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

import pandas as pd

# For .xlsx files (openpyxl engine, the default)
df = pd.read_excel("data.xlsx", sheet_name=0)  # or sheet_name="Sheet1"

# For legacy .xls files, install xlrd (pip install xlrd) or pass engine="calamine"
# df = pd.read_excel("legacy.xls", sheet_name=0, engine="calamine")

# 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

# 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:

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

Aggregation:

summary = df.groupby("category").agg(
    count=("id", "count"),
    total=("amount", "sum"),
    average=("amount", "mean")
).reset_index()

Pivot tables:

pivot = df.pivot_table(
    values="revenue",
    index="region",
    columns="quarter",
    aggfunc="sum",
    margins=True
)

Cleaning:

df["name"] = df["name"].str.strip().str.title()
df["email"] = df["email"].str.lower()
# State the format: without it pandas guesses per value and warns
df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d", errors="coerce")
df = df.drop_duplicates(subset=["id"])
df = df.dropna(subset=["required_field"])

Step 4: Export results

# To .xlsx (openpyxl writes the values; pandas does not apply cell styling)
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?). Without format=, pandas 3 parses each value separately and emits a "Could not infer format" warning, which is slower and can silently mix day-first and month-first dates.
• .xls (Excel 97-2003) needs xlrd or engine="calamine"; .xlsx needs openpyxl. A sheet_name that does not exist raises ValueError: Worksheet named ... not found.
• Excel rows are capped at 1,048,576 per sheet; split larger exports across sheets or CSV files.
• 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.