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docs/404.rst
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@@ -1,3 +1,4 @@
:orphan:
#################
404 — Not Found
docs/conf.py
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@@ -279,5 +279,5 @@ epub_exclude_files = [
todo_include_todos = True
intersphinx_mapping = {
'python': ('http://docs.python.org/', None),
'python': ('https://docs.python.org/3', None),
}
docs/contents.rst.inc
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@@ -33,7 +33,7 @@ New to Python? Let's properly setup up your Python environment:
Python Development Environments
-------------------------------
This part of the guide focus on the Python development environment,
This part of the guide focuses on the Python development environment,
and the best-practice tools that are available for writing Python code.
.. toctree::
@@ -110,7 +110,7 @@ Additional Notes
----------------
This part of the guide, which is mostly prose, begins with some
background information about Python, then focuses on next steps.
background information about Python, and then focuses on next steps.
.. toctree::
:maxdepth: 2
@@ -137,8 +137,3 @@ Contribution notes and legal information (for those interested).
notes/contribute
notes/license
notes/styleguide
docs/dev/env.rst
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@@ -7,7 +7,7 @@ Your Development Environment
Text Editors
::::::::::::
Just about anything that can edit plain text will work for writing Python code,
Just about anything that can edit plain text will work for writing Python code;
however, using a more powerful editor may make your life a bit easier.
@@ -97,7 +97,7 @@ using ``<Tab>`` key or any other customized keys.
Emacs
-----
Emacs is another powerful text editor. It is fully programmable (lisp), but
Emacs is another powerful text editor. It is fully programmable (Lisp), but
it can be some work to wire up correctly. A good start if you're already an
Emacs user is `Python Programming in Emacs`_ at EmacsWiki.
@@ -109,7 +109,7 @@ TextMate
--------
`TextMate <http://macromates.com/>`_ brings Apple's approach to operating
systems into the world of text editors. By bridging UNIX underpinnings and
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.
@@ -117,8 +117,8 @@ Sublime Text
------------
`Sublime Text <http://www.sublimetext.com/>`_ is a sophisticated text
editor for code, markup and prose. You'll love the slick user interface,
extraordinary features and amazing performance.
editor for code, markup, and prose. You'll love the slick user interface,
extraordinary features, and amazing performance.
Sublime Text has excellent support for editing Python code and uses Python for
its plugin API. It also has a diverse variety of plugins,
@@ -133,7 +133,7 @@ Atom
editors.
Atom is web native (HTML, CSS, JS), focusing on modular design and easy plugin
development. It comes with native package control and plethora of packages.
development. It comes with native package control and a plethora of packages.
Recommended for Python development is
`Linter <https://github.com/AtomLinter/Linter>`_ combined with
`linter-flake8 <https://github.com/AtomLinter/linter-flake8>`_.
@@ -156,7 +156,7 @@ Python (on Visual Studio Code)
------------------------------
`Python for Visual Studio <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ is an extension for the `Visual Studio Code IDE <https://code.visualstudio.com>`_.
This is a free, light weight, open source IDE, with support for Mac, Windows, and Linux.
This is a free, lightweight, open source IDE, with support for Mac, Windows, and Linux.
Built using open source technologies such as Node.js and Python, with compelling features such as Intellisense (autocompletion), local and remote debugging, linting, and the like.
MIT licensed.
@@ -192,7 +192,7 @@ toward working with scientific Python libraries (namely
`pylint <http://www.logilab.org/857>`_ and
`rope <https://github.com/python-rope/rope>`_.
Spyder is open-source (free), offers code completion, syntax highlighting,
Spyder is open source (free), offers code completion, syntax highlighting,
a class and function browser, and object inspection.
@@ -200,7 +200,7 @@ WingIDE
-------
`WingIDE <http://wingware.com/>`_ is a Python specific IDE. It runs on Linux,
Windows and Mac (as an X11 application, which frustrates some Mac users).
Windows, and Mac (as an X11 application, which frustrates some Mac users).
WingIDE offers code completion, syntax highlighting, source browser, graphical
debugger and support for version control systems.
@@ -211,11 +211,11 @@ NINJA-IDE
`NINJA-IDE <http://www.ninja-ide.org/>`_ (from the recursive acronym: "Ninja-IDE
Is Not Just Another IDE") is a cross-platform IDE, specially designed to build
Python applications, and runs on Linux/X11, Mac OS X and Windows desktop
Python applications, and runs on Linux/X11, Mac OS X, and Windows desktop
operating systems. Installers for these platforms can be downloaded from the
website.
NINJA-IDE is open-source software (GPLv3 licence) and is developed
NINJA-IDE is open source software (GPLv3 licence) and is developed
in Python and Qt. The source files can be downloaded from
`GitHub <https://github.com/ninja-ide>`_.
@@ -224,11 +224,11 @@ Eric (The Eric Python IDE)
--------------------------
`Eric <http://eric-ide.python-projects.org/>`_ is a full featured Python IDE
offering sourcecode autocompletion, syntax highlighting, support for version
control systems, python 3 support, integrated web browser, python shell,
integrated debugger and a flexible plug-in system. Written in python, it is
based on the Qt gui toolkit, integrating the Scintilla editor control. Eric
is an open-source software project (GPLv3 licence) with more than ten years of
offering source code autocompletion, syntax highlighting, support for version
control systems, Python 3 support, integrated web browser, python shell,
integrated debugger, and a flexible plug-in system. Written in Python, it is
based on the Qt GUI toolkit, integrating the Scintilla editor control. Eric
is an open source software project (GPLv3 licence) with more than ten years of
active development.
@@ -252,9 +252,9 @@ pyenv
of the Python interpreter to be installed at the same time. This solves the
problem of having different projects requiring different versions of Python.
For example, it becomes very easy to install Python 2.7 for compatibility in
one project, whilst still using Python 3.4 as the default interpreter.
pyenv isn't just limited to the CPython versions - it will also install PyPy,
anaconda, miniconda, stackless, jython, and ironpython interpreters.
one project, while still using Python 3.4 as the default interpreter.
pyenv isn't just limited to the CPython versions – it will also install PyPy,
Anaconda, miniconda, stackless, Jython, and IronPython interpreters.
pyenv works by filling a ``shims`` directory with fake versions of the Python
interpreter (plus other tools like ``pip`` and ``2to3``). When the system
@@ -276,7 +276,7 @@ IDLE
----
:ref:`IDLE <python:idle>` is an integrated development environment that is
part of Python standard library. It is completely written in Python and uses
part of the Python standard distribution. It is completely written in Python and uses
the 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.
@@ -294,18 +294,18 @@ IPython
`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).
* 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.
* 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.
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
.. code-block:: console
$ pip install ipython
To download and install IPython with all it's optional dependencies for the notebook, qtconsole, tests, and other functionalities
To download and install IPython with all its optional dependencies for the notebook, qtconsole, tests, and other functionalities:
.. code-block:: console
@@ -318,14 +318,14 @@ BPython
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.
* Expected parameter list for any Python function.
* "Rewind" function to pop the last line of code from memory and re-evaluate.
* Send entered code off to a pastebin.
* Save entered code to a file.
* Auto-indentation.
* Python 3 support.
* In-line syntax highlighting
* Readline-like autocomplete with suggestions displayed as you type
* Expected parameter list for any Python function
* "Rewind" function to pop the last line of code from memory and re-evaluate
* Send entered code off to a pastebin
* Save entered code to a file
* Auto-indentation
* Python 3 support
.. code-block:: console
@@ -341,12 +341,12 @@ library. It is considered to be an alternative to BPython_. Features include:
* Syntax highlighting
* Autocompletion
* Multiline editing
* Emacs and VIM Mode
* Emacs and Vim Modes
* Embedding REPL inside of your code
* Syntax Validation
* Syntax validation
* Tab pages
* Support for integrating with IPython_'s shell, by installing IPython
``pip install ipython`` and running ``ptipython``.
(``pip install ipython``) and running ``ptipython``.
.. code-block:: console
docs/dev/pip-virtualenv.rst
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@@ -1,6 +1,6 @@
.. _pip-virtualenv:
Further Configuration of Pip and Virtualenv
Further Configuration of pip and Virtualenv
===========================================
.. image:: /_static/photos/34018732105_f0e6758859_k_d.jpg
@@ -105,7 +105,7 @@ need any configuration.
When using older versions, you can configure pip in such a way that it tries to
reuse already installed packages, too.
On UNIX systems, you can add the following line to your :file:`.bashrc` or
On Unix systems, you can add the following line to your :file:`.bashrc` or
:file:`.bash_profile` file.
.. code-block:: console
@@ -124,7 +124,7 @@ add the following line to your :file:`pip.ini` file under ``[global]`` settings:
download-cache = %HOME%\pip\cache
Similarly, on UNIX systems you should simply add the following line to your
Similarly, on Unix systems you should simply add the following line to your
:file:`pip.conf` file under ``[global]`` settings:
.. code-block:: console
docs/dev/virtualenvs.rst
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@@ -212,37 +212,40 @@ Install virtualenv via pip:
$ pip install virtualenv
Test your installation
Test your installation:
.. code-block:: console
$ virtualenv --version
Basic Usage
~~~~~~~~~~~
-----------
1. Create a virtual environment for a project:
.. code-block:: console
$ cd my_project_folder
$ virtualenv my_project
$ virtualenv venv
``virtualenv my_project`` will create a folder in the current directory which will
``virtualenv venv`` will create a folder in the current directory which will
contain the Python executable files, and a copy of the ``pip`` library which you
can use to install other packages. The name of the virtual environment (in this
case, it was ``my_project``) can be anything; omitting the name will place the files
case, it was ``venv``) can be anything; omitting the name will place the files
in the current directory instead.
.. note::
'venv' is the general convention used globally. As it is readily available in ignore files (eg: .gitignore')
This creates a copy of Python in whichever directory you ran the command in,
placing it in a folder named :file:`my_project`.
placing it in a folder named :file:`venv`.
You can also use the Python interpreter of your choice (like
``python2.7``).
.. code-block:: console
$ virtualenv -p /usr/bin/python2.7 my_project
$ virtualenv -p /usr/bin/python2.7 venv
or change the interpreter globally with an env variable in ``~/.bashrc``:
@@ -254,12 +257,20 @@ or change the interpreter globally with an env variable in ``~/.bashrc``:
.. code-block:: console
$ source my_project/bin/activate
$ source venv/bin/activate
The name of the current virtual environment will now appear on the left of
the prompt (e.g. ``(my_project)Your-Computer:your_project UserName$)`` to let you know
the prompt (e.g. ``(venv)Your-Computer:your_project UserName$)`` to let you know
that it's active. From now on, any package that you install using pip will be
placed in the ``my_project`` folder, isolated from the global Python installation.
placed in the ``venv`` folder, isolated from the global Python installation.
For Windows, same command which is mentioned in step 1 can be used for creation of virtual environment. But, to activate, we use the following command.
Assuming that you are in project directory:
.. code-block:: powershell
PS C:\Users\suryav> \venv\Scripts\activate
Install packages as usual, for example:
@@ -281,11 +292,14 @@ To delete a virtual environment, just delete its folder. (In this case,
it would be ``rm -rf my_project``.)
After a while, though, you might end up with a lot of virtual environments
littered across your system, and its possible you'll forget their names or
littered across your system, and it's possible you'll forget their names or
where they were placed.
.. note::
Python has included venv module from version 3.3. For more details: `venv <https://docs.python.org/3/library/venv.html>`_.
Other Notes
~~~~~~~~~~~
-----------
Running ``virtualenv`` with the option ``--no-site-packages`` will not
include the packages that are installed globally. This can be useful
@@ -293,7 +307,7 @@ for keeping the package list clean in case it needs to be accessed later.
[This is the default behavior for ``virtualenv`` 1.7 and later.]
In order to keep your environment consistent, it's a good idea to "freeze"
the current state of the environment packages. To do this, run
the current state of the environment packages. To do this, run:
.. code-block:: console
@@ -302,7 +316,7 @@ the current state of the environment packages. To do this, run
This will create a :file:`requirements.txt` file, which contains a simple
list of all the packages in the current environment, and their respective
versions. You can see the list of installed packages without the requirements
format using "pip list". Later it will be easier for a different developer
format using ``pip list``. Later it will be easier for a different developer
(or you, if you need to re-create the environment) to install the same packages
using the same versions:
@@ -343,7 +357,7 @@ To install (make sure **virtualenv** is already installed):
$ pip install virtualenvwrapper-win
In Windows, the default path for WORKON_HOME is %USERPROFILE%\Envs
In Windows, the default path for WORKON_HOME is %USERPROFILE%\\Envs
Basic Usage
~~~~~~~~~~~
@@ -363,7 +377,7 @@ This creates the :file:`my_project` folder inside :file:`~/Envs`.
$ workon my_project
Alternatively, you can make a project, which creates the virtual environment,
and also a project directory inside ``$WORKON_HOME``, which is ``cd`` -ed into
and also a project directory inside ``$WORKON_HOME``, which is ``cd``-ed into
when you ``workon myproject``.
.. code-block:: console
docs/intro/learning.rst
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@@ -153,7 +153,7 @@ 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
approach 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.
@@ -181,7 +181,7 @@ no previous programming experience.
Learn to Program in Python with Codeacademy
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A Codeacademy course for the absolute Python beginner. This free and interactive course provides and teaches the basics (and beyond) of Python programming whilst testing the user's knowledge in between progress.
A Codeacademy course for the absolute Python beginner. This free and interactive course provides and teaches the basics (and beyond) of Python programming while testing the user's knowledge in between progress.
This course also features a built-in interpreter for receiving instant feedback on your learning.
`Learn to Program in Python with Codeacademy <http://www.codecademy.com/en/tracks/python>`_
docs/notes/styleguide.rst
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@@ -84,6 +84,8 @@ Wrap text lines at 78 characters. Where necessary, lines may exceed 78
characters, especially if wrapping would make the source text more difficult
to read.
Use Standard American English, not British English.
Use of the `serial comma <https://en.wikipedia.org/wiki/Serial_comma>`_
(also known as the Oxford comma) is 100% non-optional. Any attempt to
submit content with a missing serial comma will result in permanent banishment
docs/scenarios/crypto.rst
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@@ -48,7 +48,8 @@ GPGME bindings
The `GPGME Python bindings <https://dev.gnupg.org/source/gpgme/browse/master/lang/python/>`_ provide Pythonic access to `GPG Made Easy <https://dev.gnupg.org/source/gpgme/browse/master/>`_, a C API for the entire GNU Privacy Guard suite of projects, including GPG, libgcrypt, and gpgsm (the S/MIME engine). It supports Python 2.6, 2.7, 3.4, and above. Depends on the SWIG C interface for Python as well as the GnuPG software and libraries.
A more comprehensive `GPGME Python Bindings HOWTO <https://dev.gnupg.org/source/gpgme/browse/master/lang/python/docs/GPGMEpythonHOWTOen.org>`_ is available with the source, and an HTML version is available `here <http://files.au.adversary.org/crypto/GPGMEpythonHOWTOen.html>`_. Python 3 sample scripts from the examples in the HOWTO are also provided with the source and are accessible `here <https://dev.gnupg.org/source/gpgme/browse/master/lang/python/examples/howto/>`_.
A more comprehensive `GPGME Python Bindings HOWTO <https://dev.gnupg.org/source/gpgme/browse/master/lang/python/docs/GPGMEpythonHOWTOen.org>`_ is available with the source, and an HTML version is available `at http://files.au.adversary.org <http://files.au.adversary.org/crypto/GPGMEpythonHOWTOen.html>`_. Python 3 sample scripts from the examples in the HOWTO are also provided with the source and are accessible `at gnupg.org <https://dev.gnupg.org/source/gpgme/browse/master/lang/python/examples/howto/>`_.
Available under the same terms as the rest of the GnuPG Project: GPLv2 and LGPLv2.1, both with the "or any later version" clause.
docs/scenarios/ml.rst
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@@ -41,7 +41,7 @@ For installing the full stack, or individual packages, you can refer to the inst
scikit-learn
************
Scikit is a free and open-source machine learning library for Python. It offers off-the-shelf functions to implement many algorithms like linear regression, classifiers, SVMs, k-means, Neural Networks, etc. It also has a few sample datasets which can be directly used for training and testing.
Scikit is a free and open source machine learning library for Python. It offers off-the-shelf functions to implement many algorithms like linear regression, classifiers, SVMs, k-means, Neural Networks, etc. It also has a few sample datasets which can be directly used for training and testing.
Because of its speed, robustness, and ease of, it's one of the most widely-used libraries for many Machine Learning applications.
docs/scenarios/scientific.rst
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@@ -140,11 +140,10 @@ out.
Anaconda
--------
`Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
The `Anaconda Python Distribution <https://www.anaconda.com/>`_
includes all the common scientific Python packages as well as many packages
related to data analytics and big data. Anaconda itself is free, and
Continuum sells a number of proprietary add-ons. Free licenses for the
related to data analytics and big data. Anaconda itself is free, and a number
of proprietary add-ons are available for a fee. Free licenses for the
add-ons are available for academics and researchers.
Canopy
docs/scenarios/serialization.rst
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@@ -10,16 +10,204 @@ Data Serialization
What is data serialization?
***************************
Data serialization is the concept of converting structured data into a format
that allows it to be shared or stored in such a way that its original
structure can be recovered. In some cases, the secondary intention of data
serialization is to minimize the size of the serialized data which then
minimizes disk space or bandwidth requirements.
Data serialization is the process of converting structured data to a format
that allows sharing or storage of the data in a form that allows recovery of its original
structure. In some cases, the secondary intention of data
serialization is to minimize the data's size which then
reduces disk space or bandwidth requirements.
********************
Flat vs. Nested data
********************
Before beginning to serialize data, it is important to identify or decide how the
data should be structured during data serialization - flat or nested.
The differences in the two styles are shown in the below examples.
Flat style:
.. code-block:: python
{ "Type" : "A", "field1": "value1", "field2": "value2", "field3": "value3" }
******
Pickle
******
Nested style:
.. code-block:: python
{"A"
{ "field1": "value1", "field2": "value2", "field3": "value3" } }
For more reading on the two styles, please see the discussion on
`Python mailing list <https://mail.python.org/pipermail/python-list/2010-October/590762.html>`__,
`IETF mailing list <https://www.ietf.org/mail-archive/web/json/current/msg03739.html>`__ and
`in stackexchange <https://softwareengineering.stackexchange.com/questions/350623/flat-or-nested-json-for-hierarchal-data>`__.
****************
Serializing Text
****************
=======================
Simple file (flat data)
=======================
If the data to be serialized is located in a file and contains flat data, Python offers two methods to serialize data.
repr
----
The repr method in Python takes a single object parameter and returns a printable representation of the input:
.. code-block:: python
# input as flat text
a = { "Type" : "A", "field1": "value1", "field2": "value2", "field3": "value3" }
# the same input can also be read from a file
a = open('/tmp/file.py', 'r')
# returns a printable representation of the input;
# the output can be written to a file as well
print(repr(a))
# write content to files using repr
with open('/tmp/file.py') as f:f.write(repr(a))
ast.literal_eval
----------------
The literal_eval method safely parses and evaluates an expression for a Python datatype.
Supported data types are: strings, numbers, tuples, lists, dicts, booleans, and None.
.. code-block:: python
with open('/tmp/file.py', 'r') as f: inp = ast.literal_eval(f.read())
====================
CSV file (flat data)
====================
The CSV module in Python implements classes to read and write tabular
data in CSV format.
Simple example for reading:
.. code-block:: python
# Reading CSV content from a file
import csv
with open('/tmp/file.csv', newline='') as f:
reader = csv.reader(f)
for row in reader:
print(row)
Simple example for writing:
.. code-block:: python
# Writing CSV content to a file
import csv
with open('/temp/file.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerows(iterable)
The module's contents, functions, and examples can be found
`in the Python documentation <https://docs.python.org/3/library/csv.html>`__.
==================
YAML (nested data)
==================
There are many third party modules to parse and read/write YAML file
structures in Python. One such example is below.
.. code-block:: python
# Reading YAML content from a file using the load method
import yaml
with open('/tmp/file.yaml', 'r', newline='') as f:
try:
print(yaml.load(f))
except yaml.YAMLError as ymlexcp:
print(ymlexcp)
Documentation on the third party module can be found
`in the PyYAML Documentation <https://pyyaml.org/wiki/PyYAMLDocumentation>`__.
=======================
JSON file (nested data)
=======================
Python's JSON module can be used to read and write JSON files.
Example code is below.
Reading:
.. code-block:: python
# Reading JSON content from a file
import json
with open('/tmp/file.json', 'r') as f:
data = json.load(f)
Writing:
.. code-block:: python
# Writing JSON content to a file using the dump method
import json
with open('/tmp/file.json', 'w') as f:
json.dump(data, f, sort_keys=True)
=================
XML (nested data)
=================
XML parsing in Python is possible using the `xml` package.
Example:
.. code-block:: python
# reading XML content from a file
import xml.etree.ElementTree as ET
tree = ET.parse('country_data.xml')
root = tree.getroot()
More documentation on using the `xml.dom` and `xml.sax` packages can be found
`in the Python XML library documentation <https://docs.python.org/3/library/xml.html>`__.
*******
Binary
*******
=======================
NumPy Array (flat data)
=======================
Python's NumPy array can be used to serialize and deserialize data to and from byte representation.
Example:
.. code-block:: python
import NumPy as np
# Converting NumPy array to byte format
byte_output = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]).tobytes()
# Converting byte format back to NumPy array
array_format = np.frombuffer(byte_output)
====================
Pickle (nested data)
====================
The native data serialization module for Python is called `Pickle
<https://docs.python.org/2/library/pickle.html>`_.
docs/scenarios/web.rst
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@@ -170,7 +170,7 @@ WSGI Servers
************
Stand-alone WSGI servers typically use less resources than traditional web
servers and provide top performance [3]_.
servers and provide top performance [1]_.
.. _gunicorn-ref:
@@ -531,6 +531,4 @@ Mako is well respected within the Python web community.
.. rubric:: References
.. [1] `The mod_python project is now officially dead <http://blog.dscpl.com.au/2010/06/modpython-project-is-now-officially.html>`_
.. [2] `mod_wsgi vs mod_python <http://modpython.org/pipermail/mod_python/2007-July/024080.html>`_
.. [3] `Benchmark of Python WSGI Servers <http://nichol.as/benchmark-of-python-web-servers>`_
.. [1] `Benchmark of Python WSGI Servers <http://nichol.as/benchmark-of-python-web-servers>`_
docs/starting/install/osx.rst
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@@ -48,7 +48,7 @@ package.
commandline tools by running ``xcode-select --install`` on the terminal.
While OS X comes with a large number of UNIX utilities, those familiar with
While OS X 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://brew.sh>`_ fills this void.
docs/starting/install3/osx.rst
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@@ -41,7 +41,7 @@ package.
If you perform a fresh install of Xcode, you will also need to add the
commandline tools by running ``xcode-select --install`` on the terminal.
While OS X comes with a large number of UNIX utilities, those familiar with
While OS X comes with a large number of Unix utilities, those familiar with
Linux systems will notice one key component missing: a package manager.
`Homebrew <http://brew.sh>`_ fills this void.
docs/starting/which-python.rst
+1 -1
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@@ -88,7 +88,7 @@ written in C. It compiles Python code to intermediate bytecode which is then
interpreted by a virtual machine. CPython provides the highest
level of compatibility with Python packages and C extension modules.
If you are writing open-source Python code and want to reach the widest possible
If you are writing open source Python code and want to reach the widest possible
audience, targeting CPython is best. To use packages which rely on C extensions
to function, CPython is your only implementation option.
docs/writing/documentation.rst
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@@ -25,7 +25,7 @@ information. This file is the main entry point for readers of the code.
An :file:`INSTALL` file is less necessary with Python. The installation
instructions are often reduced to one command, such as ``pip install
module`` or ``python setup.py install`` and added to the :file:`README`
module`` or ``python setup.py install``, and added to the :file:`README`
file.
A :file:`LICENSE` file should *always* be present and specify the license
@@ -75,7 +75,7 @@ your source repository so that rebuilding your documentation will
happen automatically.
When run, Sphinx_ will import your code and using Python's introspection
features it will extract all function, method and class signatures. It will
features it will extract all function, method, and class signatures. It will
also extract the accompanying docstrings, and compile it all into well
structured and easily readable documentation for your project.
@@ -182,7 +182,7 @@ comment block is a programmer's note. The docstring describes the
Unlike block comments, docstrings are built into the Python language itself.
This means you can use all of Python's powerful introspection capabilities to
access docstrings at runtime, compared with comments which are optimised out.
access docstrings at runtime, compared with comments which are optimized out.
Docstrings are accessible from both the `__doc__` dunder attribute for almost
every Python object, as well as with the built in `help()` function.
@@ -215,7 +215,7 @@ more information about a function, what it does, any exceptions it may raise,
what it returns, or relevant details about the parameters.
For more detailed documentation of code a popular style is the one used for the
Numpy project, often called `Numpy style`_ docstrings. While it can take up more
NumPy project, often called `NumPy style`_ docstrings. While it can take up more
lines than the previous example, it allows the developer to include a lot
more information about a method, function, or class. ::
@@ -245,8 +245,8 @@ docstrings, making it easy to incorporate NumPy style docstrings into your
project.
At the end of the day, it doesn't really matter what style is used for writing
docstrings, their purpose is to serve as documentation for anyone who may need
to read or make changes to your code. As long as it is correct, understandable
docstrings; their purpose is to serve as documentation for anyone who may need
to read or make changes to your code. As long as it is correct, understandable,
and gets the relevant points across then it has done the job it was designed to
do.
docs/writing/gotchas.rst
+1 -1
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@@ -205,7 +205,7 @@ will automatically write a bytecode version of that file to disk, e.g.
These ``.pyc`` files should not be checked into your source code repositories.
Theoretically, this behavior is on by default, for performance reasons.
Theoretically, this behavior is on by default for performance reasons.
Without these bytecode files present, Python would re-generate the bytecode
every time the file is loaded.
docs/writing/license.rst
+3 -3
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@@ -19,18 +19,18 @@ In general, these licenses tend to fall into one of two categories:
1. licenses that focus more on the user's freedom to do with the
software as they please (these are the more permissive open
source licenses such as the MIT, BSD, & Apache).
source licenses such as the MIT, BSD, and Apache)
2. licenses that focus more on making sure that the code itself —
including any changes made to it and distributed along with it —
always remains free (these are the less permissive free software
licenses such as the GPL and LGPL).
licenses such as the GPL and LGPL)
The latter are less permissive in the sense that they don't permit
someone to add code to the software and distribute it without also
including the source code for their changes.
To help you choose one for your project, there's a `license chooser <http://choosealicense.com/>`_,
To help you choose one for your project, there's a `license chooser <http://choosealicense.com/>`_;
**use it**.
**More Permissive**
docs/writing/logging.rst
+2 -2
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@@ -58,7 +58,7 @@ hierarchy of loggers using dot notation, so using ``__name__`` ensures
no name collisions.
Here is an example of best practice from the `requests source`_ -- place
this in your ``__init__.py``
this in your ``__init__.py``:
.. code-block:: python
@@ -83,7 +83,7 @@ There are at least three ways to configure a logger:
- Using an INI-formatted file:
- **Pro**: possible to update configuration while running using the
function :func:`logging.config.listen` to listen on a socket.
- **Con**: less control (*e.g.* custom subclassed filters or loggers)
- **Con**: less control (e.g. custom subclassed filters or loggers)
than possible when configuring a logger in code.
- Using a dictionary or a JSON-formatted file:
- **Pro**: in addition to updating while running, it is possible to load
docs/writing/reading.rst
+3 -3
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@@ -25,7 +25,7 @@ reading. Each one of these projects is a paragon of Python coding.
best intentions in mind.
- `Diamond <https://github.com/python-diamond/Diamond>`_
Diamond is a python daemon that collects metrics
Diamond is a Python daemon that collects metrics
and publishes them to Graphite or other backends.
It is capable of collecting CPU, memory, network, I/O, load, and disk metrics.
Additionally, it features an API for implementing custom collectors
@@ -36,7 +36,7 @@ reading. Each one of these projects is a paragon of Python coding.
applications and has become one of the most advanced WSGI utility modules.
It includes a powerful debugger, full-featured request and response objects,
HTTP utilities to handle entity tags, cache control headers, HTTP dates,
cookie handling, file uploads, a powerful URL routing system and a bunch
cookie handling, file uploads, a powerful URL routing system, and a bunch
of community-contributed addon modules.
- `Requests <https://github.com/kennethreitz/requests>`_
@@ -49,4 +49,4 @@ reading. Each one of these projects is a paragon of Python coding.
.. todo:: Include code examples of exemplary code from each of the projects listed. Explain why it is excellent code. Use complex examples.
.. todo:: Explain techniques to rapidly identify data structures, algorithms and determine what the code is doing.
.. todo:: Explain techniques to rapidly identify data structures and algorithms and determine what the code is doing.
docs/writing/structure.rst
+12 -12
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@@ -211,7 +211,7 @@ I highly recommend the latter. Requiring a developer to run
codebase also requires them to have an isolated environment setup for
each instance of the codebase.
To give the individual tests import context, create a tests/context.py
To give the individual tests import context, create a ``tests/context.py``
file:
::
@@ -247,8 +247,8 @@ Makefile
If you look at most of my projects or any Pocoo project, you'll notice a
Makefile laying around. Why? These projects aren't written in C... In
short, make is a incredibly useful tool for defining generic tasks for
Makefile lying around. Why? These projects aren't written in C... In
short, make is an incredibly useful tool for defining generic tasks for
your project.
**Sample Makefile:**
@@ -334,7 +334,7 @@ include:
- Multiple and messy circular dependencies: if your classes
Table and Chair in :file:`furn.py` need to import Carpenter from
:file:`workers.py` to answer a question such as ``table.isdoneby()``,
and if conversely the class Carpenter needs to import Table and Chair,
and if conversely the class Carpenter needs to import Table and Chair
to answer the question ``carpenter.whatdo()``, then you
have a circular dependency. In this case you will have to resort to
fragile hacks such as using import statements inside
@@ -402,7 +402,7 @@ If you'd like you could name your module :file:`my_spam.py`, but even our
friend the underscore should not be seen often in module names. However, using other
characters (spaces or hyphens) in module names will prevent importing
(- is the subtract operator), so try to keep module names short so there is
no need to separate words. And, most of all, don't namespace with underscores, use submodules instead.
no need to separate words. And, most of all, don't namespace with underscores; use submodules instead.
.. code-block:: python
@@ -473,7 +473,7 @@ typing.
As mentioned in the :ref:`code_style` section, readability is one of the main
features of Python. Readability means to avoid useless boilerplate text and
clutter, therefore some efforts are spent trying to achieve a certain level of
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 where a class or function comes from, as in the
``modu.func`` idiom, greatly improves code readability and understandability in
@@ -494,7 +494,7 @@ used to gather all package-wide definitions.
A file :file:`modu.py` in the directory :file:`pack/` is imported with the
statement ``import pack.modu``. This statement will look for an
:file:`__init__.py` file in :file:`pack`, execute all of its top-level
:file:`__init__.py` file in :file:`pack` and execute all of its top-level
statements. Then it will look for a file named :file:`pack/modu.py` and
execute all of its top-level statements. After these operations, any variable,
function, or class defined in :file:`modu.py` is available in the pack.modu
@@ -549,9 +549,9 @@ programming, comes from the "state" part of the equation.
In some architectures, typically web applications, multiple instances of Python
processes are spawned to respond to external requests that can happen at the
same time. In this case, holding some state into instantiated objects, which
same time. In this case, holding some state in instantiated objects, which
means keeping some static information about the world, is prone to concurrency
problems or race-conditions. Sometimes, between the initialization of the state
problems or race conditions. Sometimes, 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 methods, the world may have changed, and
the retained state may be outdated. For example, a request may load an item in
@@ -580,7 +580,7 @@ logic (called pure functions) allow the following benefits:
- 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
- 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, and pass around.
@@ -622,7 +622,7 @@ clearer and thus preferred.
# bar() is decorated
This mechanism is useful for separating concerns and avoiding
external un-related logic 'polluting' the core logic of the function
external unrelated logic 'polluting' the core logic of the function
or method. A good example of a piece of functionality that is better handled
with decoration is `memoization <https://en.wikipedia.org/wiki/Memoization#Overview>`__ or caching: you want to store the results of an
expensive function in a table and use them directly instead of recomputing
@@ -874,7 +874,7 @@ like above or in cases where you are adding to an existing string, using
.. note::
You can also use the :ref:`% <python:string-formatting>` formatting operator
to concatenate a pre-determined number of strings besides :py:meth:`str.join`
and ``+``. However, :pep:`3101`, discourages the usage of the ``%`` operator
and ``+``. However, :pep:`3101` discourages the usage of the ``%`` operator
in favor of the :py:meth:`str.format` method.
.. code-block:: python
docs/writing/style.rst
+23 -23
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@@ -115,7 +115,7 @@ compared to the more straightforward calls to ``send('Hello', 'World')`` and
optional, and evaluate to ``None`` when they are not passed another value.
Calling a function with keyword arguments can be done in multiple ways in
Python, for example it is possible to follow the order of arguments in the
Python; for example, it is possible to follow the order of arguments in the
definition without explicitly naming the arguments, like in
``send('Hello', 'World', 'Cthulhu', 'God')``, sending a blind carbon copy to
God. It would also be possible to name arguments in another order, like in
@@ -124,7 +124,7 @@ possibilities are better avoided without any strong reason to not follow the
syntax that is the closest to the function definition:
``send('Hello', 'World', cc='Cthulhu', bcc='God')``.
As a side note, following `YAGNI <http://en.wikipedia.org/wiki/You_ain't_gonna_need_it>`_
As a side note, following the `YAGNI <http://en.wikipedia.org/wiki/You_ain't_gonna_need_it>`_
principle, it is often harder to remove an optional argument (and its logic
inside the function) that was added "just in case" and is seemingly never used,
than to add a new optional argument and its logic when needed.
@@ -134,7 +134,7 @@ than to add a new optional argument and its logic when needed.
an extensible number of positional arguments, it can be defined with the
``*args`` constructs. In the function body, ``args`` will be a tuple of all
the remaining positional arguments. For example, ``send(message, *args)``
can be called with each recipient as an argument:``send('Hello', 'God',
can be called with each recipient as an argument: ``send('Hello', 'God',
'Mom', 'Cthulhu')``, and in the function body ``args`` will be equal to
``('God', 'Mom', 'Cthulhu')``.
@@ -181,7 +181,7 @@ possible to do each of the following:
* change how the Python interpreter imports modules
* it is even possible (and recommended if needed) to embed C routines in Python.
* 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
@@ -208,7 +208,7 @@ are all responsible users".
This doesn't mean that, for example, no properties are considered private, and
that no proper encapsulation is possible in Python. Rather, instead of relying
on concrete walls erected by the developers between their code and other's, the
on concrete walls erected by the developers between their code and others', the
Python community prefers to rely on a set of conventions indicating that these
elements should not be accessed directly.
@@ -359,7 +359,7 @@ Instead, use a list comprehension:
four_lists = [[] for __ in xrange(4)]
Note: Use range() instead of xrange() in Python 3
Note: Use range() instead of xrange() in Python 3.
Create a string from a list
~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -462,7 +462,7 @@ group <http://artifex.org/~hblanks/talks/2011/pep20_by_example.pdf>`_.
PEP 8
*****
:pep:`8` is the de-facto code style guide for Python. A high quality,
:pep:`8` is the de facto code style guide for Python. A high quality,
easy-to-read version of PEP 8 is also available at `pep8.org <http://pep8.org/>`_.
This is highly recommended reading. The entire Python community does their
@@ -520,10 +520,10 @@ Conventions
Here are some conventions you should follow to make your code easier to read.
Check if variable equals a constant
Check if a variable equals a constant
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You don't need to explicitly compare a value to True, or None, or 0 - you can
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.
@@ -608,7 +608,7 @@ Never remove items from a list while you are iterating through it.
a.remove(i)
Don't make multiple passes through the list.
.. code-block:: python
while i in a:
@@ -617,7 +617,7 @@ Don't make multiple passes through the list.
**Good**:
Python has a few standard ways of filtering lists.
The approach you use depends on
The approach you use depends on:
* Python 2.x vs. 3.x
* Lists vs. iterators
@@ -626,19 +626,19 @@ The approach you use depends on
Python 2.x vs. 3.x
::::::::::::::::::
Starting with Python 3.0, the :py:func:`filter` function returns an iterator instead of a list.
Starting with Python 3.0, the :py:func:`filter` function returns an iterator instead of a list.
Wrap it in :py:func:`list` if you truly need a list.
.. code-block:: python
list(filter(...))
List comprehensions and generator expressions work the same in both 2.x and 3.x (except that comprehensions in 2.x "leak" variables into the enclosing namespace)
List comprehensions and generator expressions work the same in both 2.x and 3.x (except that comprehensions in 2.x "leak" variables into the enclosing namespace):
* comprehensions create a new list object
* generators iterate over the original list
* Comprehensions create a new list object.
* Generators iterate over the original list.
The :py:func:`filter` function
The :py:func:`filter` function:
* in 2.x returns a list (use itertools.ifilter if you want an iterator)
* in 3.x returns an iterator
@@ -653,14 +653,14 @@ Creating a new list requires more work and uses more memory. If you a just going
# comprehensions create a new list object
filtered_values = [value for value in sequence if value != x]
# Or (2.x)
filtered_values = filter(lambda i: i != x, sequence)
filtered_values = filter(lambda i: i != x, sequence)
# generators don't create another list
filtered_values = (value for value in sequence if value != x)
# Or (3.x)
filtered_values = filter(lambda i: i != x, sequence)
# Or (2.x)
filtered_values = itertools.ifilter(lambda i: i != x, sequence)
filtered_values = itertools.ifilter(lambda i: i != x, sequence)
@@ -678,7 +678,7 @@ Modifying the original list can be risky if there are other variables referencin
# Or
sequence[::] = filter(lambda value: value != x, sequence)
Modifying the values in a list
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Bad**:
@@ -690,26 +690,26 @@ Remember that assignment never creates a new object. If two or more variables re
# Add three to all list members.
a = [3, 4, 5]
b = a # a and b refer to the same list object
for i in range(len(a)):
a[i] += 3 # b[i] also changes
**Good**:
It's safer to create a new list object and leave the original alone.
It's safer to create a new list object and leave the original alone.
.. code-block:: python
a = [3, 4, 5]
b = a
# assign the variable "a" to a new list without changing "b"
a = [i + 3 for i in a]
# Or (Python 2.x):
a = map(lambda i: i + 3, a)
# Or (Python 3.x)
a = list(map(lambda i: i + 3, a))
Use :py:func:`enumerate` keep a count of your place in the list.
docs/writing/tests.rst
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@@ -81,7 +81,7 @@ The Basics
**********
Unittest
unittest
--------
:mod:`unittest` is the batteries-included test module in the Python standard
@@ -170,7 +170,7 @@ functions:
def test_answer():
assert func(3) == 5
and then running the `py.test` command
and then running the `py.test` command:
.. code-block:: console
@@ -236,14 +236,14 @@ tox
---
tox is a tool for automating test environment management and testing against
multiple interpreter configurations
multiple interpreter configurations.
.. code-block:: console
$ pip install tox
tox allows you to configure complicated multi-parameter test matrices via a
simple ini-style configuration file.
simple INI-style configuration file.
`tox <https://tox.readthedocs.io/en/latest/>`_
@@ -254,7 +254,7 @@ Unittest2
unittest2 is a backport of Python 2.7's unittest module which has an improved
API and better assertions over the one available in previous versions of Python.
If you're using Python 2.6 or below, you can install it with pip
If you're using Python 2.6 or below, you can install it with pip:
.. code-block:: console
@@ -326,4 +326,3 @@ always returns the same result (but only for the duration of the test).
Mock has many other ways you can configure it and control its behavior.
`mock <http://www.voidspace.org.uk/python/mock/>`_