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https://github.com/lvgig/tubular
Python package implementing transformers for pre processing steps for machine learning.
https://github.com/lvgig/tubular
feature-engineering pre-processing transformers
Last synced: 25 days ago
JSON representation
Python package implementing transformers for pre processing steps for machine learning.
- Host: GitHub
- URL: https://github.com/lvgig/tubular
- Owner: lvgig
- License: bsd-3-clause
- Created: 2021-04-23T12:58:45.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-01-15T13:11:02.000Z (about 1 month ago)
- Last Synced: 2025-01-19T06:08:12.719Z (about 1 month ago)
- Topics: feature-engineering, pre-processing, transformers
- Language: Python
- Homepage: https://tubular.readthedocs.io/en/latest/index.html
- Size: 2.3 MB
- Stars: 53
- Watchers: 9
- Forks: 15
- Open Issues: 64
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.rst
Awesome Lists containing this project
README
Tubular pre-processing for machine learning!
----
[](https://mybinder.org/v2/gh/lvgig/tubular/HEAD?labpath=examples)
`tubular` implements pre-processing steps for tabular data commonly used in machine learning pipelines.
The transformers are compatible with [scikit-learn](https://scikit-learn.org/) [Pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). Each has a `transform` method to apply the pre-processing step to data and a `fit` method to learn the relevant information from the data, if applicable.
The transformers in `tubular` work with data in [pandas](https://pandas.pydata.org/) [DataFrames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).
There are a variety of transformers to assist with;
- capping
- dates
- imputation
- mapping
- categorical encoding
- numeric operations
Here is a simple example of applying capping to two columns;
```python
from tubular.capping import CappingTransformer
import pandas as pd
from sklearn.datasets import fetch_california_housing
# load the california housing dataset
cali = fetch_california_housing()
X = pd.DataFrame(cali['data'], columns=cali['feature_names'])
# initialise a capping transformer for 2 columns
capper = CappingTransformer(capping_values = {'AveOccup': [0, 10], 'HouseAge': [0, 50]})
# transform the data
X_capped = capper.transform(X)
```
## Installation
The easiest way to get `tubular` is directly from [pypi](https://pypi.org/project/tubular/) with;
`pip install tubular`
## Documentation
The documentation for `tubular` can be found on [readthedocs](https://tubular.readthedocs.io/en/latest/).
Instructions for building the docs locally can be found in [docs/README](https://github.com/lvgig/tubular/blob/main/docs/README.md).
## Examples
To help get started there are example notebooks in the [examples](https://github.com/lvgig/tubular/tree/main/examples) folder in the repo that show how to use each transformer.
To open the example notebooks in [binder](https://mybinder.org/) click [here](https://mybinder.org/v2/gh/lvgig/tubular/HEAD?labpath=examples) or click on the `launch binder` shield above and then click on the directory button in the side bar to the left to navigate to the specific notebook.
## Issues
For bugs and feature requests please open an [issue](https://github.com/lvgig/tubular/issues).
## Build and test
The test framework we are using for this project is [pytest](https://docs.pytest.org/en/stable/). To build the package locally and run the tests follow the steps below.
First clone the repo and move to the root directory;
```shell
git clone https://github.com/lvgig/tubular.git
cd tubular
```
Next install `tubular` and development dependencies;
```shell
pip install . -r requirements-dev.txt
```
Finally run the test suite with `pytest`;
```shell
pytest
```
## Contribute
`tubular` is under active development, we're super excited if you're interested in contributing!
See the [CONTRIBUTING](https://github.com/lvgig/tubular/blob/main/CONTRIBUTING.rst) file for the full details of our working practices.