The Zen of Python
http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html
http://www.python.org/dev/peps/pep-0020/
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one—and preferably only one—obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than
right now.
If the implementation is hard to explain, it's a bad idea.
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!
.. -*- coding: utf-8 -*-
.. include::
.. |==>| unicode:: U+02794 .. thick rightwards arrow
==========================================
Code Like a Pythonista: Idiomatic Python
==========================================
.. sidebar:: Contents
:class: handout
.. contents:: :local:
.. class:: center big
| *David Goodger*
| [email protected]
| http://python.net/~goodger
In this interactive tutorial, we'll cover many essential Python idioms
and techniques in depth, adding immediately useful tools to your belt.
There are 3 versions of this presentation:
* `S5 presentation `__
* `Plain HTML handout `__
* `reStructuredText source `__
.. class:: small
©2006-2008, licensed under a `Creative Commons
Attribution/Share-Alike (BY-SA) license
", line 1
SyntaxError: not a chance
What a bunch of comedians! :-)
Coding Style: Readability Counts
================================
Programs must be written for people to read, and only incidentally
for machines to execute.
-- Abelson & Sussman, *Structure and Interpretation of Computer Programs*
.. container:: handout
Try to make your programs easy to read and obvious.
PEP 8: Style Guide for Python Code
==================================
Worthwhile reading:
http://www.python.org/dev/peps/pep-0008/
PEP = Python Enhancement Proposal
.. container:: handout
A PEP is a design document providing information to the Python
community, or describing a new feature for Python or its processes
or environment.
The Python community has its own standards for what source code
should look like, codified in PEP 8. These standards are different
from those of other communities, like C, C++, C#, Java,
VisualBasic, etc.
Because indentation and whitespace are so important in Python, the
Style Guide for Python Code approaches a standard. It would be
wise to adhere to the guide! Most open-source projects and
(hopefully) in-house projects follow the style guide quite
closely.
Whitespace 1
============
.. class:: incremental
* 4 spaces per indentation level.
* No hard tabs.
* **Never** mix tabs and spaces.
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This is exactly what IDLE and the Emacs Python mode support.
Other editors may also provide this support.
* One blank line between functions.
* Two blank lines between classes.
Whitespace 2
============
.. class:: incremental
* Add a space after "," in dicts, lists, tuples, & argument lists, and
after ":" in dicts, but not before.
* Put spaces around assignments & comparisons (except in argument
lists).
* No spaces just inside parentheses or just before argument
lists.
* No spaces just inside docstrings.
::
def make_squares(key, value=0):
"""Return a dictionary and a list..."""
d = {key: value}
l = [key, value]
return d, l
Naming
======
.. class:: incremental
* ``joined_lower`` for functions, methods, attributes
* ``joined_lower`` or ``ALL_CAPS`` for constants
* ``StudlyCaps`` for classes
* ``camelCase`` **only** to conform to pre-existing conventions
* Attributes: ``interface``, ``_internal``, ``__private``
.. container:: handout
But try to avoid the ``__private`` form. I never use it.
Trust me. If you use it, you **WILL** regret it later.
Explanation:
People coming from a C++/Java background are especially prone to
overusing/misusing this "feature". But ``__private`` names don't
work the same way as in Java or C++. They just trigger a `name
mangling`__ whose purpose is to prevent accidental namespace
collisions in subclasses: ``MyClass.__private`` just becomes
``MyClass._MyClass__private``. (Note that even this breaks down
for subclasses with the same name as the superclass,
e.g. subclasses in different modules.) It *is* possible to
access ``__private`` names from outside their class, just
inconvenient and fragile (it adds a dependency on the exact name
of the superclass).
__ http://docs.python.org/dev/reference/expressions.html#atom-identifiers
The problem is that the author of a class may legitimately think
"this attribute/method name should be private, only accessible
from within this class definition" and use the ``__private``
convention. But later on, a user of that class may make a
subclass that legitimately needs access to that name. So either
the superclass has to be modified (which may be difficult or
impossible), or the subclass code has to use manually mangled
names (which is ugly and fragile at best).
There's a concept in Python: "we're all consenting adults here".
If you use the ``__private`` form, who are you protecting the
attribute from? It's the responsibility of subclasses to use
attributes from superclasses properly, and it's the
responsibility of superclasses to document their attributes
properly.
It's better to use the single-leading-underscore convention,
``_internal``. This isn't name mangled at all; it just
indicates to others to "be careful with this, it's an internal
implementation detail; don't touch it if you don't **fully**
understand it". It's only a convention though.
There are some good explanations in the answers here:
* http://stackoverflow.com/questions/70528/why-are-pythons-private-methods-not-actually-private
* http://stackoverflow.com/questions/1641219/does-python-have-private-variables-in-classes
Long Lines & Continuations
==========================
.. class:: incremental
Keep lines below 80 characters in length.
Use implied line continuation inside parentheses/brackets/braces::
def __init__(self, first, second, third,
fourth, fifth, sixth):
output = (first + second + third
+ fourth + fifth + sixth)
Use backslashes as a last resort::
VeryLong.left_hand_side \
= even_longer.right_hand_side()
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Backslashes are fragile; they must end the line they're on. If you
add a space after the backslash, it won't work any more. Also,
they're ugly.
Long Strings
============
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Adjacent literal strings are concatenated by the parser:
.. class:: incremental
>>> print 'o' 'n' "e"
one
.. container:: handout
The spaces between literals are not required, but help with
readability. Any type of quoting can be used:
>>> print 't' r'\/\/' """o"""
t\/\/o
The string prefixed with an "r" is a "raw" string. Backslashes are
not evaluated as escapes in raw strings. They're useful for
regular expressions and Windows filesystem paths.
Note named string objects are **not** concatenated:
>>> a = 'three'
>>> b = 'four'
>>> a b
File "", line 1
a b
^
SyntaxError: invalid syntax
That's because this automatic concatenation is a feature of the
Python parser/compiler, not the interpreter. You must use the "+"
operator to concatenate strings at run time.
.. class:: incremental
::
text = ('Long strings can be made up '
'of several shorter strings.')
.. container:: handout
The parentheses allow implicit line continuation.
Multiline strings use triple quotes:
.. class:: incremental
::
"""Triple
double
quotes"""
::
'''\
Triple
single
quotes\
'''
.. container:: handout
In the last example above (triple single quotes), note how the
backslashes are used to escape the newlines. This eliminates extra
newlines, while keeping the text and quotes nicely left-justified.
The backslashes must be at the end of their lines.
Compound Statements
===================
Good::
if foo == 'blah':
do_something()
do_one()
do_two()
do_three()
Bad::
if foo == 'blah': do_something()
do_one(); do_two(); do_three()
.. container:: handout
Whitespace & indentations are useful visual indicators of the
program flow. The indentation of the second "Good" line above
shows the reader that something's going on, whereas the lack of
indentation in "Bad" hides the "if" statement.
Multiple statements on one line are a cardinal sin. In Python,
*readability counts*.
Docstrings & Comments
=====================
Docstrings = **How to use** code
Comments = **Why** (rationale) & **how code works**
.. container:: handout
Docstrings explain **how** to use code, and are for the **users**
of your code. Uses of docstrings:
* Explain the purpose of the function even if it seems obvious to
you, because it might not be obvious to someone else later on.
* Describe the parameters expected, the return values, and any
exceptions raised.
* If the method is tightly coupled with a single caller, make some
mention of the caller (though be careful as the caller might
change later).
Comments explain **why**, and are for the **maintainers** of your
code. Examples include notes to yourself, like::
# !!! BUG: ...
# !!! FIX: This is a hack
# ??? Why is this here?
Both of these groups include **you**, so write good docstrings and
comments!
Docstrings are useful in interactive use (``help()``) and for
auto-documentation systems.
False comments & docstrings are worse than none at all. So keep
them up to date! When you make changes, make sure the comments &
docstrings are consistent with the code, and don't contradict it.
There's an entire PEP about docstrings, PEP 257, "Docstring
Conventions":
http://www.python.org/dev/peps/pep-0257/
Practicality Beats Purity
=========================
A foolish consistency is the hobgoblin of little minds.
-- Ralph Waldo Emerson
.. container:: handout
(*hobgoblin*: Something causing superstitious fear; a bogy.)
There are always exceptions. From PEP 8:
But most importantly: know when to be inconsistent -- sometimes
the style guide just doesn't apply. When in doubt, use your
best judgment. Look at other examples and decide what looks
best. And don't hesitate to ask!
Two good reasons to break a particular rule:
(1) When applying the rule would make the code less readable,
even for someone who is used to reading code that follows
the rules.
(2) To be consistent with surrounding code that also breaks it
(maybe for historic reasons) -- although this is also an
opportunity to clean up someone else's mess (in true XP
style).
`... but practicality shouldn't beat purity to a pulp!`
Idiom Potpourri
===============
A selection of small, useful idioms.
.. container:: handout
Now we move on to the meat of the tutorial: lots of idioms.
We'll start with some easy ones and work our way up.
Swap Values
===========
In other languages::
temp = a
a = b
b = temp
.. class:: incremental
In Python::
b, a = a, b
.. container:: handout
Perhaps you've seen this before. But do you know how it works?
.. class:: incremental
* The **comma** is the tuple constructor syntax.
* A tuple is created on the right (tuple packing).
* A tuple is the target on the left (tuple unpacking).
.. container:: handout
The right-hand side is **unpacked** into the names in the tuple on
the left-hand side.
Further examples of unpacking:
>>> l =['David', 'Pythonista', '+1-514-555-1234']
>>> name, title, phone = l
>>> name
'David'
>>> title
'Pythonista'
>>> phone
'+1-514-555-1234'
Useful in loops over structured data:
``l`` (L) above is the list we just made (David's info). So
``people`` is a list containing two items, each a 3-item list.
>>> people = [l, ['Guido', 'BDFL', 'unlisted']]
>>> for (name, title, phone) in people:
... print name, phone
...
David +1-514-555-1234
Guido unlisted
Each item in ``people`` is being unpacked into the ``(name, title,
phone)`` tuple.
Arbitrarily nestable (just be sure to match the structure on the
left & right!):
>>> david, (gname, gtitle, gphone) = people
>>> gname
'Guido'
>>> gtitle
'BDFL'
>>> gphone
'unlisted'
>>> david
['David', 'Pythonista', '+1-514-555-1234']
More About Tuples
=================
.. container:: handout
We saw that the **comma** is the tuple constructor, not the
parentheses. Example:
.. class:: incremental
>>> 1,
(1,)
.. container:: handout
The Python interpreter shows the parentheses for clarity, and I
recommend you use parentheses too:
.. class:: incremental
>>> (1,)
(1,)
.. container:: handout
Don't forget the comma!
.. class:: incremental
>>> (1)
1
.. container:: handout
In a one-tuple, the trailing comma is required; in 2+-tuples, the
trailing comma is optional. In 0-tuples, or empty tuples, a pair
of parentheses is the shortcut syntax:
.. class:: incremental
>>> ()
()
>>> tuple()
()
.. container:: handout
A common typo is to leave a comma even though you don't want a
tuple. It can be easy to miss in your code:
.. class:: incremental
>>> value = 1,
>>> value
(1,)
.. container:: handout
So if you see a tuple where you don't expect one, look for a comma!
Interactive "_"
===============
.. container:: handout
This is a really useful feature that surprisingly few people know.
In the interactive interpreter, whenever you evaluate an expression
or call a function, the result is bound to a temporary name, ``_``
(an underscore):
.. class:: incremental
>>> 1 + 1
2
>>> _
2
``_`` stores the last *printed* expression.
.. container:: handout
When a result is ``None``, nothing is printed, so ``_`` doesn't
change. That's convenient!
This only works in the interactive interpreter, not within a
module.
It is especially useful when you're working out a problem
interactively, and you want to store the result for a later step:
.. class:: incremental
>>> import math
>>> math.pi / 3
1.0471975511965976
>>> angle = _
>>> math.cos(angle)
0.50000000000000011
>>> _
0.50000000000000011
Building Strings from Substrings
================================
.. container:: handout
Start with a list of strings:
::
colors = ['red', 'blue', 'green', 'yellow']
.. container:: handout
We want to join all the strings together into one large string.
Especially when the number of substrings is large...
.. class:: incremental
Don't do this::
result = ''
for s in colors:
result += s
.. container:: handout
This is very inefficient.
It has terrible memory usage and performance patterns. The
"summation" will compute, store, and then throw away each
intermediate step.
.. class:: incremental
Instead, do this::
result = ''.join(colors)
.. container:: handout
The ``join()`` string method does all the copying in one pass.
When you're only dealing with a few dozen or hundred strings, it
won't make much difference. But get in the habit of building
strings efficiently, because with thousands or with loops, it
**will** make a difference.
Building Strings, Variations 1
==============================
.. container:: handout
Here are some techniques to use the ``join()`` string method.
.. class:: incremental
If you want spaces between your substrings::
result = ' '.join(colors)
Or commas and spaces::
result = ', '.join(colors)
Here's a common case::
colors = ['red', 'blue', 'green', 'yellow']
print 'Choose', ', '.join(colors[:-1]), \
'or', colors[-1]
.. container:: handout
To make a nicely grammatical sentence, we want commas between all
but the last pair of values, where we want the word "or". The
slice syntax does the job. The "slice until -1" (``[:-1]``) gives
all but the last value, which we join with comma-space.
Of course, this code wouldn't work with corner cases, lists of
length 0 or 1.
.. container:: handout
Output:
.. class:: incremental
::
Choose red, blue, green or yellow
Building Strings, Variations 2
==============================
If you need to apply a function to generate the substrings::
result = ''.join(fn(i) for i in items)
.. container:: handout
This involves a *generator expression*, which we'll cover later.
.. class:: incremental
If you need to compute the substrings incrementally, accumulate
them in a list first::
items = []
...
items.append(item) # many times
...
# items is now complete
result = ''.join(fn(i) for i in items)
.. container:: handout
We accumulate the parts in a list so that we can apply the ``join``
string method, for efficiency.
Use ``in`` where possible (1)
=============================
Good::
for key in d:
print key
.. container:: handout
* ``in`` is generally faster.
* This pattern also works for items in arbitrary containers (such
as lists, tuples, and sets).
* ``in`` is also an operator (as we'll see).
Bad::
for key in d.keys():
print key
.. container:: handout
This is limited to objects with a ``keys()`` method.
Use ``in`` where possible (2)
=============================
But ``.keys()`` is **necessary** when mutating the dictionary::
for key in d.keys():
d[str(key)] = d[key]
.. container:: handout
``d.keys()`` creates a static list of the dictionary keys.
Otherwise, you'll get an exception "RuntimeError: dictionary
changed size during iteration".
.. class:: incremental
For consistency, use ``key in dict``, not ``dict.has_key()``::
# do this:
if key in d:
...do something with d[key]
# not this:
if d.has_key(key):
...do something with d[key]
.. container:: handout
This usage of ``in`` is as an operator.
Dictionary ``get`` Method
==========================
We often have to initialize dictionary entries before use:
.. container:: handout
This is the naïve way to do it:
::
navs = {}
for (portfolio, equity, position) in data:
if portfolio not in navs:
navs[portfolio] = 0
navs[portfolio] += position * prices[equity]
.. class:: incremental
``dict.get(key, default)`` removes the need for the test::
navs = {}
for (portfolio, equity, position) in data:
navs[portfolio] = (navs.get(portfolio, 0)
+ position * prices[equity])
.. container:: handout
Much more direct.
Dictionary ``setdefault`` Method (1)
====================================
.. container:: handout
Here we have to initialize mutable dictionary values. Each
dictionary value will be a list. This is the naïve way:
Initializing mutable dictionary values::
equities = {}
for (portfolio, equity) in data:
if portfolio in equities:
equities[portfolio].append(equity)
else:
equities[portfolio] = [equity]
.. class:: incremental
``dict.setdefault(key, default)`` does the job much more
efficiently::
equities = {}
for (portfolio, equity) in data:
equities.setdefault(portfolio, []).append(
equity)
.. container:: handout
``dict.setdefault()`` is equivalent to "get, or set & get". Or
"set if necessary, then get". It's especially efficient if your
dictionary key is expensive to compute or long to type.
The only problem with ``dict.setdefault()`` is that the default
value is always evaluated, whether needed or not. That only
matters if the default value is expensive to compute.
If the default value **is** expensive to compute, you may want to
use the ``defaultdict`` class, which we'll cover shortly.
Dictionary ``setdefault`` Method (2)
====================================
.. container:: handout
Here we see that the ``setdefault`` dictionary method can also be
used as a stand-alone statement:
``setdefault`` can also be used as a stand-alone statement::
navs = {}
for (portfolio, equity, position) in data:
navs.setdefault(portfolio, 0)
navs[portfolio] += position * prices[equity]
.. container:: handout
The ``setdefault`` dictionary method returns the default value, but
we ignore it here. We're taking advantage of ``setdefault``'s side
effect, that it sets the dictionary value only if there is no value
already.
``defaultdict``
===============
New in Python 2.5.
.. container:: handout
``defaultdict`` is new in Python 2.5, part of the ``collections``
module. ``defaultdict`` is identical to regular dictionaries,
except for two things:
* it takes an extra first argument: a default factory function; and
* when a dictionary key is encountered for the first time, the
default factory function is called and the result used to
initialize the dictionary value.
There are two ways to get ``defaultdict``:
* import the ``collections`` module and reference it via the
module,
.. container:: spoken
|==>|
* or import the ``defaultdict`` name directly:
.. container:: spoken
|==>|
.. class:: incremental
::
import collections
d = collections.defaultdict(...)
::
from collections import defaultdict
d = defaultdict(...)
.. container:: handout
Here's the example from earlier, where each dictionary value must
be initialized to an empty list, rewritten as with ``defaultdict``:
.. class:: incremental
::
from collections import defaultdict
equities = defaultdict(list)
for (portfolio, equity) in data:
equities[portfolio].append(equity)
.. container:: handout
There's no fumbling around at all now. In this case, the default
factory function is ``list``, which returns an empty list.
This is how to get a dictionary with default values of 0: use
``int`` as a default factory function:
.. class:: incremental
::
navs = defaultdict(int)
for (portfolio, equity, position) in data:
navs[portfolio] += position * prices[equity]
.. container:: handout
You should be careful with ``defaultdict`` though. You cannot get
``KeyError`` exceptions from properly initialized ``defaultdict``
instances. You have to use a "key in dict" conditional if you need
to check for the existence of a specific key.
Building & Splitting Dictionaries
=================================
.. container:: handout
Here's a useful technique to build a dictionary from two lists (or
sequences): one list of keys, another list of values.
.. class:: incremental
::
given = ['John', 'Eric', 'Terry', 'Michael']
family = ['Cleese', 'Idle', 'Gilliam', 'Palin']
::
pythons = dict(zip(given, family))
::
>>> pprint.pprint(pythons)
{'John': 'Cleese',
'Michael': 'Palin',
'Eric': 'Idle',
'Terry': 'Gilliam'}
.. container:: handout
The reverse, of course, is trivial:
.. class:: incremental
::
>>> pythons.keys()
['John', 'Michael', 'Eric', 'Terry']
>>> pythons.values()
['Cleese', 'Palin', 'Idle', 'Gilliam']
.. container:: handout
Note that the order of the results of .keys() and .values() is
different from the order of items when constructing the dictionary.
The order going in is different from the order coming out. This is
because a dictionary is inherently unordered. However, the order
is guaranteed to be consistent (in other words, the order of keys
will correspond to the order of values), as long as the dictionary
isn't changed between calls.
Testing for Truth Values
========================
::
# do this: # not this:
if x: if x == True:
pass pass
.. container:: handout
It's elegant and efficient to take advantage of the intrinsic truth
values (or Boolean values) of Python objects.
.. class:: incremental
Testing a list::
# do this: # not this:
if items: if len(items) != 0:
pass pass
# and definitely not this:
if items != []:
pass
Truth Values
============
.. container:: handout
The ``True`` and ``False`` names are built-in instances of type
``bool``, Boolean values. Like ``None``, there is only one
instance of each.
================================= ================================
False True
================================= ================================
``False`` (== 0) ``True`` (== 1)
``""`` (empty string) any string but ``""`` (``" "``,
``"anything"``)
``0``, ``0.0`` any number but ``0`` (1, 0.1, -1, 3.14)
``[]``, ``()``, ``{}``, ``set()`` any non-empty container
(``[0]``, ``(None,)``, ``['']``)
``None`` almost any object that's not
explicitly False
================================= ================================
.. container:: handout
Example of an object's truth value:
>>> class C:
... pass
...
>>> o = C()
>>> bool(o)
True
>>> bool(C)
True
(Examples: execute ``__.)
To control the truth value of instances of a user-defined class,
use the ``__nonzero__`` or ``__len__`` special methods. Use
``__len__`` if your class is a container which has a length::
class MyContainer(object):
def __init__(self, data):
self.data = data
def __len__(self):
"""Return my length."""
return len(self.data)
If your class is not a container, use ``__nonzero__``::
class MyClass(object):
def __init__(self, value):
self.value = value
def __nonzero__(self):
"""Return my truth value (True or False)."""
# This could be arbitrarily complex:
return bool(self.value)
In Python 3.0, ``__nonzero__`` has been renamed to ``__bool__`` for
consistency with the ``bool`` built-in type. For compatibility,
add this to the class definition::
__bool__ = __nonzero__
Index & Item (1)
================
.. container:: handout
Here's a cute way to save some typing if you need a list of words:
>>> items = 'zero one two three'.split()
>>> print items
['zero', 'one', 'two', 'three']
.. class:: incremental
Say we want to iterate over the items, and we need both the item's
index and the item itself::
- or -
i = 0
for item in items: for i in range(len(items)):
print i, item print i, items[i]
i += 1
Index & Item (2): ``enumerate``
===============================
The ``enumerate`` function takes a list and returns (index, item)
pairs:
>>> print list(enumerate(items))
[(0, 'zero'), (1, 'one'), (2, 'two'), (3, 'three')]
.. container:: handout
We need use a ``list`` wrapper to print the result because
``enumerate`` is a lazy function: it generates one item, a pair, at
a time, only when required. A ``for`` loop is one place that
requires one result at a time. ``enumerate`` is an example of a
*generator*, which we'll cover in greater detail later. ``print``
does not take one result at a time -- we want the entire result, so
we have to explicitly convert the generator into a list when we
print it.
.. class:: incremental
Our loop becomes much simpler::
for (index, item) in enumerate(items):
print index, item
::
# compare: # compare:
index = 0 for i in range(len(items)):
for item in items: print i, items[i]
print index, item
index += 1
.. container:: handout
The ``enumerate`` version is much shorter and simpler than the
version on the left, and much easier to read and understand than
either.
An example showing how the ``enumerate`` function actually returns
an iterator (a generator is a kind of iterator):
>>> enumerate(items)
>>> e = enumerate(items)
>>> e.next()
(0, 'zero')
>>> e.next()
(1, 'one')
>>> e.next()
(2, 'two')
>>> e.next()
(3, 'three')
>>> e.next()
Traceback (most recent call last):
File "", line 1, in ?
StopIteration
Other languages have "variables"
================================
.. container:: handout
In many other languages, assigning to a variable puts a value into
a box.
.. list-table::
:class: incremental borderless
* - ::
int a = 1;
- .. image:: a1box.png
:class: incremental
.. container:: handout
Box "a" now contains an integer 1.
Assigning another value to the same variable replaces the contents
of the box:
.. list-table::
:class: incremental borderless
* - ::
a = 2;
- .. image:: a2box.png
:class: incremental
.. container:: handout
Now box "a" contains an integer 2.
Assigning one variable to another makes a copy of the value and
puts it in the new box:
.. list-table::
:class: incremental borderless
* - ::
int b = a;
- .. image:: b2box.png
:class: incremental
- .. image:: a2box.png
:class: incremental
.. container:: handout
"b" is a second box, with a copy of integer 2. Box "a" has a
separate copy.
Python has "names"
==================
.. container:: handout
In Python, a "name" or "identifier" is like a parcel tag (or
nametag) attached to an object.
.. list-table::
:class: incremental borderless
* - ::
a = 1
- .. image:: a1tag.png
:class: incremental
.. container:: handout
Here, an integer 1 object has a tag labelled "a".
If we reassign to "a", we just move the tag to another object:
.. list-table::
:class: incremental borderless
* - ::
a = 2
- .. image:: a2tag.png
:class: incremental
- .. image:: 1.png
:class: incremental
.. container:: handout
Now the name "a" is attached to an integer 2 object.
The original integer 1 object no longer has a tag "a". It may live
on, but we can't get to it through the name "a". (When an object
has no more references or tags, it is removed from memory.)
If we assign one name to another, we're just attaching another
nametag to an existing object:
.. list-table::
:class: incremental borderless
* - ::
b = a
- .. image:: ab2tag.png
:class: incremental
.. container:: handout
The name "b" is just a second tag bound to the same object as "a".
.. container:: handout
Although we commonly refer to "variables" even in Python (because
it's common terminology), we really mean "names" or "identifiers".
In Python, "variables" are nametags for values, not labelled boxes.
If you get nothing else out of this tutorial, I hope you understand
how Python names work. A good understanding is certain to pay
dividends, helping you to avoid cases like this:
.. container:: spoken
|==>|
Default Parameter Values
========================
.. container:: handout
This is a common mistake that beginners often make. Even more
advanced programmers make this mistake if they don't understand
Python names.
::
def bad_append(new_item, a_list=[]):
a_list.append(new_item)
return a_list
.. container:: handout
The problem here is that the default value of ``a_list``, an empty
list, is evaluated at function definition time. So every time you
call the function, you get the **same** default value. Try it
several times:
.. class:: incremental
::
>>> print bad_append('one')
['one']
::
>>> print bad_append('two')
['one', 'two']
.. container:: handout
Lists are a mutable objects; you can change their contents. The
correct way to get a default list (or dictionary, or set) is to
create it at run time instead, **inside the function**:
.. class:: incremental
::
def good_append(new_item, a_list=None):
if a_list is None:
a_list = []
a_list.append(new_item)
return a_list
% String Formatting
===================
.. container:: handout
Python's ``%`` operator works like C's ``sprintf`` function.
.. container:: handout
Although if you don't know C, that's not very helpful. Basically,
you provide a template or format and interpolation values.
In this example, the template contains two conversion
specifications: "%s" means "insert a string here", and "%i" means
"convert an integer to a string and insert here". "%s" is
particularly useful because it uses Python's built-in ``str()``
function to to convert any object to a string.
The interpolation values must match the template; we have two
values here, a tuple.
::
name = 'David'
messages = 3
text = ('Hello %s, you have %i messages'
% (name, messages))
print text
.. class:: incremental
Output::
Hello David, you have 3 messages
.. container:: handout
Details are in the *Python Library Reference*, section 2.3.6.2,
"String Formatting Operations". Bookmark this one!
.. container:: handout
If you haven't done it already, go to python.org, download the HTML
documentation (in a .zip file or a tarball), and install it on your
machine. There's nothing like having the definitive resource at
your fingertips.
Advanced % String Formatting
============================
.. container:: handout
What many people don't realize is that there are other, more
flexible ways to do string formatting:
.. class:: incremental
By name with a dictionary::
values = {'name': name, 'messages': messages}
print ('Hello %(name)s, you have %(messages)i '
'messages' % values)
.. container:: handout
Here we specify the names of interpolation values, which are looked
up in the supplied dictionary.
Notice any redundancy? The names "name" and "messages" are already
defined in the local namespace. We can take advantage of this.
.. class:: incremental
By name using the local namespace::
print ('Hello %(name)s, you have %(messages)i '
'messages' % locals())
.. container:: handout
The ``locals()`` function returns a dictionary of all
locally-available names.
This is very powerful. With this, you can do all the string
formatting you want without having to worry about matching the
interpolation values to the template.
But power can be dangerous. ("With great power comes great
responsibility.") If you use the ``locals()`` form with an
externally-supplied template string, you expose your entire local
namespace to the caller. This is just something to keep in mind.
.. container:: handout
To examine your local namespace:
>>> from pprint import pprint
>>> pprint(locals())
.. container:: handout
``pprint`` is a very useful module. If you don't know it already,
try playing with it. It makes debugging your data structures much
easier!
Advanced % String Formatting
============================
.. container:: handout
The namespace of an object's instance attributes is just a
dictionary, ``self.__dict__``.
.. class:: incremental
By name using the instance namespace::
print ("We found %(error_count)d errors"
% self.__dict__)
Equivalent to, but more flexible than::
print ("We found %d errors"
% self.error_count)
.. container:: handout
Note: Class attributes are in the class __dict__. Namespace
lookups are actually chained dictionary lookups.
List Comprehensions
===================
.. container:: handout
List comprehensions ("listcomps" for short) are syntax shortcuts
for this general pattern:
.. class:: incremental
The traditional way, with ``for`` and ``if`` statements::
new_list = []
for item in a_list:
if condition(item):
new_list.append(fn(item))
As a list comprehension::
new_list = [fn(item) for item in a_list
if condition(item)]
.. container:: handout
Listcomps are clear & concise, up to a point. You can have
multiple ``for``-loops and ``if``-conditions in a listcomp, but
beyond two or three total, or if the conditions are complex, I
suggest that regular ``for`` loops should be used. Applying the
Zen of Python, choose the more readable way.
.. container:: handout
For example, a list of the squares of 0–9:
>>> [n ** 2 for n in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
A list of the squares of odd 0–9:
>>> [n ** 2 for n in range(10) if n % 2]
[1, 9, 25, 49, 81]
Generator Expressions (1)
=========================
.. container:: handout
Let's sum the squares of the numbers up to 100:
.. class:: incremental
As a loop::
total = 0
for num in range(1, 101):
total += num * num
.. container:: handout
We can use the ``sum`` function to quickly do the work for us, by
building the appropriate sequence.
.. class:: incremental
As a list comprehension::
total = sum([num * num for num in range(1, 101)])
As a generator expression::
total = sum(num * num for num in xrange(1, 101))
.. container:: handout
Generator expressions ("genexps") are just like list
comprehensions, except that where listcomps are greedy, generator
expressions are lazy. Listcomps compute the entire result list all
at once, as a list. Generator expressions compute one value at a
time, when needed, as individual values. This is especially useful
for long sequences where the computed list is just an intermediate
step and not the final result.
In this case, we're only interested in the sum; we don't need the
intermediate list of squares. We use ``xrange`` for the same
reason: it lazily produces values, one at a time.
Generator Expressions (2)
=========================
.. container:: handout
For example, if we were summing the squares of several billion
integers, we'd run out of memory with list comprehensions, but
generator expressions have no problem. This does take time,
though!
.. class:: incremental
::
total = sum(num * num
for num in xrange(1, 1000000000))
.. container:: handout
The difference in syntax is that listcomps have square brackets,
but generator expressions don't. Generator expressions sometimes
do require enclosing parentheses though, so you should always use
them.
.. container:: handout
Rule of thumb:
* Use a list comprehension when a computed list is the desired end
result.
* Use a generator expression when the computed list is just an
intermediate step.
.. container:: handout
Here's a recent example I saw at work.
.. container:: spoken
|==>|
We needed a dictionary mapping month numbers (both as string and as
integers) to month codes for futures contracts. It can be done in
one logical line of code.
.. container:: spoken
|==>|
The way this works is as follows:
* The ``dict()`` built-in takes a list of key/value pairs
(2-tuples).
* We have a list of month codes (each month code is a single
letter, and a string is also just a list of letters). We
enumerate over this list to get both the month code and the
index.
* The month numbers start at 1, but Python starts indexing at 0, so
the month number is one more than the index.
* We want to look up months both as strings and as integers. We
can use the ``int()`` and ``str()`` functions to do this for us,
and loop over them.
.. class:: incremental
Recent example::
month_codes = dict((fn(i+1), code)
for i, code in enumerate('FGHJKMNQUVXZ')
for fn in (int, str))
``month_codes`` result::
{ 1: 'F', 2: 'G', 3: 'H', 4: 'J', ...
'1': 'F', '2': 'G', '3': 'H', '4': 'J', ...}
Sorting
=======
.. container:: handout
It's easy to sort a list in Python:
::
a_list.sort()
.. container:: handout
(Note that the list is sorted in-place: the original list is
sorted, and the ``sort`` method does **not** return the list or a
copy.)
But what if you have a list of data that you need to sort, but it
doesn't sort naturally (i.e., sort on the first column, then the
second column, etc.)? You may need to sort on the second column
first, then the fourth column.
.. class:: incremental
We can use list's built-in ``sort`` method with a custom function::
def custom_cmp(item1, item2):
return cmp((item1[1], item1[3]),
(item2[1], item2[3]))
a_list.sort(custom_cmp)
.. container:: handout
This works, but it's extremely slow for large lists.
Sorting with DSU *
==================
DSU = Decorate-Sort-Undecorate
\* Note: DSU is often no longer necessary. See the next section,
`Sorting With Keys`_ for the new approach.
.. container:: handout
Instead of creating a custom comparison function, we create an
auxiliary list that *will* sort naturally:
.. class:: incremental
::
# Decorate:
to_sort = [(item[1], item[3], item)
for item in a_list]
# Sort:
to_sort.sort()
# Undecorate:
a_list = [item[-1] for item in to_sort]
.. container:: handout
The first line creates a list containing tuples: copies of the sort
terms in priority order, followed by the complete data record.
The second line does a native Python sort, which is very fast and
efficient.
The third line retrieves the **last** value from the sorted list.
Remember, this last value is the complete data record. We're
throwing away the sort terms, which have done their job and are no
longer needed.
.. container:: handout
This is a tradeoff of space and complexity against time. Much
simpler and faster, but we do need to duplicate the original list.
Sorting With Keys
=================
.. container:: handout
Python 2.4 introduced an optional argument to the ``sort`` list
method, "key", which specifies a function of one argument that is
used to compute a comparison key from each list element. For
example:
.. class:: incremental
::
def my_key(item):
return (item[1], item[3])
to_sort.sort(key=my_key)
The function ``my_key`` will be called once for each item in the
``to_sort`` list.
You can make your own key function, or use any existing
one-argument function if applicable:
* ``str.lower`` to sort alphabetically regarless of case.
* ``len`` to sort on the length of the items (strings or containers).
* ``int`` or ``float`` to sort numerically, as with numeric strings
like "2", "123", "35".
Generators
==========
.. container:: handout
We've already seen generator expressions. We can devise our own
arbitrarily complex generators, as functions:
::
def my_range_generator(stop):
value = 0
while value < stop:
yield value
value += 1
for i in my_range_generator(10):
do_something(i)
.. container:: handout
The ``yield`` keyword turns a function into a generator. When you
call a generator function, instead of running the code immediately
Python returns a generator object, which is an iterator; it has a
``next`` method. ``for`` loops just call the ``next`` method on
the iterator, until a ``StopIteration`` exception is raised. You
can raise ``StopIteration`` explicitly, or implicitly by falling
off the end of the generator code as above.
Generators can simplify sequence/iterator handling, because we
don't need to build concrete lists; just compute one value at a
time. The generator function maintains state.
.. container:: handout
This is how a ``for`` loop really works. Python looks at the
sequence supplied after the ``in`` keyword. If it's a simple
container (such as a list, tuple, dictionary, set, or user-defined
container) Python converts it into an iterator. If it's already an
iterator, Python uses it directly.
Then Python repeatedly calls the iterator's ``next`` method,
assigns the return value to the loop counter (``i`` in this case),
and executes the indented code. This is repeated over and over,
until ``StopIteration`` is raised, or a ``break`` statement is
executed in the code.
A ``for`` loop can have an ``else`` clause, whose code is executed
after the iterator runs dry, but **not** after a ``break``
statement is executed. This distinction allows for some elegant
uses. ``else`` clauses are not always or often used on ``for``
loops, but they can come in handy. Sometimes an ``else`` clause
perfectly expresses the logic you need.
For example, if we need to check that a condition holds on some
item, any item, in a sequence::
for item in sequence:
if condition(item):
break
else:
raise Exception('Condition not satisfied.')
Example Generator
=================
Filter out blank rows from a CSV reader (or items from a list)::
def filter_rows(row_iterator):
for row in row_iterator:
if row:
yield row
data_file = open(path, 'rb')
irows = filter_rows(csv.reader(data_file))
Reading Lines From Text/Data Files
==================================
::
datafile = open('datafile')
for line in datafile:
do_something(line)
.. container:: handout
This is possible because files support a ``next`` method, as do
other iterators: lists, tuples, dictionaries (for their keys),
generators.
There is a caveat here: because of the way the buffering is done,
you cannot mix ``.next`` & ``.read*`` methods unless you're using
Python 2.5+.
EAFP vs. LBYL
=============
.. class:: incremental
It's easier to ask forgiveness than permission
Look before you leap
.. container:: handout
Generally EAFP is preferred, but not always.
* Duck typing
If it walks like a duck, and talks like a duck, and looks like a
duck: it's a duck. `(Goose? Close enough.)`
* Exceptions
.. container:: handout
Use coercion if an object must be a particular type. If ``x``
must be a string for your code to work, why not call
.. class:: incremental
::
str(x)
.. container:: handout
instead of trying something like
.. class:: incremental
::
isinstance(x, str)
EAFP ``try/except`` Example
===========================
.. container:: handout
You can wrap exception-prone code in a ``try/except`` block to
catch the errors, and you will probably end up with a solution
that's much more general than if you had tried to anticipate every
possibility.
.. class:: incremental
::
try:
return str(x)
except TypeError:
...
.. container:: handout
Note: Always specify the exceptions to catch. Never use bare
``except`` clauses. Bare ``except`` clauses will catch unexpected
exceptions, making your code exceedingly difficult to debug.
Importing
=========
::
from module import *
.. container:: handout
You've probably seen this "wild card" form of the import statement.
You may even like it. **Don't use it.**
To adapt `a well-known exchange
`__:
(Exterior Dagobah, jungle, swamp, and mist.)
LUKE: Is ``from module import *`` better than explicit imports?
YODA: No, not better. Quicker, easier, more seductive.
LUKE: But how will I know why explicit imports are better than
the wild-card form?
YODA: Know you will when your code you try to read six months
from now.
Wild-card imports are from the dark side of Python.
.. class:: incremental
**Never!**
.. container:: handout
The ``from module import *`` wild-card style leads to namespace
pollution. You'll get things in your local namespace that you
didn't expect to get. You may see imported names obscuring
module-defined local names. You won't be able to figure out where
certain names come from. Although a convenient shortcut, this
should not be in production code.
Moral: **don't use wild-card imports!**
.. container:: spoken
|==>|
It's much better to:
* reference names through their module
(fully qualified identifiers),
.. container:: spoken
|==>|
* import a long module using a shorter name (alias; recommended),
.. container:: spoken
|==>|
* or explicitly import just the names you need.
.. container:: spoken
|==>|
.. container:: handout
Namespace pollution alert!
.. class:: incremental
Instead,
.. container:: handout
Reference names through their module (fully qualified identifiers):
.. class:: incremental
::
import module
module.name
.. container:: handout
Or import a long module using a shorter name (alias):
.. class:: incremental
::
import long_module_name as mod
mod.name
.. container:: handout
Or explicitly import just the names you need:
.. class:: incremental
::
from module import name
name
.. container:: handout
Note that this form doesn't lend itself to use in the interactive
interpreter, where you may want to edit and "reload()" a module.
Modules & Scripts
=================
To make a simultaneously importable module and executable script::
if __name__ == '__main__':
# script code here
.. container:: handout
When imported, a module's ``__name__`` attribute is set to the
module's file name, without ".py". So the code guarded by the
``if`` statement above will not run when imported. When executed
as a script though, the ``__name__`` attribute is set to
"__main__", and the script code *will* run.
Except for special cases, you shouldn't put any major executable
code at the top-level. Put code in functions, classes, methods,
and guard it with ``if __name__ == '__main__'``.
Module Structure
================
::
"""module docstring"""
# imports
# constants
# exception classes
# interface functions
# classes
# internal functions & classes
def main(...):
...
if __name__ == '__main__':
status = main()
sys.exit(status)
.. container:: handout
This is how a module should be structured.
Command-Line Processing
=======================
Example: ``__:
.. container:: handout
.. include:: cmdline.py
:literal:
Packages
========
::
package/
__init__.py
module1.py
subpackage/
__init__.py
module2.py
.. class:: incremental
- Used to organize your project.
- Reduces entries in load-path.
- Reduces import name conflicts.
Example::
import package.module1
from package.subpackage import module2
from package.subpackage.module2 import name
.. container:: handout
In Python 2.5 we now have absolute and relative imports via a
future import::
from __future__ import absolute_import
I haven't delved into these myself yet, so we'll conveniently cut
this discussion short.
Simple is Better Than Complex
=============================
Debugging is twice as hard as writing the code in the first place.
Therefore, if you write the code as cleverly as possible, you are,
by definition, not smart enough to debug it.
-- Brian W. Kernighan, co-author of *The C Programming Language*
and the "K" in "AWK"
.. container:: handout
In other words, keep your programs simple!
Don't reinvent the wheel
========================
.. container:: handout
Before writing any code,
.. container:: spoken
|==>| |==>| |==>| |==>|
.. class:: incremental
* Check Python's standard library.
* Check the Python Package Index (the "Cheese Shop"):
http://cheeseshop.python.org/pypi
* Search the web. `Google is your friend.`
References
==========
.. class:: small
* "Python Objects", Fredrik Lundh,
http://www.effbot.org/zone/python-objects.htm
* "How to think like a Pythonista", Mark Hammond,
http://python.net/crew/mwh/hacks/objectthink.html
* "Python main() functions", Guido van Rossum,
http://www.artima.com/weblogs/viewpost.jsp?thread=4829
* "Python Idioms and Efficiency",
http://jaynes.colorado.edu/PythonIdioms.html
* "Python track: python idioms",
http://www.cs.caltech.edu/courses/cs11/material/python/misc/python_idioms.html
* "Be Pythonic", Shalabh Chaturvedi,
http://shalabh.infogami.com/Be_Pythonic2
* "Python Is Not Java", Phillip J. Eby,
http://dirtsimple.org/2004/12/python-is-not-java.html
* "What is Pythonic?", Martijn Faassen,
http://faassen.n--tree.net/blog/view/weblog/2005/08/06/0
* "Sorting Mini-HOWTO", Andrew Dalke,
http://wiki.python.org/moin/HowTo/Sorting
* "Python Idioms", http://www.gungfu.de/facts/wiki/Main/PythonIdioms
* "Python FAQs", http://www.python.org/doc/faq/