Is there a numpy builtin to do something like the following? That is, take a list d and return a list filtered_d with any outlying elements removed based on some assumed distribution of the points in d.
I want to slice a NumPy nxn array. I want to extract an arbitrary selection of m rows and columns of that array (i.e. without any pattern in the numbers of rows/columns), making it a new, mxm array. For this example let us say the array is 4×4 and I want to extract a 2×2 array from it.
How do I get the exponential weighted moving average in NumPy just like the following in pandas?
Suppose I have a pandas data frame df:
I have several matlpotlib functions rolled into some django-celery tasks.
I’m trying to save bottleneck values to a newly created hdf5 file.
The bottleneck values come in batches of shape (120,10,10, 2048).
Saving one alone batch is taking up more than 16 gigs and python seems to be freezing at that one batch. Based on recent findings (see update, it seems hdf5 taking up large memory is okay, but the freezing part seems to be a glitch.
I have been trying to count cars when crossing the line and it works, but the problem is it counts one car many times which is ridiculous because it should only be counted once.
I’m trying to store a numpy array of about 1000 floats in a sqlite3 database but I keep getting the error “InterfaceError: Error binding parameter 1 – probably unsupported type”.
I have a set of points pts which form a loop and it looks like this:
Given a NumPy array of int32, how do I convert it to float32 in place? So basically, I would like to do