Embrace the Unix Command Line and Supercharge Your PyData Workflow
summary
Argues that the Unix command line measurably speeds up everyday PyData work, showing exact incantations: bat for colored file views, fx for exploring nested JSON, and coreutils pipelines that turn 100 JSON files into CSVs or extract top errors from a 10 million‑line log in seconds. Demonstrates a small Python CLI (shebang, argv, chmod, on PATH) used while writing a book, uses Docker image for safe experiments, and recommends tools like parallel, Starship, click, rich, tldr.
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chapters (25)
- 0:00Unix command line benefits
- 1:26What is Unix command line
- 4:34Command line for data workflows
- 8:49Lindy effect and CLI
- 10:59Five CLI tool types
- 13:27Demo: bat and fx
- 16:43CSV processing with Unix
- 19:39Extending CLI with tools
- 23:17Custom CLI tools step by step
- 25:30Example: custom data tool
- 29:45Python CLI packages
- 31:34Windows and WSL setup
- 32:37CLAIM method
- 33:41Aliases and bookmarks
- 36:33Just for automation
- 38:42Learn CLI with Docker
- 39:44Manual pages and cheatsheets
- 41:10Custom shell prompts
- 42:13tmux terminal multiplexing
- 43:40CLI themes and aesthetics
- 44:23Interactive prompts reduce mistakes
- 47:35When to use CLI
- 49:01CLI in IDEs and Jupyter
- 50:27Spark pipe method
- 51:09Key takeaways and final advice
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