How to exclude date in Pandas Dataframe if not "end of month"

Question

I have the following dataset:

import datetime
import pandas as pd

df = pd.DataFrame({'PORTFOLIO': ['A', 'A', 'A', 'A','A', 'A', 'A', 'A','A', 'A','A', 'A', 'A', 'A'],
               'DATE': ['28-02-2018','31-03-2018','30-04-2018','31-05-2018','30-06-2018','31-07-2018','31-08-2018',
                        '30-09-2018','31-10-2018','30-11-2018','31-12-2018','31-01-2019','28-02-2019','05-03-2019'],
               'IRR': [.7, .8, .9, .4, .2, .3, .4, .9, .7, .8, .9, .4,.7, .8],
               })
df

   PORTFOLIO       DATE  IRR
0          A 2018-02-28  0.7
1          A 2018-03-31  0.8
2          A 2018-04-30  0.9
3          A 2018-05-31  0.4
4          A 2018-06-30  0.2
5          A 2018-07-31  0.3
6          A 2018-08-31  0.4
7          A 2018-09-30  0.9
8          A 2018-10-31  0.7
9          A 2018-11-30  0.8
10         A 2018-12-31  0.9
11         A 2019-01-31  0.4
12         A 2019-02-28  0.7
13         A 2019-05-03  0.8

s you might see, all the dates are "end of month", except for 05-03-2019. What I need is to drop a DATE-value if its not "end of month".

My poor temperary solution is

df2=df[df.TODATE < '2019-03-01']

which is not good as the code should be more general.

How do I do that?

Answer 1

This can be done in a one-liner: use pandas.Series.dt.is_month_end

df[pd.to_datetime(df["DATE"]).dt.is_month_end]

will give you your result.

Answer 2

You can use pandas.tseries.offsets.MonthEnd in order to compare the current dates with the end of month dates, and perform a boolean indexation on the dataframe to keep only those that satisfy the condition:

from pandas.tseries.offsets import MonthEnd
df.DATE = pd.to_datetime(df.DATE)

df[df.DATE == df.DATE + MonthEnd(0)]

    PORTFOLIO   DATE  IRR
0          A 2018-02-28  0.7
1          A 2018-03-31  0.8
2          A 2018-04-30  0.9
3          A 2018-05-31  0.4
4          A 2018-06-30  0.2
5          A 2018-07-31  0.3
6          A 2018-08-31  0.4
7          A 2018-09-30  0.9
8          A 2018-10-31  0.7
9          A 2018-11-30  0.8
10         A 2018-12-31  0.9
11         A 2019-01-31  0.4
12         A 2019-02-28  0.7