Reference library

Python Code Samples

Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.

74 matches
Strings & text easy

How to Convert snake_case to Title Case in Python

Convert snake_case strings to title case by splitting on underscores, capitalizing each word, and joining them with spaces.

snake-case string-formatting text-processing
Python
def to_title_case(snake_str):
    words = snake_str.split("_")
    return " ".join(word.capitalize() for word in words)

if __name__ == "__main__":
    examples = ["hello_world", "convert_snake_case", "already_title_case", "multiple__under_scores"]
    for example in examples:
        print(f"{example!r:35} -> {to_tit…
13 0 Open
Functions & basics easy

Call a Function Dynamically by Name in Python

Use globals() to look up and call a function by its name as a string, with optional arguments.

globals dynamic-dispatch reflection
Python
def greet():
    return "Hello from greet!"

def add(a, b):
    return a + b

def multiply(a, b):
    return a * b

if __name__ == "__main__":
    func_name = "add"
    args = (3, 5)
    
    # Call function dynamically by name from globals
    result = globals()[func_name](*args)
    print(f"{func_name}({', '.join(ma…
13 0 Open
Files & data medium

How to Stream Large CSV Files in Python

Process a large CSV file in memory-efficient chunks using Python's csv module, yielding batches of rows instead of loading everything at once.

csv streaming memory-efficient
Python
import csv
from pathlib import Path

def process_csv_in_chunks(file_path, chunk_size=1000):
    """Yield rows from a large CSV file in chunks without loading all into memory."""
    with open(file_path, 'r', newline='') as f:
        reader = csv.DictReader(f)
        chunk = []
        for row in reader:
            …
12 0 Open
Files & data medium

Read Parquet-Like Columnar CSV Chunks in Python

A Python generator that reads a CSV file column-by-column, yielding dictionary chunks where each key points to a list of values—mirroring how Parquet stores data columnar.

csv columnar generator
Python
```python
import csv
from pathlib import Path
from typing import Iterator, List

def read_parquet_like_columnar(csv_path: str, column_names: List[str], chunk_size: int = 2) -> Iterator[dict]:
    """Read CSV data in columnar chunks, similar to how parquet stores columns."""
    csv_file = Path(csv_path)
    with csv_f…
13 0 Open
OOP & classes medium

How to Implement the State Pattern in Python

Implement the State design pattern in Python by delegating behavior to state objects, letting a media player change actions dynamically without if-else chains.

state-pattern design-patterns oop
Python
class State:
    def play(self, player): pass
    def pause(self, player): pass
    def stop(self, player): pass

class PlayingState(State):
    def play(self, player):
        return "Already playing"
    def pause(self, player):
        player.state = PausedState()
        return "Pausing playback"
    def stop(self…
12 0 Open
OOP & classes medium

How to Use __slots__ in Python Classes for Memory Efficiency

Defines classes with __slots__ to prevent dynamic attribute creation and reduce memory usage, including inheritance with additional slots.

slots oop memory
Python
```python
class Person:
    __slots__ = ("name", "age")

    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

    def greet(self) -> str:
        return f"Hi, I'm {self.name} and I'm {self.age} years old."


class Employee(Person):
    __slots__ = ("role",)

    def __init__(se…
13 0 Open
OOP & classes easy

Slots Class: How to Reduce Memory Usage in Python

Use __slots__ to prevent dynamic attribute creation and reduce per-instance memory overhead, while keeping methods intact.

memory slots class
Python
class SlotsDemo:
    __slots__ = ("name", "age", "email")

    def __init__(self, name, age, email):
        self.name = name
        self.age = age
        self.email = email

    def describe(self):
        return f"{self.name}, {self.age}, {self.email}"

if __name__ == "__main__":
    instance = SlotsDemo("Alice", …
12 0 Open
Algorithms & data structures medium

Find Longest Increasing Subsequence Length in Python

Compute the length of the longest increasing subsequence in an array using dynamic programming.

dynamic-programming subsequence algorithm
Python
def longest_increasing_subsequence(nums):
    if not nums:
        return 0
    
    dp = [1] * len(nums)
    
    for i in range(1, len(nums)):
        for j in range(i):
            if nums[i] > nums[j]:
                dp[i] = max(dp[i], dp[j] + 1)
    
    return max(dp)

if __name__ == "__main__":
    # Demo with…
15 0 Open
Algorithms & data structures easy

Generate Pascal's Triangle Rows in Python

Builds Pascal's triangle as a list of rows, where each inner value is the sum of the two values above it.

pascal-triangle dynamic-programming algorithms
Python
def generate_pascals_triangle(rows):
    triangle = []
    for row_num in range(rows):
        row = [1] * (row_num + 1)
        for col in range(1, row_num):
            row[col] = triangle[row_num - 1][col - 1] + triangle[row_num - 1][col]
        triangle.append(row)
    return triangle

if __name__ == "__main__":
…
14 0 Open
Algorithms & data structures easy

How to Implement a Moving Average from a Data Stream in Python

Implement a MovingAverage class using a deque and running sum to compute the average of the last k values from a continuous data stream.

deque sliding-window streaming
Python
from collections import deque

class MovingAverage:
    def __init__(self, size):
        self.size = size
        self.queue = deque()
        self.window_sum = 0

    def next(self, val):
        self.queue.append(val)
        self.window_sum += val

        if len(self.queue) > self.size:
            self.window_su…
12 0 Open
Comprehensions & generators easy

Generate UUID4 Values with a Python Generator

This code defines a generator function that yields mock UUID4 values, allowing you to stream unique identifiers one at a time.

uuid generators streaming
Python
import uuid

def generate_uuids(count=5):
    """Generate a stream of mock UUID4 values."""
    for _ in range(count):
        yield uuid.uuid4()

if __name__ == "__main__":
    # Generate and print 5 UUIDs
    for uid in generate_uuids(5):
        print(uid)
15 0 Open
Comprehensions & generators medium

How to stream parse JSON arrays in Python

This code demonstrates two generators: one that streams a JSON array as individual chunks, and another that incrementally parses those chunks into Python objects using json.JSONDecoder.

json generator streaming
Python
import json


def json_array_stream(items):
    """Generator that yields JSON-encoded values one at a time."""
    yield "["
    for i, item in enumerate(items):
        if i > 0:
            yield ","
        yield json.dumps(item)
    yield "]"


def parse_json_stream(stream):
    """Consumes a stream of JSON fragme…
14 0 Open
Comprehensions & generators easy

Memory efficient map over large file in Python

A generator-based streaming map that processes a large file line by line without loading the whole file into memory.

generator file-io streaming
Python
import sys

def process_lines(file_path):
    """Memory-efficient map over a large file: yields processed lines."""
    with open(file_path, 'r') as f:
        for line in f:
            # Example mapping: strip whitespace and uppercase
            yield line.strip().upper()

if __name__ == "__main__":
    # Use a sma…
12 0 Open
AI & LLM integration patterns easy

How to Accumulate Streamed Tokens into a Final String in Python

Accumulate a stream of tokens into a single final string by concatenating each token in sequence.

streaming tokens strings
Python
def accumulate_tokens(tokens):
    """Accumulate a stream of tokens into a single final string."""
    result = ""
    for token in tokens:
        result += token
    return result


if __name__ == "__main__":
    token_stream = ["Hello", ", ", "world", "!", " This ", "is ", "accumulated."]
    final_string = accumul…
16 0 Open
AI & LLM integration patterns easy

How to Stream Tokens from a Mock LLM in Python

Simulate real-time LLM streaming by yielding tokens one at a time with a delay, making it easy to test streaming UIs.

generator llm streaming
Python
import time
from typing import Generator


def stream_tokens(text: str, delay: float = 0.05) -> Generator[str, None, None]:
    """Simulate an LLM streaming tokens word by word."""
    for word in text.split():
        yield word
        time.sleep(delay)


if __name__ == "__main__":
    sample = "Hello world! This is…
15 0 Open
Automation & scripting easy

How to Watch a Folder and Convert New Images in Python

Watch a folder for new files and mock-convert images by copying and renaming them in an output directory.

folder-watching automation pathlib
Python
import time
import hashlib
from pathlib import Path
from datetime import datetime

def mock_convert_image(source: Path, dest_dir: Path) -> Path:
    """Mock image conversion: copy bytes and add .converted suffix."""
    dest = dest_dir / f"{source.stem}.converted{source.suffix}"
    dest.write_bytes(source.read_bytes(…
12 0 Open
Automation & scripting easy

How to rename music files by ID3 tags in Python

Renames MP3 files in a folder using artist and title extracted from ID3 tags, with a mock fallback that parses filenames.

file-renaming id3-tags mp3
Python
import os
import re
from pathlib import Path

def sanitize_filename(name: str) -> str:
    return re.sub(r'[<>:"/\\|?*]', '_', name).strip()

def rename_mp3_from_id3(path: Path) -> None:
    for f in path.glob("*.mp3"):
        # Mock ID3 extraction: derive artist/title from filename
        stem = f.stem
        if "…
12 0 Open
Automation & scripting easy

Rename Files in Folder with Numeric Prefix in Python

Renames all files in a folder by adding a sequential numeric prefix (e.g., 01_, 02_) to each filename using pathlib.

file-renaming pathlib automation
Python
from pathlib import Path

def rename_with_numeric_prefix(folder_path):
    folder = Path(folder_path)
    for index, file_path in enumerate(folder.iterdir(), start=1):
        if file_path.is_file():
            new_name = f"{index:02d}_{file_path.name}"
            new_path = file_path.with_name(new_name)
           …
13 0 Open
Data pipelines & processing medium

Enrich a stream with reference data by key lookup in Python

Uses streamz to join each incoming record to a reference dictionary by name, adding department and level fields or defaults.

streamz streaming join
Python
from streamz import Stream

reference = {"alice": {"dept": "eng", "level": 3}, "bob": {"dept": "sales", "level": 5}}

def enrich(record):
    name = record.get("name")
    ref = reference.get(name)
    joined = dict(record)
    if ref:
        joined.update(ref)
    else:
        joined["dept"] = "unknown"
        joi…
14 0 Open
Data pipelines & processing easy

How to Implement a Sliding Window Average in Python

Compute the average of the most recent N values in a stream using a bounded deque, efficiently updating the total as new values arrive.

deque sliding-window streaming
Python
from collections import deque


class SlidingWindowAverage:
    def __init__(self, window_size):
        self.window_size = window_size
        self.window = deque(maxlen=window_size)
        self.total = 0

    def add(self, value):
        if len(self.window) == self.window_size:
            self.total -= self.windo…
15 0 Open
Data pipelines & processing medium

How to Stream a Large JSONL File Line by Line in Python

Process a large JSON-lines file incrementally using streaming techniques to avoid loading the entire file into memory.

streaming jsonl large-files
Python
import json

def process_large_file(filepath, chunk_size=8192):
    """
    Stream a large JSON-lines file line by line, processing each record
    without loading the entire file into memory.
    """
    total_count = 0
    total_sum = 0
    
    with open(filepath, 'r') as f:
        while True:
            chunk = …
13 0 Open
Data pipelines & processing easy

How to Track Checkpoint Offset After Batch Commit in Python

A batch processor that tracks the last successfully committed offset after processing records in batches, advancing the checkpoint only when each batch commits successfully.

batch-processing checkpoint offset
Python
import json
from typing import Any


class BatchProcessor:
    """Tracks checkpoint offset after committing batches."""

    def __init__(self, batch_size: int = 3):
        self.batch_size = batch_size
        self.offset = 0  # last successfully committed offset (exclusive)
        self.total_committed = 0

    def …
12 0 Open
Data pipelines & processing easy

How to route late-arriving data to a side output in Python

Separate late-arriving events from a streaming data batch into a dead-letter side output list using a timestamp threshold.

data pipelines streaming dead-letter
Python
from collections import defaultdict

def late_arriving_side_output(events, late_threshold_ts):
    """
    Mock a streaming pipeline that separates late-arriving data events
    into a side output list (e.g., for dead-letter analysis).

    events: list of (timestamp, data) tuples, timestamps as ints.
    late_thresho…
12 0 Open
Data pipelines & processing medium

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.

heapq sorting streaming
Python
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.…
12 0 Open

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

  1. Pick a topic section — strings, lists, files, functions, and more
  2. Open a sample, read How it works, and copy the code block
  3. 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.