All Questions

I thought numpy.load() writes directly into the array data memory. However, the results of the profiling functions (I profiled with memray and mprof) seems a bit strange to me... My array is 2GB big. ...

The numpy.arange function takes the three parameters: start, stop, step (positional args.) The default step is 1. Throughout my entire Numpy experience, the last element of the resultant array is not ...

I'm trying to assign a dataframe cell with a nested list: df.loc['y','A'] = [[2]] However, the actual assigned value is [2]. It works expected for [2], [[[2]]], [[[[2]]]], but just not for [[2]] See ...

I am wanting to identify identical vectors after a dot product calc. The below works for a single dimension but not multi-dimensionally. Single Dimension a = np.array([0.8,0.5]) b = np.array([0.8,0.5])...

Consider the following: import numpy as np a = np.random.rand(123, 45, 3) print(a[:, :, [0, 1]].shape) # (123, 45, 2) print(a[:, 0, [0, 1]].shape) # (123, 2) print(a[0, :, [0, 1]].shape) # (2, 45)...

Say I have an array as follows: arr = np.asarray([1, 2, 3, 4294967296, 100], dtype=np.uint64) I now want two arrays, one array with the lower 32 bits of every element, and one with the upper 32 bits ...

I am trying to apply target encoding to categorical features using the category_encoders.TargetEncoder in Python. However, I keep getting the following error: AttributeError: 'numpy.ndarray' object ...

I need to work with arrays that can have zeros in their shapes. However, I am encountering an issue. Here's an example: import numpy as np arr = np.array([[]]) assert arr.shape == (1,0) arr.reshape((...

numpy.vectorize conveniently converts a scalar function to vectorized functions that can be applied directly to arrays. However, when inputting a single value into the vectorized function, the output ...

Numpy library crash without console error or warning constantly In Python Console see notifications either Process finished with exit code -1073741819 (0xC0000005) or Process finished with exit code -...

I'm currently trying to implement my own version of 2D Pooling (with channels included) in Python using only NumPy, but I've run into a bit of a roadblock with vectorizing the backpropagation process. ...

Take a unitary matrix U. I want to swap the columns such that the largest element of each column (in absolute value) is on the diagonal (modulo ties). What is the best way to do this in numpy?

I have a 2D array a given by: a = np.array([[2, 3, 1, 9], [0, 5, 4, 7], [2, 4, 6, 8]]) [[2 3 1 9] [0 5 4 7] [2 4 6 8]] I would like to get row numbers of maximum elements column-wise, i.e. given ...

What I'm trying to do is essentially removing all rows h,s in a 3D numpy array a if a[h,s,v] = some value for all v More specifically, I have a loaded image from cv2 which contains some transparent ...

I'm playing around with a numpy dataframe containing two columns: 'tweet_text' and 'cyberbullying_type'. It was created through this dataset as follows: df = pd.read_csv('data/cyberbullying_tweets.csv'...