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Python Interview Questions
GIL stands for Global Interpreter Lock. It’s a mutex that protects access to Python objects, preventing multiple native threads from executing Python bytecodes concurrently in a single process. This makes multi-threading in CPU-bound Python applications suboptimal since only one thread can execute at a time.
Python uses reference counting as its primary garbage collection mechanism. Additionally, it has a cyclic garbage collector to detect and clean reference cycles.
“__str__” is meant to return a user-friendly or informal string representation of an object, while “__repr__” is meant to return an unambiguous representation of the object, ideally one that could be used to recreate the object.
List comprehensions provide a concise way to create lists. Example: “squared_numbers = [x2 for x in range(10)]”.
Arguments in Python are passed by object reference. However, the behavior can seem different based on the mutability of the objects being passed.
“*args” is used to pass a variable number of non-keyworded arguments to a function, while “kwargs” allows you to pass a variable number of keyword arguments. They’re useful for functions that can accept a varying number of inputs.
A context manager is an object that is designed to be used with the “with” statement to ensure resources are properly and automatically managed, like opening and closing files. It defines methods “__enter__” and “__exit__”.
– Instance methods: accept “self” as the first parameter and relate to a specific instance of the class.
– Static methods: don’t accept “self” or “cls”. They work like regular functions but belong to a class’s namespace.
– Class methods: accept “cls” as the first parameter and relate to the class, not the instance.
Python supports multi-inheritance, where a class can inherit from multiple classes. The method resolution order (MRO) determines the order in which base classes are accessed. You can view the MRO using the “__mro__” attribute or the “mro()” method.
“==” checks if two objects have the same value. “is” checks if two references refer to the same object in memory.
Metaclasses are “classes of classes”. They allow one to define a blueprint for class behavior. You can use metaclasses to customize class creation, enforce coding standards, or introduce new class patterns.
“yield” is used in Python to define a generator. It returns a value from a function and remembers this state for later use. This way, when the function is called again, execution continues from where it left off, unlike “return” which exits and forgets the state.
A regular function in Python is defined using the “def” keyword and can have multiple expressions. A lambda function is an anonymous function, defined using the “lambda” keyword. It can have any number of parameters but only one expression.
– “map(func, iterable)”: applies “func” to all items in “iterable”.
– “reduce(func, iterable)”: applies “func” cumulatively to items in “iterable”, reducing the iterable to a single value.
– “filter(func, iterable)”: returns items from “iterable” for which “func” evaluates to “True”.
Python is often described as an interpreted language because it executes code line-by-line, as opposed to compiling the code into machine-level instructions before execution. Here’s a breakdown of what it means for Python to be interpreted:
- Interpreter vs. Compiler:
– Compiler: A compiler translates the entire source code of a program into machine code (or an intermediate bytecode) in one go. This machine code is then executed by the computer’s hardware. Examples of traditionally compiled languages are C and C++.
– Interpreter: An interpreter, on the other hand, translates the source code into machine code on-the-fly, line-by-line, as it runs the program. This means that with an interpreted language, you can execute code directly without the need for a prior compilation step.
- Python’s Process:
– When you run a Python program, the Python interpreter reads the code and interprets it into bytecode. This bytecode is then executed by the Python Virtual Machine (PVM).
– This process might make it seem like there’s a compilation step involved (since the source code is translated to bytecode), but this is different from languages like C or Java, where source code is compiled to machine code or bytecode ahead of execution. In Python, the translation to bytecode and its execution happen seamlessly and, from a user’s perspective, appear to occur simultaneously.
- Immediate Execution:
– One of the characteristics of interpreted languages is the ability to run code immediately. For instance, you can use Python’s interactive mode (often called the REPL for Read-Eval-Print Loop) to type and execute Python statements and see their results in real-time without needing a separate compile-link-execute process.
– Since Python code isn’t compiled down to machine-specific instructions (but rather to bytecode interpreted by the PVM), it’s often easier to run the same Python code across different platforms without modification. You just need the appropriate interpreter for the target platform.
- Performance Considerations:
– Generally, interpreted languages are slower than compiled languages because the translation from source code to machine instructions happens at runtime. However, various tools and implementations (like PyPy, which uses Just-In-Time compilation) exist that can significantly speed up Python code execution.
Python is considered an interpreted language because its standard implementation, CPython, interprets Python code line-by-line during execution. However, it’s worth noting that the distinction between interpreted and compiled can sometimes be blurry, especially given the diversity of language implementations and execution strategies available today.
In Python, both tuples and lists are used to store collections of items. However, there are some key differences between them:
– List: Lists are mutable, meaning you can modify their content by adding, removing, or changing items after the list is created.
my_list = [1, 2, 3]
my_list = 99 # This will change the second item to 99
– Tuple: Tuples are immutable, meaning once a tuple is created, you cannot alter its content.
my_tuple = (1, 2, 3)
# my_tuple = 99 # This would raise a TypeError
– List: Lists are defined by enclosing the items (elements) in square brackets “”.
– Tuple: Tuples are defined by enclosing the items in parentheses “()”. A tuple with a single item needs a trailing comma to differentiate it from a regular value in parentheses: “single_item_tuple = (1,)”.
- Use Cases:
– List: Since lists are mutable, they are generally used for collections of items that might need to be changed or updated over the course of a program.
– Tuple: Given their immutability, tuples are often used for representing collections of items that shouldn’t be changed, like the keys of a dictionary. They’re also commonly used for functions that return multiple values.
– List: Due to their dynamic nature, lists generally have a slightly larger memory overhead.
– Tuple: Tuples, being immutable, can have some performance advantages in terms of iteration and fixed storage in comparison to lists.
– List: Lists have several built-in methods like “append()”, “remove()”, “insert()”, “sort()”, etc., which allow for easy modifications.
– Tuple: Tuples have a limited set of methods. Commonly used ones are “count()” and “index()”. This is because methods that modify the data structure in place (like “append()” or “remove()”) are not applicable to immutable types.
– List: Since lists are mutable, they are susceptible to unintended side effects. For instance, passing a list to a function and modifying it inside the function will affect the original list outside the function as well.
– Tuple: Tuples, due to their immutability, provide a sort of “safety” in this regard. You can be assured that a tuple passed into a function won’t be changed by that function.
- Nested Structures:
– While tuples themselves are immutable, if you have a mutable object within a tuple, such as a list, that internal object can be modified. Similarly, while lists are mutable, they can contain immutable objects like tuples.
In summary, the primary distinction between tuples and lists in Python is their mutability. Lists are mutable and have a variety of methods for in-place modification, making them suitable for dynamic collections. Tuples are immutable, making them ideal for fixed collections of items or data structures that you want to ensure don’t get modified.
In Python, the terms “deep copy” and “shallow copy” pertain to the ways in which objects are duplicated, especially when these objects are compound objects (objects that contain other objects, like lists or dictionaries). Here’s the distinction between the two:
- Shallow Copy:
– A shallow copy creates a new object, but does not create copies of objects that the original object references. Instead, it copies references to the objects.
– As a result, changes made to the nested objects in the copy will also reflect in the original, and vice-versa.
– You can create a shallow copy using the “copy” module’s “copy()” function or the object’s built-in methods (like “list.copy()” for lists).
original_list = [[1, 2, 3], [4, 5, 6]]
copied_list = copy.copy(original_list)
In the above example, “original_list” and “copied_list” are distinct objects, but their nested lists aren’t. If you modify “copied_list”, it will modify “original_list” as well.
- Deep Copy:
– A deep copy creates a new object and recursively adds copies of all the objects found in the original.
– Changes to nested objects in the copy won’t affect the original object, and vice-versa.
– You can create a deep copy using the “copy” module’s “deepcopy()” function.
original_list = [[1, 2, 3], [4, 5, 6]]
copied_list = copy.deepcopy(original_list)
In the above example, both “original_list” and “copied_list” are entirely independent. Changes in the nested lists of one won’t affect the other.
Why does this matter?
Understanding the difference between shallow and deep copying is vital when working with compound objects. If you mistakenly use a shallow copy when you meant to create a completely independent object, you might inadvertently introduce bugs by modifying data you assumed was separate.
It’s also worth noting that deep copying can be more resource-intensive (because it might involve copying a large number of objects) and can pose challenges if there are reference cycles (though “deepcopy” will handle cycles correctly without infinite loops).
In Python, a decorator is a design pattern that allows you to add new functionality to an existing object without modifying its structure. Decorators are a very powerful and useful tool in Python since it allows programmers to modify the behavior of function or class methods without directly changing the code of the function or method.
Decorators are often used to define, modify, or extend function behaviors in a clean, reusable, and semantic way. They are typically applied using the “@decorator_name” syntax above a function or method definition.
Here’s a basic example to illustrate how a decorator works:
print(“Something is happening before the function is called.”)
print(“Something is happening after the function is called.”)
When you run the above code, you’ll get the following output:
Something is happening before the function is called.
Something is happening after the function is called.
In this example:
- “simple_decorator” is a custom decorator that wraps the function it decorates.
- The “@simple_decorator” syntax is a shorthand for “say_hello = simple_decorator(say_hello)”.
- When “say_hello()” is called, it’s the “wrapper” function that gets executed.
- Inside the “wrapper” function, before and after calling the original “say_hello” function, additional behaviors (in this case, print statements) are executed.
Decorators can also take arguments, and multiple decorators can be chained. They can be used to modify functions, methods, or even classes. Common use-cases for decorators include logging, timing, access control, memoization, and more.
Unit testing is crucial to ensure that individual units (functions, methods, classes) of your codebase work as intended. In Python, the built-in “unittest” module provides the necessary tools to write unit tests. Here’s how you can perform unit testing in Python:
- Set Up:
Before starting, ensure your directory structure is organized. A common approach is to have a “tests” directory containing all test modules.
| |– test_my_module.py
- Writing a Test:
Use the “unittest” framework to write tests. A basic unit test is a class derived from “unittest.TestCase”.
For instance, if you have a function you want to test:
def add(a, b):
return a + b
You can write a test for this function:
from my_module import add
self.assertEqual(add(2, 3), 5)
self.assertEqual(add(-2, -3), -5)
- Test Discovery and Execution:
Run tests using the unittest test discovery:
$ python -m unittest discover
By default, this will discover files matching the pattern “test*.py” under the current directory and execute them.
If you want to run a specific test module:
$ python -m unittest tests.test_my_module
- Test Cases, Test Fixtures, and Assertions:
– Test Cases: Each method in your “unittest.TestCase” subclass is a test case. It should start with the word “test” to be automatically identified as a test case.
– Test Fixtures: These are code snippets run before and/or after each test method or even the whole test suite. Commonly used methods include:
– “setUp(self)”: Run before each test method in the class.
– “tearDown(self)”: Run after each test method in the class.
– “setUpClass(cls)”: Run once before any test methods in the class.
– “tearDownClass(cls)”: Run once after all test methods in the class.
– Assertions: These are the checks that let you test the behavior of your functions. Common assertions include:
– “assertEqual(a, b)”: Check “a” and “b” are equal.
– “assertTrue(expr)”: Check that “expr” is “True”.
– “assertFalse(expr)”: Check that “expr” is “False”.
– “assertRaises(exception, callable, *args, kwargs)”: Check that “callable” raises “exception” when called with specified arguments.
- Other Popular Testing Libraries:
– pytest: A popular alternative to “unittest” that supports fixtures, parameterized testing, and has a rich ecosystem of plugins. Its concise syntax for writing tests is a notable advantage.
– nose2: Successor to the now-unmaintained “nose”, it extends “unittest” to make testing easier.
When using such libraries, you’d install them via “pip” and then write and run tests following their respective conventions.
In many cases, especially when dealing with external systems or services, you’ll want to “mock” parts of your system to control its behavior during testing. The “unittest.mock” module provides tools to mock objects, functions, or methods. This can be particularly useful to simulate certain conditions or isolate the code under test.
– Granularity: Unit tests should be granular, i.e., they should test a single “unit” of work. This makes them easy to write, read, and debug.
– Isolation: Unit tests should not depend on external systems like databases or APIs. Mock or fake these systems if needed.
– Automate: Integrate testing into your CI/CD pipeline to ensure tests are run automatically during development and deployment.
By following these guidelines and using Python’s rich testing ecosystem, you can ensure that your code is robust, maintainable, and behaves as expected.
In Python, a “lambda” function is a small, anonymous function that can have any number of arguments, but can only have one expression. The expression is evaluated and returned when the lambda function is called. Lambda functions are used when you need a simple function for a short period of time and don’t want to formally define it using the “def” keyword.
lambda arguments: expression
- A lambda function that adds two numbers:
add = lambda x, y: x + y
print(add(5, 3)) # Outputs: 8
- A lambda function to get the length of a string:
length = lambda s: len(s)
print(length(“hello”)) # Outputs: 5
Why is it used?
- Simplicity: For writing small functions, using “lambda” can result in more concise and readable code compared to the standard function definition using “def”.
- Inline Usage: Lambda functions can be defined and used right where you need them, such as in function arguments. This is particularly useful for short-lived operations, like in the “key” argument of the “sorted()” function:
points = [(1, 2), (3, 3), (1, 1)]
sorted_points = sorted(points, key=lambda p: p)
print(sorted_points) # Outputs: [(1, 1), (1, 2), (3, 3)]
- Functional Programming: Lambda functions are handy in functional programming constructs like “map()”, “filter()”, and “reduce()”. For example, to square all numbers in a list:
numbers = [1, 2, 3, 4]
squared_numbers = list(map(lambda x: x2, numbers))
print(squared_numbers) # Outputs: [1, 4, 9, 16]
- Temporary Usage: If you only need a function in one place and don’t want to formally name it, a lambda function can be appropriate. This reduces the number of named functions in your code, keeping things cleaner when the function’s logic is simple and self-contained.
While lambda functions are powerful and can make code concise, they can also make code less readable if overused or used for complex operations. It’s essential to strike a balance, choosing lambda when it makes the code cleaner and more understandable and using named functions (“def”) for more complex operations or when additional documentation is beneficial.
Generators in Python are a way to produce a sequence of values, similar to an iterator. However, unlike lists or other sequences, they produce values on-the-fly and do not store them in memory. This “lazy” behavior is particularly useful when dealing with large datasets or when generating an infinite sequence.
Generators can be created in two ways:
- Generator Functions:
– These are defined like a regular function but use the “yield” keyword to return values.
– Each time the function is called with the “next()” function, execution starts or resumes until the next “yield” is encountered.
– The state of the function (i.e., variable values, instruction pointer, etc.) is saved between calls, allowing the function to pick up where it left off between “yield”s.
– When the function returns (either by completing its execution or explicitly with a “return” statement), a “StopIteration” exception is raised, indicating there are no more items to iterate.
count = 1
while count <= n:
count += 1
counter = count_up_to(5)
print(next(counter)) # 1
print(next(counter)) # 2
- Generator Expressions:
– These are a more concise way to create simple generators.
– Their syntax is similar to list comprehensions, but they use parentheses “()” instead of brackets “”.
squares = (x*x for x in range(5))
print(next(squares)) # 0
print(next(squares)) # 1
Advantages of Generators:
- Memory Efficiency: Since they produce values on-the-fly and don’t store the entire sequence in memory, they can be more memory-efficient than lists or arrays, especially for large sequences.
- Infinite Sequences: Generators can represent an infinite sequence. For example, a generator that produces consecutive integers can run indefinitely.
- Pipeline Processing: Generators can be used to set up a processing pipeline. For example, you can have multiple generators that process and forward data to the next stage (e.g., read from a file, process data, and then filter results).
Generators can be looped over with a “for” loop, which automatically handles the “StopIteration” exception and stops iteration when there are no more items:
for number in count_up_to(5):
Remember, once a generator is exhausted (all of its items have been iterated over), it cannot be restarted or reused. You’d need to create a new instance of the generator if you want to iterate again.
Both “.py” and “.pyc” files are related to Python programming, but they serve different purposes:
- .py Files:
– Nature: This is the standard file extension for Python source code files.
– Content: Contains human-readable Python code.
– Usage: When you write a Python program using any text editor or IDE and save it, it typically gets saved with a “.py” extension. This file can be executed by the Python interpreter using commands like “python filename.py”.
- .pyc Files:
– Nature: This is the standard file extension for compiled Python files.
– Content: Contains bytecode, which is a lower-level representation of your source code. Bytecode is not machine code (that would be more analogous to assembly or binary), but it’s no longer the high-level Python source. Instead, it’s an intermediate representation that is then interpreted by the Python virtual machine.
– Creation: When you run a “.py” file, Python first compiles it to bytecode, which is a low-level platform-independent representation of the source code. These “.pyc” files are usually stored in the “__pycache__” directory under the directory of the original “.py” file.
- – Usage: The purpose of “.pyc” files is to improve the performance of the interpreter. When Python sees that the bytecode (“.pyc”) is the same age or newer than the source file (“.py”), it skips the compilation step and directly uses the bytecode for execution, which speeds up the start time of the script.
In Python, an iterator is an object that adheres to the iterator protocol, which consists of two methods: “__iter__()” and “__next__()”. Iterators are used to iterate over items, such as elements of a list or characters of a string, one by one.
Basics of Iterators:
- Iterator Protocol:
– “__iter__()”: This method returns the iterator object itself (i.e., it should return “self”).
– “__next__()”: This method returns the next item from the collection. If there are no more items to return, it should raise the “StopIteration” exception.
- How Iterators Work:
– When the “iter()” function is called with an iterable (e.g., list, string, tuple), it returns an iterator for that iterable.
– The “next()” function fetches items from the iterator one by one. When there are no more items to retrieve, a “StopIteration” exception is raised.