Automated Report Generator
Generate professional data reports with charts, tables, and visualizations
Your stakeholders need a polished report by tomorrow and you're still wrestling with chart formatting in Excel. Every month it's the same cycle: pull data, build tables, create visualizations, write commentary, and format everything to look professional. Automated report generation turns raw data into presentation-ready reports.
Who it's for: data analysts producing recurring reports for business stakeholders, operations managers generating weekly or monthly performance reports, finance teams creating formatted financial report packages, marketing analysts building campaign performance reports, business intelligence teams automating executive dashboards
Example
"Generate a monthly performance report from this data" → Professional report: formatted data tables with conditional highlighting, charts and visualizations for key metrics, trend commentary, executive summary section, and export-ready formatting for PDF or presentation
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Report Generator
## Overview
This workflow enables automatic generation of professional data reports. Create dashboards, KPI summaries, and analytical reports with charts, tables, and insights from your data.
## How to Use
1. Provide data (CSV, Excel, JSON, or describe it)
2. Specify the type of report needed
3. I'll generate a formatted report with visualizations
**Example prompts:**
- "Generate a sales report from this data"
- "Create a monthly KPI dashboard"
- "Build an executive summary with charts"
- "Produce a data analysis report"
## Domain Knowledge
### Report Components
```python
# Report structure
report = {
'title': 'Monthly Sales Report',
'period': 'January 2024',
'sections': [
'executive_summary',
'kpi_dashboard',
'detailed_analysis',
'charts',
'recommendations'
]
}
```
### Using Python for Reports
```python
import pandas as pd
import matplotlib.pyplot as plt
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
def generate_report(data, output_path):
# Load data
df = pd.read_csv(data)
# Calculate KPIs
total_revenue = df['revenue'].sum()
avg_order = df['revenue'].mean()
growth = df['revenue'].pct_change().mean()
# Create charts
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
df.plot(kind='bar', ax=axes[0,0], title='Revenue by Month')
df.plot(kind='line', ax=axes[0,1], title='Trend')
plt.savefig('charts.png')
# Generate PDF
# ... PDF generation code
return output_path
```
### HTML Report Template
```python
def generate_html_report(data, title):
html = f'''
<!DOCTYPE html>
<html>
<head>
<title>{title}</title>
<style>
body {{ font-family: Arial; margin: 40px; }}
.kpi {{ display: flex; gap: 20px; }}
.kpi-card {{ background: #f5f5f5; padding: 20px; border-radius: 8px; }}
.metric {{ font-size: 2em; font-weight: bold; color: #2563eb; }}
table {{ border-collapse: collapse; width: 100%; }}
th, td {{ border: 1px solid #ddd; padding: 12px; text-align: left; }}
</style>
</head>
<body>
<h1>{title}</h1>
<div class="kpi">
<div class="kpi-card">
<div class="metric">${data['revenue']:,.0f}</div>
<div>Total Revenue</div>
</div>
<div class="kpi-card">
<div class="metric">{data['growth']:.1%}</div>
<div>Growth Rate</div>
</div>
</div>
<!-- More content -->
</body>
</html>
'''
return html
```
## Example: Sales Report
```python
import pandas as pd
import matplotlib.pyplot as plt
def create_sales_report(csv_path, output_path):
# Read data
df = pd.read_csv(csv_path)
# Calculate metrics
metrics = {
'total_revenue': df['amount'].sum(),
'total_orders': len(df),
'avg_order': df['amount'].mean(),
'top_product': df.groupby('product')['amount'].sum().idxmax()
}
# Create visualizations
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
# Revenue by product
df.groupby('product')['amount'].sum().plot(
kind='bar', ax=axes[0,0], title='Revenue by Product'
)
# Monthly trend
df.groupby('month')['amount'].sum().plot(
kind='line', ax=axes[0,1], title='Monthly Revenue'
)
plt.tight_layout()
plt.savefig(output_path.replace('.html', '_charts.png'))
# Generate HTML report
html = generate_html_report(metrics, 'Sales Report')
with open(output_path, 'w') as f:
f.write(html)
return output_path
create_sales_report('sales_data.csv', 'sales_report.html')
```
## Resources
- [Matplotlib](https://matplotlib.org/)
- [Plotly](https://plotly.com/)
- [ReportLab](https://www.reportlab.com/)Get new playbooks like this one
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What This Does
This workflow enables automatic generation of professional data reports. Create dashboards, KPI summaries, and analytical reports with charts, tables, and insights from your data.
Quick Start
Step 1: Create a Project Folder
mkdir -p ~/Documents/ReportGenerator
Step 2: Download the Template
Click Download above, then:
mv ~/Downloads/CLAUDE.md ~/Documents/ReportGenerator/
Step 3: Start Working
cd ~/Documents/ReportGenerator
claude
How to Use
- Provide data (CSV, Excel, JSON, or describe it)
- Specify the type of report needed
- I'll generate a formatted report with visualizations
Example prompts:
- "Generate a sales report from this data"
- "Create a monthly KPI dashboard"
- "Build an executive summary with charts"
- "Produce a data analysis report"