claude-code-cheat-sheet

### Claude Code Cheat Sheet: Automating CSV Data Processing for Sales Operations

| **Command**               | **Description**                          | **Example**                                                                 | **Best Practices**                          |
|---------------------------|------------------------------------------|-----------------------------------------------------------------------------|---------------------------------------------|
| `read_csv()`              | Load CSV files into DataFrames           | `df = read_csv("sales_data.csv", parse_dates=["order_date"])`            | Use `dtype` to optimize memory usage        |
| `group_by()` + `agg()`    | Aggregate sales by region                | `df.groupby("region")["revenue"].agg(["sum", "mean"])`                | Filter data before aggregation for speed    |
| `to_csv()`                | Export processed data                    | `df.to_csv("processed_sales.csv", index=False)`                           | Set `index=False` to avoid redundant columns|
| `apply()`                 | Custom transformations                   | `df["discounted_revenue"] = df.apply(lambda x: x["revenue"] * 0.9, axis=1)` | Use vectorized operations when possible     |
| `merge()`                 | Combine sales with customer data         | `merged = df.merge(customers_df, on="customer_id", how="left")`          | Specify `how="left"` to preserve all sales |

**Error Handling:**
- Use `try-except` blocks for file operations:
```python
try:
    df = read_csv("missing_file.csv")
except FileNotFoundError:
    print("File not found. Check path.")
```

**Performance Tips:**
- For large datasets, use `chunksize` in `read_csv()`
- Filter columns early: `df[["col1", "col2"]]`
- Use `dask` for out-of-memory datasets

**Workflow Integration:**
1. Schedule with `cron` (Linux/macOS) or Task Scheduler (Windows)
2. Log errors to `error_log.txt`
3. Send completion email via `smtplib`
```python
import smtplib
from email.message import EmailMessage

msg = EmailMessage()
msg.set_content("Sales data processing completed successfully.")
msg["Subject"] = "Sales Data Update"
msg["From"] = "ops@company.com"
msg["To"] = "manager@company.com"

with smtplib.SMTP("smtp.company.com", 587) as server:
    server.starttls()
    server.login("user", "pass")
    server.send_message(msg)
```