What non parametric should I choose? I Have an hypothesis that say : " London have more jobs posts more than other cities " , I have dataframe with column of the cities and the number of jobs posts (c(London,Manchester,Bristol..),c(26,14,11...)), I did the normality and found out that p-value are lower than 5% that mean I should use one of the non-parametric test , but I don't known if it is one sample or two sample and which test (image of lists of the non parametric tests).
Thanks for reading and I appreciate your help.

Comment continued: With the information given, it is not possible to answer the
question for your data. So I have shown how to analyze
similar fictitious data.
Suppose you have vector x with numbers of job postings outside
London. (Using R.)
set.seed(2022)
x = round(rnorm(50, 20, 4))
max(x)
[1] 25

In my fictitious data for 50 cities outside London have numbers of postings 25 or less.
summary(x);  sd(x)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   8.00   17.00   20.00   19.44   21.75   25.00 
 [1] 3.476804

So, a one-sample t t.test of $H_0: \mu \le 26$ against $H_a: \mu > 26$ is
overwhelming rejected with a P-value near $0.$
t.test(x, mu = 26, alt="less")$p.val
[1] 3.099603e-18

The stripchart (or dotplot) below shows the data.
stripchart(c(26,x), pch=20, meth="stack")
 abline(v = 26, col="red", lwd=2, lty="dotted")


Of course, I have no way to know the actual numbers of job postings
outside London, so the answer for your real data may differ.
Note: With the data summary I have given it should be
easy to compute the t test by hand, and I recommend you do so.
Complete output for t.test:
t.test(x, mu = 26, alt="less")

        One Sample t-test

data:  x
t = -13.342, df = 49, p-value < 2.2e-16
alternative hypothesis: true mean is less than 26
95 percent confidence interval:
     -Inf 20.26435
sample estimates:
mean of x 
    19.44 

Output from sign test in Minitab:
Sign Test for Median: x

Sign test of median =  26.00 versus < 26.00

     N  Below  Equal  Above       P  Median
x   50     50      0      0  0.0000   20.00

