完整爬蟲+ML+Line_Notify.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#1. 安裝套件"
      ],
      "metadata": {
        "id": "U_YDrwfmeJmI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axvi7HChbvPj",
        "outputId": "10807f9b-b262-4dd7-c2d1-4a5f549d47ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: Beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (4.12.3)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from Beautifulsoup4) (2.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.32.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.7.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install Beautifulsoup4\n",
        "!pip install requests\n",
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.套件與參數設定"
      ],
      "metadata": {
        "id": "EdIFkB-6o2LM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "目標網站: https://www.ptt.cc/bbs/Tech_Job/index.html\n",
        "\n",
        "上一頁: https://www.ptt.cc/bbs/Tech_Job/index4002.html\n",
        "\n",
        "上上一頁: https://www.ptt.cc/bbs/Tech_Job/index4001.html"
      ],
      "metadata": {
        "id": "gjUzW0s0sVoN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 目標網址\n",
        "target_url = \"https://www.ptt.cc/bbs/\"\n",
        "\n",
        "# 目標看板\n",
        "target_board = \"Tech_Job\"\n",
        "\n",
        "#目標頁面\n",
        "target_page = \"/index\"\n",
        "\n",
        "#目標頁數\n",
        "page_num = \"\"\n",
        "\n",
        "#頁面附屬檔名\n",
        "page_ext = \".html\"\n",
        "\n",
        "target = target_url + target_board + target_page + page_num  + page_ext"
      ],
      "metadata": {
        "id": "ZjrHz7BVo7WT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "準備 requests 用的 header"
      ],
      "metadata": {
        "id": "72QM_edivRI4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "headers = {\n",
        "    \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36\",\n",
        "}\n",
        "\n",
        "print(headers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-XWnWVAuyUO",
        "outputId": "b4c44bb5-9fe2-4888-f36d-b60cc47ef756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. 用 Requests 下載目標網頁的程式碼"
      ],
      "metadata": {
        "id": "Qe4pkzXTIXRO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "data = requests.get(target, headers= headers)\n",
        "print(data.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AAuMqR2yPAh",
        "outputId": "56102f19-c20f-44d5-8f0d-0390cb00a7b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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id=\"topbar-container\">\\n\\t<div id=\"topbar\" class=\"bbs-content\">\\n\\t\\t<a id=\"logo\" href=\"/bbs/\">\\xe6\\x89\\xb9\\xe8\\xb8\\xa2\\xe8\\xb8\\xa2\\xe5\\xaf\\xa6\\xe6\\xa5\\xad\\xe5\\x9d\\x8a</a>\\n\\t\\t<span>&rsaquo;</span>\\n\\t\\t<a class=\"board\" href=\"/bbs/Tech_Job/index.html\"><span class=\"board-label\">\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf </span>Tech_Job</a>\\n\\t\\t<a class=\"right small\" href=\"/about.html\">\\xe9\\x97\\x9c\\xe6\\x96\\xbc\\xe6\\x88\\x91\\xe5\\x80\\x91</a>\\n\\t\\t<a class=\"right small\" href=\"/contact.html\">\\xe8\\x81\\xaf\\xe7\\xb5\\xa1\\xe8\\xb3\\x87\\xe8\\xa8\\x8a</a>\\n\\t</div>\\n</div>\\n\\n<div id=\"main-container\">\\n\\t<div id=\"action-bar-container\">\\n\\t\\t<div class=\"action-bar\">\\n\\t\\t\\t<div class=\"btn-group btn-group-dir\">\\n\\t\\t\\t\\t<a class=\"btn selected\" href=\"/bbs/Tech_Job/index.html\">\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf</a>\\n\\t\\t\\t\\t<a class=\"btn\" 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href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E8%AB%8B%E7%9B%8A%5Doffer&#43;%E8%AB%8B%E7%9B%8A\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3As19273\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 s19273 \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 8/15</div>\\n\\t\\t\\t\\t<div class=\"mark\">M</div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n\\t\\t\\n            \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f3\">22</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1723726637.A.91F.html\">[\\xe6\\x96\\xb0\\xe8\\x81\\x9e] \\xe5\\x85\\xac\\xe5\\x91\\x8a\\xe4\\xba\\x86\\xef\\xbc\\x81\\xe5\\x8f\\xb0\\xe7\\xa9\\x8d\\xe9\\x9b\\xbb\\xe4\\xbb\\xa5171.4\\xe5\\x84\\x84\\xe5\\x85\\x83\\xe8\\xb2\\xb7\\xe7\\xbe\\xa4\\xe5\\x89\\xb5\\xe5\\x8d\\x97\\xe7\\xa7\\x91\\xe5\\x9b\\x9b\\xe5\\xbb\\xa0</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">hvariables</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E6%96%B0%E8%81%9E%5D&#43;%E5%85%AC%E5%91%8A%E4%BA%86%EF%BC%81%E5%8F%B0%E7%A9%8D%E9%9B%BB%E4%BB%A5171.4%E5%84%84%E5%85%83%E8%B2%B7%E7%BE%A4%E5%89%B5%E5%8D%97%E7%A7%91%E5%9B%9B%E5%BB%A0\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ahvariables\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 hvariables \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 8/15</div>\\n\\t\\t\\t\\t<div class=\"mark\"></div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n\\t\\t\\n            \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t(\\xe6\\x9c\\xac\\xe6\\x96\\x87\\xe5\\xb7\\xb2\\xe8\\xa2\\xab\\xe5\\x88\\xaa\\xe9\\x99\\xa4) [andy19970205]\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">-</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 8/15</div>\\n\\t\\t\\t\\t<div class=\"mark\"></div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n\\t\\t\\n            \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f2\">2</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1723736590.A.00B.html\">Re: [\\xe8\\xab\\x8b\\xe7\\x9b\\x8a] \\xe6\\x97\\xa5\\xe6\\x8f\\x9a\\xe7\\xa7\\x91\\xe6\\x8a\\x80-\\xe7\\xa0\\x94\\xe7\\x99\\xbc\\xe8\\xa3\\xbd\\xe7\\xa8\\x8b</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">h34143202</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E8%AB%8B%E7%9B%8A%5D&#43;%E6%97%A5%E6%8F%9A%E7%A7%91%E6%8A%80-%E7%A0%94%E7%99%BC%E8%A3%BD%E7%A8%8B\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ah34143202\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 h34143202 \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 8/15</div>\\n\\t\\t\\t\\t<div class=\"mark\"></div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n\\t\\t\\n            \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f2\">1</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1723767127.A.537.html\">[\\xe6\\x96\\xb0\\xe8\\x81\\x9e] \\xe8\\xbb\\x9f\\xe9\\x8a\\x80\\xe8\\x88\\x87\\xe8\\x8b\\xb1\\xe7\\x89\\xb9\\xe7\\x88\\xbe\\xe5\\x90\\x88\\xe4\\xbd\\x9c\\xe7\\xa0\\xb4\\xe5\\xb1\\x80\\xe3\\x80\\x80\\xe5\\xa4\\x96\\xe5\\xaa\\x92\\xe6\\x9b\\x9d\\xe8\\xbd\\x89\\xe5\\x90\\x91\\xe5\\xb0\\x8b\\xe6\\xb1\\x82\\xe5\\x92\\x8c</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">jeff0025</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E6%96%B0%E8%81%9E%5D&#43;%E8%BB%9F%E9%8A%80%E8%88%87%E8%8B%B1%E7%89%B9%E7%88%BE%E5%90%88%E4%BD%9C%E7%A0%B4%E5%B1%80%E3%80%80%E5%A4%96%E5%AA%92%E6%9B%9D%E8%BD%89%E5%90%91%E5%B0%8B%E6%B1%82%E5%92%8C\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ajeff0025\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 jeff0025 \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 8/16</div>\\n\\t\\t\\t\\t<div class=\"mark\"></div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n\\t\\t\\n        \\n        <div class=\"r-list-sep\"></div>\\n            \\n                \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f3\">47</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1393646135.A.A28.html\">[\\xe5\\x85\\xac\\xe5\\x91\\x8a] Tech_Job\\xe6\\x9d\\xbf\\xe6\\x9d\\xbf\\xe8\\xa6\\x8f 2022.05.26\\xe6\\x9b\\xb4\\xe6\\x96\\xb0</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">mmkntust</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E5%85%AC%E5%91%8A%5D&#43;Tech_Job%E6%9D%BF%E6%9D%BF%E8%A6%8F&#43;2022.05.26%E6%9B%B4%E6%96%B0\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ammkntust\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 mmkntust \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 3/01</div>\\n\\t\\t\\t\\t<div class=\"mark\">M</div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n            \\n                \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f1\">\\xe7\\x88\\x86</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1410062073.A.1B6.html\">[\\xe5\\x85\\xac\\xe5\\x91\\x8a] \\xe7\\xbd\\xae\\xe5\\xba\\x95 \\xe6\\xaa\\xa2\\xe8\\x88\\x89/\\xe6\\x8e\\xa8\\xe8\\x96\\xa6 \\xe6\\x96\\x87\\xe7\\xab\\xa0</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">mmkntust</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E5%85%AC%E5%91%8A%5D&#43;%E7%BD%AE%E5%BA%95&#43;%E6%AA%A2%E8%88%89%2F%E6%8E%A8%E8%96%A6&#43;%E6%96%87%E7%AB%A0\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ammkntust\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 mmkntust \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 9/07</div>\\n\\t\\t\\t\\t<div class=\"mark\">M</div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n            \\n                \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f2\">3</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1425268790.A.15E.html\">[\\xe5\\x85\\xac\\xe5\\x91\\x8a] \\xe5\\xa6\\x82\\xe4\\xbd\\x95\\xe6\\xb6\\x88\\xe9\\x99\\xa4\\xe9\\x80\\x80\\xe6\\x96\\x87 \\xe8\\xbd\\x89\\xe8\\x87\\xaaAsk\\xe6\\x9d\\xbf</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">mmkntust</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E5%85%AC%E5%91%8A%5D&#43;%E5%A6%82%E4%BD%95%E6%B6%88%E9%99%A4%E9%80%80%E6%96%87&#43;%E8%BD%89%E8%87%AAAsk%E6%9D%BF\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ammkntust\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 mmkntust \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 3/02</div>\\n\\t\\t\\t\\t<div class=\"mark\">M</div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n            \\n                \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f3\">34</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1585618843.A.D43.html\">[\\xe6\\x83\\x85\\xe5\\xa0\\xb1] \\xe8\\x96\\xaa\\xe8\\xb3\\x87\\xe6\\x9f\\xa5\\xe8\\xa9\\xa2\\xe5\\xb9\\xb3\\xe5\\x8f\\xb0</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">mmkntust</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E6%83%85%E5%A0%B1%5D&#43;%E8%96%AA%E8%B3%87%E6%9F%A5%E8%A9%A2%E5%B9%B3%E5%8F%B0\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ammkntust\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 mmkntust \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 3/31</div>\\n\\t\\t\\t\\t<div class=\"mark\">M</div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n            \\n                \\n        \\n        \\n\\t\\t<div class=\"r-ent\">\\n\\t\\t\\t<div class=\"nrec\"><span class=\"hl f3\">56</span></div>\\n\\t\\t\\t<div class=\"title\">\\n\\t\\t\\t\\n\\t\\t\\t\\t<a href=\"/bbs/Tech_Job/M.1653574616.A.A15.html\">[\\xe5\\x85\\xac\\xe5\\x91\\x8a] \\xe6\\x9d\\xbf\\xe8\\xa6\\x8f\\xe5\\x8d\\x81\\xe4\\xba\\x94 offer\\xe8\\xab\\x8b\\xe7\\x9b\\x8a\\xe6\\x96\\x87 m\\xe6\\x96\\x87\\xe8\\xa7\\xa3\\xe9\\x99\\xa4\\xe7\\x94\\xb3\\xe8\\xab\\x8b</a>\\n\\t\\t\\t\\n\\t\\t\\t</div>\\n\\t\\t\\t<div class=\"meta\">\\n\\t\\t\\t\\t<div class=\"author\">mmkntust</div>\\n\\t\\t\\t\\t<div class=\"article-menu\">\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t<div class=\"trigger\">&#x22ef;</div>\\n\\t\\t\\t\\t\\t<div class=\"dropdown\">\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=thread%3A%5B%E5%85%AC%E5%91%8A%5D&#43;%E6%9D%BF%E8%A6%8F%E5%8D%81%E4%BA%94&#43;offer%E8%AB%8B%E7%9B%8A%E6%96%87&#43;m%E6%96%87%E8%A7%A3%E9%99%A4%E7%94%B3%E8%AB%8B\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe5\\x90\\x8c\\xe6\\xa8\\x99\\xe9\\xa1\\x8c\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t<div class=\"item\"><a href=\"/bbs/Tech_Job/search?q=author%3Ammkntust\">\\xe6\\x90\\x9c\\xe5\\xb0\\x8b\\xe7\\x9c\\x8b\\xe6\\x9d\\xbf\\xe5\\x85\\xa7 mmkntust \\xe7\\x9a\\x84\\xe6\\x96\\x87\\xe7\\xab\\xa0</a></div>\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t</div>\\n\\t\\t\\t\\t<div class=\"date\"> 5/26</div>\\n\\t\\t\\t\\t<div class=\"mark\">M</div>\\n\\t\\t\\t</div>\\n\\t\\t</div>\\n\\n            \\n        \\n\\t</div>\\n\\n    \\n</div>\\n\\n\\t\\t\\n\\n\\n\\n<script async src=\"https://www.googletagmanager.com/gtag/js?id=G-DZ6Y3BY9GW\"></script>\\n<script>\\n      window.dataLayer = window.dataLayer || [];\\n      function gtag(){dataLayer.push(arguments);}\\n      gtag(\\'js\\', new Date());\\n\\n      gtag(\\'config\\', \\'G-DZ6Y3BY9GW\\');\\n</script>\\n<script>\\n  (function(i,s,o,g,r,a,m){i[\\'GoogleAnalyticsObject\\']=r;i[r]=i[r]||function(){\\n  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\\n  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\\n  })(window,document,\\'script\\',\\'https://www.google-analytics.com/analytics.js\\',\\'ga\\');\\n\\n  ga(\\'create\\', \\'UA-32365737-1\\', {\\n    cookieDomain: \\'ptt.cc\\',\\n    legacyCookieDomain: \\'ptt.cc\\'\\n  });\\n  ga(\\'send\\', \\'pageview\\');\\n</script>\\n\\n\\n\\t\\t\\n<script src=\"//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js\"></script>\\n<script src=\"//images.ptt.cc/bbs/v2.27/bbs.js\"></script>\\n\\n    </body>\\n</html>\\n'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. 用 BeautifulSoup 分析跟取得 html 標籤資料"
      ],
      "metadata": {
        "id": "VAGB_HoKIo13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "html_code = BeautifulSoup(data.content)"
      ],
      "metadata": {
        "id": "vwR-AmLYzKBq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "div_list = html_code.find_all('div', class_=\"title\")\n",
        "\n",
        "'''\n",
        "從 <div> list 裡面元素找尋其他標籤的範例\n",
        "並從目標標籤取的 html 屬性值 與 標籤文字\n",
        "a_tag = div_list[0].find('a')\n",
        "a_url = a_tag.attrs['href']\n",
        "a_title = a_tag.contents[0]\n",
        "'''\n",
        "\n",
        "urls= []\n",
        "for div_ in div_list:\n",
        "  try:\n",
        "    a_tag = div_.find('a')\n",
        "    #print(a_tag.attrs['href'], a_tag.contents[0])\n",
        "\n",
        "    url_ = {\n",
        "    'url': a_tag.attrs['href'],\n",
        "    'title': a_tag.contents[0]\n",
        "    }\n",
        "\n",
        "  except:\n",
        "    url_ = {\n",
        "    'url':  None,\n",
        "    'title': '文章已刪除'\n",
        "    }\n",
        "  urls.append(url_)\n",
        "\n",
        "print(urls[0]['url'])\n",
        "print(urls[0]['title'])\n",
        "print(urls[1]['url'])\n",
        "print(urls[1]['title'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plgIeBF65bAb",
        "outputId": "8aa136b1-42b3-4a2c-9cb2-58c0086aec49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bbs/Tech_Job/M.1723723186.A.404.html\n",
            "[請益]offer 請益\n",
            "/bbs/Tech_Job/M.1723726637.A.91F.html\n",
            "[新聞] 公告了！台積電以171.4億元買群創南科四廠\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. For Loop 針對每篇文章連結進行讀取"
      ],
      "metadata": {
        "id": "c1TiE9xVJCLn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#for loop\n",
        "import time\n",
        "\n",
        "ptt_data = []\n",
        "for url_ in urls:\n",
        "  if url_['url'] != None:\n",
        "    article_url = 'https://www.ptt.cc' + url_['url']\n",
        "    page_data = requests.get(article_url, headers=headers)\n",
        "    page_html_code = BeautifulSoup(page_data.content)\n",
        "    article_data = page_html_code.find_all('span', class_=\"article-meta-value\")\n",
        "    article_author = article_data[0].contents[0]\n",
        "    article_title = article_data[2].contents[0]\n",
        "    article_time = article_data[3].contents[0]\n",
        "    article_body = page_html_code.find('div', id='main-content').contents[4]\n",
        "    article_row = {\n",
        "        'url': article_url,\n",
        "        'title': article_title,\n",
        "        'author': article_author,\n",
        "        'time': article_time,\n",
        "        'content': article_body\n",
        "    }\n",
        "    ptt_data.append(article_row)\n",
        "  time.sleep(1)\n"
      ],
      "metadata": {
        "id": "JdMMyIjPQlYK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. 讀取所有文章並保存"
      ],
      "metadata": {
        "id": "DlWEKeHOVpQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas\n",
        "ptt_df = pandas.DataFrame(ptt_data)\n",
        "# ....\n",
        "\n",
        "ptt_df.to_csv()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "CYoyY-43U9ZT",
        "outputId": "42b24eb0-ead4-44d2-d2d3-70a25eb8dea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "',url,title,author,time,content\\n0,https://www.ptt.cc/bbs/Tech_Job/M.1723723186.A.404.html,[請益]offer 請益,s19273 (阿翔),Thu Aug 15 19:59:44 2024,\"\\n（朋友沒有帳號代po）\\n\\n大家好，本人有廠務十年經驗  因工作內容與主管描述不相符\\n 加上個人因素  故已離職 現在已面試多家  剩下兩家目前在考慮中\\n想請各位大大給點意見\\n\\nN=上份工作底薪\\n1.\\n公司：力積電\\n職級：廠務輪班工程師\\n地點：竹科\\n薪水：(N+13)*14 季獎金  分紅1~2\\n加班費：有\\n工時：12 做二休二\\n\\n\\n2.\\n公司：緯創\\n職級：廠務工程師\\n地點：竹科\\n薪水：(N+5k)*14 分紅2~4\\n加班費：有\\n工時：常日班 因應產線擴編 之後可能需要輪班\\n\\n力積電相對來說較穩定 只是沒有賺錢分紅較低  也比較沒有向上發展的機會\\n\\n緯創目前再擴廠 工作內容較為繁雜  但分紅比較高  也有比較多發展及學習空間\\n\\n想請版上大大給予相關意見  感謝\\n\\n感謝各位前輩回覆提供意見\\n\\n--\\n\"\\n1,https://www.ptt.cc/bbs/Tech_Job/M.1723726637.A.91F.html,[新聞] 公告了！台積電以171.4億元買群創南科四廠,hvariables (Speculative Male),Thu Aug 15 20:57:06 2024,\"\\n\"\\n2,https://www.ptt.cc/bbs/Tech_Job/M.1723736590.A.00B.html,Re: [請益] 日揚科技-研發製程,h34143202 (神犬豆豆),Thu Aug 15 23:43:08 2024,\"\\n哈嘍，我本週有收到這家邀約，請問您有去面試嗎？\\n\\n想詢問一下面試內容。\\n\\n--\\n\"\\n3,https://www.ptt.cc/bbs/Tech_Job/M.1723767127.A.537.html,[新聞] 軟銀與英特爾合作破局\\u3000外媒曝轉向尋求和,jeff0025 (無法顯示人物名稱),Fri Aug 16 08:12:03 2024,\"\\n\\n\\nETtoday財經雲  2024-08-16 00:16\\n\\n軟銀與英特爾合作破局\\u3000外媒曝轉向尋求和台積電合作\\n\\n記者高兆麟／綜合報導\\n\\n\"\\n4,https://www.ptt.cc/bbs/Tech_Job/M.1393646135.A.A28.html,[公告] Tech_Job板板規 2015.01.16,mmkntust (Blue Bleaching),Sat Mar  1 11:55:32 2014,\"\\n\"\\n5,https://www.ptt.cc/bbs/Tech_Job/M.1410062073.A.1B6.html,[公告] 置底 檢舉/推薦 文章,mmkntust (老王廚房),Sun Sep  7 11:54:29 2014,\"\\n\"\\n6,https://www.ptt.cc/bbs/Tech_Job/M.1425268790.A.15E.html,[公告] 如何消除退文 轉自Ask板,mmkntust (老王廚房),Mon Mar  2 11:59:48 2015,\"\\n\"\\n7,https://www.ptt.cc/bbs/Tech_Job/M.1585618843.A.D43.html,[情報] 薪資查詢平台,mmkntust (E=mc2),Tue Mar 31 09:40:40 2020,\"\\n本文用以整理薪資相關公開訊息平台\\n\"\\n8,https://www.ptt.cc/bbs/Tech_Job/M.1653574616.A.A15.html,[公告] 板規十五 offer請益文 m文解除申請,mmkntust (E=mc2),Thu May 26 22:16:53 2022,\"\\n此篇文章為置底offer請益m文解除申請專用\\n非解除m文申請,請勿在此文章推文,違者視情況依板規七處分\\n對公告相關意見討論請至\"\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "article_url = 'https://www.ptt.cc' + urls[0]['url']\n",
        "article_url\n",
        "\n",
        "# request 下載資料\n",
        "page_data = requests.get(article_url, headers=headers)\n",
        "\n",
        "# 解析 Requeset 結果\n",
        "page_html_code = BeautifulSoup(page_data.content)\n",
        "\n",
        "#找到文章 meta info\n",
        "article_data = page_html_code.find_all('span', class_=\"article-meta-value\")\n",
        "\n",
        "article_author = article_data[0].contents[0]\n",
        "\n",
        "article_title = article_data[2].contents[0]\n",
        "\n",
        "article_time = article_data[3].contents[0]\n",
        "\n",
        "#文章內文\n",
        "article_body = page_html_code.find('div', id='main-content').contents[4]\n",
        "\n",
        "article_row = {\n",
        "    'url': article_url,\n",
        "    'title': article_title,\n",
        "    'author': article_author,\n",
        "    'time': article_time,\n",
        "    'content': article_body\n",
        "}\n",
        "\n",
        "print(article_row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqQG5TKwHzY3",
        "outputId": "11a0efe9-9871-4128-ec6f-634493836731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'url': 'https://www.ptt.cc/bbs/Tech_Job/M.1723723186.A.404.html', 'title': '[請益]offer 請益', 'author': 's19273 (阿翔)', 'time': 'Thu Aug 15 19:59:44 2024', 'content': '\\n（朋友沒有帳號代po）\\n\\n大家好，本人有廠務十年經驗  因工作內容與主管描述不相符\\n 加上個人因素  故已離職 現在已面試多家  剩下兩家目前在考慮中\\n想請各位大大給點意見\\n\\nN=上份工作底薪\\n1.\\n公司：力積電\\n職級：廠務輪班工程師\\n地點：竹科\\n薪水：(N+13)*14 季獎金  分紅1~2\\n加班費：有\\n工時：12 做二休二\\n\\n\\n2.\\n公司：緯創\\n職級：廠務工程師\\n地點：竹科\\n薪水：(N+5k)*14 分紅2~4\\n加班費：有\\n工時：常日班 因應產線擴編 之後可能需要輪班\\n\\n力積電相對來說較穩定 只是沒有賺錢分紅較低  也比較沒有向上發展的機會\\n\\n緯創目前再擴廠 工作內容較為繁雜  但分紅比較高  也有比較多發展及學習空間\\n\\n想請版上大大給予相關意見  感謝\\n\\n感謝各位前輩回覆提供意見\\n\\n--\\n'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. 儲存到 Google Drive"
      ],
      "metadata": {
        "id": "f-WYU_n0WAeS"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M2m6al0Xcm2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GUBFNXhZt20",
        "outputId": "30b91b1b-174a-4338-f78e-6864fca7c674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ptt_df.to_csv('/content/gdrive/My Drive/Colab Notebooks/Course/ppt.csv')"
      ],
      "metadata": {
        "id": "8PKrTVRNaHaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "# 第二部分 API 撈取資料\n",
        "# 1. 準備參數\n"
      ],
      "metadata": {
        "id": "HvPxgHIo-05n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# URL\n",
        "stock_query_url = 'https://www.twse.com.tw/rwd/zh/afterTrading/STOCK_DAY?date={}&stockNo={}&response={}&_={}'\n",
        "\n",
        "#查詢參數\n",
        "query_date = '20240816'\n",
        "stockNo = '2330'\n",
        "response_ = 'json'\n",
        "timestamp_ = '1723772411167'\n",
        "\n",
        "# Requests 參數\n",
        "headers = {\n",
        "    \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36\",\n",
        "}\n",
        "\n",
        "# 合併參數與URL\n",
        "stock_param_url = stock_query_url.format(query_date, stockNo, response_, timestamp_)\n",
        "\n",
        "print(stock_param_url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2jVchO7_Ouf",
        "outputId": "a6a991cf-3b62-42e9-da9b-300265813a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://www.twse.com.tw/rwd/zh/afterTrading/STOCK_DAY?date=20240816&stockNo=2330&response=json&_=1723772411167\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. 用 Requests 取得API回應"
      ],
      "metadata": {
        "id": "Y3SfL9kHB0kI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "res = requests.get(stock_param_url, verify=False)\n",
        "res.text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "44fch6QrBzz4",
        "outputId": "966554d1-e528-4057-c791-990c9e204d25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'{\"stat\":\"OK\",\"date\":\"20240816\",\"title\":\"113年08月 2330 台積電           各日成交資訊\",\"fields\":[\"日期\",\"成交股數\",\"成交金額\",\"開盤價\",\"最高價\",\"最低價\",\"收盤價\",\"漲跌價差\",\"成交筆數\"],\"data\":[[\"113/08/01\",\"50,257,642\",\"48,211,390,158\",\"969.00\",\"969.00\",\"948.00\",\"960.00\",\"+26.00\",\"66,845\"],[\"113/08/02\",\"89,044,852\",\"81,488,499,047\",\"918.00\",\"928.00\",\"903.00\",\"903.00\",\"-57.00\",\"451,562\"],[\"113/08/05\",\"159,662,795\",\"133,482,646,539\",\"852.00\",\"857.00\",\"813.00\",\"815.00\",\"-88.00\",\"588,373\"],[\"113/08/06\",\"108,521,787\",\"94,271,532,567\",\"868.00\",\"895.00\",\"840.00\",\"880.00\",\"+65.00\",\"189,677\"],[\"113/08/07\",\"62,643,163\",\"56,900,026,373\",\"901.00\",\"920.00\",\"893.00\",\"920.00\",\"+40.00\",\"109,333\"],[\"113/08/08\",\"41,288,222\",\"37,045,507,217\",\"901.00\",\"905.00\",\"887.00\",\"896.00\",\"-24.00\",\"107,303\"],[\"113/08/09\",\"54,464,183\",\"50,586,946,042\",\"927.00\",\"934.00\",\"922.00\",\"934.00\",\"+38.00\",\"94,661\"],[\"113/08/12\",\"40,968,742\",\"38,572,439,136\",\"942.00\",\"948.00\",\"931.00\",\"940.00\",\"+6.00\",\"73,731\"],[\"113/08/13\",\"26,831,364\",\"25,227,760,336\",\"945.00\",\"945.00\",\"937.00\",\"941.00\",\"+1.00\",\"39,633\"],[\"113/08/14\",\"40,527,779\",\"38,507,151,446\",\"951.00\",\"955.00\",\"945.00\",\"948.00\",\"+7.00\",\"56,190\"],[\"113/08/15\",\"21,714,163\",\"20,528,538,286\",\"945.00\",\"950.00\",\"941.00\",\"943.00\",\"-5.00\",\"42,553\"]],\"notes\":[\"符號說明:+/-/X表示漲/跌/不比價\",\"當日統計資訊含一般、零股、盤後定價、鉅額交易，不含拍賣、標購。\",\"ETF證券代號第六碼為K、M、S、C者，表示該ETF以外幣交易。\",\"權證證券代號可重複使用，權證顯示之名稱係目前存續權證之簡稱。\"],\"total\":11}'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3 . 用 JSON 格式化輸出"
      ],
      "metadata": {
        "id": "IAfOgbhYC3VA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas\n",
        "stock_data = json.loads(res.text)\n",
        "\n",
        "print(json.dumps(stock_data, sort_keys=False, indent=4, separators=(',', ':'), ensure_ascii=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVIZ1RZbDHQQ",
        "outputId": "f594d1e6-9a56-410d-ea81-ad627d7f5911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "    \"stat\":\"OK\",\n",
            "    \"date\":\"20240816\",\n",
            "    \"title\":\"113年08月 2330 台積電           各日成交資訊\",\n",
            "    \"fields\":[\n",
            "        \"日期\",\n",
            "        \"成交股數\",\n",
            "        \"成交金額\",\n",
            "        \"開盤價\",\n",
            "        \"最高價\",\n",
            "        \"最低價\",\n",
            "        \"收盤價\",\n",
            "        \"漲跌價差\",\n",
            "        \"成交筆數\"\n",
            "    ],\n",
            "    \"data\":[\n",
            "        [\n",
            "            \"113/08/01\",\n",
            "            \"50,257,642\",\n",
            "            \"48,211,390,158\",\n",
            "            \"969.00\",\n",
            "            \"969.00\",\n",
            "            \"948.00\",\n",
            "            \"960.00\",\n",
            "            \"+26.00\",\n",
            "            \"66,845\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/02\",\n",
            "            \"89,044,852\",\n",
            "            \"81,488,499,047\",\n",
            "            \"918.00\",\n",
            "            \"928.00\",\n",
            "            \"903.00\",\n",
            "            \"903.00\",\n",
            "            \"-57.00\",\n",
            "            \"451,562\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/05\",\n",
            "            \"159,662,795\",\n",
            "            \"133,482,646,539\",\n",
            "            \"852.00\",\n",
            "            \"857.00\",\n",
            "            \"813.00\",\n",
            "            \"815.00\",\n",
            "            \"-88.00\",\n",
            "            \"588,373\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/06\",\n",
            "            \"108,521,787\",\n",
            "            \"94,271,532,567\",\n",
            "            \"868.00\",\n",
            "            \"895.00\",\n",
            "            \"840.00\",\n",
            "            \"880.00\",\n",
            "            \"+65.00\",\n",
            "            \"189,677\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/07\",\n",
            "            \"62,643,163\",\n",
            "            \"56,900,026,373\",\n",
            "            \"901.00\",\n",
            "            \"920.00\",\n",
            "            \"893.00\",\n",
            "            \"920.00\",\n",
            "            \"+40.00\",\n",
            "            \"109,333\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/08\",\n",
            "            \"41,288,222\",\n",
            "            \"37,045,507,217\",\n",
            "            \"901.00\",\n",
            "            \"905.00\",\n",
            "            \"887.00\",\n",
            "            \"896.00\",\n",
            "            \"-24.00\",\n",
            "            \"107,303\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/09\",\n",
            "            \"54,464,183\",\n",
            "            \"50,586,946,042\",\n",
            "            \"927.00\",\n",
            "            \"934.00\",\n",
            "            \"922.00\",\n",
            "            \"934.00\",\n",
            "            \"+38.00\",\n",
            "            \"94,661\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/12\",\n",
            "            \"40,968,742\",\n",
            "            \"38,572,439,136\",\n",
            "            \"942.00\",\n",
            "            \"948.00\",\n",
            "            \"931.00\",\n",
            "            \"940.00\",\n",
            "            \"+6.00\",\n",
            "            \"73,731\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/13\",\n",
            "            \"26,831,364\",\n",
            "            \"25,227,760,336\",\n",
            "            \"945.00\",\n",
            "            \"945.00\",\n",
            "            \"937.00\",\n",
            "            \"941.00\",\n",
            "            \"+1.00\",\n",
            "            \"39,633\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/14\",\n",
            "            \"40,527,779\",\n",
            "            \"38,507,151,446\",\n",
            "            \"951.00\",\n",
            "            \"955.00\",\n",
            "            \"945.00\",\n",
            "            \"948.00\",\n",
            "            \"+7.00\",\n",
            "            \"56,190\"\n",
            "        ],\n",
            "        [\n",
            "            \"113/08/15\",\n",
            "            \"21,714,163\",\n",
            "            \"20,528,538,286\",\n",
            "            \"945.00\",\n",
            "            \"950.00\",\n",
            "            \"941.00\",\n",
            "            \"943.00\",\n",
            "            \"-5.00\",\n",
            "            \"42,553\"\n",
            "        ]\n",
            "    ],\n",
            "    \"notes\":[\n",
            "        \"符號說明:+/-/X表示漲/跌/不比價\",\n",
            "        \"當日統計資訊含一般、零股、盤後定價、鉅額交易，不含拍賣、標購。\",\n",
            "        \"ETF證券代號第六碼為K、M、S、C者，表示該ETF以外幣交易。\",\n",
            "        \"權證證券代號可重複使用，權證顯示之名稱係目前存續權證之簡稱。\"\n",
            "    ],\n",
            "    \"total\":11\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. 用 Pasdas 載入資料與欄位名稱"
      ],
      "metadata": {
        "id": "SWJA1ZQHGAzK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas\n",
        "stock_df = pandas.DataFrame(stock_data['data'], columns=stock_data['fields'])\n",
        "stock_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "tlpF44bTFPCh",
        "outputId": "4a12c273-530d-4058-d2b8-800dcce6e6f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           日期         成交股數             成交金額     開盤價     最高價     最低價     收盤價  \\\n",
              "0   113/08/01   50,257,642   48,211,390,158  969.00  969.00  948.00  960.00   \n",
              "1   113/08/02   89,044,852   81,488,499,047  918.00  928.00  903.00  903.00   \n",
              "2   113/08/05  159,662,795  133,482,646,539  852.00  857.00  813.00  815.00   \n",
              "3   113/08/06  108,521,787   94,271,532,567  868.00  895.00  840.00  880.00   \n",
              "4   113/08/07   62,643,163   56,900,026,373  901.00  920.00  893.00  920.00   \n",
              "5   113/08/08   41,288,222   37,045,507,217  901.00  905.00  887.00  896.00   \n",
              "6   113/08/09   54,464,183   50,586,946,042  927.00  934.00  922.00  934.00   \n",
              "7   113/08/12   40,968,742   38,572,439,136  942.00  948.00  931.00  940.00   \n",
              "8   113/08/13   26,831,364   25,227,760,336  945.00  945.00  937.00  941.00   \n",
              "9   113/08/14   40,527,779   38,507,151,446  951.00  955.00  945.00  948.00   \n",
              "10  113/08/15   21,714,163   20,528,538,286  945.00  950.00  941.00  943.00   \n",
              "\n",
              "      漲跌價差     成交筆數  \n",
              "0   +26.00   66,845  \n",
              "1   -57.00  451,562  \n",
              "2   -88.00  588,373  \n",
              "3   +65.00  189,677  \n",
              "4   +40.00  109,333  \n",
              "5   -24.00  107,303  \n",
              "6   +38.00   94,661  \n",
              "7    +6.00   73,731  \n",
              "8    +1.00   39,633  \n",
              "9    +7.00   56,190  \n",
              "10   -5.00   42,553  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f621966-d7dd-4091-849e-3967c0a9c7a7\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>日期</th>\n",
              "      <th>成交股數</th>\n",
              "      <th>成交金額</th>\n",
              "      <th>開盤價</th>\n",
              "      <th>最高價</th>\n",
              "      <th>最低價</th>\n",
              "      <th>收盤價</th>\n",
              "      <th>漲跌價差</th>\n",
              "      <th>成交筆數</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>113/08/01</td>\n",
              "      <td>50,257,642</td>\n",
              "      <td>48,211,390,158</td>\n",
              "      <td>969.00</td>\n",
              "      <td>969.00</td>\n",
              "      <td>948.00</td>\n",
              "      <td>960.00</td>\n",
              "      <td>+26.00</td>\n",
              "      <td>66,845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>113/08/02</td>\n",
              "      <td>89,044,852</td>\n",
              "      <td>81,488,499,047</td>\n",
              "      <td>918.00</td>\n",
              "      <td>928.00</td>\n",
              "      <td>903.00</td>\n",
              "      <td>903.00</td>\n",
              "      <td>-57.00</td>\n",
              "      <td>451,562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>113/08/05</td>\n",
              "      <td>159,662,795</td>\n",
              "      <td>133,482,646,539</td>\n",
              "      <td>852.00</td>\n",
              "      <td>857.00</td>\n",
              "      <td>813.00</td>\n",
              "      <td>815.00</td>\n",
              "      <td>-88.00</td>\n",
              "      <td>588,373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>113/08/06</td>\n",
              "      <td>108,521,787</td>\n",
              "      <td>94,271,532,567</td>\n",
              "      <td>868.00</td>\n",
              "      <td>895.00</td>\n",
              "      <td>840.00</td>\n",
              "      <td>880.00</td>\n",
              "      <td>+65.00</td>\n",
              "      <td>189,677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>113/08/07</td>\n",
              "      <td>62,643,163</td>\n",
              "      <td>56,900,026,373</td>\n",
              "      <td>901.00</td>\n",
              "      <td>920.00</td>\n",
              "      <td>893.00</td>\n",
              "      <td>920.00</td>\n",
              "      <td>+40.00</td>\n",
              "      <td>109,333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>113/08/08</td>\n",
              "      <td>41,288,222</td>\n",
              "      <td>37,045,507,217</td>\n",
              "      <td>901.00</td>\n",
              "      <td>905.00</td>\n",
              "      <td>887.00</td>\n",
              "      <td>896.00</td>\n",
              "      <td>-24.00</td>\n",
              "      <td>107,303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>113/08/09</td>\n",
              "      <td>54,464,183</td>\n",
              "      <td>50,586,946,042</td>\n",
              "      <td>927.00</td>\n",
              "      <td>934.00</td>\n",
              "      <td>922.00</td>\n",
              "      <td>934.00</td>\n",
              "      <td>+38.00</td>\n",
              "      <td>94,661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>113/08/12</td>\n",
              "      <td>40,968,742</td>\n",
              "      <td>38,572,439,136</td>\n",
              "      <td>942.00</td>\n",
              "      <td>948.00</td>\n",
              "      <td>931.00</td>\n",
              "      <td>940.00</td>\n",
              "      <td>+6.00</td>\n",
              "      <td>73,731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>113/08/13</td>\n",
              "      <td>26,831,364</td>\n",
              "      <td>25,227,760,336</td>\n",
              "      <td>945.00</td>\n",
              "      <td>945.00</td>\n",
              "      <td>937.00</td>\n",
              "      <td>941.00</td>\n",
              "      <td>+1.00</td>\n",
              "      <td>39,633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>113/08/14</td>\n",
              "      <td>40,527,779</td>\n",
              "      <td>38,507,151,446</td>\n",
              "      <td>951.00</td>\n",
              "      <td>955.00</td>\n",
              "      <td>945.00</td>\n",
              "      <td>948.00</td>\n",
              "      <td>+7.00</td>\n",
              "      <td>56,190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>113/08/15</td>\n",
              "      <td>21,714,163</td>\n",
              "      <td>20,528,538,286</td>\n",
              "      <td>945.00</td>\n",
              "      <td>950.00</td>\n",
              "      <td>941.00</td>\n",
              "      <td>943.00</td>\n",
              "      <td>-5.00</td>\n",
              "      <td>42,553</td>\n",
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              "type": "dataframe",
              "variable_name": "stock_df",
              "summary": "{\n  \"name\": \"stock_df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"\\u65e5\\u671f\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"113/08/08\",\n          \"113/08/01\",\n          \"113/08/14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6210\\u4ea4\\u80a1\\u6578\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"41,288,222\",\n          \"50,257,642\",\n          \"40,527,779\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6210\\u4ea4\\u91d1\\u984d\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"37,045,507,217\",\n          \"48,211,390,158\",\n          \"38,507,151,446\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u958b\\u76e4\\u50f9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"945.00\",\n          \"918.00\",\n          \"927.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6700\\u9ad8\\u50f9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"905.00\",\n          \"969.00\",\n          \"955.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6700\\u4f4e\\u50f9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"887.00\",\n          \"948.00\",\n          \"945.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6536\\u76e4\\u50f9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"896.00\",\n          \"960.00\",\n          \"948.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6f32\\u8dcc\\u50f9\\u5dee\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"-24.00\",\n          \"+26.00\",\n          \"+7.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u6210\\u4ea4\\u7b46\\u6578\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"107,303\",\n          \"66,845\",\n          \"56,190\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算平均值\n",
        "pandas.to_numeric(stock_df['開盤價']).mean()\n",
        "\n",
        "# 計算平均值: 資料帶有格式化\n",
        "pandas.to_numeric(stock_df['成交股數'].str.replace(',','').astype(int)).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rJ_5eOKD_n5",
        "outputId": "b1b86d24-847a-4ee2-9625-1a67b2ffe910"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "919.9090909090909"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. 機器學習實現案例 (以 LinearRegression 為例)"
      ],
      "metadata": {
        "id": "9EJ8qwJ9Qf9n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 安裝套件\n",
        "!pip install scikit-learn\n",
        "\n",
        "# 參數保存套件\n",
        "!pip install joblib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USKkQiGFQee2",
        "outputId": "97cf79ce-2ddb-42be-f72e-473f729fd29d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (1.4.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 建立訓練資料 (x)\n",
        "x = stock_df.iloc[:, 3].values\n",
        "\n",
        "x = stock_df.iloc[:, 3].drop(10, inplace=False, axis=0).values\n",
        "\n",
        "x = stock_df.iloc[:, 3].drop(10, inplace=False, axis=0).values.reshape(-1, 1)\n",
        "\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5Q1Gmc5RR4n",
        "outputId": "5d1a8740-c2fd-4057-a9e6-07ebd30cd2f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['969.00'],\n",
              "       ['918.00'],\n",
              "       ['852.00'],\n",
              "       ['868.00'],\n",
              "       ['901.00'],\n",
              "       ['901.00'],\n",
              "       ['927.00'],\n",
              "       ['942.00'],\n",
              "       ['945.00'],\n",
              "       ['951.00']], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 建立Label (Y)\n",
        "y = stock_df.iloc[:,6].values\n",
        "\n",
        "y=stock_df.iloc[:, 6].drop(0, inplace=False, axis=0).values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n94MnDjaS1xd",
        "outputId": "90467b94-2bfc-4ac1-d381-1ec92e6ace77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['903.00', '815.00', '880.00', '920.00', '896.00', '934.00',\n",
              "       '940.00', '941.00', '948.00', '943.00'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 導入 sklearn 的線性回歸模型物件\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# 初始化 LR Model\n",
        "lr = LinearRegression()\n",
        "\n",
        "# 用資料學習模型\n",
        "lr.fit(x,y)\n",
        "\n",
        "# ----\n",
        "\n",
        "# 測試資料建立\n",
        "x_test = [\n",
        "    [500],\n",
        "    [100],\n",
        "    [200]\n",
        "]\n",
        "\n",
        "# 用學習完的模型進行預測\n",
        "y_test = lr.predict(x_test)\n",
        "\n",
        "# 印出預測結果\n",
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdykCe8LTz7I",
        "outputId": "dd645806-b945-4f1f-d467-1618baa11e3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([761.83012938, 620.69303294, 655.97730705])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. 模型保存與使用"
      ],
      "metadata": {
        "id": "ppNHA_8wng7R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 保存模型 (方法一)\n",
        "from google.colab import drive\n",
        "import joblib\n",
        "\n",
        "# 掛載雲端硬碟\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# 將整個物件保存成檔案\n",
        "joblib.dump(lr, '/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7_pAtPYnocG",
        "outputId": "41aef8ba-82b5-4374-9fda-aa2fb78ccd11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816']"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 保存模型 (方法二)\n",
        "import pickle\n",
        "\n",
        "filename = '/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl'\n",
        "pickle.dump(lr, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "Q0vm3Ky8obVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from time import time\n",
        "import pickle\n",
        "import _pickle as cPickle\n",
        "import joblib\n",
        "import os\n",
        "\n",
        "print(\"Joblib 載入 Pickle 檔案:\")\n",
        "t1 = time()\n",
        "lr_model_lib = joblib.load('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl')\n",
        "print(\"Joblib module 載入時間:\", time() - t1)\n",
        "print(\"Joblib module file size\", os.path.getsize('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl'), \"kB \\n\")\n",
        "\n",
        "print(\"Joblib 載入 Joblib 檔案:\")\n",
        "t1 = time()\n",
        "lr_model_lib = joblib.load('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816')\n",
        "print(\"Joblib module 載入時間:\", time() - t1)\n",
        "print(\"Joblib module file size\", os.path.getsize('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816'), \"kB \\n\")\n",
        "\n",
        "print(\"==================================\\n\")\n",
        "\n",
        "# print(\"Pickle 載入 Joblib 檔案:\\n\")\n",
        "# t1 = time()\n",
        "# lr_model_pkl =  pickle.load(open('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816', 'rb'))\n",
        "# print(\"Pickle module 載入時間:\", time() - t1)\n",
        "# print(\"Pickle module file size\", os.path.getsize('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816'), \"kB \\n\")\n",
        "\n",
        "print(\"Pickle 載入 Pickle 檔案:\")\n",
        "t1 = time()\n",
        "lr_model_pkl =  pickle.load(open('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl', 'rb'))\n",
        "print(\"Pickle module 載入時間:\", time() - t1)\n",
        "print(\"Pickle module file size\", os.path.getsize('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl'), \"kB \\n\")\n",
        "print(\"==================================\\n\")\n",
        "\n",
        "# print(\"cPickle 載入 Joblib 檔案:\\n\")\n",
        "# t1 = time()\n",
        "# lr_model_pkl =  cPickle.load(open('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816', 'rb'))\n",
        "# print(\"Pickle module 載入時間:\", time() - t1)\n",
        "# print(\"Pickle module file size\", os.path.getsize('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816'), \"kB \\n\")\n",
        "# print(time() - t1)\n",
        "\n",
        "print(\"cPickle 載入 Pickle 檔案:\")\n",
        "t1 = time()\n",
        "lr_model_pkl =  cPickle.load(open('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl', 'rb'))\n",
        "print(\"cPickle module 載入時間:\", time() - t1)\n",
        "print(\"cPickle module file size\", os.path.getsize('/content/gdrive/My Drive/Colab Notebooks/Course/lr_model2024816.pkl'), \"kB \\n\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGmftrDrvvRI",
        "outputId": "c8725846-1f29-432e-990b-ea3414cc2bca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Joblib 載入 Pickle 檔案:\n",
            "Joblib module 載入時間: 0.008245229721069336\n",
            "Joblib module file size 419 kB \n",
            "\n",
            "Joblib 載入 Joblib 檔案:\n",
            "Joblib module 載入時間: 0.00749516487121582\n",
            "Joblib module file size 560 kB \n",
            "\n",
            "==================================\n",
            "\n",
            "Pickle 載入 Pickle 檔案:\n",
            "Pickle module 載入時間: 0.005443572998046875\n",
            "Pickle module file size 419 kB \n",
            "\n",
            "==================================\n",
            "\n",
            "cPickle 載入 Pickle 檔案:\n",
            "cPickle module 載入時間: 0.01509714126586914\n",
            "cPickle module file size 419 kB \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Line Notify"
      ],
      "metadata": {
        "id": "abv6w91G4ol-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import time\n",
        "import json\n",
        "\n",
        "# 定義參數\n",
        "line_notify_url = \"https://notify-api.line.me/api/notify\"\n",
        "line_notify_token = \"YOUR TOKEN\"\n",
        "\n",
        "# Request Header => Postman 的 Authorization\n",
        "line_notify_header = {\n",
        "    'Authorization': 'Bearer {}'.format(line_notify_token)\n",
        "}\n",
        "\n",
        "# Request Body => Postman 的 Params\n",
        "# 這邊可以自訂義你的傳輸訊息或訊息樣板\n",
        "line_notify_body = {\n",
        "    'message':  \"傳輸訊息\"\n",
        "}\n",
        "\n",
        "res = requests.post(line_notify_url, headers=line_notify_header, data = line_notify_body)\n",
        "res_msg =  json.loads(res.text)\n",
        "print(\"[{}] {}\".format(res_msg['status'], res_msg['message']))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnCC_NcK25nD",
        "outputId": "b4a608ee-83c2-405e-9084-0b38d5236d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[200] ok\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e4ew3kxEOyIj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}