
Library and Extension FAQ
*************************


General Library Questions
=========================


How do I find a module or application to perform task X?
--------------------------------------------------------

Check *the Library Reference* to see if there's a relevant standard
library module.  (Eventually you'll learn what's in the standard
library and will be able to skip this step.)

For third-party packages, search the Python Package Index or try
Google or another Web search engine.  Searching for "Python" plus a
keyword or two for your topic of interest will usually find something
helpful.


Where is the math.py (socket.py, regex.py, etc.) source file?
-------------------------------------------------------------

If you can't find a source file for a module it may be a built-in 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).

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


How do I make a Python script executable on Unix?
-------------------------------------------------

You need to do two things: the script file's mode must be executable
and the first line must begin with ``#!`` followed by the path of 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 ways.  The most straightforward
way is to write

   #!/usr/local/bin/python

as the very first line of your file, using the pathname for 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.  Almost all Unix
variants support the following, assuming the Python interpreter is in
a directory on the user's ``PATH``:

   #!/usr/bin/env python

*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 minor disadvantage is that this defines the script's __doc__
string. However, you can fix that by adding

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


Is there a curses/termcap package for Python?
---------------------------------------------

For Unix variants the standard Python source distribution comes with a
curses module in the Modules subdirectory, though it's not compiled by
default. (Note that this is not available in the Windows distribution
-- there is no curses module for Windows.)

The ``curses`` module supports basic curses features as well as many
additional functions from ncurses and SYSV curses such as colour,
alternative character set support, pads, and mouse support. This means
the module isn't 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.

For Windows: use the consolelib module.


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

The ``atexit`` module provides a register function that is similar to
C's ``onexit()``.


Why don't my signal handlers 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):
       ...


Common tasks
============


How do I test a Python program or component?
--------------------------------------------

Python comes with two testing frameworks.  The ``doctest`` module
finds examples in the docstrings for a module and runs them, comparing
the output with the expected output given in the docstring.

The ``unittest`` module is a fancier testing framework modelled on
Java and Smalltalk testing frameworks.

To make testing easier, you should use good modular design in your
program. 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__":
       main_logic()

at the bottom of the main module of your program.

Once your program is organized as a tractable collection of functions
and class behaviours you should write test functions that exercise the
behaviours.  A test suite that automates a sequence of tests can be
associated with each module. 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 self-test of the module.

   if __name__ == "__main__":
       self_test()

Even programs that interact with complex external interfaces may be
tested when the external interfaces are unavailable by using "fake"
interfaces implemented in Python.


How do I create documentation from doc strings?
-----------------------------------------------

The ``pydoc`` module can create HTML from the doc strings in your
Python source code.  An alternative for creating API documentation
purely from docstrings is epydoc.  Sphinx can also include docstring
content.


How do I get a single keypress at a time?
-----------------------------------------

For Unix variants there are several solutions.  It's straightforward
to do this using curses, but curses is a fairly large module to learn.
Here's a solution without curses:

   import termios, 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)
   fcntl.fcntl(fd, fcntl.F_SETFL, oldflags | os.O_NONBLOCK)

   try:
       while 1:
           try:
               c = sys.stdin.read(1)
               print "Got character", repr(c)
           except IOError: pass
   finally:
       termios.tcsetattr(fd, termios.TCSAFLUSH, oldterm)
       fcntl.fcntl(fd, fcntl.F_SETFL, oldflags)

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.


Threads
=======


How do I program using threads?
-------------------------------

Be sure to use the ``threading`` module and not the ``thread`` module.
The ``threading`` module builds convenient abstractions on top of the
low-level primitives provided by the ``thread`` module.

Aahz has a set of slides from his threading tutorial that are helpful;
see http://www.pythoncraft.com/OSCON2001/.


None of my threads seem to run: why?
------------------------------------

As soon as the main thread exits, all threads are killed.  Your main
thread is running too quickly, giving the threads no time to do any
work.

A simple fix is to add a sleep to the end of the program that's long
enough for all the threads to finish:

   import threading, time

   def thread_task(name, n):
       for i in range(n): print name, i

   for i in range(10):
       T = threading.Thread(target=thread_task, args=(str(i), i))
       T.start()

   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:

   def thread_task(name, n):
       time.sleep(0.001) # <---------------------!
       for i in range(n): print name, i

   for i in range(10):
       T = threading.Thread(target=thread_task, args=(str(i), i))
       T.start()

   time.sleep(10)

Instead of trying to guess a good delay value for ``time.sleep()``,
it's better to use some kind of semaphore mechanism.  One idea is to
use 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.


How do I parcel out work among a bunch of worker threads?
---------------------------------------------------------

Use the ``Queue`` module to create a queue containing a list of jobs.
The ``Queue`` class maintains a list of objects and has a
``.put(obj)`` method that adds items to the queue and a ``.get()``
method to return them. The class will take care of the locking
necessary to ensure that each job is handed out exactly once.

Here's a trivial example:

   import threading, Queue, time

   # The worker thread gets jobs off the queue.  When the queue is empty, it
   # assumes there will be no more work and exits.
   # (Realistically workers will run until terminated.)
   def worker():
       print 'Running worker'
       time.sleep(0.1)
       while True:
           try:
               arg = q.get(block=False)
           except Queue.Empty:
               print 'Worker', threading.currentThread(),
               print 'queue empty'
               break
           else:
               print 'Worker', threading.currentThread(),
               print 'running with argument', arg
               time.sleep(0.5)

   # Create queue
   q = Queue.Queue()

   # Start a pool of 5 workers
   for i in range(5):
       t = threading.Thread(target=worker, name='worker %i' % (i+1))
       t.start()

   # Begin adding work to the queue
   for i in range(50):
       q.put(i)

   # Give threads time to run
   print 'Main thread sleeping'
   time.sleep(5)

When run, this will produce the following output:

   Running worker
   Running worker
   Running worker
   Running worker
   Running worker
   Main thread sleeping
   Worker <Thread(worker 1, started)> running with argument 0
   Worker <Thread(worker 2, started)> running with argument 1
   Worker <Thread(worker 3, started)> running with argument 2
   Worker <Thread(worker 4, started)> running with argument 3
   Worker <Thread(worker 5, started)> running with argument 4
   Worker <Thread(worker 1, started)> running with argument 5
   ...

Consult the module's documentation for more details; the ``Queue``
class provides a featureful interface.


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

A *global interpreter lock* (GIL) 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 switches can be set via ``sys.setcheckinterval()``.
Each bytecode instruction and therefore all the C implementation code
reached from each instruction is therefore atomic from the point of
view of a Python program.

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

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

   L.append(x)
   L1.extend(L2)
   x = L[i]
   x = L.pop()
   L1[i:j] = L2
   L.sort()
   x = y
   x.field = y
   D[x] = y
   D1.update(D2)
   D.keys()

These aren't:

   i = i+1
   L.append(L[-1])
   L[i] = L[j]
   D[x] = D[x] + 1

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!


Can't we get rid of the Global Interpreter Lock?
------------------------------------------------

The *global interpreter lock* (GIL) is often seen as a hindrance to
Python's deployment on high-end multiprocessor server machines,
because a multi-threaded Python program effectively only uses one CPU,
due to the insistence that (almost) all Python code can only run while
the GIL is held.

Back in the days of Python 1.5, Greg Stein actually implemented a
comprehensive patch set (the "free threading" patches) that removed
the GIL and replaced it with fine-grained locking.  Unfortunately,
even on Windows (where locks are very efficient) this ran ordinary
Python code about twice as slow as the interpreter using the GIL.  On
Linux the performance loss was even worse because pthread locks aren't
as efficient.

Since then, the idea of getting rid of the GIL has occasionally come
up but nobody has found a way to deal with the expected slowdown, and
users who don't use threads would not be happy if their code ran at
half the speed.  Greg's free threading patch set has not been kept up-
to-date for later Python versions.

This doesn't mean that you can't make good use of Python on multi-CPU
machines! You just have to be creative with dividing the work up
between multiple *processes* rather than multiple *threads*.
Judicious use of C extensions will also help; if you use a C extension
to perform a time-consuming task, the extension can release the GIL
while the thread of execution is in the C code and allow other threads
to get some work done.

It has been suggested that the GIL should be a per-interpreter-state
lock rather than truly global; interpreters then wouldn't be able to
share objects. Unfortunately, this isn't likely to happen either.  It
would be a tremendous amount of work, because many object
implementations currently have global state. For example, small
integers and short strings are cached; these caches would have to be
moved to the interpreter state.  Other object types have their own
free list; these free lists would have to be moved to the interpreter
state. And so on.

And I doubt that it can even be done in finite time, because the same
problem exists for 3rd party extensions.  It is likely that 3rd party
extensions are being written at a faster rate than you can convert
them to store all their global state in the interpreter state.

And finally, once you have multiple interpreters not sharing any
state, what have you gained over running each interpreter in a
separate process?


Input and Output
================


How do I delete a file? (And other file questions...)
-----------------------------------------------------

Use ``os.remove(filename)`` or ``os.unlink(filename)``; for
documentation, see the ``os`` module.  The two functions are
identical; ``unlink()`` is simply the name of the Unix system call for
this function.

To remove a directory, use ``os.rmdir()``; use ``os.mkdir()`` to
create one. ``os.makedirs(path)`` will create any intermediate
directories in ``path`` that don't exist. ``os.removedirs(path)`` will
remove intermediate directories as long as they're empty; if you want
to delete an entire directory tree and its contents, use
``shutil.rmtree()``.

To rename a file, use ``os.rename(old_path, new_path)``.

To truncate a file, open it using ``f = open(filename, "r+")``, and
use ``f.truncate(offset)``; offset defaults to the current seek
position.  There's also ``os.ftruncate(fd, offset)`` for files opened
with ``os.open()``, where *fd* is the file descriptor (a small
integer).

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


How do I copy a file?
---------------------

The ``shutil`` module contains a ``copyfile()`` function.  Note that
on MacOS 9 it doesn't copy the resource fork and Finder info.


How do I read (or write) binary data?
-------------------------------------

To read or write complex binary data formats, it's best to use the
``struct`` module.  It allows you to take a string 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 = f.read(8)
   x, y, z = struct.unpack(">hhl", s)

The '>' in the format string forces big-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.


I can't seem to use os.read() on a pipe created with os.popen(); why?
---------------------------------------------------------------------

``os.read()`` is a low-level function which takes a file descriptor, a
small integer representing the opened file.  ``os.popen()`` creates a
high-level file object, the same type returned by the built-in
``open()`` function. Thus, to read *n* bytes from a pipe *p* created
with ``os.popen()``, you need to use ``p.read(n)``.


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

Use the ``popen2`` module.  For example:

   import popen2
   fromchild, tochild = popen2.popen2("command")
   tochild.write("input\n")
   tochild.flush()
   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 by the parent expecting the child to output more text
than it does or 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 may have been written to never explicitly flush
its output, even if it is interactive, since flushing is normally
automatic.

Note that a deadlock is also possible if you use ``popen3()`` to read
stdout and stderr. If one of the two is too large for the internal
buffer (increasing the buffer size does not help) and you ``read()``
the other one first, there is a deadlock, too.

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 amount of data is very
large, the easiest 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 ``mktemp()`` function to generate unique
temporary file names.

   import tempfile
   import os

   class Popen3:
       """
       This is a deadlock-safe version of popen that returns
       an object with errorlevel, out (a string) and err (a string).
       (capturestderr may not work under windows.)
       Example: print Popen3('grep spam','\n\nhere spam\n\n').out
       """
       def __init__(self,command,input=None,capturestderr=None):
           outfile=tempfile.mktemp()
           command="( %s ) > %s" % (command,outfile)
           if input:
               infile=tempfile.mktemp()
               open(infile,"w").write(input)
               command=command+" <"+infile
           if capturestderr:
               errfile=tempfile.mktemp()
               command=command+" 2>"+errfile
           self.errorlevel=os.system(command) >> 8
           self.out=open(outfile,"r").read()
           os.remove(outfile)
           if input:
               os.remove(infile)
           if capturestderr:
               self.err=open(errfile,"r").read()
               os.remove(errfile)

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. Or you can use a Python
interface to Don Libes' "expect" library.  A Python extension that
interfaces to expect is called "expy" and available from
http://expectpy.sourceforge.net.  A pure Python solution that works
like expect is pexpect.


How do I access the serial (RS232) port?
----------------------------------------

For Win32, POSIX (Linux, BSD, etc.), Jython:

   http://pyserial.sourceforge.net

For Unix, see a Usenet post by Mitch Chapman:

   http://groups.google.com/groups?selm=34A04430.CF9@ohioee.com


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 you create in Python via the built-in ``file``
constructor, ``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 also happens automatically 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.  Running
``sys.stdout.close()`` marks the Python-level file object as being
closed, but does *not* close the associated C stream.

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
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


Network/Internet Programming
============================


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 has many
modules that will help you build server-side and client-side web
systems.

A summary of available frameworks is maintained by Paul Boddie at
http://wiki.python.org/moin/WebProgramming.

Cameron Laird maintains a useful set of pages about Python web
technologies at http://phaseit.net/claird/comp.lang.python/web_python.


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:

   #!/usr/local/bin/python

   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))
   httpobj.endheaders()
   httpobj.send(qs)
   ### find out what the server said in response...
   reply, msg, hdrs = httpobj.getreply()
   if reply != 200:
       sys.stdout.write(httpobj.getfile().read())

Note that in general for percent-encoded POST operations, query
strings must be quoted using ``urllib.urlencode()``.  For example, to
send ``name=Guy Steele, Jr.``:

   >>> import urllib
   >>> urllib.urlencode({'name': 'Guy Steele, Jr.'})
   'name=Guy+Steele%2C+Jr.'


What module should I use to help with generating HTML?
------------------------------------------------------

You can find a collection of useful links on the Web Programming wiki
page.


How do I send mail from a Python script?
----------------------------------------

Use the standard library module ``smtplib``.

Here's a very simple interactive mail sender that uses it.  This
method will work on any host that supports an SMTP listener.

   import sys, smtplib

   fromaddr = raw_input("From: ")
   toaddrs  = raw_input("To: ").split(',')
   print "Enter message, end with ^D:"
   msg = ''
   while True:
       line = sys.stdin.readline()
       if not line:
           break
       msg += line

   # The actual mail send
   server = smtplib.SMTP('localhost')
   server.sendmail(fromaddr, toaddrs, msg)
   server.quit()

A Unix-only alternative uses sendmail.  The location of the sendmail
program varies between systems; sometimes it is ``/usr/lib/sendmail``,
sometimes ``/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 -i" % SENDMAIL, "w")
   p.write("To: receiver@example.com\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


How do I avoid blocking in the connect() method of a socket?
------------------------------------------------------------

The select module is commonly used to help with asynchronous I/O on
sockets.

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 error number as ``.errno``. ``errno.EINPROGRESS``
indicates that the connection is in progress, but hasn't finished yet.
Different OSes will return different values, so you're going to have
to check what's returned on your system.

You can use the ``connect_ex()`` method 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 to
check if it's writable.


Databases
=========


Are there any interfaces to database packages in Python?
--------------------------------------------------------

Yes.

Python 2.3 includes the ``bsddb`` package which provides an interface
to the BerkeleyDB library.  Interfaces to disk-based hashes such as
``DBM`` and ``GDBM`` are also included with standard Python.

Support for most relational databases is available.  See the
DatabaseProgramming wiki page for details.


How do you implement persistent objects in Python?
--------------------------------------------------

The ``pickle`` library module solves this in a very general way
(though you still can't store things like open files, sockets or
windows), and the ``shelve`` library module uses pickle and (g)dbm to
create persistent mappings containing arbitrary Python objects.  For
better performance, you can use the ``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.  This often beats doing something more complex and general
such as using gdbm with pickle/shelve.


Why is cPickle so slow?
-----------------------

By default ``pickle`` uses a relatively old and slow format for
backward compatibility.  You can however specify other protocol
versions that are faster:

   largeString = 'z' * (100 * 1024)
   myPickle = cPickle.dumps(largeString, protocol=1)


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
explicitly be closed using the ``.close()`` method of the database.
The underlying library caches database contents which need to be
converted to on-disk form and written.

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.


I tried to open Berkeley DB file, but bsddb produces bsddb.error: (22, 'Invalid argument'). Help! How can I restore my data?
----------------------------------------------------------------------------------------------------------------------------

Don't panic! Your data is probably intact. The most frequent cause for
the error is that you tried to open an earlier Berkeley DB file with a
later version of the Berkeley DB library.

Many Linux systems now have all three versions of Berkeley DB
available.  If you are migrating from version 1 to a newer version use
db_dump185 to dump a plain text version of the database.  If you are
migrating from version 2 to version 3 use db2_dump to create a plain
text version of the database.  In either case, use db_load to create a
new native database for the latest version installed on your computer.
If you have version 3 of Berkeley DB installed, you should be able to
use db2_load to create a native version 2 database.

You should move away from Berkeley DB version 1 files because the hash
file code contains known bugs that can corrupt your data.


Mathematics and Numerics
========================


How do I generate random numbers in Python?
-------------------------------------------

The standard module ``random`` implements a random number generator.
Usage is simple:

   import random
   random.random()

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

There are also many other specialized generators in this module, such
as:

* ``randrange(a, b)`` chooses an integer in the range [a, b).

* ``uniform(a, b)`` chooses a floating point number in the range [a,
  b).

* ``normalvariate(mean, sdev)`` samples the normal (Gaussian)
  distribution.

Some higher-level functions operate on sequences directly, such as:

* ``choice(S)`` chooses random element from a given sequence

* ``shuffle(L)`` shuffles a list in-place, i.e. permutes it randomly

There's also a ``Random`` class you can instantiate to create
independent multiple random number generators.
