numpy.pad with constant mode does what you need, where we can pass a tuple as second argument to tell how many zeros to pad on each size, a (2, 3) for instance will pad 2 zeros on the left side and 3 zeros on the right side: Given A as: A = np.array([1,2,3,4,5]) np.pad(A, (2, … Read more
Computer memory is addressed linearly. Each memory cell corresponds to a number. A block of memory can be addressed in terms of a base, which is the memory address of its first element, and the item index. For example, assuming the base address is 10,000: item index 0 1 2 3 memory address 10,000 10,001 … Read more
Try numpy.array_split. From the documentation: >>> x = np.arange(8.0) >>> np.array_split(x, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])] Identical to numpy.split, but won’t raise an exception if the groups aren’t equal length. If number of chunks > len(array) you get blank arrays nested inside, to address that – if your … Read more
The answer: numpy.full((2, 2), True) Explanation: numpy creates arrays of all ones or all zeros very easily: e.g. numpy.ones((2, 2)) or numpy.zeros((2, 2)) Since True and False are represented in Python as 1 and 0, respectively, we have only to specify this array should be boolean using the optional dtype parameter and we are done: … Read more
You might be interested in the SciPy Stats package. It has the percentile function you’re after and many other statistical goodies. percentile() is available in numpy too. import numpy as np a = np.array([1,2,3,4,5]) p = np.percentile(a, 50) # return 50th percentile, e.g median. print p 3.0 This ticket leads me to believe they won’t … Read more
Your can use NumPy’s array indexing: def unison_shuffled_copies(a, b): assert len(a) == len(b) p = numpy.random.permutation(len(a)) return a[p], b[p] This will result in creation of separate unison-shuffled arrays.
Create a zeroed array b with enough columns, i.e. a.max() + 1. Then, for each row i, set the a[i]th column to 1. >>> 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
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
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
Use: np.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 convert it … Read more