How do I convert dates in a Pandas data frame to a 'date' data type?

Question

I have a Pandas data frame, one of the column contains date strings in the format YYYY-MM-DD

For e.g. \'2013-10-28\'

At the moment th

Answer 1

Essentially equivalent to @waitingkuo, but I would use 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

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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')

Answer 2

Try to convert one of the rows into timestamp using the pd.to_datetime function and then use .map to map the formular to the entire column

Answer 3

Now you can do df['column'].dt.date

Note that for datetime objects, if you don't see the hour when they're all 00:00:00, that's not pandas. That's iPython notebook trying to make things look pretty.

Answer 4

Another way to do this and this works well if you have multiple columns to convert to datetime.

cols = ['date1','date2']
df[cols] = df[cols].apply(pd.to_datetime)