This code from the Polling case study

polls['gap'] = polls.clinton_pct - polls.trump_pct
polls['state_gap'] = polls.groupby('state').gap.transform(func='mean')
polls['swing'] = (polls.state != "U.S.") & (abs(polls.state_gap) < 7)

a. adds a swing column that is True or False depending on whether:
b. the absolute value of the difference between the Clinton and Trump percents for the state is less than 7
c. the absolute value of the average difference between the Clinton and Trump percents for the state is less than 7
d. the absolute value of the difference between the Clinton and Trump percents is less than 7
e. the absolute value of the average difference between the Clinton and Trump percents is less than 7

13. This code from the Polling case study

polls['gap'] = polls.clinton_pct - polls.trump_pct
g = sns.relplot(data=polls.query('state==["Michigan", "Florida"] & enddate > "2016-09"'), kind="line", x='enddate', y='gap', ci=None, col='state', col_wrap=2)

creates a line plot for two states that:
a. shows how the Clinton and Trump percents changed over time
b. displays one subplot for each state that shows how the Clinton and Trump percents changed over time
c. shows how the gap between the Clinton and Trump percents changed over time
d. displays one subplot for each state that shows how the gap between the Clinton and Trump percents changed over time

polls['gap'] = polls.clinton_pct - polls.trump_pct: This line calculates the difference between Clinton's percentage and Trump's percentage for each poll and stores it in a new column named gap.

polls['state_gap'] = polls.groupby('state').gap.transform(func='mean'): This line groups the polls by state and calculates the mean of the gap column for each state. It then creates a new column state_gap that contains the average gap for each state.

polls['swing'] = (polls.state != "U.S.") & (abs(polls.state_gap) < 7): This line creates a new column swing that contains True or False values. It is True if the state is not "U.S." and the absolute value of the state_gap is less than 7.



Answer to the first multiple-choice question:

The code adds a swing column that is True or False depending on whether the absolute value of the average difference between the Clinton and Trump percents for the state is less than 7 (c).


Explanation of the second piece of code:

polls['gap'] = polls.clinton_pct - polls.trump_pct: Similar to before, this line calculates the gap between Clinton's percentage and Trump's percentage.

g = sns.relplot(data=polls.query('state==["Michigan", "Florida"] & enddate > "2016-09"]'), kind="line", x='enddate', y='gap', ci=None, col='state', col_wrap=2): This line creates a line plot using Seaborn's relplot function. It queries the dataset for certain conditions and plots the gap over time, separating them into subplots for each state.



Answer to the second multiple-choice question:

The line plot creates one subplot for each state that shows how the gap between the Clinton and Trump percents changed over time (d).



In summary, these pieces of code are used to analyze historical polling data to understand the voting tendencies in certain states by measuring the differences (or gaps) between the candidates' support levels. This helps political analysts and researchers determine which states might be more competitive or "swing" states in the election.

Answered by EmmaGraceJohnson | 2025-07-07

The code analyzes polling data to create a 'swing' column indicating whether a state's average polling gap is less than 7, and it creates a line plot for Michigan and Florida showing changes in this gap over time. The correct answers are (c) for the swing column and (d) for the line plot. This helps identify swing states and visualize electoral trends as elections approach.
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Answered by EmmaGraceJohnson | 2025-07-07