{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib as mlp\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# had to take out a malformed line\n",
    "path = \"data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 209: expected 23 fields, saw 25\\nSkipping line 276: expected 23 fields, saw 25\\nSkipping line 277: expected 23 fields, saw 25\\nSkipping line 280: expected 23 fields, saw 25\\nSkipping line 285: expected 23 fields, saw 25\\nSkipping line 293: expected 23 fields, saw 25\\nSkipping line 297: expected 23 fields, saw 25\\nSkipping line 304: expected 23 fields, saw 25\\nSkipping line 313: expected 23 fields, saw 25\\nSkipping line 330: expected 23 fields, saw 25\\nSkipping line 337: expected 23 fields, saw 25\\nSkipping line 342: expected 23 fields, saw 25\\nSkipping line 344: expected 23 fields, saw 25\\nSkipping line 348: expected 23 fields, saw 25\\nSkipping line 357: expected 23 fields, saw 25\\nSkipping line 361: expected 23 fields, saw 25\\nSkipping line 380: expected 23 fields, saw 25\\nSkipping line 381: expected 23 fields, saw 25\\nSkipping line 383: expected 23 fields, saw 25\\nSkipping line 421: expected 23 fields, saw 25\\nSkipping line 427: expected 23 fields, saw 25\\nSkipping line 437: expected 23 fields, saw 25\\nSkipping line 445: expected 23 fields, saw 25\\nSkipping line 469: expected 23 fields, saw 25\\nSkipping line 494: expected 23 fields, saw 25\\nSkipping line 504: expected 23 fields, saw 25\\nSkipping line 515: expected 23 fields, saw 25\\nSkipping line 744: expected 23 fields, saw 25\\nSkipping line 771: expected 23 fields, saw 25\\nSkipping line 826: expected 23 fields, saw 25\\nSkipping line 862: expected 23 fields, saw 25\\nSkipping line 960: expected 23 fields, saw 25\\nSkipping line 970: expected 23 fields, saw 25\\nSkipping line 971: expected 23 fields, saw 25\\nSkipping line 981: expected 23 fields, saw 25\\nSkipping line 1003: expected 23 fields, saw 25\\nSkipping line 1037: expected 23 fields, saw 25\\nSkipping line 1044: expected 23 fields, saw 25\\nSkipping line 1050: expected 23 fields, saw 25\\nSkipping line 1062: expected 23 fields, saw 25\\nSkipping line 1064: expected 23 fields, saw 25\\nSkipping line 1097: expected 23 fields, saw 25\\nSkipping line 1100: expected 23 fields, saw 25\\nSkipping line 1118: expected 23 fields, saw 25\\nSkipping line 1129: expected 23 fields, saw 25\\nSkipping line 1133: expected 23 fields, saw 25\\nSkipping line 1142: expected 23 fields, saw 25\\nSkipping line 1148: expected 23 fields, saw 25\\nSkipping line 1182: expected 23 fields, saw 25\\nSkipping line 1187: expected 23 fields, saw 25\\nSkipping line 1208: expected 23 fields, saw 25\\nSkipping line 1222: expected 23 fields, saw 25\\nSkipping line 1625: expected 23 fields, saw 25\\nSkipping line 1685: expected 23 fields, saw 25\\nSkipping line 2229: expected 23 fields, saw 25\\nSkipping line 2488: expected 23 fields, saw 25\\nSkipping line 2727: expected 23 fields, saw 25\\nSkipping line 2853: expected 23 fields, saw 25\\nSkipping line 2871: expected 23 fields, saw 25\\nSkipping line 2993: expected 23 fields, saw 25\\nSkipping line 3011: expected 23 fields, saw 25\\nSkipping line 3013: expected 23 fields, saw 25\\nSkipping line 3055: expected 23 fields, saw 25\\nSkipping line 3057: expected 23 fields, saw 25\\nSkipping line 3058: expected 23 fields, saw 25\\nSkipping line 3065: expected 23 fields, saw 25\\nSkipping line 3067: expected 23 fields, saw 25\\nSkipping line 3075: expected 23 fields, saw 25\\nSkipping line 3076: expected 23 fields, saw 25\\nSkipping line 3085: expected 23 fields, saw 25\\nSkipping line 3095: expected 23 fields, saw 25\\nSkipping line 3101: expected 23 fields, saw 25\\nSkipping line 3107: expected 23 fields, saw 25\\nSkipping line 3124: expected 23 fields, saw 25\\nSkipping line 3125: expected 23 fields, saw 25\\nSkipping line 3127: expected 23 fields, saw 25\\nSkipping line 3128: expected 23 fields, saw 25\\nSkipping line 3129: expected 23 fields, saw 25\\nSkipping line 3134: expected 23 fields, saw 25\\nSkipping line 3138: expected 23 fields, saw 25\\nSkipping line 3148: expected 23 fields, saw 25\\nSkipping line 3167: expected 23 fields, saw 25\\nSkipping line 3194: expected 23 fields, saw 25\\nSkipping line 3201: expected 23 fields, saw 25\\nSkipping line 3203: expected 23 fields, saw 25\\nSkipping line 3234: expected 23 fields, saw 25\\nSkipping line 3253: expected 23 fields, saw 25\\nSkipping line 3261: expected 23 fields, saw 25\\nSkipping line 3274: expected 23 fields, saw 25\\nSkipping line 3287: expected 23 fields, saw 25\\nSkipping line 3297: expected 23 fields, saw 25\\nSkipping line 3303: expected 23 fields, saw 25\\nSkipping line 3304: expected 23 fields, saw 25\\nSkipping line 3321: expected 23 fields, saw 25\\nSkipping line 3322: expected 23 fields, saw 25\\nSkipping line 3335: expected 23 fields, saw 25\\nSkipping line 3354: expected 23 fields, saw 25\\nSkipping line 3522: expected 23 fields, saw 25\\nSkipping line 3614: expected 23 fields, saw 25\\nSkipping line 3653: expected 23 fields, saw 25\\nSkipping line 3716: expected 23 fields, saw 25\\nSkipping line 3721: expected 23 fields, saw 25\\nSkipping line 3728: expected 23 fields, saw 25\\nSkipping line 3732: expected 23 fields, saw 25\\nSkipping line 3738: expected 23 fields, saw 25\\nSkipping line 3747: expected 23 fields, saw 25\\nSkipping line 3749: expected 23 fields, saw 25\\nSkipping line 3778: expected 23 fields, saw 25\\nSkipping line 3793: expected 23 fields, saw 25\\nSkipping line 3808: expected 23 fields, saw 25\\nSkipping line 3814: expected 23 fields, saw 25\\nSkipping line 3826: expected 23 fields, saw 25\\nSkipping line 3827: expected 23 fields, saw 25\\nSkipping line 3832: expected 23 fields, saw 25\\nSkipping line 3837: expected 23 fields, saw 25\\nSkipping line 3845: expected 23 fields, saw 25\\nSkipping line 3847: expected 23 fields, saw 25\\nSkipping line 3857: expected 23 fields, saw 25\\nSkipping line 3862: expected 23 fields, saw 25\\nSkipping line 3870: expected 23 fields, saw 25\\nSkipping line 3877: expected 23 fields, saw 25\\nSkipping line 3893: expected 23 fields, saw 25\\nSkipping line 3894: expected 23 fields, saw 25\\nSkipping line 3897: expected 23 fields, saw 25\\nSkipping line 3911: expected 23 fields, saw 25\\nSkipping line 3913: expected 23 fields, saw 25\\nSkipping line 3916: expected 23 fields, saw 25\\nSkipping line 3925: expected 23 fields, saw 25\\nSkipping line 3938: expected 23 fields, saw 25\\nSkipping line 3939: expected 23 fields, saw 25\\nSkipping line 3943: expected 23 fields, saw 25\\nSkipping line 3947: expected 23 fields, saw 25\\nSkipping line 3953: expected 23 fields, saw 25\\nSkipping line 3959: expected 23 fields, saw 25\\nSkipping line 3962: expected 23 fields, saw 25\\nSkipping line 3965: expected 23 fields, saw 25\\nSkipping line 4192: expected 23 fields, saw 25\\nSkipping line 4249: expected 23 fields, saw 25\\nSkipping line 4368: expected 23 fields, saw 25\\nSkipping line 4373: expected 23 fields, saw 25\\nSkipping line 4375: expected 23 fields, saw 25\\nSkipping line 4393: expected 23 fields, saw 25\\nSkipping line 4414: expected 23 fields, saw 25\\nSkipping line 4427: expected 23 fields, saw 25\\nSkipping line 4431: expected 23 fields, saw 25\\nSkipping line 4445: expected 23 fields, saw 25\\nSkipping line 4455: expected 23 fields, saw 25\\nSkipping line 4461: expected 23 fields, saw 25\\nSkipping line 4463: expected 23 fields, saw 25\\nSkipping line 4464: expected 23 fields, saw 25\\nSkipping line 4467: expected 23 fields, saw 25\\nSkipping line 4472: expected 23 fields, saw 25\\nSkipping line 4475: expected 23 fields, saw 25\\nSkipping line 4490: expected 23 fields, saw 25\\nSkipping line 4496: expected 23 fields, saw 25\\nSkipping line 4510: expected 23 fields, saw 25\\nSkipping line 4524: expected 23 fields, saw 25\\nSkipping line 4527: expected 23 fields, saw 25\\nSkipping line 4540: expected 23 fields, saw 25\\nSkipping line 4542: expected 23 fields, saw 25\\nSkipping line 4546: expected 23 fields, saw 25\\nSkipping line 4550: expected 23 fields, saw 25\\nSkipping line 4554: expected 23 fields, saw 25\\nSkipping line 4568: expected 23 fields, saw 25\\nSkipping line 4583: expected 23 fields, saw 25\\nSkipping line 4592: expected 23 fields, saw 25\\nSkipping line 4597: expected 23 fields, saw 25\\nSkipping line 4601: expected 23 fields, saw 25\\nSkipping line 4622: expected 23 fields, saw 25\\nSkipping line 4623: expected 23 fields, saw 25\\nSkipping line 4633: expected 23 fields, saw 25\\nSkipping line 4641: expected 23 fields, saw 25\\nSkipping line 4659: expected 23 fields, saw 25\\nSkipping line 4663: expected 23 fields, saw 25\\nSkipping line 4667: expected 23 fields, saw 25\\nSkipping line 4671: expected 23 fields, saw 25\\nSkipping line 4676: expected 23 fields, saw 24\\nSkipping line 4686: expected 23 fields, saw 25\\nSkipping line 4688: expected 23 fields, saw 25\\nSkipping line 4720: expected 23 fields, saw 25\\nSkipping line 4910: expected 23 fields, saw 25\\nSkipping line 5090: expected 23 fields, saw 25\\nSkipping line 5112: expected 23 fields, saw 25\\nSkipping line 5117: expected 23 fields, saw 25\\nSkipping line 5120: expected 23 fields, saw 25\\nSkipping line 5122: expected 23 fields, saw 25\\nSkipping line 5124: expected 23 fields, saw 25\\nSkipping line 5134: expected 23 fields, saw 25\\nSkipping line 5139: expected 23 fields, saw 25\\nSkipping line 5142: expected 23 fields, saw 25\\nSkipping line 5149: expected 23 fields, saw 25\\nSkipping line 5157: expected 23 fields, saw 25\\nSkipping line 5169: expected 23 fields, saw 25\\nSkipping line 5174: expected 23 fields, saw 25\\nSkipping line 5223: expected 23 fields, saw 25\\nSkipping line 5230: expected 23 fields, saw 25\\nSkipping line 5238: expected 23 fields, saw 25\\nSkipping line 5251: expected 23 fields, saw 25\\nSkipping line 5255: expected 23 fields, saw 25\\nSkipping line 5258: expected 23 fields, saw 25\\nSkipping line 5270: expected 23 fields, saw 25\\nSkipping line 5272: expected 23 fields, saw 25\\nSkipping line 5282: expected 23 fields, saw 25\\nSkipping line 5290: expected 23 fields, saw 25\\nSkipping line 5291: expected 23 fields, saw 25\\nSkipping line 5294: expected 23 fields, saw 25\\nSkipping line 5295: expected 23 fields, saw 25\\nSkipping line 5317: expected 23 fields, saw 25\\nSkipping line 5349: expected 23 fields, saw 25\\nSkipping line 5352: expected 23 fields, saw 25\\nSkipping line 5563: expected 23 fields, saw 25\\nSkipping line 5784: expected 23 fields, saw 25\\nSkipping line 5786: expected 23 fields, saw 25\\nSkipping line 5798: expected 23 fields, saw 25\\nSkipping line 5827: expected 23 fields, saw 25\\nSkipping line 5848: expected 23 fields, saw 25\\nSkipping line 5854: expected 23 fields, saw 25\\nSkipping line 5864: expected 23 fields, saw 25\\nSkipping line 5871: expected 23 fields, saw 25\\nSkipping line 5876: expected 23 fields, saw 25\\nSkipping line 5905: expected 23 fields, saw 25\\nSkipping line 5911: expected 23 fields, saw 25\\nSkipping line 5914: expected 23 fields, saw 25\\nSkipping line 5924: expected 23 fields, saw 25\\nSkipping line 5930: expected 23 fields, saw 25\\nSkipping line 5931: expected 23 fields, saw 25\\nSkipping line 5944: expected 23 fields, saw 25\\nSkipping line 5959: expected 23 fields, saw 25\\nSkipping line 5980: expected 23 fields, saw 25\\nSkipping line 5982: expected 23 fields, saw 25\\nSkipping line 5992: expected 23 fields, saw 25\\nSkipping line 5995: expected 23 fields, saw 25\\nSkipping line 6021: expected 23 fields, saw 25\\nSkipping line 6034: expected 23 fields, saw 25\\nSkipping line 6036: expected 23 fields, saw 25\\nSkipping line 6041: expected 23 fields, saw 25\\nSkipping line 6053: expected 23 fields, saw 25\\nSkipping line 6070: expected 23 fields, saw 25\\nSkipping line 6074: expected 23 fields, saw 25\\nSkipping line 6078: expected 23 fields, saw 25\\nSkipping line 6083: expected 23 fields, saw 24\\nSkipping line 6094: expected 23 fields, saw 25\\nSkipping line 6107: expected 23 fields, saw 25\\nSkipping line 6155: expected 23 fields, saw 25\\nSkipping line 6234: expected 23 fields, saw 25\\nSkipping line 6264: expected 23 fields, saw 25\\nSkipping line 6311: expected 23 fields, saw 25\\nSkipping line 6487: expected 23 fields, saw 25\\nSkipping line 6605: expected 23 fields, saw 25\\nSkipping line 6688: expected 23 fields, saw 25\\nSkipping line 6807: expected 23 fields, saw 25\\nSkipping line 6872: expected 23 fields, saw 25\\nSkipping line 6972: expected 23 fields, saw 25\\nSkipping line 7047: expected 23 fields, saw 25\\nSkipping line 7092: expected 23 fields, saw 25\\nSkipping line 7389: expected 23 fields, saw 24\\nSkipping line 7731: expected 23 fields, saw 25\\nSkipping line 7811: expected 23 fields, saw 25\\nSkipping line 7838: expected 23 fields, saw 25\\nSkipping line 7839: expected 23 fields, saw 25\\nSkipping line 7845: expected 23 fields, saw 25\\nSkipping line 7848: expected 23 fields, saw 25\\nSkipping line 7869: expected 23 fields, saw 25\\nSkipping line 7884: expected 23 fields, saw 25\\nSkipping line 7899: expected 23 fields, saw 25\\nSkipping line 7924: expected 23 fields, saw 25\\nSkipping line 7936: expected 23 fields, saw 25\\nSkipping line 7945: expected 23 fields, saw 25\\nSkipping line 7948: expected 23 fields, saw 25\\nSkipping line 7959: expected 23 fields, saw 25\\nSkipping line 7960: expected 23 fields, saw 25\\nSkipping line 7962: expected 23 fields, saw 25\\nSkipping line 7966: expected 23 fields, saw 25\\nSkipping line 7968: expected 23 fields, saw 25\\nSkipping line 7979: expected 23 fields, saw 25\\nSkipping line 7980: expected 23 fields, saw 25\\nSkipping line 7992: expected 23 fields, saw 25\\nSkipping line 7996: expected 23 fields, saw 25\\nSkipping line 8019: expected 23 fields, saw 25\\nSkipping line 8028: expected 23 fields, saw 25\\nSkipping line 8034: expected 23 fields, saw 25\\nSkipping line 8050: expected 23 fields, saw 25\\nSkipping line 8065: expected 23 fields, saw 25\\nSkipping line 8126: expected 23 fields, saw 25\\nSkipping line 8127: expected 23 fields, saw 25\\nSkipping line 8135: expected 23 fields, saw 25\\nSkipping line 8136: expected 23 fields, saw 25\\nSkipping line 8152: expected 23 fields, saw 25\\nSkipping line 8157: expected 23 fields, saw 25\\nSkipping line 8158: expected 23 fields, saw 25\\nSkipping line 8165: expected 23 fields, saw 25\\nSkipping line 8272: expected 23 fields, saw 24\\nSkipping line 8534: expected 23 fields, saw 25\\nSkipping line 8541: expected 23 fields, saw 25\\nSkipping line 8573: expected 23 fields, saw 25\\nSkipping line 8583: expected 23 fields, saw 25\\nSkipping line 8595: expected 23 fields, saw 25\\nSkipping line 8596: expected 23 fields, saw 25\\nSkipping line 8602: expected 23 fields, saw 25\\nSkipping line 8603: expected 23 fields, saw 25\\nSkipping line 8613: expected 23 fields, saw 25\\nSkipping line 8621: expected 23 fields, saw 25\\nSkipping line 8626: expected 23 fields, saw 25\\nSkipping line 8629: expected 23 fields, saw 25\\nSkipping line 8632: expected 23 fields, saw 25\\nSkipping line 8635: expected 23 fields, saw 25\\nSkipping line 8636: expected 23 fields, saw 25\\nSkipping line 8643: expected 23 fields, saw 25\\nSkipping line 8647: expected 23 fields, saw 25\\nSkipping line 8650: expected 23 fields, saw 25\\nSkipping line 8661: expected 23 fields, saw 25\\nSkipping line 8672: expected 23 fields, saw 25\\nSkipping line 8689: expected 23 fields, saw 25\\nSkipping line 8696: expected 23 fields, saw 25\\nSkipping line 8728: expected 23 fields, saw 25\\nSkipping line 8744: expected 23 fields, saw 25\\nSkipping line 8753: expected 23 fields, saw 25\\nSkipping line 8756: expected 23 fields, saw 25\\nSkipping line 8764: expected 23 fields, saw 25\\nSkipping line 8780: expected 23 fields, saw 25\\nSkipping line 8817: expected 23 fields, saw 25\\nSkipping line 8820: expected 23 fields, saw 25\\nSkipping line 8846: expected 23 fields, saw 25\\nSkipping line 8847: expected 23 fields, saw 25\\nSkipping line 8849: expected 23 fields, saw 25\\nSkipping line 8850: expected 23 fields, saw 25\\nSkipping line 8856: expected 23 fields, saw 25\\nSkipping line 8873: expected 23 fields, saw 25\\nSkipping line 9106: expected 23 fields, saw 25\\nSkipping line 9162: expected 23 fields, saw 25\\nSkipping line 9182: expected 23 fields, saw 25\\nSkipping line 9191: expected 23 fields, saw 25\\nSkipping line 9214: expected 23 fields, saw 25\\nSkipping line 9256: expected 23 fields, saw 25\\nSkipping line 9271: expected 23 fields, saw 25\\nSkipping line 9282: expected 23 fields, saw 25\\nSkipping line 9292: expected 23 fields, saw 25\\nSkipping line 9295: expected 23 fields, saw 25\\nSkipping line 9303: expected 23 fields, saw 25\\nSkipping line 9316: expected 23 fields, saw 25\\nSkipping line 9320: expected 23 fields, saw 25\\nSkipping line 9335: expected 23 fields, saw 25\\nSkipping line 9339: expected 23 fields, saw 25\\nSkipping line 9343: expected 23 fields, saw 25\\nSkipping line 9350: expected 23 fields, saw 25\\nSkipping line 9353: expected 23 fields, saw 25\\nSkipping line 9355: expected 23 fields, saw 25\\nSkipping line 9358: expected 23 fields, saw 25\\nSkipping line 9361: expected 23 fields, saw 25\\nSkipping line 9372: expected 23 fields, saw 25\\nSkipping line 9378: expected 23 fields, saw 25\\nSkipping line 9390: expected 23 fields, saw 25\\nSkipping line 9393: expected 23 fields, saw 25\\nSkipping line 9414: expected 23 fields, saw 25\\nSkipping line 9435: expected 23 fields, saw 25\\nSkipping line 9439: expected 23 fields, saw 25\\nSkipping line 9441: expected 23 fields, saw 25\\nSkipping line 9451: expected 23 fields, saw 25\\nSkipping line 9459: expected 23 fields, saw 25\\nSkipping line 9465: expected 23 fields, saw 25\\nSkipping line 9470: expected 23 fields, saw 25\\nSkipping line 9479: expected 23 fields, saw 25\\nSkipping line 9490: expected 23 fields, saw 25\\nSkipping line 9511: expected 23 fields, saw 25\\nSkipping line 9513: expected 23 fields, saw 25\\nSkipping line 9516: expected 23 fields, saw 25\\nSkipping line 9519: expected 23 fields, saw 25\\nSkipping line 9535: expected 23 fields, saw 25\\nSkipping line 9674: expected 23 fields, saw 25\\nSkipping line 9726: expected 23 fields, saw 25\\nSkipping line 9805: expected 23 fields, saw 25\\nSkipping line 9889: expected 23 fields, saw 25\\nSkipping line 9911: expected 23 fields, saw 25\\nSkipping line 9917: expected 23 fields, saw 25\\nSkipping line 9928: expected 23 fields, saw 25\\nSkipping line 9939: expected 23 fields, saw 25\\nSkipping line 9943: expected 23 fields, saw 25\\nSkipping line 9950: expected 23 fields, saw 25\\nSkipping line 9953: expected 23 fields, saw 25\\nSkipping line 9974: expected 23 fields, saw 25\\nSkipping line 9976: expected 23 fields, saw 25\\nSkipping line 9992: expected 23 fields, saw 25\\nSkipping line 9996: expected 23 fields, saw 25\\nSkipping line 10001: expected 23 fields, saw 25\\nSkipping line 10002: expected 23 fields, saw 25\\nSkipping line 10009: expected 23 fields, saw 25\\nSkipping line 10013: expected 23 fields, saw 25\\nSkipping line 10018: expected 23 fields, saw 25\\nSkipping line 10020: expected 23 fields, saw 25\\nSkipping line 10028: expected 23 fields, saw 25\\nSkipping line 10032: expected 23 fields, saw 25\\nSkipping line 10039: expected 23 fields, saw 25\\nSkipping line 10043: expected 23 fields, saw 25\\nSkipping line 10060: expected 23 fields, saw 25\\nSkipping line 10063: expected 23 fields, saw 25\\nSkipping line 10064: expected 23 fields, saw 25\\nSkipping line 10067: expected 23 fields, saw 25\\nSkipping line 10087: expected 23 fields, saw 25\\nSkipping line 10089: expected 23 fields, saw 25\\nSkipping line 10094: expected 23 fields, saw 25\\nSkipping line 10095: expected 23 fields, saw 25\\nSkipping line 10097: expected 23 fields, saw 25\\nSkipping line 10105: expected 23 fields, saw 25\\nSkipping line 10122: expected 23 fields, saw 25\\nSkipping line 10139: expected 23 fields, saw 25\\nSkipping line 10143: expected 23 fields, saw 25\\nSkipping line 10151: expected 23 fields, saw 25\\nSkipping line 10156: expected 23 fields, saw 25\\nSkipping line 10157: expected 23 fields, saw 25\\nSkipping line 10158: expected 23 fields, saw 25\\nSkipping line 10161: expected 23 fields, saw 25\\nSkipping line 10165: expected 23 fields, saw 25\\nSkipping line 10172: expected 23 fields, saw 25\\nSkipping line 10404: expected 23 fields, saw 25\\nSkipping line 10421: expected 23 fields, saw 25\\nSkipping line 10425: expected 23 fields, saw 25\\nSkipping line 10456: expected 23 fields, saw 25\\nSkipping line 10480: expected 23 fields, saw 25\\nSkipping line 10539: expected 23 fields, saw 25\\nSkipping line 10540: expected 23 fields, saw 25\\nSkipping line 10550: expected 23 fields, saw 25\\nSkipping line 10568: expected 23 fields, saw 24\\nSkipping line 10597: expected 23 fields, saw 25\\nSkipping line 10609: expected 23 fields, saw 25\\nSkipping line 10611: expected 23 fields, saw 25\\nSkipping line 10624: expected 23 fields, saw 25\\nSkipping line 10627: expected 23 fields, saw 25\\nSkipping line 10630: expected 23 fields, saw 25\\nSkipping line 10636: expected 23 fields, saw 25\\nSkipping line 10637: expected 23 fields, saw 25\\nSkipping line 10642: expected 23 fields, saw 25\\nSkipping line 10657: expected 23 fields, saw 25\\nSkipping line 10659: expected 23 fields, saw 25\\nSkipping line 10672: expected 23 fields, saw 25\\nSkipping line 10675: expected 23 fields, saw 25\\nSkipping line 10686: expected 23 fields, saw 25\\nSkipping line 10696: expected 23 fields, saw 25\\nSkipping line 10707: expected 23 fields, saw 25\\nSkipping line 10708: expected 23 fields, saw 25\\nSkipping line 10711: expected 23 fields, saw 25\\nSkipping line 10718: expected 23 fields, saw 25\\nSkipping line 10734: expected 23 fields, saw 25\\nSkipping line 10738: expected 23 fields, saw 25\\nSkipping line 10754: expected 23 fields, saw 25\\nSkipping line 10755: expected 23 fields, saw 25\\nSkipping line 10766: expected 23 fields, saw 25\\nSkipping line 10779: expected 23 fields, saw 25\\nSkipping line 10783: expected 23 fields, saw 25\\nSkipping line 10785: expected 23 fields, saw 25\\nSkipping line 10794: expected 23 fields, saw 25\\nSkipping line 10796: expected 23 fields, saw 25\\nSkipping line 10811: expected 23 fields, saw 25\\nSkipping line 10813: expected 23 fields, saw 25\\nSkipping line 10817: expected 23 fields, saw 25\\nSkipping line 10823: expected 23 fields, saw 25\\nSkipping line 10852: expected 23 fields, saw 25\\nSkipping line 11097: expected 23 fields, saw 25\\nSkipping line 11903: expected 23 fields, saw 24\\nSkipping line 12445: expected 23 fields, saw 25\\nSkipping line 12549: expected 23 fields, saw 24\\nSkipping line 12574: expected 23 fields, saw 25\\nSkipping line 12673: expected 23 fields, saw 25\\nSkipping line 12684: expected 23 fields, saw 25\\nSkipping line 12742: expected 23 fields, saw 25\\nSkipping line 12747: expected 23 fields, saw 25\\nSkipping line 12823: expected 23 fields, saw 25\\nSkipping line 12830: expected 23 fields, saw 25\\nSkipping line 12850: expected 23 fields, saw 25\\nSkipping line 12898: expected 23 fields, saw 25\\nSkipping line 12918: expected 23 fields, saw 25\\nSkipping line 12920: expected 23 fields, saw 25\\nSkipping line 12946: expected 23 fields, saw 25\\nSkipping line 12981: expected 23 fields, saw 24\\nSkipping line 12990: expected 23 fields, saw 25\\nSkipping line 13009: expected 23 fields, saw 25\\nSkipping line 13013: expected 23 fields, saw 25\\nSkipping line 13019: expected 23 fields, saw 25\\nSkipping line 13047: expected 23 fields, saw 25\\nSkipping line 13151: expected 23 fields, saw 25\\nSkipping line 13229: expected 23 fields, saw 25\\nSkipping line 13248: expected 23 fields, saw 25\\nSkipping line 13352: expected 23 fields, saw 25\\nSkipping line 13377: expected 23 fields, saw 25\\nSkipping line 13402: expected 23 fields, saw 25\\nSkipping line 13407: expected 23 fields, saw 25\\nSkipping line 13442: expected 23 fields, saw 25\\nSkipping line 13461: expected 23 fields, saw 25\\nSkipping line 13482: expected 23 fields, saw 25\\nSkipping line 13513: expected 23 fields, saw 25\\nSkipping line 13516: expected 23 fields, saw 25\\nSkipping line 13525: expected 23 fields, saw 25\\nSkipping line 13528: expected 23 fields, saw 25\\nSkipping line 13532: expected 23 fields, saw 25\\nSkipping line 13547: expected 23 fields, saw 25\\nSkipping line 13590: expected 23 fields, saw 25\\nSkipping line 13598: expected 23 fields, saw 25\\nSkipping line 13604: expected 23 fields, saw 25\\nSkipping line 13625: expected 23 fields, saw 25\\nSkipping line 13632: expected 23 fields, saw 25\\nSkipping line 13644: expected 23 fields, saw 25\\nSkipping line 13667: expected 23 fields, saw 25\\nSkipping line 13785: expected 23 fields, saw 25\\nSkipping line 13992: expected 23 fields, saw 25\\nSkipping line 14070: expected 23 fields, saw 25\\nSkipping line 14134: expected 23 fields, saw 25\\nSkipping line 14136: expected 23 fields, saw 25\\nSkipping line 14157: expected 23 fields, saw 25\\nSkipping line 14199: expected 23 fields, saw 25\\nSkipping line 14226: expected 23 fields, saw 25\\nSkipping line 14240: expected 23 fields, saw 25\\nSkipping line 14245: expected 23 fields, saw 25\\nSkipping line 14254: expected 23 fields, saw 25\\nSkipping line 14255: expected 23 fields, saw 25\\nSkipping line 14281: expected 23 fields, saw 25\\nSkipping line 14283: expected 23 fields, saw 25\\nSkipping line 14382: expected 23 fields, saw 25\\nSkipping line 14521: expected 23 fields, saw 25\\nSkipping line 14525: expected 23 fields, saw 25\\nSkipping line 14531: expected 23 fields, saw 25\\nSkipping line 14773: expected 23 fields, saw 25\\nSkipping line 14787: expected 23 fields, saw 25\\nSkipping line 14875: expected 23 fields, saw 25\\nSkipping line 14887: expected 23 fields, saw 25\\nSkipping line 14912: expected 23 fields, saw 25\\nSkipping line 14913: expected 23 fields, saw 25\\nSkipping line 14914: expected 23 fields, saw 25\\nSkipping line 14922: expected 23 fields, saw 25\\nSkipping line 14955: expected 23 fields, saw 25\\nSkipping line 14957: expected 23 fields, saw 25\\nSkipping line 14966: expected 23 fields, saw 25\\nSkipping line 14980: expected 23 fields, saw 25\\nSkipping line 14984: expected 23 fields, saw 25\\nSkipping line 14987: expected 23 fields, saw 25\\nSkipping line 14989: expected 23 fields, saw 25\\nSkipping line 14993: expected 23 fields, saw 25\\nSkipping line 14994: expected 23 fields, saw 25\\nSkipping line 15008: expected 23 fields, saw 25\\nSkipping line 15012: expected 23 fields, saw 25\\nSkipping line 15018: expected 23 fields, saw 25\\nSkipping line 15021: expected 23 fields, saw 25\\nSkipping line 15033: expected 23 fields, saw 25\\nSkipping line 15035: expected 23 fields, saw 25\\nSkipping line 15059: expected 23 fields, saw 25\\nSkipping line 15078: expected 23 fields, saw 25\\nSkipping line 15082: expected 23 fields, saw 25\\nSkipping line 15120: expected 23 fields, saw 25\\nSkipping line 15124: expected 23 fields, saw 25\\nSkipping line 15128: expected 23 fields, saw 25\\nSkipping line 15136: expected 23 fields, saw 25\\nSkipping line 15335: expected 23 fields, saw 25\\nSkipping line 15421: expected 23 fields, saw 25\\nSkipping line 15497: expected 23 fields, saw 25\\nSkipping line 15538: expected 23 fields, saw 25\\nSkipping line 15567: expected 23 fields, saw 25\\nSkipping line 15620: expected 23 fields, saw 25\\nSkipping line 15640: expected 23 fields, saw 25\\nSkipping line 15641: expected 23 fields, saw 25\\nSkipping line 15662: expected 23 fields, saw 25\\nSkipping line 15671: expected 23 fields, saw 25\\nSkipping line 15678: expected 23 fields, saw 25\\nSkipping line 15684: expected 23 fields, saw 25\\nSkipping line 15687: expected 23 fields, saw 25\\nSkipping line 15699: expected 23 fields, saw 25\\nSkipping line 15710: expected 23 fields, saw 25\\nSkipping line 15714: expected 23 fields, saw 25\\nSkipping line 15718: expected 23 fields, saw 25\\nSkipping line 15721: expected 23 fields, saw 25\\nSkipping line 15722: expected 23 fields, saw 25\\nSkipping line 15727: expected 23 fields, saw 25\\nSkipping line 15752: expected 23 fields, saw 25\\nSkipping line 15765: expected 23 fields, saw 24\\nSkipping line 15800: expected 23 fields, saw 25\\nSkipping line 15819: expected 23 fields, saw 25\\nSkipping line 15836: expected 23 fields, saw 25\\nSkipping line 15843: expected 23 fields, saw 25\\nSkipping line 15870: expected 23 fields, saw 25\\nSkipping line 15892: expected 23 fields, saw 25\\nSkipping line 16074: expected 23 fields, saw 25\\nSkipping line 16187: expected 23 fields, saw 25\\nSkipping line 16236: expected 23 fields, saw 25\\nSkipping line 17057: expected 23 fields, saw 25\\nSkipping line 17143: expected 23 fields, saw 25\\nSkipping line 17166: expected 23 fields, saw 25\\nSkipping line 17486: expected 23 fields, saw 25\\nSkipping line 17496: expected 23 fields, saw 25\\nSkipping line 17724: expected 23 fields, saw 25\\nSkipping line 17743: expected 23 fields, saw 24\\nSkipping line 17960: expected 23 fields, saw 25\\nSkipping line 17984: expected 23 fields, saw 24\\nSkipping line 18303: expected 23 fields, saw 25\\nSkipping line 18526: expected 23 fields, saw 25\\nSkipping line 18616: expected 23 fields, saw 25\\nSkipping line 18660: expected 23 fields, saw 25\\nSkipping line 18850: expected 23 fields, saw 25\\nSkipping line 19023: expected 23 fields, saw 25\\nSkipping line 19130: expected 23 fields, saw 25\\nSkipping line 19211: expected 23 fields, saw 25\\nSkipping line 19236: expected 23 fields, saw 25\\nSkipping line 19251: expected 23 fields, saw 25\\nSkipping line 19287: expected 23 fields, saw 25\\nSkipping line 19415: expected 23 fields, saw 24\\nSkipping line 19602: expected 23 fields, saw 25\\nSkipping line 19603: expected 23 fields, saw 25\\nSkipping line 19952: expected 23 fields, saw 25\\nSkipping line 19999: expected 23 fields, saw 25\\nSkipping line 20843: expected 23 fields, saw 25\\nSkipping line 21297: expected 23 fields, saw 24\\nSkipping line 21313: expected 23 fields, saw 25\\nSkipping line 21536: expected 23 fields, saw 25\\nSkipping line 21576: expected 23 fields, saw 25\\nSkipping line 21703: expected 23 fields, saw 25\\nSkipping line 21726: expected 23 fields, saw 25\\nSkipping line 22156: expected 23 fields, saw 25\\nSkipping line 22326: expected 23 fields, saw 25\\nSkipping line 22676: expected 23 fields, saw 25\\nSkipping line 22840: expected 23 fields, saw 25\\nSkipping line 22996: expected 23 fields, saw 25\\nSkipping line 23005: expected 23 fields, saw 25\\nSkipping line 23009: expected 23 fields, saw 25\\nSkipping line 23011: expected 23 fields, saw 25\\nSkipping line 23012: expected 23 fields, saw 25\\nSkipping line 23017: expected 23 fields, saw 25\\nSkipping line 23034: expected 23 fields, saw 25\\nSkipping line 23050: expected 23 fields, saw 25\\nSkipping line 23054: expected 23 fields, saw 25\\nSkipping line 23065: expected 23 fields, saw 25\\nSkipping line 23077: expected 23 fields, saw 25\\nSkipping line 23085: expected 23 fields, saw 25\\nSkipping line 23091: expected 23 fields, saw 25\\nSkipping line 23110: expected 23 fields, saw 25\\nSkipping line 23118: expected 23 fields, saw 25\\nSkipping line 23126: expected 23 fields, saw 25\\nSkipping line 23148: expected 23 fields, saw 25\\nSkipping line 23150: expected 23 fields, saw 25\\nSkipping line 23156: expected 23 fields, saw 25\\nSkipping line 23159: expected 23 fields, saw 25\\nSkipping line 23172: expected 23 fields, saw 25\\nSkipping line 23176: expected 23 fields, saw 25\\nSkipping line 23179: expected 23 fields, saw 25\\nSkipping line 23181: expected 23 fields, saw 25\\nSkipping line 23191: expected 23 fields, saw 25\\nSkipping line 23195: expected 23 fields, saw 25\\nSkipping line 23200: expected 23 fields, saw 25\\nSkipping line 23206: expected 23 fields, saw 25\\nSkipping line 23208: expected 23 fields, saw 25\\nSkipping line 23220: expected 23 fields, saw 25\\nSkipping line 23232: expected 23 fields, saw 25\\nSkipping line 23242: expected 23 fields, saw 25\\nSkipping line 23267: expected 23 fields, saw 25\\nSkipping line 23270: expected 23 fields, saw 25\\nSkipping line 23362: expected 23 fields, saw 25\\nSkipping line 23504: expected 23 fields, saw 25\\nSkipping line 23527: expected 23 fields, saw 25\\nSkipping line 23626: expected 23 fields, saw 25\\nSkipping line 23641: expected 23 fields, saw 24\\nSkipping line 23645: expected 23 fields, saw 25\\nSkipping line 23669: expected 23 fields, saw 25\\nSkipping line 23679: expected 23 fields, saw 25\\nSkipping line 23700: expected 23 fields, saw 25\\nSkipping line 23742: expected 23 fields, saw 24\\nSkipping line 23753: expected 23 fields, saw 25\\nSkipping line 23758: expected 23 fields, saw 25\\nSkipping line 23768: expected 23 fields, saw 25\\nSkipping line 23774: expected 23 fields, saw 25\\nSkipping line 23789: expected 23 fields, saw 25\\nSkipping line 23790: expected 23 fields, saw 25\\nSkipping line 23794: expected 23 fields, saw 25\\nSkipping line 23797: expected 23 fields, saw 25\\nSkipping line 23812: expected 23 fields, saw 25\\nSkipping line 23816: expected 23 fields, saw 25\\nSkipping line 23819: expected 23 fields, saw 25\\nSkipping line 23820: expected 23 fields, saw 25\\nSkipping line 23835: expected 23 fields, saw 25\\nSkipping line 23837: expected 23 fields, saw 25\\nSkipping line 23846: expected 23 fields, saw 25\\nSkipping line 23848: expected 23 fields, saw 25\\nSkipping line 23854: expected 23 fields, saw 25\\nSkipping line 23857: expected 23 fields, saw 25\\nSkipping line 23871: expected 23 fields, saw 25\\nSkipping line 23878: expected 23 fields, saw 25\\nSkipping line 23880: expected 23 fields, saw 25\\nSkipping line 23884: expected 23 fields, saw 25\\nSkipping line 23924: expected 23 fields, saw 25\\nSkipping line 23933: expected 23 fields, saw 25\\nSkipping line 23935: expected 23 fields, saw 25\\nSkipping line 23958: expected 23 fields, saw 25\\nSkipping line 23963: expected 23 fields, saw 25\\nSkipping line 23965: expected 23 fields, saw 25\\nSkipping line 23967: expected 23 fields, saw 25\\nSkipping line 23968: expected 23 fields, saw 25\\nSkipping line 23977: expected 23 fields, saw 25\\nSkipping line 23990: expected 23 fields, saw 25\\nSkipping line 23995: expected 23 fields, saw 25\\nSkipping line 24005: expected 23 fields, saw 25\\nSkipping line 24024: expected 23 fields, saw 25\\nSkipping line 24045: expected 23 fields, saw 25\\nSkipping line 24058: expected 23 fields, saw 25\\nSkipping line 24108: expected 23 fields, saw 25\\nSkipping line 24449: expected 23 fields, saw 25\\nSkipping line 24456: expected 23 fields, saw 24\\nSkipping line 24515: expected 23 fields, saw 25\\nSkipping line 24525: expected 23 fields, saw 25\\nSkipping line 24562: expected 23 fields, saw 25\\nSkipping line 24596: expected 23 fields, saw 25\\nSkipping line 24599: expected 23 fields, saw 25\\nSkipping line 24616: expected 23 fields, saw 25\\nSkipping line 24621: expected 23 fields, saw 25\\nSkipping line 24625: expected 23 fields, saw 25\\nSkipping line 24631: expected 23 fields, saw 25\\nSkipping line 24632: expected 23 fields, saw 25\\nSkipping line 24651: expected 23 fields, saw 25\\nSkipping line 24658: expected 23 fields, saw 25\\nSkipping line 24659: expected 23 fields, saw 25\\nSkipping line 24661: expected 23 fields, saw 25\\nSkipping line 24663: expected 23 fields, saw 25\\nSkipping line 24672: expected 23 fields, saw 25\\nSkipping line 24683: expected 23 fields, saw 25\\nSkipping line 24693: expected 23 fields, saw 25\\nSkipping line 24701: expected 23 fields, saw 25\\nSkipping line 24708: expected 23 fields, saw 25\\nSkipping line 24725: expected 23 fields, saw 25\\nSkipping line 24734: expected 23 fields, saw 25\\nSkipping line 24737: expected 23 fields, saw 25\\nSkipping line 24743: expected 23 fields, saw 24\\nSkipping line 24904: expected 23 fields, saw 25\\nSkipping line 25154: expected 23 fields, saw 25\\nSkipping line 25201: expected 23 fields, saw 25\\nSkipping line 25210: expected 23 fields, saw 25\\nSkipping line 25217: expected 23 fields, saw 25\\nSkipping line 25224: expected 23 fields, saw 25\\nSkipping line 25225: expected 23 fields, saw 25\\nSkipping line 25239: expected 23 fields, saw 25\\nSkipping line 25281: expected 23 fields, saw 25\\nSkipping line 25284: expected 23 fields, saw 25\\nSkipping line 25286: expected 23 fields, saw 25\\nSkipping line 25293: expected 23 fields, saw 25\\nSkipping line 25320: expected 23 fields, saw 25\\nSkipping line 25322: expected 23 fields, saw 25\\nSkipping line 25341: expected 23 fields, saw 25\\nSkipping line 25346: expected 23 fields, saw 25\\nSkipping line 25348: expected 23 fields, saw 25\\nSkipping line 25397: expected 23 fields, saw 24\\nSkipping line 25425: expected 23 fields, saw 25\\nSkipping line 25444: expected 23 fields, saw 25\\nSkipping line 25470: expected 23 fields, saw 25\\nSkipping line 25580: expected 23 fields, saw 25\\nSkipping line 25591: expected 23 fields, saw 25\\nSkipping line 25975: expected 23 fields, saw 25\\nSkipping line 26046: expected 23 fields, saw 25\\nSkipping line 26498: expected 23 fields, saw 25\\nSkipping line 26538: expected 23 fields, saw 25\\nSkipping line 26615: expected 23 fields, saw 25\\nSkipping line 27108: expected 23 fields, saw 25\\nSkipping line 27127: expected 23 fields, saw 25\\nSkipping line 27140: expected 23 fields, saw 25\\nSkipping line 27153: expected 23 fields, saw 25\\nSkipping line 27187: expected 23 fields, saw 25\\nSkipping line 27189: expected 23 fields, saw 25\\nSkipping line 27194: expected 23 fields, saw 25\\nSkipping line 27215: expected 23 fields, saw 25\\nSkipping line 27216: expected 23 fields, saw 25\\nSkipping line 27235: expected 23 fields, saw 25\\nSkipping line 27241: expected 23 fields, saw 25\\nSkipping line 27244: expected 23 fields, saw 25\\nSkipping line 27288: expected 23 fields, saw 25\\nSkipping line 27291: expected 23 fields, saw 25\\nSkipping line 27296: expected 23 fields, saw 25\\nSkipping line 27300: expected 23 fields, saw 25\\nSkipping line 27302: expected 23 fields, saw 25\\nSkipping line 27311: expected 23 fields, saw 25\\nSkipping line 27332: expected 23 fields, saw 25\\nSkipping line 27345: expected 23 fields, saw 25\\nSkipping line 27354: expected 23 fields, saw 25\\nSkipping line 27397: expected 23 fields, saw 25\\nSkipping line 27398: expected 23 fields, saw 25\\nSkipping line 27401: expected 23 fields, saw 25\\nSkipping line 27424: expected 23 fields, saw 25\\nSkipping line 27463: expected 23 fields, saw 24\\nSkipping line 27498: expected 23 fields, saw 25\\nSkipping line 27762: expected 23 fields, saw 25\\nSkipping line 27803: expected 23 fields, saw 25\\nSkipping line 27823: expected 23 fields, saw 25\\nSkipping line 27826: expected 23 fields, saw 25\\nSkipping line 27845: expected 23 fields, saw 25\\nSkipping line 27847: expected 23 fields, saw 25\\nSkipping line 27852: expected 23 fields, saw 25\\nSkipping line 27867: expected 23 fields, saw 25\\nSkipping line 27873: expected 23 fields, saw 25\\nSkipping line 27898: expected 23 fields, saw 25\\nSkipping line 27934: expected 23 fields, saw 25\\nSkipping line 27955: expected 23 fields, saw 25\\nSkipping line 27956: expected 23 fields, saw 25\\nSkipping line 27965: expected 23 fields, saw 25\\nSkipping line 27968: expected 23 fields, saw 25\\nSkipping line 27980: expected 23 fields, saw 25\\nSkipping line 27981: expected 23 fields, saw 25\\nSkipping line 27999: expected 23 fields, saw 25\\nSkipping line 28003: expected 23 fields, saw 25\\nSkipping line 28011: expected 23 fields, saw 25\\nSkipping line 28015: expected 23 fields, saw 25\\nSkipping line 28020: expected 23 fields, saw 25\\nSkipping line 28035: expected 23 fields, saw 25\\nSkipping line 28037: expected 23 fields, saw 25\\nSkipping line 28041: expected 23 fields, saw 25\\nSkipping line 28050: expected 23 fields, saw 25\\nSkipping line 28053: expected 23 fields, saw 25\\nSkipping line 28063: expected 23 fields, saw 25\\nSkipping line 28067: expected 23 fields, saw 25\\nSkipping line 28070: expected 23 fields, saw 25\\nSkipping line 28074: expected 23 fields, saw 25\\nSkipping line 28075: expected 23 fields, saw 25\\nSkipping line 28081: expected 23 fields, saw 25\\nSkipping line 28091: expected 23 fields, saw 25\\nSkipping line 28094: expected 23 fields, saw 25\\nSkipping line 28178: expected 23 fields, saw 25\\nSkipping line 28374: expected 23 fields, saw 25\\nSkipping line 28453: expected 23 fields, saw 25\\nSkipping line 28460: expected 23 fields, saw 25\\nSkipping line 28474: expected 23 fields, saw 25\\nSkipping line 28502: expected 23 fields, saw 25\\nSkipping line 28504: expected 23 fields, saw 25\\nSkipping line 28530: expected 23 fields, saw 25\\nSkipping line 28545: expected 23 fields, saw 24\\nSkipping line 28557: expected 23 fields, saw 25\\nSkipping line 28560: expected 23 fields, saw 25\\nSkipping line 28562: expected 23 fields, saw 25\\nSkipping line 28563: expected 23 fields, saw 25\\nSkipping line 28583: expected 23 fields, saw 25\\nSkipping line 28587: expected 23 fields, saw 25\\nSkipping line 28592: expected 23 fields, saw 25\\nSkipping line 28600: expected 23 fields, saw 25\\nSkipping line 28609: expected 23 fields, saw 25\\nSkipping line 28615: expected 23 fields, saw 25\\nSkipping line 28618: expected 23 fields, saw 25\\nSkipping line 28623: expected 23 fields, saw 25\\nSkipping line 28628: expected 23 fields, saw 25\\nSkipping line 28633: expected 23 fields, saw 25\\nSkipping line 28645: expected 23 fields, saw 25\\nSkipping line 28661: expected 23 fields, saw 25\\nSkipping line 28662: expected 23 fields, saw 25\\nSkipping line 28667: expected 23 fields, saw 25\\nSkipping line 28673: expected 23 fields, saw 25\\nSkipping line 28674: expected 23 fields, saw 25\\nSkipping line 28680: expected 23 fields, saw 25\\nSkipping line 28681: expected 23 fields, saw 25\\nSkipping line 28684: expected 23 fields, saw 25\\nSkipping line 28705: expected 23 fields, saw 25\\nSkipping line 28710: expected 23 fields, saw 25\\nSkipping line 28712: expected 23 fields, saw 25\\nSkipping line 28718: expected 23 fields, saw 25\\nSkipping line 28725: expected 23 fields, saw 25\\nSkipping line 28731: expected 23 fields, saw 25\\nSkipping line 28734: expected 23 fields, saw 25\\nSkipping line 28746: expected 23 fields, saw 25\\nSkipping line 28771: expected 23 fields, saw 25\\nSkipping line 28781: expected 23 fields, saw 25\\nSkipping line 28794: expected 23 fields, saw 25\\nSkipping line 28823: expected 23 fields, saw 25\\nSkipping line 28866: expected 23 fields, saw 25\\nSkipping line 28974: expected 23 fields, saw 25\\nSkipping line 29108: expected 23 fields, saw 25\\nSkipping line 29112: expected 23 fields, saw 25\\nSkipping line 29127: expected 23 fields, saw 25\\nSkipping line 29147: expected 23 fields, saw 25\\nSkipping line 29151: expected 23 fields, saw 25\\nSkipping line 29161: expected 23 fields, saw 25\\nSkipping line 29162: expected 23 fields, saw 25\\nSkipping line 29167: expected 23 fields, saw 25\\nSkipping line 29172: expected 23 fields, saw 25\\nSkipping line 29178: expected 23 fields, saw 25\\nSkipping line 29180: expected 23 fields, saw 25\\nSkipping line 29199: expected 23 fields, saw 25\\nSkipping line 29218: expected 23 fields, saw 25\\nSkipping line 29246: expected 23 fields, saw 25\\nSkipping line 29254: expected 23 fields, saw 25\\nSkipping line 29256: expected 23 fields, saw 25\\nSkipping line 29258: expected 23 fields, saw 25\\nSkipping line 29261: expected 23 fields, saw 25\\nSkipping line 29263: expected 23 fields, saw 25\\nSkipping line 29270: expected 23 fields, saw 25\\nSkipping line 29279: expected 23 fields, saw 25\\nSkipping line 29280: expected 23 fields, saw 25\\nSkipping line 29289: expected 23 fields, saw 25\\nSkipping line 29295: expected 23 fields, saw 25\\nSkipping line 29297: expected 23 fields, saw 24\\nSkipping line 29311: expected 23 fields, saw 25\\nSkipping line 29314: expected 23 fields, saw 25\\nSkipping line 29319: expected 23 fields, saw 25\\nSkipping line 29325: expected 23 fields, saw 25\\nSkipping line 29353: expected 23 fields, saw 25\\nSkipping line 29358: expected 23 fields, saw 25\\nSkipping line 29361: expected 23 fields, saw 25\\nSkipping line 29363: expected 23 fields, saw 25\\nSkipping line 29364: expected 23 fields, saw 25\\nSkipping line 29375: expected 23 fields, saw 25\\nSkipping line 29376: expected 23 fields, saw 25\\nSkipping line 29392: expected 23 fields, saw 25\\nSkipping line 29395: expected 23 fields, saw 25\\nSkipping line 29396: expected 23 fields, saw 25\\nSkipping line 29458: expected 23 fields, saw 25\\nSkipping line 29591: expected 23 fields, saw 25\\nSkipping line 29768: expected 23 fields, saw 25\\nSkipping line 29775: expected 23 fields, saw 25\\nSkipping line 29783: expected 23 fields, saw 25\\nSkipping line 29784: expected 23 fields, saw 25\\nSkipping line 29789: expected 23 fields, saw 25\\nSkipping line 29802: expected 23 fields, saw 25\\nSkipping line 29808: expected 23 fields, saw 25\\nSkipping line 29813: expected 23 fields, saw 25\\nSkipping line 29825: expected 23 fields, saw 25\\nSkipping line 29828: expected 23 fields, saw 25\\nSkipping line 29834: expected 23 fields, saw 25\\nSkipping line 29837: expected 23 fields, saw 25\\nSkipping line 29841: expected 23 fields, saw 25\\nSkipping line 29842: expected 23 fields, saw 25\\nSkipping line 29857: expected 23 fields, saw 25\\nSkipping line 29866: expected 23 fields, saw 25\\nSkipping line 29867: expected 23 fields, saw 25\\nSkipping line 29871: expected 23 fields, saw 25\\nSkipping line 29875: expected 23 fields, saw 25\\nSkipping line 29877: expected 23 fields, saw 25\\nSkipping line 29882: expected 23 fields, saw 25\\nSkipping line 29898: expected 23 fields, saw 25\\nSkipping line 29901: expected 23 fields, saw 25\\nSkipping line 29902: expected 23 fields, saw 25\\nSkipping line 29903: expected 23 fields, saw 25\\nSkipping line 29911: expected 23 fields, saw 25\\nSkipping line 29920: expected 23 fields, saw 25\\nSkipping line 29921: expected 23 fields, saw 25\\nSkipping line 29938: expected 23 fields, saw 25\\nSkipping line 29945: expected 23 fields, saw 25\\nSkipping line 29946: expected 23 fields, saw 25\\nSkipping line 29985: expected 23 fields, saw 25\\nSkipping line 29996: expected 23 fields, saw 25\\nSkipping line 30059: expected 23 fields, saw 25\\nSkipping line 30728: expected 23 fields, saw 25\\nSkipping line 30787: expected 23 fields, saw 25\\nSkipping line 31006: expected 23 fields, saw 25\\nSkipping line 31196: expected 23 fields, saw 25\\nSkipping line 31200: expected 23 fields, saw 25\\nSkipping line 31445: expected 23 fields, saw 25\\nSkipping line 31588: expected 23 fields, saw 25\\nSkipping line 31680: expected 23 fields, saw 25\\nSkipping line 31732: expected 23 fields, saw 25\\nSkipping line 31760: expected 23 fields, saw 25\\nSkipping line 31766: expected 23 fields, saw 25\\nSkipping line 31786: expected 23 fields, saw 25\\nSkipping line 31790: expected 23 fields, saw 25\\nSkipping line 31793: expected 23 fields, saw 25\\nSkipping line 31811: expected 23 fields, saw 25\\nSkipping line 31813: expected 23 fields, saw 25\\nSkipping line 31817: expected 23 fields, saw 25\\nSkipping line 31818: expected 23 fields, saw 25\\nSkipping line 31829: expected 23 fields, saw 25\\nSkipping line 31837: expected 23 fields, saw 25\\nSkipping line 31838: expected 23 fields, saw 25\\nSkipping line 31839: expected 23 fields, saw 25\\nSkipping line 31842: expected 23 fields, saw 24\\nSkipping line 31847: expected 23 fields, saw 25\\nSkipping line 31865: expected 23 fields, saw 25\\nSkipping line 31874: expected 23 fields, saw 25\\nSkipping line 31877: expected 23 fields, saw 25\\nSkipping line 31893: expected 23 fields, saw 25\\nSkipping line 31894: expected 23 fields, saw 25\\nSkipping line 31898: expected 23 fields, saw 25\\nSkipping line 31907: expected 23 fields, saw 25\\nSkipping line 31914: expected 23 fields, saw 25\\nSkipping line 31927: expected 23 fields, saw 25\\nSkipping line 31948: expected 23 fields, saw 25\\nSkipping line 31970: expected 23 fields, saw 25\\nSkipping line 31976: expected 23 fields, saw 25\\nSkipping line 31989: expected 23 fields, saw 25\\nSkipping line 32005: expected 23 fields, saw 25\\nSkipping line 32018: expected 23 fields, saw 25\\nSkipping line 32027: expected 23 fields, saw 24\\nSkipping line 32037: expected 23 fields, saw 25\\nSkipping line 32200: expected 23 fields, saw 25\\nSkipping line 32368: expected 23 fields, saw 25\\nSkipping line 32415: expected 23 fields, saw 25\\nSkipping line 32428: expected 23 fields, saw 25\\nSkipping line 32452: expected 23 fields, saw 25\\nSkipping line 32477: expected 23 fields, saw 25\\nSkipping line 32485: expected 23 fields, saw 25\\nSkipping line 32496: expected 23 fields, saw 25\\nSkipping line 32513: expected 23 fields, saw 25\\nSkipping line 32530: expected 23 fields, saw 25\\nSkipping line 32533: expected 23 fields, saw 25\\nSkipping line 32546: expected 23 fields, saw 25\\nSkipping line 32548: expected 23 fields, saw 25\\nSkipping line 32549: expected 23 fields, saw 25\\nSkipping line 32550: expected 23 fields, saw 25\\nSkipping line 32580: expected 23 fields, saw 25\\nSkipping line 32586: expected 23 fields, saw 25\\nSkipping line 32590: expected 23 fields, saw 25\\nSkipping line 32591: expected 23 fields, saw 25\\nSkipping line 32593: expected 23 fields, saw 25\\nSkipping line 32602: expected 23 fields, saw 25\\nSkipping line 32609: expected 23 fields, saw 25\\nSkipping line 32611: expected 23 fields, saw 25\\nSkipping line 32612: expected 23 fields, saw 25\\nSkipping line 32619: expected 23 fields, saw 25\\nSkipping line 32621: expected 23 fields, saw 25\\nSkipping line 32622: expected 23 fields, saw 25\\nSkipping line 32638: expected 23 fields, saw 25\\nSkipping line 32650: expected 23 fields, saw 25\\nSkipping line 32651: expected 23 fields, saw 25\\nSkipping line 32662: expected 23 fields, saw 25\\nSkipping line 32671: expected 23 fields, saw 25\\nSkipping line 32681: expected 23 fields, saw 25\\nSkipping line 32687: expected 23 fields, saw 25\\nSkipping line 32707: expected 23 fields, saw 25\\nSkipping line 32708: expected 23 fields, saw 25\\nSkipping line 32711: expected 23 fields, saw 24\\nSkipping line 32738: expected 23 fields, saw 25\\nSkipping line 32744: expected 23 fields, saw 25\\nSkipping line 32751: expected 23 fields, saw 25\\nSkipping line 32754: expected 23 fields, saw 25\\nSkipping line 32809: expected 23 fields, saw 25\\nSkipping line 33085: expected 23 fields, saw 25\\nSkipping line 33108: expected 23 fields, saw 25\\nSkipping line 33115: expected 23 fields, saw 25\\nSkipping line 33119: expected 23 fields, saw 25\\nSkipping line 33123: expected 23 fields, saw 25\\nSkipping line 33139: expected 23 fields, saw 25\\nSkipping line 33148: expected 23 fields, saw 25\\nSkipping line 33156: expected 23 fields, saw 25\\nSkipping line 33157: expected 23 fields, saw 25\\nSkipping line 33164: expected 23 fields, saw 25\\nSkipping line 33170: expected 23 fields, saw 25\\nSkipping line 33175: expected 23 fields, saw 25\\nSkipping line 33177: expected 23 fields, saw 25\\nSkipping line 33180: expected 23 fields, saw 25\\nSkipping line 33192: expected 23 fields, saw 25\\nSkipping line 33197: expected 23 fields, saw 25\\nSkipping line 33220: expected 23 fields, saw 24\\nSkipping line 33221: expected 23 fields, saw 25\\nSkipping line 33229: expected 23 fields, saw 25\\nSkipping line 33248: expected 23 fields, saw 25\\nSkipping line 33262: expected 23 fields, saw 25\\nSkipping line 33265: expected 23 fields, saw 25\\nSkipping line 33273: expected 23 fields, saw 25\\nSkipping line 33293: expected 23 fields, saw 25\\nSkipping line 33316: expected 23 fields, saw 25\\nSkipping line 33323: expected 23 fields, saw 25\\nSkipping line 33328: expected 23 fields, saw 25\\nSkipping line 33346: expected 23 fields, saw 25\\nSkipping line 33348: expected 23 fields, saw 25\\nSkipping line 33362: expected 23 fields, saw 25\\nSkipping line 33363: expected 23 fields, saw 25\\nSkipping line 33371: expected 23 fields, saw 25\\nSkipping line 33372: expected 23 fields, saw 25\\nSkipping line 33373: expected 23 fields, saw 25\\nSkipping line 33393: expected 23 fields, saw 25\\nSkipping line 33400: expected 23 fields, saw 25\\nSkipping line 33401: expected 23 fields, saw 25\\nSkipping line 33404: expected 23 fields, saw 25\\nSkipping line 33408: expected 23 fields, saw 25\\nSkipping line 33411: expected 23 fields, saw 25\\nSkipping line 33412: expected 23 fields, saw 25\\nSkipping line 33416: expected 23 fields, saw 25\\nSkipping line 33752: expected 23 fields, saw 25\\n'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 932289: expected 25 fields, saw 27\\n'\n",
      "b'Skipping line 1016603: expected 25 fields, saw 27\\n'\n",
      "b'Skipping line 1020574: expected 25 fields, saw 27\\n'\n",
      "b'Skipping line 1078293: expected 25 fields, saw 27\\n'\n",
      "b'Skipping line 1083422: expected 25 fields, saw 27\\nSkipping line 1093800: expected 25 fields, saw 27\\n'\n",
      "b'Skipping line 1128972: expected 25 fields, saw 27\\nSkipping line 1138003: expected 25 fields, saw 27\\n'\n",
      "b'Skipping line 1228495: expected 25 fields, saw 27\\nSkipping line 1242864: expected 25 fields, saw 27\\n'\n",
      "/home/felipe/tf-venv3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2683: DtypeWarning: Columns (8,9,10,11,16,19,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(path,error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case.Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary.Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location.Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Beat</th>\n",
       "      <th>District</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community.Area</th>\n",
       "      <th>FBI.Code</th>\n",
       "      <th>X.Coordinate</th>\n",
       "      <th>Y.Coordinate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated.On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10060004</th>\n",
       "      <td>HY248774</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>010XX W 79TH ST</td>\n",
       "      <td>0460</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>612</td>\n",
       "      <td>6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1170778.0</td>\n",
       "      <td>1.85248e+06</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-05-12 12:42:01</td>\n",
       "      <td>41.7507</td>\n",
       "      <td>-87.6498</td>\n",
       "      <td>(41.75066697</td>\n",
       "      <td>-87.649760051)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059982</th>\n",
       "      <td>HY248801</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>105XX S WABASH AVE</td>\n",
       "      <td>0420</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>AGGRAVATED:KNIFE/CUTTING INSTR</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>04B</td>\n",
       "      <td>1178485.0</td>\n",
       "      <td>1.83491e+06</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-05-12 12:42:01</td>\n",
       "      <td>41.7023</td>\n",
       "      <td>-87.622</td>\n",
       "      <td>(41.702292131</td>\n",
       "      <td>-87.622049984)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059953</th>\n",
       "      <td>HY248791</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>005XX N ASHLAND AVE</td>\n",
       "      <td>2027</td>\n",
       "      <td>NARCOTICS</td>\n",
       "      <td>POSS: CRACK</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1215</td>\n",
       "      <td>13</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1165594.0</td>\n",
       "      <td>1.90371e+06</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-05-12 12:42:01</td>\n",
       "      <td>41.8914</td>\n",
       "      <td>-87.6673</td>\n",
       "      <td>(41.891381064</td>\n",
       "      <td>-87.66730127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060015</th>\n",
       "      <td>HY248790</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>005XX N MARSHFIELD AVE</td>\n",
       "      <td>0275</td>\n",
       "      <td>CRIM SEXUAL ASSAULT</td>\n",
       "      <td>ATTEMPT AGG: OTHER</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1215</td>\n",
       "      <td>13</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>02</td>\n",
       "      <td>1165337.0</td>\n",
       "      <td>1.90384e+06</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-05-12 12:42:01</td>\n",
       "      <td>41.8917</td>\n",
       "      <td>-87.6682</td>\n",
       "      <td>(41.89174052</td>\n",
       "      <td>-87.668241434)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059944</th>\n",
       "      <td>HY248782</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>0000X E 72ND ST</td>\n",
       "      <td>2024</td>\n",
       "      <td>NARCOTICS</td>\n",
       "      <td>POSS: HEROIN(WHITE)</td>\n",
       "      <td>STREET</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>323</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1178073.0</td>\n",
       "      <td>1.85726e+06</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-05-12 12:42:01</td>\n",
       "      <td>41.7636</td>\n",
       "      <td>-87.6229</td>\n",
       "      <td>(41.763624424</td>\n",
       "      <td>-87.622883163)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID Case.Number                    Date Block  \\\n",
       "10060004  HY248774  2015-05-05         010XX W 79TH ST  0460   \n",
       "10059982  HY248801  2015-05-05      105XX S WABASH AVE  0420   \n",
       "10059953  HY248791  2015-05-05     005XX N ASHLAND AVE  2027   \n",
       "10060015  HY248790  2015-05-05  005XX N MARSHFIELD AVE  0275   \n",
       "10059944  HY248782  2015-05-05         0000X E 72ND ST  2024   \n",
       "\n",
       "                         IUCR                    Primary.Type Description  \\\n",
       "10060004              BATTERY                          SIMPLE    SIDEWALK   \n",
       "10059982              BATTERY  AGGRAVATED:KNIFE/CUTTING INSTR   RESIDENCE   \n",
       "10059953            NARCOTICS                     POSS: CRACK    SIDEWALK   \n",
       "10060015  CRIM SEXUAL ASSAULT              ATTEMPT AGG: OTHER       ALLEY   \n",
       "10059944            NARCOTICS             POSS: HEROIN(WHITE)      STREET   \n",
       "\n",
       "         Location.Description Arrest Domestic Beat  District  Ward  \\\n",
       "10060004                False  False      612    6      17.0  71.0   \n",
       "10059982                False  False      512    5       9.0  49.0   \n",
       "10059953                 True  False     1215   13      26.0  24.0   \n",
       "10060015                False  False     1215   13      26.0  24.0   \n",
       "10059944                 True  False      323    3       6.0  69.0   \n",
       "\n",
       "         Community.Area   FBI.Code X.Coordinate  Y.Coordinate  \\\n",
       "10060004            08B  1170778.0  1.85248e+06        2015.0   \n",
       "10059982            04B  1178485.0  1.83491e+06        2015.0   \n",
       "10059953             18  1165594.0  1.90371e+06        2015.0   \n",
       "10060015             02  1165337.0  1.90384e+06        2015.0   \n",
       "10059944             18  1178073.0  1.85726e+06        2015.0   \n",
       "\n",
       "                        Year Updated.On Latitude      Longitude  \\\n",
       "10060004 2015-05-12 12:42:01    41.7507 -87.6498   (41.75066697   \n",
       "10059982 2015-05-12 12:42:01    41.7023  -87.622  (41.702292131   \n",
       "10059953 2015-05-12 12:42:01    41.8914 -87.6673  (41.891381064   \n",
       "10060015 2015-05-12 12:42:01    41.8917 -87.6682   (41.89174052   \n",
       "10059944 2015-05-12 12:42:01    41.7636 -87.6229  (41.763624424   \n",
       "\n",
       "                 Location  \n",
       "10060004   -87.649760051)  \n",
       "10059982   -87.622049984)  \n",
       "10059953    -87.66730127)  \n",
       "10060015   -87.668241434)  \n",
       "10059944   -87.622883163)  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID',\n",
       " 'Case.Number',\n",
       " 'Date',\n",
       " 'Block',\n",
       " 'IUCR',\n",
       " 'Primary.Type',\n",
       " 'Description',\n",
       " 'Location.Description',\n",
       " 'Arrest',\n",
       " 'Domestic',\n",
       " 'Beat',\n",
       " 'District',\n",
       " 'Ward',\n",
       " 'Community.Area',\n",
       " 'FBI.Code',\n",
       " 'X.Coordinate',\n",
       " 'Y.Coordinate',\n",
       " 'Year',\n",
       " 'Updated.On',\n",
       " 'Latitude',\n",
       " 'Longitude',\n",
       " 'Location']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all column names\n",
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>District</th>\n",
       "      <th>Ward</th>\n",
       "      <th>FBI.Code</th>\n",
       "      <th>Y.Coordinate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.345016e+06</td>\n",
       "      <td>1.344642e+06</td>\n",
       "      <td>1.338409e+06</td>\n",
       "      <td>1.344921e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.654000e+01</td>\n",
       "      <td>3.684159e+01</td>\n",
       "      <td>1.128536e+06</td>\n",
       "      <td>3.800162e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.265785e+02</td>\n",
       "      <td>2.169541e+01</td>\n",
       "      <td>2.019412e+05</td>\n",
       "      <td>2.032392e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.011000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>1.151252e+06</td>\n",
       "      <td>2.011000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>1.165234e+06</td>\n",
       "      <td>2.013000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>1.176156e+06</td>\n",
       "      <td>2.014000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.535000e+03</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>1.205079e+06</td>\n",
       "      <td>1.945412e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           District          Ward      FBI.Code  Y.Coordinate\n",
       "count  1.345016e+06  1.344642e+06  1.338409e+06  1.344921e+06\n",
       "mean   5.654000e+01  3.684159e+01  1.128536e+06  3.800162e+04\n",
       "std    2.265785e+02  2.169541e+01  2.019412e+05  2.032392e+05\n",
       "min    1.000000e+00  0.000000e+00  1.000000e+00  2.011000e+03\n",
       "25%    1.100000e+01  2.200000e+01  1.151252e+06  2.011000e+03\n",
       "50%    2.400000e+01  3.100000e+01  1.165234e+06  2.013000e+03\n",
       "75%    3.500000e+01  5.600000e+01  1.176156e+06  2.014000e+03\n",
       "max    2.535000e+03  7.700000e+01  1.205079e+06  1.945412e+06"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics for numeric features\n",
    "df.describe(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case.Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary.Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location.Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Beat</th>\n",
       "      <th>Community.Area</th>\n",
       "      <th>X.Coordinate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated.On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1344251</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1338384</td>\n",
       "      <td>1345047</td>\n",
       "      <td>1338425</td>\n",
       "      <td>1344807</td>\n",
       "      <td>1338428</td>\n",
       "      <td>1338425</td>\n",
       "      <td>1338302</td>\n",
       "      <td>1338302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1344973</td>\n",
       "      <td>1586</td>\n",
       "      <td>32134</td>\n",
       "      <td>363</td>\n",
       "      <td>33</td>\n",
       "      <td>336</td>\n",
       "      <td>134</td>\n",
       "      <td>38</td>\n",
       "      <td>48</td>\n",
       "      <td>579</td>\n",
       "      <td>166</td>\n",
       "      <td>77</td>\n",
       "      <td>147563</td>\n",
       "      <td>933556</td>\n",
       "      <td>617578</td>\n",
       "      <td>646994</td>\n",
       "      <td>609509</td>\n",
       "      <td>609280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>HV217424</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>001XX N STATE ST</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>11</td>\n",
       "      <td>06</td>\n",
       "      <td>08B</td>\n",
       "      <td>10/31/2014 03:20:56 PM</td>\n",
       "      <td>2011</td>\n",
       "      <td>-87.741385133</td>\n",
       "      <td>(41.754644364</td>\n",
       "      <td>-87.741385133)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>1453</td>\n",
       "      <td>2811</td>\n",
       "      <td>122660</td>\n",
       "      <td>300101</td>\n",
       "      <td>136521</td>\n",
       "      <td>306616</td>\n",
       "      <td>918257</td>\n",
       "      <td>1081737</td>\n",
       "      <td>30076</td>\n",
       "      <td>86504</td>\n",
       "      <td>291533</td>\n",
       "      <td>10420</td>\n",
       "      <td>18176</td>\n",
       "      <td>11982</td>\n",
       "      <td>1010</td>\n",
       "      <td>1062</td>\n",
       "      <td>1062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID Case.Number              Date    Block     IUCR Primary.Type  \\\n",
       "count    1345047     1345047           1345047  1345047  1345047      1345047   \n",
       "unique   1344973        1586             32134      363       33          336   \n",
       "top     HV217424  2011-01-01  001XX N STATE ST     0820    THEFT       SIMPLE   \n",
       "freq           3        1453              2811   122660   300101       136521   \n",
       "\n",
       "       Description Location.Description   Arrest Domestic     Beat  \\\n",
       "count      1344251              1345047  1345047  1345047  1338384   \n",
       "unique         134                   38       48      579      166   \n",
       "top         STREET                false    false    false       11   \n",
       "freq        306616               918257  1081737    30076    86504   \n",
       "\n",
       "       Community.Area X.Coordinate                    Year Updated.On  \\\n",
       "count         1345047      1338425                 1344807    1338428   \n",
       "unique             77       147563                  933556     617578   \n",
       "top                06          08B  10/31/2014 03:20:56 PM       2011   \n",
       "freq           291533        10420                   18176      11982   \n",
       "\n",
       "             Latitude      Longitude         Location  \n",
       "count         1338425        1338302          1338302  \n",
       "unique         646994         609509           609280  \n",
       "top     -87.741385133  (41.754644364   -87.741385133)  \n",
       "freq             1010           1062             1062  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics for categorical features\n",
    "df.describe(include=[object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many crimes per year?\n",
    "\n",
    "# some rows have bad dates so we must use coerce to force a NaT value\n",
    "df[\"Year\"] = pd.to_datetime(df['Year'], format='%m/%d/%Y %H:%M:%S %p',errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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PD1vaYw2wV5oUPf6NtkJR+ilPGj7b8HnrTcvV+n0tPWC0mhem38RNXC79Rut+bZVeS1rv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\n85ocdthhW50dgIWsGVBqrX0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kMh+ZzEku85HJnOQyH5nMSS7zkcmc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\nAgAAAGDI/wctiamNwI3qVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f111349a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Year\"].groupby([df.Year.dt.year]).size().plot(kind=\"bar\",figsize=(20,10), title=\"number of dataset entries by year\")\n",
    "fig = plt.gcf()\n",
    "fig.axes[0].title.set_size(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-05-12 12:42:01')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like crime is going down. If the population is increasing, this drop is even more significant\n",
    "\n",
    "\n",
    "# 2015 has had fewer crimes, but maybe we just stopped collecting data in the middle of the year\n",
    "df[\"Year\"].max()\n",
    "# yes - last date was may 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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56fn89Hx+ej6/ZWYo7chV3gAAAADYHAIlAAAA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\nAAwRKAEAAAAwRKAEAAAAwJD/B/X+0r0lgHNWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f111340fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how many crimes per month?\n",
    "df[\"Year\"].groupby([df.Year.dt.month]).size().plot(kind=\"bar\",figsize=(20,10), title=\"number of dataset entries by month\")\n",
    "fig = plt.gcf()\n",
    "fig.axes[0].title.set_size(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# february and december have had fewer crimes.\n",
    "# maybe because february is shorter and december has more holidays?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10e5083208>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qYGdmfnNi2+nAP2Xm/4uI3waeBpweEfcCbpSZ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vNLAec418C+K+uExYjg53OA04muv7sSv6trbY+97K0r96mk84Ede78DSTsBbyC6NH48a87RFdqY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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10e5097c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how many crimes, per month, per IUCR?\n",
    "df.groupby([df.IUCR, df.Year.dt.month]).size().unstack().plot(kind='bar',stacked=True,figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# theft and battery are the largest ones throughout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10cf244c18>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10cf28f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the other way around: how many crimes, per IUCR, per MONTH\n",
    "# need more colors\n",
    "colors = plt.cm.Paired(np.linspace(0, 1, 40))\n",
    "\n",
    "df.groupby([df.Year.dt.month,df.IUCR]).size().unstack().plot(kind='bar',stacked=True,figsize=(40,40), color=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# it's worth noting that there are three IUCRs that are probably the same. (NON-CRIMINAL, NON - CRIMINAL, \n",
    "# and NON-CRIMINAL (SUBJECT-SPECIFIED)) - adding them up would make it a major category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10cd5dbeb8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10b33859b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how many crimes, per IUCR, per YEAR ?\n",
    "\n",
    "df.groupby([df.Year.dt.year,df.IUCR]).size().unstack().plot(kind='bar',stacked=True,figsize=(40,20), color=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# note: crimes seemed to have dropped as a whole, not just a few categories\n",
    "# note: there are some random datapoints in 2001 and 2002 as well. they are probably noise. i'll take them out.\n",
    "\n",
    "df = df[df.Year.dt.year > 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 1303100\n",
       "unique                 920308\n",
       "top       2014-10-31 03:20:56\n",
       "freq                    18176\n",
       "first     2011-01-01 01:51:30\n",
       "last      2015-05-12 12:42:01\n",
       "Name: Year, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Year\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "other things, questions that are worth asking to get a high-level view:\n",
    "\n",
    "- what types of crimes (IUCR) have had the largest (relative, or percentage) drop/rise year on year?\n",
    "\n",
    "- what locations (Community.Area) have had the largest (relative, or percentage) drop/rise year on year?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 17.,   9.,  26.,   6.,  43.,  28.,  35.,  27.,  45.,  23.,  48.,\n",
       "        38.,   7.,  34.,  42.,  37.,  44.,  15.,  22.,  11.,  46.,  20.,\n",
       "        50.,  16.,   5.,   8.,  40.,  30.,  18.,  14.,  36.,  21.,  39.,\n",
       "        32.,   1.,  47.,   2.,  29.,  24.,  25.,   4.,  19.,  33.,   3.,\n",
       "        31.,  12.,  10.,  49.,  41.,  13.])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to cluster districts by crimes, I will represent each district as a vector with the counts for each crime \n",
    "# that happened in there\n",
    "\n",
    "# but District is a floating point number\n",
    "# how many different Districts are there?\n",
    "districts = df.District.unique()\n",
    "\n",
    "# ok so even though it's a float it's probably a categorical column\n",
    "\n",
    "# remove NaNs because they may be noise\n",
    "not_nans = np.invert(np.isnan(districts))\n",
    "\n",
    "districts = districts[not_nans]\n",
    "districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ARSON', 'ASSAULT', 'BATTERY', 'BURGLARY',\n",
       "       'CONCEALED CARRY LICENSE VIOLATION', 'CRIM SEXUAL ASSAULT',\n",
       "       'CRIMINAL DAMAGE', 'CRIMINAL TRESPASS', 'DECEPTIVE PRACTICE',\n",
       "       'GAMBLING', 'HOMICIDE', 'HUMAN TRAFFICKING',\n",
       "       'INTERFERENCE WITH PUBLIC OFFICER', 'INTIMIDATION', 'KIDNAPPING',\n",
       "       'LIQUOR LAW VIOLATION', 'MOTOR VEHICLE THEFT', 'NARCOTICS',\n",
       "       'NON - CRIMINAL', 'NON-CRIMINAL',\n",
       "       'NON-CRIMINAL (SUBJECT SPECIFIED)', 'OBSCENITY',\n",
       "       'OFFENSE INVOLVING CHILDREN', 'OTHER NARCOTIC VIOLATION',\n",
       "       'OTHER OFFENSE', 'PROSTITUTION', 'PUBLIC INDECENCY',\n",
       "       'PUBLIC PEACE VIOLATION', 'ROBBERY', 'SEX OFFENSE', 'STALKING',\n",
       "       'THEFT', 'WEAPONS VIOLATION'], dtype=object)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# we need this because some districts may not have had all types of crime\n",
    "# in which case the vector would not match the others\n",
    "# (they need to be in the same order too)\n",
    "all_iucrs = df[\"IUCR\"].unique()\n",
    "\n",
    "all_iucrs.sort()\n",
    "\n",
    "all_iucrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "district_vectors = []\n",
    "\n",
    "for district_index in districts:\n",
    "    \n",
    "    iucrs = df[df[\"District\"] == district_index].groupby(\"IUCR\")[\"IUCR\"]\n",
    "    \n",
    "    vector = []\n",
    "    \n",
    "    for iucr_name in all_iucrs:\n",
    "        \n",
    "        # if there's a key error, it means this district has had no crimes of this IUCR code\n",
    "        try:\n",
    "            count = len(iucrs.get_group(iucr_name))\n",
    "        except KeyError:\n",
    "            count = 0\n",
    "            \n",
    "        vector.append(count)    \n",
    "     \n",
    "    district_vectors.append(vector)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 33)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.vstack(district_vectors)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/felipe/tf-venv3/lib/python3.5/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# we normalize it to prevent large absolute values from affecting more\n",
    "sc = preprocessing.MinMaxScaler()\n",
    "X_scaled = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.24246988,  0.16862496,  0.13985198,  0.23488367,  0.140625  ,\n",
       "         0.20694124,  0.25852757,  0.11596351,  0.08180516,  0.01520497,\n",
       "         0.14105505,  0.03125   ,  0.06276483,  0.28340517,  0.24744898,\n",
       "         0.34809783,  0.33114388,  0.04620808,  0.140625  ,  0.09765625,\n",
       "         0.0625    ,  0.34375   ,  0.31133178,  0.09375   ,  0.2246132 ,\n",
       "         0.04969484,  0.11458333,  0.208625  ,  0.18620937,  0.17963287,\n",
       "         0.1722561 ,  0.10192487,  0.10181025],\n",
       "       [ 0.63052209,  0.73713158,  0.63685102,  0.65400645,  0.33333333,\n",
       "         0.7265745 ,  0.78690544,  0.41476832,  0.14144381,  0.34468339,\n",
       "         0.69469929,  0.22222222,  0.40936911,  0.52490421,  0.57369615,\n",
       "         0.27149758,  0.72615795,  0.32251545,  0.16666667,  0.02083333,\n",
       "         0.05555556,  0.49074074,  0.85237106,  0.11111111,  0.71227749,\n",
       "         0.1987021 ,  0.11111111,  0.60311111,  0.66823879,  0.3951049 ,\n",
       "         0.3102981 ,  0.17405026,  0.62097721]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=2, random_state=0).fit(X_scaled)\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what I would do if I had more time\n",
    "\n",
    "- try to project the points into a lower dimensionality to see whether they make sense and whether we can get any insight from looking at it\n",
    "\n",
    "- use other clustering methods\n",
    "\n",
    "- use cluster metrics (such as entropy) to see whether the clusters are good at splitting the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "general strategy for a quick model: \n",
    "\n",
    "1) extract features, train\n",
    "\n",
    "  - every date becomes: number of days from today\n",
    "   - this is because the \"test set\" is the next 6 months (from the end of the dataset), so we don't have data in that time frame, but if we consider the number of days from today, we still get some generalization and will be able to spot trends\n",
    "  \n",
    "  - districts are one hot encoded\n",
    "  - murders are encoded with a 1 if IUCR=='HOMICIDE', 0 otherwise\n",
    "  \n",
    "2) at inference time, generate rows with all districts and murder == 1, for the 6 months. (6 x 50 == 300 so it's not much data) then we run a simple algorithm like Logistic Regression and get the probability, for each (district, month) that murder == 1. Then we select the district with the highest probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059982</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059953</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060015</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059944</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059939</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059946</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060610</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060003</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059990</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059996</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059932</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>10059981</th>\n",
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       "    <tr>\n",
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       "\n",
       "[1303100 rows x 50 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract features and try out a simple model just to get some results quickly\n",
    "\n",
    "# one-hot-encode categorical features\n",
    "districts = pd.get_dummies(df[\"District\"])\n",
    "\n",
    "## DID NOT FINISH"
   ]
  }
 ],
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