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  2. This is because under the hood, mean() uses statistics._sum() which returns a data type to convert the mean into (and Decimal is not on Python's number hierarchy), while fmean() uses math.fsum() which just adds the numbers up (which is also much faster than built-in sum() function).

  3. I have L1, L2, L3, L4...LX lists which tells me the index whose mean I need for columns C[1, 2, 3]. For ex: L1 = [0, 2, 3] , means I need mean of rows 0,2,3 and store it in 1st row of a new df/matrix. Then L2 = [1,4] for which again I will calculate mean and store it in 2nd row of the new df/matrix.

  4. I am trying to calculate the number of samples, mean, standard deviation, coefficient of variation, lower and upper 95% confidence limits, and quartiles of this data set across each column and put it into a new data frame. The numbers below are not necessarily all correct & I didn't fill them all in, just provides an example.

  5. I'm practicing dplyr package using famous dataset from ggplot2, 'diamonds' data. I am trying to calculate mean 'price' of diamonds grouped by variable 'cut'. My code is as following. price.cut &l...

  6. DF['Mean'] <- apply(DF[,2:4], 1, mean) Notice I'm doing a slightly different assignment than the first example. This approach makes it easier to incorporate it into for loops.

  7. Try df.mean(axis=0), axis=0 argument calculates the column wise mean of the dataframe so the result will be axis=1 is row wise mean so you are getting multiple values. Share Improve this answer

  8. r - Calculate the mean by group - Stack Overflow

    stackoverflow.com/questions/11562656

    Calculate a mean by groups in R. 1. R compute mean and sum of value in dataframe using group_by. 0 ...

  9. If you're not using numpy, the obvious way to calculate the arithmetic mean of a list of values is to divide the sum of all elements by the number of elements, which is easily achieved using the two built-ins sum() and len(), e.g.:

  10. The proper answer to your question is to use statistics.mean. But for fun, here is a version of mean that does not use the len() function, so it (like statistics.mean) can be used on generators, which do not support len():

  11. def custom_mean(df): return df.mean(skipna=False) group.agg({"your_col_name_to_be_aggregated":custom_mean}) That's it! You can customize your own aggregation the way you want, and I'd expect this to be fairly efficient, but I did not dig into it. It was also discussed here, but I thought I'd help spread the good news!