45 Python Interview Questions and Answers for 2024

Carefully analyze your job requirements first

An effective job posting will help you to get plenty of candidates for the interview stage. Include the following in such a job posting:

Company descriptions

Introduce your company to the potential candidates with lots of facts, however, make the introduction appealing. Talk about the growth opportunities, professional development opportunities, organization environment, and work culture in your company. Explain the compensation and benefits policies.

Job descriptions for Python developers

Describe your project. Talk about the kind of projects you have, e.g., web development, “Artificial Intelligence” (AI)/”Machine Learning” (ML), etc. Describe whether you have development or maintenance projects. Explain how these projects are important to your organization, and how the contribution of Python developers will help your team.

Roles and responsibilities of a Python developer

You need a Python developer to fulfill the following responsibilities:

• Analyzing the functional and non-functional requirements;
• Studying the technical solutions in detail;
• Working closely with testing and DevOps teams to establish an effective “Continuous Integration” (CI)/”Continuous Delivery” (CD) environment;
• Creating specs for new apps;
• Coding new Python apps and unit-testing them;
• Collaborating with testers and DevOps engineers to test and deploy new apps;
• Maintaining existing apps;
• Reporting the status of the work and communicating effectively;
• Participating in process improvement initiatives.

Skills and competencies that you need in a Python developer

Look for the following skills in a Python programmer:

• Knowledge of Python, a general-purpose programming language, its syntax, and its features;
• Experience in frameworks like Django, Flask, etc.;
• Familiarity with “Object Relational Mapper” (ORM) libraries;
• Knowledge of server-side templating languages;
• Understanding of popular software architecture patterns;
• Experience in SQL databases like MySQL, PostgreSQL, etc.;
• Knowledge of NoSQL databases like MongoDB, Cassandra, etc.;
• Familiarity with front-end technologies like JavaScript, HTML, CSS, etc.;
• Knowledge to create a python script executable;
• Experience in creating applications that meet key non-functional requirements like scalability, security, performance, etc.
• Ability to write source code with good readability, which makes it easy to maintain;
• Knowledge of developing RESTful APIs;
• Understanding of testing and DevOps concepts and processes;
• Knowledge of code versioning tools like Git.

You need Python developers with the following competencies:

• Commitment to project objectives;
• Passion for excellence;
• Communication skills;
• Problem-solving skills;
• Teamwork;
• The ability to see the bigger picture.

Python interview questions for junior developers

Use the following questions when you interview junior Python programmers or freshers:

Question 1: Which standard data types does Python support?

Answer: Python supports the following standard data types:

• List;
• Number;
• String;
• Dictionary;
• “Tuples”.

Question 2: What is Tuple in Python?

Answer: Python allows developers to use “Tuples”. A “Tuple” is a sequence data type in this programming language. While writing Python source code, programmers need to separate the number of values in “Tuples” by commas.

Question 3: Explain “PEP 8” in the context of Python language.

Answer: Python developers need to write readable code. They can use Python Enhancement Proposal or“PEP 8”, a coding convention for this. Guido van Rossum, Barry Warsaw, and Nick Coghlan created it. They published it in July 2001.

PEP 8 provides useful recommendations to help programmers to write easily maintainable code. The PEP 8 documentation on the Python website covers many aspects e.g., format python code, comments, naming conventions, code layout, etc.,

Question 4: What is “Flask”?

Answer: “Flask” is a popular framework for web development using Python. Like other popular web development frameworks based on other languages, Flask has a code library to facilitate software development.

Python programmers can use “Flask” to create reliable, maintainable, and scalable web applications. Flask provides reusable pieces of code and extensions for many common programming operations. It’s licensed under a 3-clause BSD license, which gives flexibility to developers.

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This Python framework has two dependencies, namely, “Werkzeug” and “Jinja 2”. Since “Flask” has very few external dependencies, it’s a lightweight framework. Developers don’t need to update it very frequently. Fewer external dependencies also result in fewer application security-related vulnerabilities.

Question 5: Explain the key differences between Python 2 and Python 3.

Answer: Python 3 is the modern version of Python, and it was released in December 2008. Python 2 was the earlier version, and it’s not under development any longer.

Python 3 brought in considerable changes, and some of these weren’t compatible with Python 2. Organizations that used Python 2 earlier had to make several changes to migrate to Python 3.

The key differences are as follows:

• Developers find the syntax of Python 3 easier than Python 2.
• Python 3 uses “Unicode” by default to store strings. However, developers need to define “Unicode” string values with a “u” in Python 2.
• Values of variables never change in Python 3. For Python 2, the values of global variables change while using them inside a “for-loop”.
• Exceptions in Python 2 are enclosed in notations. Developers need to use parentheses to enclose exceptions in Python 3.
• Python 3 uses simpler rules of ordering comparisons than Python 2.
• Programmers use the “xrange()” function in Python 2 for iterations, whereas Python 3 offers the “Range()” function for this.

Question 6: Explain a “list comprehension” in Python.

Answer: Python developers might need to write programs that create a new list based on the values of an existing list. The program might need to evaluate certain conditions before creating the new list. E.g., from a series of names, a program might need to create a list with names starting with the letter “M”.

Normally, programmers will need to write several lines of code. This will include an evaluation of conditions. The “list comprehension” feature simplifies that. It contains built-in conditional statements.

The following is the syntax of “list comprehension”:

newlist = [expression for item in iterable if condition == True]

The “condition” is what the statement evaluates. The “iterable” in the above-mentioned code can be a list, set, or tuple.

Question 7: What is “Pythonpath”?

Answer: “Pythonpath” is an environment variable. Python allows developers to set it to add directories where Python will look for modules and Python packages.

Developers don’t need to set “Pythonpath” in most cases since Python knows where to find its standard libraries. Setting “Pythonpath” helps when one needs to maintain the Python source library directory and other directories of custom Python libraries. Developers might not want to install these custom libraries in the global default location, and they can use “Pythonpath” in that case. Programmers can pre-set “Pythonpath” as an installer to install Python.

Question 8: Mention the key differences between “range()” and “xrange()” in Python.

Answer: “Range()” and “xrange()” are Python functions. Developers can use them to iterate a certain number of times in “for loops” in Python. They have the following differences:

• Supported versions of Python: “xrange()” is available in Python 2. In Python 3, the “range()” function provides similar functionality.
• Outputs: “range()” returns a range object. The “xrange()” function returns a generator object, which can be used for displaying numbers. The Python program needs to execute a loop for this though.
• Memory: The “range()” function requires more memory than the “xrange()” function.
• Limitations: The “range()” function returns a list, which supports all operations. On the other hand, the “xrange()” function returns the “xrange” object. It doesn’t allow operations associated with lists.
• Speed: The “xrange()” function is faster than “range()”.

Question 9: Explain “positive indexes” and “negative indexes” in Python.

Answer: You can apply a positive index for searches that proceed from left to right. Here, 0 is the first index. The second index is 1, and the process continues this way.

Programmers apply a negative index when the search starts from the right and proceeds to the left. A negative index starts from “-1”. The next index is “-2”, and it continues in this manner.

Question 10: What is a “Python function”?

Answer: Python developers can create a block of code that will be executed when it’s called. Such a block of code is called a “function” in Python. Python programmers use the keyword “def” to define a function.

Question 11: Explain the utility of the “dict()” function in Python.

Answer: The “dict()” function in Python creates a dictionary. A Python dictionary is a collection, and it’s unordered. Dictionaries in Python are indexed, and they can be changed.

The syntax of the “dict()” function is as follows:

dict(keyword arguments)

Question 12: How does the “false” keyword in Python work?

Answer: In the Python programming language, the type “bool” represents Boolean values. These values can be “True” or “False”. A Boolean type can only hold “True” or “False”, and it can’t hold anything else. In Python, the “false” keyword is a Boolean value. It’s the result of a comparison operation.

Question 13: Explain the key difference between a “list” and a “tuple” in Python.

Answer: A “tuple” isn’t mutable. It’s an immutable sequence of objects. On the other hand, a “list” is mutable. A list is a mutable sequence of objects.

Question 14:Write a program to reverse a list in Python.

Answer: Python offers a method called “reverse()”. Developers can use it to reverse a list. The following code snippet provides an example.

def reverse(s);
 str = “”
 for i in s:
  str = i + str
return str

Question 15: What does the “append” method do in Python?

Answer: The “append()” method in Python adds a single item to an existing list. This method doesn’t return a new list of items. However, it modifies the original Python list object by adding an item to the end of the list. The syntax of this method is as follows:

list_name.append(item)

Question 16: Explain how indentation is done in Python code.

Answer: Many programming languages use indentation for readability, however, it’s different in the case of Python. Indentation in Python indicates a block of code. Python shows an error if a programmer omits indentation.

Question 17: How to convert all of the characters in a Python string to lowercase?

Answer: Python developers can use the “.lower()” string method to convert all characters in a string to lowercase.

Question 18: How to convert an integer to a string in Python?

Answer: Python offers a built-in function “str()” to convert an integer to a string. This function can take any Python data type and convert it into a string. The syntax of this function is as follows:

str(integer_value)

The following code snippet provides an example:

num = 10
converted_num = str(num)

Question 19: Which Python data type allows decimal values?

Answer: The “float” data type in Python allows decimal values. It can accommodate up to 15 decimal places.

Question 20: What does the Python “hex()” function do?

Answer: The “hex()” function in Python converts an integer to the corresponding hexadecimal value.

Question 21: What are the numeric data types in Python?

Answer: The commonly-used numeric data types in Python are “int” and “float”. There’s another called “complex”, however, it’s not used that commonly. The “complex” data type represents imaginary numbers in a complex pair.

Question 22: What is the difference between a .py and a .pyc file?

Answer: A .py file contains the source code while a .pyc contains the bytecode of your program.

Frequently asked Python interview questions and answers for interviewing mid-level developers

Use the following questions when you interview mid-level Python developers:

Question 23: What is a “docstring” in Python?

Answer: A Python “docstring” (“documentation string”) is a string literal enclosed in single or double quotes. Developers can use this while defining classes, modules, functions, or methods.

One can access a “docstring” from the “doc” attribute (“__doc__”) of a Python object. Programmers can also access them using built-in functions like “help()”.

A developer can define a “docstring” by including a string constant as the first statement in the definition of an object. “Docstrings” help to understand the functionality of a class, module, or function.

Question 24: What is a Python “iterator”?

Answer: In Python, an “iterator” is an object. An “iterator” contains a countable number of values. Programmers use “iterators” in programs where they need to “iterate” upon the object. In other words, the program will traverse through all the values in this object.

The “iterator” object in Python implements the “iterator” protocol. This protocol has the following methods:

• “__iter()__”;
• “__next()__”.

Question 25: What are “args” in Python?

Answer: Python developers can pass a variable number of arguments to a function, and they use special symbols for this. This programming language offers two such symbols, namely, “*args” and “**kwargs”.

They work as follows:

• “*args”: Python programmers can use this special syntax to pass a variable number of arguments to a function. This symbol allows passing an argument list with “non-key worded arguments”. The length of the list of arguments can vary.
• “**kwargs”: Developers use this symbol to pass a “keyworded” and “variable-length” argument list. They can pass any number of “keyworded” arguments.

Question 26: How do “deepcopy” and “shallow copy” vary from each other in Python?

Answer: “Deepcopy”, also known as “deep copy”, refers to the copying process being recursive. This involves constructing a new collection object first. Subsequently, the process populates it recursively with copies of the “child” objects found in the original.

Programmers use “deep copy” to store values that are already copied. This process doesn’t copy the reference pointers to the Python objects. The “deep copy” process refers to an object, furthermore, it stores the new object that’s already pointed by another object.

The process called “shallow copy” involves constructing a new collection object. It then populates the object with references to the “child” objects found in the original. This isn’t a recursive copying process, therefore, it doesn’t create copies of the “child” objects.

Python developers use “shallow copy” when they create a new instance type. The copying process keeps the values that are copied in the new instance. Additionally, it copies the reference pointers.

Python programs involving the “shallow copy” process run faster than programs using the “deep copy” process. This happens because the “deep copy” process makes copies of objects that are called.

Question 27: What are the key differences between popular Python frameworks like Django, Pyramid, and Flask?

Answer: The key differences between these frameworks are as follows:

• Use cases: Flask suits small applications with straightforward requirements. Pyramid is better for large applications with flexibility. Programmers prefer Django for even larger applications with many components and less flexibility.
• Community support: Django enjoys the support of a large and vibrant community of developers. Flask and Pyramid have developer communities, however, they are smaller than the Django community.
• Learning curve: Django offers many built-in tools, which is an advantage. However, its complexities and features also mean a steep learning curve. Pyramid doesn’t depend on extensions that much. Developers don’t need the kind of learning curve as they need for Django. Flask offers simplicity, and developers can learn it quickly.

Question 28: Explain the concept of a “namespace” in Python.

Answer: A “namespace” in Python refers to a collection of names. Conceptually, you can visualize it as a mapping of every name defined in a Python program to their corresponding objects. There can be different namespaces, however, they are isolated.

When you start the Python interpreter, a namespace containing all the built-in names is created. This namespace remains as long as the interpreter runs. If the program searches for global or local variables, it searches a namespace to get the corresponding object.

Question 29: What are the “Python modules”? Provide a few examples of them.

Answer: “Python modules” are files containing pieces of Python code. These include functions and variables. Python programs can execute Python modules, and these Python files have a “.py” extension. Python modules can be imported in three ways. For example, the three ways to import an array module are import array, import array as arr, and from array import *

The following are a few examples of Python modules:

• “os”;
• “sys”;
• “math”;
• “random”;
• “data time”;
• “JSON”.

Question 30: How does the Python memory manager work?

Answer: The memory manager in Python manages “blocks”, which are chunks of memory. A “pool” consists of a set of blocks that are of the same size. Pools are created on Arenas. Arenas are chunks of 256kb memory allocated on heap=64 pools.

Memory management in Python is performed by Python private heap space. The Python memory manager manages a private heap, and it does that on an “on-demand” basis. That means the allocation of heap space for Python objects is performed on demand. It contains all the Python objects and data structures.

The memory manager fills the space of a destroyed object with a new object. The new object needs to be of the same size.

The Python memory manager doesn’t automatically release the memory back to the operating system. It returns the memory to the Python interpreter. There might be incremental reserves of unused memory in the case of long-running Python processes.

Question 31: What does the “dir()” function do in Python?

Answer: The “dir()” function in Python displays the content of an object. It returns the properties and methods of an object, however, it doesn’t return the values. The syntax is as follows:

dir(object)

Question 32: What is an identifier in Python?

Answer: A Python identifier is a name given to entities like classes, functions, variables, etc. Using an identifier makes it easier to differentiate between entities.

Question 33: Can Python be used in an interactive manner?

Answer: Python is a high-level interpreted language. The Python interpreter can be used from an interactive shell. This interactive interpreter makes it easy to check Python commands. A developer can invoke it by typing the command “python” without any parameter, and the developer should press the “return” key at the shell prompt.

Question 34: What does the expression “func()” mean in Python?

Answer: The “func()” expression in Python makes the program call the function assigned to the variable “func”.

Questions and answers to hire senior Python developers

Planning to hire senior Python developers? Use the following interview questions:

Question 35: Explain the utility of the “__init__()” function in Python classes.

Answer: Developers need to initialize a Python class when they initiate it. They use the “def __init__()” for this. This method is also considered as the constructor in Python since constructors are used for instantiating an object. The following example shows the use of def __init__ self:

>>> class Audi:
     def__init__(self):
                   self.color=”blue”
                   self.type=”automatic”
    def show(self):
           print(f"color:" {self.color}, "type:" {self.type})
>>> o=orange()
>>> o.show()

Question 36: How does the “Lambda function” work in Python?

Answer: A “Lambda” function in Python is an anonymous function. Lambda functions can have many parameters, however, this function can have only one statement. The following is an example:

a = lambda m,n : m+n
print(a(10, 11))

Question 37: What is a “decorator” in Python?

Answer: Python programmers can use a “decorator”, a design pattern in Python that enables them to add new functionalities to existing objects. You can call a “decorator” before defining a function that you want to “decorate”.

The following code snippet shows the syntax of creating a “decorator”:

def uppercase_decorator(function):
    def wrapper():
           func = function()
           make_uppercase = func.upper()
           return make_uppercase
return wrapper

Question 38: Explain “pickling” and “unpickling” in Python.

Answer: Python offers a “pickle” module. This allows accepting any object, furthermore, it allows converting it into a string representation. This Python module can then use the “dump” function to dump it into a file. The entire process is known as “pickling”.

Python developers can also retrieve original Python objects from a string representation stored in a file. This process is known as “unpickling”, and it works exactly opposite to “pickling”.

Question 39: Explain why “Lambda” forms in Python don’t have statements.

Answer: Python programmers use the “Lambda” form to create new function objects. The “Lambda” form returns these objects at runtime. Since the syntax of the “Lambda” forms is restricted to one expression, these forms don’t have statements.

Question 40: Mention a few differences between NumPy and SciPy.

Answer: NumPy and SciPy are two of the most important libraries that Python offers. These libraries have the following key differences:

• Use cases: NumPy stands for Numerical Python, whereas SciPy stands for scientific Python. NumPy has utilities in a wide range of numerical operations. On the other hand, SciPy has utilities in scientific research-related projects.
• Foundation: SciPy is built using NumPy. This Python library includes all of the functions of NumPy.
• Speed: NumPy is faster than SciPy. NumPy is written in C, whereas SciPy is written in Python.
• Arrays: NumPy arrays have homogenous elements. The Python array object in a NumPy array keeps track of the array data types, the boundary of the array, and the shape of the array. Numpy arrays are also faster than nested arrays in Python that execute type dispatching code on each array element. SciPy arrays can be heterogeneous, alternatively, they can be homogeneous. SciPy arrays don’t have restrictions concerning shape, size, memory, or dimensions.

Question 41: What are the different classes of Python algorithms?

Answer: Python algorithms can be categorized into the following 3 classes:

• “Divide and conquer”: These algorithms divide a problem into parts. They then go on to solve each part separately.
• “Dynamic programming”: These algorithms divide problems into parts, however, they remember the results of the individual parts. They apply these results to similar parts of the problem.
• “Greedy algorithms”: These algorithms take the easiest step while solving a problem. They don’t concern themselves about the complexities in the subsequent steps in the problem-resolution process.

Question 42: Provide examples of tasks where Python can be used for automation.

Answer: The following are examples of tasks where Python can be used for automation:

• Reading and writing files;
• Sending emails;
• Web scraping;
• Interacting with an API.

Question 43: What does the “oct()” function in Python do?

Answer: The “oct()” function in Python takes an integer as an input, and it returns the octal representation of it. This function returns the output in a string format.

Question 44: What does the “PyPi OBJ” project offer?

Answer: The PyPi OBJ project provides a library containing program objects. It provides an object class, which has all the basic “dict” methods. These methods are provided as functions. This provides a clean namespace to the object. In turn, this makes it easier to initialize with data from disk.

Question 45: What does the Python garbage collector do?

Answer: The garbage collector in Python keeps a track of all objects in memory. Python has three generations of garbage collectors.

A new Python object starts its life in the first generation. It may survive the garbage collection process for a generation, and it moves to the second generation in that case.

The Python garbage collector module implements a limit to the number of objects. If the number of objects exceeds that limit, then the garbage collector starts a garbage collection process.

Summary

You can use these Python interview questions and answers to interview your job candidates. Please contact us at DevTeam.Space if you need more help.