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Intermediate Python Interview Questions and Answers

Intermediate Python interview questions with explanations and code: mutable default arguments, shallow vs deep copy, *args and **kwargs, generators, late-binding closures, scope, class vs instance attributes, and exceptions.

Intermediate6 min readUpdated
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Intermediate questions test whether you understand how Python behaves, not just its syntax. Many are "predict the output" puzzles built on a single surprising rule. Explain the rule, not just the answer.

1. What does this print? (Mutable default arguments)#

Python
def add(item, items=[]):
    items.append(item)
    return items

print(add(1))
print(add(2))
Show hint

When is the default list created?

Show solution
Output
[1]
[1, 2]

Default values are evaluated once, when the function is defined, so every call shares the same list. Fix: items=None, then if items is None: items = [] inside. See Parameters.

2. Shallow copy vs deep copy?#

Show solution

A shallow copy creates a new outer object but shares the inner objects; a deep copy copies everything recursively:

Python
import copy

grid = [[1, 2], [3, 4]]
shallow = grid.copy()
deep = copy.deepcopy(grid)

grid[0][0] = 99
print(shallow[0][0], deep[0][0])
Output
99 1

The shallow copy still points at the same inner lists, so it sees the change.

3. What are *args and **kwargs?#

Show solution

*args collects extra positional arguments into a tuple; **kwargs collects extra keyword arguments into a dict:

Python
def report(*args, **kwargs):
    print(args, kwargs)

report(1, 2, unit="kg")
Output
(1, 2) {'unit': 'kg'}

They are also used to forward arguments, for example in decorators.

4. List comprehension vs generator expression?#

Show solution

A list comprehension builds the whole list in memory; a generator expression produces values lazily, one at a time, and can be consumed only once:

Python
gen = (n * n for n in range(3))
print(list(gen), list(gen))
Output
[0, 1, 4] []

Use generators for large or one-pass data. See Generators.

5. What does this print? (Late binding in closures)#

Python
funcs = [lambda: i for i in range(3)]
print([f() for f in funcs])
Show hint

When does each lambda look up i?

Show solution
Output
[2, 2, 2]

Closures look up variables when they are called, not when they are created. By then the loop has finished and i is 2. Capture the current value with a default argument:

Python
funcs = [lambda i=i: i for i in range(3)]
print([f() for f in funcs])
Output
[0, 1, 2]

6. Why does this raise UnboundLocalError?#

Python
x = 10
def f():
    print(x)
    x = 5
f()
Show solution

Because x is assigned somewhere in f, Python treats it as a local variable for the whole function. The print(x) then reads the local x before it has a value. Use a different name, pass x as a parameter, or (rarely) declare global x. See Scope.

7. Class attributes vs instance attributes: what is the bug?#

Python
class A:
    def __init__(self):
        self.items = []

class B:
    items = []

a1, a2 = A(), A()
a1.items.append(1)
b1, b2 = B(), B()
b1.items.append(1)
print(a2.items, b2.items)
Show solution
Output
[] [1]

A creates a new list per object in __init__. B.items is a class attribute, shared by every instance, so appending through b1 also changes what b2 sees. Per-object data belongs in __init__. See Classes.

8. What is the difference between sort() and sorted()?#

Show solution
Python
print([1, 2, 3][::-1], sorted([3, 1, 2]), [3, 1, 2].sort())
Output
[3, 2, 1] [1, 2, 3] None

sorted() returns a new list and works on any iterable. list.sort() sorts in place and returns None, so x = x.sort() loses your list.

9. How do try, except, else and finally work together?#

Show solution
  • try: code that might fail.
  • except: runs if a matching exception occurs.
  • else: runs only if no exception occurred.
  • finally: always runs, for clean-up.
Python
try:
    {}["missing"]
except KeyError as e:
    print("caught", repr(e))
finally:
    print("finally always runs")
Output
caught KeyError('missing')
finally always runs

See Exceptions.

10. Which collections module tools do you use?#

Show solution
  • Counter: counts items.
  • defaultdict: a dict with an automatic default for missing keys.
  • deque: fast appends and pops at both ends (queues).
  • namedtuple: lightweight records with named fields.
Python
from collections import Counter, defaultdict

print(Counter("mississippi").most_common(2))

groups = defaultdict(list)
for w in ["apple", "avocado", "banana"]:
    groups[w[0]].append(w)
print(dict(groups))
Output
[('i', 4), ('s', 4)]
{'a': ['apple', 'avocado'], 'b': ['banana']}

Next: Advanced Questions.

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