In one of my recent projects, I used Python extensively for data cleaning and transformation. The project involved analyzing regional car sales performance, where I needed to handle a large dataset containing sales records from various regions. I started by using Python libraries like Pandas and NumPy to load the data and perform initial cleaning tasks such as removing duplicates, handling missing values, and correcting data types. For transformation, I used Pandas to create new calculated fields that helped in understanding sales trends, such as year-over-year growth and regional performance metrics. I also utilized Python's Matplotlib library to visualize the data, which assisted in identifying patterns and anomalies. This process not only improved data quality but also provided valuable insights that contributed to a 15% increase in total sales.