Basics of Python Programming for Pharmaceutical Sciences syllabus
Unit 1: Introduction to Python programming
- Installing Python
- 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.
- Question Bank : Unit 1 Python Programming Basics
Unit 2: 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.
- Question Bank : Unit 2 Control Structures & Functions
Unit 3: 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.
- Question Bank : Unit 3 Data Structures & File Handling
Unit 4: 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.
- Question Bank : Unit 4 Data Handling with Pandas
Unit 5: 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
- Question Bank : Unit 5 Data Visualization with Matplotlib
Recommendations:
First Year B. Pharm (From 2026-2027 onwards NEP PCI Syllabus)
Basics of Python Programming for Pharmaceutical Sciences F. Y. B Pharmacy Semester 1 Syllabus, Notes, MCQ’s, Books, pdf, downloads
