Summary  
This chapter introduces column-based data manipulation in Polars, demonstrating how to use column expressions (pl.col) to create new columns, select specific columns, and operate on data without relying on row indices.

General domain of usage  
Tabular data analysis and transformation (e.g., processing CSV datasets)

This video shows you how to load a `games_df` DataFrame using Polars, highlights the absence of a row index, demonstrates how to add a `total_reviews` column by summing `positive_reviews` and `negative_reviews` with column expressions, and covers simple column selection techniques.
-- video metadata (use this info when making a video) -- 

Cut all examples of code to a minimum. Show only the essential code. Show code no longer than 10 lines. Remove all possible pieces to demonstrate some code feature. You may show not full code that will not start, but still demonstrates some feature theoritacally.
If code is longer than 8 lines, move code output  blocks to second column.


## Row-based (Pandas) vs Column-based (Polars) Operations



| Aspect                   | Pandas (Row-based)                              | Polars (Column-based)                                    |
|--------------------------|------------------------------------------------|----------------------------------------------------------|
| Index Usage              | Uses row index for selection and alignment      | No row index; columns accessed directly                  |
| Column Expressions       | Often uses `apply` or loops for calculations   | Vectorized column expressions (`pl.col`, arithmetic)     |
| Adding Columns           | Assigns via `df['new'] = ...`                  | Uses `with_columns` or `select` with expressions         |
| Performance Implications | Slower with large datasets due to row-wise ops | Faster, as operations leverage columnar memory layout    |
| Syntax                   | More verbose for custom logic                  | Concise, declarative expressions                         |

Which statements describe Pandas (row-based) operations?

Dive into the fundamentals of data wrangling with Polars, using real-world Steam game datasets. Learn Polars' columnar paradigm, selection, aggregation, joining, reshaping, and essential string, date, and missing data operations.

Explore the core differences between Polars and traditional row-based DataFrames, focusing on columnar operations, selection, and conditional logic using the Steam games dataset.

Gain a solid understanding of Polars' parallel group-by, aggregation, joining, and reshaping capabilities using the Steam games and spy insights datasets.

Explore the essentials of data cleaning: text normalization, date parsing, and robust handling of missing values in Polars.

Moving from Rows to Columns

Row-based (Pandas) vs Column-based (Polars) Operations