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While searching for ways to debug code in a Jupyter notebook, I found a lot of outdated posts. So I decided to quickly write up my findings. (Just show me the answer…) Let’s say we have this piece of code and we want to set a breakpoint between the original answer to the ultimate question of life, the universe and everything stored in answer and the addition to that answer: def add_to_life_universe_everything(x): answer = 42 # we want a breakpoint here answer += x return answer add_to_life_universe_everything(12) pdb The built-in Python debugger pdb works just fine in a Jupyter notebook. With import pdb; pdb.set_trace() we can enter the debugger and get a little interactive prompt. ...

There are multiple reasons why you would want to share a VPN connection from your Mac with another device. Maybe you have to install a proprietary VPN client which does not run on your main computer or you just don’t want to run/install it there. Maybe your main computer doesn’t “comply” (i.e. you would need to install additional AV software – who wants that?). Or you simply need it for streaming video to some TV box. ...

AWS Lambda is Amazon’s “serverless” compute platform that basically lets you run code without thinking (too much) of servers. I used Lambda in the past, though only in the Node.js environment. Wanting to deploy my first Python function, I ran into a couple of problems. Deployment scenarios There are two deployment scenarios: Simple scenario Advanced scenario The simple scenario applies to you when your function code only requires the AWS SDK library (Boto 3) and no other external resources. Just use Lambda’s inline code editor and you are good to go. No need to read the rest of this article :-) ...

The following code snippets should serve as an (incomplete) cheat sheet for accessing NumPy arrays. All examples expect an import numpy as np. Basic access NumPy arrays can be accessed just like lists with array[start:stop:step] a = np.array([1,2,3,4], int) # => array([1, 2, 3, 4]) a[2] # => 3 a[:2] # => array([1, 2]) a[::2] # => array([1, 3]) When working with multidimensional arrays, a comma can be used to access values for the different axes: ...

This is an old post. You may also be interested in accessing your FileMaker database via the new Data API. I wrote a Python wrapper to make it easier: python-fmrest A lot of my clients have a substantial amount of their data in FileMaker databases. Analyzing that data or running machine learning algorithms on it is often impractical to do in FileMaker itself due to the lack of available modules, data structures as well as for performance considerations (although it can be a fun exercise, as constraints make you creative :-)). So what to do when you want to work with FileMaker data but still run your algorithms in python? This article helps you setup an ODBC connection, read the data from a FileMaker data source and transform it into a Pandas DataFrame. ...