Python/Pandas: Combining columns currently set up by Year-Month into an average of the year

Question

I currently have a dataframe that has median sales values for locations broken down by each month of the year going back to the 60s (roughly 260 columns). I'd like to create a column for each year containing the mean average of each year but I'm unsure of how to go about doing that. An example of the data:

 RegionID    ZIP      City State     Metro CountyName  SizeRank  1996-04  \
0      61639  10025  New York    NY  New York   New York         1      NaN   
2      61637  10023  New York    NY  New York   New York         3      NaN   
13     61703  10128  New York    NY  New York   New York        14      NaN   
14     61625  10011  New York    NY  New York   New York        15      NaN   
20     61617  10003  New York    NY  New York   New York        21      NaN   

    1996-05  1996-06  ...  2016-09  2016-10  2016-11    2016-12  2017-01  \
0       NaN      NaN  ...  1374400  1364100  1366300  1354800.0  1327500   
2       NaN      NaN  ...  1993500  1980700  1960900  1951300.0  1937800   
13      NaN      NaN  ...  1526000  1523700  1527200  1541600.0  1557800   
14      NaN      NaN  ...  2354000  2355500  2352200  2332100.0  2313300   
20      NaN      NaN  ...  1932800  1930400  1937500  1935100.0  1915700   

Most of the datatypes are int64 but I also have these Region Codes / location info.

Answer 1

(df
 # stacks each column one on top of the other
 .stack()
 .reset_index(level=1)
 # give the columns sensible names
 .rename(columns={'level_1': 'year_month', 0: 'sales'})
 # convert to datetime format
 .assign(year_month=lambda x: x.year_month.astype('datetime64[ns]'),
         # pull out the year part of the data
         year_col=lambda x: x['year_month'].dt.year)
 # run a groupby for each year and calculate the average sales
 .groupby('year_col', as_index=False)['sales'].mean()
 )

year_col
2017    854096
Name: 0, dtype: int64

Answer 2

Here is one approach

df.stack().groupby(level=1).mean()

2017-02    824100
2017-03    832480
2017-04    850160
2017-05    872880
2017-06    890860