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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Difference in Differences Mock in Python
Generate mock panel data with a known treatment effect and compute a difference-in-differences estimate using group and period means.
import numpy as np
import pandas as pd
# Generate mock panel data: 2 groups (control=0, treatment=1) × 2 periods (pre=0, post=1)
rng = np.random.default_rng(42)
n_per_cell = 50
data = []
for group in [0, 1]:
for period in [0, 1]:
# True effect: treatment increases outcome by 5 in the post period
…Epsilon Greedy Bandit Mock in Python
A simple epsilon-greedy multi-armed bandit simulation that balances exploration and exploitation to estimate true means of several Bernoulli-like reward distributions.
import random
class Bandit:
def __init__(self, true_mean):
self.true_mean = true_mean
self.estimated_mean = 0.0
self.n_pulls = 0
def pull(self):
return random.gauss(self.true_mean, 1.0)
def update(self, reward):
self.n_pulls += 1
self.estimated_mean += (r…Generate a Mock Multi-Armed Bandit Report in Python
Simulate a multi-armed bandit experiment with random pulls and rewards, then output a JSON report with per-arm statistics.
import random
import json
def generate_mock_bandit_report(num_arms=5, num_rounds=100, seed=42):
random.seed(seed)
arms = ["A", "B", "C", "D", "E"][:num_arms]
true_means = {arm: random.uniform(0.3, 0.7) for arm in arms}
pulls = {arm: 0 for arm in arms}
rewards = {arm: 0 for arm in arms}
for _ …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 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.
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)
…How to Simulate Fixed-Horizon Testing in Python
Simulate a fixed-horizon experiment by labeling data before the horizon as warmup and after as active/inactive, then summarize via CSV.
import csv
import io
def fixed_horizon_mock(data: list[tuple[float, float, float]], horizon: int) -> str:
"""Simulate fixed-horizon testing, then summarize with CSV output."""
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(["day", "value", "signal", "status"])
for day, value,…How to Simulate Geo Experiments in Python
Build a mock geo experiment simulator with ramp-up/down periods, measuring weekly lift between treatment and control markets.
import random
import math
from dataclasses import dataclass
@dataclass
class GeoMarket:
name: str
base_demand: float
geo_coefficient: float
def simulate_geo_experiment(markets, weeks=12, control_weeks=6):
"""
Simulates a geo experiment with ramp-up and ramp-down periods.
Returns weekly lift p…How to simulate a contextual bandit in Python
Simulate a contextual multi-armed bandit with random features and epsilon-greedy action selection in Python.
import random
class ContextualBandit:
def __init__(self, n_actions=3, n_features=4):
self.n_actions = n_actions
self.n_features = n_features
self.theta = [random.random() for _ in range(n_actions * n_features)]
def mock_context(self):
return [random.uniform(-1, 1) for _ in ra…Simulate a Ramp Rollout Percentage in Python
Simulates a percentage-based ramp rollout with deterministic seeding, returning success/failure/in-progress counts for a mock user population.
import random
from enum import Enum
class RolloutStatus(Enum):
SUCCESS = "success"
FAILED = "failed"
IN_PROGRESS = "in_progress"
def simulate_ramp_rollout(total_users: int, percentage: int, seed: int = 42) -> dict:
"""
Simulates a mock ramp rollout for a given percentage of users.
Returns sta…Cross Shard Query Scatter Gather Mock in Python
Simulate a distributed database cross-shard query using a scatter-gather pattern with a mock Python implementation.
from dataclasses import dataclass
from typing import List, Dict
@dataclass
class NodeResponse:
node_id: int
data: Dict[str, float]
def mock_query_shard(shard_id: int, shard_data: Dict[str, float], query: str) -> NodeResponse:
"""Simulate querying a single shard, returning matches whose value > 50."""
…How to Mock Replica Lag Monitoring in Python
Simulates database replica lag with a mock monitor class that generates realistic lag metrics and health statuses.
import time
import random
from datetime import datetime, timedelta
class MockReplicaLagMonitor:
def __init__(self, replicas=3, base_lag=0.5, jitter=0.2):
self.replicas = [f"replica-{i}" for i in range(replicas)]
self.base_lag = base_lag
self.jitter = jitter
self.last_write = dateti…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…How to Simulate Distributed Transactions in Python with a Mock
Model distributed transaction behavior with a mock Transaction class that supports commit, rollback, and failure simulation.
class Transaction:
def __init__(self, id):
self.id = id
self.operations = []
self.committed = False
def add_operation(self, op, data):
self.operations.append((op, data))
def commit(self):
if not self.operations:
raise ValueError("No operations to commit…How to mock directory-based sharding in Python
Simulates distributing files into logical shards using a deterministic hash of each filename, mocking how a database might shard rows across nodes.
import os
import hashlib
from collections import defaultdict
from pathlib import Path
def get_shard_for_key(key: str, num_shards: int) -> int:
"""Return a deterministic shard index (0..num_shards-1) for a key."""
digest = hashlib.md5(key.encode('utf-8')).hexdigest()
return int(digest, 16) % num_shards
…Monitor Database Index Bloat in Python
Simulates index bloat checks for database tables using random ratio thresholds and reports alerts per index.
import random
import time
class IndexBloatMonitor:
def __init__(self, thresholds=(0.5, 0.8, 0.9)):
self.thresholds = thresholds
self.indices = {
"users_pk": 48.2,
"orders_created_idx": 124.7,
"products_name_idx": 15.3,
"payments_user_idx": 203.9,
…UUID vs sequential primary key in Python
Simulate and compare UUID vs sequential primary key generation in Python to understand trade-offs in ordering and uniqueness.
import uuid
import time
def create_record_with_uuid(name):
record_id = uuid.uuid4()
return {"id": record_id, "name": name}
def create_record_with_sequential_id(name, counter):
counter += 1
return {"id": counter, "name": name}
if __name__ == "__main__":
# Simulate users inserting records
sequ…Auto Rollback on Error Rate Exceeded in Python
Simulate a service that monitors a rolling window of request errors and automatically rolls back when the error rate exceeds a threshold.
import random
import time
def simulate_requests(total_requests=1000, rollback_threshold=0.2):
"""
Simulate a service that automatically rolls back when the error rate
exceeds a threshold within a rolling window.
"""
window_size = 100
errors_seen = []
rolled_back = False
for req_num i…How to Build a Synthetic Monitor Mock in Python
Simulates a synthetic monitoring system in Python that collects latency samples, averages them, and reports service status as UP or DEGRADED.
import random
import time
from dataclasses import dataclass, field
from statistics import mean
@dataclass
class SyntheticMonitor:
service: str
endpoint: str
latency_ms: list[float] = field(default_factory=list)
def check(self) -> float:
latency = random.uniform(50.0, 250.0)
self.late…How to Implement a Manual Approval Gate Mock in Python
Simulates a manual approval workflow with threshold-based rules, random decisions for medium amounts, and logs each result with timing.
import random
import time
def approve_request(amount: float) -> bool:
if amount <= 1000:
return True
if amount <= 5000:
return random.random() < 0.7
return False
def main():
requests = [500, 1200, 7500, 3000, 50]
for amount in requests:
start = time.perf_counter()
…How to Mock Canary Deployment Traffic Split in Python
Simulate a canary deployment's stable/canary traffic split using deterministic request hashing to mock rollout behavior with precise percentage control.
class CanaryDeployment:
def __init__(self, stable_weight: float = 0.9, canary_weight: float = 0.1):
self.stable_weight = stable_weight
self.canary_weight = canary_weight
self.total_weight = stable_weight + canary_weight
def route_request(self, request_id: int) -> str:
"""Route …How to Mock Image Signing Cost in Python
Create a deterministic mock signing cost calculator that predicts resource usage for image signatures before real signing infrastructure is staged.
import math
import struct
def sign_image_cost(image_signature: bytes) -> int:
"""Deterministic mock signing cost based on image signature bytes."""
if not image_signature:
raise ValueError("Empty image signature")
digest = 0
for byte in image_signature:
digest = (digest * 31 + byte) &…How to Mock Multi-Stage Docker Builds in Python
Simulate a multi-stage Docker build in pure Python using classes and temp directories to understand how build stages copy artifacts into a final image.
# Simulate multi-stage Docker build with pure Python
from pathlib import Path
import tempfile
import shutil
class BuildContext:
"""Mimics a Docker build context with stages."""
def __init__(self, name):
self.name = name
self.files = {}
def add_file(self, dest, content):
s…How to Mock Terraform Plan and Apply in Python
This code provides a lightweight Python mock of Terraform's plan and apply commands, helping you simulate infrastructure changes without real cloud resources.
class MockTerraform:
def __init__(self):
self.plans = [
{"id": 1, "action": "create", "resource": "aws_instance.web"},
{"id": 2, "action": "update", "resource": "aws_s3_bucket.data"},
{"id": 3, "action": "destroy", "resource": "aws_iam_user.legacy"}
]
sel…How to Mock a CI Pipeline with Build, Test, and Deploy Stages in Python
Simulate a three-stage CI pipeline (build, test, deploy) in Python with random pass/fail logic, early exit on failure, and measured stage durations.
import time
import random
from dataclasses import dataclass
@dataclass
class StageResult:
name: str
status: str
duration: float
def run_stage(name: str, success_chance: float = 0.9) -> StageResult:
"""Simulate a pipeline stage with random success/failure."""
start = time.time()
time.sleep(r…Browse by section
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