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Classe « DataFrame »

Méthode pandas.DataFrame.select_dtypes

Signature de la méthode select_dtypes

def select_dtypes(self, include=None, exclude=None) -> 'Self' 

Description

help(DataFrame.select_dtypes)

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
* See the `numpy dtype hierarchy
  <https://numpy.org/doc/stable/reference/arrays.scalars.html>`__
* 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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