Contributing to pandas

Where to start?

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

If you are simply looking to start working with the pandas codebase, navigate to the GitHub “issues” tab and start looking through interesting issues. There are a number of issues listed under Docs and Difficulty Novice where you could start out.

Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!

Feel free to ask questions on the mailing list or on Gitter.

Bug reports and enhancement requests

Bug reports are an important part of making pandas more stable. Having a complete bug report will allow others to reproduce the bug and provide insight into fixing. Because many versions of pandas are supported, knowing version information will also identify improvements made since previous versions. Trying the bug-producing code out on the master branch is often a worthwhile exercise to confirm the bug still exists. It is also worth searching existing bug reports and pull requests to see if the issue has already been reported and/or fixed.

Bug reports must:

1. Include a short, self-contained Python snippet reproducing the problem. You can format the code nicely by using GitHub Flavored Markdown:

   ```python
   >>> from pandas import DataFrame
   >>> df = DataFrame(...)
   ...
   ```
   

2. Include the full version string of pandas and its dependencies. You can use the built in function:

   >>> import pandas as pd
   >>> pd.show_versions()
   

3. Explain why the current behavior is wrong/not desired and what you expect instead.

The issue will then show up to the pandas community and be open to comments/ideas from others.

Working with the code

Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the pandas code base.

Version control, Git, and GitHub

To the new user, working with Git is one of the more daunting aspects of contributing to pandas. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help.

The code is hosted on GitHub. To contribute you will need to sign up for a free GitHub account. We use Git for version control to allow many people to work together on the project.

Some great resources for learning Git:

• the GitHub help pages.
• the NumPy’s documentation.
• Matthew Brett’s Pydagogue.

Getting started with Git

GitHub has instructions for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub.

Forking

You will need your own fork to work on the code. Go to the pandas project page and hit the Fork button. You will want to clone your fork to your machine:

git clone git@github.com:your-user-name/pandas.git pandas-yourname
cd pandas-yourname
git remote add upstream git://github.com/pandas-dev/pandas.git

This creates the directory pandas-yourname and connects your repository to the upstream (main project) pandas repository.

Creating a branch

You want your master branch to reflect only production-ready code, so create a feature branch for making your changes. For example:

git branch shiny-new-feature
git checkout shiny-new-feature

The above can be simplified to:

git checkout -b shiny-new-feature

This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to pandas. You can have many shiny-new-features and switch in between them using the git checkout command.

To update this branch, you need to retrieve the changes from the master branch:

git fetch upstream
git rebase upstream/master

This will replay your commits on top of the latest pandas git master. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to stash them prior to updating. This will effectively store your changes and they can be reapplied after updating.

Creating a development environment

An easy way to create a pandas development environment is as follows.

• Install either Anaconda or miniconda
• Make sure that you have cloned the repository
• cd to the pandas source directory

Tell conda to create a new environment, named pandas_dev, or any other name you would like for this environment, by running:

conda create -n pandas_dev --file ci/requirements_dev.txt

For a python 3 environment:

conda create -n pandas_dev python=3 --file ci/requirements_dev.txt

Warning

If you are on Windows, see here for a fully compliant Windows environment.

This will create the new environment, and not touch any of your existing environments, nor any existing python installation. It will install all of the basic dependencies of pandas, as well as the development and testing tools. If you would like to install other dependencies, you can install them as follows:

conda install -n pandas_dev -c pandas pytables scipy

To install all pandas dependencies you can do the following:

conda install -n pandas_dev -c pandas --file ci/requirements_all.txt

To work in this environment, Windows users should activate it as follows:

activate pandas_dev

Mac OSX / Linux users should use:

source activate pandas_dev

You will then see a confirmation message to indicate you are in the new development environment.

To view your environments:

conda info -e

To return to your home root environment in Windows:

deactivate

To return to your home root environment in OSX / Linux:

source deactivate

See the full conda docs here.

At this point you can easily do an in-place install, as detailed in the next section.

Creating a Windows development environment

To build on Windows, you need to have compilers installed to build the extensions. You will need to install the appropriate Visual Studio compilers, VS 2008 for Python 2.7, VS 2010 for 3.4, and VS 2015 for Python 3.5 and 3.6.

For Python 2.7, you can install the mingw compiler which will work equivalently to VS 2008:

conda install -n pandas_dev libpython

or use the Microsoft Visual Studio VC++ compiler for Python. Note that you have to check the x64 box to install the x64 extension building capability as this is not installed by default.

For Python 3.4, you can download and install the Windows 7.1 SDK. Read the references below as there may be various gotchas during the installation.

For Python 3.5 and 3.6, you can download and install the Visual Studio 2015 Community Edition.

Here are some references and blogs:

• https://blogs.msdn.microsoft.com/pythonengineering/2016/04/11/unable-to-find-vcvarsall-bat/
• https://github.com/conda/conda-recipes/wiki/Building-from-Source-on-Windows-32-bit-and-64-bit
• https://cowboyprogrammer.org/building-python-wheels-for-windows/
• https://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/
• https://support.enthought.com/hc/en-us/articles/204469260-Building-Python-extensions-with-Canopy

Making changes

Before making your code changes, it is often necessary to build the code that was just checked out. There are two primary methods of doing this.

1. The best way to develop pandas is to build the C extensions in-place by running:

   python setup.py build_ext --inplace
   

   If you startup the Python interpreter in the pandas source directory you will call the built C extensions

2. Another very common option is to do a develop install of pandas:

   python setup.py develop
   

   This makes a symbolic link that tells the Python interpreter to import pandas from your development directory. Thus, you can always be using the development version on your system without being inside the clone directory.

Contributing to the documentation

If you’re not the developer type, contributing to the documentation is still of huge value. You don’t even have to be an expert on pandas to do so! Something as simple as rewriting small passages for clarity as you reference the docs is a simple but effective way to contribute. The next person to read that passage will be in your debt!

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 simple way to ensure it will help the next person.

About the pandas documentation

The documentation is written in reStructuredText, which is almost like writing in plain English, and built using Sphinx. The Sphinx Documentation has an excellent introduction to reST. 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 pandas/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 the Numpy Docstring Standard, which is used widely in the Scientific Python community. This standard specifies the format of the different sections of the docstring. See this document for a detailed explanation, or look at some of the existing functions to extend it in a similar manner.

• The tutorials make heavy use of the ipython directive 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.

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/contributing.rst. Then, to generate CONTRIBUTING.md, use pandoc with the following command:

pandoc doc/source/contributing.rst -t markdown_github > CONTRIBUTING.md

The utility script scripts/api_rst_coverage.py can be used to compare the list of methods documented in doc/source/api.rst (which is used to generate the API Reference page) and the actual public methods. This will identify methods documented in in doc/source/api.rst that are not actually class methods, and existing methods that are not documented in doc/source/api.rst.

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 creating a development environment above). Further, to build the docs, there are some extra requirements: you will need to have sphinx and ipython installed. numpydoc is used to parse the docstrings that follow the Numpy Docstring Standard (see above), but you don’t need to install this because a local copy of numpydoc is included in the pandas source code. nbsphinx is required to build the Jupyter notebooks included in the documentation.

If you have a conda environment named pandas_dev, you can install the extra requirements with:

conda install -n pandas_dev sphinx ipython nbconvert nbformat
conda install -n pandas_dev -c conda-forge nbsphinx

Furthermore, it is recommended to have all optional dependencies. installed. This is not strictly necessary, but be aware that you will see some error messages when building the docs. This happens because all the code in the documentation is executed during the doc build, and so code examples using optional dependencies will generate errors. Run pd.show_versions() to get an overview of the installed version of all dependencies.

Warning

You need to have sphinx version >= 1.3.2.

Building the documentation

So how do you build the docs? Navigate to your local pandas/doc/ directory in the console and run:

python make.py html

Then you can find the HTML output in the folder pandas/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

Starting with pandas 0.13.1 you can tell make.py to compile only a single section of the docs, greatly reducing the turn-around time for checking your changes. You will be prompted to delete .rst files that aren’t required. This is okay because the prior versions of these files can be checked out from git. However, you must make sure not to commit the file deletions to your Git repository!

#omit autosummary and API section
python make.py clean
python make.py --no-api

# compile the docs with only a single
# section, that which is in indexing.rst
python make.py clean
python make.py --single indexing

For comparison, a full documentation build may take 10 minutes, a -no-api build may take 3 minutes and a single section may take 15 seconds. Subsequent builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built:

pandas/docs/build/html/index.html

And you’ll have the satisfaction of seeing your new and improved documentation!

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, see also the Continuous Integration section.

Contributing to the code base

Code standards

Writing good code is not just about what you write. It is also about how you write it. During Continuous Integration 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.

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.

Additional standards are outlined on the code style wiki page.

C (cpplint)

pandas uses the Google standard. Google provides an open source style checker called cpplint, but we use a fork of it that can be found here. 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

Continuous Integration. will run the 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 tool, which can be downloaded here. 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, just 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.

Python (PEP8)

pandas uses the PEP8 standard. There are several tools to ensure you abide by this standard. Here are some of the more common PEP8 issues:

• we restrict line-length to 79 characters to promote readability
• passing arguments should have spaces after commas, e.g. foo(arg1, arg2, kw1='bar')

Continuous Integration will run the flake8 tool and report any stylistic errors in your code. Therefore, it is helpful before submitting code to run the check yourself on the diff:

git diff master --name-only -- '*.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 will take longer:

git diff master --name-only -- '*.py' | grep 'pandas/' | 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 master --name-only -- '*.py' | grep 'pandas/' | xargs flake8

Note that on Windows, grep, xargs, and other tools are likely unavailable. However, this has been shown to work on smaller commits in the standard Windows command line:

git diff master -u -- "*.py" | flake8 --diff

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.

Testing With Continuous Integration

The pandas test suite will run automatically on Travis-CI, Appveyor, and Circle CI 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, Appveyor , and CircleCI.

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.

Note

Each time you push to your fork, a new run of the tests will be triggered on the CI. Appveyor will auto-cancel any non-currently-running tests for that same pull-request. You can enable the auto-cancel feature for Travis-CI here and for CircleCI here.

Test-driven development/code writing

pandas is serious about testing and strongly encourages contributors to embrace test-driven development (TDD). 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 and the convenient extensions in numpy.testing.

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 of the wiki.

The pandas.util.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)

Transitioning to pytest

pandas existing test structure is mostly classed based, meaning that you will typically find tests wrapped in a class.

class TestReallyCoolFeature(object):
    ....

Going forward, we are moving to a more functional style using the pytest 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:

def test_really_cool_feature():
    ....

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
• using parametrize: allow testing of multiple cases
• 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.

import pytest
import numpy as np
import pandas as pd
from pandas.util import testing as tm

@pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
def test_dtypes(dtype):
    assert str(np.dtype(dtype)) == dtype

@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

((pandas) bash-3.2$ pytest  test_cool_feature.py  -v
=========================== test session starts ===========================
platform darwin -- Python 3.5.2, pytest-3.0.5, py-1.4.31, pluggy-0.4.0
collected 8 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_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.

((pandas) bash-3.2$ pytest  test_cool_feature.py  -v -k int8
=========================== test session starts ===========================
platform darwin -- Python 3.5.2, pytest-3.0.5, py-1.4.31, pluggy-0.4.0
collected 8 items

test_cool_feature.py::test_dtypes[int8] PASSED
test_cool_feature.py::test_series[int8] PASSED

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, 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 documentation.

New in version 0.20.0.

Furthermore one can run

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 to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. asv supports both python2 and python3.

To use all features of asv, you will need either conda or virtualenv. For more details please check the asv installation webpage.

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 test suite can take up to one hour and use up to 3GB of RAM. 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 tests 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 tests from a file, you can do it using . as a separator. For example:

asv continuous -f 1.1 upstream/master HEAD -b groupby.groupby_agg_builtins

will only run the groupby_agg_builtins 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:python3.5

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.

Documenting your code

Changes should be reflected in the release notes located in doc/source/whatsnew/vx.y.z.txt. 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 `` GH1234 `` 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 above. Further, to let users know when this feature was added, the versionadded directive is used. The sphinx syntax for that is:

.. versionadded:: 0.17.0

This will put the text New in version 0.17.0 wherever you put the sphinx directive. This should also be put in the docstring when adding a new function or method (example) or a new keyword argument (example).

Contributing your changes to pandas

Committing your code

Keep style fixes to a separate commit to make your pull request more readable.

Once you’ve made changes, you can see them by typing:

git status

If you have created a new file, it is not being tracked by git. Add it by typing:

git add path/to/file-to-be-added.py

Doing ‘git status’ again should give something like:

# On branch shiny-new-feature
#
#       modified:   /relative/path/to/file-you-added.py
#

Finally, commit your changes to your local repository with an explanatory message. Pandas uses a convention for commit message prefixes and layout. Here are some common prefixes along with general guidelines for when to use them:

• ENH: Enhancement, new functionality
• BUG: Bug fix
• DOC: Additions/updates to documentation
• TST: Additions/updates to tests
• BLD: Updates to the build process/scripts
• PERF: Performance improvement
• CLN: Code cleanup

The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either style is fine, but the former is generally preferred:

• a subject line with < 80 chars.
• One blank line.
• Optionally, a commit message body.

Now you can commit your changes in your local repository:

git commit -m

Combining commits

If you have multiple commits, you may want to combine them into one commit, often referred to as “squashing” or “rebasing”. This is a common request by package maintainers when submitting a pull request as it maintains a more compact commit history. To rebase your commits:

git rebase -i HEAD~#

Where # is the number of commits you want to combine. Then you can pick the relevant commit message and discard others.

To squash to the master branch do:

git rebase -i master

Use the s option on a commit to squash, meaning to keep the commit messages, or f to fixup, meaning to merge the commit messages.

Then you will need to push the branch (see below) forcefully to replace the current commits with the new ones:

git push origin shiny-new-feature -f

Pushing your changes

When you want your changes to appear publicly on your GitHub page, push your forked feature branch’s commits:

git push origin shiny-new-feature

Here origin is the default name given to your remote repository on GitHub. You can see the remote repositories:

git remote -v

If you added the upstream repository as described above you will see something like:

origin  git@github.com:yourname/pandas.git (fetch)
origin  git@github.com:yourname/pandas.git (push)
upstream        git://github.com/pandas-dev/pandas.git (fetch)
upstream        git://github.com/pandas-dev/pandas.git (push)

Now your code is on GitHub, but it is not yet a part of the pandas project. For that to happen, a pull request needs to be submitted on GitHub.

Review your code

When you’re ready to ask for a code review, file a pull request. Before you do, once again make sure that you have followed all the guidelines outlined in this document regarding code style, tests, performance tests, and documentation. You should also double check your branch changes against the branch it was based on:

1. Navigate to your repository on GitHub – https://github.com/your-user-name/pandas
2. Click on Branches
3. Click on the Compare button for your feature branch
4. Select the base and compare branches, if necessary. This will be master and shiny-new-feature, respectively.

Finally, make the pull request

If everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the GitHub community and can be looked at and eventually merged into the master version. This pull request and its associated changes will eventually be committed to the master branch and available in the next release. To submit a pull request:

1. Navigate to your repository on GitHub
2. Click on the Pull Request button
3. You can then click on Commits and Files Changed to make sure everything looks okay one last time
4. Write a description of your changes in the Preview Discussion tab
5. Click Send Pull Request.

This request then goes to the repository maintainers, and they will review the code. If you need to make more changes, you can make them in your branch, push them to GitHub, and the pull request will be automatically updated. Pushing them to GitHub again is done by:

git push -f origin shiny-new-feature

This will automatically update your pull request with the latest code and restart the Continuous Integration tests.

Delete your merged branch (optional)

Once your feature branch is accepted into upstream, you’ll probably want to get rid of the branch. First, merge upstream master into your branch so git knows it is safe to delete your branch:

git fetch upstream
git checkout master
git merge upstream/master

Then you can just do:

git branch -d shiny-new-feature

Make sure you use a lower-case -d, or else git won’t warn you if your feature branch has not actually been merged.

The branch will still exist on GitHub, so to delete it there do:

git push origin --delete shiny-new-feature