The code

pse$\_$data $=$ np$.$array$($info$\_$df$[["$pse$-$same","pse$-$small"$]])$

grabs data from the Pandas dataframe to create a $2$ x n numpy array that contains the PSEs for the same and small inducers condition, respectively.

$($A$)$ Please write a function subject$\_$count that takes pse$\_$data as input and applies the shape method to get the number of participants in your dataset and return that as an integer variable.

$($B$)$ Please write a function summary$\_$stats that takes pse$\_$data as input and uses the methods mean and std or their corresponding numpy commands to compute the mean and standard deviation, seperately for the two conditions, and returns them as two separate variables. Note that both mean and standard deviation can be computed in one line of code.

$($C$)$ Please write a function compute$\_$err that takes pse$\_$data as input and computes the standard error and $95\%$ confidence interval, seperately for the two conditions, and returns them as two separate variables. Watch this video $($also on eClass$)$ to learn how to compute standard error using the sample size $(=$number of subjects$),$ and how to convert standard error to the $95\%$ confidence interval. You can use the command np$.$sqrt to take the square root of a number. Your function should return two variables named:

pse$\_$stderr # standard error

pse$\_$ci # $95\%$ confidence interval

$($D$)$ Then use the following code to plot your data as a bar plot with error bars. Try replacing pse$\_$ci with pse$\_$stderr in the below and observe how the error bars change:

plt$.$bar$((0,1),$ pse$\_$means, yerr$=$pse$\_$stderr, capsize$=5)$

plt$.$ylim$([15,30])$