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 contributing to the documentation but if you skip creating the development environment you won’t be able to build the documentation locally before pushing your changes.

Creating an environment using Docker#

Instead of manually setting up a development environment, you can use 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

Build the Docker image:

# Build the image pandas-yourname-env
docker build --tag pandas-yourname-env .
# Or build the image by passing your GitHub username to use your own fork
docker build --build-arg gh_username=yourname --tag pandas-yourname-env .

Run Container:

# Run a container and bind your local repo to the container
docker run -it -w /home/pandas --rm -v path-to-local-pandas-repo:/home/pandas pandas-yourname-env

Note

If you bind your local repo for the first time, you have to build the C extensions afterwards. Run the following command inside the container:

python setup.py build_ext -j 4

You need to rebuild the C extensions anytime the Cython code in pandas/_libs changes. This most frequently occurs when changing or merging branches.

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.

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 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 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), 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.

Creating a Python environment#

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
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
>>> 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.

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 minimum Python 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 3.7.6 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

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

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