Find out the age and number of winnings of prefectural governors nationwide

From the Governor's File of the National Governors' Association, the incumbent governor Scraping

import datetime
import re

import pandas as pd
import matplotlib.pyplot as plt


def wareki2date(s):

    m = re.search("(Showa|Heisei|Reiwa)([ 0-9 yuan]{1,2})Year( [0-9]{1,2})Moon([ 0-9]{1,2})Day", s)

    if m:

        year, month, day = [1 if i == "Former" else int(i.strip()) for i in m.group(2, 3, 4)]

        if m.group(1) == "Showa":
            year += 1925
        elif m.group(1) == "Heisei":
            year += 1988
        elif m.group(1) == "Reiwa":
            year += 2018

        return pd.Timestamp(year, month, day)

    else:
        return pd.NaT


df = pd.read_html("http://www.nga.gr.jp/app/chijifile/", attrs={"summary": "Search result list"})[0]

#Convert Japanese calendar to Western calendar
df["Birthday"] = df["Birthday"].apply(wareki2date)
df["Election date"] = df["Election date"].apply(wareki2date)
df["Term expiration date"] = df["Term expiration date"].apply(wareki2date)
df["Inauguration date"] = df["Inauguration date"].apply(wareki2date)

df["age"] = df["age"].str.rstrip("age").astype(int)

age

df["age"].value_counts(bins=[20,40,45,50,55,60,65,70,75,80]).sort_index().plot.bar()

df["age"].describe()

count 47.000000 mean 61.680851 std 9.273868 min 39.000000 25% 56.000000 50% 60.000000 75% 69.500000 max 78.000000 Name: age, dtype: float64

Number of wins

df["Number of wins"].describe()

count 47.000000 mean 2.765957 std 1.447828 min 1.000000 25% 1.000000 50% 3.000000 75% 4.000000 max 7.000000 Name: Number of wins, dtype: float64

Japan Association of City Mayors

The list of Japan Association of City Mayors only lists names.