pandas.DataFrame.select_dtypes — pandas 2.2.3 documentation (original) (raw)

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

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

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
• 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
• 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