Data Handling using Pandas - II | Class 12 Informatics Practices Notes

3.5 GROUP BY FUNCTIONS

The GROUP BY function in Pandas is used to split data into groups based on one or more criteria, enabling group-wise analysis. It follows a split-apply-combine strategy: first, data is split into groups; then, functions are applied to each group; finally, results are combined into a new DataFrame. For example, grouping student marks by 'Name' allows analyzing each student's performance separately. The groupby() method creates a GroupBy object, which can be used to access group data, size, first entries, and apply aggregation functions. Grouping by multiple columns is also possible, such as grouping by 'Name' and 'UT' to analyze marks per student per unit test. Aggregation functions like mean(), var(), std(), and quantile() can be applied on groups to get detailed statistics. Grouping is essential for summarizing and comparing subsets within a dataset.

📊 Diagram: Figure 3.1: A DataFrame with two columns

🧪 Activity: Activity 3.5: Write Python statements to print average marks in Science by all students in each Unit Test.

🔗 Connection: Leads to altering DataFrame indexes for better data access and manipulation.