{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "import pandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "df = pandas.read_csv('../data/gapminder.tsv', sep='\\t')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
1AfghanistanAsia195730.3329240934820.853030
2AfghanistanAsia196231.99710267083853.100710
3AfghanistanAsia196734.02011537966836.197138
4AfghanistanAsia197236.08813079460739.981106
\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314\n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030\n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710\n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138\n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "df = pd.read_csv('../data/gapminder.tsv', sep='\\t')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
1AfghanistanAsia195730.3329240934820.853030
2AfghanistanAsia196231.99710267083853.100710
3AfghanistanAsia196734.02011537966836.197138
4AfghanistanAsia197236.08813079460739.981106
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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314\n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030\n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710\n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138\n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1704, 6)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object is not callable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: 'tuple' object is not callable" ] } ], "source": [ "df.shape()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=1704, step=1)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['Afghanistan', 'Asia', 1952, 28.801, 8425333, 779.4453145],\n", " ['Afghanistan', 'Asia', 1957, 30.331999999999997, 9240934,\n", " 820.8530296],\n", " ['Afghanistan', 'Asia', 1962, 31.997, 10267083, 853.1007099999999],\n", " ..., \n", " ['Zimbabwe', 'Africa', 1997, 46.809, 11404948, 792.4499602999999],\n", " ['Zimbabwe', 'Africa', 2002, 39.989000000000004, 11926563,\n", " 672.0386227000001],\n", " ['Zimbabwe', 'Africa', 2007, 43.486999999999995, 12311143,\n", " 469.70929810000007]], dtype=object)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.values" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country object\n", "continent object\n", "year int64\n", "lifeExp float64\n", "pop int64\n", "gdpPercap float64\n", "dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1704 entries, 0 to 1703\n", "Data columns (total 6 columns):\n", "country 1704 non-null object\n", "continent 1704 non-null object\n", "year 1704 non-null int64\n", "lifeExp 1704 non-null float64\n", "pop 1704 non-null int64\n", "gdpPercap 1704 non-null float64\n", "dtypes: float64(2), int64(2), object(2)\n", "memory usage: 80.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Subsetting" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "country_df = df['country']" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Afghanistan\n", "1 Afghanistan\n", "2 Afghanistan\n", "3 Afghanistan\n", "4 Afghanistan\n", "Name: country, dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "country_df.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1699 Zimbabwe\n", "1700 Zimbabwe\n", "1701 Zimbabwe\n", "1702 Zimbabwe\n", "1703 Zimbabwe\n", "Name: country, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "country_df.tail()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(country_df)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "subset = df[['country', 'continent', 'year']]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyear
0AfghanistanAsia1952
1AfghanistanAsia1957
2AfghanistanAsia1962
3AfghanistanAsia1967
4AfghanistanAsia1972
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" ], "text/plain": [ " country continent year\n", "0 Afghanistan Asia 1952\n", "1 Afghanistan Asia 1957\n", "2 Afghanistan Asia 1962\n", "3 Afghanistan Asia 1967\n", "4 Afghanistan Asia 1972" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.20.3'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pandas.__version__" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'[0 1] not in index'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2001\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2002\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2003\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[0;32m 1229\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1230\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1231\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%s not in index'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1233\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: '[0 1] not in index'" ] } ], "source": [ "df[[0, 1]]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
1AfghanistanAsia195730.3329240934820.853030
2AfghanistanAsia196231.99710267083853.100710
3AfghanistanAsia196734.02011537966836.197138
4AfghanistanAsia197236.08813079460739.981106
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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314\n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030\n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710\n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138\n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country Afghanistan\n", "continent Asia\n", "year 1952\n", "lifeExp 28.801\n", "pop 8425333\n", "gdpPercap 779.445\n", "Name: 0, dtype: object" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[0]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
99BangladeshAsia196743.45362821884721.186086
\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314\n", "99 Bangladesh Asia 1967 43.453 62821884 721.186086" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[[0, 99]]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'the label [-1] is not in the [index]'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1433\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1434\u001b[1;33m \u001b[0merror\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1435\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36merror\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1428\u001b[0m raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[1;32m-> 1429\u001b[1;33m (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[0;32m 1430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'the label [-1] is not in the [index]'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1328\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1549\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1550\u001b[0m \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1551\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1552\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1440\u001b[0m \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1441\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1442\u001b[1;33m \u001b[0merror\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1443\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1444\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36merror\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1427\u001b[0m \"key\")\n\u001b[0;32m 1428\u001b[0m raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[1;32m-> 1429\u001b[1;33m (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[0;32m 1430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1431\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'the label [-1] is not in the [index]'" ] } ], "source": [ "df.loc[-1]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country Afghanistan\n", "continent Asia\n", "year 1952\n", "lifeExp 28.801\n", "pop 8425333\n", "gdpPercap 779.445\n", "Name: 0, dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[0]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
99BangladeshAsia196743.45362821884721.186086
\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314\n", "99 Bangladesh Asia 1967 43.453 62821884 721.186086" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[[0, 99]]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country Zimbabwe\n", "continent Africa\n", "year 2007\n", "lifeExp 43.487\n", "pop 12311143\n", "gdpPercap 469.709\n", "Name: 1703, dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[-1]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country Afghanistan\n", "continent Asia\n", "year 1952\n", "lifeExp 28.801\n", "pop 8425333\n", "gdpPercap 779.445\n", "Name: 0, dtype: object" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ix[0]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "subset = df.loc[:, ['year', 'pop']]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearpop
019528425333
119579240934
2196210267083
3196711537966
4197213079460
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" ], "text/plain": [ " year pop\n", "0 1952 8425333\n", "1 1957 9240934\n", "2 1962 10267083\n", "3 1967 11537966\n", "4 1972 13079460" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset.head()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " year pop\n", "0 1952 8425333\n", "1 1957 9240934\n", "2 1962 10267083\n", "3 1967 11537966\n", "4 1972 13079460\n" ] } ], "source": [ "subset = df.iloc[:, [2, 4]]\n", "print(subset.head())" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
1699ZimbabweAfrica198762.3519216418706.157306
1701ZimbabweAfrica199746.80911404948792.449960
1703ZimbabweAfrica200743.48712311143469.709298
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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306\n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960\n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset = df.iloc[-5::2, :]\n", "subset.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# grouped calculations" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
1AfghanistanAsia195730.3329240934820.853030
2AfghanistanAsia196231.99710267083853.100710
3AfghanistanAsia196734.02011537966836.197138
4AfghanistanAsia197236.08813079460739.981106
\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314\n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030\n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710\n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138\n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year\n", "1952 49.057620\n", "1957 51.507401\n", "1962 53.609249\n", "1967 55.678290\n", "1972 57.647386\n", "1977 59.570157\n", "1982 61.533197\n", "1987 63.212613\n", "1992 64.160338\n", "1997 65.014676\n", "2002 65.694923\n", "2007 67.007423\n", "Name: lifeExp, dtype: float64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('year')['lifeExp'].mean()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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lifeExpgdpPercap
yearcontinent
1952Africa39.1355001252.572466
Americas53.2798404079.062552
Asia46.3143945195.484004
Europe64.4085005661.057435
Oceania69.25500010298.085650
1957Africa41.2663461385.236062
Americas55.9602804616.043733
Asia49.3185445787.732940
Europe66.7030676963.012816
Oceania70.29500011598.522455
1962Africa43.3194421598.078825
Americas58.3987604901.541870
Asia51.5632235729.369625
Europe68.5392338365.486814
Oceania71.08500012696.452430
1967Africa45.3345382050.363801
Americas60.4109205668.253496
Asia54.6636405971.173374
Europe69.73760010143.823757
Oceania71.31000014495.021790
1972Africa47.4509422339.615674
Americas62.3949206491.334139
Asia57.3192698187.468699
Europe70.77503312479.575246
Oceania71.91000016417.333380
1977Africa49.5804232585.938508
Americas64.3915607352.007126
Asia59.6105567791.314020
Europe71.93776714283.979110
Oceania72.85500017283.957605
1982Africa51.5928652481.592960
Americas66.2288407506.737088
Asia62.6179397434.135157
Europe72.80640015617.896551
Oceania74.29000018554.709840
1987Africa53.3447882282.668991
Americas68.0907207793.400261
Asia64.8511827608.226508
Europe73.64216717214.310727
Oceania75.32000020448.040160
1992Africa53.6295772281.810333
Americas69.5683608044.934406
Asia66.5372128639.690248
Europe74.44010017061.568084
Oceania76.94500020894.045885
1997Africa53.5982692378.759555
Americas71.1504808889.300863
Asia68.0205159834.093295
Europe75.50516719076.781802
Oceania78.19000024024.175170
2002Africa53.3252312599.385159
Americas72.4220409287.677107
Asia69.23387910174.090397
Europe76.70060021711.732422
Oceania79.74000026938.778040
2007Africa54.8060383089.032605
Americas73.60812011003.031625
Asia70.72848512473.026870
Europe77.64860025054.481636
Oceania80.71950029810.188275
\n", "
" ], "text/plain": [ " lifeExp gdpPercap\n", "year continent \n", "1952 Africa 39.135500 1252.572466\n", " Americas 53.279840 4079.062552\n", " Asia 46.314394 5195.484004\n", " Europe 64.408500 5661.057435\n", " Oceania 69.255000 10298.085650\n", "1957 Africa 41.266346 1385.236062\n", " Americas 55.960280 4616.043733\n", " Asia 49.318544 5787.732940\n", " Europe 66.703067 6963.012816\n", " Oceania 70.295000 11598.522455\n", "1962 Africa 43.319442 1598.078825\n", " Americas 58.398760 4901.541870\n", " Asia 51.563223 5729.369625\n", " Europe 68.539233 8365.486814\n", " Oceania 71.085000 12696.452430\n", "1967 Africa 45.334538 2050.363801\n", " Americas 60.410920 5668.253496\n", " Asia 54.663640 5971.173374\n", " Europe 69.737600 10143.823757\n", " Oceania 71.310000 14495.021790\n", "1972 Africa 47.450942 2339.615674\n", " Americas 62.394920 6491.334139\n", " Asia 57.319269 8187.468699\n", " Europe 70.775033 12479.575246\n", " Oceania 71.910000 16417.333380\n", "1977 Africa 49.580423 2585.938508\n", " Americas 64.391560 7352.007126\n", " Asia 59.610556 7791.314020\n", " Europe 71.937767 14283.979110\n", " Oceania 72.855000 17283.957605\n", "1982 Africa 51.592865 2481.592960\n", " Americas 66.228840 7506.737088\n", " Asia 62.617939 7434.135157\n", " Europe 72.806400 15617.896551\n", " Oceania 74.290000 18554.709840\n", "1987 Africa 53.344788 2282.668991\n", " Americas 68.090720 7793.400261\n", " Asia 64.851182 7608.226508\n", " Europe 73.642167 17214.310727\n", " Oceania 75.320000 20448.040160\n", "1992 Africa 53.629577 2281.810333\n", " Americas 69.568360 8044.934406\n", " Asia 66.537212 8639.690248\n", " Europe 74.440100 17061.568084\n", " Oceania 76.945000 20894.045885\n", "1997 Africa 53.598269 2378.759555\n", " Americas 71.150480 8889.300863\n", " Asia 68.020515 9834.093295\n", " Europe 75.505167 19076.781802\n", " Oceania 78.190000 24024.175170\n", "2002 Africa 53.325231 2599.385159\n", " Americas 72.422040 9287.677107\n", " Asia 69.233879 10174.090397\n", " Europe 76.700600 21711.732422\n", " Oceania 79.740000 26938.778040\n", "2007 Africa 54.806038 3089.032605\n", " Americas 73.608120 11003.031625\n", " Asia 70.728485 12473.026870\n", " Europe 77.648600 25054.481636\n", " Oceania 80.719500 29810.188275" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(['year', 'continent'])[['lifeExp', 'gdpPercap']].mean()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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lifeExpgdpPercap
yearcontinent
1952Africa39.1355001252.572466
Americas53.2798404079.062552
Asia46.3143945195.484004
Europe64.4085005661.057435
Oceania69.25500010298.085650
1957Africa41.2663461385.236062
Americas55.9602804616.043733
Asia49.3185445787.732940
Europe66.7030676963.012816
Oceania70.29500011598.522455
1962Africa43.3194421598.078825
Americas58.3987604901.541870
Asia51.5632235729.369625
Europe68.5392338365.486814
Oceania71.08500012696.452430
1967Africa45.3345382050.363801
Americas60.4109205668.253496
Asia54.6636405971.173374
Europe69.73760010143.823757
Oceania71.31000014495.021790
1972Africa47.4509422339.615674
Americas62.3949206491.334139
Asia57.3192698187.468699
Europe70.77503312479.575246
Oceania71.91000016417.333380
1977Africa49.5804232585.938508
Americas64.3915607352.007126
Asia59.6105567791.314020
Europe71.93776714283.979110
Oceania72.85500017283.957605
1982Africa51.5928652481.592960
Americas66.2288407506.737088
Asia62.6179397434.135157
Europe72.80640015617.896551
Oceania74.29000018554.709840
1987Africa53.3447882282.668991
Americas68.0907207793.400261
Asia64.8511827608.226508
Europe73.64216717214.310727
Oceania75.32000020448.040160
1992Africa53.6295772281.810333
Americas69.5683608044.934406
Asia66.5372128639.690248
Europe74.44010017061.568084
Oceania76.94500020894.045885
1997Africa53.5982692378.759555
Americas71.1504808889.300863
Asia68.0205159834.093295
Europe75.50516719076.781802
Oceania78.19000024024.175170
2002Africa53.3252312599.385159
Americas72.4220409287.677107
Asia69.23387910174.090397
Europe76.70060021711.732422
Oceania79.74000026938.778040
2007Africa54.8060383089.032605
Americas73.60812011003.031625
Asia70.72848512473.026870
Europe77.64860025054.481636
Oceania80.71950029810.188275
\n", "
" ], "text/plain": [ " lifeExp gdpPercap\n", "year continent \n", "1952 Africa 39.135500 1252.572466\n", " Americas 53.279840 4079.062552\n", " Asia 46.314394 5195.484004\n", " Europe 64.408500 5661.057435\n", " Oceania 69.255000 10298.085650\n", "1957 Africa 41.266346 1385.236062\n", " Americas 55.960280 4616.043733\n", " Asia 49.318544 5787.732940\n", " Europe 66.703067 6963.012816\n", " Oceania 70.295000 11598.522455\n", "1962 Africa 43.319442 1598.078825\n", " Americas 58.398760 4901.541870\n", " Asia 51.563223 5729.369625\n", " Europe 68.539233 8365.486814\n", " Oceania 71.085000 12696.452430\n", "1967 Africa 45.334538 2050.363801\n", " Americas 60.410920 5668.253496\n", " Asia 54.663640 5971.173374\n", " Europe 69.737600 10143.823757\n", " Oceania 71.310000 14495.021790\n", "1972 Africa 47.450942 2339.615674\n", " Americas 62.394920 6491.334139\n", " Asia 57.319269 8187.468699\n", " Europe 70.775033 12479.575246\n", " Oceania 71.910000 16417.333380\n", "1977 Africa 49.580423 2585.938508\n", " Americas 64.391560 7352.007126\n", " Asia 59.610556 7791.314020\n", " Europe 71.937767 14283.979110\n", " Oceania 72.855000 17283.957605\n", "1982 Africa 51.592865 2481.592960\n", " Americas 66.228840 7506.737088\n", " Asia 62.617939 7434.135157\n", " Europe 72.806400 15617.896551\n", " Oceania 74.290000 18554.709840\n", "1987 Africa 53.344788 2282.668991\n", " Americas 68.090720 7793.400261\n", " Asia 64.851182 7608.226508\n", " Europe 73.642167 17214.310727\n", " Oceania 75.320000 20448.040160\n", "1992 Africa 53.629577 2281.810333\n", " Americas 69.568360 8044.934406\n", " Asia 66.537212 8639.690248\n", " Europe 74.440100 17061.568084\n", " Oceania 76.945000 20894.045885\n", "1997 Africa 53.598269 2378.759555\n", " Americas 71.150480 8889.300863\n", " Asia 68.020515 9834.093295\n", " Europe 75.505167 19076.781802\n", " Oceania 78.190000 24024.175170\n", "2002 Africa 53.325231 2599.385159\n", " Americas 72.422040 9287.677107\n", " Asia 69.233879 10174.090397\n", " Europe 76.700600 21711.732422\n", " Oceania 79.740000 26938.778040\n", "2007 Africa 54.806038 3089.032605\n", " Americas 73.608120 11003.031625\n", " Asia 70.728485 12473.026870\n", " Europe 77.648600 25054.481636\n", " Oceania 80.719500 29810.188275" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df\\\n", " .groupby(['year', 'continent'])[['lifeExp', 'gdpPercap']]\\\n", " .mean()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearcontinentlifeExpgdpPercap
01952Africa39.1355001252.572466
11952Americas53.2798404079.062552
21952Asia46.3143945195.484004
31952Europe64.4085005661.057435
41952Oceania69.25500010298.085650
51957Africa41.2663461385.236062
61957Americas55.9602804616.043733
71957Asia49.3185445787.732940
81957Europe66.7030676963.012816
91957Oceania70.29500011598.522455
101962Africa43.3194421598.078825
111962Americas58.3987604901.541870
121962Asia51.5632235729.369625
131962Europe68.5392338365.486814
141962Oceania71.08500012696.452430
151967Africa45.3345382050.363801
161967Americas60.4109205668.253496
171967Asia54.6636405971.173374
181967Europe69.73760010143.823757
191967Oceania71.31000014495.021790
201972Africa47.4509422339.615674
211972Americas62.3949206491.334139
221972Asia57.3192698187.468699
231972Europe70.77503312479.575246
241972Oceania71.91000016417.333380
251977Africa49.5804232585.938508
261977Americas64.3915607352.007126
271977Asia59.6105567791.314020
281977Europe71.93776714283.979110
291977Oceania72.85500017283.957605
301982Africa51.5928652481.592960
311982Americas66.2288407506.737088
321982Asia62.6179397434.135157
331982Europe72.80640015617.896551
341982Oceania74.29000018554.709840
351987Africa53.3447882282.668991
361987Americas68.0907207793.400261
371987Asia64.8511827608.226508
381987Europe73.64216717214.310727
391987Oceania75.32000020448.040160
401992Africa53.6295772281.810333
411992Americas69.5683608044.934406
421992Asia66.5372128639.690248
431992Europe74.44010017061.568084
441992Oceania76.94500020894.045885
451997Africa53.5982692378.759555
461997Americas71.1504808889.300863
471997Asia68.0205159834.093295
481997Europe75.50516719076.781802
491997Oceania78.19000024024.175170
502002Africa53.3252312599.385159
512002Americas72.4220409287.677107
522002Asia69.23387910174.090397
532002Europe76.70060021711.732422
542002Oceania79.74000026938.778040
552007Africa54.8060383089.032605
562007Americas73.60812011003.031625
572007Asia70.72848512473.026870
582007Europe77.64860025054.481636
592007Oceania80.71950029810.188275
\n", "
" ], "text/plain": [ " year continent lifeExp gdpPercap\n", "0 1952 Africa 39.135500 1252.572466\n", "1 1952 Americas 53.279840 4079.062552\n", "2 1952 Asia 46.314394 5195.484004\n", "3 1952 Europe 64.408500 5661.057435\n", "4 1952 Oceania 69.255000 10298.085650\n", "5 1957 Africa 41.266346 1385.236062\n", "6 1957 Americas 55.960280 4616.043733\n", "7 1957 Asia 49.318544 5787.732940\n", "8 1957 Europe 66.703067 6963.012816\n", "9 1957 Oceania 70.295000 11598.522455\n", "10 1962 Africa 43.319442 1598.078825\n", "11 1962 Americas 58.398760 4901.541870\n", "12 1962 Asia 51.563223 5729.369625\n", "13 1962 Europe 68.539233 8365.486814\n", "14 1962 Oceania 71.085000 12696.452430\n", "15 1967 Africa 45.334538 2050.363801\n", "16 1967 Americas 60.410920 5668.253496\n", "17 1967 Asia 54.663640 5971.173374\n", "18 1967 Europe 69.737600 10143.823757\n", "19 1967 Oceania 71.310000 14495.021790\n", "20 1972 Africa 47.450942 2339.615674\n", "21 1972 Americas 62.394920 6491.334139\n", "22 1972 Asia 57.319269 8187.468699\n", "23 1972 Europe 70.775033 12479.575246\n", "24 1972 Oceania 71.910000 16417.333380\n", "25 1977 Africa 49.580423 2585.938508\n", "26 1977 Americas 64.391560 7352.007126\n", "27 1977 Asia 59.610556 7791.314020\n", "28 1977 Europe 71.937767 14283.979110\n", "29 1977 Oceania 72.855000 17283.957605\n", "30 1982 Africa 51.592865 2481.592960\n", "31 1982 Americas 66.228840 7506.737088\n", "32 1982 Asia 62.617939 7434.135157\n", "33 1982 Europe 72.806400 15617.896551\n", "34 1982 Oceania 74.290000 18554.709840\n", "35 1987 Africa 53.344788 2282.668991\n", "36 1987 Americas 68.090720 7793.400261\n", "37 1987 Asia 64.851182 7608.226508\n", "38 1987 Europe 73.642167 17214.310727\n", "39 1987 Oceania 75.320000 20448.040160\n", "40 1992 Africa 53.629577 2281.810333\n", "41 1992 Americas 69.568360 8044.934406\n", "42 1992 Asia 66.537212 8639.690248\n", "43 1992 Europe 74.440100 17061.568084\n", "44 1992 Oceania 76.945000 20894.045885\n", "45 1997 Africa 53.598269 2378.759555\n", "46 1997 Americas 71.150480 8889.300863\n", "47 1997 Asia 68.020515 9834.093295\n", "48 1997 Europe 75.505167 19076.781802\n", "49 1997 Oceania 78.190000 24024.175170\n", "50 2002 Africa 53.325231 2599.385159\n", "51 2002 Americas 72.422040 9287.677107\n", "52 2002 Asia 69.233879 10174.090397\n", "53 2002 Europe 76.700600 21711.732422\n", "54 2002 Oceania 79.740000 26938.778040\n", "55 2007 Africa 54.806038 3089.032605\n", "56 2007 Americas 73.608120 11003.031625\n", "57 2007 Asia 70.728485 12473.026870\n", "58 2007 Europe 77.648600 25054.481636\n", "59 2007 Oceania 80.719500 29810.188275" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df\n", " .groupby(['year', 'continent'])[['lifeExp', 'gdpPercap']]\n", " .mean()\n", " .reset_index()\n", ")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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bz9N4voicWDhX/C2AWcHOSzHAdHf/0Mz+ZmZ9CQ3dbAZuBjCzVsCL7j7a3XPM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\nzKpGugaRwij4JeqZ2cPH1mQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "# probably don't need the magic anymore\n", "%matplotlib inline\n", "\n", "(df\n", " .groupby(['year'])\n", " [['lifeExp']]\n", " .mean()\n", " .plot()\n", ")\n", "plt.show()" ] } ], "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 }