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A data preprocessing helper consists of your basic preprocessing needs

Project description

PreProcessing Ninja

A PreProcessing library for your basic PreProcessing needs

forthebadge

Python 3.6

Features of the package

  • impute_missing_value: Helps to impute missing values
  • scale_data: Scales the data
  • encode_categorical_data: Helps in encoding categorical variables
  • remove_outliers: Helps in removing outliers

Use

pip install PreProcessingNinja

Example

from preprocessing_ninja import PreProcessingNinja
ninja = PreProcessingNinja()

#creating column dictionary
d = {
    'column1':'mean',
    'column2':'mean',
    'column3':'most_frequent'
}

#calling method
df = ninja.impute_missing_value(datafra,e, d)

Note

There might be bugs.

Authors

Project details


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PreProcessingNinja-0.0.1.tar.gz (3.0 kB view hashes)

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