3-6_USDPHP_PSEI-Saplala_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "114aaf0d-2e4a-481c-b09c-52bf444f1284",
   "metadata": {},
   "source": [
    "# INTRODUCTION #\n",
    "\n",
    "Appending and Merging datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae3b094f-726d-4c5a-9e1e-0380924de79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PACKAGES / PYTHON MODULES\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import openpyxl\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37669019-e426-4e72-b917-3db4d11f67fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New working directory is:  /Users/dino/Desktop/2022-Intersession/ECON197\n"
     ]
    }
   ],
   "source": [
    "#SET WORKING DIRECTORY\n",
    "absolute_path = '/Users/dino/Desktop/2022-Intersession/ECON197'\n",
    "os.chdir(absolute_path)\n",
    "\n",
    "print('New working directory is: ', os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4be2b7fe-285c-4fcc-b62e-ff41474deeb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Working Directory is:  /Users/dino/Desktop/2022-Intersession/ECON197\n"
     ]
    }
   ],
   "source": [
    "#GET WORKING DIRECTORY\n",
    "cwd = os.getcwd()\n",
    "print('Current Working Directory is: ', cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e2194f7-dd85-4c42-bc39-4834a5b1cac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_Index</th>\n",
       "      <th>Date</th>\n",
       "      <th>USD/PHP - Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122.0</td>\n",
       "      <td>7/31/12</td>\n",
       "      <td>41.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123.0</td>\n",
       "      <td>8/31/12</td>\n",
       "      <td>42.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.0</td>\n",
       "      <td>9/28/12</td>\n",
       "      <td>41.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125.0</td>\n",
       "      <td>10/31/12</td>\n",
       "      <td>41.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>126.0</td>\n",
       "      <td>11/30/12</td>\n",
       "      <td>40.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>238.0</td>\n",
       "      <td>3/31/22</td>\n",
       "      <td>51.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>239.0</td>\n",
       "      <td>4/29/22</td>\n",
       "      <td>52.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>240.0</td>\n",
       "      <td>5/31/22</td>\n",
       "      <td>52.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>241.0</td>\n",
       "      <td>6/30/22</td>\n",
       "      <td>54.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month_Index      Date  USD/PHP - Close\n",
       "0          122.0   7/31/12            41.74\n",
       "1          123.0   8/31/12            42.08\n",
       "2          124.0   9/28/12            41.72\n",
       "3          125.0  10/31/12            41.20\n",
       "4          126.0  11/30/12            40.89\n",
       "..           ...       ...              ...\n",
       "116        238.0   3/31/22            51.74\n",
       "117        239.0   4/29/22            52.20\n",
       "118        240.0   5/31/22            52.39\n",
       "119        241.0   6/30/22            54.98\n",
       "120          NaN       NaN              NaN\n",
       "\n",
       "[121 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#csv example: PSEI \n",
    "psei_1 = pd.read_csv('datasets/finance/PSEI_2002-2012.csv')\n",
    "psei_2 = pd.read_csv('datasets/finance/PSEI_2012-2022.csv')\n",
    "\n",
    "#csv example: USDPHP 2012-2022\n",
    "usdphp_1 = pd.read_csv('datasets/finance/USDPHP_2002-2012.csv')\n",
    "usdphp_2 = pd.read_csv('datasets/finance/USDPHP_2012-2022.csv')\n",
    "\n",
    "#Display dataset\n",
    "usdphp_2\n",
    "#psei_2\n",
    "\n",
    "#View whole dataframe\n",
    "#print(usdphp_1, psei_1)\n",
    "\n",
    "#print(psei_1.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "618a9695-0e4a-4dc3-9847-c9ea990be896",
   "metadata": {},
   "outputs": [],
   "source": [
    "dollar_stocks1 = usdphp_1.set_index('Month_Index').join(psei_1.set_index('Month_Index'),lsuffix='_USD',rsuffix='_PSEI')\n",
    "dollar_stocks2 = usdphp_2.set_index('Month_Index').join(psei_2.set_index('Month_Index'),lsuffix='_USD',rsuffix='_PSEI')\n",
    "\n",
    "#print(dollar_stocks1.to_string())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33cc9dcf-ba49-4b54-8187-34c52c8153b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(dollar_stocks2.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18b2bc37-a092-4b96-903f-159596d42fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_USD</th>\n",
       "      <th>USD/PHP - Close</th>\n",
       "      <th>Date_PSEI</th>\n",
       "      <th>Philippines PSE PSEi - Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6/28/02</td>\n",
       "      <td>50.32</td>\n",
       "      <td>6/28/02</td>\n",
       "      <td>1156.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>7/31/02</td>\n",
       "      <td>51.27</td>\n",
       "      <td>7/31/02</td>\n",
       "      <td>1123.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>8/30/02</td>\n",
       "      <td>51.84</td>\n",
       "      <td>8/30/02</td>\n",
       "      <td>1103.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>9/30/02</td>\n",
       "      <td>52.43</td>\n",
       "      <td>9/30/02</td>\n",
       "      <td>1129.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>10/31/02</td>\n",
       "      <td>53.13</td>\n",
       "      <td>10/30/02</td>\n",
       "      <td>1048.53</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>237.0</th>\n",
       "      <td>2/28/22</td>\n",
       "      <td>51.27</td>\n",
       "      <td>2/28/22</td>\n",
       "      <td>7311.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238.0</th>\n",
       "      <td>3/31/22</td>\n",
       "      <td>51.74</td>\n",
       "      <td>3/31/22</td>\n",
       "      <td>7203.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239.0</th>\n",
       "      <td>4/29/22</td>\n",
       "      <td>52.20</td>\n",
       "      <td>4/29/22</td>\n",
       "      <td>6731.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240.0</th>\n",
       "      <td>5/31/22</td>\n",
       "      <td>52.39</td>\n",
       "      <td>5/31/22</td>\n",
       "      <td>6774.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241.0</th>\n",
       "      <td>6/30/22</td>\n",
       "      <td>54.98</td>\n",
       "      <td>6/30/22</td>\n",
       "      <td>6155.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date_USD  USD/PHP - Close Date_PSEI  Philippines PSE PSEi - Close\n",
       "Month_Index                                                                   \n",
       "1.0           6/28/02            50.32   6/28/02                       1156.35\n",
       "2.0           7/31/02            51.27   7/31/02                       1123.24\n",
       "3.0           8/30/02            51.84   8/30/02                       1103.36\n",
       "4.0           9/30/02            52.43   9/30/02                       1129.34\n",
       "5.0          10/31/02            53.13  10/30/02                       1048.53\n",
       "...               ...              ...       ...                           ...\n",
       "237.0         2/28/22            51.27   2/28/22                       7311.01\n",
       "238.0         3/31/22            51.74   3/31/22                       7203.47\n",
       "239.0         4/29/22            52.20   4/29/22                       6731.25\n",
       "240.0         5/31/22            52.39   5/31/22                       6774.68\n",
       "241.0         6/30/22            54.98   6/30/22                       6155.43\n",
       "\n",
       "[241 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = pd.concat([dollar_stocks1,dollar_stocks2])\n",
    "\n",
    "#Remove rows with empty cells\n",
    "combined_data = combined_data.dropna(axis=0)\n",
    "\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "522cfc74-bdfd-4db6-bc35-6527a672d3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
       "               10.0,\n",
       "              ...\n",
       "              232.0, 233.0, 234.0, 235.0, 236.0, 237.0, 238.0, 239.0, 240.0,\n",
       "              241.0],\n",
       "             dtype='float64', name='Month_Index', length=241)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e52e05b-91bb-4902-b47e-06a36bb10735",
   "metadata": {},
   "source": [
    "## EXERCISE ##\n",
    "\n",
    "(1) Rename the index in combined_data to a label representing month, year from the Data_USD and Date_PSEI columns.\n",
    "\\\n",
    "(2) Drop the extra date columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57cdb821-f630-4c39-9da1-87b92ceba049",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_USD</th>\n",
       "      <th>USD/PHP - Close</th>\n",
       "      <th>Date_PSEI</th>\n",
       "      <th>Philippines PSE PSEi - Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6/28/02</td>\n",
       "      <td>50.32</td>\n",
       "      <td>6/28/02</td>\n",
       "      <td>1156.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>7/31/02</td>\n",
       "      <td>51.27</td>\n",
       "      <td>7/31/02</td>\n",
       "      <td>1123.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>8/30/02</td>\n",
       "      <td>51.84</td>\n",
       "      <td>8/30/02</td>\n",
       "      <td>1103.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>9/30/02</td>\n",
       "      <td>52.43</td>\n",
       "      <td>9/30/02</td>\n",
       "      <td>1129.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>10/31/02</td>\n",
       "      <td>53.13</td>\n",
       "      <td>10/30/02</td>\n",
       "      <td>1048.53</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>237.0</th>\n",
       "      <td>2/28/22</td>\n",
       "      <td>51.27</td>\n",
       "      <td>2/28/22</td>\n",
       "      <td>7311.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238.0</th>\n",
       "      <td>3/31/22</td>\n",
       "      <td>51.74</td>\n",
       "      <td>3/31/22</td>\n",
       "      <td>7203.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239.0</th>\n",
       "      <td>4/29/22</td>\n",
       "      <td>52.20</td>\n",
       "      <td>4/29/22</td>\n",
       "      <td>6731.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240.0</th>\n",
       "      <td>5/31/22</td>\n",
       "      <td>52.39</td>\n",
       "      <td>5/31/22</td>\n",
       "      <td>6774.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241.0</th>\n",
       "      <td>6/30/22</td>\n",
       "      <td>54.98</td>\n",
       "      <td>6/30/22</td>\n",
       "      <td>6155.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date_USD  USD/PHP - Close Date_PSEI  Philippines PSE PSEi - Close\n",
       "Month_Index                                                                   \n",
       "1.0           6/28/02            50.32   6/28/02                       1156.35\n",
       "2.0           7/31/02            51.27   7/31/02                       1123.24\n",
       "3.0           8/30/02            51.84   8/30/02                       1103.36\n",
       "4.0           9/30/02            52.43   9/30/02                       1129.34\n",
       "5.0          10/31/02            53.13  10/30/02                       1048.53\n",
       "...               ...              ...       ...                           ...\n",
       "237.0         2/28/22            51.27   2/28/22                       7311.01\n",
       "238.0         3/31/22            51.74   3/31/22                       7203.47\n",
       "239.0         4/29/22            52.20   4/29/22                       6731.25\n",
       "240.0         5/31/22            52.39   5/31/22                       6774.68\n",
       "241.0         6/30/22            54.98   6/30/22                       6155.43\n",
       "\n",
       "[241 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6556770f-891e-41b2-8a38-2d19d606d94e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_USD</th>\n",
       "      <th>USD/PHP - Close</th>\n",
       "      <th>Date_PSEI</th>\n",
       "      <th>Philippines PSE PSEi - Close</th>\n",
       "      <th>parsed_dates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6/28/02</td>\n",
       "      <td>50.32</td>\n",
       "      <td>6/28/02</td>\n",
       "      <td>1156.35</td>\n",
       "      <td>2002-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>7/31/02</td>\n",
       "      <td>51.27</td>\n",
       "      <td>7/31/02</td>\n",
       "      <td>1123.24</td>\n",
       "      <td>2002-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>8/30/02</td>\n",
       "      <td>51.84</td>\n",
       "      <td>8/30/02</td>\n",
       "      <td>1103.36</td>\n",
       "      <td>2002-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>9/30/02</td>\n",
       "      <td>52.43</td>\n",
       "      <td>9/30/02</td>\n",
       "      <td>1129.34</td>\n",
       "      <td>2002-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>10/31/02</td>\n",
       "      <td>53.13</td>\n",
       "      <td>10/30/02</td>\n",
       "      <td>1048.53</td>\n",
       "      <td>2002-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237.0</th>\n",
       "      <td>2/28/22</td>\n",
       "      <td>51.27</td>\n",
       "      <td>2/28/22</td>\n",
       "      <td>7311.01</td>\n",
       "      <td>2022-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238.0</th>\n",
       "      <td>3/31/22</td>\n",
       "      <td>51.74</td>\n",
       "      <td>3/31/22</td>\n",
       "      <td>7203.47</td>\n",
       "      <td>2022-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239.0</th>\n",
       "      <td>4/29/22</td>\n",
       "      <td>52.20</td>\n",
       "      <td>4/29/22</td>\n",
       "      <td>6731.25</td>\n",
       "      <td>2022-04-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240.0</th>\n",
       "      <td>5/31/22</td>\n",
       "      <td>52.39</td>\n",
       "      <td>5/31/22</td>\n",
       "      <td>6774.68</td>\n",
       "      <td>2022-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241.0</th>\n",
       "      <td>6/30/22</td>\n",
       "      <td>54.98</td>\n",
       "      <td>6/30/22</td>\n",
       "      <td>6155.43</td>\n",
       "      <td>2022-06-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date_USD  USD/PHP - Close Date_PSEI  \\\n",
       "Month_Index                                        \n",
       "1.0           6/28/02            50.32   6/28/02   \n",
       "2.0           7/31/02            51.27   7/31/02   \n",
       "3.0           8/30/02            51.84   8/30/02   \n",
       "4.0           9/30/02            52.43   9/30/02   \n",
       "5.0          10/31/02            53.13  10/30/02   \n",
       "...               ...              ...       ...   \n",
       "237.0         2/28/22            51.27   2/28/22   \n",
       "238.0         3/31/22            51.74   3/31/22   \n",
       "239.0         4/29/22            52.20   4/29/22   \n",
       "240.0         5/31/22            52.39   5/31/22   \n",
       "241.0         6/30/22            54.98   6/30/22   \n",
       "\n",
       "             Philippines PSE PSEi - Close parsed_dates  \n",
       "Month_Index                                             \n",
       "1.0                               1156.35   2002-06-28  \n",
       "2.0                               1123.24   2002-07-31  \n",
       "3.0                               1103.36   2002-08-30  \n",
       "4.0                               1129.34   2002-09-30  \n",
       "5.0                               1048.53   2002-10-31  \n",
       "...                                   ...          ...  \n",
       "237.0                             7311.01   2022-02-28  \n",
       "238.0                             7203.47   2022-03-31  \n",
       "239.0                             6731.25   2022-04-29  \n",
       "240.0                             6774.68   2022-05-31  \n",
       "241.0                             6155.43   2022-06-30  \n",
       "\n",
       "[241 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data['parsed_dates'] = pd.to_datetime(combined_data['Date_USD'], \\\n",
    "                                               format=\"%m/%d/%y\")\n",
    "\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d4f9dcff-a4eb-4509-abf5-eac188e2fe7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_USD</th>\n",
       "      <th>USD/PHP - Close</th>\n",
       "      <th>Date_PSEI</th>\n",
       "      <th>Philippines PSE PSEi - Close</th>\n",
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       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
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       "    <tr>\n",
       "      <th>YearMonth_Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200206</th>\n",
       "      <td>6/28/02</td>\n",
       "      <td>50.32</td>\n",
       "      <td>6/28/02</td>\n",
       "      <td>1156.35</td>\n",
       "      <td>2002-06-28</td>\n",
       "      <td>6</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200207</th>\n",
       "      <td>7/31/02</td>\n",
       "      <td>51.27</td>\n",
       "      <td>7/31/02</td>\n",
       "      <td>1123.24</td>\n",
       "      <td>2002-07-31</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200208</th>\n",
       "      <td>8/30/02</td>\n",
       "      <td>51.84</td>\n",
       "      <td>8/30/02</td>\n",
       "      <td>1103.36</td>\n",
       "      <td>2002-08-30</td>\n",
       "      <td>8</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200209</th>\n",
       "      <td>9/30/02</td>\n",
       "      <td>52.43</td>\n",
       "      <td>9/30/02</td>\n",
       "      <td>1129.34</td>\n",
       "      <td>2002-09-30</td>\n",
       "      <td>9</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200210</th>\n",
       "      <td>10/31/02</td>\n",
       "      <td>53.13</td>\n",
       "      <td>10/30/02</td>\n",
       "      <td>1048.53</td>\n",
       "      <td>2002-10-31</td>\n",
       "      <td>10</td>\n",
       "      <td>2002</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202202</th>\n",
       "      <td>2/28/22</td>\n",
       "      <td>51.27</td>\n",
       "      <td>2/28/22</td>\n",
       "      <td>7311.01</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>2</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202203</th>\n",
       "      <td>3/31/22</td>\n",
       "      <td>51.74</td>\n",
       "      <td>3/31/22</td>\n",
       "      <td>7203.47</td>\n",
       "      <td>2022-03-31</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202204</th>\n",
       "      <td>4/29/22</td>\n",
       "      <td>52.20</td>\n",
       "      <td>4/29/22</td>\n",
       "      <td>6731.25</td>\n",
       "      <td>2022-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202205</th>\n",
       "      <td>5/31/22</td>\n",
       "      <td>52.39</td>\n",
       "      <td>5/31/22</td>\n",
       "      <td>6774.68</td>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>5</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202206</th>\n",
       "      <td>6/30/22</td>\n",
       "      <td>54.98</td>\n",
       "      <td>6/30/22</td>\n",
       "      <td>6155.43</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>6</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date_USD  USD/PHP - Close Date_PSEI  \\\n",
       "YearMonth_Index                                        \n",
       "200206            6/28/02            50.32   6/28/02   \n",
       "200207            7/31/02            51.27   7/31/02   \n",
       "200208            8/30/02            51.84   8/30/02   \n",
       "200209            9/30/02            52.43   9/30/02   \n",
       "200210           10/31/02            53.13  10/30/02   \n",
       "...                   ...              ...       ...   \n",
       "202202            2/28/22            51.27   2/28/22   \n",
       "202203            3/31/22            51.74   3/31/22   \n",
       "202204            4/29/22            52.20   4/29/22   \n",
       "202205            5/31/22            52.39   5/31/22   \n",
       "202206            6/30/22            54.98   6/30/22   \n",
       "\n",
       "                 Philippines PSE PSEi - Close parsed_dates  Month  Year  \n",
       "YearMonth_Index                                                          \n",
       "200206                                1156.35   2002-06-28      6  2002  \n",
       "200207                                1123.24   2002-07-31      7  2002  \n",
       "200208                                1103.36   2002-08-30      8  2002  \n",
       "200209                                1129.34   2002-09-30      9  2002  \n",
       "200210                                1048.53   2002-10-31     10  2002  \n",
       "...                                       ...          ...    ...   ...  \n",
       "202202                                7311.01   2022-02-28      2  2022  \n",
       "202203                                7203.47   2022-03-31      3  2022  \n",
       "202204                                6731.25   2022-04-29      4  2022  \n",
       "202205                                6774.68   2022-05-31      5  2022  \n",
       "202206                                6155.43   2022-06-30      6  2022  \n",
       "\n",
       "[241 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data['Month'] = combined_data['parsed_dates'].dt.month\n",
    "combined_data['Year'] = combined_data['parsed_dates'].dt.year\n",
    "\n",
    "\n",
    "combined_data['YearMonth_Index'] = 100*combined_data['Year'] + combined_data['Month']\n",
    "\n",
    "combined_data2 = combined_data.set_index('YearMonth_Index')\n",
    "\n",
    "combined_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d805d21-c906-4a2f-b518-a0395efac7c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD/PHP - Close</th>\n",
       "      <th>Philippines PSE PSEi - Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMonth_Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200206</th>\n",
       "      <td>50.32</td>\n",
       "      <td>1156.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200207</th>\n",
       "      <td>51.27</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>200208</th>\n",
       "      <td>51.84</td>\n",
       "      <td>1103.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200209</th>\n",
       "      <td>52.43</td>\n",
       "      <td>1129.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200210</th>\n",
       "      <td>53.13</td>\n",
       "      <td>1048.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202202</th>\n",
       "      <td>51.27</td>\n",
       "      <td>7311.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202203</th>\n",
       "      <td>51.74</td>\n",
       "      <td>7203.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202204</th>\n",
       "      <td>52.20</td>\n",
       "      <td>6731.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202205</th>\n",
       "      <td>52.39</td>\n",
       "      <td>6774.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202206</th>\n",
       "      <td>54.98</td>\n",
       "      <td>6155.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 USD/PHP - Close  Philippines PSE PSEi - Close\n",
       "YearMonth_Index                                               \n",
       "200206                     50.32                       1156.35\n",
       "200207                     51.27                       1123.24\n",
       "200208                     51.84                       1103.36\n",
       "200209                     52.43                       1129.34\n",
       "200210                     53.13                       1048.53\n",
       "...                          ...                           ...\n",
       "202202                     51.27                       7311.01\n",
       "202203                     51.74                       7203.47\n",
       "202204                     52.20                       6731.25\n",
       "202205                     52.39                       6774.68\n",
       "202206                     54.98                       6155.43\n",
       "\n",
       "[241 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop = ['Date_USD','Date_PSEI','parsed_dates','Month','Year']\n",
    "\n",
    "combined_data2 = combined_data2.drop(columns_to_drop, axis=1,inplace=False)\n",
    "\n",
    "combined_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "db9869e4-050d-43c5-8184-769740fdd094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35625cd3-d73e-4fe6-80dd-394779ef7fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearMonth_Index'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.lineplot(data=combined_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ab47df76-8e09-403c-a28c-439ae4c57303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mCall signature:\u001b[0m  \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mType:\u001b[0m            ufunc\n",
       "\u001b[0;31mString form:\u001b[0m     <ufunc 'log'>\n",
       "\u001b[0;31mFile:\u001b[0m            ~/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages/numpy/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m      \n",
       "log(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n",
       "\n",
       "Natural logarithm, element-wise.\n",
       "\n",
       "The natural logarithm `log` is the inverse of the exponential function,\n",
       "so that `log(exp(x)) = x`. The natural logarithm is logarithm in base\n",
       "`e`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x : array_like\n",
       "    Input value.\n",
       "out : ndarray, None, or tuple of ndarray and None, optional\n",
       "    A location into which the result is stored. If provided, it must have\n",
       "    a shape that the inputs broadcast to. If not provided or None,\n",
       "    a freshly-allocated array is returned. A tuple (possible only as a\n",
       "    keyword argument) must have length equal to the number of outputs.\n",
       "where : array_like, optional\n",
       "    This condition is broadcast over the input. At locations where the\n",
       "    condition is True, the `out` array will be set to the ufunc result.\n",
       "    Elsewhere, the `out` array will retain its original value.\n",
       "    Note that if an uninitialized `out` array is created via the default\n",
       "    ``out=None``, locations within it where the condition is False will\n",
       "    remain uninitialized.\n",
       "**kwargs\n",
       "    For other keyword-only arguments, see the\n",
       "    :ref:`ufunc docs <ufuncs.kwargs>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "y : ndarray\n",
       "    The natural logarithm of `x`, element-wise.\n",
       "    This is a scalar if `x` is a scalar.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "log10, log2, log1p, emath.log\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Logarithm is a multivalued function: for each `x` there is an infinite\n",
       "number of `z` such that `exp(z) = x`. The convention is to return the\n",
       "`z` whose imaginary part lies in `[-pi, pi]`.\n",
       "\n",
       "For real-valued input data types, `log` always returns real output. For\n",
       "each value that cannot be expressed as a real number or infinity, it\n",
       "yields ``nan`` and sets the `invalid` floating point error flag.\n",
       "\n",
       "For complex-valued input, `log` is a complex analytical function that\n",
       "has a branch cut `[-inf, 0]` and is continuous from above on it. `log`\n",
       "handles the floating-point negative zero as an infinitesimal negative\n",
       "number, conforming to the C99 standard.\n",
       "\n",
       "References\n",
       "----------\n",
       ".. [1] M. Abramowitz and I.A. Stegun, \"Handbook of Mathematical Functions\",\n",
       "       10th printing, 1964, pp. 67.\n",
       "       https://personal.math.ubc.ca/~cbm/aands/page_67.htm\n",
       ".. [2] Wikipedia, \"Logarithm\". https://en.wikipedia.org/wiki/Logarithm\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> np.log([1, np.e, np.e**2, 0])\n",
       "array([  0.,   1.,   2., -Inf])\n",
       "\u001b[0;31mClass docstring:\u001b[0m\n",
       "Functions that operate element by element on whole arrays.\n",
       "\n",
       "To see the documentation for a specific ufunc, use `info`.  For\n",
       "example, ``np.info(np.sin)``.  Because ufuncs are written in C\n",
       "(for speed) and linked into Python with NumPy's ufunc facility,\n",
       "Python's help() function finds this page whenever help() is called\n",
       "on a ufunc.\n",
       "\n",
       "A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.\n",
       "\n",
       "**Calling ufuncs:** ``op(*x[, out], where=True, **kwargs)``\n",
       "\n",
       "Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.\n",
       "\n",
       "The broadcasting rules are:\n",
       "\n",
       "* Dimensions of length 1 may be prepended to either array.\n",
       "* Arrays may be repeated along dimensions of length 1.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "*x : array_like\n",
       "    Input arrays.\n",
       "out : ndarray, None, or tuple of ndarray and None, optional\n",
       "    Alternate array object(s) in which to put the result; if provided, it\n",
       "    must have a shape that the inputs broadcast to. A tuple of arrays\n",
       "    (possible only as a keyword argument) must have length equal to the\n",
       "    number of outputs; use None for uninitialized outputs to be\n",
       "    allocated by the ufunc.\n",
       "where : array_like, optional\n",
       "    This condition is broadcast over the input. At locations where the\n",
       "    condition is True, the `out` array will be set to the ufunc result.\n",
       "    Elsewhere, the `out` array will retain its original value.\n",
       "    Note that if an uninitialized `out` array is created via the default\n",
       "    ``out=None``, locations within it where the condition is False will\n",
       "    remain uninitialized.\n",
       "**kwargs\n",
       "    For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "r : ndarray or tuple of ndarray\n",
       "    `r` will have the shape that the arrays in `x` broadcast to; if `out` is\n",
       "    provided, it will be returned. If not, `r` will be allocated and\n",
       "    may contain uninitialized values. If the function has more than one\n",
       "    output, then the result will be a tuple of arrays.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4bbd053-dbf2-4fba-9107-c47c7ea4899f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Philippines PSE PSEi - Close</th>\n",
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       "      <th>YearMonth_Index</th>\n",
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       "      <th>200206</th>\n",
       "      <td>50.32</td>\n",
       "      <td>1156.35</td>\n",
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       "    <tr>\n",
       "      <th>200207</th>\n",
       "      <td>51.27</td>\n",
       "      <td>1123.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200208</th>\n",
       "      <td>51.84</td>\n",
       "      <td>1103.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200209</th>\n",
       "      <td>52.43</td>\n",
       "      <td>1129.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200210</th>\n",
       "      <td>53.13</td>\n",
       "      <td>1048.53</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <th>202202</th>\n",
       "      <td>51.27</td>\n",
       "      <td>7311.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202203</th>\n",
       "      <td>51.74</td>\n",
       "      <td>7203.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202204</th>\n",
       "      <td>52.20</td>\n",
       "      <td>6731.25</td>\n",
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       "    <tr>\n",
       "      <th>202205</th>\n",
       "      <td>52.39</td>\n",
       "      <td>6774.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202206</th>\n",
       "      <td>54.98</td>\n",
       "      <td>6155.43</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 USD/PHP - Close  Philippines PSE PSEi - Close\n",
       "YearMonth_Index                                               \n",
       "200206                     50.32                       1156.35\n",
       "200207                     51.27                       1123.24\n",
       "200208                     51.84                       1103.36\n",
       "200209                     52.43                       1129.34\n",
       "200210                     53.13                       1048.53\n",
       "...                          ...                           ...\n",
       "202202                     51.27                       7311.01\n",
       "202203                     51.74                       7203.47\n",
       "202204                     52.20                       6731.25\n",
       "202205                     52.39                       6774.68\n",
       "202206                     54.98                       6155.43\n",
       "\n",
       "[241 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27e382ac-dcf0-4ee0-ad15-e300caf6bbe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.log(combined_data2['Philippines PSE PSEi - Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0baaace-31b8-4cc9-a209-78e5b055af2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearMonth_Index\n",
       "200206    7.053024\n",
       "200207    7.023973\n",
       "200208    7.006115\n",
       "200209    7.029389\n",
       "200210    6.955144\n",
       "            ...   \n",
       "202202    8.897137\n",
       "202203    8.882318\n",
       "202204    8.814516\n",
       "202205    8.820947\n",
       "202206    8.725090\n",
       "Name: Philippines PSE PSEi - Close, Length: 241, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4c240c7-0eab-4d48-9291-26c76ba1bb76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>202206</th>\n",
       "      <td>54.98</td>\n",
       "      <td>6155.43</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 USD/PHP - Close  Philippines PSE PSEi - Close\n",
       "YearMonth_Index                                               \n",
       "200206                     50.32                       1156.35\n",
       "200207                     51.27                       1123.24\n",
       "200208                     51.84                       1103.36\n",
       "200209                     52.43                       1129.34\n",
       "200210                     53.13                       1048.53\n",
       "...                          ...                           ...\n",
       "202202                     51.27                       7311.01\n",
       "202203                     51.74                       7203.47\n",
       "202204                     52.20                       6731.25\n",
       "202205                     52.39                       6774.68\n",
       "202206                     54.98                       6155.43\n",
       "\n",
       "[241 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f7629ef1-a0fa-4fde-a0fb-de7450566d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data2['B'] = B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cc336dd4-e32c-4493-b970-0ad8678dc80c",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>200210</th>\n",
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       "      <td>6.955144</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>202203</th>\n",
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       "      <td>7203.47</td>\n",
       "      <td>8.882318</td>\n",
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       "    <tr>\n",
       "      <th>202204</th>\n",
       "      <td>52.20</td>\n",
       "      <td>6731.25</td>\n",
       "      <td>8.814516</td>\n",
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       "    <tr>\n",
       "      <th>202205</th>\n",
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       "<p>241 rows × 3 columns</p>\n",
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      ],
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       "                 USD/PHP - Close  Philippines PSE PSEi - Close         B\n",
       "YearMonth_Index                                                         \n",
       "200206                     50.32                       1156.35  7.053024\n",
       "200207                     51.27                       1123.24  7.023973\n",
       "200208                     51.84                       1103.36  7.006115\n",
       "200209                     52.43                       1129.34  7.029389\n",
       "200210                     53.13                       1048.53  6.955144\n",
       "...                          ...                           ...       ...\n",
       "202202                     51.27                       7311.01  8.897137\n",
       "202203                     51.74                       7203.47  8.882318\n",
       "202204                     52.20                       6731.25  8.814516\n",
       "202205                     52.39                       6774.68  8.820947\n",
       "202206                     54.98                       6155.43  8.725090\n",
       "\n",
       "[241 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "id": "14fc3a85-3305-416a-b56c-57e8b0576dd4",
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   "outputs": [],
   "source": []
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