Category: Python for Data Science for Dummies

Source Code Placement

This is an update of a post that originally appeared on October 12, 2015.

I always recommend that you download the source code for my books. The Verifying Your Hand Typed Code post discusses some of the issues that readers encounter when typing code by hand. However, I also understand that many people learn best when they type the code by hand and that’s the point of getting my books—to learn something really interesting (see my principles for creating book source code in the Handling Source Code in Books post). Even if you do need to type the source code in order to learn, having the downloadable source code handy will help you locate errors in your code with greater ease. I won’t usually have time to debug your hand typed code for you.

Depending on your platform, you might encounter a situation the IDE chooses an unfortunate place to put the source code you want to save. For example, a Windows System might choose the Program Files folder, which contains a space and doesn’t allow saving of files unless you specifically override the default rights. Fortunately, modern IDEs do manage to avoid many of these problems, but you still need to be aware that they could exist, especially when using an older IDE.

My recommendation for fixing these, and other source code placement problems, is to create a folder that you can access and have full rights to work with to store your source code. My books usually make a recommendation for the source code file path, but you can use any path you want. The point is to create a path that’s:

  • Easy to access
  • Allows full rights
  • Lacks spaces in any of the pathname elements
  • On a local drive, rather than a cloud drive in many cases

As long as you follow these rules, you likely won’t experience problems with your choice of source code location. If you do experience source code placement problems when working with my books, please be sure to let me know at [email protected].

Verifying Your Hand Typed Code

This is an update of a post that originally appeared on January 10, 2014.

I maintain statistics for each of my books that are based on reviews and reader e-mails (so those e-mails you send really are important). These statistics help me write better books in the future and also help me determine the sorts of topics I need to address in my blog. It turns out that one of the most commonly asked questions is why a reader’s hand typed code doesn’t work. Some readers simply ask the question without giving me any details at all, which makes the question impossible to answer. In some cases, the reader sends the hand typed code, expecting that I’ll take time to troubleshoot it. However, this isn’t a realistic request because it defeats the very purpose behind typing the code by hand. If I take the time to diagnose the problems in the code you typed, I’ll be the one to learn an interesting lesson, not you. If you learn better by doing—that is, by typing the code by hand and then running it, then you need to be the one to troubleshoot any problems with the resulting code.

My advice to readers is to use the downloadable source code when working through the book text. If you want to type the code by hand after that as part of your learning experience, at least you’ll know that the example works on your system and you’ll also understand how the example works well enough to troubleshoot any errors in your own code. However, you need to be the one to diagnose the errors. If nothing else, perform a character-by-character comparison of your code to the example code that you downloaded from the publisher’s site. Often, a reader will write back after I suggest this approach and mention that they had no idea that a particular special symbol or method of formatting content was important. These are the sorts of lessons that this kind of exercise provide.

Now, it has happened that the downloadable source code doesn’t always work on a particular user’s system. When the error is in the code or something I can determine about the coding environment, you can be certain that I’ll post information about it on my blog. This should be the first place you look for such information. Simply click on the book title in question under the Technical category. You’ll find a list of posts for that book. Always feel free to contact me about a book-specific question. I want to be sure you have a good learning experience.

There are some situations where a reader tries to run application code that won’t work on a particular system. My books provide information on the kind of system you should use, but I can’t always determine exceptions to the rule in advance. When I post system requirements, your system must meet those requirements because the examples are guaranteed to fail on lesser systems. If you encounter a situation where the downloadable code won’t run on your system, but none of the fixes I post for that code work and your system does meet the requirements, then please feel free to contact me. There are times where an example simply won’t run because you can’t use the required software or the system won’t support it for whatever reason.

The point of this post is that you need to work with the downloadable source code whenever possible. The downloadable source code has been tested by a number of people, usually on a range of systems, to ensure it will work on your system too. I understand that typing the code by hand is an important and viable way to learn, but you should reserve this method as the second learning tier—used after you have tried the downloadable source code. Please let me know if you have any questions or concerns at [email protected].

Working with Code in e-Books

This is an update of a post that originally appeared on March 16, 2016.

Most of my technical readers now use e-books instead of paper books. Of course, there is a convenience factor to storing your entire library on a Kindle, even if it’s a software version of the Kindle. Of course, there are all sorts of e-book formats for your desktop system as well. The point is that electronic format makes a lot of sense when dealing with technical books.

However, e-books can cause some interesting problems and I’ve encountered a few with a number of readers now. The most important consideration is that you can’t cut and paste code from an e-book directly into your IDE and expect it to work. There are all sorts of reasons for this exclusion. For example, cutting and pasting may insert special characters into the output stream or the resulting paste may not have white space in the right places. A common problem is that publishers often convert regular single and double quotes into curly quote equivalents. The two kinds of quotes (both single and double) are completely different and the second type definitely won’t compile.

The best option when working with an e-book is to view the code in the e-book, but still get the downloadable source code for the book from my website or the publisher’s website as described in the book’s introduction. If you can’t find the downloadable source, always feel free to contact me at [email protected]. I want to be sure you have a great reading experience, which means having source code that actually runs in your development environment.

Another potential problem with e-books is that you may see unfortunate code breaks (despite the efforts of the publisher and myself). When you need to understand how white space works with a programming language, always review the downloadable source. The fact that the downloadable source compiles and runs tells you that all the of white space is in the right place and of the correct type. Typing the source code directly out of your e-book could result in added carriage returns or other white space errors that will cause the code to fail, even though the commands, variables, and other parts of the code are all correct.

As always, I’m open to your questions about my books. If you don’t understand how things work, please contact me—that’s why I’m here.

Mac Gatekeeper Error

This is an update of a post that originally appeared on March 21, 2016.

A number of my books ask readers to download an IDE or other code and install it on their Mac systems. The problem is that the Mac system won’t always cooperate. For example, you might see an error dialog like the one shown for Code::Blocks:

The Gatekeeper error tells you that it won't allow you to install software from unknown publishers.
Your Mac won’t let you install software.

The problem is one of permissions. The default permissions set for newer Mac systems restrict you to getting your apps from the Mac App Store or from vendors who have signed their files. Fortunately, you can overcome this problem either temporarily or permanently, depending on how you want to use your Mac. The blog post What is Gatekeeper & How to Disable Gatekeeper on Monterey? provides you with illustrated, step-by-step directions to perform the task using either method. Let me know if you encounter any other problems getting your Mac to install the software required to use my books at [email protected].

UnZIPping the Downloadable Source

A number of readers have written me to say that they are using the downloadable source and can see the files on their hard drive. However, when they go to access the file using their IDE, the IDE won’t open it. Modern operating systems make things easier for people by displaying the contents of .zip and other archive files as if they’re another directory or folder on the system. However, the IDE doesn’t have the same advantage. You need to remove the files from the .zip archive and place them somewhere on your hard drive that the IDE can locate. You can tell that you’re looking at a .zip file by looking at the path. If you see something like C:\Temp\A4D2E.zip\A4D2E, where A4D2E.zip is the name of the archive, then you know that you’re looking in an archive file. If you still have problems getting the files to open, make sure you remove any spaces from the path that your IDE is using. You can contact me at [email protected] if you have any additional questions about this issue.

Spaces in Paths

This is an update of the previous post originally created on April 20, 2016.

A number of readers have recently written me about an error they see when attempting to compile or execute an application or script in books such as, C++ All-In-One for Dummies, 4th Edition, Beginning Programming with Python For Dummies, 2nd Edition, Python for Data Science for Dummies, and Machine Learning for Dummies, 2nd Edition. Development environments often handle spaces differently because they’re designed to perform tasks such as compiling applications and running scripts. When you see an error message that tells you that a file or path isn’t found, you need to start looking at the path and determine whether it contains any spaces. The best option is to create a directory to hold your source code and to place that directory off the root directory of your drive if at all possible. Keeping the path small and simple is your best way to avoid potential problems compiling code or running scripts.

The problem for many readers is that the error message is buried inside a whole bunch of nonsensical looking text. The output from your compiler or interpreter can contain all sorts of useful debugging information, such as a complete listing of calls that the compiler, interpreter, or application made. However, unless you know how to read this information, which is often arcane at best, it looks like gobbledygook. Simply keep scanning through the output until you see something that humans can read and understand. More often than not, you see an error message that helps you understand what went wrong, such as not being able to find a file or path. Please let me know if you ever have problems making the code examples in my books work, but also be sure to save yourself some time and effort by reading those error messages. Let me know if you have any thoughts or concerns about spaces in directory paths at [email protected].

Getting a Good Anaconda Install

Some people may have misinterpreted the content at the beginning of Chapter 3 in Python for Data Science for Dummies. It isn’t necessary to install the products listed in the Considering the Off-the-Shelf Cross-Platform Scientific Distributions section starting on Page 39. These products are for those of you who would like to try a development environment other than the one used in the book, which is Anaconda 2.1.0. However, unless you’re an advanced user, it’s far better to install Anaconda 2.1.0 so that you can follow the exercises in the book without problem. Installing all of the products listed in Chapter 3 will result in a setup that won’t work at all because the various products will conflict with each other.

Because Continuum has upgraded Anaconda, you need to download the 2.1.0 version from the archive at https://repo.continuum.io/archive/.There are separate downloads for Windows, Mac OS X, and Linux.  The chapter tells you precisely which file to download.  For example, for Windows you’d download Anaconda-2.1.0-Windows-x86_64.exe. The point is to use the same version of Anaconda as you find in the book. You can find the installation instructions on Page 41 if you have a Windows system, Page 45 if you have a Linux system, or Page 46 if you have a Mac OS X system.  Make sure you download the databases for the book by using the procedures that start on page 47.

Following this process is the best way to ensure you get a good installation for Python for Data Science for Dummies. Luca and I want to make certain that you can use the book to discover the wonders of data science without having to jump through a lot of hoops to do it. Please feel free to contact me at [email protected] if you have any questions about the installation process.

 

Python for Data Science for Dummies Errata on Page 221

The downloadable source for Python for Data Science for Dummies contains a problem that doesn’t actually appear in the book. If you look at page 221, the code block in the middle of the page contains a line saying import numpy as np. This line is essential because the code won’t run without it. The downloadable source for Chapter 12 is missing this line so the example doesn’t run. This P4DS4D; 12; Stretching Pythons Capabilities link provides you with a .ZIP file that contains the replacement source code. Simple remove the P4DS4D; 12; Stretching Pythons Capabilities.ipynb file from the archive and use it in place of your existing file.

Luca and I always want you to have a great experience with our book, so keep those emails coming. Please let me know if you have any questions about source code file update at [email protected]. I’m sorry about any errors that appear in the downloadable source and appreciate the readers who have pointed them out.

 

Python for Data Science for Dummies Errata on Page 145

Python for Data Science for Dummies contains two errors on page 145. The first error appears in the second paragraph on that page. You can safely disregard the sentence that reads, “The use_idf controls the use of inverse-document-frequency reweighting, which is turned off in this case.” The code doesn’t contain a reference to the use_idf parameter. However, you can read about it on the Scikit-Learn site. This parameter defaults to being turned on, which is how it’s used for the example.

The second error is also in the second paragraph. The discussion references the tf_transformer.transform() method call. The actual method call is tfidf.transform(), which does appear in the sample code. The discussion about how the method works is correct, just the name of the object is wrong.

Please let me know if you have any questions about either of these changes at [email protected]. I’m sorry about any errors that appear in the book and appreciate the readers who have pointed them out.

 

Using Jupyter with Anaconda (Updated)

A few readers have recently written to me regarding the use of Jupyter with the downloadable source for Python for Data Science for Dummies. The version of Anaconda recommended for the book, 2.1.0, doesn’t rely on Jupyter, which is why the book doesn’t mention Jupyter. The book relies on IPython Notebook, which is what you should use to obtain the best reading experience. You can obtain the proper version from the Continuum archive. However, if you choose to download the current version of Anaconda, then using Jupyter becomes a possibility; although, many of the procedures found in the book will require tweaking and the screenshots won’t match precisely.

In order to use Jupyter, you must still import the downloaded files into your repository. The source code comes in an archive file that you extract to a location on your hard drive. The archive contains a list of .ipynb (IPython Notebook) files containing the source code for this book (see the Introduction for details on downloading the source code). The following steps tell how to import these files into your repository:

  1. Click Upload at the top of the page. What you see depends on your browser. In most cases, you see some type of File Upload dialog box that provides access to the files on your hard drive.
  2. Navigate to the directory containing the files you want to import into Notebook.
  3. Highlight one or more files to import and click the Open (or other, similar) button to begin the upload process. You see the file added to an upload list, as shown here. The file isn’t part of the repository yet—you’ve simply selected it for upload.

    Click Upload when you want to upload files to the repository.
    Upload Source Files to the Repository
  4. Click Upload. Notebook places the file in the repository so that you can begin using it.

It’s important to both Luca and me that you have the best possible learning experience with our book. This means using the right version of Anaconda for most people. Using the latest version shouldn’t cause problems, but we’d like to know if it does. Please feel free contact me at [email protected] with your book-specific questions.

Update

It has come to our attention since this post first published that using the latest version of Anaconda with Python for Data Science for Dummies is problematic. Some of the examples won’t work without rewriting because the Pandas Categorical class has changed. This is the only change we’ve confirmed so far, but there are no doubt other changes. In order to get the proper results from the examples in the book, you must use the correct version of Anaconda, version 2.1.0.

Please do keep those questions coming. It’s because a reader took time to write that Luca and I became aware of this problem. We truly do want you to have a great learning experience, so these questions are important!