pandas: convert datetime to end-of-month

you can also use numpy to do it faster:

import numpy as np
date_array = np.array(['2013-01-01', '2013-01-15', '2013-01-30']).astype('datetime64[ns]')
month_start_date = date_array.astype('datetime64[M]')

If the date column is in datetime format and is set to starting day of the month, this will add one month of time to it:

df['date1']=df['date'] + pd.offsets.MonthEnd(0) 

import pandas as pd
import numpy as np
import datetime as dt    

df0['Calendar day'] = pd.to_datetime(df0['Calendar day'], format='%m/%d/%Y')
df0['Calendar day'] = df0['Calendar day'].apply(pd.datetools.normalize_date)    
df0['Month Start Date'] = df0['Calendar day'].dt.to_period('M').apply(lambda r: r.start_time)

This code should work. Calendar Day is a column in which date is given in the format %m/%d/%Y. For example: 12/28/2014 is 28 December, 2014. The output comes out to be 2014-12-01 in class 'pandas.tslib.Timestamp' type.

Revised, converting to period and then back to timestamp does the trick

In [104]: df = DataFrame(dict(date = [Timestamp('20130101'),Timestamp('20130131'),Timestamp('20130331'),Timestamp('20130330')],value=randn(4))).set_index('date')

In [105]: df
Out[105]: 
               value
date                
2013-01-01 -0.346980
2013-01-31  1.954909
2013-03-31 -0.505037
2013-03-30  2.545073

In [106]: df.index = df.index.to_period('M').to_timestamp('M')

In [107]: df
Out[107]: 
               value
2013-01-31 -0.346980
2013-01-31  1.954909
2013-03-31 -0.505037
2013-03-31  2.545073

Note that this type of conversion can also be done like this, the above would be slightly faster, though.

In [85]: df.index + pd.offsets.MonthEnd(0) 
Out[85]: DatetimeIndex(['2013-01-31', '2013-01-31', '2013-03-31', '2013-03-31'], dtype='datetime64[ns]', name=u'date', freq=None, tz=None)