Authors: Mark Lutz and David Ascher
Publisher: O'Reilly & Associates
Price: $29.95 US
Reviewer: Phil Hughes
To understand what this review means, you will need to know about two things: what Python is and my background. Python is an object-oriented, interpreted programming language suitable for scripting tasks as well as serious programming projects. I see Python as the interpreted language for those who expect to be able to go back and understand their program a year after they have written it.
Now, about me. You may know me as a magazine publisher, but I am truly just a geek who found a way to make less money. I have been writing in assembly language and FORTRAN since the '60s, and in more UNIX-like languages such as C and AWK since 1980. I have seriously used at least a dozen languages and am generally very comfortable around most anything except Cobol.
What I am not is an object-oriented programmer. I understand the concepts, but have never worked in an object-oriented language such as C++ or Modula.
Python is interpreted like Perl or awk, but it is object-oriented. I was ready to give it a try. The problem is that I am not a full-time programmer, because I have this publishing job to do, but many times I do end up writing code.
Armed with O'Reilly's Programming Python, I was off to become a Python expert. Well, to make a short story shorter, it didn't work. While it is a good book to use as a reference or to take with you to a desert isle along with your Python-equipped laptop, it wasn't the book for a part-time programmer with over 30 years of non-OOP experience to use.
Enter Learning Python. My executive summary is that this is the right book for me and probably for many others as well. While Learning Python doesn't tell you everything, it is a good 366 pages that will get you up and running. Written in a textbook style with examples and exercises, it introduces both object-oriented programming and the Python language.
Both authors have done Python training, and it shows. Examples appear where you need them, and the exercises actually test your understanding of important concepts. This is a book to read with a computer nearby. You will learn a great deal from the exercises.
The book is divided into three parts. The first part covers the core of the Python language, explaining types and operators, basic statements, functions, modules, classes and exceptions. Part two moves you out a little into Python's built-in tools, common tasks and finally how to build real programs. Part three covers Python resources on the Net, platform specifics and answers to the exercises.
I have been very thorough in going through this book (because I actually want to add Python to my language set), and have found the book to be extremely accurate. The examples all work and you won't be misled by the text—a problem far too common for first printings of technical books.
Who, besides me, should get this book? I would say anyone who is comfortable with computers and wants to learn a very cool object-oriented language. By “comfortable”, knowing one programming language or at least a scripting language is going to help a lot. While the book covers the basics, if expressions like “dynamic typing” or “syntax rules” scare you, then you may need to get a little more comfortable before attempting to learn a real programming language.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide