What are the basics of python programming?
  1. What is Python?

 Python is a high level computer programming language and famous for its plainness. Late in the 1980s Rossum shaped python and unconfined in 1991. Python ropes several programming paradigms, as well as ritual object-oriented and purposeful programming. Python is very vast and regular collection which provides numerous correspondence, framework and many practical relevance like web development, data analysis AI (artificial intelligence) scientific computing and much more.

I. Introduction

  • Why learn Python?

 There are several reasons to learn Python:

  • effortlessness of Learning: Python’s straightforward and sparkling syntax makes it reachable for beginners.
  • resourcefulness: It’s applicable in diverse domains like web development, data analysis, machine learning, artificial intelligence, scientific computing, etc.
  • Large Community and Libraries: Python has a massive community that contributes to its ecosystem by creating libraries and frameworks, allowing developers to accomplish tasks more efficiently.
  • Career Opportunities: Python is widely used across industries, and proficiency in Python opens up job opportunities in software development, data science, machine learning, and more.
  • High Demand: Due to its versatility and ease of use, Python developers are in high demand in the job market.

C. Brief history and popularity

  • History: Python was conceived in the late 1980s by Guido van Rossum, and its implementation began in December 1989. It was officially released in 1991 as Python 0.9. Python 2.x and Python 3.x are the two major versions coexisting for some time, with Python 2.x being officially discontinued in 2020 in favor of Python 3.x.
  • Popularity: Python’s popularity has surged over the years due to its simplicity, readability, versatility, and an extensive community-driven ecosystem. It’s used by both beginners and experienced developers for various purposes, contributing to its widespread adoption across industries. Its popularity is evident in fields like web development (Django, Flask), data science (Pandas, NumPy), machine learning (TensorFlow, PyTorch), and more.

II. Setting Up Python

A. Installing Python:

  1. Download Python: Visit the official Python website at python.org,
  2. navigate to the Downloads section, and select the version of Python suitable for your operating system (Windows, macOS, or Linux).
  1. Install Python: Run the installer and follow the installation instructions. Make sure to check the box that says “Add Python to PATH” during installation on Windows. This makes it easier to run Python from the command line.

B. Using Integrated Development Environments (IDEs) or Text Editors:

  1. IDEs: Integrated Development Environments like PyCharm, VSCode with Python extensions, Jupyter Notebook, or Spyder provide an all-in-one solution with features like code highlighting, debugging tools, and project management. Install an IDE of your choice by downloading it from the respective website and follow the setup instructions.
  2. Text Editors: Text editors like Sublime Text, Atom, or Notepad++ are simpler compared to IDEs but still support Python development. You write code and execute it separately. After installing a text editor, create a new file and save it with a .py extension (e.g., hello.py) to write Python code.

C. Running the First Python Program (Hello, World!):

  1. Using IDEs:
    1. Open your IDE.
    1. Create a new Python file.
    1. Type the following code:


print(“Hello, World!”)

  • Save the file.
    • Run the code using the “Run” or “Execute” button in the IDE. You should see “Hello, World!” printed in the output console.
  • Using Text Editors:
    • Open your chosen text editor.
    • Create a new file and type:

print(“Hello, World!”)

  • Save the file with a .py extension (e.g., hello.py).
    • Open a command line or terminal.
    • Navigate to the directory where your Python file is saved using cd (change directory) command.
    • Type python hello.py (replace hello.py with your file name) and press Enter.
    • You should see “Hello, World!” printed in the terminal.

Congratulations! You’ve successfully installed Python, chosen an environment to write code (IDE or text editor), and executed your first Python program displaying “Hello, World!”

III. Basics of Python Programming

A. Syntax and Indentation:

  • Syntax: Python’s syntax is clear and readable. It uses indentation to define blocks of code instead of using curly braces {} or keywords like end in other languages. Proper indentation (usually four spaces) is crucial for Python to understand the code structure correctly.
  • Example:

if 5 > 2: print(“Five is greater than two”)

B. Variables and Data Types:

  1. Variables: In Python, variables are used to store data. They can be assigned different data types and values during the program’s execution.
  2. Data Types: Python has several data types:
    1. Integers (int): Whole numbers without decimals.
    1. Floats (float): Numbers with decimals.
    1. Strings (str): Ordered sequences of characters enclosed in single (‘ ‘) or double (” “) quotes.
    1. Booleans (bool): Represents True or False values.
  3. Example:

# Variable assignment my_integer = 5 my_float = 3.14 my_string = “Hello, World!” my_boolean = True

C. Operators:

  1. Arithmetic Operators: Used for basic mathematical operations such as addition, subtraction, multiplication, division, etc.


# Examples of arithmetic operators a = 10 b = 5 print(a + b) # Addition print(a – b) # Subtraction print(a * b) # Multiplication print(a / b) # Division print(a % b) # Modulus (remainder) print(a ** b) # Exponentiation

  • Comparison Operators: Used to compare values and return True or False.


# Examples of comparison operators x = 10 y = 5 print(x == y) # Equal to print(x != y) # Not equal to print(x > y) # Greater than print(x < y) # Less than print(x >= y) # Greater than or equal to print(x <= y) # Less than or equal to

  • Logical Operators: Used to combine conditional statements.


# Examples of logical operators p = True q = False print(p and q) # Logical AND print(p or q) # Logical OR print(not p) # Logical NOT

D. Control Structures:

  1. Conditionals (if, elif, else): Used to make decisions in the code based on certain conditions.


# Example of conditional statements age = 18 if age >= 18: print(“You are an adult”) elif age >= 13: print(“You are a teenager”) else: print(“You are a child”)

  • Loops (for, while): Used for iterating over a sequence (for loop) or executing a block of code while a condition is True (while loop).


# Example of loops # For loop for i in range(5): print(i) # While loop count = 0 while count < 5: print(count) count += 1

IV. Data Structures in Python

A. Lists:

  • Definition: Lists are ordered collections of items or elements in Python. They are mutable, meaning the elements within a list can be changed or modified after the list is created.
  • Syntax: Lists are created by enclosing elements within square brackets [], separated by commas.
  • Example:


# Creating a list my_list = [1, 2, 3, 4, 5]

B. Tuples:

  • Definition: Tuples are similar to lists but are immutable, meaning the elements cannot be changed once the tuple is created.
  • Syntax: Tuples are created by enclosing elements within parentheses (), separated by commas.
  • Example:


# Creating a tuple my_tuple = (1, 2, 3, 4, 5)

C. Dictionaries:

  • Definition: Dictionaries are unordered collections of key-value pairs. They are mutable and indexed by unique keys. Each key is associated with a value, similar to a real-life dictionary where words (keys) have definitions (values).
  • Syntax: Dictionaries are created by enclosing key-value pairs within curly braces {}, separated by commas and using a colon : to separate keys and values.
  • Example:


# Creating a dictionary my_dict = {‘name’: ‘Alice’, ‘age’: 25, ‘city’: ‘New York’}

D. Sets:

  • Definition: Sets are unordered collections of unique elements. They do not allow duplicate elements.
  • Syntax: Sets are created by enclosing elements within curly braces {}, separated by commas.
  • Example:


# Creating a set my_set = {1, 2, 3, 4, 5}

Key Points:

  • Lists and tuples are ordered collections, but lists are mutable while tuples are immutable.
  • Dictionaries use key-value pairs to store data, allowing quick retrieval of values using their associated keys.
  • Sets are unordered collections of unique elements; they are useful for mathematical set operations like union, intersection, etc., and do not allow duplicate elements.

These data structures provide flexibility in storing and manipulating data in Python, each with its own characteristics and best-use cases. Understanding how to use them effectively can greatly enhance your ability to work with data in Python programs.

V. Functions and Modules

A. Defining Functions:

  • Definition: Functions in Python are blocks of reusable code designed to perform a specific task. They improve code modularity and reusability.
  • Syntax: Functions are defined using the def keyword, followed by the function name and parentheses containing optional parameters. The block of code inside the function is indented.
  • Example:


# Defining a function def greet(): print(“Hello, welcome!”)

B. Passing Arguments and Returning Values:

  • Arguments: Functions can accept parameters (arguments) to perform their tasks dynamically.
    • Positional Arguments: Defined based on the order they are passed.
    • Keyword Arguments: Defined by specifying the parameter name when calling the function.
  • Return Values: Functions can return values using the return statement.
  • Example:


# Function with arguments and return value def add(a, b): return a + b result = add(3, 5) # Passing arguments print(“Result:”, result) # Output: Result: 8

C. Working with Modules and Libraries:

  • Modules: Python modules are files containing Python code, which can define functions, classes, and variables. They can be imported into other Python scripts to reuse the code.
  • Libraries: Libraries are collections of modules that provide pre-written functionalities to ease development tasks.
  • Importing Modules/Libraries: Use the import keyword to import modules and libraries in your Python script.
  • Example:


# Importing a module import math # Importing the math module # Using functions from the imported module print(math.sqrt(16)) # Output: 4.0 (square root function from math module)

  • Creating and Using Your Own Modules: You can create your own modules by writing Python code in a separate file and importing it into your script.

VI. File Handling in Python

A. Reading from and Writing to Files:

Reading from Files (open() and read()):

  • To read from a file, you can use the open() function in Python, which opens a file and returns a file object. The read() method is used to read the contents of the file.
  • Syntax for Reading:


# Reading from a file file = open(‘file.txt’, ‘r’) # Opens the file in read mode (‘r’) content = file.read() # Reads the entire file content print(content) file.close() # Close the file after reading

Writing to Files (open() and write()):

  • To write to a file, open it with the appropriate mode (‘w’ for write, ‘a’ for append). The write() method is used to write content to the file.
  • Syntax for Writing:


# Writing to a file file = open(‘file.txt’, ‘w’) # Opens the file in write mode (‘w’) file.write(‘Hello, World!\n’) # Writes content to the file file.close() # Close the file after writing

B. File Modes and Operations:

File Modes:

  • Read Mode (‘r’): Opens a file for reading. Raises an error if the file does not exist.
  • Write Mode (‘w’): Opens a file for writing. Creates a new file if it doesn’t exist or truncates the file if it exists.
  • Append Mode (‘a’): Opens a file for appending new content. Creates a new file if it doesn’t exist.
  • Read and Write Mode (‘r+’): Opens a file for both reading and writing.
  • Binary Mode (‘b’): Used in conjunction with other modes (e.g., ‘rb’, ‘wb’) to handle binary files.

File Operations:

  • read(): Reads the entire content of the file or a specified number of bytes.
  • readline(): Reads a single line from the file.
  • readlines(): Reads all the lines of a file and returns a list.
  • write(): Writes content to the file.
  • close(): Closes the file when finished with file operations.

Using with Statement (Context Manager):

  • The with statement in Python is used to automatically close the file when the block of code is exited. It’s a good practice to use it to ensure proper file handling.
  • Syntax:


with open(‘file.txt’, ‘r’) as file: content = file.read() print(content) # File is automatically closed outside the ‘with’ block

VII. Object-Oriented Programming (OOP) Basics

A. Classes and Objects:


  • Classes are blueprints for creating objects in Python. They encapsulate data (attributes) and behaviors (methods) into a single unit.
  • Syntax for Class Declaration:


# Class declaration class MyClass: # Class constructor (initializer) def __init__(self, attribute1, attribute2): self.attribute1 = attribute1 self.attribute2 = attribute2 # Class method def my_method(self): return “This is a method in MyClass”


  • Objects are instances of classes. They represent real-world entities and have attributes and behaviors defined by the class.
  • Creating Objects from a Class:


# Creating an object of MyClass obj = MyClass(“value1”, “value2”)

B. Inheritance and Polymorphism:


  • Inheritance allows a class (subclass/child class) to inherit attributes and methods from another class (superclass/parent class).
  • Syntax for Inheritance:


# Parent class class Animal: def sound(self): return “Some sound” # Child class inheriting from Animal class Dog(Animal): def sound(self): # Overriding the method return “Woof!”


  • Polymorphism allows objects of different classes to be treated as objects of a common superclass. It enables the same method name to behave differently for each class.
  • Example of Polymorphism:


# Polymorphism example def animal_sound(animal): return animal.sound() # Same method name, different behaviors # Creating instances of classes animal1 = Animal() dog = Dog() # Calling the function with different objects print(animal_sound(animal1)) # Output: “Some sound” print(animal_sound(dog)) # Output: “Woof!”

VIII. Error Handling (Exceptions)

A. Understanding Exceptions:

What are Exceptions?

  • Exceptions are errors that occur during the execution of a program, disrupting the normal flow of the code.
  • Examples include dividing by zero, trying to access an undefined variable, or attempting to open a non-existent file.

Types of Exceptions:

  • Python has built-in exception types that represent different errors that can occur during program execution, like ZeroDivisionError, NameError, FileNotFoundError, etc.

B. Using Try-Except Blocks:

Handling Exceptions with Try-Except Blocks:

  • Try-except blocks in Python provide a way to handle exceptions gracefully, preventing the program from crashing when errors occur.
  • Syntax:


try: # Code that might raise an exception result = 10 / 0 # Example: Division by zero except ExceptionType as e: # Code to handle the exception print(“An exception occurred:”, e)

Handling Specific Exceptions:

  • You can catch specific exceptions by specifying the exception type after the except keyword.
  • Example:


try: file = open(‘nonexistent_file.txt’, ‘r’) except FileNotFoundError as e: print(“File not found:”, e)

Using Multiple Except Blocks:

  • You can use multiple except blocks to handle different types of exceptions separately.
  • Example:


try: result = 10 / 0 except ZeroDivisionError as e: print(“Division by zero error:”, e) except Exception as e: print(“An exception occurred:”, e)

Handling Exceptions with Else and Finally:

  • The else block runs if no exceptions are raised in the try block, while the finally block always runs, whether an exception is raised or not.
  • Example:


try: result = 10 / 2 except ZeroDivisionError as e: print(“Division by zero error:”, e) else: print(“No exceptions occurred!”) finally: print(“Finally block always executes”)

IX. Introduction to Python Libraries

A. Overview of Popular Libraries:

  1. NumPy:
    1. Description: NumPy is a fundamental package for scientific computing in Python. It provides support for arrays, matrices, and mathematical functions to operate on these data structures efficiently.
    1. Key Features:
      1. Multi-dimensional arrays and matrices.
      1. Mathematical functions for array manipulation.
      1. Linear algebra, Fourier transforms, and random number capabilities.
    1. Example:


import numpy as np # Creating a NumPy array arr = np.array([1, 2, 3, 4, 5])

  • Pandas:
    • Description: Pandas is a powerful library for data manipulation and analysis. It provides data structures like Series and DataFrame, making it easy to handle structured data.
    • Key Features:
      • Data manipulation tools for reading, writing, and analyzing data.
      • Data alignment, indexing, and handling missing data.
      • Time-series functionality.
    • Example:


import pandas as pd # Creating a DataFrame data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’], ‘Age’: [25, 30, 35]} df = pd.DataFrame(data)

  • Matplotlib:
    • Description: Matplotlib is a comprehensive library for creating static, interactive, and animated visualizations in Python. It provides functionalities to visualize data in various formats.
    • Key Features:
      • Plotting 2D and 3D graphs, histograms, scatter plots, etc.
      • Customizable visualizations.
      • Integration with Jupyter Notebook for interactive plotting.
    • Example:


import matplotlib.pyplot as plt # Plotting a simple line graph x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] plt.plot(x, y) plt.xlabel(‘X-axis’) plt.ylabel(‘Y-axis’) plt.title(‘Simple Line Graph’) plt.show()

B. Installing and Importing Libraries:

Installing Libraries using pip:

  • Open a terminal or command prompt and use the following command to install libraries:

pip install numpy pandas matplotlib

Importing Libraries in Python:

  • Once installed, import the libraries in your Python script using import statements:

Python import numpy as np import pandas as pd import matplotlib.pyplot as plt

  • After importing, you can use the functionalities provided by these libraries in your Python code.

X. Real-life Examples and Projects

A. Simple Projects for Practice:

  1. To-Do List Application:
    1. Create a command-line to-do list application that allows users to add tasks, mark them as completed, delete tasks, and display the list.
  2. Temperature Converter:
    1. Build a program that converts temperatures between Celsius and Fahrenheit or other temperature scales.
  3. Web Scraper:
    1. Develop a web scraper that extracts information from a website and stores it in a structured format like a CSV file.
  4. Simple Calculator:
    1. Create a basic calculator that performs arithmetic operations such as addition, subtraction, multiplication, and division.
  5. Hangman Game:
    1. Implement a command-line version of the Hangman game where players guess letters to reveal a hidden word.
  6. Address Book:
    1. Develop an address book application that stores contacts with details like name, phone number, and email address.
  7. File Organizer:
    1. Write a script that organizes files in a directory based on their file extensions or other criteria.

B. Exploring Python’s Applications in Different Fields:

  1. Web Development (Django, Flask):
    1. Python is widely used for web development. Explore frameworks like Django or Flask to build web applications, REST APIs, or dynamic websites.
  2. Data Science and Machine Learning:
    1. Use libraries like NumPy, Pandas, Scikit-learn, or TensorFlow to perform data analysis, create machine learning models, or work on predictive analytics projects.
  3. Scientific Computing:
    1. Python is used extensively in scientific computing for simulations, modeling, and solving complex mathematical problems. Use libraries like SciPy or SymPy for scientific computations.
  4. Natural Language Processing (NLP):
    1. Explore NLP with Python using libraries like NLTK or spaCy for text processing, sentiment analysis, or language translation tasks.
  5. Game Development:
    1. Develop simple games using Python libraries like Pygame, allowing you to create 2D games and learn game development concepts.
  6. Automation and Scripting:
    1. Create scripts to automate repetitive tasks like file manipulation, data processing, or system administration using Python’s scripting capabilities.
  7. IoT (Internet of Things) and Raspberry Pi Projects:
    1. Experiment with Python for IoT projects by controlling sensors, actuators, or devices using Raspberry Pi and Python libraries like GPIO Zero.

XI. Conclusion

A. Recap of Key Points:

  1. Python Basics: Python is a high-level, versatile programming language known for its simplicity, readability, and vast ecosystem of libraries and frameworks.
  2. Core Concepts: Understanding Python’s syntax, data types, control structures, functions, and handling exceptions is crucial for effective programming.
  3. Popular Libraries: Libraries like NumPy, Pandas, Matplotlib, etc., offer specialized functionalities for data manipulation, scientific computing, visualization, and more.
  4. Project Ideas: Simple projects, such as to-do lists, calculators, web scrapers, etc., provide practical experience and reinforce learning.
  5. Real-world Applications: Python’s applications span diverse fields like web development, data science, machine learning, scientific computing, automation, IoT, and more.

B. Encouragement for Further Exploration:

  1. Continuous Learning: Python’s versatility and vast ecosystem offer endless opportunities for learning and growth.
  2. Practice and Projects: Build upon your knowledge by working on more complex projects, contributing to open-source, and experimenting with different libraries and domains.
  3. Community Engagement: Engage with the Python community through forums, meetups, conferences, and online platforms to learn, share experiences, and collaborate.
  4. Stay Curious: Python evolves continuously, and exploring new libraries, updates, or trends keeps your skills up-to-date and opens doors to new possibilities.
  5. Persistence: Embrace challenges as learning opportunities. Persistence and dedication in learning Python will yield rewarding results in the long run.

C. Final Thoughts:

Python is an exceptional programming language renowned for its simplicity, readability, and versatility. Its applications span across numerous fields, from web development to scientific computing, data analysis, machine learning, and beyond. Whether you’re a beginner starting your programming journey or an experienced developer seeking new avenues, Python offers a rich ecosystem and a supportive community to aid your exploration and growth.