Learning Python - Mark Lutz [26]
Dynamic typing
Python keeps track of the kinds of objects your program uses when it runs; it doesn’t require complicated type and size declarations in your code. In fact, as you’ll see in Chapter 6, there is no such thing as a type or variable declaration anywhere in Python. Because Python code does not constrain data types, it is also usually automatically applicable to a whole range of objects.
Automatic memory management
Python automatically allocates objects and reclaims (“garbage collects”) them when they are no longer used, and most can grow and shrink on demand. As you’ll learn, Python keeps track of low-level memory details so you don’t have to.
Programming-in-the-large support
For building larger systems, Python includes tools such as modules, classes, and exceptions. These tools allow you to organize systems into components, use OOP to reuse and customize code, and handle events and errors gracefully.
Built-in object types
Python provides commonly used data structures such as lists, dictionaries, and strings as intrinsic parts of the language; as you’ll see, they’re both flexible and easy to use. For instance, built-in objects can grow and shrink on demand, can be arbitrarily nested to represent complex information, and more.
Built-in tools
To process all those object types, Python comes with powerful and standard operations, including concatenation (joining collections), slicing (extracting sections), sorting, mapping, and more.
Library utilities
For more specific tasks, Python also comes with a large collection of precoded library tools that support everything from regular expression matching to networking. Once you learn the language itself, Python’s library tools are where much of the application-level action occurs.
Third-party utilities
Because Python is open source, developers are encouraged to contribute precoded tools that support tasks beyond those supported by its built-ins; on the Web, you’ll find free support for COM, imaging, CORBA ORBs, XML, database access, and much more.
Despite the array of tools in Python, it retains a remarkably simple syntax and design. The result is a powerful programming tool with all the usability of a scripting language.
It’s Mixable
Python programs can easily be “glued” to components written in other languages in a variety of ways. For example, Python’s C API lets C programs call and be called by Python programs flexibly. That means you can add functionality to the Python system as needed, and use Python programs within other environments or systems.
Mixing Python with libraries coded in languages such as C or C++, for instance, makes it an easy-to-use frontend language and customization tool. As mentioned earlier, this also makes Python good at rapid prototyping; systems may be implemented in Python first, to leverage its speed of development, and later moved to C for delivery, one piece at a time, according to performance demands.
It’s Easy to Use
To run a Python program, you simply type it and run it. There are no intermediate compile and link steps, like there are for languages such as C or C++. Python executes programs immediately, which makes for an interactive programming experience and rapid turnaround after program changes—in many cases, you can witness the effect of a program change as fast as you can type it.
Of course, development cycle turnaround is only one aspect of Python’s ease of use. It also provides a deliberately simple syntax and powerful built-in tools. In fact, some have gone so far as to call Python “executable pseudocode.” Because it eliminates much of the complexity in other tools, Python programs are simpler, smaller, and more flexible than equivalent programs in languages like C, C++, and Java.
It’s Easy to Learn
This brings us to a key point of this book: compared to other programming languages, the core Python language is remarkably easy to learn. In fact, you can