根据另一列中的多个条件填充列中的值(Python)

您可以将正则表达式用于(..MP.{13})个字符(2个字符，MP，13个字符):

df['Serial Number Final'] = df['Serial Number + Keyword'].str.extract(r'(..MP.{13})')

或者，如果x只能是数字\d\dMP\d{13}(2位，MP，13位):

df['Serial Number Final'] = df['Serial Number + Keyword'].str.extract(r'(\d\dMP\d{13})')

输出:

   Serial Number New       Serial Number + Keyword  Serial Number Old Serial Number Final
0  12MP3221156732243  12MP3221156732243 Restaurant  12MP3221156732243   12MP3221156732243
1                  0      Retail 12MP3251453730827      3251453730827   12MP3251453730827
2                  0           K312MP3251773832657      3251773832657   12MP3251773832657
3  11MP3221156732243             11MP3221156732243    MP3221156732243   11MP3221156732243

use several columns (pick first match):

cols = ['Serial Number New', 'Serial Number + Keyword']

df['Serial Number Final'] = (df[cols]
 .apply(lambda s: s.str.extract(r'(\d\dMP\d{13})', expand=False))
 .bfill().iloc[:, 0]
)

输出:

   Serial Number New       Serial Number + Keyword  Serial Number Old Serial Number Final
0  12MP3221156732243  12MP3221156732243 Restaurant  12MP3221156732243   12MP3221156732243
1                  0      Retail 12MP3251453730827      3251453730827   11MP3221156732243
2                  0           K312MP3251773832657      3251773832657   11MP3221156732243
3  11MP3221156732243             11MP3221156732243    MP3221156732243   11MP3221156732243
4  11MP3251156732267                             0    MP3251156732267   11MP3251156732267