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12. Virtual Environments and PackagesÂ¶

12.1. IntroductionÂ¶

Python applications will often use packages and modules that donât
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 libraryâs 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 Aâs environment.

12.2. Creating Virtual EnvironmentsÂ¶

The module used to create and manage virtual environments is called
venv . venv will usually install the most recent version of
Python that you have available. If you have multiple versions of Python on your
system, you can select a specific Python version by running python3 or
whichever version you want.

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:

       python3 -m venv tutorial-env

      

This will create the tutorial-env directory if it doesnât 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 youâve created a virtual environment, you may activate it.

On Windows, run:

       tutorial-env\Scripts\activate.bat

      

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 shellâs prompt to show what
virtual environment youâre 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 pipÂ¶

You can install, upgrade, and remove packages using a program called
pip . By default pip will install packages from the Python
Package Index, <https://pypi.org>. You can browse the Python
Package Index by going to it in your web browser.

pip has a number of subcommands: âinstallâ, âuninstallâ,
âfreezeâ, etc. (Consult the Installing Python Modules guide for
complete documentation for pip .)

You can install the latest version of a package by specifying a packageâs 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 youâve written
a package and want to make it available on the Python Package Index,
consult the Distributing Python Modules guide.

13. What Now?

13. What Now?Â¶

Reading this tutorial has probably reinforced your interest in using Python â
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 Pythonâs documentation set. Some other documents in
the set are:

The Python Standard Library :

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 whatâs available.

Installing Python Modules explains how to install additional modules written
by other Python users.

The Python Language Reference : A detailed explanation of Pythonâs syntax and
semantics. Itâs heavy reading, but is useful as a complete guide to the
language itself.

More Python resources:

https://www.python.org: The major Python web site. It contains code,
documentation, and pointers to Python-related pages around the web.

https://docs.python.org: Fast access to Pythonâs 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 (OâReilly & 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

1: âCheese Shopâ is a Monty Pythonâs sketch: a customer enters a cheese shop,
but whatever cheese he asks for, the clerk says itâs missing.

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14. Interactive Input Editing and History Substitution

14. Interactive Input Editing and History SubstitutionÂ¶

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 wonât duplicate here.

14.1. Tab Completion and History EditingÂ¶

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 InterpreterÂ¶

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 interpreterâs
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.

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15. Floating Point Arithmetic: Issues and Limitations

15. Floating Point Arithmetic: Issues and LimitationsÂ¶

Floating-point numbers are represented in computer hardware as base 2 (binary)
fractions. For example, the decimal fraction 0.125
has value 1/10 + 2/100 + 5/1000, and in the same way the binary fraction 0.001
has value 0/2 + 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 youâre 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 youâre 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. Youâll see the same kind of
thing in all languages that support your hardwareâs 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'

     

Itâs important to realize that this is, in a real sense, an illusion: youâre
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:

      >>> .1 + .1 + .1 == .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(.1, 1) + round(.1, 1) + round(.1, 1) == round(.3, 1)
False

Though the numbers cannot be made closer to their intended exact values,
the round() function can be useful for post-rounding so that results
with inexact values become comparable to one another:

      >>> round(.1 + .1 + .1, 10) == round(.3, 10)
True

Binary floating-point arithmetic holds many surprises like this. The problem
with â0.1â is explained in precise detail below, in the âRepresentation Errorâ
section. See The Perils of Floating Point
for a more complete account of other common surprises.

As that says near the end, âthere are no easy answers.â Still, donât 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. Thatâs more than adequate for most
tasks, but you do need to keep in mind that itâs 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 youâll 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()
methodâs 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 math.fsum() function which helps mitigate
loss-of-precision during summation. It tracks âlost digitsâ 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:

      >>> sum([0.1] * 10) == 1.0
False
>>> math.fsum([0.1] * 10) == 1.0
True

     

15.1. Representation ErrorÂ¶

This section explains the â0.1â 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 wonât display the exact decimal number you expect.

Why is that? 1/10 is not exactly representable as a binary fraction. Almost all
machines today (November 2000) use IEEE-754 floating point arithmetic, and
almost all platforms map Python floats to IEEE-754 âdouble precisionâ. 754
doubles 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)

      

       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 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 âseesâ 1/10: what it sees is the exact fraction given
above, the best 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'

      

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16. Appendix

16. AppendixÂ¶

16.1. Interactive ModeÂ¶

16.1.1. Error HandlingÂ¶

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; 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 ScriptsÂ¶

On BSDâish Unix systems, Python scripts can be made directly executable, like
shell scripts, by putting the line

#!/usr/bin/env python3.5

(assuming that the interpreter is on the userâs 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 âexecutable modeâ. 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 FileÂ¶

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 ModulesÂ¶

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/python3.5/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

1: A problem with the GNU Readline package may prevent this.

Read article

Python Setup and Usage

Python Setup and UsageÂ¶

This part of the documentation is devoted to general information on the setup
of the Python environment on different platforms, the invocation of the
interpreter and things that make working with Python easier.

1. Command line and environment
- 1.1. Command line
  - 1.1.1. Interface options
  - 1.1.2. Generic options
  - 1.1.3. Miscellaneous options
  - 1.1.4. Options you shouldnât use
- 1.2. Environment variables
  - 1.2.1. Debug-mode variables

2. Using Python on Unix platforms
- 2.1. Getting and installing the latest version of Python
  - 2.1.1. On Linux
  - 2.1.2. On FreeBSD and OpenBSD
  - 2.1.3. On OpenSolaris
- 2.2. Building Python
- 2.3. Python-related paths and files
- 2.4. Miscellaneous
- 2.5. Custom OpenSSL

3. Configure Python
- 3.1. Configure Options
  - 3.1.1. General Options
  - 3.1.2. WebAssembly Options
  - 3.1.3. Install Options
  - 3.1.4. Performance options
  - 3.1.5. Python Debug Build
  - 3.1.6. Debug options
  - 3.1.7. Linker options
  - 3.1.8. Libraries options
  - 3.1.9. Security Options
  - 3.1.10. macOS Options
  - 3.1.11. Cross Compiling Options
- 3.2. Python Build System
  - 3.2.1. Main files of the build system
  - 3.2.2. Main build steps
  - 3.2.3. Main Makefile targets
  - 3.2.4. C extensions
- 3.3. Compiler and linker flags
  - 3.3.1. Preprocessor flags
  - 3.3.2. Compiler flags
  - 3.3.3. Linker flags

4. Using Python on Windows
- 4.1. The full installer
  - 4.1.1. Installation steps
  - 4.1.2. Removing the MAX_PATH Limitation
  - 4.1.3. Installing Without UI
  - 4.1.4. Installing Without Downloading
  - 4.1.5. Modifying an install
- 4.2. The Microsoft Store package
  - 4.2.1. Known issues
    - 4.2.1.1. Redirection of local data, registry, and temporary paths
- 4.3. The nuget.org packages
- 4.4. The embeddable package
  - 4.4.1. Python Application
  - 4.4.2. Embedding Python
- 4.5. Alternative bundles
- 4.6. Configuring Python
  - 4.6.1. Excursus: Setting environment variables
  - 4.6.2. Finding the Python executable
- 4.7. UTF-8 mode
- 4.8. Python Launcher for Windows
  - 4.8.1. Getting started
    - 4.8.1.1. From the command-line
    - 4.8.1.2. Virtual environments
    - 4.8.1.3. From a script
    - 4.8.1.4. From file associations
  - 4.8.2. Shebang Lines
  - 4.8.3. Arguments in shebang lines
  - 4.8.4. Customization
    - 4.8.4.1. Customization via INI files
    - 4.8.4.2. Customizing default Python versions
  - 4.8.5. Diagnostics
  - 4.8.6. Dry Run
  - 4.8.7. Install on demand
  - 4.8.8. Return codes
- 4.9. Finding modules
- 4.10. Additional modules
  - 4.10.1. PyWin32
  - 4.10.2. cx_Freeze
- 4.11. Compiling Python on Windows
- 4.12. Other Platforms

5. Using Python on a Mac
- 5.1. Getting and Installing MacPython
  - 5.1.1. How to run a Python script
  - 5.1.2. Running scripts with a GUI
  - 5.1.3. Configuration
- 5.2. The IDE
- 5.3. Installing Additional Python Packages
- 5.4. GUI Programming on the Mac
- 5.5. Distributing Python Applications on the Mac
- 5.6. Other Resources

6. Editors and IDEs

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12. Virtual Environments and PackagesÂ¶

12.1. IntroductionÂ¶

12.2. Creating Virtual EnvironmentsÂ¶

12.3. Managing Packages with pipÂ¶

13. What Now?Â¶

14. Interactive Input Editing and History SubstitutionÂ¶

14.1. Tab Completion and History EditingÂ¶

14.2. Alternatives to the Interactive InterpreterÂ¶

15. Floating Point Arithmetic: Issues and LimitationsÂ¶

15.1. Representation ErrorÂ¶

16. AppendixÂ¶

16.1. Interactive ModeÂ¶

16.1.1. Error HandlingÂ¶

16.1.2. Executable Python ScriptsÂ¶

16.1.3. The Interactive Startup FileÂ¶

16.1.4. The Customization ModulesÂ¶

Python Setup and UsageÂ¶

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