Semester 1 · Theory · 2 credits

BP101T — Basics of Python Programming for Pharmaceutical Sciences

Is subject ka poora unit-wise syllabus PCI ke NEP 2020 curriculum ke hisaab se, neeche diya gaya hai.

Unit-wise Syllabus

1Introduction to Python programming

Installing 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)
Basic string operations and manipulation techniques. Introduction to standard libraries and third-party libraries, installing and uninstalling libraries

2Control 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

3Data 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

4Data 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

5Data Visualization with Matplotlib

Introduction 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