Running get_dummies on several DataFrame columns?

With pandas 0.19, you can do that in a single line :

pd.get_dummies(data=df, columns=['A', 'B'])

Columns specifies where to do the One Hot Encoding.

>>> df   A  B  C0  a  c  11  b  c  22  a  b  3>>> pd.get_dummies(data=df, columns=['A', 'B'])   C  A_a  A_b  B_b  B_c0  1  1.0  0.0  0.0  1.01  2  0.0  1.0  0.0  1.02  3  1.0  0.0  1.0  0.0

Since pandas version 0.15.0, pd.get_dummies can handle a DataFrame directly (before that, it could only handle a single Series, and see below for the workaround):

In [1]: df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],   ...:                 'C': [1, 2, 3]})In [2]: dfOut[2]:   A  B  C0  a  c  11  b  c  22  a  b  3In [3]: pd.get_dummies(df)Out[3]:   C  A_a  A_b  B_b  B_c0  1    1    0    0    11  2    0    1    0    12  3    1    0    1    0

Workaround for pandas < 0.15.0

You can do it for each column seperate and then concat the results:

In [111]: dfOut[111]:    A  B0  a  x1  a  y2  b  z3  b  x4  c  x5  a  y6  b  y7  c  zIn [112]: pd.concat([pd.get_dummies(df[col]) for col in df], axis=1, keys=df.columns)Out[112]:    A        B         a  b  c  x  y  z0  1  0  0  1  0  01  1  0  0  0  1  02  0  1  0  0  0  13  0  1  0  1  0  04  0  0  1  1  0  05  1  0  0  0  1  06  0  1  0  0  1  07  0  0  1  0  0  1

If you don't want the multi-index column, then remove the keys=.. from the concat function call.

Somebody may have something more clever, but here are two approaches. Assuming you have a dataframe named df with columns 'Name' and 'Year' you want dummies for.

First, simply iterating over the columns isn't too bad:

In [93]: for column in ['Name', 'Year']:    ...:     dummies = pd.get_dummies(df[column])    ...:     df[dummies.columns] = dummies

Another idea would be to use the patsy package, which is designed to construct data matrices from R-type formulas.

In [94]: patsy.dmatrix(' ~ C(Name) + C(Year)', df, return_type="dataframe")