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zeros Returns a new array of given shape and type, filled with zeros. np.zeros((5,), dtype=int) >>> array([0, 0, 0, 0, 0]) s = (2,2) np.zeros(s) >>> array([[ 0., 0.], [ 0., 0.]]) zeros_like Return an array of zeros with the same shape and type as a given array. x = np.arange(6) x = x.reshape((2, 3)) x >>> array([[0, 1, 2], [3, 4, 5]]) np.zeros_like(x) array([[0, 0, 0], [0, 0, 0]]) y = np.arange(..

pad Pad an array. numpy.pad(array, pad_width, mode='constant', **kwargs) Example 1. (1,2) : low_before, low_after (6,7) : col_before, col_after b=[[1,2],[3,4],] np.pad(b, ((1,2),(6,7)),'constant', constant_values=0) >>> array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,..

ogrid VS mgrid - ogrid np.ogrid[row(list of size), column(a single list)] np.ogrid[0:3, 0:5] >>> array([[0], [1], [2]]) array([[0, 1, 2, 3, 4]]) - mgrid np.mgrid[row(filled with the same number by row), column(filled with the same number by column)] np.mgrid[0:3, 0:5] >>> array([[[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]], [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]]) ogrid VS mg..

EasyDict It allows to access dict values as attributes. Very useful when exploiting parsed JSON content . from easydict import EasyDict as edict d = edict({'foo':3, 'bar':{'x':1, 'y':2}}) d.foo >>> 3 d.bar.x >>> 1 d = edict(foo=3) d.foo >>> 3

axis : int, optionalThe axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0. axis=0 It concatenates along the row axis. a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6]]) np.concatenate((a, b), axis=0) >>> array([[1, 2], [3, 4], [5, 6]]) axis=1 It concatenates along the column axis. np.concatenate((a, b), axis=1) >>> ValueError: all the i..