如何分割日期和时间并创建单独的列(how to split date and time and create separate columns)

如何分割日期和时间并创建单独的列(how to split date and time and create separate columns)

我想拆分DATE_H_REAL并创建两列。 一个日期和一个小时，我用这个：

from datetime import datetime df_picru = datetime.strptime(df_picru['DATE_H_REAL'], '%Y-%m-%d %H:%M:%S') df_picru['day'] = df_picru.strftime('%Y-%m-%d') df_picru['hour'] = df_picru.strftime('%H:%M:%S')

我的数据看起来像这样

0 NaN 1 NaN 2 NaN 3 02/02/2016 16:16 4 02/02/2016 16:17 5 02/02/2016 16:18

I want to split DATE_H_REAL and create two columns. one for date and one hour, i use this :

from datetime import datetime df_picru = datetime.strptime(df_picru['DATE_H_REAL'], '%Y-%m-%d %H:%M:%S') df_picru['day'] = df_picru.strftime('%Y-%m-%d') df_picru['hour'] = df_picru.strftime('%H:%M:%S')

My data look like this

0 NaN 1 NaN 2 NaN 3 02/02/2016 16:16 4 02/02/2016 16:17 5 02/02/2016 16:18

最满意答案

在pandas中需要to_datetime + Series.dt.strftime - 如果需要输出为strings ：

df_picru = pd.DataFrame({'DATE_H_REAL':['02/02/2016 16:16', '02/02/2016 16:17', np.nan]}) df_picru['DATE_H_REAL'] = pd.to_datetime(df_picru['DATE_H_REAL']) df_picru['day'] = df_picru['DATE_H_REAL'].dt.strftime('%Y-%m-%d') df_picru['hour'] = df_picru['DATE_H_REAL'].dt.strftime('%H:%M:%S') print (df_picru) DATE_H_REAL day hour 0 2016-02-02 16:16:00 2016-02-02 16:16:00 1 2016-02-02 16:17:00 2016-02-02 16:17:00 2 NaT NaT NaT print (type(df_picru.loc[0, 'day'])) <class 'str'> print (type(df_picru.loc[0, 'hour'])) <class 'str'> print (df_picru['DATE_H_REAL'].dtypes) datetime64[ns]

或者Series.dt.date + Series.dt.time如果需要输出python date和python time ：

df_picru['DATE_H_REAL'] = pd.to_datetime(df_picru['DATE_H_REAL']) df_picru['day'] = df_picru['DATE_H_REAL'].dt.date df_picru['hour'] = df_picru['DATE_H_REAL'].dt.time print (df_picru) DATE_H_REAL day hour 0 2016-02-02 16:16:00 2016-02-02 16:16:00 1 2016-02-02 16:17:00 2016-02-02 16:17:00 2 NaT NaN NaN print (type(df_picru.loc[0, 'day'])) <class 'datetime.date'> print (type(df_picru.loc[0, 'hour'])) <class 'datetime.time'> print (df_picru['DATE_H_REAL'].dtypes) datetime64[ns]

In pandas need to_datetime + Series.dt.strftime - if need output as strings:

df_picru = pd.DataFrame({'DATE_H_REAL':['02/02/2016 16:16', '02/02/2016 16:17', np.nan]}) df_picru['DATE_H_REAL'] = pd.to_datetime(df_picru['DATE_H_REAL']) df_picru['day'] = df_picru['DATE_H_REAL'].dt.strftime('%Y-%m-%d') df_picru['hour'] = df_picru['DATE_H_REAL'].dt.strftime('%H:%M:%S') print (df_picru) DATE_H_REAL day hour 0 2016-02-02 16:16:00 2016-02-02 16:16:00 1 2016-02-02 16:17:00 2016-02-02 16:17:00 2 NaT NaT NaT print (type(df_picru.loc[0, 'day'])) <class 'str'> print (type(df_picru.loc[0, 'hour'])) <class 'str'> print (df_picru['DATE_H_REAL'].dtypes) datetime64[ns]

Or Series.dt.date + Series.dt.time if need output python date and python time:

df_picru['DATE_H_REAL'] = pd.to_datetime(df_picru['DATE_H_REAL']) df_picru['day'] = df_picru['DATE_H_REAL'].dt.date df_picru['hour'] = df_picru['DATE_H_REAL'].dt.time print (df_picru) DATE_H_REAL day hour 0 2016-02-02 16:16:00 2016-02-02 16:16:00 1 2016-02-02 16:17:00 2016-02-02 16:17:00 2 NaT NaN NaN print (type(df_picru.loc[0, 'day'])) <class 'datetime.date'> print (type(df_picru.loc[0, 'hour'])) <class 'datetime.time'> print (df_picru['DATE_H_REAL'].dtypes) datetime64[ns]