Data Types in Python - 8 Data Types in Python With Examples

When starting with Python, it's important to understand the data types in Python like numbers, strings, lists, and dictionaries. These data types in Python help you store and manage real-life information such as names, steps walked, or expenses.

For example, if you want to count how many steps you walked today, you can use an integer type (int). If you want to store your name, Python uses a string (str). To keep track of your daily expenses, you might use a list to store multiple numbers. And if you want to store a person's details like name, age, and city, a dictionary (dict) can help you organize that information neatly.

In this Python tutorial, we’ll explore the most commonly used data types in Python and provide practical insights into their applications.

What are the Data Types in Python?

The data type in Python tells us what kind of value a variable in Python can hold. They help the Python interpreter understand how to handle and process the data.Using the correct data types in Python makes your code more accurate and easier to work with.

Key Features of Data Types in Python

• Dynamically Typed: You don’t need to declare the data type of a variable. Python automatically understands it when you assign a value.

• Built-in Data Types: Python provides many built-in data types, such as int, float, str, bool, list, tuple, dict, and set.

• Flexible and Easy to Use: You can easily switch between data types using built-in functions like int(), str(), or list().

• Supports Type Conversion: Python allows changing the data type of a value (for example, from string to integer) using type casting.

• Memory Efficient: Python manages memory for data types automatically, so you don’t have to worry about memory allocation.

• Object-Oriented: All data types in Python are treated as objects, which means they come with built-in functions and properties.

Types of Data Types in Python

Understanding the different data types in Python is important for writing correct and flexible code. This section will explain the different types of data types in Python, how they work, and where to use each type in real programs.

1. Numeric Data Types in Python

When you work with numbers in Python, you use numeric data types in Python. They let you handle different types of numeric values like whole numbers, decimals, or even complex numbers. Let's learn the numeric data types in Python:

1. int: Used for whole numbers. These are signed integers like 10, -5, or 0.
2. long: Python 2 supports long integers, which can represent vast numbers, including octal and hexadecimal. (In Python 3, all integers are of type int.)
3. float: Used for decimal numbers, like 3.14 or -0.99.
4. complex: Represents numbers with a real and imaginary part, like 3+4j.

Example

# integer variable.
a=150
print("The type of variable having value", a, " is ", type(a))
# float variable.
b=20.846
print("The type of variable having value", b, " is ", type(b))
# complex variable.
c=18+3j
print("The type of variable having value", c, " is ", type(c))

Output

 The type of variable having value 150 is <class 'int'>
 The type of variable having value 20.846 is <class 'float'>
 The type of variable having value (18+3j) is <class 'complex'>

Explanation

• The program defines three variables: a, b, and c, with integer, float, and complex values respectively.
• The type() function is used to determine the data type of each variable.
• It prints the variable's value and its corresponding data type to the console.
• This helps in understanding how Python recognizes and categorizes different types of data.

2. Python String Data Type

If you’re working with text in Python, you’ll use the string data type in Python. A string is a collection of characters enclosed in single, double, or even triple quotes. It’s one of the most commonly used data types in Python, and it’s excellent how Python makes handling text so easy!

Key Features of Python Strings

• Immutable: Once you create a string, you can’t change its content.
• Multi-line Support: Use triple quotes (''' or """) for strings that span multiple lines.
• Indexing and Slicing: Access individual characters or parts of the string using indices.
• String Methods: Python offers built-in methods like lower(), upper(), and replace() for string manipulation.

Example

str = 'Hello World!'
print (str) # Prints complete string
print (str[0]) # Prints first character of the string
print (str[2:5]) # Prints characters starting from 3rd to 5th
print (str[2:]) # Prints string stating from 3rd character
print (str * 2) # Prints string two times
print (str + "TEST") # Prints concatenated string

Output

 Hello World!
 H
 llo
 llo World!
 Hello World!Hello World!
 Hello World!TEST

Explanation

• The program demonstrates string operations in Python.
• It shows how to print the complete string, access specific characters, extract substrings using slicing in Python, repeat the string, and concatenate it with another string using the string in Python.

3. Python List Data Type

When you need to store multiple items in a single variable, you’ll use the list data type in Python. A list can hold items of different data types in Python, and you can easily modify, add, or remove elements. Isn’t that flexible? For more details, you can explore how to work with lists in Python.

Key Features of Python Lists

• Ordered: Lists maintain the order of items as you add them.
• Mutable: You can modify, add, or remove items after creating the list.
• Supports Multiple Data Types: A single list can hold integers, strings, and even other lists.
• Built-in Methods: To manipulate lists, use methods like append(), remove(), and sort().

Example

list = [ 'abcd', 786 , 2.23, 'Scholar-Hat', 70.2 ]
tinylist = [123, 'Scholar-Hat']
print (list) # Prints complete list
print (list[0]) # Prints first element of the list
print (list[1:3]) # Prints elements starting from 2nd till 3rd 
print (list[2:]) # Prints elements starting from 3rd element
print (tinylist * 2) # Prints list two times
print (list + tinylist) # Prints concatenated lists

Output

 ['abcd', 786, 2.23, 'Scholar-Hat', 70.2]
 abcd
 [786, 2.23]
 [2.23, ‘Scholar-Hat', 70.2]
 [123, 'Scholar-Hat', 123, 'john']
 ['abcd', 786, 2.23, 'Scholar-Hat', 70.2, 123, 'Scholar-Hat']

Explanation

• This program demonstrates list operations in Python, such as printing the entire list, accessing specific elements, slicing the list, repeating it, and concatenating two lists.
• It uses two lists,list and finalist, and performs various operations like slicing and concatenation.

4. Python Tuple Data Type

A tuple is one of the data types in Python that represents an ordered list of elements, just like a list. However, unlike lists, tuples are immutable, meaning that once created, they cannot be changed. Isn’t it great to have an ordered, unchangeable collection of items?

• As one of the commonly used data types in Python, tuples are stored using () parentheses, and you can access items in a tuple using their index, just like with lists.

Key Features of Python Tuples

• Ordered: Tuples maintain the order of elements as you add them.
• Immutable: Once created, you cannot modify, add, or remove items.
• Supports Multiple Data Types: A tuple can hold items of different data types, like integers, strings, or even other tuples.
• Indexing: You can access elements by their index number, similar to how you use indexing with lists.

Example

tuple = ( 'abcd', 786 , 2.23, 'Scholar-Hat', 70.2 )
tinytuple = (123, 'Scholar-Hat')
print (tuple) # Prints the complete tuple
print (tuple[0]) # Prints first element of the tuple
print (tuple[1:3]) # Prints elements of the tuple starting from 2nd till 3rd 
print (tuple[2:]) # Prints elements of the tuple starting from 3rd element
print (tinytuple * 2) # Prints the contents of the tuple twice
print (tuple + tinytuple) # Prints concatenated tuples

Output

 ('abcd', 786, 2.23, 'Scholar-Hat', 70.2)
 abcd
 (786, 2.23)
 (2.23, 'Scholar-Hat', 70.2)
 (123, ‘Scholar-Hat', 123, 'Scholar-Hat')
 ('abcd', 786, 2.23, 'Scholar-Hat', 70.2, 123, 'Scholar-Hat')

Explanation

• The program defines two tuples, tuple, and tinytuple, containing mixed data types.
• Considering both tuples, we just perform the operations on tuple data types in Python.

5. Python Range Data Type

The range data type in Python represents a sequence of numbers commonly used in loops in Python. It generates numbers starting from a given value up to, but not including, another value. As one of the useful data types in Python, it’s very convenient when you need to iterate over a specific range of values without manually creating a list.

Key Features of Python Range

• Efficient: It generates numbers on the fly, which is memory efficient compared to lists.
• Supports Indexing: You can access individual elements of a range using indexing.
• Customizable: You can specify the start, stop, and step values when creating a range.
• Used in Loops: Commonly used in Python for loops to iterate over a sequence of numbers.

Example

for i in range(1, 5):
print(i)

Output

 1
 2
 3
 4

6. Python Dictionary Data Type

A dictionary data type in Python is a collection of key-value pairs. It allows you to store data in a way that associates a unique key with a corresponding value. As one of the most powerful data types in Python, it’s beneficial to look up data based on a unique identifier quickly.

Key Features of Python Dictionaries

• Unordered: The items in a dictionary are not stored in any particular order.
• Mutable: You can modify, add, or remove key-value pairs after the dictionary is created.
• Unique Keys: Each key in a dictionary is unique, and it maps to a specific value.
• Efficient Lookup: You can quickly retrieve the value associated with a key.

Example

dict = {}
dict['one'] = "This is one"
dict[2] = "This is two"
tinydict = {'name': 'Scholar-Hat','code':6734, 'dept': 'sales'}
print (dict['one']) # Prints value for 'one' key
print (dict[2]) # Prints value for 2 key
print (tinydict) # Prints complete dictionary
print (tinydict.keys()) # Prints all the keys
print (tinydict.values()) # Prints all the values

Output

 This is one
 This is two
 {'name': 'Scholar-Hat', 'code': 6734, 'dept': 'sales'}
 dict_keys(['name', 'code', 'dept'])
 dict_values(['Scholar-Hat', 6734, 'sales'])

Explanation

• This program creates two dictionaries, dict, and tinydict, with key-value pairs.
• And we did print operations on these dictionary data types in Python.

7. Python Boolean Data Type

The boolean data type in Python represents one of two possible values: True or False. It is one of the most important data types in Python, especially for conditional statements, where decisions are made based on whether a condition is true or false. As a part of the core data types in Python, it plays a key role in controlling the flow of a program.

Key Features of Python Booleans

• True or False: The two boolean values are True and False.
• Conditional Logic: Used extensively in if and while statements to control program flow.
• Result of Comparisons: Comparisons between values often result in a boolean value.
• Logical Operations: You can use logical operators in Python like and, or, and not with boolean values.

Example

a = True
# display the value of a
print(a)

# display the data type of a
print(type(a))

Output

true
<class 'bool'>

Explanation

• This program defines a variable a and assigns it the boolean value True.
• print(a): Displays the value of a, which is True.
• print(type(a)): Displays the data type of a, which is <class 'bool'>.

8. Python Set Data Type

A set is one of the data types in Python that stores an unordered collection of unique elements. It’s a very handy data type in Python when you need to store values without duplicates and perform operations like union, intersection, and difference. Among the many data types in Python, sets are efficient when you want each element to be unique and easy to work with.

Key Features of Python Sets

• Unordered: Sets do not maintain any order of elements.
• Unique Elements: A set automatically removes duplicate values, so each element is unique.
• Mutable: You can add or remove elements from a set after it is created.
• Supports Set Operations: Python sets support operations like union(), intersection(), and difference().

Example of Set Data Type in Python

        my_set = {1, 2, 3, 4, 5}
print(my_set)

Output

 {1, 2, 3, 4, 5}

Explanation

• This program defines a set my_set containing the elements 1, 2, 3, 4, 5.
• print(my_set): Displays the content of the set my_set.
• Since sets are unordered collections, the elements might appear in any order when printed.

Data Type Conversion Function in Python

The data types in Python can be changed from one type to another using built-in conversion functions. This process is called type casting or type conversion. For example, you can convert an integer to a string using str() or a float to an integer using int().

Reasons why data type conversion is helpful in Python:

• It helps you work with different data types in Python without errors.
• It makes your code more flexible and reusable by handling different input types.
• It is essential when reading data from files, user input, or performing calculations.
• Understanding this helps you switch between various data types in Python easily.

Example

a = str(1)      # a will be "1"
b = str(2.2)    # b will be "2.2"
c = str("3.3")  # c will be "3.3"
print (a)
print (b)
print (c)

Output

 1
 2.2
 3.3

Explanation

• This program converts different data types into strings using the str() function.

How do You check the data type in Python?

To check the data types in Python, you can use the built-in type() function. It tells you what kind of data types in Python a value or variable has. This is very helpful when working with different data types in Python, especially when you're not sure what kind of data you're dealing with.

Example:

value = 10
print(type(value))

Output

 <class 'int'>

Explanation

• This code creates a variable value with the number 10 and uses print(type(value)) to display its data type, which is <class 'int'> (an integer).

Conclusion

Now you’ve learned the basics of data types in Python, and that’s a big step for writing better code. Each data type is helpful in its way, and knowing how to use them makes your work easier. Keep practicing with different data types in Python, and don’t worry if you make mistakes; that’s how you learn. Just enjoy the process and keep exploring more about data types in Python as you grow in your Python journey.

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