Simple utilitiy convert name of japanese prefectures
Project description
jp_prefecture.
Japan prefecture and city names and codes
Simple utility to convert the name of japanese prefectures.
- full_name from/to code (JIS X 0401-1973, JIX X 0402).
- short_name to full_name
- alphabet_name from/to full_name
- validate for full_name and short_name, alphabet_name and city name.
- allow code as str or int.
- support lists and pandas serires as input.
- support checkdigits for citycode.
currently alphabet_name and short_name only works for prefectures.
Reference
- https://www.soumu.go.jp/denshijiti/code.html (in japanese)
Install
pip install jp_prefecture
How to use
from jp_prefecture import jp_prefectures as jp
Dataframe of jp.prefectures
>>> from jp_prefecture import jp_prefectures as jp
>>> jp.prefectures
name short_name alphabet_name
code
1 北海道 北海 Hokkaido
2 青森県 青森 Aomori
3 岩手県 岩手 Iwate
4 宮城県 宮城 Miyagi
5 秋田県 秋田 Akita
6 山形県 山形 Yamagata
7 福島県 福島 Fukushima
8 茨城県 茨城 Ibaraki
9 栃木県 栃木 Tochigi
10 群馬県 群馬 Gunma
11 埼玉県 埼玉 Saitama
12 千葉県 千葉 Chiba
13 東京都 東京 Tokyo
14 神奈川県 神奈川 Kanagawa
15 新潟県 新潟 Niigata
16 富山県 富山 Toyama
17 石川県 石川 Ishikawa
18 福井県 福井 Fukui
19 山梨県 山梨 Yamanashi
20 長野県 長野 Nagano
21 岐阜県 岐阜 Gifu
22 静岡県 静岡 Shizuoka
23 愛知県 愛知 Aichi
24 三重県 三重 Mie
25 滋賀県 滋賀 Shiga
26 京都府 京都 Kyoto
27 大阪府 大阪 Osaka
28 兵庫県 兵庫 Hyogo
29 奈良県 奈良 Nara
30 和歌山県 和歌山 Wakayama
31 鳥取県 鳥取 Tottori
32 島根県 島根 Shimane
33 岡山県 岡山 Okayama
34 広島県 広島 Hiroshima
35 山口県 山口 Yamaguchi
36 徳島県 徳島 Tokushima
37 香川県 香川 Kagawa
38 愛媛県 愛媛 Ehime
39 高知県 高知 Kochi
40 福岡県 福岡 Fukuoka
41 佐賀県 佐賀 Saga
42 長崎県 長崎 Nagasaki
43 熊本県 熊本 Kumamoto
44 大分県 大分 Oita
45 宮崎県 宮崎 Miyazaki
46 鹿児島県 鹿児島 Kagoshima
47 沖縄県 沖縄 Okinawa
>>> jp.prefectures.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 47 entries, 1 to 47
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 47 non-null object
1 short_name 47 non-null object
2 alphabet_name 47 non-null object
dtypes: object(3)
memory usage: 1.2+ KB
>>>
Dataframe of Cities
In [6]: city.cities
Out[6]:
prefCode cityCode cityName bigCityFlag
0 1 1100 札幌市 2
1 1 1101 札幌市中央区 1
2 1 1102 札幌市北区 1
3 1 1103 札幌市東区 1
4 1 1104 札幌市白石区 1
... ... ... ... ...
1917 47 47361 久米島町 0
1918 47 47362 八重瀬町 0
1919 47 47375 多良間村 0
1920 47 47381 竹富町 0
1921 47 47382 与那国町 0
In [7]: city.cities.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1922 entries, 0 to 1921
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 prefCode 1922 non-null int64
1 cityCode 1922 non-null int64
2 cityName 1922 non-null object
3 bigCityFlag 1922 non-null int64
dtypes: int64(3), object(1)
memory usage: 75.1+ KB
In [8]:
Conversion
from jp_prefecture import jp_prefectures as jp
import pandas as pd
assert ( jp.name2code('京都府')
== jp.name2code('京都')
== jp.name2code('Kyoto')
== jp.name2code('KYOTO')
== jp.name2code('kyoto')
== 26 )
assert ( jp.name2code(['京都府', '大阪府', '奈良県'])
== jp.name2code(['京都', '大阪', '奈良'])
== jp.name2code(['Kyoto', 'Osaka', 'Nara'])
== jp.name2code(['KYOTO', 'OSAKA', 'NARA'])
== jp.name2code(['kyoto', 'osaka', 'nara'])
== [26, 27, 29] )
s1 = jp.name2code(pd.Series(['京都府', '大阪府', '奈良県']))
s2 = jp.name2code(pd.Series(['京都', '大阪', '奈良']))
s3 = jp.name2code(pd.Series(['Kyoto', 'Osaka', 'Nara']))
s4 = jp.name2code(pd.Series(['KYOTO', 'OSAKA', 'NARA']))
s5 = jp.name2code(pd.Series(['kyoto', 'osaka', 'nara']))
s6 = pd.Series([26, 27, 29])
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== s5.equals(s6)
== True )
assert jp.code2name(26) == '京都府'
assert jp.code2name("26") == '京都府'
assert jp.code2name([26, 27, 29]) == ['京都府', '大阪府', '奈良県']
assert jp.code2name(["26", "27", "29"]) == ['京都府', '大阪府', '奈良県']
s1 = jp.code2name(pd.Series([26, 27, 29]))
s2 = pd.Series(['京都府', '大阪府', '奈良県'])
assert s1.equals(s2) == True
assert jp.code2alphabet(26) == 'Kyoto'
assert jp.code2alphabet([26, 27, 29]) == ['Kyoto', 'Osaka', 'Nara']
s1 = jp.code2alphabet(pd.Series([26, 27, 29]))
s2 = pd.Series(['Kyoto', 'Osaka', 'Nara'])
assert s1.equals(s2) == True
s1 = jp.code2alphabet(pd.Series(["26", "27", "29"]))
s2 = pd.Series(['Kyoto', 'Osaka', 'Nara'])
assert s1.equals(s2) == True
assert ( jp.name2alphabet('京都府')
== jp.name2alphabet('京都')
== jp.name2alphabet('Kyoto')
== jp.name2alphabet('KYOTO')
== jp.name2alphabet('kyoto')
== 'Kyoto' )
assert ( jp.name2alphabet(['京都府', '大阪府', '奈良県'])
== jp.name2alphabet(['京都', '大阪', '奈良'])
== jp.name2alphabet(['Kyoto', 'Osaka', 'Nara'])
== jp.name2alphabet(['KYOTO', 'OSAKA', 'NARA'])
== jp.name2alphabet(['kyoto', 'osaka', 'nara'])
== ['Kyoto', 'Osaka', 'Nara'] )
s1 = jp.name2alphabet(pd.Series(['京都府', '大阪府', '奈良県']))
s2 = jp.name2alphabet(pd.Series(['京都', '大阪', '奈良']))
s3 = jp.name2alphabet(pd.Series(['Kyoto', 'Osaka', 'Nara']))
s4 = jp.name2alphabet(pd.Series(['KYOTO', 'OSAKA', 'NARA']))
s5 = jp.name2alphabet(pd.Series(['kyoto', 'osaka', 'nara']))
s6 = pd.Series(['Kyoto', 'Osaka', 'Nara'])
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== s5.equals(s6)
== True )
assert ( jp.alphabet2name('Kyoto')
== jp.alphabet2name('KYOTO')
== jp.alphabet2name('kyoto')
== jp.alphabet2name('京都府')
== jp.alphabet2name('京都')
== '京都府' )
assert ( jp.alphabet2name(['京都府', '大阪府', '奈良県'])
== jp.alphabet2name(['京都', '大阪', '奈良'])
== jp.alphabet2name(['Kyoto', 'Osaka', 'Nara'])
== jp.alphabet2name(['KYOTO', 'OSAKA', 'NARA'])
== jp.alphabet2name(['kyoto', 'osaka', 'nara'])
== ['京都府', '大阪府', '奈良県'] )
s1 = jp.alphabet2name(pd.Series(['京都府', '大阪府', '奈良県']))
s2 = jp.alphabet2name(pd.Series(['京都', '大阪', '奈良']))
s3 = jp.alphabet2name(pd.Series(['Kyoto', 'Osaka', 'Nara']))
s4 = jp.alphabet2name(pd.Series(['KYOTO', 'OSAKA', 'NARA']))
s5 = jp.alphabet2name(pd.Series(['kyoto', 'osaka', 'nara']))
s6 = pd.Series(['京都府', '大阪府', '奈良県'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== s5.equals(s6)
== True )
assert ( jp.validate('京都府')
== jp.validate('京都')
== jp.validate('Kyoto')
== jp.validate('KYOTO')
== jp.validate('kyoto')
== True )
assert ( jp.validate('京都県')
== jp.validate('都京')
== jp.validate('KyOto')
== jp.validate('KYoTO')
== jp.validate('kyotofu')
== False )
assert ( jp.validate(['京都府', '大阪府', '奈良県'])
== jp.validate(['京都', '大阪', '奈良'])
== jp.validate(['Kyoto', 'Osaka', 'Nara'])
== jp.validate(['KYOTO', 'OSAKA', 'NARA'])
== jp.validate(['kyoto', 'osaka', 'nara'])
== [True, True, True] )
s1 = jp.validate(pd.Series(['京都府', '大阪府', '奈良県']))
s2 = jp.validate(pd.Series(['京都', '大阪', '奈良']))
s3 = jp.validate(pd.Series(['Kyoto', 'Osaka', 'Nara']))
s4 = jp.validate(pd.Series(['KYOTO', 'OSAKA', 'NARA']))
s5 = jp.validate(pd.Series(['kyoto', 'osaka', 'nara']))
s6 = pd.Series([True, True, True])
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== s5.equals(s6)
== True )
from jp_prefecture.jp_cities import jp_cities as city
import pandas as pd
assert ( city.name2citycode('京都市') == 26100 )
assert ( city.name2citycode('京都市', with_checkdigit=True) == 261009 )
assert ( city.name2citycode(['京都市北区', '京都市左京区', '京都市右京区'])
== [26101, 26103, 26108] )
s1 = city.name2citycode( pd.Series(
['京都市北区', '京都市左京区', '京都市右京区']))
s2 = pd.Series([26101, 26103, 26108])
assert ( s1.equals(s2) == True )
assert ( city.name2prefcode('京都市') == 26 )
assert ( city.name2prefcode(['京都市北区', '大阪市中央区']) == [26, 27] )
assert ( city.name2prefecture('京都市') == '京都府' )
assert ( city.name2prefecture(['京都市北区', '大阪市中央区'])
== ['京都府', '大阪府'] )
s1 = city.name2prefcode(pd.Series(['京都市北区', '大阪市中央区']))
s2 = pd.Series([26, 27])
assert ( s1.equals(s2) == True )
assert city.citycode2name(26100) == '京都市'
assert city.citycode2name("26100") == '京都市'
assert city.citycode2name(261009) == '京都市'
assert city.citycode2name("261009") == '京都市'
assert ( city.citycode2name([26101, 26103, 26108])
== ['京都市北区', '京都市左京区', '京都市右京区'] )
s1 = city.citycode2name(pd.Series([26101, 26103, 26108]))
s2 = pd.Series( ['京都市北区', '京都市左京区', '京都市右京区'] )
assert s1.equals(s2) == True
assert ( city.validate_city('京都市') == True )
assert ( city.validate_city('京都県')
== city.validate_city('都京市')
== city.validate_city('KyOto')
== city.validate_city('KYoTO')
== city.validate_city('kyotoshi')
== False )
assert ( city.validate_city(['京都市北区', '京都市左京区', '京都市右京区'])
== [True, True, True] )
assert ( city.validate_city(['京都県', '大阪府', '奈良県'])
== city.validate_city(['都京', '大阪', '奈良'])
== city.validate_city(['KyOto', 'Osaka', 'Nara'])
== city.validate_city(['KYoTO', 'OSAKA', 'NARA'])
== city.validate_city(['kyotofu', 'osaka', 'nara'])
== [False, False, False] )
s1 = city.validate_city(pd.Series(
['京都市北区', '京都市左京区', '京都市右京区']))
s2 = pd.Series([True, True, True])
assert ( s1.equals(s2)
== True )
s1 = city.validate_city(pd.Series(['京都県', '大阪府', '奈良県']))
s2 = pd.Series([False, False, False])
assert ( s1.equals(s2) == True )
Trivia Kyoto, Osaka and Nara are the place where the emperor established their capitals.
BONUS: simpledispatchmethod
As of python 3.8 funtools.singledispatchmethod allows singledispatch on methods, class methods, and staticmethods.
For older python version, you can use as follows.
from jp_prefecture.singledispatchmethod import singledispatchmethod
class Patchwork(object):
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
@singledispatchmethod
def get(self, arg):
return getattr(self, arg, None)
@get.register(list)
def _(self, arg):
return [self.get(x) for x in arg]
if __name__ == '__main__':
pw = Patchwork(a=1, b=2, c=3)
print(pw.get('b'))
print(pw.get(['a', 'c']))
See Also StackOverflow
BONUS: ImmutableDict
If you want to use immutable dictionary. try as follows.
In [1]: from jp_prefecture.immutable_dict import ImmutableDict
In [2]: d = ImmutableDict({1: 'A', 2: 'B', 3: 'C'})
In [3]: d
Out[3]: {1: 'A', 2: 'B', 3: 'C'}
In [4]: d.pop(1)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [5], in <cell line: 1>()
----> 1 d.pop(1)
File ~/Projects/GitHub/jp_prefecture/jp_prefecture/immutable_dict.py:10, in ImmutableDict.__getattribute__(self, attribute)
8 def __getattribute__(self, attribute):
9 if attribute in ('clear', 'update', 'pop', 'popitem', 'setdefault'):
---> 10 raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attribute))
11 return dict.__getattribute__(self, attribute)
AttributeError: 'ImmutableDict' object has no attribute 'pop'
In [5]:
BONUS: checkdigit.validate_checkdigit, checkdigit.calc_cehckdigit
small utility to compute modulus 11 check digit.
from jp_prefecture.checkdigit import validate_checkdigit, calc_checkdigit
# 26100 is CityCode of Kyoto City
assert validate_checkdigit(261009) == 26100
assert validate_checkdigit("261009") == "26100"
assert validate_checkdigit(261008) == None
assert validate_checkdigit("261008") == None
assert validate_checkdigit("2610", 5) == None
assert validate_checkdigit("2610", 5) == None
assert validate_checkdigit(261009, 5) == 26100
assert validate_checkdigit("261009", 5) == "26100"
assert calc_checkdigit(26100) == 261009
assert calc_checkdigit("26100") == "261009"
assert calc_checkdigit(26100, only_checkdigit=True) == 9
assert calc_checkdigit("26100", only_checkdigit=True) == "9"
assert validate_checkdigit(261009, weights=[6,5,4,3,2]) == 26100
assert calc_checkdigit("26100", weights=[6,5,4,3,2]) == "261009"
# for ISDB10
assert( validate_checkdigit(4-900900672) == 490090067)
assert( validate_checkdigit("4-900900672") == "490090067")
# for ISDB13
assert( validate_checkdigit("978-4-906649-006") == "978490664900")
The CityCode Number (JIS X 0402)
The CityCode number consists of a five-digit number assigned to each local public entity (prefecture, municipality, etc.) in Japan, as well as to counties that are not solely local public entities but are used as statistical divisions, in accordance with certain rules. Among the five-digit numbers The first two digits represent prefectures, numbered from north to south, from "01" (Hokkaido) to "47" (Okinawa). The third digit indicates whether the area belongs to a city or a county. The third digit indicates whether the area belongs to a city or a county. The last two digits are 3-digit numbers. The last two digits are the number of the respective group represented by the third digit ("1": special wards, wards of ordinance-designated cities, "2": a group of cities, "3-": a group of counties, "4-": a group of towns and villages belonging to counties, "5-": a group of cities). 3-": counties and towns/villages within each county), and the last two digits are assigned to each city, county, town, or village according to the arrangement of the third digit. The arrangement of cities, counties, towns, and villages is fixed for each prefecture and ordinance-designated city. In most prefectures, cities are arranged in the order in which they were established, but in some cases, such as Wakayama Prefecture, cities are arranged from north to south regardless of the order in which they were established. Thus, each city, county, town, village is represented by the third digit and the last two digits combined. For example, Nagaokakyo City in Kyoto Prefecture is represented by the citycode number "26209", of which the upper two digits "26" represent Kyoto Prefecture and the lower three digits The last 3-digit "209" represents Nagaokakyo City, which is the 9th city (10th if Kyoto City is included) in Kyoto Prefecture.
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