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.
Table of contents:
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.
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 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
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
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
See https://code.visualstudio.com/docs/remote/containers for details.
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
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
You will need Build Tools for Visual Studio 2019.
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.
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:
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:
Make sure your conda is up to date (
conda update conda)
Make sure that you have cloned the repository
cdto the pandas source directory
We’ll now kick off a three-step process:
Install the build dependencies
Build and install pandas
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:
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
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