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I have a numpy array subclass, and I’d like to be able to concatenate them.

I have a numpy array subclass, and I’d like to be able to concatenate them.

%timeit -n 1000 -r 7 np.zeros(100000000, dtype=bool) gives 4.22 ms %timeit -n 1000 -r 7 np.zeros(1000000000, dtype=bool) # 1 more zero = 10 times longer array gives 3.13 μs (not even milliseconds, microseconds!?) How can this be possible? Is this hardware dependent? python numpy numpy-ndarray

I recently timed several different array allocation and initialization procedures in numpy. I however fail to interpret these timings. Here is a plot of my measurements (size of array in number of elements “n” for a given data type vs time of execution).

I recently timed several different array allocation and initialization procedures in numpy. I however fail to interpret these timings. Here is a plot of my measurements (size of array in number of elements “n” for a given data type vs time of execution).

I’m a bit confused by the behavior of the below code and wondering if someone could shed some light on this. Basically, I have a matrix called mat which is a numpy ndarray. I get its diagonal using mat.diagonal() and assign it to the variable diag. I changed all diagonal values of mat to 100. Now I find diag has its values all changed to 100 too, which seems to indicate that diag directly references elements in mat. Yet, when I check the memory address of the first element in diag and compare it to that of mat, they are different. What’s the right way to look at this?

I have a two dimensional numpy array: myarray=[[0,0],[1,1],[2,2]]
I also have a grid of points: mygrid=[[70,70],[100,100],[30,30]]

I have a circle “map”, which is basically an np.array() of coordinates which represent a circle with a fixed radius. I also have another np.array: screen=np.ones((dimensions of the screen), dtype=np.uint8). This is essentially a black canvas.

Does Numpy provide built-in capabilities to print the indices/coordinates of a 2-d Numpy array at its borders?