is there a more efficient way to take an average of an array in prespecified bins? for example, i have an arra
When installing scipy through pip with : pip install scipy Pip fails to build scipy and throws the followin
I am getting the following error while trying to import from sklearn: >>> from sklearn import svm T
In R I can create the desired output by doing: data = c(rep(1.5, 7), rep(2.5, 2), rep(3.5, 8), rep(
Numpy, scipy, matplotlib, and pylab are common terms among they who use python for scientific computation. I
I can't seem to find any python libraries that do multiple regression. The only things I find only do sim
I have sample data which I would like to compute a confidence interval for, assuming a normal distribution. I
I am trying to read an image with scipy. However it does not accept the scipy.misc.imread part. What could be
I can write something myself by finding zero-crossings of the first derivative or something, but it seems like
INTRODUCTION: I have a list of more than 30,000 integer values ranging from 0 to 47, inclusive, e.g.[0,0,0,0,.
After doing some processing on an audio or image array, it needs to be normalized within a range before it can
numpy.distutils.system_info.BlasNotFoundError: Blas (http://www.netlib.org/blas/) libraries not found.
Say I have an image of size 3841 x 7195 pixels. I would like to save the contents of the figure to disk, resul
Using standard Python arrays, I can do the following: arr = [] arr.append([1,2,3]) arr.append([4,5,6]) # arr
I have a set of data and I want to compare which line describes it best (polynomials of different orders, expo