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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Bloom Filter Join Mock in Python
A mock hash join that uses a Bloom filter to pre-filter one table before performing an exact match, reducing the number of comparisons in large dataset joins.
import hashlib
import random
import string
class BloomFilter:
def __init__(self, size: int = 200, num_hashes: int = 3):
self.bits = [False] * size
self.size = size
self.num_hashes = num_hashes
def _hashes(self, item: str):
result = []
for seed in range(self.num_hashes…
Compare Model A vs Model B Metrics in Python
A script that simulates and compares metrics between two ML models, showing a formatted diff table for quick insight.
import random
def compare_a_b(samples=5):
"""Mock comparison of model A vs model B predictions."""
metrics = ["accuracy", "precision", "recall", "f1"]
print(f"{'Metric':<12}{'Model A':>10}{'Model B':>10}{'Diff':>10}")
print("-" * 42)
random.seed(42)
for metric in metrics:
a = round(r…
How to Mock ROC AUC in Python
Compute ROC AUC from scratch in Python using pairwise comparisons between positive and negative score distributions, ideal for testing ML models without sklearn.
import random
from math import comb
def mock_roc_auc(scores, labels):
"""Compute mock ROC AUC by simulating a classifier's score distribution."""
random.seed(42)
n = len(labels)
pos_scores = [scores[i] for i in range(n) if labels[i] == 1]
neg_scores = [scores[i] for i in range(n) if labels[i] == …
Bonferroni Correction in Python
Applies the Bonferroni correction to a list of p-values to control the family-wise error rate when performing multiple comparisons.
import numpy as np
def bonferroni_correction(p_values, alpha=0.05):
"""Apply Bonferroni correction to a list of p-values."""
n = len(p_values)
corrected_alpha = alpha / n
significant = [p < corrected_alpha for p in p_values]
return corrected_alpha, significant
if __name__ == "__main__":
# Moc…
How to Count Star vs Estimate Matches in Python
Count how many times 'star' and 'estimate' annotations match their actual labels in a list of mock comparison results.
def count_star_vs_estimate(mock_scores):
"""
Count the number of times 'star' wins and 'estimate' wins
from a list of mock comparison results.
Args:
mock_scores: list of tuples, each (annotation, actual)
where annotation is 'star' or 'estimate'
Returns:
dict w…
How to Hash Passwords and Authenticate Users in Python
A beginner-friendly dataclass-based design that hashes passwords with PBKDF2 and verifies them securely using constant-time comparisons.
import hashlib
import hmac
import secrets
from dataclasses import dataclass
from typing import Optional
@dataclass
class User:
id: int
username: str
password_hash: str
salt: str
def hash_password(password: str) -> tuple[str, str]:
salt = secrets.token_hex(16)
password_hash = hashlib.pbkdf2_…
How to Hash and Verify Passwords in Python
Hash passwords securely with PBKDF2-SHA256 and verify them using a constant-time comparison.
import hashlib
import hmac
import secrets
from typing import Tuple
def hash_password(password: str, salt: str = None) -> Tuple[str, str]:
"""Hash a password with a random salt using PBKDF2-SHA256."""
salt = salt or secrets.token_hex(16)
hashed = hashlib.pbkdf2_hmac(
"sha256", password.encode("utf…
How to Salt Passwords per User in Python
Hash each user's password with a unique random salt using hashlib, and verify logins with timing-safe comparison.
import hashlib
import secrets
def hash_password(password: str, salt: str | None = None) -> tuple[str, str]:
"""Hash a password with a random salt (or provided salt).
Returns:
(salt_hex, password_hash_hex)
"""
if salt is None:
salt = secrets.token_hex(16)
salted = (salt + password)…
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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.