The random module (http://docs.python.org/2/library/random.html) has several fixed functions to randomly sample from. For example random.gauss will sample random point from a normal distribution with a given mean and sigma values.
I have a set of points in 2-dimensional space and need to calculate the distance from each point to each other point.
We are making some kNN and SVD implementations in Python. Others picked Java. Our execution times are very different. I used cProfile to see where I make mistakes but everything is quite fine actually. Yes, I use numpy also. But I would like to ask simple question.
I want to create an array which holds all the max()es of a window moving through a given numpy array. I’m sorry if this sounds confusing. I’ll give an example. Input:
What’s an efficient way, given a NumPy matrix (2D array), to return the minimum/maximum n values (along with their indices) in the array?
What (if any) performance advantages are offered by using iterators. It seems like the ‘Right Way’ to solve many problems, but does it create faster/more memory-conscious code? I’m thinking specifically in Python, but don’t restrict answers to just that.
I read in a comment here on Stack Overflow that it is more memory efficient to do slice assignment when changing lists. For example,
I’m referring to this: http://docs.python.org/tutorial/datastructures.html
If I have the following dataframe, derived like so: df = pd.DataFrame(np.random.randint(0, 10, size=(10, 1)))
I want to do exactly what this guy did: