1_Demo_Data_Explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "plt.style.use('seaborn-colorblind')\n",
    "%matplotlib inline\n",
    "from data_exploration import explore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "use_cols = [\n",
    "    'Pclass', 'Sex', 'Age', 'Fare', 'SibSp',\n",
    "    'Survived'\n",
    "]\n",
    "\n",
    "data = pd.read_csv('./data/titanic.csv', usecols=use_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Fare</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>7.2500</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>71.2833</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>7.9250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp     Fare\n",
       "0         0       3    male  22.0      1   7.2500\n",
       "1         1       1  female  38.0      1  71.2833\n",
       "2         1       3  female  26.0      0   7.9250"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get dtypes for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "str_var_list, num_var_list, all_var_list = explore.get_dtypes(data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sex']\n",
      "['Survived', 'Pclass', 'Age', 'SibSp', 'Fare']\n",
      "['Sex', 'Survived', 'Pclass', 'Age', 'SibSp', 'Fare']\n"
     ]
    }
   ],
   "source": [
    "print(str_var_list) # string type\n",
    "print(num_var_list) # numeric type\n",
    "print(all_var_list) # all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General data description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result saved at: ./output/describe.csv\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        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>Fare</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>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</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>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived      Pclass   Sex         Age       SibSp        Fare\n",
       "count   891.000000  891.000000   891  714.000000  891.000000  891.000000\n",
       "unique         NaN         NaN     2         NaN         NaN         NaN\n",
       "top            NaN         NaN  male         NaN         NaN         NaN\n",
       "freq           NaN         NaN   577         NaN         NaN         NaN\n",
       "mean      0.383838    2.308642   NaN   29.699118    0.523008   32.204208\n",
       "std       0.486592    0.836071   NaN   14.526497    1.102743   49.693429\n",
       "min       0.000000    1.000000   NaN    0.420000    0.000000    0.000000\n",
       "25%       0.000000    2.000000   NaN   20.125000    0.000000    7.910400\n",
       "50%       0.000000    3.000000   NaN   28.000000    0.000000   14.454200\n",
       "75%       1.000000    3.000000   NaN   38.000000    1.000000   31.000000\n",
       "max       1.000000    3.000000   NaN   80.000000    8.000000  512.329200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explore.describe(data=data,output_path=r'./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete variable barplot\n",
    "draw the barplot of a discrete variable x against y(target variable). \n",
    "By default the bar shows the mean value of y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved at "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yimeng.zhang\\AppData\\Roaming\\Python\\Python36\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./output/Barplot_Pclass_Survived.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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90JKDAQAAcOK2/Jy97j7a3T+e5FU5/hiGqxebCgAAgJOypdirqr9ZVa+pqg8m+ckk/yvJ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\nBwAAMND/BxnIK61uNoWMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explore.discrete_var_barplot(x='Pclass',y='Survived',data=data,output_path='./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete variable countplot\n",
    "draw the countplot of a discrete variable x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved at ./output/Countplot_Pclass.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explore.discrete_var_countplot(x='Pclass',data=data,output_path='./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete variable boxplot\n",
    "draw the boxplot of a discrete variable x against y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved at ./output/Boxplot_Pclass_Fare.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explore.discrete_var_boxplot(x='Pclass',y='Fare',data=data,output_path='./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous variable distplot\n",
    "draw the distplot of a continuous variable x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yimeng.zhang\\AppData\\Roaming\\Python\\Python36\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved at ./output/Distplot_Fare.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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gCAEHAAAwhIADAAAYQsABAAAMIeAAAACGOG3AVdW7q+p4Vd2zauwnq+pPququ5fa6Va+9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\nMISAAwAAGOL/Az5q2F6rhJbjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explore.continuous_var_distplot(x=data['Fare'],output_path='./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Scatter plot\n",
    "draw the scatter-plot of two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved at ./output/Scatter_plot_Fare_Pclass.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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6IwgCAAB0RhAEAADojCAIAADQGUEQAACgM4IgAABAZwRBAACAzgiCAAAAnREEAQAAOiMI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\nEAAAoBkhCAAA0IwQBAAAaOYLeJ9d7qYpCfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explore.scatter_plot(x=data.Fare,y=data.Pclass,data=data,output_path='./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation plot\n",
    "draw the correlation plot between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved at ./output/Corr_plot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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iUsrKIvvutxsnfeuABKvOLsP/9FMOObAXbVo2Ze7EO7jmlscZ/ugrSZe1Qxp0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\nQZiYSZIkFcT/B8ELsusQfEFfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explore.correlation_plot(data=data,output_path='./output/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year     month  passengers\n",
      "0  1949   January         112\n",
      "1  1949  February         118\n",
      "2  1949     March         132\n",
      "3  1949     April         129\n",
      "4  1949       May         121\n",
      "Image saved at ./output/Heatmap.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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dzurBb4KGmPu7FF8WySiFGRkcnf4ZODJa6sfjX7tOieNP/TAbW34e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\nyXNa0/fswrNhY5SVpxddVQQM2XaARzfspoa3J6Eeb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acuADCoWmXaBpXDpDXLz8fyy7kr\ndKxYnkfCKgGQYzIxM/o0ZzKyrZrRyaD4rFN9nAwKo0Gx9nwinx4+T9NAb15uEI6DQXEsJZN3d57A\nZDkf+shG4dxT0Y9cUxFv7zjOsZQsq2YsS3drhc8mnU1lPmX+dK31q5bfRwAeWuu3bXH7N2XJ1Fp7\n/HVL6+vbtyMDBnRn9OgZxdvmzVtCy5b1GDy4P/PmLWbevCWMHDkQAJPJxNSpC2jduqHNMvZ5oD2P\nPd6NMWNmF2+rVq0yc2aPYty4T267z8QJX3HvvbbL2LNPKx5+rD1vjf3qhu2xV5LZse0IFYL8irdl\npGcz4b1v+fDTFwmq6E9yUrpNMt7fuwn9Hm3Fu69/X7ztvSkDii/PnrocDw8XALx93Jk8+ynKB3hz\n+mQsw5/7jGVr3rR6xm69mtD3kVZ88Ma1jJ/MXMmTz95Hi9Y12bH5KJ/MXMmsL55j55ZjXDyfyKJl\nozly6DzTx//MJ9+8aNV8RqORf7/Ui6o1g8nOyuXlJ2bQsFl11qzYTf2m1ej/ZEcWL1jL4gXreGpY\nD+o3rUbzNpEopTh78jKTXlvIJ4vHWDWjwWjk4ed7U6VGMDnZubz79AxqN61Ot0fb0/fpbgD8uWQT\ny+f/wRMj+tPt0Q50e7QDAPu3RvPHjxut2tE0ZzTQYdADVKgaQl52LvOHTyGsQQ36jH6quM3aL37B\n2c38eDQ6OXDv491JPH+FhHNXrJqtOKOjA/e+/hIOLi4UFZrY+O40KtSP5OSqtVTt1oGQlk3Y98W3\nxGzYRninNgAEt2hMg4EP2yTf1Yz3jH25OOPm96YSUD8Sv2oRVGhQly0fTL+hffyBaLLi4uk49R1S\nTp/lwFff0fad0VbNWFCkeXXXYXJNRRiVYlaLuuxKTKGyuxvlXZ0ZuGkvGvBxcgTgSnYuw3ceIrPQ\nRLNyPrxSpypDtx+0asb8Is2QdQfJKSzCQSm+6FSP7bEpvN28Bs+tP8T5jByG1K1Cj7BAlp6J454g\nX0I8XemzIoo6/p6MbVKVJ/88YNWM4r9nq052HtBXKVXORrdnFUqpMu2cN21aB29vzxu2rV27kz59\nOgLQp09H1qzZUXzd11+voEuXVvj7e5dljL/IGInPTRkjIoIJC6902/Zr1uwkJCSQqlVDbBEPgMZN\nquPtfesb9LRJP/Lyqw+irjvXxKqVu+jQqSFBFf0B8PP3sknGho3D8fJyu+11WmvW/XGA+7o1AKBG\nrUqUDzD/H4dXDSQ/v5D8/EKrZ6zfOBzPmzIqpcjOygUgMzMX//Lm+2vLhmi69GiMUorIelXIzMgl\nKcG6HXe/cl5UrWmunrq5uxASFkhSQho7N0XTsXtTADp2b8qOjYcBcHVzLv6/z83Jt8k5R3zKeVGl\nRrDl9l0IqhJAakIaru4uxW3yc2+fZefavTTvZP0PaR5+3lSwPD+d3VzwDwkkIymt+HqtNce27KN2\n28YAOLk4ExIZgdHR0erZrlJK4eBivs+KTCaKTCZQkBB9nErNzPdR5TYtuBxlv07GzRm1yQQofEJD\ncCvvf0v7K3sPENK6BUop/KqGU5CdTW5q2i3tylquqQgAB6VwUAqtoVflCnx96gJXvzgxNb8AgCOp\nGWQWmoovl3dxsno+gJxCS0aDwsFgoEhDQVER5zNyANgRm0KHEHP3oW2wPytjzBX4w0kZeDg5UM7F\ndo/N/5ZS9vmxN1sNoxcC84DhwOvXX6GUqgJ8CZQHEoCngDTgABCutS5SSrkBx4FwoDIw19I+G3hG\na31MKTUfyAFqAlUsx3kSaAns1FoPvO42pwHtgRTgEa11glIqopTjJgMNgb3Aq2V5x9wsKSmVgABz\nJS4gwI/k5FQA4uKSWLNmOwsWjOfQoRPWjPAfy87O5fPPfuGLL8fx1ZdL7Zpl47r9BAT6UL3mjZ3e\nczFxFBaaeGbgVLKycnlsQEd69G5pp5Rm+/eexc/fk5Aq5W+5bv2aQ1SvWREnJ/vMeBk6shcjn/+c\nj6avQBdp5i4YCkBifDoBFXyK25UP9CYhPq24M2ptcZeTOXP8EjUiq5CanIFfOfPt+pXzIjUls7jd\ntvWHWPjRSlJTMhk3/WmbZLsq8Uoy509eIrx2FQB++uw3tv0ehZuHCyNnPX9D27zcfA7vPMbjL/e1\nacbUuCTiT1+iYo0qxdsuRJ/G3ccTv4oBNs1yM11UxLrXJ5IZl0DEfW1wDyyPo7sbBqMRAFc/H3JT\nUovbX9q9j8RjJ/GoEEi9fz2Im79fSYcu04wb3pxAVlwCYZ3a4lc1rMS2uSmpuPr5Fv/u6udLTnIq\nLj7WLR4YgI/vqU8lN1eWnr/CsbRMKrq50C6oHK0D/UnLL+DDI2e4lJ17w37dQgLZlZB6+4OWdUYF\n33RpSIiHKz+evMzhpAwcDIpafh4cTc6kU0g5Krg5AxDg6kRcVl7xvvHZ+ZR3cyYxt8AmWcV/xpbT\nB+YCjyulbn5mfQgs1FrXAxYBs7XWVzubbS1tegKrtdYFmDutw7TWjYERwEfXHcsX6IC5U7scmAFE\nAnWVUg0sbdyBvVrrRsBGYJxle2nHrQ50ujoN4HpKqcFKqSilVNS8efP+2T3yD4wf/xkjRgzEaHmh\nvRN9OOd7nhzYE3d3V7vmyMnJ44t5vzFkaK9brjOZTBw9co7ZHw1j7ryX+OyTlZyLibNDymvWrNpH\np64Nbtl+5lQsH81cyag3H7RDKrOli7czdERPlqx+gxdG9GLyOz8C5urXzZSNPj7nZOfxwZgFPPNK\nb9w8XEpt26p9XT5ZPIY3Jj/FN5/+bpN8ALnZecx9cz6PDutTXNV88Jn7mfbTW7S4rxHrft5yQ/sD\nW6OpWjfM6kPo18vPyeOXCV/Q8Zm+OLtde84e3bSHWm0a2yxHSZTBQMcJr9FtzniST8eQcSn2dq0A\nqNCoLl1nvkeniW8QUKcGez5ZaLOM7ce/TpdZH5B6Job0C5dKbnzrU8YmFaci4NmtB3h4/W5qensS\n6uGGo8FAgamI57cdYOWFWEbeNO++gZ833YID+ex4jPUDAkUaHvt9H92W7qSOvycR3m6M3XqMVxuG\ns6BzA7IKTBRaXnPUbdZz3+blSNxhbNbZ1FqnAwuBmyd2tQS+tVz+GmhtufwDcHUCziPAD0opD6AV\nsFgptR/4FAi67ljLtfld8BAQp7U+pLUuAqKBUEubIsuxAb4BWv+N4y7WWptK+Lvmaa2baK2bDB48\n+G/cE6Xz9/chPj4ZgPj4ZPz8zNWjw4dP8sorU+jQ4d+sXr2Nd975mDVrtv/Xt1eWDh48ydQpC+nY\n4VkWLlzBvHk/s+ib32ye4+KFBC5dSuKRvu/R/b6xxMel8Hi/90lMSCMw0JdWrSNxdXPG19eTRk2q\nceL4BZtnvKqw0MSGtYfp1LX+Ddvj41IZO3wBb73/CMEh9pt9snr5Htp0rAtA+871OHrYfF+VD/Qm\nPvZa1SMhLo1yNqhqFhaa+GD0fNp1aUSr9vUA8PHzJDnRPISfnJiOj++tU7LrNIog9mISaamZt1xn\njYxz35xPi/sa0bhtvVuub96pEXs23jgPbue6fTTvaLt5zqZCE79M+ILIdk2o0eraY6/IZOL49oPU\nsuGc67/i5O5G+VrVST51loKsbPOQOpirgr7m2oWzp0fxMH9Yh9aknD1v04yO7m7416xG/MEjJbZx\n8fMhJzml+Pec5BRcfH1KbF/WsgpN7E9Oo2l5HxJy89gUlwTAlrhkwjyvfcgJ93Tj1boRvLXnKOkF\n1p++c73MAhNR8Wm0CvLlUFIGT689yJN/7GdfQhoXLEPqcTl5BLo7F+8T4OZEYk5eSYe8Ayk7/diX\nrRdGzQT+jbm6WJKrn1GWAd2UUn5AY2Ad5rypWusG1/3Uum7fq4+4ousuX/29pHFI/TeOa7Olbh06\nNOPXX9cC8Ouva+nYsTkA69Z9UfzTpUsrxo17jk6d7Dv8e7NvFo1n7bpPWbvuU554ogeDB/fl8QH3\n2zxHterBrN08jZV/TmDlnxMICPRl0ZI3KFfem7YdGrBvzykKC03k5ORx+OBZwsKD/vqgVhK18yRV\nwgIICLz2ppORnsOIoV8y5KVu1GtY8rCcLfiX92J/1BkA9u46RXBlc8f3nraRrF6xB6010QfP4e7h\nYvUhdK01s977gZCwQB54vG3x9uZtIlm7cjcAa1fupnmbSAAuX0gsrsCeOnaRgsJCvG4zt7esM341\n6QeCqgTQ5eF2xdvjLiQUX96/NZoKla8NUWdn5nBi/2katq5j1WzXZ/xt9rf4hwTSrE+HG66L2X8c\n/0oBeJXzLWFv28hLzyA/y7wK2pSfT3z0MTwrVqB87epc2rUPgPObdhDU2NyZz0m5Nvfx8p6DeFas\nYJOMBddlTIg+hkcpt1uhUT0ubNmB1prkU2dwdHO1+hC6t5MD7g7m0TAng4HG/t5cyMxha1wyDS1z\n/+v7eXExy9yRC3Bx4u2GNZlw4CQXbxpWtxYfZ0c8HM0ZnY0Gmgf6EJOeg6+z+cODo0HxZK0Qfjpl\nXpy26VIS3UPNz586/p5kFphkCP1/gE0ngmmtk5VSP2LucH5p2bwNc+Xya+BxYIulbaZSahcwC1hh\nqSymK6XOKqX6a60XK/O4XT2t9T+ZJW4A+gHfA48BW7TWZXHcf+yVV6awa9chUlLSadNmIMOGPcbg\nwf14+eVJLFnyJ0FB5Zk1y7qrZ//Kq69MZ9fuw6SmZNCu7dMMHfYI3t4ejH//c5KT0xkyZDw1a4bx\n+Rdv2S3j2BGfsWf3cVJTM+naYRRDXuhFnwdb37ZteEQQrVpH8vAD72IwKPo82Jqq1W6/2KksvTV6\nEfuiTpOamkXv+97n6ec607NvM9b8vp/7bhpCX/L9Vi6eT2T+vDXMn7cGgBkfD8bP37onUXhnzCL2\nR50mLTWLfp3f56nnOjPyrX7MmbwUk6kIJycHRrzZD4AW99Zkx5ajPNZzIs4uTox55yGrZgM4cuAs\n61ftIbRqEMMenwbAE8/fT78nOjDxtYX8sWwX5QN9GDvhSQC2rTvIut+iMDoYcXJ2ZPT4f1l9qP/k\nobNsXx1FcHgQ4wZNBczD55tX7iT2QgJKKfwr+PLEq/2K99m7+RCRTWvg7Opc0mHL1MUjZ4hev5vy\noRX58sVJALR9ogcRTSI5smlv8cKg633077fJz87FVFjIyR0Hefjd5ylX2Xof0nJT04j6ZCG6qAi0\nplLzxgQ1qotXcBC75nzBkcXL8akSTGi7VgCcXr2eK3sPYTAacHR3o8mQJ6yW7fqM++YtQBdpdFER\nlZo3pkLDupxevY5TK/8kLy2d9a+9T2D9SBo+/S8C69chbv9h1ox4C6OTEw2fsX5Gf2cnRtWrhhGF\nUrAxNokdCSkcSknntfrVeTC0IrmFJqYdPgXAv6pWxsvJkZcizacIM2l4fpt1F2GVc3XknRY1MCrz\nAPma84lsvpzMSw3CuLeiH0rBklNX2B1n/kCx5XIK9wT5sbRHE/Opj3bemWsYSnK7aQB3A3W7uVdl\nfiPXnW5IKRUInAUma63fVkqFYu54lsOyQEhrfd7Sth+wGGintd5o2RYGfIx5mNsR+F5r/a5lIc8K\nrfUSyzFXaK3rWPa5/rpMzHM578e8EOlhywKhvzzu3/hTNdzJD/zqABTpaDvnKJlBmatSWYUb7Zyk\nZO4ObUnKXWbvGKXyd+lFbM6dnbGCay9Opq2wd4xSVfPuwda4lfaOUap7Arvz1YnV9o5Rqqeqd2Fs\n1Fp7xyjRhCbmM4CM2rXOzklKNrmZuQrdcdVWOycp2dpu5nObNv5us52TlGzPo/eCHceVU/JW2GWG\nqa9zD7v2cm1S2bz+vJZa6zjA7brfYzAv6rndfku46UGhtT4LdL1N24E3HbNOCdddzXLDiQv/znGF\nEEIIIf5TSt2dp3W/O/9qIYQQQghhE9LZFEIIIYQQViPfjS6EEEIIYRN35wIhqWwKIYQQQgirkcqm\nEEIIIYQN3K2nPpLKphBCCCGEsBqpbAohhBBC2IRUNoUQQgghhChT0tkUQgghhBBWI8PoQgghhBA2\nIN8gJIQQQgghRBmTyqYQQgghhE3IAiEhhBBCCCHKlFQ2hRBCCCFsQE7qLoQQQgghRBmTzqYQQggh\nhLAaGUYXQgghhLABGUYXQgghhBCijCmttb0z/H8id6YQQghxZ7NbeTGzYINd+gkeju3sWlKVyqYQ\nQgghhLAambNZxkz6oL0jlMio6gGQXbjVzklK5uZwDwApeSvsnKRkvs49iMlYbu8YpQr17Mmp9Ds7\nY1WvnuxOWGnvGKVqWr47nxz9w94xSjWkVmfGRq21d4xSTWjSkQEbN9o7Rom+adsWgMc23LkZv21n\nzhj51SY7JylZ9FNtAKg8fb2dk5Ts/Cvt7Xr7SsmcTSGEEEIIIcqUdDaFEEKI/2vvvsOjKtM+jn/v\nyQAhhZBQQoBAIEDoNYCKNFEUNSh2ioqorK5YFrEiiIoCYkFXd5EVBVRU0N1FcBFUuoLUhRCaNEGE\nJJAEEtInz/vHOQkBA69lzsy43J/rOlcmzynzy2RmznPu55wZpZRjdBhdKaWUUsondBhdKaWUUkop\nr9LKplJKKaWUD+iHuiullFJKKeVlWtlUSimllPKJ87PGd37+1UoppZRSyie0s6mUUkoppRyjw+hK\nKaWUUj6gFwgppZRSSinlZVrZVEoppZTyAf1udKWUUkoppbxMK5tKKaWUUj6hlU2llFJKKaW8Sjub\nSimllFLKMTqMrpRSSinlA3Ke1vjOz79aKaWUUkr5hFY2lVJKKaV84vy8QEg7m340+sm/sXzZBqJq\nRPDZ/FcA+OKL1bz5xhz27jnEx3Mm0LpNPABFRcWMfWoq27btxeMpof81PRn+pwGOZxz31DusWL6Z\nqKhqfDLvudPmzXr3C159aQ5LVr1GZGQ4xhhenDCbb1YkE1y1Ms88fyctWjZ0POP4sR/xzfLtREaF\nMftfj5S1z5m9kk8+/IYgt4uLurfg/pFJAHy/6ycmPfsJJ0/m4xLhnQ8fokqVSo5mfPmZj/lu1Taq\nR4YxbY6Vcc+un/jrhE/Jyy0gum4kjz03mNCw4LJ10o5kcveNkxkyvC833trL0XzpR7J4edyHZB7L\nxiXCFQMu4JqB3ck+nsvEJ98j7XAmtWMieXzCrYRXC+Hg/jSmPPsxu3f8yG339uN6h/MBHEvNZOr4\n2RzPyEZE6N3/Qq64qQez3/yMTd9sw10piNp1azD8yYGEhleluKiY6ZPnsm/HQVwiDHlwAC07NnE0\nY3Z6Jl+89h65WSdAhDZ9u9ExqRefT36HzENpABSczKNKaFWGTHkcT7GHL9+cTdqeg5iSElr06kKX\nG/o6mtFTWMSK516hpLiYEk8J9bp0oOUNV3My7Shr33iHwpyTVI+LpfOfh+JyW7uIH9dsYPunn4MI\nEQ3q0WXEMEczlhQVsWPyZEqKizEeD1GdOlGvf3/2zZzJyR9+AGMIjo6m0dChBAVbr5mM9es5NH8+\nACGxscTfdZfjGXe+NBljZ4zs2Im6/fuzf9ZMcu2MVaKjibvdynhwzsdk79xprVtYSHF2Nu2nvOZo\nxlIugTlJHUnNLeC+r1KoFxbMS72aE1GlEtuOZfPEip0UlRiubRLNw50bkXayEIDZ23/i0++P+Czj\ngsGJpOYUcMe/k3mxbwJto6shwL7MXEYu2kFukYe7OsYysE0MxSWGjLwiRi3azqHsAp9kVL+dTzqb\nIuIBkoFKQDEwE5hijCnxxf2fkSXHGBPm6/utyIABvRg8+Aoef/yNsramTWN5/fVRjHt62mnLLvpi\nNYVFRcyb/wp5eQUkXfUXrrqqG/Xq13Y0Y9K13bh5UB/GPPH2ae1HDmew5tsU6sTUKGtbtTKZAz+k\nMm/hBJK37OWFZ2fx3kdjHM0HcFX/ztxwy8U8O/rDsrYNa3ezYmkK7386isqV3WQcywaguNjDuCdm\nM+6FQTRNqMvxrJO43UGOZ+yblEj/m7sxeeypjFPGz+HuB5No2ymeRfPW8sl7y7j93ivK5k99+TM6\nX9Tc8WwAQW4Xdz2URJPm9ck9mc+Dt02hQ9emfLVgPe06N+WmoZcwZ8YS5s5cwrD7rya8WlX+9PA1\nrF6e4pN8AK6gIAaNuIZGCfXJy81nzLBXadO5GW06J3Dzn64iyB3ER3+bz/z3vuKWPyex9LM1AEyc\n9SjHM7OZ/PA/ePbth3C5nDt7SIJc9LhjANHxsRTm5fPBwy/SsH0CVz1yqnO2/J1/UiW0KgDff7MJ\nT1Ext73+JEUFhcwa8TwJ3TsREV3jbHfxu7kquek++kHcwcGUFHtY/uzL1GnXiu8Xfk2TfpcQe2Ei\nm6bPZv+yb2l8aQ9yjqSx87NF9Bw3isqhIeQfz3YsWylxu0kYOZKg4GBKiovZ8eKLRLRuTYObbiKo\nqvXYHZgzh7SlS4np14/81FQOL1xIi0cfxR0aStGJEz7J2OwvVkbjsTJWa92a2BtPZTw4Zw7py5ZS\n54p+xN50c9m6aUuWkHvwgOMZS93ash57s3IJrWy9141MbMSslEMs3JfO2AubcF3TOny88zAAX+xL\n5/k1e3yWrdSwDrHszsgl3M747LLd5BR6ABjTswlD29fjb+sOkJKezVUfHCK/uIQhbevyZI947vt8\nm8/z/lb6oe7OyjPGtDfGtAIuA64EnvbRfXuNiHi1c57YuSUREaf3e+Pj69Oocb2K7pu83AKKiz0U\n5BdSqZKb0LCq3oxToU6JCUREhP6s/aVJH/LgwzdS/nWzfMkmru5/ESJC23bxZGfnkp6e5XjGDonx\nVIsIOa3tn3O+5bY7L6FyZetfFlUjHIC1q3fRpFkMTRPqAhBRPZSgIOdfBm06xhNe7fSMP/6QTpuO\njQHo0LUZq5ZsKZv37bKtxNSPomHjaMezAUTVrEaT5vUBCAkNJjYummPpJ1izPIVLr04E4NKrE1mz\nzOpcVo8Kp1mrBrjdvjvtO7JmNRolWBmrhgRTN642GUeP06ZLAkH2AUN8q4ZkpB8H4ND+VFp1agpA\nRGQ4IeFV2bfjoKMZw6IiiI6PBaBy1WCi6tch59jxsvnGGHZ9s4mE7p2sBoGi/EJKPB6KC4pwVQqi\nSkhwRZv2GhHBbVcDSzweSjweEEhP2Um9Lh0AaNDjAn5avxmAfUtW0fiynlQOtZ6/wRHhjuYrzVha\nsTQeD8ZjdTpKO3HGGEqKiih9A0pfuZLavXrhDrXeqypVq+aXjCIVZKxg2DRj3VqiOndxPCNAdEhl\netSPOq1C2TWmOov3pwMwb3cqfRo6d3DzS9QJq0KfxjX4KPmnsrbSjiZAsNuFsW+vPphFfrFVp9p0\n+AQxYc6+XpR3+HwY3RiTJiLDgXUiMg6rwzsR6AVUAd40xrwFICKPArcCJcBCY8zjIhIPvAnUAnKB\nu40xO0RkBpAHNAcaAncAtwMXAt8ZY4aWZhCRl4HeQCZwizEm/f/ZbgbQAdgIPOzMI3NufS+/gCVL\n1tGz+93k5xfy2OO3U72682/6FVm2ZBO1oyNJaN7gtPa0tEzq1Ikq+z06Ooq01Exq1aru64gc+CGd\nzRv2MvX1hVSp4ub+h5No2boBB/anIyI8eM9bZGac5LIr2nPrsEt8ng+gYXwdVi9P4aJerVn51WbS\nU61OSX5eAXNmLmXCm8P55L1lPs+V+lMGe3ceIqFVA7Iysomqae24o2pWIyszx+d5KpJ+OIMfdh0i\n/ozTNFZ8vpaufdoD0KBJXTauTOHCPh04lpbF/p0HOZaW9bN1nHI89Rjpe3+kTsE2C9kAABTnSURB\nVLNT93do2x5CqocTWdcakWh6UQf2rE1m2h1PUVRQSM9h1xEc/vODO28zJSUsGT2RnNR04i/rQWh0\nLSqFhuAKsjrtVaOqk59pHSjmHLGG/5eNe8ka6r/+Kuq0a+WTjCnjx1OQnk7tXr0Ia2wdmO2bMYOs\n5GSqxsQQe8MNAOSnpgKwfdIkTEkJ9ZKSiGjd2icZtz9vZazVsxehjayM+2fM4PjWZIJjYoi98YbT\n1ik4doyCo0cJb+6bUYvHu8bz8vp9hFay/rfVq7jJLizGY/feUnMLqR1SpWz5yxrWpFN0BD+cyGPS\n2r0cOen8EPW4Xk14YcVuQiuf3iV5qW9zejeqwfcZJ3lu+e6frXdzmxiW7j/meD71+/nlanRjzF77\nvmsDdwLHjTGdgc7A3SLSSET6AdcCXY0x7YAX7dWnAfcbYzoBo4C/ldt0JHAJ8BdgPvAq0ApoIyLt\n7WVCgY3GmI7Ack5VWM+13WbApcaYn3U0RWS4iKwXkfXTpk07c7bXJCfvxuVysWzFNBZ/9SYz3p3P\nwYOpjt3f2eTlFTB92gLuHXHtz+YZ8/Pl/TVk4Cku4UR2HtM/eIARI5MYPeo9jDF4PB42b9zHMxMG\nM23mCJYv2cq6Nbv8knHk2JuZP/db7hvyKnm5BbjtncGstxYzYFB3qpbbAfhKXm4Bzz82k7tHXkNI\ngFYM8nMLeG30DIY8eC0hoacyzpv5Ja4gF936WlXDnld1Iap2BGPuepX3X/83TVvHERTk/CkTAIV5\nBSyYNJ2ed15HlZBTIxA7V26geWlVEzjy/Q+4XC7ufmc8d741jo3zlpB15Kjj+cTlos+EJ+n31+fJ\n2LOf7EMVnZdnvXaNp4Sc1HR6PPUXuowYxsZ/fEDhyVyfZGw9diztJk3i5L595B46BECjoUNpP3ky\nwTExZKxfb2UsKSE/LY2Ehx8m/u672TdrFsW5vsnYcsxY2kycxMn9+8izM8YNHUrbFydTNSaGjHXr\nT1snc906Ijt2Qhw8naNUz/pRZOQVse3YqYNEqaDSWvrWvfTgMS6bu5br5m1k9U9ZvNA9wfGMfRrV\n4GhuEclpPz+QHbV4B52nfcPuY7kkJZx+ytiAFtG0jQ7nrfW+Ox3BO8RPk3/58wKh0r++L9BWREoP\n/yKApsClwLvGmFwAY0yGiIQBFwFzy3Viyu+R5xtjjIgkA6nGmGQAEUkB4oD/YlVJP7aXfx/45y/Y\n7lxjjIcKGGOmYXVUAYzHbKlosd/t8wWr6N69PZUqualRI4IOHZuzdeseYmN9M8xa6seD6Rw6dJSb\nr7P66GmpmQy64Rne+2gM0dGRHDmSUbZsamoGtWr7vqoJUDs6gl592iAitGrTAJdLyMo8Se3o6nRI\nbEz1SOv0hYu6t2Dn9kN0vqCZzzM2iKvNhDeHA9aQ+nertgOwY+sBVn29hemvf05Odh7iEipXdnPN\nzRc7mqe42MMLj82k9xUd6XZJG8AaLs84eoKomtXIOHqi7HHzl+JiD689NYOL+nakc8+2Ze0rFq5j\n07fbeOK1e8sOcILcQQx54NRB0TP3vE6d+jUdz+gp9rBg0ts075lI0wvbl7WXeDzsXr2ZQS+fuoht\n54r1NOzQgiB3ECHVw6nbojGpuw9QvY7zOQEqh4ZQq0UzMnbvo+hkLiUeD66gIPIysgiOjACsKmdU\nk0a43EGE1q5JeN1oco6kERUf55OM7pAQwhMSOJ6SQkg96xQjcbmISkzkyOLF1OrWjcqRkYQ1bozL\n7aZKzZoE16lDfloaYXE+zNjMyli1XMbIxERSFy+mZrduZctmrF9Hg4GDfJKrQ3Q1ejWoQff6UVQJ\nchFaOYjHu8YTXtlNkIDHWMPs6blW9fJ4QXHZup/sOszIxEaOZ0ysF8Fl8TXo3egCqrhdhFd2M6Vf\nCx5aaL0flhiYvyuNexJjmZtiHRRd3CCSEV0actOcTRR6KqhyqIDjl8qmiDQGPEAaVqfzfvuczvbG\nmEbGmMV2+5nPIheQVW7Z9saYFuXml9b7S8rdLv39bB1r8wu2e/I3/aFeFBNTkzVrtmKMITc3n82b\nd9G4gnM7nda0WX2WrHyN/3w5mf98OZna0ZHM/uRpataKoGfv9iz47FuMMWzZvIewsBC/DKED9Lik\nNRvWWsMuB/anU1RUTPXIULp2S2D3rsPk5xVSXOxh4/o9NIr3bYe9VFaGdaFFSUkJs6d/xdXXXwjA\nK2/fx6z5o5k1fzQDBnbnljv6ON7RNMbw2nNziI2LZsDgnmXtXXu05KsFVmXmqwXruaCn88OnZ2OM\n4e0JH1O3YW2uvKVXWfvmNdtZ8MESRk68kyrBlcvaC/ILyc+z3gaS1+3EFeSiXqM6jmf88o0PiKpf\nh07XnH56xoHNO4msH014zciytvBakRxM3oUxhqL8Ag7v3E9UfWefjwUnsssqk57CQtJSdhBetw61\nWjbj0NpNVtYVa4jpZHXmYxLbkb7dqv4XZOeQcziV0NrOdoaLsrPLKpMlhYWc2L6dqtHR5KdZQ/rG\nGLK2bCG4jvX/jGzfnhP2ld5F2dnkp6YSXNO3GbN3bCf4jIzHy2UEyD9yBE9uLqH2KQFOm7JhP33m\nfEffT9Yyavl2vjucxWMrdrD2cBZ942oBcE2TaJYcsIaia1Y99frpHVuDvVnOV4cnrdpL13+sptv0\nNYz4fBvfHszkoYXbaVj91IjApY1rsDvDytKqVhgTLk3gznnJHMsrcjyftwkuv0z+5vPKpojUAqYC\nb9hVyEXAvSKyxBhTJCLNgEPAYmCsiMw2xuSKSJRd3dwnIjcaY+aKVcJoa4zZ/CsiuIAbgI+AQcAq\nY8wJL2z3Vxs1cgpr16WQlZlN755/YsT9NxEREcbz498hI+ME994zgebN4/jH9KcYOOhyRj/5N/on\njcQYw4DrepOQ4Py5Z4+PmsqGdTvJysrh8kse5p77rmHA9T0qXPbiHm1ZtWIL/fs9TnBwZcaNd/bj\nUUqNefQ9Nq7fQ1bWSZIufZa7/3w5SQO6MH7sxwwaMBl3pSDGjh+IiFCtWggDb+vJHYOmIAgXdm9O\ntx4tHc844cn32bJhD8ezTjL4yue4dXhf8vIKmT/3GwC69W5D3/6dHc9xNts272fJfzYQ1ySGEYOs\nj+G6/b5+3Hj7JUx84j2+/GwttaKr88TE2wDIOHqCh25/jVz746PmfbSSqR8/4ujQ+64t+1i1aD2x\n8TE8OfQlAG7605XMmvIvios8TPzLVACatGrIsEdu5ERmDpNGvoXLJUTWjODeMc5Xk37avpfty9ZR\ns2Fd3n9oIgDdhiTRKLEVO1duOHVhkK1dvx4s/uv7zHrgBTDQqk9XasU5exCZn3Wc9VNnYUpKwBjq\nde1ETMc2VKsfw9q/Tmfb3PlUb1ifuF4XARDdtiVpydv58pFnraHtQddRJdzZCnfR8ePse/fdsoyR\niYlEtGnDjsmT8eTlAVC1fn3iBg8GoFqrVhzfto3kp59GRIi9/nrcYc5n3D/jXSgpwRhDZCcr486X\nTmUMqV+fBoMGl62TsW4tkYmd/X5F8ivr9/FSr+Y80DGO7cdy+HSXVTEc0rIuvWNr4DGG4wXFjF61\n0y/5BHj18haEVQlCgG3pOYz+2jrgGd0jnpBKQfz9auvA96fsAu6cl+yXnOqXE1PRiXbevpOff/TR\ne8ArxpgSEXEB44EkrOdYOnCtMea4iDwO3AYUAv8xxjwpIo2AvwMx9vY+MsY8a1/Is8AY84mIxNm3\nW9v3X35eDta5nFcCx4Gb7QuE/t/t/oI/1bFhdG8IEqtSkVv8jZ+TnF2I2xpuyixY4OckZxdZ5Wr2\nZ8/3d4xzigtPYveJwM7YpFoS69I/93eMc+pc6yqmbl/s7xjndE+Lvjyx/mt/xzinCYl9GLJ8ub9j\nnNX7Pa1q/qBlgZtxdi8rY6t3V/g5ydml3GEVIhq8stTPSc7uwMje4MeTGItK/uuXcf9KrvZ+PcLx\nSWXTGHPWs/Ltz9p80p7OnDcR60r18m37gCsqWHZoudv7gdZnmVd6uHvaB0D+ku0qpZRSSqlfx/8D\n+UoppZRS6n+Wfl2lUkoppZQPVPTRU+cDrWwqpZRSSinHaGVTKaWUUsoH/P1JBP6ilU2llFJKKeUY\nrWwqpZRSSvnE+VnjOz//aqWUUkop5RPa2VRKKaWUUo7RYXSllFJKKR/Qjz5SSimllFLKy7SyqZRS\nSinlE1rZVEoppZRSyqu0sqmUUkop5QP6oe5KKaWUUkp5mXY2lVJKKaWUY3QYXSmllFLKJ87PGt/5\n+VcrpZRSSimf0MqmUkoppZQPnK8f6i7GGH9n+F+iD6ZSSikV2PzY49vlp35CM7/2crWzGcBEZLgx\nZpq/c5yLZvQOzegdmtE7NKN3aEbv+CNkVOem52wGtuH+DvALaEbv0IzeoRm9QzN6h2b0jj9CRnUO\n2tlUSimllFKO0c6mUkoppZRyjHY2A9sf4RwVzegdmtE7NKN3aEbv0Ize8UfIqM5BLxBSSimllFKO\n0cqmUkoppZRyjHY2lVJKKaWUY7Sz6WMi8o6IpInI1nJt7URktYgki8h8Eal2xjoNRCRHREaVa3tQ\nRLaKSIqIPOSvjCISJyJ5IvJfe5pabp1O9vK7ReR1EfHah8p6MePzInJQRHK8lc2bGUUkREQ+F5Ed\n9v96YqBltOd9ISKb7YxTRSQokPKVW/ez8tsKpIwiskxEdpabVzsAM1YWkWkisst+Tl4fSBlFJLxc\n239F5KiITAmkjPa8gfbyW+zXTs0AzHiznS9FRF70Vr5fm9Ge19ael2LPD7bbHdvHKC8zxujkwwno\nAXQEtpZrWwf0tG8PA547Y51PgbnAKPv31sBWIATrK0e/Apr6IyMQV365M7azFrgQ69saFgL9AjDj\nBUAMkOPP//XZMtr/49727crAygB9HKvZP8V+vt4SSPns+dcBs8+1jJ8fw2VAorefh17O+Aww3r7t\nAmoGWsYztrkB6BFIGbHes9NKHzvgRWBcgGWsARwAatm/zwT6+CmjG9gCtCuXLci+7dg+RifvTlrZ\n9DFjzAog44zmBGCFfftLoKxaICLXAnuBlHLLtwDWGGNyjTHFwHJggL8yVkREYrA6IKuN9a4wC7g2\nkDLa21ljjDnsrVxnbPt3Z7T/x0vt24XARqB+IGW0t3PCvunG6hR75cpDb+UTkTBgJDDeG7nK81ZG\nJ3kx4zBggr3NEmPM0QDMCICINAVqYx2geYWXMoo9hdqVuGrATwGWsTGwyxiTbv/+1S9Yx6mMfYEt\nxpjN9rrHjDEep/cxyru0sxkYtgL97ds3ArEAIhIKPIZVTThz+R4iUkNEQoArS9fxdUZbIxHZJCLL\nRaS73VYP+LHcMj/abYGU0R9+c0YRqQ4kAV8HYkYRWYRVsckGPgmwfM8BLwO5Dub6vRkB3rWHM8f4\nYEjwV2W0n38Az4nIRhGZKyLRgZTxDAOBj+2OSMBkNMYUAfcCyVidzJbA9EDKCOwGmtvD7G6sTpy/\n9jHNACMii+zn3aN2uz/2Meo30s5mYBgG3CciG4BwoNBufwZ41Rhz2vmExpjtwCSso78vgM1AsZ8y\nHgYaGGM6YFWOZtvn2lS0o3T6Tf/XZvSH35TRfsP/EHjdGLM3EDMaYy7HOiWhCnBJoOQTkfZAE2PM\nvxzM9Lsy2vMGG2PaAN3t6dYAy+jGqqp/Y4zpCKwGXgqwjOXdgvWacdqvfT5WwupsdgDqYg0RPxFI\nGY0xmXbGj7Eqw/vx3z7GDVwMDLZ/DhCRPvhnH6N+K3+P45+PE+c+J6oZsNa+Xfoi3w9kYQ07jKhg\nnReAP/sjYwXzlgGJWJ2OHeXaBwJvBVLGM9q8fs6mNzMC72B1NAM2Y7n224E3AiUf1k7zJ/t19CPW\nTmxZgD+GQ735GHrpcRTgJOCy22OBlEDKWO73dljDwF7L5sXHsTPwdbn2HsB/AiljBe3DgRf9kRHr\noGFGuXljgEfwwT5GJ+9NWtkMAGJfdSoiLuApYCqAMaa7MSbOGBMHTAFeMMa8ccY6DbAufHD0CP5s\nGUWklthXHotIY6ApsNdY50Fmi8gF9nDgbcC8QMroZBZvZhSR8UAE4NVPHfBWRhEJE+v8qdIK7JXA\njkDJZ4z5uzGmrv06uhirE9LLqXy/JaOIuMW+ItmufF2NNawYMBmNtUefD/SyN9EH2BZIGcutOhDf\nVDV/S8ZDQEsRqWVv4jJge4BlLL9OJPBn4G1/ZAQWAW3F+mQON9AT2OaPfYz6Hfzd2z3fJqw3wMNA\nEVaV5U7gQWCXPU3E/manM9Ybh301uv37Sqw3+s148SrBX5sR6yTuFDvHRiCp3HYSsXaYe4A3Kvq7\nAiDji/b6JfbPcYGUEWvY0mDtjP5rT3cFWMZorCtJt9jz/wq4AyXfGduLw/tXo3vjMQzFunK69DF8\nDfuK20DJaM9riHURxxasc4cbBFpGe/5eoLk3/89efhzvwXpNb8HqwNcIwIwfYu1jtuGlT5f4LRnt\n5YfYObdSrsKKg/sYnbw76ddVKqWUUkopx+gwulJKKaWUcox2NpVSSimllGO0s6mUUkoppRyjnU2l\nlFJKKeUY7WwqpZRSSinHaGdTKaWUUko5RjubSinlBaUfjq2UUup02tlUSp13ROQ5EXmw3O/Pi8gD\nIvKIiKwTkS0i8ky5+f8WkQ0ikiIiw8u154jIsyLyHXChj/8MpZT6Q9DOplLqfDQd6zvcS78e7xYg\nFevr+roA7YFOItLDXn6YMaYT1jeWPCAiNez2UKxvJOpqjFnlyz9AKaX+KNz+DqCUUr5mjNkvIsdE\npAPW121uAjoDfe3bAGFYnc8VWB3MAXZ7rN1+DPAAn/oyu1JK/dFoZ1Mpdb56GxgK1AHeAfoAE4wx\nb5VfSER6AZcCFxpjckVkGRBsz843xnh8FVgppf6IdBhdKXW++hdwBVZFc5E9DRORMAARqScitYEI\nINPuaDYHLvBXYKWU+iPSyqZS6rxkjCkUkaVAll2dXCwiLYDVIgKQAwwBvgDuEZEtwE5gjb8yK6XU\nH5EYY/ydQSmlfM6+MGgjcKMx5nt/51FKqf9VOoyulDrviEhLYDfwtXY0lVLKWVrZVEoppZRSjtHK\nplJKKaWUcox2NpVSSimllGO0s6mUUkoppRyjnU2llFJKKeUY7WwqpZRSSinH/B/9UuFuq3C08QAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights = sns.load_dataset(\"flights\")\n",
    "print(flights.head(5))\n",
    "# explore.heatmap(data=data[['Sex','Survived']])\n",
    "flights = flights.pivot(\"month\", \"year\", \"passengers\")\n",
    "explore.heatmap(data=flights,output_path='./output/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}