percentile

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 am currently using xarray to make probability maps. I want to use a statistical assessment like a “counting” exercise. Meaning, for all data points in NEU count how many times both variables jointly exceed their threshold. That means 1th percentile of the precipitation data and 99th percentile of temperature data. Then the probability (P) of join occurrence is simply the number of joint exceedances divided by the number of data points in your dataset. <xarray.Dataset> Dimensions: (latitude:

问题 I am currently using xarray to make probability maps. I want to use a statistical assessment like a “counting” exercise. Meaning, for all data points in NEU count how many times both variables jointly exceed their threshold. That means 1th percentile of the precipitation data and 99th percentile of temperature data. Then the probability (P) of join occurrence is simply the number of joint exceedances divided by the number of data points in your dataset. <xarray.Dataset> Dimensions: (latitude:

问题 I have the following code: SELECT DISTINCT SQ.COMP_ID, SQ.JT, CAST(MIN(SQ.SUM_SALES) OVER (PARTITION BY SQ.JT) AS DECIMAL (10,2)) AS MINIMUM, CAST(PERCENTILE_CONT(0.15) WITHIN GROUP (ORDER BY SQ.SUM_SALES) OVER (PARTITION BY SQ.JT) AS DECIMAL (10,2)) AS P15, CAST(PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY SQ.SUM_SALES) OVER (PARTITION BY SQ.JT) AS DECIMAL (10,2)) AS P25, CAST(AVG(SQ.SUM_SALES) OVER (PARTITION BY SQ.JT) AS DECIMAL (10,2)) AS AVERAGE, CAST(PERCENTILE_CONT(0.5) WITHIN GROUP

问题 I am using the following code to calculate the quartiles of a given data set: #!/usr/bin/python import numpy as np series = [1,2,2,2,2,2,2,2,2,2,2,5,5,6,7,8] p1 = 25 p2 = 50 p3 = 75 q1 = np.percentile(series, p1) q2 = np.percentile(series, p2) q3 = np.percentile(series, p3) print('percentile(' + str(p1) + '): ' + str(q1)) print('percentile(' + str(p2) + '): ' + str(q2)) print('percentile(' + str(p3) + '): ' + str(q3)) The percentile function returns the quartiles, however, I would also like

问题 I am using the following code to calculate the quartiles of a given data set: #!/usr/bin/python import numpy as np series = [1,2,2,2,2,2,2,2,2,2,2,5,5,6,7,8] p1 = 25 p2 = 50 p3 = 75 q1 = np.percentile(series, p1) q2 = np.percentile(series, p2) q3 = np.percentile(series, p3) print('percentile(' + str(p1) + '): ' + str(q1)) print('percentile(' + str(p2) + '): ' + str(q2)) print('percentile(' + str(p3) + '): ' + str(q3)) The percentile function returns the quartiles, however, I would also like

问题 I am using the following code to calculate the quartiles of a given data set: #!/usr/bin/python import numpy as np series = [1,2,2,2,2,2,2,2,2,2,2,5,5,6,7,8] p1 = 25 p2 = 50 p3 = 75 q1 = np.percentile(series, p1) q2 = np.percentile(series, p2) q3 = np.percentile(series, p3) print('percentile(' + str(p1) + '): ' + str(q1)) print('percentile(' + str(p2) + '): ' + str(q2)) print('percentile(' + str(p3) + '): ' + str(q3)) The percentile function returns the quartiles, however, I would also like

问题 I am using the following code to calculate the quartiles of a given data set: #!/usr/bin/python import numpy as np series = [1,2,2,2,2,2,2,2,2,2,2,5,5,6,7,8] p1 = 25 p2 = 50 p3 = 75 q1 = np.percentile(series, p1) q2 = np.percentile(series, p2) q3 = np.percentile(series, p3) print('percentile(' + str(p1) + '): ' + str(q1)) print('percentile(' + str(p2) + '): ' + str(q2)) print('percentile(' + str(p3) + '): ' + str(q3)) The percentile function returns the quartiles, however, I would also like

问题 I have two columns of data representing the same quantity; one column is from my training data, the other is from my validation data. I know how to calculate the percentile rankings of the training data efficiently using: pandas.DataFrame(training_data).rank(pct = True).values My question is, how can I efficiently get a similar set of percentile rankings of the validation data column relative to the training data column? That is, for each value in the validation data column, how can I find