Reference library

Concurrency & performance

asyncio, threading, multiprocessing, and profiling-friendly performance patterns.

4 matches

Build a Python Performance Profiler That Generates Readable Reports

Use cProfile and pstats to profile Python functions and print a sorted performance report showing the top time-consuming calls.

profiling cprofile pstats

Python

import cProfile
import pstats
import io
from pathlib import Path

def slow_function():
    total = 0
    for i in range(500_000):
        total += i ** 2
    return total

def fast_function():
    total = sum(i * i for i in range(500_000))
    return total

def profile_functions():
    profiler = cProfile.Profile()
  …

44 0 Open

Concurrency & performance medium

How to Profile CPU Hot Path in Python with cProfile and sort_stats cumtime

Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.

cprofile profiling performance

Python

import cProfile
import pstats
import io


def slow_function():
    total = 0
    for i in range(100_000):
        total += i * i
    return total


def fast_function():
    return sum(i for i in range(100))


def main():
    slow_function()
    fast_function()


if __name__ == "__main__":
    profiler = cProfile.Profi…

12 0 Open

Concurrency & performance easy

How to Use bisect.insort in Python to Maintain a Sorted List

Insert items into an already sorted list using Python's bisect.insort to keep it sorted efficiently in O(n) time.

bisect sorted insertion

Python

import bisect

def maintain_sorted_list():
    data = [3, 1, 4, 1, 5, 9, 2, 6]
    sorted_list = []
    
    for num in data:
        bisect.insort(sorted_list, num)
    
    print("Original data:", data)
    print("Sorted list maintained with insort:", sorted_list)
    
    # Insert new values to maintain sorted orde…

13 0 Open

Concurrency & performance medium

Merge K Sorted Lists in Python with heapq

Merge k sorted lists into one sorted list in O(N log k) time using a min-heap of current elements.

heapq merge sorted-lists

Python

import heapq

def merge_k_sorted_lists(lists):
    heap = []
    for i, lst in enumerate(lists):
        if lst:  # only push non-empty lists
            heapq.heappush(heap, (lst[0], i, 0))
    result = []
    while heap:
        val, list_idx, elem_idx = heapq.heappop(heap)
        result.append(val)
        if elem…

13 0 Open

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