Python is widely used for data cleaning in data science due to its powerful libraries, ease of use, and versatility. Here’s why: Rich Libraries: Python offers robust libraries like Pandas, NumPy, and Dask, which provide efficient tools for data manipulation, handling missing data, and transforming datasets. Ease of Use: Python's syntax is straightforward and readable, making it accessible for both beginners and experienced developers. This simplicity helps in writing and understanding code for data cleaning tasks. Community Support: Python has a large and active community, ensuring continuous improvements, abundant resources, and quick troubleshooting for data cleaning challenges. Integration Capabilities: Python seamlessly integrates with other tools and platforms, allowing for smooth workflows when handling data from various sources, making it ideal for end-to-end data science processes. These factors make Python a preferred choice for data cleaning in data science. Data Science Training in Pune