Easy pipelines for pandas DataFrames (learn how!).
Website: https://pdpipe.github.io/pdpipe/
Documentation: https://pdpipe.github.io/pdpipe/doc/pdpipe/
>>> 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
Contents
This is the repository of the pdpipe
package, and this readme file is aimed to help potential contributors to the project.
To learn more about how to use pdpipe
, either visit pdpipe's homepage or read the online documentation of pdpipe.
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 (shay.palachy@gmail.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.
Clone:
git clone git@github.com: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! :)
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.
Please notice that for pdoc3
- the Python package used to generate the html documentation files for pdpipe
- to successfully include doctests in the generated documentation files, the whole doctest must be indented in relation to the opening multi-string indentation, like so:
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
"""
Created by Shay Palachy (shay.palachy@gmail.com).