notebooks/ExampleQueries_colab.ipynb

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   "source": [
    "# Cypher Query Examples\n",
    "This notebook installs a Neo4j Graph Database, imports data in the form of CSV files, and runs a few example queries."
   ]
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   "source": [
    "#@title Check if Notebook is running in Google Colab\n",
    "in_colab = False\n",
    "try:\n",
    "    import google.colab\n",
    "    in_colab = True\n",
    "except:\n",
    "    pass"
   ]
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   "source": [
    "#@title Install software (Google Colab only)\n",
    "if in_colab:\n",
    "    # enable third party widgets in Colab\n",
    "    from google.colab import output\n",
    "    output.enable_custom_widget_manager()\n",
    "    output.no_vertical_scroll()\n",
    "\n",
    "    # copy required files\n",
    "    !wget -q https://raw.githubusercontent.com/pwrose/neo4j-ipycytoscape/master/notebooks/neo4j_utils.py\n",
    "    import os\n",
    "    if not os.path.exists(\"kg-import\"):\n",
    "        !git clone --quiet https://github.com/sbl-sdsc/kg-import.git\n",
    "\n",
    "    !cp /content/kg-import/notebooks/* /content\n",
    "\n",
    "    # install software\n",
    "    !apt -qq install openjdk-17-jre-headless 2>/dev/null > /dev/null\n",
    "    %pip install -q papermill > /dev/null\n",
    "    %pip install -q py2neo > /dev/null\n",
    "    %pip install -q ipycytoscape > /dev/null\n",
    "    %pip install -q python-dotenv > /dev/null\n",
    "\n",
    "    # set environment variables\n",
    "    from dotenv import load_dotenv\n",
    "    load_dotenv(\"kg-import/.env.colab\")\n",
    "else:\n",
    "    !curl -O https://raw.githubusercontent.com/pwrose/neo4j-ipycytoscape/master/notebooks/neo4j_utils.py\n",
    "    from dotenv import load_dotenv\n",
    "    load_dotenv(\"../.env\", override=True)"
   ]
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   "execution_count": 3,
   "id": "08c31e38-cb94-422d-b6e0-9ad7b351df87",
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   "source": [
    "#@title Imports\n",
    "import os\n",
    "import pandas as pd\n",
    "from py2neo import Graph\n",
    "import papermill as pm\n",
    "import neo4j_utils\n",
    "import neo4j_bulk_importer\n",
    "\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c5764c-33a5-4359-be0b-c5b7930c5ac9",
   "metadata": {
    "id": "57c5764c-33a5-4359-be0b-c5b7930c5ac9"
   },
   "source": [
    "### Download and install Neo4j Database\n",
    "Install the Neo4j Community Edition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a995db5-fcea-4137-8847-565895d75e19",
   "metadata": {
    "id": "5a995db5-fcea-4137-8847-565895d75e19"
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   "outputs": [],
   "source": [
    "neo4j_utils.download_neo4j_community()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b136af-8ed7-407e-b02d-6c25a96cd755",
   "metadata": {
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   "source": [
    "### Import example Knowledge Graph\n",
    "It used the CSV data files in the example_metadata and example_data files to create a Neo4J KG"
   ]
  },
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       "Executing:   0%|          | 0/85 [00:00<?, ?cell/s]"
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     "text": [
      "Launching server........ running.\n"
     ]
    }
   ],
   "source": [
    "neo4j_bulk_importer.import_from_csv_to_neo4j_community()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bc4783-38ff-47af-ab21-6a90eb3343ff",
   "metadata": {
    "id": "c3bc4783-38ff-47af-ab21-6a90eb3343ff"
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   "source": [
    "### Connect to the local Neo4j Graph database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37928585-7936-4e25-9108-eb33f73a512d",
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   "source": [
    "database = os.environ.get(\"NEO4J_DATABASE\")\n",
    "username = os.environ.get(\"NEO4J_USERNAME\")\n",
    "password = os.environ.get(\"NEO4J_PASSWORD\")\n",
    "stylesheet = os.environ.get(\"NEO4J_STYLESHEET\")\n",
    "\n",
    "graph = Graph(\"bolt://localhost:7687\", name=database, user=username, password=password)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e53d63e-0477-4cc0-8594-caf9a446568f",
   "metadata": {
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   "source": [
    "### Show Node metadata\n",
    "The MetaNodes define the properties and their types of the Nodes in the KG."
   ]
  },
  {
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       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-4f5e408e-56d2-4987-b2b3-36253d255c00 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "  <div id=\"id_30fcefb0-c4ca-4b55-a51b-dcd5f4fef892\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
       "        background-color: #E8F0FE;\n",
       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('metadata')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_30fcefb0-c4ca-4b55-a51b-dcd5f4fef892 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('metadata');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "  nodeName                             synonyms  \\\n",
       "0     City   Alternate names of city (string[])   \n",
       "1  Patient                                        \n",
       "2    State  Alternate names of state (string[])   \n",
       "3  Disease                                        \n",
       "4  Symptom                                        \n",
       "\n",
       "                                                   name  \\\n",
       "0                                 Name of city (string)   \n",
       "1                                                         \n",
       "2                                Name of state (string)   \n",
       "3  Name of disease from Human Disease Ontology (string)   \n",
       "4                              Name of symptom (string)   \n",
       "\n",
       "                                                      location  \\\n",
       "0  Latitude and longitude in WGS-84 format (point{crs:WGS-84})   \n",
       "1                                                                \n",
       "2  Latitude and longitude in WGS-84 format (point{crs:WGS-84})   \n",
       "3                                                                \n",
       "4                                                                \n",
       "\n",
       "                                                id        population  \\\n",
       "0            Geonames.org id for location (string)  Population (int)   \n",
       "1                       Unique patient id (string)                     \n",
       "2            Geonames.org id for location (string)  Population (int)   \n",
       "3  Disease id from Human Disease Ontology (string)                     \n",
       "4        Symptom id from Symptom Ontology (string)                     \n",
       "\n",
       "             firstName            lastName                         smoker  \\\n",
       "0                                                                           \n",
       "1  First name (string)  Last name (string)  Patient is a smoker (boolean)   \n",
       "2                                                                           \n",
       "3                                                                           \n",
       "4                                                                           \n",
       "\n",
       "                       sex        age  \n",
       "0                                      \n",
       "1  Biological sex (string)  Age (int)  \n",
       "2                                      \n",
       "3                                      \n",
       "4                                      "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (n:MetaNode) RETURN n;\n",
    "\"\"\"\n",
    "df = graph.run(query).to_data_frame()\n",
    "metadata = df[\"n\"].tolist()\n",
    "metadata = pd.DataFrame(metadata)\n",
    "metadata.fillna(\"\", inplace=True)\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4063e5c-8172-4c5b-a984-e03cdbc52e17",
   "metadata": {
    "id": "c4063e5c-8172-4c5b-a984-e03cdbc52e17"
   },
   "source": [
    "### Visualize the Metagraph\n",
    "The metagraph shows the node labels and relationship types of the KG. Click on a node to display the node metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a49b3dfc-bd6e-4402-b093-59a56c1f737c",
   "metadata": {
    "id": "a49b3dfc-bd6e-4402-b093-59a56c1f737c"
   },
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "MATCH p=(:MetaNode)-->(:MetaNode) RETURN p\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ad1f2d0-ba60-44f9-9a28-16cdce189ca0",
   "metadata": {
    "id": "1ad1f2d0-ba60-44f9-9a28-16cdce189ca0"
   },
   "outputs": [],
   "source": [
    "subgraph1 = graph.run(query).to_subgraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c72b910-069d-46db-8ab6-beac15d48853",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 817,
     "referenced_widgets": [
      "7b63ea6c651f489ab70ac06906439039",
      "b15c5ecf2b804258a95e763875a20726",
      "55a84db0f0ff48f88879820e2e5d5d60",
      "6e81eae511684ed8bc640f7702365212",
      "eab128c9cd8343b0ba89ee6e7a12fdfa",
      "cc750c130a514afcb1810215d6d25a43",
      "2c629bda2b044189bb7bfe066a4c180e",
      "79711b8a16804a4a82dac23e25b17ac9",
      "4a52482101a646ef8f18b028117ab6be",
      "6c77e27c5aaf4390b6fea5cdde7744b5",
      "b71c58739545418c9a709ff02ff195c1",
      "dc3503707b504290984016cb27d2d511",
      "68fc52b083f74535a20fb5e5ac177ae5"
     ]
    },
    "id": "9c72b910-069d-46db-8ab6-beac15d48853",
    "outputId": "2ee8dfbf-b8b3-4255-f4bf-470d2a85e258"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b63ea6c651f489ab70ac06906439039",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "CytoscapeWidget(cytoscape_layout={'name': 'cola', 'padding': 40, 'nodeSpacing': 65, 'nodeDimensionsIncludeLabe…"
      ]
     },
     "metadata": {
      "application/vnd.jupyter.widget-view+json": {
       "colab": {
        "custom_widget_manager": {
         "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
        }
       }
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "widget1 = neo4j_utils.draw_graph(subgraph1, stylesheet)\n",
    "widget1.layout.height = \"800px\"\n",
    "widget1.set_layout(name='cola', padding=40, nodeSpacing=65, nodeDimensionsIncludeLabels=True, unconstrIter=15000)\n",
    "widget1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ba02310-b648-4600-9b8e-660f34551feb",
   "metadata": {
    "id": "6ba02310-b648-4600-9b8e-660f34551feb"
   },
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "MATCH p=(a)-->(b) WHERE NOT \"MetaNode\" in LABELS(a) RETURN p\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "770f01b9-4e31-468b-9f88-116661f65b31",
   "metadata": {
    "id": "770f01b9-4e31-468b-9f88-116661f65b31"
   },
   "outputs": [],
   "source": [
    "### Visualize the Knowledge Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9a4bdf5-0a95-4f93-92d5-7d7d012d6296",
   "metadata": {
    "id": "a9a4bdf5-0a95-4f93-92d5-7d7d012d6296"
   },
   "outputs": [],
   "source": [
    "subgraph2 = graph.run(query).to_subgraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "980f5a21-1f9a-4d97-89c0-ff47b11a19e7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "e01a1b0e2a7140c6a7c7502583da187e",
      "fd283ba84d514dc98efab07d6513eeb2",
      "8071871041fe4434afc971e7cd089ed6",
      "fc9ddff841ad43e29a8a5f3f8598688c",
      "fa6f4d3fbde2497bafc1fe8b42e63c67",
      "85f7e93e116147868c59fa255ef5e436",
      "e46662cb2bf94d178969a66303ff6d32",
      "1130ce97da9b4fd2b9e63b4bb6276137",
      "69d0db26f2084c54bd27db01c3417955",
      "fa115d902bf14df8886a34eea904564c",
      "cab69e7f4e7e427cad70c537369f42ce",
      "8d5c2f6c67cb4a00ac3a29b8804955e9",
      "950df8183cde46ce985ecb20b4e875e1",
      "d9437c32ae0a421dba8549d870dbde16",
      "ca1cc5f948bf4681af168205df368ce6",
      "4c4fd1bc26fb4762b9ac82562a4b2e71",
      "30baf983887346d785ba30cfc1f72a97",
      "c77e99ec98e24550a6d052004738cb0e",
      "6254c1865de64f4da3cb7b18b88937db",
      "66dffe79994c44bdb316f57231674d78",
      "10d55a2e05fe466c97b40a3bbe296e3f",
      "28d8d778c867446e84c034eadfc3217c",
      "816fe01534b7428bb816669c6b3586f7",
      "85135b17c01d4cdd8c0d49dd7f074aed",
      "90209cedccd649169c82e9a0562627ca",
      "08029b349022459c807a31d33e1d6477",
      "faa9ebae3fd84553a0e6afca71f71b48",
      "290075353c134e8085c1d08da2b2c851",
      "65c1f458f35a413d8a440087989864e1",
      "3bce820d1d014d3fa428f9b2c63f43a7",
      "6138861cc29b483f950b6b1683ed30d2",
      "dd89151b6d414366b9fab62ace4417f4",
      "1c9e9c107d4d4916b0569f2e25de1200",
      "0754cc7216fc4cb48e57a306442db933"
     ]
    },
    "id": "980f5a21-1f9a-4d97-89c0-ff47b11a19e7",
    "outputId": "e06c75a0-9f80-45c0-9dfc-3548d4b4047f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e01a1b0e2a7140c6a7c7502583da187e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "CytoscapeWidget(cytoscape_layout={'name': 'cola', 'padding': 0, 'nodeSpacing': 40, 'nodeDimensionsIncludeLabel…"
      ]
     },
     "metadata": {
      "application/vnd.jupyter.widget-view+json": {
       "colab": {
        "custom_widget_manager": {
         "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
        }
       }
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "widget2 = neo4j_utils.draw_graph(subgraph2, stylesheet)\n",
    "widget2.layout.height = \"1000px\"\n",
    "widget2.set_layout(name='cola', padding=0, nodeSpacing=40, nodeDimensionsIncludeLabels=True, unconstrIter=15000)\n",
    "widget2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13eca098-2fd5-4c4a-8533-489dd5a9e0cc",
   "metadata": {
    "id": "13eca098-2fd5-4c4a-8533-489dd5a9e0cc"
   },
   "source": [
    "### Get number of Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "787283c7-114f-495f-ac74-ebd115e07f06",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "787283c7-114f-495f-ac74-ebd115e07f06",
    "outputId": "a10dec1f-9bba-4022-acfa-cf0a15612990"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of nodes: 19\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (n)\n",
    "RETURN COUNT(n);\n",
    "\"\"\"\n",
    "n = graph.evaluate(query)\n",
    "print(f'Total number of nodes: {n}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc48a488-1987-4310-aa7f-020aa29810b9",
   "metadata": {
    "id": "dc48a488-1987-4310-aa7f-020aa29810b9"
   },
   "source": [
    "### Get number of nodes by node label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b928985-9122-49f6-bd2c-ffb890dece42",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "5b928985-9122-49f6-bd2c-ffb890dece42",
    "outputId": "0ec060a4-06c9-4f53-826f-d15ba249d4fe"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "  <div id=\"df-8a554421-08a8-455c-b232-43684f004dba\" class=\"colab-df-container\">\n",
       "    <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>Node</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MetaNode</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Disease</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>State</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a554421-08a8-455c-b232-43684f004dba')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-8a554421-08a8-455c-b232-43684f004dba button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-8a554421-08a8-455c-b232-43684f004dba');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-a5700645-7434-4bd0-b7e7-0f7c2378052e\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a5700645-7434-4bd0-b7e7-0f7c2378052e')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --hover-fill-color: #174EA6;\n",
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       "      --disabled-bg-color: #DDD;\n",
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       "\n",
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       "\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
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       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a5700645-7434-4bd0-b7e7-0f7c2378052e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       Node  Count\n",
       "0  MetaNode      5\n",
       "1   Symptom      4\n",
       "2   Disease      3\n",
       "3   Patient      3\n",
       "4      City      3\n",
       "5     State      1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (n) RETURN labels(n)[0] AS Node, COUNT(n) AS Count\n",
    "ORDER BY Count DESC\n",
    "\"\"\"\n",
    "graph.run(query).to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1510b5b4-09d8-4ba8-8f4d-f022e9e4c67c",
   "metadata": {
    "id": "1510b5b4-09d8-4ba8-8f4d-f022e9e4c67c"
   },
   "source": [
    "### Where do the patients live?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3cfc24ac-3c32-40b1-95a5-8986e874a866",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "3cfc24ac-3c32-40b1-95a5-8986e874a866",
    "outputId": "e504b1e6-1dfc-489e-9bbe-0589a1f15893"
   },
   "outputs": [
    {
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       "        const dataTable =\n",
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       "                                                    [key], {});\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "\n",
       "  .colab-df-spinner {\n",
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       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
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       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
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       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "  Firstname Lastname           City\n",
       "0      John      Doe  San Francisco\n",
       "1      Jane      Doe    Los Angeles\n",
       "2     James     Bond      San Diego"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Patient)-[:LIVES_IN]->(c:City)\n",
    "RETURN p.firstName AS Firstname, p.lastName AS Lastname, c.name AS City;\n",
    "\"\"\"\n",
    "graph.run(query).to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17268732-e77b-4775-b407-958dbf9ef35b",
   "metadata": {
    "id": "17268732-e77b-4775-b407-958dbf9ef35b"
   },
   "source": [
    "### Which patients smoke?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0bb74fb-d7d8-4691-a6c2-04037f0e2153",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "c0bb74fb-d7d8-4691-a6c2-04037f0e2153",
    "outputId": "6e7b9251-c466-4f70-a174-6e4d8d0fdf6b"
   },
   "outputs": [
    {
     "data": {
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       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      display: none;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
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       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "  </style>\n",
       "\n",
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       "      const buttonEl =\n",
       "        document.querySelector('#df-d6edf22f-8370-4007-929c-9e1538c91933 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-d6edf22f-8370-4007-929c-9e1538c91933');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
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       "            title=\"Suggest charts\"\n",
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       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-b8d80e29-3214-456d-93d3-1f95a960cc58 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "  Firstname Lastname\n",
       "0      John      Doe\n",
       "1     James     Bond"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Patient)\n",
    "WHERE p.smoker = True\n",
    "RETURN p.firstName AS Firstname, p.lastName AS Lastname;\n",
    "\"\"\"\n",
    "graph.run(query).to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77a798e-de58-4baf-a7f2-ef0afc1328c4",
   "metadata": {
    "id": "b77a798e-de58-4baf-a7f2-ef0afc1328c4"
   },
   "source": [
    "## What diseases do the smokers have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a37d73f-c174-411d-bedb-584e7697bd88",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "7a37d73f-c174-411d-bedb-584e7697bd88",
    "outputId": "62407e4b-49c0-4387-fc99-e180e76a5523"
   },
   "outputs": [
    {
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       "    0% {\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "  Firstname Lastname                   Disease\n",
       "0      John      Doe  type 2 diabetes mellitus\n",
       "1     James     Bond                  COVID-19"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Patient)-[:DIAGNOSED_WITH]->(d:Disease)\n",
    "WHERE p.smoker = True\n",
    "RETURN p.firstName AS Firstname, p.lastName AS Lastname, d.name as Disease;\n",
    "\"\"\"\n",
    "graph.run(query).to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe19b92-cb32-4671-8d74-1ce2fc801471",
   "metadata": {
    "id": "dbe19b92-cb32-4671-8d74-1ce2fc801471"
   },
   "source": [
    "### Which patients live in California?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b7f21df-4820-469c-a525-d842d5070b31",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "5b7f21df-4820-469c-a525-d842d5070b31",
    "outputId": "bc918dc6-4a4d-4c7e-fb7a-3dd05e14dcfc"
   },
   "outputs": [
    {
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       "        document.querySelector('#df-488a3424-e84a-47b2-b8b5-a49c32e128f4 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "      async function convertToInteractive(key) {\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "  @keyframes spin {\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "  Firstname Lastname           City\n",
       "0     James     Bond      San Diego\n",
       "1      Jane      Doe    Los Angeles\n",
       "2      John      Doe  San Francisco"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Patient)-[:LIVES_IN]->(c:City)-[:LOCATED_IN]->(s:State)\n",
    "WHERE s.name = 'California'\n",
    "RETURN p.firstName AS Firstname, p.lastName AS Lastname, c.name AS City;\n",
    "\"\"\"\n",
    "graph.run(query).to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eeddb57-8c1b-4fa7-8af0-6b35617feb75",
   "metadata": {
    "id": "7eeddb57-8c1b-4fa7-8af0-6b35617feb75"
   },
   "source": [
    "### How many patients live in California?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e5653735-f8e7-4779-95d6-6573c7bce8c4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5653735-f8e7-4779-95d6-6573c7bce8c4",
    "outputId": "ec0332d4-17c3-4ca6-a3e0-078802299d3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patients in California: 3\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Patient)-[:LIVES_IN]->(:City)-[:LOCATED_IN]->(s:State)\n",
    "WHERE s.name = 'California'\n",
    "RETURN COUNT(p)\n",
    "\"\"\"\n",
    "n = graph.evaluate(query)\n",
    "print(f'Patients in California: {n}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76726091-e244-49bb-a659-7d98cb5abfeb",
   "metadata": {
    "id": "76726091-e244-49bb-a659-7d98cb5abfeb"
   },
   "source": [
    "### Full text query for Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "04fae75e-8f9a-4fde-b2ef-864d44972c0b",
   "metadata": {
    "id": "04fae75e-8f9a-4fde-b2ef-864d44972c0b"
   },
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "CALL db.index.fulltext.queryNodes('fulltext', $keyword) YIELD node, score\n",
    "RETURN LABELS(node)[0] as type, node.id as id, node.name as disease, score\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6db5522-cbd4-4d30-987b-c7903f3a1b8c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "d6db5522-cbd4-4d30-987b-c7903f3a1b8c",
    "outputId": "1c823b55-3cc6-4303-d4ae-bf620bac5fea"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>type</th>\n",
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       "      <th>disease</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disease</td>\n",
       "      <td>DOID:9352</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>0.704895</td>\n",
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       "\n",
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       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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      "text/plain": [
       "      type         id                   disease     score\n",
       "0  Disease  DOID:9352  type 2 diabetes mellitus  0.704895"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword = \"Diabetes\"\n",
    "graph.run(query, keyword=keyword).to_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GeCsLpcyUYXc",
   "metadata": {
    "id": "GeCsLpcyUYXc"
   },
   "source": [
    "## Shutdown Neo4j before closing this notebook.\n",
    "If you run this notebook locally, uncomment the last line and run neo4j_utils.stop() to stop the database. Otherwise, the database server will keep running.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "11eb5bb3-634b-47f2-b61e-5aea7e990934",
   "metadata": {
    "id": "11eb5bb3-634b-47f2-b61e-5aea7e990934"
   },
   "outputs": [],
   "source": [
    "#neo4j_utils.stop()"
   ]
  }
 ],
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