Python Concurrency and Parallelism

Python Concurrency and Parallelism - Important Points

A. NumPy

B. Pandas

C. TensorFlow

D. Flask

A. To provide a way to execute code concurrently using threads and processes.

B. To provide a way to execute code concurrently using coroutines.

C. To provide a way to manage and control concurrency in Python code.

D. To provide a way to create and manage threads and processes.

A. To ensure only one thread can access a shared resource at a time.

B. To prevent deadlock in multithreaded code.

C. To provide a way to communicate between threads.

D. To provide a way to synchronize tasks between threads.

A. To ensure only one thread can access a shared resource at a time.

B. To prevent deadlock in multithreaded code.

C. To provide a way to communicate between threads.

D. To provide a way to limit the number of threads that can access a resource at a time.

A. They can cause deadlock if used improperly.

B. They can be less efficient than using other synchronization mechanisms.

C. They can be more difficult to use than other synchronization mechanisms.

D. They can be difficult to implement in Python code.