What is the difference between Process and Pool in multiprocessing?

Question

Accepted Answer

**Process** — manual process management. **Pool** — managed worker pool with convenient methods.

```python
from multiprocessing import Process, Pool

# Process — manual control
def worker(n):
    return n ** 2

processes = []
for i in range(4):
    p = Process(target=worker, args=(i,))
    p.start()
    processes.append(p)

for p in processes:
    p.join()

# Pool — managed workers
with Pool(4) as pool:
    # map — apply function to each item
    results = pool.map(worker, range(10))

# apply_async — single call
    result = pool.apply_async(worker, (5,))
    print(result.get())  # 25

# starmap — unpack arguments
    def add(a, b):
        return a + b
    results = pool.starmap(add, [(1, 2), (3, 4)])

# imap — lazy map
    for result in pool.imap(worker, range(10)):
        print(result)
```

**Use Pool** when:
- You have many similar tasks.
- You want automatic process management.
- You need map/reduce patterns.

Python: What is the difference between Process and Pool in multiprocessing?