diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 42430fb1fbba0..1525afcac87f7 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -35,7 +35,7 @@ These are some neat pandas ``idioms``
    )
    df
 
-if-then...
+If-then...
 **********
 
 An if-then on one column
@@ -176,7 +176,7 @@ One could hard code:
 Selection
 ---------
 
-Dataframes
+DataFrames
 **********
 
 The :ref:`indexing <indexing>` docs.
@@ -1489,7 +1489,7 @@ of the data values:
    )
    df
 
-Constant series
+Constant Series
 ---------------
 
 To assess if a series has a constant value, we can check if ``series.nunique() <= 1``.
diff --git a/doc/source/user_guide/gotchas.rst b/doc/source/user_guide/gotchas.rst
index 26eb656357bf6..842f30f06676e 100644
--- a/doc/source/user_guide/gotchas.rst
+++ b/doc/source/user_guide/gotchas.rst
@@ -121,7 +121,7 @@ Below is how to check if any of the values are ``True``:
     if pd.Series([False, True, False]).any():
         print("I am any")
 
-Bitwise boolean
+Bitwise Boolean
 ~~~~~~~~~~~~~~~
 
 Bitwise boolean operators like ``==`` and ``!=`` return a boolean :class:`Series`
diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 8c80fa7052dd5..acb5a2b7919ac 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -618,7 +618,7 @@ this will make an extra copy.
 
 .. _groupby.aggregate.udf:
 
-Aggregation with User-Defined Functions
+Aggregation with user-defined functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Users can also provide their own User-Defined Functions (UDFs) for custom aggregations.
@@ -1261,7 +1261,7 @@ with
     df.groupby("A", group_keys=False).apply(lambda x: x, include_groups=False)
 
 
-Numba Accelerated Routines
+Numba accelerated routines
 --------------------------
 
 .. versionadded:: 1.1
@@ -1696,7 +1696,7 @@ introduction <categorical>` and the
 
     dfg.groupby(["A", [0, 0, 0, 1, 1]]).ngroup()
 
-Groupby by indexer to 'resample' data
+GroupBy by indexer to 'resample' data
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.
diff --git a/doc/source/user_guide/integer_na.rst b/doc/source/user_guide/integer_na.rst
index 76a2f22b7987d..8d35d1583d3bd 100644
--- a/doc/source/user_guide/integer_na.rst
+++ b/doc/source/user_guide/integer_na.rst
@@ -147,7 +147,7 @@ Reduction and groupby operations such as :meth:`~DataFrame.sum` work as well.
    df.sum()
    df.groupby("B").A.sum()
 
-Scalar NA Value
+Scalar NA value
 ---------------
 
 :class:`arrays.IntegerArray` uses :attr:`pandas.NA` as its scalar
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index fa64bce60caf4..7c165c87adb46 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -5996,7 +5996,7 @@ Full documentation can be found `here <https://pandas-gbq.readthedocs.io/en/late
 
 .. _io.stata:
 
-Stata format
+STATA format
 ------------
 
 .. _io.stata_writer:
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 0a5716f52c836..7354e2bafacc0 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -47,7 +47,7 @@ Fixed regressions
 
 .. ---------------------------------------------------------------------------
 
-Indexing with nullable boolean arrays
+Indexing with nullable Boolean arrays
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Previously indexing with a nullable Boolean array containing ``NA`` would raise a ``ValueError``, however this is now permitted with ``NA`` being treated as ``False``. (:issue:`31503`)