Metadata-Version: 1.1
Name: dupandas
Version: 0.3
Summary: python package to deduplicate text data in pandas dataframe using flexible string matching and cleaning
Home-page: https://github.com/shivam5992/dupandas
Author: Shivam Bansal
Author-email: shivam5992@gmail.com
License: MIT
Description: # **dupandas:** data deduplication of text records in a pandas dataframe
        
        
        [![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](http://www.repostatus.org/badges/latest/wip.svg)](https://github.com/shivam5992/dupandas) [![Twitter Follow](https://img.shields.io/twitter/follow/shields_io.svg?style=social&label=Follow&maxAge=2592000)](https://twitter.com/shivamshaz)
        
        dupandas is a python package to perform data deduplication on columns of a pandas dataframe using flexible text matching. It is compatible with both versions of python (2.x and 3.x). dupandas can find duplicate any kinds of text records in the pandas data. It comprises of sophisticated Matchers that can handle spelling differences and phonetics. It also comprises of several Cleaners, which can be used to clean up the noise present in the text data such as punctuations, digits, casing etc.
        
        For fast computations, dupandas uses lucene based text indexing. In the input_config, if "indexing" = True, then it indexes the dataset in RAMDirectory which is used to identify and search similar strings. Check out the instructions of installing PyLucene below.
        
        The good part of dupandas is that it's Matchers and Cleaner functions can be used as standalone packages while working with different problems of text data.
        
        
        ## Installation
        Following python modules are required to use dupandas: **pandas, fuzzy, python-levenshtein** . These modules can be installed using pip command:
        
        ```python
            pip install dupandas pandas fuzzy python-levenshtein
        ```
        **OR** if dependencies are already installed:
        
        ```
            pip install dupandas
        ```
        
        **OPTIONAL** For faster implementation dupandas with indexing feature is recommended. **PyLucene** Installation : Refer to [this](http://installion.co.uk/ubuntu/vivid/universe/p/pylucene/install/index.html) link. 
        
        **Note:** The use of indexing can reduce the overall computation time of completion to one third.
        
        ## Usage : dupandas
        dupandas using default Matchers and Cleaners, Default Matcher and Cleaners are Exact Match and No Cleaning respectively.
        
        ``` python
            from dupandas import Dedupe
            dupe = Dedupe()
            
            input_config = {
                'input_data' : pandas_dataframe,
                'column' : 'column_name_to_deduplicate',
                '_id' : 'unique_id_column_of_dataset',
                }
            results = dupe.dedupe(input_config)
        ```
        
        dupandas using custom Cleaner and Matcher configs
        
        ```  python
            from dupandas import Dedupe
        
            clean_config = { 'lower' : True, 'punctuation' : True, 'whitespace' : True, 'digit' : True }
            match_config = { 'exact' : False, 'levenshtein' : True, 'soundex' : False, 'nysiis' : False}
            dupe = Dedupe(clean_config = clean_config, match_config = match_config)
        
            input_config = {
                'input_data' : pandas_dataframe,
                'column' : 'column_name_to_deduplicate',
                '_id' : 'unique_id_column_of_dataset',
                }
            results = dupe.dedupe(input_config)
        ```
        
        Other options in input_config 
        
        ```python
            input_config = {
                'input_data' : pandas_dataframe,
                'column' : 'column_name_to_deduplicate',
                '_id' : 'unique_id_column_of_dataset',
                'score_column' : 'name_of_the_column_for_confidence_score',
                'threshold' : 0.75, # float value of threshold
                'unique_pairs' : True, # boolean to get unique (A=B) or duplicate (A=B and B=A) results
                'indexing' : False # Boolean to set lucene indexing = True / False, Default: False
                }
        ```
        
        ## Usage : standalone Cleaner class
        
        ```python
            from dupandas import Cleaner
            clean_config = { 'lower' : True, 'punctuation' : True, 'whitespace' : True, 'digit' : True }
            clean = Cleaner(clean_config)
            clean.clean_text("new Delhi 3#! 34 ")
        ```
        
        ## Usage: standalone Matcher class
        
        ```python
            from dupandas import Matcher
            match_config = { 'exact' : False, 'levenshtein' : True, 'soundex' : False, 'nysiis' : False}
            match = Matcher(match_config)
            match.match_elements("new delhi", "newdeli")
        ```
        
        ## Issues
        
        Thanks for checking this work, Yes ofcourse there is a scope of improvement, Feel free to submit issues and enhancement requests.
        
        ## Contributing
        #### ToDos
        
        1. [ ]  V2: Add Support for multi column match
        2. [ ]  V2: Add More Matchers, Cleaners
        3. [ ]  V2: Remove Library Dependencies
        4. [ ]  V2: Handle Longer Texts, Optimize Speed
        
        #### Steps 
         1. **Fork** the repo on GitHub
         2. **Clone** the project to your own machine
         3. **Commit** changes to your own branch
         4. **Push** your work back up to your fork
         5. Submit a **Pull request** 
Keywords: pandas,deduplication,text cleaning,flexible matching
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