Data-Analysis.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "source": "### Import the JSON File with Industry Lab Sensor Data", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "\nimport sys\nimport types\nimport pandas as pd\nfrom botocore.client import Config\nimport ibm_boto3\n\ndef __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.\nclient_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\nbody = client_915ea66450e44183938b1aab8572887f.get_object(Bucket='dsbootcampac3431d743f2492ebe1cfe6103674873',Key='floorsensordata2604.json')['Body']\n# add missing __iter__ method, so pandas accepts body as file-like object \n\nif 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\ndf_data_1 = pd.read_json(body, orient='values')\ndf_data_1.head()\n\n", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                    itemname sensorname                      time  value\n0  52760_5_temperature_value   item0001  2018-02-19T07:05:00.165Z   20.5\n1  47406_3_temperature_value   item0002  2018-02-19T07:05:00.240Z  20.61\n2  452B5_3_temperature_value   item0003  2018-02-19T07:05:00.316Z  19.89\n3  452F9_3_temperature_value   item0004  2018-02-19T07:05:00.388Z   19.7\n4  4521C_3_temperature_value   item0005  2018-02-19T07:05:00.460Z  18.89", 
                        "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>"
                    }, 
                    "execution_count": 1, 
                    "metadata": {}
                }
            ], 
            "execution_count": 1
        }, 
        {
            "source": "### Check the Structure of the Data Frame", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df = df_data_1", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 2
        }, 
        {
            "source": "df.head(10)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                    itemname sensorname                      time   value\n0  52760_5_temperature_value   item0001  2018-02-19T07:05:00.165Z    20.5\n1  47406_3_temperature_value   item0002  2018-02-19T07:05:00.240Z   20.61\n2  452B5_3_temperature_value   item0003  2018-02-19T07:05:00.316Z   19.89\n3  452F9_3_temperature_value   item0004  2018-02-19T07:05:00.388Z    19.7\n4  4521C_3_temperature_value   item0005  2018-02-19T07:05:00.460Z   18.89\n5  51A51_5_temperature_value   item0006  2018-02-19T07:05:00.534Z  21.812\n6  512F5_5_temperature_value   item0007  2018-02-19T07:05:00.607Z      21\n7  45974_3_temperature_value   item0008  2018-02-19T07:05:00.687Z   21.06\n8  52686_5_temperature_value   item0009  2018-02-19T07:05:00.760Z      21\n9  45152_3_temperature_value   item0010  2018-02-19T07:05:00.839Z    20.2", 
                        "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    <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>"
                    }, 
                    "execution_count": 3, 
                    "metadata": {}
                }
            ], 
            "execution_count": 3
        }, 
        {
            "source": "df.tail(10)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                           itemname sensorname                      time  \\\n760514  476CA_4_carbonDioxide_value   item0131  2018-04-26T14:00:03.218Z   \n760515  46CF4_4_carbonDioxide_value   item0104  2018-04-26T14:00:03.259Z   \n760516  45974_4_carbonDioxide_value   item0122  2018-04-26T14:00:03.280Z   \n760517  4692E_4_carbonDioxide_value   item0114  2018-04-26T14:00:03.301Z   \n760518         South_Workarea_3_Air   item0157  2018-04-26T14:00:03.321Z   \n760519  47406_4_carbonDioxide_value   item0126  2018-04-26T14:00:03.341Z   \n760520  47888_4_carbonDioxide_value   item0083  2018-04-26T14:00:03.362Z   \n760521  45C72_4_carbonDioxide_value   item0109  2018-04-26T14:00:03.383Z   \n760522  46C8A_4_carbonDioxide_value   item0072  2018-04-26T14:00:03.403Z   \n760523            Loisach_Occupancy   item0146  2018-04-26T14:00:52.918Z   \n\n           value  \n760514       798  \n760515       543  \n760516       744  \n760517       795  \n760518       500  \n760519       641  \n760520       896  \n760521       730  \n760522       565  \n760523  occupied  ", 
                        "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>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>"
                    }, 
                    "execution_count": 4, 
                    "metadata": {}
                }
            ], 
            "execution_count": 4
        }, 
        {
            "source": "df.itemname.unique()", 
            "cell_type": "code", 
            "metadata": {
                "scrolled": false
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "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": 5, 
                    "metadata": {}
                }
            ], 
            "execution_count": 5
        }, 
        {
            "source": "### Format Yanzi Sensor Data ", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df['id'] = df['itemname'].str.split('_').str[0]", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 6
        }, 
        {
            "source": "df['temperature'] = df[df['itemname'].str.contains('temperature')]['value'].astype(float)\ndf['carbonDioxide'] = df[df['itemname'].str.contains('carbonDioxide')]['value'].astype(float)\ndf['humidity'] = df[df['itemname'].str.contains('humidity')]['value'].astype(float)\ndf['illuminance'] = df[df['itemname'].str.contains('illuminance')]['value'].astype(float)\ndf['pressure'] = df[df['itemname'].str.contains('pressure')]['value'].astype(float)\ndf['Occupancy'] = df[df['itemname'].str.contains('Occupancy')]['value']", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 7
        }, 
        {
            "source": "df.head()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                    itemname sensorname                      time  value  \\\n0  52760_5_temperature_value   item0001  2018-02-19T07:05:00.165Z   20.5   \n1  47406_3_temperature_value   item0002  2018-02-19T07:05:00.240Z  20.61   \n2  452B5_3_temperature_value   item0003  2018-02-19T07:05:00.316Z  19.89   \n3  452F9_3_temperature_value   item0004  2018-02-19T07:05:00.388Z   19.7   \n4  4521C_3_temperature_value   item0005  2018-02-19T07:05:00.460Z  18.89   \n\n      id  temperature  carbonDioxide  humidity  illuminance  pressure  \\\n0  52760        20.50            NaN       NaN          NaN       NaN   \n1  47406        20.61            NaN       NaN          NaN       NaN   \n2  452B5        19.89            NaN       NaN          NaN       NaN   \n3  452F9        19.70            NaN       NaN          NaN       NaN   \n4  4521C        18.89            NaN       NaN          NaN       NaN   \n\n  Occupancy  \n0       NaN  \n1       NaN  \n2       NaN  \n3       NaN  \n4       NaN  ", 
                        "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>"
                    }, 
                    "execution_count": 8, 
                    "metadata": {}
                }
            ], 
            "execution_count": 8
        }, 
        {
            "source": "df.tail()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                           itemname sensorname                      time  \\\n760519  47406_4_carbonDioxide_value   item0126  2018-04-26T14:00:03.341Z   \n760520  47888_4_carbonDioxide_value   item0083  2018-04-26T14:00:03.362Z   \n760521  45C72_4_carbonDioxide_value   item0109  2018-04-26T14:00:03.383Z   \n760522  46C8A_4_carbonDioxide_value   item0072  2018-04-26T14:00:03.403Z   \n760523            Loisach_Occupancy   item0146  2018-04-26T14:00:52.918Z   \n\n           value       id  temperature  carbonDioxide  humidity  illuminance  \\\n760519       641    47406          NaN          641.0       NaN          NaN   \n760520       896    47888          NaN          896.0       NaN          NaN   \n760521       730    45C72          NaN          730.0       NaN          NaN   \n760522       565    46C8A          NaN          565.0       NaN          NaN   \n760523  occupied  Loisach          NaN            NaN       NaN          NaN   \n\n        pressure Occupancy  \n760519       NaN       NaN  \n760520       NaN       NaN  \n760521       NaN       NaN  \n760522       NaN       NaN  \n760523       NaN  occupied  ", 
                        "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>"
                    }, 
                    "execution_count": 9, 
                    "metadata": {}
                }
            ], 
            "execution_count": 9
        }, 
        {
            "source": "df.describe()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "         temperature  carbonDioxide       humidity    illuminance  \\\ncount  342704.000000   50509.000000  160660.000000  102881.000000   \nmean       22.124608     531.344414      16.940346     162.919545   \nstd         1.815222     168.712879       5.128360     449.858628   \nmin        12.930000     395.000000       4.000000       0.000000   \n25%        21.000000     412.000000      14.000000       0.460000   \n50%        22.000000     472.000000      17.000000      14.720000   \n75%        23.230000     579.000000      20.000000     162.380000   \nmax        32.780000    2215.000000      34.000000   15857.580000   \n\n           pressure  \ncount  58900.000000  \nmean     943.730369  \nstd        8.895958  \nmin      922.680000  \n25%      936.760000  \n50%      944.010000  \n75%      950.040000  \nmax      967.260000  ", 
                        "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>"
                    }, 
                    "execution_count": 10, 
                    "metadata": {}
                }
            ], 
            "execution_count": 10
        }, 
        {
            "source": "### Visualize Data with Pixidust", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "import pixiedust", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Pixiedust database opened successfully\nTable VERSION_TRACKER created successfully\nTable METRICS_TRACKER created successfully\n\nShare anonymous install statistics? (opt-out instructions)\n\nPixieDust 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}\nYou can opt out by calling pixiedust.optOut() in a new cell.\n"
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "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        "
                    }, 
                    "metadata": {}
                }, 
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Pixiedust runtime updated. Please restart kernel\nTable USER_PREFERENCES created successfully\nTable service_connections created successfully\n"
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "text/html": "<div>Warning: You are not running the latest version of PixieDust. Current is 1.1.14, Latest is 1.1.15</div>"
                    }, 
                    "metadata": {}
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "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            "
                    }, 
                    "metadata": {}
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "text/html": "<div>Please restart kernel after upgrading.</div>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 11
        }, 
        {
            "source": "display(df)", 
            "cell_type": "code", 
            "metadata": {
                "pixiedust": {
                    "displayParams": {
                        "no_margin": "true", 
                        "lineChartType": "subplots", 
                        "title": "Shinano Meeting Room", 
                        "filter": "{\"regex\": \"false\", \"constraint\": \"None\", \"field\": \"id\", \"case_matter\": \"false\", \"value\": \"4674C\"}", 
                        "handlerId": "lineChart", 
                        "valueFields": "temperature", 
                        "rendererId": "matplotlib", 
                        "timeseries": "true", 
                        "keyFields": "time", 
                        "legend": "true"
                    }
                }
            }, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "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>"
                    }, 
                    "metadata": {
                        "pixieapp_metadata": null
                    }
                }
            ], 
            "execution_count": 12
        }, 
        {
            "source": "### Filter on Shinano and Fill Missing Values", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df_shinano = df[df['id'].isin(['4674C', 'Shinano'])].copy()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 13
        }, 
        {
            "source": "df_shinano.sort_values(by='time', inplace=True)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 14
        }, 
        {
            "source": "df_shinano.index = pd.to_datetime(df_shinano.time)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 15
        }, 
        {
            "source": "df_shinano.fillna(method='ffill', inplace=True)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 16
        }, 
        {
            "source": "df_shinano.fillna(method='bfill', inplace=True)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 17
        }, 
        {
            "source": "df_shinano.head()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                                            itemname sensorname  \\\ntime                                                              \n2018-02-19 07:05:05.008    4674C_3_temperature_value   item0066   \n2018-02-19 07:10:00.118  4674C_4_carbonDioxide_value   item0095   \n2018-02-19 07:15:01.061       4674C_6_pressure_value   item0096   \n2018-02-19 07:15:01.460       4674C_3_humidity_value   item0094   \n2018-02-19 07:15:01.574    4674C_3_temperature_value   item0066   \n\n                                             time   value     id  temperature  \\\ntime                                                                            \n2018-02-19 07:05:05.008  2018-02-19T07:05:05.008Z    18.5  4674C        18.50   \n2018-02-19 07:10:00.118  2018-02-19T07:10:00.118Z     455  4674C        18.50   \n2018-02-19 07:15:01.061  2018-02-19T07:15:01.061Z  950.51  4674C        18.50   \n2018-02-19 07:15:01.460  2018-02-19T07:15:01.460Z      16  4674C        18.50   \n2018-02-19 07:15:01.574  2018-02-19T07:15:01.574Z   18.58  4674C        18.58   \n\n                         carbonDioxide  humidity  illuminance  pressure  \\\ntime                                                                      \n2018-02-19 07:05:05.008          455.0      16.0          NaN    950.51   \n2018-02-19 07:10:00.118          455.0      16.0          NaN    950.51   \n2018-02-19 07:15:01.061          455.0      16.0          NaN    950.51   \n2018-02-19 07:15:01.460          455.0      16.0          NaN    950.51   \n2018-02-19 07:15:01.574          455.0      16.0          NaN    950.51   \n\n                        Occupancy  \ntime                               \n2018-02-19 07:05:05.008      free  \n2018-02-19 07:10:00.118      free  \n2018-02-19 07:15:01.061      free  \n2018-02-19 07:15:01.460      free  \n2018-02-19 07:15:01.574      free  ", 
                        "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>"
                    }, 
                    "execution_count": 18, 
                    "metadata": {}
                }
            ], 
            "execution_count": 18
        }, 
        {
            "source": "df_shinano.drop(['illuminance'], axis=1, inplace=True)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 19
        }, 
        {
            "source": "df_shinano.describe()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "        temperature  carbonDioxide      humidity      pressure\ncount  22744.000000   22744.000000  22744.000000  22744.000000\nmean      21.716864     617.600114     17.815732    941.909597\nstd        2.472216     232.130746      3.968186      8.905109\nmin       15.210000     398.000000      7.000000    922.980000\n25%       20.280000     431.000000     15.000000    934.630000\n50%       21.720000     558.000000     18.000000    942.615000\n75%       22.810000     706.000000     20.000000    948.130000\nmax       32.780000    2215.000000     31.000000    962.590000", 
                        "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>"
                    }, 
                    "execution_count": 20, 
                    "metadata": {}
                }
            ], 
            "execution_count": 20
        }, 
        {
            "source": "df_shinano.Occupancy.unique()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array(['free', 'occupied'], dtype=object)"
                    }, 
                    "execution_count": 21, 
                    "metadata": {}
                }
            ], 
            "execution_count": 21
        }, 
        {
            "source": "df_shinano.groupby('Occupancy')['time'].nunique()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "Occupancy\nfree        16948\noccupied     5796\nName: time, dtype: int64"
                    }, 
                    "execution_count": 22, 
                    "metadata": {}
                }
            ], 
            "execution_count": 22
        }, 
        {
            "source": "### Plot Sensor Values depending on Occupancy State", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "import matplotlib.pyplot as plt", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 23
        }, 
        {
            "source": "df_shinano.temperature.plot()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2ba6caf0eac8>"
                    }, 
                    "execution_count": 24, 
                    "metadata": {}
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": "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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x2ba6caf168d0>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 24
        }, 
        {
            "source": "### Build Simple Prediction Model", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn.model_selection import train_test_split", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 25
        }, 
        {
            "source": "df_shinano_model = df_shinano.copy()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 26
        }, 
        {
            "source": "df_shinano_model.loc[df_shinano_model.Occupancy == 'free', 'Occupancy'] = 0\ndf_shinano_model.loc[df_shinano_model.Occupancy == 'occupied', 'Occupancy'] = 1", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 27
        }, 
        {
            "source": "y = df_shinano_model['Occupancy'].values\ny", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array([0, 0, 0, ..., 0, 0, 0])"
                    }, 
                    "execution_count": 28, 
                    "metadata": {}
                }
            ], 
            "execution_count": 28
        }, 
        {
            "source": "X = df_shinano_model.loc[:, ['temperature', 'carbonDioxide', 'humidity', 'pressure']].values\nX", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "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": 29, 
                    "metadata": {}
                }
            ], 
            "execution_count": 29
        }, 
        {
            "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 30
        }, 
        {
            "source": "import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import learning_curve", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 31
        }, 
        {
            "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", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 32
        }, 
        {
            "source": "clf = RandomForestClassifier(n_estimators=1)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 39
        }, 
        {
            "source": "clf.fit(X_train, y_train)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "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=1, n_jobs=1,\n            oob_score=False, random_state=None, verbose=0,\n            warm_start=False)"
                    }, 
                    "execution_count": 40, 
                    "metadata": {}
                }
            ], 
            "execution_count": 40
        }, 
        {
            "source": "clf.score(X_test, y_test)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "0.91382721477247741"
                    }, 
                    "execution_count": 41, 
                    "metadata": {}
                }
            ], 
            "execution_count": 41
        }, 
        {
            "source": "predictions = clf.predict(X_test)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 42
        }, 
        {
            "source": "print(classification_report(y_test, predictions))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "             precision    recall  f1-score   support\n\n          0       0.94      0.94      0.94      3377\n          1       0.83      0.83      0.83      1172\n\navg / total       0.91      0.91      0.91      4549\n\n"
                }
            ], 
            "execution_count": 43
        }, 
        {
            "source": "title = 'Learning Curves (Random Forest)'\nestimator = clf\nplot_learning_curve(estimator, title, X_train, y_train)\nplt.show()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": "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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x2ba6caca0a58>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 38
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.5.5", 
            "name": "python", 
            "pygments_lexer": "ipython3", 
            "file_extension": ".py", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}