{"url":"https://kiito.hatenablog.com/entry/2020/04/05/115700","width":"100%","categories":["python","numpy","nptyping"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fkiito.hatenablog.com%2Fentry%2F2020%2F04%2F05%2F115700\" title=\"numpy\u306e\u578b\u30d2\u30f3\u30c8\u3092nptyping\u3067\u4ed8\u3051\u3088\u3046 - \u6b69\u3044\u305f\u3089\u4f11\u3081\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_url":"https://blog.hatena.ne.jp/takeshi0406/","description":"stackoverflow\u306eNumpy type hints in Python (PEP 484)\u3068\u3044\u3046\u8a18\u4e8b\u3067 nptyping \u3068\u3044\u3046\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u7d39\u4ecb\u3055\u308c\u3066\u3044\u307e\u3057\u305f\u3002 import numpy as np from nptyping import Array def foo(array: Array[np.float64]) -> str: ... \u307e\u305f\u3001shape\u3082\u542b\u3081\u3066\u6307\u5b9a\u3067\u304d\u308b\u3088\u3046\u3067\u3059\u3002\u305f\u3060\u3057\u3001\u73fe\u6642\u70b9\u3067mypy\u306a\u3069\u3067shape\u3082\u542b\u3081\u3066\u30c1\u30a7\u30c3\u30af\u3059\u308b\u3053\u3068\u306f\u3067\u304d\u305a\u3001\u3042\u304f\u307e\u3067\u5b9f\u884c\u6642\u306bisinstance\u3067\u30c1\u30a7\u30c3\u30af\u3059\u308b\u3001\u3082\u3057\u304f\u306f\u53ef\u8aad\u6027\u306e\u305f\u3081\u306e\u8a18\u8ff0\u3059\u308b\u305f\u3081\u306b\u7528\u3044\u308b\u3088\u3046\u3067\u3059\u3002 arr = np.a\u2026","blog_title":"\u6b69\u3044\u305f\u3089\u4f11\u3081","image_url":null,"height":"190","provider_name":"Hatena Blog","published":"2020-04-05 11:57:00","type":"rich","blog_url":"https://kiito.hatenablog.com/","author_name":"takeshi0406","provider_url":"https://hatena.blog","title":"numpy\u306e\u578b\u30d2\u30f3\u30c8\u3092nptyping\u3067\u4ed8\u3051\u3088\u3046","version":"1.0"}