Pandas: Create new column in DataFrame based on other column in DataFrame

Question

I have a pandas DataFrame called df that has the following data:

Index SourceDate 0 AUG_2013 1 SEP_2013 2 JAN_2012

I need to add an additional column that turns each of these dates into the following ConvertedDate Column. This column will have the date in YYYY-MM-DD format with the day always being 01.

Index SourceDate ConvertedDate 0 AUG_2013 2013-08-01 1 SEP_2013 2013-09-01 2 JAN_2012 2012-01-01

I attempted doing this with:

df['ConvertedDate'] = time.strptime(str.replace(str.rsplit(df.SourceDate,'_',1)[0],'_','-01-'),'%b-%d-%Y')

Unfortunately this does not work since df.SourceDate is a Series, and string functions won't work on a Series.

EdChum · Accepted Answer · 2015-11-09 13:22:15Z

Use to_datetime and pass a format string:

In [64]: df['ConvertedDate'] =pd.to_datetime(df['SourceDate'], format='%b_%Y') df Out[64]: Index SourceDate ConvertedDate 0 0 AUG_2013 2013-08-01 1 1 SEP_2013 2013-09-01 2 2 JAN_2012 2012-01-01

The python datetime format string specifiers can be found here

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