data_visual_d15_01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "data_visual_d15_01.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "5h2SZgNt6OUI",
        "B2uHOWsT7Ofo",
        "u8nuCWN5DlPF",
        "2ewAK2cRF6aU",
        "jLjy49S4IKPj",
        "poAvOMmFOcAa",
        "ZHGWV84AQ_HR",
        "k37YoFqmRKi2",
        "QoXiZyt0R2aT",
        "cPLmtyatR78_",
        "By8Av5X6SkxL",
        "Nirppr_DT-CF",
        "GUtSPVy9UOII",
        "YFpCoFSbXjKS",
        "M4E9moNzZa1-",
        "b4r2Tp2Ta9mm",
        "OJudN7IbcOA0",
        "TZ1JJjKekOLL",
        "e64N7fd-lUhK"
      ],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPiegMC0H1ag7KoELUEzfSa",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OHTORO87/ai_class/blob/main/data_visual_d15_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5h2SZgNt6OUI"
      },
      "source": [
        "# seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqhYK6j05AR7"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0j_3mmkc5SWT"
      },
      "source": [
        "a = [1, 2, 3, 4]\n",
        "b= [1, 4, 9, 16]\n",
        "c = ['kbs', 'mbc', 'sbs', 'ytn']\n",
        "d = {'이름':'손흥민', '나이': 30, '직업':'운동선수','연봉':200} #딕셔너리\n",
        "s1 = {1, 2, 3, 4} \n",
        "s2 = {2, 4, 6, 8}"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2uHOWsT7Ofo"
      },
      "source": [
        "## ndarray는 int전용 행렬이 되는것?!\n",
        "## 같은 자료만 되는건데 숫자만 된다고 기억하자"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nO5diast6XYb"
      },
      "source": [
        "t1 = np.ndarray(a)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdbr4EXG6c1B",
        "outputId": "3969ffc3-9282-45c4-a1a9-7dda2fa742de"
      },
      "source": [
        "type(t1)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHkrF0I46djI"
      },
      "source": [
        "t3 = pd.Series(b)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlGIDIyJ7oqt",
        "outputId": "5eef1b9d-649d-4685-e779-55f486efd1da"
      },
      "source": [
        "t3"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1\n",
              "1     4\n",
              "2     9\n",
              "3    16\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBPC0-II7pUv",
        "outputId": "21a4b523-495e-40fe-ed35-805b0a228e47"
      },
      "source": [
        "type(t3)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hf8XtuKG8p0J",
        "outputId": "4699c355-3399-49c7-8eb2-85daa6c14161"
      },
      "source": [
        "t3.index"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=4, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTGw0YbA8r9x",
        "outputId": "607410ab-7e31-4f91-eeea-03a688962809"
      },
      "source": [
        "t3.values"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  4,  9, 16])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55OX48_T8uUw"
      },
      "source": [
        "t3.index = ['k','b','s','d']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5wVClw28zT3",
        "outputId": "135c3d28-3530-4612-ab6e-a520f79f1ad7"
      },
      "source": [
        "t3"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "k     1\n",
              "b     4\n",
              "s     9\n",
              "d    16\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2LtUfNV8zz_"
      },
      "source": [
        "t4 = pd.Series(c)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c88TNYXE84_f",
        "outputId": "ad75b52b-ef3d-42e6-8b24-e9aa9a23f13f"
      },
      "source": [
        "t4"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    kbs\n",
              "1    mbc\n",
              "2    sbs\n",
              "3    ytn\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cncmRb3I85l_"
      },
      "source": [
        "t5 = pd.Series(d)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0NY4caX887f",
        "outputId": "a6204b35-cfb5-411d-a766-68efb82a2664"
      },
      "source": [
        "t5"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "이름     손흥민\n",
              "나이      30\n",
              "직업    운동선수\n",
              "연봉     200\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJx2ACUr89mD",
        "outputId": "b28f7595-6012-4356-f73a-b1d6acf20e19"
      },
      "source": [
        "t5.index"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['이름', '나이', '직업', '연봉'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8AYIjLr8_Xn"
      },
      "source": [
        "tips = sns.load_dataset('tips')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "xpWerrECAdQL",
        "outputId": "cb658fad-82d3-4378-eaeb-0d7368c255ac"
      },
      "source": [
        "tips"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>29.03</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>27.18</td>\n",
              "      <td>2.00</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>22.67</td>\n",
              "      <td>2.00</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>17.82</td>\n",
              "      <td>1.75</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>18.78</td>\n",
              "      <td>3.00</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 7 columns</p>\n",
              "</div>"
            ],
            "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\n",
              "..          ...   ...     ...    ...   ...     ...   ...\n",
              "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
              "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
              "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
              "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
              "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
              "\n",
              "[244 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36PoQSAtAiW0",
        "outputId": "622e1dc6-bb82-41b8-d183-6731ee3d9983"
      },
      "source": [
        "type(tips)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpCaHlIpAq3R",
        "outputId": "fc2777a9-8146-403f-868c-5eaaaeb49805"
      },
      "source": [
        "tips.ndim"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "WMuGQrdyAsqn",
        "outputId": "6ccecdf1-1eae-49d2-e667-6a3303118250"
      },
      "source": [
        "tips.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19.785943</td>\n",
              "      <td>2.998279</td>\n",
              "      <td>2.569672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.902412</td>\n",
              "      <td>1.383638</td>\n",
              "      <td>0.951100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.070000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.347500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.795000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.127500</td>\n",
              "      <td>3.562500</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.810000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       total_bill         tip        size\n",
              "count  244.000000  244.000000  244.000000\n",
              "mean    19.785943    2.998279    2.569672\n",
              "std      8.902412    1.383638    0.951100\n",
              "min      3.070000    1.000000    1.000000\n",
              "25%     13.347500    2.000000    2.000000\n",
              "50%     17.795000    2.900000    2.000000\n",
              "75%     24.127500    3.562500    3.000000\n",
              "max     50.810000   10.000000    6.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Za_wWRzpBBpO",
        "outputId": "1cb5471f-34b3-4663-a35f-e82db06f49e5"
      },
      "source": [
        "tips.index"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=244, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zs5I0YFBGAt",
        "outputId": "76c247ad-d161-41cf-f3c5-1775bac67907"
      },
      "source": [
        "tips.columns"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7I3SVb7DBIlE",
        "outputId": "382cbbdc-9938-4496-97fc-6635bd50d127"
      },
      "source": [
        "tips.size"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1708"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IFggi2J1BNwt",
        "outputId": "c34edc99-3fca-4430-bd20-35bf1b2b3d21"
      },
      "source": [
        "tips.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>"
            ],
            "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Cpma27WvBRai",
        "outputId": "d8524d9a-fa39-4ba7-ce71-e5f5552a7241"
      },
      "source": [
        "tips.tail()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>239</th>\n",
              "      <td>29.03</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>27.18</td>\n",
              "      <td>2.00</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>22.67</td>\n",
              "      <td>2.00</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>17.82</td>\n",
              "      <td>1.75</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>18.78</td>\n",
              "      <td>3.00</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     total_bill   tip     sex smoker   day    time  size\n",
              "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
              "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
              "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
              "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
              "243       18.78  3.00  Female     No  Thur  Dinner     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCLfHTflC2cF"
      },
      "source": [
        "t = tips"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8nuCWN5DlPF"
      },
      "source": [
        "#seaborn 데이터시각화\n",
        "## hue 에 주목하자"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "xABqvvNGBUY9",
        "outputId": "bfe0ce28-1bf0-4b2b-884f-ecd44aa35edd"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.relplot(x='total_bill', y='tip',data=t, hue='smoker')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 417.475x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "Yw04ahEbCcmW",
        "outputId": "90c5b703-56c2-4765-efc3-7c3937371d58"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.relplot(x='total_bill', y='tip',data=t, hue='sex')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 439.725x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "sBlSfHG7De-h",
        "outputId": "a58cc641-9938-40fd-be72-e391d9015c38"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.relplot(x='day', y='total_bill',data=t, kind = 'line')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "P7g0fPhKD1e-",
        "outputId": "bcd2d892-780b-4e0c-b16f-64523cbf4e53"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "Dr8Z8FmOEy3d",
        "outputId": "fbe6b5a0-3c8d-42ca-e368-4a9a7474e4a7"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, kind = 'box')\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ewAK2cRF6aU"
      },
      "source": [
        "## 위 그래프 값은 아래와 비교하면 이해가 쉽다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MgmuXWswFDqu",
        "outputId": "1cdf2f30-12cb-46ce-8482-b77f22e1f5d0"
      },
      "source": [
        "t.describe()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19.785943</td>\n",
              "      <td>2.998279</td>\n",
              "      <td>2.569672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.902412</td>\n",
              "      <td>1.383638</td>\n",
              "      <td>0.951100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.070000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.347500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.795000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.127500</td>\n",
              "      <td>3.562500</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.810000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       total_bill         tip        size\n",
              "count  244.000000  244.000000  244.000000\n",
              "mean    19.785943    2.998279    2.569672\n",
              "std      8.902412    1.383638    0.951100\n",
              "min      3.070000    1.000000    1.000000\n",
              "25%     13.347500    2.000000    2.000000\n",
              "50%     17.795000    2.900000    2.000000\n",
              "75%     24.127500    3.562500    3.000000\n",
              "max     50.810000   10.000000    6.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ZwHWL6frFy2c",
        "outputId": "853cd78a-f653-4e30-f402-5f319c9b8b70"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, kind = 'boxen')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "cY7T45nJGDpU",
        "outputId": "7f8fba70-a6bb-4584-84d9-76fa9fcaca52"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, kind = 'swarm')\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "Guq48p4cGJBs",
        "outputId": "ff569180-d765-4dc9-e135-61ed21f1d291"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, kind = 'bar')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "qeX3PU1XGQEz",
        "outputId": "8273e54d-f8ae-44ed-93cc-276fae9a9bd2"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, hue = 'sex', kind = 'box')\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 439.725x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "yBvJc71kGaC8",
        "outputId": "618b0d86-8084-4cfb-bf39-445b0126d5ce"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, hue = 'sex', kind = 'point')\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 439.725x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ORcp28deGeUW",
        "outputId": "3e9c0a9a-ba19-4686-a2fa-4f022e4a0a65"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill',data=t, hue = 'sex', kind = 'violin')\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFtCAYAAAB80t9VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeZgjdZ3/399vVc6+r7kHRlmQQVwQBmd1YZFRDh853WeV5fECQZFdRVEOcRkQddmZYVl0PdhZXZ9HRNldBOc3gwKyo1yOQAuDzMAcfV/pTtJJp3NXkqrfH5VvJZ1OuitJVSXFfF/Pw0N3Okl9p1Kp9/dzE0VRFHA4HA6H8xaHNnoBHA6Hw+FYARc8DofD4RwTcMHjcDgczjEBFzwOh8PhHBNwweNwOBzOMQEXPA6Hw+EcE4iNXoARzM7GIMu8uoLD4bx16Otra/QS3nJwC4/D4XA4xwRc8DgcDodzTMAFj8PhcDjHBFzwOBwOh3NMwAWPw+FwOMcEXPA4HA6Hc0zABY/D4XA4xwSW1eGl02n88z//M/bt2weXy4XTTz8d3/zmNzE8PIzbbrsNc3Nz6OzsxLZt27BhwwarlsXhcDicYwTLBG/Hjh1wuVx48sknQQhBMBgEANx555246qqrcNlll2HXrl3YunUrfvrTn1q1LA6Hw+EcIxArBsDG43Gce+65eOaZZ9DS0qI9Pjs7iwsvvBAvvvgiBEFALpfD5s2b8dRTT6G7u1v3+/NOKxwO560G77RiPJZYeOPj4+js7MT3vvc9vPjii2hpacGNN94It9uNlStXQhAEAIAgCFixYgV8Pl9VgsfhcDgcznJYIni5XA7j4+M45ZRTcOutt+K1117D9ddfj+985zuGvH9PT6sh78PhcDicty6WCN7q1ashiiIuvvhiAMBpp52Grq4uuN1uzMzMIJfLaS5Nv9+P1atXV/X+3KXJsTuxWBSZTAZdXdyzwVHhLk3jsaQsobu7G5s3b8YLL7wAABgeHsbs7Cw2bNiAjRs3Ys+ePQCAPXv2YOPGjdydyTnm+Na37sQtt9zY6GVwOG9pLElaAdQ43u233465uTmIoogvfelLOPfcczE4OIjbbrsN8/PzaG9vx7Zt2/D2t7+9qvfmFh7H7lxzzVUAgP/6r583eCWcZoFbeMZjmeCZCRc8jt3hgscphQue8fBOKxwOh8M5JuCCx+FwOJxjAi54HA6Hwzkm4ILH4TQRsiw3egkczlsWLngcThORy2UbvQQO5y0LFzwOp4nIZnONXgKH85aFCx6H00RwC4/DMQ8ueBxOE5HNcsHjcMyCCx6H02CKez9kMpkGroTDeWvDBY/DaTDZbKbszxwOx1i44HE4DSaTyRb9zAWvGiRJ4qUcHN1wweNwGkwmIxX9zAVPL4qi4Itf/Cx+8pOdjV4KxyZwweNwGkyxyHHB008ul4MkSXjhhWcbvRSOTeCCx+E0GEmSyv7MWRpF4a5MTnVwweNwGsxClyYXPL3w2B2nWrjgcTgNJp1Oaz9zC08/uRwXPE51cMHjcBpMscgVix9naWSZt2HjVAcXPA6nwUgSt/BqgVt4nGrhgsfhNBhu4dUG7zvKqRYueBxOg1kYw+OCpxeetMKpFi54HE6DSadTZX/mLA1vtM2pFi54HE6DYRYeFd3cpVkFuRxPWuFUBxc8DqfBpNNpEEJBBCcXvCrgjbY51cIFj8NpMOl0GkQQASI0JIYXiUQwOjpi+XHrhbs0OdXCBY/DaTDpdAqEqoKXSlkfw/vXf70H3/jG7bazLrngcaqFCx6H02DS6TRARICKDRG8iYkxAPZra8YbbXOqhQsex1CeeGIPvvjFzyEcDjV6KbZBFTwBhIoNtbLslubPY3icauGCxzGUJ554HLFYFLOzwUYvxTak06mChddAwbNb1mOxS1NRlAauhGMXuOBxDEa98djNWmgkqXQaoCIIESFxwdNNsUvTbmvnNAYueBxT4Dcg/aRTKRAqALQxWZoMu31mxS3Z7BZ/5DQGLngcU+AWnn7SkqRaeFREJiM1zD1nb8Hj8TzO8nDB45iC3W6ejSQjpUGIABBB/b1BN2+7pfkXJ63wKRMcPXDB45gCn1WmH0mSACqobk00roG03aYPcAuPUy1c8DimwGeV6SebzTSFhWc3q7xY8LiFx9EDF7wmJJfLYe/e39q6ls1uN89Gkcvl1HgnKbbwGnPztpuVxJNWONUiWnWgLVu2wOl0wuVyAQC++tWv4pxzzsH+/fuxdetWpNNprF27Fjt27EBPT49Vy2pKRkaG8bOf/QQDA4fx2c/+Y6OXUxUs38Ju7rFGwUSG0Gaw8Oz1mRWLHLfwOHqwTPAA4Lvf/S5OOukk7XdZlnHzzTfjnnvuwaZNm/CDH/wA9957L+655x4rl9V0sBtPMGjf4m1u4elDS7wggurWROM6iGQy9hK8hS5Ne/UB5TSGhro0Dxw4AJfLhU2bNgEArrzySjzxxBONXFJTwMTCzt0jeFmCPjSRIVT9D9ZaeMXXmN1cmgstPHutndMYLLXwvvrVr0JRFJx55pm46aab4PP5sGbNGu3v3d3dkGUZc3Nz6OzstHJpTQUTC0JIg1dSO3ZLcW8UzJonRYJnpXVcfCy79aZMpyUQwQUll+YWHkcXlgneQw89hNWrV0OSJHz729/G3XffjfPPP9+Q9+7paTXkfZqF1lYnAEAUKfr62hq8muqgVBVpt1uw3dobgSTNqz8UCV5Li8Oyc5dIJLSf7feZ5QDBCeTScLns913hWI9lgrd69WoAgNPpxFVXXYXPf/7z+OQnP4mpqSntOaFQCJTSqq272dkYZNm+7r9SQqEYACCblREIRBu8murI5dTPIRKJ227tjSAQKAgeyQve7GzUsnM3Pz+v/RwKWXdcI4jFEiCCEwqAUGjeVmvXAxdw47EkhpdIJBCNqhejoij49a9/jY0bN+LUU09FKpVCf38/AODhhx/GRRddZMWSliUcDuNHP/oh/P4Zy49t7xieumbu0tRHwaVI8v8BimJd/LPYjWk7l6akujQBnqXJ0YclFt7s7Cy+8IUvaDVHJ5xwAu68805QSrF9+3bceeedC8oSmoH+/hfxhz88h46OTvzd3/29pccuxHXsF8OT8yLNszT1oYkbIUUxPOsEz87F25IkgVA3AGK7hBtOY7BE8NavX49f/epXZf92xhlnYPfu3VYsoypYa6xG1CbZWSzk/Nq5hacPLUEJVBU9WGvhFQuF3UQjI0kgpAWECrYTa05j4J1WlqERXkUmFnZ0abK128091ii02HORMW/l516c2m87wctmFkyZ4HCWgwteBRqpNXZ1aSqKgmx+7XYrYm4UC6059fO2MgFroYVnL9HIZjJaSza7iTWnMXDBq0AjtYaJhd0svFwup+0UuIVXLaTIyLPuc7frxAFFUfJNt9VyDu7S5OiBC14TYlexKI7b8RiePbBr0koul1M3hFRtycavN44euOBVgGgJBNZbWXaN4RULtZ2shWbD2hie+jmJ1F6fmdZ0mwhQCLWdO5bTGLjgVaCR7b00wbNZP8riuJ1drdRmwMprjgmFW6S2FDy1lMNea+c0Di54FWiklcXEwm6iwS28+ihcaVYKnvo5uQWCTMY+/SiLp0yAcMHj6IMLXgUaGRNglpLdvsTF67VTPKiRFKw5BUzyrHQqLLDwbPSZse+nmrTCszQ5+uCCVwFWGsAK0K2E3YTsFpewcxFz41isbta6NPMWnkhstUnRNqT5HqQ8aYWjBy54FWBWViO+SOwmZLdaNk2g+Y5bNwsMvMKjlh2ffU4ugdhqg1UseOCCx9EJF7wKsBhBI27cdu1WomXOUYetrIVGwiYkAIpWw8hGLFlBNpuFSAlESmwlGqVzBLMNaAHIsR9c8CpQEB3rv0iFpBV7fYk1wROcatsnzrKUEzerszRFSiFQezVg1vrN5l2auax9+89yrIMLXgXYl78RVpbmTrVZE2nNJSY4bHXzbCz5es+ipBUrXZrZbBYCJRCJvTZYi1ya3MLj6IALXgXYF6oxLk31mLIs22pyQsGl6VT7HHKWRbPmimJ4Vro0M5kMRAoItnNpFiw8EIqcjdbOaRxc8CpQSBxpgOAVHdNON6Fil6ad1t1IFpQlKNZbeLlc3sKjxFYehYVlCRQ5mzVp4DQGLngVYFaW1ADByywQPPtYSixRhQgO5HJZrVsNpzLlwnVW1uFls1kIBBCI6lGwy2emzanMx/BkG4k1p3FwwauAFsNrhIWXKxY8+3yRi12axb9zmpdsNqcKXt6N2oiBx7VQcPUTAAS5BtTLcuwHF7wKpNNqm6V0A9Lrc1l79qQsdmmqv/PShOUpV4hncdJK3sJjv9sBJnjMpQlFsY11ymkcXPAq0NAYXtFNx15JKxIAAlAx/7t9xLrxNGYAYy6XBSUAJczCs8f1Vpq0oj5mD7HmNA4ueBWQWHuvRlh4uRxEyn62z5dYkiQQqk6gZr9zmptSC89+gke04n07uf85jUFs9AKalYzELLzGCJ6DEmRlxTY3IEA9V4QKABG13znLsXgah5UTOmQ5B0oIqLbBssf1VlqHB1i7OUwmE/jJT/4Tbrcbn/70daCU2w52gH9KFSgUnltvYTHBU3+2T1wik8mAUFHbcXOX5vLIcjlxs07wcrmcTV2axWUJ1q99bGwU/f0v4vnnn8HcXNiy43LqgwteBZh10oj0elmWIdosaw7ICxyhAHdpVkFR7V3+xl1eBM1BzguefV2axRaedWtPp1Paz8lk0rLjcuqDC14F1OxI9S5gtaVSLHh2yjyTJAmECOpQTnALTw/s81UL0PNtxix0aeaYS5PY63orTBKhDfEopFIFwSsWP05zwwWvDLlcDrIsgwgOANbHonKyDCH/ydjlBgSoNxyFCKrogcfw9FD4fIstPOs+czkn512a6u/2sfCKY3jq9WZl+KHYquMWnn3gglcGbaeoFVBb61ZUFAUCsX63Xy+ZjMRdmlWiiVuRhWel4LEYHiuKsMsGK5PJgBCqWsZalqa1SSvlfuY0N1zwysAsk0YVUCuKorWXsssNCMgL3AILj7s0l6NY8Ihm4VlnZcmybFOXZkYrfyGahWfd9RaPx7WfEwkueHaBC14ZCh1DHAt+twrVwiv8bBekTGkMj1t4y1FwITYm+UKWc2pzLpttsLLZguChATG8RCKuxdnj8Zhlx+XUBxe8MmgWHrXewlMUJW/h2WvHDeRrF4sKz612BduRQiyKgH0drRW8hTE8RbHH9aZmBC+08Kx0ocfjMXS4RQiEIBbjgmcXuOCVodEWHmDPD0bKqC5NbuHph3UHaVQ9mSyrmyu7xfAkSdJa2IFaf71Fo1F4RMDrFLiFZyN4p5UyaJZJA7r+ay5MrbWifVyamUwGRBQ0FxNPWlme4noy0oCOIYoigwK2i+FJUlq7zkhe+Ky83qLzEXhFgowMRKPzlh2XUx92NCRMp5C0olp4jbhxaz307aN36iglKqgWAxVs03m/kSxMr7c+27DUpWkXwVOnmbAYnlj0mDXMz8+j1SmgRSSYn49YdlxOfXDBK0PpXLdG3Lgb0zu/PjLZjGalECpwC08H2rVGhIYIXk6W1Q2KJnj2qMNLpVKaS5PFjK0qAJdlGbF4DC0OihYHRYS3FrMNXPDKwG44jSg8L3Vp2iVLU5Zldep0USKBnWb5NYriJsgsjmepC12z8Ozl0kymUporkwlfcfcTM4lGo5BlGa1OilYnRWR+3jbf02MdLnhlKFh4quBxC295tE2BlirOLTw9aJsCZhkTa13BObm004o9BC+VTAL57ychFISKlgleJKJadG1OijYnRSaT4d1WbILlgve9730P73jHO3DkyBEAwP79+3HppZfiwgsvxDXXXIPZ2Vmrl7QI7ebdgCzNxTtFe+wcF7jmAIAIPEtTByxBip03NfZp3fVWKDxXf7dLa7FUKgklk0RmbhgAQAWHZR1P2HQEJngAEA6HLDk2pz4sFbyDBw9i//79WLt2LQD1y3bzzTdj69atePLJJ7Fp0ybce++9Vi6pLItjeNa75ojNbDx2znKpkHoTopQnrehAu7ZowcKzaoOlKEohaQXWd3mpFVmWkU6nIUvzyESG1AepwzIrKxxmgiegzSXkH+OCZwcsEzxJknD33Xfjrrvu0h47cOAAXC4XNm3aBAC48sor8cQTT1i1pIosjuE1LhZll9CAJnjJ2fxNiLs09VC4tvJfRUotu96YNScQaM3K7WDhqZacormBAQDUgUQiXvE1RhIKzYJAtfDauYVnKyyrw/vOd76DSy+9FOvWrdMe8/l8WLNmjfZ7d3c3ZFnG3NwcOjs7db93T0+roWt1uVimoSp4TidFX1+boceoRME1qP7e3u627Nj1kEiwL3x+4YQCkG2x9kbicKglHKyzDiECKFUsOW8s5kUJ0ZqVe72Opv/Mcrm8sJEiLwh1Ip1OWrL2RGIebS4RAiVod7EM0VjTnzeORYL36quv4sCBA/jqV79qyvvPzsYMHZoZDkfVH4gAQijm5mIIBKKGvf9SaIKX/z0SSVp27HqYmZkDUOSKJQLicXusvZFEIvFCT0gACqhl541ZRAItWHjhsHXXeq2MjU0DAEiRg4oIToTDEUvWPjnpQ7tLvc5FStDqEjE+PmX4sbmAGo8lgvfyyy9jcHAQH/jABwAA09PT+MxnPoNPfOITmJqa0p4XCoVAKa3KujODBani1LqYiooq3MRmZQlagopm4HGXph4yGQmEiFryBSyM4bHrXCiy8Oww4ULrXVlk4RHBiXjcb8nxZ4MBrHAWxLbDSTA7G7Tk2Jz6sCSG99nPfhbPP/889u7di71792LVqlX48Y9/jGuvvRapVAr9/f0AgIcffhgXXXSRFUtaEjZ6pNAxxMqsubzgaY/YRfDYOSpYeHa4eTYatQkyRSYypMY+CbVso8AET6SkKIbX/IlGsRjzwBRbeC6kUknTE6VkWUY4HEKnu2CVd7gogoEZU4/LMYaG9tKklGL79u248847kU6nsXbtWuzYsaORSwLAdt2NqidjFh7LmrOL4LFzxGJRVG0mzVmSTEYCoUJhW0MESBZtFNhnJlBAtJGFp7XyKhY80Q1ALQrv6uoy7diRSATZXA4droLgdboFHPGF1IxXykubm5mGCN7evXu1n8844wzs3r27EcuoiCRJC4ZLNsLCEzQTzy6Cxwqo8w9QAdl08988G00mk4FSHIuiAjIWbbDYZybm6/AI7NHwe35+Pt/Crtil6c7/LWKq4M3OBgAAXUUWXqdLQDaXQyQyh66ubtOOzakfvh0pQybfBBkAFCJAkqwsPFc7XditmW/hRsldmtVQPNcNgKUehXRaPY5DUKetO0Rqi2YB8/MRUIdnYZKmWBA8MwkE1DhhsUuTiV8wGDD12Jz64YJXhoUuTWtvAkzgBGqv3oalMTxCBGSyGdsk3TQKdYZgkYVHrKvDY9e1I3+tOSixhYUXicwBgmvBY8ylOWdyI2cmap0lLs3iv3GaFy54ZZCkDJQGxfBKXZp2ETztHLFtNxUAReHdVpZBSksLLTxqXUs2Nk7Hkb/YHIJ1CTP1EAqFQATPgscKgjdn6rEDgQBaXaJ2zoCC+HHBa3644JVB/dLnTw0RLJ2zxVo7ibaz8EqTVsSSxznlYBnBGkRAxqJNAhun48xfa05q3YidegiHQyBiieBREVR0IRw2txdvMOhHp3Nh2z+HoNbiBQJc8JodLnhlkKS0FsMjVLRY8PIuzbylZIdWT0CZZIf8+bODxdBIJGmhhUeIgGxGssQVzDqtOJmFR60bsVMrkiQhmUyAODyL/kZEj/kuzYB/QfyO0ekiCAatqQPk1A4XvDKk02nNQoHFBdRM4JjLxE4WHqGi5tFkMVAueEsjFceLAYAKUBTFko0OEzdX/lpzCcSyiQO1EgqpFhx1eBf/UfQiGDSvAFyWZYTCIXS6F982O10Cr8WzAVzwypBOF1l4RERass7CYzc60UbNfAE142+Bay4/lFOy8NzZkYwkFWYIwtqNAhM3l1gkeAl7CB4RFwseFb3a380gHFZr7bpcZSw8N0UoHLbNBvVYhQteGdJSumiasnV1UUCRhUeZS9MeSR9S8TkDNPHjFt7SZLKLyxIAa2KfyWQCToFq087dIrFs4kCtsBZe1NGy6G/E4UUiETctDsmSUjrKujSFfON7c12qnPrgglcGKZ0G8i5NQkVkMpJlOzeW1cjiKnax8DKlrjnu0lwWWZaRy2YXWMZWbhTi8Tg8jsItwC1SJJp8crcqeASkjEuTiaBZbk0mtp1lLTyeqWkHuOCVoCiKFo8CoAmfVdmGpYJnl7T+dDoNZYGFJ2qPc8qj1dsVnbeCK9j86y2RiMMtFjIOPaJaA9jMmbXBYACC05PvtLKQguCZIzqzs6q7tKOM4LHHeBPp5oYLXgnZbFa15rQsTTbvypobN3NhMpemnQQP4DG8amDnhpByMTzzz1s0GoWn6CPzOtRrLhZrXrdmIOAHxMXuTAAgJgteKBREi1NYUIPHYIIXCvFBsM0MF7wSmLAxC4X936p0bW1kCyUQKbGN4KVSqbLJF9zCq4x2bujCLE3AGgsvFo3A66DYP5PE/pkkvHn3pjaNoAnxB/yasJVCRDcIFUwUvFltwnkpToHA4xAQCnELr5nhgldCKqXGMNi0c+T/b5WlwhpVi4RAsJHgpdMLk1bAXZrLom2uSDlXsPkbrGg0Cq+D4tWZJF4tErxodN70Y9dCJpNBZG4O1NFa9u+EEFBnq2n1cKFQEO2uyrfMdhdFOMyTVpoZLnglFHbdjbHwMhlm4SEvePZowKxaeI25cdsVzaVZvFEg1mwUstks4okEWouSVtjPZjdgrhU1PqaUzdDUELyYmTGnHi4cClW08ACg3UG4hdfkcMErgQlboSzBWktFs/DyLk0mgM1OOp1aYKmwLM1m79zRSLRzU2ajYPZ5Y6LWWnQDb8n/HImY24+yVtikAuKsLHjU2YpAwG94pxpJkpBIJtHmVK9r5gYups0lYC7MY3jNDBe8EjSLZJGFZ026tjajjBKIhDR1xlwxaSkNIhTduAkBFRzcpbkE7FrT3OeA5kI3+7yxerFiwXML6ibL7AbMtcIEjzraKj6HOlqRTqcQj8cMPTY7X215lyZzAxfT5qSIxmK2KSU6FuGCV0JpDI/93zqXpip4AlG7rdjBpZnNZpHLZjV3HINQ0bKNgh1ZysJLmlwPx27g7c7iRCOCdpfYtMXTgYAfhAraZIRyMOvP7zfWrcms3rYlXJqtTgpFUTA/35wxUA4XvEVUcmlal6VZsPAEAlu4NDVLRXAs/AN1cJfmEjBRK7bwCBVACDV9o8CSK9pKkjDaHDC1PVc9BAJ+UGcrCFlcFsBgCS1GZ2pGInkXsGMJwXMwl3Bzbhg4XPAWwW7QmdgUMnPDDUhaKY7h2WO8TjlLhf3OLbzKsF6Wxa5g9Xen6U2cQ6FZCJSgpeQG3uEWEJptzm4hM/6ZijV4DOpUBY+5P42CWXgty1h4QPMm/XC44C2CWSvZ+XFkIkNq8gUhDYjhIZ+00vwuzYJVXGLhEdF015ydSSYT6rTzUlew4DD9vM3OBtDhErU+mowOl4Dw3FzTxaEURUEw4F86QxOqZ0ZweAx3aUaj8yCAVrpRjhYty5W7NJsVLnglqDcagsIgUwJqoWsuk8lAoASUqBaeHTqVaJZKieARav6N284kEglQwbnYRUcdpjdxDgYC6HAudg12uihkWUa4ybINY7Eo0umUZsEtieg1fBhrNDoPj0PQ5lSWo4VbeE0PF7wSUqkUqOBA8XVNBIdlFp4kSXBQ9WNxUHtkaS4q1mcIjqZvRtxI4vH44rgnAFAHYnFzBS8QmEGXZ3FPyK58E2SjXYL1wmJylbqsFEMcLYZbePPz80u6MwF1crxISVN3qjnW4YJXQiqVLJN8YV0sKpORIOT7aIqUWDqaqFYSify5ERZbeDyGV5lYLApQ56LHieBCLGreTTOZTCIai2niVkyXR3WvNqvgVeqyUgx1tGJuLmSoWzYWm0eZ/cECCCFocQqImvjZceqDC14JqVRyYecLACCipS5NFiYQKYEkNX8Mr+DSLC1LUAXP6CLgtwqxWKy84FGn4XVkxfj90wCAnjJ38A4XhUAIZmamTTt+LRQET5+FZ7RbNjo/v2T8juEVuYXXzHDBKyGZTEIhi7MNExZNgpYkCWK+G7vDJlmaBcEruXkLDiiybIs4ZCOIRqMgomvR40R0IZFImDaDkYlZOcGjhKDLIzal4FHRVd4FXAITRSNH9cRiUV2C5xEJojxppWnhgldCMplclF5vZfJFJiMhP6VFdWnaoHm0uhkgZc+b+nfu1ixHLBYFEcoInuACoKgWoAlMT/tAAHS7xbJ/73FT+HwTphy7VoLBIIi4eOhrOWh+OKxRgqcoCmLxOLxi5YQVhtdBEItxwWtWuOCVkEwmFydfUIfpdVEMSZIgFrk0tfl8TUwymQQVHYuyDQuC17zz1RpFOp1SBw1XFDzzphZMTU2i0+MoO9cNAHq9Avx+f1OVJgSDAd2CRwwWvFQqCVmWF0yHr4RHpKZtVDj1wwWvBNXCK5d8YVEMT5IgEubSVP/f7LV4iUR8sTsTagE1AMs2C3aCde6gZdpksdZZZqW3T02Oo9dd2Vrp84rI5XKGZzrWQyg0qwnZcqi1eG7DBI8JmFfUEcNzUCRTqabfpB6rcMErIZlMLK4nExxIS2lLLmJJSsORD62ImuA1dxwvmUws2iQAhVZjVsU/7QQTs3J9IdljTBSNJJfLYXpmGn3e8u5MANrfpqaaw62ZTCbUGjydggcAEL2GtUhjgudxLO/S9IhqP00zk444tVP5qgewb98+XW/y3ve+15DFNBpZlpGW0nC2LbbwoChIp1PweKr40tWAJElopYWkFfZYMxOPxxe3FQO0DETu0lwMEzMiehb9jeYfM6Mno98/g2w2ixUtla/jPq8IAmBycgJnnvkew9dQLaGQmm2p16XJnjs7a5TgqVmXurI086IYi8XQ1tZuyPE5xrGk4H39619f9g0IIfi///s/wxbUSNLpFKAoZWN4gOruNFvwMhkJjnxwnGVrNruFF49XcmlyC68ScwGuA5gAACAASURBVHPsJr5Y8EAdIFQwZUzP5OQ4AGDFEhaeUyDo8jowMTFm+PFrIRxWhUuvSxNQz2s4bIyFqgmeTpdm8Ws4zcWSgrd3716r1tEUaJmYZVyagHrj7u7uMXUNkiRBdC6M4aXTzS94EDoXPc5EMG5y1xA7Eg6HAULLJ60QAurwmtLea2xsFARAX8uSX32s8FCMjY0YfvxaYBZeNS5N4vAiFU7mN6llNhVVUI2F5xGZ4HGXZjPCY3hFMNdbaa0P0Sw88y0VSZK07DmHTWJ4iWRCS1AphlABhArcpVmGcDgEweGpPOpGcJsieBMTY+j1OrRrqxKrWkQEAv7CQOQGws7DUnPwSmFuYWZJ10M0GgUB4NZVlkDzr+GlCc3Iktu8c889d8nZU4zf//73Rq2noTDXW6l7jt3MzbZUFEWBlMnAkRdYdlNq5hheNptFRkrDWSZpBQCo4OKCV4ZQaBYQKlseRPQiaFAMqpix0WGsaVl+n7uyRYSiABMT4zjhhBMNX0c1qJsDNwhZprdXEcxVHA6HsXr12rqOH4tF4XUKiyZLlKOFuzSbmiUFb8eOHYYd6IYbbsDExAQopfB6vbjjjjuwceNGDA8P47bbbsPc3Bw6Ozuxbds2bNiwwbDjVkPBwisveGZbeLlcDoqiaMkqzNJrZsGrdM40BPM7/9uR4OzSafbU4UVkbgKyLINSYxwxsVgMoXAYZ2xYvh/lqlZ1AzM2NtoUgrfU5qAczP1phJUcjerrsgKo8U+HQPiIoCZlScF7z3uMy9Datm0b2traAABPP/00br/9djz22GO48847cdVVV+Gyyy7Drl27sHXrVvz0pz817LjVwCy4xc2j1Zu52X55JmxiUfNo9fHmbc1VOGcVBI84eAyvBLXP4yzEzpMqPoc4WiDncohE5tDV1W3IccfHRwEAq1uXjt8B6pggj0NoijheKBQCyiX3LEHBwqtf8ObnI1gm5LmAFofAXZpNypIf4w9/+EN8/vOfBwB85zvfqfi8G2+8cdkDMbEDVOEghGB2dhZvvPEGfvKTnwAALr74Ynzzm99EKBRCd7cxX/JqKNy8FyYSFJJWzL1xM2Fz2KgsYXkLz8kD+CVEInOQc7klR90wCyUYDBgmeKOjIwCAVS3L96MkhGBVi4DR0WFDjl0PoXAI1Lm6qtcQKoKKLjU5qE7mI2H06rTwAKDFQfhMvCZlScGbnp4u+3OtfP3rX8cLL7wARVHwox/9CD6fDytXroQgqL55QRCwYsUK+Hy+Bgle/sZcmqVJKKgFN24mbFrSig1cmtomoUxZAqAKYTzO4xnF6On8z8QwGAzgxBPfYchxx8aG0eEWl53rxljdIuKliXFks1mIYhUmjoFIkoRkIg5nS/WZlkT0GJK0Epmfx4buKgUvYnxJCad+lryKv/GNb2g/33PPPXUf7Nvf/jYA4Fe/+hW2b9+uyzLUQ0+PjinIOshmUxBEV9lEHSq6kM2m0NfXVuaVxhCPq0kKBQsv/38HTD1uPQhCvt9iBQuPUCeSyUTTrr8RvP666u5aSvDY3+LxOcPO3fjYMFZ59Sd+rG4VkZ1MIJWaw9ve9jZD1lAtU1NTAABaRdG5huBBZL6+85dKpZBKpdCqZ9J6nlangJk6j8sxh6q2bSMjI/jNb34Dv9+PFStW4EMf+lBNCSaXX345tm7dilWrVmFmZga5XA6CIOT79/mxenV17ovZ2Rhkuf6Za4FACChTFwUACnUiGAwhEDDPWpmeVnejzpKyhFBo3tTj1sP0dL4ouJLg5afFT0/PaZb8sc7goBpLW8qlSagIwenFyMi4IZ99MpnElM+Hc9cvP0+OsTqfuLJ//0G0tvbWvYZaGBxUC+WJo3oLjzo88PsDdZ0/1k+0VadVDABtTor5mRh8vnBdljEXTOPR/Snu3r0bV1xxBQ4fPgyPx4MjR47giiuuwO7du5d9bTweh8/n037fu3cvOjo60NPTg40bN2LPnj0AgD179mDjxo0NcWcC+dqZCq45CC7MmzzJuDRpRaAEAiFIp5s3aaVS7SKDN5BejN8/DcHZAkKX2QCILYbNpRsfH4WiAGvalo/fMbo9ApwCbWgcj/XDrMXCI6IXseh8Xc3X5+bUGGBblYKnKAqP4zUhurcf999/P3bu3ImzzjpLe6y/vx+33HILLrnkkiVfm0wmceONN6pjZChFR0cHHnjgARBCcNddd+G2227DD37wA7S3t2Pbtm21/2vqJBKJVCxuJYIL8/Pm+uVZka+zaGyLQ6RNHcNLJBIgVKxYI1XcbaW1le9YAcA3PQ04lneRUWebIbFzAJporaoi3ZASglUtIkZHhgxZQy0wwaumrRijuDRhxYqVNR2fZXm2OfV7J9hzw+Gw6Z2ZONWh++qPx+M4/fTTFzx22mmn6eqT2Nvbi//5n/8p+7cTTjgB//u//6t3GaYyPx8BqZANRkU3onPzUBRFVzF+LTBLrrgLhlNobgsvHo+DihWsYhQsPCv6ab7yyst45ZV+nH/+RTj++MbEnJZDURTMTPtAPcsXQ1NnG+JzQ4jHY2hpqS9OPTo6glaXiHZXdW7l1a0iXh0bNbQesBpCoVl10nm55uTLUDwXr3bBUy28dpf+fzt7rhmdcjj1oftTvPrqq3HfffdpN99UKoV/+7d/w9VXX23a4qwkm82qc90qWXiiG3IuZ+rYD3Zuiy08JyWQpMa3d6pEIhEvOxpIw6KSDgB48slf4w9/eA4vvPCs6ceqlfn5CFKpJKhz+U767DnT075lnrk8oyNDWOWtXrBWt4qQMhn4fFN1r6EW1MGv+uOOxbDEn3rm4oVCs3AKFO4Kw3LLwTYVRo0n4hiH7tZiiqIgGAziwQcfRHt7O+bnVWunr68Pn/vc5yxZrJksNZ8MAIigPj43N2eaa66sS5PCsuGztZBIxAFSWfCsbCCdyG9Gmnk6AxMOXYLnatdeU0+3E0mS4Jv24ey11Sd+rNY6roxg7dp1Na+hVgLBYE3uTKAwToiVgdRCKDSLDrdQlVfHK6rdVrjgNR+WtRZrdpj7oVJwnGWJzc2FsG7delPWkEqVs/DQFA18K6FOSljepWlF0gqzvpt5+KbPNwkAoK7lN03E0QJCKKamJus65sTEGGRZ1tVhpZRerwCREoyOjuC97z27rnVUi6IomA0GQNrfXtPrCRUgOFvqsvBmgwF0OKsLYRBC0OESDZu4zjEOQ1uLffazn8XOnTvrWlCjYL76SunPTAiN6NxQiVQqCYESLUsTAJwiKYwtakJi8TgIXSK93iKXpqIoWmOAZm7rNDExASo4dA0zJYSCutoxOVnfXDetw0qr/gxNhkAIVraIDcnUjETmkM1m4NKR4FMR0auVFtRCMOjHxvbqy2k6XATBgL/m43LMwdAodH9/v5FvZynLpT+z3nxmuilSqRScwsKPxCUQpJo4pT+ZiFduKwYARAQIMV3wkskksjm1CL6Z08EnJ8dBnB26XWTE2Y6JifG6jjk+Pgq3Q0BnFYkXxaxqETE+NgJFqb/WtRqYUNEqir5LIY5WTNdY2pFMJhFPJNDhrl7wOl0CgkEueM0Gn4eXZ3Y2qGaCVewYIkBweDBrwsgWRiqVhFssFTzatDE8WZaRTqcWT4gvghACKjiRSJhrpUbyrZy8DoL5SMTym7MeFEXB+MS4FpvTA3V1IhyerSsuOTaqdlipNbt4VauIRDJpuYuO1SDWI3jU2YrofKSmsACL/XXVIHhdbgHxRILXnzYZXPDyBIMBUGfL0jcF0VtXAHw5kskESrPGXU3s0kynU2qZxlIWHtQ4XjJproXHBG9NqwNSJoNUqvnOWTgcQjIRB3V16X6N4FInyU9MjNV0TFmWMT4xjpXVtPsvgdXujY/XtoZa8fun1anwS3SkWQ7qVGOltRTwMwuzVsEDgAB3azYVXPDy+AN+QFg6rkIcLfAHao8HLEcikYBbAPbPJLF/Rr1huwWKTDZbV7cIs9CEeKmyhPzfzbbwWNLR2nwnkVCo+WqgxsbUlmLU3an7Ney5bLRPtfj9M8hkMlUVnJeyIv/aWkW3VqanfRCcrSCk9tsUE7xaCvgD+e96dy2C51Ff4/dzwWsmDBW8ZnQj6UFRFAQD/mVdJ9TRinA4hGw2a8o64vEoXCLFqzNJvMoET1QtzmZMtdcmxFdoK6ZBRCRMtvBYbHV9u7qWcLj5UsLZbDlmtemBiB5Q0Y3R0doEj4lUPRaeS6Do9jhqFt1amZycBHGUz2ZVFAVyJgk5PQ8pPFDx3lMQvOrrCP3+GXgcAjxVjAZiMJE0qjUcxxgMFbzrr7/eyLezjPn5eaTTKe3LUQnqbIMiy6a5NeOxGLziQpeqJx/Ta8ZUexafWCqGx/6eiJsr2LOzQXgdAvq86o09GGy+lPCRkWEIrvblNwhFEEJAXJ0YqbG918TEOAignZdaWeGlmLBQ8HK5HAIBf8XyjczcAJRMDEoujfR0PzJzA2Wfpzbhbq2ptGN62odud223SLdI0eoUVbesTdm5cyfOOeccvPvd78aFF16Iffv2QZZl7Ny5Ex/84AexefNm3HjjjZibU8MJd955J77whS9or9+xYwc+9alPNZUhtOS3YKmhr8WwMT92LUCfmVE7WegRPED9IqxaVd1Eh+VQFAXxeByeVieAnPa4x6EKYCzWfNMSmEuT3cDZrhtyBlJ4AI7OE9SYqOBAMmnu+gMBPzrdFG1OCoEQU2OttTI8MgRShXXHENxdmJo6DEmS4HQuHS8tZWJiDD1ehzZbsVZWeEUcnvDXtIZa8PtnkMtl4XB2lP17Njq16HdnV4XifEcbJmsQvJnpKaz31D7ho9tNDOmS0wiGhobw0EMP4ZFHHsHKlSsxMTEBWZbx4IMP4umnn8bPfvYzdHd341vf+hbuvvtu3Hfffbjttttw+eWX49FHH8Vxxx2HRx55BLt27TKtFWMt6B4A+1am0P1Cv+AZTTqdRiabhdexsNNLS96dEjV5UkMtaIkheQuP7boBID3dD0CBs+tEEOowPVst4J9Gr4uCEoIOj1hX7ZUZzM2FEZkLw7VyQ9WvpZ4eyLMyxsZG8Bd/cVJVr50YH0Ofp35HzooWEYqiwOebwvHHb6j7/ZZjakqtPaSu8oIHJbv070VQVwempweq6geaTqcQnpvD6cfXnjDT4xEwWIMrtRkQBAGSJGFwcBDd3d1Yt07tsvPwww9ro90A4B//8R9x3nnnIZvNwuPxYPv27bjuuuvQ0tKCO+64Q3tes7Ck4Bkx9NUO+HyTIFRYNhuMiC5Q0a11yzASVjvWUhIv8GqC13zF1JqFlxe8SrtuQh1IS2nTGm/ncjkEZ2dx0lp1s9DlIk3nShoeVl2Sgrv67vnsNcPDQ1UJXiYjIRAMYOP6yslYiqIgms4hnVPwsi+BTas8ZT+jFXmX6NTUhCWCx4rtqynhqITgakcmm4XfP6PbM8M2+72e2l3BvV4Rr85EDWn+bTXHH388br/9dvz7v/87BgYGcPbZZ+O2227D1NQU/uEf/mHBxoFSitnZWaxcuRKnnXYa1q1bh1AohA996EMN/BeUp+qtXywWw/j4+IL/7M7k5ASos13XzZg42zFRZ+eLcmiCVzJ3iwkgS7tvJgqCl78pVNp1UwcUWYYkmTP1YXY2CFmW0eNW19HjFuD3zzRV7GBo6ChAaFUZmgzq8EBweDE0VD5OVQmfz6f2u10iftfvSyKUkhHPKHh8IIp+X/ls2m6PAEpI3V1f9DIxMQ7B1VbTlIRSaN6NPDmp/17FvD69dcQ+mVg2qvF2vVxyySX4xS9+gd/97ncghODee+/FqlWr8J//+Z/o7+/X/nv99dexcqU6jeKhhx5CJpPBihUr8KMf/ajB/4LF6Ba8gYEBXH755di0aRMuuOACnH/++bjgggtwwQUXmLk+SxgfHwfR0cwXUN0jkxPjkGXZ0DWwwG/poEmREnidQlMKXqlLsxJEEPPPN6eAnrmYu/Pxlm6PiFQqhfn55rGKBwaOQnB31nwDJ+4eHD16pKrXsESNPm/lONThUHrJ3xkiJejxiHX39dTL+PgYiKN+6w4oWInV1BFOTU2AoHBN1QI771adMyMZGhrCvn37tJity+UCpRR///d/j/vvvx+Tk+q/KRQK4emnnwYADA8P4/7778eOHTuwfft2/OhHP8Kbb77ZyH/GInQL3je+8Q1s3rwZL730ElpbW/Hyyy/jYx/7GP7lX/7FzPWZTjQ6j/n5OQhufcXA1NUJSUobnhRRmKy8+AvW7qRNOVsrlUrmh78ubRkzl6dZxeAs6agnv6Pu8QgLHm80uVwOQ0ODoO7emt9D8PQiFApW1cvV55sEQeG8lCMjK0v+Xkyvh2LaZ76FJ0kS/P7pmqzhchAqQnBV16LN55tEt9exYDYlg7mBg4ksXvYlKnoSOt1q4207Cp4kSfjXf/1XbN68GWeffTZCoRBuuukmfPKTn8SWLVtwzTXX4N3vfjc++tGP4s9//jOy2SxuvvlmXHfddTj55JOxYcMGfPnLX8Ytt9zSVAOsdW83Dx06hP/6r/+Cw+GAoihoa2vDLbfcgosvvhiXXXaZmWs0FfYloDqz5wStEHis5qGS5QiFghApQYtj8Res3UkQasLO66lUClRPij0138JzOwTt3DE31PS0DyeddLIpx6yGsbERZDIS3N46BC//2oGBwzjrrL/S9RqfbxJdHseCZuT10OsVcXgygGw2C1Gs39VYCZ9vEoqiVE5YqQHibMdYFWUVSyX7MDcwADw+EAUU4Kw1i+OklBD0eUVMTdkv7HPyySfjkUceKfu3q6++uuwc1NLnX3XVVbjqqqtMWV+t6LbwXC6XVnDd1dWFqakpyLKsueLsCuskr3c3qX4JiVZEbBSzs7PocJe3ljpcQlOm2adSyeW7rKDYwjNH8Hy+KfS4qXbuOlwUIiU1FRubwdGjhwEAgqev5veg7i4QKmrvpQefbwq9HuOShHq9AmRZ1jqQmAVzPVZToL8c1NWJYCCgq6dmJiPBH/BXLNbX6wYGVLfmhMUt2TiV0S14Z555Jn7zm98AAC688EJcd911+MQnPoG/+it9u81mZWxsGIKzBbTC4NdSCBUhuNsxMmLsuBT/zDQ6XeVvTl1uAclUqumKz1OplGa9LQWLW5nVE3TaN4neolgLJdbGm5bjyJFDEJytoBVGT+npGkIIBXV349AhfTERWZYxMzO9pDuzWqxKwpiYGFOzputoGl2KuqFVMDGxvEt2akq1MFdUSFipxg28skXEXCSija7iNBbd34biIvSbbroJJ554IuLxOK644gpTFmYVg4ODIM7qdpLE1YXh4UHD0uwVRUEgMINTOssHyFkjWr9/Bm97W/OkN6dSKYDoCOrnLTwzBtkmkwnMRSI4c8PC89LjofA1geDJsow3D70J4llR8TmV6hdLEbwrMDFxUFeaeyg0i2w2ix5P9VPOK1GIjZpb8jE+Pgbq6qirh2YpxU24TzjhL5Z8Lut5Wk87NsbKoj6kJ598St3vx6kP3VfUj3/848KLKMVll12Gq666Cg8//LApC7OCRCKOQGAG1FNdbZTg7kEsFjVsNl40Oo9EMlmx5oft0puta0MyldLp0lTXn04bX5bAzklvSTZdr0dEcDbY8KbbU1MTSCbiEL2VBa9c/WI5BO8KAIoutyYTpXrqyEpxixQtTsEawavQYaVWiKMFVHDoSlwZHx+DQ6B1ZWgymOAxEeU0Ft2C9/3vf7/s4z/84Q8NW4zVaMXAnu6qXseeX21dVCWYi6inwhdMrYFqvnqeVCoJosPCI1rSivEuzUr1Ur1eAYqiNLx575tvvgEAEFoqC57eriGCpweECjh06I1lj1taqmEUPW5q6nUYiUQQi0UNy9BkEEJAnB26GmCPj41ghVetO6yXNqeAVpdoeMyfUxvLbv/27dsHQHXN/PGPf1wQX5iYmEBLS+2tdxrN4OBRANV3v6DuThAqYGhoUHfG3FKwXeeKCi4UkRJ0e/TtTq0knUqDUB2fv4kWns83CUrIopllhUzNKaxbt97w4+rl0KE38vG7+r8nhAqg7h688ebBZZ87MzMNp0AX1XXWS7dHwLCJ5R6sONzIDE0GzZcmLBWKkGUZo2MjOLXLOMt4lZdibNTYmD+nNpb9VL/+9a8DUG9Wt99+u/Y4IQR9fX34p3/6J/NWZzIDg0chuDuq6l4PAIQIoO6uqjLmlmJychweh4D2JW5OK70UE+MjhhzPKNLpFKCjEwUhFIQKpmRpTk1NodsjLkq9Z668RiauyLKMQ4feAPUYV74ieFdgYvwgYrEoWlsr9371+6fR7al9ynklut0i9s/MI5VKwe3Wl+hVDdWWCVUDdXUiMTeESGQOnZ3l624DgRmkUimsXuLcVsvqVgdemJyyrPE2pzLL3q327t0LALjllluwfft20xdkFbIsY2DgKKi7tuam1N2L0dEBZDIZOBzVCWYpoyPDWOld+ua0ssWBg6OzSCTi8Hobb1UrigJJSsPRqm8nTKhoStKKb2ocvWXqpZwCQWeDMzXHxkaQTCbg7jJQ8FpWAsEDOHToTWza9J6Kz/PP+NDlMn6+c4822HQaxx23wfD3n5qaABXdurOmq4FZjZOTExUFj2Vfr2mt7ztdzJpWUZ08P758wsxy3Pb1rQjOGl8K1tvTiX/59t3LPm/Lli2QJAnPPPMMBEG9Fh599FF87Wtfwx133IGPf/zjFV/7iU98Atdccw3OO+88w9ZdLbrt9u3btyObzeLVV1/FzMwMVq1ahdNPP93UAlQz8fmmkEom4O6srRhY8PQiEzqE0dHhqjvYF5PNZjE+MYb3rFx657emTT3Po6Mj2LjxnTUfzygymYzq3tbZKksVPGNdmplMBv5AACetLZ+J2OemmKqif6LRvJl3PQpeAwXP0wNCRbz55sGKgifLMgLBIE5Y7TLsuIziSd5mCN7k5ATIMlNLaoUJ3tTUJN75zneVfc7w8BBESiqWJNTCmjZVPEdGBusWvODsHBLtm41YVsn7vqj7uStWrMDzzz+Pc889FwDw2GOP4Z3vbPw9SQ+6P9WhoSFcf/31qrm/ejV8Ph9cLhceeOABnHDCCWau0RS0YmDv4mLginPdimCdL44ePVyX4E1OjiObzWJN29JWG9txDg0NNIXgMfek3t6QhIqGJ61MT/sgy7LWHHl/fkr86StVAexrEfGSz4dcLqftRq3kjTcOQnB1VKy/qwVCKKinDwffeL3ic0KhWeRyuUVxTSNgk7zNKD5XFAWTU5OgrjWGvzcAEMEFKrq00UPlGB4awKpWEYJB3WkAtTVgq0uN+X/gA4a9bcO44oor8Oijj+Lcc8/F+Pg4EokETjpJvQfu27cP999/P9LpNHK5HK6//np8+MMfXvQesVgM99xzDw4fPox0Oo3Nmzfja1/7munfU90+j7vuugsf/ehH8cwzz+C///u/8eyzz+LKK6/EXXfdZeLyzOPo0cOgDg+IY3E9k55pylR0Q3C14ciR+uJ4AwNqQ+B1bUu7ULwOih6vAwMDR+s6nlEw96RewVOIYLiFNzGhdrBgqd+vziTx6kxBVFd6RWRzuYaUc2SzWRw5cgh0iXKEWhFbVsI/M12xv2og4AdQECcjcYsUXodgyrzB+fl5pJIJQ0YClYMQAuJoq5hlmsvlMDI6jLU63fTVHHdti4ihweb47tbLe97zHhw5cgSRSASPPfYYLr/8cu1vp5xyCn7+85/jV7/6FX7yk59g27ZtiEQii97jnnvuwVlnnaUNiQ2FQvjlL39p+tp1C96hQ4dw9dVXL7B0PvWpT+HQoUOmLMxsDh8+BOruKRs301sXRdy9OHL0cF2TE44ePYI2l4gOHfGW9W0CBgYON8XYG0289Hb/J6LhSStjY6NqF/8K0wBW5W9celLRjWZoaACZjGSoO5MhtKjv+WaFbE0meF0G1uAV0+Wm2jGMhLWCW24Qcz0QZ2XBm5gYRyaTWXbzWQvr2h2Y8c80XbekWiCE4EMf+hAef/xxPP7447j44ou1v4VCIXzxi1/ExRdfjM985jOIRCIYHl6cobp37178+Mc/xmWXXYYrrrgCBw8eLPs8o9EteCtWrMBLL7204LH+/n6sWGH8DtZsQqFZhELBsu5MALrrokRvH5KJeM11SYqi4PChgzi+ffmJAwBwfLsT8Xi8KVpmVWvhgRoveKOjQ1jZIkKocO5689mbRreB0wMTI3Gp+rsaoa5OUNG1hODNgBKCdhOSVgBV8PwmlCawmkkzBY862xGNziOZTCz629CQaoGtazc+k3JtGwtJDBr+3o3giiuuwHe/+12cdNJJ6OoqJADdddddeM973oPdu3dj165dWLVqVVnPjqIo+MEPfoBdu3Zh165dePLJJ3Hrrbeavm7dW8CbbroJN9xwA97//vdjzZo1mJqawu9//3vs2LHDzPWZghHNfIFC/O/o0cNYu3Zd1a+fmZlGZH4e5/yFvi/48R3ql+bQoTdqOp6RVGvhGZ20IssyRoaH8K7uyscXKMGqVhHDw9bfZN588yAETzeIYPzNkxAC6umrWI8XCPjR6a68EaiXLreINybDhsdGZ2am1RIWR+UJ7fVC8/05A4HFSTcDA0fR6hTQacJGYW2bCELU2t93ves0w9/fatavX48vf/nL+Mu//MsFj0ejUaxduxaEELzwwgsYHS3vXdmyZQt27tyJu+66C4IgIBQKIR6PY/16c2tmdX+yw8PDeOyxx7QemieeeCIeffRRW048P3LkMKjgqLubA3GoDYGPHKnNrfvGGwcAAG/r1HdT7HIL6HSL2usaiWbhkcYkrUxOTiCVTi/rflrX6sDI8KA26cMKJEnC4OAAaJ0bqqUQvCsQDs2WdS0G/P6KjciNoNNNIcuy4TMaA4EZUGeLoT00S2GCVy4GOThwJC9Mxp87l0CxssWBgSqH+DYzH/vYx7Bx48YFj33lK1/B9u3b5tQ39gAAIABJREFUcdlll+E3v/kN3vGOd5R97e233661qLzkkktw7bXXYmbG3CkcQBUW3ve//3185jOfwQ033LDg8Y997GNlZyM1M0eOHAJxd9f9xSKEgLp7cLhmwXsdnW5Rd3IBIQRv73TgzTcPNCzzkFFtliaoCClp3CDII0fUqQHHdSy9WTiuw4E/TiUwPDyIE08s/+UzmqGhAeRyWThNSFhhsDje4cNvoq9v4XGCQT9ObDXv2uhysUxNP3p7jRN1v98PiObWmLKON6Xjtubn5+EP+PGXG8xrzr6uTcSBoaOQZRmU1nbv6e3prKqEoJr31QOryy6leBD4U089VfY5Dz74oPZza2srvvGNb1SxQmM45lqLJRIJTE5OwNlrTGq/4O1DeOZVhMPhBb7s5chms3jjjQM4pdNR1Y7yhE4nXpmOYGhowLIbeDmqdmkSEZKUNmzCxBtvHESnW1w29X5DhxMEqovRqvPFLP6KMWIDoM52UNGFI0cO4eyzz9UeT6fTiMZi6Owx78bdmT/nswYPJQ7OBkEdxif5FEMEJ6jgXCR4rM3g+nbjE1YY69sc6PfNY2pqAuvWHVfTe+gpDudUpq7WYr29vbZrLaYGphUIntqnTxfD3qeaSdSA+gVLpVI4sau6noFv73KCEuD1119rsOBVb+EpioJMJlN3eyV1s/A6Tu1a/ubkdVCsbnPg9T/vx6WXfqSu4+rl8OE3Ibi7TInfMVTvQu+i+XhMhDrd5rkF210CCBZbSfUgSRIS8RicfW/T9Xwll4Hb7cYFF1yAp556ClJO/1QM4vAumnQyOHgUlBBDO6yUsi4vpgMDR2sWPE59WNJaLBwO45ZbbsHY2BicTieOP/543H333eju7sb+/fuxdetWpNNprF27Fjt27EBPT3XNnKuB1bEJVY4EqgRrJD04eLQqwXvttVcgEKI7fsfwiBTr2514bf+f8JGPfLTa5RpGLUkrgOoKrVfwDh06iHQ6jRO79W0WTupy4pnhAUQiEXR0GN+UuJhcLqfG71rMv6EJ3j4E/fsX/LuY4HW4zHNpipSg1SUaNh4LgBYPpKK+hBVFzuCCD12A6667DoqiYM8T5V1tZRE8mA0tjD8ODBzG6lYRDsG82Ge3W0CLU71XvP/9b4EKdBuiextYTx9NQgiuvfZaPPnkk9i9ezfWr1+Pe++9F7Is4+abb8bWrVvx5JNPYtOmTbj33ntrPo4eBocGILg7q24YXQmtkXSVBeH7X/0Tju9wwC1WvxM/qduJ8Ylxw11K1ZBKpdSp1DrjoEaOCOrvfwlOgeKETn2ts07udUFRgFdeeWn5J9fJ5OQ4JCltmAdhKdgxBgcLiRAFC8/c+G6Hixhq4c3NhQEARNTXlYZQB5566ins3LkTv/3tb0F0zGVkUNGzIOEml8thZHgI69rMbZNICMHaVgEDBjWd51SPeX6PIjo7O7F5c6H/2+mnn46pqSkcOHAALpcLmzZtAgBceeWVeOKJJ0xbh6IoGBoaBHVVN/9uOai7B+Njo7ozAX2+KUzPTOMd3bX1OmSv27//TzW93gjS6TRoFTcZNihWkuorTchkJPS//Ee8o9upeze+0iuir8WBfX94vq5j62FwUO3KY5QHYSmouwuEUO2YgFpjSgC0GjwWqJQOJ0Vo1jjBY904iM6m0URwIJVKYffu3ermq4oNLBHdiMeiWsOIiYlxSCYVnJeyrs2JGf8MYjH7F6DbEUsErxhZlvGLX/wCW7Zsgc/nw5o1hb553d3dkGUZc3PGdwMH1KyyZCIOWuXA1+UQ3N3IZjOYnKzco6+YV17pB6BaHrXQ6xXR53XgT396uabXG0E6ndLfZQULXZr18Oqrf0IimcTpK/V30yeE4LQ+FwYGj5o+RHdkZAhUdIEYMP9uOdT5eJ0YGRnSHguHQ2gzsQaP0e4SEJ6bM6zrTzRaneDVAxHdUBRF63rC6jTXWiF4+TheI2pDOVWUJRjFN7/5TXi9Xnz84x/Hb3/7W0Pes0dnRtqRI38GoAqUkQhuNTszHJ7Gpk3lu7AX8+fX+rGmzVFXnOXkHideOHIILpeC9nZzeg8uhaJk9SesAJo4ut0UfX21d9J4/vnfodMjVh37PG2lG3tH43jppedw7bXX1nz85RgbGwFxdZtSy1UO6urGyMgwentbQQhBLBZBu9P8Y7e7KDKZDNxuoL29/s4ouZxq+ZuZ6MMggrrRFMUc+vra4PONwesUTGm2XcqafLu7QGASfX1nm348zkIsFbxt27ZhdHQUDzzwACilWL16NaamCjvuUCgESik6O6srCJ+djUGWl99pHjhwCCDE8GnKxNkGQkUcPHgIp5++dOLK7GwQRwcG8IE663029rjw3HgcTz/9DM455/11vVctRKNxKET/DYKJ48xMGIFAtKZjjo+P4cCBA/jghlbQKgWlzSlgY48TTz35JC688DJThpdms1mMj49B7LIue5a6O5GcG8ChQ8Po7e2Df8aPTof5jps2p/rZDw6OG5JxGAiEQQWnqUXnDCaqExN+eDxdOPTmIaxuMX5YbjncIkWv14EDB97Ali1Lfw/KbQzv/qevIRIyPnbf0d2Lrd+6Z9nnbdmyBU6nEy6XumnYvHnzgux9M9iyZQseeOABbSJDPVgmePfddx8OHDiAnTt3all6p556KlKpFPr7+7Fp0yY8/PDDuOiii0xbw8TEOARXOwg1didH8iLKpjUvBXNDnlKjO5OxulVEp1tEf/+LDRG8VCoF6OyyAhQEr54hsE8++TgcAsWZq2obt/NXa704+FoYzz33O5x//odqXkclpqen1KJigzdUSyHkJ4NPTIyht7cPkcgc1ndaIXjqMebm5gwRvEQibol1BxQELx6PI5PJYHJqEu9ba74rlbGqRcDYaG39XSOhIK7aYHzz+J+P6BdR1kPTjlgieEePHsV//Md/YMOGDbjyyisBAOvWrcP3v/99bN++HXfeeeeCsgSzmJgcB3GY4/6jzg5MTCwfw+t/eR9WtjjQU6GTvaIoiKZzSOcUvOxLYNMqT9mdJyEEG3uceOmNAw2Zgp5MJWtyadbaTzMYDOCPf3wBZ61yw1OjBbO+3YnjOpx44ok9OO+88w0fXsxiuFYKHhulMzk5gXe+811IJJNoXWH+tcAELxIxJt6eqvZ6qod8AlUqlcLU1ARkWcaqFvPjd4xVLSIOjIQRi8XQ2mpegwCreOyxx/Dzn/8cuVwOra2tuOuuu/D2t78djz76KPbs2YO2tjYcPnwYK1euxB133IFt27ZhbGwMp556Ku69914QQrB792789Kc/RSaj1lPeeuuteO9737voWH6/H9/61rcwNTWFdDqND3/4w7j++ut1r9WSK+zEE0/E4cPlU3HPOOMM7N692/Q1ZLNZzAYDcPScYsr7U1c74pGhJS/icDiEgcEBnHd85RtSvy+JUErNHnt8IAoowFlrytcmvbPXjX2TCbz66p/w13/9N/X/I6oglUoDVL+lVW9Zwm9+swdEUfDetfU1Fj5nnRcPHQzjD394Dn/zN+fV9V6lWNHtvxQiOCE4PJiZmUE0qrrIWkzO0ASAlvymIxqdN+T90ul0VS7yemAeHklKa14ZNlPRCtixpqYmcNJJJ1t2XKP44he/qLk0zz//fPz5z3/GQw89BKfTiWeeeQa33347Hn74YQDA66+/jt27d2PVqlX43Oc+h6985St48MEH4fV6ccUVV2Dfvn143/veh7PPPhsXX3wxCCEYGhrCpz/9aTz77LOLjn3rrbfihhtuwFlnnQVJkvDpT38a73rXu/DXf/3XutZuedJKowgGA1AURWseazRE68I+U1Hw+vvVOrBTeiu7Tw6H0ot+ryR4a9tEdLhF9L/8ouWCl06lQMQqbuykdgsvFJrFc8/uxWkr3XXXl/1FlxNr2hzYs/sxvO995xhq5c3MTENwtlhnqTAcrZiZ8WF+Xs10bLEghucUCERKMD9vjOBJkgRYJXhF1+LcXBgCJej2WNeXts+rHn9y0p6CV+zS3L59Ow4dOoS/+7u/A6B6qIqviTPOOAOrVq0CAGzcuBFr167VkuxOPvlkjI6O4n3vex/Gx8fxla98BTMzMxBFEcFgEIFAAH19hfZ8iUQCL730EkJFTQPi8TgGBwe54JXCuqOXm3BuBNRRGDvytredUPY5/S//EStaHNoFX45MSfJN6e/FMLdm/8E/I5FIwOs1b6xKKWkpDTiriOERUvOIoMcf3wVFlvE36+t31RFC8P7jWvDzg0G88MKzOPfcLXW/JyMQ8AM6O4UUU0+bLAAgjhb4A36ttstTRTODdFZecOx0Vt+xCSHwOgXEYrUlIJWSyWYtEzzkE2NyuRymp33o9phfxlFMu4vCIRDMmDBT0GoURcHf/u3f4sYbbyz7d2YJAoAgCIt+z+VyANTxc7fddhs++MEPQpZlnHbaaYvuFbIsgxCCRx55BA5HbS5oy+vwGoXWusikWVvsfUOh8iNT5ubCGBg4glN6jA3Mv7PXhWwuh9dee8XQ910KWZaRkdJVWzJUEKtOWgkGA3j22d/h3QZYd4wTu5xY2+bA7v/3qKFjg0LhEEgtgidncMEFapus888/H4pcneBR0YP5+UhNgpfKKguOncrqT4hwCwSJxOJBqrWQy2YBq0Qnf5xcLgf/jA/dJg3KrQQlBN1uUXOB25ktW7Zg165dmJ5W/y25XA4HDlQ/viwajWLdOnXG5y9/+UvV4i+htbUVZ555Jnbu3Kk95vP5EAjob4BwzFh4TPD0ti6qGuoAoWLFGWGvvNIPBUu7M2thbZsD7S4RL7/8It77XmvqetjFWLXrrgYLb8+eXwGKjHOOMy4RgxCC845vwc8OhPDcc7/Heed9sO73VBQFkcgcaHv1LcVYmyxFUfJtsqq7RojohSLLCOXT1d2ifuFwi2TBsTurea0ArXi7XhQABFZZWepxFEVGcDaAdT3Wj9nqcBHM1tCaraO7t6qMymretxbOOussfOlLX8LnP/955HI5ZDIZXHTRRTj11FOrep+vfe1ruOGGG9DR0YFzzjmnYmnavffei3vuuQeXXHIJAKClpQXf/va3F7g+l+KYEbxYLKp2wDCpzocQAiq6K7p4+vtfRK/XgRUGB8cpIdjY48CfDryGZDIJj8ckQS9CE60qyhLY86vptBIMBvD888/gzJVuw5shn9DpxLp2J/bseQznnPP+umN5qVQKuWwWolh9uQkRHEgl5rTkLcFbXdILyR+TxfBcVTRAdokUqXhKO7arQ7+ryCkQpA0a6ksJgQLj0+3Lox4nl8sinZbQ7rI+U7LDJWCihubbemrlzKTcPLxLL70Ul1566aLHP/KRj+AjHylMKPnCF76w4O/FM/Quv/xyXH755drvN910U9lj9vX14b777qtt8TiGXJqxWFTrsGAaglPLlCsmGp3HkSNvGu7OZGzsdSObzeLAgddMef9Sqh4NxCBCVb00WWbm2QbE7hYthRC8/zgvwmE1Y7Neksm8a49aU0tWDKGFujIApnb8L8YpkJrLTEohhAAGtSlbHvU4bO3V9h1lcc9LL70Ubrcb6axc9QpanRSJZFJLw+dYwzEjeKlUdb0fa4KISKUX73j3738FsqzU3DtzOY5rd6DFKeCVV6zpramJVsn5ZMkX7EaglCZfEEH3DXJ+PoLnnvsdTjPBumOc0KlmbP7617u0RsK1kkioYmPUFI5qYMdMpZIQKam6C02tOCipuxk4w+l0Akp9n4FelPxnncup//dWObGknrgng2XSGpX0w9HHMSN46XRadxbYsjfuSlAR6dTiG8D+/X9Ch/v/t3fmsY1d973/Xu6kKC4SRYraRe3raDQazYzjZexJPXY88VYUg9oI0vSPFwQGghTtH0Fd2K7b9woDLQwkrYuiQIO0DhrEqINxJimc9xC/FEjrOk78ZmyPPYs9uzTaJe53O+f9cXkpaqG43XtJDc/nH49k8t6ry8vzPb/dgrBOtT4mjsOg34rz5z7QNAkjH6pobe9YUzD5wlS8S/MXv/jfkCQJ91RYd7cXHMfhnnYXFhcXce7cBxUdSxSlzDGNjwepz7UkSTCbjMs2NHHIZtlVitVqBag2xyoIVT4rdZNTSsxTfX3uaKJS3w8AdrOy9KZS2riEGcVRN4KnCEFxf265WXMcZ9ohOKIo4OOPzmPQb9W1V99Qsx2pdBqXDZi1lc2g2hbDKzSjjDNZds2+2o4kSfjl//0/6PfbENijhEMLRgJ2eBwW/OIXP6/oOLKc+dwN6AW5HTUuLUmSYdYdAJg5TjPBczgc4AwSPErUzYlyr2zm0j4zu8W0ZTSRvYyZlraM27mSVnuM0qkbwaOUFp32XP5wSW7HuJSLFz+FIIoYLHP2XbFEfHaYTRzOn/9/up4HyM3S3GrNFJxRxpmLEryPPjqHjWgUM+HiE3DUlmzLSQm/nk8WPbbGzHGYDtpx4eMPKxqou+kSNU5wsnBq1qFRMTD1vNqd0+VqACWFnw1NyGxgLRbl+TUo5LkFVWO12jAwiqNuBE+huC9n+cMldx7/ww/PwWLi0OPVN5nBZubQ7bHiw/OVueaKQRRVC6809x1nMkMQCy9q7777n3DZzBjwF79JUFuyJUSKn16J4f354l1Fk0EHKID33vuvot+zHbNZtUSNiUPlQjOxL5PJbFzeB5QcE5NJmyXE6XSBysYInnoeq1X5ThpoFGdRT1lp7JhRGnUjeEqMQN+Hi1KyowPAhY/Po8tjNSRzrs9vw9z8HNbW1nQ9Tz4LryCcuWBWmiRJOH/+Aww32UqKR+3Wkq1YmpwWhN1WfFBB0o9qLVCDEi+2kDmn2WwGMVDxCKWaCZ7H4wGRxay7UU+opDwbDQ1KOYLRhjEAqA2UzOYqxHzrmDoSPJv+QXEiZ0cfAcDGxgZuz91GpMRhpeWiDkX99NOPdT1PVrRKtfA4M6QCrauuXfsc6XQa/f7S7lkpLdl2o99vw+dXP98sLygRmy1jjRqwYG9HFQmHww5RJoa5NkWCLc97JXg8yoQJKukf0yKZczQ2Kj0dS31WtEDKnNNiMT6rt56pG8FzuZzg9F6MqLiln6WaQKK3O1OltcECh8WEixc/0fU8WdEqNUGDM4ESsqcb58qVSwCAbo+x9WzdXisIIbh2rbw5ZQ0NSq1g0Rm9WpI5p8vVAAqgjCz5shBkArtGg3R9Pj8AYwSPSknY7Y5sE2NBNl7w+Mw59RhEzMhPHQmeAUFxIsLp3BS8K1cuwWLi0Oo2pqGNiePQ3mjBZ59d1vU8myn4pQqeOfP+/KJw8+YNeByWkkfcVFoMHM7MQ7t583pJ71NR5xFSWZu6tFJQn2u3W+nQwhukeLxM4XBoUzbS3Ky0tiJiQpPj7QUVk2hqDmQ3KakyCscrRa3dU6+BYQx1I3iNjR7IIq9rjIVK6axrBgCufn4FYbcFFgNrozoarZibu1VSC69SyWaWlSh4XE7T3nwsLS6U1cy30mJgl5WDw2JSJh6UgcVigdPVYIiFsh0iKQk6gYDST9CoBTwlbboFKyUQMFDwpCRaAoHsdzUuGC94cVGGyWQyfHBzvVM3vTS9Xh8ACirx4Kza95ukRAKRRXi9ypeIEIKbN69josnYWxx2W0ApcPv2TfT1DehyjrJrznLqxfIRja4jaCt9g1BJE2RAEeMGm7mi+W5+nx+LUeMLiamYgt3uQFNTMwAgIRIU10q3MpIS1Wxit93uQGOjB2mhyM4j2/u4FtnXlVIKIsQQDLbC4/GC4zhE+dIEz7ptA7v952KI8gRej1ezpB9GcdTN3fb7mwAouzs9oGJyy3lWV1eQ5nlDJykDQCjrmruh2zlUC610l6byekLyW3iCIJS1gGhSDGzKKbkog0AgoNvztRdUTMDvb8ps6oyxWGRKkeCl7Dm1IBxuAxGLEzxLY9ueP+eDSmlQIqG1NQyLxQKf14sNvrRktqFtNbXbfy6GDV5GoCVY8vsYlVE3gtfcrOx+iajPgqQeV41FqLOuAk5jBc9rN8Fs4sp2zRVDKUX8WylcIM1xnGE987dT6XmDwVYQIWZ4ATiV4giHw1kLr9QFvBziAgHF5gZPC8LhNtAi75/V1w/O6gZntsPeOgOrr7+ocxBhI3suAAi1hrGSLu1+zYSdaHKY0GDl8Fh/Y0kNElRW0hShUGvJ72NURt0IXiCg7KaIqM38ru2ox23J7NpUwfE7ja2zMXEc/A5LdsK7HhBCyptdxm2+Px92u70qWXMAIMiKa61cWlvDoEQClYxza1JKQIQ4QqEwnE4nHHa7IYK3kREJVWS1oL29E0Tii4qDchwHk9UJk90Dm7+/6LZ9JL2RPRcAhMPtWE6VVsrBcRwa7WYEXBYcDrtKbhmYkgjivGJlMoylbgTP5XKhoaERtNgYQYkQIQaLxZpNr97YWAdQ+ugRLWi0ctnz1xJcERae1+uvShIBpRQxQc7GYMuhra0dAED4Da0uqyBUiIMSGeFwGziOQzAYwmpKf8FbzQheKBTS7JidnV0AAMLr1zhB5tfhbvRkSxLa2zuRFmVEDXzmFhNKDLujo9OwczIU6kbwACAcDoPw+gleMBjKBqFjsSicVjPMJe7+tJi15bJyiEWNW3S1JNASxCpvvIUXFwlEmWQ9AeWgLtgyX+Jmo8wEjNxzdXX1AABCrW1YTRd//8pNwFhJKVmGzc3apcd0dnYDAOS0foJH+TX09PTmnFP5zO7EjWsYoJ5L/XsZxlFXgtfe3gEqRgu7L8pZgMTolh1bKpUqK3FCi1lbDoup7I4h1aatrR1xXkJSNNbKU3fdqpVWDg0Nbvj8TSAlLtjlJmAAAEmvwWQyZa87HG7DWkqEWKRbuNwEjKWkhJZAS8WT4nNxuVxoaQmVfP+KhRIJcnoDPd2bgtfV1Q2TicPtmHENA27HRXg8nqw3iGEcdSV4bW3tSoxA3jtGUOoCRIkEmY9vWSxlWSqrC7sWs7a0nFO26/FNprISM2hOz8d89PREAMDQBUg5nyJ4qqVULn2RftD0aknvKTcBAwDk1Ao6O7uzPVw7OjpBoQhSMZSbgLGYJOjs0t5C6evrA02v6pL4o1iOFL29kezv7HYH2ts6cMtQwZMRiQzoOi6MsTt1JXgdHZkYQXpvl1OpCxDJuJVU9wigNKQt53HWIr2e03Bsy24obltaxjlozvt3p7e3DyaTCdejBo2KyXAjKiAcDldcV9bfPwBZiGeLwYuh3AQMSglIehX9/YPZ36nP+EKiOMErJwGDlwnWUmL2XFrS1zcAWUzqUt5BUsvZc+TSPzCMWzEJsgHZtTFBxkpSxMDAYOEXMzSnrgQvGyMoEGMpdQGS06rgbe54bTZbVZrSAoAoU82a+u7GpoVW4t+XWVD2svAcDgcivRF8vm7cjlsiFNejEkZHJys+lrqYysnyZ+sVC0mvgxJpywIeDIZgt9swF9fv/t2JS6Co3BreDT3vn5xcRiAQ3NINCQAGB4cgyMSQON6NDTFzzmHdz8XYSV0Jntvthr+MGEshSHoNDqcrW4MHKAs3X6X0eqXHoX5NabNxm1LbtGVdmnvHfcYnpjAfExEXjBmOeW1DgCgTjI9XLng9PRHYbHbISf3qIFXUcwwPj2R/ZzKZ0NMdwZyOi7fqbu7t7dP82J2d3cr9Sy1pelxKKUh6GUNDO4VmeHgUAHB1XX+vwtV1AQ67Peu6ZxhLXQkeoCxIVOO0Z8Kvoae7Z4sl6PX6kBblopMHtCQuEHh92hUEb2dT8EoTJJp5faFEh6mpaVCUNtOuEj5d4WGzWjEyMlbxsSwWCwYGhkBS+guelFhAMNS6I/mhN9KPOwlJNw/D7ZiIJr+/ohKOfJjN5sz901bwiBAFkfhdLSuv14e2cBs+N0DwPt8QMTg0wubgVYm6FDyZj2k2XZlSGSS9vmO3q3agMKIIeDtRgWpaELwddfYb3d4irFB2K5HBcVxBwevs7EZLIIALy/oLHqEUn64KmJic0swNPDY2Djm9oVtXH0C59yS1hLHRiR3/b2BgEDKhmNchEYNSihsxGQODI4VfXCbDw6PK/dOwEbecUBoxDA3tft3jEwdwIyrq2vRgNSVhNSVhfPyAbudg7E3dCZ6aoaVVrQ9Jb4BSsqW2B0C2i8JyqjTXUqWNaQWZYiMt6dq2KCsM2yy8QtmtlMqwWG0FY6Icx+Hw7D24ui4goXN5wvUNEXFexuzsMc2OqbpGpcS8ZsfcjpxaAiUSJiZ2umH7+pSEiBtR7QVvnSeI8dKWRBmtGRlRXIxauoXl5CKamgIIBncvlB8fPwCJUFzb0M/Ku7KmHHtsbOcmhWEMdSd4qu9cTq1ocjz1ONstvLa2DgDFZ8upVNqYdjGpJBTo2cVh08Lb+rcVzG4lEuy24v6e2dljIBS4sKTvuJ0Pl9Kw222YnDyo2THb2zvh9fohx/UTPCk+D5PZjOHhnW5Yj8eD1lCrLov39cwx9Uy66O7uhd3ugJy4o8nxKCUgqSWMjo7nfc3Q0DDsNhsu6ehGv7TKI9gSZC3FqkjdCZ7b7VaKW7USvPQK3O7GLQkrAOB0OhEKhkquJ6u0Me3tzK6+u7u3wCvLR02IoWTr31You5USsehkms7OLrSF23BeR8ETCcWFZR7T07Ow20vveJ8PjuMwNXUQJLmw0+2rESQxj+Hh0bz3c2R0HDeiEmSN43hX1wU0NDSgvb1D0+PmYjabMTIyBpJc1KS8hqTXQCR+T8GzWm0YHZvA5TVRl5IeQaa4tiFi8sA0q7+rInUneADQ19cPymtT3ErTq+jr272IdHBoBDdjMoiBjWmvRwU0+Zt2CLCWOJ0ZEZZLE3NKpM33FoDjOBy7537cjIpYKcItXI4r+NIKj7REcOzYvUVdUylMTR0CkUVdsjUJH4XMR3Fw6lDe1wwNjUKQiablCZRSXItKGBoa1X2O29jYBGQhDqpBs3cpYymOju6dlDQ1dQgbaQl3SvTKFMNnazwkQnGCX9vvAAAgAElEQVTwYP7PjKE/dSp4A5DFVMXFrVTmIfNR9PfvPmh1eHgUKVE2rE8fURekTJq1XqhTmrdbeAWRBTQ0FF/YfezYF8ABOL9Y2MorxxV8bjENn9e7586/XEZGxmCz2SHFbml+bDFzzKmp6T3OrzwDWmYerqZlbKQljI1pf7+2Mz6uxLmkeOVuTTlxB52d3Tvq77Zz4MBBcByHT1e0d2t+usLD5XRiYGBI82MziqdOBU+JLVUax1PfH4ns3ollbGwSHAdd4wK53I6JSAoyDhzQLh61Gy6XCwBKz3SlIhoaXEW/vKmpGSMjYzi/xBe0xkt1BccFgitrAo4eu08Xa8Vms2FycgpyYi7bUk0r5PhtdHdH9rTiGxs96Ors0rSA/7NM0oUeG4TtBIOtaG5uqTiOR2UBcmoFk5OFMyM9Hi/6+wfx6aq2sU+ZUFxaE3Fg6pCmvUcZpWOI4L3yyit46KGHMDQ0hEuXLmV/f/XqVZw+fRonT57E6dOnce3aNSMuBx0dXbBYrRV3c5BTK+A4Lm8BrsfjQaS3Hxc1/gLl49MVHiaTSZMC6r3IWnilCl6JFh4A3POF+7GWkgpmHJbqCv5oKQ1CKe65576SrqcUZmZmQcQU5JR2XUOImICcWsHhw7MFXzs2fgC3oiJ4WRvB/XxdQHNTE4JB/QeXchyHyckDkJML2frNcpASiwAlRZcCHDo0i4V4cW70Yrm2ISAlypiePqzZMRnlYYjgnThxAj/4wQ/Q3r61E/2LL76IZ555Bm+//TaeeeYZvPDCC0ZcDiwWCyK9fSDpSgVvGe3tnXsmYhyePYb5uIjlIpv5lguhFB8tCxgbm8gKkl5YLBY4HE5QuXjLlVIKWeLR2NhY0rmmpw/DbrPhXBFuzVI4v5RGV2eXrtmsk5MHYbFYIUVvanZM9ViHDhUheGMTkCnFNQ2sPJlQXN0QMTZ+wLCki/HxA0pj9go2pnJiHna7Y0f/zHxMT88AAD7RsAb0k2WlsYHeG1FGYQwRvJmZGYTDW1NxV1ZWcOHCBZw6dQoAcOrUKVy4cAGrq6V1mi+X/v5ByJlehOWgNu4t1AT28OGj4Lji4lCVcDMqYiMt4ciRe3Q9j4rb3ViS4IGIACVoaChN8BwOBw5OH8aFZV6zziFLSQlzMRH3fOF+TY6XD4fDobg147c0c2tKsZvo7Oopqs5yYGAINqsVn61VvnjfiongpeItJS0YGRmD2WyBVGZ5B6UUJHkHY2PjRbsSA4EW9PT04hON4niEUnyyKmBi8qCmmcCM8qiaQ3l+fh6hUCjbYsdsNiMYDGJ+fh5NTaW1xWpuLr3D/aFDB/Czn70FOb0Ki6v0oZ+E3wCVRRw8OImWlvyLeEtLI6amDuLcJx/ieHcDTDrtjj9YSMFht+Phhx8sOhOyEgKBJqzdKH5zQjNdMzo6Qnver9149NGH8e67v8LlVR6jgcp7hJ5fTIPjOHzpSw/D7y/tWkrlxInj+O1vfw05uQxLQ/nDZYFNd+aDxx8r+h5OHpjEZxfOV3ReQInfmUwm3HffkYonShRPI8bGRvHxpRtlvZsIUchCAseOHSnpmXvggfvx/e9/H+tpGT5HZS3AbkRFJAQZJ04cL/m5Z2jPXRFBXVmJg5S4+w8EFPeqnFwpS/DUuEww2Imlpb2nqB89eh8++OADfLYmYKDEQvJiSEsEHy8LOPaFBxCPS4jH9ZnqnovL1QiQ4nfeJDODkOPsBe/XdtraImh0N+KjpXTFgkcpxUfLPEZGxiBJlpKvpVQikRFYrTZI0RsVC54YVRb+sbHpoq97YGAM77//G6ymJDQ5y/+6X1kXEOmNIJWiSKX0f75UhofHcf78eRAxCZO1+IQnANnC/97e4ZI+55GRKQDfx4XlNO7pqCw8cGEpDavFgp6e0q4BABNIHahalmY4HMbCwkJ2UKksy1hcXNzh+tSLxkYPgqHWsruyy8llNDZ60dJSeBE7eHAGnsZG/Hq++BlppfD/FtIQZYIHHjihy/F3w+v1Za22YlBfW07DYbPZjMOzR3FpVQAvVeYanItLWEsZ5/q12x2YmprWxK0pR2+gp7evqGdOZWJCcUGqGZblkBAJ5mMixiemyj5Guagu1HLcmlJiHq3htpL7ygaDIXR1duHCSv57djDkxMHQ3p6UTXfmlK7TSxjFUzXBa25uxsjICM6ePQsAOHv2LEZGRkp2Z1bC4MAQaHqlvOnd6RUMDAwWFcC3WCx44PgXcXmVx6qG2V+A8qX69Z00IpG+LZOc9cbn8yvT44vsJEIzA1F9ZU5xmJ09BonQiks8Pl5Ow2w2Z5MTjODIkWMgUjrbwLgcCB+FnF7D0SOl9fwMhVrR3NyMKxXU432+xoMCVUm6aG/vUNq0lVieQIkEklzCgTJbxs0cPopbUSFv8/epkBNTBQTvZlREjJcxM3O0rGtgaI8hgveXf/mXuP/++3Hnzh187Wtfw2OPPQYAeOmll/D666/j5MmTeP311/Hnf/7nRlxOlv7+QWXRFkpzNRAxBVmIlzS1+PjxE+BMJrw3p62Vd2VNwEpSxBe/+Iimxy2Ez+cDsClkhSBiCmaLBQ0N5bmI+vsH4fV4KpqgQCnFJysCRkfGSy6PqISJiQOw2x2QouXFogDVncnh8OHSFk+O4zAxMYVrG2LZbcaurAlocLmqMsNNLU8gyYWSLGQ5sQhKy59xePjwEQDAheXyk80uLPOwWCy618UyiscQwfuzP/sz/Md//AcuXLiAX/3qV/jpT38KAOjr68Mbb7yBt99+G2+88QYiEWO/UGrH91LrpNTXl9Ix3u9vwuzsMXywmEa6QrdcLu/eTsLn82Fm5ohmxywG1U1EiuxWQ6UkfL6mslPaTSYTpg/N4sq6WPaMwTsJxZ15aKZwSr+WWK02HDp0GHL8dtm9NeX4TQwMDGbHTpXC2NgEeIngVhnjgiil+HxDxOjYhO7txPIxMXEAJFNAXixSYh4WqxWDg+V1NgmFwuho78Any+VZxqo7c3z8gCFJZIziqMtOKyqtrWE4XQ2Qk6XF8eTUMiwWa8kNmk+e/BJ4ieA3d7Sx8u7ERXy+LuDEiUcM7+CgLrx028w3qzcCq3fnxoWKKTRXOKNvevowRJmU3S7r4goPjtu7JZdezM4eVRbtMtyaMr8BOb1RdtxxZGQMHMeVFcdbSsqI8TLGxqpXQzY8rFx/KW5NklzA8NAorNbyZxzOHD6Km1EBMaH0TcrtmIhoWsKMwZsrxt7UteCZTCYM9A+ApEtrMUZSy+jtjZQsMt3dvRgeGsF/z6X3dC8VExAHgP+8nYTdbsODDxqXrKKStfC2C56vF1bfLhsBOYnm5soEb2hoBA67vexJ6JfWBER6+wv2VNSD0dEJOJ0uiLHS3ZpS9AY4jiuq2Hw3XK4G9Pb04vMyxgV9tq7cayPaieXD7XYrg5uLFDwiJiDz0YpjjocOzYIC+LQMN/onKzzMZnNVNleM/NS14AFKca7MR0Gk4h5qSiTI6bWym8A+8ugpRHkJH+0x9qaYgPgGL+OjJR733feQ7p1VdsPhcMDpathh4e0GpQSymKx4goPFYsHY+CSulDHCJSbImIsp41mqgcViwaFDsyAlujUppZBjNzE4OFJWhqvK6Ngk5mJiye70q+sCgi1BBAItZZ9bC8bGJiCnVotqZ7c5HaEykW5ra0drqBUXSixCp5Ti0xUBI8OjVfluMvJT94KnxuFIkXE8Ob0KUJJ3QkIhxscPoC3chv+aS1U0nui/bycBDnj44UfLPkalBAItIGKi4OuolAIoLTk9fDcmJqYQ5SUsltiqTXXnTU4an1qvMjMzq4wMKsGtSQRlFNDsbGUx2pGRMRBa2hR0mVJcj0oYqaJ1p6JMCadFhR/kxAIaG70Vz+zjOA7Th2ZxfUNAUix+o7CYlLCakjBdpkXO0I+6F7ze3j6YTKaiE1fUvn6lJKzkYjKZcPKRU9n4WzmkJYLfLKQxM3O0qjvvYEsLIBe28IigiGIp9WP5UN1UpcajPlsT0OhuRGdnV8XXUC6jo+NwOJwQYzt7a+aLfSq9M7mKGw/39w/AYrHgagnP3J24BF4iGNZ53FQxRCL9sFitkApsFiilIKkljI2NadLzc3p6BoQCl0two3+6woMD2Oy7GqTuBc9ms6Grq2eH4OVbgOTUMkKhMNzu8rsgHD36BXgaG/Fft8ubx/fBQgq8RHDy5JfKvgYtaG5uARESBS1VdYinFuLc1NSM1lBrSZsFSpXGx9XMNAQUt+bBg4dAErd3pNjni33K8Vvo7x+A1+ur6NxWqw2RSB+ul2DhXcvE/IaGRio6txZYrVYM9A+BpPYeqEuEGIiYwvDw3sNei6WnJwKvx1NS3PjiqoBIpL/iz4yhPXUveIASxyPp1S1jSHZbgCil2YLzSrBarXjoxElcWROwVKJrjlCK/55LY2BgMO9YIqNoaWkBJRKovHetkuL25DSbwj4yOoEbUanourKlpIy4IGNkRJtFsBIOHpwBkYSiXHNEiENOr2s2VmZwcATz8eLjeNc3BASDQfh8fk3OXynDw6OQ0+vZePtum1J1wrxWVqnJZMLUwRlcWRMhFfG8RXklVjzFrLuahAkeFHcPJTJIen3P11EhBiLxZbszczl+/IuwWCx4b640K+/iCo/1tISHH66udQdsuiipsHccj4gJ+Px+zUonRkZGIcgEc/HirBXVUqkF19z4+CTMFguk+O2Cr1Vfo5VrbHBwGJQqKfOFIJTiZkzG4GD175nK0NAwAGTbAe62KZWTi/B4fZq4z1UOHDgIQSa4XkSWq+r6PFCl5CjG3jDBA7KzsgoVtqr/v9jZWnvh8XgwO3sM5xb5kjLn3ptPwe/3Y2qq+jtIdVEhGZdlPqgYR1DDBWhwUFn4rm8UJ3jXNwT4NF4Ey8XhcGBkeAwkUbg3pBSfQygULmoUUDFEIv3gOKXlVSFWUjJSolyxN0NLenv7YLZY8lrHigdmGcNDw5rO7BsZUcYLXS4ibnx5TUCTv6nihBmGPjDBgxIX8vr8BRNX5NQyHA4nwuE2Tc574sRJCDIpelbeclLC1XUBDz30cHasUjVRYnIciLC34EFMaDol2+PxojXUiuvRwgsQpRQ3YjKGhkcNG1xaiAMHpiDzMZA9WtpRIoIklzSt43K5XGhv6yhK8NTXlJuNrAdWqxW9PZG8GdVUSkIWkmWXDOXDbrdjaGgEV9b2vm8Sofh8XcTkgYM186wxtsIEL0N/3wBoeu/5biS9ikikT7PEh97eCLq6uvGbO+miShR+eycFk8mEe+99QJPzV4rVaoPX59vTwqNEgiymNLeuBgaHcSsmgxS4b+s8QYyXNF8EK2EiM3VgrwkAai9IddqBVkT6BjCXkAs+b7djIlxOJ0IhY6aXFEt//yBIem3XWkbVA6NFyGE7ExMHsJwU8zaTBpQhuYJMqtqVhrE3TPAy9PX1QxbiIHlG3igF5+uauDNzeeCBE1hIiJiL7528IhOKc0s8pqYO1VT2VygYymZh7oZq/QWD2gpef/8gUqKM5eTeRdw3M1ZgLVkqwWAIgUBwz3o8KbEAi9WquVBHIv1IiTJW03vft7m4hJ4e7TZ3WtHX1w9KCQi/tuP/yallWKxWtLd3an7e0dEJAHuXw3y+JoDjOIyM1E7ck7GV2nqaq0gk0g8AIHnieHJ6FQDVPDPyyJFjsFgsOLe4d3/NK2sCEoKMe++9X9PzV0owGAL2KD5Xrb9gMKTpeVUBK9QQ+WZUhN1uQ0dH9ervdmNsbBwktZh3AgBJLWBocBhWq1XT86oTD+b2uG8SoVhISOgxcNxUsajfPzm10xtD0qvo7urRpa9se3sHPI2Ne9YxXt0Q0NPdy7qr1DBM8DJ0d/coDWrzuDVJ5gsWiWgreC5XAw4ePISPlgXIe7iZzi+m0Oh2Zwdi1gotLSHIYgqU7G6h0oyF19KireC1trbB5XRuEbzdepDeikno7e2vOUtlZGQcRBZB0jstFSKlIac3dOlf2dbWDovZjPlEfo/CYkICoRQ9PaU1RzcCv78J7kbPju8ppQQkva7bTEiO4zA0PIZrUWlXd7AgU9yOSRiugdIXRn5qaxWoIna7A63h9l13joBi4fn9Tbo0Hp6dvQdJQca1PLtHQaa4tCZi5vBRw6ciFEJ1VeZLXCFiHA6HE263tvPnOI5DpG8At2ObC/f2HqSirFgqfX39mp5bC9SxNbtlHKq/U7NRtcRisaCtvQN39nChzyeUTURXV4/m568UjuPQ2xMB5beWEBEhBkqkkieYlMLw8AhivIS1XdzBt6ICCKU1UaTPyA8TvBwivRFQYW3XHRzl13Qr9J6YOAC7zZZ3uOmVNR6iTAyfeVcMqqsyX+IKEeJo0didqRKJ9GMxKYKXd3cLzidEEEoRidRO/E7F5/Mrcbw8gmexlj5+qli6unqwsEfscyEhwW63Vb1hdD66uroh89EtiStqDa2eIj0woGxAdutHeiMqguP0SZhhaAcTvBx6enpBxPSOKd5UFiDzMd1cPDabDeMTU7iUZwrAxRUeDS6XLjv+SlFdlXmLz6UkQhonrKhEIn2gFJiP7W6tqAXWWruhtWJwcAiEX93xmZP0Cnp7Sh8/VSwdHZ1ICDLiwu4bhYWEjPb2zppzA6t0dnYBlIAI0ezvCL8Ok9msWcnQbrS1tcPpcOxa1nEzJqK9rQMul0u38zMqpzaf6Cqh7g63x1VkXt09dut27qmpacR4aUdshVCKK+siJiYP1kTt3XYaGhrgcDh3tfAoJYqFp3H8TqWnRxGy23k6rtyOSfD7fDWV1ZpLb28fiJgCzZkaT6kMwq/r6oZVsxjzTZxYSso1l+STi3r9hN/I/k7mNxAKhXV1+ZtMJsWNvs0dTCnF7biMPmbd1TxM8HJQv+TyNsFT3SWdnT26nVtNRvl8W9rznYSEhCBrXo+lFRzHoSUY2jWGR6UUKCW6dTjxeDxobmrK2yprLi6jN1J78TsVNcEiN25M0hugRM6KuR60tbUDUBoZbCchECRFOfuaWiQUagVnMoHwmxYexBg6DOhu0tMTwWJChChvWuWraRlpUa56b1tGYZjg5eB0OhEIBLfsHAHFXeJyNcDn089S8Hq9aG9rx9Vt/frUNOhaaHycj2BLEJB29gQl2QxN/Vp69fT2Yz6xMx6VEglWU2JNL0IdHV3gOA4kJwFD/Xd3t37eBJ/PD4fdvmvj8uWU8rvWVv1cg5VisVjQEghmXZqUyJD5uK7uTJWenl4QqsQ5VeYzFp+eCTMMbWCCt42Ojk5QMbrld4TfQGdnl+7tgoZHxpQpADkxnesbAlpDoZrpWL8bLS0tIGJ8RyyKitrNwctHT08Eaylpx4DO+Yybs5YXIZvNhmCwdUvTcjm9DqvVppsbGFCs8tZwG1ZSOzcKm4JXWx1WthMOh7MNDxR3OtWs5+heqGGPO4lNr8KdhAiTycT6Z+4DmOBto729Q8kAy4wKopSCClFDHua+vkGIMsFiZvdIKcWtuIz+gdpLVsklEAiCEhl0W5caIibAcZwmk87zoSYS3dkWx1NjobVYS5ZLV1c3qLjpUSD8BtraO3RPGAmFwljldyatrKZkmM1mzUY56UUo1AoixDPfT7W5gf6C19wcgMNu32LhLSQkhMNtNVcyxNgJE7xttLW1A5Rm3XFUSoHIItrajBA8Jd6kFlOvpWUkBTnbBaZWCQSUxZFu67hCxAS8Pu3GAu1Gd3cPAOxI9pmPi2jy+ysa1GsE4XAbZD6xmWIvxdBuQPwsFGrFRkraMeNtNSWjJRCoyQSpXFpagplZjHw2YcqIaRgmkwlt7R1YyinrWEoRXdqZMbSHCd421DiAGhBX4wRGxAcCgRa4nM5sUfCdxP6IDTQ3K/Va2zM1qZhAi861XG53I/x+/45C6jsJgq7u2muNtR0lVkaVwmlZhCwkDXnWWlqCoMCOZshrPEFLsLbdmQCyFigVEyBiElarDY2Nxmxu2to6sJxSrGNBplhPSTWd5MPYhAneNtQ4gGrhqf81Ij7AcRw6u3qyQncnLoHjuJqPDaiLDxG3Ja5IKUOKl7cXUgsyxUpS1LWMRCuyz5sYz24YjHjWVGtoe9eQ9bSMlpbaLDjPZXOTlQAVE2hqajZsJE9raxhxQUJaIlhN74+YJ0OBCd42nE4X3O5GUFGZVUaEGCwWq2FJI+3tHVhOKeNblpISgi1B2Gw2Q85dLg6HA05XA6iYW09GIIsJQ2JBHR1dWE6KWffcUlICBWq6lkxFFRcqJEAyxftGbBLUc6yl5WwP0pREkJZIzXZYycXvV76PVEqBSik0N+sXJ96OuiFZTctYzST+aN0cnaEPTPB2IbeujApxNAdaDOs6EQ63gZcI4iLBSpogvE9cJc1NzSC5BdRSCqBU14QVlY6OThC6WVemJv3UumUMAA0NbtjtDiXLNWPhBQL6x6J8Pj9MJhOiPMn2IFXdm7WesAIo981ssYCIKUBOwe9vMuzc6ueznpaxnrGQjYgfMiqHCd4uBFuCgKws3lRKKj8bde5MptlqSsZaWjYk80wLAoEAkNOSjYrKv5ua9F+IVGFT68qWkhIsFsu+2HVzHAe/v0lJjpJSsFptaGjQf7yMyWSC3+fLLtgAsJFW4lJNTbUveBzHwePxgUopyGJal6bu+VCTtFTBczgcaGjQtjk6Qx+Y4O1Cc3MAREgqKc9SMvuAG4HqTrodEyHKZF/EUwDA72/e0iJLtfb8fv0tvFAoDI7jsplzSykJoVBrzWcaqjQ3K/eOikn4/H7DYlH+pgBiOf00o4Jy/4ywyrXA5/WCCDGAEni9xgmey9UAu82GKE+wIRA0GWhdMiqDCd4u+P1NoJSAinEQiTd0AVDFVe3Ivh9224DiIiMSn02vVxtwG+FqslqtCDQHsJIpml5JU4TD+8MVDCj3DrLStNzIxbOpqQlRcbMsIcoTmEwmQ8WjErxeb7YrUmOjx7DzKla5H1FBRkwgaNoHLmCGAhO8XVDdcGqPQyO7nFitNriczmx/SDU4X+vkJhEAABFTMFsshrjnACDUGsZqmkCmSpp4KFT77kwVj8cLIqYBIhgqNj5fE2L85kDTmCDD09hYs1MSttPQ4AYyDSKMrrf0+poQFwjiIq3pLkiMreyPJ9tg1O76hFeaSBv9QHt9vqyryevdH18mtc8oyQgelVLwen2GueeCwRDW0jKiPAGhdN/EPgGlCTalBDIfRWOjkYLngyhT8LIqeAQ+3/5xz+VupozaWKl4vT7ERSDOy4bGDxmVwQRvF1TBU6cmGD1exuPZPJ9RxbSVogqz2l6MSin4DdwoBAJBpCWS7aG5H1LrVXKtE60nw++F+lyrm6u4SOHbJx4FQImlqRgteB6PB6spZcCwx2OcO5VRGUzwdkGNB6jdVoyMD+Sez+lw7Jv+fOrimR2eS3hdp0tsR63DurEhZn7eP3GVrZaKcYKnei7UQbAJke4ra8XpdO36byPI3aSwDM39Q00I3tWrV3H69GmcPHkSp0+fxrVr16p6PTabDTabPbt4G7nrBjYXQKN3rZXgdrvBcVyOhZfeYqnqjZocc2ufxT6B6lkq6sYqISpu4AQv7TPBc+76byPIFbla79fK2KQmBO/FF1/EM888g7fffhvPPPMMXnjhhWpfElyZhcfhcBqe3q4ugLkLYa1jMpnQ4G4EldPKpHOJN9TVowreXFxEg8sFq7W2u9Pk4nLlWirGLdzq55MQCFIiBQX2leA5HJv3ymKxGnruqalp3HvvcRw/fgIDA0OGnptRPlX3l62srODChQv43ve+BwA4deoU/uIv/gKrq6uGFC3nw93gxvraalb4jOTeex+AIAgYGxs3/NyV4Gn0YDHKg0o8AGNdwdlEI2p8zLVSchduI11zbncjOI5DUiJIZOYJ7qd4lMNhz/7bqOQolebmAP7wD/+HoedkVE7VBW9+fh6hUChrRZnNZgSDQczPz1dV8FSha6iClRUOt+HZZ79q+HkrxePxYGFtEVRWBc84V4/FYoHD4UA6nUbjPrJSAKUXqYrdbt/jldpiMpngcjqREEl2gO5+cs/ZbMbdK8bdQdUFTwuam7WPsfl9yk7X6/WgpWX/LALVpLnZjyvXbmUFr6MjZOi983oakU6nEQg07avPzOvdXLhbW429dp/Pi6SwiqSkCF5XV+u+uXfr65uW/H65ZkZ1qbrghcNhLCwsQJaVScuyLGNxcRHhcPHjNlZW4iDbBllWCscpt8ZisWJpKabpse9WrFYHiCSAygIAQJLMht47p7MBwBIsFvu++szUwm8ASCQkg++ZG6nkClIZC08QuH1z7xI5Q3/3yzWXAhNx7al60kpzczNGRkZw9uxZAMDZs2cxMjJSVXcmsOlmstsdBV7JUHG7G5X2YhkLz+gs097MZPhaH5i7ndz4k9GjoBrcbqQkipSkiO5+SrHfLyU7jNqhJp6Yl156Cd/+9rfx2muvwePx4JVXXqn2JWFsbAKfXvwEExMHqn0p+wYlq5Rm5+IZLXhf+cof4itf+UNDz6k1RmcbulwNSEsUKYnAYjbX/OzFXKxWY+8VY/9TE4LX19eHN954o9qXsYVDh2Zx6NBstS9jX6Gm1xMxAZPZzJIKysDoRbyhQRU8CpfLZXi2YyU0NnrQ1dWD8fHJal8KY59QE4LHuDtQ6waJmIDD4dxXi2e1efHF/4l0Om24m06x8GSkRLKlHnA/YLVa8dJL/6val8HYRzDBY2iGWjRNxSRcvv0TC6oFqhV3VD+zqEDgbNw/jQ4YjHKoetIK4+4hK3hSEs59Zi3UK2qhe5SX2WfGuOthgsfQjC0dQxwsu3U/oG5SYgIxvAEzg2E0TPAYmpHbMSRX/Bi1y9a2ZuwzY9zdMMFjaEZuW6zcPoeM2mVrWzNmlTPubpjgMTQjd8G02djiuR/I/czYJoVxt8MEj6EZFosFJpPySBnZBJlRPrmfE7PwGHc7TJRhVvwAAAhwSURBVPAYmqLOodtPHTvqmVyXJmsUwLjbYYLH0BS1NRaz8PYHuRsT9pkx7naY4DE0xWRSuquwPof7g9zJ8MwqZ9ztMMFjaIo66CZ3IWXULrmtzNhnxrjbYYLH0JbMbDc2umX/YbMxq5xxd8MEj6EpX/jC/WhuDuy7mXQMZuEx7n7YNpyhKadPP4vTp5+t9mUwyoDFXRl3O8zCYzAYAJgbmnH3wwSPwWAAYILHuPthgsdgMAAAZjMTPMbdDRM8BoMBADCbzdW+BAZDV5jgMRgMAMylybj7YYLHYDAAMJcm4+6HCR6DUed0d/cAYL00GXc/HKWUFn5ZbbOyEgch+/7PYDCqQjweRyy2gXC4vdqXwsihpaWx2pdw18F8GAxGneN2u+F2u6t9GQyG7jCXJoPBYDDqAiZ4DAaDwagLmOAxGAwGoy5ggsdgMBiMuoAJHoPBYDDqAiZ4DAaDwagLmOAxGAwGoy5ggsdgMBiMuoAJHoPBYDDqAiZ4DAaDwagL7orWYiYTV+1LYDAYDEaNc1c0j2YwGAwGoxDMpclgMBiMuoAJHoPBYDDqAiZ4DAaDwagLmOAxGAwGoy5ggsdgMBiMuoAJHoPBYDDqAiZ4DAaDwagLmOAxGAwGoy5ggsdgMBiMuuCuaC1mBL/3e78HQRAgiiKuXbuGgYEBAEAsFoPP58Obb75Z5Svcfzz00EOw2Wyw2+0AgCNHjuBP//RPt7zm+eefx1NPPYWZmZlqXGJN8e///u/4h3/4B1BKwfM8xsbG8Dd/8zd5X3/r1i386le/wunTpw28ytqh1PvFqAMooyRu3rxJZ2dnsz+/++679Kmnnqr4uJIkVXyM/caDDz5IL168mPf/1+M9ycfCwgI9cuQInZubo5RSSgihH3/88Z7v0erZ3I+Uc78Ydz/MpakBsizjhRdewJe//GU8/vjj+OyzzwAAb775Jr75zW9mX5f785tvvok/+IM/wHPPPYdTp07h0qVLVbn2WmK3e/KVr3wF77zzTrUvreosLy/DYrHA5/MBADiOw+joKADgj//4j/H000/jy1/+Mp577jlsbGwAAF5++WV89tlneOKJJ7Y8h/VAvvt169YtHDlyJPu63J/Vf7/66qt48skncfLkSbz//vtVuX6GPjCXpgZcuXIFf/VXf4WXX34Zf//3f4/XXnutKNfJuXPncObMGXR1dRlwlbXJN7/5zaxL8/d///fZPcnD8PAwJicncfz4cRw5cgTT09N44okn4Pf78fzzz6OpqQkA8Oqrr+If//Ef8Sd/8id44YUX8Morr9Sluz3f/SrE+vo6pqam8Ed/9Ed466238Nd//df44Q9/aMAVM4yAWXga0Nvbm91tT01N4ebNm0W9b3p6uu4X9u985zs4c+YMzpw5A5vNxu5JHkwmE1577TX8y7/8C44cOYJf/vKXePzxx7G+vo4zZ85kLbyzZ8/ik08+qfblVp1890u1fvPhcrnw4IMPAijtu8zYHzALTwNsNlv23yaTCZIkAQDMZjMIIdn/x/P8lvc1NDQYc4H7CHZP9mZwcBCDg4N49tln8aUvfQmvv/463nrrLfzwhz9EU1MTfvKTn+BHP/pRtS+zZth+vy5fvgyaMxFt+3cy33eZcXfALDwd6e7uxsWLFyEIAgRBwNtvv13tS2LsUxYWFvDBBx9kf75z5w5WV1fBcRzcbjd8Ph8EQcC//du/ZV/jdrsRj8ercblVJ9/9ikQiEEUR169fBwCcPXu2WpfIqALMwtORqakpHDt2DI899hiCwSCGh4extLRU7cti7EMkScJ3v/td3L59Gw6HA4QQfOtb38Lv/u7v4vLlyzh58iT8fj9mZmbw4YcfAgCGhobQ29uLU6dOIRKJ4Dvf+U6V/wrjyHe/Jicn8fzzz+NrX/sampqacPz48WpfKsNA2MRzBoPBYNQFzKXJYDAYjLqACR6DwWAw6gImeAwGg8GoC5jgMRgMBqMuYILHYDAYjLqACR6DsQvf/va38eqrr1b7MhgMhoYwwWMwGAxGXcAEj8FgMBh1ARM8BgPAhQsX8NRTT+HgwYP41re+le2xuLGxga9//es4evQoDh8+jK9//eu4c+cOAGXA6NNPP73lON/73vfwjW98w/DrZzAYhWGCx6h7BEHAc889hyeeeALvvfceHnnkEfz85z8HABBC8PTTT+Odd97BO++8A7vdjpdffhkAcOLECdy6dSs7/xAAzpw5gyeffLIqfweDwdgbJniMuufcuXMQRRFf/epXYbVa8cgjj2BiYgIA4Pf7cfLkSTidTrjdbnzjG9/Ar3/9awBKZ/1HH30Ub731FgDg8uXLuH37dna8DIPBqC2Y4DHqnsXFRYRCIXAcl/1dW1sbACCVSuGFF17Agw8+iOnpaTz77LOIRqOQZRkA8NRTT+EnP/kJKKU4c+YMHn300S0jZhgMRu3ABI9R97S0tGBhYWHLnLS5uTkAwD/90z/h6tWr+NGPfoTf/va3+MEPfgAA2ddOTU3BarXi/fffx9mzZ/H4448b/wcwGIyiYILHqHumpqZgsVjwz//8zxBFET//+c+zI3YSiQTsdjs8Hg/W19fxt3/7tzve/+STT+Lll1+GxWLBzMyM0ZfPYDCKhAkeo+6x2Wz47ne/ix//+MeYnZ3Fz372M/zO7/wOAOCrX/0qeJ7H0aNHcfr0adx333073v/EE0/g8uXLzLpjMGocNg+PwaiQdDqNY8eO4cc//jF6enqqfTkMBiMPzMJjMCrkX//1XzExMcHEjsGocSzVvgAGYz/z0EMPgVKKv/u7v6v2pTAYjAIwlyaDwWAw6gLm0mQwGAxGXcAEj8FgMBh1ARM8BoPBYNQFTPAYDAaDURcwwWMwGAxGXcAEj8FgMBh1wf8HOhQgknMSK+MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 439.725x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "CCVLg8unGlPp",
        "outputId": "e1871dc7-e3c4-41b0-8967-dcc0bf3af044"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill', data=t, hue='sex', kind='violin', inner='stick')\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 439.725x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmmur86PHHeG",
        "outputId": "de54e5a3-ddd1-4dfc-d206-59cd562e0e3f"
      },
      "source": [
        "t.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(244, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "bEV3T0NEHapD",
        "outputId": "bf7a51f6-524e-4fbd-c4ef-f59f6be48bb0"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.catplot(x='day', y='total_bill', data=t, hue='smoker', kind='violin', inner='stick')\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 417.475x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLjy49S4IKPj"
      },
      "source": [
        "#seaborn 예제"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "t-BOWXkOH8Oy",
        "outputId": "329f8cb3-b583-42ca-eb29-700f9b7678da"
      },
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "# Load the example tips dataset\n",
        "tips = sns.load_dataset(\"tips\")\n",
        "\n",
        "# Draw a nested violinplot and split the violins for easier comparison\n",
        "sns.violinplot(data=tips, x=\"day\", y=\"total_bill\", hue=\"smoker\",\n",
        "               split=True, inner=\"quart\", linewidth=1,\n",
        "               palette={\"Yes\": \"b\", \"No\": \".85\"})\n",
        "\n",
        "\n",
        "sns.despine(left=True)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poAvOMmFOcAa"
      },
      "source": [
        "# 타이타닉 데이터 활용하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXTKR296IIl7"
      },
      "source": [
        "titanic = sns.load_dataset('titanic')"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paKcYWBHOuXR"
      },
      "source": [
        "tt = titanic.copy()"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "ODZoBqJdO0gX",
        "outputId": "f530dbab-ce46-4a81-a953-3d132b9c572b"
      },
      "source": [
        "tt"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Second</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>B</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survived  pclass     sex   age  ...  deck  embark_town  alive  alone\n",
              "0           0       3    male  22.0  ...   NaN  Southampton     no  False\n",
              "1           1       1  female  38.0  ...     C    Cherbourg    yes  False\n",
              "2           1       3  female  26.0  ...   NaN  Southampton    yes   True\n",
              "3           1       1  female  35.0  ...     C  Southampton    yes  False\n",
              "4           0       3    male  35.0  ...   NaN  Southampton     no   True\n",
              "..        ...     ...     ...   ...  ...   ...          ...    ...    ...\n",
              "886         0       2    male  27.0  ...   NaN  Southampton     no   True\n",
              "887         1       1  female  19.0  ...     B  Southampton    yes   True\n",
              "888         0       3  female   NaN  ...   NaN  Southampton     no  False\n",
              "889         1       1    male  26.0  ...     C    Cherbourg    yes   True\n",
              "890         0       3    male  32.0  ...   NaN   Queenstown     no   True\n",
              "\n",
              "[891 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "CPggstl9PcaX",
        "outputId": "c9441b26-e62e-494f-f587-3fea8979c066"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.countplot(x='class', data=tt, hue='who')\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "RmvDVnD3P_dL",
        "outputId": "0168735b-f6e4-407e-9eb5-84841b09145c"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.countplot(x='who', hue='class', data=tt)\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "MuwXQcORQbUS",
        "outputId": "34c99b43-c655-4f22-e43c-e31f33b76ef0"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.countplot(x='class', hue='sex', data=tt)\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHGWV84AQ_HR"
      },
      "source": [
        "## palette로 그래프 색변화를 줄수있다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "s4vDRi_tQlqT",
        "outputId": "dfe28ccb-a31a-4144-e42c-071e8a56da3e"
      },
      "source": [
        "sns.set(style = 'darkgrid')\n",
        "sns.countplot(x='class', hue='sex', data=tt, palette='RdBu')\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEMCAYAAADXiYGSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de2AU5b3/8ffuxg0EAiEh4AY5wgEDgUgjREEELbEW9ABBkcKJt8pBEctFkZsiiQViSAioCDEICsWitFYgB0RBiwha4XcQERBUSrmalZAbJiHksju/P1K3RW4TSXY3yef1384zl+/sQD47z8w8YzEMw0BEROQyrL4uQERE6gYFhoiImKLAEBERUxQYIiJiigJDRERMUWCIiIgpCgwRETElwNcF1LaCghLcbj1qIiJihtVqoUWLJhdsq/eB4XYbCgwRkRqgLikRETFFgSEiIqZ4rUvq8ccf58SJE1itVoKCgpgxYwZRUVHExcVht9sJDAwEYNKkSfTt2xeA3bt3k5iYSFlZGW3atGHu3LmEhYVdUR2GYVBQcIry8rOAuqouz4Ld3ogWLcKxWCy+LkZEfMjircEHi4qKCA4OBuDDDz9k0aJFrFmzhri4ODIzM4mMjDxnfrfbTf/+/UlJSSE2NpaMjAyOHz9OSkpKtbabl1d8zjWMoqJCKisrCAkJw2LRCdblGIabwsJcAgLsBAeH+LocEallVquFsLCmF27zVhE/hgVAcXHxZX+t7tu3j8DAQGJjYwEYMWIE77///hXXUVpaTHBwiMLCJIvFSnBwC0pLi31dioj4mFfvkpo+fTqffvophmGwdOlSz/RJkyZhGAY9evRg4sSJNGvWDKfTSUREhGee0NBQ3G43hYWFhISY/6X706TMyTEIDLSre6UabDY7YBAeHnzZeUWk/vJqYCQnJwOwdu1a0tLSWLJkCStXrsThcFBeXk5ycjIzZ84kPT29xrb50y4pt9uNy2Wg6xfV43a7OXWqyNdliEgtu1SXlE+ewxgyZAiJiYkUFBTgcDgAsNvtJCQkMGbMGAAcDgfZ2dmeZfLz87FardU6uxAR/xca0gjbVVf5uoxqcVVUkF941tdleJ1XAqOkpIQffvjBEw6bN2+mefPmBAYGei6GG4bBhg0biIqKAiA6OpqzZ8+yc+dOYmNjWbVqFQMGDPBGuSLiRbarriL3wzd9XUa1tPxVAqDAqBWlpaVMmDCB0tJSrFYrzZs3JzMzk7y8PMaNG4fL5cLtdtOhQweSkpIAsFqtpKWlkZSUdM5ttSIi4hteu63WV356DeP7749y9dXX+qSWP/5xOX/5y58oKSmhZcuWPPXUNLp3j2XlyhWsW7eG4uJievS4kcmTn6ZZs+akp6dQUJBPcnJVUGZkLOCbbw7w4osZXr9o78vvTeq38PDgOnmGUV+v6fnFbbUN3bFjR1i9+m2WLl3BBx9sZf78hTgcEfzlL39i27YtLFz4KmvXvkdwcDDz5qUCMHbskxw6dIgNG9bx5Zdf8O67WUyf/pzu8BIRn6j3gw/6C6vVRnl5OYcP/4OQkBY4HFW3DGdlvcOTT06hVavWAIwcOZqhQ/+LyspKGjVqxIwZM5k0aTxBQUE88cRkz3wiIt6mwPCSa65py/jxT/H6669y+PA/6NmzF+PGTeT7750888xkrNZ/nTXYbDYKCvIJD29F167RRES0oaAgn7i4O3y4ByLS0CkwvOjXvx7Ar389gJKSYtLSnueVVxbQqlVrnn46kW7dYi64zDvv/JmKinJatgznzTdX8MADD3u5ahGRKrqG4SXHjh3h88//j/Lycuz2QAIDA7FYrAwZMpRXX83g+++dABQUFLBt25Z/LnOUJUteYcaMWcyYMZOVK1dw8OA3PtwLEWnIdIbhJeXlFWRmvsyRI0cICAjg+uu7MWXKdEJDwzAMgyef/B25ubm0aNGC22//NTff3IdZs2Zw330Pcd11VQMzjh79O2bNSmTp0jew2+0+3iMRaWh0W62You9Naotuq/Uvuq1WRESumAJDRERMUWCIiIgpCgwRETFFgSEiIqYoMERExBQFhoiImKLAEBERU/SkN7X3ikhvvcbxtdcWU1paytixT9T6tkSk4VJgUHuviGyor3EUkfpJgeFjffrE8sgjY9i27WNOnz7N1KnT2bnz/7Fjx9+orKxk1qxU2rVrT15eLs89N52SkhLKy8vp3fsWHn98wgXX+cc/Lufjjzfjcrlo2bIVU6dOJyyspZf3TETqG13D8ANNmwazdOkKxowZx9NPP8X11/+CZcveZMCA/2LFitc986SmvsDrr/+R5cvf5OuvD7B9+9/OW9fGjRv47rvvWLx4Oa+/vpKbb76FhQtf9PYuiUg9pDMMP3D77b8GoFOnzoCFW27p+8/PUXz88UcAuN1uMjJeYu/ePYBBXl4eBw9+S69evc9Z1yefbOXrrw8wcuT9ALhclTRteuGBxEREqkOB4Qd+HKrcarVit//r4rvVasXlcgHwpz+tpKjoB159dTmBgYGkpiZTXl523roMw+Chh0YycGC8d4oXkQbDa11Sjz/+OIMHD2bIkCEkJCRw4MABAA4fPszw4cPp378/w4cP58iRI55lLtXW0BQVFREW1pLAwEBOncrhk08+vuB8ffrcypo1f+GHH34AoLy8nIMHv/VmqSJST3ntDCM1NZXg4GAAPvzwQ5555hnWrFlDUlISCQkJxMfHk5WVRWJiIitWrAC4ZFtNclVU/POOpppfb00ZNmwEM2ZM5YEHfkN4eGt69LjxgvMNGPBfnD5dyLhxjwJVXVl33z3M8xImEZGfyycvUFq7di0rVqxgyZIl9O/fnx07dmCz2XC5XPTs2ZNNmzZhGMZF20JDQ01vSy9Qqhn63qS26AVK/uVSL1Dy6jWM6dOn8+mnn2IYBkuXLsXpdNK6dWtsNhsANpuNVq1a4XQ6MQzjom3VCYyf7nhOjpWAAN0cVl1Wq5Xw8GBflyHiNxri/wevBkZycjJQdYaRlpbGhAkXfo6gJv30DMPtdlNZ6a717dY3bre73v6iEt+qq3946+v/B797ReuQIUPYsWMHV199NSdPnvTcCeRyucjJycHhcOBwOC7aJiIi3ueVwCgpKcHpdHo+b968mebNmxMWFkZUVBTr168HYP369URFRREaGnrJNhER8T6vdEmVlpYyYcIESktLsVqtNG/enMzMTCwWC8899xzTpk0jIyODZs2akZqa6lnuUm0iIuJdPrlLypt0l1TN0PcmtUV3SfkXv7lLyl+FtAjiqgBbja+3otJFYcGZy863desWFi9eiN1u5/e/f57/+I92NV7Lj5KTn6Nz5yiGDh1ea9sQkfpJgQFcFWBj7a5DNb7eId07mJovK2s1//M/jxEX96sar0FEpKYoMHxswYJ57NnzBceOHWXNmrd57LFxZGa+TElJCQCjRj1G7959cDqzGTXqAQYNupsdO/5GWVkZiYmzycp6h/3792G3BzJnzjzCwlpy6NDfmTdvDmfPllJeXs7gwXfzm9+c/yR7RUUFr76awe7dn1NeXkHHjh156qmnCQoK8vbXICJ1gJ5g87Hx45+iU6connhiEs8/n056+vMkJSXz+ut/JC3tRebOfZ6ioqq+0tOnT9OtWwzLlr3JwIHxPPHEGO65Zxh/+MMqOnWK4p13/gyAw+HgxRczeP31lbz66h/43/9dw5Ejh8/b9sqVf6BJkyYsWbKCP/zhLcLCwnnjjWVe3X8RqTt0huFH9u37Eqczm0mTxnumWSwWvvvuOM2bh9C4cRC9e/cBIDKyM+Hhrbjuuk4AdO7cmf/7vx0AnD17loUL5/D3v3+LxWIlN/cUf//7t7Rr1/6c7X366VZKSkrYsmUzABUV5XTseJ03dlVE6iAFhh8xDOjQ4ToWLVpyXpvTmX3e0Od2e+C/fbZ5HnJcvHgRoaFhvP76SgICAnjyyd9RXl5+we099dS0iw5kKCLy79Ql5Ueio7tx4sQxdu3a6Zl24MBXVPfO5+LiIlq1ak1AQAD/+Mff+fLL3Recr0+fW/nTn1ZSVlb13vEzZ0ou2HUlIgI6w/ArzZo1Y86c+Sxa9BIvvTSPysoKIiLakJr6QrXW89BD/8OsWYm8+24Wbdv+BzExN1xwvvvv/y2vvbaYUaMexGq1AhZGjnzkvK4rERHQg3uA75/DqAv04J7UFj2451/04N5l1Jc/6iIitUnXMERExBQFhoiImNIgA6OeX7apcfq+RAQaYGAEBNgpKflBfwRNMgyDkpIfCAiw+7oUEfGxBnfRu0WLcAoKTlFcXOjrUuqMgAA7LVqE+7oMEfGxBhcYNlsALVvqNa8iItXV4LqkRETk51FgiIiIKQoMERExRYEhIiKmeOWid0FBAVOmTOHYsWPY7XauvfZaZs6cSWhoKJ06dSIyMvKfg99BWloanTpVveNh8+bNpKWl4XK56Nq1KykpKTRu3NgbJYuIyE945QzDYrEwatQoNm7cyLp162jbti3p6eme9lWrVpGVlUVWVpYnLEpKSpgxYwaZmZl88MEHNGnShNdee80b5YqIyAV4JTBCQkLo2bOn53NMTAzZ2dmXXGbr1q1ER0fTrl07AEaMGMF7771Xm2WKiMgleP05DLfbzVtvvUVcXJxn2gMPPIDL5eLWW29l3Lhx2O12nE4nERERnnkiIiJwOp3eLldERP7J64Exa9YsgoKCuP/++wHYsmULDoeD4uJiJk+ezKJFi3jyySdrbHsXG9ddRORKhIcH+7oEr/NqYKSmpnL06FEyMzM9F7kdjqqnrps2bcqwYcNYtmyZZ/qOHTs8y2ZnZ3vmrY6fvkBJRPxLXf3D2xBfoOS122rnz5/Pvn37WLRoEXZ71UB2p0+f5uzZqvdJV1ZWsnHjRqKiogDo27cve/fu5ciRI0DVhfE777zTW+WKiMhPeOUM4+DBgyxevJh27doxYsQIAK655hpGjRpFYmIiFouFyspKbrjhBiZMmABUnXHMnDmT0aNH43a7iYqKYvr06d4oV0RELqDBvdNbRPyL3untX/yiS0pEROo2BYaIiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKQoMERExRYEhIiKmKDBERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKQoMERExRYEhIiKmKDBERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETDEdGK+99toFpy9btuyyyxYUFPDII4/Qv39/Bg0axNixY8nPzwdg9+7dDB48mP79+zNy5Ejy8vI8y12qTUREvMt0YCxatOiC01955ZXLLmuxWBg1ahQbN25k3bp1tG3blvT0dNxuN5MnTyYxMZGNGzcSGxtLeno6wCXbRETE+y4bGJ999hmfffYZbreb7du3ez5/9tlnvP322zRp0uSyGwkJCaFnz56ezzExMWRnZ7Nv3z4CAwOJjY0FYMSIEbz//vsAl2wTERHvC7jcDNOnTwegrKyMZ555xjPdYrEQHh7Os88+W60Nut1u3nrrLeLi4nA6nURERHjaQkNDcbvdFBYWXrItJCSkWtsUEZErd9nA2Lx5MwBTpkwhLS3tijc4a9YsgoKCuP/++/nggw+ueH2XExbWtNa3ISINT3h4sK9L8LrLBsaP/j0s3G73OW1Wq7lLIampqRw9epTMzEysVisOh4Ps7GxPe35+PlarlZCQkEu2VUdeXjFut1GtZUTEe+rqH95Tp4p8XUKtsFotF/2hbfqi91dffcXw4cOJiYmha9eudO3alS5dutC1a1dTy8+fP599+/axaNEi7HY7ANHR0Zw9e5adO3cCsGrVKgYMGHDZNhER8T6LYRimfn4PGjSIfv36ER8fT6NGjc5pa9OmzSWXPXjwIAMHDqRdu3aeZa+55hoWLVrErl27SEpKoqysjDZt2jB37lxatmwJcMk2s3SGIeLfwsODyf3wTV+XUS0tf5XQIM8wTAdG9+7d+fzzz7FYLDVaXG1TYIj4NwWGf6mRLqk77riDTz75pMaKEhGRusX0Re+ysjLGjh1Ljx49zusWqom7p0RExL+ZDoyOHTvSsWPH2qxFRET8mOnAGDt2bG3WISIifs50YHz22WcXbbv55ptrpBgREfFfpgPjxyFCflRQUEBFRQWtW7fmr3/9a40XJiIi/sV0YPw4RMiPXC4Xr7zyiqnBB0VEpO772S9QstlsPPbYYyxdurQm6xERET91RW/c+/TTT+vcg3wiIvLzmO6Suu22284Jh9LSUsrLy0lKSqqVwkRExL+YDoy5c+ee87lx48a0b9+epk01fLiISENgOjBuuukmoGpo89zcXFq2bGl6WHMREan7TP/FLy4uZsqUKXTr1o1bb72Vbt26MXXqVIqK6ucAXCIici7TgTF79mxKS0tZt24de/bsYd26dZSWljJ79uzarE9ERPyE6S6pbdu28eGHH9K4cWMA2rdvT0pKCnfccUetFSciIv7D9BlGYGAg+fn550wrKCjwvD1PRETqN9NnGPfeey8jR47kt7/9LREREWRnZ7N8+XKGDRtWm/WJiIifMB0YY8aMoXXr1qxbt46cnBxatWrFqFGjFBgiIg2E6S6p5ORk2rdvz/Lly9mwYQPLly+nQ4cOJCcn12Z9IiLiJ0wHxvr164mOjj5nWnR0NOvXr6/xokRExP+YDgyLxYLb7T5nmsvlOm+aiIjUT6YDIzY2lpdeeskTEG63m5dffpnY2FhTy6emphIXF0enTp349ttvPdPj4uIYMGAA8fHxxMfHs23bNk/b7t27GTx4MP3792fkyJHk5eWZLVdERGpYtV6gNHr0aPr06UNERAROp5Pw8HAyMzNNLX/77bfz4IMPct99953XtmDBAiIjI8+Z5na7mTx5MikpKcTGxpKRkUF6ejopKSlmSxYRkRpkOjCuvvpq1qxZw549e3A6nTgcDrp162Z6PCmzZyI/2rdvH4GBgZ7lRowYwe23367AEBHxEdOBAWC1WomJiSEmJqZGi5g0aRKGYdCjRw8mTpxIs2bNcDqdREREeOYJDQ3F7XZTWFhISEhIjW5fREQur1qBURtWrlyJw+GgvLyc5ORkZs6cSXp6eo2tPyxMw6+LSM0LDw/2dQle5/PAcDgcANjtdhISEhgzZoxnenZ2tme+/Px8rFZrtc8u8vKKcbuNmitYRGpUXf3De+pU/Ryp22q1XPSHtk9faHHmzBnP8OiGYbBhwwaioqKAqmc8zp49y86dOwFYtWoVAwYM8FmtIiINndfOMGbPns2mTZvIzc3l4YcfJiQkhMzMTMaNG+d5nqNDhw6eV75arVbS0tJISkqirKyMNm3anPfWPxERX3C53XXqzKii0kVhwZkrXo/FMIx63V+jLikR/xYeHkzuh2/6uoxqafmrBNbuOuTrMkwb0r2D6S40v+2SEhGRukOBISIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFJ8Pb+4vQkMaYbvqKl+XUS2uigryC8/6ugwRaSAUGP9ku+qqOjkAGigwRMQ71CUlIiKmKDBERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKXoOQ+o8PXQp4h1eCYzU1FQ2btzId999x7p164iMjATg8OHDTJs2jcLCQkJCQkhNTaVdu3aXbRP5d3roUsQ7vNIldfvtt7Ny5UratGlzzvSkpCQSEhLYuHEjCQkJJCYmmmoTERHv80pgxMbG4nA4zpmWl5fH/v37GThwIAADBw5k//795OfnX7JNRER8w2fXMJxOJ61bt8ZmswFgs9lo1aoVTqcTwzAu2hYaGuqrkkVEGrR6f9E7LKypr0uoVeHhwb4uQX4mHTvxppr49+azwHA4HJw8eRKXy4XNZsPlcpGTk4PD4cAwjIu2VVdeXjFut3HZ+erqf95Tp4p8XYLP6djVbXX1+NU1Zv+9Wa2Wi/7Q9tlzGGFhYURFRbF+/XoA1q9fT1RUFKGhoZdsExER3/DKGcbs2bPZtGkTubm5PPzww4SEhPDuu+/y3HPPMW3aNDIyMmjWrBmpqameZS7VJlVcbned+nVWUemisOCMr8sQkZ/JK4Hx7LPP8uyzz543vUOHDrz99tsXXOZSbVLFZrWydtchX5dh2pDuHXxdgohcAQ0NIiIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIiptT70WpF/FFdG9YFNLSLKDBEfKKuDesCGtpF1CUlIiImKTBERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKQoMERExRYEhIiKmKDBERMQUvxgaJC4uDrvdTmBgIACTJk2ib9++7N69m8TERMrKymjTpg1z584lLCzMx9WKiDRMfhEYAAsWLCAyMtLz2e12M3nyZFJSUoiNjSUjI4P09HRSUlJ8WKWISMPlt11S+/btIzAwkNjYWABGjBjB+++/7+OqREQaLr85w5g0aRKGYdCjRw8mTpyI0+kkIiLC0x4aGorb7aawsJCQkBDT6w0La1ob5crPVNeG9JZz6fjVXTVx7PwiMFauXInD4aC8vJzk5GRmzpzJHXfcUSPrzssrxu02Ljuf/iN4x6lTRTW+Th0779Hxq7vMHjur1XLRH9p+0SXlcDgAsNvtJCQksGvXLhwOB9nZ2Z558vPzsVqt1Tq7EBGRmuPzwDhz5gxFRVXJZxgGGzZsICoqiujoaM6ePcvOnTsBWLVqFQMGDPBlqSIiDZrPu6Ty8vIYN24cLpcLt9tNhw4dSEpKwmq1kpaWRlJS0jm31YqIiG/4PDDatm3L2rVrL9jWvXt31q1b5+WKRETkQnzeJSUiInWDAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipvh9YBw+fJjhw4fTv39/hg8fzpEjR3xdkohIg+T3gZGUlERCQgIbN24kISGBxMREX5ckItIgBfi6gEvJy8tj//79LFu2DICBAwcya9Ys8vPzCQ0NNbUOq9VienvWRk1+Vp2+FGT360N4nuocj2qtV8fOK3T8/qWuHT+zx+5S81kMwzBqqqCatm/fPqZOncq7777rmXbXXXcxd+5cunbt6sPKREQaHr/vkhIREf/g14HhcDg4efIkLpcLAJfLRU5ODg6Hw8eViYg0PH4dGGFhYURFRbF+/XoA1q9fT1RUlOnrFyIiUnP8+hoGwKFDh5g2bRo//PADzZo1IzU1lf/8z//0dVkiIg2O3weGiIj4B7/ukhIREf+hwBAREVMUGCIiYooCQ0RETKlbz7Y3AHFxcdjtdgIDAwGwWCzcfPPNTJ06tVrrWb58OYMGDSIsLKw2ymyQ3nvvPRYvXoxhGJSVldG1a1fmzZvns3p27NhBamoqq1ev9lkNdd2wYcMoLy+noqKCI0eOcN111wFQVFRESEjIBb/bvXv3snz5ctPH/oEHHmDkyJH069evRmv3BQWGH1qwYAGRkZGXnc/lcmGz2S7YtmLFCnr37q3AqCE5OTn8/ve/Z82aNTgcDgzD4MCBA74uS67Q22+/DcCJEycYOnQoWVlZwL/C+EKuv/76i4ZFZWUlAQH1989q/d2zemL16tVs2bKFBQsWsGPHDmbPnk10dDT79+/niSeeICcnh+XLl2O323G73bz44ots2rSJnJwcxo8fT2BgIPPmzaNjx46+3pU6LTc3l4CAAEJCQoCqM78uXboA8OWXX5Kenk5JSQkA48eP55e//CUAH330ES+//DKVlZVYrVbmzJlD586d2bp1K/Pnz8flchEaGsrMmTO59tpr2bFjB88//zy/+MUv+OKLL7BYLLzwwgt06NABgBdeeIENGzbQrFkzbrrpJu9/EQ2Iy+UiMTHxvOPw72d2PwbNPffcw/bt2/nNb37DjTfeyNNPP82ZM2eIjIykrKzM17tScwzxK/369TP69+9vDB482Bg8eLDx1ltvGePGjTMMwzC2b99udO7c2di1a5dn/u7duxsnT540DMMwysrKjDNnznjW880333h/B+opl8tljBkzxrjpppuMcePGGcuWLTPy8/ON06dPG/Hx8Z5jcPLkSaNv377G6dOnjX/84x9G7969jcOHDxuGUXV8ioqKjNzcXKNnz57GwYMHDcMwjD//+c/GvffeaxhG1THu0qWL8dVXXxmGYRgZGRnGxIkTDcMwjL/+9a/GwIEDjeLiYqOystIYPXq0cffdd3v5m6ifjh8/btx0002ez5c6Dtu3b/d878ePHzciIyONd99917Ps3XffbaxevdowDMP44osvjM6dOxubN2/21q7UKl309kMLFiwgKyuLrKws7Hb7OW3XXnstN9xwg+dzr169mDZtGm+88QYnT56kcePG3i63QbBarWRkZPDGG2/Qs2dPPv74YwYPHszHH3/MiRMneOSRR4iPj+eRRx7BYrFw9OhR/va3v3HrrbfSrl07AOx2O02bNuXLL7+kc+fOnrO+oUOHcuDAAYqLiwFo37695+wlJiaG48ePA1XdJHfddRdNmjTBZrNx7733ev+LaEAudhx+KjAwkDvvvBOA4uJivv32W+Lj4z3LmelerivUJVXHBAUFnfN54cKF7N27l+3bt/Pggw/y3HPPcdttt/mouvovMjKSyMhI7rvvPu666y4Mw6BTp06sXLnyvHn37Nnzs7bx7z8SrFYrlZWVP7te+fnMHofGjRtjsdTOe0L8jc4w6rDKykqOHz9Ot27dePTRR7nllls8F2KbNGlCUVGRjyusP06ePMkXX3zh+fz999+Tn59Px44dOXr0KNu3b/e07dmzB8MwuOWWW9i6davntcLl5eUUFxcTExPD119/zaFDhwBYs2YNXbp0oWnTppesoVevXrz33nucOXMGl8vFO++8U/M7KlekadOmREZGsm7dOqDq38K3337r46pqjs4w6jC32820adMoKirCYrHgcDh46qmnAHjwwQd55plnaNSokS5614DKykpefvllvvvuOyma4ioAAANvSURBVBo1aoTb7eaJJ56gS5cuZGRkMHfuXJ5//nkqKipo27YtmZmZtGvXjlmzZvHkk0967mibM2cOnTp1Ii0tjUmTJlFZWUloaChz5869bA39+vVj9+7dxMfHey56nzx50gt7L9WRlpbG008/zZIlS4iMjOT666/3dUk1RoMPioiIKeqSEhERUxQYIiJiigJDRERMUWCIiIgpCgwRETFFgSFSC1avXs1///d/+7oMkRqlwBAREVMUGCIiYooCQ+QKOZ1Oxo4dS69evejZsyczZ848b57Zs2dz22230b17d+655x527tzpaduzZw/33HMP3bt3p3fv3qSkpABQVlbGpEmT6NmzJ7GxsQwdOpTc3Fyv7ZfIT2loEJEr4HK5GD16NL169WLz5s3YbDb27t3LsWPHzpnv+uuv53e/+x3BwcGsWLGCCRMmsHnzZgIDA0lOTubBBx9kyJAhlJSUcPDgQaBqjKni4mK2bNmC3W7nwIEDNGrUyBe7KQLoDEPkiuzZs4ecnBymTJlCUFAQgYGBxMbGnjdffHw8LVq0ICAggJEjR1JeXs7hw4cBCAgI4NixY+Tn59OkSRNiYmI80wsLCzl69Cg2m43o6OjLDlAoUpsUGCJXwOl0EhERcdnXcr722mvceeed9OjRg9jYWIqKiigoKAAgOTmZI0eOcOeddzJ06FA++ugjoCpk+vTpw8SJE+nTpw9paWlUVFTU+j6JXIy6pESugMPhwOl0XvJdzjt37mTp0qUsX76c6667DqvVyo033siP4362a9eO+fPn43a72bRpE+PHj2fHjh0EBQUxduxYxo4dy4kTJ3j00Udp3749w4YN8+YuinjoDEPkCnTr1o3w8HDmzZvHmTNnKCsr4/PPPz9nnpKSEmw2G6GhoVRWVrJw4ULP2/UAsrKyyM/Px2q10qxZM6DqhT3bt2/nm2++weVy0bRpUwICArBa9V9WfEdnGCJXwGazkZmZyezZs+nXrx8AgwYN8rzaE6BPnz707duX/v37ExQUxEMPPYTD4fC0b9u2jTlz5nD27FkiIiJ44YUXaNSoEbm5uSQlJXHy5EmCgoK46667PK/+FPEFvQ9DRERM0fmtiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFAWGiIiY8v8BX3CcN+uyQ8MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k37YoFqmRKi2"
      },
      "source": [
        "## 생존률 알아보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "tS4RelYEQ41E",
        "outputId": "bd060935-a571-40d9-c2fe-fa3cf428db50"
      },
      "source": [
        "sns.catplot(x='sex', y='survived', hue='class', data=tt, kind ='bar', palette='RdBu')\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoXiZyt0R2aT"
      },
      "source": [
        "## 생존자 구하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "XpB2Uq0_RSLZ",
        "outputId": "64351d09-7e9a-4646-cf5b-7c237b2ab915"
      },
      "source": [
        "tt.head(3)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass     sex   age  ...  deck  embark_town  alive  alone\n",
              "0         0       3    male  22.0  ...   NaN  Southampton     no  False\n",
              "1         1       1  female  38.0  ...     C    Cherbourg    yes  False\n",
              "2         1       3  female  26.0  ...   NaN  Southampton    yes   True\n",
              "\n",
              "[3 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPLmtyatR78_"
      },
      "source": [
        "## survived가 1인사람을 세면 되겠네"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AbaIxgIR6lf",
        "outputId": "25ed8a09-9759-4d26-f721-b01c5dbe6f79"
      },
      "source": [
        "tt.survived.sum()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "342"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "By8Av5X6SkxL"
      },
      "source": [
        "### 전체 행렬에서 행의 갯수가 총인원!\n",
        "### 거기에서 살아남은 사람 빼면 사망자!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEsdZS9MSPvx",
        "outputId": "b6593c6f-1da7-4bca-a2ef-e6d3a74fa553"
      },
      "source": [
        "tt.shape[0] - tt.survived.sum()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQA2TeV5Scbx",
        "outputId": "0a6a6992-8905-4fc0-ea25-2479bce2d543"
      },
      "source": [
        "tt.shape"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMaF-ronSijO",
        "outputId": "49aadd47-031b-4d44-d8de-f0c304a64501"
      },
      "source": [
        "tt['pclass'].value_counts()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: pclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SqlRytnTAel",
        "outputId": "9c8b45da-20c6-4451-f61d-4fa7132df5ca"
      },
      "source": [
        "tt['alone'].value_counts()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     537\n",
              "False    354\n",
              "Name: alone, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQPCWSXgTID9",
        "outputId": "a79a960c-0e00-4747-8dde-54d5a60e47a1"
      },
      "source": [
        "tt['embark_town'].value_counts()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Southampton    644\n",
              "Cherbourg      168\n",
              "Queenstown      77\n",
              "Name: embark_town, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yUb1MvWETjgo",
        "outputId": "0fab8fc1-7ea6-48df-ea07-ade4298fb16b"
      },
      "source": [
        "tt['sibsp'].value_counts()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    608\n",
              "1    209\n",
              "2     28\n",
              "4     18\n",
              "3     16\n",
              "8      7\n",
              "5      5\n",
              "Name: sibsp, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9AffXkITm04",
        "outputId": "7e4cdb2b-40f6-4913-ae37-51a0ff084aa2"
      },
      "source": [
        "tt['parch'].value_counts()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    678\n",
              "1    118\n",
              "2     80\n",
              "5      5\n",
              "3      5\n",
              "4      4\n",
              "6      1\n",
              "Name: parch, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nirppr_DT-CF"
      },
      "source": [
        "###나이 중앙값구하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQHQKtboTrD8",
        "outputId": "f6f9c309-fe33-4dba-f7ed-481ced7e1c79"
      },
      "source": [
        "tt['age'].median()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUtSPVy9UOII"
      },
      "source": [
        "### 나잇대별 생존"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Tzddn7fULJ_",
        "outputId": "daa883a2-0861-4849-e89f-9f9285937a9e"
      },
      "source": [
        "k1 = [0, 18, 35, 60, 100]\n",
        "k2 = ['아기','청년','장년','노년']\n",
        "tt['연령대'] = pd.cut(tt['age'], bins=k1, labels=k2)\n",
        "tt['연령대'].value_counts()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "청년    358\n",
              "장년    195\n",
              "아기    139\n",
              "노년     22\n",
              "Name: 연령대, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "TLtcE5_jVWV4",
        "outputId": "92951b95-eb4e-487a-cdfc-ae276fc3b94e"
      },
      "source": [
        "tt[tt['age'] <= 18]"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "      <th>연령대</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>C</td>\n",
              "      <td>Second</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>G</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>852</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15.2458</td>\n",
              "      <td>C</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>853</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>39.4000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>D</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>855</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.3500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>869</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>875</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>C</td>\n",
              "      <td>Third</td>\n",
              "      <td>child</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>아기</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>139 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  ...  deck  embark_town alive  alone 연령대\n",
              "7           0       3    male   2.0      3  ...   NaN  Southampton    no  False  아기\n",
              "9           1       2  female  14.0      1  ...   NaN    Cherbourg   yes  False  아기\n",
              "10          1       3  female   4.0      1  ...     G  Southampton   yes  False  아기\n",
              "14          0       3  female  14.0      0  ...   NaN  Southampton    no   True  아기\n",
              "16          0       3    male   2.0      4  ...   NaN   Queenstown    no  False  아기\n",
              "..        ...     ...     ...   ...    ...  ...   ...          ...   ...    ...  ..\n",
              "852         0       3  female   9.0      1  ...   NaN    Cherbourg    no  False  아기\n",
              "853         1       1  female  16.0      0  ...     D  Southampton   yes  False  아기\n",
              "855         1       3  female  18.0      0  ...   NaN  Southampton   yes  False  아기\n",
              "869         1       3    male   4.0      1  ...   NaN  Southampton   yes  False  아기\n",
              "875         1       3  female  15.0      0  ...   NaN    Cherbourg   yes   True  아기\n",
              "\n",
              "[139 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mX1uT02Wn6I",
        "outputId": "3612dfa0-d594-490f-a417-4c39373b56b7"
      },
      "source": [
        "tt['age'].max()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbAWSV9LXLMq",
        "outputId": "47294f6d-eae2-42e4-c02c-90949c49861c"
      },
      "source": [
        "tt['age'].min()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFpCoFSbXjKS"
      },
      "source": [
        "# 연령대를 바그래프로 나타내기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bc0KWno6YuUG",
        "outputId": "8702d6cc-7aec-40a7-edd4-7ab9d65e8c74"
      },
      "source": [
        "%matplotlib inline  \n",
        "\n",
        "import matplotlib as mpl \n",
        "import matplotlib.pyplot as plt \n",
        "import matplotlib.font_manager as fm  \n",
        "\n",
        "!apt-get update -qq\n",
        "!apt-get install fonts-nanum* -qq\n",
        "\n",
        "path = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf' \n",
        "font_name = fm.FontProperties(fname=path, size=10).get_name()\n",
        "print(font_name)\n",
        "plt.rc('font', family=font_name)\n",
        "\n",
        "fm._rebuild()\n",
        "mpl.rcParams['axes.unicode_minus'] = False"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NanumBarunGothic\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ao6UFRXkXP9W",
        "outputId": "b8f67364-f1ec-4b7b-88e3-7f38b108f0ee"
      },
      "source": [
        "sns.catplot(x='연령대', y='survived', hue='class', kind='bar', data=tt)\n",
        "plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50500 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44592 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 52397 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45380 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51109 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45432 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50672 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47161 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45824 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50500 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44592 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 52397 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45380 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51109 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45432 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50672 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47161 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45824 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4E9moNzZa1-"
      },
      "source": [
        "# 다시 시작"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SMfRNFHZK7b"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "DGD6T1weZLnO",
        "outputId": "b7bc2b16-d8b2-4aa5-eaa0-5f391a23f221"
      },
      "source": [
        "sns.countplot(x='alive',data=tt)\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEMCAYAAADXiYGSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQz0lEQVR4nO3df5BdZX3H8ffuRpKYRITNwhjUAK35ltqABkE6AzpFaG1nmEp1GIP8qB2rQQYdnUp1/FFohzYjtqNANFHsFEWYDuNgqZ0OtpUWUqRSBH8hX1I0IfzQLAuDCSWh5m7/OCe4xtzd5+7uPfdm9/2a2dk9z3POnu/OnLmffc6P5wyMj48jSdJUBntdgCTp4GBgSJKKGBiSpCIGhiSpiIEhSSqyoNcFdNFC4CTgcWBvj2uRpIPFEPAS4G5gz8SOuRwYJwF39LoISTpInQZsntgwlwPjcYCnnnqGVstnTSSpxODgAIcdtgTqz9CJ5nJg7AVotcYNDEnq3C+dyveitySpiIEhSSpiYEiSihgYkqQiBoYkqYiBIUkqYmBIkorM5ecwZmzZixaxaOELel2G+szuPf/Hzp/u7nUZUuMMjEksWvgCzr30S70uQ33mho+/jZ0YGJp/PCUlSSpiYEiSihgYkqQiBoYkqYiBIUkqYmBIkooYGJKkIgaGJKmIgSFJKmJgSJKKGBiSpCIGhiSpiIEhSSrS2Gy1EbEV2F1/AfxpZt4aEacAm4DFwFbgvMzcUW/Ttk+S1KymRxhvycxX1V+3RsQgcD1wcWauAm4H1gNM1idJal6vT0mdCOzOzM318kbgnII+SVLDmg6ML0XEdyLi0xHxYuDlwLZ9nZn5BDAYEYdP0SdJaliTb9w7LTO3R8RC4JPANcDN3d7p8PDSbu9C89DIyLJelyA1rrHAyMzt9fc9EfFp4BbgU8DKfetExHKglZlPRsTD7fo62e/Y2C5arfFp1eyHgtoZHd3Z6xKkrhgcHGj7j3Yjp6QiYklEHFr/PAC8FbgPuAdYHBGn1quuA26qf56sT5LUsKZGGEcCX46IIWAIuB94d2a2IuJ8YFNELKK+dRZgsj5JUvMaCYzM/CHw6jZ9dwKrO+2TJDWr17fVSpIOEgaGJKmIgSFJKmJgSJKKGBiSpCIGhiSpiIEhSSpiYEiSihgYkqQiBoYkqYiBIUkqYmBIkooYGJKkIgaGJKmIgSFJKmJgSJKKGBiSpCIGhiSpiIEhSSpiYEiSihgYkqQiBoYkqYiBIUkqYmBIkooYGJKkIgaGJKmIgSFJKmJgSJKKGBiSpCILmt5hRPwZcBmwOjO/FxGnAJuAxcBW4LzM3FGv27ZPktSsRkcYEbEGOAXYVi8PAtcDF2fmKuB2YP1UfZKk5jUWGBGxENgAXDSh+URgd2Zurpc3AucU9EmSGtbkKak/B67PzK0Rsa/t5dSjDYDMfCIiBiPi8Mn6MvPJ0p0ODy+dneqlCUZGlvW6BKlxjQRGRPwm8Brgg03sb6KxsV20WuPT2tYPBbUzOrqz1yVIXTE4OND2H+2mTkm9HjgO+FFEbAVeCtwK/Cqwct9KEbEcaNUjiIcn6ZMkNayRwMjM9Zm5IjOPzsyjgUeA3wGuBBZHxKn1quuAm+qf75mkT5LUsJ4+h5GZLeB84DMRsYVqJPLBqfokSc1r/DkMgHqUse/nO4HVbdZr2ydJapZPekuSihgYkqQiBoYkqYiBIUkqYmBIkooYGJKkIgaGJKmIgSFJKmJgSJKKGBiSpCIGhiSpiIEhSSpiYEiSihgYkqQiBoYkqYiBIUkq0pMXKEmaucMOPYQFhyzsdRnqMz97bg9PPf1cV363gSEdpBYcspB7Pv6OXpehPnPipdcC3QkMT0lJkooYGJKkIgaGJKmIgSFJKlIcGBHxJ23a3z975UiS+lUnI4yPtWn/yGwUIknqb1PeVhsRp9c/DkXEbwEDE7qPBXZ2ozBJUn8peQ7j8/X3RcDfTmgfB34MXDLbRUmS+s+UgZGZxwBExBcy84LulyRJ6kfFT3pPDIuIGNyvrzWbRUmS+k9xYETEGmADcDzV6SmormeMA0MF238FOAZoAbuASzLzvohYBVwHDANjwAWZuaXepm2fJKlZndwldR1wG/Aaqovdx1IFwLGF21+YmSdk5quBT/Dz6yEbgQ2ZuYoqkDZN2GayPklSgzqZfHAl8OHMHJ/OjjLz6QmLhwKtiDgCWAOcWbffCFwTESNUo5cD9mXm6HRqkCRNXycjjJuB357JziLi2oh4GLgCuBB4GfBoZu4FqL8/VrdP1idJalgnI4xFwM0RsZnqdtrnld49lZnvAIiI84ErgY92sP9pGR5e2u1daB4aGVnW6xKktrp1fHYSGPfXXzOWmV+MiM8CjwBHRcRQZu6NiCFgBbCd6pRUu75iY2O7aLWmdRbNDwW1NTra++dVPT7VzkyOz8HBgbb/aHdyW+3l0y0gIpYCh2Xm9nr5LOBJYAdwH7AWuL7+fu++axQR0bZPktSsTm6rPb1dX2Z+fYrNlwA3RcQSYC9VWJyVmeMRsQ64LiI+BjwFTDy9NVmfJKlBnZyS+vx+yyPAIVSnlSa9tTYzfwKc0qbvAeC1nfZJkprVySmpYyYu19cUPoKTD0rSvDDtFyjVt7leAVw6e+VIkvrVTN+4dybVVB+SpDmuk4ve26nmjdrnhVTPZrx7touSJPWfTi56n7ff8jPAg5n501msR5LUpzq56P0f8PzU5kcCP3Fac0maP4qvYUTEsoj4AvAs8CjwbERcFxGHdq06SVLf6OSi99VUD+CtBhbX318IXNWFuiRJfaaTaxhvBI7NzP+tlx+MiLcDD81+WZKkftPJCGM31dPdEy0H9sxeOZKkftXJCONa4F8i4m+AbVQvVHof8LluFCZJ6i+dBMYVVBe730Y1zfhjwMczc/85piRJc1Anp6Q+BWRmnpGZv56ZZwA/iIhPdqk2SVIf6SQw1gL/vV/bPcC5s1eOJKlfdRIY48DQfm1DHf4OSdJBqpMP+zuAv6if9N73xPdldbskaY7r5KL3e4GvAo9HxDbg5cDjwFndKEyS1F86mUvqkYhYA5wMvAzYDnzT+aQkaX7oZIRBHQ531V+SpHnEC9aSpCIGhiSpiIEhSSpiYEiSihgYkqQiBoYkqYiBIUkqYmBIkooYGJKkIgaGJKlIR1ODTFdEDANfBH4FeA7YArwrM0cj4hRgE7AY2Aqcl5k76u3a9kmSmtXUCGOc6nWukZmrgYeA9fUU6dcDF2fmKuB2YD08P336AfskSc1rJDAy88nM/PcJTXcBK4ETgd2Zublu3wicU/88WZ8kqWGNX8OoRw4XAbdQvVNj276+zHwCGIyIw6fokyQ1rJFrGPu5GtgFXAOc3e2dDQ8v7fYuNA+NjCzrdQlSW906PhsNjIj4BPAK4KzMbEXEw1Snpvb1LwdamfnkZH2d7HNsbBet1vi06vVDQe2Mju7sdQken2prJsfn4OBA23+0GzslFRF/SXVd4k2ZuaduvgdYHBGn1svrgJsK+iRJDWvqttpXAh8CHgTujAiAH2Xm2RFxPrApIhZR3zoL1dv92vVJkprXSGBk5veBgTZ9dwKrO+2TJDXLJ70lSUUMDElSEQNDklTEwJAkFTEwJElFDAxJUhEDQ5JUxMCQJBUxMCRJRQwMSVIRA0OSVMTAkCQVMTAkSUUMDElSEQNDklTEwJAkFTEwJElFDAxJUhEDQ5JUxMCQJBUxMCRJRQwMSVIRA0OSVMTAkCQVMTAkSUUMDElSEQNDklTEwJAkFTEwJElFFjSxk4j4BPBm4GhgdWZ+r25fBVwHDANjwAWZuWWqPklS85oaYXwFeB2wbb/2jcCGzFwFbAA2FfZJkhrWSGBk5ubM3D6xLSKOANYAN9ZNNwJrImJksr4m6pUk/bJeXsN4GfBoZu4FqL8/VrdP1idJ6oFGrmH00vDw0l6XoDloZGRZr0uQ2urW8dnLwNgOHBURQ5m5NyKGgBV1+8AkfR0ZG9tFqzU+rQL9UFA7o6M7e12Cx6famsnxOTg40PYf7Z6dksrMHcB9wNq6aS1wb2aOTtbXfKWSJGgoMCLiqoh4BHgp8K8R8f26ax1wSUQ8CFxSL1PQJ0lqWCOnpDLzPcB7DtD+APDaNtu07ZMkNc8nvSVJRQwMSVIRA0OSVMTAkCQVMTAkSUUMDElSEQNDklTEwJAkFTEwJElFDAxJUhEDQ5JUxMCQJBUxMCRJRQwMSVIRA0OSVMTAkCQVMTAkSUUMDElSEQNDklTEwJAkFTEwJElFDAxJUhEDQ5JUxMCQJBUxMCRJRQwMSVIRA0OSVMTAkCQVMTAkSUUW9LqAqUTEKuA6YBgYAy7IzC29rUqS5p+DYYSxEdiQmauADcCmHtcjSfNSX48wIuIIYA1wZt10I3BNRIxk5ugUmw8BDA4OzKiG5YctmdH2mptmelzNlkNeNNzrEtSHZnJ8Tth2aP++gfHx8Wn/4m6LiBOBL2TmKye03Q+cl5nfmmLzU4E7ulmfJM1hpwGbJzb09Qhjhu6m+oMfB/b2uBZJOlgMAS+h+gz9Bf0eGNuBoyJiKDP3RsQQsKJun8oe9ktHSVKRhw7U2NcXvTNzB3AfsLZuWgvcW3D9QpI0y/r6GgZARPwa1W21hwFPUd1Wm72tSpLmn74PDElSf+jrU1KSpP5hYEiSihgYkqQiBoYkqYiBIUkqYmBIkor0+5Pe6oGIGAc+DJxNNa38BzLzy3XfG4G/opo+YBR4V2b+T69q1dwXER8Ajs7Mi+vlI4HvAK8APgq8HlhYt12Umbsi4p3A+6hmfBgEzsnMB3pR/1ziCEPt/DQzTwLOB66C52cP/iLwtsw8HrgB+FLvStQ88XngzRGxtF5+J9Wx9x7g6cw8OTNPAB4DPlSvcyVwema+CjgJeLjhmuckH9zTL6lHGCOZ+UQ9f9fPgMVU08y/NzPPqNcbBJ4Flmfmzp4VrDkvIj4L3At8Dvgh8Aaqf1ZeBOyuV1sIfDsz3xoRNwNLgH8E/ikzf9h81XOPp6TUzm6AetJH8FhRb11NFRA7gB9k5paIGADenZlfP8D6f0A1sjgduC0i1mXmPzdX7tzkKSl14i7ghHp+L4ALqSaDdHShrsrM71K9ovmTVG/eBLgFeH9ELAaIiGURcVxELACOzcxvZuZ64GvAq3tR91xjYKhYPUvw+cANEfEd4Lz6S2rCtUAL+Gq9vB74NnB3fTxuBo6juiHj7yLiuxHxbap3O/hq51ngNQxJB4WIuBbIzLyy17XMV56XltTXImIFcBvwY6o7o9QjjjAkSUW8hiFJKmJgSJKKGBiSpCIGhtQlEfGHEbF5wvKuiDi2lzVJM+FdUlJDMnPp1GtJ/csRhiSpiCMMaYYi4oPAHwNHANuBD2fmzQdYb5xqSu5h4B+AozJzb913NnB5Zh5fT+p4af07Xwz8G7AuM59s4u+R2nGEIc3cQ8BpwKHA5cD1EfGSditn5n8Bz1BNjLfPuVRTdgNcAryJ6j0PK4Cn+Pn8SVLPGBjSDGXmTZn5WGa2MvPvgS3AyVNsdiOwFqpJ84Dfq9sA1lGNUh7JzD3AZcBb6kn1pJ7xAJRmKCIuAN4PHF03LQWWA3sn2ewG4M6IuIhqKu5vZea2um8lcHNEtCasvxc4Enh0FkuXOmJgSDMQESupXurzBuAb9ftD7gMGJtsuM++PiG3A7/KLp6Ogug7yR5n5n10qW5oWT0lJM7MEGKd6vzkR8XbgNwq3vQF4L/A64KYJ7RuBK+owIiJGIuL3Z61iaZoMDGkGMvN+4K+BbwA/AVYDpSODG6kubH89M5+Y0P4pqpcDfS0idlK9uOq1s1a0NE3OVitJKuIIQ5JUxMCQJBUxMCRJRQwMSVIRA0OSVMTAkCQVMTAkSUUMDElSEQNDklTk/wElj7v1bGET7AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "bJfA3WdzZkVU",
        "outputId": "67e670c6-2ec9-471c-a8c9-5534e9a48c5b"
      },
      "source": [
        "sns.countplot(x='alone',data=tt)\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "k-vyixwQZnZI",
        "outputId": "78647688-be62-4cbf-dd38-5d2a6c79b1cf"
      },
      "source": [
        "sns.countplot(x='class',data=tt)\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "KmSuAkf_ZscX",
        "outputId": "29820d4b-9153-4951-9e62-78449ff17b00"
      },
      "source": [
        "tt.groupby('class').std()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>0.484026</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.802856</td>\n",
              "      <td>0.611898</td>\n",
              "      <td>0.693997</td>\n",
              "      <td>78.380373</td>\n",
              "      <td>0.498555</td>\n",
              "      <td>0.501140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0.500623</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.001077</td>\n",
              "      <td>0.601633</td>\n",
              "      <td>0.690963</td>\n",
              "      <td>13.417399</td>\n",
              "      <td>0.499911</td>\n",
              "      <td>0.497081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0.428949</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.495398</td>\n",
              "      <td>1.374883</td>\n",
              "      <td>0.888861</td>\n",
              "      <td>11.778142</td>\n",
              "      <td>0.477552</td>\n",
              "      <td>0.474233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        survived  pclass        age  ...       fare  adult_male     alone\n",
              "class                                ...                                 \n",
              "First   0.484026     0.0  14.802856  ...  78.380373    0.498555  0.501140\n",
              "Second  0.500623     0.0  14.001077  ...  13.417399    0.499911  0.497081\n",
              "Third   0.428949     0.0  12.495398  ...  11.778142    0.477552  0.474233\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFHDzgRtZ1vx",
        "outputId": "85e1e192-ec5a-4436-a3bd-7bae6c70d1b5"
      },
      "source": [
        "tt.groupby('class')['fare'].median()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "First     60.2875\n",
              "Second    14.2500\n",
              "Third      8.0500\n",
              "Name: fare, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auoJWHtcaFLV",
        "outputId": "2280f4cb-2a82-4c51-d55d-e661c786bdaa"
      },
      "source": [
        "tt.query(\"alive == 'yes'\").count()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived       342\n",
              "pclass         342\n",
              "sex            342\n",
              "age            290\n",
              "sibsp          342\n",
              "parch          342\n",
              "fare           342\n",
              "embarked       340\n",
              "class          342\n",
              "who            342\n",
              "adult_male     342\n",
              "deck           136\n",
              "embark_town    340\n",
              "alive          342\n",
              "alone          342\n",
              "연령대            290\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4r2Tp2Ta9mm"
      },
      "source": [
        "## 클래스로 그룹지어서 카운트해보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "LyzsLihcaYPZ",
        "outputId": "c517ae80-455a-4cab-d6c8-c024648f75d5"
      },
      "source": [
        "tt.query(\"alive == 'yes'\").groupby('class').count()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "      <th>연령대</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>122</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>134</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>117</td>\n",
              "      <td>134</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>83</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>13</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>85</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>6</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>119</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        survived  pclass  sex  age  sibsp  ...  deck  embark_town  alive  alone  연령대\n",
              "class                                      ...                                      \n",
              "First        136     136  136  122    136  ...   117          134    136    136  122\n",
              "Second        87      87   87   83     87  ...    13           87     87     87   83\n",
              "Third        119     119  119   85    119  ...     6          119    119    119   85\n",
              "\n",
              "[3 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "jOVzM8qMa77H",
        "outputId": "b192d6fd-1835-4ece-a035-258cf6e2abb3"
      },
      "source": [
        "tt.groupby('class')['age'].describe()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>186.0</td>\n",
              "      <td>38.233441</td>\n",
              "      <td>14.802856</td>\n",
              "      <td>0.92</td>\n",
              "      <td>27.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>173.0</td>\n",
              "      <td>29.877630</td>\n",
              "      <td>14.001077</td>\n",
              "      <td>0.67</td>\n",
              "      <td>23.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>355.0</td>\n",
              "      <td>25.140620</td>\n",
              "      <td>12.495398</td>\n",
              "      <td>0.42</td>\n",
              "      <td>18.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>74.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count       mean        std   min   25%   50%   75%   max\n",
              "class                                                            \n",
              "First   186.0  38.233441  14.802856  0.92  27.0  37.0  49.0  80.0\n",
              "Second  173.0  29.877630  14.001077  0.67  23.0  29.0  36.0  70.0\n",
              "Third   355.0  25.140620  12.495398  0.42  18.0  24.0  32.0  74.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "iT8ngcJ0bec_",
        "outputId": "b4e228da-a72b-4273-96af-cc7d43d8ec00"
      },
      "source": [
        "tt.groupby('sex')['age'].aggregate([min, np.median, max])"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>min</th>\n",
              "      <th>median</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.75</td>\n",
              "      <td>27.0</td>\n",
              "      <td>63.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.42</td>\n",
              "      <td>29.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         min  median   max\n",
              "sex                       \n",
              "female  0.75    27.0  63.0\n",
              "male    0.42    29.0  80.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJudN7IbcOA0"
      },
      "source": [
        "## 40세 이상인 사람?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "QCbZmFF2bzKW",
        "outputId": "c363fe2b-a2c6-4fb1-e5e1-8e847f4be40d"
      },
      "source": [
        "tt.query('age > 30')"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "      <th>연령대</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>장년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>청년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>청년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>E</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>장년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>장년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>873</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>장년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>879</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>83.1583</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>장년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>881</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8958</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>청년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>장년</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>청년</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>305 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  ...  deck  embark_town alive  alone 연령대\n",
              "1           1       1  female  38.0      1  ...     C    Cherbourg   yes  False  장년\n",
              "3           1       1  female  35.0      1  ...     C  Southampton   yes  False  청년\n",
              "4           0       3    male  35.0      0  ...   NaN  Southampton    no   True  청년\n",
              "6           0       1    male  54.0      0  ...     E  Southampton    no   True  장년\n",
              "11          1       1  female  58.0      0  ...     C  Southampton   yes   True  장년\n",
              "..        ...     ...     ...   ...    ...  ...   ...          ...   ...    ...  ..\n",
              "873         0       3    male  47.0      0  ...   NaN  Southampton    no   True  장년\n",
              "879         1       1  female  56.0      0  ...     C    Cherbourg   yes  False  장년\n",
              "881         0       3    male  33.0      0  ...   NaN  Southampton    no   True  청년\n",
              "885         0       3  female  39.0      0  ...   NaN   Queenstown    no  False  장년\n",
              "890         0       3    male  32.0      0  ...   NaN   Queenstown    no   True  청년\n",
              "\n",
              "[305 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcaxiQy_cA6F",
        "outputId": "5e73e7f0-5540-450a-a92f-55373c14bbc1"
      },
      "source": [
        "tt.query('age > 30').groupby('class')"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f3b4a6b0050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "CUjK5JOPcj2F",
        "outputId": "73c51860-b069-45cc-994b-7153fa8345ef"
      },
      "source": [
        "tt.query('age > 30').groupby('class').median()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>56.9292</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        survived  pclass   age  sibsp  parch     fare  adult_male  alone\n",
              "class                                                                   \n",
              "First          1       1  45.0      0      0  56.9292        True  False\n",
              "Second         0       2  39.0      0      0  14.0000        True   True\n",
              "Third          0       3  38.0      0      0   8.0500        True   True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m356r3MFcoDV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdPd3gPDiTsH"
      },
      "source": [
        "# 다시 이런저런 그래프 그려보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "Dj04PkcMiU7i",
        "outputId": "84253642-ea17-4a10-8f2c-23cd3595a823"
      },
      "source": [
        "sns.catplot(x='sex', y='fare', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "8j_UyDHYiaZZ",
        "outputId": "6899dc71-c7d3-4787-fd42-c72b1a9b125e"
      },
      "source": [
        "sns.catplot(x='who', y='fare', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "KALefoKtiiSj",
        "outputId": "6cd80b82-a40c-4ddb-af30-ea798119f463"
      },
      "source": [
        "sns.catplot(x='who', y='survived', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZ1JJjKekOLL"
      },
      "source": [
        "## 연령대별 구분해보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFRz-nMDj5Ob"
      },
      "source": [
        "age = pd.cut(tt['age'],[0, 18, 40, 80])"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "t--EHCPakYfe",
        "outputId": "a9164484-0f23-4912-9f5f-8ea95f148d1d"
      },
      "source": [
        "tt.pivot_table('survived',['who', age], 'class')"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>who</th>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>child</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.431034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">man</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.086957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 40]</th>\n",
              "      <td>0.478261</td>\n",
              "      <td>0.063492</td>\n",
              "      <td>0.146199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(40, 80]</th>\n",
              "      <td>0.280000</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.064516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">woman</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.461538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 40]</th>\n",
              "      <td>0.979167</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(40, 80]</th>\n",
              "      <td>0.961538</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "class              First    Second     Third\n",
              "who   age                                   \n",
              "child (0, 18]   0.833333  1.000000  0.431034\n",
              "man   (0, 18]   0.500000  0.000000  0.086957\n",
              "      (18, 40]  0.478261  0.063492  0.146199\n",
              "      (40, 80]  0.280000  0.095238  0.064516\n",
              "woman (0, 18]   1.000000  1.000000  0.461538\n",
              "      (18, 40]  0.979167  0.914894  0.480000\n",
              "      (40, 80]  0.961538  0.846154  0.111111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e64N7fd-lUhK"
      },
      "source": [
        "## [ cut과 qcut의 차이? ]\n",
        "#### qcut은 cut 함수와는 조금 다르다. cut 함수는 bins를 직접 수치로 지정해주었다면, qcut은 나누고자 하는 범주 갯수만 정해주면 판다스가 각 범주에 동등한 갯수가 되도록 알아서 나누어준다\n",
        "####  - cut 동일 길이로 나누어서\n",
        "####  - qcut 동일 갯수로 나누어서\n",
        "####    그룹별 통계량을 구한다\n",
        "\n",
        "https://t1.daumcdn.net/cfile/tistory/99DEAF3B5C1E4AAA1E"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdgI7u-wkqfm"
      },
      "source": [
        "age = pd.qcut(tt['fare'], 3)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "VQFKwcfslIOM",
        "outputId": "6e936fe3-6fd1-4109-89ca-c6d800d7ee94"
      },
      "source": [
        "tt.pivot_table('survived',['who', age], 'class')"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>who</th>\n",
              "      <th>fare</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">child</th>\n",
              "      <th>(-0.001, 8.662]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(8.662, 26.0]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.703704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(26.0, 512.329]</th>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">man</th>\n",
              "      <th>(-0.001, 8.662]</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.108333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(8.662, 26.0]</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.103896</td>\n",
              "      <td>0.109375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(26.0, 512.329]</th>\n",
              "      <td>0.385321</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">woman</th>\n",
              "      <th>(-0.001, 8.662]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(8.662, 26.0]</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.461538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(26.0, 512.329]</th>\n",
              "      <td>0.977528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.083333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "class                     First    Second     Third\n",
              "who   fare                                         \n",
              "child (-0.001, 8.662]       NaN       NaN  0.666667\n",
              "      (8.662, 26.0]         NaN  1.000000  0.703704\n",
              "      (26.0, 512.329]  0.833333  1.000000  0.080000\n",
              "man   (-0.001, 8.662]  0.000000  0.000000  0.108333\n",
              "      (8.662, 26.0]    0.000000  0.103896  0.109375\n",
              "      (26.0, 512.329]  0.385321  0.000000  0.333333\n",
              "woman (-0.001, 8.662]       NaN       NaN  0.620000\n",
              "      (8.662, 26.0]    1.000000  0.888889  0.461538\n",
              "      (26.0, 512.329]  0.977528  1.000000  0.083333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfFZXzkgo9LC"
      },
      "source": [
        "## np.linspace"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xigHQTk_lLV5",
        "outputId": "1f528905-90b7-4939-f965-063bf5b9699b"
      },
      "source": [
        "np.linspace(1, 21, 7)\n",
        "# 1부터 21까지 7등분해줘"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.        ,  4.33333333,  7.66666667, 11.        , 14.33333333,\n",
              "       17.66666667, 21.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smlRlEcjpFzQ"
      },
      "source": [
        "### 탑승지별 생존률"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "AXrudxUKo1S_",
        "outputId": "ee77276a-47bc-4d6b-a29f-4754eeb0181e"
      },
      "source": [
        "sns.catplot(x='survived', y='embark_town', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLZdhprGpZNJ"
      },
      "source": [
        "### 자식유무에 따른 생존률"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "DB73UkgbpSMU",
        "outputId": "eec752ce-c6e4-420e-d989-9285556af8bd"
      },
      "source": [
        "sns.catplot(x='sibsp', y='survived', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "FuD7Oys2pilW",
        "outputId": "0d067f9a-5f62-437c-a957-e62139f4700d"
      },
      "source": [
        "sns.catplot(x='parch', y='survived', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "VCXA8i1xpsKy",
        "outputId": "e6c20514-0d5b-49fe-b825-4cd0435ecf70"
      },
      "source": [
        "sns.catplot(x='alone', y='survived', hue='class', data=tt, kind ='bar')\n",
        "plt.show()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 440.35x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkmD6EZxp7oI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}