20 Python Interview Questions in Data Science

  • Riya Kumari
  • Jan 17, 2021
  • Python Programming
  • Updated on: Jun 18, 2021
20 Python Interview Questions in Data Science title banner

Python is rapidly becoming far-famed among the students of data science and it is precise for reasons as python programming skill is very important for the jobs in data science. It is the high-level programming language used in data science which is known for its accessibility, simplicity, and versatility.

 

Thus, Python's readability and basic sentence structure make it moderately simple to learn. If you are fond of data science then you need to learn to work with more advanced tools. 

 

Python necessities for data scientists are not the same as those for designers and software engineers. Data scientists ought to be comfortable with primary Python syntax, built-in data types, and the most famous libraries for data analysis process

 

These are some of the points that are generally shrouded in the Python interview questions for data science. Let’s start preparing you for the interview questions with appropriate answers!

 

 

Top Interview Questions-Answers for Python in Data Science

 

We have mentioned 20 most often asked Python interview questions and answers that will encourage you to be ready for the interview. You will find all the basic and advanced Python programming questions with thorough answers here.

 

Q1: What is Python and its benefits?

 

Ans: Python is a high-level, interpreted, general-purpose programming language with automatic memory management, modules, objects, exceptions, and threads.

 

As it is a general-purpose language, it can be used to assemble almost any type of application with the correct tools or libraries.

 

There are several benefits of Python like it is a simple, easy, extensible, portable, built-in data structure, and also it is open-source. Due to this open-source, there is a huge community that is backing it. Also, this language supports third-party packages motivating modularity and code-reuse.


(Must read: Mickey- the voice assistant)

 

Q2: What are the data types used in Python?

 

Ans: Python has numbers, strings, tuples, lists, sets, and dictionaries in which numbers, strings, and tuples are immutable which means they cannot be modified during runtime.

 

Lists, sets, and dictionaries are just opposite to that which means they are mutable as they can be modified during runtime.

 

(Also read: Data Types in Python)

 

Q3: What is a Python dictionary?

 

Ans: Dictionary is one of the data types of Python which means an unordered mapping of unique keys to values. It falls under the category mutable which means it can be modified. 

 

A dictionary is built with curly braces and listed by using the square bracket notation. For example,


my_dict = {'name': 'Chris Evans', 'age':39, 'films': ['Captain America', 'The Avengers', 'Knives Out']} my_dict['age']

Here, name, age, and films are the keys. We can clearly see that the corresponding values can be of diverse data types, comprising numbers, strings, and lists and we also have discerned how the value 39 is accessed via the related key age.

 

Q4: How is memory managed in Python?

 

Ans: There is a private heap space in Python where memory is managed. Therefore, it shows that all the objects and data structures will be placed in a private heap. (as stating data structure, learn about data structure in R programming)

 

Nevertheless, the developer won't be permitted to get to this heap rather, the Python mediator will deal with it. Simultaneously, the core API will empower access to some Python tools for the developer to begin coding. 

 

The memory manager will dispense the heap space for the Python objects while the inbuilt garbage collector will reuse all the memory that is not being utilized to boost accessible heap space.

 

Q5: What are the differences between lists and tuples?

 

Ans: Lists and tuples both are the values of any data type but there are some differences between them.

 

  • The basic difference between lists and tuples is that lists are mutable whereas tuples are immutable.

  • Lists are slower than tuples.

  • Lists are built with square brackets while tuples are enclosed in parentheses.

 

(Also check: Principal Component Analysis(PCA) with Python Code Example)

 

Q6: What are lambda functions?

 

Ans: In Python, anonymous functions are called lambda functions. This is very useful when you want to describe any function in one short paragraph.

 

Thus, rather than formally illustrating the small function with a particular name, body, and return statement, you can put down everything in one short line of code by using a lambda function. For example,


(lambda a, b, c: (a+b) ** c) (3,2,2)

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In this example, we have characterized an anonymous function that has three arguments and takes the amount of the initial two arguments (a and b) to the intensity of the third argument (c).

 

As we can see, the syntax of a lambda function is considerably more succinct than that of a standard function.

 

Q7: What is Pandas?

 

Ans: Pandas is a python open-source library that gives superior and adaptable information structure and data analysis tools that make working with relational or labelled information both simple and instinctive. Learn how to do EDA using pandas profiling.

 

It is an outstanding tool for data analytics as it can translate highly complicated operations with data into only one or two commands. It comes with various built-in techniques for merging, filtering, and grouping data.

 

Q8: What are Python modules? Name some generally used built-in modules in Python.

 

Ans: Files which contain python codes are known as Python modules and this code can any be functions, classes or variables. A Python module is a .py file consisting of executable code.

 

sys, os, math, random, JSON, and data time are some of the generally used built-in modules in Python.

 

Q9: Which are the popular Python data analysis libraries?

 

Ans: Some of the popular Python data analysis libraries are:

 

  • Pandas

  • NumPy

  • Seaborn

  • Matplotlib

  • SciKit

 

These libraries will assist you in working with arrays and DataFrames, develop professional-looking plots, and also run machine learning models.

 

Q10: What libraries do data scientists use to plot data in Python?

 

Ans: The primary library used for plotting data in Python is Matplotlib. The plots built with this library require lots of fine-tuning to seem shiny and professional.

 

Seaborn is also preferred by several data scientists for some good reasons. It permits you to build appealing and meaningful plots with just one line of code.

 

(Read also: Implementing beautiful soup for web scraping)

 

Q11: What is the difference between range & xrange?

 

Ans: If we see range and xrange in terms of functionality then they are quite the same. The lone distinction is that range returns a Python list object and x reach returns an xrange object. This implies that xrange doesn't produce a static rundown at run-time as the range does.

 

It builds the values as you require them with a particular technique named yielding and this technique is used with a type of object called generators.

 

Q12: Explain what is the common way for the Flask script to work?

 

Ans: The popular way for the flask script to work is:

  • Either it should be the import way for your application.

  • Or the way to a Python file.

 

Q13: What are the key features of Python?

 

Ans: Following are key features of Python;

  • Python is powerfully composed, this implies that you don't have to express the kinds of factors when you pronounce them or anything like that.

  • In Python, functions are five-class objects which imply that they can be allowed to variables, gotten back from different functions, and passed into functions.

  • Composing Python code is brisk yet running it is frequently slower than compiled languages. Fortunately, Python permits the illusion of C-based extensions so bottlenecks can be advanced away and regularly are.

  • Python is an interpreted language and it does need to be compiled before it runs, like other languages.

  • It finds uses in many spheres like web applications, scientific modelling, big data applications, automation,  and so on.

 

Q14: How can you generate random numbers in Python?

 

Ans: To generate random numbers in Python, you have to import the command as, import random, random. random(). This returns a random floating-point number in the range [0,1)

 

(Related blog: 4 modules of selenium framework)

 

Q15: What is Multithreading achieved in Python?

 

Ans: Python has a multi-threading package however if you need multi-thread to speed your code up, at that point, it's normally not a smart thought to use it. 

  • Python has a construct called the Global Interpreter Lock (GIL). The GIL ensures that just one of your 'threads' can execute at any one time.

  • This happens rapidly so to the natural eye it might seem like your threads are executing in equal, however, they are truly alternating using a similar CPU core.

 

Q16: What are Python namespaces?

 

Ans: Python namespace is a naming system that ensures the names should be unique and helps in avoiding name conflict. Local namespaces, Global namespaces, Built-in namespaces are the example of Python namespaces.

 

(Referred blog: How does Python represent output?)

 

Q17: Write the differences between Django, Pyramid, and Flask.

 

Ans: 

  • Flask is a "microframework" that basically works for a little application with easier prerequisites. In a flask, you don't need to use outer libraries and the flask is prepared to use.

  • Pyramids work for bigger applications. It gives flexibility and lets the designer use the correct tools for their task. Pyramids are heavily configurable. 

  • The pyramid is a common, open-source, web application expansion framework built-in python which enables python creators to build web applications with comfort.

  • Like Pyramid, Django can likewise be used for bigger applications. It incorporates an ORM.

 

Q18: Explain pickling and unpickling.

 

Ans:

  • Pickling

In Python, pickling is the name of the serialization cycle. Any object in Python can be serialized into a byte stream and dumbed as a record in the memory. The way toward pickling is conservative yet pickle articles can be packed further. Also, the pickle monitors the objects it has serialized and the serialization is compact across versions.

  • Unpickling

Unpickling is the complete inverse of pickling. It deserializes the byte stream to regenerate the objects stored in the document and loads the objects to memory.

 

(Recommended blog: Word Embedding In NLP with Python Code)

 

Q19: What is the use of help() and dir() functions?

 

Ans: Both Help() and dir() functions are available from the Python interpreter which are used for sighting a close dump of built-in functions.

  • The help() function is used to show the documentation string and moreover stimulates you to discern the benefit related to modules, keywords, attributes, many more.

  • The dir() function is used to show the defined symbols.

 

Q20: What is slicing in Python?

 

Ans: A mechanism to choose a range of items from sequence types such as list, tuple, strings, and any more is called slicing.

 

A slice object is used to indicate how to slice a sequence and you can indicate where to begin the cutting, and where to end. Also, you can indicate the progression, which permits you to for example slice just every other thing.


 

Summary

 

Hope so, you will feel more confident in your data science job interview after reading these questions and answers. We just tried to support you in clearing your job interview easily.

 

Hence, these are some of the most asked python interview questions but keep in mind, these are just entry-level interview questions, they might ask some other technical questions also. So, be prepared for all the challenges. All the best!

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