histogram

问题 According to Prometheus documentation in order to have a 95th percentile using histogram metric I can use following query: histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) Source: https://prometheus.io/docs/practices/histograms/#quantiles Since each bucket of histogram is a counter we can calculate rate each of the buckets as: per-second average rate of increase of the time series in the range vector. See: https://prometheus.io/docs/prometheus/latest

问题 According to Prometheus documentation in order to have a 95th percentile using histogram metric I can use following query: histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) Source: https://prometheus.io/docs/practices/histograms/#quantiles Since each bucket of histogram is a counter we can calculate rate each of the buckets as: per-second average rate of increase of the time series in the range vector. See: https://prometheus.io/docs/prometheus/latest

问题 I have a list x = [90, 100, 121, 123, 88]. These values in the list are frequencies of occurrence of an event in 5 different trials. I am looking to create a histogram from this list. I simply tried: plt.hist(x) plt.show() I get this: I need something like this: Note: Very New to Python. And still learning when and how to use Stackoverflow. 回答1: Since the list x contains the frequencies then use a bar plot: x = [90, 100, 121, 123, 88] plt.bar(range(1,6), x) plt.ylabel('frequency') plt.show()

问题 Using ggplot2 , I want to create a histogram where anything above X is grouped into the final bin. For example, if most of my distribution was between 100 and 200, and I wanted to bin by 10, I would want anything above 200 to be binned in "200+". # create some fake data id <- sample(1:100000, 10000, rep=T) visits <- sample(1:1200,10000, rep=T) #merge to create a dataframe df <- data.frame(cbind(id,visits)) #plot the data hist <- ggplot(df, aes(x=visits)) + geom_histogram(binwidth=50) How can

问题 I have a dataset that dates and call volume per day. When I plotted them using the plotly R package, all except for 1 of them had each date separated into a different bin. However, this one tricky subset of the data instead grouped bins into 2 day intervals, which isn't very useful information. I'm sure it's an easy fix, but I'm not quite sure how to change the bin width. a <- as.Date(c("2019-02-01", "2019-01-14", "2019-01-15", "2019-01-24", "2019-01-31", "2019-01-22","2019-01-14", "2019-01

问题 What I am trying to do is to analyse the frequency of the letters in a text. As an example, I will use here a small sentence, but all that is thought to analyse huge texts (so it's better to be efficient). Well, I have the following text: test = "quatre jutges dun jutjat mengen fetge dun penjat" Then I created a function which counts the frequencies def create_dictionary2(txt): dictionary = {} i=0 for x in set(txt): dictionary[x] = txt.count(x)/len(txt) return dictionary And then import numpy

问题 I have data stored in some column (Say, Column A). The length of Column A is not fixed (depends on previous steps in the code). I need a histogram for the values in Column A, and have it in the same sheet. I need to take the values in column A, and automatically compute M Bins, then give the plot. I looked online for a "simple" code, but all codes are really fancy, with tons of details that I don't need, to the extent that I am not even able to use it. (I am a VBA beginner.) I found the

问题 The following plot and its code were generated in R (source). How can I replicate this quality of a histogram in Python code using scipy.stats ? x = rgamma(1000, 3, .1) hist(x, prob=T, br=30, col="skyblue2", main="n = 1000: GAMMA(3, .1)") curve(dgamma(x, 3, .1), add=T, lwd=2, col="orange") abline(v = 55.81, lwd=2, col="blue") abline(v = 53.2232, lwd=2, col="brown", lty="dotted") The R plot above is alot better than Python's scipy.stats histograms, one example shown below, but I know there are

问题 The following plot and its code were generated in R (source). How can I replicate this quality of a histogram in Python code using scipy.stats ? x = rgamma(1000, 3, .1) hist(x, prob=T, br=30, col="skyblue2", main="n = 1000: GAMMA(3, .1)") curve(dgamma(x, 3, .1), add=T, lwd=2, col="orange") abline(v = 55.81, lwd=2, col="blue") abline(v = 53.2232, lwd=2, col="brown", lty="dotted") The R plot above is alot better than Python's scipy.stats histograms, one example shown below, but I know there are

问题 library(ggplot2) set.seed(1) dataset <- data.frame(X = rnorm(1000)) dfun <- function(x, a, b) 1/(sqrt(2*pi)*b)*exp(-0.5*((x-a)^2/(2*b^2))) ggplot(dataset, aes(x = X)) + geom_histogram(aes(y = ..density..), binwidth = 0.5)+ stat_function(fun = dfun, args = list(a = , b = )) How can I calculate suitable values of a and b in case like this? 回答1: You can compute values for the arguments a and b with nls . Something like the following. dens <- density(dataset$X, n = nrow(dataset)) df_dens <- data