Python：多处理中单个锁的死锁(Python: Deadlock of a single lock in multiprocessing)

 你有很多选择。 使用moments或e1071包测试偏度和峰度的两种最佳方法： 
 
duration <- data$variable # I'm going to call it duration

library(moments)
kurtosis(duration)
skewness(duration)

library(e1071)                    
skewness(duration)  
kurtosis(duration) 
 
 我应该提到偏斜和峰度几乎总是存在（只有绝对完全正态的分布才会出现）并且它们被解释为更多的渐变。 小值近似正常，值越大意味着它来自Weibull等其他分布等。 
 
 所以，你通常不会在获得p值的意义上“测试”它，就像你“测量”它并解释系数一样，看它最接近代表的分布。 话虽如此，如果你想通过使用Galton的测量而不是Pearson的测试来测试它，那么测试从零开始的显着差异。 但我不认为这会有所帮助，因为几乎所有的经验数据都会有一些显着的偏斜和峰度，因此它实际上只是一个问题（即足以使数据看起来更像是另一个分布或者是数据仍然最接近正态分布）。 
 
 如果您想使用Galton的测量，您可以找到预先打包的实现，我相信会提供它，或者执行这样的自定义函数： 
 
galtonskew.proc <- function(x){
  #
  #  Compute Galton's skewness measure for x
  #  NOTE: this procedure assumes no x values are missing
  #
  quarts <- as.numeric(quantile(x, probs = c(0.25, 0.5, 0.75)))
  num <- quarts[1] + quarts[3] - 2*quarts[2]
  denom <- quarts[3] - quarts[1]
  gskew <- num/denom
  gskew
}

You have many options. Two of the best ways to test skewness and kurtosis using the moments or e1071 package:
 
duration <- data$variable # I'm going to call it duration

library(moments)
kurtosis(duration)
skewness(duration)

library(e1071)                    
skewness(duration)  
kurtosis(duration) 
 
I should mention that skewness and kurtosis are almost always present (only in an absolutely perfectly normal distribution would it not be) and they are interpreted as more of a gradient. Small values are approximately normal and larger values mean it's from some other distribution like Weibull, etc, etc.
 
So, you normally don't "test" for it in the sense of getting a p-value, so much as you "measure" it and interpret the coefficients to see which distribution it most closely represents. Having said that, if you wanted to you could test for it by using Galton's measures instead of Pearson's, then testing for siginficant difference from zero. But I don't think that would be really helpful as almost all empirical data would have some significant skewness and kurtosis, thus it's really just a matter of how much (i.e. is it enough to make the data look more like another distribution or is the data still closest to the normal distribution).
 
In case you want to use Galton's measures you can either find a prepacked implementation, I believe moments provides it, or do a custom function like this:
 
galtonskew.proc <- function(x){
  #
  #  Compute Galton's skewness measure for x
  #  NOTE: this procedure assumes no x values are missing
  #
  quarts <- as.numeric(quantile(x, probs = c(0.25, 0.5, 0.75)))
  num <- quarts[1] + quarts[3] - 2*quarts[2]
  denom <- quarts[3] - quarts[1]
  gskew <- num/denom
  gskew
}