07_Titanic_seaborn_pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/mohd-faizy/_Seaborn__CodeDump/blob/main/07_Titanic_seaborn_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "--- \r\n",
        "<strong> \r\n",
        "    <h1 align='center'>TITANIC</h1> \r\n",
        "</strong>\r\n",
        "\r\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "We will be working with a famous titanic data set for these exercises. Later on in the Machine Learning section of the course, we will revisit this data, and use it to predict survival rates of passengers."
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "laGNL345AfJd"
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      "source": [
        "**Questions that we should ask ourselves about the data set:**\r\n",
        "\r\n",
        "1. What were the passenger types(**Ages, Gender, Class**) on Titanic?\r\n",
        "2. **What is the deck of the passengers and how does it relate to the Class and Survival rate?**\r\n",
        "3. What is the **embarking place** of the passengers?\r\n",
        "4. How many people were **traveling alone or with family**, and **how does it relate to survival**?\r\n",
        "5. What **factors helped someone survive the sinking**?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "qxoPJIQ_zJ2u"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_theme(style='whitegrid')\n",
        "#plt.style.use('ggplot') # style\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9GMxEB4FK7x"
      },
      "source": [
        "# **Titanic Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "4Vx_bEvHzJ2w"
      },
      "source": [
        "titanic = sns.load_dataset('titanic')"
      ],
      "execution_count": null,
      "outputs": []
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        "outputId": "0368d4b2-2afb-4094-d32f-a9dd765d2764"
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      "source": [
        "titanic"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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              "      <th>embarked</th>\n",
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              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
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              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
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              "      <th>0</th>\n",
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              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>71.2833</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>8.0500</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
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              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
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              "      <th>889</th>\n",
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              "      <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",
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              "      <th>890</th>\n",
              "      <td>0</td>\n",
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              "      <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": 3
        }
      ]
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        "id": "4s8jdHC31r9x",
        "outputId": "8de710f8-6fac-4465-bad9-42673da64eda"
      },
      "source": [
        "titanic.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "deck           688\n",
              "embark_town      2\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "TbEZytnv0tZE",
        "outputId": "946277aa-431c-4606-b9d2-15c7b657b6cd"
      },
      "source": [
        "titanic.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype   \n",
            "---  ------       --------------  -----   \n",
            " 0   survived     891 non-null    int64   \n",
            " 1   pclass       891 non-null    int64   \n",
            " 2   sex          891 non-null    object  \n",
            " 3   age          714 non-null    float64 \n",
            " 4   sibsp        891 non-null    int64   \n",
            " 5   parch        891 non-null    int64   \n",
            " 6   fare         891 non-null    float64 \n",
            " 7   embarked     889 non-null    object  \n",
            " 8   class        891 non-null    category\n",
            " 9   who          891 non-null    object  \n",
            " 10  adult_male   891 non-null    bool    \n",
            " 11  deck         203 non-null    category\n",
            " 12  embark_town  889 non-null    object  \n",
            " 13  alive        891 non-null    object  \n",
            " 14  alone        891 non-null    bool    \n",
            "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
            "memory usage: 80.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4cK_evnGjnT"
      },
      "source": [
        "### **Survived**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgTaAxSc2M5Q",
        "outputId": "86c764b1-30aa-4f47-d449-234e29376eba"
      },
      "source": [
        "# Survived - 1\r\n",
        "titanic.survived.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 358
        },
        "id": "3tSJti2SoNVt",
        "outputId": "f3521507-1523-425a-f0da-4667cd9e0e0b"
      },
      "source": [
        "cat_sur = titanic['survived'].replace([0,1],['not_survived','survived'])\r\n",
        "sns.countplot(cat_sur)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac12aadd10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "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": "vpeXPf49Gpg2"
      },
      "source": [
        "###  **Pclass**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7Xnk_Kk2SMw",
        "outputId": "6c4d20cc-f1d1-4a0a-c1b7-86b6da4c2a15"
      },
      "source": [
        "# Pclass\r\n",
        "titanic.pclass.value_counts()"
      ],
      "execution_count": null,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JkBWsxZGs5x"
      },
      "source": [
        "### **class**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7U1BVqq8XaQ",
        "outputId": "80e8615e-1ec9-4931-8ccd-0f0b236d6b18"
      },
      "source": [
        "# class\r\n",
        "titanic['class'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Third     491\n",
              "First     216\n",
              "Second    184\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "m00ptNt6vp8W",
        "outputId": "eb337da4-06d9-4ea6-88c4-4d90ccaa8b50"
      },
      "source": [
        "sns.countplot('class', hue='alive', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac11d26cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "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": "t-roe7wIwQMO"
      },
      "source": [
        "- First class  - survival rate is **high**\r\n",
        "- Second class - survival rate is **medium**\r\n",
        "- Third class  - survival rate is **low**\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "fIq3hAzHqL30",
        "outputId": "a30ca86b-14ce-4a84-eef2-44da3ff70521"
      },
      "source": [
        "sns.countplot('sex', hue='alive', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac11df4fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "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": 357
        },
        "id": "pwxWPSVfuPRK",
        "outputId": "85c7e383-67d6-430c-9e8d-3fc33e0af627"
      },
      "source": [
        "sns.countplot('class', hue='sex', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac11eded10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "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": 357
        },
        "id": "w5JePmLP_-fQ",
        "outputId": "ac851e30-5f15-482a-a87d-d62862f328a3"
      },
      "source": [
        "sns.countplot('class', hue='who', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac0a8a8c10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "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": "8snVbVixG3h-"
      },
      "source": [
        "### **sex**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V03CGofK20U7",
        "outputId": "d10593cb-4eee-45ad-e750-4fb96bebf995"
      },
      "source": [
        "# sex - Male/Female\r\n",
        "titanic.sex.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "SNxr1hELsge5",
        "outputId": "b40ca7db-0618-4cfd-b9c8-b1d2d7db94c2"
      },
      "source": [
        "sns.countplot('sex', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac129a1e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "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": 357
        },
        "id": "T5b7uXcT0X0F",
        "outputId": "ccbbc637-b718-4c06-bca9-0190db80946e"
      },
      "source": [
        "sns.countplot('sex', hue='alive', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac12f0d8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        },
        {
          "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": "EJbpgcR2G859"
      },
      "source": [
        "### __who__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuOL54WK81NF",
        "outputId": "25997125-786a-4602-f6a2-cc855a2ecc93"
      },
      "source": [
        "# who\r\n",
        "titanic.who.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "man      537\n",
              "woman    271\n",
              "child     83\n",
              "Name: who, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "YTlPKpoisqvy",
        "outputId": "3c95b150-ca14-42a2-8160-0d7d68c7bf6e"
      },
      "source": [
        "sns.countplot('who', hue='alive', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac121337d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "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": 357
        },
        "id": "QRL6rHLM_uZk",
        "outputId": "3e9f2d61-7e76-4c04-f507-c134459634c6"
      },
      "source": [
        "sns.countplot('who', hue='class', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac0a90d2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        },
        {
          "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": "g99NV6uXHMxQ"
      },
      "source": [
        "### **sibsp**\r\n",
        "\r\n",
        "**sibsp:** The dataset defines family relations in this way\r\n",
        "- **Sibling** = brother, sister, stepbrother, stepsister\r\n",
        "- **Spouse** = husband, wife (mistresses and fiancés were ignored)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Z81Ib2X2-m8",
        "outputId": "f13141c4-23b0-4a1d-f41f-49d2a32d9efc"
      },
      "source": [
        "# Number Siblings/Spouses Aboard - sibsp\r\n",
        "titanic.sibsp.value_counts()"
      ],
      "execution_count": null,
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "XtHRX4i4tCh3",
        "outputId": "d2efe62f-f582-415e-eb68-0a32aa5a8c35"
      },
      "source": [
        "sns.countplot('sibsp', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac11b47b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "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": "Lnwb5Bz-HQjy"
      },
      "source": [
        "### **parch**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJBtzeqZ5DVx",
        "outputId": "92b2b9d9-bf76-4af4-ab72-ccb2ca957230"
      },
      "source": [
        "# Number of Parents/Children Aboard\r\n",
        "titanic.parch.value_counts()"
      ],
      "execution_count": null,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "yadRZwqyxQeT",
        "outputId": "9d59a2ac-e853-4170-9ad1-89ac31ef8cf0"
      },
      "source": [
        "sns.countplot('parch', hue='sex', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac11a4f910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "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": "Ci-jgkDMHW0X"
      },
      "source": [
        "###  **embarked**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74g25tDG8NNY",
        "outputId": "8792c5cd-8e83-4351-8933-6c39760fccca"
      },
      "source": [
        "#  Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\r\n",
        "'''\r\n",
        "Embarked implies where the traveler mounted from. There are  three  possible \r\n",
        "values for Embark — Southampton, Cherbourg, and Queenstown. More than 70% of \r\n",
        "the people boarded from Southampton.\r\n",
        "'''\r\n",
        "titanic.embarked.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f55XpAF_Hel6"
      },
      "source": [
        "### **embark_town**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9fmdtkBFkCe",
        "outputId": "08857865-ef24-451a-b9ae-b6fcb3b16b6a"
      },
      "source": [
        "# embark_town\r\n",
        "titanic.embark_town.value_counts()"
      ],
      "execution_count": null,
      "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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "UF7QR-2Nxqhl",
        "outputId": "1fcf5bc3-2631-4ec2-b795-a4cab5ce5d58"
      },
      "source": [
        "sns.countplot('embark_town', hue='alive', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac11980610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "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": "Qr532SfbHmfn"
      },
      "source": [
        "### **adult_male**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQZijRtQ9Fqv",
        "outputId": "a903d9ac-6d18-4df4-b053-221d7ba8fb4c"
      },
      "source": [
        "# adult_male\r\n",
        "titanic.adult_male.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     537\n",
              "False    354\n",
              "Name: adult_male, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "1F6ovqd7zzQq",
        "outputId": "36196a81-4e52-4f43-cb86-303e97ba2108"
      },
      "source": [
        "sns.countplot('adult_male', hue='alive', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac0d0ab5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "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": "d-vTP4nZHpzs"
      },
      "source": [
        "### **deck**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-VW-ts-9bSa",
        "outputId": "06a2001e-31ab-4e70-d00f-1852a4fcfe34"
      },
      "source": [
        "# deck\r\n",
        "'''\r\n",
        "The first letter of the cabin indicates deck. ...  It also makes  sense  that \r\n",
        "the people towards the top (higher decks, higher pclass) more likely survived,\r\n",
        "because they were closer to the lifeboats.\r\n",
        "'''\r\n",
        "titanic.deck.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C    59\n",
              "B    47\n",
              "D    33\n",
              "E    32\n",
              "A    15\n",
              "F    13\n",
              "G     4\n",
              "Name: deck, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ExBYI8Sn0mKN",
        "outputId": "47ce9d05-76ab-43c0-cd19-9f8ef29de2c1"
      },
      "source": [
        "sns.countplot('deck', hue='alive',data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabfdc6f7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        },
        {
          "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": "gi5FPbAgHuR5"
      },
      "source": [
        "### **alive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxyYIRQQGKH5",
        "outputId": "2f119406-09f0-4af6-cc72-b7065b4abde9"
      },
      "source": [
        "# alive\r\n",
        "titanic.alive.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     549\n",
              "yes    342\n",
              "Name: alive, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZjUcdmAHxHb"
      },
      "source": [
        "### **alone**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iNtgDWGGY1b",
        "outputId": "1678fb57-6f54-49c4-89c1-7f2de2ad323d"
      },
      "source": [
        "# alone\r\n",
        "titanic.alone.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1        537\n",
              "False    354\n",
              "Name: alone, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1hsOlYH3BPX"
      },
      "source": [
        "# **Titanic Data Summary**\r\n",
        "\r\n",
        "```\r\n",
        "# survived     int64         0 = No; 1 = Yes  \r\n",
        "# pclass       int64         1 = 1st; 2 = 2nd; 3 = 3rd\r\n",
        "# sex          object        male/female\r\n",
        "# age          float64       age\r\n",
        "# sibsp        int64         No. Siblings/Spouses Aboard \r\n",
        "# parch        int64         Number of Parents/Children Aboard\r\n",
        "# fare         float64       Passenger Fare\r\n",
        "# embarked     object        Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\r\n",
        "# class        category      Same as Pclass\r\n",
        "# who          object        Man/Women/Child\r\n",
        "# adult_male   bool          True/False\r\n",
        "# deck         category      A,B,C,D,E,F,G\r\n",
        "# embark_town  object        Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\r\n",
        "# alive        object        yes/no\r\n",
        "# alone        bool          true/false \r\n",
        "```\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOcPRo9OzJ2z"
      },
      "source": [
        "# **EDA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "5Id_lQ9rzJ20",
        "outputId": "1d04c419-760e-4f18-92dd-7104ba4abe76"
      },
      "source": [
        "sns.set_style()\r\n",
        "g = sns.jointplot(x='fare', y='age', data=titanic)\r\n",
        "g.fig.set_figwidth(9)\r\n",
        "g.fig.set_figheight(9)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IuKJIJlk+E4n2JPcDAJB9CLiiSCZZPhOJ9iT3AwCQfQi4omisLdHduzYEg5hATlS0ZPlkj83EeAEAgD1Imo/C6XTo+tZGNTduVt/gqKrL4qv6W9ZQps/fvlGFhfmqLiuMmmif6fECqUK1LABcQcAVA6fToaa6UjXVlcZ8jFW1YEON/U+aEhkvkEpUywJAKF4p2oRqQSxk/PwDue/e3W26/84bNOH1zfks3DZExhMum0SqFuTJE+Y7fv6B3PeVxzo0Mu4P+9n9d96Q5tHkPp5w2YRqQSxk/PwDQCgCrhj5fH51dg/pyMmL6uweks8XPuoPyKVqwXjvDYgml37+ASAdeKUYg0QSgHOlWpDkZtghV37+ASBdeMIVg0QTgAPVgq0rFqmprjQrf9mQ3Ay75MLPPwCkCwFXDObzcjnz+d4AAMgWBFwxmM8JwPP53gAAyBbkcMUgkAD86LPHdOP6JXI6pbddXaP66uKox3q9Pp06N6CegVHVVhRp+eIK5ednT5wbuLcHHj+oshKXbr3uKi1rKJPf75fP5+c1EAAAKUDAFQOn06FNaxrknfTpof2HYk4u93p9ev7gWe196nDwmD3b1mnzhiVZE3QFkpuvXrxZr73Zp4efPEzyPAAAKZYdv/VzwIW+kWCwJcWWXH7q3EAw2Aocs/epwzp1biAtY46V0+mQ369gsCWRPA8AQCoRcMUokeTynoHwx/QOZF9COsnzAADYh4ArRokkl9dWhD+mpiL7EtJJngcAwD4EXDFKpHP28sUV2rNtXcgxe7at0/LFFWkZczzoDA4AgH1Imo/A5/Orq2dYfZdHVV1epE1rGvTgPbF3zs7Pd2rzhiVa1lCm3oFR1UxXKUrS8bcuZVXlIp3BAQCwDwGXhUhL3jTVlcZ8nvx8p1qWVUmqkpTdlYuBzuDx3B8AAIiOV4oW7FryJlcqFwEAQOoQcFmwq2ovlyoXAQBAahBwWbCrai+XKhcBAEBqpC2HyzCMQkkPSLpVkkfSL03T/KRhGC2SHpVUI6lX0u2maZ5I17iszFzyZmYOV7xVe7MT75sbyrVn27o5OVzNDeXq7B4K7peOhPWZY6upKNKkz6/+QU/arg8AwEKRzqT5L2sq0GoxTdNvGEb99Pa9kr5mmuZ3DMP4kKRHJN2SxnGFlYqqPavE+5vWN4VULjY3lOvl31wIm6BvV9Azc2xlJS7d9s6rte/AcZb1AQDABmkJuAzDKJV0u6Qlpmn6Jck0zQuGYdRJ2iCpfXrXxyX9vWEYi0zTvJiOsUWSbNWeVeL9g/dsDqlc7OweCrtfc+Nm2yoGZ45tS9uKYLCVrusDALLbvbvb5Ha7w3424fXJle8M/o3o0vWEa4WmXhf+hWEY75I0JOnPJI1K6jRNc1KSTNOcNAzjnKSlkmIOuI4ePZrSwXZ0dKTkPB5nVdgE+Tc6u3X+jBn3fqkUck2HUn79VM3hQsYcJo85TB5zmLxMzGFbW1vS5/jS3p9ryOOLuM9Dn3s3PyMzRJr3dAVceZKWS3rVNM17DcPYJOlfJH0gFSdfu3atZRQer46OjpT8oEpTT67crryQYMbtylNzU52a6pbHvV8qzb5mKq+fyjlcqJjD5DGHyWMOk5fLc1hSUiqHyx91v1y9v3RL13PAtyR5NfXKUKZpviSpR1NPuJoMw8iTpOm/F0s6k6ZxReXz+dXZPaQjJy+qs3tIPp8/7LZwYl0uJxPL6sy85k9feUs721tY1gcAAJuk5QmXaZo9hmH8TFO5Wv97ujKxTtJxSYck7ZL0nem/X82G/C0pfNL7fbs3asLriynBPdbE+0wsqzP7mjXlRdq0tlH9Qx6W9QEAIMXSWaW4R9K3DcP4W0kTknabptlvGMYeSY8ahvFFSZc0lVyfFcIlvZ8406+nnz8Zc4J5rIn3mVhWJ9w1l9aXpe36AAAsFGkLuEzTPCVpc5jtr0nalK5xxCNct3mf32/ZgZ6KPgAAEA61nBGE6zbvdDhs6UAPAADmLwKuCMIls69aWpn2BHcAAJDb0pnDlXOsktklxZzgHlg+Z2DYo/y8PHnGvKqpICl99pJHds5HuGtJStv1AQAg4IrCKpk9lgT3QJXjo88eU/umq/QES+dIsl7yyI75SLbSFACAVOCVoo0CVY43rl8SDLakK5WNXT3DGR5hZlgteWTHfFhVmqbr+gAASARctgpWOVosndM3OJqhkWVWuOpPu+Yj3kpTAADsQMBlo5lVjlQ2XhGu+tOu+aDSFACQDQi44uDz+XXmwqBe+fV5HX29R+cuWi/rI12pcnzh1bPawdI5QelcyohKUwBANiBpPkbhkq93trdoWUO5Nq6uD5tsfaXKsVwDIx7dv+oGeca9qlngVXHpXMooFZWmAAAki4ArRuGSr/cdOK6tm1eqaZF1xWKwylF0oZ8pnUsZJVNpCgBAKvBKMUZWid4+v59kawAAEBEBV4ysEr2dDgfJ1gAAICJeKcYokHwdLocrUrL1zC7nVWWFcjod6h2Y6m5eX12sC30jWdntPJ2d4AEAmO8IuGIUSL5e1nCzLvQOq9Cdr+ryQjXURF7WJ1yi/bMvnpYrz6nttxra+9ThrOt2ns5O8AAALAQEXHFwOh1aWl+mpfVlMe1vlWi/5aYVkhQMtgKfPfD4QTU3bs54IrdVJ/hsGBsAID3u3d0mt9sdcZ8Jr0+ufGfwb1gj4IpBoq/XrBLt5Zjx71mf9Q2OZjyoidQJPtNjAwCkx1ce69DIuHWvyZnuv/MGm0eT+wi4okjm9Vog0X5m8OJ25Ul+SQ6F/SwbEvCtxp0NYwMAIBfx/C+KSAst+3x+dXYP6cjJi+rsntt1fnaX88aaYt29a4MKXE6VFObr0+9bl5XdztPZCR4AgIWAJ1xRWL1e6708qje6Lkd88jWzy/nAiEcX+zwh+9+3e6MeuPtm9Q95sqrbeTo7wQMAsBDwhCsKq/5bhQX5lk++Zgp0Oa8oLtRD+w+F7P/lx16R0+FQ64pFaqorzaqAJjDubBwbAAC5hoArCqvXa97JScvE8nAiJaIDAID5jVeKUVi9XuvqGY4rsZxEdAAAFi6ecMUg3Ou1eBPLSUQHAGDh4glXEpY1lOnzt29UYWG+qsuudJ0P9O3qHRiVK9+pIc+EKkrcum51vR68J7lEdKueYMksxePz+XW+d1h9lz3yjHnVUFOixYvI2wIAIFUIuBJg1ZuroaYk7Gc72lt04KU3tf1WQ5s3LEm4eajVdTetadBLx84n1CvM5/Prld9c0FvnL2vfgeMs5QMAgA14pZiASL25wn32xIHjunH9Eu196rBOnRtI+XVPnRuIqWLS6pwnzvQHg614jwcAANERcCUgUsVhpOV8xiYm1TuQeFWi1bl7BhKvgOy7PCqf308FJQAANiLgSoBVb67qsiLLz+Sf+rumIvGqRKtz11ZYjyeWczodjoSPBwAA0RFwJSBSxWG4z3a0t+iFQ2e1Z9s6LV9ckdR179u9UTvbDW2/tUU72w3dt3ujli+uSLgCsrG2RKuWVmpnewsVlAAA2ISk+QREW/om8FmgSnHYM6E/+dBUYJSfn1yMO+H16ennT4YktyezFI/T6dDG1fVaUleqa5qr5Rnzqr6mRE1UKQIAkDIEXAkK9OYKV3EY6bNkWCXNNzduDl4vkWs6nQ4tXlSqxYtSO14AADCFV4o5hOWBAADITQRcOSRSsj4AAMheBFwJ8Hp9Ov7WJb145JyOv3VJXq8vpuN8Pr86u4d05ORFdXYPyefzx3VdO5cH8vn8OnNhUK/8+ryOvt6jcxcjjy/ZewEAYCEhhytOXq9Pzx88q71PHQ4mru/Ztk6bNyyJmBBv1SU+nm7uySTHRxJubDvbW7SsoVwbV9fPOX8q7gUAgIWEJ1xxOnVuIBhsSVM5VLF0kI/UnT4e4RbSTla4se07cFwnzvSHHV+q7gUAgIWCJ1xxsurqPtVBvsryuEgJ76muZoyX1dh8fn/Y8WXzvQAAUuPe3W1yu90x7Tvh9ckVQ9ujWPebjwi44hTo6j4z4Iilg3wg4X32cdmQ8G41NqfDEXZ82XwvAIDU+MpjHRoZT21+7v133pDS8+WShRlmJmH54grt2bYuJHE9lg7ydia8Jyvc2Ha2t2jV0sqw48vmewEAIBvxhCsKn8+vzotD6rw4JHdBnsqKC3RDa6OaFpWod8CjmopCrWiqtEyY9/n86uoZVt/lUV3VUKav/vFm9V5OXcJ7pOtVl8d+jWUNZfrc7RvlduXJ5XKqosSthprwx9qVvA8AwHxFwBWBz+fXi0fO6e8efzWkeq+zqkjP/uKUfvNmf8QKvXRX8yVyPatjjGXVEcdoVzd9AADmI14pRtDVMxwMtqQr1Xtnu4f1+zeuCG6zqtBLdzVfItej4hAAAPsRcEUQqXrPMz4Zsi3c8jrpXoonkeuxXBAAAPYj4IrAaikdp8OhwoK8kG2RqvlmH29XNV8i12O5IAAA7Je2HC7DMN6Q5Jn+I0mfM03zx4ZhvEPSI5KKJL0h6UOmaXana1yRNNaW6L/uujYkh+vT71+nqtICOZ1O3XHbannGfZbVfPXVxbpr+3o9tP9Q8Pg7379ODsdU7lQq8rhmJslXlRXqvt0b9eXHXgnJx4pUPRioOJydw5WqisNEk/gzdV6Ex3wDQHLSnTT/ftM0jwa+MAzDKek7ku4wTfMXhmH8maS/lvTRNI8rLKfToXesadQXPpKvk2f61bSoTM/8/ISuW9OoJw4cDwlQZvP5/Hrp2Hl990e/0ZabVsjplJobK/SDnx3Xw08eTknyvFXCezyVkHZWHNpVNMDSQunFfANA8jL9SrFNksc0zV9Mf71X0vYMjmeOC30juv8fXtbYuE8PPH5QrSvrgsGWZJ1kHkhG7+od0f6fHNe+A8f1wOMHdW1LfcoS060S3v1+xbX0jx3LBUUan133TaK/PZhvAEheup9wfdcwDIekX0j6gqRlkt4MfGiaZo9hGE7DMKpN0+yL9aRHjx6NvlMcOjo6gv/2OKumftE4FPr3DGMTk3qjs1vnz5hzj5u1nxzWx8TL6hrJnjcVOjo6bBtfNt93Ks38OcykXJ7vbJnDXMYcJi8Tc9jW1pb0OYaHhzTk8aVgNKHm889UpHlPZ8B1o2maZwzDcEv6O0l/L+npVJx47dq1Ma/3FE1HR0fIhHV2D4V0VA/8PXtZm+amOjXVLZ9z3Oz95Lc+Jl5W10j2vMkKzKFd48vW+06l2T+HmZSr851Nc5irmMPk5fIclpSUyuFK7dI+UmqCwVyUtoDLNM0z03+PGYbxsKR/lvSgpKsC+xiGUSvJF8/TrVQJJAV7nFXq7B4K5jEFksofffaYdrS36MBLb2pHe8ucHK7ZSeaNtSW6b/dGnTjTL5/fL6fDoYoSl37w/MmUJaaHu4ZVAn+0+46WDJ1I0nS4hPz7dm+Uz+/XkZMXE06+tjvRH6GYbwBIXloCLsMwSiTlm6Y5MP1KcaekQ5I6JBUZhvHb03lceyR9Px1jmilaUvBUUnm5+oc9Wru8RiNjE/qrT12vCa9PNRXWQcOE16ennz8ZPOdd29fr3t1tqiguTFli+uxrhEvgT/S+491vttkJ+TXlRTp97rLufuDnSSVfs7RQejHfAJC8dCXN10t63jCMw5KOSmqRdKdpmj5JuyV93TCME5JulvT5NI0pKFpScCCpfM3VtVp9dY3armnQ6qtrtG6VdZJ5uHM+tP+QKooLU5aYnmwyc6zHJ3OdmQn5fr9SlnxtV6I/wmO+ASA5aXnCZZrmKUnXWnz2oqTWdIzDSqRu64muFWjHOVN9jViPT9W9pGNOAADIRpluC5EV7Oi2no4O7sleI9bjU3UvdLUHACxUBFy6khQ8swox2aRgO86Z6mvEenyq7iUdcwIAQDZKdx+urDQzKfiNzm41N9WFJAXPrtCrry7Whb6RiBV7TqdDm9Y06P5Pv1MX+0dVUeZWTXlh8PNEzhnuuE1rGvTgPeGTmaNVFgbHeOcN6hkYVW1FkZYvrgh7L6lImib5GgCwUBFwTQskBZ8/Y4b0FgpXobdn2zrtf85UV+9IxMq+l46dDzluZ3uLljWUa4NRN+ezWM9pVS04OwcqlsrCcGO0qhoMzE+yuVapOg8AALmEgCuKcOxiqIkAACAASURBVBV6e586rC03rdD+nxwPVto1N24OCSLCHbfvwHFt3bxSlWXulJ0z3H6x7hvP+QAAC8u9u9tS1lQ8YMLrkyvfGXXbfETAFYVVZZ0coV/HWtnn8/vVM5Dac4ar8otlX6oGAQBWvvJYh0bGU99pfrb777zB9mtkg/kfUibJqrJO/tCvY63sczocqq1I7TnDVfnFsi9VgwAApAcBVxT11cW6a/t6NdYUa/vvtGhne4s+f/tGHTnZrdrKQu1sN/SZHevl8/vl812JmBprS/Rfd10bUpH3wd+7RquWVmr54oo51Xp7tq3TC4fOBr+OVC04cyx/+pG3q766eM64Y6kIpGoQAID04JViBIGk8md/8bq2bl6p//nPx4LJ5Xe+f50c8utrTx6xTDgvyM/T1s0rg+scNtYUa4NRJ6fTIVe+M+Sz0qJ8tW+6Sp7xSTkdDsv32cWFedr2rlX61jNHIya6x1IRSNUgAADpQcAVQSCpfMtNK4LBljSV5/Twk4e1dfNKy4Tzrp5hffmxV0JypNyuPD14z2ZJCvtZIGl+5r6zk+Z/ffpScO3EcNedKZaKQKoGAQCwH68UIwgmlTtkmQA/e1vf4GjosWE+jycRf/Z4fH6/5XkBAEB2IuCKYGZSuVUC/OxtgYTzSAnpySTiOx0OEt0BAMgxBFwRBJLKX3j1rHa0t4QEX59+3zpVlLgsE84jJaSH+yzWpPlVSyu1c9ZYZu7r8/nV2T2kIycvqrN7KCSRP5JEjwMAANGRwxXBlaTycg2MeHT/qhvkGfeqZnopnu5LI2peXCHPmFf1NSVqWlQaknA+OzE+kAgfLlm9vrpYq5urIyavO50ObVxdryV1pbqmuXrOdWPpLh9OoscBAIDYEHBFEUwq19yk8sWLSrV4Ufhk80hJ8011pWGT1WNJXnc6HZbXTbRzPB3nAQCwF68Uo0j0VZtVYnxXj32v6yIl6ttxHAAAiA1PuCJI5lVbIDF+9hMu861+jU34bHldZ3XNaAn1iR4HAABiwxOuCKxetXX1DEc9Nlxi/I72Fj338psxnyNeiXaOp+M8AAD24glXBIFXbauWVui9N6+UZ2xSRYV5Ghkf15kLg7rQO6xCd76qywvVUBO+i3tt5Ts1PDqhy8Pjcrmcqipzq6ffo77BUTXWlqirZ1h9l0dVXZ58l/dEO8dbJfGncmy5yufzMw8AgKQRcEVQVVao1VdV6pbrrtJXnziksYnJqXUMb23R3qeuLOmzs71FyxrKtXF1fcgvY6/Xp7cuDOqRGft+fMta5TvfVFVZoS2VgYl2jp95HFWLU5gHAECq8EoxAqfToV2/tzq4bqEk3bh+STDYkqZeM+47cFwnzvTPeU34emd/MNgK7PutZ47qg+9+m0Y93oRfV9otmVep8wnzAABIFQKuCHoHRtU7MKuCL8IyP7Or+noGPGH3HRga08XZ51X2VAZStTiFeQAApAoBVwTV5UVyF+SHX4Zn1tdOh2NOVV9tRWHYfcuKXaqtsF76J9MiLUu0kDAPAIBUIYcrgsbaEnVfGtbndm9QXl6eLg+Pq6rCraZFJXr4ycMhOVxNi0pVX10ccnxzQ7k+d/tGHX/rknx+6YVXz+oDt7bI7XZq+eIK3b1rw5z8oEQqA1Od2B2oWpw9tvrqYnV2Dy2YBHKreaB6EwAQLwKuCJxOh1ZfVaMXftWpR56+8kv3ru2/pb/97I3qvDisyUmfLvSN6B/+9Zh8fgUTqr1en/7jSJf2PnUlMLvz/et05ES3it2LE64onM2OxG6rqsWXjp1fUAnkqfoeAQBAwBXF6a4BPfJ0aOL7Q/t/pf/+yXfof3zvYEiOz8zlcE6dGwgGW4HjHn7ysD6zY33IfolUFM5k17I8s6sdO7uHFuTyP4lWfQJArrt3d5vcbrft15nwTsqVnxd9xxxHwBWFVeJ732WL7YOjaqorVY9FUrxnfDJkv2RFSuxOZZCQrusAALLDVx7r0Mi4PUvRzXT/nTfYfo1sQNJ8FFaJ79XlFtunE6qtkuILC/JSmnidrsRuEsgBAEgcAVcUVzdW6HO3b9TO9hZtv7VFjTXF2rOtVSsWV0ZcDmf54grt2bYu5PM971unfz94Jq7E62iLZ6drWR6W/8keiS6oDgDIHF4pRuDz+dVhdockin98y1pVV7hVUJAXMaE6P9+pm9Y3qarcrdfe6JPPJ+0/YOqP3r1am9Y0xJR4HUtCfLoSu0kgzw50vweA3ETAFUG4hPRvPXNUWzevVH1VadSk94v9o/p//vHlkNynh/Yf0sollTHlPcWaEJ+uxG4SyDPPriIJAIC9eKUYgVWieLiu8vEcH2uncjqdYzZ+JgAgNxFwRWCVKB6uq3w8x8eaaE6iOmbjZwIAchOvFCMIJIo/+uwx3bh+iZxOadXSKhUXOXV5ZEynDg2otqJQK5oqVVAwt4dIfXWxvvjxTRoYHNPo2KQuj4xpWX15zInmVp3OnU7p6Os98ox51VBTosWLSsnfWSAS7X6f6tUIAADxIeCKwOl0aEPLInnGDX39B1NNTBtrivWB32kJNkN1u/L0qa2tuvnaJSFBl8/n10GzW2+dv6x9B46HdJv3+fwx/bKbnaheWVqonv4R/cevzoWck6TphSOR4gUS7QEg83ilGMWproFgsCVJN65fMqfz/CNPH9Hrnf0hx3X1DOvEmf5gYBTY9+EnD+vUuYGYrx9IVG9dsUhOh0O/Pn1pzjkfePygunqGU3G7yAEzfyaa6qI/3bRKtOdnBgDSh4Arit7ZneYdCpu03DvgCdnWd3lUPr/fYt/EEpwjnZOkaVgh0R4AMo+AK4oai07zs7+uqSgM2VZdXiSnw2Gxb2IJzpHOSdI0rJBoDwCZF3PAZRiGwzCMTxiG8VPDMA5Pb7vJMIzt9g0v81YsrtSntrYGf2G98OrZkK8DOVwrmipDjmusLdGqpZXa2d4Ssu+n37dOyxdXJDQWq3PS8R2RsEoAAGRePEnzfympXdLfSdo7ve2spAck7U/xuLJGQUGermmu0p999O0a8XhVWebWVXWlWlJfqkuXx1RcmK/ayiI5nQ6duTCoC73DKnTnq7q8UBuMOi1eVKJVy6o04plQbUWRrl5cofz8uXFuLFVkTqdDG1fXa0ldqa5prpZnzKv6mhI1LdAqRSrvYsMqAQCQefEEXHdIutY0zR7DML4+ve20pOUpH1WWsKruallapb6BsZDte7at0/7nTHX1jsjtytPO9hYtayifDpDKErpOuCoyp9OhxYtKtXjRwu4qTuVdfFglAAAyK54crjxJQ9P/DqyWWzpj27xjVd11qrN/zva9Tx3WjeuXBL/ed+C4Tpzpj6kSjCqy+DFnAIBcEk/A9W+S/odhGG5pKqdL0l9J+hc7BpYNrKq7zveNhN0uR+jX6VoCaCFizgAAuSSegOseSY2SBiRVaOrJ1lWSPmfDuLKCVXWXw6JSMPjcT+ldAmghYs4AALkk5hwu0zQvS9pqGEa9pGWSzpimeT7eCxqG8ReSviSp1TTNo4ZhvEPSI5KKJL0h6UOmaXbHe95kBRKwPc4qdXYPqbG2RI21Jfo/PvBb+tr3fxXME/rEe9fqpy+/qU9tbQ3tNr+tVd9/7rgkheRwNdaWyOv16dS5AfUOjKqy1C2f36/K0sJg4nJjbYnu271RJ870y+f3y+lwaNXSSqrIIkh0iZsAEu4BAOkUc8BlGEbgadjF6T8yDMNpmqYvjnNskPQOSW/OOOd3JN1hmuYvDMP4M0l/LemjsZ4zFawSsDetaVChK09bN68MBkITE5Nqu6ZeE95JbblphZxOqbmxQj975U3t2bZOkz6/iqarFBtqSuTz+fX8wbPa+9Th4Ll3tLfowEtv6sO3rdH1rY2SpAmvT08/fzLk+ojMle8M+d64wlR/hkPCPQAg3eKpUvQq5KXZFMMwvJLOSXpK0l+Yphk2iX469+trknZJen56c5skj2mav5j+eq+mnnKlNeCySsC+/84b9LffOxiSK+SeDsBc+Q7t/8mVJ1pbblqh+//xZd1/5w1qWVYV3P/k2f5gsBU49xMHjmvLTSv0wOMH1dy4WZLCXr+5cTNVZRa6eob15cdemfO9efCe6HNm9f1mvgHgint3t8ntdtt+nQmvz/L/MEf6LNfEE3DdJem9mnoCdUZTrxXvk/SsJFPSX2iqR9fHLY7/S0nfMU3zDcMwAtuWafpplyRNt5xwGoZRbZpmX6wDO3r0aBy3MZfHWRU2AftC71DY7VPL6/hDtgWW/LnQO6TBi6eCn4046ywT7McmJvVGZ/eVbbP2eaOzW+fPmEndW6Z0dHTYen6r71ksc5bMselk9xwuBMxh8pjD5GViDtva2pI+x5f2/lxDnphfYtnioc+9O6d+BiPNezwB1z2SNpimGVh5+bhhGK9I6jBNc4VhGEckhZ0VwzCul7RR0ufjuF7M1q5dm1QU3tk9JLcrb87Tkvqa0rDbnQ6HfD5/yDb5rxzTsmxp8LPjb10Ke47A/s1NdcFts/dpbqpTU13utTnr6OhIyf/YI7H6nsUyZ8kcmy7pmMP5jjlMHnOYvFyew5KSUjlcc15spV2uzt9s8QRc5ZKKNVWlGFCsqYpFSTqvqcT3cG6WtFrS6emnW0sk/VjSVzVV6ShJMgyjVpIvnqdbqWCVtL58ccWcxOyd7S1qbizT2IRfd31gvYoK8zU6NqEnf3JCe7ZdWbbH5/Or8+KQBobG9IU7rtP53mENjU5IklYtrdKZC5f1hTuu06LKIuXnOxNOAE9l8vfMc9VUFGnS51f/oCelSeWpGm8ySfPJJtwDABCveAKuf5J0wDCMBzX1SnGJpM9KenT689/V1KvFOUzT/GtNvYqUJBmG8Yak35f0a0mfNAzjt6fzuPZI+n58t5Aa4ZLWnU6Hrltdry/ccZ16BzxyF+RpYsKrS4PjcyoUP3/HdVpWV678fOecpOzGmmK9712rtP+5EyGJ83ufOqzttxravGFJQkuvpDL5e+a5ykpcuu2dV2vfgeMpTSpP5XiTWa6GpW4AAOkWTybavZL+XtJOTa2fuEtTSfD3Tn/+M009yYrZdIXjbklfNwzjxPTxtrx2jCRS1/I3zl/W/f/4sr66/5C+8p0OuVz5wWArsO8jTx2R1+sPrpE4+3w3rl+ibz5zdE7i/I3rl2jvU4d16txAcOmV1hWL1FQX29qIqey2PvNct7QtCwZbyZ7XrvFKSmjOUnEsAADxiqcPl88wjKc1VZFYO+OjOyR92zRNTxznap7x7xcltcZ6rB0idS0fHJkI+cwzNhl2396BUUlV4c/nCJ8UH9g+89hUjTvearuQc1mMN5Hz2jVeAABySTx9uN4r6TFJJyWtkXRM0lpJv5D0bVtGlyaBruWzk6iry4rkduWHfFZUmBd235qKoqjns0qcn3lsqsad7LlSdV67xgsAQC6J55Xi/yXpo6ZpXitpePrvT8qiMjGXBJKoA0vFzEyiXr64Qnu2rQt+9i///ro+tbU1ZN+ZyfLhzvfCq2f1iS1rQ47Z0d6iFw6dnXNsqsadzLl++spb2tnekpLz2jVeAABySTxJ88tM05yd0P6opqoT/yR1Q0o/p9OhTWsadP+dN+hC75Dqa0q1fHFFMK9n9dVV+ouPb1L/0Liqy91qri/TkrpS9Q54VFNRqBVNlcH8rcD5rm9t1LKGm3Whd1iF7nzVVBTqgbtvVt9lj/LzHBoamdDduzbMOTbecceb/G1VJThzDnoHRrWoqlib1jaqf8gTPK801VIh0QrDbEpWX2hL+yy0+wWAbBNPwNVtGEa9aZoXJL0x3VurR1JelOOyns/n10vHzodd2ueg2a23zl8Oqdjbs61V+587rq7eEctKO6fToaX1ZVpaXxZynbfOD6Z0SZlA8ncsOVCRqgQlhZ2DwNhSVWEYz3jtstCW9llo9wsA2SieRyvflPTb0/9+QFNVib+S9HCqB5VuVtVzp84N6MSZ/jkVe3ufOqIb1y8J2TeWSrtUV+nFK9L1o40t02NPpfl0L7FYaPcLANko5oDLNM2/MU3zB9P//idJLZLaTNP8c7sGly5W1XO9A6PTy/iErzCc+XXf4GjC14nl2FSIdP1oY8v02FNpPt1LLBba/QJANornlWII0zTfSuVAMsmqeq6mokinOi9bVhjO/DqWSrtMV+lFu36kzzI99lSaT/cSi4V2vwCQjebHEtxJsqqeW764QquWVmpne4saa4q1/XdatLO9RZ+/faOOnOwO2TdcpZ3P51dn95COnLyosxcG5Zdfn915rXa2G6qtLEy4Ss/r9en4W5f04pFzOv7WJXm9sS0uGqlKMFoF4XyqMJxP9xKLhXa/AJCNEn7CNZ/MrJ57o7NbzU11wSquDUadSktcqiov1Nd/cDiYdHzX9vW64w8LVVFcGLbiK1yi8s72Fj374mkNDk/ozvev0zVXVauhJr5qMa/Xp+cPntXep66MZc+2ddq8YUnUasdoVYKRPsumCsNkzad7icVCu18AyEY84ZoWqJ4r9F0KWerlQt+IXn3tYjDYkqbyXx7af0gVxYWWy8KES1Ted+C4bmlbprGJST385GH5/Yr7l96pcwPBYCtw3sDyQPHcZ7glbaItdzOflsOZT/cSi4V2vwCQbQi4oui7bJ04Hynp2CpROZBsn2jScs+AdYI/AADITgRcUVSXF8npcATzXwKiJR1XlRWGPSaQbJ9o0nJtRVHY8ya6PBAAALAfOVzTAp24Pc4qdXYPBXNc6quL1bqyRk2LStR5cVjPvfzmdA7Wb+l875D6hzwaHvWquDBf1eWFwZysvDyH7vngBp0+NyCff2p5n52/a2hy0qfGmmJ9+LY1c5KWY+kGHlhqaHYOV6zLA1ldIxs6kXu9Pp06N6CegVHVVhRp+eKKqHlp2TBuAEgU/w1bOAi4ZN2Je9Oahjnd1z/53rWqqyrS9378mt7Ruljf+7EZkhS/rKFcG4w6vfbmJX3t+78KfvaxP1yjH714Wre+/Sp98r2tWt9SF/I/qli7gefnO7V5wxItayhT78CoamIMTOK9z3R3Ik+kGIAO6gByGf8NW1gIuGTdifv+O2+Ys/0bPzyqrZtXqnVlXTDYCny278Bxbd28UpVl7mCwFfjsf/7zMW25aYW+8cOj+uyOa3WhbyRkeRurMTQ3bp6zDE5+vlMty6okVdl2n1bXtotVMcCyhrLpe439ftI5bgBIVLb/N+ze3W1yu90ZHcOE1ydXgusNZ+K8kRBwyTrB3SpB3ef3Sw5ZfmZ1XOCY0XGv+gZHQ/4HFakbeKr+hxfvfaby2tFELgYIH3ClY84AwC7Z/t+wrzzWoZFxf/Qdc9D9d96Q9muSNK8rnbhncrvyLBPUnQ5H8N/hPrM6Tv6pv4sK8uckzFuNIZXdwOO9z3R2Ik+kGCAdcwYAduG/YQsLAZcid5qfvf0jf/A2Lakr0QuvntUHf88I+Wxne4tWLa0Me9yO9ha9cOisPvnetSotzp+TMJ+ObuDx3Ge6O5EHigFmjiFaMQAd1AHkMv4btrA4/P7cfVzY0dHRLOn02rVrk37PHKgUmd1pfnx8Uqe7BjTi8Wp03KuKkgKNTXhV4MrX+LhX7oJ8DXu8Knbnq7LMLc/4pLovjaiuqliuPKf6h0eV58jTpUGPqssLVVNZqLrK8FUowWqVCN3AfT6/Oi8O6ULvsArdoZWR8dzn7GvM3l5fXawLfSMhlTOSYqqm6ejoUFtbW9zfg0CVYjzFAIkckwsSnUNcwRwmjzlMXrQ5HB+f1Oud/eoZ8Ki2olArmipVUJBnuX8cEs66D/xu/V+vjvFKMX6W804O17RAJ+7zZ0w11S2XNPXL/KB5QQND4/rmM0eDVSQ72lt04KU39eHb1mh9S42cTkfYKrs//uC1ujwyoW/+8MqxU5V3xWEDlcAYrN7dWy0XtKyhXBtX18cUdFldY+b2cNe5b/dGTXh9tlbTxFsM4PP5M15dCQCJ8np9+vdDnQkt1Ybcw3c0glPTPbQCwZY0ldD4xIHjunH9Ej3w+EF19QwH951dZeedVDDYCmyLZxme2ayWCzpxpj84jlQId50TZ/rDVtOk8rqpGGemxwQAsUp2qTbkFgKuCAaGxzQ2MRmx4jCwPE+4KrvRMW9Kl+Gxqmjx+f0JLRMUz3USWd7IbpEqfAAg27FU28JCwBVBaaFL5y4OR6w4DFSThKuyKyrMT+kyPFYVLU6Hw/ZqxkSWN7IbFT4AchlLtS0sBFwRTHh9eu7lN7WjvSWkiuRjf7hGLxw6qz3b1mlgxKPOi4NqbijXn/zRBt33oY266wPrdd/ujSovztOntrXOqbxrbihXZ/eQjpy8qM7uIfl8sSUlhqtoCVRG2l3NuGppZdZV01DhAyCXJVKdjdxF0nwE1eWFGhye0L+9eFpbblohOaae9CxeVKKbNyzVd3/8Gw0OT+gTW9ZqUdWwxr0+ffWJQzOSH1tV6HLqszuu1ei4V0UF+SopztOhExf15cdeiTvR2+l06PrWRi1ruDnhKsVYBK7T3Lg5pJpR0pxtmUxOtxonCfMAckEyS7Uh9xBwReB0OoILRe//yfHg062vff9X6uodCe73zWemlus5c2FoVvLjEW3dvFL7DpjBfd2uPG3dvDLhpRycToeW1pdpaX1Ziu927nXCVTNGqqLMhGiVnQCQzRJdqg25h4Argt6BUbnyHMGnW/JLw56JkGBLurJcj29WT7PgMkAxbMuWpRwAAEDqEXBFUF1epP6hcT3z768Hn0htv3Uqn2tmZUlguZ7Akj8zt8e6jURvAADmLwIuCz6fXz6/X06n9GcfuU4nzg7IMz6pInee7trxW3roiV8Fc7A+sWWtitx5WtFUrp3txtRxDodWNJXJ4XBq93uuUWNNiS72j+jq6WTIQNCWSKJ3sCt8lI7vqZqHwLWqygrldDrUO2D/dQEAmE8IuMKY2Wm9rMSl2955tZ44cDwYIO1+zzX6849ep8FRr8pLCtTbP6L9z5l69zuv1tPPnwxJmt//3HF19Y4EO9R//QeH9eHb1uirf7xZvZfjT/QO1wXeru7qVp3tn33xtAaHJ+jqDgBAjCiFCGNmB/Nb2pZp33SwJU3lWz32v17T0VOXdLrzsv7yWy/pbPeIWlfW6eEnZ3cMPqIb1y8Jfj2zQ73fL7WuWKSmutK4ApZ0dle36mx/S9syuroDABAHAq4wQjqYT3eUnymY+D79WYHLabnfzGUsZ3ao7+qJvf+W5dhmnNeO7upW1wrcE13dAQCIDQFXGLM7mFt1dw90m19SV6bCAqdlR/rZX7tdeTLf6tcvj3TFHXSls7u61bUC90SyPwAAsSHgCmNmB/OfvvKWds7qNL+zvUU1FW69cOisdrS36NFnjynP6QzbMfiFQ2eDX+9obwke89zLbyb0Si6d3dWtOtv/tOMturoDABAHkubDmN3BvLzEpWuaq9V32aPq8kIVFjrVeX5YH39vqwrynaoqc6uhpkQrmyq1urk62PV8UWWRljWUqWdgVOUlBeq6OKQPvWe1unqGdcvGZfrpK2/F3X9r9tiqygo16vHqP491qTaOLsWB6sPegVEVuvPlnZxURUlhSAL/7GtVlhYqz+mQ0VxFV3cAAOJAwGUh0MG8ojhfvzx2QY88fSRYqfepra0qKpD6Bjz61jNHZ1QlrtPmDUvUVFc6p8KvsaZY73vXqpClf3a2t6imPP5XcoGx1VcX6/mDZ7X3qcNzxhAp6ApXfbijvUUHXnpTH75tTUjlYbhO7osX0aAVAOa7e3e3ye12Z3oYtpjw+uSK8Hsy2ueJIOCK4o0LQ8FgS5pKFH/k6SP60ifeob974j9nVSUe1rKGMrUsq5pT4Xfj+iX65nRwFth/34Hj2rS2MeGxnTo3EAy2wo3BSrjqwycOHNeWm1bEtcwQAGD++spjHRoZj7+4az64/84bUn5Ocrii6LvsCV8VaLG9d2B0+rhZFX4WVYz9Q56Ex9YzEL6KMDAGK5GqD6k8BAAg9Qi4oqipKAxfFVgefntNxdQrQssKv9nnSaLKr7Yi/DUCY7ASqfqQykMAAFKPgCuKq+pK9en3hVYffvp96+TKd+j//MBvaWe7odrKwmBn+TynQ4dPXJTDId29a4NWX1Wpez/UpsaaYn3xY5u0+qrK4Hnu3rVB9dXF6uwe0pGTF9XZHbk3l8/n15kLg3rl1+d19PUelRbl6873zx3b8unlg6yEqz4MVFCmq/LQ5/PHfN+ZlCvjBABkN3K4osjLy1N+nkNbN68MrpGYn+fQj148rede6QwGWhWlbl28NKLP/f0vgonoX/z42/W772gOSZTfs61VH3tvqUoL3aqvLtZLx87HtExPuET3D/7eNVpcWxwytrLigqiVgzOrD3svj6qwIF9e36Te2bo4LZWH6VyeKBm5Mk4AQPbjCVcUr58b0Hd/9FrwyYbP79d3f/Sa3rXxKklXlvAZHfPq2//y65BE9IHBCe196sispPYjcsipprpSXegbiXmZnnCJ7t/78Wt6vfOy9h0wtf+549p3wNSXH3slpt5egerDdSsXqWVZld7WXBv3MkOJSufyRMnIlXECALIfT7iiGB4ZV/umq0IWr97R3qIRz0Rwn7GJSXnGJ+ckoo+OeSMktVdFXKZndpWg1b4+v3/Otnh7e6VbPPedSbkyTgBA9uMJVxQlxQXBYEu60kKhtLgguI/blafCgrw5iehFhfkJJdaHS1q32tfpcMzZlu1J7+lcnigZuTJOAED2S9sTLsMwfijpakk+SUOS7jJN85BhGC2SHpVUI6lX0u2maZ5I17iiGRodt2jnMKbtt7bI6XBoWUOpBofH9dE/eJsuDY7L5/erpDBfVeVu7dnWGnyt2FhTrE+8t1WjHq86u4dUX12su3dtmJMjFC5pPZDoPjuHK5CwH+34RAU60vddHlV1+ZXu8lbbYzk+3L1k4zJBuTJOAED2S+cr0FRC8AAAIABJREFUxQ+bpjkgSYZhbJH0bUkbJO2V9DXTNL9jGMaHJD0i6ZY0jsvS+PikXHnOYEAT4Hblye+T9j93XG5Xnj65da0OvnZB72hdrKefP6myEpdue+fV+tI3/lNlJS5t3bxSVzeWaWRsUn/zT6+E/PLetKZBD96zObgckFXgEkh0X9Zwsy70DqvQna/KsqlE/ZlJ86nsjGuVNL5pTUNMyf6Rks5nLhmUrcsEzV7aKFvHCQDIfml7pRgItqZVSPIZhlGnqaDr8entj0vaYBjGonSNK5LXO/v1yNNHtGPW4tV7tq3T0z+fegg3NjGpbzx9VP/lhuXBJ1m3tC3TvunXkD39Hu07YOp01+CcrvAPPH5QF/pG1FRXqtYVi6ImrTudDi2tL9PGtzVo7YpaOeTQ//0PLyeUNB8Lq6TxU+cGYkomj5R0Hkjaj+W+MylXxgkAyG5pTZo3DONbkn5XkkPSuyUtldRpmuakJJmmOWkYxrnp7RdjPe/Ro0dTOs6Ojg5J0rCzXl29I/q3F09ry00rpkbtl0ZGx3XizJX4cWxiUpdHZrx6DNNV3uf3h301+UZnt86fMRMap8dZlfJzxnL+C71DUa/b0dFh+/jmu8DPIRLHHCaPOUxeJuawra0t6XMMDw9pyONLwWhyUyLft0jzntaAyzTNj0uSYRi7JX1F0p+n4rxr165N2QKbHR0dwQn7zeleuV156un3aP9PjkuaesK1dfPKkGPcrjyVFxeEvHqc/RrS6XCEfTXZ3FSnprrlCY21s3so5eeM5fz1NaURrxuYQ7vHN5/N/DlEYpjD5DGHycvlOSwpKZXDtXCbPaf6+5aRKkXTNB+T9C5JZyU1GYaRJ0nTfy+WdCYT45ptRVPlnE7un3jvWi1rKA3Z9qmtrbp0eTS4709feUs7Z72GrChx6UPvuSZkW7IJ2OE6xs8+ZzKd0q3Ov3xxRdTrxjo+xI/u9wCQe9LyhMswjFJJVaZpnpn++g8k9UnqlnRI0i5J35n++1XTNGN+nWin/HynKksL9IU7rtPgyITOdg9p3wFTrjynPv/hjfKMT8ohh37ws+MaGpnQH73nmisJ7E6HvvCR6zQwNKay4gJ1942ovDA/pQnu0ZK6k+2UHun8sSSTk3SeenS/B4DclK5XiiWSvm8YRomkSU0FW39gmqbfMIw9kh41DOOLki5Juj1NY4qqq2dYv3mjX5L09PMnQ16N/fWjr2jr5pXad2AqF2n777TooSd+Nef12Wd2rNdfP/qKtty0Qs/8++tzPn/wns1JNdEMJHWHO4dV0npzY+zXtDp/pOsmsh9ik4rvKQAg/dIScJmmeUHSOyw+e03SpnSMI159l0eDndzDJX+7C5za/jstkkNqbixTWYlLY/2TIfsEO9CHSaS3u2s5ndLnH76nAJCbWNongqqywmAn99nJ3401xSotKtC+/300+GpnZ3uLnn3xtHr6PcFjZnagD5dAbmfX8kCn9HReE/biewoAuYmlfSJwOh2qry5URYlrThL8x/5wrb71zNGQVzv7DhzXrdddFdznU1tb9a8vvK4d7S164dWzc/p52Z1ATtL6/MP3FAByE0+4IugdGNV3f2zqnl3rJUee7rt9o4oK8jQy5pXblRf2FeLKJRX6049cp6qyQk14fdr6rlUqK3bp6sXlKi1yaf2qGzQ+OSlXXp48Y16dPNsv7+SkKkoKwyaUR1tCJ9LniSatx7psD9KPQgQAyE0EXBFUlxep/bolOt/r0fd/clztm64KLmRt9Qpx8aJSdfUM68jJnmC3+al1Dw396Jdv6ONbWjXh9YVUme1ob9GBl97Uh29bE1JtFq0iLZaKtXiT1qmCy34UIgBIh3t3t6Wsx2WumfBONXxN5XJ5BFwRNNaWaN2qen3pm/+pLTetCAZb0pVXiIFKRbcrTx/fslZjE5M6caY/pKpxbGJS3/uxqS03rQj72RMHjmvLTSvmVJtFq0izo2KNKjgAgCR95bEOjYwv3D5/9995Q0rPRw5XBE6nQ32XPRGrDBdVFmn7rS36b3dcp6HRcXVfGrFcxkcO6yV+AufvGxwNbo9UkRbL54mw45wAACx0BFxR1FQUhiQoz+R25elC34ie+fnrckgaG/eptqIouIzP7H3lV8TPZlebBSrSZu8b2Cfa54mw45wAACx0BFxRNNeX6c8+cp1allToU1tb5XblqbayUDvbDd21fb1WLavQn3/0Ovl8k1q1tFJlxS5d1Vg6p6rx41vW6s2ufrWurJmzXNCO9ha9cOjsnGozq4q0+upidXYPqXdgVH/6kbersaY45PN4KtZmLxNTX11MFVyWY2kfAMg95HBF4PP5dfR0n85cGNS+A8e1rKFU9+3eqL7LnmBLiEDyfF11kSrL8nXX//u8ykpc+sAtq/S52zfq+FuX5PNJP335Tf3uO5r1l996SWUlLm3dvFLLGsrUUF2sCd+k3tm6eE61WbiKtPrqYr107HxIUvtd29drUXWhKorDVzpGur9wCfKb1jTowXuogstGFDUAQG7iCVcEXT3DOnl2IFhteG1LvU6c6Q/bf+vMhWENDE5obGJSPf0e9Q6M6W/+6RXtO3Bc+39yXK0r67T3qSPBz/cdMPXgvldVXOjS25pr1VRXGvYXZqAirXXFIjXVlepC38icpPaH9h9SRXGh5Tki3V+4BPkLfSMh1+QXefaw+p519QxneGQAgEgIuCIILO0TTCKPkPTu8/s1Ou69snF2kn2EpX3iHVOqktpJkM89fM8AIDcRcEVQXT43Ad4q6d3pcKioIH/O9mhfx5uMnsqkdhLkcw/fMwDITQRcETTWlmjlkgp99A/WaGe7oYJ8p1qWVWr3e64JSSrf2d6ipfUlqqsp0me2r9d9u9tUU+4OSY5/4dWz+syO9drZbmj7rS3a2W7ovt0b405GD5dIf9f29RoY9sSdQB3uXHe+f50cDpGInaVY2gcAchNJ8xE4nQ6tXlal/++1C8FmpW5Xnj67a73+/GNv14jHK1e+U16vT0/97ITevqZRz754WoPDE7rjttX6/9u78/i4yvve458ZLaNdsiRbkuVFWLYewAvCIjjg2LhJdElDUtVO4oXGSWhIMU4hMS301XCbcJNXfLmQXuqQxdyGpqkTMNBASUIa6oaQmNKQIGy84DwCL3iTF8nWao2k0cz9Q9JYI82MRtKMZiR9368XL3vOnHPmmaMx/vnM93l+KclOVq+aj9fnIzMtGU+PN+A8WzYsHdWY+oP0jS0d9PT4+O6P36S+8dKIA9T955pTfBPH6ls4fqaVHf9+iNb2bgWxE5Ra+4iITEwquIZx/HybP+wOvXmZbU/u5cufXcbf/+gNalaW8/Qv6wA4crrV/7i5vZt/fuGQ/7i1H6jgiRdtVFZw7w/SA3zh/748pnM6nQ6cDgfbdu4JyAZpdfnEpdY+IiITj75SHIZ/pfkBOrt7aGrr9K8QP3B7/+Mh4fooheYDxxadALWC2CIiIrGlgmsYA1ea7+dKSSIvy+VfIX7g9v7HIVeUH/Q4EVaFVxBbREQktlRwDWPO9Cz/CvPQW4jcsXoxv3r9XT5Xs4jde0/6t6+vruCl2uO4UpLIzUzhzo8FhuY3rVkS1bBztALUCmKLiIjEljJcfbxeH/UN7bid0zh1rs0fRE5JSWZ2URb3/NlS0lKTSHclk5GexMzpmTS1dXL3+mvp8fSQ7krhkrubLeuXkpLiJDfTRX52GjMLM2lodlOYm0ZZcQ5zirNpbO6gIDedeTNzh4Sd+8dxoaWD/JzhA9FXzMzhgc+9F3enh6KCTEqnj3yh0pEEsUc6vkQ0Gd6DiIhMLCq4CN0uZWnFdP5r/2l/aL7/LlZRQQa/rj3Ba2+d89/xeuaX+wJmCpbPzOM3e0+x/dl9/mPvWL2YXa8d49C7TUFnFI6kbUuofUunjy5IHUkQezK0lZkM70FERCYefaVI6HYph083D5mhuHNXHcfPtPHh5fP82x57bj8rKmcFHnuqyV9sDdzvIyvKA/Yb2JJlJG1b4tHiZTK0lZkM70FERCYeFVyEmaUXYoai1+ej9VJXwLbBsxUbmoMf6+7qCXg8cCbgSGYLxmNm4WSYzTgZ3oOIiEw8KrgIPUsv1AxFp8PBtOy0gG2DZysWhjg2LTUp4PHAmYAjmS0Yj5mFk2E242R4DyIiMvGo4CL0LL15JblsWhM4Q3F9dQXzZ+fg6enhkx+6kvs2Xsdf3bqUheXT+NodN/CFdZV85fZlzC3KHnLsHWsW85s3TgS8xsCZgOFmC3q9Pk6da2P/O+c5da6NovyMYWcWBh7TytvHL/Lq/tPUHb9IV1dPwPk8Hm/Yx16vb1LMZpwM7yEeBn/+Imn9NJpjREQmK4XmCZyld+zUOcpKZ1BSmInH42V2cSYPfO69NLd3kZ6aRHObm1PnLvHEi3/wh65vvbm3z2L/yvL9Afm3jpzn/tuup6GpA1dqEj/bfZhb3lfOmg8sIDcjbcjsuFCzBYGgQe9lC4vZdk/wmYUDw+HZmSnccuMV7NxVR2d3DyUFGXziAxU89tzlyQCb1izh6f+01DdeoqQgg7UfNAGB//5g+URvK6PWOCM3mokGmpwgMvHdu7EKl8sV72HETbfHS0ry8PelIt1PBVef/ll6Z05YSmf0BuIPn2rikSf38OlbFvr/4lj7gQqe/83hgND1Ey9aVq+aPyQgf/e6Sr7+/d8FZIaOnN7LtntCt8wJNlvw1Lm2oEHv/vMEO9fAcHhNVbm/2AJYUTnLX2z1n2/7s/v8bYlWVM4aEvgf2OpnoreVUWuckQk10SBc66fRHCMiieXhHbVc6tKd6eFs3bw8ov30lWIYDc1u6hsvcfJc6+WiKUSLHq/PN2Sbu6snbq13Ao4ZPOYQ78Ef/I9BGyKZuMb8+YvwGBGRyUwFVxiFeb3B9y6PNyBoHSpIP3hbWmpS3FrvDD4mkjZDQ9oUjXHcMjlE4/MXyTEiIpOZCq4wsjNSufXmK+ns7OLvPruMu9dWUjE7l41/fGVA6PrWmw25mSlD2v/8bPdh1lVXxKX1TlF+BnetrcSVksRLrx9n/YBx7N5zcki7ok1rlvjbFMWiDdFgClRPHKP5/GlygohIIGW4wrjY4qbhYhtXlE7ja4+/5g//3rXuGjatXkRjSyczC7NIToLsrFS+escNNLV2UpiXTllxDleW5XOh6VJv650uD0X5sW+9A73FzGsHz/CjXxyiZmU5TieYsny2br6RxmY3Bbm947v6igL/+YryM7iqLD/k42gGyxWonlhGO9EgJdnJ6lXz8fp8OB2OiEKlIiKTlQquMPJz0nnv4lK+2ldsQW8O5dGn3uRLn3kP255+E+j91/uXPvMeDh1rZNXSWf5Q8OyibGYXZUdlLCMJeg8MLD/9yzoAnnv5MNvuWUXFnHz/foPPN9zjaFGgeuIZ6USD+oZ2HtrxekCOy5WSFHbCiIjIZKZ/coZRUtjboDpY+Ldl0ErzLZe68Pp8CREKTvTAcqKPT8ZOP2MRkUAquMJwOh0hV5vPyUgd8tjpcCREKDjRA8uJPj4ZO/2MRUQCqeAaRvnMvCEB8ztWL+bn/3Uk4HHrpU4WzM5LiFBwogeWE318Mnb6GYuIBFKGaxgej4eKslwe+Nx7ae/oIjM9lY6ubv70jxZQvWwumempuFxJZLpSKC4IH2Svb2jnQksH+TmhQ8eh9gu2HQi678CQc/MlN8nOJNydHuob2hNiVfVorvYe6XUd6zEyMlrRX0QkkAquMNra3Bw42khzWzc//tXbVC+by1N9K7a7UpL44vprufqKgoj+go9kVl6o/ZYtLOa1g2eGbE9JdvqDyYPP6XQ6KCnM5Nj+loScDRiN1d7VciaxaUV/EZHL9JViGMfOttHtgX98/gArKmf5iy3oDQD/w8491De0D3ueULPyBh8bar8jp5uDbn/7RFPYc0b6uhPVaN7fZL8mIiKSmFRwhXGhxU1Hp8ff9ma0s64inbEVar/G5uDbg7UTGnjOyT5TTC1nRERkolDBFUZBbhrpackBwd+BIp11FemMrVD7FeQG3x6sndDAc072mWJqOSMiIhOFCq4w5s7IIjXZwd9sXMqS+QVsWhM4W/Ev11bidDJse5pgM7bu23gdXp8v4NhQM7vmzcwNun3B7Lyws8DCzRTzeLzUHb/Iq/tPc/hUE3XHL/Dq/tPUHb+Ix+ONzQWNMrWcSVxq3SQiEkih+TAyMlzk5aRyvL6dx557gznFWWzZsJRj9c14vfDELw7xP5bN5YVXj9La3h0yfD14xlZBTjpHT7ew5ZFfDwluh5rZFWw7wLZ7Qs8CC3Wc1+vj5TdOsv3ZfWRnpnDLjVewc8BkgE1rlrBq6SySE7wVy2hmwmn2XOxpYoKIyFAquMKob2inqwsee24/nd09XFtRFBC4Bti5q46aleU8/cu6sO1pBs7YOnWuLWxrm2Azu0LN+BpuFliw49452cT2Z/fR2d1DTVW5v9jqH8v2Z/cxpzibijnTRn7RxtloZsJp9lxsqXWTiMhQiX0LI84utHRwsdV9ucAKEZzHcfn30QzRx0rDwBB+iPfU2KwQuYxOvD/fIiKJSAVXGNOy08jPCWztEyxwje/y76MZoo+VwkEh/FBBfZHRiPfnW0QkEY1LwWWMKTDG/NwYY40x+40xzxpjpvc9915jzJvGmDpjzH8YY2aMx5iG4/X6cHf3kOT08sUNldz2kauZV5rLX37iGtZXGwrzegux9dUVvFR7fETh63gHt+fNzGXTmiW4UpJ46fXjrK+uCBjLpjVLmDczd1zGIpNPvD/fIiKJaLwyXD7gIWvtywDGmIeBB40xnwN+CHzGWvuKMeZ/Ag8Cfz5O4wqqP/T7gxcOsu6DC+js9uH1+njkiTcCguUlhRnYdy/y/uvm4HQ4SIkwZB7v4HZyspNVS2cxpzibxuYOZkzLYMmCQhqb3RTkpjNvZm7CB+YlccX78y0ikojGpeCy1l4AXh6w6bfAnUAV4LbWvtK3fTtwjDgXXP2h35qV5Zy54AbguZffGRIs/8K6a/n+z97yH+dKSWLbPZEFg+Md3E5OdvaF4hM/GC8TT7w/3yIydvdurMLlcsV7GAmv2+ON6IbLuM9SNMY46S22fgLMAd7tf85a22CMcRpj8vuKtIgcOHAgqmM8duqcPwzfv5p7sBBwR5dnyLZjp85x5oSN6ngmotra2ngPYcLTNRw7XcOx0zUcu3hcw6qqqjGf44Htv6bNPTHWZRyrR//mQ1H5OYW77vFYFuJRoA34FrA6GidctGhR1Krw2tpaykpn+PMn/au5u1KSAoouV0oS6amBl8+VkkRZ6QxKZ8yLylgmqtra2qj8YZ/KdA3HTtdw7HQNx24iX8PMzCwcKVNn0eJY/5zGNahjjPkGsABYZ631AseBuQOeLwS8I7m7FQv9od/de05SNC2N3MyUIcHyOz+2hMyMJAWDRUREZFjjdofLGLOV3szWLdbazr7NtUC6MeZ9fTmuTcAz4zWmgbxeH/UN7bid06hvaGfZwmLKSnJoanOTnpZEe0cPW25dSmZaMq5UJ0lOJw4H/N1nl9Hd3UNRQSal07MCgsEej5cjp5o439RBbraLwtw0uj0+mlrd5OdcXi2+vqGdCy0d/m3DhYv7xzqSY6Ilnq8tE4s+KyIil41LwWWMWQj8LVAHvGqMAThqrV1tjNkIPGaMSaM3MP/J8RjTQKFakSxbWIy7p4vDx1v8q827UpK4vWYRL/3+Xa5fWBLQ1qd0+uWAsMfj9bfP6ezuoaQgg098oCLgPFs2LCUl2clDO16PuAVKPNumqGWLREqfFRGRQOM1S/Eg/vXYhzz3KrB4PMYRSqhWJFs3L6er2+svkvqf+97zB7h7XSXffGpvyLY+R043+4stgBWVs4ac55En32D1qvkjaoESz7YpatkikdJnRUQkkBZbInQrksbmDi60uIM+5+7qCdvWJ6B9DoRsodM/C3LgtnAtUOLZNkUtWyRS+qyIiARSwUXoViQFuenk56YFfS4tNSlsW5/B7XP69xn8uH8W5MBt4VqgxLNtilq2SKT0WRERCaSCi9CtSObNzCU7K5nNn1gS8Nzmjy+hu9vD/bdd3zeD0XDPhms539TOoaONHDzSgAO4a22lvw3Q7j0nuWP14iGvsWB23ohmOkarbYrX6+PUuTb2v3OeU+fa8Hp9QbdF67WHO7fE1nhf/6L8DO6/7XrWV1ew9oMVlBRkaBaviExp8ViHK+EMbEVy7NQ5ykpnUFKYicfj5cy5S6QlO9myYSnH6pvxemHnf1g+umIeT/34TaqXzeXXb5wg76ZyGlvc/HT3EaqXzeWpXXUBbYBKZ2QyLdvFI1tuoqnN7W93ArDtnshboESjbUqwQPN9G6+j2+MNG3Ie7WsrQB1f4339vV4frx08E/B6d62tZNnCYv28RWTKUsHVp78VyZkT1r9w6eFTTXR1+zh+ti2gtQ/AD144RM3Kcp7aVUfNynK+/9O3WL1qPisqZ/mLLbjcBmjbPasoLugNC88uyg547ZG2QBlr25Rggea3TzQNaV8ULOQ8mtdWgDq+xvv6B3u9R5/ey/xZefp5i8iUpa8Uw2hodtPR6cHr8wUNAPuD8I4BAfgQ4fhECgsHCzSHeo/RGLcC1PE13tdfP28RkaFUcIVRmJtGeloyTocjeADeF/jrwDZAg/dNpLBwsEBzqPcYjXErQB1f43399fMWERlKBVcY5aV5pKY4uKIki9trFgWExT99y1Xs3nuSddUV7N57kts+ejU5mSns3tO7bbxa/owmDB0s/L5gdl5UwvjDvV5hXhrrqw1fWH8tPp9P4flxEK2JFon6eiIiE4EyXMPweX20u3t49ldvU7OyHKcTzNx88HnZtGYJ3T1eVt9UTk6Wi6z0ZLZsWIrX52Pr5uW4uzwUxLClyWjD0KHC78CYwvjhpCQ7ufVmQ7ormcd/clDh+XEUjYkWifx6IiITgQquMA6fauLwqVZ/mPzpX9YBvf9iv3tdJQ//8He4UpKoWVnO4z99i233jG8IfCxh6FDh97GE8cON86Edr1OzspwnXrQKz8fBWCdaJPrriYgkOn2lGEZDsztkmNzddblo6A/Kj3coeKKEk/3jnAATCkRERGJBBVcYhblpIcPkaamX8yn9ofnxDgVPlHDywHFOhPGKiIhEmwquMMpL85hVlMmtN18ZUDBsWrOY5jY3JQUZrKuuYP8757j/tutpanNTd/wi+97uDbB7PN6QgfbBYfeurh7qjl/k1f2nqTt+EY/HG3S/gecIFk6+a20lRfkZUbsG0VihvH+c4z2hQEREJFEowxVGamoSVQums+edBtZVVzAtO42zFy7xoxf/QGt7N5//xDXMKspkxrQMvtu36nz/oqclBRms/aBh+7P7hgTEgYCw+1Vz86heVsZjz+0PWJ1+ZWUpvz90NmQo3ul0sGxhMfffdj1vHW3E64Uf/eIQyUnOqATRo7VC+eUQdQ7Nl9xsXRD7CQUiIiKJRAXXMI6dbWPbzr3UrCxn+659ARmkbz/zJls3L+fRp/f6V53vf35F5Sx/sQWBAXEgIOz+kRXlfPOpvUNWpy+dnjlsKP7shUt8/fu/CxhXtILo0Vyh3B+iRiFqEZGJ4N6NVbhcrngPY1x0e7ykJMf2Sz8VXMO40OIOG/hubA4RCA8XEPcFPufu7AlxbnfIc/QXPOGC82MtuGJ5bhERSWwP76jlUlds1krcunl5TM47WrEutkAZrmEV5KaFDXwX5IYPhA9+nJ+dPiTsnp6WFOLcacOGzGMZnJ8ooXwREZFEp4JrGPNKcrlj9eKgge8vbriWeTNzgwbCd+85yaY1S4IGxAeH3X/6m8PcsXrxoGD+EspLh1/9PZaremvFcBERkejQV4rDSElJYm5xNp/56EIyXcl8bdMNtLZ3UVyQSXKSk0PHGplbnM0Dn7uBpvbAQHhRfgZXleUHXW178Erc0/PSmVuSQ2NzBwW56cybmUtysnPYFbtjuaq3VgxPTF6vj/qGdi60dJCviQciIhOCCq4wPB4vL79xMmCm4edqFlEyPYMTZ9simr0XarXtYCtxV8yZBkwbdr9IzhUtWjE8sURr5qiIiIwvfaUYxpHTzUNmGv7j8wfweHxBZ+/VN7THc7gyBYSaOarPnohIYlPBFUZDc/BZeq2XuoLPKmxRixqJrYnSzklERAKp4AqjMDf4LL1p2cFnD6al6htaiS3NHBURmZhUcIUxb2bukJmGf7F6MT6fd8iMxXXVFXi8PeFOF7ZNTqjnotFaZ6KYSu91tDRzVERkYtItmTCSk52srCylKD+DCy1ucrNSeeIXh1i8YAa795ykZmU5OAAf7HrtXW5cPDPkucKFnYGgzy1bWMxrB89MiYC0wuCR0cxREZGJSQXXMM43dfC/vvdbalaW8/xvDtPZ3cP5ZjcfvvEKfyufSO4yhGuTAwR9buvm5VFrrZPootlGaLLTzFERkYlHBdcw/CHlAa16Gprc/PzVo9SsLGf+7FzmFOUMe5chbNjZN0zboCDHTLa/bNVGSEREJjNluIYxMKQ8MKzc0OTm+d8cZk5RDqUzsob9Sidc2DnUcwUhQvuTMSCtMLiIiExmKriGUZSfwf23XY8rxcmWW5dSUpABDA0rDxf4Dhd2DvVcf9ugqRCQVhhcREQmM32lGIbX6xsSWv/8J65hRn46eZlp/q8RIwl8Dxd2DvXcVAlIT6X3KiIiU48KrjCCBbm//cybbLsnMMgdaeA7XNg51HNTKSA9ld6riIhMLfpKMYxIV/XW6t8iIiISjgquMCINcivwLSIiIuGo4Aoj0iC3At8iIiISjjJcYQwOcudlpZHkdHDwSAP5OZdD3Qp8i4iISDgquIbRH+QuKcwMOxNRgW8REZlM7t1Yhcvlism5uz1eUpKn1pdsKrgipNYzIiIylTy8o5ZLXb5h99u6efmIzz3Vii2eZhx9AAAVXElEQVRQhitimokoIiIio6WCK0KaiSgiIiKjpYIrQsPNRByutU+/SPcb7f4T2VR6ryIiMrUowxWhcDMRI2ntA0S832j3n8im0nsVEZGpR3e4RqB/JuLi8umUzsjyFwKhAvX1De0Bx0e632j3n8im0nsVEZGpRwVXFMSqBdBUCupPpfcqIiJTjwquKIhVC6CpFNSfSu9VRESmnnEpuIwx3zDGHDXG+IwxiwZsrzDG/Lcxpq7v1wXjMZ5oCxWodzhg/zvnOX2+jRNnW2ls7uD+266npCAjYL9QLYCmUsugqfReRURk6hmv0Py/AduA3YO2bwe+ba39oTHmk8BjwPvHaUxRE6wFUH1DO3f//ctkZ6Zwy41XsHNXnT8MftfaSqbnp5GbkRa2BdBUahk0ld6riIhMPeNyh8ta+4q19sTAbcaYGcBS4Mm+TU8CS40x08djTNE2MFDvdDh4aMfrdHb38P6qOf5iC3pzSY8+vZfcjLSA4H0k541k/4lsKr1XERGZWuK5LMRs4JS1tgfAWttjjDndt/38SE504MCBqA6strZ2TMe7ndMuB8AdBA2DHzt1jjMn7JheJ5GN9RqKrmE06BqOna7h2MXjGlZVVY35HO3tbbS5vRHtq89Jr3DXfVKsw7Vo0aKoNdisra0d8wf11Lk2XClJ/kJr4O/7H5eVzqB0xrwxvU6iisY1nOp0DcdO13DsdA3HbiJfw8zMLBwpkS1APVHf43iK5yzFE0CpMSYJoO/XmX3bJ7SBAfCXXj/O+uoKhcFFRESmsLjd4bLWnjPG7AU2AD/s+3WPtXZEXyfGk8fj5cjpZhqaOyjMTaesOIfzTR1caOngipk5PLLlJpra3BTmprNkfiGNLW4KctJITnHy2wP1TMt24XRCVrorZEDc6/VR39DOhZYO8nMUJBcREZmIxqXgMsZ8E1gDFAP/aYxptNYuBDYBPzDGfBm4CHxqPMYTDR6Pl5ffOMn2Z/f5Zx/esXoxu147xqF3m/x3spYtLOa1g2cCWtasr67ghVeP0treze01i3jp9weouWnBmFsBiYiISGIal4LLWns3cHeQ7X8Alo3HGKLtyOlmf7EFvUH4x57bz93rKjn0bq2/Nc3WzcuHtKzZuauOmpXlPP3LOr73/AHuXlfJI0++QVnJKkpnZPlfI1S7m8H7iYiISGLTSvOj1NAcvBWNu6sn4HGo/XAEHhONVkAiIiKSmFRwjVJhbvBWNGmpSQGPQ+2HL/CYaLQCEhERkcSkgmuU5s3MZdOaJQGzD+9YvZif7T7sf7xlw1LKinO4a20lJQUZrP1ABeurK7jn1qXsqTuLKyWJz398CU2tbr6w/lp8Ph9e7+UpuGp3IyIiMjlMinW44iE52cmqpbOYU5xNY3MHBTlpuLs9XFNRxOIFM3A6HGSkJfH7Q2d54ZXDrF41n8d/ctAffr/zY0uYU5zNyXNt/MvP/xA0FK92NyIiEi/3bqyKaI3Lbk8PKclJw+431angGoPkZCcVc6YB0zh1ro0vfffVgMzV+mrDcy+/Q83Kcn+xBb05rO/+eB9bNy/n28+8GTYU39/uRiF5EREZTw/vqOVS1/ALn27dvHwcRjPx6SvFKAkWcPf6fP6AfLDwe2OIQL1C8SIiIpOLCq4oCRZwdzocAfmrgVwpSRSECNQrFC8iIjK5qOCKkmAB9wWz87hv43VkpiVze82iIeH3eTNzhxyz+eNLcDgICM+LiIjIxKYMV5QEC7gX5Wfw2sEzPPGiJTszhdWr5lOUn8HFVjcpyU7/MXOKb+JYfQvHz7Sy498P0drerRXlRUREJhHd4Yqi/oD74vLplM7I4uyFS/6V4hua3OzcZdn+7D7cnT08tON16hvae2cjOhxs27mHnbssDU1uf3i+vqE93m9JREREokAFVwyFWim+P0TfH47XivIiIiKTmwquGAq1Ujy+wHC8VpQXERGZ3FRwxVCwIP266gp27z0ZsGK8VpQXERGZ3BSaHyGv10d9QzsXWjrIzwm/8vvAIH1jSwdpqcn48FK5YDpNbZ28dbSR/Jw0igsytaK8iIjIJKaCawS8Xh//vb/eH4Qf3IonmIErxQc7fn11BXOKc7juqiKtKC8iIjJJ6SvFEahvaPcXS8CIZxMGO37nrjrePtGkGYkiIiKTmAquERjrbMJQx3t9Ps1IFBERmcRUcI3AWGcThjre6XBoRqKIiMgkpgzXCJQUZnLfxut4+0QTXp8Pp8PBgtl5lBRmhg3T9z/X3O7mrrWVPPr03iEZLs1IFBERmbxUcI1Qt8fLcy+/ExCa93p9vHbwTNAwPeAPymdnpvCxVfNZV11BbqaLdFcSudkuri4r0IxEERGRSUwF1wiECs1v3bw86PayklUA/udqqsr55xcOBeS4XClJbLtnlWYnioiITGLKcI1AqNB7Y3PoMH3AMX0tfYLtJyIiIpOXCq4RCBV6L8gNHaYffIxa+IiIiEw9+kpxBIryM7j/tut562gjXh/s3nOSP/vQVeRkprD540v4zr/uo7O7h5KCDP5i9WLqG9ooKsjk/tvew9HTLcwszOSutddw8lz7iEL34y2RxiIiIjIZqOCKULBg/Gf/ZCEvvHKY8xdLeHX/adZVVzBrehbN7V08+IPX/ftt/vgSUpOd/NNPD/KhG8pGFLof70JnNKvpi4jI5HPvxipcLtew+3V7vKQkR+cLs2ieK9Go4IpQsMD84z85SM3KcnbuqqNmZTnuzh6OnG7xF1T9+33nX/exetV8VlTO4okX7YhC9+Mdpg81MSAeYxERkfh5eEctl7p84/qaWzcvH9fXG0+Ts4yMgVCBeX8Q3gE4wOvzhVxNPlRoPlzofryNdTV9ERERGUoFV4RCBebxXf4VwOlwhFxN3n/MoOfChe7H21hX0xcREZGhVHBFqKQwky0blvqLEVdKEuuqK9i99yS31yxkWk4qhTku5s/OZdOaxQH7bfrYEirm5LH/7XPcerMJeG7LhqXMm5k75NybP74Eh6M3UxXv97llw1KthC8iIjIGynBFyOl0cMPiEspKVtHY0kFaShIXWzu5aels/uO1d3nfNbN44sVD/lmKWzYspcfr5eyFS/zoF4dobe/mixuuBXysXjXfP0sxJdnpP/ec4ps4Vt/C8TOt7Pj33mPGO7A+8H1eaO0gP1uzFEVERMZKBdcIOJ0OSmdkUToji1Pn2vjSd1+ls7uHtR+o4IkX/+DPPtU3XuKRJ9/oLay8Phqa3AC8W98aEKiHwJXmnQ4H23buCXg+HoH1ge9TRERExk5fKY5SJCvI9wfl+4UK1PcH0hVYFxERmZxUcI1SJCvIOx0Of5geQgfq+wPpCqyLiIhMTiq4RmlguPyl149z681XBgTN11dXMLsoi917T/q3LZidFzaQrsC6iIjI5KQM1wh4vT7ONLZzocWNu9PDnOJsvvlXvSH6gpx0rrtqBvUN7aSmJpGdkUppQSbT89Jpae8iKyOFjk4P03JS+fqdN9LY7KYwL515M3NxOh3+djrZGSls3bwcj7eH3Iw0BdZjQK2LRERkvKngipDX6+P1Q2c5fqaFnbvqhrS9AQJa4lw1N4/qZWU888s6qpfN5akBx6yvruCFV4/6ZyEuW1gctLXPlXMKVAhEmVoXiYhIPOgrxQjVN7Tz9okmf7EFl9ve1De0D2mJ85EV5Tz23H5WVM7yF1v9x+zcVcf7q+b4jz9yujloO536hvb4vNlJLFTrIl1rERGJJRVcEbrQ0hF2luHgGYbuzp7A1j+DjumfvdjZ3UNDArX2mew0E1REROJBBVeE8nPSw84yHDzDMD0tKSD8PviY/tmLrpQkChOotc9kp5mgIiISDyq4IlRSmMmC2Xmsr64IKKTuWltJc7sbn8/H/be9h/XVhrUfrOBiSyd3rb2G3XtOsm7QMeurK3ip9jiulCTu23gd6a5kvrC+kvXVhsK8NFwpSXxxw7WUFGbi9fo4da6N/e+c59S5tnFv9TPZaCaoiIjEg0LzI+R0Orh7XSXdHi+5Wan8v+f2U994iQ9eV8ryyln037r62StH+NSHr+a+T72HplY3X7vjBlovdeFKTSYtNYmKOdMozEvn6OkWtjzya3+A+7N/spCOTg9erw+Px8vvD51VwDuKnE4HyxYWs3XzchqaOyjMvTxTVEREJFZUcEWovqGdh3a87s//fPrDV3Gm8RIrrp3FjLw0kpOTePAHr/sLo3XVFfzLz9/iK7ffwDxTFHRm3LSctCEB7sd/cpCaleU88eJept+RHjTgPd6tfiYTr9cXdEaoilgREYklfaUYAa/Xx8VWNzU3lbP2gxUsmJ1LZnoKz738Dk//Zx0NzZ1898f7Agqjp3bVsaJyFhdaO0LOjDvb2B4yUN8bpncr4B1lmqUoIiLxoDtcwwi2btNn/2RhQBPqULMXnU7Iz04POTMuLS0ZV0rSkGbW+PrD9GlBn1fAe/TCzVLUXUMREYmVhLjDZYypMMb8tzGmru/XBfEeU79gd0Qe/8lBVlTOCtgv2Mw3MzefksLMMDPj0oYEuNdVV7B770k2rVlCeWn4VkAycpqlKCIi8ZAod7i2A9+21v7QGPNJ4DHg/XEeExD6johzQKn60uvHWV9dEbAC/Z0fW8KiK3pXiu+fGTc4N1RckElxQSZlJatobO4gJdlJu7ubv/7kdcybmUtyspMbFpdQVrKKC60d5GerDc1YhfpZqIgVEQl078YqXC7XuL5mt8dLSnJC3AuKurgXXMaYGcBSoLpv05PAt4wx06215+M3sl79d0QGf6139RUF/u2t7d3MLs7ma5tuoLHJTUFuGuWleaSm9t5JcTodYQun0hlZIb/OcjodYZ+XkRnuZyEiIvEzWYstAIfPF991nYwxVcC/WGsXDtj2FvBJa+0b4Y6tra0tA47GcnzJyck0dGbz3Wffunz3as3VFGV20OPMpM3tJSvNCV3NeDyeWA5FREQkIlVVVaP+V+R4/N06WYW77nG/wxUNixYtitptz9raWqqqqgK2eb0+riyboTsiEQp2DWVkdA3HTtdw7HQNx24iX8No/t0qiRGaPwGUGmOSAPp+ndm3PSH0f623uHw6pTOyVGyJiIjIiMS94LLWngP2Ahv6Nm0A9iRCfktEREQkGhLlK8VNwA+MMV8GLgKfivN4RERERKImIQoua+0fgGXxHoeIiIhILMT9K0URERGRyU4Fl4iIiEiMqeASERERiTEVXCIiIiIxpoJLREREJMZUcImIiIjEmAouERERkRhTwSUiIiISYyq4RERERGJMBZeIiIhIjKngEhEREYkxFVwiIiIiMZYQzavHIAmgq6srqift7OyM6vmmIl3DsdM1HDtdw7HTNRy7eFzDAwcOlAEnq6qqPOP+4hKUw+fzxXsMo1ZbW/s+YHe8xyEiIpKArqiqqjo20oNqa2vLgKOjPV6Cm+h3uH4PrADqgZ44j0VERCSRnBzDcVeM4XgJYkLf4RIRERGZCBSaFxEREYkxFVwiIiIiMaaCS0RERCTGVHCJiIiIxJgKLhEREZEYU8ElIiIiEmMquERERERiTAWXiIiISIxN9JXmo8oYUwH8ACgAGoFPWWvfju+oEosx5hvAx4AyYLG19kDf9pDXTtf1MmNMAbADKAe6gLeBO6y1540x7wUeA9KBY8AnrbXn+o4L+dxUZIz5N3pXwvYCbcBd1tq9+hyOnDHmK8AD9P151ucwcsaYY4C77z+Av7HWvqhrKMHoDleg7cC3rbUVwLfp/UMhgf4NWAm8O2h7uGun63qZD3jIWmustYuBw8CDxhgn8EPg833X6TfAgwDhnpvCPm2tvcZaey3wDeCf+rbrczgCxpilwHvp+/Osz+GofNxaW9n334u6hhKKCq4+xpgZwFLgyb5NTwJLjTHT4zeqxGOtfcVae2LgtnDXTtc1kLX2grX25QGbfgvMBaoAt7X2lb7t24G1fb8P99yUZK1tHvAwF/DqczgyxhgXvYXnnQM263M4drqGEpQKrstmA6estT0Afb+e7tsu4YW7drquIfT9a/dO4CfAHAbcNbTWNgBOY0z+MM9NWcaY7xljjgNfBz6NPocj9VXgh9baYwO26XM4cj8yxuwzxnzHGJOHrqGEoIJLJH4epTd/9K14D2Qistbebq2dA3wJeDje45lIjDE3ANcB34n3WCa4Fdbaa4D3AA70Z1nCUMF12Qmg1BiTBND368y+7RJeuGun6xpE3+SDBcA6a60XOE7vV4v9zxcCXmvthWGem/KstTuAPwJOos9hpG4CrgKO9gW/ZwEvAvPR5zBi/fEKa20nvcXrcvRnWUJQwdWnb5bIXmBD36YNwB5r7fn4jWpiCHftdF2HMsZspTfL8ad9/6MGqAXSjTHv63u8CXgmguemHGNMljFm9oDHHwUuAPocRsha+6C1dqa1tsxaW0ZvsXozvXcK9TmMgDEm0xiT2/d7B7Ce3s+Y/ixLUA6fzxfvMSQMY8yV9E4bnwZcpHfauI3vqBKLMeabwBqgGGgAGq21C8NdO13Xy4wxC4EDQB3Q0bf5qLV2tTHmRnpnzqVxebr42b7jQj431RhjioDngUygh95i66+ttW/oczg6fXe5PtK3LIQ+hxEwxswDfgwk9f33FnC3tbZe11CCUcElIiIiEmP6SlFEREQkxlRwiYiIiMSYCi4RERGRGFPBJSIiIhJjKrhEREREYiw53gMQkcRmjDHAU0A5cL+19ptxHpKIyISjgktEhnMf8CtrbWW8ByIiMlHpK0URGc5c4OBIDzLG6B90IiJ9tPCpiIRkjHmJ3r573YAHuB/4DL1fLzYDj1trH+jbtww4CtwOfAU4Zq1daYz5c+BeersT/A74C2vtu+P6RkRE4kx3uEQkJGvt+4HdwF9aa7OAN4FPAXnALcCdxpg/HXRYf2Pkm40xNcCX6G0HNb3vXE+O0/BFRBKGCi4RiZi19mVr7X5rrddau4/e4ummQbs9YK1tt9Z20Nuc939baw9Zaz3AVqDSGDN3nIcuIhJXyliISMSMMcuAB4FFQCrgAp4ZtNuJAb+fC2wzxvz9gG0OoBTQ14oiMmWo4BKRkXgC+Bbwx9ZatzHmH4DCQfsMDIaeAL5urf3ReA1QRCQR6StFERmJbOBCX7F1PXDrMPtvB/7WGLMQwBiTa4z5RKwHKSKSaFRwichIbAa+aoxpBb4MPB1uZ2vtc8D/AXYaY1qAA8Afx3yUIiIJRstCiIiIiMSY7nCJiIiIxJgKLhEREZEYU8ElIiIiEmMquERERERiTAWXiIiISIyp4BIRERGJMRVcIiIiIjGmgktEREQkxv4/myY0/Ibjms8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "WGqB5OSCzJ21",
        "outputId": "02162360-c0dd-4203-d106-fa1b8dfa60c1"
      },
      "source": [
        "plt.figure(figsize=(12, 7),facecolor='lightgrey')\r\n",
        "sns.distplot(titanic['fare'], bins=30, kde=False, color='tab:red')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac161e7790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "LUB0jai1zJ23",
        "outputId": "19cfa3f8-c662-4303-b3bc-aaedf1302a5c"
      },
      "source": [
        "plt.figure(figsize=(12, 7),facecolor='lightgrey')\r\n",
        "sns.boxplot(x='class', y='age', \r\n",
        "            data=titanic, \r\n",
        "            palette='tab10')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac15c7f810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "0CSdQlIwzJ24",
        "outputId": "277dcbdf-d5c0-49e9-b0ab-d651bf72d4f6"
      },
      "source": [
        "plt.figure(figsize=(12, 7),facecolor='lightgrey')\r\n",
        "sns.swarmplot(x='class',y='age',\r\n",
        "              data=titanic,\r\n",
        "              palette='Set2')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac15caa210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "KdqSZ3CbzJ25",
        "outputId": "c90dbfac-2e6b-42ee-b11c-bf6c0ba3b1b7"
      },
      "source": [
        "plt.figure(figsize=(12, 7),facecolor='lightgrey')\r\n",
        "sns.countplot(x='sex',data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac15384790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "44hU-jJ1zJ26",
        "outputId": "a0244885-3ead-4700-f553-bf430cf38588"
      },
      "source": [
        "plt.figure(figsize=(9, 7))\n",
        "sns.heatmap(titanic.corr(),cmap='viridis')\n",
        "plt.title('titanic.corr()')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'titanic.corr()')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "C0QnmKrtzJ27",
        "outputId": "753c76f8-f9b9-4eab-e69b-230e83acf53f"
      },
      "source": [
        "g = sns.FacetGrid(data=titanic, col='sex')\n",
        "g.map(plt.hist,'age')\n",
        "g.fig.set_figwidth(12)\n",
        "g.fig.set_figheight(6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyQAAAFeCAYAAAB0LNuKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfs0lEQVR4nO3df7Skd10f8PdmkywRlGhCsCGQILIfMZtjzMWCCihH0KOSRqSoqSRYVAzF0Na0VWgpoIUGJPUUiCbVUoFgUPxBiEqheEQEBPWSqAv6SYQsJIAQCHgAyQ822z9mFi4h+3Pm3u/M3dfrnHv23mdmnuc9M/fOd9/zfZ5ntuzZsycAAAAjHDU6AAAAcORSSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYY4eHQCYv6r6ziRXdPcpo7MAcGBVtSXJy5P8QJLru/ufb+C2fz3JTd39XzZqm7CWQgIAMN4jkjw2ySnd/dnRYWAj2WULAGC8U5PsUkY4Epkh4YhTVT+b5BlJvirJh5P8m+7+o6o6Ksl/SvKTSY5P8kdJLujuW6rqV5Kc1N1PmK7jhUkemuQx3b1nTrmem+T0JLclOSfJriRPmH79++nyH+/uN02v/6+neU9JcnOSF3b35ftY98lJXprkUUk+k+SXuvsl88gNsEwWcQyoqh9PcmmSY6rqM0ku6e7nVNXjkvy3JKclee80z19Pb7NrepvzkjwoyWuSPCvJr2cy2/KuJE/s7k9Or//aJI9MclySv0rytO5+zz7y7HO7sB4UEo4oVVVJfjrJt3T3h6vqtCRbpxdfmMm+u9+RyX/wX5LJi/25SS5Kcm1V/ViS9yX58SRn3t1AVFWPSPL7+4nxuO5+2z4uOzuTMvJjmexL/MYkv5bkftNllyd54PS6H0vyuCTvz6RovKGq/qK7332XPEcluTrJVdP7ckqSN1dVd/cb95MTYFNZ1DGgu/93Ve1O8hPd/Yjper45k3Hg7CR/meRJSV5fkxfv26Y3fUImu3kdneSaJN88zfa3Sf4wk+L1vOl135DkKUluT/LCJK9Ocubd5D+Y7cJcKSQcaXYn2ZbkG6vq5u7eteayC5L8dHfflHxhxuKDVXVed/9TVZ2XyQv6p5NcuPd6dzUdaI4/zHx/urckTN/N+sEkF3f37qp6TZL/VVXHd/enuvsP1tzuT6rqTZm8+/Xuu6zzW5Lcp7t/fvrz+6vqV5P8SCaFB+BIsehjwFpPTXJ5d79r+vMrqupZSR6e5E+my17a3R+d5v3TJB/r7mumP/9eku9ak+vle7+f3rdPVtW9u/sfD2O7MFcKCUeU7v77qvp3SZ6b5PSqemOSn+nuD2ey/+7vVdWda26yO8l9k3you99VVe9PclKS31qniB9d8/3nkny8u3ev+TlJ7pXkU1X1vUmek2R7JseDfUWSv7mbdZ6a5OSq+tSaZVuT/Ok8gwMsuiUYA9Y6NcmTq+rCNcuOTXLymp/vOmbc9ed7JUlVbU3y/CRPTHKfJHvv44lJ7lpIDma7MFcKCUec7v6NJL9RVV+VyS5QL8xkH9wbkzylu99+d7erqqdn8s7ahzPZz/i/7+N6j8zkXbR9+d7unqkMVNW2JL+T5PwkV3X3HVX1uiRb7ubqNya5obsfPMs2ATaDJRoDbkzy/O5+/kFc90D+VSa7Az8mk+MT753kk9n3mDGv7cJBUUg4okz3H75fkrcnuTWTd5D27j98WZLnV9WTu/sDVXWfJN/W3VdV1fZMDvD7ziT/lOTPq+oN3X3tXbcxHWjutc535dhMBsabk3x+Olvy3Ul23s11/zzJp6cHcr4kk/2HH5LkuO7+i3XOCbAwlmwM+NVMZmzenMnr+FdMt//W7v70Ia7rKzM5Mconput5wQZtFw6K0/5ypNmW5OIkH0/yD5lMvT9zetn/TPL6JG+qqk8neWeSh1XV0UmuyOQsVn/V3ddnciaTV01nKjbcdFB4Ria7DXwyk3e/Xr+P6+7O5OD3M5PckMl9/7VM3iEDOJIszRjQ3X+ZyRm/XpbJ6/zfZ3Jyk8PxyiQfSPKhTM6a9c4N2i4clC179szljKUAAACHzAwJAAAwjEICAAAMo5AAAADDKCQAAMAwC3na39XV1aOTnJLkppWVlc+PzgPAWMYFgM1rUWdITklyw44dO+5IsudQv97znvcc8m1GfMkp56J/LUtWORcy57wdEePCMmWVU85F/lqWnMuUdT3HhkUtJDO59dZbR0c4KHLOl5zztyxZ5ZyvZcl5KJbpPi1LVjnnS875WpacyfJkXc+cm7KQAAAAy+GgjiGpqhcneUKS05Kc0d07q+qEJK9K8qAktye5PslPdffN09s8PMnlSY5LsivJk7r7Y/O+AwAAwPI62BmS1yV5VJIPrFm2J8mLuru6+4wk70tycZJU1VFJrkjy9O7enuStey8DAADY66BmSLr7bUlSVWuX3ZLkLWuu9s4kT5t+v5Lk1r23S3JZJrMkT5kpLQAAsKls2bNnnwe8f5mq2pXkcd298y7Lj0rypiSv7+6XVNUTkjylu79/zXX+Kckp0yKzX6urq6clueGggwGwUFZWVrbMc33GBYDlt6+xYV6fQ/LSJJ9J8rI5rS9JsmPHjmzbtu2Qb7e6upqVlZV5RlkXcs6XnPO3LFnlnK9FzrnZx4VkebLKOV9yztey5EyWJ+t65py5kEwPeH9wkrO7+87p4g8mOXXNdU5McufBzI4AAABHjplO+1tVL8jkeJEf6O7b1ly0muS4qnrE9OcLkrx2lm0BAACbz8Ge9vclSX4wydcmeXNVfSLJDyV5ZpLrkrxjesD7Dd39+O6+s6rOS3J5Vd0j09P+rkN+AABgiR3sWbaekeQZd3PRPg9a7O53JDnjMHMBAABHAJ/UDgAADKOQsGFuv2P3uq7/YM/8sN45AAA4ePM67S8c0LHHbM3ZF101OkauvuSc0REAAJgyQwIAAAyjkAAAAMMoJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAAAwjEICAAAMo5AAAADDKCQAAMAwCgkAADCMQgIAAAyjkAAAAMMoJAAAwDAKCQAAMIxCcoS4/Y7dh33blZWVOSYBAIAvOnp0ADbGscdszdkXXTU0w9WXnDN0+wAALB4zJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAAAwjEICAAAMo5AAAADDKCQAAMAwCgkAADCMQgIAAAyjkAAAAMMoJAAAwDAKCQAAMIxCAgAADKOQAAAAwxx9oCtU1YuTPCHJaUnO6O6d0+Xbk7wiyQlJPpHk/O6+/kCXAQAA7HUwMySvS/KoJB+4y/LLklza3duTXJrk8oO8DAAAIMlBFJLuflt337h2WVWdlOSsJFdOF12Z5Kyqus/+LptfbAAAYDPYsmfPnoO6YlXtSvK47t5ZVStJXtndp6+5/L1JnpRky74u6+53H8y2VldXT0tyw0HeBw7CyspKzr7oqqEZrr7knOEZ9uZYXV0dHQM2tZWVlS3zXJ9xAWD57WtsOOAxJCPt2LEj27ZtO+Tbra6uZmVlZR0Szdey5NyMRj7uy/S8L0tWOedrkXNu9nEhWZ6scs6XnPO1LDmT5cm6njkP9yxbNya5X1VtTZLpvydPl+/vMgAAgC84rELS3R9Lcm2Sc6eLzk1yTXffvL/LZg0LAABsLgcsJFX1kqq6KckpSd5cVe+ZXnRBkgur6rokF05/zkFcBgAAkOQgjiHp7mckecbdLP+7JA/bx232eRkAAMBePqkdAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYRQSAABgGIUEAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYRQSAABgGIUEAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYRQSAABgGIUEAAAY5uhZV1BVj0vyC0m2TL+e192/W1Xbk7wiyQlJPpHk/O6+ftbtAQAAm8dMMyRVtSXJq5Kc191nJjkvySuq6qgklyW5tLu3J7k0yeWzhgUAADaXeeyydWeSe0+/Pz7JR5KcmOSsJFdOl1+Z5Kyqus8ctgcAAGwSW/bs2TPTCqrqu5L8ZpLPJvnKJN+X5I4kr+zu09dc771JntTd7z7QOldXV09LcsNMwfgSKysrOfuiq4ZmuPqSc4Zn2JtjdXV1dAzY1FZWVrbMc33GBYDlt6+xYaZjSKrq6CTPTHJOd7+9qr49yW9lsuvWzHbs2JFt27Yd8u1WV1ezsrIyjwjrallybkYjH/dlet6XJauc87XIOTf7uJAsT1Y550vO+VqWnMnyZF3PnLPusnVmkpO7++1JMv33s0luTXK/qtqaJNN/T05y44zbAwAANpFZC8lNSU6pqkqSqnpIkvsmuT7JtUnOnV7v3CTXdPfNM24PAADYRGYqJN39D0meluS3q+qvkrwmyVO6+5YkFyS5sKquS3Lh9GcAAIAvmPlzSLr71UlefTfL/y7Jw2ZdPwAAsHn5pHYAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYRQSjji337F76PZXVlaGZwAAWBQzf1I7LJtjj9masy+6amiGqy85Z+j2AQAWhRkSAABgGIUEAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYRQSAABgGIUEAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFIAACAYRQSAABgGIUEAAAYRiEBAACGOXrWFVTVPZL8UpLHJLk1yZ9191OranuSVyQ5Icknkpzf3dfPuj0AAGDzmMcMyYsyKSLbu/uMJM+eLr8syaXdvT3JpUkun8O2AACATWSmQlJV90pyfpJnd/eeJOnuj1bVSUnOSnLl9KpXJjmrqu4zy/YAAIDNZdZdth6Uye5Yz6mqRyf5TJL/kuRzST7U3buTpLt3V9WHk9w/yc0zbhMAANgkZi0kW5N8XZJruvs/VtXDklyd5IkzJ0uyc+fOw77t6urqPCKsu43KubKysiHb4eD5HZ0vOedr1pzr9ZpzJIwLyfJklXO+5JyvZcmZLE/W9RobZi0kH0zy+Ux3zerud1XVxzOZIblfVW2dzo5sTXJykhsPZeU7duzItm3bDjnU6urqUvwHfFlysj6W4blflt9ROedrkXNu9nEhWZ6scs6XnPO1LDmT5cm6njlnOoakuz+e5I+TPDZJpmfWOinJdUmuTXLu9KrnZjKLYnctAADgC+Zxlq0Lkjyrqv4myWuSnNfdn5ouv7Cqrkty4fRnAACAL5j5c0i6+/1JvvNulv9dkofNun4AAGDz8kntAADAMAoJAAAwjEICAAAMo5AAAADDKCQAAMAwCgkAADCMQgIAAAyjkAAAAMMoJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAAAwjEICAAAMo5AAAADDKCQAAMAwCgkAADCMQgIAAAyjkAAAAMMoJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAHBQbr9j9xe+X1lZ2bBtAZvb0aMDAADL4dhjtubsi67akG1dfck5G7IdYDwzJDDAorzztyg5AIAjlxkSGGAj32XcH+9AAgCjmSEBAACGUUgAAIBhFBIAAGAYhQQAWDjzOunGgU5P7OQeMJ6D2gGAhbNRJ/9wcg8YzwwJAAAwjEICAAAMo5AAAADDKCQAAMAwczuovaqek+S5Sc7o7p1V9fAklyc5LsmuJE/q7o/Na3sAAMDym8sMSVWdleThST4w/fmoJFckeXp3b0/y1iQXz2NbAMCXcupaYJnNPENSVduSXJrk3CRvmS5eSXJrd79t+vNlmcySPGXW7QEAX8opcoFlNo9dtn4+yRXdvauq9i57QKazJUnS3R+vqqOq6mu6+5aDXfHOnTsPO9Tq6uph33YjbVTOA30wFEeuA/0O+luaryMl53q95hwJ40Jy6Fm9xs9mUX43FiXHgcg5f8uSdb3GhpkKSVV9a5KHJvm5WdazLzt27Mi2bdsO+Xarq6tL8eK8LDnZ3Pb3O7gsv6Nyztci59zs40KyXFk3i0V4vJfleZdz/pYl63rmnPUYku9I8pAkN1TVriSnJHljkq9PcureK1XViUnuPJTZEQAAYPObqZB098XdfXJ3n9bdpyW5Kcn3JPnFJMdV1SOmV70gyWtnSgoAAGw66/I5JN19Z5LzkvxKVV2fyUzKuuzWBQAALK+5fQ5JkkxnSfZ+/44kZ8xz/QAAwObik9oBAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgDgiHX7Hbs31XZgGc31tL8AAMvk2GO25uyLrlr37Vx9yTnrvg1YVmZIAACAYRQSAABgGIUEAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYBiFBAAAGEYhAQAAhlFI4Ah2+x2793v5ysrK8AwAwOZ29OgAwDjHHrM1Z1901dAMV19yztDtAwBjmSEBAACG2bSFZFF2A1mUHAAAsIg27S5bi7ArSmJ3FAAA2J9NO0MCAAAsPoUEAAAYRiEBAACGUUgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAA1tmBPih5ZWVlQ7YDi2jTfjAiAMCi2KgPbPaBzCwjMyTrbH/vVMzr3RAADs1GvYvs3WqAAzNDss426h2RA/GOCcAXebcaYHGYIQEAAIZRSAAAgGFm2mWrqk5I8qokD0pye5Lrk/xUd99cVQ9PcnmS45LsSvKk7v7YbHEBAIDNZNYZkj1JXtTd1d1nJHlfkour6qgkVyR5endvT/LWJBfPuC0AAGCTmamQdPct3f2WNYvemeTUJCtJbu3ut02XX5bkh2bZFgAAsPnM7Sxb01mRpyV5fZIHJPnA3su6++NVdVRVfU1333Kw69y5c+e84gELbHV1dSHWsRGOlJzrdVrzWcaFtfdpI0+7fjiP5aHexmnkWWu9X2eOlNexjbQsWddrbJjnaX9fmuQzSV6W5PHzWOGOHTuybdu2Q77dsjypwMSs/5laXV1div+QyTm7WcaFUffpULe7yI8/y2E9f3+W5fdzWXImy5N1PXPO5SxbVfXiJA9O8sPdfWeSD2ay69bey09McuehzI4AAHBo1vvDONf+h9QHfzIvM8+QVNULMjlm5Pu7+7bp4tUkx1XVI6bHkVyQ5LWzbgsAgH3byA9k9sGfzMusp/09Pckzk1yX5B1VlSQ3dPfjq+q8JJdX1T0yPe3vjFkBAIBNZqZC0t3vSbJlH5e9I8kZs6wfAADY3HxSOwAAMIxCAgAADKOQAAAAwygkwFDzOG3kPM6L7vSVADDGPD8YEeCQbeQpKvfH6SsBYAwzJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAAAwjEICAAAMo5AAAADDKCQAAMAwCgkAADCMQgIA6+T2O3Yf8m1WVlbWIQnA4jp6dAAA2KyOPWZrzr7oqnXfztWXnLPu2wBYL2ZIAACAYRQSAABgGIUEAAAYRiEByOEdfHyoDnSw8kZkAJiXjXrN8tq4+TmoHSAbd/Dx/jgwGVgmTtrAvJghAQBgYW3U6bPNxIxjhgQAgIVlJmbzM0MCAAAMo5AAAADDKCQAAMAwCgkAADCMQgIAAAyjkAAAAMMoJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAAAwjEICsCBuv2P36AhJkm94yOmjIwBwBDl6PVdeVduTvCLJCUk+keT87r5+PbcJsKyOPWZrzr7oqtExcvUl54yOAMARZL1nSC5Lcml3b09yaZLL13l7AADAElm3GZKqOinJWUkeO110ZZKXVdV9uvvmA9x8a5Lcfvvth7392267Lcffc+th335e5FisDIuSYxEyLEqORciwKDkWIcPeHLPauXPnaUluWllZ+fzMK5uYy7iw1kY81hv1nG627Wzktmxn8be1kdsZZeS2D8WsOfc1NmzZs2fPTCvel6paSfLK7j59zbL3JnlSd797f7ddXV19RJI/XZdgAGyUB66srOyax4qMCwCbxpeNDet6DMkM/iLJI5N8JMliHOUJwKG6aY7rMi4AbA5fNjas5wzJSUmuS3JCd++uqq2ZHNj+4IPYZQsAADgCrNtB7d39sSTXJjl3uujcJNcoIwAAwF7rNkOSJFX1DZmc9verk3wyk9P+9rptEAAAWCrrWkgAAAD2xye1AwAAwygkAADAMAoJAAAwjEICAAAMs6gfjHhYqmp7Jmf1OiGTzzw5v7uvH5sqqaoXJ3lCktOSnNHdO6fLFypvVZ2Q5FVJHpTk9iTXJ/mp7r65qh6e5PIkxyXZleRJ01M7j8r6uiQPTHJnks8kubC7r120x3SvqnpOkudm+vwv4OO5K8mt068k+dnufuMC5rxHkl9K8phMsv5Zdz910Z73qjotyevWLDo+yVd199csYNbHJfmFJFumX8/r7t9dtJyzWNT7YmxYl6zGhvnm2xVjw7wynhbjwj5tthmSy5Jc2t3bk1yayR/LInhdkkcl+cBdli9a3j1JXtTd1d1nJHlfkour6qgkVyR5+jTrW5NcPDBnkjy5u7+pu785yYuTvHy6fNEe01TVWUkenunzv6CPZ5L8y+4+c/r1xgXN+aJMBpvt09/RZ0+XL9Tz3t271jyWZ2byGvAb04sXJmtVbcnkP5rnTXOel+QV0+d+YXLOwaLeF2PD/Bkb5s/YMAfGhf3bNIVk+snwZyW5crroyiRnVdV9xqWa6O63dfeNa5ctYt7uvqW737Jm0TuTnJpkJcmt3f226fLLkvzQBsf7Et39j2t+vHeSOxfxMa2qbZn80T5tzeKFezz3YaFyVtW9kpyf5NndvSdJuvuji/i8r1VVxyb50SQvX9Csd2byN5RM3rH7SJITs3g5D8uCPuZJjA3rwdiwIRYq5zKODcaFL7dpCkmS+yf5UHfvTpLpvx+eLl9EC5132oSfluT1SR6QNe/gdffHkxxVVV8zKF6SpKp+rao+mOT5SZ6cxXxMfz7JFd29a82yhXw8k7y6qv66qn65qo7P4uV8UCZTxM+pqr+sqrdU1SOymM/7Wv8ik3zvzoJlnQ7eP5Tkqqr6QCbv2J2/aDlntGz3ZaHzGhvmxtgwP8s4NhgX7mIzFRLm66WZ7H/7stFB9qW7f6K7H5DkWUl+cXSeu6qqb03y0CS/PDrLQXhkd39Tkm/JZH/RRXzetyb5uiTXdPdDk/xskt9Ncq+hqQ7sKfnibiMLpaqOTvLMJOd096lJzk7yW1n8x5RxjA0zMjbM3TKODcaFu9hMheTGJPerqq1JMv335OnyRbSweacHWj44yQ93951JPpjJ9Pzey09Mcmd33zIo4pfo7lcleXSSm7JYj+l3JHlIkhumBwaekuSNSb4+C/Z47t1tpLtvy2SQ/PYs3vP+wSSfz3S6uLvfleTjST6XxXrev6Cq7pfJ78Grp4sW7e/+zCQnd/fbk2T672cz2Rd7kXLOYtEe8wNZ2LzGhrkxNszXUo0NxoW7t2kKSU/O7nBtknOni87NpC3fPC7Vvi1q3qp6QSb7h/7A9AUoSVaTHDedAk2SC5K8dkS+ZLK/aFXdf83PZye5JclCPabdfXF3n9zdp3X3aZkMit+TyTt2i/R43rOq7j39fkuSH8nkcVyo5326W8AfJ3ls8oUzEZ2U5Los0PN+F09O8gfd/YlkIf/ub0pySlVVklTVQ5LcN5OzKC1SzsO2gI/5fi1qXmPD/Bgb5msJxwbjwt3YsmfPnnmsZyFU1Tdkcjqyr07yyUxOR9ZjUyVV9ZIkP5jkazNp7Z/o7tMXLW9VnZ5kZyZ/xJ+bLr6hux9fVd+WydkU7pEvnuLvo4Ny3jfJVUnumWR3JgPOf+judy/aY7rW9J2wx/Xk1I6L9Hh+XZLfyWTae2uS9yZ5Rnd/ZJFyrsn68kxOOXhHkv/c3W9Y1Oe9qq7L5LH8v2uWLVTWqvrRJD+XyUGMSfKc7n7douWcxaLeF2PD3HMaG+aby9iwDowLd29TFRIAAGC5bJpdtgAAgOWjkAAAAMMoJAAAwDAKCQAAMIxCAgAADKOQAAAAwygkAADAMAoJAAAwzNGjA8BmU1U/l+Qnk5yU5MZMPjH296pqa5IXJXlykk8nuSTJS5Mc092fr6p7J/kfSb4vk09H/T+ZfDrq7gF3A4A5MS7A/ikkMH/vS/LIJP+Q5IlJrqiqr09yTpLvTXJmks8mee1dbvfrST6W5OuT3DPJ72cycF2+IakBWC/GBdiPLXv27BmdATa1qro2yXOS/Nskv9ndl0+XPybJ/0tyTJITknwwyfHd/bnp5ecmeWp3P3pIcADWhXEBvpQZEpizqjo/yc8kOW266F5JTkxycibvbO219vtTMxmAPlJVe5cddZfrALCEjAuwfwoJzFFVnZrkV5N8V5I/6+7d03fCtiT5SJJT1lz9/mu+vzHJbUlO7O7Pb1ReANaXcQEOzC5bMEdV9Y1J3p3km5L8fZLzMxmILsjkna6fTvLd+eK+wo/JFw9evCrJriTPTvKZJA9Mckp3/8kG3w0A5sS4AAfmtL8wR9393kzOkvJnST6a5Iwkb59e/KtJ3pTkr5Nck+QPk3w+yd6zpZyf5Ngk703yySS/neSfbVR2AObPuAAHZoYEBqmq701yWXefOjoLAOMZFzhSOYYENkhVHZfk0Zm8G3bfTM6w8ntDQwEwjHEBJuyyBRtnS5LnZTLtfk2Sv03yX4cmAmAk4wLELlsAAMBAZkgAAIBhFBIAAGAYhQQAABhGIQEAAIZRSAAAgGEUEgAAYJj/Dx1r77mE0K7lAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2Xrus1EKAeB"
      },
      "source": [
        "### **Male/Female survival based on the age**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "b9fG8uQnEnC2",
        "outputId": "646b8aca-2093-4ebd-ba43-a93f6f100bfc"
      },
      "source": [
        "women = titanic[titanic['sex']=='female']\r\n",
        "ws = women[women['survived']==1].age.dropna()\r\n",
        "wd = women[women['survived']==0].age.dropna()\r\n",
        "\r\n",
        "men = titanic[titanic['sex']=='male']\r\n",
        "ms = men[men['survived']==1].age.dropna() \r\n",
        "md = men[men['survived']==0].age.dropna()\r\n",
        "\r\n",
        "sns.set_theme(style='whitegrid')\r\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(18, 7))\r\n",
        "\r\n",
        "ax = sns.distplot(ws, bins=18, label='survived', ax=axes[0], kde =False)\r\n",
        "ax = sns.distplot(wd, bins=40, label='not survived', ax=axes[0], kde =False)\r\n",
        "ax.set_title('female')\r\n",
        "ax.legend()\r\n",
        "\r\n",
        "\r\n",
        "ax = sns.distplot(ms, bins=18, label='survived', ax = axes[1], kde = False)\r\n",
        "ax = sns.distplot(md, bins=40, label='not survived', ax = axes[1], kde = False)\r\n",
        "ax.set_title('male')\r\n",
        "ax.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fac13a88090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1296x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xTCbbtOMmm8"
      },
      "source": [
        "men have a high probability of survival when they are between 18 and 30 years old, which is also a little bit true for women but not fully. For women the survival chances are higher between 14 and 40.\r\n",
        "\r\n",
        "For men the probability of survival is very low between the age of 5 and 18, but that isn’t true for women. Another thing to note is that infants also have a little bit higher probability of survival.\r\n",
        "\r\n",
        "Since there seem to be certain ages, which have increased odds of survival and because I want every feature to be roughly on the same scale, I will create age groups later on."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBbz2PPFM8QB"
      },
      "source": [
        "### **Embarked, Pclass and Sex:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CLpqG8CZIr1m",
        "outputId": "60ffb192-d531-402c-dbe6-a4a2c822abf2"
      },
      "source": [
        "sns.set_theme()\r\n",
        "g= sns.FacetGrid(data=titanic, row='embark_town', size=4.5, aspect=1.6)\r\n",
        "g.map(sns.pointplot, 'pclass', 'survived', 'sex', palette=None,  order=None, hue_order=None )\r\n",
        "g.add_legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fac139439d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 596.375x972 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZleljROUOCVb"
      },
      "source": [
        "**Embarked** seems to be correlated with **survival**, depending on the **gender**.\r\n",
        " \r\n",
        "- Women on port **Queenstown** and on port **Southampton** have a ***higher chance of survival***.\r\n",
        "\r\n",
        "- The inverse is true, if they are at port **Cherbourg**. \r\n",
        "\r\n",
        "- Men have a high survival probability if they are on port **Cherbourg** , but a low probability if they are on port **Queenstown** or **Southampton**.\r\n",
        "\r\n",
        "- **Pclass** also seems to be **correlated** with **survival**. We will generate another plot of it below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjMP01zxPORb"
      },
      "source": [
        "### __Pclass__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "GfpIVx2HPF6a",
        "outputId": "d2543af5-62a7-47b5-c7a7-30b94356cbd4"
      },
      "source": [
        "plt.figure(figsize=(9, 6),facecolor='lightgrey')\r\n",
        "sns.barplot(x='pclass', y='survived', data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac139a37d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwnYxLzHfmLj"
      },
      "source": [
        "Here we see clearly, that **Pclass** is contributing to a persons chance of survival, especially if this person is in **class 1**. *We will create another pclass plot below.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CyttBFXPcKm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "c489adce-9e9a-4b7f-8269-049e5de20452"
      },
      "source": [
        "# survival|age|class\r\n",
        "grid = sns.FacetGrid(data=titanic, col='survived', row='pclass', size=2.2, aspect=1.6)\r\n",
        "grid.map(plt.hist, 'age', alpha=.5, bins=20)\r\n",
        "grid.add_legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fac162c4890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 515.68x475.2 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXrj4rR1gb9u"
      },
      "source": [
        "The plot above confirms our assumption about pclass 1, but we can also spot a high probability that a person in pclass 3 will not survive.\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZxdxn8Ig1iI"
      },
      "source": [
        "### **SibSp and Parch:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLzx7y3XhGIN"
      },
      "source": [
        "SibSp and Parch would make more sense as a combined feature, that shows the total number of relatives, a person has on the Titanic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2xPhH8hlq7y",
        "outputId": "cb9d0c95-f306-4c31-f1f1-a5b987a4eb2c"
      },
      "source": [
        "data = [titanic]\r\n",
        "for dataset in data:\r\n",
        "    dataset['relatives'] = dataset['sibsp'] + dataset['parch']\r\n",
        "    dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0\r\n",
        "    dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1\r\n",
        "    dataset['not_alone'] = dataset['not_alone'].astype(int)\r\n",
        "titanic['not_alone'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    537\n",
              "0    354\n",
              "Name: not_alone, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "vcEyRbcpmD50",
        "outputId": "ee408bd1-8ecf-4afd-b31f-384b329c71ff"
      },
      "source": [
        "axes = sns.factorplot(x='relatives',\r\n",
        "                      y='survived', \r\n",
        "                      data=titanic,\r\n",
        "                      aspect = 2.5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:3714: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcmZ5yCumQED"
      },
      "source": [
        "Here we can see that you had a **high probabilty of survival** with ${1}$ to ${3}$ realitves, but a lower one if you had less than ${1}$ or more than ${3}$ (**except for some cases with 6 relatives**)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SR0RAeb5FKyM"
      },
      "source": [
        "### __How the age varies with gender__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "9SXMVTMVEp0l",
        "outputId": "438f9eb1-8d12-4db0-8912-e418d2ee8469"
      },
      "source": [
        "# Another value to visualize the data is to use FacetGrid to plot multiple kdeplots on one plot\r\n",
        "# setting the upper limit by the oldest passenger\r\n",
        "# since we know that no one can be of negative age, we will set the lowest limit as zero\r\n",
        "\r\n",
        "grid = sns.FacetGrid(titanic, hue='sex', aspect=4)\r\n",
        "grid.map(sns.kdeplot, 'age', shade=True)\r\n",
        "\r\n",
        "oldest = titanic['age'].max()\r\n",
        "grid.set(xlim=(0, oldest))\r\n",
        "grid.add_legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 941.975x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2DlnVNHIOP8"
      },
      "source": [
        "### **Let’s see which age group of people were the most present in each class.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "b_x6kpVYIR7t",
        "outputId": "47f78d66-7165-4e9d-82d2-abd05acbaeae"
      },
      "source": [
        "# Let's do the same for class by changing the hue argument:\r\n",
        "fig = sns.FacetGrid(titanic, hue=\"class\",aspect=4)\r\n",
        "fig.map(sns.kdeplot,'age',shade= True)\r\n",
        "oldest = titanic['age'].max()\r\n",
        "fig.set(xlim=(0,oldest))\r\n",
        "fig.add_legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 944.35x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxDTrTgJQYlj"
      },
      "source": [
        "# **Data Preprocessing/Statistical summary**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "N2LCAlL4QYCJ",
        "outputId": "61a7791c-81b2-421e-e2c0-7c3e9542370c"
      },
      "source": [
        "titanic.describe()"
      ],
      "execution_count": null,
      "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>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>0.904602</td>\n",
              "      <td>0.602694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>1.613459</td>\n",
              "      <td>0.489615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         survived      pclass         age  ...        fare   relatives   not_alone\n",
              "count  891.000000  891.000000  714.000000  ...  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.699118  ...   32.204208    0.904602    0.602694\n",
              "std      0.486592    0.836071   14.526497  ...   49.693429    1.613459    0.489615\n",
              "min      0.000000    1.000000    0.420000  ...    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   20.125000  ...    7.910400    0.000000    0.000000\n",
              "50%      0.000000    3.000000   28.000000  ...   14.454200    0.000000    1.000000\n",
              "75%      1.000000    3.000000   38.000000  ...   31.000000    1.000000    1.000000\n",
              "max      1.000000    3.000000   80.000000  ...  512.329200   10.000000    1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "wb1QRk_3Ps8H",
        "outputId": "61cff9c3-296f-4d9e-c911-b5c17a72ca5f"
      },
      "source": [
        "titanic.groupby(['sex']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</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>female</th>\n",
              "      <td>0.742038</td>\n",
              "      <td>2.159236</td>\n",
              "      <td>27.915709</td>\n",
              "      <td>0.694268</td>\n",
              "      <td>0.649682</td>\n",
              "      <td>44.479818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.343949</td>\n",
              "      <td>0.401274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.188908</td>\n",
              "      <td>2.389948</td>\n",
              "      <td>30.726645</td>\n",
              "      <td>0.429809</td>\n",
              "      <td>0.235702</td>\n",
              "      <td>25.523893</td>\n",
              "      <td>0.930676</td>\n",
              "      <td>0.665511</td>\n",
              "      <td>0.712305</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        survived    pclass        age  ...  adult_male  relatives  not_alone\n",
              "sex                                    ...                                  \n",
              "female  0.742038  2.159236  27.915709  ...    0.000000   1.343949   0.401274\n",
              "male    0.188908  2.389948  30.726645  ...    0.930676   0.665511   0.712305\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "2o_HGgJqPvFk",
        "outputId": "61bf4376-2975-4d2d-af2f-d060c623e134"
      },
      "source": [
        "titanic.groupby(['sex', 'class']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\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></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>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
              "      <th>First</th>\n",
              "      <td>0.968085</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.611765</td>\n",
              "      <td>0.553191</td>\n",
              "      <td>0.457447</td>\n",
              "      <td>106.125798</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.010638</td>\n",
              "      <td>0.361702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0.921053</td>\n",
              "      <td>2.0</td>\n",
              "      <td>28.722973</td>\n",
              "      <td>0.486842</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>21.970121</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.092105</td>\n",
              "      <td>0.421053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.750000</td>\n",
              "      <td>0.895833</td>\n",
              "      <td>0.798611</td>\n",
              "      <td>16.118810</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.694444</td>\n",
              "      <td>0.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
              "      <th>First</th>\n",
              "      <td>0.368852</td>\n",
              "      <td>1.0</td>\n",
              "      <td>41.281386</td>\n",
              "      <td>0.311475</td>\n",
              "      <td>0.278689</td>\n",
              "      <td>67.226127</td>\n",
              "      <td>0.975410</td>\n",
              "      <td>0.590164</td>\n",
              "      <td>0.614754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0.157407</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30.740707</td>\n",
              "      <td>0.342593</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>19.741782</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.564815</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0.135447</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.507589</td>\n",
              "      <td>0.498559</td>\n",
              "      <td>0.224784</td>\n",
              "      <td>12.661633</td>\n",
              "      <td>0.919308</td>\n",
              "      <td>0.723343</td>\n",
              "      <td>0.760807</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               survived  pclass        age  ...  adult_male  relatives  not_alone\n",
              "sex    class                                ...                                  \n",
              "female First   0.968085     1.0  34.611765  ...    0.000000   1.010638   0.361702\n",
              "       Second  0.921053     2.0  28.722973  ...    0.000000   1.092105   0.421053\n",
              "       Third   0.500000     3.0  21.750000  ...    0.000000   1.694444   0.416667\n",
              "male   First   0.368852     1.0  41.281386  ...    0.975410   0.590164   0.614754\n",
              "       Second  0.157407     2.0  30.740707  ...    0.916667   0.564815   0.666667\n",
              "       Third   0.135447     3.0  26.507589  ...    0.919308   0.723343   0.760807\n",
              "\n",
              "[6 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcwvdAZrTAWy"
      },
      "source": [
        "__Deck__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "txHz2AIrQyPR",
        "outputId": "6c871082-d569-4445-8397-947381a1b38d"
      },
      "source": [
        "titanic.groupby(['deck', 'class']).mean().dropna()"
      ],
      "execution_count": null,
      "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></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>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deck</th>\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <th>First</th>\n",
              "      <td>0.466667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>44.833333</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>39.623887</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <th>First</th>\n",
              "      <td>0.744681</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.955556</td>\n",
              "      <td>0.361702</td>\n",
              "      <td>0.574468</td>\n",
              "      <td>113.505764</td>\n",
              "      <td>0.404255</td>\n",
              "      <td>0.936170</td>\n",
              "      <td>0.468085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <th>First</th>\n",
              "      <td>0.593220</td>\n",
              "      <td>1.0</td>\n",
              "      <td>36.086667</td>\n",
              "      <td>0.644068</td>\n",
              "      <td>0.474576</td>\n",
              "      <td>100.151341</td>\n",
              "      <td>0.525424</td>\n",
              "      <td>1.118644</td>\n",
              "      <td>0.372881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">D</th>\n",
              "      <th>First</th>\n",
              "      <td>0.758621</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.037037</td>\n",
              "      <td>0.482759</td>\n",
              "      <td>0.344828</td>\n",
              "      <td>63.324286</td>\n",
              "      <td>0.448276</td>\n",
              "      <td>0.827586</td>\n",
              "      <td>0.310345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>32.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.166675</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
              "      <th>First</th>\n",
              "      <td>0.720000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.083333</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>55.740168</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>0.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>38.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.587500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
              "      <th>Second</th>\n",
              "      <td>0.875000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.687500</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>23.750000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>28.666667</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>10.611660</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <th>Third</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.750000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13.581250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             survived  pclass        age  ...  adult_male  relatives  not_alone\n",
              "deck class                                ...                                  \n",
              "A    First   0.466667     1.0  44.833333  ...    0.866667   0.266667   0.800000\n",
              "B    First   0.744681     1.0  34.955556  ...    0.404255   0.936170   0.468085\n",
              "C    First   0.593220     1.0  36.086667  ...    0.525424   1.118644   0.372881\n",
              "D    First   0.758621     1.0  40.037037  ...    0.448276   0.827586   0.310345\n",
              "     Second  0.750000     2.0  32.250000  ...    0.500000   0.000000   1.000000\n",
              "E    First   0.720000     1.0  40.083333  ...    0.600000   0.720000   0.480000\n",
              "     Second  0.750000     2.0  38.833333  ...    0.000000   0.000000   1.000000\n",
              "     Third   1.000000     3.0  21.666667  ...    0.333333   0.666667   0.333333\n",
              "F    Second  0.875000     2.0  16.687500  ...    0.125000   1.500000   0.375000\n",
              "     Third   0.200000     3.0  28.666667  ...    0.800000   0.400000   0.800000\n",
              "G    Third   0.500000     3.0  14.750000  ...    0.000000   1.750000   0.000000\n",
              "\n",
              "[11 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "zyKUbk61SXGK",
        "outputId": "5762a647-aa37-44fb-f36e-6661e5da5612"
      },
      "source": [
        "# Classes on deck\r\n",
        "sns.countplot('deck', hue='class',data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabfcbf5cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        },
        {
          "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": 357
        },
        "id": "lWndrdKCSxNG",
        "outputId": "d6feb7cf-d7f0-41f0-ceb6-5a1747c89755"
      },
      "source": [
        "# survival deck\r\n",
        "sns.countplot('deck', hue='alive',data=titanic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac05978150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        },
        {
          "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": 257
        },
        "id": "ZVIXAJBVPxW_",
        "outputId": "641e82f2-bab7-411a-a1ec-bcbeca2c733c"
      },
      "source": [
        "titanic[titanic['age']<18].groupby(['sex', 'class']).mean()"
      ],
      "execution_count": null,
      "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></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>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
              "      <th>First</th>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.125000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>104.083337</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>0.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>1.083333</td>\n",
              "      <td>26.241667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0.542857</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.428571</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.057143</td>\n",
              "      <td>18.727977</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.628571</td>\n",
              "      <td>0.228571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
              "      <th>First</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.230000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>116.072900</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>0.818182</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.757273</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>25.659473</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>1.727273</td>\n",
              "      <td>0.181818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>0.232558</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.963256</td>\n",
              "      <td>2.069767</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>22.752523</td>\n",
              "      <td>0.348837</td>\n",
              "      <td>3.069767</td>\n",
              "      <td>0.232558</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               survived  pclass        age  ...  adult_male  relatives  not_alone\n",
              "sex    class                                ...                                  \n",
              "female First   0.875000     1.0  14.125000  ...    0.000000   1.375000   0.125000\n",
              "       Second  1.000000     2.0   8.333333  ...    0.000000   1.666667   0.166667\n",
              "       Third   0.542857     3.0   8.428571  ...    0.000000   2.628571   0.228571\n",
              "male   First   1.000000     1.0   8.230000  ...    0.250000   2.500000   0.000000\n",
              "       Second  0.818182     2.0   4.757273  ...    0.181818   1.727273   0.181818\n",
              "       Third   0.232558     3.0   9.963256  ...    0.348837   3.069767   0.232558\n",
              "\n",
              "[6 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUS9ABVdXmwI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbkJ_CZ5XoaM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0rNM2_HXoKb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}