Check which columns in DataFrame are Categorical

The way I found was updating to Pandas v0.16.0, then excluding number dtypes with:

df.select_dtypes(exclude=["number","bool_","object_"])

Which works, providing no types are changed and no more are added to NumPy. The suggestion in the question's comments by @Jeff suggests include=["category"], but that didn't seem to work.

NumPy Types: link

You could use df._get_numeric_data() to get numeric columns and then find out categorical columns

In [66]: cols = df.columns

In [67]: num_cols = df._get_numeric_data().columns

In [68]: num_cols
Out[68]: Index([u'0', u'1', u'2'], dtype='object')

In [69]: list(set(cols) - set(num_cols))
Out[69]: ['3', '4']

For posterity. The canonical method to select dtypes is .select_dtypes. You can specify an actual numpy dtype or convertible, or 'category' which not a numpy dtype.

In [1]: df = DataFrame({'A' : Series(range(3)).astype('category'), 'B' : range(3), 'C' : list('abc'), 'D' : np.random.randn(3) })

In [2]: df
Out[2]: 
   A  B  C         D
0  0  0  a  0.141296
1  1  1  b  0.939059
2  2  2  c -2.305019

In [3]: df.select_dtypes(include=['category'])
Out[3]: 
   A
0  0
1  1
2  2

In [4]: df.select_dtypes(include=['object'])
Out[4]: 
   C
0  a
1  b
2  c

In [5]: df.select_dtypes(include=['object']).dtypes
Out[5]: 
C    object
dtype: object

In [6]: df.select_dtypes(include=['category','int']).dtypes
Out[6]: 
A    category
B       int64
dtype: object

In [7]: df.select_dtypes(include=['category','int','float']).dtypes
Out[7]: 
A    category
B       int64
D     float64
dtype: object