{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = [1, 2, 3]\n", "y = [1, 4, 9]\n", "z = [10, 5, 0]\n", "plt.plot(x, y)\n", "plt.plot(x, z)\n", "plt.title(\"test plot\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y and z\")\n", "plt.legend([\"this is y\", \"this is z\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sample_data = pd.read_csv('sample_data.csv')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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column_acolumn_bcolumn_c
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" ], "text/plain": [ " column_a column_b column_c\n", "0 1 1 10\n", "1 2 4 8\n", "2 3 9 6\n", "3 4 16 4\n", "4 5 25 2" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(sample_data)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_data.column_c.iloc[0]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(sample_data.column_a, sample_data.column_b, 'o')\n", "plt.plot(sample_data.column_a, sample_data.column_c)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('countries.csv')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryyearpopulation
0Afghanistan19528425333
1Afghanistan19579240934
2Afghanistan196210267083
3Afghanistan196711537966
4Afghanistan197213079460
5Afghanistan197714880372
6Afghanistan198212881816
7Afghanistan198713867957
8Afghanistan199216317921
9Afghanistan199722227415
10Afghanistan200225268405
11Afghanistan200731889923
12Albania19521282697
13Albania19571476505
14Albania19621728137
15Albania19671984060
16Albania19722263554
17Albania19772509048
18Albania19822780097
19Albania19873075321
20Albania19923326498
21Albania19973428038
22Albania20023508512
23Albania20073600523
24Algeria19529279525
25Algeria195710270856
26Algeria196211000948
27Algeria196712760499
28Algeria197214760787
29Algeria197717152804
............
1674Yemen, Rep.19829657618
1675Yemen, Rep.198711219340
1676Yemen, Rep.199213367997
1677Yemen, Rep.199715826497
1678Yemen, Rep.200218701257
1679Yemen, Rep.200722211743
1680Zambia19522672000
1681Zambia19573016000
1682Zambia19623421000
1683Zambia19673900000
1684Zambia19724506497
1685Zambia19775216550
1686Zambia19826100407
1687Zambia19877272406
1688Zambia19928381163
1689Zambia19979417789
1690Zambia200210595811
1691Zambia200711746035
1692Zimbabwe19523080907
1693Zimbabwe19573646340
1694Zimbabwe19624277736
1695Zimbabwe19674995432
1696Zimbabwe19725861135
1697Zimbabwe19776642107
1698Zimbabwe19827636524
1699Zimbabwe19879216418
1700Zimbabwe199210704340
1701Zimbabwe199711404948
1702Zimbabwe200211926563
1703Zimbabwe200712311143
\n", "

1704 rows × 3 columns

\n", "
" ], "text/plain": [ " country year population\n", "0 Afghanistan 1952 8425333\n", "1 Afghanistan 1957 9240934\n", "2 Afghanistan 1962 10267083\n", "3 Afghanistan 1967 11537966\n", "4 Afghanistan 1972 13079460\n", "5 Afghanistan 1977 14880372\n", "6 Afghanistan 1982 12881816\n", "7 Afghanistan 1987 13867957\n", "8 Afghanistan 1992 16317921\n", "9 Afghanistan 1997 22227415\n", "10 Afghanistan 2002 25268405\n", "11 Afghanistan 2007 31889923\n", "12 Albania 1952 1282697\n", "13 Albania 1957 1476505\n", "14 Albania 1962 1728137\n", "15 Albania 1967 1984060\n", "16 Albania 1972 2263554\n", "17 Albania 1977 2509048\n", "18 Albania 1982 2780097\n", "19 Albania 1987 3075321\n", "20 Albania 1992 3326498\n", "21 Albania 1997 3428038\n", "22 Albania 2002 3508512\n", "23 Albania 2007 3600523\n", "24 Algeria 1952 9279525\n", "25 Algeria 1957 10270856\n", "26 Algeria 1962 11000948\n", "27 Algeria 1967 12760499\n", "28 Algeria 1972 14760787\n", "29 Algeria 1977 17152804\n", "... ... ... ...\n", "1674 Yemen, Rep. 1982 9657618\n", "1675 Yemen, Rep. 1987 11219340\n", "1676 Yemen, Rep. 1992 13367997\n", "1677 Yemen, Rep. 1997 15826497\n", "1678 Yemen, Rep. 2002 18701257\n", "1679 Yemen, Rep. 2007 22211743\n", "1680 Zambia 1952 2672000\n", "1681 Zambia 1957 3016000\n", "1682 Zambia 1962 3421000\n", "1683 Zambia 1967 3900000\n", "1684 Zambia 1972 4506497\n", "1685 Zambia 1977 5216550\n", "1686 Zambia 1982 6100407\n", "1687 Zambia 1987 7272406\n", "1688 Zambia 1992 8381163\n", "1689 Zambia 1997 9417789\n", "1690 Zambia 2002 10595811\n", "1691 Zambia 2007 11746035\n", "1692 Zimbabwe 1952 3080907\n", "1693 Zimbabwe 1957 3646340\n", "1694 Zimbabwe 1962 4277736\n", "1695 Zimbabwe 1967 4995432\n", "1696 Zimbabwe 1972 5861135\n", "1697 Zimbabwe 1977 6642107\n", "1698 Zimbabwe 1982 7636524\n", "1699 Zimbabwe 1987 9216418\n", "1700 Zimbabwe 1992 10704340\n", "1701 Zimbabwe 1997 11404948\n", "1702 Zimbabwe 2002 11926563\n", "1703 Zimbabwe 2007 12311143\n", "\n", "[1704 rows x 3 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Compare the population growth in the US and China" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryyearpopulation
1608United States1952157553000
1609United States1957171984000
1610United States1962186538000
1611United States1967198712000
1612United States1972209896000
1613United States1977220239000
1614United States1982232187835
1615United States1987242803533
1616United States1992256894189
1617United States1997272911760
1618United States2002287675526
1619United States2007301139947
\n", "
" ], "text/plain": [ " country year population\n", "1608 United States 1952 157553000\n", "1609 United States 1957 171984000\n", "1610 United States 1962 186538000\n", "1611 United States 1967 198712000\n", "1612 United States 1972 209896000\n", "1613 United States 1977 220239000\n", "1614 United States 1982 232187835\n", "1615 United States 1987 242803533\n", "1616 United States 1992 256894189\n", "1617 United States 1997 272911760\n", "1618 United States 2002 287675526\n", "1619 United States 2007 301139947" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data.country == 'United States']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "us = data[data.country == 'United States']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "china = data[data.country == 'China']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryyearpopulation
288China1952556263527
289China1957637408000
290China1962665770000
291China1967754550000
292China1972862030000
293China1977943455000
294China19821000281000
295China19871084035000
296China19921164970000
297China19971230075000
298China20021280400000
299China20071318683096
\n", "
" ], "text/plain": [ " country year population\n", "288 China 1952 556263527\n", "289 China 1957 637408000\n", "290 China 1962 665770000\n", "291 China 1967 754550000\n", "292 China 1972 862030000\n", "293 China 1977 943455000\n", "294 China 1982 1000281000\n", "295 China 1987 1084035000\n", "296 China 1992 1164970000\n", "297 China 1997 1230075000\n", "298 China 2002 1280400000\n", "299 China 2007 1318683096" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "china" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(us.year, us.population / 10**6)\n", "plt.plot(china.year, china.population / 10**6)\n", "plt.legend(['United States', 'China'])\n", "plt.xlabel('year')\n", "plt.ylabel('population')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1608 157553000\n", "1609 171984000\n", "1610 186538000\n", "1611 198712000\n", "1612 209896000\n", "1613 220239000\n", "1614 232187835\n", "1615 242803533\n", "1616 256894189\n", "1617 272911760\n", "1618 287675526\n", "1619 301139947\n", "Name: population, dtype: int64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "us.population" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1608 100.000000\n", "1609 109.159457\n", "1610 118.396984\n", "1611 126.123908\n", "1612 133.222471\n", "1613 139.787246\n", "1614 147.371256\n", "1615 154.109114\n", "1616 163.052553\n", "1617 173.219018\n", "1618 182.589685\n", "1619 191.135648\n", "Name: population, dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "us.population / us.population.iloc[0] * 100" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(us.year, us.population / us.population.iloc[0] * 100)\n", "plt.plot(china.year, china.population / china.population.iloc[0] * 100)\n", "plt.legend(['United States', 'China'])\n", "plt.xlabel('year')\n", "plt.ylabel('population growth (first year = 100)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# thanks for watching! :)" ] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }