This project is a Python-based implementation of Quadratic Funding (QF) options. It uses Jupyter notebooks and Python scripts to process and analyze data related to Q1 2024 funding rounds.
This Jupyter notebook is the main file where the data processing and analysis are performed. It includes the following key sections:
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Data Import and Preparation: The notebook imports necessary libraries and prepares the data for analysis. It reads data from CSV files and performs data cleaning and transformation operations. See lines 1-37 and 45-71.
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Data Analysis: The notebook performs various data analysis tasks, such as calculating average and median donations, and preparing data for different strategies. See lines 100-112 and 121-142.
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QF Matching Calculation: The notebook calculates QF matching for different strategies using the
fundingutils.get_qf_matchingfunction. See lines 124-142. -
Data Visualization: The notebook generates various plots to visualize the results of the QF matching calculation. See lines 226-271 and 279-296.
This Python script contains helper functions used in the zuzalu_qf_options.ipynb notebook. Key functions include:
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prep_donations_data: Prepares the donations data for analysis. See lines 347-350.
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pivot_votes: Transforms the votes data into a pivot table. See lines 352-354.
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get_qf_matching: Calculates QF matching for different strategies. See lines 356-376.
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COCM: Implements the Connection-Oriented Cluster Matching algorithm for QF. See lines 215-284.
To run the analysis, open the zuzalu_qf_options.ipynb notebook in a Jupyter environment and execute the cells in order. Make sure the necessary data files are available in the specified paths. The fundingutils.py script should be in the same directory as the notebook.