Welcome to the Data Analysis with Python MCQs Page

Dive deep into the fascinating world of Data Analysis with Python with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Data Analysis with Python, a crucial aspect of Data Science. In this section, you will encounter a diverse range of MCQs that cover various aspects of Data Analysis with Python, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Science.

Check out the MCQs below to embark on an enriching journey through Data Analysis with Python. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Science.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Data Analysis with Python. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Data Analysis with Python MCQs | Page 4 of 7

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Developing Data Products

Q31.

Which of the following thing can be data in Pandas?

a python dict

an ndarray

a scalar value

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q32.

Point out the correct statement.

If data is a list, if index is passed the values in data corresponding to the labels in the index will be pulled out

NaN is the standard missing data marker used in pandas

Series acts very similarly to a array

None of the mentioned

Discuss

Answer: (b).NaN is the standard missing data marker used in pandas

Q33.

The result of an operation between unaligned Series will have the ________ of the indexes involved.

intersection

union

total

all of the mentioned

Discuss

Answer: (b).union

Q34.

Which of the following input can be accepted by DataFrame?

Structured ndarray

Series

DataFrame

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q35.

Point out the wrong statement.

A DataFrame is like a fixed-size dict in that you can get and set values by index label

Series can be be passed into most NumPy methods expecting an ndarray

A key difference between Series and ndarray is that operations between Series automatically align the data based on label

None of the mentioned

Discuss

Answer: (a).A DataFrame is like a fixed-size dict in that you can get and set values by index label

Q36.

Which of the following takes a dict of dicts or a dict of array-like sequences and returns a DataFrame?

DataFrame.from_items

DataFrame.from_records

DataFrame.from_dict

All of the mentioned

Discuss

Answer: (a).DataFrame.from_items

Q37.

Series is a one-dimensional labeled array capable of holding any data type.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True

Q38.

Which of the following works analogously to the form of the dict constructor?

DataFrame.from_items

DataFrame.from_records

DataFrame.from_dict

All of the mentioned

Discuss

Answer: (a).DataFrame.from_items

Q39.

Which of the following operation works with the same syntax as the analogous dict operations?

Getting columns

Setting columns

Deleting columns

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q40.

If data is an ndarray, index must be the same length as data.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True

Page 4 of 7

Welcome to the Data Analysis with Python MCQs Page

Data Analysis with Python MCQs | Page 4 of 7

Explore more Topics under Data Science

Data Science Basics

Data Manipulation

Data Analysis and Research

Statistical Inference and Regression Models

Machine Learning

Developing Data Products

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