percentile

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I want to pass the numpy percentile() function through pandas' agg() function as I do below with various other numpy statistics functions. Right now I have a dataframe that looks like this: AGGREGATE MY_COLUMN A 10 A 12 B 5 B 9 A 84 B 22 And my code looks like this: grouped = dataframe.groupby('AGGREGATE') column = grouped['MY_COLUMN'] column.agg([np.sum, np.mean, np.std, np.median, np.var, np.min, np.max]) The above code works, but I want to do something like column.agg([np.sum, np.mean, np.percentile(50), np.percentile(95)]) i.e. specify

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'd like to pass weights to scipy.stats.percentileofscore . For example: from scipy import stats a = [1, 2, 3, 4] val = 3 stats.percentileofscore(a, val) Returns 75, as 75% of the values in a lie at or below the val 3. I'd like to add weights, for example: weights = [2, 2, 3, 3] weightedpercentileofscore(a, val, weights) Should return 70, since (2 + 2 + 3) / (2 + 2 + 3 + 3) = 7 / 10 of the weights fall at or below 3. This should also work for decimal weights and large weights, so just expanding the arrays isn't ideal. Weighted percentile

Has anyone found a PostgreSQL equivalent of Oracle's PERCENTILE_CONT function? I searched, and could not find one, so I wrote my own. Here is the solution that I hope helps you out. The company I work for wanted to migrate a Java EE web application from using an Oracle database over to using PostgreSQL. Several stored procedures relied heavily upon using Oracle's unique PERCENTILE_CONT() function. This function does not exist in PostgreSQL. I tried searching to see if anyone had "ported over" that function into PG to no avail. thatdevguy After more searching I found a page that listed the

I'm having a weird situation, where pd.describe is giving me percentile markers that disagree with scipy.stats percentileofscore, because of NaNs, I think. My df is: f_recommend 0 3.857143 1 4.500000 2 4.458333 3 NaN 4 3.600000 5 NaN 6 4.285714 7 3.587065 8 4.200000 9 NaN When I run df.describe(percentiles=[.25, .5, .75]) I get: f_recommend count 7.000000 mean 4.069751 std 0.386990 min 3.587065 25% 3.728571 50% 4.200000 75% 4.372024 max 4.500000 I get the same values when I run with NaN removed. When I want to look up a specific value, however, when I run scipy.stats.percentileofscore(df['f

I've made an SQL query which rank pages by how many times they have been viewed. For instance, ╔══════╦═══════╗ ║ PAGE ║ VIEWS ║ ╠══════╬═══════╣ ║ J ║ 100 ║ ║ Q ║ 77 ║ ║ 3 ║ 55 ║ ║ A ║ 23 ║ ║ 2 ║ 6 ║ ╚══════╩═══════╝ Now what I would like to do is find the percentile rank of each page using an SQL query. The math I would like to use for this is simple enough, I just want to take the row number of the already generated table divided by the total number of rows. Or 1 minus this value, depending on my interests. Can I do a COUNT(pages) on an already generated table like this? I realize that's

NumPy 提供了很多统计函数，用于从数组中查找最小元素，最大元素，百分位标准差和方差等。 带轴向就是计算整行或整列的最大值和最小值，不带轴向，就是整个数组的最大值和最小值 numpy.amin() 和 numpy.amax() numpy.amin() 用于计算数组中的元素沿指定轴的最小值。 numpy.amax() 用于计算数组中的元素沿指定轴的最大值。 import numpy as np a = np.array([[3, 7, 5], [8, 4, 3], [2, 4, 9]]) print('我们的数组是：') print(a) print('\n') print('调用 amin() 函数：') print(np.amin(a, 1)) print('\n') print('再次调用 amin() 函数：') print(np.amin(a, 0)) print('\n') print('调用 amax() 函数：') print(np.amax(a, 1)) print('\n') print('再次调用 amax() 函数：') print(np.amax(a, axis=0)) print('\n') print('整个数组中最大和最小值为：') print(np.amin(a),np.amax(a)) 输出结果为： 我们的数组是： [[3 7 5] [8 4