Python Data functions in TIBCO Spotfire
Starting with Spotfire 10.7 there is native support for using Python for machine learning and other advanced analytics in Spotfire using Spotfire's Data functions. This provides a very tight integration between advanced analytics and visual analytics and enables building easy to use analytic applications for business users utilizing Pythons extensive library of advanced analytics capabilities.
As an example you can use Python data functions for Text Analytics, see for example Sentiment Analysis and Topic Identification using Python data functions in TIBCO Spotfire
Note that using Python for data functions is different than using Iron Python and the Spotfire API for automating an analytical application. Read more about the latter here.
Also note that if you are using Spotfire 10.6 or an an older version, the Python data function community extension provides a solution for using Python for data science in Spotfire.
If you are using 10.7 or later you should use the native Python data functions instead.
Python data functions
Spotfire data functions provides an interface between Visual analytics and advanced analytics. Data functions have inputs and outputs that can be mapped to columns, data tables, document properties etc, and may be responsive to the users marking and filtering. This enables creating dynamic analytical applications that uses familiar concepts such as drill down, marking, filtering and drag and drop configuration of visualizations in combination with machine learning or other advanced analytics. This enables providing end users a familiar and easy to use experience powered by data science. See this short video that shows text analytics in combination with visual analytics as an example.
Using Python packages
There are many packages for machine learning, text analytics etc built for Python. Some well known examples are NLTK, Gensim and Tensorflow. You can use these in your Spotfire data functions, but you need to install them into your Python environment first. Read more about this, and about how an admin can manage the usage of Python packages in your team in this article.
Also see the FAQ about Python data functions.