Metadata-Version: 1.1
Name: badfish
Version: 0.1.1a0
Summary: Badfish - A missing data analysis and wrangling library in Python
Home-page: https://github.com/harshnisar/badfish
Author: Harsh Nisar and Deshana Desai
Author-email: nisar.harsh@gmail.com
License: License :: OSI Approved :: MIT License
Description: # Badfish - A missing data wrangling library in Python
        
        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.
        
        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. 
        
        
        ## Installation
        `pip install badfish`
        
        ## Usage
            >>> import badfish as bf
            >>> mf = bf.MissFrame(df)
        
        ### Example
        Will add an exmaple IPython notebook soon.
        
        ### Counts
        Basic counts of missing data per column.
        
            >>> mf.counts(where=['gender', 'age'], how='all', columns=['Income', 'Marital Status'])
        
        ### Pattern
        Get counts on different combinations of columns with missing data. `True` means missing and `False` means present.
        
            >>> mf.pattern()
        
        The same can be visualized in the form of a plot (inspired by VIM package in R)
        
            >>> mf.plot(kind='pattern')
        
            Example plot:
        
        <img src="https://raw.githubusercontent.com/harshnisar/badfish/master/images/patternplot.png" width=500 />
        
        Note: Both `where` and `how` can be used in this method.
        
        ### Itemset Mining
        Use frequency item set mining to find subgroups where data goes missing together.
        Note: This uses the PyMining package.
        
            >>> itemsets, rules = mf.frequency_item_set()
        
        ### Cohort
        Tries to find signigicant group differences between values of columns other than the ones specified in the group clause. Group 
        made on the basis of missing or non-missing of columns in the group clause. Internally uses `scipy.stats.ttest_ind`.
        
        This method works on the values in each column instead of column names.
        
        Note: Experimental method. 
        
            >>> mf.cohort(group=['gender'], columns=['Income'])
        
        
        ## License
        Please see the [repository license](https://github.com/harshnisar/badfish/blob/master/LICENSE).
        
        Generally, we have licensed badfish to make it as widely usable as possible. 
        
        ## Call for contribution
        If you have any ideas, issues or feature requests, feel free to open an issue, send a PR or contact us.
        
        
        ## Authors
        [Harsh Nisar](http://github.com/harshnisar) & [Deshana Desai](http://github.com/deshna)
        
        
        
        ## Interesting links
         - https://github.com/tierneyn/ggmissing
         - https://github.com/tierneyn/visdat
         - http://www.njtierney.com/blag/rbloggers/2016/03/06/wombat-2016-wrap-up/
        
        
        
Keywords: data cleaning,missing,machine learning,data analysis,imputation,data wrangling
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Utilities
