Before we get involved with higher level design patterns, let's take a deep dive into one of Python's most common objects: the string. We'll see that there is a lot more to the string than meets the eye, and also cover searching strings for patterns and serializing data for storage or transmission.
In particular, we'll visit:
The complexities of strings, bytes, and byte arrays
The ins and outs of string formatting
A few ways to serialize data
The mysterious regular expression
Strings are a basic primitive in Python; we've used them in nearly every example we've discussed so far. All they do is represent an immutable sequence of characters. However, though you may not have considered it before, "character" is a bit of an ambiguous word; can Python strings represent sequences of accented characters? Chinese characters? What about Greek, Cyrillic, or Farsi?
In Python 3, the answer is yes. Python strings are all represented in Unicode, a character definition standard that can represent virtually any character in any language on the planet (and some made-up languages and random characters as well). This is done seamlessly, for the most part. So, let's think of Python 3 strings as an immutable sequence of Unicode characters. So what can we do with this immutable sequence? We've touched on many of the ways strings can be manipulated in previous examples, but let's quickly cover it all in one place: a crash course in string theory!
You know what's really hard to do using object-oriented principles? Parsing strings to match arbitrary patterns, that's what. There have been a fair number of academic papers written in which object-oriented design is used to set up string parsing, but the result is always very verbose and hard to read, and they are not widely used in practice.
In the real world, string parsing in most programming languages is handled by regular expressions. These are not verbose, but, boy, are they ever hard to read, at least until you learn the syntax. Even though regular expressions are not object oriented, the Python regular expression library provides a few classes and objects that you can use to construct and run regular expressions.
Regular expressions are used to solve a common problem: Given a string, determine whether that string matches a given pattern and, optionally, collect substrings that contain relevant information. They can be used to answer questions like:
Is this string...
Nowadays, we take the ability to write data to a file and retrieve it at an arbitrary later date for granted. As convenient as this is (imagine the state of computing if we couldn't store anything!), we often find ourselves converting data we have stored in a nice object or design pattern in memory into some kind of clunky text or binary format for storage, transfer over the network, or remote invocation on a distant server.
The Python pickle module is an object-oriented way to store objects directly in a special storage format. It essentially converts an object (and all the objects it holds as attributes) into a sequence of bytes that can be stored or transported however we see fit.
For basic work, the pickle module has an extremely simple interface. It is comprised of four basic functions for storing and loading data; two for manipulating file-like objects, and two for manipulating bytes objects (the latter are just shortcuts to the file-like interface, so we don't have...
Let's build a basic regular expression-powered templating engine in Python. This engine will parse a text file (such as an HTML page) and replace certain directives with text calculated from the input to those directives. This is about the most complicated task we would want to do with regular expressions; indeed, a full-fledged version of this would likely utilize a proper language parsing mechanism.
Consider the following input file:
/** include header.html **/ <h1>This is the title of the front page</h1> /** include menu.html **/ <p>My name is /** variable name **/. This is the content of my front page. It goes below the menu.</p> <table> <tr><th>Favourite Books</th></tr> /** loopover book_list **/ <tr><td>/** loopvar **/</td></tr> /** endloop **/ </table> /** include footer.html **/ Copyright © Today
This file contains "tags" of the form /** <directive> <data> **/ where the...
We've covered a wide variety of topics in this chapter, from strings to regular expressions, to object serialization, and back again. Now it's time to consider how these ideas can be applied to your own code.
Python strings are very flexible, and Python is an extremely powerful tool for string-based manipulations. If you don't do a lot of string processing in your daily work, try designing a tool that is exclusively intended for manipulating strings. Try to come up with something innovative, but if you're stuck, consider writing a web log analyzer (how many requests per hour? How many people visit more than five pages?) or a template tool that replaces certain variable names with the contents of other files.
Spend a lot of time toying with the string formatting operators until you've got the syntax memorized. Write a bunch of template strings and objects to pass into the format function, and see what kind of output you get. Try the exotic formatting operators, such as percentage...
We've covered string manipulation, regular expressions, and object serialization in this chapter. Hardcoded strings and program variables can be combined into outputtable strings using the powerful string formatting system. It is important to distinguish between binary and textual data and bytes and str have specific purposes that must be understood. Both are immutable, but the bytearray type can be used when manipulating bytes.
Regular expressions are a complex topic, but we scratched the surface. There are many ways to serialize Python data; pickles and JSON are two of the most popular.
In the next chapter, we'll look at a design pattern that is so fundamental to Python programming that it has been given special syntax support: the iterator pattern.
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