I have an 1D array of numbers, and want to calculate all pairwise euclidean distances. I have a method (thanks to SO) of doing this with broadcasting, but it’s inefficient because it calculates each distance twice. And it doesn’t scale well.
I’m using a 2D shape array to store pairs of longitudes+latitudes. At one point, I have to merge two of these 2D arrays, and then remove any duplicated entry. I’ve been searching for a function similar to numpy.unique, but I’ve had no luck. Any implementation I’ve been
thinking on looks very “unoptimizied”. For example, I’m trying with converting the array to a list of tuples, removing duplicates with set, and then converting to an array again:
How can I do the “in” operation on a numpy array?
(Return True if an element is present in the given numpy array)
I have a numpy 2d array [medium/large sized – say 500×500]. I want to find the eigenvalues of the element-wise exponent of it. The problem is that some of the values are quite negative (-800,-1000, etc), and their exponents underflow (meaning they are so close to zero, so that numpy treats them as zero). Is there anyway to use arbitrary precision in numpy?
I am baffled by this
I have dataframe like this:
I just noticed that the zeros function of numpy has a strange behavior :
I have the following code. It is taking forever in Python. There must be a way to translate this calculation into a broadcast…
This sounds simple, and I think I’m overcomplicating this in my mind.
I have a 2D matrix and I want to take norm of each row. But when I use numpy.linalg.norm(X) directly, it takes the norm of the whole matrix.