Python Programming Tutorial for Beginners

Gaining knowledge in Python programming has several advantages, making it a beneficial skill in the technologically advanced world of today. Because of its adaptability, Python can be used in a variety of projects and sectors. Leverage this Python programming tutorial for beginners to kickstart your versatile career. Explore more with our Python course syllabus.

Getting Started to Python Programming

Numerous libraries and frameworks for Python offer pre-built tools and functions. This makes complicated processes simpler and enables you to create applications more rapidly. We cover the following in our Python programming tutorial for beginners:

• Core Python Programming Concepts
• Setting Up Python
• Python Programming Examples
• Data Structures in Python
• Key Modules in Python
• Advanced Concepts in Python
• Importance of Learning Python

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Core Python Programming Concepts

Let’s start with an introductory Python programming basics. 

What is Python?

Python is an interpreted high-level programming language.

• “Interpreted” indicates that the code is run line by line without first requiring compilation into machine code.
• “High-level” indicates that it is made to be simple for people to read and write.

It is well-known for being readable and having a simple syntax, which makes it ideal for novices.

Web development, data analysis, artificial intelligence, and scripting are just a few of the domains where Python finds application.

Python Basics

Here are the fundamentals of Python programming:

Variables and Data Types in Python

Variables in Python are places in memory where values of a particular data type are stored. Data types are groups of various data items according to their attributes. 

Data Types in Python: 

• Tuple: An organized and unchanging data sequence.
• List: It is a changeable arrangement of items that can hold any kind of object.

my_list = [1, 2, 3, “apple”, “banana”]

print(my_list[0])  # Accessing the first element (1)

my_list.append(“orange”) # add orange to the end of the list.

for item in my_list:

    print(item)

• Dictionary: An unordered and modifiable set of key-pair values.

my_dict = {“name”: “Charlie”, “age”: 25, “city”: “London”}

print(my_dict[“name”])  # Accessing the value associated with the key “name”

my_dict[“job”] = “Engineer” # adding a new key value pair.

print(my_dict)

• Set: It is a collection of data.
• Boolean: An integer sub-data type with just two constant values: Is it true or false?
• String: A text-storing data type.
• Numeric: An integer, floating number, or even a complex number can be a numeric data type. 

Variables in Python:

• Python variables can be classified as local, global, instance, or class.
• Functions create local variables, which are only accessible within those functions.
• Global variables can be utilized across the program and are defined outside of any function.

Making use of the type() function: The built-in type() function can be used to determine a variable’s data type.

Operators in Python

Arithmetic, logical, bitwise, assignment, relational, comparison, increment and decrement, and unary operators are only a few of the several kinds of operators used in programming.

• Arithmetic Operators: + (addition), – (subtraction), * (multiplication), / (division), % (modulo), ** (exponent).
• Comparison Operators: == (equal to), != (not equal to), > (greater than), < (less than), >= (greater than or equal to), <= (less than or equal to).   
• Logical Operators: and, or, not.   
• Assignment Operators: = (assignment), +=, -=, *=, etc.

Control Flow in Python

The sequence in which statements, commands, and function calls are carried out is known as the control flow in Python. Loops, function calls, and conditional statements govern it. 

Conditional Statements:

• If-elif-else: A set of phrases that sequentially check several conditions. 
• If: It determines if a condition is true by evaluating an expression.
• Elif: This shorthand for “else if” evaluates several criteria sequentially.
• Else: A catch-all block that runs in the event that none of the conditions above are met. 

Example:

    if age >= 18:

        print(“Adult”)

    else:

        print(“Minor”)

Loops in Python: 

• Break: A line of code that ends a loop.
• Continue: A control statement that permits a loop to continue is called “continue.”
• Pass: A command for control. 

Example 1:

for i in range(5):

    print(i) #prints 0,1,2,3,4

Example 2:

count = 0

while count < 3:

    print(count)

    count += 1

Function Calls: An execution diversion is analogous to a function call. After executing all of the statements in the function’s body, the flow returns to resume where it left off. 

def greet(name):

        print(“Hello, ” + name)

    greet(“Charlie”)

Control Flow in Python Example: The if-elif-else statement can be used to determine whether a given integer is zero, positive, or negative.

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Setting Up Python

Python setup is a simple step that must be completed before you can begin writing code. This is a thorough guide for many operating systems:

Downloading Python

Official Website:

• Visit python.org, the official Python website.
• Proceed to the “Downloads” area.
• A button for the most recent version of Python will be visible. To download your operating system’s installer, click it.
• Usually, the website recognizes your operating system and provides the relevant download.

Installation (Windows)

Step 1: Run the installer: To launch the installer, double-click the downloaded.exe file.

Step 2: “Add Python to PATH”: 

• First, select the “Add Python to PATH” box. To run Python from the command prompt, this is necessary.
• You’ll have to manually add it later if you neglect to do this.

Step 3: “Install Now” instead of “Customize Installation”:

• For most users, “Install Now” is the simplest option.
• The “Customize Installation” option lets you select the optional features and installation location.

Step 4: Complete Installation: To finish the installation, adhere to the on-screen directions.

Installation (macOS)

Step 1: Download the Installer here: Get the installer (.pkg file) for macOS from the Python website.

Step 2: Run the installer: Double-click the package file, then proceed with the installation process.

Step 3: Verify Installation: 

• An older version of Python may occasionally come preinstalled on macOS. It is preferable to utilize the terminal to make sure you are using the most recent version that you downloaded.
• Open Terminal (Applications > Utilities > Terminal).
• Press Enter after typing python3 –version. The version you installed ought to be visible.

Installation in Linux

Package Manager: Python can be found in the package repository of the majority of Linux distributions.

• Step 1: Launch a terminal and use the package manager for your distribution:
  • Ubuntu/Debian: sudo apt update && sudo apt install python3.
  • Fedora/CentOS: sudo dnf install python3 
  • Arch Linux: sudo pacman -S python 
• Step 2: Check the installation:
  • Enter python3 –version in the terminal and hit Enter.

Verification (Across All OSs)

Step 1: Launch a Terminal or Command Prompt:

• Windows Type cmd and hit Enter after pressing Win + R.
• To access Terminal on macOS/Linux, navigate to Applications > Utilities > Terminal.

Step 2: Verify the Python Version:

• Press Enter after entering python3 –version (or python –version if you’re using Windows and it doesn’t recognize Python3).
• The installed version of Python ought to be visible.

Step 3: Verify the pip Version:

• The Python package installer is called Pip. It’s crucial.
• Press Enter after typing pip3 –version (or pip –version on Windows).
• The pip version will be displayed. You may need to reinstall Python if pip is not installed, being sure to tick the pip installation box.

Important Notes

• Python 2 is no longer supported in comparison to Python 3. Use Python 3 at all times.
• Virtual Environments: Using virtual environments, such as Venv or Conda, is strongly advised for larger projects. Conflicts are avoided and project dependencies are isolated in virtual environments.

You can easily install Python and get started with your programming endeavors by following these steps!

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Python Programming Examples

To help you grasp the fundamentals, let’s look at some real-world Python programming examples.

Basic “Hello, User!” Program:

name = input(“Enter your name: “)

print(“Hello, ” + name + “!”)

The user is greeted by this program after entering their name.

Calculating the Area of a Rectangle:

width = float(input(“Enter the width: “))

height = float(input(“Enter the height: “))

area = width * height

print(“The area of the rectangle is:”, area)

This program uses the user-supplied width and height to determine a rectangle’s area.

Checking if a Number is Even or Odd:

number = int(input(“Enter a number: “))

if number % 2 == 0:

    print(number, “is even.”)

else:

    print(number, “is odd.”)

This program determines whether a number is even or odd by using the modulo operator (%).

Looping Through a List

fruits = [“apple”, “banana”, “cherry”]

for fruit in fruits:

    print(fruit)

A list of fruits is iterated through by this program, which prints each one.

Creating a Simple Function:

def square(number):

    return number * number

result = square(5)

print(“The square of 5 is:”, result)

To find the square of a given number, this program constructs a function called square.

Working with Dictionaries:

person = {“name”: “Alice”, “age”: 30, “city”: “New York”}

print(“Name:”, person[“name”])

print(“Age:”, person[“age”])

person[“job”] = “Software Engineer”

print(person)

This program shows how to add and retrieve dictionary elements.

Simple Number Guessing Game

import random

number = random.randint(1, 10)

guess = 0

while guess != number:

    guess = int(input(“Guess a number between 1 and 10: “))

    if guess < number:

        print(“Too low!”)

    elif guess > number:

        print(“Too high!”)

print(“You guessed it! The number was:”, number)

The user attempts to predict the random number that is generated by this application using the random module.

Creating a list and performing list operations:

numbers = [1, 2, 3, 4, 5]

print(“Original list:”, numbers)

numbers.append(6)

print(“List after appending 6:”, numbers)

numbers.pop(0)

print(“List after removing the first element:”, numbers)

print(“The length of the list is:”, len(numbers))

print(“The sum of the list is:”, sum(numbers))

Trying these programs help you understand the fundamentals.

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Data Structures in Python

Data structures are fundamental building blocks of Python programming that let you efficiently arrange and store data. Python offers a number of pre-built data structures in addition to enabling the implementation of more intricate ones. 

Built-In Data Structures

Here are the list of built-in data structures in Python:

Lists:

• Lists are arranged, changeable collections of items.
• It may include components of various data kinds.
• Square brackets [] were used in its creation.
• Example: my_list = [1, “hello”, 3.14]

Tuples:

• Tuples are immutable, ordered sets of elements.
• Like lists, except once created, they cannot be changed.
• Parentheses were used in its creation.
• Example: my_tuple = (1, “hello”, 3.14)

Sets:

• Sets are disorganized groups of distinct components.
• It is beneficial for operations such as difference, intersection, and union.
• It is created with the set() constructor or curly braces {}.
• Example: my_set = {1, 2, 3, 4}

Dictionaries:

• Dictionaries are collections of key-value pairs that are not in order.
• Keys need to be distinct and unchangeable.
• Values can be any kind of data.
• Example: my_dict = {“name”: “Alice”, “age”: 30}

Key Features of Data Structures:

• Mutability:
  • After being created, mutable data structures, such as lists and dictionaries, can be altered.
  • Strings and tuples are examples of immutable data structures that cannot be altered.
• Ordering: 
  • Elements are kept in their proper order via ordered data structures, such as lists and tuples.
  • Sets and dictionaries are examples of unordered data structures that do not ensure any particular order.

Advanced Data Structures

Python enables the development of more complex data structures, frequently with the use of libraries or unique implementations:

Stacks:

• The Last-In-First-Out (LIFO) data structure is used in stacks.
• Lists can be used to implement it.

Queues: 

• FIFO (First-In-First-Out) data structures are used in queues.
• Lists or collections can be used to implement it.deque class.

Linked Lists:

• Linear data structures with linked items are called linked lists.
• beneficial for allocating memory dynamically.

Trees:

• Trees are hierarchical data structures that consist of child nodes and a root node.
• It is utilized in many different applications, including search algorithms and file systems.

Graphs:

• They are collections of edges and nodes, or vertices.
• It is used to depict the connections between items.

Important Notes:

• For effective data administration and manipulation, data structures are essential.
• Your code’s performance can be greatly impacted by the data format you use.
• Comprehending data structures is crucial for resolving intricate programming issues.

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Key Modules in Python

Python’s vast module library is what gives it its power. Below is a summary of the key modules, arranged for ease of understanding:

Core Modules in Python

math: 

• Offers mathematical operations, such as sin, cos, sqrt, and pi.
• It is necessary for calculations using numbers.

random: 

• Produces numbers that aren’t actually random.
• It is beneficial for gaming, security, and simulations.

datetime: 

• Utilizes times and dates.
• It manages parsing, formatting, and time computations.

os: 

• It offers an operating system interface.
• It permits process management, environment variable access, and file and directory manipulation.

sys:

• Access to system-specific parameters and functions is made possible by sys.
• It manages standard input/output, system exit, and command-line parameters.

io: 

• Manages streams of input and output (such as file I/O).
• It is used to write and read data.

json:

• JavaScript Object Notation (JSON) data is encoded and decoded using the json function.
• It is necessary for data serialization and web API development.

re: 

• It offers matching procedures for regular expressions.
• It is utilized for text modification and pattern matching.

collections:

• It offers specific container data types, such as Counter and defaultdict.
• It provides specialized and more effective data structures.

typing: 

• It offers type hints runtime support.
• It helps in error detection and code clarity.

pathlib: 

• It makes managing file paths a lot simpler.
• It is working with file paths in an object-oriented manner.

Data Manipulation and Analysis

pandas: 

• It offers data structures for data analysis, such as DataFrames.
• It is vital for transforming, cleaning, and analyzing data.

NumPy:

• Large, multi-dimensional arrays and matrices are supported by numpy.
• It is vital for scientific computing and numerical calculations.

statistics:

• Its functions for computing mathematical statistics are provided by statistics.
• Standard deviation, mean, median, mode, etc.

Web Development

requests: 

• Makes HTTP requests simpler.
• It is used to communicate with APIs and submit web requests.

flask: 

• A web framework that is lightweight.
• It is used to create APIs and online apps.

django:

• A high-level web framework is called Django.
• utilized for creating intricate web applications.

urllib:

• URL handling module.
• It is utilized to open URLs.

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GUI Development

tkinter:

• It is the Tk GUI toolkit’s standard Python interface.
• It is utilized in the development of desktop programs.

PyQt:

• A cross-platform GUI toolkit is called PyQt.
• It is utilized in the development of complex desktop programs.

Scientific Computing and Machine Learning

Scipy: 

• It offers interpolation, integration, optimization, and other techniques.
• It is needed for computing in science.

Scikit-learn: 

• It offers machine learning tools, such as clustering, regression, and classification.
• It is necessary for creating models for machine learning.

Tensorflow: It is a potent machine learning library, particularly for deep learning.

Pytorch: Another strong machine learning package that emphasizes adaptability and user-friendliness is called Pytorch.

Networking

socket: 

• An interface for low-level networking.
• It is utilized in the development of network applications.

Testing

unittest: 

• The framework for unit testing in Python.
• It is used to create and execute unit tests.

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Advanced Concepts in Python

Beyond the fundamentals of Python, you come across a number of advanced concepts that provide more capability and adaptability. Below are some advanced concepts of Python:

Advanced Object-Oriented Programming (OOP) Concepts

• Metaclasses: The “classes of classes” that govern the formation of classes are called metaclasses. They make it possible to change class behavior dynamically.
• Magic (Dunder) Methods: A Python method with double underscores before and after its name is referred to as a “dunder” or “magic” method. Knowing how to modify built-in functions and operators (such as +, -, and []) using __add__, __getitem__, and __str__.
• Advanced Inheritance: It includes multiple inheritance, MRO, or Method Resolution Order, Super() function, and ABCs (Abstract Base Classes)

Functional Programming

• Higher-Order Functions: Functions that return other functions as results or accept them as arguments are known as higher-order functions (e.g., map, filter, reduce).
• Closures: Closures are operations that, even after the outer function has completed running, “remember” variables from their enclosing scope.
• Decorators: Without altering the source code, decorators are a potent technique to change or expand the functionality of functions or classes.

Iterators and Generators

• Iterators: Knowing how to construct custom iterators and comprehending the iterator protocol (__iter__, __next__).
• Generators: It is creating memory-efficient routines that generate a series of values upon demand by utilizing the yield keyword.

Concurrency and Parallelism

• Threading: Running several threads at once with the help of the threading module.
• Multiprocessing: For CPU-bound activities, the multiprocessing module can be used to run numerous processes concurrently.
• Asynchronous Programming (asyncio): Writing concurrent code that can effectively handle I/O-bound activities requires the use of async and await in asynchronous programming.

Advanced Data Handling

• Data Serialization: It is the process of storing and exchanging data using formats like JSON, XML, and protocol buffers.
• Memory Management: Being aware of how Python handles memory, including reference counting and garbage collection.

Metaprogramming

• Reflection: A program’s capacity to analyze and alter its own behavior and structure while it is running.

Context Managers

• Creating your own context managers and using the “with” statement.

Importance of Advanced Python Programming Concepts

• Performance: Your applications’ performance can be greatly enhanced via concurrency and parallelism.
• Code Clarity: You can write more understandable and succinct code by using decorators and functional programming.
• Flexibility: When it comes to creating and implementing complicated systems, metaprogramming and OOP ideas offer more flexibility.
• Resource Management: Appropriate resource cleanup is ensured by context managers.

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Importance of Learning Python Programming

Learning Python programming has several advantages in the technologically advanced world of today. The following justifies learning Python programming for a bright future:

Versatility and Wide Range of Use: Since Python is a general-purpose language, it may be applied to a wide variety of tasks.

Easy to Learn: Python is very easy to learn, especially for beginners.

Strong Community and Extensive Libraries: Developers can save time and effort by utilizing pre-built tools and functionalities provided by its vast standard library and multiple third-party libraries, like NumPy, pandas, and scikit-learn.

High Demand and Career Options: Python developers are in high demand on the job market as a result of Python’s popularity. Being proficient in Python can lead to a variety of job opportunities. 

Data Science and Machine Learning Dominance: It is perfect for data analysis, visualization, and model creation because of its robust libraries and frameworks.

Automation and Efficiency: Python is a great tool for automating repetitive processes and improving workflow efficiency because of its scripting capabilities.

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Conclusion

Gaining knowledge of Python offers prospects for both professional and personal development since it offers a useful skill set applicable to many different fields. Leverage this Python programming tutorial for beginners to understand the basics. Kickstart your career with our Python training in Chennai.