Understanding the data types in Python is essential for writing efficient programs. Data types determine the type of value a variable can store, including a number, text, or lists. They help organize and process data effectively. They also ensure that the operations are performed correctly. In this article, we’ll explore various Python Data Types with Examples of how to use them effectively.
Table of Contents:
What are Python Data Types?
In Python, data types define a variable’s value, including numbers, strings, or sets. The most commonly used data types are integers(int), floating point numbers(float), string(str), list(list), and dictionary(dict). Python is known for its simplicity and powerful functionality. One of the important features of data type is dynamic typing, which allows variables to change their data type as needed. This flexibility makes Python highly versatile and easy to work with.
| Data Type |
Class |
Value |
| Numeric |
Int, float, complex |
Numeric value |
| String |
str |
Sequence of characters |
| Sequence |
List, tuple, range |
Collection of items |
| Mapping |
dict |
Data in key-value pair form |
| Set |
Set, frozen set |
Unordered, unique collection |
| Boolean |
bool |
Boolean value “True” or “False” |
Numeric Data Types in Python
Numeric data types in Python can hold numeric values such as integers, decimal numbers(floating numbers), and complex numbers.
- Integer: Integers are represented by the int class. It can contain positive and negative values. The size of an integer is not limited by a fixed maximum it can grow as long as memory permits
- Floating point: Floating point variables are represented by the float class. It can only hold decimal values.
- Complex Numbers: Complex numbers are represented by complex classes.
| Data Type |
Class |
Example |
| Integer |
int |
x = 10 |
| Floating Point |
float |
y = 10.5 |
| Complex Number |
complex |
z = 5 + 6j |
Example:
Output:
Sequence Data Types in Python
Sequence data types in Python are capable of representing ordered collections of items. They are iterable, support indexing and slicing, and provide multiple manipulation methods. These are the following sequence data types in Python:
| Data Type |
Class |
Example |
| String |
str |
str_example = “Hello, World!” |
| List |
list |
list_example = [1, 2, 3, “Python”] |
| Tuple |
tuple |
tuple_example = (10, 20, “AI”) |
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1. Python String Data Type
Python strings are a combination of letters or characters enclosed in single quotes or double quotes. In Python, there is no character data type. Here, character is also included in the string class. A string of length 1 can be called a character string. We can access individual characters with the help of an index. The str class represents it.
- Creation of String in Python:
In Python, a string can be created by placing characters or letters within either single quotes (‘ ‘) or double quotes (” “).
Example:
Output:
- Accessing Strings in Python
Individual characters within a string can be accessed using an index. Python uses zero-based indexing, meaning the first character is at index 0. Negative indexing can also be used to access the characters from the end of the string.
Example:
Output:
2. Python List Data Types
Lists in Python are created using a square bracket [ ]. They can hold various data types, such as numbers, text, or even other lists. Each entry within a list is separated by a comma. Lists allow you to combine multiple items into one collection and give easy access and modification as required.
- Creating a List in Python
Output:
- Access List Items in Python
We can access the list of items with the help of the index. In Python, negative indices are used for reverse traversal, like: a[-1] represents the first element from the end, a[-2] represents the second element from the end.
Example:
Output:
3. Python Tuple Data Type
Tuple data types are also the same as list data types in Python. The only difference between them is that tuples are immutable, which means we can not change the tuple elements after they are created. Tuples are defined using parentheses.
- Creating a Tuple in Python
In Python, tuples are created using parentheses with different types of values separated by commas.
Example:
Output:
- Access Tuple Items in Python
We can access tuple elements with the help of an index. With the help of the index and subscript operators, we can access the tuple elements easily.
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Example:
Output:
4. Python Range Data Type
In Python, range() is a sequence data type that gives an output for any series of numbers within a specified range. The range() becomes more efficient with the help of a loop.
Example:
Output:
Python Set Data Type
A set in Python is an unsorted (unordered) collection of elements that contains unique elements. They are unordered and do not allow duplicates. Sets are defined using curly brackets.
A set is created in Python with the set() function and an iterable object or list of objects separated by commas. Sets can also contain different types of data.
Example:
Output:
Set Items cannot be accessed with an index, we have to use a loop to iterate through the set items.
Example:
Output:
Python Dictionary Data Type
The dictionary in Python uses data types to store elements in key-value pairs like maps. An unordered collection of data values is stored in the dictionary in the form of key-value pairs. In the key-value pair format, the key values are separated from each other by a colon.
- Create a Dictionary in Python
In Python, a dictionary can be built using the built-in function dict(). Any object can be used to take the values provided in the dictionary. Duplicates are allowed in a dictionary, but the keys must be unique. The keys of the dictionary are case-sensitive.
Example:
Output:
- Access Dictionary Values Using Keys in Python
The values in the dictionary can be accessed using their keys. The built-in function get() can be used to access the values.
Example:
Output:
Boolean Data Type in Python
Python also provides one built-in data type, Boolean, that gives us two values, True and False. It can take the value of either True or False and is implemented by the class bool. The true expressions will be returned as True by Python, while expressions that are not true will be returned as False.
Example:
Output:
None Data Type in Python
None is a data type that represents null, which is commonly used to indicate that a variable does not contain a value.
Example:
Complex Data Types in Python
Complex data types can be defined as a combination of integers, strings, lists, and dictionaries joined together.
Python allows you to build more structured data using nested data structures such as a list inside a dictionary or a dictionary inside another dictionary. These complex types help store and manage information efficiently in Python.
Example: Storing Information in a Nested Dictionary
Output:
Mutable vs Immutable Data Types in Python
| Feature |
Mutable Data Type |
Immutable Data Type |
| Definition |
Can be changed even after the creation |
Cannot be changed once created |
| Data Types |
Lists, Dictionaries, Sets |
Strings, Tuples, Numbers |
| Memory Usage |
Can change memory location if resized |
Stays in the same memory location even after resizing |
| Modification |
Supports add, remove, and update |
Need to create a new object to make the changes |
| Performance |
Slightly slower due to modification |
Faster as data remains constant |
| Methods Available |
Methods like append(), remove() modify the data |
Methods like replace() return a new object |
| Example |
intellipaat_list = [1, 2, 3]
intellipaat_list.append(4)
print(intellipaat_list)
# Output: [1, 2, 3, 4] |
intellipaat_tuple = (1, 2, 3)
intellipaat_tuple[0] = 5
# Output: TypeError: ‘tuple’ object does not support item assignment |
Using isinstance() to Check Data Types in Python
The isinstance() function verifies a specific type of variable. Types in Python int, float, str, list, and tuple, are supported. Sometimes it is necessary to check what type the variable it is before you go further in your Python program. The isinstance() method checks whether an object is an instance of a specified class or type, helping the programmer avoid errors in the code.
Example: Checking Data Types with isinstance()
Output:
Type Conversion in Python
Type conversion in Python is used to change a variable from one data type into another.
If you want to do some operations on specific types of data, you may need to convert data from one type to another to perform specific operations. Python makes type conversion simple and easy to accomplish by providing built-in functions like int(), float(), str(), list(), tuple(), and much more. Type conversion makes data types accessible to each other in your Python program.
Example: Converting Different Data Types
Output:
Why Check Data Types in Python?
- Ensures Correct Operations: Certain operations require a specific type of data to be used.
- Prevents Errors: Knowing the data type helps avoid type mismatch errors during the execution of the program.
- Debugging: Checking the data type makes it easier to trace errors when your program gives unexpected output.
- Improves Code Readability: Knowing the type of data gives an idea about the purpose of the variable.
- Optimizes Performance: A proper check of the data type makes it possible for the program to execute with ease and efficiency, without producing unnecessary errors.
Python Data Type Exercise Questions
Here are some of the practice questions on Data Types in Python that you should prefer for revising the concept learned in this article.
1. Implement Basic Dictionary Operations in Python
Output:
2. Implement Basic List Operations in Python
Output:
3. Implement Basic Set Operations in Python
Output:
4. Implement Basic Tuple Operations in Python
Output:
5. Implement Basic String Operations in Python
Output:
Conclusion
Python offers a wide range of data types such as int, float, string, list, and tuple. Each serves a unique purpose in your code. Understanding Python data types and dynamic typing is key to writing efficient, clean, and optimized programs. With its simple syntax and flexible features, Python is ideal for both beginners and experienced programmers.
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