我有一個日志 df，在那個 df 我有列描述。好像。DescriptionMachine x : Turn offAnother action hereAnother action hereMachine y : Turn offMachine x : Turn onAnother action here我只需要用“：”分割行喜歡：Description Machine ActionMachine x : Turn off Machine x Turn offAnother action hereAnother action hereMachine y : Turn off Machine y Turn offMachine x : Turn on Machine x Turn onAnother action here我已經(jīng)嘗試過：s = df["Description"].apply(lambda x:x.split(":"))df["Action"] = s.apply(lambda x: x[1])df["Machine"] = s.apply(lambda x: x[0])還有一些帶有“startswith”的東西。
3 回答
qq_花開花謝_0
TA貢獻1835條經(jīng)驗 獲得超7個贊
您可以使用str.extract合適的regex. 這將找到周圍的所有值:（也去除冒號周圍的空格）：
df[['Machine', 'Action']] = df.Description.str.extract('(.*) : (.*)',expand=True)
>>> df
Description Machine Action
0 Machine x : Turn off Machine x Turn off
1 Another action here NaN NaN
2 Another action here NaN NaN
3 Machine y : Turn off Machine y Turn off
4 Machine x : Turn on Machine x Turn on
5 Another action here NaN NaN
# df[['Machine', 'Action']] = df.Description.str.extract('(.*) : (.*)',expand=True).fillna('')
婷婷同學_
TA貢獻1844條經(jīng)驗 獲得超8個贊
只需使用split與expand=True
df[['Machine', 'Action']] =df.Description.str.split(':',expand=True).dropna()
df
Description Machine Action
0 Machine x : Turn off Machine x Turn off
1 Another action here NaN NaN
2 Another action here NaN NaN
3 Machine y : Turn off Machine y Turn off
4 Machine x : Turn on Machine x Turn on
5 Another action here NaN NaN
陪伴而非守候
TA貢獻1757條經(jīng)驗 獲得超8個贊
給定一個數(shù)據(jù)框
>>> df
Description
0 Machine x : Turn off
1 Another action here
2 Another action here
3 Machine y : Turn off
4 Machine x : Turn on
5 Another action here
我會通過Series.str.split(splitter, expand=True).
>>> has_colon = df['Description'].str.contains(':')
>>> df[['Machine', 'Action']] = df.loc[has_colon, 'Description'].str.split('\s*:\s*', expand=True)
>>> df
Description Machine Action
0 Machine x : Turn off Machine x Turn off
1 Another action here NaN NaN
2 Another action here NaN NaN
3 Machine y : Turn off Machine y Turn off
4 Machine x : Turn on Machine x Turn on
5 Another action here NaN NaN
如果您更喜歡空字符串，可以NaN通過以下方式替換單元格
>>> df.fillna('')
Description Machine Action
0 Machine x : Turn off Machine x Turn off
1 Another action here
2 Another action here
3 Machine y : Turn off Machine y Turn off
4 Machine x : Turn on Machine x Turn on
5 Another action here
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