BP101T

Basics of Python Programming for Pharmaceutical Sciences

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BP101T

Basics of Python Programming for Pharmaceutical Sciences (Theory)

Core Course

2 Hours / Week
2 Credits
30 Maximum Hours

Unit I – Introduction to Python Programming

6 Hours
  • Installing Python and an Integrated Development Environment (IDE)
  • Jupyter Notebook, PyCharm and VS Code
  • Advantages of IDEs over text editors
  • Python variables and data types: integers, floats, strings and booleans
  • Type casting
  • Basic operators: arithmetic, comparison and logical operators
  • Input and output operations
  • Basic string operations and manipulation techniques
  • Introduction to standard libraries and third-party libraries
  • Installing and uninstalling libraries
📖 E-Book ▶ Videos 📝 Tests

Unit II – Control Structures & Functions

6 Hours
  • Conditional statements
    • if statement
    • if-else statement
    • if-elif-else statement
    • 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
  • BMI calculation
📄 Notes ▶ Videos 📝 Tests

Unit III – Data Structures & File Handling

6 Hours
  • Lists, tuples and dictionaries
  • Indexing and slicing lists
  • Basic operations on lists and dictionaries
  • String manipulation techniques
  • Introduction to NumPy arrays
  • Basic NumPy operations
  • Array creation and arithmetic operations
  • Reading and writing CSV files
  • Understanding structured healthcare datasets
  • Importing small pharmaceutical datasets
  • Basic data access and manipulation tasks
📄 Notes ▶ Videos 📝 Tests

Unit IV – Data Handling with Pandas

6 Hours
  • Introduction to Pandas library
  • Pandas Series and DataFrame structures
  • Reading CSV and Excel files
  • PK study datasets and ADR reports
  • Inspecting datasets using:
    • head()
    • tail()
    • info()
    • describe()
  • Data cleaning techniques
  • Handling missing values
  • Filtering and selecting data based on conditions
  • Grouping data and performing aggregation functions
📄 Notes ▶ Videos 📝 Tests

Unit V – Data Visualization with Matplotlib

6 Hours
  • Introduction to Matplotlib
  • Creating line plots
  • Creating histograms
  • Creating scatter plots
  • Creating box plots
  • Labelling axes
  • Adding titles and legends
  • Visualizing pharmaceutical datasets
  • Concentration-time curves for oral and IV administration
  • ADR reporting rates across drugs
  • Dissolution profiles
  • Scientific interpretation of plots
📄 Notes ▶ Videos 📝 Tests

🎯 Course Outcomes

  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.