Day 24 – GroupBy & Aggregation in Pandas | AI Course in English

Day 24 of your Complete AI Course in English dives deep into pandas.groupby() and aggregation – the powerhouse combination for summarizing data by categories in business analysis, AI feature engineering, and data science workflows. Building on Day 23's basic statistics, this session unlocks group-level insights that transform raw data into actionable intelligence.

You'll understand the split-apply-combine paradigm: groupby() splits your DataFrame into groups based on column values, applies aggregation functions (sum(), mean(), count(), min(), max(), std()), and combines results into a structured summary DataFrame. Real-world examples like sales by region, employee stats by department, and customer metrics by segment make the concept immediately practical.

Single column grouping is first: df.groupby('category')['revenue'].sum() gives total revenue per category, df.groupby('city')['orders'].mean() shows average orders per city. You'll see how these operations reveal hidden patterns like top-performing segments or underperforming regions that basic statistics miss.

Multiple column grouping takes it further: df.groupby(['year', 'quarter'])['sales'].sum() creates hierarchical analysis, perfect for time-series breakdowns or multi-dimensional business reporting. You'll master reset_index() to flatten multi-index results back to regular DataFrames for further analysis or visualization.

The .agg() method is a highlight: df.groupby('department').agg({'salary': ['mean', 'median', 'max'], 'count': 'size'}) computes multiple statistics simultaneously across columns. You'll rename aggregated columns, apply custom functions, and create professional summary tables for stakeholders or model feature sets.

AI/ML applications show groupby in action: engineering features like average transaction value per customer type, category conversion rates, user engagement by segment, and regional performance metrics – all crucial for classification, clustering, and recommendation systems.

By the end of Day 24, you'll transform any tabular dataset into group-wise insights using groupby() and aggregation: split by categories, compute statistics, handle multi-level groups, and create business-ready summaries for AI modeling and decision-making.