Python development
30 important interview questions.

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• Simple, readable syntax that uses indentation for blocks.
• Interpreted and dynamically typed: you don't declare variable types.
• A large standard library and a huge ecosystem of packages (pip).
• Supports procedural, object-oriented and functional styles.
• Used for web backends, automation, data analysis, machine learning and scripting.

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Mutable objects can be changed after creation: lists, dicts, sets. Immutable objects can't: ints, floats, strings, tuples. When you "change" an immutable value, Python creates a new object. Only immutable (hashable) objects can be dictionary keys or set members.

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• == checks whether two values are equal.
• is checks whether two names point to the same object in memory.

Use is only for singletons such as None: if value is None:.

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A shallow copy (list.copy(), copy.copy()) creates a new outer object but shares the nested objects inside it. A deep copy (copy.deepcopy()) copies everything recursively.

import copy

team = {"name": "QA", "members": ["Anu", "Ravi"]}
shallow = copy.copy(team)
deep = copy.deepcopy(team)
team["members"].append("Meera")
# shallow["members"] now has Meera too; deep["members"] does not

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A short way to build a list from another iterable, optionally with a condition:

squares = [n * n for n in range(10) if n % 2 == 0]
# [0, 4, 16, 36, 64]

There are also dict comprehensions ({k: v for ...}) and set comprehensions. Keep them simple; use a loop when the logic gets long.

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Python uses reference counting: each object tracks how many references point to it and is freed when the count reaches zero. A cyclic garbage collector also cleans up groups of objects that refer to each other. Memory is managed by the interpreter, so you rarely free memory yourself.

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A class is a blueprint; an object is an instance of it. __init__ sets up a new object, and self refers to the current instance.

class Account:
    def __init__(self, owner, balance=0):
        self.owner = owner
        self.balance = balance

    def deposit(self, amount):
        self.balance += amount

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• @classmethod receives the class (cls) as its first argument. Often used for alternative constructors such as Account.from_csv(row).
• @staticmethod receives neither the class nor the instance. It's a plain function grouped inside the class.

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Special methods with double underscores that let your objects work with Python's built-in behaviour: __init__ (create), __str__ and __repr__ (display), __len__ (len), __eq__ (==), __iter__ (loops). For example, defining __len__ lets you call len(obj).

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try:
    value = int(user_input)
except ValueError:
    print("Please enter a number")
else:
    print("Valid number")      # runs if no exception
finally:
    print("Always runs")        # cleanup

Catch specific exceptions rather than a bare except:, and raise your own with raise.

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An object used with with that sets something up and always cleans up, even on errors — closing files, releasing locks, committing transactions.

with open("report.csv") as f:
    data = f.read()
# the file is closed here

You can write your own with __enter__/__exit__ or the contextlib.contextmanager decorator.

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A virtual environment (python -m venv .venv) is an isolated folder with its own Python packages. Each project gets its own dependency versions, so projects don't conflict. Save dependencies with pip freeze > requirements.txt and reinstall them with pip install -r requirements.txt.

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pytest is the most common tool. Test functions start with test_ and use plain assert:

def add(a, b):
    return a + b

def test_add():
    assert add(2, 3) == 5

Use fixtures for setup and unittest.mock to replace external services such as APIs or databases.

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async def defines a coroutine, and await pauses it while waiting for I/O so the event loop can run other tasks. It lets one thread handle many network requests at once. It helps I/O-bound work, not CPU-heavy work.

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• threading: threads share memory; good for I/O-bound tasks. The GIL limits CPU-bound speed-ups.
• multiprocessing: separate processes with their own memory and interpreter; good for CPU-bound tasks, but with more overhead.

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PEP 8 is Python's style guide: 4-space indentation, snake_case for functions and variables, PascalCase for classes, readable line lengths and clear spacing. Tools such as Black, Ruff or Flake8 format and check code automatically.

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1. Measure with cProfile or time.perf_counter() before changing anything.
2. Use the right data structure — a set or dict lookup is much faster than searching a list.
3. Avoid repeated work inside loops; use built-ins and comprehensions.
4. Use NumPy or pandas for heavy numeric work.
5. Cache results (functools.lru_cache) and use async or multiprocessing where they fit.