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name: xlsx description: Read, analyze, and create Excel spreadsheets (.xlsx, .xlsm, .csv, .tsv). Use this skill when working with spreadsheet data, extracting tables, or creating Excel files with formulas.

Excel Spreadsheet Handling

This skill enables reading, analyzing, and creating Excel files using pandas and openpyxl.

Reading Spreadsheets

Read with pandas

import pandas as pd

# Read entire Excel file
df = pd.read_excel("file.xlsx")

# Read specific sheet
df = pd.read_excel("file.xlsx", sheet_name="Sheet1")

# Read all sheets into dict
sheets = pd.read_excel("file.xlsx", sheet_name=None)
for name, df in sheets.items():
    print(f"Sheet: {name}, Rows: {len(df)}")

Read with openpyxl (for formulas/formatting)

from openpyxl import load_workbook

# Load with values (computed)
wb = load_workbook("file.xlsx", data_only=True)
sheet = wb.active

# Access cells
value = sheet["A1"].value
value = sheet.cell(row=1, column=1).value

# Iterate rows
for row in sheet.iter_rows(min_row=2, values_only=True):
    print(row)

Read CSV/TSV

import pandas as pd

# CSV
df = pd.read_csv("file.csv")

# TSV
df = pd.read_csv("file.tsv", sep="\t")

# With encoding
df = pd.read_csv("file.csv", encoding="utf-8")

Creating Spreadsheets

Create with pandas

import pandas as pd

data = {"Name": ["Alice", "Bob"], "Score": [95, 87]}
df = pd.DataFrame(data)

# Write to Excel
df.to_excel("output.xlsx", index=False)

# Multiple sheets
with pd.ExcelWriter("output.xlsx") as writer:
    df1.to_excel(writer, sheet_name="Data", index=False)
    df2.to_excel(writer, sheet_name="Summary", index=False)

Create with openpyxl (for formulas)

from openpyxl import Workbook

wb = Workbook()
ws = wb.active
ws.title = "Data"

# Write headers
ws["A1"] = "Item"
ws["B1"] = "Price"
ws["C1"] = "Quantity"
ws["D1"] = "Total"

# Write data with formulas
ws["A2"] = "Widget"
ws["B2"] = 10.00
ws["C2"] = 5
ws["D2"] = "=B2*C2"  # Formula

# Save
wb.save("output.xlsx")

Working with Formulas

Important: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python. This keeps spreadsheets dynamic.

# WRONG - hardcoded calculation
total = price * quantity
ws["D2"] = total

# CORRECT - Excel formula
ws["D2"] = "=B2*C2"

Common Formula Patterns

# SUM
ws["B10"] = "=SUM(B2:B9)"

# AVERAGE
ws["B11"] = "=AVERAGE(B2:B9)"

# IF statement
ws["C2"] = '=IF(B2>100,"High","Low")'

# VLOOKUP
ws["D2"] = "=VLOOKUP(A2,LookupTable!A:B,2,FALSE)"

# Conditional formatting via formula
ws["E2"] = "=IF(D2>0,D2,0)"

Recalculate Formulas

After creating files with formulas, recalculate using LibreOffice:

python recalc.py output.xlsx

This ensures all formula values are computed and checks for errors.

Formatting

Cell Formatting

from openpyxl.styles import Font, Alignment, PatternFill, Border, Side

# Bold header
ws["A1"].font = Font(bold=True)

# Currency format
ws["B2"].number_format = "$#,##0.00"

# Percentage
ws["C2"].number_format = "0.0%"

# Date format
ws["D2"].number_format = "YYYY-MM-DD"

# Center alignment
ws["A1"].alignment = Alignment(horizontal="center")

# Background color
ws["A1"].fill = PatternFill(start_color="FFFF00", fill_type="solid")

Financial Model Color Coding

Follow standard conventions:

• Blue text: Inputs and hardcoded values
• Black text: Formulas and calculations
• Green text: Links within workbook
• Red text: External links
• Yellow background: Key assumptions

from openpyxl.styles import Font

# Input cell (blue)
ws["B2"].font = Font(color="0000FF")

# Formula cell (black)
ws["C2"].font = Font(color="000000")

Data Analysis

Basic Analysis with pandas

import pandas as pd

df = pd.read_excel("data.xlsx")

# Summary statistics
print(df.describe())

# Group by
summary = df.groupby("Category")["Amount"].sum()

# Filter
filtered = df[df["Amount"] > 100]

# Pivot table
pivot = pd.pivot_table(df, values="Amount", index="Category", columns="Month", aggfunc="sum")

Export Analysis Results

# Write analysis back to Excel
with pd.ExcelWriter("analysis.xlsx") as writer:
    df.to_excel(writer, sheet_name="Raw Data", index=False)
    summary.to_excel(writer, sheet_name="Summary")
    pivot.to_excel(writer, sheet_name="Pivot")

Error Handling

Excel Error Types

Check for these errors after recalculation:

• #VALUE! - Wrong value type
• #DIV/0! - Division by zero
• #REF! - Invalid reference
• #NAME? - Unrecognized formula name
• #NULL! - Incorrect range
• #NUM! - Invalid numeric value
• #N/A - Value not available

Detect Errors

from openpyxl import load_workbook

wb = load_workbook("file.xlsx", data_only=True)
errors = []

for sheet in wb.sheetnames:
    ws = wb[sheet]
    for row in ws.iter_rows():
        for cell in row:
            if cell.value and str(cell.value).startswith("#"):
                errors.append(f"{sheet}!{cell.coordinate}: {cell.value}")

if errors:
    print("Errors found:", errors)

Important Notes

• Use pandas for data analysis and bulk operations
• Use openpyxl for formulas, formatting, and Excel-specific features
• Always run recalc.py after creating files with formulas
• Keep formulas dynamic - avoid hardcoding calculated values
• Match existing format/style when editing templates