Python is an interpreted, object-oriented, high-level programming
language with dynamic semantics. Its high-level built in data
structures, combined with dynamic typing and dynamic binding, make it
very attractive for Rapid Application Development, as well as for use
as a scripting or glue language to connect existing components
together. Python's simple, easy to learn syntax emphasizes
readability and therefore reduces the cost of program maintenance.
Python supports modules and packages, which encourages program
modularity and code reuse. The Python interpreter and the extensive
standard library are available in source or binary form without charge
for all major platforms, and can be freely distributed.
Often, programmers fall in love with Python because of the
increased productivity it provides. Since there is no compilation
step, the edit-test-debug cycle is incredibly fast. Debugging Python
programs is easy: a bug or bad input will never cause a segmentation
fault. Instead, when the interpreter discovers an error, it raises an
exception. When the program doesn't catch the exception, the
interpreter prints a stack trace. A source level debugger allows
inspection of local and global variables, evaluation of arbitrary
expressions, setting breakpoints, stepping through the code a line at
a time, and so on. The debugger is written in Python itself,
testifying to Python's introspective power. On the other hand, often
the quickest way to debug a program is to add a few print statements
to the source: the fast edit-test-debug cycle makes this simple
approach very effective.
Comparing Python to Other Languages
Python is often compared to other interpreted languages such as Java,
JavaScript, Perl, . Comparisons to C++, Common Lisp
and Scheme can also be enlightening. In this section I will briefly
compare Python to each of these languages. These comparisons
concentrate on language issues only. In practice, the choice of a
programming language is often dictated by other real-world constraints
such as cost, availability, training, and prior investment, or even
emotional attachment. Since these aspects are highly variable, it
seems a waste of time to consider them much for this comparison.
Java
Python programs are generally expected to run slower than Java
programs, but they also take much less time to develop. Python
programs are typically 3-5 times shorter than equivalent Java
programs. This difference can be attributed to Python's built-in
high-level data types and its dynamic typing. For example, a Python
programmer wastes no time declaring the types of arguments or
variables, and Python's powerful polymorphic list and dictionary
types, for which rich syntactic support is built straight into the
language, find a use in almost every Python program. Because of the
run-time typing, Python's run time must work harder than Java's. For
example, when evaluating the expression a+b, it must first inspect the
objects a and b to find out their type, which is not known at compile
time. It then invokes the appropriate addition operation, which may be
an overloaded user-defined method. Java, on the other hand, can
perform an efficient integer or floating point addition, but requires
variable declarations for a and b, and does not allow overloading of
the + operator for instances of user-defined classes.
For these reasons, Python is much better suited as a "glue" language,
while Java is better characterized as a low-level implementation
language. In fact, the two together make an excellent
combination. Components can be developed in Java and combined to form
applications in Python; Python can also be used to prototype
components until their design can be "hardened" in a Java
implementation. To support this type of development, a Python
implementation written in Java is under development, which allows
calling Python code from Java and vice versa. In this implementation,
Python source code is translated to Java bytecode (with help from a
run-time library to support Python's dynamic semantics).
Javascript
Python's "object-based" subset is roughly equivalent to
JavaScript. Like JavaScript (and unlike Java), Python supports a
programming style that uses simple functions and variables without
engaging in class definitions. However, for JavaScript, that's all
there is. Python, on the other hand, supports writing much larger
programs and better code reuse through a true object-oriented
programming style, where classes and inheritance play an important
role.
Perl
Python and Perl come from a similar background (Unix scripting, which
both have long outgrown), and sport many similar features, but have a
different philosophy. Perl emphasizes support for common
application-oriented tasks, e.g. by having built-in regular
expressions, file scanning and report generating features. Python
emphasizes support for common programming methodologies such as data
structure design and object-oriented programming, and encourages
programmers to write readable (and thus maintainable) code by
providing an elegant but not overly cryptic notation. As a
consequence, Python comes close to Perl but rarely beats it in its
original application domain; however Python has an applicability well
beyond Perl's niche.
C++
Almost everything said for Java also applies for C++, just more so:
where Python code is typically 3-5 times shorter than equivalent Java
code, it is often 5-10 times shorter than equivalent C++ code!
Anecdotal evidence suggests that one Python programmer can finish in
two months what two C++ programmers can't complete in a year. Python
shines as a glue language, used to combine components written in C++.
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