Web Scraping - Kurs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Package "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip install dateparser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import dateparser\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Content "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_get = requests.get('https://monexnews.com/kurs-valuta-asing.htm?kurs=JPY&searchdatefrom=01-01-2019&searchdateto=31-12-2019')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">\\n<html xmlns=\"http://www.w3.org/1999/xhtml\">\\n<head>\\n<meta http-equiv=\"Content-Language\" content=\"en,in\" />\\n<link rel=\"alternate\" href=\"https://monexnews.com/kurs-valuta-asing.php\" hreflang=\"en-ID\" />\\n<base href=\"https://monexnews.com/\" target=\"_self\" />\\n<title>Kurs Yen Jepang | JPY | Harga Nilai Tukar Yen Hari Ini | Monexnews</title>\\n<meta name=\"description\" content=\"Kurs yen jepang JPY, nilai tukar rupiah terhadap harga yen hari ini berdasar kurs tengah BI, Bank Indonesia. \" />\\n<meta name=\"keywords\" content=\"Kurs yen, jpy, nilai tukar yen, monexnews\" />\\n<meta property=\"og:image\" content=\"favicon.jpg\" />\\n<meta name=\"author\" content=\"Ultimaengine.co'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_get.content[1:777]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bs4.BeautifulSoup'>\n"
     ]
    }
   ],
   "source": [
    "soup = BeautifulSoup(url_get.content,\"html.parser\")\n",
    "print(type(soup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">\n",
      "<html xmlns=\"http://www.w3.org/1999/xhtml\">\n",
      " <head>\n",
      "  <meta content=\"en,in\" http-equiv=\"Content-Language\"/>\n",
      "  <link href=\"https://monexnews.com/kurs-valuta-asing.php\" hreflang=\"en-ID\" rel=\"alternate\"/>\n",
      "  <base href=\"https://monexnews.com/\" target=\"_self\"/>\n",
      "  <title>\n",
      "   Kurs Yen Jepang | JPY | Harga Nilai Tukar Yen Hari Ini | Monexnews\n",
      "  </title>\n",
      "  <meta content=\"Kurs yen jepang JPY, nilai tukar rupiah terhadap harga yen hari ini berdasar kurs tengah BI, Bank Indonesia. \" name=\"description\"/>\n",
      "  <meta content=\"Kurs yen, jpy, nilai tukar yen, monexnews\" name=\"keywords\"/>\n",
      "  <meta content=\"favicon.jpg\" property=\"og:image\"/>\n",
      "  <meta content=\"Ultimaengine.com\" name=\"author\"/>\n",
      "  <meta content=\"follow, index\" name=\"robots\"/>\n",
      "  <meta content=\"7 days\" name=\"revisit-after\"/>\n",
      "  <meta content=\"width=device-width, initial-scale=1.0\" name=\"viewport\"/>\n",
      "  <meta content=\"M7NOlgqcEadhlLSiEssmvNI08vl1BowAztIyERgEgMQ\" name=\"google-\n"
     ]
    }
   ],
   "source": [
    "print(soup.prettify()[:1045])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parse Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "table class=\"table\">\n",
      " <tr>\n",
      "  <td>\n",
      "   TANGGAL\n",
      "  </td>\n",
      "  <td>\n",
      "   ASK\n",
      "  </td>\n",
      "  <td>\n",
      "   BID\n",
      "  </td>\n",
      " </tr>\n",
      " <tr class=\"verySoftGreyBg\">\n",
      "  <td>\n",
      "   31 Desember 2019\n",
      "  </td>\n",
      "  <td>\n",
      "   128,62\n",
      "  </td>\n",
      "  <td>\n",
      "   127,32\n",
      "  </td>\n",
      " </tr>\n",
      " <tr>\n",
      "  <td>\n",
      "   30 Desember 2019\n",
      "  </td>\n",
      "  <td>\n",
      "   128,30\n",
      "  </td>\n",
      "  <td>\n",
      "   127,02\n",
      "  </td>\n",
      " </tr>\n",
      " <tr class=\"verySoftGreyBg\">\n",
      "  <td>\n",
      "   27 Desember 2019\n",
      "  </td>\n",
      "  <td>\n",
      "   128,11\n",
      "  </td>\n",
      "  <td>\n",
      "   126,83\n",
      "  </td>\n",
      " </tr>\n",
      " <tr>\n",
      "  <td>\n",
      "   26 Desember 2019\n",
      "  </td>\n",
      "  <td>\n",
      "   128,\n"
     ]
    }
   ],
   "source": [
    "table = soup.find('table', attrs={'class':'table'})\n",
    "print(table.prettify()[1:500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tr>\n",
       " <td>TANGGAL</td>\n",
       " <td>ASK</td>\n",
       " <td>BID</td>\n",
       " </tr>, <tr class=\"verySoftGreyBg\">\n",
       " <td>31 Desember 2019</td>\n",
       " <td>128,62</td>\n",
       " <td>127,32</td>\n",
       " </tr>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr = table.find_all('tr')\n",
    "tr[:2] # just call 2 founded tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('31\\xa0Desember\\xa02019', '128,62', '127,32'),\n",
       " ('30\\xa0Desember\\xa02019', '128,30', '127,02'),\n",
       " ('27\\xa0Desember\\xa02019', '128,11', '126,83'),\n",
       " ('26\\xa0Desember\\xa02019', '128,27', '126,98'),\n",
       " ('23\\xa0Desember\\xa02019', '128,39', '127,10'),\n",
       " ('20\\xa0Desember\\xa02019', '128,63', '127,34'),\n",
       " ('19\\xa0Desember\\xa02019', '128,24', '126,93'),\n",
       " ('18\\xa0Desember\\xa02019', '128,64', '127,32'),\n",
       " ('17\\xa0Desember\\xa02019', '128,62', '127,33'),\n",
       " ('16\\xa0Desember\\xa02019', '128,67', '127,36'),\n",
       " ('13\\xa0Desember\\xa02019', '128,30', '127,02'),\n",
       " ('12\\xa0Desember\\xa02019', '130,04', '128,71'),\n",
       " ('11\\xa0Desember\\xa02019', '129,65', '128,34'),\n",
       " ('10\\xa0Desember\\xa02019', '129,56', '128,26'),\n",
       " ('09\\xa0Desember\\xa02019', '129,76', '128,44'),\n",
       " ('06\\xa0Desember\\xa02019', '129,78', '128,45'),\n",
       " ('05\\xa0Desember\\xa02019', '130,10', '128,77'),\n",
       " ('04\\xa0Desember\\xa02019', '130,79', '129,45'),\n",
       " ('03\\xa0Desember\\xa02019', '130,15', '128,82'),\n",
       " ('02\\xa0Desember\\xa02019', '129,36', '128,04'),\n",
       " ('29\\xa0November\\xa02019', '129,44', '128,12'),\n",
       " ('28\\xa0November\\xa02019', '129,47', '128,15'),\n",
       " ('27\\xa0November\\xa02019', '129,84', '128,53'),\n",
       " ('26\\xa0November\\xa02019', '129,82', '128,49'),\n",
       " ('25\\xa0November\\xa02019', '130,20', '128,89'),\n",
       " ('22\\xa0November\\xa02019', '130,44', '129,10'),\n",
       " ('21\\xa0November\\xa02019', '130,73', '129,39'),\n",
       " ('20\\xa0November\\xa02019', '130,56', '129,23'),\n",
       " ('19\\xa0November\\xa02019', '130,34', '129,03'),\n",
       " ('18\\xa0November\\xa02019', '130,02', '128,70'),\n",
       " ('15\\xa0November\\xa02019', '130,23', '128,90'),\n",
       " ('14\\xa0November\\xa02019', '130,34', '129,01'),\n",
       " ('13\\xa0November\\xa02019', '129,86', '128,53'),\n",
       " ('12\\xa0November\\xa02019', '129,45', '128,15'),\n",
       " ('11\\xa0November\\xa02019', '129,37', '128,07'),\n",
       " ('08\\xa0November\\xa02019', '129,03', '127,73'),\n",
       " ('07\\xa0November\\xa02019', '129,71', '128,41'),\n",
       " ('06\\xa0November\\xa02019', '128,96', '127,64'),\n",
       " ('05\\xa0November\\xa02019', '129,67', '128,34'),\n",
       " ('04\\xa0November\\xa02019', '130,04', '128,71'),\n",
       " ('01\\xa0November\\xa02019', '130,88', '129,54'),\n",
       " ('31\\xa0Oktober\\xa02019', '129,57', '128,25'),\n",
       " ('30\\xa0Oktober\\xa02019', '129,67', '128,34'),\n",
       " ('29\\xa0Oktober\\xa02019', '129,39', '128,09'),\n",
       " ('28\\xa0Oktober\\xa02019', '129,57', '128,27'),\n",
       " ('25\\xa0Oktober\\xa02019', '130,11', '128,78'),\n",
       " ('24\\xa0Oktober\\xa02019', '129,52', '128,22'),\n",
       " ('23\\xa0Oktober\\xa02019', '130,35', '129,05'),\n",
       " ('22\\xa0Oktober\\xa02019', '130,06', '128,73'),\n",
       " ('21\\xa0Oktober\\xa02019', '130,94', '129,60'),\n",
       " ('18\\xa0Oktober\\xa02019', '130,91', '129,60'),\n",
       " ('17\\xa0Oktober\\xa02019', '130,94', '129,61'),\n",
       " ('16\\xa0Oktober\\xa02019', '131,20', '129,89'),\n",
       " ('15\\xa0Oktober\\xa02019', '131,19', '129,87'),\n",
       " ('14\\xa0Oktober\\xa02019', '131,11', '129,77'),\n",
       " ('11\\xa0Oktober\\xa02019', '131,66', '130,31'),\n",
       " ('10\\xa0Oktober\\xa02019', '132,29', '130,94'),\n",
       " ('09\\xa0Oktober\\xa02019', '133,01', '131,64'),\n",
       " ('08\\xa0Oktober\\xa02019', '132,68', '131,32'),\n",
       " ('07\\xa0Oktober\\xa02019', '133,19', '131,83'),\n",
       " ('04\\xa0Oktober\\xa02019', '133,05', '131,71'),\n",
       " ('03\\xa0Oktober\\xa02019', '133,13', '131,80'),\n",
       " ('02\\xa0Oktober\\xa02019', '132,55', '131,19'),\n",
       " ('01\\xa0Oktober\\xa02019', '131,87', '130,52'),\n",
       " ('30\\xa0September\\xa02019', '132,03', '130,68'),\n",
       " ('27\\xa0September\\xa02019', '132,49', '131,14'),\n",
       " ('26\\xa0September\\xa02019', '132,20', '130,86'),\n",
       " ('25\\xa0September\\xa02019', '132,44', '131,07'),\n",
       " ('24\\xa0September\\xa02019', '131,73', '130,39'),\n",
       " ('23\\xa0September\\xa02019', '131,37', '130,03'),\n",
       " ('20\\xa0September\\xa02019', '131,14', '129,80'),\n",
       " ('19\\xa0September\\xa02019', '131,00', '129,69'),\n",
       " ('18\\xa0September\\xa02019', '130,75', '129,43'),\n",
       " ('17\\xa0September\\xa02019', '131,09', '129,75'),\n",
       " ('16\\xa0September\\xa02019', '130,78', '129,47'),\n",
       " ('13\\xa0September\\xa02019', '129,63', '128,30'),\n",
       " ('12\\xa0September\\xa02019', '130,75', '129,42'),\n",
       " ('11\\xa0September\\xa02019', '131,30', '129,96'),\n",
       " ('10\\xa0September\\xa02019', '131,34', '130,00'),\n",
       " ('09\\xa0September\\xa02019', '132,50', '131,16'),\n",
       " ('06\\xa0September\\xa02019', '132,73', '131,39'),\n",
       " ('05\\xa0September\\xa02019', '133,37', '132,00'),\n",
       " ('04\\xa0September\\xa02019', '134,85', '133,47'),\n",
       " ('03\\xa0September\\xa02019', '134,39', '133,01'),\n",
       " ('02\\xa0September\\xa02019', '134,30', '132,92'),\n",
       " ('30\\xa0Agustus\\xa02019', '134,33', '132,96'),\n",
       " ('29\\xa0Agustus\\xa02019', '135,32', '133,95'),\n",
       " ('28\\xa0Agustus\\xa02019', '135,51', '134,15'),\n",
       " ('27\\xa0Agustus\\xa02019', '135,32', '133,94'),\n",
       " ('26\\xa0Agustus\\xa02019', '136,20', '134,81'),\n",
       " ('23\\xa0Agustus\\xa02019', '134,45', '133,08'),\n",
       " ('22\\xa0Agustus\\xa02019', '134,38', '133,01'),\n",
       " ('21\\xa0Agustus\\xa02019', '134,63', '133,26'),\n",
       " ('20\\xa0Agustus\\xa02019', '134,48', '133,14'),\n",
       " ('19\\xa0Agustus\\xa02019', '134,29', '132,93'),\n",
       " ('16\\xa0Agustus\\xa02019', '134,86', '133,51'),\n",
       " ('15\\xa0Agustus\\xa02019', '135,65', '134,27'),\n",
       " ('14\\xa0Agustus\\xa02019', '134,60', '133,24'),\n",
       " ('13\\xa0Agustus\\xa02019', '136,19', '134,80'),\n",
       " ('12\\xa0Agustus\\xa02019', '135,50', '134,14'),\n",
       " ('09\\xa0Agustus\\xa02019', '134,60', '133,25'),\n",
       " ('08\\xa0Agustus\\xa02019', '134,67', '133,30'),\n",
       " ('07\\xa0Agustus\\xa02019', '135,34', '133,96'),\n",
       " ('06\\xa0Agustus\\xa02019', '135,85', '134,48'),\n",
       " ('05\\xa0Agustus\\xa02019', '134,95', '133,57'),\n",
       " ('02\\xa0Agustus\\xa02019', '133,31', '131,95'),\n",
       " ('01\\xa0Agustus\\xa02019', '129,80', '128,49'),\n",
       " ('31\\xa0Juli\\xa02019', '129,87', '128,54'),\n",
       " ('30\\xa0Juli\\xa02019', '129,68', '128,36'),\n",
       " ('29\\xa0Juli\\xa02019', '129,81', '128,48'),\n",
       " ('26\\xa0Juli\\xa02019', '129,56', '128,23'),\n",
       " ('25\\xa0Juli\\xa02019', '129,98', '128,65'),\n",
       " ('24\\xa0Juli\\xa02019', '130,15', '128,84'),\n",
       " ('23\\xa0Juli\\xa02019', '130,00', '128,67'),\n",
       " ('22\\xa0Juli\\xa02019', '129,98', '128,65'),\n",
       " ('19\\xa0Juli\\xa02019', '130,04', '128,72'),\n",
       " ('18\\xa0Juli\\xa02019', '130,45', '129,14'),\n",
       " ('17\\xa0Juli\\xa02019', '129,63', '128,30'),\n",
       " ('16\\xa0Juli\\xa02019', '129,62', '128,30'),\n",
       " ('15\\xa0Juli\\xa02019', '129,93', '128,60'),\n",
       " ('12\\xa0Juli\\xa02019', '130,67', '129,35'),\n",
       " ('11\\xa0Juli\\xa02019', '131,10', '129,77'),\n",
       " ('10\\xa0Juli\\xa02019', '130,61', '129,27'),\n",
       " ('09\\xa0Juli\\xa02019', '130,49', '129,15'),\n",
       " ('08\\xa0Juli\\xa02019', '131,13', '129,80'),\n",
       " ('05\\xa0Juli\\xa02019', '131,87', '130,54'),\n",
       " ('04\\xa0Juli\\xa02019', '131,60', '130,24'),\n",
       " ('03\\xa0Juli\\xa02019', '132,30', '130,94'),\n",
       " ('02\\xa0Juli\\xa02019', '131,09', '129,75'),\n",
       " ('01\\xa0Juli\\xa02019', '131,12', '129,77'),\n",
       " ('28\\xa0Juni\\xa02019', '132,07', '130,71'),\n",
       " ('27\\xa0Juni\\xa02019', '131,98', '130,63'),\n",
       " ('26\\xa0Juni\\xa02019', '132,56', '131,20'),\n",
       " ('25\\xa0Juni\\xa02019', '132,65', '131,28'),\n",
       " ('24\\xa0Juni\\xa02019', '132,61', '131,25'),\n",
       " ('21\\xa0Juni\\xa02019', '132,42', '131,05'),\n",
       " ('20\\xa0Juni\\xa02019', '132,78', '131,43'),\n",
       " ('19\\xa0Juni\\xa02019', '132,20', '130,85'),\n",
       " ('18\\xa0Juni\\xa02019', '133,01', '131,64'),\n",
       " ('17\\xa0Juni\\xa02019', '132,79', '131,42'),\n",
       " ('14\\xa0Juni\\xa02019', '132,73', '131,36'),\n",
       " ('13\\xa0Juni\\xa02019', '132,53', '131,18'),\n",
       " ('12\\xa0Juni\\xa02019', '131,88', '130,56'),\n",
       " ('11\\xa0Juni\\xa02019', '131,96', '130,64'),\n",
       " ('10\\xa0Juni\\xa02019', '131,84', '130,49'),\n",
       " ('31\\xa0Mei\\xa02019', '132,32', '130,96'),\n",
       " ('29\\xa0Mei\\xa02019', '132,48', '131,15'),\n",
       " ('28\\xa0Mei\\xa02019', '132,02', '130,69'),\n",
       " ('27\\xa0Mei\\xa02019', '131,86', '130,51'),\n",
       " ('24\\xa0Mei\\xa02019', '132,51', '131,16'),\n",
       " ('23\\xa0Mei\\xa02019', '132,34', '130,98'),\n",
       " ('22\\xa0Mei\\xa02019', '131,71', '130,37'),\n",
       " ('21\\xa0Mei\\xa02019', '131,94', '130,60'),\n",
       " ('20\\xa0Mei\\xa02019', '132,03', '130,69'),\n",
       " ('17\\xa0Mei\\xa02019', '132,30', '130,95'),\n",
       " ('16\\xa0Mei\\xa02019', '132,73', '131,40'),\n",
       " ('15\\xa0Mei\\xa02019', '132,51', '131,16'),\n",
       " ('14\\xa0Mei\\xa02019', '132,48', '131,16'),\n",
       " ('13\\xa0Mei\\xa02019', '131,54', '130,19'),\n",
       " ('10\\xa0Mei\\xa02019', '131,14', '129,80'),\n",
       " ('09\\xa0Mei\\xa02019', '131,14', '129,80'),\n",
       " ('08\\xa0Mei\\xa02019', '130,69', '129,34'),\n",
       " ('07\\xa0Mei\\xa02019', '129,91', '128,60'),\n",
       " ('06\\xa0Mei\\xa02019', '130,14', '128,80'),\n",
       " ('03\\xa0Mei\\xa02019', '128,83', '127,52'),\n",
       " ('02\\xa0Mei\\xa02019', '128,31', '127,01'),\n",
       " ('30\\xa0April\\xa02019', '128,02', '126,72'),\n",
       " ('29\\xa0April\\xa02019', '127,77', '126,49'),\n",
       " ('26\\xa0April\\xa02019', '127,70', '126,42'),\n",
       " ('25\\xa0April\\xa02019', '127,11', '125,82'),\n",
       " ('24\\xa0April\\xa02019', '126,78', '125,49'),\n",
       " ('23\\xa0April\\xa02019', '126,60', '125,31'),\n",
       " ('22\\xa0April\\xa02019', '126,20', '124,91'),\n",
       " ('18\\xa0April\\xa02019', '125,84', '124,55'),\n",
       " ('16\\xa0April\\xa02019', '126,30', '125,02'),\n",
       " ('15\\xa0April\\xa02019', '126,25', '124,96'),\n",
       " ('12\\xa0April\\xa02019', '127,35', '126,06'),\n",
       " ('11\\xa0April\\xa02019', '128,15', '126,83'),\n",
       " ('10\\xa0April\\xa02019', '128,00', '126,69'),\n",
       " ('09\\xa0April\\xa02019', '127,75', '126,46'),\n",
       " ('08\\xa0April\\xa02019', '127,60', '126,29'),\n",
       " ('05\\xa0April\\xa02019', '127,41', '126,11'),\n",
       " ('04\\xa0April\\xa02019', '127,92', '126,61'),\n",
       " ('02\\xa0April\\xa02019', '128,55', '127,24'),\n",
       " ('01\\xa0April\\xa02019', '128,68', '127,37'),\n",
       " ('29\\xa0Maret\\xa02019', '129,21', '127,92'),\n",
       " ('28\\xa0Maret\\xa02019', '130,04', '128,72'),\n",
       " ('27\\xa0Maret\\xa02019', '129,19', '127,88'),\n",
       " ('26\\xa0Maret\\xa02019', '129,38', '128,07'),\n",
       " ('25\\xa0Maret\\xa02019', '130,23', '128,90'),\n",
       " ('22\\xa0Maret\\xa02019', '128,50', '127,19'),\n",
       " ('21\\xa0Maret\\xa02019', '128,16', '126,84'),\n",
       " ('20\\xa0Maret\\xa02019', '128,14', '126,86'),\n",
       " ('19\\xa0Maret\\xa02019', '128,60', '127,30'),\n",
       " ('18\\xa0Maret\\xa02019', '128,26', '126,96'),\n",
       " ('15\\xa0Maret\\xa02019', '128,69', '127,36'),\n",
       " ('14\\xa0Maret\\xa02019', '128,55', '127,24'),\n",
       " ('13\\xa0Maret\\xa02019', '129,01', '127,70'),\n",
       " ('12\\xa0Maret\\xa02019', '128,56', '127,25'),\n",
       " ('11\\xa0Maret\\xa02019', '129,64', '128,33'),\n",
       " ('08\\xa0Maret\\xa02019', '128,19', '126,88'),\n",
       " ('06\\xa0Maret\\xa02019', '127,01', '125,73'),\n",
       " ('05\\xa0Maret\\xa02019', '127,10', '125,82'),\n",
       " ('04\\xa0Maret\\xa02019', '127,04', '125,74'),\n",
       " ('01\\xa0Maret\\xa02019', '126,99', '125,67'),\n",
       " ('28\\xa0Februari\\xa02019', '127,46', '126,19'),\n",
       " ('27\\xa0Februari\\xa02019', '127,29', '125,99'),\n",
       " ('26\\xa0Februari\\xa02019', '126,92', '125,61'),\n",
       " ('25\\xa0Februari\\xa02019', '127,16', '125,89'),\n",
       " ('22\\xa0Februari\\xa02019', '127,77', '126,47'),\n",
       " ('21\\xa0Februari\\xa02019', '127,57', '126,29'),\n",
       " ('20\\xa0Februari\\xa02019', '127,45', '126,15'),\n",
       " ('19\\xa0Februari\\xa02019', '128,42', '127,10'),\n",
       " ('18\\xa0Februari\\xa02019', '128,26', '126,97'),\n",
       " ('15\\xa0Februari\\xa02019', '128,61', '127,31'),\n",
       " ('14\\xa0Februari\\xa02019', '127,61', '126,33'),\n",
       " ('13\\xa0Februari\\xa02019', '127,47', '126,18'),\n",
       " ('12\\xa0Februari\\xa02019', '128,16', '126,88'),\n",
       " ('11\\xa0Februari\\xa02019', '128,07', '126,79'),\n",
       " ('08\\xa0Februari\\xa02019', '128,15', '126,85'),\n",
       " ('07\\xa0Februari\\xa02019', '127,92', '126,63'),\n",
       " ('06\\xa0Februari\\xa02019', '127,81', '126,52'),\n",
       " ('04\\xa0Februari\\xa02019', '128,24', '126,95'),\n",
       " ('01\\xa0Februari\\xa02019', '129,05', '127,75'),\n",
       " ('31\\xa0Januari\\xa02019', '129,92', '128,60'),\n",
       " ('30\\xa0Januari\\xa02019', '129,79', '128,47'),\n",
       " ('29\\xa0Januari\\xa02019', '129,74', '128,43'),\n",
       " ('28\\xa0Januari\\xa02019', '128,97', '127,65'),\n",
       " ('25\\xa0Januari\\xa02019', '129,68', '128,37'),\n",
       " ('24\\xa0Januari\\xa02019', '129,79', '128,48'),\n",
       " ('23\\xa0Januari\\xa02019', '130,05', '128,75'),\n",
       " ('22\\xa0Januari\\xa02019', '130,39', '129,07'),\n",
       " ('21\\xa0Januari\\xa02019', '130,31', '128,98'),\n",
       " ('18\\xa0Januari\\xa02019', '130,37', '129,03'),\n",
       " ('17\\xa0Januari\\xa02019', '130,61', '129,27'),\n",
       " ('16\\xa0Januari\\xa02019', '131,11', '129,75'),\n",
       " ('15\\xa0Januari\\xa02019', '130,56', '129,23'),\n",
       " ('14\\xa0Januari\\xa02019', '130,52', '129,19'),\n",
       " ('11\\xa0Januari\\xa02019', '130,62', '129,31'),\n",
       " ('10\\xa0Januari\\xa02019', '131,22', '129,88'),\n",
       " ('09\\xa0Januari\\xa02019', '130,31', '129,00'),\n",
       " ('08\\xa0Januari\\xa02019', '129,84', '128,52'),\n",
       " ('07\\xa0Januari\\xa02019', '130,86', '129,51'),\n",
       " ('04\\xa0Januari\\xa02019', '133,51', '132,17'),\n",
       " ('03\\xa0Januari\\xa02019', '135,44', '134,08'),\n",
       " ('02\\xa0Januari\\xa02019', '132,89', '131,54')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = [] #initiating a tuple\n",
    "\n",
    "for i in range(1, len(tr)):\n",
    "    row = table.find_all('tr')[i]\n",
    "    \n",
    "    #get tanggal\n",
    "    tanggal = row.find_all('td')[0].text\n",
    "    tanggal = tanggal.strip() #for removing the excess whitespace\n",
    "    \n",
    "    #get ask\n",
    "    ask = row.find_all('td')[1].text\n",
    "    ask = ask.strip() #for removing the excess whitespace\n",
    "    \n",
    "    #get bid\n",
    "    bid = row.find_all('td')[2].text\n",
    "    bid = bid.strip() #for removing the excess whitespace\n",
    "    \n",
    "    temp.append((tanggal, ask, bid)) \n",
    "    \n",
    "temp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('02\\xa0Januari\\xa02019', '132,89', '131,54'),\n",
       " ('03\\xa0Januari\\xa02019', '135,44', '134,08'),\n",
       " ('04\\xa0Januari\\xa02019', '133,51', '132,17'),\n",
       " ('07\\xa0Januari\\xa02019', '130,86', '129,51'),\n",
       " ('08\\xa0Januari\\xa02019', '129,84', '128,52'),\n",
       " ('09\\xa0Januari\\xa02019', '130,31', '129,00'),\n",
       " ('10\\xa0Januari\\xa02019', '131,22', '129,88'),\n",
       " ('11\\xa0Januari\\xa02019', '130,62', '129,31'),\n",
       " ('14\\xa0Januari\\xa02019', '130,52', '129,19'),\n",
       " ('15\\xa0Januari\\xa02019', '130,56', '129,23'),\n",
       " ('16\\xa0Januari\\xa02019', '131,11', '129,75'),\n",
       " ('17\\xa0Januari\\xa02019', '130,61', '129,27'),\n",
       " ('18\\xa0Januari\\xa02019', '130,37', '129,03'),\n",
       " ('21\\xa0Januari\\xa02019', '130,31', '128,98'),\n",
       " ('22\\xa0Januari\\xa02019', '130,39', '129,07'),\n",
       " ('23\\xa0Januari\\xa02019', '130,05', '128,75'),\n",
       " ('24\\xa0Januari\\xa02019', '129,79', '128,48'),\n",
       " ('25\\xa0Januari\\xa02019', '129,68', '128,37'),\n",
       " ('28\\xa0Januari\\xa02019', '128,97', '127,65'),\n",
       " ('29\\xa0Januari\\xa02019', '129,74', '128,43'),\n",
       " ('30\\xa0Januari\\xa02019', '129,79', '128,47'),\n",
       " ('31\\xa0Januari\\xa02019', '129,92', '128,60'),\n",
       " ('01\\xa0Februari\\xa02019', '129,05', '127,75'),\n",
       " ('04\\xa0Februari\\xa02019', '128,24', '126,95'),\n",
       " ('06\\xa0Februari\\xa02019', '127,81', '126,52'),\n",
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       " ('08\\xa0Februari\\xa02019', '128,15', '126,85'),\n",
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       " ('12\\xa0Februari\\xa02019', '128,16', '126,88'),\n",
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       " ('14\\xa0Februari\\xa02019', '127,61', '126,33'),\n",
       " ('15\\xa0Februari\\xa02019', '128,61', '127,31'),\n",
       " ('18\\xa0Februari\\xa02019', '128,26', '126,97'),\n",
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       " ('20\\xa0Februari\\xa02019', '127,45', '126,15'),\n",
       " ('21\\xa0Februari\\xa02019', '127,57', '126,29'),\n",
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       " ('27\\xa0Februari\\xa02019', '127,29', '125,99'),\n",
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       " ('01\\xa0Maret\\xa02019', '126,99', '125,67'),\n",
       " ('04\\xa0Maret\\xa02019', '127,04', '125,74'),\n",
       " ('05\\xa0Maret\\xa02019', '127,10', '125,82'),\n",
       " ('06\\xa0Maret\\xa02019', '127,01', '125,73'),\n",
       " ('08\\xa0Maret\\xa02019', '128,19', '126,88'),\n",
       " ('11\\xa0Maret\\xa02019', '129,64', '128,33'),\n",
       " ('12\\xa0Maret\\xa02019', '128,56', '127,25'),\n",
       " ('13\\xa0Maret\\xa02019', '129,01', '127,70'),\n",
       " ('14\\xa0Maret\\xa02019', '128,55', '127,24'),\n",
       " ('15\\xa0Maret\\xa02019', '128,69', '127,36'),\n",
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       " ('19\\xa0Maret\\xa02019', '128,60', '127,30'),\n",
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       " ('21\\xa0Maret\\xa02019', '128,16', '126,84'),\n",
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       " ('25\\xa0Maret\\xa02019', '130,23', '128,90'),\n",
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       " ('28\\xa0Maret\\xa02019', '130,04', '128,72'),\n",
       " ('29\\xa0Maret\\xa02019', '129,21', '127,92'),\n",
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       " ('12\\xa0April\\xa02019', '127,35', '126,06'),\n",
       " ('15\\xa0April\\xa02019', '126,25', '124,96'),\n",
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       " ('24\\xa0April\\xa02019', '126,78', '125,49'),\n",
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       " ('26\\xa0April\\xa02019', '127,70', '126,42'),\n",
       " ('29\\xa0April\\xa02019', '127,77', '126,49'),\n",
       " ('30\\xa0April\\xa02019', '128,02', '126,72'),\n",
       " ('02\\xa0Mei\\xa02019', '128,31', '127,01'),\n",
       " ('03\\xa0Mei\\xa02019', '128,83', '127,52'),\n",
       " ('06\\xa0Mei\\xa02019', '130,14', '128,80'),\n",
       " ('07\\xa0Mei\\xa02019', '129,91', '128,60'),\n",
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       " ('19\\xa0Juli\\xa02019', '130,04', '128,72'),\n",
       " ('22\\xa0Juli\\xa02019', '129,98', '128,65'),\n",
       " ('23\\xa0Juli\\xa02019', '130,00', '128,67'),\n",
       " ('24\\xa0Juli\\xa02019', '130,15', '128,84'),\n",
       " ('25\\xa0Juli\\xa02019', '129,98', '128,65'),\n",
       " ('26\\xa0Juli\\xa02019', '129,56', '128,23'),\n",
       " ('29\\xa0Juli\\xa02019', '129,81', '128,48'),\n",
       " ('30\\xa0Juli\\xa02019', '129,68', '128,36'),\n",
       " ('31\\xa0Juli\\xa02019', '129,87', '128,54'),\n",
       " ('01\\xa0Agustus\\xa02019', '129,80', '128,49'),\n",
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       " ('26\\xa0Agustus\\xa02019', '136,20', '134,81'),\n",
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       " ('29\\xa0Agustus\\xa02019', '135,32', '133,95'),\n",
       " ('30\\xa0Agustus\\xa02019', '134,33', '132,96'),\n",
       " ('02\\xa0September\\xa02019', '134,30', '132,92'),\n",
       " ('03\\xa0September\\xa02019', '134,39', '133,01'),\n",
       " ('04\\xa0September\\xa02019', '134,85', '133,47'),\n",
       " ('05\\xa0September\\xa02019', '133,37', '132,00'),\n",
       " ('06\\xa0September\\xa02019', '132,73', '131,39'),\n",
       " ('09\\xa0September\\xa02019', '132,50', '131,16'),\n",
       " ('10\\xa0September\\xa02019', '131,34', '130,00'),\n",
       " ('11\\xa0September\\xa02019', '131,30', '129,96'),\n",
       " ('12\\xa0September\\xa02019', '130,75', '129,42'),\n",
       " ('13\\xa0September\\xa02019', '129,63', '128,30'),\n",
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       " ('18\\xa0September\\xa02019', '130,75', '129,43'),\n",
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       " ('24\\xa0September\\xa02019', '131,73', '130,39'),\n",
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       " ('30\\xa0Oktober\\xa02019', '129,67', '128,34'),\n",
       " ('31\\xa0Oktober\\xa02019', '129,57', '128,25'),\n",
       " ('01\\xa0November\\xa02019', '130,88', '129,54'),\n",
       " ('04\\xa0November\\xa02019', '130,04', '128,71'),\n",
       " ('05\\xa0November\\xa02019', '129,67', '128,34'),\n",
       " ('06\\xa0November\\xa02019', '128,96', '127,64'),\n",
       " ('07\\xa0November\\xa02019', '129,71', '128,41'),\n",
       " ('08\\xa0November\\xa02019', '129,03', '127,73'),\n",
       " ('11\\xa0November\\xa02019', '129,37', '128,07'),\n",
       " ('12\\xa0November\\xa02019', '129,45', '128,15'),\n",
       " ('13\\xa0November\\xa02019', '129,86', '128,53'),\n",
       " ('14\\xa0November\\xa02019', '130,34', '129,01'),\n",
       " ('15\\xa0November\\xa02019', '130,23', '128,90'),\n",
       " ('18\\xa0November\\xa02019', '130,02', '128,70'),\n",
       " ('19\\xa0November\\xa02019', '130,34', '129,03'),\n",
       " ('20\\xa0November\\xa02019', '130,56', '129,23'),\n",
       " ('21\\xa0November\\xa02019', '130,73', '129,39'),\n",
       " ('22\\xa0November\\xa02019', '130,44', '129,10'),\n",
       " ('25\\xa0November\\xa02019', '130,20', '128,89'),\n",
       " ('26\\xa0November\\xa02019', '129,82', '128,49'),\n",
       " ('27\\xa0November\\xa02019', '129,84', '128,53'),\n",
       " ('28\\xa0November\\xa02019', '129,47', '128,15'),\n",
       " ('29\\xa0November\\xa02019', '129,44', '128,12'),\n",
       " ('02\\xa0Desember\\xa02019', '129,36', '128,04'),\n",
       " ('03\\xa0Desember\\xa02019', '130,15', '128,82'),\n",
       " ('04\\xa0Desember\\xa02019', '130,79', '129,45'),\n",
       " ('05\\xa0Desember\\xa02019', '130,10', '128,77'),\n",
       " ('06\\xa0Desember\\xa02019', '129,78', '128,45'),\n",
       " ('09\\xa0Desember\\xa02019', '129,76', '128,44'),\n",
       " ('10\\xa0Desember\\xa02019', '129,56', '128,26'),\n",
       " ('11\\xa0Desember\\xa02019', '129,65', '128,34'),\n",
       " ('12\\xa0Desember\\xa02019', '130,04', '128,71'),\n",
       " ('13\\xa0Desember\\xa02019', '128,30', '127,02'),\n",
       " ('16\\xa0Desember\\xa02019', '128,67', '127,36'),\n",
       " ('17\\xa0Desember\\xa02019', '128,62', '127,33'),\n",
       " ('18\\xa0Desember\\xa02019', '128,64', '127,32'),\n",
       " ('19\\xa0Desember\\xa02019', '128,24', '126,93'),\n",
       " ('20\\xa0Desember\\xa02019', '128,63', '127,34'),\n",
       " ('23\\xa0Desember\\xa02019', '128,39', '127,10'),\n",
       " ('26\\xa0Desember\\xa02019', '128,27', '126,98'),\n",
       " ('27\\xa0Desember\\xa02019', '128,11', '126,83'),\n",
       " ('30\\xa0Desember\\xa02019', '128,30', '127,02'),\n",
       " ('31\\xa0Desember\\xa02019', '128,62', '127,32')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = temp[::-1]\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>tanggal</th>\n",
       "      <th>ask</th>\n",
       "      <th>bid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02 Januari 2019</td>\n",
       "      <td>132,89</td>\n",
       "      <td>131,54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03 Januari 2019</td>\n",
       "      <td>135,44</td>\n",
       "      <td>134,08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04 Januari 2019</td>\n",
       "      <td>133,51</td>\n",
       "      <td>132,17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07 Januari 2019</td>\n",
       "      <td>130,86</td>\n",
       "      <td>129,51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08 Januari 2019</td>\n",
       "      <td>129,84</td>\n",
       "      <td>128,52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tanggal     ask     bid\n",
       "0  02 Januari 2019  132,89  131,54\n",
       "1  03 Januari 2019  135,44  134,08\n",
       "2  04 Januari 2019  133,51  132,17\n",
       "3  07 Januari 2019  130,86  129,51\n",
       "4  08 Januari 2019  129,84  128,52"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(temp, columns = ('tanggal', 'ask', 'bid'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ask'] = df['ask'].str.replace(\",\",\".\")\n",
    "df['ask'] = df['ask'].astype('float64')\n",
    "df['bid'] = df['bid'].str.replace(\",\",\".\")\n",
    "df['bid'] = df['bid'].astype('float64')\n",
    "df['bid'] = df['bid'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>tanggal</th>\n",
       "      <th>ask</th>\n",
       "      <th>bid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>132.89</td>\n",
       "      <td>131.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>135.44</td>\n",
       "      <td>134.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>133.51</td>\n",
       "      <td>132.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>130.86</td>\n",
       "      <td>129.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>129.84</td>\n",
       "      <td>128.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>128.39</td>\n",
       "      <td>127.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>128.27</td>\n",
       "      <td>126.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>128.11</td>\n",
       "      <td>126.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>128.30</td>\n",
       "      <td>127.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>128.62</td>\n",
       "      <td>127.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tanggal     ask     bid\n",
       "0   2019-01-02  132.89  131.54\n",
       "1   2019-01-03  135.44  134.08\n",
       "2   2019-01-04  133.51  132.17\n",
       "3   2019-01-07  130.86  129.51\n",
       "4   2019-01-08  129.84  128.52\n",
       "..         ...     ...     ...\n",
       "241 2019-12-23  128.39  127.10\n",
       "242 2019-12-26  128.27  126.98\n",
       "243 2019-12-27  128.11  126.83\n",
       "244 2019-12-30  128.30  127.02\n",
       "245 2019-12-31  128.62  127.32\n",
       "\n",
       "[246 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tanggal'] = df['tanggal'].replace('Januari', 'January', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Februari', 'February', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Maret', 'March', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('April', 'April', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Mei', 'May', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Juni', 'June', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Juli', 'July', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Agustus', 'August', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('September', 'September', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Oktober', 'October', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('November', 'November', regex=True)\n",
    "df['tanggal'] = df['tanggal'].replace('Desember', 'December', regex=True)\n",
    "df['tanggal'] = df['tanggal'].astype('datetime64')\n",
    "df.dtypes\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2540f34c2e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(x='tanggal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Movement of IDR vs JPY fluctuated in a relatively volatile manner in the range of 10 - 15 bps throughout 2019 due to internal and external factors such as:\n",
    "1. General presidential election\n",
    "2. Large demonstration of students rejecting bills related to Corruption Eradication Committee\n",
    "3. Volatility in world's market due to ongoing trade war"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:bebas]",
   "language": "python",
   "name": "conda-env-bebas-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}