What is the Difference between "Yield" and "Return" Keywords in Python?

If you're learning Python, there's a good chance you've run into the keywords yield and return and wondered what makes them different. At first glance, they both seem to "send data out" of a function, so why does Python have two ways to do this?

For many beginners, myself included, the difference between yield and return can be confusing. They both come from functions, they both output values, but they behave very differently under the hood. In this post, I’ll break down what each keyword does, how they affect a function's behavior, and when you'd want to use one over the other. By the end, you'll not only understand how generators work, but also why they exist and how they can make your Python code cleaner and more efficient.

Let’s clear up the confusion together!

Python Generators

To understand the difference between these keywords it is important to first understand the concept pf Python Generators. Python generators are a special type of function that returns an iterator that can be used to iterate through values. It uses the yield keyword instead of return

How Generators Work

1. Lazy Evaluation - Generators produce values on demand, only when needed. This makes them memory efficient. In comparison, normal functions produce all values at once, uses memory to store the values.
2. State Retention - When a generator encounters the yield keyword, it saves its internal state, including execution position and internal variables, and resumes the next time it is called.
3. Iterator Protocol - generators implement the iterator protocol, meaning they have the __next__() method. Meaning they can be used with iterators such as for loops.
4. Yield keyword - generators use the yield keyword which produces a value and pauses execution. It returns to its present state when the generator is called again.
5. Generator Object - When a generator function is called, it doesn't execute immediately. Instead, it returns a generator object, which is an iterator.

For Example:

   def my_generator(n):
    i = 0
    while i < n:
        yield i
        i += 1

gen = my_generator(5)

for value in gen:
    print(value)

# Output:
# 0
# 1
# 2
# 3
# 4

Key Differences with Normal Functions

1. Return vs Yield

Feature	return	yield
Stops function?	Yes	No — pauses and can resume
Values produced	One	Many (lazy sequence)
Memory efficiency	Not memory efficient for large data	Very efficient
Function type	Regular function	Generator function

1. state

Regular functions lose their state after returning. Generators retain their state between successive yield statements.
Usually, a normal funcction exits after the return statement and everything after is not reachable. In comparison, code after yield can still be executed as in the examples below.

Example 1: Using yield

def yield_example():
    print("Start")
    yield 1
    print("After first yield")
    yield 2
    print("After second yield")

gen = yield_example()

for val in gen:
    print("Received:", val)

# Output:
  # Start
  # Received: 1
  # After first yield
  # Received: 2
  # After second yield

Explanation:

• "Start" is printed at the start.

• First yield 1 pauses the function and gives control to the loop.

• "After first yield" is run when the loop calls next() again.

• This continues until the generator is exhausted.

Example 2: Using return

def return_example():
    print("Start")
    return 1
    print("This will NOT run")
    return 2

val = return_example()
print("Received:", val)

# Output:
  # Start
  # Received: 1

Explanation:

• "Start" is printed.

• return 1 ends the function.

• The line print("This will NOT run") is never reached.

Use Cases

1. Working with large datasets: Generators can process large datasets without loading them entirely into memory.
2. Creating infinite sequences: Generators can produce infinite sequences of values, such as the Fibonacci sequence.
3. Stream processing: Generators can handle data streams, processing each item as it arrives.
4. Generating synthetic data: Generators can create synthetic data for testing and development.
5. Simplifying complex logic: Generators can help simplify code by breaking down complex logic into smaller, manageable parts.