From 46d3ca359ce4b2f14ce1ba53e71ee7f939614925 Mon Sep 17 00:00:00 2001
From: Robin Cole <robincole@noths.com>
Date: Wed, 4 Nov 2020 05:12:31 +0000
Subject: [PATCH] Bump pandas and version

---
 .gitignore                                    |    2 +
 README.md                                     |    8 +
 .../Getting started with detective.ipynb      | 1599 +++++++++--------
 requirements.txt                              |    2 +-
 setup.py                                      |    4 +-
 5 files changed, 888 insertions(+), 727 deletions(-)

diff --git a/.gitignore b/.gitignore
index e39db62..eb5b185 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,5 @@ dist/*
 
 .pytest_cache/*
 .idea/*
+
+venv*
diff --git a/README.md b/README.md
index 3c9cd63..164ec33 100644
--- a/README.md
+++ b/README.md
@@ -25,5 +25,13 @@ You can now navigate to the notebooks directory and start using the detective pa
 ## Try out detective online
 You can try out the latest version of detective from pypi without installing anything. If you click on the 'launch binder' button above, detective will be started in a Docker container online using the [Binderhub](https://binderhub.readthedocs.io) service. Run the example notebook to explore detective, and use the `Upload` button to upload your own `home-assistant_v2.db` database file for analysis. Note that all data is deleted when the container closes down, so this service is just for trying out detective.
 
+## Development
+* Create a venv: `python3 -m venv venv`
+* Activate venv: `source venv/bin/activate`
+* Install requirements: `pip3 install -r requirements.txt`
+* Install detective in development mode: `pip3 install -e .`
+* Optional install Jupyter to run the notebooks: `pip3 install jupyterlab`
+* Run jupyter, ensuring from venv: `venv/bin/jupyter lab`
+
 ## Contributors
 Big thanks to [@balloob](https://github.com/balloob) and [@frenck](https://github.com/frenck), checkout their profiles!
diff --git a/notebooks/Getting started with detective.ipynb b/notebooks/Getting started with detective.ipynb
index 72cd703..2e09eb8 100644
--- a/notebooks/Getting started with detective.ipynb	
+++ b/notebooks/Getting started with detective.ipynb	
@@ -1,739 +1,890 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Notebook showing usage of the data detective."
-      ],
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# To import detective from relative folder\n",
-        "import detective.core as detective\n",
-        "import detective.functions as functions\n",
-        "import pandas as pd"
-      ],
-      "outputs": [],
-      "execution_count": 17,
-      "metadata": {}
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Supply the db_url as described in the [recorder integration docs](https://www.home-assistant.io/integrations/recorder/)."
-      ],
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "db_url = 'my_url'"
-      ],
-      "outputs": [],
-      "execution_count": 18,
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "db = detective.HassDatabase(db_url) # To init without fetching entities fetch_entities=False"
-      ],
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Successfully connected to database postgresql://postgres:***@192.168.1.164/homeassistant\n",
-            "There are 120 entities with data\n"
-          ]
-        }
-      ],
-      "execution_count": 20,
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "Alternatively, detective can discover your database credentials"
-      ],
-      "outputs": [],
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false,
-        "outputHidden": false,
-        "inputHidden": false
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "db = detective.db_from_hass_config()"
-      ],
-      "outputs": [],
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false,
-        "outputHidden": false,
-        "inputHidden": false
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Entities are listed on an attribute"
-      ],
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "db.entities[:10]"
-      ],
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "execution_count": 21,
-          "data": {
-            "text/plain": [
-              "['sensor.netatmo_master_bedroom_health',\n",
-              " 'sensor.mqtt_test',\n",
-              " 'sensor.hue_front_porch_sensor_temperature',\n",
-              " 'sensor.blink_living_room_temperature',\n",
-              " 'persistent_notification.http_login',\n",
-              " 'sensor.netatmo_master_bedroom_humidity',\n",
-              " 'sensor.garden_sensor_temperature',\n",
-              " 'sensor.battery_state',\n",
-              " 'device_tracker.robin_robin',\n",
-              " 'sensor.netatmo_master_bedroom_wifi']"
-            ]
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 21,
-      "metadata": {}
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Use `fetch_all_sensor_data()` to fetch all your sensor data into a pandas dataframe in memory. Note that by default the number of states returned is limited but this can optionally be over-ridden as shown below. "
-      ],
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df = db.fetch_all_sensor_data(limit=100000)"
-      ],
-      "outputs": [],
-      "execution_count": null,
-      "metadata": {}
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Take a minute to read about the differences between [wide-form and long-form data](https://altair-viz.github.io/user_guide/data.html#long-form-vs-wide-form-data). The Pandas dataframe we have is in long form."
-      ],
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.head()"
-      ],
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "execution_count": 23,
-          "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>domain</th>\n",
-              "      <th>entity_id</th>\n",
-              "      <th>state</th>\n",
-              "      <th>last_changed</th>\n",
-              "      <th>attributes</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>binary_sensor</td>\n",
-              "      <td>binary_sensor.office_sensor_motion</td>\n",
-              "      <td>off</td>\n",
-              "      <td>2020-01-05 15:10:49.039974+00:00</td>\n",
-              "      <td>{\"battery_level\": 50, \"sensitivity\": 2, \"sensi...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_co2</td>\n",
-              "      <td>449</td>\n",
-              "      <td>2020-01-05 15:10:36.232081+00:00</td>\n",
-              "      <td>{\"unit_of_measurement\": \"ppm\", \"friendly_name\"...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_temperature</td>\n",
-              "      <td>15.9</td>\n",
-              "      <td>2020-01-05 15:10:36.228490+00:00</td>\n",
-              "      <td>{\"unit_of_measurement\": \"\\u00b0C\", \"friendly_n...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_min_temp</td>\n",
-              "      <td>15.9</td>\n",
-              "      <td>2020-01-05 15:10:36.223739+00:00</td>\n",
-              "      <td>{\"unit_of_measurement\": \"\\u00b0C\", \"friendly_n...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_pressure</td>\n",
-              "      <td>1032.4</td>\n",
-              "      <td>2020-01-05 15:10:36.215898+00:00</td>\n",
-              "      <td>{\"unit_of_measurement\": \"mbar\", \"friendly_name...</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "          domain                                  entity_id   state  \\\n",
-              "0  binary_sensor         binary_sensor.office_sensor_motion     off   \n",
-              "1         sensor          sensor.netatmo_master_bedroom_co2     449   \n",
-              "2         sensor  sensor.netatmo_master_bedroom_temperature    15.9   \n",
-              "3         sensor     sensor.netatmo_master_bedroom_min_temp    15.9   \n",
-              "4         sensor     sensor.netatmo_master_bedroom_pressure  1032.4   \n",
-              "\n",
-              "                      last_changed  \\\n",
-              "0 2020-01-05 15:10:49.039974+00:00   \n",
-              "1 2020-01-05 15:10:36.232081+00:00   \n",
-              "2 2020-01-05 15:10:36.228490+00:00   \n",
-              "3 2020-01-05 15:10:36.223739+00:00   \n",
-              "4 2020-01-05 15:10:36.215898+00:00   \n",
-              "\n",
-              "                                          attributes  \n",
-              "0  {\"battery_level\": 50, \"sensitivity\": 2, \"sensi...  \n",
-              "1  {\"unit_of_measurement\": \"ppm\", \"friendly_name\"...  \n",
-              "2  {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_n...  \n",
-              "3  {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_n...  \n",
-              "4  {\"unit_of_measurement\": \"mbar\", \"friendly_name...  "
-            ]
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 23,
-      "metadata": {}
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "It is necessary to do some formatting of the data before we can plot it, and detective provides several functions to assist. You should familiarise yourself with these functions and create your own."
-      ],
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df = df[df['domain']=='sensor']\n",
-        "df = functions.generate_features(df)\n",
-        "df = functions.format_dataframe(df)"
-      ],
-      "outputs": [],
-      "execution_count": 24,
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.head()"
-      ],
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "execution_count": 25,
-          "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>domain</th>\n",
-              "      <th>entity_id</th>\n",
-              "      <th>state</th>\n",
-              "      <th>last_changed</th>\n",
-              "      <th>attributes</th>\n",
-              "      <th>device_class</th>\n",
-              "      <th>unit_of_measurement</th>\n",
-              "      <th>friendly_name</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_co2</td>\n",
-              "      <td>449.00</td>\n",
-              "      <td>2020-01-05 15:10:36.232081</td>\n",
-              "      <td>{'unit_of_measurement': 'ppm', 'friendly_name'...</td>\n",
-              "      <td>unknown</td>\n",
-              "      <td>ppm</td>\n",
-              "      <td>netatmo Master Bedroom CO2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_temperature</td>\n",
-              "      <td>15.90</td>\n",
-              "      <td>2020-01-05 15:10:36.228490</td>\n",
-              "      <td>{'unit_of_measurement': '°C', 'friendly_name':...</td>\n",
-              "      <td>temperature</td>\n",
-              "      <td>°C</td>\n",
-              "      <td>netatmo Master Bedroom Temperature</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_min_temp</td>\n",
-              "      <td>15.90</td>\n",
-              "      <td>2020-01-05 15:10:36.223739</td>\n",
-              "      <td>{'unit_of_measurement': '°C', 'friendly_name':...</td>\n",
-              "      <td>unknown</td>\n",
-              "      <td>°C</td>\n",
-              "      <td>netatmo Master Bedroom Min Temp.</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_pressure</td>\n",
-              "      <td>1032.40</td>\n",
-              "      <td>2020-01-05 15:10:36.215898</td>\n",
-              "      <td>{'unit_of_measurement': 'mbar', 'friendly_name...</td>\n",
-              "      <td>unknown</td>\n",
-              "      <td>mbar</td>\n",
-              "      <td>netatmo Master Bedroom Pressure</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.garden_sensor_light_level</td>\n",
-              "      <td>373.25</td>\n",
-              "      <td>2020-01-05 15:10:13.039212</td>\n",
-              "      <td>{'battery_level': 100, 'lightlevel': 25721, 'd...</td>\n",
-              "      <td>illuminance</td>\n",
-              "      <td>lx</td>\n",
-              "      <td>Garden sensor light level</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   domain                                  entity_id    state  \\\n",
-              "1  sensor          sensor.netatmo_master_bedroom_co2   449.00   \n",
-              "2  sensor  sensor.netatmo_master_bedroom_temperature    15.90   \n",
-              "3  sensor     sensor.netatmo_master_bedroom_min_temp    15.90   \n",
-              "4  sensor     sensor.netatmo_master_bedroom_pressure  1032.40   \n",
-              "5  sensor           sensor.garden_sensor_light_level   373.25   \n",
-              "\n",
-              "                last_changed  \\\n",
-              "1 2020-01-05 15:10:36.232081   \n",
-              "2 2020-01-05 15:10:36.228490   \n",
-              "3 2020-01-05 15:10:36.223739   \n",
-              "4 2020-01-05 15:10:36.215898   \n",
-              "5 2020-01-05 15:10:13.039212   \n",
-              "\n",
-              "                                          attributes device_class  \\\n",
-              "1  {'unit_of_measurement': 'ppm', 'friendly_name'...      unknown   \n",
-              "2  {'unit_of_measurement': '°C', 'friendly_name':...  temperature   \n",
-              "3  {'unit_of_measurement': '°C', 'friendly_name':...      unknown   \n",
-              "4  {'unit_of_measurement': 'mbar', 'friendly_name...      unknown   \n",
-              "5  {'battery_level': 100, 'lightlevel': 25721, 'd...  illuminance   \n",
-              "\n",
-              "  unit_of_measurement                       friendly_name  \n",
-              "1                 ppm          netatmo Master Bedroom CO2  \n",
-              "2                  °C  netatmo Master Bedroom Temperature  \n",
-              "3                  °C    netatmo Master Bedroom Min Temp.  \n",
-              "4                mbar     netatmo Master Bedroom Pressure  \n",
-              "5                  lx           Garden sensor light level  "
-            ]
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 25,
-      "metadata": {}
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebook showing usage of the data detective."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To import detective from relative folder\n",
+    "import detective.core as detective\n",
+    "import detective.functions as functions\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Supply the db_url as described in the [recorder integration docs](https://www.home-assistant.io/integrations/recorder/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_url = \"my_url\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip3 install psycopg2-binary # required for postgres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "Notice the new feature columns added. It is straightforward to create your own features, for example to add a `day_of_week` column"
-      ],
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully connected to database postgresql://postgres:***@192.168.1.164/homeassistant\n",
+      "There are 127 entities with data\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = detective.HassDatabase(db_url) # To init without fetching entities fetch_entities=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "inputHidden": false,
+    "jupyter": {
+     "outputs_hidden": false
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['day_of_week'] = df['last_changed'].apply(lambda x : x.dayofweek)"
-      ],
-      "outputs": [],
-      "execution_count": 26,
-      "metadata": {}
+    "outputHidden": false
+   },
+   "outputs": [],
+   "source": [
+    "Alternatively, detective can discover your database credentials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "inputHidden": false,
+    "jupyter": {
+     "outputs_hidden": false
     },
+    "outputHidden": false
+   },
+   "outputs": [],
+   "source": [
+    "db = detective.db_from_hass_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Entities are listed on an attribute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "df.head()"
-      ],
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "execution_count": 27,
-          "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>domain</th>\n",
-              "      <th>entity_id</th>\n",
-              "      <th>state</th>\n",
-              "      <th>last_changed</th>\n",
-              "      <th>attributes</th>\n",
-              "      <th>device_class</th>\n",
-              "      <th>unit_of_measurement</th>\n",
-              "      <th>friendly_name</th>\n",
-              "      <th>day_of_week</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_co2</td>\n",
-              "      <td>449.00</td>\n",
-              "      <td>2020-01-05 15:10:36.232081</td>\n",
-              "      <td>{'unit_of_measurement': 'ppm', 'friendly_name'...</td>\n",
-              "      <td>unknown</td>\n",
-              "      <td>ppm</td>\n",
-              "      <td>netatmo Master Bedroom CO2</td>\n",
-              "      <td>6</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_temperature</td>\n",
-              "      <td>15.90</td>\n",
-              "      <td>2020-01-05 15:10:36.228490</td>\n",
-              "      <td>{'unit_of_measurement': '°C', 'friendly_name':...</td>\n",
-              "      <td>temperature</td>\n",
-              "      <td>°C</td>\n",
-              "      <td>netatmo Master Bedroom Temperature</td>\n",
-              "      <td>6</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_min_temp</td>\n",
-              "      <td>15.90</td>\n",
-              "      <td>2020-01-05 15:10:36.223739</td>\n",
-              "      <td>{'unit_of_measurement': '°C', 'friendly_name':...</td>\n",
-              "      <td>unknown</td>\n",
-              "      <td>°C</td>\n",
-              "      <td>netatmo Master Bedroom Min Temp.</td>\n",
-              "      <td>6</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.netatmo_master_bedroom_pressure</td>\n",
-              "      <td>1032.40</td>\n",
-              "      <td>2020-01-05 15:10:36.215898</td>\n",
-              "      <td>{'unit_of_measurement': 'mbar', 'friendly_name...</td>\n",
-              "      <td>unknown</td>\n",
-              "      <td>mbar</td>\n",
-              "      <td>netatmo Master Bedroom Pressure</td>\n",
-              "      <td>6</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>sensor</td>\n",
-              "      <td>sensor.garden_sensor_light_level</td>\n",
-              "      <td>373.25</td>\n",
-              "      <td>2020-01-05 15:10:13.039212</td>\n",
-              "      <td>{'battery_level': 100, 'lightlevel': 25721, 'd...</td>\n",
-              "      <td>illuminance</td>\n",
-              "      <td>lx</td>\n",
-              "      <td>Garden sensor light level</td>\n",
-              "      <td>6</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   domain                                  entity_id    state  \\\n",
-              "1  sensor          sensor.netatmo_master_bedroom_co2   449.00   \n",
-              "2  sensor  sensor.netatmo_master_bedroom_temperature    15.90   \n",
-              "3  sensor     sensor.netatmo_master_bedroom_min_temp    15.90   \n",
-              "4  sensor     sensor.netatmo_master_bedroom_pressure  1032.40   \n",
-              "5  sensor           sensor.garden_sensor_light_level   373.25   \n",
-              "\n",
-              "                last_changed  \\\n",
-              "1 2020-01-05 15:10:36.232081   \n",
-              "2 2020-01-05 15:10:36.228490   \n",
-              "3 2020-01-05 15:10:36.223739   \n",
-              "4 2020-01-05 15:10:36.215898   \n",
-              "5 2020-01-05 15:10:13.039212   \n",
-              "\n",
-              "                                          attributes device_class  \\\n",
-              "1  {'unit_of_measurement': 'ppm', 'friendly_name'...      unknown   \n",
-              "2  {'unit_of_measurement': '°C', 'friendly_name':...  temperature   \n",
-              "3  {'unit_of_measurement': '°C', 'friendly_name':...      unknown   \n",
-              "4  {'unit_of_measurement': 'mbar', 'friendly_name...      unknown   \n",
-              "5  {'battery_level': 100, 'lightlevel': 25721, 'd...  illuminance   \n",
-              "\n",
-              "  unit_of_measurement                       friendly_name  day_of_week  \n",
-              "1                 ppm          netatmo Master Bedroom CO2            6  \n",
-              "2                  °C  netatmo Master Bedroom Temperature            6  \n",
-              "3                  °C    netatmo Master Bedroom Min Temp.            6  \n",
-              "4                mbar     netatmo Master Bedroom Pressure            6  \n",
-              "5                  lx           Garden sensor light level            6  "
-            ]
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 27,
-      "metadata": {}
-    },
+     "data": {
+      "text/plain": [
+       "['sensor.netatmo_netatmo_pressure',\n",
+       " 'sensor.robins_iphone_storage',\n",
+       " 'sensor.robins_ipad_last_update_trigger',\n",
+       " 'sensor.hue_front_porch_sensor_temperature',\n",
+       " 'sensor.blink_living_room_temperature',\n",
+       " 'persistent_notification.http_login',\n",
+       " 'sensor.garden_sensor_temperature',\n",
+       " 'sensor.robins_ipad_storage',\n",
+       " 'sensor.netatmo_netatmo_noise',\n",
+       " 'water_heater.hot_water']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.entities[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `fetch_all_data_of()` to get data for a single entity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Plot some data\n",
-        "First plot using [Seaborn](https://seaborn.pydata.org/)"
-      ],
-      "metadata": {}
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The returned Pandas dataframe has 4690 rows of data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df1 = db.fetch_all_data_of(('sensor.netatmo_netatmo_noise',))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "#!pip install seaborn # Uncomment to install if required"
-      ],
-      "outputs": [],
-      "execution_count": 35,
-      "metadata": {}
-    },
+     "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>domain</th>\n",
+       "      <th>entity_id</th>\n",
+       "      <th>state</th>\n",
+       "      <th>last_changed</th>\n",
+       "      <th>attributes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.netatmo_netatmo_noise</td>\n",
+       "      <td>37</td>\n",
+       "      <td>2020-11-04 04:57:42.156591+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"dB\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.netatmo_netatmo_noise</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2020-11-04 04:46:42.141964+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"dB\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.netatmo_netatmo_noise</td>\n",
+       "      <td>35</td>\n",
+       "      <td>2020-11-03 22:23:40.991715+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"dB\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.netatmo_netatmo_noise</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2020-11-03 22:13:40.955074+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"dB\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.netatmo_netatmo_noise</td>\n",
+       "      <td>35</td>\n",
+       "      <td>2020-11-03 21:02:40.789568+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"dB\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   domain                     entity_id state  \\\n",
+       "0  sensor  sensor.netatmo_netatmo_noise    37   \n",
+       "1  sensor  sensor.netatmo_netatmo_noise    36   \n",
+       "2  sensor  sensor.netatmo_netatmo_noise    35   \n",
+       "3  sensor  sensor.netatmo_netatmo_noise    36   \n",
+       "4  sensor  sensor.netatmo_netatmo_noise    35   \n",
+       "\n",
+       "                      last_changed  \\\n",
+       "0 2020-11-04 04:57:42.156591+00:00   \n",
+       "1 2020-11-04 04:46:42.141964+00:00   \n",
+       "2 2020-11-03 22:23:40.991715+00:00   \n",
+       "3 2020-11-03 22:13:40.955074+00:00   \n",
+       "4 2020-11-03 21:02:40.789568+00:00   \n",
+       "\n",
+       "                                          attributes  \n",
+       "0  {\"unit_of_measurement\": \"dB\", \"friendly_name\":...  \n",
+       "1  {\"unit_of_measurement\": \"dB\", \"friendly_name\":...  \n",
+       "2  {\"unit_of_measurement\": \"dB\", \"friendly_name\":...  \n",
+       "3  {\"unit_of_measurement\": \"dB\", \"friendly_name\":...  \n",
+       "4  {\"unit_of_measurement\": \"dB\", \"friendly_name\":...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `fetch_all_sensor_data()` to fetch all your sensor data into a pandas dataframe in memory. Note that by default the number of states returned is limited but this can optionally be over-ridden as shown below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "fig, ax = plt.subplots(1, figsize=(10,6))\n",
-        "sns.lineplot(\n",
-        "    x='last_changed', \n",
-        "    y='state', \n",
-        "    hue='entity_id', \n",
-        "    data=df[df['device_class'] == 'temperature'], \n",
-        "    ax=ax);"
-      ],
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": [
-              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\n"
-            ],
-            "text/plain": [
-              "<Figure size 720x432 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "execution_count": 29,
-      "metadata": {}
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The returned Pandas dataframe has 100000 rows of data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = db.fetch_all_sensor_data(limit=100000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take a minute to read about the differences between [wide-form and long-form data](https://altair-viz.github.io/user_guide/data.html#long-form-vs-wide-form-data). The Pandas dataframe we have is in long form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "Now using [Altair](https://altair-viz.github.io/index.html)"
-      ],
-      "metadata": {}
-    },
+     "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>domain</th>\n",
+       "      <th>entity_id</th>\n",
+       "      <th>state</th>\n",
+       "      <th>last_changed</th>\n",
+       "      <th>attributes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-11-04 05:07:53.407843+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"pm\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-11-04 05:07:53.407420+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"pm\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.mqtt_sensor</td>\n",
+       "      <td>{\"temperature\": 18, \"pressure\": 102780, \"humid...</td>\n",
+       "      <td>2020-11-04 05:07:53.406743+00:00</td>\n",
+       "      <td>{\"friendly_name\": \"MQTT Sensor\"}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2020-11-04 05:07:51.640630+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"pm\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2020-11-04 05:07:51.640131+00:00</td>\n",
+       "      <td>{\"unit_of_measurement\": \"pm\", \"friendly_name\":...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   domain           entity_id  \\\n",
+       "0  sensor   sensor.enviro_pm2   \n",
+       "1  sensor  sensor.enviro_pm10   \n",
+       "2  sensor  sensor.mqtt_sensor   \n",
+       "3  sensor   sensor.enviro_pm2   \n",
+       "4  sensor  sensor.enviro_pm10   \n",
+       "\n",
+       "                                               state  \\\n",
+       "0                                                  1   \n",
+       "1                                                  1   \n",
+       "2  {\"temperature\": 18, \"pressure\": 102780, \"humid...   \n",
+       "3                                                  2   \n",
+       "4                                                  2   \n",
+       "\n",
+       "                      last_changed  \\\n",
+       "0 2020-11-04 05:07:53.407843+00:00   \n",
+       "1 2020-11-04 05:07:53.407420+00:00   \n",
+       "2 2020-11-04 05:07:53.406743+00:00   \n",
+       "3 2020-11-04 05:07:51.640630+00:00   \n",
+       "4 2020-11-04 05:07:51.640131+00:00   \n",
+       "\n",
+       "                                          attributes  \n",
+       "0  {\"unit_of_measurement\": \"pm\", \"friendly_name\":...  \n",
+       "1  {\"unit_of_measurement\": \"pm\", \"friendly_name\":...  \n",
+       "2                   {\"friendly_name\": \"MQTT Sensor\"}  \n",
+       "3  {\"unit_of_measurement\": \"pm\", \"friendly_name\":...  \n",
+       "4  {\"unit_of_measurement\": \"pm\", \"friendly_name\":...  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is necessary to do some formatting of the data before we can plot it, and detective provides several functions to assist. You should familiarise yourself with these functions and create your own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[df['domain']=='sensor']\n",
+    "df = functions.generate_features(df)\n",
+    "df = functions.format_dataframe(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "# !pip install altair # Uncomment to install altair"
-      ],
-      "outputs": [],
-      "execution_count": 34,
-      "metadata": {}
-    },
+     "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>domain</th>\n",
+       "      <th>entity_id</th>\n",
+       "      <th>state</th>\n",
+       "      <th>last_changed</th>\n",
+       "      <th>attributes</th>\n",
+       "      <th>device_class</th>\n",
+       "      <th>unit_of_measurement</th>\n",
+       "      <th>friendly_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-11-04 05:07:53.407843</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm10</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-11-04 05:07:53.407420</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2020-11-04 05:07:51.640630</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm10</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2020-11-04 05:07:51.640131</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-11-04 05:07:51.639580</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   domain           entity_id  state               last_changed  \\\n",
+       "0  sensor   sensor.enviro_pm2    1.0 2020-11-04 05:07:53.407843   \n",
+       "1  sensor  sensor.enviro_pm10    1.0 2020-11-04 05:07:53.407420   \n",
+       "3  sensor   sensor.enviro_pm2    2.0 2020-11-04 05:07:51.640630   \n",
+       "4  sensor  sensor.enviro_pm10    2.0 2020-11-04 05:07:51.640131   \n",
+       "5  sensor   sensor.enviro_pm1    1.0 2020-11-04 05:07:51.639580   \n",
+       "\n",
+       "                                          attributes device_class  \\\n",
+       "0  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "1  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "3  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "4  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "5  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "\n",
+       "  unit_of_measurement friendly_name  \n",
+       "0                  pm    enviro_pm2  \n",
+       "1                  pm   enviro_pm10  \n",
+       "3                  pm    enviro_pm2  \n",
+       "4                  pm   enviro_pm10  \n",
+       "5                  pm    enviro_pm1  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice the new feature columns added. It is straightforward to create your own features, for example to add a `day_of_week` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['day_of_week'] = df['last_changed'].apply(lambda x : x.dayofweek)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "import altair as alt\n",
-        "alt.data_transformers.enable('default', max_rows=None)\n",
-        "\n",
-        "alt.Chart(df[df['device_class'] == 'temperature']).mark_line().encode(\n",
-        "    x='last_changed',\n",
-        "    y='state',\n",
-        "    color='entity_id',\n",
-        "    tooltip=['entity_id', 'state', 'last_changed']\n",
-        ").properties(\n",
-        "    width=800,\n",
-        "    height=300\n",
-        ").interactive()"
-      ],
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "execution_count": 33,
-          "data": {
-            "text/html": [
-              "\n",
-              "<div id=\"altair-viz-1\"></div>\n",
-              "<script type=\"text/javascript\">\n",
-              "  (function(spec, embedOpt){\n",
-              "    const outputDiv = document.getElementById(\"altair-viz-1\");\n",
-              "    const paths = {\n",
-              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
-              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
-              "    };\n",
-              "\n",
-              "    function loadScript(lib) {\n",
-              "      return new Promise(function(resolve, reject) {\n",
-              "        var s = document.createElement('script');\n",
-              "        s.src = paths[lib];\n",
-              "        s.async = true;\n",
-              "        s.onload = () => resolve(paths[lib]);\n",
-              "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-              "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-              "      });\n",
-              "    }\n",
-              "\n",
-              "    function showError(err) {\n",
-              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-              "      throw err;\n",
-              "    }\n",
-              "\n",
-              "    function displayChart(vegaEmbed) {\n",
-              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-              "    }\n",
-              "\n",
-              "    if(typeof define === \"function\" && define.amd) {\n",
-              "      requirejs.config({paths});\n",
-              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-              "    } else if (typeof vegaEmbed === \"function\") {\n",
-              "      displayChart(vegaEmbed);\n",
-              "    } else {\n",
-              "      loadScript(\"vega\")\n",
-              "        .then(() => loadScript(\"vega-lite\"))\n",
-              "        .then(() => loadScript(\"vega-embed\"))\n",
-              "        .catch(showError)\n",
-              "        .then(() => displayChart(vegaEmbed));\n",
-              "    }\n",
-              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-5af4ad3f17a64a2ed31d9acd7493ce70\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"entity_id\"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"entity_id\"}, {\"type\": \"quantitative\", \"field\": \"state\"}, {\"type\": \"temporal\", \"field\": \"last_changed\"}], \"x\": {\"type\": \"temporal\", \"field\": \"last_changed\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"state\"}}, \"height\": 300, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"width\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-5af4ad3f17a64a2ed31d9acd7493ce70\": [{\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_master_bedroom_temperature\", \"state\": 15.9, \"last_changed\": \"2020-01-05T15:10:36.228490\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 11.78, \"last_changed\": \"2020-01-05T15:09:07.058902\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 7.44, \"last_changed\": \"2020-01-05T15:06:49.034204\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 12.06, \"last_changed\": \"2020-01-05T15:04:07.034699\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.36, \"last_changed\": \"2020-01-05T15:02:31.035706\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 7.3, \"last_changed\": \"2020-01-05T15:01:49.035635\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 12.34, \"last_changed\": \"2020-01-05T14:59:07.035124\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 7.44, \"last_changed\": \"2020-01-05T14:56:49.035774\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 12.2, \"last_changed\": \"2020-01-05T14:54:07.033578\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 7.58, \"last_changed\": \"2020-01-05T14:51:49.035113\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 9.51, \"last_changed\": \"2020-01-05T14:49:37.044269\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.22, \"last_changed\": \"2020-01-05T14:47:37.032980\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_master_bedroom_temperature\", \"state\": 16.0, \"last_changed\": \"2020-01-05T14:45:19.231725\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 11.92, \"last_changed\": \"2020-01-05T14:44:07.033461\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.08, \"last_changed\": \"2020-01-05T14:42:37.032579\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.22, \"last_changed\": \"2020-01-05T14:37:37.033931\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 12.06, \"last_changed\": \"2020-01-05T14:34:07.034271\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.36, \"last_changed\": \"2020-01-05T14:32:37.032184\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 7.44, \"last_changed\": \"2020-01-05T14:31:55.034866\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 11.78, \"last_changed\": \"2020-01-05T14:29:07.062577\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.22, \"last_changed\": \"2020-01-05T14:27:43.038263\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.17, \"last_changed\": \"2020-01-05T14:26:43.042077\", \"attributes\": {\"battery_level\": 61, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 11.92, \"last_changed\": \"2020-01-05T14:24:19.034872\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 7.58, \"last_changed\": \"2020-01-05T14:21:55.034455\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_master_bedroom_temperature\", \"state\": 16.1, \"last_changed\": \"2020-01-05T14:20:32.414605\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 6}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 15.08, \"last_changed\": \"2020-01-05T14:17:43.055292\", \"attributes\": {\"battery_level\": 50, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor 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17.6, \"last_changed\": \"2019-12-28T07:43:08.204703\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 9.22, \"last_changed\": \"2019-12-28T07:38:56.038753\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.59, \"last_changed\": \"2019-12-28T07:38:38.033841\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_master_bedroom_temperature\", \"state\": 17.7, \"last_changed\": \"2019-12-28T07:32:49.438081\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 12.91, \"last_changed\": \"2019-12-28T07:31:08.034928\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 9.37, \"last_changed\": \"2019-12-28T07:28:56.032688\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.73, \"last_changed\": \"2019-12-28T07:28:38.031829\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 13.05, \"last_changed\": \"2019-12-28T07:26:08.043568\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 17.91, \"last_changed\": \"2019-12-28T07:24:26.033245\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 9.22, \"last_changed\": \"2019-12-28T07:23:56.032501\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 13.19, \"last_changed\": \"2019-12-28T07:21:14.034231\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.05, \"last_changed\": \"2019-12-28T07:19:26.032872\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.87, \"last_changed\": \"2019-12-28T07:18:38.032532\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 13.33, \"last_changed\": \"2019-12-28T07:16:14.036897\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_master_bedroom_temperature\", \"state\": 17.6, \"last_changed\": \"2019-12-28T07:15:49.620946\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.19, \"last_changed\": \"2019-12-28T07:14:26.033234\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.73, \"last_changed\": \"2019-12-28T07:13:38.030883\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 13.19, \"last_changed\": \"2019-12-28T07:11:14.032179\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.33, \"last_changed\": \"2019-12-28T07:09:26.032760\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.59, \"last_changed\": \"2019-12-28T07:08:38.031993\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 12.91, \"last_changed\": \"2019-12-28T07:06:14.033880\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.61, \"last_changed\": \"2019-12-28T07:04:26.032869\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 6.17, \"last_changed\": \"2019-12-28T06:58:44.052737\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.75, \"last_changed\": \"2019-12-28T06:54:32.033604\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 6.31, \"last_changed\": \"2019-12-28T06:53:44.033528\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.45, \"last_changed\": \"2019-12-28T06:53:38.053183\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 13.05, \"last_changed\": \"2019-12-28T06:51:14.032064\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.89, \"last_changed\": \"2019-12-28T06:49:32.031637\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_master_bedroom_temperature\", \"state\": 17.7, \"last_changed\": \"2019-12-28T06:48:57.530851\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"netatmo Master Bedroom Temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.47, \"last_changed\": \"2019-12-28T06:48:56.247883\", \"attributes\": {\"battery_level\": 53, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_motion_sensor_1_temperature\", \"state\": 13.19, \"last_changed\": \"2019-12-28T06:48:56.245785\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 16.59, \"last_changed\": \"2019-12-28T06:48:56.242987\", \"attributes\": {\"battery_level\": 62, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 6.17, \"last_changed\": \"2019-12-28T06:48:56.240924\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 5}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 9.08, \"last_changed\": \"2019-12-28T06:48:56.238486\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
-              "</script>"
-            ],
-            "text/plain": [
-              "alt.Chart(...)"
-            ]
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 33,
-      "metadata": {}
-    },
+     "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>domain</th>\n",
+       "      <th>entity_id</th>\n",
+       "      <th>state</th>\n",
+       "      <th>last_changed</th>\n",
+       "      <th>attributes</th>\n",
+       "      <th>device_class</th>\n",
+       "      <th>unit_of_measurement</th>\n",
+       "      <th>friendly_name</th>\n",
+       "      <th>day_of_week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-11-04 05:07:53.407843</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm10</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-11-04 05:07:53.407420</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2020-11-04 05:07:51.640630</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm10</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2020-11-04 05:07:51.640131</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sensor</td>\n",
+       "      <td>sensor.enviro_pm1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-11-04 05:07:51.639580</td>\n",
+       "      <td>{'unit_of_measurement': 'pm', 'friendly_name':...</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>pm</td>\n",
+       "      <td>enviro_pm1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   domain           entity_id  state               last_changed  \\\n",
+       "0  sensor   sensor.enviro_pm2    1.0 2020-11-04 05:07:53.407843   \n",
+       "1  sensor  sensor.enviro_pm10    1.0 2020-11-04 05:07:53.407420   \n",
+       "3  sensor   sensor.enviro_pm2    2.0 2020-11-04 05:07:51.640630   \n",
+       "4  sensor  sensor.enviro_pm10    2.0 2020-11-04 05:07:51.640131   \n",
+       "5  sensor   sensor.enviro_pm1    1.0 2020-11-04 05:07:51.639580   \n",
+       "\n",
+       "                                          attributes device_class  \\\n",
+       "0  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "1  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "3  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "4  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "5  {'unit_of_measurement': 'pm', 'friendly_name':...      unknown   \n",
+       "\n",
+       "  unit_of_measurement friendly_name  day_of_week  \n",
+       "0                  pm    enviro_pm2            2  \n",
+       "1                  pm   enviro_pm10            2  \n",
+       "3                  pm    enviro_pm2            2  \n",
+       "4                  pm   enviro_pm10            2  \n",
+       "5                  pm    enviro_pm1            2  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot some data\n",
+    "First plot using [Seaborn](https://seaborn.pydata.org/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install seaborn # Uncomment to install if required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "outputs": [],
-      "execution_count": null,
-      "metadata": {}
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.6"
-    },
-    "nteract": {
-      "version": "0.15.0"
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(1, figsize=(20,6))\n",
+    "sns.lineplot(\n",
+    "    x='last_changed', \n",
+    "    y='state', \n",
+    "    hue='entity_id', \n",
+    "    data=df[df['device_class'] == 'temperature'], \n",
+    "    ax=ax);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now using [Altair](https://altair-viz.github.io/index.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install altair # Uncomment to install altair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-25522a4637944292836733369999a9a9\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-25522a4637944292836733369999a9a9\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-25522a4637944292836733369999a9a9\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function loadScript(lib) {\n",
+       "      return new Promise(function(resolve, reject) {\n",
+       "        var s = document.createElement('script');\n",
+       "        s.src = paths[lib];\n",
+       "        s.async = true;\n",
+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-4e98f4caeaf61bddf3b751f897c4568a\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"entity_id\"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"entity_id\"}, {\"type\": \"quantitative\", \"field\": \"state\"}, {\"type\": \"temporal\", \"field\": \"last_changed\"}], \"x\": {\"type\": \"temporal\", \"field\": \"last_changed\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"state\"}}, \"height\": 300, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"width\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-4e98f4caeaf61bddf3b751f897c4568a\": [{\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_netatmo_temperature\", \"state\": 18.1, \"last_changed\": \"2020-11-04T05:07:42.185869\", \"attributes\": 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\"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.dark_sky_temperature\", \"state\": 10.0, \"last_changed\": \"2020-11-03T12:37:55.122808\", \"attributes\": {\"attribution\": \"Powered by Dark Sky\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.19, \"last_changed\": \"2020-11-03T12:36:12.026890\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 19.28, \"last_changed\": \"2020-11-03T12:36:12.026500\", \"attributes\": {\"battery_level\": 7, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 8.71, \"last_changed\": \"2020-11-03T12:31:32.030667\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.33, \"last_changed\": \"2020-11-03T12:31:12.028250\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 19.42, \"last_changed\": \"2020-11-03T12:31:12.027887\", \"attributes\": {\"battery_level\": 7, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.netatmo_netatmo_temperature\", \"state\": 20.0, \"last_changed\": \"2020-11-03T12:29:39.418768\", \"attributes\": {\"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Netatmo Netatmo Temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Netatmo Netatmo Temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.downstairs_hallway_sensor_temperature\", \"state\": 17.71, \"last_changed\": \"2020-11-03T12:28:42.031740\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.dark_sky_temperature\", \"state\": 10.0, \"last_changed\": \"2020-11-03T12:27:55.047819\", \"attributes\": {\"attribution\": \"Powered by Dark Sky\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 13.04, \"last_changed\": \"2020-11-03T12:27:22.029456\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.19, \"last_changed\": \"2020-11-03T12:26:12.029501\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 19.56, \"last_changed\": \"2020-11-03T12:26:12.029136\", \"attributes\": {\"battery_level\": 7, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.downstairs_hallway_sensor_temperature\", \"state\": 17.43, \"last_changed\": \"2020-11-03T12:23:42.032056\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 12.61, \"last_changed\": \"2020-11-03T12:22:22.027292\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 8.57, \"last_changed\": \"2020-11-03T12:21:32.031536\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.05, \"last_changed\": \"2020-11-03T12:21:12.029055\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.downstairs_hallway_sensor_temperature\", \"state\": 17.15, \"last_changed\": \"2020-11-03T12:18:42.027401\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.dark_sky_temperature\", \"state\": 9.9, \"last_changed\": \"2020-11-03T12:17:55.060332\", \"attributes\": {\"attribution\": \"Powered by Dark Sky\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 12.05, \"last_changed\": \"2020-11-03T12:17:27.029671\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 8.71, \"last_changed\": \"2020-11-03T12:16:32.027521\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.19, \"last_changed\": \"2020-11-03T12:16:17.028680\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 19.42, \"last_changed\": \"2020-11-03T12:16:17.028175\", \"attributes\": {\"battery_level\": 7, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.downstairs_hallway_sensor_temperature\", \"state\": 17.0, \"last_changed\": \"2020-11-03T12:13:42.029752\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.33, \"last_changed\": \"2020-11-03T12:11:17.028211\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.downstairs_hallway_sensor_temperature\", \"state\": 16.86, \"last_changed\": \"2020-11-03T12:08:42.034285\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.dark_sky_temperature\", \"state\": 9.8, \"last_changed\": \"2020-11-03T12:07:55.092448\", \"attributes\": {\"attribution\": \"Powered by Dark Sky\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"icon\": \"mdi:thermometer\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Dark Sky Temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.garden_sensor_temperature\", \"state\": 9.0, \"last_changed\": \"2020-11-03T12:06:32.029239\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Garden sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.office_sensor_temperature\", \"state\": 18.19, \"last_changed\": \"2020-11-03T12:06:17.027206\", \"attributes\": {\"battery_level\": 1, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Office sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.living_room_sensor_temperature\", \"state\": 19.56, \"last_changed\": \"2020-11-03T12:06:17.026644\", \"attributes\": {\"battery_level\": 7, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Living room sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.downstairs_hallway_sensor_temperature\", \"state\": 17.0, \"last_changed\": \"2020-11-03T12:03:42.032537\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Downstairs hallway sensor temperature\", \"day_of_week\": 1}, {\"domain\": \"sensor\", \"entity_id\": \"sensor.hue_front_porch_sensor_temperature\", \"state\": 11.91, \"last_changed\": \"2020-11-03T12:02:27.026727\", \"attributes\": {\"battery_level\": 100, \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"device_class\": \"temperature\"}, \"device_class\": \"temperature\", \"unit_of_measurement\": \"\\u00b0C\", \"friendly_name\": \"Hue front porch sensor temperature\", \"day_of_week\": 1}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "import altair as alt\n",
+    "alt.data_transformers.enable('default', max_rows=None)\n",
+    "\n",
+    "alt.Chart(df[df['device_class'] == 'temperature']).mark_line().encode(\n",
+    "    x='last_changed',\n",
+    "    y='state',\n",
+    "    color='entity_id',\n",
+    "    tooltip=['entity_id', 'state', 'last_changed']\n",
+    ").properties(\n",
+    "    width=800,\n",
+    "    height=300\n",
+    ").interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
+  "nteract": {
+   "version": "0.15.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index 2efffa4..9ea3707 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-pandas>=0.25.0
+pandas>=1.1.4
 ruamel.yaml>=0.15.78
 SQLAlchemy>=1.2.8
 pytz
diff --git a/setup.py b/setup.py
index 55867ed..569384b 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 REQUIRES = [
-    "pandas>=0.25.0",
+    "pandas>=1.1.4",
     "ruamel.yaml>=0.15.78",
     "SQLAlchemy>=1.2.8",
     "pytz",
@@ -18,7 +18,7 @@
 
 setup(
     name="HASS-data-detective",
-    version="2.3",
+    version="2.4",
     packages=find_packages(exclude=("tests",)),
     url="https://github.com/robmarkcole/HASS-data-detective",
     keywords=["home", "automation"],