accidents_data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>AAPL_x</th>\n",
       "      <th>AAPL_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>77.445395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>77.045575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>74.896972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>75.856461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>75.091947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2014-12-08</td>\n",
       "      <td>113.653345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>109.755497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2014-12-10</td>\n",
       "      <td>113.960331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2014-12-11</td>\n",
       "      <td>111.817477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2014-12-12</td>\n",
       "      <td>110.027139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AAPL_x      AAPL_y\n",
       "0    2014-01-02   77.445395\n",
       "1    2014-01-03   77.045575\n",
       "2    2014-01-06   74.896972\n",
       "3    2014-01-07   75.856461\n",
       "4    2014-01-08   75.091947\n",
       "..          ...         ...\n",
       "235  2014-12-08  113.653345\n",
       "236  2014-12-09  109.755497\n",
       "237  2014-12-10  113.960331\n",
       "238  2014-12-11  111.817477\n",
       "239  2014-12-12  110.027139\n",
       "\n",
       "[240 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')\n",
    "\n",
    "df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.0rc2 64-bit ('3.11.0rc2')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0rc2"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "fa07e5dff76123542c28abca75c0e47ea742948dbeeb360ccfbd2a5c7c277f8b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}