Dropping time from datetime <[M8] in Pandas

The quickest way is to use DatetimeIndex's normalize (you first need to make the column a DatetimeIndex):

In [11]: df = pd.DataFrame({"t": pd.date_range('2014-01-01', periods=5, freq='H')})

In [12]: df
Out[12]:
                    t
0 2014-01-01 00:00:00
1 2014-01-01 01:00:00
2 2014-01-01 02:00:00
3 2014-01-01 03:00:00
4 2014-01-01 04:00:00

In [13]: pd.DatetimeIndex(df.t).normalize()
Out[13]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-01-01, ..., 2014-01-01]
Length: 5, Freq: None, Timezone: None

In [14]: df['date'] = pd.DatetimeIndex(df.t).normalize()

In [15]: df
Out[15]:
                    t       date
0 2014-01-01 00:00:00 2014-01-01
1 2014-01-01 01:00:00 2014-01-01
2 2014-01-01 02:00:00 2014-01-01
3 2014-01-01 03:00:00 2014-01-01
4 2014-01-01 04:00:00 2014-01-01

DatetimeIndex also has some other useful attributes, e.g. .year, .month, .day.

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From 0.15 they'll be a dt attribute, so you can access this (and other methods) with:

df.t.dt.normalize()
# equivalent to
pd.DatetimeIndex(df.t).normalize()

Another option

df['my_date_column'].dt.date

Would give

0        2019-06-15
1        2019-06-15
2        2019-06-15
3        2019-06-15
4        2019-06-15