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Generators


Generators are very easy to implement, but a bit difficult to understand.

Generators are used to create iterators, but with a different approach. Generators are simple functions which return an iterable set of items, one at a time, in a special way.

When an iteration over a set of item starts using the for statement, the generator is run. Once the generator's function code reaches a "yield" statement, the generator yields its execution back to the for loop, returning a new value from the set. The generator function can generate as many values (possibly infinite) as it wants, yielding each one in its turn.

Here is a simple example of a generator function which returns 7 random integers:

This function decides how to generate the random numbers on its own, and executes the yield statements one at a time, pausing in between to yield execution back to the main for loop.

Exercise

Write a generator function which returns the Fibonacci series. They are calculated using the following formula: The first two numbers of the series is always equal to 1, and each consecutive number returned is the sum of the last two numbers. Hint: Can you use only two variables in the generator function? Remember that assignments can be done simultaneously. The code

will simultaneously switch the values of a and b.

This site is generously supported by DataCamp. DataCamp offers online interactive Python Tutorials for Data Science. Join over a million other learners and get started learning Python for data science today!

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