Question27 The Pandas DataFrame df is generated using the below code. import pandas as pddf=pd.DataFrame({'Salary':[50,100,75]}) Which option(s) can sort the DataFrame df by Salary column from top row being the lowest Salary to the last row being the highest Salary, and modify the DataFrame df directly? Select one or more alternatives: df.sort_values(by = 'Salary', ascending = False, inplace = True) df.sort_values(by = 'Salary', inplace = True) df.sort_values(by = 'Salary', ascending = True, inplace = True) df.sort_values(by = 'Salary') ResetMaximum marks: 3 Flag question undefined

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BlackTom AI · Accepted Answer

We are asked which options can sort the DataFrame df by the Salary column from the lowest salary at the top to the highest salary at the bottom, and modify df directly.

Option 1: df.sort_values(by = 'Salary', ascending = False, inplace = True)
- This uses ascending = False, which sorts in descending order (highest to lowest). Since the requirement is lowest to highest, this option sorts in the opposite direction and does not meet the goal.
- It also sets inplace = True, so it would modify df in place, but the direction is incorrect, making this option inappropriate for the stated goal.

Option 2: df.sort_values(by = 'Salary', inplace = True)
- By omitting ascending, the default is ascending = True, so this sorts the Salary column from smallest to largest.
- The inplace = True argument ensures df is modified in place, fulfilling both the direction and in-place modification requirements.

Option 3: df.sort_values(by = 'Salary', ascending = True, inplace = True)
- This explicitly sorts in ascending order (lowest to highest) and with inplace = True, it modifies df directly.
- This exactly matches the desired behavior of in-place modification in ascending order.

Option 4: df.sort_values(by = 'Salary')
- This call returns a new DataFrame sorted by Salary in ascending order by default, but it does not modify df in place since inplace is not specified (and defaults to False).
- Therefore, df itself remains unchanged, which does not satisfy the requirement of modifying the DataFrame directly.

Putting it together, the options that meet both criteria—sorting from lowest to highest and modifying df in place—are Option 2 and Option 3. The other options fail either in the sort direction or in-place modification requirement.

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