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Add granger_causality_testing.ipynb

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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pandas as pd"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 6,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "df_apple = pd.read_csv('../coal-price-data/investing/AAPL Historical Data.csv')\n",
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+ "df_walmart = pd.read_csv('../coal-price-data/investing/WMT Historical Data.csv')\n",
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+ "df_tesla = pd.read_csv('../coal-price-data/investing/TSLA Historical Data.csv')"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 7,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>Date</th>\n",
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+ " <th>Price</th>\n",
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+ " <th>Open</th>\n",
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+ " <th>High</th>\n",
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+ " <th>Low</th>\n",
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+ " <th>Vol.</th>\n",
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+ " <th>Change %</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>02/01/2024</td>\n",
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+ " <td>182.52</td>\n",
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+ " <td>183.97</td>\n",
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+ " <td>191.00</td>\n",
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+ " <td>179.26</td>\n",
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+ " <td>45.12M</td>\n",
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+ " <td>-1.02%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>01/01/2024</td>\n",
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+ " <td>184.40</td>\n",
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+ " <td>187.15</td>\n",
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+ " <td>196.38</td>\n",
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+ " <td>180.17</td>\n",
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+ " <td>1.19B</td>\n",
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+ " <td>-4.22%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>12/01/2023</td>\n",
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+ " <td>192.53</td>\n",
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+ " <td>190.33</td>\n",
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+ " <td>199.62</td>\n",
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+ " <td>187.45</td>\n",
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+ " <td>1.06B</td>\n",
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+ " <td>1.36%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>11/01/2023</td>\n",
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+ " <td>189.95</td>\n",
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+ " <td>171.00</td>\n",
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+ " <td>192.93</td>\n",
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+ " <td>170.12</td>\n",
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+ " <td>1.10B</td>\n",
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+ " <td>11.23%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>10/01/2023</td>\n",
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+ " <td>170.77</td>\n",
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+ " <td>171.22</td>\n",
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+ " <td>182.34</td>\n",
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+ " <td>165.67</td>\n",
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+ " <td>1.17B</td>\n",
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+ " <td>-0.26%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>...</th>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>513</th>\n",
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+ " <td>05/01/1981</td>\n",
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+ " <td>0.15</td>\n",
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+ " <td>0.13</td>\n",
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+ " <td>0.15</td>\n",
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+ " <td>0.12</td>\n",
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+ " <td>590.42M</td>\n",
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+ " <td>15.38%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>514</th>\n",
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+ " <td>04/01/1981</td>\n",
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+ " <td>0.13</td>\n",
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+ " <td>0.11</td>\n",
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+ " <td>0.13</td>\n",
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+ " <td>0.11</td>\n",
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+ " <td>536.93M</td>\n",
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+ " <td>18.18%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>515</th>\n",
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+ " <td>03/01/1981</td>\n",
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+ " <td>0.11</td>\n",
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+ " <td>0.12</td>\n",
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+ " <td>0.12</td>\n",
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+ " <td>0.10</td>\n",
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+ " <td>700.72M</td>\n",
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+ " <td>-8.33%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>516</th>\n",
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+ " <td>02/01/1981</td>\n",
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+ " <td>0.12</td>\n",
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+ " <td>0.12</td>\n",
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+ " <td>0.13</td>\n",
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+ " <td>0.11</td>\n",
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+ " <td>321.62M</td>\n",
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+ " <td>-7.69%</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>517</th>\n",
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+ " <td>01/01/1981</td>\n",
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+ " <td>0.13</td>\n",
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+ " <td>0.15</td>\n",
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+ " <td>0.16</td>\n",
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+ " <td>0.13</td>\n",
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+ " <td>608.99M</td>\n",
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+ " <td>-13.33%</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "<p>518 rows × 7 columns</p>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " Date Price Open High Low Vol. Change %\n",
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+ "0 02/01/2024 182.52 183.97 191.00 179.26 45.12M -1.02%\n",
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+ "1 01/01/2024 184.40 187.15 196.38 180.17 1.19B -4.22%\n",
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+ "2 12/01/2023 192.53 190.33 199.62 187.45 1.06B 1.36%\n",
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+ "3 11/01/2023 189.95 171.00 192.93 170.12 1.10B 11.23%\n",
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+ "4 10/01/2023 170.77 171.22 182.34 165.67 1.17B -0.26%\n",
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+ ".. ... ... ... ... ... ... ...\n",
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+ "513 05/01/1981 0.15 0.13 0.15 0.12 590.42M 15.38%\n",
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+ "514 04/01/1981 0.13 0.11 0.13 0.11 536.93M 18.18%\n",
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+ "515 03/01/1981 0.11 0.12 0.12 0.10 700.72M -8.33%\n",
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+ "516 02/01/1981 0.12 0.12 0.13 0.11 321.62M -7.69%\n",
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+ "517 01/01/1981 0.13 0.15 0.16 0.13 608.99M -13.33%\n",
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+ "\n",
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+ "[518 rows x 7 columns]"
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+ ]
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+ },
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+ "execution_count": 7,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "df_apple"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "df = pd.merge(df_apple[['Date', 'Adj Close']], df_walmart[['Date', 'Adj Close']], on='Date', how='right').rename(columns = {'Adj Close_x':'apple', 'Adj Close_y':'walmart'})\n",
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+ "df = df.merge(df_tesla[['Date', 'Adj Close']], on='Date', how='right').rename(columns={'Adj Close':'tesla'})"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "py311-kfp240-airflow251",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.12.1"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+ }