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Introduction to Python Programming for Pharmaceutical Sciences: Tools, Applications, and Setup

Overview of Python basics, IDEs, and applications in pharmacy including pharmacokinetics, pharmacovigilance, and data analysis with Jupyter, PyCharm, and VS Code.

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Mr. V. Dharshan Assistant Professor SBV Chennai Campus Introduction to Python Programming SCHOOL OF PHARMACY (Approved by PCI, New Delhi) BP101T: Basics of Python Programming for Pharmaceutical Sciences (Theory) UNIT – I B. PHARMACY (2026) As Per NEP 2020 I SEMESTER
Learning Outcomes • Define Python and explain its key characteristics as a programming language. • Describe why Python is relevant to pharmaceutical sciences and research. • Differentiate the Python interpreter from an Integrated Development Environment (IDE). • Explain the purpose and basic workflow of Jupyter Notebook, PyCharm and VS Code. • Install Python and verify the installation from a terminal. • Select an appropriate development environment for beginner pharmacy data work.
What is Python? Definition Python is a high-level, interpreted, object-oriented programming language with dynamic semantics. • Readable syntax designed to emphasize clarity and reduce program-maintenance effort. • Supports modules and packages, encouraging modularity and code reuse. • Includes a broad standard library and a large ecosystem of third-party packages. • Used for scripting, automation, scientific computing, data analysis, visualization and machine learning.
Why Python in Pharmacy? Drug & formulation data Analyze experimental datasets, dissolution profiles and formulation results. Pharmacokinetics Process concentration–time data and estimate PK parameters. Pharmacovigilance Clean, merge and summarize adverse drug reaction datasets. Research & AI Support statistics, visualization, machine learning and cheminformatics workflows.
Why Choose Python? Feature Why it matters to pharmacy students Readable syntax Beginner-friendly; code is comparatively easy to read and maintain. Free & widely available Can be installed without purchasing a commercial programming-language licence. Scientific ecosystem NumPy, Pandas, Matplotlib, scikit-learn, RDKit and PyTorch support scientific workflows. Rapid development Interactive execution and fast edit–test–debug cycles support experimentation. Reusable code Functions, modules and packages allow repeated analyses and workflows.
Python Across the Pharmaceutical Pipeline Discovery Preformulatio n & formulation Analytical research Clinical & PK Pharmacovigi lance Data & reporting Key message Python is not limited to one pharmaceutical function; it can connect data, computation and visualization across the research lifecycle.
Python Interpreter vs IDE Component Primary role Example Python interpreter Reads and executes Python code. Python installed from python.org IDE / development environment Provides an environment for writing, running, testing and debugging code. Jupyter Notebook, PyCharm, VS Code Text editor Mainly edits text/code; fewer programming-specific tools. Notepad / basic editor Remember You need a Python interpreter to execute Python. An IDE is a productivity environment that makes coding easier.
Advantages of IDEs over Basic Text Editors • Syntax highlighting improves code readability. • Code completion / IntelliSense reduces typing and helps discover functions and methods. • Error detection and linting can identify potential problems early. • Debugger supports breakpoints, variable inspection and step-by-step execution. • Integrated terminal / console supports interactive execution. • Project and environment management helps organize larger programs. • Notebook integration supports interactive data analysis in modern environments.
Installing Python: Basic Workflow 1 Download the official Python distribution for your operating system. 2 Install On Windows, enable “Add Python to PATH” during installation . 3 Verify run python --version. 4 Choose Useenviron ment Use Jupyter, PyCharm or VS Code according to your task. 5 Test Run a simple Python progra m to confirm the setup. python --version Output Python 3.x.x
Important Windows Installation Step Add Python to PATH During the Windows installer, select the option to add Python to PATH. This allows Python to be called from Command Prompt/Terminal. python --version Output Python 3.x.x python Output >>> If it fails A “command not found” / “not recognized” message commonly indicates that Python is not on the PATH or the wrong environment is selected.
Jupyter Notebook • Browser-based notebook environment designed for interactive computing. • Work is organized into cells; code can be executed cell- by-cell. • Outputs can appear immediately below code: numbers, tables, plots and other rich results. • Combines executable code with narrative text, equations and visualizations. • Especially suitable for exploratory pharmaceutical data analysis and teaching. Typical pharmacy use Explore a PK dataset calculate summary → values visualize concentration–time data → document interpretation in the same → notebook. Suggested starting point For beginners and exploratory data work, the supplied textbook recommends starting with Jupyter Notebook.
Installing / Launching Jupyter Route What it provides Typical use Anaconda Python + Jupyter + many scientific/data packages in one distribution. Beginner-friendly scientific computing setup. pip Install Jupyter into an existing Python environment. Users who already manage Python environments. VS Code + Jupyter extension Notebook editing and execution inside VS Code. Users who want notebooks + full code editor. pip install jupyter
PyCharm • Dedicated Python IDE from JetBrains. • Designed for structured projects that may contain multiple files and modules. • Provides code completion, inspections, navigation, testing and debugging. • Supports Python environments and package management workflows. • Jupyter Notebook integration is available in the current PyCharm product. Best suited for Larger Python projects, structured application development, reusable modules and debugging-heavy work. Teaching point An IDE becomes more valuable as the project grows beyond a single notebook or short script.
Visual Studio Code (VS Code) • Lightweight, extensible code editor that can be configured as a Python development environment. • Install the Microsoft Python extension to add Python- specific capabilities. • Supports IntelliSense, linting, debugging, testing and environment selection. • Jupyter support can be added with the Jupyter extension. • Works across major operating systems and with multiple Python interpreters/environments. Best suited for Students who want one flexible editor for Python scripts, notebooks, projects and other programming languages. Extensions → Python Extensions → Jupyter
Jupyter vs PyCharm vs VS Code Criterion Jupyter Notebook PyCharm VS Code Learning / teaching Excellent Good Excellent Exploratory data analysis Excellent Very good Very good Large multi-file projects Limited / less natural Excellent Excellent Interactive cells Core feature Supported Supported with Jupyter Debugging Good Excellent Excellent Extensibility Moderate High Very high Recommended first choice ✓ Later Alternative Practical recommendation For B.Pharm beginners: start with Jupyter for interactive learning and data exploration; move to VS Code or PyCharm as projects become larger.
Your First Pharmaceutical Python Program drug = "Paracetamol" dose_mg = 500 print("Dispensing", drug, "-", dose_mg, "mg tablet.") Output Dispensing Paracetamol - 500 mg tablet. • Comments begin with # and are ignored by Python. • Variables store values such as drug names and dose values. • print() displays information to the user.
Pharmaceutical Examples: What Python Can Automate Dissolution Organize time–percentage data and visualize dissolution profiles. Pharmacokinetics Calculate / visualize concentration– time relationships and PK parameters. Pharmacovigilance Standardize ADR files, identify duplicates and summarize reports. Clinical research Prepare datasets, perform calculations and support simulation workflows. Formulation R&D Compare formulation batches and analyze experimental measurements. Data visualization Generate publication-ready plots such as line, scatter, histogram and box plots.
Python Libraries: The Pharmaceutical Toolkit Library / module Primary purpose Pharmacy-oriented example math Mathematical functions Half-life, logarithms, exponentials NumPy Numerical arrays and operations Experimental / PK numerical datasets Pandas Tabular data analysis ADR reports, PK datasets, CSV/Excel files Matplotlib Scientific plotting Concentration–time and dissolution curves scikit-learn Machine learning Predictive modelling / classification RDKit Cheminformatics Molecular structure and descriptor workflows
Common Setup Problems Problem 1: Python not recognized Check installation and PATH; verify with python --version. Problem 2: Library will not import The library may have been installed into a different Python environment than the one used by the IDE. Problem 3: Multiple Python installations Choose one primary installation/environment unless you have a clear reason to manage several. Problem 4: Wrong interpreter in VS Code Use Python: Select Interpreter and choose the intended environment. import sys print(sys.executable)
Unit I — Key Takeaways • Python is a readable, high-level language with a large scientific ecosystem. • Pharmaceutical applications include PK, dissolution, pharmacovigilance, formulation data and research workflows. • Python interpreter executes code; an IDE improves coding, testing and debugging. • Jupyter is ideal for interactive notebooks and exploratory data work. • PyCharm is a full-featured Python IDE suited to structured projects. • VS Code is a lightweight, extensible environment with strong Python and Jupyter support. • Always verify the interpreter/environment before installing or importing packages.
Quick Check / Discussion • What is the difference between a Python interpreter and an IDE? • Why is Jupyter Notebook particularly useful for pharmaceutical data exploration? • Name two advantages of an IDE over a basic text editor. • Which environment would you choose for a large multi-file Python project? Why? • What does python --version verify? • Give two pharmaceutical tasks that could benefit from Python.
References  Rajgude KD, Avachat VU, Swamy MR. Basics of Python Programming for Pharmaceutical Sciences. Wiseleaf Scientific Ventures Pvt. Ltd., 2026. (Supplied course text; BP101T, Unit I).  Pavadai P. Basics of Python Programming for Pharmaceutical Sciences. PharmaMed Press/BSP Books; 2026. ISBN: 978-81-6962014-7.  Ahirwar R, Soni S, Singh AP, Vishwakarma N. A Text Book of Basics of Python Programming for Pharmaceutical Sciences. Shashwat Publication; 2026. ISBN: 978-93-7462-551-4.