SciPy appears to provide most (but not all [1]) of NumPy’s functions in its own namespace. In other words, if there’s a function named numpy.foo, there’s almost certainly a scipy.foo. Most of the time, the two appear to be exactly the same, oftentimes even pointing to the same function object.
I’m using Python+Numpy (can maybe also use Scipy) and have three 2D points
How do I install SciPy on my system?
Is there a library module or other straightforward way to implement multivariate spline interpolation in python?
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:
I want to be able to pick values from a normal distribution that only ever fall between 0 and 1. In some cases I want to be able to basically just return a completely random distribution, and in other cases I want to return values that fall in the shape of a gaussian.
I was wondering if there is an elegant and shorthand way in Pandas DataFrames to select columns by data type (dtype). i.e. Select only int64 columns from a DataFrame.
So, I have three numpy arrays which store latitude, longitude, and some property value on a grid — that is, I have LAT(y,x), LON(y,x), and, say temperature T(y,x), for some limits of x and y. The grid isn’t necessarily regular — in fact, it’s tripolar.