I have the following code import matplotlib.pyplot as plt import numpy as np array = np.pad(np.random.rand(300,300),10,'constant', constant_values = nan) fi
I'm trying to implement a function that computes the Relu derivative for each element in a matrix, and then return the result in a matrix. I'm using Python and
Here is the way how numpy.mgrid is used. grid = np.mgrid[x1:y1:100j , x2:y2:100j, ..., xn:yn:100j] However, I find this structure very irritating. Therefore, I
I'm focusing on the special case where A is a n x d matrix (where k < d) representing an orthogonal basis for a subspace of R^d and b is known to be inside t
Seemingly simple question: I have an array with two columns, the first represents an ID and the second a count. I'd like to update it with another, similar arr
If I create an array X = np.random.rand(D, 1) it has shape (3,1): [[ 0.31215124] [ 0.84270715] [ 0.41846041]] If I create my own array A = np.array([0,1,2]
I have searched high and low and just can't find a way to do it. (It's possible I was searching for the wrong terms.) I would like to create a mask (eg: [True F
I use pandas and statsmodels to do linear regression. However, i can't find any possible way to read the results. the results are displayed but i need to do som
I would like to install Python Pandas library (0.8.1) on Mac OS X 10.6.8. This library needs Numpy>=1.6. I tried this $ sudo easy_install pandas Searching
I have been searching for an answer to this question but cannot find anything useful. I am working with the python scientific computing stack (scipy,numpy,matp
Is there a fast way in numpy to add a vector to every row or column of a matrix. Lately, I have been tiling the vector to the size of the matrix, which can use
Given a self-indexing (not sure if this is the correct term) numpy array, for example: a = np.array([3, 2, 0, 1]) This represents this permutation (=> is
I want to read a zipfile into memory and extract its content into a numpy array (as numpy-datatypes). This needs to happen in an extremely efficient/fast manner
I have a NumPy record array of floats: import numpy as np ar = np.array([(238.03, 238.0, 237.0), (238.02, 238.0, 237.01), (238.05
numpy seems to not be a good friend of complex infinities While we can evaluate: In[2]: import numpy as np In[3]: np.mean([1, 2, np.inf]) Out[3]: inf The f
I am using Python Numpy arrays (rasters converted to 2D arrays, specifically) and what I want to do is take one array that has arbitrary dummy values of -999 re
I have a NumPy ndarray to which I would like to add row/column headers. The data is actually 7x12x12, but I can represent it like this: A=[[[0, 1, 2, 3, 4, 5]
I would like to do this : I have this python code : import numpy as np import pylab as plt a = np.array([1,2,3,4,5,6,7,8,9,10]) b = np.array([7,8,6,3,2,1,5,8,4
If I have the upper triangular portion of a matrix, offset above the diagonal, stored as a linear array, how can the (i,j) indices of a matrix element be extrac
So I have an array of values of x (in increasing order) and the corresponding y values. Numpy's interp function takes in the X value, and the x and y arrays. Ho