| Name | tubular JSON |
| Version |
2.1.0
JSON |
| download |
| home_page | None |
| Summary | Package to perform pre processing steps for machine learning models |
| upload_time | 2025-10-30 14:40:23 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License
Copyright (c) 2021, Liverpool Victoria General Insurance Group.
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| keywords |
data science
feature engineering
data transforms
pipeline
sklearn
machine learning
ml
ds
|
| VCS |
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<p align="center">
<img src="https://github.com/azukds/tubular/raw/main/logo.png">
</p>
Tubular pre-processing for machine learning!
----
[](https://mybinder.org/v2/gh/azukds/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` are written in narwhals [narwhals](https://narwhals-dev.github.io/narwhals/), so are agnostic between [pandas](https://pandas.pydata.org/) and [polars](https://pola.rs/) dataframes, and will utilise the chosen (pandas/polars) API under the hood.
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
import polars as pl
transformer=CappingTransformer(
capping_values={'a': [10, 20], 'b': [1,3]},
)
test_df=pl.DataFrame({'a': [1,15,18,25], 'b': [6,2,7,1], 'c':[1,2,3,4]})
transformer.transform(test_df)
# ->
# shape: (4, 3)
# ┌─────┬─────┬─────┐
# │ a ┆ b ┆ c │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ 10 ┆ 3 ┆ 1 │
# │ 15 ┆ 2 ┆ 2 │
# │ 18 ┆ 3 ┆ 3 │
# │ 20 ┆ 1 ┆ 4 │
# └─────┴─────┴─────┘
```
## 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/azukds/tubular/blob/main/docs/README.md).
## Examples
We utilise [doctest](https://docs.python.org/3/library/doctest.html) to keep valid usage examples in the docstrings of transformers in the package, so please see these for getting started!
## Issues
For bugs and feature requests please open an [issue](https://github.com/azukds/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/azukds/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/azukds/tubular/blob/main/CONTRIBUTING.rst) file for the full details of our working practices.
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"description": "<p align=\"center\">\n <img src=\"https://github.com/azukds/tubular/raw/main/logo.png\">\n</p>\n\nTubular pre-processing for machine learning!\n\n----\n\n\n\n\n\n\n\n[](https://mybinder.org/v2/gh/azukds/tubular/HEAD?labpath=examples)\n\n`tubular` implements pre-processing steps for tabular data commonly used in machine learning pipelines.\n\nThe 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.\n\nThe transformers in `tubular` are written in narwhals [narwhals](https://narwhals-dev.github.io/narwhals/), so are agnostic between [pandas](https://pandas.pydata.org/) and [polars](https://pola.rs/) dataframes, and will utilise the chosen (pandas/polars) API under the hood.\n\nThere are a variety of transformers to assist with;\n\n- capping\n- dates\n- imputation\n- mapping\n- categorical encoding\n- numeric operations\n\nHere is a simple example of applying capping to two columns;\n\n```python\nimport polars as pl\n\ntransformer=CappingTransformer(\ncapping_values={'a': [10, 20], 'b': [1,3]},\n )\n\ntest_df=pl.DataFrame({'a': [1,15,18,25], 'b': [6,2,7,1], 'c':[1,2,3,4]})\n\ntransformer.transform(test_df)\n# ->\n# shape: (4, 3)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 a \u2506 b \u2506 c \u2502\n# \u2502 --- \u2506 --- \u2506 --- \u2502\n# \u2502 i64 \u2506 i64 \u2506 i64 \u2502\n# \u255e\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2561\n# \u2502 10 \u2506 3 \u2506 1 \u2502\n# \u2502 15 \u2506 2 \u2506 2 \u2502\n# \u2502 18 \u2506 3 \u2506 3 \u2502\n# \u2502 20 \u2506 1 \u2506 4 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n## Installation\n\nThe easiest way to get `tubular` is directly from [pypi](https://pypi.org/project/tubular/) with;\n\n `pip install tubular`\n\n## Documentation\n\nThe documentation for `tubular` can be found on [readthedocs](https://tubular.readthedocs.io/en/latest/).\n\nInstructions for building the docs locally can be found in [docs/README](https://github.com/azukds/tubular/blob/main/docs/README.md).\n\n## Examples\n\nWe utilise [doctest](https://docs.python.org/3/library/doctest.html) to keep valid usage examples in the docstrings of transformers in the package, so please see these for getting started!\n\n## Issues\n\nFor bugs and feature requests please open an [issue](https://github.com/azukds/tubular/issues).\n\n## Build and test\n\nThe 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.\n\nFirst clone the repo and move to the root directory;\n\n```shell\ngit clone https://github.com/azukds/tubular.git\ncd tubular\n```\n\nNext install `tubular` and development dependencies;\n\n```shell\npip install . -r requirements-dev.txt\n```\n\nFinally run the test suite with `pytest`;\n\n```shell\npytest\n```\n\n## Contribute\n\n`tubular` is under active development, we're super excited if you're interested in contributing! \n\nSee the [CONTRIBUTING](https://github.com/azukds/tubular/blob/main/CONTRIBUTING.rst) file for the full details of our working practices.\n",
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