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.

This method allows for filtering columns based on their data types. It is useful when working with heterogeneous DataFrames where operations need to be performed on a specific subset of data types.

Parameters:

include, excludescalar 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

TypeError

• 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