Introduction to generator expressions

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Hugo Bowne-Anderson

Data Scientist at DataCamp

Generator expressions

  • Recall list comprehension
[2 * num for num in range(10)]

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
  • Use ( ) instead of [ ]
(2 * num for num in range(10))

<generator object <genexpr> at 0x1046bf888>
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List comprehensions vs. generators

  • List comprehension - returns a list
  • Generators - returns a generator object
  • Both can be iterated over
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Printing values from generators (1)

result = (num for num in range(6))

for num in result:
    print(num)

0
1
2
3
4
5

result = (num for num in range(6))

print(list(result))

[0, 1, 2, 3, 4, 5]
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Printing values from generators (2)

result = (num for num in range(6))
  • Lazy evaluation
print(next(result))

0

print(next(result))

1

print(next(result))

2

print(next(result))

3

print(next(result))

4
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Generators vs. list comprehensions

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Generators vs. list comprehensions

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Generators vs. list comprehensions

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Conditionals in generator expressions

even_nums = (num for num in range(10) if num % 2 == 0)

print(list(even_nums))

[0, 2, 4, 6, 8]
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Generator functions

  • Produces generator objects when called
  • Defined like a regular function - def
  • Yields a sequence of values instead of returning a single value
  • Generates a value with yield keyword
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Build a generator function

  • sequence.py
def num_sequence(n):
    """Generate values from 0 to n."""
    i = 0
    while i < n:
        yield i
        i += 1
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Use a generator function

result = num_sequence(5)

print(type(result))

<class 'generator'>

for item in result:
    print(item)

0
1
2
3
4
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Let's practice!

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