Python Programming for C and C++ Developers

Discover why Python follows C and C++, and explore its uses in web development, AI, and robotics; learn environment setup, input-output, variables, data types, and operators.

Introduce Python as a high-level, interpreted language with readable syntax and bytecode executed by a Python virtual machine, highlighting its open-source libraries for web development, data science, and object-oriented programming.

Set up your Python environment by downloading Python from python.org, installing it, and using a lightweight editor like VS Code to write and run a Hello world program in test.py.

Compare Python and C/C++ environment setup, including installing Python and using VS Code for Python and Dev-C++ for C/C++, with hello world under include, int main, and cout or printf.

Learn how to read user input in Python with the input function, optionally displaying a prompt string, and display results using print, including converting strings to integers with int.

Learn Python concepts after C and C++, including programs and statements, indentation, colon-based blocks, semicolon rules on a line, and reserved words like if and else, with VS Code examples.

Explore Python variables as memory references, with in-line declaration and initialization. Follow rules: start with a letter or underscore, use lowercase with underscores, avoid reserved words, and keep names descriptive.

Learn how to write comments in Python, using the hash for single-line and triple quotes for multi-line, and understand how comments help explain code in VS code.

Learn how to declare and initialize variables in C and C++, using data types like int, float, double, string, and char, while focusing on Python programming.

Explore the ten data types in Python, including numeric types (int, float, complex), boolean, string, set, dictionary, list, and tuple, and distinguish mutable versus immutable and sequence versus non-sequence behavior.

Explore chapter 2 of the Python course for beginners, covering decision making with if, else, elif, and loop basics with while and for loops, plus practical exercises and solutions.

Explore decision making in Python, including if, if else, and elif structures. Apply conditions with indentation and colon syntax through practical examples like age checks and grade logic.

Learn the Python while loop: its condition-driven repetition, syntax, and practical examples that iterate strings and lists, compare with for loops, and generate numbers in VS Code.

Master the three loop types in C and C++ - for, while, and do while - through examples printing 1 to 5. Compare C++ io stream usage with printf.

Practice beginner Python basics with arithmetic exercises from chapter 2, covering if/else and loops, implementing sum, difference, product, and division for two numbers using user input.

write a python program that checks if a number is even or odd using input, converts to int, and uses if-else with modulus by two; print results.

Learn to write a Python program that prints the squares of numbers from 1 to 10 using a for loop and range, with string formatting to display each result.

Write a python program that checks whether a user input is positive, negative, or zero using if and elif conditions, with example runs like 9, -5, and 0.

Learn to sum the first five natural numbers in Python using a while loop and the addition assignment operator. The example yields 15.

This lecture covers chapter three on Python function basics: create and call functions, differentiate argument and parameter, return statements and pass, and explore keyword, default, and required arguments with exercises.

Define and call Python functions with def, encapsulate logic to enable reuse and readability, and distinguish built-in from user-defined functions.

Explore how the return statement passes values from Python functions back to callers, how the pass keyword acts as a placeholder, and how parameters influence results.

Explore the difference between parameters and arguments in Python functions, and learn to define, pass values, and call functions with examples like squares and rectangle area.

Learn to create a function in C and C++, using a simple add example with parameters and a return value, and call it from main with scanf and printf.

Explore keyword arguments, default arguments, and required arguments in Python functions. Use keyword arguments to control parameter order and avoid misassignment of name and num.

Learn how default arguments in Python provide values in function definitions and why defaults must be last. Explore examples with a, b, c and overriding defaults by providing different arguments.

Explore default parameters as optional and why required arguments must receive values in Python functions, with examples and common VSCode error messages.

Practice Python programming by building a function to check if a user-input number is even or odd, using a check_num function, input handling, and a sample run.

Write a Python program that reads three numbers, defines a max_num function with three parameters, uses a list and max to find the maximum, and prints it.

Learn to write a Python program that reads a user string, defines a reverse function using slicing s[::-1], and prints the reversed result with an example.

Explore Python's object oriented programming concepts in chapter four. Learn about classes and objects, self, constructors and destructors, inheritance and its types, super, polymorphism, and access specifiers with practical examples.

Explore Python object-oriented programming by building classes and objects, encapsulation, inheritance, and polymorphism; define data members and methods, and learn to create and access class instances.

Learn how data members and member functions define Python classes, access via object instances, and update attributes using an example with a student class.

Understand how the self keyword links methods to the current class instance in Python, enabling access to instance variables and other methods.

Explore Python constructors, including non parameterized and parameterized __init__ methods, and the role of destructor and garbage collection. See practical class examples with self, data member, and object creation.

Explore a Python OOP destructor, showing that __del__ runs when an object is destroyed or the program ends, after the constructor __init__ initializes the object.

Master Python inheritance in object-oriented programming, showing how child classes reuse attributes and methods from a base class, with base and derived classes and common inheritance types.

Explore the Python super keyword in object-oriented programming, showing how to call parent class methods and constructors from a child class when names collide and methods are overridden.

Explore polymorphism in Python within object-oriented programming, showing method overloading and overriding, and how default arguments and variable arguments with super enable similar behavior.

Explore Python access specifiers in OOP, including public, private (double underscore), and protected (single underscore), with examples inside a class, in child classes, and from outside.

Explore Python libraries and modules, including standard libraries, creating custom modules, and using external libraries via pip such as pandas, numpy, matplotlib, and scikit learn, with best practices.

Learn how Python modules and libraries organize code with examples like math and date, and see how numpy and pandas enable machine learning, data handling, and artificial intelligence projects.

Explore how libraries and modules boost code reusability by enabling imports like math for square root and factorial, while organizing reusable, well-tested components that save time.

Learn how Python's standard libraries, including math, date time, and os, come pre-installed and how to import and use them, with notes on third party packages installed via pip.

Learn how to create and use custom Python modules, import them into other scripts, and reuse functions across projects with examples of greet, math operations, and aliasing.

Manage external libraries with pip, installing numpy, pandas, matplotlib, and requests for Python projects; use requests for web access, check 200 status, and visualize data with matplotlib.

Learn practices for using Python libraries, including virtual environments and requirements file for dependencies. Follow version control, read library documentation, use standard libraries when possible, and monitor security vulnerabilities.

Learn to open, read, write, and close files in Python, manage read, write, and read-write modes, handle directories, and implement exception management with custom errors.

Learn python file handling by opening, reading, writing, and appending text files, and exploring file types like text, csv, excel, and word with built-in open and close operations.

Explore how to open, read, write, append, and close files in Python using the open function. Learn different read and write operations, file modes, and handling existing files.

Learn to open, read, write, and append to a file using Python's open method, then close the file and observe how append preserves existing data.

Explore Python file handling by mastering modes such as read, write, append, and read/write (r+), including behavior when files exist or not and how to append without overwriting.

Learn to handle Python exceptions using try, except, else, and finally, and manage built-in and custom errors with raise in file operations and age verification scenarios.

Explore NumPy and pandas essentials for data analysis, including NumPy arrays, operations, and pandas dataframes, with hands-on data cleaning and transformation skills for machine learning and Python development.

Explore data analysis with pandas and numpy to clean and analyze Python datasets. Learn how to handle missing values, duplicates, and outliers and generate insightful reports from csv data.

Learn how to work with NumPy arrays, a fixed-size, homogeneous data structure for fast mathematical operations. Use mean and max to analyze multi-dimensional temperature data across days and sensors.

Explore numpy array operations for data analysis by creating arrays from lists, tuples, and ranges; generate zeros; reshape and flatten arrays; and index and slice two-dimensional arrays.

Explore how pandas, a Python library for machine learning data processing, creates data frames from dictionaries or files, reads and writes CSV and Excel data, and displays results.

This lecture teaches data cleaning and transformation with pandas, covering missing values, duplicates, incorrect formats, and outliers, to pre-process data for reliable model training.