Python Data Types

Data types are used to define the type of data or value stored in a variable. They determine how the data is stored, processed, and manipulated in a program. Based on the value provided, the data type will be assigned automatically in Python.

To check the type of data it is: use type(data) -> A Predefined function

Python contains the following built-in data types

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Types of Data types

Numeric Datatypes

Integer (int)

Used to represent whole numbers (non-decimal values)

Example:

a = 10
print(type(a))

Output:

class <int>

Float (float)

Used to represent decimal values (point values).

Example:

f = 1.2234
print(type(f))

Output:

class <float>

Bool datatype

This data type is used to represent Boolean values.
The only possible values for this data type are True and False.
Internally, Python represents True as 1 and False as 0.

t = True
type(t)  # bool

Example:

a = 100
b = 200
c = a < b
print(c)

Output:

True

Note:

# Demonstrating arithmetic with boolean values
True + True    # Output: 2
True - False   # Output: 1

None datatype

The None data type represents an object with no defined value. If an object has no value, we can assign it the None type.

a = None
print(a) # None
print(type(a))

Output:

<class ‘NoneType’>

Sequential data types

String (str)

str is a short form representation of String.
It is a sequence of characters enclosed in either single quotes (‘KSR’) or double quotes (”KSR”) or triple quotes (” ” ” KSR” ” ”) or (”’ KSR ”’).
It is immutable in nature (which means once created, it can’t be changed by using code)
It allows duplicates
We will discuss strings in more detail in the upcoming chapter on string data type.
All types of quotes are used to represent single-string literals. (mostly single & double quotes used).

Example:

s = 'KSR Datavizon'
s = ''KSR Datavizon''

Triple single or double quotes are used to represent multiple lines of string literals.

Example:

s = '''KSR 
      Datavizon
      Python Class '''

s = '' '' ''KSR 
      Datavizon
      Python Class '' '' ''

List

A list is a data structure in Python that stores a group of values.
A group of data enclosed in square brackets [] and each value separated with a comma ‘, ‘.
It is mutable in nature (meaning once created, we can change it by using code)
It allows duplicates
We will discuss lists in more detail in the upcoming chapter on list data type.

Example:

lst=[10, 20, 30, 40]
print(lst)
print(type(lst))

Output:

[10,20,30,40]
<class 'list'>

Tuple

A tuple data type stores a group of values and is enclosed by parentheses.
It is immutable in nature and allows duplicates.
We will cover tuples in more detail in the upcoming chapter on the tuple data type.

Example:

t=(1, 2, 3, 4)
print(t)
print(type(t))

Output:

(1,2,3,4)
<class 'tuple'>

Range

It represents a sequence of numbers.
It is immutable in nature, and duplicates are not allowed.
It is commonly used in loops and other places that need a sequence of numbers.
While it doesn’t store all the numbers in memory like other data types, it creates an object that represents the sequence.

Syntax:

range(stop): gives numbers from 0 to stop – 1.
range(start, stop): gives numbers from start to stop – 1.
range(start, stop, step): gives numbers from start to stop – 1, incrementing by step.

Example 1: Basic Range

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

Output:

This generates numbers from 0 to 4.

Example 2: Range with Start and Stop

for i in range(2, 6):
    print(i)

Output:

This generates numbers from 2 to 5.

Example 3: Range with Step

for i in range(1, 10, 2):
    print(i)

Output:

This generates numbers ranging from 1 to 9, incrementing by 2.

Example 4: Using range to Create a List

numbers = list(range(5))
print(numbers)

Output:

[0, 1, 2, 3, 4]

Here, range(5) generates a list of numbers from 0 to 4.

Set:

A set data type stores a collection of unique values within curly braces {}.
Sets are unordered and do not allow duplicate values.
It is mutable in nature.
We will cover the set in more detail in the upcoming chapter on set data structures.

Example 1: Basic Set Creation

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

Output:

{1, 2, 3, 4, 5}

Example 2: Creating a Set with Duplicate Values

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

Output:

{1, 2, 3, 4}

Example 3: Using a Set with Mixed Data Types

my_set = {1, 'hello', 3.14}
print(my_set)

Output:

{1, 'hello', 3.14}

Dictionary (dict)

A dictionary data type stores a collection of key-value pairs within curly braces {}.
Each key in a dictionary must be unique, and it maps to a specific value.
It is mutable in nature.
We will cover the dict in more detail in the upcoming chapter on dict data structures.

Example 1: Basic Dictionary Creation

d = {'name': 'Rohit', 'age': 30, 'city': 'New York'}
print(d)

Output:

{'name': 'Rohit', 'age': 30, 'city': 'New York'}

Here, d is a dictionary with keys ‘name’, ‘age’, and ‘city’, each associated with a corresponding value.

Example 2: Accessing Values

print(d['name'])
print(d['age'])

Output:

Rohit
30

Values are accessed using their keys.

Example 3: Adding or Updating Key-Value Pairs

d['email'] = 'rohit@example.com'  # Adding a new key-value pair
d['age'] = 32                     # Updating an existing key-value pair
print(d)

Output:

{'name': 'Rohit', 'age': 32, 'city': 'New York', 'email': 'rohit@example.com'}

Here, a new key-value pair is added, and an existing key’s value is updated.

Conclusion

Understanding data types is fundamental in Python as they dictate how data is stored, manipulated, and used in operations. Python offers a good set of data types for handling various kinds of data, making it versatile for a wide range of programming needs.