Briefly introduce my data.
It's on the United Nations Population website https://www.un.org/en/development/desa/population/index.asp
This web site presents the main findings of the 2018 Revision of World Urbanization Prospects
,
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).
Find out about
in various countries of the world in 1980-2015
.
Next, based on this set of data, I will briefly analyze
China's
urbanization,Aging
andAge distribution
,Gender
and compare them with major countries in the world.
Aiming at illustrate the current situation of China
import pandas as pd
data=pd.read_excel('/Users/liaoying/Desktop/population.xlsx')
data
RowID | LocationName | LocationID | LocationType | IsSmallCountry | ParentID | Year | Sex | AreaType | SortOrder | ... | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | World | 900 | 0 | False | 0 | 1980 | Female | Rural | 1 | ... | 66377.635405 | 62188.927206 | 56887.096881 | 51112.965754 | 44213.385057 | 35633.110873 | 30612.055005 | 23004.611925 | 14860.076507 | 11662.619964 |
1 | NaN | World | 900 | 0 | False | 0 | 1985 | Female | Rural | 1 | ... | 74951.288161 | 62874.433954 | 59565.085094 | 53709.506420 | 47700.370884 | 39864.776614 | 30542.893972 | 24542.247680 | 16585.120771 | 13947.127653 |
2 | NaN | World | 900 | 0 | False | 0 | 1990 | Female | Rural | 1 | ... | 89283.592335 | 71504.248648 | 59762.585369 | 55860.587503 | 50130.022679 | 43536.257464 | 34926.918619 | 24880.883230 | 18102.057136 | 16440.693725 |
3 | NaN | World | 900 | 0 | False | 0 | 1995 | Female | Rural | 1 | ... | 92664.652593 | 84440.019106 | 67107.946540 | 55914.303258 | 51417.496034 | 44701.254487 | 37295.062166 | 27806.986623 | 17676.140259 | 18208.346547 |
4 | NaN | World | 900 | 0 | False | 0 | 2000 | Female | Rural | 1 | ... | 103958.095674 | 88528.173095 | 81176.263539 | 64197.670735 | 52963.480532 | 47842.199869 | 40109.574009 | 31249.043782 | 21157.141465 | 19624.278688 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
12667 | NaN | Wallis and Futuna Islands | 876 | 4 | True | 957 | 1995 | Male | Urban | 796 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12668 | NaN | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2000 | Male | Urban | 796 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12669 | NaN | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2005 | Male | Urban | 796 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12670 | NaN | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2010 | Male | Urban | 796 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12671 | NaN | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2015 | Male | Urban | 796 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12672 rows × 28 columns
data.isnull().any(axis=0)
RowID True LocationName False LocationID False LocationType False IsSmallCountry False ParentID False Year False Sex False AreaType False SortOrder False Total False 00-04 False 05-09 False 10-14 False 15-19 False 20-24 False 25-29 False 30-34 False 35-39 False 40-44 False 45-49 False 50-54 False 55-59 False 60-64 False 65-69 False 70-74 False 75-79 False 80+ False dtype: bool
The RowID represents the number of columns,however,it is meaningless in the data,so we have to delect it.
datas=data.drop('RowID',axis=1)
datas
LocationName | LocationID | LocationType | IsSmallCountry | ParentID | Year | Sex | AreaType | SortOrder | Total | ... | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | World | 900 | 0 | False | 0 | 1980 | Female | Rural | 1 | 1.331308e+06 | ... | 66377.635405 | 62188.927206 | 56887.096881 | 51112.965754 | 44213.385057 | 35633.110873 | 30612.055005 | 23004.611925 | 14860.076507 | 11662.619964 |
1 | World | 900 | 0 | False | 0 | 1985 | Female | Rural | 1 | 1.409292e+06 | ... | 74951.288161 | 62874.433954 | 59565.085094 | 53709.506420 | 47700.370884 | 39864.776614 | 30542.893972 | 24542.247680 | 16585.120771 | 13947.127653 |
2 | World | 900 | 0 | False | 0 | 1990 | Female | Rural | 1 | 1.496395e+06 | ... | 89283.592335 | 71504.248648 | 59762.585369 | 55860.587503 | 50130.022679 | 43536.257464 | 34926.918619 | 24880.883230 | 18102.057136 | 16440.693725 |
3 | World | 900 | 0 | False | 0 | 1995 | Female | Rural | 1 | 1.560483e+06 | ... | 92664.652593 | 84440.019106 | 67107.946540 | 55914.303258 | 51417.496034 | 44701.254487 | 37295.062166 | 27806.986623 | 17676.140259 | 18208.346547 |
4 | World | 900 | 0 | False | 0 | 2000 | Female | Rural | 1 | 1.610373e+06 | ... | 103958.095674 | 88528.173095 | 81176.263539 | 64197.670735 | 52963.480532 | 47842.199869 | 40109.574009 | 31249.043782 | 21157.141465 | 19624.278688 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
12667 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 1995 | Male | Urban | 796 | 0.000000e+00 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12668 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2000 | Male | Urban | 796 | 0.000000e+00 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12669 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2005 | Male | Urban | 796 | 0.000000e+00 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12670 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2010 | Male | Urban | 796 | 0.000000e+00 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12671 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2015 | Male | Urban | 796 | 0.000000e+00 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
12672 rows × 27 columns
As what I showed above,the data has 28 columns and 12670 rows,it is a large data. We can see the basic structure of the data by getting the first 5 rows, which directly matches the population file.
datas.head()
LocationName | LocationID | LocationType | IsSmallCountry | ParentID | Year | Sex | AreaType | SortOrder | Total | ... | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | World | 900 | 0 | False | 0 | 1980 | Female | Rural | 1 | 1.331308e+06 | ... | 66377.635405 | 62188.927206 | 56887.096881 | 51112.965754 | 44213.385057 | 35633.110873 | 30612.055005 | 23004.611925 | 14860.076507 | 11662.619964 |
1 | World | 900 | 0 | False | 0 | 1985 | Female | Rural | 1 | 1.409292e+06 | ... | 74951.288161 | 62874.433954 | 59565.085094 | 53709.506420 | 47700.370884 | 39864.776614 | 30542.893972 | 24542.247680 | 16585.120771 | 13947.127653 |
2 | World | 900 | 0 | False | 0 | 1990 | Female | Rural | 1 | 1.496395e+06 | ... | 89283.592335 | 71504.248648 | 59762.585369 | 55860.587503 | 50130.022679 | 43536.257464 | 34926.918619 | 24880.883230 | 18102.057136 | 16440.693725 |
3 | World | 900 | 0 | False | 0 | 1995 | Female | Rural | 1 | 1.560483e+06 | ... | 92664.652593 | 84440.019106 | 67107.946540 | 55914.303258 | 51417.496034 | 44701.254487 | 37295.062166 | 27806.986623 | 17676.140259 | 18208.346547 |
4 | World | 900 | 0 | False | 0 | 2000 | Female | Rural | 1 | 1.610373e+06 | ... | 103958.095674 | 88528.173095 | 81176.263539 | 64197.670735 | 52963.480532 | 47842.199869 | 40109.574009 | 31249.043782 | 21157.141465 | 19624.278688 |
5 rows × 27 columns
What is more,we can get the last 5 rows to see the basic structure.
datas.tail()
LocationName | LocationID | LocationType | IsSmallCountry | ParentID | Year | Sex | AreaType | SortOrder | Total | ... | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
12667 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 1995 | Male | Urban | 796 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
12668 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2000 | Male | Urban | 796 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
12669 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2005 | Male | Urban | 796 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
12670 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2010 | Male | Urban | 796 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
12671 | Wallis and Futuna Islands | 876 | 4 | True | 957 | 2015 | Male | Urban | 796 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 27 columns
datas.describe()
LocationID | LocationType | ParentID | Year | SortOrder | Total | 00-04 | 05-09 | 10-14 | 15-19 | ... | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 1.267200e+04 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | ... | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 | 12672.000000 |
mean | 528.143939 | 3.871212 | 1155.450758 | 1997.500000 | 402.132576 | 3.771441e+04 | 3998.032330 | 3808.557811 | 3667.989631 | 3510.189088 | ... | 2486.047194 | 2231.443639 | 1964.117372 | 1698.487612 | 1436.568377 | 1179.011927 | 929.755681 | 700.795493 | 484.033960 | 467.566883 |
std | 517.050039 | 0.475243 | 1031.455841 | 11.456891 | 227.580234 | 1.962581e+05 | 21035.558177 | 20087.649114 | 19348.437950 | 18460.732249 | ... | 13214.851397 | 11939.411099 | 10514.043165 | 9018.274865 | 7560.869892 | 6186.793153 | 4794.857220 | 3602.522567 | 2516.336316 | 2561.457717 |
min | 4.000000 | 0.000000 | 0.000000 | 1980.000000 | 1.000000 | 0.000000e+00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
25% | 245.000000 | 4.000000 | 911.000000 | 1988.750000 | 204.250000 | 1.232616e+02 | 12.892912 | 12.293409 | 11.843851 | 11.380352 | ... | 7.627487 | 6.537985 | 5.484059 | 4.549367 | 3.680750 | 2.878921 | 2.136500 | 1.540148 | 0.973652 | 0.814832 |
50% | 497.000000 | 4.000000 | 920.000000 | 1997.500000 | 404.500000 | 1.544067e+03 | 153.453843 | 148.282441 | 146.002000 | 143.094060 | ... | 92.144097 | 80.414882 | 69.848554 | 59.480519 | 49.716614 | 39.139143 | 30.185808 | 21.679333 | 13.627476 | 10.351877 |
75% | 734.000000 | 4.000000 | 925.000000 | 2006.250000 | 596.500000 | 7.893457e+03 | 919.634237 | 852.179000 | 788.860760 | 750.548273 | ... | 469.323000 | 407.059547 | 359.216254 | 313.611203 | 271.282473 | 224.537825 | 177.078000 | 129.830960 | 87.653060 | 76.001301 |
max | 5501.000000 | 5.000000 | 5501.000000 | 2015.000000 | 796.000000 | 3.693185e+06 | 344424.504000 | 328108.756000 | 322750.612000 | 320351.663000 | ... | 251774.573000 | 242941.972000 | 226662.469000 | 201050.008000 | 170963.664000 | 149031.553000 | 111840.292000 | 82009.050000 | 64028.780000 | 76951.735000 |
8 rows × 23 columns
datas.dtypes
LocationName object LocationID int64 LocationType int64 IsSmallCountry bool ParentID int64 Year int64 Sex object AreaType object SortOrder int64 Total float64 00-04 float64 05-09 float64 10-14 float64 15-19 float64 20-24 float64 25-29 float64 30-34 float64 35-39 float64 40-44 float64 45-49 float64 50-54 float64 55-59 float64 60-64 float64 65-69 float64 70-74 float64 75-79 float64 80+ float64 dtype: object
After knowing the structure, type, first five lines of data and last five lines of data,
we can start to analyze!
First,I want to look at the population in rural,urban and total .
num = (
datas
.reset_index()
.pivot_table(index=["AreaType","Year"], columns="LocationName", values="Total")
)
num.head()
LocationName | Afghanistan | Africa | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | ... | Viet Nam | Wallis and Futuna Islands | Western Africa | Western Asia | Western Europe | Western Sahara | World | Yemen | Zambia | Zimbabwe | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AreaType | Year | |||||||||||||||||||||
Rural | 1980 | 5556.7755 | 175343.6480 | 905.7335 | 5497.6775 | 4.1655 | 1.4310 | 3063.7975 | 0.0 | 22.9820 | 2406.1250 | ... | 22165.6915 | 5.6160 | 52301.1230 | 27159.7335 | 23373.5825 | 17.0115 | 1.349755e+06 | 3299.8300 | 1759.5735 | 2829.2260 |
1985 | 4786.9620 | 195472.4090 | 999.0465 | 5943.9960 | 4.3640 | 1.0795 | 3510.3235 | 0.0 | 21.3600 | 2269.8270 | ... | 24798.5510 | 6.7670 | 57481.0015 | 27865.5290 | 23145.7800 | 14.4270 | 1.430276e+06 | 3939.0330 | 2063.3045 | 3304.2665 | |
1990 | 4791.2695 | 216531.8520 | 1095.6200 | 6286.3550 | 4.4815 | 1.4415 | 3845.1440 | 0.0 | 19.9875 | 2123.2625 | ... | 27476.1015 | 6.9400 | 62686.4890 | 28799.9320 | 22960.5155 | 15.0090 | 1.517893e+06 | 4661.2015 | 2376.6215 | 3714.5410 | |
1995 | 7055.8300 | 239800.4315 | 1025.6345 | 6449.7785 | 3.8915 | 2.0235 | 4304.5350 | 0.0 | 22.5685 | 2061.4270 | ... | 29576.8885 | 7.0715 | 69487.3530 | 31202.7035 | 22983.1190 | 16.2715 | 1.586880e+06 | 5724.9080 | 2780.4135 | 3972.9915 | |
2000 | 8106.1535 | 264767.2485 | 962.7170 | 6356.7990 | 3.2825 | 2.4870 | 4705.3025 | 0.0 | 26.3510 | 2003.4650 | ... | 30586.2410 | 7.2485 | 76299.8000 | 33197.4490 | 22752.9970 | 24.6620 | 1.635785e+06 | 6459.9625 | 3292.7940 | 4141.3335 |
5 rows × 264 columns
China has been promoting urbanization. To see the rural and urban population distribution and the proportion of urban population to total population in China.
China=num.loc[:,"China"]
China
AreaType Year Rural 1980 396766.2860 1985 409655.9600 1990 428631.0385 1995 427187.7220 2000 410522.7375 2005 378829.5530 2010 345217.6200 2015 311053.9925 Total 1980 492007.8385 1985 531149.6085 1990 582714.4835 1995 618765.7145 2000 640214.2915 2005 659088.4175 2010 679910.7325 2015 700793.3045 Urban 1980 95241.5525 1985 121493.6485 1990 154083.4450 1995 191577.9925 2000 229691.5540 2005 280258.8645 2010 334693.1125 2015 389739.3120 Name: China, dtype: float64
import matplotlib.pyplot as plt
labels='Urban','Rural'
sizes=China.loc["Urban",1980],China.loc["Rural",1980]
colors='lightgreen','gold'
explode=0,0
plt.pie(sizes,explode=explode,labels=labels,
colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)
plt.axis('equal')
plt.show()
ax.set_title("Proportion of urban and rural population in China in 1980 ")
Text(0.5, 1, 'Proportion of urban and rural population in China in 1980 ')
labels='Urban','Rural'
sizes=China.loc["Urban",2015],China.loc["Rural",2015]
colors='lightskyblue','lightcoral'
explode=0,0
plt.pie(sizes,explode=explode,labels=labels,
colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)
plt.axis('equal')
plt.show()
ax.set_title("Proportion of urban and rural population in China in 2015 ")
Text(0.5, 1, 'Proportion of urban and rural population in China in 2015 ')
a=China.loc["Urban",2015]/China.loc["Total",2015]
print("The proportion of urban population to total population in 2015 is ",a)
b=China.loc["Urban",1980]/China.loc["Total",1980]
print("The proportion of urban population to total population in 1980 is ",b)
c=a-b
print(f"The difference between 1980 and 2005 is {c*100}%")
The proportion of urban population to total population in 2015 is 0.5561401764220195 The proportion of urban population to total population in 1980 is 0.1935773072038162 The difference between 1980 and 2005 is 36.25628692182033%
Compared to America,which is the richest country all over the world.
America = [
"Central America","South America","Northern America",
"United States of America", "Latin America and the Caribbean"
]
Ame = num[America]
Ame
LocationName | Central America | South America | Northern America | United States of America | Latin America and the Caribbean | |
---|---|---|---|---|---|---|
AreaType | Year | |||||
Rural | 1980 | 18530.9090 | 39269.4750 | 33213.3575 | 30224.4560 | 64977.5765 |
1985 | 19370.9290 | 39060.8100 | 33906.8185 | 30845.1165 | 65599.5530 | |
1990 | 20112.7655 | 38345.1510 | 34675.4870 | 31430.9760 | 65663.3945 | |
1995 | 20965.0825 | 37279.4400 | 33755.6780 | 30479.9550 | 65628.0425 | |
2000 | 21725.9350 | 35830.1325 | 32956.3310 | 29800.9040 | 65000.9745 | |
2005 | 22034.4570 | 35669.6680 | 33134.0630 | 29923.2630 | 64871.1920 | |
2010 | 22286.3725 | 35226.7900 | 33276.1990 | 30018.8050 | 64274.9455 | |
2015 | 22540.0135 | 34721.0710 | 33146.8290 | 29883.3220 | 63648.0315 | |
Total | 1980 | 46691.8210 | 120508.8190 | 127399.8295 | 115088.1805 | 182075.2085 |
1985 | 51913.4340 | 134275.0815 | 133915.2935 | 120935.0010 | 202164.5565 | |
1990 | 57552.9600 | 147917.5365 | 141143.0585 | 127253.3235 | 222601.3250 | |
1995 | 63909.0385 | 161026.0190 | 148729.0350 | 134019.8270 | 243172.6185 | |
2000 | 69798.0255 | 174123.2245 | 157708.5510 | 142297.1975 | 263139.1140 | |
2005 | 74897.6530 | 186293.8495 | 165273.0585 | 149082.8985 | 281273.1445 | |
2010 | 80272.9980 | 197010.3485 | 173250.4500 | 156123.5580 | 298095.7225 | |
2015 | 85967.1155 | 207526.6345 | 180563.9095 | 162563.8170 | 315044.4585 | |
Urban | 1980 | 28160.9120 | 81239.3440 | 94186.4720 | 84863.7245 | 117097.6320 |
1985 | 32542.5050 | 95214.2715 | 100008.4750 | 90089.8845 | 136565.0035 | |
1990 | 37440.1945 | 109572.3855 | 106467.5715 | 95822.3475 | 156937.9305 | |
1995 | 42943.9560 | 123746.5790 | 114973.3570 | 103539.8720 | 177544.5760 | |
2000 | 48072.0905 | 138293.0920 | 124752.2200 | 112496.2935 | 198138.1395 | |
2005 | 52863.1960 | 150624.1815 | 132138.9955 | 119159.6355 | 216401.9525 | |
2010 | 57986.6255 | 161783.5585 | 139974.2510 | 126104.7530 | 233820.7770 | |
2015 | 63427.1020 | 172805.5635 | 147417.0805 | 132680.4950 | 251396.4270 |
d=Ame.loc["Urban",2015].mean()/Ame.loc["Total",2015].mean()
d
0.8067186601567283
d-a
0.2505784837347088
plt.figure(figsize=(8, 6), dpi=80)
plt.subplot(1, 1, 1)
N = 2
values = (a,d)
index = np.arange(N)
width = 0.2
p2 = plt.bar(index, values, width, label="num", color="#87CEFA")
plt.xlabel('Country')
plt.ylabel('Urbanization Rate')
plt.title('Urbanization rate of China and America in 2015 ')
plt.xticks(index, ('China', 'America'))
plt.legend(loc="upper right")
plt.show()
We can see the proportion of urban population to total population of American is about 25.1%
higher than
the proportion of urban population to total population of China in 2015.
we should work harder to promote urbanization. Aiming at create more beautiful lives to Chinese people.
What is more,many developing countries also vigorously develop urbanization,
take Brazil
and Russia
for example.
def urbanization(x,y):
a=num.loc["Urban",x]/num.loc["Total",x]
b=num.loc["Urban",y]/num.loc["Total",y]
c=a-b
return c
x=urbanization("Brazil","China")
print(x)
x.plot(kind='bar')
Year 1980 0.461098 1985 0.469887 1990 0.474796 1995 0.466486 2000 0.453147 2005 0.403121 2010 0.351094 2015 0.300732 dtype: float64
<matplotlib.axes._subplots.AxesSubplot at 0x12416de50>
y=urbanization("Russian Federation","China")
print(y)
y.plot(kind='bar')
Year 1980 0.503938 1985 0.490495 1990 0.469512 1995 0.424105 2000 0.374726 2005 0.309406 2010 0.244611 2015 0.183937 dtype: float64
<matplotlib.axes._subplots.AxesSubplot at 0x123bca450>
Even compared with developing countries, China's urbanization is insufficient.
However,the difference is decreasing.
Totally,we can see the difference by the picture.
countries=["China","Russian Federation","Brazil"]
u_c=num.loc["Urban",countries]
t_c=num.loc["Total",countries]
countries_urbanization=u_c/t_c
countries_urbanization.plot(figsize=(8,6))
<matplotlib.axes._subplots.AxesSubplot at 0x122d1cad0>
worldu=num.loc[("Urban",2015),:]/num.loc[("Total",2015),:]
def urbanization(x):
if x>0.7:
out="High"
elif 0.3<=x<=0.7:
out="Median"
else:
out="Low"
return out
worldclass=worldu.agg(urbanization)
def count(lst,x):
count=0
for ele in lst:
if(ele==x):
count=count+1
return count
print("The number of countries with low urbanizationt is",count(worldclass,"Low"))
print("The number of countries with median urbanizationt is",count(worldclass,"Median"))
print("The number of countries with high urbanizationt is",count(worldclass,"High"))
The number of countries with low urbanizationt is 35 The number of countries with median urbanizationt is 126 The number of countries with high urbanizationt is 103
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
name=["Low","Median","High"]
number=[count(worldclass,"Low"),count(worldclass,"Median"),count(worldclass,"High")]
fig, ax = plt.subplots()
ax.bar(name,number,align="center")
ax.set_title("World urbanization rate")
fig.tight_layout()
Above all,there are more
countries with high and medium urbanization rate, and less
countries with low urbanization rate
Another problem of the international community is the aging of the population。
The population aged over 60 in a country or region accounts for 10%
of the total population,
which is the standard of population aging
datas["old"]=datas["60-64"]+datas["65-69"]+datas["70-74"]+datas["75-79"]+datas["80+"]
datas["oldtotal"]=datas["old"]/datas["Total"]
aging=datas.pivot_table(
index=["LocationName","Year"],
columns=["AreaType"],
values=['oldtotal']
)
aging
oldtotal | ||||
---|---|---|---|---|
AreaType | Rural | Total | Urban | |
LocationName | Year | |||
Afghanistan | 1980 | 0.038050 | 0.037457 | 0.034267 |
1985 | 0.034210 | 0.033611 | 0.030675 | |
1990 | 0.031403 | 0.030831 | 0.028276 | |
1995 | 0.035018 | 0.034333 | 0.031549 | |
2000 | 0.034441 | 0.033706 | 0.030985 | |
... | ... | ... | ... | ... |
Zimbabwe | 1995 | 0.057030 | 0.047517 | 0.026753 |
2000 | 0.061302 | 0.050177 | 0.028015 | |
2005 | 0.067016 | 0.054626 | 0.030316 | |
2010 | 0.068769 | 0.056164 | 0.030402 | |
2015 | 0.069503 | 0.057030 | 0.030490 |
2112 rows × 3 columns
aging1=aging>0.1
aging1.all(axis=1)
aging2=aging[aging1.all(axis=1)]
aging2
oldtotal | ||||
---|---|---|---|---|
AreaType | Rural | Total | Urban | |
LocationName | Year | |||
Albania | 2005 | 0.115562 | 0.123603 | 0.132790 |
2010 | 0.135169 | 0.141915 | 0.148121 | |
2015 | 0.155378 | 0.162582 | 0.167947 | |
Andorra | 1985 | 0.137183 | 0.119252 | 0.118340 |
1990 | 0.196106 | 0.138612 | 0.135385 | |
... | ... | ... | ... | ... |
Western Europe | 2005 | 0.238195 | 0.226562 | 0.222911 |
2010 | 0.253911 | 0.242092 | 0.238644 | |
2015 | 0.270955 | 0.259675 | 0.256604 | |
World | 2010 | 0.101989 | 0.110673 | 0.118771 |
2015 | 0.111968 | 0.122325 | 0.131114 |
652 rows × 3 columns
From this picture, we know which country has a serious aging population in which year.
What is the current situation of China's aging population
chinaage=aging2.loc["China"]
chinaage
oldtotal | |||
---|---|---|---|
AreaType | Rural | Total | Urban |
Year | |||
2010 | 0.139195 | 0.124475 | 0.109291 |
2015 | 0.166820 | 0.149390 | 0.135478 |
Since 2010
, China has entered an aging society, so a series of policies such as the two child policy are meaningful.
chinaage.idxmax()
AreaType oldtotal Rural 2015 Total 2015 Urban 2015 dtype: int64
import pyecharts.options as opts
from pyecharts.charts import Line
def ageing_trend(legion, area, time, data):
time_start = time[0]
time_end = time[-1]
x = [str(i) for i in list(range(int(time_start), int(time_end)+5, 5))]
y = []
hash_area = ['Rural', 'Total', 'Urban']
hash_time = [str(i) for i in list(range(1980, 2015+5, 5))]
for l in legion:
for a in area:
y_la = [round(data.loc[l].values.tolist()[i][hash_area.index(a)], 4)
for i in range(hash_time.index(time_start), hash_time.index(time_end)+1)]
y.append(y_la)
c = (Line()
.set_global_opts(
tooltip_opts = opts.TooltipOpts(
is_show = True,
trigger = 'axis',
),
xaxis_opts = opts.AxisOpts(type_ = 'category'),
yaxis_opts = opts.AxisOpts(type_ = 'value')
)
.add_xaxis(xaxis_data = x))
for i, y_i in enumerate(y):
(c
.add_yaxis(
series_name = legion[i // len(area)] + '-' + area[i % len(area)],
y_axis = y_i,
label_opts = opts.LabelOpts(is_show = False)
))
return c
chinad= ageing_trend(['China'], ['Urban', 'Rural','Total'], ['2010','2015'], aging)
chinad.render_notebook()
The figure shows that the aging in Chinese urban,rural and total in 2015 is more serious than that in 2010.
And rural areas are more serious than urban areas.
As we all know,Japan's
aging population is very serious.
janage=aging2.loc["Japan"]
janage
oldtotal | |||
---|---|---|---|
AreaType | Rural | Total | Urban |
Year | |||
1980 | 0.164674 | 0.127924 | 0.116417 |
1985 | 0.186134 | 0.145938 | 0.133720 |
1990 | 0.220314 | 0.173543 | 0.159822 |
1995 | 0.251016 | 0.202847 | 0.189255 |
2000 | 0.277478 | 0.232308 | 0.220025 |
2005 | 0.301641 | 0.264410 | 0.258330 |
2010 | 0.348104 | 0.306213 | 0.301826 |
2015 | 0.375979 | 0.330875 | 0.327737 |
As shown in the figure above, Japan has entered an aging society since 1980
c = ageing_trend(['Japan','China'], ['Urban', 'Rural'], ['1985', '2010'], aging)
c.render_notebook()
Show by picture,China's aging level is significantly lower than Japan's.
country=["More developed regions",
"Less developed regions",
"Least developed countries"
]
answerone=aging2.loc[country]
answerone
oldtotal | ||||
---|---|---|---|---|
AreaType | Rural | Total | Urban | |
LocationName | Year | |||
More developed regions | 1980 | 0.172530 | 0.154211 | 0.146441 |
1985 | 0.183525 | 0.163804 | 0.155894 | |
1990 | 0.199471 | 0.175749 | 0.166698 | |
1995 | 0.208004 | 0.183172 | 0.174137 | |
2000 | 0.219407 | 0.193883 | 0.185013 | |
2005 | 0.222378 | 0.200694 | 0.193771 | |
2010 | 0.234835 | 0.217469 | 0.212317 | |
2015 | 0.252410 | 0.235876 | 0.231286 |
world= ageing_trend(['More developed regions', 'Less developed regions','Least developed countries'], ['Urban', 'Rural'], ['1985', '2010'], aging)
world.render_notebook()
The more developed
regions, the more serious
the problem of aging.
aging_dist = datas.pivot_table(
index = ['LocationName', 'AreaType', 'Year'],
values = ['Total', '00-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '40-44',
'45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80+'],
aggfunc = sum
)
aging_dist
00-04 | 05-09 | 10-14 | 15-19 | 20-24 | 25-29 | 30-34 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | Total | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LocationName | AreaType | Year | |||||||||||||||||
Afghanistan | Rural | 1980 | 2134.670152 | 1674.161759 | 1379.329322 | 1142.958692 | 952.153897 | 788.494444 | 699.302280 | 456.024494 | 370.688023 | 299.547044 | 235.868330 | 174.212760 | 119.305452 | 73.490445 | 37.352873 | 17.808094 | 11113.551 |
1985 | 1888.780559 | 1494.126835 | 1211.516285 | 992.624719 | 818.826639 | 689.027515 | 581.596084 | 376.098685 | 286.279450 | 218.336788 | 175.824602 | 139.589563 | 93.380761 | 54.697222 | 27.355329 | 11.525314 | 9573.924 | ||
1990 | 1898.349640 | 1512.253823 | 1247.427465 | 1009.086829 | 820.870240 | 668.665143 | 566.148344 | 424.401314 | 292.592115 | 208.771959 | 149.153328 | 117.226940 | 90.842491 | 54.063233 | 25.844764 | 11.684509 | 9582.539 | ||
1995 | 2831.482138 | 2178.244471 | 1778.224886 | 1477.282874 | 1214.872262 | 991.876815 | 809.210440 | 558.830013 | 486.170485 | 355.269990 | 263.592437 | 191.204216 | 137.974688 | 91.013977 | 47.568130 | 25.220248 | 14111.660 | ||
2000 | 3394.743578 | 2579.193145 | 2035.260598 | 1649.032815 | 1351.763975 | 1107.274803 | 911.526849 | 621.211158 | 510.714592 | 435.324771 | 307.932125 | 219.608650 | 149.484543 | 99.234447 | 57.144391 | 31.785319 | 16212.307 | ||
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Zimbabwe | Urban | 1995 | 525.906392 | 425.870791 | 396.766722 | 417.556199 | 457.046560 | 397.108653 | 312.367921 | 179.320236 | 109.255051 | 82.778999 | 57.161274 | 42.910222 | 24.810057 | 18.027940 | 7.663734 | 5.669582 | 3693.381 |
2000 | 558.334132 | 456.118889 | 441.337794 | 513.118486 | 581.256762 | 473.902028 | 337.852853 | 191.458791 | 145.422263 | 85.572126 | 68.983082 | 50.585392 | 28.741296 | 22.744105 | 8.642400 | 7.823765 | 4220.985 | ||
2005 | 570.734520 | 455.511208 | 444.729567 | 530.767011 | 651.547967 | 533.229824 | 340.827977 | 162.948544 | 129.541270 | 101.241801 | 64.673103 | 56.501767 | 31.517830 | 24.561939 | 10.191089 | 8.893701 | 4335.602 | ||
2010 | 588.131412 | 447.702109 | 424.216728 | 507.028396 | 642.936672 | 588.761577 | 389.690159 | 137.203176 | 102.862617 | 85.551082 | 74.734273 | 51.117511 | 34.299586 | 26.080256 | 10.582869 | 9.987000 | 4341.045 | ||
2015 | 609.148657 | 469.454738 | 419.403991 | 501.195050 | 681.884339 | 688.309343 | 547.593104 | 197.950250 | 114.208427 | 79.925844 | 69.337407 | 63.423180 | 32.772526 | 29.562644 | 11.623216 | 11.078155 | 4871.253 |
6336 rows × 17 columns
aging_dist.loc['China']
00-04 | 05-09 | 10-14 | 15-19 | 20-24 | 25-29 | 30-34 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | Total | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AreaType | Year | |||||||||||||||||
Rural | 1980 | 83282.677002 | 105203.656999 | 106257.041631 | 87683.844761 | 65170.695665 | 68488.249391 | 50234.810965 | 36028.274944 | 33929.844292 | 29526.614953 | 25299.963913 | 22315.795016 | 16985.378642 | 11982.725466 | 7409.428787 | 4599.764432 | 793532.572 |
1985 | 86860.461240 | 78810.041314 | 99349.591817 | 98897.072030 | 79612.373846 | 63687.146179 | 65303.895682 | 36947.933856 | 34469.428764 | 32173.465552 | 27364.523251 | 22381.801230 | 18666.853181 | 13080.045753 | 8183.783145 | 6008.900285 | 819311.920 | |
1990 | 108383.934101 | 84066.569787 | 75930.071609 | 92266.402672 | 91198.050259 | 72899.894087 | 56934.359128 | 44561.857987 | 34257.767682 | 31234.966200 | 29149.434439 | 24437.600047 | 19341.065038 | 15069.674021 | 9307.334880 | 7210.062984 | 857262.077 | |
1995 | 81972.516869 | 102985.509246 | 79476.121725 | 69888.048834 | 84206.151482 | 83852.417087 | 68644.227344 | 56046.695833 | 40812.782176 | 31310.194203 | 27466.028368 | 24404.107553 | 19400.181449 | 14079.975944 | 9737.968801 | 7486.140632 | 854375.444 | |
2000 | 57238.010902 | 77611.097054 | 97566.278277 | 64115.721691 | 55267.087493 | 71561.515515 | 76221.781774 | 49745.412656 | 53962.235564 | 39391.017665 | 29938.973153 | 26157.829809 | 22552.447786 | 16639.508991 | 10807.425855 | 9296.357012 | 821045.475 | |
2005 | 50497.205255 | 54431.495784 | 72609.132870 | 72971.051319 | 54044.372308 | 50044.228225 | 64699.602675 | 56067.726085 | 46501.028892 | 48029.599792 | 34553.815807 | 25842.339743 | 22583.944834 | 17934.098200 | 11772.406237 | 10112.231432 | 757659.106 | |
2010 | 51217.759520 | 45163.484790 | 47304.689303 | 51386.724722 | 60657.016759 | 46296.291165 | 40930.009704 | 60366.057077 | 51323.359586 | 41272.412691 | 44157.336840 | 31294.239365 | 22505.361400 | 17678.828825 | 12907.843001 | 11551.583783 | 690435.240 | |
2015 | 47757.145629 | 43009.110715 | 38906.343200 | 33955.739122 | 42254.228710 | 50667.389524 | 39271.863164 | 50915.430229 | 52242.944273 | 44767.641347 | 37463.749003 | 37754.250453 | 25420.247574 | 16570.250683 | 12183.215314 | 11692.576879 | 622107.985 | |
Total | 1980 | 98790.309000 | 123126.870000 | 126427.152000 | 107938.656000 | 86492.061000 | 88737.972000 | 64293.045000 | 47247.224000 | 44487.426000 | 37856.536000 | 31387.007000 | 27303.533000 | 20614.082000 | 14578.406000 | 9044.731000 | 5709.592000 | 984015.677 |
1985 | 108906.013000 | 97333.901000 | 122457.909000 | 125884.252000 | 107271.802000 | 85828.619000 | 87987.402000 | 49271.389000 | 46289.346000 | 43120.160000 | 36051.891000 | 28989.858000 | 24043.509000 | 16834.087000 | 10574.886000 | 7838.158000 | 1062299.217 | |
1990 | 136834.851000 | 107720.932000 | 96908.899000 | 121984.088000 | 125158.824000 | 106503.052000 | 85142.496000 | 62786.287000 | 48340.803000 | 44962.825000 | 41189.899000 | 33438.375000 | 25667.568000 | 19718.739000 | 12265.331000 | 9668.704000 | 1165428.967 | |
1995 | 109707.576000 | 135717.236000 | 107329.934000 | 96502.955000 | 121206.101000 | 124204.245000 | 105623.393000 | 86048.792000 | 61658.432000 | 47035.216000 | 43060.527000 | 38355.385000 | 29739.250000 | 21155.357000 | 14437.453000 | 11428.906000 | 1237531.429 | |
2000 | 83372.183000 | 109038.689000 | 135323.217000 | 106946.991000 | 95921.855000 | 120353.899000 | 123272.914000 | 83350.916000 | 84620.724000 | 60121.376000 | 45174.572000 | 40256.694000 | 34277.514000 | 24613.812000 | 15589.309000 | 13484.173000 | 1280428.583 | |
2005 | 78477.373000 | 83055.222000 | 108795.693000 | 134743.841000 | 106031.432000 | 94936.462000 | 119245.181000 | 103575.341000 | 82106.866000 | 82882.750000 | 58208.415000 | 42815.370000 | 36713.628000 | 29195.381000 | 18961.029000 | 16261.607000 | 1318176.835 | |
2010 | 85578.986000 | 78235.086000 | 82893.073000 | 108370.459000 | 133873.687000 | 105153.612000 | 94140.898000 | 121039.748000 | 102224.679000 | 80577.301000 | 80467.263000 | 55392.410000 | 39275.246000 | 31531.588000 | 22758.080000 | 19980.314000 | 1359821.465 | |
2015 | 91238.085000 | 85290.706000 | 78080.777000 | 82604.506000 | 107827.031000 | 133206.619000 | 104616.291000 | 117516.305000 | 119760.437000 | 100504.348000 | 78300.252000 | 76554.742000 | 50808.615000 | 33870.273000 | 24610.100000 | 23168.305000 | 1401586.609 | |
Urban | 1980 | 15507.631998 | 17923.213001 | 20170.110369 | 20254.811239 | 21321.365335 | 20249.722609 | 14058.234035 | 11218.949056 | 10557.581708 | 8329.921047 | 6087.043087 | 4987.737984 | 3628.703358 | 2595.680534 | 1635.302213 | 1109.827568 | 190483.105 |
1985 | 22045.551760 | 18523.859686 | 23108.317183 | 26987.179970 | 27659.428154 | 22141.472821 | 22683.506318 | 12323.455144 | 11819.917236 | 10946.694448 | 8687.367749 | 6608.056770 | 5376.655819 | 3754.041247 | 2391.102855 | 1829.257715 | 242987.297 | |
1990 | 28450.916899 | 23654.362213 | 20978.827391 | 29717.685328 | 33960.773741 | 33603.157913 | 28208.136872 | 18224.429013 | 14083.035318 | 13727.858800 | 12040.464561 | 9000.774953 | 6326.502962 | 4649.064979 | 2957.996120 | 2458.641016 | 308166.890 | |
1995 | 27735.059131 | 32731.726754 | 27853.812275 | 26614.906166 | 36999.949518 | 40351.827913 | 36979.165656 | 30002.096167 | 20845.649824 | 15725.021797 | 15594.498632 | 13951.277447 | 10339.068551 | 7075.381056 | 4699.484199 | 3942.765368 | 383155.985 | |
2000 | 26134.172098 | 31427.591946 | 37756.938723 | 42831.269309 | 40654.767507 | 48792.383485 | 47051.132226 | 33605.503344 | 30658.488436 | 20730.358335 | 15235.598847 | 14098.864191 | 11725.066214 | 7974.303009 | 4781.883145 | 4187.815988 | 459383.108 | |
2005 | 27980.167745 | 28623.726216 | 36186.560130 | 61772.789681 | 51987.059692 | 44892.233775 | 54545.578325 | 47507.614915 | 35605.837108 | 34853.150208 | 23654.599193 | 16973.030257 | 14129.683166 | 11261.282800 | 7188.622763 | 6149.375568 | 560517.729 | |
2010 | 34361.226480 | 33071.601210 | 35588.383697 | 56983.734278 | 73216.670241 | 58857.320835 | 53210.888296 | 60673.690923 | 50901.319414 | 39304.888309 | 36309.926160 | 24098.170635 | 16769.884600 | 13852.759175 | 9850.236999 | 8428.730217 | 669386.225 | |
2015 | 43480.939371 | 42281.595285 | 39174.433800 | 48648.766878 | 65572.802290 | 82539.229476 | 65344.427836 | 66600.874771 | 67517.492727 | 55736.706653 | 40836.502997 | 38800.491547 | 25388.367426 | 17300.022317 | 12426.884686 | 11475.728121 | 779478.624 |
aging_dist.loc['Japan']
00-04 | 05-09 | 10-14 | 15-19 | 20-24 | 25-29 | 30-34 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 | 80+ | Total | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AreaType | Year | |||||||||||||||||
Rural | 1980 | 1952.958589 | 2276.075590 | 2070.270454 | 1830.290978 | 1542.196029 | 1964.175241 | 2212.944790 | 1834.816488 | 1972.136535 | 1948.898640 | 1587.262372 | 1286.859733 | 1166.213795 | 922.062771 | 647.998392 | 539.698365 | 27615.502 |
1985 | 1734.519675 | 2036.536650 | 2299.269110 | 1825.024132 | 1430.929050 | 1625.940946 | 2017.081354 | 1856.454968 | 1807.286850 | 1932.186822 | 1895.690919 | 1529.033396 | 1205.713098 | 1043.778020 | 752.071819 | 690.309561 | 27942.380 | |
1990 | 1451.400695 | 1801.767456 | 2047.699370 | 2029.306183 | 1397.172798 | 1451.212305 | 1637.076343 | 2252.250449 | 1821.655674 | 1766.836239 | 1876.403444 | 1825.220981 | 1441.381111 | 1092.967997 | 877.602475 | 889.078931 | 27703.483 | |
1995 | 1275.195408 | 1535.666151 | 1816.662494 | 1816.237788 | 1621.793437 | 1439.094869 | 1492.867718 | 2053.505603 | 2224.399059 | 1784.784075 | 1723.526057 | 1805.757171 | 1719.251381 | 1318.365207 | 933.137693 | 1117.489893 | 27366.317 | |
2000 | 1177.502371 | 1329.648334 | 1544.326479 | 1609.616389 | 1428.364410 | 1640.619355 | 1460.323271 | 1688.907556 | 2024.474558 | 2185.017839 | 1753.690409 | 1683.994558 | 1712.681918 | 1576.532453 | 1152.931677 | 1348.360931 | 26841.781 | |
2005 | 749.945776 | 830.076198 | 905.159268 | 939.077427 | 890.185385 | 1002.421027 | 1122.429812 | 1031.379176 | 1133.317086 | 1354.603324 | 1445.862102 | 1171.197621 | 1090.390481 | 1059.628239 | 919.372153 | 1148.660464 | 17804.779 | |
2010 | 480.741079 | 532.526621 | 581.469323 | 564.202652 | 536.815435 | 592.266658 | 675.487073 | 699.581763 | 706.259420 | 778.529426 | 932.915440 | 985.760823 | 802.224943 | 724.301016 | 672.709744 | 1028.218327 | 12070.342 | |
2015 | 324.124948 | 349.612652 | 374.188109 | 378.095424 | 328.696312 | 371.623018 | 411.255378 | 532.289403 | 521.996785 | 543.995470 | 546.201594 | 581.462210 | 625.987438 | 550.020097 | 485.567042 | 866.280275 | 8245.551 | |
Total | 1980 | 8425.462000 | 10023.593000 | 8861.847000 | 8199.056000 | 7778.115000 | 9054.658000 | 10669.960000 | 8273.704000 | 8007.568000 | 7113.685000 | 5523.713000 | 4375.630000 | 3912.158000 | 2978.445000 | 2003.147000 | 1591.386000 | 115912.104 |
1985 | 7406.792000 | 8504.840000 | 10033.840000 | 8884.920000 | 8132.473000 | 7766.015000 | 9070.608000 | 9053.998000 | 8173.900000 | 7851.159000 | 6915.436000 | 5319.246000 | 4108.403000 | 3514.634000 | 2453.968000 | 2160.195000 | 119988.663 | |
1990 | 6430.434000 | 7460.626000 | 8498.302000 | 9995.297000 | 8702.295000 | 7997.355000 | 7726.074000 | 10554.906000 | 8934.009000 | 8023.815000 | 7641.491000 | 6662.651000 | 5021.324000 | 3738.316000 | 2975.016000 | 2871.904000 | 122249.285 | |
1995 | 5970.705000 | 6528.210000 | 7471.766000 | 8530.933000 | 9884.930000 | 8694.585000 | 8056.326000 | 9019.486000 | 10519.841000 | 8841.513000 | 7891.922000 | 7392.964000 | 6317.749000 | 4619.441000 | 3216.916000 | 3762.988000 | 124483.305 | |
2000 | 5858.083000 | 5992.304000 | 6534.552000 | 7481.678000 | 8394.685000 | 9782.756000 | 8684.281000 | 7741.920000 | 8928.982000 | 10342.985000 | 8654.066000 | 7673.926000 | 7024.429000 | 5825.320000 | 4082.687000 | 4665.812000 | 125714.674 | |
2005 | 5639.483000 | 5877.963000 | 6010.086000 | 6582.365000 | 7549.657000 | 8470.903000 | 9789.363000 | 8012.676000 | 7724.264000 | 8860.128000 | 10101.564000 | 8453.822000 | 7351.925000 | 6540.869000 | 5171.887000 | 6136.503000 | 126978.754 | |
2010 | 5393.342000 | 5598.901000 | 5928.424000 | 6055.616000 | 6795.206000 | 7425.856000 | 8338.003000 | 8668.379000 | 7940.356000 | 7657.775000 | 8754.496000 | 9842.212000 | 8260.959000 | 6969.029000 | 5927.028000 | 8085.933000 | 127352.833 | |
2015 | 5341.202000 | 5399.185000 | 5603.801000 | 5960.789000 | 6111.554000 | 6843.872000 | 7455.781000 | 9688.001000 | 8620.321000 | 7859.541000 | 7528.520000 | 8527.540000 | 9467.962000 | 7773.420000 | 6284.234000 | 10007.646000 | 126818.019 | |
Urban | 1980 | 6472.503411 | 7747.517410 | 6791.576546 | 6368.765022 | 6235.918971 | 7090.482759 | 8457.015210 | 6438.887512 | 6035.431465 | 5164.786360 | 3936.450628 | 3088.770267 | 2745.944205 | 2056.382229 | 1355.148608 | 1051.687635 | 88296.602 |
1985 | 5672.272325 | 6468.303350 | 7734.570890 | 7059.895868 | 6701.543950 | 6140.074054 | 7053.526646 | 7197.543032 | 6366.613150 | 5918.972178 | 5019.745081 | 3790.212604 | 2902.689902 | 2470.855980 | 1701.896181 | 1469.885439 | 92046.283 | |
1990 | 4979.033305 | 5658.858544 | 6450.602630 | 7965.990817 | 7305.122202 | 6546.142695 | 6088.997657 | 8302.655551 | 7112.353326 | 6256.978761 | 5765.087556 | 4837.430019 | 3579.942889 | 2645.348003 | 2097.413525 | 1982.825069 | 94545.802 | |
1995 | 4695.509592 | 4992.543849 | 5655.103506 | 6714.695212 | 8263.136563 | 7255.490131 | 6563.458282 | 6965.980397 | 8295.441941 | 7056.728925 | 6168.395943 | 5587.206829 | 4598.497619 | 3301.075793 | 2283.778307 | 2645.498107 | 97116.988 | |
2000 | 4680.580629 | 4662.655666 | 4990.225521 | 5872.061611 | 6966.320590 | 8142.136645 | 7223.957729 | 6053.012444 | 6904.507442 | 8157.967161 | 6900.375591 | 5989.931442 | 5311.747082 | 4248.787547 | 2929.755323 | 3317.451069 | 98872.893 | |
2005 | 4889.537224 | 5047.886802 | 5104.926732 | 5643.287573 | 6659.471615 | 7468.481973 | 8666.933188 | 6981.296824 | 6590.946914 | 7505.524676 | 8655.701898 | 7282.624379 | 6261.534519 | 5481.240761 | 4252.514847 | 4987.842536 | 109173.975 | |
2010 | 4912.600921 | 5066.374379 | 5346.954677 | 5491.413348 | 6258.390565 | 6833.589342 | 7662.515927 | 7968.797237 | 7234.096580 | 6879.245574 | 7821.580560 | 8856.451177 | 7458.734057 | 6244.727984 | 5254.318256 | 7057.714673 | 115282.491 | |
2015 | 5017.077052 | 5049.572348 | 5229.612891 | 5582.693576 | 5782.857688 | 6472.248982 | 7044.525622 | 9155.711597 | 8098.324215 | 7315.545530 | 6982.318406 | 7946.077790 | 8841.974562 | 7223.399903 | 5798.666958 | 9141.365725 | 118572.468 |
Next, let's draw a picture to see the specific situation
def aging_dist_curve(location, area, time, age, data):
hash_x = ['00-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '40-44',
'45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80+']
age_start = age[0]
age_end = age[-1]
x = hash_x[hash_x.index(age_start): hash_x.index(age_end)+1]
y = []
hash_area = ['Rural', 'Total', 'Urban']
hash_time = [str(i) for i in list(range(1980, 2015+5, 5))]
for l in location:
for a in area:
for t in time:
y_lat = [round(data.loc[l].values.tolist()[hash_area.index(a)*8 + hash_time.index(t)][i] /
data.loc[l].values.tolist()[hash_area.index(a)*8 + hash_time.index(t)][-1], 4)
for i in range(hash_x.index(age_start), hash_x.index(age_end)+1)]
y.append(y_lat)
c = (Line()
.set_global_opts(
tooltip_opts = opts.TooltipOpts(
is_show = True,
trigger = 'axis',
),
xaxis_opts = opts.AxisOpts(type_ = 'category'),
yaxis_opts = opts.AxisOpts(type_ = 'value')
)
.add_xaxis(xaxis_data = x))
for i, y_i in enumerate(y):
(c
.add_yaxis(
series_name = location[i // (len(area)+len(time))]
+ '-'
+ area[(i % (len(area)+len(time))) // len(time)]
+ '-'
+ time[(i % (len(area)+len(time))) % len(time)],
y_axis = y_i,
label_opts = opts.LabelOpts(is_show = False)
))
return c
c_dist = aging_dist_curve(['China'], ['Urban', 'Rural'], ['2015', '1985'], ['00-04', '80+'], aging_dist)
c_dist.render_notebook()
In 1985, the age distribution of rural and urban areas in China was relatively consistent.
In 2015, the proportion of the elderly increased and the proportion of children decreased,
it is obvious that there are more young people in cities and more old people in rural areas.
How to rationalize age distribution is a problem.
j_dist = aging_dist_curve(['Japan'], ['Urban', 'Rural'], ['2015', '1980'], ['00-04', '80+'], aging_dist)
j_dist.render_notebook()
g_dist = aging_dist_curve(['Germany'], ['Urban', 'Rural'], ['2015', '1980'], ['00-04', '80+'], aging_dist)
g_dist.render_notebook()
Janpan and Germany
is the first and second most aging country in the world,
and the proportion of the elderly is significantly higher
than that of China
t_dist = aging_dist_curve(["More developed regions",
"Least developed countries"], ['Urban','Rural'], ['2015','1980'], ['00-04', '80+'], aging_dist)
t_dist.render_notebook()
There are more senior citizens in developed countries,
less senior citizens and more children in underdeveloped countries.
gender = (
datas
.reset_index()
.pivot_table(index=["Year","Sex"], columns="LocationName", values="Total")
)
gender
LocationName | Afghanistan | Africa | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | ... | Viet Nam | Wallis and Futuna Islands | Western Africa | Western Asia | Western Europe | Western Sahara | World | Yemen | Zambia | Zimbabwe | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Year | Sex | |||||||||||||||||||||
1980 | Female | 4318.727333 | 160004.983333 | 888.802000 | 6446.278000 | 10.662000 | 11.054000 | 2587.265333 | 2.303333 | 24.029333 | 9501.424667 | ... | 18645.439333 | 3.705333 | 45455.865333 | 37550.003333 | 59191.926667 | 46.548000 | 1.474806e+06 | 2695.914667 | 1962.147333 | 2443.164000 |
Male | 4468.226667 | 158967.996000 | 934.382000 | 6537.191333 | 10.975333 | 12.988000 | 2504.162000 | 2.162000 | 22.838000 | 9245.332000 | ... | 17952.818667 | 3.782667 | 45876.272667 | 38149.517333 | 55027.100667 | 54.033333 | 1.491227e+06 | 2575.403333 | 1936.013333 | 2416.215333 | |
1985 | Female | 3754.215333 | 183741.164667 | 1000.489333 | 7551.522000 | 12.807333 | 13.932000 | 3065.923333 | 2.295333 | 22.589333 | 10278.320000 | ... | 20915.040000 | 4.438000 | 52169.356000 | 43192.138000 | 59793.644000 | 57.116000 | 1.611151e+06 | 3269.180667 | 2296.683333 | 2967.245333 |
Male | 3931.769333 | 182822.692000 | 1051.464000 | 7680.102667 | 13.357333 | 15.799333 | 2976.507333 | 2.146000 | 21.240000 | 9948.626000 | ... | 20190.521333 | 4.584667 | 52622.582667 | 44153.929333 | 55613.510000 | 64.498000 | 1.631250e+06 | 3165.028000 | 2262.244667 | 2939.642000 | |
1990 | Female | 3806.994667 | 210459.271333 | 1120.574000 | 8656.730000 | 15.258000 | 17.029333 | 3497.577333 | 2.793333 | 21.375333 | 11078.128000 | ... | 23345.090000 | 4.624000 | 59613.342000 | 48673.341333 | 60846.088667 | 68.788000 | 1.760802e+06 | 3970.336000 | 2634.827333 | 3502.952667 |
Male | 4013.800667 | 209532.047333 | 1177.347333 | 8836.408667 | 16.104667 | 19.311333 | 3391.652000 | 2.762667 | 19.895333 | 10671.788000 | ... | 22594.832000 | 4.629333 | 60170.228667 | 50152.609333 | 57144.138000 | 75.764000 | 1.786409e+06 | 3889.830000 | 2594.850000 | 3471.568667 | |
1995 | Female | 5738.666667 | 239364.112000 | 1106.889333 | 9673.002667 | 17.213333 | 20.132000 | 4098.069333 | 3.284667 | 23.476667 | 11837.552667 | ... | 25722.717333 | 4.762667 | 68117.522667 | 54306.054000 | 62382.800667 | 81.056667 | 1.899263e+06 | 4937.012000 | 2964.933333 | 3906.594667 |
Male | 5985.382000 | 238305.974667 | 1131.682667 | 9870.639333 | 18.036000 | 22.437333 | 3971.898667 | 3.253333 | 22.089333 | 11384.559333 | ... | 24957.311333 | 4.666000 | 68828.768667 | 56468.108667 | 59091.020667 | 87.811333 | 1.928618e+06 | 5075.122000 | 2929.292000 | 3852.981333 | |
2000 | Female | 6744.332667 | 269848.722667 | 1103.970667 | 10460.362667 | 18.762000 | 20.974000 | 4705.198667 | 3.744000 | 27.491333 | 12553.784667 | ... | 27373.415333 | 4.833333 | 77427.042000 | 60269.883333 | 63101.577333 | 97.060000 | 2.027485e+06 | 5774.849333 | 3380.488667 | 4197.236667 |
Male | 6985.907333 | 269020.835333 | 1099.328000 | 10685.936667 | 19.586000 | 22.625333 | 4578.088000 | 3.636667 | 24.274000 | 12048.260000 | ... | 26551.837333 | 4.831333 | 78441.550667 | 62065.366667 | 59925.706667 | 106.683333 | 2.057649e+06 | 5906.842000 | 3353.498667 | 4138.531333 | |
2005 | Female | 8124.601333 | 304196.310667 | 1061.858667 | 11200.038000 | 19.334667 | 25.804667 | 5577.074000 | 4.261333 | 28.758667 | 13159.482000 | ... | 28744.817333 | 4.760667 | 88143.125333 | 66713.788000 | 64333.790667 | 134.502000 | 2.154663e+06 | 6647.628000 | 3834.680667 | 4282.012667 |
Male | 8449.302000 | 303488.802000 | 1068.894667 | 11440.564000 | 20.076667 | 28.344000 | 5452.510000 | 4.163333 | 26.284667 | 12605.754000 | ... | 27887.084000 | 4.736667 | 89428.794667 | 70127.164667 | 61176.128667 | 150.686000 | 2.188066e+06 | 6778.812667 | 3812.000667 | 4191.713333 | |
2010 | Female | 9322.180000 | 343948.270000 | 1045.177333 | 12213.640667 | 18.333333 | 25.435333 | 6576.864667 | 4.629333 | 30.388667 | 13747.272667 | ... | 30051.390000 | 4.534000 | 100867.051333 | 74225.139333 | 65027.472000 | 162.401333 | 2.287167e+06 | 7521.761333 | 4414.215333 | 4423.864667 |
Male | 9609.694667 | 343440.840667 | 1054.918000 | 12494.906000 | 18.757333 | 26.502667 | 6455.884667 | 4.549333 | 27.766667 | 13168.876667 | ... | 29313.541333 | 4.509333 | 102525.058000 | 80221.891333 | 62051.267333 | 180.697333 | 2.323622e+06 | 7653.577333 | 4397.108000 | 4294.120667 | |
2015 | Female | 10522.397333 | 388566.660667 | 1065.007333 | 13398.476000 | 18.427333 | 26.610667 | 7666.132667 | 4.906000 | 31.952667 | 14346.528667 | ... | 31485.550667 | 4.386000 | 115432.534667 | 80882.534000 | 65684.301333 | 191.421333 | 2.421065e+06 | 8443.296667 | 5188.309333 | 5068.007333 |
Male | 10815.461333 | 388926.210000 | 1066.313333 | 13690.500000 | 18.598000 | 27.356000 | 7547.151333 | 4.836667 | 29.262000 | 13756.747333 | ... | 30772.202667 | 4.382667 | 117756.148000 | 88761.884667 | 62974.310000 | 211.444000 | 2.462123e+06 | 8580.094000 | 5158.093333 | 4962.727333 |
16 rows × 264 columns
x_g=gender.loc[pd.IndexSlice[:, ["Female"]], :]
y_g=gender.loc[pd.IndexSlice[:, ["Male"]], :]
female=x_g.mean(axis=0)
male=y_g.mean(axis=0)
z_g=female/male
z_g["China"]
0.9348049380175202
labels='Men','Women'
sizes=female['China'],male['China']
colors='lightgreen','gold'
explode=0,0
plt.pie(sizes,explode=explode,labels=labels,
colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)
plt.axis('equal')
plt.show()
ax.set_title("Proportion of women and men population in China ")
Text(0.5, 1, 'Proportion of women and men population in China ')
print("The country with the largest share of women is",z_g.idxmax())
The country with the largest share of women is Latvia
labels='Women','Men'
sizes=female['Latvia'],male['Latvia']
colors='lightskyblue','lightcoral'
explode=0,0
plt.pie(sizes,explode=explode,labels=labels,
colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)
plt.axis('equal')
plt.show()
ax.set_title("Proportion of women and men population in Latvia ")
Text(0.5, 1, 'Proportion of women and men population in Latvia ')
print("The country with the largest share of men is",z_g.idxmin())
The country with the largest share of men is Qatar
labels='Women','Men'
sizes=female['Qatar'],male['Qatar']
colors='lightskyblue','lightcoral'
explode=0,0
plt.pie(sizes,explode=explode,labels=labels,
colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)
plt.axis('equal')
plt.show()
ax.set_title("Proportion of women and men population in Latvia ")
Text(0.5, 1, 'Proportion of women and men population in Latvia ')
based on the above analysis, we have a rough understanding of the basic situation of the data set population.
As a Chinese, under the guidance of the party and the state,
we should also make efforts for
match the national policies, and make bold suggestions to contribute to the socialist modernization.