Summary  
This chapter explains how to use Polars’ select method to produce a new DataFrame containing only specified computed columns and its with_columns method to augment an existing DataFrame by adding or updating columns.  

General domain of usage  
Data analysis

You will often need to create new columns or modify existing ones in your Polars DataFrames. Two essential methods for this are **`select`** and **`with_columns`**. Each serves a different purpose, and knowing when to use which will help you write clearer, more efficient code. Imagine you have a `games_df` DataFrame with columns for `positive_reviews`, `negative_reviews`, and `total_reviews`. Suppose you want to calculate the percentage of positive reviews for each game. You can use `select` to create a new DataFrame with just the calculated column, or use `with_columns` to add new columns to the existing DataFrame.

In a video lesson, you would see a demonstration of both approaches. First, using **`select`** to create a DataFrame with a new column called `positive_pct`, calculated as `positive_reviews / total_reviews`:




Watch how to use Polars' select and with_columns methods to create and modify columns in a DataFrame. You'll see step-by-step how to calculate the percentage of positive reviews (positive_pct) using select, and how to add a column for negative review percentage (negative_pct) with with_columns, all using a sample games_df DataFrame. This demonstration makes it clear when to use each method for your data wrangling tasks.
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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.
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import polars as pl

# Sample DataFrame
games_df = pl.DataFrame({
    "game": ["Game A", "Game B"],
    "positive_reviews": [80, 50],
    "negative_reviews": [20, 50],
    "total_reviews": [100, 100]
})

# Using select to create a new DataFrame with only the calculated column
positive_pct_df = games_df.select(
    (pl.col("positive_reviews") / pl.col("total_reviews")).alias("positive_pct")
)
print("Result of select (only positive_pct column):")
print(positive_pct_df)

# Using with_columns to add a new column to the existing DataFrame
games_df = games_df.with_columns(
    (pl.col("negative_reviews") / pl.col("total_reviews")).alias("negative_pct")
)
print("\nResult of with_columns (original columns plus negative_pct):")
print(games_df)



Next, you would see how **`with_columns`** can be used to add a new column, such as `negative_pct`, to the existing DataFrame. This column is calculated as `negative_reviews / total_reviews`:



# Using with_columns to add a new column to the existing DataFrame
games_df = games_df.with_columns(
    (pl.col("negative_reviews") / pl.col("total_reviews")).alias("negative_pct")
)
print(games_df)



Notice how **`select`** returns a new DataFrame with only the columns you specify, while **`with_columns`** modifies the existing DataFrame by adding or updating columns. This distinction is important as you decide how to structure your data transformations.
To clarify the differences between **`select`** and **`with_columns`**, consider the following comparison grid. This table outlines the core distinctions and provides a concise example for each method.


| Method         | Effect on DataFrame                                      | Typical Use Case                          | Example Code Snippet                                                                 |
|----------------|---------------------------------------------------------|-------------------------------------------|--------------------------------------------------------------------------------------|
| **select**         | Returns a new DataFrame with only the specified columns | Create a DataFrame with specific columns  | `df.select((pl.col("positive_reviews") / pl.col("total_reviews")).alias("positive_pct"))` |
| **with_columns**   | Adds or modifies columns in the existing DataFrame      | Add or update columns in place            | `df.with_columns((pl.col("negative_reviews") / pl.col("total_reviews")).alias("negative_pct"))` |


When you use **`select`**, you are creating a new DataFrame that contains only the columns you specify. This is useful when you want to focus on a subset of columns or calculated values. In contrast, **`with_columns`** is ideal for adding new columns or updating existing ones within the same DataFrame, preserving all other columns.


Which statement best describes the difference between select and with_columns in Polars?

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.

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

Core Contexts select vs with_columns