🎯 07 – Interview Questions: Python Basics

These are real questions asked in Data Science and Python developer interviews.

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🔢 Variables & Data Types

Q1: What is the difference between is and == in Python?

Answer: == compares values; is compares identity (same object in memory).

a = [1, 2, 3]
b = [1, 2, 3]
print(a == b)   # True  (same values)
print(a is b)   # False (different objects)

c = a           # c points to the same object
print(a is c)   # True

Rule: Use is only for None, True, False comparisons. Use == for value comparisons.

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Q2: What is the output of 0.1 + 0.2 == 0.3? Why?

Answer: False. Floating point numbers can't be represented exactly in binary. Use abs(0.1 + 0.2 - 0.3) < 1e-9 to compare floats.

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Q3: What is the difference between int and float? When does division return a float?

Answer: int stores whole numbers; float stores decimals. In Python 3, / always returns a float (even 4 / 2 = 2.0). Use // for integer division.

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🔀 Control Flow

Q4: What is the difference between break and continue?

Answer: break exits the loop entirely. continue skips the rest of the current iteration and jumps to the next one.

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Q5: What does this code print?

for i in range(3):
    pass
print(i)

Answer: 2 — the loop variable i retains its last value after the loop ends, even with pass.

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Q6: What is a list comprehension? Write one that filters and transforms.

Answer: A compact syntax for building lists. Example: get squares of even numbers from 1–20:

result = [x**2 for x in range(1, 21) if x % 2 == 0]

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🔧 Functions

Q7: What is the difference between *args and **kwargs?

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

def f(*args, **kwargs):
    print(args)    # (1, 2, 3)
    print(kwargs)  # {'x': 10, 'y': 20}
f(1, 2, 3, x=10, y=20)

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Q8: What is a lambda function? Write one that sorts a list of tuples by the second element.

Answer: An anonymous one-line function.

data = [(1, 'banana'), (2, 'apple'), (3, 'cherry')]
sorted_data = sorted(data, key=lambda x: x[1])
# [(2, 'apple'), (1, 'banana'), (3, 'cherry')]

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Q9: What is a mutable default argument trap?

Answer: Using a mutable object (list, dict) as a default parameter is a classic bug:

# BUG — the list is created once and reused!
def append_to(item, lst=[]):
    lst.append(item)
    return lst

print(append_to(1))   # [1]
print(append_to(2))   # [1, 2]  ← unexpected!

# FIX — use None as default
def append_to(item, lst=None):
    if lst is None:
        lst = []
    lst.append(item)
    return lst

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📚 Data Structures

Q10: What is the difference between a list and a tuple?

Answer: | | List | Tuple | |---|------|-------| | Mutable | ✅ | ❌ | | Syntax | [1,2,3] | (1,2,3) | | Use case | Dynamic data | Fixed data, dict keys | | Speed | Slightly slower | Slightly faster |

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Q11: How do you safely get a value from a dict that might not have the key?

Answer: Use .get() with a default:

d = {"name": "Alice"}
email = d.get("email", "no email")   # "no email"

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Q12: What is the time complexity of in for a list vs a set?

Answer: List: O(n) — must check every element. Set: O(1) — uses a hash table. For large membership tests, always use a set.

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Q13: How do you remove duplicates from a list while preserving order?

Answer:

data = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3]
unique = list(dict.fromkeys(data))
# [3, 1, 4, 5, 9, 2, 6]

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Q14: What is the difference between append() and extend()?

Answer:

a = [1, 2, 3]
a.append([4, 5])     # [1, 2, 3, [4, 5]]  ← adds list as one element

b = [1, 2, 3]
b.extend([4, 5])     # [1, 2, 3, 4, 5]    ← adds each element