Cleaner way of getting date out of pandas timestamp

Suppose I have a df like so:

foo = pd.DataFrame(
    {
        'a': [1, 2, 3],
        'b': ['2021-01-05 05:15', '2021-01-06 11:10', '2021-03-01 09:00']
    }
)

And I want to convert column b to datetime and extract only the date part. I can do something like:

foo['date'] = pd.to_datetime(foo.b).dt.date

But even though this returns a Numpy array of datetime objects, Pandas doesn’t recognise this and assigns an object dtype to the column:

foo.dtypes

Out: 
a        int64
b       object
date    object
dtype: object

I can of course get it to be a datetime by casting it to datetime again:

foo['date'] = pd.to_datetime(pd.to_datetime(foo.b).dt.date)

I can also get it with string slicing

foo['date2'] = pd.to_datetime(foo.b.str[:11])

But I feel like there must be a cleaner way of getting a date out of datetime column.

>Solution :

You can use dt.normalize:

foo['date'] = pd.to_datetime(foo['b']).dt.normalize()

Output:

>>> foo
   a                 b       date
0  1  2021-01-05 05:15 2021-01-05
1  2  2021-01-06 11:10 2021-01-06
2  3  2021-03-01 09:00 2021-03-01

>>> foo.dtypes
a                int64
b               object
date    datetime64[ns]
dtype: object

However your last solution is a good solution pd.to_datetime(foo.b.str[:11]).