Standartinė biblioteka

"os" - OS interfeisas

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)

"shutil" - darbas su shell'u

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

"glob" - bylų sąrašo gavimas naudojant wildcard'ą

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

Komandinės eilutės argumentai

Priėjimas prie STDERR

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

Darbo baigimas - exit()

import sys

"re" - regexp'ai

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')

"math" - matematiniai žaislai

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

"random" - randomizer'is

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

"urllib2" - HTTP klientas

import urllib2
for line in urllib2.urlopen(''):
	if 'EST' in line or 'EDT' in line:  # look for Eastern Time
		print line

"smtplib" - SMTP klientas

import smtplib
server = smtplib.SMTP('localhost')
server.sendmail('', '',
Beware the Ides of March.

"datetime" - data ir laikas

from datetime import date
now =
print now #, 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

Duomenų suspaudimas


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

Kiti moduliai

"timeit" - performance'o matavimas

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 ( and pstats modules provide tools for identifying time critical sections in larger blocks of code.


"doctest" - automatinis testavimas

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])
	return sum(values, 0.0) / len(values)
import doctest
doctest.testmod()	# automatically validate the embedded tests

"unittest" - dar kietesnis testavimas

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:

FIXME 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

"repr" - unikalūs simboliai

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

"pprint" - įvairių duomenų struktūrų spausdinimas

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']]]

"textwrap" - wrap'ina tekstą

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.

"locale" - lokalės panaudojimas

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'

Šablonai (templates)

Ish string modulio (

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.

Delimiter'io nustatymas

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)

"struct" - pack() ir unpack() funkcijos

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('', '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

"threading" - multithreading'as

import threading, zipfile
class AsyncZip(threading.Thread):
	def __init__(self, infile, outfile):
		self.infile = infile
		self.outfile = outfile
	def run(self):
		f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
		print 'Finished background zip of: ', self.infile
background = AsyncZip('mydata.txt', '')
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.'

"Queue" - alternatyva multithreading'ui

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 ( 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.

"logging" - log'ai

import logging
logging.debug('Debugging information')'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

"weakref" - weak references

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

Kitos duomenų struktūros

"array" - masyvai

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])


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"])
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


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')]


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]

"decimal" - skaičiai su kableliu

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")

Kiti moduliai


El. paštui



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