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 1 of 7

Explore more Topics under Data Science

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Data Science Basics

02

Data Manipulation

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Data Analysis and Research

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Statistical Inference and Regression Models

05

Machine Learning

06

Developing Data Products

Q1.

Which of the following operations are supported on Time Frames?

idxmax

ixmax

ixmin

none of the mentioned

Discuss

Answer: (a).idxmax

Q2.

Point out the correct statement.

Timedeltas are differences in times, expressed in difference units

You can construct a Timedelta scalar through various argument

DateOffsets cannot be used in construction

All of the mentioned

Discuss

Answer: (a).Timedeltas are differences in times, expressed in difference units

Q3.

Numeric reduction operation for timedelta64[ns] will return _________ objects.

Timeseries

Timeplus

Timedelta

None of the mentioned

Discuss

Answer: (c).Timedelta

Q4.

Which of the following scalars can be converted to other ‘frequencies’ by as typing to a specific timedelta type?

Timedelta Series

TimedeltaIndex

Timedelta

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q5.

Point out the wrong statement.

min, max, idxmin, idxmax operations are supported on Series

You cannot pass a timedelta to get a particular value

Division by the numpy scalar is true division

None of the mentioned

Discuss

Answer: (b).You cannot pass a timedelta to get a particular value

Q6.

Which of the following is used to generate an index with time delta?

TimeIndex

TimedeltaIndex

LeadIndex

None of the mentioned

Discuss

Answer: (b).TimedeltaIndex

Q7.

Combination of TimedeltaIndex with DatetimeIndex allow certain combination operations that are NaT preserving.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True

Q8.

Using _________ on categorical data will produce similar output to a Series or DataFrame of type string.

.desc()

.describe()

.rank()

none of the mentioned

Discuss

Answer: (b)..describe()

Q9.

Which of the following method can be used to rename categorical data?

Categorical.rename_categories()

Categorical.rename()

Categorical.mv_categories()

None of the mentioned

Discuss

Answer: (a).Categorical.rename_categories()

Q10.

All values of categorical data are either in categories or np.nan.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True

Page 1 of 7

Welcome to the Data Analysis with Python MCQs Page

Data Analysis with Python MCQs | Page 1 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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