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
I know I could implement a root mean squared error function like this: def rmse(predictions, targets): re
It is possible to install NumPy with pip using pip install numpy. Is there a similar possibility with SciPy?
I am looking for a function that takes as input two lists, and returns the Pearson correlation, and the signif
Is there a SciPy function or NumPy function or module for Python that calculates the running mean of a 1D arra
Lets assume we have a dataset which might be given approximately by import numpy as np x = np.linspace(0,2*np