pd.to_datetime returns a Series of datetime64 dtype, as described here:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html

df['DATE'] = df['Date'].dt.date

or this:

df['Date'].map(datetime.datetime.date) 

To remove timezone, use tz_localize:

df['timestamp'] = pd.to_datetime(df.timestamp).dt.tz_localize(None)

Output:

                timestamp
0 2020-07-09 04:23:50.267
1 2020-07-09 11:21:55.536
2 2020-07-09 11:23:18.015
3 2020-07-09 04:03:28.581
4 2020-07-09 04:03:33.874

Since version 0.15.0 this can now be easily done using .dt to access just the date component:

Copydf['just_date'] = df['dates'].dt.date

The above returns datetime.date, so object dtype. If you want to keep the dtype as datetime64 then you can just normalize:

Copydf['normalised_date'] = df['dates'].dt.normalize()

This sets the time component to midnight, i.e. 00:00:00, but the display shows just the date value.

• pandas.Series.dt

You can convert Series of dtype datetime64[ns] to a NumPy array of datetime.datetime objects by calling the .dt.to_pydatetime() method:

In [75]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 252 entries, 0 to 251
Data columns (total 1 columns):
time    252 non-null datetime64[ns]<--the `time` column has dtype `datetime64[ns]`
dtypes: datetime64ns
memory usage: 2.0 KB

In [77]: df.head()
Out[77]: 
        time
0 2009-01-02
1 2009-01-05
2 2009-01-06
3 2009-01-07
4 2009-01-08


In [76]: df['time'].dt.to_pydatetime()[:5]
Out[76]: 
array([datetime.datetime(2009, 1, 2, 0, 0),
       datetime.datetime(2009, 1, 5, 0, 0),
       datetime.datetime(2009, 1, 6, 0, 0),
       datetime.datetime(2009, 1, 7, 0, 0),
       datetime.datetime(2009, 1, 8, 0, 0)], dtype=object)

Note that NDFrames (such as Series and DataFrames) can only hold datetime-like objects as objects of dtype datetime64[ns]. The automatic conversion of all datetime-likes to a common dtype simplifies subsequent date computations. But it makes it impossible to store, say, Python datetime.datetime objects in a DataFrame column. Pandas core developer, Jeff Reback explains,

"We don't allow direct conversions because its simply too complicated to keep anything other than datetime64[ns] internally (nor necessary at all)."

Usually I'd try using

ds["time"] = ds["time"].astype("datetime64[ns]")

If that does not work, could you post a example I can use to reproduce your issue?

For anyone who also stumbled across this when comparing a dataframe date to a variable date, and other answers did not exactly answer your question; you can use the code below.

Instead of:

self.df["date"] = pd.to_datetime(self.df["date"])

You can add .dt.date to the end like this:

self.df["date"] = pd.to_datetime(self.df["date"]).dt.date