Python 3.2: What's New, Release History & EOL Status

What Is New in Python 3.2

Python 3.2 was released on February 20, 2011. Key additions include the concurrent.futures module for high-level thread and process pools, improved argparse as the successor to optparse, functools.lru_cache as a memoization decorator, and significant improvements to the ssl module. The __pycache__ directory system also debuted here, keeping compiled bytecode organized.

concurrent.futures -- High-Level Concurrency (PEP 3148)

The concurrent.futures module provides a simple, unified API for running work in threads or processes without managing pools manually. ThreadPoolExecutor for I/O-bound work; ProcessPoolExecutor for CPU-bound work that needs to bypass the GIL.

from concurrent.futures import ThreadPoolExecutor, as_completed
import urllib.request

urls = ["https://example.com", "https://python.org"]

def fetch(url):
    return urllib.request.urlopen(url).read()

with ThreadPoolExecutor(max_workers=5) as executor:
    futures = {executor.submit(fetch, url): url for url in urls}
    for future in as_completed(futures):
        url = futures[future]
        print(f"{url}: {len(future.result())} bytes")

The executor.map() method is the simplest pattern for applying a function to an iterable in parallel. submit() + as_completed() gives you results in completion order rather than submission order.

functools.lru_cache -- Memoization

@functools.lru_cache(maxsize=128) caches the return values of a function based on its arguments. Once the cache reaches maxsize entries, the least recently used entry is evicted. Use maxsize=None for an unbounded cache (functools.cache in 3.9+, which is identical).

from functools import lru_cache

@lru_cache(maxsize=256)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

fibonacci(100)   # Completes instantly after first call
fibonacci.cache_info()  # CacheInfo(hits=98, misses=101, ...)

__pycache__ and Bytecode Management (PEP 3147)

Compiled .pyc files are now stored in a __pycache__ subdirectory alongside the source file, with the Python version embedded in the filename (e.g., module.cpython-32.pyc). This prevents conflicts when switching between Python versions sharing the same source tree, and avoids permission errors on read-only source directories.