BP101T Basics of Python Programming for Pharmaceutical Sciences | Introduction to Python programming
Book Link - https://shashwatpublication.com/a-tex...
Class 01 BP101T Basics of #python #programming for Pharmaceutical Sciences | Introduction to #pythonprogramming
Course Objectives:
The objectives of this course are to:
1. Introduce the fundamentals of Python programming for pharmaceutical sciences.
2. Develop basic programming skills using control structures, functions, and data
structures.
3. Provide knowledge of data handling techniques for structured dataset management.
4. Familiarize students with data analysis tools such as #numpy and #pandaslibrary for healthcare
datasets.
5. Enable students to visualize and interpret pharmaceutical data.
Course Outcomes (CO):
CO
No. Upon successful completion of this course, the students will be able to:
1 Explain the fundamentals of Python programming, including variables, data types,
operators, and libraries.
2 Analyze program logic using control structures and functions.
3 Organize, manipulate, and retrieve data using data structures and file handling
techniques.
4 Analyze pharmaceutical datasets using Python libraries.
5 Visualize and interpret pharmaceutical data using graphical tools.
Detailed Syllabus:
Unit No. Topics No. of Lectures
I
Introduction 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,
comparison, logical), Input and output operations.
B.Pharm Syllabus
• Basic string operations and manipulation techniques.
Introduction to standard libraries and third-party
libraries, installing and uninstalling libraries.
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.
6 hours
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.
6 hours
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.
6 Hours
V
Data 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.