Python Introduction

Why Learn Python?

Learning Python can be a useful skill for both personal and professional growth. It is a popular and versatile language used by many industries, with a large and supportive developer community.

Below are some reasons why one should Learn Python: 

• Python is a widely used programming language; hence, learning Python can lead to better future career opportunities. 
• Python is a high-level programming language that is intended to be efficient and simple to use. This can help you write code more quickly and efficiently. 
• Using Python may lead to writing more readable and efficient code as it contains many standard libraries. 

History of Python

• Creation of Python: Guido van Rossum started developing Python in the late 1980s at Centrum Wiskunde & Informatica (CWI) in the Netherlands and released it in 1991.
   
• Successor to ABC: Python was designed as a successor to the ABC programming language
   
• Origin of the Name: The name “Python” comes from the BBC comedy sketch series Monty Python’s Flying Circus, not the snake.
   
• Early Popularity: Python gained popularity due to its ease of use and simple syntax.
   
• Python 1.0 (1994): Introduced functional programming tools like lambda, map, filter, and reduce.
   
• 1990s Evolution: Added features such as keyword arguments, data hiding, and built-in support for complex numbers.
   
• Python 2.0 (2000): Introduced list comprehensions and a garbage collector.
   
• Python 2.0 Deprecation: Officially deprecated as of January 2020.
   
• Python 3.0 (2008): The latest actively maintained version, fixing fundamental design flaws and improving Python’s overall functionality.

Also see, Swapcase in Python

Data types in Python

Python data types define the type of data you want to store in a variable. Like, you will need a string data type to store your name and an integer data type for storing a count of something.

There are numerous built-in data types in python:

Text: str

Numeric: int, float, complex

Boolean: bool

Binary: bytes, bytearray, memoryview

Sequence: list, tuple, range

Set: set, frozenset

Mapping: dict

Along with the built-in data types, you can also create custom data types by using classes and objects in python.

String Data Type

A string in Python is a sequence of characters enclosed within quotes and is 0-indexed. 

You can define a string in python using single quotes, double quotes, or triple quotes. 

It is important to note that strings in python are immutable; that is, once defined, you cant update the value of a string.

Example

str1 = ‘Coding Ninjas’
str2 = “Coding Ninjas Studio”
str3 = ‘’’ A multiline
string.’’’

You can access any character of a string by specifying its index. 

Suppose you want to find a substring from a start index to an end index; then you can use the slice operator([ ] or ([:]). 

Python supports string concatenation by using plus(+) operator.

Example

• Python

Python

str = ‘Coding Ninjas’
print(str[1]) #Access a character from a string
print(str[7:]) #Prints the substring from 7th index till the end
print(str[0:6]) #Prints the substring starting from index 0 and ending at index 5
print(str + ' Platform') #Prints the concatenated string
print(len(str)) #Prints the length of the string

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Output

o
Ninjas
Coding
Coding Ninjas Platform
13

Numeric Data Type

There are three bulti-in numeric data types in python, namely, integer, floating-point numbers, and complex numbers:

• Integers(int)
  Integers data type store both positive and negative whole numbers having no decimal point.
• Floating-point numbers(float)
  Float data types store both whole and fractional numbers having decimal points.
• Complex numbers(complex)
  It stores numbers having both real and imaginary parts, represented by a+bj, where a and b are the real and imaginary parts, respectively. Here, j is the square root of -1.

Example

• Python

Python

# Integer Data Type(int)
a = 5
print(type(a))  

# Floating-point numbers(float)
b = 3.14
print(type(b))  

# Complex numbers(complex)
c = 17 + 32j
print(type(c))

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Output

<class 'int'>
<class 'float'>
<class 'complex'>

Boolean Data Type

Boolean data types are used to store the truth values i.e. ‘True’ and ‘False’. You can also evaluate the truth value of an expression using the bool() function.

Example

• Python

Python

a = True # Boolean Data Type
print(type(a))  
b= 5 
c= 7
print(b>c)

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Output

<class 'bool'>
False

List Data Type

List Data Type stores the list of items separated by commas within square brackets []. All the list items need not be of the same data type. Lists in python are mutable, i.e. you can update the lists after creation.

Example

• Python

Python

list = [1, 2, 'Hello', 7, 9.4] #List Data Type
print(type(list))
print(list) #Prints the entire list
print(list + ['World', 3]) #Prints concatenated list

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Output

<class 'list'>
[1, 2, 'Hello', 7, 9.4]
[1, 2, 'Hello', 7, 9.4, 'World', 3]

Tuple Data Type

Tuple data type also holds a collection of items like lists, but here the elements are stored within parentheses unline square brackets in the list. Tuples are immutable, so you cant update(insert, delete, etc.) them.

Example

• Python

Python

tuple = ('Hello', 7, 9.4, 'World') #tuple Data Type
print(type(tuple))
print(tuple) #Prints the entire tuple
print(tuple[1]) #Prints the element at index 1

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Output

<class 'tuple'>
('Hello', 7, 9.4, 'World')
7

Dictionary Data Type

Dictionary in python consists of key-value pairs, where the key and value can be of any data type. The dictionary is enclosed within curly braces.

Example

• Python

Python

dict = {'Python': 1, 2: 'Coding'}
print(type(dict))
print(dict['Python'])
print(dict[2])

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Output

<class 'dict'>
1
Coding

Operators in Python

Operators in Python are used to perform operations on variables and values. Python provides different types of operators like Arithmetic operators, Comparison operators, Logical Operators, Bitwise operators, Assignment operators, Identity operators, and Membership operators.

Let's see each of the operators in detail:

Arithmetic Operators

You can perform basic mathematical operations like addition, multiplication, subtraction, division, exponentiation, floor division, and modulus using the arithmetic operators.

Comparison Operators

These operators compare two values and return a boolean value (True or False) accordingly. 

Logical Operators

The logical operators in python combine conditional statements and return a boolean value accordingly. 

Bitwise Operators

Bitwise operators perform bitwise operations on binary numbers.

Assignment Operators

Assignment Operators assign values to left-hand side variables.

Identity Operators

Identity operators compare the memory locations of two objects in python.

Membership Operators

Membership operators help to check if a sequence is present in an object or not.

Different Versions of Python

Since when python was created, it has undergone continuous development and reached several milestones. Several versions of Python have been released, each new version outshining the previous version with additional features and improvements.

Different versions of python are as follows:

Python 1.x

Python 1.0 was released in 1994, followed by several sub-versions like Python 1.5, 1.6, and so on. 

Python 2.x

Some of the notable advancements in version 2 comprise the unification of classes and python types, Unicode support, augmented assignments, etc. 

Python 3.x

The third version made Python more efficient by eliminating redundancy and removing duplicate constructs and modules. The new features include modification in integer division, removal of the python 2 input function, support for functional annotations, removal of several backward compatibility features, etc. 

Different IDEs for Python

Integrated Development Environment(IDE) is a software application that facilitates software development.  

There are several popular IDEs that support python, some of which are:

Visual Studio Code

It is a cross-platform code editor equipped with numerous extensions.  It supports multiple programming languages, including python. You can install the python extension and enjoy the features such as debugging, auto-completion, unit testing, and code formatting.

Jupyter Notebook

It provides an interactive development environment and supports over 40 programming languages along with Python. You can execute the code then and there and visualize data effectively through plots supported by the Jupyter Notebook.

PyCharm

It is one of the dedicated IDEs for Python, supporting a variety of libraries and frameworks.

Spyder

It is an open-source IDE famous for scientific computing in python.

IDLE

IDLE stands for Integrated Development and Learning Environment, which itself is coded purely in python. 

Atom

You can use Atom for coding in python by installing an extension. It is quite lightweight owing to its small size and also loads faster.

Sublime Text

It is another popular IDE for development in python. Sublime text has built-in support for python code editing, allows customization, and provides plenty of helpful extensions.

Eclipse with PyDev

Eclipse is very popular in the open-source community. You can install the PyDev extension to develop in python. It helps in python debugging and provides code completion. 

Different Frameworks in Python

Frameworks in Python help to automate the various development tasks and also provides a boilerplate for application development. There can be many types of frameworks, like micro, full-stack, or asynchronous. Frameworks allow code reuse and easier integration. 

Here are some popular frameworks in python:

Django

Django is a high-level Python web framework for creating secure, scalable, and maintainable web applications. It uses the model-template-views architectural pattern. 
It follows the MVC architectural pattern and emphasizes the "don't repeat yourself" (DRY) principle, which encourages developers to write reusable code.

Flask

Flask is a lightweight Python web framework that gives developers the fundamental tools and components they need to create web applications. 
Unlike some other web frameworks, Flask is designed to be flexible and extensible, allowing developers to customize their applications to meet their specific requirements.

CherryPy

CherryPy is a lightweight and flexible Python web framework that gives developers a lightweight and flexible way to build web applications. It is intended to be simple to use and configure, with an emphasis on extensibility.

Bottle

Bottle is a lightweight Python web framework that is intended to be simple and easy to use, with an emphasis on simplicity and minimalism. 
It is excellent for developing small web applications and APIs, and it is frequently used for prototyping and testing.

Pyramid

Pyramid is a well-known Python web framework that focuses on simplicity, modularity, and extensibility. It is commonly used in enterprise and production environments and is ideal for building large-scale web applications and APIs.

Characteristics of Python

Main characteristics of Python are as follows:

• Object-Oriented Language and Procedure-Oriented 
  Python is an object-oriented programming language that focuses on data and objects rather than functions and logic. Although python supports both, i.,e, Object Oriented and Procedure oriented paradigms. 
   
• Easy to Learn and Code
  Python is designed in a way to make it easy to learn and code. Python has a simple and intuitive syntax that is simple to learn and use, which makes it a great language for both beginners and experienced developers.

• Dynamically Typed Language
  Python is a dynamically typed programming language. In Python, the data type of a variable is determined at runtime based on the value assigned to it, so there is no need to declare it explicitly.
   
• Open-Source and Free to Use
  Python is a programming language that is open-source and free to use. This means that the Python source code is freely available to the public and can be used, modified, and distributed by anyone.
   
• Interpreted Language
  Python is an interpreted language, which means that code can be executed without needing to be compiled. This allows for faster development and easier debugging. This behavior of python saves a lot of time for programmers that they can use in building logic. 

Control flow statement in Python

The control flow of a program determines the order in which the code executes. Normally, the program execution begins from the first statement and continues till the bottom, where each statement is executed one at a time.

But you can alter the control flow of a Python program using control structures.

Python has namely three types of control structures:

• Sequential
  It is the default mode of execution that executes a block of code line by line from top to bottom.
• Selection
  It includes conditional statements like if, if-else, if-elif-else, and nested if/else, which allow decision-making and branching,
• Repetition
  It includes iterative statements like for and while loops execute a block of code a certain number of times

Let's see the conditional statements in python.

If Statement

If statements allow you to execute a block of code only when the condition specified is true. 

The general syntax is:

if condition:
   # Code block

If-Else Statement

If the condition is true, then it executes the block of code followed by the if statement. But if the condition is false, it executes the else block.

The general syntax is:

if condition:
   # Code block
else:
   # Code block

If-Elif-Else Statement

Using the if-elif-else statement, you can check multiple conditions and execute the codes accordingly. 

The general syntax is:

if condition:
   # Code block
elif condition:
   # Code block
else:
   # Code block

Nested If Statement

It allows checking conditions inside other conditions. 

The general syntax is:

if condition:
  #If block
elif condition:
   # Code block
else:
   # Code block

Example Of Conditional Statements

• Python

Python

bmi = 22
if bmi < 18.5:
    print("Underweight")
elif bmi >= 18.5 and bmi <= 24.9:
    print("Healthy Weight")
elif bmi >= 25 and bmi <= 29.9:
    print("Overweight")
else:
    print("Obese")

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Output

Healthy Weight

We will see the looping statements in detail in the next section.

Read about Bitwise Operators in C here.

You can practice by yourself with the help of online python compiler for better understanding.

Loops in Python

One of the control structures in python are loops. Python supports two types of looping statements, namely, for and while loops.

Loops allow you to run a block of code a certain number of times on the basis of the specified conditions.

For Loop

A, for loop iterates over the elements of a sequence like an array, list, tuple, string etc. 

The general syntax is:

for variable in sequence:
    # Code Block

While Loop

While loop executes a code block until the specified condition remains true. 

The general syntax is:

while condition:
    # Code BlockYou can change the flow of execution of a loop using break and continue statements.

Break Statement

The break statement terminates a loop immediately. You can use it to pass the control outside the loop. i.e. the statement following the loop is executed.

Continue Statement

You can use the continue statement to skip the current iteration and continue with the next one. The statements after the continue statement are not executed, and the control flow is passed to the next iteration. 

Example

• Python

Python

control_structures = ['Sequential', 'Selection', 'Repetition']
for val in control_structures:
    if val=='Repetition':
        break
    print(val)
i=0
while(i<=5):
    i+=1
    if(i==4):
        continue
    print(i)

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Output

Sequential
Selection
1
2
3
5
6

Collections in Python

Collection in python are used to group a set of elements together as one object.

Python provides various data structures for creating collections:

• List
  A list is an ordered collection of items that can be of different data types and is mutable in nature. It is declared using square brackets[]. 
  Example: my_list = [1, ‘Two’, 3, ‘Four’]
   
• Tuple
  A tuple is also an ordered collection of items but is immutable in nature. It is declared using parentheses.
  Example: my_tuple = (‘Coding’, ‘Ninjas’, [1,2,3])
   
• Set
  Unlike lists and tuples, a set consists of unique items. It is declared using curly braces{} and is immutable. It is not an ordered collection and hence not indexed.
  Example: my_set = {1,2,3,4}
   
• Dictionary
  A dictionary is a collection of key-value pairs. It is ordered, mutable, and does not contain duplicate entries. It is declared using curly braces. 
  Example: my_dict = {1: ‘One’, 2: ‘Two’, 3: ‘Three’}

Functions in Python

A function executes a block of code when called in a program. It may take any number of input parameters or arguments and may or may not return a value. 

Python follows dynamic binding, which implies that the function call is bound to the function body at runtime. 

The basic syntax for defining a function in python is:

def name_of_function(arguments):
    # body of the function 
    return

Example

• Python

Python

def find_divisor(num):
    for i in range(1, num+1):
        if(num%i==0):
            print(i)
            
find_divisor(20)

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Output

1
2
4
5
10
20 

Error handling in Python

While running a software application, you may encounter some unexpected errors. So, if you fail to handle those errors properly, it may lead the application to crash. 

For this purpose, error handling in python is important. It also helps to debug the code efficiently.

In Python, you can use the try, except, and finally keywords to handle the errors.

Let’s have a look at the syntax of the try-except-finally block:

try:
    # Code that can raise an exception
except ExceptionType:
    # Handle the exception
finally:
    # Execute this part irrespective of the result of the try-except block

It’s not necessary to use the finally block always. 

Firstly, the code in the try block executes. If there is no error, then it skips the except block, and the execution continues after the except block. 

If an exception occurs while executing the try block, then it is matched with the exception names specified after the except keyword. If it matches, then the except block is executed.

Here, you can also handle multiple exceptions by specifying the names of all the exceptions you want to handle in parentheses in this way:

except (ExceptionType1, ExceptionType2,... so on)

Files in Python

A file is a container used to store data in computer storage devices.

When we want to read or write to a file, we must first open it. 

When we're finished, it needs to be closed so that the resources associated with the file can be released.

As a result, in Python, a file operation occurs in the following order:

• Open a file
  In python, the open() method is used to open the file. open() accepts two parameters, one is the file name, and the other is the mode that is optional. By default, the read mode is set when we don’t pass any mode to the function. 

Example

# write in text mode
file1 = open("test.txt",'w')

• Read a file 
  After the file is opened, we read the content of the file using the read() function. 
  Example

# read the file
read_content_of_file = file1.read()
print(read_content_of_file)

• Writing to the file
  
  There are two things that you need to remember while playing with files: 
  
  1. If the file doesn’t exist, then a new file is generated. 
  2. Whenever you want to write something in your file, the existing content gets erased, and new content will be added. 
  
  You can write anything to your file using the write() function. To write to a file in Python, we must first open it in write mode by passing "w" as a second argument to open().
  Example 

with open(test.txt', 'w') as file2:


    # write contents to the test.txt file
    file2.write('Coding Ninjas is the best platform to learn and grow')
    file2.write('Programming is fun with Coding Ninjas')

If you need to read the content, you can use the read() function. 

• Close the file
  Once we are done with the file operations, we can close the file using close() function. 
  Example 

# close the file
file1.close()

Data Structures in Python

A data structure in computer science is a method of organizing and storing data in a computer program so that it can be accessed and manipulated efficiently. Depending on your use case, each data structure offers a unique method of organizing data so that it can be accessed efficiently. Python includes a large number of data structures in its standard library.

Lists in Python 

A list is an ordered collection of items in Python. The items in a list can be of any data type. Lists are mutable, which means that their contents can be changed after they are created.

In Python, you can make a list by using square brackets [] and separating the items with commas.

Example

• Python

Python

my_list = [1, 2, 3, 4, 5]
# adds 6 to the end of the list
my_list.append(6)    
print(my_list)


 # removes the first occurrence of 3 from the list
my_list.remove(3)    
print(my_list)


 # removes and returns the last item in the list
print(my_list.pop())
print(my_list)

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Output 

[1, 2, 3, 4, 5, 6]
[1, 2, 3, 4, 5, 6]
6
[1, 2, 4, 5, 6]
[1, 2, 4, 5]

Tuples in Python 

A tuple in Python is an ordered collection of items, similar to a list. Tuples, unlike lists, are immutable, which means their contents cannot be changed once they have been created.

In Python, you can use parentheses () to create a tuple and commas to separate the items.

Example 

• Python

Python

# create a new tuple 
my_tuple = (1, 2, 3)


# print my_tuple
print(my_tuple)


# print the type
print(type(my_tuple))


# print the len 
print(len(my_tuple))


print(my_tuple[0])
print(my_tuple[2])

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Output

(1, 2, 3)
<class 'tuple'>
3
1
3

Arrays in Python 

An array in Python is a collection of items stored in a contiguous memory block. The items in an array can be of any data type, and integers index them. Arrays are commonly used to store collections of data, such as lists of numbers, strings, or other objects.

In Python, arrays can be created using the built-in "array" module. The "array" class has a variety of methods and attributes that can be used to manipulate the data stored in the array.

Example 

• Python

Python

# import array module 
import array as arr


# 'i' indicates that the array will hold integers
my_array = arr.array('i', [1, 2, 3, 4, 5]) 
print(my_array)

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Output

array('i', [1, 2, 3, 4, 5])

There are several operations you can perform with arrays like add, update, delete and many more. The Array module includes several in-built functions that you can use in your program. 

Dictionaries in Python 

A dictionary is a collection of unordered and mutable key-value pairs. It is constructed with curly braces { } and can hold elements of various data types. 

Example 

• Python

Python

# define a dictionary with key and value 
my_dict = {'name': 'Ninja', 'age': 20, 'city': 'New Delhi'}
#print my_dict
print(my_dict)
#print my_dict keys 
print(my_dict.keys())
#print my_dict values 
print(my_dict.values())
#print my_dict items inside it
print(my_dict.items())

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Output   

{'name': 'Ninja', 'age': 20, 'city': 'New Delhi'}
dict_keys(['name', 'age', 'city'])
dict_values(['Ninja', 20, 'New Delhi'])
dict_items([('name', 'Ninja'), ('age', 20), ('city', 'New Delhi')])

There are several operations you can perform with dictionaries like add, update, delete and many more.  

Sets in Python 

A set in Python is an unordered collection of distinct elements. A set can be implemented using curly braces {} or the set() function. 

Example 

• Python

Python

my_set = {1, 2, 3, 4, 5}
# print my_set on console
print(my_set)
# print type of my_set
print(type(my_set))
# print length of my_set
print(len(my_set))

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Output   

{1, 2, 3, 4, 5}
<class 'set'>
5

There are several operations you can perform with sets like add, union, remove and many more. 

Some Popular Python Libraries

Python includes a number of libraries that are widely used in data analysis, machine learning, and scientific computing.

Here are some popular Python libraries:

NumPy: NumPy (Numerical Python) is an open-source Python library that is used in almost every field of science and engineering. It is the universal standard for working with numerical data in Python, and it is at the heart of the scientific Python and PyData ecosystems.
 

Pandas: Pandas is a library for data manipulation and analysis. It includes data structures for storing and manipulating large datasets in an efficient manner, as well as data cleaning, filtering, and transformation functions.

TensorFlow: TensorFlow is a Python library for fast numerical computing developed and released by Google. It is a foundation library that can be used to create Deep Learning models directly or via wrapper libraries built on top of TensorFlow.

Matplotlib: Matplotlib is a Python library that allows you to create static, animated, and interactive visualisations. Matplotlib makes simple things simple and difficult things possible. Make plots that are suitable for publication. Create interactive figures that can be zoomed, panned, and updated.

PyTorch: PyTorch is an optimised Deep Learning tensor library based on Python and Torch that is primarily used for GPU and CPU applications. PyTorch is preferred over other Deep Learning frameworks, such as TensorFlow and Keras, because it employs dynamic computation graphs and is entirely Pythonic.

Some Popular Python Modules

Python has a vast library of modules that can be used to perform various tasks. 

Below are some of the popular Python modules are:

• NumPy - for numerical computing and data analysis.
• Pandas - for data manipulation and analysis.
• Matplotlib - for creating data visualizations and plots.
• Scikit-learn - for machine learning algorithms and tools.
• TensorFlow - for building and training machine learning models.
• Keras - for building and training deep learning models.
• Flask - for building web applications.
• Django - for building web applications and APIs.
• Requests - for making HTTP requests.
• BeautifulSoup - for web scraping and parsing HTML/XML documents.
• Pygame - for game development.
• OpenCV - for computer vision tasks.
• Pillow - for image processing and manipulation.
• PyQT - for building desktop applications with GUIs.
• Pygame - for game development.
   

Also See, Fibonacci Series in Python

Features of Python

• Easy to Learn and Use
  Python has a simple and readable syntax, making it beginner-friendly. Its high-level nature allows developers to focus on problem-solving rather than syntax complexities.
   
• Interpreted Language
  Python is an interpreted language, meaning code execution happens line-by-line. This makes debugging easier but may slow down execution compared to compiled languages.
   
• Dynamically Typed
  Unlike statically typed languages, Python doesn’t require explicit type declarations. Variables can hold values of different types without defining them beforehand.
   
• Extensive Library Support
  Python has a rich standard library and third-party modules for various tasks, including web development, data science, machine learning, and automation.
   
• Platform-Independent
  Python code runs on multiple operating systems without modification, making it highly portable and suitable for cross-platform applications.
   
• Object-Oriented & Functional Programming
  Python supports multiple programming paradigms, including object-oriented, procedural, and functional programming, providing flexibility in coding styles.
   
• Automatic Memory Management
  Python uses garbage collection to manage memory allocation and deallocation automatically, reducing memory leaks and simplifying development.

Python vs. Other Programming Languages

Applications of Python 

Python is a versatile programming language that can be used for a wide range of applications such as machine learning, Artificial Intelligence, Scientific Computing, Automation and Scripting, Gaming, and many more. 

Below are some of the popular applications of Python that are being used on a daily basis: 

Machine Learning and Artificial Intelligence

• Definition: Machine Learning (ML) and Artificial Intelligence (AI) focus on building intelligent systems that can perform tasks without explicit programming.
   
• Python for ML & AI: Python is widely used in ML and AI due to its simplicity, flexibility, and extensive libraries.
   
• Machine Learning Overview: ML is a subset of AI that trains models on large datasets to make predictions or decisions.
   
• Types of ML Algorithms: ML includes supervised learning, unsupervised learning, and reinforcement learning.
   
• Popular Python Libraries: Python offers robust ML and AI libraries like TensorFlow, Keras, Scikit-learn, and PyTorch.

Web Scraping

• Definition: Web scraping is the automated process of extracting data from websites using software programs.
   
• Python for Web Scraping: Python is preferred for web scraping due to its ease of use, flexibility, and strong developer community.
   
• Data Types Scraped: Python can scrape text, images, tables, and other web data.
   
• Popular Libraries: Common Python tools for web scraping include BeautifulSoup, Scrapy, Selenium, and Requests.

Data Analysis

• Definition: Data analysis involves exploring and analyzing large datasets to derive meaningful insights and improve decision-making.
   
• Purpose: It helps answer questions, test hypotheses, and extract valuable information from data.
   
• Data Processing: The process includes collecting, cleaning, and transforming data for analysis.
   
• Python for Data Analysis: Python is widely used due to its powerful libraries and easy-to-read syntax.
   
• Popular Libraries: Python offers Pandas, NumPy, Matplotlib, and Seaborn for efficient data analysis.

Limitations of Python

• Limited Mobile App Development: Python has fewer tools and libraries for mobile development, making it less common for building mobile applications.
   
• Limited Low-Level Programming Support: As a high-level language, Python is not well-suited for low-level tasks like operating system or device driver development.
   
• Limited Compatibility: Python's evolution has led to backward compatibility issues, making transitions between versions challenging.

Advantages of Python

• Simplicity: Python has an easy-to-learn, intuitive syntax, making it ideal for beginners and experienced developers.
   
• Huge Library Collection: Python has an extensive set of standard libraries that simplify development by reducing the need to write code from scratch.
   
• Dynamic Nature: Python does not require variable declarations, enhancing flexibility.
   
• Versatility: Python is widely used in web development, data science, machine learning, and more.
   
• Large Community Support: Python has a vast and active developer community, ensuring solutions to problems are easily available online.

Disadvantages of Python

• Performance Issues: Python is slower than compiled languages like C or Java, and it is an interpreted language.
   
• Complexity in Advanced Topics: While Python is beginner-friendly, concepts like OOP and concurrency can be complex.
   
• Dependency Management: Handling third-party libraries and packages can be challenging in large projects.
   
• Debugging Challenges: Python’s dynamic typing and lack of strict type-checking can make debugging difficult.

Careers in Python

Python is a versatile language with many applications, so pursuing a career in Python can be a rewarding choice, as there are many job opportunities and a strong community to help you grow. 

Some common job titles for Python professionals include:

• Web Developer,
• Software Engineer,
• DevOps Engineer,
• Python Developer,
• Data Analyst/Scientist,
• Machine Learning Engineer
   

You can also check out Data Analyst vs Data Scientist here.

Tips and best practices for Python development

Below are some tips and best practices for Python developers: 

• Write simple code with descriptive names for variables and functions.
• Use a version control tool to track code changes and collaborate with others.
• Install and manage Python packages and dependencies with a package manager tool.
• Create code documentation to help others understand how to use it.
• Use a code editor or integrated development environment (IDE) to write and debug code more efficiently.
• Coding conventions are rules for writing code that make it more readable and consistent.
• Stick to coding conventions, which are rules for writing readable code. 
  Read more,   reverse a string in python

Frequently Asked Questions

Where is Python used?

Python is used extensively in a wide range of applications like web development, machine learning, artificial intelligence, data science, and various other fields.

What is Python and its features?

Python is an interpreted, object-oriented, high-level programming language known for its refined semantics. It supports a variety of programming paradigms, including imperative, functional, and object-oriented programming. It has many significant features: easy to learn, powerful, versatile, open source, portable, and extensible.

How to use Python for beginners?

To use Python, first of all, install it from the official website. And then install an IDE(Integrated Development Environment) for Python. Then, you are good to go to use Python. Finally, start learning Python from your trusted resources.

Conclusion

This article has covered everything you need to know about the Python programming language. We discussed the history of Python till its applications in the real programming world. You can use this article as a reference for quick revision. In the last, we also discussed some tips and practices to help you master the python programming language.