A Comprehensive Guide on Python Data Types with Examples

Think of Python as the Swiss Army knife of programming languages, versatile and multifaceted. Its importance is akin to the backbone of tech wizardry, seamlessly woven into web development, data analysis, artificial intelligence, and more. Python isn’t just a language; it’s a powerhouse that empowers everyone, from coding enthusiasts to seasoned developers. Now, let’s talk about its types, the building blocks that make Python a programming marvel. From traditional numeric types like integers and floats to the enigmatic complexities of sets, dictionaries, and strings, Python’s got it all. It’s not just a language; it’s an ecosystem of possibilities waiting to be explored.

So, whether you’re scripting a website, crunching numbers, or diving into the world of machine learning, Python is your trusty companion, making the complex seem as easy as Sunday morning brunch.

What is Python?

Python, a versatile and powerful programming language, is widely used in various fields, including web development, data science, artificial intelligence, and more. One of the key features that make Python so flexible is its robust support for different data types.

Understanding Python data types is fundamental to writing efficient and effective code. In this comprehensive guide, you will explore the various data types in Python, providing detailed explanations and examples to help you grasp their usage.

In programming, data types are classifications that specify which type of value a variable can hold. Python, being a dynamically typed language, allows the interpreter to assign data types during runtime. This flexibility simplifies coding but also demands a clear understanding of the available data types.

Common Data Types in Python 

Python supports several built-in data types, categorised into the following:

• Numeric 
• Sequence
• Set 
• Dictionary 
• Boolean 

Data Types in Python with Examples

Numeric Types

Numeric Python types refer to the fundamental data types in Python that handle numerical values. These types play a crucial role in mathematical and scientific computations, making Python a powerful language for data analysis, machine learning, and scientific research.

Integer (int)

The integer class represents this value, encompassing both positive and negative whole numbers without fractions or decimals. In Python, there exists no restriction on the length of an integer value, allowing for limitless magnitude.

Float (float)

The float class represents this value, denoting a real number with a floating-point representation characterised by a decimal point. Optionally, one can append the character ‘e’ or ‘E’ followed by a positive or negative integer to indicate scientific notation.

• Complex (complex)
  

  The complex class represents a complex number specified in the form of (real part) + (imaginary part)j. An illustrative example is 2 + 3j.

  For example: 

  This code demonstrates how to determine the data type of variables in Python using the type() function. It prints the data types of three variables: a (integer), b (float), and c (complex). The output shows the respective data types for each variable.

  Python

  a = 5

  print(“Type of a: “, type(a)) 

  b = 5.0

  print(“nType of b: “, type(b)) 

    

  c = 2 + 4j

  print(“nType of c: “, type(c))

   

  Output:

  Type of a:  <class ‘int’>

  Type of b:  <class ‘float’>

  Type of c:  <class ‘complex’>

   

Sequence Types

The sequence data type in Python refers to an ordered collection of either similar or different data types. Sequences provide a structured and efficient means of storing multiple values.

 

• List (list)
  

  Lists are mutable sequences, allowing the modification of elements. Here’s an example that demonstrates the use of a list to store and manipulate a sequence of numbers:

  # Creating a list of numbers

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

  # Accessing elements in the list

  first_element = numbers[0]

  second_element = numbers[1]

  # Modifying elements in the list

  numbers[2] = 10

   

  # Adding elements to the list

  numbers.append(6)

  # Removing elements from the list

  removed_element = numbers.pop(3)

  # Iterating through the list

  for num in numbers:

      print(num)

  # Checking if an element is in the list

  is_present = 7 in numbers

  # Finding the length of the list

  list_length = len(numbers)

  # Slicing the list

  subset = numbers[1:4]

  # Concatenating two lists

  other_numbers = [7, 8, 9]

  combined_list = numbers + other_numbers

  # Sorting the list

  numbers.sort()

  # Reversing the list

  numbers.reverse()

  # Clearing all elements from the list

  numbers.clear()

   

  # Printing the results

  print(“List after modifications:”, numbers)

  print(“Removed element:”, removed_element)

  print(“Is 7 present in the list?”, is_present)

  print(“Length of the list:”, list_length)

  print(“Subset of the list:”, subset)

  print(“Combined list:”, combined_list)

  This example covers various operations on a list, including accessing elements, modifying the list, adding and removing elements, iterating through the list, checking membership, finding the length, slicing, concatenating, sorting, reversing, and clearing the list.

   

• Tuple (tuple)

   

  Tuples are immutable sequences, and once created, their elements cannot be changed.  Here’s an example demonstrating the use of a tuple:

  Examples:

  # Creating a tuple

  fruits_tuple = (‘apple’, ‘banana’, ‘orange’, ‘grape’)

  # Accessing elements in the tuple

  first_fruit = fruits_tuple[0]

  second_fruit = fruits_tuple[1]

  # Iterating through the tuple

  for fruit in fruits_tuple:

      print(fruit)

  # Checking if an element is in the tuple

  is_present = ‘kiwi’ in fruits_tuple

  # Finding the length of the tuple

  tuple_length = len(fruits_tuple)

  # Slicing the tuple

  subset = fruits_tuple[1:3]

  # Concatenating two tuples

  other_fruits_tuple = (‘pear’, ‘melon’)

  combined_tuple = fruits_tuple + other_fruits_tuple

  # Multiplying the tuple

  repeated_tuple = fruits_tuple * 2

  # Nested tuple

  nested_tuple = (‘red’, (‘green’, ‘blue’), ‘yellow’)

  # Printing the results

  print(“First fruit:”, first_fruit)

  print(“Is ‘kiwi’ present in the tuple?”, is_present)

  print(“Length of the tuple:”, tuple_length)

  print(“Subset of the tuple:”, subset)

  print(“Combined tuple:”, combined_tuple)

  print(“Repeated tuple:”, repeated_tuple)

  print(“Nested tuple:”, nested_tuple)

  This example covers various operations on a tuple, including accessing elements, iterating through the tuple, checking membership, finding the length, slicing, concatenating, multiplying, and using nested tuples. Note that unlike lists, tuples are immutable, meaning their elements cannot be modified after creation.

   

• String (str)

   

  Strings are not only a sequence type but also serve as the primary text type in Python.

  Examples:

  # Creating a string

  message = “Hello, Python!”

  # Accessing characters in the string

  first_char = message[0]

  second_char = message[7]

  # Iterating through the string

  for char in message:

      print(char, end=’ ‘)

  # Concatenating strings

  greeting = “Hello, “

  name = “Alice”

  full_greeting = greeting + name + “!”

  # Finding the length of the string

  message_length = len(message)

  # Checking if a substring is present in the string

  is_present = “Python” in message

  # String methods

  uppercase_message = message.upper()

  lowercase_message = message.lower()

  capitalized_message = message.capitalize()

  # String formatting

  formatted_message = “My name is {} and I am {} years old.”.format(“Bob”, 25)

  # String slicing

  substring = message[7:13]

  # Repeating a string

  repeated_message = message * 3

  # Escaping special characters

  escaped_string = “This is a newline character:nSee?”

  # Printing the results

  print(“nFirst character:”, first_char)

  print(“Concatenated greeting:”, full_greeting)

  print(“Length of the string:”, message_length)

  print(“Is ‘Python’ present in the string?”, is_present)

  print(“Uppercase:”, uppercase_message)

  print(“Lowercase:”, lowercase_message)

  print(“Capitalized:”, capitalized_message)

  print(“Formatted string:”, formatted_message)

  print(“Substring:”, substring)

  print(“Repeated string:”, repeated_message)

  print(“Escaped string:”, escaped_string)

  # Creating a string

  message = “Hello, Python!”

  # Accessing characters in the string

  first_char = message[0]

  second_char = message[7]

  # Iterating through the string

  for char in message:

      print(char, end=’ ‘)

  # Concatenating strings

  greeting = “Hello, “

  name = “Alice”

  full_greeting = greeting + name + “!”

  # Finding the length of the string

  message_length = len(message)

   

  # Checking if a substring is present in the string

  is_present = “Python” in message

  # String methods

  uppercase_message = message.upper()

  lowercase_message = message.lower()

  capitalized_message = message.capitalize()

  # String formatting

  formatted_message = “My name is {} and I am {} years old.”.format(“Bob”, 25)

  # String slicing

  substring = message[7:13]

  # Repeating a string

  repeated_message = message * 3

  # Escaping special characters

  escaped_string = “This is a newline character:nSee?”

  # Printing the results

  print(“nFirst character:”, first_char)

  print(“Concatenated greeting:”, full_greeting)

  print(“Length of the string:”, message_length)

  print(“Is ‘Python’ present in the string?”, is_present)

  print(“Uppercase:”, uppercase_message)

  print(“Lowercase:”, lowercase_message)

  print(“Capitalized:”, capitalized_message)

  print(“Formatted string:”, formatted_message)

  print(“Substring:”, substring)

  print(“Repeated string:”, repeated_message)

  print(“Escaped string:”, escaped_string)

   

• Set Types
  

  In Python, a set is a versatile data type representing an unordered collection that is both iterable and mutable while also ensuring the absence of duplicate elements. Sets provide a flexible and efficient way to manage unique values without concern for their order, as the arrangement of elements within a set is undefined. A set can encompass various data types, making it a dynamic container for distinct elements. 

   

  Example:

   

  # create a set named student_id

  student_id = {112, 114, 116, 118, 115}

   

  # display student_id elements

  print(student_id)

   

  # display type of student_id

  print(type(student_id))

  Run Code

  Output

   

  {112, 114, 115, 116, 118}

  <class ‘set’>

   

Dictionary Types

 

In Python, a dictionary is a dynamic and versatile data type that serves as an ordered collection of items. Unlike other sequential data structures, a Python dictionary stores elements in key/value pairs, providing a flexible and efficient means of organising and retrieving information

 

Example:

# create a dictionary named capital_city

capital_city = {‘Nepal’: ‘Kathmandu’, ‘Italy’: ‘Rome’, ‘England’: ‘London’}

 

print(capital_city)

Output

{‘Nepal’: ‘Kathmandu’, ‘Italy’: ‘Rome’, ‘England’: ‘London’}

In the above example, we have created a dictionary named capital_city. Here,

• Keys are ‘Nepal’, ‘Italy’, ‘England’
• Values are ‘Kathmandu’, ‘Rome’, ‘London’

 

• Boolean

   

  The Boolean type in Python has two default values: True and False. These values play a crucial role in evaluating the truthfulness or falsity of a given statement. This principle is illustrated in the programming language’s documentation. The value False can be denoted by either 0 or the letter “F,” whereas True can be represented by any non-zero value.

   

  For example:

   

  # Python program to check the boolean type  

  print(type(True))  

  print(type(False))  

  print(false)  

   

  Output:

  <class ‘bool’>

  <class ‘bool’>

  NameError: name ‘false’ is not defined

   

   

In A Nutshell

Python’s rich variety of data types empowers developers with the flexibility and efficiency needed to tackle a broad spectrum of programming challenges. The availability of diverse data structures, such as lists, tuples, strings, dictionaries, and more, equips programmers with versatile tools to handle and manipulate information. Today, Python is a language of immense significance across various industries, from web development to artificial intelligence and data science. Its popularity lies not only in its simplicity and readability but also in the robustness of its data types, enabling the creation of scalable and efficient solutions. As the demand for Python expertise continues to soar, gaining proficiency in Python’s data types is a valuable investment for anyone entering the field of programming.

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