DOC: normalize usage of word "pandas"

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

@@ -155,7 +155,7 @@ Using a Docker container
 
 Instead of manually setting up a development environment, you can use `Docker
 <https://docs.docker.com/get-docker/>`_ to automatically create the environment with just several
-commands. Pandas provides a ``DockerFile`` in the root directory to build a Docker image
+commands. pandas provides a ``DockerFile`` in the root directory to build a Docker image
 with a full pandas development environment.
 
 **Docker Commands**
@@ -190,7 +190,7 @@ Note that you might need to rebuild the C extensions if/when you merge with upst
 Installing a C compiler
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-Pandas uses C extensions (mostly written using Cython) to speed up certain
+pandas uses C extensions (mostly written using Cython) to speed up certain
 operations. To install pandas from source, you need to compile these C
 extensions, which means you need a C compiler. This process depends on which
 platform you're using.
@@ -1219,7 +1219,7 @@ This test shows off several useful features of Hypothesis, as well as
 demonstrating a good use-case: checking properties that should hold over
 a large or complicated domain of inputs.
 
-To keep the Pandas test suite running quickly, parametrized tests are
+To keep the pandas test suite running quickly, parametrized tests are
 preferred if the inputs or logic are simple, with Hypothesis tests reserved
 for cases with complex logic or where there are too many combinations of
 options or subtle interactions to test (or think of!) all of them.

doc/source/development/maintaining.rst

@@ -207,7 +207,7 @@ Only core team members can merge pull requests. We have a few guidelines.
 1. You should typically not self-merge your own pull requests. Exceptions include
    things like small changes to fix CI (e.g. pinning a package version).
 2. You should not merge pull requests that have an active discussion, or pull
-   requests that has any ``-1`` votes from a core maintainer. Pandas operates
+   requests that has any ``-1`` votes from a core maintainer. pandas operates
    by consensus.
 3. For larger changes, it's good to have a +1 from at least two core team members.
 

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>`__
@@ -199,7 +199,7 @@ Jupyter notebooks can be converted to a number of open standard output formats
 Python) through 'Download As' in the web interface and ``jupyter convert``
 in a shell.
 
-Pandas DataFrames implement ``_repr_html_``and ``_repr_latex`` methods
+pandas DataFrames implement ``_repr_html_``and ``_repr_latex`` methods
 which are utilized by Jupyter Notebook for displaying
 (abbreviated) HTML or LaTeX tables. LaTeX output is properly escaped.
 (Note: HTML tables may or may not be
@@ -227,7 +227,7 @@ Its `Variable Explorer <https://docs.spyder-ide.org/variableexplorer.html>`__
 allows users to view, manipulate and edit pandas ``Index``, ``Series``,
 and ``DataFrame`` objects like a "spreadsheet", including copying and modifying
 values, sorting, displaying a "heatmap", converting data types and more.
-Pandas objects can also be renamed, duplicated, new columns added,
+pandas objects can also be renamed, duplicated, new columns added,
 copyed/pasted to/from the clipboard (as TSV), and saved/loaded to/from a file.
 Spyder can also import data from a variety of plain text and binary files
 or the clipboard into a new pandas DataFrame via a sophisticated import wizard.
@@ -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>`__
@@ -355,7 +355,7 @@ Out-of-core
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Blaze provides a standard API for doing computations with various
-in-memory and on-disk backends: NumPy, Pandas, SQLAlchemy, MongoDB, PyTables,
+in-memory and on-disk backends: NumPy, pandas, SQLAlchemy, MongoDB, PyTables,
 PySpark.
 
 `Dask <https://dask.readthedocs.io/en/latest/>`__
@@ -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
@@ -422,7 +422,7 @@ Increasingly, packages are being built on top of pandas to address specific need
 Extension data types
 --------------------
 
-Pandas provides an interface for defining
+pandas provides an interface for defining
 :ref:`extension types <extending.extension-types>` to extend NumPy's type
 system. The following libraries implement that interface to provide types not
 found in NumPy or pandas, which work well with pandas' data containers.

doc/source/getting_started/comparison/comparison_with_r.rst

@@ -5,11 +5,11 @@
 Comparison with R / R libraries
 *******************************
 
-Since ``pandas`` aims to provide a lot of the data manipulation and analysis
+Since pandas aims to provide a lot of the data manipulation and analysis
 functionality that people use `R <https://www.r-project.org/>`__ for, this page
 was started to provide a more detailed look at the `R language
 <https://en.wikipedia.org/wiki/R_(programming_language)>`__ and its many third
-party libraries as they relate to ``pandas``. In comparisons with R and CRAN
+party libraries as they relate to pandas. In comparisons with R and CRAN
 libraries, we care about the following things:
 
 * **Functionality / flexibility**: what can/cannot be done with each tool
@@ -21,7 +21,7 @@ libraries, we care about the following things:
 This page is also here to offer a bit of a translation guide for users of these
 R packages.
 
-For transfer of ``DataFrame`` objects from ``pandas`` to R, one option is to
+For transfer of ``DataFrame`` objects from pandas to R, one option is to
 use HDF5 files, see :ref:`io.external_compatibility` for an
 example.
 
@@ -118,7 +118,7 @@ or by integer location
    df <- data.frame(matrix(rnorm(1000), ncol=100))
    df[, c(1:10, 25:30, 40, 50:100)]
 
-Selecting multiple columns by name in ``pandas`` is straightforward
+Selecting multiple columns by name in pandas is straightforward
 
 .. ipython:: python
 
@@ -235,7 +235,7 @@ since the subclass sizes are possibly irregular. Using a data.frame called
    tapply(baseball$batting.average, baseball.example$team,
           max)
 
-In ``pandas`` we may use :meth:`~pandas.pivot_table` method to handle this:
+In pandas we may use :meth:`~pandas.pivot_table` method to handle this:
 
 .. ipython:: python
 
@@ -268,7 +268,7 @@ column's values are less than another column's values:
    subset(df, a <= b)
    df[df$a <= df$b,]  # note the comma
 
-In ``pandas``, there are a few ways to perform subsetting. You can use
+In pandas, there are a few ways to perform subsetting. You can use
 :meth:`~pandas.DataFrame.query` or pass an expression as if it were an
 index/slice as well as standard boolean indexing:
 
@@ -295,7 +295,7 @@ An expression using a data.frame called ``df`` in R with the columns ``a`` and
    with(df, a + b)
    df$a + df$b  # same as the previous expression
 
-In ``pandas`` the equivalent expression, using the
+In pandas the equivalent expression, using the
 :meth:`~pandas.DataFrame.eval` method, would be:
 
 .. ipython:: python
@@ -347,7 +347,7 @@ summarize ``x`` by ``month``:
          mean = round(mean(x), 2),
          sd = round(sd(x), 2))
 
-In ``pandas`` the equivalent expression, using the
+In pandas the equivalent expression, using the
 :meth:`~pandas.DataFrame.groupby` method, would be:
 
 .. ipython:: python

doc/source/getting_started/comparison/comparison_with_stata.rst

@@ -146,7 +146,7 @@ the pandas command would be:
    # alternatively, read_table is an alias to read_csv with tab delimiter
    tips = pd.read_table("tips.csv", header=None)
 
-Pandas can also read Stata data sets in ``.dta`` format with the :func:`read_stata` function.
+pandas can also read Stata data sets in ``.dta`` format with the :func:`read_stata` function.
 
 .. code-block:: python
 
@@ -172,7 +172,7 @@ Similarly in pandas, the opposite of ``read_csv`` is :meth:`DataFrame.to_csv`.
 
    tips.to_csv("tips2.csv")
 
-Pandas can also export to Stata file format with the :meth:`DataFrame.to_stata` method.
+pandas can also export to Stata file format with the :meth:`DataFrame.to_stata` method.
 
 .. code-block:: python
 
@@ -583,7 +583,7 @@ should be used for comparisons.
    outer_join[pd.isna(outer_join["value_x"])]
    outer_join[pd.notna(outer_join["value_x"])]
 
-Pandas also provides a variety of methods to work with missing data -- some of
+pandas also provides a variety of methods to work with missing data -- some of
 which would be challenging to express in Stata. For example, there are methods to
 drop all rows with any missing values, replacing missing values with a specified
 value, like the mean, or forward filling from previous rows. See the
@@ -674,7 +674,7 @@ Other considerations
 Disk vs memory
 ~~~~~~~~~~~~~~
 
-Pandas and Stata both operate exclusively in memory. This means that the size of
+pandas and Stata both operate exclusively in memory. This means that the size of
 data able to be loaded in pandas is limited by your machine's memory.
 If out of core processing is needed, one possibility is the
 `dask.dataframe <https://dask.pydata.org/en/latest/dataframe.html>`_

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
 ----------------------
@@ -249,7 +249,7 @@ Recommended dependencies
 Optional dependencies
 ~~~~~~~~~~~~~~~~~~~~~
 
-Pandas has many optional dependencies that are only used for specific methods.
+pandas has many optional dependencies that are only used for specific methods.
 For example, :func:`pandas.read_hdf` requires the ``pytables`` package, while
 :meth:`DataFrame.to_markdown` requires the ``tabulate`` package. If the
 optional dependency is not installed, pandas will raise an ``ImportError`` when

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/reference/arrays.rst

@@ -16,7 +16,7 @@ For some data types, pandas extends NumPy's type system. String aliases for thes
 can be found at :ref:`basics.dtypes`.
 
 =================== ========================= ================== =============================
-Kind of Data        Pandas Data Type          Scalar             Array
+Kind of Data        pandas Data Type          Scalar             Array
 =================== ========================= ================== =============================
 TZ-aware datetime   :class:`DatetimeTZDtype`  :class:`Timestamp` :ref:`api.arrays.datetime`
 Timedeltas          (none)                    :class:`Timedelta` :ref:`api.arrays.timedelta`
@@ -29,7 +29,7 @@ Strings             :class:`StringDtype`      :class:`str`       :ref:`api.array
 Boolean (with NA)   :class:`BooleanDtype`     :class:`bool`      :ref:`api.arrays.bool`
 =================== ========================= ================== =============================
 
-Pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
+pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
 The top-level :meth:`array` method can be used to create a new array, which may be
 stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFrame`.
 
@@ -43,7 +43,7 @@ stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFra
 Datetime data
 -------------
 
-NumPy cannot natively represent timezone-aware datetimes. Pandas supports this
+NumPy cannot natively represent timezone-aware datetimes. pandas supports this
 with the :class:`arrays.DatetimeArray` extension array, which can hold timezone-naive
 or timezone-aware values.
 
@@ -162,7 +162,7 @@ If the data are tz-aware, then every value in the array must have the same timez
 Timedelta data
 --------------
 
-NumPy can natively represent timedeltas. Pandas provides :class:`Timedelta`
+NumPy can natively represent timedeltas. pandas provides :class:`Timedelta`
 for symmetry with :class:`Timestamp`.
 
 .. autosummary::
@@ -217,7 +217,7 @@ A collection of timedeltas may be stored in a :class:`TimedeltaArray`.
 Timespan data
 -------------
 
-Pandas represents spans of times as :class:`Period` objects.
+pandas represents spans of times as :class:`Period` objects.
 
 Period
 ------
@@ -352,7 +352,7 @@ Nullable integer
 ----------------
 
 :class:`numpy.ndarray` cannot natively represent integer-data with missing values.
-Pandas provides this through :class:`arrays.IntegerArray`.
+pandas provides this through :class:`arrays.IntegerArray`.
 
 .. autosummary::
    :toctree: api/
@@ -378,7 +378,7 @@ Pandas provides this through :class:`arrays.IntegerArray`.
 Categorical data
 ----------------
 
-Pandas defines a custom data type for representing data that can take only a
+pandas defines a custom data type for representing data that can take only a
 limited, fixed set of values. The dtype of a ``Categorical`` can be described by
 a :class:`pandas.api.types.CategoricalDtype`.
 

doc/source/reference/series.rst

@@ -280,7 +280,7 @@ Time Series-related
 Accessors
 ---------
 
-Pandas provides dtype-specific methods under various accessors.
+pandas provides dtype-specific methods under various accessors.
 These are separate namespaces within :class:`Series` that only apply
 to specific data types.