I am not aware of anything in the standard library. However, you could use something like: def mean(numbers): return float(sum(numbers)) / max(len(numbers), 1) >>> mean([1,2,3,4]) 2.5 >>> mean([]) 0.0 In numpy, there’s numpy.mean().
library(dplyr) dat%>% group_by(custid)%>% summarise(Mean=mean(value), Max=max(value), Min=min(value), Median=median(value), Std=sd(value)) # custid Mean Max Min Median Std #1 1 2.666667 5 1 2.5 1.632993 #2 2 5.500000 10 1 5.5 6.363961 #3 3 2.666667 5 1 2.0 2.081666 For bigger datasets, data.table would be faster setDT(dat)[,list(Mean=mean(value), Max=max(value), Min=min(value), Median=as.numeric(median(value)), Std=sd(value)), by=custid] # custid Mean Max Min Median … Read more
a.mean() takes an axis argument: In [1]: import numpy as np In [2]: a = np.array([[40, 10], [50, 11]]) In [3]: a.mean(axis=1) # to take the mean of each row Out[3]: array([ 25. , 30.5]) In [4]: a.mean(axis=0) # to take the mean of each col Out[4]: array([ 45. , 10.5]) Or, as a standalone … Read more
You can use colMeans: ### Sample data set.seed(1) m <- data.frame(matrix(sample(100, 20, replace = TRUE), ncol = 4)) ### Your error mean(m) # [1] NA # Warning message: # In mean.default(m) : argument is not numeric or logical: returning NA ### The result using `colMeans` colMeans(m) # X1 X2 X3 X4 # 47.0 64.4 44.8 … Read more
Have a look at the ave function. Something like df$grp.mean.values <- ave(df$value, df$group) If you want to use ave to calculate something else per group, you need to specify FUN = your-desired-function, e.g. FUN = min: df$grp.min <- ave(df$value, df$group, FUN = min)
If you want to first take mean on the combination of [‘cluster’, ‘org’] and then take mean on cluster groups, you can use: In [59]: (df.groupby([‘cluster’, ‘org’], as_index=False).mean() .groupby(‘cluster’)[‘time’].mean()) Out[59]: cluster 1 15 2 54 3 6 Name: time, dtype: int64 If you want the mean of cluster groups only, then you can use: In … Read more
Call your column this way. mean(X1888B_PSet03_Dataset1$FillerAcc)
I would give you some hints to get you started up: Use filter function to get the odd members of the given list. Cube the resultant list using map function. Find the mean of them by summing the list and dividing it by the length of the list.