In Python coding, grasping how functions accept and manage arguments is vital. Numerous programmers, particularly those with backgrounds in different languages, frequently wonder if Python employs call by value, call by reference, a mix of both, or a completely distinct model. This differentiation is crucial as it influences whether modifications made within a function affect the original data outside of it. In this article, you will learn the meanings of call by value and call by reference, how Python deals with mutable and immutable data during function invocations, supplemented by comprehensive examples.
Call by value indicates a scenario wherein a function obtains a duplicate of the argument’s value, rather than the original variable itself. In this approach, changes made to the parameter within the function do not affect the original variable outside of it. While Python does not apply call by value in the identical manner as C, immutable types like integers, floats, and strings exhibit similar characteristics. Any alteration made to these types within a function results in the creation of a new object, leaving the original data unaffected. This behavior resembles call by value, even though Python does not strictly adhere to this method.
What is Call by Reference in Python?
Call by reference describes the technique of invoking functions where the reference to the original variable is transmitted, rather than a copy of its value. This allows the function to modify the actual data in memory. Although Python does not use pointers for call by reference like C++, it demonstrates reference-like behavior when mutable objects (such as lists, dictionaries, or user-defined types) are provided as arguments. Any modification to such an object within the function is also observable outside the function. This happens because both the original variable and the function parameter refer to the same object in memory.
Unlock Python: Enhance Your Programming Abilities!
Engage in real-world coding projects and gain confidence in writing clean, efficient Python code.
Python employs call by object reference (also referred to as call by sharing), where a function is provided with a reference to the original object in memory. Nevertheless, the reference itself is passed by value, indicating that the function receives a copy of the reference, rather than the actual variable. As a result, the function operates directly on the same object, meaning any adjustments to mutable objects (such as lists or dictionaries) inside the function will influence the original object outside. Conversely, if the function reassigns the parameter to a new object, this reassignment will only modify the local reference within the function and will not affect the original variable externally. This model clarifies why Python behaves differently from classical call by value or call by reference methodologies utilized in other programming languages.
Examples of Call by Value and Call by Reference in Python
To discern the behavior of function arguments in Python, a distinction must be made between immutable and mutable data types. The examples below illustrate how Python manages object references during function calls.
Example 1: Immutable Types (int, str)
In this case, immutable types such as integers and strings are presented to functions. Any modifications to these objects within the function lead to the creation of a new object instead of altering the original.
Example:
Python
Code Copied!
var isMobileDevice = window.innerWidth "");
editor98329.setValue(contentDecoded); // Set the initial text
editor98329.clearSelection();
editor98329.setOptions({
maxLines: Infinity
});
function decodeHTML98329(input) {
var documentFragment = new DOMParser().parseFromString(input, "text/html");
return documentFragment.documentElement.textContent;
}
// Function to store code on clipboard
function copyCodeToClipboard98329() {
const code = editor98329.getValue(); // Retrieve code from the editor
navigator.clipboard.writeText(code).then(() => {
jQuery(".maineditor98329 .copymessage").show();
setTimeout(function() {
jQuery(".maineditor98329 .copymessage").hide();
}, 2000);
}).catch(err => {
console.error("Error copying code: ", err);
});
}
function executeCode98329() {
var code = editor98329.getSession().getValue();
"+outputData+"");
jQuery(".maineditor98329 .code-editor-output").show();
jQuery("#runBtn98329 i.run-code").hide();
}
});
}
function hideOutput98329() {
var code = editor98329.getSession().getValue();
jQuery(".maineditor98329 .code-editor-output").hide();
}
// Add event listeners to buttons
document.getElementById("copyBtn98329").addEventListener("click", copyCodeToClipboard98329);
document.getElementById("runBtn98329").addEventListener("click", executeCode98329);
document.getElementById("closeoutputBtn98329").addEventListener("click", hideOutput98329);
Output:
Clarification: In this case, the function receives an integer 5. Integers in Python are immutable, and the operation x + 10 within the function's scope generates a new integer object. This illustrates the call-by-value mechanism since the original variable number remains unchanged post function execution.
Illustration 2: Mutable Types (lists, dicts)
This example highlights the mutability of objects like lists and dictionaries, showcasing how modifications can be made in-place, with alterations visible outside the function's confines.
Illustration:
Python
Code Copied!
Output:
```
Clarification: In this instance, the list [1, 2, 3] is passed to the function. Since lists are mutable, the append() method alters the original list directly. Consequently, the alteration is observable even after the function invocation, outside of the function's scope, resembling call-by-reference behavior.
Associating Names with Objects in Python
In Python, variable identifiers are merely references (bindings) to objects residing in memory. When you send a variable to a function:
A new name (parameter) is established within the function's local context.
This new name points to the identical object in memory as the initial variable.
As both names target the same object, modifications to mutable entities inside the function will influence the original object externally.
Nevertheless, if the parameter is reassigned to a different object within the function, it will only modify the local reference, leaving the original variable untouched.
This elucidates why alterations to mutable entities within functions affect the original, while assigning a new object only alters the local reference.
Distinction Between Call by Value and Call by Reference in Python
Characteristic
Call by Value
Call by Object Reference
Data Transferred
A duplication of the actual value is sent
A reference to the object is conveyed by value
Ability to Change Data
No, modifications within the function do not impact the original
Yes, if the object is mutable
Impact on Caller
The original variable remains unchanged
Mutable objects can be altered; immutables remain the same
Application
Utilized to avert unintended side effects
Facilitates the modification of immutable objects without duplication
Applicable to Mutable Types
A copy of the value is altered without modifying the original
Changes made to the object endure outside the function
Applicable to Immutable Types
The original variable stays unchanged
Rebinding results in a new object; the original remains unchanged
Frequent Errors with Call by Value and Call by Reference in Python
Here are several prevalent errors that developers encounter when passing arguments to functions in Python, often stemming from misconceptions regarding call by value and call by reference.
Error 1: Believing Python Employs Traditional Call by Reference or Call by Value
Many newcomers assume that Python adheres to conventional call by value or call by reference principles, similar to other programming languages. In truth, Python transmits references to objects, rather than duplicates of data. The reference itself is passed by value, enabling mutable objects to be altered within functions. This distinction frequently leads to confusion and difficulties in debugging.
Illustration:
Python
Code Copied!
Output:
```html
Resolution: Understand that Python utilizes a call by object reference, which is frequently termed as the call by assignment approach. This means that these are pointers to actual objects, and binding them in the function does not affect the original object in cases of immutable types such as int, str, or tuple.
Refined Code:
Python
Code Duplicated!
Result:
Error 2: Altering Immutable Objects with the Expectation of Changes to Persist
Some programmers frequently believe that changing an immutable object within a function will also alter it outside the function.
Illustration:
Python
Code Duplicated!
var isMobile = window.innerWidth ");
editor87311.setValue(decodedContent); // Set the default text
editor87311.clearSelection();
editor87311.setOptions({
maxLines: Infinity
});
function decodeHTML87311(input) {
var doc = new DOMParser().parseFromString(input, "text/html");
return doc.documentElement.textContent;
}
// Function to copy code to clipboard
function copyCodeToClipboard87311() {
const code = editor87311.getValue(); // Get code from the editor
navigator.clipboard.writeText(code).then(() => {
jQuery(".maineditor87311 .copymessage").show();
setTimeout(function () {
jQuery(".maineditor87311 .copymessage").hide();
}, 2000);
}).catch(err => {
console.error("Error duplicating code: ", err);
});
}
``````html
{
jQuery(".maineditor87311 .copymessage").hide();
}, 2000);
}).catch(err => {
console.error("An error occurred while copying the code: ", err);
});
}
function executeCode87311() {
var sourceCode = editor87311.getSession().getValue();
"+outputData+"");
jQuery(".maineditor87311 .code-editor-output").show();
jQuery("#runBtn87311 i.run-code").hide();
}
})
}
function hideOutput87311() {
var sourceCode = editor87311.getSession().getValue();
jQuery(".maineditor87311 .code-editor-output").hide();
}
// Link event listeners to the buttons
document.getElementById("copyBtn87311").addEventListener("click", copyCodeToClipboard87311);
document.getElementById("runBtn87311").addEventListener("click", executeCode87311);
document.getElementById("closeoutputBtn87311").addEventListener("click", hideOutput87311);
Result:
Solution: Recognize that strings are immutable in Python. Upon modification, a new string object is generated within the function, while the original remains intact. If you need to return the altered version, ensure it is done explicitly.
Revised Code:
Python
Code Copied!
Result:
Error 3: Failing to Recognize Mutations on Mutable Objects Impact Original Values
This situation is the converse of the prior error. In this case, developers assume that alterations to mutable objects within a function won't influence the original object, when in actuality they do. Numerous programmers inadvertently modify lists or dictionaries within a function, anticipating the original object to remain the same.
function closeoutput43293() {
var code = editor43293.getSession().getValue();
jQuery(".maineditor43293 .code-editor-output").hide();
}
// Attach event listeners to the buttons
document.getElementById("copyBtn43293").addEventListener("click", copyCodeToClipboard43293);
document.getElementById("runBtn43293").addEventListener("click", runCode43293);
document.getElementById("closeoutputBtn43293").addEventListener("click", closeoutput43293);
Result:
Solution: Understand that mutable items such as lists and dictionaries will display modifications made within the function. If this is not your intention, create a copy before altering.
Updated Code:
Python
Code Duplicated!
var isMobile = window.innerWidth ");
editor31148.setValue(decodedContent); // Set the initial text
editor31148.clearSelection();
editor31148.setOptions({
maxLines: Infinity
});
function decodeHTML31148(input) {
var doc = new DOMParser().parseFromString(input, "text/html");
return doc.documentElement.textContent;
}
// Function to duplicate code to clipboard
function copyCodeToClipboard31148() {
const code = editor31148.getValue(); // Retrieve code from the editor
navigator.clipboard.writeText(code).then(() => {
// alert("Code duplicated to clipboard!");
How Memory Structure Influences Function Parameters in Python
To grasp how parameters are processed in Python, it's crucial to understand how Python handles variables in memory. Unlike languages such as C++ or Java, Python does not transmit the precise value or a direct reference. Instead, Python implements call by object reference (also referred to as call by sharing or call by assignment). This implies that both the variable inside the function and the argument external to it direct towards the same object in memory, albeit the reference itself is conveyed as a copy. Whether modifications within the function influence the original data hinges on the object's mutability.
Learn Python for Free: Begin Coding Smarter Today!
Embark on your programming adventure with practical Python tutorials at no cost.
Comprehending how Python processes function parameters is vital for crafting clean and dependable code. Python employs a system referred to as call by object reference or call by assignment, in which functions obtain a reference to the object, but the reference itself is passed by value. Consequently, alterations made to mutable objects within a function influence the original data, while reassigning immutable objects does not modify the original variable. This distinction aids you in understanding how your code operates and helps avert unforeseen issues.
Advance your skills by enrolling in the Python Course today and gaining practical experience. Additionally, prepare for job interviews with Python Interview Questions curated by industry experts.
Call by Value and Call by Reference in Python – FAQs
Q1. Does Python utilize call by value or call by reference?
Python implements call by object reference; hence functions receive a reference to the object. Mutable adjustments influence the original, whereas reassigning immutable objects does not change the original.
Q2. Why doesn’t changing a variable within a function impact the original variable?
If a variable refers to an immutable object such as an int, str, or tuple, any modification inside a function generates a new object. The original variable remains unchanged since the function's parameter now points to a disparate object.
Q3. Is it feasible to alter the original list passed as an argument to a function or a dictionary?
Indeed. Lists and dictionaries are mutable entities, and any in-place alteration, such as addition, deletion, or modification of an element, will update the object beyond the scope of the function.
Q4. How can I prevent a mutable argument from being altered by a function?
To avoid modifying the mutable object, pass a copy of it instead (e.g., list.copy() or dict.copy()) and work with that copy within the function.
Q5. Why does Python seem to function as both call by value and call by reference?
In Python, immutable types behave like call by value while mutable types imitate call by reference. This is a result of Python's call by object reference model, where conduct is contingent on mutability.
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional
Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
