Feb 5, 2016 · What is the most efficient way to map a function over a numpy array? I am currently doing: import numpy as np x = np.array([1, 2, 3, 4, 5]) # Obtain array of square ...

Jan 11, 2009 · One can safely assume that the index() function in Python is implemented so that it stops after finding the first match, and this results in an optimal average performance. For finding an …

Using numpy, how can I do the following: ln(x) Is it equivalent to: np.log(x) I apologise for such a seemingly trivial question, but my understanding of the difference between log and ln is that ...

Jul 13, 2020 · How can I apply a function to every single item in the array and replace that item with the return? Also, the function's return will be a tuple, so the array will become 3d.

Sep 19, 2020 · numpy() -> numpy.ndarray Returns :attr:`self` tensor as a NumPy :class:`ndarray`. This tensor and the returned :class:`ndarray` share the same underlying storage. Changes to :attr:`self` …

Jun 24, 2016 · 153 Is there any function in numpy to group this array down below by the first column? I couldn't find any good answer over the internet..

216 I'm trying to run over the parameters space of a six-parameter function to study its numerical behavior before trying to do anything complex with it, so I'm searching for an efficient way to do this. …

Aug 8, 2010 · I have a set of data and I want to compare which line describes it best (polynomials of different orders, exponential or logarithmic). I use Python and Numpy and for polynomial fitting there …

253 There doesn’t seem to be any function in NumPy or SciPy that simply calculate the moving average, leading to convoluted solutions. My question is twofold: What's the easiest way to (correctly) …

I suggest you to start with simple polynomial fit, scipy.optimize.curve_fit tries to fit a function f that you must know to a set of points. This is a simple 3 degree polynomial fit using numpy.polyfit and poly1d, …