Скачать или смотреть Create & Modify Columns Fast in Polars (with_columns, .str, .dt)

Learn how to create and modify columns in Polars using expressions — with practical examples of `with_columns`, `.str`, `.dt`, and `when/then/otherwise` for conditional logic, string, and date transformations.
Perfect for beginners who want to master Polars DataFrames and write fast, expressive, and chainable data transformations in Python.

This video is part of my beginner-friendly *Polars Data Science Series*, designed to help you start working with Polars or transition smoothly from pandas to Polars.

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🚀 What you’ll learn

Add or update multiple columns with `df.with_columns([...])` (computed in parallel )
Avoid column shadowing with `.alias("new_name")`
Write conditional logic using `pl.when(...).then(...).otherwise(...)`
Calculate KPIs with common arithmetic: `+`, `-`, `*`, `/` and chaining
Use the string namespace `.str` (`starts_with`, `contains`, `extract`, `replace`, case changes)
Use the temporal namespace `.dt` (`strftime`, `weekday`, `ordinal_day`, `is_business_day`)
Generate programmatic expressions with list comprehensions; compare parallel vs sequential performance

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💻 Code & Resources

👉 GitHub Repo: https://github.com/zerotodatadev-hub/...
👉 Polars documentation: https://docs.pola.rs

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🧠 Before you start
Make sure you’re comfortable with selecting and filtering in Polars.
If not, watch the previous video:
👉 Polars for Beginners: Selecting and Filtering Data with Expressions:    • Polars for Beginners: Selecting and Filter...  
👉 Full Polars for Beginners Playlist:   • Beginner-Friendly Polars Data Science  

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Chapters:
00:00 Intro — Polars Expressions & `with_columns`
01:03 Expressions in Context: `select` vs `with_columns`
04:34 Load a Realistic Dataset (Coffee Sales)
06:22 Conditional Logic with `when` / `then` / `otherwise`
07:55 Arithmetic Columns: Revenue, Net Price, Chaining
10:59 String Manipulation with `.str` (patterns, regex, case)
15:33 Temporal Features with `.dt` (weekday, business day)
18:20 Bonus: Programmatic Expressions & Performance
21:11 Wrap-Up: Key Takeaways

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