pandas 在python pandas中将时间对象转换为日期时间格式
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/53470304/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Convert time object to datetime format in python pandas
提问by Nadeem Haque
I have a dataset of column name DateTimehaving dtype object.
我有一个列名DateTime具有 dtype object的数据集。
df['DateTime'] = pd.to_datetime(df['DateTime'])
I have used the above code to convert to datetimeformat then did a split in the column to have Dateand Timeseparately
我用上面的代码转换为日期时间格式，然后做在列拆分有日期和时间分别
df['date'] = df['DateTime'].dt.date
df['time'] = df['DateTime'].dt.time
but after the split the format changes to object type and while converting it to datetime it showing error for the timecolumn name as: TypeError: is not convertible to datetime
但是在拆分后，格式更改为对象类型，并且在将其转换为日期时间时，它显示时间列名称的错误为：TypeError: is not convertible to datetime
How to convert it to datetime format the timecolumn
如何将其转换为日期时间格式的时间列
采纳答案by jezrael
You can use combinein list comprehension with zip:
您可以combine在列表理解中使用zip：
df = pd.DataFrame({'DateTime': ['2011-01-01 12:48:20', '2014-01-01 12:30:45']})
df['DateTime'] = pd.to_datetime(df['DateTime'])
df['date'] = df['DateTime'].dt.date
df['time'] = df['DateTime'].dt.time
import datetime
df['new'] = [datetime.datetime.combine(a, b) for a, b in zip(df['date'], df['time'])]
print (df)
DateTime date time new
0 2011-01-01 12:48:20 2011-01-01 12:48:20 2011-01-01 12:48:20
1 2014-01-01 12:30:45 2014-01-01 12:30:45 2014-01-01 12:30:45
Or convert to strings, join together and convert again:
或转换为字符串，连接在一起并再次转换：
df['new'] = pd.to_datetime(df['date'].astype(str) + ' ' +df['time'].astype(str))
print (df)
DateTime date time new
0 2011-01-01 12:48:20 2011-01-01 12:48:20 2011-01-01 12:48:20
1 2014-01-01 12:30:45 2014-01-01 12:30:45 2014-01-01 12:30:45
But if use floorfor remove times with converting times to timedeltas then use +only:
但是，如果floor用于将时间转换为 timedeltas 的删除时间，则+仅使用：
df['date'] = df['DateTime'].dt.floor('d')
df['time'] = pd.to_timedelta(df['DateTime'].dt.strftime('%H:%M:%S'))
df['new'] = df['date'] + df['time']
print (df)
DateTime date time new
0 2011-01-01 12:48:20 2011-01-01 12:48:20 2011-01-01 12:48:20
1 2014-01-01 12:30:45 2014-01-01 12:30:45 2014-01-01 12:30:45
回答by jpp
How to convert it back to datetime format the time column
如何将其转换回日期时间格式的时间列
There appears to be a misunderstanding. Pandas datetimeseries mustinclude date and time components. This is non-negotiable. You can simply use pd.to_datetimewithout specifying a date and use the default 1900-01-01date:
似乎有误会。Pandasdatetime系列必须包含日期和时间组件。这是没有商量余地的。您可以简单地使用pd.to_datetime而不指定日期并使用默认1900-01-01日期：
# date from jezrael
print(pd.to_datetime(df['time'], format='%H:%M:%S'))
0 1900-01-01 12:48:20
1 1900-01-01 12:30:45
Name: time, dtype: datetime64[ns]
Or use another date component, for example today's date:
或者使用另一个日期组件，例如今天的日期：
today = pd.Timestamp('today').strftime('%Y-%m-%d')
print(pd.to_datetime(today + ' ' + df['time'].astype(str)))
0 2018-11-25 12:48:20
1 2018-11-25 12:30:45
Name: time, dtype: datetime64[ns]
Or recombine from your dateand timeseries:
或者从你的date和time系列中重新组合：
print(pd.to_datetime(df['date'].astype(str) + ' ' + df['time'].astype(str)))
0 2011-01-01 12:48:20
1 2014-01-01 12:30:45
dtype: datetime64[ns]
