Python Data Types

Every value has a datatype, and variables can hold values. Python is a powerfully composed language; consequently, we don’t have to characterize the sort of variable while announcing it. The interpreter binds the value implicitly to its type.

a = 5  

We did not specify the type of the variable a, which has the value five from an integer. The Python interpreter will automatically interpret the variable as an integer.

We can verify the type of the program-used variable thanks to Python. The type() function in Python returns the type of the passed variable.

Consider the following illustration when defining and verifying the values of various data types.

a=10  

b="Hi Python"  

c = 10.5  

print(type(a))  

print(type(b))  

print(type(c))

Output:<type ‘int’> <type ‘str’> <type ‘float’>

Standard data types

A variable can contain a variety of values. On the other hand, a person’s id must be stored as an integer, while their name must be stored as a string.

The storage method for each of the standard data types that Python provides is specified by Python. The following is a list of the Python-defined data types.

1. Numbers
2. Sequence Type
3. Boolean
4. Set
5. Dictionary

The data types will be briefly discussed in this tutorial section. We will talk about every single one of them exhaustively later in this instructional exercise.

Numbers

Numeric values are stored in numbers. The whole number, float, and complex qualities have a place with a Python Numbers datatype. Python offers the type() function to determine a variable’s data type. The instance () capability is utilized to check whether an item has a place with a specific class.

a = 5  

print("The type of a", type(a))  

  

b = 40.5  

print("The type of b", type(b))  

  

c = 1+3j  

print("The type of c", type(c))  

print(" c is a complex number", isinstance(1+3j,complex))

Output:The type of a <class ‘int’> The type of b <class ‘float’> The type of c <class ‘complex’> c is complex number: True

Python supports three kinds of numerical data.

• Int: Whole number worth can be any length, like numbers 10, 2, 29, – 20, – 150, and so on. An integer can be any length you want in Python. Its worth has a place with int.
• Float: Float stores drifting point numbers like 1.9, 9.902, 15.2, etc. It can be accurate to within 15 decimal places.
• Complex: An intricate number contains an arranged pair, i.e., x + iy, where x and y signify the genuine and non-existent parts separately. The complex numbers like 2.14j, 2.0 + 2.3j, etc.

Sequence Type

String

The sequence of characters in the quotation marks can be used to describe the string. A string can be defined in Python using single, double, or triple quotes.

String dealing with Python is a direct undertaking since Python gives worked-in capabilities and administrators to perform tasks in the string.

When dealing with strings, the operation “hello”+” python” returns “hello python,” and the operator + is used to combine two strings.

Because the operation “Python” *2 returns “Python,” the operator * is referred to as a repetition operator.

The Python string is demonstrated in the following example.

Example – 1

str = "string using double quotes"  

print(str)  

s = '''''A multiline 

string'''  

print(s)

Output:string using double quotes A multiline string

Look at the following illustration of string handling.

Example – 2

str1 = 'hello javatpoint' #string str1    

str2 = ' how are you' #string str2    

print (str1[0:2]) #printing first two character using slice operator    

print (str1[4]) #printing 4th character of the string    

print (str1*2) #printing the string twice    

print (str1 + str2) #printing the concatenation of str1 and str2

Output:he o hello javatpointhello javatpoint hello javatpoint how are you

List

Lists in Python are like arrays in C, but lists can contain data of different types. The things put away in the rundown are isolated with a comma (,) and encased inside square sections [].

To gain access to the list’s data, we can use slice [:] operators. Like how they worked with strings, the list is handled by the concatenation operator (+) and the repetition operator (*).

Look at the following example.

Example:

list1  = [1, "hi", "Python", 2]    

#Checking type of given list  

print(type(list1))  

  

#Printing the list1  

print (list1)  

  

# List slicing  

print (list1[3:])  

  

# List slicing  

print (list1[0:2])   

  

# List Concatenation using + operator  

print (list1 + list1)  

  

# List repetation using * operator  

print (list1 * 3)

Output:[1, ‘hi’, ‘Python’, 2] [2] [1, ‘hi’] [1, ‘hi’, ‘Python’, 2, 1, ‘hi’, ‘Python’, 2] [1, ‘hi’, ‘Python’, 2, 1, ‘hi’, ‘Python’, 2, 1, ‘hi’, ‘Python’, 2]

Tuple

In many ways, a tuple is like a list. Tuples, like lists, also contain a collection of items from various data types. A parenthetical space () separates the tuple’s components from one another.

Because we cannot alter the size or value of the items in a tuple, it is a read-only data structure.

Let’s look at a straightforward tuple in action.

Example:

tup  = ("hi", "Python", 2)    

# Checking type of tup  

print (type(tup))    

  

#Printing the tuple  

print (tup)  

  

# Tuple slicing  

print (tup[1:])    

print (tup[0:1])    

  

# Tuple concatenation using + operator  

print (tup + tup)    

  

# Tuple repatation using * operator  

print (tup * 3)     

  

# Adding value to tup. It will throw an error.  

t[2] = "hi"

Output:<class ‘tuple’> (‘hi’, ‘Python’, 2) (‘Python’, 2) (‘hi’,) (‘hi’, ‘Python’, 2, ‘hi’, ‘Python’, 2) (‘hi’, ‘Python’, 2, ‘hi’, ‘Python’, 2, ‘hi’, ‘Python’, 2) Traceback (most recent call last): File “main.py”, line 14, in <module> t[2] = “hi”; TypeError: ‘tuple’ object does not support item assignment

Dictionary

A dictionary is a key-value pair set arranged in any order. It stores a specific value for each key, like an associative array or a hash table. Value is any Python object, while the key can hold any primitive data type.

The comma (,) and the curly braces are used to separate the items in the dictionary.

Look at the following example.

d = {1:'Jimmy', 2:'Alex', 3:'john', 4:'mike'}     



  



# Printing dictionary  



print (d)  



  



# Accesing value using keys  



print("1st name is "+d[1])   



print("2nd name is "+ d[4])    



  



print (d.keys())    



print (d.values())    

Output:1st name is Jimmy 2nd name is mike {1: ‘Jimmy’, 2: ‘Alex’, 3: ‘john’, 4: ‘mike’} dict_keys([1, 2, 3, 4]) dict_values([‘Jimmy’, ‘Alex’, ‘john’, ‘mike’])

Boolean

True and False are the two default values for the Boolean type. These qualities are utilized to decide the given assertion valid or misleading. The class book indicates this. False can be represented by the 0 or the letter “F,” while true can be represented by any value that is not zero.

Look at the following 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

Set

The data type’s unordered collection is Python Set. It is iterable, mutable(can change after creation), and has remarkable components. The elements of a set have no set order; It might return the element’s altered sequence. Either a sequence of elements is passed through the curly braces and separated by a comma to create the set or the built-in function set() is used to create the set. It can contain different kinds of values.

Look at the following example.

# Creating Empty set  

set1 = set()  

  

set2 = {'James', 2, 3,'Python'}  

  

#Printing Set value  

print(set2)  

  

# Adding element to the set  

  

set2.add(10)  

print(set2)  

  

#Removing element from the set  

set2.remove(2)  

print(set2)

Output:{3, ‘Python’, ‘James’, 2} {‘Python’, ‘James’, 3, 2, 10} {‘Python’, ‘James’, 3, 10}