excel-processing

name	excel-processing
description	Best practices for robust Excel data processing with Pandas and OpenPyXL

Excel Processing Skill

Guide for efficient, safe, and standards-compliant Excel data processing.

📖 Reading Excel Files

1. Engine Selection

Always determine the appropriate engine:

• .xlsx: Use openpyxl (Default modern format).
• .xls: Use xlrd (Legacy format).
• .csv: Use pandas.read_csv.

2. Robust Reading Pattern

def read_excel_safe(filepath):
    try:
        if filepath.lower().endswith('.xlsx'):
            return pd.read_csv(filepath, engine='openpyxl')
        elif filepath.lower().endswith('.xls'):
            return pd.read_csv(filepath, engine='xlrd')
        return None
    except Exception as e:
        print(f"Error: {e}")
        return None
    ```

### 3. Handling Temp Files
Always skip Excel temp files (`~$filename.xlsx`):
```python
if filename.startswith('~$'):
    continue

---

💾 Writing Excel Files

1. Preserving Data

Use index=False unless index has meaning:

df.to_excel("output.xlsx", index=False, engine='openpyxl')

2. Large Data Sets

For large datasets (>100k rows), openpyxl can be slow. Consider:

• Splitting into multiple files.
• Using CSV if formatting is not needed.

---

🛡️ Error Handling Patterns

1. File Locking

Excel file open by user will be locked. Solution: Catch PermissionError.

try:
    df.to_excel("output.xlsx")
except PermissionError:
    print("Error: File is open. Please close Excel and try again.")

2. Corrupted Files

File downloaded from internet or corrupted format. Solution: Catch BadZipFile or ValueError.

3. Encoding (CSV)

CSV might have encoding issues (e.g. non-ASCII characters). Solution: Try list of common encodings.

encodings = ['utf-8', 'utf-8-sig', 'cp1252', 'latin1']
for enc in encodings:
    try:
        return pd.read_csv(file, encoding=enc)
    except:
        continue

---

🔎 Data Validation

Check Empty

if df.empty:
    print("File has no data")
    return

Check Columns

Ensure input file has required columns:

required = ['Name', 'Email']
if not all(col in df.columns for col in required):
    print("Missing required columns")

---

🚀 Performance Tips

1. Read specific columns: pd.read_excel(..., usecols=['A', 'B']) to reduce RAM usage.
2. Specify dtypes: dtype={'Phone': str} to avoid losing leading zeros.
3. Process chunking: For huge files (GB), read by chunk (mostly with CSV).

---

✅ Checklist

• Select correct engine (openpyxl vs xlrd)
• Skip temp files ~$
• Handle PermissionError (File locked)
• Handle UnicodeDecodeError (Encoding)
• Check df.empty before processing

---

🤖 Agentic Protocol

Skill Metadata

• Version: 1.0.0
• Last Updated: 2026-01-27

1. Activation Log

When activating this skill, print: "🎯 [SKILL ACTIVATED] excel-processing v1.0.0" "📋 Parameters:" " - Input: [file_path]" " - Operation: [read|write|validate]" " - Expected Rows: [count_if_known]"

2. User Confirmation

Before writing/modifying files: "I'll use excel-processing to [action] on [file]. Proceed? [Y/n]"

3. Completion Log

• Success: "✅ [excel-processing] Processed [row_count] rows in [time]s"
• Error: "❌ [excel-processing] Error: [message]"
• Warning: "⚠️ [excel-processing] Warning: [message]"