Pandas cast all object columns to category

use apply and pd.Series.astype with dtype='category'

Consider the pd.DataFrame df

df = pd.DataFrame(dict(
        A=[1, 2, 3, 4],
        B=list('abcd'),
        C=[2, 3, 4, 5],
        D=list('defg')
    ))
df

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
A    4 non-null int64
B    4 non-null object
C    4 non-null int64
D    4 non-null object
dtypes: int64(2), object(2)
memory usage: 200.0+ bytes

Lets use select_dtypes to include all 'object' types to convert and recombine with a select_dtypes to exclude them.

df = pd.concat([
        df.select_dtypes([], ['object']),
        df.select_dtypes(['object']).apply(pd.Series.astype, dtype='category')
        ], axis=1).reindex_axis(df.columns, axis=1)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
A    4 non-null int64
B    4 non-null category
C    4 non-null int64
D    4 non-null category
dtypes: category(2), int64(2)
memory usage: 208.0 bytes

I think that this is a more elegant way:

df = pd.DataFrame(dict(
        A=[1, 2, 3, 4],
        B=list('abcd'),
        C=[2, 3, 4, 5],
        D=list('defg')
    ))

df.info()

df.loc[:, df.dtypes == 'object'] =\
    df.select_dtypes(['object'])\
    .apply(lambda x: x.astype('category'))

df.info()