Standartinė biblioteka

http://docs.python.org/lib/module-os.html

import os
 
# Vykdome komanda
print os.system ('ls -la') # Rodo listing'a, gale - 0
 
# Kuriame kataloge esame?
print os.getcwd()
 
# Keichiame kataloga?
os.chdir ('.')
 
# Rodome visas funkcijas, esanchias modulyje
print dir(os)
 
# Rodome modulio funkcijos dokumentacija
# Galima ir: help(os)
print help(os.getcwd)

http://docs.python.org/lib/module-shutil.html

import shutil
 
shutil.copyfile ('/tmp/test1', '/tmp/test10')
shutil.move ('/tmp/test10', '/tmp/test11')

http://docs.python.org/lib/module-glob.html

import glob
 
# Rodome tik kai kuriuos failus
print glob.glob('*.py')

Dar žiūrėti: http://docs.python.org/lib/module-getopt.html ir http://docs.python.org/lib/module-optparse.html

import sys
 
print sys.argv

import sys
 
# I STDERR ishvedame klaida
sys.stderr.write ('Klaida, klaida!\n')

import sys
 
sys.exit()

http://docs.python.org/lib/module-re.html

import re
 
print re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest') # ['foot', 'fell', 'fastest']
print re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat') # 'cat in the hat'
 
# Eilutes keisti galima papraschiau
eilute = 'vienas keturi trys'
print eilute.replace ('keturi', 'du')

http://docs.python.org/lib/module-math.html

import math
 
print math.cos(math.pi / 4.0) # 0.70710678118654757
print math.log(1024, 2) # 10.0

http://docs.python.org/lib/module-random.html

import random
 
print random.choice(['apple', 'pear', 'banana'])
# 'apple'
 
print random.sample(xrange(100), 10)	# sampling without replacement
# [30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
 
print random.random()	# random float
# 0.17970987693706186 arba kitas
 
print random.randrange(6)	# random integer chosen from range(6)
# 4

http://docs.python.org/lib/module-urllib2.html

import urllib2
 
for line in urllib2.urlopen('http://tycho.usno.navy.mil/cgi-bin/timer.pl'):
	if 'EST' in line or 'EDT' in line:  # look for Eastern Time
		print line

http://docs.python.org/lib/module-smtplib.html

import smtplib
 
server = smtplib.SMTP('localhost')
server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
"""To: jcaesar@example.org
From: soothsayer@example.org
 
Beware the Ides of March.
""")
server.quit()

http://docs.python.org/lib/module-datetime.html

from datetime import date
 
now = date.today()
print now # datetime.date(2003, 12, 2)
 
print now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
# '12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'
 
# dates support calendar arithmetic
birthday = date(1964, 7, 31)
age = now - birthday
print age.days # 14368

http://docs.python.org/lib/module-zlib.html

import zlib
 
s = 'witch which has which witches wrist watch'
print len(s) # 41
 
t = zlib.compress(s)
print len(t) # 37
 
print zlib.decompress(t) # 'witch which has which witches wrist watch'
print zlib.crc32(s) # 226805979

• gzip - http://docs.python.org/lib/module-gzip.html
• bz2 - http://docs.python.org/lib/module-bz2.html
• zipfile - http://docs.python.org/lib/module-zipfile.html
• tarfile - http://docs.python.org/lib/module-tarfile.html

http://docs.python.org/lib/module-timeit.html

from timeit import Timer
 
print Timer('t=a; a=b; b=t', 'a=1; b=2').timeit() # 0.57535828626024577
print Timer('a,b = b,a', 'a=1; b=2').timeit() # 0.54962537085770791

In contrast to timeit's fine level of granularity, the profile (http://docs.python.org/lib/module-profile.html) and pstats modules provide tools for identifying time critical sections in larger blocks of code.

http://docs.python.org/lib/module-doctest.html

The doctest module provides a tool for scanning a module and validating tests embedded in a program's docstrings. Test construction is as simple as cutting-and-pasting a typical call along with its results into the docstring. This improves the documentation by providing the user with an example and it allows the doctest module to make sure the code remains true to the documentation:

def average(values):
	"""Computes the arithmetic mean of a list of numbers.
 
	print average([20, 30, 70])
	40.0
	"""
	return sum(values, 0.0) / len(values)
 
import doctest
doctest.testmod()	# automatically validate the embedded tests

http://docs.python.org/lib/module-unittest.html

The unittest module is not as effortless as the doctest module, but it allows a more comprehensive set of tests to be maintained in a separate file:

pavyzdys kažkodėl neveikia.

import unittest
 
class TestStatisticalFunctions(unittest.TestCase):
 
	def test_average(self):
		self.assertEqual(average([20, 30, 70]), 40.0)
		self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
		self.assertRaises(ZeroDivisionError, average, [])
		self.assertRaises(TypeError, average, 20, 30, 70)
 
unittest.main() # Calling from the command line invokes all tests

http://docs.python.org/lib/module-repr.html

import repr   
print repr.repr(set('supercalifragilisticexpialidocious'))
# "set(['a', 'c', 'd', 'e', 'f', 'g', ...])"

http://docs.python.org/lib/module-pprint.html

Grazhiai spausdina ivairias strukturas, o jos lieka skaitomos parser'iui.

import pprint
 
t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta', 'yellow'], 'blue']]]
 
print pprint.pprint (t, width=30)
 
#	[[[['black', 'cyan'],
#		'white',
#		['green', 'red']],
#		[['magenta', 'yellow'],
#		'blue']]]

http://docs.python.org/lib/module-textwrap.html

import textwrap
 
doc = """The wrap() method is just like fill() except that it returns
a list of strings instead of one big string with newlines to separate
the wrapped lines."""
 
print textwrap.fill(doc, width=40)
 
# The wrap() method is just like fill()
# except that it returns a list of strings
# instead of one big string with newlines
# to separate the wrapped lines.

http://docs.python.org/lib/module-locale.html

import locale
 
print locale.setlocale(locale.LC_ALL, 'lt_LT.UTF-8') # 'English_United States.1252'
 
conv = locale.localeconv()	# get a mapping of conventions
x = 1234567.8
 
print locale.format("%d", x, grouping=True) # '1,234,567'
print locale.format("%s%.*f", (conv['currency_symbol'], conv['frac_digits'], x), grouping=True) # '$1,234,567.80'

Ish string modulio (http://docs.python.org/lib/module-string.html)

The format uses placeholder names formed by „$“ with valid Python identifiers (alphanumeric characters and underscores). Surrounding the placeholder with braces allows it to be followed by more alphanumeric letters with no intervening spaces. Writing „$$“ creates a single escaped „$“:

from string import Template
 
t = Template('${village}folk send $$10 to $cause.')
 
print t.substitute(village='Nottingham', cause='the ditch fund') # 'Nottinghamfolk send $10 to the ditch fund.'

The substitute method raises a KeyError when a placeholder is not supplied in a dictionary or a keyword argument. For mail-merge style applications, user supplied data may be incomplete and the safe_substitute method may be more appropriate – it will leave placeholders unchanged if data is missing.

Galima nustatyti ir kitoki delimiter'i:

import time, os.path
 
photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
 
class BatchRename(Template):
	delimiter = '%'
	fmt = raw_input('Enter rename style (%d-date %n-seqnum %f-format):  ')
 
	t = BatchRename(fmt)
	date = time.strftime('%d%b%y')
 
	for i, filename in enumerate(photofiles):
		base, ext = os.path.splitext(filename)
		newname = t.substitute(d=date, n=i, f=ext)
 
		print '%s --> %s' % (filename, newname)

http://docs.python.org/lib/module-struct.html

The following example shows how to loop through header information in a ZIP file (with pack codes „H“ and „L“ representing two and four byte unsigned numbers respectively):

import struct
 
data = open('myfile.zip', 'rb').read()
start = 0
 
for i in range(3):	# show the first 3 file headers
	start += 14
	fields = struct.unpack('LLLHH', data[start:start+16])
	crc32, comp_size, uncomp_size, filenamesize, extra_size = fields
 
	start += 16
	filename = data[start:start+filenamesize]
	start += filenamesize
	extra = data[start:start+extra_size]
 
	print filename, hex(crc32), comp_size, uncomp_size
 
	start += extra_size + comp_size	# skip to the next header

http://docs.python.org/lib/module-threading.html

import threading, zipfile
 
class AsyncZip(threading.Thread):
 
	def __init__(self, infile, outfile):
		threading.Thread.__init__(self)        
		self.infile = infile
		self.outfile = outfile
 
	def run(self):
		f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
		f.write(self.infile)
		f.close()
		print 'Finished background zip of: ', self.infile
 
background = AsyncZip('mydata.txt', 'myarchive.zip')
background.start()
 
print 'The main program continues to run in foreground.'
 
background.join()    # Wait for the background task to finish
print 'Main program waited until background was done.'

While those tools are powerful, minor design errors can result in problems that are difficult to reproduce. So, the preferred approach to task coordination is to concentrate all access to a resource in a single thread and then use the Queue module (http://docs.python.org/lib/module-Queue.html) to feed that thread with requests from other threads. Applications using Queue objects for inter-thread communication and coordination are easier to design, more readable, and more reliable.

http://docs.python.org/lib/module-logging.html

import logging
 
logging.debug('Debugging information')
logging.info('Informational message')
logging.warning('Warning:config file %s not found', 'server.conf')
logging.error('Error occurred')
logging.critical('Critical error -- shutting down')
 
# WARNING:root:Warning:config file server.conf not found
# ERROR:root:Error occurred
# CRITICAL:root:Critical error -- shutting down

http://docs.python.org/lib/module-weakref.html

Python does automatic memory management (reference counting for most objects and garbage collection to eliminate cycles). The memory is freed shortly after the last reference to it has been eliminated.

This approach works fine for most applications but occasionally there is a need to track objects only as long as they are being used by something else. Unfortunately, just tracking them creates a reference that makes them permanent. The weakref module provides tools for tracking objects without creating a reference. When the object is no longer needed, it is automatically removed from a weakref table and a callback is triggered for weakref objects. Typical applications include caching objects that are expensive to create:

import weakref, gc
 
class A:
	def __init__(self, value):
		self.value = value
	def __repr__(self):
		return str(self.value)
 
a = A(10)	# create a reference
d = weakref.WeakValueDictionary()
 
d['primary'] = a	# does not create a reference
print d['primary']	# fetch the object if it is still alive
# 10
 
del a	# remove the one reference
print gc.collect()	# run garbage collection right away
# 0
 
d['primary']	# entry was automatically removed
# Raise'ina exception'a

http://docs.python.org/lib/module-array.html

The array module provides an array() object that is like a list that stores only homogenous data and stores it more compactly. The following example shows an array of numbers stored as two byte unsigned binary numbers (typecode „H“) rather than the usual 16 bytes per entry for regular lists of python int objects:

from array import array
 
a = array('H', [4000, 10, 700, 22222])
print sum(a) # 26932
 
print a[1:3] # array('H', [10, 700])

http://docs.python.org/lib/module-collections.html

The collections module provides a deque() object that is like a list with faster appends and pops from the left side but slower lookups in the middle. These objects are well suited for implementing queues and breadth first tree searches:

from collections import deque
 
d = deque(["task1", "task2", "task3"])
d.append("task4")
 
print "Handling", d.popleft() # Handling task1
 
unsearched = deque([starting_node])
 
def breadth_first_search(unsearched):
	node = unsearched.popleft()
	for m in gen_moves(node):
		if is_goal(m):
			return m
		unsearched.append(m)

http://docs.python.org/lib/module-bisect.html

In addition to alternative list implementations, the library also offers other tools such as the bisect module with functions for manipulating sorted lists:

import bisect
 
scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]
bisect.insort(scores, (300, 'ruby'))
 
print scores # [(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]

http://docs.python.org/lib/module-heapq.html

The heapq module provides functions for implementing heaps based on regular lists. The lowest valued entry is always kept at position zero. This is useful for applications which repeatedly access the smallest element but do not want to run a full list sort:

from heapq import heapify, heappop, heappush
 
data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
 
heapify(data)	# rearrange the list into heap order
heappush(data, -5)	# add a new entry
[heappop(data) for i in range(3)]	# fetch the three smallest entries
# [-5, 0, 1]

http://docs.python.org/lib/module-decimal.html

The decimal module offers a Decimal datatype for decimal floating point arithmetic. Compared to the built-in float implementation of binary floating point, the new class is especially helpful for financial applications and other uses which require exact decimal representation, control over precision, control over rounding to meet legal or regulatory requirements, tracking of significant decimal places, or for applications where the user expects the results to match calculations done by hand.

from decimal import *
 
print Decimal('0.70') * Decimal('1.05') # Decimal("0.7350")
print .70 * 1.05 # 0.73499999999999999

Exact representation enables the Decimal class to perform modulo calculations and equality tests that are unsuitable for binary floating point:

print Decimal('1.00') % Decimal('.10') # Decimal("0.00")
print 1.00 % 0.10 # 0.09999999999999995
 
print sum([Decimal('0.1')]*10) == Decimal('1.0') # True
print sum([0.1]*10) == 1.0 # False

The decimal module provides arithmetic with as much precision as needed:

getcontext().prec = 36
print Decimal(1) / Decimal(7) # Decimal("0.142857142857142857142857142857142857")

• http://docs.python.org/lib/module-xmlrpclib.html
• http://docs.python.org/lib/module-SimpleXMLRPCServer.html

• http://docs.python.org/lib/module-email.html

• http://docs.python.org/lib/module-xml.dom.html
• http://docs.python.org/lib/module-xml.sax.html

• http://docs.python.org/lib/module-gettext.html
• http://docs.python.org/lib/module-codecs.html