Excessive memory usage in Python can occur due to large data structures, memory leaks, or retaining references to objects longer than necessary. Optimize data structures and review your code for unnecessary references.

High memory usage in Python can lead to performance issues and application crashes, particularly in data-intensive applications. Start by examining your data structures; using lists, dictionaries, or sets to store large datasets can quickly consume memory. Consider optimizing these structures by using NumPy arrays or pandas DataFrames, which are designed to handle large amounts of data efficiently. Memory leaks may also contribute to excessive memory usage, often occurring when references to objects are retained longer than necessary. Review your code to identify instances where objects are not being released, such as global variables holding onto large datasets. Using weak references can help mitigate this issue, as they allow the garbage collector to reclaim memory when objects are no longer in use. Additionally, consider profiling your memory usage with tools like memory_profiler or objgraph to identify which objects are consuming the most memory and where leaks may be occurring. By following these strategies, you can effectively manage memory usage in your Python applications.