diff --git a/solution.ipynb b/solution.ipynb new file mode 100644 index 0000000..50d2569 --- /dev/null +++ b/solution.ipynb @@ -0,0 +1,354 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "743289bd", + "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": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['figure.figsize'] = (10, 8)\n", + " \n", + "food_consumption = pd.read_csv('./food_consumption.csv', index_col=0)\n", + "food_consumption.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7859490a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " country food_category consumption co2_emission\n", + "397 Belgium pork 38.65 136.80\n", + "398 Belgium poultry 12.20 13.11\n", + "399 Belgium beef 15.63 482.31\n", + "400 Belgium lamb_goat 1.32 46.23\n", + "401 Belgium fish 18.97 30.29\n", + " country food_category consumption co2_emission\n", + "56 USA pork 27.64 97.83\n", + "57 USA poultry 50.01 53.72\n", + "58 USA beef 36.24 1118.29\n", + "59 USA lamb_goat 0.43 15.06\n", + "60 USA fish 12.35 19.72\n" + ] + } + ], + "source": [ + "#filter for Belgium\n", + "be_consumption = food_consumption[food_consumption['country'] == 'Belgium']\n", + "be_consumption.head()\n", + "print(be_consumption.head())\n", + "# Filter for USA\n", + "usa_consumption = food_consumption[food_consumption['country'] == 'USA']\n", + "print(usa_consumption.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "bc426426", + "metadata": {}, + "outputs": [], + "source": [ + "df_be_consumption = pd.DataFrame(be_consumption)\n", + "df_usa_consumption = pd.DataFrame(usa_consumption)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "620d99bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42.13272727272727\n", + "12.59\n", + "44.650000000000006\n", + "14.58\n" + ] + } + ], + "source": [ + "#Q-1) Calculate mean and median consumption in Belgium\n", + "print(df_be_consumption['consumption'].mean())\n", + "print(df_be_consumption['consumption'].median())\n", + "#Q-2) Calculate mean and median consumption of USA\n", + "print(df_usa_consumption['consumption'].mean())\n", + "print(df_usa_consumption['consumption'].median())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "10604251", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanmedian
country
Belgium42.13272712.59
USA44.65000014.58
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" + ], + "text/plain": [ + " mean median\n", + "country \n", + "Belgium 42.132727 12.59\n", + "USA 44.650000 14.58" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Q-3) Group by country, select consumption column, and compute mean and median\n", + "\n", + "be_and_usa = food_consumption[(food_consumption['country'] == 'Belgium') | (food_consumption['country'] == 'USA')]\n", + "df_be_and_usa = pd.DataFrame(be_and_usa)\n", + "df_groupby_country = df_be_and_usa.groupby('country')\n", + "df_groupby_country['consumption'].agg(['mean','median'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "094384b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Q-4)Plot the histogram of co2_emission for rice\n", + "\n", + "rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']\n", + "rice_consumption['co2_emission'].plot.hist()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "8a1b5e41", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 37.591615\n", + "median 15.200000\n", + "Name: co2_emission, dtype: float64" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Q-5) Calculate mean and median of co2_emission with .agg()\n", + "# rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']\n", + "rice_consumption['co2_emission'].agg(['mean','median'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "1ac143be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 3.54 11.026 25.59 99.978 1712. ]\n" + ] + } + ], + "source": [ + "#Q-6) Calculate the quintiles of co2_emission\n", + "co2_emission = food_consumption['co2_emission']\n", + "print(np.quantile(co2_emission,np.linspace(0, 1, 6)))\n", + "\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + }, + "vscode": { + "interpreter": { + "hash": "25806a9c2e3dc763ae4258cd2f35b673ccac1f110c3a8dc1fe65620c13ebb849" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}