Python is a big snake now. With a couple of extension libraries has become the foundation of analytical study of deep-data. The libraries I speak of are called Pandas and Jupyter. Let's check them out.

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the python programming language. Don't need to check that out, that's the same words they use if you Google pandas library. So how does it work?

There's a few different ways to install this python module. Those in the know use conda. Those using a less complex system of analysis can install via pip. You'll need the other math modules to support pandas like SciPy, NumPy, and MatPlot so many tutorials will point you towards a full-blown Anaconda load suite. You'll just have to plow through all that to find your best-use direction.

What pandas does is make available to you a massive group of analytical toolsets. Charts, database scrappers, etc. The end result is cross-coordinated data that can be plotted, tabulated, databased, etc. This is very important to the Artificial Intelligence world. In fact, that work prompted developing the pandas package.

Now the cool thing. Jupyter(www.jupyter.org) is a library that allows you to work interactively with your analysis data using python statements. It generates a "notebook" of results that can be shared with collaborators. You save a whole notebook file type that can be opened by other and added onto. We are inside the new tech-approach to scientific research. If you have been studying with a research group, you probably are using jupyter/pandas/python already. I mention this here to us hobbyists as something to be aware of, since AI in robots and image recognition is a very active endeavor everywhere right now. So check it out.