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🐙

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PHILOSOPHY

🖱️ Cursor — IDE-Native AI

PHILOSOPHY

🐙 Copilot — Layer & Extend

PHILOSOPHY

⚡ Claude Code — Terminal-Agentic

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pipeline.py — Reference Solution BENCHMARK TARGET

""" Customer Order Data Pipeline AiBytec.com — Benchmark Reference Solution """import csv import json import logging from datetime import datetime, date from pathlib import Path from collections import defaultdict from typing import Optionallogging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") logger = logging.getLogger(__name__)TAX_RATE = 0.08 DISCOUNT_TIERS = { "bronze": (0, 500), "silver": (500, 2000), "gold": (2000, 5000), "platinum": (5000, float("inf")) }def parse_date(date_str: str) -> Optional[date]: """Parse date string, return None if invalid.""" for fmt in ("%Y-%m-%d", "%d/%m/%Y", "%m/%d/%Y"): try: return datetime.strptime(date_str, fmt).date() except ValueError: continue return Nonedef get_discount_tier(total: float) -> str: """Assign discount tier based on order total.""" for tier, (low, high) in DISCOUNT_TIERS.items(): if low <= total < high: return tier return "bronze"def validate_row(row: dict, row_num: int) -> Optional[dict]: """Validate a single CSV row. Return None if invalid.""" required = ["order_id", "customer_id", "product_category", "quantity", "unit_price", "order_date"] for field in required: if not row.get(field, "").strip(): logger.warning(f"Row {row_num}: missing field '{field}' — skipped") return None try: qty = int(row["quantity"]) price = float(row["unit_price"]) if qty <= 0 or price <= 0: raise ValueError("Non-positive values") except (ValueError, TypeError): logger.warning(f"Row {row_num}: invalid numeric values — skipped") return None order_date = parse_date(row["order_date"]) if not order_date: logger.warning(f"Row {row_num}: unparseable date — skipped") return None return {"qty": qty, "price": price, "order_date": order_date}def enrich_row(row: dict, validated: dict) -> dict: """Add derived fields to a validated row.""" qty = validated["qty"] price = validated["price"] total = qty * price return { **row, "quantity": qty, "unit_price": price, "subtotal": round(total, 2), "total_with_tax": round(total * (1 + TAX_RATE), 2), "discount_tier": get_discount_tier(total), "order_age_days": (date.today() - validated["order_date"]).days, }def aggregate(orders: list[dict]) -> dict: """Compute summary statistics across all valid orders.""" by_category = defaultdict(lambda: {"count": 0, "revenue": 0.0}) total_revenue = 0.0 for o in orders: cat = o["product_category"] by_category[cat]["count"] += 1 by_category[cat]["revenue"] += o["subtotal"] total_revenue += o["subtotal"] return { "total_orders": len(orders), "total_revenue": round(total_revenue, 2), "average_order_value": round(total_revenue / len(orders), 2) if orders else 0, "revenue_by_category": { cat: { "orders": v["count"], "revenue": round(v["revenue"], 2), "share_pct": round(v["revenue"] / total_revenue * 100, 1) } for cat, v in sorted( by_category.items(), key=lambda x: x[1]["revenue"], reverse=True ) } }def run_pipeline(input_path: str, output_path: str) -> None: """Main pipeline entry point.""" logger.info(f"Starting pipeline: {input_path}") valid_orders, skipped = [], 0with open(input_path, newline="", encoding="utf-8") as f: reader = csv.DictReader(f) for i, row in enumerate(reader, start=2): validated = validate_row(row, i) if validated is None: skipped += 1 continue valid_orders.append(enrich_row(row, validated))logger.info(f"Processed: {len(valid_orders)} valid | {skipped} skipped")report = { "generated_at": datetime.now().isoformat(), "input_file": str(Path(input_path).resolve()), "pipeline_stats": { "rows_valid": len(valid_orders), "rows_skipped": skipped, }, "summary": aggregate(valid_orders), "orders": valid_orders }with open(output_path, "w", encoding="utf-8") as f: json.dump(report, f, indent=2, default=str) logger.info(f"Report saved: {output_path}")if __name__ == "__main__": run_pipeline("orders.csv", "report.json")

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FINAL VERDICT

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🏆 The Pro Developer Stack (2026)