Excel Analysis

Quick start

Read Excel files with pandas:

import pandas as pd

# Read Excel file
df = pd.read_excel("data.xlsx", sheet_name="Sheet1")

# Display first few rows
print(df.head())

# Basic statistics
print(df.describe())

Reading multiple sheets

Process all sheets in a workbook:

import pandas as pd

# Read all sheets
excel_file = pd.ExcelFile("workbook.xlsx")

for sheet_name in excel_file.sheet_names:
    df = pd.read_excel(excel_file, sheet_name=sheet_name)
    print(f"\n{sheet_name}:")
    print(df.head())

Data analysis

Perform common analysis tasks:

import pandas as pd

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

# Group by and aggregate
sales_by_region = df.groupby("region")["sales"].sum()
print(sales_by_region)

# Filter data
high_sales = df[df["sales"] > 10000]

# Calculate metrics
df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]

# Sort by column
df_sorted = df.sort_values("sales", ascending=False)

Creating Excel files

Write data to Excel with formatting:

import pandas as pd

df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [100, 200, 150],
    "Profit": [20, 40, 30]
})

# Write to Excel
writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
df.to_excel(writer, sheet_name="Sales", index=False)

# Get worksheet for formatting
worksheet = writer.sheets["Sales"]

# Auto-adjust column widths
for column in worksheet.columns:
    max_length = 0
    column_letter = column[0].column_letter
    for cell in column:
        if len(str(cell.value)) > max_length:
            max_length = len(str(cell.value))
    worksheet.column_dimensions[column_letter].width = max_length + 2

writer.close()

Pivot tables

Create pivot tables programmatically:

import pandas as pd

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

# Create pivot table
pivot = pd.pivot_table(
    df,
    values="sales",
    index="region",
    columns="product",
    aggfunc="sum",
    fill_value=0
)

print(pivot)

# Save pivot table
pivot.to_excel("pivot_report.xlsx")

Charts and visualization

Generate charts from Excel data:

import pandas as pd
import matplotlib.pyplot as plt

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

# Create bar chart
df.plot(x="category", y="value", kind="bar")
plt.title("Sales by Category")
plt.xlabel("Category")
plt.ylabel("Sales")
plt.tight_layout()
plt.savefig("chart.png")

# Create pie chart
df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
plt.title("Market Share")
plt.ylabel("")
plt.savefig("pie_chart.png")

Data cleaning

Clean and prepare Excel data:

import pandas as pd

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

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df = df.fillna(0)  # or df.dropna()

# Remove whitespace
df["name"] = df["name"].str.strip()

# Convert data types
df["date"] = pd.to_datetime(df["date"])
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")

# Save cleaned data
df.to_excel("cleaned_data.xlsx", index=False)

Merging and joining

Combine multiple Excel files:

import pandas as pd

# Read multiple files
df1 = pd.read_excel("sales_q1.xlsx")
df2 = pd.read_excel("sales_q2.xlsx")

# Concatenate vertically
combined = pd.concat([df1, df2], ignore_index=True)

# Merge on common column
customers = pd.read_excel("customers.xlsx")
sales = pd.read_excel("sales.xlsx")

merged = pd.merge
how to use excel-analysis
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use excel-analysis on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add excel-analysis
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill excel-analysiscopy
The skills CLI fetches excel-analysis from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/excel-analysis
Reload or restart Cursor to activate excel-analysis. Access the skill through slash commands (e.g., /excel-analysis) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?