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世界の国別の人口を知りたい。

Posted at 2020-05-08

目標

こういう感じに、年、地域、サブ地域、国、国番号、人口が書いてある表が欲しい時どうするかという話。

LocID,Location,Time,PopTotal,SubRegName,GeoRegName
392,Japan,2019,126860299,Eastern Asia,Asia

結論

https://population.un.org/wpp/ から必要なデータをダウンロード出来ます。

All variants (CSV, 21.35 MB) WPP2019_TotalPopulationBySex.csv より、人口データをダウンロード出来ます。
Locations (XLSX, 131 KB) WPP2019_F01_LOCATIONS.XLSX より、国コードのデータをダウンロード出来ます。

WPP2019_TotalPopulationBySex.csv の中身はこんな感じです。

LocID,Location,VarID,Variant,Time,MidPeriod,PopMale,PopFemale,PopTotal,PopDensity
...
392,Japan,2,Medium,1950,1950.5,40602.499,42199.585,82802.084,227.132
392,Japan,2,Medium,1951,1951.5,41380.556,42935.709,84316.265,231.285

LocID: ISO 3166-1 の数字による国コード
Location: 国や地域の名前
VarID, Variant: 未来の値について出生率や死亡率や移民率を色々変えて予測している。標準は Medium。
Time,MidPeriod: 年
PopMale,PopFemale,PopTotal: 男女別及び総人口 (1000人)
PopDensity: 1km^2 あたり人口密度 (1000人)

この LocID や Location の項目には国名の他に Asia や East Asia のような地域名も含まれているため、このままでは使えないです。地域名を除き国だけに揃えるために WPP2019_F01_LOCATIONS.XLSX を参照します。このファイルには

Location
DB
NOTES

の3つのシートがあり、そのうち DB がちょうど機械読み取りしやすい形になっています。例えば Japan の項目は

Index, Location, Notes, LocID, ISO3_Code, LocType, LocTypeName, ParentID, WorldID, SubRegID, SubRegName, SDGSubRegID, SDGSubRegName, SDGRegID, SDGRegName, GeoRegID, GeoRegName
133, Japan, 392, JPN, 4, Country/Area, 906, 900, 906, Eastern Asia, 1832, Eastern and South-Eastern Asia, 935, Asia

です。という事で、次の事が分かります。

LocType = 4 または LocTypeName = Country/Area の項目は国を表す。
SubRegName はその国のサブ地域を表す。(例: Eastern Asia)
GeoRegName はその国の地域を表す。(例: Asia)

Jupyter でデータを作る

(ノートブックのリンク: https://colab.research.google.com/drive/160xZ5tAGKb1enC0LU2JYEOA6m3l3w1cn?usp=sharing)

ここまで調べてようやく作業開始です。まず WPP2019_TotalPopulationBySex.csv の読み込み。

import pandas as pd

population_src = pd.read_csv("WPP2019_TotalPopulationBySex.csv")
population_src.head()

	LocID	Location	VarID	Variant	Time	MidPeriod	PopMale	PopFemale	PopTotal	PopDensity
0	4	Afghanistan	2	Medium	1950	1950.5	4099.243	3652.874	7752.117	11.874
1	4	Afghanistan	2	Medium	1951	1951.5	4134.756	3705.395	7840.151	12.009
2	4	Afghanistan	2	Medium	1952	1952.5	4174.450	3761.546	7935.996	12.156
3	4	Afghanistan	2	Medium	1953	1953.5	4218.336	3821.348	8039.684	12.315
4	4	Afghanistan	2	Medium	1954	1954.5	4266.484	3884.832	8151.316	12.486

必要な情報だけ抜き出す。

population = population_src[population_src.Variant == "Medium"][["LocID", "Location", "Time", "PopTotal"]]
population["PopTotal"] = (population["PopTotal"] * 1000).astype(int)
population

	LocID	Location	Time	PopTotal
0	4	Afghanistan	1950	7752116
1	4	Afghanistan	1951	7840151
2	4	Afghanistan	1952	7935996
3	4	Afghanistan	1953	8039684
4	4	Afghanistan	1954	8151316

WPP2019_F01_LOCATIONS.XLSX の読み込み。

locations_src = pd.read_excel('WPP2019_F01_LOCATIONS.XLSX', sheet_name="DB")
locations_src.head()

	Index	Location	Notes	LocID	ISO3_Code	LocType	LocTypeName	ParentID	WorldID	SubRegID	SubRegName	SDGSubRegID	SDGSubRegName	SDGRegID	SDGRegName	GeoRegID	GeoRegName	MoreDev	LessDev	LeastDev	oLessDev	LessDev_ExcludingChina	LLDC	SIDS	WB_HIC	WB_MIC	WB_UMIC	WB_LMIC	WB_LIC	WB_NoIncomeGroup	MaxHIV_Male	MaxHIV_Female	MaxHIV_BothSexes	YearMaxHIV_BothSexes	HIVAIDSMortalityImpact_AgePattern	HIVAIDSMortalityImpact_e0	TotPop2019LessThan90k
0	1	WORLD	NaN	900	NaN	NaN	NaN	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2	UN development groups	a	1803	NaN	25.0	Label/Separator	900	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	3	More developed regions	b	901	NaN	5.0	Development group	1803	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	4	Less developed regions	c	902	NaN	5.0	Development group	1803	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	5	Least developed countries	d	941	NaN	5.0	Development group	902	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

必要な情報だけ抜き出す。

location = locations_src[locations_src.LocType == 4][["LocID", "SubRegName", "GeoRegName"]]
location.head()

	LocID	SubRegName	GeoRegName
26	108	Eastern Africa	Africa
27	174	Eastern Africa	Africa
28	262	Eastern Africa	Africa
29	232	Eastern Africa	Africa
30	231	Eastern Africa	Africa

人口データと国データを結合します。

population_by_countries = population.merge(location)
population_by_countries.head()

	LocID	Location	Time	PopTotal	SubRegName	GeoRegName
0	4	Afghanistan	1950	7752116	Southern Asia	Asia
1	4	Afghanistan	1951	7840151	Southern Asia	Asia
2	4	Afghanistan	1952	7935996	Southern Asia	Asia
3	4	Afghanistan	1953	8039684	Southern Asia	Asia
4	4	Afghanistan	1954	8151316	Southern Asia	Asia

日本のデータを探してみます。

population_by_countries[(population_by_countries.Location == "Japan") & (population_by_countries.Time == 2019)]

	LocID	Location	Time	PopTotal	SubRegName	GeoRegName
16377	392	Japan	2019	126860299	Eastern Asia	Asia

良さそうなので保存します。

population_by_countries.to_csv("population_by_countries.csv", index=False)
!head population_by_countries.csv

LocID,Location,Time,PopTotal,SubRegName,GeoRegName
4,Afghanistan,1950,7752116,Southern Asia,Asia
4,Afghanistan,1951,7840151,Southern Asia,Asia
4,Afghanistan,1952,7935996,Southern Asia,Asia
4,Afghanistan,1953,8039684,Southern Asia,Asia
4,Afghanistan,1954,8151316,Southern Asia,Asia
4,Afghanistan,1955,8270992,Southern Asia,Asia
4,Afghanistan,1956,8398873,Southern Asia,Asia
4,Afghanistan,1957,8535157,Southern Asia,Asia
4,Afghanistan,1958,8680097,Southern Asia,Asia

おまけの苦労話

最初日本語版 Wikipedia や WolframAlpha で国の人口順リストを調べると http://data.un.org/ というサイトが出てくるのでここから引っ張ってこようと思った。
Population, surface area and density のリンクにある CSV は機械読み込み出来る感じがしない。
どうやらこれは Statistical Yearbook という本の抜粋という事が分かった。
Yearbook の Annex I によると、このデータの国番号や国名は United Nations Standard Country Codes M 49 と呼ぶ事が分かった。M49 の解説は https://unstats.un.org/unsd/methodology/m49/ に見つかった。これによると、次のような階層構造になっている。
- Global Name: World (1)
- Region Name: Asia (142)
- Sub-region Name: Eastern Asia (30)
- Intermediate Region Name: なし
- Country or Area: Japan (392)
M49 とは ISO 3166-1 の元になった規格らしい。
M49 の機械読み込み出来る形式のファイルが見つからない。一応 CSV のリンクはあるが、国名にコンマが入っていてカラムがずれている。
なんか嫌な気分になった。
他の例を探すと datahub.io では data.worldbank.org のデータを使っていた。
- 実際にダウンロードするコード例:
  - https://github.com/datasets/population/blob/master/scripts/process.py
  - クエリ: http://api.worldbank.org/countries/all/indicators/SP.POP.TOTL?format=csv
- World Bank: http://data.worldbank.org/indicator/SP.POP.TOTL
world population という語で検索すると https://www.worldometers.info/world-population/ が出てくる。
- 資料として https://population.un.org/wpp/ を使っていた！
- どうもダウンロードして使うデータとしてはこちらのほうが治安が良さそうだ。

ということで、たかが世界人口というありふれたデータを取得するだけのためにめちゃくちゃ時間を使ってしまったという事をここに記録する。

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