pandas-redistrict 0.0.3

# 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
```