Anyone know how can I calculate the mean of one these columns (on linux)??
sda 2.91 20.44 6.13 2.95 217.53 186.67 44.55 0.84 92.97 sda 0.00 0.00 2.00 0.00 80.00 0.00 40.00 0.22 110.00 sda 0.00 0.00 2.00 0.00 144.00 0.00 72.00 0.71 100.00 sda 0.00 64.00 0.00 1.00 0.00 8.00 8.00 2.63 10.00 sda 0.00 1.84 0.31 1.38 22.09 104.29 74.91 3.39 2291.82 sda 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 For example: mean(column 2)
Awk:
awk '{ total += $2 } END { print total/NR }' yourFile.whatever Read as:
NR==2,NR==6 { total += ..... (see: gnu.org/software/gawk/manual/html_node/Ranges.html)Perl solution:
perl -lane '$total += $F[1]; END{print $total/$.}' file -a autosplits the line into the @F array, which is indexed starting at 0
$. is the line number
If your fields are separated by commas instead of whitespace:
perl -F, -lane '$total += $F[1]; END{print $total/$.}' file To print mean values of all columns, assign totals to array @t:
perl -lane 'for $c (0..$#F){$t[$c] += $F[$c]}; END{for $c (0..$#t){print $t[$c]/$.}}' output:
0 0.485 14.38 1.74 0.888333333333333 77.27 49.8266666666667 39.91 1.29833333333333 434.131666666667 You can use python for that, is available in Linux.
If that comes from a file, take a look at this question, just use float instead.
For instance:
#mean.py def main(): with open("mean.txt", 'r') as f: data = [map(float, line.split()) for line in f] columnTwo = [] for row in data: columnTwo.append( row[1] ) print sum(columnTwo,0.0) / len( columnTwo ) if __name__=="__main__": main() Prints 14.38
I just include the data in the mean.txt file, not the row header: "sda"
with open("mean.txt", 'r') as f: n,t = map(sum, zip(*((1, float(line.split()[1])) for line in f))); print t/n)David Zaslavsky for the fun of it:
with open("mean.txt", 'r') as f: n,t = map(sum, zip(*((1, float(line.split()[1])) for line in f))) print t/n Simple-r will calculate the mean with the following line:
r -k2 mean file.txt for the second column. It can also do much more sophisticated statistical analysis, since it uses R environment for all of its statistical analysis.
