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# pandas.api.types.is_int64_dtype

pandas.api.types.is_int64_dtype(arr_or_dtype)

  Check whether the provided array or dtype is of the int64 dtype.

  Deprecated since version 2.1.0: is_int64_dtype is deprecated and will be removed in a future version. Use dtype == np.int64 instead.

  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 the int64 dtype.

  See also

  `api.types.is_float_dtype`

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

  `api.types.is_bool_dtype`

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

  `api.types.is_object_dtype`

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

  `numpy.int64`

    Numpy’s 64-bit integer type.

  Notes

  Depending on system architecture, the return value of is_int64_dtype( int) will be True if the OS uses 64-bit integers and False if the OS uses 32-bit integers.

  Examples

      >>> from pandas.api.types import is_int64_dtype
      >>> is_int64_dtype(str)
      False
      >>> is_int64_dtype(np.int32)
      False
      >>> is_int64_dtype(np.int64)
      True
      >>> is_int64_dtype("int8")
      False
      >>> is_int64_dtype("Int8")
      False
      >>> is_int64_dtype(pd.Int64Dtype)
      True
      >>> is_int64_dtype(float)
      False
      >>> is_int64_dtype(np.uint64)  # unsigned
      False
      >>> is_int64_dtype(np.array(["a", "b"]))
      False
      >>> is_int64_dtype(np.array([1, 2], dtype=np.int64))
      True
      >>> is_int64_dtype(pd.Index([1, 2.0]))  # float
      False
      >>> is_int64_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned
      False