Adding AI — Let Claude Analyze Your Spreadsheets

$ pip install anthropic

# Mac/Linux — add to ~/.zshrc or ~/.bashrc
export ANTHROPIC_API_KEY="sk-ant-..."

# Windows PowerShell
$env:ANTHROPIC_API_KEY="sk-ant-..."

import pandas as pd
import anthropic

df = pd.read_csv("sales.csv")

# Generate a summary with pandas
by_region = df.groupby("region")["revenue"].agg(["sum","mean","count"])
monthly = df.groupby("month")["revenue"].sum()
top_reps = df.groupby("rep")["revenue"].sum().nlargest(5)

# Build the prompt
prompt = f"""Analyze this sales data and provide:
1. Key trends you notice
2. Any anomalies or concerns
3. Top 3 actionable recommendations

Regional summary:
{by_region.to_string()}

Monthly revenue:
{monthly.to_string()}

Top 5 reps:
{top_reps.to_string()}

Be specific. Reference actual numbers."""

# Call Claude
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": prompt}]
)

analysis = response.content[0].text
print(analysis)

# Save to file
with open("analysis.txt", "w") as f:
    f.write(analysis)

def analyze_data(df, context=""):
    """Analyze a DataFrame with Claude."""

    stats = {
        "total_rows": len(df),
        "date_range": f"{df['date'].min()} to {df['date'].max()}",
        "total_revenue": f"${df['revenue'].sum():,.0f}",
        "avg_deal": f"${df['revenue'].mean():,.0f}"
    }

    prompt = f"""You are a senior data analyst reviewing sales performance.

Context: {context}

Dataset overview:
- Total records: {stats['total_rows']}
- Period: {stats['date_range']}
- Total revenue: {stats['total_revenue']}
- Average deal size: {stats['avg_deal']}

Detailed breakdown:
{df.groupby('region')['revenue'].describe().to_string()}

Provide analysis in exactly this format:
## Executive Summary (2-3 sentences)
## Key Findings (bullet points with specific numbers)
## Red Flags (if any)
## Recommendations (3 specific, actionable items)"""

    client = anthropic.Anthropic()
    response = client.messages.create(
        model="claude-opus-4-5",
        max_tokens=1500,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text

# Use it
df = pd.read_csv("sales.csv")
analysis = analyze_data(df, context="Q4 2024 sales data, US regions")
print(analysis)