diff --git a/homwork.ipynb b/homwork.ipynb new file mode 100644 index 0000000..3bc6056 --- /dev/null +++ b/homwork.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import statistics\n", + "import numpy as np\n", + "import scipy.stats\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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
\n", + "
" + ], + "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": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#read the data\n", + "#./dataset/ is a path. Copy and paste the path of the CSV file in your computer to read the data. \n", + "food_consumption = pd.read_csv('D:/codes/Class7-MathStats-Module-Week16/food_consumption.csv', index_col=0)\n", + "food_consumption.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42.13272727272727\n", + "12.59\n" + ] + } + ], + "source": [ + "# Q-1) Calculate mean and median consumption in Belgium\n", + "\n", + "print(be_consumption['consumption'].mean())\n", + "print(be_consumption['consumption'].median())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44.650000000000006\n", + "14.58\n" + ] + } + ], + "source": [ + "# Q-2) Calculate mean and median consumption of USA\n", + "print(usa_consumption['consumption'].mean())\n", + "print(usa_consumption['consumption'].median())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Work with both countries together\n", + "be_and_usa = food_consumption[(food_consumption['country'] == 'Belgium') | \n", + " (food_consumption['country'] == 'USA')]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean median\n", + "country \n", + "Belgium 42.132727 12.59\n", + "USA 44.650000 14.58\n" + ] + } + ], + "source": [ + "# Q-3) Group by country, select consumption column, and compute mean and median\n", + "print(be_and_usa.groupby('country')['consumption'].agg([np.mean, np.median]))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rice_consumption = food_consumption[food_consumption['food_category'] == 'rice']\n", + "\n", + "# Q-4)Plot the histogram of co2_emission for rice\n", + "rice_consumption['co2_emission'].hist();" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 37.591615\n", + "median 15.200000\n", + "Name: co2_emission, dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Q-5) Calculate mean and median of co2_emission with .agg()\n", + "rice_consumption['co2_emission'].agg([np.mean, np.median])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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)))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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\n" + ] + } + ], + "source": [ + "# Q-7) Calculate the variance and standard deviation of co2_emission for food_categories\n", + "print(food_consumption.groupby('food_category')['co2_emission'].agg([np.var, np.std]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Q-8) Create histogram of co2_emission for food_category 'beef'\n", + "food_consumption[food_consumption['food_category'] == 'beef']['co2_emission'].hist();" + ] + } + ], + "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": "bac75f92805af78f4c3d905bf4b7769093ac074cfb83aa4dc437efeee303fdc9" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}