Top 25 Python Interview Questions

Python is an in-demand programming language in the field of data science.

In Stack Overflow’s 2019 Developer Survey, which studied the habits and preferences of over 90,000 developers, Python was found to be the fourth most-popular programming language favored by survey recipients. That’s not all: Python was the second “most loved” programming language in the study.

While Python has a wide variety of use cases, from web development to financial analysis, the language has become immensely popular among data scientists.

This is due not only to the language’s simple syntax, which makes iteration faster, but also because there is a massive repository of third-party libraries available that allows you to effectively analyze datasets at scale. As a result, Python questions are an essential part of data science interviews.

In this guide, we’re going to explore a list of the top 25 Python interview questions that you may encounter in your data science job interview. We’ll also provide you with the answers to these questions, so you can test yourself on your knowledge.

#1: What is the difference between a list and a tuple?

Tuples and lists are both collection data types in Python, but each of these data types functions in a different way.

Lists are mutable, and can be modified after initially being created. Tuples are immutable, which means that once it has been created its contents cannot be changed.

#2: Explain how the range function works.

The Python range function generates a list of integers in a given range.

The range function accepts three arguments. With only one argument specified, range generates numbers between 0 and the number specified; with two arguments specified, range generates integers between the two integers; with three arguments specified, range generates integers between two numbers, at a certain interval.

#3: How can string interpolation be performed in Python?

String interpolation refers to inserting values into a string. There are three ways to insert values into a string using interpolation, which are:

• Using f strings (only supported in Python 3.x)
• Using the % operator
• Using the .format() function
  
  

#4: Explain the Python map function and how it works.

The Python map() function returns an iterator which can iterate over a list of returned values and execute a function on those values.

For instance, you could use map() to add 5 to every item in a list of numbers, or to reverse every item in a list of strings.

#5: Explain the Python filter function and how it works.

The Python filter() function filters elements in a sequence. Every item passed into the filter function will be read, then filtered based on whether a specified condition is met. This condition is often evaluated in a function.

For instance, you could use filter() to remove every even number in a list, or delete every name in a list that begins with a particular character.

#6: What does the “self” keyword represent in a Python class?

The “self” keyword is an instance of a class. By using the self keyword, we can give methods within a class access to the object that they belong to.

One of the most common use cases of “self” is to assign custom values to an instance to a class.

#7: What are Python decorators?

Decorators are functions that add features to an existing function in Python without changing the structure of the function. Decorators are specified using the @name syntax, and are placed before a “def” statement in a program.

#8: What are list comprehensions?

List comprehensions allow you to create a new list based on the contents of an existing list.

Suppose you had a list of numbers that you wanted to multiply by two. You could use list comprehension to accomplish this task. Or if you want to convert a sequence of characters into a list, you could use list comprehension.

#8: How do you slice a list using Python?

To slice a list in Python, you have to use the slicing notation. This appears as follows:

list[start:stop:step]

Start is the index position at which you want to start the slice, stop is the index position at which you want to stop the slice, and step is the interval at which elements will be sliced.

#9: What is the difference between Python dictionaries and JSON?

Dictionaries are a data type in Python which store keys that are mapped to values. JSON, on the other hand, is a data structure that follows the JSON format and is used to transfer data.

#10: How can you remove duplicate items from a list?

To remove duplicate items from a list, you can convert a list to a set, then back to a list. Here is a program that accomplishes this task:

numbers = [1, 3, 3, 9]

new_numbers = list(set(numbers)

Our code returns:

[1, 3, 9]

#11: What is the difference between append and extend?

The append() function adds a value to a list, whereas extend() adds values from an existing list to a new list.

#12: What is “scope” in Python?

Every object in Python has its own “scope”. Scope refers to where a certain variable or block of code can be accessed within a program.

There are two types of scope in Python: local and global.

Local scope refers to objects that are only available in a specific function. An example of this would be a variable declared within a function. Global scope refers to objects available throughout a program. An example of this would be a variable declared at the start of a main program.

#13: How can you remove whitespace from a string?

There are a few ways you can remove whitespace from a string. One of the most common is to use the replace() function to replace any whitespaces with no character.

Here’s the code you could use to remove whitespace from a string:

string = “This is    a string.”

string.replace(“ “, “”)

Our code returns: Thisisastring.

#14: What is the Python ternary operator?

The Python ternary operator is a one-line if/else statement. The syntax for the ternary operator is as follows:

a if condition else b

#15: How do you return a list of keys and values from a dictionary?

To retrieve the keys in a dictionary, you can use the keys() method. To retrieve a list of values in a dictionary, you can use the values() method.

You can also use the items() method to generate an iterator which contains two lists: one of a dictionary’s keys, and another of its values.

#16: What is the difference between del and pop?

The del keyword allows you to delete any object in Python. For instance, del can be used to remove an item from a dictionary, or remove an element by index in an array. pop() is a function that allows you to remove an element by index in an array, and returns the removed element

#17: What is the purpose of the try/except statement? How does it work?

The try/except statement allows you to handle exceptions in your code.

First, the code in the try statement is run. If that code cannot run successfully, the code in the except statement is run. In addition, you can also specify a finally statement which states code that will run whether or not the try statement executes successfully.

The try/except statement is used for error handling because it allows you to perform a certain task if a block of code fails. For instance, you could have a custom error message print to the console.

#18: What is the Python lambda statement? Why is it used?

Lambda is an anonymous function that can accept any arguments. The lambda function can only have one expression, and will return one result. Here’s an example of a lambda function that multiplies two numbers:

multiply = lambda a, b : a * b

print(multiply(2, 9))

Our code returns: 18.

The lambda function is commonly used to perform simple functions in a program.

For instance, you could use a lambda function to multiply two numbers together. Doing so would eliminate the need to write an entire function using def(), which would take up more lines of code. In other words, Python’s lambda statement is commonly used to help coders make their work more concise.

#19: What is the difference between a deep and a shallow copy of an object?

A shallow copy is a bitwise copy of an object. A shallow copy of an object will contain all the same values as the original object, and the reference addresses from the original object are copied.

Deep copies, on the other hand, copy all values recursively from one object to another, and create new reference addresses.

#20: What are the benefits of using modules in Python?

Python supports creating modules, which are Python files with a .py extension that can store their own functions, variables, and classes. To use a module in a program, you need to import it using the Python import statement.

The primary benefits of using modules in Python are as follows:

• Modules can be reused in a program.
• Working on a module helps you block together your code more effectively, thereby making the intent behind a specific module clearer.
• Using modules allows you to easily maintain your code. Because you can call a module multiple times, if there is an issue in your code you will only need to change the module.
  
  

#21: What are the break, continue, and pass statements?

The break statement terminates a loop as soon as it is executed. When the statement is executed, control of the program goes back to the statement immediately after the body of a loop.

The continue statement terminates the current iteration of a loop. When the statement is executed, it skips over the rest of the code in the current iteration of the loop, and control moves to the next loop iteration.

The pass statement is a placeholder term used to fill empty blocks of code. Typically, pass is used to prevent syntax errors while a block of code is empty.

#22: What are Python iterators?

An iterator is an object that can be iterated upon in a program. In other words, an iterator is an object that will return data, one element at a time.

Python iterators must have two methods: __iter__() and __next__(), which allow you to initialize an iterator and return the next item in an iterator, respectively.

#23: What is the purpose of the join and split functions?

The split() function allows you to convert a string to an array based on a specified delimiter.

The join() function allows you to merge a list into a string, and separate out each value based on a specified character.

#24: What is the purpose of the *args and **kwargs keywords?

The *args keyword allows you to pass a non-keyworded variable-length argument list to a function. The **kwargs keyword, on the other hand, allows you to pass a keyworded, variable-length argument dictionary to a function.

#25: How can you combine two lists into a list of tuples?

To combine two lists into a list of tuples, you use the zip() function.

Suppose we want to combine a list of student names with a list of student ages. We could do so using this code:

names = [‘Henry’, ‘Julie’, ‘April’]

ages = [7, 8, 8]

new_list = (key, value for key, value in zip(names, ages)]

Our code returns:

[(‘Henry’, 7), (‘Julie’, 8), (‘April’, 8)]

Conclusion

Job interviews are stressful – there’s no way around it.

If you spend some time ahead of your interview preparing for the types of questions that could come up, you are more likely to feel confident going in. Preparation will help you stay focused, and beat your interview stress.

The questions we have covered in this article delve deep into various facets of Python, from working with modules to string interpolation. These questions will help you prepare for what could come up in an interview.

Test yourself on the questions by asking yourself, writing down an answer, then comparing your answer with ours. You could also have a friend or a fellow programmer ask you a few questions, which gives you the added benefit of having a real person for feedback.

This article has only scratched the surface of the types of questions you could be asked about Python in a data science interview. Python simply has too many features – we could not cover all in one article. With that said, we have highlighted many of the core Python concepts used frequently in data science.