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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Automatically Generate Hardware Inventory Reports in Python
Generate a system hardware report including OS version, CPU cores, RAM, and disk usage using platform and psutil.
import platform
import psutil # requires: pip install psutil
from datetime import datetime
def generate_hardware_report():
report_lines = []
report_lines.append(f"Report Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
report_lines.append(f"System: {platform.system()} {platform.release()} ({pl…Automatically Log CPU, RAM, and Disk Usage Every Minute in Python
This script logs CPU, RAM, and disk usage to a CSV file every 60 seconds using psutil and Python's standard library.
import psutil
import time
import csv
from pathlib import Path
LOG_FILE = Path("system_usage_log.csv")
INTERVAL_SECONDS = 60
def log_system_usage():
"""Write CPU, RAM, and disk usage to CSV every minute."""
file_exists = LOG_FILE.exists()
with open(LOG_FILE, mode="a", newline="") as f:
writer = cs…Build a Terminal Dashboard That Displays Real-Time System Performance in Python
A Python script that reads Linux system files to display a real-time terminal dashboard with CPU usage, memory usage, and CPU temperature.
import os, time, sys
from collections import deque
def get_cpu_temp():
try:
with open("/sys/class/thermal/thermal_zone0/temp") as f:
return round(int(f.read().strip()) / 1000, 1)
except:
return None
def get_mem_usage():
with open("/proc/meminfo") as f:
lines = f.readli…Stress CPU Threads with a Mock Compute in Python
Simulates CPU-intensive work across multiple threads to test how Python schedules parallel compute.
import threading
import time
def stress_cpu(iterations: int):
result = 0
for i in range(iterations):
result += i * i % 1000
return result
def run_mock_stress(thread_count: int, iterations: int):
threads = []
for tid in range(thread_count):
t = threading.Thread(target=lambda: str…Map Partition Over Chunks in Python with Multiprocessing and Mock
Process data in chunks across multiple CPU cores using multiprocessing Pool.map, and mock the chunk function to test partitioning behavior without heavy computation.
from multiprocessing import Pool
from unittest.mock import patch, Mock
def process_chunk(chunk):
return [x * x for x in chunk]
def map_partition_over_chunks(data, chunk_size, process_func=process_chunk):
chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]
with Pool() as pool:
…How to Demonstrate the GIL with Python Threads vs Processes
Measure and compare wall-clock time for CPU-bound work using Python threads (limited by the GIL) versus multiprocessing (which bypasses the GIL).
import threading
import multiprocessing
import time
import os
def cpu_heavy(n):
return sum(i * i for i in range(n))
def run_threads(n):
threads = [threading.Thread(target=cpu_heavy, args=(n,)) for _ in range(2)]
start = time.perf_counter()
for t in threads:
t.start()
for t in threads:
…How to Profile CPU Hot Path in Python with cProfile and sort_stats cumtime
Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.
import cProfile
import pstats
import io
def slow_function():
total = 0
for i in range(100_000):
total += i * i
return total
def fast_function():
return sum(i for i in range(100))
def main():
slow_function()
fast_function()
if __name__ == "__main__":
profiler = cProfile.Profi…How to Use ProcessPoolExecutor for CPU Parallel Map in Python
Run a function over a sequence of inputs in parallel across multiple CPU cores with ProcessPoolExecutor.map.
from concurrent.futures import ProcessPoolExecutor
import math
def compute_square(num):
return num * num
def is_prime(n):
if n < 2:
return False
for i in range(2, int(math.sqrt(n)) + 1):
if n % i == 0:
return False
return True
if __name__ == "__main__":
numbers = rang…How to Use ThreadPoolExecutor and ProcessPoolExecutor in Python
Compares ThreadPoolExecutor and ProcessPoolExecutor by running CPU-bound and I/O-tolerant tasks over a large list, printing elapsed times and first results.
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import math
numbers = list(range(1, 1000001))
def compute_square(n):
return n * n
def compute_sqrt(n):
return math.sqrt(n)
def run_executor(executor, func, data):
start = time.perf_counter()
results = list(executo…How to Use multiprocessing Pool map and starmap in Python
Parallelize functions over iterables with Pool.map, and unpack multiple arguments via Pool.starmap.
from multiprocessing import Pool
def square(x):
return x * x
def add_and_multiply(a, b, c):
return (a + b) * c
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5]
with Pool(processes=2) as pool:
squares = pool.map(square, numbers)
print(f"squares: {squares}")
starmap_arg…How to Use pool.map for CPU-Bound Tasks in Python
Distribute CPU-intensive functions across processes with multiprocessing.Pool.map and measure the performance gain.
from multiprocessing import Pool
import time
def cpu_bound_task(n):
"""Mock CPU-bound work: compute sum of squares."""
total = 0
for i in range(n):
total += i * i
return total
if __name__ == "__main__":
numbers = [10_000_000, 12_000_000, 8_000_000, 15_000_000]
start = time.perf_count…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 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…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
…How to mock resource request limits in Python
A Python class that simulates CPU and memory limit checks for resource requests, returning clear acceptance or rejection messages.
class ResourceLimits:
def __init__(self, cpu_limit, memory_limit):
self.cpu_limit = cpu_limit
self.memory_limit = memory_limit
def check_request(self, cpu, memory):
if cpu > self.cpu_limit:
return "CPU limit exceeded: {cpu} > {limit}".format(cpu=cpu, limit=self.cpu_limit)
…Mock Kubernetes HPA CPU Scaling in Python
Python function that simulates CPU utilization and calculates desired replicas using the Kubernetes HPA formula.
import random
import time
def simulate_cpu_utilization(target_utilization=50, samples=10):
"""Simulate CPU utilization readings for HPA mock."""
utilizations = []
for _ in range(samples):
# Simulate fluctuating CPU with random noise around target
current = target_utilization + random.unif…Browse by section
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