10.1. Operating system interface
The
module provides dozens of functions for interacting with the operating system:
>>> importos>>> os.getcwd()# Return the current working directory'C:\\Python314'>>> os.chdir('/server/accesslogs')# Change current working directory>>> os.system('mkdir today')# Run the command mkdir in the system shell0Be sure to use the importos style instead of fromosimport*. This will keep
from shadowing the built-in
function which operates much differently.
The built-in
and
functions are useful as interactive aids for working with large modules like
:
>>> importos>>> 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
module provides a higher-level interface that is easier to use:
>>> importshutil>>> shutil.copyfile('data.db','archive.db')'archive.db'>>> shutil.move('/build/executables','installdir')'installdir'10.2. File wildcards
The
module provides a function for making file lists from directory wildcard searches:
>>> importglob>>> glob.glob('*.py')['primes.py', 'random.py', 'quote.py']10.3. Command-line arguments
Common utility scripts often need to process command-line arguments. These arguments are stored in the
module’s argv attribute as a list. For instance, let’s take the following demo.py file:
# File demo.pyimportsysprint(sys.argv)Here is the output from running pythondemo.pyonetwothree at the command line:
['demo.py','one','two','three']The
module provides a more sophisticated mechanism to process command-line arguments. The following script extracts one or more filenames and an optional number of lines to be displayed:
importargparseparser=argparse.ArgumentParser(prog='top',description='Show top lines from each file')parser.add_argument('filenames',nargs='+')parser.add_argument('-l','--lines',type=int,default=10)args=parser.parse_args()print(args)When run at the command line with pythontop.py--lines=5alpha.txtbeta.txt, the script sets args.lines to 5 and args.filenames to ['alpha.txt','beta.txt'].
10.4. Error output redirection and program termination
The
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 oneThe most direct way to terminate a script is to use sys.exit().
10.5. String pattern matching
The
module provides regular expression tools for advanced string processing. For complex matching and manipulation, regular expressions offer succinct, optimized solutions:
>>> importre>>> 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'10.6. Mathematics
The
module gives access to the underlying C library functions for floating-point math:
>>> importmath>>> math.cos(math.pi/4)0.70710678118654757>>> math.log(1024,2)10.0The
module provides tools for making random selections:
>>> importrandom>>> 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 from the interval [0.0, 1.0)0.17970987693706186>>> random.randrange(6)# random integer chosen from range(6)4The
module calculates basic statistical properties (the mean, median, variance, etc.) of numeric data:
>>> importstatistics>>> 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)1.3720238095238095The SciPy project <
> has many other modules for numerical computations.
10.7. Internet access
There are a number of modules for accessing the internet and processing internet protocols. Two of the simplest are
for retrieving data from URLs and
for sending mail:
>>> fromurllib.requestimporturlopen>>> withurlopen('https://docs.python.org/3/')asresponse:... forlineinresponse:... line=line.decode()# Convert bytes to a str... if'updated'inline:... print(line.rstrip())# Remove trailing newline... Last updated on Nov 11, 2025 (20:11 UTC).>>> importsmtplib>>> server=smtplib.SMTP('localhost')>>> server.sendmail('[email protected]','[email protected]',... """To: [email protected]... From: [email protected]...... Beware the Ides of March.... """)>>> server.quit()(Note that the second example needs a mailserver running on localhost.)
10.8. Dates and times
The
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>>> importdatetimeasdt>>> now=dt.date.today()>>> nowdatetime.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=dt.date(1964,7,31)>>> age=now-birthday>>> age.days1436810.9. Data compression
Common data archiving and compression formats are directly supported by modules including:
,
,
,
,
and
.
>>> importzlib>>> 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)22680597910.10. 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.
For example, it may be tempting to use the tuple packing and unpacking feature instead of the traditional approach to swapping arguments. The
module quickly demonstrates a modest performance advantage:
>>> fromtimeitimportTimer>>> Timer('t=a; a=b; b=t','a=1; b=2').timeit()0.57535828626024577>>> Timer('a,b = b,a','a=1; b=2').timeit()0.54962537085770791In contrast to
’s fine level of granularity, the
and
modules provide tools for identifying time critical sections in larger blocks of code.
10.11. Quality control
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
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:
defaverage(values):"""Computes the arithmetic mean of a list of numbers. >>> print(average([20, 30, 70])) 40.0 """returnsum(values)/len(values)importdoctestdoctest.testmod()# automatically validate the embedded testsThe
module is not as effortless as the
module, but it allows a more comprehensive set of tests to be maintained in a separate file:
importunittestclassTestStatisticalFunctions(unittest.TestCase):deftest_average(self):self.assertEqual(average([20,30,70]),40.0)self.assertEqual(round(average([1,5,7]),1),4.3)withself.assertRaises(ZeroDivisionError):average([])withself.assertRaises(TypeError):average(20,30,70)unittest.main()# Calling from the command line invokes all tests10.12. Batteries included
Python has a “batteries included” philosophy. This is best seen through the sophisticated and robust capabilities of its larger packages. For example:
The
and
modules make implementing remote procedure calls into an almost trivial task. Despite the modules’ names, no direct knowledge or handling of XML is needed.
The
package is a library for managing email messages, including MIME and other
-based message documents. Unlike
and
which actually send and receive messages, the email package has a complete toolset for building or decoding complex message structures (including attachments) and for implementing internet encoding and header protocols.
The
package provides robust support for parsing this popular data interchange format. The
module supports direct reading and writing of files in Comma-Separated Value format, commonly supported by databases and spreadsheets. XML processing is supported by the
,
and
packages. Together, these modules and packages greatly simplify data interchange between Python applications and other tools.
The
module is a wrapper for the SQLite database library, providing a persistent database that can be updated and accessed using slightly nonstandard SQL syntax.
Internationalization is supported by a number of modules including
,
, and the
package.