Day 24/90 – Pandas GroupBy & Aggregation (Sales, Segments, Business Analysis) | AI Course in Tamil

Описание: Day 24 – GroupBy & Aggregation in Pandas | AI Course in Tamil
Day 24 of your Complete AI Course in Tamil introduces one of Pandas' most powerful features: groupby() and aggregation. Building on Day 23 (sorting, value_counts, basic stats), this session teaches you how to group data by categories and compute statistics for each group – essential for business analysis, AI feature engineering, and ML model preparation.

You’ll first learn the split-apply-combine concept behind groupby(): split your DataFrame by column values (like region, department, product category), apply functions like sum(), mean(), count(), min(), max() to each group, and combine results into a new DataFrame. Tamil explanations make this workflow crystal clear with real sales/customer datasets.

Next, you'll practice single column grouping – for example, df.groupby('region')['sales'].sum() to get total sales by region, or df.groupby('department')['salary'].mean() for average salary per department. You'll see how easy it is to get business insights like top-performing regions, average order value by customer segment, or employee count by role.

The session then covers multiple column grouping using lists: df.groupby(['region', 'product'])['revenue'].sum() creates a multi-level index showing revenue by region AND product combination. You'll learn how to flatten results with reset_index() and handle complex business scenarios like sales performance by region and quarter.

You'll also master advanced aggregation with .agg(): compute multiple statistics at once like df.groupby('category').agg({'sales': ['sum', 'mean', 'count'], 'profit': 'max'}). This creates powerful summary tables for stakeholder reports and model feature creation. Custom functions and renaming aggregated columns are also covered.

Real AI/ML examples show group-based feature engineering: creating average purchase value per customer segment, transaction frequency by user type, or category-wise conversion rates – all critical features for classification and recommendation models.

By the end of Day 24 (Tamil), you'll confidently use groupby() and aggregation to split any dataset by categories, compute group statistics, and generate business insights ready for AI modeling and reporting.

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