I have a matrix A of size 256x256. I have divided it in 16 arrays of 32 by 32, keeping its original array connections.
I have a 100 by 100 2D numpy array. and I also have the index of such array. Is there any way that I can extract or get the "unique indexes" along each side of the array? (North_Bound, East_
I do have a array of shape (100,500,400) where the first two dimensions are the row and the column of an image data and the third dimension is the channels of the image file. I want to plot the histog
I\'ve played around with ax.view_init and for some reason, I can get every other image except for this one:
I am trying to model a relationship in python using multiple terms including a time term but the model I am trying to recreate is one that uses a cubic polynomial in one variable and a linear polynomi
Suppose I have a numpy array nparr = np.array([10,20,30,40,50]) The output of the operation nparr[[1,3,3,4]] += 1 on this array is:
I would like to add a new consecutive date row at the end of a new category based on a column. Ex. based on the Area column.
I referred the following page to learn gradient-descent-algortithm by Python: https://realpyt开发者_JAVA百科hon.com/gradient-descent-algorithm-python/#implementation-of-basic-gradient-descent
I want to convert this Pandas DataFrame to a TensorDataset import pandas as pd df = pd.DataFrame({\'A\': [[1, 2, 3], [1, 2, 3], [1, 2, 3]], \'B\': [0, 1, 0]})
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