Python Automation for Business Workflows

Select files by extension, name pattern, age, and size.

Read file metadata and represent it as structured records.

Design a dry-run preview before changing files.

Rename batches of files while preventing naming collisions.

Sort files into dated or categorized folders.

Copy, move, and archive files while preserving the originals when required.

Detect duplicate files by computing content hashes.

Build a configurable folder-organizer script with an audit log.

Load CSV data into a pandas DataFrame with explicit parsing options and data types, preserving identifiers such as account numbers as text.

Identify missing, duplicated, and malformed values in a dataset.

Parse and normalize inconsistent date strings with pandas.

Standardize text fields and convert formatted currency strings to numeric values.

Filter and sort business records using multiple conditions.

Combine tables with joins while checking and reporting unmatched records.

Calculate grouped totals and summary metrics.

Validate a transformed dataset against explicit business rules.

Read and write ODS worksheets using pandas with an OpenDocument-compatible engine.

Read and write XLSX workbooks that can be opened in OpenOffice Calc.

Select worksheets, header rows, and cell ranges from multi-sheet workbooks.

Write formulas that Calc can recalculate and verify their referenced input cells.

Apply number formats, column widths, and conditional styles programmatically.

Create a summary table and chart from cleaned business data.

Populate a reusable workbook template without overwriting source data.

Generate and inspect a dated spreadsheet report from raw input files.

Render a business document from structured data using a text template.

Create a styled ODT document containing headings, paragraphs, and tables.

Convert a generated ODT document to PDF with LibreOffice command-line tools and verify the output.

Extract text and metadata from text-based PDF files.

Detect when a scanned PDF requires OCR rather than ordinary text extraction.

Extract selected pages from a PDF into a new file.

Merge multiple PDF files in a specified order.

Build a batch workflow that names, converts, and files generated documents consistently.

Construct plain-text and HTML email messages with Python’s email package.

Attach generated reports using the correct MIME type.

Send email securely through a configured SMTP server.

Search an IMAP mailbox using sender, subject, and date criteria.

Download email attachments while sanitizing their filenames.

Prevent duplicate processing of previously handled messages.

Add dry-run and recipient-allowlist safeguards to an email workflow.

Build a workflow that processes an incoming attachment and prepares a result email.

Send HTTP GET requests with query parameters, timeouts, and response-status validation.

Authenticate to an API using a credential stored outside the source code.

Parse nested JSON responses into normalized Python records.

Retrieve all records from a paginated API endpoint.

Submit a validated JSON payload with an HTTP POST request and inspect the response.

Respect API rate limits using controlled delays and bounded retry rules.

Convert API records into a validated pandas DataFrame.

Build an API-to-spreadsheet reporting script with configurable inputs.

Evaluate a website’s terms, robots guidance, and data sensitivity before automating collection.

Inspect HTML to identify stable elements containing the required data.

Retrieve a static web page with appropriate headers, timeouts, and error handling.

Extract text, links, and attributes with Beautiful Soup CSS selectors.

Convert an HTML table into a cleaned DataFrame.

Follow pagination while preventing duplicate records and infinite loops.

Save provenance fields such as source URL and collection time with each record.

Build a change-monitoring script for a permitted public web page.

Install Playwright browsers and launch a controlled browser session.

Choose resilient role, label, text, or test-ID locators for page elements.

Fill and submit a web form in a permitted test workflow.

Replace fixed sleeps with explicit state-based waits.

Download a file and save it under a validated filename.

Reuse authenticated browser state without storing passwords in source code.

Capture screenshots and page details when an automation step fails.

Build a browser workflow that submits test data and verifies the resulting page state.

Write a pytest unit test for a pure data-transformation function.

Test file operations safely with pytest temporary directories.

Mock an HTTP response to test API success and failure paths.

Exclude secrets, generated files, and local environments with .gitignore.

Make a workflow idempotent so rerunning it does not duplicate its effects.

Record checkpoints so an interrupted batch can resume safely.

Validate inputs before performing irreversible actions.

Create a requirements file with reproducible dependency versions.

Organize an automation as an importable project with a clear entry point.

Create a Windows launcher that activates the correct virtual environment.

Run an automation manually from a clean terminal session.

Configure Windows Task Scheduler with an explicit program, arguments, and working directory.

Diagnose scheduled-task failures using exit codes and log files.

Prevent overlapping runs with a lock mechanism.

Apply a retention rule to old logs and generated reports.

Write an operations runbook covering setup, scheduling, recovery, and credential updates.

Define a realistic business problem, scope boundaries, and measurable acceptance criteria.

Diagram the capstone’s inputs, processing stages, outputs, and failure paths.

Implement one validated input adapter for files, an API, or a permitted browser workflow.

Implement testable transformation functions for the selected business rules.

Generate an OpenOffice-compatible spreadsheet and a human-readable summary report.

Add a safeguarded delivery step using email or an organized output folder.

Add automated tests for one success path and two important failure paths.

Release a sanitized Git repository containing setup instructions, sample data, an operations guide, and a concise demonstration.