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How to Generate Project Statistics Including Lines of Code and Complexity in Python
Walk through a Python script that scans a project directory for Python files, counts lines of code excluding blanks and comments, and estimates cyclomatic complexity by counting decision keywords.
import os
from pathlib import Path
def count_lines_of_code(filepath):
"""Counts lines of code in a Python file, excluding blank lines and comments."""
try:
with open(filepath, 'r') as f:
lines = f.readlines()
code_lines = [line for line in lines if line.strip() and not line.strip()…How to Track GitHub Stars, Forks, and Watchers in Python
Automatically fetch and track stars, forks, and watchers for multiple GitHub repositories, saving snapshots locally as JSON files for historical analysis.
import os
import time
import json
import requests
from pathlib import Path
from datetime import datetime
REPOS = [
"psf/requests",
"python/cpython",
"pallets/flask",
]
DATA_DIR = Path("github_metrics")
def fetch_repo_stats(repo):
url = f"https://api.github.com/repos/{repo}"
resp = requests.get(ur…Count Records Processed per Category in Python
Use a Counter dictionary to track how many records of each type (ok, error, retry) were processed in a data pipeline.
from collections import Counter
import random
processed_counter = Counter()
def process_records(records):
for record in records:
processed_counter[record] += 1
return len(records)
if __name__ == "__main__":
sample_records = [random.choice(["ok", "error", "retry"]) for _ in range(10)]
print(f…How to Mock a Metrics Decorator in Python with unittest.mock
This code demonstrates a timing decorator that wraps a function to measure execution time and prints the duration, with a unit test using unittest.mock to patch the print function and assert it was called.
import time
from functools import wraps
from unittest.mock import patch
def add_metrics(func):
@wraps(func)
def wrapper(*args, **kwargs):
start = time.perf_counter()
result = func(*args, **kwargs)
elapsed = time.perf_counter() - start
print(f"{func.__name__} took {elapsed:.6f}s…Observer Pattern with Mock Metrics in Python
Implement the Observer pattern with a mock metrics collector to track state changes and verify notifications.
import unittest
from unittest.mock import Mock
class Subject:
def __init__(self):
self._state = 0
self._observers = []
def attach(self, observer):
self._observers.append(observer)
def set_state(self, value):
if value != self._state:
self._state = value
…Export Metrics with OTLP Mock in Python
Simulates system metric collection and exports them as an OTLP-like JSON payload using only Python's standard library.
from dataclasses import dataclass, asdict
import json
import random
import time
@dataclass
class Metric:
name: str
value: float
timestamp: int
unit: str = "1"
def collect_system_metrics() -> list[Metric]:
"""Mock metric collection for OTLP export simulation."""
now = int(time.time())
re…Generate Mock CPU and Memory Metrics in Python
Build a mock_host_metrics() generator that outputs realistic CPU and memory usage percentages for monitoring demos and tests.
import time
import random
def mock_host_metrics():
"""Generate mock CPU and memory metrics for a host."""
cpu_percent = round(random.uniform(10.0, 95.0), 1)
memory_percent = round(random.uniform(20.0, 90.0), 1)
memory_used_mb = round(random.uniform(512, 8192), 1)
return {
"timestamp": in…Generate Prometheus Text Exposition Format in Python
Mock a Prometheus metrics endpoint by formatting metrics into the text exposition format with HELP, TYPE, and sample lines.
import time
from random import randint
# Mock a Prometheus metrics endpoint output
metrics = {
"http_requests_total": {
"help": "Total number of HTTP requests",
"type": "counter",
"samples": [
{"labels": {"method": "get", "code": "200"}, "value": randint(1000, 9999)},
…Generate Synthetic CPU Utilization Metrics in Python
Creates realistic time-series CPU utilization samples with timestamps, noise, and output as structured JSON for observability demos and testing.
from datetime import datetime, timedelta
import random
import json
def generate_metric_samples(base_value, noise, count=60, interval_minutes=1):
"""Generate realistic CPU utilization samples for a given time window."""
timestamps = []
values = []
now = datetime.utcnow()
start_time = now - timede…Generate Synthetic SRE Metrics and Calculate Availability in Python
Create realistic service metrics with random latency, error rate, and request counts, then compute availability and summarize the stream for SLO checks.
from datetime import datetime, timedelta
import random
def generate_service_metrics(service_name: str, minutes: int = 30) -> list[dict]:
"""Generate synthetic SRE metrics for a service across recent minutes."""
metrics = []
now = datetime.now()
for i in range(minutes):
timestamp = now - t…How to Build a Metrics Counter with Increment and Snapshot in Python
A simple dict-backed MetricsCounter class that increments named counters and returns a snapshot of the current values.
class MetricsCounter:
def __init__(self):
self._metrics = {}
def increment(self, key, delta=1):
self._metrics[key] = self._metrics.get(key, 0) + delta
def snapshot(self):
return dict(self._metrics)
if __name__ == "__main__":
counter = MetricsCounter()
counter.increment("…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 Build an HTTP Server Request Duration Histogram in Python
Create a small HTTP server that times each GET request, buckets the duration, and prints a histogram on shutdown.
import time
import random
from collections import Counter
from http.server import HTTPServer, BaseHTTPRequestHandler
class HistogramHandler(BaseHTTPRequestHandler):
response_times = Counter()
def do_GET(self):
start = time.perf_counter()
time.sleep(random.uniform(0.001, 0.1))
duratio…How to Compute SRE Metrics Like Error Rate and Availability in Python
Tracks log events in a sliding time window and calculates error rate per second and availability percentage using an easy-to-follow class.
from collections import deque
from datetime import datetime, timedelta
from typing import Dict, Deque
class LogMetrics:
"""Simple observability helper to track log events and calculate SRE metrics."""
def __init__(self, window_seconds: int = 60):
self.window_seconds = window_seconds
self.eve…How to Create a StatsD UDP Metric Mock Server in Python
Run a lightweight mock UDP server that captures StatsD metrics over a short window for local testing.
import socket
import threading
import time
def start_mock_statsd_server(host="127.0.0.1", port=8125, timeout=3):
"""Run a mock StatsD UDP server that captures metrics for a short window."""
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind((host, port))
sock.settimeout(timeout)
me…How to Flush Metrics on Graceful Shutdown in Python
Register an atexit handler to automatically flush collected metrics when a Python process exits gracefully.
import atexit
import time
import random
class MetricsCollector:
def __init__(self):
self._metrics = []
atexit.register(self.flush)
def record(self, name, value):
self._metrics.append((name, value, time.time()))
def flush(self):
print(f"Flushing {len(self._metrics)} metri…How to Mock Database Query Duration in Python
Simulate realistic database query durations with random jitter for testing dashboards, alerts, and SLO calculations.
import random
import time
def mock_query_duration(db_name, avg_ms, jitter_ms=5, runs=3):
"""Simulate database query durations with realistic variation."""
durations = []
for _ in range(runs):
# Base duration plus random jitter (can be negative)
duration = avg_ms + random.uniform(-jitter_m…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
…Mocking a Metrics Gauge's set_value Method in Python
Demonstrates using unittest.mock.Mock with wraps to intercept a gauge's set_value call while verifying arguments and preserving real behavior.
from unittest.mock import Mock
class MetricsGauge:
def __init__(self, name):
self.name = name
self.value = 0.0
def set_value(self, new_value):
self.value = float(new_value)
return self.value
# Usage demonstration with a mock
gauge = MetricsGauge("cpu_usage")
gauge_mock = Mock…Python Observability Data Helper for Beginners
A beginner-friendly Python helper to log events, record metrics, summarize observability data, and export it as JSON.
import json
from datetime import datetime
from collections import defaultdict
class ObservabilityDataHelper:
"""Helper for exploring basic observability data patterns."""
def __init__(self):
self.events = []
self.metrics = defaultdict(list)
def log_event(self, service, level, message):
…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):
…Mock a Sidecar Logger with Python Metrics
Simulate a sidecar logger that tracks request counts, error rates, and endpoint hits, producing a metrics snapshot.
import random
import time
from collections import defaultdict
class SidecarLogger:
def __init__(self):
self.metrics = defaultdict(int)
self.total_requests = 0
self.error_count = 0
def log_request(self, endpoint, status_code):
"""Simulate logging a request and updating metrics…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 Compute a Confusion Matrix in Python
Compute a multi-class confusion matrix from true and predicted labels using pure Python dictionaries and nested lists, then format it for readable output.
from collections import defaultdict
def compute_confusion_matrix(y_true, y_pred, labels):
"""Compute confusion matrix using Python dicts and nested lists."""
label_index = {label: i for i, label in enumerate(labels)}
matrix = [[0] * len(labels) for _ in range(len(labels))]
for true, pred in zip(y…Browse by section
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