ansible/files/materials/5-Reductions (Solutions).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reductions\n",
    "\n",
    "Reductions allow us to compute summary statistics and other useful aggregations over our data.\n",
    "\n",
    "The dataset for these exercises contains minutely price and volume observations for 5 stocks for October of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>MCD</th>\n",
       "      <th>BK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</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>2017-10-02 13:31:00</th>\n",
       "      <td>154.34</td>\n",
       "      <td>74.880</td>\n",
       "      <td>342.330</td>\n",
       "      <td>156.380</td>\n",
       "      <td>52.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:32:00</th>\n",
       "      <td>154.07</td>\n",
       "      <td>74.832</td>\n",
       "      <td>341.480</td>\n",
       "      <td>156.660</td>\n",
       "      <td>52.686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:33:00</th>\n",
       "      <td>153.72</td>\n",
       "      <td>74.835</td>\n",
       "      <td>341.830</td>\n",
       "      <td>156.324</td>\n",
       "      <td>52.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:34:00</th>\n",
       "      <td>153.69</td>\n",
       "      <td>74.890</td>\n",
       "      <td>341.240</td>\n",
       "      <td>156.660</td>\n",
       "      <td>52.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:35:00</th>\n",
       "      <td>153.45</td>\n",
       "      <td>74.810</td>\n",
       "      <td>341.873</td>\n",
       "      <td>156.670</td>\n",
       "      <td>52.706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       AAPL    MSFT     TSLA      MCD      BK\n",
       "dt                                                           \n",
       "2017-10-02 13:31:00  154.34  74.880  342.330  156.380  52.736\n",
       "2017-10-02 13:32:00  154.07  74.832  341.480  156.660  52.686\n",
       "2017-10-02 13:33:00  153.72  74.835  341.830  156.324  52.756\n",
       "2017-10-02 13:34:00  153.69  74.890  341.240  156.660  52.726\n",
       "2017-10-02 13:35:00  153.45  74.810  341.873  156.670  52.706"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>MCD</th>\n",
       "      <th>BK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</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>2017-10-02 13:31:00</th>\n",
       "      <td>420042.0</td>\n",
       "      <td>409211.0</td>\n",
       "      <td>49907.0</td>\n",
       "      <td>85774.0</td>\n",
       "      <td>30276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:32:00</th>\n",
       "      <td>161960.0</td>\n",
       "      <td>49207.0</td>\n",
       "      <td>18480.0</td>\n",
       "      <td>6866.0</td>\n",
       "      <td>4511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:33:00</th>\n",
       "      <td>118283.0</td>\n",
       "      <td>24043.0</td>\n",
       "      <td>47039.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:34:00</th>\n",
       "      <td>103544.0</td>\n",
       "      <td>62383.0</td>\n",
       "      <td>13444.0</td>\n",
       "      <td>4364.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 13:35:00</th>\n",
       "      <td>88012.0</td>\n",
       "      <td>40175.0</td>\n",
       "      <td>36556.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>1500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         AAPL      MSFT     TSLA      MCD       BK\n",
       "dt                                                                \n",
       "2017-10-02 13:31:00  420042.0  409211.0  49907.0  85774.0  30276.0\n",
       "2017-10-02 13:32:00  161960.0   49207.0  18480.0   6866.0   4511.0\n",
       "2017-10-02 13:33:00  118283.0   24043.0  47039.0   3000.0   3001.0\n",
       "2017-10-02 13:34:00  103544.0   62383.0  13444.0   4364.0    900.0\n",
       "2017-10-02 13:35:00   88012.0   40175.0  36556.0    820.0   1500.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "prices_df = pd.read_csv('prices.csv', index_col='dt', parse_dates=['dt'])\n",
    "volumes_df = pd.read_csv('volumes.csv', index_col='dt', parse_dates=['dt'])\n",
    "\n",
    "display(prices_df.head())\n",
    "display(volumes_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = prices_df.values\n",
    "volumes = volumes_df.values\n",
    "timestamps = prices_df.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017-10-02T13:31:00.000000000', '2017-10-02T13:32:00.000000000',\n",
       "       '2017-10-02T13:33:00.000000000', ...,\n",
       "       '2017-10-31T19:58:00.000000000', '2017-10-31T19:59:00.000000000',\n",
       "       '2017-10-31T20:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timestamps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Compute the average price for each stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([157.75380932,  77.94156573, 344.60197273, 162.96289044,\n",
       "        53.29279184])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Compute the average volume for each stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47083.83321678, 39618.75745921, 10911.53310023,  5934.88356643,\n",
       "        7774.04487179])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volumes.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Compute the number of times that each stock's price increased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4095, 3957, 4257, 4018, 3570])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.diff(prices, axis=0) > 0).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Compute the volume-weighted average price of all 5 stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([158.70686244,  79.33035404, 343.7402141 , 163.62733541,\n",
       "        52.92935945])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(prices, axis=0, weights=volumes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Compute the timestamps where the lowest price occurred for each stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2017-10-04T14:47:00.000000000' '2017-10-04T14:41:00.000000000'\n",
      " '2017-10-27T13:50:00.000000000' '2017-10-02T14:02:00.000000000'\n",
      " '2017-10-25T16:31:00.000000000']\n"
     ]
    }
   ],
   "source": [
    "print(timestamps[np.argmin(prices, axis=0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Compute the average volume for each minute of the day, aggregated across all 5 stocks.\n",
    "\n",
    "A correct answer should show that volumes are at their highest at the beginning and end of the day, with a relatively low and flat curve during the day.\n",
    "\n",
    "(**HINT:** There are exactly 390 trading minutes in each day in this dataset.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29031100.,  7509214.,  8226928.,  6759109.,  6249242.,  7035908.,\n",
       "        6147214.,  5436660.,  5119207.,  5092034.,  4980700.,  5113490.,\n",
       "        5077632.,  4817123.,  4823413.,  4639989.,  4981677.,  4286049.,\n",
       "        4408734.,  4037619.,  4362682.,  4528514.,  4820237.,  4497222.,\n",
       "        4490837.,  4366405.,  4032038.,  3616295.,  3717997.,  4449946.,\n",
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       "        2189391.,  2633241.,  2377778.,  2374142.,  2368715.,  2270894.,\n",
       "        2336476.,  2544212.,  2231665.,  2075682.,  2095414.,  2125369.,\n",
       "        2408165.,  2259440.,  2389797.,  2169454.,  2067085.,  2260844.,\n",
       "        2402653.,  2348331.,  2175834.,  2054040.,  2208926.,  2044661.,\n",
       "        1949514.,  2195284.,  2362278.,  2827917.,  2816800.,  2497086.,\n",
       "        2488366.,  2099106.,  2245509.,  2329265.,  2250200.,  2119793.,\n",
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       "        2298634.,  2030207.,  2520596.,  2176988.,  2016094.,  2334164.,\n",
       "        2128887.,  1860061.,  1653006.,  1903559.,  1837598.,  1892727.,\n",
       "        1660324.,  1725313.,  1985316.,  1809700.,  1888473.,  1616434.,\n",
       "        1738710.,  1740252.,  1608184.,  1844160.,  1875661.,  1771723.,\n",
       "        1518234.,  1835500.,  2205115.,  1878274.,  1776105.,  1816007.,\n",
       "        2150912.,  2049735.,  1790722.,  1831312.,  1802970.,  1943399.,\n",
       "        1942988.,  2213133.,  1825263.,  1743015.,  1860783.,  1947934.,\n",
       "        1638557.,  1992525.,  1616757.,  1933246.,  1935926.,  2199723.,\n",
       "        1789883.,  1607529.,  1736227.,  1886336.,  1839374.,  1786884.,\n",
       "        1910635.,  1545523.,  1563907.,  1468053.,  1484494.,  1671860.,\n",
       "        1772113.,  1760445.,  1468472.,  1492345.,  1559045.,  1511868.,\n",
       "        1801478.,  1813479.,  1488275.,  1691375.,  1594023.,  1405076.,\n",
       "        1451852.,  1402035.,  1613413.,  1911499.,  1425631.,  1493400.,\n",
       "        1741217.,  1438658.,  1354565.,  1285003.,  1439648.,  1689842.,\n",
       "        1482078.,  1456281.,  1435488.,  1143776.,  1344898.,  1393555.,\n",
       "        1556774.,  1577470.,  1414463.,  1442720.,  1529813.,  1328790.,\n",
       "        1192029.,  1321632.,  1227169.,  1390815.,  1445138.,  1525520.,\n",
       "        1202458.,  1338046.,  1210080.,  1299163.,  1286128.,  1379127.,\n",
       "        1325625.,  1314708.,  1406879.,  1175260.,  1309793.,  1161057.,\n",
       "        1031351.,  1265158.,  1168737.,  1222584.,  1098823.,  1228154.,\n",
       "        1192653.,  1359893.,  1334701.,  1373063.,  1648012.,  1562849.,\n",
       "        1781631.,  1343443.,  1475379.,  1399320.,  1573404.,  1482295.,\n",
       "        1491137.,  1308754.,  1340524.,  1266278.,  1463001.,  1295432.,\n",
       "        1373277.,  1387304.,  1631019.,  1416622.,  1558786.,  1309834.,\n",
       "        1619332.,  1240829.,  1320693.,  1364475.,  1311718.,  1554986.,\n",
       "        1469745.,  1542980.,  1763068.,  1344174.,  1581648.,  1421958.,\n",
       "        1378294.,  1530556.,  1567515.,  1469045.,  1608769.,  1549782.,\n",
       "        1367397.,  1361735.,  1629582.,  1514521.,  1538718.,  1704179.,\n",
       "        1446991.,  1677431.,  1591889.,  1420205.,  1364769.,  1522322.,\n",
       "        1525449.,  1474556.,  1444513.,  1410552.,  1389979.,  1327615.,\n",
       "        1770332.,  1503637.,  1404904.,  1522234.,  1561958.,  1474601.,\n",
       "        1436117.,  1484878.,  1382702.,  1325616.,  1685679.,  1466452.,\n",
       "        1649608.,  1627549.,  1372719.,  1493225.,  1718116.,  1492760.,\n",
       "        1473174.,  1725628.,  1592345.,  1674012.,  1794186.,  1630582.,\n",
       "        1565529.,  1669630.,  1506318.,  1669440.,  1442504.,  1585869.,\n",
       "        1953728.,  1951608.,  2030684.,  1949447.,  2126642.,  1916432.,\n",
       "        1941660.,  1819488.,  1760810.,  1571886.,  1908270.,  1622653.,\n",
       "        2214695.,  1839335.,  1962295.,  1964683.,  1962985.,  1683421.,\n",
       "        1771263.,  2033422.,  2054925.,  2035344.,  1681015.,  2015125.,\n",
       "        1795851.,  2267103.,  2019874.,  1952792.,  2130451.,  2325864.,\n",
       "        2748636.,  2281443.,  2415792.,  2290525.,  2369597.,  2594936.,\n",
       "        2594080.,  2341752.,  2581459.,  2393025.,  2578781.,  2838027.,\n",
       "        2963701.,  2960261.,  3689202.,  4170073.,  3598627.,  3685444.,\n",
       "        3711191.,  4269719.,  9719773.,  4873808.,  4668155.,  4949033.,\n",
       "        5215562.,  7205035.,  6967033.,  7954880.,  9807452., 23346964.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the volume across all stocks for each minute.\n",
    "# shape: (8580,)\n",
    "total_volume = volumes.sum(axis=1)\n",
    "\n",
    "# Reshape so that we have a row per day, \n",
    "# and a column per minute within that day.\n",
    "# shape: (22, 390)\n",
    "grouped_volumes = total_volume.reshape(22, 390)\n",
    "\n",
    "average_volume_per_minute = grouped_volumes.sum(axis=0)\n",
    "average_volume_per_minute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly to many people, volume isn't"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efee83f1ba8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(average_volume_per_minute)"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}