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
Contents
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.
- A simple interface.
- Informative prints and errors on pipeline application.
- Chaining pipeline stages constructor calls for easy, one-liners pipelines.
- Pipeline arithmetics.
- Easier handling of mixed data (numeric, categorical and others).
- Fully tested on Linux, macOS and Windows systems.
- Compatible with Python 3.5+.
- Pure Python.
- Extra infromative naming: Meant to make pipelines very readable, understanding their entire flow by pipeline stages names; e.g. ColDrop vs. ValDrop instead of an all-encompassing Drop stage emulating the
pandas.DataFrame.drop
method. - Data science-oriented naming (rather than statistics).
- A functional approach: Pipelines never change input DataFrames. Nothing is done "in place".
- Opinionated operations: Help novices avoid mistake by default appliance of good practices; e.g., one-hot-encoding (creating dummy variables) a column will drop one of the resulting columns by default, to avoid the dummy variable trap (perfect multicollinearity).
- Machine learning-oriented: The target use case is transforming tabular data into a vectorized dataset on which a machine learning model will be trained; e.g., column transformations will drop the source columns to avoid strong linear dependence.
The awesome Tirthajyoti Sarkar wrote an excellent practical introduction on how to use pdpipe. Read it now on Towards Data Science!
You can create stages with the following syntax:
import pdpipe as pdp
drop_name = pdp.ColDrop("Name")
All pipeline stages have a predefined precondition function that returns True for dataframes to which the stage can be applied. By default, pipeline stages raise an exception if a DataFrame not meeting their precondition is piped through. This behaviour can be set per-stage by assigning exraise
with a bool in the constructor call. If exraise
is set to False
the input DataFrame is instead returned without change:
drop_name = pdp.ColDrop("Name", exraise=False)
You can apply a pipeline stage to a DataFrame using its apply
method:
res_df = pdp.ColDrop("Name").apply(df)
Pipeline stages are also callables, making the following syntax equivalent:
drop_name = pdp.ColDrop("Name")
res_df = drop_name(df)
The initialized exception behaviour of a pipeline stage can be overridden on a per-application basis:
drop_name = pdp.ColDrop("Name", exraise=False)
res_df = drop_name(df, exraise=True)
Additionally, to have an explanation message print after the precondition is checked but before the application of the pipeline stage, pass verbose=True
:
res_df = drop_name(df, verbose=True)
All pipeline stages also adhere to the scikit-learn
transformer API, and so have fit_transform
and transform
methods; these behave exactly like apply
, and accept the input dataframe as parameter X
. For the same reason, pipeline stages also have a fit
method, which applies them but returns the input dataframe unchanged.
Some pipeline stages can be fitted, meaning that some transformation parameters are set the first time a dataframe is piped through the stage, while later applications of the stage use these now-set parameters without changing them; the Encode
scikit-learn-dependent stage is a good example.
For these type of stages the first call to apply
will both fit the stage and transform the input dataframe, while subsequent calls to apply
will transform input dataframes according to the already-fitted transformation parameters.
Additionally, for fittable stages the scikit-learn
transformer API methods behave as expected:
fit
sets the transformation parameters of the stage but returns the input dataframe unchanged.fit_transform
both sets the transformation parameters of the stage and returns the input dataframe after transformation.transform
transforms input dataframes according to already-fitted transformation parameters; if the stage is not fitted, anUnfittedPipelineStageError
is raised.
Again, apply
, fit_transform
and transform
are all of equivalent for non-fittable pipeline stages. And in all cases the y
parameter of these methods is ignored.
Pipelines can be created by supplying a list of pipeline stages:
pipeline = pdp.PdPipeline([pdp.ColDrop("Name"), pdp.OneHotEncode("Label")])
Additionally, the make_pdpipeline
method can be used to give stages as positional arguments.
pipeline = pdp.make_pdpipeline(pdp.ColDrop("Name"), pdp.OneHotEncode("Label"))
A pipeline structre can be clearly displayed by printing the object:
>>> drop_name = pdp.ColDrop("Name")
>>> binar_label = pdp.OneHotEncode("Label")
>>> map_job = pdp.MapColVals("Job", {"Part": True, "Full":True, "No": False})
>>> pipeline = pdp.PdPipeline([drop_name, binar_label, map_job])
>>> print(pipeline)
A pdpipe pipeline:
[ 0] Drop column Name
[ 1] OneHotEncode Label
[ 2] Map values of column Job with {'Part': True, 'Full': True, 'No': False}.
Alternatively, you can create pipelines by adding pipeline stages together:
pipeline = pdp.ColDrop("Name") + pdp.OneHotEncode("Label")
Or even by adding pipelines together or pipelines to pipeline stages:
pipeline = pdp.ColDrop("Name") + pdp.OneHotEncode("Label")
pipeline += pdp.MapColVals("Job", {"Part": True, "Full":True, "No": False})
pipeline += pdp.PdPipeline([pdp.ColRename({"Job": "Employed"})])
Pipeline stages can also be chained to other stages to create pipelines:
pipeline = pdp.ColDrop("Name").OneHotEncode("Label").ValDrop([-1], "Children")
Pipelines are Python Sequence objects, and as such can be sliced using Python's slicing notation, just like lists:
>>> pipeline = pdp.ColDrop("Name").OneHotEncode("Label").ValDrop([-1], "Children").ApplyByCols("height", math.ceil)
>>> pipeline[0]
Drop column Name
>>> pipeline[1:2]
A pdpipe pipeline:
[ 0] OneHotEncode Label
Pipelines are pipeline stages themselves, and can be applied to a DataFrame using the same syntax, applying each of the stages making them up, in order:
pipeline = pdp.ColDrop("Name") + pdp.OneHotEncode("Label")
res_df = pipeline(df)
Assigning the exraise
parameter to a pipeline apply call with a bool sets or unsets exception raising on failed preconditions for all contained stages:
pipeline = pdp.ColDrop("Name") + pdp.OneHotEncode("Label")
res_df = pipeline.apply(df, exraise=False)
Additionally, passing verbose=True
to a pipeline apply call will apply all pipeline stages verbosely:
res_df = pipeline.apply(df, verbose=True)
Finally, fit
, transform
and fit_transform
all call the corresponding pipeline stage methods of all stages composing the pipeline
All built-in stages are thoroughly documented, including examples; if you find any documentation lacking please open an issue. A list of briefly described available built-in stages follows:
- AdHocStage - Define custom pipeline stages on the fly.
- ColDrop - Drop columns by name.
- ValDrop - Drop rows by by their value in specific or all columns.
- ValKeep - Keep rows by by their value in specific or all columns.
- ColRename - Rename columns.
- DropNa - Drop null values. Supports all parameter supported by pandas.dropna function.
- FreqDrop - Drop rows by value frequency threshold on a specific column
- ColReorder - Reorder columns.
- RowDrop - Drop rows by callable conditions.
- Bin - Convert a continuous valued column to categoric data using binning.
- OneHotEncode - Convert a categorical column to the several binary columns corresponding to it.
- MapColVals - Replace column values by a map.
- ApplyToRows - Generate columns by applying a function to each row.
- ApplyByCols - Generate columns by applying an element-wise function to columns.
- ColByFrameFunc - Add a column by applying a dataframe-wide function.
- AggByCols - Generate columns by applying an series-wise function to columns.
- Log - Log-transform numeric data, possibly shifting data before.
- Encode - Encode a categorical column to corresponding number values.
- Scale - Scale data with any of the sklearn scalers.
- TokenizeWords - Tokenize a sentence into a list of tokens by whitespaces.
- UntokenizeWords - Joins token lists into whitespace-seperated strings.
- RemoveStopwords - Remove stopwords from a tokenized list.
- SnowballStem - Stems tokens in a list using the Snowball stemmer.
- DropRareTokens - Drop rare tokens from token lists.
To use other stages than the built-in ones (see Types of Pipeline Stages) you can extend the PdPipelineStage
class. The constructor must pass the PdPipelineStage
constructor the exmsg
, appmsg
and desc
keyword arguments to set the exception message, application message and description for the pipeline stage, respectively. Additionally, the _prec
and _transform
abstract methods must be implemented to define the precondition and the effect of the new pipeline stage, respectively.
Fittable custom pipeline stages should implement, additionally to the _transform
method, the _fit_transform
method, which should both fit pipeline stage by the input dataframe and transform transform the dataframe, while also setting self.is_fitted = True
.
To create a custom pipeline stage without creating a proper new class, you can instantiate the AdHocStage
class which takes a function in its transform
constructor parameter to define the stage's operation, and the optional prec
parameter to define a precondition (an always-true function is the default).
Package author and current maintainer is Shay Palachy ([email protected]); 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.
Clone:
git clone [email protected]:shaypal5/pdpipe.git
Install in development mode with test dependencies:
cd pdpipe
pip install -e ".[test]"
To run the tests, use:
python -m pytest --cov=pdpipe
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.
Created by Shay Palachy ([email protected]).