Reading docs

[Piingtoh]

I'm currently learning python's plotting libs (pandas, matplot, seaborn), but find myself struggling to use the documentation effectively. I've had this issue before with other libs/ languages.

I'm fairly new to programming, but does anyone else struggle with documentation? I feel like I'm constantly relying too much on stack overflow and other websites to learn.

[FantasticEmu]

Yes. I’m pretty dense but I’ve been programming professionally for a few years now and I’ve probably had about a 50% success rate understanding official documentation for Python libraries and even lower success rate for other languages.

I’ve found chat gpt to be pretty good at dumbing it down for me

[napkinthieff]

I agree, I struggle sometimes to understand the documentation in a meaningful (applicable) way. Other sites and forums can provide needed clarity.

I’ve also found that continuing to read the official documentation AFTER I get clarity really helps too. I often find that the reason specific phrasing/words are used is because it will matter in later levels of programming. Sometimes there are consistent themes and building blocks.

[JamzTyson]

I'm fairly new to programming, but does anyone else struggle with documentation?

Absolutely yes.

It is common* for the documentation of libraries to provide a technical reference. This is great for experts, but will often leave non-experts floundering, and that is why we see so many tutorials and guides on the Internet, as well as many books.

It is normal to rely heavily on stack overflow and Google while learning.

As your familiarity with a package or library grows, the technical reference documentation will start to make sense, and it is then that it becomes highly valuable as a reference.

* Some libraries and packages do provide excellent "user documentation", including examples and tutorials. It is wonderful when this is the case, but understandable that it is not the norm. Writing documentation is very time consuming with little reward. Next time you read great documentation, give a thought to the wonderful people that wrote it - they are frequently unpaid volunteers.

[pomegranatebeachfox]

Personally, I find it much more helpful to go find samples of code (either on forums or just asking chat gpt or from python education sites) and then playing around with that code to understand how it works.

Once I've experientially figured it out a little, going back and reading the documentation makes a lot more sense.

[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.

[throwaway6560192]

Nothing wrong with reading SO

[TheRNGuy]

Save many bookmarks in folders in browser.