Python Data Science Interview Questions You Need To Prepare
If youâre looking to build a career in data science, you already know how important Python will be. The significance of Python data science interview questions at interviews has risen exponentially. After all, it is the most widely used language in data science.
When preparing for a data science Python interview, youâll need to cover all of the major Python concepts so that youâre fully prepared to answer any Python data science interview questions that come your way.
If youâre a software engineer, coding engineer, software developer, engineering manager, or tech lead preparing for tech interviews, check out our technical interview checklist,interview questions page, and salary negotiation e-book to get interview-ready!
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Q95 You Are Required To Scrap Data From Imdb Top 250 Movies Page It Should Only Have Fields Movie Name Year And Rating
Ans: We will use the following lines of code:
from bs4 import BeautifulSoupimport requestsimport sysurl = 'http://www.imdb.com/chart/top'response = requests.getsoup = BeautifulSouptr = soup.findChildrentr = iternextfor movie in tr:title = movie.find.find.contentsyear = movie.find.find.contentsrating = movie.find.find.contentsrow = title + ' - ' + year + ' ' + ' ' + ratingprint
The above code will help scrap data from IMDbs top 250 list
Next in this Python Interview Questions blog, lets have a look at questions related to Data Analysis in Python.
What Are Some Common Machine Learning Models And Algorithms
There are quite a few types of models and algorithms used in machine learning. Artificial neural networks, often referred to as simply neural networks, are one of the most common. An artificial neural network is a type of computing system modeled by connecting nodes similar to those in a human brain.
Logistic regression models are similar to artificial neural networks. The primary difference between logistic regression models and neural networks is that logistic regression models are simpler and consist of only one layer. In either case, model performance should be tested with cross-validation, where data outside the sample is recruited to see how well the model holds up.
Algorithms include the decision tree algorithm, the clustering algorithm, and the binary classification algorithm. Decision tree algorithms split nodes into multiple nodes like trees, while clustering algorithms create groupings of data points. Two common clustering techniques are DBSCAN clustering and k-means clustering, each of which has its own algorithm.
Finally, binary classification algorithms separate data into two groups depending on a classification definition. It is important to determine the performance of your chosen models and algorithms in order to weed out weak models for your specific data sets.
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How Do You Access Parent Members In The Child Class
Following are the ways using which you can access parent class members within a child class:
- You can use the name of the parent class to access the attributes as shown in the example below:
classParent:# Constructordef__init__: self.name = name classChild:# Constructordef__init__: Parent.name = name self.age = agedefdisplay:print# Driver Codeobj = Childobj.display
- The parent class members can be accessed in child class using the super keyword.
classParent:# Constructordef__init__: self.name = name classChild:# Constructordef__init__:''' In Python 3.x, we can also use super.__init__ ''' super.__init__ self.age = agedefdisplay:# Note that Parent.name cant be used # here since super is used in the constructorprint# Driver Codeobj = Childobj.display
How To Prepare For A Data Science Interview:
If you are here, you probably already have a Data Science interview scheduled and are looking for tips on how to prepare so you can crush it. If thats the case, congratulations on getting past the first two stages of the recruitment pipeline. You have submitted an application and your resume, and perhaps done a take home test. Youve been offered an interview and you want to make sure you go in ready to blow the minds of your interviewers and walk away with a job offer. Below are tips to help you prepare for your technical phone screens and on-site interviews.
Read the Job Description for the Particular Position You are Interviewing for
Data Scientist roles are still pretty new and the responsibilities vary wildly across industries and across companies. Look at the skills required and the responsibilities for the particular position you are applying for. Make sure that the majority of these are skills that you have, or are willing to learn. For example, if you know Python, you could easily learn R if thats the language Data Scientists at Company X use. Do you care for web-scraping and inspecting web pages to write web-crawlers? Does analyzing text using different nlp modules excite you? Do you mostly want to write queries to pull dataca from SQL and NoSQL databases and analyse/build models based on this data? Set yourself up for success by leveraging your strengths and interests.
Review your Resume before each Stage of the Interviewing Process
Do Mock Interviews
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General Python Interview Questions
Not all questions will be technical. Questions about your work history, skill set and goals fall under the general category. Other general questions during a Python interview may include:
What are the benefits of using Python?
Discuss the drawbacks of using Python
What kind of Python experience do you have?
How did you learn Python?
What do you like about Python?
What do you dislike about Python?
What interests you about Python?
What inspires you to work in the coding industry?
What skills are you bringing on board to the team?
Are There Any Tools For Identifying Bugs And Performing Static Analysis In Python
Yes, there are tools like PyChecker and Pylint which are used as static analysis and linting tools respectively. PyChecker helps find bugs in python source code files and raises alerts for code issues and their complexity. Pylint checks for the modules coding standards and supports different plugins to enable custom features to meet this requirement.
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Functions And Lambda Expressions
This chapter will focus on the functional aspects of Python. We’ll start by defining functions with a variable amount of positional as well as keyword arguments. Next, we’ll cover lambda functions and in which cases they can be helpful. Especially, we’ll see how to use them with such functions as map, filter, and reduce. Finally, we’ll recall what is recursion and how to correctly implement one.
What Is An Interpreted Language
|Mutable unordered collection of distinct hashable objects.|
|frozenset||Immutable collection of distinct hashable objects.|
Note:set is mutable and thus cannot be used as key for a dictionary. On the other hand, frozenset is immutable and thus, hashable, and can be used as a dictionary key or as an element of another set.
- Modules:Module is an additional built-in type supported by the Python Interpreter. It supports one special operation, i.e., attribute access: mymod.myobj, where mymod is a module and myobj references a name defined in m’s symbol table. The module’s symbol table resides in a very special attribute of the module __dict__, but direct assignment to this module is neither possible nor recommended.
- Callable types are the types to which function call can be applied. They can be user-defined functions, instance methods, generator functions, and some other built-in functions, methods and classes.Refer to the documentation at docs.python.org for a detailed view of the .
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Preparing For The Coding Test
Like every interview, preparation is key. When it comes to the coding test, alongside general interview skills it is a great idea to practice specific coding skills, such as data structures, trees, sorting and search algorithms, arrays, and the most used functions.
When you practice coding challenges, make it a realistic testing scenario.
Give yourself a time limit and, even if the problem is simple, spend time planning and coding by hand on paper or a whiteboard.
You should develop a reliable process to deconstruct the question in the test you might be presented with a challenge that is not simple or straightforward. A great process will make sure that even if the test is difficult, you wont be flustered.
The best thing you can do to prepare for a coding interview is to know your programming. Make sure that you practice using Python wherever you can and find online practice challenges.
How Is Memory Managed In Python
- Memory management in Python is handled by the Python Memory Manager. The memory allocated by the manager is in form of a private heap space dedicated to Python. All Python objects are stored in this heap and being private, it is inaccessible to the programmer. Though, python does provide some core API functions to work upon the private heap space.
- Additionally, Python has an in-built garbage collection to recycle the unused memory for the private heap space.
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Q68 What Is Monkey Patching In Python
Ans: In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.
Consider the below example:
# m.pyclass MyClass:def f:print "f"
We can then run the monkey-patch testing like this:
import mdef monkey_f:print "monkey_f"m.MyClass.f = monkey_fobj = m.MyClassobj.f
The output will be as below:
As we can see, we did make some changes in the behavior of f in MyClass using the function we defined, monkey_f, outside of the module m.
How To Completely Prepare For Tech Interviews
A majority of college students hold a vision of getting into their dream company after completing their graduation. And why only college students, even many working professionals want to get rid out of their current job and get into their dream job or company eagerly.
Butwhat comes between these individuals and their dream job?? Okay, it is the INTERVIEW PROCESS!
Yes, to secure the desired job in your targeted company like Amazon, Microsoft, Swiggy, etc. youre first required to crack their multiple interview rounds. And to prepare for these tech interviews is not an easy-to-go task, especially for the freshers who often come across numerous problems such as what to prepare, where to prepare, etc.
However, things are not that difficult too as it seems. One can conveniently prepare for the tech interviews, whether it be of a startup or an IT giant if he follows the right preparation roadmap and strategies with sheer determination and consistency. Here, in this article, well get to know what skills you need to learn or what things you need to do, to crack the interview of any big tech company. Lets get started:
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Q88 Explain How You Can Set Up The Database In Django
Ans: You can use the command edit mysite/setting.py, it is a normal python module with module level representing Django settings.
Django uses SQLite by default it is easy for Django users as such it wont require any other type of installation. In the case your database choice is different that you have to the following keys in the DATABASE default item to match your database connection settings.
- Engines: you can change the database by using django.db.backends.sqlite3 , django.db.backeneds.mysql, django.db.backends.postgresql_psycopg2, django.db.backends.oracle and so on
- Name: The name of your database. In the case if you are using SQLite as your database, in that case, database will be a file on your computer, Name should be a full absolute path, including the file name of that file.
- If you are not choosing SQLite as your database then settings like Password, Host, User, etc. must be added.
Django uses SQLite as a default database, it stores data as a single file in the filesystem. If you do have a database serverPostgreSQL, MySQL, Oracle, MSSQLand want to use it rather than SQLite, then use your databases administration tools to create a new database for your Django project. Either way, with your database in place, all that remains is to tell Django how to use it. This is where your projects settings.py file comes in.
We will add the following lines of code to the setting.py file:
DATABASES = }
Faqs On Python Data Science Interview Questions
Q1. How do I prepare for Python data science interview questions?
While there is no fixed way to prepare for Python data science interview questions, having a good grasp of the basics can never go wrong. Some important topics you should keep in mind for Python interview questions for data science are: basic control flow for loops, while loops, if-else-elif statements, different data types and data structures of Python, Pandas and its various functions, and how to use list comprehension and dictionary comprehension.
Q2. Will Python be allowed in coding interviews?
While the simple answer is yes, it can vary from company to company. Python can be allowed in coding rounds, and several companies even use platforms such as HackerRank to conduct Python data science interview questions.
Q3. Explain Arrays in Python data science interview questions.
Arrays are a data structure, just like lists. With a number of objects of different data types, Python arrays can be repeated and have several built-in functions to handle them. Such conceptual questions play a vital role in Python data science interview questions. So keep this in mind when preparing.
Q4. Which resources to use to prepare for Python data science interview questions?
Some free resources to prepare for Python data science interview questions are CodeAcademy, FreeCodeCamp, DataCamp, Udacity, and Geeks for Geeks.
Q5. How long does it take to learn Python?
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Showcase Your Example Projects
I realize that you have been busy with school, a coding bootcamp, or your current / previous job, but I cannot stress the importance of this enough. When you are applying for a Python developer job you are effectively trying to convince the hiring manager that you possess the skills they need to make a significant contribution to a product or a project that will someday be a product that brings value to that company.
From my experience the best way to prove you can code is to hand over a reasonable amount of code that demonstrates your ability to produce a usable piece of software. This could be a simple web application, data processing script, or minimal desktop application. The key here is to give an idea of your ability to write code that is well organized, idiomatic, and readable.
The best way to do this is to have a public GitHub, BitBucket, or GitLab repository that houses your example project. This does a few things for you:
- It puts you in the open source community which in and of itself is a great thing.
- It demonstrates that you also know the basics of Git version control.
- It gets your name out there and increases your chance of being contacted for jobs as well.
Format Strings With F
Python has a lot of different ways to handle string formatting, and it can be tricky to know what to use. In fact, we tackle formatting in depth in two separate articles: one about string formatting in general and one specifically focused on f-strings. In a coding interview, where youre using Python 3.6+, the suggested formatting approach is Pythons f-strings.
f-strings support use of the string formatting mini-language, as well as powerful string interpolation. These features allow you to add variables or even valid Python expressions and have them evaluated at runtime before being added to the string:
> > > defget_name_and_decades:... returnf"My name is and I'm decades old."...> > > get_name_and_decadesMy name is Maria and I'm 3.10000 decades old.
The f-string allows you to put Maria into the string and add her age with the desired formatting in one succinct operation.
The one risk to be aware of is that if youre outputting user-generated values, then that can introduce security risks, in which case Template Strings may be a safer option.
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What Do You Know About Pandas
- Pandas is an open-source, python-based library used in data manipulation applications requiring high performance. The name is derived from Panel Data having multidimensional data. This was developed in 2008 by Wes McKinney and was developed for data analysis.
- Pandas are useful in performing 5 major steps of data analysis – Load the data, clean/manipulate it, prepare it, model it, and analyze the data.
What Are Dict And List Comprehensions
Python comprehensions, like decorators, are syntactic sugar constructs that help build altered and filtered lists, dictionaries, or sets from a given list, dictionary, or set. Using comprehensions saves a lot of time and code that might be considerably more verbose . Let’s check out some examples, where comprehensions can be truly beneficial:
- Performing mathematical operations on the entire list
my_list = squared_list = # list comprehension# output => squared_dict = # dict comprehension# output =>
- Performing conditional filtering operations on the entire list
my_list = squared_list = # list comprehension# output => squared_dict = # dict comprehension# output =>
- Combining multiple lists into one
Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one.
a = b = # parallel iterators# output => # nested iterators# output =>
- Flattening a multi-dimensional list
A similar approach of nested iterators can be applied to flatten a multi-dimensional list or work upon its inner elements.
my_list = ,,]flattened = # output =>
Note: List comprehensions have the same effect as the map method in other languages. They follow the mathematical set builder notation rather than map and filter functions in Python.
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