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{
"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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"display_name": "py311-kfp240-airflow251",
"language": "python",
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