Master Pythonic Loops: Elegant Techniques for Cleaner Code

This note, based on Raymond Hettinger's 2013 PyCon talk, shows how to write Pythonic code that is concise, readable, and efficient.

Looping over a range

Use range for Python 3 (or xrange for Python 2) to avoid creating an intermediate list.

for i in range(6):
    print(i * 2)

Iterating over a collection

Iterate directly over the collection instead of using indices.

colors = ['red', 'green', 'blue']
for i in range(len(colors)):
    print(colors[i])
# Better
for color in colors:
    print(color)

Enumerate and zip

When you need both index and value, use enumerate. To iterate two sequences in parallel, use zip (or izip in Python 2).

for i, color in enumerate(colors):
    print(i, '-->', color)

names = ['raymond', 'rachel']
for name, color in zip(names, colors):
    print(name, '-->', color)

Dictionary iteration

Iterate over keys with for k in d or d.keys(). For key‑value pairs, use d.items() (or iteritems() in Python 2).

d = {'matthew': 'blue', 'rachel': 'green'}
for k, v in d.items():
    print(k, '-->', v)

Counting with dictionaries

Simple counting can be done with a normal dict, but defaultdict(int) or collections.Counter is more idiomatic.

from collections import defaultdict
counts = defaultdict(int)
for color in colors:
    counts[color] += 1

Grouping items

Use defaultdict(list) or setdefault to group values by a computed key.

from collections import defaultdict
by_len = defaultdict(list)
for name in names:
    by_len[len(name)].append(name)

ChainMap for configuration merging

Combine defaults, environment variables, and command‑line arguments without copying data.

from collections import ChainMap
config = ChainMap(cli_args, os.environ, defaults)

Readability tricks

Prefer keyword arguments and namedtuple for multiple return values.

from collections import namedtuple
Result = namedtuple('Result', ['failed', 'attempted'])
res = Result(failed=0, attempted=4)

Simultaneous variable updates

Swap or update several variables in one statement.

x, y, dx, dy = x + dx*t, y + dy*t, influence(..., partial='x'), influence(..., partial='y')

Efficient sequence updates

When you need fast insert/remove at both ends, use collections.deque instead of a list.

from collections import deque
names = deque(['raymond', 'rachel'])
names.appendleft('mark')
names.popleft()

Decorators and context managers

Separate business logic from management concerns with decorators (e.g., @cache) and the with statement.

@cache
def web_lookup(url):
    return urllib.urlopen(url).read()

Use built‑in context managers such as redirect_stdout or create your own with contextlib.contextmanager.

from contextlib import redirect_stdout
with open('help.txt', 'w') as f, redirect_stdout(f):
    help(pow)

File handling and locks

Prefer the with open(...) pattern for automatic closing, and acquire locks with with lock: instead of manual acquire / release.

with open('data.txt') as f:
    data = f.read()

with lock:
    critical_section()

These idioms collectively make Python code more Pythonic, easier to read, and often faster.