32 – Real-World Python Projects – Automated Report Generator

Purpose

Automatically collect data, process it, create charts, generate a PDF or Excel report, and send it — without manual effort.

Real-World Use Cases

• Weekly Sales Reports
• HR Monthly Attendance Reports
• Finance KPI Dashboards
• Website Traffic Reports
• Automated Email Reports

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🧠 What the Project Will Do

✔ Load data from CSV/Excel/Database
✔ Process KPIs (totals, growth %, averages)
✔ Create charts (matplotlib)
✔ Generate a final PDF report
✔ Auto-save with a timestamp
✔ Optional Email Sending Feature

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🧰 Tech Stack

• pandas
• matplotlib
• reportlab
• datetime
• smtplib (optional for email)

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📁 Folder Structure

Automated_Report_Generator/
│── data/
│     ├── sales.csv
│── output/
│     ├── reports.pdf
│── report_generator.py

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📄 Sample Data (sales.csv)

date,product,units_sold,revenue
2025-01-01,Mobile,40,400000
2025-01-02,Laptop,25,750000
2025-01-03,Tablet,18,270000

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🧠 Full Python Code: report_generator.py

✔ Simple, clean, real-world ready.

import pandas as pd
import matplotlib.pyplot as plt
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image
from reportlab.lib.styles import getSampleStyleSheet
from datetime import datetime

# Load data
df = pd.read_csv("data/sales.csv")

# Summary metrics
total_revenue = df["revenue"].sum()
total_units = df["units_sold"].sum()
avg_daily_sales = round(df["revenue"].mean(), 2)

# Generate chart
plt.figure(figsize=(6, 4))
plt.plot(df["date"], df["revenue"])
plt.title("Daily Revenue Trend")
plt.xlabel("Date")
plt.ylabel("Revenue")
chart_path = "output/chart.png"
plt.savefig(chart_path)
plt.close()

# Build PDF
timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M")
pdf_path = f"output/Report_{timestamp}.pdf"

styles = getSampleStyleSheet()
doc = SimpleDocTemplate(pdf_path)

content = []

content.append(Paragraph("<b>Automated Sales Report</b>", styles["Title"]))
content.append(Spacer(1, 20))

content.append(Paragraph(f"Total Revenue: ₹{total_revenue:,}", styles["BodyText"]))
content.append(Paragraph(f"Total Units Sold: {total_units}", styles["BodyText"]))
content.append(Paragraph(f"Average Daily Revenue: ₹{avg_daily_sales:,}", styles["BodyText"]))
content.append(Spacer(1, 20))

content.append(Paragraph("<b>Revenue Trend</b>", styles["Heading2"]))
content.append(Image(chart_path, width=500, height=300))

doc.build(content)

print("Report Created:", pdf_path)

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📊 Output PDF Contains:

✔ Title: “Automated Sales Report”
✔ Key metrics
✔ Line chart of daily revenue
✔ Auto-generated timestamp
✔ Professionally formatted output

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🚀 Advanced Add-Ons (Optional)

1️⃣ Email the PDF Automatically

Use smtplib to send a report to the manager every morning.

2️⃣ Add Multiple Charts

• Product-wise revenue
• Weekly averages
• Pie chart for categories

3️⃣ Use Database Instead of CSV

MySQL / PostgreSQL / MongoDB → pandas → report.

4️⃣ Build a Scheduler

Use schedule module:

schedule.every().day.at("09:00").do(generate_report)

5️⃣ Create a Streamlit Dashboard

PDF + Live charts + Filters