numpy

问题 Is there a simpler and more memory efficient way to do the following in numpy alone. import numpy as np ar = np.array(a[l:r]) ar += c a = a[0:l] + ar.tolist() + a[r:] It may look primitive but it involves obtaining a subarray copy of the given array, then prepare two more copies of the same to append in left and right direction in addition to the scalar add. I was hoping to find some more optimized way of doing this. I would like a solution that is completely in Python list or NumPy array,

问题 The AI must predict the next number in a given sequence of incremental integers using Python, but so far I haven't gotten the intended result. I tried changing the learning rate and iterations but so far without any luck. The next number is supposed to be predicted based on this PATTERN : First number in the sequence (1) is a random int in the interval of [2^0 (current index), 2^1(next index) and so on so forth... The AI should be able to make the decision of which number to choose from the

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 6 months ago . Improve this question I often want to quickly save some Python data, but I would also like to save it in a stable file format in case the date lingers for a long time. And so I have the question, how can I save my data? In data science, there are three kinds of data I want to

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 6 months ago . Improve this question I often want to quickly save some Python data, but I would also like to save it in a stable file format in case the date lingers for a long time. And so I have the question, how can I save my data? In data science, there are three kinds of data I want to

问题 I am trying to calculate all the values contained within a particular radius from a central lat lon position.The code which I am using is as given: import numpy as np import matplotlib.pylab as pl import netCDF4 as nc import haversine f = nc.Dataset('air_temp.nc') def haversine(lon1, lat1, lon2, lat2): # convert decimal degrees to radians lon1 = np.deg2rad(lon1) lon2 = np.deg2rad(lon2) lat1 = np.deg2rad(lat1) lat2 = np.deg2rad(lat2) # haversine formula dlon = lon2 - lon1 dlat = lat2 - lat1 a

问题 I am trying to calculate all the values contained within a particular radius from a central lat lon position.The code which I am using is as given: import numpy as np import matplotlib.pylab as pl import netCDF4 as nc import haversine f = nc.Dataset('air_temp.nc') def haversine(lon1, lat1, lon2, lat2): # convert decimal degrees to radians lon1 = np.deg2rad(lon1) lon2 = np.deg2rad(lon2) lat1 = np.deg2rad(lat1) lat2 = np.deg2rad(lat2) # haversine formula dlon = lon2 - lon1 dlat = lat2 - lat1 a

问题 I have a Python script, which is used in ABAQUS and calls numpy and scipy. I switched from a 32-bit Windows machine to a 64-bit machine. I have problems installing scipy for ABAQUS 6.13.1 on my new 64-bit system. Could anybody give me some tips on how to get scipy running for ABAQUS and provide me with the compatible version of scipy? Regards. 回答1: Download and Install a 64 bit version of Anaconda. https://store.continuum.io/cshop/anaconda/ Using Conda, downgrade the version of SciPy to be

问题 I have an array (dtype=object) with the first column containing tuples of arrays and the second column containing scalars. I want all scalars from the second column where the tuples in the first column equal a certain tuple. Say >>> X array([[(array([ 21.]), array([ 13.])), 0.29452519286647716], [(array([ 25.]), array([ 9.])), 0.9106600600510809], [(array([ 25.]), array([ 13.])), 0.8137344043493814], [(array([ 25.]), array([ 14.])), 0.8143093864975313], [(array([ 25.]), array([ 15.])), 0

问题 A NaN of type decimal.Decimal causes: math.isnan to return True numpy.isnan to throw a TypeError exception. pandas.isnull to return False What is the difference between math.isnan, numpy.isnan and pandas.isnull? 回答1: The only difference between math.isnan and numpy.isnan is that numpy.isnan can handle lists, arrays, tuples whereas math.isnan can ONLY handle single integers or floats. However , I suggest using math.isnan when you just want to check if a number is nan because numpy takes

问题 I am having a hard time with a data-structure related problem. I've tried quite a lot recently, but I don't know how to proceed. The problem is that I have the right output, but the timing is too slow and I don't pass the automated tests. To solve the problem, I am using a min-heap to implement the priority queue with next free times for the workers -- how could I make it more efficient? Efficiency is critical here. Task description You have a program which is parallelized and uses m