I think you might have these two backwards.

Could you please comment a bit more on what you mean by backwards?

q1 is the maximum and vals[0] is the minimum.

I am sorry, I do have them backwards. Changing that now

I think you can use the axis parameter and multiple q to compute all three of these at the same time without the list comprehension.

To compute all of those in one call (without list comprehensions), I could do something like:

for sarr in dat:
    median, q1, q3 = np.percentile(sarr, [50, 25, 75])
    med.append(median)
    iqr.append([q1, q3])
    avs.append(adjacent_values(sarr))

Is that fine?

I think you can pass the entire array and use the axis parameter to get it to work on the right dimension. avs would probably need to stay as the list comprehension.

Got it.
I could replace
med = [np.percentile(sarr, 50) for sarr in dat]
with
med = np.percentile(dat, 50, axis=1)

Since iqr is a list of tuples (or rather, list of lists of size 2), I am unable to see how we could do all 3 percentiles with a single call. I could still replace
iqr = [[np.percentile(sarr, 25), np.percentile(sarr, 75)] for sarr in dat]
with
iqr = zip(*(np.percentile(dat, [25, 75], axis=1)))
if that is fine.

No need for the zip; just use .T to swap the axes. You could do it in one call with a temporary variable, but I'm not sure it's any better looking:

tmp = np.percentile(dat, [25, 50, 75], axis=1))).T
med = tmp[:, 1]
iqr = tmp[:, [0, 2]]

What about removing this comment and instead changing to clearer variable names:
interquartile_range = q3 - q1

same, change to
upper_adajcent_values = q3 - interquartile_range*1.5

doesn't need this comment 'cause clear variable name. Also, still in favor of vlines for this (like in #6814) because I think that's a clearer indication of what's being plotted.

@story645 👍 for better variable names, I have made changes and pushed a commit. I also intend to include the change to vlines in a separate commit, if we go ahead with that.

since this is the whiskers, why not change the variable name to whiskers? and again, then removing the comment

Don't need the list(zip; use .T as noted before.

Agreed 👍 , have made the change as suggested in latest commit

Do you actually need a transpose here or would untransposed be the same and then: median = quantiles[1], inter quartile range = qauntiles[[0,2]]

Without using the transpose would work for the medians. For the inter quartile ranges we do need a transpose.

That makes no sense. Either the rows are turned into columns or they aren't. I could be doing the selection wrong on the rows for inter quartile range though. What's the shape of quantiles before the transpose?

The list inter_quartile_ranges is supposed to be a list of pairs.
Before the transpose, the shape of quantiles would be 3 x (some N). Let's say these three rows are numbered 0, 1, 2. Row 1 would be the medians, no problem there. If we do
inter_quartile_ranges = tmp[[0, 2]]
(without the initial transpose), it's shape would be 2 x (some N). We need it's shape to be (some N) x (2) for it to be a list of pairs.
Please correct me if I'm wrong.

Hmm, then maybe save the transpose to inter_quartile_ranges = tmp[[0,2]].T? And I think tmp should be named qaurtiles since that's what they are. That being said, if you go the vlines approach, it simplifies to quartile1, median, quartile3 = np.percentile(data, [25, 50, 75], axis=1) where qaurtile1 is iqrmin, etc.
Also, currently iqr is getting computed both here and in adjacent values and that should probably be cleaned up. My first pass suggestion is change the function sig of adjacent_values to adjacent_vals(vals, q1, q3), drop the first two lines in adjacent_vals, and the call is
whiskers = [adjacent_values(sorted_array, q1, qr ) for sorted_array, q1, q3 in zip(data, quartile1, quartile3)]

Right, makes sense. Firstly, I have changed the variable name to 'quartiles' and altered the transpose calculation, as suggested. I'm working on the vlines approach and the adjacent_values() function.

@story645 I could do something like this, as per your code in the original issue and your suggestions above:

quartile1, medians, quartile3 = np.percentile(data, [25, 50, 75], axis=1)
inter_quartile_ranges = np.vstack([quartile1, quartile3]).T
whiskers = [
    adjacent_values(sorted_array, q1, q3)
    for sorted_array, q1, q3 in zip(data, quartile1, quartile3)]
whiskersMin, whiskersMax = list(zip(*whiskers))
# plot medians as points,
# whiskers as thin lines, quartiles as fat lines
inds = np.arange(1, len(medians) + 1)
ax2.scatter(inds, medians, marker='o', color='white', s=30, zorder=3)
ax2.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5)
ax2.vlines(inds, whiskersMin, whiskersMax, color='k', linestyle='-', lw=1)

I think this looks good. Any comments?

I committed the changes for now. If you have any suggestions, please put them in a review, thanks!

Pretty sure this variable is no longer used and so can be removed

Can remove these comments, probably

Hmm right, will remove these comments and the unused variable

Probabky doesn't need to be bracketed [], wonder if it can be used to remove zip* stuff in the unpacking...

To avoid the unpacking, I could also optionally make 'whiskers' an np.array and then do:
whiskersMin, whiskersMax = whiskers[:, 0], whiskers[:, 1]

Irrespective of whether I do this or not, the brackets can be removed

Then I think it makes sense to do that.

Yes, updated in latest commit

stray redundant comment

Hmm, right. Guess the title we set for the axes are self explanatory.
Updated with comments removed @story645

probably also unnecessary comment

variable name = comment