Chapter_7_Clustering.ipynb

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        "<a href=\"https://colab.research.google.com/github/pawan-cpu/AI-enabled-Generative-Quizzing/blob/main/Chapter_7_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Chapter 7: Clustering"
      ]
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      "source": [
        "## 7.2 How clustering works?"
      ]
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      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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      "metadata": {
        "id": "teq_K1h-GKAS"
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      "source": [
        "import pandas as pd\n",
        "#customers_df = pd.read_csv( \"customers.csv\" )\n",
        "customers_df=pd.read_csv(\"Income Data.csv\")"
      ]
    },
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      "source": [
        "customers_df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 300 entries, 0 to 299\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   income  300 non-null    float64\n",
            " 1   age     300 non-null    float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 4.8 KB\n"
          ]
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              "    income    age\n",
              "0  41100.0  48.75\n",
              "1  54100.0  28.10\n",
              "2  47800.0  46.75\n",
              "3  19100.0  40.25\n",
              "4  18200.0  35.80"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "customers_df",
              "summary": "{\n  \"name\": \"customers_df\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16099.21442234752,\n        \"min\": 3100.0,\n        \"max\": 60200.0,\n        \"num_unique_values\": 213,\n        \"samples\": [\n          31900.0,\n          42000.0,\n          30400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.523574425053557,\n        \"min\": 26.7,\n        \"max\": 50.4,\n        \"num_unique_values\": 204,\n        \"samples\": [\n          41.15,\n          43.05,\n          48.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 7
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      "source": [
        "customers_df.head( 5 )"
      ]
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      "execution_count": null,
      "metadata": {
        "id": "c7gaocvMGKAT"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sn.lmplot(x ='age', y ='income', data = customers_df, fit_reg = False);"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GW5qY5UGKAU"
      },
      "source": [
        "## 7.3 K-means Clustering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "gK3CAzysGKAU",
        "outputId": "d2009d46-b186-41cd-a5ca-b154514d2335"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "clusters = KMeans( 3 )\n",
        "clusters.fit( customers_df )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yLTiPzpKGKAU"
      },
      "outputs": [],
      "source": [
        "customers_df[\"clusterid\"] = clusters.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aduMUqwIGKAV",
        "outputId": "c67544d5-df83-452e-953f-6b29ce284525"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    income    age  clusterid\n",
              "0  41100.0  48.75          2\n",
              "1  54100.0  28.10          0\n",
              "2  47800.0  46.75          2\n",
              "3  19100.0  40.25          1\n",
              "4  18200.0  35.80          1"
            ],
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"customers_df[0:5]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16547.598013004786,\n        \"min\": 18200.0,\n        \"max\": 54100.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          54100.0,\n          18200.0,\n          47800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.38768442420195,\n        \"min\": 28.1,\n        \"max\": 48.75,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28.1,\n          35.8,\n          46.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clusterid\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "customers_df[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "markers = ['+','^','.']\n",
        "sn.lmplot(x ='age', y ='income', data = customers_df, fit_reg = False, hue = \"clusterid\", markers = markers);\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "VZXqetx7Omt4",
        "outputId": "7e3ebdbc-6177-4119-cf91-037494242970"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 570.986x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hgq3ZIfzGKAV"
      },
      "source": [
        "### 7.3.1 Plotting the customers with their segments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "sVvsqaCdGKAV",
        "outputId": "5439e030-f884-412b-91fe-36eb13af5148"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "lmplot() got multiple values for argument 'data'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-2968fb44eb78>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'+'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'^'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m sn.lmplot( \"age\", \"income\", \n\u001b[0m\u001b[1;32m      4\u001b[0m           \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mhue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"clusterid\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: lmplot() got multiple values for argument 'data'"
          ]
        }
      ],
      "source": [
        "markers = ['+','^','.']\n",
        "\n",
        "sn.lmplot( \"age\", \"income\",\n",
        "          data=customers_df,\n",
        "          hue = \"clusterid\",\n",
        "          fit_reg=False,\n",
        "          markers = markers,\n",
        "          size = 4 );"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cLVaQwfGKAV"
      },
      "source": [
        "### 7.3.2 Normalizing the features\n",
        "\n",
        "normalised x=value-mean/s.d"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cDrt-jl0GKAW"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3uJfrUcGKAW",
        "outputId": "5b9c03b7-6f93-495c-e7c7-75066366313b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.3701637 ,  0.09718548],\n",
              "       [-1.3791283 ,  0.90602749],\n",
              "       [ 1.10388844,  0.51405021],\n",
              "       [ 0.23849387, -1.27162408],\n",
              "       [-0.35396857, -1.32762083]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "scaler = StandardScaler()\n",
        "scaled_customers_df = scaler.fit_transform(\n",
        "    customers_df[[\"age\", \"income\"]] )\n",
        "\n",
        "scaled_customers_df[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aIQhg4YUGKAW"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "clusters_new = KMeans( 3, random_state=42 )\n",
        "clusters_new.fit( scaled_customers_df )\n",
        "customers_df[\"clusterid_new\"] = clusters_new.labels_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "markers = ['+','^','.']\n",
        "sn.lmplot(x ='age', y ='income', data = customers_df, fit_reg = False, hue = \"clusterid_new\", markers = markers);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "o6Zxe18FQfK8",
        "outputId": "399f3c7c-924b-4bb5-810b-54659364ab55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 606.611x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "markers = ['+','^','.']\n",
        "\n",
        "sn.lmplot( \"age\", \"income\",\n",
        "          data=customers_df,\n",
        "          hue = \"clusterid_new\",\n",
        "          fit_reg=False,\n",
        "          markers = markers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "jlozlhh1QX1C",
        "outputId": "06f1a3d4-e7d6-4e6a-ed98-1237ec035109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "lmplot() got multiple values for argument 'data'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-30a4f92ffc94>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'+'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'^'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m sn.lmplot( \"age\", \"income\", \n\u001b[0m\u001b[1;32m      4\u001b[0m           \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mhue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"clusterid_new\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: lmplot() got multiple values for argument 'data'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "GPQ30U0UGKAX",
        "outputId": "0c428a49-06f7-403f-a562-6b4a6ff7cf59"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "lmplot() got multiple values for argument 'data'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-0807e9a4524e>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmarkers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'+'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'^'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m sn.lmplot( \"age\", \"income\", \n\u001b[0m\u001b[1;32m      4\u001b[0m           \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mhue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"clusterid_new\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: lmplot() got multiple values for argument 'data'"
          ]
        }
      ],
      "source": [
        "markers = ['+','^','.']\n",
        "\n",
        "sn.lmplot( \"age\", \"income\",\n",
        "          data=customers_df,\n",
        "          hue = \"clusterid_new\",\n",
        "          fit_reg=False,\n",
        "          markers = markers,\n",
        "          size = 4 );\n",
        "#plt.title( \"Fig 3: Customer segments created after normalization\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VrkRnFRGKAX",
        "outputId": "b1d64ab8-c390-4cec-8eb5-5b436411191d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.46756522e+04, 3.17004348e+01],\n",
              "       [1.81447917e+04, 3.91744792e+01],\n",
              "       [4.30539326e+04, 4.64191011e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "clusters.cluster_centers_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "li1MxOAfGKAX"
      },
      "source": [
        "### 7.3.3 Cluster Centers and Interpreting the Clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "p0Hrm_PyGKAX",
        "outputId": "baf5c783-ae04-4b8a-8a5d-681c79ca04db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  clusterid        age                  income             \n",
              "                  mean       std          mean          std\n",
              "0         0  31.700435  6.122122  54675.652174  2362.224320\n",
              "1         1  39.174479  3.626068  18144.791667  6745.241906\n",
              "2         2  46.419101  2.289620  43053.932584  3613.769632"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91cb44f6-2a8f-4ed3-9535-795b8de5c4ef\" 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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>clusterid</th>\n",
              "      <th colspan=\"2\" halign=\"left\">age</th>\n",
              "      <th colspan=\"2\" halign=\"left\">income</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>31.700435</td>\n",
              "      <td>6.122122</td>\n",
              "      <td>54675.652174</td>\n",
              "      <td>2362.224320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>39.174479</td>\n",
              "      <td>3.626068</td>\n",
              "      <td>18144.791667</td>\n",
              "      <td>6745.241906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>46.419101</td>\n",
              "      <td>2.289620</td>\n",
              "      <td>43053.932584</td>\n",
              "      <td>3613.769632</td>\n",
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              "      const quickchartButtonEl =\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"                                            'std'] )\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"clusterid\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.359631168418951,\n        \"min\": 31.700434782608696,\n        \"max\": 46.41910112359551,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          31.700434782608696,\n          39.174479166666664,\n          46.41910112359551\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9452698486633175,\n        \"min\": 2.2896196788437844,\n        \"max\": 6.12212157804167,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6.12212157804167,\n          3.6260678629790997,\n          2.2896196788437844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"income\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18663.83953385618,\n        \"min\": 18144.791666666668,\n        \"max\": 54675.65217391304,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          54675.65217391304,\n          18144.791666666668,\n          43053.93258426966\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"income\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2257.702646501606,\n        \"min\": 2362.2243199164805,\n        \"max\": 6745.241906499203,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2362.2243199164805,\n          6745.241906499203,\n          3613.769631554968\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "customers_df.groupby( 'clusterid' )['age',\n",
        "                            'income'].agg( [\"mean\",\n",
        "                                            'std'] ).reset_index()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98lBM34SGKAX"
      },
      "source": [
        "## 7.4 Creating Product Segments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_z2ZlJpUGKAX"
      },
      "source": [
        "### 7.4.1 Beer Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "viWvUlvkGKAY"
      },
      "outputs": [],
      "source": [
        "beer_df = pd.read_csv( 'beer.csv' )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "xQ1lDZroGKAY",
        "outputId": "994dcbad-589d-4e9a-e125-c7aeedf476d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    name  calories  sodium  alcohol  cost\n",
              "0              Budweiser       144      15      4.7  0.43\n",
              "1                Schlitz       151      19      4.9  0.43\n",
              "2              Lowenbrau       157      15      0.9  0.48\n",
              "3            Kronenbourg       170       7      5.2  0.73\n",
              "4               Heineken       152      11      5.0  0.77\n",
              "5          Old_Milwaukee       145      23      4.6  0.28\n",
              "6             Augsberger       175      24      5.5  0.40\n",
              "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
              "8            Miller_Lite        99      10      4.3  0.43\n",
              "9        Budweiser_Light       113       8      3.7  0.40\n",
              "10                 Coors       140      18      4.6  0.44\n",
              "11           Coors_Light       102      15      4.1  0.46\n",
              "12        Michelob_Light       135      11      4.2  0.50\n",
              "13                 Becks       150      19      4.7  0.76\n",
              "14                 Kirin       149       6      5.0  0.79\n",
              "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
              "16                 Hamms       139      19      4.4  0.43\n",
              "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
              "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
              "19         Schlitz_Light        97       7      4.2  0.47"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-39eef441-f68c-47e0-a558-c22e659e74de\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>calories</th>\n",
              "      <th>sodium</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Budweiser</td>\n",
              "      <td>144</td>\n",
              "      <td>15</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Schlitz</td>\n",
              "      <td>151</td>\n",
              "      <td>19</td>\n",
              "      <td>4.9</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lowenbrau</td>\n",
              "      <td>157</td>\n",
              "      <td>15</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kronenbourg</td>\n",
              "      <td>170</td>\n",
              "      <td>7</td>\n",
              "      <td>5.2</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Heineken</td>\n",
              "      <td>152</td>\n",
              "      <td>11</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Old_Milwaukee</td>\n",
              "      <td>145</td>\n",
              "      <td>23</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Augsberger</td>\n",
              "      <td>175</td>\n",
              "      <td>24</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Srohs_Bohemian_Style</td>\n",
              "      <td>149</td>\n",
              "      <td>27</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Miller_Lite</td>\n",
              "      <td>99</td>\n",
              "      <td>10</td>\n",
              "      <td>4.3</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Budweiser_Light</td>\n",
              "      <td>113</td>\n",
              "      <td>8</td>\n",
              "      <td>3.7</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Coors</td>\n",
              "      <td>140</td>\n",
              "      <td>18</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Coors_Light</td>\n",
              "      <td>102</td>\n",
              "      <td>15</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Michelob_Light</td>\n",
              "      <td>135</td>\n",
              "      <td>11</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Becks</td>\n",
              "      <td>150</td>\n",
              "      <td>19</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kirin</td>\n",
              "      <td>149</td>\n",
              "      <td>6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pabst_Extra_Light</td>\n",
              "      <td>68</td>\n",
              "      <td>15</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Hamms</td>\n",
              "      <td>139</td>\n",
              "      <td>19</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Heilemans_Old_Style</td>\n",
              "      <td>144</td>\n",
              "      <td>24</td>\n",
              "      <td>4.9</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Olympia_Goled_Light</td>\n",
              "      <td>72</td>\n",
              "      <td>6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Schlitz_Light</td>\n",
              "      <td>97</td>\n",
              "      <td>7</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.47</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-39eef441-f68c-47e0-a558-c22e659e74de')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
              "    }\n",
              "\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-39eef441-f68c-47e0-a558-c22e659e74de 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-39eef441-f68c-47e0-a558-c22e659e74de');\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-a16ca559-9067-40db-9412-2ff30fc2faa6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a16ca559-9067-40db-9412-2ff30fc2faa6')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    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-a16ca559-9067-40db-9412-2ff30fc2faa6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "beer_df",
              "summary": "{\n  \"name\": \"beer_df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Budweiser\",\n          \"Heilemans_Old_Style\",\n          \"Pabst_Extra_Light\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 68,\n        \"max\": 175,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          144,\n          151,\n          99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 6,\n        \"max\": 27,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          24,\n          15,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0879241459625857,\n        \"min\": 0.9,\n        \"max\": 5.5,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          4.1,\n          2.3,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14485837003232177,\n        \"min\": 0.28,\n        \"max\": 0.79,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          0.5,\n          0.79,\n          0.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "beer_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xhto1A_yGKAY"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaled_beer_df = scaler.fit_transform( beer_df[['calories',\n",
        "                                                'sodium',\n",
        "                                                'alcohol',\n",
        "                                                'cost']] )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pRfTMPCGKAZ"
      },
      "source": [
        "### 7.4.2 How many clusters exist?\n",
        "\n",
        "#### 7.4.2.1 Using Dendrogram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "JbzobrMUGKAZ",
        "outputId": "cffe2a42-1cd2-4cbb-a047-b8fc7fd48b48"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cmap = sn.cubehelix_palette(as_cmap=True, rot=-.3, light=1)\n",
        "sn.clustermap(scaled_beer_df, cmap=cmap, linewidths=.2,\n",
        "              figsize = (8,8) );\n",
        "#plt.title( \"Fig 4: Dendrogram of Beer Dataset\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "eTOwSRDuGKAZ",
        "outputId": "51577292-ba42-4c26-cc2e-333d9ac75d53"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'DataFrame' object has no attribute 'ix'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-46e98a444eea>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbeer_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m         ):\n\u001b[1;32m   5901\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ix'"
          ]
        }
      ],
      "source": [
        "beer_df.ix[[10, 16]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "oFJaIDRfGKAZ",
        "outputId": "13fac509-b460-456f-eb51-97fd0f1a9809"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'DataFrame' object has no attribute 'ix'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-53-cbc545ec9188>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbeer_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m         ):\n\u001b[1;32m   5901\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ix'"
          ]
        }
      ],
      "source": [
        "beer_df.ix[[2,18]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWmiKMEbGKAZ"
      },
      "source": [
        "### 7.4.2.2 Finding Optimal Number of Clusters using Elbow Method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "LV3GxvF8GKAa",
        "outputId": "8a54fb7b-6e03-4ecf-c339-91bd0b74ddb2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cluster_range = range( 1, 10 )\n",
        "cluster_errors = []\n",
        "\n",
        "for num_clusters in cluster_range:\n",
        "  clusters = KMeans( num_clusters )\n",
        "  clusters.fit( scaled_beer_df )\n",
        "  cluster_errors.append( clusters.inertia_ )\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.plot( cluster_range, cluster_errors, marker = \"o\" );\n",
        "#plt.title( \"Fig 5: Elbow Diagram\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCCAxQPqGKAa"
      },
      "source": [
        "### 7.4.2.3 Normalizing Features\n",
        "\n",
        "#### Rescaling the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "igduyMGvGKAa"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "scaled_beer_df = scaler.fit_transform( beer_df[['calories',\n",
        "                                                'sodium',\n",
        "                                                'alcohol',\n",
        "                                                'cost']] )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MJXZxS6GKAa"
      },
      "source": [
        "### 7.4.3 Creating Clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "36KmuwREGKAa"
      },
      "outputs": [],
      "source": [
        "k = 3\n",
        "\n",
        "clusters = KMeans( k, random_state = 42 )\n",
        "clusters.fit( scaled_beer_df )\n",
        "beer_df[\"clusterid\"] = clusters.labels_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rj2VkeqGGKAa"
      },
      "source": [
        "### 7.4.4 Interpreting the Clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scf1kTZeGKAa"
      },
      "source": [
        "#### Cluster 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SsNkWKvZGKAa",
        "outputId": "98bd65ff-6825-4465-bb0c-301c5acbfb20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    name  calories  sodium  alcohol  cost  h_clusterid  \\\n",
              "0              Budweiser       144      15      4.7  0.43            1   \n",
              "1                Schlitz       151      19      4.9  0.43            1   \n",
              "5          Old_Milwaukee       145      23      4.6  0.28            1   \n",
              "6             Augsberger       175      24      5.5  0.40            1   \n",
              "7   Srohs_Bohemian_Style       149      27      4.7  0.42            1   \n",
              "10                 Coors       140      18      4.6  0.44            1   \n",
              "16                 Hamms       139      19      4.4  0.43            1   \n",
              "17   Heilemans_Old_Style       144      24      4.9  0.43            1   \n",
              "\n",
              "    clusterid  \n",
              "0           0  \n",
              "1           0  \n",
              "5           0  \n",
              "6           0  \n",
              "7           0  \n",
              "10          0  \n",
              "16          0  \n",
              "17          0  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>calories</th>\n",
              "      <th>sodium</th>\n",
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              "      <th>0</th>\n",
              "      <td>Budweiser</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Schlitz</td>\n",
              "      <td>151</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Old_Milwaukee</td>\n",
              "      <td>145</td>\n",
              "      <td>23</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.28</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Augsberger</td>\n",
              "      <td>175</td>\n",
              "      <td>24</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Srohs_Bohemian_Style</td>\n",
              "      <td>149</td>\n",
              "      <td>27</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Coors</td>\n",
              "      <td>140</td>\n",
              "      <td>18</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Hamms</td>\n",
              "      <td>139</td>\n",
              "      <td>19</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Heilemans_Old_Style</td>\n",
              "      <td>144</td>\n",
              "      <td>24</td>\n",
              "      <td>4.9</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1</td>\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"beer_df[beer_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Schlitz\",\n          \"Coors\",\n          \"Budweiser\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 139,\n        \"max\": 175,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          144,\n          151,\n          140\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 15,\n        \"max\": 27,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          15,\n          19,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33139316313320133,\n        \"min\": 4.4,\n        \"max\": 5.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          4.4,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052847489465982604,\n        \"min\": 0.28,\n        \"max\": 0.44,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.28,\n          0.44,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h_clusterid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clusterid\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "beer_df[beer_df.clusterid == 0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QNYc6ICGKAn"
      },
      "source": [
        "#### Cluster 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OGj1w_T8GKAn",
        "outputId": "c2086161-d99f-419d-8299-220881f90ba0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   name  calories  sodium  alcohol  cost  h_clusterid  \\\n",
              "2             Lowenbrau       157      15      0.9  0.48            0   \n",
              "8           Miller_Lite        99      10      4.3  0.43            0   \n",
              "9       Budweiser_Light       113       8      3.7  0.40            0   \n",
              "11          Coors_Light       102      15      4.1  0.46            0   \n",
              "12       Michelob_Light       135      11      4.2  0.50            0   \n",
              "15    Pabst_Extra_Light        68      15      2.3  0.38            0   \n",
              "18  Olympia_Goled_Light        72       6      2.9  0.46            0   \n",
              "19        Schlitz_Light        97       7      4.2  0.47            0   \n",
              "\n",
              "    clusterid  \n",
              "2           1  \n",
              "8           1  \n",
              "9           1  \n",
              "11          1  \n",
              "12          1  \n",
              "15          1  \n",
              "18          1  \n",
              "19          1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5250a2e6-d793-4235-95f4-b41329bd1a49\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>calories</th>\n",
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              "      <th>clusterid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lowenbrau</td>\n",
              "      <td>157</td>\n",
              "      <td>15</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Miller_Lite</td>\n",
              "      <td>99</td>\n",
              "      <td>10</td>\n",
              "      <td>4.3</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Budweiser_Light</td>\n",
              "      <td>113</td>\n",
              "      <td>8</td>\n",
              "      <td>3.7</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Coors_Light</td>\n",
              "      <td>102</td>\n",
              "      <td>15</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Michelob_Light</td>\n",
              "      <td>135</td>\n",
              "      <td>11</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pabst_Extra_Light</td>\n",
              "      <td>68</td>\n",
              "      <td>15</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Olympia_Goled_Light</td>\n",
              "      <td>72</td>\n",
              "      <td>6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Schlitz_Light</td>\n",
              "      <td>97</td>\n",
              "      <td>7</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0</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-5250a2e6-d793-4235-95f4-b41329bd1a49')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
              "    }\n",
              "\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-5250a2e6-d793-4235-95f4-b41329bd1a49 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-5250a2e6-d793-4235-95f4-b41329bd1a49');\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-e755d012-b2de-44fd-be45-72d813e7259d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e755d012-b2de-44fd-be45-72d813e7259d')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    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-e755d012-b2de-44fd-be45-72d813e7259d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"beer_df[beer_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Miller_Lite\",\n          \"Pabst_Extra_Light\",\n          \"Lowenbrau\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 68,\n        \"max\": 157,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          99,\n          68,\n          157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 6,\n        \"max\": 15,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          15,\n          10,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2150837713625229,\n        \"min\": 0.9,\n        \"max\": 4.3,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9,\n          4.3,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04097037257057138,\n        \"min\": 0.38,\n        \"max\": 0.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.48,\n          0.43,\n          0.38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h_clusterid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clusterid\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "beer_df[beer_df.clusterid == 1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0LKzbRUGKAn"
      },
      "source": [
        "#### Cluster 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "CJqEfw9xGKAn",
        "outputId": "e2066e87-288e-4acc-840d-8ff36eae5539"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           name  calories  sodium  alcohol  cost  h_clusterid  clusterid\n",
              "3   Kronenbourg       170       7      5.2  0.73            2          2\n",
              "4      Heineken       152      11      5.0  0.77            2          2\n",
              "13        Becks       150      19      4.7  0.76            2          2\n",
              "14        Kirin       149       6      5.0  0.79            2          2"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>calories</th>\n",
              "      <th>sodium</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cost</th>\n",
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              "      <th>clusterid</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kronenbourg</td>\n",
              "      <td>170</td>\n",
              "      <td>7</td>\n",
              "      <td>5.2</td>\n",
              "      <td>0.73</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Heineken</td>\n",
              "      <td>152</td>\n",
              "      <td>11</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.77</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Becks</td>\n",
              "      <td>150</td>\n",
              "      <td>19</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.76</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kirin</td>\n",
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              "      <td>6</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      width: 32px;\n",
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              "\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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-2492040c-a44e-4117-baeb-268e2c3f5208 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-2492040c-a44e-4117-baeb-268e2c3f5208');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
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              "    80% {\n",
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              "      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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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"beer_df[beer_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Heineken\",\n          \"Kirin\",\n          \"Kronenbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 149,\n        \"max\": 170,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          152,\n          149,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 6,\n        \"max\": 19,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11,\n          6,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.206155281280883,\n        \"min\": 4.7,\n        \"max\": 5.2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.2,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025000000000000022,\n        \"min\": 0.73,\n        \"max\": 0.79,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.77,\n          0.79,\n          0.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h_clusterid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clusterid\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "beer_df[beer_df.clusterid == 2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBAklvlXGKAo"
      },
      "source": [
        "## 7.5 Hierarchical clustering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ge967EgIGKAo"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import AgglomerativeClustering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wp8Lh6AlGKAo"
      },
      "outputs": [],
      "source": [
        "h_clusters = AgglomerativeClustering( 3 )\n",
        "h_clusters.fit( scaled_beer_df )\n",
        "beer_df[\"h_clusterid\"] = h_clusters.labels_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4RFL8LdNGKAo",
        "outputId": "b96fe08c-1fc3-44f0-9c9d-ef3b90f6aec6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   name  calories  sodium  alcohol  cost  h_clusterid\n",
              "2             Lowenbrau       157      15      0.9  0.48            0\n",
              "8           Miller_Lite        99      10      4.3  0.43            0\n",
              "9       Budweiser_Light       113       8      3.7  0.40            0\n",
              "11          Coors_Light       102      15      4.1  0.46            0\n",
              "12       Michelob_Light       135      11      4.2  0.50            0\n",
              "15    Pabst_Extra_Light        68      15      2.3  0.38            0\n",
              "18  Olympia_Goled_Light        72       6      2.9  0.46            0\n",
              "19        Schlitz_Light        97       7      4.2  0.47            0"
            ],
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              "\n",
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              "        document.querySelector('#df-05a5131e-af3d-426c-bf10-89f7ed58cbcb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            }
          },
          "metadata": {},
          "execution_count": 56
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      ],
      "source": [
        "beer_df[beer_df.h_clusterid == 0]"
      ]
    },
    {
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              "                    name  calories  sodium  alcohol  cost  h_clusterid\n",
              "0              Budweiser       144      15      4.7  0.43            1\n",
              "1                Schlitz       151      19      4.9  0.43            1\n",
              "5          Old_Milwaukee       145      23      4.6  0.28            1\n",
              "6             Augsberger       175      24      5.5  0.40            1\n",
              "7   Srohs_Bohemian_Style       149      27      4.7  0.42            1\n",
              "10                 Coors       140      18      4.6  0.44            1\n",
              "16                 Hamms       139      19      4.4  0.43            1\n",
              "17   Heilemans_Old_Style       144      24      4.9  0.43            1"
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"beer_df[beer_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Schlitz\",\n          \"Coors\",\n          \"Budweiser\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 139,\n        \"max\": 175,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          144,\n          151,\n          140\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 15,\n        \"max\": 27,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          15,\n          19,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33139316313320133,\n        \"min\": 4.4,\n        \"max\": 5.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          4.4,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052847489465982604,\n        \"min\": 0.28,\n        \"max\": 0.44,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.28,\n          0.44,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h_clusterid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "beer_df[beer_df.h_clusterid == 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "EOMgY5-5GKAo",
        "outputId": "fa1ec598-57eb-495a-875d-7cde7057d4ff"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           name  calories  sodium  alcohol  cost  h_clusterid\n",
              "3   Kronenbourg       170       7      5.2  0.73            2\n",
              "4      Heineken       152      11      5.0  0.77            2\n",
              "13        Becks       150      19      4.7  0.76            2\n",
              "14        Kirin       149       6      5.0  0.79            2"
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              "      <th>13</th>\n",
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              "      <th>14</th>\n",
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              "        document.querySelector('#df-d26aab65-e64a-4e0a-81bc-7cd43b19d45a 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-d26aab65-e64a-4e0a-81bc-7cd43b19d45a');\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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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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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              "      border-right-color: var(--fill-color);\n",
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              "      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-712220dd-1ffe-46f6-a689-413bfd18fab2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"beer_df[beer_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Heineken\",\n          \"Kirin\",\n          \"Kronenbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 149,\n        \"max\": 170,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          152,\n          149,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 6,\n        \"max\": 19,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11,\n          6,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.206155281280883,\n        \"min\": 4.7,\n        \"max\": 5.2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.2,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025000000000000022,\n        \"min\": 0.73,\n        \"max\": 0.79,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.77,\n          0.79,\n          0.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h_clusterid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "beer_df[beer_df.h_clusterid == 2]"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
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    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}