diff --git a/assignment.ipynb b/assignment.ipynb new file mode 100644 index 0000000..5901301 --- /dev/null +++ b/assignment.ipynb @@ -0,0 +1,564 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['figure.figsize'] = (10, 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryfood_categoryconsumptionco2_emission
1Argentinapork10.5137.20
2Argentinapoultry38.6641.53
3Argentinabeef55.481712.00
4Argentinalamb_goat1.5654.63
5Argentinafish4.366.96
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" + ], + "text/plain": [ + " country food_category consumption co2_emission\n", + "1 Argentina pork 10.51 37.20\n", + "2 Argentina poultry 38.66 41.53\n", + "3 Argentina beef 55.48 1712.00\n", + "4 Argentina lamb_goat 1.56 54.63\n", + "5 Argentina fish 4.36 6.96" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food_consumption = pd.read_csv('food_consumption.csv', index_col=0)\n", + "food_consumption.head()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#filter for Belgium\n", + "be_consumption = food_consumption[food_consumption['country'] == 'Belgium']\n", + "\n", + "# Filter for USA\n", + "usa_consumption = food_consumption[food_consumption['country'] == 'USA']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q-1) Calculate mean and median consumption in Belgium" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42.13272727272727\n", + "12.59\n" + ] + } + ], + "source": [ + "print (be_consumption['consumption'].mean())\n", + "print(be_consumption['consumption'].median())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q-2) Calculate mean and median consumption of USA" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44.650000000000006\n", + "14.58\n" + ] + } + ], + "source": [ + "print (usa_consumption['consumption'].mean())\n", + "print(usa_consumption['consumption'].median())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Work with both countries together\n", + "be_and_usa = food_consumption[(food_consumption['country'] == 'Belgium') | \n", + " (food_consumption['country'] == 'USA')]\n", + "\n", + "# Q-3) Group by country, select consumption column, and compute mean and median\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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", + "
meanmedian
country
Belgium42.13272712.59
USA44.65000014.58
\n", + "
" + ], + "text/plain": [ + " mean median\n", + "country \n", + "Belgium 42.132727 12.59\n", + "USA 44.650000 14.58" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "be_and_usa = food_consumption[(food_consumption['country'] == 'Belgium') | \n", + " (food_consumption['country'] == 'USA')]\n", + "be_and_usa.groupby('country')['consumption'].agg(['mean','median'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']\n", + "\n", + "Q-4)Plot the histogram of co2_emission for rice" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([77., 16., 13., 6., 5., 5., 3., 2., 1., 2.]),\n", + " array([ 1.22 , 23.074, 44.928, 66.782, 88.636, 110.49 , 132.344,\n", + " 154.198, 176.052, 197.906, 219.76 ]),\n", + " )" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(rice_consumption['co2_emission'], edgecolor='black')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q-5) Calculate mean and median of co2_emission with .agg()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 37.591615\n", + "median 15.200000\n", + "Name: co2_emission, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']\n", + "rice_consumption['co2_emission'].agg(['mean', 'median'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q-6) Calculate the quintiles of co2_emission\n", + "print(np.quantile(missing part, np.linspace(0, 1, 6)))\n", + "\n", + "You only need to fill in the missing part." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 3.54 11.026 25.59 99.978 1712. ]\n" + ] + } + ], + "source": [ + "print(np.quantile(food_consumption['co2_emission'], np.linspace(0, 1, 6)))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q-7) Calculate the variance and standard deviation of co2_emission \n", + "for food_categories" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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varstd
food_category
beef88748.408132297.906710
dairy17671.891985132.935669
eggs21.3718194.622966
fish921.63734930.358481
lamb_goat16475.518363128.356996
nuts35.6396525.969895
pork3094.96353755.632396
poultry245.02680115.653332
rice2281.37624347.763754
soybeans0.8798820.938020
wheat71.0239378.427570
\n", + "
" + ], + "text/plain": [ + " var std\n", + "food_category \n", + "beef 88748.408132 297.906710\n", + "dairy 17671.891985 132.935669\n", + "eggs 21.371819 4.622966\n", + "fish 921.637349 30.358481\n", + "lamb_goat 16475.518363 128.356996\n", + "nuts 35.639652 5.969895\n", + "pork 3094.963537 55.632396\n", + "poultry 245.026801 15.653332\n", + "rice 2281.376243 47.763754\n", + "soybeans 0.879882 0.938020\n", + "wheat 71.023937 8.427570" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food_consumption.groupby('food_category')['co2_emission'].agg(['var','std'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q-8) Create histogram of co2_emission for food_category 'beef'" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([77., 16., 13., 6., 5., 5., 3., 2., 1., 2.]),\n", + " array([ 1.22 , 23.074, 44.928, 66.782, 88.636, 110.49 , 132.344,\n", + " 154.198, 176.052, 197.906, 219.76 ]),\n", + " )" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "beef_consumption = food_consumption[food_consumption['food_category'] == 'beef']\n", + "\n", + "plt.hist(rice_consumption['co2_emission'], edgecolor='black')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4f58e81c2e092e1deee7556cb116668ef0b81c0e3f6aa3662faaf88ffce4bde0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}