excel-processor

---
name: "excel-processor"
description: "Read, transform, analyze, and generate Excel and CSV files using Python. This skill covers data loading, cleaning, filtering, aggregation, formula generation, and export to multiple formats."
license: "Apache-2.0"
---

# Excel Processor

## Overview

Read, transform, analyze, and generate Excel and CSV files using Python. This skill covers data loading, cleaning, filtering, aggregation, formula generation, and export to multiple formats.

## Instructions

When a user asks you to work with spreadsheets, Excel files, or CSV data, follow these steps:

### Step 1: Load the data

```python
import pandas as pd

# For Excel files
df = pd.read_excel("data.xlsx", sheet_name=0)  # or sheet_name="Sheet1"

# For CSV files
df = pd.read_csv("data.csv")

# Show shape and preview
print(f"Shape: {df.shape[0]} rows x {df.shape[1]} columns")
print(f"Columns: {list(df.columns)}")
print(df.head())
```

Always print the shape, column names, and first few rows so the user can verify the data loaded correctly.

### Step 2: Assess data quality

```python
# Check for issues
print(f"Missing values:\n{df.isnull().sum()}")
print(f"\nDuplicates: {df.duplicated().sum()}")
print(f"\nData types:\n{df.dtypes}")
```

Report any issues found before proceeding with transformations.

### Step 3: Apply requested transformations

Common operations:

**Filtering:**
```python
filtered = df[df["status"] == "active"]
filtered = df[df["amount"] > 1000]
filtered = df[df["date"].between("2024-01-01", "2024-12-31")]
```

**Aggregation:**
```python
summary = df.groupby("category").agg(
    count=("id", "count"),
    total=("amount", "sum"),
    average=("amount", "mean")
).reset_index()
```

**Pivot tables:**
```python
pivot = df.pivot_table(
    values="revenue",
    index="region",
    columns="quarter",
    aggfunc="sum",
    margins=True
)
```

**Cleaning:**
```python
df["name"] = df["name"].str.strip().str.title()
df["email"] = df["email"].str.lower()
df["date"] = pd.to_datetime(df["date"], errors="coerce")
df = df.drop_duplicates(subset=["id"])
df = df.dropna(subset=["required_field"])
```

### Step 4: Export results

```python
# To Excel with formatting
with pd.ExcelWriter("output.xlsx", engine="openpyxl") as writer:
    df.to_excel(writer, sheet_name="Data", index=False)
    summary.to_excel(writer, sheet_name="Summary", index=False)

# To CSV
df.to_csv("output.csv", index=False)
```

### Step 5: Report what was done

Always summarize the operations performed, rows affected, and output file location.

## Examples

### Example 1: Clean and deduplicate a customer list

**User request:** "Clean up customers.xlsx -- remove duplicates, fix phone formatting, and split into active/inactive sheets"

**Actions taken:**
1. Load `customers.xlsx` (2,340 rows, 8 columns)
2. Remove 156 duplicate rows based on email
3. Standardize phone numbers to (XXX) XXX-XXXX format
4. Split into active (1,847 rows) and inactive (337 rows)
5. Export to `customers_clean.xlsx` with two sheets

**Output:**
```
Loaded: 2,340 rows x 8 columns
Removed: 156 duplicate rows (by email)
Fixed: 892 phone numbers reformatted
Split: 1,847 active, 337 inactive

Saved to customers_clean.xlsx:
  - Sheet "Active": 1,847 rows
  - Sheet "Inactive": 337 rows
```

### Example 2: Generate a monthly sales summary

**User request:** "Create a pivot table from sales.csv showing revenue by region and month"

**Actions taken:**
1. Load `sales.csv` (15,200 rows)
2. Parse date column, extract month
3. Build pivot table: regions as rows, months as columns, sum of revenue
4. Add row and column totals
5. Export to `sales_summary.xlsx`

**Output:**
```
| Region    | Jan      | Feb      | Mar      | Total     |
|-----------|----------|----------|----------|-----------|
| North     | $45,200  | $52,100  | $48,900  | $146,200  |
| South     | $38,700  | $41,300  | $44,600  | $124,600  |
| East      | $51,900  | $49,800  | $55,200  | $156,900  |
| West      | $42,100  | $46,700  | $43,500  | $132,300  |
| Total     | $177,900 | $189,900 | $192,200 | $560,000  |

Saved to sales_summary.xlsx
```

## Guidelines

- Always show data shape and column names after loading so the user can verify correctness.
- When dealing with dates, explicitly parse them with `pd.to_datetime()` and specify the format when ambiguous (e.g., is 01/02/03 Jan 2 or Feb 1?).
- For large files (100k+ rows), warn about memory and suggest chunked processing.
- Preserve original files. Write output to new files unless the user explicitly asks to overwrite.
- When merging multiple files, check that column names match and report any discrepancies before proceeding.
- If the user asks for "formulas", generate the Excel formula strings (e.g., `=SUM(B2:B100)`) rather than computing values, so the spreadsheet stays dynamic.
- Default to UTF-8 encoding for CSV. If the data contains special characters, test with `encoding="utf-8-sig"` for Excel compatibility.