Python標準ライブラリ珠玉の10選｜datetime・pathlib・itertoolsで実務が回るチートシート

from datetime import datetime, timedelta
from zoneinfo import ZoneInfo  # Python 3.9+

JST = ZoneInfo("Asia/Tokyo")
UTC = ZoneInfo("UTC")

# 取得：常に tz-aware

now\_jst = datetime.now(JST)

# 変換：UTCへ→ISO8601で保存

now\_utc = now\_jst.astimezone(UTC)
iso = now\_utc.isoformat()

# 解析：ISOなら fromisoformat、その他は strptime

parsed = datetime.fromisoformat("2025-09-01T12:00:00+09:00")
naive = datetime.strptime("2025/09/01 12:00", "%Y/%m/%d %H:%M").replace(tzinfo=JST)

# 範囲生成（週次レポート）

start = datetime(2025, 8, 1, tzinfo=JST)
end   = datetime(2025, 9, 1, tzinfo=JST)
cur = start
while cur < end:
print(cur.date())
cur += timedelta(days=7)

from pathlib import Path
root = Path("data")

# 作成/列挙/読み書き

(root / "out").mkdir(parents=True, exist\_ok=True)
for p in root.glob("\*.csv"):
text = p.read\_text(encoding="utf-8")
(root / "out" / p.name).write\_text(text, encoding="utf-8")

# 原子置換は：一時ファイルで書いてから置き換える（詳しくは「ファイル操作（307）」を参照）

from itertools import islice, chain, groupby

# 先頭N件だけ（大きいファイルでも安全）

first10 = list(islice(range(1\_000\_000), 10))

# 2シーケンスを直列結合

merged = list(chain(\[1, 2], \[3, 4]))

# 事前に key でソート → groupby

rows = sorted(\[
{"store": "A", "qty": 2}, {"store": "B", "qty": 1}, {"store": "A", "qty": 5}
], key=lambda r: r\["store"])
for k, g in groupby(rows, key=lambda r: r\["store"]):
total = sum(r\["qty"] for r in g)
print(k, total)

# 小技：3.10+ の pairwise で隣接差分、accumulate で累積和

from collections import Counter, defaultdict, deque, namedtuple

# カウント

c = Counter(\["A", "B", "A"]).most\_common(1)  # \[('A', 2)]

# 集計（0初期化不要）

sum\_by = defaultdict(int)
for s, v in \[("A", 10), ("A", 3), ("B", 5)]:
sum\_by\[s] += v

# 直近N件のスライディング

buf = deque(maxlen=3)
for x in \[1, 2, 3, 4]:
buf.append(x)
print(list(buf))  # \[2, 3, 4]

# 名前付きの軽量レコード

Point = namedtuple("Point", "x y")
p = Point(2, 5)
print(p.x)  # 2

from functools import lru_cache, partial, singledispatch
from math import floor

@lru\_cache(maxsize=1024)
def fib(n: int) -> int:
return n if n < 2 else fib(n-1) + fib(n-2)

# 部分適用（割引率を固定）

def discount(price, rate):
return floor(price \* (1 - rate))
bf = partial(discount, rate=0.3)

# 型ごとに処理を切替（軽い多相）

@singledispatch
def dump(x):
return str(x)

@dump.register(list)
def \_(x: list):
return ",".join(map(str, x))

from statistics import mean, median, quantiles, pstdev, stdev, correlation

xs = \[1, 2, 3, 4, 5]
print(mean(xs), median(xs), quantiles(xs, n=4))  # 3 3 \[2.0, 3.0, 4.0]

# 分散の母/標本に注意

print(pstdev(xs), stdev(xs))  # 母標準偏差 / 標本標準偏差

# 相関（3.10+）

ys = \[2, 4, 6, 8, 10]
print(correlation(xs, ys))  # ほぼ1.0

from dataclasses import dataclass, field
from datetime import date
from typing import Iterable

@dataclass(frozen=True)
class Order:
id: int
price: int
created: date
tags: list\[str] = field(default\_factory=list)

def total(orders: Iterable\[Order]) -> int:
return sum(o.price for o in orders)

from typing import TypedDict, Literal, Optional, Protocol

class User(TypedDict):
id: int
name: str
role: Literal\["admin", "staff"]

class Notifier(Protocol):
def send(self, msg: str) -> None: ...

def greet(u: User, notifier: Notifier, prefix: Optional\[str] = None) -> None:
pre = prefix or "Hello"
notifier.send(f"{pre}, {u\['name']}")

import re
text = "id=42, price=1,234円"

# 名前付きグループ＋先読み/後読み

m = re.search(r"id=(?P\\d+),\s\*price=(?P\\[\d,]+)円", text)
if m:
id\_ = int(m.group("id"))
price = int(m.group("price").replace(",", ""))

# コンパイル＋フラグ

pat = re.compile(r"^\w+@\w+.\w+$", flags=re.IGNORECASE)
assert pat.match("[USER@EXAMPLE.com](mailto:USER@EXAMPLE.com)")

import heapq, bisect

# 上位K（大きい順3件）

xs = \[5, 1, 9, 3, 7]
print(heapq.nlargest(3, xs))  # \[9, 7, 5]

# 優先度付きキュー（最小ヒープ）

h = \[]
heapq.heappush(h, (1, "low"))
heapq.heappush(h, (0, "urgent"))
prio, item = heapq.heappop(h)  # (0, 'urgent')

# ソート済みリストへの挿入位置（重複は右側）

arr = \[10, 20, 20, 30]
idx = bisect.bisect\_right(arr, 20)  # 3
arr.insert(idx, 20)  # \[10, 20, 20, 20, 30]