diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..e234f18 --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,1897 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "\n", + "np.arange(1,7,1)\n", + "dices_throw = []\n", + "for i in range(10):\n", + " dices_throw.append(random.choice(np.arange(1,7,1)))\n", + " \n", + "pd.DataFrame(dices_throw)\n", + "\n", + "def dice_throw(number_throws=10, number_faces = 6):\n", + " throws = []\n", + " \n", + " for i in range(number_throws):\n", + " throws.append(random.choice(np.arange(1, number_faces+1,1)))\n", + " \n", + " throws_df = pd.DataFrame(throws, columns=[\"number\"])\n", + " \n", + " return throws_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " number\n", + "0 2\n", + "1 1\n", + "2 3\n", + "3 5\n", + "4 2\n", + "5 5\n", + "6 1\n", + "7 2\n", + "8 3\n", + "9 5" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dices = dice_throw(10)\n", + "dices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.plot(dices.index, dices[\"number\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dices = dices.sort_values(by=\"number\")\n", + "\n", + "plt.plot(\n", + " dices[\"number\"],\n", + " dices.index,\n", + " color = 'red',\n", + " marker = \"x\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "dices= dices.sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " dices,\n", + " bins = 6,\n", + " range= (1,7),\n", + " color = \"orange\",\n", + " rwidth = .90\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dices.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nin terms of frequency both plots show similar results. howver a histogram plots frequesncy in a much easier form\\n'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "in terms of frequency both plots show similar results. howver a histogram plots frequesncy in a much easier form\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " number\n", + "0 2\n", + "1 1\n", + "2 3\n", + "3 5\n", + "4 2" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "dices.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sum(dices[\"number\"]) / len(dices[\"number\"])\n", + "\n", + "def average(data):\n", + " \n", + " sum_values = sum(data)\n", + " len_values = len(data)\n", + " return sum_values/len_values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average([1,2,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2, 1, 3, 5, 2, 5, 1, 2, 3, 5]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "aux_list = list(dices[\"number\"])\n", + "aux_list" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "freq_d = {}\n", + "\n", + "for dice_number in aux_list:\n", + " if dice_number in freq_d:\n", + " freq_d[dice_number] += 1 \n", + " \n", + " else:\n", + " freq_d[dice_number] = 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2: 3, 1: 2, 3: 2, 5: 3}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "freq_d" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency(values):\n", + " \n", + " freq_d = {}\n", + "\n", + " for dice_number in values:\n", + " if dice_number in freq_d:\n", + " freq_d[dice_number] += 1 \n", + " \n", + " else:\n", + " freq_d[dice_number] = 1\n", + "\n", + " return freq_d" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 3, 2: 1, 3: 1, 4: 1}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency([1,2,3,4,1,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2, 1, 3, 5, 2, 5, 1, 2, 3, 5]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "aux_list" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sort_aux = sorted(aux_list)\n", + "len_aux = len(sort_aux)\n", + "\n", + "mid_aux = (len_aux - 1) //2\n", + "\n", + "mid_aux" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 2, 2, 2, 3, 3, 5, 5, 5]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sort_aux" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sort_aux[mid_aux] + sort_aux[mid_aux + 1] / 2 #median value" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def median_value(values):\n", + " sorted_values = sorted(values) #sorting array\n", + " len_values = len(sorted_values) #check lenght of the array\n", + " \n", + " middle_idx = (len_values -1) //2 #pick= middle index\n", + " \n", + " if middle_idx % 2:\n", + " return sorted_values[middle_idx]\n", + " else:\n", + " return(sorted_values[middle_idx] + sorted_values[middle_idx+1])/2" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_value([1,2,3,4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 2, 2, 2, 3, 3, 5, 5, 5]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "aux_list = sorted(list(dices[\"number\"]))\n", + "\n", + "half = median_value(aux_list)\n", + "half\n", + "\n", + "aux_list" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def quartiles(aux_list):\n", + " half = median_value(aux_list)\n", + " \n", + " #cases wherre array is even\n", + " if len(aux_list) %2 == 0:\n", + " first_quartile = median_value(aux_list[ : int(len(aux_list)/2)])\n", + " third_quartile = median_value(aux_list[int(len(aux_list)/2) : ])\n", + " second_quartile = half\n", + " \n", + " #cases wherre array is odd\n", + " else:\n", + " first_quartile = median_value(aux_list[ : int(len(aux_list)/2-1)])\n", + " third_quartile = median_value(aux_list[int(len(aux_list)/2-1) : ])\n", + " second_quartile = half\n", + " \n", + " return first_quartile, second_quartile, third_quartile\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.5, 2.5, 4.0)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quartiles(dices[\"number\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rollvalue
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100 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " roll value\n", + "0 0 1\n", + "1 1 2\n", + "2 2 6\n", + "3 3 1\n", + "4 4 6\n", + ".. ... ...\n", + "95 95 4\n", + "96 96 6\n", + "97 97 1\n", + "98 98 3\n", + "99 99 6\n", + "\n", + "[100 rows x 2 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "roll_dice = pd.read_csv(\"../data/roll_the_dice_hundred.csv\", index_col=[0])\n", + "roll_dice" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 6, 5, 4, 3], dtype=int64)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_dice[\"value\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 23\n", + "4 22\n", + "2 17\n", + "3 14\n", + "1 12\n", + "5 12\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_dice[\"value\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.666666666666668" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(roll_dice) / 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "we have 100 dice throws where values are from 1 to 6.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "roll_dice[\"value\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "frequency(roll_dice[\"value\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(\n", + " roll_dice[\"value\"],\n", + " bins=6,\n", + " range=(1,7),\n", + " color= \"blue\",\n", + " rwidth= 0.90\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "since the dices face 4 and 6 have higher frequency than the rest\n", + "it is expected that the mean falls over the expected mean which is 3.74.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0rollvalue
0005
1116
2221
3336
4445
............
9959959951
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9999999996
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1000 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 5\n", + "1 1 1 6\n", + "2 2 2 1\n", + "3 3 3 6\n", + "4 4 4 5\n", + ".. ... ... ...\n", + "995 995 995 1\n", + "996 996 996 4\n", + "997 997 997 4\n", + "998 998 998 3\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "roll_dice_thousand = pd.read_csv(\"../data/roll_the_dice_thousand.csv\")\n", + "roll_dice_thousand" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " roll_dice_thousand[\"value\"],\n", + " bins=6,\n", + " range=(1,7),\n", + " color= \"orange\",\n", + " rwidth= 0.90\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "166.66666666666666" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(roll_dice_thousand) / 6" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.447" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_dice_thousand[\"value\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", + "\n", + "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "363 82.0\n", + "339 73.0\n", + "493 71.0\n", + "437 70.0\n", + "523 69.0\n", + ".. ...\n", + "338 4.0\n", + "451 2.0\n", + "301 2.0\n", + "209 1.0\n", + "489 1.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population1.sort_values(by=\"observation\", ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36.56" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population1[\"observation\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.816499625976762" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population1[\"observation\"].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "ages_population2 = pd.read_csv(\"../data/ages_population2.csv\")\n", + "ages_population2.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "the majority of values fall in a range way shorter than the population1 histogram, \n", + "Also the range of ages fall from 18 to 37\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27.155" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population2[\"observation\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.969813932689186" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population2[\"observation\"].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "ages_population3 = pd.read_csv(\"../data/ages_population3.csv\")\n", + "ages_population3.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "41.989" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "ages_population3[\"observation\"].mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.144705959865934" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population3[\"observation\"].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 30.0\n", + "Name: 0.25, dtype: float64\n", + "observation 53.0\n", + "Name: 0.75, dtype: float64\n", + "observation 77.0\n", + "Name: 1.0, dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population3.quantile(0.25))\n", + "print(ages_population3.quantile(0.75))\n", + "print(ages_population3.quantile(1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 36.0\n", + "Name: 0.4, dtype: float64\n", + "observation 45.0\n", + "Name: 0.6, dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population3.quantile(0.40))\n", + "print(ages_population3.quantile(0.60))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 5759add..e234f18 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -1,522 +1,1897 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Understanding Descriptive Statistics\n", - "\n", - "Import the necessary libraries here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 1\n", - "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", - "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Plot the results sorted by value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 2\n", - "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", - "\n", - "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", - "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 3\n", - "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", - "#### 1.- Sort the values and plot them. What do you see?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Now, calculate the frequency distribution.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 4\n", - "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", - "\n", - "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 5\n", - "Now is the turn of `ages_population3.csv`.\n", - "\n", - "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bonus challenge\n", - "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ironhack-3.7", - "language": "python", - "name": "ironhack-3.7" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "\n", + "np.arange(1,7,1)\n", + "dices_throw = []\n", + "for i in range(10):\n", + " dices_throw.append(random.choice(np.arange(1,7,1)))\n", + " \n", + "pd.DataFrame(dices_throw)\n", + "\n", + "def dice_throw(number_throws=10, number_faces = 6):\n", + " throws = []\n", + " \n", + " for i in range(number_throws):\n", + " throws.append(random.choice(np.arange(1, number_faces+1,1)))\n", + " \n", + " throws_df = pd.DataFrame(throws, columns=[\"number\"])\n", + " \n", + " return throws_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.plot(dices.index, dices[\"number\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dices = dices.sort_values(by=\"number\")\n", + "\n", + "plt.plot(\n", + " dices[\"number\"],\n", + " dices.index,\n", + " color = 'red',\n", + " marker = \"x\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "dices= dices.sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " dices,\n", + " bins = 6,\n", + " range= (1,7),\n", + " color = \"orange\",\n", + " rwidth = .90\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dices.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nin terms of frequency both plots show similar results. howver a histogram plots frequesncy in a much easier form\\n'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "in terms of frequency both plots show similar results. howver a histogram plots frequesncy in a much easier form\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " number\n", + "0 2\n", + "1 1\n", + "2 3\n", + "3 5\n", + "4 2" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "dices.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sum(dices[\"number\"]) / len(dices[\"number\"])\n", + "\n", + "def average(data):\n", + " \n", + " sum_values = sum(data)\n", + " len_values = len(data)\n", + " return sum_values/len_values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average([1,2,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2, 1, 3, 5, 2, 5, 1, 2, 3, 5]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "aux_list = list(dices[\"number\"])\n", + "aux_list" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "freq_d = {}\n", + "\n", + "for dice_number in aux_list:\n", + " if dice_number in freq_d:\n", + " freq_d[dice_number] += 1 \n", + " \n", + " else:\n", + " freq_d[dice_number] = 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2: 3, 1: 2, 3: 2, 5: 3}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "freq_d" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency(values):\n", + " \n", + " freq_d = {}\n", + "\n", + " for dice_number in values:\n", + " if dice_number in freq_d:\n", + " freq_d[dice_number] += 1 \n", + " \n", + " else:\n", + " freq_d[dice_number] = 1\n", + "\n", + " return freq_d" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 3, 2: 1, 3: 1, 4: 1}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency([1,2,3,4,1,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2, 1, 3, 5, 2, 5, 1, 2, 3, 5]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "aux_list" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sort_aux = sorted(aux_list)\n", + "len_aux = len(sort_aux)\n", + "\n", + "mid_aux = (len_aux - 1) //2\n", + "\n", + "mid_aux" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 2, 2, 2, 3, 3, 5, 5, 5]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sort_aux" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sort_aux[mid_aux] + sort_aux[mid_aux + 1] / 2 #median value" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def median_value(values):\n", + " sorted_values = sorted(values) #sorting array\n", + " len_values = len(sorted_values) #check lenght of the array\n", + " \n", + " middle_idx = (len_values -1) //2 #pick= middle index\n", + " \n", + " if middle_idx % 2:\n", + " return sorted_values[middle_idx]\n", + " else:\n", + " return(sorted_values[middle_idx] + sorted_values[middle_idx+1])/2" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_value([1,2,3,4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 2, 2, 2, 3, 3, 5, 5, 5]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "aux_list = sorted(list(dices[\"number\"]))\n", + "\n", + "half = median_value(aux_list)\n", + "half\n", + "\n", + "aux_list" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def quartiles(aux_list):\n", + " half = median_value(aux_list)\n", + " \n", + " #cases wherre array is even\n", + " if len(aux_list) %2 == 0:\n", + " first_quartile = median_value(aux_list[ : int(len(aux_list)/2)])\n", + " third_quartile = median_value(aux_list[int(len(aux_list)/2) : ])\n", + " second_quartile = half\n", + " \n", + " #cases wherre array is odd\n", + " else:\n", + " first_quartile = median_value(aux_list[ : int(len(aux_list)/2-1)])\n", + " third_quartile = median_value(aux_list[int(len(aux_list)/2-1) : ])\n", + " second_quartile = half\n", + " \n", + " return first_quartile, second_quartile, third_quartile\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.5, 2.5, 4.0)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quartiles(dices[\"number\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rollvalue
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100 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " roll value\n", + "0 0 1\n", + "1 1 2\n", + "2 2 6\n", + "3 3 1\n", + "4 4 6\n", + ".. ... ...\n", + "95 95 4\n", + "96 96 6\n", + "97 97 1\n", + "98 98 3\n", + "99 99 6\n", + "\n", + "[100 rows x 2 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "roll_dice = pd.read_csv(\"../data/roll_the_dice_hundred.csv\", index_col=[0])\n", + "roll_dice" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 6, 5, 4, 3], dtype=int64)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_dice[\"value\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 23\n", + "4 22\n", + "2 17\n", + "3 14\n", + "1 12\n", + "5 12\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_dice[\"value\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.666666666666668" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(roll_dice) / 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "we have 100 dice throws where values are from 1 to 6.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "roll_dice[\"value\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "frequency(roll_dice[\"value\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(\n", + " roll_dice[\"value\"],\n", + " bins=6,\n", + " range=(1,7),\n", + " color= \"blue\",\n", + " rwidth= 0.90\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "since the dices face 4 and 6 have higher frequency than the rest\n", + "it is expected that the mean falls over the expected mean which is 3.74.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0rollvalue
0005
1116
2221
3336
4445
............
9959959951
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9999999996
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1000 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 5\n", + "1 1 1 6\n", + "2 2 2 1\n", + "3 3 3 6\n", + "4 4 4 5\n", + ".. ... ... ...\n", + "995 995 995 1\n", + "996 996 996 4\n", + "997 997 997 4\n", + "998 998 998 3\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "roll_dice_thousand = pd.read_csv(\"../data/roll_the_dice_thousand.csv\")\n", + "roll_dice_thousand" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " roll_dice_thousand[\"value\"],\n", + " bins=6,\n", + " range=(1,7),\n", + " color= \"orange\",\n", + " rwidth= 0.90\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "166.66666666666666" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(roll_dice_thousand) / 6" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.447" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_dice_thousand[\"value\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", + "\n", + "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "363 82.0\n", + "339 73.0\n", + "493 71.0\n", + "437 70.0\n", + "523 69.0\n", + ".. ...\n", + "338 4.0\n", + "451 2.0\n", + "301 2.0\n", + "209 1.0\n", + "489 1.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population1.sort_values(by=\"observation\", ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36.56" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population1[\"observation\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.816499625976762" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population1[\"observation\"].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "ages_population2 = pd.read_csv(\"../data/ages_population2.csv\")\n", + "ages_population2.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "the majority of values fall in a range way shorter than the population1 histogram, \n", + "Also the range of ages fall from 18 to 37\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27.155" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population2[\"observation\"].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.969813932689186" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population2[\"observation\"].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "ages_population3 = pd.read_csv(\"../data/ages_population3.csv\")\n", + "ages_population3.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "41.989" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "ages_population3[\"observation\"].mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.144705959865934" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population3[\"observation\"].std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 30.0\n", + "Name: 0.25, dtype: float64\n", + "observation 53.0\n", + "Name: 0.75, dtype: float64\n", + "observation 77.0\n", + "Name: 1.0, dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population3.quantile(0.25))\n", + "print(ages_population3.quantile(0.75))\n", + "print(ages_population3.quantile(1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 36.0\n", + "Name: 0.4, dtype: float64\n", + "observation 45.0\n", + "Name: 0.6, dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population3.quantile(0.40))\n", + "print(ages_population3.quantile(0.60))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}