Metadata-Version: 2.1
Name: tubular
Version: 0.2.14
Summary: Package to perform pre processing steps for machine learning models
Home-page: UNKNOWN
Author: LV GI Data Science Team
Author-email: #DataSciencePackages@lv.co.uk
License: UNKNOWN
Description: # tubular 
        
        ----
        
        `tubular` implements transformers for pre processing steps commonly used in machine learning pipelines. 
        
        The transformers are compatible with scikit-learn [Pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), having 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 DataFrames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html). 
        
        There are a variety of transformers to assist with;
        
        - capping
        - imputation
        - mapping
        - date differencing 
        - categorical encoding
        - numeric operations
        
        Here is a simple example of capping 2 columns at a specified value;
        
        ```python
        from tubular.capping import CappingTransformer
        import pandas as pd
        from sklearn.datasets import load_boston
        
        # load the boston dataset
        boston = load_boston()
        y = boston.target
        X = pd.DataFrame(boston.data, columns=boston.feature_names)
        
        # initialise a capping transformer for 2 columns
        capper = CappingTransformer(columns=['INDUS', 'RM'], cap_value_max = 20)
        
        # transform the data
        X_capped = capper.transform(X)
        ```
        
        # Installation
        
        tubular can be installed from PyPI simply with;
        
         `pip install tubular`
        
        # Documentation
        
        Documentation for tubular can be found on [readthedocs](https://tubular.readthedocs.io/en/latest/).
        
        # Examples
        
        To help get started there are example notebooks in the [examples](https://github.com/lvgig/tubular/tree/master/examples) folder that show how to use each transformer as well as an example of putting several together in a Pipeline.
        
        # Build and test
        
        The test framework we are using for this project is [pytest](https://docs.pytest.org/en/stable/), to 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
        ```
        
        Then install tubular in editable mode;
        
        ```shell
        pip install -e . -r requirements-dev.txt
        ```
        
        Then run the tests simply with pytest
        
        ```shell
        pytest
        ```
        
        # Contribute
        
        `tubular` is under active development, we're super excited if you're interested in contributing! See the `CONTRIBUTING.md` for the full details of our working practices. 
        
        For bugs and feature requests please open an [issue](https://github.com/lvgig/tubular/issues).
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
