Reversing 'one-hot' encoding in Pandas

I'd do:

cols = df.columns.to_series().values
pd.DataFrame(np.repeat(cols[None, :], len(df), 0)[df.astype(bool).values], df.index[df.any(1)])

---

Timing

MaxU's method has edge for large dataframes

Small df 5 x 3

Large df 1000000 x 52

I would use apply to decode the columns:

In [2]: animals = pd.DataFrame({"monkey":[0,1,0,0,0],"rabbit":[1,0,0,0,0],"fox":[0,0,1,0,0]})

In [3]: def get_animal(row):
   ...:     for c in animals.columns:
   ...:         if row[c]==1:
   ...:             return c

In [4]: animals.apply(get_animal, axis=1)
Out[4]: 
0    rabbit
1    monkey
2       fox
3      None
4      None
dtype: object

This works with both single and multiple labels.

We can use advanced indexing to tackle this problem. Here is the link.

import pandas as pd

df = pd.DataFrame({"monkey":[1,1,0,1,0],"rabbit":[1,1,1,1,0],\
    "fox":[1,0,1,0,0], "cat":[0,0,0,0,1]})

df['tags']='' # to create an empty column

for col_name in df.columns:
    df.ix[df[col_name]==1,'tags']= df['tags']+' '+col_name

print df

And the result is:

   cat  fox  monkey  rabbit                tags
0    0    1       1       1   fox monkey rabbit
1    0    0       1       1       monkey rabbit
2    0    1       0       1          fox rabbit
3    0    0       1       1       monkey rabbit
4    1    0       0       0                 cat

Explanation: We iterate over the columns on the dataframe.

df.ix[selection criteria, columns to write value] = value
df.ix[df[col_name]==1,'tags']= df['tags']+' '+col_name

The above line basically finds you all the places where df[col_name] == 1, selects column 'tags' and set it to the RHS value which is df['tags']+' '+ col_name

Note: .ix has been deprecated since Pandas v0.20. You should instead use .loc or .iloc, as appropriate.

UPDATE: i think ayhan is right and it should be:

df.idxmax(axis=1)

Demo:

In [40]: s = pd.Series(['dog', 'cat', 'dog', 'bird', 'fox', 'dog'])

In [41]: s
Out[41]:
0     dog
1     cat
2     dog
3    bird
4     fox
5     dog
dtype: object

In [42]: pd.get_dummies(s)
Out[42]:
   bird  cat  dog  fox
0   0.0  0.0  1.0  0.0
1   0.0  1.0  0.0  0.0
2   0.0  0.0  1.0  0.0
3   1.0  0.0  0.0  0.0
4   0.0  0.0  0.0  1.0
5   0.0  0.0  1.0  0.0

In [43]: pd.get_dummies(s).idxmax(1)
Out[43]:
0     dog
1     cat
2     dog
3    bird
4     fox
5     dog
dtype: object

OLD answer: (most probably, incorrect answer)

try this:

In [504]: df.idxmax().reset_index().rename(columns={'index':'animal', 0:'idx'})
Out[504]:
   animal  idx
0     fox    2
1  monkey    1
2  rabbit    0

data:

In [505]: df
Out[505]:
   fox  monkey  rabbit
0    0       0       1
1    0       1       0
2    1       0       0
3    0       0       0
4    0       0       0