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Course Detail

Course Name Python Programming
Course Code 19CSE282
Program B. Tech. in Mechanical Engineering
Year Taught 2019


Introduction to Python: motivation for learning Python in scenarios like rapid prototyping.

Installing Python: basic syntax, interactive shell, editing, saving, and running a script.

The concept of data types: variables, assignments; immutable variables; numerical types; arithmetic operators and expressions; comments in the program; understanding error messages;

Conditions, boolean logic, logical operators: ranges; Control statements: if-else, loops (for, while); short-circuit (lazy) evaluation

Working with text files: manipulating files and directories, os and sys modules; text files: reading/writing text and numbers from/to a file; creating and reading a formatted file (csv or tab-separated).

Lists, tuples, and dictionaries: basic list operators, replacing, inserting, removing an element; searching and sorting lists; dictionary literals, adding and removing keys, accessing and replacing values; traversing dictionaries.

Design with functions: hiding redundancy, complexity; arguments and return values; formal vs actual arguments, named arguments. Recursive functions.

Use of popular Python packages for scientific computing: Exercises to understand usage of libraries like Numpy, SciPy, Pandas, Scikit-learn in interpreted and script modes.

Use of libraries like gpiozero, mraa, paho-mqtt, and requests for IoT applications and hands-on exercises.

Objectives and Outcomes

Course Objectives

  • This course provides the foundations of programming using Python programming language

Course Outcomes

  • CO1: Understand the given programming language constructs and solve and implement known problems using the same
  • CO2: Understand and apply advanced libraries in this programming language for real-time applications

CO – PO Mapping

CO1 1 2 3 1
CO2 1 1 1 1 3 3

Textbook / References


  • Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data Second Edition. MIT Press, 2016. ISBN: 9780262529624.
  • William McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython, Second edition (27 October 2017), Shroff/O’Reilly, ISBN-10: 9789352136414, ISBN-13: 978-9352136414
  • Hans Fangohr, Faculty of Engineering and the Environment, University of Southampton, Introduction to Python for Computational Science and Engineering (A beginner’s guide), September 7, 2015. Online version available


Evaluation Pattern

Assessment Internal External
*Continuous Assessment (CA) 80
End Semester 20
*CA – Can be Quizzes, Assignment, Projects, and Reports.

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