pandas groupby dropping columns

Question

I'm doing a simple group by operation, trying to compare group means. As you can see below, I have selected specific columns from a larger dataframe, from which all missing values have been removed.

But when I group by, I am losing a couple of columns:

I have never encountered this with pandas, and I'm not finding anything else on stack overflow that is all that similar. Does anybody have any insight?

Answer 1

I think it is Automatic exclusion of 'nuisance' columns, what described here.

Sample:

df = pd.DataFrame({'C': {0: -0.91985400000000006, 1: -0.042379, 2: 1.2476419999999999, 3: -0.00992, 4: 0.290213, 5: 0.49576700000000001, 6: 0.36294899999999997, 7: 1.548106}, 'A': {0: 'foo', 1: 'bar', 2: 'foo', 3: 'bar', 4: 'foo', 5: 'bar', 6: 'foo', 7: 'foo'}, 'B': {0: 'one', 1: 'one', 2: 'two', 3: 'three', 4: 'two', 5: 'two', 6: 'one', 7: 'three'}, 'D': {0: -1.131345, 1: -0.089328999999999992, 2: 0.33786300000000002, 3: -0.94586700000000001, 4: -0.93213199999999996, 5: 1.9560299999999999, 6: 0.017587000000000002, 7: -0.016691999999999999}}) 

print (df)      A      B         C         D 0  foo    one -0.919854 -1.131345 1  bar    one -0.042379 -0.089329 2  foo    two  1.247642  0.337863 3  bar  three -0.009920 -0.945867 4  foo    two  0.290213 -0.932132 5  bar    two  0.495767  1.956030 6  foo    one  0.362949  0.017587 7  foo  three  1.548106 -0.016692  print( df.groupby('A').mean())             C         D A                       bar  0.147823  0.306945 foo  0.505811 -0.344944 

I think you can check DataFrame.dtypes.