I've been given a dataframe df and a function foo(int x) that returns a Series. I'd like to join df with the result of foo() for each row using a vectorized operation.
For example, given the following dataframe, I call foo() on the values of column col_1
| col_1 | col_2 | col_3 |
|---|---|---|
| 1 | 1 | 'a' |
| 12 | 2 | 'b' |
| 13 | 3 | 'd' |
| 4 | 4 | 'c' |
If we assume
foo(1) = Series('col4': 0, 'col5': 2)foo(12) = Series('col4': 1, 'col5': 3)foo(13) = Series('col4': 1, 'col5': 4)foo(4) = Series('col4': 0, 'col5': 5)then the output should be
| col_1 | col_2 | col_3 | col4 | col5 |
|---|---|---|---|---|
| 1 | 1 | 'a' | 0 | 2 |
| 12 | 2 | 'b' | 1 | 3 |
| 13 | 3 | 'd' | 1 | 4 |
| 4 | 4 | 'c' | 0 | 5 |
EDIT: Looks like .from_records will handle the map cleanly. You could try that with the pd.concat instead:
In [118]: pd.DataFrame.from_records(df['col_1'].map(foo))
Out[118]:
col4 col5
0 0 2
1 1 3
2 1 4
3 0 5
Normally, I'd use .map() for something like this since it's generally faster than .apply(), but the output comes out a little funky, so unless you've got a giant dataframe, I'd just use the straightforward .apply() option with pd.concat:
In [18]: def foo(n):
...: return {1: pd.Series({'col4': 0, 'col5': 2}), 12: pd.Series({'col4': 1, 'col5': 3}), 13: pd.Series({'col4': 1, 'col5': 4}), 4: pd.Series
...: ({'col4': 0, 'col5': 5})}[n]
...:
In [19]: df
Out[19]:
col_1 col_2 col_3
0 1 1 'a'
1 12 2 'b'
2 13 3 'd'
3 4 4 'c'
In [20]: pd.concat([df, df['col_1'].apply(foo)], axis=1)
Out[20]:
col_1 col_2 col_3 col4 col5
0 1 1 'a' 0 2
1 12 2 'b' 1 3
2 13 3 'd' 1 4
3 4 4 'c' 0 5
Another option you might try is to have the function return a dictionary rather than a Series