BP101T • PCI 2026Pharmaceutical Computing & InformaticsCredits: 4 (45 Hours Theory)

BP101T – Basics of Python Programming for Pharmaceutical Sciences

Python syntax, posology calculations, clinical data arrays, and pharmacokinetic graphing for B.Pharm students.

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📖 Unit-wise Notes📋 Official Syllabus❓ Solved MCQs📝 Important Questions🧪 Viva & Practicals

📌 Subject Overview

BP101T introduces foundational programming and algorithmic thinking using Python 3 within pharmaceutical contexts. Students learn core programming constructs, laboratory calculations, pediatric dosage formulas (Young's, Dilling's, Clark's rules), data analysis with NumPy/Pandas, and pharmacokinetic graphing with Matplotlib.

BP101T एक नया विषय है जिसके अंतर्गत बी.फार्मा के छात्र पायथन प्रोग्रामिंग, डोज़ कैलकुलेशन (Young's Rule, Dilling's Rule), और फ़ार्मास्युटिकल डेटा एनालिसिस सीखते हैं।

Prescribed under the new PCI 2026 curriculum (NEP 2020), BP101T consists of 5 modular units bridging computational logic and pharmaceutical science applications.

Unit-wise Notes & Study Materials

Select any unit below to access in-depth lecture notes, examples, and summaries.

Unit 1

Introduction to Python & Basic Concepts

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History of computing, Python installation, IDE setup (VS Code, Jupyter, PyCharm), variables, data types, type casting, operators, and posology solvers.यूनिट 1 में पायथन की शुरुआत, सॉफ्टवेयर इंस्टालेशन, वेरिएबल्स, डेटा टाइप्स और बच्चों की दवा की खुराक (Posology) निकालने के प्रोग्राम शामिल हैं।

Key Topics Covered:
•History of Computing & Programming Languages in Pharmacy
•Python 3 Installation on Windows, macOS, and Linux
•Python IDEs: IDLE, VS Code, Jupyter Notebook, PyCharm
•Advantages of IDEs over Plain Text Editors
•Variables, Naming Rules, and Primitive Data Types (int, float, str, bool)
•Type Casting and User Input for Lab Calculators
•Operators: Arithmetic, Relational, Logical, and Assignment
•Pediatric Posology Solvers: Young's Rule, Dilling's Rule, Clark's Rule
Unit 2

Data Manipulation: NumPy & Pandas

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Multidimensional arrays in NumPy for dosage matrices, batch calculations, Pandas DataFrames for clinical formulations, and statistical quality control limits.यूनिट 2 में NumPy और Pandas का उपयोग करके दवा निर्माण के डेटा और क्लीनिकल ट्रायल के टेबल्स का विश्लेषण करना सिखाया जाता है।

Key Topics Covered:
•Introduction to NumPy & ndarray Creation
•Array Indexing, Slicing, and Vectorized Operations
•Matrix Mathematics for Multi-Component Formulations
•Pandas Series & DataFrames for Clinical Data
•Importing CSV & Excel Stability Data
•Handling Missing Values and Data Filtering in Quality Control
•Statistical Summary: Mean, Standard Deviation, and Variance
Unit 3

Data Visualization & Pharmacokinetics

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Plotting drug plasma concentration vs. time curves (Cp vs t), Area Under the Curve (AUC), elimination half-life, and dissolution profile comparison (f1 & f2).यूनिट 3 में दवाओं के रक्त में सघनता (Plasma Concentration vs Time Curve) का ग्राफ बनाना और डिसोल्यूशन प्रोफाइल की तुलना करना शामिल है।

Key Topics Covered:
•Matplotlib & Seaborn Fundamentals for Scientific Plotting
•Plotting Drug Plasma Concentration vs. Time Curves (Cp vs t)
•Calculating Cmax, Tmax, and Area Under the Curve (AUC)
•One-Compartment Open Pharmacokinetic Modeling
•Dissolution Profile Comparison: Difference Factor (f1) & Similarity Factor (f2)
•Customizing Charts: Labels, Legends, Error Bars, and Subplots
Unit 4

Functions, Modules & Pharma Algorithms

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Modular programming in Python, writing reusable posology functions, bioequivalence calculations, and building custom pharmaceutical packages.यूनिट 4 में रीयूजेबल फंक्शन्स, कस्टम मॉड्यूल्स और एरर हैंडलिंग के ज़रिये फ़ार्मेसी कैलकुलेटर बनाना सिखाया जाता है।

Key Topics Covered:
•Function Definition, Parameters, and Return Statements
•Posology Functions with Default & Keyword Arguments
•Scope of Variables: Local vs Global in Lab Scripts
•Creating Custom Pharmacy Modules (.py files)
•Exception Handling (try-except-finally) for Robust Formula Inputs
•Bioequivalence & Bioavailability Algorithmic Functions
Unit 5

Statistical Quality Control & Database Integration

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Statistical analysis for pharmaceutical quality control, tablet weight uniformity, content uniformity, SQLite database integration, and automated QC reports.यूनिट 5 में टैबलेट वेट वेरिएशन, कंटेंट यूनिफॉर्मिटी और डेटाबेस (SQLite) मैनेजमेंट के प्रोग्राम बनाए जाते हैं।

Key Topics Covered:
•Statistical Quality Control (SQC) Principles in Pharmaceutical Manufacturing
•Tablet Weight Variation Testing & USP/IP Limit Checking
•Content Uniformity Testing with Python Scripting
•ANOVA & Student's t-test for Drug Batch Comparison
•SQLite Database Fundamentals for Pharmacy Inventory
•Automated QC Report Generation (Exporting PDF & Markdown Summaries)

Official PCI Syllabus Breakdown

The Basics of Python Programming for Pharmaceutical Sciences (BP101T) course is prescribed for 45 theory hours and 15 tutorial/practical hours per semester. The syllabus is structured to provide foundational scientific understanding and practical applications required for subsequent professional semesters.

Download Full PCI Curriculum PDF↗
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BP101T MCQs

Practice multiple-choice questions with answer explanations designed for sessional and semester tests.

Practice MCQs➔
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Important Questions

Targeted 10-mark long essays, 5-mark short answers, and 2-mark definitions with answer outlines.

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Practicals & Viva Voce

Lab experiment procedures, safety rules, and frequently asked oral viva questions with model answers.

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