Prepare for Python interviews by learning to explain how the language behaves, not by memorizing one-line definitions. This guide covers the fundamentals interviewers can probe—from collections and imports to exceptions, classes, iterators, generators, and type hints—with examples and version-aware qualifications.
How to use these Python interview questions
There is no evidence here establishing which questions employers ask most often, so this is a study guide, not a ranked list or a prediction of interview frequency. Use each question to practise explaining a concept, choosing between alternatives, and reasoning about a small example.
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The official Python Tutorial describes Python as “an easy to learn, powerful programming language.” It is intended for programmers new to Python, and explicitly is not comprehensive. Its coverage is a useful foundation for revision, not a complete interview syllabus.
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Python fundamentals and data structures
What is the difference between a list and a tuple?
Both are ordered sequences. A list is mutable, so elements can be added, removed, or replaced; a tuple is immutable, so its sequence of references cannot be changed after creation. Use a list when the collection itself needs to change and a tuple when a fixed grouping is appropriate.
names = ["Ari", "Bo"]
names.append("Cy")
point = (4, 7)Immutability is shallow: a tuple can contain a mutable object, and that object may still change. For example, a tuple containing a list does not make the list immutable.
What is the difference between a set and a dictionary?
A set stores distinct elements and is useful for membership checks and set operations such as intersection. A dictionary stores key-value associations; keys are unique, and looking up a key retrieves its associated value.
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profile = {"name": "Ari", "active": True}How do you choose among Python’s built-in collections?
- List: an ordered, changeable sequence.
- Tuple: an ordered, non-changeable sequence.
- Set: distinct elements, with membership and set operations.
- Dictionary: keys mapped to values.
Explain the behavior your code needs rather than claiming one collection is universally fastest or best. The right choice depends on whether order, mutation, uniqueness, or key-based lookup matters.
Modules, packages, and imports
What is a module?
A module is a file of Python definitions and statements that can be imported. Importing makes names from that module available according to the import form used. For example, import math binds the module name, while from math import sqrt binds the selected name.
What is a package?
A package organizes modules under a package name so related code can be grouped and imported. The precise layout and import behavior can depend on how a project is structured; consult the Python Tutorial’s modules chapter for the relevant rules.
What is the difference between importing a file and running it as a script?
When a file is run as the main program, Python executes it in the main-module context. When imported, Python executes its module-level code as part of loading the module and makes its definitions available under the module’s name. A common guard, if __name__ == "__main__":, lets a file provide script behavior without running that block on import.
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What is the module search path?
It is the set of locations Python consults when resolving imports. The environment and program configuration affect the path, so an import that works in one project or shell may fail in another. When diagnosing an import error, check the active environment, working setup, package layout, and whether a local file is shadowing the intended module.
Errors and exception handling
What is the difference between a syntax error and an exception?
A syntax error is detected while Python parses code that does not conform to the language grammar. An exception can occur while syntactically valid code is executing—for example, a lookup may raise KeyError. The distinction is about when the problem is detected, not whether the failure matters.
How does Python match an exception handler?
Exceptions are class instances. An except clause matches the specified exception class or a derived class. If a handler does not match, the exception can propagate outward to a caller that may handle it.
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try:
value = settings["mode"]
except KeyError:
value = "default"Catch the narrowest exception your code can handle meaningfully. Catching broad exceptions can hide unrelated defects, especially if the handler silently continues.
What do try, except, else, and finally do?
tryencloses code that may raise an exception.excepthandles a matching exception.elseruns when thetrysuite completes without an exception.finallyruns as control leaves the construct, including when an exception is propagating.
Use finally for cleanup that must happen across success and failure paths; for resource management, context managers are often clearer.
How do you define a custom exception?
Define a class derived from Exception (or an appropriate subclass), then raise it where the application-specific failure occurs. A custom type lets callers handle that category deliberately instead of inspecting message text.
class InvalidRecordError(Exception):
pass
raise InvalidRecordError("record is incomplete")Exception message contents are not a stable Python API and can change between versions. Code that needs reliable handling should use exception types and documented attributes rather than matching prose messages. See the Python 3.14 execution model reference.
Classes, instances, and object behavior
What is a class, and what is an instance?
A class defines behavior and can define data attributes; an instance is an object created from that class. Instance attributes typically hold per-object state, while class attributes are associated with the class and can be shared or shadowed by instance attributes.
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class Counter:
kind = "event"
def __init__(self):
self.value = 0
def increment(self):
self.value += 1Here, kind is a class attribute and value is initialized separately on each instance.
What is self?
By convention, self is the first parameter of an instance method and refers to the instance on which the method is called. It is a conventional parameter name, not a reserved keyword.
What is inheritance?
Inheritance lets a class derive from another class, reuse or specialize behavior, and participate in method lookup through its base classes. Use it when the relationship and shared behavior are meaningful; composition—holding another object and delegating work to it—can be simpler when the relationship is not truly “is a.”
What are namespaces and scope?
A namespace maps names to objects; scope describes where a name can be resolved. Python’s execution model defines how names are bound and looked up. For an interview answer, distinguish a name from the object it refers to, and explain whether a name is local, enclosing, global, or built in when that distinction affects the example.
Iterators and generators
What is an iterator?
An iterator produces values one at a time as it is consumed. A for loop works with an iterable by obtaining an iterator and requesting successive values until iteration is exhausted. Iterators are useful when processing a sequence of values without requiring the consumer to handle the entire result at once.
What is a generator?
A generator is a convenient way to create an iterator. A generator function uses yield to produce a value and suspend its execution; iteration resumes it to produce later values.
def squares(limit):
for number in range(limit):
yield number * numberCalling squares(3) creates a generator; its values are produced as it is iterated, rather than all being returned as a list at the call site. This can suit streaming or large inputs, but a generator is consumed as it advances and is not automatically reusable like a freshly constructed collection.
Type hints and Python’s typing system
Is Python statically or dynamically typed?
Python is dynamically typed: objects have types, and names can be bound to objects of different types over time. Type annotations and the typing ecosystem support static analysis, helping tools reason about intended types without changing Python into a language that enforces those annotations on every ordinary runtime assignment.
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Do type hints enforce types at runtime?
Not by themselves. An annotation communicates type information, but ordinary Python execution does not generally reject a value solely because it does not match an annotation. A static type checker or other tooling may report a mismatch. Avoid presenting annotations as runtime validation unless the program explicitly uses a runtime validation mechanism.
What are generic types?
Generic types express a type relationship involving parameters, such as a sequence whose elements have a stated type. The typing specification describes generic classes and assignability, and details evolve across Python versions. For version-sensitive syntax or rules, use the current Python typing specification and identify the version relevant to your code.
How to prepare beyond definitions
- Explain the behavior first, then give a small example.
- State the trade-off behind a data-structure or design choice.
- Trace what happens when code succeeds and when it fails.
- Separate language guarantees from conventions and implementation details.
- For version-sensitive behavior, name the Python version rather than offering a universal claim.
The Python Tutorial’s topics—data structures, modules, exceptions, classes, iterators, generators, and standard-library facilities—make a grounded revision outline. It is not a guarantee of what any particular employer will ask.
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