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Mahmoud Hossam
2012-05-06 06:26:52 +02:00
parent 555311d1ec c1a57aed2a
commit 20452dbb46
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docs/conf.py
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@@ -25,7 +25,7 @@ sys.path.append(os.path.abspath('_themes'))
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.ifconfig', 'sphinx.ext.todo']
extensions = ['sphinx.ext.ifconfig', 'sphinx.ext.todo', 'sphinx.ext.intersphinx']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
@@ -260,3 +260,7 @@ epub_copyright = u'2010, Kenneth Reitz'
#epub_tocdup = True
todo_include_todos = True
intersphinx_mapping = {
'python': ('http://docs.python.org/', None),
}
docs/contents.rst.inc
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@@ -53,6 +53,8 @@ different scenarios.
scenarios/ci
scenarios/speed
scenarios/scientific
scenarios/imaging
scenarios/xml
Shipping Great Code
docs/dev/env.rst
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@@ -12,10 +12,12 @@ however, using a more powerful editor may make your life a bit easier.
VIM
---
There exist a couple of plugins and settings for the VIM editor to aid python
development. If you only develop in Python, a good start is to set the default
settings for indentation and linewrapping to values compliant with PEP8::
Vim is a text editor which uses keyboard shortcuts for editing instead of menus
or icons. There exist a couple of plugins and settings for the VIM editor to
aid python development. If you only develop in Python, a good start is to set
the default settings for indentation and linewrapping to values compliant with
`PEP 8 <http://www.python.org/dev/peps/pep-0008/>`_. In your home directory,
open a file called `.vimrc` and add the following lines: ::
set textwidth=79
set shiftwidth=4
@@ -24,25 +26,26 @@ settings for indentation and linewrapping to values compliant with PEP8::
set softtabstop=4
set shiftround
With these settings newlines are inserted after 79 characters and indentation
With these settings, newlines are inserted after 79 characters and indentation
is set to 4 spaces per tab. If you also use VIM for other languages, there is a
handy plugin at indent_, which handles indentation settings for python source
files.
Additionally there is also a handy syntax plugin at syntax_ featuring some
improvements over the syntax file included in VIM 6.1.
There is also a handy syntax plugin at syntax_ featuring some improvements over
the syntax file included in VIM 6.1.
These plugins supply you with a basic environment for developing in Python.
However in order to improve the programming flow we also want to continually
check for PEP8 compliance and check syntax. Luckily there exist PEP8_ and
Pyflakes_ to do this for you. If your VIM is compiled with `+python` you can
also utilize some very handy plugins to do these checks from within the editor.
For PEP8 checking install vim-pep8_. Now you can map the vim function
`Pep8()` to any hotkey or action you want. Similarly for pyflakes you can
install vim-pyflakes_. Now you can map `Pyflakes()` like the PEP8 function and
have it called quickly. Both plugins will display errors in a quickfix list and
provide an easy way to jump to the corresponding line. A very handy setting is
calling these functions whenever a buffer is saved. In order to do this, enter
the following lines into your vimrc::
To get the most out of Vim, you should continually check your code for syntax
errors and PEP8 compliance. Luckily PEP8_ and Pyflakes_ will do this for you.
If your VIM is compiled with `+python` you can also utilize some very handy
plugins to do these checks from within the editor.
For PEP8 checking, install the vim-pep8_ plugin, and for pyflakes you can
install vim-pyflakes_. Now you can map the functions `Pep8()` or `Pyflakes()`
to any hotkey or action you want in Vim. Both plugins will display errors at
the bottom of the screen, and provide an easy way to jump to the corresponding
line. It's very handy to call these functions whenever you save a file. In
order to do this, add the following lines to your `vimrc`::
autocmd BufWritePost *.py call Pyflakes()
autocmd BufWritePost *.py call Pep8()
@@ -60,7 +63,10 @@ the following lines into your vimrc::
TextMate
--------
"`TextMate <http://macromates.com/>`_ brings Apple's approach to operating systems into the world of text editors. By bridging UNIX underpinnings and GUI, TextMate cherry-picks the best of both worlds to the benefit of expert scripters and novice users alike."
"`TextMate <http://macromates.com/>`_ brings Apple's approach to operating
systems into the world of text editors. By bridging UNIX underpinnings and GUI,
TextMate cherry-picks the best of both worlds to the benefit of expert
scripters and novice users alike."
Sublime Text
------------
@@ -72,7 +78,8 @@ extraordinary features."
Sublime Text has excellent support for editing Python code and uses Python for
its plugin API.
`Sublime Text 2 <http://www.sublimetext.com/blog/articles/sublime-text-2-beta>`_ is currently in beta.
`Sublime Text 2 <http://www.sublimetext.com/blog/articles/sublime-text-2-beta>`_
is currently in beta.
IDEs
::::
@@ -80,7 +87,10 @@ IDEs
PyCharm / IntelliJ IDEA
-----------------------
`PyCharm <http://www.jetbrains.com/pycharm/>`_ is developed by JetBrains, also known for IntelliJ IDEA. Both share the same code base and most of PyCharm's features can be brought to IntelliJ with the free `Python Plug-In <http://plugins.intellij.net/plugin/?id=631/>`_.
`PyCharm <http://www.jetbrains.com/pycharm/>`_ is developed by JetBrains, also
known for IntelliJ IDEA. Both share the same code base and most of PyCharm's
features can be brought to IntelliJ with the free `Python Plug-In <http://plugins.intellij.net/plugin/?id=631/>`_.
Eclipse
-------
@@ -91,19 +101,29 @@ The most popular Eclipse plugin for Python development is Aptana's
Komodo IDE
-----------
`Komodo IDE <http://www.activestate.com/komodo-ide>`_ is developed by ActiveState and is a commerical IDE for Windows, Mac
`Komodo IDE <http://www.activestate.com/komodo-ide>`_ is developed by
ActiveState and is a commercial IDE for Windows, Mac
and Linux.
Spyder
------
`Spyder <http://code.google.com/p/spyderlib/>`_ an IDE specifically geared toward working with scientific python libraries (namely `Scipy <http://www.scipy.org/>`_).
`Spyder <http://code.google.com/p/spyderlib/>`_ an IDE specifically geared
toward working with scientific python libraries (namely `Scipy <http://www.scipy.org/>`_).
Includes integration with pyflakes_, `pylint <http://www.logilab.org/857>`_,
and `rope <http://rope.sourceforge.net/>`_.
Spyder is open-source (free), offers code completion, syntax highlighting, class and function browser, and object inspection.
Spyder is open-source (free), offers code completion, syntax highlighting,
class and function browser, and object inspection.
WingIDE
-------
`WingIDE <http://wingware.com/>`_ a python specific IDE. Runs for Linux,
Windows, and Mac (as an X11 application, which frustrates some Mac users).
Interpreter Tools
:::::::::::::::::
@@ -112,8 +132,10 @@ Interpreter Tools
virtualenv
----------
Virtualenv is a tool to keep the dependencies required by different projects in separate places, by creating virtual Python environments for them.
It solves the "Project X depends on version 1.x but, Project Y needs 4.x" dilemma and keeps your global site-packages directory clean and manageable.
Virtualenv is a tool to keep the dependencies required by different projects
in separate places, by creating virtual Python environments for them.
It solves the "Project X depends on version 1.x but, Project Y needs 4.x"
dilemma and keeps your global site-packages directory clean and manageable.
`virtualenv <http://www.virtualenv.org/en/latest/index.html>`_ creates
a folder which contains all the necessary executables to contain the
@@ -185,7 +207,8 @@ control by adding it to the ignore list.
virtualenvwrapper
-----------------
`Virtualenvwrapper <http://pypi.python.org/pypi/virtualenvwrapper>`_ makes virtualenv a pleasure to use by wrapping the command line API with a nicer CLI.
`Virtualenvwrapper <http://pypi.python.org/pypi/virtualenvwrapper>`_ makes
virtualenv a pleasure to use by wrapping the command line API with a nicer CLI.
::
@@ -198,18 +221,38 @@ Put this into your `~/.bash_profile` (Linux/Mac) file:
$ export VIRTUALENVWRAPPER_VIRTUALENV_ARGS='--no-site-packages'
This will prevent your virtualenvs from relying on your (global) site packages directory, so that they are completely separate..
This will prevent your virtualenvs from relying on your (global) site packages
directory, so that they are completely separate..
Other Tools
:::::::::::
IDLE
----
`IDLE <http://docs.python.org/library/idle.html>`_ is an integrated
development environment that is part of Python standard library. It is
completely written in Python and uses Tkinter GUI toolkit. Though IDLE
is not suited for full-blown development using Python , it is quite
helpful to try out small Python snippets and experiment with different
features in Python.
It provides following features:
* Python Shell Window (interpreter)
* Multi window text editor that colorizes Python code
* Minimal debugging facility
IPython
-------
`IPython <http://ipython.org/>`_ provides a rich toolkit to help you make the most out of using Python interactively. Its main components are:
`IPython <http://ipython.org/>`_ provides a rich toolkit to help you make the
most out of using Python interactively. Its main components are:
* Powerful Python shells (terminal- and Qt-based).
* A web-based notebook with the same core features but support for rich media, text, code, mathematical expressions and inline plots.
* A web-based notebook with the same core features but support for rich media,
text, code, mathematical expressions and inline plots.
* Support for interactive data visualization and use of GUI toolkits.
* Flexible, embeddable interpreters to load into your own projects.
* Tools for high level and interactive parallel computing.
@@ -223,7 +266,8 @@ IPython
BPython
-------
`bpython <http://bpython-interpreter.org/>`_ is an alternative interface to the Python interpreter for Unix-like operating systems. It has the following features:
`bpython <http://bpython-interpreter.org/>`_ is an alternative interface to the
Python interpreter for Unix-like operating systems. It has the following features:
* In-line syntax highlighting.
* Readline-like autocomplete with suggestions displayed as you type.
@@ -238,4 +282,3 @@ BPython
$ pip install bpython
docs/dev/virtualenvs.rst
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@@ -137,3 +137,21 @@ Other useful commands
Shows contents of ``site-packages`` directory.
`Full list of virtualenvwrapper commands <http://www.doughellmann.com/docs/virtualenvwrapper/command_ref.html#managing-environments>`_.
autoenv
-------
When you ``cd`` into a directory containing a ``.env`` `autoenv <https://github.com/kennethreitz/autoenv>`_
automagically activates the environment.
Install it on Mac OS X using ``brew``:
.. code-block:: console
$ brew install autoenv
And on Linux:
.. code-block:: console
$ git clone git://github.com/kennethreitz/autoenv.git ~/.autoenv
$ echo 'source ~/.autoenv/activate.sh' >> ~/.bashrc
docs/index.rst
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@@ -6,10 +6,13 @@
The Hitchhiker's Guide to Python!
=================================
Welcome to The Hitchhiker's Guide to Python. **DON'T PANIC!**
Welcome to The Hitchhiker's Guide to Python. `DON'T PANIC! <http://www.amazon.com/gp/product/B0043M4ZH0/ref=as_li_ss_tl?ie=UTF8&tag=bookforkind-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=B0043M4ZH0>`_
**This guide is currently under heavy development, and is mostly a skeleton at the moment.** If you'd like to help, `fork us on GitHub <https://github.com/kennethreitz/python-guide>`_!
**This guide is currently under heavy development, and is mostly a skeleton at
the moment.** If you'd like to help, `fork us on GitHub <https://github.com/kennethreitz/python-guide>`_!
This *opinionated* guide exists to provide both novice and expert Python developers a best-practice handbook to the installation, configuration, and usage of Python on a daily basis.
This *opinionated* guide exists to provide both novice and expert Python
developers a best-practice handbook to the installation, configuration, and
usage of Python on a daily basis.
.. include:: contents.rst.inc
docs/intro/community.rst
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@@ -1,17 +1,22 @@
.. _the_community:
The Community
=============
BDFL
----
Guido van Rossum, the creator of Python, is often referred to as the BDFL — the Benevolent Dictator For Life.
Guido van Rossum, the creator of Python, is often referred to as the BDFL — the
Benevolent Dictator For Life.
Python Software Foundation
--------------------------
The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers.
The mission of the Python Software Foundation is to promote, protect, and
advance the Python programming language, and to support and facilitate the
growth of a diverse and international community of Python programmers.
`Learn More about the PSF <http://www.python.org/psf/>`_.
@@ -19,7 +24,8 @@ The mission of the Python Software Foundation is to promote, protect, and advanc
PEPs
----
PEPs are *Python Enhancement Proposals*. They describe changes to Python itself, or the standards around it.
PEPs are *Python Enhancement Proposals*. They describe changes to Python itself,
or the standards around it.
There are three different types of PEPs (as defined by `PEP1 <http://www.python.org/dev/peps/pep-0001/>`_):
@@ -27,7 +33,8 @@ There are three different types of PEPs (as defined by `PEP1 <http://www.python.
Describes a new feature or implementation.
**Informational**
Describes a design issue, general guidelines, or information to the community.
Describes a design issue, general guidelines, or information to the
community.
**Process**
Describes a process related to Python.
@@ -45,24 +52,28 @@ There are a few PEPs that could be considered required reading:
A list of 19 statements that briefly explain the philosophy behind Python.
- `PEP257 <http://www.python.org/dev/peps/pep-0257/>`_: Docstring Conventions.
Gives guidelines for semantics and conventions associated with Python docstrings.
Gives guidelines for semantics and conventions associated with Python
docstrings.
You can read more at `The PEP Index <http://www.python.org/dev/peps/>`_.
Submitting a PEP
~~~~~~~~~~~~~~~~
PEPs are peer-reviewed and accepted/rejected after much discussion. Anyone can write and submit a PEP for review.
PEPs are peer-reviewed and accepted/rejected after much discussion. Anyone
can write and submit a PEP for review.
Here's an overview of the PEP acceptance workflow:
.. image:: http://www.python.org/dev/peps/pep-0001/pep-0001-1.png
Python Conferences
Python Conferences
--------------------------
The major events for the Python community are developer conferences. The two most notable conferences are PyCon, which is held in the US, and its European sibling, EuroPython.
The major events for the Python community are developer conferences. The two
most notable conferences are PyCon, which is held in the US, and its European
sibling, EuroPython.
A comprehensive list of conferences is maintained `at pycon.org <http://www.pycon.org/>`_.
@@ -70,4 +81,6 @@ A comprehensive list of conferences is maintained `at pycon.org <http://www.pyco
Python User Groups
--------------------------
User Groups are where a bunch of Python developers meet to present or talk about Python topics of interest. A list of local user groups is maintained at the `Python Software Foundation Wiki <http://wiki.python.org/moin/LocalUserGroups>`_.
User Groups are where a bunch of Python developers meet to present or talk
about Python topics of interest. A list of local user groups is maintained at
the `Python Software Foundation Wiki <http://wiki.python.org/moin/LocalUserGroups>`_.
docs/intro/documentation.rst
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@@ -13,7 +13,8 @@ The official Python Language and Library documentation can be found here:
Read the Docs
-------------
Read the Docs is a popular community project, providing a single location for all documentation of popular and even more exotic Python modules.
Read the Docs is a popular community project, providing a single location for
all documentation of popular and even more exotic Python modules.
`Read the Docs <http://readthedocs.org/>`_
docs/intro/duction.rst
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@@ -1,7 +1,56 @@
Introduction
============
.. todo:: write a general blurb introducing the Python language
From the `official Python website <http://python.org/about/>`_:
Python is a general-purpose, high-level programming language similar
to Tcl, Perl, Ruby, Scheme, or Java. Some of its main key features
include:
* very clear, readable syntax
Python's philosophy focuses on readability, from code blocks
delineated with significant whitespace to intuitive keywords in
place of inscrutable punctuation
* extensive standard libraries and third party modules for virtually
any task
Python is sometimes described with the words "batteries included"
for its extensive
`stanadard library <http://docs.python.org/library/>`_, which can
includes modules for regular expressions, file IO, fraction handling,
object serialization, and much more.
Additionally, the
`Python Package Index <http://pypi.python.org/pypi/>`_ is available
for users to submit their packages for widespread use, similar to
Perl's `CPAN <http://www.cpan.org>`_. There is a thriving community
of very powerful Python frameworks and tools like
the `Django <http://www.djangoproject.com>`_ web framework and the
`NumPy <http://numpy.scipy.org>`_ set of math routines.
* integration with other systems
Python can integrate with `Java libraries <http://www.jython.org>`_,
enabling it to be used with the rich Java environment that corporate
programmers are used to. It can also be
`extended by C or C++ modules <http://docs.python.org/extending/>`_
when speed is of the essence.
* ubiquity on computers
Python is available on Windows, \*nix, and Mac. It runs wherever the
Java virtual machine runs, and the reference implementation CPython
can help bring Python to wherever there is a working C compiler.
* friendly community
Python has a vibrant and large :ref:`community <the-community>`
which maintains wikis, conferences, countless repositories,
mailing lists, IRC channels, and so much more. Heck, the Python
community is even helping to write this guide!
.. _about-ref:
docs/intro/learning.rst
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@@ -7,14 +7,18 @@ Beginner
Learn Python Interactive Tutorial
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Learnpython.org is an easy non-intimidating way to get introduced to python. The website takes the same approach used on the popular `Try Ruby <http://tryruby.org/>`_ website, it has an interactive python interpreter built into the site that allows you to go through the lessons without having to install Python locally.
Learnpython.org is an easy non-intimidating way to get introduced to python.
The website takes the same approach used on the popular `Try Ruby <http://tryruby.org/>`_
website, it has an interactive python interpreter built into the site that
allows you to go through the lessons without having to install Python locally.
`Learn Python <http://www.learnpython.org/>`_
Learn Python the Hard Way
~~~~~~~~~~~~~~~~~~~~~~~~~
This is an excellent beginner programmer's guide to Python. It covers "hello world" from the console to the web.
This is an excellent beginner programmer's guide to Python. It covers "hello
world" from the console to the web.
`Learn Python the Hard Way <http://learnpythonthehardway.org/book/>`_
@@ -22,7 +26,8 @@ This is an excellent beginner programmer's guide to Python. It covers "hello wor
Crash into Python
~~~~~~~~~~~~~~~~~
Also known as *Python for Programmers with 3 Hours*, this guide gives experienced developers from other languages a crash course on Python.
Also known as *Python for Programmers with 3 Hours*, this guide gives
experienced developers from other languages a crash course on Python.
`Crash into Python <http://stephensugden.com/crash_into_python/>`_
@@ -30,8 +35,8 @@ Also known as *Python for Programmers with 3 Hours*, this guide gives experience
Dive Into Python 3
~~~~~~~~~~~~~~~~~~
Dive Into Python 3 is a good book for those ready to jump in to Python 3. It's a
good read if you are moving from Python 2 to 3 or if you already have some
Dive Into Python 3 is a good book for those ready to jump in to Python 3. It's
a good read if you are moving from Python 2 to 3 or if you already have some
experience programming in another language.
`Dive Into Python 3 <http://diveintopython3.ep.io/>`_
@@ -39,20 +44,41 @@ experience programming in another language.
Think Python: How to Think Like a Computer Scientist
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Think Python attempts to give an introduction to basic concepts in computer science through the
use of the python language. The focus was to create a book with plenty of exercises, minimal jargon and
a section in each chapter devoted to the subject of debugging.
Think Python attempts to give an introduction to basic concepts in computer
science through the use of the python language. The focus was to create a book
with plenty of exercises, minimal jargon and a section in each chapter devoted
to the subject of debugging.
While exploring the various features available in the python language the author weaves in various design
patterns and best practices.
While exploring the various features available in the python language the
author weaves in various design patterns and best practices.
The book also includes several case studies which have the reader explore the topics discussed in the book
in greater detail by applying those topics to real-world examples. Case studies include assignments in GUI
and Markov Analysis.
The book also includes several case studies which have the reader explore the
topics discussed in the book in greater detail by applying those topics to
real-world examples. Case studies include assignments in GUI and Markov
Analysis.
`Think Python <http://greenteapress.com/thinkpython/html/index.html>`_
Python Koans
~~~~~~~~~~~~
Python Koans is a port of Edgecase's Ruby Koans. It uses a test-driven
approach, q.v. TEST DRIVEN DESIGN SECTION to provide an interactive tutorial
teaching basic python concepts. By fixing assertion statements that fail in a
test script, this provides sequential steps to learning python.
For those used to languages and figuring out puzzles on their own, this can be
a fun, attractive option. For those new to python and programming, having an
additional resource or reference will be helpful.
`Python Koans <http://bitbucket.org/gregmalcolm/python_koans>`_
More information about test driven development can be found at these resources:
`Test Driven Development <http://en.wikipedia.org/wiki/Test-driven_development>`
Advanced
--------
@@ -63,5 +89,27 @@ Pro Python
Expert Python Programming
~~~~~~~~~~~~~~~~~~~~~~~~~
Expert Python Programming deals with best practices in programming Python and
is focused on the more advanced crowd.
.. todo:: Write about `Expert Python Programming <http://www.packtpub.com/expert-python-programming/book>`_
It starts with topics like decorators (with caching, proxy, and context manager
case-studies), method resolution order, using super() and meta-programming, and
general PEP8 best practices.
It has a detailed, multi-chapter case study on writing and releasing a package
and eventually an application, including a chapter on using zc.buildout. Later
chapters detail best practices with writing documentation, test-driven
development, version control, and optimization/profiling.
`Expert Python Programming <http://www.packtpub.com/expert-python-programming/book>`_
References
----------
Python in a Nutshell
~~~~~~~~~~~~~~~~~~~~
Python in a Nutshell, written by Alex Martelli, covers most cross-platform
python's usage, from its syntax to built-in libraries to advanced topics such
as writing C extensions.
docs/notes/styleguide.rst
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@@ -12,7 +12,7 @@ approriate.
The Guide is written as :ref:`restructuredtext-ref`.
.. note:: Parts of The Guide may not yet match this style guide. Feel free
to update those parts to by in sync with The Guide Style Guide
to update those parts to be in sync with The Guide Style Guide
.. note:: On any page of the rendered HTML you can click "Show Source" to
see how authors have styled the page.
@@ -20,7 +20,7 @@ The Guide is written as :ref:`restructuredtext-ref`.
Relevancy
---------
Stride to keep any contributions relevant to the :ref:`purpose of The Guide
Strive to keep any contributions relevant to the :ref:`purpose of The Guide
<about-ref>`.
* Avoid including too much information on subjects that don't directly
docs/scenarios/ci.rst
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@@ -5,20 +5,29 @@ Continuous Integration
Why?
----
Martin Fowler, who first wrote about `Continuous Integration <http://martinfowler.com/articles/continuousIntegration.html>`_ (short: CI) together with Kent Beck, describes the CI as follows:
Martin Fowler, who first wrote about `Continuous Integration <http://martinfowler.com/articles/continuousIntegration.html>`_
(short: CI) together with Kent Beck, describes the CI as follows:
Continuous Integration is a software development practice where members of a team integrate their work frequently, usually each person integrates at least daily - leading to multiple integrations per day. Each integration is verified by an automated build (including test) to detect integration errors as quickly as possible. Many teams find that this approach leads to significantly reduced integration problems and allows a team to develop cohesive software more rapidly.
Continuous Integration is a software development practice where members of
a team integrate their work frequently, usually each person integrates at
least daily - leading to multiple integrations per day. Each integration is
verified by an automated build (including test) to detect integration errors
as quickly as possible. Many teams find that this approach leads to
significantly reduced integration problems and allows a team to develop
cohesive software more rapidly.
Jenkins
-------
`Jenkins CI <http://jenkins-ci.org>`_ is an extensible continuous integration engine. Use it.
`Jenkins CI <http://jenkins-ci.org>`_ is an extensible continuous integration
engine. Use it.
Buildbot
--------
`Buildbot <http://buildbot.net/buildbot/docs/current>`_ is a Python system to automate the compile/test cycle to validate code changes.
`Buildbot <http://buildbot.net/buildbot/docs/current>`_ is a Python system to
automate the compile/test cycle to validate code changes.
Mule?
@@ -29,9 +38,20 @@ Mule?
Tox
---
`tox <https://bitbucket.org/hpk42/tox>`_ is an automation tool providing packaging, testing and deployment of Python software right from the console or CI server.
It is a generic virtualenv management and test command line tool which provides the following features:
`tox <https://bitbucket.org/hpk42/tox>`_ is an automation tool providing
packaging, testing and deployment of Python software right from the console or
CI server. It is a generic virtualenv management and test command line tool
which provides the following features:
* Checking that packages install correctly with different Python versions and interpreters
* Running tests in each of the environments, configuring your test tool of choice
* Acting as a frontend to Continuous Integration servers, reducing boilerplate and merging CI and shell-based testing.
* Checking that packages install correctly with different Python versions and
interpreters
* Running tests in each of the environments, configuring your test tool of
choice
* Acting as a frontend to Continuous Integration servers, reducing boilerplate
and merging CI and shell-based testing.
Travis-CI
---------
.. todo:: Write about travis-ci
docs/scenarios/client.rst
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@@ -6,14 +6,23 @@ Network Applications
HTTP
::::
The Hypertext Transfer Protocol (HTTP) is an application protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web.
The Hypertext Transfer Protocol (HTTP) is an application protocol for
distributed, collaborative, hypermedia information systems. HTTP is the
foundation of data communication for the World Wide Web.
Requests
--------
Python’s standard urllib2 module provides most of the HTTP capabilities you need, but the API is thoroughly broken. It was built for a different time — and a different web. It requires an enormous amount of work (even method overrides) to perform the simplest of tasks.
Python’s standard urllib2 module provides most of the HTTP capabilities you
need, but the API is thoroughly broken. It was built for a different time —
and a different web. It requires an enormous amount of work (even method
overrides) to perform the simplest of tasks.
Requests takes all of the work out of Python HTTP — making your integration with web services seamless. There’s no need to manually add query strings to your URLs, or to form-encode your POST data. Keep-alive and HTTP connection pooling are 100% automatic, powered by urllib3, which is embedded within Requests
Requests takes all of the work out of Python HTTP — making your integration
with web services seamless. There’s no need to manually add query strings to
your URLs, or to form-encode your POST data. Keep-alive and HTTP connection
pooling are 100% automatic, powered by urllib3, which is embedded within
Requests.
- `Documention <http://docs.python-requests.org/en/latest/index.html>`_
- `PyPi <http://pypi.python.org/pypi/requests>`_
@@ -27,4 +36,8 @@ Distributed Systems
ZeroMQ
------
ØMQ (also spelled ZeroMQ, 0MQ or ZMQ) is a high-performance asynchronous messaging library aimed at use in scalable distributed or concurrent applications. It provides a message queue, but unlike message-oriented middleware, a ØMQ system can run without a dedicated message broker. The library is designed to have a familiar socket-style API.
ØMQ (also spelled ZeroMQ, 0MQ or ZMQ) is a high-performance asynchronous
messaging library aimed at use in scalable distributed or concurrent
applications. It provides a message queue, but unlike message-oriented
middleware, a ØMQ system can run without a dedicated message broker. The
library is designed to have a familiar socket-style API.
docs/scenarios/db.rst
+7 -5
View File
@@ -14,9 +14,9 @@ Nearly all Python database modules such as `sqlite3`, `psycopg` and
SQLAlchemy
----------
`SQLAlchemy <http://www.sqlalchemy.org/>`_ is a commonly used database toolkit. Unlike many database libraries
it not only provides an ORM layer but also a generalized API for writing
database-agnostic code without SQL.
`SQLAlchemy <http://www.sqlalchemy.org/>`_ is a commonly used database toolkit.
Unlike many database libraries it not only provides an ORM layer but also a
generalized API for writing database-agnostic code without SQL.
::
@@ -25,8 +25,10 @@ database-agnostic code without SQL.
Django ORM
----------
The Django ORM is the interface used by `Django <http://www.djangoproject.com>`_ to provide database access.
The Django ORM is the interface used by `Django <http://www.djangoproject.com>`_
to provide database access.
It's based on the idea of models, an abstraction that makes it easier to manipulate data in Python.
It's based on the idea of models, an abstraction that makes it easier to
manipulate data in Python.
Documentation can be found `here <https://docs.djangoproject.com/en/1.3/#the-model-layer>`_
docs/scenarios/gui.rst
+7 -6
View File
@@ -4,7 +4,8 @@ GUI Applications
Qt
::
Qt is a cross-platform application framework that is widely used for developing software with a GUI but can also be used for non-GUI applications.
Qt is a cross-platform application framework that is widely used for developing
software with a GUI but can also be used for non-GUI applications.
PySide
------
@@ -24,21 +25,21 @@ PyObjC
------
.. note:: Only available on Mac OSX. Don't pick this if you're writing a cross-platform application.
WXPython
wxPython
::::::::
Install (Stable)
----------------
*Go to http://www.wxpython.org/download.php#stable and download the appropriate package for your OS.*
*Go to http://www.wxpython.org/download.php#stable and download the appropriate
package for your OS.*
Gtk
:::
PyGTK provides Python bindings for the GTK+ toolkit. Like the GTK+ library
itself, it is currently licensed under the GNU LGPL. It is worth noting that
PyGTK only currenty supports the Gtk-2.X API (NOT Gtk-3.0). It is currently
recommended that PyGTK is not used for new projects and existing applications be
ported from PyGTK to PyGObject.
recommended that PyGTK is not used for new projects and existing applications
be ported from PyGTK to PyGObject.
Tk
::
docs/scenarios/imaging.rst
+44
View File
@@ -0,0 +1,44 @@
==================
Image Manipulation
==================
.. todo::
Add introduction about image manipulation and its Python libraries.
Python Imaging Library
----------------------
The `Python Imaging Library <http://www.pythonware.com/products/pil/>`_, or PIL
for short, is *the* library for image manipulation in Python.
It works with Python 1.5.2 and above, including 2.5, 2.6 and 2.7. Unfortunately,
it doesn't work with 3.0+ yet.
Installation
~~~~~~~~~~~~
PIL has a reputation of not being very straightforward to install. Listed below
are installation notes on various systems.
Installing on Linux
~~~~~~~~~~~~~~~~~~~
Ubuntu 11.04
````````````
.. todo::
Notes on installing on Ubuntu 11.04
Installing on Mac OS X
~~~~~~~~~~~~~~~~~~~~~~
.. todo::
Notes on installing on Mac OS X
Installing on Windows
~~~~~~~~~~~~~~~~~~~~~
.. todo::
Notes on installing on Windows machines
docs/scenarios/scientific.rst
+43 -18
View File
@@ -5,46 +5,71 @@ Scientific Applications
Context
:::::::
Python is frequently used for high-performance scientific applications. Python is widely used in academia
and scientific projects because it is easy to write, and it performs really well.
Python is frequently used for high-performance scientific applications. Python
is widely used in academia and scientific projects because it is easy to write,
and it performs really well.
Due to its high performance nature, scientific computing in python often refers to external libraries, typically
written in faster languages (like C, or FORTRAN for matrix operations). The main libraries used are NumPy and SciPy
Due to its high performance nature, scientific computing in python often refers
to external libraries, typically written in faster languages (like C, or FORTRAN
for matrix operations). The main libraries used are NumPy and SciPy
Libraries
:::::::::
Numpy
-----
`NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and FORTRAN) for high level mathematical functions.
NumPy cleverly overcomes the problem of running slower algorithms on Python by using multidimensional arrays and functions that operate on arrays.
Any algorithm can then be expressed as a function on arrays, allowing the algorithms to be run quickly.
`NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and
FORTRAN) for high level mathematical functions. NumPy cleverly overcomes the
problem of running slower algorithms on Python by using multidimensional arrays
and functions that operate on arrays. Any algorithm can then be expressed as a
function on arrays, allowing the algorithms to be run quickly.
NumPy is part of the SciPy project, and is released as a separate library so people who only need the basic requirements can just use NumPy.
NumPy is part of the SciPy project, and is released as a separate library so
people who only need the basic requirements can just use NumPy.
NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
SciPy
-----
`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical function. SciPy uses NumPy arrays as its basic data structure.
SciPy comes with modules for various commonly used tasks in scientific programing like linear algebra, integration (calculus),
ordinary differential equation solvers and signal processing.
`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
function. SciPy uses NumPy arrays as its basic data structure. SciPy comes with
modules for various commonly used tasks in scientific programing like linear
algebra, integration (calculus), ordinary differential equation solvers and
signal processing.
Enthought
---------
Installing NumPy and SciPy can be a daunting task. Which is why the `Enthought Python distribution <http://enthought.com/>`_ was created. With Enthought,
scientific python has never been easier (one click to install about 100 scientific python packages). User beware: Enthought is not free.
Installing NumPy and SciPy can be a daunting task. Which is why the
`Enthought Python distribution <http://enthought.com/>`_ was created. With
Enthought, scientific python has never been easier (one click to install about
100 scientific python packages). User beware: Enthought is not free.
Matplotlib
----------
.. todo:: write about matplotlib.
`matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
library for creating interactive 2D and 3D plots that can also be saved as
manuscript-quality figures. The API in many ways reflects that of `MATLAB <http://www.mathworks.com/products/matlab/>`_,
easing transition of MATLAB users to Python. Many examples, along with the
source code to re-create them, can be browsed at the `matplotlib gallery <http://matplotlib.sourceforge.net/gallery.html>`_.
PyQwt
-----
`PyQwt <http://pyqwt.sourceforge.net/>`_ is a solid library for plotting
numerical data. It is built on top of the popular `PyQt <http://www.riverbankcomputing.co.uk/software/pyqt/intro>`_
GUI framework. It typically has better performance than matplotlib, but the
range of built-in chart/plot types is slightly smaller than matplotlib.
Resources
:::::::::
Many people who do scientific computing are on Windows. And yet many of the scientific computing packages are notoriously difficult to build and install.
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has compiled a list of Windows binaries for many useful Python packages.
The list of packages has grown from a mainly scientific python resource to a more general list. It might be a good idea to check it out if you're on Windows.
Many people who do scientific computing are on Windows. And yet many of the
scientific computing packages are notoriously difficult to build and install.
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
compiled a list of Windows binaries for many useful Python packages. The list
of packages has grown from a mainly scientific python resource to a more
general list. It might be a good idea to check it out if you're on Windows.
docs/scenarios/speed.rst
+18 -12
View File
@@ -1,13 +1,16 @@
Speed
=====
CPython, the most commonly used implementation of Python, is slow for CPU bound tasks. `PyPy`_ is fast.
CPython, the most commonly used implementation of Python, is slow for CPU bound
tasks. `PyPy`_ is fast.
Using a slightly modified version of `David Beazleys`_ CPU bound test code(added loop for multiple tests), you can see the difference between CPython and PyPy's processing.
Using a slightly modified version of `David Beazleys`_ CPU bound test code
(added loop for multiple tests), you can see the difference between CPython
and PyPy's processing.
::
PyPy
PyPy
$ ./pypy -V
Python 2.7.1 (7773f8fc4223, Nov 18 2011, 18:47:10)
[PyPy 1.7.0 with GCC 4.4.3]
@@ -23,7 +26,7 @@ Using a slightly modified version of `David Beazleys`_ CPU bound test code(added
CPython
$ ./python -V
Python 2.7.1
$ ./python measure2.py
$ ./python measure2.py
1.06774401665
1.45412397385
1.51485204697
@@ -37,13 +40,16 @@ Context
The GIL
-------
`The GIL`_ (Global Interpreter Lock) is how Python allows multiple threads to operate at the same time. Python's
memory management isn't entirely thread-safe, so the GIL is requried to prevents multiple threads from running
the same Python code at once.
`The GIL`_ (Global Interpreter Lock) is how Python allows multiple threads to
operate at the same time. Python's memory management isn't entirely thread-safe,
so the GIL is requried to prevents multiple threads from running the same
Python code at once.
David Beazley has a great `guide`_ on how the GIL operates. He also covers the `new GIL`_ in Python 3.2. His
results show that maximizing performance in a Python application requires a strong understanding of the GIL,
how it affects your specific application, how many cores you have, and where your application bottlenecks are.
David Beazley has a great `guide`_ on how the GIL operates. He also covers the
`new GIL`_ in Python 3.2. His results show that maximizing performance in a
Python application requires a strong understanding of the GIL, how it affects
your specific application, how many cores you have, and where your application
bottlenecks are.
C Extentions
------------
@@ -52,8 +58,8 @@ C Extentions
The GIL
-------
`Special care`_ must be taken when writing C extensions to make sure you register your threads
with the interpreter.
`Special care`_ must be taken when writing C extensions to make sure you r
egister your threads with the interpreter.
C Extentions
::::::::::::
docs/scenarios/web.rst
+55 -32
View File
@@ -2,6 +2,9 @@
Web Applications
================
As a powerful scripting language adapted to both fast prototyping
and bigger projects, Python is widely used in Web applications
development.
Context
:::::::
@@ -14,17 +17,17 @@ The Web Server Gateway Interface (or "WSGI" for short) is a standard
interface between web servers and Python web application frameworks. By
standardizing behavior and communication between web servers and Python web
frameworks, WSGI makes it possible to write portable Python web code that
can be deployed in any :ref:`WSGI-compliant web server <wsgi-servers-ref>`. WSGI is
documented in `PEP-3333 <http://www.python.org/dev/peps/pep-3333/>`_.
can be deployed in any :ref:`WSGI-compliant web server <wsgi-servers-ref>`.
WSGI is documented in `PEP-3333 <http://www.python.org/dev/peps/pep-3333/>`_.
Frameworks
::::::::::
Broadly speaking, a web framework is a set of libraries upon which you can
build custom code to implement a web application (i.e. an interactive web
site). Most web frameworks include patterns and utilities to accomplish at
least the following:
Broadly speaking, a web framework consist of a set of libraries and a main
handler within which you can build custom code to implement a web application
(i.e. an interactive web site). Most web frameworks include patterns and
utilities to accomplish at least the following:
URL Routing
Matches an incoming HTTP request to a particular piece of Python code to
@@ -71,8 +74,8 @@ you may need, such as database access or form generation and validation. For
many popular modules, `Extensions <http://flask.pocoo.org/extensions/>`_ may
already exist to suit your needs.
**Support** for flask can best be found in its mailing list. Just shoot an email to
flask@librelist.com and reply to the confirmation email.
**Support** for flask can best be found in its mailing list. Just shoot an
email to flask@librelist.com and reply to the confirmation email.
.. todo:: Explain Pyramid
@@ -126,8 +129,8 @@ The majority of self hosted Python applications today are hosted with a WSGI
server such as :ref:`gUnicorn <gunicorn-ref>`, either directly or behind a
lightweight web server such as :ref:`nginx <nginx-ref>`.
The WSGI servers serve the Python applications while the web server handles tasks
better suited for it such as static file serving, request routing, DDoS
The WSGI servers serve the Python applications while the web server handles
tasks better suited for it such as static file serving, request routing, DDoS
protection, and basic authentication.
Hosting
@@ -158,10 +161,10 @@ Heroku
`Cedar stack <http://devcenter.heroku.com/articles/cedar>`_ offers first class
support for Python 2.7 applications.
Heroku allows you to run as many Python web applications as you like, 24/7 and free
of charge. Heroku is best described as a horizontal scaling platform. They start
to charge you once you "scale" you application to run on more than one Dyno
(abstacted servers) at a time.
Heroku allows you to run as many Python web applications as you like, 24/7 and
free of charge. Heroku is best described as a horizontal scaling platform. They
start to charge you once you "scale" you application to run on more than one
Dyno (abstacted servers) at a time.
Heroku publishes `step-by-step instructions
<http://devcenter.heroku.com/articles/python>`_ on how to set up your first
@@ -190,24 +193,6 @@ See the `DotCloud documentation on Python
getting started.
ep.io
~~~~~
`ep.io <https://www.ep.io/>`_ is a PaaS designed specifically for Python web
applications. It supports Python versions 2.6 and 2.7, and has Pythonic
integrations with a variety of services.
ep.io has a free plan with bandwidth and disk space limitations. Also, in the
free plan, the web process is only loaded when needed. This means that the
first request after some inactivity may take up to 15 seconds.
ep.io publishes `step-by-step instructions
<https://www.ep.io/docs/quickstart/>`_ on how to get started with their
platform and how to deploy Django, Flask, or generic WSGI applications.
ep.io is currently in invite-only beta.
Gondor
~~~~~~
@@ -221,6 +206,44 @@ Gondor publishes guides to deploying `Django projects
<https://gondor.io/support/setting-up-pinax/>`_ on their platform.
Templating
::::::::::
Most WSGI applications are responding to HTTP requests to serve
content in HTML or other markup languages. Instead of generating directly
textual content from Python, the concept of separation of concerns
advise us to use templates. A template engine manage a suite of
template files, with a system of hierarchy and inclusion to
avoid unnecessary repetition, and is in charge of rendering
(generating) the actual content, filling the static content
of the templates with the dynamic content generated by the
application.
As template files are
sometime written by designers or front-end developpers,
it can be difficult to handle increasing complexity.
Some general good pratices apply to the part of the
application passing dynamic content to the template engine,
and to the templates themselves.
- Template files should be passed only the dynamic
content that is needed for rendering the template. Avoid
to be tempted to pass additional content "just in case":
it is easier to add some missing variable when needed than to remove
a likely unused variable later.
- Many template engine allow for complex statements
or assignments in the template itself, and many
allow some Python code to be evaluated in the
templates. This convenience can lead to uncontrolled
increase in complexity, and often harder to find bugs.
- It is often possible or necessary to mix javascript templates with
HTML templates. A sane approach to this design is to isolate
the parts where the HTML template passes some variable content
to the javascript code.
.. rubric:: References
.. [1] `The mod_python project is now officially dead <http://blog.dscpl.com.au/2010/06/modpython-project-is-now-officially.html>`_
docs/scenarios/xml.rst
+34
View File
@@ -0,0 +1,34 @@
XML parsing
===========
untangle
--------
`untangle <http://0chris.com/untangle>`_ is a simple library which takes
an XML document and returns a Python object which mirrors the nodes and
attributes in its structure.
For example, an xml file like this:
.. code-block:: xml
<?xml version="1.0"?>
<root>
<child name="child1">
</root>
can be loaded like this:
.. code-block:: python
import untangle
obj = untangle.parse('path/to/file.xml')
and then you can get the child elements name like this:
.. code-block:: python
obj.root.child['name']
untangle also supports loading XML from a string or an URL.
docs/shipping/packaging.rst
+22 -12
View File
@@ -8,7 +8,8 @@ You'll need to package your code first before sharing it with other developers.
For Python Developers
:::::::::::::::::::::
If you're writing an open source Python module, `PyPI <http://pypi.python.org>`_, more properly known as *The Cheeseshop*, is the place to host it.
If you're writing an open source Python module, `PyPI <http://pypi.python.org>`_,
more properly known as *The Cheeseshop*, is the place to host it.
@@ -27,7 +28,8 @@ running from the directory which holds those packages which need to be installed
**Showing an example is always beneficial**
Say if you are after installing a package called MyPackage.tar.gz, and assuming this is your directory structure
Say if you are after installing a package called MyPackage.tar.gz, and
assuming this is your directory structure:
- archive
@@ -35,27 +37,35 @@ Say if you are after installing a package called MyPackage.tar.gz, and assuming
- MyPackage.tar.gz
Go to your command prompt and type:
::
$ cd archive
$ python -m SimpleHTTPServer 9000
.. code-block:: console
This runs a simple http server running on port 9000 and will list all packages (like **MyPackage**). Now you can install **MyPackage** using any python package installer. Using Pip, you would do it like:
::
$ cd archive
$ python -m SimpleHTTPServer 9000
$ pip install --extra-index-url=http://127.0.0.1:9000/ MyPackage
This runs a simple http server running on port 9000 and will list all packages
(like **MyPackage**). Now you can install **MyPackage** using any python
package installer. Using Pip, you would do it like:
.. code-block:: console
$ pip install --extra-index-url=http://127.0.0.1:9000/ MyPackage
Having a folder with the same name as the package name is **crucial** here.
I got fooled by that, one time. But if you feel that creating a folder called
**MyPackage** and keeping **MyPackage.tar.gz** inside that, is *redundant*, you can still install MyPackage using:
::
**MyPackage** and keeping **MyPackage.tar.gz** inside that, is *redundant*,
you can still install MyPackage using:
$ pip install http://127.0.0.1:9000/MyPackage.tar.gz
.. code-block:: console
$ pip install http://127.0.0.1:9000/MyPackage.tar.gz
Chishop
+++++++
`Chishop <https://github.com/benliles/djangopypi>`_ is a simple PyPI server written in django which allows you to register/upload with distutils and install with easy_install/pip.
`Chishop <https://github.com/benliles/djangopypi>`_ is a simple PyPI server
written in django which allows you to register/upload with distutils and
install with easy_install/pip.
For Linux Distributions
::::::::::::::::::::::::
docs/starting/install/linux.rst
+45 -12
View File
@@ -5,19 +5,32 @@ Installing Python on Linux
The latest version of Ubuntu, **comes with Python 2.7 out of the box**.
You do not need to install or configure anything else to use Python. Having said that, I would strongly recommend that you install the tools and libraries described in the next section before you start building Python applications for real-world use. In particular, you should always install Distribute, as it makes it much easier for you to use other third-party Python libraries.
You do not need to install or configure anything else to use Python. Having
said that, I would strongly recommend that you install the tools and libraries
described in the next section before you start building Python applications
for real-world use. In particular, you should always install Distribute, as
it makes it much easier for you to use other third-party Python libraries.
Distribute & Pip
----------------
The most crucial third-party Python software of all is Distribute, which extends the packaging and installation facilities provided by the distutils in the standard library. Once you add Distribute to your Python system you can download and install any compliant Python software product with a single command. It also enables you to add this network installation capability to your own Python software with very little work.
The most crucial third-party Python software of all is Distribute, which
extends the packaging and installation facilities provided by the distutils
in the standard library. Once you add Distribute to your Python system you can
download and install any compliant Python software product with a single
command. It also enables you to add this network installation capability to
your own Python software with very little work.
To obtain the latest version of Distribute for Linux, run the python script available here:
http://python-distribute.org/distribute_setup.py
To obtain the latest version of Distribute for Linux, run the python script
available here: `python-distribute <http://python-distribute.org/distribute_setup.py>`_
The new``easy_install`` command you have available is considered by many to be deprecated, so we will install its replacement: **pip**. Pip allows for uninstallation of packages, and is actively maintained, unlike easy_install.
The new``easy_install`` command you have available is considered by many to be
deprecated, so we will install its replacement: **pip**. Pip allows for
uninstallation of packages, and is actively maintained, unlike easy_install.
To install pip, simply open a command prompt and run::
To install pip, simply open a command prompt and run
.. code-block:: console
$ easy_install pip
@@ -25,22 +38,42 @@ To install pip, simply open a command prompt and run::
Virtualenv
----------
After Distribute & Pip, the next development tool that you should install is `virtualenv <http://pypi.python.org/pypi/virtualenv/>`_. Use pip::
After Distribute & Pip, the next development tool that you should install is
`virtualenv <http://pypi.python.org/pypi/virtualenv/>`_. Use pip
.. code-block:: console
$ pip install virtualenv
The virtualenv kit provides the ability to create virtual Python environments that do not interfere with either each other, or the main Python installation. If you install virtualenv before you begin coding then you can get into the habit of using it to create completely clean Python environments for each project. This is particularly important for Web development, where each framework and application will have many dependencies.
The virtualenv kit provides the ability to create virtual Python environments
that do not interfere with either each other, or the main Python installation.
If you install virtualenv before you begin coding then you can get into the
habit of using it to create completely clean Python environments for each
project. This is particularly important for Web development, where each
framework and application will have many dependencies.
To set up a new Python environment, change the working directory to where ever you want to store the environment, and run the virtualenv utility in your project's directory::
To set up a new Python environment, change the working directory to where ever
you want to store the environment, and run the virtualenv utility in your
project's directory
.. code-block:: console
$ virtualenv --distribute venv
To use an environment, run ``source venv/bin/activate``. Your command prompt will change to show the active environment. Once you have finished working in the current virtual environment, run ``deactivate`` to restore your settings to normal.
To use an environment, run ``source venv/bin/activate``. Your command prompt
will change to show the active environment. Once you have finished working in
the current virtual environment, run ``deactivate`` to restore your settings
to normal.
Each new environment automatically includes a copy of ``pip``, so that you can setup the third-party libraries and tools that you want to use in that environment. Put your own code within a subdirectory of the environment, however you wish. When you no longer need a particular environment, simply copy your code out of it, and then delete the main directory for the environment.
Each new environment automatically includes a copy of ``pip``, so that you can
setup the third-party libraries and tools that you want to use in that
environment. Put your own code within a subdirectory of the environment,
however you wish. When you no longer need a particular environment, simply
copy your code out of it, and then delete the main directory for the environment.
--------------------------------
This page is a remixed version of `another guide <http://www.stuartellis.eu/articles/python-development-windows/>`_, which is available under the same license.
This page is a remixed version of `another guide <http://www.stuartellis.eu/articles/python-development-windows/>`_,
which is available under the same license.
docs/starting/install/osx.rst
+71 -18
View File
@@ -5,24 +5,41 @@ Installing Python on Mac OS X
The latest version of Mac OS X, Lion, **comes with Python 2.7 out of the box**.
You do not need to install or configure anything else to use Python. Having said that, I would strongly recommend that you install the tools and libraries described in the next section before you start building Python applications for real-world use. In particular, you should always install Distribute, as it makes it much easier for you to use other third-party Python libraries.
You do not need to install or configure anything else to use Python. Having
said that, I would strongly recommend that you install the tools and libraries
described in the next section before you start building Python applications
for real-world use. In particular, you should always install Distribute, as it
makes it much easier for you to use other third-party Python libraries.
The version of Python that ships with OS X is great for learning, but it's not good for development. It's slightly out of date, and Apple has made significant changes that can cause hidden bugs.
The version of Python that ships with OS X is great for learning, but it's not
good for development. It's slightly out of date, and Apple has made significant
changes that can cause hidden bugs.
Doing it Right
--------------
Let's install a real version of Python.
First, you'll need to have GCC installed to compile Python. You can either get this from `XCode <http://developer.apple.com/xcode/>`_ or the smaller `OSX-GCC-Installer <https://github.com/kennethreitz/osx-gcc-installer#readme>`_ package.
First, you'll need to have GCC installed to compile Python. You can either get
this from `XCode <http://developer.apple.com/xcode/>`_ or the smaller
`OSX-GCC-Installer <https://github.com/kennethreitz/osx-gcc-installer#readme>`_ package.
While Lion comes with a large number of UNIX utilities, those familiar with Linux systems will notice one key component missing: a decent package manager. `Homebrew <http://mxcl.github.com/homebrew/>`_ fills this void.
While Lion comes with a large number of UNIX utilities, those familiar with
Linux systems will notice one key component missing: a decent package manager.
`Homebrew <http://mxcl.github.com/homebrew/>`_ fills this void.
To `install Homebrew <https://github.com/mxcl/homebrew/wiki/installation>`_, simply run::
To `install Homebrew <https://github.com/mxcl/homebrew/wiki/installation>`_,
simply run
$ ruby -e "$(curl -fsS https://raw.github.com/gist/323731)"
.. code-block:: console
Then, insert the hombrew directory at the top of your ``PATH`` enviornment variable. You can do this by adding the following line at the bottom of your ``~/.bashrc`` file::
$ ruby -e "$(curl -fsS https://raw.github.com/mxcl/homebrew/master/Library/Contributions/install_homebrew.rb)"
Then, insert the Homebrew directory at the top of your ``PATH`` enviornment
variable. You can do this by adding the following line at the bottom of your
``~/.bashrc`` file
.. code-block:: console
export PATH=/usr/local/bin:$PATH
@@ -30,21 +47,37 @@ Now, we can install Python 2.7: ::
$ brew install python --framework
This will take a minute or two. Once that's complete, you'll have to add the new Python scripts directory to your ``PATH``::
This will take a minute or two. Once that's complete, you'll have to add the
new Python scripts directory to your ``PATH``
.. code-block:: console
export PATH=/usr/local/share/python:$PATH
The ``--framework`` option tells Homebrew to compile a Framework-style Python build, rather than a UNIX-style build. The outdated version of Python that Snow Leopard comes packaged with is built as a Framework, so this helps avoid some future module installation bugs.
The ``--framework`` option tells Homebrew to compile a Framework-style Python
build, rather than a UNIX-style build. The outdated version of Python that
Snow Leopard comes packaged with is built as a Framework, so this helps avoid
some future module installation bugs.
Distribute & Pip
----------------
The most crucial third-party Python software of all is Distribute, which extends the packaging and installation facilities provided by the distutils in the standard library. Once you add Distribute to your Python system you can download and install any compliant Python software product with a single command. It also enables you to add this network installation capability to your own Python software with very little work.
The most crucial third-party Python software of all is Distribute, which
extends the packaging and installation facilities provided by the distutils
in the standard library. Once you add Distribute to your Python system you can
download and install any compliant Python software product with a single
command. It also enables you to add this network installation capability to
your own Python software with very little work.
Hombrew already installed Distribute for you. Its ``easy_install`` command is considered by many to be deprecated, so we will install its replacement: **pip**. Pip allows for uninstallation of packages, and is actively maintained, unlike easy_install.
Homebrew already installed Distribute for you. Its ``easy_install`` command is
considered by many to be deprecated, so we will install its replacement:
**pip**. Pip allows for uninstallation of packages, and is actively maintained,
unlike easy_install.
To install pip, simply open a command prompt and run::
To install pip, simply open a command prompt and run
.. code-block:: console
$ easy_install pip
@@ -52,21 +85,41 @@ To install pip, simply open a command prompt and run::
Virtualenv
----------
After Distribute & Pip, the next development tool that you should install is `virtualenv <http://pypi.python.org/pypi/virtualenv/>`_. Use pip::
After Distribute & Pip, the next development tool that you should install is
`virtualenv <http://pypi.python.org/pypi/virtualenv/>`_. Use pip
.. code-block:: console
$ pip install virtualenv
The virtualenv kit provides the ability to create virtual Python environments that do not interfere with either each other, or the main Python installation. If you install virtualenv before you begin coding then you can get into the habit of using it to create completely clean Python environments for each project. This is particularly important for Web development, where each framework and application will have many dependencies.
The virtualenv kit provides the ability to create virtual Python environments
that do not interfere with either each other, or the main Python installation.
If you install virtualenv before you begin coding then you can get into the
habit of using it to create completely clean Python environments for each
project. This is particularly important for Web development, where each
framework and application will have many dependencies.
To set up a new Python environment, change the working directory to where ever you want to store the environment, and run the virtualenv utility in your project's directory::
To set up a new Python environment, change the working directory to where ever
you want to store the environment, and run the virtualenv utility in your
project's directory
.. code-block:: console
$ virtualenv --distribute venv
To use an environment, run ``source venv/bin/activate``. Your command prompt will change to show the active environment. Once you have finished working in the current virtual environment, run ``deactivate`` to restore your settings to normal.
To use an environment, run ``source venv/bin/activate``. Your command prompt
will change to show the active environment. Once you have finished working in
the current virtual environment, run ``deactivate`` to restore your settings
to normal.
Each new environment automatically includes a copy of ``pip``, so that you can setup the third-party libraries and tools that you want to use in that environment. Put your own code within a subdirectory of the environment, however you wish. When you no longer need a particular environment, simply copy your code out of it, and then delete the main directory for the environment.
Each new environment automatically includes a copy of ``pip``, so that you can
setup the third-party libraries and tools that you want to use in that
environment. Put your own code within a subdirectory of the environment,
however you wish. When you no longer need a particular environment, simply
copy your code out of it, and then delete the main directory for the environment.
--------------------------------
This page is a remixed version of `another guide <http://www.stuartellis.eu/articles/python-development-windows/>`_, which is available under the same license.
This page is a remixed version of `another guide <http://www.stuartellis.eu/articles/python-development-windows/>`_,
which is available under the same license.
docs/starting/install/win.rst
+63 -16
View File
@@ -3,13 +3,24 @@
Installing Python on Windows
============================
First, download the `latest version <http://python.org/ftp/python/2.7.2/python-2.7.2.msi>`_ of Python 2 from the official Website.
First, download the `latest version <http://python.org/ftp/python/2.7.2/python-2.7.2.msi>`_
of Python 2 from the official Website.
The Windows version is provided as an MSI package. To install it manually, just double-click the file. The MSI package format allows Windows administrators to automate installation with their standard tools.
The Windows version is provided as an MSI package. To install it manually, just
double-click the file. The MSI package format allows Windows administrators to
automate installation with their standard tools.
By design, Python installs to a directory with the version number embedded, e.g. ``C:\Python27\``, so that you can have multiple versions of Python on the same system without conflicts. Of course, only one interpreter can be the default application for Python file types. It also does not automatically modify the ``PATH`` environment variable, so that you always have control over which copy of Python is run.
By design, Python installs to a directory with the version number embedded,
e.g. ``C:\Python27\``, so that you can have multiple versions of Python on the
same system without conflicts. Of course, only one interpreter can be the
default application for Python file types. It also does not automatically
modify the ``PATH`` environment variable, so that you always have control over
which copy of Python is run.
Typing the full path name for a Python interpreter each time quickly gets tedious, so add the directories for your default Python version to the PATH. Assuming that your Python installation is in ``C:\Python27\``, add this to your PATH::
Typing the full path name for a Python interpreter each time quickly gets
tedious, so add the directories for your default Python version to the PATH.
Assuming that your Python installation is in ``C:\Python27\``, add this to your
PATH::
C:\Python27\;C:\Python27\Scripts\
@@ -17,20 +28,34 @@ You can do this easily by running the following in ``powershell``::
[Environment]::SetEnvironmentVariable("Path", "$env:Path;C:\Python27\;C:\Python27\Scripts\", "User")
You do not need to install or configure anything else to use Python. Having said that, I would strongly recommend that you install the tools and libraries described in the next section before you start building Python applications for real-world use. In particular, you should always install Distribute, as it makes it much easier for you to use other third-party Python libraries.
You do not need to install or configure anything else to use Python. Having
said that, I would strongly recommend that you install the tools and libraries
described in the next section before you start building Python applications for
real-world use. In particular, you should always install Distribute, as it
makes it much easier for you to use other third-party Python libraries.
Distribute + Pip
----------------
The most crucial third-party Python software of all is Distribute, which extends the packaging and installation facilities provided by the distutils in the standard library. Once you add Distribute to your Python system you can download and install any compliant Python software product with a single command. It also enables you to add this network installation capability to your own Python software with very little work.
The most crucial third-party Python software of all is Distribute, which
extends the packaging and installation facilities provided by the distutils in
the standard library. Once you add Distribute to your Python system you can
download and install any compliant Python software product with a single
command. It also enables you to add this network installation capability to
your own Python software with very little work.
To obtain the latest version of Distribute for Windows, run the python script available here:
http://python-distribute.org/distribute_setup.py
To obtain the latest version of Distribute for Windows, run the python script
available here: `python-distribute <http://python-distribute.org/distribute_setup.py>`_
You'll now have a new command available to you: **easy_install**. It is considered by many to be deprecated, so we will install its replacement: **pip**. Pip allows for uninstallation of packages, and is actively maintained, unlike easy_install.
You'll now have a new command available to you: **easy_install**. It is
considered by many to be deprecated, so we will install its replacement:
**pip**. Pip allows for uninstallation of packages, and is actively maintained,
unlike easy_install.
To install pip, simply open a command prompt and run::
To install pip, simply open a command prompt and run
.. code-block:: console
> easy_install pip
@@ -38,23 +63,45 @@ To install pip, simply open a command prompt and run::
Virtualenv
----------
After Distribute & Pip, the next development tool that you should install is `virtualenv <http://pypi.python.org/pypi/virtualenv/>`_. Use pip::
After Distribute & Pip, the next development tool that you should install is
`virtualenv <http://pypi.python.org/pypi/virtualenv/>`_. Use pip
.. code-block:: console
> pip install virtualenv
The virtualenv kit provides the ability to create virtual Python environments that do not interfere with either each other, or the main Python installation. If you install virtualenv before you begin coding then you can get into the habit of using it to create completely clean Python environments for each project. This is particularly important for Web development, where each framework and application will have many dependencies.
The virtualenv kit provides the ability to create virtual Python environments
that do not interfere with either each other, or the main Python installation.
If you install virtualenv before you begin coding then you can get into the
habit of using it to create completely clean Python environments for each
project. This is particularly important for Web development, where each
framework and application will have many dependencies.
To set up a new Python environment, change the working directory to where ever you want to store the environment, and run the virtualenv utility in your project's directory::
To set up a new Python environment, change the working directory to where ever
you want to store the environment, and run the virtualenv utility in your
project's directory
.. code-block:: console
> virtualenv --distribute venv
To use an environment, run the ``activate.bat`` batch file in the ``Scripts`` subdirectory of that environment. Your command prompt will change to show the active environment. Once you have finished working in the current virtual environment, run the ``deactivate.bat`` batch file to restore your settings to normal.
To use an environment, run the ``activate.bat`` batch file in the ``Scripts``
subdirectory of that environment. Your command prompt will change to show the
active environment. Once you have finished working in the current virtual
environment, run the ``deactivate.bat`` batch file to restore your settings to
normal.
Each new environment automatically includes a copy of ``pip`` in the ``Scripts`` subdirectory, so that you can setup the third-party libraries and tools that you want to use in that environment. Put your own code within a subdirectory of the environment, however you wish. When you no longer need a particular environment, simply copy your code out of it, and then delete the main directory for the environment.
Each new environment automatically includes a copy of ``pip`` in the
``Scripts`` subdirectory, so that you can setup the third-party libraries and
tools that you want to use in that environment. Put your own code within a
subdirectory of the environment, however you wish. When you no longer need a
particular environment, simply copy your code out of it, and then delete the
main directory for the environment.
--------------------------------
This page is a remixed version of `another guide <http://www.stuartellis.eu/articles/python-development-windows/>`_, which is available under the same license.
This page is a remixed version of `another guide <http://www.stuartellis.eu/articles/python-development-windows/>`_,
which is available under the same license.
docs/starting/installation.rst
+8 -2
View File
@@ -3,12 +3,18 @@ Properly Installing Python
There's a good chance that you already have Python on your operating system.
If so, you do not need to install or configure anything else to use Python. Having said that, I would strongly recommend that you install the tools and libraries described in the guides below before you start building Python applications for real-world use. In particular, you should always install Distribute, Pip, and Virtualenv — they make it much easier for you to use other third-party Python libraries.
If so, you do not need to install or configure anything else to use Python.
Having said that, I would strongly recommend that you install the tools and
libraries described in the guides below before you start building Python
applications for real-world use. In particular, you should always install
Distribute, Pip, and Virtualenv — they make it much easier for you to use
other third-party Python libraries.
Installation Guides
-------------------
These guides go over the proper installation of :ref:`Python 2.7 <which-python>` for development purproses, as well as distribute, pip, and virtualenv setup.
These guides go over the proper installation of :ref:`Python 2.7 <which-python>`
for development purposes, as well as distribute, pip, and virtualenv setup.
- :ref:`Mac OS X <install-osx>`.
- :ref:`Microsoft Windows<install-windows>`.
docs/starting/which-python.rst
+2 -1
View File
@@ -16,7 +16,8 @@ Which Python to use?
Today
-----
If you're choosing a Python interpreter to use, I *highly* recommend you Use Python 2.7.x, unless you have a strong reason not to.
If you're choosing a Python interpreter to use, I *highly* recommend you Use
Python 2.7.x, unless you have a strong reason not to.
The Future
docs/writing/documentation.rst
+145 -7
View File
@@ -1,9 +1,141 @@
Documenting Your Code
=====================
Documenting your code is extremely important. It is debatebly even
more important than testing.
With readability of the code being a main focus for Python developers, proper
commenting is naturally important. Some best practice apply to code comments
and project documents, depending on the scope of the project.
Project documents
-----------------
A README file at the root directory give general information to the users and
the maintainers. It should be raw text or written in some very easy to read
markup, such as reStructuredText and Markdown. It should contain a few lines
explaining the purpose of the project or the library (without assuming the user
knows anything about the project), the url of the main source for the software,
and some basic credit information. This file is the main entry point for
readers of the code.
An INSTALL file is less often necessary with python, except if the dependencies
are complex or unusual, or if some C modules need to be compiled before use.
The installation instructions are often reduced to one command, such as ``pip
install module`` or ``python setup.py install`` and added to the README file.
A LICENSE file should always be present and specify the license under which the
software is made available to the public.
A TODO file or a TODO section in README should list the planned modifications
of the code.
A CHANGELOG file or section in README should compile a short overview of the
changes in the code base for the latest versions.
Documentation
-------------
As the project or library reaches a certain level of complexity, it may require
a fuller documentation, which can be of different flavors:
The introduction may show a very short overview of what can be done with the
product, using one or two extremely simplified use cases.
The tutorials will show in more details some main use cases. The reader will
follow a step-by-step procedure to set-up a working prototype.
The API reference, which is often generated automatically from the code, will
list all publicly available interfaces, their parameters and their return
values, with an explanation of their use.
Some documents intended for developers might give guidance about code
convention and general design decision of the project.
Comments
--------
Comments are written directly inside the code, either using the hash sign (#)
or a docstring_.
Finding the correct balance between undocumented code and verbose and useless
comment boilerplates is difficult, and is the subject of heated discussion
among developers.
The following guidelines seem to be most commonly agreed upon:
**Wrong or outdated comments are worse than no comments at all.** Following the
saying that it is better, on a boat, to know that we do not know were we are
than to wrongly believe we know where we are, wrong or outdated comments can be
misleading for the maintainers, slow down considerably bug hunting or
refactoring, and then, when discovered wrong, they will throw suspicion on all
other comments in the code, regardless of their individual correctness.
**No need comments for perfect code...** An hypothetical perfectly readable
code, with a crystal clear logic stream, expressive variable and function
names, orthogonal segmentation passing exactly between the flesh and the bones,
and no implicit assumptions of any kind, would not require any comment at all.
When striving for coding excellence, it is useful to see any existing comment,
or any feeling of a need for a comment, as the sign that the code do not
express clearly enough its intent and can be improved.
**.. but no code is perfect.** Perfect code is a chimere, it exists only in
our dreams. In real life, a code base is full of trade offs, and comments are
often needed in the most difficult parts. Moreover, any special case, any
obscure hack, any monkey patch and any ugly workaround MUST be signaled and
explained by a proper comment. This should be enforced by the law!
**TODOs** are special comments that a developer write as a reminder for later
use. It is said that its original intent was that someone might, one day,
search for the string "TODO" in the code base and actually roll their sleeves
and start *to do the TODOs*. There is no avalaible record that it ever
happened. However, TODOs comment are still very useful, because they mark the
current limits of the code, and it is not unlikely that, when required to add a
new behavior to the actual code, looking at the TODOs will show where to start.
**Do not use triple-quote strings to comment code.** A common operation when
modifiying code is to comment out some lines or even a full function or class
definition. This can be done by adding triple-quotes around the code block to
be skipped, but this is not a good pratice, because line-oriented command-line
tools such as ``grep`` will not be aware that the commented code is inactive.
It is better to add hashes at the proper indentation level for every commented
line. Good editors allow to do this with few keystrokes (ctrl-v on Vim).
**Bad**
.. code-block:: python
def tricky_function():
'''
Commented out because its breaks something.
if foo:
do_bar()
'''
return baz
def tricky_function():
# Commented out because its breaks something.
#if foo:
#do_bar()
return baz
def tricky_function():
# Commented out because its breaks something.
# if foo:
# do_bar()
return baz
**Good**
.. code-block:: python
def tricky_function():
# Commented out because its breaks something.
#if foo:
# do_bar()
return baz
Note that comment text is properly written and separated from the hash by a
space. Commented code is not separated from the hash by an additional space;
this helps when uncommented the code.
The Basics
::::::::::
@@ -33,12 +165,13 @@ Inline comments are used for individual lines and should be used sparingly.: ::
But sometimes, this is useful: ::
x = x + 1 # Compensate for border
Doc Strings
Docstrings
-----------
PEP 257 is the primary reference for docstrings. (http://www.python.org/dev/peps/pep-0257/)
There are two types of docstrings, one-line and multi-line. Their names should be fairly self explanatory.
There are two types of docstrings, one-line and multi-line. Their names
should be fairly self explanatory.
One-line docstrings: ::
def kos_root():
@@ -63,7 +196,9 @@ Multi-line docstrings: ::
Sphinx
------
Sphinx_ is a tool which converts documentation in the :ref:`restructuredtext-ref` markup language into a range of output formats including HTML, LaTeX (for printable PDF versions), manual pages and plain text.
Sphinx_ is a tool which converts documentation in the :ref:`restructuredtext-ref`
markup language into a range of output formats including HTML, LaTeX (for
printable PDF versions), manual pages and plain text.
.. note:: This Guide is built with Sphinx_
@@ -75,7 +210,10 @@ Sphinx_ is a tool which converts documentation in the :ref:`restructuredtext-ref
reStructuredText
----------------
Most Python documentation is written with reStructuredText_. The `reStructuredText Primer <http://sphinx.pocoo.org/rest.html>`_ and the `reStructuredText Quick Reference <http://docutils.sourceforge.net/docs/user/rst/quickref.html>`_ should help you familiarize yourself with its syntax.
Most Python documentation is written with reStructuredText_. The
`reStructuredText Primer <http://sphinx.pocoo.org/rest.html>`_ and the
`reStructuredText Quick Reference <http://docutils.sourceforge.net/docs/user/rst/quickref.html>`_
should help you familiarize yourself with its syntax.
.. _reStructuredText: http://docutils.sourceforge.net/rst.html
@@ -85,7 +223,7 @@ Other Tools
that old thing
--------------
pocco / docco / shocco
pycco / docco / shocco
----------------------
Ronn
docs/writing/license.rst
+4 -2
View File
@@ -3,9 +3,11 @@ Choosing a License
Open source.
There are plenty of `open source licenses <http://opensource.org/licenses/alphabetical>`_ available to choose from.
There are plenty of `open source licenses <http://opensource.org/licenses/alphabetical>`_
available to choose from.
To help you choose one for your project, there's a `license chooser <http://three.org/openart/license_chooser/>`_, use it.
To help you choose one for your project, there's a `license chooser <http://three.org/openart/license_chooser/>`_,
use it.
Non-Restrictive
docs/writing/structure.rst
+240 -1
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@@ -8,6 +8,245 @@ Structuring your project properly is extremely important.
Structure is Key
----------------
Thanks to the way imports and module are handled in Python, it is
relatively easy to structure a python project. Easy, here, means
actually that you have not many constraints and that the module
importing model is easy grasp. Therefore, you are left with the
pure architectural task of drawing the different parts of your
project and their interactions.
Easy structuration of a project means it is also easy
to do it poorly. Some signs of a poorly structured projects
include:
- Multiple and messy circular dependencies: if your classes
Table and Chair in furn.py need to import Carpenter from workers.py
to answer to a question such as table.isdoneby(),
and if convertly the class Carpenter need to import Table and Chair,
for example to answer to carpenter.whatdo(), then you
have a circular dependency, and will have to resort to
fragile hacks such has using import statements inside
methods or functions.
- Hidden coupling. Each and every change in Table implementation
breaks 20 tests in unrelated test cases because it breaks Carpenter's code,
which requires very careful surgery to adapt the change. This means
you have too many assumptions about Table in Carpenter's code or the
reverse.
- Heavy usage of global state or context: Instead of explicitely
passing ``(height, width, type, wood)`` to each other, Table
and Carpenter rely on global variables that can be modified
and are modified on the fly by different agent. You need to
scrutinize all access to this global variables to understand why
a rectangular table became a sqaure, and discover that a remote
template code is also modifying this context, messing with
table dimensions.
- Spaghetti code: Multiple pages of nested if clauses and for loops
with a lot of copy-pasted procedural code and no
proper segmentation are known as spaghetti code. Python's
meaningful indentation (one of its most controversial feature) make
it very hard to maintain this kind of code. So the good news is that
you might not see too much of it.
- Ravioli code is more likely in Python: it consists of hundreds of
similar little pieces of logic, often classes or objects, without
proper structure. If you never can remember if you have to use
FurnitureTable, AssetTable or Table, or even TableNew for your
task at hand, you might be swimming in ravioli code.
Modules
-------
Python modules are one of the main abstraction layer available and probably the
most natural one. Abstraction layers allow separating code into parts holding
related data and functionalities.
For example, a layer of a project can handle interfacing with user actions,
while another would handle low-level manipulation of data. The most natural way
to separate these two layers is to regroup all interfacing functionalities
in one file, and all low-level operations in another file. In this case,
the interface file need to import the low-level file. This is done with the
`import` and `from ... import` statements.
As soon as you use `import` statements you use modules, either builtin modules
such as `os` and `sys`, or third-party modules you have installed in your
environment, or project's internal modules.
Nothing special is required for a Python file to be a module, but the import
mechanism need to be understood in order to use this concept properly and avoid
some issues.
Concretely, the `import modu` statement will look for the proper file, which is
`modu.py` in the same directory as the caller if it exists. If it is not
found, the Python interpreter with search for `modu.py` in the "path"
recursively and raise an ImportError exception if it is not found.
Once `modu.py` is found, the Python interpreter will execute the module in an
isolated scope. Any top-level statement in `modu.py` will be executed,
including other imports if any. Function and classes definitions are stored in
the module's dictionary.
Then modules variables, functions and classes will be available to the caller
through the module's namespace, a central concept in programming that is
particularly helpful and powerful in Python.
In many languages, a `include file` directive is used by the preprocessor to
take all code found in the file and 'copy' it in the caller's code. It is
different in Python: the included code is isolated in a module namespace, which
means that you generally don't have to worry that the included code could have
unwanted effect, eg override an existing function with the same name.
It is possible to simulate the more standard behavior by using a special syntax
of the import statement: `from modu import *`. This is generally considered bad
practice, **using import * makes code harder to read and dependencies less
compartimented**.
Using `from modu import func` is a way to pinpoint the function you want to
import and put it is the global namespace. While much less harmful than `import
*` because it shows explicitely what is imported in the global namespace, it's
advantage over a simpler `import modu` is only that it will save some typing.
**Very bad**
.. code-block:: python
[...]
from modu import *
[...]
x = sqrt(4) # Is sqrt part of modu? A builtin? Defined above?
**Better**
.. code-block:: python
from modu import sqrt
[...]
x = sqrt(4) # sqrt may be part of modu, if not redefined in between
**Best**
.. code-block:: python
import modu
[...]
x = modu.sqrt(4) # sqrt is visibly part of modu's namespace
As said in the section about style, readability is one of the main feature of
Python. Readability means to avoid useless boilerplate text and clutter,
therefore some efforts are spent trying to achieve a certain level of brevity.
But terseness and obscurity are the limits where brevity should stop: being
able to tell immediately from where comes a class or a function, as in the
`modu.func` idiom, improves greatly code readability and understandability in
most cases but the simplest single file projects.
Packages --------
Python provides a very straightforward packaging system, which is simply an
extension of the module mechanism to a directory.
Any directory with a __init__.py file is considered a Python package. The
different modules in the package are imported in a similar manner as plain
modules, will a special behavior for the __init__.py file, that is used to
gather all package-wide definitions.
A file modu.py in the directory pack/ is imported with the statement `import
pack.modu`. This statement will look for a __init__.py file in `pack`, execute
all its top-level statements. Then it will look for a file `pack/modu.py` and
execute all its top-level statements. After these operations, any variable,
function or class defined in modu.py is available in pack.modu namespace.
A commonly seen issue is to add too many code and functions in __init__.py
files. When the project complexity grows, there may be sub-packages and
sub-sub-packages in a deep directory structure, and then, import a single item
from a sub-sub-package will require to execute all __init__.py file met while
descending the tree.
Leaving a __init__.py file empty is considered normal and even a good pratice,
if the package's modules and sub-packages do not need to share any code.
Lastly, a convenient syntax is available for importing deeply nested packages:
`import very.deep.module as mod` allow to use `mod` in place of the verbose
repetition of `very.deep.module` in front of each calls to module items.
Object-oriented programming
---------------------------
Python is sometime described as an object-oriented programming language. This
can be somewhat misleading and need to be clarified.
In Python, everything is an object, and can be handled as such. This is what is
meant when we say that, for example, functions are first-class objects.
Functions, classes, strings, and even types are objects in Python: like any
objects, they have a type, they can be passed as function arguments, they may
have methods and properties. In this understanding, Python is an
object-oriented language.
However, unlike Java, Python do not impose object-oriented programming as the
main programming paradigm. It is perfectly viable for a Python project to not
be object-oriented, ie. to use no or very few class definitions, class
inheritance, and any other mechanism that are specific to object-oriented
programming.
Moreover, as seen in the modules_ section, the way Python handles modules and
namespaces gives directly to the developer a natural way to ensure
encapsulation and separation of abstraction layers, both being the most common
reasons to use object-orientation. Therefore, Python programmers have more
latitude to not use object-orientation, when it is not required by the business
model to be constructed.
There are some reasons to avoid unnecessary object-orientation. Definining
custom classes is useful when we want to glue together some state and some
functionality. The problem, as pointed out by the discussions about functional
programming, comes from the "state" part of the equation.
In some architectures, typically web applications, instances of Python
processes are spawned simultaneously to answer to external requests that can
happen at the same time. In this case, holding some state into instanciated
objects, which means keeping some static information about the world, is prone
to concurrency problems or race-conditions: between the initialization of the
state of an object, usually done with the __init__() method, and the actual use
of the object state through one of its method, the world may have changed, and
the retained state may be outdated. For example, a request may load an item in
memory and mark it as read by a user. If another request requires the deletion
of this item at the same, it may happen that the deletion actually occur after
the first process loaded the item, and then we have to mark as read a deleted
object.
This and other issues led to the idea that using stateless functions is a
better programming paradigm.
Another way to say the same thing is to propose to use functions and procedures
with as few implicit context and side-effects as possible. A function's
implicit context is decelable when the function body refers to some global
variables or fetches data from the persistence layer. Side-effects are the
opposite: if a function body modifies the global context or save or delete data
on the persistence layer, it is said to have side-effect.
Isolating carefully functions with context and side-effects from functions with
logic (called pure functions) allow the following benefits:
- Pure functions are more likely to be deterministic: given a fixed input,
the output will always be the same.
- Pure functions are much easier to change or replace if they need to
be refactored or optimized.
- Pure functions are easier to test with unit-tests: There is less
need for complex context setup and data cleaning afterwards.
- Pure functions are easier to manipulate, decorate_, pass-around.
In summary, pure functions, without any context or side-effects, are more
efficient building blocks than classes and objects for some architectures.
Obviously, object-orientation is useful and even necessary in many cases, for
example when developing graphical desktop applications or games, where the
things that are manipulated (windows, buttons, avatars, vehicles) have a
relatively long life of their own in the computer's memory.
Vendorizing Dependencies
@@ -20,4 +259,4 @@ Runners
Further Reading
---------------
---------------
docs/writing/style.rst
+446 -11
View File
@@ -1,12 +1,220 @@
Code Style
==========
If you ask to Python programmers what they like the most in Python, they will
often say it is its high readability. Indeed, a high level of readability of
the code is at the heart of the design of the Python language, following the
recognised fact that code is read much more often than it is written.
One reason for Python code to be easily read and understood is its relatively
complete set of Code Style guidelines and "Pythonic" idioms.
On the opposite, when a veteran Python developper (a Pythonistas) point to some
parts of a code and say it is not "Pythonic", it usually means that these lines
of code do not follow the common guidelines and fail to express the intent is
what is considered the best (hear: most readable) way.
On some border cases, no best way has been agreed upon on how to express
an intent in Python code, but these cases are rare.
General concepts
----------------
Explicit code
~~~~~~~~~~~~~
While any kind of black magic is possible with Python, the
most explicit and straightforward manner is preferred.
**Bad**
.. code-block:: python
def make_complex(\*args):
x, y = args
return dict(\**locals())
**Good**
.. code-block:: python
def make_complex(x, y):
return {'x': x, 'y': y}
In the good code above, x and y are explicitely received from
the caller, and an explicit dictionary is returned. The developer
using this function knows exactly what to do by reading the
first and last lines, which is not the case with the bad example.
One statement per line
~~~~~~~~~~~~~~~~~~~~~~
While some compound statements such as list comprehensions are
allowed and appreciated for their brevity and their expressivity,
it is bad practice to have two disjoint statements on the same line.
**Bad**
.. code-block:: python
print 'one'; print 'two'
if x == 1: print 'one'
if <complex comparison> and <other complex comparison>:
# do something
**Good**
.. code-block:: python
print 'one'
print 'two'
if x == 1:
print 'one'
cond1 = <complex comparison>
cond2 = <other complex comparison>
if cond1 and cond2:
# do something
Avoid the magical wand
~~~~~~~~~~~~~~~~~~~~~~
A powerful tool for hackers, Python comes with a very rich set of hooks and
tools allowing to do almost any kind of tricky tricks. For instance, it is
possible to change how objects are created and instanciated, it is possible to
change how the Python interpreter imports modules, it is even possible (and
recommended if needed) to embed C routines in Python.
However, all these options have many drawbacks and it is always better to use
the most straightforward way to achieve your goal. The main drawback is that
readability suffers deeply from them. Many code analysis tools, such as pylint
or pyflakes, will be unable to parse this "magic" code.
We consider that a Python developer should know about these nearly infinite
possibilities, because it grows the confidence that no hard-wall will be on the
way. However, knowing how to use them and particularly when **not** to use
them is the most important.
Like a Kungfu master, a pythonistas knows how to kill with a single finger, and
never do it.
We are all consenting adults
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
As seen above, Python allows many tricks, and some of them are potentially
dangerous. A good example is that any client code can override an object's
properties and methods: There is no "private" keyword in Python. This
philosophy, very different from highly defensive languages like Java, which
give a lot of mechanism to prevent any misuse, is expressed by the saying: "We
are consenting adults".
This doesn't mean that, for example, no properties are considered private, and
that no proper encapsulation is possible in Python. But, instead of relying on
concrete walls erected by the developers between their code and other's, the
Python community prefers to rely on a set of convention indicating that these
elements should not be accessed directly.
The main convention for private properties and implementation details is to
prefix all "internals" with an underscore. If the client code breaks this rule
and access to these marked elements, any misbehavior or problems encountered if
the code is modified is the responsibility of the client code.
Using this convention generously is encouraged: any method or property that is
not intended to be used by client code should be prefixed with an underscore.
This will guarantee a better separation of duties and easier modifications of
existing code, and it will always be possible to publicize a private property,
while privatising a public property might be a much harder operation.
Idioms
::::::
------
Idiomatic Python code is often referred to as being *pythonic*.
Idiomatic Python code is often referred to as being *Pythonic*.
.. _unpacking-ref:
Unpacking
~~~~~~~~~
If you know the length of a list or tuple, you can assign names to its
elements with unpacking:
.. code-block:: python
for index, item in enumerate(some_list):
# do something with index and item
You can use this to swap variables, as well:
.. code-block:: python
a, b = b, a
Create an ignored variable
~~~~~~~~~~~~~~~~~~~~~~~~~~
If you need to assign something (for instance, in :ref:`unpacking-ref`) but
will not need that variable, use ``_``:
.. code-block:: python
filename = 'foobar.txt'
basename, _, ext = filename.rpartition()
.. note::
"``_``" is commonly used as an alias for the :func:`~gettext.gettext`
function. If your application uses (or may someday use) :mod:`gettext`,
you may want to avoid using ``_`` for ignored variables, as you may
accidentally shadow :func:`~gettext.gettext`.
Create a length-N list of the same thing
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Use the Python list ``*`` operator:
.. code-block:: python
four_nones = [None] * 4
Create a length-N list of lists
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Because lists are mutable, the ``*`` operator (as above) will create a list
of N references to the `same` list, which is not likely what you want.
Instead, use a list comprehension:
.. code-block:: python
four_lists = [[] for _ in xrange(4)]
A common idiom for creating strings is to use `join <http://docs.python.org/library/string.html#string.join>`_ on an empty string.::
letters = ['s', 'p', 'a', 'm']
word = ''.join(letters)
This will set the value of the variable *word* to 'spam'. This idiom can be applied to lists and tuples.
Sometimes we need to search through a collection of things. Let's look at two options: lists and dictionaries.
Take the following code for example::
d = {'s': [], 'p': [], 'a': [], 'm': []}
l = ['s', 'p', 'a', 'm']
def lookup_dict(d):
return 's' in d
def lookup_list(l):
return 's' in l
Even though both functions look identical, because *lookup_dict* is utilizing the fact that dictionaries in python are hashtables, the lookup performance between the two is very different.
Python will have to go through each item in the list to find a matching case, which is time consuming. By analysing the hash of the dictionary finding keys in the dict can be done very quickly.
For more information see this `StackOverflow <http://stackoverflow.com/questions/513882/python-list-vs-dict-for-look-up-table>`_ page.
Zen of Python
-------------
@@ -38,8 +246,10 @@ Also known as PEP 20, the guiding principles for Python's design.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
See `<http://stackoverflow.com/questions/228181/the-zen-of-python>`_ for some
examples.
For some examples of good Python style, see `this Stack Overflow question
<http://stackoverflow.com/questions/228181/the-zen-of-python>`_ or `these
slides from a Python user group
<http://artifex.org/~hblanks/talks/2011/pep20_by_example.pdf>`_.
PEP 8
-----
@@ -48,16 +258,18 @@ PEP 8 is the de-facto code style guide for Python.
`PEP 8 <http://www.python.org/dev/peps/pep-0008/>`_
There exists a command-line program, `pep8` that can check your code for
conformance.
Conforming your Python code to PEP 8 is generally a good idea and helps make
code more consistent when working on projects with other developers. There
exists a command-line program, `pep8 <https://github.com/jcrocholl/pep8>`_,
that can check your code for conformance. Install it by running the following
command in your Terminal:
::
pip install pep8
$ pip install pep8
Simply run it on a file or series of files and get a report of any
violations
Then run it on a file or series of files to get a report of any violations.
::
@@ -71,5 +283,228 @@ violations
optparse.py:472:29: E221 multiple spaces before operator
optparse.py:544:21: W601 .has_key() is deprecated, use 'in'
Conforming your style to PEP 8 is generally a good idea and helps make code a lot
more consistent when working on projects with other developers.
Conventions
:::::::::::
Here are some conventions you should follow to make your code easier to read.
Check if variable equals a constant
-----------------------------------
You don't need to explicitly compare a value to True, or None, or 0 - you can
just add it to the if statement. See `Truth Value Testing
<http://docs.python.org/library/stdtypes.html#truth-value-testing>`_ for a
list of what is considered false.
**Bad**:
.. code-block:: python
if attr == True:
print 'True!'
if attr == None:
print 'attr is None!'
**Good**:
.. code-block:: python
# Just check the value
if attr:
print 'attr is truthy!'
# or check for the opposite
if not attr:
print 'attr is falsey!'
# or, since None is considered false, explicity check for it
if attr is None:
print 'attr is None!'
Access a Dictionary Element
---------------------------
Don't use the ``has_key`` function. Instead use ``x in d`` syntax, or pass
a default argument to ``get``.
**Bad**:
.. code-block:: python
d = {'hello': 'world'}
if d.has_key('hello'):
print d['hello'] # prints 'world'
else:
print 'default_value'
**Good**:
.. code-block:: python
d = {'hello': 'world'}
print d.get('hello', 'default_value') # prints 'world'
print d.get('thingy', 'default_value') # prints 'default_value'
# Or:
if 'hello' in d:
print d['hello']
Short Ways to Manipulate Lists
------------------------------
`List comprehensions
<http://docs.python.org/tutorial/datastructures.html#list-comprehensions>`_
provide a powerful, concise way to work with lists. Also, the `map
<http://docs.python.org/library/functions.html#map>`_ and `filter
<http://docs.python.org/library/functions.html#filter>`_ functions can perform
operations on lists using a different concise syntax.
**Bad**:
.. code-block:: python
# Filter elements greater than 4
a = [3, 4, 5]
b = []
for i in a:
if i > 4:
b.append(i)
**Good**:
.. code-block:: python
b = [i for i in a if i > 4]
b = filter(lambda x: x > 4, a)
**Bad**:
.. code-block:: python
# Add three to all list members.
a = [3, 4, 5]
count = 0
for i in a:
a[count] = i + 3
count = count + 1
**Good**:
.. code-block:: python
a = [3, 4, 5]
a = [i + 3 for i in a]
# Or:
a = map(lambda i: i + 3, a)
Use `enumerate <http://docs.python.org/library/functions.html#enumerate>`_ to
keep a count of your place in the list.
.. code-block:: python
for i, item in enumerate(a):
print i + ", " + item
# prints
# 0, 3
# 1, 4
# 2, 5
The ``enumerate`` function has better readability than handling a counter
manually. Moreover,
it is better optimized for iterators.
Read From a File
----------------
Use the ``with open`` syntax to read from files. This will automatically close
files for you.
**Bad**:
.. code-block:: python
f = open('file.txt')
a = f.read()
print a
f.close()
**Good**:
.. code-block:: python
with open('file.txt') as f:
for line in f:
print line
The ``with`` statement is better because it will ensure you always close the
file, even if an exception is raised.
Returning Multiple Values from a Function
-----------------------------------------
Python supports returning multiple values from a function as a comma-separated
list, so you don't have to create an object or dictionary and pack multiple
values in before you return
**Bad**:
.. code-block:: python
def math_func(a):
return {'square': a ** 2, 'cube': a ** 3}
d = math_func(3)
s = d['square']
c = d['cube']
**Good**:
.. code-block:: python
def math_func(a):
return a ** 2, a ** 3
square, cube = math_func(3)
Line Continuations
~~~~~~~~~~~~~~~~~~
When a logical line of code is longer than the accepted limit, you need to
split it over multiple physical lines. Python interpreter will join consecutive
lines if the last character of the line is a backslash. This is helpful
sometime but is preferably avoided, because of its fragility: a white space
added to the end of the line, after the backslash, will break the code and may
have unexpected results.
A prefered solution is to use parenthesis around your elements. Left with an
unclosed parenthesis on an end-of-line the Python interpreter will join the
next line until the parenthesis is closed. The same behavior holds for curly
and square braces.
**Bad**:
.. code-block:: python
my_very_big_string = """For a long time I used to go to bed early. Sometimes, \
when I had put out my candle, my eyes would close so quickly that I had not even \
time to say “I’m going to sleep.”"""
from some.deep.module.inside.a.module import a_nice_function, another_nice_function, \
yet_another_nice_functio
**Good**:
.. code-block:: python
my_very_big_string = ("For a long time I used to go to bed early. Sometimes, "
"when I had put out my candle, my eyes would close so quickly that I had not even "
"time to say “I’m going to sleep.”")
from some.deep.module.inside.a.module import (a_nice_function, another_nice_function,
yet_another_nice_functio)
However, more often than not having to split long logical line is a sign that
you are trying to do too many things at the same time, which may hinder
readability.
docs/writing/tests.rst
+98 -6
View File
@@ -3,6 +3,70 @@ Testing Your Code
Testing your code is very important.
Getting used to writting the testing code and the running code in parallel is
now considered a good habit. Used wisely, this method helps you define more
precisely your code's intent and have a more decoupled architecture.
Some general rules of testing:
- A testing unit should focus on one tiny bit of functionality and prove it
correct.
- Each test unit must be fully independent. Each of them must be able to run
alone, and also within the test suite, regardless of the order they are called.
The implication of this rule is that each test must be loaded with a fresh
dataset and may have to do some cleanup afterwards. This is usually
handled by setUp() and tearDown() methods.
- Try hard to make tests that run fast. If one single test needs more than a
few millisecond to run, development will be slowed down or the tests will not
be run as often as desirable. In some cases, test can't be fast because they
need a complex data structure to work on, and this data structure must be
loaded every time the test runs. Keep these heavier tests in a separate test
suite that is run by some scheduled task, and run all other tests as often
as needed.
- Learn your tools and learn how to run a single test or a test case. Then,
when developing a function inside a module, run this function's tests very
often, ideally automatically when you save the code.
- Always run the full test suite before a coding session, and run it again
after. This will give you more confidence that you did not break anything in
the rest of the code.
- It is a good idea to implement a hook that runs all test before pushing code
to a shared repository.
- If you are in the middle of a development and have to interrupt your work, it
is a good idea to write a broken unit test about what you want to develop next.
When comming back to work, you will have a pointer to where you were and get
faster on tracks.
- The first step when you are debugging your code is to write a new test
pinpointing the bug. While it is not always possible to do, those bug
catching test are among the most valuable piece of code in your project.
- Use long and descriptive names for testing functions. The style guide here is
slighlty different than that of running code, where short names are often
preferred. The reason is testing functions are never called explicitely.
``square()`` or even ``sqr()`` is ok in running code, but in testing code you
would has names such as ``test_square_of_number_2()``,
``test_square_negative_number()``. These function names are displayed when a
test fail, and should be as descriptive as possible.
- When something goes wrong or has to be changed, and if your code has a good
set of tests, you or other maintainers will rely largely on the testing suite
to fix the problem or modify a given behavior. Therefore the testing code will
be read as much as or even more than the running code. A unit test whose
purpose is unclear is not very helpful is this case.
- Another use of the testing code is as an introduction to new developers. When
someone will have to work on the code base, runnning and reading the related
testing code is often the best they can do. They will or should discover the
hot spots, where most difficulties arise, and the corner cases. If they have
to add some functionality, the first step should be to add a test and, by this
mean, ensure the new functionality is not already a working path that has not
been plugged in the interface.
The Basics
::::::::::
@@ -36,10 +100,38 @@ As of Python 2.7 unittest also includes its own test discovery mechanisms.
Doctest
-------
The doctest module searches for pieces of text that look like interactive Python
sessions, and then executes those sessions to verify that they work exactly as
shown.
The doctest module searches for pieces of text that look like interactive
Python sessions in docstrings, and then executes those sessions to verify that
they work exactly as shown.
Doctests have a different use case than proper unit tests: they are usually
less detailed and don't catch special cases or obscure regression bugs. They
are useful as an expressive documentation of the main use cases of a module and
its components. However, doctests should run automatically each time the full
test suite runs.
A simple doctest in a function:
::
def square(x):
"""Squares x.
>>> square(2)
4
>>> square(-2)
4
"""
return x * x
if __name__ == '__main__':
import doctest
doctest.testmod()
When running this module from the command line as in ``python module.py``, the
doctests will run and complain if anything is not behaving as described in the
docstrings.
Tools
:::::
@@ -54,7 +146,7 @@ py.test is a no-boilerplate alternative to Python's standard unittest module.
$ pip install pytest
Despite being a fully-featured and extensible test tool it boasts a simple
Despite being a fully-featured and extensible test tool it boasts a simple
syntax. Creating a test suite is as easy as writing a module with a couple of
functions
@@ -115,8 +207,8 @@ xUnit-compatible test output, coverage reporting, and test selection.
tox
---
tox is a tool for automating test environment management and testing against multiple
interpreter configurations
tox is a tool for automating test environment management and testing against
multiple interpreter configurations
::