Generator Expressions
Overview
Generators are similar to list comprehensions, but they behave differently. Instead of storing all values in memory, a generator creates values one by one as needed.
- List Comprehension: Creates a list and stores all values in memory.
- Generator: Generates each value on the fly when needed.
Example: List comprehension
numbers = [x**2 for x in range(5)]
print(numbers)
Output:
[0, 1, 4, 9, 16]
Example: Generator
numbers_gen = (x**2 for x in range(5))
print(numbers_gen)
Output:
<generator object <genexpr> at 0x7f7e7c9b2f90>
Using Generators
You can loop over a generator just like a list. You can also convert it to a list or use next() to retrieve individual values.
Example:
numbers_gen = (x**2 for x in range(5))
for num in numbers_gen:
print(num)
Output:
0
1
4
9
16
You can also pass a generator to the function list to create a list.
numbers_list = (x**2 for x in range(5))
print(list(numbers_list))
Output:
[0, 1, 4, 9, 16]
Lazy Evaluation
Generators are useful for large datasets where storing the entire list in memory is impractical. This is called lazy evaluation, where values are computed only when needed.
Example:
# Does not create the entire list in memory
large_gen = (x**2 for x in range(10**6))
print(next(large_gen)) # 0
print(next(large_gen)) # 1
Conditionals
Just like list comprehensions, you can use conditionals in generator expressions.
Example:
numbers_gen = (x**2 if x % 2 == 0 else 0 for x in range(5))
for num in numbers_gen:
print(num)
Output:
0
0
4
0
16
Generator Functions
A generator function uses yield instead of return to produce values one at a time. This is useful for creating more complex generators.
In this example, count_up_to is a generator function that yields values one by one until it reaches n.
def count_up_to(n):
"""Generate values from 0 to N"""
i = 0
while i <= n:
yield i
i += 1
gen = count_up_to(5)
for num in gen:
print(num)
Output:
0
1
2
3
4
5