从列中的字符串中提取一组n个数字

你可以做到 extract :

df =pd.DataFrame({'text':["Who would have thought this would be so 4347009 difficult",
                          "24 is me"]})

df['new_col'] = df['text'].str.extract(r'(\d+)')

    text                                                new_col
0   Who would have thought this would be so 434700...   4347009
1   24 is me                                            24

你可以使用 extract 带数字捕获组 (\d+) :

import pandas as pd

data = ["AU/4347001",
        "Who would have thought this would be so 4347009 difficult",
        "Another with a no numbers",
        "131242143"]

df = pd.DataFrame(data=data, columns=['txt'])
result = df.assign(res=df.txt.str.extract('(\d+)')).fillna('')
print(result)

产量

                                                 txt        res
0                                         AU/4347001    4347001
1  Who would have thought this would be so 434700...    4347009
2                          Another with a no numbers           
3                                          131242143  131242143

注意,在上面的示例中,使用 fillna 在本例中,用空字符串填充那些没有找到数字组的列。

这是我们的测试数据框架:

### Create an example Pandas Dataframe
df = pd.DataFrame(data=['something123', 'some456thing', '789somthing', 
                        'Lots of numbers 82849585 make a long sentence'], columns = ['strings'])

### Create a function for identifying, joining and then turning the string to an integer
def get_numbers(string):
    return int(''.join([s for s in string if s.isdigit()]))

### Now lets apply the get_numbers function to the strings column
df.loc[:,'strings_wo_numbers'] = df.loc[:,'strings']apply(get_numbers)

注意:这将连接字符串中的所有数字,即“10个橄榄和5个苹果”将变为105而不是10、5。

使用 str.finall

df.text.str.findall('\d+').str[0]
0    4347009
1         24
Name: text, dtype: object