String representation of numpy objects has changed?
Numpy has changed its string representations. With numpy 1.26.4, I get:
What exactly is slowing np.sum down?
It is know that np.sum(arr) is quite a lot slower than arr.sum(). For example:
Strange behaviour of the in-operator on NumPy arrays
This behavior of the in operator on NumPy arrays I don’t understand:
Strange behaviour of the Python in-operator when used on numpy arrays
I have encountered a behavior of the in operator in the context of numpy arrays which I don’t understand.
Strange behaviour of the **in** operator with numoy arrays
I have encountered a behavior of the in operator in the context of numpy arrays which i don’t understand.
Cellular automata to simulate car traffic dont move cars
I have wrote a simulation in python based on Nagel–Schreckenberg model but It dont seems to work do you know why?
Why numpy data type kind returns void when it was created as float64?
I have this code:
Why numpy data structure field kind returns void when it was created as float64?
I have this code:
In-place addition with duplicate indices in Numpy
For checking the accuracy of a classifier, I was setting up a confusion matrix, i.e. a matrix that shows the numbers of correctly predicted vs. wrongly predicted labels.
numpy 1.17.3 : f-ordered arrays have slower assignment than their transpose
If I have an array: x = np.array(np.random.rand(2000, 400), order='F'), and create a mask: y = (x > .5) (which also happens to become F-contiguous), then the following code: