Reference library

Python Code Samples

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

9 matches
Testing & modern typing easy

How to Compare Floats in pytest with approx

Uses pytest.approx to compare floating-point numbers with tolerance, avoiding precision issues.

pytest floating-point testing
Python
import pytest

def test_float_addition():
    result = 0.1 + 0.2
    expected = 0.3
    assert result == pytest.approx(expected)
13 0 Open
Caching & Redis medium

How to Implement Probabilistic Early Expiration in Python

A Python mock of probabilistic early expiration for caches, using a heap-based expiry queue and random eviction to approximate cache stampede protection.

caching expiration heap
Python
import heapq
import random
import time


class ProbabilisticEarlyExpirationMock:
    def __init__(self, capacity=1024, expiration_probability=0.1):
        self.capacity = capacity
        self.expiration_probability = expiration_probability
        self._items = {}
        self._expiry_heap = []
        self._next_id…
14 0 Open
Reliability & rate limiting easy

How to Implement a Sliding Window Counter in Python

This code implements an approximate sliding window counter using a deque of time-based buckets to track event counts within a recent time window.

sliding-window rate-limiting deque
Python
from collections import deque
from time import time


class SlidingWindowCounter:
    def __init__(self, window_size, bucket_size=1):
        self.window_size = window_size
        self.bucket_size = bucket_size
        self.buckets = deque()

    def _evict_expired(self, now):
        while self.buckets and self.buck…
13 0 Open
Observability & SRE medium

Summary Quantile Mock Sketch in Python

Build a memory-efficient sketch that stores sorted bins of data points to answer approximate quantile queries like median without keeping all values in memory.

quantile sketch statistics
Python
import random
import statistics
from collections import Counter

class SummaryQuantileSketch:
    """
    A simple sketch that stores a fixed-size summary of data (min, max, deciles)
    using sorted bins, then answers approximate quantile queries.
    """
    def __init__(self, bins=10):
        self.bins = bins
    …
13 0 Open
Big data & Spark medium

Approximate Distinct Count in Python with HyperLogLog

Mock a large data stream and estimate the number of distinct items with a HyperLogLog-style probabilistic counter to save memory.

hyperloglog distinct-count probabilistic
Python
import random
import string
from collections import Counter
import math

class ApproxCountDistinct:
    def __init__(self, num_buckets=16):
        self.num_buckets = num_buckets
        self.max_zeros = [0] * num_buckets
        
    def _hash(self, item):
        # Simple string hash to a 32-bit integer
        h = …
15 0 Open
A/B testing & experimentation medium

Chi-Square Test in Python for Conversion Mock Data

Compute the chi-square statistic and approximate p-value for a mock A/B conversion test using the standard library.

chi-square statistics ab-testing
Python
import math
from collections import Counter

def chi_square_statistic(observed):
    """
    Compute chi-square statistic for a mock conversion test.
    observed: dict mapping outcomes to observed frequencies.
    """
    observed = Counter(observed)
    n = sum(observed.values())
    expected = n / len(observed) if …
12 0 Open
A/B testing & experimentation medium

How to Compute Mann-Whitney U Test in Python

Compute the Mann-Whitney U statistic and p-value manually in Python with tie correction and a normal approximation for independent samples.

statistics hypothesis-testing ab-testing
Python
import numpy as np
from scipy import stats

def mann_whitney_u_mock(sample_a, sample_b):
    """Compute Mann-Whitney U and p-value manually."""
    # Combine and rank
    combined = sample_a + sample_b
    n_a, n_b = len(sample_a), len(sample_b)
    n_total = n_a + n_b
    
    # Rank with ties handling (average ranks…
12 0 Open
A/B testing & experimentation easy

How to Mock a Confidence Interval for a Proportion in Python

Simulate a Bernoulli sample and compute a 95% confidence interval for a proportion using the normal approximation in Python.

confidence-interval simulation statistics
Python
import random
import math

def mock_ci(n=100, p_true=0.5, z=1.96, seed=42):
    """Simulate a sample proportion and compute its 95% confidence interval."""
    random.seed(seed)
    successes = sum(1 for _ in range(n) if random.random() < p_true)
    p_hat = successes / n
    se = math.sqrt(p_hat * (1 - p_hat) / n)
  …
15 0 Open
Database scaling & optimization medium

Approximate Count with HyperLogLog in Python

A mock HyperLogLog implementation uses hash-based registers to estimate cardinality of large datasets with sublinear memory.

hyperloglog cardinality hash
Python
import hashlib

class HyperLogLog:
    def __init__(self, precision=4):
        if precision < 4 or precision > 16:
            raise ValueError("precision must be between 4 and 16")
        self.precision = precision
        self.registers = [0] * (1 << precision)

    def _hash(self, value):
        return int(hashl…
15 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.