Python Interview Questions
Iterables, iterators, generators, and the built-in functional tools -- enumerate, zip, map, filter, reduce.
Ans
An iterable is an object that can provide an iterator, allowing it to be traversed with a for loop. Lists, tuples, strings, dictionaries, sets, and ranges are common iterables.
Example
pythonfor x in [1, 2, 3]: print(x)
Important Point
An iterable is not necessarily itself an iterator.
Ans
An iterator implements the iterator protocol, providing __iter__() returning itself and __next__() returning the next value or raising StopIteration.
Example
pythonit = iter([10, 20]) print(next(it)) print(next(it))
Important Point
An iterator is stateful and is normally consumed as values are requested.
Ans
A generator is a lazy iterator commonly created with a function containing yield or a generator expression. It produces values on demand instead of building the entire result at once.
Example
pythondef numbers(): yield 1 yield 2 for n in numbers(): print(n)
Important Point
Generators are useful for large or streaming data because they can keep memory usage low.
Ans
return ends a normal function and provides one final result. yield pauses a generator function and preserves its execution state so it can continue later.
Example
pythondef count(): yield 1 yield 2 print(list(count()))
Important Point
A function containing yield returns a generator iterator when called.
Ans
A list comprehension creates the complete list immediately. A generator expression produces values lazily as they are iterated.
Example
pythonlist_values = [x*x for x in range(5)] gen_values = (x*x for x in range(5)) print(list_values) print(list(gen_values))
Important Point
Use generators when immediate materialization is unnecessary or the input is large.
Ans
enumerate() produces pairs containing an index and an item while iterating, avoiding manual counter management.
Example
pythonfor index, name in enumerate(["A", "B"], start=1): print(index, name)
Important Point
The start value changes the reported index; it does not modify the underlying sequence.
Ans
zip() combines elements from multiple iterables into tuples, stopping when the shortest input is exhausted by default.
Example
pythonnames = ["A", "B"] marks = [80, 90] for name, mark in zip(names, marks): print(name, mark)
Important Point
Modern Python also provides `zip(..., strict=True)` when unequal lengths should raise an error.
Ans
map() applies a function to each item of an iterable and returns a lazy map iterator in Python 3.
Example
pythonvalues = map(str, [1, 2, 3]) print(list(values))
Important Point
A comprehension is often easier to read when the transformation is simple.
Ans
filter() lazily keeps elements for which a predicate is true.
Example
pythonvalues = filter(lambda x: x % 2 == 0, range(6)) print(list(values))
Important Point
Comprehensions can be clearer for many filtering tasks.
Ans
functools.reduce() repeatedly combines iterable values using a binary function to produce one accumulated result.
Example
pythonfrom functools import reduce print(reduce(lambda a, b: a + b, [1, 2, 3, 4], 0))
Important Point
For common operations such as sum, prefer the built-in function when available.
Ans
range() represents an arithmetic progression of integers and produces values lazily when iterated.
Example
pythonfor i in range(1, 5): print(i)
Important Point
The stop value is excluded, and range objects support efficient membership/index-related operations without storing every integer.
Ans
enumerate() directly provides both the item and its index, making iteration clearer and avoiding manual indexing.
Example
pythonfor i, value in enumerate(values): print(i, value)
Important Point
Use range(len(...)) when you genuinely need index-based mutation or multiple indexed accesses.
Ans
The start argument controls the initial index returned by enumerate without changing the underlying iterable.
Example
pythonfor i, value in enumerate(["A", "B"], start=100): print(i, value)
Important Point
It is useful when displayed numbering should begin at a domain-specific value.
Ans
zip(..., strict=True) raises ValueError if the input iterables do not have equal lengths, helping detect silent data truncation.
Example
pythonfor a, b in zip([1,2], [10,20], strict=True): print(a, b)
Important Point
Use strict mode when unequal lengths indicate a data error.