C3M5 Graded assignment excercise 4 and 6

Course Q&A Data Analytics Professional Certificate Python for Data Analytics
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    • The grader said excercise 4

      2. Date (from DatetimeIndex) of that largest absolute percent change

      Failed test case: max_pct_change_date has incorrect type. This should be a DatetimeIndex; make sure you are using the correct column.I have tried to make corrections, but it says df is not defined. I have used df, df_bur13 and df_bur13_chl in the first line of code

    • For exercise 6: Failed test case: predictors_simple has incorrect type. Failed test case: predictors_simple has incorrect type. Part of the code I wrote is predictors_simple=df_bur13_chl[[“idx”]]

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Hi! If you’re still having issues with this lab, can you post the latest grader feedback (with the error messages) so the Mentors will know the latest status? Thanks.

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Also, have you checked all the extra hints provided under these exercises? The ungraded labs can also help as reference. If you’re still stuck, just let us know here. Thanks.

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As Chris said you should check out the extra hints,

The instructions don’t say it outright, but it wanted a plain list of column names (like [‘age’, ‘income’]), not a DataFrame.

Your predictors_simple and predictors_multi were DataFrames which is normally fine (sklearn loves them for X), but the checker was strict and only accepted a list.

Quick reason why list > DataFrame here: It is probably just asking for the feature names, not the data itself. Lists are super easy to check exactly (assert your_list == expected_list), while DataFrames have rows, index, etc. that can trip up equality checks.

Think of it like this:

  • df[[‘age’]] = handing over a mini spreadsheet with the “age” column (still 2D, full collection of values)
  • [‘age’] = just the label “age” on a sticky note

The grader wanted the sticky note, not the spreadsheet or even a 1-column DataFrame counts as “the whole collection,”

the checker fails because it’s looking for a simple list, not a pandas object. Even though later you might use that list to select columns, for example, X = dataframe[predictors], the variable itself has to be the list.

Why they do this:

  • The question is testing your understanding of which features/columns you’re choosing to use as predictors → so just the names (list of strings).
  • Auto-graders were finding exact matches on lists (easy to compare with == or set()).
  • DataFrames/Series carry extra stuff (shape, index, dtype, etc.) that makes strict checking harder or impossible without custom logic
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Apologies for the late notice. Can you try to post your grader output again here? Are you still experiencing the issue?