Python Academy · Lesson

Batch Processing Reports

Generate reports automatically.

Batch Processing Reports is a free Python Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Python Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Automating Repetitive Reports

The real payoff of openpyxl is automation: generating dozens of similar reports without manual work. Think monthly sales sheets per region, or a summary file per client.

The pattern is: loop over data groups, build a workbook for each, save with a unique name.

Grouping the Data

Batch reports start by grouping records, for example by region. Standard Python handles this cleanly before any Excel work begins.

records = [
    {'region': 'North', 'sales': 100},
    {'region': 'South', 'sales': 80},
    {'region': 'North', 'sales': 120},
]
groups = {}
for r in records:
    groups.setdefault(r['region'], []).append(r['sales'])
for region, sales in groups.items():
    print(region, sales, 'total', sum(sales))

One File Per Group

For each group you create a workbook, write the rows, and save to a name derived from the group key. The loop body is the same code you wrote for a single report, just repeated.

Safe Filenames

Group keys may contain characters illegal in filenames. Sanitize them first so the save never fails. This is the same slugify idea used elsewhere.

import re
name = 'North/West Region 2024'
safe = re.sub(r'[^A-Za-z0-9]+', '_', name).strip('_')
print('report_' + safe + '.xlsx')

A Reusable Template Function

Factor the report layout into one function that takes data and returns a workbook. Calling it in the loop keeps every report consistent and the code DRY (do not repeat yourself).

The function would set headers, write rows, add totals and styles, then return wb.

Adding a Summary Row

Most reports end with totals. Compute aggregates in Python or with an Excel formula, and write them as the final row. Here we compute the summary values.

sales = [100, 120, 90, 110]
summary = {
    'count': len(sales),
    'total': sum(sales),
    'average': sum(sales) / len(sales),
    'max': max(sales),
}
for k, v in summary.items():
    print(k.ljust(8), v)

Multiple Sheets in One File

Sometimes one file with a tab per group is better than many files. Loop over groups and call wb.create_sheet(title=group_name), writing each group to its own tab. Save once at the end.

Organizing Output Folders

Write reports into a dated output directory so runs do not overwrite each other. Use os.makedirs(path, exist_ok=True) to create it safely, then join filenames onto it.

import os
base = 'reports'
run_date = '2026-05-30'
out_dir = os.path.join(base, run_date)
path = os.path.join(out_dir, 'north.xlsx')
print('Would create dir:', out_dir)
print('File path:', path)

Error Handling in Batches

In a long batch, one bad group should not stop the rest. Wrap each report in a try/except, log the failure, and continue. A partial run that finishes is better than a full run that crashes midway.

Scheduling the Job

Once the script works, schedule it (with cron, Task Scheduler, or a CI job) to run nightly or monthly. The same code then produces fresh reports automatically with no human in the loop.

Logging Progress

Long batches benefit from progress output. Print or log which group is being processed and how many remain, so you can tell a slow run from a stuck one. A simple counter against the total is enough.

Logging also gives you a record of which reports succeeded if you need to rerun only the failures.

Quick Check

Test your batch automation knowledge.

Recap

You can now automate report generation:

Group data, then loop creating one workbook (or one sheet) per group
Factor layout into a reusable template function
Sanitize filenames and write into dated output folders
Add summary rows, wrap each report in try/except, and schedule the script

This turns hours of manual spreadsheet work into a one-command job.

Frequently asked questions

Is the “Batch Processing Reports” lesson free?

Yes — the full text of “Batch Processing Reports” is free to read here on the web, and the Python Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Python Academy course, upgrade to CoddyKit PRO.

What will I learn in “Batch Processing Reports”?

Generate reports automatically. You practise Python Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Python Academy?

No prior experience is required. Python Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Batch Processing Reports” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Python Academy lesson?

Yes. Every Python Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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