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

Question

I have a Data-frame df which is as follows:

| date      | Revenue |
|-----------|---------|
| 6/2/2017  | 100     |
| 5/23/2017 | 200     |
| 5/20/2017 | 300             


        

Answer 1

This will work better.

Try this:

#explicitly convert to date
df['Date'] = pd.to_datetime(df['Date'])
# set your date column as index 
df.set_index('Date',inplace=True) 

# For monthly use 'M', If needed for other freq you can change.
df[revenue].resample('M').sum()

This code gives same result as @shivsn answer on first post.

But thing is we can do lot more operations in this mentioned code. Recommended to use this:

>>> df['Date'] = pd.to_datetime(df['Date'])
>>> df.set_index('Date',inplace=True)
>>> df['withdrawal'].resample('M').sum().sort_values()
Date
2019-10-31     28710.00
2019-04-30     31437.00
2019-07-31     39728.00
2019-11-30     40121.00
2019-05-31     46495.00
2020-02-29     57751.10
2019-12-31     72469.13
2020-01-31     76115.78
2019-06-30     76947.00
2019-09-30     79847.04
2020-03-31     97920.18
2019-08-31    205279.45
Name: withdrawal, dtype: float64

where @shivsn code's does same.

>>> df.groupby(df['Date'].dt.strftime('%B'))['withdrawal'].sum().sort_values()
Date
October       28710.00
April         31437.00
July          39728.00
November      40121.00
May           46495.00
February      57751.10
December      72469.13
January       76115.78
June          76947.00
September     79847.04
March         97920.18
August       205279.45
Name: withdrawal, dtype: float64