The os module provides dozens of functions for interacting with the operating system:
>>> import os >>> os.getcwd() # Return the current working directory 'C:\\Python34' >>> os.chdir('/server/accesslogs') # Change current working directory >>> os.system('mkdir today') # Run the command mkdir in the system shell 0
>>> import os >>> dir(os) <returns a list of all module functions> >>> help(os) <returns an extensive manual page created from the module's docstrings>
For daily file and directory management tasks, the shutil module provides a higher level interface that is easier to use:
>>> import shutil >>> shutil.copyfile('data.db', 'archive.db') 'archive.db' >>> shutil.move('/build/executables', 'installdir') 'installdir'
The glob module provides a function for making file lists from directory wildcard searches:
>>> import glob >>> glob.glob('*.py') ['primes.py', 'random.py', 'quote.py']
Common utility scripts often need to process command line arguments. These arguments are stored in the sys module’s argv attribute as a list. For instance the following output results from running python demo.py one two three at the command line:
>>> import sys >>> print(sys.argv) ['demo.py', 'one', 'two', 'three']
The sys module also has attributes for stdin, stdout, and stderr. The latter is useful for emitting warnings and error messages to make them visible even when stdout has been redirected:
>>> sys.stderr.write('Warning, log file not found starting a new one\n') Warning, log file not found starting a new one
The most direct way to terminate a script is to use sys.exit().
The re module provides regular expression tools for advanced string processing. For complex matching and manipulation, regular expressions offer succinct, optimized solutions:
>>> import re >>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest') ['foot', 'fell', 'fastest'] >>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat') 'cat in the hat'
When only simple capabilities are needed, string methods are preferred because they are easier to read and debug:
>>> 'tea for too'.replace('too', 'two') 'tea for two'
The math module gives access to the underlying C library functions for floating point math:
>>> import math >>> math.cos(math.pi / 4) 0.70710678118654757 >>> math.log(1024, 2) 10.0
The random module provides tools for making random selections:
>>> import random >>> random.choice(['apple', 'pear', 'banana']) 'apple' >>> random.sample(range(100), 10) # sampling without replacement [30, 83, 16, 4, 8, 81, 41, 50, 18, 33] >>> random.random() # random float 0.17970987693706186 >>> random.randrange(6) # random integer chosen from range(6) 4
The statistics module calculates basic statistical properties (the mean, median, variance, etc.) of numeric data:
>>> import statistics >>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5] >>> statistics.mean(data) 1.6071428571428572 >>> statistics.median(data) 1.25 >>> statistics.variance(data) 1.3720238095238095
The SciPy project <http://scipy.org> has many other modules for numerical computations.
>>> from urllib.request import urlopen >>> with urlopen('http://tycho.usno.navy.mil/cgi-bin/timer.pl') as response: ... for line in response: ... line = line.decode('utf-8') # Decoding the binary data to text. ... if 'EST' in line or 'EDT' in line: # look for Eastern Time ... print(line) <BR>Nov. 25, 09:43:32 PM EST >>> import smtplib >>> server = smtplib.SMTP('localhost') >>> server.sendmail('email@example.com', 'firstname.lastname@example.org', ... """To: email@example.com ... From: firstname.lastname@example.org ... ... Beware the Ides of March. ... """) >>> server.quit()
(Note that the second example needs a mailserver running on localhost.)
The datetime module supplies classes for manipulating dates and times in both simple and complex ways. While date and time arithmetic is supported, the focus of the implementation is on efficient member extraction for output formatting and manipulation. The module also supports objects that are timezone aware.
>>> # dates are easily constructed and formatted >>> from datetime import date >>> now = date.today() >>> now datetime.date(2003, 12, 2) >>> 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 >>> age.days 14368
>>> import zlib >>> s = b'witch which has which witches wrist watch' >>> len(s) 41 >>> t = zlib.compress(s) >>> len(t) 37 >>> zlib.decompress(t) b'witch which has which witches wrist watch' >>> zlib.crc32(s) 226805979
Some Python users develop a deep interest in knowing the relative performance of different approaches to the same problem. Python provides a measurement tool that answers those questions immediately.
For example, it may be tempting to use the tuple packing and unpacking feature instead of the traditional approach to swapping arguments. The timeit module quickly demonstrates a modest performance advantage:
>>> from timeit import Timer >>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit() 0.57535828626024577 >>> Timer('a,b = b,a', 'a=1; b=2').timeit() 0.54962537085770791
One approach for developing high quality software is to write tests for each function as it is developed and to run those tests frequently during the development process.
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) / len(values) import doctest doctest.testmod() # automatically validate the embedded tests
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) with self.assertRaises(ZeroDivisionError): average() with self.assertRaises(TypeError): average(20, 30, 70) unittest.main() # Calling from the command line invokes all tests
Python has a “batteries included” philosophy. This is best seen through the sophisticated and robust capabilities of its larger packages. For example: