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Notes from Raymond Hettinger's talk at pycon US 2013 video, slides.
The code examples and direct quotes are all from Raymond's talk. I've reproduced them here for my own edification and the hopes that others will find them as handy as I have!
Looping over a range of numbers
for i in [0, 1, 2, 3, 4, 5]:
print i**2
for i in range(6):
print i**2
Better
for i in xrange(6):
print i**2
xrange
creates an iterator over the range producing the values one at a time. This approach is much more memory efficient than range
. xrange
was renamed to range
in python 3.
Looping over a collection
colors = ['red', 'green', 'blue', 'yellow']
for i in range(len(colors)):
print colors[i]
Better
for color in colors:
print color
Looping backwards
colors = ['red', 'green', 'blue', 'yellow']
for i in range(len(colors)-1, -1, -1):
print colors[i]
Better
for color in reversed(colors):
print color
Looping over a collection and indices
colors = ['red', 'green', 'blue', 'yellow']
for i in range(len(colors)):
print i, '--->', colors[i]
Better
for i, color in enumerate(colors):
print i, '--->', color
It's fast and beautiful and saves you from tracking the individual indices and incrementing them.
Whenever you find yourself manipulating indices [in a collection], you're probably doing it wrong.
Looping over two collections
names = ['raymond', 'rachel', 'matthew']
colors = ['red', 'green', 'blue', 'yellow']
n = min(len(names), len(colors))
for i in range(n):
print names[i], '--->', colors[i]
for name, color in zip(names, colors):
print name, '--->', color
Better
for name, color in izip(names, colors):
print name, '--->', color
zip
creates a new list in memory and takes more memory. izip
is more efficient than zip
.
Note: in python 3 izip
was renamed to zip
and promoted to a builtin replacing the old zip
.
Looping in sorted order
colors = ['red', 'green', 'blue', 'yellow']
# Forward sorted order
for color in sorted(colors):
print colors
# Backwards sorted order
for color in sorted(colors, reverse=True):
print colors
Custom Sort Order
colors = ['red', 'green', 'blue', 'yellow']
def compare_length(c1, c2):
if len(c1) < len(c2): return -1
if len(c1) > len(c2): return 1
return 0
print sorted(colors, cmp=compare_length)
Better
print sorted(colors, key=len)
The original is slow and unpleasant to write. Also, comparison functions are no longer available in python 3.
Call a function until a sentinel value
blocks = []
while True:
block = f.read(32)
if block == '':
break
blocks.append(block)
Better
blocks = []
for block in iter(partial(f.read, 32), ''):
blocks.append(block)
iter
takes two arguments. The first you call over and over again and the second is a sentinel value.
Distinguishing multiple exit points in loops
def find(seq, target):
found = False
for i, value in enumerate(seq):
if value == target:
found = True
break
if not found:
return -1
return i
Better
def find(seq, target):
for i, value in enumerate(seq):
if value == target:
break
else:
return -1
return i
Inside of every for
loop is an else
.
Looping over dictionary keys
d = {'matthew': 'blue', 'rachel': 'green', 'raymond': 'red'}
for k in d:
print k
for k in d.keys():
if k.startswith('r'):
del d[k]
When should you use the second and not the first? When you're mutating the dictionary.
If you mutate something while you're iterating over it, you're living in a state of sin and deserve what ever happens to you.
d.keys()
makes a copy of all the keys and stores them in a list. Then you can modify the dictionary.
Note: in python 3 to iterate through a dictionary you have to explicidly write: list(d.keys())
because d.keys()
returns a "dictionary view" (an iterable that provide a dynamic view on the dictionary’s keys). See documentation.
Looping over dictionary keys and values
# Not very fast, has to re-hash every key and do a lookup
for k in d:
print k, '--->', d[k]
# Makes a big huge list
for k, v in d.items():
print k, '--->', v
Better
for k, v in d.iteritems():
print k, '--->', v
iteritems()
is better as it returns an iterator.
Note: in python 3 there is no iteritems()
and items()
behaviour is close to what iteritems()
had. See documentation.
Construct a dictionary from pairs
names = ['raymond', 'rachel', 'matthew']
colors = ['red', 'green', 'blue']
d = dict(izip(names, colors))
# {'matthew': 'blue', 'rachel': 'green', 'raymond': 'red'}
For python 3: d = dict(zip(names, colors))
Counting with dictionaries
colors = ['red', 'green', 'red', 'blue', 'green', 'red']
# Simple, basic way to count. A good start for beginners.
d = {}
for color in colors:
if color not in d:
d[color] = 0
d[color] += 1
# {'blue': 1, 'green': 2, 'red': 3}
Better
d = {}
for color in colors:
d[color] = d.get(color, 0) + 1
# Slightly more modern but has several caveats, better for advanced users
# who understand the intricacies
d = defaultdict(int)
for color in colors:
d[color] += 1
Grouping with dictionaries -- Part I and II
names = ['raymond', 'rachel', 'matthew', 'roger',
'betty', 'melissa', 'judith', 'charlie']
# In this example, we're grouping by name length
d = {}
for name in names:
key = len(name)
if key not in d:
d[key] = []
d[key].append(name)
# {5: ['roger', 'betty'], 6: ['rachel', 'judith'], 7: ['raymond', 'matthew', 'melissa', 'charlie']}
d = {}
for name in names:
key = len(name)
d.setdefault(key, []).append(name)
Better
d = defaultdict(list)
for name in names:
key = len(name)
d[key].append(name)
Is a dictionary popitem() atomic?
d = {'matthew': 'blue', 'rachel': 'green', 'raymond': 'red'}
while d:
key, value = d.popitem()
print key, '-->', value
popitem
is atomic so you don't have to put locks around it to use it in threads.
Linking dictionaries
defaults = {'color': 'red', 'user': 'guest'}
parser = argparse.ArgumentParser()
parser.add_argument('-u', '--user')
parser.add_argument('-c', '--color')
namespace = parser.parse_args([])
command_line_args = {k:v for k, v in vars(namespace).items() if v}
# The common approach below allows you to use defaults at first, then override them
# with environment variables and then finally override them with command line arguments.
# It copies data like crazy, unfortunately.
d = defaults.copy()
d.update(os.environ)
d.update(command_line_args)
Better
d = ChainMap(command_line_args, os.environ, defaults)
ChainMap
has been introduced into python 3. Fast and beautiful.
Improving Clarity
- Positional arguments and indicies are nice
- Keywords and names are better
- The first way is convenient for the computer
- The second corresponds to how human’s think
Clarify function calls with keyword arguments
twitter_search('@obama', False, 20, True)
Better
twitter_search('@obama', retweets=False, numtweets=20, popular=True)
Is slightly (microseconds) slower but is worth it for the code clarity and developer time savings.
Clarify multiple return values with named tuples
# Old testmod return value
doctest.testmod()
# (0, 4)
# Is this good or bad? You don't know because it's not clear.
Better
# New testmod return value, a namedTuple
doctest.testmod()
# TestResults(failed=0, attempted=4)
A namedTuple is a subclass of tuple so they still work like a regular tuple, but are more friendly.
To make a namedTuple:
TestResults = namedTuple('TestResults', ['failed', 'attempted'])
Unpacking sequences
p = 'Raymond', 'Hettinger', 0x30, 'python@example.com'
# A common approach / habit from other languages
fname = p[0]
lname = p[1]
age = p[2]
email = p[3]
Better
fname, lname, age, email = p
The second approach uses tuple unpacking and is faster and more readable.
Updating multiple state variables
def fibonacci(n):
x = 0
y = 1
for i in range(n):
print x
t = y
y = x + y
x = t
Better
def fibonacci(n):
x, y = 0, 1
for i in range(n):
print x
x, y = y, x + y
Problems with first approach
- x and y are state, and state should be updated all at once or in between lines that state is mis-matched and a common source of issues
- ordering matters
- it's too low level
The second approach is more high-level, doesn't risk getting the order wrong and is fast.
Simultaneous state updates
tmp_x = x + dx * t
tmp_y = y + dy * t
tmp_dx = influence(m, x, y, dx, dy, partial='x')
tmp_dy = influence(m, x, y, dx, dy, partial='y')
x = tmp_x
y = tmp_y
dx = tmp_dx
dy = tmp_dy
Better
x, y, dx, dy = (x + dx * t,
y + dy * t,
influence(m, x, y, dx, dy, partial='x'),
influence(m, x, y, dx, dy, partial='y'))
Efficiency
- An optimization fundamental rule
- Don’t cause data to move around unnecessarily
- It takes only a little care to avoid O(n**2) behavior instead of linear behavior
Basically, just don't move data around unecessarily.
Concatenating strings
names = ['raymond', 'rachel', 'matthew', 'roger',
'betty', 'melissa', 'judith', 'charlie']
s = names[0]
for name in names[1:]:
s += ', ' + name
print s
Better
print ', '.join(names)
Updating sequences
names = ['raymond', 'rachel', 'matthew', 'roger',
'betty', 'melissa', 'judith', 'charlie']
del names[0]
# The below are signs you're using the wrong data structure
names.pop(0)
names.insert(0, 'mark')
Better
names = deque(['raymond', 'rachel', 'matthew', 'roger',
'betty', 'melissa', 'judith', 'charlie'])
# More efficient with deque
del names[0]
names.popleft()
names.appendleft('mark')
Decorators and Context Managers
- Helps separate business logic from administrative logic
- Clean, beautiful tools for factoring code and improving code reuse
- Good naming is essential.
- Remember the Spiderman rule: With great power, comes great responsibility!
Using decorators to factor-out administrative logic
# Mixes business / administrative logic and is not reusable
def web_lookup(url, saved={}):
if url in saved:
return saved[url]
page = urllib.urlopen(url).read()
saved[url] = page
return page
Better
@cache
def web_lookup(url):
return urllib.urlopen(url).read()
Note: since python 3.2 there is a decorator for this in the standard library: functools.lru_cache
.
Factor-out temporary contexts
# Saving the old, restoring the new
old_context = getcontext().copy()
getcontext().prec = 50
print Decimal(355) / Decimal(113)
setcontext(old_context)
Better
with localcontext(Context(prec=50)):
print Decimal(355) / Decimal(113)
How to open and close files
f = open('data.txt')
try:
data = f.read()
finally:
f.close()
Better
with open('data.txt') as f:
data = f.read()
How to use locks
# Make a lock
lock = threading.Lock()
# Old-way to use a lock
lock.acquire()
try:
print 'Critical section 1'
print 'Critical section 2'
finally:
lock.release()
Better
# New-way to use a lock
with lock:
print 'Critical section 1'
print 'Critical section 2'
Factor-out temporary contexts
try:
os.remove('somefile.tmp')
except OSError:
pass
Better
with ignored(OSError):
os.remove('somefile.tmp')
ignored
is is new in python 3.4, documentation.
Note: ignored
is actually called suppress
in the standard library.
To make your own ignored
context manager in the meantime:
@contextmanager
def ignored(*exceptions):
try:
yield
except exceptions:
pass
Stick that in your utils directory and you too can ignore exceptions
Factor-out temporary contexts
# Temporarily redirect standard out to a file and then return it to normal
with open('help.txt', 'w') as f:
oldstdout = sys.stdout
sys.stdout = f
try:
help(pow)
finally:
sys.stdout = oldstdout
Better
with open('help.txt', 'w') as f:
with redirect_stdout(f):
help(pow)
redirect_stdout
is proposed for python 3.4, bug report.
To roll your own redirect_stdout
context manager
@contextmanager
def redirect_stdout(fileobj):
oldstdout = sys.stdout
sys.stdout = fileobj
try:
yield fieldobj
finally:
sys.stdout = oldstdout
Concise Expressive One-Liners
Two conflicting rules:
- Don’t put too much on one line
- Don’t break atoms of thought into subatomic particles
Raymond’s rule:
- One logical line of code equals one sentence in English
List Comprehensions and Generator Expressions
result = []
for i in range(10):
s = i ** 2
result.append(s)
print sum(result)
Better
print sum(i**2 for i in xrange(10))
First way tells you what to do, second way tells you what you want.