diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e411e53 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +my_environment/ +.env \ No newline at end of file diff --git a/.ipynb_checkpoints/regex_practice_problems-checkpoint.ipynb b/.ipynb_checkpoints/regex_practice_problems-checkpoint.ipynb new file mode 100644 index 0000000..376cadf --- /dev/null +++ b/.ipynb_checkpoints/regex_practice_problems-checkpoint.ipynb @@ -0,0 +1,1317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lbrLwTw4r9jM" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mtZQDWxxos3J" + }, + "source": [ + "# 30 REGULAR EXPRESSION EXERCISES!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6jEYHJH3os3Q" + }, + "source": [ + "Regex, you never know when they might come in handy. It's one of the \"good programmer\"'s fundamental yet a few people actually masters them.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "ghj1l7sQos3R" + }, + "outputs": [], + "source": [ + "import re" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_tO-OHUBos3T" + }, + "source": [ + "1) Write a Python program to check that a string contains only a certain set of characters (in this case a-z, A-Z and 0-9)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cXaMTykzos3V", + "outputId": "a73c9558-3ac4-44e9-ad0f-685a8f627983" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Solution\n", + "def is_allowed_specific_char(text):\n", + " # Define a regex pattern\n", + " pattern = r'[a-zA-Z0-9]'\n", + " # Use the pattern to search for ....\n", + " result=re.search(pattern, text)\n", + " if result == None:\n", + " return False\n", + " else:\n", + " return True\n", + "\n", + "print(is_allowed_specific_char(\"ABCDEFabcdef123450\")) # True\n", + "print(is_allowed_specific_char(\"*&%@#!}{\")) # False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2oae-B8tos3W" + }, + "source": [ + "2) Write a Python program that matches a string that has an a followed by zero or more b's" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "id": "1LHEOOMkos3X" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern=re.match(r\"ab?\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + " \n", + "print(text_match(\"ac\")) # Found a match!\n", + "print(text_match(\"abc\")) # Found a match!\n", + "print(text_match(\"abbc\")) # Found a match!\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AQRTCmJZos3Z" + }, + "source": [ + "3) Write a Python program that matches a string that has an a followed by one or more b's" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "id": "H1X2NPQYos3a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"ab+\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"ab\")) # Found a match!\n", + "print(text_match(\"abc\")) # Found a match!\n", + "print(text_match(\"ac\")) # Not matched\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E902uTcRos3a" + }, + "source": [ + "4) Write a Python program that matches a string that has an a followed by zero or one 'b'" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "id": "xeva0fRNos3b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"ab?\",text)\n", + " if pattern == None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + " \n", + "print(text_match(\"ab\")) # Found a match!\n", + "print(text_match(\"abc\")) # Found a match!\n", + "print(text_match(\"abbc\")) # Found a match!\n", + "print(text_match(\"aabbc\")) # Found a match!\n", + "print(text_match(\"ac\")) # Found a match!\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "whnPaW2kos3c" + }, + "source": [ + "5) Write a Python program that matches a string that has an a followed by three 'b'" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": { + "id": "0c_7S3h_os3d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Not matched\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"abbb\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + " \n", + "\n", + "print(text_match(\"abbb\")) # Found a match!\n", + "print(text_match(\"aabbbbbc\")) # Found a match!\n", + "print(text_match(\"aabbc\")) # Not matched\n", + "print(text_match(\"ac\")) # Not matched\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V4AsjJiCos3e" + }, + "source": [ + "6) Write a Python program that matches a string that has an a followed by two to three 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": { + "id": "VjA5CXi1os3e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Found a match!\n", + "Found a match!\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"abb\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"ab\")) # Not matched\n", + "print(text_match(\"aabbbbbc\")) # Found a match!\n", + "print(text_match(\"aabbc\")) # Found a match!\n", + "print(text_match(\"ac\")) # Not matched\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kX7sLRavos3f" + }, + "source": [ + "7) Write a Python program to find sequences of lowercase letters joined with a underscore." + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rbxk35n7os3g", + "outputId": "3ddca986-1bd6-45f1-902c-eee85e8853e8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"^[a-z]+_[a-z]+$\", text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"aab_cbbbc\")) # Found a match!\n", + "print(text_match(\"aab_Abbbc\")) # Not matched\n", + "print(text_match(\"Aaab_abbbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H4sat-Rfos3g" + }, + "source": [ + "8) Write a Python program to find the sequences of one upper case letter followed by lower case letters." + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "id": "oeUiVHGVos3h" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Found a match!\n", + "Found a match!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"[A-Z][a-z]+\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"aab_cbbbc\")) # Not matched\n", + "print(text_match(\"aab_Abbbc\")) # Found a match!\n", + "print(text_match(\"Aaab_abbbc\")) # Found a match!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RgNBl7aEos3h" + }, + "source": [ + "9) Write a Python program that matches a string that has an 'a' followed by anything, ending in 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "id": "YbCsos7Jos3i" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Not matched\n", + "Found a match!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"^a[aA-zZ]+b$\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"aabbbbd\")) # Not matched\n", + "print(text_match(\"aabAbbbc\")) # Not matched\n", + "print(text_match(\"accddbbjjjb\")) # Found a match!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0oHZnx0Gos3i" + }, + "source": [ + "10) Write a Python program that matches a word at the beginning of a string." + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "id": "1tlZiGMaos3j" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.match(r\"The\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\" The quick brown fox jumps over the lazy dog.\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mG0jspZFos3j" + }, + "source": [ + "11) Write a Python program that matches a word at the end of string, with optional punctuation." + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "id": "cmhnhbZnos3j" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(\"\\w+\\S*$\", text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match\"\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog. \")) # Not matched\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog \")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DBgbyYHXos3k" + }, + "source": [ + "12) Write a Python program that matches a word containing 'z'" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "id": "WxuMRyCbos3k" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"z\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + " \n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\"Python Exercises.\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V_9_nk8Kos3l" + }, + "source": [ + "13) Write a Python program that matches a word containing 'z', not at the start or end of the word." + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "id": "VEhCCh7yos3l" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"\\Bz\\B\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\"Python Exercises.\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L0lV07OCos3m" + }, + "source": [ + "14) Write a Python program to match a string that contains only upper and lowercase letters, numbers, and underscores." + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "id": "E9NcoWDhos3m" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Found a match!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"[aA-zZ]+[0-9_]+\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Not matched\n", + "print(text_match(\"Python_Exercises_1\")) # Found a match!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EvKJ8WOoos3n" + }, + "source": [ + "15) Write a Python program where a string will start with a specific number. " + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "id": "nWp5QysTos3n" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Solution\n", + "def match_num(string):\n", + " \n", + " pattern = re.match(r\"^5\",string)\n", + " if pattern is None:\n", + " return False\n", + " else:\n", + " return True\n", + " \n", + "print(match_num('5-2345861')) # True\n", + "print(match_num('6-2345861')) # False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r_I1cbvios3t" + }, + "source": [ + "16) Write a Python program to remove leading zeros from an IP address" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "id": "cRLX4hc3os3u" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "216.8.94.196\n" + ] + } + ], + "source": [ + "# Solution\n", + "def rewrite_ip(ip):\n", + " # your code here\n", + " \n", + " newstring= re.sub(r\"0*\",\"\",ip)\n", + " if newstring is None:\n", + " return None\n", + " else:\n", + " return newstring\n", + "ip = \"216.08.094.196\"\n", + "string = rewrite_ip(ip)\n", + "print(string) # 216.8.94.196" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZCn9r3o2os3u" + }, + "source": [ + "17) Write a Python program to check for a number at the end of a string." + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "id": "Q5t9U6n8os3u" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# Solution\n", + "def end_num(string):\n", + " \n", + " pattern = re.search(r\"\\d$\",string)\n", + " if pattern is None:\n", + " return False\n", + " else:\n", + " return True\n", + "\n", + "print(end_num('abcdef')) # False\n", + "print(end_num('abcdef6')) # True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0tM4eW8Wos3v" + }, + "source": [ + "18) Write a Python program to search the numbers (0-9) of length between 1 to 3 in a given string. " + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "id": "7QGwzVxDos3v" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of length 1 to 3\n", + "1\n", + "12\n", + "13\n", + "345\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_digits(string):\n", + "\n", + " pattern = re.findall(r\"\\d+\",string)\n", + " \n", + " print(\"Number of length 1 to 3\")\n", + " \n", + " for numbers in pattern:\n", + " print(numbers)\n", + "string = \"Exercises number 1, 12, 13, and 345 are important\"\n", + "print_digits(string)\n", + "# Number of length 1 to 3\n", + "# 1\n", + "# 12\n", + "# 13\n", + "# 345" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "43nNb7d_os3v" + }, + "source": [ + "19) Write a Python program to search some literals strings in a string. \n", + "Sample text : 'The quick brown fox jumps over the lazy dog.'\n", + "Searched words : 'fox', 'dog', 'horse'" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "id": "KIVHxWN9os3w" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Searching for \"fox\" in \"The quick brown fox jumps over the lazy dog.\"->\n", + "Matched!\n", + "Searching for \"dog\" in \"The quick brown fox jumps over the lazy dog.\"->\n", + "Matched!\n", + "Searching for \"horse\" in \"The quick brown fox jumps over the lazy dog.\"->\n", + "Not Matched!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_match(patterns, text):\n", + " \n", + " for name in patterns:\n", + " result = re.search(name,text)\n", + " print('Searching for \"{}\" in \"{}\"->'.format(name,text))\n", + " if result is None:\n", + " print(\"Not Matched!\")\n", + " else:\n", + " print(\"Matched!\")\n", + "patterns = [ 'fox', 'dog', 'horse' ]\n", + "text = 'The quick brown fox jumps over the lazy dog.'\n", + " \n", + "print_match(patterns, text)\n", + "# Searching for \"fox\" in \"The quick brown fox jumps over the lazy dog.\" ->\n", + "# Matched!\n", + "# Searching for \"dog\" in \"The quick brown fox jumps over the lazy dog.\" ->\n", + "# Matched!\n", + "# Searching for \"horse\" in \"The quick brown fox jumps over the lazy dog.\" ->\n", + "# Not Matched!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XjdROmMhos3w" + }, + "source": [ + "20) Write a Python program to search a literals string in a string and also find the location within the original string where the pattern occurs\n", + "\n", + "Sample text : 'The quick brown fox jumps over the lazy dog.'\n", + "Searched words : 'fox'" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": { + "id": "vWViImYios3w" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"fox\" in \"The quick brown fox jumps over the lazy dog.\" from 16 to 19\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_match_location(pattern, text):\n", + " \n", + " position = re.search(pattern,text)\n", + " s = position.start()\n", + " e = position.end()\n", + " print('Found \"{}\" in \"{}\" from {} to {}'.format(pattern,text,s,e))\n", + "\n", + "pattern = 'fox'\n", + "text = 'The quick brown fox jumps over the lazy dog.'\n", + "print_match_location(pattern, text)\n", + "# Found \"fox\" in \"The quick brown fox jumps over the lazy dog.\" from 16 to 19" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLRotk-Oos3x" + }, + "source": [ + "21) Write a Python program to find the substrings within a string.\n", + "\n", + "Sample text :\n", + "\n", + "'Python exercises, PHP exercises, C# exercises'\n", + "\n", + "Pattern :\n", + "\n", + "'exercises'\n", + "\n", + "Note: There are three instances of exercises in the input string." + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "id": "XU8613rTos3x" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"exercises\"\n", + "Found \"exercises\"\n", + "Found \"exercises\"\n" + ] + } + ], + "source": [ + "# Solution\n", + "def find_all_matches(pattern, text):\n", + " \n", + " for substring in re.findall(pattern, text):\n", + " print('Found \"%s\"' %substring)\n", + "\n", + "text = 'Python exercises, PHP exercises, C# exercises'\n", + "pattern = 'exercises'\n", + "find_all_matches(pattern, text)\n", + "# Found \"exercises\"\n", + "# Found \"exercises\"\n", + "# Found \"exercises\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uvAY6v7Oos3x" + }, + "source": [ + "22) Write a Python program to find the occurrence and position of the substrings within a string." + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": { + "id": "UFg6rXJnos3y" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"exercises\" at 7:16\n", + "Found \"exercises\" at 22:31\n", + "Found \"exercises\" at 36:45\n" + ] + } + ], + "source": [ + "# Solution\n", + "def find_all_matches_location(pattern, text):\n", + " \n", + " text = 'Python exercises, PHP exercises, C# exercises'\n", + " pattern = 'exercises'\n", + " for location in re.finditer(pattern,text):\n", + " s=location.start()\n", + " e=location.end()\n", + " print('Found \"%s\" at %d:%d' % (text[s:e], s, e))\n", + " \n", + "\n", + "find_all_matches_location(pattern, text)\n", + "# Found \"exercises\" at 7:16\n", + "# Found \"exercises\" at 22:31\n", + "# Found \"exercises\" at 36:45" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sb3zKxE-os3y" + }, + "source": [ + "23) Write a Python program to replace whitespaces with an underscore and vice versa." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "id": "nUxR5Xvjos3z" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python_Exercises\n", + "Python Exercises\n" + ] + } + ], + "source": [ + "text = 'Python Exercises'\n", + "\n", + "# Your code\n", + "text = text.replace(\" \",\"_\")\n", + "\n", + "print(text) # Python_Exercises\n", + "text = text.replace(\"_\",\" \")\n", + "# Your code\n", + "result = re.sub(\"_\",\" \",text)\n", + "print(text) # Python Exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "grqoW-pgos3z" + }, + "source": [ + "24) Write a Python program to extract year, month and date from a an url. " + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": { + "id": "K2-pacqmos3z" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('2016', '09', '02')]\n" + ] + } + ], + "source": [ + "# Solution\n", + "def extract_date(url):\n", + " year_month_date=re.findall(r\"/(\\d{4})/(\\d{2})/(\\d{2})\",url1)\n", + " return year_month_date\n", + " \n", + "url1= \"https://www.washingtonpost.com/news/football-insider/wp/2016/09/02/odell-beckhams-fame-rests-on-one-stupid-little-ball-josh-norman-tells-author/\"\n", + "print(extract_date(url1)) # [('2016', '09', '02')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gyD5AZW7os3z" + }, + "source": [ + "25) Write a Python program to convert a date of yyyy-mm-dd format to dd-mm-yyyy format." + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": { + "id": "bo-jpYqZos30" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original date in YYY-MM-DD Format: 2026-01-02\n", + "New date in DD-MM-YYYY Format: 02-01-2026\n" + ] + } + ], + "source": [ + "# Solution\n", + "def change_date_format(dt):\n", + " \n", + " date_format=re.sub(r\"(\\d{4})-(\\d{2})-(\\d{2})\",\"\\\\3-\\\\2-\\\\1\",dt1)\n", + " return date_format\n", + "dt1 = \"2026-01-02\"\n", + "print(\"Original date in YYY-MM-DD Format: \",dt1) # Original date in YYY-MM-DD Format: 2026-01-02\n", + "print(\"New date in DD-MM-YYYY Format: \",change_date_format(dt1)) # New date in DD-MM-YYYY Format: 02-01-2026" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IQ2SNL-gos30" + }, + "source": [ + "26) Write a Python program to match if any words from a list of words starting with letter 'P'." + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "id": "2hP4Twe0os31" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Python', 'PHP')\n" + ] + } + ], + "source": [ + "# Sample strings.\n", + "def print_words_starting_with_P(words):\n", + " \n", + " for word in words:\n", + " matchwords=re.search(\"(P\\w+)\\s(P\\w+)\",word)\n", + " if matchwords:\n", + " print(matchwords.groups())\n", + "\n", + "words = [\"Python PHP\", \"Java JavaScript\", \"c c++\"]\n", + "print_words_starting_with_P(words) # ('Python', 'PHP')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MEUMKMn0os31" + }, + "source": [ + "27) Write a Python program to separate and print the numbers of a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "id": "xG8c9PtUos32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "10\n", + "20\n", + "30\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_all_numbers(text):\n", + " \n", + "# Sample string.\n", + " text = \"Ten 10, Twenty 20, Thirty 30\"\n", + " numbers=re.split(\"\\D+\",text)\n", + " for n in numbers:\n", + " print(n)\n", + "print_all_numbers(text)\n", + "# 10\n", + "# 20\n", + "# 30" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "krdKu3Mvos32" + }, + "source": [ + "28) Write a Python program to find all words starting with 'a' or 'e' in a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "id": "3hsR5278os32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['example', 'eates', 'an', 'ayList', 'apacity', 'elements', 'elements', 'are', 'en', 'added', 'ayList', 'and', 'ayList', 'ed', 'accordingly']\n" + ] + } + ], + "source": [ + "# Solution\n", + "def get_all_words_containing_a_or_e(text):\n", + " \n", + " result = re.findall(r\"[ae]\\w+\",text)\n", + "\n", + " return result\n", + "\n", + "# Input.\n", + "text = \"The following example creates an ArrayList with a capacity of 50 elements. Four elements are then added to the ArrayList and the ArrayList is trimmed accordingly.\"\n", + "print(get_all_words_containing_a_or_e(text))\n", + "# ['example', 'eates', 'an', 'ayList', 'apacity', 'elements', 'elements', 'are', 'en', 'added', 'ayList', 'and', 'ayList', 'ed', 'accordingly']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CxfIZ5o9os33" + }, + "source": [ + "29) Write a Python program to separate and print the numbers and their position of a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "id": "XIuiRvmAos33" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n", + "Index Position: 62\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_all_numbers_and_their_position(text):\n", + " \n", + " for number_position in re.finditer(r\"\\d+\",text):\n", + " print(number_position.group())\n", + " print(\"Index Position: \", number_position.start())\n", + "\n", + "# Input.\n", + "text = \"The following example creates an ArrayList with a capacity of 50 elements. Four elements are then added to the ArrayList and the ArrayList is trimmed accordingly.\"\n", + "print_all_numbers_and_their_position(text)\n", + "# 50\n", + "# Index position: 62" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JguO0fUjos34" + }, + "source": [ + "30) Write a Python program to abbreviate 'Road' as 'Rd.' in a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "id": "VJE1Xm2Bos34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21 Ramkrishna Rd\n" + ] + } + ], + "source": [ + "# Solution\n", + "def abbreviate_road(street):\n", + " \n", + " return re.sub(r\"Road\",\"Rd\",street)\n", + "street = '21 Ramkrishna Road'\n", + "\n", + "print(abbreviate_road(street)) # 21 Ramkrishna Rd.\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "vscode": { + "interpreter": { + "hash": "ccc0ef284f665b5fb033224ed4334dd4c810861a1ab44a6ce7ff637cb973648c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/12-12-2022(Generators_Lambda).py b/12-12-2022(Generators_Lambda).py new file mode 100644 index 0000000..81bc4a3 --- /dev/null +++ b/12-12-2022(Generators_Lambda).py @@ -0,0 +1,97 @@ +#**************************************************Generators Exercise******************************************************* + +#1. Create a generator, primes_gen that generates prime numbers starting from 2. + +def primes_gen(): + + for i in range(2,30): + for j in range(2,i): + if i%j==0: + break + else: + yield i + +gen = primes_gen() + +for _ in range(10): + print(next(gen), end=' ') + + + +# 2. Create a generator, unique_letters that generates unique letters from the input string. It should generate the letters in the same order as +# from the input string. + +def unique_letters(name): + uniqueLetter=set() + for i in name: + if i not in uniqueLetter: + yield i + uniqueLetter.add(i) +for letter in unique_letters('hello'): + print(letter, end=' ') + +#********************************************************Lambda Function Excersices******************************************************** + +#1. Sort the list by each language's version in ascending order. + +prog_lang = [('Python', 3.8), + ('Java', 13), + ('JavaScript', 2019), + ('Scala', 2.13)] + +prog_lang.sort(key=lambda x: x[1]) +print(prog_lang) + +#2. Sort the list by the length of the name of each language in descending order. + +prog_lang = [('Python', 3.8), + ('Java', 13), + ('JavaScript', 2019), + ('Scala', 2.13)] + +prog_lang.sort(reverse = True,key=lambda x: x[1]) +print(prog_lang) + +# 3. Filter the list so that it only contains languages with 'a' in it. + +prog_lang = [('Python', 3.8), + ('Java', 13), + ('JavaScript', 2019), + ('Scala', 2.13)] + +a_word = list(filter(lambda x: 'a' in x[0], prog_lang)) +print(a_word) + + + +# 4. Filter the list so that it only contains languages whose version is in integer form. + +prog_lang = [('Python', 3.8), + ('Java', 13), + ('JavaScript', 2019), + ('Scala', 2.13)] + +print(list(filter(lambda x: type(x[1])==int,prog_lang))) + + + +# 5. Transform the list so that it contains the tuples in the form("language in all lower case", length of the language string) + +prog_lang = [('Python', 3.8), + ('Java', 13), + ('JavaScript', 2019), + ('Scala', 2.13)] +print(list(map(lambda x: (x[0].lower(),len(x[0])),prog_lang))) + + + +# 6. Generate a tuple in the form,("All languages separated by commas","All versions separated by commas") + +prog_lang = [('Python', 3.8), + ('Java', 13), + ('JavaScript', 2019), + ('Scala', 2.13)] + +a = tuple(map(lambda x: (x[0]), prog_lang)) +b = tuple(map(lambda x : (str(x[1])),prog_lang)) +print((' '.join(a),''.join(b))) \ No newline at end of file diff --git a/12_09_practice.py b/12_09_practice.py index 7914563..9e8e52d 100644 --- a/12_09_practice.py +++ b/12_09_practice.py @@ -1,29 +1,156 @@ #Biggie Size - Given a list, write a function that changes all positive numbers in the list to "big". Example: make_it_big([-1, 3, 5, -5]) returns that same list, #changed to [-1, "big", "big", -5]. +list1 = [-1,3,5,-5] +def make_it_big(list1): + for i, value in enumerate(list1): + if value >0: + list1[i] = "big" + return list1 +make_it_big(list1) +print(list1) + #Count Positives - Given a list of numbers, create a function to replace last value with number of positive values. Example, count_positives([-1,1,1,1]) changes list #to [-1,1,1,3] and returns it. (Note that zero is not considered to be a positive number). +list1= [-1,1,1,1] +def count_positives(list1): + result = 0 + for index, value in enumerate(list1): + if value > 0: + result+=value + list1[-1]=result +count_positives(list1) +print(list1) + #SumTotal - Create a function that takes a list as an argument and returns the sum of all the values in the list. For example sum_total([1,2,3,4]) should return 10 +list1 = [1,2,3,4] +def sum_total(list1): + total = 0 + for i in list1: + total+=i + return total +sum=sum_total(list1) +print(sum) + #Average - Create a function that takes a list as an argument and returns the average of all the values in the list. For example multiples([1,2,3,4]) should return #2.5 +list1 = [1,2,3,4] +def Average(list1): + total = 0 + for i in list1: + total+=i + avg=total/len(list1) + return avg +avg=Average(list1) +print(avg) + #Length - Create a function that takes a list as an argument and returns the length of the list. For example length([1,2,3,4]) should return 4 +list1 = [1,2,3,4] +def length(list1): + l = len(list1) + return l +l=length(list1) +print(l) + #Minimum - Create a function that takes a list as an argument and returns the minimum value in the list. If the passed list is empty, have the function return false. #For example minimum([1,2,3,4]) should return 1; minimum([-1,-2,-3]) should return -3. -# + +list1 = [1,2,3,4] +def minimum(list1): + if list1 ==[]: + return False + return min(list1) +min = minimum(list1) +print(min) + #Maximum - Create a function that takes a list as an argument and returns the maximum value in the list. If the passed list is empty, have the function return false. #For example maximum([1,2,3,4]) should return 4; maximum([-1,-2,-3]) should return -1. +list1 = [1,2,3,4] +def maximum(list1): + if list1 == []: + return False + return max(list1) +max = maximum(list1) +print(max) + #Ultimateaalyze - Create a function that takes a list as an argument and returns a dictionary that has the sumTotal, average, minimum, maximum ad length of the list. +list1 = [1,2,3,4] +def ultimateaalyze(list1): + result = {} + + sum = 0 + for i in list1: + sum= sum+i + s = sum + a= sum/len(list1) + m = min(list1) + m1= max(list1) + l= len(list1) + result["Sum"] = s + result["Avg"]= a + result["Min"]= m + result["Max"]= m1 + result["Len"] = l + return result +result=ultimateaalyze(list1) +print(result) + #ReverseList - Create a function that takes a list as a argument and return a list in a reversed order. Do this without creating a empty temporary list. For example #reverse([1,2,3,4]) should return [4,3,2,1]. This challenge is known to appear during basic technical interviews. +list1 = [1,2,3,4] +def reverselist(list1): + return list1.reverse() +reverselist(list1) +print(list1) + #Ispalindrome- Given a string, write a python function to check if it is palindrome or not. A string is said to be palindrome if the reverse of the string is the same as string. For example, “radar” is a palindrome, but “radix” is not a palindrome. +str1="radar" +def ispalindrome(str1): + str2=str1[::-1] + if str1 == str2: + print(str1 +" is Palindrome") + else: + print(str1+" is Not Palindrome") +ispalindrome(str1) + #Fizzbuzz- Create a function that will print numbers from 1 to 100, with certain exceptions: #If the number is a multiple of 3, print “Fizz” instead of the number. #If the number is a multiple of 5, print “Buzz” instead of the number. #If the number is a multiple of 3 and 5, print “FizzBuzz” instead of the number. +def fizzbuzz(): + for number in range(1,101): + if number % 3 == 0: + print("Fizz ") + elif number % 5 == 0: + print("Buzz ") + elif number % 3 == 0 and number % 5 == 0: + print("FizzBuzz ") + else: + print(number) +fizzbuzz() + #Fibonacci- The Fibonacci numbers, commonly denoted F(n) form a sequence, called the Fibonacci sequence, such that each number is the sum of the two preceding ones, #starting from 0 and 1. That is, #F(0) = 0, F(1) = 1 #F(n) = F(n - 1) + F(n - 2), for n > 1. - #Create a function that accepts any number and will create a sequence based on the fibonacci sequence. \ No newline at end of file + #Create a function that accepts any number and will create a sequence based on the fibonacci sequence. + + +def fibonacci(n): + n1 = 0 + n2 = 1 + if n == 0: + print(n1) + elif n == 1: + print(n2) + else: + print(n1) + print(n2) + for i in range(2,n): + n3 = n1 + n2 + n1 = n2 + n2 = n3 + print(n3) +fibonacci(10) diff --git a/74 or so Exercises-checkpoint.ipynb b/74 or so Exercises-checkpoint.ipynb new file mode 100644 index 0000000..6839403 --- /dev/null +++ b/74 or so Exercises-checkpoint.ipynb @@ -0,0 +1,2614 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fc4bd4f0", + "metadata": {}, + "source": [ + "1) Demonstrate how to import Pandas and check the version of the installation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3a3bc97b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.5.2'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bd8ed1ff", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\chamu\\python-practice\\my_environment\\Lib\\site-packages\\_distutils_hack\\__init__.py:33: UserWarning: Setuptools is replacing distutils.\n", + " warnings.warn(\"Setuptools is replacing distutils.\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "INSTALLED VERSIONS\n", + "------------------\n", + "commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7\n", + "python : 3.11.0.final.0\n", + "python-bits : 64\n", + "OS : Windows\n", + "OS-release : 10\n", + "Version : 10.0.22621\n", + "machine : AMD64\n", + "processor : Intel64 Family 6 Model 126 Stepping 5, GenuineIntel\n", + "byteorder : little\n", + "LC_ALL : None\n", + "LANG : None\n", + "LOCALE : English_United States.1252\n", + "\n", + "pandas : 1.5.2\n", + "numpy : 1.24.1\n", + "pytz : 2022.7.1\n", + "dateutil : 2.8.2\n", + "setuptools : 65.5.0\n", + "pip : 22.3\n", + "Cython : None\n", + "pytest : None\n", + "hypothesis : None\n", + "sphinx : None\n", + "blosc : None\n", + "feather : None\n", + "xlsxwriter : None\n", + "lxml.etree : None\n", + "html5lib : None\n", + "pymysql : None\n", + "psycopg2 : None\n", + "jinja2 : None\n", + "IPython : 8.8.0\n", + "pandas_datareader: None\n", + "bs4 : None\n", + "bottleneck : None\n", + "brotli : None\n", + "fastparquet : None\n", + "fsspec : None\n", + "gcsfs : None\n", + "matplotlib : 3.6.3\n", + "numba : None\n", + "numexpr : None\n", + "odfpy : None\n", + "openpyxl : None\n", + "pandas_gbq : None\n", + "pyarrow : None\n", + "pyreadstat : None\n", + "pyxlsb : None\n", + "s3fs : None\n", + "scipy : None\n", + "snappy : None\n", + "sqlalchemy : None\n", + "tables : None\n", + "tabulate : None\n", + "xarray : None\n", + "xlrd : None\n", + "xlwt : None\n", + "zstandard : None\n", + "tzdata : None\n" + ] + } + ], + "source": [ + "pd.show_versions()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6909c73c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"system\": {\n", + " \"commit\": \"8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7\",\n", + " \"python\": \"3.11.0.final.0\",\n", + " \"python-bits\": 64,\n", + " \"OS\": \"Windows\",\n", + " \"OS-release\": \"10\",\n", + " \"Version\": \"10.0.22621\",\n", + " \"machine\": \"AMD64\",\n", + " \"processor\": \"Intel64 Family 6 Model 126 Stepping 5, GenuineIntel\",\n", + " \"byteorder\": \"little\",\n", + " \"LC_ALL\": null,\n", + " \"LANG\": null,\n", + " \"LOCALE\": {\n", + " \"language-code\": \"English_United States\",\n", + " \"encoding\": \"1252\"\n", + " }\n", + " },\n", + " \"dependencies\": {\n", + " \"pandas\": \"1.5.2\",\n", + " \"numpy\": \"1.24.1\",\n", + " \"pytz\": \"2022.7.1\",\n", + " \"dateutil\": \"2.8.2\",\n", + " \"setuptools\": \"65.5.0\",\n", + " \"pip\": \"22.3\",\n", + " \"Cython\": null,\n", + " \"pytest\": null,\n", + " \"hypothesis\": null,\n", + " \"sphinx\": null,\n", + " \"blosc\": null,\n", + " \"feather\": null,\n", + " \"xlsxwriter\": null,\n", + " \"lxml.etree\": null,\n", + " \"html5lib\": null,\n", + " \"pymysql\": null,\n", + " \"psycopg2\": null,\n", + " \"jinja2\": null,\n", + " \"IPython\": \"8.8.0\",\n", + " \"pandas_datareader\": null,\n", + " \"bs4\": null,\n", + " \"bottleneck\": null,\n", + " \"brotli\": null,\n", + " \"fastparquet\": null,\n", + " \"fsspec\": null,\n", + " \"gcsfs\": null,\n", + " \"matplotlib\": \"3.6.3\",\n", + " \"numba\": null,\n", + " \"numexpr\": null,\n", + " \"odfpy\": null,\n", + " \"openpyxl\": null,\n", + " \"pandas_gbq\": null,\n", + " \"pyarrow\": null,\n", + " \"pyreadstat\": null,\n", + " \"pyxlsb\": null,\n", + " \"s3fs\": null,\n", + " \"scipy\": null,\n", + " \"snappy\": null,\n", + " \"sqlalchemy\": null,\n", + " \"tables\": null,\n", + " \"tabulate\": null,\n", + " \"xarray\": null,\n", + " \"xlrd\": null,\n", + " \"xlwt\": null,\n", + " \"zstandard\": null,\n", + " \"tzdata\": null\n", + " }\n", + "}" + ] + } + ], + "source": [ + "pd.show_versions(as_json=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b8fc4f4a", + "metadata": {}, + "source": [ + "2) Transform this list, numpy array, and dictionary into a series." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "21b30b4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "0 a\n", + "1 b\n", + "2 c\n", + "3 e\n", + "4 d\n", + "Name: my_list, dtype: object\n", + "\n", + "0 0\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "Name: myarr, dtype: int32\n", + "\n", + "a 0\n", + "b 1\n", + "c 2\n", + "e 3\n", + "d 4\n", + "Name: mydict, dtype: int32\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "mylist = list('abcedfghijklmnopqrstuvwxyz')\n", + "series_list=pd.Series(mylist,name='my_list')\n", + "print(type(series_list))\n", + "print(series_list.head())\n", + "myarr = np.arange(26)\n", + "series_array=pd.Series(myarr,name='myarr')\n", + "print(type(series_array))\n", + "print(series_array.head())\n", + "mydict = dict(zip(mylist, myarr))\n", + "series_dict=pd.Series(mydict,name=\"mydict\")\n", + "print(type(series_dict))\n", + "print(series_dict.head())" + ] + }, + { + "cell_type": "markdown", + "id": "a17155a8", + "metadata": {}, + "source": [ + "3) Convert the index of your previous series into a column of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "db0f1395", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " index mydict\n", + "0 a 0\n", + "1 b 1\n", + "2 c 2\n", + "3 e 3\n", + "4 d 4\n" + ] + } + ], + "source": [ + "df=series_dict.to_frame().reset_index()\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "6f625f24", + "metadata": {}, + "source": [ + "4) Combine the series below into one dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d268747c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1\n", + "0 a 0\n", + "1 b 1\n", + "2 c 2\n", + "3 e 3\n", + "4 d 4\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Letters Numbers\n", + "0 a 0\n", + "1 b 1\n", + "2 c 2\n", + "3 e 3\n", + "4 d 4" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "ser1 = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))\n", + "ser2 = pd.Series(np.arange(26))\n", + "df=pd.concat([ser1,ser2],axis=1)\n", + "print(df.head())\n", + "\n", + "df=pd.DataFrame({'Letters' : ser1, 'Numbers':ser2})\n", + "df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4bdd8618", + "metadata": {}, + "source": [ + "5) Give the series below a name." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b18f9c01", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 a\n", + "1 b\n", + "2 c\n", + "3 e\n", + "4 d\n", + "Name: My_List, dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))\n", + "ser.name='My_List'\n", + "ser.head()" + ] + }, + { + "cell_type": "markdown", + "id": "cdc8d244", + "metadata": {}, + "source": [ + "6) Find the elements in the first series (ser1) not in the second series (ser2)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f1276978", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 3\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser1 = pd.Series([1, 2, 3, 4, 5])\n", + "ser2 = pd.Series([4, 5, 6, 7, 8])\n", + "\n", + "ser1[~ser1.isin(ser2)]\n" + ] + }, + { + "cell_type": "markdown", + "id": "d336f7dd", + "metadata": {}, + "source": [ + "7) Find the elements in both series that are not in common (remove them if they exist in both)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "28768232", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 3\n", + "5 6\n", + "6 7\n", + "7 8\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser1 = pd.Series([1, 2, 3, 4, 5])\n", + "ser2 = pd.Series([4, 5, 6, 7, 8])\n", + "\n", + "set_u=pd.Series(np.union1d(ser1,ser2)) #union\n", + "set_i=pd.Series(np.intersect1d(ser1,ser2)) #intersection\n", + "\n", + "set_u[~set_u.isin(set_i)]\n" + ] + }, + { + "cell_type": "markdown", + "id": "5d7188b5", + "metadata": {}, + "source": [ + "8) Find the minimum, max, 25th percentile, and 75th percentile in the series." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "5a1a3ce7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum: 0.44928746454486834\n", + "Maximum: 21.088214585529457\n", + "25th Percentile: 5.589429439808722\n", + "75th Percentile: 13.108334465903383\n" + ] + } + ], + "source": [ + "ser = pd.Series(np.random.normal(10, 5, 25))\n", + "print(\"Minimum:\", ser.min())\n", + "print(\"Maximum:\", ser.max())\n", + "print(\"25th Percentile:\", ser.quantile(0.25))\n", + "print(\"75th Percentile:\", ser.quantile(0.75))" + ] + }, + { + "cell_type": "markdown", + "id": "2e3c1803", + "metadata": {}, + "source": [ + "9) Obtain the count of each unique item in the series." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "16a2880a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "d 8\n", + "e 5\n", + "c 5\n", + "b 3\n", + "g 3\n", + "h 2\n", + "a 2\n", + "f 2\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(np.take(list('abcdefgh'), np.random.randint(8, size=30)))\n", + "ser.value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "449036ea", + "metadata": {}, + "source": [ + "10) Keep the two most frequent items in the series and change all items that are not those two into \"Other\"." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e86f2a9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most frequent items:\n", + "1 6\n", + "3 2\n", + "4 2\n", + "2 2\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "0 1\n", + "1 1\n", + "2 1\n", + "3 3\n", + "4 Other\n", + "5 Other\n", + "6 1\n", + "7 1\n", + "8 1\n", + "9 3\n", + "10 Other\n", + "11 Other\n", + "dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.RandomState(100)\n", + "ser = pd.Series(np.random.randint(1, 5, [12]))\n", + "print(\"Most frequent items:\")\n", + "print(ser.value_counts())\n", + "ser[~ser.isin(ser.value_counts().index[:2])]='Other'\n", + "ser" + ] + }, + { + "cell_type": "markdown", + "id": "b5928875", + "metadata": {}, + "source": [ + "11) Bin the series below into 10 equal deciles and replace the values with the bin name." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "979b3d36", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5th\n", + "1 3rd\n", + "2 6th\n", + "3 9th\n", + "4 10th\n", + "dtype: category\n", + "Categories (10, object): ['1st' < '2nd' < '3rd' < '4th' ... '7th' < '8th' < '9th' < '10th']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(np.random.random(20))\n", + "pd.qcut(ser,q=[0,.10,.20,.3,.4,.5,.6,.7,.8,.9,1],\n", + " labels=['1st','2nd','3rd','4th','5th','6th','7th','8th','9th','10th']).head()" + ] + }, + { + "cell_type": "markdown", + "id": "bee79a87", + "metadata": {}, + "source": [ + "12) Reshape the series ser into a dataframe with 7 rows and 5 columns." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "17a3513a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4\n", + "0 7 8 2 1 9\n", + "1 4 5 7 1 3\n", + "2 3 1 7 6 7\n", + "3 4 4 3 5 1\n", + "4 9 1 8 8 2\n", + "5 9 4 3 5 7\n", + "6 8 5 1 9 2\n" + ] + } + ], + "source": [ + "ser = pd.Series(np.random.randint(1, 10, 35))\n", + "df=pd.DataFrame(ser.values.reshape(7,5))\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "id": "28c62e92", + "metadata": {}, + "source": [ + "13) Find the positions of numbers that are multiples of 3 from ser." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7ae884a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 8\n", + "1 9\n", + "2 1\n", + "3 7\n", + "4 3\n", + "5 2\n", + "6 8\n", + "dtype: int32\n" + ] + } + ], + "source": [ + "ser = pd.Series(np.random.randint(1, 10, 7))\n", + "\n", + "print(ser)\n", + "# np.argwhere(ser % 3==0)" + ] + }, + { + "cell_type": "markdown", + "id": "7a60f247", + "metadata": {}, + "source": [ + "14) From ser, extract the items at positions in list pos." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "42338aff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 a\n", + "4 e\n", + "8 i\n", + "14 o\n", + "20 u\n", + "dtype: object" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))\n", + "pos = [0, 4, 8, 14, 20]\n", + "ser.take(pos)" + ] + }, + { + "cell_type": "markdown", + "id": "5da1836c", + "metadata": {}, + "source": [ + "15) Stack ser1 and ser2 vertically and horizontally (to form a dataframe)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5dfea534", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1\n", + "0 0 a\n", + "1 1 b\n", + "2 2 c\n", + "3 3 d\n", + "4 4 e\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\chamu\\AppData\\Local\\Temp\\ipykernel_12676\\2949033645.py:5: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " ser1.append(ser2)\n" + ] + } + ], + "source": [ + "ser1 = pd.Series(range(5))\n", + "ser2 = pd.Series(list('abcde'))\n", + "\n", + "# Vertical\n", + "ser1.append(ser2)\n", + "\n", + "# Horizontal\n", + "df = pd.concat([ser1, ser2], axis=1)\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "id": "c252d7bb", + "metadata": {}, + "source": [ + "16) Get the positions of items of ser2 in ser1 as a list." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "da51891e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5, 4, 0, 8]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser1 = pd.Series([10, 9, 6, 5, 3, 1, 12, 8, 13])\n", + "ser2 = pd.Series([1, 3, 10, 13])\n", + "\n", + "# [np.where(i == ser1)[0].tolist()[0] for i in ser2]\n", + "\n", + "#Other Way using pandas\n", + "\n", + "[pd.Index(ser1).get_loc(i) for i in ser2]" + ] + }, + { + "cell_type": "markdown", + "id": "098e34c8", + "metadata": {}, + "source": [ + "17) Compute the mean squared error of truth and pred series." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "df7ff599", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3717714177017205" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "truth = pd.Series(range(10))\n", + "pred = pd.Series(range(10)) + np.random.random(10)\n", + "\n", + "np.mean((truth-pred)**2)" + ] + }, + { + "cell_type": "markdown", + "id": "7acf6f79", + "metadata": {}, + "source": [ + "18) Change the first character of each word to upper case in each word of ser." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c210bbe9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 How\n", + "1 To\n", + "2 Kick\n", + "3 Ass?\n", + "dtype: object" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(['how', 'to', 'kick', 'ass?'])\n", + "\n", + "# Ans1:\n", + "ser.map(lambda x: x.title())\n", + "\n", + "#Ans2:\n", + "ser.map(lambda x: x[0].upper()+x[1:])" + ] + }, + { + "cell_type": "markdown", + "id": "053f198a", + "metadata": {}, + "source": [ + "19) Caluculate the number of characters for each element in the series." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "216f9a3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3\n", + "1 2\n", + "2 4\n", + "3 4\n", + "dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(['how', 'to', 'kick', 'ass?'])\n", + "\n", + "ser.map(lambda x: len(x))" + ] + }, + { + "cell_type": "markdown", + "id": "a26bcf59", + "metadata": {}, + "source": [ + "20) Caluculate the difference of differences between the consequtive numbers of ser." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c6dba0cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nan, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0, 8.0]\n", + "[nan, nan, 1.0, 1.0, 1.0, 1.0, 0.0, 2.0]\n" + ] + } + ], + "source": [ + "ser = pd.Series([1, 3, 6, 10, 15, 21, 27, 35])\n", + "\n", + "print(ser.diff().tolist())\n", + "print(ser.diff().diff().tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "c64a2901", + "metadata": {}, + "source": [ + "21) Convert the date-strings to a timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "db82de89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2010-01-01 00:00:00\n", + "1 2011-02-02 00:00:00\n", + "2 2012-03-03 00:00:00\n", + "3 2013-04-04 00:00:00\n", + "4 2014-05-05 00:00:00\n", + "5 2015-06-06 12:20:00\n", + "dtype: datetime64[ns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])\n", + "\n", + "pd.to_datetime(ser)" + ] + }, + { + "cell_type": "markdown", + "id": "5a5f83c0", + "metadata": {}, + "source": [ + "22) Get the day of month, week number, day of year and day of week from ser." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "4bcc124e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date: [1, 2, 3, 4, 5, 6]\n", + "Week number: [53, 5, 9, 14, 19, 23]\n", + "Day number of year: [1, 33, 63, 94, 125, 157]\n", + "Day of week: [4, 2, 5, 3, 0, 5]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\chamu\\AppData\\Local\\Temp\\ipykernel_12676\\3181641852.py:10: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n", + " print(\"Week number: \", ser_ts.dt.weekofyear.tolist())\n" + ] + } + ], + "source": [ + "ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])\n", + "\n", + "from dateutil.parser import parse\n", + "ser_ts = ser.map(lambda x: parse(x))\n", + "\n", + "# day of month\n", + "print(\"Date: \", ser_ts.dt.day.tolist())\n", + "\n", + "# week number\n", + "print(\"Week number: \", ser_ts.dt.weekofyear.tolist())\n", + "\n", + "# day of year\n", + "print(\"Day number of year: \", ser_ts.dt.dayofyear.tolist())\n", + "\n", + "# day of week\n", + "print(\"Day of week: \", ser_ts.dt.day_of_week.tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "c79f97b7", + "metadata": {}, + "source": [ + "23) Change ser to dates that start with 4th of the respective months." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a6384074", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2010-01-04\n", + "1 2011-02-04\n", + "2 2012-03-04\n", + "dtype: datetime64[ns]\n" + ] + }, + { + "data": { + "text/plain": [ + "['2010-01-04', '2011-02-04', '2012-03-04']" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(['Jan 2010', 'Feb 2011', 'Mar 2012'])\n", + "\n", + "# Solution 1\n", + "print(ser.map(lambda x: parse('04 ' + x)))\n", + "\n", + "# Solution 2\n", + "from dateutil.parser import parse\n", + "# Parse the date\n", + "ser_ts = ser.map(lambda x: parse(x))\n", + "\n", + "# Construct date string with date as 4\n", + "ser_datestr = ser_ts.dt.year.astype('str') + '-' + ser_ts.dt.month.astype('str') + '-' + '04'\n", + "\n", + "# Format it.\n", + "[parse(i).strftime('%Y-%m-%d') for i in ser_datestr]" + ] + }, + { + "cell_type": "markdown", + "id": "3dbb2f54", + "metadata": {}, + "source": [ + "24) From ser, extract words that contain at least 2 vowels." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "32dec09a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Apple\n", + "1 Orange\n", + "4 Money\n", + "dtype: object" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(['Apple', 'Orange', 'Plan', 'Python', 'Money'])\n", + "\n", + "from collections import Counter\n", + "mask = ser.map(lambda x: sum([Counter(x.lower()).get(i, 0) for i in list('aeiou')]) >= 2)\n", + "ser[mask]" + ] + }, + { + "cell_type": "markdown", + "id": "44177063", + "metadata": {}, + "source": [ + "25) Extract the valid emails from the series emails. The regex pattern for valid emails is provided as reference." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "3e85fe14", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 []\n", + "1 [rameses@egypt.com]\n", + "2 [matt@t.co]\n", + "3 [narendra@modi.com]\n", + "dtype: object\n", + "*************************************************\n" + ] + }, + { + "data": { + "text/plain": [ + "1 rameses@egypt.com\n", + "2 matt@t.co\n", + "3 narendra@modi.com\n", + "dtype: object" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emails = pd.Series(['buying books at amazom.com', 'rameses@egypt.com', 'matt@t.co', 'narendra@modi.com'])\n", + "pattern ='[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\\\.[A-Za-z]{2,4}'\n", + "\n", + "# Ans 1:\n", + "import re\n", + "print(emails.str.findall(pattern, flags=re.IGNORECASE))\n", + "\n", + "print(\"*************************************************\")\n", + "\n", + "# Ans 2:\n", + "\n", + "mask=emails.map(lambda x: bool(re.match(pattern,x)))\n", + "emails[mask]" + ] + }, + { + "cell_type": "markdown", + "id": "66fadf17", + "metadata": {}, + "source": [ + "26) Compute the mean of weights of each fruit." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "75855cd1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n", + "['carrot', 'banana', 'carrot', 'banana', 'carrot', 'banana', 'apple', 'apple', 'banana', 'carrot']\n" + ] + }, + { + "data": { + "text/plain": [ + "apple 7.50\n", + "banana 5.25\n", + "carrot 4.75\n", + "dtype: float64" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fruit = pd.Series(np.random.choice(['apple', 'banana', 'carrot'], 10))\n", + "weights = pd.Series(np.linspace(1, 10, 10))\n", + "print(weights.tolist())\n", + "print(fruit.tolist())\n", + "\n", + "weights.groupby(fruit).mean()\n", + "#examples\n", + "#> [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n", + "#> ['banana', 'carrot', 'apple', 'carrot', 'carrot', 'apple', 'banana', 'carrot', 'apple', 'carrot']" + ] + }, + { + "cell_type": "markdown", + "id": "41c8ebe7", + "metadata": {}, + "source": [ + "27) Compute the euclidean distance between series (points) p and q, without using a packaged formula." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "0c9cbfc2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.16590212458495\n", + "18.16590212458495\n" + ] + } + ], + "source": [ + "p = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "q = pd.Series([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])\n", + "\n", + "# Solution \n", + "print(sum((p - q)**2)**.5)\n", + "\n", + "# Solution (using func)\n", + "print(np.linalg.norm(p-q))" + ] + }, + { + "cell_type": "markdown", + "id": "dc8326a3", + "metadata": {}, + "source": [ + "28) Get the positions of peaks (values surrounded by smaller values on both sides) in ser." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "3ea30aeb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 5, 7], dtype=int64)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series([2, 10, 3, 4, 9, 10, 2, 7, 3])\n", + "\n", + "dd = np.diff(np.sign(np.diff(ser)))\n", + "peak_locs = np.where(dd == -2)[0] + 1\n", + "peak_locs" + ] + }, + { + "cell_type": "markdown", + "id": "04f5675f", + "metadata": {}, + "source": [ + "29) Replace the spaces in my_str with the least frequent character." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "1d4aff8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d 4\n", + "b 3\n", + " 3\n", + "e 3\n", + "a 2\n", + "c 1\n", + "g 1\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "'dbcgdebgabedggade'" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_str = 'dbc deb abed gade'\n", + "ser = pd.Series(list('dbc deb abed gade'))\n", + "freq = ser.value_counts()\n", + "print(freq)\n", + "least_freq = freq.dropna().index[-1]\n", + "\"\".join(ser.replace(' ', least_freq))" + ] + }, + { + "cell_type": "markdown", + "id": "146b95f0", + "metadata": {}, + "source": [ + "30) Create a timeseries starting at ‘2000-01-01’ and 10 weekends (saturdays) after that having random numbers as values." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "e4cd8dd6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01-01 5\n", + "2000-01-08 3\n", + "2000-01-15 5\n", + "2000-01-22 9\n", + "2000-01-29 8\n", + "2000-02-05 4\n", + "2000-02-12 1\n", + "2000-02-19 7\n", + "2000-02-26 9\n", + "2000-03-04 7\n", + "Freq: W-SAT, dtype: int32" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series(np.random.randint(1,10,10), pd.date_range('2000-01-01', periods=10, freq='W-SAT'))\n", + "ser" + ] + }, + { + "cell_type": "markdown", + "id": "b8a17176", + "metadata": {}, + "source": [ + "31) Series ser has missing dates and values. Make all missing dates appear and fill up with value from previous date." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "c8a10d5d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2000-01-01 1.0\n", + "2000-01-03 10.0\n", + "2000-01-06 3.0\n", + "2000-01-08 NaN\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "2000-01-01 1.0\n", + "2000-01-02 1.0\n", + "2000-01-03 10.0\n", + "2000-01-04 10.0\n", + "2000-01-05 10.0\n", + "2000-01-06 3.0\n", + "2000-01-07 3.0\n", + "2000-01-08 NaN\n", + "Freq: D, dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser = pd.Series([1,10,3,np.nan], index=pd.to_datetime(['2000-01-01', '2000-01-03', '2000-01-06', '2000-01-08']))\n", + "print(ser)\n", + "\n", + "ser.resample('D').ffill() # fill with previous value\n", + "\n", + "#> 2000-01-01 1.0\n", + "#> 2000-01-03 10.0\n", + "#> 2000-01-06 3.0\n", + "#> 2000-01-08 NaN\n", + "#> dtype: float64" + ] + }, + { + "cell_type": "markdown", + "id": "95b261ac", + "metadata": {}, + "source": [ + "32) Compute autocorrelations for the first 10 lags of ser. Find out which lag has the largest correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "052982b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.22, 0.24, 0.61, 0.25, 0.36, 0.43, 0.18, -0.03, 0.44, 0.23]\n", + "Lag having highest correlation: 3\n" + ] + } + ], + "source": [ + "ser = pd.Series(np.arange(20) + np.random.normal(1, 10, 20))\n", + "\n", + "autocorrelations = [ser.autocorr(i).round(2) for i in range(11)]\n", + "print(autocorrelations[1:])\n", + "print('Lag having highest correlation: ', np.argmax(np.abs(autocorrelations[1:]))+1)" + ] + }, + { + "cell_type": "markdown", + "id": "1ae48ebc", + "metadata": {}, + "source": [ + "33) Import every 50th row of BostonHousing dataset as a dataframe.\n", + " https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "09888bc6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " crim zn indus chas nox rm age dis rad tax \\\n", + "0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n", + "50 0.08873 21.0 5.64 0.0 0.439 5.963 45.7 6.8147 4.0 243.0 \n", + "100 0.14866 0.0 8.56 0.0 0.520 6.727 79.9 2.7778 5.0 384.0 \n", + "150 1.65660 0.0 19.58 0.0 0.871 6.122 97.3 1.6180 5.0 403.0 \n", + "200 0.01778 95.0 1.47 0.0 0.403 7.135 13.9 7.6534 3.0 402.0 \n", + "250 0.14030 22.0 5.86 0.0 0.431 6.487 13.0 7.3967 7.0 330.0 \n", + "300 0.04417 70.0 2.24 0.0 0.400 6.871 47.4 7.8278 5.0 358.0 \n", + "350 0.06211 40.0 1.25 0.0 0.429 6.490 44.4 8.7921 1.0 335.0 \n", + "400 25.04610 0.0 18.10 0.0 0.693 5.987 100.0 1.5888 24.0 666.0 \n", + "450 6.71772 0.0 18.10 0.0 0.713 6.749 92.6 2.3236 24.0 666.0 \n", + "500 0.22438 0.0 9.69 0.0 0.585 6.027 79.7 2.4982 6.0 391.0 \n", + "\n", + " ptratio b lstat medv \n", + "0 15.3 396.90 4.98 24.0 \n", + "50 16.8 395.56 13.45 19.7 \n", + "100 20.9 394.76 9.42 27.5 \n", + "150 14.7 372.80 14.10 21.5 \n", + "200 17.0 384.30 4.45 32.9 \n", + "250 19.1 396.28 5.90 24.4 \n", + "300 14.8 390.86 6.07 24.8 \n", + "350 19.7 396.90 5.98 22.9 \n", + "400 20.2 396.90 26.77 5.6 \n", + "450 20.2 0.32 17.44 13.4 \n", + "500 19.2 396.90 14.33 16.8 \n" + ] + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', chunksize=50)\n", + "df2 = pd.concat([chunk.iloc[0] for chunk in df], axis=1)\n", + "df2 = df2.transpose()\n", + "print(df2)" + ] + }, + { + "cell_type": "markdown", + "id": "0f10cbec", + "metadata": {}, + "source": [ + "34) Import the boston housing dataset, but while importing change the 'medv' (median house value) column so that values < 25 becomes ‘Low’ and > 25 becomes ‘High’." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "c9c39256", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " crim zn indus chas nox rm age dis rad tax ptratio \\\n", + "0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 \n", + "1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 \n", + "2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 \n", + "3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 \n", + "4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 \n", + "\n", + " b lstat medv \n", + "0 396.90 4.98 Low \n", + "1 396.90 9.14 Low \n", + "2 392.83 4.03 High \n", + "3 394.63 2.94 High \n", + "4 396.90 5.33 High \n" + ] + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', \n", + " converters={'medv': lambda x: 'High' if float(x) > 25 else 'Low'})\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "a83078db", + "metadata": {}, + "source": [ + "35) Create a dataframe with rows as strides from the series \"L\"." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "ea9dfeba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 2, 3, 4, 5],\n", + " [ 4, 5, 6, 7],\n", + " [ 6, 7, 8, 9],\n", + " [ 8, 9, 10, 11],\n", + " [10, 11, 12, 13]], dtype=int64)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L = pd.Series(range(15))\n", + "\n", + "def gen_strides(a, stride_len=5, window_len=5):\n", + " n_strides = ((a.size-window_len)//stride_len) + 1\n", + " return np.array([a[s:(s+window_len)] for s in np.arange(0, a.size, stride_len)[:n_strides]])\n", + "\n", + "gen_strides(L, stride_len=2, window_len=4)" + ] + }, + { + "cell_type": "markdown", + "id": "4b36f890", + "metadata": {}, + "source": [ + "36) Import ‘crim’ and ‘medv’ columns of the BostonHousing dataset as a dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "33ecddbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " crim medv\n", + "0 0.00632 24.0\n", + "1 0.02731 21.6\n", + "2 0.02729 34.7\n", + "3 0.03237 33.4\n", + "4 0.06905 36.2\n" + ] + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', usecols=['crim', 'medv'])\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "d830c591", + "metadata": {}, + "source": [ + "37) Get the number of rows, columns, datatype and summary statistics of each column of the Cars93 dataset. Also get the numpy array and list equivalent of the dataframe.\n", + "\n", + "https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "2c1fe83a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(93, 27)\n", + "Manufacturer object\n", + "Model object\n", + "Type object\n", + "Min.Price float64\n", + "Price float64\n", + "Max.Price float64\n", + "MPG.city float64\n", + "MPG.highway float64\n", + "AirBags object\n", + "DriveTrain object\n", + "Cylinders object\n", + "EngineSize float64\n", + "Horsepower float64\n", + "RPM float64\n", + "Rev.per.mile float64\n", + "Man.trans.avail object\n", + "Fuel.tank.capacity float64\n", + "Passengers float64\n", + "Length float64\n", + "Wheelbase float64\n", + "Width float64\n", + "Turn.circle float64\n", + "Rear.seat.room float64\n", + "Luggage.room float64\n", + "Weight float64\n", + "Origin object\n", + "Make object\n", + "dtype: object\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'DataFrame' object has no attribute 'get_dtype_counts'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[73], line 10\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[39mprint\u001b[39m(df\u001b[39m.\u001b[39mdtypes)\n\u001b[0;32m 9\u001b[0m \u001b[39m# how many columns under each dtype\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m \u001b[39mprint\u001b[39m(df\u001b[39m.\u001b[39;49mget_dtype_counts())\n\u001b[0;32m 11\u001b[0m \u001b[39mprint\u001b[39m(df\u001b[39m.\u001b[39mdtypes\u001b[39m.\u001b[39mvalue_counts())\n\u001b[0;32m 13\u001b[0m \u001b[39m# summary statistics\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\chamu\\python-practice\\my_environment\\Lib\\site-packages\\pandas\\core\\generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5895\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 5896\u001b[0m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_internal_names_set\n\u001b[0;32m 5897\u001b[0m \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_metadata\n\u001b[0;32m 5898\u001b[0m \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accessors\n\u001b[0;32m 5899\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_info_axis\u001b[39m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m 5900\u001b[0m ):\n\u001b[0;32m 5901\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m[name]\n\u001b[1;32m-> 5902\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mobject\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__getattribute__\u001b[39;49m(\u001b[39mself\u001b[39;49m, name)\n", + "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'get_dtype_counts'" + ] + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')\n", + "\n", + "# number of rows and columns\n", + "print(df.shape)\n", + "\n", + "# datatypes\n", + "print(df.dtypes)\n", + "\n", + "# how many columns under each dtype\n", + "print(df.get_dtype_counts())\n", + "print(df.dtypes.value_counts())\n", + "\n", + "# summary statistics\n", + "df_stats = df.describe()\n", + "\n", + "# numpy array \n", + "df_arr = df.values\n", + "\n", + "# list\n", + "df_list = df.values.tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "e458e997", + "metadata": {}, + "source": [ + "38) Which manufacturer, model and type has the highest Price? What is the row and column number of the cell with the highest Price value?" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "f70df19d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.9" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')\n", + "\n", + "\n", + "# Solution\n", + "# Get Manufacturer with highest price\n", + "df.loc[df.Price == np.max(df.Price), ['Manufacturer', 'Model', 'Type']]\n", + "\n", + "# Get Row and Column number\n", + "row, col = np.where(df.values == np.max(df.Price))\n", + "\n", + "# Get the value\n", + "df.iat[row[0], col[0]]\n", + "df.iloc[row[0], col[0]]\n", + "\n", + "# Alternates\n", + "df.at[row[0], 'Price']\n", + "# df.get_value(row[0], 'Price')" + ] + }, + { + "cell_type": "markdown", + "id": "f4e81856", + "metadata": {}, + "source": [ + "39) Rename the column \"Type\" as \"CarType\" in the previous data and replace the ‘.’ in column names with ‘_’." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "33e517e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Manufacturer', 'Model', 'CarType', 'Min_Price', 'Price', 'Max_Price',\n", + " 'MPG_city', 'MPG_highway', 'AirBags', 'DriveTrain', 'Cylinders',\n", + " 'EngineSize', 'Horsepower', 'RPM', 'Rev_per_mile', 'Man_trans_avail',\n", + " 'Fuel_tank_capacity', 'Passengers', 'Length', 'Wheelbase', 'Width',\n", + " 'Turn_circle', 'Rear_seat_room', 'Luggage_room', 'Weight', 'Origin',\n", + " 'Make'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# print(df.columns)\n", + "\n", + "# Step 1:\n", + "\n", + "df=df.rename(columns = {'Type':'CarType'})\n", + "# or\n", + "df.columns.values[2] = \"CarType\"\n", + "\n", + "\n", + "# Step 2:\n", + "df.columns = df.columns.map(lambda x: x.replace('.', '_'))\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "id": "5989022c", + "metadata": {}, + "source": [ + "40) Check if the data from #37 has any missing values." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "e9f7e0ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Input\n", + "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')\n", + "\n", + "# Solution\n", + "df.isnull().values.any()" + ] + }, + { + "cell_type": "markdown", + "id": "ed881438", + "metadata": {}, + "source": [ + "41) Count the number of missing values in each column of the previous data. Which column has the maximum number of missing values?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abf14525", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b5abbc84", + "metadata": {}, + "source": [ + "42) Replace missing values in Min.Price and Max.Price columns with their respective mean." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91acfe3c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c1d26407", + "metadata": {}, + "source": [ + "43) In the previous data, use apply method to replace the missing values in Min.Price with the column’s mean and those in Max.Price with the column’s median." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdb6209b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "80c193bc", + "metadata": {}, + "source": [ + "44) Get the first column (a) in the data as a dataframe (rather than as a Series)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8be2fbaa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "83334c4f", + "metadata": {}, + "source": [ + "45) Actually 3 questions.\n", + "\n", + "In the data, interchange columns 'a' and 'c'.\n", + "\n", + "Create a generic function to interchange two columns, without hardcoding column names.\n", + "\n", + "Sort the columns in reverse alphabetical order, that is colume 'e' first through column 'a' last." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8706e8d3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "bd957988", + "metadata": {}, + "source": [ + "46) Change the pandas display settings on printing the dataframe so it shows a maximum of 10 rows and 10 columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8bd512a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f69a323b", + "metadata": {}, + "source": [ + "47) Suppress scientific notations like ‘e-03’ in df and print upto 4 numbers after decimal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "485aa0b2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "bfa22aec", + "metadata": {}, + "source": [ + "48) Format the values in column 'random' of df as percentages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3d4f4d9", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.random(4), columns=['random'])\n", + "df\n", + "#> random\n", + "#> 0 .689723\n", + "#> 1 .957224\n", + "#> 2 .159157\n", + "#> 3 .21082" + ] + }, + { + "cell_type": "markdown", + "id": "829030ce", + "metadata": {}, + "source": [ + "49) From the cars data, filter the 'Manufacturer', 'Model' and 'Type' for every 20th row starting from 1st (row 0)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b439593c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d38155b7", + "metadata": {}, + "source": [ + "50) In the cars data, Replace NaNs with ‘missing’ in columns 'Manufacturer', 'Model' and 'Type' and create a index as a combination of these three columns and check if the index is a primary key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cf131e7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d01c9b73", + "metadata": {}, + "source": [ + "51) Find the row position of the 5th largest value of column 'a' in df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15321302", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1, 30, 30).reshape(10,-1), columns=list('abc'))" + ] + }, + { + "cell_type": "markdown", + "id": "9df76d19", + "metadata": {}, + "source": [ + "52) In ser, find the position of the 2nd largest value greater than the mean." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61330d9f", + "metadata": {}, + "outputs": [], + "source": [ + "ser = pd.Series(np.random.randint(1, 100, 15))" + ] + }, + { + "cell_type": "markdown", + "id": "0aaae1b8", + "metadata": {}, + "source": [ + "53) Get the last two rows of df whose row sum is greater than 100." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab30612", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(10, 40, 60).reshape(-1, 4))" + ] + }, + { + "cell_type": "markdown", + "id": "ac4dac7e", + "metadata": {}, + "source": [ + "54) Replace all values of ser in the lower 5%ile and greater than 95%ile with respective 5th and 95th %ile value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78bc91ec", + "metadata": {}, + "outputs": [], + "source": [ + "ser = pd.Series(np.logspace(-2, 2, 30))" + ] + }, + { + "cell_type": "markdown", + "id": "407723d9", + "metadata": {}, + "source": [ + "55) Reshape df to the largest possible square with negative values removed. Drop the smallest values if need be. The order of the positive numbers in the result should remain the same as the original." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ebc8dec", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(-20, 50, 100).reshape(10,-1))" + ] + }, + { + "cell_type": "markdown", + "id": "5aa891f3", + "metadata": {}, + "source": [ + "56) Swap rows 1 and 2 in df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ca60052", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.arange(25).reshape(5, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "97144928", + "metadata": {}, + "source": [ + "57) Reverse all the rows of dataframe df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aac3cc14", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.arange(25).reshape(5, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "3f09f84f", + "metadata": {}, + "source": [ + "58) Get one-hot encodings for column 'a' in the dataframe df and append it as columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59860b7a", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.arange(25).reshape(5,-1), columns=list('abcde'))\n", + " a b c d e\n", + "0 0 1 2 3 4\n", + "1 5 6 7 8 9\n", + "2 10 11 12 13 14\n", + "3 15 16 17 18 19\n", + "4 20 21 22 23 24" + ] + }, + { + "cell_type": "markdown", + "id": "b5295b8b", + "metadata": {}, + "source": [ + "59) Obtain the column name with the highest number of row-wise maximum’s in df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5083160c", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 40).reshape(10, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "15fcf24b", + "metadata": {}, + "source": [ + "60) Create a new column such that, each row contains the row number of nearest row-record by euclidean distance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0719c737", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 40).reshape(10, -1), columns=list('pqrs'), index=list('abcdefghij'))\n", + "df\n", + "# p q r s\n", + "# a 57 77 13 62\n", + "# b 68 5 92 24\n", + "# c 74 40 18 37\n", + "# d 80 17 39 60\n", + "# e 93 48 85 33\n", + "# f 69 55 8 11\n", + "# g 39 23 88 53\n", + "# h 63 28 25 61\n", + "# i 18 4 73 7\n", + "# j 79 12 45 34" + ] + }, + { + "cell_type": "markdown", + "id": "f1eb1791", + "metadata": {}, + "source": [ + "61) Compute maximum possible absolute correlation value of each column against other columns in df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "baee6e5f", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1), columns=list('pqrstuvwxy'), index=list('abcdefgh'))" + ] + }, + { + "cell_type": "markdown", + "id": "828ef471", + "metadata": {}, + "source": [ + "62) Compute the minimum-by-maximum for every row of df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7368340", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "971d2276", + "metadata": {}, + "source": [ + "63) Create a new column 'penultimate' which has the second largest value of each row of df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66c6865b", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "529c1042", + "metadata": {}, + "source": [ + "64) Normalize all columns of df by subtracting the column mean and divide by standard deviation.\n", + "Range all columns of df such that the minimum value in each column is 0 and max is 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5da28502", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "af26e808", + "metadata": {}, + "source": [ + "65) Compute the correlation of each row of df with its succeeding row." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5fca730", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))" + ] + }, + { + "cell_type": "markdown", + "id": "57341a09", + "metadata": {}, + "source": [ + "66) Replace both values in both diagonals of df with 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "721432ca", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1,100, 100).reshape(10, -1))\n", + "df\n", + "# 0 1 2 3 4 5 6 7 8 9\n", + "# 0 11 46 26 44 11 62 18 70 68 26\n", + "# 1 87 71 52 50 81 43 83 39 3 59\n", + "# 2 47 76 93 77 73 2 2 16 14 26\n", + "# 3 64 18 74 22 16 37 60 8 66 39\n", + "# 4 10 18 39 98 25 8 32 6 3 29\n", + "# 5 29 91 27 86 23 84 28 31 97 10\n", + "# 6 37 71 70 65 4 72 82 89 12 97\n", + "# 7 65 22 97 75 17 10 43 78 12 77\n", + "# 8 47 57 96 55 17 83 61 85 26 86\n", + "# 9 76 80 28 45 77 12 67 80 7 63\n" + ] + }, + { + "cell_type": "markdown", + "id": "202c483b", + "metadata": {}, + "source": [ + "67) Using a key in a grouped data frame. From df_grouped, get the group belonging to 'apple' as a dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8028ee1", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({'col1': ['apple', 'banana', 'orange'] * 3,\n", + " 'col2': np.random.rand(9),\n", + " 'col3': np.random.randint(0, 15, 9)})\n", + "\n", + "df_grouped = df.groupby(['col1'])" + ] + }, + { + "cell_type": "markdown", + "id": "8d2cb901", + "metadata": {}, + "source": [ + "68) In df, find the second largest value of 'taste' for 'banana'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7209bc19", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,\n", + " 'rating': np.random.rand(9),\n", + " 'price': np.random.randint(0, 15, 9)})" + ] + }, + { + "cell_type": "markdown", + "id": "3bde89cb", + "metadata": {}, + "source": [ + "69) In df, Compute the mean price of every fruit, while keeping the fruit as another column instead of an index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69a4c8f6", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,\n", + " 'rating': np.random.rand(9),\n", + " 'price': np.random.randint(0, 15, 9)})" + ] + }, + { + "cell_type": "markdown", + "id": "e5eaa372", + "metadata": {}, + "source": [ + "70) Join dataframes df1 and df2 by ‘fruit-pazham’ and ‘weight-kilo’." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a250adf", + "metadata": {}, + "outputs": [], + "source": [ + "df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,\n", + " 'weight': ['high', 'medium', 'low'] * 3,\n", + " 'price': np.random.randint(0, 15, 9)})\n", + "\n", + "df2 = pd.DataFrame({'pazham': ['apple', 'orange', 'pine'] * 2,\n", + " 'kilo': ['high', 'low'] * 3,\n", + " 'price': np.random.randint(0, 15, 6)})" + ] + }, + { + "cell_type": "markdown", + "id": "57adb8fe", + "metadata": {}, + "source": [ + "71) Get the positions where the value of two columns match." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b43cc070", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({'fruit1': np.random.choice(['apple', 'orange', 'banana'], 10),\n", + " 'fruit2': np.random.choice(['apple', 'orange', 'banana'], 10)})" + ] + }, + { + "cell_type": "markdown", + "id": "b7da5c86", + "metadata": {}, + "source": [ + "72) Create two new columns in df, one of which is a lag1 (shift column a down by 1 row) of column ‘a’ and the other is a lead1 (shift column b up by 1 row)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e531adc8", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1, 100, 20).reshape(-1, 4), columns = list('abcd'))\n", + "\n", + " a b c d\n", + "0 66 34 76 47\n", + "1 20 86 10 81\n", + "2 75 73 51 28\n", + "3 1 1 9 83\n", + "4 30 47 67 4" + ] + }, + { + "cell_type": "markdown", + "id": "501a01f7", + "metadata": {}, + "source": [ + "73) Get the frequency of unique values in the entire dataframe df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c22b1df8", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(np.random.randint(1, 10, 20).reshape(-1, 4), columns = list('abcd'))" + ] + }, + { + "cell_type": "markdown", + "id": "ff5d182d", + "metadata": {}, + "source": [ + "74) Split the string column in df to form a dataframe with 3 columns as shown." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d8a2bcd", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame([\"STD, City State\",\n", + "\"33, Kolkata West Bengal\",\n", + "\"44, Chennai Tamil Nadu\",\n", + "\"40, Hyderabad Telengana\",\n", + "\"80, Bangalore Karnataka\"], columns=['row'])\n", + "\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a90037ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "print(np.arange(7))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "my_environment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "357ec78291cf388eeacd62e271c6b188c6b9f92b882d7927ff21aa77b4c7f673" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Closure_Decorators.py b/Closure_Decorators.py new file mode 100644 index 0000000..c6af626 --- /dev/null +++ b/Closure_Decorators.py @@ -0,0 +1,142 @@ +#************************************************************Closure-Exercise******************************************************************* + +# Using a closure, create a function, multiples_of(n) which we can use to create generators that generate multiples of n less than a given +# number. + +def multiples_of(n): + def multiply(k): + for i in range(1,(k//n)+1): + if i*n + +print(*m3_under30) +# output: 3 6 9 12 15 18 21 24 27 + +print(*m7_under30) + +#*************************************************Decorators-Exercises************************************************************************************* + +# 1. make_upper – make every letter of a string returned from the decorated function uppercase. + +def make_upper(func): + def sam(*args): + return func(*args).upper() + return sam + +@make_upper +def hello_world(): + return 'hello young, good day!!' + +print(hello_world()) + +# 2. print_func_name – print the name of the decorated function before executing the function. + +def print_func_name(func): + def wrapper(): + print(func.__name__, "is Running") + func() + return wrapper + +@print_func_name +def my_func(): + print('Python is fun!!') + +my_func()# output: my_func is running... + + +# 3. give_name(name) – concatenate the given name at the end of a string returned from the decorated function. + +def give_name(n): + def inner(func): + def wrapper(): + return (func() + ' ' + n) + return wrapper + return inner + +@give_name('Theresa') +def greeting(): + return 'Hello' + +print(greeting()) + +# 4. print_input_type – print a data type of the input argument before executing the decorated function. + +def print_input_type(func): + def wrapper(a): + print("The output data type is ",type(func(a))) + return func(a) + return wrapper + + +@print_input_type +def square(n): + return n ** 2 + +print(square(3.5)) + + +#5. check_return_type(return_type) – check if the return type of the decorated function is return_type and print +#the result before executing the function. + +def check_return_type(dtype): + def inner(func): + def wrapper(n): + if type(n) != dtype: + print("================Error!!\n==============The return type is NOT {}".format(dtype)) + + elif type(n) == dtype: + return ("The return type is {}".format(dtype)) # + type(n)) + return func(n) + return wrapper + return inner +@check_return_type(str) +def square(n): + return n ** 2 +print(square(6)) # output: =========Error!! + #=========The return type is NOT + #36 +@check_return_type(float) +def square(n): + return n ** 2 +print(square(2.9)) + +#6. execute_log – write a function execution log on the log file. + +from datetime import datetime, timezone + +def execute_log(func): + + def inner(*args, **kwargs): + called_at = datetime.now() + to_execute = func(*args, **kwargs) + return ('{0} {1} {2}'.format(called_at,func.__name__,to_execute)) + return inner + + +@execute_log +def multiply(*nums): + mult = 1 + for n in nums: + mult *= n + return mult +# return multiply + +@execute_log +def hello_world(): + return 'hello world!!' + +print(multiply(6, 2, 3)) # 36 +print(hello_world()) # hello world!! +print(multiply(2.2, 4)) # 8.8 +print(hello_world()) + + diff --git a/DA0101EN-2-Review-Data-Wrangling.ipynb b/DA0101EN-2-Review-Data-Wrangling.ipynb new file mode 100644 index 0000000..ff00e57 --- /dev/null +++ b/DA0101EN-2-Review-Data-Wrangling.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["

\n"," \n"," \"Skills\n"," \n","

\n","\n","# Data Wrangling\n","\n","Estimated time needed: **30** minutes\n","\n","## Objectives\n","\n","After completing this lab you will be able to:\n","\n","* Handle missing values\n","* Correct data format\n","* Standardize and normalize data\n"]},{"cell_type":"markdown","metadata":{},"source":["

Table of Contents

\n","\n","
\n","\n","\n","
\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

What is the purpose of data wrangling?

\n"]},{"cell_type":"markdown","metadata":{},"source":["Data wrangling is the process of converting data from the initial format to a format that may be better for analysis.\n"]},{"cell_type":"markdown","metadata":{},"source":["

What is the fuel consumption (L/100k) rate for the diesel car?

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Import data

\n","

\n","You can find the \"Automobile Dataset\" from the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data. \n","We will be using this dataset throughout this course.\n","

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Import pandas

\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#install specific version of libraries used in lab\n","#! mamba install pandas==1.3.3\n","#! mamba install numpy=1.21.2\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import matplotlib as plt"]},{"cell_type":"markdown","metadata":{},"source":["

Reading the dataset from the URL and adding the related headers

\n"]},{"cell_type":"markdown","metadata":{},"source":["First, we assign the URL of the dataset to \"filename\".\n"]},{"cell_type":"markdown","metadata":{},"source":["This dataset was hosted on IBM Cloud object. Click HERE for free storage.\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["filename = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/auto.csv\""]},{"cell_type":"markdown","metadata":{},"source":["Then, we create a Python list headers containing name of headers.\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n"," \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n"," \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n"," \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]"]},{"cell_type":"markdown","metadata":{},"source":["Use the Pandas method read_csv() to load the data from the web address. Set the parameter \"names\" equal to the Python list \"headers\".\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["df=pd.read_csv(filename,names=headers)"]},{"cell_type":"markdown","metadata":{},"source":["Use the method head() to display the first five rows of the dataframe.\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/html":["
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"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 3 ? alfa-romero gas std two \n","1 3 ? alfa-romero gas std two \n","2 1 ? alfa-romero gas std two \n","3 2 164 audi gas std four \n","4 2 164 audi gas std four \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 convertible rwd front 88.6 ... 130 \n","1 convertible rwd front 88.6 ... 130 \n","2 hatchback rwd front 94.5 ... 152 \n","3 sedan fwd front 99.8 ... 109 \n","4 sedan 4wd front 99.4 ... 136 \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000 21 \n","1 mpfi 3.47 2.68 9.0 111 5000 21 \n","2 mpfi 2.68 3.47 9.0 154 5000 19 \n","3 mpfi 3.19 3.40 10.0 102 5500 24 \n","4 mpfi 3.19 3.40 8.0 115 5500 18 \n","\n"," highway-mpg price \n","0 27 13495 \n","1 27 16500 \n","2 26 16500 \n","3 30 13950 \n","4 22 17450 \n","\n","[5 rows x 26 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# To see what the data set looks like, we'll use the head() method.\n","df.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["As we can see, several question marks appeared in the dataframe; those are missing values which may hinder our further analysis.\n","\n","
So, how do we identify all those missing values and deal with them?
\n","\n","How to work with missing data?\n","\n","Steps for working with missing data:\n","\n","
    \n","
  1. Identify missing data
    2. \n","
  2. Deal with missing data
    3. \n","
  3. Correct data format
    4. \n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Identify and handle missing values

\n","\n","

Identify missing values

\n","

Convert \"?\" to NaN

\n","In the car dataset, missing data comes with the question mark \"?\".\n","We replace \"?\" with NaN (Not a Number), Python's default missing value marker for reasons of computational speed and convenience. Here we use the function: \n"," 
.replace(A, B, inplace = True)
\n","to replace A by B.\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/html":["
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5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 3 NaN alfa-romero gas std two \n","1 3 NaN alfa-romero gas std two \n","2 1 NaN alfa-romero gas std two \n","3 2 164 audi gas std four \n","4 2 164 audi gas std four \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 convertible rwd front 88.6 ... 130 \n","1 convertible rwd front 88.6 ... 130 \n","2 hatchback rwd front 94.5 ... 152 \n","3 sedan fwd front 99.8 ... 109 \n","4 sedan 4wd front 99.4 ... 136 \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000 21 \n","1 mpfi 3.47 2.68 9.0 111 5000 21 \n","2 mpfi 2.68 3.47 9.0 154 5000 19 \n","3 mpfi 3.19 3.40 10.0 102 5500 24 \n","4 mpfi 3.19 3.40 8.0 115 5500 18 \n","\n"," highway-mpg price \n","0 27 13495 \n","1 27 16500 \n","2 26 16500 \n","3 30 13950 \n","4 22 17450 \n","\n","[5 rows x 26 columns]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["import numpy as np\n","\n","# replace \"?\" to NaN\n","df.replace(\"?\",np.nan,inplace=True)\n","df.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["

Evaluating for Missing Data

\n","\n","The missing values are converted by default. We use the following functions to identify these missing values. There are two methods to detect missing data:\n","\n","
    \n","
  1. .isnull()
    2. \n","
  2. .notnull()
    3. \n","
\n","The output is a boolean value indicating whether the value that is passed into the argument is in fact missing data.\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/html":["
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3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 False True False False False False \n","1 False True False False False False \n","2 False True False False False False \n","3 False False False False False False \n","4 False False False False False False \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 False False False False ... False \n","1 False False False False ... False \n","2 False False False False ... False \n","3 False False False False ... False \n","4 False False False False ... False \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm \\\n","0 False False False False False False \n","1 False False False False False False \n","2 False False False False False False \n","3 False False False False False False \n","4 False False False False False False \n","\n"," city-mpg highway-mpg price \n","0 False False False \n","1 False False False \n","2 False False False \n","3 False False False \n","4 False False False \n","\n","[5 rows x 26 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["missing_data=df.isnull()\n","missing_data.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["\"True\" means the value is a missing value while \"False\" means the value is not a missing value.\n"]},{"cell_type":"markdown","metadata":{},"source":["

Count missing values in each column

\n","

\n","Using a for loop in Python, we can quickly figure out the number of missing values in each column. As mentioned above, \"True\" represents a missing value and \"False\" means the value is present in the dataset. In the body of the for loop the method \".value_counts()\" counts the number of \"True\" values. \n","

\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["symboling\n","False 205\n","Name: symboling, dtype: int64\n","\n","normalized-losses\n","False 164\n","True 41\n","Name: normalized-losses, dtype: int64\n","\n","make\n","False 205\n","Name: make, dtype: int64\n","\n","fuel-type\n","False 205\n","Name: fuel-type, dtype: int64\n","\n","aspiration\n","False 205\n","Name: aspiration, dtype: int64\n","\n","num-of-doors\n","False 203\n","True 2\n","Name: num-of-doors, dtype: int64\n","\n","body-style\n","False 205\n","Name: body-style, dtype: int64\n","\n","drive-wheels\n","False 205\n","Name: drive-wheels, dtype: int64\n","\n","engine-location\n","False 205\n","Name: engine-location, dtype: int64\n","\n","wheel-base\n","False 205\n","Name: wheel-base, dtype: int64\n","\n","length\n","False 205\n","Name: length, dtype: int64\n","\n","width\n","False 205\n","Name: width, dtype: int64\n","\n","height\n","False 205\n","Name: height, dtype: int64\n","\n","curb-weight\n","False 205\n","Name: curb-weight, dtype: int64\n","\n","engine-type\n","False 205\n","Name: engine-type, dtype: int64\n","\n","num-of-cylinders\n","False 205\n","Name: num-of-cylinders, dtype: int64\n","\n","engine-size\n","False 205\n","Name: engine-size, dtype: int64\n","\n","fuel-system\n","False 205\n","Name: fuel-system, dtype: int64\n","\n","bore\n","False 201\n","True 4\n","Name: bore, dtype: int64\n","\n","stroke\n","False 201\n","True 4\n","Name: stroke, dtype: int64\n","\n","compression-ratio\n","False 205\n","Name: compression-ratio, dtype: int64\n","\n","horsepower\n","False 203\n","True 2\n","Name: horsepower, dtype: int64\n","\n","peak-rpm\n","False 203\n","True 2\n","Name: peak-rpm, dtype: int64\n","\n","city-mpg\n","False 205\n","Name: city-mpg, dtype: int64\n","\n","highway-mpg\n","False 205\n","Name: highway-mpg, dtype: int64\n","\n","price\n","False 201\n","True 4\n","Name: price, dtype: int64\n","\n"]}],"source":["for column in missing_data.columns.values.tolist():\n"," print(column)\n"," print (missing_data[column].value_counts())\n"," print(\"\") "]},{"cell_type":"markdown","metadata":{},"source":["Based on the summary above, each column has 205 rows of data and seven of the columns containing missing data:\n","\n","
    \n","
  1. \"normalized-losses\": 41 missing data
    2. \n","
  2. \"num-of-doors\": 2 missing data
    3. \n","
  3. \"bore\": 4 missing data
    4. \n","
  4. \"stroke\" : 4 missing data
    5. \n","
  5. \"horsepower\": 2 missing data
    6. \n","
  6. \"peak-rpm\": 2 missing data
    7. \n","
  7. \"price\": 4 missing data
    8. \n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Deal with missing data

\n","How to deal with missing data?\n","\n","
    \n","
  1. Drop data
    \n"," a. Drop the whole row
    \n"," b. Drop the whole column\n","
    2. \n","
  2. Replace data
    \n"," a. Replace it by mean
    \n"," b. Replace it by frequency
    \n"," c. Replace it based on other functions\n","
    3. \n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["Whole columns should be dropped only if most entries in the column are empty. In our dataset, none of the columns are empty enough to drop entirely.\n","We have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:\n","\n","Replace by mean:\n","\n","
    \n","
  • \"normalized-losses\": 41 missing data, replace them with mean
    • \n","
  • \"stroke\": 4 missing data, replace them with mean
    • \n","
  • \"bore\": 4 missing data, replace them with mean
    • \n","
  • \"horsepower\": 2 missing data, replace them with mean
    • \n","
  • \"peak-rpm\": 2 missing data, replace them with mean
    • \n","
\n","\n","Replace by frequency:\n","\n","
    \n","
  • \"num-of-doors\": 2 missing data, replace them with \"four\". \n","
      \n","
    • Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occur
      • \n","
    \n","
    • \n","
\n","\n","Drop the whole row:\n","\n","
    \n","
  • \"price\": 4 missing data, simply delete the whole row\n","
      \n","
    • Reason: price is what we want to predict. Any data entry without price data cannot be used for prediction; therefore any row now without price data is not useful to us
      • \n","
    \n","
    • \n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Calculate the mean value for the \"normalized-losses\" column

\n"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["avg_norm_loss = df[\"normalized-losses\"].astype(\"float\").mean(axis=0)\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Average of normalized-losses: 122.0\n"]}],"source":["print(\"Average of normalized-losses:\", avg_norm_loss)"]},{"cell_type":"markdown","metadata":{},"source":["

Replace \"NaN\" with mean value in \"normalized-losses\" column

\n"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["
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symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-base...engine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212713495
13122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212716500
21122.0alfa-romerogasstdtwohatchbackrwdfront94.5...152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8...109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4...136mpfi3.193.408.01155500182217450
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 3 122.0 alfa-romero gas std two \n","1 3 122.0 alfa-romero gas std two \n","2 1 122.0 alfa-romero gas std two \n","3 2 164 audi gas std four \n","4 2 164 audi gas std four \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 convertible rwd front 88.6 ... 130 \n","1 convertible rwd front 88.6 ... 130 \n","2 hatchback rwd front 94.5 ... 152 \n","3 sedan fwd front 99.8 ... 109 \n","4 sedan 4wd front 99.4 ... 136 \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000 21 \n","1 mpfi 3.47 2.68 9.0 111 5000 21 \n","2 mpfi 2.68 3.47 9.0 154 5000 19 \n","3 mpfi 3.19 3.40 10.0 102 5500 24 \n","4 mpfi 3.19 3.40 8.0 115 5500 18 \n","\n"," highway-mpg price \n","0 27 13495 \n","1 27 16500 \n","2 26 16500 \n","3 30 13950 \n","4 22 17450 \n","\n","[5 rows x 26 columns]"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df[\"normalized-losses\"].replace(np.nan, avg_norm_loss, inplace=True)\n","df.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["

Calculate the mean value for the \"bore\" column

\n"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Average of bore: 3.3297512437810943\n"]}],"source":["average_bore=df['bore'].astype(float).mean(axis=0)\n","print(\"Average of bore: \",average_bore)\n"]},{"cell_type":"markdown","metadata":{},"source":["

Replace \"NaN\" with the mean value in the \"bore\" column

\n"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"text/html":["
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03122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212713495
13122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212716500
21122.0alfa-romerogasstdtwohatchbackrwdfront94.5...152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8...109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4...136mpfi3.193.408.01155500182217450
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 3 122.0 alfa-romero gas std two \n","1 3 122.0 alfa-romero gas std two \n","2 1 122.0 alfa-romero gas std two \n","3 2 164 audi gas std four \n","4 2 164 audi gas std four \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 convertible rwd front 88.6 ... 130 \n","1 convertible rwd front 88.6 ... 130 \n","2 hatchback rwd front 94.5 ... 152 \n","3 sedan fwd front 99.8 ... 109 \n","4 sedan 4wd front 99.4 ... 136 \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000 21 \n","1 mpfi 3.47 2.68 9.0 111 5000 21 \n","2 mpfi 2.68 3.47 9.0 154 5000 19 \n","3 mpfi 3.19 3.40 10.0 102 5500 24 \n","4 mpfi 3.19 3.40 8.0 115 5500 18 \n","\n"," highway-mpg price \n","0 27 13495 \n","1 27 16500 \n","2 26 16500 \n","3 30 13950 \n","4 22 17450 \n","\n","[5 rows x 26 columns]"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["df['bore'].replace(np.nan,average_bore,inplace=True)\n","df.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #1:

\n","\n","Based on the example above, replace NaN in \"stroke\" column with the mean value.\n","\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","#Calculate the mean vaule for \"stroke\" column\n","avg_stroke = df[\"stroke\"].astype(\"float\").mean(axis = 0)\n","print(\"Average of stroke:\", avg_stroke)\n","\n","# replace NaN by mean value in \"stroke\" column\n","df[\"stroke\"].replace(np.nan, avg_stroke, inplace = True)\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Calculate the mean value for the \"horsepower\" column

\n"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Average of hoursepower: 104.25615763546799\n"]}],"source":["avg_horsepower=df['horsepower'].astype(float).mean(axis=0)\n","print(\"Average of hoursepower: \",avg_horsepower)"]},{"cell_type":"markdown","metadata":{},"source":["

Replace \"NaN\" with the mean value in the \"horsepower\" column

\n"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["df['horsepower'].replace(np.nan,avg_horsepower,inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["

Calculate the mean value for \"peak-rpm\" column

\n"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["5125.369458128079\n"]}],"source":["avg_peakrpm=df['peak-rpm'].astype(float).mean(axis=0)\n","print(avg_peakrpm)"]},{"cell_type":"markdown","metadata":{},"source":["

Replace \"NaN\" with the mean value in the \"peak-rpm\" column

\n"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"data":{"text/html":["
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03122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212713495
13122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212716500
21122.0alfa-romerogasstdtwohatchbackrwdfront94.5...152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8...109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4...136mpfi3.193.408.01155500182217450
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 3 122.0 alfa-romero gas std two \n","1 3 122.0 alfa-romero gas std two \n","2 1 122.0 alfa-romero gas std two \n","3 2 164 audi gas std four \n","4 2 164 audi gas std four \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 convertible rwd front 88.6 ... 130 \n","1 convertible rwd front 88.6 ... 130 \n","2 hatchback rwd front 94.5 ... 152 \n","3 sedan fwd front 99.8 ... 109 \n","4 sedan 4wd front 99.4 ... 136 \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000 21 \n","1 mpfi 3.47 2.68 9.0 111 5000 21 \n","2 mpfi 2.68 3.47 9.0 154 5000 19 \n","3 mpfi 3.19 3.40 10.0 102 5500 24 \n","4 mpfi 3.19 3.40 8.0 115 5500 18 \n","\n"," highway-mpg price \n","0 27 13495 \n","1 27 16500 \n","2 26 16500 \n","3 30 13950 \n","4 22 17450 \n","\n","[5 rows x 26 columns]"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["df['peak-rpm'].replace(np.nan,avg_peakrpm,inplace=True)\n","df.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["To see which values are present in a particular column, we can use the \".value_counts()\" method:\n"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/plain":["four 114\n","two 89\n","Name: num-of-doors, dtype: int64"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["df['num-of-doors'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["We can see that four doors are the most common type. We can also use the \".idxmax()\" method to calculate the most common type automatically:\n"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["'four'"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["df['num-of-doors'].value_counts().idxmax()"]},{"cell_type":"markdown","metadata":{},"source":["The replacement procedure is very similar to what we have seen previously:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#replace the missing 'num-of-doors' values by the most frequent \n","df['num-of-doors'].replace(np.nan,'four',inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["Finally, let's drop all rows that do not have price data:\n"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[],"source":["# simply drop whole row with NaN in \"price\" column\n","df.dropna(subset=['price'],axis=0,inplace=True)\n","\n","\n","# reset index, because we droped two rows\n","df.reset_index(drop=True,inplace=True)\n"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-base...engine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212713495
13122.0alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212716500
21122.0alfa-romerogasstdtwohatchbackrwdfront94.5...152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8...109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4...136mpfi3.193.408.01155500182217450
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n","0 3 122.0 alfa-romero gas std two \n","1 3 122.0 alfa-romero gas std two \n","2 1 122.0 alfa-romero gas std two \n","3 2 164 audi gas std four \n","4 2 164 audi gas std four \n","\n"," body-style drive-wheels engine-location wheel-base ... engine-size \\\n","0 convertible rwd front 88.6 ... 130 \n","1 convertible rwd front 88.6 ... 130 \n","2 hatchback rwd front 94.5 ... 152 \n","3 sedan fwd front 99.8 ... 109 \n","4 sedan 4wd front 99.4 ... 136 \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000 21 \n","1 mpfi 3.47 2.68 9.0 111 5000 21 \n","2 mpfi 2.68 3.47 9.0 154 5000 19 \n","3 mpfi 3.19 3.40 10.0 102 5500 24 \n","4 mpfi 3.19 3.40 8.0 115 5500 18 \n","\n"," highway-mpg price \n","0 27 13495 \n","1 27 16500 \n","2 26 16500 \n","3 30 13950 \n","4 22 17450 \n","\n","[5 rows x 26 columns]"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{},"source":["Good! Now, we have a dataset with no missing values.\n"]},{"cell_type":"markdown","metadata":{},"source":["

Correct data format

\n","We are almost there!\n","

The last step in data cleaning is checking and making sure that all data is in the correct format (int, float, text or other).

\n","\n","In Pandas, we use:\n","\n","

.dtype() to check the data type

\n","

.astype() to change the data type

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Let's list the data types for each column

\n"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/plain":["symboling int64\n","normalized-losses object\n","make object\n","fuel-type object\n","aspiration object\n","num-of-doors object\n","body-style object\n","drive-wheels object\n","engine-location object\n","wheel-base float64\n","length float64\n","width float64\n","height float64\n","curb-weight int64\n","engine-type object\n","num-of-cylinders object\n","engine-size int64\n","fuel-system object\n","bore object\n","stroke object\n","compression-ratio float64\n","horsepower object\n","peak-rpm object\n","city-mpg int64\n","highway-mpg int64\n","price object\n","dtype: object"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"markdown","metadata":{},"source":["

As we can see above, some columns are not of the correct data type. Numerical variables should have type 'float' or 'int', and variables with strings such as categories should have type 'object'. For example, 'bore' and 'stroke' variables are numerical values that describe the engines, so we should expect them to be of the type 'float' or 'int'; however, they are shown as type 'object'. We have to convert data types into a proper format for each column using the \"astype()\" method.

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Convert data types to proper format

\n"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[],"source":["df[['bore','stroke']]=df[['bore','stroke']].astype(float)\n","df['normalized-losses']=df['normalized-losses'].astype(int)\n","df['price']=df['price'].astype(float)\n","df['peak-rpm']=df['peak-rpm'].astype(float)"]},{"cell_type":"markdown","metadata":{},"source":["

Let us list the columns after the conversion

\n"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"data":{"text/plain":["symboling int64\n","normalized-losses int32\n","make object\n","fuel-type object\n","aspiration object\n","num-of-doors object\n","body-style object\n","drive-wheels object\n","engine-location object\n","wheel-base float64\n","length float64\n","width float64\n","height float64\n","curb-weight int64\n","engine-type object\n","num-of-cylinders object\n","engine-size int64\n","fuel-system object\n","bore float64\n","stroke float64\n","compression-ratio float64\n","horsepower object\n","peak-rpm float64\n","city-mpg int64\n","highway-mpg int64\n","price float64\n","dtype: object"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"markdown","metadata":{},"source":["Wonderful!\n","\n","Now we have finally obtained the cleaned dataset with no missing values with all data in its proper format.\n"]},{"cell_type":"markdown","metadata":{},"source":["

Data Standardization

\n","

\n","Data is usually collected from different agencies in different formats.\n","(Data standardization is also a term for a particular type of data normalization where we subtract the mean and divide by the standard deviation.)\n","

\n","\n","What is standardization?\n","\n","

Standardization is the process of transforming data into a common format, allowing the researcher to make the meaningful comparison.\n","

\n","\n","Example\n","\n","

Transform mpg to L/100km:

\n","

In our dataset, the fuel consumption columns \"city-mpg\" and \"highway-mpg\" are represented by mpg (miles per gallon) unit. Assume we are developing an application in a country that accepts the fuel consumption with L/100km standard.

\n","

We will need to apply data transformation to transform mpg into L/100km.

\n"]},{"cell_type":"markdown","metadata":{},"source":["

The formula for unit conversion is:

\n","L/100km = 235 / mpg\n","

We can do many mathematical operations directly in Pandas.

\n"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-base...engine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03122alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000.0212713495.0
13122alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000.0212716500.0
21122alfa-romerogasstdtwohatchbackrwdfront94.5...152mpfi2.683.479.01545000.0192616500.0
32164audigasstdfoursedanfwdfront99.8...109mpfi3.193.4010.01025500.0243013950.0
42164audigasstdfoursedan4wdfront99.4...136mpfi3.193.408.01155500.0182217450.0
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration \\\n","0 3 122 alfa-romero gas std \n","1 3 122 alfa-romero gas std \n","2 1 122 alfa-romero gas std \n","3 2 164 audi gas std \n","4 2 164 audi gas std \n","\n"," num-of-doors body-style drive-wheels engine-location wheel-base ... \\\n","0 two convertible rwd front 88.6 ... \n","1 two convertible rwd front 88.6 ... \n","2 two hatchback rwd front 94.5 ... \n","3 four sedan fwd front 99.8 ... \n","4 four sedan 4wd front 99.4 ... \n","\n"," engine-size fuel-system bore stroke compression-ratio horsepower \\\n","0 130 mpfi 3.47 2.68 9.0 111 \n","1 130 mpfi 3.47 2.68 9.0 111 \n","2 152 mpfi 2.68 3.47 9.0 154 \n","3 109 mpfi 3.19 3.40 10.0 102 \n","4 136 mpfi 3.19 3.40 8.0 115 \n","\n"," peak-rpm city-mpg highway-mpg price \n","0 5000.0 21 27 13495.0 \n","1 5000.0 21 27 16500.0 \n","2 5000.0 19 26 16500.0 \n","3 5500.0 24 30 13950.0 \n","4 5500.0 18 22 17450.0 \n","\n","[5 rows x 26 columns]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-base...fuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100km
03122alfa-romerogasstdtwoconvertiblerwdfront88.6...mpfi3.472.689.01115000.0212713495.011.190476
13122alfa-romerogasstdtwoconvertiblerwdfront88.6...mpfi3.472.689.01115000.0212716500.011.190476
21122alfa-romerogasstdtwohatchbackrwdfront94.5...mpfi2.683.479.01545000.0192616500.012.368421
32164audigasstdfoursedanfwdfront99.8...mpfi3.193.4010.01025500.0243013950.09.791667
42164audigasstdfoursedan4wdfront99.4...mpfi3.193.408.01155500.0182217450.013.055556
\n","

5 rows × 27 columns

\n","
"],"text/plain":[" symboling normalized-losses make fuel-type aspiration \\\n","0 3 122 alfa-romero gas std \n","1 3 122 alfa-romero gas std \n","2 1 122 alfa-romero gas std \n","3 2 164 audi gas std \n","4 2 164 audi gas std \n","\n"," num-of-doors body-style drive-wheels engine-location wheel-base ... \\\n","0 two convertible rwd front 88.6 ... \n","1 two convertible rwd front 88.6 ... \n","2 two hatchback rwd front 94.5 ... \n","3 four sedan fwd front 99.8 ... \n","4 four sedan 4wd front 99.4 ... \n","\n"," fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n","0 mpfi 3.47 2.68 9.0 111 5000.0 21 \n","1 mpfi 3.47 2.68 9.0 111 5000.0 21 \n","2 mpfi 2.68 3.47 9.0 154 5000.0 19 \n","3 mpfi 3.19 3.40 10.0 102 5500.0 24 \n","4 mpfi 3.19 3.40 8.0 115 5500.0 18 \n","\n"," highway-mpg price city-L/100km \n","0 27 13495.0 11.190476 \n","1 27 16500.0 11.190476 \n","2 26 16500.0 12.368421 \n","3 30 13950.0 9.791667 \n","4 22 17450.0 13.055556 \n","\n","[5 rows x 27 columns]"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["# Convert mpg to L/100km by mathematical operation (235 divided by mpg)\n","df['city-L/100km']=235/df['city-mpg']\n","# check your transformed data \n","df.head()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #2:

\n","\n","According to the example above, transform mpg to L/100km in the column of \"highway-mpg\" and change the name of column to \"highway-L/100km\".\n","\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n","\n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","# transform mpg to L/100km by mathematical operation (235 divided by mpg)\n","df[\"highway-mpg\"] = 235/df[\"highway-mpg\"]\n","\n","# rename column name from \"highway-mpg\" to \"highway-L/100km\"\n","df.rename(columns={'\"highway-mpg\"':'highway-L/100km'}, inplace=True)\n","\n","# check your transformed data \n","df.head()\n","\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Data Normalization

\n","\n","Why normalization?\n","\n","

Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling the variable so the variable values range from 0 to 1.\n","

\n","\n","Example\n","\n","

To demonstrate normalization, let's say we want to scale the columns \"length\", \"width\" and \"height\".

\n","

Target: would like to normalize those variables so their value ranges from 0 to 1

\n","

Approach: replace original value by (original value)/(maximum value)

\n"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[],"source":["# replace (original value) by (original value)/(maximum value)\n","df['length']=df['length']/df['length'].max()\n","df['width']=df['width']/df['width'].max()\n"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #3:

\n","\n","According to the example above, normalize the column \"height\".\n","\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","df['height'] = df['height']/df['height'].max() \n","\n","# show the scaled columns\n","df[[\"length\",\"width\",\"height\"]].head()\n","\n","\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["Here we can see we've normalized \"length\", \"width\" and \"height\" in the range of \\[0,1].\n"]},{"cell_type":"markdown","metadata":{},"source":["

Binning

\n","Why binning?\n","

\n"," Binning is a process of transforming continuous numerical variables into discrete categorical 'bins' for grouped analysis.\n","

\n","\n","Example: \n","\n","

In our dataset, \"horsepower\" is a real valued variable ranging from 48 to 288 and it has 59 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three ‘bins' to simplify analysis?

\n","\n","

We will use the pandas method 'cut' to segment the 'horsepower' column into 3 bins.

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Example of Binning Data In Pandas

\n"]},{"cell_type":"markdown","metadata":{},"source":["Convert data to correct format:\n"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[],"source":["df[\"horsepower\"]=df[\"horsepower\"].astype(int, copy=True)"]},{"cell_type":"markdown","metadata":{},"source":["Let's plot the histogram of horsepower to see what the distribution of horsepower looks like.\n"]},{"cell_type":"code","execution_count":50,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'horsepower bins')"]},"execution_count":50,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["%matplotlib inline\n","import matplotlib as plt\n","from matplotlib import pyplot\n","plt.pyplot.hist(df[\"horsepower\"])\n","\n","# set x/y labels and plot title\n","plt.pyplot.xlabel(\"horsepower\")\n","plt.pyplot.ylabel(\"count\")\n","plt.pyplot.title(\"horsepower bins\")"]},{"cell_type":"markdown","metadata":{},"source":["

We would like 3 bins of equal size bandwidth so we use numpy's linspace(start_value, end_value, numbers_generated function.

\n","

Since we want to include the minimum value of horsepower, we want to set start_value = min(df[\"horsepower\"]).

\n","

Since we want to include the maximum value of horsepower, we want to set end_value = max(df[\"horsepower\"]).

\n","

Since we are building 3 bins of equal length, there should be 4 dividers, so numbers_generated = 4.

\n"]},{"cell_type":"markdown","metadata":{},"source":["We build a bin array with a minimum value to a maximum value by using the bandwidth calculated above. The values will determine when one bin ends and another begins.\n"]},{"cell_type":"code","execution_count":39,"metadata":{},"outputs":[{"data":{"text/plain":["array([ 48. , 119.33333333, 190.66666667, 262. ])"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)\n","bins"]},{"cell_type":"markdown","metadata":{},"source":["We set group names:\n"]},{"cell_type":"code","execution_count":41,"metadata":{},"outputs":[],"source":["group_names = ['Low', 'Medium', 'High']"]},{"cell_type":"markdown","metadata":{},"source":["We apply the function \"cut\" to determine what each value of `df['horsepower']` belongs to.\n"]},{"cell_type":"code","execution_count":52,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
horsepowerhorsepower-binned
0111Low
1111Low
2154Medium
3102Low
4115Low
\n","
"],"text/plain":[" horsepower horsepower-binned\n","0 111 Low\n","1 111 Low\n","2 154 Medium\n","3 102 Low\n","4 115 Low"]},"execution_count":52,"metadata":{},"output_type":"execute_result"}],"source":["df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names)\n","df[['horsepower','horsepower-binned']].head(5)"]},{"cell_type":"markdown","metadata":{},"source":["Let's see the number of vehicles in each bin:\n"]},{"cell_type":"code","execution_count":53,"metadata":{},"outputs":[{"data":{"text/plain":["Low 152\n","Medium 43\n","High 5\n","Name: horsepower-binned, dtype: int64"]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["df['horsepower-binned'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["Let's plot the distribution of each bin:\n"]},{"cell_type":"code","execution_count":54,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'horsepower bins')"]},"execution_count":54,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["%matplotlib inline\n","import matplotlib as plt\n","from matplotlib import pyplot\n","pyplot.bar(group_names, df[\"horsepower-binned\"].value_counts())\n","\n","# set x/y labels and plot title\n","plt.pyplot.xlabel(\"horsepower\")\n","plt.pyplot.ylabel(\"count\")\n","plt.pyplot.title(\"horsepower bins\")"]},{"cell_type":"markdown","metadata":{},"source":["

\n"," Look at the dataframe above carefully. You will find that the last column provides the bins for \"horsepower\" based on 3 categories (\"Low\", \"Medium\" and \"High\"). \n","

\n","

\n"," We successfully narrowed down the intervals from 59 to 3!\n","

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Bins Visualization

\n","Normally, a histogram is used to visualize the distribution of bins we created above. \n"]},{"cell_type":"code","execution_count":57,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'horsepower bins')"]},"execution_count":57,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["\n","%matplotlib inline\n","import matplotlib as plt\n","from matplotlib import pyplot\n","\n","\n","# draw historgram of attribute \"horsepower\" with bins = 3\n","plt.pyplot.hist(df[\"horsepower\"], bins = 3)\n","\n","# set x/y labels and plot title\n","plt.pyplot.xlabel(\"horsepower\")\n","plt.pyplot.ylabel(\"count\")\n","plt.pyplot.title(\"horsepower bins\")\n"]},{"cell_type":"markdown","metadata":{},"source":["The plot above shows the binning result for the attribute \"horsepower\".\n"]},{"cell_type":"markdown","metadata":{},"source":["

Indicator Variable (or Dummy Variable)

\n","What is an indicator variable?\n","

\n"," An indicator variable (or dummy variable) is a numerical variable used to label categories. They are called 'dummies' because the numbers themselves don't have inherent meaning. \n","

\n","\n","Why we use indicator variables?\n","\n","

\n"," We use indicator variables so we can use categorical variables for regression analysis in the later modules.\n","

\n","Example\n","

\n"," We see the column \"fuel-type\" has two unique values: \"gas\" or \"diesel\". Regression doesn't understand words, only numbers. To use this attribute in regression analysis, we convert \"fuel-type\" to indicator variables.\n","

\n","\n","

\n"," We will use pandas' method 'get_dummies' to assign numerical values to different categories of fuel type. \n","

\n"]},{"cell_type":"code","execution_count":58,"metadata":{},"outputs":[{"data":{"text/plain":["Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n"," 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n"," 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n"," 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n"," 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n"," 'highway-mpg', 'price', 'city-L/100km', 'horsepower-binned'],\n"," dtype='object')"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["df.columns"]},{"cell_type":"markdown","metadata":{},"source":["Get the indicator variables and assign it to data frame \"dummy_variable\\_1\":\n"]},{"cell_type":"code","execution_count":59,"metadata":{},"outputs":[{"data":{"text/html":["
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"],"text/plain":[" diesel gas\n","0 0 1\n","1 0 1\n","2 0 1\n","3 0 1\n","4 0 1"]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])\n","dummy_variable_1.head()"]},{"cell_type":"markdown","metadata":{},"source":["Change the column names for clarity:\n"]},{"cell_type":"code","execution_count":60,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
fuel-type-dieselfuel-type-gas
001
101
201
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401
\n","
"],"text/plain":[" fuel-type-diesel fuel-type-gas\n","0 0 1\n","1 0 1\n","2 0 1\n","3 0 1\n","4 0 1"]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["dummy_variable_1.rename(columns={'gas':'fuel-type-gas', 'diesel':'fuel-type-diesel'}, inplace=True)\n","dummy_variable_1.head()"]},{"cell_type":"markdown","metadata":{},"source":["In the dataframe, column 'fuel-type' has values for 'gas' and 'diesel' as 0s and 1s now.\n"]},{"cell_type":"code","execution_count":61,"metadata":{},"outputs":[],"source":["# merge data frame \"df\" and \"dummy_variable_1\" \n","df = pd.concat([df, dummy_variable_1], axis=1)\n","\n","# drop original column \"fuel-type\" from \"df\"\n","df.drop(\"fuel-type\", axis = 1, inplace=True)\n"]},{"cell_type":"code","execution_count":62,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselength...compression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhorsepower-binnedfuel-type-dieselfuel-type-gas
03122alfa-romerostdtwoconvertiblerwdfront88.60.811148...9.01115000.0212713495.011.190476Low01
13122alfa-romerostdtwoconvertiblerwdfront88.60.811148...9.01115000.0212716500.011.190476Low01
21122alfa-romerostdtwohatchbackrwdfront94.50.822681...9.01545000.0192616500.012.368421Medium01
32164audistdfoursedanfwdfront99.80.848630...10.01025500.0243013950.09.791667Low01
42164audistdfoursedan4wdfront99.40.848630...8.01155500.0182217450.013.055556Low01
\n","

5 rows × 29 columns

\n","
"],"text/plain":[" symboling normalized-losses make aspiration num-of-doors \\\n","0 3 122 alfa-romero std two \n","1 3 122 alfa-romero std two \n","2 1 122 alfa-romero std two \n","3 2 164 audi std four \n","4 2 164 audi std four \n","\n"," body-style drive-wheels engine-location wheel-base length ... \\\n","0 convertible rwd front 88.6 0.811148 ... \n","1 convertible rwd front 88.6 0.811148 ... \n","2 hatchback rwd front 94.5 0.822681 ... \n","3 sedan fwd front 99.8 0.848630 ... \n","4 sedan 4wd front 99.4 0.848630 ... \n","\n"," compression-ratio horsepower peak-rpm city-mpg highway-mpg price \\\n","0 9.0 111 5000.0 21 27 13495.0 \n","1 9.0 111 5000.0 21 27 16500.0 \n","2 9.0 154 5000.0 19 26 16500.0 \n","3 10.0 102 5500.0 24 30 13950.0 \n","4 8.0 115 5500.0 18 22 17450.0 \n","\n"," city-L/100km horsepower-binned fuel-type-diesel fuel-type-gas \n","0 11.190476 Low 0 1 \n","1 11.190476 Low 0 1 \n","2 12.368421 Medium 0 1 \n","3 9.791667 Low 0 1 \n","4 13.055556 Low 0 1 \n","\n","[5 rows x 29 columns]"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{},"source":["The last two columns are now the indicator variable representation of the fuel-type variable. They're all 0s and 1s now.\n"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #4:

\n","\n","Similar to before, create an indicator variable for the column \"aspiration\"\n","\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","# get indicator variables of aspiration and assign it to data frame \"dummy_variable_2\"\n","dummy_variable_2 = pd.get_dummies(df['aspiration'])\n","\n","# change column names for clarity\n","dummy_variable_2.rename(columns={'std':'aspiration-std', 'turbo': 'aspiration-turbo'}, inplace=True)\n","\n","# show first 5 instances of data frame \"dummy_variable_1\"\n","dummy_variable_2.head()\n","\n","\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #5:

\n","\n","Merge the new dataframe to the original dataframe, then drop the column 'aspiration'.\n","\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","# merge the new dataframe to the original datafram\n","df = pd.concat([df, dummy_variable_2], axis=1)\n","\n","# drop original column \"aspiration\" from \"df\"\n","df.drop('aspiration', axis = 1, inplace=True)\n","\n","\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["Save the new csv:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.to_csv('clean_df.csv')"]},{"cell_type":"markdown","metadata":{},"source":["### Thank you for completing this lab!\n","\n","## Author\n","\n","Joseph Santarcangelo\n","\n","### Other Contributors\n","\n","Mahdi Noorian PhD\n","\n","Bahare Talayian\n","\n","Eric Xiao\n","\n","Steven Dong\n","\n","Parizad\n","\n","Hima Vasudevan\n","\n","Fiorella Wenver\n","\n","Yi Yao.\n","\n","## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","| ----------------- | ------- | ---------- | ----------------------------------- |\n","| 2020-10-30 | 2.2 | Lakshmi | Changed URL of csv |\n","| 2020-09-09 | 2.1 | Lakshmi | Updated Indicator Variables section |\n","| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n","\n","
\n","\n","##

© IBM Corporation 2020. All rights reserved.

\n"]}],"metadata":{"anaconda-cloud":{},"kernelspec":{"display_name":"my_environment","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]"},"vscode":{"interpreter":{"hash":"357ec78291cf388eeacd62e271c6b188c6b9f92b882d7927ff21aa77b4c7f673"}}},"nbformat":4,"nbformat_minor":4} diff --git a/DA0101EN-3-Review-Exploratory-Data-Analysis.ipynb b/DA0101EN-3-Review-Exploratory-Data-Analysis.ipynb new file mode 100644 index 0000000..3754d0d --- /dev/null +++ b/DA0101EN-3-Review-Exploratory-Data-Analysis.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["

\n"," \n"," \"Skills\n"," \n","

\n","\n","# Data Analysis with Python\n","\n","Estimated time needed: **30** minutes\n","\n","## Objectives\n","\n","After completing this lab you will be able to:\n","\n","* Explore features or charecteristics to predict price of car\n"]},{"cell_type":"markdown","metadata":{},"source":["

Table of Contents

\n","\n","
\n","
    \n","
  1. Import Data from Module
    2. \n","
  2. Analyzing Individual Feature Patterns using Visualization
    3. \n","
  3. Descriptive Statistical Analysis
    4. \n","
  4. Basics of Grouping
    5. \n","
  5. Correlation and Causation
    6. \n","
  6. ANOVA
    7. \n","
\n","\n","
\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

What are the main characteristics that have the most impact on the car price?

\n"]},{"cell_type":"markdown","metadata":{},"source":["

1. Import Data from Module 2

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Setup

\n"]},{"cell_type":"markdown","metadata":{},"source":["Import libraries:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np"]},{"cell_type":"markdown","metadata":{},"source":["Load the data and store it in dataframe `df`:\n"]},{"cell_type":"markdown","metadata":{},"source":["This dataset was hosted on IBM Cloud object. Click HERE for free storage.\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/plain":["symboling object\n","normalized-losses object\n","make object\n","fuel-type object\n","aspiration object\n","num-of-doors object\n","body-style object\n","drive-wheels object\n","engine-location object\n","wheel-base object\n","length object\n","width object\n","height object\n","curb-weight object\n","engine-type object\n","num-of-cylinders object\n","engine-size object\n","fuel-system object\n","bore object\n","stroke object\n","compression-ratio object\n","horsepower object\n","peak-rpm object\n","city-mpg object\n","highway-mpg object\n","price object\n","dtype: object"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["filename = 'C:/Users/SamanthaGrzegorzewsk/pandas_demos/coursera_labs/automobileEDA.csv'\n","headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n"," \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n"," \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n"," \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n","df = pd.read_csv(filename, names=headers)\n","df.dtypes"]},{"cell_type":"markdown","metadata":{},"source":["

2. Analyzing Individual Feature Patterns Using Visualization

\n"]},{"cell_type":"markdown","metadata":{},"source":["To install Seaborn we use pip, the Python package manager.\n"]},{"cell_type":"markdown","metadata":{},"source":["Import visualization packages \"Matplotlib\" and \"Seaborn\". Don't forget about \"%matplotlib inline\" to plot in a Jupyter notebook.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n"]},{"cell_type":"markdown","metadata":{},"source":["

How to choose the right visualization method?

\n","

When visualizing individual variables, it is important to first understand what type of variable you are dealing with. This will help us find the right visualization method for that variable.

\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# list the data types for each column\n"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #1:

\n","\n","What is the data type of the column \"peak-rpm\"? \n","\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","float64\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["For example, we can calculate the correlation between variables of type \"int64\" or \"float64\" using the method \"corr\":\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["The diagonal elements are always one; we will study correlation more precisely Pearson correlation in-depth at the end of the notebook.\n"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question #2:

\n","\n","

Find the correlation between the following columns: bore, stroke, compression-ratio, and horsepower.

\n","

Hint: if you would like to select those columns, use the following syntax: df[['bore','stroke','compression-ratio','horsepower']]

\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Continuous Numerical Variables:

\n","\n","

Continuous numerical variables are variables that may contain any value within some range. They can be of type \"int64\" or \"float64\". A great way to visualize these variables is by using scatterplots with fitted lines.

\n","\n","

In order to start understanding the (linear) relationship between an individual variable and the price, we can use \"regplot\" which plots the scatterplot plus the fitted regression line for the data. This will be useful later on for visualizing the fit of the simple linear regression model as well.

\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's see several examples of different linear relationships:\n"]},{"cell_type":"markdown","metadata":{},"source":["

Positive Linear Relationship

\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's find the scatterplot of \"engine-size\" and \"price\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":["# Engine size as potential predictor variable of price\n"]},{"cell_type":"markdown","metadata":{},"source":["

As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line.

\n"]},{"cell_type":"markdown","metadata":{},"source":["We can examine the correlation between 'engine-size' and 'price' and see that it's approximately 0.87.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["Highway mpg is a potential predictor variable of price. Let's find the scatterplot of \"highway-mpg\" and \"price\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

As highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price.

\n"]},{"cell_type":"markdown","metadata":{},"source":["We can examine the correlation between 'highway-mpg' and 'price' and see it's approximately -0.704.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

Weak Linear Relationship

\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's see if \"peak-rpm\" is a predictor variable of \"price\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore, it's not a reliable variable.

\n"]},{"cell_type":"markdown","metadata":{},"source":["We can examine the correlation between 'peak-rpm' and 'price' and see it's approximately -0.101616.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question 3 a):

\n","\n","

Find the correlation between x=\"stroke\" and y=\"price\".

\n","

Hint: if you would like to select those columns, use the following syntax: df[[\"stroke\",\"price\"]].

\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute\n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","\n","#The correlation is 0.0823, the non-diagonal elements of the table.\n","\n","df[[\"stroke\",\"price\"]].corr()\n","\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["
\n","

Question 3 b):

\n","\n","

Given the correlation results between \"price\" and \"stroke\", do you expect a linear relationship?

\n","

Verify your results using the function \"regplot()\".

\n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
Click here for the solution\n","\n","```python\n","\n","#There is a weak correlation between the variable 'stroke' and 'price.' as such regression will not work well. We can see this using \"regplot\" to demonstrate this.\n","\n","#Code: \n","sns.regplot(x=\"stroke\", y=\"price\", data=df)\n","\n","```\n","\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["

Categorical Variables

\n","\n","

These are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. The categorical variables can have the type \"object\" or \"int64\". A good way to visualize categorical variables is by using boxplots.

\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's look at the relationship between \"body-style\" and \"price\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

We see that the distributions of price between the different body-style categories have a significant overlap, so body-style would not be a good predictor of price. Let's examine engine \"engine-location\" and \"price\":

\n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

Here we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price.

\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's examine \"drive-wheels\" and \"price\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# drive-wheels\n"]},{"cell_type":"markdown","metadata":{},"source":["

Here we see that the distribution of price between the different drive-wheels categories differs. As such, drive-wheels could potentially be a predictor of price.

\n"]},{"cell_type":"markdown","metadata":{},"source":["

3. Descriptive Statistical Analysis

\n"]},{"cell_type":"markdown","metadata":{},"source":["

Let's first take a look at the variables by utilizing a description method.

\n","\n","

The describe function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.

\n","\n","This will show:\n","\n","
    \n","
  • the count of that variable
    • \n","
  • the mean
    • \n","
  • the standard deviation (std)
    • \n","
  • the minimum value
    • \n","
  • the IQR (Interquartile Range: 25%, 50% and 75%)
    • \n","
  • the maximum value
    • \n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["We can apply the method \"describe\" as follows:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["The default setting of \"describe\" skips variables of type object. We can apply the method \"describe\" on the variables of type 'object' as follows:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

      Value Counts

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Value counts is a good way of understanding how many units of each characteristic/variable we have. We can apply the \"value_counts\" method on the column \"drive-wheels\". Don’t forget the method \"value_counts\" only works on pandas series, not pandas dataframes. As a result, we only include one bracket df['drive-wheels'], not two brackets df[['drive-wheels']].

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df['drive-wheels'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["We can convert the series to a dataframe as follows:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df['drive-wheels'].value_counts().to_frame()"]},{"cell_type":"markdown","metadata":{},"source":["Let's repeat the above steps but save the results to the dataframe \"drive_wheels_counts\" and rename the column 'drive-wheels' to 'value_counts'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\n","drive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\n","drive_wheels_counts"]},{"cell_type":"markdown","metadata":{},"source":["Now let's rename the index to 'drive-wheels':\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["drive_wheels_counts.index.name = 'drive-wheels'\n","drive_wheels_counts"]},{"cell_type":"markdown","metadata":{},"source":["We can repeat the above process for the variable 'engine-location'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# engine-location as variable\n","engine_loc_counts = df['engine-location'].value_counts().to_frame()\n","engine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\n","engine_loc_counts.index.name = 'engine-location'\n","engine_loc_counts.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["

      After examining the value counts of the engine location, we see that engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, so this result is skewed. Thus, we are not able to draw any conclusions about the engine location.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      4. Basics of Grouping

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      The \"groupby\" method groups data by different categories. The data is grouped based on one or several variables, and analysis is performed on the individual groups.

      \n","\n","

      For example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df['drive-wheels'].unique()"]},{"cell_type":"markdown","metadata":{},"source":["

      If we want to know, on average, which type of drive wheel is most valuable, we can group \"drive-wheels\" and then average them.

      \n","\n","

      We can select the columns 'drive-wheels', 'body-style' and 'price', then assign it to the variable \"df_group_one\".

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df_group_one = df[['drive-wheels','body-style','price']]"]},{"cell_type":"markdown","metadata":{},"source":["We can then calculate the average price for each of the different categories of data.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# grouping results\n","df_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\n","df_group_one"]},{"cell_type":"markdown","metadata":{},"source":["

      From our data, it seems rear-wheel drive vehicles are, on average, the most expensive, while 4-wheel and front-wheel are approximately the same in price.

      \n","\n","

      You can also group by multiple variables. For example, let's group by both 'drive-wheels' and 'body-style'. This groups the dataframe by the unique combination of 'drive-wheels' and 'body-style'. We can store the results in the variable 'grouped_test1'.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# grouping results\n","df_gptest = df[['drive-wheels','body-style','price']]\n","grouped_test1 = df_gptest.groupby(['drive-wheels','body-style'],as_index=False).mean()\n","grouped_test1"]},{"cell_type":"markdown","metadata":{},"source":["

      This grouped data is much easier to visualize when it is made into a pivot table. A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. We can convert the dataframe to a pivot table using the method \"pivot\" to create a pivot table from the groups.

      \n","\n","

      In this case, we will leave the drive-wheels variable as the rows of the table, and pivot body-style to become the columns of the table:

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["grouped_pivot = grouped_test1.pivot(index='drive-wheels',columns='body-style')\n","grouped_pivot"]},{"cell_type":"markdown","metadata":{},"source":["

      Often, we won't have data for some of the pivot cells. We can fill these missing cells with the value 0, but any other value could potentially be used as well. It should be mentioned that missing data is quite a complex subject and is an entire course on its own.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":["grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\n","grouped_pivot"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question 4:

      \n","\n","

      Use the \"groupby\" function to find the average \"price\" of each car based on \"body-style\".

      \n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","# grouping results\n","df_gptest2 = df[['body-style','price']]\n","grouped_test_bodystyle = df_gptest2.groupby(['body-style'],as_index= False).mean()\n","grouped_test_bodystyle\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["If you did not import \"pyplot\", let's do it again.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","%matplotlib inline "]},{"cell_type":"markdown","metadata":{},"source":["

      Variables: Drive Wheels and Body Style vs. Price

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's use a heat map to visualize the relationship between Body Style vs Price.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#use the grouped results\n","plt.pcolor(grouped_pivot, cmap='RdBu')\n","plt.colorbar()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["

      The heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' on the vertical and horizontal axis, respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'.

      \n","\n","

      The default labels convey no useful information to us. Let's change that:

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = plt.subplots()\n","im = ax.pcolor(grouped_pivot, cmap='RdBu')\n","\n","#label names\n","row_labels = grouped_pivot.columns.levels[1]\n","col_labels = grouped_pivot.index\n","\n","#move ticks and labels to the center\n","ax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\n","ax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n","\n","#insert labels\n","ax.set_xticklabels(row_labels, minor=False)\n","ax.set_yticklabels(col_labels, minor=False)\n","\n","#rotate label if too long\n","plt.xticks(rotation=90)\n","\n","fig.colorbar(im)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["

      Visualization is very important in data science, and Python visualization packages provide great freedom. We will go more in-depth in a separate Python visualizations course.

      \n","\n","

      The main question we want to answer in this module is, \"What are the main characteristics which have the most impact on the car price?\".

      \n","\n","

      To get a better measure of the important characteristics, we look at the correlation of these variables with the car price. In other words: how is the car price dependent on this variable?

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      5. Correlation and Causation

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Correlation: a measure of the extent of interdependence between variables.

      \n","\n","

      Causation: the relationship between cause and effect between two variables.

      \n","\n","

      It is important to know the difference between these two. Correlation does not imply causation. Determining correlation is much simpler the determining causation as causation may require independent experimentation.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Pearson Correlation

      \n","

      The Pearson Correlation measures the linear dependence between two variables X and Y.

      \n","

      The resulting coefficient is a value between -1 and 1 inclusive, where:

      \n","
        \n","
      • 1: Perfect positive linear correlation.
        • \n","
      • 0: No linear correlation, the two variables most likely do not affect each other.
        • \n","
      • -1: Perfect negative linear correlation.
        • \n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Pearson Correlation is the default method of the function \"corr\". Like before, we can calculate the Pearson Correlation of the of the 'int64' or 'float64' variables.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.corr()"]},{"cell_type":"markdown","metadata":{},"source":["Sometimes we would like to know the significant of the correlation estimate.\n"]},{"cell_type":"markdown","metadata":{},"source":["P-value\n","\n","

      What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.

      \n","\n","By convention, when the\n","\n","
        \n","
      • p-value is $<$ 0.001: we say there is strong evidence that the correlation is significant.
        • \n","
      • the p-value is $<$ 0.05: there is moderate evidence that the correlation is significant.
        • \n","
      • the p-value is $<$ 0.1: there is weak evidence that the correlation is significant.
        • \n","
      • the p-value is $>$ 0.1: there is no evidence that the correlation is significant.
        • \n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["We can obtain this information using \"stats\" module in the \"scipy\" library.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from scipy import stats"]},{"cell_type":"markdown","metadata":{},"source":["

      Wheel-Base vs. Price

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","

      Since the p-value is $<$ 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Horsepower vs. Price

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","\n","

      Since the p-value is $<$ 0.001, the correlation between horsepower and price is statistically significant, and the linear relationship is quite strong (~0.809, close to 1).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Length vs. Price

      \n","\n","Let's calculate the Pearson Correlation Coefficient and P-value of 'length' and 'price'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","

      Since the p-value is $<$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Width vs. Price

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the Pearson Correlation Coefficient and P-value of 'width' and 'price':\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value ) "]},{"cell_type":"markdown","metadata":{},"source":["#### Conclusion:\n","\n","Since the p-value is < 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (\\~0.751).\n"]},{"cell_type":"markdown","metadata":{},"source":["### Curb-Weight vs. Price\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the Pearson Correlation Coefficient and P-value of 'curb-weight' and 'price':\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\n","print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","

      Since the p-value is $<$ 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Engine-Size vs. Price

      \n","\n","Let's calculate the Pearson Correlation Coefficient and P-value of 'engine-size' and 'price':\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","\n","

      Since the p-value is $<$ 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Bore vs. Price

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the Pearson Correlation Coefficient and P-value of 'bore' and 'price':\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","

      Since the p-value is $<$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["We can relate the process for each 'city-mpg' and 'highway-mpg':\n"]},{"cell_type":"markdown","metadata":{},"source":["

      City-mpg vs. Price

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\n","print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion:

      \n","

      Since the p-value is $<$ 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of about -0.687 shows that the relationship is negative and moderately strong.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Highway-mpg vs. Price

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\n","print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) "]},{"cell_type":"markdown","metadata":{},"source":["#### Conclusion:\n","\n","Since the p-value is < 0.001, the correlation between highway-mpg and price is statistically significant, and the coefficient of about -0.705 shows that the relationship is negative and moderately strong.\n"]},{"cell_type":"markdown","metadata":{},"source":["

      6. ANOVA

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      ANOVA: Analysis of Variance

      \n","

      The Analysis of Variance (ANOVA) is a statistical method used to test whether there are significant differences between the means of two or more groups. ANOVA returns two parameters:

      \n","\n","

      F-test score: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.

      \n","\n","

      P-value: P-value tells how statistically significant our calculated score value is.

      \n","\n","

      If our price variable is strongly correlated with the variable we are analyzing, we expect ANOVA to return a sizeable F-test score and a small p-value.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Drive Wheels

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand.

      \n","\n","

      To see if different types of 'drive-wheels' impact 'price', we group the data.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["grouped_test2=df_gptest[['drive-wheels', 'price']].groupby(['drive-wheels'])\n","grouped_test2.head(2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df_gptest"]},{"cell_type":"markdown","metadata":{},"source":["We can obtain the values of the method group using the method \"get_group\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["grouped_test2.get_group('4wd')['price']"]},{"cell_type":"markdown","metadata":{},"source":["We can use the function 'f_oneway' in the module 'stats' to obtain the F-test score and P-value.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# ANOVA\n","f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'], grouped_test2.get_group('4wd')['price']) \n"," \n","print( \"ANOVA results: F=\", f_val, \", P =\", p_val) "]},{"cell_type":"markdown","metadata":{},"source":["This is a great result with a large F-test score showing a strong correlation and a P-value of almost 0 implying almost certain statistical significance. But does this mean all three tested groups are all this highly correlated?\n","\n","Let's examine them separately.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### fwd and rwd\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price']) \n"," \n","print( \"ANOVA results: F=\", f_val, \", P =\", p_val )"]},{"cell_type":"markdown","metadata":{},"source":["Let's examine the other groups.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### 4wd and rwd\n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":["f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('rwd')['price']) \n"," \n","print( \"ANOVA results: F=\", f_val, \", P =\", p_val) "]},{"cell_type":"markdown","metadata":{},"source":["

      4wd and fwd

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('fwd')['price']) \n"," \n","print(\"ANOVA results: F=\", f_val, \", P =\", p_val) "]},{"cell_type":"markdown","metadata":{},"source":["We notice that ANOVA for the categories `4wd` and `fwd` yields a high p-value > 0.1, so the calculated F-test score is not very statistically significant. This suggests we can't reject the assumption that the means of these two groups are the same, or, in other words, we can't conclude the difference in correlation to be significant.\n"]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion: Important Variables

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:

      \n","\n","Continuous numerical variables:\n","\n","
        \n","
      • Length
        • \n","
      • Width
        • \n","
      • Curb-weight
        • \n","
      • Engine-size
        • \n","
      • Horsepower
        • \n","
      • City-mpg
        • \n","
      • Highway-mpg
        • \n","
      • Wheel-base
        • \n","
      • Bore
        • \n","
      \n","\n","Categorical variables:\n","\n","
        \n","
      • Drive-wheels
        • \n","
      \n","\n","

      As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["### Thank you for completing this lab!\n","\n","## Author\n","\n","Joseph Santarcangelo\n","\n","### Other Contributors\n","\n","Mahdi Noorian PhD\n","\n","Bahare Talayian\n","\n","Eric Xiao\n","\n","Steven Dong\n","\n","Parizad\n","\n","Hima Vasudevan\n","\n","Fiorella Wenver\n","\n","Yi Yao.\n","\n","## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","| ----------------- | ------- | ---------- | ---------------------------------- |\n","| 2020-10-30 | 2.1 | Lakshmi | changed URL of csv |\n","| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n","\n","
      \n","\n","##

      © IBM Corporation 2020. All rights reserved.

      \n"]}],"metadata":{"anaconda-cloud":{},"kernelspec":{"display_name":"my_environment","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]"},"vscode":{"interpreter":{"hash":"1468b46fbbf336438d2a9c265dd02fb55be29fdd63084011561d11061c2774ae"}}},"nbformat":4,"nbformat_minor":4} diff --git a/DA0101EN-4-Review-Model-Development.ipynb b/DA0101EN-4-Review-Model-Development.ipynb new file mode 100644 index 0000000..8810cd9 --- /dev/null +++ b/DA0101EN-4-Review-Model-Development.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["

      \n"," \n"," \"Skills\n"," \n","

      \n","\n","# Model Development\n","\n","Estimated time needed: **30** minutes\n","\n","## Objectives\n","\n","After completing this lab you will be able to:\n","\n","* Develop prediction models\n"]},{"cell_type":"markdown","metadata":{},"source":["

      In this section, we will develop several models that will predict the price of the car using the variables or features. This is just an estimate but should give us an objective idea of how much the car should cost.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Some questions we want to ask in this module\n","\n","
        \n","
      • Do I know if the dealer is offering fair value for my trade-in?
        • \n","
      • Do I know if I put a fair value on my car?
        • \n","
      \n","

      In data analytics, we often use Model Development to help us predict future observations from the data we have.

      \n","\n","

      A model will help us understand the exact relationship between different variables and how these variables are used to predict the result.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Setup

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Import libraries:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#install specific version of libraries used in lab\n","#! mamba install pandas==1.3.3-y\n","#! mamba install numpy=1.21.2-y\n","#! mamba install sklearn=0.20.1-y"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt"]},{"cell_type":"markdown","metadata":{},"source":["Load the data and store it in dataframe `df`:\n"]},{"cell_type":"markdown","metadata":{},"source":["This dataset was hosted on IBM Cloud object. Click HERE for free storage.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# path of data \n","path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'\n","df = pd.read_csv(path)\n","df.head()"]},{"cell_type":"markdown","metadata":{},"source":["

      1. Linear Regression and Multiple Linear Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Linear Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      One example of a Data Model that we will be using is:

      \n","Simple Linear Regression\n","\n","
      \n","

      Simple Linear Regression is a method to help us understand the relationship between two variables:

      \n","
        \n","
      • The predictor/independent variable (X)
        • \n","
      • The response/dependent variable (that we want to predict)(Y)
        • \n","
      \n","\n","

      The result of Linear Regression is a linear function that predicts the response (dependent) variable as a function of the predictor (independent) variable.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["$$\n","Y: Response \\ Variable\\\\\\\\\n","X: Predictor \\ Variables\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["Linear Function\n","$$\n","Yhat = a + b X\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["
        \n","
      • a refers to the intercept of the regression line, in other words: the value of Y when X is 0
        • \n","
      • b refers to the slope of the regression line, in other words: the value with which Y changes when X increases by 1 unit
        • \n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Let's load the modules for linear regression:

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import LinearRegression"]},{"cell_type":"markdown","metadata":{},"source":["

      Create the linear regression object:

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm = LinearRegression()\n","lm"]},{"cell_type":"markdown","metadata":{},"source":["

      How could \"highway-mpg\" help us predict car price?

      \n"]},{"cell_type":"markdown","metadata":{},"source":["For this example, we want to look at how highway-mpg can help us predict car price.\n","Using simple linear regression, we will create a linear function with \"highway-mpg\" as the predictor variable and the \"price\" as the response variable.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["X = df[['highway-mpg']]\n","Y = df['price']"]},{"cell_type":"markdown","metadata":{},"source":["Fit the linear model using highway-mpg:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm.fit(X,Y)"]},{"cell_type":"markdown","metadata":{},"source":["We can output a prediction:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Yhat=lm.predict(X)\n","Yhat[0:5] "]},{"cell_type":"markdown","metadata":{},"source":["

      What is the value of the intercept (a)?

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm.intercept_"]},{"cell_type":"markdown","metadata":{},"source":["

      What is the value of the slope (b)?

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":["lm.coef_"]},{"cell_type":"markdown","metadata":{},"source":["

      What is the final estimated linear model we get?

      \n"]},{"cell_type":"markdown","metadata":{},"source":["As we saw above, we should get a final linear model with the structure:\n"]},{"cell_type":"markdown","metadata":{},"source":["$$\n","Yhat = a + b X\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["Plugging in the actual values we get:\n"]},{"cell_type":"markdown","metadata":{},"source":["Price = 38423.31 - 821.73 x highway-mpg\n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #1 a):

      \n","\n","Create a linear regression object called \"lm1\".\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","lm1 = LinearRegression()\n","lm1\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #1 b):

      \n","\n","Train the model using \"engine-size\" as the independent variable and \"price\" as the dependent variable?\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","lm1.fit(df[['engine-size']], df[['price']])\n","lm1\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #1 c):

      \n","\n","Find the slope and intercept of the model.\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Slope

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Intercept

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","# Slope \n","lm1.coef_\n","\n","# Intercept\n","lm1.intercept_\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #1 d):

      \n","\n","What is the equation of the predicted line? You can use x and yhat or \"engine-size\" or \"price\".\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","# using X and Y \n","Yhat=-7963.34 + 166.86*X\n","\n","Price=-7963.34 + 166.86*engine-size\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Multiple Linear Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      What if we want to predict car price using more than one variable?

      \n","\n","

      If we want to use more variables in our model to predict car price, we can use Multiple Linear Regression.\n","Multiple Linear Regression is very similar to Simple Linear Regression, but this method is used to explain the relationship between one continuous response (dependent) variable and two or more predictor (independent) variables.\n","Most of the real-world regression models involve multiple predictors. We will illustrate the structure by using four predictor variables, but these results can generalize to any integer:

      \n"]},{"cell_type":"markdown","metadata":{},"source":["$$\n","Y: Response \\ Variable\\\\\\\\\n","X\\_1 :Predictor\\ Variable \\ 1\\\\\\\\\n","X\\_2: Predictor\\ Variable \\ 2\\\\\\\\\n","X\\_3: Predictor\\ Variable \\ 3\\\\\\\\\n","X\\_4: Predictor\\ Variable \\ 4\\\\\\\\\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["$$\n","a: intercept\\\\\\\\\n","b\\_1 :coefficients \\ of\\ Variable \\ 1\\\\\\\\\n","b\\_2: coefficients \\ of\\ Variable \\ 2\\\\\\\\\n","b\\_3: coefficients \\ of\\ Variable \\ 3\\\\\\\\\n","b\\_4: coefficients \\ of\\ Variable \\ 4\\\\\\\\\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["The equation is given by:\n"]},{"cell_type":"markdown","metadata":{},"source":["$$\n","Yhat = a + b\\_1 X\\_1 + b\\_2 X\\_2 + b\\_3 X\\_3 + b\\_4 X\\_4\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["

      From the previous section we know that other good predictors of price could be:

      \n","
        \n","
      • Horsepower
        • \n","
      • Curb-weight
        • \n","
      • Engine-size
        • \n","
      • Highway-mpg
        • \n","
      \n","Let's develop a model using these variables as the predictor variables.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Z = df[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']]"]},{"cell_type":"markdown","metadata":{},"source":["Fit the linear model using the four above-mentioned variables.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm.fit(Z, df['price'])"]},{"cell_type":"markdown","metadata":{},"source":["What is the value of the intercept(a)?\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm.intercept_"]},{"cell_type":"markdown","metadata":{},"source":["What are the values of the coefficients (b1, b2, b3, b4)?\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm.coef_"]},{"cell_type":"markdown","metadata":{},"source":["What is the final estimated linear model that we get?\n"]},{"cell_type":"markdown","metadata":{},"source":["As we saw above, we should get a final linear function with the structure:\n","\n","$$\n","Yhat = a + b\\_1 X\\_1 + b\\_2 X\\_2 + b\\_3 X\\_3 + b\\_4 X\\_4\n","$$\n","\n","What is the linear function we get in this example?\n"]},{"cell_type":"markdown","metadata":{},"source":["Price = -15678.742628061467 + 52.65851272 x horsepower + 4.69878948 x curb-weight + 81.95906216 x engine-size + 33.58258185 x highway-mpg\n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #2 a):

      \n","Create and train a Multiple Linear Regression model \"lm2\" where the response variable is \"price\", and the predictor variable is \"normalized-losses\" and \"highway-mpg\".\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","lm2 = LinearRegression()\n","lm2.fit(df[['normalized-losses' , 'highway-mpg']],df['price'])\n","\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #2 b):

      \n","Find the coefficient of the model.\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","lm2.coef_\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      2. Model Evaluation Using Visualization

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Now that we've developed some models, how do we evaluate our models and choose the best one? One way to do this is by using a visualization.\n"]},{"cell_type":"markdown","metadata":{},"source":["Import the visualization package, seaborn:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# import the visualization package: seaborn\n","import seaborn as sns\n","%matplotlib inline "]},{"cell_type":"markdown","metadata":{},"source":["

      Regression Plot

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      When it comes to simple linear regression, an excellent way to visualize the fit of our model is by using regression plots.

      \n","\n","

      This plot will show a combination of a scattered data points (a scatterplot), as well as the fitted linear regression line going through the data. This will give us a reasonable estimate of the relationship between the two variables, the strength of the correlation, as well as the direction (positive or negative correlation).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's visualize **highway-mpg** as potential predictor variable of price:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["width = 12\n","height = 10\n","plt.figure(figsize=(width, height))\n","sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)\n","plt.ylim(0,)"]},{"cell_type":"markdown","metadata":{},"source":["

      We can see from this plot that price is negatively correlated to highway-mpg since the regression slope is negative.\n","\n","One thing to keep in mind when looking at a regression plot is to pay attention to how scattered the data points are around the regression line. This will give you a good indication of the variance of the data and whether a linear model would be the best fit or not. If the data is too far off from the line, this linear model might not be the best model for this data.\n","\n","Let's compare this plot to the regression plot of \"peak-rpm\".

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize=(width, height))\n","sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)\n","plt.ylim(0,)"]},{"cell_type":"markdown","metadata":{},"source":["

      Comparing the regression plot of \"peak-rpm\" and \"highway-mpg\", we see that the points for \"highway-mpg\" are much closer to the generated line and, on average, decrease. The points for \"peak-rpm\" have more spread around the predicted line and it is much harder to determine if the points are decreasing or increasing as the \"peak-rpm\" increases.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #3:

      \n","Given the regression plots above, is \"peak-rpm\" or \"highway-mpg\" more strongly correlated with \"price\"? Use the method \".corr()\" to verify your answer.\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","# The variable \"highway-mpg\" has a stronger correlation with \"price\", it is approximate -0.704692 compared to \"peak-rpm\" which is approximate -0.101616. You can verify it using the following command:\n","\n","df[[\"peak-rpm\",\"highway-mpg\",\"price\"]].corr()\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Residual Plot

      \n","\n","

      A good way to visualize the variance of the data is to use a residual plot.

      \n","\n","

      What is a residual?

      \n","\n","

      The difference between the observed value (y) and the predicted value (Yhat) is called the residual (e). When we look at a regression plot, the residual is the distance from the data point to the fitted regression line.

      \n","\n","

      So what is a residual plot?

      \n","\n","

      A residual plot is a graph that shows the residuals on the vertical y-axis and the independent variable on the horizontal x-axis.

      \n","\n","

      What do we pay attention to when looking at a residual plot?

      \n","\n","

      We look at the spread of the residuals:

      \n","\n","

      - If the points in a residual plot are randomly spread out around the x-axis, then a linear model is appropriate for the data.\n","\n","Why is that? Randomly spread out residuals means that the variance is constant, and thus the linear model is a good fit for this data.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["width = 12\n","height = 10\n","plt.figure(figsize=(width, height))\n","sns.residplot(x=df['highway-mpg'], y=df['price'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["What is this plot telling us?\n","\n","

      We can see from this residual plot that the residuals are not randomly spread around the x-axis, leading us to believe that maybe a non-linear model is more appropriate for this data.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Multiple Linear Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      How do we visualize a model for Multiple Linear Regression? This gets a bit more complicated because you can't visualize it with regression or residual plot.

      \n","\n","

      One way to look at the fit of the model is by looking at the distribution plot. We can look at the distribution of the fitted values that result from the model and compare it to the distribution of the actual values.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["First, let's make a prediction:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Y_hat = lm.predict(Z)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize=(width, height))\n","\n","\n","ax1 = sns.distplot(df['price'], hist=False, color=\"r\", label=\"Actual Value\")\n","sns.distplot(Y_hat, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n","\n","\n","plt.title('Actual vs Fitted Values for Price')\n","plt.xlabel('Price (in dollars)')\n","plt.ylabel('Proportion of Cars')\n","\n","plt.show()\n","plt.close()"]},{"cell_type":"markdown","metadata":{},"source":["

      We can see that the fitted values are reasonably close to the actual values since the two distributions overlap a bit. However, there is definitely some room for improvement.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      3. Polynomial Regression and Pipelines

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Polynomial regression is a particular case of the general linear regression model or multiple linear regression models.

      \n","

      We get non-linear relationships by squaring or setting higher-order terms of the predictor variables.

      \n","\n","

      There are different orders of polynomial regression:

      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Quadratic - 2nd Order
      \n","$$\n","Yhat = a + b_1 X +b_2 X^2 \n","$$\n","\n","
      Cubic - 3rd Order
      \n","$$\n","Yhat = a + b_1 X +b_2 X^2 +b_3 X^3\\\\\\\\\n","$$\n","\n","
      Higher-Order:
      \n","$$\n","Y = a + b_1 X +b_2 X^2 +b_3 X^3 ....\\\\\\\\\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["

      We saw earlier that a linear model did not provide the best fit while using \"highway-mpg\" as the predictor variable. Let's see if we can try fitting a polynomial model to the data instead.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      We will use the following function to plot the data:

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def PlotPolly(model, independent_variable, dependent_variabble, Name):\n"," x_new = np.linspace(15, 55, 100)\n"," y_new = model(x_new)\n","\n"," plt.plot(independent_variable, dependent_variabble, '.', x_new, y_new, '-')\n"," plt.title('Polynomial Fit with Matplotlib for Price ~ Length')\n"," ax = plt.gca()\n"," ax.set_facecolor((0.898, 0.898, 0.898))\n"," fig = plt.gcf()\n"," plt.xlabel(Name)\n"," plt.ylabel('Price of Cars')\n","\n"," plt.show()\n"," plt.close()"]},{"cell_type":"markdown","metadata":{},"source":["Let's get the variables:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["x = df['highway-mpg']\n","y = df['price']"]},{"cell_type":"markdown","metadata":{},"source":["Let's fit the polynomial using the function polyfit, then use the function poly1d to display the polynomial function.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Here we use a polynomial of the 3rd order (cubic) \n","f = np.polyfit(x, y, 3)\n","p = np.poly1d(f)\n","print(p)"]},{"cell_type":"markdown","metadata":{},"source":["Let's plot the function:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["PlotPolly(p, x, y, 'highway-mpg')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["np.polyfit(x, y, 3)"]},{"cell_type":"markdown","metadata":{},"source":["

      We can already see from plotting that this polynomial model performs better than the linear model. This is because the generated polynomial function \"hits\" more of the data points.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4:

      \n","Create 11 order polynomial model with the variables x and y from above.\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","# Here we use a polynomial of the 11rd order (cubic) \n","f1 = np.polyfit(x, y, 11)\n","p1 = np.poly1d(f1)\n","print(p1)\n","PlotPolly(p1,x,y, 'Highway MPG')\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      The analytical expression for Multivariate Polynomial function gets complicated. For example, the expression for a second-order (degree=2) polynomial with two variables is given by:

      \n"]},{"cell_type":"markdown","metadata":{},"source":["$$\n","Yhat = a + b\\_1 X\\_1 +b\\_2 X\\_2 +b\\_3 X\\_1 X\\_2+b\\_4 X\\_1^2+b\\_5 X\\_2^2\n","$$\n"]},{"cell_type":"markdown","metadata":{},"source":["We can perform a polynomial transform on multiple features. First, we import the module:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.preprocessing import PolynomialFeatures"]},{"cell_type":"markdown","metadata":{},"source":["We create a PolynomialFeatures object of degree 2:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pr=PolynomialFeatures(degree=2)\n","pr"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Z_pr=pr.fit_transform(Z)"]},{"cell_type":"markdown","metadata":{},"source":["In the original data, there are 201 samples and 4 features.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Z.shape"]},{"cell_type":"markdown","metadata":{},"source":["After the transformation, there are 201 samples and 15 features.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Z_pr.shape"]},{"cell_type":"markdown","metadata":{},"source":["

      Pipeline

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Data Pipelines simplify the steps of processing the data. We use the module Pipeline to create a pipeline. We also use StandardScaler as a step in our pipeline.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import StandardScaler"]},{"cell_type":"markdown","metadata":{},"source":["We create the pipeline by creating a list of tuples including the name of the model or estimator and its corresponding constructor.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]"]},{"cell_type":"markdown","metadata":{},"source":["We input the list as an argument to the pipeline constructor:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pipe=Pipeline(Input)\n","pipe"]},{"cell_type":"markdown","metadata":{},"source":["First, we convert the data type Z to type float to avoid conversion warnings that may appear as a result of StandardScaler taking float inputs.\n","\n","Then, we can normalize the data, perform a transform and fit the model simultaneously.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Z = Z.astype(float)\n","pipe.fit(Z,y)"]},{"cell_type":"markdown","metadata":{},"source":["Similarly, we can normalize the data, perform a transform and produce a prediction simultaneously.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ypipe=pipe.predict(Z)\n","ypipe[0:4]"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #5:

      \n","Create a pipeline that standardizes the data, then produce a prediction using a linear regression model using the features Z and target y.\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","Input=[('scale',StandardScaler()),('model',LinearRegression())]\n","\n","pipe=Pipeline(Input)\n","\n","pipe.fit(Z,y)\n","\n","ypipe=pipe.predict(Z)\n","ypipe[0:10]\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      4. Measures for In-Sample Evaluation

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      When evaluating our models, not only do we want to visualize the results, but we also want a quantitative measure to determine how accurate the model is.

      \n","\n","

      Two very important measures that are often used in Statistics to determine the accuracy of a model are:

      \n","
        \n","
      • R^2 / R-squared
        • \n","
      • Mean Squared Error (MSE)
        • \n","
      \n","\n","R-squared\n","\n","

      R squared, also known as the coefficient of determination, is a measure to indicate how close the data is to the fitted regression line.

      \n","\n","

      The value of the R-squared is the percentage of variation of the response variable (y) that is explained by a linear model.

      \n","\n","Mean Squared Error (MSE)\n","\n","

      The Mean Squared Error measures the average of the squares of errors. That is, the difference between actual value (y) and the estimated value (ŷ).

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Model 1: Simple Linear Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the R^2:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true},"outputs":[],"source":["#highway_mpg_fit\n","lm.fit(X, Y)\n","# Find the R^2\n","print('The R-square is: ', lm.score(X, Y))"]},{"cell_type":"markdown","metadata":{},"source":["We can say that \\~49.659% of the variation of the price is explained by this simple linear model \"horsepower_fit\".\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the MSE:\n"]},{"cell_type":"markdown","metadata":{},"source":["We can predict the output i.e., \"yhat\" using the predict method, where X is the input variable:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Yhat=lm.predict(X)\n","print('The output of the first four predicted value is: ', Yhat[0:4])"]},{"cell_type":"markdown","metadata":{},"source":["Let's import the function mean_squared_error from the module metrics:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.metrics import mean_squared_error"]},{"cell_type":"markdown","metadata":{},"source":["We can compare the predicted results with the actual results:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mse = mean_squared_error(df['price'], Yhat)\n","print('The mean square error of price and predicted value is: ', mse)"]},{"cell_type":"markdown","metadata":{},"source":["

      Model 2: Multiple Linear Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the R^2:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# fit the model \n","lm.fit(Z, df['price'])\n","# Find the R^2\n","print('The R-square is: ', lm.score(Z, df['price']))"]},{"cell_type":"markdown","metadata":{},"source":["We can say that \\~80.896 % of the variation of price is explained by this multiple linear regression \"multi_fit\".\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the MSE.\n"]},{"cell_type":"markdown","metadata":{},"source":["We produce a prediction:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Y_predict_multifit = lm.predict(Z)"]},{"cell_type":"markdown","metadata":{},"source":["We compare the predicted results with the actual results:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["print('The mean square error of price and predicted value using multifit is: ', \\\n"," mean_squared_error(df['price'], Y_predict_multifit))"]},{"cell_type":"markdown","metadata":{},"source":["

      Model 3: Polynomial Fit

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the R^2.\n"]},{"cell_type":"markdown","metadata":{},"source":["Let’s import the function r2\\_score from the module metrics as we are using a different function.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.metrics import r2_score"]},{"cell_type":"markdown","metadata":{},"source":["We apply the function to get the value of R^2:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["r_squared = r2_score(y, p(x))\n","print('The R-square value is: ', r_squared)"]},{"cell_type":"markdown","metadata":{},"source":["We can say that \\~67.419 % of the variation of price is explained by this polynomial fit.\n"]},{"cell_type":"markdown","metadata":{},"source":["

      MSE

      \n"]},{"cell_type":"markdown","metadata":{},"source":["We can also calculate the MSE:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mean_squared_error(df['price'], p(x))"]},{"cell_type":"markdown","metadata":{},"source":["

      5. Prediction and Decision Making

      \n","

      Prediction

      \n","\n","

      In the previous section, we trained the model using the method fit. Now we will use the method predict to produce a prediction. Lets import pyplot for plotting; we will also be using some functions from numpy.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","%matplotlib inline "]},{"cell_type":"markdown","metadata":{},"source":["Create a new input:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["new_input=np.arange(1, 100, 1).reshape(-1, 1)"]},{"cell_type":"markdown","metadata":{},"source":["Fit the model:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lm.fit(X, Y)\n","lm"]},{"cell_type":"markdown","metadata":{},"source":["Produce a prediction:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["yhat=lm.predict(new_input)\n","yhat[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["We can plot the data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.plot(new_input, yhat)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["

      Decision Making: Determining a Good Model Fit

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Now that we have visualized the different models, and generated the R-squared and MSE values for the fits, how do we determine a good model fit?\n","

        \n","
      • What is a good R-squared value?
        • \n","
      \n","

      \n","\n","

      When comparing models, the model with the higher R-squared value is a better fit for the data.\n","

        \n","
      • What is a good MSE?
        • \n","
      \n","

      \n","\n","

      When comparing models, the model with the smallest MSE value is a better fit for the data.

      \n","\n","

      Let's take a look at the values for the different models.

      \n","

      Simple Linear Regression: Using Highway-mpg as a Predictor Variable of Price.\n","

        \n","
      • R-squared: 0.49659118843391759
        • \n","
      • MSE: 3.16 x10^7
        • \n","
      \n","

      \n","\n","

      Multiple Linear Regression: Using Horsepower, Curb-weight, Engine-size, and Highway-mpg as Predictor Variables of Price.\n","

        \n","
      • R-squared: 0.80896354913783497
        • \n","
      • MSE: 1.2 x10^7
        • \n","
      \n","

      \n","\n","

      Polynomial Fit: Using Highway-mpg as a Predictor Variable of Price.\n","

        \n","
      • R-squared: 0.6741946663906514
        • \n","
      • MSE: 2.05 x 10^7
        • \n","
      \n","

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Simple Linear Regression Model (SLR) vs Multiple Linear Regression Model (MLR)

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Usually, the more variables you have, the better your model is at predicting, but this is not always true. Sometimes you may not have enough data, you may run into numerical problems, or many of the variables may not be useful and even act as noise. As a result, you should always check the MSE and R^2.

      \n","\n","

      In order to compare the results of the MLR vs SLR models, we look at a combination of both the R-squared and MSE to make the best conclusion about the fit of the model.\n","

        \n","
      • MSE: The MSE of SLR is 3.16x10^7 while MLR has an MSE of 1.2 x10^7. The MSE of MLR is much smaller.
        • \n","
      • R-squared: In this case, we can also see that there is a big difference between the R-squared of the SLR and the R-squared of the MLR. The R-squared for the SLR (~0.497) is very small compared to the R-squared for the MLR (~0.809).
        • \n","
      \n","

      \n","\n","This R-squared in combination with the MSE show that MLR seems like the better model fit in this case compared to SLR.\n"]},{"cell_type":"markdown","metadata":{},"source":["

      Simple Linear Model (SLR) vs. Polynomial Fit

      \n"]},{"cell_type":"markdown","metadata":{},"source":["
        \n","
      • MSE: We can see that Polynomial Fit brought down the MSE, since this MSE is smaller than the one from the SLR.
        • \n","
      • R-squared: The R-squared for the Polynomial Fit is larger than the R-squared for the SLR, so the Polynomial Fit also brought up the R-squared quite a bit.
        • \n","
      \n","

      Since the Polynomial Fit resulted in a lower MSE and a higher R-squared, we can conclude that this was a better fit model than the simple linear regression for predicting \"price\" with \"highway-mpg\" as a predictor variable.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Multiple Linear Regression (MLR) vs. Polynomial Fit

      \n"]},{"cell_type":"markdown","metadata":{},"source":["
        \n","
      • MSE: The MSE for the MLR is smaller than the MSE for the Polynomial Fit.
        • \n","
      • R-squared: The R-squared for the MLR is also much larger than for the Polynomial Fit.
        • \n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Conclusion

      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Comparing these three models, we conclude that the MLR model is the best model to be able to predict price from our dataset. This result makes sense since we have 27 variables in total and we know that more than one of those variables are potential predictors of the final car price.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["### Thank you for completing this lab!\n","\n","## Author\n","\n","Joseph Santarcangelo\n","\n","### Other Contributors\n","\n","Mahdi Noorian PhD\n","\n","Bahare Talayian\n","\n","Eric Xiao\n","\n","Steven Dong\n","\n","Parizad\n","\n","Hima Vasudevan\n","\n","Fiorella Wenver\n","\n","Yi Yao.\n","\n","## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","| ----------------- | ------- | ---------- | --------------------------------------------- |\n","| 2020-10-30 | 2.2 | Lakshmi | Changed url of csv |\n","| 2020-09-09 | 2.1 | Lakshmi | Fixes made in Polynomial Regression Equations |\n","| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n","\n","
      \n","\n","##

      © IBM Corporation 2020. All rights reserved.

      \n"]}],"metadata":{"kernelspec":{"display_name":"my_environment","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]"},"vscode":{"interpreter":{"hash":"1468b46fbbf336438d2a9c265dd02fb55be29fdd63084011561d11061c2774ae"}}},"nbformat":4,"nbformat_minor":4} diff --git a/DA0101EN-5-Review-Model-Evaluation-and-Refinement.ipynb b/DA0101EN-5-Review-Model-Evaluation-and-Refinement.ipynb new file mode 100644 index 0000000..b52d62d --- /dev/null +++ b/DA0101EN-5-Review-Model-Evaluation-and-Refinement.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["

      \n"," \n"," \"Skills\n"," \n","

      \n","\n","# Model Evaluation and Refinement\n","\n","Estimated time needed: **30** minutes\n","\n","## Objectives\n","\n","After completing this lab you will be able to:\n","\n","* Evaluate and refine prediction models\n"]},{"cell_type":"markdown","metadata":{},"source":["

      Table of Contents

      \n","\n"]},{"cell_type":"markdown","metadata":{},"source":["This dataset was hosted on IBM Cloud object. Click HERE for free storage.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#install specific version of libraries used in lab\n","#! mamba install pandas==1.3.3 -y\n","#! mamba install numpy=1.21.2 -y\n","#! mamba install sklearn=0.20.1 -y\n","#! mamba install ipywidgets=7.4.2 -y"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","\n","# Import clean data \n","path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/module_5_auto.csv'\n","df = pd.read_csv(path)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.to_csv('module_5_auto.csv')"]},{"cell_type":"markdown","metadata":{},"source":["First, let's only use numeric data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df=df._get_numeric_data()\n","df.head()"]},{"cell_type":"markdown","metadata":{},"source":["Libraries for plotting:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from ipywidgets import interact, interactive, fixed, interact_manual"]},{"cell_type":"markdown","metadata":{},"source":["

      Functions for Plotting

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n"," width = 12\n"," height = 10\n"," plt.figure(figsize=(width, height))\n","\n"," ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n"," ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n","\n"," plt.title(Title)\n"," plt.xlabel('Price (in dollars)')\n"," plt.ylabel('Proportion of Cars')\n","\n"," plt.show()\n"," plt.close()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n"," width = 12\n"," height = 10\n"," plt.figure(figsize=(width, height))\n"," \n"," \n"," #training data \n"," #testing data \n"," # lr: linear regression object \n"," #poly_transform: polynomial transformation object \n"," \n"," xmax=max([xtrain.values.max(), xtest.values.max()])\n","\n"," xmin=min([xtrain.values.min(), xtest.values.min()])\n","\n"," x=np.arange(xmin, xmax, 0.1)\n","\n","\n"," plt.plot(xtrain, y_train, 'ro', label='Training Data')\n"," plt.plot(xtest, y_test, 'go', label='Test Data')\n"," plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n"," plt.ylim([-10000, 60000])\n"," plt.ylabel('Price')\n"," plt.legend()"]},{"cell_type":"markdown","metadata":{},"source":["

      Part 1: Training and Testing

      \n","\n","

      An important step in testing your model is to split your data into training and testing data. We will place the target data price in a separate dataframe y_data:

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["y_data = df['price']"]},{"cell_type":"markdown","metadata":{},"source":["Drop price data in dataframe **x_data**:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["x_data=df.drop('price',axis=1)"]},{"cell_type":"markdown","metadata":{},"source":["Now, we randomly split our data into training and testing data using the function train_test_split.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","\n","x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n","\n","\n","print(\"number of test samples :\", x_test.shape[0])\n","print(\"number of training samples:\",x_train.shape[0])\n"]},{"cell_type":"markdown","metadata":{},"source":["The test_size parameter sets the proportion of data that is split into the testing set. In the above, the testing set is 10% of the total dataset.\n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #1):

      \n","\n","Use the function \"train_test_split\" to split up the dataset such that 40% of the data samples will be utilized for testing. Set the parameter \"random_state\" equal to zero. The output of the function should be the following: \"x_train1\" , \"x_test1\", \"y_train1\" and \"y_test1\".\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0) \n","print(\"number of test samples :\", x_test1.shape[0])\n","print(\"number of training samples:\",x_train1.shape[0])\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's import LinearRegression from the module linear_model.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import LinearRegression"]},{"cell_type":"markdown","metadata":{},"source":["We create a Linear Regression object:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lre=LinearRegression()"]},{"cell_type":"markdown","metadata":{},"source":["We fit the model using the feature \"horsepower\":\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lre.fit(x_train[['horsepower']], y_train)"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate the R^2 on the test data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lre.score(x_test[['horsepower']], y_test)"]},{"cell_type":"markdown","metadata":{},"source":["We can see the R^2 is much smaller using the test data compared to the training data.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lre.score(x_train[['horsepower']], y_train)"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #2):

      \n"," \n","Find the R^2 on the test data using 40% of the dataset for testing.\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0)\n","lre.fit(x_train1[['horsepower']],y_train1)\n","lre.score(x_test1[['horsepower']],y_test1)\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["Sometimes you do not have sufficient testing data; as a result, you may want to perform cross-validation. Let's go over several methods that you can use for cross-validation.\n"]},{"cell_type":"markdown","metadata":{},"source":["

      Cross-Validation Score

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's import model_selection from the module cross_val_score.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.model_selection import cross_val_score"]},{"cell_type":"markdown","metadata":{},"source":["We input the object, the feature (\"horsepower\"), and the target data (y_data). The parameter 'cv' determines the number of folds. In this case, it is 4.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=4)"]},{"cell_type":"markdown","metadata":{},"source":["The default scoring is R^2. Each element in the array has the average R^2 value for the fold:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Rcross"]},{"cell_type":"markdown","metadata":{},"source":["We can calculate the average and standard deviation of our estimate:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\" , Rcross.std())"]},{"cell_type":"markdown","metadata":{},"source":["We can use negative squared error as a score by setting the parameter 'scoring' metric to 'neg_mean_squared_error'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["-1 * cross_val_score(lre,x_data[['horsepower']], y_data,cv=4,scoring='neg_mean_squared_error')"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #3):

      \n"," \n","Calculate the average R^2 using two folds, then find the average R^2 for the second fold utilizing the \"horsepower\" feature: \n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","Rc=cross_val_score(lre,x_data[['horsepower']], y_data,cv=2)\n","Rc.mean()\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["You can also use the function 'cross_val_predict' to predict the output. The function splits up the data into the specified number of folds, with one fold for testing and the other folds are used for training. First, import the function:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.model_selection import cross_val_predict"]},{"cell_type":"markdown","metadata":{},"source":["We input the object, the feature \"horsepower\", and the target data y_data. The parameter 'cv' determines the number of folds. In this case, it is 4. We can produce an output:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["yhat = cross_val_predict(lre,x_data[['horsepower']], y_data,cv=4)\n","yhat[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["

      Part 2: Overfitting, Underfitting and Model Selection

      \n","\n","

      It turns out that the test data, sometimes referred to as the \"out of sample data\", is a much better measure of how well your model performs in the real world. One reason for this is overfitting.\n","\n","Let's go over some examples. It turns out these differences are more apparent in Multiple Linear Regression and Polynomial Regression so we will explore overfitting in that context.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's create Multiple Linear Regression objects and train the model using 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg' as features.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lr = LinearRegression()\n","lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)"]},{"cell_type":"markdown","metadata":{},"source":["Prediction using training data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n","yhat_train[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["Prediction using test data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n","yhat_test[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["Let's perform some model evaluation using our training and testing data separately. First, we import the seaborn and matplotlib library for plotting.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","%matplotlib inline\n","import seaborn as sns"]},{"cell_type":"markdown","metadata":{},"source":["Let's examine the distribution of the predicted values of the training data.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Title = 'Distribution Plot of Predicted Value Using Training Data vs Training Data Distribution'\n","DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"]},{"cell_type":"markdown","metadata":{},"source":["Figure 1: Plot of predicted values using the training data compared to the actual values of the training data.\n"]},{"cell_type":"markdown","metadata":{},"source":["So far, the model seems to be doing well in learning from the training dataset. But what happens when the model encounters new data from the testing dataset? When the model generates new values from the test data, we see the distribution of the predicted values is much different from the actual target values.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Title='Distribution Plot of Predicted Value Using Test Data vs Data Distribution of Test Data'\n","DistributionPlot(y_test,yhat_test,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)"]},{"cell_type":"markdown","metadata":{},"source":["Figure 2: Plot of predicted value using the test data compared to the actual values of the test data.\n"]},{"cell_type":"markdown","metadata":{},"source":["

      Comparing Figure 1 and Figure 2, it is evident that the distribution of the test data in Figure 1 is much better at fitting the data. This difference in Figure 2 is apparent in the range of 5000 to 15,000. This is where the shape of the distribution is extremely different. Let's see if polynomial regression also exhibits a drop in the prediction accuracy when analysing the test dataset.

      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.preprocessing import PolynomialFeatures"]},{"cell_type":"markdown","metadata":{},"source":["

      Overfitting

      \n","

      Overfitting occurs when the model fits the noise, but not the underlying process. Therefore, when testing your model using the test set, your model does not perform as well since it is modelling noise, not the underlying process that generated the relationship. Let's create a degree 5 polynomial model.

      \n"]},{"cell_type":"markdown","metadata":{},"source":["Let's use 55 percent of the data for training and the rest for testing:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)"]},{"cell_type":"markdown","metadata":{},"source":["We will perform a degree 5 polynomial transformation on the feature 'horsepower'.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pr = PolynomialFeatures(degree=5)\n","x_train_pr = pr.fit_transform(x_train[['horsepower']])\n","x_test_pr = pr.fit_transform(x_test[['horsepower']])\n","pr"]},{"cell_type":"markdown","metadata":{},"source":["Now, let's create a Linear Regression model \"poly\" and train it.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["poly = LinearRegression()\n","poly.fit(x_train_pr, y_train)"]},{"cell_type":"markdown","metadata":{},"source":["We can see the output of our model using the method \"predict.\" We assign the values to \"yhat\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["yhat = poly.predict(x_test_pr)\n","yhat[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["Let's take the first five predicted values and compare it to the actual targets.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["print(\"Predicted values:\", yhat[0:4])\n","print(\"True values:\", y_test[0:4].values)"]},{"cell_type":"markdown","metadata":{},"source":["We will use the function \"PollyPlot\" that we defined at the beginning of the lab to display the training data, testing data, and the predicted function.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train, y_test, poly,pr)"]},{"cell_type":"markdown","metadata":{},"source":["Figure 3: A polynomial regression model where red dots represent training data, green dots represent test data, and the blue line represents the model prediction.\n"]},{"cell_type":"markdown","metadata":{},"source":["We see that the estimated function appears to track the data but around 200 horsepower, the function begins to diverge from the data points.\n"]},{"cell_type":"markdown","metadata":{},"source":["R^2 of the training data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["poly.score(x_train_pr, y_train)"]},{"cell_type":"markdown","metadata":{},"source":["R^2 of the test data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["poly.score(x_test_pr, y_test)"]},{"cell_type":"markdown","metadata":{},"source":["We see the R^2 for the training data is 0.5567 while the R^2 on the test data was -29.87. The lower the R^2, the worse the model. A negative R^2 is a sign of overfitting.\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's see how the R^2 changes on the test data for different order polynomials and then plot the results:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Rsqu_test = []\n","\n","order = [1, 2, 3, 4]\n","for n in order:\n"," pr = PolynomialFeatures(degree=n)\n"," \n"," x_train_pr = pr.fit_transform(x_train[['horsepower']])\n"," \n"," x_test_pr = pr.fit_transform(x_test[['horsepower']]) \n"," \n"," lr.fit(x_train_pr, y_train)\n"," \n"," Rsqu_test.append(lr.score(x_test_pr, y_test))\n","\n","plt.plot(order, Rsqu_test)\n","plt.xlabel('order')\n","plt.ylabel('R^2')\n","plt.title('R^2 Using Test Data')\n","plt.text(3, 0.75, 'Maximum R^2 ') "]},{"cell_type":"markdown","metadata":{},"source":["We see the R^2 gradually increases until an order three polynomial is used. Then, the R^2 dramatically decreases at an order four polynomial.\n"]},{"cell_type":"markdown","metadata":{},"source":["The following function will be used in the next section. Please run the cell below.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def f(order, test_data):\n"," x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=test_data, random_state=0)\n"," pr = PolynomialFeatures(degree=order)\n"," x_train_pr = pr.fit_transform(x_train[['horsepower']])\n"," x_test_pr = pr.fit_transform(x_test[['horsepower']])\n"," poly = LinearRegression()\n"," poly.fit(x_train_pr,y_train)\n"," PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train,y_test, poly, pr)"]},{"cell_type":"markdown","metadata":{},"source":["The following interface allows you to experiment with different polynomial orders and different amounts of data.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["interact(f, order=(0, 6, 1), test_data=(0.05, 0.95, 0.05))"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4a):

      \n","\n","We can perform polynomial transformations with more than one feature. Create a \"PolynomialFeatures\" object \"pr1\" of degree two.\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","pr1=PolynomialFeatures(degree=2)\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4b):

      \n","\n"," \n"," Transform the training and testing samples for the features 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg'. Hint: use the method \"fit_transform\".\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","x_train_pr1=pr1.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n","\n","x_test_pr1=pr1.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n","\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["\n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4c):

      \n"," \n","How many dimensions does the new feature have? Hint: use the attribute \"shape\".\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","x_train_pr1.shape #there are now 15 features\n","\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4d):

      \n","\n"," \n","Create a linear regression model \"poly1\". Train the object using the method \"fit\" using the polynomial features.\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","poly1=LinearRegression().fit(x_train_pr1,y_train)\n","\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4e):

      \n","Use the method \"predict\" to predict an output on the polynomial features, then use the function \"DistributionPlot\" to display the distribution of the predicted test output vs. the actual test data.\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","yhat_test1=poly1.predict(x_test_pr1)\n","\n","Title='Distribution Plot of Predicted Value Using Test Data vs Data Distribution of Test Data'\n","\n","DistributionPlot(y_test, yhat_test1, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #4f):

      \n","\n","Using the distribution plot above, describe (in words) the two regions where the predicted prices are less accurate than the actual prices.\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","#The predicted value is higher than actual value for cars where the price $10,000 range, conversely the predicted price is lower than the price cost in the $30,000 to $40,000 range. As such the model is not as accurate in these ranges.\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Part 3: Ridge Regression

      \n"]},{"cell_type":"markdown","metadata":{},"source":["In this section, we will review Ridge Regression and see how the parameter alpha changes the model. Just a note, here our test data will be used as validation data.\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's perform a degree two polynomial transformation on our data.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pr=PolynomialFeatures(degree=2)\n","x_train_pr=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])\n","x_test_pr=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])"]},{"cell_type":"markdown","metadata":{},"source":["Let's import Ridge from the module linear models.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import Ridge"]},{"cell_type":"markdown","metadata":{},"source":["Let's create a Ridge regression object, setting the regularization parameter (alpha) to 0.1\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["RigeModel=Ridge(alpha=1)"]},{"cell_type":"markdown","metadata":{},"source":["Like regular regression, you can fit the model using the method fit.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["RigeModel.fit(x_train_pr, y_train)"]},{"cell_type":"markdown","metadata":{},"source":["Similarly, you can obtain a prediction:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["yhat = RigeModel.predict(x_test_pr)"]},{"cell_type":"markdown","metadata":{},"source":["Let's compare the first five predicted samples to our test set:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["print('predicted:', yhat[0:4])\n","print('test set :', y_test[0:4].values)"]},{"cell_type":"markdown","metadata":{},"source":["We select the value of alpha that minimizes the test error. To do so, we can use a for loop. We have also created a progress bar to see how many iterations we have completed so far.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from tqdm import tqdm\n","\n","Rsqu_test = []\n","Rsqu_train = []\n","dummy1 = []\n","Alpha = 10 * np.array(range(0,1000))\n","pbar = tqdm(Alpha)\n","\n","for alpha in pbar:\n"," RigeModel = Ridge(alpha=alpha) \n"," RigeModel.fit(x_train_pr, y_train)\n"," test_score, train_score = RigeModel.score(x_test_pr, y_test), RigeModel.score(x_train_pr, y_train)\n"," \n"," pbar.set_postfix({\"Test Score\": test_score, \"Train Score\": train_score})\n","\n"," Rsqu_test.append(test_score)\n"," Rsqu_train.append(train_score)"]},{"cell_type":"markdown","metadata":{},"source":["We can plot out the value of R^2 for different alphas:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["width = 12\n","height = 10\n","plt.figure(figsize=(width, height))\n","\n","plt.plot(Alpha,Rsqu_test, label='validation data ')\n","plt.plot(Alpha,Rsqu_train, 'r', label='training Data ')\n","plt.xlabel('alpha')\n","plt.ylabel('R^2')\n","plt.legend()"]},{"cell_type":"markdown","metadata":{},"source":["**Figure 4**: The blue line represents the R^2 of the validation data, and the red line represents the R^2 of the training data. The x-axis represents the different values of Alpha.\n"]},{"cell_type":"markdown","metadata":{},"source":["Here the model is built and tested on the same data, so the training and test data are the same.\n","\n","The red line in Figure 4 represents the R^2 of the training data. As alpha increases the R^2 decreases. Therefore, as alpha increases, the model performs worse on the training data\n","\n","The blue line represents the R^2 on the validation data. As the value for alpha increases, the R^2 increases and converges at a point.\n"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #5):

      \n","\n","Perform Ridge regression. Calculate the R^2 using the polynomial features, use the training data to train the model and use the test data to test the model. The parameter alpha should be set to 10.\n","\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","RigeModel = Ridge(alpha=10) \n","RigeModel.fit(x_train_pr, y_train)\n","RigeModel.score(x_test_pr, y_test)\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["

      Part 4: Grid Search

      \n"]},{"cell_type":"markdown","metadata":{},"source":["The term alpha is a hyperparameter. Sklearn has the class GridSearchCV to make the process of finding the best hyperparameter simpler.\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's import GridSearchCV from the module model_selection.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.model_selection import GridSearchCV"]},{"cell_type":"markdown","metadata":{},"source":["We create a dictionary of parameter values:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["parameters1= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n","parameters1"]},{"cell_type":"markdown","metadata":{},"source":["Create a Ridge regression object:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["RR=Ridge()\n","RR"]},{"cell_type":"markdown","metadata":{},"source":["Create a ridge grid search object:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Grid1 = GridSearchCV(RR, parameters1,cv=4, iid=None)"]},{"cell_type":"markdown","metadata":{},"source":["In order to avoid a deprecation warning due to the iid parameter, we set the value of iid to \"None\".\n","\n","Fit the model:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Grid1.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)"]},{"cell_type":"markdown","metadata":{},"source":["The object finds the best parameter values on the validation data. We can obtain the estimator with the best parameters and assign it to the variable BestRR as follows:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["BestRR=Grid1.best_estimator_\n","BestRR"]},{"cell_type":"markdown","metadata":{},"source":["We now test our model on the test data:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["BestRR.score(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_test)"]},{"cell_type":"markdown","metadata":{},"source":["
      \n","

      Question #6):

      \n","Perform a grid search for the alpha parameter and the normalization parameter, then find the best values of the parameters:\n","
      \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Write your code below and press Shift+Enter to execute \n"]},{"cell_type":"markdown","metadata":{},"source":["
      Click here for the solution\n","\n","```python\n","parameters2= [{'alpha': [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],'normalize':[True,False]} ]\n","Grid2 = GridSearchCV(Ridge(), parameters2,cv=4)\n","Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']],y_data)\n","Grid2.best_estimator_\n","\n","\n","```\n","\n","
      \n"]},{"cell_type":"markdown","metadata":{},"source":["### Thank you for completing this lab!\n","\n","## Author\n","\n","Joseph Santarcangelo\n","\n","### Other Contributors\n","\n","Mahdi Noorian PhD\n","\n","Bahare Talayian\n","\n","Eric Xiao\n","\n","Steven Dong\n","\n","Parizad\n","\n","Hima Vasudevan\n","\n","Fiorella Wenver\n","\n","Yi Yao.\n","\n","## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","| ----------------- | ------- | ---------- | ----------------------------------- |\n","| 2020-10-30 | 2.3 | Lakshmi | Changed URL of csv |\n","| 2020-10-05 | 2.2 | Lakshmi | Removed unused library imports |\n","| 2020-09-14 | 2.1 | Lakshmi | Made changes in OverFitting section |\n","| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n","\n","
      \n","\n","##

      © IBM Corporation 2020. All rights reserved.

      \n"]}],"metadata":{"anaconda-cloud":{},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"},"metadata":{"interpreter":{"hash":"ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc"}}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots.jupyterlite.ipynb b/DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots.jupyterlite.ipynb new file mode 100644 index 0000000..e402392 --- /dev/null +++ b/DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots.jupyterlite.ipynb @@ -0,0 +1,2447 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "
      \n", + " \"cognitiveclass.ai\n", + "
      \n", + "\n", + "# Data Visualization\n", + "\n", + "Estimated time needed: **30** minutes\n", + "\n", + "## Objectives\n", + "\n", + "After completing this lab you will be able to:\n", + "\n", + "* Create Data Visualization with Python\n", + "* Use various Python libraries for visualization\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "## Introduction\n", + "\n", + "The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible.\n", + "Speaking of consistency, because there is no *best* data visualization library available for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualization technique and tool for a given problem *and* audience.\n", + "\n", + "Please make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n", + "\n", + "**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "## Table of Contents\n", + "\n", + "
      \n", + "\n", + "1. [Exploring Datasets with *pandas*](#0)
      \n", + "\n", + "1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)
      \n", + "1.2 [*pandas* Basics](#4)
      \n", + "1.3 [*pandas* Intermediate: Indexing and Selection](#6)
      \n", + "2\\. [Visualizing Data using Matplotlib](#8)
      \n", + "2.1 [Matplotlib: Standard Python Visualization Library](#10)
      \n", + "3\\. [Line Plots](#12)\n", + "\n", + "
      \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Exploring Datasets with *pandas* \n", + "\n", + "*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01):\n", + "\n", + "> *pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n", + "\n", + "The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familiarize yourself with the *pandas* API Reference: [http://pandas.pydata.org/pandas-docs/stable/api.html](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "## The Dataset: Immigration to Canada from 1980 to 2013 \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n", + "\n", + "The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n", + "\n", + "In this lab, we will focus on the Canadian immigration data.\n", + "\n", + "![Data Preview](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/DataSnapshot.png)\n", + "\n", + "The Canada Immigration dataset can be fetched from here.\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "## *pandas* Basics\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing we'll do is install **openpyxl** (formerly **xlrd**), a module that *pandas* requires to read Excel files.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "#pip install openpyxl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Next, we'll do is import two key data analysis modules: *pandas* and *numpy*.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "import numpy as np # useful for many scientific computing in Python\n", + "import pandas as pd # primary data structure library" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Let's download and import our primary Canadian Immigration dataset using *pandas*'s `read_excel()` method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data downloaded and read into a dataframe!\n" + ] + } + ], + "source": [ + "#from js import fetch\n", + "#import io\n", + "\n", + "URL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx'\n", + "#resp = await fetch(URL)\n", + "#text = io.BytesIO((await resp.arrayBuffer()).to_py())\n", + "\n", + "df_can = pd.read_excel(URL,\n", + " sheet_name='Canada by Citizenship',\n", + " skiprows=range(20),\n", + " skipfooter=2)\n", + "print('Data downloaded and read into a dataframe!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Let's view the top 5 rows of the dataset using the `head()` function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      TypeCoverageOdNameAREAAreaNameREGRegNameDEVDevName1980...2004200520062007200820092010201120122013
      0ImmigrantsForeignersAfghanistan935Asia5501Southern Asia902Developing regions16...2978343630092652211117461758220326352004
      1ImmigrantsForeignersAlbania908Europe925Southern Europe901Developed regions1...14501223856702560716561539620603
      2ImmigrantsForeignersAlgeria903Africa912Northern Africa902Developing regions80...3616362648073623400553934752432537744331
      3ImmigrantsForeignersAmerican Samoa909Oceania957Polynesia902Developing regions0...0010000000
      4ImmigrantsForeignersAndorra908Europe925Southern Europe901Developed regions0...0011000011
      \n", + "

      5 rows × 43 columns

      \n", + "
      " + ], + "text/plain": [ + " Type Coverage OdName AREA AreaName REG \\\n", + "0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n", + "1 Immigrants Foreigners Albania 908 Europe 925 \n", + "2 Immigrants Foreigners Algeria 903 Africa 912 \n", + "3 Immigrants Foreigners American Samoa 909 Oceania 957 \n", + "4 Immigrants Foreigners Andorra 908 Europe 925 \n", + "\n", + " RegName DEV DevName 1980 ... 2004 2005 2006 \\\n", + "0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n", + "1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n", + "2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n", + "3 Polynesia 902 Developing regions 0 ... 0 0 1 \n", + "4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n", + "\n", + " 2007 2008 2009 2010 2011 2012 2013 \n", + "0 2652 2111 1746 1758 2203 2635 2004 \n", + "1 702 560 716 561 539 620 603 \n", + "2 3623 4005 5393 4752 4325 3774 4331 \n", + "3 0 0 0 0 0 0 0 \n", + "4 1 0 0 0 0 1 1 \n", + "\n", + "[5 rows x 43 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_can.head()\n", + "# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "We can also view the bottom 5 rows of the dataset using the `tail()` function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      TypeCoverageOdNameAREAAreaNameREGRegNameDEVDevName1980...2004200520062007200820092010201120122013
      190ImmigrantsForeignersViet Nam935Asia920South-Eastern Asia902Developing regions1191...1816185231532574178421711942172317312112
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      192ImmigrantsForeignersYemen935Asia922Western Asia902Developing regions1...124161140122133128211160174217
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      " + ], + "text/plain": [ + " Type Coverage OdName AREA AreaName REG \\\n", + "190 Immigrants Foreigners Viet Nam 935 Asia 920 \n", + "191 Immigrants Foreigners Western Sahara 903 Africa 912 \n", + "192 Immigrants Foreigners Yemen 935 Asia 922 \n", + "193 Immigrants Foreigners Zambia 903 Africa 910 \n", + "194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n", + "\n", + " RegName DEV DevName 1980 ... 2004 2005 2006 \\\n", + "190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n", + "191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n", + "192 Western Asia 902 Developing regions 1 ... 124 161 140 \n", + "193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n", + "194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n", + "\n", + " 2007 2008 2009 2010 2011 2012 2013 \n", + "190 2574 1784 2171 1942 1723 1731 2112 \n", + "191 0 0 0 0 0 0 0 \n", + "192 122 133 128 211 160 174 217 \n", + "193 71 64 60 102 69 46 59 \n", + "194 663 611 508 494 434 437 407 \n", + "\n", + "[5 rows x 43 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_can.tail()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe.\n", + "\n", + "Choose a method that can be used to get a short summary of the dataframe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Get the list of all columb headers\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Similarly, get the list of indices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Note: The default type of intance variables `index` and `columns` are **NOT** `list`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "print(type(df_can.columns))\n", + "print(type(df_can.index))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "print the index and columns as lists, also check the type\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "view the dimensions of the dataframe\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "**Note**: The main types stored in *pandas* objects are `float`, `int`, `bool`, `datetime64[ns]`, `datetime64[ns, tz]`, `timedelta[ns]`, `category`, and `object` (string). In addition, these dtypes have item sizes, e.g. `int64` and `int32`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      OdNameAreaNameRegNameDevName198019811982198319841985...2004200520062007200820092010201120122013
      0AfghanistanAsiaSouthern AsiaDeveloping regions1639394771340...2978343630092652211117461758220326352004
      1AlbaniaEuropeSouthern EuropeDeveloped regions100000...14501223856702560716561539620603
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      2 rows × 38 columns

      \n", + "
      " + ], + "text/plain": [ + " OdName AreaName RegName DevName 1980 1981 \\\n", + "0 Afghanistan Asia Southern Asia Developing regions 16 39 \n", + "1 Albania Europe Southern Europe Developed regions 1 0 \n", + "\n", + " 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n", + "0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n", + "1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n", + "\n", + " 2011 2012 2013 \n", + "0 2203 2635 2004 \n", + "1 539 620 603 \n", + "\n", + "[2 rows x 38 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n", + "df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n", + "df_can.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n", + " 1981, 1982, 1983, 1984, 1985,\n", + " 1986, 1987, 1988, 1989, 1990,\n", + " 1991, 1992, 1993, 1994, 1995,\n", + " 1996, 1997, 1998, 1999, 2000,\n", + " 2001, 2002, 2003, 2004, 2005,\n", + " 2006, 2007, 2008, 2009, 2010,\n", + " 2011, 2012, 2013],\n", + " dtype='object')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n", + "df_can.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Check to see how many null objects we have in the dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Finally, let's view a quick summary of each column in our dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "***\n", + "\n", + "## *pandas* Intermediate: Indexing and Selection (slicing)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Select Column\n", + "\n", + "**There are two ways to filter on a column name:**\n", + "\n", + "Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n", + "\n", + "```python\n", + " df.column_name # returns series\n", + "```\n", + "\n", + "Method 2: More robust, and can filter on multiple columns.\n", + "\n", + "```python\n", + " df['column'] # returns series\n", + "```\n", + "\n", + "```python\n", + " df[['column 1', 'column 2']] # returns dataframe\n", + "```\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Example: Let's try filtering on the list of countries ('Country').\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "df_can.Country # returns a series" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Let's try filtering on the list of countries ('Country') and the data for years: 1980 - 1985.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Select Row\n", + "\n", + "There are main 2 ways to select rows:\n", + "\n", + "```python\n", + " df.loc[label] # filters by the labels of the index/column\n", + " df.iloc[index] # filters by the positions of the index/column\n", + "```\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Before we proceed, notice that the default index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corresponding index value.\n", + "\n", + "This can be fixed very easily by setting the 'Country' column as the index.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "\n", + "# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "df_can.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n", + "1\\. The full row data (all columns)\n", + "2\\. For year 2013\n", + "3\\. For years 1980 to 1985\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "# 1. the full row data (all columns)\n", + "df_can.loc['Japan']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# alternate methods\n", + "df_can.iloc[87]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "df_can[df_can.index == 'Japan']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "# 2. for year 2013\n", + "df_can.loc['Japan', 2013]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# alternate method\n", + "# year 2013 is the last column, with a positional index of 36\n", + "df_can.iloc[87, 36]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# 3. for years 1980 to 1985\n", + "df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# Alternative Method\n", + "df_can.iloc[87, [3, 4, 5, 6, 7, 8]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index.\n", + "\n", + "To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "\n", + "# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# useful for plotting later on\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Filtering based on a criteria\n", + "\n", + "To filter the dataframe based on a condition, we simply pass the condition as a boolean vector.\n", + "\n", + "For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "# 1. create the condition boolean series\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# 2. pass this condition into the dataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# we can pass multiple criteria in the same line.\n", + "# let's filter for AreaNAme = Asia and RegName = Southern Asia\n", + "\n", + "\n", + "\n", + "# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n", + "# don't forget to enclose the two conditions in parentheses" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Before we proceed: let's review the changes we have made to our dataframe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "print('data dimensions:', df_can.shape)\n", + "print(df_can.columns)\n", + "df_can.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "***\n", + "\n", + "# Visualizing Data using Matplotlib\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "## Matplotlib: Standard Python Visualization Library\n", + "\n", + "The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01). As mentioned on their website:\n", + "\n", + "> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n", + "\n", + "If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Matplotlib.Pyplot\n", + "\n", + "One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Let's start by importing `matplotlib` and `matplotlib.pyplot` as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# we are using the inline backend\n", + "%matplotlib inline \n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "\\*optional: check if Matplotlib is loaded.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "\\*optional: apply a style to Matplotlib.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "print(plt.style.available)\n", + "mpl.style.use(['ggplot']) # optional: for ggplot-like style" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Plotting in *pandas*\n", + "\n", + "Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n", + "\n", + "Documentation:\n", + "\n", + "* [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#plotting)
      \n", + "* [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#api-dataframe-plotting)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Line Pots (Series/Dataframe) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "**What is a line plot and why use it?**\n", + "\n", + "A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n", + "Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "**Let's start with a case study:**\n", + "\n", + "In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n", + "\n", + "**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "First, we will extract the data series for Haiti.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n", + "\n", + "Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n", + "\n", + "Quick note on x and y values in `plt.text(x, y, label)`:\n", + "\n", + "```\n", + " Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n", + "```\n", + "\n", + "```python\n", + " plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n", + "```\n", + "\n", + "```\n", + "If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n", + "```\n", + "\n", + "```python\n", + " plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n", + "```\n", + "\n", + "```\n", + "We will cover advanced annotation methods in later modules.\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "We can easily add more countries to line plot to make meaningful comparisons immigration from different countries.\n", + "\n", + "**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Step 1: Get the data set for China and India, and display the dataframe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "### type your answer here\n", + "# df_CI = ...\n", + "# df_CI\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "
      Click here for a sample python solution\n", + "\n", + "```python\n", + " #The correct answer is:\n", + " df_CI = df_can.loc[['India', 'China'], years]\n", + " df_CI\n", + "```\n", + "\n", + "
      \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "scrolled": true, + "trusted": true + }, + "outputs": [], + "source": [ + "### type your answer here\n", + "# df_CI. ...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "
      Click here for a sample python solution\n", + "\n", + "```python\n", + " #The correct answer is:\n", + " df_CI.plot(kind='line')\n", + "```\n", + "\n", + "
      \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "That doesn't look right...\n", + "\n", + "Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "df_CI = df_CI.transpose()\n", + "df_CI.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "*pandas* will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "### type your answer here\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "
      Click here for a sample python solution\n", + "\n", + "```python\n", + " #The correct answer is:\n", + " df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n", + " df_CI.plot(kind='line')\n", + "\n", + " plt.title('Immigrants from China and India')\n", + " plt.ylabel('Number of Immigrants')\n", + " plt.xlabel('Years')\n", + "\n", + " plt.show()\n", + "```\n", + "\n", + "
      \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "
      From the above plot, we can observe that the China and India have very similar immigration trends through the years.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "*Note*: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n", + "\n", + "That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below.\n", + "\n", + "```python\n", + "print(type(haiti))\n", + "print(haiti.head(5))\n", + "```\n", + "\n", + "> class 'pandas.core.series.Series'
      \n", + "> 1980 1666
      \n", + "> 1981 3692
      \n", + "> 1982 3498
      \n", + "> 1983 2860
      \n", + "> 1984 1418
      \n", + "> Name: Haiti, dtype: int64
      \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + }, + "trusted": true + }, + "outputs": [], + "source": [ + "### type your answer here\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "
      Click here for a sample python solution\n", + "\n", + "```python\n", + " #The correct answer is: \n", + " #Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n", + " #We will sort on this column to get our top 5 countries using pandas sort_values() method.\n", + " \n", + " inplace = True # paramemter saves the changes to the original df_can dataframe\n", + " df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n", + "\n", + " # get the top 5 entries\n", + " df_top5 = df_can.head(5)\n", + "\n", + " # transpose the dataframe\n", + " df_top5 = df_top5[years].transpose() \n", + "\n", + " print(df_top5)\n", + "\n", + "\n", + " #Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n", + " df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n", + " df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n", + "\n", + "\n", + "\n", + " plt.title('Immigration Trend of Top 5 Countries')\n", + " plt.ylabel('Number of Immigrants')\n", + " plt.xlabel('Years')\n", + "\n", + "\n", + " plt.show()\n", + "\n", + "```\n", + "\n", + "
      \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Other Plots\n", + "\n", + "Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n", + "\n", + "* `bar` for vertical bar plots\n", + "* `barh` for horizontal bar plots\n", + "* `hist` for histogram\n", + "* `box` for boxplot\n", + "* `kde` or `density` for density plots\n", + "* `area` for area plots\n", + "* `pie` for pie plots\n", + "* `scatter` for scatter plots\n", + "* `hexbin` for hexbin plot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "### Thank you for completing this lab!\n", + "\n", + "## Author\n", + "\n", + "Alex Aklson\n", + "\n", + "### Other Contributors\n", + "\n", + "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n", + "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n", + "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n", + "[Weiqing Wang](https://www.linkedin.com/in/weiqing-wang-641640133/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01)\n", + "\n", + "## Change Log\n", + "\n", + "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", + "| ----------------- | ------- | ------------- | ---------------------------------- |\n", + "| 2021-05-29 | 2.4 | Weiqing Wang | Fixed typos and code smells. |\n", + "| 2021-01-20 | 2.3 | Lakshmi Holla | Changed TOC cell markdown |\n", + "| 2020-11-20 | 2.2 | Lakshmi Holla | Changed IBM box URL |\n", + "| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL and info method |\n", + "| 2020-08-27 | 2.0 | Lavanya | Moved Lab to course repo in GitLab |\n", + "\n", + "##

      © IBM Corporation 2020. All rights reserved.

      \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "my_environment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "vscode": { + "interpreter": { + "hash": "1468b46fbbf336438d2a9c265dd02fb55be29fdd63084011561d11061c2774ae" + } + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Data_Analytics_Projects.ipynb b/Data_Analytics_Projects.ipynb new file mode 100644 index 0000000..494dce8 --- /dev/null +++ b/Data_Analytics_Projects.ipynb @@ -0,0 +1,6386 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1a99cdef", + "metadata": {}, + "source": [ + "# Real Time Projects\n" + ] + }, + { + "cell_type": "markdown", + "id": "54e6434d", + "metadata": {}, + "source": [ + "## The Weather Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "1623ee8e", + "metadata": {}, + "source": [ + "Here, The Weather Dataset is a time-series data set with per-hour information about the weather conditions at a particular \n", + "location. it records temperature, dew point Temprature, Relative Humidity, Wind Speed, Visibility, Presure, and conditions.\n", + "\n", + "This data is available as a CSV file. we are going to analyze this data set using the Pandas DataFrame.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "254fc4c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\users\\chamu\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.5.2)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\chamu\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\chamu\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas) (2022.6)\n", + "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\chamu\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas) (1.24.0)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\chamu\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "03b0488c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bfba3bdd", + "metadata": {}, + "outputs": [], + "source": [ + "data=pd.read_csv(r\"C:\\Users\\chamu\\Downloads\\1. Weather Data.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6fc08a5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather
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      21/1/2012 2:00-1.8-3.48974.0101.26Freezing Drizzle,Fog
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      41/1/2012 4:00-1.5-3.38874.8101.23Fog
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      877912/31/2012 19:000.1-2.781309.7100.13Snow
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      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
      21/1/2012 2:00-1.8-3.48974.0101.26Freezing Drizzle,Fog
      31/1/2012 3:00-1.5-3.28864.0101.27Freezing Drizzle,Fog
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      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "2 1/1/2012 2:00 -1.8 -3.4 89 7 \n", + "3 1/1/2012 3:00 -1.5 -3.2 88 6 \n", + "4 1/1/2012 4:00 -1.5 -3.3 88 7 \n", + "\n", + " Visibility_km Press_kPa Weather \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog \n", + "2 4.0 101.26 Freezing Drizzle,Fog \n", + "3 4.0 101.27 Freezing Drizzle,Fog \n", + "4 4.8 101.23 Fog " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "2a9b5bc4", + "metadata": {}, + "source": [ + "## .shape" + ] + }, + { + "cell_type": "markdown", + "id": "8c29aee1", + "metadata": {}, + "source": [ + "it shows the total no. of rows and no. of columns of the dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ab513769", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 8)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "id": "103d28ed", + "metadata": {}, + "source": [ + "## .index" + ] + }, + { + "cell_type": "markdown", + "id": "3dfba29d", + "metadata": {}, + "source": [ + "This attribute provides the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9c1b21cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=8784, step=1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.index" + ] + }, + { + "cell_type": "markdown", + "id": "19cf19cf", + "metadata": {}, + "source": [ + "## .columns" + ] + }, + { + "cell_type": "markdown", + "id": "f28e5152", + "metadata": {}, + "source": [ + "it shows the name of each column" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9630e091", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Date/Time', 'Temp_C', 'Dew Point Temp_C', 'Rel Hum_%',\n", + " 'Wind Speed_km/h', 'Visibility_km', 'Press_kPa', 'Weather'],\n", + " dtype='object')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "markdown", + "id": "94772c50", + "metadata": {}, + "source": [ + "## .dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "5e80b700", + "metadata": {}, + "source": [ + "it shows the data types of each column" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9e3139cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date/Time object\n", + "Temp_C float64\n", + "Dew Point Temp_C float64\n", + "Rel Hum_% int64\n", + "Wind Speed_km/h int64\n", + "Visibility_km float64\n", + "Press_kPa float64\n", + "Weather object\n", + "dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "5e419480", + "metadata": {}, + "source": [ + "## .unique()" + ] + }, + { + "cell_type": "markdown", + "id": "371ce7ec", + "metadata": {}, + "source": [ + "In a column, it shows all the unique values. it can be applied on a single column only. not on the whole dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "038b552b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Fog', 'Freezing Drizzle,Fog', 'Mostly Cloudy', 'Cloudy', 'Rain',\n", + " 'Rain Showers', 'Mainly Clear', 'Snow Showers', 'Snow', 'Clear',\n", + " 'Freezing Rain,Fog', 'Freezing Rain', 'Freezing Drizzle',\n", + " 'Rain,Snow', 'Moderate Snow', 'Freezing Drizzle,Snow',\n", + " 'Freezing Rain,Snow Grains', 'Snow,Blowing Snow', 'Freezing Fog',\n", + " 'Haze', 'Rain,Fog', 'Drizzle,Fog', 'Drizzle',\n", + " 'Freezing Drizzle,Haze', 'Freezing Rain,Haze', 'Snow,Haze',\n", + " 'Snow,Fog', 'Snow,Ice Pellets', 'Rain,Haze', 'Thunderstorms,Rain',\n", + " 'Thunderstorms,Rain Showers', 'Thunderstorms,Heavy Rain Showers',\n", + " 'Thunderstorms,Rain Showers,Fog', 'Thunderstorms',\n", + " 'Thunderstorms,Rain,Fog',\n", + " 'Thunderstorms,Moderate Rain Showers,Fog', 'Rain Showers,Fog',\n", + " 'Rain Showers,Snow Showers', 'Snow Pellets', 'Rain,Snow,Fog',\n", + " 'Moderate Rain,Fog', 'Freezing Rain,Ice Pellets,Fog',\n", + " 'Drizzle,Ice Pellets,Fog', 'Drizzle,Snow', 'Rain,Ice Pellets',\n", + " 'Drizzle,Snow,Fog', 'Rain,Snow Grains', 'Rain,Snow,Ice Pellets',\n", + " 'Snow Showers,Fog', 'Moderate Snow,Blowing Snow'], dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Weather'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "a9e37388", + "metadata": {}, + "source": [ + "## .nunique()" + ] + }, + { + "cell_type": "markdown", + "id": "6c42c4ff", + "metadata": {}, + "source": [ + "it shows the total no.of unique values in each column. it can be applied on a single column as well as on whole dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1bd63f00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date/Time 8784\n", + "Temp_C 533\n", + "Dew Point Temp_C 489\n", + "Rel Hum_% 83\n", + "Wind Speed_km/h 34\n", + "Visibility_km 24\n", + "Press_kPa 518\n", + "Weather 50\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.nunique()" + ] + }, + { + "cell_type": "markdown", + "id": "df50cba6", + "metadata": {}, + "source": [ + "## .count" + ] + }, + { + "cell_type": "markdown", + "id": "065dbb6b", + "metadata": {}, + "source": [ + "it shows the total no. of non-null values in each column. it can be applied on a single column as well as on whole dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e9e500ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date/Time 8784\n", + "Temp_C 8784\n", + "Dew Point Temp_C 8784\n", + "Rel Hum_% 8784\n", + "Wind Speed_km/h 8784\n", + "Visibility_km 8784\n", + "Press_kPa 8784\n", + "Weather 8784\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.count()" + ] + }, + { + "cell_type": "markdown", + "id": "8fd4f755", + "metadata": {}, + "source": [ + "## .value_counts" + ] + }, + { + "cell_type": "markdown", + "id": "9937247b", + "metadata": {}, + "source": [ + "In a column, it shows all the unique values with their count. it can be applied on single column only." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3c37577a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Mainly Clear 2106\n", + "Mostly Cloudy 2069\n", + "Cloudy 1728\n", + "Clear 1326\n", + "Snow 390\n", + "Rain 306\n", + "Rain Showers 188\n", + "Fog 150\n", + "Rain,Fog 116\n", + "Drizzle,Fog 80\n", + "Snow Showers 60\n", + "Drizzle 41\n", + "Snow,Fog 37\n", + "Snow,Blowing Snow 19\n", + "Rain,Snow 18\n", + "Thunderstorms,Rain Showers 16\n", + "Haze 16\n", + "Drizzle,Snow,Fog 15\n", + "Freezing Rain 14\n", + "Freezing Drizzle,Snow 11\n", + "Freezing Drizzle 7\n", + "Snow,Ice Pellets 6\n", + "Freezing Drizzle,Fog 6\n", + "Snow,Haze 5\n", + "Freezing Fog 4\n", + "Snow Showers,Fog 4\n", + "Moderate Snow 4\n", + "Rain,Snow,Ice Pellets 4\n", + "Freezing Rain,Fog 4\n", + "Freezing Drizzle,Haze 3\n", + "Rain,Haze 3\n", + "Thunderstorms,Rain 3\n", + "Thunderstorms,Rain Showers,Fog 3\n", + "Freezing Rain,Haze 2\n", + "Drizzle,Snow 2\n", + "Rain Showers,Snow Showers 2\n", + "Thunderstorms 2\n", + "Moderate Snow,Blowing Snow 2\n", + "Rain Showers,Fog 1\n", + "Thunderstorms,Moderate Rain Showers,Fog 1\n", + "Snow Pellets 1\n", + "Rain,Snow,Fog 1\n", + "Moderate Rain,Fog 1\n", + "Freezing Rain,Ice Pellets,Fog 1\n", + "Drizzle,Ice Pellets,Fog 1\n", + "Thunderstorms,Rain,Fog 1\n", + "Rain,Ice Pellets 1\n", + "Rain,Snow Grains 1\n", + "Thunderstorms,Heavy Rain Showers 1\n", + "Freezing Rain,Snow Grains 1\n", + "Name: Weather, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Weather'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "4f3d3adf", + "metadata": {}, + "source": [ + "## .info()" + ] + }, + { + "cell_type": "markdown", + "id": "c5bc09ec", + "metadata": {}, + "source": [ + "provides basic information about the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e2ef9a38", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8784 entries, 0 to 8783\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date/Time 8784 non-null object \n", + " 1 Temp_C 8784 non-null float64\n", + " 2 Dew Point Temp_C 8784 non-null float64\n", + " 3 Rel Hum_% 8784 non-null int64 \n", + " 4 Wind Speed_km/h 8784 non-null int64 \n", + " 5 Visibility_km 8784 non-null float64\n", + " 6 Press_kPa 8784 non-null float64\n", + " 7 Weather 8784 non-null object \n", + "dtypes: float64(4), int64(2), object(2)\n", + "memory usage: 549.1+ KB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "id": "09a07a97", + "metadata": {}, + "source": [ + "## Q) 1. Find all the unique \"Wind Speed\" values in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "cea0fc5a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather
      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
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      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "\n", + " Visibility_km Press_kPa Weather \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "37deb0fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date/Time 8784\n", + "Temp_C 533\n", + "Dew Point Temp_C 489\n", + "Rel Hum_% 83\n", + "Wind Speed_km/h 34\n", + "Visibility_km 24\n", + "Press_kPa 518\n", + "Weather 50\n", + "dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "08233ff6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Wind Speed_km/h'].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "68eef3d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4, 7, 6, 9, 15, 13, 20, 22, 19, 24, 30, 35, 39, 32, 33, 26, 44,\n", + " 43, 48, 37, 28, 17, 11, 0, 83, 70, 57, 46, 41, 52, 50, 63, 54, 2],\n", + " dtype=int64)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Wind Speed_km/h'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "1c62fc2b", + "metadata": {}, + "source": [ + "## Q)2. Find the number of times when the 'Weather is exactly Clear'" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "81371e65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Mainly Clear 2106\n", + "Mostly Cloudy 2069\n", + "Cloudy 1728\n", + "Clear 1326\n", + "Snow 390\n", + "Rain 306\n", + "Rain Showers 188\n", + "Fog 150\n", + "Rain,Fog 116\n", + "Drizzle,Fog 80\n", + "Snow Showers 60\n", + "Drizzle 41\n", + "Snow,Fog 37\n", + "Snow,Blowing Snow 19\n", + "Rain,Snow 18\n", + "Thunderstorms,Rain Showers 16\n", + "Haze 16\n", + "Drizzle,Snow,Fog 15\n", + "Freezing Rain 14\n", + "Freezing Drizzle,Snow 11\n", + "Freezing Drizzle 7\n", + "Snow,Ice Pellets 6\n", + "Freezing Drizzle,Fog 6\n", + "Snow,Haze 5\n", + "Freezing Fog 4\n", + "Snow Showers,Fog 4\n", + "Moderate Snow 4\n", + "Rain,Snow,Ice Pellets 4\n", + "Freezing Rain,Fog 4\n", + "Freezing Drizzle,Haze 3\n", + "Rain,Haze 3\n", + "Thunderstorms,Rain 3\n", + "Thunderstorms,Rain Showers,Fog 3\n", + "Freezing Rain,Haze 2\n", + "Drizzle,Snow 2\n", + "Rain Showers,Snow Showers 2\n", + "Thunderstorms 2\n", + "Moderate Snow,Blowing Snow 2\n", + "Rain Showers,Fog 1\n", + "Thunderstorms,Moderate Rain Showers,Fog 1\n", + "Snow Pellets 1\n", + "Rain,Snow,Fog 1\n", + "Moderate Rain,Fog 1\n", + "Freezing Rain,Ice Pellets,Fog 1\n", + "Drizzle,Ice Pellets,Fog 1\n", + "Thunderstorms,Rain,Fog 1\n", + "Rain,Ice Pellets 1\n", + "Rain,Snow Grains 1\n", + "Thunderstorms,Heavy Rain Showers 1\n", + "Freezing Rain,Snow Grains 1\n", + "Name: Weather, dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.Weather.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "8b5c9090", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather
      671/3/2012 19:00-16.9-24.8502425.0101.74Clear
      1141/5/2012 18:00-7.1-14.4561125.0100.71Clear
      1151/5/2012 19:00-9.2-15.461725.0100.80Clear
      1161/5/2012 20:00-9.8-15.762925.0100.83Clear
      1171/5/2012 21:00-9.0-14.8631325.0100.83Clear
      ...........................
      864612/26/2012 6:00-13.4-14.889425.0102.47Clear
      869812/28/2012 10:00-6.1-8.6821924.1101.27Clear
      871312/29/2012 1:00-11.9-13.6871125.0101.31Clear
      871412/29/2012 2:00-11.8-13.1901325.0101.33Clear
      875612/30/2012 20:00-13.8-16.5802425.0101.52Clear
      \n", + "

      1326 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "67 1/3/2012 19:00 -16.9 -24.8 50 24 \n", + "114 1/5/2012 18:00 -7.1 -14.4 56 11 \n", + "115 1/5/2012 19:00 -9.2 -15.4 61 7 \n", + "116 1/5/2012 20:00 -9.8 -15.7 62 9 \n", + "117 1/5/2012 21:00 -9.0 -14.8 63 13 \n", + "... ... ... ... ... ... \n", + "8646 12/26/2012 6:00 -13.4 -14.8 89 4 \n", + "8698 12/28/2012 10:00 -6.1 -8.6 82 19 \n", + "8713 12/29/2012 1:00 -11.9 -13.6 87 11 \n", + "8714 12/29/2012 2:00 -11.8 -13.1 90 13 \n", + "8756 12/30/2012 20:00 -13.8 -16.5 80 24 \n", + "\n", + " Visibility_km Press_kPa Weather \n", + "67 25.0 101.74 Clear \n", + "114 25.0 100.71 Clear \n", + "115 25.0 100.80 Clear \n", + "116 25.0 100.83 Clear \n", + "117 25.0 100.83 Clear \n", + "... ... ... ... \n", + "8646 25.0 102.47 Clear \n", + "8698 24.1 101.27 Clear \n", + "8713 25.0 101.31 Clear \n", + "8714 25.0 101.33 Clear \n", + "8756 25.0 101.52 Clear \n", + "\n", + "[1326 rows x 8 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Filtering\n", + "data[data.Weather == 'Clear']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0c74dea0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather
      671/3/2012 19:00-16.9-24.8502425.0101.74Clear
      1141/5/2012 18:00-7.1-14.4561125.0100.71Clear
      1151/5/2012 19:00-9.2-15.461725.0100.80Clear
      1161/5/2012 20:00-9.8-15.762925.0100.83Clear
      1171/5/2012 21:00-9.0-14.8631325.0100.83Clear
      ...........................
      864612/26/2012 6:00-13.4-14.889425.0102.47Clear
      869812/28/2012 10:00-6.1-8.6821924.1101.27Clear
      871312/29/2012 1:00-11.9-13.6871125.0101.31Clear
      871412/29/2012 2:00-11.8-13.1901325.0101.33Clear
      875612/30/2012 20:00-13.8-16.5802425.0101.52Clear
      \n", + "

      1326 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "67 1/3/2012 19:00 -16.9 -24.8 50 24 \n", + "114 1/5/2012 18:00 -7.1 -14.4 56 11 \n", + "115 1/5/2012 19:00 -9.2 -15.4 61 7 \n", + "116 1/5/2012 20:00 -9.8 -15.7 62 9 \n", + "117 1/5/2012 21:00 -9.0 -14.8 63 13 \n", + "... ... ... ... ... ... \n", + "8646 12/26/2012 6:00 -13.4 -14.8 89 4 \n", + "8698 12/28/2012 10:00 -6.1 -8.6 82 19 \n", + "8713 12/29/2012 1:00 -11.9 -13.6 87 11 \n", + "8714 12/29/2012 2:00 -11.8 -13.1 90 13 \n", + "8756 12/30/2012 20:00 -13.8 -16.5 80 24 \n", + "\n", + " Visibility_km Press_kPa Weather \n", + "67 25.0 101.74 Clear \n", + "114 25.0 100.71 Clear \n", + "115 25.0 100.80 Clear \n", + "116 25.0 100.83 Clear \n", + "117 25.0 100.83 Clear \n", + "... ... ... ... \n", + "8646 25.0 102.47 Clear \n", + "8698 24.1 101.27 Clear \n", + "8713 25.0 101.31 Clear \n", + "8714 25.0 101.33 Clear \n", + "8756 25.0 101.52 Clear \n", + "\n", + "[1326 rows x 8 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#groupby()\n", + "data.groupby('Weather').get_group('Clear')" + ] + }, + { + "cell_type": "markdown", + "id": "70ca0b91", + "metadata": {}, + "source": [ + "## Q)3. Find the number of times when the 'Wind Speed was exactly 4 km/h'." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d228b999", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather
      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
      961/5/2012 0:00-8.8-11.77949.7100.32Snow
      1011/5/2012 5:00-7.0-9.58244.0100.19Snow
      1461/7/2012 2:00-8.1-11.179419.3100.15Cloudy
      ...........................
      876812/31/2012 8:00-8.6-10.38743.2101.14Snow Showers
      876912/31/2012 9:00-8.1-9.68942.4101.09Snow
      877012/31/2012 10:00-7.4-8.98946.4101.05Snow,Fog
      877212/31/2012 12:00-5.8-7.588412.9100.78Snow
      877312/31/2012 13:00-4.6-6.686412.9100.63Snow
      \n", + "

      474 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "96 1/5/2012 0:00 -8.8 -11.7 79 4 \n", + "101 1/5/2012 5:00 -7.0 -9.5 82 4 \n", + "146 1/7/2012 2:00 -8.1 -11.1 79 4 \n", + "... ... ... ... ... ... \n", + "8768 12/31/2012 8:00 -8.6 -10.3 87 4 \n", + "8769 12/31/2012 9:00 -8.1 -9.6 89 4 \n", + "8770 12/31/2012 10:00 -7.4 -8.9 89 4 \n", + "8772 12/31/2012 12:00 -5.8 -7.5 88 4 \n", + "8773 12/31/2012 13:00 -4.6 -6.6 86 4 \n", + "\n", + " Visibility_km Press_kPa Weather \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog \n", + "96 9.7 100.32 Snow \n", + "101 4.0 100.19 Snow \n", + "146 19.3 100.15 Cloudy \n", + "... ... ... ... \n", + "8768 3.2 101.14 Snow Showers \n", + "8769 2.4 101.09 Snow \n", + "8770 6.4 101.05 Snow,Fog \n", + "8772 12.9 100.78 Snow \n", + "8773 12.9 100.63 Snow \n", + "\n", + "[474 rows x 8 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data['Wind Speed_km/h']==4]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ce4a4e99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather
      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
      961/5/2012 0:00-8.8-11.77949.7100.32Snow
      1011/5/2012 5:00-7.0-9.58244.0100.19Snow
      1461/7/2012 2:00-8.1-11.179419.3100.15Cloudy
      ...........................
      876812/31/2012 8:00-8.6-10.38743.2101.14Snow Showers
      876912/31/2012 9:00-8.1-9.68942.4101.09Snow
      877012/31/2012 10:00-7.4-8.98946.4101.05Snow,Fog
      877212/31/2012 12:00-5.8-7.588412.9100.78Snow
      877312/31/2012 13:00-4.6-6.686412.9100.63Snow
      \n", + "

      474 rows × 8 columns

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      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "96 1/5/2012 0:00 -8.8 -11.7 79 4 \n", + "101 1/5/2012 5:00 -7.0 -9.5 82 4 \n", + "146 1/7/2012 2:00 -8.1 -11.1 79 4 \n", + "... ... ... ... ... ... \n", + "8768 12/31/2012 8:00 -8.6 -10.3 87 4 \n", + "8769 12/31/2012 9:00 -8.1 -9.6 89 4 \n", + "8770 12/31/2012 10:00 -7.4 -8.9 89 4 \n", + "8772 12/31/2012 12:00 -5.8 -7.5 88 4 \n", + "8773 12/31/2012 13:00 -4.6 -6.6 86 4 \n", + "\n", + " Visibility_km Press_kPa Weather \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog \n", + "96 9.7 100.32 Snow \n", + "101 4.0 100.19 Snow \n", + "146 19.3 100.15 Cloudy \n", + "... ... ... ... \n", + "8768 3.2 101.14 Snow Showers \n", + "8769 2.4 101.09 Snow \n", + "8770 6.4 101.05 Snow,Fog \n", + "8772 12.9 100.78 Snow \n", + "8773 12.9 100.63 Snow \n", + "\n", + "[474 rows x 8 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby('Wind Speed_km/h').get_group(4)" + ] + }, + { + "cell_type": "markdown", + "id": "365b3e37", + "metadata": {}, + "source": [ + "## Q)4. Find out all the Null Values in the data" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8f4a70eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date/Time 0\n", + "Temp_C 0\n", + "Dew Point Temp_C 0\n", + "Rel Hum_% 0\n", + "Wind Speed_km/h 0\n", + "Visibility_km 0\n", + "Press_kPa 0\n", + "Weather 0\n", + "dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "a2c2691e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date/Time 8784\n", + "Temp_C 8784\n", + "Dew Point Temp_C 8784\n", + "Rel Hum_% 8784\n", + "Wind Speed_km/h 8784\n", + "Visibility_km 8784\n", + "Press_kPa 8784\n", + "Weather 8784\n", + "dtype: int64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.notnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "07e4d441", + "metadata": {}, + "source": [ + "## Q)5. Rename the column name 'Weather' of the dataframe to 'Weather Condition'." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "4bd91532", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
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      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog " + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.rename(columns={'Weather':'Weather Condition'},inplace=True)\n", + "data.head(2)" + ] + }, + { + "cell_type": "markdown", + "id": "363cc978", + "metadata": {}, + "source": [ + "## Q) 6. What is the mean 'Visibility'?" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "f0cb096b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
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      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "1504601a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27.664446721311478" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.Visibility_km.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "1c4ca524", + "metadata": {}, + "source": [ + "## Q)7. What is the standard deviation of 'Pressure' in this data?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1728f4a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8440047459486483" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.Press_kPa.std()" + ] + }, + { + "cell_type": "markdown", + "id": "7c135490", + "metadata": {}, + "source": [ + "## Q)8. What is the Variance of 'Relative Humidity' in this data?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "6e267ec1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "286.24855019850196" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Rel Hum_%'].var()" + ] + }, + { + "cell_type": "markdown", + "id": "7153a775", + "metadata": {}, + "source": [ + "## Q)9. Find all instances when 'Snow' was recorded" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "445890ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Mainly Clear 2106\n", + "Mostly Cloudy 2069\n", + "Cloudy 1728\n", + "Clear 1326\n", + "Snow 390\n", + "Rain 306\n", + "Rain Showers 188\n", + "Fog 150\n", + "Rain,Fog 116\n", + "Drizzle,Fog 80\n", + "Snow Showers 60\n", + "Drizzle 41\n", + "Snow,Fog 37\n", + "Snow,Blowing Snow 19\n", + "Rain,Snow 18\n", + "Thunderstorms,Rain Showers 16\n", + "Haze 16\n", + "Drizzle,Snow,Fog 15\n", + "Freezing Rain 14\n", + "Freezing Drizzle,Snow 11\n", + "Freezing Drizzle 7\n", + "Snow,Ice Pellets 6\n", + "Freezing Drizzle,Fog 6\n", + "Snow,Haze 5\n", + "Freezing Fog 4\n", + "Snow Showers,Fog 4\n", + "Moderate Snow 4\n", + "Rain,Snow,Ice Pellets 4\n", + "Freezing Rain,Fog 4\n", + "Freezing Drizzle,Haze 3\n", + "Rain,Haze 3\n", + "Thunderstorms,Rain 3\n", + "Thunderstorms,Rain Showers,Fog 3\n", + "Freezing Rain,Haze 2\n", + "Drizzle,Snow 2\n", + "Rain Showers,Snow Showers 2\n", + "Thunderstorms 2\n", + "Moderate Snow,Blowing Snow 2\n", + "Rain Showers,Fog 1\n", + "Thunderstorms,Moderate Rain Showers,Fog 1\n", + "Snow Pellets 1\n", + "Rain,Snow,Fog 1\n", + "Moderate Rain,Fog 1\n", + "Freezing Rain,Ice Pellets,Fog 1\n", + "Drizzle,Ice Pellets,Fog 1\n", + "Thunderstorms,Rain,Fog 1\n", + "Rain,Ice Pellets 1\n", + "Rain,Snow Grains 1\n", + "Thunderstorms,Heavy Rain Showers 1\n", + "Freezing Rain,Snow Grains 1\n", + "Name: Weather Condition, dtype: int64" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#value_counts()\n", + "data['Weather Condition'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "d5d706d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      551/3/2012 7:00-14.0-19.5631925.0100.95Snow
      841/4/2012 12:00-13.7-21.7511124.1101.25Snow
      861/4/2012 14:00-11.3-19.053719.3100.97Snow
      871/4/2012 15:00-10.2-16.361119.7100.89Snow
      881/4/2012 16:00-9.4-15.5611319.3100.79Snow
      ...........................
      877912/31/2012 19:000.1-2.781309.7100.13Snow
      878012/31/2012 20:000.2-2.483249.7100.03Snow
      878112/31/2012 21:00-0.5-1.593284.899.95Snow
      878212/31/2012 22:00-0.2-1.889289.799.91Snow
      878312/31/2012 23:000.0-2.1863011.399.89Snow
      \n", + "

      390 rows × 8 columns

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      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "55 1/3/2012 7:00 -14.0 -19.5 63 19 \n", + "84 1/4/2012 12:00 -13.7 -21.7 51 11 \n", + "86 1/4/2012 14:00 -11.3 -19.0 53 7 \n", + "87 1/4/2012 15:00 -10.2 -16.3 61 11 \n", + "88 1/4/2012 16:00 -9.4 -15.5 61 13 \n", + "... ... ... ... ... ... \n", + "8779 12/31/2012 19:00 0.1 -2.7 81 30 \n", + "8780 12/31/2012 20:00 0.2 -2.4 83 24 \n", + "8781 12/31/2012 21:00 -0.5 -1.5 93 28 \n", + "8782 12/31/2012 22:00 -0.2 -1.8 89 28 \n", + "8783 12/31/2012 23:00 0.0 -2.1 86 30 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "55 25.0 100.95 Snow \n", + "84 24.1 101.25 Snow \n", + "86 19.3 100.97 Snow \n", + "87 9.7 100.89 Snow \n", + "88 19.3 100.79 Snow \n", + "... ... ... ... \n", + "8779 9.7 100.13 Snow \n", + "8780 9.7 100.03 Snow \n", + "8781 4.8 99.95 Snow \n", + "8782 9.7 99.91 Snow \n", + "8783 11.3 99.89 Snow \n", + "\n", + "[390 rows x 8 columns]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Filtering\n", + "data[data['Weather Condition']=='Snow']\n", + "\n", + "#groupby\n", + "# data.groupby('Weather Condition').get_group('Snow')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c79522c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      868012/27/2012 16:00-4.5-6.288372.0100.44Snow,Blowing Snow
      868112/27/2012 17:00-4.2-5.988323.2100.47Snow,Blowing Snow
      868212/27/2012 18:00-4.0-5.788288.0100.49Snow,Blowing Snow
      868312/27/2012 19:00-3.9-5.688269.7100.52Snow,Blowing Snow
      868412/27/2012 20:00-3.7-5.3893716.1100.58Snow
      868512/27/2012 21:00-3.7-4.892244.8100.62Freezing Drizzle,Snow
      868612/27/2012 22:00-3.8-4.694204.8100.65Freezing Drizzle,Snow
      868712/27/2012 23:00-4.0-5.689249.7100.70Snow
      868812/28/2012 0:00-4.2-5.789198.0100.78Freezing Drizzle,Snow
      868912/28/2012 1:00-4.4-6.685156.4100.83Freezing Drizzle,Snow
      869012/28/2012 2:00-4.3-6.3861112.9100.93Freezing Drizzle,Snow
      869112/28/2012 3:00-4.6-5.991134.0101.01Snow
      869212/28/2012 4:00-4.9-5.99399.7101.00Snow
      872312/29/2012 11:00-10.9-12.29076.4101.09Snow Showers,Fog
      872412/29/2012 12:00-10.5-11.692118.0100.93Snow Showers,Fog
      872512/29/2012 13:00-10.0-11.192229.7100.63Snow Showers,Fog
      872612/29/2012 14:00-9.3-10.591224.8100.60Snow,Fog
      872712/29/2012 15:00-8.8-10.091201.2100.55Snow,Fog
      872812/29/2012 16:00-8.5-9.990241.2100.49Snow,Fog
      872912/29/2012 17:00-9.0-10.490192.4100.46Snow,Fog
      873012/29/2012 18:00-9.3-10.988266.4100.38Snow,Fog
      873112/29/2012 19:00-9.5-11.287263.2100.33Snow,Fog
      873212/29/2012 20:00-9.7-11.686249.7100.25Snow,Fog
      873312/29/2012 21:00-9.8-11.885248.0100.24Snow,Fog
      873412/29/2012 22:00-10.1-11.689152.4100.20Snow,Fog
      873512/29/2012 23:00-10.0-12.085206.4100.19Snow,Fog
      873612/30/2012 0:00-9.6-11.387133.2100.23Snow,Fog
      873712/30/2012 1:00-9.4-10.59292.4100.22Snow,Fog
      873812/30/2012 2:00-9.3-10.49294.0100.28Snow,Fog
      873912/30/2012 3:00-9.1-10.490113.6100.30Snow,Fog
      874012/30/2012 4:00-9.3-10.690139.7100.28Snow,Fog
      874112/30/2012 5:00-9.1-10.490114.0100.32Snow,Fog
      874212/30/2012 6:00-9.3-10.889178.0100.39Snow,Fog
      876712/31/2012 7:00-9.3-11.385019.3101.19Snow Showers
      876812/31/2012 8:00-8.6-10.38743.2101.14Snow Showers
      876912/31/2012 9:00-8.1-9.68942.4101.09Snow
      877012/31/2012 10:00-7.4-8.98946.4101.05Snow,Fog
      877112/31/2012 11:00-6.7-7.99199.7100.93Snow
      877212/31/2012 12:00-5.8-7.588412.9100.78Snow
      877312/31/2012 13:00-4.6-6.686412.9100.63Snow
      877412/31/2012 14:00-3.4-5.784611.3100.57Snow
      877512/31/2012 15:00-2.3-4.68499.7100.47Snow
      877612/31/2012 16:00-1.4-4.0821312.9100.40Snow
      877712/31/2012 17:00-1.1-3.385199.7100.30Snow
      877812/31/2012 18:00-1.3-3.188179.7100.19Snow
      877912/31/2012 19:000.1-2.781309.7100.13Snow
      878012/31/2012 20:000.2-2.483249.7100.03Snow
      878112/31/2012 21:00-0.5-1.593284.899.95Snow
      878212/31/2012 22:00-0.2-1.889289.799.91Snow
      878312/31/2012 23:000.0-2.1863011.399.89Snow
      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "8680 12/27/2012 16:00 -4.5 -6.2 88 37 \n", + "8681 12/27/2012 17:00 -4.2 -5.9 88 32 \n", + "8682 12/27/2012 18:00 -4.0 -5.7 88 28 \n", + "8683 12/27/2012 19:00 -3.9 -5.6 88 26 \n", + "8684 12/27/2012 20:00 -3.7 -5.3 89 37 \n", + "8685 12/27/2012 21:00 -3.7 -4.8 92 24 \n", + "8686 12/27/2012 22:00 -3.8 -4.6 94 20 \n", + "8687 12/27/2012 23:00 -4.0 -5.6 89 24 \n", + "8688 12/28/2012 0:00 -4.2 -5.7 89 19 \n", + "8689 12/28/2012 1:00 -4.4 -6.6 85 15 \n", + "8690 12/28/2012 2:00 -4.3 -6.3 86 11 \n", + "8691 12/28/2012 3:00 -4.6 -5.9 91 13 \n", + "8692 12/28/2012 4:00 -4.9 -5.9 93 9 \n", + "8723 12/29/2012 11:00 -10.9 -12.2 90 7 \n", + "8724 12/29/2012 12:00 -10.5 -11.6 92 11 \n", + "8725 12/29/2012 13:00 -10.0 -11.1 92 22 \n", + "8726 12/29/2012 14:00 -9.3 -10.5 91 22 \n", + "8727 12/29/2012 15:00 -8.8 -10.0 91 20 \n", + "8728 12/29/2012 16:00 -8.5 -9.9 90 24 \n", + "8729 12/29/2012 17:00 -9.0 -10.4 90 19 \n", + "8730 12/29/2012 18:00 -9.3 -10.9 88 26 \n", + "8731 12/29/2012 19:00 -9.5 -11.2 87 26 \n", + "8732 12/29/2012 20:00 -9.7 -11.6 86 24 \n", + "8733 12/29/2012 21:00 -9.8 -11.8 85 24 \n", + "8734 12/29/2012 22:00 -10.1 -11.6 89 15 \n", + "8735 12/29/2012 23:00 -10.0 -12.0 85 20 \n", + "8736 12/30/2012 0:00 -9.6 -11.3 87 13 \n", + "8737 12/30/2012 1:00 -9.4 -10.5 92 9 \n", + "8738 12/30/2012 2:00 -9.3 -10.4 92 9 \n", + "8739 12/30/2012 3:00 -9.1 -10.4 90 11 \n", + "8740 12/30/2012 4:00 -9.3 -10.6 90 13 \n", + "8741 12/30/2012 5:00 -9.1 -10.4 90 11 \n", + "8742 12/30/2012 6:00 -9.3 -10.8 89 17 \n", + "8767 12/31/2012 7:00 -9.3 -11.3 85 0 \n", + "8768 12/31/2012 8:00 -8.6 -10.3 87 4 \n", + "8769 12/31/2012 9:00 -8.1 -9.6 89 4 \n", + "8770 12/31/2012 10:00 -7.4 -8.9 89 4 \n", + "8771 12/31/2012 11:00 -6.7 -7.9 91 9 \n", + "8772 12/31/2012 12:00 -5.8 -7.5 88 4 \n", + "8773 12/31/2012 13:00 -4.6 -6.6 86 4 \n", + "8774 12/31/2012 14:00 -3.4 -5.7 84 6 \n", + "8775 12/31/2012 15:00 -2.3 -4.6 84 9 \n", + "8776 12/31/2012 16:00 -1.4 -4.0 82 13 \n", + "8777 12/31/2012 17:00 -1.1 -3.3 85 19 \n", + "8778 12/31/2012 18:00 -1.3 -3.1 88 17 \n", + "8779 12/31/2012 19:00 0.1 -2.7 81 30 \n", + "8780 12/31/2012 20:00 0.2 -2.4 83 24 \n", + "8781 12/31/2012 21:00 -0.5 -1.5 93 28 \n", + "8782 12/31/2012 22:00 -0.2 -1.8 89 28 \n", + "8783 12/31/2012 23:00 0.0 -2.1 86 30 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "8680 2.0 100.44 Snow,Blowing Snow \n", + "8681 3.2 100.47 Snow,Blowing Snow \n", + "8682 8.0 100.49 Snow,Blowing Snow \n", + "8683 9.7 100.52 Snow,Blowing Snow \n", + "8684 16.1 100.58 Snow \n", + "8685 4.8 100.62 Freezing Drizzle,Snow \n", + "8686 4.8 100.65 Freezing Drizzle,Snow \n", + "8687 9.7 100.70 Snow \n", + "8688 8.0 100.78 Freezing Drizzle,Snow \n", + "8689 6.4 100.83 Freezing Drizzle,Snow \n", + "8690 12.9 100.93 Freezing Drizzle,Snow \n", + "8691 4.0 101.01 Snow \n", + "8692 9.7 101.00 Snow \n", + "8723 6.4 101.09 Snow Showers,Fog \n", + "8724 8.0 100.93 Snow Showers,Fog \n", + "8725 9.7 100.63 Snow Showers,Fog \n", + "8726 4.8 100.60 Snow,Fog \n", + "8727 1.2 100.55 Snow,Fog \n", + "8728 1.2 100.49 Snow,Fog \n", + "8729 2.4 100.46 Snow,Fog \n", + "8730 6.4 100.38 Snow,Fog \n", + "8731 3.2 100.33 Snow,Fog \n", + "8732 9.7 100.25 Snow,Fog \n", + "8733 8.0 100.24 Snow,Fog \n", + "8734 2.4 100.20 Snow,Fog \n", + "8735 6.4 100.19 Snow,Fog \n", + "8736 3.2 100.23 Snow,Fog \n", + "8737 2.4 100.22 Snow,Fog \n", + "8738 4.0 100.28 Snow,Fog \n", + "8739 3.6 100.30 Snow,Fog \n", + "8740 9.7 100.28 Snow,Fog \n", + "8741 4.0 100.32 Snow,Fog \n", + "8742 8.0 100.39 Snow,Fog \n", + "8767 19.3 101.19 Snow Showers \n", + "8768 3.2 101.14 Snow Showers \n", + "8769 2.4 101.09 Snow \n", + "8770 6.4 101.05 Snow,Fog \n", + "8771 9.7 100.93 Snow \n", + "8772 12.9 100.78 Snow \n", + "8773 12.9 100.63 Snow \n", + "8774 11.3 100.57 Snow \n", + "8775 9.7 100.47 Snow \n", + "8776 12.9 100.40 Snow \n", + "8777 9.7 100.30 Snow \n", + "8778 9.7 100.19 Snow \n", + "8779 9.7 100.13 Snow \n", + "8780 9.7 100.03 Snow \n", + "8781 4.8 99.95 Snow \n", + "8782 9.7 99.91 Snow \n", + "8783 11.3 99.89 Snow " + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#str. contains\n", + "data[data['Weather Condition'].str.contains('Snow')].tail(50)" + ] + }, + { + "cell_type": "markdown", + "id": "20adbc26", + "metadata": {}, + "source": [ + "## Q)10. Find all instances when 'Wind Speed is above 24' and 'Visibility' is 25'" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "8f0d488a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      231/1/2012 23:005.32.0793025.099.31Cloudy
      241/2/2012 0:005.21.5773525.099.26Rain Showers
      251/2/2012 1:004.60.0723925.099.26Cloudy
      261/2/2012 2:003.9-0.9713225.099.26Mostly Cloudy
      271/2/2012 3:003.7-1.5693325.099.30Mostly Cloudy
      ...........................
      870512/28/2012 17:00-8.6-12.0762625.0101.34Mainly Clear
      875312/30/2012 17:00-12.1-15.8742825.0101.26Mainly Clear
      875512/30/2012 19:00-13.4-16.5772625.0101.47Mainly Clear
      875912/30/2012 23:00-12.1-15.1782825.0101.52Mostly Cloudy
      876012/31/2012 0:00-11.1-14.4772625.0101.51Cloudy
      \n", + "

      308 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "23 1/1/2012 23:00 5.3 2.0 79 30 \n", + "24 1/2/2012 0:00 5.2 1.5 77 35 \n", + "25 1/2/2012 1:00 4.6 0.0 72 39 \n", + "26 1/2/2012 2:00 3.9 -0.9 71 32 \n", + "27 1/2/2012 3:00 3.7 -1.5 69 33 \n", + "... ... ... ... ... ... \n", + "8705 12/28/2012 17:00 -8.6 -12.0 76 26 \n", + "8753 12/30/2012 17:00 -12.1 -15.8 74 28 \n", + "8755 12/30/2012 19:00 -13.4 -16.5 77 26 \n", + "8759 12/30/2012 23:00 -12.1 -15.1 78 28 \n", + "8760 12/31/2012 0:00 -11.1 -14.4 77 26 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "23 25.0 99.31 Cloudy \n", + "24 25.0 99.26 Rain Showers \n", + "25 25.0 99.26 Cloudy \n", + "26 25.0 99.26 Mostly Cloudy \n", + "27 25.0 99.30 Mostly Cloudy \n", + "... ... ... ... \n", + "8705 25.0 101.34 Mainly Clear \n", + "8753 25.0 101.26 Mainly Clear \n", + "8755 25.0 101.47 Mainly Clear \n", + "8759 25.0 101.52 Mostly Cloudy \n", + "8760 25.0 101.51 Cloudy \n", + "\n", + "[308 rows x 8 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data.head(2)\n", + "data[(data['Wind Speed_km/h']>24) & (data['Visibility_km']==25)]" + ] + }, + { + "cell_type": "markdown", + "id": "9c101919", + "metadata": {}, + "source": [ + "## Q)11. What is the Mean value of each column against each'Weather Condition'?" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "ca99f3d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\chamu\\AppData\\Local\\Temp\\ipykernel_5140\\4248410862.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", + " data.groupby('Weather Condition').mean()\n" + ] + }, + { + "data": { + "text/html": [ + "
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      Temp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPa
      Weather Condition
      Clear6.8257160.08936764.49773810.55731530.153243101.587443
      Cloudy7.9705442.37581069.59259316.12731526.625752100.911441
      Drizzle7.3536595.50487888.24390216.09756117.931707100.435366
      Drizzle,Fog8.0675007.03375093.27500011.8625005.257500100.786625
      Drizzle,Ice Pellets,Fog0.400000-0.70000092.00000020.0000004.000000100.790000
      Drizzle,Snow1.0500000.15000093.50000014.00000010.500000100.890000
      Drizzle,Snow,Fog0.6933330.12000095.86666715.5333335.51333399.281333
      Fog4.3033333.15933392.2866677.9466676.248000101.184067
      Freezing Drizzle-5.657143-8.00000083.57142916.5714299.200000100.202857
      Freezing Drizzle,Fog-2.533333-4.18333388.50000017.0000005.266667100.441667
      Freezing Drizzle,Haze-5.433333-8.00000082.00000010.3333332.666667100.316667
      Freezing Drizzle,Snow-5.109091-7.07272786.09090916.2727275.872727100.520909
      Freezing Fog-7.575000-9.25000087.7500004.7500000.650000102.320000
      Freezing Rain-3.885714-6.07857184.64285719.2142868.24285799.647143
      Freezing Rain,Fog-2.225000-3.75000089.50000015.5000007.55000099.945000
      Freezing Rain,Haze-4.900000-7.45000082.5000007.5000002.400000100.375000
      Freezing Rain,Ice Pellets,Fog-2.600000-3.70000092.00000028.0000008.000000100.950000
      Freezing Rain,Snow Grains-5.000000-7.30000084.00000032.0000004.80000098.560000
      Haze-0.200000-2.97500081.62500010.4375007.831250101.482500
      Mainly Clear12.5589274.58167160.66714214.14482434.264862101.248832
      Moderate Rain,Fog1.7000000.80000094.00000017.0000006.40000099.980000
      Moderate Snow-5.525000-7.25000087.75000033.7500000.750000100.275000
      Moderate Snow,Blowing Snow-5.450000-6.50000092.50000040.0000000.600000100.570000
      Mostly Cloudy10.5742873.13117462.10246515.81392031.253842101.025288
      Rain9.7862757.04281083.62418319.25490218.856536100.233333
      Rain Showers13.7223409.18776675.15957417.13297922.816489100.404043
      Rain Showers,Fog12.80000012.10000096.00000013.0000006.40000099.830000
      Rain Showers,Snow Showers2.150000-1.50000076.50000022.50000021.700000101.100000
      Rain,Fog8.2732767.21982893.18965514.7931036.873276100.500862
      Rain,Haze4.6333332.06666783.33333311.6666676.700000100.540000
      Rain,Ice Pellets0.600000-0.60000092.00000024.0000009.700000100.120000
      Rain,Snow1.055556-0.56666789.00000028.38888911.67222299.951111
      Rain,Snow Grains1.900000-2.10000075.00000026.00000025.000000100.600000
      Rain,Snow,Fog0.8000000.30000096.0000009.0000006.400000100.730000
      Rain,Snow,Ice Pellets1.100000-0.17500091.50000023.2500006.000000100.105000
      Snow-4.524103-7.62333379.30769220.03846211.171795100.536103
      Snow Pellets0.700000-6.40000059.00000035.0000002.40000099.700000
      Snow Showers-3.506667-7.86666772.35000019.23333320.158333100.963500
      Snow Showers,Fog-10.675000-11.90000090.75000013.7500007.025000101.292500
      Snow,Blowing Snow-5.410526-7.62105384.47368434.8421054.10526399.704737
      Snow,Fog-5.075676-6.36486590.67567617.3243244.537838100.688649
      Snow,Haze-4.020000-6.86000080.6000005.0000004.640000100.782000
      Snow,Ice Pellets-1.883333-3.66666787.66666723.8333337.416667100.548333
      Thunderstorms24.15000019.75000077.0000007.50000024.550000100.230000
      Thunderstorms,Heavy Rain Showers10.9000009.00000088.0000009.0000002.400000100.260000
      Thunderstorms,Moderate Rain Showers,Fog19.60000018.50000093.00000015.0000003.200000100.010000
      Thunderstorms,Rain20.43333318.53333389.00000015.66666719.833333100.420000
      Thunderstorms,Rain Showers20.03750017.61875086.37500018.31250015.893750100.233750
      Thunderstorms,Rain Showers,Fog21.60000018.70000084.00000019.6666679.700000100.063333
      Thunderstorms,Rain,Fog20.60000018.60000088.00000019.0000004.800000100.080000
      \n", + "
      " + ], + "text/plain": [ + " Temp_C Dew Point Temp_C \\\n", + "Weather Condition \n", + "Clear 6.825716 0.089367 \n", + "Cloudy 7.970544 2.375810 \n", + "Drizzle 7.353659 5.504878 \n", + "Drizzle,Fog 8.067500 7.033750 \n", + "Drizzle,Ice Pellets,Fog 0.400000 -0.700000 \n", + "Drizzle,Snow 1.050000 0.150000 \n", + "Drizzle,Snow,Fog 0.693333 0.120000 \n", + "Fog 4.303333 3.159333 \n", + "Freezing Drizzle -5.657143 -8.000000 \n", + "Freezing Drizzle,Fog -2.533333 -4.183333 \n", + "Freezing Drizzle,Haze -5.433333 -8.000000 \n", + "Freezing Drizzle,Snow -5.109091 -7.072727 \n", + "Freezing Fog -7.575000 -9.250000 \n", + "Freezing Rain -3.885714 -6.078571 \n", + "Freezing Rain,Fog -2.225000 -3.750000 \n", + "Freezing Rain,Haze -4.900000 -7.450000 \n", + "Freezing Rain,Ice Pellets,Fog -2.600000 -3.700000 \n", + "Freezing Rain,Snow Grains -5.000000 -7.300000 \n", + "Haze -0.200000 -2.975000 \n", + "Mainly Clear 12.558927 4.581671 \n", + "Moderate Rain,Fog 1.700000 0.800000 \n", + "Moderate Snow -5.525000 -7.250000 \n", + "Moderate Snow,Blowing Snow -5.450000 -6.500000 \n", + "Mostly Cloudy 10.574287 3.131174 \n", + "Rain 9.786275 7.042810 \n", + "Rain Showers 13.722340 9.187766 \n", + "Rain Showers,Fog 12.800000 12.100000 \n", + "Rain Showers,Snow Showers 2.150000 -1.500000 \n", + "Rain,Fog 8.273276 7.219828 \n", + "Rain,Haze 4.633333 2.066667 \n", + "Rain,Ice Pellets 0.600000 -0.600000 \n", + "Rain,Snow 1.055556 -0.566667 \n", + "Rain,Snow Grains 1.900000 -2.100000 \n", + "Rain,Snow,Fog 0.800000 0.300000 \n", + "Rain,Snow,Ice Pellets 1.100000 -0.175000 \n", + "Snow -4.524103 -7.623333 \n", + "Snow Pellets 0.700000 -6.400000 \n", + "Snow Showers -3.506667 -7.866667 \n", + "Snow Showers,Fog -10.675000 -11.900000 \n", + "Snow,Blowing Snow -5.410526 -7.621053 \n", + "Snow,Fog -5.075676 -6.364865 \n", + "Snow,Haze -4.020000 -6.860000 \n", + "Snow,Ice Pellets -1.883333 -3.666667 \n", + "Thunderstorms 24.150000 19.750000 \n", + "Thunderstorms,Heavy Rain Showers 10.900000 9.000000 \n", + "Thunderstorms,Moderate Rain Showers,Fog 19.600000 18.500000 \n", + "Thunderstorms,Rain 20.433333 18.533333 \n", + "Thunderstorms,Rain Showers 20.037500 17.618750 \n", + "Thunderstorms,Rain Showers,Fog 21.600000 18.700000 \n", + "Thunderstorms,Rain,Fog 20.600000 18.600000 \n", + "\n", + " Rel Hum_% Wind Speed_km/h \\\n", + "Weather Condition \n", + "Clear 64.497738 10.557315 \n", + "Cloudy 69.592593 16.127315 \n", + "Drizzle 88.243902 16.097561 \n", + "Drizzle,Fog 93.275000 11.862500 \n", + "Drizzle,Ice Pellets,Fog 92.000000 20.000000 \n", + "Drizzle,Snow 93.500000 14.000000 \n", + "Drizzle,Snow,Fog 95.866667 15.533333 \n", + "Fog 92.286667 7.946667 \n", + "Freezing Drizzle 83.571429 16.571429 \n", + "Freezing Drizzle,Fog 88.500000 17.000000 \n", + "Freezing Drizzle,Haze 82.000000 10.333333 \n", + "Freezing Drizzle,Snow 86.090909 16.272727 \n", + "Freezing Fog 87.750000 4.750000 \n", + "Freezing Rain 84.642857 19.214286 \n", + "Freezing Rain,Fog 89.500000 15.500000 \n", + "Freezing Rain,Haze 82.500000 7.500000 \n", + "Freezing Rain,Ice Pellets,Fog 92.000000 28.000000 \n", + "Freezing Rain,Snow Grains 84.000000 32.000000 \n", + "Haze 81.625000 10.437500 \n", + "Mainly Clear 60.667142 14.144824 \n", + "Moderate Rain,Fog 94.000000 17.000000 \n", + "Moderate Snow 87.750000 33.750000 \n", + "Moderate Snow,Blowing Snow 92.500000 40.000000 \n", + "Mostly Cloudy 62.102465 15.813920 \n", + "Rain 83.624183 19.254902 \n", + "Rain Showers 75.159574 17.132979 \n", + "Rain Showers,Fog 96.000000 13.000000 \n", + "Rain Showers,Snow Showers 76.500000 22.500000 \n", + "Rain,Fog 93.189655 14.793103 \n", + "Rain,Haze 83.333333 11.666667 \n", + "Rain,Ice Pellets 92.000000 24.000000 \n", + "Rain,Snow 89.000000 28.388889 \n", + "Rain,Snow Grains 75.000000 26.000000 \n", + "Rain,Snow,Fog 96.000000 9.000000 \n", + "Rain,Snow,Ice Pellets 91.500000 23.250000 \n", + "Snow 79.307692 20.038462 \n", + "Snow Pellets 59.000000 35.000000 \n", + "Snow Showers 72.350000 19.233333 \n", + "Snow Showers,Fog 90.750000 13.750000 \n", + "Snow,Blowing Snow 84.473684 34.842105 \n", + "Snow,Fog 90.675676 17.324324 \n", + "Snow,Haze 80.600000 5.000000 \n", + "Snow,Ice Pellets 87.666667 23.833333 \n", + "Thunderstorms 77.000000 7.500000 \n", + "Thunderstorms,Heavy Rain Showers 88.000000 9.000000 \n", + "Thunderstorms,Moderate Rain Showers,Fog 93.000000 15.000000 \n", + "Thunderstorms,Rain 89.000000 15.666667 \n", + "Thunderstorms,Rain Showers 86.375000 18.312500 \n", + "Thunderstorms,Rain Showers,Fog 84.000000 19.666667 \n", + "Thunderstorms,Rain,Fog 88.000000 19.000000 \n", + "\n", + " Visibility_km Press_kPa \n", + "Weather Condition \n", + "Clear 30.153243 101.587443 \n", + "Cloudy 26.625752 100.911441 \n", + "Drizzle 17.931707 100.435366 \n", + "Drizzle,Fog 5.257500 100.786625 \n", + "Drizzle,Ice Pellets,Fog 4.000000 100.790000 \n", + "Drizzle,Snow 10.500000 100.890000 \n", + "Drizzle,Snow,Fog 5.513333 99.281333 \n", + "Fog 6.248000 101.184067 \n", + "Freezing Drizzle 9.200000 100.202857 \n", + "Freezing Drizzle,Fog 5.266667 100.441667 \n", + "Freezing Drizzle,Haze 2.666667 100.316667 \n", + "Freezing Drizzle,Snow 5.872727 100.520909 \n", + "Freezing Fog 0.650000 102.320000 \n", + "Freezing Rain 8.242857 99.647143 \n", + "Freezing Rain,Fog 7.550000 99.945000 \n", + "Freezing Rain,Haze 2.400000 100.375000 \n", + "Freezing Rain,Ice Pellets,Fog 8.000000 100.950000 \n", + "Freezing Rain,Snow Grains 4.800000 98.560000 \n", + "Haze 7.831250 101.482500 \n", + "Mainly Clear 34.264862 101.248832 \n", + "Moderate Rain,Fog 6.400000 99.980000 \n", + "Moderate Snow 0.750000 100.275000 \n", + "Moderate Snow,Blowing Snow 0.600000 100.570000 \n", + "Mostly Cloudy 31.253842 101.025288 \n", + "Rain 18.856536 100.233333 \n", + "Rain Showers 22.816489 100.404043 \n", + "Rain Showers,Fog 6.400000 99.830000 \n", + "Rain Showers,Snow Showers 21.700000 101.100000 \n", + "Rain,Fog 6.873276 100.500862 \n", + "Rain,Haze 6.700000 100.540000 \n", + "Rain,Ice Pellets 9.700000 100.120000 \n", + "Rain,Snow 11.672222 99.951111 \n", + "Rain,Snow Grains 25.000000 100.600000 \n", + "Rain,Snow,Fog 6.400000 100.730000 \n", + "Rain,Snow,Ice Pellets 6.000000 100.105000 \n", + "Snow 11.171795 100.536103 \n", + "Snow Pellets 2.400000 99.700000 \n", + "Snow Showers 20.158333 100.963500 \n", + "Snow Showers,Fog 7.025000 101.292500 \n", + "Snow,Blowing Snow 4.105263 99.704737 \n", + "Snow,Fog 4.537838 100.688649 \n", + "Snow,Haze 4.640000 100.782000 \n", + "Snow,Ice Pellets 7.416667 100.548333 \n", + "Thunderstorms 24.550000 100.230000 \n", + "Thunderstorms,Heavy Rain Showers 2.400000 100.260000 \n", + "Thunderstorms,Moderate Rain Showers,Fog 3.200000 100.010000 \n", + "Thunderstorms,Rain 19.833333 100.420000 \n", + "Thunderstorms,Rain Showers 15.893750 100.233750 \n", + "Thunderstorms,Rain Showers,Fog 9.700000 100.063333 \n", + "Thunderstorms,Rain,Fog 4.800000 100.080000 " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data.head(2)\n", + "data.groupby('Weather Condition').mean()\n" + ] + }, + { + "cell_type": "markdown", + "id": "099fd839", + "metadata": {}, + "source": [ + "## Q)12. What is the Minimum & Maximum value of each column against each 'Weather Condition'" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "bf676409", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPa
      Weather Condition
      Clear1/11/2012 1:00-23.3-28.520011.399.52
      Cloudy1/1/2012 17:00-21.4-26.818011.398.39
      Drizzle1/23/2012 21:001.1-0.27406.497.84
      Drizzle,Fog1/23/2012 20:000.0-1.68501.098.65
      Drizzle,Ice Pellets,Fog12/17/2012 9:000.4-0.792204.0100.79
      Drizzle,Snow12/17/2012 15:000.90.19299.7100.63
      Drizzle,Snow,Fog12/18/2012 21:000.3-0.19272.497.79
      Fog1/1/2012 0:00-16.0-17.28000.298.31
      Freezing Drizzle1/13/2012 10:00-9.0-12.27864.898.44
      Freezing Drizzle,Fog1/1/2012 2:00-6.4-9.08263.698.74
      Freezing Drizzle,Haze2/1/2012 11:00-5.8-8.38192.0100.28
      Freezing Drizzle,Snow1/13/2012 3:00-8.3-10.47962.499.19
      Freezing Fog1/22/2012 6:00-19.0-22.97100.2101.97
      Freezing Rain1/13/2012 11:00-6.5-9.08172.898.22
      Freezing Rain,Fog1/17/2012 23:00-6.1-8.78272.898.32
      Freezing Rain,Haze2/1/2012 14:00-4.9-7.58262.0100.34
      Freezing Rain,Ice Pellets,Fog12/17/2012 3:00-2.6-3.792288.0100.95
      Freezing Rain,Snow Grains1/13/2012 9:00-5.0-7.384324.898.56
      Haze1/22/2012 12:00-11.5-16.06804.8100.35
      Mainly Clear1/10/2012 11:00-22.8-28.020012.998.67
      Moderate Rain,Fog12/10/2012 8:001.70.894176.499.98
      Moderate Snow1/12/2012 15:00-6.3-7.683260.699.88
      Moderate Snow,Blowing Snow12/27/2012 10:00-5.5-6.692390.6100.50
      Mostly Cloudy1/1/2012 16:00-23.2-28.518011.398.36
      Rain1/1/2012 18:000.3-5.74004.097.52
      Rain Showers1/1/2012 22:001.6-7.23706.498.51
      Rain Showers,Fog10/20/2012 3:0012.812.196136.499.83
      Rain Showers,Snow Showers11/4/2012 8:002.1-1.8751719.3101.09
      Rain,Fog1/23/2012 18:000.0-1.28302.098.61
      Rain,Haze3/13/2012 7:004.01.08174.0100.50
      Rain,Ice Pellets12/18/2012 5:000.6-0.692249.7100.12
      Rain,Snow1/10/2012 5:000.6-1.781132.498.18
      Rain,Snow Grains12/21/2012 0:001.9-2.1752625.0100.60
      Rain,Snow,Fog12/8/2012 21:000.80.39696.4100.73
      Rain,Snow,Ice Pellets12/21/2012 1:000.9-0.788174.899.85
      Snow1/10/2012 1:00-16.7-24.64101.097.75
      Snow Pellets11/24/2012 15:000.7-6.459352.499.70
      Snow Showers1/12/2012 7:00-13.3-19.35202.499.49
      Snow Showers,Fog12/26/2012 9:00-11.3-12.78974.0100.63
      Snow,Blowing Snow1/13/2012 21:00-12.0-16.270240.698.11
      Snow,Fog12/16/2012 15:00-10.1-12.07741.299.38
      Snow,Haze2/1/2012 17:00-4.3-7.28004.0100.61
      Snow,Ice Pellets12/10/2012 3:00-4.3-5.976192.899.40
      Thunderstorms7/16/2012 1:0021.619.467024.199.84
      Thunderstorms,Heavy Rain Showers5/29/2012 6:0010.99.08892.4100.26
      Thunderstorms,Moderate Rain Showers,Fog7/17/2012 6:0019.618.593153.2100.01
      Thunderstorms,Rain5/25/2012 20:0019.418.283416.1100.19
      Thunderstorms,Rain Showers5/29/2012 16:0011.07.06876.499.65
      Thunderstorms,Rain Showers,Fog6/29/2012 3:0019.516.18079.799.71
      Thunderstorms,Rain,Fog7/17/2012 5:0020.618.688194.8100.08
      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C \\\n", + "Weather Condition \n", + "Clear 1/11/2012 1:00 -23.3 \n", + "Cloudy 1/1/2012 17:00 -21.4 \n", + "Drizzle 1/23/2012 21:00 1.1 \n", + "Drizzle,Fog 1/23/2012 20:00 0.0 \n", + "Drizzle,Ice Pellets,Fog 12/17/2012 9:00 0.4 \n", + "Drizzle,Snow 12/17/2012 15:00 0.9 \n", + "Drizzle,Snow,Fog 12/18/2012 21:00 0.3 \n", + "Fog 1/1/2012 0:00 -16.0 \n", + "Freezing Drizzle 1/13/2012 10:00 -9.0 \n", + "Freezing Drizzle,Fog 1/1/2012 2:00 -6.4 \n", + "Freezing Drizzle,Haze 2/1/2012 11:00 -5.8 \n", + "Freezing Drizzle,Snow 1/13/2012 3:00 -8.3 \n", + "Freezing Fog 1/22/2012 6:00 -19.0 \n", + "Freezing Rain 1/13/2012 11:00 -6.5 \n", + "Freezing Rain,Fog 1/17/2012 23:00 -6.1 \n", + "Freezing Rain,Haze 2/1/2012 14:00 -4.9 \n", + "Freezing Rain,Ice Pellets,Fog 12/17/2012 3:00 -2.6 \n", + "Freezing Rain,Snow Grains 1/13/2012 9:00 -5.0 \n", + "Haze 1/22/2012 12:00 -11.5 \n", + "Mainly Clear 1/10/2012 11:00 -22.8 \n", + "Moderate Rain,Fog 12/10/2012 8:00 1.7 \n", + "Moderate Snow 1/12/2012 15:00 -6.3 \n", + "Moderate Snow,Blowing Snow 12/27/2012 10:00 -5.5 \n", + "Mostly Cloudy 1/1/2012 16:00 -23.2 \n", + "Rain 1/1/2012 18:00 0.3 \n", + "Rain Showers 1/1/2012 22:00 1.6 \n", + "Rain Showers,Fog 10/20/2012 3:00 12.8 \n", + "Rain Showers,Snow Showers 11/4/2012 8:00 2.1 \n", + "Rain,Fog 1/23/2012 18:00 0.0 \n", + "Rain,Haze 3/13/2012 7:00 4.0 \n", + "Rain,Ice Pellets 12/18/2012 5:00 0.6 \n", + "Rain,Snow 1/10/2012 5:00 0.6 \n", + "Rain,Snow Grains 12/21/2012 0:00 1.9 \n", + "Rain,Snow,Fog 12/8/2012 21:00 0.8 \n", + "Rain,Snow,Ice Pellets 12/21/2012 1:00 0.9 \n", + "Snow 1/10/2012 1:00 -16.7 \n", + "Snow Pellets 11/24/2012 15:00 0.7 \n", + "Snow Showers 1/12/2012 7:00 -13.3 \n", + "Snow Showers,Fog 12/26/2012 9:00 -11.3 \n", + "Snow,Blowing Snow 1/13/2012 21:00 -12.0 \n", + "Snow,Fog 12/16/2012 15:00 -10.1 \n", + "Snow,Haze 2/1/2012 17:00 -4.3 \n", + "Snow,Ice Pellets 12/10/2012 3:00 -4.3 \n", + "Thunderstorms 7/16/2012 1:00 21.6 \n", + "Thunderstorms,Heavy Rain Showers 5/29/2012 6:00 10.9 \n", + "Thunderstorms,Moderate Rain Showers,Fog 7/17/2012 6:00 19.6 \n", + "Thunderstorms,Rain 5/25/2012 20:00 19.4 \n", + "Thunderstorms,Rain Showers 5/29/2012 16:00 11.0 \n", + "Thunderstorms,Rain Showers,Fog 6/29/2012 3:00 19.5 \n", + "Thunderstorms,Rain,Fog 7/17/2012 5:00 20.6 \n", + "\n", + " Dew Point Temp_C Rel Hum_% \\\n", + "Weather Condition \n", + "Clear -28.5 20 \n", + "Cloudy -26.8 18 \n", + "Drizzle -0.2 74 \n", + "Drizzle,Fog -1.6 85 \n", + "Drizzle,Ice Pellets,Fog -0.7 92 \n", + "Drizzle,Snow 0.1 92 \n", + "Drizzle,Snow,Fog -0.1 92 \n", + "Fog -17.2 80 \n", + "Freezing Drizzle -12.2 78 \n", + "Freezing Drizzle,Fog -9.0 82 \n", + "Freezing Drizzle,Haze -8.3 81 \n", + "Freezing Drizzle,Snow -10.4 79 \n", + "Freezing Fog -22.9 71 \n", + "Freezing Rain -9.0 81 \n", + "Freezing Rain,Fog -8.7 82 \n", + "Freezing Rain,Haze -7.5 82 \n", + "Freezing Rain,Ice Pellets,Fog -3.7 92 \n", + "Freezing Rain,Snow Grains -7.3 84 \n", + "Haze -16.0 68 \n", + "Mainly Clear -28.0 20 \n", + "Moderate Rain,Fog 0.8 94 \n", + "Moderate Snow -7.6 83 \n", + "Moderate Snow,Blowing Snow -6.6 92 \n", + "Mostly Cloudy -28.5 18 \n", + "Rain -5.7 40 \n", + "Rain Showers -7.2 37 \n", + "Rain Showers,Fog 12.1 96 \n", + "Rain Showers,Snow Showers -1.8 75 \n", + "Rain,Fog -1.2 83 \n", + "Rain,Haze 1.0 81 \n", + "Rain,Ice Pellets -0.6 92 \n", + "Rain,Snow -1.7 81 \n", + "Rain,Snow Grains -2.1 75 \n", + "Rain,Snow,Fog 0.3 96 \n", + "Rain,Snow,Ice Pellets -0.7 88 \n", + "Snow -24.6 41 \n", + "Snow Pellets -6.4 59 \n", + "Snow Showers -19.3 52 \n", + "Snow Showers,Fog -12.7 89 \n", + "Snow,Blowing Snow -16.2 70 \n", + "Snow,Fog -12.0 77 \n", + "Snow,Haze -7.2 80 \n", + "Snow,Ice Pellets -5.9 76 \n", + "Thunderstorms 19.4 67 \n", + "Thunderstorms,Heavy Rain Showers 9.0 88 \n", + "Thunderstorms,Moderate Rain Showers,Fog 18.5 93 \n", + "Thunderstorms,Rain 18.2 83 \n", + "Thunderstorms,Rain Showers 7.0 68 \n", + "Thunderstorms,Rain Showers,Fog 16.1 80 \n", + "Thunderstorms,Rain,Fog 18.6 88 \n", + "\n", + " Wind Speed_km/h Visibility_km \\\n", + "Weather Condition \n", + "Clear 0 11.3 \n", + "Cloudy 0 11.3 \n", + "Drizzle 0 6.4 \n", + "Drizzle,Fog 0 1.0 \n", + "Drizzle,Ice Pellets,Fog 20 4.0 \n", + "Drizzle,Snow 9 9.7 \n", + "Drizzle,Snow,Fog 7 2.4 \n", + "Fog 0 0.2 \n", + "Freezing Drizzle 6 4.8 \n", + "Freezing Drizzle,Fog 6 3.6 \n", + "Freezing Drizzle,Haze 9 2.0 \n", + "Freezing Drizzle,Snow 6 2.4 \n", + "Freezing Fog 0 0.2 \n", + "Freezing Rain 7 2.8 \n", + "Freezing Rain,Fog 7 2.8 \n", + "Freezing Rain,Haze 6 2.0 \n", + "Freezing Rain,Ice Pellets,Fog 28 8.0 \n", + "Freezing Rain,Snow Grains 32 4.8 \n", + "Haze 0 4.8 \n", + "Mainly Clear 0 12.9 \n", + "Moderate Rain,Fog 17 6.4 \n", + "Moderate Snow 26 0.6 \n", + "Moderate Snow,Blowing Snow 39 0.6 \n", + "Mostly Cloudy 0 11.3 \n", + "Rain 0 4.0 \n", + "Rain Showers 0 6.4 \n", + "Rain Showers,Fog 13 6.4 \n", + "Rain Showers,Snow Showers 17 19.3 \n", + "Rain,Fog 0 2.0 \n", + "Rain,Haze 7 4.0 \n", + "Rain,Ice Pellets 24 9.7 \n", + "Rain,Snow 13 2.4 \n", + "Rain,Snow Grains 26 25.0 \n", + "Rain,Snow,Fog 9 6.4 \n", + "Rain,Snow,Ice Pellets 17 4.8 \n", + "Snow 0 1.0 \n", + "Snow Pellets 35 2.4 \n", + "Snow Showers 0 2.4 \n", + "Snow Showers,Fog 7 4.0 \n", + "Snow,Blowing Snow 24 0.6 \n", + "Snow,Fog 4 1.2 \n", + "Snow,Haze 0 4.0 \n", + "Snow,Ice Pellets 19 2.8 \n", + "Thunderstorms 0 24.1 \n", + "Thunderstorms,Heavy Rain Showers 9 2.4 \n", + "Thunderstorms,Moderate Rain Showers,Fog 15 3.2 \n", + "Thunderstorms,Rain 4 16.1 \n", + "Thunderstorms,Rain Showers 7 6.4 \n", + "Thunderstorms,Rain Showers,Fog 7 9.7 \n", + "Thunderstorms,Rain,Fog 19 4.8 \n", + "\n", + " Press_kPa \n", + "Weather Condition \n", + "Clear 99.52 \n", + "Cloudy 98.39 \n", + "Drizzle 97.84 \n", + "Drizzle,Fog 98.65 \n", + "Drizzle,Ice Pellets,Fog 100.79 \n", + "Drizzle,Snow 100.63 \n", + "Drizzle,Snow,Fog 97.79 \n", + "Fog 98.31 \n", + "Freezing Drizzle 98.44 \n", + "Freezing Drizzle,Fog 98.74 \n", + "Freezing Drizzle,Haze 100.28 \n", + "Freezing Drizzle,Snow 99.19 \n", + "Freezing Fog 101.97 \n", + "Freezing Rain 98.22 \n", + "Freezing Rain,Fog 98.32 \n", + "Freezing Rain,Haze 100.34 \n", + "Freezing Rain,Ice Pellets,Fog 100.95 \n", + "Freezing Rain,Snow Grains 98.56 \n", + "Haze 100.35 \n", + "Mainly Clear 98.67 \n", + "Moderate Rain,Fog 99.98 \n", + "Moderate Snow 99.88 \n", + "Moderate Snow,Blowing Snow 100.50 \n", + "Mostly Cloudy 98.36 \n", + "Rain 97.52 \n", + "Rain Showers 98.51 \n", + "Rain Showers,Fog 99.83 \n", + "Rain Showers,Snow Showers 101.09 \n", + "Rain,Fog 98.61 \n", + "Rain,Haze 100.50 \n", + "Rain,Ice Pellets 100.12 \n", + "Rain,Snow 98.18 \n", + "Rain,Snow Grains 100.60 \n", + "Rain,Snow,Fog 100.73 \n", + "Rain,Snow,Ice Pellets 99.85 \n", + "Snow 97.75 \n", + "Snow Pellets 99.70 \n", + "Snow Showers 99.49 \n", + "Snow Showers,Fog 100.63 \n", + "Snow,Blowing Snow 98.11 \n", + "Snow,Fog 99.38 \n", + "Snow,Haze 100.61 \n", + "Snow,Ice Pellets 99.40 \n", + "Thunderstorms 99.84 \n", + "Thunderstorms,Heavy Rain Showers 100.26 \n", + "Thunderstorms,Moderate Rain Showers,Fog 100.01 \n", + "Thunderstorms,Rain 100.19 \n", + "Thunderstorms,Rain Showers 99.65 \n", + "Thunderstorms,Rain Showers,Fog 99.71 \n", + "Thunderstorms,Rain,Fog 100.08 " + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(\"Weather Condition\").min()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "5c1c72fb", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPa
      Weather Condition
      Clear9/9/2012 5:0032.820.4993348.3103.63
      Cloudy9/9/2012 23:0030.522.6995448.3103.65
      Drizzle9/30/2012 3:0018.817.7963025.0101.56
      Drizzle,Fog9/30/2012 2:0019.919.1100289.7102.07
      Drizzle,Ice Pellets,Fog12/17/2012 9:000.4-0.792204.0100.79
      Drizzle,Snow12/19/2012 18:001.20.2951911.3101.15
      Drizzle,Snow,Fog12/22/2012 3:001.10.698329.7100.15
      Fog9/22/2012 0:0020.819.6100229.7103.04
      Freezing Drizzle2/1/2012 5:00-2.3-3.3932612.9101.02
      Freezing Drizzle,Fog12/10/2012 5:00-0.3-2.394338.0101.27
      Freezing Drizzle,Haze2/1/2012 13:00-5.0-7.783114.0100.36
      Freezing Drizzle,Snow3/2/2012 12:00-3.3-4.6942412.9101.18
      Freezing Fog3/17/2012 6:00-0.1-0.39990.8102.85
      Freezing Rain2/1/2012 7:000.3-1.7922816.1101.00
      Freezing Rain,Fog12/17/2012 1:000.1-0.993269.7101.01
      Freezing Rain,Haze2/1/2012 15:00-4.9-7.48392.8100.41
      Freezing Rain,Ice Pellets,Fog12/17/2012 3:00-2.6-3.792288.0100.95
      Freezing Rain,Snow Grains1/13/2012 9:00-5.0-7.384324.898.56
      Haze3/13/2012 23:0014.111.186179.7102.97
      Mainly Clear9/9/2012 9:0033.021.2996348.3103.59
      Moderate Rain,Fog12/10/2012 8:001.70.894176.499.98
      Moderate Snow12/27/2012 9:00-4.9-6.793390.8100.67
      Moderate Snow,Blowing Snow12/27/2012 12:00-5.4-6.493410.6100.64
      Mostly Cloudy9/9/2012 2:0032.424.41008348.3103.65
      Rain9/5/2012 2:0022.820.4995248.3102.26
      Rain Showers9/8/2012 16:0026.423.0974148.3102.31
      Rain Showers,Fog10/20/2012 3:0012.812.196136.499.83
      Rain Showers,Snow Showers12/5/2012 10:002.2-1.2782824.1101.11
      Rain,Fog9/30/2012 23:0021.719.5100469.7101.77
      Rain,Haze3/13/2012 9:005.52.986179.7100.61
      Rain,Ice Pellets12/18/2012 5:000.6-0.692249.7100.12
      Rain,Snow4/23/2012 3:001.70.5945225.0101.07
      Rain,Snow Grains12/21/2012 0:001.9-2.1752625.0100.60
      Rain,Snow,Fog12/8/2012 21:000.80.39696.4100.73
      Rain,Snow,Ice Pellets12/21/2012 5:001.30.194286.4100.47
      Snow4/27/2012 9:003.70.3965725.0102.73
      Snow Pellets11/24/2012 15:000.7-6.459352.499.70
      Snow Showers3/4/2012 21:002.9-0.7943748.3102.50
      Snow Showers,Fog12/29/2012 13:00-10.0-11.192229.7102.52
      Snow,Blowing Snow2/25/2012 9:00-1.4-2.991489.7100.62
      Snow,Fog3/14/2012 19:001.10.899359.7102.07
      Snow,Haze2/1/2012 21:00-3.6-6.481156.4100.99
      Snow,Ice Pellets3/3/2012 4:000.8-1.7923311.3100.96
      Thunderstorms7/4/2012 16:0026.720.1871525.0100.62
      Thunderstorms,Heavy Rain Showers5/29/2012 6:0010.99.08892.4100.26
      Thunderstorms,Moderate Rain Showers,Fog7/17/2012 6:0019.618.593153.2100.01
      Thunderstorms,Rain7/23/2012 18:0021.319.1933024.1100.83
      Thunderstorms,Rain Showers9/8/2012 4:0025.523.1983225.0101.06
      Thunderstorms,Rain Showers,Fog7/31/2012 20:0022.921.391359.7100.64
      Thunderstorms,Rain,Fog7/17/2012 5:0020.618.688194.8100.08
      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C \\\n", + "Weather Condition \n", + "Clear 9/9/2012 5:00 32.8 \n", + "Cloudy 9/9/2012 23:00 30.5 \n", + "Drizzle 9/30/2012 3:00 18.8 \n", + "Drizzle,Fog 9/30/2012 2:00 19.9 \n", + "Drizzle,Ice Pellets,Fog 12/17/2012 9:00 0.4 \n", + "Drizzle,Snow 12/19/2012 18:00 1.2 \n", + "Drizzle,Snow,Fog 12/22/2012 3:00 1.1 \n", + "Fog 9/22/2012 0:00 20.8 \n", + "Freezing Drizzle 2/1/2012 5:00 -2.3 \n", + "Freezing Drizzle,Fog 12/10/2012 5:00 -0.3 \n", + "Freezing Drizzle,Haze 2/1/2012 13:00 -5.0 \n", + "Freezing Drizzle,Snow 3/2/2012 12:00 -3.3 \n", + "Freezing Fog 3/17/2012 6:00 -0.1 \n", + "Freezing Rain 2/1/2012 7:00 0.3 \n", + "Freezing Rain,Fog 12/17/2012 1:00 0.1 \n", + "Freezing Rain,Haze 2/1/2012 15:00 -4.9 \n", + "Freezing Rain,Ice Pellets,Fog 12/17/2012 3:00 -2.6 \n", + "Freezing Rain,Snow Grains 1/13/2012 9:00 -5.0 \n", + "Haze 3/13/2012 23:00 14.1 \n", + "Mainly Clear 9/9/2012 9:00 33.0 \n", + "Moderate Rain,Fog 12/10/2012 8:00 1.7 \n", + "Moderate Snow 12/27/2012 9:00 -4.9 \n", + "Moderate Snow,Blowing Snow 12/27/2012 12:00 -5.4 \n", + "Mostly Cloudy 9/9/2012 2:00 32.4 \n", + "Rain 9/5/2012 2:00 22.8 \n", + "Rain Showers 9/8/2012 16:00 26.4 \n", + "Rain Showers,Fog 10/20/2012 3:00 12.8 \n", + "Rain Showers,Snow Showers 12/5/2012 10:00 2.2 \n", + "Rain,Fog 9/30/2012 23:00 21.7 \n", + "Rain,Haze 3/13/2012 9:00 5.5 \n", + "Rain,Ice Pellets 12/18/2012 5:00 0.6 \n", + "Rain,Snow 4/23/2012 3:00 1.7 \n", + "Rain,Snow Grains 12/21/2012 0:00 1.9 \n", + "Rain,Snow,Fog 12/8/2012 21:00 0.8 \n", + "Rain,Snow,Ice Pellets 12/21/2012 5:00 1.3 \n", + "Snow 4/27/2012 9:00 3.7 \n", + "Snow Pellets 11/24/2012 15:00 0.7 \n", + "Snow Showers 3/4/2012 21:00 2.9 \n", + "Snow Showers,Fog 12/29/2012 13:00 -10.0 \n", + "Snow,Blowing Snow 2/25/2012 9:00 -1.4 \n", + "Snow,Fog 3/14/2012 19:00 1.1 \n", + "Snow,Haze 2/1/2012 21:00 -3.6 \n", + "Snow,Ice Pellets 3/3/2012 4:00 0.8 \n", + "Thunderstorms 7/4/2012 16:00 26.7 \n", + "Thunderstorms,Heavy Rain Showers 5/29/2012 6:00 10.9 \n", + "Thunderstorms,Moderate Rain Showers,Fog 7/17/2012 6:00 19.6 \n", + "Thunderstorms,Rain 7/23/2012 18:00 21.3 \n", + "Thunderstorms,Rain Showers 9/8/2012 4:00 25.5 \n", + "Thunderstorms,Rain Showers,Fog 7/31/2012 20:00 22.9 \n", + "Thunderstorms,Rain,Fog 7/17/2012 5:00 20.6 \n", + "\n", + " Dew Point Temp_C Rel Hum_% \\\n", + "Weather Condition \n", + "Clear 20.4 99 \n", + "Cloudy 22.6 99 \n", + "Drizzle 17.7 96 \n", + "Drizzle,Fog 19.1 100 \n", + "Drizzle,Ice Pellets,Fog -0.7 92 \n", + "Drizzle,Snow 0.2 95 \n", + "Drizzle,Snow,Fog 0.6 98 \n", + "Fog 19.6 100 \n", + "Freezing Drizzle -3.3 93 \n", + "Freezing Drizzle,Fog -2.3 94 \n", + "Freezing Drizzle,Haze -7.7 83 \n", + "Freezing Drizzle,Snow -4.6 94 \n", + "Freezing Fog -0.3 99 \n", + "Freezing Rain -1.7 92 \n", + "Freezing Rain,Fog -0.9 93 \n", + "Freezing Rain,Haze -7.4 83 \n", + "Freezing Rain,Ice Pellets,Fog -3.7 92 \n", + "Freezing Rain,Snow Grains -7.3 84 \n", + "Haze 11.1 86 \n", + "Mainly Clear 21.2 99 \n", + "Moderate Rain,Fog 0.8 94 \n", + "Moderate Snow -6.7 93 \n", + "Moderate Snow,Blowing Snow -6.4 93 \n", + "Mostly Cloudy 24.4 100 \n", + "Rain 20.4 99 \n", + "Rain Showers 23.0 97 \n", + "Rain Showers,Fog 12.1 96 \n", + "Rain Showers,Snow Showers -1.2 78 \n", + "Rain,Fog 19.5 100 \n", + "Rain,Haze 2.9 86 \n", + "Rain,Ice Pellets -0.6 92 \n", + "Rain,Snow 0.5 94 \n", + "Rain,Snow Grains -2.1 75 \n", + "Rain,Snow,Fog 0.3 96 \n", + "Rain,Snow,Ice Pellets 0.1 94 \n", + "Snow 0.3 96 \n", + "Snow Pellets -6.4 59 \n", + "Snow Showers -0.7 94 \n", + "Snow Showers,Fog -11.1 92 \n", + "Snow,Blowing Snow -2.9 91 \n", + "Snow,Fog 0.8 99 \n", + "Snow,Haze -6.4 81 \n", + "Snow,Ice Pellets -1.7 92 \n", + "Thunderstorms 20.1 87 \n", + "Thunderstorms,Heavy Rain Showers 9.0 88 \n", + "Thunderstorms,Moderate Rain Showers,Fog 18.5 93 \n", + "Thunderstorms,Rain 19.1 93 \n", + "Thunderstorms,Rain Showers 23.1 98 \n", + "Thunderstorms,Rain Showers,Fog 21.3 91 \n", + "Thunderstorms,Rain,Fog 18.6 88 \n", + "\n", + " Wind Speed_km/h Visibility_km \\\n", + "Weather Condition \n", + "Clear 33 48.3 \n", + "Cloudy 54 48.3 \n", + "Drizzle 30 25.0 \n", + "Drizzle,Fog 28 9.7 \n", + "Drizzle,Ice Pellets,Fog 20 4.0 \n", + "Drizzle,Snow 19 11.3 \n", + "Drizzle,Snow,Fog 32 9.7 \n", + "Fog 22 9.7 \n", + "Freezing Drizzle 26 12.9 \n", + "Freezing Drizzle,Fog 33 8.0 \n", + "Freezing Drizzle,Haze 11 4.0 \n", + "Freezing Drizzle,Snow 24 12.9 \n", + "Freezing Fog 9 0.8 \n", + "Freezing Rain 28 16.1 \n", + "Freezing Rain,Fog 26 9.7 \n", + "Freezing Rain,Haze 9 2.8 \n", + "Freezing Rain,Ice Pellets,Fog 28 8.0 \n", + "Freezing Rain,Snow Grains 32 4.8 \n", + "Haze 17 9.7 \n", + "Mainly Clear 63 48.3 \n", + "Moderate Rain,Fog 17 6.4 \n", + "Moderate Snow 39 0.8 \n", + "Moderate Snow,Blowing Snow 41 0.6 \n", + "Mostly Cloudy 83 48.3 \n", + "Rain 52 48.3 \n", + "Rain Showers 41 48.3 \n", + "Rain Showers,Fog 13 6.4 \n", + "Rain Showers,Snow Showers 28 24.1 \n", + "Rain,Fog 46 9.7 \n", + "Rain,Haze 17 9.7 \n", + "Rain,Ice Pellets 24 9.7 \n", + "Rain,Snow 52 25.0 \n", + "Rain,Snow Grains 26 25.0 \n", + "Rain,Snow,Fog 9 6.4 \n", + "Rain,Snow,Ice Pellets 28 6.4 \n", + "Snow 57 25.0 \n", + "Snow Pellets 35 2.4 \n", + "Snow Showers 37 48.3 \n", + "Snow Showers,Fog 22 9.7 \n", + "Snow,Blowing Snow 48 9.7 \n", + "Snow,Fog 35 9.7 \n", + "Snow,Haze 15 6.4 \n", + "Snow,Ice Pellets 33 11.3 \n", + "Thunderstorms 15 25.0 \n", + "Thunderstorms,Heavy Rain Showers 9 2.4 \n", + "Thunderstorms,Moderate Rain Showers,Fog 15 3.2 \n", + "Thunderstorms,Rain 30 24.1 \n", + "Thunderstorms,Rain Showers 32 25.0 \n", + "Thunderstorms,Rain Showers,Fog 35 9.7 \n", + "Thunderstorms,Rain,Fog 19 4.8 \n", + "\n", + " Press_kPa \n", + "Weather Condition \n", + "Clear 103.63 \n", + "Cloudy 103.65 \n", + "Drizzle 101.56 \n", + "Drizzle,Fog 102.07 \n", + "Drizzle,Ice Pellets,Fog 100.79 \n", + "Drizzle,Snow 101.15 \n", + "Drizzle,Snow,Fog 100.15 \n", + "Fog 103.04 \n", + "Freezing Drizzle 101.02 \n", + "Freezing Drizzle,Fog 101.27 \n", + "Freezing Drizzle,Haze 100.36 \n", + "Freezing Drizzle,Snow 101.18 \n", + "Freezing Fog 102.85 \n", + "Freezing Rain 101.00 \n", + "Freezing Rain,Fog 101.01 \n", + "Freezing Rain,Haze 100.41 \n", + "Freezing Rain,Ice Pellets,Fog 100.95 \n", + "Freezing Rain,Snow Grains 98.56 \n", + "Haze 102.97 \n", + "Mainly Clear 103.59 \n", + "Moderate Rain,Fog 99.98 \n", + "Moderate Snow 100.67 \n", + "Moderate Snow,Blowing Snow 100.64 \n", + "Mostly Cloudy 103.65 \n", + "Rain 102.26 \n", + "Rain Showers 102.31 \n", + "Rain Showers,Fog 99.83 \n", + "Rain Showers,Snow Showers 101.11 \n", + "Rain,Fog 101.77 \n", + "Rain,Haze 100.61 \n", + "Rain,Ice Pellets 100.12 \n", + "Rain,Snow 101.07 \n", + "Rain,Snow Grains 100.60 \n", + "Rain,Snow,Fog 100.73 \n", + "Rain,Snow,Ice Pellets 100.47 \n", + "Snow 102.73 \n", + "Snow Pellets 99.70 \n", + "Snow Showers 102.50 \n", + "Snow Showers,Fog 102.52 \n", + "Snow,Blowing Snow 100.62 \n", + "Snow,Fog 102.07 \n", + "Snow,Haze 100.99 \n", + "Snow,Ice Pellets 100.96 \n", + "Thunderstorms 100.62 \n", + "Thunderstorms,Heavy Rain Showers 100.26 \n", + "Thunderstorms,Moderate Rain Showers,Fog 100.01 \n", + "Thunderstorms,Rain 100.83 \n", + "Thunderstorms,Rain Showers 101.06 \n", + "Thunderstorms,Rain Showers,Fog 100.64 \n", + "Thunderstorms,Rain,Fog 100.08 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(\"Weather Condition\").max()" + ] + }, + { + "cell_type": "markdown", + "id": "3b3cd884", + "metadata": {}, + "source": [ + "## Q)13. Show all the Records where Weather Condition is Fog." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "f2696d6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      01/1/2012 0:00-1.8-3.98648.0101.24Fog
      11/1/2012 1:00-1.8-3.78748.0101.24Fog
      41/1/2012 4:00-1.5-3.38874.8101.23Fog
      51/1/2012 5:00-1.4-3.38796.4101.27Fog
      61/1/2012 6:00-1.5-3.18976.4101.29Fog
      ...........................
      871612/29/2012 4:00-16.0-17.29069.7101.25Fog
      871712/29/2012 5:00-14.8-15.99146.4101.25Fog
      871812/29/2012 6:00-13.8-15.38849.7101.25Fog
      871912/29/2012 7:00-14.8-16.48878.0101.22Fog
      872212/29/2012 10:00-12.0-13.39076.4101.15Fog
      \n", + "

      150 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "0 1/1/2012 0:00 -1.8 -3.9 86 4 \n", + "1 1/1/2012 1:00 -1.8 -3.7 87 4 \n", + "4 1/1/2012 4:00 -1.5 -3.3 88 7 \n", + "5 1/1/2012 5:00 -1.4 -3.3 87 9 \n", + "6 1/1/2012 6:00 -1.5 -3.1 89 7 \n", + "... ... ... ... ... ... \n", + "8716 12/29/2012 4:00 -16.0 -17.2 90 6 \n", + "8717 12/29/2012 5:00 -14.8 -15.9 91 4 \n", + "8718 12/29/2012 6:00 -13.8 -15.3 88 4 \n", + "8719 12/29/2012 7:00 -14.8 -16.4 88 7 \n", + "8722 12/29/2012 10:00 -12.0 -13.3 90 7 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "0 8.0 101.24 Fog \n", + "1 8.0 101.24 Fog \n", + "4 4.8 101.23 Fog \n", + "5 6.4 101.27 Fog \n", + "6 6.4 101.29 Fog \n", + "... ... ... ... \n", + "8716 9.7 101.25 Fog \n", + "8717 6.4 101.25 Fog \n", + "8718 9.7 101.25 Fog \n", + "8719 8.0 101.22 Fog \n", + "8722 6.4 101.15 Fog \n", + "\n", + "[150 rows x 8 columns]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data[data['Weather Condition']=='Fog']\n", + "data.groupby('Weather Condition').get_group('Fog')" + ] + }, + { + "cell_type": "markdown", + "id": "40e76b1a", + "metadata": {}, + "source": [ + "## Q)14. Find all instances when 'Weather is Clear' or 'Visibility' is above 40'." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "33b49a46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      671/3/2012 19:00-16.9-24.8502425.0101.74Clear
      1061/5/2012 10:00-6.0-10.0731748.3100.45Mainly Clear
      1071/5/2012 11:00-5.6-10.2702248.3100.41Mainly Clear
      1081/5/2012 12:00-4.7-9.6692048.3100.38Mainly Clear
      1091/5/2012 13:00-4.4-9.7662648.3100.40Mainly Clear
      ...........................
      874912/30/2012 13:00-12.4-16.2733748.3100.92Mostly Cloudy
      875012/30/2012 14:00-11.8-16.1703748.3100.96Mainly Clear
      875112/30/2012 15:00-11.3-15.6703248.3101.05Mainly Clear
      875212/30/2012 16:00-11.4-15.5722648.3101.15Mainly Clear
      875612/30/2012 20:00-13.8-16.5802425.0101.52Clear
      \n", + "

      3027 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "67 1/3/2012 19:00 -16.9 -24.8 50 24 \n", + "106 1/5/2012 10:00 -6.0 -10.0 73 17 \n", + "107 1/5/2012 11:00 -5.6 -10.2 70 22 \n", + "108 1/5/2012 12:00 -4.7 -9.6 69 20 \n", + "109 1/5/2012 13:00 -4.4 -9.7 66 26 \n", + "... ... ... ... ... ... \n", + "8749 12/30/2012 13:00 -12.4 -16.2 73 37 \n", + "8750 12/30/2012 14:00 -11.8 -16.1 70 37 \n", + "8751 12/30/2012 15:00 -11.3 -15.6 70 32 \n", + "8752 12/30/2012 16:00 -11.4 -15.5 72 26 \n", + "8756 12/30/2012 20:00 -13.8 -16.5 80 24 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "67 25.0 101.74 Clear \n", + "106 48.3 100.45 Mainly Clear \n", + "107 48.3 100.41 Mainly Clear \n", + "108 48.3 100.38 Mainly Clear \n", + "109 48.3 100.40 Mainly Clear \n", + "... ... ... ... \n", + "8749 48.3 100.92 Mostly Cloudy \n", + "8750 48.3 100.96 Mainly Clear \n", + "8751 48.3 101.05 Mainly Clear \n", + "8752 48.3 101.15 Mainly Clear \n", + "8756 25.0 101.52 Clear \n", + "\n", + "[3027 rows x 8 columns]" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[(data['Weather Condition']=='Clear') | (data['Visibility_km']>40)]" + ] + }, + { + "cell_type": "markdown", + "id": "057c3a19", + "metadata": {}, + "source": [ + "## Q)15. Find all instances when:\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "14a66784", + "metadata": {}, + "source": [ + "### A. 'Weather is Clear' and 'Relative Humidity >50'\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c6aa2484", + "metadata": {}, + "source": [ + "### or" + ] + }, + { + "cell_type": "markdown", + "id": "faaf9fd9", + "metadata": {}, + "source": [ + "### B. 'Visibility is above 40" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "7c5218aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      Date/TimeTemp_CDew Point Temp_CRel Hum_%Wind Speed_km/hVisibility_kmPress_kPaWeather Condition
      1061/5/2012 10:00-6.0-10.0731748.3100.45Mainly Clear
      1071/5/2012 11:00-5.6-10.2702248.3100.41Mainly Clear
      1081/5/2012 12:00-4.7-9.6692048.3100.38Mainly Clear
      1091/5/2012 13:00-4.4-9.7662648.3100.40Mainly Clear
      1101/5/2012 14:00-5.1-10.7652248.3100.46Mainly Clear
      ...........................
      874912/30/2012 13:00-12.4-16.2733748.3100.92Mostly Cloudy
      875012/30/2012 14:00-11.8-16.1703748.3100.96Mainly Clear
      875112/30/2012 15:00-11.3-15.6703248.3101.05Mainly Clear
      875212/30/2012 16:00-11.4-15.5722648.3101.15Mainly Clear
      875612/30/2012 20:00-13.8-16.5802425.0101.52Clear
      \n", + "

      2921 rows × 8 columns

      \n", + "
      " + ], + "text/plain": [ + " Date/Time Temp_C Dew Point Temp_C Rel Hum_% Wind Speed_km/h \\\n", + "106 1/5/2012 10:00 -6.0 -10.0 73 17 \n", + "107 1/5/2012 11:00 -5.6 -10.2 70 22 \n", + "108 1/5/2012 12:00 -4.7 -9.6 69 20 \n", + "109 1/5/2012 13:00 -4.4 -9.7 66 26 \n", + "110 1/5/2012 14:00 -5.1 -10.7 65 22 \n", + "... ... ... ... ... ... \n", + "8749 12/30/2012 13:00 -12.4 -16.2 73 37 \n", + "8750 12/30/2012 14:00 -11.8 -16.1 70 37 \n", + "8751 12/30/2012 15:00 -11.3 -15.6 70 32 \n", + "8752 12/30/2012 16:00 -11.4 -15.5 72 26 \n", + "8756 12/30/2012 20:00 -13.8 -16.5 80 24 \n", + "\n", + " Visibility_km Press_kPa Weather Condition \n", + "106 48.3 100.45 Mainly Clear \n", + "107 48.3 100.41 Mainly Clear \n", + "108 48.3 100.38 Mainly Clear \n", + "109 48.3 100.40 Mainly Clear \n", + "110 48.3 100.46 Mainly Clear \n", + "... ... ... ... \n", + "8749 48.3 100.92 Mostly Cloudy \n", + "8750 48.3 100.96 Mainly Clear \n", + "8751 48.3 101.05 Mainly Clear \n", + "8752 48.3 101.15 Mainly Clear \n", + "8756 25.0 101.52 Clear \n", + "\n", + "[2921 rows x 8 columns]" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data.head(2)\n", + "data[(data['Weather Condition']=='Clear')&(data['Rel Hum_%']>50)|(data['Visibility_km']>40)]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Dict_Examples.py b/Dict_Examples.py new file mode 100644 index 0000000..41ac012 --- /dev/null +++ b/Dict_Examples.py @@ -0,0 +1,50 @@ +x = {} +print(type(x)) +file_counts = {"jpig":10,"txt":14,"csv":2,"py":23} +print(len(file_counts)) +print(file_counts) +print(file_counts["txt"]) +print("jpig" in file_counts) +file_counts["cfg"] = 8 +file_counts["csv"]=17 +print(file_counts) +del file_counts["cfg"] +print(file_counts) + +#*************************************************************************** +file_counts = {"jpig":10,"txt":14,"csv":2,"py":23} +for extension in file_counts: + print(extension) +for ext,amount in file_counts.items(): + print("There are {} files with .{} extension".format(amount,ext)) +print(file_counts.keys()) +print(file_counts.values()) + +#*************************************************************** + +cool_beasts = {"octopuses":"tentacles", "dolphins":"fins", "rhinos":"horns"} +for key, value in cool_beasts.items(): + print("{} have {}".format(key,value)) + +#********************************************************************* + +dict = {1:'one',2:'two',2:'twoooo'} +dict[2] = "three" +dict[4]='four' +print(dict[2]) +print(dict.keys()) +print(dict.values()) + + +#******************************************************************** +def count_letters(text): + result={} + for letter in text: + if letter not in result: + result[letter]=0 + result[letter]+=1 + return result +print(count_letters("sssss gg")) + +#***********************************Dictionary are mutable ********************************** + diff --git a/House_Sales_in_King_Count_USA.ipynb b/House_Sales_in_King_Count_USA.ipynb new file mode 100644 index 0000000..01a15b7 --- /dev/null +++ b/House_Sales_in_King_Count_USA.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["

      \n"," \n"," \"Skills\n"," \n","

      \n","\n","

      Data Analysis with Python

      \n"]},{"cell_type":"markdown","metadata":{},"source":["# House Sales in King County, USA\n"]},{"cell_type":"markdown","metadata":{},"source":["This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n"]},{"cell_type":"markdown","metadata":{},"source":["| Variable | Description |\n","| ------------- | ----------------------------------------------------------------------------------------------------------- |\n","| id | A notation for a house |\n","| date | Date house was sold |\n","| price | Price is prediction target |\n","| bedrooms | Number of bedrooms |\n","| bathrooms | Number of bathrooms |\n","| sqft_living | Square footage of the home |\n","| sqft_lot | Square footage of the lot |\n","| floors | Total floors (levels) in house |\n","| waterfront | House which has a view to a waterfront |\n","| view | Has been viewed |\n","| condition | How good the condition is overall |\n","| grade | overall grade given to the housing unit, based on King County grading system |\n","| sqft_above | Square footage of house apart from basement |\n","| sqft_basement | Square footage of the basement |\n","| yr_built | Built Year |\n","| yr_renovated | Year when house was renovated |\n","| zipcode | Zip code |\n","| lat | Latitude coordinate |\n","| long | Longitude coordinate |\n","| sqft_living15 | Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area |\n","| sqft_lot15 | LotSize area in 2015(implies-- some renovations) |\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#After executing the below command restart the kernel and run all cells.\n","!pip3 install scikit-learn --upgrade --user"]},{"cell_type":"markdown","metadata":{},"source":["You will require the following libraries:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import seaborn as sns\n","from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n","from sklearn.linear_model import LinearRegression\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{},"source":["# Module 1: Importing Data Sets\n"]},{"cell_type":"markdown","metadata":{},"source":["Load the csv:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["We use the method head to display the first 5 columns of the dataframe.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 1\n","\n","Display the data types of each column using the function dtypes, then take a screenshot and submit it, include your code in the image.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["We use the method describe to obtain a statistical summary of the dataframe.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# Module 2: Data Wrangling\n"]},{"cell_type":"markdown","metadata":{},"source":["### Question 2\n","\n","Drop the columns \"id\" and \"Unnamed: 0\" from axis 1 using the method drop(), then use the method describe() to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the inplace parameter is set to True\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["We can see we have missing values for the columns bedrooms and bathrooms \n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":["#find the total of null values for the columns above.\n"]},{"cell_type":"markdown","metadata":{},"source":["We can replace the missing values of the column 'bedrooms' with the mean of the column 'bedrooms' using the method replace(). Don't forget to set the inplace parameter to True\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["We also replace the missing values of the column 'bathrooms' with the mean of the column 'bathrooms' using the method replace(). Don't forget to set the inplace parameter top True \n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":["#double check for null values here to show the modification has been made."]},{"cell_type":"markdown","metadata":{},"source":["# Module 3: Exploratory Data Analysis\n"]},{"cell_type":"markdown","metadata":{},"source":["### Question 3\n","\n","Use the method value_counts to count the number of houses with unique floor values, use the method .to_frame() to convert it to a dataframe.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 4\n","\n","Use the function boxplot in the seaborn library to determine whether houses with a waterfront view or without a waterfront view have more price outliers.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 5\n","\n","Use the function regplot in the seaborn library to determine if the feature sqft_above is negatively or positively correlated with price.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["We can use the Pandas method corr() to find the feature other than price that is most correlated with price.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# Module 4: Model Development\n"]},{"cell_type":"markdown","metadata":{},"source":["We can Fit a linear regression model using the longitude feature 'long' and caculate the R^2.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 6\n","\n","Fit a linear regression model to predict the 'price' using the feature 'sqft_living' then calculate the R^2. Take a screenshot of your code and the value of the R^2.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 7\n","\n","Fit a linear regression model to predict the 'price' using the list of features:\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"] "]},{"cell_type":"markdown","metadata":{},"source":["Then calculate the R^2. Take a screenshot of your code.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### This will help with Question 8\n","\n","Create a list of tuples, the first element in the tuple contains the name of the estimator:\n","\n","'scale'\n","\n","'polynomial'\n","\n","'model'\n","\n","The second element in the tuple contains the model constructor\n","\n","StandardScaler()\n","\n","PolynomialFeatures(include_bias=False)\n","\n","LinearRegression()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"]},{"cell_type":"markdown","metadata":{},"source":["### Question 8\n","\n","Use the list to create a pipeline object to predict the 'price', fit the object using the features in the list features, and calculate the R^2.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# Module 5: Model Evaluation and Refinement\n"]},{"cell_type":"markdown","metadata":{},"source":["Import the necessary modules:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":["from sklearn.model_selection import cross_val_score\n","from sklearn.model_selection import train_test_split\n","print(\"done\")"]},{"cell_type":"markdown","metadata":{},"source":["We will split the data into training and testing sets:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 9\n","\n","Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import Ridge"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Question 10\n","\n","Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["

      About the Authors:

      \n","\n","Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"]},{"cell_type":"markdown","metadata":{},"source":["Other contributors: Michelle Carey, Mavis Zhou\n"]},{"cell_type":"markdown","metadata":{},"source":["## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","| ----------------- | ------- | --------------- | -------------------------------------------- |\n","| 2022-07-29 | 2.3 | Lakshmi Holla | Added library import |\n","| 2020-12-01 | 2.2 | Aije Egwaikhide | Coverted Data describtion from text to table |\n","| 2020-10-06 | 2.1 | Lakshmi Holla | Changed markdown instruction of Question1 |\n","| 2020-08-27 | 2.0 | Malika Singla | Added lab to GitLab |\n","\n","
      \n","\n","##

      © IBM Corporation 2020. All rights reserved.

      \n","\n","

      \n"]}],"metadata":{"kernelspec":{"display_name":"my_environment","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]"},"vscode":{"interpreter":{"hash":"1468b46fbbf336438d2a9c265dd02fb55be29fdd63084011561d11061c2774ae"}},"widgets":{"state":{},"version":"1.1.2"}},"nbformat":4,"nbformat_minor":4} diff --git a/List_Examples.py b/List_Examples.py new file mode 100644 index 0000000..5fc8134 --- /dev/null +++ b/List_Examples.py @@ -0,0 +1,128 @@ +x = ['Now', 'We', 'are', 'cooking'] +print(type(x)) +print(x) +print(len(x)) +print('are' in x) +print('am' in x) +print('Now' in x) +print(x[2],x[3]) +print(x[1:3]) +print(x[1:]) +#****************************Using Split() and indexing *************************************** +def get_word(sentence, n): + # Only proceed if n is positive + if n > 0: + words = sentence.split() + # Only proceed if n is not more than the number of words + if n <= len(words): + return(words[n-1]) + return("") + +print(get_word("This is a lesson about lists", 4)) # Should print: lesson +print(get_word("This is a lesson about lists", -4)) # Nothing +print(get_word("Now we are cooking!", 1)) # Should print: Now +print(get_word("Now we are cooking!", 5)) # Nothing + +#********************************List's are Mutable & List Methods******************************************************** + +fruits = ['pineapple','banana','orange','apple','melon'] +fruits.append("kiwi") +print(fruits) +fruits.insert(1,"mango") +print(fruits) +fruits.insert(25,"peach") +print(fruits) +fruits.remove("banana") +print(fruits) +fruits.remove(fruits[2]) +print(fruits) +fruits.pop(0) +print(fruits) +fruits[2]='Strawbury' +print(fruits) + +#*****************************Tuple is Immutable******************************** + +fullname = [('Samundeeswari',"Velusamy")] +print(fullname) + +#********************************************************* + +def convert_seconds(seconds): + hours = seconds//3600 + minitues = (seconds - hours *3600)//60 + remaining_seconds = seconds-hours * 3600 -minitues*60 + return hours,minitues,remaining_seconds +result = convert_seconds(5000) +print(result) +print(type(result)) + + +#******************************************************************** + +def file_size(file_info): + name, type, size = file_info + return("{:.2f}".format(size/ 1024)) + +print(file_size(('Class Assignment', 'docx', 17875))) # Should print 17.46 +print(file_size(('Notes', 'txt', 496))) # Should print 0.48 +print(file_size(('Program', 'py', 1239))) # Should print 1.21 + +#************************************************************************** + +animals= ['lion','zebra','dolphin','monkey','tiger','cow'] +chars = 0 +for animal in animals: + chars+=len(animal) +print("Total Characters: {}, Average Length: {}".format(chars,chars/len(animals))) + +#********************************enumerate function************************************************** + +winners = ['samundeeswari velusamy','deepesh maniyan','ritisha maniyan','maniyan mariappan'] +for index,person in enumerate(winners): + print("{}-{}".format(index+1,person)) + +#**************************************************************************** +def full_emails(people): + result = [] + for email,name in people: + result.append("{}<{}>".format(name,email)) + return result +print(full_emails([("chamu.grm@gmail.com", "sam eshwari"),("shay@example.com","shay brandt")])) + +#*************************************************************** +def skip_elements(elements): + # code goes here + element = [] + for i,name in enumerate(elements): + if i %2 ==0: + element.append(name) + return element + +print(skip_elements(["a", "b", "c", "d", "e", "f", "g"])) # Should be ['a', 'c', 'e', 'g'] +print(skip_elements(['Orange', 'Pineapple', 'Strawberry', 'Kiwi', 'Peach'])) # Should be ['Orange', 'Strawberry', 'Peach'] + +#*******************************List Comprehension******************************************************** +multiples = [] +for x in range(1,11): + multiples.append(x*7) +print(multiples) + +#*****************************Just one line using list comprehension *************** +multiples = [x*7 for x in range(1,11)] +print(multiples) +#************************************************ +languages = ['python','java','c++','ruby'] +lengths = [len(language) for language in languages] +print(lengths) +#********************************************** +def odd_numbers(n): + return [x for x in range(0,n+1) if x %2 != 0] + +print(odd_numbers(5)) # Should print [1, 3, 5] +print(odd_numbers(10)) # Should print [1, 3, 5, 7, 9] +print(odd_numbers(11)) # Should print [1, 3, 5, 7, 9, 11] +print(odd_numbers(1)) # Should print [1] +print(odd_numbers(-1)) # Should print [] + +#************************************************************ diff --git a/Matplotlib.ipynb b/Matplotlib.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/Oops.py b/Oops.py new file mode 100644 index 0000000..2dc3ee3 --- /dev/null +++ b/Oops.py @@ -0,0 +1,151 @@ +#****************************Classes and Objects(Practice Program)***************************************** +class Employee: + def __new__(cls): + print ("__new__ magic method is called") + inst = object.__new__(cls) + return inst + def __init__(self): + print ("__init__ magic method is called") + self.name='Satya' +#************************************************************************* + +print(help(int)) + +#************************************************************************* +class Flower: + color = 'unknown' + +rose = Flower() +rose.color = "Red" + +violet = Flower() +violet.color = "blue" + +this_pun_is_for_you = "sugar is sweet and so you are" + +print("Roses are {},".format(rose.color)) +print("violets are {},".format(violet.color)) +print(this_pun_is_for_you) +#********************************************************************** + +a,b,c=1,2,3 +print(a) +print(b) +print(c) +a = "hello" +b="hai" +x=a +a=b +b=x + +print(a) +print(b) + +#******************************************************************************** +class Dog: + years = 0 + def dog_years(breed): + return breed.years * 7 + +fido=Dog() +fido.years=3 +print(fido.dog_years()) + +#*************************************************************************************** +class Person: + def __init__(self, name): + self.name = name + def greeting(self): + # Should return "hi, my name is " followed by the name of the Person. + return "hi, my name is {}".format(some_person.name) + +# Create a new instance with a name of your choice +some_person = Person("sam") +# Call the greeting method +print(some_person.greeting()) +#********************************************************************************************** + +class Person: + def __init__(self, name): + self.name = name + def greeting(self): + """Outputs a message with the name of the person""" + print("Hello! My name is {name}.".format(name=self.name)) + +help(Person) + +#******************************************************************************************** +class Clothing: + material = "" + def __init__(self,name): + self.name = name + def checkmaterial(self): + print("This {} is made of {}".format(self.name,self.material)) + +class Shirt(Clothing): + material="Cotton" + +polo = Shirt("Polo") +polo.checkmaterial() + +#*********************************************************************************************** + +class Clothing: + stock={ 'name': [],'material' :[], 'amount':[]} + def __init__(self,name): + material = "" + self.name = name + def add_item(self, name, material, amount): + Clothing.stock['name'].append(self.name) + Clothing.stock['material'].append(self.material) + Clothing.stock['amount'].append(amount) + def Stock_by_Material(self, material): + count=0 + n=0 + for item in Clothing.stock['material']: + if item == material: + count += Clothing.stock['amount'][n] + n+=1 + return count + +class shirt(Clothing): + material="Cotton" +class pants(Clothing): + material="Cotton" + +polo = shirt("Polo") +sweatpants = pants("Sweatpants") +polo.add_item(polo.name, polo.material, 4) +sweatpants.add_item(sweatpants.name, sweatpants.material, 6) +current_stock = polo.Stock_by_Material("Cotton") +print(current_stock) + +#*************************************************************************************** +import datetime, random +now = datetime.datetime.now() +print(now) +print(random.randint(1,20)) +#************************************************************** + +class Dog: + def __init__(self, name, age): + self.name=name + self.age=age + print("Hello",name) + def get_name(self): + print(self.name) + return self.name + def get_age(self): + print(self.age) + def bark(self): + print("bark") +d=Dog("Tim",25) +d1=Dog("Bill",36) +d.get_name() +d.get_age() +d1.get_age() +d1.get_name() +d.bark() +d.bark() +print(type(d)) + diff --git a/Rock_paper_scissors.py b/Rock_paper_scissors.py new file mode 100644 index 0000000..c651171 --- /dev/null +++ b/Rock_paper_scissors.py @@ -0,0 +1,39 @@ +import random + +user_wins = 0 +computer_wins = 0 + +options = ["rock", "paper", "scissors"] + +while True: + user_input = input("Type Rock/Paper/Scissors or Q to quit: ").lower() + if user_input == "q": + break + + if user_input not in options: + continue + + random_number = random.randint(0, 2) + # rock: 0, paper: 1, scissors: 2 + computer_pick = options[random_number] + print("Computer picked", computer_pick + ".") + + if user_input == "rock" and computer_pick == "scissors": + print("You won!") + user_wins += 1 + + elif user_input == "paper" and computer_pick == "rock": + print("You won!") + user_wins += 1 + + elif user_input == "scissors" and computer_pick == "paper": + print("You won!") + user_wins += 1 + + else: + print("You lost!") + computer_wins += 1 + +print("You won", user_wins, "times.") +print("The computer won", computer_wins, "times.") +print("Goodbye!") \ No newline at end of file diff --git a/Sam_DAV_SBA/01/Sam_DAV_SBA.ipynb b/Sam_DAV_SBA/01/Sam_DAV_SBA.ipynb new file mode 100644 index 0000000..218e64f --- /dev/null +++ b/Sam_DAV_SBA/01/Sam_DAV_SBA.ipynb @@ -0,0 +1,1140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Read Data from the woocommerce-product-export.csv file using the python library." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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      " + ], + "text/plain": [ + " month_number facecream facewash toothpaste bathingsoap shampoo \\\n", + "0 1 2500 1500 5200 9200 1200 \n", + "1 2 2630 1200 5100 6100 2100 \n", + "2 3 2140 1340 4550 9550 3550 \n", + "3 4 3400 1130 5870 8870 1870 \n", + "4 5 3600 1740 4560 7760 1560 \n", + "5 6 2760 1555 4890 7490 1890 \n", + "6 7 2980 1120 4780 8980 1780 \n", + "7 8 3700 1400 5860 9960 2860 \n", + "8 9 3540 1780 6100 8100 2100 \n", + "9 10 1990 1890 8300 10300 2300 \n", + "10 11 2340 2100 7300 13300 2400 \n", + "11 12 2900 1760 7400 14400 1800 \n", + "\n", + " moisturizer total_units total_profit \n", + "0 1500 21100 211000 \n", + "1 1200 18330 183300 \n", + "2 1340 22470 224700 \n", + "3 1130 22270 222700 \n", + "4 1740 20960 209600 \n", + "5 1555 20140 201400 \n", + "6 1120 29550 295500 \n", + "7 1400 36140 361400 \n", + "8 1780 23400 234000 \n", + "9 1890 26670 266700 \n", + "10 2100 41280 412800 \n", + "11 1760 30020 300200 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(r\"C:\\Users\\Chamu\\Downloads\\woocommerce-product-export.csv\")\n", + "df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Show a concise summary of the columns using info() method." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 12 entries, 0 to 11\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 month_number 12 non-null int64\n", + " 1 facecream 12 non-null int64\n", + " 2 facewash 12 non-null int64\n", + " 3 toothpaste 12 non-null int64\n", + " 4 bathingsoap 12 non-null int64\n", + " 5 shampoo 12 non-null int64\n", + " 6 moisturizer 12 non-null int64\n", + " 7 total_units 12 non-null int64\n", + " 8 total_profit 12 non-null int64\n", + "dtypes: int64(9)\n", + "memory usage: 992.0 bytes\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Show a summary of statistics pertaining to the columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      max12.0000003700.0000002100.0000008300.00000014400.0000003550.0000002100.00000041280.00000412800.000000
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      " + ], + "text/plain": [ + " month_number facecream facewash toothpaste bathingsoap \\\n", + "count 12.000000 12.000000 12.000000 12.000000 12.000000 \n", + "mean 6.500000 2873.333333 1542.916667 5825.833333 9500.833333 \n", + "std 3.605551 584.595172 316.733745 1242.032486 2348.095779 \n", + "min 1.000000 1990.000000 1120.000000 4550.000000 6100.000000 \n", + "25% 3.750000 2460.000000 1305.000000 4862.500000 8015.000000 \n", + "50% 6.500000 2830.000000 1527.500000 5530.000000 9090.000000 \n", + "75% 9.250000 3435.000000 1765.000000 6400.000000 10045.000000 \n", + "max 12.000000 3700.000000 2100.000000 8300.000000 14400.000000 \n", + "\n", + " shampoo moisturizer total_units total_profit \n", + "count 12.000000 12.000000 12.00000 12.000000 \n", + "mean 2117.500000 1542.916667 26027.50000 260275.000000 \n", + "std 617.724931 316.733745 7014.36594 70143.659404 \n", + "min 1200.000000 1120.000000 18330.00000 183300.000000 \n", + "25% 1795.000000 1305.000000 21065.00000 210650.000000 \n", + "50% 1995.000000 1527.500000 22935.00000 229350.000000 \n", + "75% 2325.000000 1765.000000 29667.50000 296675.000000 \n", + "max 3550.000000 2100.000000 41280.00000 412800.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Print the first five rows by default." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " month_number facecream facewash toothpaste bathingsoap shampoo \\\n", + "0 1 2500 1500 5200 9200 1200 \n", + "1 2 2630 1200 5100 6100 2100 \n", + "2 3 2140 1340 4550 9550 3550 \n", + "3 4 3400 1130 5870 8870 1870 \n", + "4 5 3600 1740 4560 7760 1560 \n", + "\n", + " moisturizer total_units total_profit \n", + "0 1500 21100 211000 \n", + "1 1200 18330 183300 \n", + "2 1340 22470 224700 \n", + "3 1130 22270 222700 \n", + "4 1740 20960 209600 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Print the last five rows by default." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " month_number facecream facewash toothpaste bathingsoap shampoo \\\n", + "7 8 3700 1400 5860 9960 2860 \n", + "8 9 3540 1780 6100 8100 2100 \n", + "9 10 1990 1890 8300 10300 2300 \n", + "10 11 2340 2100 7300 13300 2400 \n", + "11 12 2900 1760 7400 14400 1800 \n", + "\n", + " moisturizer total_units total_profit \n", + "7 1400 36140 361400 \n", + "8 1780 23400 234000 \n", + "9 1890 26670 266700 \n", + "10 2100 41280 412800 \n", + "11 1760 30020 300200 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Print the \"total_profit,\" and “month_number” columns only." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      total_profitmonth_number
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      62955007
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      82340009
      926670010
      1041280011
      1130020012
      \n", + "
      " + ], + "text/plain": [ + " total_profit month_number\n", + "0 211000 1\n", + "1 183300 2\n", + "2 224700 3\n", + "3 222700 4\n", + "4 209600 5\n", + "5 201400 6\n", + "6 295500 7\n", + "7 361400 8\n", + "8 234000 9\n", + "9 266700 10\n", + "10 412800 11\n", + "11 300200 12" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[[\"total_profit\",\"month_number\"]]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Read the total profit of all months and show it using the Bar plot. \n", + " The generated Bar plot must include the following properties: \n", + " X label name = Month number.\n", + " Y label name = Total profit.\n", + " Title = Company profit per month." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x= df[\"month_number\"]\n", + "y=df[\"total_profit\"]\n", + "plt.bar(x,y)\n", + "plt.xlabel(\"Month Number\")\n", + "plt.ylabel(\"Total Profit\")\n", + "plt.title(\"Company Profit Per Month\")\n", + "plt.grid(axis=\"both\",ls='dotted',lw=1)\n", + "plt.yticks([100000,200000,300000,400000,500000])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. Read the total profit of all of the months, and show the line plot with the following style properties:\n", + " The generated line plot must include the following Style properties: \n", + " The line style should be dotted and the line color should be red.\n", + " Show the legend at the lower-right location.\n", + " X label name = Month number\n", + " Y label name = Total profit.\n", + " Title = Company profit per month.\n", + " Add a circle marker; the line marker color = red. \n", + " The line width should be 3.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x= df[\"month_number\"]\n", + "y=df[\"total_profit\"]\n", + "plt.plot(x,y,color=\"red\",linestyle=\"dotted\",lw=3,marker=\"o\",markerfacecolor=\"black\")\n", + "plt.legend([\"Profit data of last year\"], loc = \"lower right\")\n", + "plt.xlabel(\"Month Number\")\n", + "plt.ylabel(\"Total Profit\")\n", + "plt.title(\"Company Profit Per Month\")\n", + "plt.xticks([1,2,3,4,5,6,7,8,9,10,11,12])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. Print all of the product sales data and show it using a multi-line plot.\n", + " Display the number of units sold per month for each product using multi-line plots. (i.e., show a separate Plotline for each product)." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df[\"month_number\"],df[\"facecream\"],label=\"Face Cream Sales Data\",color=\"blue\")\n", + "plt.plot(df[\"month_number\"],df[\"facewash\"],label=\"Face Wash Sales Data\",color=\"orange\")\n", + "plt.plot(df[\"month_number\"],df[\"toothpaste\"],label=\"ToothPaste Sales Data\",color=\"green\")\n", + "plt.plot(df[\"month_number\"],df[\"bathingsoap\"],label=\"BathingSoap Sales Data\",color=\"red\")\n", + "plt.plot(df[\"month_number\"],df[\"shampoo\"],label=\"Shampoo Sales Data\",color=\"purple\")\n", + "plt.plot(df[\"month_number\"],df[\"moisturizer\"],label=\"Moisturizer Sales Data\",color=\"brown\")\n", + "plt.xlabel(\"Month Number\")\n", + "plt.ylabel(\"Sales units in Number\")\n", + "plt.title(\"Sales Data\")\n", + "plt.xticks([1,2,3,4,5,6,7,8,9,10,11,12])\n", + "plt.legend(loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10. Read “bathingsoap” sales data for each month and show it using a scatter plot.\n", + " Add a grid to the plot. The grid line style should be “–“." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df['month_number'],df['bathingsoap'])\n", + "plt.xlabel(\"Month Number\")\n", + "plt.xticks([1,2,3,4,5,6,7,8,9,10,11,12])\n", + "plt.ylabel(\"Number of units Sold\")\n", + "plt.title(\"Bathingsoap Sales Data\")\n", + "plt.legend([\"Bathing Soap Sales Data\"],loc=\"upper left\")\n", + "plt.grid(axis=\"both\",ls=\"dotted\")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Section Two\n", + "Below are the Tasks:\n", + "\n", + " 1. Given Dataset:\n", + " date=[\"25/12\",\"26/12\",\"27/12\"]\n", + " temp=[8.5,10.5,6.8]\n", + "\n", + " A line chart of date versus temperature must include the following properties: \n", + " X label name = Date\n", + " Y label name = temperature\n", + " Title = Date-wise Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "date=[\"25/12\",\"26/12\",\"27/12\"]\n", + "temp=[8.5,10.5,6.8]\n", + "plt.plot(date,temp)\n", + "plt.xlabel(\"Date\")\n", + "plt.ylabel(\"Temperature\")\n", + "plt.title(\"Date-wise Temperature\")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Given Dataset:\n", + " height=[121.9,124.5,129.5,134.6,139.7,147.3,152.4,157.5,162.6]\n", + " weight=[19.7,21.3,23.5,25.9,28.5,32.1,35.7,39.6,43.2]\n", + "\n", + " Show the average weight against the average height (use a line chart or any other chart):\n", + " The generated line plot must include the following Style properties:\n", + " The line style should be dash-dot and the line color should be green.\n", + " Show the legend at the lower-right location.\n", + " X label name =Weight in kg.\n", + " Y label name = Height in cm.\n", + " Title = Average weight with respect to average height.\n", + " Add a circle marker: marker size =10; circle marker color = green" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "height=[121.9,124.5,129.5,134.6,139.7,147.3,152.4,157.5,162.6]\n", + "weight=[19.7,21.3,23.5,25.9,28.5,32.1,35.7,39.6,43.2]\n", + "plt.plot(weight,height,color=\"green\",linestyle=\"dashdot\",markersize=10,marker=\"o\")\n", + "plt.xlabel(\"Weight in Kg\")\n", + "plt.ylabel(\"Height in Cm\")\n", + "plt.title(\"Average weight with respect to average height\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "my_environment", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "357ec78291cf388eeacd62e271c6b188c6b9f92b882d7927ff21aa77b4c7f673" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam_Python_Practice_on_JupyterNotebook.ipynb b/Sam_Python_Practice_on_JupyterNotebook.ipynb new file mode 100644 index 0000000..45be466 --- /dev/null +++ b/Sam_Python_Practice_on_JupyterNotebook.ipynb @@ -0,0 +1,870 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a9caeaf0", + "metadata": {}, + "source": [ + "# For Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "89b17e07", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello Python!\n" + ] + } + ], + "source": [ + "print(\"Hello Python!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "45a6e2a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "58c60aa7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Sam\n", + "1 Shiva\n", + "2 Tom\n", + "3 John\n", + "4 Jennifer\n" + ] + } + ], + "source": [ + "mylist=[\"Sam\",\"Shiva\",\"Tom\",\"John\",\"Jennifer\"]\n", + "for i in range(len(mylist)):\n", + " print(i,mylist[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7c4ba949", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n", + "95\n", + "90\n", + "85\n", + "80\n", + "75\n", + "70\n", + "65\n", + "60\n", + "55\n", + "50\n", + "45\n", + "40\n", + "35\n", + "30\n", + "25\n", + "20\n", + "15\n" + ] + } + ], + "source": [ + "for i in range(100,10,-5):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "52120ae7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 X 7 = 7\n", + "2 X 7 = 14\n", + "3 X 7 = 21\n", + "4 X 7 = 28\n", + "5 X 7 = 35\n", + "6 X 7 = 42\n", + "7 X 7 = 49\n", + "8 X 7 = 56\n", + "9 X 7 = 63\n", + "10 X 7 = 70\n" + ] + } + ], + "source": [ + "for i in range(1,11):\n", + " \n", + " print(i, \"X\", 7, \"=\",i*j)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6df96479", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 X 7 = 7\n", + "2 X 7 = 14\n", + "3 X 7 = 21\n", + "4 X 7 = 28\n", + "5 X 7 = 35\n", + "6 X 7 = 42\n", + "7 X 7 = 49\n", + "8 X 7 = 56\n", + "9 X 7 = 63\n", + "10 X 7 = 70\n" + ] + } + ], + "source": [ + "\n", + "i=1\n", + "while i <=10:\n", + " print(i,\"X\",7,\"=\",i*j)\n", + " i+=1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a151781f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 : Sam\n", + "2 : Tom\n", + "3 : Jenny\n" + ] + } + ], + "source": [ + "mydict={1:\"Sam\",2:\"Tom\",3:\"Jenny\"}\n", + "for key in mydict:\n", + " print(key,\":\",mydict[key])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "931c249e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 => S\n", + "1 => a\n", + "2 => m\n", + "3 => p\n", + "4 => l\n", + "5 => e\n", + "6 => \n", + "7 => T\n", + "8 => e\n", + "9 => x\n", + "10 => t\n" + ] + } + ], + "source": [ + "mystr=\"Sample Text\"\n", + "for i in range(len(mystr)):\n", + " print(i,\"=>\",mystr[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b02a78c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 S\n", + "1 a\n", + "2 m\n", + "3 p\n", + "4 l\n", + "5 e\n", + "6 \n", + "7 T\n", + "8 e\n", + "9 x\n", + "10 t\n" + ] + } + ], + "source": [ + "mystr=\"Sample Text\"\n", + "for index,value in enumerate(mystr):\n", + " print(index,value)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e15ea386", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 'S')\n", + "(1, 'a')\n", + "(2, 'm')\n", + "(3, 'p')\n", + "(4, 'l')\n", + "(5, 'e')\n", + "(6, ' ')\n", + "(7, 'T')\n", + "(8, 'e')\n", + "(9, 'x')\n", + "(10, 't')\n" + ] + } + ], + "source": [ + "mystr=\"Sample Text\"\n", + "for i in enumerate(mystr):\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "id": "ddfb08ce", + "metadata": {}, + "source": [ + "# While loop" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "eacbb755", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n", + "90\n", + "80\n", + "70\n", + "60\n", + "50\n", + "40\n", + "30\n", + "20\n", + "10\n" + ] + } + ], + "source": [ + "i=100\n", + "while i >=10:\n", + " print(i)\n", + " i=i-10" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7b9c69cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n", + "hello\n", + "j\n", + "j\n", + "z\n" + ] + } + ], + "source": [ + "a=input()\n", + "while a.lower()!='z':\n", + " print(a)\n", + " a=input()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "219f73f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "i=2\n", + "while i <=30:\n", + " if i%2==0:\n", + " print(i)\n", + " i+=2\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "58c46ab4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "4\n", + "6\n", + "8\n", + "10\n", + "12\n", + "14\n", + "16\n", + "18\n" + ] + } + ], + "source": [ + "i=2\n", + "while i <=30:\n", + " if i%2==0:\n", + " print(i)\n", + " i+=2\n", + "# continue\n", + " if i%20 ==0:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "520ed704", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55\n", + "41\n", + "2\n", + "3\n", + "6\n", + "5\n", + "2\n", + "1\n", + "9\n", + "8\n", + "7\n", + "[1, 2, 2, 3, 5, 6, 7, 8, 9, 41, 55]\n" + ] + } + ], + "source": [ + "a=[]\n", + "for i in range(11):\n", + " i=int(input())\n", + " a.append(i)\n", + "a=sorted(a)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "89fc75c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* * * * * * * * * * \n", + "* * * * * * * * * * \n", + "* * * * * * * * * * \n", + "* * * * * * * * * * \n", + "* * * * * * * * * * \n" + ] + } + ], + "source": [ + "for i in range(5):\n", + " for j in range(10):\n", + " print(\"*\",end=\" \")\n", + " \n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "0fbfb105", + "metadata": {}, + "source": [ + "# Patter Program using loops\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "b2ea587d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the value5\n", + "* \n", + "* * \n", + "* * * \n", + "* * * * \n", + "* * * * * \n" + ] + } + ], + "source": [ + "n=int(input(\"Enter the value\"))\n", + "for i in range(1,n+1):\n", + " for j in range(1,i+1):\n", + " print(\"*\",end=\" \")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "44299e1e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the value7\n", + "1 \n", + "2 2 \n", + "3 3 3 \n", + "4 4 4 4 \n", + "5 5 5 5 5 \n", + "6 6 6 6 6 6 \n", + "7 7 7 7 7 7 7 \n" + ] + } + ], + "source": [ + "n=int(input(\"Enter the value\"))\n", + "for i in range(1,n+1):\n", + " for j in range(1,i+1):\n", + " print(i,end=\" \")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "f27dbd48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the value7\n", + "1 \n", + "1 2 \n", + "1 2 3 \n", + "1 2 3 4 \n", + "1 2 3 4 5 \n", + "1 2 3 4 5 6 \n", + "1 2 3 4 5 6 7 \n" + ] + } + ], + "source": [ + "n=int(input(\"Enter the value\"))\n", + "for i in range(1,n+1):\n", + " for j in range(1,i+1):\n", + " print(j,end=\" \")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "85f1d79e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of rows6\n", + " * \n", + " * * \n", + " * * * \n", + " * * * * \n", + " * * * * * \n", + "* * * * * * \n" + ] + } + ], + "source": [ + "rows=int(input(\"Enter the number of rows\"))\n", + "space =rows-1\n", + "for i in range(0,rows):\n", + " for j in range(0,space):\n", + " print(end=\" \")\n", + " space-=1\n", + " for k in range(0,i+1):\n", + " print(\"*\",end=\" \")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "fa32af8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of rows5\n", + "\n", + "* \n", + "* * \n", + "* * * \n", + "* * * * \n", + "* * * * * \n", + "* * * * \n", + "* * * \n", + "* * \n", + "* \n", + "\n" + ] + } + ], + "source": [ + "n=int(input(\"Enter the number of rows\"))\n", + "for i in range(0,n+1):\n", + " for j in range(0,i):\n", + " print(\"*\",end=\" \")\n", + " print()\n", + "for i in range(n,0,-1):\n", + " for j in range(0,i-1):\n", + " print(\"*\", end=\" \")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "021ee101", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of Rows5\n", + "A \n", + "B C \n", + "D E F \n", + "G H I J \n", + "K L M N O \n" + ] + } + ], + "source": [ + "row=int(input(\"Enter the number of Rows\"))\n", + "ascii = 65\n", + "for i in range(0,row):\n", + " for j in range(0,i+1):\n", + " val = chr(ascii)\n", + " print(val, end=\" \")\n", + " ascii+=1\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "4268e9fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of Rows5\n", + "A \n", + "B B \n", + "C C C \n", + "D D D D \n", + "E E E E E \n" + ] + } + ], + "source": [ + "row=int(input(\"Enter the number of Rows\"))\n", + "ascii = 65\n", + "for i in range(0,row):\n", + " for j in range(0,i+1):\n", + " val = chr(ascii)\n", + " print(val, end=\" \")\n", + " ascii+=1\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "2ac71c86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of Rows5\n", + " A \n", + " B B \n", + " C C C \n", + " D D D D \n", + "E E E E E \n" + ] + } + ], + "source": [ + "row=int(input(\"Enter the number of Rows\"))\n", + "ascii = 65\n", + "space=row-1\n", + "for i in range(0,row):\n", + " for s in range(0,space):\n", + " print(end=\" \")\n", + " space=space-1\n", + " for j in range(0,i+1):\n", + " val = chr(ascii)\n", + " print(val, end=\" \")\n", + " ascii+=1\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "c8145e9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of Rows5\n", + "A \n", + "B B \n", + "C C C \n", + "D D D D \n", + "E E E E E \n", + "D D D D \n", + "C C C \n", + "B B \n", + "A \n", + "\n" + ] + } + ], + "source": [ + "row=int(input(\"Enter the number of Rows\"))\n", + "ascii = 65\n", + "for i in range(0,row):\n", + " for j in range(0,i+1):\n", + " val = chr(ascii)\n", + " print(val, end=\" \")\n", + " ascii+=1\n", + " print()\n", + "for i in range(row,0,-1):\n", + " for j in range(0,i-1):\n", + " val=chr(ascii-2)\n", + " print(val,end=\" \")\n", + " ascii-=1\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "6469ef05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the number of rows5\n", + " * \n", + " * * \n", + " * * * \n", + " * * * * \n", + "* * * * * \n", + " * * * * \n", + " * * * \n", + " * * \n", + " * \n" + ] + } + ], + "source": [ + "rows=int(input(\"Enter the number of rows\"))\n", + "space =rows-1\n", + "for i in range(0,rows):\n", + " for j in range(0,space):\n", + " print(end=\" \")\n", + " space-=1\n", + " for k in range(0,i+1):\n", + " print(\"*\",end=\" \")\n", + " print()\n", + "for i in range(rows-1,0,-1):\n", + " for j in range(space,0):\n", + " print(end=\" \")\n", + " space-=1\n", + " for k in range(0,i):\n", + " print(\"*\", end=\" \")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "c8bd9bfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " * * * * \n", + "* * * \n", + "* * \n", + " * * \n", + " * * \n", + " * \n" + ] + } + ], + "source": [ + "for row in range(6):\n", + " for col in range(7):\n", + " if (row==0 and col%3!=0) or (row==1 and col%3==0) or (row-col==2) or (row+col==8):\n", + " print(\"*\",end=\" \")\n", + " else:\n", + " print(end=\" \")\n", + " print()\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd1af315", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/StudentJson.py b/StudentJson.py new file mode 100644 index 0000000..c81b107 --- /dev/null +++ b/StudentJson.py @@ -0,0 +1,43 @@ +import json + + +with open("studentfile.json","r") as file: + student_data = json.load(file) +print(student_data) + +print(type(student_data)) + +print(student_data[0]) + +student_dict = {} +student_list=[] + +for item in student_data: + tamil=item['course']['marks']['tamil'] + english=item['course']['marks']['english'] + computer=item['course']['marks']['computer'] + total = tamil+english+computer + item['course']["total"]=total + item['course']["average"]=total/3 + item["total"]=total + item["average"]=total/3 + student_list.append(item) + if item['course']['average'] > 75 and item['course']['average'] < 100: + item['course']['grade']="A" + elif item['course']['average'] >65 and item['course']['average'] < 75: + item['course']['grade']="B" + elif item['course']['average'] >45 and item['course']['average'] < 65: + item['course']['grade']="C" + else: + item['course']['grade']="Fail" + + student_list.append(item) + +print(student_list) + +sorted_student_list=sorted(student_list, key= lambda x: x['course']['grade']) +print(sorted_student_list) + +with open("student_result.json","w") as student_file: + json.dump(sorted_student_list,student_file,indent=4) + \ No newline at end of file diff --git a/Tuple.py b/Tuple.py new file mode 100644 index 0000000..4e9e37d --- /dev/null +++ b/Tuple.py @@ -0,0 +1,50 @@ +import sys +mytuple = ("tim","john","jenny","jack") +print(type(mytuple)) + +print(mytuple[0]) + +# mylist=list(mytuple) +# print(mylist) +# mytuple=tuple(mylist) +# print(mytuple) + +ct=mytuple.count("sam") +print(ct) + +print(mytuple[::-1]) + +a=("john",26,"Boston") +print(type(a)) +name,age,place=a + +print(type(name)) +print(type(age)) +print(place) +print(type(a)) + +number_list=[1,2,3,4,5,6,7,8,9] +number = (1,2,3,4,5,6,7,8,9) +*i1,i2=number +print(i2) +print(sys.getsizeof(number_list),bytes) +print(sys.getsizeof(number),bytes) + +import timeit + +print(timeit.timeit(stmt="[1,2,3,4,5]",number=100))#7.499994... +print(timeit.timeit(stmt="(1,2,3,4,5)",number=100)) # tuple is more efficient than list- 2.00092 +print(timeit.timeit(stmt='{"a":1,"b":2,"c":3,"d":4,"e":5}',number=100))#1.22 +print(timeit.timeit(stmt='"12345"',number=100))#2.20001675.. + + + + + + + + + + + + diff --git a/UserJson.py b/UserJson.py new file mode 100644 index 0000000..5dc13a0 --- /dev/null +++ b/UserJson.py @@ -0,0 +1,31 @@ +# Fetching only the specified field(id,name and phone) from JSON Data + +import json +import requests + +response = requests.get("https://jsonplaceholder.typicode.com/users") +users = json.loads(response.text) + +print(type(users)) + +print(users[:1]) + +user3=[] +users_phonenumbers = {"id":None,"name":None,"phone":None} + +for item in users: + users_phonenumbers={} + + users_phonenumbers["id"]=item["id"] + users_phonenumbers["name"]=item["name"] + users_phonenumbers["phone"]=item["phone"] + + user3.append(users_phonenumbers) +print(user3) + + +orderby_users = sorted(user3, key=lambda x: x['name']) +print(orderby_users) +with open("users.json","w") as file: + + json.dump(orderby_users,file,indent=4) diff --git a/auto.csv b/auto.csv new file mode 100644 index 0000000..dd61579 --- /dev/null +++ b/auto.csv @@ -0,0 +1,205 @@ +3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9.0,111,5000,21,27,13495 +3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9.0,111,5000,21,27,16500 +1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9.0,154,5000,19,26,16500 +2,164,audi,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.40,10.0,102,5500,24,30,13950 +2,164,audi,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.40,8.0,115,5500,18,22,17450 +2,?,audi,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.40,8.5,110,5500,19,25,15250 +1,158,audi,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.40,8.5,110,5500,19,25,17710 +1,?,audi,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.40,8.5,110,5500,19,25,18920 +1,158,audi,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.40,8.3,140,5500,17,20,23875 +0,?,audi,gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52.0,3053,ohc,five,131,mpfi,3.13,3.40,7.0,160,5500,16,22,? +2,192,bmw,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.50,2.80,8.8,101,5800,23,29,16430 +0,192,bmw,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.50,2.80,8.8,101,5800,23,29,16925 +0,188,bmw,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9.0,121,4250,21,28,20970 +0,188,bmw,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9.0,121,4250,21,28,21105 +1,?,bmw,gas,std,four,sedan,rwd,front,103.5,189.0,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9.0,121,4250,20,25,24565 +0,?,bmw,gas,std,four,sedan,rwd,front,103.5,189.0,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8.0,182,5400,16,22,30760 +0,?,bmw,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8.0,182,5400,16,22,41315 +0,?,bmw,gas,std,four,sedan,rwd,front,110.0,197.0,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8.0,182,5400,15,20,36880 +2,121,chevrolet,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151 +1,98,chevrolet,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52.0,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295 +0,81,chevrolet,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52.0,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575 +1,118,dodge,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 +1,118,dodge,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377 +1,118,dodge,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957 +1,148,dodge,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 +1,148,dodge,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 +1,148,dodge,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 +1,148,dodge,gas,turbo,?,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558 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+1,154,plymouth,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 +1,154,plymouth,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 +-1,74,plymouth,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921 +3,?,plymouth,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7.0,145,5000,19,24,12764 +3,186,porsche,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018 +3,?,porsche,gas,std,two,hardtop,rwd,rear,89.5,168.9,65.0,51.6,2756,ohcf,six,194,mpfi,3.74,2.90,9.5,207,5900,17,25,32528 +3,?,porsche,gas,std,two,hardtop,rwd,rear,89.5,168.9,65.0,51.6,2756,ohcf,six,194,mpfi,3.74,2.90,9.5,207,5900,17,25,34028 +3,?,porsche,gas,std,two,convertible,rwd,rear,89.5,168.9,65.0,51.6,2800,ohcf,six,194,mpfi,3.74,2.90,9.5,207,5900,17,25,37028 +1,?,porsche,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10.0,288,5750,17,28,? +0,?,renault,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.90,8.7,?,?,23,31,9295 +2,?,renault,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.90,8.7,?,?,23,31,9895 +3,150,saab,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 +2,104,saab,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170 +3,150,saab,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040 +2,104,saab,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510 +3,150,saab,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9.0,160,5500,19,26,18150 +2,104,saab,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9.0,160,5500,19,26,18620 +2,83,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9.0,69,4900,31,36,5118 +2,83,subaru,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053 +2,83,subaru,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603 +0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172.0,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126 +0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172.0,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775 +0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172.0,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9.0,94,5200,26,32,9960 +0,102,subaru,gas,std,four,sedan,4wd,front,97.0,172.0,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9.0,82,4800,24,25,9233 +0,102,subaru,gas,turbo,four,sedan,4wd,front,97.0,172.0,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259 +0,89,subaru,gas,std,four,wagon,fwd,front,97.0,173.5,65.4,53.0,2290,ohcf,four,108,2bbl,3.62,2.64,9.0,82,4800,28,32,7463 +0,89,subaru,gas,std,four,wagon,fwd,front,97.0,173.5,65.4,53.0,2455,ohcf,four,108,mpfi,3.62,2.64,9.0,94,5200,25,31,10198 +0,85,subaru,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9.0,82,4800,23,29,8013 +0,85,subaru,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694 +1,87,toyota,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9.0,62,4800,35,39,5348 +1,87,toyota,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9.0,62,4800,31,38,6338 +1,74,toyota,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9.0,62,4800,31,38,6488 +0,77,toyota,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9.0,62,4800,31,37,6918 +0,81,toyota,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9.0,62,4800,27,32,7898 +0,91,toyota,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9.0,62,4800,27,32,8778 +0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53.0,2081,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,30,37,6938 +0,91,toyota,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,30,37,7198 +0,91,toyota,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53.0,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898 +0,91,toyota,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788 +0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53.0,2094,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,38,47,7738 +0,91,toyota,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,28,34,8358 +0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,28,34,9258 +1,168,toyota,gas,std,two,sedan,rwd,front,94.5,168.7,64.0,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,29,34,8058 +1,168,toyota,gas,std,two,hatchback,rwd,front,94.5,168.7,64.0,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9.0,70,4800,29,34,8238 +1,168,toyota,gas,std,two,sedan,rwd,front,94.5,168.7,64.0,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298 +1,168,toyota,gas,std,two,hatchback,rwd,front,94.5,168.7,64.0,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538 +2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52.0,2540,ohc,four,146,mpfi,3.62,3.50,9.3,116,4800,24,30,8449 +2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52.0,2536,ohc,four,146,mpfi,3.62,3.50,9.3,116,4800,24,30,9639 +2,134,toyota,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52.0,2551,ohc,four,146,mpfi,3.62,3.50,9.3,116,4800,24,30,9989 +2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52.0,2679,ohc,four,146,mpfi,3.62,3.50,9.3,116,4800,24,30,11199 +2,134,toyota,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52.0,2714,ohc,four,146,mpfi,3.62,3.50,9.3,116,4800,24,30,11549 +2,134,toyota,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53.0,2975,ohc,four,146,mpfi,3.62,3.50,9.3,116,4800,24,30,17669 +-1,65,toyota,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948 +-1,65,toyota,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698 +-1,65,toyota,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988 +-1,65,toyota,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898 +-1,65,toyota,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248 +3,197,toyota,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52.0,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558 +3,197,toyota,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52.0,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998 +-1,90,toyota,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690 +-1,?,toyota,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750 +2,122,volkswagen,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.40,23.0,52,4800,37,46,7775 +2,122,volkswagen,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.40,9.0,85,5250,27,34,7975 +2,94,volkswagen,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.40,23.0,52,4800,37,46,7995 +2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.40,9.0,85,5250,27,34,8195 +2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.40,9.0,85,5250,27,34,8495 +2,94,volkswagen,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.40,23.0,68,4500,37,42,9495 +2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.40,10.0,100,5500,26,32,9995 +3,?,volkswagen,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.40,8.5,90,5500,24,29,11595 +3,256,volkswagen,gas,std,two,hatchback,fwd,front,94.5,165.7,64.0,51.4,2221,ohc,four,109,mpfi,3.19,3.40,8.5,90,5500,24,29,9980 +0,?,volkswagen,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.40,8.5,110,5500,19,24,13295 +0,?,volkswagen,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.40,23.0,68,4500,33,38,13845 +0,?,volkswagen,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.40,9.0,88,5500,25,31,12290 +-2,103,volvo,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940 +-1,74,volvo,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415 +-2,103,volvo,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985 +-1,74,volvo,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515 +-2,103,volvo,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420 +-1,74,volvo,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950 +-1,95,volvo,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845 +-1,95,volvo,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045 +-1,95,volvo,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485 +-1,95,volvo,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.40,23.0,106,4800,26,27,22470 +-1,95,volvo,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625 diff --git a/complex_data.json b/complex_data.json new file mode 100644 index 0000000..7ecf087 --- /dev/null +++ b/complex_data.json @@ -0,0 +1,13 @@ +[ + { + "__complex__": true, + "real": 42, + "imag": 36 + }, + + { + "__complex__": true, + "real": 88, + "imag": 21 + } +] \ No newline at end of file diff --git a/download_project.py b/download_project.py new file mode 100644 index 0000000..18ab50b --- /dev/null +++ b/download_project.py @@ -0,0 +1,57 @@ +import os +from pprint import pprint +import collections +video_extension=['mp4','mpg','mpeg','avi','mov','flv','mwv','mkv','m4v','h264'] +audio_extension=['mp3','wav','raw','wma','mid','midi'] +image_extension=['png','jpg','jpeg','gif','svg','bmp','psd','tif','tiff'] +document_extension=['txt','pdf','csv','xls','xlsx','ods','doc','docx','html','odt','tex','ppt','pptx','log'] +compression_extension=['zip','z','tar','7z','gz','bz','rar','rpm','pkg','deb'] +install_extension=['dmg','exe','iso'] + +base_path=os.path.expanduser('~') +dest_dirs=['Video_folder','Audio_folder','Image_folder','Document_folder','Compression_folder','Other_folder'] +for d in dest_dirs: + dir_path=os.path.join(base_path,d) + if not os.path.isdir(dir_path): + os.mkdir(dir_path) + +download_path=os.path.join(base_path,"Downloads") +files_mapping=collections.defaultdict(list) +file_list=os.listdir(download_path) + + +for filename in file_list: + if filename != '.': + file_ext=filename.split('.')[-1] + files_mapping[file_ext].append(filename) +pprint(files_mapping) + +for f_ext,f_list in files_mapping.items(): + if f_ext in video_extension: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,'Video_folder',file)) + elif f_ext in audio_extension: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,'Audio_folder',file)) + elif f_ext in image_extension: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,'Image_folder',file)) + elif f_ext in document_extension: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,"Document_folder",file)) + elif f_ext in compression_extension: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,"Compression_folder",file)) + elif f_ext in install_extension: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,"Installation_folder",file)) + else: + for file in f_list: + os.rename(os.path.join(download_path,file),os.path.join(base_path,"Other_Folder",file)) + + + + + + + diff --git a/error_exception.py b/error_exception.py new file mode 100644 index 0000000..44e1c5d --- /dev/null +++ b/error_exception.py @@ -0,0 +1,54 @@ +# x = 5 +# if x<0: + +# print("hello") +# print(5+8) +# print("see the raise exception below") +# print("enter the value of x:") +# x= int(input()) +# # if x <0: +# # raise Exception("The value of x should be positive") + +# # x=45 +# assert(x>35), "the value of x should be more than 35" +# x=int(input()) + +# try: +# # 5/k +# # 5+10 +# a=a+b + +# except Exception as e: +# print(e) +# except TypeError as e: +# print(e) +# else: +# print("this is else block") +# finally: +# print("try-catch successfully executed") + +class Negative_numbers(Exception): + pass +class toosmall(Exception): + def __init__(self, message,value): + self.message = message + self.value = value + +def test_data(x): + if x<0: + raise Negative_numbers("this is the negative number") + if x<100: + raise toosmall("this is the small number than 100",x) +try: + # test_data(-5) + test_data(50) +except Negative_numbers as e: + print(e) +except Exception as e: + # print(e) + print(e.message,e.value) + + + + + diff --git a/filtered_data_file.json b/filtered_data_file.json new file mode 100644 index 0000000..11d0220 --- /dev/null +++ b/filtered_data_file.json @@ -0,0 +1,146 @@ +[ + { + "userId": 5, + "id": 81, + "title": "suscipit qui totam", + "completed": true + }, + { + "userId": 5, + "id": 83, + "title": "quidem at rerum quis ex aut sit quam", + "completed": true + }, + { + "userId": 5, + "id": 85, + "title": "et quia ad iste a", + "completed": true + }, + { + "userId": 5, + "id": 86, + "title": "incidunt ut saepe autem", + "completed": true + }, + { + "userId": 5, + "id": 87, + "title": "laudantium quae eligendi consequatur quia et vero autem", + "completed": true + }, + { + "userId": 5, + "id": 89, + "title": "sequi ut omnis et", + "completed": true + }, + { + "userId": 5, + "id": 90, + "title": "molestiae nisi accusantium tenetur dolorem et", + "completed": true + }, + { + "userId": 5, + "id": 91, + "title": "nulla quis consequatur saepe qui id expedita", + "completed": true + }, + { + "userId": 5, + "id": 92, + "title": "in omnis laboriosam", + "completed": true + }, + { + "userId": 5, + "id": 93, + "title": "odio iure consequatur molestiae quibusdam necessitatibus quia sint", + "completed": true + }, + { + "userId": 5, + "id": 95, + "title": "vel nihil et molestiae iusto assumenda nemo quo ut", + "completed": true + }, + { + "userId": 5, + "id": 98, + "title": "debitis accusantium ut quo facilis nihil quis sapiente necessitatibus", + "completed": true + }, + { + "userId": 10, + "id": 182, + "title": "inventore saepe cumque et aut illum enim", + "completed": true + }, + { + "userId": 10, + "id": 183, + "title": "omnis nulla eum aliquam distinctio", + "completed": true + }, + { + "userId": 10, + "id": 188, + "title": "vel non beatae est", + "completed": true + }, + { + "userId": 10, + "id": 189, + "title": "culpa eius et voluptatem et", + "completed": true + }, + { + "userId": 10, + "id": 190, + "title": "accusamus sint iusto et voluptatem exercitationem", + "completed": true + }, + { + "userId": 10, + "id": 191, + "title": "temporibus atque distinctio omnis eius impedit tempore molestias pariatur", + "completed": true + }, + { + "userId": 10, + "id": 193, + "title": "rerum debitis voluptatem qui eveniet tempora distinctio a", + "completed": true + }, + { + "userId": 10, + "id": 195, + "title": "rerum ex veniam mollitia voluptatibus pariatur", + "completed": true + }, + { + "userId": 10, + "id": 196, + "title": "consequuntur aut ut fugit similique", + "completed": true + }, + { + "userId": 10, + "id": 197, + "title": "dignissimos quo nobis earum saepe", + "completed": true + }, + { + "userId": 10, + "id": 198, + "title": "quis eius est sint explicabo", + "completed": true + }, + { + "userId": 10, + "id": 199, + "title": "numquam repellendus a magnam", + "completed": true + } +] \ No newline at end of file diff --git a/json_practice.py b/json_practice.py new file mode 100644 index 0000000..56b6634 --- /dev/null +++ b/json_practice.py @@ -0,0 +1,156 @@ +import json +import requests +from pprint import pp +#serialize a json, encoding data into json format +#deserialize a jason, decoding json into another format + + +data = { + "user" : { "name": "william williams", + "age": 93 +} +} + +#print(data["user"["age"]]) + +with open("data_file.json","w") as write_file: + json.dump(data,write_file, indent=4) + +json_str = json.dumps(data, indent=4) +print(json_str) + +#let's check and see if serialization and deserialization are perfect inverses + +blkjk_hand = (8,"Q") +encoded = json.dumps(blkjk_hand) +decoded = json.loads(encoded) + +print(type(blkjk_hand)) +print(type(decoded)) +print(blkjk_hand == tuple(decoded)) + +response = requests.get("http://jsonplaceholder.typicode.com/todos") +todos = json.loads(response.text) + +print(type(todos)) + +pp(todos[:10]) + +todos_by_user={} +for item in todos: + if item["completed"]==True: + + if item["userId"] not in todos_by_user.keys(): + todos_by_user[item["userId"]]=1 + else: + todos_by_user[item["userId"]]+=1 +print(todos_by_user) + +top_users = sorted(todos_by_user.items(), key=lambda x: x[1], reverse=True) +print(top_users) + +# with that code above we are sorting by the values due to the lamda function sorting at x[1] +max_complete = top_users[0][1] +print(max_complete) +users = [] +for user, value in top_users: + if value== max_complete: + users.append(str(user)) +max_users = " and ".join(users) +print("users {} completed {} ".format(max_users,max_complete)) + + +def keep(todos): + is_complete = todos["completed"] + has_max_count = str(todos["userId"]) in users + return is_complete and has_max_count + +with open("filtered_data_file.json", "w") as data_file: + filtered_todos = list(filter(keep, todos)) + json.dump(filtered_todos, data_file, indent=2) + +class Elf: + def __init__(self, level, ability_scores=None): + self.level = level + self.ability_scores = { + "str": 11, "dex": 12, "con": 10, + "int": 16, "wis": 14, "cha": 13 + } if ability_scores is None else ability_scores + self.hp = 10 + self.ability_scores["con"] + + + elf = Elf(level=4) + json.dumps(elf) +# # print(elf) +# z=8+3j +# print(type(z)) +# # json.dumps(z) +# print(z.real) +# print(z.imag) + +# complex(8,3)==z + + + def encode_complex(z): + if isinstance(z, complex): + return (z.real, z.imag) + else: + type_name = z.__class__.__name__ + raise TypeError(f"Object of type '{type_name}' is not JSON serializable") + # result=json.dumps(9 + 5j, default=encode_complex) + # print(result) + # r=json.dumps(elf, default=encode_complex) + # print(r) + +class ComplexEncoder(json.JSONEncoder): + def default(self, z): + if isinstance(z, complex): + return (z.real, z.imag) + else: + return super().default(z) +result=json.dumps(2 + 5j, cls=ComplexEncoder) +print(result) + +encoder = ComplexEncoder() +r=encoder.encode(3 + 6j) +print(r) + +complex_json = json.dumps(4 + 17j, cls=ComplexEncoder) +# json.loads(complex_json) +print(complex_json) + + +def decode_complex(dct): + if "__complex__" in dct: + return complex(dct["real"], dct["imag"]) + return dct + +with open("complex_data.json") as complex_data: + data = complex_data.read() + z = json.loads(data, object_hook=decode_complex) + + print(z) + +# with open("complex_data.json") as complex_data: +# data = complex_data.read() +# numbers = json.loads(data, object_hook=decode_complex) + +# print(numbers) + + + + + + + + + + + + + + + + + + diff --git a/kc_house_data_NaN.txt b/kc_house_data_NaN.txt new file mode 100644 index 0000000..fe2f408 --- /dev/null +++ b/kc_house_data_NaN.txt @@ -0,0 +1,21614 @@ 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+21215,1760650950,20150423T000000,309000.0,3.0,2.5,1950,3825,2.0,0,0,3,7,1950,0,2013,0,98042,47.3588,-122.08200000000001,1950,3825 +21216,5100403818,20150220T000000,369500.0,3.0,2.0,1108,1128,3.0,0,0,3,7,1108,0,2009,0,98115,47.6961,-122.318,1285,1253 +21217,2325400170,20150211T000000,391000.0,4.0,2.25,2190,3850,2.0,0,0,3,7,2190,0,2006,0,98059,47.4861,-122.161,2190,3980 +21218,5700000446,20141029T000000,465000.0,3.0,1.75,1590,1322,2.0,0,0,3,8,1060,530,2014,0,98144,47.5753,-122.294,1530,5400 +21219,9492500010,20140606T000000,879950.0,4.0,2.75,3010,7215,2.0,0,0,3,9,3010,0,2014,0,98033,47.6952,-122.178,3010,7215 +21220,2461900446,20141023T000000,372000.0,3.0,2.0,1330,1042,2.0,0,0,3,8,1060,270,2014,0,98136,47.5522,-122.382,1440,2428 +21221,8669160170,20140522T000000,259000.0,3.0,2.5,1550,3569,2.0,0,0,3,7,1550,0,2011,0,98002,47.3528,-122.211,2095,3402 +21222,3644100101,20140707T000000,374000.0,2.0,1.5,1260,1575,2.0,0,0,3,7,1260,0,2001,0,98144,47.5914,-122.295,1220,1740 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+21247,7169500020,20141205T000000,510000.0,2.0,2.25,1470,1101,2.0,0,0,3,8,1340,130,2005,0,98115,47.6768,-122.301,1470,1582 +21248,2911700010,20150303T000000,1080000.0,3.0,2.5,2240,21477,2.0,0,2,3,8,2240,0,1995,0,98006,47.5745,-122.18,2930,21569 +21249,9578060470,20140508T000000,494000.0,3.0,2.5,2310,4729,2.0,0,0,3,8,2310,0,2011,0,98028,47.7734,-122.23700000000001,2440,4711 +21250,1776460190,20140626T000000,429900.0,3.0,2.5,2370,5353,2.0,0,0,3,8,2370,0,2009,0,98019,47.7333,-121.975,2130,6850 +21251,3449500050,20141015T000000,505000.0,4.0,2.75,2980,9825,1.0,0,0,3,8,1910,1070,2007,0,98056,47.5073,-122.17200000000001,2580,12231 +21252,2309710130,20140715T000000,272000.0,4.0,2.0,1870,6551,1.0,0,3,3,7,1870,0,2009,0,98022,47.1934,-121.977,2280,5331 +21253,1972201511,20150210T000000,671500.0,3.0,2.5,1770,1714,3.0,0,0,3,8,1770,0,2012,0,98103,47.6532,-122.348,1720,3360 +21254,7852120050,20150311T000000,729950.0,4.0,3.5,3510,10010,2.0,0,0,3,10,3510,0,2001,0,98065,47.5412,-121.876,4200,9935 +21255,3814900660,20140721T000000,471835.0,4.0,2.5,3281,5354,2.0,0,0,3,9,3281,0,2014,0,98092,47.3273,-122.163,2598,4815 +21256,8141310080,20141103T000000,249950.0,3.0,2.0,1670,4438,1.0,0,0,3,7,1670,0,2014,0,98022,47.1948,-121.97399999999999,1670,4558 +21257,7207900050,20140808T000000,424950.0,5.0,3.5,2760,3846,2.5,0,0,3,8,2760,0,2013,0,98056,47.5047,-122.17,2760,4587 +21258,2424059163,20140709T000000,1240000.0,5.0,3.5,5430,10327,2.0,0,2,3,10,4010,1420,2007,0,98006,47.5476,-122.116,4340,10324 +21259,2140950130,20140911T000000,440000.0,4.0,2.5,2990,7928,2.0,0,0,3,9,2990,0,2011,0,98010,47.3139,-122.024,2810,7401 +21260,1776230190,20150408T000000,495000.0,4.0,3.5,3170,3858,2.0,0,0,3,8,2530,640,2008,0,98059,47.5049,-122.155,2640,3844 +21261,3524039224,20140513T000000,870000.0,4.0,2.5,3520,6773,2.5,0,0,3,9,2650,870,2006,0,98136,47.5317,-122.391,2930,6458 +21262,5694500840,20141125T000000,559000.0,2.0,3.0,1650,960,3.0,0,0,3,8,1350,300,2015,0,98103,47.6611,-122.346,1650,3000 +21263,4014400381,20140507T000000,495000.0,4.0,2.75,2656,21195,2.0,0,0,3,9,2656,0,2014,0,98001,47.3162,-122.272,1860,16510 +21264,2838000130,20150213T000000,722000.0,3.0,2.5,2230,4850,2.0,0,0,3,8,2230,0,2014,0,98133,47.7295,-122.334,2230,4513 +21265,8562770430,20140702T000000,567500.0,3.0,2.5,2280,2502,2.0,0,0,3,8,1880,400,2006,0,98027,47.5364,-122.073,2280,2812 +21266,1402970020,20141217T000000,440000.0,4.0,2.5,2798,5085,2.0,0,0,3,9,2798,0,2011,0,98092,47.3308,-122.18700000000001,2502,5707 +21267,3943600020,20140829T000000,400000.0,4.0,2.5,2398,5988,2.0,0,0,3,8,2398,0,2008,0,98055,47.452,-122.204,2370,5988 +21268,1438000430,20141006T000000,459995.0,4.0,2.5,2350,3760,2.0,0,0,3,8,2350,0,2014,0,98059,47.4786,-122.12299999999999,2590,4136 +21269,1601600167,20140507T000000,365000.0,5.0,2.75,2410,5003,1.0,0,0,3,7,1410,1000,2008,0,98118,47.5298,-122.274,1590,5003 +21270,1773100541,20150417T000000,389950.0,3.0,2.25,1580,920,3.0,0,0,3,8,1580,0,2015,0,98106,47.5578,-122.363,1250,1150 +21271,1773100924,20140708T000000,320000.0,3.0,3.25,1450,1387,2.0,0,0,3,8,1180,270,2013,0,98106,47.5556,-122.36200000000001,1450,1198 +21272,982850080,20140613T000000,415500.0,4.0,2.5,1750,4779,2.0,0,0,3,7,1750,0,2009,0,98028,47.7608,-122.23200000000001,1580,4687 +21273,7628700050,20150309T000000,775000.0,3.0,2.5,3020,4120,2.0,0,0,3,9,2360,660,2008,0,98126,47.5714,-122.37299999999999,2280,4120 +21274,8673400020,20150311T000000,590000.0,3.0,3.0,1740,1100,3.0,0,0,3,8,1740,0,2007,0,98107,47.67,-122.391,1370,1180 +21275,8725950170,20150123T000000,950000.0,2.0,2.25,2200,2043,2.0,0,0,3,9,1760,440,2007,0,98004,47.6213,-122.2,2020,1957 +21276,6306800080,20140806T000000,378950.0,4.0,2.5,1867,15314,2.0,0,0,3,9,1867,0,2013,0,98030,47.3524,-122.198,2616,8048 +21277,3362401763,20140508T000000,441750.0,2.0,1.5,1020,1060,3.0,0,0,3,8,1020,0,2008,0,98103,47.6801,-122.348,1340,1415 +21278,301401630,20141031T000000,335900.0,4.0,2.75,2475,4000,2.0,0,0,3,7,2475,0,2014,0,98002,47.345,-122.209,2475,4000 +21279,6056110780,20140627T000000,229800.0,2.0,1.75,1110,1773,2.0,0,0,3,8,1110,0,2014,0,98108,47.5647,-122.29299999999999,1420,2855 +21280,6819100352,20150310T000000,645000.0,3.0,2.5,1900,1258,2.5,0,0,3,7,1700,200,2007,0,98119,47.6465,-122.35799999999999,1780,1877 +21281,9297302031,20150423T000000,448000.0,3.0,3.25,1560,1345,2.0,0,0,3,8,1260,300,2009,0,98126,47.5637,-122.375,1560,4800 +21282,7203150080,20141216T000000,706000.0,4.0,2.5,2510,5436,2.0,0,0,3,8,2510,0,2011,0,98053,47.6894,-122.016,2520,5436 +21283,2937300050,20150227T000000,988990.0,4.0,4.75,4150,6303,3.0,0,0,3,9,4150,0,2014,0,98052,47.7047,-122.12299999999999,3570,6285 +21284,9521100029,20140716T000000,716000.0,3.0,3.0,1660,1849,3.0,0,0,3,9,1660,0,2013,0,98103,47.6649,-122.353,1660,3300 +21285,832700170,20150421T000000,319000.0,2.0,1.5,1090,847,3.0,0,0,3,8,1090,0,2009,0,98133,47.7235,-122.352,1090,1118 +21286,6817750440,20141014T000000,300000.0,4.0,2.5,1914,3272,2.0,0,0,3,8,1914,0,2009,0,98055,47.4297,-122.189,1714,3250 +21287,123059127,20140502T000000,625000.0,4.0,3.25,2730,54014,1.0,0,0,3,9,1560,1170,2007,0,98059,47.5133,-122.11,2730,111274 +21288,3630200430,20140514T000000,773000.0,3.0,2.75,2470,3600,2.0,0,0,3,9,2470,0,2007,0,98029,47.5406,-121.994,2570,3600 +21289,3448740430,20140925T000000,392000.0,5.0,2.5,2340,5670,2.0,0,0,3,7,2340,0,2009,0,98059,47.4913,-122.152,2190,4869 +21290,1438000190,20140911T000000,549995.0,4.0,3.5,2660,5690,2.0,0,0,3,8,1920,740,2014,0,98059,47.4775,-122.12200000000001,2970,5690 +21291,7853320250,20140920T000000,480000.0,3.0,2.5,2410,4656,2.0,0,0,3,7,2410,0,2009,0,98065,47.5203,-121.874,2410,4840 +21292,100300280,20141020T000000,355000.0,3.0,2.25,1430,4777,2.0,0,0,3,7,1430,0,2010,0,98059,47.4867,-122.152,1639,3854 +21293,8862500280,20141230T000000,208400.0,2.0,2.5,1570,1268,3.0,0,0,3,7,1570,0,2007,0,98106,47.534,-122.365,1570,1300 +21294,1042700080,20140822T000000,831548.0,5.0,2.75,3010,4919,2.0,0,0,3,9,3010,0,2014,0,98074,47.6067,-122.052,3230,5415 +21295,4051150080,20141117T000000,279500.0,4.0,2.5,1613,4338,2.0,0,0,3,7,1613,0,2009,0,98042,47.3859,-122.162,1427,4341 +21296,5592200010,20150227T000000,445000.0,3.0,2.5,2380,5269,2.0,0,0,3,8,2380,0,2008,0,98056,47.5066,-122.19200000000001,2150,7600 +21297,7787920080,20140616T000000,492500.0,5.0,2.5,2570,9962,2.0,0,0,3,8,2570,0,2006,0,98019,47.7275,-121.95700000000001,2890,9075 +21298,3448740190,20140709T000000,435000.0,4.0,2.5,2550,5200,2.0,0,0,3,7,2550,0,2009,0,98059,47.4919,-122.15299999999999,2550,4660 +21299,8822900122,20150512T000000,325000.0,3.0,2.25,1330,969,3.0,0,0,3,7,1330,0,2007,0,98125,47.7177,-122.292,1310,1941 +21300,4083300098,20141117T000000,453000.0,2.0,1.5,1160,1269,2.0,0,0,3,7,970,190,2005,0,98103,47.6608,-122.335,1700,3150 +21301,1438000170,20140822T000000,612995.0,5.0,3.5,3240,6919,2.0,0,0,3,8,2760,480,2014,0,98059,47.4779,-122.12200000000001,2970,5690 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+21310,2154970020,20140703T000000,2351960.0,4.0,4.25,5010,19412,2.0,0,1,3,11,4000,1010,2014,0,98040,47.5455,-122.211,3820,17064 +21311,5694500497,20150116T000000,539900.0,3.0,3.25,1300,1325,2.0,0,0,3,8,1080,220,2005,0,98103,47.6584,-122.346,1290,1323 +21312,7708200670,20140723T000000,490000.0,4.0,2.5,2510,4349,2.0,0,0,3,8,2510,0,2010,0,98059,47.4927,-122.147,2510,4314 +21313,8562770050,20140527T000000,627000.0,3.0,3.5,2710,3475,2.0,0,0,3,8,1650,1060,2005,0,98027,47.5359,-122.072,2440,2867 +21314,1441000470,20140728T000000,458000.0,4.0,3.5,3217,4000,2.0,0,0,3,8,2587,630,2008,0,98055,47.4483,-122.20299999999999,2996,5418 +21315,6056100293,20141110T000000,440000.0,3.0,2.5,1650,4929,2.0,0,0,3,7,1520,130,2007,0,98108,47.5634,-122.29799999999999,1520,2287 +21316,6600000050,20150310T000000,1698000.0,4.0,3.5,3950,6240,2.0,0,0,3,11,3950,0,2015,0,98112,47.6221,-122.29,2040,6240 +21317,1732800199,20150511T000000,935000.0,2.0,2.5,1680,977,3.0,0,0,3,9,1680,0,2009,0,98119,47.632,-122.361,1680,977 +21318,7853360470,20150417T000000,641000.0,5.0,3.5,3420,6403,2.0,0,2,3,8,2700,720,2013,0,98065,47.5162,-121.874,2710,6038 +21319,3438501583,20140911T000000,452000.0,3.0,2.75,2300,5090,2.0,0,0,3,8,1700,600,2007,0,98106,47.545,-122.36,1530,9100 +21320,7853370250,20141223T000000,625000.0,4.0,2.75,3010,6854,2.0,0,2,3,9,2570,440,2012,0,98065,47.5171,-121.876,1830,2952 +21321,2387600010,20150303T000000,1350000.0,4.0,3.5,4680,12495,2.0,0,0,3,10,3040,1640,2008,0,98033,47.6984,-122.206,3240,10749 +21322,8946780080,20140908T000000,834950.0,5.0,3.5,3630,4911,2.0,0,0,3,9,2790,840,2014,0,98034,47.718,-122.156,3600,4992 +21323,9402800005,20141028T000000,1500000.0,3.0,3.5,3530,3610,2.0,0,0,3,10,2370,1160,2008,0,98103,47.6857,-122.339,1780,3610 +21324,5422950080,20140825T000000,305000.0,4.0,2.5,2280,3800,2.0,0,0,3,7,2280,0,2006,0,98038,47.3586,-122.036,2630,4045 +21325,2826079027,20141112T000000,659000.0,3.0,2.5,3090,384634,2.0,0,0,3,8,3090,0,2007,0,98019,47.7072,-121.927,2200,292645 +21326,6003000851,20140522T000000,353000.0,1.0,1.0,550,1279,2.0,0,0,3,7,550,0,2008,0,98122,47.61600000000001,-122.314,1460,1385 +21327,7394400080,20150304T000000,535000.0,4.0,3.25,2840,4000,2.0,0,3,3,9,2330,510,2014,0,98108,47.5529,-122.29299999999999,2160,4867 +21328,1238501184,20140708T000000,999000.0,4.0,2.5,3130,10849,2.0,0,0,3,10,3130,0,2013,0,98033,47.6828,-122.186,2470,9131 +21329,263000009,20150129T000000,375000.0,3.0,2.5,1440,1102,3.0,0,0,3,8,1440,0,2009,0,98103,47.6995,-122.346,1440,1434 +21330,5101408889,20140616T000000,685000.0,4.0,3.5,2840,4637,3.0,0,0,3,8,2840,0,2008,0,98125,47.7033,-122.321,1730,5279 +21331,7299601410,20140808T000000,333000.0,4.0,2.5,2623,7184,2.0,0,0,3,8,2623,0,2012,0,98092,47.25899999999999,-122.20200000000001,2010,4939 +21332,9266700190,20150511T000000,245000.0,1.0,1.0,390,2000,1.0,0,0,4,6,390,0,1920,0,98103,47.6938,-122.34700000000001,1340,5100 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+21612,1523300157,20141015T000000,325000.0,2.0,0.75,1020,1076,2.0,0,0,3,7,1020,0,2008,0,98144,47.5941,-122.29899999999999,1020,1357 diff --git a/main.py b/main.py new file mode 100644 index 0000000..60846a9 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +from decouple import config + +user=config('username') +password=config('password') + +print(user,password) diff --git a/movies.xml b/movies.xml new file mode 100644 index 0000000..135af75 --- /dev/null +++ b/movies.xml @@ -0,0 +1,131 @@ + + + + + DVD + 1981 + PG + + 'Archaeologist and adventurer Indiana Jones + is hired by the U.S. government to find the Ark of the + Covenant before the Nazis.' + + + + DVD,Online + 1984 + PG + None provided. + + + Blu-ray + 1985 + PG + Marty McFly + + + + + Online + 1992 + R + WhAtEvER I Want!!!?! + + + + dvd, digital + 2000 + PG-13 + Two mutants come to a private academy for their kind whose resident superhero team must + oppose a terrorist organization with similar powers. + + + + + + + DVD + 1979 + R + """"""""" + + + + + DVD + 1986 + PG13 + Funny movie about a funny guy + + + blue-ray + 2000 + Unrated + psychopathic Bateman + + + + + + + + DVD,VHS + 1966 + PG + What a joke! + + + + + DVD + 2010 + PG--13 + Emma Stone = Hester Prynne + + + DVD,digital,Netflix + 2011 + Unrated + Tim (Rudd) is a rising executive + who 'succeeds' in finding the perfect guest, + IRS employee Barry (Carell), for his boss' monthly event, + a so-called 'dinner for idiots,' which offers certain + advantages to the exec who shows up with the biggest buffoon. + + + + + + Online,VHS + 1984 + PG + Who ya gonna call? + + + + + Blu_Ray + 1991 + Unknown + Robin Hood slaying + + + + + + + DVD + 2017R + an intellectually gifted seven-year-old who becomes the subject + of a custody battle between her maternal + uncle and maternal grandmother. + + + VHS + 1992 + PG13 + NA. + + + + \ No newline at end of file diff --git a/phNumber_main.py b/phNumber_main.py new file mode 100644 index 0000000..f4fde29 --- /dev/null +++ b/phNumber_main.py @@ -0,0 +1,19 @@ +import phonenumbers +from test_phNumber import number + +from phonenumbers import geocoder +ch_number=phonenumbers.parse(number,"CH") +print(geocoder.description_for_number(ch_number,"ta")) # finding the place for the phone number + +from phonenumbers import carrier + + +service_number=phonenumbers.parse(number,"R0") +print(carrier.name_for_number(service_number,"en"))# finding the service provider for the phone number + +from phonenumbers import timezone +phnumber=phonenumbers.parse(number) +timezone=timezone.time_zones_for_number(phnumber)# finding the timezone for the phone number +print(timezone) + + diff --git a/random_numbers.py b/random_numbers.py new file mode 100644 index 0000000..5d4bdd6 --- /dev/null +++ b/random_numbers.py @@ -0,0 +1,72 @@ +# import random +# a=random.randint(1,20) +# print(a) + +# b = random.random() +# print(b) + +import random +a = random.random() +print(a) +a = random.uniform(1, 10) +print(a) +a = random.randint(1, 10) # This will include the upper bound +print(a) +a = random.randrange(1, 10) #upper range not included +print(a) +a = random.normalvariate(0, 1)# return a random with a mean of zero and std. deviation of 1 +print(a) +mylist = list("ABCDEFGH") +print(mylist) +a = random.choice(mylist) # Pick a random element +print(a) +a = random.sample(mylist, 3) +print(a) +a = random.choices(mylist, k=3) # to pick elements multiple times +print(a) +a = random.shuffle(mylist) #it will shuffle the order pf elements in the list +print(mylist) +random.seed(1) # Can reproduce the data and print the exact same results with the seed number +print(random.random()) +print(random.randint(1, 10)) +random.seed(2) +print(random.random()) +print(random.randint(1, 10)) +random.seed(1) +print(random.random()) +print(random.randint(1, 10)) +random.seed(2) +print(random.random()) +print(random.randint(1, 10)) +# Since the seed can reproduce the random numbers it is advisable to use +# the secret module for security purposes +import secrets +a = secrets.randbelow(10) # upper bound not included +print(a) +a =secrets.randbits(4) +# 4 bits means it can have 4 different random binary values +# the highest possible numbe rin this case is 1111 +# which is 15. It will generate a number from 0-15 +print(a) +mylist = list("ABCDEFGH") +a = secrets.choice(mylist) +print(a) +# import numpy as np +# # numpy random generator uses a different random number generator +# a = np,random.rand(3) +# print(a) +# a = np,random.rand(3, 3) +# print(a) +# a = np,random.randint(0, 10, 3) +# print(a) +# a = np,random.randint(0, 10, (3, 4)) +# print(a) +# arr = np.array([1,2,3], [4,5,6], [7,8,9]) +# print(arr) +# np.random.shuffle(arr) +# print(arr) +# np.random.seed(1) +# print(np.random.rand(3,3)) +# np.random.seed(1) +# print(np.random.rand(3,3)) + diff --git a/regex_practice_problems.ipynb b/regex_practice_problems.ipynb new file mode 100644 index 0000000..376cadf --- /dev/null +++ b/regex_practice_problems.ipynb @@ -0,0 +1,1317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lbrLwTw4r9jM" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mtZQDWxxos3J" + }, + "source": [ + "# 30 REGULAR EXPRESSION EXERCISES!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6jEYHJH3os3Q" + }, + "source": [ + "Regex, you never know when they might come in handy. It's one of the \"good programmer\"'s fundamental yet a few people actually masters them.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "ghj1l7sQos3R" + }, + "outputs": [], + "source": [ + "import re" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_tO-OHUBos3T" + }, + "source": [ + "1) Write a Python program to check that a string contains only a certain set of characters (in this case a-z, A-Z and 0-9)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cXaMTykzos3V", + "outputId": "a73c9558-3ac4-44e9-ad0f-685a8f627983" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Solution\n", + "def is_allowed_specific_char(text):\n", + " # Define a regex pattern\n", + " pattern = r'[a-zA-Z0-9]'\n", + " # Use the pattern to search for ....\n", + " result=re.search(pattern, text)\n", + " if result == None:\n", + " return False\n", + " else:\n", + " return True\n", + "\n", + "print(is_allowed_specific_char(\"ABCDEFabcdef123450\")) # True\n", + "print(is_allowed_specific_char(\"*&%@#!}{\")) # False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2oae-B8tos3W" + }, + "source": [ + "2) Write a Python program that matches a string that has an a followed by zero or more b's" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "id": "1LHEOOMkos3X" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern=re.match(r\"ab?\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + " \n", + "print(text_match(\"ac\")) # Found a match!\n", + "print(text_match(\"abc\")) # Found a match!\n", + "print(text_match(\"abbc\")) # Found a match!\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AQRTCmJZos3Z" + }, + "source": [ + "3) Write a Python program that matches a string that has an a followed by one or more b's" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "id": "H1X2NPQYos3a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"ab+\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"ab\")) # Found a match!\n", + "print(text_match(\"abc\")) # Found a match!\n", + "print(text_match(\"ac\")) # Not matched\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E902uTcRos3a" + }, + "source": [ + "4) Write a Python program that matches a string that has an a followed by zero or one 'b'" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "id": "xeva0fRNos3b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"ab?\",text)\n", + " if pattern == None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + " \n", + "print(text_match(\"ab\")) # Found a match!\n", + "print(text_match(\"abc\")) # Found a match!\n", + "print(text_match(\"abbc\")) # Found a match!\n", + "print(text_match(\"aabbc\")) # Found a match!\n", + "print(text_match(\"ac\")) # Found a match!\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "whnPaW2kos3c" + }, + "source": [ + "5) Write a Python program that matches a string that has an a followed by three 'b'" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": { + "id": "0c_7S3h_os3d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Found a match!\n", + "Not matched\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"abbb\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + " \n", + "\n", + "print(text_match(\"abbb\")) # Found a match!\n", + "print(text_match(\"aabbbbbc\")) # Found a match!\n", + "print(text_match(\"aabbc\")) # Not matched\n", + "print(text_match(\"ac\")) # Not matched\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V4AsjJiCos3e" + }, + "source": [ + "6) Write a Python program that matches a string that has an a followed by two to three 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": { + "id": "VjA5CXi1os3e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Found a match!\n", + "Found a match!\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"abb\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"ab\")) # Not matched\n", + "print(text_match(\"aabbbbbc\")) # Found a match!\n", + "print(text_match(\"aabbc\")) # Found a match!\n", + "print(text_match(\"ac\")) # Not matched\n", + "print(text_match(\"bbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kX7sLRavos3f" + }, + "source": [ + "7) Write a Python program to find sequences of lowercase letters joined with a underscore." + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rbxk35n7os3g", + "outputId": "3ddca986-1bd6-45f1-902c-eee85e8853e8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"^[a-z]+_[a-z]+$\", text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"aab_cbbbc\")) # Found a match!\n", + "print(text_match(\"aab_Abbbc\")) # Not matched\n", + "print(text_match(\"Aaab_abbbc\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H4sat-Rfos3g" + }, + "source": [ + "8) Write a Python program to find the sequences of one upper case letter followed by lower case letters." + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "id": "oeUiVHGVos3h" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Found a match!\n", + "Found a match!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"[A-Z][a-z]+\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"aab_cbbbc\")) # Not matched\n", + "print(text_match(\"aab_Abbbc\")) # Found a match!\n", + "print(text_match(\"Aaab_abbbc\")) # Found a match!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RgNBl7aEos3h" + }, + "source": [ + "9) Write a Python program that matches a string that has an 'a' followed by anything, ending in 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "id": "YbCsos7Jos3i" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Not matched\n", + "Found a match!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"^a[aA-zZ]+b$\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"aabbbbd\")) # Not matched\n", + "print(text_match(\"aabAbbbc\")) # Not matched\n", + "print(text_match(\"accddbbjjjb\")) # Found a match!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0oHZnx0Gos3i" + }, + "source": [ + "10) Write a Python program that matches a word at the beginning of a string." + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "id": "1tlZiGMaos3j" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.match(r\"The\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\" The quick brown fox jumps over the lazy dog.\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mG0jspZFos3j" + }, + "source": [ + "11) Write a Python program that matches a word at the end of string, with optional punctuation." + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "id": "cmhnhbZnos3j" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match\n", + "Not matched\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(\"\\w+\\S*$\", text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match\"\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog. \")) # Not matched\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog \")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DBgbyYHXos3k" + }, + "source": [ + "12) Write a Python program that matches a word containing 'z'" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "id": "WxuMRyCbos3k" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"z\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + " \n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\"Python Exercises.\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V_9_nk8Kos3l" + }, + "source": [ + "13) Write a Python program that matches a word containing 'z', not at the start or end of the word." + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "id": "VEhCCh7yos3l" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found a match!\n", + "Not matched\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"\\Bz\\B\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Found a match!\n", + "print(text_match(\"Python Exercises.\")) # Not matched" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L0lV07OCos3m" + }, + "source": [ + "14) Write a Python program to match a string that contains only upper and lowercase letters, numbers, and underscores." + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "id": "E9NcoWDhos3m" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not matched\n", + "Found a match!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def text_match(text):\n", + " \n", + " pattern = re.search(r\"[aA-zZ]+[0-9_]+\",text)\n", + " if pattern is None:\n", + " return \"Not matched\"\n", + " else:\n", + " return \"Found a match!\"\n", + "print(text_match(\"The quick brown fox jumps over the lazy dog.\")) # Not matched\n", + "print(text_match(\"Python_Exercises_1\")) # Found a match!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EvKJ8WOoos3n" + }, + "source": [ + "15) Write a Python program where a string will start with a specific number. " + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "id": "nWp5QysTos3n" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Solution\n", + "def match_num(string):\n", + " \n", + " pattern = re.match(r\"^5\",string)\n", + " if pattern is None:\n", + " return False\n", + " else:\n", + " return True\n", + " \n", + "print(match_num('5-2345861')) # True\n", + "print(match_num('6-2345861')) # False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r_I1cbvios3t" + }, + "source": [ + "16) Write a Python program to remove leading zeros from an IP address" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "id": "cRLX4hc3os3u" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "216.8.94.196\n" + ] + } + ], + "source": [ + "# Solution\n", + "def rewrite_ip(ip):\n", + " # your code here\n", + " \n", + " newstring= re.sub(r\"0*\",\"\",ip)\n", + " if newstring is None:\n", + " return None\n", + " else:\n", + " return newstring\n", + "ip = \"216.08.094.196\"\n", + "string = rewrite_ip(ip)\n", + "print(string) # 216.8.94.196" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZCn9r3o2os3u" + }, + "source": [ + "17) Write a Python program to check for a number at the end of a string." + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "id": "Q5t9U6n8os3u" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# Solution\n", + "def end_num(string):\n", + " \n", + " pattern = re.search(r\"\\d$\",string)\n", + " if pattern is None:\n", + " return False\n", + " else:\n", + " return True\n", + "\n", + "print(end_num('abcdef')) # False\n", + "print(end_num('abcdef6')) # True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0tM4eW8Wos3v" + }, + "source": [ + "18) Write a Python program to search the numbers (0-9) of length between 1 to 3 in a given string. " + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "id": "7QGwzVxDos3v" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of length 1 to 3\n", + "1\n", + "12\n", + "13\n", + "345\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_digits(string):\n", + "\n", + " pattern = re.findall(r\"\\d+\",string)\n", + " \n", + " print(\"Number of length 1 to 3\")\n", + " \n", + " for numbers in pattern:\n", + " print(numbers)\n", + "string = \"Exercises number 1, 12, 13, and 345 are important\"\n", + "print_digits(string)\n", + "# Number of length 1 to 3\n", + "# 1\n", + "# 12\n", + "# 13\n", + "# 345" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "43nNb7d_os3v" + }, + "source": [ + "19) Write a Python program to search some literals strings in a string. \n", + "Sample text : 'The quick brown fox jumps over the lazy dog.'\n", + "Searched words : 'fox', 'dog', 'horse'" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "id": "KIVHxWN9os3w" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Searching for \"fox\" in \"The quick brown fox jumps over the lazy dog.\"->\n", + "Matched!\n", + "Searching for \"dog\" in \"The quick brown fox jumps over the lazy dog.\"->\n", + "Matched!\n", + "Searching for \"horse\" in \"The quick brown fox jumps over the lazy dog.\"->\n", + "Not Matched!\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_match(patterns, text):\n", + " \n", + " for name in patterns:\n", + " result = re.search(name,text)\n", + " print('Searching for \"{}\" in \"{}\"->'.format(name,text))\n", + " if result is None:\n", + " print(\"Not Matched!\")\n", + " else:\n", + " print(\"Matched!\")\n", + "patterns = [ 'fox', 'dog', 'horse' ]\n", + "text = 'The quick brown fox jumps over the lazy dog.'\n", + " \n", + "print_match(patterns, text)\n", + "# Searching for \"fox\" in \"The quick brown fox jumps over the lazy dog.\" ->\n", + "# Matched!\n", + "# Searching for \"dog\" in \"The quick brown fox jumps over the lazy dog.\" ->\n", + "# Matched!\n", + "# Searching for \"horse\" in \"The quick brown fox jumps over the lazy dog.\" ->\n", + "# Not Matched!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XjdROmMhos3w" + }, + "source": [ + "20) Write a Python program to search a literals string in a string and also find the location within the original string where the pattern occurs\n", + "\n", + "Sample text : 'The quick brown fox jumps over the lazy dog.'\n", + "Searched words : 'fox'" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": { + "id": "vWViImYios3w" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"fox\" in \"The quick brown fox jumps over the lazy dog.\" from 16 to 19\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_match_location(pattern, text):\n", + " \n", + " position = re.search(pattern,text)\n", + " s = position.start()\n", + " e = position.end()\n", + " print('Found \"{}\" in \"{}\" from {} to {}'.format(pattern,text,s,e))\n", + "\n", + "pattern = 'fox'\n", + "text = 'The quick brown fox jumps over the lazy dog.'\n", + "print_match_location(pattern, text)\n", + "# Found \"fox\" in \"The quick brown fox jumps over the lazy dog.\" from 16 to 19" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLRotk-Oos3x" + }, + "source": [ + "21) Write a Python program to find the substrings within a string.\n", + "\n", + "Sample text :\n", + "\n", + "'Python exercises, PHP exercises, C# exercises'\n", + "\n", + "Pattern :\n", + "\n", + "'exercises'\n", + "\n", + "Note: There are three instances of exercises in the input string." + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "id": "XU8613rTos3x" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"exercises\"\n", + "Found \"exercises\"\n", + "Found \"exercises\"\n" + ] + } + ], + "source": [ + "# Solution\n", + "def find_all_matches(pattern, text):\n", + " \n", + " for substring in re.findall(pattern, text):\n", + " print('Found \"%s\"' %substring)\n", + "\n", + "text = 'Python exercises, PHP exercises, C# exercises'\n", + "pattern = 'exercises'\n", + "find_all_matches(pattern, text)\n", + "# Found \"exercises\"\n", + "# Found \"exercises\"\n", + "# Found \"exercises\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uvAY6v7Oos3x" + }, + "source": [ + "22) Write a Python program to find the occurrence and position of the substrings within a string." + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": { + "id": "UFg6rXJnos3y" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"exercises\" at 7:16\n", + "Found \"exercises\" at 22:31\n", + "Found \"exercises\" at 36:45\n" + ] + } + ], + "source": [ + "# Solution\n", + "def find_all_matches_location(pattern, text):\n", + " \n", + " text = 'Python exercises, PHP exercises, C# exercises'\n", + " pattern = 'exercises'\n", + " for location in re.finditer(pattern,text):\n", + " s=location.start()\n", + " e=location.end()\n", + " print('Found \"%s\" at %d:%d' % (text[s:e], s, e))\n", + " \n", + "\n", + "find_all_matches_location(pattern, text)\n", + "# Found \"exercises\" at 7:16\n", + "# Found \"exercises\" at 22:31\n", + "# Found \"exercises\" at 36:45" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sb3zKxE-os3y" + }, + "source": [ + "23) Write a Python program to replace whitespaces with an underscore and vice versa." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "id": "nUxR5Xvjos3z" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python_Exercises\n", + "Python Exercises\n" + ] + } + ], + "source": [ + "text = 'Python Exercises'\n", + "\n", + "# Your code\n", + "text = text.replace(\" \",\"_\")\n", + "\n", + "print(text) # Python_Exercises\n", + "text = text.replace(\"_\",\" \")\n", + "# Your code\n", + "result = re.sub(\"_\",\" \",text)\n", + "print(text) # Python Exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "grqoW-pgos3z" + }, + "source": [ + "24) Write a Python program to extract year, month and date from a an url. " + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": { + "id": "K2-pacqmos3z" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('2016', '09', '02')]\n" + ] + } + ], + "source": [ + "# Solution\n", + "def extract_date(url):\n", + " year_month_date=re.findall(r\"/(\\d{4})/(\\d{2})/(\\d{2})\",url1)\n", + " return year_month_date\n", + " \n", + "url1= \"https://www.washingtonpost.com/news/football-insider/wp/2016/09/02/odell-beckhams-fame-rests-on-one-stupid-little-ball-josh-norman-tells-author/\"\n", + "print(extract_date(url1)) # [('2016', '09', '02')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gyD5AZW7os3z" + }, + "source": [ + "25) Write a Python program to convert a date of yyyy-mm-dd format to dd-mm-yyyy format." + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": { + "id": "bo-jpYqZos30" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original date in YYY-MM-DD Format: 2026-01-02\n", + "New date in DD-MM-YYYY Format: 02-01-2026\n" + ] + } + ], + "source": [ + "# Solution\n", + "def change_date_format(dt):\n", + " \n", + " date_format=re.sub(r\"(\\d{4})-(\\d{2})-(\\d{2})\",\"\\\\3-\\\\2-\\\\1\",dt1)\n", + " return date_format\n", + "dt1 = \"2026-01-02\"\n", + "print(\"Original date in YYY-MM-DD Format: \",dt1) # Original date in YYY-MM-DD Format: 2026-01-02\n", + "print(\"New date in DD-MM-YYYY Format: \",change_date_format(dt1)) # New date in DD-MM-YYYY Format: 02-01-2026" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IQ2SNL-gos30" + }, + "source": [ + "26) Write a Python program to match if any words from a list of words starting with letter 'P'." + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "id": "2hP4Twe0os31" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Python', 'PHP')\n" + ] + } + ], + "source": [ + "# Sample strings.\n", + "def print_words_starting_with_P(words):\n", + " \n", + " for word in words:\n", + " matchwords=re.search(\"(P\\w+)\\s(P\\w+)\",word)\n", + " if matchwords:\n", + " print(matchwords.groups())\n", + "\n", + "words = [\"Python PHP\", \"Java JavaScript\", \"c c++\"]\n", + "print_words_starting_with_P(words) # ('Python', 'PHP')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MEUMKMn0os31" + }, + "source": [ + "27) Write a Python program to separate and print the numbers of a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "id": "xG8c9PtUos32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "10\n", + "20\n", + "30\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_all_numbers(text):\n", + " \n", + "# Sample string.\n", + " text = \"Ten 10, Twenty 20, Thirty 30\"\n", + " numbers=re.split(\"\\D+\",text)\n", + " for n in numbers:\n", + " print(n)\n", + "print_all_numbers(text)\n", + "# 10\n", + "# 20\n", + "# 30" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "krdKu3Mvos32" + }, + "source": [ + "28) Write a Python program to find all words starting with 'a' or 'e' in a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "id": "3hsR5278os32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['example', 'eates', 'an', 'ayList', 'apacity', 'elements', 'elements', 'are', 'en', 'added', 'ayList', 'and', 'ayList', 'ed', 'accordingly']\n" + ] + } + ], + "source": [ + "# Solution\n", + "def get_all_words_containing_a_or_e(text):\n", + " \n", + " result = re.findall(r\"[ae]\\w+\",text)\n", + "\n", + " return result\n", + "\n", + "# Input.\n", + "text = \"The following example creates an ArrayList with a capacity of 50 elements. Four elements are then added to the ArrayList and the ArrayList is trimmed accordingly.\"\n", + "print(get_all_words_containing_a_or_e(text))\n", + "# ['example', 'eates', 'an', 'ayList', 'apacity', 'elements', 'elements', 'are', 'en', 'added', 'ayList', 'and', 'ayList', 'ed', 'accordingly']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CxfIZ5o9os33" + }, + "source": [ + "29) Write a Python program to separate and print the numbers and their position of a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "id": "XIuiRvmAos33" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n", + "Index Position: 62\n" + ] + } + ], + "source": [ + "# Solution\n", + "def print_all_numbers_and_their_position(text):\n", + " \n", + " for number_position in re.finditer(r\"\\d+\",text):\n", + " print(number_position.group())\n", + " print(\"Index Position: \", number_position.start())\n", + "\n", + "# Input.\n", + "text = \"The following example creates an ArrayList with a capacity of 50 elements. Four elements are then added to the ArrayList and the ArrayList is trimmed accordingly.\"\n", + "print_all_numbers_and_their_position(text)\n", + "# 50\n", + "# Index position: 62" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JguO0fUjos34" + }, + "source": [ + "30) Write a Python program to abbreviate 'Road' as 'Rd.' in a given string." + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "id": "VJE1Xm2Bos34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21 Ramkrishna Rd\n" + ] + } + ], + "source": [ + "# Solution\n", + "def abbreviate_road(street):\n", + " \n", + " return re.sub(r\"Road\",\"Rd\",street)\n", + "street = '21 Ramkrishna Road'\n", + "\n", + "print(abbreviate_road(street)) # 21 Ramkrishna Rd.\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "vscode": { + "interpreter": { + "hash": "ccc0ef284f665b5fb033224ed4334dd4c810861a1ab44a6ce7ff637cb973648c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b4bf459 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +certifi==2022.12.7 +charset-normalizer==3.0.1 +idna==3.4 +python-decouple==3.7 +requests==2.28.2 +urllib3==1.26.14 diff --git a/sets.py b/sets.py new file mode 100644 index 0000000..1905ecb --- /dev/null +++ b/sets.py @@ -0,0 +1,76 @@ +myset=set("hello") +print(myset) + +myset1=set()#set is mutable +print(type(myset1)) + +myset1.add(1) +myset1.add(20) +myset1.add(30) + +print(myset1) + +myset1.remove(1) +print(myset1) +# myset1.remove(40) +# myset1.discard(40) +myset1.pop() + +print(myset1) + +odds = {1,3,5,7,9} +evens = {0,2,4,6,8} +primes={2,3,5,7} + +u=odds.union(evens) +print(u) + +i = odds.intersection(evens) +print(i) + +i1=odds.intersection(primes) +print(i1) + + +setA = {1,2,3,4,5,6,7,8,9,13} +setB={1,2,3,10,11,12,13} + +diff = setB.difference(setA) +print(diff) + +symdiff = setB.symmetric_difference(setA) +print(symdiff) + +# setA.update(setB) +# print(setA) + +# setA.intersection_update(setB) +# print(setA) + +# setB.difference_update(setA) +# print(setB) + +# setA.symmetric_difference(setB) +# print(setA) +setA = {1,2,3,4,5,6,7,8,9,13} +setB={1,2,3,13} +setC={45,50} +print(setB.issuperset(setA)) +print(setB.issubset(setA)) + +print(setC.isdisjoint(setA)) + +# setD=setC +setD=setC.copy() +setC.add(100) +print(setD) +print(setC) + +#frozenset is immutable--"we cannot modify or add or delete the element" + +a=frozenset([1,2,3,4]) +b=frozenset([8,9]) + +print(a.union(b)) + +print(a) diff --git a/student_result.json b/student_result.json new file mode 100644 index 0000000..93f2f74 --- /dev/null +++ b/student_result.json @@ -0,0 +1,406 @@ +[ + { + "id": "102", + "name": "abc", + "age": 31, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 100, + "english": 100, + "computer": 95, + "science": 85 + }, + "total": 295, + "average": 98.33333333333333, + "grade": "A" + }, + "address": "90 Main Street", + "hobbies": "painting", + "total": 295, + "average": 98.33333333333333 + }, + { + "id": "102", + "name": "abc", + "age": 31, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 100, + "english": 100, + "computer": 95, + "science": 85 + }, + "total": 295, + "average": 98.33333333333333, + "grade": "A" + }, + "address": "90 Main Street", + "hobbies": "painting", + "total": 295, + "average": 98.33333333333333 + }, + { + "id": "104", + "name": "ghi", + "age": 21, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 48, + "english": 85, + "computer": 95 + }, + "total": 228, + "average": 76.0, + "grade": "A" + }, + "address": "100 Plano Dr", + "hobbies": "Origami", + "total": 228, + "average": 76.0 + }, + { + "id": "104", + "name": "ghi", + "age": 21, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 48, + "english": 85, + "computer": 95 + }, + "total": 228, + "average": 76.0, + "grade": "A" + }, + "address": "100 Plano Dr", + "hobbies": "Origami", + "total": 228, + "average": 76.0 + }, + { + "id": "106", + "name": "Deepesh", + "age": 19, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 99, + "english": 98, + "computer": 100 + }, + "total": 297, + "average": 99.0, + "grade": "A" + }, + "address": "221 Commodo Blvd.", + "hobbies": "sports", + "total": 297, + "average": 99.0 + }, + { + "id": "106", + "name": "Deepesh", + "age": 19, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 99, + "english": 98, + "computer": 100 + }, + "total": 297, + "average": 99.0, + "grade": "A" + }, + "address": "221 Commodo Blvd.", + "hobbies": "sports", + "total": 297, + "average": 99.0 + }, + { + "id": "109", + "name": "Tom", + "age": 20, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 90, + "english": 94, + "computer": 92 + }, + "total": 276, + "average": 92.0, + "grade": "A" + }, + "address": "39 Bridge Dr", + "hobbies": "Sewing", + "total": 276, + "average": 92.0 + }, + { + "id": "109", + "name": "Tom", + "age": 20, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 90, + "english": 94, + "computer": 92 + }, + "total": 276, + "average": 92.0, + "grade": "A" + }, + "address": "39 Bridge Dr", + "hobbies": "Sewing", + "total": 276, + "average": 92.0 + }, + { + "id": "107", + "name": "wesley", + "age": 40, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 48, + "english": 95, + "computer": 79 + }, + "total": 222, + "average": 74.0, + "grade": "B" + }, + "address": "23 Miskin Dr", + "hobbies": "swimming", + "total": 222, + "average": 74.0 + }, + { + "id": "107", + "name": "wesley", + "age": 40, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 48, + "english": 95, + "computer": 79 + }, + "total": 222, + "average": 74.0, + "grade": "B" + }, + "address": "23 Miskin Dr", + "hobbies": "swimming", + "total": 222, + "average": 74.0 + }, + { + "id": "108", + "name": "Randy", + "age": 28, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 78, + "english": 73, + "computer": 65 + }, + "total": 216, + "average": 72.0, + "grade": "B" + }, + "address": "15 Cosmo Blvd.", + "hobbies": "drawing", + "total": 216, + "average": 72.0 + }, + { + "id": "108", + "name": "Randy", + "age": 28, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 78, + "english": 73, + "computer": 65 + }, + "total": 216, + "average": 72.0, + "grade": "B" + }, + "address": "15 Cosmo Blvd.", + "hobbies": "drawing", + 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"average": 52.666666666666664 + }, + { + "id": "103", + "name": "def", + "age": 20, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 68, + "english": 55, + "computer": 35 + }, + "total": 158, + "average": 52.666666666666664, + "grade": "C" + }, + "address": "9519 Kirk Street ", + "hobbies": "drawing", + "total": 158, + "average": 52.666666666666664 + }, + { + "id": "105", + "name": "jkl", + "age": 19, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 58, + "english": 73, + "computer": 23 + }, + "total": 154, + "average": 51.333333333333336, + "grade": "C" + }, + "address": "49 Simmons Dr", + "hobbies": "Sewing", + "total": 154, + "average": 51.333333333333336 + }, + { + "id": "105", + "name": "jkl", + "age": 19, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 58, + "english": 73, + "computer": 23 + }, + "total": 154, + "average": 51.333333333333336, + "grade": "C" + }, + "address": "49 Simmons Dr", + "hobbies": "Sewing", + "total": 154, + "average": 51.333333333333336 + }, + { + "id": "101", + "name": "Samu", + "age": 36, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 100, + "english": 100, + "computer": 100, + "science": 95 + }, + "total": 300, + "average": 100.0, + "grade": "Fail" + }, + "address": "95 Commons Dr", + "hobbies": "sports", + "total": 300, + "average": 100.0 + }, + { + "id": "101", + "name": "Samu", + "age": 36, + "course": { + "course_name": "Computer Science", + "marks": { + "tamil": 100, + "english": 100, + "computer": 100, + "science": 95 + }, + "total": 300, + "average": 100.0, + "grade": "Fail" + }, + "address": "95 Commons Dr", + "hobbies": "sports", + "total": 300, + "average": 100.0 + } +] \ No newline at end of file diff --git a/studentfile.json b/studentfile.json new file mode 100644 index 0000000..a93a209 --- /dev/null +++ b/studentfile.json @@ -0,0 +1,138 @@ +[ + { + "id" : "101", + "name" : "Samu", + "age" : 36, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 100, + "english" : 100, + "computer" : 100, + "science" : 95 + } }, + "address" : "95 Commons Dr", + "hobbies" : "sports" + }, + { + "id" : "102", + "name" : "abc", + "age" : 31, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 100, + "english" : 100, + "computer" : 95, + "science" : 85 + } }, + "address" : "90 Main Street", + "hobbies" : "painting" + }, + { + "id" : "103", + "name" : "def", + "age" : 20, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 68, + "english" : 55, + "computer" : 35 + } }, + "address" : "9519 Kirk Street ", + "hobbies" : "drawing" + }, + { + "id" : "104", + "name" : "ghi", + "age" : 21, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 48, + "english" : 85, + "computer" : 95 + } }, + "address" : "100 Plano Dr", + "hobbies" : "Origami" + }, + { + "id" : "105", + "name" : "jkl", + "age" : 19, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 58, + "english" : 73, + "computer" : 23 + } }, + "address" : "49 Simmons Dr", + "hobbies" : "Sewing" + }, + { + "id" : "106", + "name" : "Deepesh", + "age" : 19, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 99, + "english" : 98, + "computer" : 100 + } }, + "address" : "221 Commodo Blvd.", + "hobbies" : "sports" + }, + { + "id" : "107", + "name" : "wesley", + "age" : 40, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 48, + "english" : 95, + "computer" : 79 + } }, + "address" : "23 Miskin Dr", + "hobbies" : "swimming" + }, + { + "id" : "108", + "name" : "Randy", + "age" : 28, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 78, + "english" : 73, + "computer" : 65 + } }, + "address" : "15 Cosmo Blvd.", + "hobbies" : "drawing" + }, + { + "id" : "109", + "name" : "Tom", + "age" : 20, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 90, + "english" : 94, + "computer" : 92 + } }, + "address" : "39 Bridge Dr", + "hobbies" : "Sewing" + }, + { + "id" : "110", + "name" : "Hercules", + "age" : 30, + "course" : {"course_name" : "Computer Science", + "marks": + { "tamil" : 59, + "english" : 82, + "computer" : 63 + } }, + "address" : "9526 Costco Street", + "hobbies" : "painting" + } +] + + + + \ No newline at end of file diff --git a/users.json b/users.json new file mode 100644 index 0000000..6846958 --- /dev/null +++ b/users.json @@ -0,0 +1,52 @@ +[ + { + "id": 5, + "name": "Chelsey Dietrich", + "phone": "(254)954-1289" + }, + { + "id": 10, + "name": "Clementina DuBuque", + "phone": "024-648-3804" + }, + { + "id": 3, + "name": "Clementine Bauch", + "phone": "1-463-123-4447" + }, + { + "id": 2, + "name": "Ervin Howell", + "phone": "010-692-6593 x09125" + }, + { + "id": 9, + "name": "Glenna Reichert", + "phone": "(775)976-6794 x41206" + }, + { + "id": 7, + "name": "Kurtis Weissnat", + "phone": "210.067.6132" + }, + { + "id": 1, + "name": "Leanne Graham", + "phone": "1-770-736-8031 x56442" + }, + { + "id": 6, + "name": "Mrs. Dennis Schulist", + "phone": "1-477-935-8478 x6430" + }, + { + "id": 8, + "name": "Nicholas Runolfsdottir V", + "phone": "586.493.6943 x140" + }, + { + "id": 4, + "name": "Patricia Lebsack", + "phone": "493-170-9623 x156" + } +] \ No newline at end of file diff --git a/xml_practice.py b/xml_practice.py new file mode 100644 index 0000000..3f4341c --- /dev/null +++ b/xml_practice.py @@ -0,0 +1,82 @@ +import xml.etree.ElementTree as ET +import re + +tree = ET.parse('movies.xml') +root = tree.getroot() + + +print(tree) +print(root) +print(root.tag) +print(root.attrib) + +for child in root: + print(child.tag, child.attrib) + +# print([elem.tag for elem in root.iter()]) + +# print(ET.tostring(root, encoding='utf8').decode('utf8')) + +for movie in root.iter('movie'): + # print(movie.attrib) + pass +for description in root.iter('description'): + # print(description.text) + pass +for movie in root.findall("./genre/decade/movie/format/[@multiple='Yes']..."): + # print(movie.attrib) + pass + +# back = root.find("./genre/decade/movie[@title='Back 2 the Future']") +# print(back) +# back.attrib["title"]="Back to the Future" +# print(back.attrib) + +# tree.write('movies.xml') + +# tree=ET.parse('movies.xml') +# root = tree.getroot() +# for movie in root.iter('movies'): +# print(movie.attrib) + +for form in root.findall("./genre/decade/movie/format"): + # print(form.attrib, form.text) + #search for the commas in the format text + match = re.search(',',form.text) + if match: + form.set('multiple','Yes') + else: + form.set('multiple','No') + print(form.attrib, form.text) + +for decade in root.findall("./genre/decade"): + print(decade.attrib) + for year in decade.findall("./movie/year"): + print(year.text,'\n') + +for movie in root.findall("./genre/decade/movie/[year = '2000']"): + print(movie.attrib) + +add=root.find("./genre/[@category='Action']") +print(add) + +new_dec = ET.SubElement(add,'decade') +print(new_dec) +print() + +new_dec.attrib["years"] = '2000s' + +print(ET.tostring(add, encoding='utf8').decode('utf8')) + +# xmen = root.find("./genre/decade/movie[@title = 'X-Men']") +# dec2000=root.find("./genre[@category='Action']") +xmen = root.find("./genre/decade/movie[@title='X-Men']") +dec2000s = root.find("./genre[@category='Action']/decade[@years='2000s']") +dec2000s.append(xmen) +dec1990s = root.find("./genre[@category='Action']/decade[@years='1990s']") +dec1990s.remove(xmen) + +print(ET.tostring(add, encoding='utf8').decode('utf8')) + +tree.write('movies.xml') + diff --git a/xml_practice2.py b/xml_practice2.py new file mode 100644 index 0000000..df8b354 --- /dev/null +++ b/xml_practice2.py @@ -0,0 +1,85 @@ +import xml.etree.ElementTree as ET + +tree= ET.parse('movies1.xml') +root= tree.getroot() + +# print(tree) +# print(ET.tostring(root, encoding = 'utf8').decode('utf8')) +# print(root.tag) #giving us the name of the element tag +# print(root.attrib) #giving us the element attributes in a dictionary +# ###############blank dicitonary means no attributes +# print(len(root)) ##how many children the element has +# print([elem.tag for elem in root.iter()]) #gives me a list of all the elements + +for rating in root.iter('rating'): + # print(rating.text) + pass + + +for rating_pg in root.findall("./genre/decade/movie/[rating='PG']"): + #print(rating_pg.attrib) + pass + +kkid= root.find("./genre/decade/movie[@title= 'THE KARATE KID']") +kkid.attrib['title']= "The Karate Kid" +#print(kkid.attrib) + +#Creating "Anime" category under +anime_genre = ET.SubElement(root, 'genre') +print(ET.tostring(root, encoding = 'utf8').decode('utf8')) +anime_genre.attrib['category']='Anime' + +#Creating "decade" under +new_decade = root.find("./genre[@category='Anime']") +print(new_decade) +print() +new_decade_add = ET.SubElement(new_decade,'decade') +print(new_decade_add) +print() +new_decade_add.attrib['years']='2017'#Adding the attribute value '2017' +print(ET.tostring(new_decade_add, encoding='utf8').decode('utf8')) + + +#Creating "movie" under +new_movie= root.find("./genre[@category='Anime']/decade/[@years='2017']") + +new_movie_add = ET.SubElement(new_movie,'movie') +new_movie_add.attrib['favorite']='True' +new_movie_add.attrib['title']='Gifted' + + +#Creating sub-tags ,,and under "movie" tag +Movie_path = root.find("./genre[@category='Anime']/decade[@years='2017']/movie[@title='Gifted']") + +new_format = ET.SubElement(Movie_path,'format') +new_year=ET.SubElement(Movie_path,'year') +new_rating=ET.SubElement(Movie_path,'rating') +new_description = ET.SubElement(Movie_path,'description') + +# Giving the value of attributes +new_format.attrib['multiple']='yes' +new_format.text ='DVD' +new_year.text='2017' +new_rating.text='R' +new_description.text='an intellectually gifted seven-year-old who becomes the subject of a custody battle between her maternal uncle and maternal grandmother.' + +#Writing the changes into the file +tree.write('movies1.xml') + +#Changing the movie "Batman Returns" from Action categroy to Anime category +batman=root.find("./genre/decade/movie[@title='Batman Returns']") +decade_2017= root.find("./genre[@category='Anime']/decade[@years='2017']") +decade_2017.append(batman) + +#Remove the movie "Batman Returns" from Action category +decade_1990= root.find("./genre[@category='Action']/decade[@years='1990s']") +decade_1990.remove(batman) + +#Again writing the changes to the file +tree.write('movies1.xml') + +#View the changes +print(ET.tostring(root, encoding = 'utf8').decode('utf8')) + + +