diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst
index 2b42eebf762e1..3d89fe171a343 100644
--- a/doc/source/gotchas.rst
+++ b/doc/source/gotchas.rst
@@ -215,8 +215,28 @@ arrays. For example:
    s2.dtype
 
 This trade-off is made largely for memory and performance reasons, and also so
-that the resulting ``Series`` continues to be "numeric". One possibility is to
-use ``dtype=object`` arrays instead.
+that the resulting ``Series`` continues to be "numeric".
+
+If you need to represent integers with possibly missing values, use one of
+the nullable-integer extension dtypes provided by pandas
+
+* :class:`Int8Dtype`
+* :class:`Int16Dtype`
+* :class:`Int32Dtype`
+* :class:`Int64Dtype`
+
+.. ipython:: python
+
+   s_int = pd.Series([1, 2, 3, 4, 5], index=list('abcde'),
+                     dtype=pd.Int64Dtype())
+   s_int
+   s_int.dtype
+
+   s2_int = s_int.reindex(['a', 'b', 'c', 'f', 'u'])
+   s2_int
+   s2_int.dtype
+
+See :ref:`integer_na` for more.
 
 ``NA`` type promotions
 ~~~~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/index.rst.template b/doc/source/index.rst.template
index 7fe26d68c6fd0..145acbc55b85f 100644
--- a/doc/source/index.rst.template
+++ b/doc/source/index.rst.template
@@ -143,6 +143,7 @@ See the package overview for more detail about what's in the library.
     timeseries
     timedeltas
     categorical
+    integer_na
     visualization
     style
     io
diff --git a/doc/source/integer_na.rst b/doc/source/integer_na.rst
new file mode 100644
index 0000000000000..befcf7016f155
--- /dev/null
+++ b/doc/source/integer_na.rst
@@ -0,0 +1,101 @@
+.. currentmodule:: pandas
+
+{{ header }}
+
+ .. _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, an identifier, casting to float can
+be problematic. Some integers cannot even be represented as floating point
+numbers.
+
+Pandas can represent integer data with possibly missing values using
+:class:`arrays.IntegerArray`. 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 :meth:`array` or :class:`Series`:
+
+.. ipython:: python
+
+   arr = pd.array([1, 2, np.nan], 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")
+
+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
+
+These dtypes can operate as part of of ``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 'sum' work as well.
+
+.. ipython:: python
+
+   df.sum()
+   df.groupby('B').A.sum()
diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst
index 6a089decde3f5..a9234b83c78ab 100644
--- a/doc/source/missing_data.rst
+++ b/doc/source/missing_data.rst
@@ -19,32 +19,6 @@ pandas.
 
 See the :ref:`cookbook<cookbook.missing_data>` for some advanced strategies.
 
-Missing data basics
--------------------
-
-When / why does data become missing?
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Some might quibble over our usage of *missing*. By "missing" we simply mean
-**NA** ("not available") or "not present for whatever reason". Many data sets simply arrive with
-missing data, either because it exists and was not collected or it never
-existed. For example, in a collection of financial time series, some of the time
-series might start on different dates. Thus, values prior to the start date
-would generally be marked as missing.
-
-In pandas, one of the most common ways that missing data is **introduced** into
-a data set is by reindexing. For example:
-
-.. ipython:: python
-
-   df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],
-                     columns=['one', 'two', 'three'])
-   df['four'] = 'bar'
-   df['five'] = df['one'] > 0
-   df
-   df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
-   df2
-
 Values considered "missing"
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -62,6 +36,16 @@ arise and we wish to also consider that "missing" or "not available" or "NA".
 
 .. _missing.isna:
 
+.. ipython:: python
+
+   df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],
+                     columns=['one', 'two', 'three'])
+   df['four'] = 'bar'
+   df['five'] = df['one'] > 0
+   df
+   df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
+   df2
+
 To make detecting missing values easier (and across different array dtypes),
 pandas provides the :func:`isna` and
 :func:`notna` functions, which are also methods on
@@ -90,6 +74,23 @@ Series and DataFrame objects:
 
       df2['one'] == np.nan
 
+Integer Dtypes and Missing Data
+-------------------------------
+
+Because ``NaN`` is a float, a column of integers with even one missing values
+is cast to floating-point dtype (see :ref:`gotchas.intna` for more). Pandas
+provides a nullable integer array, which can be used by explicitly requesting
+the dtype:
+
+.. ipython:: python
+
+   pd.Series([1, 2, np.nan, 4], dtype=pd.Int64Dtype())
+
+Alternatively, the string alias ``dtype='Int64'`` (note the capital ``"I"``) can be
+used.
+
+See :ref:`integer_na` for more.
+
 Datetimes
 ---------
 
@@ -751,3 +752,19 @@ However, these can be filled in using :meth:`~DataFrame.fillna` and it will work
 
    reindexed[crit.fillna(False)]
    reindexed[crit.fillna(True)]
+
+Pandas provides a nullable integer dtype, but you must explicitly request it
+when creating the series or column. Notice that we use a capital "I" in
+the ``dtype="Int64"``.
+
+.. ipython:: python
+
+   s = pd.Series(np.random.randn(5), index=[0, 2, 4, 6, 7],
+                 dtype="Int64")
+   s > 0
+   (s > 0).dtype
+   crit = (s > 0).reindex(list(range(8)))
+   crit
+   crit.dtype
+
+See :ref:`integer_na` for more.
diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 1734666efe669..c0860ba7993bf 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -159,7 +159,9 @@ Reduction and groupby operations such as 'sum' work.
 
 .. warning::
 
-   The Integer NA support currently uses the captilized dtype version, e.g. ``Int8`` as compared to the traditional ``int8``. This may be changed at a future date.
+   The Integer NA support currently uses the capitalized dtype version, e.g. ``Int8`` as compared to the traditional ``int8``. This may be changed at a future date.
+
+See :ref:`integer_na` for more.
 
 .. _whatsnew_0240.enhancements.array: