diff --git a/ci/run_tests.sh b/ci/run_tests.sh
index d73940c1010ad..f5e3420b8c9b3 100755
--- a/ci/run_tests.sh
+++ b/ci/run_tests.sh
@@ -10,7 +10,7 @@ if [[ "not network" == *"$PATTERN"* ]]; then
 fi
 
 if [ "$COVERAGE" ]; then
-    COVERAGE="-s --cov=pandas --cov-report=xml"
+    COVERAGE="-s --cov=pandas --cov-report=xml --cov-append"
 fi
 
 # If no X server is found, we use xvfb to emulate it
diff --git a/codecov.yml b/codecov.yml
index 3f3df474956da..893e40db004a6 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -8,7 +8,7 @@ coverage:
   status:
     project:
       default:
-        target: '72'
+        target: '82'
     patch:
       default:
         target: '50'
diff --git a/doc/source/development/contributing.rst b/doc/source/development/contributing.rst
index b4fa6b008be74..b9afbe387799e 100644
--- a/doc/source/development/contributing.rst
+++ b/doc/source/development/contributing.rst
@@ -140,266 +140,6 @@ Note that performing a shallow clone (with ``--depth==N``, for some ``N`` greate
 or equal to 1) might break some tests and features as ``pd.show_versions()``
 as the version number cannot be computed anymore.
 
-.. _contributing.dev_env:
-
-Creating a development environment
-----------------------------------
-
-To test out code changes, you'll need to build pandas from source, which
-requires a C/C++ compiler and Python environment. If you're making documentation
-changes, you can skip to :ref:`contributing.documentation` but if you skip
-creating the development environment you won't be able to build the documentation
-locally before pushing your changes.
-
-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
-with a full pandas development environment.
-
-**Docker Commands**
-
-Pass your GitHub username in the ``DockerFile`` to use your own fork::
-
-    # Build the image pandas-yourname-env
-    docker build --tag pandas-yourname-env .
-    # Run a container and bind your local forked repo, pandas-yourname, to the container
-    docker run -it --rm -v path-to-pandas-yourname:/home/pandas-yourname pandas-yourname-env
-
-Even easier, you can integrate Docker with the following IDEs:
-
-**Visual Studio Code**
-
-You can use the DockerFile to launch a remote session with Visual Studio Code,
-a popular free IDE, using the ``.devcontainer.json`` file.
-See https://code.visualstudio.com/docs/remote/containers for details.
-
-**PyCharm (Professional)**
-
-Enable Docker support and use the Services tool window to build and manage images as well as
-run and interact with containers.
-See https://www.jetbrains.com/help/pycharm/docker.html for details.
-
-Note that you might need to rebuild the C extensions if/when you merge with upstream/master using::
-
-    python setup.py build_ext -j 4
-
-.. _contributing.dev_c:
-
-Installing a C compiler
-~~~~~~~~~~~~~~~~~~~~~~~
-
-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.
-
-If you have setup your environment using ``conda``, the packages ``c-compiler``
-and ``cxx-compiler`` will install a fitting compiler for your platform that is
-compatible with the remaining conda packages. On Windows and macOS, you will
-also need to install the SDKs as they have to be distributed separately.
-These packages will be automatically installed by using ``pandas``'s
-``environment.yml``.
-
-**Windows**
-
-You will need `Build Tools for Visual Studio 2017
-<https://visualstudio.microsoft.com/downloads/>`_.
-
-.. warning::
-	You DO NOT need to install Visual Studio 2019.
-	You only need "Build Tools for Visual Studio 2019" found by
-	scrolling down to "All downloads" -> "Tools for Visual Studio 2019".
-	In the installer, select the "C++ build tools" workload.
-
-You can install the necessary components on the commandline using
-`vs_buildtools.exe <https://aka.ms/vs/16/release/vs_buildtools.exe>`_:
-
-.. code::
-
-    vs_buildtools.exe --quiet --wait --norestart --nocache ^
-        --installPath C:\BuildTools ^
-        --add "Microsoft.VisualStudio.Workload.VCTools;includeRecommended" ^
-        --add Microsoft.VisualStudio.Component.VC.v141 ^
-        --add Microsoft.VisualStudio.Component.VC.v141.x86.x64 ^
-        --add Microsoft.VisualStudio.Component.Windows10SDK.17763
-
-To setup the right paths on the commandline, call
-``"C:\BuildTools\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.16 10.0.17763.0``.
-
-**macOS**
-
-To use the ``conda``-based compilers, you will need to install the
-Developer Tools using ``xcode-select --install``. Otherwise
-information about compiler installation can be found here:
-https://devguide.python.org/setup/#macos
-
-**Linux**
-
-For Linux-based ``conda`` installations, you won't have to install any
-additional components outside of the conda environment. The instructions
-below are only needed if your setup isn't based on conda environments.
-
-Some Linux distributions will come with a pre-installed C compiler. To find out
-which compilers (and versions) are installed on your system::
-
-    # for Debian/Ubuntu:
-    dpkg --list | grep compiler
-    # for Red Hat/RHEL/CentOS/Fedora:
-    yum list installed | grep -i --color compiler
-
-`GCC (GNU Compiler Collection) <https://gcc.gnu.org/>`_, is a widely used
-compiler, which supports C and a number of other languages. If GCC is listed
-as an installed compiler nothing more is required. If no C compiler is
-installed (or you wish to install a newer version) you can install a compiler
-(GCC in the example code below) with::
-
-    # for recent Debian/Ubuntu:
-    sudo apt install build-essential
-    # for Red Had/RHEL/CentOS/Fedora
-    yum groupinstall "Development Tools"
-
-For other Linux distributions, consult your favourite search engine for
-compiler installation instructions.
-
-Let us know if you have any difficulties by opening an issue or reaching out on
-`Gitter`_.
-
-.. _contributing.dev_python:
-
-Creating a Python environment
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Now create an isolated pandas development environment:
-
-* Install either `Anaconda <https://www.anaconda.com/download/>`_, `miniconda
-  <https://conda.io/miniconda.html>`_, or `miniforge <https://github.com/conda-forge/miniforge>`_
-* Make sure your conda is up to date (``conda update conda``)
-* Make sure that you have :ref:`cloned the repository <contributing.forking>`
-* ``cd`` to the pandas source directory
-
-We'll now kick off a three-step process:
-
-1. Install the build dependencies
-2. Build and install pandas
-3. Install the optional dependencies
-
-.. code-block:: none
-
-   # Create and activate the build environment
-   conda env create -f environment.yml
-   conda activate pandas-dev
-
-   # or with older versions of Anaconda:
-   source activate pandas-dev
-
-   # Build and install pandas
-   python setup.py build_ext -j 4
-   python -m pip install -e . --no-build-isolation --no-use-pep517
-
-At this point you should be able to import pandas from your locally built version::
-
-   $ python  # start an interpreter
-   >>> import pandas
-   >>> print(pandas.__version__)
-   0.22.0.dev0+29.g4ad6d4d74
-
-This will create the new environment, and not touch any of your existing environments,
-nor any existing Python installation.
-
-To view your environments::
-
-      conda info -e
-
-To return to your root environment::
-
-      conda deactivate
-
-See the full conda docs `here <https://conda.pydata.org/docs>`__.
-
-.. _contributing.pip:
-
-Creating a Python environment (pip)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If you aren't using conda for your development environment, follow these instructions.
-You'll need to have at least Python 3.7.0 installed on your system. If your Python version
-is 3.8.0 (or later), you might need to update your ``setuptools`` to version 42.0.0 (or later)
-in your development environment before installing the build dependencies::
-
-      pip install --upgrade setuptools
-
-**Unix**/**macOS with virtualenv**
-
-.. code-block:: bash
-
-   # Create a virtual environment
-   # Use an ENV_DIR of your choice. We'll use ~/virtualenvs/pandas-dev
-   # Any parent directories should already exist
-   python3 -m venv ~/virtualenvs/pandas-dev
-
-   # Activate the virtualenv
-   . ~/virtualenvs/pandas-dev/bin/activate
-
-   # Install the build dependencies
-   python -m pip install -r requirements-dev.txt
-
-   # Build and install pandas
-   python setup.py build_ext -j 4
-   python -m pip install -e . --no-build-isolation --no-use-pep517
-
-**Unix**/**macOS with pyenv**
-
-Consult the docs for setting up pyenv `here <https://github.com/pyenv/pyenv>`__.
-
-.. code-block:: bash
-
-   # Create a virtual environment
-   # Use an ENV_DIR of your choice. We'll use ~/Users/<yourname>/.pyenv/versions/pandas-dev
-
-   pyenv virtualenv <version> <name-to-give-it>
-
-   # For instance:
-   pyenv virtualenv 3.7.6 pandas-dev
-
-   # Activate the virtualenv
-   pyenv activate pandas-dev
-
-   # Now install the build dependencies in the cloned pandas repo
-   python -m pip install -r requirements-dev.txt
-
-   # Build and install pandas
-   python setup.py build_ext -j 4
-   python -m pip install -e . --no-build-isolation --no-use-pep517
-
-**Windows**
-
-Below is a brief overview on how to set-up a virtual environment with Powershell
-under Windows. For details please refer to the
-`official virtualenv user guide <https://virtualenv.pypa.io/en/stable/userguide/#activate-script>`__
-
-Use an ENV_DIR of your choice. We'll use ~\\virtualenvs\\pandas-dev where
-'~' is the folder pointed to by either $env:USERPROFILE (Powershell) or
-%USERPROFILE% (cmd.exe) environment variable. Any parent directories
-should already exist.
-
-.. code-block:: powershell
-
-   # Create a virtual environment
-   python -m venv $env:USERPROFILE\virtualenvs\pandas-dev
-
-   # Activate the virtualenv. Use activate.bat for cmd.exe
-   ~\virtualenvs\pandas-dev\Scripts\Activate.ps1
-
-   # Install the build dependencies
-   python -m pip install -r requirements-dev.txt
-
-   # Build and install pandas
-   python setup.py build_ext -j 4
-   python -m pip install -e . --no-build-isolation --no-use-pep517
-
 Creating a branch
 -----------------
 
@@ -429,1060 +169,6 @@ When you want to update the feature branch with changes in master after
 you created the branch, check the section on
 :ref:`updating a PR <contributing.update-pr>`.
 
-.. _contributing.documentation:
-
-Contributing to the documentation
-=================================
-
-Contributing to the documentation benefits everyone who uses pandas.
-We encourage you to help us improve the documentation, and
-you don't have to be an expert on pandas to do so! In fact,
-there are sections of the docs that are worse off after being written by
-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.
-
-.. contents:: Documentation:
-   :local:
-
-
-About the pandas documentation
---------------------------------
-
-The documentation is written in **reStructuredText**, which is almost like writing
-in plain English, and built using `Sphinx <https://www.sphinx-doc.org/en/master/>`__. The
-Sphinx Documentation has an excellent `introduction to reST
-<https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`__. Review the Sphinx docs to perform more
-complex changes to the documentation as well.
-
-Some other important things to know about the docs:
-
-* The pandas documentation consists of two parts: the docstrings in the code
-  itself and the docs in this folder ``doc/``.
-
-  The docstrings provide a clear explanation of the usage of the individual
-  functions, while the documentation in this folder consists of tutorial-like
-  overviews per topic together with some other information (what's new,
-  installation, etc).
-
-* The docstrings follow a pandas convention, based on the **Numpy Docstring
-  Standard**. Follow the :ref:`pandas docstring guide <docstring>` for detailed
-  instructions on how to write a correct docstring.
-
-  .. toctree::
-     :maxdepth: 2
-
-     contributing_docstring.rst
-
-* The tutorials make heavy use of the `IPython directive
-  <https://matplotlib.org/sampledoc/ipython_directive.html>`_ sphinx extension.
-  This directive lets you put code in the documentation which will be run
-  during the doc build. For example::
-
-      .. ipython:: python
-
-          x = 2
-          x**3
-
-  will be rendered as::
-
-      In [1]: x = 2
-
-      In [2]: x**3
-      Out[2]: 8
-
-  Almost all code examples in the docs are run (and the output saved) during the
-  doc build. This approach means that code examples will always be up to date,
-  but it does make the doc building a bit more complex.
-
-* Our API documentation files in ``doc/source/reference`` house the auto-generated
-  documentation from the docstrings. For classes, there are a few subtleties
-  around controlling which methods and attributes have pages auto-generated.
-
-  We have two autosummary templates for classes.
-
-  1. ``_templates/autosummary/class.rst``. Use this when you want to
-     automatically generate a page for every public method and attribute on the
-     class. The ``Attributes`` and ``Methods`` sections will be automatically
-     added to the class' rendered documentation by numpydoc. See ``DataFrame``
-     for an example.
-
-  2. ``_templates/autosummary/class_without_autosummary``. Use this when you
-     want to pick a subset of methods / attributes to auto-generate pages for.
-     When using this template, you should include an ``Attributes`` and
-     ``Methods`` section in the class docstring. See ``CategoricalIndex`` for an
-     example.
-
-  Every method should be included in a ``toctree`` in one of the documentation files in
-  ``doc/source/reference``, else Sphinx
-  will emit a warning.
-
-.. note::
-
-    The ``.rst`` files are used to automatically generate Markdown and HTML versions
-    of the docs. For this reason, please do not edit ``CONTRIBUTING.md`` directly,
-    but instead make any changes to ``doc/source/development/contributing.rst``. Then, to
-    generate ``CONTRIBUTING.md``, use `pandoc <https://johnmacfarlane.net/pandoc/>`_
-    with the following command::
-
-      pandoc doc/source/development/contributing.rst -t markdown_github > CONTRIBUTING.md
-
-The utility script ``scripts/validate_docstrings.py`` can be used to get a csv
-summary of the API documentation. And also validate common errors in the docstring
-of a specific class, function or method. The summary also compares the list of
-methods documented in the files in ``doc/source/reference`` (which is used to generate
-the `API Reference <https://pandas.pydata.org/pandas-docs/stable/api.html>`_ page)
-and the actual public methods.
-This will identify methods documented in ``doc/source/reference`` that are not actually
-class methods, and existing methods that are not documented in ``doc/source/reference``.
-
-
-Updating a pandas docstring
------------------------------
-
-When improving a single function or method's docstring, it is not necessarily
-needed to build the full documentation (see next section).
-However, there is a script that checks a docstring (for example for the ``DataFrame.mean`` method)::
-
-    python scripts/validate_docstrings.py pandas.DataFrame.mean
-
-This script will indicate some formatting errors if present, and will also
-run and test the examples included in the docstring.
-Check the :ref:`pandas docstring guide <docstring>` for a detailed guide
-on how to format the docstring.
-
-The examples in the docstring ('doctests') must be valid Python code,
-that in a deterministic way returns the presented output, and that can be
-copied and run by users. This can be checked with the script above, and is
-also tested on Travis. A failing doctest will be a blocker for merging a PR.
-Check the :ref:`examples <docstring.examples>` section in the docstring guide
-for some tips and tricks to get the doctests passing.
-
-When doing a PR with a docstring update, it is good to post the
-output of the validation script in a comment on github.
-
-
-How to build the pandas documentation
----------------------------------------
-
-Requirements
-~~~~~~~~~~~~
-
-First, you need to have a development environment to be able to build pandas
-(see the docs on :ref:`creating a development environment above <contributing.dev_env>`).
-
-Building the documentation
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-So how do you build the docs? Navigate to your local
-``doc/`` directory in the console and run::
-
-    python make.py html
-
-Then you can find the HTML output in the folder ``doc/build/html/``.
-
-The first time you build the docs, it will take quite a while because it has to run
-all the code examples and build all the generated docstring pages. In subsequent
-evocations, sphinx will try to only build the pages that have been modified.
-
-If you want to do a full clean build, do::
-
-    python make.py clean
-    python make.py html
-
-You can tell ``make.py`` to compile only a single section of the docs, greatly
-reducing the turn-around time for checking your changes.
-
-::
-
-    # omit autosummary and API section
-    python make.py clean
-    python make.py --no-api
-
-    # compile the docs with only a single section, relative to the "source" folder.
-    # For example, compiling only this guide (doc/source/development/contributing.rst)
-    python make.py clean
-    python make.py --single development/contributing.rst
-
-    # compile the reference docs for a single function
-    python make.py clean
-    python make.py --single pandas.DataFrame.join
-
-    # compile whatsnew and API section (to resolve links in the whatsnew)
-    python make.py clean
-    python make.py --whatsnew
-
-For comparison, a full documentation build may take 15 minutes, but a single
-section may take 15 seconds. Subsequent builds, which only process portions
-you have changed, will be faster.
-
-The build will automatically use the number of cores available on your machine
-to speed up the documentation build. You can override this::
-
-    python make.py html --num-jobs 4
-
-Open the following file in a web browser to see the full documentation you
-just built::
-
-    doc/build/html/index.html
-
-And you'll have the satisfaction of seeing your new and improved documentation!
-
-.. _contributing.dev_docs:
-
-Building master branch documentation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-When pull requests are merged into the pandas ``master`` branch, the main parts of
-the documentation are also built by Travis-CI. These docs are then hosted `here
-<https://pandas.pydata.org/docs/dev/>`__, see also
-the :ref:`Continuous Integration <contributing.ci>` section.
-
-Previewing changes
-------------------
-
-Once, the pull request is submitted, GitHub Actions will automatically build the
-documentation. To view the built site:
-
-#. Wait for the ``CI / Web and docs`` check to complete.
-#. Click ``Details`` next to it.
-#. From the ``Artifacts`` drop-down, click ``docs`` or ``website`` to download
-   the site as a ZIP file.
-
-.. _contributing.code:
-
-Contributing to the code base
-=============================
-
-.. contents:: Code Base:
-   :local:
-
-Code standards
---------------
-
-Writing good code is not just about what you write. It is also about *how* you
-write it. During :ref:`Continuous Integration <contributing.ci>` testing, several
-tools will be run to check your code for stylistic errors.
-Generating any warnings will cause the test to fail.
-Thus, good style is a requirement for submitting code to pandas.
-
-There is a tool in pandas to help contributors verify their changes before
-contributing them to the project::
-
-   ./ci/code_checks.sh
-
-The script verifies the linting of code files, it looks for common mistake patterns
-(like missing spaces around sphinx directives that make the documentation not
-being rendered properly) and it also validates the doctests. It is possible to
-run the checks independently by using the parameters ``lint``, ``patterns`` and
-``doctests`` (e.g. ``./ci/code_checks.sh lint``).
-
-In addition, because a lot of people use our library, it is important that we
-do not make sudden changes to the code that could have the potential to break
-a lot of user code as a result, that is, we need it to be as *backwards compatible*
-as possible to avoid mass breakages.
-
-In addition to ``./ci/code_checks.sh``, some extra checks are run by
-``pre-commit`` - see :ref:`here <contributing.pre-commit>` for how to
-run them.
-
-Additional standards are outlined on the :ref:`pandas code style guide <code_style>`.
-
-.. _contributing.pre-commit:
-
-Pre-commit
-----------
-
-You can run many of these styling checks manually as we have described above. However,
-we encourage you to use `pre-commit hooks <https://pre-commit.com/>`_ instead
-to automatically run ``black``, ``flake8``, ``isort`` when you make a git commit. This
-can be done by installing ``pre-commit``::
-
-    pip install pre-commit
-
-and then running::
-
-    pre-commit install
-
-from the root of the pandas repository. Now all of the styling checks will be
-run each time you commit changes without your needing to run each one manually.
-In addition, using ``pre-commit`` will also allow you to more easily
-remain up-to-date with our code checks as they change.
-
-Note that if needed, you can skip these checks with ``git commit --no-verify``.
-
-If you don't want to use ``pre-commit`` as part of your workflow, you can still use it
-to run its checks with::
-
-    pre-commit run --files <files you have modified>
-
-without needing to have done ``pre-commit install`` beforehand.
-
-If you want to run checks on all recently committed files on upstream/master you can use::
-
-    pre-commit run --from-ref=upstream/master --to-ref=HEAD --all-files
-
-without needing to have done ``pre-commit install`` beforehand.
-
-.. note::
-
-    If you have conflicting installations of ``virtualenv``, then you may get an
-    error - see `here <https://github.com/pypa/virtualenv/issues/1875>`_.
-
-    Also, due to a `bug in virtualenv <https://github.com/pypa/virtualenv/issues/1986>`_,
-    you may run into issues if you're using conda. To solve this, you can downgrade
-    ``virtualenv`` to version ``20.0.33``.
-
-Optional dependencies
----------------------
-
-Optional dependencies (e.g. matplotlib) should be imported with the private helper
-``pandas.compat._optional.import_optional_dependency``. This ensures a
-consistent error message when the dependency is not met.
-
-All methods using an optional dependency should include a test asserting that an
-``ImportError`` is raised when the optional dependency is not found. This test
-should be skipped if the library is present.
-
-All optional dependencies should be documented in
-:ref:`install.optional_dependencies` and the minimum required version should be
-set in the ``pandas.compat._optional.VERSIONS`` dict.
-
-C (cpplint)
-~~~~~~~~~~~
-
-pandas uses the `Google <https://google.github.io/styleguide/cppguide.html>`_
-standard. Google provides an open source style checker called ``cpplint``, but we
-use a fork of it that can be found `here <https://github.com/cpplint/cpplint>`__.
-Here are *some* of the more common ``cpplint`` issues:
-
-* we restrict line-length to 80 characters to promote readability
-* every header file must include a header guard to avoid name collisions if re-included
-
-:ref:`Continuous Integration <contributing.ci>` will run the
-`cpplint <https://pypi.org/project/cpplint>`_ tool
-and report any stylistic errors in your code. Therefore, it is helpful before
-submitting code to run the check yourself::
-
-   cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,-build/include_subdir modified-c-file
-
-You can also run this command on an entire directory if necessary::
-
-   cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,-build/include_subdir --recursive modified-c-directory
-
-To make your commits compliant with this standard, you can install the
-`ClangFormat <https://clang.llvm.org/docs/ClangFormat.html>`_ tool, which can be
-downloaded `here <https://llvm.org/builds/>`__. To configure, in your home directory,
-run the following command::
-
-    clang-format style=google -dump-config  > .clang-format
-
-Then modify the file to ensure that any indentation width parameters are at least four.
-Once configured, you can run the tool as follows::
-
-    clang-format modified-c-file
-
-This will output what your file will look like if the changes are made, and to apply
-them, run the following command::
-
-    clang-format -i modified-c-file
-
-To run the tool on an entire directory, you can run the following analogous commands::
-
-    clang-format modified-c-directory/*.c modified-c-directory/*.h
-    clang-format -i modified-c-directory/*.c modified-c-directory/*.h
-
-Do note that this tool is best-effort, meaning that it will try to correct as
-many errors as possible, but it may not correct *all* of them. Thus, it is
-recommended that you run ``cpplint`` to double check and make any other style
-fixes manually.
-
-.. _contributing.code-formatting:
-
-Python (PEP8 / black)
-~~~~~~~~~~~~~~~~~~~~~
-
-pandas follows the `PEP8 <https://www.python.org/dev/peps/pep-0008/>`_ standard
-and uses `Black <https://black.readthedocs.io/en/stable/>`_ and
-`Flake8 <http://flake8.pycqa.org/en/latest/>`_ to ensure a consistent code
-format throughout the project. We encourage you to use :ref:`pre-commit <contributing.pre-commit>`.
-
-:ref:`Continuous Integration <contributing.ci>` will run those tools and
-report any stylistic errors in your code. Therefore, it is helpful before
-submitting code to run the check yourself::
-
-   black pandas
-   git diff upstream/master -u -- "*.py" | flake8 --diff
-
-to auto-format your code. Additionally, many editors have plugins that will
-apply ``black`` as you edit files.
-
-You should use a ``black`` version 20.8b1 as previous versions are not compatible
-with the pandas codebase.
-
-One caveat about ``git diff upstream/master -u -- "*.py" | flake8 --diff``: this
-command will catch any stylistic errors in your changes specifically, but
-be beware it may not catch all of them. For example, if you delete the only
-usage of an imported function, it is stylistically incorrect to import an
-unused function. However, style-checking the diff will not catch this because
-the actual import is not part of the diff. Thus, for completeness, you should
-run this command, though it may take longer::
-
-   git diff upstream/master --name-only -- "*.py" | xargs -r flake8
-
-Note that on OSX, the ``-r`` flag is not available, so you have to omit it and
-run this slightly modified command::
-
-   git diff upstream/master --name-only -- "*.py" | xargs flake8
-
-Windows does not support the ``xargs`` command (unless installed for example
-via the `MinGW <http://www.mingw.org/>`__ toolchain), but one can imitate the
-behaviour as follows::
-
-    for /f %i in ('git diff upstream/master --name-only -- "*.py"') do flake8 %i
-
-This will get all the files being changed by the PR (and ending with ``.py``),
-and run ``flake8`` on them, one after the other.
-
-Note that these commands can be run analogously with ``black``.
-
-.. _contributing.import-formatting:
-
-Import formatting
-~~~~~~~~~~~~~~~~~
-pandas uses `isort <https://pypi.org/project/isort/>`__ to standardise import
-formatting across the codebase.
-
-A guide to import layout as per pep8 can be found `here <https://www.python.org/dev/peps/pep-0008/#imports/>`__.
-
-A summary of our current import sections ( in order ):
-
-* Future
-* Python Standard Library
-* Third Party
-* ``pandas._libs``, ``pandas.compat``, ``pandas.util._*``, ``pandas.errors`` (largely not dependent on ``pandas.core``)
-* ``pandas.core.dtypes`` (largely not dependent on the rest of ``pandas.core``)
-* Rest of ``pandas.core.*``
-* Non-core ``pandas.io``, ``pandas.plotting``, ``pandas.tseries``
-* Local application/library specific imports
-
-Imports are alphabetically sorted within these sections.
-
-As part of :ref:`Continuous Integration <contributing.ci>` checks we run::
-
-    isort --check-only pandas
-
-to check that imports are correctly formatted as per the ``setup.cfg``.
-
-If you see output like the below in :ref:`Continuous Integration <contributing.ci>` checks:
-
-.. code-block:: shell
-
-   Check import format using isort
-   ERROR: /home/travis/build/pandas-dev/pandas/pandas/io/pytables.py Imports are incorrectly sorted
-   Check import format using isort DONE
-   The command "ci/code_checks.sh" exited with 1
-
-You should run::
-
-    isort pandas/io/pytables.py
-
-to automatically format imports correctly. This will modify your local copy of the files.
-
-Alternatively, you can run a command similar to what was suggested for ``black`` and ``flake8`` :ref:`right above <contributing.code-formatting>`::
-
-    git diff upstream/master --name-only -- "*.py" | xargs -r isort
-
-Where similar caveats apply if you are on OSX or Windows.
-
-You can then verify the changes look ok, then git :ref:`commit <contributing.commit-code>` and :ref:`push <contributing.push-code>`.
-
-Backwards compatibility
-~~~~~~~~~~~~~~~~~~~~~~~
-
-Please try to maintain backward compatibility. pandas has lots of users with lots of
-existing code, so don't break it if at all possible.  If you think breakage is required,
-clearly state why as part of the pull request.  Also, be careful when changing method
-signatures and add deprecation warnings where needed. Also, add the deprecated sphinx
-directive to the deprecated functions or methods.
-
-If a function with the same arguments as the one being deprecated exist, you can use
-the ``pandas.util._decorators.deprecate``:
-
-.. code-block:: python
-
-    from pandas.util._decorators import deprecate
-
-    deprecate('old_func', 'new_func', '1.1.0')
-
-Otherwise, you need to do it manually:
-
-.. code-block:: python
-
-    import warnings
-
-
-    def old_func():
-        """Summary of the function.
-
-        .. deprecated:: 1.1.0
-           Use new_func instead.
-        """
-        warnings.warn('Use new_func instead.', FutureWarning, stacklevel=2)
-        new_func()
-
-
-    def new_func():
-        pass
-
-You'll also need to
-
-1. Write a new test that asserts a warning is issued when calling with the deprecated argument
-2. Update all of pandas existing tests and code to use the new argument
-
-See :ref:`contributing.warnings` for more.
-
-.. _contributing.type_hints:
-
-Type hints
-----------
-
-pandas strongly encourages the use of :pep:`484` style type hints. New development should contain type hints and pull requests to annotate existing code are accepted as well!
-
-Style guidelines
-~~~~~~~~~~~~~~~~
-
-Types imports should follow the ``from typing import ...`` convention. So rather than
-
-.. code-block:: python
-
-   import typing
-
-   primes: typing.List[int] = []
-
-You should write
-
-.. code-block:: python
-
-   from typing import List, Optional, Union
-
-   primes: List[int] = []
-
-``Optional`` should be used where applicable, so instead of
-
-.. code-block:: python
-
-   maybe_primes: List[Union[int, None]] = []
-
-You should write
-
-.. code-block:: python
-
-   maybe_primes: List[Optional[int]] = []
-
-In some cases in the code base classes may define class variables that shadow builtins. This causes an issue as described in `Mypy 1775 <https://github.com/python/mypy/issues/1775#issuecomment-310969854>`_. The defensive solution here is to create an unambiguous alias of the builtin and use that without your annotation. For example, if you come across a definition like
-
-.. code-block:: python
-
-   class SomeClass1:
-       str = None
-
-The appropriate way to annotate this would be as follows
-
-.. code-block:: python
-
-   str_type = str
-
-   class SomeClass2:
-       str: str_type = None
-
-In some cases you may be tempted to use ``cast`` from the typing module when you know better than the analyzer. This occurs particularly when using custom inference functions. For example
-
-.. code-block:: python
-
-   from typing import cast
-
-   from pandas.core.dtypes.common import is_number
-
-   def cannot_infer_bad(obj: Union[str, int, float]):
-
-       if is_number(obj):
-           ...
-       else:  # Reasonably only str objects would reach this but...
-           obj = cast(str, obj)  # Mypy complains without this!
-	   return obj.upper()
-
-The limitation here is that while a human can reasonably understand that ``is_number`` would catch the ``int`` and ``float`` types mypy cannot make that same inference just yet (see `mypy #5206 <https://github.com/python/mypy/issues/5206>`_. While the above works, the use of ``cast`` is **strongly discouraged**. Where applicable a refactor of the code to appease static analysis is preferable
-
-.. code-block:: python
-
-   def cannot_infer_good(obj: Union[str, int, float]):
-
-       if isinstance(obj, str):
-           return obj.upper()
-       else:
-           ...
-
-With custom types and inference this is not always possible so exceptions are made, but every effort should be exhausted to avoid ``cast`` before going down such paths.
-
-pandas-specific types
-~~~~~~~~~~~~~~~~~~~~~
-
-Commonly used types specific to pandas will appear in `pandas._typing <https://github.com/pandas-dev/pandas/blob/master/pandas/_typing.py>`_ and you should use these where applicable. This module is private for now but ultimately this should be exposed to third party libraries who want to implement type checking against pandas.
-
-For example, quite a few functions in pandas accept a ``dtype`` argument. This can be expressed as a string like ``"object"``, a ``numpy.dtype`` like ``np.int64`` or even a pandas ``ExtensionDtype`` like ``pd.CategoricalDtype``. Rather than burden the user with having to constantly annotate all of those options, this can simply be imported and reused from the pandas._typing module
-
-.. code-block:: python
-
-   from pandas._typing import Dtype
-
-   def as_type(dtype: Dtype) -> ...:
-       ...
-
-This module will ultimately house types for repeatedly used concepts like "path-like", "array-like", "numeric", etc... and can also hold aliases for commonly appearing parameters like ``axis``. Development of this module is active so be sure to refer to the source for the most up to date list of available types.
-
-Validating type hints
-~~~~~~~~~~~~~~~~~~~~~
-
-pandas uses `mypy <http://mypy-lang.org>`_ to statically analyze the code base and type hints. After making any change you can ensure your type hints are correct by running
-
-.. code-block:: shell
-
-   mypy pandas
-
-.. _contributing.ci:
-
-Testing with continuous integration
------------------------------------
-
-The pandas test suite will run automatically on `Travis-CI <https://travis-ci.org/>`__ and
-`Azure Pipelines <https://azure.microsoft.com/en-us/services/devops/pipelines/>`__
-continuous integration services, once your pull request is submitted.
-However, if you wish to run the test suite on a branch prior to submitting the pull request,
-then the continuous integration services need to be hooked to your GitHub repository. Instructions are here
-for `Travis-CI <http://about.travis-ci.org/docs/user/getting-started/>`__ and
-`Azure Pipelines <https://docs.microsoft.com/en-us/azure/devops/pipelines/>`__.
-
-A pull-request will be considered for merging when you have an all 'green' build. If any tests are failing,
-then you will get a red 'X', where you can click through to see the individual failed tests.
-This is an example of a green build.
-
-.. image:: ../_static/ci.png
-
-.. note::
-
-   Each time you push to *your* fork, a *new* run of the tests will be triggered on the CI.
-   You can enable the auto-cancel feature, which removes any non-currently-running tests for that same pull-request, for
-   `Travis-CI here <https://docs.travis-ci.com/user/customizing-the-build/#Building-only-the-latest-commit>`__.
-
-.. _contributing.tdd:
-
-
-Test-driven development/code writing
-------------------------------------
-
-pandas is serious about testing and strongly encourages contributors to embrace
-`test-driven development (TDD) <https://en.wikipedia.org/wiki/Test-driven_development>`_.
-This development process "relies on the repetition of a very short development cycle:
-first the developer writes an (initially failing) automated test case that defines a desired
-improvement or new function, then produces the minimum amount of code to pass that test."
-So, before actually writing any code, you should write your tests.  Often the test can be
-taken from the original GitHub issue.  However, it is always worth considering additional
-use cases and writing corresponding tests.
-
-Adding tests is one of the most common requests after code is pushed to pandas.  Therefore,
-it is worth getting in the habit of writing tests ahead of time so this is never an issue.
-
-Like many packages, pandas uses `pytest
-<https://docs.pytest.org/en/latest/>`_ and the convenient
-extensions in `numpy.testing
-<https://numpy.org/doc/stable/reference/routines.testing.html>`_.
-
-.. note::
-
-   The earliest supported pytest version is 5.0.1.
-
-Writing tests
-~~~~~~~~~~~~~
-
-All tests should go into the ``tests`` subdirectory of the specific package.
-This folder contains many current examples of tests, and we suggest looking to these for
-inspiration.  If your test requires working with files or
-network connectivity, there is more information on the `testing page
-<https://github.com/pandas-dev/pandas/wiki/Testing>`_ of the wiki.
-
-The ``pandas._testing`` module has many special ``assert`` functions that
-make it easier to make statements about whether Series or DataFrame objects are
-equivalent. The easiest way to verify that your code is correct is to
-explicitly construct the result you expect, then compare the actual result to
-the expected correct result::
-
-    def test_pivot(self):
-        data = {
-            'index' : ['A', 'B', 'C', 'C', 'B', 'A'],
-            'columns' : ['One', 'One', 'One', 'Two', 'Two', 'Two'],
-            'values' : [1., 2., 3., 3., 2., 1.]
-        }
-
-        frame = DataFrame(data)
-        pivoted = frame.pivot(index='index', columns='columns', values='values')
-
-        expected = DataFrame({
-            'One' : {'A' : 1., 'B' : 2., 'C' : 3.},
-            'Two' : {'A' : 1., 'B' : 2., 'C' : 3.}
-        })
-
-        assert_frame_equal(pivoted, expected)
-
-Please remember to add the Github Issue Number as a comment to a new test.
-E.g. "# brief comment, see GH#28907"
-
-Transitioning to ``pytest``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-pandas existing test structure is *mostly* class-based, meaning that you will typically find tests wrapped in a class.
-
-.. code-block:: python
-
-    class TestReallyCoolFeature:
-        pass
-
-Going forward, we are moving to a more *functional* style using the `pytest <https://docs.pytest.org/en/latest/>`__ framework, which offers a richer testing
-framework that will facilitate testing and developing. Thus, instead of writing test classes, we will write test functions like this:
-
-.. code-block:: python
-
-    def test_really_cool_feature():
-        pass
-
-Using ``pytest``
-~~~~~~~~~~~~~~~~
-
-Here is an example of a self-contained set of tests that illustrate multiple features that we like to use.
-
-* functional style: tests are like ``test_*`` and *only* take arguments that are either fixtures or parameters
-* ``pytest.mark`` can be used to set metadata on test functions, e.g. ``skip`` or ``xfail``.
-* using ``parametrize``: allow testing of multiple cases
-* to set a mark on a parameter, ``pytest.param(..., marks=...)`` syntax should be used
-* ``fixture``, code for object construction, on a per-test basis
-* using bare ``assert`` for scalars and truth-testing
-* ``tm.assert_series_equal`` (and its counter part ``tm.assert_frame_equal``), for pandas object comparisons.
-* the typical pattern of constructing an ``expected`` and comparing versus the ``result``
-
-We would name this file ``test_cool_feature.py`` and put in an appropriate place in the ``pandas/tests/`` structure.
-
-.. code-block:: python
-
-   import pytest
-   import numpy as np
-   import pandas as pd
-
-
-   @pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
-   def test_dtypes(dtype):
-       assert str(np.dtype(dtype)) == dtype
-
-
-   @pytest.mark.parametrize(
-       'dtype', ['float32', pytest.param('int16', marks=pytest.mark.skip),
-                 pytest.param('int32', marks=pytest.mark.xfail(
-                     reason='to show how it works'))])
-   def test_mark(dtype):
-       assert str(np.dtype(dtype)) == 'float32'
-
-
-   @pytest.fixture
-   def series():
-       return pd.Series([1, 2, 3])
-
-
-   @pytest.fixture(params=['int8', 'int16', 'int32', 'int64'])
-   def dtype(request):
-       return request.param
-
-
-   def test_series(series, dtype):
-       result = series.astype(dtype)
-       assert result.dtype == dtype
-
-       expected = pd.Series([1, 2, 3], dtype=dtype)
-       tm.assert_series_equal(result, expected)
-
-
-A test run of this yields
-
-.. code-block:: shell
-
-   ((pandas) bash-3.2$ pytest  test_cool_feature.py  -v
-   =========================== test session starts ===========================
-   platform darwin -- Python 3.6.2, pytest-3.6.0, py-1.4.31, pluggy-0.4.0
-   collected 11 items
-
-   tester.py::test_dtypes[int8] PASSED
-   tester.py::test_dtypes[int16] PASSED
-   tester.py::test_dtypes[int32] PASSED
-   tester.py::test_dtypes[int64] PASSED
-   tester.py::test_mark[float32] PASSED
-   tester.py::test_mark[int16] SKIPPED
-   tester.py::test_mark[int32] xfail
-   tester.py::test_series[int8] PASSED
-   tester.py::test_series[int16] PASSED
-   tester.py::test_series[int32] PASSED
-   tester.py::test_series[int64] PASSED
-
-Tests that we have ``parametrized`` are now accessible via the test name, for example we could run these with ``-k int8`` to sub-select *only* those tests which match ``int8``.
-
-
-.. code-block:: shell
-
-   ((pandas) bash-3.2$ pytest  test_cool_feature.py  -v -k int8
-   =========================== test session starts ===========================
-   platform darwin -- Python 3.6.2, pytest-3.6.0, py-1.4.31, pluggy-0.4.0
-   collected 11 items
-
-   test_cool_feature.py::test_dtypes[int8] PASSED
-   test_cool_feature.py::test_series[int8] PASSED
-
-
-.. _using-hypothesis:
-
-Using ``hypothesis``
-~~~~~~~~~~~~~~~~~~~~
-
-Hypothesis is a library for property-based testing.  Instead of explicitly
-parametrizing a test, you can describe *all* valid inputs and let Hypothesis
-try to find a failing input.  Even better, no matter how many random examples
-it tries, Hypothesis always reports a single minimal counterexample to your
-assertions - often an example that you would never have thought to test.
-
-See `Getting Started with Hypothesis <https://hypothesis.works/articles/getting-started-with-hypothesis/>`_
-for more of an introduction, then `refer to the Hypothesis documentation
-for details <https://hypothesis.readthedocs.io/en/latest/index.html>`_.
-
-.. code-block:: python
-
-    import json
-    from hypothesis import given, strategies as st
-
-    any_json_value = st.deferred(lambda: st.one_of(
-        st.none(), st.booleans(), st.floats(allow_nan=False), st.text(),
-        st.lists(any_json_value), st.dictionaries(st.text(), any_json_value)
-    ))
-
-
-    @given(value=any_json_value)
-    def test_json_roundtrip(value):
-        result = json.loads(json.dumps(value))
-        assert value == result
-
-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
-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.
-
-.. _contributing.warnings:
-
-Testing warnings
-~~~~~~~~~~~~~~~~
-
-By default, one of pandas CI workers will fail if any unhandled warnings are emitted.
-
-If your change involves checking that a warning is actually emitted, use
-``tm.assert_produces_warning(ExpectedWarning)``.
-
-
-.. code-block:: python
-
-   import pandas._testing as tm
-
-
-   df = pd.DataFrame()
-   with tm.assert_produces_warning(FutureWarning):
-       df.some_operation()
-
-We prefer this to the ``pytest.warns`` context manager because ours checks that the warning's
-stacklevel is set correctly. The stacklevel is what ensure the *user's* file name and line number
-is printed in the warning, rather than something internal to pandas. It represents the number of
-function calls from user code (e.g. ``df.some_operation()``) to the function that actually emits
-the warning. Our linter will fail the build if you use ``pytest.warns`` in a test.
-
-If you have a test that would emit a warning, but you aren't actually testing the
-warning itself (say because it's going to be removed in the future, or because we're
-matching a 3rd-party library's behavior), then use ``pytest.mark.filterwarnings`` to
-ignore the error.
-
-.. code-block:: python
-
-   @pytest.mark.filterwarnings("ignore:msg:category")
-   def test_thing(self):
-       ...
-
-If the test generates a warning of class ``category`` whose message starts
-with ``msg``, the warning will be ignored and the test will pass.
-
-If you need finer-grained control, you can use Python's usual
-`warnings module <https://docs.python.org/3/library/warnings.html>`__
-to control whether a warning is ignored / raised at different places within
-a single test.
-
-.. code-block:: python
-
-   with warnings.catch_warnings():
-       warnings.simplefilter("ignore", FutureWarning)
-       # Or use warnings.filterwarnings(...)
-
-Alternatively, consider breaking up the unit test.
-
-
-Running the test suite
-----------------------
-
-The tests can then be run directly inside your Git clone (without having to
-install pandas) by typing::
-
-    pytest pandas
-
-The tests suite is exhaustive and takes around 20 minutes to run.  Often it is
-worth running only a subset of tests first around your changes before running the
-entire suite.
-
-The easiest way to do this is with::
-
-    pytest pandas/path/to/test.py -k regex_matching_test_name
-
-Or with one of the following constructs::
-
-    pytest pandas/tests/[test-module].py
-    pytest pandas/tests/[test-module].py::[TestClass]
-    pytest pandas/tests/[test-module].py::[TestClass]::[test_method]
-
-Using `pytest-xdist <https://pypi.org/project/pytest-xdist>`_, one can
-speed up local testing on multicore machines. To use this feature, you will
-need to install ``pytest-xdist`` via::
-
-    pip install pytest-xdist
-
-Two scripts are provided to assist with this.  These scripts distribute
-testing across 4 threads.
-
-On Unix variants, one can type::
-
-    test_fast.sh
-
-On Windows, one can type::
-
-    test_fast.bat
-
-This can significantly reduce the time it takes to locally run tests before
-submitting a pull request.
-
-For more, see the `pytest <https://docs.pytest.org/en/latest/>`_ documentation.
-
-Furthermore one can run
-
-.. code-block:: python
-
-   pd.test()
-
-with an imported pandas to run tests similarly.
-
-Running the performance test suite
-----------------------------------
-
-Performance matters and it is worth considering whether your code has introduced
-performance regressions. pandas is in the process of migrating to
-`asv benchmarks <https://github.com/spacetelescope/asv>`__
-to enable easy monitoring of the performance of critical pandas operations.
-These benchmarks are all found in the ``pandas/asv_bench`` directory, and the
-test results can be found `here <https://pandas.pydata.org/speed/pandas/#/>`__.
-
-To use all features of asv, you will need either ``conda`` or
-``virtualenv``. For more details please check the `asv installation
-webpage <https://asv.readthedocs.io/en/latest/installing.html>`_.
-
-To install asv::
-
-    pip install git+https://github.com/spacetelescope/asv
-
-If you need to run a benchmark, change your directory to ``asv_bench/`` and run::
-
-    asv continuous -f 1.1 upstream/master HEAD
-
-You can replace ``HEAD`` with the name of the branch you are working on,
-and report benchmarks that changed by more than 10%.
-The command uses ``conda`` by default for creating the benchmark
-environments. If you want to use virtualenv instead, write::
-
-    asv continuous -f 1.1 -E virtualenv upstream/master HEAD
-
-The ``-E virtualenv`` option should be added to all ``asv`` commands
-that run benchmarks. The default value is defined in ``asv.conf.json``.
-
-Running the full benchmark suite can be an all-day process, depending on your
-hardware and its resource utilization. However, usually it is sufficient to paste
-only a subset of the results into the pull request to show that the committed changes
-do not cause unexpected performance regressions.  You can run specific benchmarks
-using the ``-b`` flag, which takes a regular expression. For example, this will
-only run benchmarks from a ``pandas/asv_bench/benchmarks/groupby.py`` file::
-
-    asv continuous -f 1.1 upstream/master HEAD -b ^groupby
-
-If you want to only run a specific group of benchmarks from a file, you can do it
-using ``.`` as a separator. For example::
-
-    asv continuous -f 1.1 upstream/master HEAD -b groupby.GroupByMethods
-
-will only run the ``GroupByMethods`` benchmark defined in ``groupby.py``.
-
-You can also run the benchmark suite using the version of ``pandas``
-already installed in your current Python environment. This can be
-useful if you do not have virtualenv or conda, or are using the
-``setup.py develop`` approach discussed above; for the in-place build
-you need to set ``PYTHONPATH``, e.g.
-``PYTHONPATH="$PWD/.." asv [remaining arguments]``.
-You can run benchmarks using an existing Python
-environment by::
-
-    asv run -e -E existing
-
-or, to use a specific Python interpreter,::
-
-    asv run -e -E existing:python3.6
-
-This will display stderr from the benchmarks, and use your local
-``python`` that comes from your ``$PATH``.
-
-Information on how to write a benchmark and how to use asv can be found in the
-`asv documentation <https://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_.
-
-Documenting your code
----------------------
-
-Changes should be reflected in the release notes located in ``doc/source/whatsnew/vx.y.z.rst``.
-This file contains an ongoing change log for each release.  Add an entry to this file to
-document your fix, enhancement or (unavoidable) breaking change.  Make sure to include the
-GitHub issue number when adding your entry (using ``:issue:`1234``` where ``1234`` is the
-issue/pull request number).
-
-If your code is an enhancement, it is most likely necessary to add usage
-examples to the existing documentation.  This can be done following the section
-regarding documentation :ref:`above <contributing.documentation>`.
-Further, to let users know when this feature was added, the ``versionadded``
-directive is used. The sphinx syntax for that is:
-
-.. code-block:: rst
-
-  .. versionadded:: 1.1.0
-
-This will put the text *New in version 1.1.0* wherever you put the sphinx
-directive. This should also be put in the docstring when adding a new function
-or method (`example <https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/frame.py#L1495>`__)
-or a new keyword argument (`example <https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/generic.py#L568>`__).
-
 Contributing your changes to pandas
 =====================================
 
@@ -1605,7 +291,7 @@ automatically updated.  Pushing them to GitHub again is done by::
     git push origin shiny-new-feature
 
 This will automatically update your pull request with the latest code and restart the
-:ref:`Continuous Integration <contributing.ci>` tests.
+:any:`Continuous Integration <contributing.ci>` tests.
 
 Another reason you might need to update your pull request is to solve conflicts
 with changes that have been merged into the master branch since you opened your
diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst
new file mode 100644
index 0000000000000..d6ff48ed5fd39
--- /dev/null
+++ b/doc/source/development/contributing_codebase.rst
@@ -0,0 +1,836 @@
+.. _contributing_codebase:
+
+{{ header }}
+
+=============================
+Contributing to the code base
+=============================
+
+.. contents:: Table of Contents:
+   :local:
+
+Code standards
+--------------
+
+Writing good code is not just about what you write. It is also about *how* you
+write it. During :ref:`Continuous Integration <contributing.ci>` testing, several
+tools will be run to check your code for stylistic errors.
+Generating any warnings will cause the test to fail.
+Thus, good style is a requirement for submitting code to pandas.
+
+There is a tool in pandas to help contributors verify their changes before
+contributing them to the project::
+
+   ./ci/code_checks.sh
+
+The script verifies the linting of code files, it looks for common mistake patterns
+(like missing spaces around sphinx directives that make the documentation not
+being rendered properly) and it also validates the doctests. It is possible to
+run the checks independently by using the parameters ``lint``, ``patterns`` and
+``doctests`` (e.g. ``./ci/code_checks.sh lint``).
+
+In addition, because a lot of people use our library, it is important that we
+do not make sudden changes to the code that could have the potential to break
+a lot of user code as a result, that is, we need it to be as *backwards compatible*
+as possible to avoid mass breakages.
+
+In addition to ``./ci/code_checks.sh``, some extra checks are run by
+``pre-commit`` - see :ref:`here <contributing.pre-commit>` for how to
+run them.
+
+Additional standards are outlined on the :ref:`pandas code style guide <code_style>`.
+
+.. _contributing.pre-commit:
+
+Pre-commit
+----------
+
+You can run many of these styling checks manually as we have described above. However,
+we encourage you to use `pre-commit hooks <https://pre-commit.com/>`_ instead
+to automatically run ``black``, ``flake8``, ``isort`` when you make a git commit. This
+can be done by installing ``pre-commit``::
+
+    pip install pre-commit
+
+and then running::
+
+    pre-commit install
+
+from the root of the pandas repository. Now all of the styling checks will be
+run each time you commit changes without your needing to run each one manually.
+In addition, using ``pre-commit`` will also allow you to more easily
+remain up-to-date with our code checks as they change.
+
+Note that if needed, you can skip these checks with ``git commit --no-verify``.
+
+If you don't want to use ``pre-commit`` as part of your workflow, you can still use it
+to run its checks with::
+
+    pre-commit run --files <files you have modified>
+
+without needing to have done ``pre-commit install`` beforehand.
+
+If you want to run checks on all recently committed files on upstream/master you can use::
+
+    pre-commit run --from-ref=upstream/master --to-ref=HEAD --all-files
+
+without needing to have done ``pre-commit install`` beforehand.
+
+.. note::
+
+    If you have conflicting installations of ``virtualenv``, then you may get an
+    error - see `here <https://github.com/pypa/virtualenv/issues/1875>`_.
+
+    Also, due to a `bug in virtualenv <https://github.com/pypa/virtualenv/issues/1986>`_,
+    you may run into issues if you're using conda. To solve this, you can downgrade
+    ``virtualenv`` to version ``20.0.33``.
+
+Optional dependencies
+---------------------
+
+Optional dependencies (e.g. matplotlib) should be imported with the private helper
+``pandas.compat._optional.import_optional_dependency``. This ensures a
+consistent error message when the dependency is not met.
+
+All methods using an optional dependency should include a test asserting that an
+``ImportError`` is raised when the optional dependency is not found. This test
+should be skipped if the library is present.
+
+All optional dependencies should be documented in
+:ref:`install.optional_dependencies` and the minimum required version should be
+set in the ``pandas.compat._optional.VERSIONS`` dict.
+
+C (cpplint)
+~~~~~~~~~~~
+
+pandas uses the `Google <https://google.github.io/styleguide/cppguide.html>`_
+standard. Google provides an open source style checker called ``cpplint``, but we
+use a fork of it that can be found `here <https://github.com/cpplint/cpplint>`__.
+Here are *some* of the more common ``cpplint`` issues:
+
+* we restrict line-length to 80 characters to promote readability
+* every header file must include a header guard to avoid name collisions if re-included
+
+:ref:`Continuous Integration <contributing.ci>` will run the
+`cpplint <https://pypi.org/project/cpplint>`_ tool
+and report any stylistic errors in your code. Therefore, it is helpful before
+submitting code to run the check yourself::
+
+   cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,-build/include_subdir modified-c-file
+
+You can also run this command on an entire directory if necessary::
+
+   cpplint --extensions=c,h --headers=h --filter=-readability/casting,-runtime/int,-build/include_subdir --recursive modified-c-directory
+
+To make your commits compliant with this standard, you can install the
+`ClangFormat <https://clang.llvm.org/docs/ClangFormat.html>`_ tool, which can be
+downloaded `here <https://llvm.org/builds/>`__. To configure, in your home directory,
+run the following command::
+
+    clang-format style=google -dump-config  > .clang-format
+
+Then modify the file to ensure that any indentation width parameters are at least four.
+Once configured, you can run the tool as follows::
+
+    clang-format modified-c-file
+
+This will output what your file will look like if the changes are made, and to apply
+them, run the following command::
+
+    clang-format -i modified-c-file
+
+To run the tool on an entire directory, you can run the following analogous commands::
+
+    clang-format modified-c-directory/*.c modified-c-directory/*.h
+    clang-format -i modified-c-directory/*.c modified-c-directory/*.h
+
+Do note that this tool is best-effort, meaning that it will try to correct as
+many errors as possible, but it may not correct *all* of them. Thus, it is
+recommended that you run ``cpplint`` to double check and make any other style
+fixes manually.
+
+.. _contributing.code-formatting:
+
+Python (PEP8 / black)
+~~~~~~~~~~~~~~~~~~~~~
+
+pandas follows the `PEP8 <https://www.python.org/dev/peps/pep-0008/>`_ standard
+and uses `Black <https://black.readthedocs.io/en/stable/>`_ and
+`Flake8 <http://flake8.pycqa.org/en/latest/>`_ to ensure a consistent code
+format throughout the project. We encourage you to use :ref:`pre-commit <contributing.pre-commit>`.
+
+:ref:`Continuous Integration <contributing.ci>` will run those tools and
+report any stylistic errors in your code. Therefore, it is helpful before
+submitting code to run the check yourself::
+
+   black pandas
+   git diff upstream/master -u -- "*.py" | flake8 --diff
+
+to auto-format your code. Additionally, many editors have plugins that will
+apply ``black`` as you edit files.
+
+You should use a ``black`` version 20.8b1 as previous versions are not compatible
+with the pandas codebase.
+
+One caveat about ``git diff upstream/master -u -- "*.py" | flake8 --diff``: this
+command will catch any stylistic errors in your changes specifically, but
+be beware it may not catch all of them. For example, if you delete the only
+usage of an imported function, it is stylistically incorrect to import an
+unused function. However, style-checking the diff will not catch this because
+the actual import is not part of the diff. Thus, for completeness, you should
+run this command, though it may take longer::
+
+   git diff upstream/master --name-only -- "*.py" | xargs -r flake8
+
+Note that on OSX, the ``-r`` flag is not available, so you have to omit it and
+run this slightly modified command::
+
+   git diff upstream/master --name-only -- "*.py" | xargs flake8
+
+Windows does not support the ``xargs`` command (unless installed for example
+via the `MinGW <http://www.mingw.org/>`__ toolchain), but one can imitate the
+behaviour as follows::
+
+    for /f %i in ('git diff upstream/master --name-only -- "*.py"') do flake8 %i
+
+This will get all the files being changed by the PR (and ending with ``.py``),
+and run ``flake8`` on them, one after the other.
+
+Note that these commands can be run analogously with ``black``.
+
+.. _contributing.import-formatting:
+
+Import formatting
+~~~~~~~~~~~~~~~~~
+pandas uses `isort <https://pypi.org/project/isort/>`__ to standardise import
+formatting across the codebase.
+
+A guide to import layout as per pep8 can be found `here <https://www.python.org/dev/peps/pep-0008/#imports/>`__.
+
+A summary of our current import sections ( in order ):
+
+* Future
+* Python Standard Library
+* Third Party
+* ``pandas._libs``, ``pandas.compat``, ``pandas.util._*``, ``pandas.errors`` (largely not dependent on ``pandas.core``)
+* ``pandas.core.dtypes`` (largely not dependent on the rest of ``pandas.core``)
+* Rest of ``pandas.core.*``
+* Non-core ``pandas.io``, ``pandas.plotting``, ``pandas.tseries``
+* Local application/library specific imports
+
+Imports are alphabetically sorted within these sections.
+
+As part of :ref:`Continuous Integration <contributing.ci>` checks we run::
+
+    isort --check-only pandas
+
+to check that imports are correctly formatted as per the ``setup.cfg``.
+
+If you see output like the below in :ref:`Continuous Integration <contributing.ci>` checks:
+
+.. code-block:: shell
+
+   Check import format using isort
+   ERROR: /home/travis/build/pandas-dev/pandas/pandas/io/pytables.py Imports are incorrectly sorted
+   Check import format using isort DONE
+   The command "ci/code_checks.sh" exited with 1
+
+You should run::
+
+    isort pandas/io/pytables.py
+
+to automatically format imports correctly. This will modify your local copy of the files.
+
+Alternatively, you can run a command similar to what was suggested for ``black`` and ``flake8`` :ref:`right above <contributing.code-formatting>`::
+
+    git diff upstream/master --name-only -- "*.py" | xargs -r isort
+
+Where similar caveats apply if you are on OSX or Windows.
+
+You can then verify the changes look ok, then git :any:`commit <contributing.commit-code>` and :any:`push <contributing.push-code>`.
+
+Backwards compatibility
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Please try to maintain backward compatibility. pandas has lots of users with lots of
+existing code, so don't break it if at all possible.  If you think breakage is required,
+clearly state why as part of the pull request.  Also, be careful when changing method
+signatures and add deprecation warnings where needed. Also, add the deprecated sphinx
+directive to the deprecated functions or methods.
+
+If a function with the same arguments as the one being deprecated exist, you can use
+the ``pandas.util._decorators.deprecate``:
+
+.. code-block:: python
+
+    from pandas.util._decorators import deprecate
+
+    deprecate('old_func', 'new_func', '1.1.0')
+
+Otherwise, you need to do it manually:
+
+.. code-block:: python
+
+    import warnings
+
+
+    def old_func():
+        """Summary of the function.
+
+        .. deprecated:: 1.1.0
+           Use new_func instead.
+        """
+        warnings.warn('Use new_func instead.', FutureWarning, stacklevel=2)
+        new_func()
+
+
+    def new_func():
+        pass
+
+You'll also need to
+
+1. Write a new test that asserts a warning is issued when calling with the deprecated argument
+2. Update all of pandas existing tests and code to use the new argument
+
+See :ref:`contributing.warnings` for more.
+
+.. _contributing.type_hints:
+
+Type hints
+----------
+
+pandas strongly encourages the use of :pep:`484` style type hints. New development should contain type hints and pull requests to annotate existing code are accepted as well!
+
+Style guidelines
+~~~~~~~~~~~~~~~~
+
+Types imports should follow the ``from typing import ...`` convention. So rather than
+
+.. code-block:: python
+
+   import typing
+
+   primes: typing.List[int] = []
+
+You should write
+
+.. code-block:: python
+
+   from typing import List, Optional, Union
+
+   primes: List[int] = []
+
+``Optional`` should be used where applicable, so instead of
+
+.. code-block:: python
+
+   maybe_primes: List[Union[int, None]] = []
+
+You should write
+
+.. code-block:: python
+
+   maybe_primes: List[Optional[int]] = []
+
+In some cases in the code base classes may define class variables that shadow builtins. This causes an issue as described in `Mypy 1775 <https://github.com/python/mypy/issues/1775#issuecomment-310969854>`_. The defensive solution here is to create an unambiguous alias of the builtin and use that without your annotation. For example, if you come across a definition like
+
+.. code-block:: python
+
+   class SomeClass1:
+       str = None
+
+The appropriate way to annotate this would be as follows
+
+.. code-block:: python
+
+   str_type = str
+
+   class SomeClass2:
+       str: str_type = None
+
+In some cases you may be tempted to use ``cast`` from the typing module when you know better than the analyzer. This occurs particularly when using custom inference functions. For example
+
+.. code-block:: python
+
+   from typing import cast
+
+   from pandas.core.dtypes.common import is_number
+
+   def cannot_infer_bad(obj: Union[str, int, float]):
+
+       if is_number(obj):
+           ...
+       else:  # Reasonably only str objects would reach this but...
+           obj = cast(str, obj)  # Mypy complains without this!
+	   return obj.upper()
+
+The limitation here is that while a human can reasonably understand that ``is_number`` would catch the ``int`` and ``float`` types mypy cannot make that same inference just yet (see `mypy #5206 <https://github.com/python/mypy/issues/5206>`_. While the above works, the use of ``cast`` is **strongly discouraged**. Where applicable a refactor of the code to appease static analysis is preferable
+
+.. code-block:: python
+
+   def cannot_infer_good(obj: Union[str, int, float]):
+
+       if isinstance(obj, str):
+           return obj.upper()
+       else:
+           ...
+
+With custom types and inference this is not always possible so exceptions are made, but every effort should be exhausted to avoid ``cast`` before going down such paths.
+
+pandas-specific types
+~~~~~~~~~~~~~~~~~~~~~
+
+Commonly used types specific to pandas will appear in `pandas._typing <https://github.com/pandas-dev/pandas/blob/master/pandas/_typing.py>`_ and you should use these where applicable. This module is private for now but ultimately this should be exposed to third party libraries who want to implement type checking against pandas.
+
+For example, quite a few functions in pandas accept a ``dtype`` argument. This can be expressed as a string like ``"object"``, a ``numpy.dtype`` like ``np.int64`` or even a pandas ``ExtensionDtype`` like ``pd.CategoricalDtype``. Rather than burden the user with having to constantly annotate all of those options, this can simply be imported and reused from the pandas._typing module
+
+.. code-block:: python
+
+   from pandas._typing import Dtype
+
+   def as_type(dtype: Dtype) -> ...:
+       ...
+
+This module will ultimately house types for repeatedly used concepts like "path-like", "array-like", "numeric", etc... and can also hold aliases for commonly appearing parameters like ``axis``. Development of this module is active so be sure to refer to the source for the most up to date list of available types.
+
+Validating type hints
+~~~~~~~~~~~~~~~~~~~~~
+
+pandas uses `mypy <http://mypy-lang.org>`_ to statically analyze the code base and type hints. After making any change you can ensure your type hints are correct by running
+
+.. code-block:: shell
+
+   mypy pandas
+
+.. _contributing.ci:
+
+Testing with continuous integration
+-----------------------------------
+
+The pandas test suite will run automatically on `Travis-CI <https://travis-ci.org/>`__ and
+`Azure Pipelines <https://azure.microsoft.com/en-us/services/devops/pipelines/>`__
+continuous integration services, once your pull request is submitted.
+However, if you wish to run the test suite on a branch prior to submitting the pull request,
+then the continuous integration services need to be hooked to your GitHub repository. Instructions are here
+for `Travis-CI <http://about.travis-ci.org/docs/user/getting-started/>`__ and
+`Azure Pipelines <https://docs.microsoft.com/en-us/azure/devops/pipelines/>`__.
+
+A pull-request will be considered for merging when you have an all 'green' build. If any tests are failing,
+then you will get a red 'X', where you can click through to see the individual failed tests.
+This is an example of a green build.
+
+.. image:: ../_static/ci.png
+
+.. note::
+
+   Each time you push to *your* fork, a *new* run of the tests will be triggered on the CI.
+   You can enable the auto-cancel feature, which removes any non-currently-running tests for that same pull-request, for
+   `Travis-CI here <https://docs.travis-ci.com/user/customizing-the-build/#Building-only-the-latest-commit>`__.
+
+.. _contributing.tdd:
+
+
+Test-driven development/code writing
+------------------------------------
+
+pandas is serious about testing and strongly encourages contributors to embrace
+`test-driven development (TDD) <https://en.wikipedia.org/wiki/Test-driven_development>`_.
+This development process "relies on the repetition of a very short development cycle:
+first the developer writes an (initially failing) automated test case that defines a desired
+improvement or new function, then produces the minimum amount of code to pass that test."
+So, before actually writing any code, you should write your tests.  Often the test can be
+taken from the original GitHub issue.  However, it is always worth considering additional
+use cases and writing corresponding tests.
+
+Adding tests is one of the most common requests after code is pushed to pandas.  Therefore,
+it is worth getting in the habit of writing tests ahead of time so this is never an issue.
+
+Like many packages, pandas uses `pytest
+<https://docs.pytest.org/en/latest/>`_ and the convenient
+extensions in `numpy.testing
+<https://numpy.org/doc/stable/reference/routines.testing.html>`_.
+
+.. note::
+
+   The earliest supported pytest version is 5.0.1.
+
+Writing tests
+~~~~~~~~~~~~~
+
+All tests should go into the ``tests`` subdirectory of the specific package.
+This folder contains many current examples of tests, and we suggest looking to these for
+inspiration.  If your test requires working with files or
+network connectivity, there is more information on the `testing page
+<https://github.com/pandas-dev/pandas/wiki/Testing>`_ of the wiki.
+
+The ``pandas._testing`` module has many special ``assert`` functions that
+make it easier to make statements about whether Series or DataFrame objects are
+equivalent. The easiest way to verify that your code is correct is to
+explicitly construct the result you expect, then compare the actual result to
+the expected correct result::
+
+    def test_pivot(self):
+        data = {
+            'index' : ['A', 'B', 'C', 'C', 'B', 'A'],
+            'columns' : ['One', 'One', 'One', 'Two', 'Two', 'Two'],
+            'values' : [1., 2., 3., 3., 2., 1.]
+        }
+
+        frame = DataFrame(data)
+        pivoted = frame.pivot(index='index', columns='columns', values='values')
+
+        expected = DataFrame({
+            'One' : {'A' : 1., 'B' : 2., 'C' : 3.},
+            'Two' : {'A' : 1., 'B' : 2., 'C' : 3.}
+        })
+
+        assert_frame_equal(pivoted, expected)
+
+Please remember to add the Github Issue Number as a comment to a new test.
+E.g. "# brief comment, see GH#28907"
+
+Transitioning to ``pytest``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+pandas existing test structure is *mostly* class-based, meaning that you will typically find tests wrapped in a class.
+
+.. code-block:: python
+
+    class TestReallyCoolFeature:
+        pass
+
+Going forward, we are moving to a more *functional* style using the `pytest <https://docs.pytest.org/en/latest/>`__ framework, which offers a richer testing
+framework that will facilitate testing and developing. Thus, instead of writing test classes, we will write test functions like this:
+
+.. code-block:: python
+
+    def test_really_cool_feature():
+        pass
+
+Using ``pytest``
+~~~~~~~~~~~~~~~~
+
+Here is an example of a self-contained set of tests that illustrate multiple features that we like to use.
+
+* functional style: tests are like ``test_*`` and *only* take arguments that are either fixtures or parameters
+* ``pytest.mark`` can be used to set metadata on test functions, e.g. ``skip`` or ``xfail``.
+* using ``parametrize``: allow testing of multiple cases
+* to set a mark on a parameter, ``pytest.param(..., marks=...)`` syntax should be used
+* ``fixture``, code for object construction, on a per-test basis
+* using bare ``assert`` for scalars and truth-testing
+* ``tm.assert_series_equal`` (and its counter part ``tm.assert_frame_equal``), for pandas object comparisons.
+* the typical pattern of constructing an ``expected`` and comparing versus the ``result``
+
+We would name this file ``test_cool_feature.py`` and put in an appropriate place in the ``pandas/tests/`` structure.
+
+.. code-block:: python
+
+   import pytest
+   import numpy as np
+   import pandas as pd
+
+
+   @pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
+   def test_dtypes(dtype):
+       assert str(np.dtype(dtype)) == dtype
+
+
+   @pytest.mark.parametrize(
+       'dtype', ['float32', pytest.param('int16', marks=pytest.mark.skip),
+                 pytest.param('int32', marks=pytest.mark.xfail(
+                     reason='to show how it works'))])
+   def test_mark(dtype):
+       assert str(np.dtype(dtype)) == 'float32'
+
+
+   @pytest.fixture
+   def series():
+       return pd.Series([1, 2, 3])
+
+
+   @pytest.fixture(params=['int8', 'int16', 'int32', 'int64'])
+   def dtype(request):
+       return request.param
+
+
+   def test_series(series, dtype):
+       result = series.astype(dtype)
+       assert result.dtype == dtype
+
+       expected = pd.Series([1, 2, 3], dtype=dtype)
+       tm.assert_series_equal(result, expected)
+
+
+A test run of this yields
+
+.. code-block:: shell
+
+   ((pandas) bash-3.2$ pytest  test_cool_feature.py  -v
+   =========================== test session starts ===========================
+   platform darwin -- Python 3.6.2, pytest-3.6.0, py-1.4.31, pluggy-0.4.0
+   collected 11 items
+
+   tester.py::test_dtypes[int8] PASSED
+   tester.py::test_dtypes[int16] PASSED
+   tester.py::test_dtypes[int32] PASSED
+   tester.py::test_dtypes[int64] PASSED
+   tester.py::test_mark[float32] PASSED
+   tester.py::test_mark[int16] SKIPPED
+   tester.py::test_mark[int32] xfail
+   tester.py::test_series[int8] PASSED
+   tester.py::test_series[int16] PASSED
+   tester.py::test_series[int32] PASSED
+   tester.py::test_series[int64] PASSED
+
+Tests that we have ``parametrized`` are now accessible via the test name, for example we could run these with ``-k int8`` to sub-select *only* those tests which match ``int8``.
+
+
+.. code-block:: shell
+
+   ((pandas) bash-3.2$ pytest  test_cool_feature.py  -v -k int8
+   =========================== test session starts ===========================
+   platform darwin -- Python 3.6.2, pytest-3.6.0, py-1.4.31, pluggy-0.4.0
+   collected 11 items
+
+   test_cool_feature.py::test_dtypes[int8] PASSED
+   test_cool_feature.py::test_series[int8] PASSED
+
+
+.. _using-hypothesis:
+
+Using ``hypothesis``
+~~~~~~~~~~~~~~~~~~~~
+
+Hypothesis is a library for property-based testing.  Instead of explicitly
+parametrizing a test, you can describe *all* valid inputs and let Hypothesis
+try to find a failing input.  Even better, no matter how many random examples
+it tries, Hypothesis always reports a single minimal counterexample to your
+assertions - often an example that you would never have thought to test.
+
+See `Getting Started with Hypothesis <https://hypothesis.works/articles/getting-started-with-hypothesis/>`_
+for more of an introduction, then `refer to the Hypothesis documentation
+for details <https://hypothesis.readthedocs.io/en/latest/index.html>`_.
+
+.. code-block:: python
+
+    import json
+    from hypothesis import given, strategies as st
+
+    any_json_value = st.deferred(lambda: st.one_of(
+        st.none(), st.booleans(), st.floats(allow_nan=False), st.text(),
+        st.lists(any_json_value), st.dictionaries(st.text(), any_json_value)
+    ))
+
+
+    @given(value=any_json_value)
+    def test_json_roundtrip(value):
+        result = json.loads(json.dumps(value))
+        assert value == result
+
+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
+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.
+
+.. _contributing.warnings:
+
+Testing warnings
+~~~~~~~~~~~~~~~~
+
+By default, one of pandas CI workers will fail if any unhandled warnings are emitted.
+
+If your change involves checking that a warning is actually emitted, use
+``tm.assert_produces_warning(ExpectedWarning)``.
+
+
+.. code-block:: python
+
+   import pandas._testing as tm
+
+
+   df = pd.DataFrame()
+   with tm.assert_produces_warning(FutureWarning):
+       df.some_operation()
+
+We prefer this to the ``pytest.warns`` context manager because ours checks that the warning's
+stacklevel is set correctly. The stacklevel is what ensure the *user's* file name and line number
+is printed in the warning, rather than something internal to pandas. It represents the number of
+function calls from user code (e.g. ``df.some_operation()``) to the function that actually emits
+the warning. Our linter will fail the build if you use ``pytest.warns`` in a test.
+
+If you have a test that would emit a warning, but you aren't actually testing the
+warning itself (say because it's going to be removed in the future, or because we're
+matching a 3rd-party library's behavior), then use ``pytest.mark.filterwarnings`` to
+ignore the error.
+
+.. code-block:: python
+
+   @pytest.mark.filterwarnings("ignore:msg:category")
+   def test_thing(self):
+       ...
+
+If the test generates a warning of class ``category`` whose message starts
+with ``msg``, the warning will be ignored and the test will pass.
+
+If you need finer-grained control, you can use Python's usual
+`warnings module <https://docs.python.org/3/library/warnings.html>`__
+to control whether a warning is ignored / raised at different places within
+a single test.
+
+.. code-block:: python
+
+   with warnings.catch_warnings():
+       warnings.simplefilter("ignore", FutureWarning)
+       # Or use warnings.filterwarnings(...)
+
+Alternatively, consider breaking up the unit test.
+
+
+Running the test suite
+----------------------
+
+The tests can then be run directly inside your Git clone (without having to
+install pandas) by typing::
+
+    pytest pandas
+
+The tests suite is exhaustive and takes around 20 minutes to run.  Often it is
+worth running only a subset of tests first around your changes before running the
+entire suite.
+
+The easiest way to do this is with::
+
+    pytest pandas/path/to/test.py -k regex_matching_test_name
+
+Or with one of the following constructs::
+
+    pytest pandas/tests/[test-module].py
+    pytest pandas/tests/[test-module].py::[TestClass]
+    pytest pandas/tests/[test-module].py::[TestClass]::[test_method]
+
+Using `pytest-xdist <https://pypi.org/project/pytest-xdist>`_, one can
+speed up local testing on multicore machines. To use this feature, you will
+need to install ``pytest-xdist`` via::
+
+    pip install pytest-xdist
+
+Two scripts are provided to assist with this.  These scripts distribute
+testing across 4 threads.
+
+On Unix variants, one can type::
+
+    test_fast.sh
+
+On Windows, one can type::
+
+    test_fast.bat
+
+This can significantly reduce the time it takes to locally run tests before
+submitting a pull request.
+
+For more, see the `pytest <https://docs.pytest.org/en/latest/>`_ documentation.
+
+Furthermore one can run
+
+.. code-block:: python
+
+   pd.test()
+
+with an imported pandas to run tests similarly.
+
+Running the performance test suite
+----------------------------------
+
+Performance matters and it is worth considering whether your code has introduced
+performance regressions. pandas is in the process of migrating to
+`asv benchmarks <https://github.com/spacetelescope/asv>`__
+to enable easy monitoring of the performance of critical pandas operations.
+These benchmarks are all found in the ``pandas/asv_bench`` directory, and the
+test results can be found `here <https://pandas.pydata.org/speed/pandas/#/>`__.
+
+To use all features of asv, you will need either ``conda`` or
+``virtualenv``. For more details please check the `asv installation
+webpage <https://asv.readthedocs.io/en/latest/installing.html>`_.
+
+To install asv::
+
+    pip install git+https://github.com/spacetelescope/asv
+
+If you need to run a benchmark, change your directory to ``asv_bench/`` and run::
+
+    asv continuous -f 1.1 upstream/master HEAD
+
+You can replace ``HEAD`` with the name of the branch you are working on,
+and report benchmarks that changed by more than 10%.
+The command uses ``conda`` by default for creating the benchmark
+environments. If you want to use virtualenv instead, write::
+
+    asv continuous -f 1.1 -E virtualenv upstream/master HEAD
+
+The ``-E virtualenv`` option should be added to all ``asv`` commands
+that run benchmarks. The default value is defined in ``asv.conf.json``.
+
+Running the full benchmark suite can be an all-day process, depending on your
+hardware and its resource utilization. However, usually it is sufficient to paste
+only a subset of the results into the pull request to show that the committed changes
+do not cause unexpected performance regressions.  You can run specific benchmarks
+using the ``-b`` flag, which takes a regular expression. For example, this will
+only run benchmarks from a ``pandas/asv_bench/benchmarks/groupby.py`` file::
+
+    asv continuous -f 1.1 upstream/master HEAD -b ^groupby
+
+If you want to only run a specific group of benchmarks from a file, you can do it
+using ``.`` as a separator. For example::
+
+    asv continuous -f 1.1 upstream/master HEAD -b groupby.GroupByMethods
+
+will only run the ``GroupByMethods`` benchmark defined in ``groupby.py``.
+
+You can also run the benchmark suite using the version of ``pandas``
+already installed in your current Python environment. This can be
+useful if you do not have virtualenv or conda, or are using the
+``setup.py develop`` approach discussed above; for the in-place build
+you need to set ``PYTHONPATH``, e.g.
+``PYTHONPATH="$PWD/.." asv [remaining arguments]``.
+You can run benchmarks using an existing Python
+environment by::
+
+    asv run -e -E existing
+
+or, to use a specific Python interpreter,::
+
+    asv run -e -E existing:python3.6
+
+This will display stderr from the benchmarks, and use your local
+``python`` that comes from your ``$PATH``.
+
+Information on how to write a benchmark and how to use asv can be found in the
+`asv documentation <https://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_.
+
+Documenting your code
+---------------------
+
+Changes should be reflected in the release notes located in ``doc/source/whatsnew/vx.y.z.rst``.
+This file contains an ongoing change log for each release.  Add an entry to this file to
+document your fix, enhancement or (unavoidable) breaking change.  Make sure to include the
+GitHub issue number when adding your entry (using ``:issue:`1234``` where ``1234`` is the
+issue/pull request number).
+
+If your code is an enhancement, it is most likely necessary to add usage
+examples to the existing documentation.  This can be done following the section
+regarding :ref:`documentation <contributing_documentation>`.
+Further, to let users know when this feature was added, the ``versionadded``
+directive is used. The sphinx syntax for that is:
+
+.. code-block:: rst
+
+  .. versionadded:: 1.1.0
+
+This will put the text *New in version 1.1.0* wherever you put the sphinx
+directive. This should also be put in the docstring when adding a new function
+or method (`example <https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/frame.py#L1495>`__)
+or a new keyword argument (`example <https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/generic.py#L568>`__).
diff --git a/doc/source/development/contributing_documentation.rst b/doc/source/development/contributing_documentation.rst
new file mode 100644
index 0000000000000..a4a4f781d9dad
--- /dev/null
+++ b/doc/source/development/contributing_documentation.rst
@@ -0,0 +1,222 @@
+.. _contributing_documentation:
+
+{{ header }}
+
+=================================
+Contributing to the documentation
+=================================
+
+Contributing to the documentation benefits everyone who uses pandas.
+We encourage you to help us improve the documentation, and
+you don't have to be an expert on pandas to do so! In fact,
+there are sections of the docs that are worse off after being written by
+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.
+
+.. contents:: Documentation:
+   :local:
+
+
+About the pandas documentation
+--------------------------------
+
+The documentation is written in **reStructuredText**, which is almost like writing
+in plain English, and built using `Sphinx <https://www.sphinx-doc.org/en/master/>`__. The
+Sphinx Documentation has an excellent `introduction to reST
+<https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`__. Review the Sphinx docs to perform more
+complex changes to the documentation as well.
+
+Some other important things to know about the docs:
+
+* The pandas documentation consists of two parts: the docstrings in the code
+  itself and the docs in this folder ``doc/``.
+
+  The docstrings provide a clear explanation of the usage of the individual
+  functions, while the documentation in this folder consists of tutorial-like
+  overviews per topic together with some other information (what's new,
+  installation, etc).
+
+* The docstrings follow a pandas convention, based on the **Numpy Docstring
+  Standard**. Follow the :ref:`pandas docstring guide <docstring>` for detailed
+  instructions on how to write a correct docstring.
+
+  .. toctree::
+     :maxdepth: 2
+
+     contributing_docstring.rst
+
+* The tutorials make heavy use of the `IPython directive
+  <https://matplotlib.org/sampledoc/ipython_directive.html>`_ sphinx extension.
+  This directive lets you put code in the documentation which will be run
+  during the doc build. For example::
+
+      .. ipython:: python
+
+          x = 2
+          x**3
+
+  will be rendered as::
+
+      In [1]: x = 2
+
+      In [2]: x**3
+      Out[2]: 8
+
+  Almost all code examples in the docs are run (and the output saved) during the
+  doc build. This approach means that code examples will always be up to date,
+  but it does make the doc building a bit more complex.
+
+* Our API documentation files in ``doc/source/reference`` house the auto-generated
+  documentation from the docstrings. For classes, there are a few subtleties
+  around controlling which methods and attributes have pages auto-generated.
+
+  We have two autosummary templates for classes.
+
+  1. ``_templates/autosummary/class.rst``. Use this when you want to
+     automatically generate a page for every public method and attribute on the
+     class. The ``Attributes`` and ``Methods`` sections will be automatically
+     added to the class' rendered documentation by numpydoc. See ``DataFrame``
+     for an example.
+
+  2. ``_templates/autosummary/class_without_autosummary``. Use this when you
+     want to pick a subset of methods / attributes to auto-generate pages for.
+     When using this template, you should include an ``Attributes`` and
+     ``Methods`` section in the class docstring. See ``CategoricalIndex`` for an
+     example.
+
+  Every method should be included in a ``toctree`` in one of the documentation files in
+  ``doc/source/reference``, else Sphinx
+  will emit a warning.
+
+.. note::
+
+    The ``.rst`` files are used to automatically generate Markdown and HTML versions
+    of the docs. For this reason, please do not edit ``CONTRIBUTING.md`` directly,
+    but instead make any changes to ``doc/source/development/contributing.rst``. Then, to
+    generate ``CONTRIBUTING.md``, use `pandoc <https://johnmacfarlane.net/pandoc/>`_
+    with the following command::
+
+      pandoc doc/source/development/contributing.rst -t markdown_github > CONTRIBUTING.md
+
+The utility script ``scripts/validate_docstrings.py`` can be used to get a csv
+summary of the API documentation. And also validate common errors in the docstring
+of a specific class, function or method. The summary also compares the list of
+methods documented in the files in ``doc/source/reference`` (which is used to generate
+the `API Reference <https://pandas.pydata.org/pandas-docs/stable/api.html>`_ page)
+and the actual public methods.
+This will identify methods documented in ``doc/source/reference`` that are not actually
+class methods, and existing methods that are not documented in ``doc/source/reference``.
+
+
+Updating a pandas docstring
+-----------------------------
+
+When improving a single function or method's docstring, it is not necessarily
+needed to build the full documentation (see next section).
+However, there is a script that checks a docstring (for example for the ``DataFrame.mean`` method)::
+
+    python scripts/validate_docstrings.py pandas.DataFrame.mean
+
+This script will indicate some formatting errors if present, and will also
+run and test the examples included in the docstring.
+Check the :ref:`pandas docstring guide <docstring>` for a detailed guide
+on how to format the docstring.
+
+The examples in the docstring ('doctests') must be valid Python code,
+that in a deterministic way returns the presented output, and that can be
+copied and run by users. This can be checked with the script above, and is
+also tested on Travis. A failing doctest will be a blocker for merging a PR.
+Check the :ref:`examples <docstring.examples>` section in the docstring guide
+for some tips and tricks to get the doctests passing.
+
+When doing a PR with a docstring update, it is good to post the
+output of the validation script in a comment on github.
+
+
+How to build the pandas documentation
+---------------------------------------
+
+Requirements
+~~~~~~~~~~~~
+
+First, you need to have a development environment to be able to build pandas
+(see the docs on :ref:`creating a development environment <contributing_environment>`).
+
+Building the documentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+So how do you build the docs? Navigate to your local
+``doc/`` directory in the console and run::
+
+    python make.py html
+
+Then you can find the HTML output in the folder ``doc/build/html/``.
+
+The first time you build the docs, it will take quite a while because it has to run
+all the code examples and build all the generated docstring pages. In subsequent
+evocations, sphinx will try to only build the pages that have been modified.
+
+If you want to do a full clean build, do::
+
+    python make.py clean
+    python make.py html
+
+You can tell ``make.py`` to compile only a single section of the docs, greatly
+reducing the turn-around time for checking your changes.
+
+::
+
+    # omit autosummary and API section
+    python make.py clean
+    python make.py --no-api
+
+    # compile the docs with only a single section, relative to the "source" folder.
+    # For example, compiling only this guide (doc/source/development/contributing.rst)
+    python make.py clean
+    python make.py --single development/contributing.rst
+
+    # compile the reference docs for a single function
+    python make.py clean
+    python make.py --single pandas.DataFrame.join
+
+    # compile whatsnew and API section (to resolve links in the whatsnew)
+    python make.py clean
+    python make.py --whatsnew
+
+For comparison, a full documentation build may take 15 minutes, but a single
+section may take 15 seconds. Subsequent builds, which only process portions
+you have changed, will be faster.
+
+The build will automatically use the number of cores available on your machine
+to speed up the documentation build. You can override this::
+
+    python make.py html --num-jobs 4
+
+Open the following file in a web browser to see the full documentation you
+just built::
+
+    doc/build/html/index.html
+
+And you'll have the satisfaction of seeing your new and improved documentation!
+
+.. _contributing.dev_docs:
+
+Building master branch documentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When pull requests are merged into the pandas ``master`` branch, the main parts of
+the documentation are also built by Travis-CI. These docs are then hosted `here
+<https://pandas.pydata.org/docs/dev/>`__, see also
+the :any:`Continuous Integration <contributing.ci>` section.
+
+Previewing changes
+------------------
+
+Once, the pull request is submitted, GitHub Actions will automatically build the
+documentation. To view the built site:
+
+#. Wait for the ``CI / Web and docs`` check to complete.
+#. Click ``Details`` next to it.
+#. From the ``Artifacts`` drop-down, click ``docs`` or ``website`` to download
+   the site as a ZIP file.
diff --git a/doc/source/development/contributing_environment.rst b/doc/source/development/contributing_environment.rst
new file mode 100644
index 0000000000000..bc0a3556b9ac1
--- /dev/null
+++ b/doc/source/development/contributing_environment.rst
@@ -0,0 +1,265 @@
+.. _contributing_environment:
+
+{{ header }}
+
+==================================
+Creating a development environment
+==================================
+
+To test out code changes, you'll need to build pandas from source, which
+requires a C/C++ compiler and Python environment. If you're making documentation
+changes, you can skip to :ref:`contributing to the documentation <contributing_documentation>` but if you skip
+creating the development environment you won't be able to build the documentation
+locally before pushing your changes.
+
+.. contents:: Table of contents:
+   :local:
+
+
+Creating an environment using Docker
+--------------------------------------
+
+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
+with a full pandas development environment.
+
+**Docker Commands**
+
+Pass your GitHub username in the ``DockerFile`` to use your own fork::
+
+    # Build the image pandas-yourname-env
+    docker build --tag pandas-yourname-env .
+    # Run a container and bind your local forked repo, pandas-yourname, to the container
+    docker run -it --rm -v path-to-pandas-yourname:/home/pandas-yourname pandas-yourname-env
+
+Even easier, you can integrate Docker with the following IDEs:
+
+**Visual Studio Code**
+
+You can use the DockerFile to launch a remote session with Visual Studio Code,
+a popular free IDE, using the ``.devcontainer.json`` file.
+See https://code.visualstudio.com/docs/remote/containers for details.
+
+**PyCharm (Professional)**
+
+Enable Docker support and use the Services tool window to build and manage images as well as
+run and interact with containers.
+See https://www.jetbrains.com/help/pycharm/docker.html for details.
+
+Note that you might need to rebuild the C extensions if/when you merge with upstream/master using::
+
+    python setup.py build_ext -j 4
+
+
+Creating an environment without Docker
+---------------------------------------
+
+Installing a C compiler
+~~~~~~~~~~~~~~~~~~~~~~~
+
+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.
+
+If you have setup your environment using ``conda``, the packages ``c-compiler``
+and ``cxx-compiler`` will install a fitting compiler for your platform that is
+compatible with the remaining conda packages. On Windows and macOS, you will
+also need to install the SDKs as they have to be distributed separately.
+These packages will automatically be installed by using the ``pandas``
+``environment.yml`` file.
+
+**Windows**
+
+You will need `Build Tools for Visual Studio 2017
+<https://visualstudio.microsoft.com/downloads/>`_.
+
+.. warning::
+	You DO NOT need to install Visual Studio 2019.
+	You only need "Build Tools for Visual Studio 2019" found by
+	scrolling down to "All downloads" -> "Tools for Visual Studio 2019".
+	In the installer, select the "C++ build tools" workload.
+
+You can install the necessary components on the commandline using
+`vs_buildtools.exe <https://aka.ms/vs/16/release/vs_buildtools.exe>`_:
+
+.. code::
+
+    vs_buildtools.exe --quiet --wait --norestart --nocache ^
+        --installPath C:\BuildTools ^
+        --add "Microsoft.VisualStudio.Workload.VCTools;includeRecommended" ^
+        --add Microsoft.VisualStudio.Component.VC.v141 ^
+        --add Microsoft.VisualStudio.Component.VC.v141.x86.x64 ^
+        --add Microsoft.VisualStudio.Component.Windows10SDK.17763
+
+To setup the right paths on the commandline, call
+``"C:\BuildTools\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.16 10.0.17763.0``.
+
+**macOS**
+
+To use the ``conda``-based compilers, you will need to install the
+Developer Tools using ``xcode-select --install``. Otherwise
+information about compiler installation can be found here:
+https://devguide.python.org/setup/#macos
+
+**Linux**
+
+For Linux-based ``conda`` installations, you won't have to install any
+additional components outside of the conda environment. The instructions
+below are only needed if your setup isn't based on conda environments.
+
+Some Linux distributions will come with a pre-installed C compiler. To find out
+which compilers (and versions) are installed on your system::
+
+    # for Debian/Ubuntu:
+    dpkg --list | grep compiler
+    # for Red Hat/RHEL/CentOS/Fedora:
+    yum list installed | grep -i --color compiler
+
+`GCC (GNU Compiler Collection) <https://gcc.gnu.org/>`_, is a widely used
+compiler, which supports C and a number of other languages. If GCC is listed
+as an installed compiler nothing more is required. If no C compiler is
+installed (or you wish to install a newer version) you can install a compiler
+(GCC in the example code below) with::
+
+    # for recent Debian/Ubuntu:
+    sudo apt install build-essential
+    # for Red Had/RHEL/CentOS/Fedora
+    yum groupinstall "Development Tools"
+
+For other Linux distributions, consult your favorite search engine for
+compiler installation instructions.
+
+Let us know if you have any difficulties by opening an issue or reaching out on `Gitter <https://gitter.im/pydata/pandas/>`_.
+
+
+Creating a Python environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now create an isolated pandas development environment:
+
+* Install either `Anaconda <https://www.anaconda.com/download/>`_, `miniconda
+  <https://conda.io/miniconda.html>`_, or `miniforge <https://github.com/conda-forge/miniforge>`_
+* Make sure your conda is up to date (``conda update conda``)
+* Make sure that you have :any:`cloned the repository <contributing.forking>`
+* ``cd`` to the pandas source directory
+
+We'll now kick off a three-step process:
+
+1. Install the build dependencies
+2. Build and install pandas
+3. Install the optional dependencies
+
+.. code-block:: none
+
+   # Create and activate the build environment
+   conda env create -f environment.yml
+   conda activate pandas-dev
+
+   # or with older versions of Anaconda:
+   source activate pandas-dev
+
+   # Build and install pandas
+   python setup.py build_ext -j 4
+   python -m pip install -e . --no-build-isolation --no-use-pep517
+
+At this point you should be able to import pandas from your locally built version::
+
+   $ python  # start an interpreter
+   >>> import pandas
+   >>> print(pandas.__version__)
+   0.22.0.dev0+29.g4ad6d4d74
+
+This will create the new environment, and not touch any of your existing environments,
+nor any existing Python installation.
+
+To view your environments::
+
+      conda info -e
+
+To return to your root environment::
+
+      conda deactivate
+
+See the full conda docs `here <https://conda.pydata.org/docs>`__.
+
+
+Creating a Python environment (pip)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you aren't using conda for your development environment, follow these instructions.
+You'll need to have at least the :ref:`minimum Python version <install.version>` that pandas supports. If your Python version
+is 3.8.0 (or later), you might need to update your ``setuptools`` to version 42.0.0 (or later)
+in your development environment before installing the build dependencies::
+
+      pip install --upgrade setuptools
+
+**Unix**/**macOS with virtualenv**
+
+.. code-block:: bash
+
+   # Create a virtual environment
+   # Use an ENV_DIR of your choice. We'll use ~/virtualenvs/pandas-dev
+   # Any parent directories should already exist
+   python3 -m venv ~/virtualenvs/pandas-dev
+
+   # Activate the virtualenv
+   . ~/virtualenvs/pandas-dev/bin/activate
+
+   # Install the build dependencies
+   python -m pip install -r requirements-dev.txt
+
+   # Build and install pandas
+   python setup.py build_ext -j 4
+   python -m pip install -e . --no-build-isolation --no-use-pep517
+
+**Unix**/**macOS with pyenv**
+
+Consult the docs for setting up pyenv `here <https://github.com/pyenv/pyenv>`__.
+
+.. code-block:: bash
+
+   # Create a virtual environment
+   # Use an ENV_DIR of your choice. We'll use ~/Users/<yourname>/.pyenv/versions/pandas-dev
+
+   pyenv virtualenv <version> <name-to-give-it>
+
+   # For instance:
+   pyenv virtualenv 3.7.6 pandas-dev
+
+   # Activate the virtualenv
+   pyenv activate pandas-dev
+
+   # Now install the build dependencies in the cloned pandas repo
+   python -m pip install -r requirements-dev.txt
+
+   # Build and install pandas
+   python setup.py build_ext -j 4
+   python -m pip install -e . --no-build-isolation --no-use-pep517
+
+**Windows**
+
+Below is a brief overview on how to set-up a virtual environment with Powershell
+under Windows. For details please refer to the
+`official virtualenv user guide <https://virtualenv.pypa.io/en/stable/userguide/#activate-script>`__
+
+Use an ENV_DIR of your choice. We'll use ~\\virtualenvs\\pandas-dev where
+'~' is the folder pointed to by either $env:USERPROFILE (Powershell) or
+%USERPROFILE% (cmd.exe) environment variable. Any parent directories
+should already exist.
+
+.. code-block:: powershell
+
+   # Create a virtual environment
+   python -m venv $env:USERPROFILE\virtualenvs\pandas-dev
+
+   # Activate the virtualenv. Use activate.bat for cmd.exe
+   ~\virtualenvs\pandas-dev\Scripts\Activate.ps1
+
+   # Install the build dependencies
+   python -m pip install -r requirements-dev.txt
+
+   # Build and install pandas
+   python setup.py build_ext -j 4
+   python -m pip install -e . --no-build-isolation --no-use-pep517
diff --git a/doc/source/development/index.rst b/doc/source/development/index.rst
index abe2fc1409bfb..fb50a88c6637f 100644
--- a/doc/source/development/index.rst
+++ b/doc/source/development/index.rst
@@ -13,6 +13,9 @@ Development
     :maxdepth: 2
 
     contributing
+    contributing_environment
+    contributing_documentation
+    contributing_codebase
     code_style
     maintaining
     internals
diff --git a/doc/source/getting_started/install.rst b/doc/source/getting_started/install.rst
index a9c3d637a41e3..f56391ab568ac 100644
--- a/doc/source/getting_started/install.rst
+++ b/doc/source/getting_started/install.rst
@@ -15,6 +15,8 @@ Instructions for installing from source,
 `PyPI <https://pypi.org/project/pandas>`__, `ActivePython <https://www.activestate.com/activepython/downloads>`__, various Linux distributions, or a
 `development version <https://github.com/pandas-dev/pandas>`__ are also provided.
 
+.. _install.version:
+
 Python version support
 ----------------------
 
@@ -184,7 +186,7 @@ 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_environment>` if you wish to create a pandas development environment.
 
 Running the test suite
 ----------------------
diff --git a/environment.yml b/environment.yml
index feea3445cb4fe..136b032e15dbe 100644
--- a/environment.yml
+++ b/environment.yml
@@ -78,6 +78,7 @@ dependencies:
   - bottleneck>=1.2.1
   - ipykernel
   - ipython>=7.11.1
+  - decorator=4  # temporary pin (dependency of IPython), see GH-40768
   - jinja2  # pandas.Styler
   - matplotlib>=2.2.2  # pandas.plotting, Series.plot, DataFrame.plot
   - numexpr>=2.6.8
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 349b176253acb..4d0bdb7f6cb03 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -50,6 +50,7 @@ blosc
 bottleneck>=1.2.1
 ipykernel
 ipython>=7.11.1
+decorator==4
 jinja2
 matplotlib>=2.2.2
 numexpr>=2.6.8