pandas.api.types.is_numeric_dtype#

pandas.api.types.is_numeric_dtype(arr_or_dtype)[source]#

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

Parameters:

arr_or_dtypearray-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 an 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=np.timedelta64))
False