7 Pandas Performance Tricks Every Data Scientist Should Know

Pandas remains a fundamental tool for data scientists, and making it more efficient is crucial for handling larger datasets. This guide presents seven practical ways to speed up pandas workflows, starting with smarter data loading using `read_csv` and specifying data types. It emphasizes replacing slow loops with vectorized operations and using smaller data types like `category` to reduce memory usage. Further optimizations include proper indexing with `loc` and `iloc`, cleaner filtering with `query()`, and processing massive files in manageable chunks to avoid memory issues.