What are generators in Python?
Assesses fundamental understanding of Python conventions, runtime behavior, and memory/performance considerations.
Hiring managers look for precision, avoidance of ambiguous jargon, and ability to explain trade-offs under real production conditions.
Generators are functions that return an iterable collection of items, one at a time, in a set manner. Generators, in general, are used to create iterators with a different approach. They employ the use of yield keyword rather than return to return a generator object.
Let's try and build a generator for fibonacci numbers –
## generate fibonacci numbers upto n
def fib(n):
p, q = 0, 1
while(p < n):
yield p
p, q = q, p + q
x = fib(10) # create generator object
## iterating using __next__(), for Python2, use next()
x.__next__() # output => 0
x.__next__() # output => 1
x.__next__() # output => 1
x.__next__() # output => 2
x.__next__() # output => 3
x.__next__() # output => 5
x.__next__() # output => 8
x.__next__() # error
## iterating using loop
for i in fib(10):
print(i) # output => 0 1 1 2 3 5 8
Candidate Response Strategy & Interview Tips
- Start with a concise one-sentence summary: Deliver a direct, confident answer first before expanding into nuances.
- Demonstrate real-world trade-offs: Discuss where this approach excels and when you would avoid it in production systems.
- Discuss complexity & edge cases: Proactively explain time/space complexity or boundary conditions (null values, scale limits).
- Prepare for interviewer follow-ups: Technical hiring panels frequently probe deeper into concurrency, backward compatibility, or alternative libraries.