Python realizes recognizing provinces and cities in text and drawing

1. Get ready

2. Basic use

3. Advanced use

Doing it NLP（ natural language processing ） Related tasks , We often encounter the need to identify and extract provinces 、 City 、 The needs of the administrative region . Although we can search the keyword table one by one to achieve the purpose of extraction , But we need to collect the keyword list of provinces and cities first , Relatively cumbersome .

Today I will introduce a module to you , You just need to pass the string to this module , He can return the province in this string to you 、 City 、 Zone keywords , And can mark it on the picture for you , It is Cpca modular .

Before the start , You have to make sure that Python and pip Has been successfully installed on the computer , without , You can visit this article ： Hyperdetail Python Installation guide   Installation .

( Optional 1)  If you use Python The goal is data analysis , It can be installed directly Anaconda, It has... Built in Python and pip.

( Optional 2)  Besides , Recommended VSCode Editor , It has many advantages

Please choose one of the following ways to enter the command to install the dependency ：

1. Windows Environmental Science open Cmd ( Start - function -CMD).

2. MacOS Environmental Science open Terminal (command+ Space input Terminal).

3. If you're using a VSCode Editor or Pycharm, You can directly use the Terminal.

Be careful , at present cpca The module only supports Python3 And above .

stay windows The following problems may occur on the ：

First read the original text to download Microsoft Visual C++ Build Tools install VC++ Building tools , Again pip install cpca, Problem solvable .

Through two lines of code, you can achieve the most basic provincial and urban extraction ：

The effect is as follows ：

province City District Address adcode
0 Guangdong province, shenzhen Futian district Shennan Middle Road, bating street 1025 New town building No 1 layer 440304
1 Shanghai None None .310000
2 Sichuan Province deyang Guanghan City By the Duck River in Sanxingdui town in the west of the city , It is a bronze age cultural site 510681

Pay attention to Article 3 of Guanghan City ,cpca Not only the county-level city Guanghan City in the sentence is recognized , It can also be automatically matched to Deyang City, which is the entrusted city , I have to say it's very powerful .

If you want to know that the program extracts the name of the province or city from the position of the string , You can add one  pos_sensitive=True  Parameters :

The effect is as follows ：

(base) G:\push\20220623>python 1.py
      province City District Address adcode province _pos City _pos District _pos
0   Guangdong province, shenzhen Futian district Shennan Middle Road, bating street 1025 New town building No 1 layer  440304      0      3      6
1   Shanghai None None .310000     38     -1     -1
2   Sichuan Province deyang Guanghan City By the Duck River in Sanxingdui town in the west of the city , It is a bronze age cultural site  510681      9     -1     12

It marks the identification to the province 、 City 、 Key location of the zone （index）, Of course, if it is Deyang City, this special identification will be marked as -1.

It can also batch identify multiple regions from large pieces of text ：

The effect is as follows ：

(base) G:\push\20220623>python 1.py
           province City District Address adcode province _pos City _pos District _pos
0        Guangdong province, guangzhou  None     440100     -1     44     -1
1    Hong Kong Special Administrative Region  None  None     810000     47     -1     -1
2        Guangdong province, shenzhen  None     440300     -1     58     -1
3        The Beijing municipal  None  None     110000     71     -1     -1
4        Guangdong province, guangzhou  None     440100     -1     86     -1
5        Guangdong province, shenzhen  None     440300     -1     89     -1
6    Hong Kong Special Administrative Region  None  None     810000     92     -1     -1
7        The Beijing municipal  None  None     110000    100     -1     -1
8        Guangdong province, guangzhou  None     440100     -1    110     -1
9    Hong Kong Special Administrative Region  None  None     810000    115     -1     -1
10       Guangdong province, shenzhen  None     440300     -1    120     -1
11       The Beijing municipal  None  None     110000    128     -1     -1
12       Guangdong province, guangzhou  None     440100     -1    143     -1

More Than This , The module also comes with some simple drawing tools , The data output above can be drawn in the form of thermal diagram on the map ：

This error may be reported when running ：

(base) G:\push\20220623>python 1.py
Traceback (most recent call last):
  File "1.py", line 12, in <module>
    drawer.draw_locations(df[cpca._ADCODE], "df.html")
  File "G:\Anaconda3\lib\site-packages\cpca\drawer.py", line 41, in draw_locations
    import folium
ModuleNotFoundError: No module named 'folium'

Use pip Can be installed ：

Then rerun the code , Will generate... In the current directory df.html, Double-click to open , The effect is as follows ：

How to use it? , Does it feel very convenient ？ In the future, this module will be sufficient for location identification .

For more details, you can visit the Github Home page reading , The project README Written entirely in Chinese , It's very easy to read ：

This is about Python This is the end of the article on how to recognize provinces and cities in characters and draw them , More about Python Please search the previous articles of the software development network or continue to browse the following related articles. I hope you will support the software development network in the future ！