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Efficient Data Handling with Generators In modern backend development, especially when dealing with large volumes of financial or time-series data, efficiency, responsiveness, and scalability are non-negotiable. Python's generators provide a clean and memory-efficient solution for such scenarios. In this article, we'll explore: What Python generators are (and what they are not) How generators work under the hood Practical use cases (like stock data streaming) Examples from MJ-API-Development, JobFinders, and more ✅ What Are Python Generators? A generator is a special type of iterable, like a list or a tuple, but instead of returning all items at once, it yields one item at a time — only when requested. You define a generator with a function that contains the yield keyword: def generate_numbers(n): for i in range(n): yield i When this function is called, it returns a generator object that you can iterate over using next() or a loop: gen = generate_numbers(3) print(next(gen)) # 0 print(next(gen)) # 1 print(next(gen)) # 2 When there are no more items to yield, the generator raises a StopIteration exception.
Efficient Data Handling with Generators
In modern backend development, especially when dealing with large volumes of financial or time-series data, efficiency, responsiveness, and scalability are non-negotiable. Python's generators provide a clean and memory-efficient solution for such scenarios.
In this article, we'll explore:
- What Python generators are (and what they are not)
- How generators work under the hood
- Practical use cases (like stock data streaming)
- Examples from MJ-API-Development, JobFinders, and more
✅ What Are Python Generators?
A generator is a special type of iterable, like a list or a tuple, but instead of returning all items at once, it yields one item at a time — only when requested.
You define a generator with a function that contains the yield keyword:
def generate_numbers(n):
for i in range(n):
yield i
When this function is called, it returns a generator object that you can iterate over using next() or a loop:
gen = generate_numbers(3)
print(next(gen)) # 0
print(next(gen)) # 1
print(next(gen)) # 2
When there are no more items to yield, the generator raises a StopIteration exception.
