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# pandas.api.types.is_numeric_dtype

pandas.api.types.is_numeric_dtype(arr_or_dtype)

  Check whether the provided array or dtype is of a numeric dtype.

  Numeric dtypes include integer, float, complex, and boolean types. Datetime and timedelta dtypes are not considered numeric.

  Parameters:
    arr_or_dtype array-like or dtype

      The array or dtype to check.

  Returns:
    boolean

      Whether or not the array or dtype is of a numeric dtype.

  See also

  `api.types.is_integer_dtype`

    Check whether the provided array or dtype is of an integer dtype.

  `api.types.is_unsigned_integer_dtype`

    Check whether the provided array or dtype is of an unsigned integer dtype.

  `api.types.is_signed_integer_dtype`

    Check whether the provided array or dtype is of a signed integer dtype.

  Examples

      >>> from pandas.api.types import is_numeric_dtype
      >>> is_numeric_dtype(str)
      False
      >>> is_numeric_dtype(int)
      True
      >>> is_numeric_dtype(float)
      True
      >>> is_numeric_dtype(np.uint64)
      True
      >>> is_numeric_dtype(np.datetime64)
      False
      >>> is_numeric_dtype(np.timedelta64)
      False
      >>> is_numeric_dtype(np.array(["a", "b"]))
      False
      >>> is_numeric_dtype(pd.Series([1, 2]))
      True
      >>> is_numeric_dtype(pd.Index([1, 2.0]))
      True
      >>> is_numeric_dtype(np.array([], dtype="m8[ns]"))
      False