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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Normalize a List of Numbers in Python
This Python function normalizes a list of numeric values to the range [0, 1] using min-max scaling, returning a new list and leaving the original unchanged.
def normalize(data):
"""
Normalize a list of numeric values to the range [0, 1].
Returns a new list, leaving the original unchanged.
"""
if not data:
return []
min_val = min(data)
max_val = max(data)
# Handle the edge case where all values are identical
if min_val …How to Normalize a List of Numbers to the 0-1 Range in Python
Scale a list of numbers so the minimum becomes 0 and the maximum becomes 1 using min-max normalization.
def min_max_normalize(values):
"""Normalize a list of numbers to the [0, 1] range."""
if not values:
return []
min_val = min(values)
max_val = max(values)
if min_val == max_val:
return [0.0] * len(values)
return [(x - min_val) / (max_val - min_val) for x in values]
if __name__…How to Mock Auto Scaling Policy Scale Out in Python
Define a mock auto-scaling function that scales out capacity by a factor up to a max, simulating AWS-like events.
def mock_scale_out(current_capacity: int, max_capacity: int, scale_factor: int = 1) -> tuple:
"""
Mock auto-scaling policy: scales out by the specified factor
if capacity allows, capped at max_capacity.
"""
if current_capacity >= max_capacity:
return current_capacity, False
new_cap…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…StandardScaler mock in Python
A pure-Python StandarScaler class that standardizes features to zero mean and unit variance without sklearn.
import math
class StandardScaler:
def __init__(self):
self.mean_ = None
self.std_ = None
def fit(self, X):
n = len(X)
self.mean_ = [sum(col) / n for col in zip(*X)]
self.std_ = []
for col in zip(*X):
variance = sum((x - self.mean_[i]) ** 2 for i, x …Database Helper in Python with SQLite Scaling Optimization
Build a beginner-friendly SQLite database helper class with WAL, indexed queries, and efficient batch inserts for scaling.
import sqlite3
from contextlib import contextmanager
class DatabaseHelper:
"""Beginner-friendly helper for SQLite database operations with scaling tips."""
def __init__(self, db_path):
self.db_path = db_path
@contextmanager
def connection(self):
"""Context manager for automatic comm…How to Convert Data with Scaling for Database Optimization in Python
A beginner-friendly helper that normalizes and scales numeric fields in a list of dicts, reducing storage footprint for database efficiency.
import json
from datetime import datetime
def convert_data(data: list[dict], scale_factor: int = 1) -> list[dict]:
"""Convert a list of dicts to a scaled, normalized format for database efficiency."""
converted = []
for row in data:
normalized = {}
for key, value in row.items():
…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 Validate Data Before Scaling in Python
A reusable Python helper that validates required fields and constraint checks on data rows before entering a database pipeline, improving data quality and throughput.
def validate_data(data, required_fields, constraints=None):
"""
Basic validation helper demonstrating data-quality workflows
before scaling (catches bad rows early, improves throughput).
"""
constraints = constraints or {}
errors = []
for field in required_fields:
if field not in d…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
…Simulate Shard Key Cardinality in Python
Generate mock data with configurable cardinality to evaluate shard key distribution and detect hotspots in database scaling design.
import random
import string
def calculate_cardinality(values):
"""Return the number of distinct values in the given list."""
return len(set(values))
def generate_mock_data(num_records, cardinality):
"""Generate mock records for a shard key with given cardinality."""
possible_keys = [f"key_{i:04d}" fo…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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