Question18 What is the structure of ‘ser3‘ after executing the following Pandas code: Select one alternative: A 5.0B 7.0C 9.0 AD 5BC 7CB 9 A 1.0B 8.0C 8.0D 4.0 A NaNB 8.0C 8.0D NaN None of the above ResetMaximum marks: 3 Flag question undefinedSingle choice
A
A 5.0 B 7.0 C 9.0
B
AD 5 BC 7 CB 9
C
A 1.0 B 8.0 C 8.0 D 4.0
D
A NaN B 8.0 C 8.0 D NaN
E
None of the above

Question

Question18 What is the structure of ‘ser3‘ after executing the following Pandas code: Select one alternative: A 5.0B 7.0C 9.0 AD 5BC 7CB 9 A 1.0B 8.0C 8.0D 4.0 A NaNB 8.0C 8.0D NaN None of the above ResetMaximum marks: 3 Flag question undefinedSingle choice

A

A 5.0 B 7.0 C 9.0

B

AD 5 BC 7 CB 9

C

A 1.0 B 8.0 C 8.0 D 4.0

D

A NaN B 8.0 C 8.0 D NaN

E

None of the above

BlackTom AI · Accepted Answer

To tackle this, I’ll map out what each piece of code creates and then see how the addition behaves at each index.
Option A corresponds to the result with values at A, B, C, and D as: A 5.0, B 7.0, C 9.0. This would require ser1 and ser2 to contribute those exact numbers at those positions, which doesn’t align with the actual data given for ser1 (1, 2, 3) and ser2 (4, 5, 6).
Option B shows A D 5, B C 7, C B 9. Here the labels seem scrambled (A, D, B, C) and the numbers 5, 7, 9 don’t correspond to the sum of matching indices across ser1 and ser2. Since ser1 has no D, and ser2 has D with value 4, a sum at D would be NaN for A and D, not 5, 7, or 9.
Option C displays A 1.0, B 8.0, C 8.0, D 4.0. This implies that A would be 1.0 and D would be 4.0, which would occur if we only took ser1 values for A and ser2 values for D without summing across overlapping indices, which is not how the addition between two aligned Series works in pandas when both have missing positions. Both A and D would be NaN because one side is missing for those indices.
Option D reads A NaN, B 8.0, C 8.0, D NaN. This matches the behavior of adding two Series with overlapping indices: for B and C the values sum to 8.0 each (2+6 and 3+5), while A and D lack a counterpart in one of the Series, yielding NaN for those positions. After sorting by index, the order becomes A, B, C, D with the corresponding values NaN, 8.0, 8.0, NaN.
The final option, None of the above, would be chosen if none of the listed patterns matched the actual result. Given the above, Option D precisely reflects the correct summed values at each index after the operation and the sorted order.
In detail, the operation ser3 = ser1 + ser2 performs alignment on the index labels. For index A, ser1 has 1 but ser2 has no A, so the sum is NaN. For B, 2 + 6 = 8.0. For C, 3 + 5 = 8.0. For D, ser1 has no D, so NaN. After sort_index(inplace=True), the index order is A, B, C, D, producing the sequence: NaN, 8.0, 8.0, NaN.
Thus, Option D is the one that matches the described result, while the other options misstate the values or ordering, or incorrectly assign values to indices that don’t align with the actual addition behavior.

Question18 What is the structure of ‘ser3‘ after executing the following Pandas code: Select one alternative: A 5.0B 7.0C 9.0 AD 5BC 7CB 9 A 1.0B 8.0C 8.0D 4.0 A NaNB 8.0C 8.0D NaN None of the above ResetMaximum marks: 3 Flag question undefinedSingle choice

Similar Questions

What type of data structure is a Pandas Series exactly?

What type of data structure is a Pandas Series?

Question21 Which of the following correctly describes a Series in Python using the Pandas library? Select one alternative: A table with rows and columns A fixed-size 2D array A 3D matrix holder A one-dimensional labeled array ResetMaximum marks: 2 Flag question undefined

Q11 .apply operation will perform more efficiently when applied to which of the following pandas data structures?

True or False? While Series.head() will return us the first five rows of the Series by default, Series.nlargest() will return us the five rows corresponding to the largest values in the Series by default.

Which of the following can you use to obtain the maximum value in a Pandas Series, s? Assume there are no missing values in s.

Question15 Which of the following is a key feature of a Pandas Series? Select one alternative: (a) Multidimensional structure (b) Labeled index (c) Mixed types in a single column (d) SQL integration ResetMaximum marks: 3 Flag question undefined

Question7 Pandas automatically aligns Series by index when performing arithmetic. Select one alternative: True False ResetMaximum marks: 2 Flag question undefined

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler