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Python interview preparation is strongest when you can explain what code does and why—not when you memorize definitions. Use these questions to review core language behavior, collections, imports, exceptions, classes, iterators, generators, and typing. The examples focus on Python 3; when behavior depends on a particular release, check the version used by the role or project.
What should you know before using a question bank?
The official Python Tutorial describes Python as “an easy to learn, powerful programming language.” It is aimed at programmers new to Python, however, and explicitly is not comprehensive. Its topics make a grounded revision outline, not a complete interview syllabus. No reliable evidence establishes which questions employers ask most often, so the questions below are practice prompts rather than a ranking or a prediction of interview frequency.
Python fundamentals and data structures
1. What is the difference between a list and a tuple?
Both are ordered sequences. A list is mutable: its items can be replaced, added, or removed. A tuple is immutable as a sequence: its item slots cannot be reassigned after creation. Choose based on whether the collection needs in-place changes and whether the fixed-sequence intent is useful to readers of the code.
2. What does mutable mean in Python?
A mutable object can be changed after it is created. Lists, dictionaries, and sets are common mutable built-ins. Integers, strings, and tuples are immutable. Immutability applies to the object itself: a tuple can contain a mutable object, and that contained object may still change.
3. When would you use a set?
A set stores distinct hashable elements and is useful for membership checks, removing duplicates, and set operations such as union and intersection. It is not a sequence for positional indexing. Use a list instead when order or repeated elements matter.
4. What is a dictionary?
A dictionary maps keys to values. Keys must be hashable; values can be any objects. Use a dictionary when values are best retrieved by a meaningful key rather than by a numeric position.
5. How do you choose among a list, tuple, set, and dictionary?
- List: an ordered, changeable sequence, including when duplicates matter.
- Tuple: an ordered sequence whose item positions should not be reassigned.
- Set: distinct hashable values, especially for membership and set algebra.
- Dictionary: key-to-value lookup.
Selection is a design choice; explain the required behavior rather than claiming one container is universally best.
Modules, packages, and running Python files
6. What is a module?
A module is a unit of Python code that can define names such as functions, classes, and variables and can be imported by other code. Importing a module makes its names available through the module object, for example math.sqrt after import math.
7. What is a package?
A package organizes related modules under a package name, allowing imports such as package.module. Package structure and import behavior are explained in the Tutorial’s modules chapter.
8. What happens when Python imports a module?
Python locates the module using its import machinery and search path, then executes the module code when it is loaded. Imports are not merely declarations: top-level statements in a module can run as a consequence. Keep reusable definitions separate from work that should happen only when a file is launched directly.
9. How can a file behave differently when imported and when run as a script?
Python sets a module’s __name__ to '__main__' when that file is run as the program entry point. A common pattern is to put script-only behavior behind if __name__ == '__main__':. When imported under its module name, that guarded block does not run, while definitions outside it are still evaluated.
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10. What is the difference between a syntax error and an exception?
A syntax error is detected while Python parses code that does not follow the language grammar. An exception occurs when syntactically valid code is executed and an error condition arises. For example, 1 / 0 parses successfully but raises ZeroDivisionError when evaluated.
11. What is an exception in Python?
Exceptions are class instances. A raised exception can be handled by an except clause matching its class or a base class. If the current code has no matching handler, the exception propagates outward to its caller; if it remains unhandled, execution ends with a traceback.
12. How does try/except work?
Put code that may raise an expected exception in a try block, then handle specific exception classes in except clauses. Catch the narrowest useful class so unrelated programming errors are not silently hidden. The Tutorial’s errors and exceptions chapter describes the control flow.
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13. What is finally used for?
A finally clause runs as control leaves the associated try statement, whether or not an exception occurred. It is useful for cleanup that must be attempted across success and failure paths. For resource management such as files, a context manager with with is often clearer.
14. How do you define a custom exception?
Define a class derived from Exception (or a more specific exception class) and raise an instance when the program encounters the condition it represents. A custom exception gives callers a meaningful type to catch. Do not make callers depend on exact exception message text: Python’s documentation notes that exception messages are not a stable API and may vary between versions.
Classes, instances, and object behavior
15. What is the difference between a class and an instance?
A class defines a kind of object and its behavior; an instance is an object created from that class. For example, a class can define a Dog type, while my_dog = Dog() creates an instance.
16. What is self?
In a typical instance method, self is the parameter that receives the instance on which the method was called. It is a conventional parameter name, not a reserved keyword. Python passes the instance when an instance method is called through that instance.
17. What is the difference between an instance variable and a class variable?
An instance variable belongs to an individual object, commonly assigned through self.name. A class variable is defined on the class and can be shared by instances unless an instance attribute shadows it. Use instance attributes for per-object state; avoid mutable class attributes for data that should be independent across instances.
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Inheritance lets a class derive from another class, reusing or extending its behavior. A derived class can override inherited methods. Use it when the subtype relationship is meaningful; shared functionality alone does not always make inheritance the clearest design.
19. What are namespaces and scope?
A namespace maps names to objects; scope determines where a name can be resolved. Python code commonly encounters local, enclosing, global, and built-in namespaces. A name assigned in a function is local by default unless declared otherwise with global or nonlocal.
Iterators and generators
20. What is an iterator?
An iterator is an object that produces values one at a time. Iteration obtains an iterator and repeatedly requests its next value; when there are no more values, the iterator signals completion with StopIteration. A for loop handles this protocol for you.
21. What is an iterable?
An iterable is an object from which an iterator can be obtained, for example with iter(obj). Lists and strings are common iterables. An iterable is not necessarily itself an iterator: a list can produce a fresh iterator for each traversal.
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22. 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; it continues from that point when the next value is requested. Generators can process streams of values without building a complete result list in memory.
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23. What is the difference between return and yield?
return ends a function call and supplies its result. yield produces a value from a generator while preserving its state so iteration can resume later. A function containing yield creates a generator when called rather than immediately running through all its body.
Type hints and Python’s typing model
24. Is Python statically or dynamically typed?
Python is dynamically typed: values have types at runtime, and a name can be bound to objects of different types over its lifetime. Python also supports gradual typing through annotations and the typing ecosystem, which can help static analysis tools reason about code.
25. Do type hints enforce types at runtime?
No. An annotation such as def greet(name: str) -> str: documents intended types and can be checked by external tools, but Python does not automatically reject a call with a different argument type. Add explicit runtime validation when the program must enforce an input contract.
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26. What is a generic type?
A generic type can be parameterized by other types, expressing relationships among the types of its contents or inputs and outputs. For example, a type annotation can describe a list of strings rather than a list of arbitrary objects. The exact syntax and available features depend on the Python version and typing specification in use; consult the Python typing specification for current definitions, including generic types and assignability.
Quick Recap
How to prepare without memorizing brittle answers
- Use the Tutorial as a topic map. Review its sections on data structures, modules, errors, classes, iterators, and standard-library facilities; it is a starting outline, not a full reference.
- Explain behavior in your own words. For each concept, state what Python does, why it matters, and one case where the distinction changes a design choice.
- Run small examples on the version relevant to the role. Pay special attention to behavior that can vary by Python release, and avoid treating an observed exception message as a contractual interface.
- Practice follow-up questions. Be ready to modify an example, explain an edge case, or compare alternatives rather than recite a one-line definition.
- Check claims against primary documentation. Use the Tutorial for core language topics and the typing specification for typing terminology and rules.
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