Pandas transform() vs apply()

Question

I don\'t understand why apply and transform return different dtypes when called on the same data frame. The way I explained the two functions to my

Answer 1

It looks like SeriesGroupBy.transform() tries to cast the result dtype to the same one as the original column has, but DataFrameGroupBy.transform() doesn't seem to do that:

In [139]: df.groupby('id')['cat'].transform(lambda x: (x == 1).any())
Out[139]:
0    1
1    1
2    1
3    1
4    1
5    1
6    1
7    0
8    0
9    1
Name: cat, dtype: int64

#                         v       v
In [140]: df.groupby('id')[['cat']].transform(lambda x: (x == 1).any())
Out[140]:
     cat
0   True
1   True
2   True
3   True
4   True
5   True
6   True
7  False
8  False
9   True

In [141]: df.dtypes
Out[141]:
cat    int64
id     int64
dtype: object