Iterators are objects that implement __iter__() and __next__() methods. The generator is a simplified version of iterators, which automatically implement these methods through the yield keyword. 1. Each time the iterator calls next(), the StopIteration exception is thrown when there are no more elements. 2. The generator uses function definition to generate data on demand, saving memory and supporting infinite sequences. 3. Use iterators when processing existing sets, use a generator when dynamically generating big data or lazy evaluation, such as loading line by line when reading large files. Note: Iterable objects such as lists are not iterators. They need to be recreated after the iterator reaches its end, and the generator can only traverse it once.
Python generators and iterators are very practical tools for processing data streams, especially in scenarios where large amounts of data are processed or lazy evaluation is required. They can help you save memory, improve performance, and make your code more concise.
What is an iterator?
In Python, as long as an object implements __iter__() and __next__() methods , it is an iterator.
-
__iter__()returns the iterator itself. -
__next__()returns an element at a time, andStopIterationexception will be thrown when there are no more elements.
You may have used a lot of built-in iterators, such as lists, strings, dictionaries and other iterable objects. They will actually be converted into iterators for use in the for loop.
Let's give a simple example:
my_list = [1, 2, 3] it = iter(my_list) print(next(it)) # Output 1 print(next(it)) # Output 2
But usually you don't need to call next() manually, just leave it to for loop to process.
What is a generator? What does it have to do with iterators?
You can understand the generator as a "simplified version of iterator". It does not require you to manually implement __iter__ and __next__ , but is automatically generated by a function with the yield keyword.
For example:
def my_generator():
yield 1
yield 2
yield 3
gen = my_generator()
print(next(gen)) # Output 1
print(next(gen)) # Output 2The benefits of generators are:
- Lazy evaluation, generate data on demand, save memory
- More concise, it feels like a normal function
- Can be used to represent infinite sequences (such as a function that continuously generates numbers)
For example, if you want to process 100 million numbers, you will definitely not be able to bear it if it exists in the list, but you can use the generator to generate it while using it.
When should I use a generator and when should I use iterator?
This question is actually a bit like asking: “Do I take a bicycle by myself or buy one directly?”
If you just want to iterate over an existing collection, such as lists, file lines, and database result sets, then it is enough to just use the built-in iterator or for loop.
And when you:
- Need to generate data dynamically
- Too large data volume is not suitable for one-time loading
- Want to keep the code simple and clear
That's more suitable for using generators.
Let’s give a practical example: read large files.
def read_large_file(file_path):
with open(file_path) as f:
for line in f:
yield line.strip()This way, only one line is read at a time, and the entire file will not be loaded into memory at once.
Pay attention to the small details
- Not all iterable objects are iterators, such as lists are iterable, but they are not iterators themselves.
- Once the iterator reaches its end (throws
StopIteration), it cannot be used anymore and must be recreated. - The generator can only be traversed once and cannot be reset unless the generator function is called again.
In general, generators and iterators are the basic tools in Python for handling data flows. Understanding how they work will allow you to write more efficient and elegant code.
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