Copyright

Copyright¶
Python and this documentation is:
Copyright © 2001 Python Software Foundation. All rights reserved.
Copyright © 2000 BeOpen.com. All rights reserved.
Copyright © 1995-2000 Corporation for National Research Initiatives. All rights reserved.
Copyright © 1991-1995 Stichting Mathematisch Centrum. All rights reserved.
See History and License for complete license and permissions information.


Appendix

16. Appendix¶
16.1. Interactive Mode¶
There are two variants of the interactive REPL. The classic basic interpreter is supported on all platforms with minimal line control capabilities.
Since Python 3.13, a new interactive shell is used by default.
This one supports color, multiline editing, history browsing, and
paste mode. To disable color, see Controlling color for
details. Function keys provide some additional functionality.
F1 enters the interactive help browser pydoc
.
F2 allows for browsing command-line history with neither output nor the
>>> and … prompts. F3 enters “paste mode”, which
makes pasting larger blocks of code easier. Press F3 to return to
the regular prompt.
When using the new interactive shell, exit the shell by typing exit or quit. Adding call parentheses after those commands is not required.
If the new interactive shell is not desired, it can be disabled via
the PYTHON_BASIC_REPL
environment variable.
16.1.1. Error Handling¶
When an error occurs, the interpreter prints an error message and a stack trace.
In interactive mode, it then returns to the primary prompt; when input came from
a file, it exits with a nonzero exit status after printing the stack trace.
(Exceptions handled by an except
clause in a try
statement
are not errors in this context.) Some errors are unconditionally fatal and
cause an exit with a nonzero exit status; this applies to internal inconsistencies and
some cases of running out of memory. All error messages are written to the
standard error stream; normal output from executed commands is written to
standard output.
Typing the interrupt character (usually Control-C or Delete) to the primary or
secondary prompt cancels the input and returns to the primary prompt. [1]
Typing an interrupt while a command is executing raises the
KeyboardInterrupt
exception, which may be handled by a try
statement.
16.1.2. Executable Python Scripts¶
On BSD’ish Unix systems, Python scripts can be made directly executable, like shell scripts, by putting the line
#!/usr/bin/env python3
(assuming that the interpreter is on the user’s PATH
) at the beginning
of the script and giving the file an executable mode. The #!
must be the
first two characters of the file. On some platforms, this first line must end
with a Unix-style line ending ('\n'
), not a Windows ('\r\n'
) line
ending. Note that the hash, or pound, character, '#'
, is used to start a
comment in Python.
The script can be given an executable mode, or permission, using the chmod command.
$ chmod +x myscript.py
On Windows systems, there is no notion of an “executable mode”. The Python
installer automatically associates .py
files with python.exe
so that
a double-click on a Python file will run it as a script. The extension can
also be .pyw
, in that case, the console window that normally appears is
suppressed.
16.1.3. The Interactive Startup File¶
When you use Python interactively, it is frequently handy to have some standard
commands executed every time the interpreter is started. You can do this by
setting an environment variable named PYTHONSTARTUP
to the name of a
file containing your start-up commands. This is similar to the .profile
feature of the Unix shells.
This file is only read in interactive sessions, not when Python reads commands
from a script, and not when /dev/tty
is given as the explicit source of
commands (which otherwise behaves like an interactive session). It is executed
in the same namespace where interactive commands are executed, so that objects
that it defines or imports can be used without qualification in the interactive
session. You can also change the prompts sys.ps1
and sys.ps2
in this
file.
If you want to read an additional start-up file from the current directory, you
can program this in the global start-up file using code like if
os.path.isfile('.pythonrc.py'): exec(open('.pythonrc.py').read())
.
If you want to use the startup file in a script, you must do this explicitly
in the script:
import os
filename = os.environ.get('PYTHONSTARTUP')
if filename and os.path.isfile(filename):
with open(filename) as fobj:
startup_file = fobj.read()
exec(startup_file)
16.1.4. The Customization Modules¶
Python provides two hooks to let you customize it: sitecustomize and usercustomize. To see how it works, you need first to find the location of your user site-packages directory. Start Python and run this code:
>>> import site
>>> site.getusersitepackages()
'/home/user/.local/lib/python3.x/site-packages'
Now you can create a file named usercustomize.py
in that directory and
put anything you want in it. It will affect every invocation of Python, unless
it is started with the -s
option to disable the automatic import.
sitecustomize works in the same way, but is typically created by an
administrator of the computer in the global site-packages directory, and is
imported before usercustomize. See the documentation of the site
module for more details.
Footnotes


15. Floating-Point Arithmetic: Issues and Limitations¶
Floating-point numbers are represented in computer hardware as base 2 (binary)
fractions. For example, the decimal fraction 0.625
has value 6/10 + 2/100 + 5/1000, and in the same way the binary fraction 0.101
has value 1/2 + 0/4 + 1/8. These two fractions have identical values, the only
real difference being that the first is written in base 10 fractional notation,
and the second in base 2.
Unfortunately, most decimal fractions cannot be represented exactly as binary fractions. A consequence is that, in general, the decimal floating-point numbers you enter are only approximated by the binary floating-point numbers actually stored in the machine.
The problem is easier to understand at first in base 10. Consider the fraction 1/3. You can approximate that as a base 10 fraction:
0.3
or, better,
0.33
or, better,
0.333
and so on. No matter how many digits you’re willing to write down, the result will never be exactly 1/3, but will be an increasingly better approximation of 1/3.
In the same way, no matter how many base 2 digits you’re willing to use, the decimal value 0.1 cannot be represented exactly as a base 2 fraction. In base 2, 1/10 is the infinitely repeating fraction
0.0001100110011001100110011001100110011001100110011...
Stop at any finite number of bits, and you get an approximation. On most
machines today, floats are approximated using a binary fraction with
the numerator using the first 53 bits starting with the most significant bit and
with the denominator as a power of two. In the case of 1/10, the binary fraction
is 3602879701896397 / 2 ** 55
which is close to but not exactly
equal to the true value of 1/10.
Many users are not aware of the approximation because of the way values are displayed. Python only prints a decimal approximation to the true decimal value of the binary approximation stored by the machine. On most machines, if Python were to print the true decimal value of the binary approximation stored for 0.1, it would have to display:
>>> 0.1
0.1000000000000000055511151231257827021181583404541015625
That is more digits than most people find useful, so Python keeps the number of digits manageable by displaying a rounded value instead:
>>> 1 / 10
0.1
Just remember, even though the printed result looks like the exact value of 1/10, the actual stored value is the nearest representable binary fraction.
Interestingly, there are many different decimal numbers that share the same
nearest approximate binary fraction. For example, the numbers 0.1
and
0.10000000000000001
and
0.1000000000000000055511151231257827021181583404541015625
are all
approximated by 3602879701896397 / 2 ** 55
. Since all of these decimal
values share the same approximation, any one of them could be displayed
while still preserving the invariant eval(repr(x)) == x
.
Historically, the Python prompt and built-in repr()
function would choose
the one with 17 significant digits, 0.10000000000000001
. Starting with
Python 3.1, Python (on most systems) is now able to choose the shortest of
these and simply display 0.1
.
Note that this is in the very nature of binary floating point: this is not a bug in Python, and it is not a bug in your code either. You’ll see the same kind of thing in all languages that support your hardware’s floating-point arithmetic (although some languages may not display the difference by default, or in all output modes).
For more pleasant output, you may wish to use string formatting to produce a limited number of significant digits:
>>> format(math.pi, '.12g') # give 12 significant digits
'3.14159265359'
>>> format(math.pi, '.2f') # give 2 digits after the point
'3.14'
>>> repr(math.pi)
'3.141592653589793'
It’s important to realize that this is, in a real sense, an illusion: you’re simply rounding the display of the true machine value.
One illusion may beget another. For example, since 0.1 is not exactly 1/10, summing three values of 0.1 may not yield exactly 0.3, either:
>>> 0.1 + 0.1 + 0.1 == 0.3
False
Also, since the 0.1 cannot get any closer to the exact value of 1/10 and
0.3 cannot get any closer to the exact value of 3/10, then pre-rounding with
round()
function cannot help:
>>> round(0.1, 1) + round(0.1, 1) + round(0.1, 1) == round(0.3, 1)
False
Though the numbers cannot be made closer to their intended exact values,
the math.isclose()
function can be useful for comparing inexact values:
>>> math.isclose(0.1 + 0.1 + 0.1, 0.3)
True
Alternatively, the round()
function can be used to compare rough
approximations:
>>> round(math.pi, ndigits=2) == round(22 / 7, ndigits=2)
True
Binary floating-point arithmetic holds many surprises like this. The problem with “0.1” is explained in precise detail below, in the “Representation Error” section. See Examples of Floating Point Problems for a pleasant summary of how binary floating point works and the kinds of problems commonly encountered in practice. Also see The Perils of Floating Point for a more complete account of other common surprises.
As that says near the end, “there are no easy answers.” Still, don’t be unduly wary of floating point! The errors in Python float operations are inherited from the floating-point hardware, and on most machines are on the order of no more than 1 part in 2**53 per operation. That’s more than adequate for most tasks, but you do need to keep in mind that it’s not decimal arithmetic and that every float operation can suffer a new rounding error.
While pathological cases do exist, for most casual use of floating-point
arithmetic you’ll see the result you expect in the end if you simply round the
display of your final results to the number of decimal digits you expect.
str()
usually suffices, and for finer control see the str.format()
method’s format specifiers in Format String Syntax.
For use cases which require exact decimal representation, try using the
decimal
module which implements decimal arithmetic suitable for
accounting applications and high-precision applications.
Another form of exact arithmetic is supported by the fractions
module
which implements arithmetic based on rational numbers (so the numbers like
1/3 can be represented exactly).
If you are a heavy user of floating-point operations you should take a look at the NumPy package and many other packages for mathematical and statistical operations supplied by the SciPy project. See <https://scipy.org>.
Python provides tools that may help on those rare occasions when you really
do want to know the exact value of a float. The
float.as_integer_ratio()
method expresses the value of a float as a
fraction:
>>> x = 3.14159
>>> x.as_integer_ratio()
(3537115888337719, 1125899906842624)
Since the ratio is exact, it can be used to losslessly recreate the original value:
>>> x == 3537115888337719 / 1125899906842624
True
The float.hex()
method expresses a float in hexadecimal (base
16), again giving the exact value stored by your computer:
>>> x.hex()
'0x1.921f9f01b866ep+1'
This precise hexadecimal representation can be used to reconstruct the float value exactly:
>>> x == float.fromhex('0x1.921f9f01b866ep+1')
True
Since the representation is exact, it is useful for reliably porting values across different versions of Python (platform independence) and exchanging data with other languages that support the same format (such as Java and C99).
Another helpful tool is the sum()
function which helps mitigate
loss-of-precision during summation. It uses extended precision for
intermediate rounding steps as values are added onto a running total.
That can make a difference in overall accuracy so that the errors do not
accumulate to the point where they affect the final total:
>>> 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 == 1.0
False
>>> sum([0.1] * 10) == 1.0
True
The math.fsum()
goes further and tracks all of the “lost digits”
as values are added onto a running total so that the result has only a
single rounding. This is slower than sum()
but will be more
accurate in uncommon cases where large magnitude inputs mostly cancel
each other out leaving a final sum near zero:
>>> arr = [-0.10430216751806065, -266310978.67179024, 143401161448607.16,
... -143401161400469.7, 266262841.31058735, -0.003244936839808227]
>>> float(sum(map(Fraction, arr))) # Exact summation with single rounding
8.042173697819788e-13
>>> math.fsum(arr) # Single rounding
8.042173697819788e-13
>>> sum(arr) # Multiple roundings in extended precision
8.042178034628478e-13
>>> total = 0.0
>>> for x in arr:
... total += x # Multiple roundings in standard precision
...
>>> total # Straight addition has no correct digits!
-0.0051575902860057365
15.1. Representation Error¶
This section explains the “0.1” example in detail, and shows how you can perform an exact analysis of cases like this yourself. Basic familiarity with binary floating-point representation is assumed.
Representation error refers to the fact that some (most, actually) decimal fractions cannot be represented exactly as binary (base 2) fractions. This is the chief reason why Python (or Perl, C, C++, Java, Fortran, and many others) often won’t display the exact decimal number you expect.
Why is that? 1/10 is not exactly representable as a binary fraction. Since at least 2000, almost all machines use IEEE 754 binary floating-point arithmetic, and almost all platforms map Python floats to IEEE 754 binary64 “double precision” values. IEEE 754 binary64 values contain 53 bits of precision, so on input the computer strives to convert 0.1 to the closest fraction it can of the form J/2**N where J is an integer containing exactly 53 bits. Rewriting
1 / 10 ~= J / (2**N)
as
J ~= 2**N / 10
and recalling that J has exactly 53 bits (is >= 2**52
but < 2**53
),
the best value for N is 56:
>>> 2**52 <= 2**56 // 10 < 2**53
True
That is, 56 is the only value for N that leaves J with exactly 53 bits. The best possible value for J is then that quotient rounded:
>>> q, r = divmod(2**56, 10)
>>> r
6
Since the remainder is more than half of 10, the best approximation is obtained by rounding up:
>>> q+1
7205759403792794
Therefore the best possible approximation to 1/10 in IEEE 754 double precision is:
7205759403792794 / 2 ** 56
Dividing both the numerator and denominator by two reduces the fraction to:
3602879701896397 / 2 ** 55
Note that since we rounded up, this is actually a little bit larger than 1/10; if we had not rounded up, the quotient would have been a little bit smaller than 1/10. But in no case can it be exactly 1/10!
So the computer never “sees” 1/10: what it sees is the exact fraction given above, the best IEEE 754 double approximation it can get:
>>> 0.1 * 2 ** 55
3602879701896397.0
If we multiply that fraction by 10**55, we can see the value out to 55 decimal digits:
>>> 3602879701896397 * 10 ** 55 // 2 ** 55
1000000000000000055511151231257827021181583404541015625
meaning that the exact number stored in the computer is equal to the decimal value 0.1000000000000000055511151231257827021181583404541015625. Instead of displaying the full decimal value, many languages (including older versions of Python), round the result to 17 significant digits:
>>> format(0.1, '.17f')
'0.10000000000000001'
The fractions
and decimal
modules make these calculations
easy:
>>> from decimal import Decimal
>>> from fractions import Fraction
>>> Fraction.from_float(0.1)
Fraction(3602879701896397, 36028797018963968)
>>> (0.1).as_integer_ratio()
(3602879701896397, 36028797018963968)
>>> Decimal.from_float(0.1)
Decimal('0.1000000000000000055511151231257827021181583404541015625')
>>> format(Decimal.from_float(0.1), '.17')
'0.10000000000000001'


14. Interactive Input Editing and History Substitution¶
Some versions of the Python interpreter support editing of the current input line and history substitution, similar to facilities found in the Korn shell and the GNU Bash shell. This is implemented using the GNU Readline library, which supports various styles of editing. This library has its own documentation which we won’t duplicate here.
14.1. Tab Completion and History Editing¶
Completion of variable and module names is
automatically enabled at interpreter startup so
that the Tab key invokes the completion function; it looks at
Python statement names, the current local variables, and the available
module names. For dotted expressions such as string.a
, it will evaluate
the expression up to the final '.'
and then suggest completions from
the attributes of the resulting object. Note that this may execute
application-defined code if an object with a __getattr__()
method
is part of the expression. The default configuration also saves your
history into a file named .python_history
in your user directory.
The history will be available again during the next interactive interpreter
session.
14.2. Alternatives to the Interactive Interpreter¶
This facility is an enormous step forward compared to earlier versions of the
interpreter; however, some wishes are left: It would be nice if the proper
indentation were suggested on continuation lines (the parser knows if an
INDENT
token is required next). The completion mechanism might
use the interpreter’s symbol table. A command to check (or even suggest)
matching parentheses, quotes, etc., would also be useful.
One alternative enhanced interactive interpreter that has been around for quite some time is IPython, which features tab completion, object exploration and advanced history management. It can also be thoroughly customized and embedded into other applications. Another similar enhanced interactive environment is bpython.


What Now?

13. What Now?¶
Reading this tutorial has probably reinforced your interest in using Python — you should be eager to apply Python to solving your real-world problems. Where should you go to learn more?
This tutorial is part of Python’s documentation set. Some other documents in the set are:
-
You should browse through this manual, which gives complete (though terse) reference material about types, functions, and the modules in the standard library. The standard Python distribution includes a lot of additional code. There are modules to read Unix mailboxes, retrieve documents via HTTP, generate random numbers, parse command-line options, compress data, and many other tasks. Skimming through the Library Reference will give you an idea of what’s available.
Installing Python Modules explains how to install additional modules written by other Python users.
The Python Language Reference: A detailed explanation of Python’s syntax and semantics. It’s heavy reading, but is useful as a complete guide to the language itself.
More Python resources:
https://www.python.org: The major Python website. It contains code, documentation, and pointers to Python-related pages around the web.
https://docs.python.org: Fast access to Python’s documentation.
https://pypi.org: The Python Package Index, previously also nicknamed the Cheese Shop [1], is an index of user-created Python modules that are available for download. Once you begin releasing code, you can register it here so that others can find it.
https://code.activestate.com/recipes/langs/python/: The Python Cookbook is a sizable collection of code examples, larger modules, and useful scripts. Particularly notable contributions are collected in a book also titled Python Cookbook (O’Reilly & Associates, ISBN 0-596-00797-3.)
https://pyvideo.org collects links to Python-related videos from conferences and user-group meetings.
https://scipy.org: The Scientific Python project includes modules for fast array computations and manipulations plus a host of packages for such things as linear algebra, Fourier transforms, non-linear solvers, random number distributions, statistical analysis and the like.
For Python-related questions and problem reports, you can post to the newsgroup comp.lang.python, or send them to the mailing list at python-list@python.org. The newsgroup and mailing list are gatewayed, so messages posted to one will automatically be forwarded to the other. There are hundreds of postings a day, asking (and answering) questions, suggesting new features, and announcing new modules. Mailing list archives are available at https://mail.python.org/pipermail/.
Before posting, be sure to check the list of Frequently Asked Questions (also called the FAQ). The FAQ answers many of the questions that come up again and again, and may already contain the solution for your problem.
Footnotes


Virtual Environments and Packages

12. Virtual Environments and Packages¶
12.1. Introduction¶
Python applications will often use packages and modules that don’t come as part of the standard library. Applications will sometimes need a specific version of a library, because the application may require that a particular bug has been fixed or the application may be written using an obsolete version of the library’s interface.
This means it may not be possible for one Python installation to meet the requirements of every application. If application A needs version 1.0 of a particular module but application B needs version 2.0, then the requirements are in conflict and installing either version 1.0 or 2.0 will leave one application unable to run.
The solution for this problem is to create a virtual environment, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.
Different applications can then use different virtual environments. To resolve the earlier example of conflicting requirements, application A can have its own virtual environment with version 1.0 installed while application B has another virtual environment with version 2.0. If application B requires a library be upgraded to version 3.0, this will not affect application A’s environment.
12.2. Creating Virtual Environments¶
The module used to create and manage virtual environments is called
venv
. venv
will install the Python version from which
the command was run (as reported by the --version
option).
For instance, executing the command with python3.12
will install
version 3.12.
To create a virtual environment, decide upon a directory where you want to
place it, and run the venv
module as a script with the directory path:
python -m venv tutorial-env
This will create the tutorial-env
directory if it doesn’t exist,
and also create directories inside it containing a copy of the Python
interpreter and various supporting files.
A common directory location for a virtual environment is .venv
.
This name keeps the directory typically hidden in your shell and thus
out of the way while giving it a name that explains why the directory
exists. It also prevents clashing with .env
environment variable
definition files that some tooling supports.
Once you’ve created a virtual environment, you may activate it.
On Windows, run:
tutorial-env\Scripts\activate
On Unix or MacOS, run:
source tutorial-env/bin/activate
(This script is written for the bash shell. If you use the
csh or fish shells, there are alternate
activate.csh
and activate.fish
scripts you should use
instead.)
Activating the virtual environment will change your shell’s prompt to show what
virtual environment you’re using, and modify the environment so that running
python
will get you that particular version and installation of Python.
For example:
$ source ~/envs/tutorial-env/bin/activate
(tutorial-env) $ python
Python 3.5.1 (default, May 6 2016, 10:59:36)
...
>>> import sys
>>> sys.path
['', '/usr/local/lib/python35.zip', ...,
'~/envs/tutorial-env/lib/python3.5/site-packages']
>>>
To deactivate a virtual environment, type:
deactivate
into the terminal.
12.3. Managing Packages with pip¶
You can install, upgrade, and remove packages using a program called
pip. By default pip
will install packages from the Python
Package Index. You can browse the Python
Package Index by going to it in your web browser.
pip
has a number of subcommands: “install”, “uninstall”,
“freeze”, etc. (Consult the Installing Python Modules guide for
complete documentation for pip
.)
You can install the latest version of a package by specifying a package’s name:
(tutorial-env) $ python -m pip install novas
Collecting novas
Downloading novas-3.1.1.3.tar.gz (136kB)
Installing collected packages: novas
Running setup.py install for novas
Successfully installed novas-3.1.1.3
You can also install a specific version of a package by giving the
package name followed by ==
and the version number:
(tutorial-env) $ python -m pip install requests==2.6.0
Collecting requests==2.6.0
Using cached requests-2.6.0-py2.py3-none-any.whl
Installing collected packages: requests
Successfully installed requests-2.6.0
If you re-run this command, pip
will notice that the requested
version is already installed and do nothing. You can supply a
different version number to get that version, or you can run python
-m pip install --upgrade
to upgrade the package to the latest version:
(tutorial-env) $ python -m pip install --upgrade requests
Collecting requests
Installing collected packages: requests
Found existing installation: requests 2.6.0
Uninstalling requests-2.6.0:
Successfully uninstalled requests-2.6.0
Successfully installed requests-2.7.0
python -m pip uninstall
followed by one or more package names will
remove the packages from the virtual environment.
python -m pip show
will display information about a particular package:
(tutorial-env) $ python -m pip show requests
---
Metadata-Version: 2.0
Name: requests
Version: 2.7.0
Summary: Python HTTP for Humans.
Home-page: http://python-requests.org
Author: Kenneth Reitz
Author-email: me@kennethreitz.com
License: Apache 2.0
Location: /Users/akuchling/envs/tutorial-env/lib/python3.4/site-packages
Requires:
python -m pip list
will display all of the packages installed in
the virtual environment:
(tutorial-env) $ python -m pip list
novas (3.1.1.3)
numpy (1.9.2)
pip (7.0.3)
requests (2.7.0)
setuptools (16.0)
python -m pip freeze
will produce a similar list of the installed packages,
but the output uses the format that python -m pip install
expects.
A common convention is to put this list in a requirements.txt
file:
(tutorial-env) $ python -m pip freeze > requirements.txt
(tutorial-env) $ cat requirements.txt
novas==3.1.1.3
numpy==1.9.2
requests==2.7.0
The requirements.txt
can then be committed to version control and
shipped as part of an application. Users can then install all the
necessary packages with install -r
:
(tutorial-env) $ python -m pip install -r requirements.txt
Collecting novas==3.1.1.3 (from -r requirements.txt (line 1))
...
Collecting numpy==1.9.2 (from -r requirements.txt (line 2))
...
Collecting requests==2.7.0 (from -r requirements.txt (line 3))
...
Installing collected packages: novas, numpy, requests
Running setup.py install for novas
Successfully installed novas-3.1.1.3 numpy-1.9.2 requests-2.7.0
pip
has many more options. Consult the Installing Python Modules
guide for complete documentation for pip
. When you’ve written
a package and want to make it available on the Python Package Index,
consult the Python packaging user guide.


11. Brief Tour of the Standard Library — Part II¶
This second tour covers more advanced modules that support professional programming needs. These modules rarely occur in small scripts.
11.1. Output Formatting¶
The reprlib
module provides a version of repr()
customized for
abbreviated displays of large or deeply nested containers:
>>> import reprlib
>>> reprlib.repr(set('supercalifragilisticexpialidocious'))
"{'a', 'c', 'd', 'e', 'f', 'g', ...}"
The pprint
module offers more sophisticated control over printing both
built-in and user defined objects in a way that is readable by the interpreter.
When the result is longer than one line, the “pretty printer” adds line breaks
and indentation to more clearly reveal data structure:
>>> import pprint
>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
... 'yellow'], 'blue']]]
...
>>> pprint.pprint(t, width=30)
[[[['black', 'cyan'],
'white',
['green', 'red']],
[['magenta', 'yellow'],
'blue']]]
The textwrap
module formats paragraphs of text to fit a given screen
width:
>>> 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.
The locale
module accesses a database of culture specific data formats.
The grouping attribute of locale’s format function provides a direct way of
formatting numbers with group separators:
>>> import locale
>>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')
'English_United States.1252'
>>> conv = locale.localeconv() # get a mapping of conventions
>>> x = 1234567.8
>>> locale.format_string("%d", x, grouping=True)
'1,234,567'
>>> locale.format_string("%s%.*f", (conv['currency_symbol'],
... conv['frac_digits'], x), grouping=True)
'$1,234,567.80'
11.2. Templating¶
The string
module includes a versatile Template
class
with a simplified syntax suitable for editing by end-users. This allows users
to customize their applications without having to alter the application.
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.')
>>> 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:
>>> t = Template('Return the $item to $owner.')
>>> d = dict(item='unladen swallow')
>>> t.substitute(d)
Traceback (most recent call last):
...
KeyError: 'owner'
>>> t.safe_substitute(d)
'Return the unladen swallow to $owner.'
Template subclasses can specify a custom delimiter. For example, a batch renaming utility for a photo browser may elect to use percent signs for placeholders such as the current date, image sequence number, or file format:
>>> import time, os.path
>>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
>>> class BatchRename(Template):
... delimiter = '%'
...
>>> fmt = input('Enter rename style (%d-date %n-seqnum %f-format): ')
Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f
>>> 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('{0} --> {1}'.format(filename, newname))
img_1074.jpg --> Ashley_0.jpg
img_1076.jpg --> Ashley_1.jpg
img_1077.jpg --> Ashley_2.jpg
Another application for templating is separating program logic from the details of multiple output formats. This makes it possible to substitute custom templates for XML files, plain text reports, and HTML web reports.
11.3. Working with Binary Data Record Layouts¶
The struct
module provides pack()
and
unpack()
functions for working with variable length binary
record formats. The following example shows
how to loop through header information in a ZIP file without using the
zipfile
module. Pack codes "H"
and "I"
represent two and four
byte unsigned numbers respectively. The "<"
indicates that they are
standard size and in little-endian byte order:
import struct
with open('myfile.zip', 'rb') as f:
data = f.read()
start = 0
for i in range(3): # show the first 3 file headers
start += 14
fields = struct.unpack('<IIIHH', 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
11.4. Multi-threading¶
Threading is a technique for decoupling tasks which are not sequentially dependent. Threads can be used to improve the responsiveness of applications that accept user input while other tasks run in the background. A related use case is running I/O in parallel with computations in another thread.
The following code shows how the high level threading
module can run
tasks in background while the main program continues to run:
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.')
The principal challenge of multi-threaded applications is coordinating threads that share data or other resources. To that end, the threading module provides a number of synchronization primitives including locks, events, condition variables, and semaphores.
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.
11.5. Logging¶
The logging
module offers a full featured and flexible logging system.
At its simplest, log messages are sent to a file or to sys.stderr
:
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')
This produces the following output:
WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down
By default, informational and debugging messages are suppressed and the output
is sent to standard error. Other output options include routing messages
through email, datagrams, sockets, or to an HTTP Server. New filters can select
different routing based on message priority: DEBUG
,
INFO
, WARNING
, ERROR
,
and CRITICAL
.
The logging system can be configured directly from Python or can be loaded from a user editable configuration file for customized logging without altering the application.
11.6. 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
>>> d['primary'] # fetch the object if it is still alive
10
>>> del a # remove the one reference
>>> gc.collect() # run garbage collection right away
0
>>> d['primary'] # entry was automatically removed
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
d['primary'] # entry was automatically removed
File "C:/python314/lib/weakref.py", line 46, in __getitem__
o = self.data[key]()
KeyError: 'primary'
11.7. Tools for Working with Lists¶
Many data structure needs can be met with the built-in list type. However, sometimes there is a need for alternative implementations with different performance trade-offs.
The array
module provides an array
object that is like
a list that stores only homogeneous 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])
>>> sum(a)
26932
>>> 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"])
>>> 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)
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'))
>>> 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]
11.8. Decimal Floating-Point Arithmetic¶
The decimal
module offers a Decimal
datatype for
decimal floating-point arithmetic. Compared to the built-in float
implementation of binary floating point, the 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
applications where the user expects the results to match calculations done by hand.
For example, calculating a 5% tax on a 70 cent phone charge gives different results in decimal floating point and binary floating point. The difference becomes significant if the results are rounded to the nearest cent:
>>> from decimal import *
>>> round(Decimal('0.70') * Decimal('1.05'), 2)
Decimal('0.74')
>>> round(.70 * 1.05, 2)
0.73
The Decimal
result keeps a trailing zero, automatically
inferring four place significance from multiplicands with two place
significance. Decimal reproduces mathematics as done by hand and avoids
issues that can arise when binary floating point cannot exactly represent
decimal quantities.
Exact representation enables the Decimal
class to perform
modulo calculations and equality tests that are unsuitable for binary floating
point:
>>> Decimal('1.00') % Decimal('.10')
Decimal('0.00')
>>> 1.00 % 0.10
0.09999999999999995
>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
True
>>> 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 == 1.0
False
The decimal
module provides arithmetic with as much precision as needed:
>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)
Decimal('0.142857142857142857142857142857142857')


Brief Tour of the Standard Library

10. Brief Tour of the Standard Library¶
10.1. Operating System Interface¶
The os
module provides dozens of functions for interacting with the
operating system:
>>> import os
>>> 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 shell
0
Be sure to use the import os
style instead of from os import *
. This
will keep os.open()
from shadowing the built-in open()
function which
operates much differently.
The built-in dir()
and help()
functions are useful as interactive
aids for working with large modules like os
:
>>> 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'
10.2. File Wildcards¶
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']
10.3. 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. For
instance, let’s take the following demo.py
file:
# File demo.py
import sys
print(sys.argv)
Here is the output from running python demo.py one two three
at the command
line:
['demo.py', 'one', 'two', 'three']
The argparse
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:
import argparse
parser = 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 python top.py --lines=5 alpha.txt
beta.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 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()
.
10.5. String Pattern Matching¶
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'
10.6. 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
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 from the interval [0.0, 1.0)
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 <https://scipy.org> 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 urllib.request
for retrieving data
from URLs and smtplib
for sending mail:
>>> from urllib.request import urlopen
>>> with urlopen('https://docs.python.org/3/') as response:
... for line in response:
... line = line.decode() # Convert bytes to a str
... if 'updated' in line:
... print(line.rstrip()) # Remove trailing newline
...
Last updated on Nov 11, 2025 (20:11 UTC).
>>> 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()
(Note that the second example needs a mailserver running on localhost.)
10.8. Dates and Times¶
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
10.9. 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
10.10. 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 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
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.
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 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
10.12. 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
xmlrpc.client
andxmlrpc.server
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
email
package is a library for managing email messages, including MIME and other RFC 5322-based message documents. Unlikesmtplib
andpoplib
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
json
package provides robust support for parsing this popular data interchange format. Thecsv
module supports direct reading and writing of files in Comma-Separated Value format, commonly supported by databases and spreadsheets. XML processing is supported by thexml.etree.ElementTree
,xml.dom
andxml.sax
packages. Together, these modules and packages greatly simplify data interchange between Python applications and other tools.The
sqlite3
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
gettext
,locale
, and thecodecs
package.


Classes

9. Classes¶
Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.
Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modified further after creation.
In C++ terminology, normally class members (including the data members) are public (except see below Private Variables), and all member functions are virtual. As in Modula-3, there are no shorthands for referencing the object’s members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.
(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)
9.1. A Word About Names and Objects¶
Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change — this eliminates the need for two different argument passing mechanisms as in Pascal.
9.2. Python Scopes and Namespaces¶
Before introducing classes, I first have to tell you something about Python’s scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.
Let’s begin with some definitions.
A namespace is a mapping from names to objects. Most namespaces are currently
implemented as Python dictionaries, but that’s normally not noticeable in any
way (except for performance), and it may change in the future. Examples of
namespaces are: the set of built-in names (containing functions such as abs()
, and
built-in exception names); the global names in a module; and the local names in
a function invocation. In a sense the set of attributes of an object also form
a namespace. The important thing to know about namespaces is that there is
absolutely no relation between names in different namespaces; for instance, two
different modules may both define a function maximize
without confusion —
users of the modules must prefix it with the module name.
By the way, I use the word attribute for any name following a dot — for
example, in the expression z.real
, real
is an attribute of the object
z
. Strictly speaking, references to names in modules are attribute
references: in the expression modname.funcname
, modname
is a module
object and funcname
is an attribute of it. In this case there happens to be
a straightforward mapping between the module’s attributes and the global names
defined in the module: they share the same namespace! [1]
Attributes may be read-only or writable. In the latter case, assignment to
attributes is possible. Module attributes are writable: you can write
modname.the_answer = 42
. Writable attributes may also be deleted with the
del
statement. For example, del modname.the_answer
will remove
the attribute the_answer
from the object named by modname
.
Namespaces are created at different moments and have different lifetimes. The
namespace containing the built-in names is created when the Python interpreter
starts up, and is never deleted. The global namespace for a module is created
when the module definition is read in; normally, module namespaces also last
until the interpreter quits. The statements executed by the top-level
invocation of the interpreter, either read from a script file or interactively,
are considered part of a module called __main__
, so they have their own
global namespace. (The built-in names actually also live in a module; this is
called builtins
.)
The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have their own local namespace.
A scope is a textual region of a Python program where a namespace is directly accessible. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.
Although scopes are determined statically, they are used dynamically. At any time during execution, there are 3 or 4 nested scopes whose namespaces are directly accessible:
the innermost scope, which is searched first, contains the local names
the scopes of any enclosing functions, which are searched starting with the nearest enclosing scope, contain non-local, but also non-global names
the next-to-last scope contains the current module’s global names
the outermost scope (searched last) is the namespace containing built-in names
If a name is declared global, then all references and assignments go directly to
the next-to-last scope containing the module’s global names. To rebind variables
found outside of the innermost scope, the nonlocal
statement can be
used; if not declared nonlocal, those variables are read-only (an attempt to
write to such a variable will simply create a new local variable in the
innermost scope, leaving the identically named outer variable unchanged).
Usually, the local scope references the local names of the (textually) current function. Outside functions, the local scope references the same namespace as the global scope: the module’s namespace. Class definitions place yet another namespace in the local scope.
It is important to realize that scopes are determined textually: the global scope of a function defined in a module is that module’s namespace, no matter from where or by what alias the function is called. On the other hand, the actual search for names is done dynamically, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on dynamic name resolution! (In fact, local variables are already determined statically.)
A special quirk of Python is that – if no global
or nonlocal
statement is in effect – assignments to names always go into the innermost scope.
Assignments do not copy data — they just bind names to objects. The same is true
for deletions: the statement del x
removes the binding of x
from the
namespace referenced by the local scope. In fact, all operations that introduce
new names use the local scope: in particular, import
statements and
function definitions bind the module or function name in the local scope.
The global
statement can be used to indicate that particular
variables live in the global scope and should be rebound there; the
nonlocal
statement indicates that particular variables live in
an enclosing scope and should be rebound there.
9.2.1. Scopes and Namespaces Example¶
This is an example demonstrating how to reference the different scopes and
namespaces, and how global
and nonlocal
affect variable
binding:
def scope_test():
def do_local():
spam = "local spam"
def do_nonlocal():
nonlocal spam
spam = "nonlocal spam"
def do_global():
global spam
spam = "global spam"
spam = "test spam"
do_local()
print("After local assignment:", spam)
do_nonlocal()
print("After nonlocal assignment:", spam)
do_global()
print("After global assignment:", spam)
scope_test()
print("In global scope:", spam)
The output of the example code is:
After local assignment: test spam
After nonlocal assignment: nonlocal spam
After global assignment: nonlocal spam
In global scope: global spam
Note how the local assignment (which is default) didn’t change scope_test's
binding of spam. The nonlocal
assignment changed scope_test's
binding of spam, and the global
assignment changed the module-level
binding.
You can also see that there was no previous binding for spam before the
global
assignment.
9.3. A First Look at Classes¶
Classes introduce a little bit of new syntax, three new object types, and some new semantics.
9.3.1. Class Definition Syntax¶
The simplest form of class definition looks like this:
class ClassName:
<statement-1>
.
.
.
<statement-N>
Class definitions, like function definitions (def
statements) must be
executed before they have any effect. (You could conceivably place a class
definition in a branch of an if
statement, or inside a function.)
In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful — we’ll come back to this later. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods — again, this is explained later.
When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.
When a class definition is left normally (via the end), a class object is
created. This is basically a wrapper around the contents of the namespace
created by the class definition; we’ll learn more about class objects in the
next section. The original local scope (the one in effect just before the class
definition was entered) is reinstated, and the class object is bound here to the
class name given in the class definition header (ClassName
in the
example).
9.3.2. Class Objects¶
Class objects support two kinds of operations: attribute references and instantiation.
Attribute references use the standard syntax used for all attribute references
in Python: obj.name
. Valid attribute names are all the names that were in
the class’s namespace when the class object was created. So, if the class
definition looked like this:
class MyClass:
"""A simple example class"""
i = 12345
def f(self):
return 'hello world'
then MyClass.i
and MyClass.f
are valid attribute references, returning
an integer and a function object, respectively. Class attributes can also be
assigned to, so you can change the value of MyClass.i
by assignment.
__doc__
is also a valid attribute, returning the docstring
belonging to the class: "A simple example class"
.
Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):
x = MyClass()
creates a new instance of the class and assigns this object to the local
variable x
.
The instantiation operation (“calling” a class object) creates an empty object.
Many classes like to create objects with instances customized to a specific
initial state. Therefore a class may define a special method named
__init__()
, like this:
def __init__(self):
self.data = []
When a class defines an __init__()
method, class instantiation
automatically invokes __init__()
for the newly created class instance. So
in this example, a new, initialized instance can be obtained by:
x = MyClass()
Of course, the __init__()
method may have arguments for greater
flexibility. In that case, arguments given to the class instantiation operator
are passed on to __init__()
. For example,
>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
9.3.3. Instance Objects¶
Now what can we do with instance objects? The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names: data attributes and methods.
Data attributes correspond to “instance variables” in Smalltalk, and to “data
members” in C++. Data attributes need not be declared; like local variables,
they spring into existence when they are first assigned to. For example, if
x
is the instance of MyClass
created above, the following piece of
code will print the value 16
, without leaving a trace:
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print(x.counter)
del x.counter
The other kind of instance attribute reference is a method. A method is a function that “belongs to” an object.
Valid method names of an instance object depend on its class. By definition,
all attributes of a class that are function objects define corresponding
methods of its instances. So in our example, x.f
is a valid method
reference, since MyClass.f
is a function, but x.i
is not, since
MyClass.i
is not. But x.f
is not the same thing as MyClass.f
— it
is a method object, not a function object.
9.3.4. Method Objects¶
Usually, a method is called right after it is bound:
x.f()
If x = MyClass()
, as above, this will return the string 'hello world'
.
However, it is not necessary to call a method right away: x.f
is a method
object, and can be stored away and called at a later time. For example:
xf = x.f
while True:
print(xf())
will continue to print hello world
until the end of time.
What exactly happens when a method is called? You may have noticed that
x.f()
was called without an argument above, even though the function
definition for f()
specified an argument. What happened to the argument?
Surely Python raises an exception when a function that requires an argument is
called without any — even if the argument isn’t actually used…
Actually, you may have guessed the answer: the special thing about methods is
that the instance object is passed as the first argument of the function. In our
example, the call x.f()
is exactly equivalent to MyClass.f(x)
. In
general, calling a method with a list of n arguments is equivalent to calling
the corresponding function with an argument list that is created by inserting
the method’s instance object before the first argument.
In general, methods work as follows. When a non-data attribute of an instance is referenced, the instance’s class is searched. If the name denotes a valid class attribute that is a function object, references to both the instance object and the function object are packed into a method object. When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.
9.3.5. Class and Instance Variables¶
Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:
class Dog:
kind = 'canine' # class variable shared by all instances
def __init__(self, name):
self.name = name # instance variable unique to each instance
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind # shared by all dogs
'canine'
>>> e.kind # shared by all dogs
'canine'
>>> d.name # unique to d
'Fido'
>>> e.name # unique to e
'Buddy'
As discussed in A Word About Names and Objects, shared data can have possibly surprising effects involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances:
class Dog:
tricks = [] # mistaken use of a class variable
def __init__(self, name):
self.name = name
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks # unexpectedly shared by all dogs
['roll over', 'play dead']
Correct design of the class should use an instance variable instead:
class Dog:
def __init__(self, name):
self.name = name
self.tricks = [] # creates a new empty list for each dog
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks
['roll over']
>>> e.tricks
['play dead']
9.4. Random Remarks¶
If the same attribute name occurs in both an instance and in a class, then attribute lookup prioritizes the instance:
>>> class Warehouse:
... purpose = 'storage'
... region = 'west'
...
>>> w1 = Warehouse()
>>> print(w1.purpose, w1.region)
storage west
>>> w2 = Warehouse()
>>> w2.region = 'east'
>>> print(w2.purpose, w2.region)
storage east
Data attributes may be referenced by methods as well as by ordinary users (“clients”) of an object. In other words, classes are not usable to implement pure abstract data types. In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)
Clients should use data attributes with care — clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that clients may add data attributes of their own to an instance object without affecting the validity of the methods, as long as name conflicts are avoided — again, a naming convention can save a lot of headaches here.
There is no shorthand for referencing data attributes (or other methods!) from within methods. I find that this actually increases the readability of methods: there is no chance of confusing local variables and instance variables when glancing through a method.
Often, the first argument of a method is called self
. This is nothing more
than a convention: the name self
has absolutely no special meaning to
Python. Note, however, that by not following the convention your code may be
less readable to other Python programmers, and it is also conceivable that a
class browser program might be written that relies upon such a convention.
Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:
# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)
class C:
f = f1
def g(self):
return 'hello world'
h = g
Now f
, g
and h
are all attributes of class C
that refer to
function objects, and consequently they are all methods of instances of
C
— h
being exactly equivalent to g
. Note that this practice
usually only serves to confuse the reader of a program.
Methods may call other methods by using method attributes of the self
argument:
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
Methods may reference global names in the same way as ordinary functions. The global scope associated with a method is the module containing its definition. (A class is never used as a global scope.) While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope, and in the next section we’ll find some good reasons why a method would want to reference its own class.
Each value is an object, and therefore has a class (also called its type).
It is stored as object.__class__
.
9.5. Inheritance¶
Of course, a language feature would not be worthy of the name “class” without supporting inheritance. The syntax for a derived class definition looks like this:
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
The name BaseClassName
must be defined in a
namespace accessible from the scope containing the
derived class definition. In place of a base class name, other arbitrary
expressions are also allowed. This can be useful, for example, when the base
class is defined in another module:
class DerivedClassName(modname.BaseClassName):
Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.
There’s nothing special about instantiation of derived classes:
DerivedClassName()
creates a new instance of the class. Method references
are resolved as follows: the corresponding class attribute is searched,
descending down the chain of base classes if necessary, and the method reference
is valid if this yields a function object.
Derived classes may override methods of their base classes. Because methods
have no special privileges when calling other methods of the same object, a
method of a base class that calls another method defined in the same base class
may end up calling a method of a derived class that overrides it. (For C++
programmers: all methods in Python are effectively virtual
.)
An overriding method in a derived class may in fact want to extend rather than
simply replace the base class method of the same name. There is a simple way to
call the base class method directly: just call BaseClassName.methodname(self,
arguments)
. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as BaseClassName
in the global
scope.)
Python has two built-in functions that work with inheritance:
Use
isinstance()
to check an instance’s type:isinstance(obj, int)
will beTrue
only ifobj.__class__
isint
or some class derived fromint
.Use
issubclass()
to check class inheritance:issubclass(bool, int)
isTrue
sincebool
is a subclass ofint
. However,issubclass(float, int)
isFalse
sincefloat
is not a subclass ofint
.
9.5.1. Multiple Inheritance¶
Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
For most purposes, in the simplest cases, you can think of the search for
attributes inherited from a parent class as depth-first, left-to-right, not
searching twice in the same class where there is an overlap in the hierarchy.
Thus, if an attribute is not found in DerivedClassName
, it is searched
for in Base1
, then (recursively) in the base classes of Base1
,
and if it was not found there, it was searched for in Base2
, and so on.
In fact, it is slightly more complex than that; the method resolution order
changes dynamically to support cooperative calls to super()
. This
approach is known in some other multiple-inheritance languages as
call-next-method and is more powerful than the super call found in
single-inheritance languages.
Dynamic ordering is necessary because all cases of multiple inheritance exhibit
one or more diamond relationships (where at least one of the parent classes
can be accessed through multiple paths from the bottommost class). For example,
all classes inherit from object
, so any case of multiple inheritance
provides more than one path to reach object
. To keep the base classes
from being accessed more than once, the dynamic algorithm linearizes the search
order in a way that preserves the left-to-right ordering specified in each
class, that calls each parent only once, and that is monotonic (meaning that a
class can be subclassed without affecting the precedence order of its parents).
Taken together, these properties make it possible to design reliable and
extensible classes with multiple inheritance. For more detail, see
The Python 2.3 Method Resolution Order.
9.6. Private Variables¶
“Private” instance variables that cannot be accessed except from inside an
object don’t exist in Python. However, there is a convention that is followed
by most Python code: a name prefixed with an underscore (e.g. _spam
) should
be treated as a non-public part of the API (whether it is a function, a method
or a data member). It should be considered an implementation detail and subject
to change without notice.
Since there is a valid use-case for class-private members (namely to avoid name
clashes of names with names defined by subclasses), there is limited support for
such a mechanism, called name mangling. Any identifier of the form
__spam
(at least two leading underscores, at most one trailing underscore)
is textually replaced with _classname__spam
, where classname
is the
current class name with leading underscore(s) stripped. This mangling is done
without regard to the syntactic position of the identifier, as long as it
occurs within the definition of a class.
See also
The private name mangling specifications for details and special cases.
Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls. For example:
class Mapping:
def __init__(self, iterable):
self.items_list = []
self.__update(iterable)
def update(self, iterable):
for item in iterable:
self.items_list.append(item)
__update = update # private copy of original update() method
class MappingSubclass(Mapping):
def update(self, keys, values):
# provides new signature for update()
# but does not break __init__()
for item in zip(keys, values):
self.items_list.append(item)
The above example would work even if MappingSubclass
were to introduce a
__update
identifier since it is replaced with _Mapping__update
in the
Mapping
class and _MappingSubclass__update
in the MappingSubclass
class respectively.
Note that the mangling rules are designed mostly to avoid accidents; it still is possible to access or modify a variable that is considered private. This can even be useful in special circumstances, such as in the debugger.
Notice that code passed to exec()
or eval()
does not consider the
classname of the invoking class to be the current class; this is similar to the
effect of the global
statement, the effect of which is likewise restricted
to code that is byte-compiled together. The same restriction applies to
getattr()
, setattr()
and delattr()
, as well as when referencing
__dict__
directly.
9.7. Odds and Ends¶
Sometimes it is useful to have a data type similar to the Pascal “record” or C
“struct”, bundling together a few named data items. The idiomatic approach
is to use dataclasses
for this purpose:
from dataclasses import dataclass
@dataclass
class Employee:
name: str
dept: str
salary: int
>>> john = Employee('john', 'computer lab', 1000)
>>> john.dept
'computer lab'
>>> john.salary
1000
A piece of Python code that expects a particular abstract data type can often be
passed a class that emulates the methods of that data type instead. For
instance, if you have a function that formats some data from a file object, you
can define a class with methods read()
and
readline()
that get the
data from a string buffer instead, and pass it as an argument.
Instance method objects have attributes, too:
m.__self__
is the instance
object with the method m()
, and m.__func__
is
the function object
corresponding to the method.
9.8. Iterators¶
By now you have probably noticed that most container objects can be looped over
using a for
statement:
for element in [1, 2, 3]:
print(element)
for element in (1, 2, 3):
print(element)
for key in {'one':1, 'two':2}:
print(key)
for char in "123":
print(char)
for line in open("myfile.txt"):
print(line, end='')
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the for
statement
calls iter()
on the container object. The function returns an iterator
object that defines the method __next__()
which accesses
elements in the container one at a time. When there are no more elements,
__next__()
raises a StopIteration
exception which tells the
for
loop to terminate. You can call the __next__()
method
using the next()
built-in function; this example shows how it all works:
>>> s = 'abc'
>>> it = iter(s)
>>> it
<str_iterator object at 0x10c90e650>
>>> next(it)
'a'
>>> next(it)
'b'
>>> next(it)
'c'
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
next(it)
StopIteration
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an __iter__()
method which
returns an object with a __next__()
method. If the class
defines __next__()
, then __iter__()
can just return self
:
class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
... print(char)
...
m
a
p
s
9.9. Generators¶
Generators are a simple and powerful tool for creating iterators. They
are written like regular functions but use the yield
statement
whenever they want to return data. Each time next()
is called on it, the
generator resumes where it left off (it remembers all the data values and which
statement was last executed). An example shows that generators can be trivially
easy to create:
def reverse(data):
for index in range(len(data)-1, -1, -1):
yield data[index]
>>> for char in reverse('golf'):
... print(char)
...
f
l
o
g
Anything that can be done with generators can also be done with class-based
iterators as described in the previous section. What makes generators so
compact is that the __iter__()
and __next__()
methods
are created automatically.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like self.index
and self.data
.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise StopIteration
. In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.
9.10. Generator Expressions¶
Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.
Examples:
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> unique_words = set(word for line in page for word in line.split())
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']
Footnotes


Python Frequently Asked Questions

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Installing Python Modules

Installing Python Modules¶
- Email:
As a popular open source development project, Python has an active supporting community of contributors and users that also make their software available for other Python developers to use under open source license terms.
This allows Python users to share and collaborate effectively, benefiting from the solutions others have already created to common (and sometimes even rare!) problems, as well as potentially contributing their own solutions to the common pool.
This guide covers the installation part of the process. For a guide to creating and sharing your own Python projects, refer to the Python packaging user guide.
Note
For corporate and other institutional users, be aware that many organisations have their own policies around using and contributing to open source software. Please take such policies into account when making use of the distribution and installation tools provided with Python.
Key terms¶
pip
is the preferred installer program. Starting with Python 3.4, it is included by default with the Python binary installers.A virtual environment is a semi-isolated Python environment that allows packages to be installed for use by a particular application, rather than being installed system wide.
venv
is the standard tool for creating virtual environments, and has been part of Python since Python 3.3. Starting with Python 3.4, it defaults to installingpip
into all created virtual environments.virtualenv
is a third party alternative (and predecessor) tovenv
. It allows virtual environments to be used on versions of Python prior to 3.4, which either don’t providevenv
at all, or aren’t able to automatically installpip
into created environments.The Python Package Index is a public repository of open source licensed packages made available for use by other Python users.
the Python Packaging Authority is the group of developers and documentation authors responsible for the maintenance and evolution of the standard packaging tools and the associated metadata and file format standards. They maintain a variety of tools, documentation, and issue trackers on GitHub.
distutils
is the original build and distribution system first added to the Python standard library in 1998. While direct use ofdistutils
is being phased out, it still laid the foundation for the current packaging and distribution infrastructure, and it not only remains part of the standard library, but its name lives on in other ways (such as the name of the mailing list used to coordinate Python packaging standards development).
Changed in version 3.5: The use of venv
is now recommended for creating virtual environments.
Basic usage¶
The standard packaging tools are all designed to be used from the command line.
The following command will install the latest version of a module and its dependencies from the Python Package Index:
python -m pip install SomePackage
Note
For POSIX users (including macOS and Linux users), the examples in this guide assume the use of a virtual environment.
For Windows users, the examples in this guide assume that the option to adjust the system PATH environment variable was selected when installing Python.
It’s also possible to specify an exact or minimum version directly on the
command line. When using comparator operators such as >
, <
or some other
special character which get interpreted by shell, the package name and the
version should be enclosed within double quotes:
python -m pip install SomePackage==1.0.4 # specific version
python -m pip install "SomePackage>=1.0.4" # minimum version
Normally, if a suitable module is already installed, attempting to install it again will have no effect. Upgrading existing modules must be requested explicitly:
python -m pip install --upgrade SomePackage
More information and resources regarding pip
and its capabilities can be
found in the Python Packaging User Guide.
Creation of virtual environments is done through the venv
module.
Installing packages into an active virtual environment uses the commands shown
above.
How do I …?¶
These are quick answers or links for some common tasks.
… install pip
in versions of Python prior to Python 3.4?¶
Python only started bundling pip
with Python 3.4. For earlier versions,
pip
needs to be “bootstrapped” as described in the Python Packaging
User Guide.
… install packages just for the current user?¶
Passing the --user
option to python -m pip install
will install a
package just for the current user, rather than for all users of the system.
… install scientific Python packages?¶
A number of scientific Python packages have complex binary dependencies, and
aren’t currently easy to install using pip
directly. At this point in
time, it will often be easier for users to install these packages by
other means
rather than attempting to install them with pip
.
… work with multiple versions of Python installed in parallel?¶
On Linux, macOS, and other POSIX systems, use the versioned Python commands
in combination with the -m
switch to run the appropriate copy of
pip
:
python2 -m pip install SomePackage # default Python 2
python2.7 -m pip install SomePackage # specifically Python 2.7
python3 -m pip install SomePackage # default Python 3
python3.4 -m pip install SomePackage # specifically Python 3.4
Appropriately versioned pip
commands may also be available.
On Windows, use the py
Python launcher in combination with the -m
switch:
py -2 -m pip install SomePackage # default Python 2
py -2.7 -m pip install SomePackage # specifically Python 2.7
py -3 -m pip install SomePackage # default Python 3
py -3.4 -m pip install SomePackage # specifically Python 3.4
Common installation issues¶
Installing into the system Python on Linux¶
On Linux systems, a Python installation will typically be included as part
of the distribution. Installing into this Python installation requires
root access to the system, and may interfere with the operation of the
system package manager and other components of the system if a component
is unexpectedly upgraded using pip
.
On such systems, it is often better to use a virtual environment or a
per-user installation when installing packages with pip
.
Pip not installed¶
It is possible that pip
does not get installed by default. One potential fix is:
python -m ensurepip --default-pip
There are also additional resources for installing pip.
Installing binary extensions¶
Python has typically relied heavily on source based distribution, with end users being expected to compile extension modules from source as part of the installation process.
With the introduction of support for the binary wheel
format, and the
ability to publish wheels for at least Windows and macOS through the
Python Package Index, this problem is expected to diminish over time,
as users are more regularly able to install pre-built extensions rather
than needing to build them themselves.
Some of the solutions for installing scientific software
that are not yet available as pre-built wheel
files may also help with
obtaining other binary extensions without needing to build them locally.