AMZN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing data, converting date to date-time format and replacing index with date\n",
    "\n",
    "data= pd.read_csv('AMZN.csv',parse_dates=['Date'], index_col= 'Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2019-04-12</th>\n",
       "      <td>1848.400024</td>\n",
       "      <td>1851.500000</td>\n",
       "      <td>1841.300049</td>\n",
       "      <td>1843.060059</td>\n",
       "      <td>1843.060059</td>\n",
       "      <td>3114400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-15</th>\n",
       "      <td>1842.000000</td>\n",
       "      <td>1846.849976</td>\n",
       "      <td>1818.900024</td>\n",
       "      <td>1844.869995</td>\n",
       "      <td>1844.869995</td>\n",
       "      <td>3724400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-16</th>\n",
       "      <td>1851.349976</td>\n",
       "      <td>1869.770020</td>\n",
       "      <td>1848.000000</td>\n",
       "      <td>1863.040039</td>\n",
       "      <td>1863.040039</td>\n",
       "      <td>3044600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-17</th>\n",
       "      <td>1872.989990</td>\n",
       "      <td>1876.469971</td>\n",
       "      <td>1860.439941</td>\n",
       "      <td>1864.819946</td>\n",
       "      <td>1864.819946</td>\n",
       "      <td>2893500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-18</th>\n",
       "      <td>1868.790039</td>\n",
       "      <td>1870.819946</td>\n",
       "      <td>1859.479980</td>\n",
       "      <td>1861.689941</td>\n",
       "      <td>1861.689941</td>\n",
       "      <td>2749900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2019-04-12  1848.400024  1851.500000  1841.300049  1843.060059  1843.060059   \n",
       "2019-04-15  1842.000000  1846.849976  1818.900024  1844.869995  1844.869995   \n",
       "2019-04-16  1851.349976  1869.770020  1848.000000  1863.040039  1863.040039   \n",
       "2019-04-17  1872.989990  1876.469971  1860.439941  1864.819946  1864.819946   \n",
       "2019-04-18  1868.790039  1870.819946  1859.479980  1861.689941  1861.689941   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2019-04-12  3114400  \n",
       "2019-04-15  3724400  \n",
       "2019-04-16  3044600  \n",
       "2019-04-17  2893500  \n",
       "2019-04-18  2749900  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A short overview of the data\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-04-23', '2018-04-24', '2018-04-25', '2018-04-26',\n",
       "               '2018-04-27', '2018-04-30', '2018-05-01', '2018-05-02',\n",
       "               '2018-05-03', '2018-05-04',\n",
       "               ...\n",
       "               '2019-04-05', '2019-04-08', '2019-04-09', '2019-04-10',\n",
       "               '2019-04-11', '2019-04-12', '2019-04-15', '2019-04-16',\n",
       "               '2019-04-17', '2019-04-18'],\n",
       "              dtype='datetime64[ns]', name='Date', length=250, freq=None)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking index\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2019-01-02</th>\n",
       "      <td>1465.199951</td>\n",
       "      <td>1553.359985</td>\n",
       "      <td>1460.930054</td>\n",
       "      <td>1539.130005</td>\n",
       "      <td>1539.130005</td>\n",
       "      <td>7983100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>1520.010010</td>\n",
       "      <td>1538.000000</td>\n",
       "      <td>1497.109985</td>\n",
       "      <td>1500.280029</td>\n",
       "      <td>1500.280029</td>\n",
       "      <td>6953200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>1530.000000</td>\n",
       "      <td>1594.000000</td>\n",
       "      <td>1518.310059</td>\n",
       "      <td>1575.390015</td>\n",
       "      <td>1575.390015</td>\n",
       "      <td>9182600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>1602.310059</td>\n",
       "      <td>1634.560059</td>\n",
       "      <td>1589.189941</td>\n",
       "      <td>1629.510010</td>\n",
       "      <td>1629.510010</td>\n",
       "      <td>7993200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>1664.689941</td>\n",
       "      <td>1676.609985</td>\n",
       "      <td>1616.609985</td>\n",
       "      <td>1656.579956</td>\n",
       "      <td>1656.579956</td>\n",
       "      <td>8881400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>1652.979980</td>\n",
       "      <td>1667.800049</td>\n",
       "      <td>1641.400024</td>\n",
       "      <td>1659.420044</td>\n",
       "      <td>1659.420044</td>\n",
       "      <td>6348800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>1641.010010</td>\n",
       "      <td>1663.250000</td>\n",
       "      <td>1621.619995</td>\n",
       "      <td>1656.219971</td>\n",
       "      <td>1656.219971</td>\n",
       "      <td>6507700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11</th>\n",
       "      <td>1640.550049</td>\n",
       "      <td>1660.290039</td>\n",
       "      <td>1636.219971</td>\n",
       "      <td>1640.560059</td>\n",
       "      <td>1640.560059</td>\n",
       "      <td>4686200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-14</th>\n",
       "      <td>1615.000000</td>\n",
       "      <td>1648.199951</td>\n",
       "      <td>1595.150024</td>\n",
       "      <td>1617.209961</td>\n",
       "      <td>1617.209961</td>\n",
       "      <td>6005900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-15</th>\n",
       "      <td>1632.000000</td>\n",
       "      <td>1675.160034</td>\n",
       "      <td>1626.010010</td>\n",
       "      <td>1674.560059</td>\n",
       "      <td>1674.560059</td>\n",
       "      <td>5998500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-16</th>\n",
       "      <td>1684.219971</td>\n",
       "      <td>1705.000000</td>\n",
       "      <td>1675.880005</td>\n",
       "      <td>1683.780029</td>\n",
       "      <td>1683.780029</td>\n",
       "      <td>6366900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-17</th>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1700.170044</td>\n",
       "      <td>1677.500000</td>\n",
       "      <td>1693.219971</td>\n",
       "      <td>1693.219971</td>\n",
       "      <td>4208900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-18</th>\n",
       "      <td>1712.000000</td>\n",
       "      <td>1716.199951</td>\n",
       "      <td>1691.540039</td>\n",
       "      <td>1696.199951</td>\n",
       "      <td>1696.199951</td>\n",
       "      <td>6020500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-22</th>\n",
       "      <td>1681.000000</td>\n",
       "      <td>1681.869995</td>\n",
       "      <td>1610.199951</td>\n",
       "      <td>1632.170044</td>\n",
       "      <td>1632.170044</td>\n",
       "      <td>6416800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-23</th>\n",
       "      <td>1656.000000</td>\n",
       "      <td>1657.430054</td>\n",
       "      <td>1612.000000</td>\n",
       "      <td>1640.020020</td>\n",
       "      <td>1640.020020</td>\n",
       "      <td>5225200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-24</th>\n",
       "      <td>1641.069946</td>\n",
       "      <td>1657.260010</td>\n",
       "      <td>1631.780029</td>\n",
       "      <td>1654.930054</td>\n",
       "      <td>1654.930054</td>\n",
       "      <td>4089900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-25</th>\n",
       "      <td>1670.500000</td>\n",
       "      <td>1683.479980</td>\n",
       "      <td>1661.609985</td>\n",
       "      <td>1670.569946</td>\n",
       "      <td>1670.569946</td>\n",
       "      <td>4945900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-28</th>\n",
       "      <td>1643.589966</td>\n",
       "      <td>1645.000000</td>\n",
       "      <td>1614.089966</td>\n",
       "      <td>1637.890015</td>\n",
       "      <td>1637.890015</td>\n",
       "      <td>4837700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-29</th>\n",
       "      <td>1631.270020</td>\n",
       "      <td>1632.380005</td>\n",
       "      <td>1590.719971</td>\n",
       "      <td>1593.880005</td>\n",
       "      <td>1593.880005</td>\n",
       "      <td>4632800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30</th>\n",
       "      <td>1623.000000</td>\n",
       "      <td>1676.949951</td>\n",
       "      <td>1619.680054</td>\n",
       "      <td>1670.430054</td>\n",
       "      <td>1670.430054</td>\n",
       "      <td>5783800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>1692.849976</td>\n",
       "      <td>1736.410034</td>\n",
       "      <td>1679.079956</td>\n",
       "      <td>1718.729980</td>\n",
       "      <td>1718.729980</td>\n",
       "      <td>10910300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2019-01-02  1465.199951  1553.359985  1460.930054  1539.130005  1539.130005   \n",
       "2019-01-03  1520.010010  1538.000000  1497.109985  1500.280029  1500.280029   \n",
       "2019-01-04  1530.000000  1594.000000  1518.310059  1575.390015  1575.390015   \n",
       "2019-01-07  1602.310059  1634.560059  1589.189941  1629.510010  1629.510010   \n",
       "2019-01-08  1664.689941  1676.609985  1616.609985  1656.579956  1656.579956   \n",
       "2019-01-09  1652.979980  1667.800049  1641.400024  1659.420044  1659.420044   \n",
       "2019-01-10  1641.010010  1663.250000  1621.619995  1656.219971  1656.219971   \n",
       "2019-01-11  1640.550049  1660.290039  1636.219971  1640.560059  1640.560059   \n",
       "2019-01-14  1615.000000  1648.199951  1595.150024  1617.209961  1617.209961   \n",
       "2019-01-15  1632.000000  1675.160034  1626.010010  1674.560059  1674.560059   \n",
       "2019-01-16  1684.219971  1705.000000  1675.880005  1683.780029  1683.780029   \n",
       "2019-01-17  1680.000000  1700.170044  1677.500000  1693.219971  1693.219971   \n",
       "2019-01-18  1712.000000  1716.199951  1691.540039  1696.199951  1696.199951   \n",
       "2019-01-22  1681.000000  1681.869995  1610.199951  1632.170044  1632.170044   \n",
       "2019-01-23  1656.000000  1657.430054  1612.000000  1640.020020  1640.020020   \n",
       "2019-01-24  1641.069946  1657.260010  1631.780029  1654.930054  1654.930054   \n",
       "2019-01-25  1670.500000  1683.479980  1661.609985  1670.569946  1670.569946   \n",
       "2019-01-28  1643.589966  1645.000000  1614.089966  1637.890015  1637.890015   \n",
       "2019-01-29  1631.270020  1632.380005  1590.719971  1593.880005  1593.880005   \n",
       "2019-01-30  1623.000000  1676.949951  1619.680054  1670.430054  1670.430054   \n",
       "2019-01-31  1692.849976  1736.410034  1679.079956  1718.729980  1718.729980   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2019-01-02   7983100  \n",
       "2019-01-03   6953200  \n",
       "2019-01-04   9182600  \n",
       "2019-01-07   7993200  \n",
       "2019-01-08   8881400  \n",
       "2019-01-09   6348800  \n",
       "2019-01-10   6507700  \n",
       "2019-01-11   4686200  \n",
       "2019-01-14   6005900  \n",
       "2019-01-15   5998500  \n",
       "2019-01-16   6366900  \n",
       "2019-01-17   4208900  \n",
       "2019-01-18   6020500  \n",
       "2019-01-22   6416800  \n",
       "2019-01-23   5225200  \n",
       "2019-01-24   4089900  \n",
       "2019-01-25   4945900  \n",
       "2019-01-28   4837700  \n",
       "2019-01-29   4632800  \n",
       "2019-01-30   5783800  \n",
       "2019-01-31  10910300  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Showing all data from Jan-2019\n",
    "data['2019-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1639.405007181818"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the mean of closing price of one month of January-2019\n",
    "data['2019-01-01':'2019-02-01'].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1632.6422674545452"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the mean of opening price of one month of January-2019\n",
    "data['2019-01-01':'2019-02-01'].Open.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-04-30    1515.804993\n",
       "2018-05-31    1594.903637\n",
       "2018-06-30    1698.823812\n",
       "2018-07-31    1784.649042\n",
       "2018-08-31    1897.851308\n",
       "2018-09-30    1966.077900\n",
       "2018-10-31    1782.058265\n",
       "2018-11-30    1625.483823\n",
       "2018-12-31    1559.443154\n",
       "2019-01-31    1640.032389\n",
       "2019-02-28    1626.937365\n",
       "2019-03-31    1722.491909\n",
       "2019-04-30    1839.970694\n",
       "Freq: M, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resample the total data monthly wise\n",
    "data.Close.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing matplotlib for plotting data\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdfb76ec400>"
      ]
     },
     "execution_count": 81,
     "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": [
    "#Plottng resampled data\n",
    "data.Close.resample('M').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdfb76ff518>"
      ]
     },
     "execution_count": 82,
     "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": [
    "#plotting Quartary resampled data\n",
    "data.Close.resample('Q').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdfb77b4080>"
      ]
     },
     "execution_count": 83,
     "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": [
    "#plotting bar chart\n",
    "data.Close.resample('Q').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdfb76ff400>"
      ]
     },
     "execution_count": 84,
     "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": [
    "#plotting daily basis\n",
    "data.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}