Metadata-Version: 2.1
Name: imputena
Version: 0.3
Summary: Package that allows both automated and customized treatment of missing values in datasets using Python.
Home-page: http://github.com/macarro/imputena
Author: Miguel Macarro
Author-email: migmackle@alum.us.es
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Financial and Insurance Industry
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: statsmodels
Requires-Dist: sklearn


This package allows both automated and customized treatment of missing
values in datasets using Python. The treatments that are implemented in this
package are:

* Listwise deletion
* Pairwise deletion
* Dropping variables
* Random sample imputation
* Random hot-deck imputation
* LOCF
* NOCB
* Most frequent substitution
* Mean and median substitution
* Constant value imputation
* Random value imputation
* Interpolation
* Interpolation with seasonal adjustment
* Linear regression imputation
* Stochastic regression imputation
* Logistic regression imputation
* K-nearest neighbors imputation
* Sequential regression multiple imputation
* Multiple imputation by chained equations

All these treatments can be applied to whole datasets or parts of them and
 allow for extensive customization. The package can also recommend a
  treatment for a given dataset, inform about the treatments that are
   applicable to it, and automatically apply the best treatment.


