Metadata-Version: 2.1
Name: scikit-learn-pipeline-utils
Version: 0.0.7
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
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Requires-Dist: scikit-learn
Requires-Dist: pandas

# 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 

