heart_data_visualization.ipynb

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    "colab": {
      "name": "heart-data-visualization.ipynb",
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "df = pd.read_csv(\"/content/heart.csv\")"
      ],
      "metadata": {
        "id": "Mvh9ItJbbwdC"
      },
      "execution_count": 34,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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      "execution_count": 21,
      "outputs": [
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
              "4   3     2       0  "
            ],
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
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              "      <th>slope</th>\n",
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              "      <td>140</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>125</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>148</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>294</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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      "cell_type": "code",
      "source": [
        "print(df)"
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        "colab": {
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        },
        "id": "yAYMPF_Lbwvj",
        "outputId": "26d1df55-63da-486c-f7a0-9103715f5cd9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
            "0      52    1   0       125   212    0        1      168      0      1.0   \n",
            "1      53    1   0       140   203    1        0      155      1      3.1   \n",
            "2      70    1   0       145   174    0        1      125      1      2.6   \n",
            "3      61    1   0       148   203    0        1      161      0      0.0   \n",
            "4      62    0   0       138   294    1        1      106      0      1.9   \n",
            "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
            "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
            "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
            "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
            "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
            "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
            "\n",
            "      slope  ca  thal  target  \n",
            "0         2   2     3       0  \n",
            "1         0   0     3       0  \n",
            "2         0   0     3       0  \n",
            "3         2   1     3       0  \n",
            "4         1   3     2       0  \n",
            "...     ...  ..   ...     ...  \n",
            "1020      2   0     2       1  \n",
            "1021      1   1     3       0  \n",
            "1022      1   1     2       0  \n",
            "1023      2   0     2       1  \n",
            "1024      1   1     3       0  \n",
            "\n",
            "[1025 rows x 14 columns]\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "df.target.value_counts()"
      ],
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        "id": "o3pBCGkQbwyq",
        "outputId": "de73f77c-63d8-41a3-e2a5-e788477efd38"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    526\n",
              "0    499\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"target\", data=df, palette=\"bwr\")\n",
        "plt.show()"
      ],
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        "id": "8JWCetAacD_4",
        "outputId": "407b1f80-6299-45f6-b8f8-54382cd7d57f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countNoDisease = len(df[df.target == 0])\n",
        "countHaveDisease = len(df[df.target == 1])\n",
        "print(\"Percentage of Patients That Don't Heart Disease: {:.2f}%\".format((countNoDisease / (len(df.target))*100)))\n",
        "print(\"Percentage of Patients Having Heart Disease: {:.2f}%\".format((countHaveDisease / (len(df.target))*100)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fv7JPWrucECr",
        "outputId": "053a2f26-62b0-4a86-ff27-8774230b9e03"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of Patients That Don't Heart Disease: 48.68%\n",
            "Percentage of Patients Having Heart Disease: 51.32%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='sex', data=df, palette=\"mako_r\")\n",
        "plt.xlabel(\"Sex (0 = female, 1= male)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Xbrd9qK2cEFW",
        "outputId": "62cab71a-9c21-4857-9d5e-3d03763ac6d9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('target').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "cPRFQDbCcSz2",
        "outputId": "2b65acd9-b625-4376-aa69-5a05dc67122b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age       sex        cp    trestbps        chol       fbs  \\\n",
              "target                                                                    \n",
              "0       56.569138  0.827655  0.482966  134.106212  251.292585  0.164329   \n",
              "1       52.408745  0.570342  1.378327  129.245247  240.979087  0.134981   \n",
              "\n",
              "         restecg     thalach     exang   oldpeak     slope        ca      thal  \n",
              "target                                                                          \n",
              "0       0.456914  139.130261  0.549098  1.600200  1.166333  1.158317  2.539078  \n",
              "1       0.598859  158.585551  0.134981  0.569962  1.593156  0.370722  2.119772  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d749417-9f31-4770-a9fb-3e6e51f6a581\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56.569138</td>\n",
              "      <td>0.827655</td>\n",
              "      <td>0.482966</td>\n",
              "      <td>134.106212</td>\n",
              "      <td>251.292585</td>\n",
              "      <td>0.164329</td>\n",
              "      <td>0.456914</td>\n",
              "      <td>139.130261</td>\n",
              "      <td>0.549098</td>\n",
              "      <td>1.600200</td>\n",
              "      <td>1.166333</td>\n",
              "      <td>1.158317</td>\n",
              "      <td>2.539078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52.408745</td>\n",
              "      <td>0.570342</td>\n",
              "      <td>1.378327</td>\n",
              "      <td>129.245247</td>\n",
              "      <td>240.979087</td>\n",
              "      <td>0.134981</td>\n",
              "      <td>0.598859</td>\n",
              "      <td>158.585551</td>\n",
              "      <td>0.134981</td>\n",
              "      <td>0.569962</td>\n",
              "      <td>1.593156</td>\n",
              "      <td>0.370722</td>\n",
              "      <td>2.119772</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d749417-9f31-4770-a9fb-3e6e51f6a581')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6d749417-9f31-4770-a9fb-3e6e51f6a581 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-6d749417-9f31-4770-a9fb-3e6e51f6a581');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.age,df.target).plot(kind=\"bar\",figsize=(20,6))\n",
        "plt.title('Heart Disease Frequency for Ages')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.savefig('heartDiseaseAndAges.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "yMXx9YxqcS2g",
        "outputId": "432c8e33-0dde-428d-9c99-feddbf7da16a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.sex,df.target).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
        "plt.title('Heart Disease Frequency for Sex')\n",
        "plt.xlabel('Sex (0 = Female, 1 = Male)')\n",
        "plt.legend([\"Doesn't Have Disease\", \"Has Disease\"])\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "VY8ods9ScWps",
        "outputId": "b8c87455-2a27-4be3-892a-01e941567eb6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x=df.age[df.target==1], y=df.thalach[(df.target==1)], c=\"red\")\n",
        "plt.scatter(x=df.age[df.target==0], y=df.thalach[(df.target==0)])\n",
        "plt.legend([\"Disease\", \"Not Disease\"])\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Maximum Heart Rate\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "6I263032cWsv",
        "outputId": "7085b0f0-5360-43df-efeb-bb29229510fd"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.slope,df.target).plot(kind=\"bar\",figsize=(15,6),color=['#DAF7A6','#FF5733' ])\n",
        "plt.title('Heart Disease Frequency for Slope')\n",
        "plt.xlabel('The Slope of The Peak Exercise ST Segment ')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "LGYz0V2QckjI",
        "outputId": "f38587bf-809a-42ba-d77e-36d07055cf44"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.fbs,df.target).plot(kind=\"bar\",figsize=(15,6),color=['#FFC300','#581845' ])\n",
        "plt.title('Heart Disease Frequency According To FBS')\n",
        "plt.xlabel('FBS - (Fasting Blood Sugar > 120 mg/dl) (1 = true; 0 = false)')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.legend([\"Haven't Disease\", \"Have Disease\"])\n",
        "plt.ylabel('Frequency of Disease or Not')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "L4k8TbtTckl_",
        "outputId": "4d9bf307-802f-44b3-b1eb-0458ca1d7903"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.cp,df.target).plot(kind=\"bar\",figsize=(15,6),color=['#11A5AA','#AA1190' ])\n",
        "plt.title('Heart Disease Frequency According To Chest Pain Type')\n",
        "plt.xlabel('Chest Pain Type')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.ylabel('Frequency of Disease or Not')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "clTUPZ8ockof",
        "outputId": "9bb60357-3f01-4898-8846-bec25f2f8bf2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}