Learn group_by & Aggregations | Combining, Aggregating

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Grouping and aggregating data is a core part of data wrangling, especially when you want to summarize information by categories. With Polars, you can efficiently perform group-by operations and aggregate results in parallel, making it ideal for large datasets. Suppose you have a DataFrame called games_df with columns such as developer, price, positive_reviews, and negative_reviews. You might want to find the average price and total reviews for each developer. In Polars, this can be done using the group_by method, followed by aggregation functions like mean and sum.

Here's how you can group games_df by the developer column, calculate the average price, and sum the total reviews (combining both positive and negative reviews):


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import polars as pl

# Sample data
games_df = pl.DataFrame({
    "developer": ["DevA", "DevB", "DevA", "DevC", "DevB"],
    "price": [10.0, 20.0, 15.0, 30.0, 25.0],
    "positive_reviews": [100, 150, 200, 80, 120],
    "negative_reviews": [10, 20, 15, 5, 8]
})

# Group by developer, calculate average price and total reviews
result = (
    games_df
    .group_by("developer")
    .agg([
        pl.col("price").mean().alias("avg_price"),
        (pl.col("positive_reviews") + pl.col("negative_reviews")).sum().alias("total_reviews")
    ])
)

print(result)

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Section 2. Chapter 1