Python Yield(): A Comprehensive Guide to Understanding and Implementing the Keyword
In this article, we will learn what yield keyword in python is, how to use yield to create generators. Later in the article, we will also discuss best practices and common pitfalls to avoid using yield keyword in python.
Yield() is one of the most powerful keywords used in python generators to create the iterator function. This comprehensive article will discuss the yield() keyword, how it works, and how it differs from the return keyword in python.
Table of Content
- What is the yield keyword in Python?
- How to use yield to create generators
- Advanced yield usage: sending values and exceptions
- Best Practices for using the yield keyword
- Common pitfalls to avoid using yield keyword in Python
What is the Yield keyword in Python?
Definition and Syntax
The yield keyword is a powerful feature in Python that allows you to create generators. When used in a function, the yield keyword indicates that the function is a generator. The generator will produce a sequence of values, one at a time, as needed. Each time the yield keyword is used, the function generates a new value and returns it to the caller. When the function is called again, it picks up where it left off, remembering the previous state and continuing from there.
Syntax
# syntax for yield keyword in python
def my_generator_function(): # some code here yield value # some more code here
Must Read: Keywords in Python
Difference between return and yield keyword in Python
| return | yield |
| used to return a value from a function and terminate its execution. | used to return a value from a generator function and pause its execution until the next value is requested. |
| When a function is called, it executes its code and returns a value to the caller, who can then use it as needed. The function is then terminated, and its state is lost. | When a generator function is called, it does not execute its code immediately. Instead, it returns an iterator object that can iterate over the sequence of values the generator function produces. The function state is preserved between calls to resume execution from where it left off when the next value is requested. |
| A function can only return a single value that is usually specified using the return keyword. | Using the yield keyword, a generator function can return multiple values, one at a time. The function can then be called repeatedly to generate the next value in the sequence. |
| Once a function returns a value, it cannot be called again to generate additional values. | A generator function can be called repeatedly to generate additional values until it has produced all the values in the sequence. |
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How to use yield to create generators
A generator is a special function that produces a sequence of values on the fly without loading the entire sequence into memory at once. Generators use the yield keyword, which allows a function to “pause” its execution and return a value to the caller while preserving its state between calls.
Must Read: Introduction to Generators in Python
Now, let’s take some examples of a generator function.
Example -1: Define a function to generate the first n Fibonacci numbers.
# define the function to generate the Fibonacci number
def fibonacci(n): a, b = 0, 1 for _ in range(n): yield a a, b = b, a + b
In this example, the fibonacci() function uses a for loop to generate the first n Fibonacci numbers. The yield keyword returns each number to the caller while preserving the function’s state between calls. The function repeatedly calls to generate the next number in the sequence.
Here’s an example of how to use the fibonacci() generator:
#find the first 10 numbers from the Fibonacci series# n = 10for num in fibonacci(10): print(num)
Output
When the fibonacci() generator is called, it does not execute its code immediately. Instead, it returns an iterator object that can iterate over the sequence of values the generator function produces. The actual execution of the function code only happens when the next value in the sequence is requested by iterating over the iterator.
Must Read: Fibonacci Number in Python
Must Read: for loop in Python
Example-2: Define a function to generate the square of a number up to the given limit.
#define a function to generate the square of a number upto the defined limit
def square_generator(limit): for i in range(1, limit + 1): yield i ** 2
In this example, the square_generator() function uses a for loop to generate the squares of numbers from 1 to limit. The yield keyword returns each square to the caller while preserving the function’s state between calls.
To use this generator function, you can call it in a loop or use it with other iterator functions. Let’s have a look:
# find the square of first 10 numbers
for square in square_generator(10): print(square)
Output
Example-3: Generating Infinite Sequences
# function that generates an infinite sequence of numbers, starting from a specified value and incrementing by a specified amount
def infinite_sequence(start=0, step=1): num = start while True: yield num num += step
In this example, the infinite_sequence() function takes two optional arguments: start, which specifies the starting value of the sequence (defaulting to 0), and step, which specifies the amount by which the sequence increments (defaulting to 1). The function then uses a while loop to generate an infinite sequence of numbers, where each number is the previous number plus the specified step. The yield statement returns each number in the sequence to the caller, and the loop continues indefinitely.
Now print the first 10 numbers in the sequence, starting from 1 and incrementing by 2:
seq = infinite_sequence(1, 2)for i in range(10): print(next(seq))
Output
Advanced yield usage: sending values and exceptions
Sending values to a generator function
In addition to generating values, a generator function can receive values from the caller using the send() method. This allows the caller to interact with the generator function and provide input values that affect its behavior. Here’s an example of a generator function that receives values from the caller using send().
def echo_generator(): while True: value = yield print("Received value:", value)
In this example, the echo_generator() function uses a while loop to receive values from the caller using yield continuously. The print() statement then outputs each value received from the caller. To send a value to this generator function, you can use the send() method like this:
gen = echo_generator()next(gen)gen.send("Hello, world!")
Output
Raising exceptions in a generator function:
In addition to generating values, a generator function can raise exceptions using the throw() method. The caller can signal an error condition and interrupt the generator function’s execution. Here’s an example of a generator function that raises an exception when an invalid input value is received:
def validate_generator(): while True: value = yield if not isinstance(value, int): raise ValueError("Invalid input value!")
In this example, the validate_generator() function uses a while loop to receive values from the caller using yield continuously. The if statement then checks whether each input value is an integer and raises a ValueError exception if it is not. To raise an exception in this generator function, you can use the throw() method like this:
gen = validate_generator()next(gen)try: gen.send("Hello, world!")except ValueError as e: print("Error:", e)
Output
Best practices for using yield keyword in Python
- Use meaningful function and variable names:
When writing a generator function that uses yield, it’s important to use meaningful functions and variable names that accurately describe what the function does and what the variables represent. It can make the code easier to understand and maintain, especially when working with other developers.
- Use a for loop to consume the generator:
When you use a yield generator function, you can consume the sequence of values it generates using a for a loop. It is generally the preferred way to consume a generator, as it’s more concise and less error-prone than manually calling next() on the generator object.
- Avoid modifying the state outside of the generator function:
When writing a generator function that uses yield, it’s generally best to avoid modifying the generator’s state outside of the function. It can lead to unexpected behavior and make the code harder to understand and maintain. If you need to modify the generator’s state, consider using a coroutine pattern or passing values into the generator using send().
- Document the generator’s behavior:
When writing a generator function that uses yield, it’s important to document its behavior, including what values it generates, how it handles errors and exceptions, and any other relevant information. It can help other developers understand how to use the generator correctly and avoid common pitfalls.
- Use the yield from the statement for delegation:
If you’re writing a generator function that needs to delegate to another generator function, you can use the yield from the statement to simplify the code. This statement allows you to delegate to another generator function without manually looping over its values and yield each one.
- Test your generator functions:
Like any other code, it should be tested to ensure they behave correctly under various conditions. Consider writing unit tests that exercise your generator function with different inputs and test its behavior under different scenarios.
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Common pitfalls to avoid when using the Python yield keyword
- Forgetting to iterate over the generator: When you create a yield generator, you need to iterate over it to get the values it generates. If you forget to iterate over the generator, you won’t get any values, and the generator function will appear to do nothing.
- Reusing generator objects: Once a generator has been consumed, you cannot reuse it. If you try to iterate over a generator object more than once, it will not generate any values. Instead, creating a new generator object each time you want to iterate over the sequence of values would be best.
- Modifying the generator outside the function: When you use yield, the generator’s state is stored internally within the generator function. If you modify the state of the generator outside of the function, it can lead to unexpected behavior and errors. Instead, you should pass any necessary information into the generator using the send() method.
- Mixing up return and yield: return and yield have different behavior and should not be used interchangeably. Using return instead of yield in a generator function will terminate the function and return a single value instead of generating a sequence of values.
- Not handling exceptions: When you use yield, exceptions can be raised both inside and outside the generator function. If you don’t handle exceptions properly, they can cause your program to crash or behave unexpectedly. Handle exceptions appropriately by catching them inside the generator or propagating them to the caller.
- Confusing yield with a list: yield differs from returning a list. A yield function returns a generator object, which generates values one at a time, while returning a list generates all values simultaneously. This means that generators can be more memory-efficient and generate large or infinite sequences of values.
Conclusion
In this article, we have discussed what is yield() keyword in python, how to use it with generators. Later in the article, we have also discussed the best practices and some pitfalls of using yield keyword in python.
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Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio
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