How do NumPy and Pandas work with GIL?

GIL (Global Interpreter Lock) in Python restricts the concurrent execution of Python bytecode within a single process, which affects multithreading.

NumPy and Pandas are implemented in C/C++ and use extensions that can release the GIL during compute-intensive operations. This allows such operations to run in parallel despite the GIL.

For example, when calling a NumPy function that performs heavy computations, the GIL can be released so that other threads can work concurrently.

However, if operations involve calls to pure Python code, the GIL still blocks concurrent execution.

Example:

import numpy as np
import threading

def compute():
    a = np.random.rand(1000000)
    b = np.fft.fft(a)  # releases GIL during computation

threads = [threading.Thread(target=compute) for _ in range(4)]
for t in threads:
    t.start()
for t in threads:
    t.join()

Thus, NumPy and Pandas partially bypass GIL limitations through low-level implementation.