Simple utilitiy convert name of japanese prefectures
Project description
jp_prefecture.
Japan prefecture and city names and codes, geodetic.
Simple utility to convert the name of japanese prefectures and cities.
- parser for japanese address.
- full_name from/to code (JIS X 0401-1973, JIX X 0402).
- short_name to full_name (prefecture only)
- alphabet_name from/to full_name
- validate for full_name and short_name, alphabet_name.
- allow str or int for input code.
- support lists and pandas serires as input.
- support checkdigits for citycode.
- support regular expression for cityName and town.
- get geodetic(latitude, longitude) from cityCode or cityName, street address.
Reference
- https://www.soumu.go.jp/denshijiti/code.html (in japanese)
- Geolpnia OpenData for japanese address.
Install
pip install jp_prefecture
How to use
from jp_prefecture import jp_prefectures as jp
# or
from jp_prefecture.jp_cities import jp.jp_cities as jp
# If you want to town data.
del jp
jp = JpCity(enable_town=True)
# or Set Shell Environment variable
export JP_PREFECTURE_ENABLE_TOWN=1
# and, japanese address parser.
from jp_prefecture.address import JpAddressParser, JpAddress
parser = JpAddressParser()
class JpAddressParser
parse_address()
from jp_prefecture.address import JpAddressParser, JpAddress
parser = JpAddressParser()
data = '〒617-0826 京都府長岡京市開田1丁目-2-3 アパート123号室'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == '6170826' )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '開田1丁目-2-3 アパート123号室')
assert ( addr.prefCode == 26)
assert ( addr.cityCode == 26209)
assert ( addr.geodetic == (34.928769, 135.696847))
data = '〒617-0824 長岡京市天神２丁目１５−１３'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == '6170824' )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '天神２丁目１５−１３')
assert ( addr.geodetic == (34.923314, 135.685162))
assert ( addr.__str__()
== '〒617-0824 京都府長岡京市天神２丁目１５−１３')
data = '6170824 長岡京市天神２丁目１５−１３'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == '6170824' )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '天神２丁目１５−１３')
assert ( addr.geodetic == (34.923314, 135.685162))
assert ( addr.__str__()
== '〒617-0824 京都府長岡京市天神２丁目１５−１３')
data = '長岡京市天神２丁目１５−１３'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '天神２丁目１５−１３')
assert ( addr.geodetic == (34.923314, 135.685162))
assert ( addr.__str__()
== '京都府長岡京市天神２丁目１５−１３')
data = '京都長岡京市天神２丁目１５−１３'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '天神２丁目１５−１３')
assert ( addr.geodetic == (34.923314, 135.685162))
assert ( addr.__str__()
== '京都府長岡京市天神２丁目１５−１３')
data = '京都 長岡京市天神２丁目１５−１３'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '天神２丁目１５−１３')
assert ( addr.geodetic == (34.923314, 135.685162))
assert ( addr.__str__()
== '京都府長岡京市天神２丁目１５−１３')
data = '京都府 長岡京市天神２丁目１５−１３'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '長岡京市' )
assert ( addr.street == '天神２丁目１５−１３')
assert ( addr.geodetic == (34.923314, 135.685162))
assert ( addr.__str__()
== '京都府長岡京市天神２丁目１５−１３')
data = '京都市下京区烏丸通七条下ル 東塩小路町 721-1'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '京都市下京区' )
assert ( addr.street == '烏丸通七条下ル 東塩小路町 721-1')
assert ( addr.prefCode == 26)
assert ( addr.cityCode == 26106)
assert ( addr.geodetic == (35.002973, 135.764009))
data = '京都市 下京区烏丸通七条下ル 東塩小路町 721-1'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '京都市下京区' )
assert ( addr.street == '烏丸通七条下ル 東塩小路町 721-1')
assert ( addr.prefCode == 26)
assert ( addr.cityCode == 26106)
assert ( addr.geodetic == (35.002973, 135.764009))
assert ( addr.__str__()
== '京都府京都市下京区烏丸通七条下ル 東塩小路町 721-1' )
data = '京都 下京区 烏丸通七条下ル 東塩小路町 721-1'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '京都府' )
assert ( addr.city == '京都市下京区' )
assert ( addr.street == '烏丸通七条下ル 東塩小路町 721-1')
assert ( addr.prefCode == 26)
assert ( addr.cityCode == 26106)
assert ( addr.geodetic == (35.002973, 135.764009))
assert ( addr.__str__()
== '京都府京都市下京区烏丸通七条下ル 東塩小路町 721-1' )
data = '千代田区丸の内1-9-2グラントウキョウサウスタワー23階'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '東京都')
assert ( addr.city == '千代田区' )
assert ( addr.street == '丸の内1-9-2グラントウキョウサウスタワー23階')
assert ( addr.prefCode == 13)
assert ( addr.cityCode == 13101)
assert ( addr.geodetic == (35.68156, 139.767201))
assert ( addr.__str__()
== '東京都千代田区丸の内1-9-2グラントウキョウサウスタワー23階' )
data = '千代田区 丸の内1-9-2グラントウキョウサウスタワー23階'
addr = self.parser.parse_address(data)
assert ( addr.zipCode == None )
assert ( addr.prefecture == '東京都')
assert ( addr.city == '千代田区' )
assert ( addr.street == '丸の内1-9-2グラントウキョウサウスタワー23階')
assert ( addr.prefCode == 13)
assert ( addr.cityCode == 13101)
assert ( addr.geodetic == ( 35.68156, 139.767201))
assert ( addr.__str__()
== '東京都千代田区丸の内1-9-2グラントウキョウサウスタワー23階' )
Dataframe of jp.prefectures
In [2]: jp.prefectures
Out[2]:
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
In [3]: 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
In [4]:
>>>
Dataframe of Cities
In [1]: from jp_prefecture.jp_cities import jp_cities as jp
In [2]: jp.cities
Out[2]:
prefCode cityCode cityName cityAlphabet latitude longitude bigCityFlag
0 1 1100 札幌市 Sapporo-shi 43.0351 141.2049 2
1 1 1101 札幌市中央区 Sapporo-shi Chuo-ku 43.0422 141.3197 1
2 1 1102 札幌市北区 Sapporo-shi Kita-ku 43.1571 141.3902 1
3 1 1103 札幌市東区 Sapporo-shi Higashi-ku 43.1208 141.3944 1
4 1 1104 札幌市白石区 Sapporo-shi Shiroishi-ku 43.0716 141.4370 1
... ... ... ... ... ... ... ...
1909 47 47361 島尻郡久米島町 Shimajiri-gun Kumejim... 26.3474 126.7697 0
1910 47 47362 島尻郡八重瀬町 Shimajiri-gun Yaese-cho 26.1260 127.7472 0
1911 47 47375 宮古郡多良間村 Miyako-gun Tarama-son 24.6578 124.6854 0
1912 47 47381 八重山郡竹富町 Yaeyama-gun Taketomi-cho 24.2371 124.0119 0
1913 47 47382 八重山郡与那国町 Yaeyama-gun Yonaguni-cho 24.4559 122.9877 0
In [3]: jp.cities.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1914 entries, 0 to 1913
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 prefCode 1914 non-null int8
1 cityCode 1914 non-null int32
2 cityName 1914 non-null object
3 cityAlphabet 1914 non-null object
4 latitude 1914 non-null float64
5 longitude 1914 non-null float64
6 bigCityFlag 1914 non-null int8
dtypes: float64(2), int32(1), int8(2), object(2)
memory usage: 86.0+ KB
In [4]:
Dataframe of Towns
In [1]: import os
In [2]: os.environ.update({'JP_PREFECTURE_ENABLE_TOWN': '1'})
In [3]: from jp_prefecture.jp_cities import jp_cities as jp
In [4]: jp.towns
Out[4]:
prefCode cityCode town townAlphabet latitude longitude bigCityFlag
0 1 1101 旭ケ丘一丁目 Asahigaoka 1 43.0422 141.3197 0
1 1 1101 南二十五条西十一丁目 Minami25-Jonishi 11 43.0261 141.3446 0
2 1 1101 南二十五条西十二丁目 Minami25-Jonishi 12 43.0259 141.3430 0
3 1 1101 南二十五条西十三丁目 Minami25-Jonishi 13 43.0258 141.3412 0
4 1 1101 南二十五条西十四丁目 Minami25-Jonishi 14 43.0255 141.3401 0
... ... ... ... ... ... ... ...
277186 47 47381 字鳩間 NaN 24.4723 123.8204 0
277187 47 47381 字竹富 NaN 24.3261 124.0891 0
277188 47 47381 字南風見仲 NaN 24.2882 123.8880 0
277189 47 47381 字新城 NaN 24.2340 123.9449 0
277190 47 47382 字与那国 NaN 24.4559 122.9877 0
In [5]: jp.towns.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 277191 entries, 0 to 277190
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 prefCode 277191 non-null int8
1 cityCode 277191 non-null int32
2 town 277191 non-null object
3 townAlphabet 237491 non-null object
4 latitude 277191 non-null float64
5 longitude 277191 non-null float64
6 bigCityFlag 277191 non-null int8
dtypes: float64(2), int32(1), int8(2), object(2)
memory usage: 12.2+ MB
In [6]:
class JpPrefecture
name2code()code2name()name2normalize()validate()
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
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.code2name(26, ascii=True) == 'Kyoto'
assert jp.code2name("26", ascii=True) == 'Kyoto'
assert ( jp.code2name([26, 27, 29], ascii=True)
== ['Kyoto', 'Osaka', 'Nara'] )
assert ( jp.code2name(["26", "27", "29"], ascii=True)
== ['Kyoto', 'Osaka', 'Nara'] )
s1 = jp.code2name(pd.Series([26, 27, 29]), ascii=True)
s2 = pd.Series(['Kyoto', 'Osaka', 'Nara'])
assert s1.equals(s2) == True
s1 = jp.code2name(pd.Series(["26", "27", "29"]), ascii=True)
s2 = pd.Series(['Kyoto', 'Osaka', 'Nara'])
assert s1.equals(s2) == True
assert ( jp.name2normalize('京都府')
== jp.name2normalize('京都')
== jp.name2normalize('Kyoto')
== jp.name2normalize('KYOTO')
== jp.name2normalize('kyoto')
== '京都府' )
assert ( jp.name2normalize(['京都府', '大阪府', '奈良県'])
== jp.name2normalize(['京都', '大阪', '奈良'])
== jp.name2normalize(['Kyoto', 'Osaka', 'Nara'])
== jp.name2normalize(['KYOTO', 'OSAKA', 'NARA'])
== jp.name2normalize(['kyoto', 'osaka', 'nara'])
== ['京都府', '大阪府', '奈良県'] )
s1 = jp.name2normalize(pd.Series(['京都府', '大阪府', '奈良県']))
s2 = jp.name2normalize(pd.Series(['京都', '大阪', '奈良']))
s3 = jp.name2normalize(pd.Series(['Kyoto', 'Osaka', 'Nara']))
s4 = jp.name2normalize(pd.Series(['KYOTO', 'OSAKA', 'NARA']))
s5 = jp.name2normalize(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.name2normalize('京都府', ascii=True)
== jp.name2normalize('京都', ascii=True)
== jp.name2normalize('Kyoto', ascii=True)
== jp.name2normalize('KYOTO', ascii=True)
== jp.name2normalize('kyoto', ascii=True)
== 'Kyoto' )
assert ( jp.name2normalize(['京都府', '大阪府', '奈良県'], ascii=True)
== jp.name2normalize(['京都', '大阪', '奈良'], ascii=True)
== jp.name2normalize(['Kyoto', 'Osaka', 'Nara'], ascii=True)
== jp.name2normalize(['KYOTO', 'OSAKA', 'NARA'], ascii=True)
== jp.name2normalize(['kyoto', 'osaka', 'nara'], ascii=True)
== ['Kyoto', 'Osaka', 'Nara'] )
s1 = jp.name2normalize(
pd.Series(['京都府', '大阪府', '奈良県']), ascii=True)
s2 = jp.name2normalize(
pd.Series(['京都', '大阪', '奈良']), ascii=True)
s3 = jp.name2normalize(
pd.Series(['Kyoto', 'Osaka', 'Nara']), ascii=True)
s4 = jp.name2normalize(
pd.Series(['KYOTO', 'OSAKA', 'NARA']), ascii=True)
s5 = jp.name2normalize(
pd.Series(['kyoto', 'osaka', 'nara']), ascii=True)
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.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] )
assert ( jp.validate(['京都県', '大阪府', '奈良県'])
== jp.validate(['都京', '大阪', '奈良'])
== jp.validate(['KyOto', 'Osaka', 'Nara'])
== jp.validate(['KYoTO', 'OSAKA', 'NARA'])
== jp.validate(['kyotofu', 'osaka', 'nara'])
== [False, 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 )
s1 = jp.validate(pd.Series(['京都県', '大阪府', '奈良県']))
s2 = jp.validate(pd.Series(['都京', '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(['kyotofu', 'osaka', 'nara']))
s6 = pd.Series([False, True, True])
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== s5.equals(s6)
== True )
class JpCity
JpCity class is subclass of JpPrefecture.
citycode2name()cityname2code()cityname2normalize()citycode2normalize()cityname2prefcode()cityname2preffecture()cityname2geodetic()citycode2geodetic()findcity()validate_city()
from jp_prefecture.jp_cities import jp_cities as jp
import pandas as pd
assert ( jp.cityname2code('札幌市')
== jp.cityname2code('Sapporo-shi')
== 1100 )
assert ( jp.cityname2code('札幌市', as_str=True)
== jp.cityname2code('Sapporo-shi', as_str=True)
== "01100" )
assert ( jp.cityname2code('京都市')
== jp.cityname2code('Kyoto-shi')
== 26100 )
assert ( jp.cityname2code('KYOTO-SHI')
== jp.cityname2code('kyoto-shi')
== None )
assert ( jp.cityname2code('京都市', ignore_case=True)
== jp.cityname2code('Kyoto-shi', ignore_case=True)
== jp.cityname2code('KYOTO-SHI', ignore_case=True)
== jp.cityname2code('kyoto-shi', ignore_case=True)
== 26100 )
assert ( jp.cityname2code('京都市', checkdigit=True)
== jp.cityname2code('Kyoto-shi', checkdigit=True)
== 261009 )
assert ( jp.cityname2code(
['京都市北区', '京都市左京区', '京都市右京区'])
== jp.cityname2code(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
== [26101, 26103, 26108] )
assert ( jp.cityname2code(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] )
== jp.cityname2code(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'] )
== [None, None, None] )
assert ( jp.cityname2code(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'], ignore_case=True )
== jp.cityname2code(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'], ignore_case=True )
== [26101, 26103, 26108] )
s1 = jp.cityname2code( pd.Series(
['京都市北区', '京都市左京区', '京都市右京区']))
s2 = jp.cityname2code( pd.Series(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] ))
s3 = pd.Series([26101, 26103, 26108])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2code( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] ))
s2 = jp.cityname2code( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] ))
s3 = pd.Series([None, None, None])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2code( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU']), ignore_case=True )
s2 = jp.cityname2code( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU']), ignore_case=True )
s3 = pd.Series([26101, 26103, 26108])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
assert ( jp.cityname2normalize('京都市')
== jp.cityname2normalize('Kyoto-shi')
== '京都市')
assert ( jp.cityname2normalize('KYOTO-SHI')
== jp.cityname2normalize('kyoto-shi')
== None )
assert ( jp.cityname2normalize('京都市')
== jp.cityname2normalize('KYOTO-SHI', ignore_case=True)
== jp.cityname2normalize('kyoto-shi', ignore_case=True)
== '京都市')
assert ( jp.cityname2normalize(
['京都市北区',
'京都市左京区',
'京都市右京区'])
== jp.cityname2normalize(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
== ['京都市北区',
'京都市左京区',
'京都市右京区'])
assert ( jp.cityname2normalize(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] )
== jp.cityname2normalize(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'] )
== [None, None, None] )
assert ( jp.cityname2normalize(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'], ignore_case=True )
== jp.cityname2normalize(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'], ignore_case=True)
== ['京都市北区',
'京都市左京区',
'京都市右京区'])
s1 = jp.cityname2normalize( pd.Series(
['京都市北区',
'京都市左京区',
'京都市右京区']))
s2 = jp.cityname2normalize( pd.Series(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] ))
s3 = pd.Series( ['京都市北区',
'京都市左京区',
'京都市右京区'])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2normalize( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] ))
s2 = jp.cityname2normalize( pd.Series(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'] ))
s3 = pd.Series( [None, None, None] )
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2normalize( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU']), ignore_case=True )
s2 = jp.cityname2normalize( pd.Series(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku']), ignore_case=True)
s3 = pd.Series( ['京都市北区',
'京都市左京区',
'京都市右京区'])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
assert ( jp.cityname2normalize('京都市',ascii=True)
== jp.cityname2normalize('Kyoto-shi',ascii=True)
== "Kyoto-shi" )
assert ( jp.cityname2normalize('KYOTO-SHI',ascii=True)
== jp.cityname2normalize('kyoto-shi',ascii=True)
== None )
assert ( jp.cityname2normalize('KYOTO-SHI',
ascii=True, ignore_case=True)
== jp.cityname2normalize('kyoto-shi',
ascii=True, ignore_case=True)
== "Kyoto-shi" )
assert ( jp.cityname2normalize(
['京都市北区',
'京都市左京区',
'京都市右京区'], ascii=True)
== jp.cityname2normalize(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'], ascii=True)
== ['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
assert ( jp.cityname2normalize(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'], ascii=True)
== jp.cityname2normalize(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'], ascii=True)
== [None, None, None] )
assert ( jp.cityname2normalize(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'], ascii=True, ignore_case=True)
== jp.cityname2normalize(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'], ascii=True, ignore_case=True)
== ['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
s1 = jp.cityname2normalize( pd.Series(
['京都市北区',
'京都市左京区',
'京都市右京区']), ascii=True)
s2 = jp.cityname2normalize( pd.Series(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] ), ascii=True)
s3 = pd.Series( ['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2normalize( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] ), ascii=True)
s2 = jp.cityname2normalize( pd.Series(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'] ), ascii=True)
s3 = pd.Series([None, None, None])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2normalize( pd.Series(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] ), ascii=True, ignore_case=True)
s2 = jp.cityname2normalize( pd.Series(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'] ), ascii=True, ignore_case=True)
s3 = pd.Series( ['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
assert jp.citycode2normalize(26100) == 26100
assert jp.citycode2normalize(261009) == 26100
assert jp.citycode2normalize(26100, as_str=True) == '26100'
assert jp.citycode2normalize(261009, as_str=True) == '26100'
assert jp.citycode2normalize("26100") == 26100
assert jp.citycode2normalize("261009") == 26100
assert jp.citycode2normalize("26100", as_str=True) == '26100'
assert jp.citycode2normalize("261009", as_str=True) == '26100'
assert jp.citycode2name(26100) == '京都市'
assert jp.citycode2name("26100") == '京都市'
assert jp.citycode2name(261009) == '京都市'
assert jp.citycode2name("261009") == '京都市'
assert ( jp.citycode2name([26101, 26103, 26108])
== ['京都市北区', '京都市左京区', '京都市右京区'] )
assert jp.citycode2name(26100, ascii=True) == 'Kyoto-shi'
assert jp.citycode2name("26100", ascii=True) == 'Kyoto-shi'
assert jp.citycode2name(261009, ascii=True) == 'Kyoto-shi'
assert jp.citycode2name("261009", ascii=True) == 'Kyoto-shi'
assert ( jp.citycode2name([26101, 26103, 26108], ascii=True)
== ['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
s1 = jp.citycode2name(pd.Series([26101, 26103, 26108]), ascii=True)
s2 = pd.Series( ['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
assert s1.equals(s2) == True
assert ( jp.cityname2prefcode('京都市')
== jp.cityname2prefcode('Kyoto-shi')
== 26 )
assert ( jp.cityname2prefcode('KYOTO-SHI')
== jp.cityname2prefcode('kyoto-shi')
== None )
assert ( jp.cityname2prefcode('京都市', ignore_case=True)
== jp.cityname2prefcode('KYOTO-SHI', ignore_case=True)
== jp.cityname2prefcode('kyoto-shi', ignore_case=True)
== 26 )
assert ( jp.cityname2prefcode(['京都市北区', '大阪市中央区'])
== jp.cityname2prefcode(['Kyoto-shi Kita-ku',
'Osaka-shi Chuo-ku'])
== [26, 27] )
assert ( jp.cityname2prefcode(['KYOTO-SHI KITA-KU',
'OSAKA-SHI CHUO-KU'])
== jp.cityname2prefcode(['kyoto-shi kita-ku',
'osaka-shi chuo-ku'])
== [None, None] )
assert ( jp.cityname2prefcode(
['京都市北区', '大阪市中央区'],
ignore_case=True)
== jp.cityname2prefcode(
['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU'],
ignore_case=True)
== jp.cityname2prefcode(
['kyoto-shi kita-ku', 'osaka-shi chuo-ku'],
ignore_case=True)
== [26, 27] )
s1 = jp.cityname2prefcode(
pd.Series(['京都市北区', '大阪市中央区']))
s2 = jp.cityname2prefcode(
pd.Series(['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku']))
s3 = pd.Series([26, 27])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2prefcode(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']))
s2 = jp.cityname2prefcode(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']))
s3 = pd.Series([None, None])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2prefcode(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']),
ignore_case=True)
s2 = jp.cityname2prefcode(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']),
ignore_case=True)
s3 = pd.Series([26, 27])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
assert ( jp.cityname2prefecture('京都市')
== jp.cityname2prefecture('Kyoto-shi')
== '京都府' )
assert ( jp.cityname2prefecture('KYOTO-SHI')
== jp.cityname2prefecture('kyoto-shi')
== None )
assert ( jp.cityname2prefecture('京都市', ignore_case=True)
== jp.cityname2prefecture('Kyoto-shi', ignore_case=True)
== jp.cityname2prefecture('KYOTO-SHI', ignore_case=True)
== jp.cityname2prefecture('kyoto-shi', ignore_case=True)
== '京都府' )
assert ( jp.cityname2prefecture(['京都市北区', '大阪市中央区'])
== jp.cityname2prefecture(['Kyoto-shi Kita-ku',
'Osaka-shi Chuo-ku'])
== ['京都府', '大阪府'] )
assert ( jp.cityname2prefecture(
['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU'])
== jp.cityname2prefecture(
['kyoto-shi kita-ku', 'osaka-shi chuo-ku'])
== [None, None] )
assert ( jp.cityname2prefecture(
['京都市北区', '大阪市中央区'],
ignore_case=True)
== jp.cityname2prefecture(
['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku'],
ignore_case=True)
== jp.cityname2prefecture(
['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU'],
ignore_case=True)
== jp.cityname2prefecture(
['kyoto-shi kita-ku', 'osaka-shi chuo-ku'],
ignore_case=True)
== ['京都府', '大阪府'] )
s1 = jp.cityname2prefecture(
pd.Series( ['京都市北区', '大阪市中央区']))
s2 = jp.cityname2prefecture(
pd.Series(['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku']))
s3 = pd.Series(['京都府', '大阪府'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2prefecture(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']))
s2 = jp.cityname2prefecture(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']))
s3 = pd.Series([None, None] )
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2prefecture(
pd.Series( ['京都市北区', '大阪市中央区']),
ignore_case=True)
s2 = jp.cityname2prefecture(
pd.Series(['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku']),
ignore_case=True)
s3 = jp.cityname2prefecture(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']),
ignore_case=True)
s4 = jp.cityname2prefecture(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']),
ignore_case=True)
s5 = pd.Series(['京都府', '大阪府'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== True )
assert ( jp.cityname2prefecture('京都市', ascii=True)
== jp.cityname2prefecture('Kyoto-shi', ascii=True)
== 'Kyoto' )
assert ( jp.cityname2prefecture('KYOTO-SHI', ascii=True)
== jp.cityname2prefecture('kyoto-shi', ascii=True)
== None )
assert ( jp.cityname2prefecture('京都市',
ascii=True, ignore_case=True)
== jp.cityname2prefecture('Kyoto-shi',
ascii=True, ignore_case=True)
== jp.cityname2prefecture('KYOTO-SHI',
ascii=True, ignore_case=True)
== jp.cityname2prefecture('kyoto-shi',
ascii=True, ignore_case=True)
== 'Kyoto' )
assert ( jp.cityname2prefecture(
['京都市北区', '大阪市中央区'],
ascii=True)
== jp.cityname2prefecture(
['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku'],
ascii=True)
== ['Kyoto', 'Osaka'] )
assert ( jp.cityname2prefecture(
['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU'],
ascii=True)
== jp.cityname2prefecture(
['kyoto-shi kita-ku', 'osaka-shi chuo-ku'],
ascii=True)
== [None, None] )
assert ( jp.cityname2prefecture(
['京都市北区', '大阪市中央区'],
ascii=True, ignore_case=True)
== jp.cityname2prefecture(
['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku'],
ascii=True, ignore_case=True)
== jp.cityname2prefecture(
['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU'],
ascii=True, ignore_case=True)
== jp.cityname2prefecture(
['kyoto-shi kita-ku', 'osaka-shi chuo-ku'],
ascii=True, ignore_case=True)
== ['Kyoto', 'Osaka'] )
s1 = jp.cityname2prefecture(
pd.Series(['京都市北区', '大阪市中央区']),
ascii=True)
s2 = jp.cityname2prefecture(
pd.Series(['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku']),
ascii=True)
s3 = pd.Series(['Kyoto', 'Osaka'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2prefecture(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']),
ascii=True)
s2 = jp.cityname2prefecture(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']),
ascii=True)
s3 = pd.Series([None, None])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.cityname2prefecture(
pd.Series(['京都市北区', '大阪市中央区']),
ascii=True, ignore_case=True)
s2 = jp.cityname2prefecture(
pd.Series(['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku']),
ascii=True, ignore_case=True)
s3 = jp.cityname2prefecture(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']),
ascii=True, ignore_case=True)
s4 = jp.cityname2prefecture(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']),
ascii=True, ignore_case=True)
s5 = pd.Series(['Kyoto', 'Osaka'] )
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== True )
assert ( jp.validate_city('京都市')
== jp.validate_city('Kyoto-shi')
== True )
assert ( jp.validate_city('KYOTO-SHI')
== jp.validate_city('kyoto-shi')
== False )
assert ( jp.validate_city('京都市', ignore_case=True)
== jp.validate_city('Kyoto-shi', ignore_case=True)
== jp.validate_city('KYOTO-SHI', ignore_case=True)
== jp.validate_city('kyoto-shi', ignore_case=True)
== True )
assert ( jp.validate_city('京都県')
== jp.validate_city('都京市')
== jp.validate_city('Kyoto')
== jp.validate_city('kyotoshi')
== False )
assert ( jp.validate_city(
['京都市北区',
'京都市左京区',
'京都市右京区'])
== jp.validate_city(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] )
== [True, True, True] )
assert ( jp.validate_city(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'] )
== jp.validate_city(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'] )
== [False, False, False] )
assert ( jp.validate_city(
['京都市北区',
'京都市左京区',
'京都市右京区'], ignore_case=True)
== jp.validate_city(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'], ignore_case=True )
== jp.validate_city(
['KYOTO-SHI KITA-KU',
'KYOTO-SHI SAKYO-KU',
'KYOTO-SHI UKYO-KU'], ignore_case=True )
== jp.validate_city(
['kyoto-shi kita-ku',
'kyoto-shi sakyo-ku',
'kyoto-shi ukyo-ku'], ignore_case=True )
== [True, True, True] )
assert ( jp.validate_city(['京都県', '大阪府', '奈良県'])
== jp.validate_city(['都京', '大阪', '奈良'])
== jp.validate_city(['Kyoto', 'OSAKA', 'NARA'])
== jp.validate_city(['kyotofu', 'osaka', 'nara'])
== [False, False, False] )
s1 = jp.validate_city(pd.Series(
['京都市北区',
'京都市左京区',
'京都市右京区']))
s2 = jp.validate_city( pd.Series(
['Kyoto-shi Kita-ku',
'Kyoto-shi Sakyo-ku',
'Kyoto-shi Ukyo-ku'] ))
s3 = pd.Series([True, True, True])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.validate_city(pd.Series(['京都県', '大阪府', '奈良県']))
s2 = pd.Series([False, False, False])
assert ( s1.equals(s2)
== True )
s1 = jp.validate_city(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']))
s2 = jp.validate_city(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']))
s3 = pd.Series([False, False])
assert ( s1.equals(s2)
== s2.equals(s3)
== True )
s1 = jp.validate_city(
pd.Series(['京都市北区', '大阪市中央区']),
ignore_case=True)
s2 = jp.validate_city(
pd.Series(['Kyoto-shi Kita-ku', 'Osaka-shi Chuo-ku']),
ignore_case=True)
s3 = jp.validate_city(
pd.Series(['KYOTO-SHI KITA-KU', 'OSAKA-SHI CHUO-KU']),
ignore_case=True)
s4 = jp.validate_city(
pd.Series(['kyoto-shi kita-ku', 'osaka-shi chuo-ku']),
ignore_case=True)
s5 = pd.Series([True, True] )
assert ( s1.equals(s2)
== s2.equals(s3)
== s3.equals(s4)
== s4.equals(s5)
== True )
assert ( jp.cityname2geodetic('京都市')
== jp.cityname2geodetic('Kyoto-shi')
== (35.0117,135.452 ) )
assert ( jp.cityname2geodetic(['京都市', '大阪市'])
== jp.cityname2geodetic(['Kyoto-shi','Osaka-shi'])
== [(35.0117,135.452),(34.4138,135.3808)] )
d1 = jp.cityname2geodetic(pd.Series(['京都市', '大阪市']))
d2 = pd.DataFrame([ ['京都市', 35.0117,135.452],
['大阪市', 34.4138,135.3808]],
columns=['cityName', 'latitude', 'longitude'])
check = pd.concat([d1,d2]).drop_duplicates(keep=False)
assert ( len(check) == 0 )
d3 = jp.cityname2geodetic(pd.Series(['Kyoto-shi', 'Osaka-shi']))
d4 = pd.DataFrame([['Kyoto-shi', 35.0117,135.452],
['Osaka-shi', 34.4138,135.3808]],
columns=['cityName', 'latitude', 'longitude'])
check = pd.concat([d3,d4]).drop_duplicates(keep=False)
assert ( len(check) == 0 )
assert ( jp.citycode2geodetic(26100) == (35.0117,135.452) )
assert ( jp.citycode2geodetic("26100") == (35.0117,135.452) )
assert ( jp.citycode2geodetic(261009) == (35.0117,135.452) )
assert ( jp.citycode2geodetic("261009") == (35.0117,135.452) )
assert ( jp.citycode2geodetic([26100, 27100])
== [(35.0117,135.452), (34.4138,135.3808)] )
assert ( jp.citycode2geodetic(["26100", "27100"])
== [(35.0117,135.452), (34.4138,135.3808)] )
assert ( jp.citycode2geodetic([261009, 271004])
== [(35.0117,135.452), (34.4138,135.3808)] )
assert ( jp.citycode2geodetic(["26100", "271004"])
== [(35.0117,135.452), (34.4138,135.3808)] )
d1 = jp.citycode2geodetic(pd.Series([26100,27100]))
d2 = pd.DataFrame([ [26100, 35.0117,135.452],
[27100, 34.4138,135.3808] ],
columns=['cityCode', 'latitude', 'longitude'])
check = pd.concat([d1,d2]).drop_duplicates(keep=False)
assert ( len(check) == 0 )
d1 = jp.citycode2geodetic(pd.Series([261009,271004]))
d2 = pd.DataFrame([ [26100, 35.0117,135.452],
[27100, 34.4138,135.3808] ],
columns=['cityCode', 'latitude', 'longitude'])
check = pd.concat([d1,d2]).drop_duplicates(keep=False)
assert ( len(check) == 0 )
d1 = jp.citycode2geodetic(pd.Series(["26100","27100"]))
d2 = pd.DataFrame([ [26100, 35.0117,135.452],
[27100, 34.4138,135.3808] ],
columns=['cityCode', 'latitude', 'longitude'])
check = pd.concat([d1,d2]).drop_duplicates(keep=False)
assert ( len(check) == 0 )
d1 = jp.citycode2geodetic(pd.Series(["261009","271004"]))
d2 = pd.DataFrame([ [26100, 35.0117,135.452],
[27100, 34.4138,135.3808] ],
columns=['cityCode', 'latitude', 'longitude'])
check = pd.concat([d1,d2]).drop_duplicates(keep=False)
assert ( len(check) == 0 )
Trivia Kyoto, Osaka and Nara are the place where the emperor established their capitals.
Regular Expression
findcity() and cityname2code() allow to regexpression.
from jp_prefecture.jp_cities import jp_cities as jp
import re
name=re.compile('.*長岡.*')
expect = ['長岡市', '長岡京市', '長岡郡本山町', '長岡郡大豊町']
result = jp.findcity(name)
assert ( result == expect )
name=re.compile('Kyoto.*')
expect = ['Kyoto-Shi',
'Kyoto-Shi Kita-Ku',
'Kyoto-Shi Kamigyo-Ku',
'Kyoto-Shi Sakyo-Ku',
'Kyoto-Shi Nakagyo-Ku',
'Kyoto-Shi Higashiyama-Ku',
'Kyoto-Shi Shimogyo-Ku',
'Kyoto-Shi Minami-Ku',
'Kyoto-Shi Ukyo-Ku',
'Kyoto-Shi Fushimi-Ku',
'Kyoto-Shi Yamashina-Ku',
'Kyoto-Shi Nishikyo-Ku']
result = jp.findcity(name)
assert ( result == expect )
pattern = re.compile('.*町町')
expect = ['杵島郡大町町']
result = jp.findcity(pattern)
assert ( result == expect )
pattern = re.compile('.*町町')
expect = ['Kishima-gun Omachi-cho']
result = jp.findcity(pattern, ascii=True)
assert ( result == expect )
pattern=re.compile('.*長岡.*')
expect = [15202, 26209, 39341, 39344]
result = jp.cityname2code(pattern)
assert ( result == expect )
pattern=re.compile('Kyoto.*')
expect = [26100, 26101, 26102, 26103, 26104, 26105,
26106, 26107, 26108, 26109, 26110, 26111]
result = jp.cityname2code(pattern)
assert ( result == expect )
class JpNumberParser
kanji2number(val)number2kanji(val, style)- style: 'kanji', 'arabic', 'mix', 'finance', 'daiji'
- `normalize_kanjinumber(val)``
n [1]: from jp_prefecture.jp_numbers import JpNumberParser
In [2]: jn = JpNumberParser()
In [3]: jn.number2kanji(87654)
Out[3]: JpNumber(number=87654, as_str='87654', as_kanji='八万七千六百五十四')
In [4]: jn.number2kanji(87654, style='arabic')
Out[4]: JpNumber(number=87654, as_str='87654', as_kanji='８７６５４')
In [5]: jn.number2kanji(87654, style='mix')
Out[5]: JpNumber(number=87654, as_str='87654', as_kanji='８万７６５４')
In [6]: jn.number2kanji(87654, style='finance')
Out[6]: JpNumber(number=87654, as_str='87654', as_kanji='８７，６５４')
In [7]: jn.number2kanji(87654, style='daiji')
Out[7]: JpNumber(number=87654, as_str='87654', as_kanji='捌萬漆仟陸佰伍拾肆')
In [8]: jn.kanji2number('八万七千六百五十四')
Out[8]: JpNumber(number=87654, as_str='87654', as_kanji='八万七千六百五十四')
In [9]: jn.kanji2number('８７６５４')
Out[9]: JpNumber(number=87654, as_str='87654', as_kanji='８７６５４')
In [10]: jn.kanji2number('８７，６５４')
Out[10]: JpNumber(number=87654, as_str='87654', as_kanji='８７，６５４')
In [11]: jn.kanji2number('捌萬漆仟陸佰伍拾肆')
Out[11]: JpNumber(number=87654, as_str='87654', as_kanji='捌萬漆仟陸佰伍拾肆')
In [12]: jn.normalize_kanjinumber('京都府長岡京市天神２丁目１５-１３')
Out[12]: '京都府長岡京市天神二丁目十五-十三'
Memory Usage
jp_prefecture: 60.05 KB.
jp_cities: 2919.74 KB.
jp_cities_with_town: 120326.30 KB.
address parser: 15.07 KB.
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 ( validate_checkdigit(26100, 5) == 26100 )
assert ( validate_checkdigit(1100, 5) == 1100 )
assert ( validate_checkdigit("1100", 5) == "01100" )
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")
Japanese address
Prefecture : ( '-To':'都', '-Dou': '道', '-Fu': '府', '-Ken': '県' )
City: { '-Shi': '市' }
District: { '-Ku': '区' }
County: {'-Gun': '郡' }
Town: { '-Machi': '町',
'-Cho': '町' }
Village: { '-Son': '村',
'-Mura': '村' }
The CityCode (JIS X 0402)
The CityCode 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 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 "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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