Metadata-Version: 1.1
Name: pandas-redistrict
Version: 0.0.2
Summary: Redistricting of pandas dataframes
Home-page: https://github.com/correctiv/pandas-redistrict
Author: Stefan Wehrmeyer
Author-email: stefan.wehrmeyer@correctiv.org
License: MIT
Description: # pandas-redistrict
        
        Uses data on redistricting to apply redistricting to older datasets to represent the districts in their current state.
        
        Supports merging and splitting of districts:
        - Merged districts are summed up under new identifier
        - Split districts are distributed by population-based ratio.
        
        Data on redistricting is in `data/` directory. Currently only available for German *Kreise* (containing reforms in NRW, Sachsen, Sachsen-Anhalt and Mecklenburg-Vorpommern).
        
        Install like this:
        
            pip install pandas-redistrict
        
        
        ## Usage
        
        ``` python
        >>> df  # Values indexed by German district identifiers
        value1  value2
        AGS
        05354       4       5
        05313       5       6
        05334       6       7
        15154       8       9
        15159      10      11
        15151      12      13
        15082      13      14
        
        >>> # Port old identifiers to new versions. Sum and distribute values on the way
        >>> from redistrict import redistrict
        >>> redistrict(df, 'de/kreise', drop=True, splits=True)
        value1  value2
        AGS
        05334   15.00   18.00
        15001    2.40    2.60
        15082   35.44   38.81
        15086    0.96    1.04
        15091    4.20    4.55
        ```
        
        When you want to preserve groups inside districts, you can use ``redistrict_grouped``:
        
        ``` python
        >>> # Specify district column (e.g. AGS)
        >>> # Also specify groups to preserve, in this case year
        >>> df
             AGS  year  value1  value2
        0  05354  2008       4       5
        1  05313  2008       5       6
        2  05334  2011       6       7
        3  15154  2005       8       9
        4  15159  2005      10      11
        5  15151  2005      12      13
        6  15082  2013      13      14
        >>> # from redistrict import redistrict_grouped
            redistrict_grouped(df, 'de/kreise', ['year'],
                                            district_col='AGS',
                                            value_cols=['value1', 'value2'],
                                            drop=True)
        
             AGS  value1  value2  year
        0  15001    2.40    2.60  2005
        1  15082   22.44   24.81  2005
        2  15086    0.96    1.04  2005
        3  15091    4.20    4.55  2005
        0  05334    9.00   11.00  2008
        0  05334    6.00    7.00  2011
        0  15082   13.00   14.00  2013
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
