Python: Idiomatic Efficiency Reference

When to Use

• Use this skill when the task matches this description: Language-specific super-code guidelines for python.

Table of Contents

1. Comprehensions & Generators
2. Unpacking & Destructuring
3. Built-ins & stdlib
4. Functions & Defaults
5. Classes & Dataclasses
6. Error Handling
7. Type Hints
8. Anti-patterns specific to Python

1. Comprehensions & Generators {#comprehensions}

# ❌ Imperative accumulation
result = []
for item in items:
    if item.active:
        result.append(item.name.upper())

# ✅
result = [item.name.upper() for item in items if item.active]

# ❌ Dict built in a loop
d = {}
for k, v in pairs:
    d[k] = v

# ✅
d = dict(pairs)
# or
d = {k: v for k, v in pairs}

# ❌ Generator converted to list unnecessarily
total = sum(list(x * 2 for x in nums))

# ✅ — generator expression works directly in sum()
total = sum(x * 2 for x in nums)

Use generator expressions (not list comprehensions) when the result is consumed once and not stored.

2. Unpacking & Destructuring {#unpacking}

# ❌ Index access
first = items[0]
rest = items[1:]

# ✅
first, *rest = items

# ❌ Temporary variable for swap
tmp = a
a = b
b = tmp

# ✅
a, b = b, a

# ❌ items() with separate indexing
for i in range(len(items)):
    print(i, items[i])

# ✅
for i, item in enumerate(items):
    print(i, item)

# ❌ zip with separate index
for i in range(len(a)):
    process(a[i], b[i])

# ✅
for x, y in zip(a, b):
    process(x, y)

3. Built-ins & stdlib {#builtins}

# ❌ Manual max search
max_val = items[0]
for item in items[1:]:
    if item > max_val:
        max_val = item

# ✅
max_val = max(items)

# ❌ Manual grouping
from collections import defaultdict
groups = defaultdict(list)
for item in items:
    groups[item.category].append(item)

# ✅ — same thing, just be explicit about defaultdict; it IS the right tool
# (this example is already correct — don't replace defaultdict with a loop)

# ❌ Manual sentinel for dict default
if key in d:
    val = d[key]
else:
    val = default

# ✅
val = d.get(key, default)

# ❌ Rolling your own counter
counts = {}
for item in items:
    counts[item] = counts.get(item, 0) + 1

# ✅
from collections import Counter
counts = Counter(items)

Use itertools (chain, islice, groupby, product) before writing nested loops for combinatorial or streaming logic.

4. Functions & Defaults {#functions}

# ❌ Mutable default argument (bug, not just style)
def append_to(item, lst=[]):
    lst.append(item)
    return lst

# ✅
def append_to(item, lst=None):
    if lst is None:
        lst = []
    lst.append(item)
    return lst

# ❌ Positional args for everything when keyword clarity helps
create_user("Alice", True, False, 30)

# ✅ — use keyword args at call site for boolean/ambiguous params
create_user("Alice", is_admin=True, is_active=False, age=30)

# ❌ Long function doing multiple things
def process_and_save(data):
    # 40 lines of transform
    # 20 lines of DB write
    ...

# ✅ — split only if each part is reused OR independently testable
def _transform(data): ...
def _save(record): ...
def process_and_save(data): _save(_transform(data))

5. Classes & Dataclasses {#classes}

# ❌ Manual __init__ for data holders
class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

# ✅
from dataclasses import dataclass

@dataclass
class Point:
    x: float
    y: float

# ❌ Class just to hold a namespace of functions
class MathUtils:
    @staticmethod
    def add(a, b): return a + b

# ✅ — module-level functions; classes for state + behavior
def add(a, b): return a + b

# ❌ __repr__ written manually when dataclass gives it free
# (see above — use @dataclass)

Use @dataclass(frozen=True) for immutable value objects. Use NamedTuple when you need tuple unpacking.

6. Error Handling {#errors}

# ❌ Bare except
try:
    risky()
except:
    pass

# ✅ — catch the specific exception; don't swallow silently
try:
    risky()
except ValueError as e:
    logger.warning("Invalid value: %s", e)

# ❌ LBYL (look before you leap) when EAFP is cleaner

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