numpy.array is just a convenience function to create an ndarray; it is not a class itself. You can also create an array using numpy.ndarray, but it is not the recommended way. From the docstring of numpy.ndarray: Arrays should be constructed using array, zeros or empty … The parameters given here refer to a low-level method … Read more
tl; dr: the numpy.matrix class is getting deprecated. There are some high-profile libraries that depend on the class as a dependency (the largest one being scipy.sparse) which hinders proper short-term deprecation of the class, but users are strongly encouraged to use the ndarray class (usually created using the numpy.array convenience function) instead. With the introduction … Read more
We could leverage views using a helper function that I have used across few Q&As. To get the presence of subarrays, we could use np.isin on the views or use a more laborious one with np.searchsorted. Approach #1 : Using np.isin – # https://stackoverflow.com/a/45313353/ @Divakar def view1D(a, b): # a, b are arrays a = … Read more
Simply put, numpy.newaxis is used to increase the dimension of the existing array by one more dimension, when used once. Thus, 1D array will become 2D array 2D array will become 3D array 3D array will become 4D array 4D array will become 5D array and so on.. Here is a visual illustration which depicts … Read more
Use tolist(): >>> import numpy as np >>> np.array([[1,2,3],[4,5,6]]).tolist() [[1, 2, 3], [4, 5, 6]] Note that this converts the values from whatever numpy type they may have (e.g. np.int32 or np.float32) to the “nearest compatible Python type” (in a list). If you want to preserve the numpy data types, you could call list() on … Read more
Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing: >>> a = np.array([1, 0, 3]) >>> b = np.zeros((a.size, a.max()+1)) >>> b[np.arange(a.size),a] = 1 >>> b array([[ 0., 1., 0., 0.], [ 1., 0., 0., 0.], [ 0., … Read more
You’re not saying how exactly putdata() is not behaving. I’m assuming you’re doing >>> pic.putdata(a) Traceback (most recent call last): File “…blablabla…/PIL/Image.py”, line 1185, in putdata self.im.putdata(data, scale, offset) SystemError: new style getargs format but argument is not a tuple This is because putdata expects a sequence of tuples and you’re giving it a numpy … Read more
The line should be: numpy.concatenate([a,b]) The arrays you want to concatenate need to be passed in as a sequence, not as separate arguments. From the NumPy documentation: numpy.concatenate((a1, a2, …), axis=0) Join a sequence of arrays together. It was trying to interpret your b as the axis parameter, which is why it complained it couldn’t … Read more
The criterion to satisfy for providing the new shape is that ‘The new shape should be compatible with the original shape’ numpy allow us to give one of new shape parameter as -1 (eg: (2,-1) or (-1,3) but not (-1, -1)). It simply means that it is an unknown dimension and we want numpy to … Read more
The current API is that: flatten always returns a copy. ravel returns a view of the original array whenever possible. This isn’t visible in the printed output, but if you modify the array returned by ravel, it may modify the entries in the original array. If you modify the entries in an array returned from … Read more