Day_44_Percentile_method.ipynb

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        "import pandas as pd\n",
        "import numpy as np"
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        "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day44-outlier-detection-using-percentiles/weight-height.csv')"
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              "  Gender     Height      Weight\n",
              "0   Male  73.847017  241.893563\n",
              "1   Male  68.781904  162.310473\n",
              "2   Male  74.110105  212.740856\n",
              "3   Male  71.730978  220.042470\n",
              "4   Male  69.881796  206.349801"
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              "Gender    0\n",
              "Height    0\n",
              "Weight    0\n",
              "dtype: int64"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Gender  10000 non-null  object \n",
            " 1   Height  10000 non-null  float64\n",
            " 2   Weight  10000 non-null  float64\n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 234.5+ KB\n"
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              "             Height        Weight\n",
              "count  10000.000000  10000.000000\n",
              "mean      66.367560    161.440357\n",
              "std        3.847528     32.108439\n",
              "min       54.263133     64.700127\n",
              "25%       63.505620    135.818051\n",
              "50%       66.318070    161.212928\n",
              "75%       69.174262    187.169525\n",
              "max       78.998742    269.989699"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['Height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "zqsXJhC9xR15",
        "outputId": "f9c427ac-4be2-4d2e-e425-09ef8a6efeaa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upper = df['Height'].quantile(0.99)"
      ],
      "metadata": {
        "id": "74CaibrSxpIg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lower = df['Height'].quantile(0.01)"
      ],
      "metadata": {
        "id": "cgd_1gExxpFb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "upper,lower"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIWaCnrwxpCs",
        "outputId": "55b0b4e4-a119-4568-b329-952dc9031c75"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(74.7857900583366, 58.13441158671655)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df[(df['Height']<74.8) & (df['Height']>=58)]"
      ],
      "metadata": {
        "id": "7LEMbv9yxoz-"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGiquABmy8w3",
        "outputId": "4c111d29-3533-402f-df28-ae11c2d89787"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9811, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "zHUTs8IJzJpj",
        "outputId": "ab9c39ed-2039-419d-d5e4-3b8c0cb0b31b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Height       Weight\n",
              "count  9811.000000  9811.000000\n",
              "mean     66.358487   161.356603\n",
              "std       3.653636    30.984787\n",
              "min      58.032173    77.523774\n",
              "25%      63.571564   136.250594\n",
              "50%      66.316886   161.176218\n",
              "75%      69.119859   186.747036\n",
              "max      74.795375   249.946283"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-acaef981-453a-4931-8da5-ce3fb023426b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9811.000000</td>\n",
              "      <td>9811.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.358487</td>\n",
              "      <td>161.356603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.653636</td>\n",
              "      <td>30.984787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>58.032173</td>\n",
              "      <td>77.523774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63.571564</td>\n",
              "      <td>136.250594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>66.316886</td>\n",
              "      <td>161.176218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>69.119859</td>\n",
              "      <td>186.747036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>74.795375</td>\n",
              "      <td>249.946283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acaef981-453a-4931-8da5-ce3fb023426b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-acaef981-453a-4931-8da5-ce3fb023426b 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-acaef981-453a-4931-8da5-ce3fb023426b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(new_df['Height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "xTtWh5_-zJdW",
        "outputId": "fc8e8dd8-e6e0-422a-83e8-3a1dfd3a6ea6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-edc1cb406df2>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(new_df['Height'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Height', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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D448/jvnz52Pjxo3O6haRpF74KgfFhiZE9/PCwuuGSB2HJBboo8bUoaEAgOc2HYOhgQtjEl0OSQunF198EQ888ADmzZtnPzLk7e2Nd955p932L730EqZPn45FixZh+PDh+Mtf/oIxY8bg1VdftbfZtWsX5s6di6uuugpxcXF48MEHkZiYeMkjWUSeYH/+ObyXkQcA+NstCfBW8zreBEwaHIwQPw0q60x4+ZtcqeMQuTXJCieTyYT9+/cjLS3tpzByOdLS0pCRkdHuYzIyMtq0B4Bp06a1aT9hwgRs3LgRRUVFEEJg+/btOHHiBK677roOszQ3N8NoNLa5EbkbU4sVT/4vC0IAt42JwuTBIVJHIhehlMtxY4Jtovj7GXnIq6yXOBGR+5Ls19HKykpYLBaEhYW12R4WFobjx4+3+5jS0tJ225eWltr//corr+DBBx9EVFQUlEol5HI53nrrLVx55ZUdZlm+fDmeeeaZy+gNkfRe33EKueV1CPJR4+kZw6WOQy5mcJgfhoT54kRZHR5Zm4l7UmIv+Zi7U2KckIzIvUg+OdzRXnnlFfz444/YuHEj9u/fjxUrVuCRRx7Btm3bOnzMkiVLYDAY7LfCwkInJia6fLlltXh1u20IZtnNIxHoo5Y4Ebmi6+PDIQNwpNiIMzzqRNQjkh1xCg4OhkKhQFlZWZvtZWVl0Ov17T5Gr9d32r6xsRFPPfUUPv30U8yYMQMAMGrUKBw8eBAvvPDCRcN8rTQaDTQazeV2iahXrN1d0On9ViHw5nenYbYIDA3zw02jwp2UjNxNmL8W4+P6YU9eNb46UorfXDkAMplM6lhEbkWyI05qtRpjx45Fenq6fZvVakV6ejpSU1PbfUxqamqb9gCwdetWe3uz2Qyz2Qy5vG23FAoFrFarg3tA5Br2nKlGQXUD1Eo5ZiZF8IuQOnX18FAo5TIUVDfgRFmd1HGI3I6kp9wsXLgQc+fOxbhx45CcnIyVK1eivr4e8+bNAwDMmTMHkZGRWL58OQBgwYIFmDJlClasWIEZM2bgo48+wr59+/Dmm28CAPz9/TFlyhQsWrQIXl5eiI2Nxbfffov3338fL774omT9JOotxkYzvjpim+M3bUQYArw5REed89eqkDogCN+frMS2Y2UYEubLYpuoGyQtnGbPno2KigosXboUpaWlSEpKwpYtW+wTwAsKCtocPZowYQLWrl2Lp59+Gk899RQGDx6MDRs2ID4+3t7mo48+wpIlS3DPPfeguroasbGxeO655/DQQw85vX9EvW3T4RI0t1gRHeiFlAFBAC49tEc0eUgIdp+pRlFNI46VGDEiQid1JCK3IRNcg/8iRqMROp0OBoMB/v68MCpJq6NC6ERZLVbvyoMMtov4RgR4OTcYubWvj5ZiR04F9P5azL96EOTtHHXiWXXkbpzx/e1xZ9UR9QVmixUbDxUDACYMDGLRRN02eVAINEo5So1NyCmtlToOkdtg4UTkhr49UYHqehP8tUqkDQ+79AOIfsZLrUBKf9vw7o6ccl4AmKiLWDgRuZnK2mZ8e6ICADBjVAQ0KoXEichdTRwUBKVchsJzjTjNdZ2IuoSFE5EbEULgs0NFsFgFhoT5Ij6Cc/Co5/y0KoyL6wcA+DanQuI0RO6BhRORG8kqMuBURT2UchluGsU1m+jyXTk4GHIZcLKiDmfPNUgdh8jlsXAichNmixVbsm1rNk0ZGoIgX652T5cvwFuNUVEBAICdJyulDUPkBlg4EbmJ73MrYWg0Q+elwpWDQ6SOQx5k4qBgAMDhIgMMjWaJ0xC5NhZORG7A2GjGtyfKAQDT4/VQKfjWJceJDPBC/2AfWAXw4+kqqeMQuTR++hK5ga+PlsFsEYjp541RkVzlmRxv4kDbUac9Z6phauG1PYk6wsKJyMUVnWtEZsE5AMCMhHBOCKdeMSzcD/181Gg0W+yvNyK6GAsnIhcmhMCmw7YVwpOiAxDdz1viROSp5DIZJgy0LYi561QlrFwQk6hdLJyIXNiX2aXIq2qASiHDtJF6qeOQhxsbGwitSo7KOhNOlPEyLETtYeFE5KJMLVYs//IYAGDy4BDovFQSJyJPp1EqMD7WtiAmlyYgah8LJyIXtW5vAQqrG+GnVXL5AXKa1IFBkMuAUxX1OFZilDoOkcth4UTkghpNFrz8zUkAwNShoVAr+VYl5wjwVmNkhO3Mzbd/OCNxGiLXw09jIhf0XkYeKmqbERXohXFxgVLHoT5m4vlJ4hsPFaOqrlniNESuhYUTkYsxNpnx+o5TAIDfpQ2BUs63KTlXdD9vRAZ4wdRixUd7C6WOQ+RS+IlM5GLe25kHQ6MZg0J9MWt0pNRxqA+SXbA0wQc/5sNs4YKYRK1YOBG5kLrmFry90zav5NGrB0Eh52KXJI2ESB2CfdUoMTTh6yNlUschchksnIhcyP9l5KOmwYwBwT64cVSE1HGoD1Mq5Lg7OQYAsHoXJ4kTtWLhROQiGkwteOv70wCAR6byaBNJ754rYqGUy7A37xyyiwxSxyFyCSyciFzE2t0FqK43IaafN2Ym8WgTSS/MX4vrE8IBAO/typM2DJGLUEodgKgvWru7oM2/LVaBV86v2zQ2JhD/3XdWilhEF7l3Qhw+P1SMzw4VY8kNw9HPRy11JCJJ8YgTkQvIOlsDQ6MZvholkmICpI5DZDcmJgAJkTqYWqz4cE/BpR9A5OFYOBFJTAiB73Nt1wWbMDAIKgXfluQ6ZDIZ7p0QB8C2NEELlyagPo6f0EQSO1leh1JjE9QKOVL6B0kdh+giNyaG/7Q0wVEuTUB9GwsnIon9cP4q9OPiAuGlVkichuhiGqUCd7UuTbAzT9owRBJj4UQkoXJjE3LL6yADMHFgsNRxiDp0T4ptaYI9edU4UsylCajvYuFEJKGM01UAgOHh/gjk2UrkwvQ6Lk1ABLBwIpJMk9mCAwU1AIDUgZzbRK7v3gmxAIANB4tRXW+SOA2RNFg4EUlkf/45mCxWhPppMCDYR+o4RJc0JiaQSxNQn8fCiUgCViHw4/lhutSBQZDJeHkVcn0ymQxzuTQB9XEsnIgkcKqiDlX1JmiUciRFB0gdh6jLbhwVjiAfLk1AfRcLJyIJ7D1TDQAYHRMAjZJLEJD70KoUuDvl/NIEnCROfRALJyInq6htxtESIwBgfFw/idMQdZ99aYIzXJqA+h4WTkRO9vH+s7AKIDrQC+E6L6njEHWbXqfF9Hg9AC6ISX0PCyciJ7Jahf1spOT+PNpE7mvexP4AgM8OFqPM2CRxGiLnYeFE5EQZp6tQUN0AjVKOhMgAqeMQ9djY2ECMjwuEyWLFOz+ckToOkdMopQ5A1Jes31cIAEiMDoBayd9byLWt3d35Wk3D9f7Ym3cOq3fl4bdTB0HnpXJSMiLp8JObyEmMTWZ8mV0KABgbEyhxGqLLN0Tvh1A/DZpbrFizO1/qOEROwcKJyEk2ZZWgucWKwaG+iArkpHByf3KZDFOGhAAA3vkhD01mi8SJiHofCyciJ2kdprt9bBRXCiePMSoqAAFeKlTWNeN/mWeljkPU6zjHichBOpsPUlHbjMyCGshZL5GHUchlmDgoGJsOl+DN707jzvExUPCFTh6MR5yInCCz4BwAYEiYH/y0nEBLnmV8XD8EeKuQX9WAL7NLpI5D1KtYOBH1MiEEDp2tAQBel448klopx9zUOADAqm9PQQghbSCiXsTCiaiXFVQ3oKbBDLVSjuHh/lLHIeoVcyfEQauSI7vIiO9yK6WOQ9RrWDgR9bLWo00jw/2hUvAtR56pn48a96TEAgD+tfUEjzqRx+KnOFEvslgFDp+1XQQ1kcN05OEemjIQXioFDhbWYHtOudRxiHoFCyeiXnSqog71Jgt81AoMDPGVOg5Rrwrx02DOBNtRpxd51Ik8FAsnol50qLAGAJAQFcBTtKlP+M2VA+GjViC7yIivjpRJHYfI4Vg4EfUSU4sVR0qMAICkKJ3EaYico5+PGvMm9gcArNx2AlYrjzqRZ2HhRNRLjpcaYWqxItBbheh+3lLHIXKa+yf3h59GieOltdjMdZ3Iw3DlcKJecqh1UnhUAC+xQh7v5yvnJ/fvh/Tj5Xjm86OoaTBDfsF74O6UGGfHI3IYHnEi6gWNJgtOlNYC4Nl01DdNHBQML5UCFbXNyDq/JAeRJ2DhRNQLsosNsAgBvb8WYf5aqeMQOZ1WpcDkwcEAgG3HytFitUqciMgxWDgR9YLWs+l4tIn6stSBQfDTKFFdb8Lu09VSxyFyCBZORA5mbDTjTGU9AGAUz6ajPkyjVOCa4WEAgG+Ol6PRZJE4EdHlY+FE5GBHig0QAKIDvRDorZY6DpGkxsYGItRPg0azBTtOcDVxcn8snIgc7HCRbe2mhEgebSJSyGWYHq8HAGScqsK5epPEiYguDwsnIgeqbTIjv8o2TBfPwokIADA0zA8DQnzQYhX4+mip1HGILgsLJyIHOlJshAAQFeiFAA7TEQEAZDIZro8PB2Bb34zLE5A7Y+FE5EDZRbZFLzlMR9RWZIAXRp8/y/Rvm4/xAsDktiQvnF577TXExcVBq9UiJSUFe/bs6bT9+vXrMWzYMGi1WiQkJGDz5s0XtTl27Bhuvvlm6HQ6+Pj4YPz48SgoKGhnb0SOU9fcYj+bLj6ChRPRz107IgxKuQw/nq7GtmOcKE7uSdLCad26dVi4cCGWLVuGzMxMJCYmYtq0aSgvb/8NtWvXLtx111247777cODAAcyaNQuzZs1Cdna2vc2pU6cwadIkDBs2DDt27EBWVhb+9Kc/QavlIoTUu1rPposM8EKgD4fpiH4uwFuNiYNsi2L+bfMxmFq4KCa5H5mQ8HhpSkoKxo8fj1dffRUAYLVaER0djUcffRRPPvnkRe1nz56N+vp6fPHFF/ZtV1xxBZKSkrBq1SoAwJ133gmVSoX/+7//63Euo9EInU4Hg8EAf3//Hu+H+pZrVuzAqYp6TB+px5VDQqSOQ+SSms0WvLbjFCrrmvH0jOG4f/IAqSORB3HG97dkR5xMJhP279+PtLS0n8LI5UhLS0NGRka7j8nIyGjTHgCmTZtmb2+1WrFp0yYMGTIE06ZNQ2hoKFJSUrBhw4ZOszQ3N8NoNLa5EXVHVV3zT8N0nN9E1CGNSoE/TBsKAHgpPRdVdc0SJyLqHskKp8rKSlgsFoSFhbXZHhYWhtLS9k9XLS0t7bR9eXk56urq8Pe//x3Tp0/H119/jVtuuQW33norvv322w6zLF++HDqdzn6Ljo6+zN5RX/P10TJYBRARoEU/DtMRdeq2sVEYGeGP2qYWvLj1hNRxiLpF8snhjmQ9fxHJmTNn4ne/+x2SkpLw5JNP4sYbb7QP5bVnyZIlMBgM9lthYaGzIpOH2Hy4BAAnhRN1hUIuw9IbRwAAPtxTgOOlPMpP7kOywik4OBgKhQJlZWVttpeVlUGv17f7GL1e32n74OBgKJVKjBgxok2b4cOHd3pWnUajgb+/f5sbUVedqzdh16kqABymI+qqlAFBuCFBD6sA/vLFUS5PQG5DssJJrVZj7NixSE9Pt2+zWq1IT09Hampqu49JTU1t0x4Atm7dam+vVqsxfvx45OTktGlz4sQJxMbGOrgHRDZbj5bBYhUI12kR7KuROg6Ry1u7uwBrdxdgRLgOCrkMO09WYelnR+zbL7wRuRqllD984cKFmDt3LsaNG4fk5GSsXLkS9fX1mDdvHgBgzpw5iIyMxPLlywEACxYswJQpU7BixQrMmDEDH330Efbt24c333zTvs9FixZh9uzZuPLKKzF16lRs2bIFn3/+OXbs2CFFF6kP2NQ6TMejTUTd0s9HjUmDgvHtiQpsPlyCwWG+UMo9agYJeSBJC6fZs2ejoqICS5cuRWlpKZKSkrBlyxb7BPCCggLIL3gTTZgwAWvXrsXTTz+Np556CoMHD8aGDRsQHx9vb3PLLbdg1apVWL58OR577DEMHToU//vf/zBp0iSn9488n6HBjJ0nKwFwfhNRT1w1JAT788+hqt6EH09VYdJgLuVBrk3SdZxcFddxoq5av68Qiz7OwjC9H+akxkkdh8gt7curxicHiqBVybHw2qHw1fz0O/3dKTESJiN349HrOBF5gi+zbUthtF7AlIi6b0xsICJ0WjSZrdh2rOzSDyCSEAsnoh4yNJrxfW4FAGDGqPbPBCWiS5PLZLhhlO2Xj71nqlFqaJI4EVHHWDgR9VD6sTKYLQKDQ30xKNRP6jhEbm1AsC9GRvhDANh0uJjLE5DLYuFE1EOti17ekMBhOiJHuD4+HAq5DKcq6nG8tFbqOETtYuFE1AO1TWZ8d8J2Nh0LJyLHaF2eALD9YtJy/moQRK6kR4XT6dOnHZ2DyK2kHyuHyWLFwBAfDAnzlToOkceYMiQEvhqlfXkCIlfTo8Jp0KBBmDp1Kj744AM0NXESH/U9Fw7TyWQyidMQeQ6tSoHrRtjW8vsmpxxVdc0SJyJqq0eFU2ZmJkaNGoWFCxdCr9fjN7/5Dfbs2ePobEQuqa65BTtO2M6m4zAdkeNduDzBi1tPSB2HqI0eFU5JSUl46aWXUFxcjHfeeQclJSWYNGkS4uPj8eKLL6KiosLROYlcxjfHy2FqsaJ/sA+G6Xk2HZGjXbg8wYd7CnC81ChxIqKfXNbkcKVSiVtvvRXr16/H888/j5MnT+KJJ55AdHQ05syZg5KSEkflJHIZm7Nah+n0HKYj6iWtyxNYBfC3zceljkNkd1mF0759+/Db3/4W4eHhePHFF/HEE0/g1KlT2Lp1K4qLizFz5kxH5SRyCfXNLdieUw6Aq4UT9bbpI/VQymX47kQFfjzNieLkGnpUOL344otISEjAhAkTUFxcjPfffx/5+fn461//iv79+2Py5MlYvXo1MjMzHZ2XSFLbc8rR3GJFbJA3RkbwOoZEvSnIV4M7k6MBAP/YcpyLYpJL6FHh9Prrr+Puu+9Gfn4+NmzYgBtvvBFyedtdhYaG4u2333ZISCJX8eXhn65Nx2E6ot732NWDoVXJkVlQg2+Ol0sdh6hnhdPWrVuxePFihIe3HaoQQqCgoAAAoFarMXfu3MtPSOQiGk0W+wf3DJ5NR+QUof5a3DuhPwDgn1/lwGrlUSeSVo8Kp4EDB6KysvKi7dXV1ejfv/9lhyJyRTtyytFotiAq0AvxkRymI3KWh6YMgJ9GieOltfj6aKnUcaiP61Hh1NE4c11dHbRa7WUFInJVm7joJZEkArzVuHdiHADgpfSTPOpEklJ2p/HChQsBADKZDEuXLoW3t7f9PovFgt27dyMpKcmhAYlcQZP5p2E6LnpJ5Hz3TeqPd3fm4ViJEVuPlWHaSL3UkaiP6lbhdODAAQC2I06HDx+GWq2236dWq5GYmIgnnnjCsQmJXMCOnAo0mCyIDPBCYpRO6jhEfU6AtxpzJ8Tite2n8HJ6Lq4bEcYjvySJbhVO27dvBwDMmzcPL730Evz9Oc+D+oYvs23DdNfHc9FLImdau7vA/vdALzXUSjmOFBuxbOMRDNNf/B10d0qMM+NRH9SjOU7vvvsuiybqM5rMFqQfO7/oJYfpiCTjrVEiJa4fAOC7E7y0F0mjy0ecbr31VqxevRr+/v649dZbO237ySefXHYwIlfxfW4l6ppbEK7TYnR0gNRxiPq0CYOCsetUFfKqGlBQVY+YIB+pI1Ef0+XCSafT2YcodDrO8aC+Y/P5s+mmx+shl3OYjkhKOi8VkmICsD//HL7LrcQvWTiRk3W5cHr33Xfb/TuRJ2tusWDb0TIAXPSSyFVMHhSM/fnncKzEiPLaJoT6cRkccp5uTQ5v1djYCCGEfTmC/Px8fPrppxgxYgSuu+46hwYkksra3QU4XmJEbXML/LW2xfdOlNVJHYuozwv112J4uD+OlRix82QVbhkdKXUk6kN6NDl85syZeP/99wEANTU1SE5OxooVKzBz5ky8/vrrDg1IJKXsYgMAYGSEDnKeTUfkMiYOCgIAHCw8hwZTi8RpqC/pUeGUmZmJyZMnAwA+/vhj6PV65Ofn4/3338fLL7/s0IBEUmmxWHGk2AgAiI/kvD4iV9I/yAfhOi3MFoF9eeekjkN9SI8Kp4aGBvj5+QEAvv76a9x6662Qy+W44oorkJ+f79CARFI5UVaH5hYr/LVKxAZ5X/oBROQ0MpkMqQNsR51+PF0FCy/DQk7So8Jp0KBB2LBhAwoLC/HVV1/Z5zWVl5dzfSfyGFlFNQCAhEgO0xG5osToAHirFahpNONYiVHqONRH9KhwWrp0KZ544gnExcUhJSUFqampAGxHn0aPHu3QgERSaDC12D+IR0UFSBuGiNqlUsiRfH5BzIzTVRKnob6iR2fV3X777Zg0aRJKSkqQmJho337NNdfglltucVg4Iql8c7wcZotAoLcKUYFeUschog4k9++Hb09U4ExlPSprm6WOQ31AjwonANDr9dDr216dOjk5+bIDEbmCzw8VA7AdbeK16YhcV4C3GkP1fjheWos9edVSx6E+oEeFU319Pf7+978jPT0d5eXlsFqtbe4/ffq0Q8IRSaG2yYztObbrYI2K4tl0RK5ufFw/HC+tRWbBOTSZLdCqFFJHIg/Wo8Lp/vvvx7fffotf/epXCA8P52/k5FG2Hi2DqcWKYF8N9P5ckZjI1Q0J84POSwVDoxlfHSnFzCQuiEm9p0eF05dffolNmzZh4sSJjs5DJLkvsmzXphsVpeMvBURuQCGXYVxsINKPl2Pt7gIWTtSrenRWXWBgIPr16+foLESSq2kw4bsT54fpuOglkdsYF9cPMgC7z1TjTGW91HHIg/WocPrLX/6CpUuXoqGhwdF5iCS1JbsULVaBYXo/hHKYjsht6LxUGBzmCwD4JPOsxGnIk/VoqG7FihU4deoUwsLCEBcXB5VK1eb+zMxMh4QjcrbWYbqbEiMkTkJE3TUmJhAnyurwv/1n8bu0IZDLOdROjtejwmnWrFkOjkHkHGt3F3R4X22TGTtPVgIABK/eQOR2hof7w1+rRLGhCRmnqzBxULDUkcgD9ahwWrZsmaNzEEkuu9gIASAq0Av9fNRSxyGiblIp5LgpMQJrdhfg4/1nWThRr+jRHCcAqKmpwX/+8x8sWbIE1dW2RccyMzNRVFTksHBEznT4bA0ATgoncme3j40CAHyZXYLaJrPEacgT9ahwysrKwpAhQ/D888/jhRdeQE1NDQDgk08+wZIlSxyZj8gpDI1m5FXZTnaIZ+FE5LaSogMwMMQHTWYrtmSXSh2HPFCPCqeFCxfi3nvvRW5uLrTan848uuGGG/Ddd985LByRsxwuMgAAYoO8EeDNYToidyWTyTDr/DpOrSd7EDlSj+Y47d27F2+88cZF2yMjI1Faygqf3E9W6zBdVICkOYjo8qzdXYDWczu+z63Am9+dhq+m/a+6u1NinBeMPEaPjjhpNBoYjcaLtp84cQIhISGXHYrImarrTTh7rhEyAPER/lLHIaLLFOyrQWSAF6wCOFJskDoOeZgeFU4333wznn32WZjNtol3MpkMBQUFWLx4MW677TaHBiTqba2TwgeE+MBPq+q8MRG5hdYLdGedZeFEjtWjwmnFihWoq6tDSEgIGhsbMWXKFAwaNAh+fn547rnnHJ2RqFdlnZ/fxGE6Is+RcP4kj7zKehgaeXYdOU6P5jjpdDps3boVO3fuxKFDh1BXV4cxY8YgLS3N0fmIelV5bRNKDE2Qy4CRHKYj8hgB3mrEBnkjv6oB2UUGrulEDtPtwslqtWL16tX45JNPkJeXB5lMhv79+0Ov10MIwavJk1s5fP4w/uBQP3ire/R7BBG5qFFRAcivasChszUsnMhhujVUJ4TAzTffjPvvvx9FRUVISEjAyJEjkZ+fj3vvvRe33HJLb+UkcjghhH3+Q+t8CCLyHPER/pABOHuuEdX1JqnjkIfo1q/Yq1evxnfffYf09HRMnTq1zX3ffPMNZs2ahffffx9z5sxxaEii3lBqbEJFXTOUchmGh3OYjsjT+GlVGBjii5MVdcg6W4OrhoZKHYk8QLeOOH344Yd46qmnLiqaAODqq6/Gk08+iTVr1jgsHFFvaj3aNCTMD1qVQuI0RNQbeHYdOVq3CqesrCxMnz69w/uvv/56HDp06LJDEfU2IYR9tXAO0xF5rpEROihkMpQam1BmbJI6DnmAbhVO1dXVCAsL6/D+sLAwnDt37rJDEfW2ohrbnAeVQoZheg7TEXkqL7UCg8N8AfCoEzlGtwoni8UCpbLjaVEKhQItLS2XHYqot7V+gA7T+0Ot7NFyZkTkJlrXaDtcVAMhROeNiS6hW5PDhRC49957odFo2r2/ubnZIaGIepP1gmG6RA7TEXm8YXo/KOQyVNaZUF7bjDB/7aUfRNSBbhVOc+fOvWQbnlFHrq6wugGGRjM0SjkGh/lJHYeIeplWpcCgEF/klNXiSLGRhRNdlm4VTu+++25v5SBymkPnh+lGhPtDpeAwHVFfMDLCHzlltThabMDVw7gsAfUcvzWoT7EKgWyeTUfU5wwLty2GWWxowjkuhkmXgYUT9SlnKutR19wCL5UCA0N9pY5DRE7iq1EiLtgHAHCkxChxGnJnLJyoT2k9m25khD+Ucr78ifqS1gt5HynmsgTUc/zmoD7DbLFeMEwXIG0YInK6EecvrVRQ1YDaJrPEachdsXCiPmPnyUo0mi3w0SjR//wheyLqOwK81YgK9IIAcKykVuo45KZconB67bXXEBcXB61Wi5SUFOzZs6fT9uvXr8ewYcOg1WqRkJCAzZs3d9j2oYcegkwmw8qVKx2cmtzNF1klAGxXTFfIZRKnISIpjAzncB1dHskLp3Xr1mHhwoVYtmwZMjMzkZiYiGnTpqG8vLzd9rt27cJdd92F++67DwcOHMCsWbMwa9YsZGdnX9T2008/xY8//oiIiIje7ga5uOYWC746UgqAw3REfdnICNvZtKcq6mBo5HAddZ/khdOLL76IBx54APPmzcOIESOwatUqeHt745133mm3/UsvvYTp06dj0aJFGD58OP7yl79gzJgxePXVV9u0KyoqwqOPPoo1a9ZApVI5oyvkwr47UYnaphb4a5WIDfKWOg4RSSTYT4NQPw2sAth+vP1f0Ik6I2nhZDKZsH//fqSlpdm3yeVypKWlISMjo93HZGRktGkPANOmTWvT3mq14le/+hUWLVqEkSNH9k54ciufHyoGACRE6iCXcZiOqC9rPbtuS3apxEnIHUlaOFVWVsJisSAsLKzN9rCwMJSWtv+CLi0tvWT7559/HkqlEo899liXcjQ3N8NoNLa5kedoNFmw7VgZAA7TEdFPw3U7TpSj0WSROA25G8mH6hxt//79eOmll7B69WrIunhkYfny5dDpdPZbdHR0L6ckZ/rmeDkaTBZEBXohKtBL6jhEJLFwnRYB3io0ma349kSF1HHIzUhaOAUHB0OhUKCsrKzN9rKyMuj1+nYfo9frO23//fffo7y8HDExMVAqlVAqlcjPz8fvf/97xMXFtbvPJUuWwGAw2G+FhYWX3zlyGV9k2YbpbhwV0eVimog8l0wms59d9/URDtdR90haOKnVaowdOxbp6en2bVarFenp6UhNTW33MampqW3aA8DWrVvt7X/1q18hKysLBw8etN8iIiKwaNEifPXVV+3uU6PRwN/fv82NPENdcwu+OT8B9KbEcInTEJGraB2u23asDKYWq8RpyJ0opQ6wcOFCzJ07F+PGjUNycjJWrlyJ+vp6zJs3DwAwZ84cREZGYvny5QCABQsWYMqUKVixYgVmzJiBjz76CPv27cObb74JAAgKCkJQUFCbn6FSqaDX6zF06FDndo4kt+1oGZpbrBgQ7IMR4f44VMi1W4gIiAnyRrCvGpV1Jvx4ugpXDgmROhK5CcnnOM2ePRsvvPACli5diqSkJBw8eBBbtmyxTwAvKChASUmJvf2ECROwdu1avPnmm0hMTMTHH3+MDRs2ID4+XqoukAv7aZgunMN0RGQnl8lw7QjbFI+vOFxH3SATQgipQ7gao9EInU4Hg8HAYTs3ZmgwY9xzW2G2CGz93ZUYHOaHtbsLpI5FRC4iMtALc9/Zg2BfDXY/dQ2vKOABnPH9LfkRJ6Le8tXRUpgtAkPD/DA4zE/qOETkYlIHBMFPq0RlXTMOFJyTOg65CRZO5LFaF73kpHAiao9aKcc1w0IBcDFM6joWTuSRquqasetUFQDbMgRERO2ZHm+b57TlSCk4c4W6goUTeaQvs0thsQokROoQF+wjdRwiclFThoRCq5Lj7LlGHCnmVSPo0lg4kUe68Gw6IqKOeKkVuGoIh+uo61g4kccpNzZh95lqAMAMFk5EdAnXJ9iG677MLrlESyIWTuSBNh0ugRDAmJgARAV6Sx2HiFzc1GGhUClkOFVRj5PltVLHIRfHwok8zhdZtt8ab0rkpHAiujR/rQqTBgUDAL48zOE66hwLJ/IoRTWN2J9/DjIZcEMCh+mIqGsuPLuOqDMsnMijbDo/KTw5rh/C/LUSpyEid3HtCD3kMuBIsREFVQ1SxyEXxsKJPMrnhzhMR0Td189HjZT+tgvE89p11BkWTuQx8irrcbjIAIVchuvPH3YnIuoqnl1HXcHCiTxG69pNEwYGIchXI3EaInI3142wFU6ZBTUoMzZJnIZcFQsn8hitw3Q3c5iOiHpAr9NiTEwAAA7XUcdYOJFHyCmtRU5ZLdQKOa4byWE6IuoZ+9l1XEWcOsDCiTzC54dsw3RThoZA56WSOA0RuavpI23LmOw+U42qumaJ05ArUkodgOhyCSHw+fn5TTybjoi6au3ugna3RwRoUVzThGe/OGo/0+7ulBhnRiMXxiNO5PYOFxmQX9UAL5UCacNDpY5DRG5uVGQAAOBQoUHaIOSSeMSJ3N4/tuQAAAaF+mLDgWKJ0xCRuxsVpcOWI6XIr6qHodHM4X9qg0ecyK1ZrQKHi2y/FSZG6SROQ0SeIMBbjdggbwgAWWdrpI5DLoaFE7m1/QXnYGg0Q6uSY0iYn9RxiMhDJEYFAACyznK4jtpi4URurfVsuhHhOigVfDkTkWPER+ogl9kuHF5Zy7Pr6Cf8piG31WKxYvNh26KXozhMR0QO5KtRYmCILwDgEIfr6AIsnMhtZZyuQmWdCd5qhf0DjojIUVqH6w6dNUAIIW0YchksnMhttQ7TxUfqoJDLJE5DRJ5mRIQ/lHIZKuuacaTYKHUcchEsnMgtNbdY7JdE4DAdEfUGrUqBoXrbSSetv6gRsXAit/T9iUoYm1oQ5q9BXJCP1HGIyEONOj9c9/mhYlitHK4jFk7kplovsXLjqAjIZRymI6LeMUzvB41SjmJDE/YXnJM6DrkAFk7kdhpNFmw9WgaA16Yjot6lUsgxItwfAPDZwSKJ05ArYOFEbueb4+VoMFkQ3c+Lq4UTUa9LjA4AAHx+qARNZou0YUhyLJzI7Ww8ZPut76ZREZBxmI6IetmgUF+E67QwNJqx7ViZ1HFIYiycyK0YGszYfrwCAIfpiMg55DIZbhsTBQBYv++sxGlIaiycyK1sOlwCk8WKYXo/DD8/74CIqLfdPtZWOH2XW4ESQ6PEaUhKLJzIrXySaftt79YxkRInIaK+JC7YB8lx/SAE8EkmJ4n3ZSycyG0UVDVgX/45yGXAzCQWTkTkXHeMax2uK+SaTn0YCydyG58esP2WN3FQMML8tRKnIaK+5oaEcPhqlMirakDG6Sqp45BEWDiRWxBC4JMDHKYjIun4aJS4ZbTt8+eDH/MlTkNSYeFEbiGzoAb5VQ3wViswbaRe6jhE1Ef98opYAMDXR8tQZmySOA1JgYUTuYVPzx9tmh6vh7daKXEaIuqrhur9MD4uEBarwEd7CqWOQxJg4UQur7nFgs8PlQAAbh0dJXEaIurr7kmxHXX6aG8BWixWidOQs7FwIpe3/XgFDI1mhPlrkDowSOo4RNTHXZ+gRz8fNUoMTfbrZlLfwcKJXF7rMN2s0ZFQyHmJFSKSlkapwF3J0QCAt384I3EacjYWTuTSztWb8M3xcgAcpiMi1zEnNQ4qhQz78s/hUGGN1HHIiVg4kUv7PKsYZovAiHB/DNX7SR2HiAgAEOavxU2jbNfL5FGnvoWnJ5HLWbu7AIBt7abXd5wCAPQP9rFvJyJyBb+e1B+fHCjC5sMlWHLDMITrvKSORE7AI07ksopqGlFiaIJSLsPomACp4xARtREfqUNK/35osQq8w6NOfQYLJ3JZe85UA7B9OHHtJiJyRb+ZMgAAsGZ3AWoaTBKnIWdg4UQuqdlsQdZZAwBgfFw/idMQEbVv6tBQDA/3R4PJgtW78qSOQ07Awolc0qGzBpgsVoT4ahAX5C11HCKidslkMjwydSAA4N2deahrbpE4EfU2Fk7kkvbm2YbpxscFQibj2k1E5Lqujw/HgGAfGBrNWMOL/3o8Fk7kcopqGlFU0wiFXIbRMYFSxyEi6pRCLsNDV9mOOr31/WnU86iTR2PhRC6n9WjTyAh/+Gg4KZyIXN8toyMRG+SNyjoT3svIkzoO9SIWTuRS6ptb7KvwclI4EbkLlUKOx9MGAwDe+PY0jE1miRNRb2HhRC7li6xiNLdYEeSjxoBgH6njEBF12c2JkRgU6gtDoxlvf891nTwVCydyKWv3FAKwHW3ipHAicicKuQwLrx0CwHYZloraZokTUW/gBBJyGYcKa3CosAYKGVcKJyLX0tVLPlmFQGSAF4pqGvGvbSfwt1sSejkZORuPOJHLeO/84nGjonTw06qkDUNE1ANymQw3JIQDAD7aU4DjpUaJE5GjsXAil1Be24TPs4oBAKkDgyROQ0TUc/2DfXBDgh5WATy36RiEEFJHIgdi4UQuYe3uApgtAmNiAhAVyJXCici9PTl9ONQKOb7PrcT2nHKp45ADsXAiyZlarFhzfv7AvRP7S5yGiOjyxQR5Y96kOADAXzcdg9lilTYQOQwLJ5LcxkPFqKhtRqifBtfH66WOQ0TkEI9MHYQgHzVOV9TzUiwehIUTSUoIgbe+Ow0AuHdiHFQKviSJyDP4a1X43fnlCVam58LQwEUxPYFLfEu99tpriIuLg1arRUpKCvbs2dNp+/Xr12PYsGHQarVISEjA5s2b7feZzWYsXrwYCQkJ8PHxQUREBObMmYPi4uLe7gb1wI4TFcgpq4WPWoF7UmKljkNE5FB3jo/GkDBf1DSY8a9tJ6SOQw4g+TpO69atw8KFC7Fq1SqkpKRg5cqVmDZtGnJychAaGnpR+127duGuu+7C8uXLceONN2Lt2rWYNWsWMjMzER8fj4aGBmRmZuJPf/oTEhMTce7cOSxYsAA333wz9u3bJ0EPqTOtR5vuTI6BzotLEBCRZ7hw3adJg0JwoqwO7+3Kg59WiXCdV5u2d6fEODseXQaZkPg8yZSUFIwfPx6vvvoqAMBqtSI6OhqPPvoonnzyyYvaz549G/X19fjiiy/s26644gokJSVh1apV7f6MvXv3Ijk5Gfn5+YiJufQL1Gg0QqfTwWAwwN/fv4c9o0vJLjLgxld+gEIuw3d/mIrIANuHSVcXmiMichdrd+cju9iI2CBvPDh5QJsrI7BwchxnfH9LOlRnMpmwf/9+pKWl2bfJ5XKkpaUhIyOj3cdkZGS0aQ8A06ZN67A9ABgMBshkMgQEBDgkNznGv3ecBADcNCrcXjQREXmiGxLCoVLIkF/VgIPnL2RO7knSwqmyshIWiwVhYWFttoeFhaG0tLTdx5SWlnarfVNTExYvXoy77rqrw+qzubkZRqOxzY16V25ZLb7Mtj1nD181SOI0RES9K8BbjalDbdNPtmSXoslskTgR9ZRLTA7vLWazGb/4xS8ghMDrr7/eYbvly5dDp9PZb9HR0U5M2Tf9e8cpCAFMGxmGoXo/qeMQEfW6SYOCEeSjRm1zC9KPlUkdh3pI0sIpODgYCoUCZWVtX0BlZWXQ69tfz0ev13epfWvRlJ+fj61bt3Y61rlkyRIYDAb7rbCwsIc9oq7Ir6rHZweLAADzpw6WOA0RkXMoFXLclBgBAMg4XYVSQ5PEiagnJC2c1Go1xo4di/T0dPs2q9WK9PR0pKamtvuY1NTUNu0BYOvWrW3atxZNubm52LZtG4KCOr/2mUajgb+/f5sb9Z5/bz8FqwCuGhqChCid1HGIiJxmSJgfRoT7wypsi//yOnbuR/KhuoULF+Ktt97Ce++9h2PHjuHhhx9GfX095s2bBwCYM2cOlixZYm+/YMECbNmyBStWrMDx48fx5z//Gfv27cP8+fMB2Iqm22+/Hfv27cOaNWtgsVhQWlqK0tJSmEwmSfpIPzlTWY+PM88CAB69mnObiKjvmTHKNlE8r6oeh84apI5D3SR54TR79my88MILWLp0KZKSknDw4EFs2bLFPgG8oKAAJSUl9vYTJkzA2rVr8eabbyIxMREff/wxNmzYgPj4eABAUVERNm7ciLNnzyIpKQnh4eH2265duyTpI/3kX1tPwGIVmDo0BGNj+0kdh4jI6QK91bjq/ETxL7NLUNvEFcXdieTrOLkiruPUO44WG3HDy98DADY9NgkjI9ofpuM6TkTk6VosVryUnouqehMemNwff5wxQupIHsHj13GivmXF1zkAgBtHhXdYNBER9QVKhRw3jrJNFH93Zx5yy2olTkRdJfklV6hvWL75GNKPl0MuA4aE+vGoEhH1eUP1fhge7o9jJUYs/ewI1j6Q0mZFcXJNPOJEvU4Iga+P2paQGBMTiGA/jcSJiIhcw40J4dAo5cg4XYXPs0ou/QCSHAsn6nU/nKzEmcp6KOQyXD3s4gs3ExH1VYE+avz2/NUTntt0FHXNLRInokth4US9SgiBf35lm9t0Rf9+CPBWS5yIiMi1/GbKAMT080aZsRmvpOdKHYcugYUT9arNh0uRddYAtUKOKUN5tImI6Oe0KgX+fLPtrLq3fziDk+WcKO7KWDhRr2kyW/C3zccAAJMGB8NXw3MRiIjac/WwMKQND0WLVWDZxiNcUdyF8ZuMes3bP5xBUU0jwnVaXDk4ROo4REQuqfUs46ToQOzIqcDOk1V46tNsJERevGzL3Skxzo5HP8MjTtQryo1N+Pf2kwCAxdOHQa3kS42IqDP9fNS4cojtl8zNh0tgtlglTkTt4bcZ9YoXvs5BvcmCpOgA3Hz+auBERNS5KUNCoPNSwdBoxq5TVVLHoXawcCKHyy4yYP1+24V8l940AnI5F3QjIuoKlUKOa0fYrtW6I6cc9VyewOWwcCKHEkLg2S+OQghgZlIExsQESh2JiMitJEUHIFynRXOLFTtyyqWOQz/DyeHUY+1dNiW7yIA9Z6qhUsgwNIyXViEi6i65TIbp8Xq8uzMPP56uxhUDghDkyysuuAoecSKHMVus+DLbdsmAyYNDuNglEVEPDQ71w+BQX1guuGQVuQYWTuQwu05V4VyDGf5aJZcfICK6TNNG6iEDcLjIgMLqBqnj0HksnMghapvM9rH4aSP1XH6AiOgyRQR4ISk6AADwZXYpF8V0Efx2I4fYerQMzS1WRAV6IfH8G52IiC7PtSPCoJTLkFdVj5xSXorFFbBwostWXNOI/fnnAAA3JoRDLuPyA0REjhDgrcaEgUEAgK3HymC18qiT1Fg40WURQmDT4RIIAKOidIgJ8pE6EhGRR7lycAg0SjlKDE3YcqRU6jh9HgsnuixHio04U1kPpVyG6SP1UschIvI43holJg4KBgC8uPUELDzqJCkWTtRjLVx+gIjIKSYODIaXSoGT5XXYeKhI6jh9Ggsn6rE2yw8MCZY6DhGRx/JSKzB5sO1z9qVtubwAsIRYOFGPVNQ2Y/v55QeuG6mHRqmQOBERkWdLHRiEIB818qoa8EnmWanj9FksnKhHXtyag+YWKyIvWGeEiIh6j0apwMNXDQQAvJx+Es0tFokT9U28Vh1125FiAz7aWwgAuHEUlx8gInIWlUIOP60SRTWNWLQ+C1cMCOq0/d0pMU5K1nfwiBN1ixACf/niKIQAEiJ1iOXyA0RETqNSyDF1aCgAYHtOOec6SYCFE3XL10fL8OPpamiUckyP5/IDRETONi42EAFeKtQ2tWD36Sqp4/Q5LJyoy5pbLPjb5mMAgAevHIBALj9AROR0SoUcVw+zHXXacaKCc52cjIUTddl7u/KQX9WAUD8NHpoyUOo4RER91uiYQAT5qNFgsiDjFI86ORMnh5Pd2t0FHd5X19yCFV+fAGBb/v+zg8XOikVERD+jkMtwzfBQ/HffWXyXW4GU/kHwUnNZGGfgESfqkm1Hy35afiAmQOo4RER93qioAIT6adBktuKHk5VSx+kzWDjRJZUYGrE3rxoAMCOByw8QEbkCuUyGtOFhAICdpypR19wicaK+gYUTdUoIgU1ZJRCwLT8QF8zlB4iIXMXICH9EBGhharHiuxMVUsfpE1g4UaeOldTidGU9lHIZpo/k8gNERK5EJpPh2uG2z+YfT1fB2GiWOJHnY+FEHWqxWLE5uwQAMGlQMAJ9uPwAEZGrGRLmi5h+3mixCvs1RKn3sHCiDmWcrkJ1vQl+GiWmDAmROg4REbVDJpPhuhG2uU5786pRXW+SOJFnY+FE7aprbsE3x22/uVw3MgwaFU9zJSJyVQNCfDEoxBdWAXxzvEzqOB6NhRO16+sjpWhusSJCp8XomECp4xAR0SVce/6o04GCGpTXNkmcxnOxcKKLFFY3YF/+OQDATYkRXH6AiMgNRPfzxnC9HwRsa+9R72DhRG1YhcDGQ7ZVwcfEBCA2iMsPEBG5i2tH6CEDkF1sRGF1g9RxPBILJ2pjb141imoaoVXJMY3LDxARuRW9Tosx56dXbM4ugRBC4kSeh4UT2dU3t+DrI7bDu2nDw+CnVUmciIiIuittRBhUChnyqxrwNYfsHI6FE9l9fbQUjWYL9P5apPQPkjoOERH1gM5LhYkDgwEAz395HKYWq8SJPAsLJwIAHCyswb4824TwmxMjoJBzQjgRkbu6ckgIfNQKnK6sx/sZeVLH8SgsnAgtFiuWfpYNAWB0dACvR0dE5Oa0KoV9nurKbblcnsCBWDgR3t2Zh6yzBmhVckyP54RwIiJPMCY2EKOidKhrbsE/tuRIHcdjsHDq4/Iq67Fiq+0NdUN8OCeEExF5CLlMhj/fPBIA8PH+s9ifXy1xIs/AwqkPs1oFnvwkC01mKyYOCsLYWK4QTkTkScbEBOKOsVEAgMX/O4zmFovEidwfC6c+7L2MPPx4uhpeKgWW3zIKMq4QTkTkcZ66YTiCfdU4WV6H13eckjqO22Ph1EedKKvF8i+PAwCevH4YYoK8JU5ERES9IdBHjWU32YbsXtt+ErlltRIncm8snPqg5hYLHvvwAEwtVlw1NARzUmOljkRERL3oxlHhSBseCrNF4Hf/Pci1nS4DC6c+6G+bjuF4aS36+ajxj9s5REdE5OlkMhmeuyUBgd4qZBcZseJrnmXXUyyc+phPD5zFexn5AIB/3j4KoX5aiRMREZEzhPlr8fxtowAAb3x3Gj/kVkqcyD2xcOpDjhYbseSTwwCAx64ehGuGh0mciIiInOm6kXrcnRIDAFjw0QEU1TRKnMj9sHDqI0oNTbj/vb1oMltx5ZAQLEgbInUkIiKSwJ9mjMCIcH9U1ZvwwHv70GBqkTqSW2Hh1AcYGs249909KDY0YUCID16+M4nXoiMi6qO81Aq8NXccgnzUOFpixKL1WbBahdSx3AYLJw9X39yCB9/fh+OltQjx0+C9eckI8FZLHYuIiCQUGeCFVb8aC5VChk2HS/DHDdkQgsVTV7Bw8mDGJjPmvLMHu89Uw1ejxOp54xHdj+s1ERERMD6uH178RRLkMuDDPQV49oujLJ66gIWThyqvbcI9b+3G/vxz8Ncq8cH9KRgZoZM6FhERuZCbEiPsZ9q9uzMPT/7vMMwWrvHUGRZOHmh//jnc+PIPOFxkQD8fNT588AokRQdIHYuIiFzQHeOisfzWBMhlwLp9hZj37l4YGs1Sx3JZLJw8SIvFilXfnsKdb2agvLYZg0N98b+HJ/BIExERdequ5Bi8NWccvNUK/HCyEje89D1+PF0ldSyXxMLJQxwqrMGsf+/E3788DrNFYPpIPT59ZCL6B/tIHY2IiNzANcPD8N/fpCKmnzeKahpx11s/4s8bj+BcvUnqaC7FJQqn1157DXFxcdBqtUhJScGePXs6bb9+/XoMGzYMWq0WCQkJ2Lx5c5v7hRBYunQpwsPD4eXlhbS0NOTm5vZmFyRz+KwBD7y/DzNf24nsIiP8tUr84/ZReP2XY+CrUUodj4iI3Eh8pA6bF0zG7HHREAJYvSsPV/5zO/694yRqGlhAAS5QOK1btw4LFy7EsmXLkJmZicTEREybNg3l5eXttt+1axfuuusu3HfffThw4ABmzZqFWbNmITs7297mH//4B15++WWsWrUKu3fvho+PD6ZNm4ampiZndatXlRqa8H8ZebjplR9w06s/YOvRMshlwK2jI7Ht91Pwi3HRvP4cERH1iK9GiedvH4X/uy8Zw/R+qG1qwT+25CDlb+lYtP4Qvj1R0acvEiwTEp97mJKSgvHjx+PVV18FAFitVkRHR+PRRx/Fk08+eVH72bNno76+Hl988YV92xVXXIGkpCSsWrUKQghERETg97//PZ544gkAgMFgQFhYGFavXo0777zzkpmMRiN0Oh0MBgP8/f0d1NOeqW9uwZnKehwvrcWhwhrszavG8dJa+/0qhQwzEsIx/+rBGBTqe1k/a+3ugsuNS0RELqT18io9ZbEKbDhQhP/8cAbHSoz27X4aJZL798OY2EDER+owKNQXETqt5L+0O+P7W9KxHJPJhP3792PJkiX2bXK5HGlpacjIyGj3MRkZGVi4cGGbbdOmTcOGDRsAAGfOnEFpaSnS0tLs9+t0OqSkpCAjI6NLhVNvySmtRXaRAc0tVphaLDBZrGg2W21/tljRaLKgut6EyrpmVNebUFVvQnU7Y8syGZAUHYAZCeG4dUwU+vlwQUsiInI8hVyG28ZG4dYxkdiXfw6fZJ7F1qPlqKxrRvrxcqQf/2l0SK2UI8RXg2BfNYJ9NQj21cBbo4BGqYBGKYdGJf/p70o5hof7Iz7S/U5ekrRwqqyshMViQVhY24vNhoWF4fjx4+0+prS0tN32paWl9vtbt3XU5ueam5vR3Nxs/7fBYABgq1wd6fN9p/By+sluPy7QW4UBwb4YGemPUVE6JPcP+qlYsjTBaHTMEGRDfe2lGxERkdtw5PfY0H5KLEmLw+KrY3Gk2IADhTU4VFCD3Io6FFQ1oKlZoLC+DoVlXdvfryf1R8y1jr1uamt/e3MwjbOHASxfvhzPPPPMRdujo6MlSHOxQgBZADZInIOIiNzLA1IH6MQzK4GLv3kdo7a2Fjpd7xzNkrRwCg4OhkKhQFlZ2/K0rKwMer2+3cfo9fpO27f+WVZWhvDw8DZtkpKS2t3nkiVL2gz/Wa1WVFdXIygoSPLx2sthNBoRHR2NwsJCyedqOZon9w1g/9yZJ/cNYP/cmSf3Dfipf0ePHkVERESv/RxJCye1Wo2xY8ciPT0ds2bNAmArWtLT0zF//vx2H5Oamor09HQ8/vjj9m1bt25FamoqAKB///7Q6/VIT0+3F0pGoxG7d+/Gww8/3O4+NRoNNBpNm20BAQGX1TdX4u/v75FvEsCz+wawf+7Mk/sGsH/uzJP7BgCRkZGQy3tv0QDJh+oWLlyIuXPnYty4cUhOTsbKlStRX1+PefPmAQDmzJmDyMhILF++HACwYMECTJkyBStWrMCMGTPw0UcfYd++fXjzzTcBADKZDI8//jj++te/YvDgwejfvz/+9Kc/ISIiwl6cEREREfWE5IXT7NmzUVFRgaVLl6K0tBRJSUnYsmWLfXJ3QUFBm8pxwoQJWLt2LZ5++mk89dRTGDx4MDZs2ID4+Hh7mz/84Q+or6/Hgw8+iJqaGkyaNAlbtmyBVqt1ev+IiIjIc0heOAHA/PnzOxya27Fjx0Xb7rjjDtxxxx0d7k8mk+HZZ5/Fs88+66iIbkmj0WDZsmUXDUN6Ak/uG8D+uTNP7hvA/rkzT+4b4Lz+Sb4AJhEREZG7kPySK0RERETugoUTERERURexcCIiIiLqIhZORERERF3EwskN/PnPf4ZMJmtzGzZsGAAgLy/vovtab+vXr+9wn/fee+9F7adPn+6sLl2kqKgIv/zlLxEUFAQvLy8kJCRg37599vuFEFi6dCnCw8Ph5eWFtLQ05ObmXnK/r732GuLi4qDVapGSkoI9e/b0Zjc61Fn/zGYzFi9ejISEBPj4+CAiIgJz5sxBcXFxp/vs7HXhTJd67nr6WnOH5w5Ah++/f/7znx3u01Weu7i4uHazP/LIIwCApqYmPPLIIwgKCoKvry9uu+22i67c8HM9fa86Wmd9q66uxqOPPoqhQ4fCy8sLMTExeOyxx+zXKe2IK31uXuq5u+qqqy6676GHHup0n67y3AGd90/y7z1BLm/ZsmVi5MiRoqSkxH6rqKgQQgjR0tLSZntJSYl45plnhK+vr6itre1wn3PnzhXTp09v87jq6mpndamN6upqERsbK+69916xe/ducfr0afHVV1+JkydP2tv8/e9/FzqdTmzYsEEcOnRI3HzzzaJ///6isbGxw/1+9NFHQq1Wi3feeUccOXJEPPDAAyIgIECUlZU5o1t2l+pfTU2NSEtLE+vWrRPHjx8XGRkZIjk5WYwdO7bT/Xb2unCWrjx3PXmtuctzJ4S46P33zjvvCJlMJk6dOtXhfl3huRNCiPLy8jYZtm7dKgCI7du3CyGEeOihh0R0dLRIT08X+/btE1dccYWYMGFCp/vsyXu1N3TWt8OHD4tbb71VbNy4UZw8eVKkp6eLwYMHi9tuu63TfbrS5+alnrspU6aIBx54oE0bg8HQ6T5d5bkTovP+Sf29x8LJDSxbtkwkJiZ2uX1SUpL49a9/3WmbuXPnipkzZ15eMAdZvHixmDRpUof3W61WodfrxT//+U/7tpqaGqHRaMSHH37Y4eOSk5PFI488Yv+3xWIRERERYvny5Y4J3kWX6l979uzZIwCI/Pz8Dtt093XRG7rSt5681tz5uZs5c6a4+uqrO23jCs9dexYsWCAGDhworFarqKmpESqVSqxfv95+/7FjxwQAkZGR0e7je/pedYYL+9ae//73v0KtVguz2dzhPlzpc/Pnft6/KVOmiAULFnT58a783Alx6efPmd97HKpzE7m5uYiIiMCAAQNwzz33oKCgoN12+/fvx8GDB3Hfffddcp87duxAaGgohg4diocffhhVVVWOjt0lGzduxLhx43DHHXcgNDQUo0ePxltvvWW//8yZMygtLUVaWpp9m06nQ0pKCjIyMtrdp8lkwv79+9s8Ri6XIy0trcPH9JZL9a89BoMBMpnsktdM7Orrord0tW/dea2583NXVlaGTZs2den9J/Vz93MmkwkffPABfv3rX0Mmk2H//v0wm81tnodhw4YhJiamw+ehJ+9VZ/h539pjMBjg7+8PpbLzdaFd5XPzQh31b82aNQgODkZ8fDyWLFmChoaGDvfhqs8dcOnnz+nfe5ddelGv27x5s/jvf/8rDh06JLZs2SJSU1NFTEyMMBqNF7V9+OGHxfDhwy+5zw8//FB89tlnIisrS3z66adi+PDhYvz48aKlpaU3utApjUYjNBqNWLJkicjMzBRvvPGG0Gq1YvXq1UIIIXbu3CkAiOLi4jaPu+OOO8QvfvGLdvdZVFQkAIhdu3a12b5o0SKRnJzcOx3pwKX693ONjY1izJgx4u677+50v915XfSWrvStu681d37unn/+eREYGHjJoQ1XeO5+bt26dUKhUIiioiIhhBBr1qwRarX6onbjx48Xf/jDH9rdR0/eq87w8779XEVFhYiJiRFPPfVUp/txpc/NC7XXvzfeeENs2bJFZGVliQ8++EBERkaKW265pcN9uOpzJ8Slnz9nf++xcHJD586dE/7+/uI///lPm+0NDQ1Cp9OJF154odv7PHXqlAAgtm3b5qiYXaZSqURqamqbbY8++qi44oorhBDuXzhdqn8XMplM4qabbhKjR4++5HyEn+voddGbutO3Vpd6rbnrcyeEEEOHDhXz58/v9s+R4rn7ueuuu07ceOON9n97UuH0875dyGAwiOTkZDF9+nRhMpm6tV8pPzcv1Fn/WqWnpwsAbebnXchVnzshOu+fFN97HKpzQwEBARgyZAhOnjzZZvvHH3+MhoYGzJkzp9v7HDBgAIKDgy/apzOEh4djxIgRbbYNHz7cPnSh1+sB4KKzecrKyuz3/VxwcDAUCkW3HtNbLtW/VmazGb/4xS+Qn5+PrVu3wt/fv1s/p6PXRW/qat8udKnXmjs+dwDw/fffIycnB/fff3+3f44Uz92F8vPzsW3btjbZ9Xo9TCYTampq2rTt7HnoyXu1t7XXt1a1tbWYPn06/Pz88Omnn0KlUnVr31J+brbqrH8XSklJAYAOs7ricwdcun9SfO+xcHJDdXV1OHXqFMLDw9tsf/vtt3HzzTcjJCSk2/s8e/YsqqqqLtqnM0ycOBE5OTlttp04cQKxsbEAgP79+0Ov1yM9Pd1+v9FoxO7du5GamtruPtVqNcaOHdvmMVarFenp6R0+prdcqn/AT0VTbm4utm3bhqCgoG7/nI5eF72pK337uUu91tztuWv19ttvY+zYsUhMTOz2z5HiubvQu+++i9DQUMyYMcO+bezYsVCpVG2eh5ycHBQUFHT4PPTkvdrb2utba67rrrsOarUaGzduhFar7fa+pfzcbNVR/37u4MGDANBhVld87oBL90+S771uH9sip/v9738vduzYIc6cOSN27twp0tLSRHBwsCgvL7e3yc3NFTKZTHz55Zft7mPo0KHik08+EUIIUVtbK5544gmRkZEhzpw5I7Zt2ybGjBkjBg8eLJqampzSpwvt2bNHKJVK8dxzz4nc3FyxZs0a4e3tLT744AN7m7///e8iICDAPj4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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Height'] = np.where(df['Height']>upper,upper,np.where(df['Height']<lower, lower, df['Height']))"
      ],
      "metadata": {
        "id": "yqMF92TPzZB4"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noGVblg3zYtD",
        "outputId": "f2b1adf7-fdf5-4ab6-f3ff-fdeb85829551"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9Y5T8ccB0HdA",
        "outputId": "b8bce6f4-baf0-4ce3-eca2-56a00dc238f1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Height        Weight\n",
              "count  10000.000000  10000.000000\n",
              "mean      66.366281    161.440357\n",
              "std        3.795717     32.108439\n",
              "min       58.134412     64.700127\n",
              "25%       63.505620    135.818051\n",
              "50%       66.318070    161.212928\n",
              "75%       69.174262    187.169525\n",
              "max       74.785790    269.989699"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.366281</td>\n",
              "      <td>161.440357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.795717</td>\n",
              "      <td>32.108439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>58.134412</td>\n",
              "      <td>64.700127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63.505620</td>\n",
              "      <td>135.818051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>66.318070</td>\n",
              "      <td>161.212928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>69.174262</td>\n",
              "      <td>187.169525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>74.785790</td>\n",
              "      <td>269.989699</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          const element = document.querySelector('#df-499d41ee-4573-430e-af03-5b600fee7848');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similarly we can work with Weight column if any outlier is present. Let's check."
      ],
      "metadata": {
        "id": "CWyna7Sb0LDw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Weight'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "mxH3ss1j0IsJ",
        "outputId": "f5aa2126-e5e0-4706-bacf-1cb09360a667"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-05ee4c8848be>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df['Weight'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Weight', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['Weight'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "RAkErWMv0d0y",
        "outputId": "ad9bcbea-2a69-4dd7-94d5-2cbc556db74b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hWIbJqq40h8C"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}