{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 疑問\n",
    "- 時間區間\n",
    "    - 每小時\n",
    "- 這些都是英文字，那地區設定要為台灣嗎?\n",
    "    - 對"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:20:54.578022Z",
     "start_time": "2021-07-11T18:20:52.518024Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:20:55.746294Z",
     "start_time": "2021-07-11T18:20:54.584029Z"
    }
   },
   "outputs": [],
   "source": [
    "import dataset\n",
    "import datetime\n",
    "import time\n",
    "# import the TrendReq method from the pytrends request module\n",
    "from pytrends.request import TrendReq\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:20:57.673496Z",
     "start_time": "2021-07-11T18:20:56.408287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>from_mid</th>\n",
       "      <th>from_title</th>\n",
       "      <th>title</th>\n",
       "      <th>mid</th>\n",
       "      <th>ttype</th>\n",
       "      <th>dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>/m/02wbm</td>\n",
       "      <td>food</td>\n",
       "      <td>Eating</td>\n",
       "      <td>/m/01f5gx</td>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-06 22:31:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>/m/02wbm</td>\n",
       "      <td>food</td>\n",
       "      <td>Chinese cuisine</td>\n",
       "      <td>/m/01xw9</td>\n",
       "      <td>Cuisine</td>\n",
       "      <td>2021-07-06 22:31:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>/m/02wbm</td>\n",
       "      <td>food</td>\n",
       "      <td>Dog food</td>\n",
       "      <td>/m/01jbnd</td>\n",
       "      <td>Food</td>\n",
       "      <td>2021-07-06 22:31:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>/m/02wbm</td>\n",
       "      <td>food</td>\n",
       "      <td>Food truck</td>\n",
       "      <td>/m/04s_6n</td>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-06 22:31:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>/m/02wbm</td>\n",
       "      <td>food</td>\n",
       "      <td>Truck</td>\n",
       "      <td>/m/07r04</td>\n",
       "      <td>Body style</td>\n",
       "      <td>2021-07-06 22:31:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>341</td>\n",
       "      <td>/m/02zkwn</td>\n",
       "      <td>Diet</td>\n",
       "      <td>ketosis</td>\n",
       "      <td>/g/11bc59565f</td>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-07 09:13:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>342</td>\n",
       "      <td>/m/02zkwn</td>\n",
       "      <td>Diet</td>\n",
       "      <td>Dietary fiber</td>\n",
       "      <td>/m/0hkwr</td>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-07 09:13:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>343</td>\n",
       "      <td>/m/02zkwn</td>\n",
       "      <td>Diet</td>\n",
       "      <td>Protein</td>\n",
       "      <td>/m/05wvs</td>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-07 09:13:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>344</td>\n",
       "      <td>/m/02zkwn</td>\n",
       "      <td>Diet</td>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>/m/01w_3</td>\n",
       "      <td>Chemical compound</td>\n",
       "      <td>2021-07-07 09:13:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>345</td>\n",
       "      <td>/m/02zkwn</td>\n",
       "      <td>Diet</td>\n",
       "      <td>Muscle</td>\n",
       "      <td>/m/04_fs</td>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-07 09:13:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   from_mid from_title            title            mid  \\\n",
       "0      1   /m/02wbm       food           Eating      /m/01f5gx   \n",
       "1      2   /m/02wbm       food  Chinese cuisine       /m/01xw9   \n",
       "2      3   /m/02wbm       food         Dog food      /m/01jbnd   \n",
       "3      4   /m/02wbm       food       Food truck      /m/04s_6n   \n",
       "4      5   /m/02wbm       food            Truck       /m/07r04   \n",
       "..   ...        ...        ...              ...            ...   \n",
       "315  341  /m/02zkwn       Diet          ketosis  /g/11bc59565f   \n",
       "316  342  /m/02zkwn       Diet    Dietary fiber       /m/0hkwr   \n",
       "317  343  /m/02zkwn       Diet          Protein       /m/05wvs   \n",
       "318  344  /m/02zkwn       Diet      Cholesterol       /m/01w_3   \n",
       "319  345  /m/02zkwn       Diet           Muscle       /m/04_fs   \n",
       "\n",
       "                 ttype                  dt  \n",
       "0                Topic 2021-07-06 22:31:16  \n",
       "1              Cuisine 2021-07-06 22:31:18  \n",
       "2                 Food 2021-07-06 22:31:18  \n",
       "3                Topic 2021-07-06 22:31:18  \n",
       "4           Body style 2021-07-06 22:31:18  \n",
       "..                 ...                 ...  \n",
       "315              Topic 2021-07-07 09:13:01  \n",
       "316              Topic 2021-07-07 09:13:01  \n",
       "317              Topic 2021-07-07 09:13:01  \n",
       "318  Chemical compound 2021-07-07 09:13:01  \n",
       "319              Topic 2021-07-07 09:13:01  \n",
       "\n",
       "[320 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_data(tabel_name='hhh_weekly_keywords'):\n",
    "    \"\"\"\n",
    "        從DB取得資料轉換成df。\n",
    "    \"\"\"\n",
    "    db = dataset.connect('mysql://choozmo:pAssw0rd@db.ptt.cx:3306/cmm_test?charset=utf8mb4')\n",
    "    table = db[tabel_name]\n",
    "    return pd.DataFrame(table)\n",
    "\n",
    "df = get_data('topic_tree')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:20:57.689018Z",
     "start_time": "2021-07-11T18:20:57.676502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Topic', 'Cuisine', 'Food', 'Body style', 'Commerce', 'People',\n",
       "       'Nutrition', 'Illness', 'Law', 'Art', 'Company', 'Website',\n",
       "       'Furniture retail company', 'Product line', 'Furniture',\n",
       "       'Retail chain company', 'Chemical element', 'Chemical series',\n",
       "       'Organization type', 'Software type', 'Marketing',\n",
       "       'Software grouping', 'System software', 'Media', 'Plan',\n",
       "       'Computing', 'Protocol', 'Field of study', 'Metadata',\n",
       "       'Application programming interface', 'Programming language',\n",
       "       'Mental disorder', 'Dish', 'Plant', 'Medical condition', 'Fruit',\n",
       "       'Restaurant', 'Poultry', 'Type of dish', 'Table sauce', 'Noodle',\n",
       "       'Japanese noodles', 'Cooking utensil', 'Meat', 'Cooking technique',\n",
       "       'Fast food restaurant company', 'Animal', 'Pet food company',\n",
       "       'Occupation', 'Subsidiary', 'Superstore company', 'Disease',\n",
       "       'E-commerce company', 'Film company role or service',\n",
       "       'Cooking method', 'Vehicle', 'City in Oregon',\n",
       "       'City in Pennsylvania', 'City in New York State',\n",
       "       'City in Florida', 'Korean restaurant in Los Angeles, California',\n",
       "       'Automaker company', 'Automobile make', 'Automobile company',\n",
       "       'Advertising company', 'Country in North America', 'Dairy product',\n",
       "       'Confection', 'Unit of mass', 'Online game', 'Restaurant company',\n",
       "       'Corporation', 'Mail company', 'Online food ordering company',\n",
       "       'Date', 'Retail company', 'Supermarket company',\n",
       "       'Postal service company', 'Fashion company', 'Currency',\n",
       "       'Football team', 'Football league', 'Armed conflict', 'Sport',\n",
       "       'Football competition', 'Footballer', 'Disorder', 'Nutrient',\n",
       "       'Pill', 'Table condiment', 'Chemical compound'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要抓的google trend標題\n",
    "\n",
    "to_titles = df['ttype'].unique()\n",
    "to_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:21:40.253769Z",
     "start_time": "2021-07-11T18:21:37.999141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>isPartial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-10 14:00:00</th>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-10 15:00:00</th>\n",
       "      <td>17</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-10 16:00:00</th>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-10 17:00:00</th>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-10 18:00:00</th>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 09:00:00</th>\n",
       "      <td>34</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 10:00:00</th>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 11:00:00</th>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 12:00:00</th>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 13:00:00</th>\n",
       "      <td>27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Topic isPartial\n",
       "date                                \n",
       "2021-07-10 14:00:00     27     False\n",
       "2021-07-10 15:00:00     17     False\n",
       "2021-07-10 16:00:00     38     False\n",
       "2021-07-10 17:00:00     15     False\n",
       "2021-07-10 18:00:00     11     False\n",
       "...                    ...       ...\n",
       "2021-07-17 09:00:00     34     False\n",
       "2021-07-17 10:00:00     26     False\n",
       "2021-07-17 11:00:00     15     False\n",
       "2021-07-17 12:00:00     23     False\n",
       "2021-07-17 13:00:00     27      True\n",
       "\n",
       "[168 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 試著爬取2020-現在的資料科學/機器學習/深度學習資料\n",
    "\n",
    "# 建立物件\n",
    "pytrend = TrendReq()\n",
    "\n",
    "# 搜尋關鍵字\n",
    "keywords = to_titles[0:1]\n",
    "\n",
    "# payload\n",
    "pytrend.build_payload(\n",
    "    kw_list=keywords,\n",
    "    cat=0,\n",
    "#     timeframe='2020-01-01 2021-05-26',\n",
    "    timeframe='now 7-d',\n",
    "    geo='TW',\n",
    "    gprop=''\n",
    ")\n",
    "\n",
    "# 隨著時間搜尋\n",
    "to_topics_interest_over_time = pytrend.interest_over_time()\n",
    "to_topics_interest_over_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:24:40.443622Z",
     "start_time": "2021-07-11T18:24:40.429627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-07-10 14:00:00'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(to_topics_interest_over_time.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:25:17.152721Z",
     "start_time": "2021-07-11T18:25:17.143720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 7, 10, 14, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime.strptime(str(to_topics_interest_over_time.index[0]), \"%Y-%m-%d %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:26:34.467056Z",
     "start_time": "2021-07-11T18:26:34.433542Z"
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    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       datetime.datetime(2021, 7, 10, 15, 0),\n",
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       "       datetime.datetime(2021, 7, 10, 22, 0),\n",
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       "       datetime.datetime(2021, 7, 11, 1, 0),\n",
       "       datetime.datetime(2021, 7, 11, 2, 0),\n",
       "       datetime.datetime(2021, 7, 11, 3, 0),\n",
       "       datetime.datetime(2021, 7, 11, 4, 0),\n",
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       "       datetime.datetime(2021, 7, 11, 6, 0),\n",
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       "       datetime.datetime(2021, 7, 11, 9, 0),\n",
       "       datetime.datetime(2021, 7, 11, 10, 0),\n",
       "       datetime.datetime(2021, 7, 11, 11, 0),\n",
       "       datetime.datetime(2021, 7, 11, 12, 0),\n",
       "       datetime.datetime(2021, 7, 11, 13, 0),\n",
       "       datetime.datetime(2021, 7, 11, 14, 0),\n",
       "       datetime.datetime(2021, 7, 11, 15, 0),\n",
       "       datetime.datetime(2021, 7, 11, 16, 0),\n",
       "       datetime.datetime(2021, 7, 11, 17, 0),\n",
       "       datetime.datetime(2021, 7, 11, 18, 0),\n",
       "       datetime.datetime(2021, 7, 11, 19, 0),\n",
       "       datetime.datetime(2021, 7, 11, 20, 0),\n",
       "       datetime.datetime(2021, 7, 11, 21, 0),\n",
       "       datetime.datetime(2021, 7, 11, 22, 0),\n",
       "       datetime.datetime(2021, 7, 11, 23, 0),\n",
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       "       datetime.datetime(2021, 7, 12, 1, 0),\n",
       "       datetime.datetime(2021, 7, 12, 2, 0),\n",
       "       datetime.datetime(2021, 7, 12, 3, 0),\n",
       "       datetime.datetime(2021, 7, 12, 4, 0),\n",
       "       datetime.datetime(2021, 7, 12, 5, 0),\n",
       "       datetime.datetime(2021, 7, 12, 6, 0),\n",
       "       datetime.datetime(2021, 7, 12, 7, 0),\n",
       "       datetime.datetime(2021, 7, 12, 8, 0),\n",
       "       datetime.datetime(2021, 7, 12, 9, 0),\n",
       "       datetime.datetime(2021, 7, 12, 10, 0),\n",
       "       datetime.datetime(2021, 7, 12, 11, 0),\n",
       "       datetime.datetime(2021, 7, 12, 12, 0),\n",
       "       datetime.datetime(2021, 7, 12, 13, 0),\n",
       "       datetime.datetime(2021, 7, 12, 14, 0),\n",
       "       datetime.datetime(2021, 7, 12, 15, 0),\n",
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       "       datetime.datetime(2021, 7, 12, 17, 0),\n",
       "       datetime.datetime(2021, 7, 12, 18, 0),\n",
       "       datetime.datetime(2021, 7, 12, 19, 0),\n",
       "       datetime.datetime(2021, 7, 12, 20, 0),\n",
       "       datetime.datetime(2021, 7, 12, 21, 0),\n",
       "       datetime.datetime(2021, 7, 12, 22, 0),\n",
       "       datetime.datetime(2021, 7, 12, 23, 0),\n",
       "       datetime.datetime(2021, 7, 13, 0, 0),\n",
       "       datetime.datetime(2021, 7, 13, 1, 0),\n",
       "       datetime.datetime(2021, 7, 13, 2, 0),\n",
       "       datetime.datetime(2021, 7, 13, 3, 0),\n",
       "       datetime.datetime(2021, 7, 13, 4, 0),\n",
       "       datetime.datetime(2021, 7, 13, 5, 0),\n",
       "       datetime.datetime(2021, 7, 13, 6, 0),\n",
       "       datetime.datetime(2021, 7, 13, 7, 0),\n",
       "       datetime.datetime(2021, 7, 13, 8, 0),\n",
       "       datetime.datetime(2021, 7, 13, 9, 0),\n",
       "       datetime.datetime(2021, 7, 13, 10, 0),\n",
       "       datetime.datetime(2021, 7, 13, 11, 0),\n",
       "       datetime.datetime(2021, 7, 13, 12, 0),\n",
       "       datetime.datetime(2021, 7, 13, 13, 0),\n",
       "       datetime.datetime(2021, 7, 13, 14, 0),\n",
       "       datetime.datetime(2021, 7, 13, 15, 0),\n",
       "       datetime.datetime(2021, 7, 13, 16, 0),\n",
       "       datetime.datetime(2021, 7, 13, 17, 0),\n",
       "       datetime.datetime(2021, 7, 13, 18, 0),\n",
       "       datetime.datetime(2021, 7, 13, 19, 0),\n",
       "       datetime.datetime(2021, 7, 13, 20, 0),\n",
       "       datetime.datetime(2021, 7, 13, 21, 0),\n",
       "       datetime.datetime(2021, 7, 13, 22, 0),\n",
       "       datetime.datetime(2021, 7, 13, 23, 0),\n",
       "       datetime.datetime(2021, 7, 14, 0, 0),\n",
       "       datetime.datetime(2021, 7, 14, 1, 0),\n",
       "       datetime.datetime(2021, 7, 14, 2, 0),\n",
       "       datetime.datetime(2021, 7, 14, 3, 0),\n",
       "       datetime.datetime(2021, 7, 14, 4, 0),\n",
       "       datetime.datetime(2021, 7, 14, 5, 0),\n",
       "       datetime.datetime(2021, 7, 14, 6, 0),\n",
       "       datetime.datetime(2021, 7, 14, 7, 0),\n",
       "       datetime.datetime(2021, 7, 14, 8, 0),\n",
       "       datetime.datetime(2021, 7, 14, 9, 0),\n",
       "       datetime.datetime(2021, 7, 14, 10, 0),\n",
       "       datetime.datetime(2021, 7, 14, 11, 0),\n",
       "       datetime.datetime(2021, 7, 14, 12, 0),\n",
       "       datetime.datetime(2021, 7, 14, 13, 0),\n",
       "       datetime.datetime(2021, 7, 14, 14, 0),\n",
       "       datetime.datetime(2021, 7, 14, 15, 0),\n",
       "       datetime.datetime(2021, 7, 14, 16, 0),\n",
       "       datetime.datetime(2021, 7, 14, 17, 0),\n",
       "       datetime.datetime(2021, 7, 14, 18, 0),\n",
       "       datetime.datetime(2021, 7, 14, 19, 0),\n",
       "       datetime.datetime(2021, 7, 14, 20, 0),\n",
       "       datetime.datetime(2021, 7, 14, 21, 0),\n",
       "       datetime.datetime(2021, 7, 14, 22, 0),\n",
       "       datetime.datetime(2021, 7, 14, 23, 0),\n",
       "       datetime.datetime(2021, 7, 15, 0, 0),\n",
       "       datetime.datetime(2021, 7, 15, 1, 0),\n",
       "       datetime.datetime(2021, 7, 15, 2, 0),\n",
       "       datetime.datetime(2021, 7, 15, 3, 0),\n",
       "       datetime.datetime(2021, 7, 15, 4, 0),\n",
       "       datetime.datetime(2021, 7, 15, 5, 0),\n",
       "       datetime.datetime(2021, 7, 15, 6, 0),\n",
       "       datetime.datetime(2021, 7, 15, 7, 0),\n",
       "       datetime.datetime(2021, 7, 15, 8, 0),\n",
       "       datetime.datetime(2021, 7, 15, 9, 0),\n",
       "       datetime.datetime(2021, 7, 15, 10, 0),\n",
       "       datetime.datetime(2021, 7, 15, 11, 0),\n",
       "       datetime.datetime(2021, 7, 15, 12, 0),\n",
       "       datetime.datetime(2021, 7, 15, 13, 0),\n",
       "       datetime.datetime(2021, 7, 15, 14, 0),\n",
       "       datetime.datetime(2021, 7, 15, 15, 0),\n",
       "       datetime.datetime(2021, 7, 15, 16, 0),\n",
       "       datetime.datetime(2021, 7, 15, 17, 0),\n",
       "       datetime.datetime(2021, 7, 15, 18, 0),\n",
       "       datetime.datetime(2021, 7, 15, 19, 0),\n",
       "       datetime.datetime(2021, 7, 15, 20, 0),\n",
       "       datetime.datetime(2021, 7, 15, 21, 0),\n",
       "       datetime.datetime(2021, 7, 15, 22, 0),\n",
       "       datetime.datetime(2021, 7, 15, 23, 0),\n",
       "       datetime.datetime(2021, 7, 16, 0, 0),\n",
       "       datetime.datetime(2021, 7, 16, 1, 0),\n",
       "       datetime.datetime(2021, 7, 16, 2, 0),\n",
       "       datetime.datetime(2021, 7, 16, 3, 0),\n",
       "       datetime.datetime(2021, 7, 16, 4, 0),\n",
       "       datetime.datetime(2021, 7, 16, 5, 0),\n",
       "       datetime.datetime(2021, 7, 16, 6, 0),\n",
       "       datetime.datetime(2021, 7, 16, 7, 0),\n",
       "       datetime.datetime(2021, 7, 16, 8, 0),\n",
       "       datetime.datetime(2021, 7, 16, 9, 0),\n",
       "       datetime.datetime(2021, 7, 16, 10, 0),\n",
       "       datetime.datetime(2021, 7, 16, 11, 0),\n",
       "       datetime.datetime(2021, 7, 16, 12, 0),\n",
       "       datetime.datetime(2021, 7, 16, 13, 0),\n",
       "       datetime.datetime(2021, 7, 16, 14, 0),\n",
       "       datetime.datetime(2021, 7, 16, 15, 0),\n",
       "       datetime.datetime(2021, 7, 16, 16, 0),\n",
       "       datetime.datetime(2021, 7, 16, 17, 0),\n",
       "       datetime.datetime(2021, 7, 16, 18, 0),\n",
       "       datetime.datetime(2021, 7, 16, 19, 0),\n",
       "       datetime.datetime(2021, 7, 16, 20, 0),\n",
       "       datetime.datetime(2021, 7, 16, 21, 0),\n",
       "       datetime.datetime(2021, 7, 16, 22, 0),\n",
       "       datetime.datetime(2021, 7, 16, 23, 0),\n",
       "       datetime.datetime(2021, 7, 17, 0, 0),\n",
       "       datetime.datetime(2021, 7, 17, 1, 0),\n",
       "       datetime.datetime(2021, 7, 17, 2, 0),\n",
       "       datetime.datetime(2021, 7, 17, 3, 0),\n",
       "       datetime.datetime(2021, 7, 17, 4, 0),\n",
       "       datetime.datetime(2021, 7, 17, 5, 0),\n",
       "       datetime.datetime(2021, 7, 17, 6, 0),\n",
       "       datetime.datetime(2021, 7, 17, 7, 0),\n",
       "       datetime.datetime(2021, 7, 17, 8, 0),\n",
       "       datetime.datetime(2021, 7, 17, 9, 0),\n",
       "       datetime.datetime(2021, 7, 17, 10, 0),\n",
       "       datetime.datetime(2021, 7, 17, 11, 0),\n",
       "       datetime.datetime(2021, 7, 17, 12, 0),\n",
       "       datetime.datetime(2021, 7, 17, 13, 0)], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def transform_time(array):\n",
    "    ans = []\n",
    "    for i in range(len(array)):\n",
    "        ans.append(datetime.datetime.strptime(str(array[i]), \"%Y-%m-%d %H:%M:%S\"))\n",
    "    return np.array(ans)\n",
    "\n",
    "transform_time(to_topics_interest_over_time.index)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:26:55.543347Z",
     "start_time": "2021-07-11T18:26:55.504395Z"
    }
   },
   "outputs": [],
   "source": [
    "def df_to_db(df):\n",
    "    columns = ['iot_kword', 'iot_date', 'iot_value', 'iot_dtime']\n",
    "    length = len(df)\n",
    "    iot_kword = df.columns[0]\n",
    "    \n",
    "    to_db_df = pd.DataFrame(columns=columns)\n",
    "    iot_kwords = len(df) * [iot_kword]\n",
    "    iot_dates = transform_time(df.index)\n",
    "    iot_values = df[iot_kword].values\n",
    "    iot_dtime = datetime.datetime.utcnow() + datetime.timedelta(hours=8)\n",
    "    \n",
    "    to_db_df['iot_kword'] = iot_kwords\n",
    "    to_db_df['iot_date'] = iot_dates\n",
    "    to_db_df['iot_value'] = iot_values\n",
    "    to_db_df['iot_dtime'] = iot_dtime\n",
    "    \n",
    "    return to_db_df\n",
    "\n",
    "\n",
    "data = df_to_db(to_topics_interest_over_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:26:56.762806Z",
     "start_time": "2021-07-11T18:26:56.737287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iot_kword</th>\n",
       "      <th>iot_date</th>\n",
       "      <th>iot_value</th>\n",
       "      <th>iot_dtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-10 14:00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-10 15:00:00</td>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-10 16:00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-10 17:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-10 18:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-17 09:00:00</td>\n",
       "      <td>34</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-17 10:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-17 11:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-17 12:00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Topic</td>\n",
       "      <td>2021-07-17 13:00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>2021-07-17 21:24:20.921178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    iot_kword            iot_date  iot_value                  iot_dtime\n",
       "0       Topic 2021-07-10 14:00:00         27 2021-07-17 21:24:20.921178\n",
       "1       Topic 2021-07-10 15:00:00         17 2021-07-17 21:24:20.921178\n",
       "2       Topic 2021-07-10 16:00:00         38 2021-07-17 21:24:20.921178\n",
       "3       Topic 2021-07-10 17:00:00         15 2021-07-17 21:24:20.921178\n",
       "4       Topic 2021-07-10 18:00:00         11 2021-07-17 21:24:20.921178\n",
       "..        ...                 ...        ...                        ...\n",
       "163     Topic 2021-07-17 09:00:00         34 2021-07-17 21:24:20.921178\n",
       "164     Topic 2021-07-17 10:00:00         26 2021-07-17 21:24:20.921178\n",
       "165     Topic 2021-07-17 11:00:00         15 2021-07-17 21:24:20.921178\n",
       "166     Topic 2021-07-17 12:00:00         23 2021-07-17 21:24:20.921178\n",
       "167     Topic 2021-07-17 13:00:00         27 2021-07-17 21:24:20.921178\n",
       "\n",
       "[168 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:27:05.443765Z",
     "start_time": "2021-07-11T18:27:04.310052Z"
    }
   },
   "outputs": [],
   "source": [
    "# 將df to db\n",
    "\n",
    "import dataset\n",
    "\n",
    "table_name = 'topic_tree_g_trend_iot'\n",
    "db = dataset.connect('mysql://choozmo:pAssw0rd@db.ptt.cx:3306/cmm_test?charset=utf8mb4')\n",
    "table = db[table_name]\n",
    "rows = []\n",
    "columns = ['iot_kword', 'iot_date', 'iot_value', 'iot_dtime']\n",
    "\n",
    "\n",
    "for i in range(len(data)):\n",
    "    rows.append({})\n",
    "    for j, column in enumerate(data.columns):\n",
    "        rows[i][column] = data.iloc[i, j]\n",
    "# rows\n",
    "table.insert_many(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:27:13.270536Z",
     "start_time": "2021-07-11T18:27:13.248537Z"
    }
   },
   "outputs": [],
   "source": [
    "# 整理上面成函式\n",
    "\n",
    "def get_table(table_name, db_name):\n",
    "    db = dataset.connect(f'mysql://choozmo:pAssw0rd@db.ptt.cx:3306/{db_name}?charset=utf8mb4')\n",
    "    table = db[table_name]\n",
    "    \n",
    "    return table\n",
    "\n",
    "def data_to_db(table, data):\n",
    "    rows = []\n",
    "    columns = ['iot_kword', 'iot_date', 'iot_value', 'iot_dtime']\n",
    "    \n",
    "    for i in range(len(data)):\n",
    "        rows.append({})\n",
    "        for j, column in enumerate(data.columns):\n",
    "            rows[i][column] = data.iloc[i, j]\n",
    "    print('db updating...')\n",
    "    table.insert_many(rows)\n",
    "    print('db updated.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:27:14.664058Z",
     "start_time": "2021-07-11T18:27:14.645540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Topic', 'Cuisine', 'Food', 'Body style', 'Commerce', 'People',\n",
       "       'Nutrition', 'Illness', 'Law', 'Art', 'Company', 'Website',\n",
       "       'Furniture retail company', 'Product line', 'Furniture',\n",
       "       'Retail chain company', 'Chemical element', 'Chemical series',\n",
       "       'Organization type', 'Software type', 'Marketing',\n",
       "       'Software grouping', 'System software', 'Media', 'Plan',\n",
       "       'Computing', 'Protocol', 'Field of study', 'Metadata',\n",
       "       'Application programming interface', 'Programming language',\n",
       "       'Mental disorder', 'Dish', 'Plant', 'Medical condition', 'Fruit',\n",
       "       'Restaurant', 'Poultry', 'Type of dish', 'Table sauce', 'Noodle',\n",
       "       'Japanese noodles', 'Cooking utensil', 'Meat', 'Cooking technique',\n",
       "       'Fast food restaurant company', 'Animal', 'Pet food company',\n",
       "       'Occupation', 'Subsidiary', 'Superstore company', 'Disease',\n",
       "       'E-commerce company', 'Film company role or service',\n",
       "       'Cooking method', 'Vehicle', 'City in Oregon',\n",
       "       'City in Pennsylvania', 'City in New York State',\n",
       "       'City in Florida', 'Korean restaurant in Los Angeles, California',\n",
       "       'Automaker company', 'Automobile make', 'Automobile company',\n",
       "       'Advertising company', 'Country in North America', 'Dairy product',\n",
       "       'Confection', 'Unit of mass', 'Online game', 'Restaurant company',\n",
       "       'Corporation', 'Mail company', 'Online food ordering company',\n",
       "       'Date', 'Retail company', 'Supermarket company',\n",
       "       'Postal service company', 'Fashion company', 'Currency',\n",
       "       'Football team', 'Football league', 'Armed conflict', 'Sport',\n",
       "       'Football competition', 'Footballer', 'Disorder', 'Nutrient',\n",
       "       'Pill', 'Table condiment', 'Chemical compound'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IndexError: index 0 is out of bounds for axis 0 with size 0\n",
    "# ----> 4     iot_kword = df.columns[0]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-11T18:28:42.804715Z",
     "start_time": "2021-07-11T18:27:18.023114Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Cuisine 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Food 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Body style 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Commerce 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "People 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Nutrition 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Illness 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Law 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Art 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Company 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Website 資料抓取中...\n",
      "db updating...\n",
      "db updated.\n",
      "Furniture retail company 資料抓取中...\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2112/3210238404.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mcrawler_iot_topic_tree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_titles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2112/3210238404.py\u001b[0m in \u001b[0;36mcrawler_iot_topic_tree\u001b[1;34m(keywords, timeframe)\u001b[0m\n\u001b[0;32m     15\u001b[0m         )\n\u001b[0;32m     16\u001b[0m         \u001b[0mto_topics_interest_over_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpytrend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterest_over_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_to_db\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_topics_interest_over_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m         \u001b[0mtable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'topic_tree_g_trend_iot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'cmm_test'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0mdata_to_db\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2112/3050356344.py\u001b[0m in \u001b[0;36mdf_to_db\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'iot_kword'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'iot_date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'iot_value'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'iot_dtime'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mlength\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0miot_kword\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mto_db_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ming0\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4295\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4296\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4297\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4299\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "def crawler_iot_topic_tree(keywords, timeframe='today 3-m'):\n",
    "    \"\"\"\n",
    "        輸入keywords, \n",
    "    \"\"\"\n",
    "    pytrend = TrendReq()\n",
    "    \n",
    "    for keyword in keywords:\n",
    "        print(f'{keyword} 資料抓取中...')\n",
    "        pytrend.build_payload(\n",
    "            kw_list=[keyword],\n",
    "            cat=0,\n",
    "            timeframe=timeframe,\n",
    "            geo='TW',\n",
    "            gprop=''\n",
    "        )\n",
    "        to_topics_interest_over_time = pytrend.interest_over_time()\n",
    "        data = df_to_db(to_topics_interest_over_time)\n",
    "        table = get_table('topic_tree_g_trend_iot', 'cmm_test')\n",
    "        data_to_db(table, data)\n",
    "        time.sleep(5)\n",
    "        \n",
    "crawler_iot_topic_tree(to_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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