Summary  
This chapter covers filtering rows using Boolean mask expressions and creating new columns through conditional logic chains (when/then/otherwise) applied across a DataFrame.  

General domain of usage  
Data wrangling

**Video: Filtering & Conditional Logic in Polars**

This short video demonstrates how to filter a DataFrame for games with a price above 20 and how to use conditional logic with `pl.when().then().otherwise()` to create a `price_tier` column that categorizes each game as "Budget", "Standard", or "Premium".

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Filtering data is a core part of data wrangling, especially when you want to focus on a specific subset of your dataset. In Polars, you can use Boolean masks to select only the rows that match your condition. Suppose you have a DataFrame called `games_df` with a `price` column. To filter for games where the price is greater than 20, you can use the following approach:

import polars as pl

# Example DataFrame
games_df = pl.DataFrame({
    "name": ["Chess", "Monopoly", "Scrabble", "Catan", "Pandemic"],
    "price": [10, 25, 15, 35, 22]
})

# Filter games with price > 20
filtered_df = games_df.filter(pl.col("price") > 20)
print(filtered_df)

In this example, only the games with a price above 20 are included in `filtered_df`. 

You can also use conditional logic to create new columns based on the values of existing columns. The `pl.when().then().otherwise()` construct allows you to categorize data efficiently. For instance, you might want to classify each game into a price tier: `"Budget"` for games priced at 15 or less, `"Standard"` for prices between 16 and 30, and `"Premium"` for prices above 30. Here is how you can add a `price_tier` column to your DataFrame:

games_with_tier = games_df.with_columns(
    pl.when(pl.col("price") <= 15)
      .then("Budget")
      .when((pl.col("price") > 15) & (pl.col("price") <= 30))
      .then("Standard")
      .otherwise("Premium")
      .alias("price_tier")
)

print(games_with_tier)

This approach assigns each game to a tier based on its price, making it easy to segment your dataset for further analysis or visualization.

Which Polars method is used to assign a new column based on multiple conditions?

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.