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
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
Newer NumPy versions (1.8 and up) have a function called argpartition for this. To get the indices of the four largest elements, do >>> a = np.array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0]) >>> a array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0]) >>> ind = np.argpartition(a, -4)[-4:] >>> ind … Read more
NumPy’s arrays are more compact than Python lists — a list of lists as you describe, in Python, would take at least 20 MB or so, while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB. Access in reading and writing items is also faster with NumPy. Maybe you … Read more