Python Beginner’s Learning Roadmap 2023?

Python Beginner’s learning roadmap 2023

Welcome to the world of Python programming! As a beginner, having a structured learning roadmap can help you progress efficiently and build a strong foundation. Here’s a Python learning roadmap for 2023:

Getting Started with Python:

  1. Install Python: Start by installing Python on your computer. You can download the latest version from the official Python website (

Text Editor or IDE: Choose a text editor or an Integrated Development Environment (IDE) to write and run your Python code. Popular choices include Visual Studio Code, PyCharm, and Atom.

Python Basics:

  1. Learn the syntax: Familiarize yourself with basic Python syntax, such as variables, data types, operators, and control flow (if-else statements, loops).
  2. Functions: Understand how to define and use functions to modularize your code.

Lists, Tuples, and Dictionaries: Learn about these fundamental data structures and how to manipulate them.

  1. Object-Oriented Programming (OOP):

Classes and Objects: Study the principles of OOP, including classes, objects, attributes, and methods.

Inheritance and Encapsulation: Explore inheritance and encapsulation concepts to build more complex and organized programs.

Python Libraries:

  1. NumPy: Learn NumPy for numerical computing, especially for arrays and mathematical operations.
  2. pandas: Understand pandas for data manipulation and analysis, particularly for handling tabular data.
  3. Matplotlib and seaborn: Get familiar with data visualization using these libraries.

Working with Files and Modules:

File I/O: Learn how to read from and write to files in Python.

  1. Modules: Understand how to create and use modules to organize your code into reusable components.
  2. Error Handling:

Study exception handling to make your programs more robust and handle errors gracefully.

Web Development (Optional):

  1. Flask or Django: If you’re interested in web development, pick one of these Python web frameworks to build web applications.

Data Science (Optional):

  1. SciPy and scikit-learn: Dive into data science libraries for scientific computing and machine learning.
  2. Jupyter Notebooks: Use Jupyter Notebooks for interactive data exploration and visualization.

Practice, Projects, and Collaboration:

Python Installation:

Visit the official Python website:

Go to the “Downloads” section.

Download the latest stable release of Python for your operating system (Windows, macOS, or Linux). Python 3.x is recommended as Python 2 is no longer supported.

Run the installer and follow the installation wizard’s instructions.

Python Example:

Once Python is installed, you can create a simple Python script using a text editor or an integrated development environment (IDE) like Visual Studio Code, PyCharm, or IDLE (comes with Python installation).

Let’s create a simple script that prints “Hello, World!” to the console:

Open a text editor (e.g., Notepad on Windows, TextEdit on macOS) or your preferred IDE.

Type or copy the following Python code into the editor:

print(“Hello, World!”)

Save the file with a .py extension, for example, Make sure the file is saved in an easily accessible location.

Open a terminal or command prompt on your computer.

Navigate to the directory where you saved the file using the cd command (e.g., cd /path/to/your/folder).

Execute the Python script by typing the following command and pressing Enter:


You should see the output:

Hello, World!

Learn the syntax ?

Sure! Learning the syntax of a programming language is a crucial step in becoming proficient in it. Python is known for its simple and easy-to-read syntax, which makes it an excellent choice for beginners and experienced developers alike. Let’s go through some of the essential Python syntax elements:


print(“Hello, World!”)

Variables and Data Types:

# Variables can store different data types

name = “John”

age = 25

height = 1.75

is_student = True

# This is a single-line comment


This is a





Python uses indentation to define blocks of code (loops, functions, conditional statements). There are no braces like in other programming languages.

if True:

print(“This is indented code”)


print(“This is not indented code”)


if age >= 18:

print(“You are an adult.”)

elif age >= 13:

print(“You are a teenager.”)


print(“You are a child.”)


# For loop

for i in range(5):


# While loop

counter = 0

while counter < 5:


counter += 1


def greet(name):

return f”Hello, {name}!”

result = greet(“Alice”)



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

print(fruits[0])  # Accessing elements

fruits.append(“grape”)  # Adding an element to the list


person = {

“name”: “John”,

“age”: 30,

“is_student”: True


print(person[“name”])  # Accessing values by key

person[“occupation”] = “Engineer”  # Adding a new key-value pair


coordinates = (10, 20)

x, y = coordinates  # Unpacking tuple

Input from User:

name = input(“Enter your name: “)

print(f”Hello, {name}!”)

Importing Modules:

import math

radius = 5

area = math.pi * radius ** 2



Functions in Python are blocks of reusable code that perform a specific task. They allow you to break down your code into smaller, more manageable parts, making your code more organized and easier to maintain. Functions in Python follow a specific syntax and can take arguments (inputs) and return values (outputs).

Let’s dive deeper into functions in Python:

Function Definition:

In Python, you define a function using the def keyword followed by the function name, a pair of parentheses (), and a colon :. The function body is indented and contains the code to be executed when the function is called.


def function_name(parameters):

# Function body (code block)

# …

return result  # Optional return statement (if you want the function to return a value)

Function Parameters:

Parameters are values that you can pass to a function to work with when the function is called. They are enclosed in the parentheses after the function name.


def greet(name):

return f”Hello, {name}!”


def greet(name):

return f”Hello, {name}!”

Function Call:

To execute a function and use its functionality, you call it by using its name followed by parentheses ().


result = greet(“Alice”)

print(result)  # Output: Hello, Alice!

Return Statement:

The return statement is optional in Python functions. It allows the function to send a value back to the caller. If a function doesn’t have a return statement, it implicitly returns None.


def add(a, b):

return a + b

result = add(5, 3)

print(result)  # Output: 8

Default Parameters:

You can provide default values for parameters in a function. If a value is not provided for a parameter during the function call, the default value will be used.


def greet(name=”Guest”):

return f”Hello, {name}!”

print(greet())       # Output: Hello, Guest!

print(greet(“Alice”)) # Output: Hello, Alice!

Variable Number of Arguments:

Python functions can also accept a variable number of arguments using *args (for non-keyword arguments) and **kwargs (for keyword arguments).


def sum_numbers(*args):

total = 0

for num in args:

total += num

return total

result = sum_numbers(1, 2, 3, 4)

print(result)  # Output: 10

Lambda Functions (Anonymous Functions):

Python allows you to create small anonymous functions using the lambda keyword. They are often used for short and simple tasks.


square = lambda x: x ** 2

print(square(5))  # Output: 25

Object-Oriented Programming (OOP)

Object-Oriented Programming (OOP) is a programming paradigm that revolves around the concept of objects. In OOP, data and the methods (functions) that operate on that data are organized into objects. Each object is an instance of a class, and a class is a blueprint that defines the structure and behavior of the objects.

The four main principles of Object-Oriented Programming are:

Encapsulation: Encapsulation is the idea of bundling data (attributes) and the methods (functions) that operate on that data within a single unit called an object. It allows you to control the access to the object’s internal data, protecting it from unwanted modifications.

Abstraction: Abstraction is the process of simplifying complex systems by breaking them down into smaller, more manageable units. In OOP, you create abstract classes that define the structure and behavior of objects without implementing all the details. The abstract classes act as a blueprint for the derived (child) classes to implement.

Inheritance: Inheritance allows you to create a new class (called the derived or child class) from an existing class (called the base or parent class). The child class inherits the attributes and methods of the parent class and can also have its own unique attributes and methods. Inheritance promotes code reuse and hierarchy in class relationships.

Polymorphism: Polymorphism allows objects of different classes to be treated as objects of a common superclass. It allows you to use a single interface to represent different data types or objects, providing a flexible and modular approach to programming.

Let’s illustrate these concepts with an example:

# Define a simple class ‘Person’

class Person:

def __init__(self, name, age): = name

self.age = age

def say_hello(self):

return f”Hello, my name is {} and I’m {self.age} years old.”

# Inheritance: Create a subclass ‘Student’ that inherits from ‘Person’

class Student(Person):

def __init__(self, name, age, major):

super().__init__(name, age)

self.major = major

# Polymorphism: Override the ‘say_hello’ method

def say_hello(self):

return f”Hello, I’m {}, a {self.major} student.”

# Create objects of the classes

person1 = Person(“Alice”, 30)

student1 = Student(“Bob”, 20, “Computer Science”)

# Encapsulation: Accessing object attributes via methods

print(person1.say_hello())   # Output: Hello, my name is Alice and I’m 30 years old.

print(student1.say_hello())  # Output: Hello, I’m Bob, a Computer Science student.

In this example, we defined two classes: Person and Student. The Student class inherits from the Person class, showcasing inheritance. Both classes have a say_hello() method, but the Student class overrides the method to provide its own implementation, demonstrating polymorphism. The data (name, age, and major) is encapsulated within the objects of their respective classes.

Object-Oriented Programming is a powerful paradigm that encourages modularity, reusability, and a more intuitive way of structuring code. By understanding and utilizing OOP principles, you can create more organized and scalable applications.


NumPy (Numerical Python) is a powerful library for numerical computations in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of functions to perform operations on these arrays efficiently. NumPy is a fundamental package for scientific computing and is widely used in fields such as data analysis, machine learning, image processing, and more.

Key features of NumPy:

Arrays: NumPy’s main object is the ndarray (N-dimensional array), which is a versatile data structure for storing homogeneous numerical data. Arrays can have one or more dimensions and can be created from lists, tuples, or other array-like objects.

Vectorized Operations: NumPy allows you to perform element-wise operations on arrays, which are much faster than traditional loops because they are implemented in C and are optimized for performance.

Broadcasting: Broadcasting is a powerful feature that allows NumPy to perform operations on arrays of different shapes and sizes, making the code more concise and readable.

Mathematical Functions: NumPy provides a wide range of mathematical functions, such as trigonometric functions, exponential and logarithmic functions, statistical functions, etc.

Indexing and Slicing: You can access elements of NumPy arrays using indexing and slicing, just like standard Python lists.

Array Manipulation: NumPy offers various functions to reshape, transpose, concatenate, and split arrays.

To use NumPy, you need to install it first. If you don’t have it installed, you can install it using pip:

pip install numpy

Once installed, you can import NumPy in your Python script or interactive session:

import numpy as np

Now, let’s see some basic examples:

Creating NumPy Arrays:

import numpy as np

# Create a 1-dimensional array

arr1d = np.array([1, 2, 3, 4, 5])


# Create a 2-dimensional array

arr2d = np.array([[1, 2, 3], [4, 5, 6]])


# Create a 3-dimensional array

arr3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])


Performing Operations:

import numpy as np

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

# Element-wise addition

result = a + b

print(result)  # Output: [5 7 9]

# Element-wise multiplication

result = a * b

print(result)  # Output: [ 4 10 18]

# Broadcasting: Add a scalar to an array

result = a + 10

print(result)  # Output: [11 12 13]

These are just basic examples to introduce you to NumPy. NumPy offers a lot more functionality for advanced operations and mathematical computations. To learn more, you can explore the official NumPy documentation:


Pandas is a popular Python library for data manipulation and analysis. It provides powerful data structures and functions designed to make working with structured data (e.g., tabular data, time series) easier and more efficient. Pandas is widely used in data science, machine learning, and other data-intensive fields.

Key features of Pandas:

DataFrame: The main data structure in Pandas is the DataFrame, which is a two-dimensional, size-mutable, and heterogeneous tabular data structure with labeled axes (rows and columns). It is similar to a spreadsheet or SQL table.

Series: Another essential data structure in Pandas is the Series, which is a one-dimensional labeled array that can hold data of any type (integers, strings, floats, etc.).

Data Alignment: Pandas automatically aligns data based on the row and column labels, making it easy to perform operations on different datasets with different labels.

Data Cleaning and Preprocessing: Pandas provides various methods to handle missing data, duplicate data, and outliers, as well as tools for data transformation and normalization.

Data Aggregation and Grouping: Pandas allows you to group data based on specific criteria and perform aggregate functions on the grouped data.

Data Input and Output: Pandas supports reading and writing data from and to various formats, including CSV, Excel, SQL databases, JSON, and more.

To use Pandas, you need to install it first. If you don’t have it installed, you can install it using pip:

pip install pandas

Once installed, you can import Pandas in your Python script or interactive session:

import pandas as pd

Now, let’s see some basic examples:

Creating a DataFrame:

import pandas as pd

# Create a DataFrame from a dictionary

data = {

‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],

‘Age’: [25, 30, 22],

‘Occupation’: [‘Engineer’, ‘Doctor’, ‘Teacher’]


df = pd.DataFrame(data)


Reading Data from a CSV file:

import pandas as pd

# Read data from a CSV file

df = pd.read_csv(‘data.csv’)

# Display the first few rows of the DataFrame


Data Manipulation:

import pandas as pd

# Create a DataFrame

data = {

‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],

‘Age’: [25, 30, 22],

‘Occupation’: [‘Engineer’, ‘Doctor’, ‘Teacher’]


df = pd.DataFrame(data)

# Select specific columns

print(df[‘Name’])          # Output: [‘Alice’, ‘Bob’, ‘Charlie’]

# Filter rows based on conditions

filtered_df = df[df[‘Age’] > 25]


# Grouping and Aggregation

grouped_df = df.groupby(‘Occupation’).mean()


These are some basic examples to introduce you to Pandas. Pandas offers a wide range of functionality for data manipulation, analysis, and visualization. To learn more, you can explore the official Pandas documentation:

Matplotlib and seaborn

Matplotlib and Seaborn are two popular Python libraries used for data visualization. They are often used in conjunction with Pandas to create insightful plots and charts to explore and communicate data effectively.


Matplotlib is a versatile 2D plotting library that allows you to create a wide variety of static, interactive, and animated plots. It provides a high-level interface for drawing attractive and informative visualizations, making it a fundamental tool for data analysts and scientists.

To install Matplotlib, you can use pip:

pip install matplotlib

Once installed, you can import Matplotlib in your Python script or interactive session:

import matplotlib.pyplot as plt

Example of a simple line plot using Matplotlib:

import matplotlib.pyplot as plt

# Sample data

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

y = [2, 4, 6, 8, 10]

# Create a line plot

plt.plot(x, y)

# Add labels and title



plt.title(‘Simple Line Plot’)

# Show the plot


Seaborn is a statistical data visualization library built on top of Matplotlib. It provides a higher-level interface and more visually appealing themes for creating attractive statistical graphics. Seaborn is particularly useful for exploring relationships and patterns in complex datasets.

To install Seaborn, you can use pip:

pip install seaborn

Once installed, you can import Seaborn in your Python script or interactive session:

import seaborn as sns

Example of a scatter plot using Seaborn:

import seaborn as sns

import matplotlib.pyplot as plt

# Sample data

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

y = [2, 4, 6, 8, 10]

# Create a scatter plot

sns.scatterplot(x, y)

# Add labels and title



plt.title(‘Scatter Plot’)

# Show the plot

Seaborn provides a wide range of built-in functions for creating various types of plots, such as bar plots, histograms, box plots, heatmaps, pair plots, etc. It is often used in combination with Pandas DataFrames to create expressive visualizations.

Both Matplotlib and Seaborn are powerful tools for data visualization, and the choice between them often depends on the specific visualization requirements and aesthetics preferences. Combining the functionalities of these libraries with Pandas allows you to gain valuable insights from your data and effectively communicate your findings.


In Python, a module is a file containing Python definitions and statements. It acts as a container for organizing related code and provides code reusability across multiple programs. Modules help keep code modular, making it easier to understand, maintain, and scale.

A module can contain functions, classes, variables, and other Python code that you can import and use in other scripts or modules. Python provides several ways to work with modules:

Built-in Modules: Python comes with a large standard library of modules that are ready to use. Some examples include math, random, datetime, os, sys, and more. You can use these modules by importing them into your script.


import math

radius = 5

area = math.pi * math.pow(radius, 2)


Custom Modules: You can create your own modules by saving Python code in a .py file and then importing it into your scripts. This allows you to organize and reuse your code across multiple projects.



def greet(name):

return f”Hello, {name}!”


import my_module

result = my_module.greet(“Alice”)


sing from Statement: Instead of importing the entire module, you can import specific functions, classes, or variables from a module using the from statement.


from math import pi, pow

radius = 5

area = pi * pow(radius, 2)


Aliasing Modules: You can give an alias to a module to use a shorter name for it, which can be helpful when working with long module names.


import math as m

radius = 5

area = m.pi * m.pow(radius, 2)


Standard Library vs. Third-Party Modules: Apart from the built-in modules, Python has a vast ecosystem of third-party modules available on platforms like PyPI (Python Package Index). You can install these modules using pip and use them in your projects. Popular third-party modules include NumPy, Pandas, Matplotlib, Requests, Django, Flask, and more.

Example (installing a third-party module):

pip install pandas

import pandas as pd

# Now you can use the Pandas library in your script

Modules are an essential part of Python programming as they allow code organization, code reuse, and extend the functionality of Python beyond its standard library. When working on larger projects, using modules helps keep your codebase clean and maintainable.

Error Handling

Error handling, also known as exception handling, is a technique used in programming to gracefully handle unexpected situations and errors that may occur during the execution of a program. In Python, when an error occurs, an exception is raised, which can be caught and handled using various error handling mechanisms.

Python provides the try, except, else, and finally blocks to implement error handling:

try-except block: The try block is used to enclose the code that might raise an exception. If an exception occurs within the try block, Python looks for a matching except block to handle the exception.


# Code that might raise an exception

result = 10 / 0

except ZeroDivisionError:

# Code to handle the specific exception (division by zero in this case)

print(“Error: Division by zero”)

Multiple except blocks: You can use multiple except blocks to handle different types of exceptions that may occur in the try block.


# Code that might raise an exception

value = int(input(“Enter an integer: “))

result = 10 / value

except ValueError:

print(“Error: Invalid input. Please enter an integer.”)

except ZeroDivisionError:

print(“Error: Division by zero”)

else block: The else block is executed if no exceptions are raised in the try block.


num1 = int(input(“Enter the first number: “))

num2 = int(input(“Enter the second number: “))

result = num1 / num2

except ValueError:

print(“Error: Invalid input. Please enter integers.”)

except ZeroDivisionError:

print(“Error: Division by zero”)


print(f”The result is: {result}”)

finally block: The finally block is used to define code that will be executed regardless of whether an exception occurred or not. It is often used to perform cleanup tasks.


file = open(“data.txt”, “r”)

# Perform some operations on the file

except IOError:

print(“Error: Cannot open the file.”)



Handling multiple exceptions together: You can catch multiple exceptions together by using a tuple in the except block.


# Code that might raise exceptions

value = int(input(“Enter an integer: “))

result = 10 / value

except (ValueError, ZeroDivisionError) as e:

print(f”Error: {e}”)

By using error handling techniques, you can make your code more robust and handle potential issues gracefully. It is essential to catch specific exceptions that you can handle properly, rather than catching all exceptions, as this can lead to obscure errors and make debugging more difficult.

Flask or Django

Flask and Django are both popular web frameworks in Python, but they have different use cases and cater to different needs. The choice between Flask and Django depends on the requirements and complexity of your web application.


Flask is a lightweight and minimalist web framework that follows the “micro-framework” philosophy. It provides the essentials needed to build a web application, but it keeps things simple and modular. Flask is excellent for small to medium-sized projects, APIs, prototypes, and applications that require a high level of customization and flexibility.

Pros of Flask:

Minimalistic and easy to learn.

Allows you to choose the components you need, making it very modular.

More freedom and flexibility in designing the project structure.

Good for small applications and APIs.

Cons of Flask:

Less built-in functionality compared to Django.

You may need to integrate third-party libraries for certain features that Django provides out-of-the-box.

Might require more effort to set up certain aspects like authentication and admin interface.


Django, on the other hand, is a full-featured web framework that follows the “batteries-included” philosophy. It comes with a lot of built-in functionalities and follows a more opinionated approach. Django is suitable for larger projects, content-heavy websites, and applications that need a lot of features out-of-the-box.

Pros of Django:

Comprehensive and provides many built-in features like authentication, admin interface, ORM, etc.

Promotes a consistent project structure and enforces best practices.

Well-suited for complex and data-driven applications.

Cons of Django:

Learning curve might be steeper for beginners due to its comprehensive nature.

Less flexibility compared to Flask as it follows a more opinionated approach.

Might have some overhead for small projects due to its rich features.

Which one to choose?

Choose Flask if you prefer a lightweight and flexible framework, and you want more control over the project structure and components. It is a good fit for smaller projects, APIs, or when you need to customize every aspect of the application.

Choose Django if you want a comprehensive and feature-rich framework that provides many functionalities out-of-the-box, and you prefer a more structured approach to web development. It is suitable for larger projects, content-heavy websites, and applications with complex data models.

Ultimately, both Flask and Django are powerful frameworks, and the decision depends on your specific project requirements, your familiarity with the frameworks, and your development preferences.

SciPy and scikit-learn

SciPy and scikit-learn are two powerful Python libraries used for different purposes in the field of data science and machine learning. While they are related and can complement each other, they have distinct functionalities and use cases.


SciPy is an open-source library built on top of NumPy that provides additional functionality for scientific and technical computing. It is a collection of mathematical algorithms and functions for optimization, integration, interpolation, linear algebra, signal and image processing, statistical functions, and more. SciPy builds on NumPy arrays to provide more advanced mathematical operations and is widely used in scientific research, engineering, and data analysis.

Key features of SciPy:

Integration, interpolation, and optimization functions.

Signal processing and image processing capabilities.

Special functions for mathematical operations.

Linear algebra operations, sparse matrix support, and eigenvalue problems.

Statistical functions and distributions.


scikit-learn, often referred to as sklearn, is a popular machine learning library built on NumPy and SciPy. It provides a wide range of tools for data mining, data preprocessing, feature engineering, model selection, and model evaluation. scikit-learn is designed to be simple and efficient and supports a variety of supervised and unsupervised learning algorithms, making it an excellent choice for beginners and experienced data scientists alike.

Key features of scikit-learn:

A consistent and user-friendly API for various machine learning algorithms.

Support for classification, regression, clustering, dimensionality reduction, and more.

Data preprocessing tools like scaling, encoding, and missing data handling.

Model evaluation and selection tools like cross-validation and hyperparameter tuning.

Integration with NumPy and Pandas for seamless data handling.

When to use SciPy:

Use SciPy when you need advanced mathematical functions and algorithms for scientific computing, numerical optimization, integration, interpolation, signal processing, and linear algebra. It is suitable for tasks where you require more in-depth mathematical operations beyond the capabilities of NumPy.

When to use scikit-learn:

Use scikit-learn when you are working on machine learning tasks like classification, regression, clustering, and more. It provides a wide range of algorithms and tools for building and evaluating machine learning models, making it a go-to library for many data science projects.

In summary, SciPy is a foundation for numerical and scientific computing in Python, while scikit-learn is a specialized library for machine learning tasks. They can be used together to combine the power of advanced mathematical operations with machine learning capabilities.

Jupyter Notebooks

Jupyter Notebook (formerly IPython Notebook) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It is an interactive computing environment that supports various programming languages, including Python, R, Julia, and more. However, Python is the most commonly used language in Jupyter Notebooks.

Key features of Jupyter Notebooks:

Interactive Environment: Jupyter Notebooks provide an interactive environment where you can write and execute code directly in the browser. Code cells allow you to run individual code blocks, making it easy to experiment and iterate on code.

Rich Text Support: You can mix code cells with Markdown cells to create rich-text documents that include explanations, images, equations, and formatted text using Markdown syntax.

Data Visualization: Jupyter Notebooks support interactive data visualization using libraries like Matplotlib, Seaborn, and Plotly. You can generate plots, charts, and graphs directly in the notebook.

Kernel Support: Jupyter Notebooks are based on the concept of kernels, which are separate computational engines that execute code in specific programming languages. When you create a notebook, you can select the kernel associated with the language you want to use.

Data Exploration and Analysis: Jupyter Notebooks are widely used in data science for data exploration, cleaning, analysis, and visualization. They allow data scientists to interactively work with data, making the analysis process more intuitive and transparent.

Reproducibility and Sharing: Jupyter Notebooks are great for reproducible research as they combine code, documentation, and results in a single document. Notebooks can be easily shared with others and are often used for teaching, collaboration, and documentation purposes.

To use Jupyter Notebooks, you need to have Jupyter installed. The recommended way to install Jupyter is using pip:

pip install jupyter

Once installed, you can start a Jupyter Notebook server by running the following command in your terminal or command prompt:

jupyter notebook

This will open Jupyter in your default web browser, allowing you to create, open, and edit notebooks. You can create a new notebook by clicking on the “New” button and selecting “Python 3” or any other kernel of your choice.

Jupyter Notebooks provide a powerful and flexible environment for interactive computing, making them a favorite tool for data scientists, researchers, educators, and developers who want to work with code, data, and visualizations in an interactive and collaborative manner.

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