<ul><li>The Polars .group_by() method is used to create groupings based on column values and aggregate related values.</li><li>Aggregation is a common technique in summarizing data, and it condenses related values into a single value.</li><li>You can use .filter() to view specific data and .group_by() to summarize one or more columns in Polars.</li><li>Time series data can be grouped using .group_by_dynamic() in Polars.</li><li>You can match summarized data with the original data using window functions in Polars.</li><li>Pivot tables in Polars allow you to group and aggregate rows and columns of data efficiently.</li><li>To start with grouping data in Polars, you need to have a familiarity with Polars DataFrames or similar libraries like pandas.</li><li>Install the Polars library using the command 'python -m pip install polars' to begin working with Polars in Python.</li><li>In Polars, you can aggregate data by applying aggregation functions directly to the columns of interest.</li><li>To find the highest number of absences among students in the math class, you can use aggregation functions provided by Polars.</li></ul>

How to Group Data Using Polars .group_by()

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