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        "author": "Stefan Wehrmeyer",
        "author_email": "stefan.wehrmeyer@correctiv.org",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering"
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        "description": "# pandas-redistrict\n\nUses data on redistricting to apply redistricting to older datasets to represent the districts in their current state.\n\nSupports merging and splitting of districts:\n- Merged districts are summed up under new identifier\n- Split districts are distributed by population-based ratio.\n\nData on redistricting is in `data/` directory. Currently only available for German *Kreise* (containing reforms in NRW, Sachsen, Sachsen-Anhalt and Mecklenburg-Vorpommern).\n\nInstall like this:\n\n    pip install pandas-redistrict\n\n\n## Usage\n\n``` python\n>>> df  # Values indexed by German district identifiers\nvalue1  value2\nAGS\n05354       4       5\n05313       5       6\n05334       6       7\n15154       8       9\n15159      10      11\n15151      12      13\n15082      13      14\n\n>>> # Port old identifiers to new versions. Sum and distribute values on the way\n>>> from redistrict import redistrict\n>>> redistrict(df, 'de/kreise', drop=True, splits=True)\nvalue1  value2\nAGS\n05334   15.00   18.00\n15001    2.40    2.60\n15082   35.44   38.81\n15086    0.96    1.04\n15091    4.20    4.55\n```\n\nWhen you want to preserve groups inside districts, you can use ``redistrict_grouped``:\n\n``` python\n>>> # Specify district column (e.g. AGS)\n>>> # Also specify groups to preserve, in this case year\n>>> df\n     AGS  year  value1  value2\n0  05354  2008       4       5\n1  05313  2008       5       6\n2  05334  2011       6       7\n3  15154  2005       8       9\n4  15159  2005      10      11\n5  15151  2005      12      13\n6  15082  2013      13      14\n>>> from redistrict import redistrict_grouped\n    redistrict_grouped(df, 'de/kreise', ['year'],\n                                    district_col='AGS',\n                                    value_cols=['value1', 'value2'],\n                                    drop=True)\n\n     AGS  value1  value2  year\n0  15001    2.40    2.60  2005\n1  15082   22.44   24.81  2005\n2  15086    0.96    1.04  2005\n3  15091    4.20    4.55  2005\n0  05334    9.00   11.00  2008\n0  05334    6.00    7.00  2011\n0  15082   13.00   14.00  2013\n```",
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