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Top 50 Pandas Interview Questions & Answers

Top 50 Pandas Interview Questions & Answers
Author: Knowledge Powerhouse
Publisher: Independently Published
Total Pages: 78
Release: 2018-09-11
Genre: Computers
ISBN: 9781720224068
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Top 50 Pandas Interview Questions This book contains Pandas (python library) interview questions that an interviewer asks. It is a compilation of easy to advanced Pandas interview questions after attending dozens of technical interviews in top-notch companies like- Oracle, Google, Ebay, Amazon etc. Each question is accompanied with an answer so that you can prepare for job interview in short time. Often, these questions and concepts are used in our daily programming work. But these are most helpful when an Interviewer is trying to test your deep knowledge of Pandas concepts. How will this book help me? By reading this book, you do not have to spend time searching the Internet for Pandas interview questions. We have already compiled the list of the most popular and the latest Pandas Interview questions. Are there answers in this book? Yes, in this book each question is followed by an answer. So you can save time in interview preparation. What is the best way of reading this book? You have to first do a slow reading of all the questions in this book. Once you go through them in the first pass, mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this book 2-3 times, you will be well prepared to face a technical interview for Data Engineer position in Pandas. What is the level of questions in this book? This book contains questions that are good for a Associate Data engineer to a Principal Software engineer. The difficulty level of question varies in the book from a Fresher to an Experienced professional. What are the sample questions in this book? What is a Series in Pandas? How can we create copy of series in Pandas? How will you create a series from dict in Python? What are operations on Series in pandas? What is a DataFrame in pandas? What are the different ways in which a DataFrame can be created in Pandas? How will you create an empty DataFrame in pandas? How will you add a column to a pandas DataFrame? How will you add a scalar column with same value for all rows to a pandas DataFrame? How will you retrieve a single column from pandas DataFrame? How will you add the value of two columns in a pandas DataFrame to create another column? How will you delete a column in a pandas DataFrame? How can we select a column in pandas DataFrame? How can we retrieve a row in pandas DataFrame? How will you slice rows in a pandas DataFrame? How will you append new rows to a pandas DataFrame? How will you delete rows from a pandas DataFrame? How will you get the number of rows and columns of a DataFrame in pandas? How will you get the top 2 rows from a DataFrame in pandas? How will you get the last 2 rows from a DataFrame in pandas? How will you get the number of elements in a DataFrame in pandas? How will you get the names of columns of a DataFrame in pandas? How will you convert a DataFrame to an array in pandas? How can you check if a DataFrame is empty in pandas? How can you get the sum of values of a column in pandas DataFrame? How will you get the average of values of a column in pandas DataFrame? How can we get the statistical summary of data in a pandas DataFrame? How will you apply a function to every data element in a DataFrame? How will you apply a function to a row of pandas DataFrame? How will you apply a function to a column of pandas DataFrame? What is reindexing in pandas? How will you rename a column in pandas DataFrame?

Pandas for Everyone

Pandas for Everyone
Author: Daniel Y. Chen
Publisher: Addison-Wesley Professional
Total Pages: 1093
Release: 2017-12-15
Genre: Computers
ISBN: 0134547055
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The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

Cracking the Data Science Interview

Cracking the Data Science Interview
Author: Maverick Lin
Publisher:
Total Pages: 120
Release: 2019-12-17
Genre:
ISBN: 9781710680133
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Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics.

Numerical Python

Numerical Python
Author: Robert Johansson
Publisher: Apress
Total Pages: 709
Release: 2018-12-24
Genre: Computers
ISBN: 1484242467
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Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.

Learn Python 3 the Hard Way

Learn Python 3 the Hard Way
Author: Zed A. Shaw
Publisher: Addison-Wesley Professional
Total Pages: 752
Release: 2017-06-26
Genre: Computers
ISBN: 0134693906
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You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3

Build a Career in Data Science

Build a Career in Data Science
Author: Emily Robinson
Publisher: Manning Publications
Total Pages: 352
Release: 2020-03-24
Genre: Computers
ISBN: 1617296244
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Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

Heard in Data Science Interviews

Heard in Data Science Interviews
Author: Kal Mishra
Publisher: Createspace Independent Publishing Platform
Total Pages: 240
Release: 2018-10-03
Genre:
ISBN: 9781727287325
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A collection of over 650 actual Data Scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Author: Jiawei Han
Publisher: Elsevier
Total Pages: 740
Release: 2011-06-09
Genre: Computers
ISBN: 0123814804
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

500 Data Science Interview Questions and Answers

500 Data Science Interview Questions and Answers
Author: Vamsee Puligadda
Publisher: Vamsee Puligadda
Total Pages:
Release:
Genre: Computers
ISBN:
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Knowledge for Free... Get that job, you aspire for! Want to switch to that high paying job? Or are you already been preparing hard to give interview the next weekend? Do you know how many people get rejected in interviews by preparing only concepts but not focusing on actually which questions will be asked in the interview? Don't be that person this time. This is the most comprehensive Data Science interview questions book that you can ever find out. It contains: 500 most frequently asked and important Data Science interview questions and answers Wide range of questions which cover not only basics in Data Science but also most advanced and complex questions which will help freshers, experienced professionals, senior developers, testers to crack their interviews.

Data Science and Machine Learning

Data Science and Machine Learning
Author: Dirk P. Kroese
Publisher: CRC Press
Total Pages: 538
Release: 2019-11-20
Genre: Business & Economics
ISBN: 1000730778
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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code