{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring the Gapminder Data set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to do it\n", "\n", "1. Import the `plotly.express` module as `px`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import plotly.express as px" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Load the data set and check its type" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gapminder = px.data.gapminder()\n", "type(gapminder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Inspect the `DataFrame` using the method `head` and `tail`" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
0AfghanistanAsia195228.8018425333779.445314AFG4
1AfghanistanAsia195730.3329240934820.853030AFG4
2AfghanistanAsia196231.99710267083853.100710AFG4
3AfghanistanAsia196734.02011537966836.197138AFG4
4AfghanistanAsia197236.08813079460739.981106AFG4
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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "\n", " iso_num \n", "0 4 \n", "1 4 \n", "2 4 \n", "3 4 \n", "4 4 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gapminder.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
1699ZimbabweAfrica198762.3519216418706.157306ZWE716
1700ZimbabweAfrica199260.37710704340693.420786ZWE716
1701ZimbabweAfrica199746.80911404948792.449960ZWE716
1702ZimbabweAfrica200239.98911926563672.038623ZWE716
1703ZimbabweAfrica200743.48712311143469.709298ZWE716
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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n", "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n", "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n", "\n", " iso_num \n", "1699 716 \n", "1700 716 \n", "1701 716 \n", "1702 716 \n", "1703 716 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gapminder.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Use the method `describe` " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearlifeExppopgdpPercapiso_num
count1704.000001704.0000001.704000e+031704.0000001704.000000
mean1979.5000059.4744392.960121e+077215.327081425.880282
std17.2653312.9171071.061579e+089857.454543248.305709
min1952.0000023.5990006.001100e+04241.1658764.000000
25%1965.7500048.1980002.793664e+061202.060309208.000000
50%1979.5000060.7125007.023596e+063531.846989410.000000
75%1993.2500070.8455001.958522e+079325.462346638.000000
max2007.0000082.6030001.318683e+09113523.132900894.000000
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" ], "text/plain": [ " year lifeExp pop gdpPercap iso_num\n", "count 1704.00000 1704.000000 1.704000e+03 1704.000000 1704.000000\n", "mean 1979.50000 59.474439 2.960121e+07 7215.327081 425.880282\n", "std 17.26533 12.917107 1.061579e+08 9857.454543 248.305709\n", "min 1952.00000 23.599000 6.001100e+04 241.165876 4.000000\n", "25% 1965.75000 48.198000 2.793664e+06 1202.060309 208.000000\n", "50% 1979.50000 60.712500 7.023596e+06 3531.846989 410.000000\n", "75% 1993.25000 70.845500 1.958522e+07 9325.462346 638.000000\n", "max 2007.00000 82.603000 1.318683e+09 113523.132900 894.000000" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gapminder.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5. Make a scatter plot to visualize the relationship between GDP per capita and Life Expectancy for the oldes and the most recent year (i.e. 1952 and 2007)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "gdpPercap=%{x}
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"paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { 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