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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.
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
…Consumer Driven Contract Pact Mock in Python
Define and verify consumer-driven contracts using Pact's Consumer and Provider classes, mocking the provider to assert expected interactions.
from pact import Consumer, Provider
pact = Consumer('OrderService').has_pact_with(Provider('InventoryService'))
@Pact.verify()
class TestInventoryContract:
def test_get_inventory(self):
expected = {"item": "widget", "quantity": 100}
(pact
.given('inventory exists for widget')
.u…How to Build an Anti-Corruption Layer in Python
Translate messy legacy system data into a clean domain model using an anti-corruption layer in Python.
class MockLegacySystem:
"""Simulates a legacy system with messy data formats."""
def get_user_data(self):
# Legacy format: fields are abbreviated and types are inconsistent
return {
"usr_id": "USR-123",
"usr_nm": "john_doe",
"email_addrs": "John.Doe@example.c…How to implement a tumbling window aggregation in Python
Build a mock tumbling window aggregator in Python that groups streaming events into fixed time intervals and computes count, sum, and average per window.
import time
from collections import deque
class TumblingWindow:
def __init__(self, duration_seconds):
self.duration = duration_seconds
self.buffer = deque()
self.window_start = None
def add(self, item):
current_time = time.time()
if self.window_start is None:
…How to use foreachBatch with a mock sink in PySpark
Demonstrates using Spark Structured Streaming's foreachBatch sink to capture and verify streaming batches by writing them into a custom mock sink object.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit
class MockSink:
def __init__(self):
self.batches = []
def write_batch(self, batch_df, batch_id):
# Collect batch data as list of dicts for verification
records = batch_df.collect()
self.batches…How to Build an sklearn Pipeline with ColumnTransformer in Python
A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…How to Stage ML Model Workflows with Python Classes
Defines a Stage class to model ML pipeline stages with variants and mocks, printing grammar for Model, Staging, and Production stages.
class Stage:
def __init__(self, name):
self.name = name
self.mocks = []
self.variants = []
def add_mock(self, mock_name):
self.mocks.append(mock_name)
def add_variant(self, variant_name, productions=()):
self.variants.append((variant_name, list(productions)))
…How to Train a Gradient Boosting Regressor in Python
Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
def train_gradient_boosting_mock():
# Toy regression dataset
np.random.seed(42)
X = np.random.rand(100, 3) * 10
y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…K-Fold Cross Validation in Python: A Simple Implementation
Implements k-fold cross validation from scratch, splitting data into folds and computing MSE scores for a baseline mean-predictor model.
import random
from statistics import mean
def cross_validation_scores(data, labels, k=5, seed=42):
random.seed(seed)
indices = list(range(len(data)))
random.shuffle(indices)
fold_size = len(indices) // k
folds = []
for i in range(k):
if i == k - 1:
folds.append(indices[i *…Train Logistic Regression From Scratch in Python
Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.
import numpy as np
# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])
# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]
# Initialize parameters
theta = np.zeros(X_b.shape[1])
# Hyperparameters
learning_rate = 0…Bayesian A/B Test Credible Interval in Python
Simulates A/B test data and computes posterior credible intervals and the probability that variant B outperforms A using Bayesian Beta-Binomial inference.
import numpy as np
from scipy import stats
# Simulated A/B test data
n_A = 1000
n_B = 1000
conversions_A = 120
conversions_B = 140
# Prior: Beta(1, 1) uniform
alpha_prior, beta_prior = 1, 1
# Posterior parameters
alpha_A = alpha_prior + conversions_A
beta_A = beta_prior + n_A - conversions_A
alpha_B = alpha_prior +…Bootstrap Confidence Interval in Python
Estimates a confidence interval for a statistic (like the mean) using bootstrap resampling in pure Python.
import random
def bootstrap_ci(data, statistic, n_bootstraps=1000, ci_level=0.95, seed=42):
random.seed(seed)
n = len(data)
boot_stats = []
for _ in range(n_bootstraps):
sample = [random.choice(data) for _ in range(n)]
boot_stats.append(statistic(sample))
boot_stats.sort()
l…Delta Method for Ratio Metrics in A/B Testing with Python
Computes the confidence interval for the difference between two ratio metrics using the delta method, with mock A/B test data.
import numpy as np
from scipy.stats import norm
def delta_method_ratio_delta(control: np.ndarray, treatment: np.ndarray, confidence: float = 0.95):
"""Estimate confidence interval for ratio metric using delta method.
Args:
control: numerator/denominator pairs from control group (n x 2 array)
…How to Create an Interrupted Time Series Mock in Python
Generate simulated interrupted time series data with a pre/post-intervention trend, level shift, and noise to test segmented regression models.
import numpy as np
# Mock interrupted time series data
np.random.seed(42)
n_pre = 50
n_post = 50
time = np.arange(0, n_pre + n_post)
# Pre-intervention: linear trend + noise
pre_trend = 0.05 * time[:n_pre] + np.random.normal(0, 0.5, n_pre)
# Post-intervention: new slope + level shift + noise
post_trend = 0.05 * tim…How to Perform Intent-to-Treat Analysis in Python
Runs an intent-to-treat analysis on mock A/B test data, comparing outcomes by initial group assignment with a t-test for significance.
import pandas as pd
import numpy as np
def intent_to_treat_analysis(data):
"""Perform intent-to-treat (ITT) analysis.
ITT compares outcomes based on initial treatment assignment,
regardless of whether participants actually received the treatment.
"""
# Create a copy to avoid mutating the origina…How to Explain SQLite Query Plans in Python
Build a Python function that runs EXPLAIN QUERY PLAN on SQLite in-memory tables and prints the optimizer's execution plan for any SELECT statement.
import sqlite3
def explain_query(sql: str) -> str:
"""Return the SQLite query plan for the given SQL statement."""
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
# Create sample data for a realistic plan
cursor.execute("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT)")
c…How to Mock SQLite executemany When Batch Inserting in Python
Batch insert many rows into SQLite with executemany and mock the cursor for isolated tests.
import sqlite3
from unittest.mock import Mock, patch
def insert_users(conn, users):
"""Insert multiple user records using executemany."""
cursor = conn.cursor()
cursor.executemany(
"INSERT INTO users (name, age) VALUES (?, ?)",
users
)
conn.commit()
return cursor.rowcount
if _…How to Mock a Hot Shard Split in Python
Simulate a database hot shard splitting into two shards by key ranges when it exceeds a threshold, with a mock class for testing.
import random
from collections import defaultdict
class HotShardMock:
"""Mock implementation of a hot shard split in a distributed database."""
def __init__(self, shard_id="shard_1", max_entries=5):
self.shard_id = shard_id
self.max_entries = max_entries
self.entries = {}
def ad…How to Simulate Colocated Shard Joins in Python
Groups shards by their node and merges co-located shards into a single logical unit, checking capacity constraints.
import random
from collections import defaultdict
def simulate_colocated_shards_join(nodes: list[dict], shards: list[dict]) -> dict:
"""
Simulates the join of co-located shards (on the same node) into a single
logical shard. Returns the resulting node-to-shard mapping.
Each node: {'id': str, 'capaci…Simulate a GIN Index for JSONB in Python
Build a mock Generalized Inverted Index (GIN) that flattens JSON documents into key-value tokens for fast lookup queries, mimicking PostgreSQL JSONB indexing.
import json
import random
from collections import defaultdict
# Mock GIN (Generalized Inverted Index) for JSONB key-value pairs
class GINIndex:
def __init__(self):
self.posting_lists = defaultdict(list) # token -> list of doc_ids
def index(self, doc_id, json_obj):
"""Index a JSON documen…Snowflake ID Generator with Cluster Index Mock in Python
A thread-safe Snowflake ID generator mock that creates unique 64-bit IDs across simulated cluster nodes and maintains a sorted in-memory index for range queries.
import time
import threading
class SnowflakeIDGenerator:
def __init__(self, machine_id, datacenter_id):
self.machine_id = machine_id
self.datacenter_id = datacenter_id
self.sequence = 0
self.last_timestamp = -1
self.machine_bits = 5
self.datacenter_bits = 5
…How to Mock HTTP Responses to Verify HSTS Headers in Python
This code demonstrates how to use unittest.mock to intercept and capture HTTP response headers, specifically the Strict-Transport-Security header, from a mocked HTTPServer handler for security validation.
from http.server import BaseHTTPRequestHandler, HTTPServer
from unittest.mock import patch
class StrictTransportMock(BaseHTTPRequestHandler):
def do_GET(self):
self.send_response(200)
self.send_header("Strict-Transport-Security", "max-age=31536000; includeSubDomains")
self.end_headers()
…How to Mock an mTLS Client Certificate in Python
Create a self-signed client certificate and key with OpenSSL, load them into an SSL context, and simulate an mTLS handshake in Python for testing.
import ssl
import socket
import subprocess
import tempfile
from pathlib import Path
def create_mock_certificates():
"""Generate self-signed client certificate and key for mTLS testing."""
with tempfile.TemporaryDirectory() as tmpdir:
cert_path = Path(tmpdir) / "client.crt"
key_path = Path(tmpd…Browse by section
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