Python 3.7.x Notes-7 Standard Library

Posted 2022-11-30 Joey-Zhang

Brief Tour of the Standard Library

Operating System Interface

>>> import os
>>> os.getcwd()      # Return the current working directory
'C:\\\\Python37'
>>> os.chdir('/server/accesslogs')   # Change current working directory
>>> os.system('mkdir today')   # Run the command mkdir in the system shell
0

>>> import shutil
>>> shutil.copyfile('data.db', 'archive.db')
'archive.db'
>>> shutil.move('/build/executables', 'installdir')
'installdir'

File Wildcards

>>> import glob
>>> glob.glob('*.py')
['primes.py', 'random.py', 'quote.py']

Command Line Arguments

Common utility scripts often need to process command line arguments. These arguments are stored in the sys module’s argv attribute as a list.

>>> import sys
>>> print(sys.argv)
['demo.py', 'one', 'two', 'three']

Error Output Redirection and Program Termination

>>> sys.stderr.write('Warning, log file not found starting a new one\\n')
Warning, log file not found starting a new one

String Pattern Matching

The re module provides regular expression tools for advanced string processing.

>>> 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'

Mathematics

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

>>> 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

>>> 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

Internet Access

There are a number of modules for accessing the internet and processing internet protocols. Two of the simplest are urllib.request for retrieving data from URLs and smtplib for sending mail:

>>> 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('[e-mail]', '[e-mail]',
... """To: [e-mail]
... From: [e-mail]
...
... Beware the Ides of March.
... """)
>>> server.quit()

Dates and Times

>>> # 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

Data Compression

Common data archiving and compression formats are directly supported by modules including: zlib, gzip, bz2, lzma, zipfile and tarfile.

>>> 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

Performance Measurement

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.

>>> 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

Quality Control

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

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:

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