search_rate_closing_price_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "colab_type": "code",
    "id": "r9KV_8mL1cVO",
    "outputId": "fcadced3-b6de-4ca9-dd84-50000726f759"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-HkrDZyq1mjq",
    "outputId": "346f95ea-ce11-43b7-ebfb-5dc370a41983"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/data\n"
     ]
    }
   ],
   "source": [
    "%cd/content/drive/My Drive/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import urllib\n",
    "import time\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "    \n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 네이버 금융에서 모나리자 종가 데이터 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "id": "Z-60x2Cm0ww2",
    "outputId": "4aa2f228-9485-4026-ff5b-4466aed23028"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2020.02.14 4,770\n",
      "2020.02.13 4,705\n",
      "2020.02.12 4,515\n",
      "2020.02.11 4,820\n",
      "2020.02.10 5,310\n",
      "2020.02.07 5,760\n",
      "2020.02.06 5,950\n",
      "2020.02.05 5,910\n",
      "2020.02.04 5,860\n",
      "2020.02.03 6,520\n",
      "2\n",
      "2020.01.31 9,130\n",
      "2020.01.30 7,420\n",
      "2020.01.29 7,400\n",
      "2020.01.28 8,090\n",
      "2020.01.23 6,230\n",
      "2020.01.22 5,980\n",
      "2020.01.21 4,600\n",
      "2020.01.20 4,430\n",
      "2020.01.17 3,980\n",
      "2020.01.16 3,975\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stockItem = '012690'\n",
    "\n",
    "timestamp = []\n",
    "closing_price = []\n",
    "\n",
    "\n",
    "url = 'http://finance.naver.com/item/sise_day.nhn?code='+ stockItem\n",
    "html = urlopen(url) \n",
    "source = BeautifulSoup(html.read(), \"html.parser\")\n",
    " \n",
    "maxPage=source.find_all(\"table\",align=\"center\")\n",
    "\n",
    "                                            \n",
    "for page in range(1, 3):\n",
    "  print (str(page) )\n",
    "  url = 'http://finance.naver.com/item/sise_day.nhn?code=' + stockItem +'&page='+ str(page)\n",
    "  html = urlopen(url)\n",
    "  source = BeautifulSoup(html.read(), \"html.parser\")\n",
    "  srlists=source.find_all(\"tr\")\n",
    "  isCheckNone = None\n",
    "   \n",
    "  if((page % 1) == 0):\n",
    "    time.sleep(1.50)\n",
    " \n",
    "  for i in range(1,len(srlists)-1):\n",
    "   if(srlists[i].span != isCheckNone):\n",
    "     \n",
    "    srlists[i].td.text\n",
    "    print(srlists[i].find_all(\"td\",align=\"center\")[0].text, srlists[i].find_all(\"td\",class_=\"num\")[0].text )\n",
    "    timestamp.append(srlists[i].find_all(\"td\",align=\"center\")[0].text)\n",
    "    closing_price.append(srlists[i].find_all(\"td\",class_=\"num\")[0].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0Njg0AWk2Kf6"
   },
   "outputs": [],
   "source": [
    "timestamp = pd.Series(timestamp, name = 'timestamp')\n",
    "closing_price = pd.Series(closing_price, name='closing_price')\n",
    "모나리자_df = pd.DataFrame([timestamp, closing_price]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## '신종 코로나'와 '우한 폐렴' 구글 트렌드 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "13X1saqa4ykv"
   },
   "outputs": [],
   "source": [
    "trend_coro = pd.read_csv('multiTimeline(신종코로나).csv')\n",
    "trend_wuhan = pd.read_csv('multiTimeline(우한폐렴).csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OgtcLn-L5ilO"
   },
   "outputs": [],
   "source": [
    "trend_coro = trend_coro.iloc[1:,:]\n",
    "trend_coro.rename(columns = {'카테고리: 모든 카테고리' : 'relative_frequency'}, inplace = True)\n",
    "\n",
    "trend_wuhan = trend_wuhan.iloc[1:,:]\n",
    "trend_wuhan.rename(columns = {'카테고리: 모든 카테고리' : 'relative_frequency'}, inplace = True)\n",
    "\n",
    "\n",
    "\n",
    "trend_coro['relative_frequency'] = trend_coro['relative_frequency'].replace(\"<1\", \"0\")\n",
    "trend_wuhan['relative_frequency'] = trend_wuhan['relative_frequency'].replace(\"<1\", \"0\")\n",
    "\n",
    "trend_coro['relative_frequency'] = trend_coro['relative_frequency'].astype(float)\n",
    "trend_wuhan['relative_frequency'] = trend_wuhan['relative_frequency'].astype(float)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "81Orn3IV-NE2"
   },
   "outputs": [],
   "source": [
    "for i in range(모나리자_df['closing_price'].shape[0]):\n",
    "    모나리자_df['closing_price'][i] = re.sub('[,]', '', 모나리자_df['closing_price'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yj0Sz5Wv97Fq"
   },
   "outputs": [],
   "source": [
    "모나리자_df['closing_price'] = 모나리자_df['closing_price'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "P0qj3xi0Addj",
    "outputId": "34ee10af-89bf-49ba-c5d8-9e12e09ff2aa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for i in range(모나리자_df['timestamp'].shape[0]):\n",
    "    모나리자_df['timestamp'][i] = re.sub('[.]', '-', 모나리자_df['timestamp'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "VwqypkLj_2eC",
    "outputId": "55d5491a-c091-45d4-e1fe-6890131693ce"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>closing_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>4770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>4705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>4515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>4820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>5310.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp  closing_price\n",
       "0  2020-02-14         4770.0\n",
       "1  2020-02-13         4705.0\n",
       "2  2020-02-12         4515.0\n",
       "3  2020-02-11         4820.0\n",
       "4  2020-02-10         5310.0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "모나리자_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "7jEHDiAZ_Kw1",
    "outputId": "bb7aade9-eece-4c66-f544-68d18187bfe8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for i in range(모나리자_df['timestamp'].shape[0]):\n",
    "    모나리자_df['timestamp'][i] = 모나리자_df['timestamp'][i][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 654
    },
    "colab_type": "code",
    "id": "657oJpKwAD6T",
    "outputId": "0dbd4cac-f5c1-4812-de1f-3cd77742269c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>timestamp</th>\n",
       "      <th>closing_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02-14</td>\n",
       "      <td>4770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-13</td>\n",
       "      <td>4705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02-12</td>\n",
       "      <td>4515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02-11</td>\n",
       "      <td>4820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02-10</td>\n",
       "      <td>5310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>02-07</td>\n",
       "      <td>5760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>02-06</td>\n",
       "      <td>5950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02-05</td>\n",
       "      <td>5910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>02-04</td>\n",
       "      <td>5860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>02-03</td>\n",
       "      <td>6520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01-31</td>\n",
       "      <td>9130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>01-30</td>\n",
       "      <td>7420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>01-29</td>\n",
       "      <td>7400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01-28</td>\n",
       "      <td>8090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01-23</td>\n",
       "      <td>6230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>01-22</td>\n",
       "      <td>5980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>01-21</td>\n",
       "      <td>4600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>01-20</td>\n",
       "      <td>4430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>01-17</td>\n",
       "      <td>3980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>01-16</td>\n",
       "      <td>3975.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp  closing_price\n",
       "0      02-14         4770.0\n",
       "1      02-13         4705.0\n",
       "2      02-12         4515.0\n",
       "3      02-11         4820.0\n",
       "4      02-10         5310.0\n",
       "5      02-07         5760.0\n",
       "6      02-06         5950.0\n",
       "7      02-05         5910.0\n",
       "8      02-04         5860.0\n",
       "9      02-03         6520.0\n",
       "10     01-31         9130.0\n",
       "11     01-30         7420.0\n",
       "12     01-29         7400.0\n",
       "13     01-28         8090.0\n",
       "14     01-23         6230.0\n",
       "15     01-22         5980.0\n",
       "16     01-21         4600.0\n",
       "17     01-20         4430.0\n",
       "18     01-17         3980.0\n",
       "19     01-16         3975.0"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "모나리자_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "luEx5Qx6BHDt"
   },
   "outputs": [],
   "source": [
    "모나리자_df.sort_values(by='timestamp', inplace = True)\n",
    "모나리자_df.set_index('timestamp', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "zd7FJhfcBjmU",
    "outputId": "e4568f5e-7c04-4d49-dd1c-b4540ae6a582"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relative_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-18</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "            relative_frequency\n",
       "2020-01-15                 0.0\n",
       "2020-01-16                 0.0\n",
       "2020-01-17                 2.0\n",
       "2020-01-18                 2.0\n",
       "2020-01-19                 0.0"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_coro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tFIQ0TKMApPt"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "trend_coro.reset_index(inplace= True)\n",
    "trend_coro.rename(columns = {'index' : 'timestamp'}, inplace = True)\n",
    "\n",
    "for i in range(trend_coro['timestamp'].shape[0]):\n",
    "      trend_coro['timestamp'][i] = trend_coro['timestamp'][i][-5:]\n",
    "        \n",
    "trend_coro.set_index('timestamp', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "colab_type": "code",
    "id": "2pBQqZNpBOL8",
    "outputId": "518ed6eb-26a4-402d-f38a-c1255ea9036a"
   },
   "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>relative_frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-15</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-16</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-17</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-18</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-19</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           relative_frequency\n",
       "timestamp                    \n",
       "01-15                     0.0\n",
       "01-16                     0.0\n",
       "01-17                     2.0\n",
       "01-18                     2.0\n",
       "01-19                     0.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_coro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177
    },
    "colab_type": "code",
    "id": "Y7C2E0mTBWJg",
    "outputId": "2f8fbb9f-2ea2-4f21-815b-a768980dee61"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df = pd.concat([trend_coro, 모나리자_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mHA6_-t-KWr3"
   },
   "outputs": [],
   "source": [
    "inp_df = pd.DataFrame({'timestamp' : ['01-17', '01-18', '01-19', '01-20', '01-23', '01-24', '01-25', '01-26', '01-27', '01-28', '01-31', '02-01', '02-02', '02-03', '02-07', '02-08', '02-09', '02-10'], 'est_closing_price': [3980, 4130, 4280, 4430, 6230,  6602, 6974, 7346, 7718, 8090, 9130, 8260, 7390, 6520, 5760, 5610, 5460, 5310]})\n",
    "inp_df.set_index('timestamp', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177
    },
    "colab_type": "code",
    "id": "oYvIaPktLE0U",
    "outputId": "8e27101c-f247-4ae9-ea01-30beb3c67cf3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df = pd.concat([df, inp_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## '신종 코로나' 트렌드와 '모나리자 종가' 겹쳐 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "colab_type": "code",
    "id": "OYLHDJod6z68",
    "outputId": "682cf008-e0c5-44ff-b407-79cc00ea696b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "plt.xticks(rotation=315)\n",
    "plt.grid(True)\n",
    "ax1.plot(df['relative_frequency'], color='red', label = 'search rate for novel corona')\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df['closing_price'], color = 'blue', label = 'closing price for monalisa')\n",
    "ax2.plot(df['est_closing_price'], linestyle='--', color = 'blue', label = 'interpolated closing price')\n",
    "\n",
    "\n",
    "bbox_props = dict(boxstyle=\"round,pad=0.5\", fc=\"w\", ec=\"k\", lw=2)\n",
    "ax2.annotate('local minimum',\n",
    "            xy=(0.5, 0.52),\n",
    "            xycoords='axes fraction',\n",
    "            xytext=(0.35, 0.52),\n",
    "            bbox=bbox_props,\n",
    "            arrowprops=\n",
    "                dict(facecolor='black', shrink=0.5),\n",
    "                horizontalalignment='left',\n",
    "                verticalalignment='center')\n",
    "\n",
    "\n",
    "ax2.annotate('maximum',\n",
    "            xy=(0.56, 0.95),\n",
    "            xycoords='axes fraction',\n",
    "            xytext=(0.45 ,0.95),\n",
    "            bbox=bbox_props,\n",
    "            arrowprops=\n",
    "                dict(facecolor='black', shrink=0.5),\n",
    "                horizontalalignment='left',\n",
    "                verticalalignment='center')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig('coro_monalisa.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## '우한 폐렴' 구글 트렌드 데이터 전처리 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "1nj0HNiZYLPP",
    "outputId": "653ca669-25c1-4853-f9f0-556dd0c95f8d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "trend_wuhan.reset_index(inplace= True)\n",
    "trend_wuhan.rename(columns = {'index' : 'timestamp'}, inplace = True)\n",
    "for i in range(trend_wuhan['timestamp'].shape[0]):\n",
    "      trend_wuhan['timestamp'][i] = trend_wuhan['timestamp'][i][-5:]\n",
    "\n",
    "trend_wuhan.set_index('timestamp', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "colab_type": "code",
    "id": "cnELV54gYLs6",
    "outputId": "4b744a99-9d39-416d-db1c-f43c80115443"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "c:\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df2 = pd.concat([trend_wuhan, 모나리자_df], axis=1)\n",
    "df2 = pd.concat([df2, inp_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## '우한 폐렴' 트렌드와 '모나리자 종가' 겹쳐 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "c0c-sA9BYL__",
    "outputId": "f52b0f28-4344-454f-aa64-4dfbc1eb05e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "plt.xticks(rotation=315)\n",
    "plt.grid(True)\n",
    "ax1.plot(df2['relative_frequency'], color='red', label = 'search rate for wuhan pneumonia')\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df2['closing_price'], color = 'blue', label = 'closing price for monalisa')\n",
    "ax2.plot(df2['est_closing_price'], linestyle='--', color = 'blue', label = 'interpolated closing price')\n",
    "\n",
    "bbox_props = dict(boxstyle=\"round,pad=0.5\", fc=\"w\", ec=\"k\", lw=2)\n",
    "ax2.annotate('local minimum',\n",
    "            xy=(0.53, 0.48),\n",
    "            xycoords='axes fraction',\n",
    "            xytext=(0.38, 0.48),\n",
    "            bbox=bbox_props,\n",
    "            arrowprops=\n",
    "                dict(facecolor='black', shrink=0.5),\n",
    "                horizontalalignment='left',\n",
    "                verticalalignment='center')\n",
    "\n",
    "ax2.annotate('maximum',\n",
    "            xy=(0.43, 0.95),\n",
    "            xycoords='axes fraction',\n",
    "            xytext=(0.32 ,0.95),\n",
    "            bbox=bbox_props,\n",
    "            arrowprops=\n",
    "                dict(facecolor='black', shrink=0.5),\n",
    "                horizontalalignment='left',\n",
    "                verticalalignment='center')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig('wuhan_monalisa.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Untitled9.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}