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ActivePython 2.5 documentation
Python Library and Extension FAQ
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 able to skip this step.)
Search the Python Package Index.
Next, check the Vaults of Parnassus,
an older index of packages.
Finally, try Google or other Web search
engine. Searching for "Python" plus a keyword or two for your topic
of interest will usually find something helpful.
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).
There are (at least) three kinds of modules in Python:
modules written in Python (.py);
modules written in C and dynamically loaded (.dll, .pyd, .so, .sl, etc);
modules written in C and linked with the interpreter; to get a list
of these, type:
import sys
print sys.builtin_module_names
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..."""
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.
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):
...
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.
For testing, it helps to write the program so that it may be easily
tested by using good modular design. 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
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 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.
The pydoc module
can create HTML from the doc strings in your Python source code. An
alternative is pythondoc.
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", `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.
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 how long a time.sleep() delay will be
enough, 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.
Use the Queue module to create a queue
containing a list of jobs. The Queue class maintains a list of
objects with .put(obj) to add an item to the queue and .get()
to return an item. 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.
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 builtin 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!
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 at 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?
Use os.remove(filename) or os.unlink(filename); for
documentation, see the POSIX 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.
The shutil module contains a copyfile() function. Note that
on MacOS 9 it doesn't copy the resource fork and Finder info.
or complex 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
thefloats), you can also use the array module.
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 builtin
open() function. Thus, to read n bytes from a pipe p created with
os.popen(), you need to use p.read(n).
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 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 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 <http://pexpect.sourceforge.net>`_.
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 builtin 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
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 URL-encoded POST operations, query
strings must be quoted by using urllib.quote(). For example to send name="Guy
Steele, Jr.":
>>> from urllib import quote
>>> x = quote("Guy Steele, Jr.")
>>> x
'Guy%20Steele,%20Jr.'
>>> query_string = "name="+x
>>> query_string
'name=Guy%20Steele,%20Jr.'
There are many different modules available:
- HTMLgen is 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.
- DocumentTemplate and Zope Page Templates are two different systems that are
part of Zope.
- Quixote's PTL uses Python syntax to assemble strings of text.
Consult the Web Programming topic guide for more links.
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 1:
line = sys.stdin.readline()
if not line:
break
msg = 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, 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 -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
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.
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 Database
Topic Guide for details.
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.
The default format used by the pickle module is a slow one that
results in readable pickles. Making it the default, but it would
break backward compatibility:
largeString = 'z' * (100 * 1024)
myPickle = cPickle.dumps(largeString, protocol=1)
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.
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.
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.
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