pandas.api.types.is_integer_dtype¶

pandas.api.types.is_integer_dtype(arr_or_dtype)[source]¶

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

Unlike in in_any_int_dtype, timedelta64 instances will return False.

Changed in version 0.24.0: The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered as integer by this function.

Parameters

arr_or_dtypearray-like: The array or dtype to check.

Returns

boolean: Whether or not the array or dtype is of an integer dtype and not an instance of timedelta64.

Examples

>>> is_integer_dtype(str)
False
>>> is_integer_dtype(int)
True
>>> is_integer_dtype(float)
False
>>> is_integer_dtype(np.uint64)
True
>>> is_integer_dtype('int8')
True
>>> is_integer_dtype('Int8')
True
>>> is_integer_dtype(pd.Int8Dtype)
True
>>> is_integer_dtype(np.datetime64)
False
>>> is_integer_dtype(np.timedelta64)
False
>>> is_integer_dtype(np.array(['a', 'b']))
False
>>> is_integer_dtype(pd.Series([1, 2]))
True
>>> is_integer_dtype(np.array([], dtype=np.timedelta64))
False
>>> is_integer_dtype(pd.Index([1, 2.]))  # float
False

pandas.api.types.is_int64_dtype pandas.api.types.is_interval_dtype