[EasyImputer] is an abstract library for missing value imputation based on missing data statistics.

Currently supported data types

• Cross sectional numeric data only

• Works for cross sectional datasets having missing values in one or more columns
• Easy to use. Takes away the need to switch between many different kinds of imputation, by acting as a one stop shop.
• It provides flexibility by allowing the user to force the kind of imputation desired.

• EasyImputer
  • Features
• Table of contents
• Installation
• Usage
  • Quick Start
  • Real Dataset Examples
  • Support and Contributions
  • Acknowledgement
  • License

Assuming that anaconda environment is already installed,

• EasyImputer can be installed from PyPI using

pip install EasyImputer

Supports imputation on all numeric cross sectional data.

from EasyImputer import CSDImputer

imputer = CSDImputer()
statistics, imputed_values, comments = imputer.impute(input_df)

Use models = [] to override the imputation strategy. Alowd imputation techniques can be obtained from constant.py

Refer to datasets folder

Please submit bug reports and feature requests as Issues. Contributions are very welcome.

For additional questions and feedback, please contact us at [email protected]

EasyImputer is developed by Emerging Tech Team at Fidelity Investments. The part of the package was developed as part of an internship program at Fidelity. We thank [Ambika Sadhu] for her contribution to the package.

EasyImputer is licensed under the GPL License 3.0.