Package pdpipe

The pdpipe Python package provides a concise interface for building pandas pipelines that have pre-conditions, are verbose, support the fit-transform design of scikit-learn transformers and are highly serializable. pdpipe pipelines have a simple interface, informative prints and errors on pipeline application, support pipeline arithmetics and enable easier handling of mixed-type data.


  • 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.

Design Decisions

  • Extra informative 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.


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 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.

Basic Use

The awesome Tirthajyoti Sarkar wrote an excellent practical introduction on how to use pdpipe. Read it now on his website!

For a thorough overview of all the capabilities of pdpipe, continue below:

Pipeline Stages

Creating Pipeline Stages

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)

Applying Pipeline Stages

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.

Fittable Pipeline Stages

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, an UnfittedPipelineStageError is raised.

Again, apply, fit_transform and are all of equivalent for non-fittable pipeline stages. And in all cases the y parameter of these methods is ignored.


Creating Pipelines

Pipelines can be created by supplying a list of pipeline stages:

    pipeline = pdp.PdPipeline([pdp.ColDrop("Name"), pdp.OneHotEncode("Label")]

Additionally, the method can be used to give stages as positional arguments.

    pipeline = pdp.make_pdpipeline(pdp.ColDrop("Name"), pdp.OneHotEncode("Label"))

Printing Pipelines

A pipeline structure 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}.

Pipeline Arithmetics

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 Chaining

Pipeline stages can also be chained to other stages to create pipelines:

  pipeline = pdp.ColDrop("Name").OneHotEncode("Label").ValDrop([-1], "Children")

Pipeline Slicing

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 can also be sliced by the stages name parameter, notice when running pipeline[['name1', 'name2']] a new pipeline will returned with all stages that they name is 'name1' or 'name2', and when running `pipeline['name'] only the first stage that has the 'name' will return.:

  >>> pipeline = pdp.ColDrop("Name", name="dropName").OneHotEncode("Label", name="encoder").ValDrop([-1], "Children").ApplyByCols("height", math.ceil)
  >>> pipeline['dropName']
  PdPipelineStage: Drop columns Name
  >>> pipeline[['dropName', 'encoder']]
  A pdpipe pipeline:
  [ 0]  Drop columns Name
  [ 1]  One-hot encode Label

Applying Pipelines

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 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.

Column Qualifiers

All pdpipe pipeline stages that possess the columns parameter can accept callables - instead of lists of labels - as valid arguments to that parameter. These callables are assumed to be column qualifiers - functions that can be applied to an input dataframe to extract the list of labels to operate on in run time.

The module pdpipe.cq provides a powerful class - ColumnQualifier - implementing this idea with various enhancements, like the ability to fit a list of labels in fit time to be retained for future transforms and support for various boolean operators between column qualifiers.

It also provides ready implementations for qualifiers qualifying columns by label, dtype and the number of missing values. This enable powerful behaviours like dropping columns by missing value frequency, scaling only integer columns or performing PCA on the subset of columns starting with the string 'tfidf_token_'.

Read more on column qualifiers in the documentation of the pdpipe.cq module.

Types of Pipeline Stages

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:

Built-in pandas methods

Ad-hoc pipeline stages that wrap any pandas.DataFrame built-in method that returns a dataframe object can be easily created using the pdpipe.df submodule:

  pipeline = pdp.PdPipeline([

Refer to the pdpipe.df module for a more detailed documentation.

Basic Stages

Refer to submodule pdpipe.basic_stages

  • 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.
  • Schematize - Learn a dataframe schema on fit and transform to it on future transforms.
  • DropDuplicates - Drop duplicate values in a subset of columns.

Column Generation

Refer to submodule pdpipe.col_generation

  • 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.

Text Stages

Refer to submodule pdpipe.text_stages

  • RegexReplace - Replace regex occurences in columns of strings.
  • DropTokensByLength - Drop tokens in token lists by token length.
  • DropTokensByList - Drop every occurence of a given set of string tokens in token lists.

Scikit-learn-dependent Stages

Refer to submodule pdpipe.sklearn_stages

  • Encode - Encode a categorical column to corresponding number values.
  • Scale - Scale data with any of the sklearn scalers.
  • TfidfVectorizeTokenLists - Transform a column of token lists into the correponding set of tfidf vector columns.

nltk-dependent Stages

Refer to submodule pdpipe.nltk_stages

  • 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.

Creating additional stages

Extending Pdpipelinestage

To use other stages than the built-in ones (see Types of Pipeline Stages) you can extend the 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 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.

Ad-Hoc Pipeline Stages

To create a custom pipeline stage without creating a proper new class, you can instantiate the 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).

Expand source code
The `pdpipe` Python package provides a concise interface for building `pandas`
pipelines that have pre-conditions, are verbose, support the fit-transform
design of scikit-learn transformers and are highly serializable. `pdpipe`
pipelines have a simple interface, informative prints and errors on pipeline
application, support pipeline arithmetics and enable easier handling of
mixed-type data.

.. include:: ./
# pylint: disable=C0413
# flake8: noqa

import warnings
import traceback

from . import core
from .core import PdPipelineStage, AdHocStage, PdPipeline, make_pdpipeline


from . import basic_stages
from .basic_stages import (


from . import col_generation
from .col_generation import (


from . import text_stages
from .text_stages import (


from . import wrappers
from .wrappers import (


    from . import sklearn_stages
    from .sklearn_stages import (

except ImportError:
    tb = traceback.format_exc()
        "pdpipe: Scikit-learn or skutil import failed. Scikit-learn"
        "-dependent pipeline stages will not be loaded."

    from . import nltk_stages
    from .nltk_stages import (

except ImportError:
    tb = traceback.format_exc()
        "pdpipe: nltk import failed. nltk-dependent  pipeline "
        "stages will not be loaded."

from . import cq
from . import cond
from . import df

from ._version import get_versions

__version__ = get_versions()["version"]

for name in [
    except KeyError:
    del name  # pylint: disable=W0631
except NameError:

# this dictates which modules are skipped on pdoc documentation generation
__pdoc__ = {
    'shared': False,



Basic pdpipe PdPipelineStages.


Column generation pdpipe PdPipelineStages.


Fittable conditions for pdpipe …


Defines pipelines for processing pandas.DataFrame-based datasets …


Column qualifiers for pdpipe …


Handles for dynamic dataframe-method-wrapping pipeline stages …


Custom exceptions for pdpipe.


PdPipeline stages dependent on the nltk Python library …


Classes for sklearn integration …


PdPipeline stages dependent on the scikit-learn Python library …


Text processing pdpipe pipeline stages.


Custom types for pdpipe.


Utility methods for pdpipe.


Wrapper-kind pdpipe pipeline stages.