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
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
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 purpose of meshgrid is to create a rectangular grid out of an array of x values and an array of y values. So, for example, if we want to create a grid where we have a point at each integer value between 0 and 4 in both the x and y directions. To create … 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
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
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