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.. currentmodule:: pandas


.. ipython:: python
    :suppress:

    import numpy as np
    import pandas as pd

 .. _integer_na:

Nullable Integer Data Type

.. versionadded:: 0.24.0

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, and identifier, casting to float can be problematic.

Pandas can represent integer data with missing values with the :class:`arrays.IntegerArray` array. This is an :ref:`extension types <extending.extension-types>` implemented within pandas. It is not the default dtype for integers, and will not be inferred; you must explicitly pass the dtype into the :meth:`array` or :class:`Series` method:

.. ipython:: python

   pd.array([1, 2, np.nan], dtype=pd.Int64Dtype())

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")

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.

.. ipython:: python

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

By default (if you don't specify dtype), NumPy is used, and you'll end up with a float64 dtype Series:

.. ipython:: python

   pd.Series([1, 2, np.nan])

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

.. ipython:: python

   # arithmetic
   s + 1

   # comparison
   s == 1

   # indexing
   s.iloc[1:3]

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

   # coerce when needed
   s + 0.01

Reduction and groupby operations such as 'sum' work as well.

.. ipython:: python

   df.sum()
   df.groupby('B').A.sum()