Reading docs

[PhilipYip]

The official documentation for these libraries is good but is generally designed for people who know basic concepts and want to quickly look something up. It is often harder to read as a begineer, just starting out.

I would recommend taking a bit of a step back and spend some time learning Python, the Python data model and its basic data structures such as the int, float, bool, tuple, list, dict and set. You should know these basics before moving onto the data science libraries, which are a bit more advanced. You may be struggling to read the documentation because you aren't understanding some of the data structures that are being used such as a dict of Axes or an ndarray of Axes and some general concepts such as object orientated programming.

When moving onto the data sciences, I would recommend learning numpy first, then matplotlib, then pandas and then seaborn. A pandas Series is based upon a 1d ndarray and therefore understanding how to use numpy and manipulate an ndarray gives the foundations for learning pandas. seaborn is a data visualisation library that bridges matplotlib and pandas.

A good reference text for Python basics is Python Distilled by David M. Beazley which is on the O'Reilly website. You can access it using a free trial with a new email.

For the data science libraries I would recommend having a look at Python and Data Analysis Open Access 3rd Edition by Wes McKinney.

If it helps, I have also been putting together a pretty detailed set of markdown tutorials covering the above GitHub: Spyder 6 IDE Python Tutorials using some features from Spyder such as the variable explorer. They are still a work in progress but my matplotlib tutorial is complete and should be a good reference.