titanic数据分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic 数据分析与预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['Songti SC']\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、数据查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### object类型值（分类变量）统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "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>sex</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>889</td>\n",
       "      <td>891</td>\n",
       "      <td>889</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>577</td>\n",
       "      <td>644</td>\n",
       "      <td>537</td>\n",
       "      <td>644</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex embarked  who  embark_town alive\n",
       "count    891      889  891          889   891\n",
       "unique     2        3    3            3     2\n",
       "top     male        S  man  Southampton    no\n",
       "freq     577      644  537          644   549"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同pclass占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a298c3e80>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.groupby('pclass').size()/len(df)).sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.age根据sex分组取中位数进行补全"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    27.0\n",
       "male      29.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_median = df.groupby('sex').age.median()\n",
    "age_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('sex',inplace=True)\n",
    "df.age.fillna(age_median,inplace=True)\n",
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.441268\n",
       "std       13.018747\n",
       "min        0.420000\n",
       "25%       22.000000\n",
       "50%       29.000000\n",
       "75%       35.000000\n",
       "max       80.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.age根据sex和pclass分组，取中位数进行补全"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass  sex   \n",
       "1       female    35.0\n",
       "        male      40.0\n",
       "2       female    28.0\n",
       "        male      30.0\n",
       "3       female    21.5\n",
       "        male      25.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_median = df.groupby(['pclass','sex']).age.median()\n",
    "age_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.112424\n",
       "std       13.304424\n",
       "min        0.420000\n",
       "25%       21.500000\n",
       "50%       26.000000\n",
       "75%       36.000000\n",
       "max       80.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(['pclass','sex'],inplace=True)\n",
    "df.age.fillna(age_median,inplace=True)\n",
    "df.reset_index(inplace=True)\n",
    "df.age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. sex和embark转为数字编码表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sex']=df['sex'].map({'male':0,'female':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'].fillna(df.groupby('embarked')['survived'].count().idxmax(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q'], dtype=object)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'] = df['embarked'].map({'S':0,'C':1,'Q':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      "pclass         891 non-null int64\n",
      "sex            891 non-null int64\n",
      "survived       891 non-null int64\n",
      "age            891 non-null float64\n",
      "sibsp          891 non-null int64\n",
      "parch          891 non-null int64\n",
      "fare           891 non-null float64\n",
      "embarked       891 non-null int64\n",
      "class          891 non-null category\n",
      "who            891 non-null object\n",
      "adult_male     891 non-null bool\n",
      "deck           203 non-null category\n",
      "embark_town    889 non-null object\n",
      "alive          891 non-null object\n",
      "alone          891 non-null bool\n",
      "dtypes: bool(2), category(2), float64(2), int64(6), object(3)\n",
      "memory usage: 80.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同pclass的生还率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['pclass','survived']].groupby('pclass').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='survived',index='pclass',aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2a893b00>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='pclass',y='survived',data=df,ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pclass越高生还率越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同性别生还率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2986a940>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='sex',y='survived',data=df,ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "female生还率高于male"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 综合pclass和sex的生还率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass  sex   \n",
       "1       female    0.968085\n",
       "        male      0.368852\n",
       "2       female    0.921053\n",
       "        male      0.157407\n",
       "3       female    0.500000\n",
       "        male      0.135447\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['pclass','sex']).survived.mean()\n",
    "#df.pivot_table(values='survived',index=['pclass','sex'],aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2d92be80>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 379
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,y='survived',x='pclass',hue='sex',ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ae7a3c8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FI8l57wid/Xg01ZvcCyIjDfZvcg43ZB6F1TPg/NvdmU9EpIJTMSAiEgyMcfYpqFQVajbLe2x6aj5XGbKOI8ne5O62Xz9RMSAi4hIVAyIi5U1kZefIb5O3jCNZ6xby6aJ0aA9gPUm9QA7tLu0MRMq0XQePsGFXKoeOHiMmIozTa1WmVlwxFv2XI5s3b+bUU09l+PDhTJ8+vbTTKRNUDIiIVFThUVC1oXPkJfOYUxDkuxh6F/iOeZB4OdhoTcRlmT7L4l9288pXW1iyfk+OmDFwwek1GNqxIRc2q0loiP4NyZ9UDIiISN5CwyCujnPkxeeDw/vyXgS968fir2mIqVG814uUM0mp6dz4yiq+2eJn8z6cJUdL1u9hyfo9tGtYlReGJlK9cqTHWUpZpWJARETcERLidPmJqQ6c6X/M2nkwZ3DxznN6j+K9XqQcSUpN56oXlrMp6VCBxn+zZT9XvbCcN2/sRDUVBAKUwf3pRUSk3GraC+Lyabean+pnuJOLSJDz+Sw3z1xd4EIg26akQ9w0czU+X+msBdq8eTPGGEaMGMHGjRvp378/1apVIzY2lp49e/Ljj84+J3v27OH666+nTp06REVF0b59exYvXpxjrh07dvDggw9y7rnnUrt2bSIiIqhbty6DBg1i7dq1hcorLS2NRx99lDZt2hATE0PlypXp1KkTs2bNcu29l0UqBkRExDuhYXDOdcWb441h8MOb7uQjEsQ+37CHFZv3Fem1KzbvY+mvpbtT+ubNm+nQoQO7du1ixIgR9OzZk0WLFtGlSxc2bNhAx44dWblyJQMGDODqq69mzZo1XHzxxWzduvX4HJ9//jn//ve/qVKlCldeeSW33XYbHTt25M0336R9+/asWbOmQLkkJydz3nnn8fe//53Q0FBGjRrF8OHD2bNnD4MGDeIf//hHSf01lDrdJiQiIt7q+FenPejmpUV7feZReGs0HNgG596ae4djkQpi5ldbivX6V5ZvoXPT0luDs2TJEh5++GHuvffe48899NBD3HfffXTo0IGrr76aSZMmERLifHfdo0cPhg0bxpNPPsmTTz4JQNeuXdm1axexsbE55l6zZg3nnnsu48aNY/78+fnmcuutt/Ltt9/yn//8h7vuuuv480eOHKFfv3488sgj9O/fnzZt2rjx1ssUXRkQERFvhUXAwFeh4XnFm2fRA/DBHU63I5EKJik1nU9+KV6b3U9/2UVSauntbN6oUSPGjRuX47nhw4cDkJ6ezoQJE44XAgCDBg0iLCyM77777vhzNWvWzFUIALRu3ZquXbuyePFiMjIy8sxj7969zJw5k3bt2uUoBACioqL4z3/+g7WW114rxu7pZZiuDIiIiPei4mHoXPjiKVjxP0gLcLtCeAzUPhO2fe0//s0UOPg79J8KETEll69IGbMp6RC2mLf8+6wzT2l1FmrTpg2hoaE5nqtbty4ATZs2zfUhPzQ0lFq1arF9+/Ycz3/wwQe88MILfPPNNyQlJXHsWM4vCJKSkqhTJ3A3tJUrV5KZmYkxhgceeCBXPLuYKOwahGChYkBEREpHWCR0uRvOuxV+fg/WfQipu8FmQnQ1aHwhnDUAImOdNQLv3OTcInSy9Qtg+iUw6HWoXNP79yFSCg6lu3NFzK15iiI+Pj7Xc2FhYQFj2fETv+mfOHEiY8eOpWrVqvTo0YMGDRoQHR2NMYZ33nmHNWvWkJ6e99WPvXv3Ak5RsHLlyoDjUlOL2Ra5jFIxICIipSssEs66yjkCadUfYuvA7GvgyIHc8R3fwuRuMPgtqNG05HIVKSMqR7rzEc6teUrDsWPHuP/++6lduzarV6/O9e3/8uXLCzRPduFx22238cQTT7ieZ1mnNQMiIhIcGp0Loz+G+Ab+48lbYUoP2FKwDwAiwaxxjcrF3kk4NMTQuEZllzLyXlJSEsnJyfzlL3/JVQikpqayevXqAs1zzjnnEBISwtKlRWxqEORUDIiISPCocQZcuwjqtPYfP5IMMy6DH+d6m5eIx6rGRNCzRa1izXFRy1pUjYlwKSPv1axZk+joaFatWpXjFp6MjAzGjh1LUlLBWqfWrFmTwYMH88033/DQQw/lWnMAsHHjRjZt2uRa7mWJigEREQkusbVgxIdwek//8cx0eHMkfDmRYq+wFCnDhnZsWKzXDynm60tbSEgIY8aMYcuWLbRq1Ypbb72Vm2++mbPOOot58+Zx4YUXFniuZ599lo4dO3LffffRokULRo0axT333MPw4cM555xzaNKkCV9/HaCRQZBTMSAiIsEnsjIMnAWJIwKP+fifMP8u8GV6lpaIlzo1rsYFRdwn4IKmNeh0WjWXM/LeQw89xOOPP06lSpV48cUXmTt3Lu3atWPFihU0aBDglkI/4uLiWLJkCc888wzVq1fnrbfe4oknnmDx4sXExsby5JNP0qNHjxJ8J6XHWH1rUiKMMavatm3bdtWqVaWdiohI+WUtfPEEfPJg4DFnXAJXToaIaO/yEvEjuzVl8+bNXZvzwOEMBry4nF92phT4Nc3rxDH7+o7EVwp3LQ8pGQX9nUlMTGT16tWrrbWJhT2HrgyIiEjwMgbOvwOueAlCAnywWfcBvHwppO7xNjcRD8RXCuf1GzvR5YyCXSHockYN5tygQkD+FLz9pERERLKddTXE1obZQyDdT+vR31fBlO5O69HqTbzPT6QExUWFM21Ee77Zsp8Zy7ew4Mc/yMj8886P8FDDxWfWYWinhrRrWBVjiteFSMoXFQMiIlI+nHoBjF4Ir14FB7blju/f7BQE18yGBh09T0+kJBljaN8ogfaNEjh45Ey2JKWRmn6MypFhNKweTVyUrgSIf7pNSEREyo+azZ29CGqf5T9+eD+83Bd+ftfbvEQ8FBcVTqtT4unUuBqtTolXISB5UjEgIiLlS1wdGPkhNOnuP56ZDq8Ph+XPeZuXiEgZpGJARETKn8hY53agtsMCDLCw8O8wf5xaj4pIhaZiQEREyqfQcOgzEbr+I/CYr5+H14dBxmHv8hIRKUNUDIiISPllDFzwN7j8RQgJ0DPjl3nwch84lORtbiIiZYCKARERKf9aD4Qhb0FknP/49pUwpQfs3ehtXiIipUzFgIiIVAyndYFRCyCunv/4vt+cgmDbSi+zEhEpVSoGRESk4qjVEq5dBLXO9B9P2+vsVrz2fW/zEhEpJSoGRESkYomrCyPnQ+Ou/uPHjsCcofDVC97mJSJSClQMiIhIxRMVB4NehzZDAgywsOBuWPB38Pk8TU1ExEsqBkREpGIKDYfLnoUufw885qvn4I3haj0qweXgH7BxMayd5zwe/KO0M/Jr4sSJtGjRgkqVKmGM4amnnirtlAptxIgRGGPYvHlzaadSZAH6rImIiFQAxkCXu6FKfXjv/8B3LPeYte/BjF0wcBbEVPM+R5GC8GXCho9g5WT4ddFJQQNNukH7a+H0nhASWiopnmj27NmMHTuWs88+m1tvvZXIyEg6duxY2mlVSCoGRERE2gyC2NowZxgcTckd3/a102loyJuQcJr3+YnkJXUPzBkC274KMMA6BcKvi6B+RxgwEyrX8DTFk82bN+/4Y926dUs1l4pOtwmJiIiAs6B41AKIDfDBZN9GmNwDtq/yNi+RvKTugakX5VEInGTbV874Ut5kb8eOHQAqBMoAFQMiIiLZap/ptB6t2dJ/PC0Jpl8Cv3zobV4i/vh88Powp1AtjH0bnY5ZpbA4/oEHHsAYw+LFiwEwxhw/sv3yyy+MGDGC+vXrExkZSa1atRg0aBDr1q3LNV/2PfubNm3i2WefpUWLFkRFRdGoUSMeeeQRrLUAvPHGG5xzzjnExMRQs2ZNbrnlFo4cOZJrvnfeeYchQ4bQtGlTYmJiqFy5MomJiUycOBFfIf++vv76a/r370/t2rWJiIigfv363HDDDccLobJCtwmJiIicKL4ejJrvfFjatCR3/NhhmDMYLv4vnHOd9/mJZNv4KWxdVrTXbl0Gv30KTbq7m1M+unTpAsD06dPZsmUL999/f474ggULuOKKK8jIyKBPnz40adKE7du3M3fuXD744AMWL15M27Ztc81755138tlnn9GnTx969uzJe++9x7333svRo0dJSEhg3Lhx9OvXj/PPP5+PP/6Y5557jszMTJ5//vkc84wbN46QkBA6dOhAvXr1OHDgAJ9++iljx45l5cqVvPLKKwV6n9OmTeO6664jMjKSvn37Ur9+fTZs2MDkyZN5//33+eqrr2jQoEHR/hJdZrIrJnGXMWZV27Zt265apcvJIiJB6dhReH8MrJkVeMxfxkD38RCiC+2Sv7Vr1wLQvHlzdyZ8bSCsn1/015/RG67J4/e7BHXp0oUlS5Zw4ufQ/fv3c9pppxEaGsrnn39OixYtjsd++uknOnToQNOmTVm9evXx50eMGMHLL79Mw4YN+fLLL6lXz9lhPDk5mSZNmnD48GGio6P5/PPPj/+9p6enc/bZZ7Nx40a2bdtGzZo1j8+3ceNGGjdunCNXn8/HyJEjmTFjBl999RUdOnTIdf5NmzbRqFEjANavX8+ZZ55JgwYNWLJkyfGcAD799FN69OhB3759efvtt/P9eyro70xiYiKrV69eba1NzHfSk+i/XiIiIv6ERUC/56Hz3YHHLJsIb42CjNy3G4iUqNQ9sH5B8eZYv8CZp4yYMWMGycnJjB8/PkchANCyZUuuu+46vv32W37++edcr/3nP/+Z40N3lSpV6Nu3L2lpadx00005PkxHRkYyYMAAjh49evzDdraTCwGAkJAQxo4dC8DChQvzfR/PP/88GRkZPP300zlyAujatSt9+/bl/fffJyXFT7OCUqDbhERERAIxBi78O8TXh/fHgs3MPeantyFlJwx8DaITvM9RKqa9vwLFvLvD+px5SrmzULbly5cDsGbNGh544IFc8fXr1wPOt+UnFwvt2rXLNT57cXJiYu4vy7M/pG/fvj3H83v37mXChAl8+OGH/Pbbbxw6dChH/Pfffy/w+1iyZAkrV67MFd+9ezeZmZmsX7/eb25eUzEgIiKSn7ZDIa4OvD4cjqbmjm9dDlN6Oq1HqzbyPD2pgI4eyn+Ml/O4YO/evQC89NJLeY5LTc39bzA+Pj7Xc2FhYfnGMjIyjj+XnJxM+/bt2bRpE+eccw7Dhg0jISGBsLAwkpOTefrpp0lPTy/w+5gwYUKh30dpUDEgIiJSEE26w8j58OpVkLozd3zvBpjcHQa9DvVyL3AUcVVk5bI1jwuyP7SvWbOGs846y/PzT548mU2bNnH//ffnujKxfPlynn766QLNk/0+Dhw4QFxcnNtpuk5rBkRERAqqzllO69EaARbzHdrjtB5dV8x7uUXyU70pmGLuJGxCnXnKiOwdiJcuXVoq5//1118BuPLKK3PFlizx01ksgNJ+H4WlYkBERKQwqtR3NidrdL7/eEYazL4GVk7xNi+pWKIToNklxZuj+aVlap3LyJEjqVKlCuPHj2fFihW54j6fj88++6zEzp/dDejkc3z77bc8+uijBZ7nlltuITw8nNtuu+34OocTHT16tEwVCrpNSEREpLAqVYEhc+G9W+D7Obnj1gcf3A7JW6Hb/Wo9KiWj/bWw9r3ivb4MqVatGm+++SaXX345HTt2pFu3brRs2ZKQkBC2bt3K8uXL2bt3r9/NwtwwbNgwJkyYwK233srixYs5/fTT2bBhA/PmzeOKK65gzhw//9b9aNasGVOnTmXUqFG0bNmSXr160bRpUzIyMti6dStLly6lRo0a/PLLLyXyPgpLxYCIiEhRhEXA5S86nYaWPuZ/zJdPwYHt0G8ShEV6m5+Uf6deAI27wcZPCv/axt0CX90qRd26deP777/nscceY+HChSxdupSIiAjq1q1L165d/d7C45a6deuydOlSxo0bxxdffMHChQtp1qwZkyZNonv37gUuBgCGDBlC69atefzxx1m8eDEfffQRMTEx1K1bl/79+zNgwIASex+FpU3HSog2HRMRqUC+mQYf3OG/9ShAw/Ng4EyoVNXbvKRMcX3TMYDDyc46lV0/Fvw1tVrBiHnOFS4p07TpmIiISDBoNxKumQ3hMf7jW76AKRc5tw2JuKlSFRj5ITTpUbDxTWddoMQAACAASURBVHrAyA9UCMhxKgZERETc0LSn86Gsci3/8aR1TuvRHd95m5eUf1HxMPgNGLkAzrwSQsJzxkPC4cz+TnzwG854kSxaMyAiIuKWum1g9MfOXgRJ63LHU3fBtN5w1XSneBBxizHQsJNzHDkA+36D9FRnH4GE01QASEC6MiAiIuKmqg1h9EJnnYA/GYdg1kBnnYFISYiKh7pnw6nnO48qBCQPKgZERETcVqkqDJ3r3Jrhj82EebfCJw+CGnmISClSMSAiIlISwiLhipfgvNsCj1n6OMy9Ho4d9S4vEZETBGUxYIw5xRgz1RizwxiTbozZbIx5yhhTqJ5txpjzjDHvZr3+iDFmqzHmQ2NMr5LKXUREKpCQEOj+AFzyBJgA/5f7w+sw8wqnRaSISBav2v8HXTFgjGkMrAJGAiuAJ4HfgLHAcmNMtQLOcxOwFOiW9fgksAToDMw3xtzrfvYiIlIhtR8NA2dBeLT/+OalMLUXJG/zNi/xlDEGAJ/PV8qZSDDILgayf29KStAVA8AkoCYwxlrbz1o7zlrbFefD/BnAv/KbwBgTDjwKHAESrbVDrbX3WGuHAu2AdOBeY4y2ixQREXec0QtGfAAxNfzH96x1Wo/+8b23eYlnIiOdjxWHDh0q5UwkGGT/nmT/3pSUoCoGjDGnAT2BzcBzJ4XvBw4BQ40xAXZ9OS4BiAfWW2tz9H6z1q4F1gOVgMoupC0iIuKo1xauXQTVTvcfT90J0y6GXxd5m5d4IjY2FoCdO3eSkpKCz+fz7FYQCQ7WWnw+HykpKezcuRP48/empATbPgNdsx4/stbmuMZmrU0xxnyJUyx0BD7JY57dwB6gqTHmdGvthuyAMaYpcDrwnbV2r6vZi4iIVG0Eoz+C2YNg6/Lc8aOp8OrV0OcpaDvM8/Sk5CQkJHDo0CHS0tLYvn17aacjQSA6OpqEhIQSPUdQXRnAuQ0InG/u/cn+UN80r0msU4b/Fef9rzLGvGyMedQYMwNnPcJPwFUFScgYs8rfATQryOtFRKQCik6Aoe9Ay8v9x20mvPd/8Om/1Hq0HAkJCaF+/frUqFGDqKioEr8XXIKTMYaoqChq1KhB/fr1CQkp2Y/rwXZlIHvXjAMB4tnPV8lvImvtG8aYHcAs4MSvXnYB03AWJYuIiJSM8Ci4cirE14dlE/2P+fy/cGAb9JkIYRHe5iclIiQkhOrVq1O9evXSTkUECL4rA/nJLrHz/RrFGDMEWITTSag5EJ31+AnwLDC7ICe01ib6O4BfivIGRESkAgkJgZ4PQe/HArceXTMLXu0PRwJ9DyYiUnTBVgxk/5cw0L7acSeN8ytrXcBUnNuBhlprf7HWHrbW/gIMxblV6CpjTJfipywiIpKPc66DAa9CWCX/8U1LnNajB3SfuYi4K9iKgezOP4HWBGS3Zwi0piBbTyAcWOJnIbIP+Dzrx8SiJCkiIlJozXo7rUejA9w+svtnmNwDdv7gbV4iUq4FWzGwOOuxpzE5r6caY2KBc4HDwFf5zJPdsDVAs+fjz2t/eBER8c4piVmtR5v4j6fsgKkXw8ZPvc1LRMqtoCoGrLUbgY+ARjjdgE40HogBZlhrj+/mYYxpZow5ubPP0qzH/saYs04MGGPaAP1x1h3ov7YiIuKthFNh9MdQv4P/+NEUePUq+PZVb/MSkXIpqIqBLDfj7BMw0RjzTlZL0E+B23BuD7r3pPFrs47jrLUrcDoGVQJWGmNmG2P+Y4yZA3wNRAFPW2t/KuH3IiIiklt0Agx7F1pc5j/uOwbv3gyf/VutR0WkWIKuGMi6OtAOmA50AO4AGgMTgU6F2ChsNDASWA5clDVPD+AL4Bpr7W3uZi4iIlII4ZWg/3TodEvgMZ89Cu/eApkZnqUlIuVLsO0zAIC1dhvOB/mCjPW7o0fWxmPTsw4REZGyJyQELvqXsxfBgnH47Zz93Uw4+DtcPQOi4nLHRUTyEHRXBkRERCqcjjfCgFcgLMp//LfFMK03HNzhbV4iEvRUDIiIiASD5n1g+DyIruY/vusHmNwddmm5m4gUnIoBERGRYFG/vdNpKOE0//GDvzubk/32madpiUjwUjEgIiISTKo1htGL4JRz/MfTD8LMK+G7Wd7mJSJBScWAiIhIsImpBsPfg2aX+o/7jsE7N8KSCWo9KiJ5UjEgIiISjMIrOR2EOtwUeMzih+H9MWo9KiIBqRgQEREJViGhcPG/4aJHAb+dtGH1DJg1ENJTPE1NRIKDigEREZFg1+lmuPrlwK1Hf10E0y6Gg394m5eIlHkqBkRERMqDFpfBsPegUoL/+M4fYEoP2L3W27xEpExTMSAiIlJeNOjgtB6t2sh//MA2mHIRbPrc07REpOxSMSAiIlKeVG/itB6tl+g/nn4AXrkCvn/D27xEpExSMSAiIlLeVK7h7FZ8xiX+474MmHstLH1crUdFKjgVAyIiIuVRRDQMeAXOuT7wmE8ehHm3QeYx7/ISkTJFxYCIiEh5FRIKF/8Xej4ceMyqaTD7GkhP9S4vESkzVAyIiIiUZ8bAX/4PrpoOoZH+x2z4CKb3hpRdnqYmIqVPxYCIiEhF0PJyGPYuVKrqP/7HGpjcHfas8zYvESlVKgZEREQqioadnNajVRr6jx/Y6uxFsPlLb/MSkVKjYkBERKQiqX46XLsI6p7tP37kALzSD35409u8RKRUqBgQERGpaCrXhBEfQNNe/uOZR+Gt0fDFU2o9KlLOqRgQERGpiCJiYMCr0G504DGL7ocP7lDrUZFyTMWAiIhIRRUaBpc8Dt3HBx7zzRSYMwSOHvIuLxHxjIoBERGRiswYOO9WuHIKhEb4H7N+Pky/FFJ3e5ubiJQ4FQMiIiICrfrD0HcgKt5/fMdqp/Vo0gZv8xKREqViQERERByNznVaj8Y38B9P3uK0Ht2y3Nu8RKTEqBgQERGRP9U4w2k9Wqe1//jh/TDjMvhxrrd5iUiJUDEgIiIiOcXWghEfwuk9/ccz0+HNkfDlRLUeFQlyKgZEREQkt8jKMHAWJI4IPObjf8L8u8CX6VlaIuIuFQMiIiLiX2gYXPoUdLsv8JgV/4M5Q+Fomnd5iYhrVAyIiIhIYMbA+XfAFS9BSLj/Mes+gJcvhdQ93uYmIsWmYkBERETyd9bVMHQuRAZoPfr7KpjSHZJ+9TYvESkWFQMiIiJSMKdeAKMXQtwp/uP7NzutR7d+7WlaIlJ0KgZERESk4Go2d1qP1m7lP354H8zoCz+/621eIlIkKgZERESkcOLqwMj50Lib//ixI/D6cFg+ydu8RKTQVAyIiIhI4UXGwqA50HZYgAEWFt4D88ep9ahIGaZiQERERIomNBz6TISu/wg85uvn4Y3hkHHYu7xEpMBUDIiIiEjRGQMX/A0ufxFCwvyPWfs+vNwHDiV5m5uI5EvFgIiIiBRf64Ew5C2IjPMf377S6TS0d6O3eYlInlQMiIiIiDtO6wKjFkBcPf/xfb85BcG2lV5mJSJ5UDEgIiIi7qnV0mk9WutM//G0vc5uxWvf9zYvEfFLxYCIiIi4K66u03r0tAv9x48dgTlD4esXvc1LRHJRMSAiIiLui4qDwW9AmyEBBliYfxcsvBd8Pk9TE5E/qRgQERGRkhEaDpc9C13uCTxm+bPw5gjIOOJZWiLyJxUDIiIiUnKMgS7j4LJJgVuP/vwuzLgM0vZ5m5uIqBgQERERD5w92LltKCLWf3zbV06noX2bvM1LpIJTMSAiIiLeaNzVaT0aW9d/fO+vMLk7bF/lbV4iFZiKAREREfFO7TOd1qM1W/qPpyXB9Evglw+9zUukglIxICIiIt6Krwej5sOpnf3Hjx2GOYNhxUve5iVSAakYEBEREe9FxcPgN6H1Nf7j1gcf3gkf/VOtR0VKkIoBERERKR1hEdDveeh8d+AxyybCW6PVelSkhKgYEBERkdJjDFz4d+j7DJhQ/2N+mguvXK7WoyIlQMWAiIiIlL62w2Dw6xBR2X986zKY0hP2b/Y0LZHyTsWAiIiIlA1NusPID6Fybf/xvRtgcg/4fbW3eYmUYyoGREREpOyo09ppPVqjuf/4od1O69F1C7zNS6ScUjEgIiIiZUuV+s7mZI3O9x/PSIPZ18A3U73NS6QcUjEgIiIiZU+lKjBkLpw1wH/c+mDebbDoAbUeFSkGFQMiIiJSNoVFwOUvwvl3Bh7zxZMw9zo4lu5dXiLliIoBERERKbuMgW7/hEufCtx69Mc34ZUr4PB+b3MTKQdUDIiIiEjZ124kXDMbwmP8x7d8AVMuguSt3uYlEuRUDIiIiEhwaNozq/VoLf/xpHUwuTvs+M7bvESCmIoBERERCR5128Doj6H6Gf7jqbtgWm/Y8LG3eYkEKRUDIiIiElyqNoTRC6Hhef7jGYfgtQGwarqnaYkEIxUDIiIiEnwqVYWhc+HMK/3HbSa8PxY+eQis9TY3kSCiYkBERESCU1gkXDEZzr018Jilj8HbN8Cxo97lJRJEVAyIiIhI8AoJgR7j4ZInwAT4WPP9HJh5BRxO9jY3kSCgYkBERESCX/vRMHAWhEf7j29eClN7wYHt3uYlUsapGBAREZHy4YxeMOIDiKnhP75nrdN69I/vvc1LpAxTMSAiIiLlR722cO0iqHa6/3jKHzDtYvh1kbd5iZRRRS4GjDE+Y0xmEY5jbr4BERERkRyqNoLRH0GDTv7jR1Ph1ath9QxP0xIpi8KK8drPgZN7dVUFzgJ8wDZgJ1AbqI9TeHwP7C/GOUVERETyF50AQ9+Bd26En97OHbeZ8N7/QfI2uPDvYIz3OYqUAUUuBqy1XU782RhTB1gGzAX+Zq3ddELsVOAx4GygV1HPKSIiIlJg4VFw5VSIrw/LJvof8/l/4cA26DMRwiK8zU+kDHBzzcB/gP3W2v4nFgIAWT/3Bw5kjRMREREpeSEh0PMh6P1Y4Naja2bBa1fBkQPe5iZSBrhZDFwELAwUtNbarLiuDIiIiIi3zrkOBsyEsEr+4799BlMvhgO/e5qWSGlzsxiIBeLzGROfNU5ERETEW80ugRHzILq6//jun5zWozt/9DYvkVLkZjGwFhhgjKnvL2iMaQgMAH528ZwiIiIiBXdKO7j2Y0ho7D+essPZnGzjp97mJVJK3CwGJuB0E/rWGHOfMaaLMaZ51uP9wCqcKwMTXDyniIiISOEknObsRVC/g//40RR49Sr49lVv8xIpBcVpLZqDtXZ2VkehR4H7TwobIAO401o7x61zioiIiBRJdAIMexfevgF+fjd33HcM3r0ZDmyHznep9aiUW67uQGytfRI4A7gPeBv4NOvxH0DTrLiIiIhI6QuvBP2nQ6dbAo/57BF49xbIzPAsLREvuXZlIJu1dgvwL7fnFREREXFdSAhc9C9nL4IF48i9nyrw3Uw4+DtcPQOi4jxPUaQkuXplwCvGmFOMMVONMTuMMenGmM3GmKeMMVWLMFcrY8wMY8y2rLl2G2OWGGOGlUTuIiIiUgZ1vBEGvAJhUf7jvy2Gab3h4A5v8xIpYa4WA8aYEGPM/xljvjLGHDDGHDshdrYxZpIxpmkxz9EYZzHySGAF8CTwGzAWWG6MqVaIuUYA3wL9gKXA48CbOGscehcnTxEREQkyzfvA8HkQHeCjxK4fnNaju37yNi+REuTabULGmAhgPtAF2AekAJVPGLIJGAXsIfcC48KYBNQExlhrnznh/E8At+HconRjAfLtCEwGfgR6WWt3nhQPL0aOIiIiEozqt4fRH8Or/WHfb7njB393Wo8OmAmndfY+PxGXuXll4G/AhcB4oBbOB+3jrLXJwOc4OxUXiTHmNKAnsBl47qTw/cAhYKgxJqYA0/0XCAWGnFwIZOWrlUIiIiIVUbXGTkFwSnv/8fSDMPNKWDPb27xESoCbxcBg4Etr7YPWWh9+V+CwCWhQjHN0zXr8KOscx1lrU4AvgWigY16TGGNOAc4HvgF+MsZcaIy50xhzhzGmmzEmKNdSiIiIiEtiqsOw96DZpf7jvgynLennE8D6+8gjEhzc7CZ0KvBBPmP2AQnFOMcZWY/rA8Q34Fw5aAp8ksc82aX+Bpz2p11Oiv9gjLnCWvtrfgkZY1YFCDXL77UiIiJShkVEOx2EFt4LXz/vf8ynD0PyVrjkCQjVHcYSfNz8BvwwUCWfMQ2A5GKcIz7r8UCAePbz+eVRM+vxaqA5cEXW3E2AV4BWwAdZ6yBERESkogoJhYv/DRc9itNfxI/VM2DWQEhP8TQ1ETe4WQx8B/QM9AHaGBOPs15ghYvnzHWarMf8rteFnvB4rbX2bWvtQWvtRmA4zu1DTYEr8zuhtTbR3wH8UsT3ICIiImVNp5vh6pcDtx79dZHTejQl1zJEkTLNzWLgJaA+8KoxJseOHMaYKsB0oCrwQjHOkf3Nf3yAeNxJ4wLZn/WYDnx4YsBaa4HsfcnPKWyCIiIiUk61uMxZR1ApwB3PO793Wo/u1veBEjxcKwastbOAaTjfpu8BbgIwxnwD/AFcBkyy1n4YcJL8rct6DLRXwelZj4HWFJw8T8rJC5GzZBcLlQqRm4iIiJR3DTo4nYaqNvIfP7ANpvSETUs9TUukqFztmmOtHY2zl8DPQA2c23baAr8Co621/1fMUyzOeux5cscfY0wscC7O2oWv8pnneyAJqG6MqeUnfmbW4+aipyoiIiLlUvUmMHoR1Ev0H08/AK9cDt+/4W1eIkXgegtNa+10a+3ZOBuOnQLEWmtbWWunuTD3RuAjoBHw15PC44EYYIa19lD2k8aYZsaYHJ19rLXHgBezfvzviYWFMaYVMAI4hrMbsYiIiEhOlWs4uxWfcYn/uC8D5l4LSx9X61Ep09zcgTjkxFturLWHcb6ld9vNwDJgojGmG7AW6ICz4dl64N6Txq/NTvGk5x8BugHDgFbGmM9wrmZcCUQBdxSktaiIiIhUUBHRMOAVmH83rHzJ/5hPHoTkbdD7MQh1s6O7iDvcvDKw3RjzH2NMSxfnzCXr6kA7nAXJHYA7gMbARKCTtXZvAedJwykGxuNsVPZXoC9OodHbWvuE68mLiIhI+RISCr0nQM+HA49ZNQ1mXwPpqd7lJVJAxrp06coYsx+ny48FVuN8WJ9d0A/n5Y0xZlXbtm3brloVaE8yERERKVd+nOvsSpx51H+8ThsY9DrE+luuKFJ0iYmJrF69enVWe/tCcfPKQC1gILAAaIPzTf3vxpi3jDF9jTGheb5aREREJJideQUMexeiAux9+sd3TuvRPev8x0VKgZutRY9aa1+31l6Cs3D4Lpx7+C8H3gb+MMY8aYw5261zioiIiJQpDf/itB6t0tB//MBWp/Xo5i+9zUskANe7CQFYa3dZax+31p4FJALPAD5gLLCyJM4pIiIiUibUaArXLoK6Ab7/PJIMr/SDH9S0UEpfiRQDJ7LWfgs8B7wMZJC7q4+IiIhI+VK5Joz4AJr28h/PPApvjYYvn1brUSlVJVYMGGPijTE3GGOWAb8Ad+K0Gp1aUucUERERKTMiYmDAq9BudOAxH98HH94Jmce8y0vkBK42vM3avKsXMBzoA0TidBf6BKe70Fxr7RE3zykiIiJSZoWGwSWPQ5UGsOh+/2NWToYDv0P/KU4BIeIh164MGGMeA34H3geuArYC/wAaWWt7WmtfUyEgIiIiFY4xcN6tcOUUCI3wP2b9fJh+KaTu9jY3qfDcvE3odpyde18CzrXWNrPWPmqt3e7iOURERESCU6v+MPRtiIr3H9+x2mk9mrTB27ykQnOzGBgE1LbW3mitXe7ivCIiIiLlQ6PznNaj8Q38x5O3wJQesEUfpcQbbu4zMNtam+7WfCIiIiLlUo0z4NqPoU5r//HD+2HGZfDT297mJRVSibcWFREREZGTxNaGER/C6T39xzPT4Y0RsOwZtR6VElXkYsAY4zPGHDPGND3h58wCHOqdJSIiIhJZGQbOgsQRgcd89A+Yfzf4Mj1LSyqW4rQW/RynbWjaST+LiIiISEGEhsGlTzmtRz950P+YFS/Cwd/hipcgItrb/KTcK3IxYK3tktfPIiIiIlIAxsD5d0B8fXjnZvBl5B7zyzx4uQ8MmgMx1b3PUcotrRkQERERKQvOuhqGzoXIAK1Hf//GaT26d6O3eUm55uamY/8xxjR3az4RERGRCufUC2D0Qog7xX98/yanINj6tbd5Sbnl5pWBvwE/GmNWGGP+aoxJcHFuERERkYqhZnO4dhHUbuU/fngfzOgLP7/rbV5SLrm96dhC4GxgIrDDGPOmMaaPMSbUxfOIiIiIlG9xdWDkfGjczX/82BF4fTgsn+RtXlLuuL3pWG/gFOBuYANwBfAOTmHwhDGmjVvnExERESnXImOdBcNnDw0wwMLCe2D+OLUelSJzfQGxtXaXtfYxa20rIBF4FjDArcAqY8x3bp9TREREpFwKDYe+z8CF/wg85uvn4Y3hkHHYu7yk3CjRbkLW2m+ttWOBujhrCo4BAW6AExEREZFcjIHOf4N+L0BIgK7wa9+Hl/vCob3e5iZBr0SLAWNMvDHmemAJ8F8gHDhYkucUERERKZfaXAND3oLIOP/x7StgilqPSuG4XgwYY0KMMRcbY2YDfwDPAx2AT4ChOFcJRERERKSwTusCoxZAXD3/8X2/wZQesP0bL7OSIObmPgOtjDETgO3APOBqYCvwT6CRtbantfZVa61uaBMREREpqlotndajtc70H0/bC9MvhbXzvM1LgpKbVwbWAHcAlYDJwLnW2mbW2kestdtdPI+IiIhIxRZX12k9etqF/uPHDsOcIfD1i97mJUHHzWLgI5y9Bmpba2+w1i53cW4REREROVFUHAx+A9oMCTDAwvy7YOG94PN5mpoEDzeLgdeA/dbadBfnFBEREZFAQsPhsmehyz2Bxyx/Ft4cCRlHvMtLgoabxcBUoJeL84mIiIhIfoyBLuPgskmBW4/+/A7MuAzS9nmbm5R5bhYDO12eT0REREQK6uzBMOh1iIj1H9/2ldNpaN+mnM8fTXPake5eCwf/AGtLPlcpMwKUj0WyALjQGBNirdWNaSIiIiJea9INRs2HV6+ClD9yx/f+CpO7w6A5kJEGKyfDLx+A79ifY+LrQ+JwaDscKtf0LncpFW5+k38vEAtMMcZUd3FeERERESmo2q2c1qM1W/iPpyU5Vwhe7gM/v5uzEAA4sA0+fRieaAFfPKkrBeWcm1cGZgEHgGHAQGPMZpxbh07+DbLW2m4unldEREREThR/irM52ZyhsGlJ7nhBbuLwZcCiB+DgDrj4v87aBCl33CwGupzw50jgjKzjZCovRUREREpaVDwMfhPeHwNrZhV9nhX/g6qnQqeb3ctNygzXbhOy1oYU8Ah165wiIiIikoewCOj3PHS+u3jzLP4XpKe4k5OUKer+IyIiIlKeGQOdx0F0MZZ0Hk2F7+e4l5OUGSoGRERERMq7LV84C4eLY9XL7uQiZYprawaMMRcUdKy19nO3zisiIiIi+fhjTfHn2PUj+HwQou+SyxM3FxB/RsEXB2vdgIiIiIhX3Ljf3/qc24Wi4oo/l5QZbhYDD+K/GKgCtAf+ArwPrHbxnCIiIiKSn/BKLs0T7c48Uma4VgxYax/IK26MGQE8g7M5mYiIiIh4JaFx8eeo0gBC3fweWcoCz276stZOB5YDj3h1ThEREREBTu8JlaoWb47Wg9zJRcoUr1eArAEKvNBYRERERFwQHgVnDy36600oJA53Lx8pM7wuBurj7joFERERESmIjjdBZHzRXps4HOLqupuPlAmeFAPGmFBjzLVAf+AbL84pIiIiIieIqwsDXoHQiMK9ruG50OvfJZOTlDo39xn4LY9z1Mp6PAr83a1zioiIiEghnNYZhsyF14fC4f35j292KVzxPwiLLPncpFS4ectOCP5bi2YAPwArgGestWtdPKeIiIiIFMap58Mtq2D1y/DNNDiw9aQBBpr2gvbXQpNuYEyppCnecLO1aCO35hIRERGREhRTDc6/Hc4dC9tWwIFtcOwIRFWBeokQX6+0MxSPlPhiXmNMdeB8IA1YZK3NLOlzioiIiEgBhIRCw05Ap9LOREqJawuIjTE3GWO+NsYknPBcIrAWeBP4EFhmjIlx65wiIiIiIlJ0bnYTGgBYa+2+E56bAFQFpuEUA+2BG108p4iIiIiIFJGbxcDpwPfZP2TdHtQZmGKtvdZa2wdYCWj7OhERERGRMsDNYqAasPuEn8/Nenz7hOeWAg1dPKeIiIiIiBSRm8XAPqD6CT93BnzAshOes0CUi+cUEREREZEicrMYWAv0McZUM8ZUwVlDsNJae/CEMY2AnS6eU0REREREisjNYuBpoA6wHdgG1AYmZQeNMaHAecAaF88pIiIiIiJF5OamY+8ZY24Ers966lVr7cwThnTHuUVooVvnFBERERGRonN10zFr7f+A/wWILcRpMyoiIiIiImWAm7cJiYiIiIhIEFExICIiIiJSQakYEBERERGpoFQMyP+3d9/hVdb3/8df7+zBJuyVsAREkKFCEmWpde/RpQLa1tGqre1XsXv9lNZW3LUVVGqdta46yxBJAJWpCMhKWGGFlRBCQnI+vz/OQQImOYeQnJOT+/m4rl53c879uc9brwvD67w/AwAAAB5FGAAAAAA8ijAAAAAAeBRhAAAAAPAowgAAAADgUYQBAAAAwKMIAwAAAIBHEQYAAAAAjyIMAAAAAB5FGAAAAAA8ijAAAAAAeBRhAAAAAPAowgAAAADgUYQBAAAAwKMIAwAAAIBHEQYAAAAAjyIMAAAAAB5FGAAAAAA8KirDgJl1NbNpZlZgZmVmlm9mU8ys9Qk88ywzqzQzZ2Z/qM96AQAAgMYoLtIFHC8z6yVpnqT2kt6QtErS6ZLukHSemWU553Yd5zObS3pW0gFJzeq3YgAAAKBxisbOwOPyB4HbnXOXOefucc6NlfSgpJMkNvKrJgAAIABJREFU/bEOz3xIUktJ99VfmQAAAEDjFlVhwMx6SjpXUr6kx455+9eSSiRdZ2apx/HMSyVNkHS7pIL6qRQAAABo/KIqDEgaG7h+4JzzVX3DOVcsKVdSiqQRoTzMzNpL+oek151zz9VnoQAAAEBjF21rBk4KXFfX8P4a+TsHfSXNDOF5f5c/EN1c14LMbFENb/Wr6zMBAACAcIi2MNAycN1Xw/uHX28V7EFmNlHSpZKudc5tr4faAAAAgKgSbWEgGAtcXa03maVLmiLpFefcyyfygc65YTV8xiJJQ0/k2QAAAEBDirY1A4e/+W9Zw/stjrmvJtMklUq6tT6KAgAAAKJRtIWBLwPXvjW83ydwrWlNwWFD5d+edGfgkDFnZk7S04H3fx547fUTKxcAAABovKJtmtDswPVcM4upuqNQ4OCwLPm/8V8Q5DnT5d916Fh9JJ0laamkRZKWnHDFAAAAQCMVVWHAObfOzD6Qf8eg2yQ9UuXt30pKlfSkc67k8Itm1i8wdlWV59xe3fPNbLz8YeBt59wv6v0fAI1epc8pr7BEew6UKzbG1KFFkjq3TJKZBR8MAAAQZaIqDATcKmmepIfNbJyklZLOkDRG/ulBPz/m/pWBK3+bQ412FB3UC59s0gufbNS2ooNHvTewSwtdN6KHLhncRckJsRGqEAAAoP5F25oBOefWSRou6Rn5Q8BdknpJeljSSOfcrshVh2j0z/n5yv7TbD04Y/XXgoAkLd9SpLtf/Vyj/jxbn+bvDn+BAAAADSQaOwNyzm2SNCHEe0PuCDjnnpE/ZMAjHpqxRg/OCLbe3G9HcZm+89THeur64Tqrb7sGrgwAAKDhRV1nAKgvby0rCDkIHFZe4dMtzy1SXmFJ8JsBAAAaOcIAPKnS5zT5vVXBb6xGSXmlHp21tp4rAgAACD/CADxpzuod2ryntM7j3/qsQHtKyuuxIgAAgPAjDMCTXvp00wmNL6/w6Y2lW+qpGgAAgMggDMCTvtxWfMLPWFUPzwAAAIgkwgA8aX9ZZT08o6IeKgEAAIgcwgA8KaUeDg/7YkuRVm4tqodqAAAAIoMwAE/q3b7ZCT8jb1eJzn9orr79jwWauXK7fD5XD5UBAACET1QeOgacqKuGddWsVTvq5Vnz1u3SvHW7lJGWqglZ6bpyaFelJvJHCwAANH50BuBJ5wzooA4tEuv1mXmFJfrVG19o5H0zdd+7K1Wwt+5blwIAAIQDYQCeFB8bozvP7tsgzy46WKEn56zXmX+arR8+v1hLNu5pkM8BAAA4UYQBeNY3T+umCVnpxzXGJH3r9G7q1S416L2VPqf/frZVlz8+T5c/nqv/flagikpf3YoFAABoAExshmeZmX510QC1Sk7QQzNXK9j63+ZJcXrom6dqbL8O8vmc5qzZqWk5eZq7pjDoZy3ZuFc/fH6JOrdM0g2Z6frm6d3VMjm+nv5JAAAA6sacYweUhmBmi4YOHTp00aJFkS4FIVi3c7/+OX+DXl20WcXHnB/QtXWyvjuih64Z3k1tUhO+Nnb19mJNy8nTf5ZsUXlFaN/8pyTE6uphXTU+K0MZacG7DAAAADUZNmyYFi9evNg5N+x4xxIGGghhIDqVlldqecE+7S4pV1yMqUOLJPXv1EKxMRZ07K79ZXr+442avmCDdhaXhfR5ZtK4fu01MTtDI3u2lVnwzwEAAKiKMNAIEQa8q6yiUm9/tlVTc/L0RUHoh5L179RCE7PSdcmpnZUYd+KHogEAAG84kTDAAmKgniXGxeqKoV313x9l68Xvj9A5AzoolC/8V24t0s/+/Zmy7p+lKTNWq3B/aN0FAACAumIBMdBAzEwjerbViJ5ttWFXiZ7OzdcrCzeppLyy1nGF+8s1ZcYaPf7hOl12amdNzM5Qv44twlQ1AADwEjoDQBj0aJuq31xysubfO06/uLC/urRKDjqmvMKnlxdu1nlT5uo7Ty3QrFXb5Qu25REAAMBxoDMAhFGLpHjddGZPjc9M1wcrtmtaTp4Wbgh+KFnu2l3KXbtLPdNSNSErXVcO66qUBP74AgCAE0NnAIiAuNgYXXBKJ/37lky9fluWLhncWXEh7Fi0vrBEv3zjC434fzN137srVbC3NAzVAgCApoowAETYqd1a6eFvDdHcu8foltG9QjqMrOhghZ6cs15n/mm2fvTCEi3ZGLy7AAAAcCzCANBIdGqZrLvP66f5k8bqD5cNVM92wQ8jq/Q5vbWsQJc/Pk9XPJ6rtz/bqorK0A4+AwAAYNIx0MikJMTpuyN66Nund9ec1Ts1LTdPc9cUBh23eONeLX5+sbq0StYNmT107WndQ+oyAAAA7yIMAI1UTIxpTL/2GtOvvb7cVqxpOXl6bekWlVfU/s3/lr2l+n/vrNKUGWt09bCumpCVofS04F0GAADgPUwTAqLASR2ba/JVgzT/nrH6yTl9ldYsMeiYA+WVenb+Bo35y4e66dmFmreuUJw4DgAAqqIzAESRts0Sdfu4PvrBqJ7677KtmpqTpxVbi2od45w0Y+V2zVi5XQM6tdDE7AxdPLiTEuNiw1Q1AABorOgMAFEoMS5WVw7rqrdvz9YL3xuhcwZ0kAXfmVQrthbpp68sU9b9s/XQjDUq3F/W8MUCAIBGi84AEMXMTCN7tdXIXm2VX1iiZ+bl6+WFm3SgvLLWcYX7y/TgjNV67MO1uuzUzpqYnaF+HVuEqWoAANBY0BkAmoj0tFT95pKTNX/SOP38gv7q0io56JjyCp9eXrhZ502Zq+8+9bFmrdoun491BQAAeAWdAaCJaZkcr++d1VMTstL1wYrtmpqTp0Ubgh9KlrO2UDlrC9WzXaomZGXoyqFdlJLAfyIAAGjK6AwATVRcbIwuOKWTXr0lU6/flqVLBndWbEzwhQXrd5bol68v18j7Zun+d1dp677SMFQLAAAigTAAeMCp3Vrp4W8NUc7dY3TzqF4hHUa2r/SQ/jZnnbInz9aPXliipZv2hqFSAAAQToQBwEM6tUzWPef30/xJY/X7ywaqZwiHkVX6nN5aVqDLHsvVlU/M09ufbVVFZe0HnwEAgOjAhGDAg1IS4nTdiB76zundNWf1Tk3NyVPO2sKg4xZt2KNFG/aoS6tkjc9M1zWndQupywAAABonwgDgYTExpjH92mtMv/Zata1IT+fk67WlW1ReUfs3/1v2luqP76zUlBmrdfXwbhqfma70ELoMAACgcWGaEABJUr+OLTT5qkGad89Y/fjsvkprlhh0TEl5pZ6Zl68xf/lQNz27UPPX7ZJzbE0KAEC0oDMA4ChpzRJ1x9l9dPPonnpr2VZNzcnTyq1FtY5xTpqxcrtmrNyuAZ1a6MbsDF00uJMS42LDVDUAAKgLOgMAqpUYF6urhnXVO7dn64XvjdDZ/TvIgu9MqhVbi3TXK8uUPXm2Hp65Rrv2lzV8sQAAoE7oDAColZlpZK+2GtmrrfIKS/RMbp5eWbRZB8orax23s7hMf/3faj06e60uP7WLJmZn6KSOzcNUNQAACAWdAQAhy0hL1W8vHaj5k8bp3gv6qUur5KBjyit8emnhJn1jykf67lMfa/aqHfL5WFcAAEBjQGcAwHFrmRyv75/VSxOzMvT+F9s1NWe9Fm8MfihZztpC5awtVM92qZqQlaErh3ZRSgL/GQIAIFLoDACos7jYGF04qJP+c2uWXrs1UxcP7qzYmOALC9bvLNEvX1+ukffN0uT3VmnrvtIwVAsAAI5FGABQL4Z0b61HvjVEc/9vjH4wqqdaJAX/xn9f6SE98eE6nTl5tm5/YYmWbQreXQAAAPWHMACgXnVulaxJ5/fXgnvH6feXnqyeIRxGVuFzenNZgS59LFdXPjFP73y+VRWVtR98BgAAThyTdQE0iJSEOF03Ml3fOaOHPly9Q1Nz8pS7dlfQcYs27NGiDXvUpVWyxmem69rTu6lFUnwYKgYAwHsIAwAaVEyMaWy/Dhrbr4NWbi3S07l5en1pgcorav/mf8veUv3xnZWaMmO1rh7eTROy0tWjbfAuAwAACB3ThACETf9OLfSnqwZr3j1j9eOz+yqtWULQMSXllXpmXr5GP/Chvjd9oRas3yXn2JoUAID6QGcAQNilNUvUHWf30c2je+rNpQWampOnVduKax3jnPS/Fdv1vxXbdXLnFpqYlaGLBndSYlxsmKoGAKDpoTMAIGIS42J19fBueveOM/X8987Q2f3by4LvTKovCop01yvLlD15th6euUa79pc1fLEAADRBdAYARJyZKbNXmjJ7pSmvsETP5ObplUWbdaC8stZxO4vL9Nf/rdZjs9fq8iFdNCErQyd1bB6mqgEAiH50BgA0KhlpqfrtpQM1/55xmnR+P3VumRR0TFmFTy9+uknfmPKRrpv6sWZ/uUM+H+sKAAAIhs4AgEapZUq8fjCql27MztB7X2zT1Jw8LdkY/FCyuWsKNXdNoXq1S9WErAxdObSrkhNYVwAAQHXoDABo1OJiY3TRoM567dYs/efWTF00qJNiY4IvLFi3s0S/eH25Rt4/U5PfW6Vt+w6GoVoAAKILnQEAUWNo99Ya+u3W2rK3VNPn5+uFjzeq6GBFrWP2HjikJz5cp398tF4XDuqkiVkZGtytVXgKBgCgkaMzACDqdGmVrEnn99f8SeP0u0tPVkZa8MPIKnxObywt0KWP5eqqJ+bp3c+3qpJ1BQAAj6MzACBqpSbG6fqR6fruGT00+8sdmpqTp3nrdgUdt3DDHi3csEddWydrfGa6rjmtm1okxYehYgAAGhfCAICoFxNjGte/g8b176AVBUV6OjdPbywtUHmlr9Zxm/eU6g9vr9SD/1utq4d304SsdPVoG7zLAABAU8E0IQBNyoDOLfTnqwcr956xuvPsPkprlhB0TEl5pZ6Zl6/RD3yo709fqAXrd8k5phABAJo+OgMAmqR2zRN159l9dfOoXnpzWYGm5eRp1bbiWsc4J32wYrs+WLFdJ3duoRuzM3TRoM5KiON7EwBA08RvOABNWlJ8rK4Z3k3v3nGmnr/pDI3r1z6kcV8UFOknLy9T1uRZemTmGu0uKW/gSgEACD86AwA8wcyU2TtNmb3TtH7nfj0zL1+vLNys0kOVtY7bWVymv/xvtR6dvVaXD+miidkZ6tuheZiqBgCgYdEZAOA5Pds10+8uHagFk8bpnvP7qVPLpKBjyip8evHTTTr3wY903dSP9eGXO+Rja1IAQJSjMwDAs1qmxOvmUb10Y3aG3lu+TVNz8rR0096g4+auKdTcNYXq1S5VE7MzdMWQrkpOiA1DxQAA1C86AwA8Lz42RhcP7qzXb8vSf27N1IWDOik2xoKOW7ezRD9/bblG3j9Tf3pvlbbtOxiGagEAqD90BgCgiqHdW2vot1try95STZ+Xr+c/2ajigxW1jtl74JAe/3Cd/v7Rel00qJMmZmdoUNdWYaoYAIC6ozMAANXo0ipZky7orwWTxum3l5ys9LYpQcdU+JxeX1qgSx7N1dV/m6d3P9+qStYVAAAaMToDAFCL1MQ43ZCZrutG9NCsVTs0LTdP89btCjru0/w9+jR/j7q2Ttb4zHRde1o3NU+KD0PFAACEjjAAACGIiTGdPaCDzh7QQSsKijQtN09vLi1QeaWv1nGb95TqD2+v1JQZa3T18K6akJmh7iF0GQAACAemCQHAcRrQuYUeuHqwcu4ZozvG9VHb1ISgY/aXVejp3HyNemC2vj99oT5ev0vOMYUIABBZdAYAoI7aN0/Sj8/pq1tG99Kbywo0LSdPq7YV1zrGOemDFdv1wYrtGtilhSZmZeiiQZ2VEMd3MwCA8OO3DwCcoKT4WF0zvJveveNM/eumMzS2X/uQxi3fUqSfvLxM2ZNn6dFZa7S7pLyBKwUA4Gh0BgCgnpiZsnqnKat3mtbv3K+nc/P170WbVXqostZxO4rL9MAHq/XIrLW6YmgXTczKUJ8OzcNUNQDAy+gMAEAD6NmumX5/2UDNnzRW95zfT51aJgUdU1bh0wufbNI5D36k66Z+rA+/3MG6AgBAg6IzAAANqFVKgm4e1Us3Zmfo3eXbNDUnT8s27Q06bu6aQs1dU6je7ZtpYlaGLh/SRckJsWGoGADgJXQGACAM4mNjdMngznrjtiy9ekumLjylk2Is+Li1O/br3tc+V+b9M/Xn91dp276DDV8sAMAz6AwAQJgN69Faw3q01uY9BzR9/ga98MlGFR+sqHXMngOH9NjsdXpyznpdNKiTbszuqVO6tgxTxQCAporOAABESNfWKbr3gv6aP2mcfnvJyUoP4TCyCp/T60sLdPGjObrmb/P13vKtqvSxrgAAUDdRGQbMrKuZTTOzAjMrM7N8M5tiZq1DHJ9qZt8xs+fNbJWZlZhZsZktNLO7zCz4CUIAUE+aJcbphsx0zbxrtP5x/XCN7Nk2pHGf5O/Wzc8t1qg/z9ZTc9er+OChBq4UANDUWLTtVGFmvSTNk9Re0huSVkk6XdIYSV9KynLO7QryjPMkvStpt6TZktZKaiPpYkkdA88f55yr8+RcM1s0dOjQoYsWLarrIwB42BcF+zQtJ19vLStQeaUvpDHNEuN0zfBuGp+Zru4hdBkAAE3DsGHDtHjx4sXOuWHHOzYaw8D7ks6VdLtz7pEqr/9V0o8lPemcuznIM06VdLKkV5xz5VVeby7pQ0lDJf3UOfeXE6iTMADghO0oPqjnFmzUvxZs0K4QDyWLMemcAR10Y3ZPnZbeWmYhrFQGAEQtz4QBM+spaZ2kfEm9nHO+Ku81l7RVkklq75wrqeNnfFvSvyT91zl38QnUShgAUG8OHqrUm0sLNDUnT19uLw553CldWmpidrouPKWzEuKicmYoACCIEwkD0fabYWzg+kHVICBJzrliSbmSUiSNOIHPODzptvatPQAgjJLiY3XNad303p1n6rkbz9CYk9qFNO7zLfv045eWKXvyLD06a412h9hdAAB4Q7RtLXpS4Lq6hvfXyD+FqK+kmXX8jImB63uh3GxmNX3136+Onw8ANTIzZfdJU3afNK3buV9P5+bp1UVbVHqostZxO4rL9MAHq/XIrLW6YmhXTcxKV58OzcNUNQCgsYq2zsDhTbX31fD+4ddb1eXhZvZDSedJWippWl2eAQDh0qtdM/3hslM0f9JY3X1eP3VskRR0TFmFTy98slHnPPiRrp/2ieas3qlomi4KAKhf0dYZCObwKrnj/s1mZldImiJpm6QrnXMh7dFX09ysQMdg6PHWAQDHq1VKgm4Z3Us3nZmhd5dv09ScPC3btDfouI9W79RHq3eqd/tmmpiVoSuGdlFSfGwYKgYANBbR1hk4/M1/TcdutjjmvpCY2WWSXpS0Q9Jo59z6upUHAJETHxujSwZ31uu3ZurVW0bqwlM6KSaEjYTW7tive1/7XCPvm6k/v79K24vqvKsyACDKRFtn4MvAtW8N7/cJXGtaU/A1Zna1pOfl7wiMdc6tqXt5ABB5ZqZhPdpoWI822rzngJ6dl68XP9mk4rLa90XYc+CQHpu9Tn//aL0uGtRZE7MydErXmr57AQA0BdG2tWgv+Q8Iy1fNW4vGSGoXytaigW1Ep0vaImlMfXYE2FoUQGOyv6xC/164SU/Py9eGXQdCHnd6ehtNzM7QOQM6KDaUNgMAIOw8s7Woc26dpA8kpUu67Zi3fyspVdL0qkHAzPqZ2dd29jGzGyT9U9JGSWcxNQhAU9YsMU7jszI0667R+vt1wzSiZ5uQxn2Sv1s3P7dIox+Yrak5eSo+GNJyKgBAlIiqzoD0VXdgnqT2kt6QtFLSGZLGyD89KNM5t6vK/U6SnHNW5bUxkmbIH4amSdpUzUftdc5NOYE66QwAaNSWb9mnp3Pz9eayLTpUGdrvgmaJcbr2tG4an5mubm1SGrhCAEAoPHMC8WFm1k3S7+TfBrSt/NODXpf0W+fc7mPurS4MjJf0dJCP2eCcSz+BGgkDAKLCjuKDem7+Bj338caQDyWLMencAR01MTtDp6W3lhlTiAAgUjwXBqIBYQBAtDl4qFJvLN2iqTl5Wr19f8jjTunSUjdmZ+iCUzopIS6qZp8CQJPgmTUDAICGkxQfq2tP66737zxLz914hsac1C6kcZ9v2ac7X1qqM/80S4/NXqs9IXYXAACRF21biwIAGpiZKbtPmrL7pGntjv16OjdPry7erIOHfLWO215Upj+//6UembVGlw/pqhuz09W7ffPj+mznnJyTYti5CADCgmlCDYRpQgCakr0HyvX8Jxs1fd4GbTuOQ8lG9W2nidkZOqtPWo3rClZtK9JzCzZoxood2rm/TD7n1CYlQVm903TdyB4a3oM1CQBQG9YMNEKEAQBN0aFKn975fKum5eRp2ebQD3vv076ZJmZn6PIhXZQUHytJ2rK3VHf/+zPlrC2sdezJnVto8pWDNLALB6ABQHUIA40QYQBAU+ac06INezQtN0/vLd8mX4i/SlqnxOs7Z/RQdu+2uv3FpdpRXBbSuJSEWD11/XBl9k47gaoBoGkiDDRChAEAXrFp9wE9Oy9fL326ScVlFQ32Oc0S4/SfWzPVt8PxrUMAgKaO3YQAABHTrU2KfnHRAM2/d5x+ffEAdW+gw8j2l1Xo9/9d0SDPBgCvIgwAAOpFs8Q4TcjK0OyfjtbfrxumMzLa1PtnzF1TqPU7Qz8DAQBQO7YWBQDUq9gY07knd9S5J3fU8i37NC03T28tK9ChyvqZlnrHi0s0pl8HpSbEKiUxTinxsUpNjFVKQpxSE2OVHB/31c8pCbFKjo9lq1IAqAFhAADQYAZ2aam/XnOq7jmvn/65YIOe/Gi9yitqP68gmM+3FOnzLUXHNSYlITbwP39ASE2M++q11IQ4JVd57cjPR+4/HDRS4uOUkui/Jyk+hi1PAUQ9wgAAoMG1b5Gku849SZ9t3qc5q3eG/fMPlFfqQHmlpPo7HdlMSon3dydSE2KVnBD3VbfC/7M/NKQEQsTR4aJqIDk6oCTGETIAhA9hAAAQNknxTWepmnNSSXmlSsorVZ/xJsYUerfi8FSpKl2OlBoCSUIsIQPA1xEGAABh07lVcqRLaPR8Tiouq/Bv0xriOQyhiIuxo7sVVQNFNdOnvlqDUeWe1KPG+QNKQlzTCXheU+lzmr1qh95cVqDNew6ovNKnlsnxGtKtta49rZu6NdDOYGhcCAMAgLC5aFBnPZ2bf0LPaJuaIDPTgfKKwNQfhKLC51R8sELFB+v3LIj4WKt9+lM1XY3DgeSr944JKCkJsYqPJWQ0FOecnluwQX+bs15b9pZ+7f3ctbv02IdrNfak9pp0QX/1bt8sAlUiXAgDAICwGdq9lQZ0aqEVW49vAfBhrVLilXvPWCXFx0qSfD6ngxWVKimr1IHyCpWUVar0UMVXPx8ITOM5UFYRWDdQcczPlSopr1Bp4HqgzP9a6SFCRqgOVTrtKz2kfaWH6vW5CXExR099qqVbkXzUfUeCRnUBJdbjO0tVVPr0f//+TP9ZsqXW+5yTZq7aoU/yduvJ64Zx+ncTRhgAAISNmenm0b10+wtL6jR+QmbGV0FAkmJiDn8rHScpsZ6q9E+fKD10JDSUlB8JDwfK/IGitEqwKDn8Xg2B5EB5pUrKKlR2gjspeUl5hU/lFT7tPVC/ISMxLkapiXFKDrIl7dHrMWpfn5ESJdvXOuf0yze+CBoEqiouq9BN0xfq5R+M1MAuLRuwOkQKYQAAEFYXD+qkhfm7NX3+huMaN/qkdrptTK8GquposTGmZolxapZYv78mK33uSMeiuu5ElfDh/7lqoAgEjGpCyolu1+olZRU+lVXU365ShyXHB7oXx05/OtzVSDx6fcaRn6sPJKmJsUqKq9+QMWf1Tr3wycbjHnegvFJ3vbxM7915JovQmyDCAAAgrMxMv7n4ZMXFxGhabl5IY84d0EEPfXOI4qJ8HnlsjKl5UryaJ8XX63MPVfr805uOmu5UJTCUVZkidThclFXteBwJKKWHp1aVV9TbQXFeUHrIP71sV0n9PrfqOorqzshISax5fUbVn1MT4vT3j9bXuY4vtxdr/rpdTBdqgggDAICwi4kx/eriARp1UjtNy8mr8eyBId1baXxmui4e1DkqpmFESnxsjFomx6hlcv2GjPIK35GA8VVg+Hq3ovRQ1U7H0dOnjg0oJeWVqvQRMkJ15IyMyPvngg2EgSaIMAAAiJhRfdtpVN92yi8s0cxVO7SzuEw+59QmNUHZvdOYoxxhCXExSoiLUcuU+gsZzjmVV/qOdCDKqnQsyqou6A5Mhzp0zD1VuhpVg0ZJeYXIGA1rzuqdcs4xVaiJIQwAACIuPS1VN2ZnRLoMhIGZKTEuVolxsWpVj9vYO+dUVuE7Mt3p0NHrMr5ac/G16VNHOhalVe6p2tVwhAxJ/i7FwUM+JSfEBr8ZUYMwAAAAop6ZKSk+VknxsWqTmlBvz3XO6eAh39Eh4qg1FzV3K762E9UxgSQa0RRoeggDAAAANTDzn9ycnBCrtvX43KpnZNS0LuPYbkXVbWqrdj6q7j7VkGdkNEuMO2prXzQNhAEAAIAwO/qMjPrjC5yRcew0qI27D+iul5fpRGY8jT6pXb3VicaDMAAAANBExMSYUhPjlJoYJzU/8vrw9DZ65/NtmrFye52fff3I9BMvEI1OdG/YDAAAgJBMzEqv89iBXVrotPTW9VcMGg3CAAAAgAdk9k7TxKzj37WreVKc/nrNqWwp2kQRBgAAADzi5xf213dHdA/5/tYp8Xpmwunq26F58JsRlVgzAAAA4BGxMabfXzpQQ7q11hNz1mntjv3V3hcfazp/YCf97BsnqVubejwQAo0OYQAAAMBDzExXDuuqK4Z20YL1u/Xmsi3avKdUZRU+tUyO15DurXT1sG5q1zwx0qUiDAgDAAAAHmRmGtmrrUb2qs8TFBBtWDMAAAAAeBRhAAAAAPAowgAAAADgUYSCBBweAAAJFklEQVQBAAAAwKMIAwAAAIBHEQYAAAAAjyIMAAAAAB5FGAAAAAA8ijAAAAAAeBRhAAAAAPAowgAAAADgUeaci3QNTZKZ7UpOTm7Tv3//SJcCAACAJmzlypUqLS3d7Zxre7xjCQMNxMzyJLWQlB/hUlA3/QLXVRGtAvAe/uwBkcGfveiWLqnIOZdxvAMJA0A1zGyRJDnnhkW6FsBL+LMHRAZ/9ryLNQMAAACARxEGAAAAAI8iDAAAAAAeRRgAAAAAPIowAAAAAHgUuwkBAAAAHkVnAAAAAPAowgAAAADgUYQBAAAAwKMIAwAAAIBHEQYAAAAAjyIMAAAAAB5FGAAAAAA8ijAABJjZVWb2iJnNNbMiM3Nm9lyk6wKaMjNra2Y3mdlrZrbWzErNbJ+Z5ZjZjWbG7ymggZjZZDObaWabAn/2dpvZEjP7tZm1jXR9CA8OHQMCzGyppMGS9kvaLKmfpH85574b0cKAJszMbpb0hKStkmZL2iipg6QrJLWU9Kqkqx2/rIB6Z2blkhZLWiFph6RUSSMkDZdUIGmEc25T5CpEOBAGgAAzGyN/CFgraZT8fzEhDAANyMzGyv8XkLedc74qr3eU9ImkbpKucs69GqESgSbLzJKccweref2Pku6V9IRz7tbwV4Zwov0KBDjnZjvn1vANJBA+zrlZzrm3qgaBwOvbJP0t8OPosBcGeEB1QSDg5cC1T7hqQeQQBgAAjdWhwLUiolUA3nNx4PpZRKtAWMRFugAAAI5lZnGSrg/8+F4kawGaOjP7qaRm8q/TGS4pW/4gcH8k60J4EAYAAI3R/ZIGSnrHOfd+pIsBmrifyr9w/7D3JI13zu2MUD0II6YJAQAaFTO7XdJdklZJui7C5QBNnnOuo3POJHWUfyevnpKWmNnQyFaGcCAMAAAaDTO7TdJD8m91OMY5tzvCJQGe4Zzb7px7TdK5ktpKmh7hkhAGhAEAQKNgZndKelTScvmDwLYIlwR4knNug/yB/GQzS4t0PWhYhAEAQMSZ2d2SHpS0VP4gsCPCJQFe1zlwrYxoFWhwhAEAQESZ2S/lXzC8SNI451xhhEsCmjwz6xc43O/Y12MCh461lzTPObcn/NUhnDiBGAgws8skXRb4saOkb0haL2lu4LVC59xPI1Eb0FSZ2Q2SnpH/28dHJO2r5rZ859wzYSwLaPIC0/L+LOkjSesk7ZJ/R6FR8i8g3iZ/OF8RsSIRFoQBIMDMfiPp17XcssE5lx6eagBvCOHPnSTNcc6NbvhqAO8ws4GSbpGUJamrpFaSSiStlvS2pIdZwO8NhAEAAADAo1gzAAAAAHgUYQAAAADwKMIAAAAA4FGEAQAAAMCjCAMAAACARxEGAAAAAI8iDAAAAAAeRRgAAAAAPIowAAAAAHgUYQAAAADwKMIAAAAA4FGEAQBARJjZeDNzZjY+0rUAgFcRBgAAAACPIgwAAAAAHkUYAAAAADyKMAAAqJGZpQfm9T9jZv3M7HUz221mJWaWY2bn1jDuWjObGbj3oJnlm9kLZjY8hM8cY2Z/N7MVZlZkZqVmttzMfm1mSdXc39zMfhm4p8jMis1snZm9ZGbDjrn3kkBdW82szMwKzGyOmd1a939LABC94iJdAAAgKmRImi9puaQnJXWSdK2kd83s2865lyTJzEzS05JukFQo6T+SdkrqKmmMpC8lLQzyWXdL6idpnqS3JSVJypL0G0mjzexs51xllc97T1JmoL6nJFVI6iZptKS5khYF7v1+oPZtkt4K1Nde0iBJEyQ9Xrd/NQAQvQgDAIBQnCXpAefczw6/YGaPyv8X8L+Z2bvOuSJJ35M/CHwq6Rzn3L4q98fK/5fvYG6VlOecc1VfNLPfS/qFpKskvRR4eaD8QeB159zlx9wfI6lllZd+IKlc0mDn3I5j7k0LoS4AaHKYJgQACMU+Sb+r+oJzbqGkf0lqJenwX8R/FLj+oGoQCNxf6ZzbGuyDnHPrjw0CAVMC129U815pNc/xOef2HPNyhaRD1dxbGKwuAGiKCAMAgFAsds4VV/P6h4HrEDNLlf+b+u3OuSV1/SAzSzWze83sUzPbZ2Y+M3PyT+uRpC5Vbl8haamkb5lZrpn9n5llmllCNY/+l6QUSV+Y2YNmdpmZtatrnQDQFDBNCAAQiu01vL4tcG0pf4dAkrbU9UPMLF7SLEmny78+4SX51xwc/jb/15ISD9/vnKs0s7GSfiX/9KHJgbeKzexZSZOcc/sD9/7VzArln4Z0u6Q7JTkzmyPpZ4FOBwB4CmEAABCKDjW83jFw3Sdpb+D/d6nh3lBcKn8QeNY5N77qG2bWSf4wcJTAVKAfS/qxmfWWNEr+9QE/lD+gXFfl3umSpptZK/nXGlwuaaKk982s/7FrCQCgqWOaEAAgFEPNrHk1r48OXJc450rk/za/g5kNqePn9A5cX63mvVHBBjvn1jrnpgbu3S9/uKjuvr3OuXecc9+T9IykNpLOrFPFABDFCAMAgFC0lH8qzlcCZwZ8R/6uwGuBlx8OXJ80s5bH3B8T+Ha/NvmB6+hjxvbUkSlAVV/PMLOTq3lOa/mnE5VWufc8M6uuI354h6MDQWoDgCaHaUIAgFB8JOkmMztDUq6OnDMQI//OQUWB+56SlC3peklrzOwN+ef8d5Y0VtI0+c8LqMlbktZK+omZnSJpiaTuki6S/8yB7sfcP1jSa2a2SP6uRIGkdvJ3BOJ1dIB4UdJBM8uRP3SY/N2A0+Q/i2BGyP82AKCJIAwAAEKRJ+lmSfcHromSFkv6nXPu/cM3BbYEvcHMPpD0fUnXBO7dKv8BYG/W9iHOuZLAguD75e8OnClpvaTfS/qr/AGkqoWS7pN/WtB58ncEdsr/l/uHnXPvVrn3Hvm3JR0q6QJJByVtkP+Qsyecc1/bchQAmjqrfitnAAAkM0uXPwh8bUEvACD6sWYAAAAA8CjCAAAAAOBRhAEAAADAo1gzAAAAAHgUnQEAAADAowgDAAAAgEcRBgAAAACPIgwAAAAAHkUYAAAAADyKMAAAAAB4FGEAAAAA8CjCAAAAAOBRhAEAAADAowgDAAAAgEcRBgAAAACPIgwAAAAAHkUYAAAAADzq/wN2pzyV/7PtegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(data=df,y='survived',x='pclass',hue='sex',ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 年龄分布直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2b78da90>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 639,
       "width": 425
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# FacetGrid可以直接生成多个子图，row,col参数表明根据哪行哪列分开，map函数的第一个参数是绘图函数，后面的参数是传给这个绘图函数的参数\n",
    "sns.FacetGrid(df,col='survived',row='pclass').map(plt.hist,'age',bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 连续变量用pd.cut离散化，分区间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "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>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>age_band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>(20.315, 40.21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>(20.315, 40.21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>(20.315, 40.21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>(20.315, 40.21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>(20.315, 40.21]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass     sex  survived   age  sibsp  parch     fare embarked  class  \\\n",
       "0       3    male         0  22.0      1      0   7.2500        S  Third   \n",
       "1       1  female         1  38.0      1      0  71.2833        C  First   \n",
       "2       3  female         1  26.0      0      0   7.9250        S  Third   \n",
       "3       1  female         1  35.0      1      0  53.1000        S  First   \n",
       "4       3    male         0  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone         age_band  \n",
       "0    man        True  NaN  Southampton    no  False  (20.315, 40.21]  \n",
       "1  woman       False    C    Cherbourg   yes  False  (20.315, 40.21]  \n",
       "2  woman       False  NaN  Southampton   yes   True  (20.315, 40.21]  \n",
       "3  woman       False    C  Southampton   yes  False  (20.315, 40.21]  \n",
       "4    man        True  NaN  Southampton    no   True  (20.315, 40.21]  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_band'] = pd.cut(df['age'],4)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20.315, 40.21]    562\n",
       "(0.34, 20.315]     179\n",
       "(40.21, 60.105]    128\n",
       "(60.105, 80.0]      22\n",
       "Name: age_band, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获得各区间数量\n",
    "df.age_band.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.34, 20.315]</th>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20.315, 40.21]</th>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.21, 60.105]</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.105, 80.0]</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 survived\n",
       "age_band                 \n",
       "(0.34, 20.315]        179\n",
       "(20.315, 40.21]       562\n",
       "(40.21, 60.105]       128\n",
       "(60.105, 80.0]         22"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各年龄区间总人数\n",
    "df.pivot_table(values='survived',index='age_band',aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看各个年龄区间的生还概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.34, 20.315]</th>\n",
       "      <td>0.458101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20.315, 40.21]</th>\n",
       "      <td>0.364769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.21, 60.105]</th>\n",
       "      <td>0.390625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.105, 80.0]</th>\n",
       "      <td>0.227273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 survived\n",
       "age_band                 \n",
       "(0.34, 20.315]   0.458101\n",
       "(20.315, 40.21]  0.364769\n",
       "(40.21, 60.105]  0.390625\n",
       "(60.105, 80.0]   0.227273"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['age_band','survived']].groupby('age_band').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '中文')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 275,
       "width": 379
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x='age_band',y='survived',ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2c0fecc0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 865
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df,col='age_band',row='sex').map(plt.hist,'survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a34c61748>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(data=df,hue='sex',y='survived',x='age_band',ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2f2acd30>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 856
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df,col='age_band').map(sns.pointplot,'pclass','survived','sex',ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同年龄区间，pclass，sex和生还率的对照图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "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>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  sex  survived   age  sibsp  parch     fare  embarked  class    who  \\\n",
       "0       3    0         0  22.0      1      0   7.2500         0  Third    man   \n",
       "1       1    1         1  38.0      1      0  71.2833         1  First  woman   \n",
       "2       3    1         1  26.0      0      0   7.9250         0  Third  woman   \n",
       "3       1    1         1  35.0      1      0  53.1000         0  First  woman   \n",
       "4       3    0         0  35.0      0      0   8.0500         0  Third    man   \n",
       "\n",
       "   adult_male deck  embark_town alive  alone  \n",
       "0        True  NaN  Southampton    no  False  \n",
       "1       False    C    Cherbourg   yes  False  \n",
       "2       False  NaN  Southampton   yes   True  \n",
       "3       False    C  Southampton   yes  False  \n",
       "4        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split,KFold,cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择\n",
    "factors=['pclass','sex','age','sibsp','parch','fare','embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df[factors], df[\"survived\"], test_size = 0.3, train_size = 0.7, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score  0.32472745085650123\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "y_predict = lr.predict(x_test)\n",
    "print('score ',lr.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct rate:  0.2835820895522388\n"
     ]
    }
   ],
   "source": [
    "y_predict[y_predict>0.5]=1\n",
    "y_predict[y_predict<=0.5]=0\n",
    "print('correct rate: ',sum(y_predict[y_predict==y_test])/len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8260381593714926"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# n_estimators表示有几棵树，min_samples_split表示树的深度\n",
    "rfc = RandomForestClassifier(random_state = 1, n_estimators = 55, min_samples_split = 8, min_samples_leaf = 2)\n",
    "\n",
    "# k折验证\n",
    "kf = KFold(n_splits=3,random_state=1)\n",
    "scores = cross_val_score(rfc,df[factors],df['survived'],cv=kf)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加familySize\n",
    "df['familySize']=df['parch']+df['sibsp']\n",
    "factors = ['pclass','sex','age','sibsp','parch','fare','embarked','familySize']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1a3721d1d0>,\n",
       "  <matplotlib.axis.XTick at 0x1a3721d4a8>,\n",
       "  <matplotlib.axis.XTick at 0x1a3721d5c0>,\n",
       "  <matplotlib.axis.XTick at 0x1a36d71940>,\n",
       "  <matplotlib.axis.XTick at 0x1a36d71e10>,\n",
       "  <matplotlib.axis.XTick at 0x1a371e3320>,\n",
       "  <matplotlib.axis.XTick at 0x1a36d71ef0>,\n",
       "  <matplotlib.axis.XTick at 0x1a371e38d0>],\n",
       " <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 365
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selector = SelectKBest(f_classif, k = 5)\n",
    "selector.fit(df[factors], df[\"survived\"])\n",
    "scores = -np.log10(selector.pvalues_)\n",
    "sns.barplot(factors, scores)\n",
    "plt.xticks(range(len(factors)), factors, rotation = \"vertical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据柱状图看，影响因子最大的特征是sex，其次是pclass，fare，embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8125701459034792\n"
     ]
    }
   ],
   "source": [
    "predictors=['pclass','sex','fare','embarked']\n",
    "rfc = RandomForestClassifier(random_state = 1, n_estimators = 65, min_samples_split = 10, min_samples_leaf = 2)\n",
    "kf = KFold(n_splits=3,random_state=1)\n",
    "scores = cross_val_score(rfc, df[predictors], df[\"survived\"], cv = kf)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "做特征选择并没有提升效果……毕竟这里总的特征数也不多，其实不是很有必要"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier \n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}