How to do Pandas dataframe rolling_mean()?

Question

I have a df :

                  sales  net_pft
STK_ID RPT_Date                 
600141 20101231  46.780    1.833
       20110331  13.725    0.384
       20110630  32.733    1.132
       20110930  50.386    1.923
       20111231  65.685    2.325
       20120331  21.088    0.656
       20120630  46.952    1.591
600809 20101231  30.166    4.945
       20110331  18.724    5.061
       20110630  28.948    6.586
       20110930  35.637    7.075
       20111231  44.882    7.805
       20120331  22.140    4.925
       20120630  38.157    7.868 

And I want to do a rolling average for all columns, after groupby STK_ID , the rule expressed by pseudocode like :

if RPT_Date[4:8] == '0331':
    all_column = rolling_mean(all_column,2)

if RPT_Date[4:8] == '0630':
    all_column = rolling_mean(all_column,3)

if RPT_Date[4:8] == '0930':
    all_column = rolling_mean(all_column,4)

if RPT_Date[4:8] == '1231':
    all_column = rolling_mean(all_column,5)

if is_the_first_row():
    keep_original_values()

all_column here stands for ' sales ', 'net_pft' . The final result would like :

                  sales  net_pft
STK_ID RPT_Date                 
600141 20101231  46.780    1.833   # same as original value
       20110331  30.253    1.109   # average of row1&row2 
       20110630  31.079    1.116   # average of row1&row2&row3
......
600809 20101231  30.166    4.945   # same as original value
       20110331  24.445    5.003   # average of row1&row2 
.....

How to write in neat Pandas expression?

Answer 1

I think you want this?

In [29]: df.groupby(level='STK_ID').apply(lambda x: pd.expanding_mean(x))
Out[29]: 
                     sales   net_pft
STK_ID RPT_Date                     
600141 20101231  46.780000  1.833000
       20110331  30.252500  1.108500
       20110630  31.079333  1.116333
       20110930  35.906000  1.318000
       20111231  41.861800  1.519400
       20120331  38.399500  1.375500
       20120630  39.621286  1.406286
600809 20101231  30.166000  4.945000
       20110331  24.445000  5.003000
       20110630  25.946000  5.530667
       20110930  28.368750  5.916750
       20111231  31.671400  6.294400
       20120331  30.082833  6.066167
       20120630  31.236286  6.323571