pandas-vet is a plugin for
flake8 that provides opinionated linting for
It began as a project during the PyCascades 2019 sprints.
pandas can be daunting. The usual internet help sites are littered with different ways to do the same thing and some features that the
pandas docs themselves discourage live on in the API.
pandas-vet is (hopefully) a way to help make
pandas a little more friendly for newcomers by taking some opinionated stances about
pandas best practices. It is designed to help users reduce the
The idea to create a linter was sparked by Ania Kapuścińska's talk at PyCascades 2019, "Lint your code responsibly!".
Many of the opinions stem from Ted Petrou's excellent Minimally Sufficient Pandas. Other ideas are drawn from
pandas docs or elsewhere. The Pandas in Black and White flashcards have a lot of the same opinions too.
pandas-vet is a plugin for
flake8. If you don't have
flake8 already, it will install automatically when you install
The plugin is on PyPI and can be installed with:
pip install pandas-vet
It can also be installed with
conda install -c conda-forge pandas-vet
pandas-vet is tested under Python 3.6, 3.7, 3.8, and 3.9 as defined in our GitHub Actions
Once installed successfully in an environment that also has
pandas-vet should run whenever
flake8 is run.
$ flake8 ...
flake8 docs for more information.
For a full list of implemented warnings, see the list below.
pandas-vet is still in the very early stages. Contributions are welcome from the community on code, tests, docs, and just about anything else.
Code of Conduct
Because this project started during the PyCascades 2019 sprints, we adopt the PyCascades minimal expectation that we "Be excellent to each another". Beyond that, we follow the Python Software Foundation's Community Code of Conduct.
Steps to contributing
Please submit an issue (or draft PR) first describing the types of changes you'd like to implement.
Fork the repo and create a new branch for your enhancement/fix.
Get a development environment set up with your favorite environment manager (
You must use at least python 3.6 to develop, for black support.
You can create one from
pip install -r requirements_dev.txtor, if you use Docker, you can build an image from the Dockerfile included in this repo.
Once your enviroment is set up you will need to install pandas-vet in development mode. Use
pip install -e .(use this if you are alreay in your virtual enviroment) or
pip install -e <path>(use this one if not in the virtual enviroment and prefer to state explicitly where it is going).
Write code, docs, etc.
blackto validate our codebase. TravisCI integration will complain on pull requests if there are any failing tests or lint violations. To check these locally, run the following commands:
flake8 pandas_vet setup.py tests --exclude tests/data
black --check pandas_vet setup.py tests --exclude tests/data
Push to your forked repo.
Submit pull request to the parent repo from your branch. Be sure to write a clear message and reference the Issue # that relates to your pull request.
Feel good about giving back to open source projects.
How to add a check to the linter
Write tests. At a minimum, you should have test cases where the linter should catch "bad"
pandasand test cases where the linter should allow "good"
Write your check function in
pyteston the linter itself (see Steps to contributing)
PyCascades 2019 sprints team
- Sam Beck
- Jacob Deppen
- Charles Simchick
- Aly Sivji
- Tim Smith
PyCascades 2020 sprints team
- Jacob Deppen
Other awesome contributors
- Earl Clark
- Leandro Leites
List of warnings
PD001: pandas should always be imported as 'import pandas as pd'
PD002: 'inplace = True' should be avoided; it has inconsistent behavior
PD003: '.isna' is preferred to '.isnull'; functionality is equivalent
PD004: '.notna' is preferred to '.notnull'; functionality is equivalent
PD005: Use arithmetic operator instead of method
PD006: Use comparison operator instead of method
PD007: '.ix' is deprecated; use more explicit '.loc' or '.iloc'
PD008: Use '.loc' instead of '.at'. If speed is important, use numpy.
PD009: Use '.iloc' instead of '.iat'. If speed is important, use numpy.
PD010 '.pivot_table' is preferred to '.pivot' or '.unstack'; provides same functionality
PD011 Use '.array' or '.to_array()' instead of '.values'; 'values' is ambiguous
PD012 '.read_csv' is preferred to '.read_table'; provides same functionality
PD013 '.melt' is preferred to '.stack'; provides same functionality
PD015 Use '.merge' method instead of 'pd.merge' function. They have equivalent functionality.
Very Opinionated Warnings
These warnings are turned off by default. To enable them, add the
-annoy flag to your command, e.g.,
$ flake8 --annoy my_file.py
PD901 'df' is a bad variable name. Be kinder to your future self.