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

df['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:

df['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

The format in your error isn't the same as the line above, no space between `%A,` and `%b`. You sure there isn't a typo in your original format. This conversion isn't really going to work without some year information though.

x = ['Thursday, Mar 9', 'Wednesday, Mar 10']
pd.to_datetime(x, format='%A, %b %d')
   DatetimeIndex(['1900-03-09', '1900-03-10'], dtype='datetime64[ns]', freq=None)

Essentially equivalent to @waitingkuo, but I would use pd.to_datetime here (it seems a little cleaner, and offers some additional functionality e.g. dayfirst):

In [11]: df
Out[11]:
   a        time
0  1  2013-01-01
1  2  2013-01-02
2  3  2013-01-03

In [12]: pd.to_datetime(df['time'])
Out[12]:
0   2013-01-01 00:00:00
1   2013-01-02 00:00:00
2   2013-01-03 00:00:00
Name: time, dtype: datetime64[ns]

In [13]: df['time'] = pd.to_datetime(df['time'])

In [14]: df
Out[14]:
   a                time
0  1 2013-01-01 00:00:00
1  2 2013-01-02 00:00:00
2  3 2013-01-03 00:00:00

Handling ValueErrors
If you run into a situation where doing

df['time'] = pd.to_datetime(df['time'])

Throws a

ValueError: Unknown string format

That means you have invalid (non-coercible) values. If you are okay with having them converted to pd.NaT, you can add an errors='coerce' argument to to_datetime:

df['time'] = pd.to_datetime(df['time'], errors='coerce')