percentile

I have a simple table of data, and I'd like to select the row that's at about the 40th percentile from the query. I can do this right now by first querying to find the number of rows and then running another query that sorts and selects the nth row: select count(*) as `total` from mydata; which may return something like 93, 93*0.4 = 37 select * from mydata order by `field` asc limit 37,1; Can I combine these two queries into a single query? This will give you approximately the 40th percentile, it returns the row where 40% of rows are less than it. It sorts rows by how far they are from the

For a project, I need to replicate some results that currently exist in Stata output files (.dta) and were computed from an older Stata script. The new version of the project needs to be written in Python. The specific part I am having difficulty with is matching quantile breakpoint calculations based on the weighted version of Stata's xtile command . Note that ties between data points won't matter with the weights, and the weights I am using come from a continuous quantity, so ties are extremely unlikely (and there are no ties in my test data set). So miscategorizing due to ties is not it. I

I have a matrix in MATLAB and I need to find the 99% value for each column. In other words, the value such that 99% of the population has a larger value than it. Is there a function in MATLAB for this? Use QUANTILE function. Y = quantile(X,P); where X is a matrix and P is scalar or vector of probabilities. For example, if P=0.01, the Y will be vector of values for each columns, so that 99% of column values are larger. gnovice The simplest solution is to use the function QUANTILE as yuk suggested . Y = quantile(X,0.01); However, you will need the Statistics Toolbox to use the function QUANTILE

I'm using SQL Server 2008 R2. I need to compute a percentile value per group, something like: SELECT id, PCTL(0.9, x) -- for the 90th percentile FROM my_table GROUP BY id ORDER BY id For example, given this DDL ( fiddle ) --- CREATE TABLE my_table (id INT, x REAL); INSERT INTO my_table VALUES (7, 0.164595), (5, 0.671311), (7, 0.0118385), (6, 0.704592), (3, 0.633521), (3, 0.337268), (0, 0.54739), (6, 0.312282), (0, 0.220618), (7, 0.214973), (6, 0.410768), (7, 0.151572), (7, 0.0639506), (5, 0.339075), (1, 0.284094), (2, 0.126722), (2, 0.870079), (3, 0.369366), (1, 0.6687), (5, 0.199456), (5, 0

From what I can see, boxplot() method expects a sequence of raw values (numbers) as input, from which it then computes percentiles to draw the boxplot(s). I would like to have a method by which I could pass in the percentiles and get the corresponding boxplot . For example: Assume that I have run several benchmarks and for each benchmark I've measured latencies ( floating point values ). Now additionally, I have precomputed the percentiles for these values. Hence for each benchmark, I have the 25th, 50th, 75th percentile along with the min and max. Now given these data, I would like to draw

I want to mimic the Excel equivalent PERCENTILE function in C# (or in some pseudo code). How can I do that? The function should take two arguments where the first is a list of values and the second is for what percentile the function should calculate for. Tanks! Edit: I'm sorry if my question came across like I had not tried it my self. I just couldn't understand how the excel function worked (yes, I tried wikipedia and wolfram first) and I thought I would understand it better if someone presented it in code. @CodeInChaos gave an answer that seem to be what I'm after. I think Wikipedia page

I have several data sets (distribution) as follows: set1 = [1,2,3,4,5] set2 = [3,4,5,6,7] set3 = [1,3,4,5,8] How do I plot a scatter plot with the data sets above with the y-axis being the probability (i.e. the percentile of the distribution in set: 0%-100% ) and the x-axis being the data set names? in JMP, it is called 'Quantile Plot'. Something like image attached: Please educate. Thanks. [EDIT] My data is in csv as such: Using JMP analysis tool, I'm able to plot the probability distribution plot (QQ-plot/Normal Quantile Plot as figure far below): I believe Joe Kington almost has my problem

I am trying to calculate percentile for every value in column a from a DataFrame x . Is there a better way to write the following piece of code? x["pcta"] = [stats.percentileofscore(x["a"].values, i) for i in x["a"].values] I would like to see better performance. It seems like you want Series.rank() : x.loc[:, 'pcta'] = x.rank(pct=True) # will be in decimal form Performance: import scipy.stats as scs %timeit [scs.percentileofscore(x["a"].values, i) for i in x["a"].values] 1000 loops, best of 3: 877 µs per loop %timeit x.rank(pct=True) 10000 loops, best of 3: 107 µs per loop 来源： https:/