What are generators in Python and how do they work
Oct 05, 2025 am 02:17 AM
生成器通過yield逐個返回值，如count_up_to(n)函數(shù)所示，每次調(diào)用返回一個數(shù)字并暫停，直到下一次請求，實現(xiàn)內(nèi)存高效的數(shù)據(jù)處理。
Generators in Python are a simple way to create iterators. Unlike regular functions that return all results at once using return, generators yield one result at a time using yield. This makes them memory efficient, especially when dealing with large datasets.
How Generators Work
When you call a generator function, it doesn't run the function immediately. Instead, it returns a generator object that can be iterated over. Each time you request the next value (like in a loop), the function runs until it hits yield, then pauses and saves its state. The next call resumes from where it left off.
Example:
def count_up_to(n):
???num = 1
???while num
??????yield num
??????num = 1
for number in count_up_to(5):
???print(number)
This prints numbers from 1 to 5, but only one at a time. The function remembers the current value of num between calls.
Benefits of Using Generators
- Memory Efficient: They don’t store all values in memory — only generate them as needed.
- Simplify Code: Writing a generator is often easier than building a class-based iterator.
- Work Well With Streams: Ideal for processing large files or data streams where loading everything is impractical.
Generator Expressions
You can also create generators using expressions, similar to list comprehensions but with parentheses.
values = (x**2 for x in range(5))
for val in values:
???print(val)
This generates squares of numbers 0 to 4 without creating a full list in memory.
Basically, generators give you lazy evaluation — values are produced on demand. That’s what makes them powerful and efficient for many real-world tasks.
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