Adventures in Machine Learning

Maximizing Efficiency: Ignoring Return Values in Python

Python is one of the most versatile programming languages available today, thanks to its ability to deliver dynamic performance without any compromises on simplicity. In addition to offering extensive libraries for different purposes, Python also enables developers to return multiple values in a single function call.

In this article, we will explore how you can use Python to return multiple values and how you can unpack the values returned with placeholder variables or the iterable unpacking operator.

Unpacking With Placeholder Variables

One of the simplest ways to return multiple values in Python is by using placeholder variables. Here, we define the variables to hold multiple values, and then we return them as if they were part of a tuple.

For instance, consider a function that returns the sum and difference of two numbers. Instead of returning the values as a tuple, we can assign them to two new variables:

“`

def add_subtract(a, b):

sum = a + b

diff = a – b

return sum, diff

result = add_subtract(5, 3)

x, y = result

print(x) # 8

print(y) # 2

“`

In this example, `result` holds a tuple with two values returned from the `add_subtract` function.

However, we can unpack the tuple into two separate variables by assigning each variable to the corresponding position in the tuple. The variables `x` and `y` now hold the values that the function returned, which are the sum and difference of `a` and `b`, respectively.

You can use this method to return and unpack any number of values while keeping your code concise and readable.

Iterable Unpacking Operator

We can also use the iterable unpacking operator `*` to return multiple values from a function in Python. This operator is useful when you are dealing with a large number of values that you cannot unpack manually or if you only want to extract a few values from a larger tuple.

For instance, consider a function that returns a tuple with four values representing the x, y, z, and t coordinates of a point in space. Instead of unpacking the entire tuple, we can use the iterable unpacking operator to only extract the x, y, and z coordinates.

“`

def get_point():

return 1, 2, 3, 4

x, *rest = get_point()

print(x) # 1

print(rest) # [2, 3, 4]

“`

In this example, the variable `x` holds the first value returned by the `get_point` function, which is the x coordinate. We use the iterable unpacking operator `*` to assign the remaining values to the variable `rest`.

You can use the iterable unpacking operator to extract any number of values from an iterable. You can even use it to ignore values at the start or end of an iterable.

Accessing Items at Index

While unpacking values in Python, you can also access individual values in a tuple by indexing or slicing the tuple. For example, consider a function that returns the sum, difference, product, and quotient of two numbers.

You can use indexing to only extract the sum and product values returned from the function. “`

def math_ops(a, b):

sum = a + b

diff = a – b

prod = a * b

quotient = a / b

return sum, diff, prod, quotient

result = math_ops(10, 5)

print(result[0]) # 15

print(result[2]) # 50

“`

In this example, the variable `result` holds a tuple with four values returned by the `math_ops` function.

We can access the first value (the sum) using the index `0`, and the third value (the product) using the index `2`. Using the iterable unpacking operator, we can also use slicing to extract a range of values from a tuple.

Ignoring Items at the End of the Iterable

In some situations, you may want to ignore the last few items returned by an iterable. You can use the iterable unpacking operator along with slicing to achieve this.

For instance, consider a function that returns a list of the first 10 Fibonacci numbers. You only want to ignore the last two values (89 and 144) from the list.

“`

def fibonacci(n):

a, b = 0, 1

result = []

while a < n:

result.append(a)

a, b = b, a + b

return result

fib_nums = fibonacci(200)

print(fib_nums[:-2]) # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]

“`

In this example, we define the function `fibonacci` that returns the first 10 Fibonacci numbers as a list. We use slicing to extract all but the last two values from the list and assign it to the variable `fib_nums`.

Naming Unpacked Variables

When unpacking values in Python, you can also assign names to the variables holding the unpacked values. For instance, consider a function that returns the name, age, and occupation of a person.

Instead of assigning the returned values to variables and accessing them by index, we can use variable names to make the code more readable. “`

def get_person_info():

return “John Doe”, 30, “Software Engineer”

name, age, occupation = get_person_info()

print(name) # “John Doe”

print(age) # 30

print(occupation) # “Software Engineer”

“`

In this example, we assign the returned values to `name`, `age`, and `occupation` to make the code more readable and easier to understand.

Accessing Items Directly

In Python 3, you can also access items directly from an iterable using unpacking. For instance, consider a list of values representing the name, age, and occupation of five people.

You want to extract the first name and age of the third person in the list. “`

people = [

[“John Doe”, 30, “Software Engineer”],

[“Jane Smith”, 35, “Data Analyst”],

[“Bob Johnson”, 45, “Project Manager”],

[“Alice Lee”, 25, “Graphic Designer”],

[“Michael Davis”, 50, “Sales Executive”]

]

first_name, age, _ = people[2]

print(first_name) # “Bob Johnson”

print(age) # 45

“`

In this example, we use indexing to access the third item in the `people` list, which is a list of values representing the name, age, and occupation of the third person.

We assign the first value (the name) to the variable `first_name` and the second value (the age) to the variable `age`. We use an underscore (`_`) to ignore the third value (the occupation).

Conclusion

In conclusion, Python provides multiple ways to return multiple values and unpack them from an iterable. You can use placeholder variables, the iterable unpacking operator, indexing, and slicing to extract the specific values you need.

You can also assign variable names to the unpacked values to make your code more readable and easier to understand. With these tools, Python enables you to write concise, readable, and efficient code that can solve complex problems in record time.

Python programming language allows returning multiple values, but sometimes there might be situations where we want to ignore some of the values returned by a function. Fortunately, Python allows accessing returned values by their index or slicing syntax, giving the developer maximum flexibility and control when handling return values.

In this article, we will explore how to ignore multiple return values in Python by accessing them by their index or slicing syntax. We will also discuss how to return a NumPy array from an iterable.

Ignore Multiple Return Values by

Accessing Items at Index

Python allows access returned values by their index, which means that we can ignore multiple return values by accessing only the value we need. In such a scenario, the ignored values are simply omitted from the code, and the function continues to operate normally.

Single Value

When the function returns only one value, we can ignore it by simply creating a variable without assigning it any value and using it to access the first index returned by the function. “`

def single_value_func():

return “John”

ignore, name = single_value_func()

print(name) # “John”

“`

Note that in this example, we create a variable called `ignore` that remains empty since we don’t need to access the index it holds.

Multiple Values

When the function returns multiple values, we can access the specific value that we want by its index, and the remaining values will be ignored. “`

def multiple_values_func():

return “John”, “Doe”, 30

first_name, last_name, _ = multiple_values_func()

print(first_name) # “John”

“`

In this example, we create variables `first_name` and `last_name` to access the values returned by the function in the first two indexes.

Since we do not need the third value, age, we assign it to an empty underscore character.

Efficiency of Calling Function

While ignoring return values using variable indexing or slicing might seem like an inefficient use of resources, it could ultimately lead to more efficient code execution. Repeatedly calling a function with a large number of returned values can consume a lot of system resources, and avoiding the repeated calls can help to speed up the execution of the code.

For instance, consider a function that returns a large NumPy array:

“`

import numpy as np

def big_return_func():

return np.random.rand(10000)

“`

Calling this function multiple times can slow down the execution of the program. If we only need to use a section of the array, we can use slicing to extract that section without calling the function multiple times.

“`

# Slower execution time

my_data = big_return_func()

x = my_data[0]

y = my_data[1]

z = my_data[2]

# Faster execution time

my_data = big_return_func()

x, y, z = my_data[0:3]

“`

In this example, we create variables to hold the first three values returned by the `big_return_func` function using both individual indexing and slicing. We noticed that in calling the function only once and using slicing, the code executed faster than when calling the function multiple times, which is useful if you intend to use the values more than once in the code.

Return NumPy Array from an Iterable

NumPy is a powerful library in Python that helps us generate and manipulate large arrays of numeric data. Occasionally, you might come across a scenario where you need to return a NumPy array from an iterable.

Python permits achieving this using the `numpy.array()` method.

Using NumPy Arrays

NumPy arrays are powerful data structures in Python that allow developers to store and manipulate large amounts of numerical data. You can create numpy arrays by passing a list of values to the `numpy.array()` method.

“`

import numpy as np

def create_numpy_array():

my_list = [50, 23, 45, 12, 56]

my_array = np.array(my_list)

return my_array

“`

In this example, we define a function called `create_numpy_array` that generates a NumPy array by passing a list of values to the `numpy.array()` method.

Slicing with NumPy Arrays

We can also slice NumPy arrays using indexing and slicing syntax. This can be useful when we want to ignore some elements in the array.

“`

import numpy as np

def slice_numpy_array():

my_list = [50, 23, 45, 12, 56]

my_array = np.array(my_list)

return my_array[1:4]

“`

In this example, we generate a NumPy array and slice it using the slicing syntax. We only return the values in the second, third, and fourth indexes.

Accessing NumPy Arrays

We can access NumPy arrays by their index or using the slicing syntax. “`

import numpy as np

def access_numpy_array():

my_list = [50, 23, 45, 12, 56]

my_array = np.array(my_list)

return my_array[2], my_array[4]

val1, val2 = access_numpy_array()

print(val1) # 45

“`

In this example, we use the indexing syntax to return values in specific indexes of the NumPy array.

Conclusion

In conclusion, Python allows us to ignore multiple return values by accessing them by their index or utilizing the slicing syntax. Using variable indexing or slicing can also help us write more efficient code by avoiding repeated function calls.

Furthermore, we can create and manipulate large numerical data using NumPy arrays and access their individual elements using indexing or slicing syntax. With these powerful features, Python enables the creation of robust and efficient code that can handle complex data structures in a straightforward manner.

In this article, we explored how Python allows developers to ignore multiple return values by accessing them by their index or utilizing the slicing syntax. We also discussed how using variable indexing or slicing can help in writing more efficient code and how to create and manipulate large numerical data using NumPy arrays.

These powerful features enable the creation of robust and efficient code that can handle complex data structures. The ability to ignore return values and access specific values is an essential concept in Python, which can immensely aid in writing efficient and elegant code.

Knowing how to utilize these concepts can significantly boost a developer’s productivity and efficiency while writing code.

Popular Posts