BP101T · Core · Semester 1
Basics of Python Programming for Pharmaceutical Sciences
Learn Python foundations, control structures, data and file handling, Pandas, and Matplotlib through carefully labelled pharmaceutical teaching datasets and browser practice.
Unit 1: Introduction to Python programming
Use an IDE conceptually, then trace variables, integers, floats, strings, Booleans, type casting, operators, and input/output in a fictional pharmacy-data example. Library installation is discussed as a controlled learning concept, not performed by this browser workspace.
Choose a suitable data type for each field in a simulated medicine record, then explain one type conversion and output.
Unit 2: Control Structures & Functions
Use conditions, nested decisions, bounded loops, break/continue, arguments, return values, and small functions to model a fictional workflow. Dosage and BMI formulas remain programming practice—not patient-specific recommendations.
Trace a bounded loop and write a function with arguments and a return value for a fictional BMI practice input.
Unit 3: Data Structures & File Handling
Organise a fictional formulary with lists, tuples, dictionaries, indexing, slicing, string handling, NumPy arrays, and a transparent CSV read/write concept before interpreting structured healthcare data.
Add and retrieve values from a practice formulary, then identify a safe CSV field and a NumPy arithmetic operation.
Unit 4: Data Handling with Pandas
Inspect fictional PK and ADR practice records as Series and DataFrames. Use head, tail, info, describe, cleaning, filtering, selecting, grouping, and aggregation without treating a teaching summary as clinical evidence.
Inspect a simulated ADR table, handle a missing follow-up value, then distinguish a filter from a grouped count.
Unit 5: Data Visualization with Matplotlib
Use line, histogram, scatter, and box-plot concepts with titles, axes, and legends to communicate fictional concentration-time, ADR, and dissolution data. Scientific interpretation remains bounded by the stated assumptions.
Choose a suitable visual display for a fictional pharmaceutical dataset and state one interpretation limit.