R23
EEE · 2-2
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-12
Python Programming Lab
JNTUK R23 · EEE · semester 2-2 · syllabus
This JNTUK R23 skill-enhancement course is listed for Electrical and Electronics Engineering (EEE). It is listed in semester 2-2. It carries 2 credits. The published coverage runs across 5 units, from Python Basics and Control Flow through Introduction to Data Science.
Verified vs. published syllabus
Checked 12 Jul 2026
SOURCE DOCKET
Published evidence
✓ Verified
Checked 12 Jul 2026
Record Pages 39–41 · 5 published units
Verification scope The title, semester placement, credits, five-unit syllabus and complete sample-experiment lists are sourced directly from JNTUK's official R23 EEE course structure and syllabus PDF; that entry does not publish a separate course-outcomes list.
5 units, from Python Basics and Control Flow to Introduction to Data Science. Tick off units as you cover them — your progress stays on this device.
Unit-wise syllabus
0 / 5 COVERED
UNIT 01 — Python Basics and Control Flow
History and thrust areas of Python; Anaconda distribution and Jupyter Notebook · Identifiers, keywords, statements, variables, operators, precedence and data types · Indentation, comments, input/output and type conversions · Control-flow statements: if/elif/else, while and for loops, break/continue, try/except · Sample experiment: find the largest of three numbers · Sample experiment: display all prime numbers within an interval · Sample experiment: swap two numbers without a temporary variable · Sample experiment: demonstrate arithmetic, relational, assignment, logical, bitwise, ternary, membership and identity operators · Sample experiment: add and multiply complex numbers · Sample experiment: print the multiplication table of a given number
UNIT 02 — Functions, Strings and Lists
Built-in functions and commonly used modules; function definition and calling · Return statement, scope and lifetime, default and keyword arguments, *args and **kwargs · Strings: operations, indexing, slicing, methods and formatting · Lists: operations, indexing and slicing, built-in functions and methods · Sample experiment: define a function with multiple return values · Sample experiment: define a function using default arguments · Sample experiment: find a string's length without library functions · Sample experiment: check whether a substring is present in a string · Sample experiment: perform addition, insertion and slicing operations on a list · Sample experiment: apply any five built-in functions to a list
UNIT 03 — Dictionaries, Tuples and Sets
Dictionaries: creating, accessing and modifying key:value pairs, built-in functions and methods · Tuples: creating, operations, indexing and slicing · Sets and set methods; the zip() function · Relation between tuples, lists and dictionaries; zip(), sets, set methods and frozensets · Sample experiment: create and concatenate name, age, address and college tuples for two members · Sample experiment: count vowels in a string without control flow · Sample experiment: check whether a key exists in a dictionary · Sample experiment: add a new key-value pair to a dictionary · Sample experiment: sum all items in a dictionary
UNIT 04 — Files and Object-Oriented Programming
File types; reading and writing text and binary files; the pickle module · Reading and writing CSV files; os and os.path modules · Classes and objects; constructor method; class vs data attributes · Encapsulation, inheritance and polymorphism · Sample experiment: sort words from one file into another as lowercase words · Sample experiment: print every line of a file in reverse order · Sample experiment: count the characters, words and lines in a file · Sample experiment: create, display, append, insert and reverse array items · Sample experiment: add, transpose and multiply two matrices · Sample experiment: implement a Shape class with area/perimeter methods and Circle, Triangle and Square subclasses
UNIT 05 — Introduction to Data Science
Functional programming in Python · JSON and XML in Python · NumPy arrays with Python · Pandas data frames · Sample experiment: check whether a JSON string contains a complex object · Sample experiment: create NumPy arrays using array() · Sample experiment: demonstrate ndarray ndim, shape, size and dtype · Sample experiment: demonstrate basic slicing plus integer and Boolean indexing · Sample experiment: find an array's minimum, maximum, sum and cumulative sum · Sample experiment: convert a five-key dictionary of ten-value lists to a Pandas data frame, apply head() and perform data-selection operations · Sample experiment: select two data-frame columns and compare them using scatter and plot operations in Matplotlib