live.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# bigdata.luisbelloch.es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>30.480</td>\n",
       "      <td>30.99</td>\n",
       "      <td>30.48</td>\n",
       "      <td>30.98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.950</td>\n",
       "      <td>31.10</td>\n",
       "      <td>30.59</td>\n",
       "      <td>30.65</td>\n",
       "      <td>38414185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.960</td>\n",
       "      <td>31.10</td>\n",
       "      <td>30.64</td>\n",
       "      <td>30.87</td>\n",
       "      <td>49758862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.770</td>\n",
       "      <td>31.08</td>\n",
       "      <td>30.52</td>\n",
       "      <td>30.88</td>\n",
       "      <td>58182332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.452</td>\n",
       "      <td>30.70</td>\n",
       "      <td>30.19</td>\n",
       "      <td>30.61</td>\n",
       "      <td>50564285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close   High    Low   Open    Volume\n",
       "Date                                             \n",
       "2010-01-01  30.480  30.99  30.48  30.98         0\n",
       "2010-01-04  30.950  31.10  30.59  30.65  38414185\n",
       "2010-01-05  30.960  31.10  30.64  30.87  49758862\n",
       "2010-01-06  30.770  31.08  30.52  30.88  58182332\n",
       "2010-01-07  30.452  30.70  30.19  30.61  50564285"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sample_data import stock_prices\n",
    "aapl = stock_prices(\"aapl\")\n",
    "msft = stock_prices(\"msft\")\n",
    "msft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msft.Close.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}