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GH-39640: [Docs] Pin pydata-sphinx-theme to 0.14.* #39758

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merged 4 commits into from
Jan 30, 2024
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GH-39640: [Docs] Pin pydata-sphinx-theme to 0.14.* #39758

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7b8b101
GH-39640: [Docs] Pin pydata-sphinx-theme to 0.14.*
jorisvandenbossche Jan 23, 2024
db58502
ScalarUdfContext -> UdfContext in the docs
jorisvandenbossche Jan 23, 2024
b725f0c
pandas compat
jorisvandenbossche Jan 23, 2024
580b355
Merge remote-tracking branch 'upstream/main' into gh-39640
jorisvandenbossche Jan 30, 2024
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2 changes: 1 addition & 1 deletion ci/conda_env_sphinx.txt
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Expand Up @@ -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
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2 changes: 1 addition & 1 deletion docs/requirements.txt
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Expand Up @@ -5,7 +5,7 @@
breathe
ipython
numpydoc
pydata-sphinx-theme==0.14.1
pydata-sphinx-theme~=0.14
sphinx-autobuild
sphinx-design
sphinx-copybutton
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2 changes: 1 addition & 1 deletion docs/source/python/api/compute.rst
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Expand Up @@ -590,4 +590,4 @@ User-Defined Functions
:toctree: ../generated/

register_scalar_function
ScalarUdfContext
UdfContext
4 changes: 2 additions & 2 deletions docs/source/python/compute.rst
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Expand Up @@ -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`:
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2 changes: 1 addition & 1 deletion docs/source/python/pandas.rst
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Expand Up @@ -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

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