Metadata-Version: 2.1
Name: scikit-learn-pipeline-utils
Version: 0.0.4
Summary: Custom Pipeline Transformers for Sklearn Pipelines
Home-page: https://github.com/deltahedge1/scikit-learn-pipeline-utils
Author: Ish Hassan
Author-email: ishassan90@gmail.com
License: MIT
Description: # Helpful Package For Custom Transformers To Use In Sklearn Pipelines
        
        ## Installation
        `pip install scikit-learn-pipeline-utils`
        
        ## Quick Start
        ```
        from sklearn_pipeline_utils import DFSelector
        from sklearn.pipeline import Pipeline
        import pandas as pd
        
        df = pd.DataFrame({
            "col1": ["a", "b", "a"],
            "col2": [1, 2, 3]
        })
        
        pipeline = Pipeline([
            ("dfselectcol1", DFSelector("col1"))
            ])
        
        print(pipeline.transform(df))
        '''
        expected result:
          col1
        0    a
        1    b
        2    a
        '''
        ```
        
        ## Dataframe Transformers List
        1. DFSelector
        2. DFObjectSelector
        3. DFFeatureUnion
        4. DFImputer
        5. DFImputerMostFrequent
        6. DFOrdinalEncoder
        7. DFStandardScaler 
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
