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integer_na.rst

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.. currentmodule:: pandas

{{ header }}

Nullable integer data type

Note

IntegerArray is currently experimental. Its API or implementation may change without warning. Uses :attr:`pandas.NA` as the missing value.

In :ref:`missing_data`, we saw that pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point. In some cases, this may not matter much. But if your integer column is, say, an identifier, casting to float can be problematic. Some integers cannot even be represented as floating point numbers.

Construction

pandas can represent integer data with possibly missing values using :class:`arrays.IntegerArray`. This is an :ref:`extension type <extending.extension-types>` implemented within pandas.

.. ipython:: python

   arr = pd.array([1, 2, None], dtype=pd.Int64Dtype())
   arr

Or the string alias "Int64" (note the capital "I") to differentiate from NumPy's 'int64' dtype:

.. ipython:: python

   pd.array([1, 2, np.nan], dtype="Int64")

All NA-like values are replaced with :attr:`pandas.NA`.

.. ipython:: python

   pd.array([1, 2, np.nan, None, pd.NA], dtype="Int64")

This array can be stored in a :class:`DataFrame` or :class:`Series` like any NumPy array.

.. ipython:: python

   pd.Series(arr)

You can also pass the list-like object to the :class:`Series` constructor with the dtype.

Warning

Currently :meth:`pandas.array` and :meth:`pandas.Series` use different rules for dtype inference. :meth:`pandas.array` will infer a nullable-integer dtype

.. ipython:: python

   pd.array([1, None])
   pd.array([1, 2])

For backwards-compatibility, :class:`Series` infers these as either integer or float dtype.

.. ipython:: python

   pd.Series([1, None])
   pd.Series([1, 2])

We recommend explicitly providing the dtype to avoid confusion.

.. ipython:: python

   pd.array([1, None], dtype="Int64")
   pd.Series([1, None], dtype="Int64")

In the future, we may provide an option for :class:`Series` to infer a nullable-integer dtype.

Operations

Operations involving an integer array will behave similar to NumPy arrays. Missing values will be propagated, and the data will be coerced to another dtype if needed.

.. ipython:: python

   s = pd.Series([1, 2, None], dtype="Int64")

   # arithmetic
   s + 1

   # comparison
   s == 1

   # slicing operation
   s.iloc[1:3]

   # operate with other dtypes
   s + s.iloc[1:3].astype("Int8")

   # coerce when needed
   s + 0.01

These dtypes can operate as part of a DataFrame.

.. ipython:: python

   df = pd.DataFrame({"A": s, "B": [1, 1, 3], "C": list("aab")})
   df
   df.dtypes

These dtypes can be merged, reshaped & casted.

.. ipython:: python

   pd.concat([df[["A"]], df[["B", "C"]]], axis=1).dtypes
   df["A"].astype(float)

Reduction and groupby operations such as :meth:`~DataFrame.sum` work as well.

.. ipython:: python

   df.sum(numeric_only=True)
   df.sum()
   df.groupby("B").A.sum()

Scalar NA Value

:class:`arrays.IntegerArray` uses :attr:`pandas.NA` as its scalar missing value. Slicing a single element that's missing will return :attr:`pandas.NA`

.. ipython:: python

   a = pd.array([1, None], dtype="Int64")
   a[1]