更新时间:2023-11-07 15:09:40
将字符串列转换为时间序列,可以使用 dt.strftime
方法:
If you convert the column of strings to a time series, you could use the dt.strftime
method:
import numpy as np
import pandas as pd
nan = np.nan
df = pd.DataFrame({'TBD': [nan, nan, nan], 'TBD.1': [nan, nan, nan], 'TBD.2': [nan, nan, nan], 'TimeStamp': ['2016/06/08 17:19:53', '2016/06/08 17:19:54', '2016/06/08 17:19:54'], 'Value': [0.062941999999999998, 0.062941999999999998, 0.062941999999999998]})
df['TimeStamp'] = pd.to_datetime(df['TimeStamp']).dt.strftime('%m/%d/%Y %H:%M:%S')
print(df)
收益
TBD TBD.1 TBD.2 TimeStamp Value
0 NaN NaN NaN 06/08/2016 17:19:53 0.062942
1 NaN NaN NaN 06/08/2016 17:19:54 0.062942
2 NaN NaN NaN 06/08/2016 17:19:54 0.062942
由于要将一列字符串转换为另一(不同的)字符串列,因此也可以使用向量化的 str.replace
方法:
import numpy as np
import pandas as pd
nan = np.nan
df = pd.DataFrame({'TBD': [nan, nan, nan], 'TBD.1': [nan, nan, nan], 'TBD.2': [nan, nan, nan], 'TimeStamp': ['2016/06/08 17:19:53', '2016/06/08 17:19:54', '2016/06/08 17:19:54'], 'Value': [0.062941999999999998, 0.062941999999999998, 0.062941999999999998]})
df['TimeStamp'] = df['TimeStamp'].str.replace(r'(\d+)/(\d+)/(\d+)(.*)', r'\2/\3/\1\4')
print(df)
因为
In [32]: df['TimeStamp'].str.replace(r'(\d+)/(\d+)/(\d+)(.*)', r'\2/\3/\1\4')
Out[32]:
0 06/08/2016 17:19:53
1 06/08/2016 17:19:54
2 06/08/2016 17:19:54
Name: TimeStamp, dtype: object
这使用正则表达式重新排列了strin g ,而无需先将
字符串解析为日期。这比第一种方法要快(主要是因为它跳过了
的解析步骤),但是它也具有不检查
日期字符串是否为有效日期的缺点。
This uses regex to rearrange pieces of the string without first parsing the string as a date. This is faster than the first method (mainly because it skips the parsing step), but it also has the disadvantage of not checking that the date strings are valid dates.