new practice ibm coders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the JSON File with Industry Lab Sensor Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>sensorname</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52760_5_temperature_value</td>\n",
       "      <td>item0001</td>\n",
       "      <td>2018-02-19T07:05:00.165Z</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47406_3_temperature_value</td>\n",
       "      <td>item0002</td>\n",
       "      <td>2018-02-19T07:05:00.240Z</td>\n",
       "      <td>20.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452B5_3_temperature_value</td>\n",
       "      <td>item0003</td>\n",
       "      <td>2018-02-19T07:05:00.316Z</td>\n",
       "      <td>19.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452F9_3_temperature_value</td>\n",
       "      <td>item0004</td>\n",
       "      <td>2018-02-19T07:05:00.388Z</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4521C_3_temperature_value</td>\n",
       "      <td>item0005</td>\n",
       "      <td>2018-02-19T07:05:00.460Z</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    itemname sensorname                      time  value\n",
       "0  52760_5_temperature_value   item0001  2018-02-19T07:05:00.165Z   20.5\n",
       "1  47406_3_temperature_value   item0002  2018-02-19T07:05:00.240Z  20.61\n",
       "2  452B5_3_temperature_value   item0003  2018-02-19T07:05:00.316Z  19.89\n",
       "3  452F9_3_temperature_value   item0004  2018-02-19T07:05:00.388Z   19.7\n",
       "4  4521C_3_temperature_value   item0005  2018-02-19T07:05:00.460Z  18.89"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import sys\n",
    "import types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share your notebook.\n",
    "client_915ea66450e44183938b1aab8572887f = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='x8LKJUWfwBvzMvyp4glOaZk6VQOcrYmbWV80lKToccNW',\n",
    "    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_915ea66450e44183938b1aab8572887f.get_object(Bucket='dsbootcampac3431d743f2492ebe1cfe6103674873',Key='floorsensordata2604.json')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object \n",
    "\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "# Since JSON data can be semi-structured and contain additional metadata, it is possible that you might face an error during data loading.\n",
    "# Please read the documentation of 'pandas.read_json()' and 'pandas.io.json.json_normalize' to learn more about the possibilities to adjust the data loading.\n",
    "# pandas documentation: http://pandas.pydata.org/pandas-docs/stable/io.html#io-json-reader\n",
    "# and http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.json.json_normalize.html\n",
    "\n",
    "df_data_1 = pd.read_json(body, orient='values')\n",
    "df_data_1.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the Structure of the Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>sensorname</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52760_5_temperature_value</td>\n",
       "      <td>item0001</td>\n",
       "      <td>2018-02-19T07:05:00.165Z</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47406_3_temperature_value</td>\n",
       "      <td>item0002</td>\n",
       "      <td>2018-02-19T07:05:00.240Z</td>\n",
       "      <td>20.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452B5_3_temperature_value</td>\n",
       "      <td>item0003</td>\n",
       "      <td>2018-02-19T07:05:00.316Z</td>\n",
       "      <td>19.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452F9_3_temperature_value</td>\n",
       "      <td>item0004</td>\n",
       "      <td>2018-02-19T07:05:00.388Z</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4521C_3_temperature_value</td>\n",
       "      <td>item0005</td>\n",
       "      <td>2018-02-19T07:05:00.460Z</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51A51_5_temperature_value</td>\n",
       "      <td>item0006</td>\n",
       "      <td>2018-02-19T07:05:00.534Z</td>\n",
       "      <td>21.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>512F5_5_temperature_value</td>\n",
       "      <td>item0007</td>\n",
       "      <td>2018-02-19T07:05:00.607Z</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45974_3_temperature_value</td>\n",
       "      <td>item0008</td>\n",
       "      <td>2018-02-19T07:05:00.687Z</td>\n",
       "      <td>21.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52686_5_temperature_value</td>\n",
       "      <td>item0009</td>\n",
       "      <td>2018-02-19T07:05:00.760Z</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45152_3_temperature_value</td>\n",
       "      <td>item0010</td>\n",
       "      <td>2018-02-19T07:05:00.839Z</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    itemname sensorname                      time   value\n",
       "0  52760_5_temperature_value   item0001  2018-02-19T07:05:00.165Z    20.5\n",
       "1  47406_3_temperature_value   item0002  2018-02-19T07:05:00.240Z   20.61\n",
       "2  452B5_3_temperature_value   item0003  2018-02-19T07:05:00.316Z   19.89\n",
       "3  452F9_3_temperature_value   item0004  2018-02-19T07:05:00.388Z    19.7\n",
       "4  4521C_3_temperature_value   item0005  2018-02-19T07:05:00.460Z   18.89\n",
       "5  51A51_5_temperature_value   item0006  2018-02-19T07:05:00.534Z  21.812\n",
       "6  512F5_5_temperature_value   item0007  2018-02-19T07:05:00.607Z      21\n",
       "7  45974_3_temperature_value   item0008  2018-02-19T07:05:00.687Z   21.06\n",
       "8  52686_5_temperature_value   item0009  2018-02-19T07:05:00.760Z      21\n",
       "9  45152_3_temperature_value   item0010  2018-02-19T07:05:00.839Z    20.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>sensorname</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>760514</th>\n",
       "      <td>476CA_4_carbonDioxide_value</td>\n",
       "      <td>item0131</td>\n",
       "      <td>2018-04-26T14:00:03.218Z</td>\n",
       "      <td>798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760515</th>\n",
       "      <td>46CF4_4_carbonDioxide_value</td>\n",
       "      <td>item0104</td>\n",
       "      <td>2018-04-26T14:00:03.259Z</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760516</th>\n",
       "      <td>45974_4_carbonDioxide_value</td>\n",
       "      <td>item0122</td>\n",
       "      <td>2018-04-26T14:00:03.280Z</td>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760517</th>\n",
       "      <td>4692E_4_carbonDioxide_value</td>\n",
       "      <td>item0114</td>\n",
       "      <td>2018-04-26T14:00:03.301Z</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760518</th>\n",
       "      <td>South_Workarea_3_Air</td>\n",
       "      <td>item0157</td>\n",
       "      <td>2018-04-26T14:00:03.321Z</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760519</th>\n",
       "      <td>47406_4_carbonDioxide_value</td>\n",
       "      <td>item0126</td>\n",
       "      <td>2018-04-26T14:00:03.341Z</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760520</th>\n",
       "      <td>47888_4_carbonDioxide_value</td>\n",
       "      <td>item0083</td>\n",
       "      <td>2018-04-26T14:00:03.362Z</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760521</th>\n",
       "      <td>45C72_4_carbonDioxide_value</td>\n",
       "      <td>item0109</td>\n",
       "      <td>2018-04-26T14:00:03.383Z</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760522</th>\n",
       "      <td>46C8A_4_carbonDioxide_value</td>\n",
       "      <td>item0072</td>\n",
       "      <td>2018-04-26T14:00:03.403Z</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760523</th>\n",
       "      <td>Loisach_Occupancy</td>\n",
       "      <td>item0146</td>\n",
       "      <td>2018-04-26T14:00:52.918Z</td>\n",
       "      <td>occupied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           itemname sensorname                      time  \\\n",
       "760514  476CA_4_carbonDioxide_value   item0131  2018-04-26T14:00:03.218Z   \n",
       "760515  46CF4_4_carbonDioxide_value   item0104  2018-04-26T14:00:03.259Z   \n",
       "760516  45974_4_carbonDioxide_value   item0122  2018-04-26T14:00:03.280Z   \n",
       "760517  4692E_4_carbonDioxide_value   item0114  2018-04-26T14:00:03.301Z   \n",
       "760518         South_Workarea_3_Air   item0157  2018-04-26T14:00:03.321Z   \n",
       "760519  47406_4_carbonDioxide_value   item0126  2018-04-26T14:00:03.341Z   \n",
       "760520  47888_4_carbonDioxide_value   item0083  2018-04-26T14:00:03.362Z   \n",
       "760521  45C72_4_carbonDioxide_value   item0109  2018-04-26T14:00:03.383Z   \n",
       "760522  46C8A_4_carbonDioxide_value   item0072  2018-04-26T14:00:03.403Z   \n",
       "760523            Loisach_Occupancy   item0146  2018-04-26T14:00:52.918Z   \n",
       "\n",
       "           value  \n",
       "760514       798  \n",
       "760515       543  \n",
       "760516       744  \n",
       "760517       795  \n",
       "760518       500  \n",
       "760519       641  \n",
       "760520       896  \n",
       "760521       730  \n",
       "760522       565  \n",
       "760523  occupied  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['52760_5_temperature_value', '47406_3_temperature_value',\n",
       "       '452B5_3_temperature_value', '452F9_3_temperature_value',\n",
       "       '4521C_3_temperature_value', '51A51_5_temperature_value',\n",
       "       '512F5_5_temperature_value', '45974_3_temperature_value',\n",
       "       '52686_5_temperature_value', '45152_3_temperature_value',\n",
       "       '51BEB_5_temperature_value', '45A95_3_temperature_value',\n",
       "       '516AF_5_temperature_value', '51711_5_temperature_value',\n",
       "       '51B62_5_temperature_value', '476CA_3_temperature_value',\n",
       "       '51174_5_temperature_value', '511D3_5_temperature_value',\n",
       "       '45A94_3_temperature_value', '5161B_5_temperature_value',\n",
       "       '454CA_3_temperature_value', '510FD_5_temperature_value',\n",
       "       '4562F_3_temperature_value', '51A63_5_temperature_value',\n",
       "       '51B5C_5_temperature_value', '47888_3_temperature_value',\n",
       "       '451E8_3_temperature_value', '5131F_5_temperature_value',\n",
       "       '51C0A_5_temperature_value', '46C8A_3_temperature_value',\n",
       "       '52259_5_temperature_value', '4548D_3_temperature_value',\n",
       "       '451B7_3_temperature_value', '51B6F_5_temperature_value',\n",
       "       '5140A_5_temperature_value', '52444_5_temperature_value',\n",
       "       '51B88_5_temperature_value', '515B5_5_temperature_value',\n",
       "       '52774_5_temperature_value', '45C72_3_temperature_value',\n",
       "       '5130A_5_temperature_value', '51B0E_5_temperature_value',\n",
       "       '51166_5_temperature_value', '5257C_5_temperature_value',\n",
       "       '45534_3_temperature_value', '4692E_3_temperature_value',\n",
       "       '51183_5_temperature_value', '522AE_5_temperature_value',\n",
       "       '455DB_3_temperature_value', '4560C_3_temperature_value',\n",
       "       '45731_3_temperature_value', '5245A_5_temperature_value',\n",
       "       '450EC_3_temperature_value', '51B56_5_temperature_value',\n",
       "       '452FC_3_temperature_value', '46CF4_3_temperature_value',\n",
       "       '52443_5_temperature_value', '45735_3_temperature_value',\n",
       "       '510E0_5_temperature_value', '52268_5_temperature_value',\n",
       "       '52439_5_temperature_value', '45590_3_temperature_value',\n",
       "       '45140_3_temperature_value', '455D9_3_temperature_value',\n",
       "       '456C6_3_temperature_value', '4674C_3_temperature_value',\n",
       "       '45A95_4_carbonDioxide_value', '4674C_4_carbonDioxide_value',\n",
       "       '476CA_4_carbonDioxide_value', '46CF4_4_carbonDioxide_value',\n",
       "       '45974_4_carbonDioxide_value', '4692E_4_carbonDioxide_value',\n",
       "       '47406_4_carbonDioxide_value', '47888_4_carbonDioxide_value',\n",
       "       '46C8A_4_carbonDioxide_value', '45C72_4_carbonDioxide_value',\n",
       "       '46C8A_3_humidity_value', '47406_3_humidity_value',\n",
       "       '45152_5_illuminance_value', '45A95_3_humidity_value',\n",
       "       '451E8_3_humidity_value', '45A94_6_pressure_value',\n",
       "       '45534_3_humidity_value', '450EC_5_illuminance_value',\n",
       "       '4521C_3_humidity_value', '45735_3_humidity_value',\n",
       "       '4560C_3_humidity_value', '454CA_5_illuminance_value',\n",
       "       '451B7_5_illuminance_value', '454CA_3_humidity_value',\n",
       "       '451E8_5_illuminance_value', '45140_3_humidity_value',\n",
       "       '46CF4_3_humidity_value', '45974_6_pressure_value',\n",
       "       '452B5_3_humidity_value', '47888_3_humidity_value',\n",
       "       '4674C_6_pressure_value', '455DB_3_humidity_value',\n",
       "       '47888_6_pressure_value', '45731_5_illuminance_value',\n",
       "       '4562F_3_humidity_value', '476CA_6_pressure_value',\n",
       "       '45731_3_humidity_value', '4674C_3_humidity_value',\n",
       "       '45590_5_illuminance_value', '450EC_3_humidity_value',\n",
       "       '476CA_3_humidity_value', '45A94_3_humidity_value',\n",
       "       '45A95_6_pressure_value', '45590_3_humidity_value',\n",
       "       '47406_6_pressure_value', '46CF4_6_pressure_value',\n",
       "       '4548D_3_humidity_value', '45974_3_humidity_value',\n",
       "       '456C6_3_humidity_value', '455D9_3_humidity_value',\n",
       "       '46C8A_6_pressure_value', '4692E_3_humidity_value',\n",
       "       '45152_3_humidity_value', '45C72_3_humidity_value',\n",
       "       '4548D_5_illuminance_value', '4562F_5_illuminance_value',\n",
       "       '455DB_5_illuminance_value', '451B7_3_humidity_value',\n",
       "       '45C72_6_pressure_value', '45534_5_illuminance_value',\n",
       "       '45140_5_illuminance_value', '452F9_5_illuminance_value',\n",
       "       '4521C_5_illuminance_value', '452FC_5_illuminance_value',\n",
       "       '452FC_3_humidity_value', '452B5_5_illuminance_value',\n",
       "       '452F9_3_humidity_value', '4692E_6_pressure_value',\n",
       "       '456C6_5_illuminance_value', '455D9_5_illuminance_value',\n",
       "       '45735_5_illuminance_value', 'Wuerm_Occupancy',\n",
       "       'Mangfall_Occupancy', 'Think_Box_Occupancy', 'Loisach_Occupancy',\n",
       "       'Port_Of_Rotterdam_Occupancy', 'Amper_Occupancy',\n",
       "       'Shinano_Occupancy', 'Isar_Occupancy', '4560C_5_illuminance_value',\n",
       "       'meetingroom_door_contact_value', 'office_door_contact_value',\n",
       "       'DEV_WASP_AGR_MESHLIUM_DEV_value', 'fridge_door_contact_value',\n",
       "       'smarthome_door_contact_value', 'liquidSensor_liquidDetected_value',\n",
       "       '46CE5_3_humidity_value', '46CE5_5_illuminance_value',\n",
       "       '46CE5_3_temperature_value', 'South_Workarea_3_Air',\n",
       "       'Yangtze_Occupancy', 'Floor23_SmartMeter_Value',\n",
       "       'Floor23_SmartMeter_Power', 'EnOceanAgri_relativeHumidity_value',\n",
       "       'EnOceanAgri_luminanceIntensity_value', 'Floor23_Curtain_Move',\n",
       "       'Floor23_Window_Handle_Status', 'Floor23_Curtain',\n",
       "       'ESP8266_600194107B29_IRRIGATOR_WATERLEVEL',\n",
       "       'ESP8266_600194107B29_IRRIGATOR_SOIL_MOIS', 'Server_Floor23_Ping',\n",
       "       'Server_Floor23_KEVAG_Upload', 'Server_Floor23_KEVAG_Download',\n",
       "       'Server_Floor23_KEVAG_Ping', 'Server_Floor23_NetDSign_Upload',\n",
       "       'Server_Floor23_NetDSign_Download', 'Server_Floor23_NetDSign_Ping'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.itemname.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format Yanzi Sensor Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['id'] = df['itemname'].str.split('_').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['temperature'] = df[df['itemname'].str.contains('temperature')]['value'].astype(float)\n",
    "df['carbonDioxide'] = df[df['itemname'].str.contains('carbonDioxide')]['value'].astype(float)\n",
    "df['humidity'] = df[df['itemname'].str.contains('humidity')]['value'].astype(float)\n",
    "df['illuminance'] = df[df['itemname'].str.contains('illuminance')]['value'].astype(float)\n",
    "df['pressure'] = df[df['itemname'].str.contains('pressure')]['value'].astype(float)\n",
    "df['Occupancy'] = df[df['itemname'].str.contains('Occupancy')]['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>sensorname</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "      <th>temperature</th>\n",
       "      <th>carbonDioxide</th>\n",
       "      <th>humidity</th>\n",
       "      <th>illuminance</th>\n",
       "      <th>pressure</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52760_5_temperature_value</td>\n",
       "      <td>item0001</td>\n",
       "      <td>2018-02-19T07:05:00.165Z</td>\n",
       "      <td>20.5</td>\n",
       "      <td>52760</td>\n",
       "      <td>20.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47406_3_temperature_value</td>\n",
       "      <td>item0002</td>\n",
       "      <td>2018-02-19T07:05:00.240Z</td>\n",
       "      <td>20.61</td>\n",
       "      <td>47406</td>\n",
       "      <td>20.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452B5_3_temperature_value</td>\n",
       "      <td>item0003</td>\n",
       "      <td>2018-02-19T07:05:00.316Z</td>\n",
       "      <td>19.89</td>\n",
       "      <td>452B5</td>\n",
       "      <td>19.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452F9_3_temperature_value</td>\n",
       "      <td>item0004</td>\n",
       "      <td>2018-02-19T07:05:00.388Z</td>\n",
       "      <td>19.7</td>\n",
       "      <td>452F9</td>\n",
       "      <td>19.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4521C_3_temperature_value</td>\n",
       "      <td>item0005</td>\n",
       "      <td>2018-02-19T07:05:00.460Z</td>\n",
       "      <td>18.89</td>\n",
       "      <td>4521C</td>\n",
       "      <td>18.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    itemname sensorname                      time  value  \\\n",
       "0  52760_5_temperature_value   item0001  2018-02-19T07:05:00.165Z   20.5   \n",
       "1  47406_3_temperature_value   item0002  2018-02-19T07:05:00.240Z  20.61   \n",
       "2  452B5_3_temperature_value   item0003  2018-02-19T07:05:00.316Z  19.89   \n",
       "3  452F9_3_temperature_value   item0004  2018-02-19T07:05:00.388Z   19.7   \n",
       "4  4521C_3_temperature_value   item0005  2018-02-19T07:05:00.460Z  18.89   \n",
       "\n",
       "      id  temperature  carbonDioxide  humidity  illuminance  pressure  \\\n",
       "0  52760        20.50            NaN       NaN          NaN       NaN   \n",
       "1  47406        20.61            NaN       NaN          NaN       NaN   \n",
       "2  452B5        19.89            NaN       NaN          NaN       NaN   \n",
       "3  452F9        19.70            NaN       NaN          NaN       NaN   \n",
       "4  4521C        18.89            NaN       NaN          NaN       NaN   \n",
       "\n",
       "  Occupancy  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>sensorname</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "      <th>temperature</th>\n",
       "      <th>carbonDioxide</th>\n",
       "      <th>humidity</th>\n",
       "      <th>illuminance</th>\n",
       "      <th>pressure</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>760519</th>\n",
       "      <td>47406_4_carbonDioxide_value</td>\n",
       "      <td>item0126</td>\n",
       "      <td>2018-04-26T14:00:03.341Z</td>\n",
       "      <td>641</td>\n",
       "      <td>47406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>641.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760520</th>\n",
       "      <td>47888_4_carbonDioxide_value</td>\n",
       "      <td>item0083</td>\n",
       "      <td>2018-04-26T14:00:03.362Z</td>\n",
       "      <td>896</td>\n",
       "      <td>47888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760521</th>\n",
       "      <td>45C72_4_carbonDioxide_value</td>\n",
       "      <td>item0109</td>\n",
       "      <td>2018-04-26T14:00:03.383Z</td>\n",
       "      <td>730</td>\n",
       "      <td>45C72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760522</th>\n",
       "      <td>46C8A_4_carbonDioxide_value</td>\n",
       "      <td>item0072</td>\n",
       "      <td>2018-04-26T14:00:03.403Z</td>\n",
       "      <td>565</td>\n",
       "      <td>46C8A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>565.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760523</th>\n",
       "      <td>Loisach_Occupancy</td>\n",
       "      <td>item0146</td>\n",
       "      <td>2018-04-26T14:00:52.918Z</td>\n",
       "      <td>occupied</td>\n",
       "      <td>Loisach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>occupied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           itemname sensorname                      time  \\\n",
       "760519  47406_4_carbonDioxide_value   item0126  2018-04-26T14:00:03.341Z   \n",
       "760520  47888_4_carbonDioxide_value   item0083  2018-04-26T14:00:03.362Z   \n",
       "760521  45C72_4_carbonDioxide_value   item0109  2018-04-26T14:00:03.383Z   \n",
       "760522  46C8A_4_carbonDioxide_value   item0072  2018-04-26T14:00:03.403Z   \n",
       "760523            Loisach_Occupancy   item0146  2018-04-26T14:00:52.918Z   \n",
       "\n",
       "           value       id  temperature  carbonDioxide  humidity  illuminance  \\\n",
       "760519       641    47406          NaN          641.0       NaN          NaN   \n",
       "760520       896    47888          NaN          896.0       NaN          NaN   \n",
       "760521       730    45C72          NaN          730.0       NaN          NaN   \n",
       "760522       565    46C8A          NaN          565.0       NaN          NaN   \n",
       "760523  occupied  Loisach          NaN            NaN       NaN          NaN   \n",
       "\n",
       "        pressure Occupancy  \n",
       "760519       NaN       NaN  \n",
       "760520       NaN       NaN  \n",
       "760521       NaN       NaN  \n",
       "760522       NaN       NaN  \n",
       "760523       NaN  occupied  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>carbonDioxide</th>\n",
       "      <th>humidity</th>\n",
       "      <th>illuminance</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>342704.000000</td>\n",
       "      <td>50509.000000</td>\n",
       "      <td>160660.000000</td>\n",
       "      <td>102881.000000</td>\n",
       "      <td>58900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.124608</td>\n",
       "      <td>531.344414</td>\n",
       "      <td>16.940346</td>\n",
       "      <td>162.919545</td>\n",
       "      <td>943.730369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.815222</td>\n",
       "      <td>168.712879</td>\n",
       "      <td>5.128360</td>\n",
       "      <td>449.858628</td>\n",
       "      <td>8.895958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.930000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>922.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>936.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>14.720000</td>\n",
       "      <td>944.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.230000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>162.380000</td>\n",
       "      <td>950.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.780000</td>\n",
       "      <td>2215.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>15857.580000</td>\n",
       "      <td>967.260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temperature  carbonDioxide       humidity    illuminance  \\\n",
       "count  342704.000000   50509.000000  160660.000000  102881.000000   \n",
       "mean       22.124608     531.344414      16.940346     162.919545   \n",
       "std         1.815222     168.712879       5.128360     449.858628   \n",
       "min        12.930000     395.000000       4.000000       0.000000   \n",
       "25%        21.000000     412.000000      14.000000       0.460000   \n",
       "50%        22.000000     472.000000      17.000000      14.720000   \n",
       "75%        23.230000     579.000000      20.000000     162.380000   \n",
       "max        32.780000    2215.000000      34.000000   15857.580000   \n",
       "\n",
       "           pressure  \n",
       "count  58900.000000  \n",
       "mean     943.730369  \n",
       "std        8.895958  \n",
       "min      922.680000  \n",
       "25%      936.760000  \n",
       "50%      944.010000  \n",
       "75%      950.040000  \n",
       "max      967.260000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Data with Pixidust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixiedust database opened successfully\n",
      "Table VERSION_TRACKER created successfully\n",
      "Table METRICS_TRACKER created successfully\n",
      "\n",
      "Share anonymous install statistics? (opt-out instructions)\n",
      "\n",
      "PixieDust will record metadata on its environment the next time the package is installed or updated. The data is anonymized and aggregated to help plan for future releases, and records only the following values:\n",
      "\n",
      "{\n",
      "   \"data_sent\": currentDate,\n",
      "   \"runtime\": \"python\",\n",
      "   \"application_version\": currentPixiedustVersion,\n",
      "   \"space_id\": nonIdentifyingUniqueId,\n",
      "   \"config\": {\n",
      "       \"repository_id\": \"https://github.com/ibm-watson-data-lab/pixiedust\",\n",
      "       \"target_runtimes\": [\"Data Science Experience\"],\n",
      "       \"event_id\": \"web\",\n",
      "       \"event_organizer\": \"dev-journeys\"\n",
      "   }\n",
      "}\n",
      "You can opt out by calling pixiedust.optOut() in a new cell.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <div style=\"margin:10px\">\n",
       "            <a href=\"https://github.com/ibm-watson-data-lab/pixiedust\" target=\"_new\">\n",
       "                <img src=\"https://github.com/ibm-watson-data-lab/pixiedust/raw/master/docs/_static/pd_icon32.png\" style=\"float:left;margin-right:10px\"/>\n",
       "            </a>\n",
       "            <span>Pixiedust version 1.1.14</span>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixiedust runtime updated. Please restart kernel\n",
      "Table USER_PREFERENCES created successfully\n",
      "Table service_connections created successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>Warning: You are not running the latest version of PixieDust. Current is 1.1.14, Latest is 1.1.15</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div>Please copy and run the following command in a new cell to upgrade: <span style=\"background-color:#ececec;font-family:monospace;padding:0 5px\">!pip install --user --upgrade pixiedust</span></div>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>Please restart kernel after upgrading.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pixiedust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pixiedust": {
     "displayParams": {
      "filter": "{\"regex\": \"false\", \"constraint\": \"None\", \"field\": \"id\", \"case_matter\": \"false\", \"value\": \"4674C\"}",
      "handlerId": "lineChart",
      "keyFields": "time",
      "legend": "true",
      "lineChartType": "subplots",
      "no_margin": "true",
      "rendererId": "matplotlib",
      "timeseries": "true",
      "title": "Shinano Meeting Room",
      "valueFields": "temperature"
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "pixieapp_metadata": null
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter on Shinano and Fill Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano = df[df['id'].isin(['4674C', 'Shinano'])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano.sort_values(by='time', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano.index = pd.to_datetime(df_shinano.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano.fillna(method='bfill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemname</th>\n",
       "      <th>sensorname</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "      <th>temperature</th>\n",
       "      <th>carbonDioxide</th>\n",
       "      <th>humidity</th>\n",
       "      <th>illuminance</th>\n",
       "      <th>pressure</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-19 07:05:05.008</th>\n",
       "      <td>4674C_3_temperature_value</td>\n",
       "      <td>item0066</td>\n",
       "      <td>2018-02-19T07:05:05.008Z</td>\n",
       "      <td>18.5</td>\n",
       "      <td>4674C</td>\n",
       "      <td>18.50</td>\n",
       "      <td>455.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.51</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-19 07:10:00.118</th>\n",
       "      <td>4674C_4_carbonDioxide_value</td>\n",
       "      <td>item0095</td>\n",
       "      <td>2018-02-19T07:10:00.118Z</td>\n",
       "      <td>455</td>\n",
       "      <td>4674C</td>\n",
       "      <td>18.50</td>\n",
       "      <td>455.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.51</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-19 07:15:01.061</th>\n",
       "      <td>4674C_6_pressure_value</td>\n",
       "      <td>item0096</td>\n",
       "      <td>2018-02-19T07:15:01.061Z</td>\n",
       "      <td>950.51</td>\n",
       "      <td>4674C</td>\n",
       "      <td>18.50</td>\n",
       "      <td>455.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.51</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-19 07:15:01.460</th>\n",
       "      <td>4674C_3_humidity_value</td>\n",
       "      <td>item0094</td>\n",
       "      <td>2018-02-19T07:15:01.460Z</td>\n",
       "      <td>16</td>\n",
       "      <td>4674C</td>\n",
       "      <td>18.50</td>\n",
       "      <td>455.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.51</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-19 07:15:01.574</th>\n",
       "      <td>4674C_3_temperature_value</td>\n",
       "      <td>item0066</td>\n",
       "      <td>2018-02-19T07:15:01.574Z</td>\n",
       "      <td>18.58</td>\n",
       "      <td>4674C</td>\n",
       "      <td>18.58</td>\n",
       "      <td>455.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.51</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            itemname sensorname  \\\n",
       "time                                                              \n",
       "2018-02-19 07:05:05.008    4674C_3_temperature_value   item0066   \n",
       "2018-02-19 07:10:00.118  4674C_4_carbonDioxide_value   item0095   \n",
       "2018-02-19 07:15:01.061       4674C_6_pressure_value   item0096   \n",
       "2018-02-19 07:15:01.460       4674C_3_humidity_value   item0094   \n",
       "2018-02-19 07:15:01.574    4674C_3_temperature_value   item0066   \n",
       "\n",
       "                                             time   value     id  temperature  \\\n",
       "time                                                                            \n",
       "2018-02-19 07:05:05.008  2018-02-19T07:05:05.008Z    18.5  4674C        18.50   \n",
       "2018-02-19 07:10:00.118  2018-02-19T07:10:00.118Z     455  4674C        18.50   \n",
       "2018-02-19 07:15:01.061  2018-02-19T07:15:01.061Z  950.51  4674C        18.50   \n",
       "2018-02-19 07:15:01.460  2018-02-19T07:15:01.460Z      16  4674C        18.50   \n",
       "2018-02-19 07:15:01.574  2018-02-19T07:15:01.574Z   18.58  4674C        18.58   \n",
       "\n",
       "                         carbonDioxide  humidity  illuminance  pressure  \\\n",
       "time                                                                      \n",
       "2018-02-19 07:05:05.008          455.0      16.0          NaN    950.51   \n",
       "2018-02-19 07:10:00.118          455.0      16.0          NaN    950.51   \n",
       "2018-02-19 07:15:01.061          455.0      16.0          NaN    950.51   \n",
       "2018-02-19 07:15:01.460          455.0      16.0          NaN    950.51   \n",
       "2018-02-19 07:15:01.574          455.0      16.0          NaN    950.51   \n",
       "\n",
       "                        Occupancy  \n",
       "time                               \n",
       "2018-02-19 07:05:05.008      free  \n",
       "2018-02-19 07:10:00.118      free  \n",
       "2018-02-19 07:15:01.061      free  \n",
       "2018-02-19 07:15:01.460      free  \n",
       "2018-02-19 07:15:01.574      free  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shinano.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano.drop(['illuminance'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>carbonDioxide</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22744.000000</td>\n",
       "      <td>22744.000000</td>\n",
       "      <td>22744.000000</td>\n",
       "      <td>22744.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.716864</td>\n",
       "      <td>617.600114</td>\n",
       "      <td>17.815732</td>\n",
       "      <td>941.909597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.472216</td>\n",
       "      <td>232.130746</td>\n",
       "      <td>3.968186</td>\n",
       "      <td>8.905109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.210000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>922.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.280000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>934.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.720000</td>\n",
       "      <td>558.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>942.615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.810000</td>\n",
       "      <td>706.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>948.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.780000</td>\n",
       "      <td>2215.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>962.590000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        temperature  carbonDioxide      humidity      pressure\n",
       "count  22744.000000   22744.000000  22744.000000  22744.000000\n",
       "mean      21.716864     617.600114     17.815732    941.909597\n",
       "std        2.472216     232.130746      3.968186      8.905109\n",
       "min       15.210000     398.000000      7.000000    922.980000\n",
       "25%       20.280000     431.000000     15.000000    934.630000\n",
       "50%       21.720000     558.000000     18.000000    942.615000\n",
       "75%       22.810000     706.000000     20.000000    948.130000\n",
       "max       32.780000    2215.000000     31.000000    962.590000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shinano.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['free', 'occupied'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shinano.Occupancy.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Occupancy\n",
       "free        16948\n",
       "occupied     5796\n",
       "Name: time, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shinano.groupby('Occupancy')['time'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Sensor Values depending on Occupancy State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b7ecaca1cf8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7ecac90908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_shinano.temperature.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Simple Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano_model = df_shinano.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shinano_model.loc[df_shinano_model.Occupancy == 'free', 'Occupancy'] = 0\n",
    "df_shinano_model.loc[df_shinano_model.Occupancy == 'occupied', 'Occupancy'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_shinano_model['Occupancy'].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  18.5 ,  455.  ,   16.  ,  950.51],\n",
       "       [  18.5 ,  455.  ,   16.  ,  950.51],\n",
       "       [  18.5 ,  455.  ,   16.  ,  950.51],\n",
       "       ..., \n",
       "       [  22.22,  815.  ,   21.  ,  949.91],\n",
       "       [  22.39,  815.  ,   21.  ,  949.91],\n",
       "       [  22.39,  812.  ,   21.  ,  949.91]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_shinano_model.loc[:, ['temperature', 'carbonDioxide', 'humidity', 'pressure']].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92701692679709824"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.95      0.95      3377\n",
      "          1       0.86      0.86      0.86      1172\n",
      "\n",
      "avg / total       0.93      0.93      0.93      4549\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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9xb5pk/OwDgScpbGxObtJpHm8hfuQ7ej4ivx8GDvWWRLZvbt1fGTdOnj6aUdkXNwxJMksEncMSWLc5s9/hrlzYfNmji8uhhtvhHPPbeneckXW73fuj2uVxIuJ6/YyDKNHkDaxUNWQiMwEXsBJnX1IVVeIyBxgiao+B1QAd4iI4lgN10RPfwo4FViO47r6p6r+NV1tTQvuA1MEcnOdJZ5wuNkica2SQMD5DIWaRUS1+eHaUaukb1845hhniUcVdu5sKSTu53/+03IMSUaGM4Yk3hrZuBEefNARPCBr2zZntHtGRusMskikefxITU1Ly8S9P24KsCsmySwUwzC6BWkdZ6GqzwPPJ+ybHbf+FI4wJJ4XBr6VzrZ1Oe7DsL25p1wxcSczDAScMSFuoNoVjY5aJSLQv7+zTJrU+pqVla2tkWRjSOJpaHDm01q82BEpdyksbLldUNA6dhLvvnOTChLb6/M1C4qbCuyKpwmKYRwwbChwd6S91FhoHXR33VuBgLMd795KDLq3ZZWIOKPOBw1qPYYkEoHNm+G445Kf29AAf/ub4/5KtCDiyclpKSCJolJQ0Pp4nz7OuY2Ney8o8S4vwzD2CxOLnkh7v6hd948rJq5ry10ikWYxSQy6t/VQ9Xic8SNDhjgxmESGDIFFi5qD+bt3Ny+7drXcjl/WrGleDwTa7m9GRmsRSRSUxKVvX2dciiuY8fGT+DEmJiiG0SFMLHobrgAks0rcoHtirMRdEkeDJ8ZKZs1yXE7xsY3sbGe/e233YT18eMfbrOrU2ZaoJArPxo3wwQfOeuKswYntLyhotlj69GneTlwKCx33XHGxs+TkOOLX1LR/GV7z5zuzG69f79yT225rnjnAMHoQJhYHE/GpwMnGXiSmAsdbJY2NcOaZzvrdd8OWLTQWF5N1441w1lnNAhI/aDF+cF+y9fjtnBxnKSnZuz41NTmzBKeyZtwyn37avN0e+fkcl5vrzOPlusgKC52lXz9nKS52Pt04UHGxMzreJfF9KZ995myDCUZbmLh2W0wsjGbamhkXmq2Sa66Bq6+GUIi3liyhYsKEliPeI5HmuIW77h6Lf9tgfLm2rtfWL/nEY67QDBnibCeOQE8mVJGIM7dWdXVLsYlbr/70U7JVne0tW5rLtNfu7OzmWMwnn7RODKivh//6L8cyci22+NmEU227ll6q8vH72ygj7nigVGnMByrF+fHH4dvfNnHtpphYGB0j2QBFr7flxIf7SvxUK/Hbe3usPaFKPAaOtdCnjxOPiT8WFaMP161jUGlpS3EKh52HmSsse/a0FJnqaseyqa6GlfHToMWxZw/ceWf7onMAmBK/kZhRFy868ccSU5vjBay9fR2pY/781m7F+nq4/noYNw6KiprjTq6L1G2ru25jd9KGiYXR9XRwLqoDhis+mzfDoYe2L1Tx64nbo0fDhg2t6x861Ek1drPZgsHmFOlgsGXKtHss3kUYv6Q6lrjPndIlHGZtZSWH9O3bnBQRX1+qbTfmlXg9N2st3p3Z0XMTY2Yu27Y5YuHzOYJRVOS4/9zP+PUBAxx34KBBTp27drUUwHiB6U5/cz0AEwvDSCT+IbI/4zjuuKP1+1JycpwR8IMGdbyeVKLUUQFLsK7WL1vGIePGtTyWaIm15zZMdAem2k4kMXZ18smOQCdSVATXXQc7djiDSrdvd9bff9/5rKtLWv1JWVlOLKktYXHjTIMGwcCBLWdjTma1HOSYWBhGunD97PsbsN3LmY47jM/n/BLfWzoqWqmEC5pFKBKB73/fcTklZtvdfDOcd15y8fF4HGvGTWyoqnKWHTvY/NFHDHNnLdi2DVatcsQl/t0yLiJOrCmZqMQnNLjCUljoJInEDxB1RaWXusNMLAwjnUyf3vuCs+lyG159tTPnWaK4XnZZc4JEvLhEIs2uusJCZ54z16UFfLJuHcNKS1u3vbbWERRXXHbudETEXXbuhI8/dj53707eVr+/tajEfw4Y4CwDBzoCk5fX491hJhaGYXQf2hJXd3BlR3Atl02bnPnNXGFxYyV9+jgP8vi4Slsus3C4tdWyc2dLgXHnW9uxIzZvWivy8jrmDhs0yFl3B4+mcocdwFRjEwvDMHoX7iSVIu2/y8UlPkbjLvGB96KiZqFpKxDvik1jY7PVsnNncnHZuBHefdc5liwjzuNpO5AfdYf1aWyEhQvhpz9tdtulOdXYxMIwjIOb+FcEdIRU4jJwYHM2m5sBlnh+/FifeJdYvFvM/VyxwlmPe73AhLbaVl/vWBomFoZhGF3M/opLXPoyxcWOqyw+bbotcWlqclxiO3fy/gcfMO77309+vfXr969/bWBiYRiGkU72R1zig/rhsDMdTjBIVW6us54s1Xhv5mXbCyx52DAMozvhiovf74z9yMlxAuQFBU7MYtAg59iddzrH4snJcYLcacDEwjAMoycyfTrMm+e4sUScz3nzLBvKMAzDSOAAjuMxy8IwDMNIiYmFYRiGkRITC8MwDCMlJhaGYRhGSkwsDMMwjJSYWBiGYRgpSatYiMhUEflIRNaIyKwkx0eIyCsi8r6ILBCRoXHHhovIiyKySkRWisjIdLbVMAzDaJu0iYWIeIH7gbOAMuDLIlKWUOwu4FFVHQfMAe6IO/Yo8DNVHQUcC2xLV1sNwzCM9kmnZXEssEZV16pqAHgCOD+hTBnwSnT9Vfd4VFR8qvoSgKrWqmrCm9wNwzCMA4Wo+6rDzq5Y5GJgqqpeFd2+HDhOVWfGlfkj8Laq3isi04Cngf7AScBVQAAoBV4GZqlqOOEaM4AZAAMHDpz4xBNPpKUv+0NtbS15eXld3Yy00Jv7Bta/nk5v7l9n9u2UU05Zqqrlqcqlc7qPZO8KTFSm7wH3iciVwGvAJiAUbddJwDHAeuBPwJXA71pUpjoPmAdQXl6uFRUVndb4zmLBggV0x3Z1Br25b2D96+n05v51Rd/S6YbaCAyL2x4KtJhPV1U3q+o0VT0GuCW6rzp67rKoCysEPEs77/swDMMw0ks6xWIxcLiIlIpIBnAp8Fx8ARHpLyJuG24CHoo7t1BEiqPbpwIr09hWwzAMox3SJhZRi2Am8AKwCnhSVVeIyBwR+UK0WAXwkYh8DAwEboueG8ZxUb0iIstxXFoPpquthmEYRvukdYpyVX0eeD5h3+y49aeAp9o49yVgXDrbZxiGYXQMG8FtGIZhpMTEwjAMw0iJiYVhGIaREhMLwzAMIyUmFoZhGEZKTCwMwzCMlJhYGIZhGCkxsTAMwzBSYmJhGIZhpMTEwjAMw0iJiYVhGIaREhMLwzAMIyUmFoZhGEZKTCwMwzCMlJhYGIZhGCkxsTAMwzBSYmJhGIZhpMTEwjAMw0iJiYVhGIaREhMLwzAMIyUmFoZhGEZKTCwMwzCMlJhYGIZhGCkxsTAMwzBSYmJhGIZhpCStYiEiU0XkIxFZIyKzkhwfISKviMj7IrJARIYmHO8jIptE5L50ttMwDMNon7SJhYh4gfuBs4Ay4MsiUpZQ7C7gUVUdB8wB7kg4/mNgYbraaBiGYXSMdFoWxwJrVHWtqgaAJ4DzE8qUAa9E11+NPy4iE4GBwItpbKNhGIbRAXxprHsIsCFueyNwXEKZ94CLgHuBC4F8EekH7AJ+DlwOnNbWBURkBjADYODAgSxYsKCz2t5p1NbWdst2dQa9uW9g/evp9Ob+dUXf0ikWkmSfJmx/D7hPRK4EXgM2ASHgv4DnVXWDSLJqopWpzgPmAZSXl2tFRcX+t7qTWbBgAd2xXZ1Bb+4bWP96Or25f13Rt3SKxUZgWNz2UGBzfAFV3QxMAxCRPOAiVa0WkROAk0Tkv4A8IENEalW1VZDcMAzDSD/pFIvFwOEiUopjMVwKXBZfQET6A1WqGgFuAh4CUNXpcWWuBMpNKAzDMLqOtAW4VTUEzAReAFYBT6rqChGZIyJfiBarAD4SkY9xgtm3pas9hmEYxr6TTssCVX0eeD5h3+y49aeAp1LU8QjwSBqaZxiGYXQQG8FtGIZhpMTEwjAMw0iJiYVhGIaREhMLwzAMIyUmFoZhGEZKOiwWIjJZRL4WXS+Ojp8wDMMwDgI6JBYi8gPgRpyBcwB+4A/papRhGIbRveioZXEh8AWgDmLTdOSnq1GGYRhG96KjYhFQVSU6EaCI5KavSYZhGEZ3o6Ni8aSI/AboKyLfBF4GHkxfswzDMIzuRIem+1DVu0TkDGAPcCQwW1VfSmvLDMMwjG5DSrGIvh71BVU9HTCBMAzDOAhJ6YZS1TBQLyIFB6A9hmEYRjeko7PONgLLReQlohlRAKr6nbS0yjAMw+hWdFQs/h5dDMMwjIOQjga4fy8iGcAR0V0fqWowfc0yDMMwuhMdEgsRqQB+D3wKCDBMRL6qqq+lr2mGYRhGd6Gj4yx+DpypqlNU9WTg88Av0tcswzAMIxXzl89n5D0j8fzIw8h7RjJ/+fy0XaujMQu/qn7kbqjqxyLiT1ObDMMwjBTMXz6fGX+dQX2wHoDPqj9jxl9nADB97PROv15HxWKJiPwOeCy6PR1Y2umtMQzD6GGoKorizIhEbL2j+yIaASCikdgSf8xdQpEQ2+q2saV2C+9te49fvPWLmFC41AfrueWVW7pULL4NXAN8Bydm8RrwQKe3xjAMo4OkfBCjNIYaY2Xjy7n73AcxtHxYJ32Ax9URIUIk4pRBAHWuKSLODHpt7FNVBIkdRyAQDrCjbgfb67ezvW472+q3sb1uO9vrt7OtbltsqWqoirW9PdZXr++kO9ySjoqFD7hXVe+G2KjuzLS0yDCMgwpVJaxhQpEQoUiIplATgXCg1cPbFYLYg1rcCkj6cA6EA6zfHX1wxj2c4x/YEq1ERFqsO6dIbL9IwnpC2WQ0BBuorKtkW902ttZujT303X2VtZVU1lWyu3F3q3O94qU4p5gBeQMY0mcIEwZPYGDuQAbkDWBg7kBqP63lJ2t+QmVdZatzhxcM3+vvoCN0VCxeAU4HaqPb2cCLwInpaJRhGL0P15USioQIhoM0hhppDDUSCAdiQgDg9XjxiKfFw9knzqMqfl8qPOIhLzOvU/ugqtQGamMP/GRCUFnrHKsJ1LQ63+/xMyB3AANyB1Dat5Tjhh7HwNyBjhDkDmBgnrNelF2E1+MlohGC4SBhDROOhGMi9enOT7n15Fu54aUbaAg1xOrP8edw22m3dWqfXToqFlmq6goFqlorIjlpaZFhGD2WZFZCY6iRplAToUgoVk5E8Hq8+Dw+cvw5HXr4pxNVZXfj7ha//NuyCBLjBABZ3iwG5jkP/KP6H8WUEVNi2/FCUJhVmLSv8aIQ0QgNQUcAfB4fWf4ssn3ZZHgz8Hl8+L1+Nns3M/PYmRRmF3LLK7ewvno9wwuGc9tpt6UlXgEdF4s6EZmgqu8AiEg50JDiHERkKnAv4AV+q6pzE46PAB4CioEq4CuqulFExgO/AvoAYeA2Vf1TB9tqGEaa2RsrwevxkuHLIEuyuqSduxp2sbVuK9tqk7uBXDFoCje1Oj/Xnxv7tX/0wKNjD/9EIeiT2adDghfRCIFQICYKbgiiLVHwSPujG6aPnZ42cUiko2JxHfB/IrIZp3slwCXtnRCNa9wPnAFsBBaLyHOqujKu2F3Ao9ER4qcCdwCXA/XAFaq6WkRKgKUi8oKqtnbuGYaRFrrCSvjzqj8z9425bK7ZTEl+CbMmz2LaqGmtyoUjYXbU73B+/bchBBt3bWT3v3e3aKtLQWZB7Nf+pCGTWrmBXCHIzdi397wlWgpunGRfRaE70K5YiMgkYIOqLhaRo4BvAdOAfwLrUtR9LLBGVddG63oCOB+IF4sy4LvR9VeBZ8EZx+EWUNXNIrINx/owsTCMTqa7WAl/XvXnFj74TTWbuP6F6/n7x3+nKLuoWQjqKtlRvyOWsRRPUXZRcwygcABHjDiilRAU5xST7c/ulDa3JwqZvkxy/Dk9ThTaQuL/GFodFHkHOF1Vq0TkZOAJ4FpgPDBKVS9u59yLgamqelV0+3LgOFWdGVfmj8DbqnqviEwDngb6q+rOuDLH4kw1Mlq15V+HiMwAZgAMHDhw4hNPPLF3vT8A1NbWkpfXuUG27kJv7hv0zv7F0kdVqa+rJzMns4UgJGbNNGhpAAAgAElEQVQIpZNgJMiGhg2srVvL2tq1PLf5OQIaSFq2yF9EUYaz9Mvs13o7o4hCfyF+T/NY4ca6RrJyO0/U4tNu3Q83S8ojnlaZUumkM/82TznllKWqWp6qXCo3lFdVq6LrlwDzVPVp4GkReTfFucnuWKIyfQ+4T0SuxBm7sQmI2YwiMhhnIOBXE4UCQFXnAfMAysvLtaKiIkWTDjwLFiygO7arM+jNfYOe2b+9sRI+WfYJZZPKDsiv3e1121m5fSWrdqxixfYVrNq+ijVVawhGnPlIM72ZbQqFICy/dvleX3PF4hWMnjR6r85JZSlk+7PJ9Gbi8/jweXx4Pd69bldn0BV/mynFQkR8qhoCTiP6K76D524EhsVtDwU2xxdQ1c04bi1EJA+4SFWro9t9cKZFv1VV30rVEcM4WOisWIL7i7gzCYQDrK5a7QjD9lUxgdhRvyNWZlDeIMqKyzit9DTKissYVTyKQwoP4cTfncimmk2t6izJL+nUNkJLUe3OotCdSPXAfxxYKCI7cLKfXgcQkcOA6hTnLgYOF5FSHIvhUuCy+AIi0h+oiloNN+FkRhGdDv0ZnOD3/+1Vjwyjl9BdYgnJUFW2129vJQqrq1bHBCvTm8mR/Y/k9NLTGVU8irLiMo7qfxRF2UVJ65w1eVarcQPZvmxmTZ61z+1MFAWn8cREoU9mHxOFDtKuWKjqbSLyCjAYeFGb/0I9OLGL9s4NichM4AWc1NmHVHWFiMwBlqjqc0AFcIeIKI4b6pro6V8CTgb6RV1UAFeqairXl2H0KHrCuISmUFOztbBjVUwgdjbEQosMzhvsWAuHONZCWf8ySgtL8Xk6mnBJLOupI9lQiSQThUjESVM1UegcUn6TyVxA8dlKKc59Hng+Yd/suPWngKeSnPcH4A8duYZh9AS6s5XgoqpU1lW2sBRWbl/Jmqo1hDUMOIPPjux/JGceeiaj+jdbC4XZhZ3ShmmjprUrDslEQVWdlFRfFvkZ+WT6MvF7/Gz2beaQokM6pV1Gx8dZGIaxF0Q0QlOoiYZQAzVNNTSFmgd8dYfRy42hRlbXruaDFR+0EIeqhqpYmSH5QxhVPIozDz3TsRaKyyjtW3pAfpXvjSiYpXBgMLEwjE7AFYfGUCM1gRoag40oitfjxe/xd/ocRR1FVdlau7WF+2jljpV8UvVJs7Xgy+Kofkcx9dCpLWILfbP6HrA2BiNBguFgLLXX6/GS7cs2UehGmFgYxj4Q0QiBcICGYAO1gVonKKuO1ZDhzegScWgMNfLxzo9ZtT2anrpjFau2r2JX465YmaF9hjKq/yjOOuws8qvzOeP4Mw6YtQCOMIQiIYKRIJFIJCao2b5sCjILyPJl4ff69yrWYRwY7BsxjA7gikNTqImaphrqQ/WoKh7x4Pf6ycs4cOKgqmyp3dIq4Lx219qYtZDty+ao/kdx9uFnt4gtFGQVxOpZsXgFhxUdlta2ujGasIZjU4Rn+bIozCoky5cVG93c1RMJGqkxsTCMJLijdasbq2Pi4JLhzSDXn3tAHnANwQY+3vlxS2HYsarFOxCG9RnGqOJRnHP4OYwqHsWo4lGMLBh5wF024UiYYCRIKByKvT8iw5tBn8w+ZPuduZD8Hr8JQw/FxMIwcMQhEA7QGGqkNlBLfbCeQCjAtrpt+L3+fRaHjk6Mp6psrt3catzC2l1rYwFe11o45/BzYgHno/ofRZ/MPvvd/73FHekcjARjFoNPfORm5JKdnU2GL4MMb0aPngvJaImJhXFQ4opDU6iJ2mAtdYE6ZxSvSCxLyePx7POso5B8YrwbXrqBYDjIkf2PbCUM1U3N41yHFwxnVP9RnHfEeYzqH7UW+o7skoevG4AORUKxF/B4PB5y/DkU+YvI8DrCYMHn3o2JhXFQ4D7wmkJN1AZqqQvWxd6h7Pf605LCOveNuS1GIwM0hBq4/sXrY9s5/hyO6n8U5x3piMLo4tEc2f/ILrEWXILhYCxt1Z0YL9uXTUF2AZm+zFicwTi4sG/c6LUEwgEC4QC1TbXUBmtjv4p9Xh/ZvuxOF4eIRlhTtYYlm5ewdPPSpPMcuTx43oOUFZcxvGB4l7pqYgHoiBMYR5xpOgoyC8j2Z+P3+i3OYAAmFkYvIhgO0hRuiomD6+t3B3J19kO5LlDHsq3LYuKwdMvSmCupb1ZfsnxZNIYaW503JH8IZx9+dqe2pSO4GV3hiDOjaiQSIRwJk5+ZT44/B7/H3+PfuWCkDxMLo8fiikNdoI66QB3BSDAWc+hscVBVNuzZwJLNS2LLqh2rYoJ0ZL8jOfeIc5k4eCITSyZyaOGhPPPhM50+Md7etDcQDsRGQIsIPvGR7c8m159Lhi+DLb4tlBaWpr0tRu/AxMLoMbgT7dUF66htqnUm2hPwipdMX2anzqXUGGpkxZ4VvL7kdcdy2LKUbXXbAOe9zBMGT+A7x36H8pJyjhl8TNLRzvszMd7eEB+AThzoVphV6IyAtoFuxn5ifz1Gt8UVh/pgPTWBGoJh50U5Po+PDG8GWf7OE4fK2sqYKCzZvITl25YTCDsv4xlRMILJwydTXlJOeUk5R/U7qsOZP6kmxtsX4ge6uQMDs3xZ9Mnu44yAjk6NYXEGozMxsTC6DaFIiEA4QH3AEYdAOIDgTLqX4c0gy9c54hCKhPhwx4ctXEob9mwAnODuuIHj+MYx32BA3QAuPPlCinOLO+W6+0I4Eo7FGdx3T2Z6M22gm3HAMbEwuoxwJExTuCkmDsFIMDazaIY3g0xfZqdcZ1fDLt7Z8g5LtjjC8O7Wd6kPOiOyB+YOpLyknK8f83XKS8oZXTw6dt0Vi1ccUKFIHOimaGwqkRx/jiMMFoA2uggTC+OA4f5Krg/WO9N2h5tir/bsLHGIaIRPqj6JWQxLtyxlddVqwIltjB4wmktHX8rEkomUl5QzJH9Il/wqTzbTqjsYsMhfFJtp1Qa6Gd0FEwsjbSR7p4MbfM3wZpDvy9/va7jpq26s4Z3N77C7yZk3qW9WX8pLypk2ahrlJeWMHzSeHH/Ofl9zb0mcaRUBj3hiM63aQDejJ2B/nUanke53OqgqG/dsbI41bFnCyu0rW6Svnn342ZSXlMfSVw+01RAvDO5AN494yPRl2kyrRo/GxMLYLxpDjTQEG2LiAJ33ToemUBPLty2PDXpbsmVJi/TVYwYfkzJ9NZ0kpqwiIEiLKbhtBLTRWzCxMPaa+mA9VfVVNIWb+Gz3Z83vdNhPcaisrYy5k5ZuWcr7le93SvpqZ9CeMORn5ZswGL0eEwujwwTCAXbU76CmqSY2/XR+5r7FHeLTV12rYX31eqBl+qo7InpA7oDO7Eq7uMIQ0Qg1TTVAc4yhT3YfMr2Z5koyDjpMLIyURDTC7sbd7KjfgVe8+yQQuxt3O+mr0XjDsq3LWqSvTiyZyJXjr6R8cDljBozptLTZVMRnJUU0Ept+O9uXjc/jY1jBMBvkZhiYWBjtoKrUBmrZVreNcCRMbkbHXgAU0Qhrd61tMegtPn21rLiMS0ZfEnMpHaj0VXccQygSQp2BDDFhcLOS4oVhtazukuwpw+iOmFgYSWkMNbKtbhsNwQay/dlk+7OBlm9+K84s5vt53+fzh36ed7e+Gxv09s6Wd2Kv/eyb1ZeJgyce8PTVRGFQbZ4vqW9WX0tXNYy9JK3/KSIyFbgX8AK/VdW5CcdHAA8BxUAV8BVV3Rg99lXg1mjRn6jq79PZVsMhFAmxs34nuxp2kenLbOFySnzz27ambXznH98BnNHGAEf0O4KzD2tOXz2k8JC0jziOF4b4acmz/dmxN7nZRHqGsX+k7b9HRLzA/cAZwEZgsYg8p6or44rdBTyqqr8XkVOBO4DLRaQI+AFQjvOG36XRc3elq70HO6rKnqY9bKvbhiDkZ+a3cg3d8cYdrd78pij5Gfk8cM4DTBg8Ie3pq+47GUKRUPNrUKNTb8dPiWHCYBidSzr/o44F1qjqWgAReQI4H4gXizLgu9H1V4Fno+ufB15S1arouS8BU4HH09jeg5b6YD2VtZUEwgFyM3KTWgKvrnuVzTWbk55fG6jl1NJTO71d4Ug4lq6KAoIJg2F0Een8LxsCbIjb3ggcl1DmPeAiHFfVhUC+iPRr49whiRcQkRnADICBAweyYMGCzmp7p1FbW9st2wWOVRCKhAhHwnjEkzTIvLNpJ79e+2sW7liIV7yENdyqTHFmMSsWr9jfxjixhWjgGZzBfe7cUR7xIBzYbKTu/N11Bta/nktX9C2dYpHsP1sTtr8H3CciVwKvAZuAUAfPRVXnAfMAysvLtaKiYj+amx4WLFhAd2tXOBJmV8MudjbsjPn2k5V59L1H+enbPyUQDvA/J/4PJfkl3PzKza3e/Pb9U7/P6FGj9+r6wUiQUNgJPrvvxc7x5TgWgy+jW0yi1x2/u87E+tdz6Yq+pVMsNgLD4raHAi38GKq6GZgGICJ5wEWqWi0iG4GKhHMXpLGtBwWJqbB5GXlJrYnllcu58eUbea/yPU4ecTK3n3p77PWbPo+vZTbUqd9v9+U+sRf1uO9jUPB7/eT6c8nOzu42wmAYRvukUywWA4eLSCmOxXApcFl8ARHpD1SpagS4CSczCuAF4HYRKYxunxk9buwjbaXCxlMbqOXOf9/Jw+8+TL/sfjxw9gN84cgvtBCU+De/rVi8ooVF0ZYwuO9j8Hv9sZHfhmH0LNImFqoaEpGZOA9+L/CQqq4QkTnAElV9Dsd6uENEFMcNdU303CoR+TGO4ADMcYPdxt7RXiqsi6ry/Ornmb1gNpW1lVx+9OXM+twsCrIK2q03ohFqm2pjabOZXqf+HH8Ofo/fXtRjGL2ItKaRqOrzwPMJ+2bHrT8FPNXGuQ/RbGkYe0lEI+xp2sP2uu2xOZySuZw2VG/gln/dwivrXqGsuIwHz3uQCYMntFlvOBKmPlhPhjcDr3gp6VNiwmAYBwGWc9gLqQ/Ws7V2K8FwsM1U2GA4yLyl87j7rbvxiIfZU2bzjWO+0W4aakOwgYhGGJw3mPzMfD7zfEZexv7NNGsYRs/AxKIXEQgH2F63nZqmGrL92WRlZiUtt3jTYma9PIsPd37I1EOnMueUOQzp0yozOUY4EqYuWEd+Rj4Dcgfg9/rT1QXDMLopJha9gMRU2D5ZfZKW29WwizveuIP5y+dTkl/Cw+c/zJmHntlu3fXBelSVIflD9nk6csMwej4mFj0YNxW2srYSRdtMhVVVnl71NHMWzmF3426unng1159wPbkZuW3WHYqEqA/UU5BVQHFusY2SNoyDHHsC9FAaQ41U1lbSEGwgNyO3zXEKa6rWcPMrN/PvDf9mwuAJPH7644wubn8AXX3Aec/E0D5D9/vtd4Zh9A5MLHoYiamwbbmcGkON3LfoPu5ffD/Zvmzmnj6X6WOnt5uxZNaEYRhtYU+DHkJEI1Q3VjupsB5PmyIB8Npnr3HzKzezbvc6LjzqQn4w5QcU5xa3W399oB4RYVjBsHbdU4ZhHJyYWPQA6oP1bK3ZSkhD5GTktGkdbK/bzpyFc/jzh39mZN+RPH7R45w84uR26w6GgzSEGijMKqR/Tn+bdsMwjKSYWHRjWqXCepOnwkY0wvzl87njded9E989/rvMPHYmWb7k5cEJetcH6/GIh+EFw+31oYZhtIuJRTeko6mwACu3r+TGl2/knS3vcMLQE5h7+lwOKzqs3fpda6Iou4h+2f3MmjAMIyUmFt0IVaWmqYZtddvaTYUFxzX18zd/zoPvPEhBVgH3Tr2Xi0Zd1GZ5t/66QB0+j48RBSOSTiZoGIaRDBOLbkJHU2EBXvzkRW79161sqtnEZWMu4+aTbqYwu7DN8uC4tBqDjfTP6U9RTpHN42QYxl5hYtHFBMNBqhqqUqbCAmyq2cTsf83mn5/8k6P6HcWzlzzLpCGT2q3fjU14PV5G9DVrwjCMfcPEoovYm1TYUCTE75b9jrvevIuIRrh58s3MmDgj5RxNTaEmAuEA/bL7mTVhGMZ+YWLRBdQF6qisrUyZCguwbMsybnz5RlZsX8Gppady26m3MbxgeLv1qyp1wToyPBmM6Dui3awowzCMjmBicQBxU2FrA7Vk+bLaTIUFqG6sZu6/5/LYe48xMHcg886dx9mHn91uABsca6Ip3MSA3AH0zepr1oRhGJ2CicUBIDEVtr3ZW1WV5z56jh8u/CE76nfw9WO+zv+c+D8pZ3yNaIS6QB2Z3kxK+5aS6cvs7G4YhnEQY2KRRlSViEZYt2tdylRYgHW71nHLv25h4WcLOXrg0fz+gt8zbuC4lNdpDDUSDAdj1kQq68MwDGNvMbFIEw3BBiprKwmGg2T6MttNhW0KNfGrJb/if9/+X/xePz855SdccfQVKQfLudZEtj+bIflDzJowDCNtmFh0MsFwkB31O6huqibLl4XH42n3of/mhje56ZWbWFO1hnOPOJcfVfyIQXmDUl6nIdhAKBIya8IwjAOCiUUn0SoVNrPtVFiAnfU7+fFrP+b/Vv4fwwuG89iFj3Fq6akduk5doI4cfw7DCoaR4c3orC4YhmG0iYlFJ7A3qbARjfCnD/7ET17/CbWBWmYeO5PrjruuQ4PlGoINhCNhBuUNok9mH7MmDiKCwSAbN26ksbGx0+osKChg1apVnVZfd6M3929f+paVlcXQoUPx+9sfn9UWJhb7QVOoiW1126gP1juzwnraH8/w0Y6PmPXKLBZtWsRxQ47jjtPu4Mj+R6a8TjgSpj5YT44/h0EFg1IOxjN6Hxs3biQ/P5+RI0d22o+Empoa8vN773vVe3P/9rZvqsrOnTvZuHEjpaWl+3RNE4t9IBwJU9VQxc6GnWR4M1KmtTYEG7jnrXv49dJfk5eRx8/P/DlfGv2lDo2BcK2JwXmDyc/MN2viIKWxsbFThcI4uBAR+vXrx/bt2/e5jrSKhYhMBe4FvMBvVXVuwvHhwO+BvtEys1T1eRHxA78FJkTb+Kiq3pHOtnaExFlh8zNSP7wXVS3iqkevYn31er40+kt8/+TvU5RdlPJa4UiYumAd+Rn5DMgdYNaEYUJh7Bf7+/eTNrEQES9wP3AGsBFYLCLPqerKuGK3Ak+q6q9EpAx4HhgJfBHIVNWxIpIDrBSRx1X103S1NxVuKmxTuIkcf07KtNattVuZ/eps/r767xxWdBhPffEpThh2QoeuVR+sR1UZkj8k5dgMwzCMA0E654I4FlijqmtVNQA8AZyfUEYBN22oANgctz9XRHxANhAA9qSxrW0SDAfZUrOFz6o/A4H8zPx2hSIcCfPQsoeY8sgUXl77Ml8d8VVe/MqLHRKKUCTEnqY95PpzKS0sNbeTse/Mnw8jR4LH43zOn79f1e3cuZPx48czfvx4Bg0axJAhQ2LbgUCgQ3V87Wtf46OPPmq3zP3338/8/WyrkR5EVdNTscjFwFRVvSq6fTlwnKrOjCszGHgRKARygdNVdWnUDfUYcBqQA3xXVeclucYMYAbAwIEDJz7xxBOd2oewhgmFQyB0KL7wcc3H3LvmXlbXrmZi34nMPGwm/SL9yMpNPZFfRCMA+D3+HjOfU21tLXl5eV3djLTRnfpXUFDAYYe1/wZEF9+TT5J17bVIQ0Nsn2Zn0/jLXxL60pdi+8LhMF7v3r8l8fbbbycvL4/vfOc7LfarKqqKx9M9/n73tX/J6C19W7NmDdXV1S32nXLKKUtVtTzVuemMWST7SZyoTF8GHlHVn4vICcBjIjIGxyoJAyU4QvK6iLysqmtbVOYIyDyA8vJyraio2O9Gu+9/iKXC+ttPhQWoaarhZ2/+jIffe5h+2f144OwH+MKRX0BEWLF4BaMnjW7z3FAkRH2gnoKsAopzi/F5ek7OwYIFC+iMe95d6U79W7VqVXP2y3XXwbvvtl34rbegqanFLmloIPuaa+Cxx2L7QuEwPveBM3483HNPh9qSmZlJZmYm+fn5rFmzhgsuuIDJkyfz9ttv87e//Y0f/ehHvPPOOzQ0NHDJJZcwe/ZsACZPnsx9993HmDFj6N+/P1dffTX/+Mc/yMnJ4S9/+QsDBgzg1ltvpX///lx33XVMnjyZyZMn869//Yvq6moefvhhTjzxROrq6rjiiitYs2YNZWVlrF69mt/+9reMHz++RTv/+7//m5deegmfz8dZZ53FT3/6U7Zu3cq3vvUt1q1bh4gwb948jjvuOO68804effRRAL71rW9x7bXXJu3b+++/z5w5c2hqauLwww/noYceIjc3t0P3rTPZ10yvrKwsjjnmmH26ZjplciMwLG57KM1uJpdvAE8CqOp/gCygP3AZ8E9VDarqNuDfQErl21+aQk1s3LORDXs24PP6yMvIa1coVJW/ffw3Kh6p4KFlD3H5uMtZeOVCzj/q/A65j+oD9QTDQYYVDGNw/uAeJRRGNyZBKFLu309WrlzJN77xDZYtW8aQIUOYO3cuS5Ys4b333uOll15i5cqVrc6prq5mypQpvPfee5xwwgk89NBDSetWVRYtWsTPfvYz5syZA8Avf/lLBg0axHvvvcesWbNYtmxZq/MqKyt58cUXWbFiBe+//z433XQTANdccw1nnHEG77//PkuXLmXUqFEsWrSI+fPns2jRIv7zn//wwAMP8P7777fqm9/vZ+7cubzyyiu88847jBs3jnvvvbezbmO3J51Pp8XA4SJSCmwCLsURgXjW47iaHhGRUThisT26/1QR+QOOG+p4oGM/e/aBiEbYWb8zlgqbavQ1wIbqDdz8r5v517p/Mbp4NA9+4UEmDJ7QoesFw0EaQg0UZhXSP6d/ymC5YbQglQUwciR89lnr/SNGwIIFsc2GThqHcOihhzJpUvMbGx9//HF+97vfEQqF2Lx5MytXrqSsrKzFOdnZ2Zx11lkATJw4kddffz1p3dOmTYuV+fTTTwF44403uPHGGwE4+uijGT26teVeVFSEx+Phm9/8Jueccw7nnnsu4FiLrrva5/PRp08fXn/9dS666CJycnIAuOCCC3jjjTc488wzW/TtzTffZOXKlZx44okABAIBJk+evPc3rIeSNrFQ1ZCIzARewEmLfUhVV4jIHGCJqj4H/D/gQRH5Lo6L6kpVVRG5H3gY+ADHnfWwqr6frrY2hZrYUb+jQ6Oig+Eg85bO4+637sYjHn4w5Qd8/Zivd8gqcF1cHvEwvGA4Of6czuqCYTRz220wYwbU1zfvy8lx9qeBeDfM6tWruffee1m0aBF9+/blK1/5StJR5xkZzdPUeL1eQqFQ0rozMzNblelInNXv97Nw4ULeeustnnjiCX71q1/x4osvAq1TSNurL75vqsrUqVN5LM6VdzCR1miNqj6vqkeo6qGqelt03+yoUKCqK1X1c6p6tKqOV9UXo/trVfWLqjpaVctU9WfpbCeA1+NNKRSLNy3m83/4PLe/cTsVIypYcOUCZkyc0SGhCIaD1ARqKMgqYGTfkSYURvqYPh3mzXMsCRHnc948Z3+a2bNnD/n5+fTp04ctW7bwwgsvdPo1Jk+ezJNPPgnA8uXLk7q5ampqqKmp4dxzz+UXv/hFzFV1yimn8Otf/xpwgsR79uzh5JNP5plnnqGhoYHa2lr+8pe/cNJJJ7Wq88QTT2ThwoWsXeuETuvq6li9enWn96+7Yk7yDrCrYRe3v347f/zgjwzJH8LD5z/MmYee2eHza5tq8Xl8jCgY0aE5oAxjv5k+/YCIQyITJkygrKyMMWPGcMghh/C5z32u069x7bXXcsUVVzBu3DgmTJjAmDFjKCgoaFGmurqaiy++mFAoRCQS4e677wbgvvvu45vf/Ca/+c1v8Pl8/OY3v+HYY4/ly1/+cszd9O1vf5uxY8eyZs2aFnUOHDiQ3/3ud1xyySWxdOHbb7+dww8/vNP72B1JW+rsgaa8vFyXLFmyT+c2BBvYsGcDeRkt0yRVladWPcWchXOobqzmmxO+yfUnXE9uRseyHwLhAB8u+ZCJJ0ykX06/HpMS21G6U7ZQOuhO/Vu1ahWjRo3q1Dp76txJoVCIUChEVlYWq1ev5swzz2T16tX4fC1/+/bU/nWEfe1bsr8jEeny1NkezZqqNdz0yk28ueFNJgyewE9P/yllxWWpT8QRmbpAHT6vj0xvJsW5xWlurWEcPNTW1nLaaacRCoVQ1ZiVYKQXu8MJNIYa+eXbv+SBJQ+Q7ctm7ulzmT52eoetgkA4QFOoiX7Z/SjKKWK9rE9ziw3j4KJv374sXbq0q5tx0HHQi8X85fO56eWb2LhnI/2y+wGwo2EH046axuwpsztsFagqdcE6MjwZjOg7gixf6lHbhmEYPYWDWizmL5/PjL/OoD7opBjuaNiBIFwz6RpuPunmDtfTFGqiKdwUe8Vpb4tNGIZhHNRPtVteuSUmFC6K8uyHz3bo/IhGqGmqQRBK+5ZSlF1kQmEYRq/koLYs1lcnjydsrkmclaQ1jaFGguFgzJqw2WENw+jNHNQ/g4cXDE+6vyS/pM1zXGvCK15G9h1JYXahCYXR7Zi/fD4j7xmJ50ceRt4zkvnL93/a761bt3LppZdy6KGHUlZWxtlnn83HH3/cCa3tfEaOHMnOnTsBYtNzJHLllVfy1FNPtVvPI488wubNzT8er7rqqqSDAA8GDmqxuO2021qNpM72ZTNr8qyk5RtDjdQH6xmQO4DhBcPJ9GUeiGYaxl7hxuI+q/4MRfms+jNm/HXGfgmGqnLhhRdSUVHBJ598wsqVK7n99tuprKxsUS4cDu9v8zudN998c5/PTRSL3/72t63mueoOtDVdSmdyUIvF9LHTmXfePIb1GYYgDMkfwp1n3Mm0UdNalHOtCb/Hb9aE0eVc98/rqHikos3lG3/5RqtYXH2wnm/85Rstyp395Nmx9ev+eV2713z11Vfx+/1cffXVsX3jx4/npJNOYsGCBZxyyilcdtlljB07FoC7776bMWPGMGbMGO6JTnxYV3fysRsAABChSURBVFfHOeecw9FHH82YMWP405/+BMCsWbMoKytj3LhxfO9732t17V/96lfccMMNse1HHnmEa6+9FnAm/Zs4cSKjR49m3rxWr7wBiL2TRFWZOXMmZWVlnHPOOWzbti1WZs6cOUyaNIkxY8YwY8YMZ0DuU0+xZMkSpk+fzvjx42loaKCiogJ38O/jjz/O2LFjGTNmTGxiQ/d6t9xyC0cffTTHH398K0EFWLhwYezlUccccww1NTUA3HnnnYwdO5ajjz6aWbOcH63vvvsuxx9/POPGjePCCy9k165dAFRUVHDzzTczZcoU7r33XrZv385FF13EpEmTmDRpEv/+97/b/kL3gYM6ZgGOYEw7alrSEdzgjO4OR8IMyhvUoYkGDaOraQonn4q8rf0d4YMPPmDixIltHl+0aBEffPABpaWlLF26lIcffpi3334bVeW4445jypQprF27lpKSEv7+978DzpQcVVVVPPPMM3z44YeICLt3725V98UXX8wJJ5zAnXfeCcCf/vQnbrnlFgAeeughioqKaGhoYNKkSVx00UX069cvaRufeeYZPvroI5YvX05lZSVlZWV8/etfB2DmzJmx925cfvnl/O1vf+Piiy/mvvvu46677qK8vOUA582bN3PjjTeydOlSCgsLOfPMM3n22We54IILqKur4/jjj+e2227jhhtu4MEHH+TWW29tcf5dd93F/fffz+c+9zlqa2vJysriH//4B88++yxvv/02OTk5VFVVAXDFFVfwy1/+kilTpjB79mx+9KMf8eMf/xiA3bt3s3DhQgAuu+wyvvvd7zJ58mTWr1/P5z//eVatWtXOt7p3HPRi0RbhSJi6QB25GbkMKhiE3+vv6iYZBgD3TG1/ivKR94x0XgOcwIiCESy4ckFsuzOnwzj22GMpLS0FnCnEL7zwwtiMrdOmTeP1119n6tSpfO973+PGG2/k3HPP5aSTTopN23HVVVe1mEo8nuLiYg455BDeeustDj/8cD766KPYnFP/+7//yzPPPAPAhg0bWL16dZti8dprr/HlL38Zr9dLSUkJp556auzYq6++yp133kl9fT1VVVWMHj2a8847r83+Ll68mIqKCoqLnXFY06dP57XXXuOCCy4gIyMj1o+JEyfy0ksvtTr/c5/7HNdffz3Tp09n2rRpDB06lJdffpmvfe1rsanSi4qKqK6uZvfu3UyZMgWAr371q3zxi1+M1XPJJZfE1l9++eUW8ZQ9e/Z06nd8ULuh2qIh2EBDsIGS/BKG9hlqQmH0KJLF4nL8Odx22r5PUT569Oh2R00nTuWdjCOOOIKlS5cyduxYbrrpJubMmYPP52PRokVcdNFFPPvss0ydOpVwOBxz0bi/9i+55BKefPJJnn76aS688EJEhAULFvDyy/+/vbMPjqq8/vjna0gI4TXU6kTjEJii09hQQyNGq2nVX1PItGWwTquDrbSWluqvI+1oxTrTqVNHsW+m/HREfGEo2mhLhTrOWNoqMrWNitLlRRoq1P5aIiovA4IQCuH0j/vselk2WRIXNruez8ydfe65z3Pvc/bcvWefl3ueP9Le3s6aNWuor6/PGA49Tqaega6uLq699lqWLFnCunXrmDlzZtbz9BZTr7S0NHWdnsKvz5kzhwceeID9+/fT2NhIR0cHZtbnnov493748GHa29tJJBIkEgk6OztzGhvLnUWM7sPdvH3gbcoHlTO2ciwjyr3bySk8kmNxY0aOQYgxI8ew4LMLmF7X/yi0l1xyCQcOHOD+++9PyVatWpXqAonT1NTEsmXL2LdvH++88w5Lly7loosu4vXXX6eiooKrrrqKG264gdWrV7N37152795NS0sLra2tJBIJSkpKUg+85Op4l112GcuWLaOtrS31b3r37t1UVlZSUVFBR0cHzz//fK86NDU18eijj9Ld3c3WrVtZsWIFQMoxnHzyyezdu/eIGVLDhw9PjSfEOe+881i5ciXbt2+nu7ubtra21L//Y2Hz5s3U1dVx00030dDQQEdHB83NzTz00EPsC+uQ7Ny5k5EjR1JZWZlaHGrx4sU9Xqe5uZm77747tZ/obendfuDdUIGD3QcR4rRhpzF88HB3Ek5BM71u+ntyDulIYunSpcyePZu5c+dSXl5OTU0Nra2tdHZ2HpF34sSJzJgxg0mTJgHRdNP6+nqWL1/OjTfeyEknnURpaSn33nsve/bsYerUqXR1dWFm3HXXXRmvX1lZSW1tLRs2bEidd/LkycyfP58JEyZw1lln0djY2KsO06ZN45lnnqGuro4zzzwz9dAdNWoUM2fOpK6ujpqamiNW/ZsxYwazZs1iyJAhtLe3p+RVVVXccccdXHzxxZgZLS0tTJ069Zi/z9bWVlasWEFJSQm1tbVMmTKFwYMHk0gkaGhooKysjJaWFm6//XYWLVrErFmz2LdvH+PGjWPhwoUZzzlv3jyuu+46JkyYwKFDh2hqakqt3ZELPEQ5UfC/Xft3MbpidM7XwR5IYa5zTTHrBgNLPw9R3neKWT8PUZ4nykrKOGXYKfmuhuM4zoDFxywcx3GcrLizcJwCoVi6jJ388F7vH3cWjlMAlJeXs2PHDncYTr8wM3bs2EF5ef/X2fExC8cpAKqrq9myZQvbtm3L2Tm7urre08NjoFPM+vVHt/Lycqqrq/t9TXcWjlMAlJaWpt6QzhXPPvss9fX1OT3nQKKY9cuHbt4N5TiO42TFnYXjOI6TFXcWjuM4TlaK5g1uSduAo0Nt5p+Tge35rsRxoph1A9ev0Clm/XKp2xgz+2C2TEXjLAYqkl46llfpC5Fi1g1cv0KnmPXLh27eDeU4juNkxZ2F4ziOkxV3FsefzAsDFwfFrBu4foVOMet3wnXzMQvHcRwnK96ycBzHcbLizsJxHMfJijuLPiLpDEkrJP1N0iuSrg/y0ZL+IOnV8FkZ5JI0T9ImSWslTYyd6+qQ/1VJV+dLp3QklUj6q6Qnw/5YSS+Eej4mqSzIB4f9TeF4TewcNwf5Rkmfzo8mRyNplKQlkjqCDc8vMtt9O9yX6yW1SSovZPtJekjSW5LWx2Q5s5ekj0laF8rM0wleT7kH/X4c7s+1kpZKGhU7ltEukiYH2SZJc2LyjLbvF2bmWx82oAqYGNLDgb8DtcCPgDlBPge4M6RbgKcAAY3AC0E+GvhH+KwM6cp86xfq9h3gl8CTYf9XwBUhPR/4ZkhfC8wP6SuAx0K6FlgDDAbGApuBknzrFeq2CPhaSJcBo4rFdsDpwGvAkJjdZhSy/YAmYCKwPibLmb2AF4HzQ5mngCkDQL9mYFBI3xnTL6NdwrYZGBfu6TVAbeweOMr2/aprvm/wQt+A3wKfAjYCVUFWBWwM6fuAK2P5N4bjVwL3xeRH5MujPtXA08AlwJPhR7Q9dvOeDywP6eXA+SE9KOQTcDNwc+ycqXx51m1EeJgqTV4stjsd+Hd4KA4K9vt0odsPqEl7mObEXuFYR0x+RL586Zd2bBrwSEhntEvcpvF8vf12+7N5N9R7IDTb64EXgFPNbCtA+Ewu6p38ASfZEmQ9yfNNK/Bd4HDY/wCwy8wOhf14PVM6hOO7Q/6Bqts4YBuwMHSzPSBpKEViOzPrBH4C/AvYSmSPlyke+yXJlb1OD+l0+UDiq0QtHui7fr39dvuMO4t+ImkY8Btgtpm93VvWDDLrRZ43JH0GeMvMXo6LM2S1LMcGnG6BQURN/nvNrB54h6gboycKSr/Qdz+VqIviNGAoMCVD1kK1Xzb6qs+A1lPSLcAh4JGkKEO2E6afO4t+IKmUyFE8YmaPB/GbkqrC8SrgrSDfApwRK14NvN6LPJ98HPicpH8CjxJ1RbUCoyQlF8qK1zOlQzg+EtjJwNQNonptMbMXwv4SIudRDLYD+B/gNTPbZmYHgceBCyge+yXJlb22hHS6PO+EQfjPANMt9CHRd/2207Pt+4w7iz4SZks8CPzNzH4WO/QEkJxlcTXRWEZS/uUwU6MR2B2azsuBZkmV4R9hc5DlDTO72cyqzayGaMDzGTObDqwALg/Z0nVL6nx5yG9BfkWYbTMWGE80kJhXzOwN4N+SzgqiS4ENFIHtAv8CGiVVhPs0qV9R2C9GTuwVju2R1Bi+ry/HzpU3JE0GbgI+Z2b7Yod6sssqYHyY+VRG9Nt9ItiyJ9v3nRM9mFPoG3AhUVNuLZAIWwtR/+DTwKvhc3TIL+AeotkK64CG2Lm+CmwK21fyrVuanp/k3dlQ48JNuQn4NTA4yMvD/qZwfFys/C1B542c4BkmWfQ6B3gp2G8Z0eyYorEdcCvQAawHFhPNnClY+wFtROMvB4n+QV+TS3sBDeG72gzcTdrkhzzpt4loDCL5fJmfzS7hGfT3cOyWmDyj7fuzebgPx3EcJyveDeU4juNkxZ2F4ziOkxV3Fo7jOE5W3Fk4juM4WXFn4TiO42TFnYVTUEj6gKRE2N6Q1BnbP6aImpIWxt616CnPdZKm56bWAwNJz0k6J9/1cAoTnzrrFCySfgDsNbOfpMlFdG8fzljwfYqk54D/NbNEvuviFB7esnCKAkkfUrSGw3xgNVAlaYGklxSt7/D9WN7nJJ0jaZCkXZLmSlojqV3SKSHPbZJmx/LPlfRiWDPggiAfKuk3oWxbuNZR/9wlnStppaSXJT0l6VRJpWH/wpDnx5JuDelbJa1K6hOcX7IeP5P0J0kbJDUoWu/g1eA4k9/DK5IWK1qn4VeShmSo05Sg72pF6xwMjdVjg6K1FO7MqZGcgsadhVNM1AIPmlm9RRFY55hZA/BR4FOSajOUGQmsNLOPAu1Eb/pmQmY2CbgRSDqebwFvhLJziSIQH1lIGgz8HPi8mX0MeBj4oUWxm74CLJDUTBSH67ZQ7Odmdi5QF+o3OXbK/WZ2EVHImWXArJDv63p3kZxa4B4zqwO6gG+k1ekUogCKl5rZRKK32a+XdCrRm8Bnm9kE4I4evgvnfYg7C6eY2Gxmq2L7V0paTdTS+DDRQzSd/WaWDAH9MtHaApl4PEOeC4kCLmJma4BXMpT7MHA28EdJCaKH9BmhzNpQ/rdEISgOhjKXSnqRaBGbT4TySZ4In+uAdWb2ppl1Af/k3aB4r5nZ8yH9cKhnnAuIvou/hDpNDzrtJApNf7+kaURReR0HiEI2O06xkHq4SRoPXA9MMrNdkh4mioWUzn9i6W56/k0cyJDnWJbgFLA2tAYy8RGidSSS3V8VRDGKJppZp6Tb0uqdrMfhWDq5n6xX+kBk+r6A35nZl46qrNRAtJjXFcA3iYLuOY63LJyiZQSwB3hbURjr47GO9HPAFwAk1ZG55bIBOF3SpJCvTNLZIf1FYBhR0MZ7JI0AhhA9+LdLGg58vh/1Givp3JC+MtQzzl+AT0gaF+oxVNL4cL0RZvYk8G0ydKs571+8ZeEUK6uJHtTridZc/vNxuMb/Ab+QtDZcbz1RKyGFmR2QdDkwLzyMBwE/lbSNaIzik6EFcR9wl5ldI2lRONf/E63C2FdeAWZKepAoAu2CtDq9Keka4LHYdOPvAfuBx8M4y0lEa7E7DuBTZx2n3yhaVGaQmXWFbq/fA+Pt3WUs81GnDwFLzMzfp3ByircsHKf/DAOeDk5DwDfy6Sgc53jiLQvHcRwnKz7A7TiO42TFnYXjOI6TFXcWjuM4TlbcWTiO4zhZcWfhOI7jZOW/O2vLxOyhax0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7eca8ea668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Learning Curves (Random Forest)'\n",
    "estimator = clf\n",
    "plot_learning_curve(estimator, title, X_train, y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}