Your data are paired, so the Mann-Whitney U test (which isn't) is immediately out.
We can easily argue for normality (the counts are large, if "$k$" means thousands, so near-normality would be reasonable), but the actual problem is that since for count data we expect the variance to be related to the mean, unless we assume that the mean isn't varying across days, we don't have constant variance, which might suggest some caution with the t-test; it will still have decent power, but its type I error rate may be somewhat affected.
You can argue for the Wilcoxon signed rank test, but there are several issues there that need to be thought about.
Note that there's also the sign test.
One could easily argue for a test that's appropriate for count data. So, for example, you could do a form of proportion test that takes account of the apparent one-tailedness of the hypothesis. (If you do a two-tailed test, you could also do it as a chi-square.)
Finally, one could also fit a Poisson, quasi-Poisson or Negative Binomial GLM.
In scipy.stats, the Mann-Whitney U test compares two populations:
Computes the Mann-Whitney rank test on samples x and y.
but the Wilcoxon test compares two PAIRED populations:
The Wilcoxon signed-rank test tests the null hypothesis that two
related paired samples come from the same distribution. In particular,
it tests whether the distribution of the differences x - y is
symmetric about zero. It is a non-parametric version of the paired
T-test.
EDITED / CORRECTED in response to ttnphns' comments.
Note that the t does not test for whether the distribution of the differences is symmetric about zero, so the Wilcoxon signed rank test is not truly a non-parametric counterpart of the paired t test.
The Mann-Whitney test, on the other hand, assumes that all the observations are independent of each other (no basis for pairing here!). It also assumes that the two distributions are the same, and the alternative is that one is stochastically greater than the other. If we make the additional assumption that the only difference between the two distributions is their location, and the distributions are continuous, then "stochastically greater than" is equivalent to such statements as "the medians are different", so you can, with the extra assumption(s), interpret it that way.
The Mann-Whitney uses a continuity correction by default, but the Wilcoxon doesn't.
The Mann-Whitney handles ties using the midrank, but the Wilcoxon offers three options for handling ties in the paired values (i.e., zero difference between the two elements of the pair.)
It sounds like the Wilcoxon test is the more appropriate for your purposes, since you do have that lack of independence between all observations. However, one might imagine that requests with similar, but not equal, lengths might exhibit similar behavior, whereas the Wilcoxon would assume that if they aren't paired, they are independent. A logistic regression model might serve you better in this case.
Quotes are from the scipy.stats doc pages, which we aren't supposed to link to, apparently.
Best Answer
The data values are paired so you would use the Wilcoxon signed rank or paired t-test. An example of unpaired values might be if you had ten corporations from the United States and you wanted to compare them with ten corporations from Japan. In the unpaired case of course the sample sizes do not have to be equal.