Representative Work

Input: sales_data.csv (2,847 rows, Jan–Dec 2025)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

REGIONAL SALES BREAKDOWN
  Region          Revenue      Share    YoY
  ──────────────────────────────────────────
  Pacific NW      $842,300     34.2%   +12.4%
  California      $624,100     25.4%    +7.1%
  Mountain West   $418,500     17.0%   +18.9%
  Southwest       $312,800     12.7%    -3.2%
  Other           $263,400     10.7%    +5.5%
  ──────────────────────────────────────────
  TOTAL         $2,461,100    100.0%    +8.7%

MONTHLY TREND (Q4 2025)
  Oct  ████████████████████  $218,400
  Nov  ██████████████████    $196,200
  Dec  ████████████████████████  $261,700  ← peak

KEY STATISTICS
  Total Revenue:      $2,461,100
  YoY Growth:         +8.7%
  Best Region:        Pacific NW ($842,300)
  Best Month:         December ($261,700)
  Avg Order Value:    $864.42
  Transactions:       2,847

ANOMALIES DETECTED
  [!] Southwest region declining -3.2% — investigate Q3 drop
  [!] 14 duplicate transaction IDs found and flagged
  [✓] Data quality score: 97.2% (cleaned 81 malformed rows)

# csv_monitor.py — Watch a folder for new CSV files,
# process them automatically, and send a summary email.
import os, time, csv, smtplib, logging
from pathlib import Path
from datetime import datetime
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

# ── Config ────────────────────────────────────────────
WATCH_DIR   = Path(os.getenv("WATCH_DIR", "./incoming"))
DONE_DIR    = Path(os.getenv("DONE_DIR",  "./processed"))
SMTP_HOST   = os.getenv("SMTP_HOST", "smtp.gmail.com")
SMTP_PORT   = int(os.getenv("SMTP_PORT", "587"))
SMTP_USER   = os.getenv("SMTP_USER")
SMTP_PASS   = os.getenv("SMTP_PASS")
NOTIFY_TO   = os.getenv("NOTIFY_EMAIL")
POLL_SEC    = 30

logging.basicConfig(
    filename="csv_monitor.log",
    level=logging.INFO,
    format="%(asctime)s %(levelname)s %(message)s"
)

def process_csv(path: Path) -> dict:
    # Read CSV and return basic stats.
    rows, errors = [], 0
    with open(path, newline="", encoding="utf-8-sig") as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                rows.append(row)
            except Exception:
                errors += 1
    return {"file": path.name, "rows": len(rows),
            "cols": len(rows[0]) if rows else 0, "errors": errors}

def send_summary(stats: list[dict]) -> None:
    # Send summary email via SMTP.
    if not all([SMTP_USER, SMTP_PASS, NOTIFY_TO]):
        logging.warning("Email not configured — skipping notification")
        return
    body = "CSV Processing Summary\n" + "="*40 + "\n"
    for s in stats:
        body += f"  {s['file']}: {s['rows']} rows, {s['cols']} columns"
        if s['errors']:
            body += f" ({s['errors']} errors)"
        body += "\n"
    msg = MIMEMultipart()
    msg["From"], msg["To"] = SMTP_USER, NOTIFY_TO
    msg["Subject"] = f"[CSV Monitor] {len(stats)} file(s) processed"
    msg.attach(MIMEText(body, "plain"))
    with smtplib.SMTP(SMTP_HOST, SMTP_PORT) as s:
        s.starttls()
        s.login(SMTP_USER, SMTP_PASS)
        s.send_message(msg)
    logging.info(f"Summary email sent for {len(stats)} file(s)")

def watch() -> None:
    WATCH_DIR.mkdir(exist_ok=True)
    DONE_DIR.mkdir(exist_ok=True)
    logging.info(f"Watching {WATCH_DIR} every {POLL_SEC}s")
    seen = set()
    while True:
        batch = []
        for f in sorted(WATCH_DIR.glob("*.csv")):
            if f.name not in seen:
                seen.add(f.name)
                stats = process_csv(f)
                batch.append(stats)
                f.rename(DONE_DIR / f.name)
                logging.info(f"Processed: {stats}")
        if batch:
            send_summary(batch)
        time.sleep(POLL_SEC)

if __name__ == "__main__":
    watch()

[
  {
    "id": "prod_001",
    "name": "TrailMaster Pro Hiking Boot",
    "price": "$189.99",
    "original_price": "$229.99",
    "discount": "17%",
    "rating": 4.7,
    "review_count": 1284,
    "availability": "in_stock",
    "sku": "TM-PRO-M10",
    "scraped_at": "2026-05-30T07:14:22Z"
  },
  {
    "id": "prod_002",
    "name": "Alpine Summit 3-Season Tent",
    "price": "$349.00",
    "original_price": "$349.00",
    "discount": null,
    "rating": 4.9,
    "review_count": 437,
    "availability": "in_stock",
    "sku": "AS-TENT-2P",
    "scraped_at": "2026-05-30T07:14:23Z"
  },
  {
    "id": "prod_003",
    "name": "Merino Wool Base Layer Set",
    "price": "$94.95",
    "original_price": "$119.00",
    "discount": "20%",
    "rating": 4.5,
    "review_count": 2891,
    "availability": "low_stock",
    "sku": "MW-BASE-M-L",
    "scraped_at": "2026-05-30T07:14:24Z"
  },
  {
    "id": "prod_004",
    "name": "Carbon Fiber Trekking Poles (pair)",
    "price": "$79.99",
    "original_price": "$79.99",
    "discount": null,
    "rating": 4.3,
    "review_count": 618,
    "availability": "in_stock",
    "sku": "CF-POLES-ADJ",
    "scraped_at": "2026-05-30T07:14:24Z"
  },
  {
    "id": "prod_005",
    "name": "Hydration Pack 2.5L",
    "price": "$67.50",
    "original_price": "$89.99",
    "discount": "25%",
    "rating": 4.6,
    "review_count": 943,
    "availability": "out_of_stock",
    "sku": "HP-25L-BLK",
    "scraped_at": "2026-05-30T07:14:25Z"
  }
]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Execution stats:
  Pages scraped:    847
  Products found:   4,231
  Runtime:          12.3s
  Errors:           3 (rate-limited, retried)
  Output:           products_2026-05-30.json

# weather_collector.py — Fetch 7-day forecast from Open-Meteo API,
# transform to business-friendly format, write to CSV.
# No API key required. Runs as-is.
import csv, requests
from datetime import datetime, timedelta

BASE_URL = "https://api.open-meteo.com/v1/forecast"

def fetch_forecast(lat: float, lon: float, city: str) -> list[dict]:
    # Fetch and transform 7-day forecast for a location.
    params = {
        "latitude":  lat,
        "longitude": lon,
        "daily": [
            "temperature_2m_max",
            "temperature_2m_min",
            "precipitation_sum",
            "windspeed_10m_max",
            "weathercode",
        ],
        "temperature_unit": "fahrenheit",
        "windspeed_unit":   "mph",
        "precipitation_unit": "inch",
        "timezone": "America/Los_Angeles",
        "forecast_days": 7,
    }
    resp = requests.get(BASE_URL, params=params, timeout=10)
    resp.raise_for_status()
    data = resp.json()["daily"]

    WMO_CODES = {0:"Clear",1:"Mostly Clear",2:"Partly Cloudy",
                 3:"Overcast",61:"Rain",71:"Snow",95:"Thunderstorm"}

    rows = []
    for i, date in enumerate(data["time"]):
        rows.append({
            "city":        city,
            "date":        date,
            "high_f":      data["temperature_2m_max"][i],
            "low_f":       data["temperature_2m_min"][i],
            "precip_in":   data["precipitation_sum"][i],
            "wind_mph":    data["windspeed_10m_max"][i],
            "condition":   WMO_CODES.get(data["weathercode"][i], "Unknown"),
            "fetched_at":  datetime.now().isoformat(timespec="seconds"),
        })
    return rows

def main():
    locations = [
        (47.6062, -122.3321, "Seattle"),
        (45.5231, -122.6765, "Portland"),
        (47.2529, -122.4443, "Tacoma"),
    ]

    all_rows = []
    for lat, lon, city in locations:
        print(f"Fetching forecast for {city}...")
        all_rows.extend(fetch_forecast(lat, lon, city))

    outfile = f"weather_{datetime.now().strftime('%Y%m%d')}.csv"
    with open(outfile, "w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=all_rows[0].keys())
        writer.writeheader()
        writer.writerows(all_rows)

    print(f"Done. {len(all_rows)} rows written to {outfile}")

if __name__ == "__main__":
    main()