Based_on_greatness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_hdf('train.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "excl = ['id', 'y', 'timestamp']\n",
    "cols_feature = [c for c in train.columns if c not in excl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ids = train['id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1424"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set_1 = [0, 11, 27, 32, 33, 40, 41, 49, 58, 72, 85, 93, 95, 99, 101, 111, 119, 122, 137, 140, 158, 170, 171, 172, 173, 216, 226, 241, 245, 255, 262, 266, 271, 277, 280, 303, 308, 316, 329, 339, 340, 353, 357, 369, 408, 411, 412, 429, 435, 437, 473, 490, 502, 505, 508, 513, 527, 529, 531, 533, 546, 569, 575, 589, 609, 624, 625, 653, 682, 703, 706, 723, 729, 752, 761, 767, 769, 793, 804, 807, 820, 824, 838, 852, 861, 869, 870, 882, 884, 909, 940, 953, 958, 980, 986, 1000, 1002, 1004, 1009, 1022, 1023, 1024, 1035, 1056, 1060, 1065, 1088, 1098, 1105, 1107, 1113, 1118, 1128, 1146, 1151, 1157, 1165, 1184, 1200, 1210, 1213, 1229, 1243, 1257, 1265, 1276, 1277, 1281, 1291, 1294, 1310, 1311, 1317, 1325, 1326, 1343, 1354, 1373, 1383, 1394, 1429, 1431, 1432, 1434, 1447, 1501, 1506, 1515, 1538, 1543, 1552, 1573, 1606, 1610, 1616, 1639, 1657, 1659, 1673, 1681, 1692, 1696, 1697, 1711, 1714, 1717, 1726, 1732, 1737, 1745, 1772, 1780, 1788, 1798, 1817, 1825, 1828, 1840, 1852, 1893, 1895, 1897, 1916, 1939, 1950, 1956, 1962, 1986, 2006, 2043, 2063, 2071, 2076, 2086, 2089, 2090, 2093, 2117, 2154]\n",
    "set_2 = [13, 14, 15, 31, 46, 48, 54, 55, 56, 65, 82, 90, 97, 103, 113, 127, 131, 141, 150, 151, 154, 163, 169, 178, 186, 188, 203, 206, 218, 220, 222, 227, 232, 258, 264, 269, 285, 288, 289, 296, 306, 317, 319, 324, 325, 334, 338, 355, 361, 363, 377, 382, 386, 388, 399, 406, 407, 423, 431, 445, 456, 458, 467, 468, 469, 482, 483, 484, 492, 501, 506, 509, 517, 528, 532, 551, 565, 568, 573, 577, 579, 584, 585, 586, 587, 592, 601, 620, 638, 644, 649, 650, 651, 667, 693, 708, 713, 720, 736, 737, 740, 748, 762, 763, 765, 773, 785, 788, 792, 797, 798, 800, 809, 811, 817, 828, 842, 856, 863, 865, 866, 875, 881, 890, 891, 897, 906, 912, 914, 960, 961, 963, 967, 994, 1012, 1013, 1018, 1030, 1033, 1045, 1046, 1050, 1064, 1070, 1076, 1090, 1120, 1126, 1135, 1139, 1144, 1153, 1166, 1174, 1175, 1195, 1197, 1209, 1215, 1227, 1228, 1230, 1238, 1244, 1246, 1249, 1255, 1260, 1261, 1263, 1272, 1285, 1295, 1296, 1297, 1304, 1306, 1313, 1318, 1323, 1331, 1332, 1357, 1365, 1374, 1376, 1377, 1387, 1398, 1401, 1402, 1416, 1422, 1427, 1441, 1442, 1459, 1465, 1479, 1493, 1495, 1502, 1511, 1537, 1539, 1548, 1554, 1569, 1575, 1577, 1579, 1583, 1585, 1591, 1602, 1614, 1624, 1625, 1637, 1646, 1652, 1664, 1667, 1685, 1686, 1690, 1695, 1699, 1705, 1730, 1731, 1739, 1793, 1794, 1802, 1803, 1810, 1830, 1831, 1845, 1853, 1858, 1862, 1863, 1866, 1888, 1896, 1899, 1904, 1911, 1918, 1926, 1927, 1931, 1941, 1945, 1951, 1963, 1965, 1968, 1972, 2004, 2013, 2015, 2018, 2024, 2029, 2036, 2037, 2044, 2055, 2056, 2057, 2064, 2065, 2069, 2075, 2097, 2102, 2107, 2108, 2112, 2118, 2121, 2124, 2129, 2136, 2140, 2150, 2151, 2155, 2156, 2158]\n",
    "set_3 = [6, 17, 20, 23, 24, 25, 30, 43, 47, 59, 60, 62, 63, 66, 68, 77, 79, 80, 88, 100, 102, 104, 105, 108, 115, 118, 129, 132, 134, 149, 156, 159, 166, 174, 175, 176, 179, 181, 183, 187, 195, 198, 205, 212, 215, 217, 223, 225, 229, 231, 233, 240, 242, 244, 248, 249, 252, 253, 256, 257, 259, 268, 270, 273, 274, 275, 290, 297, 300, 304, 305, 310, 312, 318, 333, 341, 343, 344, 346, 352, 356, 364, 372, 374, 378, 379, 381, 383, 384, 389, 391, 392, 395, 396, 398, 400, 402, 403, 404, 410, 413, 414, 416, 418, 419, 425, 430, 432, 433, 440, 446, 448, 449, 450, 459, 461, 470, 474, 480, 481, 491, 494, 520, 521, 523, 535, 536, 538, 539, 540, 543, 550, 553, 554, 555, 556, 561, 570, 572, 578, 582, 594, 595, 597, 600, 602, 604, 606, 608, 610, 613, 614, 621, 622, 629, 631, 636, 637, 641, 642, 648, 652, 654, 659, 662, 670, 676, 683, 687, 689, 690, 691, 692, 695, 696, 697, 698, 699, 700, 701, 715, 722, 724, 726, 727, 731, 741, 745, 751, 757, 760, 766, 770, 776, 777, 783, 786, 791, 794, 799, 802, 808, 810, 812, 815, 816, 819, 821, 823, 829, 831, 832, 833, 839, 841, 843, 844, 846, 848, 849, 855, 858, 859, 867, 868, 871, 872, 873, 874, 889, 898, 907, 910, 915, 918, 924, 925, 926, 929, 932, 936, 937, 938, 942, 943, 944, 946, 949, 952, 954, 955, 959, 966, 968, 970, 973, 978, 979, 982, 983, 984, 988, 989, 991, 995, 999, 1007, 1008, 1010, 1011, 1015, 1016, 1017, 1019, 1021, 1025, 1034, 1036, 1037, 1038, 1039, 1041, 1042, 1048, 1049, 1053, 1062, 1063, 1066, 1071, 1072, 1073, 1085, 1086, 1087, 1089, 1093, 1095, 1108, 1112, 1114, 1116, 1125, 1130, 1133, 1134, 1136, 1137, 1152, 1164, 1168, 1169, 1170, 1172, 1177, 1178, 1181, 1182, 1186, 1187, 1194, 1204, 1205, 1208, 1212, 1218, 1221, 1223, 1224, 1226, 1231, 1232, 1235, 1236, 1239, 1240, 1241, 1247, 1256, 1259, 1264, 1266, 1271, 1274, 1279, 1288, 1290, 1292, 1303, 1307, 1312, 1316, 1319, 1324, 1327, 1335, 1336, 1339, 1342, 1348, 1349, 1350, 1359, 1363, 1371, 1375, 1378, 1379, 1380, 1381, 1382, 1388, 1390, 1391, 1392, 1396, 1406, 1409, 1414, 1419, 1438, 1439, 1440, 1443, 1445, 1450, 1451, 1452, 1454, 1457, 1460, 1462, 1464, 1466, 1467, 1473, 1477, 1478, 1483, 1486, 1489, 1491, 1492, 1497, 1505, 1507, 1509, 1514, 1517, 1519, 1521, 1524, 1530, 1534, 1535, 1541, 1545, 1546, 1553, 1555, 1561, 1564, 1566, 1568, 1570, 1578, 1587, 1588, 1589, 1599, 1603, 1604, 1607, 1619, 1627, 1628, 1629, 1636, 1650, 1653, 1654, 1660, 1662, 1663, 1666, 1669, 1671, 1688, 1703, 1704, 1708, 1728, 1733, 1734, 1744, 1747, 1749, 1750, 1759, 1760, 1767, 1774, 1775, 1776, 1785, 1787, 1789, 1791, 1792, 1795, 1799, 1808, 1813, 1818, 1821, 1822, 1833, 1836, 1838, 1843, 1854, 1857, 1859, 1861, 1864, 1867, 1872, 1873, 1876, 1882, 1885, 1890, 1892, 1906, 1910, 1915, 1919, 1923, 1925, 1929, 1933, 1936, 1943, 1949, 1952, 1954, 1957, 1966, 1967, 1971, 1976, 1980, 1981, 1985, 1990, 1991, 1998, 2000, 2001, 2002, 2009, 2011, 2021, 2027, 2028, 2030, 2031, 2035, 2045, 2050, 2054, 2058, 2059, 2068, 2070, 2077, 2080, 2081, 2082, 2088, 2095, 2096, 2098, 2100, 2101, 2109, 2111, 2115, 2120, 2125, 2130, 2134, 2135, 2137, 2139, 2146]\n",
    "set_4 = [7, 12, 19, 26, 38, 39, 44, 52, 69, 76, 81, 83, 91, 98, 106, 112, 126, 128, 130, 133, 142, 152, 161, 177, 185, 191, 208, 219, 224, 239, 278, 284, 330, 358, 366, 393, 417, 441, 451, 479, 500, 515, 530, 545, 548, 559, 567, 571, 583, 591, 615, 618, 639, 640, 656, 672, 694, 707, 709, 743, 753, 789, 795, 805, 806, 830, 845, 853, 864, 880, 885, 895, 935, 964, 975, 976, 992, 1003, 1043, 1057, 1059, 1100, 1101, 1131, 1138, 1150, 1155, 1159, 1161, 1176, 1192, 1199, 1201, 1207, 1225, 1258, 1269, 1270, 1287, 1301, 1322, 1329, 1340, 1346, 1352, 1370, 1385, 1404, 1408, 1410, 1415, 1421, 1424, 1426, 1436, 1444, 1476, 1481, 1482, 1494, 1498, 1499, 1513, 1516, 1522, 1532, 1536, 1560, 1562, 1565, 1612, 1613, 1615, 1638, 1647, 1655, 1668, 1672, 1676, 1677, 1679, 1680, 1701, 1706, 1716, 1718, 1727, 1729, 1738, 1743, 1753, 1756, 1764, 1766, 1769, 1783, 1784, 1796, 1806, 1812, 1814, 1827, 1839, 1842, 1860, 1865, 1870, 1883, 1884, 1887, 1902, 1903, 1907, 1928, 1935, 1973, 1975, 1977, 1979, 1992, 2023, 2033, 2062, 2092, 2126, 2131, 2142, 2148]\n",
    "set_5 = [10, 16, 18, 22, 64, 70, 87, 121, 124, 160, 184, 199, 214, 221, 235, 261, 265, 279, 281, 286, 287, 294, 295, 298, 302, 307, 321, 347, 348, 349, 351, 362, 365, 380, 397, 424, 438, 439, 452, 453, 454, 462, 472, 507, 510, 512, 542, 557, 558, 576, 580, 588, 593, 599, 605, 611, 617, 619, 633, 646, 657, 663, 669, 677, 679, 684, 686, 704, 705, 711, 719, 728, 758, 780, 796, 860, 888, 892, 896, 901, 911, 913, 916, 920, 934, 941, 972, 977, 985, 990, 993, 1026, 1047, 1067, 1069, 1075, 1078, 1083, 1094, 1103, 1124, 1129, 1140, 1142, 1143, 1149, 1193, 1196, 1220, 1253, 1267, 1286, 1298, 1308, 1314, 1320, 1328, 1334, 1367, 1386, 1393, 1395, 1407, 1412, 1413, 1448, 1474, 1485, 1500, 1508, 1518, 1523, 1527, 1550, 1593, 1598, 1611, 1618, 1620, 1630, 1635, 1683, 1687, 1712, 1724, 1725, 1741, 1742, 1752, 1755, 1758, 1778, 1782, 1790, 1809, 1815, 1832, 1848, 1849, 1851, 1869, 1874, 1877, 1878, 1891, 1898, 1901, 1905, 1912, 1921, 1969, 1970, 1987, 2008, 2047, 2061, 2066, 2074, 2078, 2079, 2083, 2087, 2104, 2114, 2138, 2145, 2149, 2152]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_label(id):\n",
    "    if id in set_1:\n",
    "        return 'a'\n",
    "    elif id in set_2:\n",
    "        return 'b'\n",
    "    elif id in set_3:\n",
    "        return 'c'\n",
    "    elif id in set_4:\n",
    "        return 'd'\n",
    "    elif id in set_5:\n",
    "        return 'e'\n",
    "    else:\n",
    "        return 'n'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['label'] = train.id.map(add_label)\n",
    "label_dummy = pd.get_dummies(train.label)\n",
    "train.drop('label', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "      <th>y</th>\n",
       "      <th>label_1</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370326</td>\n",
       "      <td>-0.006316</td>\n",
       "      <td>0.222831</td>\n",
       "      <td>-0.213030</td>\n",
       "      <td>0.729277</td>\n",
       "      <td>-0.335633</td>\n",
       "      <td>0.113292</td>\n",
       "      <td>1.621238</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011753</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>-0.038064</td>\n",
       "      <td>-0.017425</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.114285</td>\n",
       "      <td>-0.210185</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001240</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.010622</td>\n",
       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
       "      <td>-0.764516</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020940</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015959</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.176693</td>\n",
       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>-0.125687</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007338</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.346856</td>\n",
       "      <td>0.166239</td>\n",
       "      <td>-6.080701</td>\n",
       "      <td>-0.992249</td>\n",
       "      <td>-0.125916</td>\n",
       "      <td>0.345812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.584239</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031425</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072036</td>\n",
       "      <td>0.014931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.193205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.032895</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300062</td>\n",
       "      <td>0.071251</td>\n",
       "      <td>-0.074451</td>\n",
       "      <td>-0.065292</td>\n",
       "      <td>-0.011286</td>\n",
       "      <td>0.026365</td>\n",
       "      <td>0.210249</td>\n",
       "      <td>0.167494</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015803</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003511</td>\n",
       "      <td>-0.034270</td>\n",
       "      <td>0.082372</td>\n",
       "      <td>-0.023937</td>\n",
       "      <td>-0.025750</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.263451</td>\n",
       "      <td>-0.241212</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.027593</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.083330</td>\n",
       "      <td>0.081935</td>\n",
       "      <td>-2.048438</td>\n",
       "      <td>-0.206856</td>\n",
       "      <td>-0.839563</td>\n",
       "      <td>-0.234100</td>\n",
       "      <td>-0.291853</td>\n",
       "      <td>-5.722336</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435826</td>\n",
       "      <td>1.797289</td>\n",
       "      <td>0.363570</td>\n",
       "      <td>-0.005651</td>\n",
       "      <td>0.442866</td>\n",
       "      <td>0.125375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292311</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001899</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.285388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.193590</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050219</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.034991</td>\n",
       "      <td>-0.019258</td>\n",
       "      <td>0.055769</td>\n",
       "      <td>-0.084496</td>\n",
       "      <td>0.259828</td>\n",
       "      <td>0.198800</td>\n",
       "      <td>0.265104</td>\n",
       "      <td>-0.160462</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.212615</td>\n",
       "      <td>-0.979520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.395701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.780873</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005203</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0.071704</td>\n",
       "      <td>-0.044019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.038046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.331778</td>\n",
       "      <td>-0.021114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.116360</td>\n",
       "      <td>0.164506</td>\n",
       "      <td>0.156510</td>\n",
       "      <td>-0.129252</td>\n",
       "      <td>1.310059</td>\n",
       "      <td>-0.264846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.370725</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017768</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026824</td>\n",
       "      <td>-0.024105</td>\n",
       "      <td>-0.028991</td>\n",
       "      <td>0.433277</td>\n",
       "      <td>-0.006366</td>\n",
       "      <td>0.295138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.285946</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001089</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.367122</td>\n",
       "      <td>0.675543</td>\n",
       "      <td>-0.008483</td>\n",
       "      <td>-0.367778</td>\n",
       "      <td>-0.015529</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.251624</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.453271</td>\n",
       "      <td>-0.036301</td>\n",
       "      <td>0.094657</td>\n",
       "      <td>-0.521106</td>\n",
       "      <td>0.049715</td>\n",
       "      <td>-0.352013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.182694</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040724</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.122734</td>\n",
       "      <td>0.038939</td>\n",
       "      <td>0.148015</td>\n",
       "      <td>2.493512</td>\n",
       "      <td>0.009746</td>\n",
       "      <td>-0.063111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162878</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003921</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.062361</td>\n",
       "      <td>-0.063724</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.032845</td>\n",
       "      <td>0.174025</td>\n",
       "      <td>0.122335</td>\n",
       "      <td>-0.133188</td>\n",
       "      <td>-0.108286</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011317</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175489</td>\n",
       "      <td>-0.024445</td>\n",
       "      <td>-0.034079</td>\n",
       "      <td>-0.020533</td>\n",
       "      <td>0.282478</td>\n",
       "      <td>-0.069417</td>\n",
       "      <td>0.247840</td>\n",
       "      <td>0.303953</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044167</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.190705</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>-0.056835</td>\n",
       "      <td>0.063464</td>\n",
       "      <td>-0.149507</td>\n",
       "      <td>0.267959</td>\n",
       "      <td>-0.392849</td>\n",
       "      <td>0.080084</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008503</td>\n",
       "      <td>0.048732</td>\n",
       "      <td>0.123793</td>\n",
       "      <td>0.266366</td>\n",
       "      <td>-0.009772</td>\n",
       "      <td>-0.248937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.157415</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012101</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043156</td>\n",
       "      <td>0.540945</td>\n",
       "      <td>0.278394</td>\n",
       "      <td>0.563343</td>\n",
       "      <td>-0.144004</td>\n",
       "      <td>-0.024376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.337090</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.070837</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0.086832</td>\n",
       "      <td>-0.022962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071911</td>\n",
       "      <td>0.112818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001766</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.082307</td>\n",
       "      <td>-0.039761</td>\n",
       "      <td>0.102000</td>\n",
       "      <td>-0.798021</td>\n",
       "      <td>0.149622</td>\n",
       "      <td>0.028448</td>\n",
       "      <td>0.062647</td>\n",
       "      <td>0.044142</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012542</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005351</td>\n",
       "      <td>-0.031086</td>\n",
       "      <td>0.078334</td>\n",
       "      <td>0.417328</td>\n",
       "      <td>-0.020146</td>\n",
       "      <td>0.390538</td>\n",
       "      <td>0.256594</td>\n",
       "      <td>-0.167223</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029697</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>0.330190</td>\n",
       "      <td>-0.816961</td>\n",
       "      <td>0.012602</td>\n",
       "      <td>-0.036462</td>\n",
       "      <td>-0.115197</td>\n",
       "      <td>-0.237428</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030815</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003925</td>\n",
       "      <td>-0.037522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.028244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049041</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.016336</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710726</th>\n",
       "      <td>2100</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.079269</td>\n",
       "      <td>-0.021573</td>\n",
       "      <td>-0.033426</td>\n",
       "      <td>-0.333376</td>\n",
       "      <td>0.061463</td>\n",
       "      <td>0.217718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.127397</td>\n",
       "      <td>...</td>\n",
       "      <td>4.784280e-03</td>\n",
       "      <td>-3.330669e-16</td>\n",
       "      <td>-0.050890</td>\n",
       "      <td>0.008358</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710727</th>\n",
       "      <td>2101</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.204339</td>\n",
       "      <td>0.134338</td>\n",
       "      <td>0.143729</td>\n",
       "      <td>0.702932</td>\n",
       "      <td>-0.290513</td>\n",
       "      <td>0.031102</td>\n",
       "      <td>-0.243759</td>\n",
       "      <td>-0.205067</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.989455e-12</td>\n",
       "      <td>-1.989691e+00</td>\n",
       "      <td>-0.001040</td>\n",
       "      <td>-0.003035</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710728</th>\n",
       "      <td>2102</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.095661</td>\n",
       "      <td>-0.051734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.004488</td>\n",
       "      <td>-0.249359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.850498e-07</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>0.005510</td>\n",
       "      <td>-0.001172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710729</th>\n",
       "      <td>2104</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.134436</td>\n",
       "      <td>-0.030198</td>\n",
       "      <td>-0.057608</td>\n",
       "      <td>0.016190</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>-0.340176</td>\n",
       "      <td>-0.104457</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>...</td>\n",
       "      <td>2.896559e-23</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>0.020394</td>\n",
       "      <td>0.003194</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710730</th>\n",
       "      <td>2107</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.180348</td>\n",
       "      <td>0.061339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.418095</td>\n",
       "      <td>-0.620948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.911819e-01</td>\n",
       "      <td>-1.660443e-06</td>\n",
       "      <td>0.052314</td>\n",
       "      <td>-0.019208</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710731</th>\n",
       "      <td>2108</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.247698</td>\n",
       "      <td>0.026739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.280510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.198069</td>\n",
       "      <td>-0.620398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.547892e-04</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>-0.019431</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710732</th>\n",
       "      <td>2109</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.425230</td>\n",
       "      <td>0.266186</td>\n",
       "      <td>0.075126</td>\n",
       "      <td>0.039726</td>\n",
       "      <td>-0.060703</td>\n",
       "      <td>-0.059133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286111</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.736235e-18</td>\n",
       "      <td>-1.660443e-06</td>\n",
       "      <td>-0.065701</td>\n",
       "      <td>-0.012707</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710733</th>\n",
       "      <td>2114</td>\n",
       "      <td>1812</td>\n",
       "      <td>-2.337000</td>\n",
       "      <td>1.459039</td>\n",
       "      <td>3.495335</td>\n",
       "      <td>-0.347759</td>\n",
       "      <td>0.257406</td>\n",
       "      <td>0.172330</td>\n",
       "      <td>-0.098562</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.850498e-07</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>-0.016923</td>\n",
       "      <td>-0.002151</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710734</th>\n",
       "      <td>2117</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.136062</td>\n",
       "      <td>-0.006129</td>\n",
       "      <td>-2.083220</td>\n",
       "      <td>-0.316265</td>\n",
       "      <td>-0.480613</td>\n",
       "      <td>-0.270387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.297367</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.442857e-05</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>-0.057015</td>\n",
       "      <td>0.034297</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710735</th>\n",
       "      <td>2118</td>\n",
       "      <td>1812</td>\n",
       "      <td>-2.458985</td>\n",
       "      <td>1.501635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.204721</td>\n",
       "      <td>-0.136476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.896965e-23</td>\n",
       "      <td>-1.989691e+00</td>\n",
       "      <td>0.022479</td>\n",
       "      <td>-0.007932</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710736</th>\n",
       "      <td>2120</td>\n",
       "      <td>1812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.398379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.222814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.554627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-0.032474</td>\n",
       "      <td>-0.033865</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710737</th>\n",
       "      <td>2121</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.126793</td>\n",
       "      <td>0.040152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.054294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.423501</td>\n",
       "      <td>-0.416516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>0.043590</td>\n",
       "      <td>-0.001192</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710738</th>\n",
       "      <td>2126</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.181550</td>\n",
       "      <td>-0.039677</td>\n",
       "      <td>-0.082102</td>\n",
       "      <td>-0.140690</td>\n",
       "      <td>-0.231005</td>\n",
       "      <td>-0.145413</td>\n",
       "      <td>-0.150222</td>\n",
       "      <td>0.158479</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.036653e-01</td>\n",
       "      <td>-1.782362e+00</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>-0.003483</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710739</th>\n",
       "      <td>2129</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.064731</td>\n",
       "      <td>-0.044927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.615191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040299</td>\n",
       "      <td>-0.058827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.547892e-04</td>\n",
       "      <td>-3.051758e-05</td>\n",
       "      <td>0.034427</td>\n",
       "      <td>0.005619</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710740</th>\n",
       "      <td>2130</td>\n",
       "      <td>1812</td>\n",
       "      <td>-8.833108</td>\n",
       "      <td>3.455564</td>\n",
       "      <td>1.174958</td>\n",
       "      <td>-0.439014</td>\n",
       "      <td>-1.401483</td>\n",
       "      <td>-0.219093</td>\n",
       "      <td>0.087497</td>\n",
       "      <td>-1.461111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-6.892265e-13</td>\n",
       "      <td>0.020224</td>\n",
       "      <td>-0.027963</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710741</th>\n",
       "      <td>2131</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.149930</td>\n",
       "      <td>0.070203</td>\n",
       "      <td>0.363126</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>0.164914</td>\n",
       "      <td>-0.262682</td>\n",
       "      <td>-0.315310</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.036645e-01</td>\n",
       "      <td>-4.279235e-09</td>\n",
       "      <td>0.002708</td>\n",
       "      <td>-0.016629</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710742</th>\n",
       "      <td>2137</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.059745</td>\n",
       "      <td>-0.052186</td>\n",
       "      <td>-0.062670</td>\n",
       "      <td>-1.842369</td>\n",
       "      <td>0.073483</td>\n",
       "      <td>0.287247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-0.024923</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710743</th>\n",
       "      <td>2138</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.219608</td>\n",
       "      <td>-0.034336</td>\n",
       "      <td>-0.031817</td>\n",
       "      <td>0.270888</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.134292</td>\n",
       "      <td>0.364995</td>\n",
       "      <td>0.064963</td>\n",
       "      <td>...</td>\n",
       "      <td>3.403606e-04</td>\n",
       "      <td>-1.660443e-06</td>\n",
       "      <td>0.010777</td>\n",
       "      <td>0.002015</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710744</th>\n",
       "      <td>2139</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.062132</td>\n",
       "      <td>0.070332</td>\n",
       "      <td>0.351273</td>\n",
       "      <td>0.478199</td>\n",
       "      <td>0.092099</td>\n",
       "      <td>0.494140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.330590</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.948457e-01</td>\n",
       "      <td>-6.892265e-13</td>\n",
       "      <td>0.027970</td>\n",
       "      <td>0.025162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710745</th>\n",
       "      <td>2140</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.213264</td>\n",
       "      <td>0.029603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.273137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.464013</td>\n",
       "      <td>-0.404886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.036501e-01</td>\n",
       "      <td>-4.279235e-09</td>\n",
       "      <td>0.042746</td>\n",
       "      <td>-0.024362</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710746</th>\n",
       "      <td>2142</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.223395</td>\n",
       "      <td>-0.042492</td>\n",
       "      <td>-0.060381</td>\n",
       "      <td>0.016019</td>\n",
       "      <td>0.218667</td>\n",
       "      <td>-0.160979</td>\n",
       "      <td>-0.138038</td>\n",
       "      <td>0.348972</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.442868e-05</td>\n",
       "      <td>-1.660443e-06</td>\n",
       "      <td>0.022390</td>\n",
       "      <td>0.008430</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710747</th>\n",
       "      <td>2145</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.154051</td>\n",
       "      <td>-0.029331</td>\n",
       "      <td>-0.010545</td>\n",
       "      <td>0.019339</td>\n",
       "      <td>-0.260369</td>\n",
       "      <td>-0.227888</td>\n",
       "      <td>0.006721</td>\n",
       "      <td>0.008255</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.989458e-12</td>\n",
       "      <td>-1.660443e-06</td>\n",
       "      <td>-0.032458</td>\n",
       "      <td>-0.000711</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710748</th>\n",
       "      <td>2146</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.238458</td>\n",
       "      <td>0.316407</td>\n",
       "      <td>0.632261</td>\n",
       "      <td>0.531651</td>\n",
       "      <td>-0.154740</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.543269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.330669e-16</td>\n",
       "      <td>-0.004821</td>\n",
       "      <td>-0.017794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710749</th>\n",
       "      <td>2148</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.089476</td>\n",
       "      <td>-0.038628</td>\n",
       "      <td>0.776538</td>\n",
       "      <td>8.809832</td>\n",
       "      <td>-0.177850</td>\n",
       "      <td>0.388419</td>\n",
       "      <td>0.193054</td>\n",
       "      <td>0.175415</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.989455e-12</td>\n",
       "      <td>-1.999969e+00</td>\n",
       "      <td>-0.022806</td>\n",
       "      <td>-0.001058</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710750</th>\n",
       "      <td>2149</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.254593</td>\n",
       "      <td>0.064668</td>\n",
       "      <td>-0.034742</td>\n",
       "      <td>-1.336611</td>\n",
       "      <td>-0.037401</td>\n",
       "      <td>0.244451</td>\n",
       "      <td>0.049125</td>\n",
       "      <td>-0.130919</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.948457e-01</td>\n",
       "      <td>-6.892265e-13</td>\n",
       "      <td>-0.027780</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710751</th>\n",
       "      <td>2150</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.123364</td>\n",
       "      <td>-0.055977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.255730</td>\n",
       "      <td>-0.108285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.061612e-27</td>\n",
       "      <td>-2.328306e-10</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710752</th>\n",
       "      <td>2151</td>\n",
       "      <td>1812</td>\n",
       "      <td>-10.437184</td>\n",
       "      <td>0.080905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.369380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.293557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.395756e-04</td>\n",
       "      <td>-1.660443e-06</td>\n",
       "      <td>0.044597</td>\n",
       "      <td>-0.009241</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710753</th>\n",
       "      <td>2154</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.077930</td>\n",
       "      <td>-0.038748</td>\n",
       "      <td>-0.031859</td>\n",
       "      <td>0.646608</td>\n",
       "      <td>-0.145526</td>\n",
       "      <td>-0.119539</td>\n",
       "      <td>-0.151587</td>\n",
       "      <td>-0.130524</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.863969e-01</td>\n",
       "      <td>-1.266820e-11</td>\n",
       "      <td>0.030816</td>\n",
       "      <td>-0.006852</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710754</th>\n",
       "      <td>2156</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.269845</td>\n",
       "      <td>-0.005322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.117539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214088</td>\n",
       "      <td>-0.307293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.142751e-37</td>\n",
       "      <td>-1.999260e+00</td>\n",
       "      <td>-0.011706</td>\n",
       "      <td>-0.000785</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710755</th>\n",
       "      <td>2158</td>\n",
       "      <td>1812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.456266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1710756 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  timestamp  derived_0  derived_1  derived_2  derived_3  \\\n",
       "0          10          0   0.370326  -0.006316   0.222831  -0.213030   \n",
       "1          11          0   0.014765  -0.038064  -0.017425   0.320652   \n",
       "2          12          0  -0.010622  -0.050577   3.379575  -0.157525   \n",
       "3          25          0        NaN        NaN        NaN        NaN   \n",
       "4          26          0   0.176693  -0.025284  -0.057680   0.015100   \n",
       "5          27          0   0.346856   0.166239  -6.080701  -0.992249   \n",
       "6          31          0   0.072036   0.014931        NaN   0.014063   \n",
       "7          38          0   0.300062   0.071251  -0.074451  -0.065292   \n",
       "8          39          0  -0.003511  -0.034270   0.082372  -0.023937   \n",
       "9          40          0  -0.083330   0.081935  -2.048438  -0.206856   \n",
       "10         41          0   0.435826   1.797289   0.363570  -0.005651   \n",
       "11         43          0        NaN        NaN        NaN        NaN   \n",
       "12         44          0   0.034991  -0.019258   0.055769  -0.084496   \n",
       "13         49          0        NaN   0.212615  -0.979520        NaN   \n",
       "14         54          0   0.071704  -0.044019        NaN   0.038046   \n",
       "15         59          0   0.116360   0.164506   0.156510  -0.129252   \n",
       "16         60          0   0.026824  -0.024105  -0.028991   0.433277   \n",
       "17         62          0   0.367122   0.675543  -0.008483  -0.367778   \n",
       "18         63          0   0.453271  -0.036301   0.094657  -0.521106   \n",
       "19         68          0  -0.122734   0.038939   0.148015   2.493512   \n",
       "20         69          0  -0.062361  -0.063724   0.021274   0.032845   \n",
       "21         70          0   0.175489  -0.024445  -0.034079  -0.020533   \n",
       "22         76          0  -0.190705   0.006221  -0.056835   0.063464   \n",
       "23         79          0   0.008503   0.048732   0.123793   0.266366   \n",
       "24         80          0   0.043156   0.540945   0.278394   0.563343   \n",
       "25         82          0   0.086832  -0.022962        NaN   0.006783   \n",
       "26         83          0  -0.082307  -0.039761   0.102000  -0.798021   \n",
       "27         85          0   0.005351  -0.031086   0.078334   0.417328   \n",
       "28         87          0   0.045536   0.054391   0.330190  -0.816961   \n",
       "29         90          0  -0.003925  -0.037522        NaN  -0.028244   \n",
       "...       ...        ...        ...        ...        ...        ...   \n",
       "1710726  2100       1812   0.079269  -0.021573  -0.033426  -0.333376   \n",
       "1710727  2101       1812  -0.204339   0.134338   0.143729   0.702932   \n",
       "1710728  2102       1812   0.095661  -0.051734        NaN   0.035816   \n",
       "1710729  2104       1812   0.134436  -0.030198  -0.057608   0.016190   \n",
       "1710730  2107       1812  -0.180348   0.061339        NaN   0.049680   \n",
       "1710731  2108       1812  -0.247698   0.026739        NaN  -0.280510   \n",
       "1710732  2109       1812  -0.425230   0.266186   0.075126   0.039726   \n",
       "1710733  2114       1812  -2.337000   1.459039   3.495335  -0.347759   \n",
       "1710734  2117       1812  -0.136062  -0.006129  -2.083220  -0.316265   \n",
       "1710735  2118       1812  -2.458985   1.501635        NaN        NaN   \n",
       "1710736  2120       1812        NaN        NaN  -0.398379        NaN   \n",
       "1710737  2121       1812   0.126793   0.040152        NaN  -0.054294   \n",
       "1710738  2126       1812  -0.181550  -0.039677  -0.082102  -0.140690   \n",
       "1710739  2129       1812  -0.064731  -0.044927        NaN   0.615191   \n",
       "1710740  2130       1812  -8.833108   3.455564   1.174958  -0.439014   \n",
       "1710741  2131       1812  -0.149930   0.070203   0.363126  -0.010869   \n",
       "1710742  2137       1812  -0.059745  -0.052186  -0.062670  -1.842369   \n",
       "1710743  2138       1812   0.219608  -0.034336  -0.031817   0.270888   \n",
       "1710744  2139       1812   0.062132   0.070332   0.351273   0.478199   \n",
       "1710745  2140       1812   0.213264   0.029603        NaN  -0.273137   \n",
       "1710746  2142       1812  -0.223395  -0.042492  -0.060381   0.016019   \n",
       "1710747  2145       1812  -0.154051  -0.029331  -0.010545   0.019339   \n",
       "1710748  2146       1812  -0.238458   0.316407   0.632261   0.531651   \n",
       "1710749  2148       1812   0.089476  -0.038628   0.776538   8.809832   \n",
       "1710750  2149       1812   0.254593   0.064668  -0.034742  -1.336611   \n",
       "1710751  2150       1812  -0.123364  -0.055977        NaN   0.010906   \n",
       "1710752  2151       1812 -10.437184   0.080905        NaN   3.369380   \n",
       "1710753  2154       1812  -0.077930  -0.038748  -0.031859   0.646608   \n",
       "1710754  2156       1812  -0.269845  -0.005322        NaN  -0.117539   \n",
       "1710755  2158       1812        NaN        NaN        NaN        NaN   \n",
       "\n",
       "         derived_4  fundamental_0  fundamental_1  fundamental_2 ...   \\\n",
       "0         0.729277      -0.335633       0.113292       1.621238 ...    \n",
       "1        -0.034134       0.004413       0.114285      -0.210185 ...    \n",
       "2        -0.068550      -0.155937       1.219439      -0.764516 ...    \n",
       "3              NaN       0.178495            NaN      -0.007262 ...    \n",
       "4         0.180894       0.139445      -0.125687      -0.018707 ...    \n",
       "5        -0.125916       0.345812            NaN      -0.584239 ...    \n",
       "6              NaN      -0.193205            NaN            NaN ...    \n",
       "7        -0.011286       0.026365       0.210249       0.167494 ...    \n",
       "8        -0.025750       0.007815       0.263451      -0.241212 ...    \n",
       "9        -0.839563      -0.234100      -0.291853      -5.722336 ...    \n",
       "10        0.442866       0.125375            NaN       0.292311 ...    \n",
       "11             NaN      -0.285388            NaN      -0.193590 ...    \n",
       "12        0.259828       0.198800       0.265104      -0.160462 ...    \n",
       "13             NaN       0.395701            NaN      -0.780873 ...    \n",
       "14             NaN       0.331778      -0.021114            NaN ...    \n",
       "15        1.310059      -0.264846            NaN       0.370725 ...    \n",
       "16       -0.006366       0.295138            NaN      -0.285946 ...    \n",
       "17       -0.015529       0.129848            NaN      -0.251624 ...    \n",
       "18        0.049715      -0.352013            NaN      -0.182694 ...    \n",
       "19        0.009746      -0.063111            NaN      -0.162878 ...    \n",
       "20        0.174025       0.122335      -0.133188      -0.108286 ...    \n",
       "21        0.282478      -0.069417       0.247840       0.303953 ...    \n",
       "22       -0.149507       0.267959      -0.392849       0.080084 ...    \n",
       "23       -0.009772      -0.248937            NaN      -0.157415 ...    \n",
       "24       -0.144004      -0.024376            NaN      -0.337090 ...    \n",
       "25             NaN       0.071911       0.112818            NaN ...    \n",
       "26        0.149622       0.028448       0.062647       0.044142 ...    \n",
       "27       -0.020146       0.390538       0.256594      -0.167223 ...    \n",
       "28        0.012602      -0.036462      -0.115197      -0.237428 ...    \n",
       "29             NaN      -0.049041       0.084827            NaN ...    \n",
       "...            ...            ...            ...            ... ...    \n",
       "1710726   0.061463       0.217718            NaN       0.127397 ...    \n",
       "1710727  -0.290513       0.031102      -0.243759      -0.205067 ...    \n",
       "1710728        NaN      -0.004488      -0.249359            NaN ...    \n",
       "1710729   0.146911      -0.340176      -0.104457       0.002421 ...    \n",
       "1710730        NaN      -0.418095      -0.620948            NaN ...    \n",
       "1710731        NaN       0.198069      -0.620398            NaN ...    \n",
       "1710732  -0.060703      -0.059133            NaN       0.286111 ...    \n",
       "1710733   0.257406       0.172330      -0.098562      -0.000398 ...    \n",
       "1710734  -0.480613      -0.270387            NaN      -0.297367 ...    \n",
       "1710735        NaN       0.204721      -0.136476            NaN ...    \n",
       "1710736        NaN      -0.222814            NaN      -0.554627 ...    \n",
       "1710737        NaN      -0.423501      -0.416516            NaN ...    \n",
       "1710738  -0.231005      -0.145413      -0.150222       0.158479 ...    \n",
       "1710739        NaN       0.040299      -0.058827            NaN ...    \n",
       "1710740  -1.401483      -0.219093       0.087497      -1.461111 ...    \n",
       "1710741   0.164914      -0.262682      -0.315310       0.019663 ...    \n",
       "1710742   0.073483       0.287247            NaN       0.001499 ...    \n",
       "1710743   0.045701       0.134292       0.364995       0.064963 ...    \n",
       "1710744   0.092099       0.494140            NaN      -0.330590 ...    \n",
       "1710745        NaN      -0.464013      -0.404886            NaN ...    \n",
       "1710746   0.218667      -0.160979      -0.138038       0.348972 ...    \n",
       "1710747  -0.260369      -0.227888       0.006721       0.008255 ...    \n",
       "1710748  -0.154740       0.069316            NaN      -0.543269 ...    \n",
       "1710749  -0.177850       0.388419       0.193054       0.175415 ...    \n",
       "1710750  -0.037401       0.244451       0.049125      -0.130919 ...    \n",
       "1710751        NaN      -0.255730      -0.108285            NaN ...    \n",
       "1710752        NaN      -0.293557            NaN            NaN ...    \n",
       "1710753  -0.145526      -0.119539      -0.151587      -0.130524 ...    \n",
       "1710754        NaN       0.214088      -0.307293            NaN ...    \n",
       "1710755        NaN            NaN      -6.456266            NaN ...    \n",
       "\n",
       "         technical_42  technical_43  technical_44         y  label_1    a  \\\n",
       "0                 NaN -2.000000e+00           NaN -0.011753        0  0.0   \n",
       "1                 NaN -2.000000e+00           NaN -0.001240        1  1.0   \n",
       "2                 NaN -2.000000e+00           NaN -0.020940        0  0.0   \n",
       "3                 NaN -2.000000e+00           NaN -0.015959        0  0.0   \n",
       "4                 NaN  0.000000e+00           NaN -0.007338        0  0.0   \n",
       "5                 NaN -2.000000e+00           NaN  0.031425        1  1.0   \n",
       "6                 NaN -2.000000e+00           NaN -0.032895        0  0.0   \n",
       "7                 NaN -2.000000e+00           NaN  0.015803        0  0.0   \n",
       "8                 NaN -2.000000e+00           NaN -0.027593        0  0.0   \n",
       "9                 NaN -2.000000e+00           NaN  0.006662        1  1.0   \n",
       "10                NaN -2.000000e+00           NaN -0.001899        1  1.0   \n",
       "11                NaN -2.000000e+00           NaN  0.050219        0  0.0   \n",
       "12                NaN -2.000000e+00           NaN -0.018991        0  0.0   \n",
       "13                NaN  0.000000e+00           NaN -0.005203        1  1.0   \n",
       "14                NaN -2.000000e+00           NaN -0.006369        0  0.0   \n",
       "15                NaN -2.000000e+00           NaN  0.017768        0  0.0   \n",
       "16                NaN  0.000000e+00           NaN -0.001089        0  0.0   \n",
       "17                NaN -2.000000e+00           NaN -0.008794        0  0.0   \n",
       "18                NaN  0.000000e+00           NaN  0.040724        0  0.0   \n",
       "19                NaN -2.000000e+00           NaN -0.003921        0  0.0   \n",
       "20                NaN  0.000000e+00           NaN -0.011317        0  0.0   \n",
       "21                NaN -2.000000e+00           NaN  0.044167        0  0.0   \n",
       "22                NaN -2.000000e+00           NaN  0.001395        0  0.0   \n",
       "23                NaN  0.000000e+00           NaN -0.012101        0  0.0   \n",
       "24                NaN  0.000000e+00           NaN -0.070837        0  0.0   \n",
       "25                NaN -2.000000e+00           NaN -0.001766        0  0.0   \n",
       "26                NaN  0.000000e+00           NaN -0.012542        0  0.0   \n",
       "27                NaN  0.000000e+00           NaN  0.029697        1  1.0   \n",
       "28                NaN  0.000000e+00           NaN  0.030815        0  0.0   \n",
       "29                NaN -2.000000e+00           NaN -0.016336        0  0.0   \n",
       "...               ...           ...           ...       ...      ...  ...   \n",
       "1710726  4.784280e-03 -3.330669e-16     -0.050890  0.008358        0  0.0   \n",
       "1710727 -5.989455e-12 -1.989691e+00     -0.001040 -0.003035        0  0.0   \n",
       "1710728 -7.850498e-07 -1.266820e-11      0.005510 -0.001172        0  0.0   \n",
       "1710729  2.896559e-23 -1.266820e-11      0.020394  0.003194        0  0.0   \n",
       "1710730 -8.911819e-01 -1.660443e-06      0.052314 -0.019208        0  0.0   \n",
       "1710731  3.547892e-04 -1.266820e-11     -0.019431  0.000383        0  0.0   \n",
       "1710732 -3.736235e-18 -1.660443e-06     -0.065701 -0.012707        0  0.0   \n",
       "1710733 -7.850498e-07 -1.266820e-11     -0.016923 -0.002151        0  0.0   \n",
       "1710734 -1.442857e-05 -1.266820e-11     -0.057015  0.034297        1  1.0   \n",
       "1710735  2.896965e-23 -1.989691e+00      0.022479 -0.007932        0  0.0   \n",
       "1710736  0.000000e+00 -2.000000e+00     -0.032474 -0.033865        0  0.0   \n",
       "1710737  0.000000e+00 -1.266820e-11      0.043590 -0.001192        0  0.0   \n",
       "1710738 -1.036653e-01 -1.782362e+00     -0.010252 -0.003483        0  0.0   \n",
       "1710739  3.547892e-04 -3.051758e-05      0.034427  0.005619        0  0.0   \n",
       "1710740 -0.000000e+00 -6.892265e-13      0.020224 -0.027963        0  0.0   \n",
       "1710741 -1.036645e-01 -4.279235e-09      0.002708 -0.016629        0  0.0   \n",
       "1710742  1.000000e+00 -2.000000e+00     -0.024923  0.008800        0  0.0   \n",
       "1710743  3.403606e-04 -1.660443e-06      0.010777  0.002015        0  0.0   \n",
       "1710744 -9.948457e-01 -6.892265e-13      0.027970  0.025162        0  0.0   \n",
       "1710745  1.036501e-01 -4.279235e-09      0.042746 -0.024362        0  0.0   \n",
       "1710746 -1.442868e-05 -1.660443e-06      0.022390  0.008430        0  0.0   \n",
       "1710747 -5.989458e-12 -1.660443e-06     -0.032458 -0.000711        0  0.0   \n",
       "1710748  0.000000e+00 -3.330669e-16     -0.004821 -0.017794        0  0.0   \n",
       "1710749 -5.989455e-12 -1.999969e+00     -0.022806 -0.001058        0  0.0   \n",
       "1710750 -9.948457e-01 -6.892265e-13     -0.027780 -0.001462        0  0.0   \n",
       "1710751  4.061612e-27 -2.328306e-10      0.001004  0.004604        0  0.0   \n",
       "1710752  3.395756e-04 -1.660443e-06      0.044597 -0.009241        0  0.0   \n",
       "1710753 -8.863969e-01 -1.266820e-11      0.030816 -0.006852        1  1.0   \n",
       "1710754  4.142751e-37 -1.999260e+00     -0.011706 -0.000785        0  0.0   \n",
       "1710755  0.000000e+00  0.000000e+00           NaN  0.003497        0  0.0   \n",
       "\n",
       "           b    c    d    e  \n",
       "0        0.0  0.0  0.0  1.0  \n",
       "1        0.0  0.0  0.0  0.0  \n",
       "2        0.0  0.0  1.0  0.0  \n",
       "3        0.0  1.0  0.0  0.0  \n",
       "4        0.0  0.0  1.0  0.0  \n",
       "5        0.0  0.0  0.0  0.0  \n",
       "6        1.0  0.0  0.0  0.0  \n",
       "7        0.0  0.0  1.0  0.0  \n",
       "8        0.0  0.0  1.0  0.0  \n",
       "9        0.0  0.0  0.0  0.0  \n",
       "10       0.0  0.0  0.0  0.0  \n",
       "11       0.0  1.0  0.0  0.0  \n",
       "12       0.0  0.0  1.0  0.0  \n",
       "13       0.0  0.0  0.0  0.0  \n",
       "14       1.0  0.0  0.0  0.0  \n",
       "15       0.0  1.0  0.0  0.0  \n",
       "16       0.0  1.0  0.0  0.0  \n",
       "17       0.0  1.0  0.0  0.0  \n",
       "18       0.0  1.0  0.0  0.0  \n",
       "19       0.0  1.0  0.0  0.0  \n",
       "20       0.0  0.0  1.0  0.0  \n",
       "21       0.0  0.0  0.0  1.0  \n",
       "22       0.0  0.0  1.0  0.0  \n",
       "23       0.0  1.0  0.0  0.0  \n",
       "24       0.0  1.0  0.0  0.0  \n",
       "25       1.0  0.0  0.0  0.0  \n",
       "26       0.0  0.0  1.0  0.0  \n",
       "27       0.0  0.0  0.0  0.0  \n",
       "28       0.0  0.0  0.0  1.0  \n",
       "29       1.0  0.0  0.0  0.0  \n",
       "...      ...  ...  ...  ...  \n",
       "1710726  0.0  1.0  0.0  0.0  \n",
       "1710727  0.0  1.0  0.0  0.0  \n",
       "1710728  1.0  0.0  0.0  0.0  \n",
       "1710729  0.0  0.0  0.0  1.0  \n",
       "1710730  1.0  0.0  0.0  0.0  \n",
       "1710731  1.0  0.0  0.0  0.0  \n",
       "1710732  0.0  1.0  0.0  0.0  \n",
       "1710733  0.0  0.0  0.0  1.0  \n",
       "1710734  0.0  0.0  0.0  0.0  \n",
       "1710735  1.0  0.0  0.0  0.0  \n",
       "1710736  0.0  1.0  0.0  0.0  \n",
       "1710737  1.0  0.0  0.0  0.0  \n",
       "1710738  0.0  0.0  1.0  0.0  \n",
       "1710739  1.0  0.0  0.0  0.0  \n",
       "1710740  0.0  1.0  0.0  0.0  \n",
       "1710741  0.0  0.0  1.0  0.0  \n",
       "1710742  0.0  1.0  0.0  0.0  \n",
       "1710743  0.0  0.0  0.0  1.0  \n",
       "1710744  0.0  1.0  0.0  0.0  \n",
       "1710745  1.0  0.0  0.0  0.0  \n",
       "1710746  0.0  0.0  1.0  0.0  \n",
       "1710747  0.0  0.0  0.0  1.0  \n",
       "1710748  0.0  1.0  0.0  0.0  \n",
       "1710749  0.0  0.0  1.0  0.0  \n",
       "1710750  0.0  0.0  0.0  1.0  \n",
       "1710751  1.0  0.0  0.0  0.0  \n",
       "1710752  1.0  0.0  0.0  0.0  \n",
       "1710753  0.0  0.0  0.0  0.0  \n",
       "1710754  1.0  0.0  0.0  0.0  \n",
       "1710755  1.0  0.0  0.0  0.0  \n",
       "\n",
       "[1710756 rows x 117 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([train, label_dummy], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_37</th>\n",
       "      <th>technical_38</th>\n",
       "      <th>technical_39</th>\n",
       "      <th>technical_40</th>\n",
       "      <th>technical_41</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "      <th>y</th>\n",
       "      <th>label_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370326</td>\n",
       "      <td>-0.006316</td>\n",
       "      <td>0.222831</td>\n",
       "      <td>-0.213030</td>\n",
       "      <td>0.729277</td>\n",
       "      <td>-0.335633</td>\n",
       "      <td>0.113292</td>\n",
       "      <td>1.621238</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.414776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011753</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>-0.038064</td>\n",
       "      <td>-0.017425</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.114285</td>\n",
       "      <td>-0.210185</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.273607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001240</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.010622</td>\n",
       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
       "      <td>-0.764516</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.175710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020940</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.211506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.176693</td>\n",
       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>-0.125687</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007338</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "0  10          0   0.370326  -0.006316   0.222831  -0.213030   0.729277   \n",
       "1  11          0   0.014765  -0.038064  -0.017425   0.320652  -0.034134   \n",
       "2  12          0  -0.010622  -0.050577   3.379575  -0.157525  -0.068550   \n",
       "3  25          0        NaN        NaN        NaN        NaN        NaN   \n",
       "4  26          0   0.176693  -0.025284  -0.057680   0.015100   0.180894   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2   ...     technical_37  \\\n",
       "0      -0.335633       0.113292       1.621238   ...              NaN   \n",
       "1       0.004413       0.114285      -0.210185   ...              NaN   \n",
       "2      -0.155937       1.219439      -0.764516   ...              NaN   \n",
       "3       0.178495            NaN      -0.007262   ...              NaN   \n",
       "4       0.139445      -0.125687      -0.018707   ...              NaN   \n",
       "\n",
       "   technical_38  technical_39  technical_40  technical_41  technical_42  \\\n",
       "0           NaN           NaN     -0.414776           NaN           NaN   \n",
       "1           NaN           NaN     -0.273607           NaN           NaN   \n",
       "2           NaN           NaN     -0.175710           NaN           NaN   \n",
       "3           NaN           NaN     -0.211506           NaN           NaN   \n",
       "4           NaN           NaN     -0.001957           NaN           NaN   \n",
       "\n",
       "   technical_43  technical_44         y  label_1  \n",
       "0          -2.0           NaN -0.011753        0  \n",
       "1          -2.0           NaN -0.001240        1  \n",
       "2          -2.0           NaN -0.020940        0  \n",
       "3          -2.0           NaN -0.015959        0  \n",
       "4           0.0           NaN -0.007338        0  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11 in set_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>54</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>63</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>68</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>69</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>70</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>76</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>79</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>80</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>82</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>83</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>87</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>90</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710726</th>\n",
       "      <td>2100</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710727</th>\n",
       "      <td>2101</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710728</th>\n",
       "      <td>2102</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710729</th>\n",
       "      <td>2104</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710730</th>\n",
       "      <td>2107</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710731</th>\n",
       "      <td>2108</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710732</th>\n",
       "      <td>2109</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710733</th>\n",
       "      <td>2114</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710734</th>\n",
       "      <td>2117</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710735</th>\n",
       "      <td>2118</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710736</th>\n",
       "      <td>2120</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710737</th>\n",
       "      <td>2121</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710738</th>\n",
       "      <td>2126</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710739</th>\n",
       "      <td>2129</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710740</th>\n",
       "      <td>2130</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710741</th>\n",
       "      <td>2131</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710742</th>\n",
       "      <td>2137</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710743</th>\n",
       "      <td>2138</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710744</th>\n",
       "      <td>2139</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710745</th>\n",
       "      <td>2140</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710746</th>\n",
       "      <td>2142</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710747</th>\n",
       "      <td>2145</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710748</th>\n",
       "      <td>2146</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710749</th>\n",
       "      <td>2148</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710750</th>\n",
       "      <td>2149</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710751</th>\n",
       "      <td>2150</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710752</th>\n",
       "      <td>2151</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710753</th>\n",
       "      <td>2154</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710754</th>\n",
       "      <td>2156</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710755</th>\n",
       "      <td>2158</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1710756 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id label\n",
       "0          10     e\n",
       "1          11     a\n",
       "2          12     d\n",
       "3          25     c\n",
       "4          26     d\n",
       "5          27     a\n",
       "6          31     b\n",
       "7          38     d\n",
       "8          39     d\n",
       "9          40     a\n",
       "10         41     a\n",
       "11         43     c\n",
       "12         44     d\n",
       "13         49     a\n",
       "14         54     b\n",
       "15         59     c\n",
       "16         60     c\n",
       "17         62     c\n",
       "18         63     c\n",
       "19         68     c\n",
       "20         69     d\n",
       "21         70     e\n",
       "22         76     d\n",
       "23         79     c\n",
       "24         80     c\n",
       "25         82     b\n",
       "26         83     d\n",
       "27         85     a\n",
       "28         87     e\n",
       "29         90     b\n",
       "...       ...   ...\n",
       "1710726  2100     c\n",
       "1710727  2101     c\n",
       "1710728  2102     b\n",
       "1710729  2104     e\n",
       "1710730  2107     b\n",
       "1710731  2108     b\n",
       "1710732  2109     c\n",
       "1710733  2114     e\n",
       "1710734  2117     a\n",
       "1710735  2118     b\n",
       "1710736  2120     c\n",
       "1710737  2121     b\n",
       "1710738  2126     d\n",
       "1710739  2129     b\n",
       "1710740  2130     c\n",
       "1710741  2131     d\n",
       "1710742  2137     c\n",
       "1710743  2138     e\n",
       "1710744  2139     c\n",
       "1710745  2140     b\n",
       "1710746  2142     d\n",
       "1710747  2145     e\n",
       "1710748  2146     c\n",
       "1710749  2148     d\n",
       "1710750  2149     e\n",
       "1710751  2150     b\n",
       "1710752  2151     b\n",
       "1710753  2154     a\n",
       "1710754  2156     b\n",
       "1710755  2158     b\n",
       "\n",
       "[1710756 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['id', 'label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1424"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set_1 + set_2 + set_3 + set_4 + set_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nan_df = np.zeros((ids.shape[0], len(cols_feature)))\n",
    "for i, id in enumerate(ids):\n",
    "    for j, col in enumerate(cols_feature):\n",
    "        feature = train.loc[train.id==id, col]\n",
    "        nan_ratio[i, j] = feature.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1424, 108)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_ratio.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1813.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_ratio.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151.43699282147315"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_ratio.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nan_sum = nan_ratio.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAbECAYAAABhcIIkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzMzMzMysav8HBH6Xz51U1gUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x182af9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ind = np.arange(len(cols_feature))\n",
    "width = 1.0\n",
    "fig, ax = plt.subplots(figsize=(12,30))\n",
    "rects = ax.barh(ind, nan_sum, color='r')\n",
    "ax.set_yticks(ind+((width)/2.))\n",
    "ax.set_yticklabels(cols_feature, rotation='horizontal')\n",
    "ax.set_xlabel(\"Count of missing values\")\n",
    "ax.set_title(\"Number of missing values in each column\")\n",
    "#autolabel(rects)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}