Consider a DataFrame named df with columns named P2010, P2011, P2012, P2013, 2014 and P2015 containing float values. We want to use the apply method to get a new DataFrame named result_df with a new column AVG. The AVG column should average the float values across P2010 to P2015. The apply method should also remove the 6 original columns (P2010 to P2015). For that, what should be the value of x and y in the given code? frames = ['P2010', 'P2011', 'P2012', 'P2013', 'P2014', 'P2015']
df['AVG'] = df[frames ].apply(lambda z: np. mean(z), axis=x)
result_df df. drop(frames, axis=y)
a. x = 1 y = 0.
b. x = 1 y = 1.
c. x = 0 y = 1.
d. x = 0 y = 0.

x = 1 ; y = 1

Explanation:

The newly created AVG column will contain the mean value of all 6 columns(P2010, P2011, P2012, P2013, 2014 and P2015) this means that we are taking the mean across the columns as our AVG column have the same length as the initial lengtb of the data Frame as all a values in each index are averaged to create the new column. To achieve this the value 1 will be passed to the axis argument in the apply method.

To drop the previous 6 columns, we se the axis argument set to 1, this will delete the columns passed to the drop method.

Hence, both x and y values of the axis argument will take the value 1.

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