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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Mock Azure Blob Upload and Download in Python
Simulate Azure Blob Storage upload and download operations with a lightweight in-memory mock class for testing.
import io
import json
from datetime import datetime, timezone
class MockBlob:
def __init__(self, name):
self.name = name
self.content = b""
self.properties = {
"last_modified": datetime.now(timezone.utc).isoformat(),
"size": 0,
}
def upload(self, data, …Mock GCP storage bucket blob upload in Python
Simulate uploading a blob to a GCP Storage bucket for testing without hitting the cloud.
import io
from datetime import datetime
from unittest.mock import MagicMock, patch
class MockBlob:
"""Simulates a GCP storage blob for unit testing."""
def __init__(self, name):
self.name = name
self.uploaded_at = None
self.content = b""
def upload_from_file(self, file_obj):
…Mock S3, GCS, and Azure storage with a Python abstract interface
Define an abstract Storage interface and implement a local, filesystem-backed mock so S3, GCS, and Azure code can be tested without cloud dependencies.
from abc import ABC, abstractmethod
from pathlib import Path
class Storage(ABC):
@abstractmethod
def put(self, name: str, data: bytes) -> None:
pass
@abstractmethod
def get(self, name: str) -> bytes:
pass
class LocalStorage(Storage):
def __init__(self, base_dir: str = "mock_sto…How to Parametrize Tests in Python with pytest
This code demonstrates how to use pytest's @pytest.mark.parametrize decorator to run a single test function against multiple input sets, ensuring comprehensive coverage with minimal code duplication.
import pytest
def multiply(a, b):
return a * b
@pytest.mark.parametrize("x, y, expected", [
(2, 3, 6),
(4, 5, 20),
(0, 10, 0),
(7, 1, 7),
])
def test_multiply(x, y, expected):
result = multiply(x, y)
assert result == expected, f"multiply({x}, {y}) = {result}, expected {expected}"
if _…How to Run Coverage Report and Generate HTML in Python
Use the coverage module to measure test coverage, save the report, and generate an HTML report in Python.
import coverage
import unittest
def add(a, b):
return a + b
class TestAdd(unittest.TestCase):
def test_add_positive(self):
self.assertEqual(add(2, 3), 5)
if __name__ == "__main__":
cov = coverage.Coverage(source=["__main__"])
cov.start()
suite = unittest.defaultTestLoader.loadTestsFro…How to set up mypy strict mode in Python
Demonstrates how to configure and run mypy in strict mode to enforce full type annotation coverage across a Python project.
from typing import Dict, Optional
def describe_user(name: str, age: int, email: Optional[str] = None) -> Dict[str, object]:
"""Build a user description dictionary with strict type annotations."""
user: Dict[str, object] = {"name": name, "age": age}
if email is not None:
user["email"] = email
…Benchmark list.append vs deque.append in Python
Measures and compares the performance of appending to a Python list versus a collections.deque using timeit.repeat, showing best and average timings.
"""Benchmark list.append vs collections.deque.append."""
import timeit
def bench(stmt, setup, repeat=5, number=1_000_000):
times = timeit.repeat(stmt, setup=setup, repeat=repeat, number=number)
return min(times), sum(times) / len(times)
if __name__ == "__main__":
number = 1_000_000
list_best, list_a…How to Use Array Typecodes for Compact Numeric Storage in Python
This code demonstrates how to use the `array` module with typecodes to store integers, floats, and bytes in a memory-efficient way compared to standard Python lists.
from array import array
def demonstrate_array_types():
# Compact integer arrays
small_ints = array('i', [1, 2, 3, 4, 5])
unsigned_ints = array('I', [10, 20, 30])
# Floating point arrays
floats = array('f', [1.5, 2.5, 3.5])
doubles = array('d', [1.123456789, 2.987654321])
# Charac…Using a Python Generator Instead of a List to Save Memory
Compare a list approach with a generator to stream values lazily, avoiding memory-heavy storage of large sequences.
def fibonacci_generator(limit):
a, b = 0, 1
count = 0
while count < limit:
yield a
a, b = b, a + b
count += 1
def sum_first_n(generator, n):
total = 0
for i, value in enumerate(generator):
if i >= n:
break
total += value
return total
if __…How to Compare Execution Speed Between Python Functions
Measure and compare the average execution time of multiple Python functions using a reusable benchmark helper with time.perf_counter.
import time
import random
def method_a(values):
"""Sort using built-in sorted."""
return sorted(values)
def method_b(values):
"""Sort using list's sort method."""
values_copy = values[:]
values_copy.sort()
return values_copy
def method_c(values):
"""Sort manually using bubble sort (slow,…How to Run Test Coverage with pytest-cov in Python
Run pytest with coverage reporting using pytest-cov on a temporary project and see line-by-line coverage output.
import os
import subprocess
import tempfile
from pathlib import Path
def sample_function(x: int) -> int:
"""A simple function to demonstrate coverage."""
if x > 0:
return x * 2
else:
return -x
def run_pytest_with_coverage() -> str:
"""Run pytest with coverage on a temp project and r…How to Take Periodic Snapshots of Aggregate State in Python
Build a Python class that accumulates values and periodically captures immutable snapshots of total, count, and average for later analysis.
import time
import random
from collections import defaultdict
class SnapshotAggregator:
def __init__(self):
self.total = 0
self.count = 0
self.history = []
def add(self, value):
self.total += value
self.count += 1
def snapshot(self):
avg = self.total / se…Create a Data Helper in Python for gRPC-style APIs
This code builds a simple DataHelper class that mimics gRPC request/response handling with in-memory storage, JSON serialization, and basic CRUD operations for beginners.
import json
from dataclasses import dataclass, asdict
from typing import Dict, Any
@dataclass
class User:
user_id: int
name: str
email: str
class DataHelper:
"""Simple helper to demonstrate gRPC-like data handling for beginners."""
def __init__(self) -> None:
self._users: Dict[int, Use…How to Aggregate Periodic Snapshot Data in Python
Generates mock snapshot data and groups values into periods to compute average aggregates with Python's standard library.
import random
from collections import defaultdict
def snapshot_aggregate(n=10, period=3):
data = defaultdict(list)
for i in range(n):
key = f"item_{i % period}"
data[key].append(random.randint(1, 100))
return dict(data)
def aggregate_periodic(snapshots, period=3):
result = {}
for …Sliding Window Average with Deque in Python
Computes the running average of a sliding window over streaming numbers using a collections.deque for O(1) pop-left operations.
from collections import deque
class SlidingAverage:
def __init__(self, window_size):
self.window_size = window_size
self.window = deque()
self.total = 0
def add(self, value):
self.window.append(value)
self.total += value
if len(self.window) > self.window_size:
…How to implement a write-behind cache with async queue in Python
Build an async write-behind cache that queues writes in memory and flushes them in batches to persistent storage.
import asyncio
from collections import deque
from dataclasses import dataclass
@dataclass
class CacheEntry:
key: str
value: str
class WriteBehindCache:
def __init__(self, flush_interval=1.0):
self.cache = {}
self.queue = deque()
self.flush_interval = flush_interval
self._f…How to Build a Consumer Lag Gauge in Python
Simulate Kafka consumer lag with a Python class that tracks lag over time and reports health and averages.
import time
import random
from collections import deque
class ConsumerLagGauge:
"""Mock consumer lag gauge measuring how far behind a consumer is."""
def __init__(self, producer_rate=10, consumer_rate=7, initial_lag=0):
self.producer_rate = producer_rate
self.consumer_rate = consumer_rate
…How to Build a Python Latency Histogram with Mock Buckets
This code implements a mock latency histogram that records request durations into configurable buckets and outputs counts, total, and average latency.
import time
import random
from collections import Counter
class LatencyHistogram:
def __init__(self, buckets):
self.buckets = sorted(buckets)
self.counts = Counter()
self.total = 0
self.sum_latency = 0
def record(self, latency_ms):
for i, boundary in enumerate(self.bu…How to Process System Metrics (RSS, CPU) in Python
Simulate and aggregate RSS and CPU system metrics to compute averages and maximums for monitoring dashboards.
import random
import time
from collections import namedtuple
Metric = namedtuple("Metric", ["name", "value", "unit"])
def generate_metrics(num_metrics: int = 5) -> list:
"""Simulate a batch of system metrics."""
metrics = []
for i in range(num_metrics):
rss = random.randint(50, 500) # MB
…Track Success Rates and Latency in Python: SRE Metrics Helper
A beginner-friendly Python class to record request outcomes and latencies, then report success rate, average latency, and p99.
import random
import time
from collections import defaultdict
class MetricsTracker:
"""Simple helper to track success rates and latencies for SRE beginners."""
def __init__(self):
self.successes = 0
self.failures = 0
self.latencies = []
def record(self, success, latency_ms):
…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:
…Mock Predicate Pushdown in Python for Big Data Queries
Simulate predicate pushdown by applying filters at the storage layer before materializing rows, showing how big data engines optimize queries.
class Query:
def __init__(self, table, rows):
self.table = table
self.rows = rows
def filter(self, predicate):
return Query(
self.table,
[row for row in self.rows if all(predicate(row) for predicate in predicate)]
)
def filter_pushdown(self, predica…Modeling a Hive Metastore Table Schema in Python
A dataclass that mimics a Hive metastore table schema—columns, partition keys, storage format, and location—with helper methods for description and mutation.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class HiveTable:
"""Simple mock of a Hive metastore table schema."""
name: str
database: str = "default"
columns: List[Dict[str, str]] = field(default_factory=list)
partition_keys: List[Dict[str, str]] = f…Sliding Window Streaming Mock in Python
A simple Python class that maintains a sliding window of recent streaming values and computes the running average.
import time
import random
class StreamingMock:
"""Produces a stream of numbers using a sliding window."""
def __init__(self, window_size=5):
self.window = []
self.window_size = window_size
def push(self, value):
"""Add a value, sliding the window forward."""
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