sk-transformers¶

A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps 🛠

Introduction¶

Every tabular data is different. Every column needs to be treated differently. Pandas is already great! And scikit-learn has a nice collection of dataset transformers. But the possibilities of data transformation are infinite. This project tries to provide a brought collection of data transformers that can be easily used together with scikit-learn - either in a pipeline or just on its own. See the usage chapter for some examples.

The idea is simple. It is like a well-equipped toolbox 🧰: You always find the tool you need and sometimes you get inspired by seeing a tool you did not know before. Please feel free to contribute your tools and ideas.

Check out some examples in the Jupyter notebook.

Installation¶

If you are using pip, you can install the package with the following command:

pip install sk-transformers

If you are using Poetry, you can install the package with the following command:

poetry add sk-transformers

installing dependencies¶

With pip:

pip install -r requirements.txt

With Poetry:

poetry install

Available transformers¶

Module	Transformer	Description
`Datetime transformer`	`DateColumnsTransformer`	Splits a date column into multiple columns.
`Datetime transformer`	`DurationCalculatorTransformer`	Calculates the duration between to given dates.
`Encoder transformer`	`MeanEncoderTransformer`	Scikit-learn API for the feature-engine MeanEncoder.
`Generic transformer`	`AggregateTransformer`	This transformer uses Pandas groupby method and aggregate to apply function on a column grouped by another column.
`Generic transformer`	`AllowedValuesTransformer`	This transformer replaces values that are not in a list with another value.
`Generic transformer`	`ColumnDropperTransformer`	Drops columns from a dataframe using Pandas drop method.
`Generic transformer`	`ColumnEvalTransformer`	Provides the possibility to use Pandas methods on columns.
`Generic transformer`	`DtypeTransformer`	Transformer that converts a column to a different dtype.
`Generic transformer`	`FunctionsTransformer`	This transformer is a plain wrapper around the sklearn.preprocessing.FunctionTransformer.
`Generic transformer`	`LeftJoinTransformer`	Uses Pandas merge function to perform a left-join based on the column of a dataframe and the index of another dataframe. The right dataframe is essentially a lookup table.
`Generic transformer`	`MapTransformer`	This transformer iterates over all columns in the `features` list and applies the given callback to the column. For this it uses the `pandas.Series.map` method.
`Generic transformer`	`NaNTransformer`	Replace NaN values with a specified value. Internally Pandas fillna method is used.
`Generic transformer`	`QueryTransformer`	Applies a list of queries to a dataframe. If it operates on a dataset used for supervised learning this transformer should be applied on the dataframe containing `X` and `y`.
`Generic transformer`	`ValueIndicatorTransformer`	Adds a column to a dataframe indicating if a value is equal to a specified value.
`Generic transformer`	`ValueReplacerTransformer`	Uses Pandas replace method to replace values in a column.
`Number transformer`	`MathExpressionTransformer`	Applies an operation to a column and a given value or column. The operation can be any operation from the `numpy` or `operator` package.
`Number transformer`	`GeoDistanceTransformer`	Calculates the distance in kilometers between two places on the earth using the latitudes and longitudes.
`String transformer`	`EmailTransformer`	Transforms an email address into multiple features.
`String transformer`	`IPAddressEncoderTransformer`	Encodes IPv4 and IPv6 strings addresses to a float representation.
`String transformer`	`PhoneTransformer`	Transforms a phone number into multiple features.
`String transformer`	`StringSimilarityTransformer`	Calculates the similarity between two strings using the `gestalt pattern matching` algorithm from the `SequenceMatcher` class.
`String transformer`	`StringSlicerTransformer`	Slices all entries of specified string features using the slice() function.
`String transformer`	`StringSplitterTransformer`	Splits a string column into multiple columns based on the occurrence of a character.
`String transformer`	`StringCombinationTransformer`	Contatenates two string columns after ordering them alphabetically first.

Usage¶

Let's assume you want to use some method from [NumPy's mathematical functions, to sum up the values of column foo and column bar. You could use the MathExpressionTransformer.

import pandas as pd
from sk_transformers import MathExpressionTransformer

X = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
transformer = MathExpressionTransformer([("foo", "np.sum", "bar", {"axis": 0})])
transformer.fit_transform(X).to_numpy()

array([[1, 4, 5],
       [2, 5, 7],
       [3, 6, 9]])

Even if we only pass one tuple to the transformer - in this example. Like with most other transformers the idea is to simplify preprocessing by giving the possibility to operate on multiple columns at the same time. In this case, the MathExpressionTransformer has created an extra column with the name foo_sum_bar.

In the next example, we additionally add the MapTransformer. Together with scikit-learn's pipelines it would look like this:

import pandas as pd
from sk_transformers import MathExpressionTransformer
from sk_transformers import MapTransformer
from sklearn.pipeline import Pipeline

X = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
map_step = MapTransformer([("foo", lambda x: x + 100)])
sum_step = MathExpressionTransformer([("foo", "np.sum", "bar", {"axis": 0})])
pipeline = Pipeline([("map_step", map_step), ("sum_step", sum_step)])
pipeline.fit_transform(X)

   foo  bar  foo_sum_bar
0  101    4          105
1  102    5          107
2  103    6          109

Contributing¶

We're all kind of in the same boat. Preprocessing/feature engineering in data science is somehow very individual - every feature is different and must be handled and processed differently. But somehow we all have the same problems: sometimes date columns have to be changed. Sometimes strings have to be formatted, sometimes durations have to be calculated, etc. There is a huge number of preprocessing possibilities but we all use the same tools.

scikit-learns pipelines help to use formalized functions. So why not also share these so-called transformers with others? This open-source project has the goal to collect useful preprocessing pipeline steps. Let us all collect what we used for preprocessing and share it with others. This way we can all benefit from each other's work and save a lot of time. So if you have a preprocessing step that you use regularly, please feel free to contribute it to this project. The idea is that this is not only a toolbox but also an inspiration for what is possible. Maybe you have not thought about this preprocessing step before.

Please check out the guide on how to contribute to this project.