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Python Variables play a crucial role in coding by enabling the storage and modification of information. In Python, variables are remarkably adaptable and straightforward, rendering Python an excellent choice for both novices and seasoned programmers.

This article will cover the basics of Python variables, how to use them, and some best practices to keep in mind.

What are Python Variables

Python variables are used to store values. Whenever we want to store any value in Python, we use Variables. In simple words Variable acts like a container to store any value in it, It can be any number, any text, or a combination of both.

This value can be of various data types, such as numbers, strings, lists, or even more complex data structures. Think of a variable as a labeled box where you can store data to be used later in your code.

Creating Variables in Python

Creating a variable in Python is straightforward. You use the assignment operator (=) to assign a value to a variable name.

x = 5
name = "Alice"
is_student = True

In this example:

• x is an integer variable with a value of 5.
• name is a string variable with a value of “Alice”.
• is_student is a boolean variable with a value of True.

Python Variables Naming Rules

A Python variables name must follow these rules:

• Names can contain letters, digits, and underscores: my_variable, var123, name_1_2_3.
• Names must start with a letter or an underscore: _variable, variable1, but not 1variable.
• Names are case-sensitive: myVariable and myvariable are two different variables.
• Avoid using Python-reserved words: Words like class, for, and if have special meanings in Python and should not be used as variable names.

Examples of Valid and Invalid Variable Names

# Valid variable names
my_variable = 10
_var = 20
name1 = "John"

# Invalid variable names
1variable = 10 # Starts with a digit
class = "Hello" # Reserved word
my-variable = 30 # Contains a hyphen

Dynamic Typing in Python

Python is a dynamically typed language, which means you don’t have to declare the type of a variable when you create it. The type is inferred from the value assigned to it. This allows for great flexibility but requires careful management to avoid unexpected behavior.

x = 5
x = "Now I'm a string"

In the above example, x starts as an integer but is later reassigned to a string. This flexibility is powerful but can lead to unexpected behavior if not managed carefully.

Checking Variable Types

You can check the type of a variable using the built-in type() function:

x = 5
print(type(x)) # <class 'int'>

x = "Hello"
print(type(x)) # <class 'str'>

Common Data Types

Python supports several built-in data types, including:

• Integers: Whole numbers, e.g., 5, -3.
• Floats: Decimal numbers, e.g., 3.14, -2.0.
• Strings: Text, e.g., "Hello, world!".
• Booleans: Truth values, True and False.
• Lists: Ordered collections, e.g., [1, 2, 3].
• Dictionaries: Key-value pairs, e.g., {'name': 'Alice', 'age': 25}.
• Tuples: Ordered, immutable collections, e.g., (1, 2, 3).
• Sets: Unordered collections of unique elements, e.g., {1, 2, 3}.

Example Usage of Different Data Types

# Integer
age = 30

# Float
height = 5.9

# String
name = "Alice"

# Boolean
is_student = True

# List
colors = ["red", "blue", "green"]

# Dictionary
person = {"name": "Alice", "age": 25}

# Tuple
coordinates = (10.0, 20.0)

# Set
unique_numbers = {1, 2, 3}

Object References in Python

In Python, variables are references to objects stored in memory. This means that variables do not hold the actual data themselves; instead, they point to the location in memory where the data is stored.

Example of Object References

a = [1, 2, 3]
b = a

print(a) # Output: [1, 2, 3]
print(b) # Output: [1, 2, 3]

a.append(4)
print(a) # Output: [1, 2, 3, 4]
print(b) # Output: [1, 2, 3, 4]

Explanation:

• a = [1, 2, 3]: a references a list object.
• b = a: b references the same list object as a.
• a.append(4): Modifies the list object in place. Since a and b reference the same object, both see the change.

This demonstrates that variables are references to objects, and changes to the object through one reference are reflected in all references to that object.

Object Identity in Python

Every object in Python has a unique identity that can be accessed using the id() function. The identity is a unique integer that remains constant for the object during its lifetime. The is operator can be used to test whether two variables reference the same object.

Example of Object Identity

x = [1, 2, 3]
y = [1, 2, 3]
z = x

print(id(x)) # Outputs the unique identity of the list referenced by x
print(id(y)) # Outputs the unique identity of the list referenced by y
print(id(z)) # Outputs the same identity as x, since z references the same object

print(x is y) # Output: False, because x and y reference different objects
print(x is z) # Output: True, because x and z reference the same object

Explanation:

• id(x): Returns the unique identity of the list object referenced by x.
• id(y): Returns the unique identity of a different list object, even though it has the same contents as x.
• id(z): Returns the same identity as x, because z references the same object as x.
• x is y: Returns False because x and y reference different objects.
• x is z: Returns True because x and z reference the same object.

Understanding object identity helps in determining whether two variables reference the same object or different objects with the same value. This is especially important when working with mutable objects.

Type Conversion

Python provides built-in functions to convert variables from one type to another. Common functions include int(), float(), str(), and bool().

x = "123"
y = int(x) # y is now an integer with value 123
z = float(x) # z is now a float with value 123.0

Explanation:

• x = “123”: A string variable representing a number.
• y = int(x): Converts the string x to an integer, resulting in y = 123.
• z = float(x): Converts the string x to a float, resulting in z = 123.0.

Variable Scope

The scope of Python variables refers to the region of the code where the variable is recognized. In Python, variables can have:

• Local Scope: Variables defined inside a function. They can only be used within that function.
• Global Scope: Variables defined outside any function. They can be accessed from anywhere in the code.

Example of Variable Scope

global_var = "I'm global"

def my_function():
local_var = "I'm local"
print(global_var) # This will work
print(local_var) # This will work

print(global_var) # This will work
print(local_var) # This will cause an error

Explanation:

• global_var = “I’m global”: A global variable that can be accessed anywhere in the code.
• local_var = “I’m local”: A local variable that is only accessible within the my_function function.
• print(global_var): This works both inside and outside the function because global_var is a global variable.
• print(local_var): This works inside the function but causes an error outside because local_var is local to my_function.

Global Keyword

If you need to modify a global variable inside a function, you can use the global keyword.

x = 5

def my_function():
global x
x = 10

my_function()
print(x) # Output: 10

Avoid Using Python Reserved Word

Python has a set of reserved words, also known as keywords, that have special meanings in the language. These words are part of the syntax and cannot be used as variable names. Here are some common reserved words in Python:

and as assert break class continue def
del elif else except False finally for
from global if import in is lambda
None nonlocal not or pass raise return
True try while with yield

Explanation:

• Reserved words are predefined and have special significance in the Python language.
• Using these words as variable names will result in a syntax error because Python will not be able to distinguish between the intended use of the word and its special meaning.

Credits:

• Photo by Stockcake

Conclusion

Python Variables are a cornerstone of programming in Python. Understanding how to effectively use Python variables, including their creation, naming, and scope, is crucial for writing clear and efficient code. By adhering to best practices and leveraging Python’s dynamic typing, you can make the most of this powerful language feature.

Mastering Python variables lays a solid foundation for tackling more complex programming concepts and challenges. Happy coding!