{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Population\n", "\n", "### Dan Qin,U201816119\n", "\n", "Briefly introduce my data. \n", "\n", "It's on the United Nations Population website https://www.un.org/en/development/desa/population/index.asp \n", "\n", "This web site presents the main findings of the `2018 Revision of World Urbanization Prospects`, \n", "which are consistent with the size of the total population of each country as estimated or projected in the 2017 Revision of World Population Prospects (United Nations, 2017). \n", "\n", "Find out about \n", "\n", "- the rural, urban, total population\n", "- gender distribution,\n", "- age distribution \n", "\n", "in various countries of the world in `1980-2015`.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, based on this set of data, I will briefly analyze \n", "\n", "- `China's` urbanization, \n", "- `Aging` and \n", "- `Age distribution`, \n", "- `Gender`\n", "and compare them with major countries in the world.\n", "\n", "Aiming at **illustrate the current situation of China**" ] }, { "cell_type": "code", "execution_count": 280, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import pandas as pd\n", "data=pd.read_excel('/Users/liaoying/Desktop/population.xlsx')" ] }, { "cell_type": "code", "execution_count": 281, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " RowID LocationName LocationID LocationType \\\n", "0 NaN World 900 0 \n", "1 NaN World 900 0 \n", "2 NaN World 900 0 \n", "3 NaN World 900 0 \n", "4 NaN World 900 0 \n", "... ... ... ... ... \n", "12667 NaN Wallis and Futuna Islands 876 4 \n", "12668 NaN Wallis and Futuna Islands 876 4 \n", "12669 NaN Wallis and Futuna Islands 876 4 \n", "12670 NaN Wallis and Futuna Islands 876 4 \n", "12671 NaN Wallis and Futuna Islands 876 4 \n", "\n", " IsSmallCountry ParentID Year Sex AreaType SortOrder ... \\\n", "0 False 0 1980 Female Rural 1 ... \n", "1 False 0 1985 Female Rural 1 ... \n", "2 False 0 1990 Female Rural 1 ... \n", "3 False 0 1995 Female Rural 1 ... \n", "4 False 0 2000 Female Rural 1 ... \n", "... ... ... ... ... ... ... ... \n", "12667 True 957 1995 Male Urban 796 ... \n", "12668 True 957 2000 Male Urban 796 ... \n", "12669 True 957 2005 Male Urban 796 ... \n", "12670 True 957 2010 Male Urban 796 ... \n", "12671 True 957 2015 Male Urban 796 ... \n", "\n", " 35-39 40-44 45-49 50-54 55-59 \\\n", "0 66377.635405 62188.927206 56887.096881 51112.965754 44213.385057 \n", "1 74951.288161 62874.433954 59565.085094 53709.506420 47700.370884 \n", "2 89283.592335 71504.248648 59762.585369 55860.587503 50130.022679 \n", "3 92664.652593 84440.019106 67107.946540 55914.303258 51417.496034 \n", "4 103958.095674 88528.173095 81176.263539 64197.670735 52963.480532 \n", "... ... ... ... ... ... \n", "12667 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12668 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12669 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12670 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12671 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "\n", " 60-64 65-69 70-74 75-79 80+ \n", "0 35633.110873 30612.055005 23004.611925 14860.076507 11662.619964 \n", "1 39864.776614 30542.893972 24542.247680 16585.120771 13947.127653 \n", "2 43536.257464 34926.918619 24880.883230 18102.057136 16440.693725 \n", "3 44701.254487 37295.062166 27806.986623 17676.140259 18208.346547 \n", "4 47842.199869 40109.574009 31249.043782 21157.141465 19624.278688 \n", "... ... ... ... ... ... \n", "12667 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12668 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12669 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12670 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12671 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "\n", "[12672 rows x 28 columns]" ] }, "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 282, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "RowID True\n", "LocationName False\n", "LocationID False\n", "LocationType False\n", "IsSmallCountry False\n", "ParentID False\n", "Year False\n", "Sex False\n", "AreaType False\n", "SortOrder False\n", "Total False\n", "00-04 False\n", "05-09 False\n", "10-14 False\n", "15-19 False\n", "20-24 False\n", "25-29 False\n", "30-34 False\n", "35-39 False\n", "40-44 False\n", "45-49 False\n", "50-54 False\n", "55-59 False\n", "60-64 False\n", "65-69 False\n", "70-74 False\n", "75-79 False\n", "80+ False\n", "dtype: bool" ] }, "execution_count": 282, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().any(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The RowID represents the number of columns,however,it is meaningless in the data,so we have to delect it." ] }, { "cell_type": "code", "execution_count": 283, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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4World9000False02000FemaleRural11.610373e+06...103958.09567488528.17309581176.26353964197.67073552963.48053247842.19986940109.57400931249.04378221157.14146519624.278688
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12672 rows × 27 columns

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" ], "text/plain": [ " LocationName LocationID LocationType IsSmallCountry \\\n", "0 World 900 0 False \n", "1 World 900 0 False \n", "2 World 900 0 False \n", "3 World 900 0 False \n", "4 World 900 0 False \n", "... ... ... ... ... \n", "12667 Wallis and Futuna Islands 876 4 True \n", "12668 Wallis and Futuna Islands 876 4 True \n", "12669 Wallis and Futuna Islands 876 4 True \n", "12670 Wallis and Futuna Islands 876 4 True \n", "12671 Wallis and Futuna Islands 876 4 True \n", "\n", " ParentID Year Sex AreaType SortOrder Total ... \\\n", "0 0 1980 Female Rural 1 1.331308e+06 ... \n", "1 0 1985 Female Rural 1 1.409292e+06 ... \n", "2 0 1990 Female Rural 1 1.496395e+06 ... \n", "3 0 1995 Female Rural 1 1.560483e+06 ... \n", "4 0 2000 Female Rural 1 1.610373e+06 ... \n", "... ... ... ... ... ... ... ... \n", "12667 957 1995 Male Urban 796 0.000000e+00 ... \n", "12668 957 2000 Male Urban 796 0.000000e+00 ... \n", "12669 957 2005 Male Urban 796 0.000000e+00 ... \n", "12670 957 2010 Male Urban 796 0.000000e+00 ... \n", "12671 957 2015 Male Urban 796 0.000000e+00 ... \n", "\n", " 35-39 40-44 45-49 50-54 55-59 \\\n", "0 66377.635405 62188.927206 56887.096881 51112.965754 44213.385057 \n", "1 74951.288161 62874.433954 59565.085094 53709.506420 47700.370884 \n", "2 89283.592335 71504.248648 59762.585369 55860.587503 50130.022679 \n", "3 92664.652593 84440.019106 67107.946540 55914.303258 51417.496034 \n", "4 103958.095674 88528.173095 81176.263539 64197.670735 52963.480532 \n", "... ... ... ... ... ... \n", "12667 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12668 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12669 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12670 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12671 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "\n", " 60-64 65-69 70-74 75-79 80+ \n", "0 35633.110873 30612.055005 23004.611925 14860.076507 11662.619964 \n", "1 39864.776614 30542.893972 24542.247680 16585.120771 13947.127653 \n", "2 43536.257464 34926.918619 24880.883230 18102.057136 16440.693725 \n", "3 44701.254487 37295.062166 27806.986623 17676.140259 18208.346547 \n", "4 47842.199869 40109.574009 31249.043782 21157.141465 19624.278688 \n", "... ... ... ... ... ... \n", "12667 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12668 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12669 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12670 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12671 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "\n", "[12672 rows x 27 columns]" ] }, "execution_count": 283, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas=data.drop('RowID',axis=1)\n", "datas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As what I showed above,the data has 28 columns and 12670 rows,it is a large data.\n", "We can see the basic structure of the data by getting the first 5 rows, which directly matches the population file." ] }, { "cell_type": "code", "execution_count": 284, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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LocationNameLocationIDLocationTypeIsSmallCountryParentIDYearSexAreaTypeSortOrderTotal...35-3940-4445-4950-5455-5960-6465-6970-7475-7980+
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1World9000False01985FemaleRural11.409292e+06...74951.28816162874.43395459565.08509453709.50642047700.37088439864.77661430542.89397224542.24768016585.12077113947.127653
2World9000False01990FemaleRural11.496395e+06...89283.59233571504.24864859762.58536955860.58750350130.02267943536.25746434926.91861924880.88323018102.05713616440.693725
3World9000False01995FemaleRural11.560483e+06...92664.65259384440.01910667107.94654055914.30325851417.49603444701.25448737295.06216627806.98662317676.14025918208.346547
4World9000False02000FemaleRural11.610373e+06...103958.09567488528.17309581176.26353964197.67073552963.48053247842.19986940109.57400931249.04378221157.14146519624.278688
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5 rows × 27 columns

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" ], "text/plain": [ " LocationName LocationID LocationType IsSmallCountry ParentID Year \\\n", "0 World 900 0 False 0 1980 \n", "1 World 900 0 False 0 1985 \n", "2 World 900 0 False 0 1990 \n", "3 World 900 0 False 0 1995 \n", "4 World 900 0 False 0 2000 \n", "\n", " Sex AreaType SortOrder Total ... 35-39 40-44 \\\n", "0 Female Rural 1 1.331308e+06 ... 66377.635405 62188.927206 \n", "1 Female Rural 1 1.409292e+06 ... 74951.288161 62874.433954 \n", "2 Female Rural 1 1.496395e+06 ... 89283.592335 71504.248648 \n", "3 Female Rural 1 1.560483e+06 ... 92664.652593 84440.019106 \n", "4 Female Rural 1 1.610373e+06 ... 103958.095674 88528.173095 \n", "\n", " 45-49 50-54 55-59 60-64 65-69 \\\n", "0 56887.096881 51112.965754 44213.385057 35633.110873 30612.055005 \n", "1 59565.085094 53709.506420 47700.370884 39864.776614 30542.893972 \n", "2 59762.585369 55860.587503 50130.022679 43536.257464 34926.918619 \n", "3 67107.946540 55914.303258 51417.496034 44701.254487 37295.062166 \n", "4 81176.263539 64197.670735 52963.480532 47842.199869 40109.574009 \n", "\n", " 70-74 75-79 80+ \n", "0 23004.611925 14860.076507 11662.619964 \n", "1 24542.247680 16585.120771 13947.127653 \n", "2 24880.883230 18102.057136 16440.693725 \n", "3 27806.986623 17676.140259 18208.346547 \n", "4 31249.043782 21157.141465 19624.278688 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 284, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is more,we can get the last 5 rows to see the basic structure." ] }, { "cell_type": "code", "execution_count": 285, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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LocationNameLocationIDLocationTypeIsSmallCountryParentIDYearSexAreaTypeSortOrderTotal...35-3940-4445-4950-5455-5960-6465-6970-7475-7980+
12667Wallis and Futuna Islands8764True9571995MaleUrban7960.0...0.00.00.00.00.00.00.00.00.00.0
12668Wallis and Futuna Islands8764True9572000MaleUrban7960.0...0.00.00.00.00.00.00.00.00.00.0
12669Wallis and Futuna Islands8764True9572005MaleUrban7960.0...0.00.00.00.00.00.00.00.00.00.0
12670Wallis and Futuna Islands8764True9572010MaleUrban7960.0...0.00.00.00.00.00.00.00.00.00.0
12671Wallis and Futuna Islands8764True9572015MaleUrban7960.0...0.00.00.00.00.00.00.00.00.00.0
\n", "

5 rows × 27 columns

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" ], "text/plain": [ " LocationName LocationID LocationType IsSmallCountry \\\n", "12667 Wallis and Futuna Islands 876 4 True \n", "12668 Wallis and Futuna Islands 876 4 True \n", "12669 Wallis and Futuna Islands 876 4 True \n", "12670 Wallis and Futuna Islands 876 4 True \n", "12671 Wallis and Futuna Islands 876 4 True \n", "\n", " ParentID Year Sex AreaType SortOrder Total ... 35-39 40-44 \\\n", "12667 957 1995 Male Urban 796 0.0 ... 0.0 0.0 \n", "12668 957 2000 Male Urban 796 0.0 ... 0.0 0.0 \n", "12669 957 2005 Male Urban 796 0.0 ... 0.0 0.0 \n", "12670 957 2010 Male Urban 796 0.0 ... 0.0 0.0 \n", "12671 957 2015 Male Urban 796 0.0 ... 0.0 0.0 \n", "\n", " 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ \n", "12667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "12668 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "12669 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "12670 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "12671 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 285, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas.tail()" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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LocationIDLocationTypeParentIDYearSortOrderTotal00-0405-0910-1415-19...35-3940-4445-4950-5455-5960-6465-6970-7475-7980+
count12672.00000012672.00000012672.00000012672.00000012672.0000001.267200e+0412672.00000012672.00000012672.00000012672.000000...12672.00000012672.00000012672.00000012672.00000012672.00000012672.00000012672.00000012672.00000012672.00000012672.000000
mean528.1439393.8712121155.4507581997.500000402.1325763.771441e+043998.0323303808.5578113667.9896313510.189088...2486.0471942231.4436391964.1173721698.4876121436.5683771179.011927929.755681700.795493484.033960467.566883
std517.0500390.4752431031.45584111.456891227.5802341.962581e+0521035.55817720087.64911419348.43795018460.732249...13214.85139711939.41109910514.0431659018.2748657560.8698926186.7931534794.8572203602.5225672516.3363162561.457717
min4.0000000.0000000.0000001980.0000001.0000000.000000e+000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%245.0000004.000000911.0000001988.750000204.2500001.232616e+0212.89291212.29340911.84385111.380352...7.6274876.5379855.4840594.5493673.6807502.8789212.1365001.5401480.9736520.814832
50%497.0000004.000000920.0000001997.500000404.5000001.544067e+03153.453843148.282441146.002000143.094060...92.14409780.41488269.84855459.48051949.71661439.13914330.18580821.67933313.62747610.351877
75%734.0000004.000000925.0000002006.250000596.5000007.893457e+03919.634237852.179000788.860760750.548273...469.323000407.059547359.216254313.611203271.282473224.537825177.078000129.83096087.65306076.001301
max5501.0000005.0000005501.0000002015.000000796.0000003.693185e+06344424.504000328108.756000322750.612000320351.663000...251774.573000242941.972000226662.469000201050.008000170963.664000149031.553000111840.29200082009.05000064028.78000076951.735000
\n", "

8 rows × 23 columns

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" ], "text/plain": [ " LocationID LocationType ParentID Year SortOrder \\\n", "count 12672.000000 12672.000000 12672.000000 12672.000000 12672.000000 \n", "mean 528.143939 3.871212 1155.450758 1997.500000 402.132576 \n", "std 517.050039 0.475243 1031.455841 11.456891 227.580234 \n", "min 4.000000 0.000000 0.000000 1980.000000 1.000000 \n", "25% 245.000000 4.000000 911.000000 1988.750000 204.250000 \n", "50% 497.000000 4.000000 920.000000 1997.500000 404.500000 \n", "75% 734.000000 4.000000 925.000000 2006.250000 596.500000 \n", "max 5501.000000 5.000000 5501.000000 2015.000000 796.000000 \n", "\n", " Total 00-04 05-09 10-14 \\\n", "count 1.267200e+04 12672.000000 12672.000000 12672.000000 \n", "mean 3.771441e+04 3998.032330 3808.557811 3667.989631 \n", "std 1.962581e+05 21035.558177 20087.649114 19348.437950 \n", "min 0.000000e+00 0.000000 0.000000 0.000000 \n", "25% 1.232616e+02 12.892912 12.293409 11.843851 \n", "50% 1.544067e+03 153.453843 148.282441 146.002000 \n", "75% 7.893457e+03 919.634237 852.179000 788.860760 \n", "max 3.693185e+06 344424.504000 328108.756000 322750.612000 \n", "\n", " 15-19 ... 35-39 40-44 45-49 \\\n", "count 12672.000000 ... 12672.000000 12672.000000 12672.000000 \n", "mean 3510.189088 ... 2486.047194 2231.443639 1964.117372 \n", "std 18460.732249 ... 13214.851397 11939.411099 10514.043165 \n", "min 0.000000 ... 0.000000 0.000000 0.000000 \n", "25% 11.380352 ... 7.627487 6.537985 5.484059 \n", "50% 143.094060 ... 92.144097 80.414882 69.848554 \n", "75% 750.548273 ... 469.323000 407.059547 359.216254 \n", "max 320351.663000 ... 251774.573000 242941.972000 226662.469000 \n", "\n", " 50-54 55-59 60-64 65-69 \\\n", "count 12672.000000 12672.000000 12672.000000 12672.000000 \n", "mean 1698.487612 1436.568377 1179.011927 929.755681 \n", "std 9018.274865 7560.869892 6186.793153 4794.857220 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 4.549367 3.680750 2.878921 2.136500 \n", "50% 59.480519 49.716614 39.139143 30.185808 \n", "75% 313.611203 271.282473 224.537825 177.078000 \n", "max 201050.008000 170963.664000 149031.553000 111840.292000 \n", "\n", " 70-74 75-79 80+ \n", "count 12672.000000 12672.000000 12672.000000 \n", "mean 700.795493 484.033960 467.566883 \n", "std 3602.522567 2516.336316 2561.457717 \n", "min 0.000000 0.000000 0.000000 \n", "25% 1.540148 0.973652 0.814832 \n", "50% 21.679333 13.627476 10.351877 \n", "75% 129.830960 87.653060 76.001301 \n", "max 82009.050000 64028.780000 76951.735000 \n", "\n", "[8 rows x 23 columns]" ] }, "execution_count": 286, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas.describe()" ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "LocationName object\n", "LocationID int64\n", "LocationType int64\n", "IsSmallCountry bool\n", "ParentID int64\n", "Year int64\n", "Sex object\n", "AreaType object\n", "SortOrder int64\n", "Total float64\n", "00-04 float64\n", "05-09 float64\n", "10-14 float64\n", "15-19 float64\n", "20-24 float64\n", "25-29 float64\n", "30-34 float64\n", "35-39 float64\n", "40-44 float64\n", "45-49 float64\n", "50-54 float64\n", "55-59 float64\n", "60-64 float64\n", "65-69 float64\n", "70-74 float64\n", "75-79 float64\n", "80+ float64\n", "dtype: object" ] }, "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After knowing the structure, type, first five lines of data and last five lines of data, \n", "\n", "we can start to analyze!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Urbanization\n", "First,I want to look at the population in rural,urban and total ." ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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LocationNameAfghanistanAfricaAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntigua and BarbudaArgentina...Viet NamWallis and Futuna IslandsWestern AfricaWestern AsiaWestern EuropeWestern SaharaWorldYemenZambiaZimbabwe
AreaTypeYear
Rural19805556.7755175343.6480905.73355497.67754.16551.43103063.79750.022.98202406.1250...22165.69155.616052301.123027159.733523373.582517.01151.349755e+063299.83001759.57352829.2260
19854786.9620195472.4090999.04655943.99604.36401.07953510.32350.021.36002269.8270...24798.55106.767057481.001527865.529023145.780014.42701.430276e+063939.03302063.30453304.2665
19904791.2695216531.85201095.62006286.35504.48151.44153845.14400.019.98752123.2625...27476.10156.940062686.489028799.932022960.515515.00901.517893e+064661.20152376.62153714.5410
19957055.8300239800.43151025.63456449.77853.89152.02354304.53500.022.56852061.4270...29576.88857.071569487.353031202.703522983.119016.27151.586880e+065724.90802780.41353972.9915
20008106.1535264767.2485962.71706356.79903.28252.48704705.30250.026.35102003.4650...30586.24107.248576299.800033197.449022752.997024.66201.635785e+066459.96253292.79404141.3335
\n", "

5 rows × 264 columns

\n", "
" ], "text/plain": [ "LocationName Afghanistan Africa Albania Algeria American Samoa \\\n", "AreaType Year \n", "Rural 1980 5556.7755 175343.6480 905.7335 5497.6775 4.1655 \n", " 1985 4786.9620 195472.4090 999.0465 5943.9960 4.3640 \n", " 1990 4791.2695 216531.8520 1095.6200 6286.3550 4.4815 \n", " 1995 7055.8300 239800.4315 1025.6345 6449.7785 3.8915 \n", " 2000 8106.1535 264767.2485 962.7170 6356.7990 3.2825 \n", "\n", "LocationName Andorra Angola Anguilla Antigua and Barbuda Argentina \\\n", "AreaType Year \n", "Rural 1980 1.4310 3063.7975 0.0 22.9820 2406.1250 \n", " 1985 1.0795 3510.3235 0.0 21.3600 2269.8270 \n", " 1990 1.4415 3845.1440 0.0 19.9875 2123.2625 \n", " 1995 2.0235 4304.5350 0.0 22.5685 2061.4270 \n", " 2000 2.4870 4705.3025 0.0 26.3510 2003.4650 \n", "\n", "LocationName ... Viet Nam Wallis and Futuna Islands Western Africa \\\n", "AreaType Year ... \n", "Rural 1980 ... 22165.6915 5.6160 52301.1230 \n", " 1985 ... 24798.5510 6.7670 57481.0015 \n", " 1990 ... 27476.1015 6.9400 62686.4890 \n", " 1995 ... 29576.8885 7.0715 69487.3530 \n", " 2000 ... 30586.2410 7.2485 76299.8000 \n", "\n", "LocationName Western Asia Western Europe Western Sahara World \\\n", "AreaType Year \n", "Rural 1980 27159.7335 23373.5825 17.0115 1.349755e+06 \n", " 1985 27865.5290 23145.7800 14.4270 1.430276e+06 \n", " 1990 28799.9320 22960.5155 15.0090 1.517893e+06 \n", " 1995 31202.7035 22983.1190 16.2715 1.586880e+06 \n", " 2000 33197.4490 22752.9970 24.6620 1.635785e+06 \n", "\n", "LocationName Yemen Zambia Zimbabwe \n", "AreaType Year \n", "Rural 1980 3299.8300 1759.5735 2829.2260 \n", " 1985 3939.0330 2063.3045 3304.2665 \n", " 1990 4661.2015 2376.6215 3714.5410 \n", " 1995 5724.9080 2780.4135 3972.9915 \n", " 2000 6459.9625 3292.7940 4141.3335 \n", "\n", "[5 rows x 264 columns]" ] }, "execution_count": 288, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num = (\n", " datas\n", " .reset_index()\n", " .pivot_table(index=[\"AreaType\",\"Year\"], columns=\"LocationName\", values=\"Total\")\n", ")\n", "num.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "China has been promoting urbanization.\n", "To see the rural and urban population distribution and the proportion of urban population to total population in China." ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "AreaType Year\n", "Rural 1980 396766.2860\n", " 1985 409655.9600\n", " 1990 428631.0385\n", " 1995 427187.7220\n", " 2000 410522.7375\n", " 2005 378829.5530\n", " 2010 345217.6200\n", " 2015 311053.9925\n", "Total 1980 492007.8385\n", " 1985 531149.6085\n", " 1990 582714.4835\n", " 1995 618765.7145\n", " 2000 640214.2915\n", " 2005 659088.4175\n", " 2010 679910.7325\n", " 2015 700793.3045\n", "Urban 1980 95241.5525\n", " 1985 121493.6485\n", " 1990 154083.4450\n", " 1995 191577.9925\n", " 2000 229691.5540\n", " 2005 280258.8645\n", " 2010 334693.1125\n", " 2015 389739.3120\n", "Name: China, dtype: float64" ] }, "execution_count": 289, "metadata": {}, "output_type": "execute_result" } ], "source": [ "China=num.loc[:,\"China\"]\n", "China" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5, 1, 'Proportion of urban and rural population in China in 1980 ')" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "labels='Urban','Rural'\n", "sizes=China.loc[\"Urban\",1980],China.loc[\"Rural\",1980]\n", "colors='lightgreen','gold'\n", "explode=0,0\n", "plt.pie(sizes,explode=explode,labels=labels,\n", " colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)\n", "plt.axis('equal')\n", "plt.show()\n", "ax.set_title(\"Proportion of urban and rural population in China in 1980 \")" ] }, { "cell_type": "code", "execution_count": 291, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5, 1, 'Proportion of urban and rural population in China in 2015 ')" ] }, "execution_count": 291, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels='Urban','Rural'\n", "sizes=China.loc[\"Urban\",2015],China.loc[\"Rural\",2015]\n", "colors='lightskyblue','lightcoral'\n", "explode=0,0\n", "plt.pie(sizes,explode=explode,labels=labels,\n", " colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)\n", "plt.axis('equal')\n", "plt.show()\n", "ax.set_title(\"Proportion of urban and rural population in China in 2015 \")" ] }, { "cell_type": "code", "execution_count": 341, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The proportion of urban population to total population in 2015 is 0.5561401764220195\n", "The proportion of urban population to total population in 1980 is 0.1935773072038162\n", "The difference between 1980 and 2005 is 36.25628692182033%\n" ] } ], "source": [ "a=China.loc[\"Urban\",2015]/China.loc[\"Total\",2015]\n", "print(\"The proportion of urban population to total population in 2015 is \",a)\n", "b=China.loc[\"Urban\",1980]/China.loc[\"Total\",1980]\n", "print(\"The proportion of urban population to total population in 1980 is \",b)\n", "c=a-b\n", "print(f\"The difference between 1980 and 2005 is {c*100}%\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compared to America,which is the richest country all over the world." ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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LocationNameCentral AmericaSouth AmericaNorthern AmericaUnited States of AmericaLatin America and the Caribbean
AreaTypeYear
Rural198018530.909039269.475033213.357530224.456064977.5765
198519370.929039060.810033906.818530845.116565599.5530
199020112.765538345.151034675.487031430.976065663.3945
199520965.082537279.440033755.678030479.955065628.0425
200021725.935035830.132532956.331029800.904065000.9745
200522034.457035669.668033134.063029923.263064871.1920
201022286.372535226.790033276.199030018.805064274.9455
201522540.013534721.071033146.829029883.322063648.0315
Total198046691.8210120508.8190127399.8295115088.1805182075.2085
198551913.4340134275.0815133915.2935120935.0010202164.5565
199057552.9600147917.5365141143.0585127253.3235222601.3250
199563909.0385161026.0190148729.0350134019.8270243172.6185
200069798.0255174123.2245157708.5510142297.1975263139.1140
200574897.6530186293.8495165273.0585149082.8985281273.1445
201080272.9980197010.3485173250.4500156123.5580298095.7225
201585967.1155207526.6345180563.9095162563.8170315044.4585
Urban198028160.912081239.344094186.472084863.7245117097.6320
198532542.505095214.2715100008.475090089.8845136565.0035
199037440.1945109572.3855106467.571595822.3475156937.9305
199542943.9560123746.5790114973.3570103539.8720177544.5760
200048072.0905138293.0920124752.2200112496.2935198138.1395
200552863.1960150624.1815132138.9955119159.6355216401.9525
201057986.6255161783.5585139974.2510126104.7530233820.7770
201563427.1020172805.5635147417.0805132680.4950251396.4270
\n", "
" ], "text/plain": [ "LocationName Central America South America Northern America \\\n", "AreaType Year \n", "Rural 1980 18530.9090 39269.4750 33213.3575 \n", " 1985 19370.9290 39060.8100 33906.8185 \n", " 1990 20112.7655 38345.1510 34675.4870 \n", " 1995 20965.0825 37279.4400 33755.6780 \n", " 2000 21725.9350 35830.1325 32956.3310 \n", " 2005 22034.4570 35669.6680 33134.0630 \n", " 2010 22286.3725 35226.7900 33276.1990 \n", " 2015 22540.0135 34721.0710 33146.8290 \n", "Total 1980 46691.8210 120508.8190 127399.8295 \n", " 1985 51913.4340 134275.0815 133915.2935 \n", " 1990 57552.9600 147917.5365 141143.0585 \n", " 1995 63909.0385 161026.0190 148729.0350 \n", " 2000 69798.0255 174123.2245 157708.5510 \n", " 2005 74897.6530 186293.8495 165273.0585 \n", " 2010 80272.9980 197010.3485 173250.4500 \n", " 2015 85967.1155 207526.6345 180563.9095 \n", "Urban 1980 28160.9120 81239.3440 94186.4720 \n", " 1985 32542.5050 95214.2715 100008.4750 \n", " 1990 37440.1945 109572.3855 106467.5715 \n", " 1995 42943.9560 123746.5790 114973.3570 \n", " 2000 48072.0905 138293.0920 124752.2200 \n", " 2005 52863.1960 150624.1815 132138.9955 \n", " 2010 57986.6255 161783.5585 139974.2510 \n", " 2015 63427.1020 172805.5635 147417.0805 \n", "\n", "LocationName United States of America Latin America and the Caribbean \n", "AreaType Year \n", "Rural 1980 30224.4560 64977.5765 \n", " 1985 30845.1165 65599.5530 \n", " 1990 31430.9760 65663.3945 \n", " 1995 30479.9550 65628.0425 \n", " 2000 29800.9040 65000.9745 \n", " 2005 29923.2630 64871.1920 \n", " 2010 30018.8050 64274.9455 \n", " 2015 29883.3220 63648.0315 \n", "Total 1980 115088.1805 182075.2085 \n", " 1985 120935.0010 202164.5565 \n", " 1990 127253.3235 222601.3250 \n", " 1995 134019.8270 243172.6185 \n", " 2000 142297.1975 263139.1140 \n", " 2005 149082.8985 281273.1445 \n", " 2010 156123.5580 298095.7225 \n", " 2015 162563.8170 315044.4585 \n", "Urban 1980 84863.7245 117097.6320 \n", " 1985 90089.8845 136565.0035 \n", " 1990 95822.3475 156937.9305 \n", " 1995 103539.8720 177544.5760 \n", " 2000 112496.2935 198138.1395 \n", " 2005 119159.6355 216401.9525 \n", " 2010 126104.7530 233820.7770 \n", " 2015 132680.4950 251396.4270 " ] }, "execution_count": 293, "metadata": {}, "output_type": "execute_result" } ], "source": [ "America = [\n", " \"Central America\",\"South America\",\"Northern America\",\n", " \"United States of America\", \"Latin America and the Caribbean\"\n", "]\n", "Ame = num[America]\n", "Ame" ] }, { "cell_type": "code", "execution_count": 294, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "0.8067186601567283" ] }, "execution_count": 294, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d=Ame.loc[\"Urban\",2015].mean()/Ame.loc[\"Total\",2015].mean()\n", "d" ] }, { "cell_type": "code", "execution_count": 295, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.2505784837347088" ] }, "execution_count": 295, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d-a" ] }, { "cell_type": "code", "execution_count": 296, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6), dpi=80)\n", "plt.subplot(1, 1, 1)\n", "N = 2\n", "values = (a,d)\n", "index = np.arange(N)\n", "width = 0.2\n", "p2 = plt.bar(index, values, width, label=\"num\", color=\"#87CEFA\")\n", "plt.xlabel('Country')\n", "plt.ylabel('Urbanization Rate')\n", "plt.title('Urbanization rate of China and America in 2015 ')\n", "plt.xticks(index, ('China', 'America'))\n", "plt.legend(loc=\"upper right\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the proportion of urban population to total population of American is about `25.1%` higher than \n", "the proportion of urban population to total population of China in 2015.\n", "\n", "- **Conclusion:**\n", "\n", "we should **work harder to promote urbanization**.\n", "Aiming at create more beautiful lives to Chinese people." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is more,many developing countries also vigorously develop urbanization，\n", "\n", "take `Brazil` and `Russia` for example." ] }, { "cell_type": "code", "execution_count": 302, "metadata": {}, "outputs": [], "source": [ "def urbanization(x,y):\n", " a=num.loc[\"Urban\",x]/num.loc[\"Total\",x]\n", " b=num.loc[\"Urban\",y]/num.loc[\"Total\",y]\n", " c=a-b\n", " return c" ] }, { "cell_type": "code", "execution_count": 307, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Year\n", "1980 0.461098\n", "1985 0.469887\n", "1990 0.474796\n", "1995 0.466486\n", "2000 0.453147\n", "2005 0.403121\n", "2010 0.351094\n", "2015 0.300732\n", "dtype: float64\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 307, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x=urbanization(\"Brazil\",\"China\")\n", "print(x)\n", "x.plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 305, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Year\n", "1980 0.503938\n", "1985 0.490495\n", "1990 0.469512\n", "1995 0.424105\n", "2000 0.374726\n", "2005 0.309406\n", "2010 0.244611\n", "2015 0.183937\n", "dtype: float64\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 305, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y=urbanization(\"Russian Federation\",\"China\")\n", "print(y)\n", "y.plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even compared with developing countries, China's urbanization is **insufficient**.\n", "\n", "However,the difference is **decreasing**.\n", "\n", "Totally,we can see the difference by the picture." ] }, { "cell_type": "code", "execution_count": 308, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 308, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "countries=[\"China\",\"Russian Federation\",\"Brazil\"]\n", "u_c=num.loc[\"Urban\",countries]\n", "t_c=num.loc[\"Total\",countries]\n", "countries_urbanization=u_c/t_c\n", "countries_urbanization.plot(figsize=(8,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- So, **what is the overall situation of urbanization in the world？**" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of countries with low urbanizationt is 35\n", "The number of countries with median urbanizationt is 126\n", "The number of countries with high urbanizationt is 103\n" ] } ], "source": [ "worldu=num.loc[(\"Urban\",2015),:]/num.loc[(\"Total\",2015),:]\n", "def urbanization(x):\n", " if x>0.7:\n", " out=\"High\"\n", " elif 0.3<=x<=0.7:\n", " out=\"Median\"\n", " else: \n", " out=\"Low\"\n", " return out\n", "worldclass=worldu.agg(urbanization)\n", "def count(lst,x):\n", " count=0\n", " for ele in lst:\n", " if(ele==x):\n", " count=count+1\n", " return count\n", "\n", "print(\"The number of countries with low urbanizationt is\",count(worldclass,\"Low\"))\n", "print(\"The number of countries with median urbanizationt is\",count(worldclass,\"Median\"))\n", "print(\"The number of countries with high urbanizationt is\",count(worldclass,\"High\"))\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "name=[\"Low\",\"Median\",\"High\"]\n", "number=[count(worldclass,\"Low\"),count(worldclass,\"Median\"),count(worldclass,\"High\")]\n", "fig, ax = plt.subplots()\n", "ax.bar(name,number,align=\"center\")\n", "ax.set_title(\"World urbanization rate\")\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above all,there are `more` countries with high and medium urbanization rate, and `less` countries with low urbanization rate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ageing of population\n", "Another problem of the international community is the aging of the population。\n", "\n", "The population aged over 60 in a country or region accounts for `10%` of the total population, \n", "\n", "which is the standard of population aging" ] }, { "cell_type": "code", "execution_count": 309, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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oldtotal
AreaTypeRuralTotalUrban
LocationNameYear
Afghanistan19800.0380500.0374570.034267
19850.0342100.0336110.030675
19900.0314030.0308310.028276
19950.0350180.0343330.031549
20000.0344410.0337060.030985
...............
Zimbabwe19950.0570300.0475170.026753
20000.0613020.0501770.028015
20050.0670160.0546260.030316
20100.0687690.0561640.030402
20150.0695030.0570300.030490
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2112 rows × 3 columns

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" ], "text/plain": [ " oldtotal \n", "AreaType Rural Total Urban\n", "LocationName Year \n", "Afghanistan 1980 0.038050 0.037457 0.034267\n", " 1985 0.034210 0.033611 0.030675\n", " 1990 0.031403 0.030831 0.028276\n", " 1995 0.035018 0.034333 0.031549\n", " 2000 0.034441 0.033706 0.030985\n", "... ... ... ...\n", "Zimbabwe 1995 0.057030 0.047517 0.026753\n", " 2000 0.061302 0.050177 0.028015\n", " 2005 0.067016 0.054626 0.030316\n", " 2010 0.068769 0.056164 0.030402\n", " 2015 0.069503 0.057030 0.030490\n", "\n", "[2112 rows x 3 columns]" ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas[\"old\"]=datas[\"60-64\"]+datas[\"65-69\"]+datas[\"70-74\"]+datas[\"75-79\"]+datas[\"80+\"]\n", "datas[\"oldtotal\"]=datas[\"old\"]/datas[\"Total\"]\n", "aging=datas.pivot_table(\n", " index=[\"LocationName\",\"Year\"],\n", " columns=[\"AreaType\"],\n", " values=['oldtotal']\n", ")\n", "aging" ] }, { "cell_type": "code", "execution_count": 310, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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oldtotal
AreaTypeRuralTotalUrban
LocationNameYear
Albania20050.1155620.1236030.132790
20100.1351690.1419150.148121
20150.1553780.1625820.167947
Andorra19850.1371830.1192520.118340
19900.1961060.1386120.135385
...............
Western Europe20050.2381950.2265620.222911
20100.2539110.2420920.238644
20150.2709550.2596750.256604
World20100.1019890.1106730.118771
20150.1119680.1223250.131114
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652 rows × 3 columns

\n", "
" ], "text/plain": [ " oldtotal \n", "AreaType Rural Total Urban\n", "LocationName Year \n", "Albania 2005 0.115562 0.123603 0.132790\n", " 2010 0.135169 0.141915 0.148121\n", " 2015 0.155378 0.162582 0.167947\n", "Andorra 1985 0.137183 0.119252 0.118340\n", " 1990 0.196106 0.138612 0.135385\n", "... ... ... ...\n", "Western Europe 2005 0.238195 0.226562 0.222911\n", " 2010 0.253911 0.242092 0.238644\n", " 2015 0.270955 0.259675 0.256604\n", "World 2010 0.101989 0.110673 0.118771\n", " 2015 0.111968 0.122325 0.131114\n", "\n", "[652 rows x 3 columns]" ] }, "execution_count": 310, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aging1=aging>0.1\n", "aging1.all(axis=1)\n", "aging2=aging[aging1.all(axis=1)]\n", "aging2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this picture, we know which country has a serious aging population in which year.\n", "\n", "What is the current situation of China's aging population" ] }, { "cell_type": "code", "execution_count": 311, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
oldtotal
AreaTypeRuralTotalUrban
Year
20100.1391950.1244750.109291
20150.1668200.1493900.135478
\n", "
" ], "text/plain": [ " oldtotal \n", "AreaType Rural Total Urban\n", "Year \n", "2010 0.139195 0.124475 0.109291\n", "2015 0.166820 0.149390 0.135478" ] }, "execution_count": 311, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chinaage=aging2.loc[\"China\"]\n", "chinaage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since `2010` , China has entered an aging society, so a series of policies such as the two child policy are meaningful." ] }, { "cell_type": "code", "execution_count": 312, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ " AreaType\n", "oldtotal Rural 2015\n", " Total 2015\n", " Urban 2015\n", "dtype: int64" ] }, "execution_count": 312, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chinaage.idxmax()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- A graphic illustration of China's aging from 2010 to 2015" ] }, { "cell_type": "code", "execution_count": 313, "metadata": {}, "outputs": [], "source": [ "import pyecharts.options as opts\n", "from pyecharts.charts import Line" ] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [], "source": [ "def ageing_trend(legion, area, time, data):\n", " \n", " time_start = time[0]\n", " time_end = time[-1]\n", " \n", " x = [str(i) for i in list(range(int(time_start), int(time_end)+5, 5))]\n", " \n", " y = []\n", " hash_area = ['Rural', 'Total', 'Urban']\n", " hash_time = [str(i) for i in list(range(1980, 2015+5, 5))]\n", " \n", " for l in legion:\n", " for a in area:\n", " y_la = [round(data.loc[l].values.tolist()[i][hash_area.index(a)], 4) \n", " for i in range(hash_time.index(time_start), hash_time.index(time_end)+1)]\n", " y.append(y_la)\n", " \n", " c = (Line()\n", " .set_global_opts(\n", " tooltip_opts = opts.TooltipOpts(\n", " is_show = True, \n", " trigger = 'axis',\n", " ),\n", " xaxis_opts = opts.AxisOpts(type_ = 'category'),\n", " yaxis_opts = opts.AxisOpts(type_ = 'value')\n", " )\n", " .add_xaxis(xaxis_data = x))\n", " \n", " for i, y_i in enumerate(y):\n", " (c\n", " .add_yaxis(\n", " series_name = legion[i // len(area)] + '-' + area[i % len(area)],\n", " y_axis = y_i,\n", " label_opts = opts.LabelOpts(is_show = False)\n", " ))\n", " \n", " return c\n", " " ] }, { "cell_type": "code", "execution_count": 315, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 315, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chinad= ageing_trend(['China'], ['Urban', 'Rural','Total'], ['2010','2015'], aging)\n", "chinad.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure shows that the aging in Chinese urban,rural and total in 2015 is more **serious** than that in 2010.\n", "\n", "And rural areas are more serious than urban areas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we all know,` Japan's` aging population is very serious." ] }, { "cell_type": "code", "execution_count": 316, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
oldtotal
AreaTypeRuralTotalUrban
Year
19800.1646740.1279240.116417
19850.1861340.1459380.133720
19900.2203140.1735430.159822
19950.2510160.2028470.189255
20000.2774780.2323080.220025
20050.3016410.2644100.258330
20100.3481040.3062130.301826
20150.3759790.3308750.327737
\n", "
" ], "text/plain": [ " oldtotal \n", "AreaType Rural Total Urban\n", "Year \n", "1980 0.164674 0.127924 0.116417\n", "1985 0.186134 0.145938 0.133720\n", "1990 0.220314 0.173543 0.159822\n", "1995 0.251016 0.202847 0.189255\n", "2000 0.277478 0.232308 0.220025\n", "2005 0.301641 0.264410 0.258330\n", "2010 0.348104 0.306213 0.301826\n", "2015 0.375979 0.330875 0.327737" ] }, "execution_count": 316, "metadata": {}, "output_type": "execute_result" } ], "source": [ "janage=aging2.loc[\"Japan\"]\n", "janage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown in the figure above, Japan has entered an aging society since 1980" ] }, { "cell_type": "code", "execution_count": 317, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 317, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = ageing_trend(['Japan','China'], ['Urban', 'Rural'], ['1985', '2010'], aging)\n", "c.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show by picture,China's aging level is significantly lower than Japan's." ] }, { "cell_type": "code", "execution_count": 318, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
oldtotal
AreaTypeRuralTotalUrban
LocationNameYear
More developed regions19800.1725300.1542110.146441
19850.1835250.1638040.155894
19900.1994710.1757490.166698
19950.2080040.1831720.174137
20000.2194070.1938830.185013
20050.2223780.2006940.193771
20100.2348350.2174690.212317
20150.2524100.2358760.231286
\n", "
" ], "text/plain": [ " oldtotal \n", "AreaType Rural Total Urban\n", "LocationName Year \n", "More developed regions 1980 0.172530 0.154211 0.146441\n", " 1985 0.183525 0.163804 0.155894\n", " 1990 0.199471 0.175749 0.166698\n", " 1995 0.208004 0.183172 0.174137\n", " 2000 0.219407 0.193883 0.185013\n", " 2005 0.222378 0.200694 0.193771\n", " 2010 0.234835 0.217469 0.212317\n", " 2015 0.252410 0.235876 0.231286" ] }, "execution_count": 318, "metadata": {}, "output_type": "execute_result" } ], "source": [ "country=[\"More developed regions\",\n", " \"Less developed regions\",\n", " \"Least developed countries\"\n", " ]\n", "answerone=aging2.loc[country]\n", "answerone\n", "\n" ] }, { "cell_type": "code", "execution_count": 319, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 319, "metadata": {}, "output_type": "execute_result" } ], "source": [ "world= ageing_trend(['More developed regions', 'Less developed regions','Least developed countries'], ['Urban', 'Rural'], ['1985', '2010'], aging)\n", "world.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **Conclusion:**\n", "\n", "The more `developed` regions, the more `serious` the problem of aging." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Age distribution" ] }, { "cell_type": "code", "execution_count": 320, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
00-0405-0910-1415-1920-2425-2930-3440-4445-4950-5455-5960-6465-6970-7475-7980+Total
LocationNameAreaTypeYear
AfghanistanRural19802134.6701521674.1617591379.3293221142.958692952.153897788.494444699.302280456.024494370.688023299.547044235.868330174.212760119.30545273.49044537.35287317.80809411113.551
19851888.7805591494.1268351211.516285992.624719818.826639689.027515581.596084376.098685286.279450218.336788175.824602139.58956393.38076154.69722227.35532911.5253149573.924
19901898.3496401512.2538231247.4274651009.086829820.870240668.665143566.148344424.401314292.592115208.771959149.153328117.22694090.84249154.06323325.84476411.6845099582.539
19952831.4821382178.2444711778.2248861477.2828741214.872262991.876815809.210440558.830013486.170485355.269990263.592437191.204216137.97468891.01397747.56813025.22024814111.660
20003394.7435782579.1931452035.2605981649.0328151351.7639751107.274803911.526849621.211158510.714592435.324771307.932125219.608650149.48454399.23444757.14439131.78531916212.307
............................................................
ZimbabweUrban1995525.906392425.870791396.766722417.556199457.046560397.108653312.367921179.320236109.25505182.77899957.16127442.91022224.81005718.0279407.6637345.6695823693.381
2000558.334132456.118889441.337794513.118486581.256762473.902028337.852853191.458791145.42226385.57212668.98308250.58539228.74129622.7441058.6424007.8237654220.985
2005570.734520455.511208444.729567530.767011651.547967533.229824340.827977162.948544129.541270101.24180164.67310356.50176731.51783024.56193910.1910898.8937014335.602
2010588.131412447.702109424.216728507.028396642.936672588.761577389.690159137.203176102.86261785.55108274.73427351.11751134.29958626.08025610.5828699.9870004341.045
2015609.148657469.454738419.403991501.195050681.884339688.309343547.593104197.950250114.20842779.92584469.33740763.42318032.77252629.56264411.62321611.0781554871.253
\n", "

6336 rows × 17 columns

\n", "
" ], "text/plain": [ " 00-04 05-09 10-14 \\\n", "LocationName AreaType Year \n", "Afghanistan Rural 1980 2134.670152 1674.161759 1379.329322 \n", " 1985 1888.780559 1494.126835 1211.516285 \n", " 1990 1898.349640 1512.253823 1247.427465 \n", " 1995 2831.482138 2178.244471 1778.224886 \n", " 2000 3394.743578 2579.193145 2035.260598 \n", "... ... ... ... \n", "Zimbabwe Urban 1995 525.906392 425.870791 396.766722 \n", " 2000 558.334132 456.118889 441.337794 \n", " 2005 570.734520 455.511208 444.729567 \n", " 2010 588.131412 447.702109 424.216728 \n", " 2015 609.148657 469.454738 419.403991 \n", "\n", " 15-19 20-24 25-29 30-34 \\\n", "LocationName AreaType Year \n", "Afghanistan Rural 1980 1142.958692 952.153897 788.494444 699.302280 \n", " 1985 992.624719 818.826639 689.027515 581.596084 \n", " 1990 1009.086829 820.870240 668.665143 566.148344 \n", " 1995 1477.282874 1214.872262 991.876815 809.210440 \n", " 2000 1649.032815 1351.763975 1107.274803 911.526849 \n", "... ... ... ... ... \n", "Zimbabwe Urban 1995 417.556199 457.046560 397.108653 312.367921 \n", " 2000 513.118486 581.256762 473.902028 337.852853 \n", " 2005 530.767011 651.547967 533.229824 340.827977 \n", " 2010 507.028396 642.936672 588.761577 389.690159 \n", " 2015 501.195050 681.884339 688.309343 547.593104 \n", "\n", " 40-44 45-49 50-54 55-59 \\\n", "LocationName AreaType Year \n", "Afghanistan Rural 1980 456.024494 370.688023 299.547044 235.868330 \n", " 1985 376.098685 286.279450 218.336788 175.824602 \n", " 1990 424.401314 292.592115 208.771959 149.153328 \n", " 1995 558.830013 486.170485 355.269990 263.592437 \n", " 2000 621.211158 510.714592 435.324771 307.932125 \n", "... ... ... ... ... \n", "Zimbabwe Urban 1995 179.320236 109.255051 82.778999 57.161274 \n", " 2000 191.458791 145.422263 85.572126 68.983082 \n", " 2005 162.948544 129.541270 101.241801 64.673103 \n", " 2010 137.203176 102.862617 85.551082 74.734273 \n", " 2015 197.950250 114.208427 79.925844 69.337407 \n", "\n", " 60-64 65-69 70-74 75-79 \\\n", "LocationName AreaType Year \n", "Afghanistan Rural 1980 174.212760 119.305452 73.490445 37.352873 \n", " 1985 139.589563 93.380761 54.697222 27.355329 \n", " 1990 117.226940 90.842491 54.063233 25.844764 \n", " 1995 191.204216 137.974688 91.013977 47.568130 \n", " 2000 219.608650 149.484543 99.234447 57.144391 \n", "... ... ... ... ... \n", "Zimbabwe Urban 1995 42.910222 24.810057 18.027940 7.663734 \n", " 2000 50.585392 28.741296 22.744105 8.642400 \n", " 2005 56.501767 31.517830 24.561939 10.191089 \n", " 2010 51.117511 34.299586 26.080256 10.582869 \n", " 2015 63.423180 32.772526 29.562644 11.623216 \n", "\n", " 80+ Total \n", "LocationName AreaType Year \n", "Afghanistan Rural 1980 17.808094 11113.551 \n", " 1985 11.525314 9573.924 \n", " 1990 11.684509 9582.539 \n", " 1995 25.220248 14111.660 \n", " 2000 31.785319 16212.307 \n", "... ... ... \n", "Zimbabwe Urban 1995 5.669582 3693.381 \n", " 2000 7.823765 4220.985 \n", " 2005 8.893701 4335.602 \n", " 2010 9.987000 4341.045 \n", " 2015 11.078155 4871.253 \n", "\n", "[6336 rows x 17 columns]" ] }, "execution_count": 320, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aging_dist = datas.pivot_table(\n", " index = ['LocationName', 'AreaType', 'Year'],\n", " values = ['Total', '00-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '40-44', \n", " '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80+'],\n", " aggfunc = sum\n", ")\n", "aging_dist" ] }, { "cell_type": "code", "execution_count": 321, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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00-0405-0910-1415-1920-2425-2930-3440-4445-4950-5455-5960-6465-6970-7475-7980+Total
AreaTypeYear
Rural198083282.677002105203.656999106257.04163187683.84476165170.69566568488.24939150234.81096536028.27494433929.84429229526.61495325299.96391322315.79501616985.37864211982.7254667409.4287874599.764432793532.572
198586860.46124078810.04131499349.59181798897.07203079612.37384663687.14617965303.89568236947.93385634469.42876432173.46555227364.52325122381.80123018666.85318113080.0457538183.7831456008.900285819311.920
1990108383.93410184066.56978775930.07160992266.40267291198.05025972899.89408756934.35912844561.85798734257.76768231234.96620029149.43443924437.60004719341.06503815069.6740219307.3348807210.062984857262.077
199581972.516869102985.50924679476.12172569888.04883484206.15148283852.41708768644.22734456046.69583340812.78217631310.19420327466.02836824404.10755319400.18144914079.9759449737.9688017486.140632854375.444
200057238.01090277611.09705497566.27827764115.72169155267.08749371561.51551576221.78177449745.41265653962.23556439391.01766529938.97315326157.82980922552.44778616639.50899110807.4258559296.357012821045.475
200550497.20525554431.49578472609.13287072971.05131954044.37230850044.22822564699.60267556067.72608546501.02889248029.59979234553.81580725842.33974322583.94483417934.09820011772.40623710112.231432757659.106
201051217.75952045163.48479047304.68930351386.72472260657.01675946296.29116540930.00970460366.05707751323.35958641272.41269144157.33684031294.23936522505.36140017678.82882512907.84300111551.583783690435.240
201547757.14562943009.11071538906.34320033955.73912242254.22871050667.38952439271.86316450915.43022952242.94427344767.64134737463.74900337754.25045325420.24757416570.25068312183.21531411692.576879622107.985
Total198098790.309000123126.870000126427.152000107938.65600086492.06100088737.97200064293.04500047247.22400044487.42600037856.53600031387.00700027303.53300020614.08200014578.4060009044.7310005709.592000984015.677
1985108906.01300097333.901000122457.909000125884.252000107271.80200085828.61900087987.40200049271.38900046289.34600043120.16000036051.89100028989.85800024043.50900016834.08700010574.8860007838.1580001062299.217
1990136834.851000107720.93200096908.899000121984.088000125158.824000106503.05200085142.49600062786.28700048340.80300044962.82500041189.89900033438.37500025667.56800019718.73900012265.3310009668.7040001165428.967
1995109707.576000135717.236000107329.93400096502.955000121206.101000124204.245000105623.39300086048.79200061658.43200047035.21600043060.52700038355.38500029739.25000021155.35700014437.45300011428.9060001237531.429
200083372.183000109038.689000135323.217000106946.99100095921.855000120353.899000123272.91400083350.91600084620.72400060121.37600045174.57200040256.69400034277.51400024613.81200015589.30900013484.1730001280428.583
200578477.37300083055.222000108795.693000134743.841000106031.43200094936.462000119245.181000103575.34100082106.86600082882.75000058208.41500042815.37000036713.62800029195.38100018961.02900016261.6070001318176.835
201085578.98600078235.08600082893.073000108370.459000133873.687000105153.61200094140.898000121039.748000102224.67900080577.30100080467.26300055392.41000039275.24600031531.58800022758.08000019980.3140001359821.465
201591238.08500085290.70600078080.77700082604.506000107827.031000133206.619000104616.291000117516.305000119760.437000100504.34800078300.25200076554.74200050808.61500033870.27300024610.10000023168.3050001401586.609
Urban198015507.63199817923.21300120170.11036920254.81123921321.36533520249.72260914058.23403511218.94905610557.5817088329.9210476087.0430874987.7379843628.7033582595.6805341635.3022131109.827568190483.105
198522045.55176018523.85968623108.31718326987.17997027659.42815422141.47282122683.50631812323.45514411819.91723610946.6944488687.3677496608.0567705376.6558193754.0412472391.1028551829.257715242987.297
199028450.91689923654.36221320978.82739129717.68532833960.77374133603.15791328208.13687218224.42901314083.03531813727.85880012040.4645619000.7749536326.5029624649.0649792957.9961202458.641016308166.890
199527735.05913132731.72675427853.81227526614.90616636999.94951840351.82791336979.16565630002.09616720845.64982415725.02179715594.49863213951.27744710339.0685517075.3810564699.4841993942.765368383155.985
200026134.17209831427.59194637756.93872342831.26930940654.76750748792.38348547051.13222633605.50334430658.48843620730.35833515235.59884714098.86419111725.0662147974.3030094781.8831454187.815988459383.108
200527980.16774528623.72621636186.56013061772.78968151987.05969244892.23377554545.57832547507.61491535605.83710834853.15020823654.59919316973.03025714129.68316611261.2828007188.6227636149.375568560517.729
201034361.22648033071.60121035588.38369756983.73427873216.67024158857.32083553210.88829660673.69092350901.31941439304.88830936309.92616024098.17063516769.88460013852.7591759850.2369998428.730217669386.225
201543480.93937142281.59528539174.43380048648.76687865572.80229082539.22947665344.42783666600.87477167517.49272755736.70665340836.50299738800.49154725388.36742617300.02231712426.88468611475.728121779478.624
\n", "
" ], "text/plain": [ " 00-04 05-09 10-14 15-19 \\\n", "AreaType Year \n", "Rural 1980 83282.677002 105203.656999 106257.041631 87683.844761 \n", " 1985 86860.461240 78810.041314 99349.591817 98897.072030 \n", " 1990 108383.934101 84066.569787 75930.071609 92266.402672 \n", " 1995 81972.516869 102985.509246 79476.121725 69888.048834 \n", " 2000 57238.010902 77611.097054 97566.278277 64115.721691 \n", " 2005 50497.205255 54431.495784 72609.132870 72971.051319 \n", " 2010 51217.759520 45163.484790 47304.689303 51386.724722 \n", " 2015 47757.145629 43009.110715 38906.343200 33955.739122 \n", "Total 1980 98790.309000 123126.870000 126427.152000 107938.656000 \n", " 1985 108906.013000 97333.901000 122457.909000 125884.252000 \n", " 1990 136834.851000 107720.932000 96908.899000 121984.088000 \n", " 1995 109707.576000 135717.236000 107329.934000 96502.955000 \n", " 2000 83372.183000 109038.689000 135323.217000 106946.991000 \n", " 2005 78477.373000 83055.222000 108795.693000 134743.841000 \n", " 2010 85578.986000 78235.086000 82893.073000 108370.459000 \n", " 2015 91238.085000 85290.706000 78080.777000 82604.506000 \n", "Urban 1980 15507.631998 17923.213001 20170.110369 20254.811239 \n", " 1985 22045.551760 18523.859686 23108.317183 26987.179970 \n", " 1990 28450.916899 23654.362213 20978.827391 29717.685328 \n", " 1995 27735.059131 32731.726754 27853.812275 26614.906166 \n", " 2000 26134.172098 31427.591946 37756.938723 42831.269309 \n", " 2005 27980.167745 28623.726216 36186.560130 61772.789681 \n", " 2010 34361.226480 33071.601210 35588.383697 56983.734278 \n", " 2015 43480.939371 42281.595285 39174.433800 48648.766878 \n", "\n", " 20-24 25-29 30-34 40-44 \\\n", "AreaType Year \n", "Rural 1980 65170.695665 68488.249391 50234.810965 36028.274944 \n", " 1985 79612.373846 63687.146179 65303.895682 36947.933856 \n", " 1990 91198.050259 72899.894087 56934.359128 44561.857987 \n", " 1995 84206.151482 83852.417087 68644.227344 56046.695833 \n", " 2000 55267.087493 71561.515515 76221.781774 49745.412656 \n", " 2005 54044.372308 50044.228225 64699.602675 56067.726085 \n", " 2010 60657.016759 46296.291165 40930.009704 60366.057077 \n", " 2015 42254.228710 50667.389524 39271.863164 50915.430229 \n", "Total 1980 86492.061000 88737.972000 64293.045000 47247.224000 \n", " 1985 107271.802000 85828.619000 87987.402000 49271.389000 \n", " 1990 125158.824000 106503.052000 85142.496000 62786.287000 \n", " 1995 121206.101000 124204.245000 105623.393000 86048.792000 \n", " 2000 95921.855000 120353.899000 123272.914000 83350.916000 \n", " 2005 106031.432000 94936.462000 119245.181000 103575.341000 \n", " 2010 133873.687000 105153.612000 94140.898000 121039.748000 \n", " 2015 107827.031000 133206.619000 104616.291000 117516.305000 \n", "Urban 1980 21321.365335 20249.722609 14058.234035 11218.949056 \n", " 1985 27659.428154 22141.472821 22683.506318 12323.455144 \n", " 1990 33960.773741 33603.157913 28208.136872 18224.429013 \n", " 1995 36999.949518 40351.827913 36979.165656 30002.096167 \n", " 2000 40654.767507 48792.383485 47051.132226 33605.503344 \n", " 2005 51987.059692 44892.233775 54545.578325 47507.614915 \n", " 2010 73216.670241 58857.320835 53210.888296 60673.690923 \n", " 2015 65572.802290 82539.229476 65344.427836 66600.874771 \n", "\n", " 45-49 50-54 55-59 60-64 \\\n", "AreaType Year \n", "Rural 1980 33929.844292 29526.614953 25299.963913 22315.795016 \n", " 1985 34469.428764 32173.465552 27364.523251 22381.801230 \n", " 1990 34257.767682 31234.966200 29149.434439 24437.600047 \n", " 1995 40812.782176 31310.194203 27466.028368 24404.107553 \n", " 2000 53962.235564 39391.017665 29938.973153 26157.829809 \n", " 2005 46501.028892 48029.599792 34553.815807 25842.339743 \n", " 2010 51323.359586 41272.412691 44157.336840 31294.239365 \n", " 2015 52242.944273 44767.641347 37463.749003 37754.250453 \n", "Total 1980 44487.426000 37856.536000 31387.007000 27303.533000 \n", " 1985 46289.346000 43120.160000 36051.891000 28989.858000 \n", " 1990 48340.803000 44962.825000 41189.899000 33438.375000 \n", " 1995 61658.432000 47035.216000 43060.527000 38355.385000 \n", " 2000 84620.724000 60121.376000 45174.572000 40256.694000 \n", " 2005 82106.866000 82882.750000 58208.415000 42815.370000 \n", " 2010 102224.679000 80577.301000 80467.263000 55392.410000 \n", " 2015 119760.437000 100504.348000 78300.252000 76554.742000 \n", "Urban 1980 10557.581708 8329.921047 6087.043087 4987.737984 \n", " 1985 11819.917236 10946.694448 8687.367749 6608.056770 \n", " 1990 14083.035318 13727.858800 12040.464561 9000.774953 \n", " 1995 20845.649824 15725.021797 15594.498632 13951.277447 \n", " 2000 30658.488436 20730.358335 15235.598847 14098.864191 \n", " 2005 35605.837108 34853.150208 23654.599193 16973.030257 \n", " 2010 50901.319414 39304.888309 36309.926160 24098.170635 \n", " 2015 67517.492727 55736.706653 40836.502997 38800.491547 \n", "\n", " 65-69 70-74 75-79 80+ \\\n", "AreaType Year \n", "Rural 1980 16985.378642 11982.725466 7409.428787 4599.764432 \n", " 1985 18666.853181 13080.045753 8183.783145 6008.900285 \n", " 1990 19341.065038 15069.674021 9307.334880 7210.062984 \n", " 1995 19400.181449 14079.975944 9737.968801 7486.140632 \n", " 2000 22552.447786 16639.508991 10807.425855 9296.357012 \n", " 2005 22583.944834 17934.098200 11772.406237 10112.231432 \n", " 2010 22505.361400 17678.828825 12907.843001 11551.583783 \n", " 2015 25420.247574 16570.250683 12183.215314 11692.576879 \n", "Total 1980 20614.082000 14578.406000 9044.731000 5709.592000 \n", " 1985 24043.509000 16834.087000 10574.886000 7838.158000 \n", " 1990 25667.568000 19718.739000 12265.331000 9668.704000 \n", " 1995 29739.250000 21155.357000 14437.453000 11428.906000 \n", " 2000 34277.514000 24613.812000 15589.309000 13484.173000 \n", " 2005 36713.628000 29195.381000 18961.029000 16261.607000 \n", " 2010 39275.246000 31531.588000 22758.080000 19980.314000 \n", " 2015 50808.615000 33870.273000 24610.100000 23168.305000 \n", "Urban 1980 3628.703358 2595.680534 1635.302213 1109.827568 \n", " 1985 5376.655819 3754.041247 2391.102855 1829.257715 \n", " 1990 6326.502962 4649.064979 2957.996120 2458.641016 \n", " 1995 10339.068551 7075.381056 4699.484199 3942.765368 \n", " 2000 11725.066214 7974.303009 4781.883145 4187.815988 \n", " 2005 14129.683166 11261.282800 7188.622763 6149.375568 \n", " 2010 16769.884600 13852.759175 9850.236999 8428.730217 \n", " 2015 25388.367426 17300.022317 12426.884686 11475.728121 \n", "\n", " Total \n", "AreaType Year \n", "Rural 1980 793532.572 \n", " 1985 819311.920 \n", " 1990 857262.077 \n", " 1995 854375.444 \n", " 2000 821045.475 \n", " 2005 757659.106 \n", " 2010 690435.240 \n", " 2015 622107.985 \n", "Total 1980 984015.677 \n", " 1985 1062299.217 \n", " 1990 1165428.967 \n", " 1995 1237531.429 \n", " 2000 1280428.583 \n", " 2005 1318176.835 \n", " 2010 1359821.465 \n", " 2015 1401586.609 \n", "Urban 1980 190483.105 \n", " 1985 242987.297 \n", " 1990 308166.890 \n", " 1995 383155.985 \n", " 2000 459383.108 \n", " 2005 560517.729 \n", " 2010 669386.225 \n", " 2015 779478.624 " ] }, "execution_count": 321, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aging_dist.loc['China']" ] }, { "cell_type": "code", "execution_count": 322, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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00-0405-0910-1415-1920-2425-2930-3440-4445-4950-5455-5960-6465-6970-7475-7980+Total
AreaTypeYear
Rural19801952.9585892276.0755902070.2704541830.2909781542.1960291964.1752412212.9447901834.8164881972.1365351948.8986401587.2623721286.8597331166.213795922.062771647.998392539.69836527615.502
19851734.5196752036.5366502299.2691101825.0241321430.9290501625.9409462017.0813541856.4549681807.2868501932.1868221895.6909191529.0333961205.7130981043.778020752.071819690.30956127942.380
19901451.4006951801.7674562047.6993702029.3061831397.1727981451.2123051637.0763432252.2504491821.6556741766.8362391876.4034441825.2209811441.3811111092.967997877.602475889.07893127703.483
19951275.1954081535.6661511816.6624941816.2377881621.7934371439.0948691492.8677182053.5056032224.3990591784.7840751723.5260571805.7571711719.2513811318.365207933.1376931117.48989327366.317
20001177.5023711329.6483341544.3264791609.6163891428.3644101640.6193551460.3232711688.9075562024.4745582185.0178391753.6904091683.9945581712.6819181576.5324531152.9316771348.36093126841.781
2005749.945776830.076198905.159268939.077427890.1853851002.4210271122.4298121031.3791761133.3170861354.6033241445.8621021171.1976211090.3904811059.628239919.3721531148.66046417804.779
2010480.741079532.526621581.469323564.202652536.815435592.266658675.487073699.581763706.259420778.529426932.915440985.760823802.224943724.301016672.7097441028.21832712070.342
2015324.124948349.612652374.188109378.095424328.696312371.623018411.255378532.289403521.996785543.995470546.201594581.462210625.987438550.020097485.567042866.2802758245.551
Total19808425.46200010023.5930008861.8470008199.0560007778.1150009054.65800010669.9600008273.7040008007.5680007113.6850005523.7130004375.6300003912.1580002978.4450002003.1470001591.386000115912.104
19857406.7920008504.84000010033.8400008884.9200008132.4730007766.0150009070.6080009053.9980008173.9000007851.1590006915.4360005319.2460004108.4030003514.6340002453.9680002160.195000119988.663
19906430.4340007460.6260008498.3020009995.2970008702.2950007997.3550007726.07400010554.9060008934.0090008023.8150007641.4910006662.6510005021.3240003738.3160002975.0160002871.904000122249.285
19955970.7050006528.2100007471.7660008530.9330009884.9300008694.5850008056.3260009019.48600010519.8410008841.5130007891.9220007392.9640006317.7490004619.4410003216.9160003762.988000124483.305
20005858.0830005992.3040006534.5520007481.6780008394.6850009782.7560008684.2810007741.9200008928.98200010342.9850008654.0660007673.9260007024.4290005825.3200004082.6870004665.812000125714.674
20055639.4830005877.9630006010.0860006582.3650007549.6570008470.9030009789.3630008012.6760007724.2640008860.12800010101.5640008453.8220007351.9250006540.8690005171.8870006136.503000126978.754
20105393.3420005598.9010005928.4240006055.6160006795.2060007425.8560008338.0030008668.3790007940.3560007657.7750008754.4960009842.2120008260.9590006969.0290005927.0280008085.933000127352.833
20155341.2020005399.1850005603.8010005960.7890006111.5540006843.8720007455.7810009688.0010008620.3210007859.5410007528.5200008527.5400009467.9620007773.4200006284.23400010007.646000126818.019
Urban19806472.5034117747.5174106791.5765466368.7650226235.9189717090.4827598457.0152106438.8875126035.4314655164.7863603936.4506283088.7702672745.9442052056.3822291355.1486081051.68763588296.602
19855672.2723256468.3033507734.5708907059.8958686701.5439506140.0740547053.5266467197.5430326366.6131505918.9721785019.7450813790.2126042902.6899022470.8559801701.8961811469.88543992046.283
19904979.0333055658.8585446450.6026307965.9908177305.1222026546.1426956088.9976578302.6555517112.3533266256.9787615765.0875564837.4300193579.9428892645.3480032097.4135251982.82506994545.802
19954695.5095924992.5438495655.1035066714.6952128263.1365637255.4901316563.4582826965.9803978295.4419417056.7289256168.3959435587.2068294598.4976193301.0757932283.7783072645.49810797116.988
20004680.5806294662.6556664990.2255215872.0616116966.3205908142.1366457223.9577296053.0124446904.5074428157.9671616900.3755915989.9314425311.7470824248.7875472929.7553233317.45106998872.893
20054889.5372245047.8868025104.9267325643.2875736659.4716157468.4819738666.9331886981.2968246590.9469147505.5246768655.7018987282.6243796261.5345195481.2407614252.5148474987.842536109173.975
20104912.6009215066.3743795346.9546775491.4133486258.3905656833.5893427662.5159277968.7972377234.0965806879.2455747821.5805608856.4511777458.7340576244.7279845254.3182567057.714673115282.491
20155017.0770525049.5723485229.6128915582.6935765782.8576886472.2489827044.5256229155.7115978098.3242157315.5455306982.3184067946.0777908841.9745627223.3999035798.6669589141.365725118572.468
\n", "
" ], "text/plain": [ " 00-04 05-09 10-14 15-19 \\\n", "AreaType Year \n", "Rural 1980 1952.958589 2276.075590 2070.270454 1830.290978 \n", " 1985 1734.519675 2036.536650 2299.269110 1825.024132 \n", " 1990 1451.400695 1801.767456 2047.699370 2029.306183 \n", " 1995 1275.195408 1535.666151 1816.662494 1816.237788 \n", " 2000 1177.502371 1329.648334 1544.326479 1609.616389 \n", " 2005 749.945776 830.076198 905.159268 939.077427 \n", " 2010 480.741079 532.526621 581.469323 564.202652 \n", " 2015 324.124948 349.612652 374.188109 378.095424 \n", "Total 1980 8425.462000 10023.593000 8861.847000 8199.056000 \n", " 1985 7406.792000 8504.840000 10033.840000 8884.920000 \n", " 1990 6430.434000 7460.626000 8498.302000 9995.297000 \n", " 1995 5970.705000 6528.210000 7471.766000 8530.933000 \n", " 2000 5858.083000 5992.304000 6534.552000 7481.678000 \n", " 2005 5639.483000 5877.963000 6010.086000 6582.365000 \n", " 2010 5393.342000 5598.901000 5928.424000 6055.616000 \n", " 2015 5341.202000 5399.185000 5603.801000 5960.789000 \n", "Urban 1980 6472.503411 7747.517410 6791.576546 6368.765022 \n", " 1985 5672.272325 6468.303350 7734.570890 7059.895868 \n", " 1990 4979.033305 5658.858544 6450.602630 7965.990817 \n", " 1995 4695.509592 4992.543849 5655.103506 6714.695212 \n", " 2000 4680.580629 4662.655666 4990.225521 5872.061611 \n", " 2005 4889.537224 5047.886802 5104.926732 5643.287573 \n", " 2010 4912.600921 5066.374379 5346.954677 5491.413348 \n", " 2015 5017.077052 5049.572348 5229.612891 5582.693576 \n", "\n", " 20-24 25-29 30-34 40-44 \\\n", "AreaType Year \n", "Rural 1980 1542.196029 1964.175241 2212.944790 1834.816488 \n", " 1985 1430.929050 1625.940946 2017.081354 1856.454968 \n", " 1990 1397.172798 1451.212305 1637.076343 2252.250449 \n", " 1995 1621.793437 1439.094869 1492.867718 2053.505603 \n", " 2000 1428.364410 1640.619355 1460.323271 1688.907556 \n", " 2005 890.185385 1002.421027 1122.429812 1031.379176 \n", " 2010 536.815435 592.266658 675.487073 699.581763 \n", " 2015 328.696312 371.623018 411.255378 532.289403 \n", "Total 1980 7778.115000 9054.658000 10669.960000 8273.704000 \n", " 1985 8132.473000 7766.015000 9070.608000 9053.998000 \n", " 1990 8702.295000 7997.355000 7726.074000 10554.906000 \n", " 1995 9884.930000 8694.585000 8056.326000 9019.486000 \n", " 2000 8394.685000 9782.756000 8684.281000 7741.920000 \n", " 2005 7549.657000 8470.903000 9789.363000 8012.676000 \n", " 2010 6795.206000 7425.856000 8338.003000 8668.379000 \n", " 2015 6111.554000 6843.872000 7455.781000 9688.001000 \n", "Urban 1980 6235.918971 7090.482759 8457.015210 6438.887512 \n", " 1985 6701.543950 6140.074054 7053.526646 7197.543032 \n", " 1990 7305.122202 6546.142695 6088.997657 8302.655551 \n", " 1995 8263.136563 7255.490131 6563.458282 6965.980397 \n", " 2000 6966.320590 8142.136645 7223.957729 6053.012444 \n", " 2005 6659.471615 7468.481973 8666.933188 6981.296824 \n", " 2010 6258.390565 6833.589342 7662.515927 7968.797237 \n", " 2015 5782.857688 6472.248982 7044.525622 9155.711597 \n", "\n", " 45-49 50-54 55-59 60-64 \\\n", "AreaType Year \n", "Rural 1980 1972.136535 1948.898640 1587.262372 1286.859733 \n", " 1985 1807.286850 1932.186822 1895.690919 1529.033396 \n", " 1990 1821.655674 1766.836239 1876.403444 1825.220981 \n", " 1995 2224.399059 1784.784075 1723.526057 1805.757171 \n", " 2000 2024.474558 2185.017839 1753.690409 1683.994558 \n", " 2005 1133.317086 1354.603324 1445.862102 1171.197621 \n", " 2010 706.259420 778.529426 932.915440 985.760823 \n", " 2015 521.996785 543.995470 546.201594 581.462210 \n", "Total 1980 8007.568000 7113.685000 5523.713000 4375.630000 \n", " 1985 8173.900000 7851.159000 6915.436000 5319.246000 \n", " 1990 8934.009000 8023.815000 7641.491000 6662.651000 \n", " 1995 10519.841000 8841.513000 7891.922000 7392.964000 \n", " 2000 8928.982000 10342.985000 8654.066000 7673.926000 \n", " 2005 7724.264000 8860.128000 10101.564000 8453.822000 \n", " 2010 7940.356000 7657.775000 8754.496000 9842.212000 \n", " 2015 8620.321000 7859.541000 7528.520000 8527.540000 \n", "Urban 1980 6035.431465 5164.786360 3936.450628 3088.770267 \n", " 1985 6366.613150 5918.972178 5019.745081 3790.212604 \n", " 1990 7112.353326 6256.978761 5765.087556 4837.430019 \n", " 1995 8295.441941 7056.728925 6168.395943 5587.206829 \n", " 2000 6904.507442 8157.967161 6900.375591 5989.931442 \n", " 2005 6590.946914 7505.524676 8655.701898 7282.624379 \n", " 2010 7234.096580 6879.245574 7821.580560 8856.451177 \n", " 2015 8098.324215 7315.545530 6982.318406 7946.077790 \n", "\n", " 65-69 70-74 75-79 80+ Total \n", "AreaType Year \n", "Rural 1980 1166.213795 922.062771 647.998392 539.698365 27615.502 \n", " 1985 1205.713098 1043.778020 752.071819 690.309561 27942.380 \n", " 1990 1441.381111 1092.967997 877.602475 889.078931 27703.483 \n", " 1995 1719.251381 1318.365207 933.137693 1117.489893 27366.317 \n", " 2000 1712.681918 1576.532453 1152.931677 1348.360931 26841.781 \n", " 2005 1090.390481 1059.628239 919.372153 1148.660464 17804.779 \n", " 2010 802.224943 724.301016 672.709744 1028.218327 12070.342 \n", " 2015 625.987438 550.020097 485.567042 866.280275 8245.551 \n", "Total 1980 3912.158000 2978.445000 2003.147000 1591.386000 115912.104 \n", " 1985 4108.403000 3514.634000 2453.968000 2160.195000 119988.663 \n", " 1990 5021.324000 3738.316000 2975.016000 2871.904000 122249.285 \n", " 1995 6317.749000 4619.441000 3216.916000 3762.988000 124483.305 \n", " 2000 7024.429000 5825.320000 4082.687000 4665.812000 125714.674 \n", " 2005 7351.925000 6540.869000 5171.887000 6136.503000 126978.754 \n", " 2010 8260.959000 6969.029000 5927.028000 8085.933000 127352.833 \n", " 2015 9467.962000 7773.420000 6284.234000 10007.646000 126818.019 \n", "Urban 1980 2745.944205 2056.382229 1355.148608 1051.687635 88296.602 \n", " 1985 2902.689902 2470.855980 1701.896181 1469.885439 92046.283 \n", " 1990 3579.942889 2645.348003 2097.413525 1982.825069 94545.802 \n", " 1995 4598.497619 3301.075793 2283.778307 2645.498107 97116.988 \n", " 2000 5311.747082 4248.787547 2929.755323 3317.451069 98872.893 \n", " 2005 6261.534519 5481.240761 4252.514847 4987.842536 109173.975 \n", " 2010 7458.734057 6244.727984 5254.318256 7057.714673 115282.491 \n", " 2015 8841.974562 7223.399903 5798.666958 9141.365725 118572.468 " ] }, "execution_count": 322, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aging_dist.loc['Japan']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's draw a picture to see the specific situation" ] }, { "cell_type": "code", "execution_count": 323, "metadata": {}, "outputs": [], "source": [ "def aging_dist_curve(location, area, time, age, data):\n", " \n", " hash_x = ['00-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '40-44', \n", " '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80+']\n", " age_start = age[0]\n", " age_end = age[-1]\n", " \n", " x = hash_x[hash_x.index(age_start): hash_x.index(age_end)+1]\n", " \n", " y = []\n", " hash_area = ['Rural', 'Total', 'Urban']\n", " hash_time = [str(i) for i in list(range(1980, 2015+5, 5))]\n", " \n", " for l in location:\n", " for a in area:\n", " for t in time:\n", " y_lat = [round(data.loc[l].values.tolist()[hash_area.index(a)*8 + hash_time.index(t)][i] / \n", " data.loc[l].values.tolist()[hash_area.index(a)*8 + hash_time.index(t)][-1], 4) \n", " for i in range(hash_x.index(age_start), hash_x.index(age_end)+1)]\n", " y.append(y_lat)\n", " \n", " c = (Line()\n", " .set_global_opts(\n", " tooltip_opts = opts.TooltipOpts(\n", " is_show = True, \n", " trigger = 'axis',\n", " ),\n", " xaxis_opts = opts.AxisOpts(type_ = 'category'),\n", " yaxis_opts = opts.AxisOpts(type_ = 'value')\n", " )\n", " .add_xaxis(xaxis_data = x))\n", " \n", " for i, y_i in enumerate(y):\n", " (c\n", " .add_yaxis(\n", " series_name = location[i // (len(area)+len(time))] \n", " + '-'\n", " + area[(i % (len(area)+len(time))) // len(time)] \n", " + '-'\n", " + time[(i % (len(area)+len(time))) % len(time)],\n", " y_axis = y_i,\n", " label_opts = opts.LabelOpts(is_show = False)\n", " ))\n", " \n", " return c\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 324, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 324, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c_dist = aging_dist_curve(['China'], ['Urban', 'Rural'], ['2015', '1985'], ['00-04', '80+'], aging_dist)\n", "c_dist.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In 1985, the age distribution of rural and urban areas in China was relatively consistent. \n", "\n", "In 2015, the proportion of the elderly increased and the proportion of children decreased,\n", "\n", "it is obvious that there are more young people in cities and more old people in rural areas. \n", "\n", "How to rationalize age distribution is a problem." ] }, { "cell_type": "code", "execution_count": 329, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 329, "metadata": {}, "output_type": "execute_result" } ], "source": [ "j_dist = aging_dist_curve(['Japan'], ['Urban', 'Rural'], ['2015', '1980'], ['00-04', '80+'], aging_dist)\n", "j_dist.render_notebook()" ] }, { "cell_type": "code", "execution_count": 331, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 331, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_dist = aging_dist_curve(['Germany'], ['Urban', 'Rural'], ['2015', '1980'], ['00-04', '80+'], aging_dist)\n", "g_dist.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Janpan and Germany` is the first and second most aging country in the world, \n", "\n", "and the proportion of the elderly is significantly `higher` than that of China" ] }, { "cell_type": "code", "execution_count": 332, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 332, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_dist = aging_dist_curve([\"More developed regions\",\n", " \"Least developed countries\"], ['Urban','Rural'], ['2015','1980'], ['00-04', '80+'], aging_dist)\n", "t_dist.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are more senior citizens in developed countries,\n", "\n", "less senior citizens and more children in underdeveloped countries.\n", "\n", "### The Gender Ratio" ] }, { "cell_type": "code", "execution_count": 333, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LocationNameAfghanistanAfricaAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntigua and BarbudaArgentina...Viet NamWallis and Futuna IslandsWestern AfricaWestern AsiaWestern EuropeWestern SaharaWorldYemenZambiaZimbabwe
YearSex
1980Female4318.727333160004.983333888.8020006446.27800010.66200011.0540002587.2653332.30333324.0293339501.424667...18645.4393333.70533345455.86533337550.00333359191.92666746.5480001.474806e+062695.9146671962.1473332443.164000
Male4468.226667158967.996000934.3820006537.19133310.97533312.9880002504.1620002.16200022.8380009245.332000...17952.8186673.78266745876.27266738149.51733355027.10066754.0333331.491227e+062575.4033331936.0133332416.215333
1985Female3754.215333183741.1646671000.4893337551.52200012.80733313.9320003065.9233332.29533322.58933310278.320000...20915.0400004.43800052169.35600043192.13800059793.64400057.1160001.611151e+063269.1806672296.6833332967.245333
Male3931.769333182822.6920001051.4640007680.10266713.35733315.7993332976.5073332.14600021.2400009948.626000...20190.5213334.58466752622.58266744153.92933355613.51000064.4980001.631250e+063165.0280002262.2446672939.642000
1990Female3806.994667210459.2713331120.5740008656.73000015.25800017.0293333497.5773332.79333321.37533311078.128000...23345.0900004.62400059613.34200048673.34133360846.08866768.7880001.760802e+063970.3360002634.8273333502.952667
Male4013.800667209532.0473331177.3473338836.40866716.10466719.3113333391.6520002.76266719.89533310671.788000...22594.8320004.62933360170.22866750152.60933357144.13800075.7640001.786409e+063889.8300002594.8500003471.568667
1995Female5738.666667239364.1120001106.8893339673.00266717.21333320.1320004098.0693333.28466723.47666711837.552667...25722.7173334.76266768117.52266754306.05400062382.80066781.0566671.899263e+064937.0120002964.9333333906.594667
Male5985.382000238305.9746671131.6826679870.63933318.03600022.4373333971.8986673.25333322.08933311384.559333...24957.3113334.66600068828.76866756468.10866759091.02066787.8113331.928618e+065075.1220002929.2920003852.981333
2000Female6744.332667269848.7226671103.97066710460.36266718.76200020.9740004705.1986673.74400027.49133312553.784667...27373.4153334.83333377427.04200060269.88333363101.57733397.0600002.027485e+065774.8493333380.4886674197.236667
Male6985.907333269020.8353331099.32800010685.93666719.58600022.6253334578.0880003.63666724.27400012048.260000...26551.8373334.83133378441.55066762065.36666759925.706667106.6833332.057649e+065906.8420003353.4986674138.531333
2005Female8124.601333304196.3106671061.85866711200.03800019.33466725.8046675577.0740004.26133328.75866713159.482000...28744.8173334.76066788143.12533366713.78800064333.790667134.5020002.154663e+066647.6280003834.6806674282.012667
Male8449.302000303488.8020001068.89466711440.56400020.07666728.3440005452.5100004.16333326.28466712605.754000...27887.0840004.73666789428.79466770127.16466761176.128667150.6860002.188066e+066778.8126673812.0006674191.713333
2010Female9322.180000343948.2700001045.17733312213.64066718.33333325.4353336576.8646674.62933330.38866713747.272667...30051.3900004.534000100867.05133374225.13933365027.472000162.4013332.287167e+067521.7613334414.2153334423.864667
Male9609.694667343440.8406671054.91800012494.90600018.75733326.5026676455.8846674.54933327.76666713168.876667...29313.5413334.509333102525.05800080221.89133362051.267333180.6973332.323622e+067653.5773334397.1080004294.120667
2015Female10522.397333388566.6606671065.00733313398.47600018.42733326.6106677666.1326674.90600031.95266714346.528667...31485.5506674.386000115432.53466780882.53400065684.301333191.4213332.421065e+068443.2966675188.3093335068.007333
Male10815.461333388926.2100001066.31333313690.50000018.59800027.3560007547.1513334.83666729.26200013756.747333...30772.2026674.382667117756.14800088761.88466762974.310000211.4440002.462123e+068580.0940005158.0933334962.727333
\n", "

16 rows × 264 columns

\n", "
" ], "text/plain": [ "LocationName Afghanistan Africa Albania Algeria \\\n", "Year Sex \n", "1980 Female 4318.727333 160004.983333 888.802000 6446.278000 \n", " Male 4468.226667 158967.996000 934.382000 6537.191333 \n", "1985 Female 3754.215333 183741.164667 1000.489333 7551.522000 \n", " Male 3931.769333 182822.692000 1051.464000 7680.102667 \n", "1990 Female 3806.994667 210459.271333 1120.574000 8656.730000 \n", " Male 4013.800667 209532.047333 1177.347333 8836.408667 \n", "1995 Female 5738.666667 239364.112000 1106.889333 9673.002667 \n", " Male 5985.382000 238305.974667 1131.682667 9870.639333 \n", "2000 Female 6744.332667 269848.722667 1103.970667 10460.362667 \n", " Male 6985.907333 269020.835333 1099.328000 10685.936667 \n", "2005 Female 8124.601333 304196.310667 1061.858667 11200.038000 \n", " Male 8449.302000 303488.802000 1068.894667 11440.564000 \n", "2010 Female 9322.180000 343948.270000 1045.177333 12213.640667 \n", " Male 9609.694667 343440.840667 1054.918000 12494.906000 \n", "2015 Female 10522.397333 388566.660667 1065.007333 13398.476000 \n", " Male 10815.461333 388926.210000 1066.313333 13690.500000 \n", "\n", "LocationName American Samoa Andorra Angola Anguilla \\\n", "Year Sex \n", "1980 Female 10.662000 11.054000 2587.265333 2.303333 \n", " Male 10.975333 12.988000 2504.162000 2.162000 \n", "1985 Female 12.807333 13.932000 3065.923333 2.295333 \n", " Male 13.357333 15.799333 2976.507333 2.146000 \n", "1990 Female 15.258000 17.029333 3497.577333 2.793333 \n", " Male 16.104667 19.311333 3391.652000 2.762667 \n", "1995 Female 17.213333 20.132000 4098.069333 3.284667 \n", " Male 18.036000 22.437333 3971.898667 3.253333 \n", "2000 Female 18.762000 20.974000 4705.198667 3.744000 \n", " Male 19.586000 22.625333 4578.088000 3.636667 \n", "2005 Female 19.334667 25.804667 5577.074000 4.261333 \n", " Male 20.076667 28.344000 5452.510000 4.163333 \n", "2010 Female 18.333333 25.435333 6576.864667 4.629333 \n", " Male 18.757333 26.502667 6455.884667 4.549333 \n", "2015 Female 18.427333 26.610667 7666.132667 4.906000 \n", " Male 18.598000 27.356000 7547.151333 4.836667 \n", "\n", "LocationName Antigua and Barbuda Argentina ... Viet Nam \\\n", "Year Sex ... \n", "1980 Female 24.029333 9501.424667 ... 18645.439333 \n", " Male 22.838000 9245.332000 ... 17952.818667 \n", "1985 Female 22.589333 10278.320000 ... 20915.040000 \n", " Male 21.240000 9948.626000 ... 20190.521333 \n", "1990 Female 21.375333 11078.128000 ... 23345.090000 \n", " Male 19.895333 10671.788000 ... 22594.832000 \n", "1995 Female 23.476667 11837.552667 ... 25722.717333 \n", " Male 22.089333 11384.559333 ... 24957.311333 \n", "2000 Female 27.491333 12553.784667 ... 27373.415333 \n", " Male 24.274000 12048.260000 ... 26551.837333 \n", "2005 Female 28.758667 13159.482000 ... 28744.817333 \n", " Male 26.284667 12605.754000 ... 27887.084000 \n", "2010 Female 30.388667 13747.272667 ... 30051.390000 \n", " Male 27.766667 13168.876667 ... 29313.541333 \n", "2015 Female 31.952667 14346.528667 ... 31485.550667 \n", " Male 29.262000 13756.747333 ... 30772.202667 \n", "\n", "LocationName Wallis and Futuna Islands Western Africa Western Asia \\\n", "Year Sex \n", "1980 Female 3.705333 45455.865333 37550.003333 \n", " Male 3.782667 45876.272667 38149.517333 \n", "1985 Female 4.438000 52169.356000 43192.138000 \n", " Male 4.584667 52622.582667 44153.929333 \n", "1990 Female 4.624000 59613.342000 48673.341333 \n", " Male 4.629333 60170.228667 50152.609333 \n", "1995 Female 4.762667 68117.522667 54306.054000 \n", " Male 4.666000 68828.768667 56468.108667 \n", "2000 Female 4.833333 77427.042000 60269.883333 \n", " Male 4.831333 78441.550667 62065.366667 \n", "2005 Female 4.760667 88143.125333 66713.788000 \n", " Male 4.736667 89428.794667 70127.164667 \n", "2010 Female 4.534000 100867.051333 74225.139333 \n", " Male 4.509333 102525.058000 80221.891333 \n", "2015 Female 4.386000 115432.534667 80882.534000 \n", " Male 4.382667 117756.148000 88761.884667 \n", "\n", "LocationName Western Europe Western Sahara World Yemen \\\n", "Year Sex \n", "1980 Female 59191.926667 46.548000 1.474806e+06 2695.914667 \n", " Male 55027.100667 54.033333 1.491227e+06 2575.403333 \n", "1985 Female 59793.644000 57.116000 1.611151e+06 3269.180667 \n", " Male 55613.510000 64.498000 1.631250e+06 3165.028000 \n", "1990 Female 60846.088667 68.788000 1.760802e+06 3970.336000 \n", " Male 57144.138000 75.764000 1.786409e+06 3889.830000 \n", "1995 Female 62382.800667 81.056667 1.899263e+06 4937.012000 \n", " Male 59091.020667 87.811333 1.928618e+06 5075.122000 \n", "2000 Female 63101.577333 97.060000 2.027485e+06 5774.849333 \n", " Male 59925.706667 106.683333 2.057649e+06 5906.842000 \n", "2005 Female 64333.790667 134.502000 2.154663e+06 6647.628000 \n", " Male 61176.128667 150.686000 2.188066e+06 6778.812667 \n", "2010 Female 65027.472000 162.401333 2.287167e+06 7521.761333 \n", " Male 62051.267333 180.697333 2.323622e+06 7653.577333 \n", "2015 Female 65684.301333 191.421333 2.421065e+06 8443.296667 \n", " Male 62974.310000 211.444000 2.462123e+06 8580.094000 \n", "\n", "LocationName Zambia Zimbabwe \n", "Year Sex \n", "1980 Female 1962.147333 2443.164000 \n", " Male 1936.013333 2416.215333 \n", "1985 Female 2296.683333 2967.245333 \n", " Male 2262.244667 2939.642000 \n", "1990 Female 2634.827333 3502.952667 \n", " Male 2594.850000 3471.568667 \n", "1995 Female 2964.933333 3906.594667 \n", " Male 2929.292000 3852.981333 \n", "2000 Female 3380.488667 4197.236667 \n", " Male 3353.498667 4138.531333 \n", "2005 Female 3834.680667 4282.012667 \n", " Male 3812.000667 4191.713333 \n", "2010 Female 4414.215333 4423.864667 \n", " Male 4397.108000 4294.120667 \n", "2015 Female 5188.309333 5068.007333 \n", " Male 5158.093333 4962.727333 \n", "\n", "[16 rows x 264 columns]" ] }, "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gender = (\n", " datas\n", " .reset_index()\n", " .pivot_table(index=[\"Year\",\"Sex\"], columns=\"LocationName\", values=\"Total\")\n", ")\n", "gender" ] }, { "cell_type": "code", "execution_count": 335, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9348049380175202" ] }, "execution_count": 335, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_g=gender.loc[pd.IndexSlice[:, [\"Female\"]], :]\n", "y_g=gender.loc[pd.IndexSlice[:, [\"Male\"]], :]\n", "female=x_g.mean(axis=0)\n", "male=y_g.mean(axis=0)\n", "z_g=female/male\n", "z_g[\"China\"]" ] }, { "cell_type": "code", "execution_count": 336, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5, 1, 'Proportion of women and men population in China ')" ] }, "execution_count": 336, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels='Men','Women'\n", "sizes=female['China'],male['China']\n", "colors='lightgreen','gold'\n", "explode=0,0\n", "plt.pie(sizes,explode=explode,labels=labels,\n", " colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)\n", "plt.axis('equal')\n", "plt.show()\n", "ax.set_title(\"Proportion of women and men population in China \")" ] }, { "cell_type": "code", "execution_count": 337, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The country with the largest share of women is Latvia\n" ] } ], "source": [ "print(\"The country with the largest share of women is\",z_g.idxmax())" ] }, { "cell_type": "code", "execution_count": 338, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5, 1, 'Proportion of women and men population in Latvia ')" ] }, "execution_count": 338, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels='Women','Men'\n", "sizes=female['Latvia'],male['Latvia']\n", "colors='lightskyblue','lightcoral'\n", "explode=0,0\n", "plt.pie(sizes,explode=explode,labels=labels,\n", " colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)\n", "plt.axis('equal')\n", "plt.show()\n", "ax.set_title(\"Proportion of women and men population in Latvia \")" ] }, { "cell_type": "code", "execution_count": 339, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The country with the largest share of men is Qatar\n" ] } ], "source": [ "print(\"The country with the largest share of men is\",z_g.idxmin())" ] }, { "cell_type": "code", "execution_count": 340, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5, 1, 'Proportion of women and men population in Latvia ')" ] }, "execution_count": 340, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels='Women','Men'\n", "sizes=female['Qatar'],male['Qatar']\n", "colors='lightskyblue','lightcoral'\n", "explode=0,0\n", "plt.pie(sizes,explode=explode,labels=labels,\n", " colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)\n", "plt.axis('equal')\n", "plt.show()\n", "ax.set_title(\"Proportion of women and men population in Latvia \")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finally, \n", "based on the above analysis, we have a rough understanding of the basic situation of the data set population. \n", "\n", "### What should we do?\n", "As a **Chinese**, under the guidance of the party and the state, \n", "\n", "we should also make efforts for \n", "- **China's urbanization**\n", "- **urban and rural population distribution balance**\n", "- **gender ratio coordination**\n", "\n", "match the national policies, and make bold suggestions to contribute to the socialist modernization." ] } ], "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }