https://github.com/jordandelbar/feature-engineering-polars

Feature engineering done with Polars
https://github.com/jordandelbar/feature-engineering-polars

data-science feature-engineering machine-learning polars python

Last synced: 2 months ago
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Feature engineering done with Polars

• Host: GitHub
• URL: https://github.com/jordandelbar/feature-engineering-polars
• Owner: jordandelbar
• License: mit
• Created: 2023-03-23T20:43:35.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-12-07T21:17:37.000Z (over 2 years ago)
• Last Synced: 2024-07-08T21:17:58.065Z (almost 2 years ago)
• Topics: data-science, feature-engineering, machine-learning, polars, python
• Language: Python
• Homepage:
• Size: 93.8 KB
• Stars: 6
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

          
# Feature Engineering with Polars

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/feature-engineering-polars?logo=Python)](https://pypi.org/project/feature-engineering-polars/)

[![GitHub](https://img.shields.io/github/license/jordandelbar/feature-engineering-polars)](https://github.com/jordandelbar/feature-engineering-polars/blob/main/LICENSE.md)

[![codecov](https://codecov.io/gh/jordandelbar/feature-engineering-polars/branch/main/graph/badge.svg?token=TUKAPUHHEV)](https://codecov.io/gh/jordandelbar/feature-engineering-polars)

Feature engineering done with Polars

![fe-polars](https://user-images.githubusercontent.com/35341015/229273836-9f87fd67-2011-4aa9-a7d8-680795d75259.png)

## How to install

```bash

pip install feature-engineering-polars

```

## How to use it

```python

import polars as pl

from fe_polars.imputing.base_imputing import Imputer

from fe_polars.encoding.target_encoding import TargetEncoder

dataframe = pl.DataFrame(

        {

            "City": ["A", "A", "B", "B", "B", "C", "C", "C"],

            "Rain": [103, None, 90, 75, None, 200, 155, 127],

            "Temperature": [30.5, 32, 25, 38, 40, 29.6, 21.3, 24.9],

        }

    )

imputer = Imputer(features_to_impute=["Rain"], strategy="mean")

encoder = TargetEncoder(smoothing=2, features_to_encode=["City"])

temp = imputer.fit_transform(x=dataframe)

encoder.fit_transform(x=temp, y=dataframe['Temperature'])

shape: (8, 3)

City    Temperature Rain

f64     f64         f64

30.706  30.5        103.0

30.706  32.0        125.0

32.665  25.0        90.0

32.665  38.0        75.0

32.665  40.0        125.0

27.225  29.6        200.0

27.225  21.3        155.0

27.225  24.9        127.0

```

## Available transformers

- Encoding:

  - Target encoding

  - One hot encoding

- Imputing:

  - Base imputing:

    - Mean imputing

    - Median imputing

    - Max imputing

    - Min imputing

    - Fixed value imputing