How to create a one-hot-encoded dataframe of all possible combinations of X columns?
Question:
I have a pandas dataframe with 0 rows and 58 columns, but for the sake of illustration let’s assume I have a dataframe with 0 rows and 3 columns that looks like this:
col1
col2
col3
I need to expand this table to represent all possible combinations of one-hot-encoded vectors like this:
col1
col2
col3
0
0
0
1
1
1
0
1
0
1
1
0
1
0
0
0
0
1
0
1
1
How can I do this in python?
Answers:
You can use itertools.product:
from itertools import product
pd.DataFrame(list(product(*([[0,1]]*len(df.columns))) ), columns=df.columns)
Output:
col1 col2 col3
0 0 0 0
1 0 0 1
2 0 1 0
3 0 1 1
4 1 0 0
5 1 0 1
6 1 1 0
7 1 1 1
I have a pandas dataframe with 0 rows and 58 columns, but for the sake of illustration let’s assume I have a dataframe with 0 rows and 3 columns that looks like this:
| col1 | col2 | col3 |
|---|
I need to expand this table to represent all possible combinations of one-hot-encoded vectors like this:
| col1 | col2 | col3 |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 1 | 1 |
| 0 | 1 | 0 |
| 1 | 1 | 0 |
| 1 | 0 | 0 |
| 0 | 0 | 1 |
| 0 | 1 | 1 |
How can I do this in python?
You can use itertools.product:
from itertools import product
pd.DataFrame(list(product(*([[0,1]]*len(df.columns))) ), columns=df.columns)
Output:
col1 col2 col3
0 0 0 0
1 0 0 1
2 0 1 0
3 0 1 1
4 1 0 0
5 1 0 1
6 1 1 0
7 1 1 1