Programming Python, Part I
A blog is not a blog if we can't add new posts, so let's do that:
>>> blog = blog + ["A new post."] >>> blog ['My first post', 'Python is cool', 'A new post.']
Here we set blog to a new value, which is the old blog, and a new post. Remembering all that merely to add a new post is not pleasant though, so we can encapsulate it in what is called a function:
>>> def add_post(blog, new_post): ... return blog + [new_post] ... >>>
def is the keyword used to define a new function or method (more on functions in structured or functional programming and methods in object-oriented programming later in this article). What follows is the name of the function. Inside the parentheses, we have the formal parameters. Those are like variables that will be defined by the caller of the function. After the colon, the prompt has changed from >>> to ... to show that we are inside a definition. The function is composed of all those lines with a level of indentation below the level of the def line.
So, where other programming languages use curly braces or begin/end keywords, Python uses indentation. The idea is that if you are a good programmer, you'd indent it anyway, so we'll use that indentation and make you a good programmer at the same time. Indeed, it's a controversial issue; I didn't like it at first, but I learned to live with it.
While working with the REPL, you safely can press Tab to make an indentation level, and although a Tab character can do it, using four spaces is the strongly recommended way. Many text editors know to put four spaces when you press Tab when editing a Python file. Whatever you do, never, I repeat, never, mix Tabs with spaces. In other programming languages, it may make the community dislike you, but in Python, it'll make your program fail with weird error messages.
Being practical, to reproduce what I did, simply type the class header, def add_post(blog, new_post):, press Enter, press Tab, type return blog + [new_post], press Enter, press Enter again, and that's it. Let's see the function in action:
>>> blog = add_post(blog, "Fourth post") >>> blog ['My first post', 'Python is cool', 'A new post.', 'Fourth post'] >>>
add_post takes two parameters. The first is the blog itself, and it gets assigned to blog. This is tricky. The blog inside the function is not the same as the blog outside the function. They are in different scopes. That's why the following:
>>> def add_post(blog, new_post): ... blog = blog + [new_post]
doesn't work. blog is modified only inside the function. By now, you might know that new_post contains the post passed to the function.
Our blog is growing, and it is time to see that the posts are simply strings, but we want to have a title and a body. One way to do this is to use tuples, like this:
>>> blog =  >>> blog = add_post(blog, ("New blog", "First post")) >>> blog = add_post(blog, ("Cool", "Python is cool")) >>> blog [('New blog', 'First post'), ('Cool', 'Python and is cool')] >>>
In the first line, I reset the blog to be an empty list. Then, I added two posts. See the double parentheses? The outside parentheses are part of the function call, and the inside parentheses are the creation of a tuple.
A tuple is created by parentheses, and its members are separated by commas. They are similar to lists, but semantically, they are different. For example, you can't update the members of a tuple. Tuples are used to build some kind of structure with a fixed set of elements. Let's see a tuple outside of our blog:
>>> (1,2,3) (1, 2, 3)
Accessing each part of the posts is similar to accessing each part of the blog:
>>> blog 'New blog' >>> blog 'This is my first post'
This might be a good solution if we want to store only a title and a body. But, how long until we want to add the date and time, excerpts, tags or messages? You may begin thinking you'll need to hang a sheet of paper on the wall, as shown in Figure 1, to remember the index of each field—not pleasant at all. To solve this problem, and some others, Python gives us object-oriented programming.
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.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.Register Now!
- SUSE LLC's SUSE Manager
- My +1 Sword of Productivity
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Managing Linux Using Puppet
- Non-Linux FOSS: Caffeine!
- Doing for User Space What We Did for Kernel Space
- SuperTuxKart 0.9.2 Released
- Google's SwiftShader Released
- Parsing an RSS News Feed with a Bash Script
- SourceClear Open
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