Tag: Jupyter Notebook

Warning Messages in Jupyter Notebook Example Code

You’re working with the downloadable source code from a book like  Algorithms for Dummies, 2nd Edition, Beginning Programming with Python For Dummies, 3rd Edition, Machine Learning for Dummies, 2nd Edition, Python for Data Science for Dummies, or Machine Learning Security Principles and see a warning message like this:

C:\Users\John\anaconda3\lib\site-packages\sklearn\feature_selection\_sequential.py:206: FutureWarning: Leaving `n_features_to_select` to None is deprecated in 1.0 and will become 'auto' in 1.3. To keep the same behaviour as with None (i.e. select half of the features) and avoid this warning, you should manually set `n_features_to_select='auto'` and set tol=None when creating an instance.
  warnings.warn(

Well, that’s pretty confusing looking and if you’re just learning to work with Python may give you the idea that you’ve done something seriously wrong. There are a couple things to note here. First, this is a warning message. In fact, it’s a FutureWarning message, which means the change mentioned in the warning hasn’t actually taken effect yet.

Second, if you’re using the version of Jupyter Notebook and Python mentioned in the book, it’s unlikely that the effects described in the message will become a problem anytime soon, so you can usually ignore them. (This is one reason that I always ask which version of Jupyter Notebook and Python you’re using because a newer version can definitely cause error messages to appear.) Of course, if this warning ever does turn into an error, Luca and I definitely want to hear about it at [email protected].

Third, the message does state a potential fix for the problem. If the fix is simple enough, you can always try to make the required change to see if it works. However, this is a do it at your own risk sort of modification. The point is that the warning isn’t keeping you from using the downloadable source today, so ignoring it is probably the best action to take.

If you really don’t want to see these warnings, you can always add two lines of code the to first cell of the downloadable source. The warning isn’t actually going away, you just won’t see it:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

So, what causes these warning messages in the first place? Is the book’s source code faulty? There is nothing wrong with the book’s source code. What you’re seeing is the result of a library upgrade. Python uses a huge number of libraries and a change in any one of them can create a warning message of the sort you’ve seen. Luca and I work hard to ensure that the source code you get with the book is functional (and warning free) on all of the supported platforms at the time of writing, but it would be impossible for us to constantly update the book’s code to keep up with these library changes.

Jupyter Notebook vs JupyterLite

There seems to be some confusion for readers of   Algorithms for Dummies, 2nd Edition, Beginning Programming with Python For Dummies, 3rd Edition, Machine Learning for Dummies, 2nd Edition, Python for Data Science for Dummies, and Machine Learning Security Principles lately due to the similarity of names of two Integrated Development Environments (IDEs) available now. Even though I’m sure that JupyterLite is a very good product, even the website states, “Not all the usual features available in JupyterLab and the Classic Notebook will work with JupyterLite, but many already do!” This lack of support becomes a problem when you try to run the downloadable source using JupyterLite. In addition, Luca and I haven’t tested the downloadable source with this product, so we can’t even tell you what will and won’t work.

The two supported IDEs for our books are Google Colab (recommended for those of you who want to use a mobile device) and Jupyter Notebook (recommended for those of you who have a desktop system). It’s actually preferred that you get Jupyter Notebook as part of the Anaconda toolset because Anaconda makes it very easy for you to perform some advanced setup tasks found in some of our books. For example, you gain access to the Anaconda prompt and the associated Conda utility that definitely makes it easier for you to manage some of the machine learning packages found in our books. Using either Google Colab or Jupyter Notebook makes it very much easier for Luca and I to help you with your book-specific questions.

Please let me know if you have any questions or concerns about how to setup your programming environment for our books at [email protected]. Remember to use the version of the products listed in the book for optimal results in working with the downloadable source. In addition, always remember to use the downloadable source to enhance your learning experience.

IPython Magic Functions

This is an update of a post that originally appeared on April 25, 2016.

All of my current Python language books (and those I collaborated on with Luca Massaron): Machine Learning Security Principles, Algorithms for Dummies, 2nd Edition, Beginning Programming with Python For Dummies, 3rd Edition, Python for Data Science for Dummies, and Machine Learning for Dummies, 2nd Edition allow use of Jupyter Notebook (through Anaconda) or Google Colab to interact with the example code. Both of these IDEs extend the development environment in a number of ways, one of which is the use of magic functions. You see the magic functions in the code of these books as calls that begin with either one or two percent signs (% or %%). The most common of these magic functions is %matplotlib, which controls how IPython Notebook or Jupyter Notebook display plot output from the code.

You can find a listing of the most common magic functions in the Python for Data Science for Dummies Cheat Sheet. None of my books use any other magic functions, so this is also a complete list of magic functions that you can expect to find in our books. However, you might want to know more. Fortunately, the site at https://damontallen.github.io/IPython-quick-ref-sheets/ provides you with a complete listing of the magic commands (and a wealth of other information about Jupyter Notebook).

There are differences in magic function support between Jupyter Notebook and Google Colab, some of which are outlined in our books as needed. None of these differences will significantly affect your learning experience. However, it pays to know that Jupyter Notebook and Google Colab are only mostly the same, not precisely the same, and you’ll encounter differences. The screenshots in my books reflect the Jupyter Notebook version supported by that book, so what you might see on your screen when using magic functions in Google Colab may differ from the book.

Of course, you might choose to use another IDE—one that isn’t quite so magical as Jupyter Notebook or Google Colab. In this case, you need to remove those magic commands. Removing the commands generally won’t affect functionality of the code. The example will still work as explained in the book. However, the way that the IDE presents output could change. For example, instead of being inline, plots could appear in a separate window. Even though using a separate window is less convenient, either method works just fine. If you ever do encounter a magic function-related problem, please be sure to let me know at [email protected].