ANN: badfish v0.1.2 - missing data analysis and wrangling library

[Harsh Nisar]

Hi,

We are happy to announce the first release of badfish - a library built over pandas to explore and analyze missing data with a convenient api.
It is our first release and we would really appreciate honest feedback about the library and how we could improve it - also if we missed something really basic.

What is it?
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Badfish introduces MissFrame, a wrapper over pandas DataFrame, to wrangle through and investigate missing data. It opens an easy to use API to summarize and explore patterns in missingness.

Badfish provides methods which make it easy to investigate any systematic issues in data wrangling, surveys, ETL processes which can lead to missing data.

Current methods:
 - Corr (correlations between missingness columns)
 - Counts
 - Cohort analysis ( group differences between columns other than the one with missing values)
 - patternplot (visualizing different permuations of missing data)
 - Association rule mining (which columns go missing together)


The API has been inspired by typical questions which arise when exploring missing data. Badfish uses the where and how api in most of its methods to prepare a subset of the data to work on.

where : Work on a subset of data where specified columns are missing.
how : Either all | any of the columns should be missing.

Eg. mf.counts(columns = ['Age', 'Gender']) would give counts of missing values in the entire dataset.

While, mf.counts(where=['Income'], columns = ['Age', 'Gender']) would give counts of missing values in subset of data where Income is already missing.

Links
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https://github.com/harshnisar/badfish

Also,
pip install badfish


Contributors
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Harsh Nisar
Deshana Desai

Looking forward to your comments and feedback,

Regards,
Team Badfish