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b/your-code/main.ipynb index 5759add..02d89c9 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -1,522 +1,1507 @@ -{ - "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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import pandas as pd\n", + "import numpy as np\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": 22, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "incomplete input (2457017082.py, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Cell \u001b[1;32mIn[22], line 3\u001b[1;36m\u001b[0m\n\u001b[1;33m '''\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "'''\n", + "def diceroll():\n", + " dice_df = pd.DataFrame(columns=['Rolls'])\n", + " i = 10\n", + " for _ in range(i):\n", + " roll = random.randint(1, 6)\n", + " dice_df = dice_df.append({'Rolls': roll}, ignore_index=True)\n", + " return dice_df\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def diceroll():\n", + " num_rolls = 10\n", + " rolls = random.choices(range(1, 7), k=num_rolls)\n", + " dice_df = pd.DataFrame({'Rolls': rolls})\n", + " return dice_df" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "lucky_df = diceroll()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rolls\n", + "0 6\n", + "1 6\n", + "2 3\n", + "3 5\n", + "4 2\n", + "5 3\n", + "6 3\n", + "7 1\n", + "8 4\n", + "9 1\n" + ] + } + ], + "source": [ + "print(lucky_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "lucky_df['Rolls'] = lucky_df['Rolls'].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Rolls int32\n", + "dtype: object" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lucky_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "lucky_sorted = lucky_df.sort_values(by='Rolls')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5,2))\n", + "sns.countplot(data=lucky_sorted, x='Rolls', order=lucky_sorted['Rolls'].value_counts().index)" + ] + }, + { + "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.\n", + "\n", + "#its already a frequency distribution? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "#its already a frequency distribution, because you can visually see the frequency of each roll sorted by value.?" + ] + }, + { + "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": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "\n", + "def dice_mean(dataframe):\n", + " total = 0\n", + " i = 0\n", + " for value in dataframe['Rolls']:\n", + " total += value\n", + " i += 1\n", + " mean = total / i\n", + " return mean" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.4" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dice_mean(lucky_sorted)" + ] + }, + { + "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": 86, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "\n", + "def calc_median(dataframe):\n", + " sorted_df = sorted(dataframe['Rolls'])\n", + " n = len(sorted_df)\n", + " if n % 2 == 0:\n", + " middle = sorted_df[n // 2 - 1]\n", + " middle2 = sorted_df[n // 2]\n", + " median = (middle + middle2) / 2 \n", + " else:\n", + " median = sorted_df[n // 2]\n", + "\n", + " return median" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calc_median(lucky_df)" + ] + }, + { + "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\n", + "\n", + "def calc_quartiles(dataframe):\n", + " q25 = " + ] + }, + { + "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": 90, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "dice_df = pd.read_csv('roll_the_dice_hundred.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5,2))\n", + "sns.countplot(data=dice_df, x='value', order=dice_df['value'].value_counts().index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "What i see is a very lucky diceroll. You would expect it to be more evenly distributed with 100 rolls. \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": 103, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Rolls'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\geert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[1;32mc:\\Users\\geert\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mc:\\Users\\geert\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'Rolls'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\geert\\Documents\\IRONHACK\\Labs\\Descriptive-Stats\\your-code\\main.ipynb Cell 35\u001b[0m line \u001b[0;36m4\n\u001b[0;32m 1\u001b[0m \u001b[39m# I need to change ''value'' to ''Rolls'' \u001b[39;00m\n\u001b[0;32m 2\u001b[0m dice_df \u001b[39m=\u001b[39m dice_df\u001b[39m.\u001b[39mrename(columns\u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mvalue \u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39mRolls\u001b[39m\u001b[39m'\u001b[39m})\n\u001b[1;32m----> 4\u001b[0m dice_mean(dice_df)\n", + "\u001b[1;32mc:\\Users\\geert\\Documents\\IRONHACK\\Labs\\Descriptive-Stats\\your-code\\main.ipynb Cell 35\u001b[0m line \u001b[0;36m6\n\u001b[0;32m 4\u001b[0m total \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m 5\u001b[0m i \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m----> 6\u001b[0m \u001b[39mfor\u001b[39;00m value \u001b[39min\u001b[39;00m dataframe[\u001b[39m'\u001b[39;49m\u001b[39mRolls\u001b[39;49m\u001b[39m'\u001b[39;49m]:\n\u001b[0;32m 7\u001b[0m total \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m value\n\u001b[0;32m 8\u001b[0m i \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\geert\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3805\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m 3806\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m 3808\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3809\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", + "File \u001b[1;32mc:\\Users\\geert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m 3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m 3806\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3807\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3808\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3809\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[1;31mKeyError\u001b[0m: 'Rolls'" + ] + } + ], + "source": [ + "# I need to change ''value'' to ''Rolls'', but its broken\n", + "dice_df = dice_df.rename(columns={'value ': 'Rolls'})\n", + "\n", + "dice_mean(dice_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "plt.figure(figsize=(5,2))\n", + "sns.countplot(data=dice_df, x='value', order=dice_df['value'].value_counts().index)" + ] + }, + { + "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": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 2))\n", + "sns.countplot(data=thousand, x='value', order=thousand['value'].value_counts().index)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nIts definitely more evenly distributed. Makes a lot of sense. The higher the sample the higher the more likely for it to be evenly distributed\\n\\n'" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Its definitely more evenly distributed. Makes a lot of sense. The higher the sample the higher the more likely for it to be evenly distributed\n", + "\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": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "ages_population = pd.read_csv('ages_population.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14,4))\n", + "sns.countplot(data=ages_population, x='observation')" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "#i would say mean around 30 and std around 15" + ] + }, + { + "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": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: observation 36.56\n", + "dtype: float64 and Std: observation 12.81009\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\geert\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3430: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n" + ] + } + ], + "source": [ + "# your code here\n", + "ages_population_mean = np.mean(ages_population)\n", + "ages_population_std = np.std(ages_population)\n", + "\n", + "print(f'Mean: {ages_population_mean} and Std: {ages_population_std}')" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 1)" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Not bad! \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": 128, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "ages_population2 = pd.read_csv('ages_population2.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 1)" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " observation\n", + "count 1000.000000\n", + "mean 27.155000\n", + "std 2.969814\n", + "min 19.000000\n", + "25% 25.000000\n", + "50% 27.000000\n", + "75% 29.000000\n", + "max 36.000000" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population2.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "The std deviation is much smaller so its likely the population is from the same age group. Also mean is lower so its a sample of younger people.\n", + "\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": 136, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "ages_population3 = pd.read_csv('ages_population3.csv')" + ] + }, + { + "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": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " observation\n", + "count 1000.000000\n", + "mean 41.989000\n", + "std 16.144706\n", + "min 1.000000\n", + "25% 30.000000\n", + "50% 40.000000\n", + "75% 53.000000\n", + "max 77.000000" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population3.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "A lot older people and way more spread out\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": [ + "# you can see them here at above. its about a 2 years (rounded) difference between the median and the mean. " + ] + }, + { + "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": "base", + "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/data/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv similarity index 88% rename from data/roll_the_dice_hundred.csv rename to 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