Hi,  I’ve been looking around and messing with map, eachrow and different things but I haven’t been able to figure it out.  Essentially, if I have a N x M dataframe, I want to return a new N x M dataframe, except each value in the new dataframe is each value in the old dataframe divided by the maximum of the row it originally sits in the old dataframe.  For a 1 row dataframe of [2,4,6] it should return [.33,.66,1].  But in my use case mapped to a dataframe with many rows.

Hi everyone,  I have been learning Julia since January by trying to implement classic machine learning algorithms from scratch. My latest project is the KMeans clustering algorithm. I have been successful with this implementation (by comparing results with the implementation sklearn with Python).  The Python implementation (using Python 3.81,  sklearn 0.22.1, and IPython 7.12.0 ) is as follows;  from sklearn.cluster import KMeans from sklearn.metrics import pairwise_distances from sklearn.datase...