Iris.ipynb

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        "<a href=\"https://colab.research.google.com/github/Aisha-Gama/Visualizing-Iris-Flower-Dataset/blob/main/Iris.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "nhxkdL2pi3tZ"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Iris.csv')"
      ],
      "metadata": {
        "id": "kan1PH_uj071"
      },
      "execution_count": 6,
      "outputs": []
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        "df.head()"
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              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
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            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  "
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              "      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-b0c7d989-b5cc-4c7d-a3fb-a2d72e77f804 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-b0c7d989-b5cc-4c7d-a3fb-a2d72e77f804');\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-78679672-c328-4b74-8e46-6fec02ec28e1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78679672-c328-4b74-8e46-6fec02ec28e1')\"\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-78679672-c328-4b74-8e46-6fec02ec28e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 146,\n        \"max\": 150,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          147,\n          150,\n          148\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30331501776206193,\n        \"min\": 5.9,\n        \"max\": 6.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.3,\n          5.9,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31937438845342625,\n        \"min\": 2.5,\n        \"max\": 3.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          2.5,\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14832396974191348,\n        \"min\": 5.0,\n        \"max\": 5.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          5.1,\n          5.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23021728866442667,\n        \"min\": 1.8,\n        \"max\": 2.3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.9,\n          1.8,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMJ9xpPUkuAQ",
        "outputId": "fefdb2a3-c5b0-4f7f-a440-4f1efcffb221"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "d2FAHbqCkyJO",
        "outputId": "679e078d-87ea-42fd-f7d3-d6a2d20f9f76"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01a35b2c-70e8-4160-a90e-4d807991ea11\" 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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</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-01a35b2c-70e8-4160-a90e-4d807991ea11')\"\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-01a35b2c-70e8-4160-a90e-4d807991ea11 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-01a35b2c-70e8-4160-a90e-4d807991ea11');\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",
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              "\n",
              "\n",
              "<div id=\"df-3eb1d269-d975-45de-b48e-8f7ec4c93562\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3eb1d269-d975-45de-b48e-8f7ec4c93562')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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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",
              "  }\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",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\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",
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              "      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",
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              "</div>\n",
              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.756293020494844,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          150.0,\n          75.5,\n          112.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "Qa4xTG0ml-nE",
        "outputId": "a4ac1d8d-0726-4379-baf4-04f019f6b89a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0\n",
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ],
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              "      <th>PetalWidthCm</th>\n",
              "      <td>0</td>\n",
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              "      <th>Species</th>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
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          },
          "metadata": {},
          "execution_count": 11
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "petals_df = df[df['Species']== 'Iris-setosa']\n",
        "petals_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5R_ukKzCmES7",
        "outputId": "c492816b-e674-42d1-cf87-bb6ce1fbd200"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "type": "dataframe",
              "variable_name": "petals_df",
              "summary": "{\n  \"name\": \"petals_df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 1,\n        \"max\": 50,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          14,\n          40,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3524896872134512,\n        \"min\": 4.3,\n        \"max\": 5.8,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          5.8,\n          5.2,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38102439795469095,\n        \"min\": 2.3,\n        \"max\": 4.4,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.5,\n          3.0,\n          3.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1735111594364455,\n        \"min\": 1.0,\n        \"max\": 1.9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          1.3,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10720950308167837,\n        \"min\": 0.1,\n        \"max\": 0.6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.2,\n          0.4,\n          0.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "N4uz9KAzxrnj",
        "outputId": "4f83b62f-6dec-4e69-fc60-20c742b16119"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.scatter(df['SepalLengthCm'], df['SepalWidthCm'])\n",
        "plt.ylabel('SepalLengthCm')\n",
        "plt.xlabel('SepalWidthCm')\n",
        "plt.title('Scatter Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "h44E9bkHmSmE",
        "outputId": "891015a8-8e07-4b8d-9ecc-c94b18a1607e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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A2ihz1ba70nQkQ1KVu1FlrtrIBQUAAKKKxAkA2qip7zxpCqUcAADo/kicAKCN1CSHqeUAAED3R+IEAG3kZqUo3elQZ4OO29Qyul5uVkokwwIAAFFE4gQAbcTZbSrIz5akdslT6/uC/GzmcwIAIIaQOAFAB6aOTNeymTlKc/rejpfmdGjZzBzmcQIAIMYwAS4AdGLqyHRNyU5TmatWNfWNSk1quT2PK00AAMQeEicA6EKc3aa8If2jHQYAAIgybtUDAAAAAD9InAAAAADADxInAAAAAPCDZ5wAhKTZYzBoAgAAiBkkTgCCVlpRpcKSSlW5G73L0p0OFeRnM0w3AADokbhVD0BQSiuqNL+43CdpkqRqd6PmF5ertKIqSpEBAACED4kTgIA1ewwVllTK6OCz1mWFJZVq9nRUAgAAoPsicQIQsDJXbbsrTUcyJFW5G1Xmqo1cUAAAABFA4gQgYDX1nSdNoZQDAADoLkicAAQsNclhajkAAIDugsQJQMBys1KU7nSos0HHbWoZXS83KyWSYQEAAIQdiROAgMXZbSrIz5akdslT6/uC/GzmcwIAAD0OiROAoEwdma5lM3OU5vS9HS/N6dCymTnM4wQAAHokJsAFELSpI9M1JTtNZa5a1dQ3KjWp5fY8rjQBAICeisQJQEji7DblDekf7TAAAAAiglv1AAAAAMAPEicAAAAA8IPECQAAAAD84BknAIAkqdljMOAHAACdsMwVp/vvv182m0033XRTl+XWrl2r4cOHy+FwaNSoUVq3bl1kAgSAHqy0okpnL31FV6x4Qzeu2a4rVryhs5e+otKKqmiHBgCAJVgicdq2bZuWL1+u0047rctyW7Zs0RVXXKGrr75a77zzjmbMmKEZM2aooqIiQpECQM9TWlGl+cXlqnI3+iyvdjdqfnE5yRMAALJA4nTgwAFdeeWVWrFihfr169dl2UceeURTp07VbbfdphEjRujee+9VTk6OHnvssQhFCwA9S7PHUGFJpYwOPmtdVlhSqWZPRyUAAIgdUU+cFixYoOnTp+v888/3W3br1q3tyl144YXaunVrp985dOiQ6urqfF4AgBZlrtp2V5qOZEiqcjeqzFUbuaAAALCgqA4OsWbNGpWXl2vbtm0Bla+urtaAAQN8lg0YMEDV1dWdfqeoqEiFhYVHFScA9FQ19Z0nTaGUAwCgp4raFadPPvlEN954o/7whz/I4XCE7d9ZuHCh3G639/XJJ5+E7d8CgO4mNSmw42+g5QAA6KmidsXp7bffVk1NjXJycrzLmpubtWnTJj322GM6dOiQ4uLifL6Tlpamffv2+Szbt2+f0tLSOv13EhISlJCQYG7wANBD5GalKN3pULW7scPnnGyS0pwtQ5MDABDLonbF6bzzztPOnTu1fft27+uMM87QlVdeqe3bt7dLmiQpLy9PL7/8ss+y9evXKy8vL1JhA0CPEme3qSA/W1JLknSk1vcF+dnM5wQAiHlRu+KUlJSkkSNH+iw75phj1L9/f+/y2bNna9CgQSoqKpIk3XjjjZo0aZIeeughTZ8+XWvWrNFbb72l3/3udxGPHwB6iqkj07VsZo4KSyp9BopIczpUkJ+tqSPToxgdAADWENXBIfzZu3ev7PZvL4qNGzdOTz/9tO666y7dcccdGjp0qJ577rl2CRgAIDhTR6ZrSnaayly1qqlvVGpSy+15XGkCAKCFzTCMmJqco66uTk6nU263W8nJydEOBwAAAECUBJMbRH0eJwAAAACwOhInAAAAAPDD0s84AUAomg57tHrrbu2pbVBGSl/NystU73h+JwIAAKEjcQLQoxStq9SKzS55jnh6c/G69zRvQpYWTsuOXmAAAKBbI3EC0GMUravU8k2udss9hrzLSZ4AAEAouHcFQI/QdNijFZvbJ01HWrHZpabDnghFBAAAehISJwA9wuqtu31uz+uIx2gpBwAAECwSJwA9wp7aBlPLAQAAHInECUCPkJHS19RyAAAARyJxAtAjzMrLlN3WdRm7raUcAABAsEicAPQIvePtmjchq8sy8yZkMZ8TAAAICcORA+gxWocabzuPk90m5nECAABHxWYYhp9xqHqWuro6OZ1Oud1uJScnRzscAGHQdNij1Vt3a09tgzJS+mpWXiZXmgAAQDvB5AZccQLQ4/SOt+vqCSdFOwwAANCD8BMsAAAAAPhB4gQAAAAAfpA4AQAAAIAfPOMExIivm5q1ZF2ldu9vUGb/vrpjWrb69I6Ldlg4Cs0eQ2WuWtXUNyo1yaHcrBTF+ZvMCgDQ41n5/GDl2PwhcQJiwLyntml9ZY33/eYPpdVv7NWU7FStmD02ipEhVKUVVSosqVSVu9G7LN3pUEF+tqaOTI9iZACAaLLy+cHKsQWCW/WAHq5t0nSk9ZU1mvfUtghHhKNVWlGl+cXlPiceSap2N2p+cblKK6qiFBkAIJqsfH6wcmyBInECerCvm5o7TZpara+s0ddNzRGKCEer2WOosKRSHU3A17qssKRSzZ6YmqIPAGKelc8PVo4tGCROQA+2ZF2lqeUQfWWu2na/1h3JkFTlblSZqzZyQQEAos7K5wcrxxYMEiegB9u9v8HUcoi+mvrOTzyhlAMA9AxWPj9YObZgkDgBPVhm/76mlkP0pSY5TC0HAOgZrHx+sHJswSBxAnqwO6Zlm1oO0ZeblaJ0p0OdDdxqU8sIRblZKZEMCwAQZVY+P1g5tmCQOAE9WJ/ecZqSndplmSnZqczn1I3E2W0qyG9JdNuegFrfF+Rnd5s5MQAA5rDy+cHKsQWDxAno4VbMHttp8sQ8Tt3T1JHpWjYzR2lO31sa0pwOLZuZ0y3mwgAAmM/K5wcrxxYom2EY1h73z2R1dXVyOp1yu91KTk6OdjhAxHzd1Kwl6yq1e3+DMvv31R3TsrnS1M1159nXAQDhY+Xzg9ViCyY3IHECAAAAEJOCyQ24VQ8AAAAA/CBxAgAAAAA/4qMdAIDIsNo9xUcyOzYr1xUAAHRPJE5ADCitqFJhSaWq3N/OyJ3udKggPzvqo9iYHZuV6woAALovbtUDerjSiirNLy73SSQkqdrdqPnF5SqtqIpSZObHZuW6AgCA7o3ECejBmj2GCksq1dHQma3LCksq1eyJ/OCaZsdm5boCAIDuj8QJ6MHKXLXtrr4cyZBU5W5Umas2ckH9H7Njs3JdAQBA90fiBPRgNfWdJxKhlDOT2bFZua4AAKD7I3ECerDUJIep5cxkdmxWrisAAOj+SJyAHiw3K0XpToc6G4jbppYR53KzUiIZliTzY7NyXQEAQPdH4gT0YHF2mwrysyWpXULR+r4gPzsqcxyZHZuV6woAALo/Eiegh5s6Ml3LZuYozel7i1qa06FlM3OiOreR2bFZua4AAKB7sxmGEVNj89bV1cnpdMrtdis5OTna4QAR0+wxVOaqVU19o1KTWm5Zs8rVF7Njs3JdAQCAdQSTG8RHKCYAURZntylvSP9oh9Ehs2Ozcl0BAED3xK16AAAAAOAHiRMAAAAA+EHiBAAAAAB+8IwTwo4H9UNDuwEAuhvOXejJSJwQVqUVVSosqVSVu9G7LN3pUEF+NkNDd4F2AwB0N5y70NNxqx7CprSiSvOLy30OoJJU7W7U/OJylVZURSkya6PdAADdDecuxAISJ4RFs8dQYUmlOpokrHVZYUmlmj0xNY2YX7QbAKC74dyFWEHihLAoc9W2+9XpSIakKnejyly1kQuqG6DdAADdDecuxAoSJ4RFTX3nB9BQysUK2g0A0N1w7kKsIHFCWKQmOUwtFytoNwBAd8O5C7GCxAlhkZuVonSnQ50NQGpTy0g7uVkpkQzL8mg3AEB3w7kLsYLECWERZ7epID9bktodSFvfF+RnM7dDG7QbAKC74dyFWEHihLCZOjJdy2bmKM3pe2k+zenQspk5zOnQCdoNANDdcO5CLLAZhhFTY0PW1dXJ6XTK7XYrOTk52uHEBGYRDw3tBgDobjh3obsJJjeIj1BMiGFxdpvyhvSPdhjdDu0GAOhuOHehJws5cdq2bZs2bNigmpoaeTwen8/+3//7f0cdGAAAAABYRUiJ05IlS3TXXXfplFNO0YABA2SzfXsJ9sj/BwAAAICeIKTE6ZFHHtETTzyhuXPnmhwOgFZNhz1avXW39tQ2KCOlr2blZap3fOjjuZi9vli5jz1W6gkAALoWUuJkt9s1fvx4s2MB8H+K1lVqxWaXPEcM3bJ43XuaNyFLC6dlR319pRVVKiypVJX721ng050OFeRn96iRk2KlngAAwL+Qfm6++eab9Zvf/Oao//Fly5bptNNOU3JyspKTk5WXl6d//OMfnZZfuXKlbDabz8vhYBZq9CxF6yq1fJNvkiNJHkNavsmlonWVUV1faUWV5heX+yQTklTtbtT84nKVVlQFtT6ripV6AgCAwIR0xel//ud/NH36dA0ZMkTZ2dnq1auXz+fPPPNMQOs54YQTdP/992vo0KEyDEOrVq3SxRdfrHfeeUennnpqh99JTk7WBx984H3PM1XoSZoOe7Ris6vLMis2u3TrBcMDus3O7PU1ewwVllSqozkMDLVMdFhYUqkp2Wnd+na2WKknAAAIXEhXnG644QZt2LBBw4YNU//+/eV0On1egcrPz9e0adM0dOhQDRs2TIsXL1ZiYqLeeOONTr9js9mUlpbmfQ0YMKDLf+PQoUOqq6vzeQFWtXrr7nZXhtryGC3lorG+MldtuyswRzIkVbkbVeaqDWh9VhUr9QQAAIEL6YrTqlWr9Ne//lXTp083LZDm5matXbtWBw8eVF5eXqflDhw4oIyMDHk8HuXk5GjJkiWdXp2SpKKiIhUWFpoWJxBOe2obLF2upr7zZCKUclYVK/UEAACBC+mKU0pKioYMGWJKADt37lRiYqISEhL005/+VM8++6yyszt+WP2UU07RE088oeeff17FxcXyeDwaN26cPv30007Xv3DhQrndbu/rk08+MSVuIBwyUvpaulxqUmDPFAZazqpipZ4AACBwISVOixYtUkFBgRoaAvuVuiunnHKKtm/frjfffFPz58/XnDlzVFnZ8cPqeXl5mj17tkaPHq1JkybpmWee0fHHH6/ly5d3uv6EhATv4BOtL8CqZuVlyt8jM3ZbS7lorC83K0XpToc6W6VNLaPO5WalBLQ+q4qVegIAgMCFlDj9+te/1j/+8Q8NGDBAo0aNUk5Ojs8rGL1799bJJ5+s008/XUVFRfrOd76jRx55JKDv9urVS2PGjNFHH30USjUAy+kdb9e8CVldlpk3ISvg+ZfMXl+c3aaC/JYrwm2Titb3BfnZ3X7AhFipJwAACFxIzzjNmDHD5DC+5fF4dOjQoYDKNjc3a+fOnZo2bVrY4gEirXVepbbzLtltCmneJbPXN3VkupbNzGk3v1FaD5vfKFbqCQAAAmMzDMPPmFvhs3DhQl100UU68cQTVV9fr6efflpLly7Viy++qClTpmj27NkaNGiQioqKJEn33HOPzjrrLJ188sn66quv9MADD+i5557T22+/3elzUW3V1dXJ6XTK7XZz2x4sremwR6u37tae2gZlpPTVrLzMgK8MRWJ9zR5DZa5a1dQ3KjWp5ba1nngFJlbqCQBALAomNwjqitOXX36p4uJizZkzp92K3W63nnrqqQ4/60xNTY1mz56tqqoqOZ1OnXbaad6kSZL27t0ru/3bP+y+/PJLzZs3T9XV1erXr59OP/10bdmyJeCkCehOesfbdfWEkyy7vji7TXlD+pu2PquKlXoCAICuBXXF6d5779W7776rtWvXdvj5j370I33nO9/RnXfeaVqAZuOKEwAAAAApuNwgqPt0/vrXv+qnP/1pp59fe+21+stf/hLMKgEAAADA8oJKnHbt2qWhQ4d2+vnQoUO1a9euow4KAAAAAKwkqGec4uLi9Nlnn+nEE0/s8PPPPvvM55kkQLL2w/VmD5hgJqsP5kC/IpKsvL3F0r4FALEsqMRpzJgxeu6553TWWWd1+Pmzzz6rMWPGmBIYeobSiqp2wzmnW2Q456J1le2G6F687r2Qhug2m9mxmd0P9CsiycrbWyztWwAQ64L6Cfa6667TQw89pMcee0zNzc3e5c3NzXr00Uf18MMPa8GCBaYHie6ptKJK84vLff4AkKRqd6PmF5ertKIqSpG1/HG9fJPvH9eS5DGk5ZtcKlpXGZ3AZH5sZvcD/YpIsvL2Fkv7FgAgyMTp0ksv1c9+9jPdcMMNSklJ0ZgxYzRmzBilpKTopptu0i233KIf/OAH4YoV3Uizx1BhSaU6GrKxdVlhSaWa2/6FGwFNhz1asdnVZZkVm11qOuyJUETfMjs2s/uBfkUkWXl7i6V9CwDQIuib/hcvXqw33nhDc+fO1cCBA5Wenq6rrrpKW7du1f333x+OGNENlblq2/1qeiRDUpW7UWWu2sgF9X9Wb93d7opEWx6jpVykmR2b2f1AvyKSrLy9xdK+BQBoEdQzTq1yc3OVm5trdizoQWrqO/8DIJRyZtpT22BqOTOZHZvZ/UC/IpKsvL3F0r4FAGgRUuIkSV999ZXKyspUU1Mjj8f31pfZs2cfdWDo3lKTHKaWM1NGSl9Ty5nJ7NjM7gf6FZFk5e0tlvYtAECLkBKnkpISXXnllTpw4ICSk5Nls307TKrNZiNxgnKzUpTudKja3djhPfs2SWnOlmF2I21WXqYWr3uvy9u67LaWcpFmdmxm9wP9ikiy8vYWS/sWAKBFSBOb3HrrrfrJT36iAwcO6KuvvtKXX37pfdXWcv81pDi7TQX5LUM/t519pPV9QX52VOYm6R1v17wJWV2WmTchKyrz/pgdm9n9QL8ikqy8vcXSvgUAaBHSXxD/+c9/dMMNN6hvX255QeemjkzXspk5SnP63lqS5nRo2cycqM5JsnBatq6dmKW2f4PYbdK1E6M734/ZsZndD/QrIsnK21ss7VsAAMlmGEbQY5tecskluvzyy/WjH/0oHDGFVV1dnZxOp9xut5KTk6MdTkxo9hgqc9Wqpr5RqUktt5pY5VfTpsMerd66W3tqG5SR0lez8jItc0XC7NjM7gf6FZFk5e0tlvYtAOhpgskNAk6cXnjhBe//f/7557rnnnt01VVXadSoUerVq5dP2e9973shhB0ZJE4AAAAApDAlTnZ7YL/W2mw2NTc3B1Q2GkicAAAAAEjB5QYBj6rXdshxAAAAAIgVId30/9RTT+nQoUPtljc1Nempp5466qAAAAAAwEpCGhwiLi5OVVVVSk1N9Vm+f/9+paamcqseYAIeOAdiE4ObhIZ2AxCKsNyqdyTDMHwmvW316aefyul0hrJKAEcorahSYUmlqtyN3mXpTocK8rNDGpLY7PUBCI+idZVasdnlM5Hz4nXvad4EhtPvCu0GIBKCSpzGjBkjm80mm82m8847T/Hx3369ublZLpdLU6dONT1IIJaUVlRpfnG52l4KrnY3an5xedDzuZi9PgDhUbSuUss3udot9xjyLicJaI92AxApQSVOM2bMkCRt375dF154oRITE72f9e7dW5mZmbr00ktNDRCIJc0eQ4Ulle2SHEkyJNkkFZZUakp2WkC32Zm9PgDh0XTYoxWb2//xf6QVm1269YLh3H52BNoNQCQFlTgVFBRIkjIzM3XZZZfJ4XD4+QaAYJS5an1up2vLkFTlblSZq1Z5Q/pHfH0AwmP11t0+t5l1xGO0lLt6wkmRCaoboN0ARFJIzzjNmTPH7DgASKqp7zzJsUI5AOGxp7bB1HKxgnYDEEkhJU79+vXrcHAIm80mh8Ohk08+WXPnztVVV1111AECsSQ1KbCruNEqByA8MlL6mlouVtBuACIppBt+7777btntdk2fPl2FhYUqLCzU9OnTZbfbtWDBAg0bNkzz58/XihUrzI4X6NFys1KU7nSos6eNbGoZDS83KyUq6wMQHrPyMuXvMUO7raUcvkW7AYikkBKn1157Tffdd59Wr16t66+/Xtdff71Wr16t++67T2+//bZWrFihBx54QL/+9a/Njhfo0eLsNhXkt4z+1PZvgdb3BfnZAQ/kYPb6AIRH73i75k3I6rLMvAlZDHDQBu0GIJJCOpK8+OKLOv/889stP++88/Tiiy9KkqZNm6aPP/746KIDYtDUkelaNjNHaU7f2+fSnI6Qhg43e30AwmPhtGxdOzGr3RUUu026diLzEXWGdgMQKTbDMPyMR9PeiSeeqJtvvlk333yzz/KHH35YDz/8sPbu3at3331XF1xwgaqrq00L1gzBzA4MRFOzx1CZq1Y19Y1KTWq5ne5orgyZvT4A4dF02KPVW3drT22DMlL6alZeJldMAkC7AQhFMLlBSIND/OIXv9D8+fO1YcMG5ebmSpK2bdumdevW6fHHH5ckrV+/XpMmTQpl9QDUcpudmUOEm70+AOHRO97O0NkhoN0AhFtIV5wk6fXXX9djjz2mDz74QJJ0yimn6Prrr9e4ceNMDdBsXHECAAAAIAWXG4ScOHVXJE4AAAAApAjcqidJHo9HH330kWpqauTxeHw+mzhxYqirRYis/PyK2bHFyn3sVu5TIBBsw9Fn9vEylvrUzLrG0jOrVo4NOFohXXF644039OMf/1h79uxR26/bbDY1NzebFqDZeuIVp9KKKhWWVKrK3ehdlu50qCA/O+ojppkdW9G6Sq3Y7JLniM3ObmsZbrYnjZxk5T4FAsE2HH1mHy9jqU/NrKvZ7WblfrBybEBnwn6r3ujRozVs2DAVFhYqPT1dNpvvLwlOpzPYVUZMT0ucSiuqNL+4XG07sbVHojnctNmxFa2r1PJNrk4/7ynDzlq5T4FAsA1Hn9nHy1jqUzPrana7WbkfrBwb0JVgcoOQrtd/+OGHWrJkiUaMGKFjjz1WTqfT54XIaPYYKiypbHeQkuRdVlhSqWZP5B9jMzu2psMerdjc+R8BkrRis0tNhz1dlrE6K/cpEAi24egz+3gZS31qZl3Nbjcr94OVYwPMFFLidOaZZ+qjjz4yOxYEqcxV63M5vC1DUpW7UWWu2sgF9X/Mjm311t3yd7z1GC3lujMr9ykQCLbh6DP7eBlLfWpmXc1uNyv3g5VjA8wU0uAQ119/vW699VZVV1dr1KhR6tWrl8/np512minBoWs19Z0fpEIpZyazY9tT22BqOauycp8CgWAbjj6zj5ex1Kdm1tXsdrNyP1g5NsBMISVOl156qSTpJz/5iXeZzWaTYRiWHxyiJ0lNcphazkxmx5aR0tfUclZl5T4FAsE2HH1mHy9jqU/NrKvZ7WblfrBybICZQrpVz+VytXt9/PHH3v8iMnKzUpTudKizQT5tahnNJjcrJZJhSTI/tll5mfI3mqnd1lKuO7NynwKBYBuOPrOPl7HUp2bW1ex2s3I/WDk2wEwhJU4ZGRldvhAZcXabCvJbRkVqe7BqfV+Qnx2V+RPMjq13vF3zJmR1WWbehKxuP5+TlfsUCATbcPSZfbyMpT41s65mt5uV+8HKsQFmCvmvzNWrV2v8+PEaOHCg9uzZI0n61a9+peeff9604ODf1JHpWjYzR2lO38vfaU5H1If+NDu2hdOyde3ErHa/pNptPWcocsnafQoEgm04+sw+XsZSn5pZV7Pbzcr9YOXYALOENI/TsmXLdPfdd+umm27S4sWLVVFRoZNOOkkrV67UqlWrtGHDhnDEaoqeNo9TKyvP1G12bE2HPVq9dbf21DYoI6WvZuVldvsrTR2xcp8CgWAbjj6zj5ex1Kdm1tXsdrNyP1g5NqAjYZ8ANzs7W0uWLNGMGTOUlJSkHTt26KSTTlJFRYUmT56sL774IuTgw62nJk4AAAAAghP2CXBdLpfGjBnTbnlCQoIOHjwYyioBAAAAwLJCSpyysrK0ffv2dstLS0s1YsSIo40JAAAAACwlpHmcbrnlFi1YsECNjY0yDENlZWX64x//qKKiIv3+9783O0YAAAAAiKqQEqdrrrlGffr00V133aWGhgb9+Mc/1sCBA/XII4/o8ssvNztGdHNWflDU7Ni+bmrWknWV2r2/QZn9++qOadnq0zvOxIgBIHBWPv6azcrHXyvHBiBwIQ0OcaSGhgYdOHBAqampamho0Pbt2zVu3Diz4jMdg0NEVmlFlQpLKlXlbvQuS3c6VJCfHfWhSc2Obd5T27S+sqbd8inZqVoxe+xRxQoAwbLy8ddsVj7+Wjk2ABEYVa8zO3bsUE5Ojpqbm81apelInCKntKJK84vL1XYDa/2tM5rzOpgdW2cnxlacIAFEkpWPv2az8vHXyrEBaBH2UfUAf5o9hgpLKtudtCV5lxWWVKrZY1reHjCzY/u6qbnLE6Mkra+s0ddN1v1BAUDPYeXjr9msfPy1cmwAQkPihLAoc9X63B7SliGpyt2oMldt5IL6P2bHtmRdpanlAOBoWPn4azYrH3+tHBuA0JA4ISxq6js/aYdSzkxmx7Z7f4Op5QDgaFj5+Gs2Kx9/rRwbgNAENareCy+80OXnLpfrqIJBz5Ga5DC1nJnMji2zf19t/jCwcgAQblY+/prNysdfK8cGIDRBJU4zZszwW8Zm65nDnCI4uVkpSnc6VO1u7PA+e5ukNGfL0LiRZnZsd0zL1uo39gZUDgDCzcrHX7NZ+fhr5dgAhCaoW/U8Ho/fl5VH1EPkxNltKshvORm0TaVb3xfkZ0dlPhGzY+vTO05TslO7LDMlO5U5OwBEhJWPv2az8vHXyrEBCA3POCFspo5M17KZOUpz+t4OkuZ0RH0oXLNjWzF7bKcnSIabBRBpVj7+ms3Kx18rxwYgeAHP4+Tv+aYjfe973ws5oHBjHqfIs/LM9WbHxuzwAKzEysdfs1n5+Gvl2IBYF5YJcO32wC5O2Ww2S9+uR+IEAAAAQAouNwh4cAiPx3PUgQEAAABAd8QzTgAAAADgR1DDkR/p4MGDevXVV7V37141NTX5fHbDDTccdWA9ndn3ncfSfexmajrs0eqtu7WntkEZKX01Ky9TveND/z3BzH6gT0Nn5bYzc5uzcj0la9fV7H0/Vpj9rI6Z/Wrl47mV8feINdAP3UPAzzgd6Z133tG0adPU0NCggwcPKiUlRV988YX69u2r1NRUffzxxwGtZ9myZVq2bJl2794tSTr11FN1991366KLLur0O2vXrtUvfvEL7d69W0OHDtXSpUs1bdq0gGO3wjNOpRVVKiypVJX721nb050OFeRnhzTSkdnrixVF6yq1YrNLniP2ALtNmjchSwtDmFfDzH6gT0Nn5bYzc5uzcj0la9fV7H0/Vsx7apvWV9a0Wx7q6HBm9quVj+dWxt8j1kA/RFdYBoc40uTJkzVs2DA9/vjjcjqd2rFjh3r16qWZM2fqxhtv1CWXXBLQekpKShQXF6ehQ4fKMAytWrVKDzzwgN555x2deuqp7cpv2bJFEydOVFFRkb773e/q6aef1tKlS1VeXq6RI0cG9G9GO3EqrajS/OLydpMStv4GEOwwsWavL1YUravU8k2uTj+/dmJwJ1sz+4E+DZ2V287Mbc7K9ZSsXVez9/1Y0VnS1CrY5MnMfrXy8dzK+HvEGuiH6AsmNwjpGvb27dt16623ym63Ky4uTocOHdLgwYP1y1/+UnfccUfA68nPz9e0adM0dOhQDRs2TIsXL1ZiYqLeeOONDss/8sgjmjp1qm677TaNGDFC9957r3JycvTYY4+FUo2Ia/YYKiyp7HAm99ZlhSWVavYElsuavb5Y0XTYoxWbOz/JStKKzS41HQ5sQBQz+4E+DZ2V287Mbc7K9ZSsXVez9/1Y8XVTc5dJkyStr6zR102BjahrZr9a+XhuZfw9Yg30Q/cTUuLUq1cv7/Dkqamp2rt3ryTJ6XTqk08+CSmQ5uZmrVmzRgcPHlReXl6HZbZu3arzzz/fZ9mFF16orVu3drreQ4cOqa6uzucVLWWuWp/Lpm0ZkqrcjSpz1UZlfbFi9dbd8nfM8Bgt5QJhZj/Qp6GzctuZuc1ZuZ6Stetq9r4fK5asqzS1nJn9auXjuZXx94g10A/dT0iDQ4wZM0bbtm3T0KFDNWnSJN1999364osvtHr16oBvmWu1c+dO5eXlqbGxUYmJiXr22WeVnd3xJfXq6moNGDDAZ9mAAQNUXV3d6fqLiopUWFgYVEzhUlPf+cZshXKxYk9tg6nlzOwH+jR0Vm47M7c5K9dTsnZdzd73Y8Xu/YG1R6DlzOxXKx/PrYy/R6yBfuh+QrritGTJEqWnt9wfuXjxYvXr10/z58/X559/rt/97ndBreuUU07R9u3b9eabb2r+/PmaM2eOKisD+9UqEAsXLpTb7fa+Qr0iZobUJIely8WKjJS+ppYzsx/o09BZue3M3OasXE/J2nU1e9+PFZn9A2uPQMuZ2a9WPp5bGX+PWAP90P2ElDidccYZOueccyS13KpXWlqquro6vf322/rOd74T1Lp69+6tk08+WaeffrqKior0ne98R4888kiHZdPS0rRv3z6fZfv27VNaWlqn609ISFBycrLPK1pys1KU7nSos8EgbWoZ9SQ3KyUq64sVs/Iy5W9ETrutpVwgzOwH+jR0Vm47M7c5K9dTsnZdzd73Y8UdAQ6sEGg5M/vVysdzK+PvEWugH7qfo5q0oqamRps3b9bmzZv1+eefmxKQx+PRoUOHOvwsLy9PL7/8ss+y9evXd/pMlNXE2W0qyG85sbTdqFvfF+RnBzzOvtnrixW94+2aNyGryzLzJmQFPP+Hmf1An4bOym1n5jZn5XpK1q6r2ft+rOjTO05TslO7LDMlOzXg+ZzM7FcrH8+tjL9HrIF+6H5COjvU19dr1qxZGjRokCZNmqRJkyZp4MCBmjlzptxud8DrWbhwoTZt2qTdu3dr586dWrhwoTZu3Kgrr7xSkjR79mwtXLjQW/7GG29UaWmpHnroIb3//vtatGiR3nrrLV133XWhVCMqpo5M17KZOUpz+l4mTXM6Qhoi0uz1xYqF07J17cSsdr9U2m2hDUdsZj/Qp6GzctuZuc1ZuZ6Stetq9r4fK1bMHttp8hTKPE5m9quVj+dWxt8j1kA/dC8hzeN02WWX6Z133tGjjz7qvdqzdetW3XjjjRo9erTWrFkT0Hquvvpqvfzyy6qqqpLT6dRpp52m22+/XVOmTJHUMl9UZmamVq5c6f3O2rVrddddd3knwP3lL3/Z7SbAlZgh2iqsPNM8fRo6K7edmduclespWbuuZu/7seLrpmYtWVep3fsblNm/r+6Ylh3wlaaOmNmvVj6eWxl/j1gD/RA9YZ8A95hjjtGLL76os88+22f55s2bNXXqVB08eDDYVUaMVRInAAAAANEV9glw+/fvL6fT2W650+lUv379QlklAAAAAFhWSInTXXfdpVtuucVn/qTq6mrddttt+sUvfmFacAAAAABgBSHdqjdmzBh99NFHOnTokE488URJ0t69e5WQkKChQ4f6lC0vLzcnUpNwqx4AAAAAKbjcID6Uf2DGjBmhfA1hxEOAQPfAACKhsfKD07EUm9msHh+ij20EVhLSFafurCdecSqtqFJhSaWq3I3eZelOhwrysxl2ErAQM/fVWNrvza6rlfvByrGZzerxIfrYRhAJYR9VT5K++uor/eUvf9GuXbt02223KSUlReXl5RowYIAGDRoUUuCR0NMSp9KKKs0vLlfbTmz9LYYx+wFrMHNfjaX93uy6WrkfrByb2aweH6KPbQSREvZR9d59910NGzZMS5cu1YMPPqivvvpKkvTMM8/4TFiL8Gr2GCosqWx3UJHkXVZYUqlmT0xdVAQsx8x9NZb2e7PrauV+sHJsZrN6fIg+thFYVUiJ0y233KK5c+fqww8/lMPx7czE06ZN06ZNm0wLDl0rc9X6XL5uy5BU5W5Umas2ckEBaMfMfTWW9nuz62rlfrBybGazenyIPrYRWFVIidO2bdt07bXXtls+aNAgnyHKEV419Z0fVEIpByA8zNxXY2m/N7uuVu4HK8dmNqvHh+hjG4FVhZQ4JSQkqK6urt3yf//73zr++OOPOigEJjXJ4b9QEOUAhIeZ+2os7fdm19XK/WDl2Mxm9fgQfWwjsKqQEqfvfe97uueee/TNN99Ikmw2m/bu3avbb79dl156qakBonO5WSlKdzrU2aCcNrWMPpOblRLJsAC0Yea+Gkv7vdl1tXI/WDk2s1k9PkQf2wisKqTE6aGHHtKBAweUmpqqr7/+WpMmTdKQIUOUmJioxYsXmx0jOhFnt6kgP1uS2h1cWt8X5Gcz3wEQZWbuq7G035tdVyv3g5VjM5vV40P0sY3Aqo5qHqfXXntN7777rg4cOKDTTz9d5513npmxhUVPG45cYp4DoLuIpTl6zBRLcyVZOTazWT0+RB/bCCIhbPM4bd26Vfv379d3v/td77JVq1apoKBADQ0NmjFjhh599FElJCSEHn2Y9cTESWJmbaC7MHNfjaX93uy6WrkfrByb2aweH6KPbQThFrbE6aKLLtLkyZN1++23S5J27typ008/XXPmzNGIESP0wAMP6Nprr9WiRYuOqgLh1FMTJwAAAADBCdsEuNu3b/e5HW/NmjXKzc3VihUrdMstt+jXv/61/vznP4cWNQAAAABYVFCJ05dffqkBAwZ437/66qu66KKLvO/Hjh2rTz75xLzoAAAAAMACgkqcBgwYIJfLJUlqampSeXm5zjrrLO/n9fX16tWrl7kRAgAAAECUxQdTeNq0afr5z3+upUuX6rnnnlPfvn01YcIE7+fvvvuuhgwZYnqQANBTmPmgc9Nhj1Zv3a09tQ3KSOmrWXmZ6h0f0iwTpsdmdWa2nZUHh2AbCV0s1TVWxFKfxlJdIymowSG++OILXXLJJXrttdeUmJioVatW6fvf/7738/POO09nnXWWpedyYnAIANFi5tC6ResqtWKzS54jjuB2mzRvQpYWTsuOamxWZ2bbWXk4craR0MVSXWNFLPVpLNXVDGEbVa+V2+1WYmKi4uLifJbX1tYqMTFRvXv3DnaVEUPiBCAaSiuqNL+4XG0PuK2//y2bmRPwCa1oXaWWb3J1+vm1E4P7w9jM2KzOzLYzu93YRqwhluoaK2KpT2OprmYJ26h6rZxOZ7ukSZJSUlIsnTQBQDQ0ewwVllS2O5FJ8i4rLKlUs8f/71hNhz1asbnzP4glacVml5oOeyIem9WZ2XZmtxvbiDXEUl1jRSz1aSzVNVpCv9EZABCQMletzy0TbRmSqtyNKnPV+l3X6q275e+c5zFaykU6Nqszs+3Mbje2EWuIpbrGiljq01iqa7SQOAFAmNXUd34iC7bcntqGgNYVaDkzY7M6M9vO7HZjG7GGWKprrIilPo2lukYLiRMAhFlqksO0chkpfQNaV6DlzIzN6sxsO7PbjW3EGmKprrEilvo0luoaLSROABBmuVkpSnc61NlAsDa1jHiUm5Xid12z8jLlb0RZu62lXKRjszoz287sdmMbsYZYqmusiKU+jaW6RguJEwCEWZzdpoL8lhHM2p7QWt8X5GcHNMdG73i75k3I6rLMvAlZAc/VY2ZsVmdm25ndbmwj1hBLdY0VsdSnsVTXaCFxAoAImDoyXctm5ijN6XuLRJrTEfTwsAunZevaiVntrirYbcEPM212bFZnZtuZ3W5sI9YQS3WNFbHUp7FU12gIaR6n7ox5nABEk5mzuTcd9mj11t3aU9ugjJS+mpWXGfBVhHDHZnVmtp3Z7cY2Yg2xVNdYEUt9Gkt1PVphnwC3OyNxAgAAACBFYAJcAAAAAIglJE4AAAAA4Ed8tAMA0D1x/3RorNxuBxoP6+Y/vaO9X36tE/v10cOXjVGiwxqnCSu3m9nMrGsstRsAhJs1zogAupXSiioVllSqyv3t7OPpTocK8rMZsacLVm637z22We9+Wud9/0F1vUYuelGnnZCsF66bEMXIrN1uZjOzrrHUbgAQCQwOASAopRVVml9crrYHjtbfsBnutGNWbre2SVNb0UyerNxuZjOzrrHUbgBwNBgcAkBYNHsMFZZUtvtjTJJ3WWFJpZo9MfV7jF9WbrcDjYe7TJok6d1P63Sg8XCEIvqWldvNbGbWNZbaDQAiicQJQMDKXLU+t/20ZUiqcjeqzFUbuaC6ASu3281/esfUcmaycruZzcy6xlK7AUAkkTgBCFhNfed/jIVSLlZYud32fvm1qeXMZOV2M5uZdY2ldgOASCJxAhCw1CSHqeVihZXb7cR+fUwtZyYrt5vZzKxrLLUbAEQSiROAgOVmpSjd6VBngxnb1DJqV25WSiTDsjwrt9vDl40xtZyZrNxuZjOzrrHUbgAQSSROAAIWZ7epID9bktr9Udb6viA/m3li2rByuyU64nXaCV2PInTaCclRmc/Jyu1mNjPrGkvtBgCRROIEIChTR6Zr2cwcpTl9b/NJczoY4rgLVm63F66b0GnyFO15nKzcbmYzs66x1G4AECnM4wQgJM0eQ2WuWtXUNyo1qeW2H37B9s/K7Xag8bBu/tM72vvl1zqxXx89fNmYqFxp6oiV281sZtY1ltoNAEIRTG5A4gQAAAAgJjEBLgAAAACYiMQJAAAAAPwgcQIAAAAAP6zx1C8AWJSVH643OzYr19VssVRXALGHY1x4kDgBQCdKK6pUWFKpKnejd1m606GC/OyoD+dsdmxWrqvZYqmuAGIPx7jwYVQ9AOhAaUWV5heXq+0BsvX3umjOhWN2bFauq9liqa4AYg/HuOAxqh4AHIVmj6HCksp2Jx5J3mWFJZVq9kT+dyezY7NyXc0WS3UFEHs4xoUfiRMAtFHmqvW5xaEtQ1KVu1FlrtrIBfV/zI7NynU1WyzVFUDs4RgXfiROANBGTX3nJ55QypnJ7NisXFezxVJdAcQejnHhR+IEAG2kJjlMLWcms2Ozcl3NFkt1BRB7OMaFH4kTALSRm5WidKdDnQ3calPLCEW5WSmRDEuS+bFZua5mi6W6Aog9HOPCj8QJANqIs9tUkJ8tSe1OQK3vC/KzozInhtmxWbmuZoulugKIPRzjwo/ECQA6MHVkupbNzFGa0/eWhjSnI+rDuZodm5XrarZYqiuA2MMxLryYxwkAumDl2dfNjs3KdTVbLNUVQOzhGBe4YHIDEicAAAAAMYkJcAEAAADARCROAAAAAOBHfLQDAHoSK99TbOXYrMzKzxHRpz1T02GPVm/drT21DcpI6atZeZnqHc/vnP6wPwAIt6gmTkVFRXrmmWf0/vvvq0+fPho3bpyWLl2qU045pdPvrFy5UldddZXPsoSEBDU2Mgsyoqu0okqFJZWqcn+7LaY7HSrIz476KDZWjs3KzG43M9dHn/ZMResqtWKzS54jnj5evO49zZuQpYXTsqMXmMWxPwCIhKj+hPXqq69qwYIFeuONN7R+/Xp98803uuCCC3Tw4MEuv5ecnKyqqirva8+ePRGKGOhYaUWV5heX+5y0Jana3aj5xeUqraiKUmTWjs3KzG43M9dHn/ZMResqtXyTb9IkSR5DWr7JpaJ1ldEJzOLYHwBESlQTp9LSUs2dO1ennnqqvvOd72jlypXau3ev3n777S6/Z7PZlJaW5n0NGDAgQhED7TV7DBWWVKqj4SlblxWWVKq57V9DEWDl2KzM7HYzc330ac/UdNijFZtdXZZZsdmlpsOeCEXUPbA/AIgkS9007Xa7JUkpKSldljtw4IAyMjI0ePBgXXzxxfrXv/7VadlDhw6prq7O5wWYqcxV2+6XziMZkqrcjSpz1UYuqP9j5diszOx2M3N99GnPtHrr7nZXmtryGC3l8C32BwCRZJnEyePx6KabbtL48eM1cuTITsudcsopeuKJJ/T888+ruLhYHo9H48aN06efftph+aKiIjmdTu9r8ODB4aoCYlRNfWDP1wVazkxWjs3KzG43M8vRpz3TntoGU8vFCvYHAJFkmcRpwYIFqqio0Jo1a7osl5eXp9mzZ2v06NGaNGmSnnnmGR1//PFavnx5h+UXLlwot9vtfX3yySfhCB8xLDXJYWo5M1k5Niszu93MLEef9kwZKX1NLRcr2B8ARJIlEqfrrrtOf/vb37RhwwadcMIJQX23V69eGjNmjD766KMOP09ISFBycrLPCzBTblaK0p0OdTborU0tozvlZnV9C2o4WDk2KzO73cxcH33aM83Ky5S/kbPttpZy+Bb7A4BIimriZBiGrrvuOj377LN65ZVXlJWVFfQ6mpubtXPnTqWnM9wooiPOblNBfsswwW1P3q3vC/KzozKfiJVjszKz283M9dGnPVPveLvmTej6HDhvQhbzObXB/gAgkqJ6BF6wYIGKi4v19NNPKykpSdXV1aqurtbXX3/tLTN79mwtXLjQ+/6ee+7RSy+9pI8//ljl5eWaOXOm9uzZo2uuuSYaVQAkSVNHpmvZzBylOX1vB0lzOrRsZk5U5xGxcmxWZna7mbk++rRnWjgtW9dOzGp35cluk66dyDxOnWF/ABApNsMwojZGp83W8S9ATz75pObOnStJmjx5sjIzM7Vy5UpJ0s0336xnnnlG1dXV6tevn04//XTdd999GjNmTED/Zl1dnZxOp9xuN7ftwXRWnrneyrFZmdntZub66NOeqemwR6u37tae2gZlpPTVrLxMrjQFgP0BQCiCyQ2imjhFA4kTAAAAACm43ICfsAAAAADADxInAAAAAPCDxAkAAAAA/IiPdgBAT8LDyT0PD+oDAACJxAkwTWlFlQpLKlXlbvQuS3c6VJCfzXC43VTRukqt2OyS54ghdBave0/zJjA0NAAAsYafTQETlFZUaX5xuU/SJEnV7kbNLy5XaUVVlCJDqIrWVWr5Jt+kSZI8hrR8k0tF6yqjExgAAIgKEifgKDV7DBWWVKqjcf1blxWWVKq57V/gsKymwx6t2OzqssyKzS41HfZEKCIAABBtJE7AUSpz1ba70nQkQ1KVu1FlrtrIBYWjsnrr7nZXmtryGC3lAABAbCBxAo5STX3nSVMo5RB9e2obTC0HAAC6PxIn4CilJjlMLYfoy0jpa2o5AADQ/ZE4AUcpNytF6U6HOht03KaW0fVys1IiGRaOwqy8TPkbRd5uaykHAABiA4kTcJTi7DYV5LcMTd32b+3W9wX52czn1I30jrdr3oSsLsvMm5DFfE4AAMQQzvqACaaOTNeymTlKc/rejpfmdGjZzBzmceqGFk7L1rUTs9pdebLbpGsnMo8TAACxxmYYRkyNkVxXVyen0ym3263k5ORoh4MeptljqMxVq5r6RqUmtdyex5Wm7q3psEert+7WntoGZaT01ay8TK40AQDQQwSTG8RHKCYgJsTZbcob0j/aYcBEvePtunrCSdEOAwAARBk/mwIAAACAHyROAAAAAOAHt+qhQzyrE330gTXQD4g0tjkAsCYSJ7RTWlGlwpJKVbkbvcvSnQ4V5GczOlyE0AfWQD8g0tjmAMC6GFUPPkorqjS/uFxtN4rW3zoZWjv86ANroB8QaWxzABB5weQGPOMEr2aPocKSynYnbUneZYUllWr2xFSuHVH0gTXQD4g0tjkAsD4SJ3iVuWp9bg9py5BU5W5Umas2ckHFGPrAGugHRBrbHABYH4kTvGrqOz9ph1IOwaMPrIF+QKSxzQGA9ZE4wSs1yWFqOQSPPrAG+gGRxjYHANZH4gSv3KwUpTsd6mzQW5taRnfKzUqJZFgxhT6wBvoBkcY2BwDWR+IErzi7TQX52ZLU7uTd+r4gP5v5RMKIPrAG+gGRxjYHANZH4gQfU0ema9nMHKU5fW8HSXM6GAo3QugDa6AfEGlscwBgbczjhA4xc3300QfWQD8g0tjmACBygskNSJwAAAAAxCQmwAUAAAAAE5E4AQAAAIAfJE4AAAAA4Ed8tAMAAMCfpsMerd66W3tqG5SR0lez8jLVO94av/0xmAMAxAYSJwCApRWtq9SKzS55jhjKaPG69zRvQpYWTsuOXmCSSiuqVFhSqSp3o3dZutOhgvxshg8HgB7GGj/XAQDQgaJ1lVq+yTdpkiSPIS3f5FLRusroBKaWpGl+cblP0iRJ1e5GzS8uV2lFVZQiAwCEA4kTAMCSmg57tGKzq8syKza71HTYE6GIvtXsMVRYUqmO5vNoXVZYUqnmthkfAKDbInECAFjS6q27211pastjtJSLtDJXbbsrTUcyJFW5G1Xmqo1cUACAsCJxAgBY0p7aBlPLmammvvOkKZRyAADrI3ECAFhSRkpfU8uZKTXJYWo5AID1kTgBACxpVl6m/I3qbbe1lIu03KwUpTsd6iw8m1pG18vNSolkWACAMCJxAgBYUu94u+ZNyOqyzLwJWVGZzynOblNBfstQ6G2Tp9b3BfnZzOcEAD0IiRMAwLIWTsvWtROz2l15stukaydGdx6nqSPTtWxmjtKcvrfjpTkdWjYzh3mcAKCHsRmGEVNjpdbV1cnpdMrtdis5OTna4QAAAtB02KPVW3drT22DMlL6alZeZlSuNHWk2WOozFWrmvpGpSa13J7HlSYA6B6CyQ3iIxQTAAAh6x1v19UTTop2GB2Ks9uUN6R/tMMAAISZNX6uAwAAAAALI3ECAAAAAD9InAAAAADADxInAAAAAPCDxAkAAAAA/CBxAgAAAAA/SJwAAAAAwA8SJwAAAADwg8QJAAAAAPwgcQIAAAAAP0icAAAAAMAPEicAAAAA8IPECQAAAAD8IHECAAAAAD9InAAAAADADxInAAAAAPCDxAkAAAAA/IiPdgAAOtbsMVTmqlVNfaNSkxzKzUpRnN0W7bAAAABiUlSvOBUVFWns2LFKSkpSamqqZsyYoQ8++MDv99auXavhw4fL4XBo1KhRWrduXQSiBSKntKJKZy99RVeseEM3rtmuK1a8obOXvqLSiqpohwYAABCTopo4vfrqq1qwYIHeeOMNrV+/Xt98840uuOACHTx4sNPvbNmyRVdccYWuvvpqvfPOO5oxY4ZmzJihioqKCEYOhE9pRZXmF5eryt3os7za3aj5xeUkTwAAAFFgMwzDiHYQrT7//HOlpqbq1Vdf1cSJEzssc9lll+ngwYP629/+5l121llnafTo0Xr88cf9/ht1dXVyOp1yu91KTk42LXbADM0eQ2cvfaVd0tTKJinN6dBrt5/LbXsAAABHKZjcwFKDQ7jdbklSSkpKp2W2bt2q888/32fZhRdeqK1bt3ZY/tChQ6qrq/N5AVZV5qrtNGmSJENSlbtRZa7ayAUFAAAA6yROHo9HN910k8aPH6+RI0d2Wq66uloDBgzwWTZgwABVV1d3WL6oqEhOp9P7Gjx4sKlxA2aqqe88aQqlHAAAAMxhmcRpwYIFqqio0Jo1a0xd78KFC+V2u72vTz75xNT1A2ZKTXKYWg4AAADmsMRw5Nddd53+9re/adOmTTrhhBO6LJuWlqZ9+/b5LNu3b5/S0tI6LJ+QkKCEhATTYgXCKTcrRelOh6rdjero4cPWZ5xyszq/nRUAAADmi+oVJ8MwdN111+nZZ5/VK6+8oqysLL/fycvL08svv+yzbP369crLywtXmEDExNltKsjPltSSJB2p9X1BfjYDQwAAAERYVBOnBQsWqLi4WE8//bSSkpJUXV2t6upqff31194ys2fP1sKFC73vb7zxRpWWluqhhx7S+++/r0WLFumtt97SddddF40qAKabOjJdy2bmKM3peztemtOhZTNzNHVkepQiAwAAiF1RHY7cZuv4V/Mnn3xSc+fOlSRNnjxZmZmZWrlypffztWvX6q677tLu3bs1dOhQ/fKXv9S0adMC+jcZjhzdRbPHUJmrVjX1jUpNark9jytNAAAA5gkmN7DUPE6RQOIEAAAAQOrG8zgBAAAAgBWROAEAAACAHyROAAAAAOAHiRMAAAAA+EHiBAAAAAB+kDgBAAAAgB8kTgAAAADgB4kTAAAAAPhB4gQAAAAAfpA4AQAAAIAf8dEOINIMw5Ak1dXVRTkSAAAAANHUmhO05ghdibnEqb6+XpI0ePDgKEcCAAAAwArq6+vldDq7LGMzAkmvehCPx6PPPvtMSUlJstls0Q7H0urq6jR48GB98sknSk5OjnY4MYk+sAb6wRroB2ugH6yBfrAG+iH6jrYPDMNQfX29Bg4cKLu966eYYu6Kk91u1wknnBDtMLqV5ORkDgZRRh9YA/1gDfSDNdAP1kA/WAP9EH1H0wf+rjS1YnAIAAAAAPCDxAkAAAAA/CBxQqcSEhJUUFCghISEaIcSs+gDa6AfrIF+sAb6wRroB2ugH6Ivkn0Qc4NDAAAAAECwuOIEAAAAAH6QOAEAAACAHyROAAAAAOAHiRMAAAAA+EHiBN1///2y2Wy66aabOi2zcuVK2Ww2n5fD4YhckD3UokWL2rXr8OHDu/zO2rVrNXz4cDkcDo0aNUrr1q2LULQ9U7B9wL4QPv/5z380c+ZM9e/fX3369NGoUaP01ltvdfmdjRs3KicnRwkJCTr55JO1cuXKyATbgwXbDxs3bmy3T9hsNlVXV0cw6p4lMzOzwzZdsGBBp9/h3GC+YPuB84P5mpub9Ytf/EJZWVnq06ePhgwZonvvvVf+xrYL17kh3pS1oNvatm2bli9frtNOO81v2eTkZH3wwQfe9zabLZyhxYxTTz1V//znP73v4+M73y23bNmiK664QkVFRfrud7+rp59+WjNmzFB5eblGjhwZiXB7pGD6QGJfCIcvv/xS48eP1znnnKN//OMfOv744/Xhhx+qX79+nX7H5XJp+vTp+ulPf6o//OEPevnll3XNNdcoPT1dF154YQSj7zlC6YdWH3zwgZKTk73vU1NTwxlqj7Zt2zY1Nzd731dUVGjKlCn64Q9/2GF5zg3hEWw/SJwfzLZ06VItW7ZMq1at0qmnnqq33npLV111lZxOp2644YYOvxPOcwOJUww7cOCArrzySq1YsUL33Xef3/I2m01paWkRiCy2xMfHB9yujzzyiKZOnarbbrtNknTvvfdq/fr1euyxx/T444+HM8weLZg+kNgXwmHp0qUaPHiwnnzySe+yrKysLr/z+OOPKysrSw899JAkacSIEXrttdf08MMPkziFKJR+aJWamqpjjz02TJHFluOPP97n/f33368hQ4Zo0qRJHZbn3BAewfaDxPnBbFu2bNHFF1+s6dOnS2q5CvjHP/5RZWVlnX4nnOcGbtWLYQsWLND06dN1/vnnB1T+wIEDysjI0ODBg3XxxRfrX//6V5gjjA0ffvihBg4cqJNOOklXXnml9u7d22nZrVu3tuuvCy+8UFu3bg13mD1aMH0gsS+EwwsvvKAzzjhDP/zhD5WamqoxY8ZoxYoVXX6H/cF8ofRDq9GjRys9PV1TpkzR66+/HuZIY0dTU5OKi4v1k5/8pNOrF+wL4RdIP0icH8w2btw4vfzyy/r3v/8tSdqxY4dee+01XXTRRZ1+J5z7A4lTjFqzZo3Ky8tVVFQUUPlTTjlFTzzxhJ5//nkVFxfL4/Fo3Lhx+vTTT8Mcac925plnauXKlSotLdWyZcvkcrk0YcIE1dfXd1i+urpaAwYM8Fk2YMAAniU4CsH2AftCeHz88cdatmyZhg4dqhdffFHz58/XDTfcoFWrVnX6nc72h7q6On399dfhDrlHCqUf0tPT9fjjj+uvf/2r/vrXv2rw4MGaPHmyysvLIxh5z/Xcc8/pq6++0ty5czstw7kh/ALpB84P5vv5z3+uyy+/XMOHD1evXr00ZswY3XTTTbryyis7/U5Yzw0GYs7evXuN1NRUY8eOHd5lkyZNMm688caA19HU1GQMGTLEuOuuu8IQYez68ssvjeTkZOP3v/99h5/36tXLePrpp32W/eY3vzFSU1MjEV5M8NcHbbEvmKNXr15GXl6ez7Lrr7/eOOusszr9ztChQ40lS5b4LPv73/9uSDIaGhrCEmdPF0o/dGTixInGzJkzzQwtZl1wwQXGd7/73S7LcG4Iv0D6oS3OD0fvj3/8o3HCCScYf/zjH413333XeOqpp4yUlBRj5cqVnX4nnOcGrjjFoLfffls1NTXKyclRfHy84uPj9eqrr+rXv/614uPjfR6E7Exr1v/RRx9FIOLYceyxx2rYsGGdtmtaWpr27dvns2zfvn3cT20if33QFvuCOdLT05Wdne2zbMSIEV3eNtnZ/pCcnKw+ffqEJc6eLpR+6Ehubi77hAn27Nmjf/7zn7rmmmu6LMe5IbwC7Ye2OD8cvdtuu8171WnUqFGaNWuWbr755i7vmArnuYHEKQadd9552rlzp7Zv3+59nXHGGbryyiu1fft2xcXF+V1Hc3Ozdu7cqfT09AhEHDsOHDigXbt2ddqueXl5evnll32WrV+/Xnl5eZEILyb464O22BfMMX78eJ+RqCTp3//+tzIyMjr9DvuD+ULph45s376dfcIETz75pFJTU70PxneGfSG8Au2Htjg/HL2GhgbZ7b7pSlxcnDweT6ffCev+cFTXq9BjtL1Vb9asWcbPf/5z7/vCwkLjxRdfNHbt2mW8/fbbxuWXX244HA7jX//6VxSi7TluvfVWY+PGjYbL5TJef/114/zzzzeOO+44o6amxjCM9v3w+uuvG/Hx8caDDz5ovPfee0ZBQYHRq1cvY+fOndGqQrcXbB+wL4RHWVmZER8fbyxevNj48MMPjT/84Q9G3759jeLiYm+Zn//858asWbO87z/++GOjb9++xm233Wa89957xm9+8xsjLi7OKC0tjUYVeoRQ+uHhhx82nnvuOePDDz80du7cadx4442G3W43/vnPf0ajCj1Gc3OzceKJJxq33357u884N0ROMP3A+cF8c+bMMQYNGmT87W9/M1wul/HMM88Yxx13nPGzn/3MWyaS5wYSJxiG0T5xmjRpkjFnzhzv+5tuusk48cQTjd69exsDBgwwpk2bZpSXl0c+0B7msssuM9LT043evXsbgwYNMi677DLjo48+8n7eth8MwzD+/Oc/G8OGDTN69+5tnHrqqcbf//73CEfdswTbB+wL4VNSUmKMHDnSSEhIMIYPH2787ne/8/l8zpw5xqRJk3yWbdiwwRg9erTRu3dv46STTjKefPLJyAXcQwXbD0uXLjWGDBliOBwOIyUlxZg8ebLxyiuvRDjqnufFF180JBkffPBBu884N0ROMP3A+cF8dXV1xo033miceOKJhsPhME466STjzjvvNA4dOuQtE8lzg80w/Ey9CwAAAAAxjmecAAAAAMAPEicAAAAA8IPECQAAAAD8IHECAAAAAD9InAAAAADADxInAAAAAPCDxAkAAAAA/CBxAgAAAAA/SJwAAN2WzWbTc889d1TrmDt3rmbMmNFlmcmTJ+umm27qsszKlSt17LHHHlUsAADrInECAATt888/1/z583XiiScqISFBaWlpuvDCC/X6669HLaazzjpLP/3pT32WPf7447LZbFq5cqXP8rlz52rChAmSpEceeaTd5/5kZmbqV7/6VUhx1tXV6c4779Tw4cPlcDiUlpam888/X88884wMwwhpnQCA8IuPdgAAgO7n0ksvVVNTk1atWqWTTjpJ+/bt08svv6z9+/dHLaZzzjlHzz77rM+yDRs2aPDgwdq4caPmzp3rXb5x40bNmTNHkuR0OiMW41dffaWzzz5bbrdb9913n8aOHav4+Hi9+uqr+tnPfqZzzz2Xq1YAYFFccQIABOWrr77S5s2btXTpUp1zzjnKyMhQbm6uFi5cqO9973veMtdcc42OP/54JScn69xzz9WOHTu861i0aJFGjx6t5cuXa/Dgwerbt69+9KMfye12e8ts27ZNU6ZM0XHHHSen06lJkyapvLy807jOOeccffDBB6qurvYue/XVV/Xzn/9cGzdu9C5zuVzas2ePzjnnHEntb9U7ePCgZs+ercTERKWnp+uhhx7y+XcmT56sPXv26Oabb5bNZpPNZvP5/MUXX9SIESOUmJioqVOnqqqqyvvZHXfcod27d+vNN9/UnDlzlJ2drWHDhmnevHnavn27EhMTJbVc0brvvvu8cWRkZOiFF17Q559/rosvvliJiYk67bTT9NZbb/nrLgCASUicAABBSUxMVGJiop577jkdOnSowzI//OEPVVNTo3/84x96++23lZOTo/POO0+1tbXeMh999JH+/Oc/q6SkRKWlpXrnnXf03//9397P6+vrNWfOHL322mt64403NHToUE2bNk319fUd/pvjx49Xr169tGHDBklSZWWlvv76a1199dXav3+/XC6XpJarUA6HQ3l5eR2u57bbbtOrr76q559/Xi+99JI2btzok7A988wzOuGEE3TPPfeoqqrKJzFqaGjQgw8+qNWrV2vTpk3au3ev/ud//keS5PF4tGbNGl155ZUaOHBgh+0aH//tjSAPP/ywxo8fr3feeUfTp0/XrFmzNHv2bM2cOVPl5eUaMmSIZs+eze19ABAhJE4AgKDEx8dr5cqVWrVqlY499liNHz9ed9xxh959911J0muvvaaysjKtXbtWZ5xxhoYOHaoHH3xQxx57rP7yl79419PY2KinnnpKo0eP1sSJE/Xoo49qzZo13itG5557rmbOnKnhw4drxIgR+t3vfqeGhga9+uqrHcZ1zDHHKDc313t1aePGjTr77LOVkJCgcePG+SzPy8tTQkJCu3UcOHBA//u//6sHH3xQ5513nkaNGqVVq1bp8OHD3jIpKSmKi4tTUlKS0tLSlJaW5v3sm2++0eOPP64zzjhDOTk5uu666/Tyyy9Lkr744gt9+eWXGj58eEDtPG3aNF177bUaOnSo7r77btXV1Wns2LH64Q9/qGHDhun222/Xe++9p3379gW0PgDA0SFxAgAE7dJLL9Vnn32mF154QVOnTtXGjRuVk5OjlStXaseOHTpw4ID69+/vvTqVmJgol8ulXbt2eddx4oknatCgQd73eXl58ng8+uCDDyRJ+/bt07x58zR06FA5nU4lJyfrwIED2rt3b6dxTZ482SdBmjx5siRp0qRJPstbb9Nra9euXWpqatKZZ57pXZaSkqJTTjkloHbp27evhgwZ4n2fnp6umpoaSQr6ytBpp53m/f8BAwZIkkaNGtVuWev6AQDhxeAQAICQOBwOTZkyRVOmTNEvfvELXXPNNSooKNB///d/Kz093ee5olbBDHwwZ84c7d+/X4888ogyMjKUkJCgvLw8NTU1dfqdc845R4sXL9Z//vMfbdy40Xub3KRJk7R8+XLt2rVLn3zyic4999xgqxuQXr16+by32WzehOn444/Xscceq/fffz/odbU+R9XRMo/Hc1QxAwACwxUnAIApsrOzdfDgQeXk5Ki6ulrx8fE6+eSTfV7HHXect/zevXv12Wefed+/8cYbstvt3qs7r7/+um644QZNmzZNp556qhISEvTFF190GcO4cePUu3dv/fa3v1VjY6NOP/10SdLYsWP1+eef64knnvDe0teRIUOGqFevXnrzzTe9y7788kv9+9//9inXu3dvNTc3B9U+drtdl19+uf7whz/41LvVgQMHfG4JBABYC4kTACAo+/fv17nnnqvi4mK9++67crlcWrt2rX75y1/q4osv1vnnn6+8vDzNmDFDL730knbv3q0tW7bozjvv9BkFzuFwaM6cOdqxY4c2b96sG264QT/60Y+8zwwNHTpUq1ev1nvvvac333xTV155pfr06dNlbH369NFZZ52lRx99VOPHj1dcXJyklkTnyOVtrwy1SkxM1NVXX63bbrtNr7zyiioqKjR37lzZ7b6ny8zMTG3atEn/+c9//CZzR1q8eLEGDx6sM888U0899ZQqKyv14Ycf6oknntCYMWN04MCBgNcFAIgsbtUDAAQlMTFRZ555ph5++GHt2rVL33zzjQYPHqx58+bpjjvukM1m07p163TnnXfqqquu0ueff660tDRNnDjR+1yOJJ188sm65JJLNG3aNNXW1uq73/2ufvvb33o//9///V/913/9l3JycjR48GAtWbLEe+tdV8455xxt2rTJ+3xTq0mTJmnDhg2dPt/U6oEHHtCBAweUn5+vpKQk3XrrrT7DpEvSPffco2uvvVZDhgzRoUOHAn5+KSUlRW+88Ybuv/9+3XfffdqzZ4/69eunUaNG6YEHHojonFIAgODYDMYxBQBE2KJFi/Tcc89p+/bt0Q4FAICAcKseAAAAAPhB4gQAAAAAfnCrHgAAAAD4wRUnAAAAAPCDxAkAAAAA/CBxAgAAAAA/SJwAAAAAwA8SJwAAAADwg8QJAAAAAPwgcQIAAAAAP0icAAAAAMCP/w/XsFLrkH+PRAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.scatter(df['SepalLengthCm'], df['SepalWidthCm'], color = 'red')\n",
        "plt.ylabel('PetalLengthCm')\n",
        "plt.xlabel('PetalWidthCm')\n",
        "plt.title('Scatter Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "uGoAqR_onj8G",
        "outputId": "bffe5b08-b8a6-4a18-bf20-d663347e6a5a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "setosa_df = df[df['Species']== 'Iris-setosa']\n",
        "setosa_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PEjXxBuGoHa6",
        "outputId": "73ccc718-2604-4481-f42c-c381f590e3e6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4e48a984-0f79-4461-abb2-ac5400521b32\" 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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "<style>\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "setosa_df",
              "summary": "{\n  \"name\": \"setosa_df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 1,\n        \"max\": 50,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          14,\n          40,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3524896872134512,\n        \"min\": 4.3,\n        \"max\": 5.8,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          5.8,\n          5.2,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38102439795469095,\n        \"min\": 2.3,\n        \"max\": 4.4,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.5,\n          3.0,\n          3.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1735111594364455,\n        \"min\": 1.0,\n        \"max\": 1.9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          1.3,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10720950308167837,\n        \"min\": 0.1,\n        \"max\": 0.6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.2,\n          0.4,\n          0.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color_map = {'Iris-setosa': 'red', 'Iris-versicolor': 'green', 'Iris-virginica': 'blue'}\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "\n",
        "for species, group in df.groupby('Species'):\n",
        " plt.scatter(group['PetalLengthCm'], group['PetalWidthCm'],\n",
        "               color=color_map[species], label=species)\n",
        "ax.set_ylabel('PetalLengthCm')\n",
        "ax.set_xlabel('PetalWidthCm')\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "ZRw1JbUWts7J",
        "outputId": "fb9c81fd-7a93-405b-d4ca-e3a10515a45e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "sns.histplot(data=df, x=\"PetalLengthCm\", hue=\"Species\", multiple=\"stack\", ax=ax)\n",
        "ax.set_xlabel(\"Petal Length (cm)\")\n",
        "ax.set_ylabel(\"Count\")\n",
        "ax.set_title(\"Distribution of Petal Length by Species\")\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "xoNUo-on1Y6p",
        "outputId": "dbc060ae-ea21-4d61-ab5a-b7c7f0e011f0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color_map = {'Iris-setosa': 'red', 'Iris-versicolor': 'green', 'Iris-virginica': 'blue'}\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "\n",
        "for species, group in df.groupby('Species'):\n",
        " plt.scatter(group['SepalLengthCm'], group['SepalWidthCm'],\n",
        "               color=color_map[species], label=species)\n",
        "ax.set_ylabel('SepalLengthCm')\n",
        "ax.set_xlabel('SepalWidthCm')\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "DnTvQmHAoiV7",
        "outputId": "031066e3-4e1e-4d7d-c69e-dcf824ec3089"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "short_petals = df[df['PetalLengthCm'] < 2.0]\n",
        "short_petals.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4JrxldyuvFRA",
        "outputId": "d8c0cd81-8c4b-4a6b-b698-8a2fc92c98db"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
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              "        document.querySelector('#df-006d0858-057a-4369-a4b5-8d05cd1b6506 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-006d0858-057a-4369-a4b5-8d05cd1b6506');\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-65421551-9055-4322-a6d5-b51aaefb4627\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65421551-9055-4322-a6d5-b51aaefb4627')\"\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-65421551-9055-4322-a6d5-b51aaefb4627 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "short_petals",
              "summary": "{\n  \"name\": \"short_petals\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 1,\n        \"max\": 50,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          14,\n          40,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3524896872134512,\n        \"min\": 4.3,\n        \"max\": 5.8,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          5.8,\n          5.2,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38102439795469095,\n        \"min\": 2.3,\n        \"max\": 4.4,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.5,\n          3.0,\n          3.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1735111594364455,\n        \"min\": 1.0,\n        \"max\": 1.9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          1.3,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10720950308167837,\n        \"min\": 0.1,\n        \"max\": 0.6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.2,\n          0.4,\n          0.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "long_petals = df[df['PetalLengthCm'] > 2.0]\n",
        "long_petals.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "jdelix-mwUT7",
        "outputId": "683f5ae0-e6a8-4e3a-87d6-1df90a3701d8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "50  51            7.0           3.2            4.7           1.4   \n",
              "51  52            6.4           3.2            4.5           1.5   \n",
              "52  53            6.9           3.1            4.9           1.5   \n",
              "53  54            5.5           2.3            4.0           1.3   \n",
              "54  55            6.5           2.8            4.6           1.5   \n",
              "\n",
              "            Species  \n",
              "50  Iris-versicolor  \n",
              "51  Iris-versicolor  \n",
              "52  Iris-versicolor  \n",
              "53  Iris-versicolor  \n",
              "54  Iris-versicolor  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc05a43b-e2ae-479d-91ef-7f5b98a297d7\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
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              "      <th>50</th>\n",
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              "      <td>7.0</td>\n",
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              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-versicolor</td>\n",
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              "      <th>51</th>\n",
              "      <td>52</td>\n",
              "      <td>6.4</td>\n",
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              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>53</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>54</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>55</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dc05a43b-e2ae-479d-91ef-7f5b98a297d7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\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",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    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",
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            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " long_sepals = df[df['SepalLengthCm'] > 6.0]\n",
        "long_sepals.head()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "qKbraXQJyD0-",
        "outputId": "7b99802f-2cf8-4ce2-b2bf-9ccf2d305593"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "50  51            7.0           3.2            4.7           1.4   \n",
              "51  52            6.4           3.2            4.5           1.5   \n",
              "52  53            6.9           3.1            4.9           1.5   \n",
              "54  55            6.5           2.8            4.6           1.5   \n",
              "56  57            6.3           3.3            4.7           1.6   \n",
              "\n",
              "            Species  \n",
              "50  Iris-versicolor  \n",
              "51  Iris-versicolor  \n",
              "52  Iris-versicolor  \n",
              "54  Iris-versicolor  \n",
              "56  Iris-versicolor  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "long_sepals",
              "summary": "{\n  \"name\": \"long_sepals\",\n  \"rows\": 61,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 51,\n        \"max\": 149,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          51,\n          59,\n          133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4776136033430797,\n        \"min\": 6.1,\n        \"max\": 7.9,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          7.0,\n          6.4,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3028191763279079,\n        \"min\": 2.2,\n        \"max\": 3.8,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          3.6,\n          3.8,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.68322603449199,\n        \"min\": 4.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          5.0,\n          5.1,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.381054045762999,\n        \"min\": 1.2,\n        \"max\": 2.5,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          2.2,\n          1.9,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Iris-virginica\",\n          \"Iris-versicolor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion:\n",
        "\n",
        "The data set has 150 datas, three species of Iris: setosa, versicolor, and virginica with 50 samples for each species.\n",
        "\n",
        "Columns:Six; (Id, sepal length, sepal width, petal length, petal width and species).\n",
        "\n",
        "Features: sepal length, sepal width, petal length, and petal width\n",
        "\n",
        "\n",
        "\n",
        "From the analysis it was seen that:\n",
        "\n",
        "-Iris-Setosa is easily separable from the others with smaller petals and sepals, while Iris-Versicolor and Iris-Virginica are more similar with overlaps, though Iris-virginica has the biggest petals Iris-versicolor has the largest sepals.\n",
        "\n",
        "-The species are less separable with multiple ovelaps based on sepal length and width.\n",
        "\n",
        "-Petal Length and Petal Width are the most informative features.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "_U31WeyDCbtc"
      }
    }
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}