pandas.DataFrame.select_dtypes

DataFrame.select_dtypes(include=None, exclude=None)[source]

Return a subset of the DataFrame’s columns based on the column dtypes.

Parameters:

include, exclude:scalar or list-like

A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

Returns:

DataFrame

The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

Raises:

ValueError

• If both of include and exclude are empty

• If include and exclude have overlapping elements

• If any kind of string dtype is passed in.

See also

DataFrame.dtypes

Return Series with the data type of each column.

Notes

• To select all numeric types, use np.number or 'number'

• To select strings you must use the object dtype, but note that this will return all object dtype columns. With pd.options.future.infer_string enabled, using "str" will work to select all string columns.

• See the numpy dtype hierarchy

• To select datetimes, use np.datetime64, 'datetime' or 'datetime64'

• To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'

• To select Pandas categorical dtypes, use 'category'

• To select Pandas datetimetz dtypes, use 'datetimetz' or 'datetime64[ns, tz]'

Examples

>>> df = pd.DataFrame({'a': [1, 2] * 3,
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0

>>> df.select_dtypes(include='bool')
   b
0  True
1  False
2  True
3  False
4  True
5  False

>>> df.select_dtypes(include=['float64'])
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0

>>> df.select_dtypes(exclude=['int64'])
       b    c
0   True  1.0
1  False  2.0
2   True  1.0
3  False  2.0
4   True  1.0
5  False  2.0