22-382-0107 PYTHON CATEGORY L T P CREDIT
PROGRAMMING
LAB LAB 0 1 2 2
Prerequisite: Programming Fundamentals
Course Outcomes: After the completion of the course the student will be able to
CO1 Apply different data types based on the requirement (Cognitive level : Apply)
CO2 Apply functions and object-oriented principles in (Cognitive level : Apply)
programming
CO3 Employ exception handling and database connectivity to (Cognitive level : Apply)
develop robust applications in python
CO4 Able to develop websites using Django framework (Cognitive level : Apply)
CO5 Analyse data using Pandas library and Numpy package (Cognitive level : Apply)
Mapping of Course Outcomes with Programme Outcomes - Low=1, Medium=2, High=3
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3 3
CO5 3 3
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22-382-0107 PYTHON PROGRAMMING LAB
UNIT I :Data Types, Control Structures, Operators and Functions:
Introduction to python, Python variables and assignments, Data types in python, Numbers,
Strings, List and List processing, Tuple, Set, Dictionary. Operators. Flow Control: –
Decision making statements and loops
UNIT 2: Functions, Classes Files and Modules:
Function and Function arguments, Anonymous functions, Recursive functions, User
defined functions, Class, Constructor and methods. Inheritance, File handling in python:-
Opening a file, Closing a file, Writing to a file, Reading from a file. Modules: - Modules
and importing modules.
UNIT 3: Exception Handling and Database Programming
Exception Handling: -Built -in-Exceptions and user defined exceptions. Database
programming:- python-SQLite connectivity
UNIT 4: Web programming with Django
Python web application framework - Django:- Introducing models, Views, Templates, urls,
Custom user models, Permissions, Static and dynamic web pages, Deployment.
UNIT 5: Data analysis with Pandas and NumPy
Accessing and preparing data - Reading a file, indexing, selecting a subset. Data pre-
processing with python: -Dropping columns in a dataframe, Changing the index of a
dataframe, Tidying up fields in the data, Cleaning columns and data, Renaming columns
and skipping rows. Numerical analysis using NumPy: - Handling arrays and analysing data
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Textbook & References
1. An Introduction to Python by Guido Van Rossum, Fred L.Drake, Network Theory
Limited.
2. Programming and Problem Solving with Python, Ashok NamdevKamthane& Amit
Ashok Kamthane, McGrawHill Education (India) Private Limited
3. Django for Beginners: Build websites with Python and Django Paperback – March 7,
2018 by William S. Vincent
4. Python Data Science Handbook - Essential Tools for Working with Data , Jake
VanderPlas,O’Reilly
Online References
http://www.tutorialspoint.com/python/,http://docs.python.org/tutorial/,
http://zetcode.com/tutorials/pythontutorial/,http://www.sthurlow.com/python/,
http://www.djangoproject.com/, http://www.djangobook.com/ ,https://realpython.co
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