Return a subset of the DataFrameâs 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.
A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.
The subset of the frame including the dtypes in include and excluding the dtypes in exclude.
If both of include and exclude are empty
If include and exclude have overlapping elements
If any kind of string dtype is passed in.
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
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