The Java Language Environment White Paper [Gosling96] lists a number of desirable features of a programming language:

Python meets and exceeds most of these expectations. We'll look closely at each of these twelve desireable attributes.

Simple and Familiar. By simple, we mean that there is no GOTO statement, we don't need to explicitly manage memory and pointers, there is no confusing preprocessor, we don't have the aliasing problems associated wuth unions. We note that this list summarizes the most confusing and bug-inducing features of the C programming language.

Python is simple. It relies on a few core data structures and statements. The rich set of features is introduced by explicit import of extension modules. Python lacks the problem-plagued GOTO statement, and includes the more reliable break , continue and exception raise statements. Python conceals the mechanics of object references from the programmer, making it impossible to corrupt a pointer. There is no language preprocessor to obscure the syntax of the language. There is no C-style union (or COBOL-style REDEFINES) to create problematic aliases for data in memory.

Python uses an English-like syntax, making it reasonably familiar to people who read and write English or related languages. There are few syntax rules, and ordinary, obvious indentation is used to make the structure of the software very clear.

Object-Oriented. Python is object oriented. Almost all language features are first class objects, and can be used in a variety of contexts. This is distinct from Java and C++ which create confusion by having objects as well as primitive data types that are not objects. The built-in type( x ) function can interrogate the types of all objects. The language permits creation of new object classes. It supports single and multiple inheritance. Polymorphism is supported via run-time interpretation, leading to some additional implementation freedoms not permitted in Java or C++.

Secure. The Python language environment is reasonably secure from tampering. Pre-compiled python modules can be distributed to prevent altering the source code. Additional security checks can be added by supplementing the built-in __import__ function.

Many security flaws are problems with operating systems or framework software (for example, database servers or web servers). There is, however, one prominent language-related security problem: the "buffer overflow" problem, where an input buffer, of finite size, is overwritten by input data which is larger than the available buffer. Python doesn't suffer from this problem.

Python is a dynamic language, and abuse of features like the exec statement or the eval function can introduce security problems. These mechanisms are easy to identify and audit in a large program.

Interpreted. An interpreted language, like Python allows for rapid, flexible, exploratory software development. Compiled languages require a sometimes lengthy edit-compile-link-execute cycle. Interpreted languages permit a simpler edit-execute cycle. Interpreted languages can support a complete debugging and diagnostic environment. The Python interpreter can be run interactively; which can help with program development and testing.

The Python interpreter can be extended with additional high-performance modules. Also, the Python interpreter can be embedded into another application to provide a handy scripting extension to that application.

Dynamic. Python executes dynamically. Python modules can be distributed as source; they are compiled (if necessary) at import time. Object messages are interpreted, and problems are reported at run time, allowing for flexible development of applications.

In C++, any change to centrally used class headers will lead to lengthy recompilation of dependent modules. In Java, a change to the public interface of a class can invalidate a number of other modules, leading to recompilation in the best case, or runtime errors in the worst case.

Portable. Since Python rests squarely on a portable C source, Python programs behave the same on a variety of platforms. Subtle issues like memory management are completely hidden. Operating system inconsistency makes it impossible to provide perfect portability of every feature. Portable GUI's are built using the widely-ported Tk GUI tools Tkinter, or the GTK+ tools and the the pyGTK bindings.

Robust. Programmers do not directly manipulate memory or pointers, making the language run-time environment very robust. Errors are raised as exceptions, allowing programs to catch and handle a variety of conditions. All Python language mistakes lead to simple, easy-to-interpret error messages from exceptions.

Multithreaded. The Python threading module is a Posix-compliant threading library. This is not completely supported on all platforms, but does provide the necessary interfaces. Beyond thread management, OS process management is also available, as are execution of shell scripts and other programs from within a Python program.

Additionally, many of the web frameworks include thread management. In products like TurboGears, individual web requests implicitly spawn new threads.

Garbage Collection. Memory-management can be done with explicit deletes or automated garbage collection. Since Python uses garbage collection, the programmer doesn't have to worry about memory leaks (failure to delete) or dangling references (deleting too early).

The Python run-time environment handles garbage collection of all Python objects. Reference counters are used to assure that no live objects are removed. When objects go out of scope, they are eligible for garbage collection.

Exceptions. Python has exceptions, and a sophisticated try statement that handles exceptions. Unlike the standard C library where status codes are returned from some functions, invalid pointers returned from others and a global error number variable used for determining error conditions, Python signals almost all errors with an exception. Even common, generic OS services are wrapped so that exceptions are raised in a uniform way.

High Performance. The Python interpreter is quite fast. However, where necessary, a class or module that is a bottleneck can be rewritten in C or C++, creating an extension to the runtime environment that improves performance.

One of the languages which strongly influenced the design of Python was Modula-2. In 1974, N. Wirth (creator of Pascal and its successor, Modula-2) wrote an article “On the Design of Programming Languages” [Wirth74], which defined some other considerations in designing a programming language. He suggests the following:

Python syntax is designed for readability; the language is quite simple, making it easy to learn and use. The Python community is always alert to ways to simplify Python. The Python 3000 project is actively working to remove a few poorly-concieved features of Python. This will mean that Python 3.0 will be simpler and easier to use, but incompatible with Python 2.x in a few areas.

Most Python features are brought in via modules, assuring that extensions do not change or break existing features. This allows tremendous flexibility and permits rapid growth in the language libraries.

The Python interpreter is very small. Typically, it is smaller than the Java Virtual Machine. Since Python is (ultimately) written in C, it has the same kind of broad access to external libraries and extensions. Also, this makes Python completely portable.