如何在 Pandas 数据框中将时间戳转换为 datetime.date？

Question

I need to merge 2 pandas dataframes together on dates, but they currently have different date types. 1 is timestamp (imported from excel) and the other is datetime.date .

Any advice?

I've tried pd.to_datetime().date but this only works on a single item(eg df.ix[0,0] ), it won't let me apply to the entire series (eg df['mydates'] ) or the dataframe.

Answer 1

I got some help from a colleague.

This appears to solve the problem posted above

pd.to_datetime(df['mydates']).apply(lambda x: x.date())

Answer 2

比上面简单得多：

df['mydates'].dt.date

Answer 3

For me this works:

from datetime import datetime
df[ts] = [datetime.fromtimestamp(x) for x in df[ts]]

Answer 4

如果您需要datetime.date对象...然后使用Timestamp的.date属性获取它们

pd.to_datetime(df['mydates']).date

Answer 5

Another question was marked as dupe pointing to this, but it didn't include this answer, which seems the most straightforward (perhaps this method did not yet exist when this question was posted/answered):

The pandas doc shows a pandas.Timestamp.to_pydatetime method to "Convert a Timestamp object to a native Python datetime object".

Answer 6

I found the following to be the most effective, when I ran into a similar issue. For instance, with the dataframe df with a series of timestmaps in column ts .

df.ts.apply(lambda x: pd.datetime.fromtimestamp(x).date())

This makes the conversion, you can leave out the .date() suffix for datetimes. Then to alter the column on the dataframe. Like so...

df.loc[:, 'ts'] = df.ts.apply(lambda x: pd.datetime.fromtimestamp(x).date())

Answer 7

Assume time column is in timestamp integer msec format

1 day = 86400000 ms

Here you go:

day_divider = 86400000

df['time'] = df['time'].values.astype(dtype='datetime64[ms]') # for msec format

df['time'] = (df['time']/day_divider).values.astype(dtype='datetime64[D]') # for day format

Answer 8

我试图将时间戳列转换为日期/时间，这是我想出的：

df['Timestamp'] = df['Timestamp'].apply(lambda timestamp: datetime.fromtimestamp(timestamp))