Python is one of the most popular and loved programming languages today. It was developed by Guido van Rossum and first released in 1991. From startups to big tech companies and freelancers, everyone is harnessing the power of Python. In fact, according to a Stack Overflow developers’ survey, Python is the third most widely used programming language in the world today. This growing popularity is partly attributable to its simplicity, ease of use, and reduced development time.
However, this also means that there is tough competition in the job market for Python developers. Many companies search for candidates with a strong understanding of Python fundamentals. Therefore, to be a competitive developer, you must have ample preparation to succeed in interviews. Yet preparing for a Python interview can be tricky if you’re unfamiliar with the kind of questions you will encounter in an interview.
But don’t fret; we’ve compiled a list of the 20 most common Python interview questions and answers you’re likely to be asked in an interview.
Note: These questions are mainly geared toward beginners/freshers.
Let’s dive right in!
PEP is an acronym for Python Enhancement Proposal. It is an official design document that contains a set of rules specifying how to format Python code and helps to achieve maximum readability. PEP 8 is useful because it documents all style guides for Python code. This is because contributing to the Python open-source community requires you to adhere to these style guidelines strictly.
The difference between a Python list and a tuple is as follows:
A list has mutable objects.
A tuple contains immutable objects.
A list can be modified, appended, or sliced.
A tuple remains constant and cannot be modified.
Data in a list is represented in square brackets . For example, [1,2,3]
Data in a tuple is represented in parentheses (). For example, (1,2,3)
A list has a variable length. It means the size of the created list can be changed.
A tuple has a fixed length. It means the size of the created tuple cannot be changed.
A scope in Python is a block of code in which a Python code lives. While namespaces uniquely identify objects inside a program, they also include a scope that enables us to use their objects without prefixes. A few examples of scope created during a program’s execution are:
Global scope: It refers to the top-most scope in a program. These are global variables available throughout the code execution since their inception in the main body of the Python code.
Local scope: It refers to the local variables available inside a current function and can only be used locally.
Note: Local scope objects and variables can be synced together with global scope objects via keywords like
Lists can store heterogeneous data, i.e., elements of different data types.
Arrays can store only homogenous data, i.e., values with uniform data types.
A list cannot handle arithmetic operations directly.
An array can handle arithmetic operations directly.
A list does not require us to explicitly import a module for declaration.
Arrays need us to explicitly import modules for declaration.
Lists use more memory as they are allocated a few extra elements to allow for quicker appending of items.
Since arrays' sizes are constant from the time they are first initialized, they stay compact in terms of memory usage.
**kwargsmean in Python?
*args to pass a variable length of non-keyword arguments in a Python function. By default, we should use an asterisk
(*) before the parameter name to pass a variable number of arguments. An asterisk means variable length and args is the name used by convention. You can use any other name.
def addition(e, f, *args):add = e + ffor num in args:add += numreturn addprint(addition(1, 2, 3, 4, 5))
**kwargs to pass a variable number of keyword arguments in Python function. By default, we should use a double-asterisk (**) before the parameter name to represent a
def forArguments(**kwargs):for key, value in kwargs.items():print(key + ": " + value)forArguments(arg0 = "arg 00", arg1 = "arg 11", arg2 = "arg 22")
A lambda function is basically an inline anonymous function (i.e., defined without a name) represented in a single expression that can take several arguments. It is used to create function objects during runtime.
However, unlike common functions, they evaluate and return only a single expression.
Also, in place of the traditional
def keyword used for creating functions, a lambda function uses the
lambda keyword. We typically use a lambda function where functions are required only for short periods. They can be used as:
add_func = lambda x,b : x+bprint(add_func(3,5))
Python is generally considered to be a general-purpose programming language, but we can also use it for scripting. A scripting language is also a programming language that is used for automating a repeated task that involves similar types of steps while executing a program. Filename extensions for Python scripting language are of different types, such as
.py, .pyc, .pyd, .pyo, .pyw, and .pyz.
We handle memory management in Python via a Python Memory Manager using a private head space. This is because all Python objects and data structures live in a private heap. Since we as programmers do not have access to this private heap, the Python interpreter handles this instead.
Additionally, the core API gives access to some tools for us to code.
Python also includes an in-built garbage collector to recycle unused memory for private heap space.
# A Sample class with init methodclass Alibi:# init method or constructordef __init__(self, name):self.name = name# Sample Methoddef hello_hi(self):print('Hello, my name is', self.name)# Creating different objectsp1 = Alibi('NIKE')p2 = Alibi('PUMA')p3 = Alibi('KANYE')p1.hello_hi()p2.hello_hi()p3.hello_hi()
Type conversion in Python is the process of changing one data type into another data type. For example
dict(): Used to transform a tuple of order (key, value) into a dictionary.
str(): Used to convert integers into strings.
An iterator in Python is an object used to iterate over a finite number of elements in data structures like lists, tuples, dicts, and sets. Iterators allow us to traverse through all the elements of a collection and return a single element at a time.
The iterator object is initialized via the
iter() method and uses the
next() method for iteration.
Code example in Python 3
print("List Iteration")l = ["educative", "for", "learning"]for i in l:print(i)
NumPy is an acronym for Numerical Python. It is one of the most popular, open-source, general-purpose, and robust Python packages used to process arrays. NumPy comes with several highly optimized functions featuring high performance and powerful n-dimensional array processing capabilities.
NumPy can be used in trigonometric operations, algebraic and statistical computations, scientific computations, and so on.
A decorator in Python is a function that allows a user to add a new piece of functionality to an existing object without modifying its structure. You usually call decorators before the definition of the function you want to decorate.
# Prints a list of numbers from 0 to 9# Note: by default the list starts from 0 and default step is of size 1print(list(range(10)))# Prints a list of numbers from 5 to 8# step is not provided hence it is 1 by defaultprint(list(range(5, 9)))# Prints a list of numbers from 2 to 20# with a step of size 2print(list(range(2, 21, 2)))# Using range to create a for loopfor i in range(10):print("Looping =", i)
Python modules are files containing Python definitions and statements to be used in a program. Modules can define functions, classes, and variables. A Python module is created by saving the file with the extension of
.py. This file contains the classes and functions that are reusable in the code as well as across the modules. They can also include runnable code.
Advantages of Python modules include:
Code organization: Code is easier to understand and use. Code reusability: Other modules can reuse functionality used in a module, hence eliminating the need for duplication.
Pickling is a process in Python where an object hierarchy is transformed into a byte stream. Unpickling, in contrast, is the opposite of pickling. It happens when a byte stream is converted back into an object hierarchy. Pickling is also known as “serialization” or “marshaling”.
Pickling uses the pickle module in Python. This module has the method
pickle.dump() to dump Python objects to disks to achieve pickling. Unpickling uses the method
pickle.load() to retrieve the data as Python objects.
Documentation strings or docstrings are multi-line strings used to document specific code segments, like Python modules, functions, and classes.
They help us understand what the function, class, module, or method does without having to read the details of the implementation. Unlike conventional code comments, the docstrings describe what a function does, not how it works.
We write docstring in three double quotation marks
(""") as shown in the example below:
Add up two integer numbers.
def add(a, b):"""Add up two integer numbers.This function simply wraps the ``+`` operator, and does notdo anything else.Examples-------->>> add(2, 2)4"""return a + b
We can access the docstring using:
__doc__ method of the object
The built-in help function
It is a unique environment variable used when a module is imported. PYTHONPATH acts as a guide to the Python interpreter to determine which module to load at a given time. It is used to check for the presence of the imported modules in various directories.
Launching the Python interpreter opens the PYTHONPATH inside the current working directory.
A Python package is a collection of modules. Modules that are related to each other are usually grouped in the same package. When you require a module from an external package in a program, it can be imported, and its specific modules are then used as required. Packages help avoid clashes between module names. You need to add the package name as a prefix to the module name, joined by a dot to import any module or its content.
Because interviews are often the most stressful part of finding a job, having in-depth preparation and ample practice will help you gain confidence and crack them successfully.
By practicing the above questions, you can familiarize yourself with common Python concepts. The next practical step should involve practicing these questions alongside coding examples. With ample practice, you will ace your interview and get closer to your dream job!
At Educative, we aim to make the interview process smoother by providing you with a hands-on interactive learning platform. You can learn more about Python, including data structures, built-in functions, web development, runtime, compilers, Python libraries, data science, machine learning in Python, and more, via our comprehensive courses and Python tutorials. Engage with the material through live, in-browser coding environments and address gaps in your knowledge to help actively retain and apply what you need to learn.
The Grokking Coding Interview Patterns in Python course will not just help prepare you for the technical portion of your interview. It will also allow you to learn algorithms and coding patterns to face any problems you may encounter in a Python interview. What you learn in this course will help you progress in your career even after landing your dream job.
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