Course

UNIT I Introduction to Python programming

• Installing Python and an Integrated Development Environment (IDE) such as Jupyter Notebook, PyCharm, or VS Code offers several advantages over using 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

• Conditional 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

• Lists, 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

• Introduction 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

• Introduction to Matplotlib.
• Creating line plots, histograms, scatter plots, and box plots.
• Labeling axes, titles, and legends.
• Creating plots and visualizing pharmaceutical datasets – concentration-time curves for oral and IV administration, ADR reporting rates across drugs, and dissolution profiles.
• Scientific interpretation of plots.

This course provides a foundational understanding of Python programming and its applications in pharmaceutical sciences, enabling students to develop computational skills for data handling, analysis, and visualization. It covers the basic ideas of Python, like programming basics, control structures, functions, data structures, and file handling, and shows how to use them for simple pharmacy problems like calculating dosages and BMI.

The course helps you become better at handling organized healthcare and pharmaceutical data with NumPy and Pandas, covering tasks like importing, cleaning, filtering, combining, and exploring the data. Students gain practical experience in reading and processing CSV and Excel datasets related to pharmacokinetic studies, adverse drug reaction (ADR) reports, and other pharmaceutical data. This course also equips learners with skills to create and interpret scientific visualizations using Matplotlib, enabling effective presentation and interpretation of concentration–time profiles, dissolution data, ADR trends, and other pharmaceutical datasets. Overall, the course establishes a strong computational foundation for evidence-based pharmaceutical research, data-driven decision-making, and advanced studies in bioinformatics, pharmacometrics, and healthcare analytics.

Upon successful completion of the subject, the student shall be able to:

• K1: Discuss the basic concepts of Python programming, including variables, data types, operators, and control structures.
• K2: Describe the use of Python functions, data structures, and file handling for simple programming tasks.
• K3: Explain the role of NumPy and Pandas in organizing and analyzing pharmaceutical datasets.
• K4: Interpret graphical representations of pharmaceutical data created using Matplotlib.
• S1: Develop simple Python programs to perform basic pharmaceutical calculations, analyze datasets, and generate scientific plots.
• A1: Demonstrate a systematic and responsible approach to handling, analyzing, and presenting pharmaceutical data using Python.

1. Weiss, C.J., 2017. Scientific Computing for Chemists with Python. Available at: https://weisscharlesj.github.io/SciCompforChemists/notebooks/introduction/intro.html
2. Perkovic, L., 2015. Introduction to Computing Using Python: An Application Development Focus. 2nd ed. Hoboken: Wiley.
3. Sweigart, A., 2025. Automate the Boring Stuff with Python. 3rd ed. Available at: https://automatetheboringstuff.com/
4. W3Schools, n.d. Python Tutorial. Available at: https://www.w3schools.com/python/
5. 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.