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Why do Python lambda defined in a loop with different values all return the same result?

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In this article, we are going to learn why Python lambda defined in a loop with different values all return the same result.

In this article, we will explore the reasons behind this behavior and discuss how to avoid it. We will start by looking at how lambda functions are defined and executed, and how they capture the values of variables that are used within their body. We will then look at an example of how this can cause lambda functions defined in a loop to all return the same result, and finally, we will discuss how to avoid this issue by using default argument values instead of closing over variables.

What is a lambda function?

Lambda functions in Python are anonymous functions that are defined without a name. They are often used when a function is required for a short period of time, such as in a loop or as a callback function. However, when using lambda functions in a loop, it is common to encounter an issue where all of the lambda functions seem to return the same result, regardless of the different values that are passed to them. This behavior can be confusing and frustrating for programmers, especially if they are not aware of the underlying mechanisms behind lambda functions.

Understanding Lambda Functions

Lambda functions are defined using the lambda keyword, followed by a list of arguments and a single expression. The expression is the return value of the lambda function, and it is automatically returned when the lambda function is called. Here is an example of a simple lambda function that takes a single argument and returns its square:

Python3




# Lambda function to return
# square of a number
square = lambda x: x ** 2
 
# Printing the square of a number
# using square lambda function
print(square(5))


Output:

25

Lambda functions are often used as a shorthand for defining small, one-line functions. They are particularly useful when the function is only needed in a specific context, such as in a loop or as a callback function. Lambda functions can also accept multiple arguments, just like regular functions. For example:

Python3




# lambda function with expression
# which takes two values as argument
add = lambda x, y: x + y
 
# Adding two values using lambda function
print(add(3, 4))


Output:

7

In addition to accepting arguments, lambda functions can also capture the values of variables that are defined outside of their scope. This is known as “closing over” a variable. When a lambda function is defined, it captures the current value of any variables that are used within its body. These variables become part of the lambda function’s environment and are retained even when the lambda function is executed outside of the context in which it was defined.

Here is an example that demonstrates how lambda functions can close over variables:

Python3




# Declaring a variable with value 5
x = 5
 
# Creating a function which returns
# a lambda function
def create_lambda():
   return lambda: x
 
# copy a function in my_lambda
my_lambda = create_lambda()
 
# printing using my_lambda
print(my_lambda())


Explanation: In this example, the create_lambda function returns a lambda function that simply returns the value of the x variable. When the lambda function is executed, it returns the value of x, which is 5. This ability to close over variables can be useful in certain situations, but it can also lead to confusion and unexpected behavior when used in a loop.

Lambda expression in a Loop

Suppose we have a loop that creates a list of lambda functions, each with a different value of a variable. In the below example, we have defined a list “lambdas” which is then populated with five lambda functions, each with a different value of i. However, when the lambda functions are executed in the second loop, they all return the same value of 4, which is the final value of “i” in the first loop. This is because the lambda functions are all using the same value of “i”, which has been closed over and is now part of their environment.

Python3




# Defining a list named lambdas
lambdas = []
 
# Running a loop which append values to list
# lambdas by calling create_lambda() function
for i in range(5):
   lambdas.append(lambda :i)
 
# Printing the items of list lambdas
for l in lambdas:
   print(l())


Output:

4
4
4
4
4

This behavior can be confusing and unexpected, especially if you are not aware of the way that lambda functions capture variables. It can also be difficult to debug, as the lambda functions may appear to be correct when they are defined, but produce unexpected results when they are executed.

To understand why this happens, it is important to realize that lambda functions are defined and executed at different times. When the lambda functions are defined in the first loop, they capture the current value of the “i” variable, which is updated in each iteration of the loop. However, the lambda functions are not executed until the second loop, at which point they all use the final value of “i”, which is 4.

Avoiding the Issue

To avoid the issue of lambda functions defined in a loop all returning the same result, you can use default argument values instead of closing over variables.

Python3




# Defining a function with argument
# with default value "None"
def create_lambda(x=None):
   return lambda: x
 
# Declare a list
lambdas = []
 
# Running a loop and append in a list
# using create_lambda function
for i in range(5):
   lambdas.append(create_lambda(i))
 
# Printing values using all functions stored
# in list "lambdas"
for l in lambdas:
   print(l())


Output:

0
1
2
3
4

Explanation: In this version of the code, the lambda function no longer closes over the x variable and instead uses the default value of None. As a result, each lambda function will return the correct value when it is executed.

Using default argument values is a more reliable way to define lambda functions that are used in a loop, as it avoids the issue of capturing variables that may change over time. It also makes the behavior of the lambda functions more predictable, as they will always use the default value unless it is explicitly overridden when the lambda function is called.

It is also worth noting that this issue is not specific to lambda functions. It can also occur with regular functions that close over variables, as well as with nested functions that are defined inside of a loop. In these cases, the same solution of using default argument values can be applied to avoid the issue.



Last Updated : 01 Feb, 2023
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