Python – Gets a list of DataFrame column names for non-floating-point columns

I’m trying to get a list of column names from a DataFrame that corresponds to a column of non-float type. Now I have

categorical = (df.dtypes.values != np.dtype('float64'))

It provides me with a bool array to determine if a column name is floating, but that’s not what I’m looking for. Specifically, I want a list of column names that correspond to the “true” values in my bool array.

Use boolean indexing df.columns:

categorical = df.columns[(df.dtypes.values != np.dtype('float64'))]

Or get difference select_dtypes Number of columns selected:

categorical = df.columns.difference(df.select_dtypes('float64').columns)

Example:

df = pd. DataFrame({'A':list('abcdef'),
                   'B':[4,5,4,5,5,4],
                   'C':[7.,8,9,4,2,3],
                   'D':[1,3,5.,7,1,0],
                   'E':[5,3,6,9,2,4],
                   'F':list('aaabbb')})

print (df)
   A  B    C    D  E  F
0  a  4  7.0  1.0  5  a
1  b  5  8.0  3.0  3  a
2  c  4  9.0  5.0  6  a
3  d  5  4.0  7.0  9  b
4  e  5  2.0  1.0  2  b
5  f  4  3.0  0.0  4  b

categorical = df.columns.difference(df.select_dtypes('float64').columns)
print (categorical)
Index(['A', 'B', 'E', 'F'], dtype='object')