apacheGH-39640: [Docs] Pin pydata-sphinx-theme to 0.14.* (apache#39758)

### Rationale for this change Fixing the pinning syntax so we get the latest 0.14.x version (which is currently 0.14.4) * Closes: apache#39640 Authored-by: Joris Van den Bossche <[email protected]> Signed-off-by: Joris Van den Bossche <[email protected]>
mathworks · Jan 30, 2024 · c6ab286 · c6ab286
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diff --git a/ci/conda_env_sphinx.txt b/ci/conda_env_sphinx.txt
@@ -20,7 +20,7 @@ breathe
 doxygen
 ipython
 numpydoc
-pydata-sphinx-theme=0.14.1
+pydata-sphinx-theme=0.14
 sphinx-autobuild
 sphinx-design
 sphinx-copybutton

diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -5,7 +5,7 @@
 breathe
 ipython
 numpydoc
-pydata-sphinx-theme==0.14.1
+pydata-sphinx-theme~=0.14
 sphinx-autobuild
 sphinx-design
 sphinx-copybutton

diff --git a/docs/source/python/api/compute.rst b/docs/source/python/api/compute.rst
@@ -590,4 +590,4 @@ User-Defined Functions
    :toctree: ../generated/
 
    register_scalar_function
-   ScalarUdfContext
+   UdfContext
diff --git a/docs/source/python/compute.rst b/docs/source/python/compute.rst
@@ -445,9 +445,9 @@ output type need to be defined. Using :func:`pyarrow.compute.register_scalar_fun
 
 The implementation of a user-defined function always takes a first *context*
 parameter (named ``ctx`` in the example above) which is an instance of
-:class:`pyarrow.compute.ScalarUdfContext`.
+:class:`pyarrow.compute.UdfContext`.
 This context exposes several useful attributes, particularly a
-:attr:`~pyarrow.compute.ScalarUdfContext.memory_pool` to be used for
+:attr:`~pyarrow.compute.UdfContext.memory_pool` to be used for
 allocations in the context of the user-defined function.
 
 You can call a user-defined function directly using :func:`pyarrow.compute.call_function`:

diff --git a/docs/source/python/pandas.rst b/docs/source/python/pandas.rst
@@ -197,7 +197,7 @@ use the ``datetime64[ns]`` type in Pandas and are converted to an Arrow
 
 .. ipython:: python
 
-   df = pd.DataFrame({"datetime": pd.date_range("2020-01-01T00:00:00Z", freq="H", periods=3)})
+   df = pd.DataFrame({"datetime": pd.date_range("2020-01-01T00:00:00Z", freq="h", periods=3)})
    df.dtypes
    df