@@ -153,14 +153,38 @@ to build the documentation locally before pushing your changes.
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Using a Docker container
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~~~~~~~~~~~~~~~~~~~~~~~~
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- Instead of manually setting up a development environment, you can use Docker to
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- automatically create the environment with just several commands. Pandas provides a `DockerFile `
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- in the root directory to build a Docker image with a full pandas development environment.
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+ Instead of manually setting up a development environment, you can use `Docker
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+ <https://docs.docker.com/get-docker/> `_ to automatically create the environment with just several
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+ commands. Pandas provides a `DockerFile ` in the root directory to build a Docker image
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+ with a full pandas development environment.
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- Even easier, you can use the DockerFile to launch a remote session with Visual Studio Code,
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+ **Docker Commands **
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+
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+ Pass your GitHub username in the `DockerFile ` to use your own fork::
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+
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+ # Build the image pandas-yourname-env
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+ docker build --tag pandas-yourname-env .
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+ # Run a container and bind your local forked repo, pandas-yourname, to the container
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+ docker run -it --rm -v path-to-pandas-yourname:/home/pandas-yourname pandas-yourname-env
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+
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+ Even easier, you can integrate Docker with the following IDEs:
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+
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+ **Visual Studio Code **
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+
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+ You can use the DockerFile to launch a remote session with Visual Studio Code,
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a popular free IDE, using the `.devcontainer.json ` file.
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See https://code.visualstudio.com/docs/remote/containers for details.
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+ **PyCharm (Professional) **
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+ Enable Docker support and use the Services tool window to build and manage images as well as
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+ run and interact with containers.
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+ See https://www.jetbrains.com/help/pycharm/docker.html for details.
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+
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+ Note that you might need to rebuild the C extensions if/when you merge with upstream/master using::
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+ python setup.py build_ext --inplace -j 4
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+
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.. _contributing.dev_c :
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Installing a C compiler
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