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# pandas.api.types.is_datetime64_ns_dtype

pandas.api.types.is_datetime64_ns_dtype(arr_or_dtype)

  Check whether the provided array or dtype is of the datetime64[ns] dtype.

  This function is more restrictive than `api.types.is_datetime64_dtype()` because it requires the dtype to have nanosecond resolution specifically, including timezone-aware `DatetimeTZDtype` with nanosecond units.

  Parameters:
    arr_or_dtype array-like or dtype

      The array or dtype to check.

  Returns:
    bool

      Whether or not the array or dtype is of the datetime64[ns] dtype.

  See also

  `api.types.is_datetime64_dtype`

    Check whether an array-like or dtype is of the datetime64 dtype.

  `api.types.is_datetime64_any_dtype`

    Check whether the provided array or dtype is of the datetime64 dtype.

  Examples

      >>> from pandas.api.types import is_datetime64_ns_dtype
      >>> from pandas.api.types import DatetimeTZDtype
      >>> is_datetime64_ns_dtype(str)
      False
      >>> is_datetime64_ns_dtype(int)
      False
      >>> is_datetime64_ns_dtype(np.datetime64)  # no unit
      False
      >>> is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern"))
      True
      >>> is_datetime64_ns_dtype(np.array(["a", "b"]))
      False
      >>> is_datetime64_ns_dtype(np.array([1, 2]))
      False
      >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64"))  # no unit
      False
      >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]"))  # wrong unit
      False
      >>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
      True