Python Programming Language

Home Search Download Documentation
Help News Community SIGs

The whole Python FAQ

See also the Python FAQ Wizard, which has a search engine and allows PSA members to update entries!

Last changed on Mon Feb 12 16:05:28 2001 EST

(Entries marked with ** were changed within the last 24 hours; entries marked with * were changed within the last 7 days.)

1. General information and availability

2. Python in the real world

3. Building Python and Other Known Bugs

4. Programming in Python

5. Extending Python

6. Python's design

7. Using Python on non-UNIX platforms

8. Python on Windows

1. General information and availability

1.1. What is Python?

Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++. It is also usable as an extension language for applications that need a programmable interface. Finally, Python is portable: it runs on many brands of UNIX, on the Mac, and on PCs under MS-DOS, Windows, Windows NT, and OS/2.

To find out more, the best thing to do is to start reading the tutorial from the documentation set (see a few questions further down).

See also question 1.17 (what is Python good for).

1.2. Why is it called Python?

Apart from being a computer scientist, I'm also a fan of "Monty Python's Flying Circus" (a BBC comedy series from the seventies, in the -- unlikely -- case you didn't know). It occurred to me one day that I needed a name that was short, unique, and slightly mysterious. And I happened to be reading some scripts from the series at the time... So then I decided to call my language Python.

By now I don't care any more whether you use a Python, some other snake, a foot or 16-ton weight, or a wood rat as a logo for Python!

1.3. How do I obtain a copy of the Python source?

The latest Python source distribution is always available from, at The latest development sources can be obtained via anonymous CVS from SourceForge, at

It is a gzipped tar file containing the complete C source, LaTeX documentation, Python library modules, example programs, and several useful pieces of freely distributable software. This will compile and run out of the box on most UNIX platforms. (See section 7 for non-UNIX information.)

Older versions of Python, including Python 1.6 and Python 1.5.2, are also available from

1.4. How do I get documentation on Python?

All documentation is available on-line, starting at

The LaTeX source for the documentation is part of the source distribution. If you don't have LaTeX, the latest Python documentation set is available, in various formats like postscript and html, by anonymous ftp - visit the above URL for links to the current versions.

PostScript for a high-level description of Python is in the file (a separate file on the ftp site).

1.5. Are there other ftp sites that mirror the Python distribution?

The following anonymous ftp sites keep mirrors of the Python distribution:


1.6. Is there a newsgroup or mailing list devoted to Python?

There is a newsgroup, comp.lang.python, and a mailing list. The newsgroup and mailing list are gatewayed into each other -- if you can read news it's unnecessary to subscribe to the mailing list. To subscribe to the mailing list ([email protected]) visit its Mailman webpage at

More info about the newsgroup and mailing list, and about other lists, can be found at

Archives of the newsgroup are kept by Deja News and accessible through the "Python newsgroup search" web page, This page also contains pointer to other archival collections.

1.7. Is there a WWW page devoted to Python?

Yes, is the official Python home page.

1.8. Is the Python documentation available on the WWW?

Yes. Python 2.0 documentation is available from and from Note that most documentation is available for on-line browsing as well as for downloading.

1.9. Are there any books on Python?

Yes, many, and more are being published. See for a list.

You can also search online bookstores for "Python" (and filter out the Monty Python references; or perhaps search for "Python" and "language").

1.10. Are there any published articles about Python that I can reference?

If you can't reference the web site, and you don't want to reference the books (see previous question), there are several articles on Python that you could reference.

Most publications about Python are collected on the Python web site:
It is no longer recommended to reference this very old article by Python's author:

    Guido van Rossum and Jelke de Boer, "Interactively Testing Remote
    Servers Using the Python Programming Language", CWI Quarterly, Volume
    4, Issue 4 (December 1991), Amsterdam, pp 283-303.

1.11. Are there short introductory papers or talks on Python?

There are several - you can find links to some of them collected at

1.12. How does the Python version numbering scheme work?

Python versions are numbered A.B.C or A.B. A is the major version number -- it is only incremented for major changes in functionality or source structure. B is the minor version number, incremented for less earth-shattering changes to a release. C is the patchlevel -- it is incremented for each new patch release. Not all releases have patch releases. Note that in the past, patches have added significant changes; in fact the changeover from 0.9.9 to 1.0.0 was the first time that either A or B changed!

Alpha, beta and release candidate versions have an additional suffixes. The suffix for an alpha version is "aN" for some small number N, the suffix for a beta version is "bN" for some small number N, and the suffix for a release candidate version is "cN" for some small number N.

Note that (for instance) all versions labeled 2.0aN precede the versions labeled 2.0bN, which precede versions labeled 2.0cN, and those precede 2.0.

As a rule, no changes are made between release candidates and the final release unless there are show-stopper bugs.

1.13. How do I get a beta test version of Python?

All releases, including alphas, betas and release candidates, are announced on comp.lang.python and comp.lang.python.announce newsgroups, which are gatewayed into the [email protected] and [email protected]. In addition, all these annoucements appear on the Python home page, at

1.14. Are there copyright restrictions on the use of Python?

Hardly. You can do anything you want with the source, as long as you leave the copyrights in, and display those copyrights in any documentation about Python that you produce. Also, don't use the author's institute's name in publicity without prior written permission, and don't hold them responsible for anything (read the actual copyright for a precise legal wording).

In particular, if you honor the copyright rules, it's OK to use Python for commercial use, to sell copies of Python in source or binary form, or to sell products that enhance Python or incorporate Python (or part of it) in some form. I would still like to know about all commercial use of Python!

1.15. Why was Python created in the first place?

Here's a very brief summary of what got me started:

I had extensive experience with implementing an interpreted language in the ABC group at CWI, and from working with this group I had learned a lot about language design. This is the origin of many Python features, including the use of indentation for statement grouping and the inclusion of very-high-level data types (although the details are all different in Python).

I had a number of gripes about the ABC language, but also liked many of its features. It was impossible to extend the ABC language (or its implementation) to remedy my complaints -- in fact its lack of extensibility was one of its biggest problems. I had some experience with using Modula-2+ and talked with the designers of Modula-3 (and read the M3 report). M3 is the origin of the syntax and semantics used for exceptions, and some other Python features.

I was working in the Amoeba distributed operating system group at CWI. We needed a better way to do system administration than by writing either C programs or Bourne shell scripts, since Amoeba had its own system call interface which wasn't easily accessible from the Bourne shell. My experience with error handling in Amoeba made me acutely aware of the importance of exceptions as a programming language feature.

It occurred to me that a scripting language with a syntax like ABC but with access to the Amoeba system calls would fill the need. I realized that it would be foolish to write an Amoeba-specific language, so I decided that I needed a language that was generally extensible.

During the 1989 Christmas holidays, I had a lot of time on my hand, so I decided to give it a try. During the next year, while still mostly working on it in my own time, Python was used in the Amoeba project with increasing success, and the feedback from colleagues made me add many early improvements.

In February 1991, after just over a year of development, I decided to post to USENET. The rest is in the Misc/HISTORY file.

1.16. Do I have to like "Monty Python's Flying Circus"?

No, but it helps. Pythonistas like the occasional reference to SPAM, and of course, nobody expects the Spanish Inquisition

The two main reasons to use Python are:

 - Portable
 - Easy to learn
The three main reasons to use Python are:

 - Portable
 - Easy to learn
 - Powerful standard library
(And nice red uniforms.)

And remember, there is no rule six.

1.17. What is Python good for?

Python is used in many situations where a great deal of dynamism, ease of use, power, and flexibility are required.

In the area of basic text manipulation core Python (without any non-core extensions) is easier to use and is roughly as fast as just about any language, and this makes Python good for many system administration type tasks and for CGI programming and other application areas that manipulate text and strings and such.

When augmented with standard extensions (such as PIL, COM, Numeric, oracledb, kjbuckets, tkinter, win32api, etc.) or special purpose extensions (that you write, perhaps using helper tools such as SWIG, or using object protocols such as ILU/CORBA or COM) Python becomes a very convenient "glue" or "steering" language that helps make heterogeneous collections of unrelated software packages work together. For example by combining Numeric with oracledb you can help your SQL database do statistical analysis, or even Fourier transforms. One of the features that makes Python excel in the "glue language" role is Python's simple, usable, and powerful C language runtime API.

Many developers also use Python extensively as a graphical user interface development aide.

1.18. Can I use the FAQ Wizard software to maintain my own FAQ?

Sure. Version 1.0 is distributed in the Tools subdirectory of the Python 1.5 source release at

1.19. Which editor has good support for editing Python source code?

On Unix, the first choice is Emacs/XEmacs. There's an elaborate mode for editing Python code, which is available from the Python source distribution (Misc/python-mode.el). It's also bundled with XEmacs (we're still working on legal details to make it possible to bundle it with FSF Emacs). And it has its own web page:
There are many other choices, for Unix, Windows or Macintosh. Richard Jones compiled a table from postings on the Python newsgroup:
See also FAQ question 7.10 for some more Mac and Win options.

1.20. I've never programmed before. Is there a Python tutorial?

There is a tutorial on Python in the standard documentation set. It can also be accessed at You are encouraged to reduce load on the web site by using the version provided in your distribution if you have already installed Python. This tutorial is probably a little too steep for a total programming newbie.

If you've never programmed before, you might try , a programming tutorial that attempts to teach programming and Python simultaneously.

The Python tutor mail list was also created for those new to Python and for those new to programming in general. See

2. Python in the real world

2.1. How many people are using Python?

Certainly thousands, and quite probably tens of thousands of users. More are seeing the light each day. The comp.lang.python newsgroup is very active, but overall there is no accurate estimate of the number of subscribers or Python users.

Jacek Artymiak has created a Python Users Counter; you can see the current count by visiting (this will not increment the counter; use the link there if you haven't added yourself already). Most Python users appear not to have registered themselves.

Another statistic is the number of accesses to the Python WWW server. Have a look at

2.2. Have any significant projects been done in Python?

At CWI (the former home of Python), we have written a 20,000 line authoring environment for transportable hypermedia presentations, a 5,000 line multimedia teleconferencing tool, as well as many many smaller programs.

At CNRI (Python's new home), we have written two large applications: Grail, a fully featured web browser (see, and the Knowbot Operating Environment, a distributed environment for mobile code.

The University of Virginia uses Python to control a virtual reality engine. See

The ILU project at Xerox PARC can generate Python glue for ILU interfaces. See ILU is a free CORBA compliant ORB which supplies distributed object connectivity to a host of platforms using a host of languages.

Mark Hammond and Greg Stein and others are interfacing Python to Microsoft's COM and ActiveX architectures. This means, among other things, that Python may be used in active server pages or as a COM controller (for example to automatically extract from or insert information into Excel or MSAccess or any other COM aware application). Mark claims Python can even be a ActiveX scripting host (which means you could embed JScript inside a Python application, if you had a strange sense of humor). Python/AX/COM is distributed as part of the PythonWin distribution.

The University of California, Irvine uses a student administration system called TELE-Vision written entirely in Python. Contact: Ray Price [email protected].

The Melbourne Cricket Ground (MCG) in Australia (a 100,000+ person venue) has it's scoreboard system written largely in Python on MS Windows. Python expressions are used to create almost every scoring entry that appears on the board. The move to Python/C++ away from exclusive C++ has provided a level of functionality that would simply not have been viable otherwise.

See also the next question.

Note: this FAQ entry is really old. See for a more recent list.

2.3. Are there any commercial projects going on using Python?

Yes, there's lots of commercial activity using Python. See for a list.

2.4. How stable is Python?

Very stable. While the current version number would suggest it is in the early stages of development, in fact new, stable releases (numbered 0.9.x through 1.5.2) have been coming out roughly every 3 to 6 or 12 months since 1991.

2.5. What new developments are expected for Python in the future?

Follow the newsgroup discussions! The workshop proceedings ( may also contain interesting looks into the future.

Also see peps/ for proposals.

2.6. Is it reasonable to propose incompatible changes to Python?

In general, no. There are already millions of lines of Python code around the world, so any changes in the language that invalidates more than a very small fraction of existing programs has to be frowned upon. Even if you can provide a conversion program, there still is the problem of updating all documentation. Providing a gradual upgrade path is the only way if a feature has to be changed.

See peps/pep-0005.html for the proposed mechanism for creating backwards-incompatibilities.

2.7. What is the future of Python?

Please see peps/ for proposals of future activities. One of the PEPs (Python Enhancement Proposals) deals with the PEP process and PEP format -- see peps/pep-0001.html if you want to submit a PEP. In peps/pep-0042.html there is a list of wishlists the Python Development team plans to tackle.

2.8. What is the PSA, anyway?

The Python Software Activity was created by a number of Python aficionados who want Python to be more than the product and responsibility of a single individual. It has found a home at CNRI

Please note that the PSA is now obsolete. There is no need to join it now.

2.9. Deleted

2.10. Deleted

2.11. Is Python Y2K (Year 2000) Compliant?

As of January, 2001 no major problems have been reported and Y2K compliance seems to be a non-issue.

Since Python is available free of charge, there are no absolute guarantees. If there are unforeseen problems, liability is the user's rather than the developers', and there is nobody you can sue for damages.

Python does few date manipulations, and what it does is all based on the Unix representation for time (even on non-Unix systems) which uses seconds since 1970 and won't overflow until 2038.

2.12. Is Python a good language in a class for beginning programmers?

Yes. This long answer attempts to address any concerns you might have with teaching Python as a programmer's first language. (If you want to discuss Python's use in education, then you may be interested in joining the edu-sig mailinglist. See )

It is still common to start students with a procedural (subset of a) statically typed language such as Pascal, C, or a subset of C++ or Java. I think that students may be better served by learning Python as their first language. Python has a very simple and consistent syntax and a large standard library. Most importantly, using Python in a beginning programming course permits students to concentrate on important programming skills, such as problem decomposition and data type design.

With Python, students can be quickly introduced to basic concepts such as loops and procedures. They can even probably work with user-defined objects in their very first course. They could implement a tree structure as nested Python lists, for example. They could be introduced to objects in their first course if desired. For a student who has never programmed before, using a statically typed language seems unnatural. It presents additional complexity that the student must master and slows the pace of the course. The students are trying to learn to think like a computer, decompose problems, design consistent interfaces, and encapsulate data. While learning to use a statically typed language is important, it is not necessarily the best topic to address in the students' first programming course.

Many other aspects of Python make it a good first language. Python has a large standard library (like Java) so that students can be assigned programming projects very early in the course that do something. Assignments aren't restricted to the standard four-function calculator and check balancing programs. By using the standard library, students can gain the satisfaction of working on realistic applications as they learn the fundamentals of programming. Using the standard library also teaches students about code reuse.

Python's interactive interpreter also enables students to test language features while they're programming. They can keep a window with the interpreter running while they enter their programs' source in another window. If they can't remember the methods for a list, they can do something like this:

 >>> L = []
 >>> dir(L)
 ['append', 'count', 'extend', 'index', 'insert', 'pop', 'remove',
 'reverse', 'sort']
 >>> print L.append.__doc__
 L.append(object) -- append object to end
 >>> L.append(1)
 >>> L
With the interpreter, documentation is never far from the student as he's programming.

There are also good IDEs for Python. Guido van Rossum's IDLE is a cross-platform IDE for Python that is written in Python using Tk. There is also a Windows specific IDE called PythonWin. Emacs users will be happy to know that there is a very good Python mode for Emacs. All of these programming environments provide syntax highlighting, auto-indenting, and access to the interactive interpreter while coding. For more information about IDEs, see XXX.

If your department is currently using Pascal because it was designed to be a teaching language, then you'll be happy to know that Guido van Rossum designed Python to be simple to teach to everyone but powerful enough to implement real world applications. Python makes a good language for first time programmers because that was one of Python's design goals. There are papers at on the Python website by Python's creator explaining his objectives for the language. One that may interest you is titled "Computer Programming for Everybody"

If you're seriously considering Python as a language for you school, Guido van Rossum may even be willing to correspond with you about how the language would fit in your curriculum. See for examples of Python's use in the "real world."

While Python, its source code, and its IDEs are are freely available, this consideration should not be weighed too heavily. There are other free languages (Java, free C compilers), and many companies are willing to waive some or all of their fees for student programming tools if it guarantees that a whole graduating class will know how to use their tools. It may also be comforting to know that Python is under the stewardship of CNRI (Corporation for National Research Initiatives), a non-profit whose mission is to foster research and development for the National Information Infrastructure. It "undertakes, fosters, and promotes research in the public interest."

While Python jobs may not be as prevalent as C/C++/Java jobs, teachers should not worry about teaching students critical job skills in their first course. The skills that win students a job are those they learn in their senior classes and internships. Their first programming courses are there to lay a solid foundation in programming fundamentals. The primary question in choosing the language for such a course should be which language permits the students to learn this material without hindering or limiting them.

Another argument for Python is that there are many tasks for which something like C++ is overkill. That's where languages like Python, Perl, Tcl, and Visual Basic thrive. It's critical for students to know something about these languages. (Every employer for whom I've worked used at least one such language.) Of the languages listed above, Python probably makes the best language in a programming curriculum since its syntax is simple, consistent, and not unlike other languages (C/C++/Java) that are probably in the curriculum. By starting students with Python, a department simultaneously lays the foundations for other programming courses and introduces students to the type of language that is often used as a "glue" language. As an added bonus, Python can be used to interface with Microsoft's COM components (thanks to Mark Hammond). There is also a JPython that can be used to connect Java components.

If you currently start students with Pascal or C/C++ or Java, you may be worried they will have trouble learning a statically typed language after starting with Python. I think that this fear most often stems from the fact that the teacher started with a statically typed language, and we tend to like to teach others in the same way we were taught. In reality, the transition from Python to one of these other languages is quite simple.

To motivate a statically typed language such as C++, begin the course by explaining that unlike Python, their first language, C++ is compiled to a machine dependent executable. Explain that the point is to make a very fast executable. To permit the compiler to make optimizations, programmers must help it by specifying the "types" of variables. By restricting each variable to a specific type, the compiler can reduce the book-keeping it has to do to permit dynamic types. The compiler also has to resolve references at compile time. Thus, the language gains speed by sacrificing some of Python's dynamic features. Then again, the C++ compiler provides type safety and catches many bugs at compile time instead of run time (a critical consideration for many commercial applications). C++ is also designed for very large programs where one may want to guarantee that others don't touch an object's implementation. C++ provides very strong language features to separate an object's implementation from its interface. Explain why this separation is a good thing.

The first day of a C++ course could then be a whirlwind introduction to what C++ requires and provides. The point here is that after a semester or two of Python, students are hopefully competent programmers. They know how to handle loops and write procedures. They've also worked with objects, thought about the benefits of consistent interfaces, and used the technique of subclassing to specialize behavior. Thus, a whirlwind introduction to C++ could show them how objects and subclassing looks in C++. The potentially difficult concepts of object-oriented design were taught without the additional obstacles presented by a language such as C++ or Java. When learning one of these languages, the students would already understand the "road map." They understand objects; they would just be learning how objects fit in a statically typed languages. Language requirements and compiler errors that seem unnatural to beginning programmers make sense in this new context. Many students will find it helpful to be able to write a fast prototype of their algorithms in Python. Thus, they can test and debug their ideas before they attempt to write the code in the new language, saving the effort of working with C++ types for when they've discovered a working solution for their assignments. When they get annoyed with the rigidity of types, they'll be happy to learn about containers and templates to regain some of the lost flexibility Python afforded them. Students may also gain an appreciation for the fact that no language is best for every task. They'll see that C++ is faster, but they'll know that they can gain flexibility and development speed with a Python when execution speed isn't critical.

If you have any concerns that weren't addressed here, try posting to the Python newsgroup. Others there have done some work with using Python as an instructional tool. Good luck. We'd love to hear about it if you choose Python for your course.

3. Building Python and Other Known Bugs

3.1. Is there a test set?

Sure. You can run it after building with "make test", or you can run it manually with the command

	import test.autotest
In 1.4 or earlier, use

	import autotest
The test set doesn't test all features of Python, but it goes a long way to confirm that Python is actually working.

NOTE: if "make test" fails, don't just mail the output to the newsgroup -- this doesn't give enough information to debug the problem. Instead, find out which test fails, and run that test manually from an interactive interpreter. For example, if "make test" reports that test_spam fails, try this interactively:

	import test.test_spam
This generally produces more verbose output which can be diagnosed to debug the problem.

3.2. When running the test set, I get complaints about floating point operations, but when playing with floating point operations I cannot find anything wrong with them.

The test set makes occasional unwarranted assumptions about the semantics of C floating point operations. Until someone donates a better floating point test set, you will have to comment out the offending floating point tests and execute similar tests manually.

3.3. Link errors after rerunning the configure script.

It is generally necessary to run "make clean" after a configuration change.

3.4. The python interpreter complains about options passed to a script (after the script name).

You are probably linking with GNU getopt, e.g. through -liberty. Don't. The reason for the complaint is that GNU getopt, unlike System V getopt and other getopt implementations, doesn't consider a non-option to be the end of the option list. A quick (and compatible) fix for scripts is to add "--" to the interpreter, like this:

        #! /usr/local/bin/python --
You can also use this interactively:

        python -- [options]
Note that a working getopt implementation is provided in the Python distribution (in Python/getopt.c) but not automatically used.

3.5. When building on the SGI, make tries to run python to create glmodule.c, but python hasn't been built or installed yet.

Comment out the line mentioning glmodule.c in Setup and build a python without gl first; install it or make sure it is in your $PATH, then edit the Setup file again to turn on the gl module, and make again. You don't need to do "make clean"; you do need to run "make Makefile" in the Modules subdirectory (or just run "make" at the toplevel).

3.6. I use VPATH but some targets are built in the source directory.

On some systems (e.g. Sun), if the target already exists in the source directory, it is created there instead of in the build directory. This is usually because you have previously built without VPATH. Try running "make clobber" in the source directory.

3.7. Trouble building or linking with the GNU readline library.

You can use the GNU readline library to improve the interactive user interface: this gives you line editing and command history when calling python interactively. Its sources are distributed with Python (at least for 2.0). Uncomment the line

#readline readline.c -lreadline -ltermcap

in Modules/Setup. The configuration option --with-readline is no longer supported, at least in Python 2.0. Some hints on building and using the readline library: On SGI IRIX 5, you may have to add the following to rldefs.h:

        #ifndef sigmask
        #define sigmask(sig) (1L << ((sig)-1))
On some systems, you will have to add #include "rldefs.h" to the top of several source files, and if you use the VPATH feature, you will have to add dependencies of the form foo.o: foo.c to the Makefile for several values of foo. The readline library requires use of the termcap library. A known problem with this is that it contains entry points which cause conflicts with the STDWIN and SGI GL libraries. The STDWIN conflict can be solved by adding a line saying '#define werase w_erase' to the stdwin.h file (in the STDWIN distribution, subdirectory H). The GL conflict has been solved in the Python configure script by a hack that forces use of the static version of the termcap library. Check the newsgroup gnu.bash.bug news:gnu.bash.bug for specific problems with the readline library (I don't read this group but I've been told that it is the place for readline bugs).

3.8. Trouble with socket I/O on older Linux 1.x versions.

Once you've built Python, use it to run the script in the Lib/linux1 directory. Apparently the files as distributed don't match the system headers on some Linux versions.

3.9. Trouble with prototypes on Ultrix.

Ultrix cc seems broken -- use gcc, or edit config.h to #undef HAVE_PROTOTYPES.

3.10. Other trouble building Python on platform X.

Please email the details to [email protected] and I'll look into it. Please provide as many details as possible. In particular, if you don't tell me what type of computer and what operating system (and version) you are using it will be difficult for me to figure out what is the matter. If you get a specific error message, please email it to me too.

3.11. How to configure dynamic loading on Linux.

This is now automatic as long as your Linux version uses the ELF object format (all recent Linuxes do).

3.12. I can't get shared modules to work on Linux 2.0 (Slackware96)?

This is a bug in the Slackware96 release. The fix is simple: Make sure that there is a link from /lib/ to /lib/ so that the following links are setup: /lib/ -> /lib/ /lib/ -> /lib/ You may have to rerun the configure script, after rm'ing the config.cache file, before you attempt to rebuild python after this fix.

3.13. Trouble when making modules shared on Linux.

This happens when you have built Python for static linking and then enable
in the Setup file. Shared library code must be compiled with "-fpic". If a .o file for the module already exist that was compiled for static linking, you must remove it or do "make clean" in the Modules directory.

3.14. How to use threads on Linux.

[Greg Stein] You need to have a very recent libc, or even better, get the LinuxThreads-0.5 distribution. Note that if you install LinuxThreads normally, then you shouldn't need to specify the directory to the -with-thread configuration switch. The configure script ought to find it without a problem. To make sure everything builds properly, do a "make clean", remove config.cache, re-run configure with that switch, and then build.

[Andy Dustman] On glibc systems (i.e. RedHat 5.0+), LinuxThreads is obsoleted by POSIX threads (-lpthread). If you upgraded from an earlier RedHat, remove LinuxThreads with "rpm -e linuxthreads linuxthreads-devel". Then run configure using --with-thread as above.

3.15. Errors when linking with a shared library containing C++ code.

Link the main Python binary with C++. Change the definition of LINKCC in Modules/Makefile to be your C++ compiler. You may have to edit config.c slightly to make it compilable with C++.

3.16. I built with tkintermodule.c enabled but get 'Tkinter not found' (note: upper case T) lives in a subdirectory of Lib, Lib/tkinter. If you are using the default module search path, you probably didn't enable the line in the Modules/Setup file defining TKPATH; if you use the environment variable PYTHONPATH, you'll have to add the proper tkinter subdirectory.

For Windows, see question 7.11.

3.17. I built with Tk 4.0 but Tkinter complains about the Tk version.

Several things could cause this. You most likely have a Tk 3.6 installation that wasn't completely eradicated by the Tk 4.0 installation (which tends to add "4.0" to its installed files). You may have the Tk 3.6 support library installed in the place where the Tk 4.0 support files should be (default /usr/local/lib/tk/); you may have compiled Python with the old tk.h header file (yes, this actually compiles!); you may actually have linked with Tk 3.6 even though Tk 4.0 is also around. Similar for Tcl 7.4 vs. Tcl 7.3.

3.18. Compilation or link errors for the _tkinter module

Most likely, there's a version mismatch between the Tcl/Tk header files (tcl.h and tk.h) and the Tcl/Tk libraries you are using e.g. "-ltk8.0" and "-ltcl8.0" arguments for _tkinter in the Setup file). It is possible to install several versions of the Tcl/Tk libraries, but there can only be one version of the tcl.h and tk.h header files. If the library doesn't match the header, you'll get problems, either when linking the module, or when importing it. Fortunately, the version number is clearly stated in each file, so this is easy to find. Reinstalling and using the latest version usually fixes the problem.

(Also note that when compiling unpatched Python 1.5.1 against Tcl/Tk 7.6/4.2 or older, you get an error on Tcl_Finalize. See the 1.5.1 patch page at

3.19. I configured and built Python for Tcl/Tk but "import Tkinter" fails.

Most likely, you forgot to enable the line in Setup that says "TKPATH=:$(DESTLIB)/tkinter".

3.20. Tk doesn't work right on DEC Alpha.

You probably compiled either Tcl, Tk or Python with gcc. Don't. For this platform, which has 64-bit integers, gcc is known to generate broken code. The standard cc (which comes bundled with the OS!) works. If you still prefer gcc, at least try recompiling with cc before reporting problems to the newsgroup or the author; if this fixes the problem, report the bug to the gcc developers instead. (As far as we know, there are no problem with gcc on other platforms -- the instabilities seem to be restricted to the DEC Alpha.) See also question 3.6.

There's also a 64-bit bugfix for Tcl/Tk; see

3.21. Several common system calls are missing from the posix module.

Most likely, all test compilations run by the configure script are failing for some reason or another. Have a look in config.log to see what could be the reason. A common reason is specifying a directory to the --with-readline option that doesn't contain the libreadline.a file.

3.22. ImportError: No module named string, on MS Windows.

Most likely, your PYTHONPATH environment variable should be set to something like:

set PYTHONPATH=c:\python;c:\python\lib;c:\python\scripts

(assuming Python was installed in c:\python)

3.23. Core dump on SGI when using the gl module.

There are conflicts between entry points in the termcap and curses libraries and an entry point in the GL library. There's a hack of a fix for the termcap library if it's needed for the GNU readline library, but it doesn't work when you're using curses. Concluding, you can't build a Python binary containing both the curses and gl modules.

3.24. "Initializer not a constant" while building DLL on MS-Windows

Static type object initializers in extension modules may cause compiles to fail with an error message like "initializer not a constant". Fredrik Lundh <[email protected]> explains:

This shows up when building DLL under MSVC. There's two ways to address this: either compile the module as C++, or change your code to something like:

  statichere PyTypeObject bstreamtype = {
      PyObject_HEAD_INIT(NULL) /* must be set by init function */
      /* Patch object type */
      bstreamtype.ob_type = &PyType_Type;
      Py_InitModule("bstream", functions);

3.25. Output directed to a pipe or file disappears on Linux.

Some people have reported that when they run their script interactively, it runs great, but that when they redirect it to a pipe or file, no output appears.

    % python
    ...some output...
    % python >file
    % cat file
    % # no output
    % python | cat
    % # no output
Nobody knows what causes this, but it is apparently a Linux bug. Most Linux users are not affected by this.

There's at least one report of someone who reinstalled Linux (presumably a newer version) and Python and got rid of the problem; so this may be the solution.

3.26. Syntax Errors all over the place in Linux with libc 5.4

``I have installed python1.4 on my Linux system. When I try run the import statement I get the following error message:''

   File "<stdin>", line 1
       import sys
   Syntax Error: "invalid syntax"
Did you compile it yourself? This usually is caused by an incompatibility between libc 5.4.x and earlier libc's. In particular, programs compiled with libc 5.4 give incorrect results on systems which had libc 5.2 installed because the ctype.h file is broken. In this case, Python can't recognize which characters are letters and so on. The fix is to install the C library which was used when building the binary that you installed, or to compile Python yourself. When you do this, make sure the C library header files which get used by the compiler match the installed C library.

[adapted from an answer by Martin v. Loewis]

PS [adapted from Andreas Jung]: If you have upgraded to libc 5.4.x, and the problem persists, check your library path for an older version of libc. Try to clean update libc with the libs and the header files and then try to recompile all.

3.27. Crash in XIO on Linux when using Tkinter.

When Python is built with threads under Linux, use of Tkinter can cause crashes like the following:

  >>> from Tkinter import *
  >>> root = Tk()
  XIO:  fatal IO error 0 (Unknown error) on X server ":0.0"
        after 45 requests (40 known processed) with 1 events remaining.
The reason is that the default Xlib is not built with support for threads. If you rebuild Xlib with threads enabled the problems go away. Alternatively, you can rebuild Python without threads ("make clean" first!).

(Disclaimer: this is from memory.)

3.28. How can I test if Tkinter is working?

Try the following:

  >>> import _tkinter
  >>> import Tkinter
  >>> Tkinter._test()
This should pop up a window with two buttons, one "Click me" and one "Quit".

If the first statement (import _tkinter) fails, your Python installation probably has not been configured to support Tcl/Tk. On Unix, if you have installed Tcl/Tk, you have to rebuild Python after editing the Modules/Setup file to enable the _tkinter module and the TKPATH environment variable.

It is also possible to get complaints about Tcl/Tk version number mismatches or missing TCL_LIBRARY or TK_LIBRARY environment variables. These have to do with Tcl/Tk installation problems.

A common problem is to have installed versions of tcl.h and tk.h that don't match the installed version of the Tcl/Tk libraries; this usually results in linker errors or (when using dynamic loading) complaints about missing symbols during loading the shared library.

3.29. Is there a way to get the interactive mode of the python interpreter to perform function/variable name completion?

(From a posting by Guido van Rossum)

On Unix, if you have enabled the readline module (i.e. if Emacs-style command line editing and bash-style history works for you), you can add this by importing the undocumented standard library module "rlcompleter". When completing a simple identifier, it completes keywords, built-ins and globals in __main__; when completing NAME.NAME..., it evaluates (!) the expression up to the last dot and completes its attributes.

This way, you can do "import string", type "string.", hit the completion key twice, and see the list of names defined by the string module.

Tip: to use the tab key as the completion key, call

    readline.parse_and_bind("tab: complete")
You can put this in a ~/.pythonrc file, and set the PYTHONSTARTUP environment variable to ~/.pythonrc. This will cause the completion to be enabled whenever you run Python interactively.

Notes (see the docstring for for more information):

* The evaluation of the NAME.NAME... form may cause arbitrary application defined code to be executed if an object with a __getattr__ hook is found. Since it is the responsibility of the application (or the user) to enable this feature, I consider this an acceptable risk. More complicated expressions (e.g. function calls or indexing operations) are not evaluated.

* GNU readline is also used by the built-in functions input() and raw_input(), and thus these also benefit/suffer from the complete features. Clearly an interactive application can benefit by specifying its own completer function and using raw_input() for all its input.

* When stdin is not a tty device, GNU readline is never used, and this module (and the readline module) are silently inactive.

3.30. Why is the Python interpreter not built as a shared library?

(This is a Unix question; on Mac and Windows, it is a shared library.)

It's just a nightmare to get this to work on all different platforms. Shared library portability is a pain. And yes, I know about GNU libtool -- but it requires me to use its conventions for filenames etc, and it would require a complete and utter rewrite of all the makefile and config tools I'm currently using.

In practice, few applications embed Python -- it's much more common to have Python extensions, which already are shared libraries. Also, serious embedders often want total control over which Python version and configuration they use so they wouldn't want to use a standard shared library anyway. So while the motivation of saving space when lots of apps embed Python is nice in theory, I doubt that it will save much in practice. (Hence the low priority I give to making a shared library.)

For Linux systems, the simplest method of producing seems to be (originally from the Minotaur project web page,

  make distclean 
  make OPT="-fpic -O2" 
  mkdir .extract 
  (cd .extract; ar xv ../libpython1.5.a) 
  gcc -shared -o .extract/*.o 
  rm -rf .extract

3.31. Build with GCC on Solaris 2.6 (SunOS 5.6) fails

If you have upgraded Solaris 2.5 or 2.5.1 to Solaris 2.6, but you have not upgraded your GCC installation, the compile may fail, e.g. like this:

 In file included from /usr/include/sys/stream.h:26,
                  from /usr/include/netinet/in.h:38,
                  from /usr/include/netdb.h:96,
                  from ./socketmodule.c:121:
 /usr/include/sys/model.h:32: #error "No DATAMODEL_NATIVE specified"
Solution: rebuild GCC for Solaris 2.6. You might be able to simply re-run fixincludes, but people have had mixed success with doing that.

3.32. Running "make clean" seems to leave problematic files that cause subsequent builds to fail.

Use "make clobber" instead.

Use "make clean" to reduce the size of the source/build directory after you're happy with your build and installation. If you have already tried to build python and you'd like to start over, you should use "make clobber". It does a "make clean" and also removes files such as the partially built Python library from a previous build.

3.33. Submitting bug reports and patches

To report a bug or submit a patch, please use the relevant service from the Python project at SourceForge.



3.34. I can't load shared libraries under Python 1.5.2, Solaris 7, and gcc 2.95.2

When trying to load shared libraries, you may see errors like: ImportError: python: fatal: relocation error: file /usr/local/lib/python1.5/site-packages/Perp/util/
 symbol PyExc_RuntimeError: referenced symbol not found

There is a problem with the configure script for Python 1.5.2 under Solaris 7 with gcc 2.95 . configure should set the make variable LINKFORSHARED=-Xlinker --export-dynamic

in Modules/Makefile,

Manually add this line to the Modules/Makefile. This builds a Python executable that can load shared library extensions ( .

4. Programming in Python

4.1. Is there a source code level debugger with breakpoints, step, etc.? *

Yes. Module pdb, documented in the Library Reference Manual is a rudimentary bu adequate debugger for Python. You can also write your own debugger by using the code for pdb as an example.

Pythonwin also has a GUI debugger available, based on bdb, which colors breakpoints and has quite a few cool features (including debugging non-Pythonwin programs). The interface needs some work, but is interesting none the less. A reference can be found in
More recent version of PythonWin are available as a part of ActivePython. See
Richard Wolff has created a modified version of pdb, called Pydb, for use with the popular Data Display Debugger (DDD). Pydb can be found at, and DDD can be found at

The IDLE interactive development environment, normally available at Tools/idle in a standard distribution, also contains a graphical debugger.

4.2. Can I create an object class with some methods implemented in C and others in Python (e.g. through inheritance)? (Also phrased as: Can I use a built-in type as base class?)

No, but you can easily create a Python class which serves as a wrapper around a built-in object, e.g. (for dictionaries):

        # A user-defined class behaving almost identical
        # to a built-in dictionary.
        class UserDict:
                def __init__(self): = {}
                def __repr__(self): return repr(
                def __cmp__(self, dict):
                        if type(dict) == type(
                                return cmp(, dict)
                                return cmp(,
                def __len__(self): return len(
                def __getitem__(self, key): return[key]
                def __setitem__(self, key, item):[key] = item
                def __delitem__(self, key): del[key]
                def keys(self): return
                def items(self): return
                def values(self): return
                def has_key(self, key): return
A2. See Jim Fulton's ExtensionClass for an example of a mechanism which allows you to have superclasses which you can inherit from in Python -- that way you can have some methods from a C superclass (call it a mixin) and some methods from either a Python superclass or your subclass. See

A3. The Boost Python Library (BPL, provides a way of doing this from C++ (i.e. you can inherit from an extension class written in C++ using the BPL).

4.3. Is there a curses/termcap package for Python?

The standard Python distribution comes with a curses module in the Modules/ subdirectory, though it's not compiled by default. In Python versions before 2.0 the module only supported plain curses; you couldn't use ncurses features like colors with it (though it would link with ncurses).

In Python 2.0, the curses module has been greatly extended, starting from Oliver Andrich's enhanced version, to provide many additional functions from ncurses and SYSV curses, such as colour, alternative character set support, pads, and mouse support. This means the module is no longer compatible with operating systems that only have BSD curses, but there don't seem to be any currently maintained OSes that fall into this category.

4.4. Is there an equivalent to C's onexit() in Python?

For Python 2.0: The new atexit module provides a register function that is similar to C's onexit. See the Library Reference for details. For 2.0 you should not assign to sys.exitfunc!

For Python 1.5.2: You need to import sys and assign a function to sys.exitfunc, it will be called when your program exits, is killed by an unhandled exception, or (on UNIX) receives a SIGHUP or SIGTERM signal.

4.5. When I define a function nested inside another function, the nested function seemingly can't access the local variables of the outer function. What is going on? How do I pass local data to a nested function?

Python does not have arbitrarily nested scopes. When you need to create a function that needs to access some data which you have available locally, create a new class to hold the data and return a method of an instance of that class, e.g.:

        class MultiplierClass:
            def __init__(self, factor):
                self.factor = factor
            def multiplier(self, argument):
                return argument * self.factor
        def generate_multiplier(factor):
            return MultiplierClass(factor).multiplier
        twice = generate_multiplier(2)
        print twice(10)
        # Output: 20
An alternative solution uses default arguments, e.g.:

        def generate_multiplier(factor):
            def multiplier(arg, fact = factor):
                return arg*fact
            return multiplier
        twice = generate_multiplier(2)
        print twice(10)
        # Output: 20

4.6. How do I iterate over a sequence in reverse order?

If it is a list, the fastest solution is

                for x in list:
                        "do something with x"
This has the disadvantage that while you are in the loop, the list is temporarily reversed. If you don't like this, you can make a copy. This appears expensive but is actually faster than other solutions:

        rev = list[:]
        for x in rev:
                <do something with x>
If it's not a list, a more general but slower solution is:

        for i in range(len(sequence)-1, -1, -1):
                x = sequence[i]
                <do something with x>
A more elegant solution, is to define a class which acts as a sequence and yields the elements in reverse order (solution due to Steve Majewski):

        class Rev:
                def __init__(self, seq):
                        self.forw = seq
                def __len__(self):
                        return len(self.forw)
                def __getitem__(self, i):
                        return self.forw[-(i + 1)]
You can now simply write:

        for x in Rev(list):
                <do something with x>
Unfortunately, this solution is slowest of all, due to the method call overhead...

4.7. My program is too slow. How do I speed it up?

That's a tough one, in general. There are many tricks to speed up Python code; I would consider rewriting parts in C only as a last resort. One thing to notice is that function and (especially) method calls are rather expensive; if you have designed a purely OO interface with lots of tiny functions that don't do much more than get or set an instance variable or call another method, you may consider using a more direct way, e.g. directly accessing instance variables. Also see the standard module "profile" (described in the Library Reference manual) which makes it possible to find out where your program is spending most of its time (if you have some patience -- the profiling itself can slow your program down by an order of magnitude).

Remember that many standard optimization heuristics you may know from other programming experience may well apply to Python. For example it may be faster to send output to output devices using larger writes rather than smaller ones in order to avoid the overhead of kernel system calls. Thus CGI scripts that write all output in "one shot" may be notably faster than those that write lots of small pieces of output.

Also, be sure to use "aggregate" operations where appropriate. For example the "slicing" feature allows programs to chop up lists and other sequence objects in a single tick of the interpreter mainloop using highly optimized C implementations. Thus to get the same effect as

  L2 = []
  for i in range[3]:
it is much shorter and far faster to use

  L2 = list(L1[:3]) # "list" is redundant if L1 is a list.
Note that the map() function, particularly used with builtin methods or builtin functions can be a convenient accellerator. For example to pair the elements of two lists together:

  >>> map(None, [1,2,3], [4,5,6])
  [(1, 4), (2, 5), (3, 6)]
or to compute a number of sines:

  >>> map( math.sin, (1,2,3,4))
  [0.841470984808, 0.909297426826, 0.14112000806,   -0.756802495308]
The map operation completes very quickly in such cases.

Other examples of aggregate operations include the join, joinfields, split, and splitfields methods of the standard string builtin module. For example if s1..s7 are large (10K+) strings then string.joinfields([s1,s2,s3,s4,s5,s6,s7], "") may be far faster than the more obvious s1+s2+s3+s4+s5+s6+s7, since the "summation" will compute many subexpressions, whereas joinfields does all copying in one pass. For manipulating strings also consider the regular expression libraries and the "substitution" operations String % tuple and String % dictionary. Also be sure to use the list.sort builtin method to do sorting, and see FAQ's 4.51 and 4.59 for examples of moderately advanced usage -- list.sort beats other techniques for sorting in all but the most extreme circumstances.

There are many other aggregate operations available in the standard libraries and in contributed libraries and extensions.

Another common trick is to "push loops into functions or methods." For example suppose you have a program that runs slowly and you use the profiler ( to determine that a Python function ff is being called lots of times. If you notice that ff

   def ff(x): something with x computing result...
       return result
tends to be called in loops like (A)

   list = map(ff, oldlist)
or (B)

   for x in sequence:
       value = ff(x) something with value...
then you can often eliminate function call overhead by rewriting ff to

   def ffseq(seq):
       resultseq = []
       for x in seq:
  something with x computing result...
       return resultseq
and rewrite (A) to

    list = ffseq(oldlist)
and (B) to

    for value in ffseq(sequence): something with value...
Other single calls ff(x) translate to ffseq([x])[0] with little penalty. Of course this technique is not always appropriate and there are other variants, which you can figure out.

You can gain some performance by explicitly storing the results of a function or method lookup into a local variable. A loop like

    for key in token:
        dict[key] = dict.get(key, 0) + 1
resolves dict.get every iteration. If the method isn't going to change, a faster implementation is

    dict_get = dict.get  # look up the method once
    for key in token:
        dict[key] = dict_get(key, 0) + 1
Default arguments can be used to determine values once, at compile time instead of at run time. This can only be done for functions or objects which will not be changed during program execution, such as replacing

    def degree_sin(deg):
        return math.sin(deg * math.pi / 180.0)

    def degree_sin(deg, factor = math.pi/180.0, sin = math.sin):
        return sin(deg * factor)
Because this trick uses default arguments for terms which should not be changed, it should only be used when you are not concerned with presenting a possibly confusing API to your users.

For an anecdote related to optimization, see

4.8. When I have imported a module, then edit it, and import it again (into the same Python process), the changes don't seem to take place. What is going on?

For reasons of efficiency as well as consistency, Python only reads the module file on the first time a module is imported. (Otherwise a program consisting of many modules, each of which imports the same basic module, would read the basic module over and over again.) To force rereading of a changed module, do this:

        import modname
Warning: this technique is not 100% fool-proof. In particular, modules containing statements like

        from modname import some_objects
will continue to work with the old version of the imported objects.

4.9. How do I find the current module name?

A module can find out its own module name by looking at the (predefined) global variable __name__. If this has the value '__main__' you are running as a script.

4.10. I have a module in which I want to execute some extra code when it is run as a script. How do I find out whether I am running as a script?

See the previous question. E.g. if you put the following on the last line of your module, main() is called only when your module is running as a script:

        if __name__ == '__main__': main()

4.11. I try to run a program from the Demo directory but it fails with ImportError: No module named ...; what gives?

This is probably an optional module (written in C!) which hasn't been configured on your system. This especially happens with modules like "Tkinter", "stdwin", "gl", "Xt" or "Xm". For Tkinter, STDWIN and many other modules, see Modules/ for info on how to add these modules to your Python, if it is possible at all. Sometimes you will have to ftp and build another package first (e.g. Tcl and Tk for Tkinter). Sometimes the module only works on specific platforms (e.g. gl only works on SGI machines).

NOTE: if the complaint is about "Tkinter" (upper case T) and you have already configured module "tkinter" (lower case t), the solution is not to rename tkinter to Tkinter or vice versa. There is probably something wrong with your module search path. Check out the value of sys.path.

For X-related modules (Xt and Xm) you will have to do more work: they are currently not part of the standard Python distribution. You will have to ftp the Extensions tar file, i.e. and follow the instructions there.

See also the next question.

4.12. I have successfully built Python with STDWIN but it can't find some modules (e.g. stdwinevents).

There's a subdirectory of the library directory named 'stdwin' which should be in the default module search path. There's a line in Modules/Setup(.in) that you have to enable for this purpose -- unfortunately in the latest release it's not near the other STDWIN-related lines so it's easy to miss it.

4.13. What GUI toolkits exist for Python?

Depending on what platform(s) you are aiming at, there are several.

Currently supported solutions:


There's a neat object-oriented interface to the Tcl/Tk widget set, called Tkinter. It is part of the standard Python distribution and well-supported -- all you need to do is build and install Tcl/Tk and enable the _tkinter module and the TKPATH definition in Modules/Setup when building Python. This is probably the easiest to install and use, and the most complete widget set. It is also very likely that in the future the standard Python GUI API will be based on or at least look very much like the Tkinter interface. For more info about Tk, including pointers to the source, see the Tcl/Tk home page at Tcl/Tk is now fully portable to the Mac and Windows platforms (NT and 95 only); you need Python 1.4beta3 or later and Tk 4.1patch1 or later.

There's an interface to wxWindows called wxPython. wxWindows is a portable GUI class library written in C++. It supports GTK, Motif, MS-Windows and Mac as targets. Ports to other platforms are being contemplated or have already had some work done on them. wxWindows preserves the look and feel of the underlying graphics toolkit, and there is quite a rich widget set and collection of GDI classes. See the wxWindows page at for more details. wxPython is a python extension module that wraps many of the wxWindows C++ classes, and is quickly gaining popularity amongst Python developers. You can get wxPython as part of the source or CVS distribution of wxWindows, or directly from its home page at

Bindings to Gnome and the GIMP Toolkit by James Henstridge exist; see

For KDE bindings, see or

For OpenGL bindings, see

Platform specific:

The Mac port has a rich and ever-growing set of modules that support the native Mac toolbox calls. See the documentation that comes with the Mac port. See Support by Jack Jansen [email protected].

Pythonwin by Mark Hammond ([email protected]) includes an interface to the Microsoft Foundation Classes and a Python programming environment using it that's written mostly in Python. See

There's an object-oriented GUI based on the Microsoft Foundation Classes model called WPY, supported by Jim Ahlstrom [email protected]. Programs written in WPY run unchanged and with native look and feel on Windows NT/95, Windows 3.1 (using win32s), and on Unix (using Tk). Source and binaries for Windows and Linux are available in

Obsolete or minority solutions:

There's an interface to X11, including the Athena and Motif widget sets (and a few individual widgets, like Mosaic's HTML widget and SGI's GL widget) available from Support by Sjoerd Mullender [email protected].

On top of the X11 interface there's the vpApp toolkit by Per Spilling, now also maintained by Sjoerd Mullender [email protected]. See

For SGI IRIX only, there are unsupported interfaces to the complete GL (Graphics Library -- low level but very good 3D capabilities) as well as to FORMS (a buttons-and-sliders-etc package built on top of GL by Mark Overmars -- ftp'able from This is probably also becoming obsolete, as OpenGL takes over (see above).

There's an interface to STDWIN, a platform-independent low-level windowing interface for Mac and X11. This is totally unsupported and rapidly becoming obsolete. The STDWIN sources are at

There is an interface to WAFE, a Tcl interface to the X11 Motif and Athena widget sets. WAFE is at

4.14. Are there any interfaces to database packages in Python?

Yes! See the Database Topic Guide at for details.

4.15. Is it possible to write obfuscated one-liners in Python?

Yes. See the following three examples, due to Ulf Bartelt:

        # Primes < 1000
        print filter(None,map(lambda y:y*reduce(lambda x,y:x*y!=0,
        map(lambda x,y=y:y%x,range(2,int(pow(y,0.5)+1))),1),range(2,1000)))
        # First 10 Fibonacci numbers
        print map(lambda x,f=lambda x,f:(x<=1) or (f(x-1,f)+f(x-2,f)): f(x,f),
        # Mandelbrot set
        print (lambda Ru,Ro,Iu,Io,IM,Sx,Sy:reduce(lambda x,y:x+y,map(lambda y,
        Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,Sy=Sy,L=lambda yc,Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,i=IM,
        Sx=Sx,Sy=Sy:reduce(lambda x,y:x+y,map(lambda x,xc=Ru,yc=yc,Ru=Ru,Ro=Ro,
        i=i,Sx=Sx,F=lambda xc,yc,x,y,k,f=lambda xc,yc,x,y,k,f:(k<=0)or (x*x+y*y
        >=4.0) or 1+f(xc,yc,x*x-y*y+xc,2.0*x*y+yc,k-1,f):f(xc,yc,x,y,k,f):chr(
        ))))(-2.1, 0.7, -1.2, 1.2, 30, 80, 24)
        #    \___ ___/  \___ ___/  |   |   |__ lines on screen
        #        V          V      |   |______ columns on screen
        #        |          |      |__________ maximum of "iterations"
        #        |          |_________________ range on y axis
        #        |____________________________ range on x axis
Don't try this at home, kids!

4.16. Is there an equivalent of C's "?:" ternary operator?

Not directly. In many cases you can mimic a?b:c with "a and b or c", but there's a flaw: if b is zero (or empty, or None -- anything that tests false) then c will be selected instead. In many cases you can prove by looking at the code that this can't happen (e.g. because b is a constant or has a type that can never be false), but in general this can be a problem.

Tim Peters (who wishes it was Steve Majewski) suggested the following solution: (a and [b] or [c])[0]. Because [b] is a singleton list it is never false, so the wrong path is never taken; then applying [0] to the whole thing gets the b or c that you really wanted. Ugly, but it gets you there in the rare cases where it is really inconvenient to rewrite your code using 'if'.

4.17. My class defines __del__ but it is not called when I delete the object.

There are several possible reasons for this.

The del statement does not necessarily call __del__ -- it simply decrements the object's reference count, and if this reaches zero __del__ is called.

If your data structures contain circular links (e.g. a tree where each child has a parent pointer and each parent has a list of children) the reference counts will never go back to zero. You'll have to define an explicit close() method which removes those pointers. Please don't ever call __del__ directly -- __del__ should call close() and close() should make sure that it can be called more than once for the same object.

If the object has ever been a local variable (or argument, which is really the same thing) to a function that caught an expression in an except clause, chances are that a reference to the object still exists in that function's stack frame as contained in the stack trace. Normally, deleting (better: assigning None to) sys.exc_traceback will take care of this. If a stack was printed for an unhandled exception in an interactive interpreter, delete sys.last_traceback instead.

There is code that deletes all objects when the interpreter exits, but it is not called if your Python has been configured to support threads (because other threads may still be active). You can define your own cleanup function using sys.exitfunc (see question 4.4).

Finally, if your __del__ method raises an exception, a warning message is printed to sys.stderr.

Starting with Python 2.0, a garbage collector capable of reclaiming the space used by many cycles with no external references. There are, however, pathological cases where it can be expected to fail, so making sure your programs break such cycles is always safest.

Question 6.14 is intended to explain the new garbage collection algorithm.

4.18. How do I change the shell environment for programs called using os.popen() or os.system()? Changing os.environ doesn't work.

You must be using either a version of python before 1.4, or on a (rare) system that doesn't have the putenv() library function.

Before Python 1.4, modifying the environment passed to subshells was left out of the interpreter because there seemed to be no well-established portable way to do it (in particular, some systems, have putenv(), others have setenv(), and some have none at all). As of Python 1.4, almost all Unix systems do have putenv(), and so does the Win32 API, and thus the os module was modified so that changes to os.environ are trapped and the corresponding putenv() call is made.

4.19. What is a class?

A class is the particular object type that is created by executing a class statement. Class objects are used as templates, to create class instance objects, which embody both the data structure and program routines specific to a datatype.

4.20. What is a method?

A method is a function that you normally call as for some object x. The term is used for methods of classes and class instances as well as for methods of built-in objects. (The latter have a completely different implementation and only share the way their calls look in Python code.) Methods of classes (and class instances) are defined as functions inside the class definition.

4.21. What is self?

Self is merely a conventional name for the first argument of a method -- i.e. a function defined inside a class definition. A method defined as meth(self, a, b, c) should be called as x.meth(a, b, c) for some instance x of the class in which the definition occurs; the called method will think it is called as meth(x, a, b, c).

4.22. What is an unbound method?

An unbound method is a method defined in a class that is not yet bound to an instance. You get an unbound method if you ask for a class attribute that happens to be a function. You get a bound method if you ask for an instance attribute. A bound method knows which instance it belongs to and calling it supplies the instance automatically; an unbound method only knows which class it wants for its first argument (a derived class is also OK). Calling an unbound method doesn't "magically" derive the first argument from the context -- you have to provide it explicitly.

Trivia note regarding bound methods: each reference to a bound method of a particular object creates a bound method object. If you have two such references (a = inst.meth; b = inst.meth), they will compare equal (a == b) but are not the same (a is not b).

4.23. How do I call a method defined in a base class from a derived class that overrides it?

If your class definition starts with "class Derived(Base): ..." then you can call method meth defined in Base (or one of Base's base classes) as Base.meth(self, arguments...). Here, Base.meth is an unbound method (see previous question).

4.24. How do I call a method from a base class without using the name of the base class?

DON'T DO THIS. REALLY. I MEAN IT. It appears that you could call self.__class__.__bases__[0].meth(self, arguments...) but this fails when a doubly-derived method is derived from your class: for its instances, self.__class__.__bases__[0] is your class, not its base class -- so (assuming you are doing this from within Derived.meth) you would start a recursive call.

Often when you want to do this you are forgetting that classes are first class in Python. You can "point to" the class you want to delegate an operation to either at the instance or at the subclass level. For example if you want to use a "glorp" operation of a superclass you can point to the right superclass to use.

  class subclass(superclass1, superclass2, superclass3):
      delegate_glorp = superclass2
      def glorp(self, arg1, arg2):
            ... subclass specific stuff ...
            self.delegate_glorp.glorp(self, arg1, arg2)
  class subsubclass(subclass):
       delegate_glorp = superclass3
Note, however that setting delegate_glorp to subclass in subsubclass would cause an infinite recursion on subclass.delegate_glorp. Careful! Maybe you are getting too fancy for your own good. Consider simplifying the design (?).

4.25. How can I organize my code to make it easier to change the base class?

You could define an alias for the base class, assign the real base class to it before your class definition, and use the alias throughout your class. Then all you have to change is the value assigned to the alias. Incidentally, this trick is also handy if you want to decide dynamically (e.g. depending on availability of resources) which base class to use. Example:

        BaseAlias = <real base class>
        class Derived(BaseAlias):
                def meth(self):

4.26. How can I find the methods or attributes of an object?

This depends on the object type.

For an instance x of a user-defined class, instance attributes are found in the dictionary x.__dict__, and methods and attributes defined by its class are found in x.__class__.__bases__[i].__dict__ (for i in range(len(x.__class__.__bases__))). You'll have to walk the tree of base classes to find all class methods and attributes.

Many, but not all built-in types define a list of their method names in x.__methods__, and if they have data attributes, their names may be found in x.__members__. However this is only a convention.

For more information, read the source of the standard (but undocumented) module newdir.

4.27. I can't seem to use on a pipe created with os.popen(). is a low-level function which takes a file descriptor (a small integer). os.popen() creates a high-level file object -- the same type used for sys.std{in,out,err} and returned by the builtin open() function. Thus, to read n bytes from a pipe p created with os.popen(), you need to use

4.28. How can I create a stand-alone binary from a Python script? *

The "freeze" tool in "Tools/freeze/" does what you want. See the README.

This works by scanning your source recursively for import statements both forms) and looking for the modules on the standard Python path as well as in the source directory (for built-in modules). It then "compiles" the modules written in Python to C code (array initializers that can be turned into code objects using the marshal module) and creates a custom-made config file that only contains those built-in modules which are actually used in the program. It then compiles the generated C code and links it with the rest of the Python interpreter to form a self-contained binary which acts exactly like your script.

Hint: the freeze program only works if your script's filename ends in ".py".

If you want to do this under windows, there are two utilities which may be helpful. The first is Gordon McMillan's installer at
and the second is Thomas Heller's py2exe at
This latter tool is still under development, but has already been used to make a standalone .exe file for a Python COM server.

4.29. What WWW tools are there for Python? *

See the chapters titled "Internet Protocols and Support" and "Internet Data Handling" in the Library Reference Manual. Python is full of good things which will help you build server-side and client-side web systems.

If you are interested in Python web frameworks, a good site to visit is
There's also a web browser written in Python, called Grail -- see It isn't clear how much maintenance this code has received in recent years, however.

4.30. How do I run a subprocess with pipes connected to both input and output?

Use the standard popen2 module. For example:

	import popen2
	fromchild, tochild = popen2.popen2("command")
	output = fromchild.readline()
Warning: in general, it is unwise to do this, because you can easily cause a deadlock where your process is blocked waiting for output from the child, while the child is blocked waiting for input from you. This can be caused because the parent expects the child to output more text than it does, or it can be caused by data being stuck in stdio buffers due to lack of flushing. The Python parent can of course explicitly flush the data it sends to the child before it reads any output, but if the child is a naive C program it can easily have been written to never explicitly flush its output, even if it is interactive, since flushing is normally automatic.

Note on a bug in popen2: unless your program calls wait() or waitpid(), finished child processes are never removed, and eventually calls to popen2 will fail because of a limit on the number of child processes. Calling os.waitpid with the os.WNOHANG option can prevent this; a good place to insert such a call would be before calling popen2 again.

In many cases, all you really need is to run some data through a command and get the result back. Unless the data is infinite in size, the easiest (and often the most efficient!) way to do this is to write it to a temporary file and run the command with that temporary file as input. The standard module tempfile exports a function mktemp() which generates unique temporary file names.

Note that many interactive programs (e.g. vi) don't work well with pipes substituted for standard input and output. You will have to use pseudo ttys ("ptys") instead of pipes. There is some undocumented code to use these in the library module -- I'm afraid you're on your own here.

A different answer is a Python interface to Don Libes' "expect" library. A Python extension that interfaces to expect is called "expy" and available from

A pure Python solution that works like expect is PIPE by John Croix. A prerelease of PIPE is available from

4.31. How do I call a function if I have the arguments in a tuple?

Use the built-in function apply(). For instance,

    func(1, 2, 3)
is equivalent to

    args = (1, 2, 3)
    apply(func, args)
Note that func(args) is not the same -- it calls func() with exactly one argument, the tuple args, instead of three arguments, the integers 1, 2 and 3.

In Python 2.0, you can also use extended call syntax:

f(*args) is equivalent to apply(f, args)

4.32. How do I enable font-lock-mode for Python in Emacs?

If you are using XEmacs 19.14 or later, any XEmacs 20, FSF Emacs 19.34 or any Emacs 20, font-lock should work automatically for you if you are using the latest python-mode.el.

If you are using an older version of XEmacs or Emacs you will need to put this in your .emacs file:

        (defun my-python-mode-hook ()
          (setq font-lock-keywords python-font-lock-keywords)
          (font-lock-mode 1))
        (add-hook 'python-mode-hook 'my-python-mode-hook)

4.33. Is there a scanf() or sscanf() equivalent?

Not as such.

For simple input parsing, the easiest approach is usually to split the line into whitespace-delimited words using string.split(), and to convert decimal strings to numeric values using string.atoi(), string.atol() or string.atof(). (Python's atoi() is 32-bit and its atol() is arbitrary precision.) string.split supports an optional "sep" parameter which is useful if the line uses something other than whitespace as a delimiter.

For more complicated input parsing, regular expressions (see module re) are better suited and more powerful than C's sscanf().

There's a contributed module that emulates sscanf(), by Steve Clift; see contrib/Misc/sscanfmodule.c of the ftp site:

4.34. Can I have Tk events handled while waiting for I/O?

Yes, and you don't even need threads! But you'll have to restructure your I/O code a bit. Tk has the equivalent of Xt's XtAddInput() call, which allows you to register a callback function which will be called from the Tk mainloop when I/O is possible on a file descriptor. Here's what you need:

        from Tkinter import tkinter
        tkinter.createfilehandler(file, mask, callback)
The file may be a Python file or socket object (actually, anything with a fileno() method), or an integer file descriptor. The mask is one of the constants tkinter.READABLE or tkinter.WRITABLE. The callback is called as follows:

        callback(file, mask)
You must unregister the callback when you're done, using

Note: since you don't know *how many bytes* are available for reading, you can't use the Python file object's read or readline methods, since these will insist on reading a predefined number of bytes. For sockets, the recv() or recvfrom() methods will work fine; for other files, use, maxbytecount).

4.35. How do I write a function with output parameters (call by reference)?

[Mark Lutz] The thing to remember is that arguments are passed by assignment in Python. Since assignment just creates references to objects, there's no alias between an argument name in the caller and callee, and so no call-by-reference per se. But you can simulate it in a number of ways:

1) By using global variables; but you probably shouldn't :-)

2) By passing a mutable (changeable in-place) object:

      def func1(a):
          a[0] = 'new-value'     # 'a' references a mutable list
          a[1] = a[1] + 1        # changes a shared object
      args = ['old-value', 99]
      print args[0], args[1]     # output: new-value 100
3) By returning a tuple, holding the final values of arguments:

      def func2(a, b):
          a = 'new-value'        # a and b are local names
          b = b + 1              # assigned to new objects
          return a, b            # return new values
      x, y = 'old-value', 99
      x, y = func2(x, y)
      print x, y                 # output: new-value 100
4) And other ideas that fall-out from Python's object model. For instance, it might be clearer to pass in a mutable dictionary:

      def func3(args):
          args['a'] = 'new-value'     # args is a mutable dictionary
          args['b'] = args['b'] + 1   # change it in-place
      args = {'a':' old-value', 'b': 99}
      print args['a'], args['b']
5) Or bundle-up values in a class instance:

      class callByRef:
          def __init__(self, **args):
              for (key, value) in args.items():
                  setattr(self, key, value)
      def func4(args):
          args.a = 'new-value'        # args is a mutable callByRef
          args.b = args.b + 1         # change object in-place
      args = callByRef(a='old-value', b=99)
      print args.a, args.b
   But there's probably no good reason to get this complicated :-).
[Python's author favors solution 3 in most cases.]

4.36. Please explain the rules for local and global variables in Python.

[Ken Manheimer] In Python, procedure variables are implicitly global, unless they are assigned anywhere within the block. In that case they are implicitly local, and you need to explicitly declare them as 'global'.

Though a bit surprising at first, a moment's consideration explains this. On one hand, requirement of 'global' for assigned vars provides a bar against unintended side-effects. On the other hand, if global were required for all global references, you'd be using global all the time. Eg, you'd have to declare as global every reference to a builtin function, or to a component of an imported module. This clutter would defeat the usefulness of the 'global' declaration for identifying side-effects.

4.37. How can I have modules that mutually import each other?

Jim Roskind recommends the following order in each module:

First: all exports (like globals, functions, and classes that don't need imported base classes).

Then: all import statements.

Finally: all active code (including globals that are initialized from imported values).

Python's author doesn't like this approach much because the imports appear in a strange place, but has to admit that it works. His recommended strategy is to avoid all uses of "from <module> import *" (so everything from an imported module is referenced as <module>.<name>) and to place all code inside functions. Initializations of global variables and class variables should use constants or built-in functions only.

4.38. How do I copy an object in Python?

There is no generic copying operation built into Python, however most object types have some way to create a clone. Here's how for the most common objects:

For immutable objects (numbers, strings, tuples), cloning is unnecessary since their value can't change. For lists (and generally for mutable sequence types), a clone is created by the expression l[:]. For dictionaries, the following function returns a clone:

        def dictclone(o):
            n = {}
            for k in o.keys(): n[k] = o[k]
            return n
Finally, for generic objects, the "copy" module defines two functions for copying objects. copy.copy(x) returns a copy as shown by the above rules. copy.deepcopy(x) also copies the elements of composite objects. See the section on this module in the Library Reference Manual.

4.39. How to implement persistent objects in Python? (Persistent == automatically saved to and restored from disk.)

The library module "pickle" now solves this in a very general way (though you still can't store things like open files, sockets or windows), and the library module "shelve" uses pickle and (g)dbm to create persistent mappings containing arbitrary Python objects. For possibly better performance also look for the latest version of the relatively recent cPickle module.

A more awkward way of doing things is to use pickle's little sister, marshal. The marshal module provides very fast ways to store noncircular basic Python types to files and strings, and back again. Although marshal does not do fancy things like store instances or handle shared references properly, it does run extremely fast. For example loading a half megabyte of data may take less than a third of a second (on some machines). This often beats doing something more complex and general such as using gdbm with pickle/shelve.

4.40. I try to use __spam and I get an error about _SomeClassName__spam.

Variables with double leading underscore are "mangled" to provide a simple but effective way to define class private variables. See the chapter "New in Release 1.4" in the Python Tutorial.

4.41. How do I delete a file? And other file questions.

Use os.remove(filename) or os.unlink(filename); for documentation, see the posix section of the library manual. They are the same, unlink() is simply the Unix name for this function. In earlier versions of Python, only os.unlink() was available.

To remove a directory, use os.rmdir(); use os.mkdir() to create one.

To rename a file, use os.rename().

To truncate a file, open it using f = open(filename, "r+"), and use f.truncate(offset); offset defaults to the current seek position. (The "r+" mode opens the file for reading and writing.) There's also os.ftruncate(fd, offset) for files opened with -- for advanced Unix hacks only.

The shutil module also contains a number of functions to work on files including copyfile, copytree, and rmtree amongst others.

4.42. How to modify urllib or httplib to support HTTP/1.1?

Recent versions of Python (2.0 and onwards) support HTTP/1.1 natively.

4.43. Unexplicable syntax errors in compile() or exec.

When a statement suite (as opposed to an expression) is compiled by compile(), exec or execfile(), it must end in a newline. In some cases, when the source ends in an indented block it appears that at least two newlines are required.

4.44. How do I convert a string to a number?

For integers, use the built-in int() function, e.g. int('144') == 144. Similarly, long() converts from string to long integer, e.g. long('144') == 144L; and float() to floating-point, e.g. float('144') == 144.0.

Note that these are restricted to decimal interpretation, so that int('0144') == 144 and int('0x144') raises ValueError. For Python 2.0 int takes the base to convert from as a second optional argument, so int('0x144', 16) == 324.

For greater flexibility, or before Python 1.5, import the module string and use the string.atoi() function for integers, string.atol() for long integers, or string.atof() for floating-point. E.g., string.atoi('100', 16) == string.atoi('0x100', 0) == 256. See the library reference manual section for the string module for more details.

While you could use the built-in function eval() instead of any of those, this is not recommended, because someone could pass you a Python expression that might have unwanted side effects (like reformatting your disk). It also has the effect of interpreting numbers as Python expressions, so that e.g. eval('09') gives a syntax error since Python regards numbers starting with '0' as octal (base 8).

4.45. How do I convert a number to a string?

To convert, e.g., the number 144 to the string '144', use the built-in function repr() or the backquote notation (these are equivalent). If you want a hexadecimal or octal representation, use the built-in functions hex() or oct(), respectively. For fancy formatting, use the % operator on strings, just like C printf formats, e.g. "%04d" % 144 yields '0144' and "%.3f" % (1/3.0) yields '0.333'. See the library reference manual for details.

4.46. How do I copy a file?

There's the shutil module which contains a copyfile() function that implements a copy loop; it isn't good enough for the Macintosh, though: it doesn't copy the resource fork and Finder info.

4.47. How do I check if an object is an instance of a given class or of a subclass of it?

If you are developing the classes from scratch it might be better to program in a more proper object-oriented style -- instead of doing a different thing based on class membership, why not use a method and define the method differently in different classes?

However, there are some legitimate situations where you need to test for class membership.

In Python 1.5, you can use the built-in function isinstance(obj, cls).

The following approaches can be used with earlier Python versions:

An unobvious method is to raise the object as an exception and to try to catch the exception with the class you're testing for:

	def is_instance_of(the_instance, the_class):
		raise the_instance
	    except the_class:
		return 1
		return 0
This technique can be used to distinguish "subclassness" from a collection of classes as well

                              raise the_instance
                except Audible:
                except Visual:
                except Olfactory:
                              raise ValueError, "dunno what to do with this!"
This uses the fact that exception catching tests for class or subclass membership.

A different approach is to test for the presence of a class attribute that is presumably unique for the given class. For instance:

	class MyClass:
	    ThisIsMyClass = 1
	def is_a_MyClass(the_instance):
	    return hasattr(the_instance, 'ThisIsMyClass')
This version is easier to inline, and probably faster (inlined it is definitely faster). The disadvantage is that someone else could cheat:

	class IntruderClass:
	    ThisIsMyClass = 1    # Masquerade as MyClass
but this may be seen as a feature (anyway, there are plenty of other ways to cheat in Python). Another disadvantage is that the class must be prepared for the membership test. If you do not "control the source code" for the class it may not be advisable to modify the class to support testability.

4.48. What is delegation?

Delegation refers to an object oriented technique Python programmers may implement with particular ease. Consider the following:

  from string import upper
  class UpperOut:
        def __init__(self, outfile):
              self.__outfile = outfile
        def write(self, str):
              self.__outfile.write( upper(str) )
        def __getattr__(self, name):
              return getattr(self.__outfile, name)
Here the UpperOut class redefines the write method to convert the argument string to upper case before calling the underlying self.__outfile.write method, but all other methods are delegated to the underlying self.__outfile object. The delegation is accomplished via the "magic" __getattr__ method. Please see the language reference for more information on the use of this method.

Note that for more general cases delegation can get trickier. Particularly when attributes must be set as well as gotten the class must define a __settattr__ method too, and it must do so carefully.

The basic implementation of __setattr__ is roughly equivalent to the following:

   class X:
        def __setattr__(self, name, value):
             self.__dict__[name] = value
Most __setattr__ implementations must modify self.__dict__ to store local state for self without causing an infinite recursion.

4.49. How do I test a Python program or component. *

We presume for the purposes of this question you are interested in standalone testing, rather than testing your components inside a testing framework. The best-known testing framework for Python is the PyUnit module, maintained at
For standalone testing, it helps to write the program so that it may be easily tested by using good modular design. In particular your program should have almost all functionality encapsulated in either functions or class methods -- and this sometimes has the surprising and delightful effect of making the program run faster (because local variable accesses are faster than global accesses). Furthermore the program should avoid depending on mutating global variables, since this makes testing much more difficult to do.

The "global main logic" of your program may be as simple as

  if __name__=="__main__":
at the bottom of the main module of your program.

Once your program is organized as a tractible collection of functions and class behaviours you should write test functions that exercise the behaviours. A test suite can be associated with each module which automates a sequence of tests. This sounds like a lot of work, but since Python is so terse and flexible it's surprisingly easy. You can make coding much more pleasant and fun by writing your test functions in parallel with the "production code", since this makes it easy to find bugs and even design flaws earlier.

"Support modules" that are not intended to be the main module of a program may include a "test script interpretation" which invokes a self test of the module.

   if __name__ == "__main__":
Even programs that interact with complex external interfaces may be tested when the external interfaces are unavailable by using "fake" interfaces implemented in Python. For an example of a "fake" interface, the following class defines (part of) a "fake" file interface:

 import string
 testdata = "just a random sequence of characters"
 class FakeInputFile:
   data = testdata
   position = 0
   closed = 0
   def read(self, n=None):
       p = self.position
       if n is None:
          result=[p: p+n]
       self.position = p + len(result)
       return result
   def seek(self, n, m=0):
       last = len(
       p = self.position
       if m==0: 
       elif m==1:
       elif m==2:
          raise ValueError, "bad m"
       if final<0:
          raise IOError, "negative seek"
       self.position = final
   def isatty(self):
       return 0
   def tell(self):
       return self.position
   def close(self):
       self.closed = 1
   def testclosed(self):
       if self.closed:
          raise IOError, "file closed"
Try f=FakeInputFile() and test out its operations.

4.50. My multidimensional list (array) is broken! What gives?

You probably tried to make a multidimensional array like this.

   A = [[None] * 2] * 3
This makes a list containing 3 references to the same list of length two. Changes to one row will show in all rows, which is probably not what you want. The following works much better:

   A = [None]*3
   for i in range(3):
        A[i] = [None] * 2
This generates a list containing 3 different lists of length two.

If you feel weird, you can also do it in the following way:

   w, h = 2, 3
   A = map(lambda i,w=w: [None] * w, range(h))
For Python 2.0 the above can be spelled using a list comprehension:

   w,h = 2,3
   A = [ [None]*w for i in range(h) ]

4.51. I want to do a complicated sort: can you do a Schwartzian Transform in Python?

Yes, and in Python you only have to write it once:

 def st(List, Metric):
     def pairing(element, M = Metric):
           return (M(element), element)
     paired = map(pairing, List)
     return map(stripit, paired)
 def stripit(pair):
     return pair[1]
This technique, attributed to Randal Schwartz, sorts the elements of a list by a metric which maps each element to its "sort value". For example, if L is a list of string then

   import string
   Usorted = st(L, string.upper)
   def intfield(s):
         return string.atoi( string.strip(s[10:15] ) )
   Isorted = st(L, intfield)
Usorted gives the elements of L sorted as if they were upper case, and Isorted gives the elements of L sorted by the integer values that appear in the string slices starting at position 10 and ending at position 15. In Python 2.0 this can be done more naturally with list comprehensions:

  import string
  tmp1 = [ (string.upper(x),x) for x in L ] # Schwartzian transform
  Usorted = [ x[1] for x in tmp1 ]
  tmp2 = [ (int(s[10:15]), s) for s in L ] # Schwartzian transform
  Isorted = [ x[1] for x in tmp2 ]

Note that Isorted may also be computed by

   def Icmp(s1, s2):
         return cmp( intfield(s1), intfield(s2) )
   Isorted = L[:]
but since this method computes intfield many times for each element of L, it is slower than the Schwartzian Transform.

4.52. How to convert between tuples and lists?

The function tuple(seq) converts any sequence into a tuple with the same items in the same order. For example, tuple([1, 2, 3]) yields (1, 2, 3) and tuple('abc') yields ('a', 'b', 'c'). If the argument is a tuple, it does not make a copy but returns the same object, so it is cheap to call tuple() when you aren't sure that an object is already a tuple.

The function list(seq) converts any sequence into a list with the same items in the same order. For example, list((1, 2, 3)) yields [1, 2, 3] and list('abc') yields ['a', 'b', 'c']. If the argument is a list, it makes a copy just like seq[:] would.

4.53. Files retrieved with urllib contain leading garbage that looks like email headers.

Extremely old versions of Python supplied libraries which did not support HTTP/1.1; the vanilla httplib in Python 1.4 only recognized HTTP/1.0. In Python 2.0 full HTTP/1.1 support is included.

4.54. How do I get a list of all instances of a given class?

Python does not keep track of all instances of a class (or of a built-in type).

You can program the class's constructor to keep track of all instances, but unless you're very clever, this has the disadvantage that the instances never get deleted,because your list of all instances keeps a reference to them.

(The trick is to regularly inspect the reference counts of the instances you've retained, and if the reference count is below a certain level, remove it from the list. Determining that level is tricky -- it's definitely larger than 1.)

4.55. A regular expression fails with regex.error: match failure.

This is usually caused by too much backtracking; the regular expression engine has a fixed size stack which holds at most 4000 backtrack points. Every character matched by e.g. ".*" accounts for a backtrack point, so even a simple search like

will fail.

This is fixed in the re module introduced with Python 1.5; consult the Library Reference section on re for more information.

4.56. I can't get signal handlers to work.

The most common problem is that the signal handler is declared with the wrong argument list. It is called as

	handler(signum, frame)
so it should be declared with two arguments:

	def handler(signum, frame):

4.57. I can't use a global variable in a function? Help! *

Did you do something like this?

   x = 1 # make a global
   def f():
         print x # try to print the global
         for j in range(100):
              if q>3:
Any variable assigned in a function is local to that function. unless it is specifically declared global. Since a value is bound to x as the last statement of the function body, the compiler assumes that x is local. Consequently the "print x" attempts to print an uninitialized local variable and will trigger a NameError.

In such cases the solution is to insert an explicit global declaration at the start of the function, making it

   def f():
         global x
         print x # try to print the global
         for j in range(100):
              if q>3:

In this case, all references to x are interpreted as references to the x from the module namespace.

4.58. What's a negative index? Why doesn't list.insert() use them?

Python sequences are indexed with positive numbers and negative numbers. For positive numbers 0 is the first index 1 is the second index and so forth. For negative indices -1 is the last index and -2 is the pentultimate (next to last) index and so forth. Think of seq[-n] as the same as seq[len(seq)-n].

Using negative indices can be very convenient. For example if the string Line ends in a newline then Line[:-1] is all of Line except the newline.

Sadly the list builtin method L.insert does not observe negative indices. This feature could be considered a mistake but since existing programs depend on this feature it may stay around forever. L.insert for negative indices inserts at the start of the list. To get "proper" negative index behaviour use L[n:n] = [x] in place of the insert method.

4.59. How can I sort one list by values from another list?

You can sort lists of tuples.

  >>> list1 = ["what", "I'm", "sorting", "by"]
  >>> list2 = ["something", "else", "to", "sort"]
  >>> pairs = map(None, list1, list2)
  >>> pairs
  [('what', 'something'), ("I'm", 'else'), ('sorting', 'to'), ('by', 'sort')]
  >>> pairs.sort()
  >>> pairs
  [("I'm", 'else'), ('by', 'sort'), ('sorting', 'to'), ('what', 'something')]
  >>> result = pairs[:]
  >>> for i in xrange(len(result)): result[i] = result[i][1]
  >>> result
  ['else', 'sort', 'to', 'something']
And if you didn't understand the question, please see the example above ;c). Note that "I'm" sorts before "by" because uppercase "I" comes before lowercase "b" in the ascii order. Also see 4.51.

In Python 2.0 this can be done like:

 >>> list1 = ["what", "I'm", "sorting", "by"]
 >>> list2 = ["something", "else", "to", "sort"]
 >>> pairs = zip(list1, list2)
 >>> pairs
 [('what', 'something'), ("I'm", 'else'), ('sorting', 'to'), ('by', 'sort')]
 >>> pairs.sort()
 >>> result = [ x[1] for x in pairs ]
 >>> result
 ['else', 'sort', 'to', 'something']

Someone asked, why not this for the last steps:

  result = []
  for p in pairs: result.append(p[1])
This is much more legible. However, a quick test shows that it is almost twice as slow for long lists. Why? First of all, the append() operation has to reallocate memory, and while it uses some tricks to avoid doing that each time, it still has to do it occasionally, and apparently that costs quite a bit. Second, the expression "result.append" requires an extra attribute lookup. The attribute lookup could be done away with by rewriting as follows:

  result = []
  append = result.append
  for p in pairs: append(p[1])
which gains back some speed, but is still considerably slower than the original solution, and hardly less convoluted.

4.60. Why doesn't dir() work on builtin types like files and lists?

It does starting with Python 1.5.

Using 1.4, you can find out which methods a given object supports by looking at its __methods__ attribute:

    >>> List = []
    >>> List.__methods__
    ['append', 'count', 'index', 'insert', 'remove', 'reverse', 'sort']

4.61. How can I mimic CGI form submission (METHOD=POST)?

I would like to retrieve web pages that are the result of POSTing a form. Is there existing code that would let me do this easily?

Yes. Here's a simple example that uses httplib.

    import httplib, sys, time
    ### build the query string
    qs = "First=Josephine&MI=Q&Last=Public"
    ### connect and send the server a path
    httpobj = httplib.HTTP('www.some-server.out-there', 80)
    httpobj.putrequest('POST', '/cgi-bin/some-cgi-script')
    ### now generate the rest of the HTTP headers...
    httpobj.putheader('Accept', '*/*')
    httpobj.putheader('Connection', 'Keep-Alive')
    httpobj.putheader('Content-type', 'application/x-www-form-urlencoded')
    httpobj.putheader('Content-length', '%d' % len(qs))
    ### find out what the server said in response...
    reply, msg, hdrs = httpobj.getreply()
    if reply != 200:
Note that in general for "url encoded posts" (the default) query strings must be "quoted" to, for example, change equals signs and spaces to an encoded form when they occur in name or value. Use urllib.quote to perform this quoting. For example to send name="Guy Steele, Jr.":

   >>> from urllib import quote
   >>> x = quote("Guy Steele, Jr.")
   >>> x
   >>> query_string = "name="+x
   >>> query_string

4.62. If my program crashes with a bsddb (or anydbm) database open, it gets corrupted. How come?

Databases opened for write access with the bsddb module (and often by the anydbm module, since it will preferentially use bsddb) must explcitly be closed using the close method of the database. The underlying libdb package caches database contents which need to be converted to on-disk form and written, unlike regular open files which already have the on-disk bits in the kernel's write buffer, where they can just be dumped by the kernel with the program exits.

If you have initialized a new bsddb database but not written anything to it before the program crashes, you will often wind up with a zero-length file and encounter an exception the next time the file is opened.

4.63. How do I make a Python script executable on Unix?

You need to do two things: the script file's mode must be executable (include the 'x' bit), and the first line must begin with #! followed by the pathname for the Python interpreter.

The first is done by executing 'chmod +x scriptfile' or perhaps 'chmod 755 scriptfile'.

The second can be done in a number of way. The most straightforward way is to write

as the very first line of your file - or whatever the pathname is where the python interpreter is installed on your platform.

If you would like the script to be independent of where the python interpreter lives, you can use the "env" program. On almost all platforms, the following will work, assuming the python interpreter is in a directory on the user's $PATH:

  #! /usr/bin/env python
Note -- *don't* do this for CGI scripts. The $PATH variable for CGI scripts is often very minimal, so you need to use the actual absolute pathname of the interpreter.

Occasionally, a user's environment is so full that the /usr/bin/env program fails; or there's no env program at all. In that case, you can try the following hack (due to Alex Rezinsky):

  #! /bin/sh
  exec python $0 ${1+"$@"}
The disadvantage is that this defines the script's __doc__ string. However, you can fix that by adding

  __doc__ = """...Whatever..."""

4.64. How do you remove duplicates from a list?

Generally, if you don't mind reordering the List

   if List:
      last = List[-1]
      for i in range(len(List)-2, -1, -1):
          if last==List[i]: del List[i]
          else: last=List[i]
If all elements of the list may be used as dictionary keys (ie, they are all hashable) this is often faster

   d = {}
   for x in List: d[x]=x
   List = d.values()
Also, for extremely large lists you might consider more optimal alternatives to the first one. The second one is pretty good whenever it can be used.

4.65. Are there any known year 2000 problems in Python?

I am not aware of year 2000 deficiencies in Python 1.5. Python does very few date calculations and for what it does, it relies on the C library functions. Python generally represent times either as seconds since 1970 or as a tuple (year, month, day, ...) where the year is expressed with four digits, which makes Y2K bugs unlikely. So as long as your C library is okay, Python should be okay. Of course, I cannot vouch for your Python code!

Given the nature of freely available software, I have to add that this statement is not legally binding. The Python copyright notice contains the following disclaimer:

The good news is that if you encounter a problem, you have full source available to track it down and fix it!

4.66. I want a version of map that applies a method to a sequence of objects! Help!

Get fancy!

  def method_map(objects, method, arguments):
       """method_map([a,b], "flog", (1,2)) gives [a.flog(1,2), b.flog(1,2)]"""
       nobjects = len(objects)
       methods = map(getattr, objects, [method]*nobjects)
       return map(apply, methods, [arguments]*nobjects)
It's generally a good idea to get to know the mysteries of map and apply and getattr and the other dynamic features of Python.

4.67. How do I generate random numbers in Python?

The standard library module "whrandom" implements a random number generator. Usage is simple:

    import whrandom
This returns a random floating point number in the range [0, 1).

There are also other specialized generators in this module:

    randint(a, b) chooses an integer in the range [a, b)
    choice(S) chooses from a given sequence
    uniform(a, b) chooses a floating point number in the range [a, b)
To force the random number generator's initial setting, use

    seed(x, y, z) set the seed from three integers in [1, 256)
There's also a class, whrandom, whoch you can instantiate to create independent multiple random number generators.

The module "random" contains functions that approximate various standard distributions.

All this is documented in the library reference manual. Note that the module "rand" is obsolete.

4.68. How do I access the serial (RS232) port?

There's a Windows serial communication module (for communication over RS 232 serial ports) at
For DOS, try Hans Nowak's Python-DX, which supports this, at:
For Unix, search Deja News (using for "serial port" with author Mitch Chapman (his post is a little too long to include here).

4.69. Images on Tk-Buttons don't work in Py15?

They do work, but you must keep your own reference to the image object now. More verbosely, you must make sure that, say, a global variable or a class attribute refers to the object.

Quoting Fredrik Lundh from the mailinglist:

  Well, the Tk button widget keeps a reference to the internal
  photoimage object, but Tkinter does not.  So when the last
  Python reference goes away, Tkinter tells Tk to release the
  photoimage.  But since the image is in use by a widget, Tk
  doesn't destroy it.  Not completely.  It just blanks the image,
  making it completely transparent...
  And yes, there was a bug in the keyword argument handling
  in 1.4 that kept an extra reference around in some cases.  And
  when Guido fixed that bug in 1.5, he broke quite a few Tkinter

4.70. Where is the (,, etc.) source file?

If you can't find a source file for a module it may be a builtin or dynamically loaded module implemented in C, C++ or other compiled language. In this case you may not have the source file or it may be something like mathmodule.c, somewhere in a C source directory (not on the Python Path).

Fredrik Lundh ([email protected]) explains (on the python-list):

There are (at least) three kinds of modules in Python: 1) modules written in Python (.py); 2) modules written in C and dynamically loaded (.dll, .pyd, .so, .sl, etc); 3) modules written in C and linked with the interpreter; to get a list of these, type:

    import sys
    print sys.builtin_module_names

4.71. How do I send mail from a Python script? *

On Unix, it's very simple, using sendmail. The location of the sendmail program varies between systems; sometimes it is /usr/lib/sendmail, sometime /usr/sbin/sendmail. The sendmail manual page will help you out. Here's some sample code:

  SENDMAIL = "/usr/sbin/sendmail" # sendmail location
  import os
  p = os.popen("%s -t" % SENDMAIL, "w")
  p.write("To: [email protected]\n")
  p.write("Subject: test\n")
  p.write("\n") # blank line separating headers from body
  p.write("Some text\n")
  p.write("some more text\n")
  sts = p.close()
  if sts != 0:
      print "Sendmail exit status", sts
On non-Unix systems (and on Unix systems too, of course!), you can use SMTP to send mail to a nearby mail server. A library for SMTP has been included since Python 1.5.1. Here's a very simple interactive mail sender that uses it. This method will work on any host that supports an SMTP listener; otherwise, you will have to ask the user for a host:

    import sys, smtplib
    fromaddr = raw_input("From: ")
    toaddrs  = string.splitfields(raw_input("To: "), ',')
    print "Enter message, end with ^D:"
    msg = ''
    while 1:
        line = sys.stdin.readline()
        if not line:
        msg = msg + line
    # The actual mail send
    server = smtplib.SMTP('localhost')
    server.sendmail(fromaddr, toaddrs, msg)

4.72. How do I avoid blocking in connect() of a socket?

The select module is widely known to help with asynchronous I/O on sockets once they are connected. However, it is less than common knowledge how to avoid blocking on the initial connect() call. Jeremy Hylton has the following advice (slightly edited):

To prevent the TCP connect from blocking, you can set the socket to non-blocking mode. Then when you do the connect(), you will either connect immediately (unlikely) or get an exception that contains the errno. errno.EINPROGRESS indicates that the connection is in progress, but hasn't finished yet. Different OSes will return different errnos, so you're going to have to check. I can tell you that different versions of Solaris return different errno values.

In Python 1.5 and later, you can use connect_ex() to avoid creating an exception. It will just return the errno value.

To poll, you can call connect_ex() again later -- 0 or errno.EISCONN indicate that you're connected -- or you can pass this socket to select (checking to see if it is writeable).

4.73. How do I specify hexadecimal and octal integers?

To specify an octal digit, precede the octal value with a zero. For example, to set the variable "a" to the octal value "10" (8 in decimal), type:

    >>> a = 010
To verify that this works, you can type "a" and hit enter while in the interpreter, which will cause Python to spit out the current value of "a" in decimal:

    >>> a
Hexadecimal is just as easy. Simply precede the hexadecimal number with a zero, and then a lower or uppercase "x". Hexadecimal digits can be specified in lower or uppercase. For example, in the Python interpreter:

    >>> a = 0xa5
    >>> a
    >>> b = 0XB2
    >>> b

4.74. How to get a single keypress at a time?

For Windows, see question 8.2. Here is an answer for Unix.

There are several solutions; some involve using curses, which is a pretty big thing to learn. Here's a solution without curses, due to Andrew Kuchling (adapted from code to do a PGP-style randomness pool):

        import termios, TERMIOS, sys, os
        fd = sys.stdin.fileno()
        old = termios.tcgetattr(fd)
        new = termios.tcgetattr(fd)
        new[3] = new[3] & ~TERMIOS.ICANON & ~TERMIOS.ECHO
        new[6][TERMIOS.VMIN] = 1
        new[6][TERMIOS.VTIME] = 0
        termios.tcsetattr(fd, TERMIOS.TCSANOW, new)
        s = ''    # We'll save the characters typed and add them to the pool.
            while 1:
                c =, 1)
                print "Got character", `c`
                s = s+c
            termios.tcsetattr(fd, TERMIOS.TCSAFLUSH, old)
You need the termios module for any of this to work, and I've only tried it on Linux, though it should work elsewhere. It turns off stdin's echoing and disables canonical mode, and then reads a character at a time from stdin, noting the time after each keystroke.

4.75. How can I overload constructors (or methods) in Python?

(This actually applies to all methods, but somehow the question usually comes up first in the context of constructors.)

Where in C++ you'd write

    class C {
        C() { cout << "No arguments\n"; }
        C(int i) { cout << "Argument is " << i << "\n"; }
in Python you have to write a single constructor that catches all cases using default arguments. For example:

    class C:
        def __init__(self, i=None):
            if i is None:
                print "No arguments"
                print "Argument is", i
This is not entirely equivalent, but close enough in practice.

You could also try a variable-length argument list, e.g.

        def __init__(self, *args):
The same approach works for all method definitions.

4.76. How do I pass keyword arguments from one method to another?

Use apply. For example:

    class Account:
        def __init__(self, **kw):
            self.accountType = kw.get('accountType')
            self.balance = kw.get('balance')
    class CheckingAccount(Account):
        def __init__(self, **kw):
            kw['accountType'] = 'checking'
            apply(Account.__init__, (self,), kw)
    myAccount = CheckingAccount(balance=100.00)
In Python 2.0 you can call it directly using the new ** syntax:

    class CheckingAccount(Account):
        def __init__(self, **kw):
            kw['accountType'] = 'checking'
            Account.__init__(self, **kw)
or more generally:

 >>> def f(x, *y, **z):
 ...  print x,y,z
 >>> Y = [1,2,3]
 >>> Z = {'foo':3,'bar':None}
 >>> f('hello', *Y, **Z)
 hello (1, 2, 3) {'foo': 3, 'bar': None}

4.77. What module should I use to help with generating HTML?

Check out HTMLgen written by Robin Friedrich. It's a class library of objects corresponding to all the HTML 3.2 markup tags. It's used when you are writing in Python and wish to synthesize HTML pages for generating a web or for CGI forms, etc.

It can be found in the FTP contrib area on or on the Starship. Use the search engines there to locate the latest version.

It might also be useful to consider DocumentTemplate, which offers clear separation between Python code and HTML code. DocumentTemplate is part of the Bobo objects publishing system (http:/ but can be used independantly of course!

4.78. How do I create documentation from doc strings?

Use gendoc, by Daniel Larson. See

It can create HTML from the doc strings in your Python source code.

4.79. How do I read (or write) binary data?

For complex data formats, it's best to use use the struct module. It's documented in the library reference. It allows you to take a string read from a file containing binary data (usually numbers) and convert it to Python objects; and vice versa.

For example, the following code reads two 2-byte integers and one 4-byte integer in big-endian format from a file:

  import struct
  f = open(filename, "rb")  # Open in binary mode for portability
  s =
  x, y, z = struct.unpack(">hhl", s)
The '>' in the format string forces bin-endian data; the letter 'h' reads one "short integer" (2 bytes), and 'l' reads one "long integer" (4 bytes) from the string.

For data that is more regular (e.g. a homogeneous list of ints or floats), you can also use the array module, also documented in the library reference.

4.80. I can't get key bindings to work in Tkinter

An oft-heard complaint is that event handlers bound to events with the bind() method don't get handled even when the appropriate key is pressed.

The most common cause is that the widget to which the binding applies doesn't have "keyboard focus". Check out the Tk documentation for the focus command. Usually a widget is given the keyboard focus by clicking in it (but not for labels; see the taketocus option).

4.81. "import crypt" fails


Starting with Python 1.5, the crypt module is disabled by default. In order to enable it, you must go into the Python source tree and edit the file Modules/Setup to enable it (remove a '#' sign in front of the line starting with '#crypt'). Then rebuild. You may also have to add the string '-lcrypt' to that same line.

4.82. Are there coding standards or a style guide for Python programs?

Yes, Guido has written the "Python Style Guide". See

4.83. How do I freeze Tkinter applications?

Freeze is a tool to create stand-alone applications (see 4.28).

When freezing Tkinter applications, the applications will not be truly stand-alone, as the application will still need the tcl and tk libraries.

One solution is to ship the application with the tcl and tk libraries, and point to them at run-time using the TCL_LIBRARY and TK_LIBRARY environment variables.

To get truly stand-alone applications, the Tcl scripts that form the library have to be integrated into the application as well. One tool supporting that is SAM (stand-alone modules), which is part of the Tix distribution ( Build Tix with SAM enabled, perform the appropriate call to Tclsam_init etc inside Python's Modules/tkappinit.c, and link with libtclsam and libtksam (you might include the Tix libraries as well).

4.84. How do I create static class data and static class methods?

[Tim Peters, [email protected]]

Static data (in the sense of C++ or Java) is easy; static methods (again in the sense of C++ or Java) are not supported directly.


For example,

    class C:
        count = 0   # number of times C.__init__ called
        def __init__(self):
            C.count = C.count + 1
        def getcount(self):
            return C.count  # or return self.count
c.count also refers to C.count for any c such that isinstance(c, C) holds, unless overridden by c itself or by some class on the base-class search path from c.__class__ back to C.

Caution: within a method of C,

    self.count = 42
creates a new and unrelated instance vrbl named "count" in self's own dict. So rebinding of a class-static data name needs the

    C.count = 314
form whether inside a method or not.


Static methods (as opposed to static data) are unnatural in Python, because

returns an unbound method object, which can't be invoked without supplying an instance of C as the first argument.

The intended way to get the effect of a static method is via a module-level function:

    def getcount():
        return C.count
If your code is structured so as to define one class (or tightly related class hierarchy) per module, this supplies the desired encapsulation.

Several tortured schemes for faking static methods can be found by searching DejaNews. Most people feel such cures are worse than the disease. Perhaps the least obnoxious is due to Pekka Pessi (mailto:[email protected]):

    # helper class to disguise function objects
    class _static:
        def __init__(self, f):
            self.__call__ = f
    class C:
        count = 0
        def __init__(self):
            C.count = C.count + 1
        def getcount():
            return C.count
        getcount = _static(getcount)
        def sum(x, y):
            return x + y
        sum = _static(sum)
    C(); C()
    c = C()
    print C.getcount()  # prints 3
    print c.getcount()  # prints 3
    print C.sum(27, 15) # prints 42

4.85. __import__('x.y.z') returns <module 'x'>; how do I get z?


For more realistic situations, you may have to do something like

   m = __import__(s)
   for i in string.split(s, ".")[1:]:
       m = getattr(m, i)

4.86. Basic thread wisdom

If you write a simple test program like this:

  import thread
  def run(name, n):
      for i in range(n): print name, i
  for i in range(10):
      thread.start_new(run, (i, 100))
none of the threads seem to run! The reason is that as soon as the main thread exits, all threads are killed.

A simple fix is to add a sleep to the end of the program, sufficiently long for all threads to finish:

  import thread, time
  def run(name, n):
      for i in range(n): print name, i
  for i in range(10):
      thread.start_new(run, (i, 100))
  time.sleep(10) # <----------------------------!
But now (on many platforms) the threads don't run in parallel, but appear to run sequentially, one at a time! The reason is that the OS thread scheduler doesn't start a new thread until the previous thread is blocked.

A simple fix is to add a tiny sleep to the start of the run function:

  import thread, time
  def run(name, n):
      time.sleep(0.001) # <---------------------!
      for i in range(n): print name, i
  for i in range(10):
      thread.start_new(run, (i, 100))
Some more hints:

Instead of using a time.sleep() call at the end, it's better to use some kind of semaphore mechanism. One idea is to use a the Queue module to create a queue object, let each thread append a token to the queue when it finishes, and let the main thread read as many tokens from the queue as there are threads.

Use the threading module instead of the thread module. It's part of Python since version 1.5.1. It takes care of all these details, and has many other nice features too!

4.87. Why doesn't closing sys.stdout (stdin, stderr) really close it?

Python file objects are a high-level layer of abstraction on top of C streams, which in turn are a medium-level layer of abstraction on top of (among other things) low-level C file descriptors.

For most file objects f you create in Python via the builtin "open" function, f.close() marks the Python file object as being closed from Python's point of view, and also arranges to close the underlying C stream. This happens automatically too, in f's destructor, when f becomes garbage.

But stdin, stdout and stderr are treated specially by Python, because of the special status also given to them by C: doing

    sys.stdout.close() # ditto for stdin and stderr
marks the Python-level file object as being closed, but does not close the associated C stream (provided sys.stdout is still bound to its default value, which is the stream C also calls "stdout").

To close the underlying C stream for one of these three, you should first be sure that's what you really want to do (e.g., you may confuse the heck out of extension modules trying to do I/O). If it is, use os.close:

    os.close(0)   # close C's stdin stream
    os.close(1)   # close C's stdout stream
    os.close(2)   # close C's stderr stream

4.88. What kinds of global value mutation are thread-safe?

[adapted from responses by Gordon McMillan & GvR]

A global interpreter lock is used internally to ensure that only one thread runs in the Python VM at a time. In general, Python offers to switch among threads only between bytecode instructions (how frequently it offers to switch can be set via sys.setcheckinterval). Each bytecode instruction-- and all the C implementation code reached from it --is therefore atomic.

In theory, this means an exact accounting requires an exact understanding of the PVM bytecode implementation. In practice, it means that operations on shared vrbls of builtin data types (ints, lists, dicts, etc) that "look atomic" really are.

For example, these are atomic (L, L1, L2 are lists, D, D1, D2 are dicts, x, y are objects, i, j are ints):

    x = L[i]
    x = L.pop()
    L1[i:j] = L2
    x = y
    x.field = y
    D[x] = y
These aren't:

    i = i+1
    L[i] = L[j]
    D[x] = D[x] + 1
Note: operations that replace other objects may invoke those other objects' __del__ method when their reference count reaches zero, and that can affect things. This is especially true for the mass updates to dictionaries and lists. When in doubt, use a mutex!

4.89. How do I modify a string in place?

Strings are immutable (see question 6.2) so you cannot modify a string directly. If you need an object with this ability, try converting the string to a list or take a look at the array module.

    >>> s = "Hello, world"
    >>> a = list(s)
    >>> print a
    ['H', 'e', 'l', 'l', 'o', ',', ' ', 'w', 'o', 'r', 'l', 'd']
    >>> a[7:] = list("there!")
    >>> import string
    >>> print string.join(a, '')
    'Hello, there!'
    >>> import array
    >>> a = array.array('c', s)
    >>> print a
    array('c', 'Hello, world')
    >>> a[0] = 'y' ; print a
    array('c', 'yello world')
    >>> a.tostring()
    'yello, world'

4.90. How to pass on keyword/optional parameters/arguments

Q: How can I pass on optional or keyword parameters from one function
   to another?
A: Use 'apply', like:

	def f1(a, *b, **c):

	def f2(x, *y, **z):
		apply(f1, (x,)+y, z)

4.91. How can I get a dictionary to display its keys in a consistent order?

In general, dictionaries store their keys in an unpredictable order, so the display order of a dictionary's elements will be similarly unpredictable. (See Question 6.12 to understand why this is so.)

This can be frustrating if you want to save a printable version to a file, make some changes and then compare it with some other printed dictionary. If you have such needs you can subclass UserDict.UserDict to create a SortedDict class that prints itself in a predictable order. Here's one simpleminded implementation of such a class:

  import UserDict, string
  class SortedDict(UserDict.UserDict):
    def __repr__(self):
      result = []
      append = result.append
      keys =
      for k in keys:
        append("%s: %s" % (`k`, `[k]`))
      return "{%s}" % string.join(result, ", ")
    ___str__ = __repr__

This will work for many common situations you might encounter, though it's far from a perfect solution. (It won't have any effect on the pprint module and does not transparently handle values that are or contain dictionaries.

4.92. Is there a Python tutorial?

Yes. See question 1.20 at

4.93. How do I compile a Python application to a stand-alone program?

Even though there are Python compilers being developed, you probably don't need a real compiler, if all you want is a stand-alone program. There are three solutions to that.

One is to use the freeze tool, which is included in the Python source tree as Tools/freeze. It converts Python byte code to C arrays. Using a C compiler, you can embed all your modules into a new program, which is then linked with the standard Python modules.

On Windows, another alternative exists which does not require a C compiler. Christian Tismer's SQFREEZE ( appends the byte code to a specially-prepared Python interpreter, which will find the byte code in executable.

Gordon McMillian offers with Installer ( a third alternative, which works similar to SQFREEZE, but allows to include arbitraty additional files in the stand-alone binary as well.

4.94. How do I get a single keypress without blocking?

There are several solutions; some involve using curses, which is a pretty big thing to learn. Here's a solution without curses.

        import termios, TERMIOS, fcntl, FCNTL, sys, os
        fd = sys.stdin.fileno()
        oldterm = termios.tcgetattr(fd)
        newattr = termios.tcgetattr(fd)
        newattr[3] = newattr[3] & ~TERMIOS.ICANON & ~TERMIOS.ECHO
        termios.tcsetattr(fd, TERMIOS.TCSANOW, newattr)
        oldflags = fcntl.fcntl(fd,FCNTL.F_GETFL)
            while 1:
                 c =
                  print "Got character", `c`
                except IOError:  pass   # Ignore IOError from empty buff
            termios.tcsetattr(fd, TERMIOS.TCSAFLUSH, oldterm)
You need the termios and the fcntl module for any of this to work, and I've only tried it on Linux, though it should work elsewhere.

In this code, characters are read and printed one at a time.

termios.tcsetattr() turns off stdin's echoing and disables canonical mode. fcntl.fnctl() is used to obtain stdin's file descriptor flags and modify them for non-blocking mode. Since reading stdin when it is empty results in an IOError, this error is caught and ignored.

4.95. Is there an equivalent to Perl chomp()? (Remove trailing newline from string)

There are two partial substitutes. If you want to remove all trailing whitespace, use the method string.rstrip(). Otherwise, if there is only one line in the string, use string.splitlines()[0].

5. Extending Python

5.1. Can I create my own functions in C?

Yes, you can create built-in modules containing functions, variables, exceptions and even new types in C. This is explained in the document "Extending and Embedding the Python Interpreter" (the LaTeX file Doc/ext.tex). Also read the chapter on dynamic loading.

There's more information on this in each of the Python books: Programming Python, Internet Programming with Python, and Das Python-Buch (in German).

5.2. Can I create my own functions in C++?

Yes, using the C-compatibility features found in C++. Basically you place extern "C" { ... } around the Python include files and put extern "C" before each function that is going to be called by the Python interpreter. Global or static C++ objects with constructors are probably not a good idea.

5.3. How can I execute arbitrary Python statements from C?

The highest-level function to do this is PyRun_SimpleString() which takes a single string argument which is executed in the context of module __main__ and returns 0 for success and -1 when an exception occurred (including SyntaxError). If you want more control, use PyRun_String(); see the source for PyRun_SimpleString() in Python/pythonrun.c.

5.4. How can I evaluate an arbitrary Python expression from C?

Call the function PyRun_String() from the previous question with the start symbol eval_input (Py_eval_input starting with 1.5a1); it parses an expression, evaluates it and returns its value.

5.5. How do I extract C values from a Python object?

That depends on the object's type. If it's a tuple, PyTupleSize(o) returns its length and PyTuple_GetItem(o, i) returns its i'th item; similar for lists with PyListSize(o) and PyList_GetItem(o, i). For strings, PyString_Size(o) returns its length and PyString_AsString(o) a pointer to its value (note that Python strings may contain null bytes so strlen() is not safe). To test which type an object is, first make sure it isn't NULL, and then use PyString_Check(o), PyTuple_Check(o), PyList_Check(o), etc.

There is also a high-level API to Python objects which is provided by the so-called 'abstract' interface -- read Include/abstract.h for further details. It allows for example interfacing with any kind of Python sequence (e.g. lists and tuples) using calls like PySequence_Length(), PySequence_GetItem(), etc.) as well as many other useful protocols.

5.6. How do I use Py_BuildValue() to create a tuple of arbitrary length?

You can't. Use t = PyTuple_New(n) instead, and fill it with objects using PyTuple_SetItem(t, i, o) -- note that this "eats" a reference count of o. Similar for lists with PyList_New(n) and PyList_SetItem(l, i, o). Note that you must set all the tuple items to some value before you pass the tuple to Python code -- PyTuple_New(n) initializes them to NULL, which isn't a valid Python value.

5.7. How do I call an object's method from C?

The PyObject_CallMethod() function can be used to call an arbitrary method of an object. The parameters are the object, the name of the method to call, a format string like that used with Py_BuildValue(), and the argument values:

    PyObject *
    PyObject_CallMethod(PyObject *object, char *method_name,
                        char *arg_format, ...);
This works for any object that has methods -- whether built-in or user-defined. You are responsible for eventually DECREF'ing the return value.

To call, e.g., a file object's "seek" method with arguments 10, 0 (assuming the file object pointer is "f"):

        res = PyObject_CallMethod(f, "seek", "(ii)", 10, 0);
        if (res == NULL) {
                ... an exception occurred ...
        else {
Note that since PyObject_CallObject() always wants a tuple for the argument list, to call a function without arguments, pass "()" for the format, and to call a function with one argument, surround the argument in parentheses, e.g. "(i)".

5.8. How do I catch the output from PyErr_Print() (or anything that prints to stdout/stderr)?

(Due to Mark Hammond):

In Python code, define an object that supports the "write()" method. Redirect sys.stdout and sys.stderr to this object. Call print_error, or just allow the standard traceback mechanism to work. Then, the output will go wherever your write() method sends it.

The easiest way to do this is to use the StringIO class in the standard library.

Sample code and use for catching stdout:

	>>> class StdoutCatcher:
	...  def __init__(self):
	... = ''
	...  def write(self, stuff):
	... = + stuff
	>>> import sys
	>>> sys.stdout = StdoutCatcher()
	>>> print 'foo'
	>>> print 'hello world!'
	>>> sys.stderr.write(
	hello world!

5.9. How do I access a module written in Python from C?

You can get a pointer to the module object as follows:

        module = PyImport_ImportModule("<modulename>");
If the module hasn't been imported yet (i.e. it is not yet present in sys.modules), this initializes the module; otherwise it simply returns the value of sys.modules["<modulename>"]. Note that it doesn't enter the module into any namespace -- it only ensures it has been initialized and is stored in sys.modules.

You can then access the module's attributes (i.e. any name defined in the module) as follows:

        attr = PyObject_GetAttrString(module, "<attrname>");
Calling PyObject_SetAttrString(), to assign to variables in the module, also works.

5.10. How do I interface to C++ objects from Python?

Depending on your requirements, there are many approaches. To do this manually, begin by reading the "Extending and Embedding" document (Doc/ext.tex, see also Realize that for the Python run-time system, there isn't a whole lot of difference between C and C++ -- so the strategy to build a new Python type around a C structure (pointer) type will also work for C++ objects.

A useful automated approach (which also works for C) is SWIG:

5.11. mSQLmodule (or other old module) won't build with Python 1.5 (or later)

Since python-1.4 "Python.h" will have the file includes needed in an extension module. Backward compatibility is dropped after version 1.4 and therefore mSQLmodule.c will not build as "allobjects.h" cannot be found. The following change in mSQLmodule.c is harmless when building it with 1.4 and necessary when doing so for later python versions:

Remove lines:

	#include "allobjects.h"
	#include "modsupport.h"
And insert instead:

	#include "Python.h"
You may also need to add

                #include "rename2.h"
if the module uses "old names".

This may happen with other ancient python modules as well, and the same fix applies.

5.12. I added a module using the Setup file and the make fails! Huh?

Setup must end in a newline, if there is no newline there it gets very sad. Aside from this possibility, maybe you have other non-Python-specific linkage problems.

5.13. I want to compile a Python module on my Red Hat Linux system, but some files are missing.

Red Hat's RPM for Python doesn't include the /usr/lib/python1.x/config/ directory, which contains various files required for compiling Python extensions. Install the python-devel RPM to get the necessary files.

5.14. What does "SystemError: _PyImport_FixupExtension: module yourmodule not loaded" mean?

This means that you have created an extension module named "yourmodule", but your module init function does not initialize with that name.

Every module init function will have a line similar to:

  module = Py_InitModule("yourmodule", yourmodule_functions);
If the string passed to this function is not the same name as your extenion module, the SystemError will be raised.

5.15. How to tell "incomplete input" from "invalid input"?

Sometimes you want to emulate the Python interactive interpreter's behavior, where it gives you a continuation prompt when the input is incomplete (e.g. you typed the start of an "if" statement or you didn't close your parentheses or triple string quotes), but it gives you a syntax error message immediately when the input is invalid.

In Python you can use the codeop module, which approximates the parser's behavior sufficiently. IDLE uses this, for example.

The easiest way to do it in C is to call PyRun_InteractiveLoop() (in a separate thread maybe) and let the Python interpreter handle the input for you. You can also set the PyOS_ReadlineFunctionPointer to point at your custom input function. See Modules/readline.c and Parser/myreadline.c for more hints.

However sometimes you have to run the embedded Python interpreter in the same thread as your rest application and you can't allow the PyRun_InteractiveLoop() to stop while waiting for user input. The one solution then is to call PyParser_ParseString() and test for e.error equal to E_EOF (then the input is incomplete). Sample code fragment, untested, inspired by code from Alex Farber:

  #include <Python.h>
  #include <node.h>
  #include <errcode.h>
  #include <grammar.h>
  #include <parsetok.h>
  #include <compile.h>
  int testcomplete(char *code)
    /* code should end in \n */
    /* return -1 for error, 0 for incomplete, 1 for complete */
    node *n;
    perrdetail e;
    n = PyParser_ParseString(code, &_PyParser_Grammar,
                             Py_file_input, &e);
    if (n == NULL) {
      if (e.error == E_EOF) 
        return 0;
      return -1;
    return 1;
Another solution is trying to compile the received string with Py_CompileString(). If it compiles fine - try to execute the returned code object by calling PyEval_EvalCode(). Otherwise save the input for later. If the compilation fails, find out if it's an error or just more input is required - by extracting the message string from the exception tuple and comparing it to the "unexpected EOF while parsing". Here is a complete example using the GNU readline library (you may want to ignore SIGINT while calling readline()):

  #include <stdio.h>
  #include <readline.h>
  #include <Python.h>
  #include <object.h>
  #include <compile.h>
  #include <eval.h>
  int main (int argc, char* argv[])
    int i, j, done = 0;                          /* lengths of line, code */
    char ps1[] = ">>> ";
    char ps2[] = "... ";
    char *prompt = ps1;
    char *msg, *line, *code = NULL;
    PyObject *src, *glb, *loc;
    PyObject *exc, *val, *trb, *obj, *dum;
    Py_Initialize ();
    loc = PyDict_New ();
    glb = PyDict_New ();
    PyDict_SetItemString (glb, "__builtins__", PyEval_GetBuiltins ());
    while (!done)
      line = readline (prompt);
      if (NULL == line)                          /* CTRL-D pressed */
        done = 1;
        i = strlen (line);
        if (i > 0)
          add_history (line);                    /* save non-empty lines */
        if (NULL == code)                        /* nothing in code yet */
          j = 0;
          j = strlen (code);
        code = realloc (code, i + j + 2);
        if (NULL == code)                        /* out of memory */
          exit (1);
        if (0 == j)                              /* code was empty, so */
          code[0] = '\0';                        /* keep strncat happy */
        strncat (code, line, i);                 /* append line to code */
        code[i + j] = '\n';                      /* append '\n' to code */
        code[i + j + 1] = '\0';
        src = Py_CompileString (code, "<stdin>", Py_single_input);       
        if (NULL != src)                         /* compiled just fine - */
          if (ps1  == prompt ||                  /* ">>> " or */
              '\n' == code[i + j - 1])           /* "... " and double '\n' */
          {                                               /* so execute it */
            dum = PyEval_EvalCode ((PyCodeObject *)src, glb, loc);
            Py_XDECREF (dum);
            Py_XDECREF (src);
            free (code);
            code = NULL;
            if (PyErr_Occurred ())
              PyErr_Print ();
            prompt = ps1;
        }                                        /* syntax error or E_EOF? */
        else if (PyErr_ExceptionMatches (PyExc_SyntaxError))           
          PyErr_Fetch (&exc, &val, &trb);        /* clears exception! */
          if (PyArg_ParseTuple (val, "sO", &msg, &obj) &&
              !strcmp (msg, "unexpected EOF while parsing")) /* E_EOF */
            Py_XDECREF (exc);
            Py_XDECREF (val);
            Py_XDECREF (trb);
            prompt = ps2;
          else                                   /* some other syntax error */
            PyErr_Restore (exc, val, trb);
            PyErr_Print ();
            free (code);
            code = NULL;
            prompt = ps1;
        else                                     /* some non-syntax error */
          PyErr_Print ();
          free (code);
          code = NULL;
          prompt = ps1;
        free (line);

5.16. How do I debug an extension?

When using gdb with dynamically loaded extensions, you can't set a breakpoint in your extension until your extension is loaded.

In your .gdbinit file (or interactively), add the command

br _PyImport_LoadDynamicModule

$ gdb /local/bin/python

gdb) run

gdb) continue # repeat until your extension is loaded

gdb) finish # so that your extension is loaded

gdb) br myfunction.c:50

gdb) continue

5.17. How do I find undefined Linux g++ symbols, __builtin_new or __pure_virtural

To dynamically load g++ extension modules, you must recompile python, relink python using g++ (change LINKCC in the python Modules Makefile), and link your extension module using g++ (e.g., "g++ -shared -o mymodule.o").

6. Python's design

6.1. Why isn't there a switch or case statement in Python?

You can do this easily enough with a sequence of if... elif... elif... else. There have been some proposals for switch statement syntax, but there is no consensus (yet) on whether and how to do range tests.

6.2. Why does Python use indentation for grouping of statements?

Basically I believe that using indentation for grouping is extremely elegant and contributes a lot to the clarity of the average Python program. Most people learn to love this feature after a while. Some arguments for it:

Since there are no begin/end brackets there cannot be a disagreement between grouping perceived by the parser and the human reader. I remember long ago seeing a C fragment like this:

        if (x <= y)
and staring a long time at it wondering why y was being decremented even for x > y... (And I wasn't a C newbie then either.)

Since there are no begin/end brackets, Python is much less prone to coding-style conflicts. In C there are loads of different ways to place the braces (including the choice whether to place braces around single statements in certain cases, for consistency). If you're used to reading (and writing) code that uses one style, you will feel at least slightly uneasy when reading (or being required to write) another style. Many coding styles place begin/end brackets on a line by themself. This makes programs considerably longer and wastes valuable screen space, making it harder to get a good overview over a program. Ideally, a function should fit on one basic tty screen (say, 20 lines). 20 lines of Python are worth a LOT more than 20 lines of C. This is not solely due to the lack of begin/end brackets (the lack of declarations also helps, and the powerful operations of course), but it certainly helps!

6.3. Why are Python strings immutable?

There are two advantages. One is performance: knowing that a string is immutable makes it easy to lay it out at construction time -- fixed and unchanging storage requirements. (This is also one of the reasons for the distinction between tuples and lists.) The other is that strings in Python are considered as "elemental" as numbers. No amount of activity will change the value 8 to anything else, and in Python, no amount of activity will change the string "eight" to anything else. (Adapted from Jim Roskind)

6.4. Delete

6.5. Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))?

Functions are used for those operations that are generic for a group of types and which should work even for objects that don't have methods at all (e.g. numbers, strings, tuples). Also, implementing len(), max(), min() as a built-in function is actually less code than implementing them as methods for each type. One can quibble about individual cases but it's really too late to change such things fundamentally now.

6.6. Why can't I derive a class from built-in types (e.g. lists or files)?

This is caused by the relatively late addition of (user-defined) classes to the language -- the implementation framework doesn't easily allow it. See the answer to question 4.2 for a work-around. This may be fixed in the (distant) future.

6.7. Why must 'self' be declared and used explicitly in method definitions and calls?

So, is your current programming language C++ or Java? :-) When classes were added to Python, this was (again) the simplest way of implementing methods without too many changes to the interpreter. The idea was borrowed from Modula-3. It turns out to be very useful, for a variety of reasons.

First, it makes it more obvious that you are using a method or instance attribute instead of a local variable. Reading "self.x" or "self.meth()" makes it absolutely clear that an instance variable or method is used even if you don't know the class definition by heart. In C++, you can sort of tell by the lack of a local variable declaration (assuming globals are rare or easily recognizable) -- but in Python, there are no local variable declarations, so you'd have to look up the class definition to be sure.

Second, it means that no special syntax is necessary if you want to explicitly reference or call the method from a particular class. In C++, if you want to use a method from base class that is overridden in a derived class, you have to use the :: operator -- in Python you can write baseclass.methodname(self, <argument list>). This is particularly useful for __init__() methods, and in general in cases where a derived class method wants to extend the base class method of the same name and thus has to call the base class method somehow.

Lastly, for instance variables, it solves a syntactic problem with assignment: since local variables in Python are (by definition!) those variables to which a value assigned in a function body (and that aren't explicitly declared global), there has to be some way to tell the interpreter that an assignment was meant to assign to an instance variable instead of to a local variable, and it should preferably be syntactic (for efficiency reasons). C++ does this through declarations, but Python doesn't have declarations and it would be a pity having to introduce them just for this purpose. Using the explicit "self.var" solves this nicely. Similarly, for using instance variables, having to write "self.var" means that references to unqualified names inside a method don't have to search the instance's directories.

6.8. Can't you emulate threads in the interpreter instead of relying on an OS-specific thread implementation?

Answer 1: Unfortunately, the interpreter pushes at least one C stack frame for each Python stack frame. Also, extensions can call back into Python at almost random moments. Therefore a complete threads implementation requires thread support for C.

Answer 2: Fortunately, there is Stackless Python, which has a completely redesigned interpreter loop that avoids the C stack. It's still experimental but looks very promising. Although it is binary compatible with standard Python, it's still unclear whether Stackless will make it into the core -- maybe it's just too revolutionary. Stackless Python currently lives here: A microthread implementation that uses it can be found here:

6.9. Why can't lambda forms contain statements?

Python lambda forms cannot contain statements because Python's syntactic framework can't handle statements nested inside expressions.

However, in Python, this is not a serious problem. Unlike lambda forms in other languages, where they add functionality, Python lambdas are only a shorthand notation if you're too lazy to define a function.

Functions are already first class objects in Python, and can be declared in a local scope. Therefore the only advantage of using a lambda form instead of a locally-defined function is that you don't need to invent a name for the function -- but that's just a local variable to which the function object (which is exactly the same type of object that a lambda form yields) is assigned!

6.10. Why don't lambdas have access to variables defined in the containing scope?

Because they are implemented as ordinary functions. See question 4.5 above.

6.11. Why can't recursive functions be defined inside other functions?

See question 4.5 above. But actually recursive functions can be defined in other functions with some trickery.

    def test():
        class factorial:
             def __call__(self, n):
                 if n<=1: return 1
                 return n * self(n-1)
        return factorial()
    fact = test()
The instance created by factorial() above acts like the recursive factorial function.

Mutually recursive functions can be passed to each other as arguments.

6.12. Why is there no more efficient way of iterating over a dictionary than first constructing the list of keys()?

Have you tried it? I bet it's fast enough for your purposes! In most cases such a list takes only a few percent of the space occupied by the dictionary. Apart from the fixed header, the list needs only 4 bytes (the size of a pointer) per key. A dictionary uses 12 bytes per key plus between 30 and 70 percent hash table overhead, plus the space for the keys and values. By necessity, all keys are distinct objects, and a string object (the most common key type) costs at least 20 bytes plus the length of the string. Add to that the values contained in the dictionary, and you see that 4 bytes more per item really isn't that much more memory...

A call to dict.keys() makes one fast scan over the dictionary (internally, the iteration function does exist) copying the pointers to the key objects into a pre-allocated list object of the right size. The iteration time isn't lost (since you'll have to iterate anyway -- unless in the majority of cases your loop terminates very prematurely (which I doubt since you're getting the keys in random order).

I don't expose the dictionary iteration operation to Python programmers because the dictionary shouldn't be modified during the entire iteration -- if it is, there's a small chance that the dictionary is reorganized because the hash table becomes too full, and then the iteration may miss some items and see others twice. Exactly because this only occurs rarely, it would lead to hidden bugs in programs: it's easy never to have it happen during test runs if you only insert or delete a few items per iteration -- but your users will surely hit upon it sooner or later.

6.13. Can Python be compiled to machine code, C or some other language?

Not easily. Python's high level data types, dynamic typing of objects and run-time invocation of the interpreter (using eval() or exec) together mean that a "compiled" Python program would probably consist mostly of calls into the Python run-time system, even for seemingly simple operations like "x+1".

Several projects described in the Python newsgroup or at past Python conferences have shown that this approach is feasible, although the speedups reached so far are only modest (e.g. 2x). JPython uses the same strategy for compiling to Java bytecode. (Jim Hugunin has demonstrated that in combination with whole-program analysis, speedups of 1000x are feasible for small demo programs. See the website for the 1997 Python conference.)

Internally, Python source code is always translated into a "virtual machine code" or "byte code" representation before it is interpreted (by the "Python virtual machine" or "bytecode interpreter"). In order to avoid the overhead of parsing and translating modules that rarely change over and over again, this byte code is written on a file whose name ends in ".pyc" whenever a module is parsed (from a file whose name ends in ".py"). When the corresponding .py file is changed, it is parsed and translated again and the .pyc file is rewritten.

There is no performance difference once the .pyc file has been loaded (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 will generally improve start-up time of Python scripts. If desired, the Lib/ module/script can be used to force creation of valid .pyc files for a given set of modules.

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.

If you are looking for a way to translate Python programs in order to distribute them in binary form, without the need to distribute the interpreter and library as well, have a look at the script in the Tools/freeze directory. This creates a single binary file incorporating your program, the Python interpreter, and those parts of the Python library that are needed by your program. Of course, the resulting binary will only run on the same type of platform as that used to create it.

6.14. How does Python manage memory? Why not full garbage collection?

The details of Python memory management depend on the implementation. The standard Python implementation (the C implementation) uses reference counting memory management. This means that when an object is no longer in use Python frees the object automatically, with a few exceptions.

On the other hand, JPython relies on the Java runtime; so it uses the JVM's garbage collector. This difference can cause some subtle porting problems if your Python code depends on the behavior of the reference counting implementation.

Two exceptions to bear in mind for standard Python are:

1) if the object lies on a circular reference path it won't be freed unless the circularities are broken. EG:

       List = [None]
       List[0] = List
List will not be freed unless the circularity (List[0] is List) is broken. The reason List will not be freed is because although it may become inaccessible the list contains a reference to itself, and reference counting only deallocates an object when all references to an object are destroyed. To break the circular reference path we must destroy the reference, as in

       List[0] = None
So, if your program creates circular references (and if it is long running and/or consumes lots of memory) it may have to do some explicit management of circular structures. In many application domains this is needed rarely, if ever.

CPython 2.0 fixes this problem by periodically executing a cycle detection algorithm which looks for inaccessible cycles and deletes the objects involved. A new gc module provides functions to perform a garbage collection, obtain debugging statistics, and tuning the collector's parameters.

Running the cycle detection algorithm takes some time, and therefore will result in some additional overhead. It is hoped that after we've gotten experience with the cycle collection from using 2.0, Python 2.1 will be able to minimize the overhead with careful tuning. It's not yet obvious how much performance is lost, because benchmarking this is tricky and depends crucially on how often the program creates and destroys objects. The detection of cycles can be disabled when Python is compiled, if you can't afford even a tiny speed penalty or suspect that the cycle collection is buggy, by specifying the "-without-cycle-gc" switch when running the configure script.

2) Sometimes objects get stuck in "tracebacks" temporarily and hence are not deallocated when you might expect. Clear the tracebacks via

       import sys
       sys.exc_traceback = sys.last_traceback = None
Tracebacks are used for reporting errors and implementing debuggers and related things. They contain a portion of the program state extracted during the handling of an exception (usually the most recent exception).

In the absence of circularities and modulo tracebacks, Python programs need not explicitly manage memory.

It is often suggested that Python could benefit from fully general garbage collection. It's looking less and less likely that Python will ever get "automatic" garbage collection (GC). For one thing, unless this were added to C as a standard feature, it's a portability pain in the ass. And yes, I know about the Xerox library. It has bits of assembler code for most common platforms. Not for all. And although it is mostly transparent, it isn't completely transparent (when I once linked Python with it, it dumped core).

"Proper" GC also becomes a problem when Python gets embedded into other applications. While in a stand-alone Python it may be 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.

In JPython, which has garbage collection, the following code (which is fine in C Python) will probably run out of file descriptors long before it runs out of memory:

        for file in <very long list of files>:
                f = open(file)
                c =
Using the current reference counting and destructor scheme, each new assignment to f closes the previous file. Using GC, this is not guaranteed. Sure, you can think of ways to fix this. But it's not off-the-shelf technology. If you want to write code that will work with any Python implementation, you should explicitly close the file; this will work regardless of GC:

       for file in <very long list of files>:
                f = open(file)
                c =

All that said, somebody has managed to add GC to Python using the GC library fromn Xerox, so you can see for yourself. See
See also question 4.17 for ways to plug some common memory leaks manually.

If you're not satisfied with the answers here, before you post to the newsgroup, please read this summary of past discussions on GC for Python by Moshe Zadka:

6.15. Why are there separate tuple and list data types?

This is done so that tuples can be immutable while lists are mutable.

Immutable tuples are useful in situations where you need to pass a few items to a function and don't want the function to modify the tuple; for example,

	point1 = (120, 140)
	point2 = (200, 300)
	record(point1, point2)
	draw(point1, point2)
You don't want to have to think about what would happen if record() changed the coordinates -- it can't, because the tuples are immutable.

On the other hand, when creating large lists dynamically, it is absolutely crucial that they are mutable -- adding elements to a tuple one by one requires using the concatenation operator, which makes it quadratic in time.

As a general guideline, use tuples like you would use structs in C or records in Pascal, use lists like (variable length) arrays.

6.16. How are lists implemented?

Despite what a Lisper might think, Python's lists are really variable-length arrays. The implementation uses a contiguous array of references to other objects, and keeps a pointer to this array (as well as its length) in a list head structure.

This makes indexing a list (a[i]) an operation whose cost is independent of the size of the list or the value of the index.

When items are appended or inserted, the array of references is resized. Some cleverness is applied to improve the performance of appending items repeatedly; when the array must be grown, some extra space is allocated so the next few times don't require an actual resize.

6.17. How are dictionaries implemented?

Python's dictionaries are implemented as resizable hash tables.

Compared to B-trees, this gives better performance for lookup (the most common operation by far) under most circumstances, and the implementation is simpler.

6.18. Why must dictionary keys be immutable?

The hash table implementation of dictionaries uses a hash value calculated from the key value to find the key. If the key were a mutable object, its value could change, and thus its hash could change. But since whoever changes the key object can't tell that is incorporated in a dictionary, it can't move the entry around in the dictionary. Then, when you try to look up the same object in the dictionary, it won't be found, since its hash value is different; and if you try to look up the old value, it won't be found either, since the value of the object found in that hash bin differs.

If you think you need to have a dictionary indexed with a list, try to use a tuple instead. The function tuple(l) creates a tuple with the same entries as the list l.

Some unacceptable solutions that have been proposed:

- Hash lists by their address (object ID). This doesn't work because if you construct a new list with the same value it won't be found; e.g.,

  d = {[1,2]: '12'}
  print d[[1,2]]
will raise a KeyError exception because the id of the [1,2] used in the second line differs from that in the first line. In other words, dictionary keys should be compared using '==', not using 'is'.

- Make a copy when using a list as a key. This doesn't work because the list (being a mutable object) could contain a reference to itself, and then the copying code would run into an infinite loop.

- Allow lists as keys but tell the user not to modify them. This would allow a class of hard-to-track bugs in programs that I'd rather not see; it invalidates an important invariant of dictionaries (every value in d.keys() is usable as a key of the dictionary).

- Mark lists as read-only once they are used as a dictionary key. The problem is that it's not just the top-level object that could change its value; you could use a tuple containing a list as a key. Entering anything as a key into a dictionary would require marking all objects reachable from there as read-only -- and again, self-referential objects could cause an infinite loop again (and again and again).

There is a trick to get around this if you need to, but use it at your own risk: You can wrap a mutable structure inside a class instance which has both a __cmp__ and a __hash__ method.

   class listwrapper:
        def __init__(self, the_list):
              self.the_list = the_list
        def __cmp__(self, other):
              return self.the_list == other.the_list
        def __hash__(self):
              l = self.the_list
              result = 98767 - len(l)*555
              for i in range(len(l)):
                        result = result + (hash(l[i]) % 9999999) * 1001 + i
                        result = (result % 7777777) + i * 333
              return result
Note that the hash computation is complicated by the possibility that some members of the list may be unhashable and also by the possibility of arithmetic overflow.

You must make sure that the hash value for all such wrapper objects that reside in a dictionary (or other hash based structure), remain fixed while the object is in the dictionary (or other structure).

Furthermore it must always be the case that if o1 == o2 (ie o1.__cmp__(o2)==0) then hash(o1)==hash(o2) (ie, o1.__hash__() == o2.__hash__()), regardless of whether the object is in a dictionary or not. If you fail to meet these restrictions dictionaries and other hash based structures may misbehave!

In the case of listwrapper above whenever the wrapper object is in a dictionary the wrapped list must not change to avoid anomalies. Don't do this unless you are prepared to think hard about the requirements and the consequences of not meeting them correctly. You've been warned!

6.19. How the heck do you make an array in Python?

["this", 1, "is", "an", "array"]

Lists are arrays in the C or Pascal sense of the word (see question 6.16). The array module also provides methods for creating arrays of fixed types with compact representations (but they are slower to index than lists). Also note that the Numerics extensions and others define array-like structures with various characteristics as well.

To get Lisp-like lists, emulate cons cells

    lisp_list = ("like",  ("this",  ("example", None) ) )
using tuples (or lists, if you want mutability). Here the analogue of lisp car is lisp_list[0] and the analogue of cdr is lisp_list[1]. Only do this if you're sure you really need to (it's usually a lot slower than using Python lists).

Think of Python lists as mutable heterogeneous arrays of Python objects (say that 10 times fast :) ).

6.20. Why doesn't list.sort() return the sorted list?

In situations where performance matters, making a copy of the list just to sort it would be wasteful. Therefore, list.sort() sorts the list in place. In order to remind you of that fact, it does not return the sorted list. This way, you won't be fooled into accidentally overwriting a list when you need a sorted copy but also need to keep the unsorted version around.

As a result, here's the idiom to iterate over the keys of a dictionary in sorted order:

	keys = dict.keys()
	for key in keys: whatever with dict[key]...

6.21. How do you specify and enforce an interface spec in Python?

An interfaces specification for a module as provided by languages such as C++ and java describes the prototypes for the methods and functions of the module. Many feel that compile time enforcement of interface specifications help aid in the construction of large programs. Python does not support interface specifications directly, but many of their advantages can be obtained by an appropriate test discipline for components, which can often be very easily accomplished in Python.

A good test suite for a module can at once provide a regression test and serve as a module interface specification (even better since it also gives example usage). Look to many of the standard libraries which often have a "script interpretation" which provides a simple "self test." Even modules which use complex external interfaces can often be tested in isolation using trivial "stub" emulations of the external interface.

An appropriate testing discipline (if enforced) can help build large complex applications in Python as well as having interface specifications would do (or better). Of course Python allows you to get sloppy and not do it. Also you might want to design your code with an eye to make it easily tested.

6.22. Why do all classes have the same type? Why do instances all have the same type?

The Pythonic use of the word "type" is quite different from common usage in much of the rest of the programming language world. A "type" in Python is a description for an object's operations as implemented in C. All classes have the same operations implemented in C which sometimes "call back" to differing program fragments implemented in Python, and hence all classes have the same type. Similarly at the C level all class instances have the same C implementation, and hence all instances have the same type.

Remember that in Python usage "type" refers to a C implementation of an object. To distinguish among instances of different classes use Instance.__class__, and also look to 4.47. Sorry for the terminological confusion, but at this point in Python's development nothing can be done!

6.23. Why isn't all memory freed when Python exits?

Objects referenced from Python module global name spaces are not always deallocated when Python exits.

This may happen if there are circular references (see question 4.17). There are also certain bits of memory that are allocated by the C library that are impossible to free (e.g. a tool like Purify will complain about these).

But in general, Python 1.5 and beyond (in contrast with earlier versions) is quite agressive about cleaning up memory on exit.

If you want to force Python to delete certain things on deallocation use the sys.exitfunc hook to force those deletions. For example if you are debugging an extension module using a memory analysis tool and you wish to make Python deallocate almost everything you might use an exitfunc like this one:

  import sys
  def my_exitfunc():
       print "cleaning up"
       import sys
       # do order dependant deletions here
       # now delete everything else in arbitrary order
       for x in sys.modules.values():
            d = x.__dict__
            for name in d.keys():
                 del d[name]
  sys.exitfunc = my_exitfunc
Other exitfuncs can be less drastic, of course.

(In fact, this one just does what Python now already does itself; but the example of using sys.exitfunc to force cleanups is still useful.)

6.24. Why no class methods or mutable class variables?

The notation

    instance.attribute(arg1, arg2)
usually translates to the equivalent of

    Class.attribute(instance, arg1, arg2)
where Class is a (super)class of instance. Similarly

    instance.attribute = value
sets an attribute of an instance (overriding any attribute of a class that instance inherits).

Sometimes programmers want to have different behaviours -- they want a method which does not bind to the instance and a class attribute which changes in place. Python does not preclude these behaviours, but you have to adopt a convention to implement them. One way to accomplish this is to use "list wrappers" and global functions.

   def C_hello():
         print "hello"
   class C:
        hello = [C_hello]
        counter = [0]
    I = C()
Here I.hello[0]() acts very much like a "class method" and I.counter[0] = 2 alters C.counter (and doesn't override it). If you don't understand why you'd ever want to do this, that's because you are pure of mind, and you probably never will want to do it! This is dangerous trickery, not recommended when avoidable. (Inspired by Tim Peter's discussion.)

6.25. Why are default values sometimes shared between objects?

It is often expected that a function CALL creates new objects for default values. This is not what happens. Default values are created when the function is DEFINED, that is, there is only one such object that all functions refer to. If that object is changed, subsequent calls to the function will refer to this changed object. By definition, immutable objects (like numbers, strings, tuples, None) are safe from change. Changes to mutable objects (like dictionaries, lists, class instances) is what causes the confusion.

Because of this feature it is good programming practice not to use mutable objects as default values, but to introduce them in the function. Don't write:

	def foo(dict={}):  # XXX shared reference to one dict for all calls
	def foo(dict=None):
		if dict is None:
			dict = {} # create a new dict for local namespace
See page 182 of "Internet Programming with Python" for one discussion of this feature. Or see the top of page 144 or bottom of page 277 in "Programming Python" for another discussion.

6.26. Why no goto?

Actually, you can use exceptions to provide a "structured goto" that even works across function calls. Many feel that exceptions can conveniently emulate all reasonable uses of the "go" or "goto" constructs of C, Fortran, and other languages. For example:

   class label: pass # declare a label
        if (condition): raise label() # goto label
   except label: # where to goto
This doesn't allow you to jump into the middle of a loop, but that's usually considered an abuse of goto anyway. Use sparingly.

6.27. How do you make a higher order function in Python?

You have two choices: you can use default arguments and override them or you can use "callable objects." For example suppose you wanted to define linear(a,b) which returns a function f where f(x) computes the value a*x+b. Using default arguments:

     def linear(a,b):
         def result(x, a=a, b=b):
             return a*x + b
         return result
Or using callable objects:

     class linear:
        def __init__(self, a, b):
            self.a, self.b = a,b
        def __call__(self, x):
            return self.a * x + self.b
In both cases:

     taxes = linear(0.3,2)
gives a callable object where taxes(10e6) == 0.3 * 10e6 + 2.

The defaults strategy has the disadvantage that the default arguments could be accidentally or maliciously overridden. The callable objects approach has the disadvantage that it is a bit slower and a bit longer. Note however that a collection of callables can share their signature via inheritance. EG

      class exponential(linear):
         # __init__ inherited
         def __call__(self, x):
             return self.a * (x ** self.b)
On comp.lang.python, [email protected] points out that an object can encapsulate state for several methods in order to emulate the "closure" concept from functional programming languages, for example:

    class counter:
        value = 0
        def set(self, x): self.value = x
        def up(self): self.value=self.value+1
        def down(self): self.value=self.value-1
    count = counter()
    inc, dec, reset = count.up, count.down, count.set
Here inc, dec and reset act like "functions which share the same closure containing the variable count.value" (if you like that way of thinking).

6.28. Why do I get a SyntaxError for a 'continue' inside a 'try'?

This is an implementation limitation, caused by the extremely simple-minded way Python generates bytecode. The try block pushes something on the "block stack" which the continue would have to pop off again. The current code generator doesn't have the data structures around so that 'continue' can generate the right code.

Note that JPython doesn't have this restriction!

6.29. Why can't raw strings (r-strings) end with a backslash?

More precisely, they can't end with an odd number of backslashes: the unpaired backslash at the end escapes the closing quote character, leaving an unterminated string.

Raw strings were designed to ease creating input for processors (chiefly regular expression engines) that want to do their own backslash escape processing. Such processors consider an unmatched trailing backslash to be an error anyway, so raw strings disallow that. In return, they allow you to pass on the string quote character by escaping it with a backslash. These rules work well when r-strings are used for their intended purpose.

If you're trying to build Windows pathnames, note that all Windows system calls accept forward slashes too:

    f = open("/mydir/file.txt") # works fine!
If you're trying to build a pathname for a DOS command, try e.g. one of

    dir = r"\this\is\my\dos\dir" "\\"
    dir = r"\this\is\my\dos\dir\ "[:-1]
    dir = "\\this\\is\\my\\dos\\dir\\"

6.30. Why can't I use an assignment in an expression?

Many people used to C or Perl complain that they want to be able to use e.g. this C idiom:

    while (line = readline(f)) { something with line...
where in Python you're forced to write this:

    while 1:
        line = f.readline()
        if not line:
            break something with line...
This issue comes up in the Python newsgroup with alarming frequency -- search Deja News for past messages about assignment expression. The reason for not allowing assignment in Python expressions is a common, hard-to-find bug in those other languages, caused by this construct:

    if (x = 0) {
        ...error handling...
    else {
        ...code that only works for nonzero x...
Many alternatives have been proposed. Most are hacks that save some typing but use arbitrary or cryptic syntax or keywords, and fail the simple criterion that I use for language change proposals: it should intuitively suggest the proper meaning to a human reader who has not yet been introduced with the construct.

The earliest time something can be done about this will be with Python 2.0 -- if it is decided that it is worth fixing. An interesting phenomenon is that most experienced Python programmers recognize the "while 1" idiom and don't seem to be missing the assignment in expression construct much; it's only the newcomers who express a strong desire to add this to the language.

One fairly elegant solution would be to introduce a new operator for assignment in expressions spelled ":=" -- this avoids the "=" instead of "==" problem. It would have the same precedence as comparison operators but the parser would flag combination with other comparisons (without disambiguating parentheses) as an error.

Finally -- there's an alternative way of spelling this that seems attractive but is generally less robust than the "while 1" solution:

    line = f.readline()
    while line: something with line...
        line = f.readline()
The problem with this is that if you change your mind about exactly how you get the next line (e.g. you want to change it into sys.stdin.readline()) you have to remember to change two places in your program -- the second one hidden at the bottom of the loop.

7. Using Python on non-UNIX platforms

7.1. Is there a Mac version of Python?

Yes, see the "mac" subdirectory of the distribution sites, e.g.

7.2. Are there DOS and Windows versions of Python?

Yes. The core windows binaries are available from There is a plethora of Windows extensions available, including a large number of not-always-compatible GUI toolkits. The core binaries include the standard Tkinter GUI extension.

Most windows extensions can be found (or referenced) at

Windows 3.1/DOS support seems to have dropped off recently. You may need to settle for an old version of Python one these platforms. One such port is WPY

WPY: Ports to DOS, Windows 3.1(1), Windows 95, Windows NT and OS/2. Also contains a GUI package that offers portability between Windows (not DOS) and Unix, and native look and feel on both.

7.3. Is there an OS/2 version of Python?

Yes, see

7.4. Is there a VMS version of Python?

Yes, there is a port of Python 1.4 to OpenVMS and a few ports of 1.2 to VMS. See

Uwe Zessin has ported Python 1.5.x to OpenVMS. See

7.5. What about IBM mainframes, or other non-UNIX platforms?

I haven't heard about these, except I remember hearing about an OS/9 port and a port to Vxworks (both operating systems for embedded systems). If you're interested in any of this, go directly to the newsgroup and ask there, you may find exactly what you need. For example, a port to MPE/iX 5.0 on HP3000 computers was just announced, see

7.6. Where are the source or Makefiles for the non-UNIX versions?

The standard sources can (almost) be used. Additional sources can be found in the platform-specific subdirectories of the distribution.

7.7. What is the status and support for the non-UNIX versions?

I don't have access to most of these platforms, so in general I am dependent on material submitted by volunteers. However I strive to integrate all changes needed to get it to compile on a particular platform back into the standard sources, so porting of the next version to the various non-UNIX platforms should be easy.

7.8. I have a PC version but it appears to be only a binary. Where's the library?

If you are running any version of Windows, then you have the wrong distribution. The FAQ lists current Windows versions. Notably, Pythonwin and wpy provide fully functional installations.

But if you are sure you have the only distribution with a hope of working on your system, then...

You still need to copy the files from the distribution directory "python/Lib" to your system. If you don't have the full distribution, you can get the file lib<version>.tar.gz from most ftp sites carrying Python; this is a subset of the distribution containing just those files, e.g.

Once you have installed the library, you need to point sys.path to it. Assuming the library is in C:\misc\python\lib, the following commands will point your Python interpreter to it (note the doubled backslashes -- you can also use single forward slashes instead):

        >>> import sys
        >>> sys.path.insert(0, 'C:\\misc\\python\\lib')
For a more permanent effect, set the environment variable PYTHONPATH, as follows (talking to a DOS prompt):

        C> SET PYTHONPATH=C:\misc\python\lib

7.9. Where's the documentation for the Mac or PC version?

The documentation for the Unix version also applies to the Mac and PC versions. Where applicable, differences are indicated in the text.

7.10. How do I create a Python program file on the Mac or PC?

Use an external editor. On the Mac, BBEdit seems to be a popular no-frills text editor. I work like this: start the interpreter; edit a module file using BBedit; import and test it in the interpreter; edit again in BBedit; then use the built-in function reload() to re-read the imported module; etc. In the 1.4 distribution you will find a BBEdit extension that makes life a little easier: it can tell the interpreter to execute the current window. See :Mac:Tools:BBPy:README.

Regarding the same question for the PC, Kurt Wm. Hemr writes: "While anyone with a pulse could certainly figure out how to do the same on MS-Windows, I would recommend the NotGNU Emacs clone for MS-Windows. Not only can you easily resave and "reload()" from Python after making changes, but since WinNot auto-copies to the clipboard any text you select, you can simply select the entire procedure (function) which you changed in WinNot, switch to QWPython, and shift-ins to reenter the changed program unit."

If you're using Windows95 or Windows NT, you should also know about PythonWin, which provides a GUI framework, with an mouse-driven editor, an object browser, and a GUI-based debugger. See
for details.

7.11. How can I use Tkinter on Windows 95/NT?

Starting from Python 1.5, it's very easy -- just download and install Python and Tcl/Tk and you're in business. See
One warning: don't attempt to use Tkinter from PythonWin (Mark Hammond's IDE). Use it from the command line interface (python.exe) or the windowless interpreter (pythonw.exe).

7.12. (or other CGI programming) doesn't work sometimes on NT or win95!

Be sure you have the latest python.exe, that you are using python.exe rather than a GUI version of python and that you have configured the server to execute

     "...\python.exe -u ..."
for the cgi execution. The -u (unbuffered) option on NT and win95 prevents the interpreter from altering newlines in the standard input and output. Without it post/multipart requests will seem to have the wrong length and binary (eg, GIF) responses may get garbled (resulting in, eg, a "broken image").

7.13. Why doesn't os.popen() work in PythonWin on NT?

The reason that os.popen() doesn't work from within PythonWin is due to a bug in Microsoft's C Runtime Library (CRT). The CRT assumes you have a Win32 console attached to the process.

You should use the win32pipe module's popen() instead which doesn't depend on having an attached Win32 console.


 import win32pipe
 f = win32pipe.popen('dir /c c:\\')
 print f.readlines()

7.14. How do I use different functionality on different platforms with the same program?

Remember that Python is extremely dynamic and that you can use this dynamism to configure a program at run-time to use available functionality on different platforms. For example you can test the sys.platform and import different modules based on its value.

   import sys
   if sys.platform == "win32":
      import win32pipe
      popen = win32pipe.popen
      import os
      popen = os.popen
(See FAQ 7.13 for an explanation of why you might want to do something like this.) Also you can try to import a module and use a fallback if the import fails:

         import really_fast_implementation
         choice = really_fast_implementation
    except ImportError:
         import slower_implementation
         choice = slower_implementation

7.15. Is there an Amiga version of Python?

Yes. See the AmigaPython homepage at

7.16. Why doesn't os.popen()/win32pipe.popen() work on Win9x?

There is a bug in Win9x that prevents os.popen/win32pipe.popen* from working. The good news is there is a way to work around this problem. The Microsoft Knowledge Base article that you need to lookup is: Q150956. You will find links to the knowledge base at:

8. Python on Windows

8.1. Using Python for CGI on Microsoft Windows

Setting up the Microsoft IIS Server/Peer Server:

On the Microsoft IIS server or on the Win95 MS Personal Web Server you set up python in the same way that you would set up any other scripting engine.

Run regedt32 and go to:


and enter the following line (making any specific changes that your system may need)

.py :REG_SZ: c:\<path to python>\python.exe -u %s %s

This line will allow you to call your script with a simple reference like: http://yourserver/scripts/ provided "scripts" is an "executable" directory for your server (which it usually is by default). The "-u" flag specifies unbuffered and binary mode for stdin - needed when working with binary data

In addition, it is recommended by people who would know that using ".py" may not be a good idea for the file extensions when used in this context (you might want to reserve *.py for support modules and use *.cgi or *.cgp for "main program" scripts). However, that issue is beyond this Windows FAQ entry.

Netscape Servers: Information on this topic exists at:

8.2. How to check for a keypress without blocking?

Use the msvcrt module. This is a standard Windows-specific extensions in Python 1.5 and beyond. It defines a function kbhit() which checks whether a keyboard hit is present; also getch() which gets one character without echo. Plus a few other goodies.

(Search for "keypress" to find an answer for Unix as well.)


In MS-DOS derived environments, a unix variable such as $PYTHONPATH is set as PYTHONPATH, without the dollar sign. PYTHONPATH is useful for specifying the location of library files.

8.4. dedent syntax errors *

The FAQ does not recommend using tabs, and Guido's Python Style Guide recommends 4 spaces for distributed Python code; this is also the Emacs python-mode default; see
Under any editor mixing tabs and spaces is a bad idea. MSVC is no different in this respect, and is easily configured to use spaces: Take Tools -> Options -> Tabs, and for file type "Default" set "Tab size" and "Indent size" to 4, and select the "Insert spaces" radio button.

If you suspect mixed tabs and spaces are causing problems in leading whitespace, run Python with the -t switch or, run Tools/Scripts/ to check a directory tree in batch mode.

8.5. How do I emulate os.kill() in Windows?

Use win32api:

    def kill(pid):
        """kill function for Win32"""
        import win32api
        handle = win32api.OpenProcess(1, 0, pid)
        return (0 != win32api.TerminateProcess(handle, 0))

8.6. Why does os.path.isdir() fail on NT shared directories?

The solution appears to be always append the "\\" on the end of shared drives.

  >>> import os
  >>> os.path.isdir( '\\\\rorschach\\public')
  >>> os.path.isdir( '\\\\rorschach\\public\\')
[Blake Winton responds:] I've had the same problem doing "Start >> Run" and then a directory on a shared drive. If I use "\\rorschach\public", it will fail, but if I use "\\rorschach\public\", it will work. For that matter, os.stat() does the same thing (well, it gives an error for "\\\\rorschach\\public", but you get the idea)...

I've got a theory about why this happens, but it's only a theory. NT knows the difference between shared directories, and regular directories. "\\rorschach\public" isn't a directory, it's _really_ an IPC abstraction. This is sort of lended credence to by the fact that when you're mapping a network drive, you can't map "\\rorschach\public\utils", but only "\\rorschach\public".

[Clarification by [email protected]] It's not actually a Python question, as Python is working just fine; it's clearing up something a bit muddled about Windows networked drives.

It helps to think of share points as being like drive letters. Example:

        k: is not a directory
        k:\ is a directory
        k:\media is a directory
        k:\media\ is not a directory
The same rules apply if you substitute "k:" with "\\conky\foo":
        \\conky\foo  is not a directory
        \\conky\foo\ is a directory
        \\conky\foo\media is a directory
        \\conky\foo\media\ is not a directory

8.7. PyRun_SimpleFile() crashes on Windows but not on Unix

I've seen a number of reports of PyRun_SimpleFile() failing in a Windows port of an application embedding Python that worked fine on Unix. PyRun_SimpleString() works fine on both platforms.

I think this happens because the application was compiled with a different set of compiler flags than Python15.DLL. It seems that some compiler flags affect the standard I/O library in such a way that using different flags makes calls fail. You need to set it for the non-debug multi-threaded DLL (/MD on the command line, or can be set via MSVC under Project Settings->C++/Code Generation then the "Use rum-time library" dropdown.)

Also note that you can not mix-and-match Debug and Release versions. If you wish to use the Debug Multithreaded DLL, then your module _must_ have an "_d" appended to the base name.

8.8. Import of _tkinter fails on Windows 95/98

Sometimes, the import of _tkinter fails on Windows 95 or 98, complaining with a message like the following:

  ImportError: DLL load failed: One of the library files needed
  to run this application cannot be found.
It could be that you haven't installed Tcl/Tk, but if you did install Tcl/Tk, and the Wish application works correctly, the problem may be that its installer didn't manage to edit the autoexec.bat file correctly. It tries to add a statement that changes the PATH environment variable to include the Tcl/Tk 'bin' subdirectory, but sometimes this edit doesn't quite work. Opening it with notepad usually reveals what the problem is.

(One additional hint, noted by David Szafranski: you can't use long filenames here; e.g. use C:\PROGRA~1\Tcl\bin instead of C:\Program Files\Tcl\bin.)

8.9. Can't extract the downloaded documentation on Windows

Sometimes, when you download the documentation package to a Windows machine using a web browser, the file extension of the saved file ends up being .EXE. This is a mistake; the extension should be .TGZ.

Simply rename the downloaded file to have the .TGZ extension, and WinZip will be able to handle it. (If your copy of WinZip doesn't, get a newer one from

8.10. Can't get Py_RunSimpleFile() to work.

This is very sensitive to the compiler vendor, version and (perhaps) even options. If the FILE* structure in your embedding program isn't the same as is assumed by the Python interpreter it won't work.

The Python 1.5.* DLLs (python15.dll) are all compiled with MS VC++ 5.0 and with multithreading-DLL options (/MD, I think).

If you can't change compilers or flags, try using Py_RunSimpleString(). A trick to get it to run an arbitrary file is to construct a call to execfile() with the name of your file as argument.

8.11. Where is Freeze for Windows?

("Freeze" is a program that allows you to ship a Python program as a single stand-alone executable file. It is not a compiler, your programs don't run any faster, but they are more easily distributable (to platforms with the same OS and CPU). Read the README file of the freeze program for more disclaimers.)

You can use freeze on Windows, but you must download the source tree (see This is recommended for Python 1.5.2 (and betas thereof) only; older versions don't quite work.

You need the Microsoft VC++ 5.0 compiler (maybe it works with 6.0 too). You probably need to build Python -- the project files are all in the PCbuild directory.

The freeze program is in the Tools\freeze subdirectory of the source tree.

8.12. Is a *.pyd file the same as a DLL?

Yes, .pyd files are dll's. But there are a few differences. If you have a DLL named foo.pyd, then it must have a function initfoo(). You can then write Python "import foo", and Python will search for foo.pyd (as well as, foo.pyc) and if it finds it, will attempt to call initfoo() to initialize it. You do not link your .exe with foo.lib, as that would cause Windows to require the DLL to be present.

Note that the search path for foo.pyd is PYTHONPATH, not the same as the path that Windows uses to search for foo.dll. Also, foo.pyd need not be present to run your program, whereas if you linked your program with a dll, the dll is required. Of course, foo.pyd is required if you want to say "import foo". In a dll, linkage is declared in the source code with __declspec(dllexport). In a .pyd, linkage is defined in a list of available functions.

8.13. Missing cw3215mt.dll (or missing cw3215.dll)

Sometimes, when using Tkinter on Windows, you get an error that cw3215mt.dll or cw3215.dll is missing.

Cause: you have an old Tcl/Tk DLL built with cygwin in your path (probably C:\Windows). You must use the Tcl/Tk DLLs from the standard Tcl/Tk installation (Python 1.5.2 comes with one).

8.14. How to make python scripts executable:

[Blake Coverett]


The standard installer already associates the .py extension with a file type (Python.File) and gives that file type an open command that runs the interpreter (D:\Program Files\Python\python.exe "%1" %*). This is enough to make scripts executable from the command prompt as ''. If you'd rather be able to execute the script by simple typing 'foo' with no extension you need to add .py to the PATHEXT environment variable.


The steps taken by the installed as described above allow you do run a script with '', but a long time bug in the NT command processor prevents you from redirecting the input or output of any script executed in this way. This is often important.

An appropriate incantation for making a Python script executable under WinNT is to give the file an extension of .cmd and add the following as the first line:

    @setlocal enableextensions & python -x %~f0 %* & goto :EOF

[Due to Bruce Eckel]

  @echo off
  rem = """
  rem run python on this bat file. Needs the full path where
  rem you keep your python files. The -x causes python to skip
  rem the first line of the file:
  python -x c:\aaa\Python\\"%0".bat %1 %2 %3 %4 %5 %6 %7 %8 %9
  goto endofpython
  rem """
  # The python program goes here:
  print "hello, Python"
  # For the end of the batch file:
  rem = """
  rem """

8.15. Warning about CTL3D32 version from installer

The Python installer issues a warning like this:

  This version uses CTL3D32.DLL whitch is not the correct version.
  This version is used for windows NT applications only.
[Tim Peters] This is a Microsoft DLL, and a notorious source of problems. The msg means what it says: you have the wrong version of this DLL for your operating system. The Python installation did not cause this -- something else you installed previous to this overwrote the DLL that came with your OS (probably older shareware of some sort, but there's no way to tell now). If you search for "CTL3D32" using any search engine (AltaVista, for example), you'll find hundreds and hundreds of web pages complaining about the same problem with all sorts of installation programs. They'll point you to ways to get the correct version reinstalled on your system (since Python doesn't cause this, we can't fix it).

David A Burton has written a little program to fix this. Go to and click on ""

8.16. How can I embed Python into a Windows application?

Edward K. Ream <[email protected]> writes

Embedding the Python interpreter in a Windows app can be summarized as follows:

1. Do _not_ build Python into your .exe file directly. On Windows, Python must be a DLL to handle importing modules that are themselves DLL's. (This is the first key undocumented fact.) Instead, link to python15.dll; it is typically installed in c:\Windows\System.

You can link to Python statically or dynamically. Linking statically means linking against python15.lib The drawback is that your app won't run if python15.dll does not exist on your system.

General note: python15.lib is the so-called "import lib" corresponding to python.dll. It merely defines symbols for the linker.

Borland note: convert python15.lib to OMF format using Coff2Omf.exe first.

Linking dynamically greatly simplifies link options; everything happens at run time. Your code must load python15.dll using the Windows LoadLibraryEx routine. The code must also use access routines and data in python15.dll (that is, Python's C API's) using pointers obtained by the Windows GetProcAddress routine. Macros can make using these pointers transparent to any C code that calls routines in Python's C API.

2. If you use SWIG, it is easy to create a Python "extension module" that will make the app's data and methods available to Python. SWIG will handle just about all the grungy details for you. The result is C code that you link _into your .exe file_ (!) You do _not_ have to create a DLL file, and this also simplifies linking.

3. SWIG will create an init function (a C function) whose name depends on the name of the extension module. For example, if the name of the module is leo, the init function will be called initleo(). If you use SWIG shadow classes, as you should, the init function will be called initleoc(). This initializes a mostly hidden helper class used by the shadow class.

The reason you can link the C code in step 2 into your .exe file is that calling the initialization function is equivalent to importing the module into Python! (This is the second key undocumented fact.)

4. In short, you can use the following code to initialize the Python interpreter with your extension module.

    #include "python.h"
    Py_Initialize();  // Initialize Python.
    initmyAppc();  // Initialize (import) the helper class. 
    PyRun_SimpleString("import myApp") ;  // Import the shadow class.
5. There are two problems with Python's C API which will become apparent if you use a compiler other than MSVC, the compiler used to build python15.dll.

Problem 1: The so-called "Very High Level" functions that take FILE * arguments will not work in a multi-compiler environment; each compiler's notion of a struct FILE will be different. Warnings should be added to the Python documentation! From an implementation standpoint these are very _low_ level functions.

Problem 2: SWIG generates the following code when generating wrappers to void functions:

    _resultobj = Py_None;
    return _resultobj;
Alas, Py_None is a macro that expands to a reference to a complex data structure called _Py_NoneStruct inside python15.dll. Again, this code will fail in a mult-compiler environment. Replace such code by:

        return Py_Build("");
It may be possible to use SWIG's %typemap command to make the change automatically, though I have not been able to get this to work (I'm a complete SWIG newbie.)

6. Using a Python shell script to put up a Python interpreter window from inside your Windows app is not a good idea; the resulting window will be independent of your app's windowing system. Rather, you (or the wxPythonWindow class) should create a "native" interpreter window. It is easy to connect that window to the Python interpreter. You can redirect Python's i/o to _any_ object that supports read and write, so all you need is a Python object (defined in your extension module) that contains read and write methods.