Quick Check 6.6 – Data 100, Summer 2020

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For the elections dataframe from class created by pd.read_csv('elections.csv'), what's the number of different entries in the % column of the original dataframe that affect the result of the following expression? elections.groupby(['Party', 'Year']).mean()['%'][:, 2016] Reminder: the data include election results from 1980 through 2016 of candidates who won at least 5% of the popular vote. *

1 for each political party whose candidate won at least 5% of the vote in 2016

1 for each year

1 for each year and each political party

1 for each year and each political party in which that political party's candidate won at least 5% of the vote

(Spring 2018 Midterm) The tables food and store contain information regarding different ingredients and whereto buy them. You may assume all strings are strings and numbers are floats. This is preview of the first 5 rows of the DataFrames. You may assume it has many more rows than what is shown, with the same structure and no missing data

Which of the following expressions would generate the following table? *

food.groupby(['food_group', 'color'])[['calories']].median()

pd.pivot_table(food, values='calories', index='food_group', columns='color', aggfunc=np.median)

food.set_index('food_group').groupby('color')[['calories']].mean())

pd.pivot_table(food, values='calories', index='color', columns='food_group', aggfunc=np.median)

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