diff --git a/module-1/lab-advanced-web-scraping/your-code/Learning.ipynb b/module-1/lab-advanced-web-scraping/your-code/Learning.ipynb index b2af0c8..2407fc8 100644 --- a/module-1/lab-advanced-web-scraping/your-code/Learning.ipynb +++ b/module-1/lab-advanced-web-scraping/your-code/Learning.ipynb @@ -298,7 +298,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-advanced-web-scraping/your-code/main.ipynb b/module-1/lab-advanced-web-scraping/your-code/main.ipynb index 257597a..3133a3b 100755 --- a/module-1/lab-advanced-web-scraping/your-code/main.ipynb +++ b/module-1/lab-advanced-web-scraping/your-code/main.ipynb @@ -235,7 +235,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-bag-of-words/your-code/main.ipynb b/module-1/lab-bag-of-words/your-code/main.ipynb index c68edab..a1ae510 100644 --- a/module-1/lab-bag-of-words/your-code/main.ipynb +++ b/module-1/lab-bag-of-words/your-code/main.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -25,13 +25,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ironhack is cool.', 'I love Ironhack.', 'I am a student at Ironhack.']\n" + ] + } + ], "source": [ "corpus = []\n", "\n", - "# Write your code here" + "# Write your code here\n", + "for e in docs:\n", + " with open(e, 'r') as y:\n", + " corpus.append(y.read())\n", + "\n", + "print (corpus)\n" ] }, { @@ -43,10 +56,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ironhack is cool', 'i love ironhack', 'i am a student at ironhack']\n" + ] + } + ], + "source": [ + "corpus=[corpus[i].lower() for i in range(len(corpus))]\n", + "corpus=[corpus[i].replace('.', '') for i in range(len(corpus))]\n", + "print(corpus)" + ] }, { "cell_type": "markdown", @@ -84,13 +109,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "bag_of_words = []\n", "\n", - "# Write your code here" + "# Write your code here\n", + "\n", + "for e in corpus:\n", + " f=e.split(' ')\n", + " for g in f:\n", + " if g not in bag_of_words : bag_of_words.append(g)" ] }, { @@ -104,10 +134,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ironhack', 'is', 'cool', 'i', 'love', 'am', 'a', 'student', 'at']\n" + ] + } + ], + "source": [ + "print(bag_of_words)" + ] }, { "cell_type": "markdown", @@ -118,13 +158,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "term_freq = []\n", "\n", - "# Write your code here" + "# Write your code here\n", + "\n", + "for e in corpus:\n", + " lista=[]\n", + " e=e.split()\n", + " for f in bag_of_words:\n", + " if f in e:\n", + " lista.append(1)\n", + " else:\n", + " lista.append(0)\n", + " term_freq.append(lista)\n", + "\n", + " " ] }, { @@ -169,7 +221,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-intro-pandas/your-code/main.ipynb b/module-1/lab-intro-pandas/your-code/main.ipynb index 03f020a..3b84a97 100755 --- a/module-1/lab-intro-pandas/your-code/main.ipynb +++ b/module-1/lab-intro-pandas/your-code/main.ipynb @@ -18,10 +18,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import numpy as nb\n", + "import pandas as pd" + ] }, { "cell_type": "markdown", @@ -32,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -41,10 +44,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 5.7\n", + "1 75.2\n", + "2 74.4\n", + "3 84.0\n", + "4 66.5\n", + "5 66.3\n", + "6 55.8\n", + "7 75.7\n", + "8 29.1\n", + "9 43.7\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "serie=pd.Series(lst)\n", + "print(serie)" + ] }, { "cell_type": "markdown", @@ -57,10 +81,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "74.4\n" + ] + } + ], + "source": [ + "print(serie[2])" + ] }, { "cell_type": "markdown", @@ -71,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -89,10 +123,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "colnames=['col1','col2','col3','col4','col5']" + ] }, { "cell_type": "markdown", @@ -103,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -112,10 +148,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Score_1 Score_2 Score_3 Score_4 Score_5\n", + "0 53.1 95.0 67.5 35.0 78.4\n", + "1 61.3 40.8 30.8 37.8 87.6\n", + "2 20.6 73.2 44.2 14.6 91.8\n", + "3 57.4 0.1 96.1 4.2 69.5\n", + "4 83.6 20.5 85.4 22.8 35.9\n", + "5 49.0 69.0 0.1 31.8 89.1\n", + "6 23.3 40.7 95.0 83.8 26.9\n", + "7 27.6 26.4 53.8 88.8 68.5\n", + "8 96.6 96.4 53.4 72.4 50.1\n", + "9 73.7 39.0 43.2 81.6 34.7\n" + ] + } + ], + "source": [ + "df=pd.DataFrame(b,columns=colnames)\n", + "print (df)" + ] }, { "cell_type": "markdown", @@ -126,16 +183,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", + "execution_count": 23, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Score_1 Score_3 Score_5\n", + "0 53.1 67.5 78.4\n", + "1 61.3 30.8 87.6\n", + "2 20.6 44.2 91.8\n", + "3 57.4 96.1 69.5\n", + "4 83.6 85.4 35.9\n", + "5 49.0 0.1 89.1\n", + "6 23.3 95.0 26.9\n", + "7 27.6 53.8 68.5\n", + "8 96.6 53.4 50.1\n", + "9 73.7 43.2 34.7\n" + ] + } + ], "source": [ - "### 7. From the original data frame, calculate the average Score_3 value." + "sub_df=df[['Score_1','Score_3','Score_5']]\n", + "print(sub_df)" ] }, { @@ -154,10 +225,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.95000000000001\n" + ] + } + ], + "source": [ + "print (df['Score_3'].mean())" + ] }, { "cell_type": "markdown", @@ -168,10 +249,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88.8\n" + ] + } + ], + "source": [ + "print (df['Score_4'].max())" + ] }, { "cell_type": "markdown", @@ -182,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -203,10 +294,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Description Quantity UnitPrice Revenue\n", + "0 LUNCH BAG APPLE DESIGN 1 1.65 1.65\n", + "1 SET OF 60 VINTAGE LEAF CAKE CASES 24 0.55 13.20\n", + "2 RIBBON REEL STRIPES DESIGN 1 1.65 1.65\n", + "3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 0.18 518.40\n", + "4 PLAYING CARDS JUBILEE UNION JACK 2 1.25 2.50\n", + "5 POPCORN HOLDER 7 0.85 5.95\n", + "6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95\n", + "7 PARTY BUNTING 4 4.95 19.80\n", + "8 JAZZ HEARTS ADDRESS BOOK 10 0.19 1.90\n", + "9 SET OF 4 SANTA PLACE SETTINGS 48 1.25 60.00\n" + ] + } + ], + "source": [ + "orders=pd.DataFrame(orders)\n", + "print(orders)" + ] }, { "cell_type": "markdown", @@ -217,10 +329,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978\n", + "637.0\n" + ] + } + ], + "source": [ + "print(orders['Quantity'].sum())\n", + "print(orders['Revenue'].sum())" + ] }, { "cell_type": "markdown", @@ -229,6 +353,24 @@ "### 12. Obtain the prices of the most expensive and least expensive items ordered and print the difference." ] }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.77\n" + ] + } + ], + "source": [ + "diff=(orders['UnitPrice'].max())-(orders['UnitPrice'].min())\n", + "print(diff)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -253,7 +395,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-lambda-functions/your-code/main.ipynb b/module-1/lab-lambda-functions/your-code/main.ipynb index 66a9984..da8421c 100755 --- a/module-1/lab-lambda-functions/your-code/main.ipynb +++ b/module-1/lab-lambda-functions/your-code/main.ipynb @@ -34,18 +34,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n" + ] + } + ], "source": [ - "l = [######]\n", - "f = lambda x: #define the lambda expression\n", + "l = range(0,10)\n", + "f = lambda x: 2*x\n", "b = []\n", - "def modify_list(lst, fudduLambda):\n", - " for x in ####:\n", - " b.append(#####(x))\n", - "#Call modify_list(##,##)\n", - "#print b" + "def modify_list(lst, lamb):\n", + " for x in lst:\n", + " b.append(lamb(x))\n", + "modify_list(l,f)\n", + "print (b)" ] }, { @@ -59,12 +67,24 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "288.15" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "k=lambda x: x+273.15\n", + "k(15)\n" ] }, { @@ -76,13 +96,29 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[285.15, 296.15, 311.15, 218.14999999999998, 297.15]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "temps = [12, 23, 38, -55, 24]\n", "\n", - "# Your code here:" + "# Your code here:\n", + "\n", + "def modify(lst, lamb):\n", + " return [lamb(e) for e in lst]\n", + "\n", + "modify (temps, kelvin)" ] }, { @@ -96,11 +132,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n" + ] + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "mod=lambda x,y: 1 if x%y==0 or y%x==0 else 0\n", + "print (mod(5,7))\n", + "print (mod(2,4))" ] }, { @@ -114,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +172,8 @@ " output: a function that returns 1 if the number is divisible by another number (to be passed later) and zero otherwise\n", " \"\"\"\n", " \n", - " # Your code here:" + " # Your code here:\n", + " return lambda x,b: 1 if b%x==0 else 0" ] }, { @@ -136,11 +185,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "def divisible5(b):\n", + " # Your code here:\n", + " mod=lambda x,b: 1 if b%x==0 else 0\n", + " return mod(5,b)" ] }, { @@ -152,18 +205,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "divisible5(10)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "divisible5(8)" ] @@ -183,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -195,7 +270,7 @@ " ('tomato', 'tomato')]" ] }, - "execution_count": 1, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -218,14 +293,27 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 2), (2, 3), (3, 4), (4, 5)]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "list1 = [1,2,3,4]\n", "list2 = [2,3,4,5]\n", "## Zip the lists together \n", - "## Print the zipped list " + "## Print the zipped list \n", + "zipped = zip(list1,list2)\n", + "list(zipped)" ] }, { @@ -237,28 +325,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 48, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'\\n\\ncompare = lambda ###: print(\"True\") if ### else print(\"False\")\\nfor ### in zip(list1,list2):\\n compare(###)\\n \\n'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "False\n", + "False\n", + "False\n" + ] } ], "source": [ - "'''\n", - "\n", - "compare = lambda ###: print(\"True\") if ### else print(\"False\")\n", - "for ### in zip(list1,list2):\n", - " compare(###)\n", - " \n", - "''' " + "compare = lambda x: print(\"True\") if x[0]>x[1] else print(\"False\")\n", + "for e in zip(list1,list2):\n", + " compare(e)" ] }, { @@ -274,14 +358,40 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('Engineering', 'Lab'), ('Computer Science', 'Homework'), ('Political Science', 'Essay'), ('Mathematics', 'Module')]\n" + ] + }, + { + "data": { + "text/plain": [ + "[('Engineering', 'Lab'),\n", + " ('Computer Science', 'Homework'),\n", + " ('Mathematics', 'Module'),\n", + " ('Political Science', 'Essay')]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "list1 = ['Engineering', 'Computer Science', 'Political Science', 'Mathematics']\n", "list2 = ['Lab', 'Homework', 'Essay', 'Module']\n", "\n", - "# Your code here:\n" + "# Your code here:\n", + "zip2 = list(zip(list1,list2))\n", + "zip2 = list(zip(list1,list2))\n", + "print(zip2)\n", + "orden = lambda x: x[-1][1][0]\n", + "sorted(zip2, key=orden)" ] }, { @@ -295,13 +405,25 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Toyota': 1995, 'Honda': 1997, 'Audi': 2001, 'BMW': 2005}\n" + ] + } + ], "source": [ "d = {'Honda': 1997, 'Toyota': 1995, 'Audi': 2001, 'BMW': 2005}\n", "\n", - "# Your code here:" + "# Your code here:\n", + "d2 = dict(sorted(d.items(), key=lambda x: x[1]))\n", + "print(d2)\n", + "\n", + "\n" ] }, { @@ -328,7 +450,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-list-comprehensions/your-code/Learning.ipynb b/module-1/lab-list-comprehensions/your-code/Learning.ipynb index 27602f6..a1d1ac9 100644 --- a/module-1/lab-list-comprehensions/your-code/Learning.ipynb +++ b/module-1/lab-list-comprehensions/your-code/Learning.ipynb @@ -287,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-list-comprehensions/your-code/main.ipynb b/module-1/lab-list-comprehensions/your-code/main.ipynb index c5931c4..f20e700 100755 --- a/module-1/lab-list-comprehensions/your-code/main.ipynb +++ b/module-1/lab-list-comprehensions/your-code/main.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -29,10 +29,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n" + ] + } + ], + "source": [ + "list=[i for i in range(1, 51)]\n", + "print (list)" + ] }, { "cell_type": "markdown", @@ -43,10 +54,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n" + ] + } + ], + "source": [ + "list2=[i for i in range(2, 201, 2)]\n", + "print (list2)" + ] }, { "cell_type": "markdown", @@ -57,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -75,10 +97,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n" + ] + } + ], + "source": [ + "list3=[i for j in a for i in j]\n", + "print (list3)" + ] }, { "cell_type": "markdown", @@ -89,10 +122,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n" + ] + } + ], + "source": [ + "list4=[i for j in a for i in j if i>0.5]\n", + "print (list4)" + ] }, { "cell_type": "markdown", @@ -103,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -125,10 +169,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.06769833, 0.96031863, 0.51293743, 0.09143215, 0.71893382, 0.45850679, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n" + ] + } + ], + "source": [ + "b1 = b.flatten()\n", + "list_b1=[ i for i in b1]\n", + "print (list_b1)" + ] }, { "cell_type": "markdown", @@ -139,10 +195,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.08147297, 0.06833034, 0.30296619, 0.45850679, 0.11434044, 0.10708681, 0.47984544, 0.23760688]\n" + ] + } + ], + "source": [ + "list_b2=[i for j in b for i in j]\n", + "l=[list_b2[i][2] for i in range(len(list_b2)) if list_b2[i][2]<=0.5]\n", + "print (l)" + ] }, { "cell_type": "markdown", @@ -153,10 +221,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sample_file_9.csv', 'sample_file_7.csv', 'sample_file_6.csv', 'sample_file_0.csv', 'sample_file_4.csv', 'sample_file_5.csv', 'sample_file_3.csv', 'sample_file_2.csv', 'sample_file_1.csv', 'sample_file_8.csv']\n" + ] + } + ], + "source": [ + "nombres=[i for i in os.listdir('../data') if i.endswith('.csv')]\n", + "print (nombres)" + ] }, { "cell_type": "markdown", @@ -167,10 +246,335 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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90.2913990.5512480.7365420.1635620.4511490.8889380.9684650.3960900.6816790.390542...0.9440770.3661390.0781880.1754820.2324890.8069640.9045930.8036290.7928510.692851
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10 rows × 21 columns

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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.215190 0.155352 0.160848 0.807736 0.363587 0.899832 0.146754 \n", + "1 0.895544 0.955196 0.089925 0.827555 0.089071 0.642883 0.996052 \n", + "2 0.413752 0.693052 0.789796 0.929164 0.536191 0.439769 0.773474 \n", + "3 0.728001 0.348156 0.935787 0.851163 0.444573 0.715080 0.988408 \n", + "4 0.134942 0.875931 0.273505 0.207588 0.080696 0.717396 0.033930 \n", + "5 0.460281 0.308551 0.735894 0.054059 0.593642 0.397679 0.019922 \n", + "6 0.065851 0.736029 0.797730 0.692722 0.167764 0.839756 0.910186 \n", + "7 0.671323 0.747296 0.892328 0.732902 0.065608 0.262364 0.712417 \n", + "8 0.445478 0.523522 0.959355 0.348292 0.761805 0.301391 0.712240 \n", + "9 0.291399 0.551248 0.736542 0.163562 0.451149 0.888938 0.968465 \n", + "\n", + " 7 8 9 ... 11 12 13 14 \\\n", + "0 0.094802 0.705133 0.882762 ... 0.687745 0.016789 0.340725 0.984182 \n", + "1 0.879020 0.421837 0.412141 ... 0.217091 0.176157 0.551236 0.834378 \n", + "2 0.982074 0.876955 0.633154 ... 0.483317 0.908288 0.756172 0.462130 \n", + "3 0.210332 0.732133 0.892383 ... 0.367595 0.846208 0.240111 0.471880 \n", + "4 0.646837 0.888722 0.922742 ... 0.861333 0.389451 0.695244 0.129955 \n", + "5 0.855673 0.339445 0.895810 ... 0.553308 0.617711 0.841575 0.336094 \n", + "6 0.643328 0.371559 0.674311 ... 0.279397 0.015081 0.788497 0.132391 \n", + "7 0.764379 0.017461 0.131970 ... 0.902157 0.034188 0.840938 0.098536 \n", + "8 0.945462 0.139241 0.105477 ... 0.160828 0.400774 0.770295 0.844655 \n", + "9 0.396090 0.681679 0.390542 ... 0.944077 0.366139 0.078188 0.175482 \n", + "\n", + " 15 16 17 18 19 20 \n", + "0 0.985461 0.412044 0.867894 0.113432 0.349845 0.249845 \n", + "1 0.419535 0.041431 0.602258 0.984628 0.516899 0.416899 \n", + "2 0.289892 0.145233 0.076819 0.797836 0.197592 0.097592 \n", + "3 0.399721 0.758196 0.665568 0.931542 0.448124 0.348124 \n", + "4 0.364114 0.428224 0.365442 0.847818 0.588319 0.488319 \n", + "5 0.951907 0.050986 0.856135 0.773953 0.295344 0.195344 \n", + "6 0.766033 0.708510 0.752866 0.493862 0.577262 0.477262 \n", + "7 0.957824 0.566209 0.655947 0.592733 0.002596 -0.097404 \n", + "8 0.772798 0.584262 0.807400 0.177419 0.926007 0.826007 \n", + "9 0.232489 0.806964 0.904593 0.803629 0.792851 0.692851 \n", + "\n", + "[10 rows x 21 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datos2 = [c-0.1 for c in datos['19'] ]\n", + "datos ['20'] = datos2\n", + "datos.index=range(len(datos))\n", + "display(datos.head(10))" + ] }, { "cell_type": "markdown", @@ -207,6 +961,25 @@ "### 10. Use a list comprehension to extract and print all values from the data set that are between 0.7 and 0.75." ] }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.7280006345208285, 0.7025657075988269, 0.7347510852128797, 0.7132714209987683, 0.736029494090402, 0.7472962776291713, 0.7258310816144985, 0.7358941798147411, 0.7365416322783028, 0.7343092314001137, 0.7088042362916818, 0.7329024831531535, 0.7023481768180954, 0.7133695725698419, 0.7476323136755818, 0.7008930873050235, 0.7150799992177622, 0.7173964318670598, 0.7268545088531969, 0.7013619954419494, 0.7357618655811269, 0.7032248108904495, 0.7124169515124726, 0.71223975981198, 0.7383982036983279, 0.7306642125209889, 0.7298115760258881, 0.7051334608870933, 0.7321333453970806, 0.7178166705957928, 0.7374449181749492, 0.7159290265157912, 0.7294980584878026, 0.7215147068185102, 0.7111584953876777, 0.7209375615129192, 0.7045750112959754, 0.7303553716733908, 0.7396561066717461, 0.7133690799930356, 0.7390312927111312, 0.7356923324968598, 0.7078738356977186, 0.7321533684342264, 0.7085098152301276, 0.7130781095091867, 0.7490680818183477, 0.7264121114538001, 0.7196096371031628, 0.7393833032570581, 0.7244863338885654, 0.7274869207330491, 0.7033930409580709, 0.7376946446827494]\n" + ] + } + ], + "source": [ + "lista6 = [datos[x][y] for x in datos for y in range(len(datos)) if datos[x][y]>=0.7 and datos[x][y]<=0.75] \n", + "\n", + "print(lista6)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -231,7 +1004,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-map-reduce-filter/your-code/Learning.ipynb b/module-1/lab-map-reduce-filter/your-code/Learning.ipynb index 08a4d02..f31ec14 100644 --- a/module-1/lab-map-reduce-filter/your-code/Learning.ipynb +++ b/module-1/lab-map-reduce-filter/your-code/Learning.ipynb @@ -487,7 +487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-map-reduce-filter/your-code/main.ipynb b/module-1/lab-map-reduce-filter/your-code/main.ipynb index 5221ab9..359b5bf 100755 --- a/module-1/lab-map-reduce-filter/your-code/main.ipynb +++ b/module-1/lab-map-reduce-filter/your-code/main.ipynb @@ -17,7 +17,9 @@ "outputs": [], "source": [ "# import reduce from functools, numpy and pandas\n", - "\n" + "from functools import reduce\n", + "import numpy as np\n", + "import pandas as pd" ] }, { @@ -33,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -55,12 +57,23 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13069\n", + "12501\n" + ] + } + ], "source": [ "# Your code here:\n", - "\n" + "print(len(prophet))\n", + "prophet=prophet[568:]\n", + "print(len(prophet))" ] }, { @@ -72,12 +85,21 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dispense', 'with', 'confidence?\\n\\nIf', 'this', 'is', 'my', 'day', 'of', 'harvest,', 'in']\n" + ] + } + ], "source": [ "# Your code here:\n", - "\n" + "\n", + "print (prophet[0:10])" ] }, { @@ -91,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +128,7 @@ " '''\n", " \n", " # Your code here:\n", - " \n", + " return x.split('{')[0]\n", " " ] }, @@ -124,7 +146,7 @@ "outputs": [], "source": [ "# Your code here:\n", - "\n" + "prophet_reference=list(map()) \n" ] }, { @@ -444,7 +466,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-parsing-api/your-code/guided-lesson.ipynb b/module-1/lab-parsing-api/your-code/guided-lesson.ipynb index 955ba8f..60ad77b 100755 --- a/module-1/lab-parsing-api/your-code/guided-lesson.ipynb +++ b/module-1/lab-parsing-api/your-code/guided-lesson.ipynb @@ -269,7 +269,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-parsing-api/your-code/main.ipynb b/module-1/lab-parsing-api/your-code/main.ipynb index 585533a..847d316 100755 --- a/module-1/lab-parsing-api/your-code/main.ipynb +++ b/module-1/lab-parsing-api/your-code/main.ipynb @@ -107,7 +107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-resolving-git-conflicts/your-code/about-me.md b/module-1/lab-resolving-git-conflicts/your-code/about-me.md index 30a999d..4b3c285 100755 --- a/module-1/lab-resolving-git-conflicts/your-code/about-me.md +++ b/module-1/lab-resolving-git-conflicts/your-code/about-me.md @@ -5,3 +5,29 @@ Ut porttitor iaculis tellus bibendum euismod. Morbi porta, ante nec tempus porta Suspendisse ut malesuada ex. Nulla ultricies nisl et nisi rhoncus sollicitudin. Vestibulum maximus iaculis ligula, nec commodo nunc ullamcorper nec. Duis quis condimentum sapien. Cras vestibulum interdum felis eu auctor. Quisque semper, magna at dapibus faucibus, felis risus semper ligula, id aliquam lectus ligula vel nisi. In hac habitasse platea dictumst. Donec arcu sapien, suscipit ac dictum et, imperdiet id tortor. Maecenas ornare sodales interdum. Mauris dictum felis eu eros vestibulum cursus. Phasellus accumsan, turpis ut malesuada sollicitudin, augue leo venenatis ante, vel convallis tellus diam sit amet lacus. Aenean eu mauris eros. Praesent ante lacus, gravida sit amet tellus nec, laoreet ultrices lacus. Integer commodo semper vestibulum. Fusce felis massa, consectetur facilisis rutrum nec, pulvinar et nisi. Morbi fermentum ultricies tortor, vehicula ultrices eros elementum a. Duis ornare aliquam facilisis. Proin aliquam tincidunt odio vitae dignissim. Sed malesuada lacinia massa, nec blandit urna auctor elementum. Duis auctor non tortor in consequat. Mauris id vestibulum risus. In eget erat sed lacus efficitur viverra sed eu est. Aliquam interdum consequat molestie. Aliquam metus nisi, blandit non semper ut, blandit vel leo. Cras dictum turpis erat, sed iaculis ligula facilisis dapibus. Aliquam posuere dignissim fermentum. Praesent at neque sit amet lectus ornare iaculis. Curabitur id urna quis lorem varius ultrices eu sit amet sapien. Curabitur maximus volutpat suscipit. Proin imperdiet elementum lacus a eleifend. Sed tempor lacus posuere diam vehicula iaculis. +lab-re# Who am I + + * Where are you from? + I was born in Cuernavaca Morelos + * What do you do? + I have 3 part time jobs. + * Do you have previous experience with technology/data? + Tecnology yes but very little with Data + + # Why am I here + I am here to learn the ways of use and manipulation of data. + + * What has brought you to Ironhack? + I have a dream, and i feel IronHack has the tools to help and intructors to guide me to become the person that will effectivly reach that goal. + * What knowledge/skills do you expect to learn in this bootcamp? + How to import and export data, and also how to analize that said data. + + # What will I do after the bootcamp? + The idea is to invest my time on making an effective e-commerce, or dive into the data analitic world by getting a job. + + * Which industry will you seek employment in? + I qant to explore the e-commerce aspect of the tech world. + * What will your future role look like? + Like a lot of hard work.. + * What is your career goal? + To become a founder and Ceo of a startup. \ No newline at end of file diff --git a/module-1/lab-tuple-set-dict/your-code/challenge-1.ipynb b/module-1/lab-tuple-set-dict/your-code/challenge-1.ipynb index 2e59d77..46d974b 100755 --- a/module-1/lab-tuple-set-dict/your-code/challenge-1.ipynb +++ b/module-1/lab-tuple-set-dict/your-code/challenge-1.ipynb @@ -15,11 +15,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "tup=(\"I\",)" ] }, { @@ -33,11 +34,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tuple" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "type(tup)" ] }, { @@ -55,13 +68,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'tuple' object has no attribute 'append'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Your code here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"o\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"h\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" + ] + } + ], "source": [ "# Your code here\n", - "\n", - "# Your explanation here\n" + "tup.append(\"r\")\n", + "tup.append(\"o\")\n", + "tup.append(\"n\")\n", + "tup.append(\"h\")\n", + "tup.append(\"a\")\n", + "tup.append(\"c\")\n", + "tup.append(\"k\")\n", + "# Your explanation here\n", + "# No se puede añadir append en tuplas" ] }, { @@ -79,13 +111,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Your code here\n", "\n", - "# Your explanation here\n" + "tup=\"Ironhack\"\n", + "\n", + "# Your explanation here\n", + "# no se pudo por el motivo de que no se puede añadir append en tuplas por que no son listas" ] }, { @@ -103,11 +138,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I', 'r', 'o', 'n')\n", + "('h', 'a', 'c', 'k')\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "tup1=tuple(tup[0]+tup[1]+tup[2]+tup[3])\n", + "tup2=tuple(tup[-4]+tup[-3]+tup[-2]+tup[-1])\n", + "print (tup1)\n", + "print (tup2)" ] }, { @@ -121,11 +169,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I', 'r', 'o', 'n', 'h', 'a', 'c', 'k')\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "tup3=tup1+tup2\n", + "print (tup3)" ] }, { @@ -137,11 +195,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "8\n", + "8\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print (len(tup1))\n", + "print (len(tup2))\n", + "print (len(tup1)+len(tup2))\n", + "print (len(tup3))" ] }, { @@ -153,11 +226,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "h=tup3[4]\n", + "print (h)" ] }, { @@ -177,11 +260,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "True\n", + "False\n", + "False\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "letters = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n", + "\n", + "for e in letters:\n", + " if e in tup3:\n", + " print (True)\n", + " else:\n", + " print (False)" ] }, { @@ -195,12 +297,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "la letra a aparece 1 veces\n", + "la letra c aparece 1 veces\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "for e in letters:\n", + " if e in tup3:\n", + " for i in tup3:\n", + " v=0\n", + " if e==i:\n", + " v+=1\n", + " print ('la letra {} aparece {} veces'.format(e, v))\n", + " else:\n", + " continue" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -219,7 +353,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/lab-tuple-set-dict/your-code/challenge-2.ipynb b/module-1/lab-tuple-set-dict/your-code/challenge-2.ipynb index 41d6911..ca9eefe 100755 --- a/module-1/lab-tuple-set-dict/your-code/challenge-2.ipynb +++ b/module-1/lab-tuple-set-dict/your-code/challenge-2.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -38,11 +38,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set1=set(sample_list_1)\n", + "print (len(set1))" ] }, { @@ -54,11 +64,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[87, 5, 97, 62, 84, 13, 81, 73, 64, 72, 73, 18, 24, 56, 70, 65, 82, 93, 79, 2, 66, 29, 100, 98, 68, 20, 1, 32, 90, 39, 85, 32, 70, 33, 93, 49, 44, 17, 75, 27, 69, 16, 61, 54, 35, 1, 78, 46, 3, 47, 8, 74, 91, 47, 52, 77, 53, 60, 73, 2, 77, 76, 86, 89, 51, 32, 13, 47, 87, 2, 75, 82, 15, 41, 98, 67, 63, 96, 94, 69]\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "sample_list_2=[]\n", + "for i in range(80):\n", + " a=random.randint(0,100)\n", + " sample_list_2.append(a)\n", + " \n", + "print (sample_list_2)" ] }, { @@ -77,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -93,11 +117,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "62\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set2=set(sample_list_2)\n", + "print (len(set2))\n" ] }, { @@ -109,11 +143,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "set3=set1.difference(set2)" ] }, { @@ -125,11 +160,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "set4=set2.difference(set1)" ] }, { @@ -141,11 +177,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "set5=set1.intersection(set2)" ] }, { @@ -165,11 +202,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 5)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m if total1==total2:print (True)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "total1=len(set1)+len(set2)+len(set5)\n", + "total2=len(set3)+len(set4)+len(set5\n", + " \n", + " if total1==total2:print (True)\n", + " else: print (False)" ] }, { @@ -181,11 +232,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set6=set()\n", + "print (type(set6))" ] }, { @@ -197,11 +258,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 59, 60, 63, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set6.update(set3)\n", + "set6.update(set5)\n", + "print (set6)" ] }, { @@ -213,11 +285,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "if set1==set6: \n", + " print (True)\n", + "else:\n", + " print (False)" ] }, { @@ -229,11 +313,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "False\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print (set2.issubset(set1))\n", + "print (set3.issubset(set1))" ] }, { @@ -247,11 +342,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "94\n", + "93\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print (len(set3.union(set4).union(set5)))\n", + "print (len(set1.union(set2)))" ] }, { @@ -263,11 +369,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 59, 60, 63, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100}\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "print (set1)\n", + "set1.pop()" ] }, { @@ -307,7 +433,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-1/python-project/your-code/blackjack.py b/module-1/python-project/your-code/blackjack.py new file mode 100644 index 0000000..6ca01b4 --- /dev/null +++ b/module-1/python-project/your-code/blackjack.py @@ -0,0 +1,33 @@ +jugador = [] +casino = [] +while len(casino) !=2: + casino.append(random.randin(1,11)) + if len(casino)== 2: + print ("Casino tiene X", casino) +while len(jugador) !=2: + jugador,append(random.randin(1,11)) + if len(jugador)==2: + print ("Tienes", jugador) + +if sum(casino)==21: + print("Casino tiene 21!") +elif sum(casino) > 21: + print("Casino se paso de 21!") + +while sum(jugador)< 21: + jugador_1 = str(input("¿Quieres otra carta Si/s o No?")) + if jugador_1 == (s): + jugador.append(random.randin(1,11)) + print("Tienes un total " + str(sum(jugador))+ jugador) + else: + print("El casino tiene un total de" + str(sum(casino)) + casino) + print("tienes un total de " + str(sum(jugador))+ jugador) + if sum(casino > sum(jugador)): + print(" ¡Casino GANA!") + else: + print("¡HAS GANADO!") + break +if sum(jugador) > 21: + print("Te has pasado, casino gana") +elif sum(jugador)==21: + print("¡¡BLACKJACK!!") \ No newline at end of file diff --git a/module-1/python-project/your-code/sample-code.ipynb b/module-1/python-project/your-code/sample-code.ipynb deleted file mode 100755 index a6f8a94..0000000 --- a/module-1/python-project/your-code/sample-code.ipynb +++ /dev/null @@ -1,248 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# define rooms and items\n", - "\n", - "couch = {\n", - " \"name\": \"couch\",\n", - " \"type\": \"furniture\",\n", - "}\n", - "\n", - "door_a = {\n", - " \"name\": \"door a\",\n", - " \"type\": \"door\",\n", - "}\n", - "\n", - "key_a = {\n", - " \"name\": \"key for door a\",\n", - " \"type\": \"key\",\n", - " \"target\": door_a,\n", - "}\n", - "\n", - "piano = {\n", - " \"name\": \"piano\",\n", - " \"type\": \"furniture\",\n", - "}\n", - "\n", - "game_room = {\n", - " \"name\": \"game room\",\n", - " \"type\": \"room\",\n", - "}\n", - "\n", - "outside = {\n", - " \"name\": \"outside\"\n", - "}\n", - "\n", - "all_rooms = [game_room, outside]\n", - "\n", - "all_doors = [door_a]\n", - "\n", - "# define which items/rooms are related\n", - "\n", - "object_relations = {\n", - " \"game room\": [couch, piano, door_a],\n", - " \"piano\": [key_a],\n", - " \"outside\": [door_a],\n", - " \"door a\": [game_room, outside]\n", - "}\n", - "\n", - "# define game state. Do not directly change this dict. \n", - "# Instead, when a new game starts, make a copy of this\n", - "# dict and use the copy to store gameplay state. This \n", - "# way you can replay the game multiple times.\n", - "\n", - "INIT_GAME_STATE = {\n", - " \"current_room\": game_room,\n", - " \"keys_collected\": [],\n", - " \"target_room\": outside\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def linebreak():\n", - " \"\"\"\n", - " Print a line break\n", - " \"\"\"\n", - " print(\"\\n\\n\")\n", - "\n", - "def start_game():\n", - " \"\"\"\n", - " Start the game\n", - " \"\"\"\n", - " print(\"You wake up on a couch and find yourself in a strange house with no windows which you have never been to before. You don't remember why you are here and what had happened before. You feel some unknown danger is approaching and you must get out of the house, NOW!\")\n", - " play_room(game_state[\"current_room\"])\n", - "\n", - "def play_room(room):\n", - " \"\"\"\n", - " Play a room. First check if the room being played is the target room.\n", - " If it is, the game will end with success. Otherwise, let player either \n", - " explore (list all items in this room) or examine an item found here.\n", - " \"\"\"\n", - " game_state[\"current_room\"] = room\n", - " if(game_state[\"current_room\"] == game_state[\"target_room\"]):\n", - " print(\"Congrats! You escaped the room!\")\n", - " else:\n", - " print(\"You are now in \" + room[\"name\"])\n", - " intended_action = input(\"What would you like to do? Type 'explore' or 'examine'?\").strip()\n", - " if intended_action == \"explore\":\n", - " explore_room(room)\n", - " play_room(room)\n", - " elif intended_action == \"examine\":\n", - " examine_item(input(\"What would you like to examine?\").strip())\n", - " else:\n", - " print(\"Not sure what you mean. Type 'explore' or 'examine'.\")\n", - " play_room(room)\n", - " linebreak()\n", - "\n", - "def explore_room(room):\n", - " \"\"\"\n", - " Explore a room. List all items belonging to this room.\n", - " \"\"\"\n", - " items = [i[\"name\"] for i in object_relations[room[\"name\"]]]\n", - " print(\"You explore the room. This is \" + room[\"name\"] + \". You find \" + \", \".join(items))\n", - "\n", - "def get_next_room_of_door(door, current_room):\n", - " \"\"\"\n", - " From object_relations, find the two rooms connected to the given door.\n", - " Return the room that is not the current_room.\n", - " \"\"\"\n", - " connected_rooms = object_relations[door[\"name\"]]\n", - " for room in connected_rooms:\n", - " if(not current_room == room):\n", - " return room\n", - "\n", - "def examine_item(item_name):\n", - " \"\"\"\n", - " Examine an item which can be a door or furniture.\n", - " First make sure the intended item belongs to the current room.\n", - " Then check if the item is a door. Tell player if key hasn't been \n", - " collected yet. Otherwise ask player if they want to go to the next\n", - " room. If the item is not a door, then check if it contains keys.\n", - " Collect the key if found and update the game state. At the end,\n", - " play either the current or the next room depending on the game state\n", - " to keep playing.\n", - " \"\"\"\n", - " current_room = game_state[\"current_room\"]\n", - " next_room = \"\"\n", - " output = None\n", - " \n", - " for item in object_relations[current_room[\"name\"]]:\n", - " if(item[\"name\"] == item_name):\n", - " output = \"You examine \" + item_name + \". \"\n", - " if(item[\"type\"] == \"door\"):\n", - " have_key = False\n", - " for key in game_state[\"keys_collected\"]:\n", - " if(key[\"target\"] == item):\n", - " have_key = True\n", - " if(have_key):\n", - " output += \"You unlock it with a key you have.\"\n", - " next_room = get_next_room_of_door(item, current_room)\n", - " else:\n", - " output += \"It is locked but you don't have the key.\"\n", - " else:\n", - " if(item[\"name\"] in object_relations and len(object_relations[item[\"name\"]])>0):\n", - " item_found = object_relations[item[\"name\"]].pop()\n", - " game_state[\"keys_collected\"].append(item_found)\n", - " output += \"You find \" + item_found[\"name\"] + \".\"\n", - " else:\n", - " output += \"There isn't anything interesting about it.\"\n", - " print(output)\n", - " break\n", - "\n", - " if(output is None):\n", - " print(\"The item you requested is not found in the current room.\")\n", - " \n", - " if(next_room and input(\"Do you want to go to the next room? Ener 'yes' or 'no'\").strip() == 'yes'):\n", - " play_room(next_room)\n", - " else:\n", - " play_room(current_room)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You wake up on a couch and find yourself in a strange house with no windows which you have never been to before. You don't remember why you are here and what had happened before. You feel some unknown danger is approaching and you must get out of the house, NOW!\n", - "You are now in game room\n", - "What would you like to do? Type 'explore' or 'examine'?explore\n", - "You explore the room. This is game room. You find couch, piano, door a\n", - "You are now in game room\n", - "What would you like to do? Type 'explore' or 'examine'?examine\n", - "What would you like to examine?door a\n", - "You examine door a. It is locked but you don't have the key.\n", - "You are now in game room\n", - "What would you like to do? Type 'explore' or 'examine'?examine\n", - "What would you like to examine?piano\n", - "You examine piano. You find key for door a.\n", - "You are now in game room\n", - "What would you like to do? Type 'explore' or 'examine'?examine\n", - "What would you like to examine?door a\n", - "You examine door a. You unlock it with a key you have.\n", - "Do you want to go to the next room? Ener 'yes' or 'no'yes\n", - "Congrats! You escaped the room!\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "game_state = INIT_GAME_STATE.copy()\n", - "\n", - "start_game()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb b/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb index 6eee69f..48a4349 100644 --- a/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb +++ b/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['admin', 'config', 'db_companies', 'local']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "client=MongoClient()\n", + "db=client.Companies\n", + "client.list_database_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "colec=db.companies" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db.list_collection_names()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(colec.find({\"name\" : \"Babelgun\"},{\"name\"}))" + ] }, { "cell_type": "markdown", @@ -40,6 +104,15 @@ "### 2. All the companies that have more than 5000 employees. Limit the search to 20 companies and sort them by **number of employees**." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list(colec.find({'number_of_employees':{'$gt':5000}}).sort('number_of_employees', -1).limit(20))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -322,7 +395,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-advance-querying-mongo/starter_code/queries.ipynb b/module-2/lab-advance-querying-mongo/starter_code/queries.ipynb index 6eee69f..48a4349 100644 --- a/module-2/lab-advance-querying-mongo/starter_code/queries.ipynb +++ b/module-2/lab-advance-querying-mongo/starter_code/queries.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['admin', 'config', 'db_companies', 'local']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "client=MongoClient()\n", + "db=client.Companies\n", + "client.list_database_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "colec=db.companies" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db.list_collection_names()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(colec.find({\"name\" : \"Babelgun\"},{\"name\"}))" + ] }, { "cell_type": "markdown", @@ -40,6 +104,15 @@ "### 2. All the companies that have more than 5000 employees. Limit the search to 20 companies and sort them by **number of employees**." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list(colec.find({'number_of_employees':{'$gt':5000}}).sort('number_of_employees', -1).limit(20))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -322,7 +395,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-df-calculation-and-transformation/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb b/module-2/lab-df-calculation-and-transformation/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb new file mode 100755 index 0000000..998c5fd --- /dev/null +++ b/module-2/lab-df-calculation-and-transformation/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1\n", + "\n", + "In this challenge you will be working on **pokemons**. You will answer a series of questions in order to practice dataframe calculation, aggregation, and transformation.\n", + "\n", + "![Pokemon](pokemon.jpg)\n", + "\n", + "Follow the instructions below and enter your code.\n", + "\n", + "### Import all required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import libraries\n", + "[Pokemon](pokemon.jpg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import data set\n", + "\n", + "Import data set `Pokemon.csv` from the `your-code` directory of this lab. Read the data into a dataframe called `pokemon`.\n", + "\n", + "*Data set attributed to [Alberto Barradas](https://www.kaggle.com/abcsds/pokemon/)*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import data set\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Print first 10 rows of `pokemon`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you look at a data set, you often wonder what each column means. Some open-source data sets provide descriptions of the data set. In many cases, data descriptions are extremely useful for data analysts to perform work efficiently and successfully.\n", + "\n", + "For the `Pokemon.csv` data set, fortunately, the owner provided descriptions which you can see [here](https://www.kaggle.com/abcsds/pokemon/home). For your convenience, we are including the descriptions below. Read the descriptions and understand what each column means. This knowledge is helpful in your work with the data.\n", + "\n", + "| Column | Description |\n", + "| --- | --- |\n", + "| # | ID for each pokemon |\n", + "| Name | Name of each pokemon |\n", + "| Type 1 | Each pokemon has a type, this determines weakness/resistance to attacks |\n", + "| Type 2 | Some pokemon are dual type and have 2 |\n", + "| Total | A general guide to how strong a pokemon is |\n", + "| HP | Hit points, or health, defines how much damage a pokemon can withstand before fainting |\n", + "| Attack | The base modifier for normal attacks (eg. Scratch, Punch) |\n", + "| Defense | The base damage resistance against normal attacks |\n", + "| SP Atk | Special attack, the base modifier for special attacks (e.g. fire blast, bubble beam) |\n", + "| SP Def | The base damage resistance against special attacks |\n", + "| Speed | Determines which pokemon attacks first each round |\n", + "| Generation | Number of generation |\n", + "| Legendary | True if Legendary Pokemon False if not |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtain the distinct values across `Type 1` and `Type 2`\n", + "\n", + "Exctract all the values in `Type 1` and `Type 2`. Then create an array containing the distinct values across both fields." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cleanup `Name` that contain \"Mega\"\n", + "\n", + "If you have checked out the pokemon names carefully enough, you should have found there are junk texts in the pokemon names which contain \"Mega\". We want to clean up the pokemon names. For instance, \"VenusaurMega Venusaur\" should be \"Mega Venusaur\", and \"CharizardMega Charizard X\" should be \"Mega Charizard X\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n", + "\n", + "\n", + "# test transformed data\n", + "\n", + "pokemon.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a new column called `A/D Ratio` whose value equals to `Attack` devided by `Defense`\n", + "\n", + "For instance, if a pokemon has the Attack score 49 and Defense score 49, the corresponding `A/D Ratio` is 49/49=1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Identify the pokemon with the highest `A/D Ratio`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Identify the pokemon with the lowest A/D Ratio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a new column called `Combo Type` whose value combines `Type 1` and `Type 2`.\n", + "\n", + "Rules:\n", + "\n", + "* If both `Type 1` and `Type 2` have valid values, the `Combo Type` value should contain both values in the form of ` `. For example, if `Type 1` value is `Grass` and `Type 2` value is `Poison`, `Combo Type` will be `Grass-Poison`.\n", + "\n", + "* If `Type 1` has valid value but `Type 2` is not, `Combo Type` will be the same as `Type 1`. For example, if `Type 1` is `Fire` whereas `Type 2` is `NaN`, `Combo Type` will be `Fire`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Identify the pokemons whose `A/D Ratio` are among the top 5 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For the 5 pokemons printed above, aggregate `Combo Type` and use a list to store the unique values.\n", + "\n", + "Your end product is a list containing the distinct `Combo Type` values of the 5 pokemons with the highest `A/D Ratio`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For each of the `Combo Type` values obtained from the previous question, calculate the mean scores of all numeric fields across all pokemons.\n", + "\n", + "Your output should look like below:\n", + "\n", + "![Aggregate](aggregated-mean.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter your code here\n" + ] + } + ], + "metadata": { + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module-2/lab-df-calculation-and-transformation/your-code/challenge-1.ipynb b/module-2/lab-df-calculation-and-transformation/your-code/challenge-1.ipynb index 426e92a..75db7a1 100755 --- a/module-2/lab-df-calculation-and-transformation/your-code/challenge-1.ipynb +++ b/module-2/lab-df-calculation-and-transformation/your-code/challenge-1.ipynb @@ -17,11 +17,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# import libraries\n" + "# import libraries\n", + "import pandas as pd\n", + "import numpy as np" ] }, { @@ -37,11 +39,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# import data set\n" + "# import data set\n", + "pokemon=pd.read_csv('Pokemon.csv')" ] }, { @@ -57,7 +60,8 @@ "metadata": {}, "outputs": [], "source": [ - "# enter your code here\n" + "# enter your code her\n", + "\n" ] }, { @@ -269,7 +273,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-matplotlib-seaborn/.ipynb_checkpoints/main-checkpoint.ipynb b/module-2/lab-matplotlib-seaborn/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100755 index 0000000..e2f451e --- /dev/null +++ b/module-2/lab-matplotlib-seaborn/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,1489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solution lab matplotlib-seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import all the libraries that are necessary" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# import libraries here\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = np.arange(0,100)\n", + "y = x*2\n", + "z = x**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot (x,y) and (x,z) on the axes.\n", + "There are 2 ways of doing this. Do in both ways.\n", + "Hint: Check out the nrows, ncols, and index arguments of subplots\n", + "\n", + "Also, play around with the linewidth and style. Use the ones you're most happy with." + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here-1st way\n", + "plt.subplot(1,2,1)\n", + "plt.plot(x,y,lw=3,color=\"b\", ls='--')\n", + "plt.subplot(1,2,2)\n", + "plt.plot(x,z,lw=3, color=\"r\",ls='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use plt.subplots(nrows=1, ncols=2) to create the plot below" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here-2st way\n", + "fig,axes=plt.subplots(1,2)\n", + "axes[0].plot(x,y,color=\"b\", lw=3, ls='--')\n", + "axes[1].plot(x,z,color=\"r\", lw=3, ls='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Augmenting your previous code, resize your previous plot.\n", + "Hint: Add the figsize argument in plt.subplots()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'z')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))\n", + "\n", + "axes[0].plot(x,y,color=\"blue\", lw=5)\n", + "axes[0].set_xlabel('x')\n", + "axes[0].set_ylabel('y')\n", + "\n", + "axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n", + "axes[1].set_xlabel('x')\n", + "axes[1].set_ylabel('z')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot both y=x^2 and y=exp(x) in the same plot using normal and logarithmic scale.\n", + "Hint: Use set_xscale and set_yscale" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Logarithmic scale (y)')" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n", + " \n", + "axes[0].plot(x, x**2,x, np.exp(x))\n", + "axes[0].set_title(\"Normal scale\")\n", + "\n", + "axes[1].plot(x, x**2,x, np.exp(x))\n", + "axes[1].set_yscale(\"log\")\n", + "axes[1].set_title(\"Logarithmic scale (y)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import the Fitbit2.csv file and name your dataset fitbit" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCalorie burnedStepsDistanceFloorsMinutes SedentaryMinutes Lightly ActiveMinutes Fairly ActiveMinutes Very ActiveActivity Calories...Distance_milesDaysDays_encodedWork_or_WeekendHours SleepSleep efficiencyYesterday_sleepYesterday_sleep_efficiencyMonthsMonths_encoded
02015-05-0819349050.6501.35546001680...0.403891Friday4.016.40000092.0863310.0000000.000000May5
12015-05-0936311892514.114611.00031661602248...8.767545Saturday5.007.56666792.4643586.40000092.086331May5
22015-05-1032041422810.571602.00022614771719...6.567891Sunday6.006.45000088.7614687.56666792.464358May5
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3 rows × 24 columns

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" + ], + "text/plain": [ + " Date Calorie burned Steps Distance Floors Minutes Sedentary \\\n", + "0 2015-05-08 1934 905 0.65 0 1.355 \n", + "1 2015-05-09 3631 18925 14.11 4 611.000 \n", + "2 2015-05-10 3204 14228 10.57 1 602.000 \n", + "\n", + " Minutes Lightly Active Minutes Fairly Active Minutes Very Active \\\n", + "0 46 0 0 \n", + "1 316 61 60 \n", + "2 226 14 77 \n", + "\n", + " Activity Calories ... Distance_miles Days Days_encoded \\\n", + "0 1680 ... 0.403891 Friday 4.0 \n", + "1 2248 ... 8.767545 Saturday 5.0 \n", + "2 1719 ... 6.567891 Sunday 6.0 \n", + "\n", + " Work_or_Weekend Hours Sleep Sleep efficiency Yesterday_sleep \\\n", + "0 1 6.400000 92.086331 0.000000 \n", + "1 0 7.566667 92.464358 6.400000 \n", + "2 0 6.450000 88.761468 7.566667 \n", + "\n", + " Yesterday_sleep_efficiency Months Months_encoded \n", + "0 0.000000 May 5 \n", + "1 92.086331 May 5 \n", + "2 92.464358 May 5 \n", + "\n", + "[3 rows x 24 columns]" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitbit=pd.read_csv('Fitbit2.csv')\n", + "fitbit.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### From the Fitbit data, we want to visually understand:\n", + "\n", + "How the average number of steps change by month. Use the appropriate visualization to show the median steps by month?\n", + "Is Fitbitter more active on weekend or workdays?\n", + "All plots must be in the same jupyter notebook cell.\n", + "Hints:\n", + "\n", + "Use Months_encoded and Week_or Weekend columns\n", + "Use matplolib.pyplot object oriented API\n", + "Set your size figure to 12,4\n", + "Explore plt.sca\n", + "Explore plt.xticks\n", + "Save your figures" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Median Steps by Month_encoded\n", + "weekday_steps = fitbit['Steps'].groupby(fitbit['Months_encoded']).median()\n", + "weekday_steps.head(5)\n", + "\n", + "fig,axes = plt.subplots(figsize = (12,4),nrows = 1, ncols = 2)\n", + "plt.sca(axes[0])\n", + "weekday_steps.plot(kind = 'line',linewidth=2)\n", + "plt.ylabel('Median number of steps')\n", + "plt.xlabel('Months')\n", + "plt.title('Median Steps walked by month')\n", + "plt.xticks(list(range(13)),['','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n", + "\n", + "plt.savefig('steps_months.png',bbox_inches='tight')\n", + "\n", + "\n", + "# Median Steps by Work_or_Weekend\n", + "plt.sca(axes[1])\n", + "weekday_steps = fitbit['Steps'].groupby(fitbit['Work_or_Weekend']).median().sort_values()\n", + "weekday_steps.plot(kind = 'bar',alpha = 0.5)\n", + "plt.ylabel('Median number of steps')\n", + "plt.title('Median Steps walked by Workdays and Weekend')\n", + "plt.xticks(list(range(2)),['Weekend','Workdays'])\n", + "\n", + "plt.savefig('steps_work_weekend.png',bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write a loop to plot 3 scatter plots of the following features:\n", + "\n", + "Minutes Lightly Active vs Steps\n", + "Minutes Very Active vs Steps\n", + "Minutes Sedentary vs Steps" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cols=['Minutes Lightly Active','Minutes Very Active','Minutes Sedentary','Steps']\n", + "df=fitbit[cols]\n", + "fig,axes = plt.subplots(figsize = (16,8), nrows = 1, ncols = 3)\n", + "for i in range(3):\n", + " plt.sca(axes[i])\n", + " plt.scatter(df.iloc[:,i],df['Steps'], alpha = 0.8)\n", + " plt.xlabel(df.iloc[:,i].name)\n", + " plt.ylabel('Steps')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Open the titanic file. Name your dataset titanic" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
010.03Braund, Mr. Owen Harrismale22.010A/5 211717.2500U0S
121.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
231.03Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250U0S
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0.0 3 \n", + "1 2 1.0 1 \n", + "2 3 1.0 3 \n", + "\n", + " Name Gender Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 U0 S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 U0 S " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "import pandas as pd\n", + "titanic = pd.read_csv('./titanic.csv',low_memory=False)\n", + "titanic.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore the titanic dataset using Pandas dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId int64\n", + "Survived float64\n", + "Pclass int64\n", + "Name object\n", + "Gender object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Ticket object\n", + "Fare float64\n", + "Cabin object\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "\n", + "titanic.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What are your numerical variables? What are your categorical variables?\n", + "Hint: Use Pandas select_dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
010.0322.0107.2500
121.0138.01071.2833
231.0326.0007.9250
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp Parch Fare\n", + "0 1 0.0 3 22.0 1 0 7.2500\n", + "1 2 1.0 1 38.0 1 0 71.2833\n", + "2 3 1.0 3 26.0 0 0 7.9250" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here- numerical variable\n", + "titanic.select_dtypes(exclude=object).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdPclassNameGenderSibSpParchTicketCabinEmbarked
013Braund, Mr. Owen Harrismale10A/5 21171U0S
121Cumings, Mrs. John Bradley (Florence Briggs Th...female10PC 17599C85C
233Heikkinen, Miss. Lainafemale00STON/O2. 3101282U0S
\n", + "
" + ], + "text/plain": [ + " PassengerId Pclass Name \\\n", + "0 1 3 Braund, Mr. Owen Harris \n", + "1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", + "2 3 3 Heikkinen, Miss. Laina \n", + "\n", + " Gender SibSp Parch Ticket Cabin Embarked \n", + "0 male 1 0 A/5 21171 U0 S \n", + "1 female 1 0 PC 17599 C85 C \n", + "2 female 0 0 STON/O2. 3101282 U0 S " + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here- categorical variable\n", + "titanic.select_dtypes(exclude=['int','float']).head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set the plot style to classic and the figure size to (12,6)\n", + "Hint: To set the style you can use matplotlib.pyplot functions or seaborn " + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your code here\n", + "plt.style.use('classic')\n", + "sns.set_style('whitegrid')\n", + "plt.rcParams['figure.figsize'] = (12, 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right visulalization to show the distribution of the column Age" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "titanic['Age'].hist(bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use subplots and plot the distribution of the Age variable with bins equal to 10,20 and 50." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.subplot(1,3,1)\n", + "titanic['Age'].hist()\n", + "plt.subplot(1,3,2)\n", + "titanic['Age'].hist(bins=20)\n", + "plt.subplot(1,3,3)\n", + "titanic['Age'].hist(bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How does the bin size affect your plot? Comment." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#your comment here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use seaborn to show the distribution of the column Age" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "sns.distplot(titanic['Age'],bins=30,kde=False,color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right plot to visualize the column Gender. There are 2 ways of doing it. Do it both ways.\n", + "Hint: Use matplotlib and seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'Count')" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here- 1st way\n", + "titanic['Gender'].value_counts().plot(kind='bar')\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('Count')" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here- 2nd way\n", + "sns.countplot(x='Gender',data=titanic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right plot to visualize the column Pclass" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'Count')" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "titanic['Pclass'].value_counts().plot(kind='bar')\n", + "plt.xlabel('Class')\n", + "plt.ylabel('Count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We would like to have in one single plot the summary statistics of the feature Age. What kind of plot would you use?" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.boxplot(column='Age')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What does the last plot tells you about the feature Age? Comment." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your comment here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now in addition to the summary statistics, we want to have in the SAME plot the distribution of Age. What kind of plot would you use?" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "sns.violinplot(\"Age\", data=titanic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What additional information the last plot provides you about the feature Age? Comment." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#your comment here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We suspect that there is a linear relationship between Fare and Age. Use the right plot to show the relationship between these 2 features. There are 2 ways, please do it both ways.\n", + "Hint: One of the ways involves using Seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#your code- 1st way" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code-2nd way\n", + "sns.jointplot(x='Fare',y='Age',data=titanic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using Seaborn plot the correlation matrix " + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Correlation matrix')" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "sns.heatmap(titanic.corr(),cmap='coolwarm')\n", + "plt.title('Correlation matrix')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What are the most correlated feature? Comment" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#your comment here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right plot to display the summary statistics of the Age in function of the Pclass" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "sns.boxplot(x='Pclass',y='Age',data=titanic,palette='rainbow')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the seaborn to plot the distribution of the Age based on the Gender\n", + "Hint: Use Facetgrid" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "g = sns.FacetGrid(data=titanic,col='Gender')\n", + "g.map(plt.hist,'Age')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/module-2/lab-matplotlib-seaborn/main.ipynb b/module-2/lab-matplotlib-seaborn/main.ipynb index ff8b438..e2f451e 100755 --- a/module-2/lab-matplotlib-seaborn/main.ipynb +++ b/module-2/lab-matplotlib-seaborn/main.ipynb @@ -71,9 +71,7 @@ { "cell_type": "code", "execution_count": 188, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -114,9 +112,7 @@ { "cell_type": "code", "execution_count": 191, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -157,9 +153,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -205,9 +199,7 @@ { "cell_type": "code", "execution_count": 87, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -259,9 +251,7 @@ { "cell_type": "code", "execution_count": 193, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -446,9 +436,7 @@ { "cell_type": "code", "execution_count": 195, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -502,9 +490,7 @@ { "cell_type": "code", "execution_count": 196, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -546,7 +532,6 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -674,9 +659,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -718,9 +701,7 @@ { "cell_type": "code", "execution_count": 94, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -807,9 +788,7 @@ { "cell_type": "code", "execution_count": 95, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -938,9 +917,7 @@ { "cell_type": "code", "execution_count": 110, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -978,9 +955,7 @@ { "cell_type": "code", "execution_count": 109, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1041,9 +1016,7 @@ { "cell_type": "code", "execution_count": 108, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1082,9 +1055,7 @@ { "cell_type": "code", "execution_count": 107, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1117,9 +1088,7 @@ { "cell_type": "code", "execution_count": 106, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1157,9 +1126,7 @@ { "cell_type": "code", "execution_count": 105, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1199,9 +1166,7 @@ { "cell_type": "code", "execution_count": 104, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1256,9 +1221,7 @@ { "cell_type": "code", "execution_count": 98, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1326,9 +1289,7 @@ { "cell_type": "code", "execution_count": 99, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1366,9 +1327,7 @@ { "cell_type": "code", "execution_count": 101, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1426,7 +1385,6 @@ "cell_type": "code", "execution_count": 102, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1467,9 +1425,7 @@ { "cell_type": "code", "execution_count": 103, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1525,7 +1481,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-3-checkpoint.ipynb b/module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-3-checkpoint.ipynb new file mode 100755 index 0000000..47a39d9 --- /dev/null +++ b/module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-3-checkpoint.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'seaborn'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'" + ] + } + ], + "source": [ + "# import libraries here\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import the titanic file. Name your dataset `titanic`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
010.03Braund, Mr. Owen Harrismale22.010A/5 211717.2500U0S
121.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
231.03Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250U0S
341.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
450.03Allen, Mr. William Henrymale35.0003734508.0500U0S
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0.0 3 \n", + "1 2 1.0 1 \n", + "2 3 1.0 3 \n", + "3 4 1.0 1 \n", + "4 5 0.0 3 \n", + "\n", + " Name Gender Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 U0 S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 U0 S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 U0 S " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic = pd.read_csv('./titanic.csv',low_memory=False)\n", + "titanic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore the titanic dataset using Pandas dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#your code here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" + ] + } + ], + "source": [ + "#your code here\n", + "df.dtypes\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What are your numerical variables? What are your categorical variables?\n", + "\n", + "*Hint: Use Pandas `select_dtypes`*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here- numerical variable\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your code here- categorical variable\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set the plot style to `classic` and the figure size to `(12,6)`\n", + "\n", + "*Hint: To set the style you can use `matplotlib.pyplot` functions or seaborn*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the appropriate visulalization to show the distribution of the column `Age`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use subplots to plot the distribution of the `Age` variable with bins equal to `10`, `20` and `50`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Whether the bin size affects your plot? Why?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#your comment here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use seaborn to show the distribution of the column `Age`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the appropriate plot to visualize the column `Gender`. There are 2 ways of doing it. Do it both ways.\n", + "\n", + "*Hint: Use matplotlib and seaborn*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here- 1st way\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here- 2nd way\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the appropriate plot to visualize the column `Pclass` \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We would like to have in one single plot the summary statistics of the feature `Age`. Choose the appropriate plot to show below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What does the previous plot tell you about the feature `Age`? Comment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your comment here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now in addition to the summary statistics, we also want to visualize the distribution of `Age`. Choose the appropriate plot to sow below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What additional information in the previous plot provide you about the feature `Age`? Comment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your comment here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We suspect that there is a linear relationship between `Fare` and `Age`. Use the appropriate plot to show the relationship between these 2 features. There are 2 ways, please do it both ways.\n", + "\n", + "*Hint: One of the ways involves using Seaborn*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your code-1st way\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code-2nd way\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using Seaborn plot the correlation matrix of various features.\n", + "\n", + "*Hint: search how to use `heatmap`*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### According to the previous plot, what are the most correlated feature? Comment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your comment here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the appropriate plot to display the summary statistics of the `Age` in function of the `Pclass`.\n", + "\n", + "*Hint: use boxplot to display summary statistics of `Age` in relation to each of the discrete values of `Pclass`*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the appropriate plot to display the distributions of `Age` in relation to `Gender`.\n", + "\n", + "*Hint: use Facetgrid to display the distribution of `Age` for each categorical value of `Gender`*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#your code here\n" + ] + } + ], + "metadata": { + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module-2/lab-matplotlib-seaborn/your-code/challenge-3.ipynb b/module-2/lab-matplotlib-seaborn/your-code/challenge-3.ipynb index 93106c2..5be3ebc 100755 --- a/module-2/lab-matplotlib-seaborn/your-code/challenge-3.ipynb +++ b/module-2/lab-matplotlib-seaborn/your-code/challenge-3.ipynb @@ -9,18 +9,37 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy.testing.nosetester'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/seaborn/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Import seaborn objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mrcmod\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m 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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
010.03Braund, Mr. Owen Harrismale22.010A/5 211717.2500U0S
121.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
231.03Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250U0S
341.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
450.03Allen, Mr. William Henrymale35.0003734508.0500U0S
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0.0 3 \n", + "1 2 1.0 1 \n", + "2 3 1.0 3 \n", + "3 4 1.0 1 \n", + "4 5 0.0 3 \n", + "\n", + " Name Gender Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 U0 S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 U0 S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 U0 S " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic = pd.read_csv('./titanic.csv',low_memory=False)\n", "titanic.head()" @@ -49,11 +211,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here\n" + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId int64\n", + "Survived float64\n", + "Pclass int64\n", + "Name object\n", + "Gender object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Ticket object\n", + "Fare float64\n", + "Cabin object\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "titanic.dtypes\n" ] }, { @@ -67,11 +253,181 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here- numerical variable\n" + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
010.0322.000000107.2500
121.0138.0000001071.2833
231.0326.000000007.9250
341.0135.0000001053.1000
450.0335.000000008.0500
........................
13041305NaN329.513190008.0500
13051306NaN139.00000000108.9000
13061307NaN338.500000007.2500
13071308NaN329.513190008.0500
13081309NaN325.3154351122.3583
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1309 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp Parch Fare\n", + "0 1 0.0 3 22.000000 1 0 7.2500\n", + "1 2 1.0 1 38.000000 1 0 71.2833\n", + "2 3 1.0 3 26.000000 0 0 7.9250\n", + "3 4 1.0 1 35.000000 1 0 53.1000\n", + "4 5 0.0 3 35.000000 0 0 8.0500\n", + "... ... ... ... ... ... ... ...\n", + "1304 1305 NaN 3 29.513190 0 0 8.0500\n", + "1305 1306 NaN 1 39.000000 0 0 108.9000\n", + "1306 1307 NaN 3 38.500000 0 0 7.2500\n", + "1307 1308 NaN 3 29.513190 0 0 8.0500\n", + "1308 1309 NaN 3 25.315435 1 1 22.3583\n", + "\n", + "[1309 rows x 7 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here- numerical variable\n", + "\n", + "titanic.select_dtypes(exclude=object)" ] }, { @@ -83,6 +439,172 @@ "#your code here- categorical variable\n" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameGenderTicketCabinEmbarked
0Braund, Mr. Owen HarrismaleA/5 21171U0S
1Cumings, Mrs. John Bradley (Florence Briggs Th...femalePC 17599C85C
2Heikkinen, Miss. LainafemaleSTON/O2. 3101282U0S
3Futrelle, Mrs. Jacques Heath (Lily May Peel)female113803C123S
4Allen, Mr. William Henrymale373450U0S
..................
1304Spector, Mr. WoolfmaleA.5. 3236U0S
1305Oliva y Ocana, Dona. FerminafemalePC 17758C105C
1306Saether, Mr. Simon SivertsenmaleSOTON/O.Q. 3101262U0S
1307Ware, Mr. Frederickmale359309U0S
1308Peter, Master. Michael Jmale2668U0C
\n", + "

1309 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Name Gender \\\n", + "0 Braund, Mr. Owen Harris male \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female \n", + "2 Heikkinen, Miss. Laina female \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female \n", + "4 Allen, Mr. William Henry male \n", + "... ... ... \n", + "1304 Spector, Mr. Woolf male \n", + "1305 Oliva y Ocana, Dona. Fermina female \n", + "1306 Saether, Mr. Simon Sivertsen male \n", + "1307 Ware, Mr. Frederick male \n", + "1308 Peter, Master. Michael J male \n", + "\n", + " Ticket Cabin Embarked \n", + "0 A/5 21171 U0 S \n", + "1 PC 17599 C85 C \n", + "2 STON/O2. 3101282 U0 S \n", + "3 113803 C123 S \n", + "4 373450 U0 S \n", + "... ... ... ... \n", + "1304 A.5. 3236 U0 S \n", + "1305 PC 17758 C105 C \n", + "1306 SOTON/O.Q. 3101262 U0 S \n", + "1307 359309 U0 S \n", + "1308 2668 U0 C \n", + "\n", + "[1309 rows x 5 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.select_dtypes(object)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -94,11 +616,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.style.use('classic')\n", + "titanic['Age'].hist(figsize=(12,6))" ] }, { @@ -114,7 +659,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "titanic['Age'].hist(figsize=(12,6))" ] }, { @@ -126,11 +672,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.subplot(1,3,1)\n", + "titanic['Age'].hist(bins=10)\n", + "plt.subplot(1,3,1)\n", + "titanic['Age'].hist(bins=20)\n", + "plt.subplot(1,3,1)\n", + "titanic['Age'].hist(bins=50)\n" ] }, { @@ -142,11 +715,24 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'sns' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#your comment here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'whitegrid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined" + ] + } + ], "source": [ - "#your comment here\n" + "#your comment here\n", + "sns.set_style('whitegrid')" ] }, { @@ -386,7 +972,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-subsetting-and-descriptive-stats/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/module-2/lab-subsetting-and-descriptive-stats/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100755 index 0000000..2754cb6 --- /dev/null +++ b/module-2/lab-subsetting-and-descriptive-stats/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Before you start :\n", + " - These exercises are related to the Subsetting and Descriptive Stats lessons.\n", + " - Keep in mind that you need to use some of the functions you learned in the previous lessons.\n", + " - All datasets are provided in the `your-code` folder of this lab.\n", + " - Elaborate your codes and outputs as much as you can.\n", + " - Try your best to answer the questions and complete the tasks and most importantly enjoy the process!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import all the libraries that are necessary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# import libraries here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### In this challenge we will use the `Temp_States.csv` file. \n", + "\n", + "#### First import it into a data frame called `temp`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print `temp`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore the data types of the Temp dataframe. What type of data do we have? Comment your result." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select the rows where state is New York" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What is the average of the temperature of cities in New York?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We want to know cities and states with Temperature above 15 degress Celcius" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now, return only the cities that have a temperature above 15 degress Celcius" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We want to know which cities have a temperature above 15 degrees Celcius and below 20 degrees Celcius\n", + "\n", + "*Hint: First write the condition then select the rows.*" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find the mean and the standard deviation of the temperature of each state.\n", + "\n", + "*Hint: Use functions from Data Manipulation lesson*" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Challenge 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the `employee.csv` file into a DataFrame. Call the dataframe `employee`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore the data types of the Temp dataframe. Comment your results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show visually the frequency distribution (histogram) of the employee dataset. In few words describe these histograms?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What's the average salary in this company?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What's the highest salary?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What's the lowest salary?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Who are the employees with the lowest salary?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Could you give all the information about an employee called David?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Could you give only David's salary?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print all the rows where job title is associate" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print the first 3 rows of your dataframe\n", + "\n", + "##### Tip : There are 2 ways to do it. Do it both ways" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here- 1 method\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here- 2nd method\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find the employees who's title is associate and the salary above 55?" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Group the employees based on their number of years of employment. What are the average salaries in each group?" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What is the average Salary per title?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show a visual summary of the data using boxplot. What Are the First and Third Quartiles? Comment your results.\n", + "##### * Hint : Quantiles vs Quartiles*\n", + "##### - `In Probability and Statistics, quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities. When division is into four parts the values of the variate corresponding to 25%, 50% and 75% of the total distribution are called quartiles.`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# draw boxplot here" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# print first quartile here" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# print third quartile here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Is the mean salary per gender different?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find the minimum, mean and the maximum of all numeric columns for each Department.\n", + "\n", + "##### Hint: Use functions from Data Manipulation lesson" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus Question\n", + "\n", + "#### For each department, compute the difference between the maximal salary and the minimal salary.\n", + "\n", + "##### * Hint: try using `agg` or `apply` and `lambda`*" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Challenge 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Open the Orders.csv dataset. Name your dataset orders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore your dataset by looking at the data types and the summary statistics. Comment your results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What is the average Purchase Price?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What were the highest and lowest purchase prices? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select all the customers we have in Spain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How many customers do we have in Spain?\n", + "##### Hint : Use value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select all the customers who have bought more than 50 items ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select orders from Spain that are above 50 items" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select all free orders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select all orders that are 'lunch bag'\n", + "#### Hint: Use string functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select all orders that are made in 2011 and are 'lunch bag' " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show the frequency distribution of the amount spent in Spain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select all orders made in the month of August" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select how many orders are made by countries in the month of August\n", + "##### Hint: Use value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What's the average amount of money spent by country" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What's the most expensive item?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What was the average amount spent per year ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# your answer here" + ] + } + ], + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/module-2/lab-subsetting-and-descriptive-stats/your-code/main.ipynb b/module-2/lab-subsetting-and-descriptive-stats/your-code/main.ipynb index 6a2cca3..d89889e 100755 --- a/module-2/lab-subsetting-and-descriptive-stats/your-code/main.ipynb +++ b/module-2/lab-subsetting-and-descriptive-stats/your-code/main.ipynb @@ -21,13 +21,14 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ - "# import libraries here" + "# import libraries here\n", + "import pandas as pd\n", + "import numpy as np\n", + "import re" ] }, { @@ -48,13 +49,12 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ - "# your answer here\n" + "# your answer here\n", + "temp=pd.read_csv(\"Temp_States.csv\")" ] }, { @@ -66,10 +66,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " City State Temperature\n", + "0 NYC New York 19.444444\n", + "1 Albany New York 9.444444\n", + "2 Buffalo New York 3.333333\n", + "3 Hartford Connecticut 17.222222\n", + "4 Bridgeport Connecticut 14.444444\n", + "5 Treton New Jersey 22.222222\n", + "6 Newark New Jersey 20.000000\n" + ] + } + ], + "source": [ + "print(temp)" + ] }, { "cell_type": "markdown", @@ -80,11 +97,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "City object\n", + "State object\n", + "Temperature float64\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "temp.dtypes" ] }, { @@ -96,11 +128,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "# your answer here\n", + "NY=temp[temp['State']=='New York']" ] }, { @@ -112,11 +145,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Temperature 10.740741\n", + "dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "NY.mean()" ] }, { @@ -128,11 +174,80 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here\n" + "outputs": [ + { + "data": { + "text/html": [ + "
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CityStateTemperature
0NYCNew York19.444444
3HartfordConnecticut17.222222
5TretonNew Jersey22.222222
6NewarkNew Jersey20.000000
\n", + "
" + ], + "text/plain": [ + " City State Temperature\n", + "0 NYC New York 19.444444\n", + "3 Hartford Connecticut 17.222222\n", + "5 Treton New Jersey 22.222222\n", + "6 Newark New Jersey 20.000000" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "temp[temp.Temperature>15][['City','State','Temperature']]" ] }, { @@ -144,11 +259,75 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "outputs": [ + { + "data": { + "text/html": [ + "
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CityTemperature
0NYC19.444444
3Hartford17.222222
5Treton22.222222
6Newark20.000000
\n", + "
" + ], + "text/plain": [ + " City Temperature\n", + "0 NYC 19.444444\n", + "3 Hartford 17.222222\n", + "5 Treton 22.222222\n", + "6 Newark 20.000000" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "temp[temp.Temperature>15][['City','Temperature']]" ] }, { @@ -162,11 +341,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 NYC\n", + "3 Hartford\n", + "Name: City, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "temp[(temp.Temperature>15) & (temp.Temperature<20)]['City']\n" ] }, { @@ -184,7 +377,9 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here\n" + "# your answer here\n", + "temp.groupby('State').mean()\n", + "temp.groupby('State').std()" ] }, { @@ -205,13 +400,12 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "# your answer here\n", + "employee=pd.read_csv('Employee.csv')" ] }, { @@ -223,11 +417,30 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here\n" + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Department object\n", + "Education object\n", + "Gender object\n", + "Title object\n", + "Years int64\n", + "Salary int64\n", + "dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "employee.dtypes" ] }, { @@ -239,11 +452,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "# your answer here\n", + "employee.hist();" ] }, { @@ -255,11 +469,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "48.888888888888886" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here" + "# your answer here\n", + "employee[\"Salary\"].mean()" ] }, { @@ -271,11 +497,23 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "70" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here" + "# your answer here\n", + "employee[\"Salary\"].max()" ] }, { @@ -287,11 +525,23 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "employee[\"Salary\"].min()\n" ] }, { @@ -303,11 +553,78 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDepartmentEducationGenderTitleYearsSalary
1MariaITMasterFanalyst230
2DavidHRMasterManalyst230
\n", + "
" + ], + "text/plain": [ + " Name Department Education Gender Title Years Salary\n", + "1 Maria IT Master F analyst 2 30\n", + "2 David HR Master M analyst 2 30" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "employee[(employee[\"Salary\"]== employee[\"Salary\"].min())]" ] }, { @@ -319,11 +636,67 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here\n" + "execution_count": 31, + "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", + "
NameDepartmentEducationGenderTitleYearsSalary
2DavidHRMasterManalyst230
\n", + "
" + ], + "text/plain": [ + " Name Department Education Gender Title Years Salary\n", + "2 David HR Master M analyst 2 30" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "employee[(employee[\"Name\"]==\"David\")]" ] }, { @@ -335,11 +708,24 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2 30\n", + "Name: Salary, dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "employee[(employee[\"Name\"]== \"David\")][\"Salary\"]\n" ] }, { @@ -351,11 +737,58 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDepartmentEducationGenderTitleYearsSalary
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Name, Department, Education, Gender, Title, Years, Salary]\n", + "Index: []" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "employee[(employee[\"Title\"]==\"Associate\")]" ] }, { @@ -369,20 +802,285 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here- 1 method\n" + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDepartmentEducationGenderTitleYearsSalary
0JoseITBachelorManalyst135
1MariaITMasterFanalyst230
2DavidHRMasterManalyst230
\n", + "
" + ], + "text/plain": [ + " Name Department Education Gender Title Years Salary\n", + "0 Jose IT Bachelor M analyst 1 35\n", + "1 Maria IT Master F analyst 2 30\n", + "2 David HR Master M analyst 2 30" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here- 1 method\n", + "\n", + "employee[0:3]\n" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here- 2nd method\n" + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDepartmentEducationGenderTitleYearsSalary
0JoseITBachelorManalyst135
1MariaITMasterFanalyst230
2DavidHRMasterManalyst230
3SoniaHRBachelorFanalyst435
\n", + "
" + ], + "text/plain": [ + " Name Department Education Gender Title Years Salary\n", + "0 Jose IT Bachelor M analyst 1 35\n", + "1 Maria IT Master F analyst 2 30\n", + "2 David HR Master M analyst 2 30\n", + "3 Sonia HR Bachelor F analyst 4 35" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here- 2nd method\n", + "employee.iloc[0:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDepartmentEducationGenderTitleYearsSalary
0JoseITBachelorManalyst135
1MariaITMasterFanalyst230
2DavidHRMasterManalyst230
3SoniaHRBachelorFanalyst435
\n", + "
" + ], + "text/plain": [ + " Name Department Education Gender Title Years Salary\n", + "0 Jose IT Bachelor M analyst 1 35\n", + "1 Maria IT Master F analyst 2 30\n", + "2 David HR Master M analyst 2 30\n", + "3 Sonia HR Bachelor F analyst 4 35" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "employee.loc[0:3]" ] }, { @@ -394,11 +1092,67 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here\n" + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDepartmentEducationGenderTitleYearsSalary
7PedroITPhdMassociate760
\n", + "
" + ], + "text/plain": [ + " Name Department Education Gender Title Years Salary\n", + "7 Pedro IT Phd M associate 7 60" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your answer here\n", + "employee[(employee['Title'] == 'associate') & (employee['Salary'] > 55)]" ] }, { @@ -881,7 +1635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-ufo/.ipynb_checkpoints/ufos-checkpoint.ipynb b/module-2/lab-ufo/.ipynb_checkpoints/ufos-checkpoint.ipynb index f539d1a..f0a6013 100644 --- a/module-2/lab-ufo/.ipynb_checkpoints/ufos-checkpoint.ipynb +++ b/module-2/lab-ufo/.ipynb_checkpoints/ufos-checkpoint.ipynb @@ -18,39 +18,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# Import libraries" + "# Import libraries\n", + "import pymongo\n", + "from pymongo import MongoClient\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# Import data" + "import pandas as pd\n", + "import folium" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# Select data" + "from folium.plugins import HeatMapWithTime as HMWT\n", + "from folium.plugins import HeatMap as HM" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import data\n", + "ufo=pd.read_csv('ufo.csv')\n", + "ufo.country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0datetimecitystatecountryshapedurationtotal_timecommentsdate_postedlatitudelongitudeyeardistance
0010/10/1949 20:30san marcostxuscylinder2700.045 minutesThis event took place in early fall around 194...4/27/200429.883056-97.94111120041242.667772
3310/10/1956 21:00ednatxuscircle20.01/2 hourMy older brother and twin sister were leaving ...1/17/200428.978333-96.64583320041211.971352
4410/10/1960 20:00kaneohehiuslight900.015 minutesAS a Marine 1st Lt. flying an FJ4B fighter/att...1/22/200421.418056-157.80361120046960.923396
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 datetime city state country shape duration \\\n", + "0 0 10/10/1949 20:30 san marcos tx us cylinder 2700.0 \n", + "3 3 10/10/1956 21:00 edna tx us circle 20.0 \n", + "4 4 10/10/1960 20:00 kaneohe hi us light 900.0 \n", + "\n", + " total_time comments date_posted \\\n", + "0 45 minutes This event took place in early fall around 194... 4/27/2004 \n", + "3 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 \n", + "4 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 \n", + "\n", + " latitude longitude year distance \n", + "0 29.883056 -97.941111 2004 1242.667772 \n", + "3 28.978333 -96.645833 2004 1211.971352 \n", + "4 21.418056 -157.803611 2004 6960.923396 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select data\n", + "ufo1=ufo.loc[ufo['country'] == 'us']\n", + "ufo2=ufo1.loc[ufo1['year'] >= 2004]\n", + "ufo2.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0datetimecitystatecountryshapedurationtotal_timecommentsdate_postedlatitudelongitudeyeardistance
0010/10/1949 20:30san marcostxuscylinder2700.045 minutesThis event took place in early fall around 194...4/27/200429.883056-97.94111120041242.667772
1110/10/1949 21:00lackland afbtxNaNlight7200.01-2 hrs1949 Lackland AFB&#44 TX. Lights racing acros...12/16/200529.384210-98.58108220051325.486319
2210/10/1955 17:00chester (uk/england)NaNgbcircle20.020 secondsGreen/Orange circular disc over Chester&#44 En...1/21/200853.200000-2.91666720086515.416577
3310/10/1956 21:00ednatxuscircle20.01/2 hourMy older brother and twin sister were leaving ...1/17/200428.978333-96.64583320041211.971352
4410/10/1960 20:00kaneohehiuslight900.015 minutesAS a Marine 1st Lt. flying an FJ4B fighter/att...1/22/200421.418056-157.80361120046960.923396
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 datetime city state country shape \\\n", + "0 0 10/10/1949 20:30 san marcos tx us cylinder \n", + "1 1 10/10/1949 21:00 lackland afb tx NaN light \n", + "2 2 10/10/1955 17:00 chester (uk/england) NaN gb circle \n", + "3 3 10/10/1956 21:00 edna tx us circle \n", + "4 4 10/10/1960 20:00 kaneohe hi us light \n", + "\n", + " duration total_time comments \\\n", + "0 2700.0 45 minutes This event took place in early fall around 194... \n", + "1 7200.0 1-2 hrs 1949 Lackland AFB, TX. Lights racing acros... \n", + "2 20.0 20 seconds Green/Orange circular disc over Chester, En... \n", + "3 20.0 1/2 hour My older brother and twin sister were leaving ... \n", + "4 900.0 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... \n", + "\n", + " date_posted latitude longitude year distance \n", + "0 4/27/2004 29.883056 -97.941111 2004 1242.667772 \n", + "1 12/16/2005 29.384210 -98.581082 2005 1325.486319 \n", + "2 1/21/2008 53.200000 -2.916667 2008 6515.416577 \n", + "3 1/17/2004 28.978333 -96.645833 2004 1211.971352 \n", + "4 1/22/2004 21.418056 -157.803611 2004 6960.923396 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the city\n", + "def generateBaseMap(default_location=[29.883056, -97.941111], default_zoom_start=7):\n", + " \n", + " base_map = folium.Map(location=default_location, \n", + " control_scale=True, \n", + " zoom_start=default_zoom_start)\n", + " \n", + " return base_map" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "base_map = generateBaseMap()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HM(data=ufo2[['latitude', 'longitude']]).add_to(base_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "# Find the city" + "base_map.save('base_map.html')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_map" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -69,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/module-2/lab-ufo/base_map.html b/module-2/lab-ufo/base_map.html new file mode 100644 index 0000000..44625e3 --- /dev/null +++ b/module-2/lab-ufo/base_map.html @@ -0,0 +1,71 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + \ No newline at end of file diff --git a/module-2/lab-ufo/ufos.ipynb b/module-2/lab-ufo/ufos.ipynb index f539d1a..f0a6013 100644 --- a/module-2/lab-ufo/ufos.ipynb +++ b/module-2/lab-ufo/ufos.ipynb @@ -18,39 +18,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# Import libraries" + "# Import libraries\n", + "import pymongo\n", + "from pymongo import MongoClient\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# Import data" + "import pandas as pd\n", + "import folium" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# Select data" + "from folium.plugins import HeatMapWithTime as HMWT\n", + "from folium.plugins import HeatMap as HM" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import data\n", + "ufo=pd.read_csv('ufo.csv')\n", + "ufo.country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0datetimecitystatecountryshapedurationtotal_timecommentsdate_postedlatitudelongitudeyeardistance
0010/10/1949 20:30san marcostxuscylinder2700.045 minutesThis event took place in early fall around 194...4/27/200429.883056-97.94111120041242.667772
3310/10/1956 21:00ednatxuscircle20.01/2 hourMy older brother and twin sister were leaving ...1/17/200428.978333-96.64583320041211.971352
4410/10/1960 20:00kaneohehiuslight900.015 minutesAS a Marine 1st Lt. flying an FJ4B fighter/att...1/22/200421.418056-157.80361120046960.923396
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 datetime city state country shape duration \\\n", + "0 0 10/10/1949 20:30 san marcos tx us cylinder 2700.0 \n", + "3 3 10/10/1956 21:00 edna tx us circle 20.0 \n", + "4 4 10/10/1960 20:00 kaneohe hi us light 900.0 \n", + "\n", + " total_time comments date_posted \\\n", + "0 45 minutes This event took place in early fall around 194... 4/27/2004 \n", + "3 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 \n", + "4 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 \n", + "\n", + " latitude longitude year distance \n", + "0 29.883056 -97.941111 2004 1242.667772 \n", + "3 28.978333 -96.645833 2004 1211.971352 \n", + "4 21.418056 -157.803611 2004 6960.923396 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select data\n", + "ufo1=ufo.loc[ufo['country'] == 'us']\n", + "ufo2=ufo1.loc[ufo1['year'] >= 2004]\n", + "ufo2.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0datetimecitystatecountryshapedurationtotal_timecommentsdate_postedlatitudelongitudeyeardistance
0010/10/1949 20:30san marcostxuscylinder2700.045 minutesThis event took place in early fall around 194...4/27/200429.883056-97.94111120041242.667772
1110/10/1949 21:00lackland afbtxNaNlight7200.01-2 hrs1949 Lackland AFB&#44 TX. Lights racing acros...12/16/200529.384210-98.58108220051325.486319
2210/10/1955 17:00chester (uk/england)NaNgbcircle20.020 secondsGreen/Orange circular disc over Chester&#44 En...1/21/200853.200000-2.91666720086515.416577
3310/10/1956 21:00ednatxuscircle20.01/2 hourMy older brother and twin sister were leaving ...1/17/200428.978333-96.64583320041211.971352
4410/10/1960 20:00kaneohehiuslight900.015 minutesAS a Marine 1st Lt. flying an FJ4B fighter/att...1/22/200421.418056-157.80361120046960.923396
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 datetime city state country shape \\\n", + "0 0 10/10/1949 20:30 san marcos tx us cylinder \n", + "1 1 10/10/1949 21:00 lackland afb tx NaN light \n", + "2 2 10/10/1955 17:00 chester (uk/england) NaN gb circle \n", + "3 3 10/10/1956 21:00 edna tx us circle \n", + "4 4 10/10/1960 20:00 kaneohe hi us light \n", + "\n", + " duration total_time comments \\\n", + "0 2700.0 45 minutes This event took place in early fall around 194... \n", + "1 7200.0 1-2 hrs 1949 Lackland AFB, TX. Lights racing acros... \n", + "2 20.0 20 seconds Green/Orange circular disc over Chester, En... \n", + "3 20.0 1/2 hour My older brother and twin sister were leaving ... \n", + "4 900.0 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... \n", + "\n", + " date_posted latitude longitude year distance \n", + "0 4/27/2004 29.883056 -97.941111 2004 1242.667772 \n", + "1 12/16/2005 29.384210 -98.581082 2005 1325.486319 \n", + "2 1/21/2008 53.200000 -2.916667 2008 6515.416577 \n", + "3 1/17/2004 28.978333 -96.645833 2004 1211.971352 \n", + "4 1/22/2004 21.418056 -157.803611 2004 6960.923396 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the city\n", + "def generateBaseMap(default_location=[29.883056, -97.941111], default_zoom_start=7):\n", + " \n", + " base_map = folium.Map(location=default_location, \n", + " control_scale=True, \n", + " zoom_start=default_zoom_start)\n", + " \n", + " return base_map" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "base_map = generateBaseMap()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HM(data=ufo2[['latitude', 'longitude']]).add_to(base_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "# Find the city" + "base_map.save('base_map.html')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_map" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -69,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4,