Grouping Data and Aggregation – B.Pharm Notes (Ch 22)

These free B.Pharm notes on groupby and aggregation functions in pandas cover chapter 22 of Basics of Python Programming for Pharmaceutical Sciences (BP101T), a first-semester subject. Read the key points below, then study the complete chapter PDF. You can browse every chapter in the Basics of Python Programming for Pharmaceutical Sciences category.

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Overview of groupby and aggregation functions in pandas

These notes explain how to summarise pharmaceutical data by categories using the split-apply-combine approach in Pandas. They cover the groupby() method, counting records with value_counts() and count(), calculating several statistics at once with agg(), grouping by multiple columns, and an end-to-end example that reads, inspects, cleans, filters and aggregates ADR and PK data and finds Tmax.

Key points on groupby and aggregation functions in pandas

In the split-apply-combine approach, data is divided into groups, an operation is applied to each group, and the results are combined into a new DataFrame.
The groupby() method itself does not calculate anything; it creates a DataFrameGroupBy object on which an aggregation, transformation or filtering operation is applied.
The value_counts() method directly counts each distinct value in a Series, while groupby() with count() keeps the grouping variable as a regular column.
The agg() method calculates several summary statistics such as count, mean, min and max for each group at once, with custom column names.
Missing-severity records are left out of grouped output because Pandas does not include NaN as a normal group by default.

Relevance to pharmacy practice

Pharmaceutical analysis often needs summaries across patients, treatment groups, drug classes, study sites or time intervals. Grouping and aggregation let a pharmacist compare adverse event counts, onset times by severity and causality, and find peak concentration in pharmacokinetic data quickly.

Every chapter in this subject builds on the one before it, so do not skip topics that look simple. Definitions, principles, procedures and examples are the parts that examiners return to most often, and a clear understanding here will also make later subjects easier to follow in the coming semesters.

Frequently asked questions on groupby and aggregation functions in pandas

What does the groupby() method do in Pandas?

groupby() divides the rows of a DataFrame into groups based on the values in one or more columns. A following operation, such as mean() or count(), is then applied to each group.

When should I use agg() instead of a single function?

Use agg() when several summary statistics are needed for the same grouping, such as count, mean, minimum and maximum onset time per severity group, calculated together.

How do you find the time of peak concentration in Pandas?

The idxmax() method finds the index of the highest concentration, and loc[] then selects that row to show Cmax and the time it occurred, known as Tmax.

Quick revision checklist

Use this self-test after reading the chapter on groupby and aggregation functions in pandas. If you can do all of the following without looking at your notes, you are ready for the exam.

I can explain: the split-apply-combine approach
I can explain: what a DataFrameGroupBy object is
I can explain: value_counts() versus groupby() with count()
I can explain: how agg() computes several statistics together
I can explain: how to group by multiple columns

Study and exam tips

Start by reading the chapter once for the overall idea, then revise groupby and aggregation functions in pandas using headings, definitions, principles, reactions and examples. Short-answer questions in the university exam usually ask for definitions, principles, procedures, reactions and uses, so write these in your own words and practise the equations, tables and flow charts that the chapter has.

For wider reading, standard books and open resources such as the NCBI Bookshelf help you confirm terms and see more examples. Revise groupby and aggregation functions in pandas again a day later and again a week later, because spaced revision improves long-term memory.

This page is part of the Basics of Python Programming for Pharmaceutical Sciences series for first-semester B.Pharm students on B.Pharm Guru, where every chapter has a short summary and a full PDF. Bookmark this page and share it with classmates who are preparing for the same exam.

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