The following code:
import
numpy as p
myarr=[[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6],[0,1],[0,6]]
copy=p.array(myarr)
p.mean(copy)[:,1]
Is generating the following error message:
Traceback (most recent call last):
File "<pyshell#3>", line 1, in <module>
p.mean(copy)[:,1]
IndexError: 0-d arrays can only use a single () or a list of newaxes (and a single ...) as an index
I looked up the syntax at this link and I seem to be using the correct syntax to slice. However, when I type
copy[:,1]
into the Python shell, it gives me the following output, which is clearly wrong, and is probably what is throwing the error:
array([1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6])
Can anyone show me how to fix my code so that I can extract the second column and then take the mean of the second column as intended in the original code above?
EDIT:
Thank you for your solutions. However, my posting was an oversimplification of my real problem. I used your solutions in my real code, and got a new error. Here is my real code with one of your solutions that I tried:
filteredSignalArray=p.array(filteredSignalArray)
logical=p.logical_and(EndTime-10.0<=matchingTimeArray,matchingTimeArray<=EndTime)
finalStageTime=matchingTimeArray.compress(logical)
finalStageFiltered=filteredSignalArray.compress(logical)
for j in range(len(finalStageTime)):
if j == 0:
outputArray=[[finalStageTime[j],finalStageFiltered[j]]]
else:
outputArray+=[[finalStageTime[j],finalStageFiltered[j]]]
print 'outputArray[:,1].mean() is: ',outputArray[:,1].mean()
And here is the error message that is now being generated by the new code:
File "mypath\myscript.py", line 1545, in WriteToOutput10SecondsBeforeTimeMarker
print 'outputArray[:,1].mean() is: ',outputArray[:,1].mean()
TypeError: list indices must be integers, not tuple
Second EDIT:
This is solved now that I added:
outputArray=p.array(outputArray)
above my code.
I have been at this too many hours and need to take a break for a while if I am making these kinds of mistakes.
