How to convert binary columns with multiple occurrences into categorical data in Pandas
Question:
I have the following example data set
A
B
C
D
foo
0
1
1
bar
0
0
1
baz
1
1
0
How could extract the column names of each 1 occurrence in a row and put that into another column E so that I get the following table:
A
B
C
D
E
foo
0
1
1
C, D
bar
0
0
1
D
baz
1
1
0
B, C
Note that there can be more than two 1s per row.
Answers:
You can use DataFrame.dot.
df['E'] = df[['B', 'C', 'D']].dot(df.columns[1:] + ', ').str.rstrip(', ')
df
A B C D E
0 foo 0 1 1 C, D
1 bar 0 0 1 D
2 baz 1 1 0 B, C
Inspired by jezrael’s answer in this post.
Another way is that you can convert each row to boolean and use it as a selection mask to filter the column names.
cols = pd.Index(['B', 'C', 'D'])
df['E'] = df[cols].astype('bool').apply(lambda row: ", ".join(cols[row]), axis=1)
df
A B C D E
0 foo 0 1 1 C, D
1 bar 0 0 1 D
2 baz 1 1 0 B, C
I have the following example data set
| A | B | C | D |
|---|---|---|---|
| foo | 0 | 1 | 1 |
| bar | 0 | 0 | 1 |
| baz | 1 | 1 | 0 |
How could extract the column names of each 1 occurrence in a row and put that into another column E so that I get the following table:
| A | B | C | D | E |
|---|---|---|---|---|
| foo | 0 | 1 | 1 | C, D |
| bar | 0 | 0 | 1 | D |
| baz | 1 | 1 | 0 | B, C |
Note that there can be more than two 1s per row.
You can use DataFrame.dot.
df['E'] = df[['B', 'C', 'D']].dot(df.columns[1:] + ', ').str.rstrip(', ')
df
A B C D E
0 foo 0 1 1 C, D
1 bar 0 0 1 D
2 baz 1 1 0 B, C
Inspired by jezrael’s answer in this post.
Another way is that you can convert each row to boolean and use it as a selection mask to filter the column names.
cols = pd.Index(['B', 'C', 'D'])
df['E'] = df[cols].astype('bool').apply(lambda row: ", ".join(cols[row]), axis=1)
df
A B C D E
0 foo 0 1 1 C, D
1 bar 0 0 1 D
2 baz 1 1 0 B, C