Variables in Python (Python Variables)

Last Updated: 29th September, 2024

One of the fundamental concepts in Python is variables . Variables store information and give it a name to be referenced later in the code . They can be defined using a single equals sign (=) and hold many different types of data, such as strings 💬, integers 🔢, and booleans . In this tutorial, we will learn about variables in Python in depth .

What is a Variable in Python?

A variable is a named storage location 📦 in a computer's memory 💾 that can hold a value 🔢, which can be changed 🔁 during the execution 💻 of a program 📝
📝 or script 📃. It is often used to store data 📊 that the program, such as user input , calculations 🧮, or intermediate results , may modify.

There are different types of variables, such as:

• Numeric variables: 🔢
• String variables
• Boolean variables: :green_circle:🔴
• List variables: 📜
• Tuple variables: 📦
• Set variables: 🧮
• Dictionary variables

Imagine you work in a showcasing office📈and have been entrusted with following the execution of a unused promoting campaign for a client's item. To do this, you'll get to use variables in Python to store and control information. For that, we ought to get it how to make a variable.

How to Create a Variable in Python?

Creating a variable is a fundamental concept in programming and can be done in several ways. The simplest way is to choose a name for your variable and use the assignment operator = to assign a value to it. This process allows you to store data in memory and reference it later in your program. To create a variable in Python, use this syntax:

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Python is a dynamically typed language that infers the variable type at runtime. This allows for greater flexibility in programming and can make code easier to read and write.💰 We can create a variable to represent the budget for the campaign. You could name this variable "campaign_budget" and assign it a value of the total amount allocated for the campaign.

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We need to track the number of impressions and clicks the campaign generates. Using multiple assignments, we can create two separate variables, "impressions" 📈 and "clicks"👆, to store this data. Additionally, Python supports multiple assignments, meaning you can assign multiple variables simultaneously.

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Creating a variable in Python is a simple process that involves choosing a name and assigning a value using the assignment operator. This assigns 1000000 to variable impressions and 5000 to variable clicks. This concept is crucial to programming in Python and is used extensively throughout the language.

Rules for Creating Variables in Python

There are a few rules we need to follow while creating a variable in Python:

• Please ensure that the name only includes letters, numbers, and underscores. No other special characters are permitted like @,#,&. ❌
• The variable name should either begin with an Uppercase(A to Z) or Lowercase(a to z) character or an underscore(_). 🔡
• The name must not begin with a number. ❌
• The name should be descriptive and easy to understand. 💡
• The name is case-sensitive, meaning "age" and "Age" are two different variables. 🔤
• Avoid using Python reserved words as variable names. ⚠️

Some valid variable names are:

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Some invalid variable names are:

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How to Re-Declare a Variable in Python?

In Python, you can redeclare a variable by assigning a new value. This is a fundamental operation in Python. Nevertheless, redeclaring a variable can cause unintended consequences, particularly if used in several places in your code. It is usually suggested to use new variable names rather than redeclaring current ones. If you need to redeclare a variable, make sure to thoroughly review your code for any potential issues that may arise.

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How to Assign the Same Value to Multiple Python Variables?

To assign the same value to multiple variables, you can either use the assignment operator for each variable or a loop to assign the value to each variable. Before assigning, please verify that the assigned value is suitable for each variable and meets any constraints, such as data type or range.

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Casting of Variables in Python

The casting of variables involves converting a variable from one data type to another, and Python has a variety of built-in functions for converting variables, including:

int()

To cast a variable to an integer data type, use the int() function. Here's an example:

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We used the int() function to convert the value of x to an integer and assign the result to a new variable called cast_to_int.

float()

To convert a variable to a float data type, use the float() function. See below for an example:

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We use the float() function to convert x to a float and assign the result to a new variable cast_to_float.

str()

To turn a variable into a string data type, use the str() function. Here's an example:

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We use the str() function to convert x to string and assign the result to a new variable cast_to_str.

bool()

To convert a variable to its boolean data type, you can use the bool() function. Here's an example:

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We use the bool() function to convert x to a boolean data type and assign the result to a new variable cast_to_bool.

Keywords in Python

Python keywords are unique words that have a specific meaning in the language. These words have a particular purpose and cannot be used as names for variables, functions, or anything else in your Python code. Here is a complete list of Python keywords:

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

Memory Management for Variables in Python

When a variable is created in Python, it is actually a reference to an object stored in memory. Python uses a dynamic memory management system based on a private heap that stores objects and variables. Python's memory manager handles the allocation of this memory. The key components of Python's memory management include:

• Reference Counting: Every object in Python maintains a reference count, which tracks how many references point to the object. When the reference count drops to zero, the object is deleted from memory.
• Garbage Collection: Python uses a built-in garbage collector to automatically manage memory, cleaning up objects that are no longer in use. It helps to free memory when there are cyclic references.

Example:

x = 42  # '42' is stored in memory, and 'x' references it.
y = x   # Now, 'y' also references the same object '42'.
del x   # The reference count for '42' decreases but is not deleted since 'y' still references it

Mutable vs. Immutable Variables

In Python, variables can reference mutable or immutable objects. Understanding the difference is critical, especially when dealing with large-scale programs:

• Immutable types (like integers, floats, strings, and tuples) cannot be altered after creation. When an immutable variable is modified, Python creates a new object and updates the reference.
• Mutable types (like lists, dictionaries, and sets) can be modified in place, meaning their internal state can change without creating a new object.

Example:

# Immutable variable example:
a = 10
b = a
a = a + 1  # This creates a new object for 'a', 'b' still points to the old value.

# Mutable variable example:
list1 = [1, 2, 3]
list2 = list1
list1.append(4)  # This modifies the original list object, so 'list2' also reflects this change.

Variable Scoping Rules

Python follows a specific set of rules for how variables are scoped and accessed within a program. These rules are commonly referred to as LEGB:

• Local: Variables declared within a function are considered local to that function.
• Enclosing: Variables in the enclosing function (the outer function in nested functions).
• Global: Variables declared at the top level of the script or as global using the global keyword.
• Built-in: Python's built-in names such as print(), len(), etc.

Example of variable scope:

x = "global"

def outer():
    x = "enclosing"
    
    def inner():
        nonlocal x  # Refers to the enclosing scope variable
        x = "local"
        print("Inner:", x)
    
    inner()
    print("Outer:", x)

outer()
print("Global:", x)

Best Practices for Variable Management

When working on larger codebases, following some best practices can make code more maintainable and readable:

1. Descriptive Names: Always use descriptive variable names that reflect their role in the program. Avoid single-character variable names, except for loop counters.

Bad Example:

Good Example:

2. Avoid Global Variables: Global variables can make it difficult to debug and track where a variable is changed. Use local variables wherever possible, and if a global variable is needed, explicitly declare it with the global keyword.
3. Use Constants: If a value should not be changed throughout the execution of the program, define it as a constant using all uppercase letters. While Python doesn’t enforce constants, it’s a good convention.
   Example:

4. Limit Variable Scope: Declare variables in the smallest scope necessary, such as within a function or loop, to avoid polluting the global namespace.
5. Immutability for Safety: Where possible, use immutable types (like tuples) to prevent accidental modifications, especially in shared data structures or when passing data between functions.

Working with Dynamic Typing

While Python's dynamic typing allows for flexibility, it can also introduce bugs if variables are reassigned to incompatible types. Consider using type annotations and tools like mypy to check for type consistency in your codebase.

Example with type hints:

def add(a: int, b: int) -> int:
    return a + b

result = add(5, 10)  # mypy can verify that 'result' is an integer

Python Variable Performance Considerations

When working with large data structures or performance-critical code, it's important to understand how Python handles variables:

• Copying Data: Be mindful of the difference between copying a variable (e.g., using copy.copy() for shallow copies or copy.deepcopy() for deep copies) and merely copying a reference. Shallow copies duplicate the reference but not the object, while deep copies duplicate the entire object hierarchy.
• Memory Usage: Large mutable objects like lists or dictionaries can consume significant memory. Consider using generators or iterators when dealing with large datasets to minimize memory usage.

Example of using a generator:

def large_dataset():
    for i in range(1000000):
        yield i

for data in large_dataset():
    print(data)

Conclusion

Variables act as containers for information of various types, counting numbers, strings, lists, sets, dictionaries, and more. All through this instructional exercise, we've secured the essential concept of variables in Python. We've talked about how to make and name variables and reassign values and assign the same value to multiple factors. By acing these basics, readers are well-equipped to start their travel into programming with Python.

Key Takeaways

• In this lesson, we covered the topic of variables in Python. Variables serve as containers for data that can take on different data types.📦
• In Python, we can create variables using the assignment operator and assign multiple ones simultaneously.
• During our discussion, we covered the guidelines for creating variables in Python and their local or global scope. 🌎
• We also learned how to redeclare a variable and assign the same value to multiple variables.🔄
• Variables are an essential concept in programming, and it is necessary to comprehend them to write effective Python code.💻

Quiz

1. How do we create a variable in Python?
   1.  Choose a name for variable and use assignment operator to assign value to it 
   2. Choose a name for variable and use equals operator to assign value to it
   3. Choose a name for variable and use semicolon to assign value to it  
   4. None of the above

Answer: a) Choose a name for variable and use assignment operator to assign value to it

2. What is the type of value inferred for a variable in Python? 
   1. static 
   2. dynamic 
   3. complex 
   4. None of the above

Answer: b) dynamic

3. What is the syntax for multiple assignment in Python? 
   1. a, b, c = 1, 2, 3  
   2. a=1,b=2,c=3 
   3. a, b = 1, 2, 3 
   4. a and b

Answer: a) a, b, c = 1, 2, 3

4. What is the scope of a variable in Python? 
   1. Determines the variable name 
   2. Determines the size of variable 
   3. Determines where it can be accessed in the program 
   4. Determines the type of variable

Answer: c) Determines where it can be accessed in the program