Automate Excel Weekly Reports with Python pyexcel

Weekly Excel reporting is a common business task. It often involves manual work. This process can be time-consuming and error-prone. Python offers a solution.

The pyexcel library simplifies Excel automation. It provides an intuitive interface for spreadsheet operations. You can automate your weekly reports with minimal code.

Why Automate Weekly Excel Reports?

Manual Excel reporting has several drawbacks. It consumes valuable employee time. Human errors can creep into calculations. Formatting inconsistencies may occur between reports.

Automation solves these problems. Python scripts run consistently every time. They process data without fatigue or distraction. This ensures report accuracy and reliability.

Weekly automation frees up your team. They can focus on analysis rather than data entry. Business decisions get faster with timely reports.

Setting Up pyexcel for Report Automation

First, install the pyexcel library. Use pip, Python's package manager. The command below installs pyexcel with Excel support.

 
# Install pyexcel with xlsx support
pip install pyexcel pyexcel-xlsx

Now you're ready to automate. Pyexcel can read various file formats. It handles xlsx, xls, csv, and more. The library provides a uniform API.

Basic Weekly Report Automation Script

Let's create a simple weekly report. This script reads source data. It processes and summarizes the information. Then it generates a formatted Excel report.

 
import pyexcel as pe

def generate_weekly_sales_report():
    # Read weekly sales data
    weekly_data = pe.get_array(file_name="weekly_sales_data.xlsx")
    
    # Calculate weekly summary
    total_sales = sum(row[1] for row in weekly_data[1:])
    average_sale = total_sales / (len(weekly_data) - 1)
    
    # Create report structure
    report_data = [
        ["Weekly Sales Report"],
        ["Total Sales", total_sales],
        ["Average Sale", average_sale],
        ["Number of Transactions", len(weekly_data) - 1]
    ]
    
    # Save the report
    pe.save_as(array=report_data, dest_file_name="weekly_sales_report.xlsx")
    print("Weekly sales report generated successfully!")

generate_weekly_sales_report()

Weekly sales report generated successfully!

This basic script demonstrates the automation concept. It reads source data and creates a summary report. The process is repeatable and consistent.

Advanced Weekly Reporting with Multiple Data Sources

Real-world reports often combine multiple files. You might have daily sales files. Or separate department reports. Pyexcel handles these scenarios well.

For combining multiple spreadsheets, you can learn about appending data to Excel files with Python pyexcel. This technique is essential for comprehensive weekly reports.

 
import pyexcel as pe
from datetime import datetime, timedelta

def generate_comprehensive_weekly_report():
    # Calculate date range for weekly report
    end_date = datetime.now()
    start_date = end_date - timedelta(days=7)
    
    report_data = [["Comprehensive Weekly Report"]]
    report_data.append(["Report Period", f"{start_date.date()} to {end_date.date()}"])
    report_data.append([])  # Empty row for spacing
    
    # Process multiple department files
    departments = ["sales", "marketing", "operations"]
    
    for dept in departments:
        try:
            filename = f"{dept}_weekly_data.xlsx"
            dept_data = pe.get_array(file_name=filename)
            
            # Add department header
            report_data.append([f"{dept.title()} Department Summary"])
            
            # Process department-specific metrics
            if dept == "sales":
                sales_total = sum(row[2] for row in dept_data[1:] if len(row) > 2)
                report_data.append(["Total Sales", f"${sales_total:,.2f}"])
            
            elif dept == "marketing":
                campaign_count = len(dept_data) - 1
                report_data.append(["Active Campaigns", campaign_count])
            
        except FileNotFoundError:
            report_data.append([f"{dept.title()} Data", "File not available"])
    
    # Save comprehensive report
    pe.save_as(array=report_data, dest_file_name="comprehensive_weekly_report.xlsx")
    return "Comprehensive weekly report generated"

print(generate_comprehensive_weekly_report())

Comprehensive weekly report generated

Automating Data Validation and Cleaning

Data quality is crucial for accurate reporting. Weekly automation should include validation. Pyexcel helps identify and handle data issues.

You can implement spreadsheet structure validation with Python pyexcel to ensure data consistency. This prevents processing errors in your weekly reports.

 
import pyexcel as pe

def validate_and_clean_weekly_data(input_file, output_file):
    """Validate and clean weekly data before reporting"""
    
    # Read the raw data
    raw_data = pe.get_array(file_name=input_file)
    
    # Data validation checks
    if len(raw_data) < 2:
        raise ValueError("Insufficient data for weekly report")
    
    headers = raw_data[0]
    cleaned_data = [headers]  # Start with headers
    
    # Clean and validate each data row
    for i, row in enumerate(raw_data[1:], start=1):
        if len(row) != len(headers):
            print(f"Warning: Row {i} has incorrect number of columns")
            continue
            
        # Validate numeric data (example: second column should be numeric)
        try:
            float(row[1])  # Try converting to float
            cleaned_data.append(row)
        except (ValueError, TypeError):
            print(f"Warning: Invalid numeric data in row {i}, column 2")
    
    # Save cleaned data
    pe.save_as(array=cleaned_data, dest_file_name=output_file)
    return f"Data validation complete. {len(cleaned_data)-1} valid rows processed."

# Usage
result = validate_and_clean_weekly_data("raw_weekly_data.xlsx", "cleaned_weekly_data.xlsx")
print(result)

Warning: Row 3 has incorrect number of columns
Data validation complete. 4 valid rows processed.

Scheduling Weekly Automated Reports

Automation is most valuable when it runs automatically. You can schedule weekly Python scripts. This ensures reports generate without manual intervention.

On Windows, use Task Scheduler. On Linux/macOS, use cron jobs. The script runs at your specified time. Reports are ready when you need them.

 
import pyexcel as pe
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders
import os

def send_weekly_report():
    """Generate and email weekly report automatically"""
    
    # Generate the report (using previous function)
    generate_comprehensive_weekly_report()
    
    # Email configuration
    sender_email = "[email protected]"
    receiver_email = "[email protected]"
    password = "your_email_password"
    
    # Create email message
    message = MIMEMultipart()
    message["From"] = sender_email
    message["To"] = receiver_email
    message["Subject"] = "Weekly Business Report"
    
    # Email body
    body = "Please find attached the weekly business report."
    message.attach(MIMEText(body, "plain"))
    
    # Attach the report file
    filename = "comprehensive_weekly_report.xlsx"
    
    with open(filename, "rb") as attachment:
        part = MIMEBase("application", "octet-stream")
        part.set_payload(attachment.read())
    
    encoders.encode_base64(part)
    part.add_header(
        "Content-Disposition",
        f"attachment; filename= {filename}",
    )
    
    message.attach(part)
    text = message.as_string()
    
    # Send email (commented out for safety)
    # with smtplib.SMTP("smtp.company.com", 587) as server:
    #     server.starttls()
    #     server.login(sender_email, password)
    #     server.sendmail(sender_email, receiver_email, text)
    
    return "Weekly report ready for distribution"

print(send_weekly_report())

Weekly report ready for distribution

Best Practices for Weekly Excel Automation

Error handling is essential. Your script should handle missing files. It should manage data format issues. Proper logging helps troubleshoot problems.

Maintain data backup procedures. Keep original data files safe. Store previous reports for comparison. This supports audit requirements.

Implement version control for your scripts. Track changes to automation logic. This helps maintain report consistency over time.

For handling multiple files efficiently, consider batch processing Excel files with Python pyexcel. This approach scales well for growing data volumes.

Conclusion

Automating weekly Excel reports with Python pyexcel saves significant time. It reduces errors and ensures consistency. The library's simple API makes automation accessible.

Start with basic report automation. Then add advanced features gradually. Include data validation and multiple source handling. Finally, implement scheduling for full automation.

Weekly reporting becomes a background process. Your team gains time for value-added analysis. Business decisions improve with reliable, timely information.

Pyexcel provides the tools you need. Begin automating your weekly reports today. The investment in setup pays dividends in efficiency.