How to group and count rows by month and year using Pandas?

To group on multiple criteria, pass a list of the columns or criteria:

df['birthdate'].groupby([df.birthdate.dt.year, df.birthdate.dt.month]).agg('count')

Example:

In [165]:
df = pd.DataFrame({'birthdate':pd.date_range(start=dt.datetime(2015,12,20),end=dt.datetime(2016,3,1))})
df.groupby([df['birthdate'].dt.year, df['birthdate'].dt.month]).agg({'count'})

Out[165]:
                    birthdate
                        count
birthdate birthdate          
2015      12               12
2016      1                31
          2                29
          3                 1

UPDATE

As of version 0.23.0 the above code no longer works due to the restriction that multi-index level names must be unique, you now need to rename the levels in order for this to work:

In[107]:
df.groupby([df['birthdate'].dt.year.rename('year'), df['birthdate'].dt.month.rename('month')]).agg({'count'})

Out[107]: 
           birthdate
               count
year month          
2015 12           12
2016 1            31
     2            29
     3             1

You can also use the "monthly" period with to_period with the dt accessor:

In [11]: df = pd.DataFrame({'birthdate': pd.date_range(start='20-12-2015', end='3-1-2016')})

In [12]: df['birthdate'].groupby(df.birthdate.dt.to_period("M")).agg('count')
Out[12]:
birthdate
2015-12    12
2016-01    31
2016-02    29
2016-03     1
Freq: M, Name: birthdate, dtype: int64

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It's worth noting if the datetime is the index (rather than a column) you can use resample:

df.resample("M").count()