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. ...