Use caching libraries like `cachetools` or Redis to store frequently accessed data in memory, reducing the need for repeated computations or database queries.

Implementing caching in Python applications can greatly enhance performance by reducing the overhead of repeated computations and database queries. Caching involves storing the results of expensive function calls or database queries so that subsequent requests for the same data can be served from memory rather than requiring a new computation or lookup. Libraries like cachetools provide in-memory caching mechanisms, including Least Recently Used (LRU) caches, which automatically evict old entries when the cache limit is reached. For distributed caching, consider using Redis, which offers fast access to cached data across multiple application instances. When implementing caching, identify which data or results are frequently accessed and could benefit from caching. Additionally, establish cache expiration policies to ensure that stale data does not persist in the cache indefinitely. By effectively implementing caching strategies, you can significantly improve the performance and responsiveness of your Python applications.