How to read e-Stat subregion data

Header of "Chiyoda Ward (10KB)" in "Age (5 years old class, 4 categories), Gender-specific population" of "2010 National Census (Small Area) 2010/10/01" data of Ministry of Internal Affairs and Communications e-Stat Try to understand the meaning of. If you make a mistake, I would appreciate it if you could comment.

Basically, I think it should be written in the "definition document", but there are many places where the meaning of the definition document is not well understood. I can't find the need to explain "serial number" or "hierarchy".

Looking at the data, you can read it as if it were a column of MultiIndex as follows.

0	1
KEY_CODE
HYOSYO
CITYNAME
NAME
HTKSYORI
HTKSAKI
GASSAN
T000573001	Total number, including age "unknown"
T000573002	Total number 0-4 years old
T000573003	5-9 years old in total
T000573004	10-14 years in total
T000573005	15-19 years old in total
T000573006	20-24 years old in total
T000573007	25-29 years old in total
T000573008	30-34 years old in total
T000573009	35-39 years old in total
T000573010	40-44 years old in total
T000573011	45-49 years old in total
T000573012	50-54 years old in total
T000573013	55-59 years old in total
T000573014	Total 60-64 years old
T000573015	65-69 years old in total
T000573016	70-74 years old in total
T000573017	Under 15 years old
T000573018	15-64 years in total
T000573019	65 years old or older
T000573020	75 years old or older
T000573021	Total number of men, including age "unknown"
T000573022	Male 0-4 years old
T000573023	Male 5-9 years old
T000573024	Male 10-14 years old
T000573025	Male 15-19 years old
T000573026	Man 20-24 years old
T000573027	Male 25-29 years old
T000573028	Male 30-34 years old
T000573029	Male 35-39 years old
T000573030	Male 40-44 years old
T000573031	Male 45-49 years old
T000573032	Male 50-54 years old
T000573033	Male 55-59 years old
T000573034	Male 60-64 years old
T000573035	Male 65-69 years old
T000573036	Male 70-74 years old
T000573037	Man under 15 years old
T000573038	Male 15-64 years old
T000573039	Male over 65 years old
T000573040	Male over 75 years old
T000573041	Total number of women, including age "unknown"
T000573042	Woman 0-4 years old
T000573043	Woman 5-9 years old
T000573044	Woman 10-14 years old
T000573045	Woman 15-19 years old
T000573046	Woman 20-24 years old
T000573047	Woman 25-29 years old
T000573048	Woman 30-34 years old
T000573049	Woman 35-39 years old
T000573050	Woman 40-44 years old
T000573051	Woman 45-49 years old
T000573052	Woman 50-54 years old
T000573053	Woman 55-59 years old
T000573054	Woman 60-64 years old
T000573055	Woman 65-69 years old
T000573056	Woman 70-74 years old
T000573057	Woman under 15 years old
T000573058	Woman 15-64 years old
T000573059	Woman over 65 years old
T000573060	Woman over 75 years old

KEY_CODE, HYOSYO, CITYNAME, NAME, HTKSYORI, HTKSAKI, GASSAN are not listed in the previous definition. These meanings are important.

KEY_CODE

Administrative code. The size of the section changes according to the number of digits. The number of digits is 5, 9, 11, and it is constructed starting from the code system of the administrative code. Both the large compartment and the small compartments it contains also exist as rows. When present in parallel, the larger parcels contain the total value.

HYOSYO

The depth of the assembled code system of KEY_CODE?

CITYNAME

Land name in the largest unit

NAME

Land name in the smallest unit

HTKSYORI

Whether "confidential processing" is being performed. There are two types, "secret area" and "total area", and these are in a mutual relationship.

HTKSAKI

A value is entered when HTKSYORI is a" secret area ". Use in concatenation with the first 5 characters of KEY_CODE on the corresponding line.

GASSAN

A value is entered when HtKSYORI is set to "There is a total area". Use in concatenation with the first 5 characters of KEY_CODE on the corresponding line. When multiple regions are combined, multiple regions are concatenated with a semicolon.

pandas

Like this?

import pandas as pd

opts = dict(
    header=[0,1],
    converters={i:str for i in range(7)}
)
txt_ = pd.concat([pd.read_csv(open(f, encoding="CP932"), **opts) for f in glob.glob("tblT000573C27*.txt")])
txt_columns = txt_.columns
txt_.columns =  [c[0] if c[1].startswith("Unnamed:") else c[1] for c in txt_.columns]
txt = pd.concat([txt_[txt_.columns[:7]],
    txt_[txt_.columns[7:]].applymap(lambda s: 0 if s in ("-","X") else int(s))], axis=1)

for ri,r in txt.iterrows():
    for s in r["GASSAN"].split(";"):
        if not s:
            continue
        t = r["KEY_CODE"][:5]+s
        assert txt[txt["KEY_CODE"]==t].shape[0] == 1