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Python in Action 60 Mini Projects to Automate Everything (Part 1) [#1012823]
Python in Action: 60 Mini Projects to Automate Everything (Part 1): Practical CLI Tools, File Automation, and Data Cleaning with CSV, Excel, and JSON
by Leo Code
English | 2026 | ASIN: B0GJMTVF23 | 218 pages | pdf | 68 MB
If you love programming, you already know the feeling: you write a few lines of Python-and suddenly your computer starts doing real work for you. Files get organized. Messy datasets become usable. Reports generate in minutes instead of hours. Repetitive tasks disappear.
Python in Action: 60 Mini Projects to Automate Everything (Part 1) is built around that feeling-because this isn't a book to skim. It's a book to do.
In this first part of the series, you'll complete 20 hands-on mini projects designed to give you real, reusable tools-and the professional habits behind them. Instead of one-off scripts, you'll learn how to build utilities you can actually keep using: structured projects, clean command-line interfaces, predictable configuration, solid logging, and practical data workflows.
What you'll build in Part 1
You'll start with professional foundations and productivity tools, including:A reusable project template (folders, virtual environments, dependencies)Clean CLI utilities with arguments, help, and exit codesEnvironment-based configuration (.env, variables, dev/prod)Production-ready logging for traceability and debuggingInput validation and safer automation patternsFormat converters (TXT/CSV/JSON)Batch renaming by pattern, folder organization, duplicate detection, and directory reportingThen you'll move into the work Python is famous for in real jobs: data cleanup and reliability, including:Cleaning "dirty" CSV files (broken columns, inconsistent separators, missing values)Normalizing names and keys (spacing, casing, accents/diacritics)Unifying multiple CSVs into a single schemaQuality audits (nulls, ranges, duplicates)Rule-based validation (configurable with JSON/YAML-style rules)Professional Excel exports (multiple sheets + summary)List reconciliation (A vs B: missing, extra, changed)Outlier detection with a clear reportGenerating realistic synthetic data for testingA mini ETL pipeline: input → cleanup → clean output + logsHow to use the book
Each mini project follows a simple flow:Read the brief.Build the solution step by step.Test it on your own files.Upgrade it using the included "Pro Version" ideas.By the end of Part 1, you'll have a practical Python automation toolbelt-and a workflow that feels closer to real development: structured, reusable, and reliable.
Start with Project #1 and feel the difference immediately. Once your first tool works, the rest becomes addictive-in the best way.
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