如何在python中将Series类型的列转换为datetime weekdays格式?
我有以下数据和python代码如何在python中将Series类型的列转换为datetime weekdays格式?,python,python-2.7,datetime,pandas,datetime-format,Python,Python 2.7,Datetime,Pandas,Datetime Format,我有以下数据和python代码 Time Started Date Submitted Status 10/29/2015 17:34 10/29/2015 17:34 Complete 10/29/2015 17:35 10/29/2015 17:35 Complete 10/29/2015 17:36 10/29/2015 17:37 Complete import pandas as pd from datetime import dateti
Time Started Date Submitted Status
10/29/2015 17:34 10/29/2015 17:34 Complete
10/29/2015 17:35 10/29/2015 17:35 Complete
10/29/2015 17:36 10/29/2015 17:37 Complete
import pandas as pd
from datetime import datetime, timedelta
from pandas import Series, DataFrame
df = pd.read_csv('sample.csv')
datetime.strptime(df['Date Submitted'],'%Y-%m-%d %H:%M').strptime('%A')
当我尝试运行下面的代码时,会收到一条TypeError消息。我只是
正在尝试将series类型的列数据转换为datetime工作日
格式
datetime.strtime(df['Session Submitted'],“%Y-%m-%d%H:%m”).strtime(“%A”)TypeError:必须是字符串,而不是序列
添加参数
parse_dates
以转换为datetime
:
import pandas as pd
import io
temp=u"""Time Started,Date Submitted,Status
10/29/2015 17:34,10/29/2015 17:34,Complete
10/29/2015 17:35,10/29/2015 17:35,Complete
10/29/2015 17:36,10/29/2015 17:37,Complete"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), parse_dates=[0,1])
print (df)
Time Started Date Submitted Status
0 2015-10-29 17:34:00 2015-10-29 17:34:00 Complete
1 2015-10-29 17:35:00 2015-10-29 17:35:00 Complete
2 2015-10-29 17:36:00 2015-10-29 17:37:00 Complete
print (df.dtypes)
Time Started datetime64[ns]
Date Submitted datetime64[ns]
Status object
dtype: object
然后使用:
另一个解决方案是(版本0.18.1中新增):
谢谢你,耶斯雷尔!你的回答很有帮助!
df['Date Submitted'] = df['Date Submitted'].dt.strftime('%A')
print (df)
Time Started Date Submitted Status
0 2015-10-29 17:34:00 Thursday Complete
1 2015-10-29 17:35:00 Thursday Complete
2 2015-10-29 17:36:00 Thursday Complete
df['Date Submitted'] = df['Date Submitted'].dt.weekday_name
print (df)
Time Started Date Submitted Status
0 2015-10-29 17:34:00 Thursday Complete
1 2015-10-29 17:35:00 Thursday Complete
2 2015-10-29 17:36:00 Thursday Complete