K_means.ipynb

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    "colab": {
      "name": "K means.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMPuisuRvt9Q7Nm89uSOR/z",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/VISHNUCHARAN27/Module1/blob/master/K_means.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0lP2OzkaQhw",
        "colab_type": "text"
      },
      "source": [
        "**NAME: B. VISHNU CHARAN**\n",
        "\n",
        "\n",
        "**REG NO: 17BCI0090**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9UD7DI9amtz",
        "colab_type": "code",
        "colab": {
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          "height": 195
        },
        "outputId": "8a2b23dc-6af9-4717-9863-e2a44bd2cdb5"
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/cars.csv')\n",
        "df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>cubicinches</th>\n",
              "      <th>hp</th>\n",
              "      <th>weightlbs</th>\n",
              "      <th>time-to-60</th>\n",
              "      <th>year</th>\n",
              "      <th>brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350</td>\n",
              "      <td>165</td>\n",
              "      <td>4209</td>\n",
              "      <td>12</td>\n",
              "      <td>1972</td>\n",
              "      <td>US.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.9</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>71</td>\n",
              "      <td>1925</td>\n",
              "      <td>14</td>\n",
              "      <td>1980</td>\n",
              "      <td>Europe.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302</td>\n",
              "      <td>140</td>\n",
              "      <td>3449</td>\n",
              "      <td>11</td>\n",
              "      <td>1971</td>\n",
              "      <td>US.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>400</td>\n",
              "      <td>150</td>\n",
              "      <td>3761</td>\n",
              "      <td>10</td>\n",
              "      <td>1971</td>\n",
              "      <td>US.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.5</td>\n",
              "      <td>4</td>\n",
              "      <td>98</td>\n",
              "      <td>63</td>\n",
              "      <td>2051</td>\n",
              "      <td>17</td>\n",
              "      <td>1978</td>\n",
              "      <td>US.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mpg   cylinders  cubicinches   hp  weightlbs   time-to-60   year     brand\n",
              "0  14.0           8          350  165       4209           12   1972       US.\n",
              "1  31.9           4           89   71       1925           14   1980   Europe.\n",
              "2  17.0           8          302  140       3449           11   1971       US.\n",
              "3  15.0           8          400  150       3761           10   1971       US.\n",
              "4  30.5           4           98   63       2051           17   1978       US."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7_3myKM-wxT",
        "colab_type": "code",
        "outputId": "fbb388c4-3f68-4eb3-e27c-dae1510a6b1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mpg', ' cylinders', ' cubicinches', ' hp', ' weightlbs', ' time-to-60',\n",
              "       ' year', ' brand'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPHhH8hm9F6i",
        "colab_type": "code",
        "outputId": "df320a18-dc0a-4a0f-b55a-d7eb787530bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "261"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_gx9YcD-1VZ",
        "colab_type": "code",
        "outputId": "db58d569-3c09-48f8-e859-af7bef2c212a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwoCnn5w_WC6",
        "colab_type": "code",
        "outputId": "8f2384b8-3238-4a35-e9a3-cb58d5560c8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg             0\n",
              " cylinders      0\n",
              " cubicinches    0\n",
              " hp             0\n",
              " weightlbs      0\n",
              " time-to-60     0\n",
              " year           0\n",
              " brand          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvjGdkfzb5U1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns=['mpg', 'cylinders', 'cubicinches', 'hp', 'weightlbs', 'time-to-60',\n",
        "       'year', 'brand']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzsZr0HhcQKi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "eebe9150-26cd-4856-da4d-20a9a48ed3c6"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg            float64\n",
              "cylinders        int64\n",
              "cubicinches     object\n",
              "hp               int64\n",
              "weightlbs       object\n",
              "time-to-60       int64\n",
              "year             int64\n",
              "brand           object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm8koJVz-mFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=df.drop(['brand'],axis='columns')\n",
        "#X=X.values\n",
        "#X=pd.DataFrame(X)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XpYlV8ZwXQY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "10d39559-98ca-458a-b9e7-19b33aadb02b"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>cubicinches</th>\n",
              "      <th>hp</th>\n",
              "      <th>weightlbs</th>\n",
              "      <th>time-to-60</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350</td>\n",
              "      <td>165</td>\n",
              "      <td>4209</td>\n",
              "      <td>12</td>\n",
              "      <td>1972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.9</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>71</td>\n",
              "      <td>1925</td>\n",
              "      <td>14</td>\n",
              "      <td>1980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302</td>\n",
              "      <td>140</td>\n",
              "      <td>3449</td>\n",
              "      <td>11</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>400</td>\n",
              "      <td>150</td>\n",
              "      <td>3761</td>\n",
              "      <td>10</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.5</td>\n",
              "      <td>4</td>\n",
              "      <td>98</td>\n",
              "      <td>63</td>\n",
              "      <td>2051</td>\n",
              "      <td>17</td>\n",
              "      <td>1978</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mpg  cylinders cubicinches   hp weightlbs  time-to-60  year\n",
              "0  14.0          8         350  165      4209          12  1972\n",
              "1  31.9          4          89   71      1925          14  1980\n",
              "2  17.0          8         302  140      3449          11  1971\n",
              "3  15.0          8         400  150      3761          10  1971\n",
              "4  30.5          4          98   63      2051          17  1978"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOO8tkIDxEzV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "386c4daf-c862-4142-e32d-c6be2eaff650"
      },
      "source": [
        "X.dtypes"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg            float64\n",
              "cylinders        int64\n",
              "cubicinches     object\n",
              "hp               int64\n",
              "weightlbs       object\n",
              "time-to-60       int64\n",
              "year             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqQ-MsgH3mmR",
        "colab_type": "text"
      },
      "source": [
        "We convert the object datatypes to float"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOX3ImF20hYs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X.dropna()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-VRgJk00y9O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "k=[]\n",
        "for i in X['cubicinches']:\n",
        "  if i!='':\n",
        "    k.append(float(i)) \n",
        "  else:\n",
        "    k.append(0)\n",
        "\n",
        "import math\n",
        "\n",
        "meank=math.floor(sum(k)/len(k))\n",
        "\n",
        "for i in range(0,len(k)):\n",
        "  if k[i]==0:\n",
        "    k[i]=meank\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__PUiDvt2JEI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X['cubicinches']=k"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ci8EMy82Vvx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "k=[]\n",
        "for i in X['weightlbs']:\n",
        "  if i!='':\n",
        "    k.append(float(i))\n",
        "  else:\n",
        "    k.append(0)\n",
        "\n",
        "import math\n",
        "\n",
        "meank=math.floor(sum(k)/len(k))\n",
        "\n",
        "for i in range(0,len(k)):\n",
        "  if k[i]==0:\n",
        "    k[i]=meank"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rv2uiod829HG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X['weightlbs']=k"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sO1MFLxr2NDs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "4812f1cb-1ce6-48bd-b1e5-ccf38ce80a4c"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>cubicinches</th>\n",
              "      <th>hp</th>\n",
              "      <th>weightlbs</th>\n",
              "      <th>time-to-60</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>165</td>\n",
              "      <td>4209.0</td>\n",
              "      <td>12</td>\n",
              "      <td>1972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.9</td>\n",
              "      <td>4</td>\n",
              "      <td>89.0</td>\n",
              "      <td>71</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302.0</td>\n",
              "      <td>140</td>\n",
              "      <td>3449.0</td>\n",
              "      <td>11</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>400.0</td>\n",
              "      <td>150</td>\n",
              "      <td>3761.0</td>\n",
              "      <td>10</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.5</td>\n",
              "      <td>4</td>\n",
              "      <td>98.0</td>\n",
              "      <td>63</td>\n",
              "      <td>2051.0</td>\n",
              "      <td>17</td>\n",
              "      <td>1978</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mpg  cylinders  cubicinches   hp  weightlbs  time-to-60  year\n",
              "0  14.0          8        350.0  165     4209.0          12  1972\n",
              "1  31.9          4         89.0   71     1925.0          14  1980\n",
              "2  17.0          8        302.0  140     3449.0          11  1971\n",
              "3  15.0          8        400.0  150     3761.0          10  1971\n",
              "4  30.5          4         98.0   63     2051.0          17  1978"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
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      },
      "source": [
        "y=df['brand']\n",
        "y.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         US.\n",
              "1     Europe.\n",
              "2         US.\n",
              "3         US.\n",
              "4         US.\n",
              "Name: brand, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9AFjwmsBRlW",
        "colab_type": "text"
      },
      "source": [
        "ELBOW METHOD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3BhqaH8_yMS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "590bb22b-b0a5-402d-fa0c-3cbacc7e29e4"
      },
      "source": [
        "# Using the elbow method to find  the optimal number of clusters\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "wcss = []\n",
        "for i in range(1,11):\n",
        "    kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UUlNDjuc3bGPLro6sSzEzG1bF0gZxMfDzvOEA7pO0UNIlAy0o6RJJLZJaWltbC1pkX5oa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\nmfXJAWFmZn1yQJiZWZ/+PztzE723he+FAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oU3dDaa3Lh9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "4d6c9457-e70d-4b16-f108-7cf9fd9e17b7"
      },
      "source": [
        "# We Apply k-means to the cars dataset\n",
        "kmeans = KMeans(n_clusters=3,init='k-means++',max_iter=300,n_init=10,random_state=0) \n",
        "y_kmeans = kmeans.fit_predict(X)\n",
        "\n",
        "X = X.as_matrix(columns=None)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHXd5_Zn3RmH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "1ebc16fb-e66e-4a35-98bf-92147e4da6f6"
      },
      "source": [
        "# We Visualize the clusters\n",
        "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=100,c='red',label='US')\n",
        "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=100,c='blue',label='Japan')\n",
        "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2,1],s=100,c='green',label='Europe')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='Centroids')\n",
        "plt.title('Clusters of car brands')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cN7MY/RletpVSr0UpLY28PM7m81RH0jLaeIKyKdZBIlai9nrq48fAs6o6FjiR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\nMcbnLFEbY4zP/X+SVAK9ZKvR2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}