PanDas PRePRocessor: Preprocess Pandas Objects for Machine Learning
Installation
$ pip install pdprpr
Usage
Assume you have following DataFrame to be preprocessed:
from pandas import DataFrame
df = DataFrame({
'num': [1, 3, float('nan')], # numerical feature, needs to be scaled in [0, 1]
'cat': ['p', 'q', 'r'], # categorical feature, needs to be transformted to dummy var
'bin': [False, False, True], # binary feature, needs to be 0 / 1
}, columns =['num', 'cat', 'bin'])
# num cat bin
# 0 1.0 p False
# 1 3.0 q False
# 2 NaN r True
Define preprocessing settings:
# preprocessing.yml
- name: num
kind: numerical
- name: cat
kind: categorical
- name: bin
kind: binary
Then create DataFramePreprocessor with them:
import yaml
with open('preprocessing.yml') as f:
settings = yaml.load(f)
from pdprpr import DataFramePreprocessor
processor = DataFramePreprocessor(settings)
Finally use it to preprocess the DataFrame:
processor.process(df)
# num__VALUE cat__p cat__q cat__r bin__TRUE
# 0 0.0 1 0 0 0
# 1 1.0 0 1 0 0
# 2 NaN 0 0 1 1
Documentation
For more options please see tests untill docs get available…
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