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Python Code Samples

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

4 matches
OOP & classes medium

Implement the Strategy Pattern with Interchangeable Algorithm Classes in Python

Uses abstract base classes to define a SortStrategy interface, then swaps between BubbleSort and QuickSort at runtime.

strategy-pattern oop abstract-class
Python
from abc import ABC, abstractmethod
from typing import List


class SortStrategy(ABC):
    @abstractmethod
    def sort(self, data: List[int]) -> List[int]:
        pass


class BubbleSort(SortStrategy):
    def sort(self, data: List[int]) -> List[int]:
        result = data[:]
        n = len(result)
        for i in…
12 0 Open
System design patterns easy

How to Implement a Data Helper Class in Python

Build a beginner-friendly DataHelper class using dataclasses and key system design patterns like Command, Strategy, and Map.

dataclass data-helper design-patterns
Python
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional


@dataclass
class DataHelper:
    """A beginner-friendly data utility with common system design patterns."""
    data: List[Dict[str, Any]] = field(default_factory=list)

    def add_record(self, r…
13 0 Open
System design patterns medium

How to Implement the Strategy Pattern in Python

This Python code demonstrates the Strategy design pattern using interchangeable sorting algorithms (bubble sort and quick sort) that can be swapped at runtime.

design-pattern strategy oop
Python
class SortingStrategy:
    def sort(self, data):
        raise NotImplementedError

class BubbleSort(SortingStrategy):
    def sort(self, data):
        result = data.copy()
        n = len(result)
        for i in range(n):
            for j in range(0, n - i - 1):
                if result[j] > result[j + 1]:
      …
13 0 Open
Big data & Spark easy

How to Mock a Hash Join on Large and Small Tables in Python

This code efficiently joins a large dataset (1000 rows) with a small lookup table (20 rows) by building a dictionary hash lookup, mimicking a hash join strategy used in big data systems.

hash-join dictionaries data-join
Python
import random
from pprint import pprint

# Large table: 1000 rows (id, group_id, value)
large = [{"id": i, "group_id": random.randint(1, 20), "value": random.random() * 100} for i in range(1000)]

# Small table: 20 rows (group_id, label)
small = [{"group_id": g, "label": f"Group-{g}"} for g in range(1, 21)]

# Mock a …
13 0 Open

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