Basics of Python Programming for Pharmaceutical Sciences
Course Objectives
- Introduce the fundamentals of Python programming for pharmaceutical sciences.
- Develop basic programming skills using control structures, functions, and data structures.
- Provide knowledge of data handling techniques for structured dataset management.
- Familiarize students with data analysis tools such as NumPy and Pandas for healthcare datasets.
- Enable students to visualize and interpret pharmaceutical data.
Course Content
Unit I: Introduction to Python programming
6 HoursInstalling Python and an Integrated Development Environment (IDE) [Jupyter Notebook, PyCharm, VS Code etc.], Advantages of IDEs over text editors.
Python variables and data types (integers, floats, strings, booleans), Type casting and basic operators (arithmetic, comparison, logical), Input and output operations.
Basic string operations and manipulation techniques. Introduction to standard libraries and third-party libraries, installing and uninstalling libraries.
Unit II: Control Structures & Functions
6 HoursConditional statements (if, if-else, if-elif-else), nested conditions
Loops (for loop, while loop).
Break and continue statements.
Defining and calling functions, passing arguments and returning values.
Writing modular programs for simple pharmaceutical applications- dosage calculation and BMI calculation.
Unit III: Data Structures & File Handling
6 HoursLists, tuples, and dictionaries.
Indexing and slicing lists, basic operations on lists and dictionaries, string manipulation techniques.
Introduction to NumPy arrays, basic operations using NumPy (array creation, arithmetic operations).
Reading and writing CSV files.
Understanding structured healthcare datasets.
Importing small pharmaceutical datasets and performing basic data access and manipulation tasks.
Unit IV: Data Handling with Pandas
6 HoursIntroduction to Pandas library.
Pandas Series and DataFrame structures.
Reading CSV and Excel files-PK study datasets and ADR reports
Inspecting datasets using functions such as head(), tail(), info(), and describe().
Data cleaning techniques and handling missing values.
Filtering and selecting data based on conditions.
Grouping data and performing aggregation functions.
Unit V: Data Visualization with Matplotlib
6 HoursIntroduction to Matplotlib.
Creating line plots, histograms, scatter plots, and box plots.
Labeling axes, titles, and legends.
Create plots and visualize pharmaceutical datasets - concentration-time curves for oral and IV administration, ADR reporting rates across drugs, dissolution profiles.
Scientific interpretation of plots.
Suggested Readings
- Weiss, C.J., 2017. Scientific Computing for Chemists with Python. Available at: https://weisscharlesj.github.io/SciCompforChemists/notebooks/introduction/intro.html
- Perkovic, L., 2015. Introduction to Computing Using Python: An Application Development Focus. 2nd ed. Hoboken: Wiley.
- Sweigart, A., 2025. Automate the Boring Stuff with Python. 3rd ed. Available at: https://automatetheboringstuff.com/
- W3Schools, n.d. Python Tutorial. Available at: https://www.w3schools.com/python/
- Datasets for Education and Research: Mentors and students can access healthcare datasets from sources such as Kaggle (healthcare records), government agencies (healthdata.gov, WHO, https://www.data.gov.in/), and clinical trial registries (https://ctri.nic.in/, https://clinicaltrials.gov/). Always use data responsibly.


