I would like to break down a pandas column consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).
I am using Pandas dataframes and want to create a new column as a function of existing columns. I have not seen a good discussion of the speed difference between df.apply() and np.vectorize(), so I thought I would ask here.
In another question, other users offered some help if I could supply the array I was having trouble with. However, I even fail at a basic I/O task, such as writing an array to a file.
In Python, I have an ndarray y
that is printed as array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1])
I have two arrays that have the shapes N X T and M X T. I’d like to compute the correlation coefficient across T between every possible pair of rows n and m (from N and M, respectively).
I am trying to install numpy with OpenBLAS , however I am at loss as to how the site.cfg file needs to be written.
Is there a Pythonic and efficient way to check whether a Numpy array contains at least one instance of a given row? By “efficient” I mean it terminates upon finding the first matching row rather than iterating over the entire array even if a result has already been found.
flatten is a method of an ndarray object and hence can only be called for true numpy arrays.
is there a more efficient way to take an average of an array in prespecified bins? for example, i have an array of numbers and an array corresponding to bin start and end positions in that array, and I want to just take the mean in those bins? I have code that does it below but i am wondering how it can be cut down and improved. thanks.
I’m taking some university classes and have been given an ‘instructional account’, which is a school account I can ssh into to do work. I want to run my computationally intensive Numpy, matplotlib, scipy code on that machine, but I cannot install these modules because I am not a system administrator.