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# pandas.arrays.IntegerArray

class pandas.arrays.IntegerArray(values, mask, copy=False)

  Array of integer (optional missing) values.

  Uses `pandas.NA` as the missing value.

  Warning

  IntegerArray is currently experimental, and its API or internal implementation may change without warning.

  We represent an IntegerArray with 2 numpy arrays:

  - data: contains a numpy integer array of the appropriate dtype

  - mask: a boolean array holding a mask on the data, True is missing

  To construct an IntegerArray from generic array-like input, use `pandas.array()` with one of the integer dtypes (see examples).

  See Nullable integer data type for more.

  Parameters:
    values numpy.ndarray

      A 1-d integer-dtype array.

    mask numpy.ndarray

      A 1-d boolean-dtype array indicating missing values.

    copy bool, default False

      Whether to copy the values and mask.

  Attributes

  None |

  Methods

  None |

  Returns:
    IntegerArray

  See also

  `array`

    Create an array using the appropriate dtype, including `IntegerArray`.

  `Int32Dtype`

    An ExtensionDtype for int32 integer data.

  `UInt16Dtype`

    An ExtensionDtype for uint16 integer data.

  Examples

  Create an IntegerArray with `pandas.array()`.

      >>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
      >>> int_array
      <IntegerArray>
      [1, <NA>, 3]
      Length: 3, dtype: Int32

  String aliases for the dtypes are also available. They are capitalized.

      >>> pd.array([1, None, 3], dtype="Int32")
      <IntegerArray>
      [1, <NA>, 3]
      Length: 3, dtype: Int32

      >>> pd.array([1, None, 3], dtype="UInt16")
      <IntegerArray>
      [1, <NA>, 3]
      Length: 3, dtype: UInt16