Original Translation
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Internally, Python source code is always translated into a bytecode representation, and this bytecode is then executed by the Python virtual machine. In order to avoid the overhead of repeatedly parsing and translating modules that rarely change, this byte code is written into a file whose name ends in ".pyc" whenever a module is parsed. When the corresponding .py file is changed, it is parsed and translated again and the .pyc file is rewritten.
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There is no performance difference once the .pyc file has been loaded, as the bytecode read from the .pyc file is exactly the same as the bytecode created by direct translation. The only difference is that loading code from a .pyc file is faster than parsing and translating a .py file, so the presence of precompiled .pyc files improves the start-up time of Python scripts. If desired, the Lib/compileall.py module can be used to create valid .pyc files for a given set of modules.
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Note that the main script executed by Python, even if its filename ends in .py, is not compiled to a .pyc file. It is compiled to bytecode, but the bytecode is not saved to a file. Usually main scripts are quite short, so this doesn't cost much speed.
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There are also several programs which make it easier to intermingle Python and C code in various ways to increase performance. See, for example, `Psyco <http://psyco.sourceforge.net/>`_, `Pyrex <http://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/>`_, `PyInline <http://pyinline.sourceforge.net/>`_, `Py2Cmod <http://sourceforge.net/projects/py2cmod/>`_, and `Weave <http://www.scipy.org/Weave>`_.
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How does Python manage memory?
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The details of Python memory management depend on the implementation. The standard C implementation of Python uses reference counting to detect inaccessible objects, and another mechanism to collect reference cycles, periodically executing a cycle detection algorithm which looks for inaccessible cycles and deletes the objects involved. The :mod:`gc` module provides functions to perform a garbage collection, obtain debugging statistics, and tune the collector's parameters.
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Jython relies on the Java runtime so the JVM's garbage collector is used. This difference can cause some subtle porting problems if your Python code depends on the behavior of the reference counting implementation.
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In the absence of circularities, Python programs do not need to manage memory explicitly.
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Why doesn't Python use a more traditional garbage collection scheme? For one thing, this is not a C standard feature and hence it's not portable. (Yes, we know about the Boehm GC library. It has bits of assembler code for *most* common platforms, not for all of them, and although it is mostly transparent, it isn't completely transparent; patches are required to get Python to work with it.)
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Traditional GC also becomes a problem when Python is embedded into other applications. While in a standalone Python it's fine to replace the standard malloc() and free() with versions provided by the GC library, an application embedding Python may want to have its *own* substitute for malloc() and free(), and may not want Python's. Right now, Python works with anything that implements malloc() and free() properly.