This project demonstrates an end-to-end workflow for cleaning and preparing a dataset related to property taxes ("taxe foncière") in Québec for the year 2023, using Python for data manipulation and PowerBI for visualization and insights. It showcases the synergy between Python's data processing capabilities and PowerBI's interactive data visualization tools 📊.
The primary objectives of this project are:
Data Cleaning: Perform comprehensive data cleaning operations on the Québec property tax dataset to ensure the data is high-quality and ready for analysis.
Hybrid Workflow Demonstration: Illustrate how Python can automate data preparation tasks within a workflow that includes PowerBI for advanced data visualization and analysis.
The dataset is sourced from Données Québec, a repository of public data. This project uses the "Québec MRC Urban Agglomeration 2023" dataset, focusing on taxation and property assessment information.
You can also find the transformed dataset on Kaggle
Data Extraction: Utilize Python to extract data from CSV files.
Data Cleaning and Transformation: Clean and transform the data using Python scripts for better analysis readiness.
Data Export: Save the cleaned and transformed data into new CSV files for analysis in PowerBI.
Visualization and Analysis: Use PowerBI to create dashboards and visualizations that provide insights into the property tax data.
Python: For data cleaning and preparation, with libraries such as Pandas and NumPy.
PowerBI: For creating interactive dashboards and visualizations.
Kaggle: The initial development environment for Python scripting.
Explore the Python Notebooks: Check out the /notebooks directory for the Jupyter notebooks used for data cleaning and preparation.
View the Cleaned Data: The /data directory holds the CSV files ready for import into PowerBI.
Interact with the PowerBI Dashboards: Find instructions and resources in the /dashboards directory.
Contributions are welcome! 🎉 If you have suggestions for improving the data cleaning process, additional analyses, or any other enhancements, feel free to open an issue or submit a pull request. I'm also keen on collaborating with non-profit organizations and individuals passionate about data analysis.
This project is available under the Creative Commons License - see the LICENSE file for details.