Welcome to the third blog post of our exciting Python series! If you’ve been following along, you already know that we’ve covered a brief introduction to Python and its various components. Now, get ready to elevate your Python skills to the next level as we explore the fascinating world of data types. This post will provide you with a sneak peek into the different data types in Python, and in the upcoming posts, we’ll deep-dive into each of them to master them like a pro. So, are you prepared to uncover the mysteries of Python data types? Let’s dive right in!
If you’re learning a modern programming language, you’ll definitely encounter the term “data types.” But what precisely does it mean, and why is understanding it so crucial? In simple terms, a data type classifies data, determining the operations that can be executed on it. Whether it’s numeric data or string data (words and sentences), data types delineate the nature of information and how a program can store, manipulate, and utilize it.
This implies that each data type, be it a string or a numeric value, possesses a distinct array of operations for manipulation and utilization. While each programming language boasts its own unique types of data, comprehending them remains pivotal for crafting efficient, error-free code. Whether you’re an experienced programmer or a novice, mastering the types of data is a foundational skill that empowers you to unleash your code’s full potential.
As previously mentioned, data classes hold a crucial role in programming, and Python follows suit. In Python, data class define the nature of data being utilized, be it numeric, string, boolean, or others.
Python adopts dynamic typing, automatically assigning data types according to stored values. For instance, if you assign “hello” to a variable, Python auto-assigns the data type as string.
Example:
To better understand this, look at the example that I have provided below:
In the provided example, variables x and y received values devoid of explicit data type specification. A numerical value and a string value were involved. To ascertain the data type of each variable, the type() function got employed. Calling this function on variable x yielded int, signifying x as an integer class of data. Likewise, invoking type() on variable y produced str, affirming y’s classification as a string data type.
Create a variable and assign a value 2,.41 to it, check the type of data using type() function and print the results
There are a total of 14 Data types in python. Now let us take a look at the data types in python one by one.
In Python, numeric types of data are used to represent numbers. There are three built-in numeric data classes: integers, floating-point numbers, and complex numbers.
Integers represent whole numbers, lacking a decimal point, whereas floating-point numbers include a decimal point. Complex numbers capture entities with both real and imaginary components.
Python facilitates various numerical bases—binary, octal, hexadecimal—and sustains arithmetic operations like addition, subtraction, multiplication, and division. Grasping distinct numeric data class and their operations is essential for executing mathematical computations and managing data within Python.
Here is an example program that demonstrates the three numeric data types in Python:
Exercise:
Write a python program to separate the real part and imaginary part of a complex data class like 2+3j and print the results separately.
Hint: you can use ‘.real’ and ‘.imag’ attributes of the complex data type to perform this.
This was a brief introduction to the numeric data types in Python.
4.String
In Python, a string is a sequence of characters that is used to represent text. It is a built-in data class that is denoted by enclosing the text in either single quotes (‘…’) or double quotes (“…”).
String is a very basic and important data type in python and we will be looking deep into it in our future posts.
For newcomers to programming, the concept of sequential type of data might raise questions. In essence, sequential data types encompass arrangements of items in a specific order. In Python, three primary types of sequential data exist, each possessing distinct attributes and applications.
The three main sequential data types in python are:
5.List
6.Tuple
7.Range
Python users frequently rely on lists, a widely employed data type. These collections, denoted by square brackets [], are mutable and orderly assortments of diverse data. Creating lists involves commas between items enclosed within square brackets. Lists facilitate indexing, slicing, and offer methods to insert, remove, and manipulate elements.
Note: In Python, mutability refers to the ability of an object to be modified after it has been created, while immutability refers to the inability of an object to be modified after it has been created.
Mutable objects are those that permit modification post-creation. Python’s mutable objects encompass lists, dictionaries, and sets.
Immutable objects are incapable of alteration after creation. Python’s immutable objects comprise numbers (integers, floats, complex), strings, and tuples.
Example: Given below is an example program on how to create a list:
Tuples resemble lists, yet they remain immutable, ensuring their content remains unaltered after creation. Created using parentheses (), tuples contain items separated by commas. Tuples are ideal for storing unchangeable data, like coordinates or image dimensions.
Example: Given below is an example program demonstrating how to create a tuple:
The range, a native data type, signifies an unmodifiable sequence of numbers. It’s frequently employed to produce numeric sequences for looping and other tasks. To create range objects, the range() function is used, requiring three arguments: start, stop, and step. Start indicates the initial number, stop indicates the last number (not encompassed), and step determines the interval between sequence numbers.
Mapping types, integral data classes in Python, embody key-value pair collections. Each key corresponds to a distinct value, enabling efficient data storage and retrieval through keys. The dictionary, often termed dict, is the predominant mapping type, while other instances encompass OrderedDict and defaultdict.
8. Dictionary
Ever pondered efficient data storage and retrieval in Python? Enter the ‘dict’ data type, a powerhouse for key-based operations. Dictionaries are mutable, unordered key-value pair collections, ideal for modeling real-world entities like user profiles and product catalogs. In today’s data-rich landscape, mastering Python’s dictionary type is crucial for any programmer.
Example:
Given Below is an example on how to create a dictionary data class in python.
Tired of duplicates in Python collections? Enter the set data class! It holds unique, unordered elements—ideal for deduplication and finding common items. Python also offers frozen sets, unchangeable versions. Amid data complexity, these types offer potent solutions for managing unique elements.
9. Set
10. Frozen Set
In Python, a set is an unordered collection of unique elements. Set elements must be immutable, ensuring they can’t change post-creation. Sets are great for tasks like deduplication, checking common elements, and set operations (union, intersection, difference). Create sets with {} or set(). Efficiently handling unique elements and set operations makes sets a valuable Python data type.
A frozen set in Python is akin to a set – an unordered collection of unique elements. Yet, unlike sets, frozen sets are immutable, barring modifications post-creation. Due to this immutability, they’re used as keys in dictionaries or elements in other sets. You can make frozen sets using the frozenset() constructor function. With their capacity to manage unique elements efficiently while remaining unalterable, frozen sets serve as a valuable Python data type, ideal for scenarios where element collections must remain unchanged after creation.
11. Bool
Python’s Boolean type is intrinsic, capable of adopting just two values: True and False. It symbolizes truth values in logical expressions, frequently employed for conditional statements and Boolean operations.
Boolean values are typically produced through comparisons or logical operations. For instance, “3 < 5” results in True, while “5 == 6” yields False.
Boolean values can also be crafted explicitly using the keywords True and False. E.g.,
Boolean values can be combined using logical operators such as and, or, and not. For example:
Boolean values are often used in control flow statements such as if and while to determine the flow of execution in a program. For example:
Exercise: Change the value of x in the above program for different types of data as see what happens
Binary types of data portray binary data in Python, composed of 0s and 1s, often applied for low-level data storage and transmission.
Python employs bytes and bytearray data types for binary data representation. bytes are immutable, unalterable after creation, while bytearrays are mutable, amendable post-creation. These types excel at encoding and decoding binary data like images, audio files, and network packets.
12. Bytes
13. Bytearray
Python employs the bytes data class to portray binary data, like file bytes, network packets, or cryptographic keys. Unlike strings, bytes represent actual data bytes.
A “bytes” object is an unchangeable byte sequence, with values ranging from 0 to 255. To create a bytes object, use the bytes() constructor with a sequence of integers denoting byte values.
The b prefix denotes a bytes object. You can also make a bytes object from a string by encoding it, like utf-8:
Bytes objects support operations like concatenation, slicing, and length checking. As they are immutable, direct modification is not possible.
In sum, the bytes data type is valuable for binary data handling in Python, particularly for low-level structures like files or network packets.
Python’s bytearray resembles the bytes data class but is mutable, enabling content modification.
A bytearray is an array-like sequence of bytes with values from 0 to 255. Create one with bytearray() and a sequence of integers representing byte values, as seen below:
Mutable bytearray objects can be altered using methods like append(), insert(), pop(), remove(), and reverse().
Performing operations like concatenation, slicing, and length checks on bytearray objects is also possible.
The bytearray data type is invaluable for handling mutable binary data, especially when modifications are required after creation.
In Python, “None” is a built-in data class that represents the absence of a value or the lack of a value. It is often used to indicate that a variable or object does not have a value assigned to it or that a function does not return a value.
14. None
The “None” type is a singleton object, which means that there is only one instance of it in the entire Python interpreter. This means that any variables or objects that are assigned the value “None” will reference the same object in memory.
Here is an example of how “None” can be used:
In the above example, we assign the value “None” to a variable called “my_variable”. We also define a function called “my_function” that does not return a value. When we call the function and store the result in a variable called “result”, we check if the result is “None” using the “is” keyword. If the result is “None”, we print a message indicating that the function did not return a value.
Congratulations on completing this blog post on data class in Python! You’ve learned about the basics of data types and how they are used in Python, but we’re not done yet. In our next blog, we’ll be taking a deep dive into the numerical data type and exploring how it can be used in a variety of applications.
So, stay tuned for more exciting content and get ready to expand your knowledge of Python programming. Thanks for reading and we hope to see you soon for our next post, and follow 1stepGrow.
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