Section 1.1: Simplicity of Lambda Functions

Lambda functions are easy to write and utilize since they do not require naming. This leads us to the first reason for their use: Simplicity.

Consider this scenario: You have a list of tuples, where each tuple contains a string and a number, and you wish to sort this list based on the numeric value. With a lambda function, this can be achieved effortlessly:

my_list = [("are", 3), ("lambda", 1), ("simple", 4), ("functions", 2)]

sorted_list = sorted(my_list, key=lambda x: x[1])

print(sorted_list)

# Output

[('lambda', 1), ('functions', 2), ('are', 3), ('simple', 4)]

In this example, lambda x: x[1] allows you to access the second element of each tuple (the number) for sorting purposes.

The simplicity of lambda functions can be broken down into two main points:

1. No Naming Required: You avoid the hassle of creating and remembering a function name.
2. No Separate Definition: Unlike standard user-defined functions, lambda functions do not need the def keyword or a separate definition.

Given these features, lambda functions are particularly beneficial for quick, one-time tasks.

Section 1.2: Conciseness of Lambda Functions

Another significant advantage of lambda functions is their Conciseness. Since they do not require a separate name or definition, you can save valuable lines of code.

For instance, if you want to create a new list by adding the cube and square of each number, you can do this:

my_list = [2, 5, 10, 21, 100]

def cube_square_sum(x):

return x**3 + x**2

# Using a normal user-defined function

result1 = list(map(cube_square_sum, my_list))

print(result1)

# Output

[12, 150, 1100, 9702, 1010000]

# Using a lambda function

result2 = list(map(lambda x: x**3 + x**2, my_list))

print(result2)

# Output

[12, 150, 1100, 9702, 1010000]

As demonstrated, the lambda function can accomplish this task in a single line, making it ideal for one-time operations. However, for recurring tasks, a standard user-defined function might be the better choice.

Section 1.3: Flexibility of Lambda Functions

Lastly, the Flexibility of lambda functions is another reason to use them. Their compact nature allows them to be applied in various contexts.

1. Inline Usage: Lambda functions can be defined where they are used, as shown in previous examples.
2. Mapping: You can easily apply a lambda function across all elements of a list using the map() function.
3. Filtering: Lambda functions can work alongside the filter() function to extract specific elements from a list based on defined conditions.

For instance, if you want to filter out numbers divisible by 7, the lambda function can do this succinctly:

my_list = [887, 569, 971, 1001, 1020, 1015]

new_list = list(filter(lambda x: x % 7 == 0, my_list))

print(new_list)

# Output

[1001, 1015]

The use of lambda functions here makes the code concise. However, if the logic becomes too complex, consider using a standard function instead.