notebook.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "## Importing Dependencies"
      ],
      "metadata": {
        "id": "yWlSCLB6aLX5"
      }
    },
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "aKQlQ3te0eQk"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/train.csv')"
      ],
      "metadata": {
        "id": "c2l67qJ9KfY3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. How big is the data?"
      ],
      "metadata": {
        "id": "UBgdYpIzCWNo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
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        "id": "a8go_s9gCXe2",
        "outputId": "b4abbdc5-5070-435c-cce1-cb678e33b937"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. How does the data look like?"
      ],
      "metadata": {
        "id": "vcGbnQSaCXwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
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        "id": "eESXlqe7CZ8Q",
        "outputId": "d56108bf-0653-4100-ac0e-1202036017c7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ],
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              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
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              "      <th>SibSp</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>A/5 21171</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. what is the data type of cols"
      ],
      "metadata": {
        "id": "M23_gkzQCayO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iz9RczwRCdQg",
        "outputId": "24abc621-3b57-4d6a-ab5c-3da2b990dbd2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Are there any missing values in the dataset?"
      ],
      "metadata": {
        "id": "sxO3kI5QCe_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nsNG4daCgzv",
        "outputId": "e7ed193c-a570-448b-d35f-68234601c358"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. How does the data look mathematically?"
      ],
      "metadata": {
        "id": "6oZBZ5w8ChOl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "kXalCyoTCi8Y",
        "outputId": "903c314a-95aa-43e2-c2ee-0cd4437f03a1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
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              "      <td>32.204208</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429855,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228578,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Are there duplicate values in the data?"
      ],
      "metadata": {
        "id": "BismGlSOCjbF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHkkwSH6ClpJ",
        "outputId": "d86ed7e5-2a20-4011-b329-f86aa9148592"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. How is the correlation betwwen cols?"
      ],
      "metadata": {
        "id": "ODBUV1WlCmzS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr(numeric_only=True)['Survived']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MC0qJKQ8CnPw",
        "outputId": "67e853ce-12e7-4bbe-aeda-16955f392ae0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId   -0.005007\n",
              "Survived       1.000000\n",
              "Pclass        -0.338481\n",
              "Age           -0.077221\n",
              "SibSp         -0.035322\n",
              "Parch          0.081629\n",
              "Fare           0.257307\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Univariate Analysis"
      ],
      "metadata": {
        "id": "0QbW_O8sM5Xb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Categorical"
      ],
      "metadata": {
        "id": "u2nofvpBNYyO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#a . countplot"
      ],
      "metadata": {
        "id": "aswLzInGOE8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "F3QLPBSRNIEJ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdI-LP99OaHx",
        "outputId": "398bc99d-566e-45b3-d608-e57b7aee31f6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    549\n",
              "1    342\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "survived_counts = df['Survived'].value_counts().reset_index()\n",
        "survived_counts.columns = ['Survived', 'Count']"
      ],
      "metadata": {
        "id": "teoCNNcNOKOx"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Survived', y='Count', data=survived_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "6lhcD5ccOmeD",
        "outputId": "95919b78-75f1-4524-ecf4-80b6beced970"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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.countplot(df['Embarked'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "OcJZemEfOsjJ",
        "outputId": "d25929a8-69a8-405c-ad2f-aa4a661bb9eb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='Embarked'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGwCAYAAACKOz5MAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAg60lEQVR4nO3de1TUdf7H8dcgMqIIeAMkATHJS4qRpMvq/jSxNfVYdtxyDRNjq/XSlulpS0vd9qzi5m6tZmnZltW22rbdTEszUys3r4FiGnkh4ZiISVzExAuf3x+us42gwjgwH/H5OIdznPl+Gd7z0TM+z3e+38FhjDECAACwmJ+vBwAAALgYggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1vP39QDeUFFRoe+++05NmzaVw+Hw9TgAAKAajDEqLS1VZGSk/PwufAylXgTLd999p6ioKF+PAQAAPJCXl6c2bdpccJ96ESxNmzaVdOYJBwcH+3gaAABQHSUlJYqKinL9P34h9SJYzr4NFBwcTLAAAHCZqc7pHJx0CwAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxXLz7p9qz/e3yxGjgDfT0GAAD1ytbZo3w9AkdYAACA/QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPWsCZbDhw9r7Nixio6OltPpVEREhAYMGKD169f7ejQAAOBj/r4e4Kxhw4bpxIkTeuWVV9SuXTsdOnRIq1ev1pEjR3w9GgAA8DErgqWoqEifffaZ1q5dqz59+kiSYmJi1KNHDx9PBgAAbGDFW0JBQUEKCgrSu+++q/Ly8ovuX15erpKSErcvAABQf1kRLP7+/lq0aJFeeeUVhYaGqlevXpoyZYq2b99e5f7p6ekKCQlxfUVFRdXxxAAAoC5ZESzSmXNYvvvuOy1dulQ333yz1q5dq+uvv16LFi2qtO/kyZNVXFzs+srLy6v7gQEAQJ1xGGOMr4c4n3vuuUerVq3S/v37L7hfSUmJQkJC1O13C9TAGVhH0wEAcGXYOntUrTzu2f+/i4uLFRwcfMF9rTnCUpXOnTurrKzM12MAAAAfs+IqoSNHjuj2229XWlqa4uPj1bRpU23ZskVPPvmkbr31Vl+PBwAAfMyKYAkKClLPnj319NNPa+/evTp58qSioqJ07733asqUKb4eDwAA+JgVweJ0OpWenq709HRfjwIAACxk9TksAAAAEsECAAAuAwQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6/r4ewJs+/dMIBQcH+3oMAADgZRxhAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWM/f1wN40/89vlgNnIG+HgO1bOvsUb4eAQBQxzjCAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACs51/dHbdv317tB42Pj/doGAAAgKpUO1iuu+46ORwOGWPkcDguuO/p06cveTAAAICzqv2WUE5Ojvbt26ecnBy99dZbio2N1XPPPaeMjAxlZGToueee09VXX6233nqrNucFAABXoGofYYmJiXH9+fbbb9fcuXM1aNAg133x8fGKiorS1KlTNXToUK8OCQAArmwenXSblZWl2NjYSvfHxsZq586dlzwUAADAT3kULJ06dVJ6erpOnDjhuu/EiRNKT09Xp06dvDYcAACAVIO3hH5qwYIFGjJkiNq0aeO6Imj79u1yOBx6//33vTogAACAR8HSo0cP7du3T6+//rq+/vprSdLw4cN15513qkmTJl4dEAAAwKNgkaQmTZrovvvu8+YsAAAAVfL4k25fe+019e7dW5GRkdq/f78k6emnn9Z7773nteEAAAAkD4Nl/vz5mjhxogYOHKgffvjB9UFxzZo109/+9jdvzgcAAOBZsDzzzDNauHChHnvsMfn7/+9dpcTERGVlZXltOAAAAMnDYMnJyVFCQkKl+51Op8rKyi55KAAAgJ/yKFhiY2OVmZlZ6f4VK1bwOSwAAMDrPLpKaOLEiRo/fryOHz8uY4w2bdqkxYsXKz09XS+++KK3ZwQAAFc4j4LlnnvuUWBgoB5//HEdO3ZMd955pyIjIzVnzhz9+te/9vaMAADgCudRsJSUlCglJUUpKSk6duyYjh49qrCwMEnSnj171L59e68OCQAArmwencMyePBglZeXS5IaN27sipXs7Gz17dvXa8MBAABIHgZLUFCQbrvtNp06dcp1365du9S3b18NGzbMa8MBAABIHgbL22+/reLiYqWkpMgYox07dqhv374aMWKE5syZ4+0ZAQDAFc6jYAkMDNTy5cuVnZ2tO+64Q8nJyRo1apSeeuopb88HAABQ/ZNuS0pK3G77+fnpjTfe0E033aRhw4Zp6tSprn2Cg4O9OyUAALiiVTtYQkND5XA4Kt1vjNGCBQv0/PPPyxgjh8Ph+t1CAAAA3lDtYFmzZk1tzgEAAHBe1Q6WPn36SJJOnTqlmTNnKi0tTW3atKm1wQAAAM6q8Um3/v7+mj17ttslzQAAALXJo6uE+vXrp3Xr1nl7FgAAgCp59NH8AwcO1KOPPqqsrCx1795dTZo0cdt+yy231Ojx8vPzNWPGDC1fvlwHDhxQWFiYrrvuOk2YMEHJycmejAgAAOoRj4Jl3LhxklTl567U9Cqhb7/9Vr169VJoaKhmz56trl276uTJk1q5cqXGjx+vr7/+2pMRAQBAPeJRsFRUVHhtgHHjxsnhcGjTpk1uR2quvfZapaWlee3nAACAy5dHweIthYWFWrFihWbMmFHpbSXpzGe/VKW8vNz1yxelyh9qBwAA6hePg6WsrEzr1q1Tbm6uTpw44bbtgQceqNZj7NmzR8YYdezYsUY/Oz09XU888USNvgcAAFy+PAqWjIwMDRo0SMeOHVNZWZmaN2+u77//Xo0bN1ZYWFi1g8UY48mP1+TJkzVx4kTX7ZKSEkVFRXn0WAAAwH4eXdb80EMPaciQIfrhhx8UGBioDRs2aP/+/erevbv+8pe/VPtx4uLi5HA4anxirdPpVHBwsNsXAACovzwKlszMTE2aNEl+fn5q0KCBysvLFRUVpSeffFJTpkyp9uM0b95cAwYM0LPPPquysrJK24uKijwZDwAA1DMeBUvDhg3l53fmW8PCwpSbmytJCgkJUV5eXo0e69lnn9Xp06fVo0cPvfXWW9q9e7d27dqluXPnKikpyZPxAABAPePROSwJCQnavHmz4uLi1KdPH02bNk3ff/+9XnvtNXXp0qVGj9WuXTt9+eWXmjFjhiZNmqSDBw+qVatW6t69u+bPn+/JeAAAoJ5xGA/OfN2yZYtKS0t14403qqCgQKNGjdJ//vMfxcXF6aWXXlK3bt1qY9bzKikpUUhIiLr9boEaOAPr9Gej7m2dPcrXIwAAvODs/9/FxcUXPR/VoyMsiYmJrj+HhYVpxYoVnjwMAABAtVzSB8cVFBQoOztbktSxY0e1atXKK0MBAAD8lEcn3ZaWluquu+7SVVddpT59+qhPnz6KjIzUyJEjVVxc7O0ZAQDAFc6jYLnnnnu0ceNGLVu2TEVFRSoqKtKyZcu0ZcsW/fa3v/X2jAAA4Arn0VtCy5Yt08qVK9W7d2/XfQMGDNDChQt18803e204AAAAycMjLC1atFBISEil+0NCQtSsWbNLHgoAAOCnPAqWxx9/XBMnTlR+fr7rvvz8fD388MOaOnWq14YDAACQavCWUEJCghwOh+v27t27FR0drejoaElSbm6unE6nDh8+zHksAADAq6odLEOHDq3FMQAAAM6v2sEyffr02pwDAADgvC7pg+Mk6ejRo6qoqHC772IfrwsAAFATHp10m5OTo8GDB6tJkyauK4OaNWum0NBQrhICAABe59ERlpEjR8oYo5deeknh4eFuJ+MCAAB4m0fBsm3bNm3dulUdOnTw9jwAAACVePSW0A033KC8vDxvzwIAAFAlj46wvPjiixozZowOHDigLl26qGHDhm7b4+PjvTIcAACA5GGwHD58WHv37tXdd9/tus/hcMgYI4fDodOnT3ttQAAAAI+CJS0tTQkJCVq8eDEn3QIAgFrnUbDs379fS5cuVfv27b09DwAAQCUenXTbr18/bdu2zduzAAAAVMmjIyxDhgzRQw89pKysLHXt2rXSSbe33HKLV4YDAACQPAyWMWPGSJL++Mc/VtrGSbcAAMDbPAqWc393EAAAQG2q0TksgwYNUnFxsev2rFmzVFRU5Lp95MgRde7c2WvDAQAASDUMlpUrV6q8vNx1e+bMmSosLHTdPnXqlLKzs703HQAAgGoYLMaYC94GAACoDR5d1gwAAFCXahQsDoej0qfa8im3AACgttXoKiFjjEaPHi2n0ylJOn78uMaMGaMmTZpIktv5LQAAAN5So2BJTU11uz1y5MhK+4waNerSJgIAADhHjYLl5Zdfrq05AAAAzouTbgEAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFivRr9LyHaf/mmEgoODfT0GAADwMo6wAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArOfv6wG86f8eX6wGzkBfj1Frts4e5esRAADwCY6wAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOv5PFjy8vKUlpamyMhIBQQEKCYmRg8++KCOHDni69EAAIAlfBos+/btU2Jionbv3q3Fixdrz549WrBggVavXq2kpCQVFhb6cjwAAGAJf1/+8PHjxysgIEAfffSRAgMDJUnR0dFKSEjQ1Vdfrccee0zz58+v9H3l5eUqLy933S4pKamzmQEAQN3z2RGWwsJCrVy5UuPGjXPFylkRERFKSUnRG2+8IWNMpe9NT09XSEiI6ysqKqquxgYAAD7gs2DZvXu3jDHq1KlTlds7deqkH374QYcPH660bfLkySouLnZ95eXl1fa4AADAh3z6lpCkKo+g/FRAQECl+5xOp5xOZ22NBAAALOOzIyzt27eXw+HQrl27qty+a9cutWrVSqGhoXU7GAAAsI7PgqVFixa66aab9Nxzz+nHH39025afn6/XX39do0eP9s1wAADAKj69rHnevHkqLy/XgAED9OmnnyovL08rVqzQTTfdpGuuuUbTpk3z5XgAAMASPg2WuLg4bd68We3atdMdd9yhmJgYDRw4UNdcc43Wr1+voKAgX44HAAAs4fNPum3btq0WLVqk/Px8VVRUaNq0afroo4+0fft2X48GAAAs4fOrhM71xBNPqG3bttqwYYN69OghPz+fNxUAAPAx64JFku6++25fjwAAACzC4QsAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANbz9/UA3vTpn0YoODjY12MAAAAv4wgLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA69WL3yVkjJEklZSU+HgSAABQXWf/3z77//iF1ItgOXLkiCQpKirKx5MAAICaKi0tVUhIyAX3qRfB0rx5c0lSbm7uRZ/wlaykpERRUVHKy8vjt1qfB2t0caxR9bBOF8caXVx9XyNjjEpLSxUZGXnRfetFsPj5nTkVJyQkpF7+hXpbcHAw63QRrNHFsUbVwzpdHGt0cfV5jap7oIGTbgEAgPUIFgAAYL16ESxOp1PTp0+X0+n09ShWY50ujjW6ONaoelini2ONLo41+h+Hqc61RAAAAD5UL46wAACA+o1gAQAA1iNYAACA9QgWAABgvXoRLM8++6zatm2rRo0aqWfPntq0aZOvR6ozn376qYYMGaLIyEg5HA69++67btuNMZo2bZpat26twMBA9e/fX7t373bbp7CwUCkpKQoODlZoaKh+85vf6OjRo3X4LGpXenq6brjhBjVt2lRhYWEaOnSosrOz3fY5fvy4xo8frxYtWigoKEjDhg3ToUOH3PbJzc3V4MGD1bhxY4WFhenhhx/WqVOn6vKp1Jr58+crPj7e9eFUSUlJ+vDDD13br/T1qcqsWbPkcDg0YcIE132sk/SHP/xBDofD7atjx46u7azRGQcOHNDIkSPVokULBQYGqmvXrtqyZYtrO6/dVTCXuSVLlpiAgADz0ksvma+++srce++9JjQ01Bw6dMjXo9WJDz74wDz22GPm7bffNpLMO++847Z91qxZJiQkxLz77rtm27Zt5pZbbjGxsbHmxx9/dO1z8803m27dupkNGzaYzz77zLRv396MGDGijp9J7RkwYIB5+eWXzY4dO0xmZqYZNGiQiY6ONkePHnXtM2bMGBMVFWVWr15ttmzZYn72s5+Zn//8567tp06dMl26dDH9+/c3GRkZ5oMPPjAtW7Y0kydP9sVT8rqlS5ea5cuXm2+++cZkZ2ebKVOmmIYNG5odO3YYY1ifc23atMm0bdvWxMfHmwcffNB1P+tkzPTp0821115rDh486Po6fPiwaztrZExhYaGJiYkxo0ePNhs3bjT79u0zK1euNHv27HHtw2t3ZZd9sPTo0cOMHz/edfv06dMmMjLSpKen+3Aq3zg3WCoqKkxERISZPXu2676ioiLjdDrN4sWLjTHG7Ny500gymzdvdu3z4YcfGofDYQ4cOFBns9elgoICI8msW7fOGHNmTRo2bGjefPNN1z67du0ykswXX3xhjDkThn5+fiY/P9+1z/z5801wcLApLy+v2ydQR5o1a2ZefPFF1uccpaWlJi4uzqxatcr06dPHFSys0xnTp0833bp1q3Iba3TGI488Ynr37n3e7bx2V+2yfkvoxIkT2rp1q/r37++6z8/PT/3799cXX3zhw8nskJOTo/z8fLf1CQkJUc+ePV3r88UXXyg0NFSJiYmuffr37y8/Pz9t3LixzmeuC8XFxZL+90szt27dqpMnT7qtU8eOHRUdHe22Tl27dlV4eLhrnwEDBqikpERfffVVHU5f+06fPq0lS5aorKxMSUlJrM85xo8fr8GDB7uth8S/o5/avXu3IiMj1a5dO6WkpCg3N1cSa3TW0qVLlZiYqNtvv11hYWFKSEjQwoULXdt57a7aZR0s33//vU6fPu32D1uSwsPDlZ+f76Op7HF2DS60Pvn5+QoLC3Pb7u/vr+bNm9fLNayoqNCECRPUq1cvdenSRdKZNQgICFBoaKjbvueuU1XreHZbfZCVlaWgoCA5nU6NGTNG77zzjjp37sz6/MSSJUv05ZdfKj09vdI21umMnj17atGiRVqxYoXmz5+vnJwc/eIXv1BpaSlr9F/79u3T/PnzFRcXp5UrV2rs2LF64IEH9Morr0jitft86sVvawaqa/z48dqxY4c+//xzX49inQ4dOigzM1PFxcX697//rdTUVK1bt87XY1kjLy9PDz74oFatWqVGjRr5ehxrDRw40PXn+Ph49ezZUzExMfrXv/6lwMBAH05mj4qKCiUmJmrmzJmSpISEBO3YsUMLFixQamqqj6ez12V9hKVly5Zq0KBBpTPMDx06pIiICB9NZY+za3Ch9YmIiFBBQYHb9lOnTqmwsLDereH999+vZcuWac2aNWrTpo3r/oiICJ04cUJFRUVu+5+7TlWt49lt9UFAQIDat2+v7t27Kz09Xd26ddOcOXNYn//aunWrCgoKdP3118vf31/+/v5at26d5s6dK39/f4WHh7NOVQgNDdU111yjPXv28G/pv1q3bq3OnTu73depUyfXW2e8dlftsg6WgIAAde/eXatXr3bdV1FRodWrVyspKcmHk9khNjZWERERbutTUlKijRs3utYnKSlJRUVF2rp1q2ufTz75RBUVFerZs2edz1wbjDG6//779c477+iTTz5RbGys2/bu3burYcOGbuuUnZ2t3Nxct3XKyspye4FYtWqVgoODK73w1BcVFRUqLy9nff4rOTlZWVlZyszMdH0lJiYqJSXF9WfWqbKjR49q7969at26Nf+W/qtXr16VPlrhm2++UUxMjCReu8/L12f9XqolS5YYp9NpFi1aZHbu3Gnuu+8+Exoa6naGeX1WWlpqMjIyTEZGhpFknnrqKZORkWH2799vjDlzaVxoaKh57733zPbt282tt95a5aVxCQkJZuPGjebzzz83cXFx9erSuLFjx5qQkBCzdu1at0stjx075tpnzJgxJjo62nzyySdmy5YtJikpySQlJbm2n73U8pe//KXJzMw0K1asMK1atao3l1o++uijZt26dSYnJ8ds377dPProo8bhcJiPPvrIGMP6nM9PrxIyhnUyxphJkyaZtWvXmpycHLN+/XrTv39/07JlS1NQUGCMYY2MOXNZvL+/v5kxY4bZvXu3ef31103jxo3NP/7xD9c+vHZXdtkHizHGPPPMMyY6OtoEBASYHj16mA0bNvh6pDqzZs0aI6nSV2pqqjHmzOVxU6dONeHh4cbpdJrk5GSTnZ3t9hhHjhwxI0aMMEFBQSY4ONjcfffdprS01AfPpnZUtT6SzMsvv+za58cffzTjxo0zzZo1M40bNza33XabOXjwoNvjfPvtt2bgwIEmMDDQtGzZ0kyaNMmcPHmyjp9N7UhLSzMxMTEmICDAtGrVyiQnJ7tixRjW53zODRbWyZjhw4eb1q1bm4CAAHPVVVeZ4cOHu32+CGt0xvvvv2+6dOlinE6n6dixo3nhhRfctvPaXZnDGGN8c2wHAACgei7rc1gAAMCVgWABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAVBvffvtt3I4HMrMzPT1KAAuEcECAACsR7AAqDUVFRV68skn1b59ezmdTkVHR2vGjBmSpKysLPXr10+BgYFq0aKF7rvvPh09etT1vX379tWECRPcHm/o0KEaPXq063bbtm01c+ZMpaWlqWnTpoqOjtYLL7zg2h4bGytJSkhIkMPhUN++fWvtuQKoXQQLgFozefJkzZo1S1OnTtXOnTv1z3/+U+Hh4SorK9OAAQPUrFkzbd68WW+++aY+/vhj3X///TX+GX/961+VmJiojIwMjRs3TmPHjlV2drYkadOmTZKkjz/+WAcPHtTbb7/t1ecHoO74+3oAAPVTaWmp5syZo3nz5ik1NVWSdPXVV6t3795auHChjh8/rldffVVNmjSRJM2bN09DhgzRn//8Z4WHh1f75wwaNEjjxo2TJD3yyCN6+umntWbNGnXo0EGtWrWSJLVo0UIRERFefoYA6hJHWADUil27dqm8vFzJyclVbuvWrZsrViSpV69eqqiocB0dqa74+HjXnx0OhyIiIlRQUOD54ACsRLAAqBWBgYGX9P1+fn4yxrjdd/LkyUr7NWzY0O22w+FQRUXFJf1sAPYhWADUiri4OAUGBmr16tWVtnXq1Enbtm1TWVmZ677169fLz89PHTp0kCS1atVKBw8edG0/ffq0duzYUaMZAgICXN8L4PJGsACoFY0aNdIjjzyi3//+93r11Ve1d+9ebdiwQX//+9+VkpKiRo0aKTU1VTt27NCaNWv0u9/9TnfddZfr/JV+/fpp+fLlWr58ub7++muNHTtWRUVFNZohLCxMgYGBWrFihQ4dOqTi4uJaeKYA6gLBAqDWTJ06VZMmTdK0adPUqVMnDR8+XAUFBWrcuLFWrlypwsJC3XDDDfrVr36l5ORkzZs3z/W9aWlpSk1N1ahRo9SnTx+1a9dON954Y41+vr+/v+bOnavnn39ekZGRuvXWW739FAHUEYc5901iAAAAy3CEBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPX+H95Atds6/lvtAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# b. PieChart"
      ],
      "metadata": {
        "id": "Dz1hYFtWO_8Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Survived'].value_counts().plot(kind='pie', autopct='%1.0f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DHcCWHToO1GA",
        "outputId": "859f2c7c-d51b-4aab-d8bc-ceff273d19a2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Numerical data"
      ],
      "metadata": {
        "id": "nl_94szyPV0i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#a. Histogram"
      ],
      "metadata": {
        "id": "YgweSLVMPeaB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "_LvsQu4zPKkb"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Age'], bins=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "Uh6cW2k1Pj6_",
        "outputId": "dd63cb7b-adc8-4851-b834-2411cdf3dd58"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([100., 346., 188.,  69.,  11.]),\n",
              " array([ 0.42 , 16.336, 32.252, 48.168, 64.084, 80.   ]),\n",
              " <BarContainer object of 5 artists>)"
            ]
          },
          "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": "markdown",
      "source": [
        "# b. Displot"
      ],
      "metadata": {
        "id": "8FWM53LCP4BM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "u49XIoSSPmDn",
        "outputId": "6b409f7a-245f-4e6e-e0a3-fa0d146da6fc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-0fafe04ea3f6>: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['Age'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aKtDDCQPVbhAA/HCKvUhEZF8YkIjslL7BiNNFlQBMy/tt0W8GsheJiOwTAxKRnfrl/BXUNRjh6ihHb0/bGl5r9OtepB/Zi0REdoQBichOpR03LaGPULtBamPDa782PsIXAHDocjnKq/UiV0NE1DoMSER2SBAEfH/CFJAG2ejwWqPens7o28sFRgH4mbtrE5GdYEAiskNH87UorKiFg0yCfr6uYpdzS7eHm85o23/xKqr13F2biGyfXOwCiHqi9Zm5HXp9+rXeo3BfNzjIbP/3nHBfV/irlCisqEXmhTKMH+ArdklERDdl+/+yEtENzhZXAQAGqMU/WqQ1JBIJbrvWi7T33BU0GIwiV0REdHMMSER2prbegLyr1QCAsF62P7zWKCpQBZWTA3R1DTicXyF2OUREN8WARGRnLpbqYBQALxcFPF0UYpfTajKpBHGhXgCAvedKIQiCyBUREbWMAYnIzpwpMQ2v2VPvUaORIV6QSyUoKK/FpSvVYpdDRNQiBiQiO3Pu2vyjMDtYvdaUi6McMUEeAIC956+IWwwR0U0wIBHZEW1NPYor6yAB0LeXi9jltMvofj4AgOMFFdw4kohsFgMSkR05d214LcDDCc4K+9ylQ61SItTHtHHkgUtXxS6HiKhZDEhEduSsHQ+v/VrjZO39F8tgMHKyNhHZHgYkIjtyoVQHAOhnhxO0f21QgDtcFDJU1jbgpEYrdjlERDdgQCKyE+XVepTX1EMqAYK9nMUup0PkUilGhJh6kfZdKBO5GiKiGzEgEdmJi9eWxQd4OEEht/9v3ZEhXpAAOFNchStVdWKXQ0Rkwf7/lSXqIS5eMQ2v9bHz3qNGXi4KhPuZhgo5WZuIbA0DEpGduHQtIIX42Ofy/uaM6GMaZjuYexVG7qxNRDaEAYnIDlTrG1CkNQ1D9fHuPgEpQu0GJwcZtLUN5hV6RES2gAGJyA7kXpt/5OOqgKujfe5/1By5TGreWTuLw2xEZEMYkIjsQOME7ZBu1HvUaHgfTwDA8UItd9YmIpvBgERkB8wTtLthQArwcIK/SgmDUcDXhwrELoeICAADEpHNqzcYkV9eAwAI8e4eK9iaGhZs6kXalHVZ5EqIiEwYkIhsXGFFLQxGAS4KGbxcFGKX0ymigzwglQCHL1fglKZS7HKIiBiQiGzd5aum+Ue9PZ0hkUhErqZzuDrKEaF2BwBsOpAncjVERAxIRDYvr+xaQPJyErmSztU4WXtrTj7qDUaRqyGinq77rBcm6qYuXzXNPwry7J7zjxr193ODq6McpVV6vPr1cQz0d2/XfR6IC7ZyZUTUE7EHiciGVesbcEVnWvre27N79yDJpBLuiURENoMBiciGNfYeebso4Kzo/h2+jcNsJzVaVNc1iFwNEfVkNhGQVq9ejZCQECiVSsTFxWHfvn03bb9p0yZERERAqVQiKioK27dvNz9XX1+PF154AVFRUXBxcUFAQABmzZqFggLL/VVCQkIgkUgsHsuXL++U90fUXnnXJmgHdZMDam/Fz10Jf5USRgE4UlAhdjlE1IOJHpA2btyI5ORkLFu2DNnZ2YiOjkZiYiKKi4ubbb93717MnDkTc+fOxcGDB5GUlISkpCQcPXoUAFBdXY3s7GwsWbIE2dnZ2LJlC06dOoW77rrrhnu9+uqrKCwsND/mz5/fqe+VqK0ul5l6kLr78NqvNQ6zHcpjQCIi8YgekN555x08+uijmDNnDgYNGoTU1FQ4Ozvjo48+arb9u+++i0mTJuG5557DwIED8dprr2HYsGFYtWoVAEClUiEtLQ3Tpk3DgAEDMGrUKKxatQpZWVnIzc21uJebmxvUarX54eLS/XYpJvslCML1HqRuPkH714b09oAEpt3DefQIEYlF1ICk1+uRlZWFhIQE8zWpVIqEhARkZGQ0+5qMjAyL9gCQmJjYYnsAqKiogEQigYeHh8X15cuXw9vbG0OHDsVbb72FhoaW5zzU1dVBq9VaPIg609XqelTrDZBJJFCrlGKX02VUTg4I8TH9snL4MnuRiEgcogak0tJSGAwG+Pn5WVz38/ODRqNp9jUajaZN7Wtra/HCCy9g5syZcHe/vmz46aefxoYNG/DDDz/g8ccfx1//+lc8//zzLdaakpIClUplfgQFBbX2bRK1S+MGkWqVEg4y0Tt7u1R0bw8AwKHL5aLWQUQ9V7deFlNfX49p06ZBEAR88MEHFs8lJyeb/zxkyBAoFAo8/vjjSElJgaOj4w33Wrx4scVrtFotQxJ1qoLyWgBAYA+af9QoMsAdXx8qQGFFLYq0tfBz7zk9aERkG0T9tdTHxwcymQxFRUUW14uKiqBWq5t9jVqtblX7xnB06dIlpKWlWfQeNScuLg4NDQ24ePFis887OjrC3d3d4kHUmQoqTBO0A1U9LyA5O8oR7ucKADjMXiQiEoGoAUmhUGD48OFIT083XzMajUhPT0d8fHyzr4mPj7doDwBpaWkW7RvD0ZkzZ/D999/D29v7lrXk5ORAKpXC19e3ne+GyHoEQUBBuSkgBXj0vIAEmA6wBYBDlysgCIK4xRBRjyP6EFtycjJmz56NESNGIDY2FitXroROp8OcOXMAALNmzUJgYCBSUlIAAAsWLMC4ceOwYsUKTJkyBRs2bMCBAwewZs0aAKZwdN999yE7Oxvbtm2DwWAwz0/y8vKCQqFARkYGMjMzMX78eLi5uSEjIwMLFy7EQw89BE9PT3E+CKJfqagxTdCWSgA/9xuHfHuCgWp3KGRSlOn0uHy1psfsBUVEtkH0gDR9+nSUlJRg6dKl0Gg0iImJwY4dO8wTsXNzcyGVXu/oGj16NNavX4+XXnoJL774IsLDw7F161ZERkYCAPLz8/HVV18BAGJiYiy+1g8//IA77rgDjo6O2LBhA15++WXU1dUhNDQUCxcutJhjRCSmxt4jP3cl5D1sgnYjhVyKQQHuyMkrR87lcgYkIupSEoF91+2i1WqhUqlQUVHB+UjUZuszc2/6fNrxIvxwqhjDgz0xdXjvLqrK9pzSaPFpxiW4OMqxaFIEZFLJLV/Dw2qJ6GZa+/O7Z/5qSmTjrs8/6tmrt8J83eCskEFX14BzJVVil0NEPQgDEpENalzB1lMnaDeSSSWIDFQBAI5w00gi6kIMSEQ2Rltbj8raBkgA+PfAJf5NDbkWkI4XatFgNIpcDRH1FAxIRDam8Nrwmo+bIxRyfouG+LjA1VGOmnoDzhXrxC6HiHoI/utLZGPyG3fQ7uHDa42kEgkiA00TKY/kc5iNiLoGAxKRjSm8Nv/IvwcdUHsrkeZhtgoOsxFRl2BAIrIxmgpTDxLnH10X4u0CN0c5auuNOFfM1WxE1PkYkIhsiL7BiDKdHgCgZg+SmVQiweDG1WwcZiOiLsCARGRDirS1EAC4Ocrh6ij6Rvc2JYqr2YioCzEgEdmQwmvDa+w9ulEfb2e4KU3DbGc5zEZEnYwBiciGaLSmCdoMSDeSSiSIDOCmkUTUNRiQiGyIuQfJnQGpOY3DbCc0WjQYOMxGRJ2HAYnIRgiCwBVstxDs7Qx3DrMRURdgQCKyEeXV9ahrMEImkaCXm6PY5dgkrmYjoq7CgERkIxqH13zdHSGTSkSuxnZZnM3GYTYi6iQMSEQ2wjxBm/OPbirIyzTMVtdgxBkOsxFRJ2FAIrIRXOLfOqaz2TjMRkSdiwGJyEZoGJBazbyarVCLeg6zEVEnYEAisgH1hl8dMcIhtlsK8nKGyskBdQ1czUZEnYMBicgGFFfWQQDgrJDxiJFWMG0a6Q6Aw2xE1DkYkIhsQJHWNLzm566ERMIVbK3BYTYi6kwMSEQ2oKjiekCi1un9q2G2M0WVYpdDRN0MAxKRDSiq5BEjbSWVSMy9SBxmIyJrY0AisgFF2joAgJ87d9Bui0jz2WyVHGYjIqtiQCISWY3egIqaegAcYmurIE8nqJwcoOcwGxFZGQMSkcgaJ2irnBygdJCJXI19kXCYjYg6CQMSkcg4/6hjOMxGRJ2BAYlIZNeX+HP+UXsEeTrBg8NsRGRlDEhEItNUNE7QZg9Se0h4NhsRdQIGJCIRCYJgsUkktU/Ur4bZausNIldDRN0BAxKRiCrrGlBTb4AEQC83DrG1V+9fDbPtOl0idjlE1A0wIBGJqPja/kdeLgo4yPjt2F6/Hmb75nChyNUQUXfAf5GJRFRcyeE1a2kcZks/UcRhNiLqMAYkIhEVV5p6kHw5vNZhjcNsOr0BP57iMBsRdQwDEpGIiq9N0PblEv8O+/Uw2/YjHGYjoo5hQCISiWkFW2MPEofYrKFxmO17DrMRUQcxIBGJRKc3mFew+biyB8kaens6IdDDCdUcZiOiDmJAIhJJ4/Cap4sCCjm/Fa1BIpFgcpQaAPANh9mIqAP4rzKRSDhBu3NMjvIHwNVsRNQxDEhEImlc4s/5R9YVE+Txq2G2YrHLISI71a6AdP78easWsXr1aoSEhECpVCIuLg779u27aftNmzYhIiICSqUSUVFR2L59u/m5+vp6vPDCC4iKioKLiwsCAgIwa9YsFBQUWNyjrKwMDz74INzd3eHh4YG5c+eiqqrKqu+L6GbME7S5gs2qLIfZNCJXQ0T2ql0BKSwsDOPHj8fnn3+O2traDhWwceNGJCcnY9myZcjOzkZ0dDQSExNRXNz8b3579+7FzJkzMXfuXBw8eBBJSUlISkrC0aNHAQDV1dXIzs7GkiVLkJ2djS1btuDUqVO46667LO7z4IMP4tixY0hLS8O2bduwe/duPPbYYx16L0RtwSG2zjNlSAAADrMRUftJBEEQ2vqinJwcfPzxx/jiiy+g1+sxffp0zJ07F7GxsW0uIC4uDiNHjsSqVasAAEajEUFBQZg/fz4WLVp0Q/vp06dDp9Nh27Zt5mujRo1CTEwMUlNTm/0a+/fvR2xsLC5duoTg4GCcOHECgwYNwv79+zFixAgAwI4dOzB58mRcvnwZAQEBt6xbq9VCpVKhoqIC7u7ubX7f1LN9uPs8Xt9+AgCw7HeD4CiXiVxR9/FAXDAEQcDYN35AfnkNUh8ahkmR/mKXRUQ2orU/v9vVgxQTE4N3330XBQUF+Oijj1BYWIixY8ciMjIS77zzDkpKWre8Vq/XIysrCwkJCdcLkkqRkJCAjIyMZl+TkZFh0R4AEhMTW2wPABUVFZBIJPDw8DDfw8PDwxyOACAhIQFSqRSZmZnN3qOurg5ardbiQdRejb1HHs4ODEedgMNsRNRRHZqkLZfLce+992LTpk144403cPbsWTz77LMICgrCrFmzUFh482W2paWlMBgM8PPzs7ju5+cHjab5f9Q0Gk2b2tfW1uKFF17AzJkzzUlRo9HA19f3hvfi5eXV4n1SUlKgUqnMj6CgoJu+N6KbMZ/BxgnanYbDbETUER0KSAcOHMAf//hH+Pv745133sGzzz6Lc+fOIS0tDQUFBbj77rutVWe71NfXY9q0aRAEAR988EGH7rV48WJUVFSYH3l5eVaqknoizj/qfNG9VVzNRkTtJm/Pi9555x18/PHHOHXqFCZPnozPPvsMkydPhlRqyluhoaH45JNPEBISctP7+Pj4QCaToaioyOJ6UVER1Gp1s69Rq9Wtat8Yji5duoSdO3dajDOq1eobJoE3NDSgrKysxa/r6OgIR0f+MCPr4BlsnU8ikWDKEH+s2X0e2w4Xch4SEbVJu3qQPvjgAzzwwAO4dOkStm7dijvvvNMcjhr5+vpi7dq1N72PQqHA8OHDkZ6ebr5mNBqRnp6O+Pj4Zl8THx9v0R4A0tLSLNo3hqMzZ87g+++/h7e39w33KC8vR1ZWlvnazp07YTQaERcXd/M3T2QF13uQOMTWmRo3jdx5shg1eg6zEVHrtasHKS0tDcHBwTeEIkEQkJeXh+DgYCgUCsyePfuW90pOTsbs2bMxYsQIxMbGYuXKldDpdJgzZw4AYNasWQgMDERKSgoAYMGCBRg3bhxWrFiBKVOmYMOGDThw4ADWrFkDwBSO7rvvPmRnZ2Pbtm0wGAzmeUVeXl5QKBQYOHAgJk2ahEcffRSpqamor6/HvHnzMGPGjFatYCPqiIrqelTWNgAAenGIrVM1DrPll9fgh1PF5sBERHQr7epB6tevH0pLS2+4XlZWhtDQ0Dbda/r06Xj77bexdOlSxMTEICcnBzt27DBPxM7NzbWY7D169GisX78ea9asQXR0NDZv3oytW7ciMjISAJCfn4+vvvoKly9fRkxMDPz9/c2PvXv3mu+zbt06REREYMKECZg8eTLGjh1rDllEnelsSSUAQOXkAKUDV7B1JolEgjujTaHoq5yCW7QmIrquXfsgSaXSZleCXbp0CYMGDYJOp7NagbaK+yBRe23Yl4tFW44g3NcVc8a07RcKurUH4oIt/n68QIvJ7/0EhVyK/X9OgMrJQaTKiMgWtPbnd5uG2JKTkwGYfitbunQpnJ2dzc8ZDAZkZmYiJiamfRUT9RBnik1H2nAFW9cY6O+GcF9XnCmuwv+OaTBtBLfoIKJba1NAOnjwIADTXKMjR45AoVCYn1MoFIiOjsazzz5r3QqJupnTRaYhNl93TtDuChKJBHfHBODt707jq5wCBiQiapU2BaQffvgBADBnzhy8++67HFoiaoez7EHqcndFB+Lt705j77lSFFfWcvUgEd1SuyZpf/zxxwxHRO1QWVuPwopreyDxh3SXCfZ2RkyQB4wC8M3hm+/wT0QEtKEH6d5778Unn3wCd3d33HvvvTdtu2XLlg4XRtQdNfYeuSnlcFJwBVtXujsmADl55fhvTgEnxxPRLbU6IKlUKkgkEvOfiajtGido8wy2rjdliD9e23YcOXnluHRFhz7eLmKXREQ2rNUB6eOPP272z0TUeo09SL14xEiX83VTYkyYD346U4qvcgowf0K42CURkQ1r1xykmpoaVFdXm/9+6dIlrFy5Et99953VCiPqjswr2DhBWxR3RZt2yt+ak492bAFHRD1IuwLS3Xffjc8++wwAUF5ejtjYWKxYsQJ33303PvjgA6sWSNSdnClqXMHGITYxJEaqoZBLca5Eh+OFWrHLISIb1q6AlJ2djdtuuw0AsHnzZqjValy6dAmfffYZ3nvvPasWSNRd6OoakF9eAwDwYw+SKNyVDpgQYToBgEePENHNtCsgVVdXw83NDQDw3Xff4d5774VUKsWoUaNw6dIlqxZI1F2cKzH1Hvm4KuDs2K5zoskK7o4xDbP9N6cABiOH2Yioee0KSGFhYdi6dSvy8vLwv//9DxMnTgQAFBcXc38kohY0Dq+F+7qJXEnPdscAX7gr5dBoa7H33I2HbhMRAe0MSEuXLsWzzz6LkJAQxMXFIT4+HoCpN2no0KFWLZCou2hc4h/u5ypyJT2b0kGGu671Iv0767LI1RCRrWpXQLrvvvuQm5uLAwcOYMeOHebrEyZMwN/+9jerFUfUnZy5toIt3JcBSWz3DTedx7bjmAba2nqRqyEiW9SugAQAarUaQ4cOhVR6/RaxsbGIiIiwSmFE3U1jD1IYh9hEF91bhTBfV9TWG7GdR48QUTPaFZB0Oh2WLFmC0aNHIywsDH379rV4EJGlGr0BeVdNe4dxiE18EokE9w3vDQDYzGE2ImpGu5bSPPLII9i1axcefvhh+Pv7m48gIaLmnSupgiAAns4O8HZRiF0OAbhnaCDe3HESBy5dxYVSHUJ9ePQIEV3XroD07bff4ptvvsGYMWOsXQ9Rt3TWPEHbjb9Q2Ag/dyVu798LP54qwZbsy/jTxAFil0RENqRdQ2yenp7w8vKydi1E3daZYk7QtkWNw2z/zroMI/dEIqJfaVdAeu2117B06VKL89iIqGWnzXsgMSDZkoSBfnBXylFQUYuM81fELoeIbEi7hthWrFiBc+fOwc/PDyEhIXBwcLB4Pjs72yrFEXUXvx5iI9uhdJDhd9EBWJeZi81ZlzEmzEfskojIRrQrICUlJVm5DKLuq7begEtXdADYg2SL7hveG+syc/Ht0UK8evdguCkdbv0iIur22hWQli1bZu06iLqtcyVVMAqAh7MDevGQWpsTE+SBfr1ccK5Eh28OF2JGbLDYJRGRDWj3RpHl5eX45z//icWLF6OsrAyAaWgtPz/fasURdQenr+2g3d+XK9hskUQiwfSRpp2112XmilwNEdmKdgWkw4cPo3///njjjTfw9ttvo7y8HACwZcsWLF682Jr1Edk98wRtbhBps+4bHgSFXIoj+RU4fLlc7HKIyAa0KyAlJyfj97//Pc6cOQOlUmm+PnnyZOzevdtqxRF1B41nsPXnBG2b5eWiwJQofwDA579cErkaIrIF7QpI+/fvx+OPP37D9cDAQGg0mg4XRdSdnGJAsgsPxpnmHn11qAAV1TzAlqina1dAcnR0hFarveH66dOn0atXrw4XRdRdVOsbkFdWAwDozyE2mza8jyci1G6orTdiy0Gez0bU07UrIN1111149dVXUV9v+i1LIpEgNzcXL7zwAqZOnWrVAonsWeP+R94uCni7cgWbLZNIJOZepHWZuRAE7qxN1JO1KyCtWLECVVVV6NWrF2pqajBu3DiEhYXBzc0Nr7/+urVrJLJbjRO0ObxmH5KGBsJZIcPZ4ipkXigTuxwiElG79kFSqVRIS0vDzz//jEOHDqGqqgrDhg1DQkKCtesjsmvmJf4cXrMLbkoH3B0TiC/25eLzXy5hVF9vsUsiIpG0OSAZjUZ88skn2LJlCy5evAiJRILQ0FCo1WoIgsB9Xoh+pTEg8YgR+/FgXDC+2JeL/x3ToKSyjpt7EvVQbRpiEwQBd911Fx555BHk5+cjKioKgwcPxqVLl/D73/8e99xzT2fVSWSXznCIze5EBqoQE+SBeoOALw/kiV0OEYmkTT1In3zyCXbv3o309HSMHz/e4rmdO3ciKSkJn332GWbNmmXVIonsUWVtPfLLuYLNHj00qg9y8sqxPjMXj9/eF3JZuw8dICI71abv+i+++AIvvvjiDeEIAH7zm99g0aJFWLdundWKI7JnZ66tYPN1c4SHs0Lkaqgt7hziD09nB+SX1+D7E0Vil0NEImhTD9Lhw4fx5ptvtvj8b3/7W7z33nsdLoqoO+AO2uJYb6Xz1B6M64NVP5zF2j0XMCnS3yr3JCL70aYepLKyMvj5+bX4vJ+fH65evdrhooi6A57BZt8eju8DB5kE+y9e5flsRD1QmwKSwWCAXN5yp5NMJkNDQ0OHiyLqDhpXsA1gD5Jd8nNX4s4hAQCAtXsuiFwNEXW1Ng2xCYKA3//+93B0bH7Za11dnVWKIuoOuMTf/s0dG4r/HMzHN4cLsei3EfBXOYldEhF1kTYFpNmzZ9+yDVewEQEV1fUo0pp+YeAQm/2KDFQhLtQLmRfKsPanC3jpzkFil0REXaRNAenjjz+2egGrV6/GW2+9BY1Gg+joaLz//vuIjY1tsf2mTZuwZMkSXLx4EeHh4XjjjTcwefJk8/NbtmxBamoqsrKyUFZWhoMHDyImJsbiHnfccQd27dplce3xxx9HamqqVd8b9Vyni029RwEqJdyVDiJXQx3xxB39kHmhDF/sy8W834RxRSJRDyHq5h4bN25EcnIyli1bhuzsbERHRyMxMRHFxcXNtt+7dy9mzpyJuXPn4uDBg0hKSkJSUhKOHj1qbqPT6TB27Fi88cYbN/3ajz76KAoLC82Pm63OI2orDq91H3f074UItRt0egP+lXFJ7HKIqIuIGpDeeecdPProo5gzZw4GDRqE1NRUODs746OPPmq2/bvvvotJkybhueeew8CBA/Haa69h2LBhWLVqlbnNww8/jKVLl97yXDhnZ2eo1Wrzw93d3arvjXq26ztoc3jN3kkkEjx5Rz8AwMd7L6JGbxC5IiLqCqIFJL1ej6ysLIsgI5VKkZCQgIyMjGZfk5GRcUPwSUxMbLH9zaxbtw4+Pj6IjIzE4sWLUV1dfdP2dXV10Gq1Fg+ilpzSsAepO5kS5Y/enk4o0+mxcb919lkiItsmWkAqLS2FwWC4YV8lPz8/aDSaZl+j0Wja1L4lDzzwAD7//HP88MMPWLx4Mf71r3/hoYceuulrUlJSoFKpzI+goKA2fU3qWc4Uc4l/dyKXSfH4OFMvUuqu86hrYC8SUXfXpkna3cVjjz1m/nNUVBT8/f0xYcIEnDt3Dv369Wv2NYsXL0ZycrL571qtliGJmlWm06O0Sg8ACPPlEFt3MW1Eb6zeeRYabS2+3J+Hh+NDxC6JiDqRaD1IPj4+kMlkKCqyPOeoqKgIarW62deo1eo2tW+tuLg4AMDZs2dbbOPo6Ah3d3eLB1FzGido9/Z0gotjj/wdpFtylMvwx/GmX6D+/uM59iIRdXOiBSSFQoHhw4cjPT3dfM1oNCI9PR3x8fHNviY+Pt6iPQCkpaW12L61cnJyAAD+/jxviTruNM9g67amjQiCn7sjCitqsenAZbHLIaJOJOoqtuTkZHz44Yf49NNPceLECTz55JPQ6XSYM2cOANOmk4sXLza3X7BgAXbs2IEVK1bg5MmTePnll3HgwAHMmzfP3KasrAw5OTk4fvw4AODUqVPIyckxz1M6d+4cXnvtNWRlZeHixYv46quvMGvWLNx+++0YMmRIF7576q5OFJoCUoSaAam7UTrI8OS1uUirdp5FbT17kYi6K1ED0vTp0/H2229j6dKliImJQU5ODnbs2GGeiJ2bm4vCwkJz+9GjR2P9+vVYs2YNoqOjsXnzZmzduhWRkZHmNl999RWGDh2KKVOmAABmzJiBoUOHmjeBVCgU+P777zFx4kRERETgT3/6E6ZOnYqvv/66C985dWcnNaYVjhH+HIbtjmbEBiNApYRGW8t9kYi6MYkgCILYRdgjrVYLlUqFiooKzkciM6NRQOTL/0O13oDvk29HmG/zvUjrM7lU3NY9EBfc4nNf7s/D8/8+DE9nB+x+fjzcuFs6kd1o7c9vUXuQiLqb3LJqVOsNUMilCPF2Ebsc6iT3DgtEv14uuFpdjw9/uiB2OUTUCRiQiKyocXhtgJ8b5DJ+e3VXcpkUz04cAAD450/nUVxZK3JFRGRtXINMnc4aw0k3G+6wJcc5QbvHmBSpRkyQB3LyyvHOd6exfCoXeRB1J/wVl8iKThaaepAGcoJ2tyeRSLDkzoEAgI0H8nC8gMcPEXUnDEhEVnTy2hlsEf7sQeoJhvfxwp1D/CEIwF++OQ6ueSHqPhiQiKyksrYeuWWmQ48HqtmD1FO8MCkCCrkUe89dwXfHi279AiKyCwxIRFbSuIO22l0JTxeFyNVQVwnycsajt4UCAF79+jhq9Nw8kqg7YEAishLzBG0Or/U4T40PQ6CHE/LLa/D3H1s+05GI7AcDEpGVcIJ2z+WskGPJnYMAAP/YdR4XSnUiV0REHcWARGQlx68FJC7x75kSB/thXP9e0BuMeHHLERiNnLBNZM8YkIisoMFgxIlrASkqUCVyNSQGiUSC1+6OhJODDBnnr+CL/TxOhsieMSARWcH5Uh1q641wUch4xEgPFuztjOcSTTtsp2w/ifzyGpErIqL2YkAisoKj+RUAgEEB7pBKJSJXQ2L6/egQjOjjiaq6BizecoR7IxHZKQYkIis4dm0X5cEBHF7r6aRSCd64bwgUcil2ny7B5qzLYpdERO3AgERkBY09SJGcf0QA+vVyRfL/9QcAvLbtOIq0PMyWyN4wIBF1kNEomM/higzkEn8yeWRsKKJ7q6CtbcCf/3OUQ21EdoYBiaiDcsuqUVnXAEe5FGG9XMUuh2yEXCbFm/dFw0EmwfcnirBhf57YJRFRGzAgEXXQ0QLT8FqEvzvkMn5L0XUD1G54dqJpVdsrXx/DmWvH0RCR7eO/5kQddH2CNofX6EaP3tYXt4X7oLbeiPlfHERtPc9qI7IHDEhEHWSeoM0VbNQMqVSCFdOi4e2iwElNJZZ/e1LskoioFeRiF0BkzwRBMPcgcYJ297I+07o7Yd85xB+fZlzCJ3svwmgUENGGM/seiAu2ai1EdGvsQSLqgLyyGpTp9FDIpBjAM9joJgao3TGmnzcAYHP2ZWhr6kWuiIhuhgGJqANyLpcDAAYGuMNRLhO3GLJ5iYPV8FcpUa03YOOBPBh4oC2RzWJAIuqAnNxyAEBMb84/oluTy6SYMTIYCpkUF0p12HmySOySiKgFDEhEHXDoWg9STLCHqHWQ/ejl5oikoYEAgB9PlXDpP5GNYkAiaqd6g9G8gi26t4e4xZBdiQnywMgQLwgANh7IQwXnIxHZHAYkonY6WViJugYjVE4OCPVxEbscsjN3DvG/Ph9pfy7nIxHZGAYkonZqnKAdHeQBiUQibjFkdxxkUsyMDYajXIqLV6rx/QnORyKyJQxIRO3ECdrUUT6ujrjn2nykXadLcEqjFbkiImrEgETUTpygTdYwpLcHRvX1AgB8eeAyyqv1IldERAADElG7aGvrca6kCgAnaFPHTY70R6CHE2rqDdiwn/sjEdkCBiSidsjJLYcgAEFeTvB2dRS7HLJz8mvzkZQOUuSWVeO7YxqxSyLq8RiQiNph/8UyAMDIPl4iV0LdhZeLAlOH9QYA/HS2FCcKOR+JSEwMSETtsO/CtYAUyoBE1jM4QIXR185r25SVh6s6zkciEotc7AKI7M1ney8i69JVAECRttbqp75TzzYpUo3csmpcvlqDL/bn4rHb+4pdElGPxB4kojbKL69Bg1GAi0KGXpx/RFYml0oxc6RpPtLlqzXYcZTzkYjEwIBE1EYXS3UAgBAfF24QSZ3C00WB+4cHAQD2nruCHUcLRa6IqOdhQCJqo4tXqgEAId48XoQ6z0B/d9wW5gMAeG7zYeRe+/+OiLoGAxJRGxiMAi6VXetBYkCiTjZxsBrBXs6orG3AU+uzUddgELskoh6DAYmoDU5qtKitN0Ihl0KtUopdDnVzMqkEM0YGwdPZAUfyK/D6NyfELomoxxA9IK1evRohISFQKpWIi4vDvn37btp+06ZNiIiIgFKpRFRUFLZv327x/JYtWzBx4kR4e3tDIpEgJyfnhnvU1tbiqaeegre3N1xdXTF16lQUFfGgSLq1/deW9/fxcoZMyvlH1Pk8nBV4Z3oMAOCzjEvYdrhA3IKIeghRA9LGjRuRnJyMZcuWITs7G9HR0UhMTERxcXGz7ffu3YuZM2di7ty5OHjwIJKSkpCUlISjR4+a2+h0OowdOxZvvPFGi1934cKF+Prrr7Fp0ybs2rULBQUFuPfee63+/qj72XP2CgAg1IfDa9R1xg/wxZN39AMALPr3EVy4tlCAiDqPRBAE0Q79iYuLw8iRI7Fq1SoAgNFoRFBQEObPn49Fixbd0H769OnQ6XTYtm2b+dqoUaMQExOD1NRUi7YXL15EaGgoDh48iJiYGPP1iooK9OrVC+vXr8d9990HADh58iQGDhyIjIwMjBo1qlW1a7VaqFQqVFRUwN3dva1vvUexxj5BD8QFW6GSjqk3GBHzynfQ6Q14anwYAj2cxC6JehCDUcDaPedx8Uo1/FVKPDGuHxxkrf8d1xa+h4hsQWt/fovWg6TX65GVlYWEhITrxUilSEhIQEZGRrOvycjIsGgPAImJiS22b05WVhbq6+st7hMREYHg4OCb3qeurg5ardbiQT3Lwdxy6PQGOCtk8Of8I+piMqkE00cGw0UhQ2FFLfdHIupkogWk0tJSGAwG+Pn5WVz38/ODRtP8N75Go2lT+5buoVAo4OHh0ab7pKSkQKVSmR9BQUGt/prUPfx0pgQAEObrCin3PyIRqJwccP8I0789Geev4GxxlcgVEXVfok/StheLFy9GRUWF+ZGXlyd2SdTFdp8pBQD093UTuRLqyfr7uSHu2hmA/86+jBo9l/4TdQbRApKPjw9kMtkNq8eKioqgVqubfY1arW5T+5buodfrUV5e3qb7ODo6wt3d3eJBPcdVnR6HL5cDMPUgEYnpt5H+8HJRoKKmnqvaiDqJaAFJoVBg+PDhSE9PN18zGo1IT09HfHx8s6+Jj4+3aA8AaWlpLbZvzvDhw+Hg4GBxn1OnTiE3N7dN96Ge5edzpRAEYICfG9ydHMQuh3o4hVyKacN7QwLgYF45juZXiF0SUbcjF/OLJycnY/bs2RgxYgRiY2OxcuVK6HQ6zJkzBwAwa9YsBAYGIiUlBQCwYMECjBs3DitWrMCUKVOwYcMGHDhwAGvWrDHfs6ysDLm5uSgoMP1WderUKQCmniO1Wg2VSoW5c+ciOTkZXl5ecHd3x/z58xEfH9/qFWzU8/x02jS8dlu4j8iVEJkEe7tgXP9e+PF0Cbbm5KOPtzPclAzvRNYi6hyk6dOn4+2338bSpUsRExODnJwc7NixwzwROzc3F4WF1w9pHD16NNavX481a9YgOjoamzdvxtatWxEZGWlu89VXX2Ho0KGYMmUKAGDGjBkYOnSoxTYAf/vb33DnnXdi6tSpuP3226FWq7Fly5YuetdkbwxGAeknTXtz3d6/l8jVEF33m4G+8FcpUa034D8H8yHiri1E3Y6o+yDZM+6D1Hr2vg/S/otluD81A+5KOQ689H/YnHVZtFqImtJoa7H6h7MwGAVMHdYbw/t4NtuO+yARmdj8PkhE9uLbI6btHxIG+kEh57cM2Ra1uxIJA0297t8cKUBFTb3IFRF1D/zXnugmBEHA/46ZAtKkyNavliTqSmPDfBDk6YTaeiP+c/Ayh9qIrIABiegmjuRXIL+8Bs4KGecfkc2SSSWYOrw35FIJThdVIevSVbFLIrJ7DEhEN/HtteMcxg/whdJBJnI1RC3zdVPi/wY1DrUVorxaL3JFRPaNAYmoBYIgmM+7SuTwGtmBMWE+CPZyRl2DEVu4qo2oQxiQiFqQk1eOC6U6OMqlGD+Aw2tk+6QSCe4bZhpqO1tchf0XOdRG1F4MSEQt2LDPdN7elCh/bsBHdsPHzRGJg009ntuPFuKqjkNtRO3BgETUjKq6Bnx97Yyr6SODRK6GqG3i+3mjj7cz9A1G/PvgZRg51EbUZgxIRM3YdqgA1XoD+vq4IPbayelE9qJxqM1BJsH5Eh32XSgTuyQiu8OARNSML/abhtemjwyCRCIRuRqitvN2vT7UtuOoBrlXqkWuiMi+MCARNXGsoAKH8sohl0pw77DeYpdD1G6j+noj1McFeoMRz20+BKORQ21ErcWARNTE3384B8C0tL+Xm6PI1RC1n1QiwdRhvaGQSZF5oQyfZVwUuyQiu8GARPQrp4sqsf1oIQBg/m/CRK6GqOO8XBTmY3KW7ziJi6U6kSsisg8MSES/smrnWQgCMGmwGhHqlk95JrInsaFeGN3PG7X1HGojai0GJKJrzhZXmZf2z5/A3iPqPqQSCd6YOgQuChn2X7yKj/deFLskIpvHgEQE07Eir39zHIIA/N8gPwwOUIldEpFVBXk5489TBgEA3txxEudLqkSuiMi2MSARAfjPwXz8cKoECrkUL0yKELscok4xMzYIt4X7oK7BiGc3HYKBQ21ELWJAoh6vuLIWr3x9HADwTEI4wnxdRa6IqHNIJBIsnzoEro5yZOeWY+2e82KXRGSz5GIXQNRUvcGIYwVa5JbpUFqlh66uAf/NyYe3qwKDA1QY1dcLQ3p7wEHW8XxfbzDiuU2HUVFTj6hAFR67ra8V3gGR7Qr0cMKSOwfihX8fwdvfncZvIvz4SwFRMxiQyGbU1Rvw4+kS7L9Yhmq9weK5wopaAMD2IxoAgKezA+6KDsDU4b0xpLdHu76e0Sjg+c2Hset0CRzlUrx53xDIrRC6iGzdtBFB2H5Eg12nS/CnTYfw7yfi+f8+URMMSGQTLl3RYVPWZZRdO3lc5eSAqEAVfN0c4aZ0wIgQTxRpa3Hg4lVkXriCq9X1+DTjEj7NuISoQBUeHtUHv4sOgJNC1qqvp28w4uWvj+E/B/Mhl0rwwUPDMNCfy/qpZzANtUVh4t9241BeOT786QKevKOf2GUR2RQGJBJdxvkr2HaoAAIADycHTBnijwi1O2TS62eg/S46AADwyG1Ag8GIPWdL8Z+D+fj2qAZH8ivw/L8P4y/fHMd9w4Nw77BADA5wb/EMtWMFFXh202GcKNRCIgFWTIvGbyL8uuKtEtkMf5UTlt45CM9tPoy/pZ3GhIG+6O/nJnZZRDaDAYlE9fPZUnxzxLRzdUyQB+6KDoDS4ea9QHKZFHcM8MUdA3yxTKfHpgN5WJeZi9yyanz08wV89PMFBHs547ZwH0T4uyNApURdgxEF5TX47ngR9l8sgyCYhun+ek8Ufhvl3xVvlcjm3De8N749qsHOk8VYuDEH/35y9C2//4h6CgYkEs3ec9fD0R39e+H/Bvm12OvTEi8XBR4f1w+P3tYXu8+UYOP+PPxwqhi5ZdVYl5nb4usmR6nxyl2RPGuNejSJRIKUe6MwaeVuHCvQ4i/fHMdfkqLELovIJjAgkSjOlVThm8OmcDR+QC8kDGx7OPo1qVRi7lWq1jdg9+kSHLpcgROFWlyp0kPpIIW70gFjwnwwcbAfens6W+utENk1P3cl/jY9BnM+2Y/Pf8nFyBAv3B0TKHZZRKJjQKIuV1FTjw37ciEAGB7s2eFw1JSzQo5Jkf6YFMmhM6LWuGOAL+aND8P7O89i8ZYjGBzgjjBfzkeino3rOqlLGYwC1mdegk5vQIBKibtiAqwajoiofZ5J6I/4vt6o1hvw5OfZqNY3iF0SkagYkKhL/Xy2FHlXa6B0kOKBuD5W2eyRiDpOJpXg3Zkx6OXmiDPFVXjpP0chCDyKhHou/nSiLnOlqg7fnygCAEyJCoCXi0Lkiojo13zdlHh/5lBIJcCWg/n4dO9FsUsiEg0DEnUJQRDwn4P5aDAKCOvlimHBHmKXRETNGNXX23xg86vbjmPnySKRKyISBwMSdYmcvHKcL9XBQSZB0tBAzjsismGP3d4X00cEwSgA89YfxLGCCrFLIupyDEjU6eoNRnx33PRb6PgBvhxaI7JxEokEf7knEmPCTJO2//DJfhRW1IhdFlGXYkCiTvfz2VJU1NTDw8m0DxER2T4HmRR/f3A4wnxdUaStw9xPDkBXx5Vt1HMwIFGnKq2qw67TJQCAiYP9uGqNyI6onBzw8e9HwsdVgeOFWjy1Phv6BqPYZRF1Cf60ok71fvoZ1DUYEejhhCG9PcQuh4jaKMjLGR/OGgGlgxQ/nirBvPXZqDcwJFH3x4BEnaawogZf7MsDAEyKVEPKidlEdmlosCc+nDUCCrkU3x0vwtNfHGRIom6PAYk6zQc/noPeYESItwv6+riIXQ4RdcBt4b3wj4eHQyGT4tujGizcmIMGhiTqxngWG3WKgvIabLjWe5Qw0JfL+olEtj4zt8P3eCAuGB88NAxPfJ6FbYcLIZdKsGJaDGRSfn9T98MeJOoUjb1HcaFe6NvLVexyiMhKJgz0w6oHhkEulWBrTgHmrc9Gbb1B7LKIrI4BiayuuLIWGw+Yeo8WJISLXA0RWVviYDVWPTDUPNw266N9qKiuF7ssIquyiYC0evVqhISEQKlUIi4uDvv27btp+02bNiEiIgJKpRJRUVHYvn27xfOCIGDp0qXw9/eHk5MTEhIScObMGYs2ISEhkEgkFo/ly5db/b31RB//fBH6BiOGBXsgvq+32OUQUSeYFOmPT/4wEm6Ocuy7UIb7/7GXm0lStyL6HKSNGzciOTkZqampiIuLw8qVK5GYmIhTp07B19f3hvZ79+7FzJkzkZKSgjvvvBPr169HUlISsrOzERkZCQB488038d577+HTTz9FaGgolixZgsTERBw/fhxKpdJ8r1dffRWPPvqo+e9ubm6d/4a7OW1tPT7PuAQAePKOMJuae2SNORhEdN3ofj748ol4zP5oH04XVeHev+/Fp3+IRX8//ltK9k/0HqR33nkHjz76KObMmYNBgwYhNTUVzs7O+Oijj5pt/+6772LSpEl47rnnMHDgQLz22msYNmwYVq1aBcDUe7Ry5Uq89NJLuPvuuzFkyBB89tlnKCgowNatWy3u5ebmBrVabX64uHClVUetz8xFZV0Dwn1dMSHixoBLRN3LQH93bPnjaIT5uqKwohZT/74XP5wsFrssog4TNSDp9XpkZWUhISHBfE0qlSIhIQEZGRnNviYjI8OiPQAkJiaa21+4cAEajcaijUqlQlxc3A33XL58Oby9vTF06FC89dZbaGhoeRv9uro6aLVaiwdZqmswYO2eCwCAx8f1g5QrW4h6hN6eztj8RDxiQ7xQWdeAP3y6Hx/8eA6CIIhdGlG7iTrEVlpaCoPBAD8/P4vrfn5+OHnyZLOv0Wg0zbbXaDTm5xuvtdQGAJ5++mkMGzYMXl5e2Lt3LxYvXozCwkK88847zX7dlJQUvPLKK217gz3Mf3MKUFJZB3+VEndFB1j13hweI7JtHs4KfP5IHJZ9dQxf7MvFGztO4nihFm9OHQInhUzs8ojaTPQ5SGJJTk42/3nIkCFQKBR4/PHHkZKSAkdHxxvaL1682OI1Wq0WQUFBXVKrPRAEAWt/MvUezRkTAoVc9NFbIupiCrkUKfdGYXCAO17+6hi+PlSA8yVVWDNrBAI9nMQuj6hNRP0p5uPjA5lMhqKiIovrRUVFUKvVzb5GrVbftH3jf9tyTwCIi4tDQ0MDLl682Ozzjo6OcHd3t3jQdT+dKcWpokq4KGSYPjJY7HKISEQPjeqDdY/EwctFgWMFWtz53k/Yc6ZU7LKI2kTUgKRQKDB8+HCkp6ebrxmNRqSnpyM+Pr7Z18THx1u0B4C0tDRz+9DQUKjVaos2Wq0WmZmZLd4TAHJyciCVSptdOUe39s9rc4+mjQyCyslB5GqISGxxfb3x1bwxiAx0x9Xqesz6KJPzksiuiD7ElpycjNmzZ2PEiBGIjY3FypUrodPpMGfOHADArFmzEBgYiJSUFADAggULMG7cOKxYsQJTpkzBhg0bcODAAaxZswYAIJFI8Mwzz+Avf/kLwsPDzcv8AwICkJSUBMA00TszMxPjx4+Hm5sbMjIysHDhQjz00EPw9PQU5XOwZ6c0ldh9ugRSCfCHMaFil0NEnaQ9cwHvHx4EhawA2blX8caOk9h2uAAbHhsFNyV/kSLbJnpAmj59OkpKSrB06VJoNBrExMRgx44d5knWubm5kEqvd3SNHj0a69evx0svvYQXX3wR4eHh2Lp1q3kPJAB4/vnnodPp8Nhjj6G8vBxjx47Fjh07zHsgOTo6YsOGDXj55ZdRV1eH0NBQLFy40GKOEbXe2j3nAQCTItUI8nIWuRoisiUOMimmDgtEkJcTth0qxLECLZJW/4x/PDwCYb48hohsl0Rgf2e7aLVaqFQqVFRU9Oj5SCWVdRizfCf0BiP+/eRoDO9zYw8cV6AREQDkllVjfeYlaGsb4KKQ4e37o/HbKH+xy6IeprU/v7nUiDrkX79cgt5gxNBgj2bDERFRo2AvZzw1PgxxoV7Q6Q14cl02ln97Eg0Go9ilEd2AAYnarbbegM9/MR0r8sjYviJXQ0T2wE3pgHWPxOGRsab5iqm7zmH2x/twpapO5MqILDEgUbttyc5HmU6P3p5OSBzsd+sXEBEBkMukeOnOQXh/5lA4K2T4+ewV/O79PTh8uVzs0ojMGJCoXYxGAf/8yTQ5+/ejQyCX8X8lImqb30UH4D9/HINQHxcUVNTivtQMfHkgT+yyiADYwCo2sk9pJ4pwvlQHN6UcM2K5MSQRtV7ThRsPj+qDTQfycEJTiec3H8ZXOQWYFKmGVNLyeY4PxPHfHepc/LWf2mXNblPv0UOj+sDVkTmbiNpP6SDDg6P64DcRpo1695wtxee/XEJdvUHkyqgnY0CiNsu6VIasS1ehkEkxZ3SI2OUQUTcglUiQMNAP00cGQS6V4KSmEqm7z+GqTi92adRDMSBRm/1jl6n3KGloAHzdlSJXQ0TdSXRvDzx6W1+4OcpRpK3D3388i0tXdGKXRT0QAxK1yfmSKqSdMB0E/NjtXNpPRNYX5OWMP44PQ4BKCZ3egH/uuYCDuVfFLot6GAYkapMPf7oAQQASBvoizNdN7HKIqJtSOTngsdv7YXCAOwxGAZuyLuN/xzQw8vAH6iIMSNRqJZV1+Hf2ZQDAY7f3E7kaIuruFHIpZsYG447+vQAAu06X4It9udA3cOdt6nwMSNRqn2VchL7BiJggD4wM4bEiRNT5pBIJJg5W4/7hvSGTSnCsQIs1P51DYUWN2KVRN8eARK2iq2vAZxmmY0Uev70vJDfZn4SIyNqGBnvikbGhcFHIUFBei7tX/YxDeeVil0XdGAMStcq6zEuoqKlHiLczJg5Wi10OEfVAfbxd8Mc7wuDn7ojiyjpM+0cGvjlcKHZZ1E0xINEt1egN5o0h/zg+DDIpe4+ISByeLgo8fns//CbCF3UNRjy1PhvvpZ+BwMnbZGUMSHRL6zIvobRKjyAvJ9wzNFDscoioh1M6yPDhrBGYOzYUAPBO2mk8szEHtdx5m6yIAYluqrbegH9c6z166o4wOPBQWiKyATKpBEvuHISUe6Mgl0rw35wCzFjzC4ora8UujboJ/rSjm/r8l0soqaxDoIcT7h3WW+xyiIgszIwNxmdzY6FyckBOXjnufG8P9l0oE7ss6gYYkKhF2tp6rP7hLABg/m/CoJDzfxcisj2j+/lg61NjEO7riuLKOsz88Bek7joHo5Hzkqj9+BOPWvTh7vO4Wl2Pfr1ccN9w9h4Rke0K9XHBf+eNwT1DA2EwClj+7Uk89q8DKK/mYbfUPgxI1KxibS3++dMFAMBziRGQc+4REdk4Z4Uc70yLxl/viYJCLsX3J4ox5b09yDh3RezSyA7xpx41a2X6GdTUGzA02AOJg/3ELoeIqFUkEgkeiAvGlidHo4+3M/LLazDzw1+w7L9HUa1vELs8siMMSHSDo/kV+GJfLgBg0aQI7ppNRHYnMlCFbfPHYmZsMADg04xLmLTyJ/xynr1J1DoMSGTBaBSw9L9HIQjAXdEBiOvrLXZJRETt4qZ0QMq9UfjX3FgEqJTILavGjDW/4MX/HMGVqjqxyyMbx4BEFv6dfRnZueVwVsjw4uSBYpdDRNRht4X3wv8W3o6ZsUEAgPWZubjjrR+xZvc51DVwc0lqHgMSmZXp9Fj+7UkAwNMTwqFWKUWuiIjIOky9SUOw8bFRGBzgjsq6Bvx1+0lM/Ntu7DhayKNK6AZysQsg27Hkv0dxRadHuK8r/jAmVOxyiIisLq6vN76eNxb/zr6MN/93CpeuVOOJz7Phr1JiXP9eiAxUQdrGeZcPxAV3UrUkJvYgEQBg2+ECfHO4EDKpBCumRXNTSCLqtqRSCe4fEYQfn70D4wf0gkIuRWFFLTbsz8PK788g69JVGLjJZI/HHiRCsbYWS7YeBQD88Y5+GNLbQ9yCiIi6gIujHP83SI0xYT7IOHcFe89dQWlVHf6dfRnfHddgZIgXRoZ4QeXkIHapJAIGpB6u3mDEvPUHcbW6HgP93TH/N+Fil0REdEvrM3Otdi9nhRwTBvphbJgPMi+U4eezpaisbcDOk8X48VQxBvq7Iy7UG317ubR5+I3sFwNSD/fGtyex72IZXB3lWP3AUA6tEVGP5eggw+39e2F0mDeOF2jxy/kyXLyiw7ECLY4VaOHh7IChQR4YGuwJH1dHsculTsaA1IP9Nycf/9xjOk7k7fuj0beXq8gVERGJTy6VYkhvDwzp7QGNthaZ568gJ68c5dX1+OFUCX44VYJgL2cMDfbAkEAPsculTsKA1EPtOl2CP315CADwxLh+mBSpFrkiIiLbo3ZX4u6YQEyO8seJQi2yc6/iTFEVcsuqkVtWjW8OFyIrtwy/GxKA8RG+UDrIxC6ZrIQBqQfKzr2KJ/6VhQajgLuiA/B84gCxSyIismkOsuu9StraehzKK8fB3HJotLXYfkSD7Uc0cFHIkDDID3cOCcDt/X3gKGdYsmcSgbtjtYtWq4VKpUJFRQXc3d3FLqfV9p4txWP/ykJVXQNuC/fB2tkjO33ekTUnUxIR2QpBEFBYUYt6gxHbDhciv7zG/JybUo7EwWpMifJHfD9v9izZkNb+/GZAaid7DEjfHC7Ewo050BuMGNXXC2tnj4SLY+d3IjIgEVF39kBcMARBwMG8cnx9yLSnXHHl9bPenBUy3Bbug4SBfvhNhC+8OcFbVAxIncyeApK+wYg3d5w0T8ieHKXGO9Niuuw3GgYkIurOmu6kbTAK2H+xDN8cLkTa8SJotLXm5yQSYHiwJxIG+WFc/14Y4OcGqZRbB3QlBqROZi8B6aRGixc2H8ahyxUAgLljQ/Hi5IGQdeE3JAMSEXVnNztqRBAEHCvQIu14Eb4/UYRjBVqL5z2dHTCqrzfi+3kjLtQbYb6uXfrvc0/U2p/fnKTdTV3V6fFu+hn865dLMBgFqJwc8NZ9QzBxMFerERF1FYlEgshAFSIDVVj4f/1RUF6D9JPFSD9RhH0XynC1uh7fHtXg26MaAICLQobIQBWigzwQ3dsDA/3dEOzlDLmMe9R1NQakbiavrBof/3wRX+zLRU29AQDw20g1ltw5CAEeTiJXR0TUswV4OOHhUX3w8Kg+qDcYcfhyBTLOlSLj/BVkXyqHTm9A5oUyZF4oM79GIZMi1McFYb6u6NfLBb09nRHg4YQADyUCPJw4AbyT2MQQ2+rVq/HWW29Bo9EgOjoa77//PmJjY1tsv2nTJixZsgQXL15EeHg43njjDUyePNn8vCAIWLZsGT788EOUl5djzJgx+OCDDxAefv0YjbKyMsyfPx9ff/01pFIppk6dinfffReurq3bLNFWhtgEQcClK9XYfaYEXx8qwP6LV83PDfJ3x+LJEbgtvJdo9QEcYiMiag2DUUBJZR0uX63G5fIa5F+tQXFlLeoNN/8x7eIoh4eTA1RODogJ9oCPiwLero7wcXWEt6sCPq4KeLs4QuXkwPlOsKMhto0bNyI5ORmpqamIi4vDypUrkZiYiFOnTsHX1/eG9nv37sXMmTORkpKCO++8E+vXr0dSUhKys7MRGRkJAHjzzTfx3nvv4dNPP0VoaCiWLFmCxMREHD9+HEqlEgDw4IMPorCwEGlpaaivr8ecOXPw2GOPYf369V36/tuiqq4BeWXVuHy1BudLqnAkvwI5eeW4fPX60lKJBBgb5oNHb+uL28J9IOG5QUREdkEmlUCtUkKtUmLEtWtGQUBFdT2KK+tQXFmL0io9Kmr0KK+uR3l1PfQGI3R1DdDVNSC/vAbHC7Ut3l8ulcDTRWEOUx7ODlA5KeDh7ACPxr87K0zPXWvjqpTDRSGH0kHa436eiN6DFBcXh5EjR2LVqlUAAKPRiKCgIMyfPx+LFi26of306dOh0+mwbds287VRo0YhJiYGqampEAQBAQEB+NOf/oRnn30WAFBRUQE/Pz988sknmDFjBk6cOIFBgwZh//79GDHC9L/hjh07MHnyZFy+fBkBAQG3rLuzepA27s/FKU0VtLX10NbUQ1tbj4qaBmgqanC1ur7Z1zjIJBgW7IkJA31xV3Qg1Cql1eqxBvYgERFZnyAIqKk3mMOStrYefXu5oLSqDleq9LhSpUepzvTniprmf360llQCuCjkcHGUw9lRBldHOZwVMrgo5HB0kEIhk0Ihl8Lh2n8VcikcZdf/LpNKIJFIIJXA4s/Sa/81/f3G52NDveDrZt2faXbRg6TX65GVlYXFixebr0mlUiQkJCAjI6PZ12RkZCA5OdniWmJiIrZu3QoAuHDhAjQaDRISEszPq1QqxMXFISMjAzNmzEBGRgY8PDzM4QgAEhISIJVKkZmZiXvuueeGr1tXV4e6uuv7WlRUmFaFabUtp/X22JJ5FhnnrrT4vMpJjkAPZwR5OiEiwA2DA1SI7u3xq/2M9NBq9VatqaOqdZVil0BE1G15OAAeKgmgUmDaiOYX4ugbjCiv1uOKrg7amgZoa+pRUVOPimu/hGtrTCGqorrBdK1aD21tPar1RgCAEUBFLVDRhe8LAFIfHo6xYT5WvWfjz+1b9Q+JGpBKS0thMBjg5+dncd3Pzw8nT55s9jUajabZ9hqNxvx847WbtWk6fCeXy+Hl5WVu01RKSgpeeeWVG64HBQW19PY6RR6Ao136FYmIyF48KnYBVjZlZefdu7KyEiqVqsXnRZ+DZC8WL15s0XNlNBpRVlYGb2/vHjcu2xparRZBQUHIy8uz6X2i7BE/287Fz7fz8LPtXPx8W0cQBFRWVt5yOo2oAcnHxwcymQxFRUUW14uKiqBWN99NqFarb9q+8b9FRUXw9/e3aBMTE2NuU1xcbHGPhoYGlJWVtfh1HR0d4ehouT28h4fHzd8gwd3dnd+onYSfbefi59t5+Nl2Ln6+t3aznqNGou48pVAoMHz4cKSnp5uvGY1GpKenIz4+vtnXxMfHW7QHgLS0NHP70NBQqNVqizZarRaZmZnmNvHx8SgvL0dWVpa5zc6dO2E0GhEXF2e190dERET2SfQhtuTkZMyePRsjRoxAbGwsVq5cCZ1Ohzlz5gAAZs2ahcDAQKSkpAAAFixYgHHjxmHFihWYMmUKNmzYgAMHDmDNmjUATDPhn3nmGfzlL39BeHi4eZl/QEAAkpKSAAADBw7EpEmT8OijjyI1NRX19fWYN28eZsyY0aoVbERERNS9iR6Qpk+fjpKSEixduhQajQYxMTHYsWOHeZJ1bm4upNLrHV2jR4/G+vXr8dJLL+HFF19EeHg4tm7dat4DCQCef/556HQ6PPbYYygvL8fYsWOxY8cO8x5IALBu3TrMmzcPEyZMMG8U+d5773XdG+/mHB0dsWzZshuGJanj+Nl2Ln6+nYefbefi52tdou+DRERERGRrePodERERURMMSERERERNMCARERERNcGARERERNQEAxJZ3erVqxESEgKlUom4uDjs27dP7JLsUkpKCkaOHAk3Nzf4+voiKSkJp06dsmhTW1uLp556Ct7e3nB1dcXUqVNv2EiVbm358uXmLUIa8bPtmPz8fDz00EPw9vaGk5MToqKicODAAfPzgiBg6dKl8Pf3h5OTExISEnDmzBkRK7YPBoMBS5YsQWhoKJycnNCvXz+89tprFueK8bO1DgYksqqNGzciOTkZy5YtQ3Z2NqKjo5GYmHjDzuV0a7t27cJTTz2FX375BWlpaaivr8fEiROh0+nMbRYuXIivv/4amzZtwq5du1BQUIB7771XxKrtz/79+/GPf/wDQ4YMsbjOz7b9rl69ijFjxsDBwQHffvstjh8/jhUrVsDT09Pc5s0338R7772H1NRUZGZmwsXFBYmJiaitrRWxctv3xhtv4IMPPsCqVatw4sQJvPHGG3jzzTfx/vvvm9vws7USgciKYmNjhaeeesr8d4PBIAQEBAgpKSkiVtU9FBcXCwCEXbt2CYIgCOXl5YKDg4OwadMmc5sTJ04IAISMjAyxyrQrlZWVQnh4uJCWliaMGzdOWLBggSAI/Gw76oUXXhDGjh3b4vNGo1FQq9XCW2+9Zb5WXl4uODo6Cl988UVXlGi3pkyZIvzhD3+wuHbvvfcKDz74oCAI/GytiT1IZDV6vR5ZWVlISEgwX5NKpUhISEBGRoaIlXUPFRUVAAAvLy8AQFZWFurr6y0+74iICAQHB/PzbqWnnnoKU6ZMsfgMAX62HfXVV19hxIgRuP/+++Hr64uhQ4fiww8/ND9/4cIFaDQai89XpVIhLi6On+8tjB49Gunp6Th9+jQA4NChQ9izZw9++9vfAuBna02i76RN3UdpaSkMBoN5F/RGfn5+OHnypEhVdQ9GoxHPPPMMxowZY941XqPRQKFQ3HBosp+fHzQajQhV2pcNGzYgOzsb+/fvv+E5frYdc/78eXzwwQdITk7Giy++iP379+Ppp5+GQqHA7NmzzZ9hc/9W8PO9uUWLFkGr1SIiIgIymQwGgwGvv/46HnzwQQDgZ2tFDEhEduCpp57C0aNHsWfPHrFL6Rby8vKwYMECpKWlWRxBRNZhNBoxYsQI/PWvfwUADB06FEePHkVqaipmz54tcnX27csvv8S6deuwfv16DB48GDk5OXjmmWcQEBDAz9bKOMRGVuPj4wOZTHbDSp+ioiKo1WqRqrJ/8+bNw7Zt2/DDDz+gd+/e5utqtRp6vR7l5eUW7fl531pWVhaKi4sxbNgwyOVyyOVy7Nq1C++99x7kcjn8/Pz42XaAv78/Bg0aZHFt4MCByM3NBQDzZ8h/K9ruueeew6JFizBjxgxERUXh4YcfxsKFC80HuvOztR4GJLIahUKB4cOHIz093XzNaDQiPT0d8fHxIlZmnwRBwLx58/Cf//wHO3fuRGhoqMXzw4cPh4ODg8XnferUKeTm5vLzvoUJEybgyJEjyMnJMT9GjBiBBx980PxnfrbtN2bMmBu2pDh9+jT69OkDAAgNDYVarbb4fLVaLTIzM/n53kJ1dbXFAe4AIJPJYDQaAfCztSqxZ4lT97JhwwbB0dFR+OSTT4Tjx48Ljz32mODh4SFoNBqxS7M7Tz75pKBSqYQff/xRKCwsND+qq6vNbZ544gkhODhY2Llzp3DgwAEhPj5eiI+PF7Fq+/XrVWyCwM+2I/bt2yfI5XLh9ddfF86cOSOsW7dOcHZ2Fj7//HNzm+XLlwseHh7Cf//7X+Hw4cPC3XffLYSGhgo1NTUiVm77Zs+eLQQGBgrbtm0TLly4IGzZskXw8fERnn/+eXMbfrbWwYBEVvf+++8LwcHBgkKhEGJjY4VffvlF7JLsEoBmHx9//LG5TU1NjfDHP/5R8PT0FJydnYV77rlHKCwsFK9oO9Y0IPGz7Zivv/5aiIyMFBwdHYWIiAhhzZo1Fs8bjUZhyZIlgp+fn+Do6ChMmDBBOHXqlEjV2g+tVissWLBACA4OFpRKpdC3b1/hz3/+s1BXV2duw8/WOiSC8KvtN4mIiIiIc5CIiIiImmJAIiIiImqCAYmIiIioCQYkIiIioiYYkIiIiIiaYEAiIiIiaoIBiYiIiKgJBiQiIiKiJhiQiIiIiJpgQCKiHiMjIwMymQxTpkwRuxQisnE8aoSIeoxHHnkErq6uWLt2LU6dOoWAgACxSyIiG8UeJCLqEaqqqrBx40Y8+eSTmDJlCj755BOL57/66iuEh4dDqVRi/Pjx+PTTTyGRSFBeXm5us2fPHtx2221wcnJCUFAQnn76aeh0uq59I0TUJRiQiKhH+PLLLxEREYEBAwbgoYcewkcffYTGDvQLFy7gvvvuQ1JSEg4dOoTHH38cf/7zny1ef+7cOUyaNAlTp07F4cOHsXHjRuzZswfz5s0T4+0QUSfjEBsR9QhjxozBtGnTsGDBAjQ0NMDf3x+bNm3CHXfcgUWLFuGbb77BkSNHzO1feuklvP7667h69So8PDzwyCOPQCaT4R//+Ie5zZ49ezBu3DjodDoolUox3hYRdRL2IBFRt3fq1Cns27cPM2fOBADI5XJMnz4da9euNT8/cuRIi9fExsZa/P3QoUP45JNP4Orqan4kJibCaDTiwoULXfNGiKjLyMUugIios61duxYNDQ0Wk7IFQYCjoyNWrVrVqntUVVXh8ccfx9NPP33Dc8HBwVarlYhsAwMSEXVrDQ0N+Oyzz7BixQpMnDjR4rmkpCR88cUXGDBgALZv327x3P79+y3+PmzYMBw/fhxhYWGdXjMRiY9zkIioW9u6dSumT5+O4uJiqFQqi+deeOEF7Ny5E19++SUGDBiAhQsXYu7cucjJycGf/vQnXL58GeXl5VCpVDh8+DBGjRqFP/zhD3jkkUfg4uKC48ePIy0trdW9UERkPzgHiYi6tbVr1yIhIeGGcAQAU6dOxYEDB1BZWYnNmzdjy5YtGDJkCD744APzKjZHR0cAwJAhQ7Br1y6cPn0at912G4YOHYqlS5dyLyWiboo9SEREzXj99deRmpqKvLw8sUshIhFwDhIREYC///3vGDlyJLy9vfHzzz/jrbfe4h5HRD0YAxIREYAzZ87gL3/5C8rKyhAcHIw//elPWLx4sdhlEZFIOMRGRERE1AQnaRMRERE1wYBERERE1AQDEhEREVETDEhERERETTAgERERETXBgERERETUBAMSERERURMMSERERERN/D8BdNz65gOAegAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Q9WVDGIQBCe",
        "outputId": "3f3cbfb4-1869-4fad-8e8b-c70405d0a7c4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1NU2-BXQdPf",
        "outputId": "4658b6f6-d1c7-46b2-c604-d9e7b427df62"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.0"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FxWKad2QieM",
        "outputId": "25a9856d-b651-470f-e149-0c94b6b83a69"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multivariate/Bivariate analysis"
      ],
      "metadata": {
        "id": "HSUvg22CRHVr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tips = sns.load_dataset('tips')"
      ],
      "metadata": {
        "id": "AF6yCjA9QmkV"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tips.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mufd39m3RjIH",
        "outputId": "6cbab3de-a702-41e8-be46-a6fc366fb33b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-98270a2f-8205-4eae-ad06-4483cbedbf4e\" 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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</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-98270a2f-8205-4eae-ad06-4483cbedbf4e')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-98270a2f-8205-4eae-ad06-4483cbedbf4e 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-98270a2f-8205-4eae-ad06-4483cbedbf4e');\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-a928dbbb-9e10-4e22-acfe-08bf39ad9c1a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a928dbbb-9e10-4e22-acfe-08bf39ad9c1a')\"\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-a928dbbb-9e10-4e22-acfe-08bf39ad9c1a 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": "tips",
              "summary": "{\n  \"name\": \"tips\",\n  \"rows\": 244,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.902411954856856,\n        \"min\": 3.07,\n        \"max\": 50.81,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          22.12,\n          20.23,\n          14.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3836381890011822,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          3.35,\n          1.5,\n          6.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Sat\",\n          \"Fri\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Lunch\",\n          \"Dinner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flights = sns.load_dataset('flights')"
      ],
      "metadata": {
        "id": "co1Am4_8RZJk"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flights.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jEcVxWoQRsss",
        "outputId": "dd7ac267-674e-4491-80f7-989ce0eb9f8c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year month  passengers\n",
              "0  1949   Jan         112\n",
              "1  1949   Feb         118\n",
              "2  1949   Mar         132\n",
              "3  1949   Apr         129\n",
              "4  1949   May         121"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64823df2-e629-4bc6-b8fa-0805f460c8cf\" 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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>passengers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949</td>\n",
              "      <td>Jan</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949</td>\n",
              "      <td>Feb</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949</td>\n",
              "      <td>Mar</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949</td>\n",
              "      <td>Apr</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949</td>\n",
              "      <td>May</td>\n",
              "      <td>121</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-64823df2-e629-4bc6-b8fa-0805f460c8cf')\"\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-64823df2-e629-4bc6-b8fa-0805f460c8cf 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-64823df2-e629-4bc6-b8fa-0805f460c8cf');\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-eef815bb-f9df-4b35-aa85-517f7a5c038f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eef815bb-f9df-4b35-aa85-517f7a5c038f')\"\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-eef815bb-f9df-4b35-aa85-517f7a5c038f 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": "flights",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Nov\",\n          \"Oct\",\n          \"Jan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119,\n        \"min\": 104,\n        \"max\": 622,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293,\n          340,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris = sns.load_dataset('iris')"
      ],
      "metadata": {
        "id": "-Xe83j1FRd07"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hT4ns9s0Ry1K",
        "outputId": "35a41876-3ba0-4603-dd5d-4a288f483f11"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
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              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "      <th>1</th>\n",
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              "      <td>setosa</td>\n",
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              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
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              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
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              "        document.querySelector('#df-c645deec-e5b1-4649-82ab-60cbe78d063e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c645deec-e5b1-4649-82ab-60cbe78d063e');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\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-5f237413-e4fd-4558-88b5-8b7d49324759 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": "iris",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numerical - Numerical"
      ],
      "metadata": {
        "id": "JgrEz9SFR-uX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Scatterplot"
      ],
      "metadata": {
        "id": "zYzg8nPNSIEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='total_bill', y='tip', data=tips, hue='sex', style = 'smoker', size='size')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "-hdnDlmWRiiD",
        "outputId": "1a964f7d-b164-4f45-f4de-11302e2ed2e0"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='total_bill', ylabel='tip'>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numerical- categotical"
      ],
      "metadata": {
        "id": "lJ11tvJNS6h9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Barplot"
      ],
      "metadata": {
        "id": "U1HLQoubS2bT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VwgfvJVjSOuE",
        "outputId": "dbc9a270-44fc-4410-d780-4c018891a81c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40176684-95a5-4d4d-a4b0-3c4ab732047b\" 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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</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-40176684-95a5-4d4d-a4b0-3c4ab732047b')\"\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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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\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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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Pclass', y='Age', data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "1_VJQDysTDZ-",
        "outputId": "4b13be20-862b-456c-be8b-9958358d942c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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gLJp9aen3v/+9MjIy9NFHH+nkyZNasGCBPvroI23ZskVlZWUXYkYAAIDTavYZmWuvvVaVlZU6efKkBg0apLfeektxcXHaunWr0tLSLsSMAAAAp9XsMzKS1KtXLz3//PMtPQsAAECzNDtk6uvrT7vd4XDI6XQqMjLyRw8FAABwLpodMl26dDnju5OSk5OVnZ2tRx99VO3a8Q0IAADgwml2yCxbtky/+93vlJ2drauvvlqS9N5772n58uV66KGHdOjQIT311FNyOp367W9/2+IDAwAAfKvZIbN8+XI9/fTTuu2220LbJkyYoEGDBum5555TSUmJUlJS9PjjjxMyAADggmr2tZ8tW7ZoyJAhP9g+ZMgQbd26VdI372z69juYAAAALpRmh4zH49GSJUt+sH3JkiXyeDySpMOHD+uSSy758dMBAACcQbMvLT311FO69dZbtW7dOg0bNkyStH37dv3nP//Ra6+9Jknatm2bJk+e3LKTAgAAfE+zQ+bmm29WVVWVFi9erP/+97+SpIyMDK1atUpHjx6VJE2bNq1lpwQAADiN8/pAvEsvvVT5+fmSvvlcmZdeekmTJ0/W9u3bderUqRYdEAAAoCnn/UEvmzZtUlZWlpKSkvT0009r9OjRKi8vb9Yx8vLyNGzYMMXExCguLk6TJk1SVVVV2D7Hjh1TTk6OunbtqujoaGVmZqq2tvZ8xwYAAG1Is0KmpqZG+fn56tOnj2699Va5XC4FAgGtWrVK+fn5oXtmzlVZWZlycnJUXl6ujRs36sSJE7rhhhvk9/tD+8yaNUtvvvmmVqxYobKyMu3fv1+33HJLs34OAABom8750tKECRO0adMm3XTTTZo/f77Gjx+v9u3ba/Hixef9w9evXx+2vmzZMsXFxamiokIjR45UXV2dlixZoqKiIo0ZM0aStHTpUl1xxRUqLy/XiBEjfnDMQCCgQCAQWm/qKxUAAID5zvmMzLp16zRlyhTNmTNHN910k9q3b9/iw9TV1UmSYmNjJUkVFRU6ceKExo0bF9qnX79+SklJCX1mzffl5eXJ7XaHlm/fEg4AANqecw6ZzZs3q6GhQWlpaRo+fLj+9Kc/6fPPP2+xQRobGzVz5kxdc801GjhwoKRvLmVFRkaqS5cuYfvGx8erpqbmtMfx+Xyqq6sLLdXV1S02IwAAsJdzDpkRI0bo+eef14EDB/Tzn/9cxcXFSkpKUmNjozZu3KiGhoYfNUhOTo527dql4uLiH3Ucp9Mpl8sVtgAAgLap2e9a6ty5s+69915t3rxZO3fu1K9//Wvl5+crLi5ON99883kNMX36dK1Zs0bvvPOOkpOTQ9sTEhJ0/PhxHTlyJGz/2tpaJSQknNfPAgAAbcd5v/1akvr27asnnnhCn332mV566aVmPz8YDGr69OlauXKl/vnPf6pnz55hj6elpalDhw4qKSkJbauqqtK+ffvk9Xp/zOgAAKANOK8PxPu+9u3ba9KkSZo0aVKznpeTk6OioiKtXr1aMTExofte3G63OnbsKLfbrSlTpig3N1exsbFyuVyaMWOGvF7vad+xBAAALi4tEjLnq7CwUJI0atSosO1Lly5Vdna2JOnZZ59Vu3btlJmZqUAgoPT0dC1atKiVJwUAAHZkacgEg8Gz7hMVFaWCggIVFBS0wkQAAMAkP+oeGQAAACsRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMZWnIbNq0SRMmTFBSUpIcDodWrVoV9nh2drYcDkfYMn78eGuGBQAAtmNpyPj9fg0ePFgFBQVN7jN+/HgdOHAgtLz00kutOCEAALCzCCt/eEZGhjIyMs64j9PpVEJCQitNBAAATGL7e2RKS0sVFxenvn37atq0aTp8+PAZ9w8EAqqvrw9bAABA22TrkBk/frxeeOEFlZSU6A9/+IPKysqUkZGhU6dONfmcvLw8ud3u0OLxeFpxYgAA0JosvbR0Nrfffnvoz4MGDVJqaqp69eql0tJSjR079rTP8fl8ys3NDa3X19cTMwAAtFG2PiPzfZdddpm6deum3bt3N7mP0+mUy+UKWwAAQNtkVMh89tlnOnz4sBITE60eBQAA2ICll5aOHj0adnZl7969qqysVGxsrGJjYzVnzhxlZmYqISFBe/bs0YMPPqjevXsrPT3dwqkBAIBdWBoy27dv1+jRo0Pr397bkpWVpcLCQu3YsUPLly/XkSNHlJSUpBtuuEHz5s2T0+m0amQAAGAjlobMqFGjFAwGm3x8w4YNrTgNAAAwjVH3yAAAAHwXIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwlqUhs2nTJk2YMEFJSUlyOBxatWpV2OPBYFCPPPKIEhMT1bFjR40bN06ffPKJNcMCAADbsTRk/H6/Bg8erIKCgtM+/sQTT2jhwoVavHix3n33XXXu3Fnp6ek6duxYK08KAADsKMLKH56RkaGMjIzTPhYMBjV//nw99NBDmjhxoiTphRdeUHx8vFatWqXbb7/9tM8LBAIKBAKh9fr6+pYfHAAA2IJt75HZu3evampqNG7cuNA2t9ut4cOHa+vWrU0+Ly8vT263O7R4PJ7WGBcAAFjAtiFTU1MjSYqPjw/bHh8fH3rsdHw+n+rq6kJLdXX1BZ0TAABYx9JLSxeC0+mU0+m0egwAANAKbHtGJiEhQZJUW1sbtr22tjb0GAAAuLjZNmR69uyphIQElZSUhLbV19fr3XffldfrtXAyAABgF5ZeWjp69Kh2794dWt+7d68qKysVGxurlJQUzZw5U//3f/+nPn36qGfPnnr44YeVlJSkSZMmWTc0AACwDUtDZvv27Ro9enRoPTc3V5KUlZWlZcuW6cEHH5Tf79f999+vI0eO6Nprr9X69esVFRVl1cgAAMBGLA2ZUaNGKRgMNvm4w+HQ3LlzNXfu3FacCgAAmMK298gAAACcDSEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFi2DpnHHntMDocjbOnXr5/VYwEAAJuIsHqAsxkwYIDefvvt0HpEhO1HBgAArcT2VRAREaGEhASrxwAAADZk60tLkvTJJ58oKSlJl112me666y7t27fvjPsHAgHV19eHLQAAoG2ydcgMHz5cy5Yt0/r161VYWKi9e/fquuuuU0NDQ5PPycvLk9vtDi0ej6cVJwYAAK3J1iGTkZGhW2+9VampqUpPT9fatWt15MgRvfLKK00+x+fzqa6uLrRUV1e34sQAAKA12f4eme/q0qWLLr/8cu3evbvJfZxOp5xOZytOBQAArGLrMzLfd/ToUe3Zs0eJiYlWjwIAAGzA1iHzwAMPqKysTJ9++qm2bNmin/70p2rfvr3uuOMOq0cDAAA2YOtLS5999pnuuOMOHT58WN27d9e1116r8vJyde/e3erRAACADdg6ZIqLi60eAQAA2JitLy0BAACcCSEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMJYRIVNQUKBLL71UUVFRGj58uN577z2rRwIAADZg+5B5+eWXlZubq0cffVTvv/++Bg8erPT0dB08eNDq0QAAgMVsHzLPPPOM7rvvPt1zzz3q37+/Fi9erE6dOumvf/2r1aMBAACLRVg9wJkcP35cFRUV8vl8oW3t2rXTuHHjtHXr1tM+JxAIKBAIhNbr6uokSfX19ec9x6nA1+f9XLRNP+b11FJ4XeK7eE3Cbn7sa/Lb5weDwTPuZ+uQ+fzzz3Xq1CnFx8eHbY+Pj9fHH3982ufk5eVpzpw5P9ju8XguyIy4OLn/ONXqEYAwvCZhNy31mmxoaJDb7W7ycVuHzPnw+XzKzc0NrTc2NuqLL75Q165d5XA4LJzMfPX19fJ4PKqurpbL5bJ6HIDXJGyH12TLCQaDamhoUFJS0hn3s3XIdOvWTe3bt1dtbW3Y9traWiUkJJz2OU6nU06nM2xbly5dLtSIFyWXy8W/oLAVXpOwG16TLeNMZ2K+ZeubfSMjI5WWlqaSkpLQtsbGRpWUlMjr9Vo4GQAAsANbn5GRpNzcXGVlZWno0KG6+uqrNX/+fPn9ft1zzz1WjwYAACxm+5CZPHmyDh06pEceeUQ1NTW68sortX79+h/cAIwLz+l06tFHH/3BpTvAKrwmYTe8JlufI3i29zUBAADYlK3vkQEAADgTQgYAABiLkAEAAMYiZAAAgLEIGZzVpk2bNGHCBCUlJcnhcGjVqlVWj4SLXF5enoYNG6aYmBjFxcVp0qRJqqqqsnosXMQKCwuVmpoa+iA8r9erdevWWT3WRYGQwVn5/X4NHjxYBQUFVo8CSJLKysqUk5Oj8vJybdy4USdOnNANN9wgv99v9Wi4SCUnJys/P18VFRXavn27xowZo4kTJ+rDDz+0erQ2j7dfo1kcDodWrlypSZMmWT0KEHLo0CHFxcWprKxMI0eOtHocQJIUGxurJ598UlOmTLF6lDbN9h+IBwBnU1dXJ+mbvzgAq506dUorVqyQ3+/n63RaASEDwGiNjY2aOXOmrrnmGg0cONDqcXAR27lzp7xer44dO6bo6GitXLlS/fv3t3qsNo+QAWC0nJwc7dq1S5s3b7Z6FFzk+vbtq8rKStXV1enVV19VVlaWysrKiJkLjJABYKzp06drzZo12rRpk5KTk60eBxe5yMhI9e7dW5KUlpambdu2acGCBXruuecsnqxtI2QAGCcYDGrGjBlauXKlSktL1bNnT6tHAn6gsbFRgUDA6jHaPEIGZ3X06FHt3r07tL53715VVlYqNjZWKSkpFk6Gi1VOTo6Kioq0evVqxcTEqKamRpLkdrvVsWNHi6fDxcjn8ykjI0MpKSlqaGhQUVGRSktLtWHDBqtHa/N4+zXOqrS0VKNHj/7B9qysLC1btqz1B8JFz+FwnHb70qVLlZ2d3brDAJKmTJmikpISHThwQG63W6mpqZo9e7auv/56q0dr8wgZAABgLD7ZFwAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgaAMUaNGqWZM2daPQYAGyFkALSq7OxsORwOORyO0LcFz507VydPnrR6NAAG4ksjAbS68ePHa+nSpQoEAlq7dq1ycnLUoUMH+Xw+q0cDYBjOyABodU6nUwkJCerRo4emTZumcePG6Y033pAk/etf/9KoUaPUqVMnXXLJJUpPT9eXX3552uP87W9/09ChQxUTE6OEhATdeeedOnjwYOjxL7/8UnfddZe6d++ujh07qk+fPlq6dKkk6fjx45o+fboSExMVFRWlHj16KC8v78L/8gBaFGdkAFiuY8eOOnz4sCorKzV27Fjde++9WrBggSIiIvTOO+/o1KlTp33eiRMnNG/ePPXt21cHDx5Ubm6usrOztXbtWknSww8/rI8++kjr1q1Tt27dtHv3bn399deSpIULF+qNN97QK6+8opSUFFVXV6u6urrVfmcALYOQAWCZYDCokpISbdiwQTNmzNATTzyhoUOHatGiRaF9BgwY0OTz77333tCfL7vsMi1cuFDDhg3T0aNHFR0drX379mnIkCEaOnSoJOnSSy8N7b9v3z716dNH1157rRwOh3r06NHyvyCAC45LSwBa3Zo1axQdHa2oqChlZGRo8uTJeuyxx0JnZM5VRUWFJkyYoJSUFMXExOgnP/mJpG8iRZKmTZum4uJiXXnllXrwwQe1ZcuW0HOzs7NVWVmpvn376pe//KXeeuutlv0lAbQKQgZAqxs9erQqKyv1ySef6Ouvv9by5cvVuXNndezY8ZyP4ff7lZ6eLpfLpRdffFHbtm3TypUrJX1z/4skZWRk6H//+59mzZql/fv3a+zYsXrggQckSVdddZX27t2refPm6euvv9Ztt92mn/3sZy3/ywK4oAgZAK2uc+fO6t27t1JSUhQR8f+vcKempqqkpOScjvHxxx/r8OHDys/P13XXXad+/fqF3ej7re7duysrK0t///vfNX/+fP35z38OPeZyuTR58mQ9//zzevnll/Xaa6/piy+++PG/IIBWwz0yAGzD5/Np0KBB+sUvfqGpU6cqMjJS77zzjm699VZ169YtbN+UlBRFRkbqj3/8o6ZOnapdu3Zp3rx5Yfs88sgjSktL04ABAxQIBLRmzRpdccUVkqRnnnlGiYmJGjJkiNq1a6cVK1YoISFBXbp0aa1fF0AL4IwMANu4/PLL9dZbb+mDDz7Q1VdfLa/Xq9WrV4edtflW9+7dtWzZMq1YsUL9+/dXfn6+nnrqqbB9IiMj5fP5lJqaqpEjR6p9+/YqLi6WJMXExIRuLh42bJg+/fRTrV27Vu3a8Z9FwCSOYDAYtHoIAACA88H/egAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADDW/wP2YMDICBs4MwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Pclass', y='Age', data=df, hue='Sex' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Pbfy8ttfTIuR",
        "outputId": "9780bc5c-ffa1-4a66-95e1-f729b7a759df"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Numerical- categotical"
      ],
      "metadata": {
        "id": "hkDH7nadTvaD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Distplot"
      ],
      "metadata": {
        "id": "Yu35b4S2T2vE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df[df['Survived']==0]['Age'], hist=False)\n",
        "sns.distplot(df[df['Survived']==1]['Age'], hist=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        },
        "id": "noYZpl2RTT2Z",
        "outputId": "afb6c6fe-fbd9-427e-a16c-a7c9f14eb8e0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-59-6a2590635ecf>: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 `kdeplot` (an axes-level function for kernel density plots).\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[df['Survived']==0]['Age'], hist=False)\n",
            "<ipython-input-59-6a2590635ecf>:2: 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 `kdeplot` (an axes-level function for kernel density plots).\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[df['Survived']==1]['Age'], hist=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Categorical- categorical"
      ],
      "metadata": {
        "id": "WBXAi4oUUt7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['Pclass'], df['Survived'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "8n1j2Nd8VV7m",
        "outputId": "288c30e1-47ef-4bfe-b47f-1f5b1bbc7c31"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0    1\n",
              "Pclass            \n",
              "1          80  136\n",
              "2          97   87\n",
              "3         372  119"
            ],
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              "  .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-8a9d4a53-2d67-4852-9b3a-3055967e0d62 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\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 80,\n        \"max\": 372,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          80,\n          97,\n          372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 87,\n        \"max\": 136,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          136,\n          87,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(pd.crosstab(df['Pclass'], df['Survived']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "lkYmfdXjT-EC",
        "outputId": "12c3d872-6115-439f-cb0d-03c756ae8c1f"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='Pclass'>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Pclass')['Survived'].mean()*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohy_kvoLU93n",
        "outputId": "289956a1-73d8-4a69-fc0c-5709ff049fb9"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    62.962963\n",
              "2    47.282609\n",
              "3    24.236253\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Sex')['Survived'].mean()*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1cJkyw5VjK6",
        "outputId": "962f0fa2-0228-4ee4-958d-bd1590f17459"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    74.203822\n",
              "male      18.890815\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Embarked')['Survived'].mean()*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I71JfgddVq2e",
        "outputId": "0db7cb53-eb1c-4123-e84b-deb0751977d0"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked\n",
              "C    55.357143\n",
              "Q    38.961039\n",
              "S    33.695652\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(df.groupby('Pclass')['Survived'].mean()*100).plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "f-YvYHd0VwL3",
        "outputId": "f955ae2c-3efc-4333-97d3-661f03667365"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass'>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PairPlot"
      ],
      "metadata": {
        "id": "VubRroY9V-OG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iris.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wZR69KdRV5FW",
        "outputId": "dd934cbf-cb73-4efe-d14c-9ad73374d8d1"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66572b60-29c5-4834-8a9e-b5b87a0bb854\" 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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</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-66572b60-29c5-4834-8a9e-b5b87a0bb854')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
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              "  <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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-66572b60-29c5-4834-8a9e-b5b87a0bb854 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-66572b60-29c5-4834-8a9e-b5b87a0bb854');\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",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e3a8b6e-ece1-451f-b818-4a7380e4666a')\"\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",
              "</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",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-top-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",
              "      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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iris",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(iris, hue='species')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jwMsm9hWWJAH",
        "outputId": "af1435d2-07dd-4ca3-d396-a62b47dd1e4a"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7e89b9ab7730>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1117.75x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Numerical-Numerical"
      ],
      "metadata": {
        "id": "8Zk82a34WeOq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lineplot"
      ],
      "metadata": {
        "id": "7xa4WI_kWiWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flights.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "blVphBi8WNa3",
        "outputId": "6576037c-05d1-4603-f743-4f5d38be195b"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year month  passengers\n",
              "0  1949   Jan         112\n",
              "1  1949   Feb         118\n",
              "2  1949   Mar         132\n",
              "3  1949   Apr         129\n",
              "4  1949   May         121"
            ],
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              "      <th>2</th>\n",
              "      <td>1949</td>\n",
              "      <td>Mar</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949</td>\n",
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              "        const element = document.querySelector('#df-96a16f7e-10f1-48a2-bbd4-0215242bcc8f');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
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              "\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-ac2f9657-7abc-46b9-94b5-49dac9be780f 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": "flights",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Nov\",\n          \"Oct\",\n          \"Jan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119,\n        \"min\": 104,\n        \"max\": 622,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293,\n          340,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new = flights.groupby('year')['passengers'].sum().reset_index()\n",
        "new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "_kczu6EqWtj0",
        "outputId": "c3cfa81b-cc34-4ba5-f108-84936dc39c21"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  passengers\n",
              "0   1949        1520\n",
              "1   1950        1676\n",
              "2   1951        2042\n",
              "3   1952        2364\n",
              "4   1953        2700\n",
              "5   1954        2867\n",
              "6   1955        3408\n",
              "7   1956        3939\n",
              "8   1957        4421\n",
              "9   1958        4572\n",
              "10  1959        5140\n",
              "11  1960        5714"
            ],
            "text/html": [
              "\n",
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              "      <td>2042</td>\n",
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              "      <th>3</th>\n",
              "      <td>1952</td>\n",
              "      <td>2364</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1953</td>\n",
              "      <td>2700</td>\n",
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              "      <td>1954</td>\n",
              "      <td>2867</td>\n",
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              "      <th>6</th>\n",
              "      <td>1955</td>\n",
              "      <td>3408</td>\n",
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              "      <th>7</th>\n",
              "      <td>1956</td>\n",
              "      <td>3939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1957</td>\n",
              "      <td>4421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1958</td>\n",
              "      <td>4572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1959</td>\n",
              "      <td>5140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1960</td>\n",
              "      <td>5714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-8d221b5e-3229-4ae6-b959-6eeee1b0aed0 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-8d221b5e-3229-4ae6-b959-6eeee1b0aed0');\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",
              "      }\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --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-dc16f1f4-31f3-464f-a585-e22aa61d819e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_58d2a82b-3279-4db4-8134-8ddbeb2dede3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_58d2a82b-3279-4db4-8134-8ddbeb2dede3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new",
              "summary": "{\n  \"name\": \"new\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1390,\n        \"min\": 1520,\n        \"max\": 5714,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          5140,\n          4572,\n          1520\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(x='year', y='passengers', data=new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "n0NqvIqxW13i",
        "outputId": "43479bb8-fb67-48be-b90b-3f4215a4a42e"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='year', ylabel='passengers'>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "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.heatmap(flights.pivot_table(values='passengers', index='month', columns='year'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "J1_tS9gxXCM8",
        "outputId": "88ab8516-b5b4-4c58-9bcd-e09f01b78534"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='year', ylabel='month'>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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BA83HjUYj+vTpg6KiIuzfvx/Lly9HcnIyZs2aVeEc0h/b0Ol0GDJkCLZt24ZTp06hRYsWGDt2LBo2bIj8/HzZ8YiIiKpOCOtsFWRvb4+AgADz5uPjAwDIycnBZ599hvfffx9du3ZFmzZtsGzZMuzfvx8//vgjAOD777/HqVOn8O9//xsPP/wwevfujTfffBOLFi0qtYbZg0gvNu6m1WrNa6NwPRQiIqoxrNSNYjAYkJuba7H9dRbtu509exZBQUFo1KgRhg4dioyMDADA4cOHUVxcjO7du5vPbd68ORo0aICUlBQAt2f3btmyJfz9/c3nxMTEIDc3F2lpaRX68qUXGwaDAatWrUKPHj3QtGlTpKamYuHChcjIyICbm5vseERERKpR1qzZSUlJZZ4bFRWF5ORkbNmyBR999BHS09Px6KOPIi8vD1lZWXB0dISHh4fFNf7+/sjKygIAZGVlWRQad47fOVYRUp9GGTt2LFavXo369etj5MiRWLVqlbmJh4iIqMaw0qOv8fHxmDJlisW+v05seUfv3r3Nv46IiEBUVBSCg4OxZs0aODs7WyVPeUktNpYsWYIGDRqgUaNG2L17N3bv3l3meevXr6/mZERERFZkpRlEy5o1u7w8PDzQtGlTnDt3Dj169EBRURGys7MtWjeuXLmCgIAAAEBAQAAOHjxo8R53nla5c055Se1GGT58OLp06QIPD49SzUJ3b0RERFQ1+fn5+PXXXxEYGIg2bdrAwcEB27dvNx8/c+YMMjIyEB0dDQCIjo5Gamoqrl69aj5n27ZtcHd3R1hYWIXuLX1SLyIiohpPwgyir7zyCvr27Yvg4GBcvnwZs2fPhp2dHYYMGQK9Xo9Ro0ZhypQp8PLygru7OyZMmIDo6Gi0b98eANCzZ0+EhYVh2LBhmDdvHrKysjBjxgyMGzeuwq0rqlgbxdrUMEdHiUn+0zQ3jfceoVxd7FSyKJ4a5u4rUsFCTFoVrDVkhPwMRSb5fy/VkKH4lp3sCHBv4yQ7QrUQEmYQvXjxIoYMGYI///wTvr6+6NSpE3788Uf4+voCAObPnw+tVovY2FgYDAbExMRg8eLF5uvt7OywadMmjBkzBtHR0XB1dUVcXBwSExMrnEUj1PCd2cp83JvKjgB7rfx/xD46+V1QdeyrdxDSvTzk4C07giqKjUYa+X8eTYvkFxv1VFCIq0HDwGzZEeDdyUF2BNRZ+J3i9yhIirPK+7jGL7fK+1S3GtmyQUREpCpciI2IiIgUpYKWTZlYbBARESmtlrdsSB2hJIRARkYGCgsLZcYgIiIiBUkvNkJDQ/H777/LjEFERKQsCUvMq4nUYkOr1aJJkyb4888/ZcYgIiJSlklYZ7NR0h/0fvvttzFt2jScPHlSdhQiIiJSgPQBosOHD8fNmzfRqlUrODo6lloc5vr165KSERERWQmfRpHrgw8+kB2BiIhIWTbcBWIN0ouNuDjrzKpGRERE6iS92AAAo9GIr776Cj///DMAICwsDE899RTs7VURj4iIqEpkrI2iJtK/m6elpaFfv37IyspCs2bNAADvvPMOfH19sXHjRoSHh0tOSEREVEW1vBtF+tMozz//PFq0aIGLFy/iyJEjOHLkCH7//XdERETgxRdflB2PiIiIqkh6y8axY8fw008/wdPT07zP09MTc+fORbt27R54vcFggMFguYKjECZoVLK0OREREVs2JGvatCmuXLlSav/Vq1cRGhr6wOuTkpKg1+sttltFN5SISkREVDnCZJ3NRkkvNpKSkjBx4kSsW7cOFy9exMWLF7Fu3Tq8/PLLeOedd5Cbm2veyhIfH4+cnByLzdnRs8xziYiIpKjlM4hK70Z58sknAQBPP/00NBoNgNtrpgBA3759za81Gg2MRmOp63U6HXQ6ncU+dqEQERGph/RiY+fOnfc8duLECURERFRjGiIiIusTNtwqYQ3Si43OnTtbvM7Ly8OqVauwdOlSHD58uMzWDCIiIptSy4sN1fQ37NmzB3FxcQgMDMS7776Lrl274scff5Qdi4iIiKpIastGVlYWkpOT8dlnnyE3NxdPP/00DAYDvv76a4SFhcmMRkREZD21fAZRaS0bffv2RbNmzXDixAl88MEHuHz5Mj788ENZcYiIiJTDp1Hk2Lx5MyZOnIgxY8agSZMmsmIQERGRwqS1bOzduxd5eXlo06YNoqKisHDhQvzxxx+y4hARESmnlrdsSCs22rdvj08//RSZmZl46aWXsHr1agQFBcFkMmHbtm3Iy8uTFY2IiMiqhBBW2WyV9KdRXF1dMXLkSOzduxepqamYOnUq3n77bfj5+aFfv36y4xEREVEVSS827tasWTPMmzcPFy9exKpVq2THISIiso5a3o0ifVKvstjZ2aF///7o37+/7ChERERVZ8OFgjWostggIiKqSThdeQ1UZCyRHQFGFUzg4ugs/49XC43sCACAYhUszeyoggUCtSr4vLOD/BCOWhX8fbCTvxSDq2+R7AjQOLvJjkDVQP53IyIiopqOLRtERESkKPmNaVLJb9clIiKiGk0VxcbOnTtlRyAiIlKMMAmrbLZKFcVGr1690LhxY8yZMwe///677DhERETWVcvn2VBFsXHp0iWMHz8e69atQ6NGjRATE4M1a9agqEj+SGkiIiKqGlUUGz4+Ppg8eTKOHTuGAwcOoGnTphg7diyCgoIwceJEHD9+XHZEIiKiyjNZabNRqig27ta6dWvEx8dj/PjxyM/Px+eff442bdrg0UcfRVpamux4REREFcYxGypRXFyMdevW4YknnkBwcDC2bt2KhQsX4sqVKzh37hyCg4MxePBg2TGJiIioglQxz8aECROwatUqCCEwbNgwzJs3D+Hh4ebjrq6uePfddxEUFCQxJRERUSXZcBeINaii2Dh16hQ+/PBDDBw4EDqdrsxzfHx8+IgsERHZJFvuArEGVRQb27dvf+A59vb26Ny5czWkISIisjK2bKjHqVOnkJGRUeqR1379+klKRERERFWlimLj/PnzGDBgAFJTU6HRaCDE7eYmjeb2iqFG471XRzQYDDAYDBb7hBDma4mIiGRTwcLTUqniaZRJkyYhJCQEV69ehYuLC9LS0rBnzx60bdsWu3btuu+1SUlJ0Ov1FltRcXa15CYiIioXzrMhX0pKChITE+Hj4wOtVgutVotOnTohKSkJEydOvO+18fHxyMnJsdgcHTyqJzgRERE9kCq6UYxGI+rUqQPg9lMnly9fRrNmzRAcHIwzZ87c91qdTlfqCRZ2oRARkZrU9m4UVRQb4eHhOH78OEJCQhAVFYV58+bB0dERn3zyCRo1aiQ7HhERUdWw2JBvxowZKCgoAAAkJCSgb9++ePTRR+Ht7Y3Vq1dLTkdERERVoYpiIyYmxvzrJk2a4PTp07h+/To8PT3ZJUJERDaP3SgSjRw5slznff755wonISIiUg6LDYmSk5MRHByMyMhI89waREREVLNILTbGjBmDVatWIT09Hc899xyeffZZeHl5yYxERERkdbW9ZUPqPBuLFi1CZmYmXn31VWzcuBH169fH008/ja1bt7Klg4iIag6hsc5mo6RP6qXT6TBkyBBs27YNp06dQosWLTB27Fg0bNgQ+fn5suMRERFVmTBZZ7NV0ouNu2m1WvPaKPdbD4WIiIhsh/Riw2AwYNWqVejRoweaNm2K1NRULFy4EBkZGXBzc5Mdj4iIqMqESWOVzVZJHSA6duxYrF69GvXr18fIkSOxatUq+Pj4yIxERERkdbbcBWINUouNJUuWoEGDBmjUqBF2796N3bt3l3ne+vXrqzkZERERWYvUYmP48OGcIZSIiGo8YcNPkliD9Em9lODioHvwSQpzsXeSHQH2GjvZERBkX0d2BACAm0b+zPxeKlgdoEmx/A+8cF2O7AgIbJorOwLs3OT/WTg0kj+vkV1UG9kRqoUaulHefvttxMfHY9KkSfjggw8AAIWFhZg6dSpWr14Ng8GAmJgYLF68GP7+/ubrMjIyMGbMGOzcuRNubm6Ii4tDUlIS7O3L/5kmfYAoERERKevQoUP4+OOPERERYbF/8uTJ2LhxI9auXYvdu3fj8uXLGDhwoPm40WhEnz59UFRUhP3792P58uVITk7GrFmzKnR/FhtEREQKk/k0Sn5+PoYOHYpPP/0Unp6e5v05OTn47LPP8P7776Nr165o06YNli1bhv379+PHH38EAHz//fc4deoU/v3vf+Phhx9G79698eabb2LRokUoKioqdwYWG0RERAoTwjqbwWBAbm6uxWYwGO5773HjxqFPnz7o3r27xf7Dhw+juLjYYn/z5s3RoEEDpKSkAABSUlLQsmVLi26VmJgY5ObmIi0trdxfP4sNIiIiG5GUlAS9Xm+xJSUl3fP81atX48iRI2Wek5WVBUdHR3h4eFjs9/f3R1ZWlvmcuwuNO8fvHCsv+SPWiIiIajhrTcgVHx+PKVOmWOzT6cp+KOL333/HpEmTsG3bNjg5yX1oQXqxkZ2djYMHD+Lq1aswmSyH6w4fPlxSKiIiIuuxVrGh0+nuWVz81eHDh3H16lW0bt3avM9oNGLPnj1YuHAhtm7diqKiImRnZ1u0bly5cgUBAQEAgICAABw8eNDifa9cuWI+Vl5Si42NGzdi6NChyM/Ph7u7u8WcGxqNhsUGERHVCDIWMu/WrRtSU1Mt9j333HNo3rw5pk+fjvr168PBwQHbt29HbGwsAODMmTPIyMhAdHQ0ACA6Ohpz587F1atX4efnBwDYtm0b3N3dERYWVu4sUouNqVOnYuTIkXjrrbfg4uIiMwoREVGNUqdOHYSHh1vsc3V1hbe3t3n/qFGjMGXKFHh5ecHd3R0TJkxAdHQ02rdvDwDo2bMnwsLCMGzYMMybNw9ZWVmYMWMGxo0bV+4WFkBysXHp0iVMnDiRhQYREdVoal1Ebf78+dBqtYiNjbWY1OsOOzs7bNq0CWPGjEF0dDRcXV0RFxeHxMTECt1HarERExODn376CY0aNZIZg4iISFFqma58165dFq+dnJywaNEiLFq06J7XBAcH47vvvqvSfaUWG3369MG0adNw6tQptGzZEg4ODhbH+/XrJykZERERWUulio2CggK8/fbb2L59e5lPkZw/f75c7/PCCy8AQJnNMRqNBkajsTLxiIiIVEUNa6PIVKli4/nnn8fu3bsxbNgwBAYGVnrl1r8WKURERDWRSSXdKLJUqtjYvHkzvv32W3Ts2NHaeSrMYDCUmqpVCBM0Gk6OSkREpAaVKjY8PT3h5VX1pYkfNJq1PKvKJSUlISEhwWKfq84bbk6+VcpGRERkLWoZICqLRoiKTzXy73//G9988w2WL19epcdWIyMjLV4XFxcjPT0d9vb2aNy4MY4cOfLA9yirZaNJ/XbSWzZc7OVODQsAPo7usiMg2MFDdgQAgI+m/M+DK8VL/oS9CCuS3+IXocuRHQGBTXNlR4Cdm/xvPg6NPB98ksLsotrIjgDnQTMUv8fppk9Y5X2a/1K1p0JkKfenX2RkpMXYjHPnzsHf3x8NGzYs9RRJeYoEADh69Gipfbm5uRgxYgQGDBhQrvcoa+pW2YUGERER/U+5i43+/fsrGON/3N3dkZCQgL59+2LYsGHVck8iIiIlyZiuXE3KXWzMnj1byRwWcnJykJMjv6mViIjIGtQ6g2h1qVQncqNGjXDo0CF4e3tb7M/Ozkbr1q3LPc/GggULLF4LIZCZmYl//etf6N27d2WiERERqQ4ffa2ECxculDnhlsFgwMWLF8v9PvPnz7d4rdVq4evri7i4OMTHx1cmGhEREalMhYqNDRs2mH+9detW6PV682uj0Yjt27cjJCSk3O+Xnp5ekdsTERHZpNr+6GuFio07g0Q1Gg3i4uIsjjk4OKBhw4Z47733Hvg+AwcOfHAwe3sEBASgR48e6Nu3b0ViEhERqQoHiFbAnenFQ0JCcOjQIfj4+FTqpne3iNzvXmfPnsXSpUvxyiuvVHg5WyIiIlKHSo3ZqGr3x7Jly8p97qZNmzB27FgWG0REZLM4QLSStm/ffs9VXz///PMqB7ujU6dOaNu2rdXej4iIqLpxzEYlJCQkIDExEW3btq3Sqq/l4eHhgfXr1yv2/kRERKSsShUbS5YsQXJyMmf4JCIiKgcOEK2EoqIidOjQwdpZiIiIaqTaPmajUiuWPf/88/jiiy+snYWIiIhqoEq1bBQWFuKTTz7BDz/8gIiIiFKrvr7//vtWCVdZznaOUu8PAHp7F9kR4GHnLDsCXDTyl1UH1LG8e4MS+asRN9Pmy44A3/p5siNA95C77AjQ+nvJjgBtRKTsCLCP6ic7QrXgANFKOHHiBB5++GEAwMmTJy2OKTlYlIiIyBbV9m6UShUbO3futHYOIiKiGquWjw+t3JiNu128eLFCi68RERFR7VKpYsNkMiExMRF6vR7BwcEIDg6Gh4cH3nzzzVITfBEREdV2JqGxymarKtWN8sYbb+Czzz7D22+/jY4dOwIA9u7di3/84x8oLCzE3LlzrRqSiIjIlnGAaCUsX74cS5cuRb9+/xtFHBERgbp162Ls2LEsNoiIiMisUsXG9evX0bx581L7mzdvjuvXr1c5FBERUU1S2wcYVGrMRqtWrbBw4cJS+xcuXIhWrVpVORQREVFNIqCxymarKtWyMW/ePPTp0wc//PADoqOjAQApKSnIyMjA5s2b73vthg0byn2fu7tpiIiIyDZVqtjo3Lkzzpw5g48++gg///wzAGDgwIEYO3YsgoKC7ntt//79y3UPjUYDo9FYmXhERESqYqrlE21Ueg5nb29v9OvXD+3btzc/7vrTTz8BuH+LBB+NJSKi2sZkw10g1lCpYmPLli0YPnw4/vzzT4i/rJvLFgkiIiK6W6WKjQkTJmDw4MGYNWsW/P39K33zxMTE+x6fNWtWpd+biIhILWx5cKc1VKrYuHLlCqZMmVKlQgMAvvrqK4vXxcXFSE9Ph729PRo3blyuYsNgMMBgMFjsE8IEjUb+CptEREQAH32tVLExaNAg7Nq1C40bN67SzY8ePVpqX25uLkaMGIEBAwaU6z2SkpKQkJBgsU/v5AdPl4AqZSMiIrKW2t6yoRF/HXRRDjdv3sTgwYPh6+uLli1bwsHBweL4xIkTqxQqNTUVffv2xYULFx54blktGxENO0pv2fBwcJN6fwDwdagjOwIC7VxlRwAA1INOdgQ0KJHf2tYG+bIjoF6jbNkR4Npa/r8Nrb+X7AjQRkTKjgD7KPlTHDj4NFL8Ht/7P2OV9+l5ZbVV3qe6VaplY9WqVfj+++/h5OSEXbt2QaP5X8Wm0WiqXGzk5OQgJyenXOfqdDrodJbfSGQXGkRERHdjN0olvPHGG0hISMBrr70Grbby39gXLFhg8VoIgczMTPzrX/9C7969K/2+REREasJioxKKiorwt7/9rUqFBgDMnz/f4rVWq4Wvry/i4uIQHx9fpfcmIiIidahUsREXF4cvv/wSr7/+epVunp6eXqXriYiIbEFtHyBaqWLDaDRi3rx52Lp1KyIiIkoNEH3//ffve/3AgQMfHMzeHgEBAejRowf69u1bmZhERESqYKrdtUblio3U1FRERt4exXzy5EmLY3cPFr0XvV7/wHNMJhPOnj2LpUuX4pVXXnngBGBERESkTpUqNnbu3Fmlmy5btqzc527atAljx45lsUFERDaLa6OoXKdOndC2bVvZMYiIiCqtli/6CtVPSOHh4YH169fLjkFERESVpPqWDSIiIlvHeTaIiIhIUaZyPDxRk7HYICIiUhjHbBAREREpiC0bpCgX2MmOAADQCflNmEXyIyC/yOHBJymsxCD/ZxyNVv4fhsZNBSsiO7nITgBRkC07AuCj/C04ZoOIiIgUVdtnEJX/IwYRERHVaGzZICIiUhhnECUiIiJF8WkUIiIiIgVJb9mIjIwsc6VYjUYDJycnhIaGYsSIEejSpYuEdERERFXHAaKS9erVC+fPn4erqyu6dOmCLl26wM3NDb/++ivatWuHzMxMdO/eHd98843sqERERJVistJmq6S3bPzxxx+YOnUqZs6cabF/zpw5+O233/D9999j9uzZePPNN/HUU09JSklERESVJb1lY82aNRgyZEip/c888wzWrFkDABgyZAjOnDlT3dGIiIisQlhps1XSiw0nJyfs37+/1P79+/fDyckJAGAymcy/JiIisjUmjXW2ivjoo48QEREBd3d3uLu7Izo6Gps3bzYfLywsxLhx4+Dt7Q03NzfExsbiypUrFu+RkZGBPn36wMXFBX5+fpg2bRpKSkoq/PVL70aZMGECRo8ejcOHD6Ndu3YAgEOHDmHp0qV4/fXXAQBbt27Fww8/LDElERFR5ckYb1GvXj28/fbbaNKkCYQQWL58OZ566ikcPXoULVq0wOTJk/Htt99i7dq10Ov1GD9+PAYOHIh9+/YBAIxGI/r06YOAgADs378fmZmZGD58OBwcHPDWW29VKItGCCG9ZWblypVYuHChuaukWbNmmDBhAv7+978DAG7dumV+OqU8QrxbKZa1vDwc3GRHgK9DHdkR0NjOXXYEAECgkL8miKcKhqO3LDLIjoDQhn/KjgCPjvL/fWpD6smOAE3Yw7IjwC60rewIcAxurfg9Pq33rFXe54WL/67S9V5eXvjnP/+JQYMGwdfXF1988QUGDRoEADh9+jQeeughpKSkoH379ti8eTOefPJJXL58Gf7+/gCAJUuWYPr06bh27RocHR3LfV/pLRsAMHToUAwdOvSex52dnasxDRERkXVZq2XDYDDAYLD8oUGn00Gn0933OqPRiLVr16KgoADR0dE4fPgwiouL0b17d/M5zZs3R4MGDczFRkpKClq2bGkuNAAgJiYGY8aMQVpaGiIjI8udW/qYDSIioppOaKyzJSUlQa/XW2xJSUn3vG9qairc3Nyg0+kwevRofPXVVwgLC0NWVhYcHR3h4eFhcb6/vz+ysrIAAFlZWRaFxp3jd45VhPSWDa1WW+akXncYjcb7Xl9WlSeECRoN6ygiIqpZ4uPjMWXKFIt992vVaNasGY4dO4acnBysW7cOcXFx2L17t9IxS5FebHz11VcWr4uLi3H06FEsX74cCQkJD7w+KSmp1Hl6Jz94ugRYNScREVFlWasbpTxdJndzdHREaGgoAKBNmzY4dOgQ/u///g9/+9vfUFRUhOzsbIvWjStXriAg4Pb3z4CAABw8eNDi/e48rXLnnPKSXmyUNVHXoEGD0KJFC3z55ZcYNWrUfa8vq8qLaNjRqhmJiIiqQi2zf5pMJhgMBrRp0wYODg7Yvn07YmNjAQBnzpxBRkYGoqOjAQDR0dGYO3curl69Cj8/PwDAtm3b4O7ujrCwsArdV3qxcS/t27fHiy+++MDzyqry2IVCRES1XXx8PHr37o0GDRogLy8PX3zxBXbt2oWtW7dCr9dj1KhRmDJlCry8vODu7o4JEyYgOjoa7du3BwD07NkTYWFhGDZsGObNm4esrCzMmDED48aNq1DrCqDSYuPWrVtYsGAB6tatKzsKERFRlcmYY+Lq1asYPnw4MjMzodfrERERga1bt6JHjx4AgPnz50Or1SI2NhYGgwExMTFYvHix+Xo7Ozts2rQJY8aMQXR0NFxdXREXF4fExMQKZ5FebHh6eloMEBVCIC8vD87Ozli5cqXEZERERNYhY5qdzz777L7HnZycsGjRIixatOie5wQHB+O7776rchbpxcYHH3xg8Vqr1cLX1xdRUVG4dOmSnFBERERkNdKLjbi4OIvXeXl5WLVqFWbPno2ffvrpgY++EhERqZ1aBojKopqRlHv27EFcXBwCAwPx7rvvokuXLvjxxx9lxyIiIqoyk5U2WyW1ZSMrKwvJycn47LPPkJubi6effhoGgwFff/11hR+rISIiUivpi5BJJq1lo2/fvmjWrBlOnDiBDz74AJcvX8aHH34oKw4REREpRFrLxubNmzFx4kSMGTMGTZo0kRWDiIhIcSpY9FkqaS0be/fuRV5eHtq0aYOoqCgsXLgQf/zxh6w4REREiqntYzakFRvt27fHp59+iszMTLz00ktYvXo1goKCYDKZsG3bNuTl5cmKRkRERFYk/WkUV1dXjBw5Env37kVqaiqmTp2Kt99+G35+fujXr5/seERERFUmrLTZKunFxt2aNWuGefPm4eLFi1i1apXsOERERFZhgrDKZqukT+pVFjs7O/Tv3x/9+/ev1PW3jEXWDVQJOjv5GQpM8jNc1NyUHQEAUKx1kh0B+XYOsiNAb1+xxZOU4JjhKTsCgBuyAwD7TstOAH1UhuwIEM1OyI4Ax3GtZUeo8VRZbBAREdUktjy40xpYbBARESnMdjtArENVYzaIiIio5mHLBhERkcLYjaISV69exZkzZwDcfirFz89PciIiIiLr4AyikuXl5WHYsGGoW7cuOnfujM6dO6Nu3bp49tlnkZOTIzseERFRldX2R1+lFxvPP/88Dhw4gE2bNiE7OxvZ2dnYtGkTfvrpJ7z00kuy4xEREVEVSe9G2bRpE7Zu3YpOnTqZ98XExODTTz9Fr169JCYjIiKyDtttk7AO6cWGt7c39Hp9qf16vR6enmqY/IeIiKhqavsAUendKDNmzMCUKVOQlZVl3peVlYVp06Zh5syZEpMRERGRNUhv2fjoo49w7tw5NGjQAA0aNAAAZGRkQKfT4dq1a/j444/N5x45ckRWTCIiokqz5cGd1iC92Kjs+idERES2onaXGiooNmbPni07AhERESlI+pgNAMjOzsbSpUsRHx+P69evA7jdZXLp0iXJyYiIiKrOZKXNVklv2Thx4gS6d+8OvV6PCxcu4IUXXoCXlxfWr1+PjIwMrFix4r7XGwwGGAwGi31CmKDRqKKOIiIiqvVjNqR/R54yZQpGjBiBs2fPwsnJybz/iSeewJ49ex54fVJSEvR6vcVWYPhTychERERUAdKLjUOHDpU5U2jdunUtHoe9l/j4eOTk5FhsrjpvJaISERFVirDSZqukd6PodDrk5uaW2v/LL7/A19e3XNfrdDqLfexCISIiNbHl8RbWIP27cr9+/ZCYmIji4mIAgEajQUZGBqZPn47Y2FjJ6YiIiKpOWOk/WyW92HjvvfeQn58PPz8/3Lp1C507d0bjxo3h5uaGuXPnyo5HREREVSS9G0Wv12Pbtm3Yu3cvTpw4gfz8fLRp0wbdunWTHY2IiMgq2I0iSUpKCjZt2mR+3alTJ7i6umLx4sUYMmQIXnzxxVKPtBIREdkiE4RVNlslrdhITExEWlqa+XVqaipeeOEF9OjRA6+99ho2btyIpKQkWfGIiIjISqQVG8eOHbPoKlm9ejUeeeQRfPrpp5gyZQoWLFiANWvWyIpHRERkNXz0VZIbN27A39/f/Hr37t3o3bu3+XW7du3w+++/y4hGRERkVbbcBWIN0lo2/P39kZ6eDgAoKirCkSNH0L59e/PxvLw8ODg4yIpHREREViKt2HjiiSfw2muv4b///S/i4+Ph4uKCRx991Hz8xIkTaNy4sax4REREVsOF2CR58803MXDgQHTu3Blubm5Yvnw5HB0dzcc///xz9OzZU1Y8IiIiq7HlCbmsQVqx4ePjgz179iAnJwdubm6ws7OzOL527Vq4ublJSkdERETWoopJvcri5eVVzUmIiIiUYctdINYgvdhQQn5RoewIEEJ+k5mdChakc1BBBkAF8/IDMGjkf9zUsXeWHQHFRvkZii7I/xvhbGeUHQEabbbsCHA3nZcdoVqwG4WIiIgUJf9HDbnkl/dERERUo7Flg4iISGEmFXSty8Rig4iISGG1u9RgNwoREREpTHqxMXLkSOTl5ZXaX1BQgJEjR0pIREREZF1cYl6y5cuX49atW6X237p1CytWrJCQiIiIyLqElf6zVdLGbOTm5kIIASEE8vLy4OTkZD5mNBrx3Xffwc/PT1Y8IiIishJpxYaHhwc0Gg00Gg2aNm1a6rhGo0FCQoKEZERERNZV2+fZkFZs7Ny5E0IIdO3aFf/5z38spid3dHREcHAwgoKCZMUjIiKyGlseb2EN0oqNzp07AwDS09PRoEEDaDQaWVGIiIgUZcvjLaxB+gDRHTt2YN26daX2r127FsuXL5eQiIiIiKxJerGRlJQEHx+fUvv9/Pzw1ltvSUhERERkXSYrbbZK+gyiGRkZCAkJKbU/ODgYGRkZD7zeYDDAYDBY7BNCsFuGiIhUQw0rgcskvWXDz88PJ06cKLX/+PHj8Pb2fuD1SUlJ0Ov1FltxSbYCSYmIiKgypBcbQ4YMwcSJE7Fz504YjUYYjUbs2LEDkyZNwjPPPPPA6+Pj45GTk2OxOdh7KB+ciIionGr7DKLSu1HefPNNXLhwAd26dYO9/e04RqMRcXFx5RqzodPpoNPpLPaxC4WIiNTElsdbWIP0YsPR0RFffvklXnnlFVy4cAHOzs5o2bIlgoODZUcjIiIiK5BabGRnZ+ONN97Al19+iRs3bgAAPD098cwzz2DOnDnw8PCQGY+IiMgqOM+GJNevX0dUVBSWL1+O2NhYvPfee3jvvfcwcOBAJCcnIzo62lyAEBER2TIZYzaSkpLQrl071KlTB35+fujfvz/OnDljcU5hYSHGjRsHb29vuLm5ITY2FleuXLE4JyMjA3369IGLiwv8/Pwwbdo0lJSUVCiLtJaNxMREODo64tdff4W/v3+pYz179kRiYiLmz58vKSEREZHt2r17N8aNG4d27dqhpKQEr7/+Onr27IlTp07B1dUVADB58mR8++23WLt2LfR6PcaPH4+BAwdi3759AG6PoezTpw8CAgKwf/9+ZGZmYvjw4XBwcKjQXFgaIenh34YNG+Ljjz9GTExMmce3bNmC0aNH48KFCxV+bzeX0vN2VDdXB92DT1KYj5NedgT4OtSRHQEAoNc6PfgkhblrHGVHQFM4y46AIKP8AdyNiw0PPklhznZG2RFQLzhbdgS4t5H/b7POwu8Uv0fv+r2t8j6bf99c6WuvXbsGPz8/7N69G4899hhycnLg6+uLL774AoMGDQIAnD59Gg899BBSUlLQvn17bN68GU8++SQuX75sbhhYsmQJpk+fjmvXrsHRsXyfa9K6UTIzM9GiRYt7Hg8PD0dWVlY1JiIiIlKGtWYQNRgMyM3Ntdj+OrHlveTk5ACAeeHTw4cPo7i4GN27dzef07x5czRo0AApKSkAgJSUFLRs2dKiByImJga5ublIS0sr99cvrdjw8fG5b6tFenq6xUqwREREtkpY6b+yJrJMSkp64P1NJhNefvlldOzYEeHh4QCArKwsODo6lnoYw9/f3/zDflZWVqmhDndeV6RBQNqYjZiYGLzxxhvYtm1bqWYYg8GAmTNnolevXpLSERERqU98fDymTJlise+vc02VZdy4cTh58iT27t2rVLT7kjpAtG3btmjSpAnGjRuH5s2bQwiBn3/+GYsXL4bBYMC//vUvWfGIiIisxlqzf5Y1keWDjB8/Hps2bcKePXtQr1498/6AgAAUFRUhOzvbonXjypUrCAgIMJ9z8OBBi/e787TKnXPKQ1o3Sr169ZCSkoKwsDDEx8ejf//+GDBgAN544w2EhYVh3759qF+/vqx4REREViOEsMpW0XuOHz8eX331FXbs2FFq0dM2bdrAwcEB27dvN+87c+YMMjIyEB0dDQCIjo5Gamoqrl69aj5n27ZtcHd3R1hYWLmzSJ3UKyQkBJs3b8aNGzdw9uxZAEBoaCjHahAREVXRuHHj8MUXX+Cbb75BnTp1zGMs9Ho9nJ2dodfrMWrUKEyZMgVeXl5wd3fHhAkTEB0djfbt2wMAevbsibCwMAwbNgzz5s1DVlYWZsyYgXHjxlWohUX6dOXA7VlDH3nkEdkxiIiIFCFjEbWPPvoIAPD4449b7F+2bBlGjBgBAJg/fz60Wi1iY2NhMBgQExODxYsXm8+1s7PDpk2bMGbMGERHR8PV1RVxcXFITEysUBZVFBtEREQ1mYzpysvT7eLk5IRFixZh0aJF9zwnODgY331XtblIamSxUVhSJDsCSkzyJ+xxspc/iZRO6yA7AgCg0FSxqXWVUGQnf0ItnZ20YVpmRjv5E94B8jPYqWCpDG2G/BCFeTdlR4A6ph6s2WpksUFERKQmJjmTdasGiw0iIiKF1e5SQ+Kjr0RERFQ7sGWDiIhIYTKeRlETVbRszJkzB+np6bJjEBERKcIEYZXNVqmi2Fi7di1CQ0PRoUMHLF68GH/88YfsSERERFYjYwZRNVFFsXH8+HGcOHECjz/+ON59910EBQWhT58++OKLL3DzpvzHooiIiKjyVFFsAECLFi3w1ltv4fz589i5cycaNmyIl19+uUILvRAREalRbe9GUeUAUVdXVzg7O8PR0RF5eXmy4xAREVWJjBlE1UQ1LRvp6emYO3cuWrRogbZt2+Lo0aNISEgwLxxDREREtkkVLRvt27fHoUOHEBERgeeeew5DhgxB3bp1ZcciIiKyClse3GkNqig2unXrhs8//xxhYWGyoxAREVmdLY+3sAZVFBtz5841//pO9afRaGTFISIiIitSzZiNFStWoGXLlnB2doazszMiIiLwr3/964HXGQwG5ObmWmy1vbmKiIjUhfNsqMD777+PMWPG4IknnsCaNWuwZs0a9OrVC6NHj8b8+fPve21SUhL0er3FJkx8goWIiNSjtj/6qhEqKJVCQkKQkJCA4cOHW+xfvnw5/vGPf9x3KnODwQCDwWCxz9O7ufRuGHutndT7A4C/q4fsCPB2dJcdAQDgqJHfY+hp5yw7AuraucqOgHpCJzsC6hvld9PaSf/kBVqgQHYEeHnKn7ix8cmtit+jVUAHq7zP8az9Vnmf6ib/ExhAZmYmOnQo/QfRoUMHZGZm3vdanU4Hnc7yw0t2oUFERHQ3zrOhAqGhoVizZk2p/V9++SWaNGkiIREREZH1mISwymarVNGykZCQgL/97W/Ys2cPOnbsCADYt28ftm/fXmYRQkREZEvYsqECsbGxOHDgALy9vfH111/j66+/ho+PDw4ePIgBAwbIjkdERERVoIqWDQBo06YNVq5cKTsGERGR1dlyF4g1SC02tFrtAwdzajQalJSUVFMiIiIi66vt3ShSi42vvvrqnsdSUlKwYMECmEymakxERERE1ia12HjqqadK7Ttz5gxee+01bNy4EUOHDkViYqKEZERERNZT27tRVDFAFAAuX76MF154AS1btkRJSQmOHTuG5cuXIzg4WHY0IiKiKhFW+s9WSS82cnJyMH36dISGhiItLQ3bt2/Hxo0bER4eLjsaERERWYHUbpR58+bhnXfeQUBAAFatWlVmtwoREZGtq+3dKFLXRtFqtXB2dkb37t1hZ3fvtUTWr19fofe1d6xb1WhVxrVRbuPaKP/DtVFu49oot3FtlNtqy9oojXwirfI+5/84apX3qW5SP4GHDx/OdUyIiIhqOKnFRnJysszbExERVQshavc0DvLblhWgs3eQHQEeOvnN1XoH+Rk87VxkRwAA2Gvkd2v5aOV3o4SooAsjQAVdGIHF8icKlD46H0BgwxzZEeASZJQdoVqYbPhJEmuokcUGERGRmkgcHqkKaiiuiYiIqAZjywYREZHC2I1CREREimI3ChEREZGCVFFsxMbG4p133im1f968eRg8eLCERERERNZjEsIqm61SRbGxZ88ePPHEE6X29+7dG3v27JGQiIiIyHq4EJsK5Ofnw9HRsdR+BwcH5ObmSkhERERE1qKKYqNly5b48ssvS+1fvXo1wsLCJCQiIiKyHiGEVTZbpYqnUWbOnImBAwfi119/RdeuXQEA27dvx6pVq7B27VrJ6YiIiKqGj76qQN++ffH111/jrbfewrp16+Ds7IyIiAj88MMP6Ny5s+x4REREVAWqKDYAoE+fPujTp4/sGERERFZny10g1qCKMRsAkJ2djaVLl+L111/H9evXAQBHjhzBpUuXJCcjIiKqmtr+6KsqWjZOnDiB7t27Q6/X48KFC3j++efh5eWF9evXIyMjAytWrLjntQaDAQaDwWKfEAIajfyVJYmIiAC2bKiiZWPKlCkYMWIEzp49CycnJ/P+J5544oHzbCQlJUGv11tsxSXyl00mIiKi21RRbBw6dAgvvfRSqf1169ZFVlbWfa+Nj49HTk6OxeZgr1cqKhERUYWZIKyy2SpVdKPodLoyJ+/65Zdf4Ovr+8BrdTqdxT52oRARkZqwG0UF+vXrh8TERBQXFwO4XSxkZGRg+vTpiI2NlZyOiIiIqkIVxcZ7772H/Px8+Pr64tatW+jcuTNCQ0NRp04dzJ07V3Y8IiKiKuHTKCqg1+uxbds27Nu3D8ePH0d+fj5at26N7t27y45GRERUZba8iJo1SC82TCYTkpOTsX79ely4cAEajQYhISEICAjgI6xEREQ1gNRuFCEE+vXrh+effx6XLl1Cy5Yt0aJFC/z2228YMWIEBgwYIDMeERGRVbAbRaLk5GTs2bMH27dvR5cuXSyO7dixA/3798eKFSswfPhwSQmJiIiqjk+jSLRq1Sq8/vrrpQoNAOjatStee+01rFy5UkIyIiIishapxcaJEyfQq1evex7v3bs3jh8/Xo2JiIiIrE9Y6T9bJbUb5fr16/D397/ncX9/f9y4caMaExEREVlfbe9GkVpsGI1G2NvfO4KdnR1KSkqqMREREZH1sdiQSAiBESNGlJpu/I6/ruZKREREtkdqsREXF/fAc/gkChER2bra3a4BQFAphYWFYvbs2aKwsJAZmIEZmIEZVJpBTTno/jRC1PKOpDLk5uZCr9cjJycH7u7uzMAMzMAMzKDCDGrKQfenioXYiIiIqOZisUFERESKYrFBREREimKxUQadTofZs2ff85FcZmAGZmAGZpCfQU056P44QJSIiIgUxZYNIiIiUhSLDSIiIlIUiw0iIiJSFIsNIiIiUhSLDSIiIlIUiw0iIiJSFIsNIiIiUpTUJebVpKCgAIcPH0ZmZia0Wi0aNWqE1q1bQ6PRVFuGkpISpKWlISsrCwAQEBCAsLAwODg4VFuG+ykpKcHly5fRoEED2VGkuHLlCgwGg9SvPyEhAePGjYOPj4+0DMXFxdL+TpaUlGDnzp3IyMhAcHAwunTpAjs7O0Xv+ccff0j9/b7DaDTit99+Q8OGDaHVamEwGPDNN9/AZDKhS5cu8Pf3r5YcaviszMrKwoEDByw+K6OiohAQEFBtGaiC5C46K5/RaBTTpk0TLi4uQqvVCq1WKzQajdBoNCI4OFhs2LChWjK88cYbwsPDw3zvO5uHh4eYMWOGMBqNiud4kGPHjgmtVqv4fRYtWiS6desmBg8eLH744QeLY9euXRMhISGK3j83N1cMHTpUNGjQQAwfPlwYDAYxduxYodFohFarFY899pjIyclRNENOTk6pLTs7Wzg4OIgDBw6Y9ynpyy+/FAaDwfz6ww8/FA0aNBBarVZ4e3uLhIQERe8vhBDjx48XGzduFEII8fvvv4vmzZsLOzs74e/vL+zs7ETLli3FxYsXFc2g1WpF165dxcqVK6UtY378+HERGBgotFqtCA8PFxkZGSI8PFy4uroKNzc34enpKQ4ePKhoBjV8Vubn54uhQ4cKOzs7YW9vL/z8/ISfn5+wt7cXdnZ24tlnnxUFBQWK56CKq/XFxvTp08VDDz0kNm7cKLZt2yYee+wx8c4774iff/5ZzJw5U+h0OrF161ZFM0ybNk34+vqKJUuWiPT0dHHz5k1x8+ZNkZ6eLj7++GPh5+cnXn31VUUzlEd1FBv/93//J1xcXMS4cePEs88+KxwdHcVbb71lPp6VlaV4hvHjx4vmzZuLBQsWiMcff1w89dRTIjw8XOzdu1fs3r1bhIWFiddff13RDHc+zP+63Sl47vxf6QxXrlwRQgjx+eefCycnJzFr1izx7bffijlz5ghXV1fx6aefKprB399fpKamCiGEePrpp0X37t3FtWvXhBBC/Pnnn+LJJ58UgwYNUjSDRqMRvXr1Eo6OjsLT01OMHz9eHD16VNF7/lVMTIwYNGiQSE1NFZMmTRIPPfSQGDx4sCgqKhLFxcXi2WefFd27d1c0gxo+K0eNGiWaNGkitmzZIkpKSsz7S0pKxNatW0XTpk3F888/r2gGqpxaX2wEBgaKPXv2mF9fvHhRuLm5mX+CSUxMFNHR0Ypm8Pf3F1u2bLnn8S1btgg/Pz9FMwghRGRk5H235s2bK/4NLiwsTKxcudL8et++fcLX11fMnDlTCFE9xUb9+vXFjh07hBBCXLp0SWg0GvNP10IIsWnTJtGsWTNFM9StW1f06dNH7NixQ+zatUvs2rVL7Ny5U9jZ2Ylly5aZ9ylJo9GYi41HHnlEzJs3z+L44sWLRWRkpKIZnJycxPnz54UQQtSrV08cOHDA4nhqaqrw8fFRNMOd34dr166Jd999V4SFhQmtVitat24tFi9erHgLkxBCeHp6ilOnTgkhhLh586aws7Oz+L04efKk8Pb2VjSDGj4rPTw8xL59++55fO/evcLDw0PRDFQ5tX6AaH5+PurWrWt+HRgYiMLCQty4cQMAEBsbi+PHjyuaIS8vD0FBQfc8HhgYiIKCAkUzAMCpU6cQERGBp556qsytc+fOimdIT09Hhw4dzK87dOiAHTt24JNPPkF8fLzi9weAq1evIjQ0FAAQFBQEZ2dnNG3a1Hw8PDwcv//+u6IZTpw4AQcHB7z55psIDQ1F586d8fjjj0Oj0eCRRx5B586dq+XP404//Pnz59GzZ0+LYz179sS5c+cUvX/Tpk1x8OBBAECdOnWQm5trcTwvLw8mk0nRDHf4+Phg6tSpSEtLw969e/Hwww9j+vTpCAwMxPDhwxW9txAC9va3h9j99f8AYGdnp/jvgxo+K00mExwdHe953NHRsdr+PlAFya52ZOvQoYOYM2eO+fWqVassKuPU1FTh6empaIYnnnhC9OzZ09w8fLdr166JXr16iT59+iiaQQgh2rRpIxYvXnzP40ePHq2WVoW7f3q6Iy0tTfj7+4vhw4crniEoKEgcPnzY/HrIkCHmn/CFuP1TpNJ/J+5YvHixCAoKEl988YUQQgh7e3uRlpZWLffWaDRixYoV4ptvvhH16tUT+/fvtzh+8uRJ4e7urmiGZcuWiXr16omdO3eKFStWiIceekj88MMP4tKlS2LHjh2iZcuWijeb392d9Ff5+fli6dKlokOHDopm6Natmxg1apS4ePGiSEhIEKGhoeK5554zHx87dqx49NFHFc2ghs/Kv//97yIyMlIcOXKk1LEjR46INm3aiKFDhyqagSqn1hcbP/zwg9DpdOKRRx4Rjz32mLC3txfz5883H//nP/8punbtqmiGO4O97O3tRWRkpOjVq5fo1auXiIyMFPb29iIiIkJkZGQomkEIISZOnCgmTZp0z+Pnzp0Tjz/+uKIZhgwZIl5++eUyj508eVL4+voqXmz06tVLLFmy5J7Hly1bpvg3l7ulpaWJVq1aiSFDhlR7sXH3dvc3GiGEWLp0qeLdKEII8d577wkXFxfh7OwsHB0dLcaw9O/fX+Tl5Sl6/7u7k2Q5ePCg8Pb2FlqtVvj6+oqTJ0+KqKgoERAQIIKCgoSzs3OpwdTWpobPyuvXr4tevXoJjUYjvLy8RPPmzUXz5s2Fl5eX0Gq1onfv3uLGjRuKZqDK4RLzAI4fP441a9bAYDAgJiYGPXr0qPYMJpMJW7duxY8//mjxOFd0dDR69uwJrbZ29HidOHEChw8fxnPPPVfm8ZMnT+I///kPZs+erViG69evQ6vVwsPDo8zjmzdvhrOzMx5//HHFMvxVUVERXnvtNezcuRPr169HSEhItd37XjZt2gQHBwfExMQofq/s7Gxs27YN58+fh8lkQmBgIDp27IgmTZoofu/ly5fjmWeegU6nU/xe91NQUIDTp0+jWbNmcHNzQ2FhIVauXIlbt26hR48eaNasmeIZ1PBZCQCnT59GSkpKqc/K5s2bS8lDD8Zig4iIiBTFSb3uoWvXrli2bBmCg4Ol3D89PR3nzp1DYGAgwsPDpWRQSw5mYAZmYAYAMBgM0Gq15knlfv31V3z++efmSd5GjRqlilY/KoPcXhz5vvnmmzI3Ozs7sXDhQvNrJY0ZM8bc73zz5k0RGxtr7ifXarWiS5cuivdL3yvH3XM6VEcOZrh3hur+O8Hfh3tnUMPvQ23M0LlzZ7F27VohxO3HXHU6nYiIiBB/+9vfRGRkpHBxcSk1kJnUodYXG3dPknSvrTonT4qPjxf16tUTO3bsEAUFBWLv3r2icePG4rXXXlM0g1pyMAMzMAMz3Iu7u7v45ZdfhBC3C4/JkydbHJ8xY4bo2LGjohmocmp9sXHnsdK/jjav7lH/d+4fHh5ufszxjm+++UY0bdq0VuRgBmZgBma4F1dXV/Hzzz8LIW5Phnjs2DGL4+fOnRNubm6KZqDKqR2PONzH5s2b0a1bN7Rt2xabNm2SluPO5ElZWVmIiIiwONaqVSvFJ5FSUw5mYAZmYIayREVFYePGjQCAxo0bl5pE7NixY/Dy8lI0A1UOB4gCmDx5Mrp06YKhQ4di48aNmD9/frVnmDlzJlxcXKDVanH58mW0aNHCfOzPP/+Eq6trrcnBDMzADMxQljlz5qB3794oKCjAkCFDMHXqVJw9exYPPfQQzpw5gwULFlTbTMNUMSw2/r+HH34YP/30EyZPnoyHH34YohqfCH7sscdw5swZAEBYWBh+++03i+PfffedxT/qmpyDGZiBGZjhXqKjo7F582ZMmTIFBw4cAADMnTsXwO2lBf7xj39g0qRJimagyuE8G2XYsGEDdu7cifj4ePj5+cmOg/Pnz8PR0RH16tWr9TmYgRmYgRkA4Nq1axaTvDVs2LBa7kuVw2KDiIiIFMVuFAC3bt3CqlWrsHfvXmRmZkKr1aJRo0bo378/unXrVmsyqCUHMzADMzCDmjNQJUh9FkYFzp49K4KDg4Wfn5+oX7++0Gg0ok+fPiIqKkrY2dmJwYMHi+Li4hqfQS05mIEZmIEZ1JyBKqfWFxu9e/cWL730kjCZTEIIId5++23Ru3dvIYQQv/zyi2jYsKGYPXt2jc+glhzMwAzMwAxqzkCVU+uLDRcXF/OMdEIIYTAYhIODg/jjjz+EEEJ8/fXXomHDhjU+g1pyMAMzMAMzqDkDVU6tn9TLw8MDeXl55tc3b95ESUkJHB0dAQARERHIzMys8RnUkoMZmIEZmEHNGahyan2x0aNHD0yZMgWnT59Geno6Ro8ejYcffhh16tQBAGRkZCj++KsaMqglBzMwAzMwg5ozUCXJblqR7cqVK6J9+/bmBdeCg4PFkSNHzMfXrl0rFixYUOMzqCUHMzADMzCDmjNQ5XCejf/v7NmzMBgMaN68Oezt5TwRrIYMasnBDMzADMyg5gxUQbKrHbXLyMgQzz33XK3PoJYczMAMzMAMas5AZWPLxgMcP34crVu3htForNUZ1JKDGZiBGZhBzRmobLW+/WnDhg33PX7+/PlakUEtOZiBGZiBGdScgSqn1rdsaLVaaDSa+67yqtFoFK2U1ZBBLTmYgRmYgRnUnIEqp9Y/+hoYGIj169fDZDKVuR05cqRWZFBLDmZgBmZgBjVnoMqp9cVGmzZtcPjw4Xsef1AVXVMyqCUHMzADMzCDmjNQ5dT6MRvTpk1DQUHBPY+HhoZi586dNT6DWnIwAzMwAzOoOQNVTq0fs0FERETKqvXdKERERKQsFhtERESkKBYbREREpCgWG0RERKQoFhtERESkKBYbRPRARqMRJpNJdgwislEsNohszIoVK+Dt7Q2DwWCxv3///hg2bBgA4JtvvkHr1q3h5OSERo0aISEhASUlJeZz33//fbRs2RKurq6oX78+xo4di/z8fPPx5ORkeHh4YMOGDQgLC4NOp0NGRkb1fIFEVOOw2CCyMYMHD4bRaLRYlOrq1av49ttvMXLkSPz3v//F8OHDMWnSJJw6dQoff/wxkpOTMXfuXPP5Wq0WCxYsQFpaGpYvX44dO3bg1VdftbjPzZs38c4772Dp0qVIS0uDn59ftX2NRFSzcFIvIhs0duxYXLhwAd999x2A2y0VixYtwrlz59CjRw9069YN8fHx5vP//e9/49VXX8Xly5fLfL9169Zh9OjR+OOPPwDcbtl47rnncOzYMbRq1Ur5L4iIajQWG0Q26OjRo2jXrh1+++031K1bFxERERg8eDBmzpwJX19f5Ofnw87Ozny+0WhEYWEhCgoK4OLigh9++AFJSUk4ffo0cnNzUVJSYnE8OTkZL730EgoLC6HRaCR+pURUE9T6tVGIbFFkZCRatWqFFStWoGfPnkhLS8O3334LAMjPz0dCQgIGDhxY6jonJydcuHABTz75JMaMGYO5c+fCy8sLe/fuxahRo1BUVAQXFxcAgLOzMwsNIrIKFhtENur555/HBx98gEuXLqF79+6oX78+AKB169Y4c+YMQkNDy7zu8OHDMJlMeO+996DV3h62tWbNmmrLTUS1D4sNIhv197//Ha+88go+/fRTrFixwrx/1qxZePLJJ9GgQQMMGjQIWq0Wx48fx8mTJzFnzhyEhoaiuLgYH374Ifr27Yt9+/ZhyZIlEr8SIqrp+DQKkY3S6/WIjY2Fm5sb+vfvb94fExODTZs24fvvv0e7du3Qvn17zJ8/H8HBwQCAVq1a4f3338c777yD8PBwrFy5EklJSZK+CiKqDThAlMiGdevWDS1atMCCBQtkRyEiuicWG0Q26MaNG9i1axcGDRqEU6dOoVmzZrIjERHdE8dsENmgyMhI3LhxA++88w4LDSJSPbZsEBERkaI4QJSIiIgUxWKDiIiIFMVig4iIiBTFYoOIiIgUxWKDiIiIFMVig4iIiBTFYoOIiIgUxWKDiIiIFMVig4iIiBT1/wDxU/t0c3mF0wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "khFQyw2DXQEU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}