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# pandas.DataFrame.select_dtypes

DataFrame.select_dtypes(include=None, exclude=None)

  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, 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

    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

  - A dtype instance (e.g. `np.dtype("int32")` or `pd.CategoricalDtype(["a", "b"])`) selects only columns with exactly that dtype, whereas a class or string selects a family of dtypes. Under-specified instances like a unitless `np.dtype("datetime64")`, a bare `pd.CategoricalDtype()`, or a `pd.IntervalDtype("int64")` without a `closed` select their whole family

  - To select datetimes, use `np.datetime64`, `'datetime'` or `'datetime64'`

  - To select timedeltas, use `np.timedelta64`, `'timedelta'` or `'timedelta64'`

  - To select datetimes or timedeltas of a specific resolution, pass a unit-qualified dtype such as `'datetime64[us]'` or `'timedelta64[ms]'`; this matches only columns with exactly that resolution, whereas an unqualified spec matches every resolution

  - To select Pandas categorical dtypes, use `'category'`

  - To select all timezone-aware datetime dtypes, use `pandas.DatetimeTZDtype`; a string such as `'datetime64[ns, US/Eastern]'` selects only that exact dtype

  - To select all period dtypes, use `pandas.PeriodDtype`; a string such as `'period[D]'` selects only that frequency

  - An `ExtensionDtype` subclass matches every instance of that subclass regardless of parametrization, e.g. `pd.ArrowDtype` selects all pyarrow-backed columns and `pd.CategoricalDtype` selects all categorical columns

  Deprecated since version 3.1.0: The strings `'datetimetz'` and `'datetime64tz'` are deprecated; pass `pandas.DatetimeTZDtype` instead.

  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