Easy pipelines for pandas.
Project description
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 01 Documentation
This is the repository of the pdpipe package. This readme 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.
2 Installation
Install pdpipe with:
pip install pdpipeSome 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.
3 Contributing
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. Intuit are nice.
3.1 Installing for development
Clone:
git clone git@github.com:shaypal5/pdpipe.gitInstall in development mode with test dependencies:
cd pdpipe
pip install -e ".[test]"3.2 Running the tests
To run the tests, use:
python -m pytest --cov=pdpipe3.3 Adding documentation
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.
3.4 Adding doctests
Please notice that for pdoc3 - the used the generate documentation for pdpipe - to successfully include doctest in the generated documentation, the whole doctest must be indented in relation to the opening multi-string indentation:
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
"""4 Credits
Created by Shay Palachy (shay.palachy@gmail.com).
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