{
 "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",
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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>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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df_apple[['Date', 'Price']], df_walmart[['Date', 'Price']], on='Date', how='right').rename(columns = {'Adj Close_x':'apple', 'Adj Close_y':'walmart'})\n",
    "df = df.merge(df_tesla[['Date', 'Price']], on='Date', how='right').rename(columns={'Adj Close':'tesla'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Price_x</th>\n",
       "      <th>Price_y</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02/01/2024</td>\n",
       "      <td>182.52</td>\n",
       "      <td>175.56</td>\n",
       "      <td>191.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/01/2024</td>\n",
       "      <td>184.40</td>\n",
       "      <td>165.25</td>\n",
       "      <td>187.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/01/2023</td>\n",
       "      <td>192.53</td>\n",
       "      <td>157.65</td>\n",
       "      <td>248.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/01/2023</td>\n",
       "      <td>189.95</td>\n",
       "      <td>155.69</td>\n",
       "      <td>240.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/01/2023</td>\n",
       "      <td>170.77</td>\n",
       "      <td>163.41</td>\n",
       "      <td>200.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>11/01/2010</td>\n",
       "      <td>11.11</td>\n",
       "      <td>54.09</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>10/01/2010</td>\n",
       "      <td>10.75</td>\n",
       "      <td>54.17</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>09/01/2010</td>\n",
       "      <td>10.13</td>\n",
       "      <td>53.52</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>08/01/2010</td>\n",
       "      <td>8.68</td>\n",
       "      <td>50.14</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>07/01/2010</td>\n",
       "      <td>9.19</td>\n",
       "      <td>51.19</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Price_x  Price_y   Price\n",
       "0    02/01/2024   182.52   175.56  191.97\n",
       "1    01/01/2024   184.40   165.25  187.29\n",
       "2    12/01/2023   192.53   157.65  248.48\n",
       "3    11/01/2023   189.95   155.69  240.08\n",
       "4    10/01/2023   170.77   163.41  200.84\n",
       "..          ...      ...      ...     ...\n",
       "159  11/01/2010    11.11    54.09    2.36\n",
       "160  10/01/2010    10.75    54.17    1.46\n",
       "161  09/01/2010    10.13    53.52    1.36\n",
       "162  08/01/2010     8.68    50.14    1.30\n",
       "163  07/01/2010     9.19    51.19    1.33\n",
       "\n",
       "[164 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] =  pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Price_x</th>\n",
       "      <th>Price_y</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>182.52</td>\n",
       "      <td>175.56</td>\n",
       "      <td>191.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>184.40</td>\n",
       "      <td>165.25</td>\n",
       "      <td>187.29</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>192.53</td>\n",
       "      <td>157.65</td>\n",
       "      <td>248.48</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-11-01</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>170.77</td>\n",
       "      <td>163.41</td>\n",
       "      <td>200.84</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>159</th>\n",
       "      <td>2010-11-01</td>\n",
       "      <td>11.11</td>\n",
       "      <td>54.09</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2010-10-01</td>\n",
       "      <td>10.75</td>\n",
       "      <td>54.17</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2010-09-01</td>\n",
       "      <td>10.13</td>\n",
       "      <td>53.52</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2010-08-01</td>\n",
       "      <td>8.68</td>\n",
       "      <td>50.14</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>9.19</td>\n",
       "      <td>51.19</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Price_x  Price_y   Price\n",
       "0   2024-02-01   182.52   175.56  191.97\n",
       "1   2024-01-01   184.40   165.25  187.29\n",
       "2   2023-12-01   192.53   157.65  248.48\n",
       "3   2023-11-01   189.95   155.69  240.08\n",
       "4   2023-10-01   170.77   163.41  200.84\n",
       "..         ...      ...      ...     ...\n",
       "159 2010-11-01    11.11    54.09    2.36\n",
       "160 2010-10-01    10.75    54.17    1.46\n",
       "161 2010-09-01    10.13    53.52    1.36\n",
       "162 2010-08-01     8.68    50.14    1.30\n",
       "163 2010-07-01     9.19    51.19    1.33\n",
       "\n",
       "[164 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('Date').rename_axis('company', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>company</th>\n",
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       "      <th>Price_y</th>\n",
       "      <th>Price</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-01</th>\n",
       "      <td>182.52</td>\n",
       "      <td>175.56</td>\n",
       "      <td>191.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>184.40</td>\n",
       "      <td>165.25</td>\n",
       "      <td>187.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>192.53</td>\n",
       "      <td>157.65</td>\n",
       "      <td>248.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-01</th>\n",
       "      <td>189.95</td>\n",
       "      <td>155.69</td>\n",
       "      <td>240.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>170.77</td>\n",
       "      <td>163.41</td>\n",
       "      <td>200.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>11.11</td>\n",
       "      <td>54.09</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>10.75</td>\n",
       "      <td>54.17</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>10.13</td>\n",
       "      <td>53.52</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>8.68</td>\n",
       "      <td>50.14</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>9.19</td>\n",
       "      <td>51.19</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "company     Price_x  Price_y   Price\n",
       "Date                                \n",
       "2024-02-01   182.52   175.56  191.97\n",
       "2024-01-01   184.40   165.25  187.29\n",
       "2023-12-01   192.53   157.65  248.48\n",
       "2023-11-01   189.95   155.69  240.08\n",
       "2023-10-01   170.77   163.41  200.84\n",
       "...             ...      ...     ...\n",
       "2010-11-01    11.11    54.09    2.36\n",
       "2010-10-01    10.75    54.17    1.46\n",
       "2010-09-01    10.13    53.52    1.36\n",
       "2010-08-01     8.68    50.14    1.30\n",
       "2010-07-01     9.19    51.19    1.33\n",
       "\n",
       "[164 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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