Python 正则表达式匹配字符串中的多个数字

发布于05月14日

I have a regex that looks like this to extract order numbers from columns:

df["Orders"].str.extract('([0-9]{9,10}[/+ #_;.-]?)')

The orders column can look like this:

12
123456789
1234567890
123456789/1234567890
123456789/1/123456789
123456789+1234567890

The resulting new column in the dataframe after the regex should look like this:

NaN
123456789
1234567890
123456789/1234567890
123456789/123456789
123456789+1234567890

However, with my current regex I'm getting the following result:

How can I get the result that I'm looking for?

推荐答案

You can use

import pandas as pd
df = pd.DataFrame({'Orders':['12','123456789','1234567890','123456789/1234567890','123456789/1/123456789','123456789+1234567890', 'Order number: 6508955960_000010_1005500']})
df["Result"] = df["Orders"].str.findall(r'[/+ #_;.-]?(?<![0-9])[0-9]{9,10}(?![0-9])').str.join('').str.lstrip('/+ #_;.-')
df.loc[df['Result'] == '', 'Result'] = np.nan

See the regex demo. Details

[/+ #_;.-]?(?<![0-9])[0-9]{9,10}(?![0-9]) - matches an optional /, +, space, #, _, ;, . or - char, and then none or ten digit number not enclosed with other digits
Series.str.findall extracts all occurrences
.str.join('') concatenates the matches into a single string
.str.lstrip('/+ #_;.-') - removes the special chars that were matched with the number at the beginning of the string
df.loc[df['Result'] == '', 'Result'] = np.nan - if needed - replaces empty strings with np.nan values in the Result column.

Output:

>>> df
                  Orders                Result
0                    NaN                   NaN
1              123456789             123456789
2             1234567890            1234567890
3   123456789/1234567890  123456789/1234567890
4  123456789/1/123456789   123456789/123456789
5   123456789+1234567890  123456789+1234567890
>>>