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Lots of metrics for quantifying gerrymandering.

License: GNU General Public License v3.0

Python 80.35% Jupyter Notebook 19.65%

gerrymandering redistricting politics

gerrymandertests's Introduction

Metrics for quantifying gerrymandering

This repository contains:

Python code for implementing a number of metrics for quantifying gerrymandering⁹:
- Mean-median difference and variant:
  - Mean-median difference^1,2
  - Equal vote weight²
- Lopsided margins (two-sample t-test on win margins)¹
- Bootstrap (Monte Carlo) simulation¹
- Mann-Whitney U test
- Declination variants³
  - Declination
  - Declination (buffered)
  - Declination variant
  - Declination variant (buffered)
- Efficiency gap variants
  - Efficiency gap⁴
  - Difference gap^5,6,7
  - Loss gap⁷
  - Surplus gap⁸
  - Vote-centric gap^6,7
  - Vote-centric gap 2^6,7
  - Tau gap³
- Partisan bias^6,7
Historical election results:
- Congressional elections, 1948–2016 (CSV)
- State legislative elections (lower house), 1971–2017 (CSV, full repository)
Jupyter notebook demonstrating how to run the tests on all elections, as well as reporting the percentile ranking for all tests of any particular election.

Installation

If using pip, do pip install gerrymetrics

References

Samuel S.-H. Wang. (2016). Three Tests for Practical Evaluation of Partisan Gerrymandering. Stanford Law Review.
Michael D. McDonald and Robin E. Best. (2015). Unfair Partisan Gerrymanders in Politics and Law: A Diagnostic Applied to Six Cases. Election Law Journal.
Gregory S. Warrington. (2018). Quantifying Gerrymandering Using the Vote Distribution. Election Law Journal.
Eric McGhee. (2014). Measuring Partisan Bias in Single‐Member District Electoral Systems. Legislative Studies Quarterly.
Whitford v. Gill, No. 15-cv-421, F. Supp. 3d. (2016). Griesbach, dissenting, 128.
Benjamin P. Cover. (2018). Quantifying Partisan Gerrymandering: An Evaluation of the Efficiency Gap Proposal. Stanford Law Review.
John F. Nagle. (2017). How Competitive Should a Fair Single Member Districting Plan Be? Election Law Journal.
Wendy K. Tam Cho. (2018). Measuring Partisan Fairness: How Well Does the Efficiency Gap Guard Against Sophisticated as well as Simple-Minded Modes of Partisan Discrimination? University of Pennsylvania Law Review.
Gregory S. Warrington. (2018). A Comparison of Gerrymandering Metrics. arXiv.

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northstar antithesisconundrum sarah-obrien tyerush awoziji tahentx janish-parikh blipblipgo

gerrymandertests's Issues

utils.py failure due to slight change in pandas syntax

Line 53 fails with newer versions of pandas due to unexpected MultiIndex 'labels' syntax. Code runs by replacing 'labels' with 'codes'.

TypeError when using g.bootstrap

I'm trying to use Wang's 3 tests. However, when trying to use Test 1 (the excess seats test), I get the following errors:
KeyError: 'bootstrap'
TypeError: object of type 'numpy.float64' has no len()

Code to replicate the error:

impute_val = 1
chambers = defaultdict(lambda: defaultdict(list))
chambers['Congressional']['filepath'] = 'election_data/congressional_election_results_post1948.csv'

metric_dict = {'bootstrap': g.bootstrap,
               't_test_diff':            g.t_test_diff,
               'mean_median_diff':       g.mean_median}
for chamber in chambers:
    chambers[chamber]['elections_df'] = g.parse_results(chambers[chamber]['filepath'])
    chambers[chamber]['tests_df'] = g.tests_df(g.run_all_tests(
        chambers[chamber]['elections_df'],
        impute_val=impute_val,
        metrics=metric_dict))

I'm using Python 3.6.
I guess the error may come from this part of the function 'bootstrap', but I'm not sure how to solve it:
n_matches = len(match_seats)

Is it also possible that I'm using the function incorrectly. I would appreciate any help!

Update: I also got the same error when using g.t_test and g.mean_median_test.

First Notebook example doesn't work: apparently expects a state data file to already be there?

I'm trying to run the gerrymandertests, but apparently it relies on my separately downloading state-specific files (I'm particularly interested in New Mexico) and I can't find any documentation on where to get them.

If I just run the notebook, here's the error:

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-1-54dcfe840d25> in <module>
     41 
     42 for chamber in chambers:
---> 43     chambers[chamber]['elections_df'] = g.parse_results(chambers[chamber]['filepath'])
     44     chambers[chamber]['tests_df'] = g.tests_df(g.run_all_tests(
     45         chambers[chamber]['elections_df'],

~/outsrc/gerrymandertests/gerrymetrics/utils.py in parse_results(input_filepath, start_year, coerce_odd_years)
     12     '''
     13 
---> 14     df = pd.read_csv(input_filepath)
     15 
     16     df = df[df['Year'] >= start_year]

~/pythonenv/gerry/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    674         )
    675 
--> 676         return _read(filepath_or_buffer, kwds)
    677 
    678     parser_f.__name__ = name

~/pythonenv/gerry/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    446 
    447     # Create the parser.
--> 448     parser = TextFileReader(fp_or_buf, **kwds)
    449 
    450     if chunksize or iterator:

~/pythonenv/gerry/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    878             self.options["has_index_names"] = kwds["has_index_names"]
    879 
--> 880         self._make_engine(self.engine)
    881 
    882     def close(self):

~/pythonenv/gerry/lib/python3.7/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
   1112     def _make_engine(self, engine="c"):
   1113         if engine == "c":
-> 1114             self._engine = CParserWrapper(self.f, **self.options)
   1115         else:
   1116             if engine == "python":

~/pythonenv/gerry/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   1889         kwds["usecols"] = self.usecols
   1890 
-> 1891         self._reader = parsers.TextReader(src, **kwds)
   1892         self.unnamed_cols = self._reader.unnamed_cols
   1893 

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()

FileNotFoundError: [Errno 2] File election_data/state_legislative/state_legislative_election_results_post1971.csv does not exist: 'election_data/state_legislative/state_legislative_election_results_post1971.csv'

election_data/congressional_election_results_post1948.csv comes as part of the repository, but election_data/state_legislative/ is an empty directory. Where can I get the files that it expected there?

In NM we're actively fighting for better redistricting (I'm webmaster for fairdistrictsnm.org) and I'd love to get some quantitative measurements I could show to legislators and display on the website.

Need to use older Pandas version

This package uses deprecated features from Pandas that are no longer available in Pandas version 1.0 or higher. Accordingly, one should use a virtual environment to install an older version of pandas e.g. pandas==0.25.1 until this issues.

Creating classes

I've been playing with the methods in the metrics.py file and have found myself mimicking the functionality in new classes. For example, I've replaced use of _stats in metrics.py with a class I call VoteShares:

I had hoped to put up an explanatory PR, but I'm not allowed to make pull requests. Would you have interest in adding some of these classes to your repo? They make some use of the methods a bit more concise e.g.
mwu: MannWhitneyU = MannWhitneyU([.1, .2, .3, .4, .5, .6, .7, .8, .9, 1.0])
mwu.get_statistic()
mwu.get_p_value()

utils.py indent throws error

Additional indent in utils.py line 37 throws error. Runs smoothly if outdented to for and else level.

princetonuniversity / gerrymandertests Goto Github PK

gerrymandertests's Introduction

Metrics for quantifying gerrymandering

Installation

References

gerrymandertests's People

Contributors

Stargazers

Watchers

Forkers

gerrymandertests's Issues

utils.py failure due to slight change in pandas syntax

TypeError when using g.bootstrap

First Notebook example doesn't work: apparently expects a state data file to already be there?

Need to use older Pandas version

Creating classes

utils.py indent throws error

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