I\'m wondering what the best way is to iterate nonzero entries of sparse matrices with scipy.sparse. For example, if I do the following:
I am not a statistician (more of a researchy web developer) but I\'ve been hearing a lot about scipy and R these days. So out of curiosity I wanted to ask this question (though it might sound silly to
I smooth a series of data points using the algorithm described here: http://www.scipy.org/Cookbook/SignalS开发者_StackOverflowmooth .
I want to plot multiple data sets on the same scatter plot: cases = scatter(x[:4], y[:4], s=10, c=\'b\', marker=\"s\")
I can\'t get simple matrix operations to work on data, for the life of me I haven\'t been able to figure out what I\'m doing incorrectly:
I\'ve got a pip requirements file that I\'m using with virtualenv to automatically grab dependencies for my application.
I am trying to write a simple low pass filter using scipy, but I need help defining the parameters. I have 3.5 million records in the time series data that needs to be filtered, and the data is sampl
I was wonderin开发者_JAVA百科g if there is a counterpart to scipy.linalg.cho_solve in R. What the function does is given the cholesky factor L of A (A = LL\') and b, it solves the original problem, Ax
I have a relatively expensive-to-calculate function which, given a single scalar, returns a numpy.array() object. When I try to integrate this function with respect to the scalar argument, using scipy
What is the fastest (or most \"Pythonic\") way to convert x = [False, False, True, True] into 12? (If there is such a way.)
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