Introduction to Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brock University ECON 4F10 - Applied Research Methods in Economics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import library and load a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # import Pandas library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx = pd.read_csv('GSPC.csv') # load data from CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close  Adj Close   Volume\n",
       "0  1950-01-03  16.66  16.66  16.66  16.66      16.66  1260000\n",
       "1  1950-01-04  16.85  16.85  16.85  16.85      16.85  1890000\n",
       "2  1950-01-05  16.93  16.93  16.93  16.93      16.93  2550000\n",
       "3  1950-01-06  16.98  16.98  16.98  16.98      16.98  2010000\n",
       "4  1950-01-09  17.08  17.08  17.08  17.08      17.08  2520000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx.head() # examine \"head\" of the dataset (top five rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx.columns # print a list of column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>1260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>1890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>2550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>2010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>2520000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Volume\n",
       "0  1950-01-03  1260000\n",
       "1  1950-01-04  1890000\n",
       "2  1950-01-05  2550000\n",
       "3  1950-01-06  2010000\n",
       "4  1950-01-09  2520000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx[['Date','Volume']].head() # select only certain columns and print their \"head\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate variable plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = spx.Close.plot() # do time-series plot of S&P 500 closing prices (index values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import numpy as np # import numpy library for logarithm functionality (Note: math log function won't work here)\n",
    "p = np.log(spx.Close).plot() # plot natural log of S&P 500 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ICV1E2pyAtbTUU0IXkTYraIldCV1EDhm1daG05zL/6RXHNylry9Pnioi0Cj/+22LK732dquq6lPcZ2qcL/Xt0yGFUrYcSuogcMl5ZtAmAAzWpJ/S2RAldRA5JzqX2XtB4jSvNtZ0P79s184BaASV0ETkkzVm7I6V6iR58xralH9a5JJuQ8k4JXUQOOSHn2HewNuP9m8vxJ/bvlvExWwPN5SIih5yzH3yLvSkndCPF1hluu2AYk2auyjiufNMduoi0qD/PWc/rS7ZktO/O/TUAaSTz+E0uzc3hUliQvDvjWUNb/vWZ3zj76JTqKaGLSIv6wQsLuemJ9F5odrC2jso96fU/PxQ8ddNIfnn1SY3KLj6xb5N6vbu0S+l4Sugi0up9/Yl5nPbT1zPaN24vlyxiSbV3TXMmjj+mfrlX53Z0inkV3vfHDmt6zhSPrYQuIq3eO8srM963tU2Re1SvTvXLDscZRx/GoKiygjjxFqb4LSihi4i0gEizyRHdG0atGkbX9sW8cdvZCfft0Sm17pTq5SIigRd705uPm/Zvjjma0YN7MbRPlyZxpBrPpScdwcMJtusOXUTy4q/zK1pkCL9Bs90W4yXSW88bkpM4CgusUTKHhrZ8v5qFlNBFJC/+488f8+CMZUnr1YWyewgZT6IEeszhXZrdBs1/OERrX9w0taYzBUFzklVXQheRnCqbOJV7X14Sd9uW3QcS7rtt70GO/uG0rM6fKGmWFKafAl1Mn5NuHZq+6ejzQ1Ltq940uGxu1pXQRSTnHp21Jm55spvdjTursj53ornPC+IMJIpNqFee2p+vjjoyrXPGHZ8UJ1OnmrxT7SmphC4irVYOWluSipc80+16Hm/EqR/94ZN9AKSU0M1snJktM7OVZjYxzvbbzGyJmS0wszfMLL2PMxEJjAM1dfzPW6uorQslr5wkUYayHMQD2fdoic3NsSHFO350X/KrywdkHUdsM09zkiZ0MysEJgHjgeHAtWY2PKbafKDcOXci8Dzw87SiFZHA+M0bK3hg+lKen1fRZNuCip2N1pMlKh/yedZuH39s/XJpl3aMO/7wpPtEJ/T6rolx28tTK6vfluy8SSOD04GVzrnVzrlq4Fng8ugKzrmZzrn93upsoH8KxxWRAIpMa1sV0yXx/leWsnTTnrSOle0wez/06FRSn5RvOWcwXznjSBb/ZGzCfeI2ucSbJCzFGFK9DKkMLOoHbIharwBGJqh/I/BKvA1mNgGYADBw4MDUIhSRQHjk7VWUFDW+h0yWqPxI534PIjIzOrVLnDoNGNCzAxu2VyX8HtNN8sn6q/v6UNTMvgyUAw/G2+6cm+ycK3fOlZeWtvwUlCLScn7y9yU8/OaKRmXVtY3b1Xfsr2b9tv3163+dX0HZxKmUTZzKa0u2EMrHU9E4kn3wvPDNUXz73MH162bGa/9xNot+Mra+WSn+Q9GmpX26tue8Y3rz40saWrYjd/zJpvdNJaFvBAZErff3yhoHZnY+cAdwmXMuePNcikjaHnp1ecLts1dv56wHZzJz2VYgPNgo4utPzE27l0uq84Znqrkb5FOP7Ml3L2w8S2L74kI6tyuq/zCIPy9707LCAuP3159G+ZE9gPBblC46oS9fGz2IOy46tukOUVJJ6HOAIWY2yMxKgGuAKY2DshHA7wgn860pHFNEpN4Nf5gTt3zWysxnWYxI1A/dD8mOHvlMyiaO4sIC7rx0eNJJupImdOdcLXALMAP4BHjOObfYzO4xs8u8ag8CnYG/mNlHZjalmcOJiKQs3dfBpdq9z0/xzpivGXtTmm3ROTcNmBZTdmfU8vk+xyUibcylv52VVv3jjujK4k93J62X6+TammZb10hREWkVFm7clfUx4vf1zvqwXH1a+DHiOcN6p72va2hzacLvDxvNhy4ivpq/YWfySj6I1/MkhXc8J/X5OC+BPr5fN9bef3HSfdsXF3CgpnFPnoS9XGIy+vC+XaP2S5/u0EUkK845yiZOZdLMlQAsqMj+TjsV8aYFKDDjoatO4pdXn8SQ3p2BxnftXxw5MGnXv85J+pgncs9lx3vnjFLfyyXx/C6v33Y2z/7bGRmfG5TQRSRLMxZvAeDBGcu47OH02sH9VmDh2RGvGNEwWD06j35rzNGsuu8iX8716FfLAZIOMkrQ4tIotsG9O9O1fcNUvJn8saGELiIZc85RsaNhYFBL3Z1DM4NsojJkrvu7DOnTOfaUcXvZjBzUs1F9gJMHdAegY7G/rd5qQxeRjD05ex33Tv2kxc738BdHcMvT8wGaTCMAjdvQI/PA5LoXSrIHsVefNoAxw3pzeLf29WXPTjiDih376dax6csxIiIvzkj2BqVoukMXkYy98GGTQeM58/w3RnHJiUfUrx/Zs2OTOmPi9EJpyT7hJw8Ij+48/9g+Uee3RskcwqNIB/dOnKjLenXiL98YxT2XH5/y+ZXQRaSJd5ZX8lFUb5XHZq3hN2+E52X520cbmfxOeMDPngM1LRZTeVnPRuv3/csJTepEmjIAfnX1CC4Y3oeywzrVP+gsiJPdH7rqJG4+p/GUAX+6MdH8g2E9vVGbV53a0F4/7PAurLrvIi48LvkUu6k4rawn7YsLU66vJhcRaeKrj30AUN9V7x7vnaDfOW8Itz77EQATzjqa1ZX78hMg0LEkcfo6oX83/td7cPn768uZumATR3Tv0KTelac2ne375IHdm5TF6tK+mBU/HU9RTFt+sl40uaSELiIpm716W/1y2cSpeYwkPf17dOTf0pi4K9Wui8UZvGQ6l1pXNCLSqs1YvLlFzjOgZ9M76Xhm/eAc/vqtz+U4mkOHErqIpOwP/1jr+zHvvjT2jZbw7vfPTWnf/j06MmJgD79DAqBX53bceOagnBw7V9TkIiISx9wfHXpzDuoOXUTyqnW8kygYlNBFpFm7D9SwfEt6L3ZO5M8Tms5V0tzr3c4/NvWZDWN7mrRVanIRkWadePerGXfD+865g/nNmysblfXu2r5JvcG9OzcpA5j8lXIu+e0slmzazcA4g4iizbnjfKpq6jKKM0h0hy7SRjnnmPzOKrbvq2bRxl38afa6uPXqUnix5wn9ujUpG3NM0zvsyJ10x5KGwTJnxZmuFqCgwCguDNf/9TUn15f/y4h+Ter26FQSt495W6M7dBGfbdxZRcfiwqTvf8yX219cyLnH9OawziXcN20ps1dv582l4VcB/+ilRRkdc/TgXk1eUHFKnN4nA3p25K5Lh3PcEd3419+9l/zA3sjO6I+UB686iXuvSH04fFuiO3QRn42+/01G/uyNfIfRrGc+WM/Xn5jLrqrwsP3Iv9lo7l2eJ/TrxvWfK2tUdsPoQZw+qPEw/qduGslRpZ2a7B9p7IluZy8ssKSjRNsqJXSRHKiuDSWv1AJq6kLU1oVj2bizigNR7cw3/GEOAPPW7UjrmOcd07vJ23uae7D592+fyW0XDk16zNGDe3HtaQOblOfrZcuHKiV0kQCZu3Y7//7s/PqpY4fc8QqD73iFf6z8jNH3v8kxP56e9Tkib96JTuqhFNrZkxkaZ5rYb40ZDDT/4FQaU0IXyYMd+6opmziVVxZuymj/tZ/t47UlW+rXP96wk7eWbeXKR97jpY8+5fVPtjaaa+VLj76fVbwPfKFhZsN4nV5CDq7/XFnckZXxZjgE+Nm/nMB9VzQc9+w4D0cvGN6HtfdfXD83uCSmhiiRHJq+aDPLNu/h1vOHNCpfWbkXgEdnrWH8CX0bb9u6h77dOtS/2qy6NsRdUxZx2wXDKO3SDoAxD71VX/+mMwfx6Kw1jY7x9Sfm+vY9RO7Ef/DCQiB+gh4xsDuXnhSeq3xV5d5Gr1Lr3K6I+644gbOG9mq0z7WnN21ikewooYtk6ZG3V3H/K0vr3zEZ8ei7q+vf5nPr+UM4UFPHwZoQ3ToW1z/si7zo+NOdVXz+5zN54Zuf4/9N+gcQTqRV1XVc8Mu3qdhRxTMfbOCd753DT6ctaXyemGSeje+cO5hTjuzB9V77ejwFUX/XF1j47jySzAEev+H0Jvt8cWRqyftPN46s/9CS9Jlr7mlGjpWXl7u5c/27ixDxy6PvrubXr69g4U/Gxt3+zvJK6pxjYM+OnPeLt7M+n1nzDxVz6eIT+jI1pskncjc+7lfvsHTznkZlQ+6YRk2d41tjjub7444BYOf+aqprQ3EHDElumNk851x5vG26Q5c2Zf22/byyaBNfHVVGh6jBLTv2VfPX+Ru5YXRZ/V11pA36SyMH8smm3YwY2IPf+3g3HNESyfz/XziUh15d3qjs5AHdmyT0iMG9O9cn9IiFd4/l5QWbuPzkhrvx7h1bZ1/7tiqlhG5m44BfA4XAo865+2O2twOeAE4FtgFXO+fW+huqSIPNuw5Q2qVdo2HpW3cfYP6GnZxx1GGNHqI559hXXcf2vdWc9eBMAH72ytK4x428mSfaU++vB+DD9TubbMu3X19zMscd0Y2/fbSR33rD7N//4Xm8ungzPTu14+anP2RYny71oyhLigrqu1ReP7qMkqICNu6sYvI7qxnet2v9cR/4wom8vGATd0VNbdu+uDDu232k9Uia0M2sEJgEXABUAHPMbIpzLvon/0Zgh3NusJldAzwAXJ2LgA9VtXUhinx6u0ko5CiI6WrgnKMu5CgsMGpDjuraEKsq99KlfTG7qmoY2qczew/UcljndmzbexAH1IYchWZ0bFfIxh1V1IUc89btoE/XdvToWEK3jsXMXrWN7fuqOX3QYQBs3n2A5+Zs4OZzB1NUYJR2aUflnoPMX7+D8rKe9OveAedg0ae7mDRzJZV7DlJe1oOxxx3O5l0H6hPpv519FL06tePxf66lV+cSNu48wBdO7Ue3DsX8fPoyRgzszvxWmEATOXNwL4b26cJj//D/Lh7gsevLOb5fN/74z7VMmhl+p+clJx5BYYHx3QuH8ds3V3Lt6QPp07U9XxlVhnOO740dxlXl/enZsYQ1n+3jpjOP4tFZqxl73OEUFxZw3efKeGB6+P/k4hMbHs52alfUpK+5tH5J29DNbBRwt3NurLd+O4Bz7mdRdWZ4dd4zsyJgM1DqEhy8y4Bh7tTvPIIj/CdnpB1x/fb9AOFRY5G964eLQZ13yAKz+kEHzoUTmiP8kCkUCq8XevNAFJrV952NhBQdWHSUoagV58LzWETOWVwQPk5sm2fIS6Yh5/hsbzUAfbq2o8AM5+BgbR079tfQvWMxHYsLCXnfR8g79s79NZQUFdClXRFm4X6+4eM7aurC9WpCIQ7UNAxWKSkqoKSwALNwgt9XrYmJcmVw784cqKmjYkcVE846iiG9OzP+hL4UFRh/mr2OP81exw2jB3GdNyLSef+nkaH/tXUhdlXV8OqSLVx5an+27a1m2ZY9DO/bleVb9jDqqMPqP6DfXLqFn079hKnf+Tzrtu2npi5Ez04lhJyjf4/EE1TV1oUoLGj4WU/Vk7PX8eOXFvGLq07iC7oDb/UStaGnktCvBMY5527y1r8CjHTO3RJVZ5FXp8JbX+XV+SzmWBOACQBdjzjq1Ot/8Xy4HKipcxQVGvPW7aBiRxWXnNi4K5fz6kX+xA65huRbYIYR/lAoMKtP9pERctFjHqJ/1qN/7MNJ1IW7ZFlku1FYAIUFBUSSa+TDI5LYQy58l1tYYBQUGDv3V9O1fTHOi88M9lXXMXXBJgb16sSpR/bAgKLCcJyFBcYT763j7KGl9O/RIfyhFHL130thgfdlxprP9lFSVMCMxZu5YkR/OrcrpMDbVlhgLNm0myG9u/Dpziqme68Ki3z4jBzUk6qaOkLOsXX3QbbuOQiEu44988H6hD8DiZR2aUf3DsWs2Lo342NEdO9YzLA+XXh/zXbaFxcw/vi+TF+0uX4WvW4dijm6tBP9enSkYsd+enQs4eZzBvP3jz/l388fgpnx4bodjDyqJx2KC3EuPKx9656DDIszaEXCQiHHW8u3cs6w3ml/GEjLazUJPZp6uYiIpC9RQk+lUXcjMCBqvb9XFreO1+TSjfDDURERaSGpJPQ5wBAzG2RmJcA1wJSYOlOA67zlK4E3E7Wfi4iI/5L2cnHO1ZrZLcAMwt0WH3POLTaze4C5zrkpwO+BJ81sJbCdcNIXEc+hOFUAAAYNSURBVJEWlFI/dOfcNGBaTNmdUcsHgKv8DU1ERNKh2RZFRAJCCV1EJCCU0EVEAkIJXUQkIPI2fa6Z7QGW5eXkyfUCmh0UlWetNbbWGhcotky01rhAsR3pnGv6eifyO33usuZGO+Wbmc1VbOlprXGBYstEa40LFFsianIREQkIJXQRkYDIZ0KfnMdzJ6PY0tda4wLFlonWGhcotmbl7aGoiIj4S00uIiIBoYQuIhIQeUnoZjbOzJaZ2Uozm9gC5xtgZjPNbImZLTazW73ynmb2mpmt8P7t4ZWbmf3Gi2+BmZ0SdazrvPorzOy65s6ZQYyFZjbfzF721geZ2fteDH/2pi7GzNp56yu97WVRx7jdK19mZmN9iKm7mT1vZkvN7BMzG9VarpmZ/Yf3f7nIzJ4xs/b5umZm9piZbfVe9BIp8+06mdmpZrbQ2+c3Zqm/VqiZ2B70/k8XmNlfzax7suvR3O9sc9c8k7iitn3XzJyZ9Wot18wr/7Z33Rab2c9b+pqlxDnXol+Ep+BdBRwFlAAfA8NzfM6+wCnechdgOTAc+Dkw0SufCDzgLV8EvEL4ZXRnAO975T2B1d6/PbzlHj7FeBvwNPCyt/4ccI23/AjwTW/5W8Aj3vI1wJ+95eHetWwHDPKucWGWMf0RuMlbLgG6t4ZrBvQD1gAdoq7V9fm6ZsBZwCnAoqgy364T8IFX17x9x2cZ24VAkbf8QFRsca8HCX5nm7vmmcTllQ8gPFX3OqBXK7pm5wCvA+289d4tfc1Sit2vA6VxsUYBM6LWbwdub+EY/gZcQHikal+vrC/hwU4AvwOujaq/zNt+LfC7qPJG9bKIpz/wBnAu8LL3Q/hZ1C9d/TXzfthHectFXj2LvY7R9TKMqRvhpGkx5Xm/ZoQT+gbvF7nIu2Zj83nNgLKYBODLdfK2LY0qb1Qvk9hitl0BPOUtx70eNPM7m+jnNNO4gOeBk4C1NCT0vF8zwkn4/Dj1WvSaJfvKR5NL5JcxosIraxHen9sjgPeBPs65Td6mzUAfb7m5GHMV+6+A7wMhb/0wYKdzrjbOeepj8Lbv8ur7HdsgoBL4g4Wbgh41s060gmvmnNsIPASsBzYRvgbzyP81i+bXdernLeciRoCvEb6DzSS2RD+naTOzy4GNzrmPYza1hms2FPi811TytpmdlmFsvl6zWG3qoaiZdQZeAP7dObc7epsLf1y2eB9OM7sE2Oqcm9fS506iiPCfnf/jnBsB7CPcdFAvj9esB3A54Q+dI4BOwLiWjiNV+bpOyZjZHUAt8FQriKUj8EPgzmR186SI8F+EZwDfA55Lp12+peQjoafy0mnfmVkx4WT+lHPuRa94i5n19bb3BbYmiTEXsY8GLjOztcCzhJtdfg10t/ALt2PP09wLuf2OrQKocM69760/TzjBt4Zrdj6wxjlX6ZyrAV4kfB3zfc2i+XWdNnrLvsZoZtcDlwBf8j5wMoltG81f83QdTfgD+mPvd6E/8KGZHZ5BXLm4ZhXAiy7sA8J/TffKIDY/r1lTfrXdpNE2VUT44cUgGh4WHJfjcxrwBPCrmPIHafzg6ufe8sU0fgjzgVfek3C7cg/vaw3Q08c4x9DwUPQvNH5w8i1v+WYaP+B7zls+jsYPZ1aT/UPRd4Fh3vLd3vXK+zUDRgKLgY7e+f4IfDuf14ymba6+XSeaPuC7KMvYxgFLgNKYenGvBwl+Z5u75pnEFbNtLQ1t6K3hmn0DuMdbHkq4OcVa+poljduvA6V5sS4i3NNkFXBHC5zvTMJ/8i4APvK+LiLcnvUGsILwE+zID4MBk7z4FgLlUcf6GrDS+7rB5zjH0JDQj/J+KFd6PwCRp+vtvfWV3vajova/w4t5GWk81U8Qz8nAXO+6veT90rSKawb8BFgKLAKe9H6h8nLNgGcIt+XXEL6Tu9HP6wSUe9/nKuBhYh5UZxDbSsIJKfK78Eiy60Ezv7PNXfNM4orZvpaGhN4arlkJ8CfvmB8C57b0NUvlS0P/RUQCok09FBURCTIldBGRgFBCFxEJCCV0EZGAUEIXEQkIJXQRkYBQQhcRCYj/A6H+G7N4kgTvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = spx['Volume'].plot() # plot index trading volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "p = spx.Close.pct_change().plot() # plot daily percent changes in S&P 500 closing values (\"daily index returns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "p = spx.Close.pct_change().hist(bins=20) # plot a histogram of daily S&P 500 returns using n=20 bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add column to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx['Return'] = spx.Close.pct_change() # add column containing daily returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary statistics for entire dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>17311.000000</td>\n",
       "      <td>17311.000000</td>\n",
       "      <td>17311.000000</td>\n",
       "      <td>17311.000000</td>\n",
       "      <td>17311.000000</td>\n",
       "      <td>1.731100e+04</td>\n",
       "      <td>17310.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>562.607749</td>\n",
       "      <td>565.917851</td>\n",
       "      <td>559.110741</td>\n",
       "      <td>562.733054</td>\n",
       "      <td>562.733054</td>\n",
       "      <td>9.114776e+08</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>667.818767</td>\n",
       "      <td>671.159450</td>\n",
       "      <td>664.203455</td>\n",
       "      <td>667.917970</td>\n",
       "      <td>667.917970</td>\n",
       "      <td>1.539713e+09</td>\n",
       "      <td>0.009603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>6.800000e+05</td>\n",
       "      <td>-0.204669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>85.739998</td>\n",
       "      <td>86.405003</td>\n",
       "      <td>85.024998</td>\n",
       "      <td>85.744999</td>\n",
       "      <td>85.744999</td>\n",
       "      <td>8.850000e+06</td>\n",
       "      <td>-0.004024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>165.550003</td>\n",
       "      <td>166.380005</td>\n",
       "      <td>164.850006</td>\n",
       "      <td>165.550003</td>\n",
       "      <td>165.550003</td>\n",
       "      <td>9.121000e+07</td>\n",
       "      <td>0.000467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1090.205017</td>\n",
       "      <td>1097.244995</td>\n",
       "      <td>1080.460022</td>\n",
       "      <td>1090.059998</td>\n",
       "      <td>1090.059998</td>\n",
       "      <td>1.168950e+09</td>\n",
       "      <td>0.004954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2936.760010</td>\n",
       "      <td>2940.909912</td>\n",
       "      <td>2927.110107</td>\n",
       "      <td>2930.750000</td>\n",
       "      <td>2930.750000</td>\n",
       "      <td>1.145623e+10</td>\n",
       "      <td>0.115800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open          High           Low         Close     Adj Close  \\\n",
       "count  17311.000000  17311.000000  17311.000000  17311.000000  17311.000000   \n",
       "mean     562.607749    565.917851    559.110741    562.733054    562.733054   \n",
       "std      667.818767    671.159450    664.203455    667.917970    667.917970   \n",
       "min       16.660000     16.660000     16.660000     16.660000     16.660000   \n",
       "25%       85.739998     86.405003     85.024998     85.744999     85.744999   \n",
       "50%      165.550003    166.380005    164.850006    165.550003    165.550003   \n",
       "75%     1090.205017   1097.244995   1080.460022   1090.059998   1090.059998   \n",
       "max     2936.760010   2940.909912   2927.110107   2930.750000   2930.750000   \n",
       "\n",
       "             Volume        Return  \n",
       "count  1.731100e+04  17310.000000  \n",
       "mean   9.114776e+08      0.000343  \n",
       "std    1.539713e+09      0.009603  \n",
       "min    6.800000e+05     -0.204669  \n",
       "25%    8.850000e+06     -0.004024  \n",
       "50%    9.121000e+07      0.000467  \n",
       "75%    1.168950e+09      0.004954  \n",
       "max    1.145623e+10      0.115800  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx.describe() # this produces another dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditional formatting / data extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "worst_day_return = spx.Return.min() # find the lowest daily return for the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9497    1987-10-19\n",
      "Name: Date, dtype: object\n"
     ]
    }
   ],
   "source": [
    "worst_day_date = spx[spx.Return == spx.Return.min()].Date # find when it hapened\n",
    "print(worst_day_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On its worst day, the idnex lost -0.20466930860972166  percent. This happened on 9497    1987-10-19\n",
      "Name: Date, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('On it''s worst day, the idnex lost', worst_day_return, ' percent. This happened on', worst_day_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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