Elegant SciPy. Juan Nunez-Iglesias, Stefan van der Walt, Harriet Dashnow

Elegant SciPy


Elegant.SciPy.pdf
ISBN: 9781491922873 | 250 pages | 7 Mb


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Elegant SciPy Juan Nunez-Iglesias, Stefan van der Walt, Harriet Dashnow
Publisher: O'Reilly Media, Incorporated



Previous message: [SciPy-User] Call for code nominations for Elegant SciPy! Continuum is a sponsor of this year's SciPy Conference in Austin, TX. Calling out SciPy on diversity (even though it hurts) Clarifications about our book, Elegant SciPy (and our call for code submissions). Rougier opened issue HarrietInc/elegant-scipy-submissions#22 · @rougier. Self- organizing map (vector quantization). Matlab, Elegant matrix support; visualization, Expensive; incomplete statistics support, No, Engineering. Key methods of the distribution classes in scipy.stats. >>> from scipy.optimize import brute >>> a,f,g,j = brute(my_func,[param1_list,param2_list,. [Numpy-discussion] fast access and normalizing of ndarray slices. I think what you want is flatten(). For a (10,10) range(100) In [201]: np.nonzero(a>100) Out[201]: (array([], dtype= int32), array([], dtype=int32)). EG: >>> import numpy as np >>> a = np.array([[1 , 2], [3, 4]]) >>> a.flatten('F') >>> array([1, 3, 2, 4]). I think scipy.optimize.brute is what you're after. Val Kalatsky Is there an >> elegant numpy way to do that? Import numpy as np import pandas as pd import scipy.stats as st A = data[data['x1' ]=='A']['x2'] Elegant numpy array shifting and NaN filling? Writing an O'Reilly book about the SciPy library and the surrounding ecosystem . Elegant-scipy-submissions - Submissions of code snippets for the book Elegant SciPy. Is there a more elegant or efficient way?





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