Rather than using the profiler, you can use visualizations within an IBM Watson notebook to present data visually to identify patterns, gain insights, and make decisions based upon your project's objectives or assumptions. As we've seen in earlier chapters, many open source visualization libraries, such as matplotlib, are already pre-installed on IBM Watson Studio for you, and all you have to do is import them.
You can install other third-party and open source visualization libraries and packages in the same manner, or take advantage of other IBM visualization libraries and tools, such as Brunel, to create interactive graphs with simple code and SPSS models to create interactive tables and charts to help evaluate and improve a predictive analytics model.
In the next few sections, we will use a notebook and Python commands to show the various ways to analyze and condition data.