poli-sci-kit 0.0.3

Political elections, appointment, analysis and visualization in Python

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Political elections, appointment, analysis and visualization in Python

Jump to: Appointment • Plotting • Examples • To-Do

poli-sci-kit is a Python package for political science appointment and election analysis. The goal is to provide a comprehensive tool for all methods needed to analyze and simulate election results.

Installation via PyPi

pip install poli-sci-kit

import poli_sci_kit

Appointment

appointment.methods includes functions to allocate parliamentary seats based on population or vote shares. Arguments to allow allocation thresholds, minimum allocations per group, tie break conditions, and other election features are also provided. Along with deriving results for visualization and reporting, these functions allow the user to analyze outcomes given systematic or situational changes. The appointment.metrics module further provides diagnostics to analyze the results of elections, apportionments, and other political science scenarios.

A basic example of political appointment using poli-sci-kit is:

from poli_sci_kit import appointment

vote_counts = [2700, 900, 3300, 1300, 2150, 500]
seats_to_allocate = 50

# Huntington-Hill is the method used to allocate House of Representatives seats to US states
ha_allocations = appointment.methods.highest_average(
    averaging_style="Huntington-Hill",
    shares=vote_counts,
    total_alloc=seats_to_allocate,
    alloc_threshold=None,
    min_alloc=1,
    tie_break="majority",
    majority_bonus=False,
    modifier=None,
)

ha_allocations
# [26, 9, 37, 12, 23, 5]

# The Gallagher method is a measure of absolute difference similar to summing square residuals
disproportionality = appointment.metrics.dispr_index(
    shares=vote_counts,
    allocations=ha_allocations,
    mertric_type='Gallagher'
)

disproportionality
# 0.01002

Plotting

poli-sci-kit provides Python only implementations of common electoral plots.

Let's visualize the above results:

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import poli_sci_kit
import stdviz

# German political parties
parties = ['CDU/CSU', 'FDP', 'Greens', 'Die Linke', 'SPD', 'AfD']
party_colors = ['#000000', '#ffed00', '#64a12d', '#be3075', '#eb001f', '#009ee0']

Baseline visualization with stdviz:

ax = stdviz.plot.bar(
    counts=ha_allocations,
    names=parties,
    faction_names=None,
    colors=party_colors,
    horizontal=False,
    stacked=False,
    label_bars=True,
    axis=None,
)

# Initialize empty handles and labels
handles, labels = stdviz.plot.legend.gen_elements()

# Add a majority line
ax.axhline(int(sum(ha_allocations) / 2) + 1, ls="--", color="black")
handles.insert(0, Line2D([0], [0], linestyle="--", color="black"))
labels.insert(0, "Majority: {} seats".format(int(sum(ha_allocations) / 2) + 1))

ax.legend(
    handles=handles,
    labels=labels,
    title="Bundestag: {} seats".format(sum(ha_allocations)),
    loc="upper left",
    bbox_to_anchor=(0, 0.9),
    title_fontsize=20,
    fontsize=15,
    frameon=True,
    facecolor="#FFFFFF",
    framealpha=1,
)

ax.set_ylabel("Seats", fontsize=15)
ax.set_xlabel("Party", fontsize=15)

plt.show()

Parliament Plots

poli_sci_kit provides implementations of both rectangular and semicircle parliament plots:

fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)

ax1 = poli_sci_kit.plot.parliament(
    allocations=seat_allocations,
    names=parties,
    colors=party_colors,
    style="rectangle",
    num_rows=4,
    marker_size=300,
    speaker=True,
    df_seat_lctns=None,
    axis=ax1,
)

ax2 = poli_sci_kit.plot.parliament(
    allocations=seat_allocations,
    names=parties,
    colors=party_colors,
    style="semicircle",
    num_rows=4,
    marker_size=175,
    speaker=False,
    df_seat_lctns=None,
    axis=ax2,
)

plt.show()

Disproportionality Bar Plot

A novel addition to social science analysis is the disproportionality bar plot, which graphically depicts the disproportionality between expected and realized results. Bar widths are the proportion of shares (ex: votes received), and heights are the difference or relative difference between shares and allocations (ex: parliament seats received).

An example follows:

ax = poli_sci_kit.plot.dispr_bar(
    shares=votes,
    allocations=ha_allocations,
    names=parties,
    colors=party_colors,
    total_shares=None,
    total_alloc=None,
    percent=True,
    axis=None,
)

handles, labels = stdviz.plot.legend.gen_elements(
    counts=[round(v / sum(votes), 4) for v in votes],
    names=parties,
    colors=party_colors,
    size=11,
    marker="o",
    padding_indexes=None,
    order=None,
)

ax.legend(
    handles=handles,
    labels=labels,
    title="Vote Percents (bar widths)",
    title_fontsize=15,
    fontsize=11,
    ncol=2,
    loc="upper left",
    bbox_to_anchor=(0, 1),
    frameon=True,
    facecolor="#FFFFFF",
    framealpha=1,
)

ax.axes.set_title('Seat to Vote Share Disproportionality', fontsize=30)
ax.set_xlabel('Parties', fontsize=20)
ax.set_ylabel('Percent Shift', fontsize=20)

plt.show()

Examples

Examples in poli-sci-kit use publicly available Wikidata statistics sourced via the Python package wikirepo. Current examples include:

• US HoR

  • Allocates seats to a version of the US House of Representatives that includes all US territories and Washington DC given census data, with this further being used to derive relative vote strengths of state citizens in the US presidential election

• Global Parliament

  • Analyzes the allocation of seats in a hypothetical global parliament given the prevalence of certain counties and organizations, the distribution of seats based on Freedom House indexes, and disproportionality metrics

To-Do

• Checks for appointment.methods implementations
• Deriving further needed arguments to assure that all current and historic appointment systems can be simulated using poli-sci-kit
• Potentially indexing preset versions of appointment.methods that coincide with the systems used by governments around the world
  • This would allow quick comparisons of actual systems with variations
• Creating, improving and sharing examples
• Finishing accurate allocations in the semicircle variation of poli_sci_kit.plot.parliament

References

Full list of references

• https://github.com/crflynn/voting

• https://blogs.reading.ac.uk/readingpolitics/2015/06/29/electoral-disproportionality-what-is-it-and-how-should-we-measure-it/

• Balinski, M. L., and Young, H. P. (1982). Fair Representation: Meeting the Ideal of One Man, One Vote. New Haven, London: Yale University Press.

• Karpov, A. (2008). "Measurement of disproportionality in proportional representation systems". Mathematical and Computer Modelling, Vol. 48, pp. 1421-1438. URL: https://www.sciencedirect.com/science/article/pii/S0895717708001933.

• Kohler, U., and Zeh, J. (2012). “Apportionment methods”. The Stata Journal, Vol. 12, No. 3, pp. 375–392. URL: https://journals.sagepub.com/doi/pdf/10.1177/1536867X1201200303.

• Taagepera, R., and Grofman, B. (2003). "Mapping the Indices of Seats-Votes Disproportionality and Inter-Election Volatility". Party Politics, Vol. 9, No. 6, pp. 659–677. URL: https://escholarship.org/content/qt0m9912ff/qt0m9912ff.pdf.