D’Hondt Election Simulator: A Powerful Tool for Electoral Analysis in Python

Today, I’m excited to introduce the D’Hondt election simulator, a powerful tool for political scientists, data analysts, and anyone interested in electoral systems. This user-friendly Python package allows users to simulate elections using the D’Hondt method, a highest averages method for allocating seats in party-list proportional representation systems.

Visit the GitHub page for more information:

D’Hondt Election Simulator Python Package

This package provides an easy-to-use and powerful simulation tool to conduct election simulations using the D’Hondt method in Python.

pip install git+https://github.com/onurgitmez/dhondt-python.git

Usage

Start by loading the dhondt package:

import pandas as pd
import importlib.resources as pkg_resources
from dhondt import simulate_election

Simulating an election

To simulate an election, you need a dataset that includes:

District names: A column with district names. Seats: A column specifying the number of seats available in each district. Party votes: Columns representing the number of votes each party received.

data_file = pkg_resources.files('dhondt.data').joinpath('example_election_data.csv')
with data_file.open('r') as f:
    election_data = pd.read_csv(f)

results = simulate_election(election_data, 
                            district_col="DistrictName", 
                            seats_col="NumberofSeats", 
                            parties=["AkpVote", "MhpVote", "ChpVote", "IyipVote", "HdpVote", "RefahVote", "ZaferVote"], threshold=0)

The ‘threshold’ argument specifies the national threshold required for a party to be eligible for seats. If desired, the resulting dataframe can be assigned to a global environment variable, ‘election_results’ for convenient access.

Function

When you run the simulation of any election using the simulate_election() function in the dhondt package, the calculations are performed in the background using the D’Hondt method.

The function takes several arguments:

• df: Your dataframe with election data.
• district_col: The name of the column with district names.
• seats_col: The name of the column with the number of seats in each district.
• parties: A vector of party names, each matching a column in your dataframe. Each column holds the votes that party got in each district.
• threshold: (Optional) A vote share threshold that parties must exceed to be eligible for seats. Default is 0.

The simulate_election() function returns:

The simulate_election() function returns two key components:

• Named Vector of Total Seats for Each Party: A dictionary that displays how many seats each party won in total.

• DataFrame of Seats Won by Each Party in Each District: A DataFrame with a detailed breakdown of the seats won by each party in each district.

You can print the seat totals like this:

for party, seats in results["totals"].items():
    print(f"{party} seats: {seats}")

Conclusion

The D’Hondt Election Simulator provides a powerful and easy-to-use method for analyzing election results using the D’Hondt method in Python. Whether you’re a political scientist, data analyst, or just someone interested in electoral systems, this package offers a straightforward way to simulate and analyze proportional representation elections.

For more details or to explore the code, check out the GitHub page. Let me know if you have any questions!