Python's yield is a very useful keyword. It returns a value temporarily and pauses the function by preserving its state. There are methods like iter() and next() that work together with yield, and we will also learn try-except and Default Parameters.

Today's agenda:

• Iterables and Iterators
• Default Parameter (fallback value) and try-except for Error Handling
• Difference between yield and return
• For Loop to use Generator

Iterables and Iterators

Python datatypes like lists, strings, tuples, dictionaries, sets, and ranges are called iterables. We use iterator iter(), a built-in function which returns the iterator object from iterables.

syntax:
iter(iterable) # iterator
next(iterator) # next value

When no values remain, next() raises StopIteration. For example:

arr = [1, 2] # arr contains 2 values, len(arr) == 3
next_number = iter(arr)

print(next(next_number)) # 1
print(next(next_number)) # 2
print(next(next_number)) # err -> StopIteration

Default Parameter (fallback value) and try-except for error handling

1. Default Parameter (fallback value)

To handle the StopIteration error we can use a fallback value directly in the next() method. This is an easier and more effective way than writing a try-except block.

syntax:
next(iterator, 'finished') # 'finished' string as a fall back value 

2. try-except

We can use try-except to handle the StopIteration error.

# syntax:
try:
    next(iterator)
except StopIteration:
    pass # handle your error here

Combined Example

arr = [1, 2] # arr contains 4 values, len(arr) is 4
next_number = iter(arr)

print(next(next_number)) # 1
print(next(next_number)) # 2

# Fall back example
print(next(next_number, 'no more item on arr')) # 'no more item on arr'

# try-except example
try:
  print(next(next_number)) # error
except StopIteration:
  print('no more item on arr') # 'no more item on arr'

Difference between yield and return

The return keyword is very commonly used while writing functions. After writing a block of code and applying logic inside the function, it simply returns the result using the return keyword. After the return line, other lines of code are not accessible.

# Uses of return
def next_number():
    numbers = [1,2,3]
    for number in numbers:
        return number # Always returns the first number -> 1
        print('this line will never execute')

print(next_number()) # 1
print(next_number()) # 1 
# next_number() always generates 1 in this case

yield is a keyword like return that provides the current result and can also preserve the function state, which means another call can reuse the same state to get another result.

# Uses of yield
def next_number():
    numbers = [1,2,3]
    for number in numbers:
        print('Starting number')
        yield number
        print('Ending number')

which_number = next_number() # Generator function (Function that contains the yield keyword)

print(next(which_number)) #Output: 1
print(next(which_number)) #Output: 2
print(next(which_number)) #Output: 3

Here, each next() call resumes the function until the next yield.

For Loop to use Generator

To extend the above example, we can use a for-in loop instead of next() to get the same results.

# Uses of yield
def next_number():
    numbers = [1,2,3]
    for number in numbers:
        print('Starting number')
        yield number
        print('Ending number')

which_number = next_number() # Generator function 

for number in which_number:
    print(number)