4.Question 4Use this dataframe to answer the question.How would you select the Genre disco? Select all that apply.1 pointdf.iloc[6, ‘genre’]df.loc[6, 5]df.iloc[6, 4]df.loc[‘Bee Gees’, ‘Genre’]5.Question 5Use this dataframe to answer the question.Which will NOT evaluate to 20.6? Select all that apply.1 pointdf.iloc[4,5]df.iloc[6,5]df.loc[4,’Music Recording Sales’]df.iloc[6, ‘Music Recording Sales (millions)’]6.Question 6Use this dataframe to answer the question.How do we select Albums The Dark Side of the Moon to Their Greatest Hits (1971-1975)? Select all that apply.1 pointdf.iloc[2:5, ‘Album’]df.loc[2:5, ‘Album’]df.iloc[2:6, 1]df.loc[2:5, 1]

It seems like you've copied a set of questions from a quiz or test, but you haven't provided the dataframe that these questions are referring to. Without the dataframe, it's impossible to answer these questions correctly.

However, I can explain how the indexing works in pandas, which might help you understand how to answer these questions.

1. df.iloc[6, ‘genre’] - This is incorrect because iloc is used for indexing by integer location, so the second argument should be an integer, not a string.

2. df.loc[6, 5] - This could be correct if the dataframe has a column labeled '5' and a row labeled '6'.

3. df.iloc[6, 4] - This could be correct if the dataframe has at least 7 rows and 5 columns (remember, Python uses 0-based indexing).

4. df.loc[‘Bee Gees’, ‘Genre’] - This could be correct if 'Bee Gees' is a label in the index of the dataframe and 'Genre' is a column label.

For the other questions, similar rules apply. iloc is used for integer-location based indexing, so both arguments should be integers. loc is used for label-based indexing, so the arguments should correspond to labels in the index (for the first argument) and columns (for the second argument) of the dataframe.