dask_expr._collection.DataFrame.select_dtypes
dask_expr._collection.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 docstring was copied from pandas.core.frame.DataFrame.select_dtypes.
Some inconsistencies with the Dask version may exist.
- 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 inexclude
.
- Raises
- ValueError
If both of
include
andexclude
are emptyIf
include
andexclude
have overlapping elementsIf 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 columnsSee 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