What are the best practices for memory management in Python?

- Generators and iterators allow you to work with large datasets or infinite sequences efficiently by generating values on the fly, reducing memory consumption compared to storing the entire dataset in memory. - Circular references prevent objects from being garbage collected, leading to memory leaks. Break circular references manually or use weak references to ensure proper memory management. - Choose appropriate data structures such as sets, dictionaries, and tuples based on your application's requirements to minimize memory usage and optimize performance. - Monitor memory usage using tools like `memory_profiler` or built-in functions to detect memory leaks and inefficiencies, allowing you to address them promptly.

Effective memory management in Python begins with comprehending its underlying mechanisms. Python employs a memory model rooted in reference counting and garbage collection. Reference counting entails assigning a counter to each object, tracking the number of references pointing to it. Upon reaching zero references, the object becomes eligible for deletion. Garbage collection supplements this process by periodically scanning memory and deallocating objects unreferenced by any pointer. This mechanism proves indispensable in handling circular references, wherein objects mutually reference each other, impeding reference counting's ability to reach zero.

Maximizing memory usage efficiency in Python can be achieved through leveraging its built-in data types, including lists, tuples, dictionaries, sets, and strings. These data types, implemented in C, prioritize both speed and memory efficiency. They offer a plethora of methods and operations, enhancing convenience and flexibility. Employ list comprehensions, generators, slices, and iterators to manipulate lists without generating redundant copies or intermediate objects. Additionally, utilize the collections module to access specialized data types like defaultdict, namedtuple, Counter, and deque, which provide enhanced functionality and performance advantages.

De manière générale, utiliser des yield plutot qu'un retour de liste ou tout autre méthode d'accumulation, utiliser des types de données avec des variables statiques, namedtuples, set. Ainsi que des générateurs plutot que des listes (voir exemple de yield)

D'après mon expérience, pour éviter les fuites mémoires, l'utilisation de with est recommandée, pour l'ouverture d'un fichier, exemple le plus banal. Attention également au librairies en .so, du C codé maison, importées il peut comporter des erreurs non géré puisque C a un accès direct à la mémoire.

Pour monitorer l'usage de la mémoire, on peut utiliser psutil qui permet de voir pas mal de chose dont l'utilisation mémoire par PID, par exemple; tracemalloc, ou memory_profiler pour le profilage.

Optimizing Object Creation and Destruction: Reuse Objects: Instead of creating new objects repeatedly, consider reusing existing objects whenever possible. For example, when working with large collections of data, use object pools or recycling mechanisms to minimize the creation and destruction of objects. Avoid Unnecessary Copies: Be mindful of unnecessary copies of objects, especially when dealing with large data structures or immutable objects. Instead of creating copies, consider using views or slices to work with subsets of data without duplicating memory.