Q14 I ran the code below and the df was unchanged. How can I make sure the sorting is applied to the df?

df.sort_values(['date', 'storeid'])

A: df.sort_values(['date','storeid'], inplace=True)

B: df = df.sort_values(['date','storeid'])

C: df.sort_index(['date','storeid'])

Question

Q14 I ran the code below and the df was unchanged. How can I make sure the sorting is applied to the df?

df.sort_values(['date', 'storeid'])

A: df.sort_values(['date','storeid'], inplace=True)

B: df = df.sort_values(['date','storeid'])

C: df.sort_index(['date','storeid'])

BlackTom AI · Accepted Answer

To determine which options actually apply the sorting to the DataFrame, we need to recall how pandas sort methods behave.
Option A: df.sort_values(['date','storeid'], inplace=True)
Using inplace=True performs the sort operation in place, meaning the original df is reordered by the specified columns without needing to reassign. This will modify the DataFrame as intended.
Option B: df = df.sort_values(['date','storeid'])
By assigning the result back to df, you replace the original DataFrame with a sorted version. This is a valid way to apply the sort, and the effect is the same as performing a sorted copy.
Option C: df.sort_index(['date','storeid'])
This call attempts to sort by the DataFrame's index using sort_index, but you’ve supplied column names instead of index labels. sort_index typically takes parameters like axis, level, or by index level, and the positional argument list ['date','storeid'] is not appropriate for sort_index. Consequently, this does not sort by the given column values and will not have the intended effect.
Exploring the implications further, there are two viable approaches for achieving the desired sort order: mutating in place with sort_values(..., inplace=True) or reassigning the sorted result back to a variable (df = df.sort_values(...)). Both approaches produce a DataFrame ordered by date and storeid, whereas sort_index with column names misapplies the function and yields incorrect behavior. 
In summary, the first option leverages in-place modification, the second leverages reassignment, and the third misuses sort_index by supplying column labels where index-related parameters are expected, which leads to failure to sort by the intended columns.

ISBA2401 - Data Analytics with Python (MW 05:45 PM, MW 7:35 PM, TTh 7:35PM) Midterm Multiple Choice (FA2025 - MW545) - Requires Respondus LockDown Browser

Q14 I ran the code below and the df was unchanged. How can I make sure the sorting is applied to the df? df.sort_values(['date', 'storeid']) A: df.sort_values(['date','storeid'], inplace=True) B: df = df.sort_values(['date','storeid']) C: df.sort_index(['date','storeid'])

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