DOC: Standardize references to pandas in the documentation for #32316

doc/source/development/code_style.rst

@@ -9,7 +9,7 @@ pandas code style guide
 .. contents:: Table of contents:
    :local:
 
-*pandas* follows the `PEP8 <https://www.python.org/dev/peps/pep-0008/>`_
+pandas follows the `PEP8 <https://www.python.org/dev/peps/pep-0008/>`_
 standard and uses `Black <https://black.readthedocs.io/en/stable/>`_
 and `Flake8 <https://flake8.pycqa.org/en/latest/>`_ to ensure a
 consistent code format throughout the project. For details see the

doc/source/development/contributing.rst

@@ -134,7 +134,7 @@ want to clone your fork to your machine::
     git remote add upstream https://github.com/pandas-dev/pandas.git
 
 This creates the directory ``pandas-yourname`` and connects your repository to
-the upstream (main project) *pandas* repository.
+the upstream (main project) pandas repository.
 
 Note that performing a shallow clone (with ``--depth==N``, for some ``N`` greater
 or equal to 1) might break some tests and features as ``pd.show_versions()``

doc/source/ecosystem.rst

@@ -98,7 +98,7 @@ With Altair, you can spend more time understanding your data and its
 meaning. Altair's API is simple, friendly and consistent and built on
 top of the powerful Vega-Lite JSON specification. This elegant
 simplicity produces beautiful and effective visualizations with a
-minimal amount of code. Altair works with Pandas DataFrames.
+minimal amount of code. Altair works with pandas DataFrames.
 
 
 `Bokeh <https://bokeh.pydata.org>`__
@@ -110,7 +110,7 @@ graphics in the style of Protovis/D3, while delivering high-performance interact
 large data to thin clients.
 
 `Pandas-Bokeh <https://github.com/PatrikHlobil/Pandas-Bokeh>`__ provides a high level API
-for Bokeh that can be loaded as a native Pandas plotting backend via
+for Bokeh that can be loaded as a native pandas plotting backend via
 
 .. code:: python
 
@@ -185,7 +185,7 @@ IDE
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 IPython is an interactive command shell and distributed computing
-environment. IPython tab completion works with Pandas methods and also
+environment. IPython tab completion works with pandas methods and also
 attributes like DataFrame columns.
 
 `Jupyter Notebook / Jupyter Lab <https://jupyter.org>`__
@@ -274,13 +274,13 @@ The following data feeds are available:
 `Quandl/Python <https://github.com/quandl/Python>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Quandl API for Python wraps the Quandl REST API to return
-Pandas DataFrames with timeseries indexes.
+pandas DataFrames with timeseries indexes.
 
 `Pydatastream <https://github.com/vfilimonov/pydatastream>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 PyDatastream is a Python interface to the
 `Refinitiv Datastream (DWS) <https://www.refinitiv.com/en/products/datastream-macroeconomic-analysis>`__
-REST API to return indexed Pandas DataFrames with financial data.
+REST API to return indexed pandas DataFrames with financial data.
 This package requires valid credentials for this API (non free).
 
 `pandaSDMX <https://pandasdmx.readthedocs.io>`__
@@ -401,7 +401,7 @@ If also displays progress bars.
 `Ray <https://ray.readthedocs.io/en/latest/pandas_on_ray.html>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Pandas on Ray is an early stage DataFrame library that wraps Pandas and transparently distributes the data and computation. The user does not need to know how many cores their system has, nor do they need to specify how to distribute the data. In fact, users can continue using their previous Pandas notebooks while experiencing a considerable speedup from Pandas on Ray, even on a single machine. Only a modification of the import statement is needed, as we demonstrate below. Once you’ve changed your import statement, you’re ready to use Pandas on Ray just like you would Pandas.
+Pandas on Ray is an early stage DataFrame library that wraps pandas and transparently distributes the data and computation. The user does not need to know how many cores their system has, nor do they need to specify how to distribute the data. In fact, users can continue using their previous Pandas notebooks while experiencing a considerable speedup from Pandas on Ray, even on a single machine. Only a modification of the import statement is needed, as we demonstrate below. Once you’ve changed your import statement, you’re ready to use Pandas on Ray just like you would Pandas.
 
 .. code:: python
 
@@ -412,7 +412,7 @@ Pandas on Ray is an early stage DataFrame library that wraps Pandas and transpar
 `Vaex <https://docs.vaex.io/>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (10\ :sup:`9`) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
+Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a python library for Out-of-Core DataFrames (similar to pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (10\ :sup:`9`) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
 
  * vaex.from_pandas
  * vaex.to_pandas_df

doc/source/getting_started/install.rst

@@ -184,7 +184,7 @@ You can find simple installation instructions for pandas in this document: ``ins
 Installing from source
 ~~~~~~~~~~~~~~~~~~~~~~
 
-See the :ref:`contributing guide <contributing>` for complete instructions on building from the git source tree. Further, see :ref:`creating a development environment <contributing.dev_env>` if you wish to create a *pandas* development environment.
+See the :ref:`contributing guide <contributing>` for complete instructions on building from the git source tree. Further, see :ref:`creating a development environment <contributing.dev_env>` if you wish to create a pandas development environment.
 
 Running the test suite
 ----------------------

doc/source/getting_started/overview.rst

@@ -6,7 +6,7 @@
 Package overview
 ****************
 
-**pandas** is a `Python <https://www.python.org>`__ package providing fast,
+pandas is a `Python <https://www.python.org>`__ package providing fast,
 flexible, and expressive data structures designed to make working with
 "relational" or "labeled" data both easy and intuitive. It aims to be the
 fundamental high-level building block for doing practical, **real-world** data

doc/source/user_guide/scale.rst

@@ -260,7 +260,7 @@ Inspecting the ``ddf`` object, we see a few things
 * There are new attributes like ``.npartitions`` and ``.divisions``
 
 The partitions and divisions are how Dask parallelizes computation. A **Dask**
-DataFrame is made up of many **Pandas** DataFrames. A single method call on a
+DataFrame is made up of many pandas DataFrames. A single method call on a
 Dask DataFrame ends up making many pandas method calls, and Dask knows how to
 coordinate everything to get the result.
 

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/v0.13.0.rst

@@ -668,7 +668,7 @@ Enhancements
 
 - ``Series`` now supports a ``to_frame`` method to convert it to a single-column DataFrame (:issue:`5164`)
 
-- All R datasets listed here http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html can now be loaded into Pandas objects
+- All R datasets listed here http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html can now be loaded into pandas objects
 
   .. code-block:: python
 

doc/source/whatsnew/v0.13.1.rst

@@ -17,7 +17,7 @@ Highlights include:
 - Will intelligently limit display precision for datetime/timedelta formats.
 - Enhanced Panel :meth:`~pandas.Panel.apply` method.
 - Suggested tutorials in new :ref:`Tutorials<tutorials>` section.
-- Our pandas ecosystem is growing, We now feature related projects in a new :ref:`Pandas Ecosystem<ecosystem>` section.
+- Our pandas ecosystem is growing, We now feature related projects in a new :ref:`pandas Ecosystem<ecosystem>` section.
 - Much work has been taking place on improving the docs, and a new :ref:`Contributing<contributing>` section has been added.
 - Even though it may only be of interest to devs, we <3 our new CI status page: `ScatterCI <http://scatterci.github.io/pydata/pandas>`__.
 

doc/source/whatsnew/v0.16.1.rst

@@ -13,7 +13,7 @@ We recommend that all users upgrade to this version.
 Highlights include:
 
 - Support for a ``CategoricalIndex``, a category based index, see :ref:`here <whatsnew_0161.enhancements.categoricalindex>`
-- New section on how-to-contribute to *pandas*, see :ref:`here <contributing>`
+- New section on how-to-contribute to pandas, see :ref:`here <contributing>`
 - Revised "Merge, join, and concatenate" documentation, including graphical examples to make it easier to understand each operations, see :ref:`here <merging>`
 - New method ``sample`` for drawing random samples from Series, DataFrames and Panels. See :ref:`here <whatsnew_0161.enhancements.sample>`
 - The default ``Index`` printing has changed to a more uniform format, see :ref:`here <whatsnew_0161.index_repr>`

doc/source/whatsnew/v0.16.2.rst

@@ -14,7 +14,7 @@ We recommend that all users upgrade to this version.
 Highlights include:
 
 - A new ``pipe`` method, see :ref:`here <whatsnew_0162.enhancements.pipe>`
-- Documentation on how to use numba_ with *pandas*, see :ref:`here <enhancingperf.numba>`
+- Documentation on how to use numba_ with pandas, see :ref:`here <enhancingperf.numba>`
 
 
 .. contents:: What's new in v0.16.2

doc/source/whatsnew/v0.17.0.rst

@@ -50,7 +50,7 @@ Highlights include:
 - Development installed versions of pandas will now have ``PEP440`` compliant version strings (:issue:`9518`)
 - Development support for benchmarking with the `Air Speed Velocity library <https://github.com/spacetelescope/asv/>`_ (:issue:`8361`)
 - Support for reading SAS xport files, see :ref:`here <whatsnew_0170.enhancements.sas_xport>`
-- Documentation comparing SAS to *pandas*, see :ref:`here <compare_with_sas>`
+- Documentation comparing SAS to pandas, see :ref:`here <compare_with_sas>`
 - Removal of the automatic TimeSeries broadcasting, deprecated since 0.8.0, see :ref:`here <whatsnew_0170.prior_deprecations>`
 - Display format with plain text can optionally align with Unicode East Asian Width, see :ref:`here <whatsnew_0170.east_asian_width>`
 - Compatibility with Python 3.5 (:issue:`11097`)

doc/source/whatsnew/v0.17.1.rst

@@ -8,7 +8,7 @@ Version 0.17.1 (November 21, 2015)
 
 .. note::
 
-   We are proud to announce that *pandas* has become a sponsored project of the (`NumFOCUS organization`_). This will help ensure the success of development of *pandas* as a world-class open-source project.
+   We are proud to announce that pandas has become a sponsored project of the (`NumFOCUS organization`_). This will help ensure the success of development of *pandas* as a world-class open-source project.
 
 .. _numfocus organization: http://www.numfocus.org/blog/numfocus-announces-new-fiscally-sponsored-project-pandas
 

doc/source/whatsnew/v0.18.0.rst

@@ -1274,7 +1274,7 @@ Bug fixes
 - Bug in ``.groupby`` where a ``KeyError`` was not raised for a wrong column if there was only one row in the dataframe (:issue:`11741`)
 - Bug in ``.read_csv`` with dtype specified on empty data producing an error (:issue:`12048`)
 - Bug in ``.read_csv`` where strings like ``'2E'`` are treated as valid floats (:issue:`12237`)
-- Bug in building *pandas* with debugging symbols (:issue:`12123`)
+- Bug in building pandas with debugging symbols (:issue:`12123`)
 
 
 - Removed ``millisecond`` property of ``DatetimeIndex``. This would always raise a ``ValueError`` (:issue:`12019`).

doc/source/whatsnew/v0.23.0.rst

@@ -884,7 +884,7 @@ Extraction of matching patterns from strings
 
 By default, extracting matching patterns from strings with :func:`str.extract` used to return a
 ``Series`` if a single group was being extracted (a ``DataFrame`` if more than one group was
-extracted). As of Pandas 0.23.0 :func:`str.extract` always returns a ``DataFrame``, unless
+extracted). As of pandas 0.23.0 :func:`str.extract` always returns a ``DataFrame``, unless
 ``expand`` is set to ``False``. Finally, ``None`` was an accepted value for
 the ``expand`` parameter (which was equivalent to ``False``), but now raises a ``ValueError``. (:issue:`11386`)
 
@@ -1175,7 +1175,7 @@ Performance improvements
 Documentation changes
 ~~~~~~~~~~~~~~~~~~~~~
 
-Thanks to all of the contributors who participated in the Pandas Documentation
+Thanks to all of the contributors who participated in the pandas Documentation
 Sprint, which took place on March 10th. We had about 500 participants from over
 30 locations across the world. You should notice that many of the
 :ref:`API docstrings <api>` have greatly improved.

doc/source/whatsnew/v0.25.0.rst

@@ -545,13 +545,13 @@ with :attr:`numpy.nan` in the case of an empty :class:`DataFrame` (:issue:`26397
 ``__str__`` methods now call ``__repr__`` rather than vice versa
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Pandas has until now mostly defined string representations in a Pandas objects's
+Pandas has until now mostly defined string representations in a pandas objects's
 ``__str__``/``__unicode__``/``__bytes__`` methods, and called ``__str__`` from the ``__repr__``
 method, if a specific ``__repr__`` method is not found. This is not needed for Python3.
-In Pandas 0.25, the string representations of Pandas objects are now generally
+In Pandas 0.25, the string representations of pandas objects are now generally
 defined in ``__repr__``, and calls to ``__str__`` in general now pass the call on to
 the ``__repr__``, if a specific ``__str__`` method doesn't exist, as is standard for Python.
-This change is backward compatible for direct usage of Pandas, but if you subclass
+This change is backward compatible for direct usage of pandas, but if you subclass
 Pandas objects *and* give your subclasses specific ``__str__``/``__repr__`` methods,
 you may have to adjust your ``__str__``/``__repr__`` methods (:issue:`26495`).
 
@@ -881,7 +881,7 @@ Other API changes
 - Bug in :meth:`DatetimeIndex.snap` which didn't preserving the ``name`` of the input :class:`Index` (:issue:`25575`)
 - The ``arg`` argument in :meth:`pandas.core.groupby.DataFrameGroupBy.agg` has been renamed to ``func`` (:issue:`26089`)
 - The ``arg`` argument in :meth:`pandas.core.window._Window.aggregate` has been renamed to ``func`` (:issue:`26372`)
-- Most Pandas classes had a ``__bytes__`` method, which was used for getting a python2-style bytestring representation of the object. This method has been removed as a part of dropping Python2 (:issue:`26447`)
+- Most pandas classes had a ``__bytes__`` method, which was used for getting a python2-style bytestring representation of the object. This method has been removed as a part of dropping Python2 (:issue:`26447`)
 - The ``.str``-accessor has been disabled for 1-level :class:`MultiIndex`, use :meth:`MultiIndex.to_flat_index` if necessary (:issue:`23679`)
 - Removed support of gtk package for clipboards (:issue:`26563`)
 - Using an unsupported version of Beautiful Soup 4 will now raise an ``ImportError`` instead of a ``ValueError`` (:issue:`27063`)

doc/source/whatsnew/v1.0.0.rst

@@ -18,7 +18,7 @@ including other versions of pandas.
 New deprecation policy
 ~~~~~~~~~~~~~~~~~~~~~~
 
-Starting with Pandas 1.0.0, pandas will adopt a variant of `SemVer`_ to
+Starting with pandas 1.0.0, pandas will adopt a variant of `SemVer`_ to
 version releases. Briefly,
 
 * Deprecations will be introduced in minor releases (e.g. 1.1.0, 1.2.0, 2.1.0, ...)
@@ -763,7 +763,7 @@ Other API changes
 - :class:`core.groupby.GroupBy.transform` now raises on invalid operation names (:issue:`27489`)
 - :meth:`pandas.api.types.infer_dtype` will now return "integer-na" for integer and ``np.nan`` mix (:issue:`27283`)
 - :meth:`MultiIndex.from_arrays` will no longer infer names from arrays if ``names=None`` is explicitly provided (:issue:`27292`)
-- In order to improve tab-completion, Pandas does not include most deprecated attributes when introspecting a pandas object using ``dir`` (e.g. ``dir(df)``).
+- In order to improve tab-completion, pandas does not include most deprecated attributes when introspecting a pandas object using ``dir`` (e.g. ``dir(df)``).
   To see which attributes are excluded, see an object's ``_deprecations`` attribute, for example ``pd.DataFrame._deprecations`` (:issue:`28805`).
 - The returned dtype of :func:`unique` now matches the input dtype. (:issue:`27874`)
 - Changed the default configuration value for ``options.matplotlib.register_converters`` from ``True`` to ``"auto"`` (:issue:`18720`).