data types in python

Understanding Data Types of Python Programming Language

Python is a forerunner in the technical domain, and the reason behind its success is its vast community. These communities are super active, trying their best to keep the language simple and updated, making learning easy. Python offers ease to the developers with the fast development speed of coding and readability. All this is possible because of the different data types of the Python programming language.

Libraries, modules, and functions are the ones that make Python multifaceted. Besides, there are basic Python data types that make a difference in the language’s design. With this article, we are about to shed light on some of the prominent built-in data types.

7 Data Types of Python Programming Language


Numeric

The Numeric data types in Python comprise integers, floating type numbers aka floats, and complex numbers.

  • Integer includes positive, negative, or zero. For example, 11, 4, -10, -100, etc. There’s no restriction on the length of the integer.
  • Floats are real numbers usually depicted in decimal form like 2.2, 10.9, etc.
  • Complex numbers include real as well as imaginary elements like a + by where a and by could be imaginary and real parts. Complex numbers could be 1.15k, 3.0 + 2.5j, etc.

Amid this, I’m wondering how to check data types in Python? Then, there’s a facility of the type() function to determine the type.
For Example:

Creating a variable with integer value

d=250

print(“The variable type”, d, “is”, type(x))

Creating a variable with float value

e=10.2356

print(“The variable type”, e, “is”, type(y))

Create a variable with complex value

f=120+6j

print(“The variable type”, f, “is”, type(f))

Now the final output would be,

  • The variable type 250 is <class ‘int’>
  • The variable type 10.2356 is <class ‘float’>
  • The variable type 120+6j is <class ‘complex’>

2. List

The sequenced items in Python are called List. Interestingly this data type of Python programming language is flexible as the List doesn’t need to be of the same data type. Due to this adaptability, it is extensively used in Python. Most importantly, in a List, data is written in a sequence using square brackets ([]) and commas (,). Let’s go through the example below for a better understanding.

If there is a list of integers

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

print(d)

If there is a list of strings

e=[“Cyber”, “Success”]

print(e)

List comprising of both strings and number

f=[“Cyber”,9,8,7, “Success”]

print(f)

Now the output will be,

  • [6,5,4,3,2,1]
  • [‘Cyber’, ‘Success’]
  • [‘Cyber’,9,8,7, ‘Success’]

3. Strings

A String comprises a sequence of Unicode characters. They are a collection of characters, either single or more, represented using single, double, or triple quotes. Alongside this, Strings are absolute. Once you declare them, they cannot be updated or deleted. If you try doing any of this, it will lead to an error. However, you can put as many as characters you want, but the limitation comes from the memory resources of your machines.

For example,

String1 = “Goodbye”

String2 = “To Emma”

print(String1+String2)

Now the output will be,

– Goodbye To Emma

This infers the joining of two Strings together.

4. Tuple

A Tuple is similar to a List data type of Python programming language. The only difference between List and Tuple is that the latter is immutable. This feature makes it impossible to alter Tuples. However, this characteristic makes them faster than the List data type in Python. Due to this, they are used to write-protected data. Tuples are represented by using parentheses (), while commas are used to differentiate the items.

For example,

Tuple with integer type of data

d=(19,20,34)

print(d)

Tuple with multiple types of data

e=(“baby”, 6,7,4,“hello”)

print(e)

Printing single element in a tuple

print(d[2]) 

The output of this example is,

  • (19,20,34)
  • (‘baby’,6,7,4,‘hello’)
  • 20

5. Set

Some unique items that are not in sequence are called a Set. In this data type of Python programming language, braces {} define Set, and a comma separates the value. There is no particular order in Set data type, but it eliminates duplicates as it maintains unique values. Alongside this, you can perform various operations on two sets like intersection and union.

For example,

Create a set using string

set1=set(“CyberSuccess”)

print(set1)

Creating a set using list

set2 = set([“Cyber”, “Success”, “Cyber”])

print(set2)

Creating a set using a mixture of all values

set3 = set([8, 2, ‘Cyber’, 2, ‘Success’])

print(set3)

The output will be

  • {‘Y’, ‘C’, ‘E’, ‘U’, ‘C’, ‘E’, ‘C’, ‘S’, ‘S’, ‘B’, ‘R’, ‘S’}
  • {‘Cyber’, ‘Success’}
  • {‘Cyber’, 2, ‘Success’, 8, 2}

6. Dictionary

Dictionary is one of the most adaptable built-in Python data types where collections are unordered. Besides, the values are in pairs called key-value pairs. Dictionaries are a tad problematic to understand, yet it is primarily used for saving an enormous volume of data. However, to recover the value, one must know the key.

Dictionaries are defined using braces {} curly brackets. The syntax for this data type is key: value and these entities can be of any data type.

For example,

Creating a Dictionary with integer keys

Dict1 = {2: ‘Cyber’, 5: ‘Success’}

print(Dict1)

Creating a dictionary using mixed keys

Dict2 = {‘Cyber’: ‘Success’, 3: [10, 20, 30, 40]}

print(Dict2)

Creating a dictionary with the dict() method

Dict3 = dict({10: ‘Cyber’, 4: ‘Success’})

print(Dict3)

The output would be 

  • {2: ‘Cyber’, 5: ‘Success’}
  • {3: [10, 20, 30, 44], ‘Cyber’: ‘Success’}
  • {10: ‘Cyber’, 4: ‘Success’}

7. Boolean

When it comes to the Boolean data type of Python, it has only two values – true or false. Objects that are equal to true in the Boolean are known as Truthy, and false objects are tagged as Falsy.

So, these were some of the prominent data types of the Python programming language. If you wish to know how many data types in Python are prominent or what is the size of data types in Python; then you must enroll in Best Python classes near me. If you are wondering where to enroll, then keep reading!  

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