Python Code
Samples
Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Call a Parent Class __init__ with super() in Python
Shows how to chain __init__ calls through a class hierarchy using super(), so each class sets its own attributes while reusing the parent's initialization logic.
class Animal:
def __init__(self, name, species):
self.name = name
self.species = species
print(f"Animal init: {self.name}, {self.species}")
class Mammal(Animal):
def __init__(self, name, species, fur_color):
super().__init__(name, species)
self.fur_color = fur_color
…How to Compare Dataclass Instances by Specific Fields in Python
Use @dataclass(order=True) with field(compare=False) to control which fields determine ordering and equality between instances.
from dataclasses import dataclass, field
from typing import Any
@dataclass(order=True)
class Person:
name: str = field(compare=False)
age: int
height_cm: float
priority: int = field(compare=False, default=0)
def __repr__(self):
return f"Person(name={self.name!r}, age={self.age}, height={s…How to Implement Rich Comparison Ordering in Python Classes
This code demonstrates how to implement rich comparison operators (like <, <=, >, >=, ==, !=) in a Python class by defining __lt__ and __eq__, enabling sorting and ordering of custom objects.
class Task:
def __init__(self, priority, name):
self.priority = priority
self.name = name
def __lt__(self, other):
if not isinstance(other, Task):
return NotImplemented
return self.priority < other.priority
def __eq__(self, other):
if not isinstance(oth…Understanding Multiple Inheritance Method Resolution Order in Python
This code demonstrates how Python's MRO determines which greet method is called in a diamond inheritance scenario, and prints the full MRO for class D.
class A:
def greet(self):
return "Hello from A"
class B(A):
def greet(self):
return "Hello from B"
class C(A):
def greet(self):
return "Hello from C"
class D(B, C):
pass
if __name__ == "__main__":
d = D()
print(d.greet())
print(D.__mro__)Depth First Search Traversal Order in Python
Recursive depth-first search that returns the visit order of nodes in an adjacency list graph starting from a given node.
def dfs_order(adj, start):
visited = set()
order = []
def dfs(node):
visited.add(node)
order.append(node)
for neighbor in adj.get(node, []):
if neighbor not in visited:
dfs(neighbor)
dfs(start)
return order
if __name__ == "__main__":
# Dem…Find Elements in One Python List but Not Another
Return a new list containing only the elements from list A that are not present in list B, preserving duplicates and order.
def difference_elements(a, b):
"""Return elements present in list a but not in list b."""
set_b = set(b)
return [item for item in a if item not in set_b]
if __name__ == "__main__":
a = [1, 2, 3, 4, 5, 3, 2]
b = [2, 4, 6]
result = difference_elements(a, b)
print(f"A: {a}")
print(f"B: {b…How to Compute the Cartesian Product of Two Lists in Python
Generates all ordered pairs from two lists using itertools.product and prints each combination.
from itertools import product
# Two small input lists
list_a = [1, 2, 3]
list_b = ["x", "y"]
# Compute the Cartesian product
result = list(product(list_a, list_b))
# Display the result
print("Cartesian product of", list_a, "and", list_b, "is:")
for pair in result:
print(pair)How to Get the Breadth-First Traversal Order of a Graph in Python
Performs a breadth-first search on an adjacency list and returns the order nodes are visited, using a deque for efficient queue operations.
from collections import deque
def bfs_order(adjacency, start=0):
"""Return the order nodes are visited in a breadth-first traversal."""
visited = set()
order = []
queue = deque([start])
visited.add(start)
while queue:
node = queue.popleft()
order.append(node)
for neig…How to Heapify a List into a Min Heap with heapq in Python
Convert any list into a valid min heap in-place using Python's heapq.heapify(), then pop the smallest element to verify heap order.
import heapq
data = [5, 3, 8, 1, 9, 2, 7, 4, 6]
print("Original list:", data)
heapq.heapify(data)
print("Min heap:", data)
popped = heapq.heappop(data)
print("Smallest element popped:", popped)
print("Heap after pop:", data)How to Remove Banned Values from a List in Python
Filters a list by removing elements present in a banned set, preserving the original order.
def remove_banned(values, banned):
banned_set = set(banned)
return [item for item in values if item not in banned_set]
if __name__ == "__main__":
values = [1, 2, 3, 4, 5, 2, 6, 3, 7]
banned = [2, 3]
result = remove_banned(values, banned)
print(result)How to Remove Duplicates in Python Preserving Order
Removes duplicate items from a list while keeping the first occurrence order intact using a set for fast membership checks.
def remove_duplicates_preserving_order(items):
seen = set()
result = []
for item in items:
if item not in seen:
seen.add(item)
result.append(item)
return result
if __name__ == "__main__":
sample = [3, 1, 2, 1, 3, 4, 2, 5]
unique_items = remove_duplicates_preserv…Move Zeroes to End in Python Maintaining Order
In-place algorithm that moves all zeroes to the end of a list while preserving the relative order of non-zero elements.
def move_zeroes(nums):
non_zero_index = 0
for i in range(len(nums)):
if nums[i] != 0:
nums[non_zero_index], nums[i] = nums[i], nums[non_zero_index]
non_zero_index += 1
return nums
if __name__ == "__main__":
example = [0, 1, 0, 3, 12]
result = move_zeroes(example)
…Reorder a List by Odd Even Indices in Python
Splits a list into two sublists based on 1-based index parity, then concatenates odd-indexed elements before even-indexed ones.
def reorder_by_odd_even(items):
"""Reorders a list so that elements at odd indices come first,
followed by elements at even indices (1-based).
Example: [0,1,2,3,4,5,6] -> [1,3,5,0,2,4,6]
"""
odds = [items[i] for i in range(1, len(items), 2)]
evens = [items[i] for i in range(0, len(items), …Segregate Negative Numbers Before Positives in Python
Reorders a list so all negative numbers appear before non-negative numbers while preserving the original relative order of elements.
def segregate_negatives(numbers):
"""Segregate negatives before positives without altering relative order."""
negatives = [n for n in numbers if n < 0]
positives = [n for n in numbers if n >= 0]
return negatives + positives
if __name__ == "__main__":
sample = [3, -1, 4, -5, 2, -9, 0]
result =…Sort list by multiple keys with tuple ordering in Python
Sort a list of dictionaries by multiple criteria — surname, age, then score descending — using a tuple key and negation.
def sort_multi_key(data):
# Sorts by surname, then age, then score descending
return sorted(
data,
key=lambda person: (
person['surname'].lower(),
person['age'],
-person['score'] # negative to reverse sort by score
)
)
if __name__ == "__main__"…Stable merge two lists by custom comparator in Python
Merge two lists into one sorted output using a custom comparator while maintaining the original order of equal elements.
from functools import cmp_to_key
def compare(x, y):
# Custom comparator: sorts by length first, then by original index for stability
if len(x) != len(y):
return len(x) - len(y)
return 0 # Equal keys preserve original order (stable)
def merge_stable(left, right, cmp_func):
result = []
i =…Stable sort preserving equal order demo in Python
Demonstrates Python's stable sort, showing that elements with equal sort keys retain their original relative order.
from operator import itemgetter
def stable_sort_demo():
data = [(3, "first"), (1, "second"), (3, "third"), (1, "fourth"), (2, "fifth")]
print("Original:", data)
# Sort by first element (the tuple's first value), keeping relative order of equal items
sorted_data = sorted(data, key=itemgetter(0))
…How to Generate Permutations of Length r in Python
Generate all ordered arrangements of length r from a given list of elements using itertools.permutations.
from itertools import permutations
def generate_permutations(elements, r):
"""Generate all r-length permutations of the given elements."""
return list(permutations(elements, r))
if __name__ == "__main__":
elements = ['A', 'B', 'C']
r = 2
result = generate_permutations(elements, r)
print(f"Ele…Merge Data with Comprehension and Generator in Python
Merge user and order data using a dictionary comprehension for lookups and a generator expression to filter and transform orders.
def merge_data(users, orders):
"""
Merge user and order data using a dictionary comprehension
and a generator expression for filtering.
"""
# Build a lookup: user_id -> user name
user_map = {user["id"]: user["name"] for user in users}
# Generator: yield orders with user names attached
…Merge Sorted Iterators with a Heap Generator in Python
Merge multiple sorted iterators into a single sorted stream using a heap and generator, yielding values lazily in order.
import heapq
def merge_sorted_iterators(*iterators):
heap = []
for idx, iterator in enumerate(iterators):
try:
value = next(iterator)
heapq.heappush(heap, (value, idx, iterator))
except StopIteration:
continue
while heap:
value, idx, iterator = …How to Sort a List of Dictionaries by Key in Python
A reusable helper function that sorts a list of dictionaries by a specified key, with optional descending order support.
from typing import List
def sort_records(records: List[dict], key: str, descending: bool = False) -> List[dict]:
"""Sort a list of dictionaries by a specified key."""
return sorted(records, key=lambda record: record[key], reverse=descending)
def demonstrate_sorting() -> None:
users = [
{"name": …How to Topologically Sort a DAG in Python
Compute a valid execution order for tasks with dependencies using Kahn's algorithm in Python.
from collections import defaultdict, deque
def topological_order(dependencies):
graph = defaultdict(list)
in_degree = defaultdict(int)
tasks = set(dependencies.keys())
for task, depends_on in dependencies.items():
for d in depends_on:
graph[d].append(task)
in_degree[t…Implement an Out-of-Order Sort Buffer with a Heap in Python
Buffers out-of-order indices from a stream and emits them in sorted order using a min-heap with a sliding window.
import heapq
from collections import deque
class OutOfOrderSorter:
def __init__(self, buffer_size):
self.buffer_size = buffer_size
self.buffer = deque(maxlen=buffer_size)
self.heap = []
self.next_expected_index = 0
self.result = []
def push(self, item):
heapq.…How to Run Coroutines Concurrently with asyncio.gather in Python
Run multiple async coroutines concurrently and collect their results in the order they were passed.
import asyncio
async def fetch_data(name: str, delay: float) -> str:
"""Simulate an async operation (e.g., API call) with a delay."""
await asyncio.sleep(delay)
return f"{name} data (after {delay}s)"
async def main() -> None:
"""Run multiple coroutines concurrently with asyncio.gather."""
resul…Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.