Easy pipelines for pandas.
Easy pipelines for pandas DataFrames (learn how!).
>>> df = pd.DataFrame( data=[[4, 165, 'USA'], [2, 180, 'UK'], [2, 170, 'Greece']], index=['Dana', 'Jane', 'Nick'], columns=['Medals', 'Height', 'Born'] ) >>> import pdpipe as pdp >>> pipeline = pdp.ColDrop('Medals').OneHotEncode('Born') >>> pipeline(df) Height Born_UK Born_USA Dana 165 0 1 Jane 180 1 0 Nick 170 0 0
This is the repository of the pdpipe package, and this readme file is aimed to help potential contributors to the project.
Install pdpipe with:
pip install pdpipe
Some pipeline stages require scikit-learn; they will simply not be loaded if scikit-learn is not found on the system, and pdpipe will issue a warning. To use them you must also install scikit-learn.
Similarly, some pipeline stages require nltk; they will simply not be loaded if nltk is not found on your system, and pdpipe will issue a warning. To use them you must additionally install nltk.
Package author and current maintainer is Shay Palachy (email@example.com); You are more than welcome to approach him for help. Contributions are very welcomed, especially since this package is very much in its infancy and many other pipeline stages can be added.
git clone firstname.lastname@example.org:pdpipe/pdpipe.git
Install in development mode with test dependencies:
cd pdpipe pip install -e ".[test]"
To run the tests, use:
python -m pytest
Notice pytest runs are configured by the pytest.ini file. Read it to understand the exact pytest arguments used.
At the time of writing, pdpipe is maintained with a test coverage of 100%. Although challenging, I hope to maintain this status. If you add code to the package, please make sure you thoroughly test it. Codecov automatically reports changes in coverage on each PR, and so PR reducing test coverage will not be examined before that is fixed.
Tests reside under the tests directory in the root of the repository. Each module has a separate test folder, with each class - usually a pipeline stage - having a dedicated file (always starting with the string “test”) containing several tests (each a global function starting with the string “test”). Please adhere to this structure, and try to separate tests cases to different test functions; this allows us to quickly focus on problem areas and use cases. Thank you! :)
pdpipe can be configured using both a configuration file - locaated at either $XDG_CONFIG_HOME/pdpipe/cfg.json or, if the XDG_CONFIG_HOME environment variable is not set, at ~/.pdpipe/cfg.json - and environment variables.
At the moment, these configuration options are only relevant for development. The available options are:
- LOAD_STAGE_ATTRIBUTES - True by default. If set to False stage attributes, which enable the chainer construction pattern, e.g. pdp.ColDrop('b').Bin('f'), are not loaded. This is used for sensible documentation generation. Set with this "LOAD_STAGE_ATTRIBUTES": false in cfg.json, or with export PDPIPE__LOAD_STAGE_ATTRIBUTES=False for environment variable-driven configuration.
pdpip code is written to adhere to the coding style dictated by flake8. Practically, this means that one of the jobs that runs on the project’s Travis for each commit and pull request checks for a successfull run of the flake8 CLI command in the repository’s root. Which means pull requests will be flagged red by the Travis bot if non-flake8-compliant code was added.
To solve this, please run flake8 on your code (whether through your text editor/IDE or using the command line) and fix all resulting errors. Thank you! :)
This project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings (in my personal opinion, of course). When documenting code you add to this project, please follow these conventions.
Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.
Doctests can be added in the traditional manner:
class ApplyByCols(PdPipelineStage): """A pipeline stage applying an element-wise function to columns. Parameters ---------- columns : str or list-like Names of columns on which to apply the given function. func : function The function to be applied to each element of the given columns. result_columns : str or list-like, default None The names of the new columns resulting from the mapping operation. Must be of the same length as columns. If None, behavior depends on the drop parameter: If drop is True, the name of the source column is used; otherwise, the name of the source column is used with the suffix '_app'. drop : bool, default True If set to True, source columns are dropped after being mapped. func_desc : str, default None A function description of the given function; e.g. 'normalizing revenue by company size'. A default description is used if None is given. Example ------- >>> import pandas as pd; import pdpipe as pdp; import math; >>> data = [[3.2, "acd"], [7.2, "alk"], [12.1, "alk"]] >>> df = pd.DataFrame(data, [1,2,3], ["ph","lbl"]) >>> round_ph = pdp.ApplyByCols("ph", math.ceil) >>> round_ph(df) ph lbl 1 4 acd 2 8 alk 3 13 alk """
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