How can I Group By Month from a Date field using Python/Pandas

try this:

In [6]: df['date'] = pd.to_datetime(df['date'])

In [7]: df
Out[7]: 
        date  Revenue
0 2017-06-02      100
1 2017-05-23      200
2 2017-05-20      300
3 2017-06-22      400
4 2017-06-21      500



In [59]: df.groupby(df['date'].dt.strftime('%B'))['Revenue'].sum().sort_values()
Out[59]: 
date
May      500
June    1000

Try a groupby using a pandas Grouper:

df = pd.DataFrame({'date':['6/2/2017','5/23/2017','5/20/2017','6/22/2017','6/21/2017'],'Revenue':[100,200,300,400,500]})
df.date = pd.to_datetime(df.date)
dg = df.groupby(pd.Grouper(key='date', freq='1M')).sum() # groupby each 1 month
dg.index = dg.index.strftime('%B')

     Revenue
 May    500
June    1000

For DataFrame with many rows, using strftime takes up more time. If the date column already has dtype of datetime64[ns] (can use pd.to_datetime() to convert, or specify parse_dates during csv import, etc.), one can directly access datetime property for groupby labels (Method 3). The speedup is substantial.

import numpy as np
import pandas as pd

T = pd.date_range(pd.Timestamp(0), pd.Timestamp.now()).to_frame(index=False)
T = pd.concat([T for i in range(1,10)])
T['revenue'] = pd.Series(np.random.randint(1000, size=T.shape[0]))
T.columns.values[0] = 'date'

print(T.shape) #(159336, 2)
print(T.dtypes) #date: datetime64[ns], revenue: int32

Method 1: strftime

%timeit -n 10 -r 7 T.groupby(T['date'].dt.strftime('%B'))['revenue'].sum()

1.47 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Method 2: Grouper

%timeit -n 10 -r 7 T.groupby(pd.Grouper(key='date', freq='1M')).sum()
#NOTE Manually map months as integer {01..12} to strings

56.9 ms ± 2.88 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Method 3: datetime properties

%timeit -n 10 -r 7 T.groupby(T['date'].dt.month)['revenue'].sum()
#NOTE Manually map months as integer {01..12} to strings

34 ms ± 3.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)