How To Yield A List In Python

In this blog post, we will discuss how to use the yield keyword in Python to return a list. The yield keyword is used in a function like a return statement, but instead of returning a value and ending the function, the yield statement returns a generator object. This allows the function to be called multiple times and return a new value each time, without losing its internal state.

Using the Yield Keyword

To use the yield keyword in a function, we simply replace the return statement with a yield statement. Let’s look at an example of a simple function that yields a list of numbers:

  def generate_numbers():
      n = 1
      while True:
          yield [n, n+1, n+2]
          n += 3
  

In this example, the generate_numbers function is an infinite generator that yields a list of three consecutive numbers, starting from 1. Each time the function is called, the next list of three numbers is returned. Here’s how we would use this generator in a loop:

  gen = generate_numbers()

  for i in range(5):
      print(next(gen))
  

This code would output the following:

  [1, 2, 3]
  [4, 5, 6]
  [7, 8, 9]
  [10, 11, 12]
  [13, 14, 15]
  

Advantages of Using Yield

There are a few major advantages to using the yield keyword in our functions:

1. Memory Efficiency: Since the generator function only returns one value at a time, it doesn’t need to store the entire list of values in memory. This can be particularly useful when working with large datasets or when generating a sequence of results that would be too big to fit in memory.
2. Lazy Evaluation: Generators are only evaluated when their values are actually requested. This means that if we don’t need all the values generated by a generator, we don’t waste time computing them. This can lead to performance improvements when working with large datasets or when generating a sequence of results that take a long time to compute.

Conclusion

Using the yield keyword in Python is a powerful way to create generator functions that can return a list of values one at a time. They offer memory efficiency and lazy evaluation, making them an excellent choice for working with large datasets or when generating a sequence of results that would be too big to fit in memory.