numpy

问题 I would like to create a numpy 2d-array based on values in a numpy 3d-array, using another numpy 2d-array to determine which element to use in axis 3. import numpy as np #-------------------------------------------------------------------- arr_3d = np.arange(2*3*4).reshape(2,3,4) print('arr_3d shape=', arr_3d.shape, '\n', arr_3d) arr_2d = np.array(([3,2,0], [2,3,2])) print('\n', 'arr_2d shape=', arr_2d.shape, '\n', arr_2d) res_2d = arr_3d[:, :, 2] print('\n','res_2d example using element 2 of

问题 I have a text file with many rows of data - the first piece of data in each row is a unix timestamp such as 1436472000 . I am using numpy.loadtxt and in the parameters for converters I want to specify for it to convert the timestamp into whatever numpy understands as a date time. I know this needs to go after the 0: in the curly brackets, but I can't work out how to convert it. I know a converter can be used from matplotlib.dates.strpdate2num for normal dates, but I this won't work for unix

问题 I am trying to plot a curve on a sphere but I can not plot them at the same time. I identified some points with Euclidean norm 10 for my curve, and some other points to plot the sphere of radius 10 , respectively as following. Points for curve: random_numbers=[] basevalues=np.linspace(-0.9,0.9,100) for i in range(len(basevalues)): t=random.random() random_numbers.append(t*10) xvalues=[random_numbers[i]*np.cos(basevalues[i]) for i in range(len(basevalues))] yvalues=[random_numbers[i]*np.sin

问题 I am a total novice in python and currently I am stumbled with a simple but tricky situation. Is it possible to remove all these zeroes and rearrange the column from this : A B C D E F 10 10 5 0 0 0 0 0 0 13 3 4 0 13 41 55 0 0 0 0 31 30 21 0 11 19 20 0 0 0 To be something like this: A B C 10 10 5 13 3 4 13 41 55 31 30 21 11 19 20 回答1: Assuming all rows have the same amount of zeros: a = df.to_numpy() a = a[a!=0].reshape(-1,3) pd.DataFrame(a, columns=df.columns[:a.shape[1]]) A B C 0 10 10 5 1

问题 I am a total novice in python and currently I am stumbled with a simple but tricky situation. Is it possible to remove all these zeroes and rearrange the column from this : A B C D E F 10 10 5 0 0 0 0 0 0 13 3 4 0 13 41 55 0 0 0 0 31 30 21 0 11 19 20 0 0 0 To be something like this: A B C 10 10 5 13 3 4 13 41 55 31 30 21 11 19 20 回答1: Assuming all rows have the same amount of zeros: a = df.to_numpy() a = a[a!=0].reshape(-1,3) pd.DataFrame(a, columns=df.columns[:a.shape[1]]) A B C 0 10 10 5 1

问题 I am a total novice in python and currently I am stumbled with a simple but tricky situation. Is it possible to remove all these zeroes and rearrange the column from this : A B C D E F 10 10 5 0 0 0 0 0 0 13 3 4 0 13 41 55 0 0 0 0 31 30 21 0 11 19 20 0 0 0 To be something like this: A B C 10 10 5 13 3 4 13 41 55 31 30 21 11 19 20 回答1: Assuming all rows have the same amount of zeros: a = df.to_numpy() a = a[a!=0].reshape(-1,3) pd.DataFrame(a, columns=df.columns[:a.shape[1]]) A B C 0 10 10 5 1

问题 update from Jan 2021: I performed a clean install of Big Sur in Jan 2021, and upgrade pip to latest version using python3 -m pip install --upgrade pip --user , and installed numpy without issues, and without the error message below. original question from Nov 2020 I'm using macOS big sur on 2019 macbook pro. I'm able to install numpy using python3 -m pip install numpy --user . Note I do not have brew; I'm just using the python3 from the Xcode command line tools. I've seen this error reported

问题 I have two numpy arrays, X and Y , with shapes (n,d) and (m,d) , respectively. Assume that we want to compute the Euclidean distances between each row of X and each row of Y and store the result in array Z with shape (n,m) . I have two implementations for this. The first implementation uses two for loops as follows: for i in range(n): for j in range(m): Z[i,j] = np.sqrt(np.sum(np.square(X[i] - Y[j]))) The second implementation uses only one loop by vectorization: for i in range(n): Z[i] = np

问题 I have two numpy arrays, X and Y , with shapes (n,d) and (m,d) , respectively. Assume that we want to compute the Euclidean distances between each row of X and each row of Y and store the result in array Z with shape (n,m) . I have two implementations for this. The first implementation uses two for loops as follows: for i in range(n): for j in range(m): Z[i,j] = np.sqrt(np.sum(np.square(X[i] - Y[j]))) The second implementation uses only one loop by vectorization: for i in range(n): Z[i] = np

问题 I want to load more than 10000 images in my 8gb ram in the form of numpy arrays.So far I have tried cv2.imread,keras.preprocessing.image.load_image,pil,imageio,scipy.I want to do it the fastest way possible but I can't figure out which on is it. 回答1: One of the Fastest way is to get your multiprocessors do your job in Parallel it asks for parallelisation of your desired job, it brings multiple processors to work on your tasks at the same time when concurrent running isn't an issue. This