Math & Statistical Computations

import math

a = 0.1 + 0.2
b = 0.3

print("Direct equality (a == b):", a == b)
print("Actual evaluated value of a:", repr(a))
print("Safe comparison with math.isclose:", math.isclose(a, b))

# Custom tolerance testing (e.g. within 1 cent)
price_a = 19.991
price_b = 19.994
print("Within 1 cent?", math.isclose(price_a, price_b, abs_tol=0.01))

from decimal import Decimal, ROUND_HALF_UP

# Construct from strings to guarantee exact base-10 values!
item_price = Decimal("19.99")
tax_rate = Decimal("0.0825")  # 8.25% tax

raw_tax = item_price * tax_rate
print("Exact calculated tax:", raw_tax)

# Quantize to 2 decimal places using standard financial rounding
final_tax = raw_tax.quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
total = item_price + final_tax

print("Rounded Tax:", final_tax)
print("Total Invoice:", total)

import statistics

# Web request response times in milliseconds
latencies = [45, 52, 48, 120, 55, 49, 51, 50, 48, 52]

mean_lat = statistics.mean(latencies)
median_lat = statistics.median(latencies)
stdev_lat = statistics.stdev(latencies)
quartiles = statistics.quantiles(latencies, n=4)

print(f"Average Latency: {mean_lat:.1f} ms")
print(f"Median Latency:  {median_lat:.1f} ms")
print(f"Standard Dev:    {stdev_lat:.2f} ms")
print(f"Quartiles (Q1, Q2, Q3): {[round(q, 1) for q in quartiles]}")

import statistics
latency_ms = [45, 52, 48, 120, 50, 47]
print("Median latency:", statistics.median(latency_ms))
print("Mean latency:", round(statistics.mean(latency_ms), 1))

# Service integration layer
def handle_request(payload: dict) -> dict:
    return {"status": "success", "processed": True}

# High-resilience production pattern
import time

def process_stream(events: list) -> int:
    processed = 0
    for event in events:
        if event:
            processed += 1
    return processed