If you want to run some analysis on a dataset that's just a little too big to load into memory on your laptop, but you don't want to leave the comfort of using Pandas dataframes in a Jupyter notebook, then Dask may be just your thing. Dask is ...
The ALS-WR algorithm works well for recommender systems involving a sparse matrix of users by items to review, which happens when most people only review a small subset of many possible items (businesses, movies, etc.). By tweaking the code from a great tutorial to take advantage of this sparsity, I was able to dramatically reduce the computation time.
In my PhD research, I do a lot of analysis of 2D and 3D grid data output by simulations I run. In my analyses, it's very helpful to restructure these data into a more useable format. A few key lines of python code do the trick.
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In my polymer field theory research, often my studies involve running a bunch of simulations where I pick one or more input parameters and change them over a range of values, then compare the results of each separate simulation to see how that/those variable(s) affect the system I’m simulating. This kind of study is called a “parameter sweep”, and can also be referred to as “embarrassingly parallel”, because the processor(s) for each for each individual job don’t need to communicate with the processor(s) from any other job. It can be very tedious to manually create input files for each job, so I wrote a bash script to help me out.
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