Tag: Python

Antiquated Technology Making Developers Faster

This is an update of a post that originally appeared on November 7, 2014.

I often wonder when I create a blog post whether the technology I’m describing will stand the test of time. In this case, I asked whether the reader would like to be able to type application code faster and with fewer keystrokes. The article, The 100 Year Old Trick to Writing at 240 Words Per Minute, probably has some good advice for you—at least, if you’re willing to learn the technique. It turns out that stenography isn’t only useful for court typists and people who print out the text for the hearing impaired on television, it’s also quite useful for developer. Yes, your IDE probably has more than a few tricks available for speeding up your typing, but I guarantee that these tricks only go so far. My personal best typing speed is 110 wpm and that’s flat out typing as fast as my fingers will go.

Since that original post, someone has come out with a book called Learn Plover! that describes how to use this stenographic technique in more detail. There is also a site devoted to Plover now.

Naturally, I haven’t ever used one of the devices mentioned in the article. However, a stenographer named Mirabai Knight has tried one of the devices and reproduced a 140 keystroke Python application using only 50 keystrokes. By the way, she has produced a series of interesting videos that you may want to review if you really think that Plover is for you. I don’t know of any IDE that can provide that sort of efficiency. Of course, it’s one thing for a trained stenographer to produce these sorts of results, but I’d like to hear from any developer who has used the technique to hear about how well it worked for them. Please contact me about your experiences at [email protected]. (Oddly enough, I did hear from at least one developer who uses it successfully.)

The part that interested me most though is that the system, called Plover, is written in Python. (If you want to see Plover in action, check out the video at http://plover.stenoknight.com/2014/10/longer-plover-coding-snippet-in-python.html. A number of Beginning Programming with Python For Dummies, 3rd Edition readers have written to ask me how they can use their new found programming skills. The book contains sections that tell you about all sorts of ways in which Python is being used, but many of these uses are in large corporations. This particular use is by a small developer—someone just like you. Yet, it has a big potential for impacting how developers work. Just imagine the look on the boss’ face when you turn in your application in half the time because you can type it in so much faster? So, Python isn’t just for really large companies or for scientists—it’s for everyone who needs a language that can help them create useful applications of the sort that Python is best suited to target (and I describe all of these uses in my book).

Understanding the Continuing Need for C++

This is an update of a post that originally appeared on February 23, 2015.

I maintain statistics on all my books, including C++ All-In-One for Dummies, 4th Edition. These statistics are based on reader e-mail and other sources of input that I get. I even take the comments on Amazon.com into account. One of the most common C++ questions I get (not the most common, but it’s up there) is why someone would want to use the language in the first place. It’s true, C++ isn’t the language to use if you’re creating a database application and schools now prefer Python as an introductory programming language for older children. However, it is the language to use if you’re writing low-level code that has to run fast. C++ also sees use in a vast number of libraries because library code has to be fast. For example, check out the Python libraries at some point and you’ll find C++ staring back at you. In fact, part of the Python documentation discusses how to use C++ to create extensions.

I decided to look through some of my past notes to see if there was some succinct discussion of just why C++ is a useful language for the average developer to know. That’s when I ran across an InfoWorld article entitled, “Stroustrup: Why the 35-year-old C++ still dominates ‘real’ dev.” Given that the guy being interviewed is Bjarne Stroustrup, the inventor of C++, it’s a great source of information. The interview is revealing because it’s obvious that Bjarne is taking a measured view of C++ and not simply telling everyone to use it for every occasion (quite the contrary, in fact).

The bottom line in C++ development is speed. Along with speed, you also get flexibility and great access to the hardware. As with anything, you pay a price for getting these features. In the case of C++, you’ll experience increased development time, greater complexity, and more difficulty in locating bugs. Some people are taking a new route to C++ speed though and that’s to write their code in one language and move it to C++ from there. For example, some Python developers are now cross-compiling their code into C++ to gain a speed advantage. You can read about it in the InfoWorld article entitled, Python to C converter tool.

A lot of readers will close a message to me asking whether there is a single language they can learn to do everything well. Unfortunately, there isn’t any such language and given the nature of computer languages, I doubt there ever will be. Every language has a niche for which it’s indispensable. The smart developer has a toolbox full of languages suited for every job the developer intends to tackle.

Do you find that you really don’t understand how the languages in my books can help you? Let me know your book-specific language questions at [email protected]. It’s always my goal that you understand how the material you’ve learned while reading one of my books will eventually help you in the long run. After all, what’s the point of reading a book that doesn’t help you in some material way? Thanks, as always, for your staunch support of my writing efforts!

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].

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.

 

Python for Data Science for Dummies Errata on Page 124

Python for Data Science for Dummies contains an error in the example that appears on the top half of page 124. In the first of the two grey boxes, the code computes the results of four print statements. The bottom-most print statement, print x[1:2, 1:2], is supposed to compute a result based on rows 1 and 2 of columns 1 and 2, and the bottom grey box seems to confirm that interpretation by the showing the result as [[[14 15 16] [17 18 19]] [[24 25 26] [27 28 29]]]. However, the answer provided for this example in the downloadable source code is [[[14 15 16]]], which doesn’t agree with that in the text.

The good news is that the downloadable source contains the correct code. The error appears only in the book. The last print statement in the book is wrong. Here is the correct code (with output) for this example:

x = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9],],
 [[11,12,13], [14,15,16], [17,18,19],],
 [[21,22,23], [24,25,26], [27,28,29]]])

print x[1,1]
print x[:,1,1]
print x[1,:,1]
print
print x[1:3, 1:3]
[14 15 16]
[ 5 15 25]
[12 15 18]

[[[14 15 16]
 [17 18 19]]

[[24 25 26]
 [27 28 29]]]

Please let me know if you have any questions about this example at [email protected]. I’m sorry about the error that appears in the book and appreciate the readers who have pointed it out.

 

Missing XMLData2.xml File

A number of readers have written to report that XMLData2.xml is missing from the downloadable source for Python for Data Science for Dummies. You encounter this file in Chapter 6, on page 108. The publisher has already added the file to the downloadable source, but you might be missing the file from your copy. If so, you can download it by clicking XMLData2.zip. I’m truly sorry about any problems that the missing file might have caused. Please be sure to let me know about your book specific question at [email protected].

 

Tip Error in Python for Data Science for Dummies

There is a small error on page 318 of Python for Data Science for Dummies. You can find it near the middle of the page in the Tip text. The current text on the second line of that paragraph says, “k as a number near the squared number of available observations.” However, the text should really read, “k as a number near the squared root number of available observations.” The word root is missing, which obviously changes the mathematical meaning of the text. Please accept our apologies for the typo. Let me know if you find any other errors of a technical nature in the book at [email protected] and I’ll be sure to provide a blog post about it here. Thank you for your support!

 

Missing File from Python for Data Science for Dummies Downloadable Source

A reader recently contacted me regarding a missing file from the downloadable source for Python for Data Science for Dummies. This is the P4DS4D; 01; Quick Overview.ipynb you need for the first chapter. Simply click here to download P4DS4D; 01; Quick Overview.ipynb. I’m also asking the publisher to add the missing file to the downloadable source found on the Dummies site at http://www.dummies.com/store/product/Python-for-Data-Science-For-Dummies.productCd-1118844181,descCd-DOWNLOAD.html. If you encounter any other problems with the book, please be sure to let me know at [email protected]. Thank you for your patience!