DOC: Making pandas always lowercase (#57464)

Author: rhshadrach

doc/source/conf.py

@@ -431,7 +431,7 @@
         "index",
         "pandas.tex",
         "pandas: powerful Python data analysis toolkit",
-        "Wes McKinney and the Pandas Development Team",
+        "Wes McKinney and the pandas Development Team",
         "manual",
     )
 ]

doc/source/development/contributing_codebase.rst

@@ -253,7 +253,7 @@ Testing type hints in code using pandas
 
 .. warning::
 
-    * Pandas is not yet a py.typed library (:pep:`561`)!
+    * pandas is not yet a py.typed library (:pep:`561`)!
       The primary purpose of locally declaring pandas as a py.typed library is to test and
       improve the pandas-builtin type annotations.
 

doc/source/development/contributing_documentation.rst

@@ -14,7 +14,7 @@ experts. If something in the docs doesn't make sense to you, updating the
 relevant section after you figure it out is a great way to ensure it will help
 the next person. Please visit the `issues page <https://github.com/pandas-dev/pandas/issues?page=1&q=is%3Aopen+sort%3Aupdated-desc+label%3ADocs>`__
 for a full list of issues that are currently open regarding the
-Pandas documentation.
+pandas documentation.
 
 
 

doc/source/development/debugging_extensions.rst

@@ -6,7 +6,7 @@
 Debugging C extensions
 ======================
 
-Pandas uses Cython and C/C++ `extension modules <https://docs.python.org/3/extending/extending.html>`_ to optimize performance. Unfortunately, the standard Python debugger does not allow you to step into these extensions. Cython extensions can be debugged with the `Cython debugger <https://docs.cython.org/en/latest/src/userguide/debugging.html>`_ and C/C++ extensions can be debugged using the tools shipped with your platform's compiler.
+pandas uses Cython and C/C++ `extension modules <https://docs.python.org/3/extending/extending.html>`_ to optimize performance. Unfortunately, the standard Python debugger does not allow you to step into these extensions. Cython extensions can be debugged with the `Cython debugger <https://docs.cython.org/en/latest/src/userguide/debugging.html>`_ and C/C++ extensions can be debugged using the tools shipped with your platform's compiler.
 
 For Python developers with limited or no C/C++ experience this can seem a daunting task. Core developer Will Ayd has written a 3 part blog series to help guide you from the standard Python debugger into these other tools:
 

doc/source/development/maintaining.rst

@@ -430,7 +430,7 @@ Release
     git checkout <branch>
     git pull --ff-only upstream <branch>
     git clean -xdf
-    git commit --allow-empty --author="Pandas Development Team <[email protected]>" -m "RLS: <version>"
+    git commit --allow-empty --author="pandas Development Team <[email protected]>" -m "RLS: <version>"
     git tag -a v<version> -m "Version <version>"  # NOTE that the tag is v1.5.2 with "v" not 1.5.2
     git push upstream <branch> --follow-tags
 
@@ -460,7 +460,7 @@ which will be triggered when the tag is pushed.
 4. Create a `new GitHub release <https://github.com/pandas-dev/pandas/releases/new>`_:
 
    - Tag: ``<version>``
-   - Title: ``Pandas <version>``
+   - Title: ``pandas <version>``
    - Description: Copy the description of the last release of the same kind (release candidate, major/minor or patch release)
    - Files: ``pandas-<version>.tar.gz`` source distribution just generated
    - Set as a pre-release: Only check for a release candidate

doc/source/user_guide/10min.rst

@@ -19,7 +19,7 @@ Customarily, we import as follows:
 Basic data structures in pandas
 -------------------------------
 
-Pandas provides two types of classes for handling data:
+pandas provides two types of classes for handling data:
 
 1. :class:`Series`: a one-dimensional labeled array holding data of any type
     such as integers, strings, Python objects etc.

doc/source/user_guide/io.rst

@@ -1044,7 +1044,7 @@ Writing CSVs to binary file objects
 
 ``df.to_csv(..., mode="wb")`` allows writing a CSV to a file object
 opened binary mode. In most cases, it is not necessary to specify
-``mode`` as Pandas will auto-detect whether the file object is
+``mode`` as pandas will auto-detect whether the file object is
 opened in text or binary mode.
 
 .. ipython:: python
@@ -1604,7 +1604,7 @@ Specifying ``iterator=True`` will also return the ``TextFileReader`` object:
 Specifying the parser engine
 ''''''''''''''''''''''''''''
 
-Pandas currently supports three engines, the C engine, the python engine, and an experimental
+pandas currently supports three engines, the C engine, the python engine, and an experimental
 pyarrow engine (requires the ``pyarrow`` package). In general, the pyarrow engine is fastest
 on larger workloads and is equivalent in speed to the C engine on most other workloads.
 The python engine tends to be slower than the pyarrow and C engines on most workloads. However,
@@ -3910,7 +3910,7 @@ The look and feel of Excel worksheets created from pandas can be modified using
 
 .. note::
 
-    As of Pandas 3.0, by default spreadsheets created with the ``to_excel`` method
+    As of pandas 3.0, by default spreadsheets created with the ``to_excel`` method
     will not contain any styling. Users wishing to bold text, add bordered styles,
     etc in a worksheet output by ``to_excel`` can do so by using :meth:`Styler.to_excel`
     to create styled excel files. For documentation on styling spreadsheets, see

doc/source/whatsnew/v0.11.0.rst

@@ -12,7 +12,7 @@ Data have had quite a number of additions, and Dtype support is now full-fledged
 There are also a number of important API changes that long-time pandas users should
 pay close attention to.
 
-There is a new section in the documentation, :ref:`10 Minutes to Pandas <10min>`,
+There is a new section in the documentation, :ref:`10 Minutes to pandas <10min>`,
 primarily geared to new users.
 
 There is a new section in the documentation, :ref:`Cookbook <cookbook>`, a collection

doc/source/whatsnew/v1.3.1.rst

@@ -14,7 +14,7 @@ including other versions of pandas.
 
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
-- Pandas could not be built on PyPy (:issue:`42355`)
+- pandas could not be built on PyPy (:issue:`42355`)
 - :class:`DataFrame` constructed with an older version of pandas could not be unpickled (:issue:`42345`)
 - Performance regression in constructing a :class:`DataFrame` from a dictionary of dictionaries (:issue:`42248`)
 - Fixed regression in :meth:`DataFrame.agg` dropping values when the DataFrame had an Extension Array dtype, a duplicate index, and ``axis=1`` (:issue:`42380`)

doc/source/whatsnew/v1.4.0.rst

@@ -607,7 +607,7 @@ Deprecated Int64Index, UInt64Index & Float64Index
 
 :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index` have been
 deprecated in favor of the base :class:`Index` class and will be removed in
-Pandas 2.0 (:issue:`43028`).
+pandas 2.0 (:issue:`43028`).
 
 For constructing a numeric index, you can use the base :class:`Index` class
 instead specifying the data type (which will also work on older pandas

doc/source/whatsnew/v1.5.0.rst

@@ -63,7 +63,7 @@ We recommend installing the latest version of PyArrow to access the most recentl
 DataFrame interchange protocol implementation
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Pandas now implement the DataFrame interchange API spec.
+pandas now implement the DataFrame interchange API spec.
 See the full details on the API at https://data-apis.org/dataframe-protocol/latest/index.html
 
 The protocol consists of two parts:

doc/source/whatsnew/v2.0.0.rst

@@ -57,7 +57,7 @@ can it now take all numpy numeric dtypes, i.e.
     pd.Index([1, 2, 3], dtype=np.uint16)
     pd.Index([1, 2, 3], dtype=np.float32)
 
-The ability for :class:`Index` to hold the numpy numeric dtypes has meant some changes in Pandas
+The ability for :class:`Index` to hold the numpy numeric dtypes has meant some changes in pandas
 functionality. In particular, operations that previously were forced to create 64-bit indexes,
 can now create indexes with lower bit sizes, e.g. 32-bit indexes.
 

doc/source/whatsnew/v2.1.0.rst

@@ -67,7 +67,7 @@ DataFrame reductions preserve extension dtypes
 
 In previous versions of pandas, the results of DataFrame reductions
 (:meth:`DataFrame.sum` :meth:`DataFrame.mean` etc.) had NumPy dtypes, even when the DataFrames
-were of extension dtypes. Pandas can now keep the dtypes when doing reductions over DataFrame
+were of extension dtypes. pandas can now keep the dtypes when doing reductions over DataFrame
 columns with a common dtype (:issue:`52788`).
 
 *Old Behavior*