{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_apple = pd.read_csv('../coal-price-data/investing/AAPL Historical Data.csv')\n",
    "df_walmart = pd.read_csv('../coal-price-data/investing/WMT Historical Data.csv')\n",
    "df_tesla = pd.read_csv('../coal-price-data/investing/TSLA Historical Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Date</th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02/01/2024</td>\n",
       "      <td>182.52</td>\n",
       "      <td>183.97</td>\n",
       "      <td>191.00</td>\n",
       "      <td>179.26</td>\n",
       "      <td>45.12M</td>\n",
       "      <td>-1.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/01/2024</td>\n",
       "      <td>184.40</td>\n",
       "      <td>187.15</td>\n",
       "      <td>196.38</td>\n",
       "      <td>180.17</td>\n",
       "      <td>1.19B</td>\n",
       "      <td>-4.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/01/2023</td>\n",
       "      <td>192.53</td>\n",
       "      <td>190.33</td>\n",
       "      <td>199.62</td>\n",
       "      <td>187.45</td>\n",
       "      <td>1.06B</td>\n",
       "      <td>1.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/01/2023</td>\n",
       "      <td>189.95</td>\n",
       "      <td>171.00</td>\n",
       "      <td>192.93</td>\n",
       "      <td>170.12</td>\n",
       "      <td>1.10B</td>\n",
       "      <td>11.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/01/2023</td>\n",
       "      <td>170.77</td>\n",
       "      <td>171.22</td>\n",
       "      <td>182.34</td>\n",
       "      <td>165.67</td>\n",
       "      <td>1.17B</td>\n",
       "      <td>-0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>05/01/1981</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>590.42M</td>\n",
       "      <td>15.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>04/01/1981</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>536.93M</td>\n",
       "      <td>18.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>03/01/1981</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>700.72M</td>\n",
       "      <td>-8.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>02/01/1981</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>321.62M</td>\n",
       "      <td>-7.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>01/01/1981</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.13</td>\n",
       "      <td>608.99M</td>\n",
       "      <td>-13.33%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>518 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Price    Open    High     Low     Vol. Change %\n",
       "0    02/01/2024  182.52  183.97  191.00  179.26   45.12M   -1.02%\n",
       "1    01/01/2024  184.40  187.15  196.38  180.17    1.19B   -4.22%\n",
       "2    12/01/2023  192.53  190.33  199.62  187.45    1.06B    1.36%\n",
       "3    11/01/2023  189.95  171.00  192.93  170.12    1.10B   11.23%\n",
       "4    10/01/2023  170.77  171.22  182.34  165.67    1.17B   -0.26%\n",
       "..          ...     ...     ...     ...     ...      ...      ...\n",
       "513  05/01/1981    0.15    0.13    0.15    0.12  590.42M   15.38%\n",
       "514  04/01/1981    0.13    0.11    0.13    0.11  536.93M   18.18%\n",
       "515  03/01/1981    0.11    0.12    0.12    0.10  700.72M   -8.33%\n",
       "516  02/01/1981    0.12    0.12    0.13    0.11  321.62M   -7.69%\n",
       "517  01/01/1981    0.13    0.15    0.16    0.13  608.99M  -13.33%\n",
       "\n",
       "[518 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "df = df.merge(df_tesla[['Date', 'Adj Close']], on='Date', how='right').rename(columns={'Adj Close':'tesla'})"
   ]
  }
 ],
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