{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start read_csv\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from bs4 import BeautifulSoup\n",
    "import urllib\n",
    "from urllib import request\n",
    "import re\n",
    "import json\n",
    "from datetime import datetime\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import matplotlib.pyplot as plt\n",
    "from random import shuffle\n",
    "import random\n",
    "#Setup\n",
    "#load dataset\n",
    "print(\"start read_csv\")\n",
    "df = pd.read_csv(\"trendresult.csv\")\n",
    "\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2)\n",
    "\n",
    "\n",
    "#df.plot(title = \"Naver Trend - Stock-Daily\", figsize = (20, 10), legend = False)\n",
    "\n",
    "print(\"start read_csv_1\")\n",
    "df2 = pd.read_csv(\"finace_285130.csv\")\n",
    "df2 = pd.DataFrame(df2)\n",
    "print(df2)\n",
    "df[\"SK케미칼\"].plot(title = \"SK-chemical\", figsize = (20,10), legend = False,ax=axes[0,0])\n",
    "print(df[\"SK케미칼\"])\n",
    "df2.plot(title = \"SK-chemical - Stock-Daily\", figsize = (20, 10), legend = False,ax=axes[0,1])\n",
    "\n",
    "df3 = pd.read_csv(\"finace_307070.csv\")\n",
    "df3 = pd.DataFrame(df3)\n",
    "df[\"삼성머스트스팩3호\"].plot(title = \"삼성머스트스팩3호\", figsize = (20,10), legend = False,ax=axes[1,0])\n",
    "print(df3)\n",
    "print(df[\"삼성머스트스팩3호\"])\n",
    "df3.plot(title = \"SK hos - Stock-Daily\", figsize = (20, 10), legend = False,ax=axes[1,1])\n",
    "\n",
    "df4 = pd.read_csv(\"finace_309930.csv\")\n",
    "df4 = pd.DataFrame(df4)\n",
    "df[\"SK4호스팩\"].plot(title = \"SK-호스팩\", figsize = (20,10), legend = False,ax=axes[2,0])\n",
    "print(df4)\n",
    "print(df[\"SK4호스팩\"])\n",
    "df4.plot(title = \"samsung - Stock-Daily\", figsize = (20, 10), legend = False,ax=axes[2,1])\n"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}