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contributing_environment.rst

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{{ 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. It's recommended to also install the :ref:`pre-commit hooks <contributing.pre-commit>`.

Table of contents:

Option 1: 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 :ref:`mamba <contributing.mamba>`, the packages c-compiler and cxx-compiler will install a fitting compiler for your platform that is compatible with the remaining mamba 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 2019.

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:

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 :ref:`mamba <contributing.mamba>`-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 :ref:`mamba <contributing.mamba>` installations, you won't have to install any additional components outside of the mamba environment. The instructions below are only needed if your setup isn't based on mamba 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), 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 our contributor community :ref:`Slack <community.slack>`.

Option 1a: using mamba (recommended)

Now create an isolated pandas development environment:

We'll now kick off a three-step process:

  1. Install the build dependencies
  2. Build and install pandas
  3. Install the optional dependencies
# Create and activate the build environment
mamba env create --file environment.yml
mamba 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
>>> import pandas
>>> print(pandas.__version__)  # note: the exact output may differ
1.5.0.dev0+1355.ge65a30e3eb.dirty

This will create the new environment, and not touch any of your existing environments, nor any existing Python installation.

To return to your root environment:

mamba deactivate

Option 1b: using pip

If you aren't using mamba for your development environment, follow these instructions. You'll need to have at least the :ref:`minimum Python version <install.version>` that pandas supports. You also need to have setuptools 51.0.0 or later to build pandas.

Unix/macOS with virtualenv

# 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.

# 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 3.9.10 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.

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.

# 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

Option 2: creating an environment using Docker

Instead of manually setting up a development environment, you can use Docker. pandas provides pre-built images that serve a variety of users. These images include:

  • alpine - a lightweight image for the absolute minimalist (note: this is experimental)
  • pip-minimal - a pip-based installation with the minimum set of packages for building / testing
  • mamba-minimal - a mamba-based installation with the minimum set of packages for building / testing
  • pip-all - a pip-based installation with all testing dependencies
  • mamba-all - a mamba-based installation with all testing dependencies

If you are a new user and the image size is no concern to you, we suggest opting for either image that includes all of the dependencies, as this will ensure you can run the test suite without any caveats.

To use any of the images, you should first start with docker pull willayd/pandas-dev:<tag>, where tag is one of alpine, pip-minimal, mamba-minimal, pip-all or mamba-all. You can then run the image without any extra configuration.

To illustrate, if you wanted to use the pip-all image, from the root of your local pandas project you would run:

docker pull willayd/pandas-dev:pip-all
docker run --rm -it -v ${PWD}:/home/pandas willayd/pandas-dev:pip-all

Similarly for mamba-all

docker pull willayd/pandas-dev:mamba-all
docker run --rm -it -v ${PWD}:/home/pandas willayd/pandas-dev:mamba-all

The mamba- images will automatically activate the appropriate virtual environment for you on entry.

Note

You may run the images from a directory besides the root of the pandas project - just be sure to substitute ${PWD} in the commands above to point to your local pandas repository