{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 计算传播与统计初步\n",
    "***\n",
    "***\n",
    "# 分析天涯论坛的帖子\n",
    "***\n",
    "***\n",
    "\n",
    "王成军 \n",
    "\n",
    "wangchengjun@nju.edu.cn\n",
    "\n",
    "计算传播网 http://computational-communication.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 使用pandas清洗泰坦尼克数据\n",
    "# 分析天涯回帖数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/matplotlib.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/pandas_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 使用pandas清洗泰坦尼克数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch            Ticket     Fare Cabin Embarked  \n",
      "0      0         A/5 21171   7.2500   NaN        S  \n",
      "1      0          PC 17599  71.2833   C85        C  \n",
      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3      0            113803  53.1000  C123        S  \n",
      "4      0            373450   8.0500   NaN        S  \n",
      "   PassengerId  Pclass                                          Name     Sex  \\\n",
      "0          892       3                              Kelly, Mr. James    male   \n",
      "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
      "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
      "3          895       3                              Wirz, Mr. Albert    male   \n",
      "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
      "\n",
      "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
      "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
      "1  47.0      1      0   363272   7.0000   NaN        S  \n",
      "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
      "3  27.0      0      0   315154   8.6625   NaN        S  \n",
      "4  22.0      1      1  3101298  12.2875   NaN        S  \n"
     ]
    }
   ],
   "source": [
    "# Import the Pandas library\n",
    "import pandas as pd\n",
    "# Load the train and test datasets to create two DataFrames\n",
    "train_url = \"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv\"\n",
    "train = pd.read_csv(train_url)\n",
    "\n",
    "test_url = \"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/test.csv\"\n",
    "test = pd.read_csv(test_url)\n",
    "#Print the `head` of the train and test dataframes\n",
    "print(train.head())\n",
    "print(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "train.to_csv('/Users/chengjun/github/cjc2016/data/tatanic_train.csv')\n",
    "test.to_csv('/Users/chengjun/github/cjc2016/data/tatanic_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "You can easily explore a DataFrame  \n",
    " - .describe() summarizes the columns/features of the DataFrame, including the count of observations, mean, max and so on. \n",
    " - Another useful trick is to look at the dimensions of the DataFrame. This is done by requesting the .shape attribute of your DataFrame object. (ex. your_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 从本机读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('/Users/chengjun/github/cjc2016/data/tatanic_train.csv', sep = \",\", header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PassengerId  Survived  Pclass  \\\n",
       "0           0            1         0       3   \n",
       "1           1            2         1       1   \n",
       "2           2            3         1       3   \n",
       "3           3            4         1       1   \n",
       "4           4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>222.500000</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>667.500000</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  PassengerId    Survived      Pclass         Age  \\\n",
       "count  891.000000   891.000000  891.000000  891.000000  714.000000   \n",
       "mean   445.000000   446.000000    0.383838    2.308642   29.699118   \n",
       "std    257.353842   257.353842    0.486592    0.836071   14.526497   \n",
       "min      0.000000     1.000000    0.000000    1.000000    0.420000   \n",
       "25%    222.500000   223.500000    0.000000    2.000000   20.125000   \n",
       "50%    445.000000   446.000000    0.000000    3.000000   28.000000   \n",
       "75%    667.500000   668.500000    1.000000    3.000000   38.000000   \n",
       "max    890.000000   891.000000    1.000000    3.000000   80.000000   \n",
       "\n",
       "            SibSp       Parch        Fare  \n",
       "count  891.000000  891.000000  891.000000  \n",
       "mean     0.523008    0.381594   32.204208  \n",
       "std      1.102743    0.806057   49.693429  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    7.910400  \n",
       "50%      0.000000    0.000000   14.454200  \n",
       "75%      1.000000    0.000000   31.000000  \n",
       "max      8.000000    6.000000  512.329200  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 13)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passengers that survived vs passengers that passed away\n",
    "train[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.616162\n",
       "1    0.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As proportions\n",
    "train[\"Survived\"].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    468\n",
       "1    109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Males that survived vs males that passed away\n",
    "train[\"Survived\"][train[\"Sex\"] == 'male'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    233\n",
       "0     81\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Females that survived vs Females that passed away\n",
    "train[\"Survived\"][train[\"Sex\"] == 'female'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.811092\n",
       "1    0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized male survival\n",
    "train[\"Survived\"][train[\"Sex\"] == 'male'].value_counts(normalize = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.742038\n",
       "0    0.257962\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized female survival\n",
    "train[\"Survived\"][train[\"Sex\"] == 'female'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.0\n",
      "1    0.0\n",
      "2    0.0\n",
      "Name: Child, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Create the column Child, and indicate whether child or not a child. Print the new column.\n",
    "train[\"Child\"] = float('NaN')\n",
    "train.Child[train.Age < 18] = 1\n",
    "train.Child[train.Age >= 18] = 0\n",
    "print train.Child[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.539823\n",
       "0    0.460177\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized Survival Rates for under 18\n",
    "train.Survived[train.Child == 1].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.618968\n",
       "1    0.381032\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized Survival Rates for over 18\n",
    "train.Survived[train.Child == 0].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    1\n",
      "2    0\n",
      "Name: Survived, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Create a copy of test: test_one\n",
    "test_one = test\n",
    "# Initialize a Survived column to 0\n",
    "test_one['Survived'] = 0\n",
    "# Set Survived to 1 if Sex equals \"female\" and print the `Survived` column from `test_one`\n",
    "test_one.Survived[test_one.Sex =='female'] = 1\n",
    "\n",
    "print test_one.Survived[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#Convert the male and female groups to integer form\n",
    "train[\"Sex\"][train[\"Sex\"] == \"male\"] = 0\n",
    "train[\"Sex\"][train[\"Sex\"] == \"female\"] = 1\n",
    "\n",
    "#Impute the Embarked variable\n",
    "train[\"Embarked\"] = train[\"Embarked\"].fillna('S')\n",
    "\n",
    "#Convert the Embarked classes to integer form\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"S\"] = 0\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"C\"] = 1\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"Q\"] = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 分析天涯回帖数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
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       "      <td>/post-free-2839539-1.shtml</td>\n",
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       "\n",
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/chengjun/github/cjc2016/data/tianya_bbs_threads_list.txt', sep = \"\\t\", header=None)\n",
    "df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>/post-free-2848797-1.shtml</td>\n",
       "      <td>牧阳光</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>82779</td>\n",
       "      <td>625</td>\n",
       "      <td>2012-10-28 19:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>【吴钩一言堂】漳州PX成\"二踢响\",说明老百姓科学素质不低</td>\n",
       "      <td>/post-free-5043260-1.shtml</td>\n",
       "      <td>挑灯看吴钩</td>\n",
       "      <td>http://www.tianya.cn/36959960</td>\n",
       "      <td>45304</td>\n",
       "      <td>219</td>\n",
       "      <td>2015-04-07 21:30</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PX是否有毒?宁波镇海事件谁在推波助澜(转载)</td>\n",
       "      <td>/post-free-2848995-1.shtml</td>\n",
       "      <td>zzjzzpgg12</td>\n",
       "      <td>http://www.tianya.cn/53134970</td>\n",
       "      <td>38132</td>\n",
       "      <td>835</td>\n",
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       "                           title                        link      author  \\\n",
       "0     【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml          贾也   \n",
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       "4        PX是否有毒?宁波镇海事件谁在推波助澜(转载)  /post-free-2848995-1.shtml  zzjzzpgg12   \n",
       "\n",
       "                     author_page   click  reply              time  \n",
       "0  http://www.tianya.cn/50499450  194675   2703  2012-10-29 07:59  \n",
       "1  http://www.tianya.cn/74341835   88244   1041  2012-10-24 12:41  \n",
       "2  http://www.tianya.cn/36535656   82779    625  2012-10-28 19:11  \n",
       "3  http://www.tianya.cn/36959960   45304    219  2015-04-07 21:30  \n",
       "4  http://www.tianya.cn/53134970   38132    835  2012-10-28 21:08  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.rename(columns = {0:'title', 1:'link', 2:'author',3:'author_page', 4:'click', 5:'reply', 6:'time'})\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "da = pd.read_csv('/Users/chengjun/github/cjc2016/data/tianya_bbs_threads_author_info.txt', sep = \"\\t\", header=None)\n",
    "da[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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     "output_type": "execute_result"
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    "da=da.rename(columns = {0:'author_page', 1:'followed_num', 2:'fans_num',3:'post_num', 4:'comment_num'})\n",
    "da[:5]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "467"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([df,da], axis=1)\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>author</th>\n",
       "      <th>author_page</th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>time</th>\n",
       "      <th>author_page</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>贾也</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>194675</td>\n",
       "      <td>2703</td>\n",
       "      <td>2012-10-29 07:59</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>152</td>\n",
       "      <td>27452</td>\n",
       "      <td>1020</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁波镇海PX项目引发群体上访 当地政府发布说明(转载)</td>\n",
       "      <td>/post-free-2839539-1.shtml</td>\n",
       "      <td>无上卫士ABC</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>88244</td>\n",
       "      <td>1041</td>\n",
       "      <td>2012-10-24 12:41</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宁波准备停止PX项目了,元芳,你怎么看?</td>\n",
       "      <td>/post-free-2848797-1.shtml</td>\n",
       "      <td>牧阳光</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>82779</td>\n",
       "      <td>625</td>\n",
       "      <td>2012-10-28 19:11</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>816</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title                        link   author  \\\n",
       "0   【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml       贾也   \n",
       "1  宁波镇海PX项目引发群体上访 当地政府发布说明(转载)  /post-free-2839539-1.shtml  无上卫士ABC   \n",
       "2         宁波准备停止PX项目了,元芳,你怎么看?  /post-free-2848797-1.shtml      牧阳光   \n",
       "\n",
       "                     author_page   click  reply              time  \\\n",
       "0  http://www.tianya.cn/50499450  194675   2703  2012-10-29 07:59   \n",
       "1  http://www.tianya.cn/74341835   88244   1041  2012-10-24 12:41   \n",
       "2  http://www.tianya.cn/36535656   82779    625  2012-10-28 19:11   \n",
       "\n",
       "                     author_page followed_num fans_num post_num comment_num  \n",
       "0  http://www.tianya.cn/50499450          152    27452     1020        1341  \n",
       "1  http://www.tianya.cn/74341835            0        1        2           5  \n",
       "2  http://www.tianya.cn/36535656           19       28      816        1268  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# extract date from datetime\n",
    "date = map(lambda x: x[:10], data.time)\n",
    "data['date'] = pd.to_datetime(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tslib.Timestamp"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert str to datetime format\n",
    "data.time = pd.to_datetime(data.time)\n",
    "data['month'] = data.time.dt.month\n",
    "data['year'] = data.time.dt.year\n",
    "data['day'] = data.time.dt.day\n",
    "type(data.time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>author</th>\n",
       "      <th>author_page</th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>time</th>\n",
       "      <th>author_page</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>贾也</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>194675</td>\n",
       "      <td>2703</td>\n",
       "      <td>2012-10-29 07:59:00</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>152</td>\n",
       "      <td>27452</td>\n",
       "      <td>1020</td>\n",
       "      <td>1341</td>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁波镇海PX项目引发群体上访 当地政府发布说明(转载)</td>\n",
       "      <td>/post-free-2839539-1.shtml</td>\n",
       "      <td>无上卫士ABC</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>88244</td>\n",
       "      <td>1041</td>\n",
       "      <td>2012-10-24 12:41:00</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宁波准备停止PX项目了,元芳,你怎么看?</td>\n",
       "      <td>/post-free-2848797-1.shtml</td>\n",
       "      <td>牧阳光</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>82779</td>\n",
       "      <td>625</td>\n",
       "      <td>2012-10-28 19:11:00</td>\n",
       "      <td>http://www.tianya.cn/36535656</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>816</td>\n",
       "      <td>1268</td>\n",
       "      <td>2012-10-28</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title                        link   author  \\\n",
       "0   【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml       贾也   \n",
       "1  宁波镇海PX项目引发群体上访 当地政府发布说明(转载)  /post-free-2839539-1.shtml  无上卫士ABC   \n",
       "2         宁波准备停止PX项目了,元芳,你怎么看?  /post-free-2848797-1.shtml      牧阳光   \n",
       "\n",
       "                     author_page   click  reply                time  \\\n",
       "0  http://www.tianya.cn/50499450  194675   2703 2012-10-29 07:59:00   \n",
       "1  http://www.tianya.cn/74341835   88244   1041 2012-10-24 12:41:00   \n",
       "2  http://www.tianya.cn/36535656   82779    625 2012-10-28 19:11:00   \n",
       "\n",
       "                     author_page followed_num fans_num post_num comment_num  \\\n",
       "0  http://www.tianya.cn/50499450          152    27452     1020        1341   \n",
       "1  http://www.tianya.cn/74341835            0        1        2           5   \n",
       "2  http://www.tianya.cn/36535656           19       28      816        1268   \n",
       "\n",
       "        date  month  year  day  \n",
       "0 2012-10-29     10  2012   29  \n",
       "1 2012-10-24     10  2012   24  \n",
       "2 2012-10-28     10  2012   28  "
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Statsmodels\n",
    "\n",
    "http://statsmodels.sourceforge.net/\n",
    "\n",
    "![](./img/statsmodels_hybi_banner.png)\n",
    "\n",
    "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. \n",
    "\n",
    "An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. \n",
    "\n",
    "Researchers across fields may find that statsmodels fully meets their needs for statistical computing and data analysis in Python. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Features include:\n",
    "\n",
    "- Linear regression models\n",
    "- Generalized linear models\n",
    "- Discrete choice models\n",
    "- Robust linear models\n",
    "- Many models and functions for time series analysis\n",
    "- Nonparametric estimators\n",
    "- A collection of datasets for examples\n",
    "- A wide range of statistical tests\n",
    "- Input-output tools for producing tables in a number of formats and for reading Stata files into NumPy and Pandas.\n",
    "- Plotting functions\n",
    "- Extensive unit tests to ensure correctness of results\n",
    "- Many more models and extensions in development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GEE   GLM   GLS   GLSAR   Logit   MNLogit   MixedLM   NegativeBinomial   NominalGEE   OLS   OrdinalGEE   PHReg   Poisson   ProbPlot   Probit   QuantReg   RLM   WLS   __builtins__   __doc__   __file__   __name__   __package__   add_constant   categorical   cov_struct   datasets   distributions   emplike   families   formula   genmod   graphics   iolib   load   nonparametric   qqline   qqplot   qqplot_2samples   regression   robust   show_versions   stats   test   tools   tsa   version   webdoc'"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'   '.join(dir(sm))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CompareCox   CompareJ   CompareMeans   DescrStatsW   Describe   FTestAnovaPower   FTestPower   GofChisquarePower   HetGoldfeldQuandt   NormalIndPower   Runs   TTestIndPower   TTestPower   __builtins__   __doc__   __file__   __name__   __package__   acorr_breush_godfrey   acorr_ljungbox   anova_lm   binom_test   binom_test_reject_interval   binom_tost   binom_tost_reject_interval   breaks_cusumolsresid   breaks_hansen   chisquare_effectsize   cochrans_q   compare_cox   compare_j   corr_clipped   corr_nearest   cov_cluster   cov_cluster_2groups   cov_hac   cov_hc0   cov_hc1   cov_hc2   cov_hc3   cov_nearest   cov_nw_panel   cov_white_simple   diagnostic   durbin_watson   fdrcorrection   fdrcorrection_twostage   gof   gof_chisquare_discrete   het_arch   het_breushpagan   het_goldfeldquandt   het_white   jarque_bera   lillifors   linear_harvey_collier   linear_lm   linear_rainbow   mcnemar   moment_helpers   multicomp   multipletests   normal_ad   omni_normtest   power_binom_tost   power_ztost_prop   powerdiscrepancy   proportion_confint   proportion_effectsize   proportions_chisquare   proportions_chisquare_allpairs   proportions_chisquare_pairscontrol   proportions_ztest   proportions_ztost   recursive_olsresiduals   runstest_1samp   runstest_2samp   sandwich_covariance   se_cov   stattools   symmetry_bowker   tt_ind_solve_power   tt_solve_power   ttest_ind   ttost_ind   ttost_paired   tukeyhsd   unitroot_adf   zconfint   zt_ind_solve_power   ztest   ztost'"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'   '.join(dir(sm.stats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1534.957173</td>\n",
       "      <td>18.907923</td>\n",
       "      <td>7.432548</td>\n",
       "      <td>2012.620985</td>\n",
       "      <td>17.961456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11099.249834</td>\n",
       "      <td>144.869921</td>\n",
       "      <td>3.084860</td>\n",
       "      <td>1.795269</td>\n",
       "      <td>9.491730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>194675.000000</td>\n",
       "      <td>2703.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               click        reply       month         year         day\n",
       "count     467.000000   467.000000  467.000000   467.000000  467.000000\n",
       "mean     1534.957173    18.907923    7.432548  2012.620985   17.961456\n",
       "std     11099.249834   144.869921    3.084860     1.795269    9.491730\n",
       "min        11.000000     0.000000    1.000000  2006.000000    1.000000\n",
       "25%        42.500000     0.000000    5.000000  2013.000000    8.000000\n",
       "50%        84.000000     0.000000    6.000000  2013.000000   23.000000\n",
       "75%       322.000000     4.000000   11.000000  2013.000000   25.000000\n",
       "max    194675.000000  2703.000000   12.000000  2015.000000   31.000000"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1534.9571734475376, 11087.35990002894, 716825)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.mean(data.click), np.std(data.click), np.sum(data.click)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1534.9571734475376, 122929549.55276975, 11087.35990002894, 716825.0)"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不加权的变量描述\n",
    "d1 = sm.stats.DescrStatsW(data.click, weights=[1 for i in data.click])\n",
    "d1.mean, d1.var, d1.std, d1.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83335.963986409959, 6297145701.6868114, 79354.556905617035, 735856562.0)"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加权的变量描述\n",
    "d1 = sm.stats.DescrStatsW(data.click, weights=data.reply)\n",
    "d1.mean, d1.var, d1.std, d1.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.0"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(data.click)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i5PxduJ3AW6rqBuAk8GdDfO5XxVwmuZz+p/qPdEcQi/L9PY5wOAEMXgRd29UEVNWz3Z/P\nAw/TPw03nWQCoDukfK7rfgK4cmD1M3M5W/1l63TfR7miql4YyYtZPBZi/l4V+3VVPV/dSWzgr+jv\nn+BczkuSZfSD4aGqeqQrL8r9cxzh8DiwLsmbk1wG3AnsHcM4Fp0kr+0+VZDkdcAG4Cn683N31+0u\n4MxOtRe4s7tD4WpgHXCwOzR9Mcn6JAE+MGOdu7r27wAHRvuqxiK8/BPTQszfPuA9SVYkWQm8p6st\ndS+by+6X1xm/DXyzazuX8/PXwOGq+tRAbXHun2O6an8r/Sv1U8CWcd5BsJgW4Gr6d28doh8KW7r6\nG4HHujnbD7xhYJ2t9O9iOAJsGKj/YvccU8CnBurLgT1d/avAVeN+3UOewy8A/wX8EPg28Af079QY\n+fx1b/Ap4FvAB8Y9FyOay88CT3b76cP0z5c7l/Obz5uAHw+8x7/e/S5ckPf3+c6pX4KTJDW8IC1J\nahgOkqSG4SBJahgOkqSG4SBJahgOkqSG4SBJahgOkqTG/wFP7mSJURLUoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c50aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.click)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d0feb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.reply, color = 'green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xLvD8zte2v8ALiytL7Fu2L2fwSGcUg36dcAaPpLVyjF6SGmfQS1LjDHpJapxBL0mN88PY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\n+/98sevrgM6isgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d19d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(data.click+1), color='green')\n",
    "plt.hist(np.log(data.reply+1), color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQhugI0IboCP/Cyma/4ZiqfQyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c7f6450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the height and weight to see\n",
    "plt.boxplot([np.log(data.click+1)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mm8OexqKw0Dek1wJ7q+pVgCTbgJuAX37oKJ2Sffv2cezYsfGfT7T37ds3pBlJH+if1b71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\nqWcGvXqaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cecd4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the height and weight to see\n",
    "plt.boxplot([data.click, data.reply])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def transformData(dat):\n",
    "    results = []\n",
    "    for i in dat:\n",
    "        if i != 'na':\n",
    "            results.append( int(i))\n",
    "        else:\n",
    "            results.append(0)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "data.fans_num = transformData(data.fans_num)\n",
    "data.followed_num = transformData(data.followed_num )\n",
    "data.post_num = transformData(data.post_num )\n",
    "data.comment_num = transformData(data.comment_num )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ee28950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the height and weight to see\n",
    "plt.boxplot([np.log(data.click+1), np.log(data.reply+1), \n",
    "             np.log(data.fans_num+1), np.log(data.followed_num + 1)], \n",
    "            labels = ['$Click$', '$Reply$', '$Fans$', '$Followed$'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas自身已经包含了boxplot的功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Usim5T4kI9ykiaomNYQCFQgEHDhwAAIyPj9cawIVCwWFUlAc8mbLzZQyB7yYmJrhfNVF/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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11eb15450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "data.boxplot(return_type='dict')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T   _AXIS_ALIASES   _AXIS_IALIASES   _AXIS_LEN   _AXIS_NAMES   _AXIS_NUMBERS   _AXIS_ORDERS   _AXIS_REVERSED   _AXIS_SLICEMAP   __abs__   __add__   __and__   __array__   __array_wrap__   __bool__   __bytes__   __class__   __contains__   __delattr__   __delitem__   __dict__   __dir__   __div__   __doc__   __eq__   __finalize__   __floordiv__   __format__   __ge__   __getattr__   __getattribute__   __getitem__   __getstate__   __gt__   __hash__   __iadd__   __idiv__   __imul__   __init__   __invert__   __ipow__   __isub__   __iter__   __itruediv__   __le__   __len__   __lt__   __mod__   __module__   __mul__   __ne__   __neg__   __new__   __nonzero__   __or__   __pow__   __radd__   __rand__   __rdiv__   __reduce__   __reduce_ex__   __repr__   __rfloordiv__   __rmod__   __rmul__   __ror__   __round__   __rpow__   __rsub__   __rtruediv__   __rxor__   __setattr__   __setitem__   __setstate__   __sizeof__   __str__   __sub__   __subclasshook__   __truediv__   __unicode__   __weakref__   __xor__   _accessors   _add_numeric_operations   _add_series_only_operations   _add_series_or_dataframe_operations   _agg_by_level   _align_frame   _align_series   _apply_broadcast   _apply_empty_result   _apply_raw   _apply_standard   _at   _box_col_values   _box_item_values   _check_inplace_setting   _check_is_chained_assignment_possible   _check_percentile   _check_setitem_copy   _clear_item_cache   _combine_const   _combine_frame   _combine_match_columns   _combine_match_index   _combine_series   _combine_series_infer   _compare_frame   _compare_frame_evaluate   _consolidate_inplace   _construct_axes_dict   _construct_axes_dict_for_slice   _construct_axes_dict_from   _construct_axes_from_arguments   _constructor   _constructor_expanddim   _constructor_sliced   _convert   _count_level   _create_indexer   _dir_additions   _dir_deletions   _ensure_valid_index   _expand_axes   _flex_compare_frame   _from_arrays   _from_axes   _get_agg_axis   _get_axis   _get_axis_name   _get_axis_number   _get_axis_resolvers   _get_block_manager_axis   _get_bool_data   _get_cacher   _get_index_resolvers   _get_item_cache   _get_numeric_data   _get_values   _getitem_array   _getitem_column   _getitem_frame   _getitem_multilevel   _getitem_slice   _iat   _iget_item_cache   _iloc   _indexed_same   _info_axis   _info_axis_name   _info_axis_number   _info_repr   _init_dict   _init_mgr   _init_ndarray   _internal_names   _internal_names_set   _is_cached   _is_datelike_mixed_type   _is_mixed_type   _is_numeric_mixed_type   _is_view   _ix   _ixs   _join_compat   _loc   _maybe_cache_changed   _maybe_update_cacher   _metadata   _needs_reindex_multi   _nsorted   _protect_consolidate   _reduce   _reindex_axes   _reindex_axis   _reindex_columns   _reindex_index   _reindex_multi   _reindex_with_indexers   _repr_fits_horizontal_   _repr_fits_vertical_   _repr_html_   _repr_latex_   _reset_cache   _reset_cacher   _sanitize_column   _series   _set_as_cached   _set_axis   _set_axis_name   _set_is_copy   _set_item   _setitem_array   _setitem_frame   _setitem_slice   _setup_axes   _slice   _stat_axis   _stat_axis_name   _stat_axis_number   _typ   _unpickle_frame_compat   _unpickle_matrix_compat   _update_inplace   _validate_dtype   _values   _xs   abs   add   add_prefix   add_suffix   align   all   any   append   apply   applymap   as_blocks   as_matrix   asfreq   assign   astype   at   at_time   author   author_page   axes   between_time   bfill   blocks   bool   boxplot   click   clip   clip_lower   clip_upper   columns   combine   combineAdd   combineMult   combine_first   comment_num   compound   consolidate   convert_objects   copy   corr   corrwith   count   cov   cummax   cummin   cumprod   cumsum   date   day   describe   diff   div   divide   dot   drop   drop_duplicates   dropna   dtypes   duplicated   empty   eq   equals   eval   ewm   expanding   fans_num   ffill   fillna   filter   first   first_valid_index   floordiv   followed_num   from_csv   from_dict   from_items   from_records   ftypes   ge   get   get_dtype_counts   get_ftype_counts   get_value   get_values   groupby   gt   head   hist   iat   icol   idxmax   idxmin   iget_value   iloc   index   info   insert   interpolate   irow   is_copy   isin   isnull   iteritems   iterkv   iterrows   itertuples   ix   join   keys   kurt   kurtosis   last   last_valid_index   le   link   loc   lookup   lt   mad   mask   max   mean   median   memory_usage   merge   min   mod   mode   month   mul   multiply   ndim   ne   nlargest   notnull   nsmallest   pct_change   pipe   pivot   pivot_table   plot   pop   post_num   pow   prod   product   quantile   query   radd   rank   rdiv   reindex   reindex_axis   reindex_like   rename   rename_axis   reorder_levels   replace   reply   resample   reset_index   rfloordiv   rmod   rmul   rolling   round   rpow   rsub   rtruediv   sample   select   select_dtypes   sem   set_axis   set_index   set_value   shape   shift   size   skew   slice_shift   sort   sort_index   sort_values   sortlevel   squeeze   stack   std   style   sub   subtract   sum   swapaxes   swaplevel   tail   take   time   title   to_clipboard   to_csv   to_dense   to_dict   to_excel   to_gbq   to_hdf   to_html   to_json   to_latex   to_msgpack   to_panel   to_period   to_pickle   to_records   to_sparse   to_sql   to_stata   to_string   to_timestamp   to_wide   to_xarray   transpose   truediv   truncate   tshift   tz_convert   tz_localize   unstack   update   values   var   where   xs   year'"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'   '.join(dir(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BQXFxMZKSkgAASUlJKC4uZkDg5dZu2orWTjGkEjnMpi/hI1dg5IjhEJnbERMdxQ+gQe71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\n+H95EyN3w1gjyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124455b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools import plotting\n",
    "\n",
    "#fig = plt.figure(figsize=(10, 10))\n",
    "plotting.scatter_matrix(data[['click', 'reply', 'post_num','comment_num']]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AbstractMethodError  Appender  AreaPlot  BarPlot  BarhPlot  BasePlotMethods  BoxPlot  FramePlotMethods  HexBinPlot  HistPlot  Index  KdePlot  LinePlot  LooseVersion  MPLPlot  MultiIndex  PandasObject  PeriodIndex  PiePlot  PlanePlot  ScatterPlot  Series  SeriesPlotMethods  _Options  __builtins__  __doc__  __file__  __name__  __package__  _all_kinds  _common_kinds  _dataframe_kinds  _flatten  _gca  _gcf  _get_all_lines  _get_layout  _get_marker_compat  _get_standard_colors  _get_standard_kind  _get_xlim  _grouped_plot  _grouped_plot_by_column  _handle_shared_axes  _klasses  _mpl_ge_1_3_1  _mpl_ge_1_4_0  _mpl_ge_1_5_0  _mpl_le_1_2_1  _plot  _plot_klass  _remove_labels_from_axis  _series_kinds  _set_ticks_props  _shared_doc_df_kwargs  _shared_doc_kwargs  _shared_doc_series_kwargs  _shared_docs  _subplots  andrews_curves  autocorrelation_plot  bootstrap_plot  boxplot  boxplot_frame_groupby  cache_readonly  ceil  colors  com  compat  contextmanager  conv  cycler  deprecate_kwarg  df_ax  df_coord  df_kind  df_note  df_unique  format_date_labels  grouped_hist  hist_frame  hist_series  klass  lag_plot  lmap  lrange  map  mpl_stylesheet  namedtuple  np  parallel_coordinates  plot_frame  plot_params  plot_series  radviz  range  re  remove_na  scatter_matrix  scatter_plot  series_ax  series_coord  series_kind  series_note  series_unique  string_types  table  warnings  zip'"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'  '.join(dir(plotting))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 更多使用pandas.plotting绘图的操作见：\n",
    "http://pandas.pydata.org/pandas-docs/version/0.15.0/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x126704590>"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CKvD3f//3HD58mM7OTv78z/+c733vexw+fJijR49is9mYNm0aP/7xjwGYNWsW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\n5bhwJILXF8AfMvPUC2/j6egFwGGzcNu6i5lTMXllL7raqIhMV4Zh0Nb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08QfLz8dmNQ+5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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1267044d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn # conda install seaborn\n",
    "seaborn.pairplot(data, vars=['click', 'reply', 'post_num', 'comment_num'],\n",
    "                  kind='reg')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x124e85610>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ph3/vHXY5lCFpDx0BHLzsymDNa6yxD/8B+HZ2+NTCAsuQsfyB/WcIWzYh28kF\nNJC9tp43ZHwyZGEiw3M6dB8bwLZtgiA4OKQ7QvtsDJMrt4e2yc0biGA7Fv4IvytypfsROqGDEZbZ\njo3vB4z228cMWb05ZEceFs8C27IJzPB4Cc+QGJJzZciicuAEqkNjmiGPBnjo0pWjb9AUVZbF2+mn\nn86ePXvYt28f9fX1bN26lUcffXRYmyVLlvDMM89wySWX8Jvf/IaamhoNmUpZiPx/DfSm0pgKCxO1\noMLCVFgQscDOHpUzbeDnSK79Xy/x3N5F2V+YQBz43mei/Elvmk+n2kk1JkmdnWRO3fkEJoPvp7Es\nn3AkzJ8Z+Kc/WPT3hplek+GznzE4AwVTbW0VnZ3x3HpOOcIx4/Nq5lI17chnCNbOquJiM+s49tTI\nqqZVkU7Fj95wjCqiVUQj+btMRSRcxSjXPS0Zf/0Xp3Ag9sdxnf2cTnvcuqWTF/7fSVQM/Lb3gQ+r\nbELhMBnbojtq4VoeszJp3p/Rw8md1XyiL0TaCXjuk22EZ9RzUqiRmlr49HmGN9v28sd4L6fNaeDc\nafWHn43qBURe6Mc+4BHMDeFeMR1CR9+GfP+RWIrxAmN480AfH/W7fGJ6hAvmVo/5bN9Z1cX1R3Ax\nKPGvgJE5jsO6detYvXo1xhhWrVpFU1MTzz77LJZlcfXVV/P5z3+enTt3ctFFF1FZWclDDz1U6LRF\n8iJ09nT8tvEfxPb0X15GkoNfxDbwldzS+oHHfz/sNZ7nkUz2ge9y3qfSGJMgHHGO+5e0bVl5OSNQ\nykcQBGRcmxk183nuxk8SAFVDioUThrQdeo7iJzg4slIJXMuhIy0WS+ecBByhmAnZuF+a/J7KcmBb\nFheeoH2XL2VZvAEsXryYxYuHH8txzTXXDJu+9957JzMlkbIXCoWorj7YCxYEAYlEP56XxvfTBMYl\nk8kUMEMpZa7rEbKrqZutURKZ2sq2eBORwrNtm+nTD/61bYyhuipMT3cLvu/iB2ls2xDJ47CilKd0\nymd6VSMVFeV/H1aRo1HxJiKTxrIsKioqmVFzsOfEdV3S6X4838UPXMAjHD7+oVYpL57nE/gRamfN\n02dCZICKNxEpqEgkQiRSm5v2fZ9kMo7npXJDraGQPf67QUjJSaczRCO1VM+YWehURIqKvg1FpKg4\njjMw1JodbjXGkEolcN0kfpDGD1wNtZY5YwzplGFGzXwikUih0xEpOireRKSoWZZFZWUVlZVVuXnp\ndJp0uh++uwk9AAAgAElEQVQ/cEmnAtx0hnAkNOpNuqV0ZDIeNlXUza7X+ykyChVvIlJyotEo0Wj2\nXpH19dWYoJtEog/fS+MHaYzxCIVtHMc5SiQpJqmUR1VlPdOmTS90KiJFTcWbiJQ8x3Gorj54XJQx\nhmQyTiaTxA9cfD+NE7IIl/rVbcuU7wf4XojZsxbopASRMdA3mYiUHcuyBnpvDvbgpFIp0m4/vu8S\nBG7ubhAamissN+0RCc9gZm3t0RuLCKDiTUSmiIqKCioqKnLTg3eD8LzsSRDGZIhE9JU4WbInJQTU\nVJ9AJBItdDoiJUXfVCIyJY12N4jAD+GmEwOXKLF0iZIJYFk2gV/J7NoG9XyKHAd9K4mIcPBuELWz\nqvG9aQOXKEniuoncJUosyxCN6hIl4zVtWhW1sw69t6iIjJWKNxGREWQvUTKNysqDt2NyXZdUqg8/\nyOD7abA8IjpuTkQmWVkWbz09Pdxxxx3s27eP+fPn89hjj1FdXT2sTUtLC3fffTcdHR3Yts1VV13F\nddddV6CMRaQUZO8GMTs37ft+9hIlvpu7G0Q47OgSJSIyocrynOxNmzaxaNEiXn/9dc4991w2btx4\nWBvHcVi7di1bt27l2Wef5ZlnnuGDDz4oQLYiUqoGL1Eyc0YDs2tPpK72ZCKhOgKvgoxrk0pmcN1M\nodMUkTJTlj1vO3bs4Ac/+AEAK1eu5Nprr2XNmjXD2tTX11NfXw9AVVUVTU1NtLa20tTUNOn5DvJ9\nH/fdn2G1jlxEds2qGHG+iBSHkS5RUlMdoa/nQO56c5YV6G4QIjIuZVm8dXZ2UldXB2SLtM7OziO2\n//jjj3nvvfdYuHDhZKQ3KsdxcBbOhZMXjLh8Vqx/kjMSkfGKRqPU1Bwcas1eoqR/2N0gwhFHF6cV\nkTEr2eLthhtuoL29/bD5t99++2HzjvQXbjwe57bbbuOee+6hqqpq1HZD1ddXH73RcWhpiXOkP8Yr\nKyPjWvdE5T0Z8Us1dqHke5sUL9/xDr9EiZtJ4vtpfN/FCUE4PPpZrbW1Y/uuGhOTv1DHovTeM8Ur\nZDwZrmSLtyeffHLUZbNnz6a9vZ26ujra2tqoHeXK3Z7ncdttt3H55Zdz4YUXjnndbW19x5zvWJkj\nfJEmk+5xr7u+vnpC857I+KUaezB+IeRzm/K9jxRvJBYwDZiGY0MqkaTHjWdPhAjS2LYhEskWc7W1\nVXR2xvOWX+2sPBaCx6D03zPFm8x4MlxZ9tNfcMEFvPDCCwBs2bKFJUuWjNjunnvu4ZRTTuH666+f\nzPRERI6ooqKSGTV11M46gfrZn6Rm+okQTMfLhEmnA1IplyAICp2miBRIWRZvN910Ez/96U9pbm7m\n7bff5uabbwagtbWVW265BYBf/epXvPzyy7z99tusWLGClStXsmvXrkKmLSIyonA4THX1LGbNnENj\n/SeZPeuTONYsfC+Km4ZUysXzvEKnKSKTpGSHTY9k5syZbN68+bD5DQ0NucuGnHXWWbz77ruTnJmI\nyPgN3g0CagAG7gaRwHWTubtBDB1qFZHyUpbFm4jIVJK9G0QVlZUHj19Lp9O4bhxv4ALCuhuESPlQ\n8VZEfN8n9bvfU9HaOuLy9umzRpwvInKoaDRKNBrNTefuBjFwiZLAZAiHdScIkVKk4q2IOI5D+MwA\nmpIjLq/dN2+SMxKRcjF4N4hBxhiSyTgV0cKcbSoix0/FWxHxfZ/oP++n4qPuEZcn62ZMckYiUq4G\n7wZRUVFZ6FRE5BipeCsijuNw4ad7OXHByHdScLunTXJGIiIiUmxUvBUR3/f54GcpOn4/8tsyr+nI\nt/kSERGR8qfirYg4jsOlf1rJn54w8uX3/tmMfKcIERERmTrK8iK9IiIiIuVKxZuIiIhICVHxJiIi\nIlJCVLyJiIiIlJCyLN56enpYvXo1zc3N3HjjjfT19Y3aNggCVq5cya233jqJGYqIiIgcn7Is3jZt\n2sSiRYt4/fXXOffcc3M3ox/JU089RVNT0yRmJyIiInL8yrJ427FjBytXrgRg5cqVvPHGGyO2a2lp\nYefOnVx11VWTmd64+L7PBx/8v1F/fN8vdIoiIiIygcryOm+dnZ3U1dUBUF9fT2fnyBe3ffDBB7n7\n7ruPOKw62TwvIONbIy4LrICPPvo3fvHVbZxQNeew5fvjLfDoJTQ1/QmrVl02bFkkEsJ1PQCee+4l\ngMPaDDWWNoPtVq26bFj80dqMJc5IBmPnK+/BNiIiIqXIMsaYQidxPG644Qba29sPm3/77bezdu1a\nfv7zn+fmnXvuubzzzjvD2v34xz9m165d3Hvvvbzzzjs8+eSTfPvb357wvEVERETGo2R73p588slR\nl82ePZv29nbq6upoa2ujtvbwOxP8+te/5s0332Tnzp2k02ni8Th33303Dz/88ESmLSIiIjIuJdvz\ndiSPPPIIM2bM4Oabb2bTpk309vayZs2aUdv//Oc/53vf+5563kRERKToleUJCzfddBM//elPaW5u\n5u233+bmm28GoLW1lVtuuaXA2YmIiIgcv7LseRMREREpV2XZ8yYiIiJSrlS8iYiIiJQQFW8iIiIi\nJUTFm4iIiEgJUfEmIiIiUkJUvImIiIiUEBVvIiIiIiVExZuIiIhICVHxJiIiIlJCVLyJiIiIlBAV\nbyIiIiIlRMWbiIiISAlR8SYiIiJSQoq6eOvr6+O2225j2bJlLF++nN/+9rf09PSwevVqmpubufHG\nG+nr68u137hxI0uXLmXZsmW89dZbufl/+MMfuPTSS2lubmbDhg25+a7rcscdd7B06VKuvvpq9u/f\nP6nbJyIiInKsirp427BhA5///Od59dVX+Yd/+AdOPvlkNm3axKJFi3j99dc599xz2bhxIwDvv/8+\nr776Ktu2beOJJ57g/vvvxxgDwNe//nU2bNjA66+/zkcffcRPfvITAJ577jlmzJjB9u3buf7663nk\nkUcKtq0iIiIiY1G0xVt/fz+//OUvufLKKwEIhUJUV1ezY8cOVq5cCcDKlSt54403AHjzzTe55JJL\nCIVCzJ8/nwULFrB7927a2tqIx+MsXLgQgBUrVuReMzRWc3MzP/vZzyZ7M0VERESOSdEWbx9//DGz\nZs1i7dq1rFy5knXr1pFMJuno6KCurg6A+vp6Ojs7AYjFYsydOzf3+sbGRmKxGLFYjDlz5hw2H6C1\ntTW3zHEcampq6O7unqxNFBERETlmRVu8eZ7Hv/zLv/DlL3+ZLVu2UFlZyaZNm7Asa1i7Q6fHY3CY\ndbxtRApJn1EpdvqMioxPqNAJjGbOnDnMmTOH008/HYClS5fyxBNPMHv2bNrb26mrq6OtrY3a2log\n26N24MCB3OtbWlpobGw8bH4sFqOxsRGAhoaGXDvf9+nv72fmzJlHzMuyLNra+o7Y5njV11eXZOyJ\njl+qsQfjT7Z8f0bzvY8Ur/jiTTZ9RhXvWOPJcEXb81ZXV8fcuXP58MMPAXj77bc55ZRTuOCCC3jh\nhRcA2LJlC0uWLAHgggsuYNu2bbiuy969e9mzZw8LFy6kvr6e6upqdu/ejTGGF198cdhrtmzZAsBr\nr73GeeedV4AtFRERERm7ou15A/jrv/5r1qxZg+d5nHjiiTz00EP4vs/tt9/O888/z7x583jssccA\nOOWUU3KXFAmFQtx33325IdV7772XtWvXkk6nWbx4MYsXLwbgqquu4q677mLp0qXMnDmTRx99tGDb\nKiIiIjIWRV28ffrTn+b5558/bP7mzZtHbH/LLbdwyy23HDb/tNNO4+WXXz5sfiQS4fHHHx93niIi\nIiKTpWiHTUVERETkcCreREREREqIijcRERGREqLiTURERKSEqHgTERERKSEq3kRERERKSFFfKkRE\nisMr33mOCis66nLP8bn4L1ZMYkYiIlOXijcROaqqXwV8sWb0O5C8ktgJfzF5+YiITGUaNhUREREp\nISreREREREpIURdvQRCwcuVKbr31VgB6enpYvXo1zc3N3HjjjfT19eXabty4kaVLl7Js2TLeeuut\n3Pw//OEPXHrppTQ3N7Nhw4bcfNd1ueOOO1i6dClXX301+/fvn7wNExERETlORV28PfXUUzQ1NeWm\nN23axKJFi3j99dc599xz2bhxIwDvv/8+r776Ktu2beOJJ57g/vvvxxgDwNe//nU2bNjA66+/zkcf\nfcRPfvITAJ577jlmzJjB9u3buf7663nkkUcmfwNFREREjlHRnrDQ0tLCzp07ufXWW3nyyScB2LFj\nBz/4wQ8AWLlyJddeey1r1qzhzTff5JJLLiEUCjF//nwWLFjA7t27OeGEE4jH4yxcuBCAFStW8MYb\nb/C5z32OHTt2cNtttwHQ3NzMAw88UJgNFSkBu7p+w4de+6jL9yUO8AVWTWJGIlLufN8nkeghFE4x\na2Z9odMpKkVbvD344IPcfffdw4ZGOzo6qKurA6C+vp7Ozk4AYrEYZ555Zq5dY2MjsVgMx3GYM2fO\nYfMBWltbc8scx6Gmpobu7m5mzpw54dsmUmpm/OnnmP+J5aMuj+/dNonZiEi5ymQyJJI9eF4Kg0sk\nEsL3nUKnVXSKsnj78Y9/TF1dHaeeeirvvPPOqO0sy8rbOgeHWceivr46b+stl9gTHb9UYxdKvrfJ\nto98hIXt2Me0znznp3ilp9j3ieJNXrx0Ok0i0UPGS+CEM9RNjwDTBn7Ay+Q1tbJQlMXbr3/9a958\n80127txJOp0mHo9z1113UVdXR3t7O3V1dbS1tVFbWwtke9QOHDiQe31LSwuNjY2HzY/FYjQ2NgLQ\n0NCQa+f7Pv39/WPudWtr6zt6o+NQX19dkrEnOn6pxh6MXwj53Kb6+mqCIDhim8APxrzOfO9zxRt/\nvEIo9n2ieBMbL5mMk3bjeF4Sy/aJRMK5ZfH48Gqtpromb7mVi6I8YeGrX/0qP/7xj9mxYwePPvoo\n5557Lo888ghf+MIXeOGFFwDYsmULS5YsAeCCCy5g27ZtuK7L3r172bNnDwsXLqS+vp7q6mp2796N\nMYYXX3xx2Gu2bNkCwGuvvcZ5541+AVIRERE5fsYY4vE+urpbaOv4kJTbhhNKE62whxVuMjZF2fM2\nmptvvpnbb7+d559/nnnz5vHYY48BcMopp7Bs2TKWL19OKBTivvvuyw2p3nvvvaxdu5Z0Os3ixYtZ\nvHgxAFdddRV33XUXS5cuZebMmTz66KMF2y4REZFyky3Yesl4CXw/RShsEQo7hMLFV3rccccd3HDD\nDSxcuJC///u/J5VK8Y//+I94nkdjYyMPPvggyWSSr33tayQSCbq6uli/fj0zZ87kv/7X/0pNTQ3X\nXXcdS5cunZR8i28PHuKcc87hnHPOAWDmzJls3rx5xHa33HILt9xyy2HzTzvtNF5++eXD5kciER5/\n/PG85ioiIjKV+b5PT28nnV2t+EGKSCREKGwXZcE21IoVK3jllVdYuHAhr732Gp7nceedd7Jw4UK+\n+93v8vzzz3Paaafx5S9/mUWLFrFt2za2bdvGl7/8ZRKJBC+99NKk5lvce1NERESKmud5xBPdeF6K\nwKSZWzmTcCQgTKTQqY3Z+eefz+OPP87+/fuZNm0aH374Id/85jeB7EX9Fy1axOLFi3nqqad46aWX\n6OvrY968eQAsWLBg0vNV8SYiIiLHJJ1Ok0r14vlJwCMSDRNxACJ5vRLEZHEch3POOYeHH36Yyy+/\nnBdffJGvfvWrnHzyyezatYtIJMLmzZu56KKLuOiii/i7v/s7uru7gfxe+WKsVLyJiIjIUaVSSVLp\nfjw/iWV5RCJhIiELKI8TDlasWMFf/MVf8M1vfpNTTjmF+++/n3Q6TSQS4Zvf/CZBELB+/Xr+7//9\nv3zmM5+hra0NUPEmIiIiRcIYQyLRTyaTIOMncBwIh0M4ZVSwDeX7fu7Ex0984hO5uzsNqqur49VX\nXz3sdU888cRkpZij4k1ERESAg5f0yHgJPD9JOGzhhB2cIj/hYLxeeuklNm/ezN/+7d8WOpUxKe93\nQ0RERI4oCAL6+rrIeMmBM0SdkjhDNJ8uu+wyLrvsskKnMWZT550RERERIHuGaCLRg+en8E0IY6UJ\nRyzCZTgcWo5UvImIiEwBruuSTGbPEDW4RKMRwg5UVIRJJNxCpyfHQMWbiIhImRr5DFGghK7BJocr\nynubQvbm8tdddx3Lly/n0ksv5amnngKgp6eH1atX09zczI033khf38Gb327cuJGlS5eybNky3nrr\nrdz8P/zhD1x66aU0NzezYcOG3HzXdbnjjjtYunQpV199Nfv375+8DRQREZkAyWSc7u4Y7R0fkUi1\n4IRSRKOW7iFaRoq2eHMch7Vr17J161aeffZZnnnmGT744AM2bdrEokWLeP311zn33HPZuHEjAO+/\n/z6vvvoq27Zt44knnuD+++/HGAPA17/+dTZs2MDrr7/ORx99xE9+8hMAnnvuOWbMmMH27du5/vrr\neeSRRwq2vSIiIsfDGEN/f++Qm7634oRdohUO4Sl00sFkyWfnUiaT4d5776W5uZlLLrmEH/3oR2PK\noWjf1fr6eurr6wGoqqqiqamJWCzGjh07+MEPfgDAypUrufbaa1mzZg1vvvkml1xyCaFQiPnz57Ng\nwQJ2797NCSecQDweZ+HChUD2InxvvPEGn/vc59ixYwe33XYbAM3NzTzwwAOF2ViRIrd77z+xN/7x\nqMt7ej4GLp68hESmuCAIiMd7pvQZooUy2Ll06qmnEo/HueKKK/jsZz/LCy+8wKJFi7jpppvYtGkT\nGzduZM2aNcM6l1paWrjhhhvYvn07lmXx7W9/m9mzZ/P6668D5O7acDQl8S5//PHHvPfee5xxxhl0\ndHRQV1cHZAu8zs5OAGKxGGeeeWbuNY2NjcRiMRzHYc6cOYfNB2htbc0tcxyHmpoauru7mTlz5mRt\nmkhJ6DrtU3Scdv6oyyve/ekkZiMyNfm+TzzeDVY3nV2dRKIhnSF6FCbt4r35Dqa9C/ukE3A+dxaW\nPb47IuSrc+mMM87g+eef57XXXsvFHmv9UfTFWzwe57bbbuOee+6hqqrqsNtQ5PO2FIPDrEdTX1+d\nt3WWS+yJjl+qsQsl39vkODbBUZYfyzrznZ/ilZ5i3yfFEs91XeKJHjKZBJbtUlsXxrIC5p6Q306G\n2tqqoo3nZY7/tf6Pf0Hw7r9ln7d1YVVV4pz9mTxlNr7OpcFh1ccee4yf//znnHTSSdx7773U1tYe\ndb1FXbx5nsdtt93G5ZdfzoUXXgjA7NmzaW9vp66ujra2ttxGNjY2cuDAgdxrW1paaGxsPGx+LBaj\nsbERgIaGhlw73/fp7+8fU9Xb1tZ31DbHo76+uiRjT3T8Uo09GL8Q8rlN9fXV+P6RSjfw/WDM68z3\nPle88ccrhGLfJ4WMN9IZooPiiQy1tVV0dsbzll+xx6uprjnu1wYdw4chTWfPeNPJGW/nkud5tLS0\ncNZZZ/Hf//t/Z/PmzfzN3/wNDz/88FHXXbQnLADcc889nHLKKVx//fW5eRdccAEvvPACAFu2bGHJ\nkiW5+du2bcN1Xfbu3cuePXtYuHAh9fX1VFdXs3v3bowxvPjii8Nes2XLFgBee+01zjvvvEneQhER\nkYEzRHtaae/4iHjygM4QzRP7k/OGTVuHTB+vI3UuAWPqXJo1axaVlZVcdNFFAFx88cW8++67Y1p/\n0RZvv/rVr3j55Zd5++23WbFiBStXrmTXrl3cdNNN/PSnP6W5uZm3336bm2++GYBTTjmFZcuWsXz5\ncm6++Wbuu+++XNV777338rWvfY3m5mYWLFjA4sWLAbjqqqvo6upi6dKlfP/73+fOO+8s2PaKiMjU\nMeIZoqE00QpHBVseOeedQejCRThnfJrQZV/A+ZMFeYmbj86lwWVvv/02AD/96U9pamoa0/qLdtj0\nrLPOGrUC3bx584jzb7nlFm655ZbD5p922mm8/PLLh82PRCI8/vjj48pTRERkLLJniGbvcOD7KUJh\ni1DY0RmiE8iyLJyFn8przMHOpU996lOsWLECy7K44447uOmmm7j99tt5/vnnmTdvHo899hgwvHMp\nFAoN61y68847ufvuu3nooYeora3loYceGlMO+sSIiMikSiYTdHXFmDmzIa8nnRUj3/fp6e2ks6uV\nwKSJREIDRZt+/ZaqfHYunXDCCbkzVI+FPj0iIjKpgsDHcpK0d+6huqqeiopphU4przKZDIlkD56X\nyt5DtHIG4UgAuqSH5ImKN5E8830f3/fxPBdjppd9z4LI8bAsi4oKm3gyRjI1jZkzSrsXLp1Ok0pl\nh0TBIxINE3EAwiW9XVKcVLyJjEG2GPPIZNIExgMTEJgACDAmwA+83HPLMlgWGAOuW0s0Gi10+iJF\nKxIJYUyajs4/Mq2yjmnTphc6pTFLJuOk3Tiel8Sy/YGbvluoh+3Y5f7oHfjxgcAYAmPIGKibXegM\ni4uKN5myfN8nlUrR399LEGQwxmCMD5bBmIDA+JiBH8sC27ZwHAfbzp6k7QyJFcIamHNwbiYzjitL\nikwhlmURrXBIua0kU73Mmjkn9/9ZMTHGkEj042bieH6SUMgiFHJwQjZFfPGGghhajLmeT0B2/wXG\nEMCwRwNgW2DZOCEHOzS8NHGDI19ncipS8SZlwxiT6yHzPDdXkEG2EAuCbGEWDPSS2RY44ekEpLEc\ni5EHNoYXZCIyccLhMODT2b2HymgtVVXHf3HWfMle0qNnyp8hmv1u9fGCbEGWK8CCbBHmG0NgwGDo\nDtL09qcOFmOj7CuVvMdvan36pOQYY/A8j2QygedlMGZweNIMDFUOFGNWttfMtsB2bBzHGVaQDf+C\nOPixD4fDWJabt3yDIDt06nk+bjo95luuichB0aiD63WS6uxjRk0jodDk/qryfZ++vs4hN30Pld1N\n3weLsYzvYYUNXf3x3DBltjAjV4wFxoBjY1kWtnN4zxiQG3sAiFRWEnb13TeRyueTKCVjsIcsk3EH\nDur3gQA/8IFsERYE/sB8g7GmkXJT2YJs4IbCFkMLssFnx9BDZgyWF0A8jdOXxLguxvUxaRcrk4GM\nh5Xxso+ei5VJg5fG8tzsjfb8NPgZLN/FBBmM75I2GTzjkbZ8MpZHKLBJnfQtKioq8rTnRKaOUMiB\nUEB3716i4ZlUV8+a0PV5nkc80Y3npfCDEMZyB276HpnQ9eaDMYYgCMhkvIGesSA3TOkPHaYcKMYM\nYGwb2x4oxmyH1AgF2XF8s04JLS0t3H333XR0dGDbNldddRXXXXcdPT093HHHHezbt4/58+fz2GOP\nUV2dvf3cxo0bef7553Ech6997Wucf/75xONx/vN//s9YloUxhpaWFi6//HLWrl171BxUvEleGGPI\nZDJ4XgbPczlYjAUEgYcZOLg/CLLHj2GB41jDCrKQQ66osowPno/l+VR0QqajP1tIuS5k0pBJYTIp\nTCYNbpogk8T306T9NG7gkjEZMri4gYdr+bh4uPi4VoBr+WRsQ9o2ZGyDa2Wfuza4NmSs7GPacnBt\nm7QVwos4ZKIOrhXCsxwyloNnOXg4+JaDb1UC0zg4EGABhpcSyTHdL1dERhaNhvD9Xto7+plRM2dg\naPXYBcbwRmwvf4z3sqCqhgsbTyTjuqRSfXh+gqFniFZURkgkC3fM6sFDQLLDlH6QLcb8INsLliJD\nZ3ci2zvG4DFjQ4qxEXoIh/aMyfg4jsPatWs59dRTicfjXHHFFXz2s5/lhRdeYNGiRdx0001s2rSJ\njRs3smbNGt5//31effVVtm3bRktLCzfccAPbt2+nqqqKF198MRf3iiuuYOnSpWPKYcoXb7t27eLB\nBx/EGMOVV16Zu92WZIcAsz1kaXwvg8mksXwP3Awm44KbwnITmHQCP2JI9PYR+Ekyvovrp3B9l4zv\nkgmyvVJukMHFI4OHazxS+KSAlA0pyyJtQdqxcW2LtGWTsR1cyyZj2biWg2fbBwsmbAIrjAmHMZEQ\nmDC2mY7DDGwTyv7gYBsHx4SwjYNNCCewcQbmD7ZxjD2w3MYJHGxjY+MQNTaVxspOGxsHG8tY2XbG\nxjbWwOsH2jC0rYVn+fSd2gfMLfRbKVLSHMfGcaCnby+R0Axqao791MM3YnvZduBDgiBgd1eM/ngH\nn6+dMSlniA4e/jG0GPMPOWB/cJgyGBymHFqMHdIrFkSjWBX+cR2Ra4zBZ/AYtYGeOdel283gc3De\n4DFsPmZI22x+vhn6k92Gg6+FcF8P8aQ7pC0HYzIk9mAMDpnOtcvGfvDUU4973/tugtZffZ9U916q\n5pxG/ZnXYFnjO9Kuvr6e+vp6AKqqqmhqaiIWi7Fjx47cBXdXrlzJtddey5o1a3jzzTe55JJLCIVC\nzJ8/nwULFrB7927OOOOMXMwPP/yQrq4uzjrrrDHlMKWLtyAIWL9+PZs3b6ahoYFVq1axZMmSMd9b\nbDIYA/vfh75ui6qZhnen7+GPiYN/OdoAnodJu/ipPt4+8G+09Xcw2zZ8siJCKpMkmUniOz49yTgJ\nL0PS90gFAenA4GKTMeDi4DFQHJkQgRUiIIRPCEOIgDDZL7dsoZT9yghhE8YyM7BMCJtGHDNQ/Bhn\noEiysckWNHZg4xgHB5tQwMCjTdhYRI2NYywcYxEyFvbAc8fYOIaBAsnCAezAyhZSGCyT/YvSMgBm\nyPPB+SPPY2BethPQwJA42X7AQ2MzbH2M4bW+7RAE8Yn6aIhMOdFomCDop609Tk11A+FIlB+17OGd\nf43huj6fqzuBi+achG1ZGAMfvw/v7usj5nTyVvhf6Q0yRGyb6pDDAS9FJFJ3xPUZc/BgfG9IYZHx\nfVzfx/U80r6PGxh8E5AZbBcYMoAXBHhApCNEfzKTLcxsC9+AZ4KBuMGw2J7J9rJ5QUBANl5wSAFk\nORauF+SKnFzhd8gQae7YNQ5OM8qpWcWsp6//uF/b+usf0PPRPwGQ7t5DqHImtZ++JF+p8fHHH/Pe\neyn0EVMAACAASURBVO9xxhln0NHRQV1d9jNVX19PZ2cnALFYjDPPPDP3msbGRmKx2LA427ZtY9my\nZWNe75Qu3nbv3s2CBQuYN28eAMuXL2fHjh1HLN5++Q9vkkx4mMDG+DZBYA382Bjfwg8GfnwwAQSe\nBYEFJvtlQmBjBdb/z969xzd134f/fx0dWb7I8l2IWyApLi35EpOGJcBMvNTwsweEYpvQtOs3MGAO\nJWOenUAaOymBtaRbabl8l6wxjIZlTZtfYzCZG3MpJs0gLuxXttUJg+8SmoyrhS9YyDLW9fz+kH3A\n2AYZZCTD+/l4OEGfc877fI4sS299zucCmgFFC7bk0J2MaAYUvfVG6fr3lZ92TWEsCdynJaCg8Ll2\nEYNG13ZQiGWcdj9fJJhIGDRIILjNAD2Ske7EQ09M+klaFHomOnSVKf2UXUlqCN4C7XG+awXofju5\nU52JwukOhIi0f9vwW/Ab0a68cwT/r+nvTHr5Ve841zz+jICmkA7MJrlru5ujfIqiKcFQKKQCqSh8\nmcxrYsAJLaA/7vl/FxB8D+5Z3vPLGwS/xsaj9Nire58+j73KtVsUra9tcO1Y+Kv345pz9xf/6jqF\ntO9V+/dZ16sP7iNuf1fds+79p5JXl1+452Q/e92Y23Gmx2OP4+xNx7qWy+WipKSEiooKzGZzr8mY\nBzI5c21tLevXrw95/7s6ebPb7YwYceWWls1m46OPPrruMfe/e+8g1+pGtGv+L6KVXwmQnJIc6WoI\nEXWmfJYZ6SqIft3os+X2f/b854lGePjmjk0c+SCdrX/QH5tHTrrO3qHz+XyUlJQwb948Zs6cCUB6\nejrNzc1kZGTQ1NREWloaEMwtzp8/rx/b2NiIzWbTH584cQK/38/9998f8vnv6uTtThQg+OVVo+un\n699djX9dZZpeBhBQtOB2NH1b8P8aASVYFkALzpGmBNAIBKfmUALB7QRACQSPUfxoXPVvJdD140dT\n/KAECI4gDR7TfVywvLvsymNNCYAhgMKV/VG04GPDlceaQnBfgK5/62UKXecIXgtd19ndLKh1HR98\n3H0cXbG7nose+xNs1gS9vDs+ioJmCJ4jEGMg17Lmdvzae7FaLWGNp6qG67aRqqphQOcMd/0knhB3\nLoPqu+lj0ycWYoxPwd12ioThE7GM/qOw1KmiooLMzEwWLVqkl+Xm5rJz506efvppqqurmTFjhl6+\ncuVK/vzP/xy73c6pU6fIysrSj3vvvfd4/PHHB3T+uzp5s9lsnDt3Tn9st9sZNmzYdY+p/oKPgEEj\nQAC/EiDQ48fflQD5g0lOd7LS9aN0/RhUDYNBQ1VBNUCM3Y8poBDrMxDnMxLrjcGomai+1w/m3/L/\nJDyIiS9iSVE42e7Aez6B+y4GuLfZiynWQIwJfI/E4X0ont9cOMupDifTP0/lgRNmINgInjgzhYvj\nFTRN0ece0zSuzEOmBbp+NLRAoKtcAy3Av7Y2cuqyiw6/nwSDgTHxZh5Ns2LoWgNKQyM9zUxLa/uV\n46DHv+keEdVVpnWXa/rA9eCuCihK91hNBUXRSE9P5OLFDhRF6fETDmlpZlpbB6dfmtfrRVUjM76r\nqckZtlhWqwW///q3t/3+QMjntFotYa+fxLu1eLfbyuw2VF+wS0HPv+SudwblSvnVjxW6vlh1Mbiv\n7vIRfNfovqWpv2KV7tuyV26+dr8z+dTg6HK6vpB1qp6uGiiYDAZMPbo9aCidoPi7vhADWoxGILY7\nes97jIErtUcxKAQCV75cXnWlPa4nWNCzZavnvZbgfw2Kgr+fuzBX3md7H9d9/l7zT/ZRFri2hU2j\n60v1lYe9YqO/jfcZL3DVPgblSrecnlF6t+yt/+NHepWFSlEUUjJzb/r4vhw9epSamhrGjx9PQUEB\niqJQVlZGcXExpaWl7Nixg1GjRrFp0yYAMjMzmTVrFnPmzMFoNPLyyy/3+Azbs2cPW7ZsGVAd7urk\n7YEHHuDUqVOcPXsWq9XKe++9x4YNG657zJzlKTRfbMdgCI7zMRpjiIkxBae8uMmE4qlX/4Wq09P0\nNxUX8NJkFz7rHoq/MIrMe6boo2Pu09KDAxgugu/CZZICfrQRKobJccQaFPJHfSkYdJwGKZ0YGv0E\nhquYpqejttxcp8+vJaXdcJ+0NAt+/619oHT/sXe/yQWXq9JISjTT2XEJTQtOgBsIBNACetsi3X/s\nAS3Q/c4VfAMJvoME31Z7JJVX3oC8HgWvJ9jBV+napqEFf5dXJ5Z09QRRrrz5K12dSLqX8RmM5FKI\nO9EPn8zkzLk/dE2SfeO/FY/Hi6YZMarxxMUmEhcXD8AT2/6bnZ+kEtf1ee8HPjMrpARUjAGNlniF\nP1g6sXk8NCZ3MKo5mXudMXSqAd65z44xyco9ajqJKV7u/UoLBy+6ON15mQnpyUyOMwe/oF4tAEk1\nRmLsBry2AJfyfSEtERDuL4lDMV5A0zjYpHG6Q+OeBIVHrUrv57cfSZboWu928uTJHD9+vM9t27dv\n77N82bJlLFu2rM9tv/71rwdch7s6eVNVle9+97ssWbIETdN44oknbjjSNCV9GN5AfFjr8c8rvsZl\nrnyjNgCvYAW+3WtfRYFRX+x+FE+/MxEZFHwPX6ln91xq0az7Tfza1qrY2NhBm+jWmmEBLbSkszuZ\nvPane1UFPbEMNmliVAM3PSeVEHcyRVEwGo3XTdw8bi8Qg1GNx2K2YTL1niy3aul4AoD5qtbIkV3b\nHJeaMfmcZMUaAQM2EgE/7fgBKCQV8AHdo/4Uvtq1+nm/yYwBLs27+Vt4dzODovAnw6L/c2iouKuT\nN4CcnBxycnIiXQ0xBNxMi1o0Lq4tRDTSNA2P24fBEItRjSM5KfmWlsVKTsrA40niktOOMcYfsS4M\nQgyGuz55E0IIERmBQACPx49qiCPGGE9aanJYv/CYTCYy0u/B6WzF7XYQGysfeeLOIK9kIYQQt5Wq\nGvH7TJhizGSkWQa9j6jFkkac14Ljkh3V6AuumyrEECb3dIQQQtxWcXHxpKWOIDEx6bYN7omJiSEj\nfTQxairuTum3JoY2Sd6EEELcNRITk0lNGYPXY8TnkyRODE0hJW/FxcXs3r0br7ffsY1CCCHEkKCq\nKmmpIzAZM+iUVjgxQI2NjSxcuJA5c+Ywd+5c3nzzTQAcDgdLliwhPz+fpUuX4nRemcmgsrKSvLw8\nZs2axaFDh/Ty2tpavva1rzF37lx+/OMfh1yHkJK3p59+moMHD5Kfn8/atWtpaGgI+QRCCCFENDKb\nLaSnjsXniZHGCREyVVUpLy/nvffe4+233+att97i5MmTbNmyhWnTprF3716mTJlCZWUlAJ9++im7\nd++mtraWrVu3snbtWjRNo62tjfXr1/Pmm29SU1NDc3Mzhw8fDqkOISVvDz/8MK+88gq1tbVMmjSJ\nkpISHn/8cbZv347H47n5Z0AIIYSIIIPBQGrqcOJjbXRe9vdefUAMaV5vB//f73/C/kPlNJx4q8ck\n7TfLarUyYcIEAMxmM+PGjcNut1NXV0dhYSEAhYWF7N+/H4ADBw4we/ZsjEYjo0ePZuzYsTQ0NHD6\n9GnuvfdeUlJSAJg6dSr79u0LqQ4hjzY9cuQI7777Lh9++CE5OTnMnj2b+vp6li9fzrZt2wZ04UII\nIUQ0iY83ExeXgMPRhNcjt1LvFL8//s+cOhe8TelwniIuNoXx980JW/wzZ85w4sQJJk2aREtLCxkZ\nGUAwwWttbQWCS28++OCD+jE2mw273c7UqVP57LPPOHfuHMOGDaOuri7kFuCQkrevfvWrjB49mvnz\n57N69Wp9tvspU6Ywf/78AV2oEEIIEY0URSElZRiWRCPNzSeJMclE20PdpfYzPR4728/1s+fAuVwu\nSkpKqKiowGw29xo5faOR1ElJSaxZs4bS0lIMBgNf+cpXOH36dEjnDil5+6d/+ifGjBnTq9xgMFBd\nXR3SiQbihz/8Ie+//z4mk4kxY8bwgx/8gMTE4NpmlZWV7NixA1VVefHFF5k+fToAx44d44UXXsDj\n8ZCTk8OLL74IgMfj4Tvf+Q7Hjh0jNTWVjRs3MnJkcAGV6upqXn/9dQCWL19OQUFB2K9FCCHE0BIX\nF0962hguOVvwutuJiZV54Yaq4dYHuej4w1WPJ4Ulrs/no6SkhHnz5jFz5kwA0tPTaW5uJiMjg6am\nJtLSguuC22w2zp8/rx/b2NiIzWYD4LHHHuOxxx4D4Je//GXIK4FcN3l76qmnrps5do+wCLfp06ez\ncuVKDAYDP/rRj6isrOS5557r0emvsbGRxYsXs2/fPhRFYc2aNaxbt46srCyKi4s5ePAgjz76KFVV\nVSQnJ7Nv3z5qa2tZv349GzduxOFw8Nprr1FdXY2maRQVFTFjxgwsFsugXJMQQoihQ1GUriW2gpP7\nmmI1aYUbgiZkFhEXm4LDeZphGRMZZfujsMStqKggMzOTRYsW6WW5ubns3LmTp59+murqambMmKGX\nr1y5kj//8z/Hbrdz6tQpsrKyAGhtbSUtLQ2Hw8HPf/5zNm/eHNL5r5u8/dVf/dXNXtct+eM//mP9\n3w8++CB79+4F+u/0N3LkSFwul/5kFBQUsH//fh599FHq6uooKSkBID8/n+9973sAHDp0iOzsbD1Z\ny87O5uDBg8yePft2XqoQQogoZjLFYs0Y07XEVhuxsTGRrpIYAEVR+MKYGWGNefToUWpqahg/fjwF\nBQUoikJZWRnFxcWUlpayY8cORo0axaZNmwDIzMxk1qxZzJkzB6PRyMsvv6w3jK1bt44TJ06gKAp/\n+Zd/ydixY0Oqw3WTt0ceeUT/9/79+zl8+DCqqpKTk0N2dvbNXveAVFVV8fjjjwP9d/pTVZXhw4f3\nKge4cOGCvk1VVSwWC21tbdjtdkaMGNHnMUIIIcTVriyx1YgxJoCqSivc3Wry5MkcP368z23bt2/v\ns3zZsmUsW7asV/lA5na7Wkh93v7u7/6O//iP/2DOnDkEAgE2b97Mxx9/3GdFQrV48WKam5t7lZeV\nlZGbmwvAT37yE2JiYvTkLRzCMQzcah28W6tDNfZgxx+qsSMl3NekqgauN8BeVQ0DOme46yfxhp5o\nf076izdyZBpOZxuuyy3ExYW+PHhamjlcVbvr4vlkCr5eQnrlHThwgF/96lfExASbi7/xjW9QUFBw\nS8nbG2+8cd3tO3fu5IMPPujRr66/Tn/Xltvtdr0z4LBhw/T9/H4/7e3tpKSkYLPZOHLkSI9YU6dO\nDanuTU3OG+90E6xWy5CMPdjxh2rs7viREM5rslot+P3XnxvJ7w+EfM5wP+cS79bjRUK0PyfXj6dC\nIJ3G83YUg5uYmOt/lKalmWltdYWtfndbvCRLUthi3SlCavdNS0vD5bryi/D7/fqkcoPhX//1X9m2\nbRs/+clPMJlMenlubi61tbV4PB5Onz6td/qzWq1YLBYaGhrQNI1du3b16CjYPSJ2z549eoI2ffp0\n6uvrcTqdOBwO6uvr9ZGrQgghxPWoqkp62khiY2SJLXH7hdTylpGRQUFBAXl5eaiqyvvvv09aWhpr\n1qwB0P8fLt///vfxer0sWbIEgEmTJrFmzZrrdvpbvXo15eXluN1ucnJyyMnJAWDBggWsWrWKvLw8\nUlJS2LBhAwDJyck888wzzJ8/H0VRWLFiBUlJkt0LIYQIndlsIT7ejMNxAU3p0O9QCTGYQkrecnNz\n9X5oAOPHjx+0CgHXXR6iv05/EydOpKample5yWTqd+htUVERRUVFN19RIYQQd73uJbY6OtpxdTQR\nG6fecIJWIW5FSMlbYWEhZ86c4dNPPyU7O5vGxkbuueeewa6bEEIIMWQkJCQSH2+mzdFEQHNhMoU+\noEGIgQipz1ttbS3Lly9n3bp1tLW18c1vfpN33313sOsmhBBCDCmKopCaMgxzvI3OzoAsdH8Hamxs\nZOHChcyZM4e5c+fqAysdDgdLliwhPz+fpUuX4nQGB720tbWxcOFCvvKVr/D973+/R6yNGzfy2GOP\n8dBDDw2oDiElb1u3buUXv/gFZrMZq9VKdXU1W7ZsGdCJhBBCiLtFXFwCGWlj0PwJeNwy18WdRFVV\nysvLee+993j77bd56623OHnyJFu2bGHatGns3buXKVOmUFlZCUBsbCylpaW88MILvWLNmDGDqqqq\nAdchpOTNYDDoa4sCWK1WWSZECCGEuA5FUUhOziA5aTTuTo1A4PpT7ojw6/B18pMT/0LF0X/k5yf3\nE9Bu/XdgtVqZMGECAGazmXHjxmG326mrq6OwsBAIdjfbv38/APHx8Tz00EM9Zs/olpWVRUZGxoDr\nENIN+S9+8Yv87Gc/w+fzcfz4cX7+85/z5S9/ecAnE0IIIe42sbFxZKSP4dKlFjyeS9IX7jb62clf\n86H9IwBOtdtJMSUy+57Q5nQNxZkzZzhx4gSTJk2ipaVFT8SsViutra1hO8+1Qmo+6+jowG63Exsb\nS0VFBYmJibz88suDVikhhBDiTpOUlI7FPAp3Z3C+VDH4zrh6ruR0tqMlbLFdLhclJSVUVFRgNpt7\njTAezBHHIaX/Z8+e5ZVXXuG5554btIoIIYQQdzqTyURG+j1dC907iI2VVrjB9GDaOP7gPKc/npQ2\nLixxfT4fJSUlzJs3j5kzZwKQnp5Oc3MzGRkZNDU1kZaWFpZz9SWkV43BYCA3N5f77ruP2NhYvfzq\npauEEEIIERqLJY14XxKOS3YMqhejUY10le5IhWMfJcWUyCnXBSam3scfZXwpLHErKirIzMxk0aJF\nellubi47d+7k6aefprq6Wl/p6Wr9jT4e6KjkkJK3VatWDSioEEIIIa7PaDSSnjaK9nYHne6LxMZK\nAhduiqKQO3Jg03DcyNGjR6mpqWH8+PEUFBSgKAplZWUUFxdTWlrKjh07GDVqFJs2bdKPyc3NxeVy\n4fV6qaurY9u2bYwbN47169fzq1/9CrfbzWOPPcYTTzzBihUrbliHkJK3Rx555Oav8hb89Kc/5Yc/\n/CGHDx/W11KtrKxkx44dqKrKiy++qK9HeuzYMV544QU8Hg85OTm8+OKLAHg8Hr7zne9w7NgxUlNT\n2bhxIyNHjgSgurqa119/HYDly5dTUFAQgasUQghxN0tMTCYhYOFiWyMG1Y3RKLdSo9nkyZM5fvx4\nn9u2b9/eZ/mBAwf6LF+1atVNNZBF7XwfjY2NfPjhh3qiBXDy5El2795NbW0tW7duZe3atXpT45o1\na1i3bh179+7l888/5+DBgwBUVVWRnJzMvn37WLRoEevXrweCk+m99tprVFVV8c477/Dqq6/qE+oJ\nIYQQt5PBYOha6N6Ku9Mvk/uK64ra5O2VV17h+eef71FWV1fH7NmzMRqNjB49mrFjx9LQ0EBTUxMu\nl4usrCwACgoK9PlVrp53JT8/n8OHDwNw6NAhsrOzsVgsJCUlkZ2drSd8QgghRCQkJCSSljoGvy8W\nr9cX6eqIKBWVyVtdXR0jRozgS1/q2bHQbrczYsQI/bHNZsNut2O32xk+fHivcoALFy7o21RVxWKx\n0NbW1m8sIYQQIpIMBgOpKTYS4mSJLdG3iN1YX7x4Mc3Nzb3KS0tLqays5Kc//emgnDccfwRWqyUM\nNbmzYg92/KEaO1LCfU2qauB685KrqmFA5wx3/STe0BPtz0l0xLOgacO4ePECPr8TU2yMviUtzRy+\nykV5PJ+sLtZLxJK3N954o8/y//7v/+bs2bPMmzcPTdOw2+0UFRXxzjvvYLPZOH/+vL5vY2MjNput\nV7ndbsdmswEwbNgwfT+/3097ezspKSnYbDaOHDnSI9bUqaHNutzUNDh946xWy5CMPdjxh2rs7viR\nEM5rslot+P3XX1LG7w+EfM5wP+cS79bjRUK0PyfRFS8Bj9tAS8sFYkwaGRkWWltdYatfWpo5quMl\nWZLCFutOEXW3TcePH8+HH35IXV0dBw4cwGazUV1dTXp6Orm5udTW1uLxeDh9+jSnTp0iKysLq9WK\nxWKhoaEBTdPYtWuXPr9Kbm4u1dXVAOzZs0dP0KZPn059fT1OpxOHw0F9fb0+clUIIYSIJt1LbKGZ\ncctC93e9qB+PrCiKfqszMzOTWbNmMWfOHIxGIy+//LK+/MTq1aspLy/H7XaTk5NDTk4OAAsWLGDV\nqlXk5eWRkpLChg0bAEhOTuaZZ55h/vz5KIrCihUrSEqS7F4IIUT0Sk7KIDk5ltaWTzHG+FFVmRvu\ndmtsbOT555+npaUFg8HAggULWLhwIQ6Hg7KyMs6ePcvo0aPZtGmT3s++pKSEjz76iPnz5/PSSy8B\n0NnZyV//9V9z6tQpjEYjX/3qV3n22WdDqkPUJ291dXU9Hi9btoxly5b12m/ixInU1NT0KjeZTGze\nvLnP2EVFRRQVFYWnokIIIcRt0HOJrTZir+oLJwafqqqUl5czYcIEXC4XRUVFZGdns3PnTqZNm0Zx\ncTFbtmyhsrKSlStXEhsbS2lpKZ988gmffPJJj1hLly7lkUcewefzsWjRIg4ePMijjz56wzpE3W1T\nIYQQQtyYxZJGsuUe3J0KPp8sdN+XDp+Xyv/6PS/92yF+8ekJAmEZtGhlwoQJAJjNZsaNG4fdbu8x\nNVlhYaE+ZVl8fDwPPfQQJpOpR5y4uDh9EQSj0cj9999PY2NjSHWQ5E0IIYQYomJiYshIH41RTcHd\nKfPCXevnnxynvvEcp9ud7Dn1GXtPfx7W+GfOnOHEiRNMmjSJlpYWMjIygGCC19raGnKcS5cu8f77\n7zNt2rSQ9pfkTQghhBjiLIkppKaMweNWZXLfq5xxtfd4fO6ax7fC5XJRUlJCRUUFZrNZ74Pf7drH\n/fH7/Tz33HMsWrSI0aNHh3SMJG9CCCHEHUBV1a4ltjLolFY4ACalW6/7+Gb5fD5KSkqYN28eM2fO\nBCA9PV2fv7apqYm0tLSQYn33u9/lvvvu46mnngr5/FE/YEEIIYQQoTObLcTHm3E4LqApHcTE3L0D\nGgruzSTFFMvpdif/Ky2DyVZbWOJWVFSQmZnJokWL9LLc3Fx27tzJ008/TXV1tT5l2dWuXShg48aN\ntLe388orrwzo/JK8CSGEEHcYg8FAaupwOjracXU0ExtnCPk23p1EURS+OmpMWGMePXqUmpoaxo8f\nT0FBAYqiUFZWRnFxMaWlpezYsYNRo0axadMm/Zjc3FxcLhder5e6ujq2bdtGYmIilZWVjBs3To/z\nrW99iyeeeOKGdZDkTQghhLhDJSQkEh9vps3RREBzYTLJx/6tmjx5MsePH+9z2/bt2/ssP3DgQJ/l\nJ06cuKk6SJ83IYQQ4g6mKAqpKcMwx9twy0L3dwRJ3oQQQoi7QFxcAulpY9D8CXjdMi/cUBa1yds/\n//M/M2vWLObOncuPfvQjvbyyspK8vDxmzZrFoUOH9PJjx44xd+5c8vPzWbdunV7u8XgoKysjLy+P\nJ598knPnzunbqquryc/PJz8/n127dt2eCxNCCCEiRFEUkpMzMJuH03lZIxAIRLpK4iZE5c3vI0eO\n8P7771NTU4PRaNQnujt58iS7d++mtraWxsZGFi9ezL59+1AUhTVr1rBu3TqysrIoLi7Wl5ioqqoi\nOTmZffv2UVtby/r169m4cSMOh4PXXnuN6upqNE2jqKiIGTNmYLFYInz1QgghxOCKjY3DGjsGp7NV\nphUZgqKy5e0Xv/gFxcXFGI3B3LJ7rpS6ujpmz56N0Whk9OjRjB07loaGBpqamnC5XGRlZQFQUFCg\nL0tx9XIV+fn5HD58GIBDhw6RnZ2NxWIhKSmJ7OxsDh48eLsvVQghhIgYiyWN9NQxuDvB75dWuKEi\nKpO3zz//nN/97nd8/etf56mnnuLjjz8GwG63M2LECH0/m82G3W7HbrczfPjwXuUAFy5c0LepqorF\nYqGtra3fWEIIIcTdJLjE1j2oShKdnd5IV0eEIGK3TRcvXqzPRHy10tJS/H4/DoeDX/7ylzQ0NPDX\nf/3X1NXVheW84RhlY7UO3q3VoRp7sOMP1diREu5rUlUD1/tOrqqGAZ0z3PWTeENPtD8nd2M8q9WC\nzzeS1ovnMahejEb1puOlpZnDVjdflOWTjY2NPP/887S0tGAwGFiwYAELFy7E4XBQVlbG2bNnGT16\nNJs2bdIbjEpKSvjoo4+YP38+L730kh7rL/7iL2hubsbn8zFp0iTWrl2r33W8noglb2+88Ua/295+\n+23y8vIAyMrKQlVVLl68iM1m4/z58/p+jY2N2Gy2XuV2ux2bLTiL8rBhw/T9/H4/7e3tpKSkYLPZ\nOHLkSI9YU6dODanuTU3OAV1rqKxWy5CMPdjxh2rs7viREM5rslotN7yd4vcHQj5nuJ9ziXfr8SIh\n2p+TuzmeQgrtly7R0dlCXNzA04S0NDOtra6w1S/JkhS2WOGgqirl5eVMmDABl8tFUVER2dnZ7Ny5\nk2nTplFcXMyWLVuorKxk5cqVxMbGUlpayieffMInn3zSI9bmzZsxm4OJbklJCbW1tXzta1+7YR2i\n8rbpzJkz9b5pn332GV6vl9TUVHJzc6mtrcXj8XD69GlOnTpFVlYWVqsVi8VCQ0MDmqaxa9cufVmK\n3NxcqqurAdizZ4+eoE2fPp36+nqcTicOh4P6+nqmT58emQsWQgghoojZnER66li8HiNeb5Q1fQ1A\nh9fP1o8aWV3/P/y//7eJQFjuvlmZMGECAGazmXHjxmG323v0sS8sLNT73sfHx/PQQw9hMpl6xepO\n3LxeLx6Ph5SUlJDqEJWjTYuKiqioqGDu3LnExMTwd3/3dwBkZmYya9Ys5syZg9Fo5OWXX9aX+1i9\nejXl5eW43W5ycnLIyckBYMGCBaxatYq8vDxSUlLYsGEDAMnJyTzzzDPMnz8fRVFYsWIFSUnRld0L\nIYQQkWIwGEhLHUFHRzsdl5sxxQ69JbZ+8X+b+O25SwCccbpJjjXyp/emhi3+mTNnOHHiBJMmAElO\n/AAAIABJREFUTaKlpYWMjAwgmOB1z5RxI0uXLuXjjz9m2rRpeu5yI1GZvMXExLB+/fo+ty1btoxl\ny5b1Kp84cSI1NTW9yk0mE5s3b+4zVlFREUVFRbdWWSGEEOIOdmWJrQsEtI4htcTW2XZPj8fnr3l8\nK1wuFyUlJVRUVGA2m3sltqEmutu2bcPj8VBaWsquXbsoKCi44TFRedtUCCGEENEjuMSWDXO8jc4h\ntMRWVkbPgRMPWBPCEtfn81FSUsK8efOYOXMmAOnp6fpAzKamJn2as1CYTCby8vL46KOPQtpfkjch\nhBBChCQuLoGMtDEEfHF43NE/ue+8cWksvH8YXx2TwooHR/JHtvAM0KmoqCAzM5NFixbpZbm5uezc\nuRMIruDU3ff+alcnvR0dHTQ1NQHBZPCDDz7gy1/+ckjnHzptn0IIIYSIOEVRSEkZhtvdySWnHVNs\nsH9cNFIUhcfuCW0QQKiOHj1KTU0N48ePp6CgAEVRKCsro7i4mNLSUnbs2MGoUaPYtGmTfkxubi4u\nlwuv10tdXR3btm0jJSWF5cuX4/V60TSN7OxsnnjiiZDqIMmbEEIIIQYsuMTWWByXmvG4nZhi746U\nYvLkyRw/frzPbdu3b++z/MCBA32WV1VV3VQdojNVFkIIIcSQkJyUgSVxVNcSW/5IV+euIMmbEEII\nIW6JyWTSl9hyy0L3g06SNyGEEEKEhcWSRlrqGDxuAz6ftMINFknehBBCCBE2MTExpKeNIkZNlVa4\nQSLJmxBCCCHCLjExmdSUMXjcKj6fJHHhFJVDQxoaGvibv/kbfD6fvgzWAw88AEBlZSU7duxAVVVe\nfPFFfT3SY8eO8cILL+DxeMjJyeHFF18EwOPx8J3vfIdjx46RmprKxo0bGTlyJBCch+X1118HYPny\n5SHNaizE3ajtxL8Te7mj3+0dZz4B/vftq5AQYkhQVZX0tJG4XE46OpuJjVWH3BJb0SgqW97Wr1+v\nLxPxV3/1V/zwhz8E4NNPP2X37t3U1taydetW1q5dq094t2bNGtatW8fevXv5/PPPOXjwIBAchpuc\nnMy+fftYtGiRvuyWw+Hgtddeo6qqinfeeYdXX30Vp9MZmQsWIsolTRyOMXtkvz8p/2tEpKsohIhi\nZrOF9NSx+H2xeL1DuxWusbGRhQsXMmfOHObOncubb74JBPOKJUuWkJ+fz9KlS/Wcoq2tjYULF/KV\nr3yF73//+33G/Pa3v83cuXNDrkNUJm/Dhg3TL9rpdGKz2YDgPCmzZ8/GaDQyevRoxo4dS0NDA01N\nTbhcLrKysgAoKChg//79ANTV1VFYWAhAfn4+hw8fBuDQoUNkZ2djsVhISkoiOztbT/iEEEIIEV4G\ng4HUFBtxJiudl/1DZomta6mqSnl5Oe+99x5vv/02b731FidPnmTLli1MmzaNvXv3MmXKFCorKwGI\njY2ltLSUF154oc94v/71r0lMTBxQHaIyeXvuuef427/9Wx577DHWr1/Pc889B4DdbmfEiCvf8G02\nG3a7HbvdzvDhw3uVA1y4cEHfpqoqFouFtra2fmMJIYQQYvAkJCSSkT42uMSWZ3BHpHo9Gr//0MOh\nX3Vy4qg3LAmj1WplwoQJAJjNZsaNG4fdbu/RWFRYWKg3IsXHx/PQQw9hMpl6xero6GD79u0sX758\nQHWIWJ+3xYsX6wu4Xq20tJSf/exnfPe732XmzJns2bOHiooK3njjjbCcNzy/uPCsjXYnxR7s+EM1\ndqSE+5pUg4HrvcWqqjKgc4a7fhJv6In250TiDX68YcOS6Oy8jOOSnRiT1u8SWz7vzdfj+O+8nPss\n+O7lbPMRG69w3/3hS33OnDnDiRMnmDRpEi0tLWRkZADBBK+1tfWGx2/evJklS5YQFxc3oPNGLHm7\nXjK2atUqffuf/umf8tJLLwHB1rHz58/r+zU2NmKz2XqV2+12/VbrsGHD9P38fj/t7e2kpKRgs9k4\ncuRIj1hTp04Nqe5NTYPTN85qtQzJ2IMdf6jG7o4fCeG8JqvVgj8QuO4+fr8W8jnD/ZxLvFuPFwnR\n/pxIvNsXz6Ck09rSjM/n6HOJrSRL0k3Xpd2hXfP4+u9lA+FyuSgpKaGiogKz2dxrIMaNBmacOHGC\nU6dOUV5ezpkzZwZ07qi8bXrvvffyb//2bwD89re/ZezYsUBwYdfa2lo8Hg+nT5/m1KlTZGVlYbVa\nsVgsNDQ0oGkau3btYsaMGfox1dXVAOzZs0dP0KZPn059fT1OpxOHw0F9fb0+clUIIYQQt09wia2R\nYV9iyzrKcM1jNSxxfT4fJSUlzJs3j5kzZwKQnp6u31FsamoiLS3tujH+4z/+g2PHjjFjxgy+9a1v\n8dlnn7Fw4cKQzh+VU4WsXbuWv/mbv8Hr9RIbG8v3vvc9ADIzM5k1axZz5szRpxDpzmxXr15NeXk5\nbrebnJwccnJyAFiwYAGrVq0iLy+PlJQUNmzYAEBycjLPPPMM8+fPR1EUVqxYQVLSzWf3QgghhLh5\nJlMsGen34HS24na3ERsbc8sxMx8wEhuv4LwYIGOEiu2e8CRvFRUVZGZmsmjRIr0sNzeXnTt38vTT\nT1NdXa03Il3t6q5b3/zmN/nmN78JwNmzZ/n2t7+tj1y9kahM3h544AHeeeedPrctW7aMZcuW9Sqf\nOHEiNTU1vcpNJhObN2/uM1ZRURFFRUW3Vlkh7gLO/zmNMb7/PhmeU6duY22EEHcyiyWNOK8FxyU7\nqvHWphVRFIUxXwxvqnP06FFqamoYP348BQUFKIpCWVkZxcXFlJaWsmPHDkaNGsWmTZv0Y3Jzc3G5\nXHi9Xurq6ti2bRvjxo276TpEZfImhIguhsQLuM2X+90en9h5G2sjhLjTxcTEkJE+Gmd7Gzfocnvb\nTZ48mePHj/e5bfv27X2WHzhw4LoxR40a1WcDVH8keRNC3JCh5TJJ/9P/LQx/S/ttrI0Q4m5hSUy5\nI0ZXh5skb0KIG8q9z80X7z/b7/Y//N+BTTAphBDi5knyJoS4odPHL3PZ3v/bxcU2aXkTQojbRZI3\nIcQNPXpPAtO/4Ol3e/3/yG0NIYS4XaJynjchhBBCCNE3Sd6EEEIIIYYQuW0qhBBCCBGixsZGnn/+\neVpaWjAYDCxYsICFCxficDgoKyvj7NmzjB49mk2bNmGxWGhra6OkpISPPvqI+fPn60t+Ajz11FM0\nNTURFxeHoihs27bthiszgCRvQgghhBAhU1WV8vJyJkyYgMvloqioiOzsbHbu3Mm0adMoLi5my5Yt\nVFZWsnLlSmJjYyktLeWTTz7hk08+6RVvw4YN3H///QOqg9w2FUIIIcQdSesM4Kluxf26He8+B1pA\nu/FBN2C1WpkwYQIAZrOZcePGYbfbqauro7CwEIDCwkL2798PQHx8PA899BAmk6nPeIGbmIU4Ysnb\nnj17ePzxx5kwYQLHjh3rsa2yspK8vDxmzZrFoUOH9PJjx44xd+5c8vPzWbdunV7u8XgoKysjLy+P\nJ598knPnzunbqquryc/PJz8/n127dunlZ86c4etf/zr5+fk8++yz+Hy3tgSHEEIIIaKLb6+DQEMH\nmt2L/7dO/EfCO63RmTNnOHHiBJMmTaKlpYWMjAwgmOC1traGFKO8vJzCwkL+4R/+IeTzRix5Gz9+\nPK+++ioPP/xwj/KTJ0+ye/duamtr2bp1K2vXrtUXcl2zZg3r1q1j7969fP755xw8eBCAqqoqkpOT\n2bdvH4sWLWL9+vUAOBwOXnvtNaqqqnjnnXd49dVXcTqdAPzoRz9i8eLF7N27F4vFQlVV1W28eiGE\nEEIMtsAFb4/HWlP4GmpcLhclJSVUVFRgNptRFKXH9msf9+XHP/4xNTU1vPXWWxw9epR33303pHNH\nrM/bF77wBQA9MetWV1fH7NmzMRqNjB49mrFjx9LQ0MDIkSNxuVxkZWUBUFBQwP79+3n00Uepq6uj\npKQEgPz8fL73ve8BcOjQIbKzs7FYgnNQZWdnc/DgQWbPns3hw4fZsGEDEGze/Pu//3u+8Y1v3JZr\nF+JO5ff7+T//Z8N19zGbY1m69C8BbrhvScmzqKp63bhmcywul1vf90509fV3X29f7uTnQIiboX4x\nDt+5K3NUGr4YF5a4Pp+PkpIS5s2bx8yZMwFIT0+nubmZjIwMmpqaQhp4MGzYMAASEhJ4/PHH+eij\nj5g3b94Nj4u6AQt2u50HH3xQf2yz2bDb7aiqyvDhw3uVA1y4cEHfpqqqPrrDbrczYsSIXsdcvHiR\n5ORkDIZgw+Pw4cO5cOHC7bg8IYakg8c0fvtf1/kWqQb4OvD553+g8sM6jGZzv7v6XC5mzJgFcN19\nfS4XX/taAePGffGGca/e94knvnbD66mq+heAPvc1mYx4PL6Q9u0r5rX7XhtvIDG79w31ee1+DoQQ\nQeqfWMCiotm9GL4Qi/rl+LDEraioIDMzk0WLFullubm57Ny5k6effprq6mpmzJjR67irG6z8fj+X\nLl0iNTUVr9fL+++/T3Z2dkjnH9TkbfHixTQ3N/cqLysrIzc3d9DOe21r3s3u05/BXCR3qMYe7PhD\nNXakhPuafrn/cIjnfYiWDz4MOW6o+w4k7gcfvB/y+SO9b6j7DfR5HQrC/RqVeHd2vJulKArGyf1/\n6bkZR48epaamhvHjx1NQUICiKJSVlVFcXExpaSk7duxg1KhRbNq0ST8mNzcXl8uF1+ulrq6Obdu2\nMXLkSJYuXYrf7ycQCDBt2jS+/vWvh1SHQU3e3njjjQEfY7PZOH/+vP64sbERm83Wq9xut2Oz2YBg\ns2P3fn6/n/b2dlJSUrDZbBw5cqRHrKlTp5KamorT6SQQCGAwGPRjhRBCCCGuZ/LkyRw/frzPbdu3\nb++z/MCBA32W79y586bqEBVThVzdCpabm0ttbS0ej4fTp09z6tQpsrKysFqtWCwWGhoa0DSNXbt2\n6U2Subm5VFdXA8FRrFOnTgVg+vTp1NfX43Q6cTgc1NfXM336dACmTJnCnj17APpt3hRCCCGEiDaK\ndiv3D2/B/v37+d73vsfFixdJSkriy1/+Mv/4j/8IBKcKqaqqwmg08uKLL+oJ18cff0x5eTlut5uc\nnBx9lmKPx8OqVas4fvw4KSkpbNiwgdGjRwPBrPb1119HURSWL19OQUEBAKdPn+bZZ5/l0qVLTJgw\ngfXr1xMTExOBZ0IIIYQQInQRS96EEEIIIcTARcVtUyGEEEIIERpJ3oQQQgghhhBJ3oQQQgghhhBJ\n3oQQQgghhhBJ3oQQQgghhhBJ3oQQQgghhhBJ3oQQQgghhhBJ3oQQQgghhhBJ3oQQQgghhhBJ3oQQ\nQgghhhBJ3oQQQgghhhBJ3oQQQgghhhBJ3oQQQgghhpCIJ29Op5OSkhJmzZrFnDlz+P3vf4/D4WDJ\nkiXk5+ezdOlSnE6nvn9lZSV5eXnMmjWLQ4cO6eXHjh1j7ty55Ofns27dOr3c4/FQVlZGXl4eTz75\nJOfOndO3VVdXk5+fT35+Prt27bo9FyyEEEIIcQsinrytW7eOP/mTP2H37t28++67fOELX2DLli1M\nmzaNvXv3MmXKFCorKwH49NNP2b17N7W1tWzdupW1a9eiaRoAa9asYd26dezdu5fPP/+cgwcPAlBV\nVUVycjL79u1j0aJFrF+/HgCHw8Frr71GVVUV77zzDq+++mqPJFEIIYQQIhpFNHlrb2/nd7/7HfPn\nzwfAaDRisVioq6ujsLAQgMLCQvbv3w/AgQMHmD17NkajkdGjRzN27FgaGhpoamrC5XKRlZUFQEFB\ngX7M1bHy8/M5fPgwAIcOHSI7OxuLxUJSUhLZ2dl6wieEEEIIEa2MkTz5mTNnSE1Npby8nBMnTjBx\n4kQqKipoaWkhIyMDAKvVSmtrKwB2u50HH3xQP95ms2G321FVleHDh/cqB7hw4YK+TVVVLBYLbW1t\n2O12RowY0ecxQgghhBDRKqItbz6fj//6r//iz/7sz6iuriY+Pp4tW7agKEqP/a59fCu6b7NG6ngh\nBpu8RkW0k9eoELcmoi1vw4cPZ/jw4TzwwAMA5OXlsXXrVtLT02lubiYjI4OmpibS0tKAYOvY+fPn\n9eMbGxux2Wy9yu12OzabDYBhw4bp+/n9ftrb20lJScFms3HkyJEesaZOnXrDOiuKQlPT4PSNs1ot\nQzL2YMcfqrG7499u4X6Nhvs5knjRF+92k9eoxBtoPNFTRFveMjIyGDFiBJ999hkAhw8fJjMzk9zc\nXHbu3AkER4TOmDEDgNzcXGpra/F4PJw+fZpTp06RlZWF1WrFYrHQ0NCApmns2rWrxzHV1dUA7Nmz\nR0/Qpk+fTn19PU6nE4fDQX19PdOnT7/dT4EQQgghxIBEtOUN4KWXXmLlypX4fD7uuecefvCDH+D3\n+yktLWXHjh2MGjWKTZs2AZCZmalPKWI0Gnn55Zf1W6qrV6+mvLwct9tNTk4OOTk5ACxYsIBVq1aR\nl5dHSkoKGzZsACA5OZlnnnmG+fPnoygKK1asICkpKTJPghBCCCFEiBRNOh8M2FC8hSe3TW9/7O74\nkRDtt0AkXnTFi4Rof04kXnTFEz1FfJ43IYQQQggROknehBBCCCGGEEnehBBCCCGGEEnehBBCCCGG\nEEnehBBCCCGGEEnehBBCCCGGEEnehBBCCCGGkIhP0iuEEELcqvd/sQd/S2e/251uF3nfLsBsNt/G\nWgkxOCR5E0IIMeRpn3Qwz/Vov9t/52igvb1dkjdxR5DbpkIIIYQQQ4gkb0IIIYQQQ0hUJG+BQIDC\nwkK+/e1vA+BwOFiyZAn5+fksXboUp/PKGmmVlZXk5eUxa9YsDh06pJcfO3aMuXPnkp+fz7p16/Ry\nj8dDWVkZeXl5PPnkk5w7d07fVl1dTX5+Pvn5+ezates2XKkQQgghxK2Jij5vb775JuPGjaO9vR2A\nLVu2MG3aNIqLi9myZQuVlZWsXLmSTz/9lN27d1NbW0tjYyOLFy9m3759KIrCmjVrWLduHVlZWRQX\nF3Pw4EEeffRRqqqqSE5OZt++fdTW1rJ+/Xo2btyIw+Hgtddeo7q6Gk3TKCoqYsaMGVgssgCuEEIM\nNf/Z9CmnLtv73X7BdYH/7Z9wG2skxOCJeMtbY2MjH3zwAQsWLNDL6urqKCwsBKCwsJD9+/cDcODA\nAWbPno3RaGT06NGMHTuWhoYGmpqacLlcZGVlAVBQUKAfc3Ws/Px8Dh8+DMChQ4fIzs7GYrGQlJRE\ndnY2Bw8evG3XLYQQInySx2cxMucv+/1JmTQXVVUjXU0hwiLiydsrr7zC888/j6IoellLSwsZGRkA\nWK1WWltbAbDb7YwYMULfz2azYbfbsdvtDB8+vFc5wIULF/RtqqpisVhoa2vrN5YQQgghRDSL6G3T\n3/zmN2RkZDBhwgSOHDnS735XJ3a3StO0W45htQ7erdWhGnuw4w/V2JES7muSeHdmvI72DjovdxIX\nHxeWeAMR7ufEZLr+x5miKGRkJIZ83mj9nd2t8URPEU3e/v3f/50DBw7wwQcf4Ha7cblcrFq1ioyM\nDJqbm8nIyKCpqYm0tDQg2Dp2/vx5/fjGxkZsNluvcrvdjs1mA2DYsGH6fn6/n/b2dlJSUrDZbD0S\nxsbGRqZOnRpSvZuanDfe6SZYrZYhGXuw4w/F2O7LnXicnYz50giMxtv/ZxbOawr3cyTxIhvPfbkT\nr8uD4taIM8SSODoOIpC8hfs58Xh8EN//Ppqm0dzcjqre+LzR9juTeJIIXiuit02fffZZfvOb31BX\nV8eGDRuYMmUK69ev56tf/So7d+4EgiNCZ8yYAUBubi61tbV4PB5Onz7NqVOnyMrKwmq1YrFYaGho\nQNM0du3a1eOY6upqAPbs2aMnaNOnT6e+vh6n04nD4aC+vp7p06dH4FkQdwqP20176yVc5x2obRrx\nvlj8fn+kqyVEr9emRUsg0WSOyBcLIcSti8q/3KeffprS0lJ27NjBqFGj2LRpEwCZmZnMmjWLOXPm\nYDQaefnll/VbqqtXr6a8vBy3201OTg45OTkALFiwgFWrVpGXl0dKSgobNmwAIDk5mWeeeYb58+ej\nKAorVqwgKSkpMhcshiyfz0en8zKKWyMmYMQSk6D/VXm93shWTtzVfF4vne2dKJ0BTJqpx2tTCDG0\nRc2f8iOPPMIjjzwCQEpKCtu3b+9zv2XLlrFs2bJe5RMnTqSmpqZXuclkYvPmzX3GKioqoqio6OYr\nLe5KgUCAy84OtM4AMX4VS0w8qAR/hIggv9/P5Usd4NaICXS9NqPmXV4IES7yZy1ECDRNo8PpQusM\noHoVzDFxKAYlCsZri7vd1V8mjD4DFpN8mRDiTifJmxD90DSNy+0dBDr9GDxgjokP3qY3Rbpm4m7X\n75cJeW0KcVeQ5E2Ia3R2XMbX4UVxa8SrcahqrHwoiojr/jLh9Ae43NguXyaEuItJ8iYE4Ol043G5\n9ekT4tUE+VAUUeGyqwP/ZR8GN8Qb4zAnxOM2BSJdLSFEBEnyJu5aXo8HZ/MlXOcvYdJigqPxYiJd\nKyF6z8VmNMqXCSHEFZK8ibvK1aPxTJoR8zALbqPMxSYiz+O+0vobi4k4SdiEEP2Q5E3c8fodjSdE\nhMlcbEKImyFvE+KOpGkal53BkaIyGk9EE5mLTQhxq+QtQ9xRLrd3de72BDt3GwwyUlREnszFJoQI\nJ0nexJDXe2oPkyRsIuK652KL8wVw213S+iuECBtJ3sSQdG3n7njp3C2iwLUTOycY4zCbZWqP2+HI\n6X9j/x/+s9/t7kutzA2U3sYaCTF4JHkTQ4beubuPReCFiKSr52KLU2NlYucIMH/5S9jHPtzvdu9n\nxzAY5D61uDNEdGXGxsZGFi5cyJw5c5g7dy5vvvkmAA6HgyVLlpCfn8/SpUtxOp36MZWVleTl5TFr\n1iwOHTqklx87doy5c+eSn5/PunXr9HKPx0NZWRl5eXk8+eSTnDt3Tt9WXV1Nfn4++fn57Nq16zZc\nsRgov9+Ps/US7fZL+Js8WHzxJKoJxMbIJ6OILPflTtqbL+E668B0yYBFS8BsSkBVJUEQQgyuiCZv\nqqpSXl7Oe++9x9tvv81bb73FyZMn2bJlC9OmTWPv3r1MmTKFyspKAD799FN2795NbW0tW7duZe3a\ntWiaBsCaNWtYt24de/fu5fPPP+fgwYMAVFVVkZyczL59+1i0aBHr168Hggnia6+9RlVVFe+88w6v\nvvpqjyRRRI6mabgutdN+4RKexg4S3LFYDAnExcRGumriLudxu2lvvYTrvAO1TcOiJZBoMmM0ShOw\nEOL2iWjyZrVamTBhAgBms5lx48Zht9upq6ujsLAQgMLCQvbv3w/AgQMHmD17NkajkdGjRzN27Fga\nGhpoamrC5XKRlZUFQEFBgX7M1bHy8/M5fPgwAIcOHSI7OxuLxUJSUhLZ2dl6widuv+7O3e1Nl7h8\nzom5MxaLkkCCqWv9RiEixOf10n7RiavxEkpLAIs/gUSjmRijLMchhIiMqPm6eObMGU6cOMGkSZNo\naWkhIyMDCCZ4ra2tANjtdh588EH9GJvNht1uR1VVhg8f3qsc4MKFC/o2VVWxWCy0tbVht9sZMWJE\nn8eI20cfKdqpkRATL1N7iKjQ51xsMrWHECJKREXy5nK5KCkpoaKiArPZ3KulJZwtL923WW+F1WoJ\nQ03urNgDid95uRNvuwetM0Cymogx9cYvw7Q0861W77bH9nq9gxI3FOH+Xd8N8QKBAB2XXDjPObB4\nDQxLyghDzYLC/RoLZ7wO3GGLNRDhfg2YTNd/H1EUhYyMxJDPG42v0bs5nugp4smbz+ejpKSEefPm\nMXPmTADS09Npbm4mIyODpqYm0tLSgGDr2Pnz5/VjGxsbsdlsvcrtdjs2mw2AYcOG6fv5/X7a29tJ\nSUnBZrNx5MiRHrGmTp0aUp2bmganb5zVahmSsUOJ7/V4cLe7UdwBTFoMpq4BBx7ccIMPj7Q0M62t\nrnBW97bE9nq9pA1LHpTYNxLO33W4XzvRFK/7dr3WGUD1KsTHxJGenkiry8VlV3heF+F+jYU7Xtzw\nyHwMhPs14PH4rruPpmk0N7ejqjc+bzS9RiWeJIJ9iWifN4CKigoyMzNZtGiRXpabm8vOnTuB4IjQ\nGTNm6OW1tbV4PB5Onz7NqVOnyMrKwmq1YrFYaGhoQNM0du3a1eOY6upqAPbs2aMnaNOnT6e+vh6n\n04nD4aC+vp7p06ffzku/4/n9ftovOmlvvITW7MPijyfRaNYTNyEi4dr+lQmXTdK/UggxpES05e3o\n0aPU1NQwfvx4CgoKUBSFsrIyiouLKS0tZceOHYwaNYpNmzYBkJmZyaxZs5gzZw5Go5GXX35Zf7Nd\nvXo15eXluN1ucnJyyMnJAWDBggWsWrWKvLw8UlJS2LBhAwDJyck888wzzJ8/H0VRWLFiBUlJSZF5\nIu4gsgyQiFb6XGweiDPIXGxCiKErosnb5MmTOX78eJ/btm/f3mf5smXLWLZsWa/yiRMnUlNT06vc\nZDKxefPmPmMVFRVRVFQUeoVFnzRNo90RnNpDFoEX0cR9uRNvhwelUyPOEIvRmAAySFQIMcRFvM+b\nGLquXgQ+bVg6FkWWqBKRd+3SaXGydJoQ4g4jyZsYkP4WgTcYIt59UtzFZOk0IcTdRN7exA3pLRld\nt57iVWnJEJHXPRdbnMePv9kjc7EJIe4akryJPuktGZ0BTJop2JIhfYWuKxAI4PX68Pn8oIHb7SbZ\nnxjpat1R+hoQY1YTcMfc+vyNQggxVEjyJnR+v5/Lzg7ovGpWeXmFAMHnxuv14/f7CfiDiYIWAFDQ\ntOC/FYOKwaCiGkwoCqAF5zEUt+baudhkQIwQ4m4X0kfzH/7wB375y1/icDh6lP/gBz8YlEqJ2ycQ\nCHC5vQPtcrAlIzEmDkVV7ppbT1pXguXrTsy6EjI0CAQ00ILJmaKoGFQVo2pC794n3fyc9giQAAAg\nAElEQVQGjaZpXG7vINDpx+CBBGNcsF+lJGxCCBFa8rZixQpmz57Nl770pcGuj7gNenwwusFsir8j\nWzI0Dfx+n95ipl2TmGkBCPjAdSmAaoxBNcTSY9zFTSSwgYBGIBDA7/fR3uEiEEgI09WErqXVTutF\nB5bEDGJihta97t4DYmQuNiGEuFZIyVtSUhIrVqwY7LqIQaZPUuqGeGNccBH42EjX6uYEApqemGla\nMBHTApp+CzN4O1MJ3sZUTagGpWdLWVdiFhebgPs6yUEgECDgD+AN+Aj4/fgJoAEawSRNUzT8GqBp\nBPCjoWAwKCgGA5e1TnwRWN/UYIAYkw+H8zSqkkBiYnpUJ3HXzsUWL1N7CCHEdYWUvBUWFrJx40am\nTp2K0XjlkIcffnjQKibCo/uDMfayH9MlQ3CS0ij/YLy6478W0DCq4HR0oHXdwgwENBQMGAwqRmMc\nBgVQuGFLWfDYYCucL+AnEAiA0U+bywWKRkADfyCApgR39hMAJbigtUE1oKhXZX+BAAbNh8HrJcYX\n/L/B5+v6Cf7b43HDHw0fxGfq+mJjYwAvl5xnMPz/7d15eFTV/fjx950lCUkm+2TCZmxBFMWo/aIg\nwfgY+CYCIquUfqsiblSlFfCLFvgKuCtYlaIPII+4VctPUoIgGJCALFIW1yjWim2RBEhCgKxkMss9\nvz8mc0lIAkMySQh8Xs+TJzPnnvu55545M/OZu2qdzqkkTq7FJoQQzRdQ8rZ7926+/fZbvvzyS6NM\n0zTeeeedVmuYaL66N4H3fzFGhoTjsrTODdjPxqlnZCrdtxvXf6yZ0kHTTMYWM98Gs3AsjSRmvq1v\nXrxej+94NXR0FLpSvpiawqvXnlygvOB1Y/Z4MSkvFl3H7PGgVWh0qjiByePBXJt0nZqImesmZe7a\nx7o3oPWtpF/wOq+ZQkItGEmcKZwoWwJmc9sf1Og/gznM5YWjXrkWWztQCpTSa983vq3HZncH3fwu\nxAUsoI/O7777jg0bNrR2W0QLeDwenBXVdS5S2vZnijZ2RiZ1tpahtIZnZPo3Zplrd1Hi20XpdtWg\nvFXgcUO1RtXxSjSvC9weNLcLk9ddm3B5sHi9mHWv77mReDXcEtYetxzXfVnpOcGXxLk4XvozZnNE\nmyRx/muxUXPyDGa5tMfZ8SdcvmMpdaqrzVRVVddO9E331VOgwHfIgO8QgpPPMeqbdIWmdExKYQKq\nqzRi23idhBAtE9DXe69evfjhhx+47LLLWrs94iy01U3g/Qf+e9xe325FvfYLRfd/QWjoHqgsc2NW\nCqvuBbcbXDWomhp0Vw2auwY8bjR/IuV2gT+x8tZu0fJ6jK1cZo8HTZ1MfOzBXaWA6CYTHrMJr1nD\nazXhsWjoFg2vWUO3gNfs+9NN4NEUulmhazoek46u6ZzwKHp5z53kzS80zAq4OF72M2ZTJFG2+KAm\ncW01Ls91uq5QSsftduN0utA9HpTXi+b1gtsDXi+aVwfdC24vmu57rnm9Rj1N18HrxaQrTLpvi7Gy\nmrBVu3z19dp4Rv3ax3rjZVojPyaOTr6jHXpHCNESASVv+fn5jBo1CrvdjtVqRSmFpmnk5ua2dvta\n3datW3n22WdRSjFmzBjuv//+9m7SaQX1mldKoTxedGc1nhPV4HKjXG5Mbjea24PmdoPLjVabTFk9\nHjSPG9yu2iTsZDJm8riJ8ga2K9FYPDpK00/+r/3zouOxeOuV1atjPPY2mOY1KXQT6CZl/CmTL6lS\nmkLX6sTFi2ZS6MoD+J6jPIAXDS8oLxpnSL68tX/GOvn/NMKB6uoxZ9Unbcl3TFxNUJK4c/pabL5N\nv1Anyan7v95jjxf3ITNaaSXK7UF5PeA5JUGqTbiMx16PkWT5kyXNqxvJkq57ifd60VTwtjYGc6P6\nseJCuv6ycxAjCiFaW0CfAa+99lprt6Nd6LrOU089xVtvvUViYiJjx45l0KBB9OjRo23boRSbDpZx\n2ZtHcRxVhF8Ugnt0JFh8+xSVUjirqn1nijp1wpUZs8dbm0iVo7nc4Hajud1obhfK5UJz16BcTvSa\nE+huJxXKjeasBq8L5a3x7YLUXWjoaOiYGkmGPLVbkbzoeE21f5rvz2PW8VpV7fM6/02+cy69Jl+Z\nrinfVilN1daj9r9C13xJjq750iVd01CAV/O1SgG6ptWb5n9et56uUfvfl2optNppNBLTjFez1Kuv\n++PUfeyP2Uj809VX2smdsxZd569tOpKa59QkLjoqAZPJFNC4POO12HybbeslSErVYDpW6SvXvWie\nhknUycTKU5sg1S33QO0WKrwe3Bp0crp8sevU8SdQyuupHTG+s4TRlO9/7ePaEe87c1hTlPvLTL56\nXk3Vjj1fuVfDN3Zrn/um+cadovbHg6ahNH8ir4xxqEy+E2P02nHiGzOqzmPt5A8A//zKH6t2mqZQ\n+M7S8T3XUMoEmm/+k+PQXFsPwFR7lRyT7zFa7TTFNWFyzJsQHU1AyduePXsaLe/atWtQG9PW8vLy\nSE5ONtZj2LBh5ObmnjZ52zTrx3rP6x1HpRqWacoorl9+yjxdgUoFlQAH3PBKlVFRU43MZzChqVB8\n1/yoP/XUulqDH/5a4/XqPlFNtBuMrwErp7axYStP7ZMmp9V91MR6N3bs2un7qJHyRl6rU/ujsfpn\nE99lVnBlE5Vb0acLjmFSvldBq80ETGhGH/lPztUUmGrLTGigzGh4OKgKjeld0Yxxaaodl5ryvcaa\nqh9L44Qvlqrtx1Om+5d9GFftNDMaZt+xkKfUaWpeTZ0cXU3XaRiHJh7XfR9q+D4Qm4wV+EvQoexP\nP9HeTRBCnKWAkrddu3YZj91uN1988QV9+/Zl5MiRrdawtlBUVETnzid3FzgcDr799tvTztP/sJwe\nd/4K/kH0B89yV3IwZP4sW1JE4I6Vl7Z3E4QQZymgTOTU22CVlpYyderUVmmQaDtNpSqNlmtnmB5A\nDFVv00X97RgBxWxi08eZ5j2b+YyyRuapX19rvLzOfC4ThEec34lUnZMYjX4+2YcndwFSZ5qqM406\n01Wdef27nxuLq+rWNR5rxrS68zU6vzFNqzM2VKO7LE9dXqPljbTd2DFbr63++OqUmHV3nZ4aT0PX\nVKPrrDfSBvDvwm1Y5vuvjPkAPBp0dbfPJYTsdltQ44WEnP7rTNM0EhIiA15usNsn8UQwNWszUnh4\nOAcPHgx2W9qcw+Hg0KFDxvOioiISExNPO8+bv1pae7q9hln5vzJMaJpvJ5CGGTQNExomzeK7J6Zm\nxmSyYDJbsVismM1WNLMVr9mCyRpC2Oc6VlMnzHoIZm8omh7CsU4hZF9UTbzpEJd4LyfEHEmV10OR\nqYJIVzhdyz0klyrCQkxYQjS8fULYkVjKZ8cOAhopByO58bCdSLMFBYReb6Pyl74dnYraXZunfKlp\ntY+Nw7YU7Dh6mK1HDlLlcVPhdmOzWomwWLkhoSvX2zsbu5tiYiM4fryqdrba44uUP7qGrvxlyohd\nt8x/SQ1Ve402hTIex0R34tjxqtpLISgjhq7rdb6QdVCgfBeKM+IrFLryr5nim/JSvig/6ltfTeN6\nu4NLLOFommYsjzp941uOv521Jy/U1oXas21rl+c7nkpD6Tq+nYIexiZcfdrx1Bo2X7nFeCFNmoZu\nMqGZfJdp0UwamEyYzCbfrRg0DUxmNAuABWVSeMwaymoidIcHqxaCpswoZcKERpXVioYJpTQsmgdn\n+HGivZFYLJ0woVHTS6emh5u8/eAsijT2O4YlVtHnl77+tdk6UV5RjaYU35RV8lVFqdHdV9ui6BNt\nq30t4buKCr4pL8c/SlOibPSOrPuloPhXTTWflxzHN6oVV9hsXBYZycmR7YtljEv/FGMonpyiFHQK\nD6GqyokG6OpkuVHXGLcng+gK37Fpvme1x6r5noWGWql2umrr+caX/6g14+oeqpEkU6mT0+qUh4ZY\ncNa4a99Z1C4RCmtqOOSsxn8sXJeQUBxhJw+nUKo2CTS6RaGhMeSK6xsOojZw5EhF0GLZ7TZcLs9p\n6yilKCmpxGw+83LtdlvQ2yfxWhZP1BdQ8nbHHXegaSc/AAoKCkhLS2vVhrWFK6+8kgMHDnDw4EHs\ndjtr167lpZdeOu08Q5OH43aHYg4LxRQagikkFGU1o5lNWCxmrFaL76DtRui6TrXHiR4C5k4WOkX6\n7nt5x7erycr/lZE87TfBGz2r0GNzGfGLLoS7ulNZaiIiRvGPCCcl+3VUTTRJVVbidS+qsxnPf4Vx\nm5ZAbFEoP1eVc1FyFJfm27EUedGTzMQMslNytPKs+6h75MXEh5nZX1VGlcdDhNnCxZHRDHZ0x1Tn\n4Hx7bDhHPK2zi9But3EkPDgfBAOVYmNRPj9XlZMcEcVv+lzO0ZKz75dAhYa2/Za3G+8Yi7P49F9k\ndXm9Xqq9TlSohiXcSlh4JwDu+Go1WfnXG+PyZxNsv6ITl5RXc2l1MScSnIRcn0+PopuwFpvxJCqq\nr/aCBhfHKD7bq1FZbiUyyk3qFWG+W5QBcXERHKu9YHTP2EQSjh4j31lN97BO3BgfV29cXRbvwH6a\n6QBpseHE7Ms/bZ2zERcXwbFjwdsa1VbxdKXYcoa+aowtou3vvyuEaJmAkrff//73xmNN04iNjaVn\nz56t1qi2Yjabefzxx7n77rtRSjF27Ngznmlq/68rcJYGfu0upRTVbideq8LUyUwnm81IhP3enXwr\n1Zz8teIAnscO/K5uJAC6cxF0OVla986ZJiAj6SLjud4ZXLWPNVPzvsxMmlYvZkd36vq05Eu+I6v3\nQyLSQkRkdIM6p47LROBeACJRKoGKimPUuBOp7qzjPOUsFLNJI+1KODlCG+9nk6ZxU0J8k+080/RA\n61wIpB+EuHAElLxdd9117Nu3j7KyMpRSHD9+nD179pwX9zZNS0trla2I1S4nHouOFmaiU0JEk1vj\nhGgrSilOuKrRQ2jyh0SgNE0jKioepeKoqDiG011OaKi52fGEEEIELqDk7cknn2TTpk10797dKJN7\nmzZU43bhMnkgVKNTXDhh7XD/SCHqUkpR7XLiDVG1PyQig/pDom4SV15xlBp3hSRxQgjRygJK3rZv\n305OTg5hYWGt3Z4Ox+V24dLcqFATodGhRIbI8SOifSmlcLprcJu9vh8S8eGtfg9TTdOIjkpAqXjK\nK47iclcQIkmcEEK0ioCSt+7du588S1D4bgKvalChGtbYECI6NTxeSIj2EmILxZzoJdTS9tckrJfE\nlR/F5ZEkTgghgi2gT/fo6GiGDRvGNddcQ0jIyXvfnHr9twuB6qThjlZEhEvCJs5NoWGhWCyuM1ds\nRZqmER2dgK77jomTJE4IIYInoOTthhtu4IYbbmjttnQIttgonB75AhIiECaTqV4SV+Mpb+8mCSFE\nhxdQ8jZq1KjTTsvOzg5ag4QQ55+6SZzy1uB0lhIWZm3vZgkhRIfU4tPO5Fg4IUSgTCYTsbGJxMde\njMcditPpPvNMQggh6mlx8ibHsAghzpbJZCI2JpG4mGQ87lBqaiSJE0KIQMmVY4UQ7cZsNhMbk0hs\ntCRxQggRKEnehBDtrn4SFyJJnBBCnEaLLwQlx7wJIYLFl8Q58Hq9lFeU4PVWESonNgghRD0BbXlr\n7GzS9957D4D7778/uC0SQlzw/ElcdFR3PC4rrhpPezdJCCHOGafd8vbWW29RWVnJ8uXLOXjwoFHu\n8Xj46KOP+O1vf8vQoUObteB58+axefNmQkJCuOiii3juueeIjIwEYMmSJfztb3/DbDYza9YsBg4c\nCMDevXv54x//iMvlIi0tjVmzZgHgcrl47LHH2Lt3L7Gxsbz88st06dIF8CWeixcvBuCBBx5g5MiR\nABQUFDBt2jTKysq44oormDdvHpZ2uCK9EKJpVquV2Ngk3G43lZVH8aoThIbKljghxIXttFvekpOT\nGy0PDQ3l+eefb9GCBw4cyNq1a/nwww9JTk5myZIlAPz00098/PHHrFu3jqVLl/LEE08Yu2bnzp3L\nM888w/r169m/fz/btm0DICsri+joaDZs2MCECROYP38+AGVlZbz22mtkZWWxYsUKXn31VSoqKgB4\n8cUXmThxIuvXr8dms5GVldWi9RFCtB5/Ehdt822Jk2PihBAXstMmbzfddBOTJ0/m7bffZvLkyUye\nPJm77rqLwYMH07dv3xYteMCAAZhMvsVfffXVFBYWArBp0yaGDh2KxWKhW7duJCcnk5eXx5EjR6iq\nqiIlJQWAkSNHsnHjRgByc3ONCwlnZmayc+dOALZv305qaio2m42oqChSU1ONhG/nzp1kZmYCvgsN\nf/LJJy1aHyFE66ubxLldFtmdKoS4IAV0zNuXX37JjBkzOHbsGEOHDuUPf/gDL7/8ctAakZWVxY03\n3ghAUVERnTt3NqY5HA6KioooKioiKSmpQTlAcXGxMc1sNmOz2SgtLW0y1vHjx4mOjjaSx6SkJIqL\ni4O2PkKI1mW1WomL7YwtsisetxwTJ4S4sAR0kNdf//pXli1bxurVqxk0aBCzZs1i3LhxTJ069bTz\nTZw4kZKSkgblU6dOJT09HYBFixZhtVq55ZZbmtH8xgVyBmxLzpK1223Nnvd8jd3a8Ttq7PYS7HU6\n1+NF2RIoLz9Se0xcSIvjxcVFBKFVHSReO10wINhjICTk9F9nmqaRkBAZ8HLP9TF/ocUT9QV8hH5M\nTAxbtmzhzjvvxGKxUFNTc8Z53nzzzdNOX7lyJVu2bOGdd94xyhwOB4cPHzaeFxYW4nA4GpQXFRXh\ncDgASExMNOp5vV4qKyuJiYnB4XCwa9euerH69+9PbGwsFRUV6LqOyWQy5g3UkSMVAdc9G3a7rUPG\nbu34HTW2P357COY6BbuPWiNeWVkNEIXHFUbp8aMonGf8Mm9KXFwEx45VBa1953y82OAmloEK9hhw\nuU6/9VUpRUlJJWbzmZfbEcb8hRZP1BfQbtOePXsyadIkCgoKuP7663n44YeNY8+aa+vWrbzxxhss\nWrSIkJCTv5TT09NZt24dLpeL/Px8Dhw4QEpKCna7HZvNRl5eHkopVq1axaBBg4x5/JczycnJoX//\n/oDvpIgdO3ZQUVFBWVkZO3bsMM5c7devHzk5OYDvjFR/LCFExxUSEkJcbGciwzvjdpnP+IUuhBAd\nUUA/TZ999lm++uorevXqRUhICKNGjTKSoOZ6+umncbvd3H333QBcddVVzJ07l549ezJkyBCGDRuG\nxWJhzpw5xv1TZ8+ezYwZM6ipqSEtLY20tDQAbrvtNqZPn05GRgYxMTG89NJLAERHR/Pggw8yZswY\nNE1j8uTJREVFAfDII48wbdo0FixYQO/evRk7dmyL1kcIce4IDQ0jNLQLNTVOqk4cQ1HT7C1xQghx\nrgno08ztdrN582aee+45vF4v/fr147rrrmvRddE2bNjQ5LRJkyYxadKkBuV9+vRhzZo1DcpDQkJY\nsGBBo7FGjx7N6NGjG5R3796dFStWnEWLhRAdTd0krrLqKGg1hITIdeKEEB1bQLtNn3zySZxOJ88+\n+ywvvPACHo+HOXPmtHbbhBAiKEJDw4iP60pkeBdcNSbcbrlOnBCi4wpo09nevXtZvXq18Xz27NnN\nvrOCEEK0F9+WuK44nSeoOnEMzeTCapUtcUKIjiWgLW9KKcrLy43n5eXlmM3mVmuUEEK0prCwcOLj\nuhEeloSrRpMtcUKIDiWgLW933XUXt912G+np6Sil2LRpk9yQXgjR4YWFhRMWFm5siTOZJYkTQpz7\nAkrehg8fzuHDh1m0aBFKKWbMmMGYMWNau21CCNEm/ElcdXUVrhoXbrcHq1XOThVCnJsC+nR6/PHH\nqampYeHChei6zocffsiBAweYNWtWa7dPCCHaTKdOEdjtSVSfsFLtLEUzuSWJE0KccwL6VPrmm2+M\nC9qC76K4wbydlRBCnEvCwyMJD4/kxIlKTlQfx2zxYrHIcb5CiHNDQCcsJCUlkZ+fbzw/duwYiYmJ\nrdYoIYQ4F4SHR5IQ351QawI1TvB4vO3dJCGECGzLm6Zp3HrrrQwYMACz2cyuXbtITEzkvvvuA2Dp\n0qWt2kghhGhP/i1xVVUVVDuPY7bosiVOCNFuAkreHnjggXrPb7/99lZpjBBCnMsiImxERNgkiRNC\ntKuAkrfrrruutdshhBAdRv0k7hgWK5jNAR2FIoQQLdbunzbLli3jsssuo7S01ChbsmQJGRkZDBky\nhO3btxvle/fuZfjw4WRmZvLMM88Y5S6Xi6lTp5KRkcGvf/1rDh06ZEzLzs4mMzOTzMxMVq1aZZQX\nFBQwbtw4MjMzmTZtGh6Pp5XXVAhxvomIsJEQn4zFFEuNU8frlWPihBCtr12Tt8LCQj777DO6dOli\nlP3rX//i448/Zt26dSxdupQnnngCpRQAc+fO5ZlnnmH9+vXs37+fbdu2AZCVlUV0dDQbNmxgwoQJ\nzJ8/H4CysjJee+01srKyWLFiBa+++ioVFRUAvPjii0ycOJH169djs9nIyspq47UXQpwvIiOjJIkT\nQrSZdk3enn32WR599NF6Zbm5uQwdOhSLxUK3bt1ITk4mLy+PI0eOUFVVRUpKCgAjR45k48aNxjyj\nRo0CIDMzk507dwKwfft2UlNTsdlsREVFkZqaaiR8O3fuJDMzE4BRo0bxySeftMk6CyHOX5GR0UYS\n55QkTgjRStotecvNzaVz585ceuml9cqLioro3Lmz8dzhcFBUVERRURFJSUkNygGKi4uNaWazGZvN\nRmlpaZOxjh8/TnR0NCaTb/WTkpIoLi5utXUVQlxYIiOjsddJ4nRdb+8mCSHOI6166fCJEydSUlLS\noHzKlCksWbKEZcuWtcpy/btZW1qnKXa7rdnznq+xWzt+R43dXoK9ThKvZXEqOpVSdeI4IaEYPxpb\nIi4uosUxDM3/KGyRYL9mISGn/zrTNI2EhMiAl3uujqkLNZ6or1WTtzfffLPR8h9//JGDBw8yYsQI\nlFIUFRUxevRoVqxYgcPh4PDhw0bdwsJCHA5Hg/KioiIcDgcAiYmJRj2v10tlZSUxMTE4HA527dpV\nL1b//v2JjY2loqICXdcxmUzGvIE6cqTibLsiIHa7rUPGbu34HTW2P357COY6BbuPLsR4TqcZsymB\n0mPHcdaUERKqNTuJi4uL4NixqqC1Ly42iIngWQh2H7tcpz/pTClFSUklZvOZl9sRxtSFFk/U1y67\nTXv16sVnn31Gbm4umzZtwuFwkJ2dTXx8POnp6axbtw6Xy0V+fj4HDhwgJSUFu92OzWYjLy8PpRSr\nVq1i0KBBgO92XdnZ2QDk5OTQv39/AAYOHMiOHTuoqKigrKyMHTt2MHDgQAD69etn3PIrOzvbiCWE\nEK3FZoslIT4ZEzZqnF7ZnSqEaJZz4o7LmqYZuzF79uzJkCFDGDZsGBaLhTlz5qBpGgCzZ89mxowZ\n1NTUkJaWRlpaGgC33XYb06dPJyMjg5iYGF566SUAoqOjefDBBxkzZgyapjF58mSioqIAeOSRR5g2\nbRoLFiygd+/ejB07th3WXAhxodE0DZstjkgVS2XlcWqc5VhDmr8lTghx4Tknkrfc3Nx6zydNmsSk\nSZMa1OvTpw9r1qxpUB4SEsKCBQsajT169GhGjx7doLx79+6sWLGimS0WQoiWOTWJczrLCA01Gz9W\nhRCiKfJTTwgh2pE/iUuIuxhNReJ0elp0QpUQ4vwnyZsQQpwD6iZx6BGSxAkhmiTJmxBCnEM0TSMq\nKp6EuItRergkcUKIBiR5E0KIc5CmaURHJRhJXE2NV5I4IQRwjpywIIQQonH+JE6peMrLjxpb4uTE\nBiEuXLLlTQghOgBN04iOTiAx4Rcobzg1sjtViAuWJG9CCNGBmEwmoqMTiItNRnnDcTrd7d0kIUQb\nk+RNCCE6IH8SFx97MV53qCRxQlxAJHkTQogOzGQyEROTSHzsxeieMEnihLgASPImhBDnAd+WODtx\nMcl43KHU1EgSJ8T5SpI3IYQ4j5jNZmJjEomNliROiPOVJG9CCHEeqp/EhUgSJ8R5pF2Tt3fffZch\nQ4YwfPhwXnzxRaN8yZIlZGRkMGTIELZv326U7927l+HDh5OZmckzzzxjlLtcLqZOnUpGRga//vWv\nOXTokDEtOzubzMxMMjMzWbVqlVFeUFDAuHHjyMzMZNq0aXg8nlZeWyGEaHu+JM5hJHGuGvmsE6Kj\na7fkbdeuXWzevJk1a9awZs0a7r77bgD+9a9/8fHHH7Nu3TqWLl3KE088YVzLaO7cuTzzzDOsX7+e\n/fv3s23bNgCysrKIjo5mw4YNTJgwgfnz5wNQVlbGa6+9RlZWFitWrODVV1+loqICgBdffJGJEyey\nfv16bDYbWVlZ7dALQgjRNvxJXJStGx6XVZI4ITqwdkve/vrXv3Lfffdhsfhu8hAXFwdAbm4uQ4cO\nxWKx0K1bN5KTk8nLy+PIkSNUVVWRkpICwMiRI9m4caMxz6hRowDIzMxk586dAGzfvp3U1FRsNhtR\nUVGkpqYaCd/OnTvJzMwEYNSoUXzyySdtt/JCCNFOrFYrsbFJRhIH58edGo4XH8L58z+b/Ks+9DNK\n6e3dTCGCot1uj7V//34+//xzXn75ZUJDQ3nsscfo06cPRUVFXH311UY9h8NBUVERZrOZpKSkBuUA\nxcXFxjSz2YzNZqO0tJSioiI6d+7cYJ7jx48THR2NyeTLXZOSkiguLm6L1RZCiHOCP4mLiba1d1OC\nwmSuwOnZ2+R0jzrM+ZKoCtGqydvEiRMpKSlpUD5lyhS8Xi9lZWV88MEH5OXl8fDDD5ObmxuU5QZy\ny5iW3FbGbm+9D7uOGru143fU2O0l2Osk8c7veO0h2OuQ0K0zxd0cTU73hIVit9sCXu65/ppdaPFE\nfa2avL355ptNTlu+fDkZGRkApKSkYDabOX78OA6Hg8OHDxv1CgsLcTgcDcqLihcQnFoAABucSURB\nVIpwOHxv1MTERKOe1+ulsrKSmJgYHA4Hu3btqherf//+xMbGUlFRga7rmEwmY95AHTlSEXDds2G3\n2zpk7NaO31Fj++O3h2CuU7D7SOKde/HaQ7DXweU6/TF8SkFJSSVm85mX2xFeswstnqiv3Y55Gzx4\nsHFs2n/+8x/cbjexsbGkp6ezbt06XC4X+fn5HDhwgJSUFOx2Ozabjby8PJRSrFq1ikGDBgGQnp5O\ndnY2ADk5OfTv3x+AgQMHsmPHDioqKigrK2PHjh0MHDgQgH79+pGTkwP4zkj1xxJCCCGEOJe12zFv\no0ePZubMmQwfPhyr1coLL7wAQM+ePRkyZAjDhg3DYrEwZ84cNM13nMLs2bOZMWMGNTU1pKWlkZaW\nBsBtt93G9OnTycjIICYmhpdeegmA6OhoHnzwQcaMGYOmaUyePJmoqCgAHnnkEaZNm8aCBQvo3bs3\nY8eObYdeEEIIIYQ4O+2WvFmtVuOSHqeaNGkSkyZNalDep08f1qxZ06A8JCSEBQsWNBpr9OjRjB49\nukF59+7dWbFixVm2WgghhBCifckdFoQQQgghOhBJ3oQQQgghOpB2220qhBBCBMuRf/+EXpjf5HR3\nyVH0jDFt2CIhWo8kb0IIITq8+B4RHL3kRJPTzf/yYjKZ27BFQrQeSd6EEEJ0eM6f/kPMv5u+ttiJ\n0mr0Qd42bJEQrUeSNyGEEB3eNckRJHY/0OT0/P2y5U2cPyR5E0II0eEVfHeYgs+rmpx+vMKDZ9jp\n78IgREchyZsQQogO7796duG62LImp39/0IvFIl954vwglwoRQgghhOhAJHkTQgghhOhAJHkTQggh\nhOhAJHkTQgghhOhA2u3ozby8PJ588kk8Hg8Wi4U5c+Zw5ZVXArBkyRL+9re/YTabmTVrFgMHDgRg\n7969/PGPf8TlcpGWlsasWbMAcLlcPPbYY+zdu5fY2FhefvllunTpAkB2djaLFy8G4IEHHmDkyJEA\nFBQUMG3aNMrKyrjiiiuYN2+eHMwqhBDnOa/Xy9atm09bJzo6nKuu6ofZLJcWEeemdstW5s+fz5Qp\nUxg4cCBbtmxh3rx5vPvuu/z00098/PHHrFu3jsLCQiZOnMiGDRvQNI25c+fyzDPPkJKSwn333ce2\nbdu44YYbyMrKIjo6mg0bNrBu3Trmz5/Pyy+/TFlZGa+99hrZ2dkopRg9ejSDBg3CZrPx4osvMnHi\nRIYMGcKcOXPIyspi/Pjx7dUdQggh2sC//vUT/++NL4i2JTZZp6yimGmz4+nV69I2bJkQgWu35C0x\nMZGKCt/VsCsqKnA4HABs2rSJoUOHYrFY6NatG8nJyeTl5dGlSxeqqqpISUkBYOTIkWzcuJEbbriB\n3Nxc/vCHPwCQmZnJU089BcD27dtJTU3FZrMBkJqayrZt2xg6dCg7d+7kpZdeAmDUqFEsXLhQkjch\nhDjPeb1etlt2YzHbmqzjsVTwsHc4Xq+X/fv/fdp4F1/8ywt+C53X62X58veM5zZbGBUVzgb1xo//\n7QXfV8HSbsnbI488wm9+8xteeOEFlFIsX74cgKKiIq6++mqjnsPhoKioCLPZTFJSUoNygOLiYmOa\n2WzGZrNRWlpKUVERnTt3bjDP8ePHiY6OxmTyHfKXlJREcXFxq6+zEEKI1vHdj6V8Ud70RXirnF6u\nnqBhNpvoW/Q5toqmk4iKE17MZhP79/+bPdPW0SUiqdF6h6oK4aWh9OhxCWPH3tpkvJAQCy6Xh6ys\n1QCnret3urr+eGeqF2jMuvECWf6p9fbv/zfP53yG1RbTZD13RSn9+19Pjx6XnLGd4sxaNXmbOHEi\nJSUlDcqnTJnCX/7yFx5//HEGDx5MTk4OM2fO5M033wzKcpVSQanTFLu96V9sLdVRY7d2/I4au70E\ne50k3vkdrz0Eex0Wvv9RgDV7sPbzYwHH7b/jVwHV27Ll9MfRtXbd9ly+3f4rCnPeDzimaLlWTd5O\nl4xNnz7dmH7zzTfzf//3f4Bv69jhw4eNeoWFhTgcjgblRUVFxq7WxMREo57X66WyspKYmBgcDge7\ndu2qF6t///7ExsZSUVGBruuYTCZjXiGEEEKIc127XSrk4osvZvfu3QD8/e9/Jzk5GYD09HTWrVuH\ny+UiPz+fAwcOkJKSgt1ux2azkZeXh1KKVatWMWjQIGOe7OxsAHJycujfvz8AAwcOZMeOHVRUVFBW\nVsaOHTuMM1f79etHTk4O4Dsj1R9LCCGEEOJcpqmW7D9sgW+//ZYnn3wSt9tNaGgoc+bM4fLLLwd8\nlwrJysrCYrHUu1TId999x4wZM6ipqSEtLc3YWudyuZg+fTr/+Mc/iImJ4aWXXqJbt24ArFy5ksWL\nF6NpWr1LheTn5zNt2jTKy8vp3bs38+fPx2q1tkNPCCGEEEIErt2SNyGEEEIIcfbkDgtCCCGEEB2I\nJG9CCCGEEB2IJG9CCCGEEB3IBX8zz1dffZUPPviA+Ph4AKZOnUpaWhrQNvdY9du6dSvPPvssSinG\njBnD/fff32h709PTiYyMxGQyYbFYyMrKoqysjKlTp3Lw4EG6devGK6+8YtxV4kzrcOjQITweDxdd\ndBFr1qzB5XIxbdo0tm3bhlKKK6+8ksWLFwccr26fzJw5k82bN+N2u4mLiyM2Nparr76ajz/+mPj4\neEpLS/F4PISHh/PAAw9QVFQUcOzCwkKmT5/O999/T01NDYmJifzlL38hIiKCqVOn8s9//pPKykoS\nExN56KGHGDlyZMBtv+eee3j00UcpKSmhqKgIq9VKcnJy0Nre0rHSGmO3KYGOy2CO0XXr1vHpp58S\nHx/PmjVrAII6xlvadydOnMDj8RjreNttt3HnnXc2u42PPPIIhw8fJjQ0FIfDwbhx47jjjjua3cZH\nH32UAwcOEBERQXx8PIMGDTLu49zcPgx0vPjNmzePzZs3ExISwkUXXcRzzz1HZGRks5Z1uvdKoGO0\nsLCQRx99lKNHjwblNfO384YbbuCLL77A4XCwePHiFsVzOp1omobZbMZkMvHss89y8cUXNztecXEx\nSikcDge9evXiueeeo7q6+qzfh5GRkYSGhuJyuejXrx8FBQVB+64ZMWIEBw4cwGKxcO211/LKK6+0\n2jg576gL3MKFC9WyZcsalP/0009qxIgRyu12q/z8fDV48GCl67pSSqmxY8eqb775Riml1L333qu2\nbt2qlFLqvffeU3PmzFFKKbV27Vo1ZcoUpZRSpaWlatCgQaq8vFyVlZUZj/28Xq8aPHiwKigoUC6X\nS916663qp59+arS96enpqrS0tF7ZvHnz1Ouvv66UUmrJkiVq/vz5Siml9u3bd8Z12LNnjxo/fry6\n6aabjHUYMWKEev3119XatWvV8OHDzype3T7Zs2eP+tOf/qSuvfZao0+GDRumli1b1qBP0tLS1C23\n3BJw7OLiYvWnP/1JzZkzR1VWVqrU1FR17733qnnz5qk///nPatCgQerPf/6zevrpp9WgQYPU119/\nHXDb16xZo77//nv13nvvqZkzZ6qMjAy1bNmyoLW9JWOlrmCO3caczbisq6VjdM+ePer7779Xt9xy\nS7PmP9MYb2nfFRcXq/Hjx6utW7eqyspKlZGRoX766admt/HTTz9V33//vbr33nvVhg0bjHgtaeOJ\nEyfUvffeqz799FN12223qc8//7xFfajUmcdLXZ999pnyer1KKaXmz5+vXnzxxWYvq6n3ilKBj9Hi\n4mL1/fffK6VUUF4zfztvvvlmdccdd6hJkyYppVo2Th977DF1yy23qK1btyq3263Ky8ubHW/jxo0q\nPT1d3X333Wrr1q3q4YcfVitXrmzW+zAlJcVY3/T0dDVjxoxmrV9j3zWTJk1SXq9XrV27Vt18882t\nNk7OR7LblMbvtpCbm9voPVaPHDnS6D1W/fOMGjUK8N1jdefOnUD9e6xGRUUZ91j1y8vLIzk5ma5d\nu2K1Whk2bBi5ublNtlXX9QZt9S931KhRRnuauk9s3XXo27cvQ4cOpbKy0ohVVlbGqFGjyMzMpLCw\n8Kzi1e2Tvn378sUXXxAdHW30SUFBAUqpBn2SmJhIr169Ao5tt9vZu3cvo0aNIiIigiuvvJKvvvqK\n3Nxc7HY7qampjB8/nm3btpGamsrbb78dcNv37NlD7969yc3NZdy4cfTo0YNLLrkkaG1vyVhprbHb\nmLMZl6e2qSVj1Gq1EhUV1ez5zzTGW9p3drud22+/nY0bNxIREUGPHj0oKipqdhtvvPFGevfuzciR\nI9m+fTs9evQwbtfX3DZ26tSJkSNHsmHDBnRdJzo6ukV9CGceL3UNGDDAuP3g1VdfTWFhYbOXdep7\n5e9//7uxnEDHqN1up3fv3gBBec1SUlIoLCzEYrEQHh5e77VpTrxf/vKXfP755/zud79j48aNWCwW\nbDZbs+P17dsXq9XKzTffzIYNG3A6nTgcjrN+H3o8HnRdN14Xl8uF1+ttVn819l3zwAMPYDKZyMzM\npKioqNXGyflIkjfgL3/5CyNGjGDWrFlUVFQANHlf1KKioqDdY9WvselN3WtV0zTuvvtuxowZw4oV\nKwA4evQoCQkJgO9D6tixY2e1DgkJCXg8HmMdKioqSEhIwGw2ExMTw9GjR5vdJ0ePHsVisRh9EhIS\nwjvvvMPzzz/Pd999Z/S3pmlomtas/i4oKOCf//wnMTExlJSUcOLECTp37mz0hf/uHM15PZVS/PDD\nD1xzzTWt0nZ/vwQ6Vk4VrLHbmLMZl3UFY4ye6tixY0Eb437B6LuCggJ++OEHrrrqqha/Dx0OB/v3\n7+eHH34wvpya20Zd11mwYAHZ2dlcd9119OzZMyjtO914aUpWVhY33nhjs5d16nslKiqK0tLSJuOd\naYwG6zV79tlnmTBhAsePHzfqNTdeQUEBsbGxrFq1io8++ojHH3+c6urqZseLjo7m7rvv5umnnyY7\nOxubzcaAAQPOOt6RI0eMz2+AqqoqysrKmtVf0PC7pu7rqus6ffv2Pat4gY6T89EFkbxNnDiR4cOH\nN/jbtGkT//M//0Nubi4ffvghCQkJPP/880FbbmO/mlvqr3/9K9nZ2SxdupT33nuPzz//vF7iADR4\n3hLBXofIyEiys7OZOHEi4eHhLe7vEydO8Ic//IGZM2c2SKKgZX2h6zqzZ89m5syZREREBL3tdTXV\nzytXruSNN95o87HbEm0xRls6fzD6zuPxGGMvIiKixevodDrZu3evEa8lbfQfMzVgwAC++OILdu3a\nFfTX4HSfq36LFi3CarVyyy23tGhZdbXkM6mqqioor9mnn35KQkICF1988WnrBRrP4/Hw/fff89//\n/d9ce+21dOrUiddff73Z7cvPz+ett95iwYIFpKamUl1dzerVq8+596HfokWLAN8Ws2Bpje/fc8kF\nccJCoDe8HzduHL/73e+AtrnHqp/D4eDQoUP1YiYmJjbaRn95XFwcgwcPJi8vj/j4eEpKSkhISODI\nkSPExcWd1TqUlJQYv64SExOpqqqipKSE2NhYysvLjV9qzemT+Ph445ew1+ulurqa2NhYHA4HiYmJ\n5OXlAb43Wt03WyCx7XY7jz32GCNGjOCmm27i8ccfJyEhgfDwcPbv32/0RWFhIZ07dz6rtns8HkpK\nShg5ciSDBw8OetsDHSsDBgygf//+DB06tNHx4NfSsduYsxmXdQVjjJ4qmGPc3za/5vTdoUOH2Ldv\nH3fffTeDBw9ucRs9Hg/z5s2jZ8+eRryWtrGoqIiuXbuSmJjId9991+I+PHW8nOlzdeXKlWzZsoV3\n3nnHKAvm56o/XqBj1J9sjxgxosWv2ZdffsmmTZv4+OOPOXHiBCaTienTp5OQkNCseElJSSQlJRER\nEYHD4SAjI4OlS5c2u33ffvstv/rVr6iqqiIpKYlrrrmGr7766qzjmUymelusIyIijMNggvFdU1hY\nyGeffcaWLVsICwur97oGc5ycjy6ILW+nc+TIEePxJ598Qq9evYC2u8cqwJVXXsmBAwc4ePAgLpeL\ntWvXNnqv1erqaqqqqgDfFqft27fTq1cv0tPTWblyJVD/Pq2BrsOGDRuMM3zS09OJiopi5cqV5OTk\n4HA4zjpe3T4ZMGCAsZk9JyeHa665xuiT7du384tf/IKysjKKi4v58ccfzyp2RUUFTqeTCRMmGP2d\nnp5OcXExO3bsYPny5Ubf33nnnWfV9pkzZ3LppZdSU1PTKm1v7lhprbHbmEDHZWuM0VN/NQdzjAej\n7+bPn0+fPn2YMGFCUNo4c+ZMKisrefDBB1vcxu3bt1NeXs6qVau44YYb2LFjB5dffnmL+/BM46Wu\nrVu38sYbb7Bo0SJCQkLq9VGwPlfh7MbozJkz6dmzZ1Bes8GDB7N582b69OnD5MmT6devH/Pnz+em\nm25qVrxDhw6RlJTE+++/z6BBg9i5cyc9e/Zsdvs8Hg9ff/01K1euJD09vdnxYmNjMZlMxusSEhJi\nHMsYjO+a1157jTfeeIPRo0dz/fXXt9o4OR9d8LfHevTRR/nHP/6ByWSia9euPPnkk8aWpra4x6rf\n1q1beeaZZ1BKMXbs2EZPd8/Pz2fy5MlomobX62X48OHcf//9lJaWMmXKFA4fPkzXrl155ZVXjAO+\nz7QO/oPwPR4PCQkJPPDAA2zZsoXPPvsMpRR9+vRh8eLFAcer2yePPPIIu3btoqSkBJPJRJcuXbjs\nssv4+eefMZlMmEwmysvLsVgsxuU2Ao39xRdfcPvttxMREYHT6cRisTB79mzS09OZMmUK+/bto7Ky\nErvdzuTJk41LhQQSf8iQIdx+++1ccsklHDp0iJqaGrp27UrPnj2D0vaWjpXWGLtNCWRcBnuMZmdn\ns2vXLkpLS0lISOD3v/89gwcP5uGHHw7KGP/973/Prl27mt13/oT90ksvNXbVT506lZSUlGa9D6dM\nmUJ+fj6xsbEkJSUZ8T766KNmtXHatGkcPnyYiIgIEhMTGTFiBPfcc0+LPicCHS9+GRkZuN1uY8vH\nVVddxdy5c5u1rNO9VwIdo/7Pi169egXlNavbzoyMDJYtW8bixYtb1McVFRW4XC7sdjvdu3fnueee\nw+v1Njte3UuFXH755Tz99NNUVVWd9fvQf0xfdHQ0gwYNIj8/P2jfNc8//zw1NTWEhITQtWtXrr32\n2lYbJ+ebCz55E0IIIYToSC743aZCCCGEEB2JJG9CCCGEEB2IJG9CCCGEEB2IJG9CCCGEEB2IJG9C\nCCGEEB2IJG9CCCGEEB2IJG/irNxxxx3s2bOH7777jscff7zJert37+aOO+5ow5YJcXZkjIr2snnz\nZt566632bobowC6I22OJ4OvTpw99+vQ5bZ1g3mNViNYgY1S0h71797Z3E0QHJ8mbOK358+ezceNG\nrFYr48aNM8p3797NwoULeffdd/nHP/7BnDlzcDqdREdH8+KLL9aL8fbbb5Obm8vSpUsJDQ1t61UQ\n57Hdu3czf/58dF2na9euhIeHs2/fPnRd57777mPo0KFkZ2ezadMmSkpKOHbsGOnp6Tz22GNGjAMH\nDjBhwgQ2b94MwJ49e3j99ddZunRpe62WOIft3r2bRYsWoZSisLCQq666iqeeeoo1a9bw1ltvoWka\nV1xxBbNnz8ZqtTJz5kx++uknAH7zm9/wq1/9iuXLlwPQtWtXRo0a1ehysrOz2bZtG2VlZeTn5zNw\n4EBmz55d77MXYMaMGfTr149rr72Whx56iO7du/Pjjz/Sp08frrvuOrKzsykvL+fVV1/ll7/8Zdt0\nkmh1sttUNCknJ4evv/6atWvXsmLFClauXMnRo0eN6f6tFtOnT+ehhx5i9erVDBs2zLgRtVKKlStX\nsnHjRkncRKv5+eefefvtt0lOTqZPnz787W9/491332XRokUUFBQA8O2337Jo0SI++ugjvvrqKzZu\n3GjMf9FFF9GtWzd27doF+L40R48e3S7rIjqGr7/+mqeeeoqcnBxqamp4/fXXef3113nvvfdYvXo1\nnTp1YuHChXz11VeUlZWxcuVKli1bxpdffkmPHj0YP34848ePbzJxq7ucV199ldWrV7Np0yb27dsH\nNL3F+J///CcPPfQQ69ev59tvv+XQoUMsX76coUOH8sEHHwS9H0T7keRNNGnPnj0MGTIEi8VCp06d\nWLVqFfHx8fXqHD9+nCNHjnDjjTcCMH78eKZPnw7Avn37mD17NnfeeackbqLV/OIXvyAyMpIdO3aw\nfPlyRo4cye23347T6TS2eGRkZBATE4PVamXYsGHs3LmzXowxY8bw4Ycf4nQ62blzJ4MHD26PVREd\nxIABA+jevTsAt956K4sWLSI9Pd24z+e4cePYuXMnvXr1Yv/+/dxzzz2sXr3a+GwM1DXXXEOnTp0I\nCwuje/fulJWVnba+3W7nsssuA8DhcBg3Z+/atesZ5xUdiyRvokkWS/296gUFBVRXV9crs1qt9Z67\nXC7y8/MBiIyMZOHChbzwwgs4nc7Wbay4YPl/GOi6zvz581m1ahWrVq3i/fffJzU1FQCz2WzU13W9\n3nOAm2++mc8++4ycnBxuvPHGBuNaiLpOHU9KKU69TbjX6yU6Opo1a9Zw55138p///IeRI0dSWVkZ\n8HJCQkLqPVdKoWlavWW53W7j8anj9tTPcHH+kORNNOnaa69lw4YNeDweqquruffeeykuLq5XJzIy\nks6dO/P3v/8dgFWrVrFw4UIAunTpwk033US/fv1YsGBBm7dfXFj69+/P+++/D8CRI0cYNWoUhYWF\ngO/svhMnTlBTU8PatWtJS0urN29YWBhpaWm88sorZ9yVJcSuXbsoKSlB13U+/PBDZsyYwaZNmygv\nLwfggw8+oF+/fnz66adMnz6dG2+8kVmzZhEREcHhw4cxm814PJ5mLTs2NpaCggJcLhelpaV88cUX\nxrRTE0hx/pK0XDRp8ODBfPfdd8aX2V133cW6desa1Js3bx5z585l3rx5xMbGMm/ePP79738b06dP\nn84tt9zCrbfeSu/evdus/eLC8tBDD/HEE08wfPhwdF1n+vTpdO/enc8//5y4uDjuueceSktLGTly\nJKmpqezevbve/EOHDuWrr74iJSWlndZAdBSJiYn87//+L8XFxaSmpnL77bfTqVMnfvvb3+L1erni\niit44oknCAkJYf369QwbNozQ0FAyMjK45JJLKCsr449//CN2u53f/va3AS3Tf5xbz549SUtL45Zb\nbqFr16707du3QZ1TH4vzj6YkVRdCnMeys7P55ptvmDt3bpN1dF3n5ZdfJj4+nrvuuqvN2iY6nt27\nd7N06VI5G1m0K9nyJoS44I0ZM4a4uDgWLVrU3k0RF5B169bx+uuv19tK5j+uLTs7ux1bJs51suVN\nCCGEEKIDkRMWhBBCCCE6EEnehBBCCCE6EEnehBBCCCE6EEnehBBCCCE6EEnehBBCCCE6kP8PKequ\nB2BFASwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124f97350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.pairplot(data, vars=['click', 'reply', 'post_num'],\n",
    "                 kind='reg', hue='year')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12324c9d0>"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DlFB9yBJZn0K3traWxsZGDhw4wLRp02hubmbkyJHRrk2ImFBK0WyzgyGjx85f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1267045d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lmplot(y='reply', x='click', data=data)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# values_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    304\n",
       "2014     63\n",
       "2007     34\n",
       "2012     33\n",
       "2015     20\n",
       "2011      6\n",
       "2009      6\n",
       "2006      1\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year\n",
       "2006     1\n",
       "2007    34\n",
       "2009     6\n",
       "2011     6\n",
       "2012    33\n",
       "2013   304\n",
       "2014    63\n",
       "2015    20"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = data.year.value_counts()\n",
    "dd = pd.DataFrame(d)\n",
    "dd = dd.sort_index(axis=0, ascending=True)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2006, 2007, 2009, 2011, 2012, 2013, 2014, 2015], dtype='int64')"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2006-01-01',\n",
       " '2007-01-01',\n",
       " '2009-01-01',\n",
       " '2011-01-01',\n",
       " '2012-01-01',\n",
       " '2013-01-01',\n",
       " '2014-01-01',\n",
       " '2015-01-01']"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_date_str = map(lambda x: str(x) +'-01-01', dd.index)\n",
    "dd_date_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2006-01-01', '2007-01-01', '2009-01-01', '2011-01-01',\n",
       "               '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_date = pd.to_datetime(dd_date_str)\n",
    "dd_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DstQk1ColARFpv6TXA7KUBFqlmoCItI87fOQj8MorobklyTZsgNGjQ30gSfMb5aHkNQEz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e6f3d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dd_date, dd.year, 'r-o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year\n",
       "2006     1\n",
       "2007    35\n",
       "2009    41\n",
       "2011    47\n",
       "2012    80\n",
       "2013   384\n",
       "2014   447\n",
       "2015   467"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = dd.cumsum()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dkw7SrWk3R4dUaclE80KIUrHF/+Wkf07iZMLJfMsupV0iKjWK92e+zxT/KfLt\nvwCZaF4IUWmcTDhJeKvwQsu7/taVF3tWrStcR5FRRIUQTucOtzscHUKVIUlACCGqMEkCQghRhUkS\nEEKIKkwahoUQDtPWsy0n9p/A3dWdO++4M99yYR9O2UW0ZcuWxMXF2TkiIURxvL29iY2Nteo2tdZ4\nf+RN8FPBMhJoKVWJLqLW/kMTQjinE0knAGj3h3YOjqTqkjYBIYTDBEcFM8R3iNwQ5kCSBIQQDrM5\najODfQc7OowqTZKAEMIhbmbdZEfcDgb6DHR0KFWaJAEhhEPsOruLuxrdRYNaDUouLGxGkoAQwiGk\nKsg5SBIQQjhEcFSwJAEnIElACGF3iWmJRCdH09Orp6NDqfIkCQgh7C4kKoTAVoHUqFZ1ZsRzVpIE\nhBB2FxwdzGAfqQpyBpIEhBB2pbU2bhJrPcTRoQgkCQgh7OzXxF9xr+GOT30fR4cikCQghLAz6Rrq\nXCQJCCHsKne8IOEcJAkIIewmPTOd3ed2E9gq0NGhCBNJAkIIu9ket527m9wtE8k7EUkCQgi7kaog\n5yNJQAhhN8HRMlSEs5EkIISwi/hr8ZxPPU/3pt0dHYrIQ5KAEMIuQqJDuN/nfqq5VHN0KCIPSQJC\nCLsIjpKhIpyRJAEhhM3l6BxCokOkPcAJSRIQQtjcoQuHaFS7Ec3rNXd0KKIASQJCCJuTCWSclyQB\nIYTNyXhBzkuSgBDCplJvpnLwwkEGeA9wdCjCAkkCQgibCo8Lx9/LH3dXd0eHIiyQJCCEsKnNpzdL\n11AnVmISUEq5KaX2KqUOKaWOKqXeMS2vr5QKVkqdUEptVkrVy/Oa15RSp5RSx5VS8ukLUYWVNFRE\nXEwMM4OCmB4YyMygIOJiYuwYnaheUgGt9U2lVKDW+oZSqhrws1KqD/AwsEVr/b5S6p/Aa8BUpdRd\nwGjAD7gT2KKUaqO11jZ8H0IIJxSbEktKRgpdmnSxuD4uJoY5gwYxMyoKdyANmL5nD1NCQvBu1cqu\nsVZVpaoO0lrfMD10M70mGRgBLDItXwQ8Ynr8MLBca52ltY4FTgH+1gpYCFFxBEcFM8hnEC7K8qlm\n4RtvmBMAgDswMyqKhW+8YbcYq7pSJQGllItS6hBwEQjTWh8DPLXWCQBa64tAY1NxL+BsnpfHm5YJ\nIaqYku4PyImPp2BzsTuQc/68TeMSvyvtlUCO1rorRvVOP6VUAFCwekeqe4QQZlk5WYTGhDLIZ1CR\nZVy8vEj0lHRfAAAgAElEQVQrsCwNcGnWzKaxid+V2CaQl9b6mlJqA3APkKCU8tRaJyilmgCJpmLx\nQN57w+80LStkxowZ5scBAQEEBASUJRwhhBPbH7+f5vWa07Ru0yLLjH/rLaZv2MDM5OTf2wR8fZny\n1lt2i9PZhYWFERYWZrPtq5Laa5VSDYFMrfVVpVQtYDMwExgMXNFazzI1DNfXWuc2DC8FemJUA4UA\nhRqGlVLSVixEJTYzbCbXb11n9uDZxZaL69CBhc2akXPsGC6NGjF+zRppFC6GUgqttbLW9kpzJdAU\nWKSUUhjVR99orbea2ghWKqUmAHEYPYLQWh9TSq0EjgGZwGQ52wtR9QRHBzMzYGbxhc6cwTsxkem/\n/AK7d8MLL4AkALsq8UrAZjuWKwEhKq2UjBSaf9icS/+4RM3qNYsu+MkncOAALFwIOTng7Q2bNkGH\nDnaLtaKx9pWA3DEshLC60JhQ+jTvU3wCAFi7Fh5+2Hjs4gKPPw7Ll9s+QGEmSUAIYXXBUcEM8R1S\nfKGUFNi7Fwbn6UI6ZoyRBKSWwG4kCQghrEprXbqhozduhP79oU6d35d1N01Cf/Cg7QIU+UgSEEJY\n1ekrp8nMzuSuRncVX3DtWhgxIv8ypX6/GhB2IUlACGFVuXcJGx0Ki3DrFmzeDMOHF173xBOwYoXR\nUCxsTpKAEMKqSlUVFB4O7dtDkyaF1911F9SvDz//bJsART6SBIQQVnMr+xbhceHc73N/8QUtVQXl\nNWYMfPutdYMTFkkSEEJYzZ5ze2j7h7Y0rN2w6EJaw7p1JSeBVasgK8v6QYp8JAkIIaymVLOIHToE\nNWsa1UFF8fEx7hzeutW6AYpCJAkIIaympFnEgN9vECuu4RiMBmLpJWRzkgSEEFZx+cZlTiad5N7m\n9xZfsKT2gFyjRxtlMzKsE6CwSJKAEMIqtkRvYYD3AFyruRZdKDYW4uOhd++SN9isGXTpYowlJGxG\nkoAQwipKmkUMMBqEH3oIqlUr3Uall5DNSRIQQtw2rXXpxgsqbVVQrpEjjSuB69dvL0BRJEkCQojb\nduzSMWpUq0HrBq2LLpScDPv3w6Cip5sspGFD6NPHuIIQNiFJQAhx24KjghnsU8JQERs3QkAAuBec\nWr4E0kvIpiQJCCFuW3B0MENaW7kqKNeIEcYwE8nJ5QtOFEuSgBDitmRkZbDzzE7ua3Vf0YVu3jQG\njHvoobLv4I47jCqk778vf5CiSJIEhBC3ZeeZnXRq3AmPmh5FFwoLMwaG8/Qs305keGmbkSQghLgt\nNukVVNCDDxpzEV+8WP5tCIskCQghbkuJQ0eXZsC4ktSqZVQlrVpV/m0IiyQJCCHK7ULqBc5cPUMP\nrx5FFzp40OgRVNyAcaXxxBNy45gNSBIQQpTblugtDGw1kOou1YsudLtVQbnuvx9OnIC4uNvfljCT\nJCCEKLdSzSJ2u1VBuVxd4Y9/NKaeFFYjSUAIUS45OoeQ6JDik0BMjNGY26uXdXYqN45ZnSQBIUS5\nRCZE4lHTg5YeLYsuVNYB40rSv7+RVE6csM72hCQBIUT5lGoWMWu1B+SqVs2YZ0CuBqxGkoAQolxK\nnEXsyhWjb//9JUw6X1a5w0trbd3tVlGSBIQQZZZ2K4198fsIbBVYdKENGyAwEGrXtu7Oe/Y0hqH4\n5RfrbreKkiQghCiz8LhwujftTh3XOkUXsnZVUC6lZLIZK5IkIIQosxJnEbt5E0JCyjdgXGnk9hKS\nKqHbJklACFFmJY4XFBoKHTtC48a2CaBTJ+Mu5N27bbP9KkSSgBCiTM5ePculG5fo2rRr0YWsdYNY\nUZSSewasRJKAEKJMgqOCud/nflxUEaePnBzbJwEw2gW++w6ys227n0pOkoAQokyCo0uoCjp40JgI\npm1b2wbSpg14eRlzFYhykyQghCi17JxstkRvYZBPMZPF26pXkCUy2cxtKzEJKKXuVEqFKqWOKqWO\nKKVeNC2vr5QKVkqdUEptVkrVy/Oa15RSp5RSx5VSJdxSKISoKA5eOEjTOk3xusOr6EJr18LDD9sn\noMcfN6advHXLPvurhEpzJZAF/E1r3QG4F3hBKdUemAps0Vq3A0KB1wCUUncBowE/YCjwqVJK2SJ4\nIYR9ldgrKDoaEhONG7rsoXlzY9rK4GD77K8SKjEJaK0vaq0Pmx5fB44DdwIjgEWmYouAR0yPHwaW\na62ztNaxwCnA38pxCyEcoMSho9euheHDrTdgXGnIZDO3pUxtAkqplsDdwB7AU2udAEaiAHI7BHsB\nZ/O8LN60TAhRgV27eY3DFw/Tz7tf0YXs2R6Qa9Qo+OknuHHDvvutJEqdBJRSdYBVwEumK4KCt+rJ\nrXtCVGLbYrbR685e1K5RxFhASUlw6JD1B4wrSePG4O8PP/5o3/1WEsXMCfc7pVR1jATwjdZ6rWlx\nglLKU2udoJRqAiSalscDzfO8/E7TskJmzJhhfhwQEEBAQECZghdC2E+J7QEbNsB99xmTwttb7o1j\no0fbf982FhYWRpgNu8EqXYqxN5RSi4HLWuu/5Vk2C7iitZ6llPonUF9rPdXUMLwU6IlRDRQCtNEF\ndqSUKrhICOHEWn/cmu8f/57Onp0tFxg1yhgraPx4u8YFQEoKeHvDmTNQr17J5SswpRRaa6t1tilN\nF9E+wFjgPqXUIaVUhFLqAWAWMEgpdQIYCLwHoLU+BqwEjgEbgMlytheiYou6EkVaZhqdGneyXCAj\nA7Zssd2AcSXx8DCGrf7hB8fsvwIrsTpIa/0zUFRTv8XKP631u8C7txGXEMKJ5M4lXGRv79BQ6NwZ\nGja0b2B5jRkDCxfC0087LoYKSO4YFkKUaHNUCVNJ2vMGsaIMH26MKnrpkmPjqGAkCQghipWZncm2\nmG0M8i1iqAh7DRhXEnd3GDYMVq1ybBwVjCQBIUSx9sXvw6e+D43di5gbYP9+qF/fGNDN0WR46TKT\nJCCEKFap7hJ29FVAriFD4Ndf4dw5R0dSYUgSEEIUq8SpJJ0pCbi5wSOPwMqVjo6kwpAkIIQo0pX0\nKxy7dIw+zftYLnD6NFy5Ytyx6yxkeOkykSQghCjS1uit9PPuh1t1N8sF1q0zeuW4ONGpJDAQ4uKM\nBCVK5ESfnBDC2QRHBZfcNdRZqoJyVa8Ojz0GK1Y4OpIKQZKAEMIirbUxlWTrIsYLunwZDh+GgQPt\nG1hpyPDSpSZJQAhh0YmkE2itafeHdpYL/PSTkQBq1rRvYKVx771w7ZrRU0gUS5KAEMKi3F5BRQ4V\n4YxVQblcXIwGYrkaKJEkASGERcUOHZ2eDlu3woMP2jeossjtJSTjVxZLkoAQopCbWTfZHredgT5F\n1Pdv3Qp33+3YAeNK0rWrMc3l/v2OjsSpSRIQQhSy6+wu/Br50aBWA8sFnLkqKJdSMoxEKUgSEEIU\nUmxVUE4OrF/v/EkAjCqhFSsgO9vRkTgtSQJCiEKKHS9o3z6jGsjX175BlYefHzRqBDt3OjoSpyVJ\nQAiRT2JaItHJ0fT06mm5QEWoCspLegkVS5KAECKfLdFbCGwVSI1qNSwXqIhJYPVqyMx0dCROSZKA\nECKfYmcRO3XKmNT9nnvsG9TtaNkSWrc2ejSJQiQJCCHMtNbFDx29dq3zDRhXGjKMRJEq2CcphLCl\nXxN/xb2GO74Nimj0rWhVQbkee8wY8TQjw9GROB1JAkIIs2J7BV26BJGRcN999g3KGpo2hW7dYMMG\nR0fidCQJCCHMiq0K+vFHGDTIOQeMKw2ZbMYiSQJCCADSM9PZfW4397Uq4pv+unUVsyoo18iRsHkz\npKY6OhKnIklACAHA9rjt3N3kbu5wu6PwyvR0CA117gHjStKgAfTrZyQzYSZJQAgBlDCL2JYtRp16\ngyLGEqoopJdQIZIEhBAAxc8itnYtPPywfQOyhYcfhh074MoVR0fiNCQJCCGIvxbP+dTzdG/avfDK\n7OyKM2BcSerWhSFDjDuIBSBJQAgBhESHMLDVQKq5VCu8cu9eaNwYfHzsH5gtSC+hfCQJCCGKHzq6\not4gVpShQyEiAi5ccHQkTkGSgBBVXI7OISQ6hEG+gywXqGxJoFYto23gu+8cHYlTkCQgRBV36MIh\nGtZuSIt6LQqvPHHC6Fff3UJbQUUmw0ubSRIQooortipo3TrjW3NFGzCuJPffD6dPQ0yMoyNxuEr2\nyQohyqrY8YIqW1VQrho1jDuIV6xwdCQOJ0lAiCos9WYqBy8cZID3gMIrExPh118hMND+gdmDTEIP\nSBIQokoLjwvH38sfd1f3witzB4xzc7N/YPbQt68xMurx446OxKFKTAJKqflKqQSlVGSeZfWVUsFK\nqRNKqc1KqXp51r2mlDqllDqulCriGlMI4Qw2ny5mFrHKWhWUq1o1ePzxKn81UJorgQVAwVajqcAW\nrXU7IBR4DUApdRcwGvADhgKfKqWU9cIVQlhTcHQRQ0ffuAHbtsGwYfYPyp5ybxzT2tGROEyJSUBr\nvRNILrB4BLDI9HgR8Ijp8cPAcq11ltY6FjgF+FsnVCGENcWmxJKSkUKXJl0KrwwJMeYRrugDxpWk\nRw/IyoJDhxwdicOUt02gsdY6AUBrfRFobFruBZzNUy7etEwI4WSCo4IZ5DMIF2XhNFDZq4JyKVXl\nh5GobqXtlOtaasaMGebHAQEBBAQEWCkcIURJgqOCebidhZFBs7ONRuE337R/UI7wxBNGtdd77znl\n/RBhYWGEhYXZbPtKl6IuTCnlDazXWnc2PT8OBGitE5RSTYBtWms/pdRUQGutZ5nKbQKma633Wtim\nLs2+hRDWl5WTRePZjTk6+ShN6zbNv/Lnn2HyZPjlF8cE5wgdO8IXX0CfPo6OpERKKbTWVmtrLW3a\nU6afXOuA8abHTwNr8ywfo5RyVUq1AloD+6wQpxDCivbH76d5veaFEwBUnaqgvKrwZDOl6SK6DNgF\ntFVKnVFKPQO8BwxSSp0ABpqeo7U+BqwEjgEbgMnydV8I51PsLGJVMQk8/rgxoFxWlqMjsbtSVQfZ\nZMdSHSSEw/T5ug8zBswoPHLob78Z4+qcPWs0mlYl/v7wn/8YN8g5MUdVBwkhKomUjBQiEyLp592v\n8MrcaSSrWgKAKttLSK4EhKgCJv1zEicTTgJwKe0SF65foLNnZ9p6tuXLWV/+XrB3b5g+3ZiCsao5\ndw66dIHz5516qAxrXwlYq4uoEMKJnUw4SXir8HzLwgmHvCMpJyTAsWNQVbtq33mn0Uto82bjaqiK\nkOogIYRh/XrjCsCJvwXbXBWcbEaSgBCVXI7O4Ur6lZILVsVeQQWNGgUbNkBamqMjsRtJAkJUUhdS\nL/DOjndo/XFropOjiy+clgbh4ZV/wLiSNGoE995r3DFdRUgSEKISyc7JZsOpDTy64lHu+vQuYpJj\nWDFqBd2bljBHcEiI0UXSw8M+gTqzKnbjmDQMC1EJnL16lq8Pfc38Q/PxrOPJpG6TWPzIYuq61QWg\nXZN2qJjCHUraerY1HkhV0O8eeQRefBFSUqpEUpQuokJUUFk5WWw4tYEvD37JrrO7GNNxDM92e5au\nTbuWbUPZ2dCkCRw4AN7etgm2ovnjH2H4cHjmGUdHUoh0ERWiiotNiWV+xHy+Pvw13vW8mdR9EitG\nrbA8RWRp7NoFXl6SAPKICwxk4euvk7N4MS5eXox/6y28W7VydFg2IUlAiAogMzuTdSfWMS9iHgfO\nH2Bsp7FsDtpMx8Ydb3/jUhWUT1xMDHM+/JCZFy/ifvEiacD0PXuYEhJSKROBVAcJ4cROXznNVxFf\nsfDwQto1bMez3Z5lpN9IatWoZZ0daA1t28KKFdCtm3W2WcHNDAri70uXkve6Kg3479ixTF+yxFFh\nmUl1kBCV3M2sm6z5bQ3zIuZxJOEI47qMI2x8GO0btrf+zo4fh5s3oWsZ2xEqsZz4eApWrLkDOTEx\nlopXeJIEhHASv13+jXkH5/FN5Dd09uzMpG6TeKT9I7hVt+EdvFV5wLgiuHh5kQaFrgRcDhyAf/4T\nXn0V/vAHB0VnfXKfgBAOlJ6ZzpLIJfRf0J/ARYG4VnNl9592s2XcFh7v+LhtEwDAunXSHlDA+Lfe\nYrqvL7n3DKcB0319Gb9tG1y7ZlSfTZ8OV686MkyrkTYBIRzg18RfmXdwHkuPLKWHVw+e7fYsw9sO\np0a1GvYL4uJF8PMzBo5zdbXffiuAuJgYFr7xBjnnz+PSrFn+3kExMfDvfxt3Ff/tbzBlCtSpY7fY\nrN0mIElACCvLO2xzXq0atqL/2P58GfElZ66e4U9d/8SErhNo6dHS/kECzJsHoaFV6u5Yqzpxwrgi\nCAuDqVPh+eehZk2b71aSgBBOLmB8QKFhmwGqhVdj6LNDmdRtEkPbDKW6i4Ob5B56CMaONYZJEOUX\nGQlvvmncbPevf8GECTa9spIkIBymqG+4hSYmqeBydA7pmemkZ6Wbf9/IvFHishuZN0jPSmfZnGVc\nuOdCoe32OtWL3Ut2O+AdWXD9OjRrBmfOVImhEexi3z4jGeReIQQFQXXrJ3rpIiocxtLEJED+iUls\nQGtNRlZGoZOtpRNwscuKOXHnXXYr+xY1q9ekVo1a1Kpei1o1alG7Rm3z41rVTc9z1+cpc4fbHbjX\nsHznrs0becsiOBh69pQEYE3+/rBpE+zYYVwRvPcezJwJjz0GLs7bB0eSgLht6Znp/Jr4a5m+NZuX\nleJknpGVgWs1V8sn4BJOyn+o9Qdq3VHK15l+16xeE3UbXSZ/mvcTpzltxSNsA3KXsO3062e0E2zZ\nYiSD//wH3nrLabviSnUQlaOaIzM7s8gTaXlPygXLXFh/gawBWYX27bbDDd8/+pbqpFzSCdjSslo1\nauGinPebVEFFtQkMiBlA2MIw+wdUUFaWMWBcRAS0aOHoaCo3rY1eRP/6l9FO8PbbMHjwbSUDqQ6y\ngZMJJwmvF07rbdAkFS7WhdOBQEL5t5mdk132E7Clb8SlfB1Q5Im04Mk3bxn3Gu40rN2wVCflJyKe\nYDeF67R73dmLsMlh5T9YlUxbz7YWq8jMwzY7iLnb49GjuOTkMD47GxkyzsaUMkYjffBBWLUKXnoJ\nGjc2kkH//o6ODpAkAMDN1HRGrIOlycZdgmnA2HNwsPtp3tz2ZrlOylk5WZZPwCWclGtVr4VHTY/S\nnczzLLNH/3LXatKXvDSc8eoxLiaGOYMGMTMqyvw3Pn3QoEo7KJrTcXGB0aONIaqXLYPx46F1a6Oa\nqGdPh4ZW5ZPAqaRTqJ8jzQkAjN9Lk+GhXy5T60YmdTwale5knmeZazXX26pXdkbO+g1XlGzhG2+Y\nEwAYf+Mzo6L47xtvOMWgaFVG9eowbpzRLXfBAmNO465djWTQpYtjQnLIXh0sMzuT9UdWc3Tef+gf\ncoKAxEyLA0Z1T87ktdEfG+OEdOiQ56c13HWXXe8SdAbO+A1XlEBrOHaMnJ9/tjwo2vnzjohK1KgB\nkyYZCeGLL+CBB4zqoZkzob0NBgosRsVpbbOCs1fP8n+LX+CrgfUJGDCOSftz6P3mV+z0bmQeJyRX\nGrCneUNITTVa+idPhoYNjTssn3/eqNdr2dKo63v1VVi0yLhZJK3gloSwM62NRt9p04xhIYYOxcXN\nzeLfuEuzZo6IUOSqWdNoJzh92hjKu39/ePppiI62WwiVvndQjs4h5Oh6fv3i3/TcEEnnKzW4FfQE\nDV+cCm3aAPDkc2PQ367lq9QMc33pxLo1UU+MYNkXyy1vODvbGEPk6NH8PydPGj0v8l05dDCye+3a\nNn+/oorKyYG9e2H1auOnWjUYOdL46dGDuNjYwm0Cvr7SJuBsrl6FDz+ETz4xqor+9S+48858ReSO\n4VJKTEtk7ffvUm3+1zx68AZpHdrS8K+vUfOPoy3e0l3sgFFlkZVlZPGCyeHUKWMKP0vJwQ7jjYhK\nKDvbuDFp9WpYswbq1fv9xN+5c6FuiFb7Gxe2l5QEs2cb4zs99RS89hpxN26w8I03mLF0qSSBomit\n+flECEfmvkm39Qdod82VG0GP0+yvb4CPj1X3VWZZWcYlX8HkEBUFzZsXTg7t2oGbE91hKpzDrVuw\nbZtx4v/hB+NbYu6J3851ycIOLl6E994jbsEC5ri4MDMlhTogSaCgqxlX2bDqXfS8Lxl24CrJd7en\n0UuvU+ePj9tk7A6rysw0rhIKJoeYGONGHkvJQYb9rVoyMoxhHlavNm48atvWOOn/8Y+O/3Ij7GLm\no4/y9x9+wB1QSBIwizi1nSP/N40O63bT6oYrqUGj8X55Jsq7EtwCc+uW0b5QMDnExRkN0gWTQ9u2\nRo8DUTlcvw4bNxon/k2b4O67jRP/o48WqiMWld/0wEBmhoUB1k8CTv41ubC0W2mEfPceOV98zn0H\nr1CnWzsa/W8h9R99gj9Uq+bo8KzH1RU6djR+8rp50xilMDcpfPut8fvsWeNbYcHk0Lq1JIeK4upV\nWL/eOPGHhkKvXsaJ/+OPjd5oosqyNOWltVSYK4HfovYS+eFU2q3ZQZNMV1KCRtH6b/+h2p3NbRhl\nBZKRkT855P6cO2ckgoLJwdfX+avKqoLLl43B3Favhp07ISDAOPEPHw4NGjg6OuEk8t7xXaXaBG5m\nZrB95WwyP59L74OJnOvRjkYvvY7nI2OdemhWp5KeDr/9Vjg5XLhgdJEtmBx8fIzuhUUw9zCJj8fF\ny0t6mBSh2ON04YLRm2f1auPeksGDjRP/gw9C3bqODVw4rdy/qQrTO0gp9QDwEcYNafO11rMKrC8y\nCcTFHOaXD/5Bm9XbqKtduTL2j/i98h41mkldqNWkpVlODgkJRuNzweTQqhVxcXHS17wULI7T06IF\nU4KC8A4PN47zgw8aJ/4hQ+T+EVEmFeI+AaWUC3ASGAicB/YDY7TWv+Upo/u0akx215Z0at2Fz975\nlN3LZ3Pz80+458AFonoa/fpbPPyUXb/1h4WFERAQYLf9lYZdY7p+HY4fL5wcLl9mppsbf09Oxh0I\nAwIwTnD/7dWL6ZMm2Se+YoT99hsBTtBNcuaXX/L3PXsKHycfH6bPmQMDBzq0+68z/o2Dc8bljDFV\nlKGk/YFTWus4AKXUcmAE8FveQptjEhmXnEjdyN+IXriAO12qc+nJR3Bd+T7dmjqmrt8ZP3S7xlSn\nDvToYfzklZpKTkAA7snJRkwYJzd3ICc2FrZvt098xQg7dIiAxERHh0FObKy5AS+MPMfJ2xuGDXNU\nWGbO+DcOzhmXM8ZkbbZKAl7A2TzPz2EkhnzcgcUpMOXmdfSyebQc8QwtK9nIm5VG3bq4+PmRFhGR\nr4dCGuAycKAxIqKjzZhh/DiYS1AQaUuXFj5OMk6PcEIOb111By7Vq0vbRyY45dRr4nfj33qL6b6+\n5oHIctsExr/1liPDcjpynERFYqs2gV7ADK31A6bnUwGdt3FYKeUcc0sKIUQFUxEahqsBJzAahi8A\n+4AntNbHrb4zIYQQ5WaTNgGtdbZS6i9AML93EZUEIIQQTsZhN4sJIYRwPKs1DCul7lRKhSqljiql\njiilXjQtr6+UClZKnVBKbVZK1cvzmteUUqeUUseVUoPzLK+hlPrC9JpjSqlHHR2XUqqOUuqQUirC\n9PuSUup/TnCsnjFt47BSaoNSqlxjDVg5pseVUr+YtvNueeIpT0xKqQam8qlKqY8LbKubUipSKXVS\nKfWRk8T0tlLqjFLqWnnjsWZMSqlaSqkfTZ/nEaXUO84Ql2ndRtP/3a9Kqa+UUuWqxbBmTHm2uU4p\nFVmeeKwdk1Jqm1LqN/X7uaphiQFora3yAzQB7jY9roPRJtAemAW8alr+T+A90+O7gEMYVVItgdP8\nfmUyA/h3nm03cIa4Cmz3ANDHkTEBNYAkoL6p3CzgTQfH1ACIy/3MgAVAoJ1iqg30BiYBHxfY1l6g\nh+nxBmCIE8TkD3gC1+z8v2cxJqAWMMD0uDqwvbzHyQbHqk6ex6uAsY6OybT+UWAJEOkkx2kb0LVM\n+7+dP74S3tgPwP0YN4h55nmzv5keTwX+maf8RqCn6fEZoJazxZVnWVsgztExYZx0TwEtTI8/AyY6\nOKZ7gJA8y4OAT+wRU55yT5P/5NYEOJbn+RjgM0fGVGDdbSUBW8RkWv8R8Cdnigvji8864AFHx4TR\nw307xgm73EnAyjFtA7qXZX82uU9AKdUSuBvYY3oTCQBa64tA7pi4BW8oiwe88lQ3vK2UOqiUWqGU\nauTouAps6nFghaNj0san/hLwK8YNeX7AfEfGhHFF0E4p1cJ0yf4IcNu3f5cypqJ4YRyfXOco/Jna\nOyabsFZMSikPYDiw1VniUkptAi4C6VrrTU4Q01vAf4H0243FijEBLDRVBf2rNIWtngSUUnUwLtde\n0lpfBwq2PJfUEl0duBPYqbXujnEwPnCCuPIaA3zr6JiUUnWBOUBnrbUXcAR43ZExaa1TgD8DK4Fw\nIAbIdmRMtlCZY1JGF+9lwEda61hniUsb9x01BdyUUuMcGZNSqgvgq7Veh3EVftv99q10nJ7UWncC\n+gH9lFJBJb3AqknA9M1vFfCN1nqtaXGCUsrTtL4JkDu4Szz5vyHeCcRrrZOANK31GtPy74Cujo4r\nz7Y6A9W01oecICY/IDrPP+pK4F4Hx4TW+ietdS+tdR+MgQRP2immohT7mTooJquyckxfAie01nOc\nLC601reA1UCPksraOKZ7ge5KqWhgB9BWKRXq4JjQWl8w/U7DSOSFhuspyNpXAl9j1L3+X55l64Dx\npsdPA2vzLB+jlHJVSrUCWmPcVAawXikVaHp8P3DMSeICeAIrXAVYKaZooL1S6g+mcoOA27kfwyrH\nKbf6TilVH5gMfGWnmPIyfzMzXUpfVUr5K6UUMK6I19gtplIut3tMSqm3gTu01i9bISarxKWUcjed\nBHNPlg8Chx0Zk9b6c631nVprH6AvRtK8z5ExKaWq5Z4LlFI1gIcwqoqLZ8XGjD4Yl/2HMXqNRAAP\nYOsuWsUAAADXSURBVPQW2YLR4h0MeOR5zWsYdcjHgcF5lrfAqEo4DIQAdzpDXKZ1p4G2TnSsnsKo\nBjps+iOp7wQxLQOOmv4AH7PzcYoBLgPXMDoYtDct7246TqeA/3OSmGZhtKtkmZaXt2eXVWLCaCfJ\nMX12uduZ4OhjhVEXvs+0nV+A2VjosWfvzy/Pem9ur3eQtY5TbYxei4dNf+sfluY4yc1iQghRhTl8\nFFEhhBCOI0lACCGqMEkCQghRhUkSEEKIKkySgBBCVGGSBIQQogqTJCCEEFWYJAEhhKjC/h/ba3A/\nHhFnvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc90810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = data.year.value_counts()\n",
    "dd = pd.DataFrame(d)\n",
    "dd = dd.sort_index(axis=0, ascending=True)\n",
    "ds = dd.cumsum()\n",
    "\n",
    "def getDate(dat):\n",
    "    dat_date_str = map(lambda x: str(x) +'-01-01', dat.index)\n",
    "    dat_date = pd.to_datetime(dat_date_str)\n",
    "    return dat_date\n",
    "\n",
    "ds.date = getDate(ds)\n",
    "dd.date = getDate(dd)\n",
    "\n",
    "plt.plot(ds_date, ds.year, 'g-s', label = '$Cumulative\\: Number\\:of\\: Threads$')\n",
    "plt.plot(dd_date, dd.year, 'r-o', label = '$Yearly\\:Number\\:of\\:Threads$')\n",
    "plt.legend(loc=2,numpoints=1,fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1214</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278</td>\n",
       "      <td>291</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>28290</td>\n",
       "      <td>514</td>\n",
       "      <td>22</td>\n",
       "      <td>137</td>\n",
       "      <td>8041</td>\n",
       "      <td>10344</td>\n",
       "      <td>281</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>18644</td>\n",
       "      <td>186</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>531</td>\n",
       "      <td>571</td>\n",
       "      <td>39</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2889</td>\n",
       "      <td>28</td>\n",
       "      <td>84</td>\n",
       "      <td>28</td>\n",
       "      <td>332</td>\n",
       "      <td>661</td>\n",
       "      <td>50</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>463720</td>\n",
       "      <td>5933</td>\n",
       "      <td>2779</td>\n",
       "      <td>59511</td>\n",
       "      <td>12315</td>\n",
       "      <td>32498</td>\n",
       "      <td>322</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>63140</td>\n",
       "      <td>937</td>\n",
       "      <td>571</td>\n",
       "      <td>43265</td>\n",
       "      <td>24359</td>\n",
       "      <td>40362</td>\n",
       "      <td>2458</td>\n",
       "      <td>6111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>57764</td>\n",
       "      <td>772</td>\n",
       "      <td>2216</td>\n",
       "      <td>16664</td>\n",
       "      <td>11266</td>\n",
       "      <td>98025</td>\n",
       "      <td>233</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>81164</td>\n",
       "      <td>436</td>\n",
       "      <td>1649</td>\n",
       "      <td>272391</td>\n",
       "      <td>11217</td>\n",
       "      <td>20186</td>\n",
       "      <td>80</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       click  reply  followed_num  fans_num  post_num  comment_num  month  \\\n",
       "year                                                                        \n",
       "2006    1214     24             0         2       278          291      8   \n",
       "2007   28290    514            22       137      8041        10344    281   \n",
       "2009   18644    186            17        12       531          571     39   \n",
       "2011    2889     28            84        28       332          661     50   \n",
       "2012  463720   5933          2779     59511     12315        32498    322   \n",
       "2013   63140    937           571     43265     24359        40362   2458   \n",
       "2014   57764    772          2216     16664     11266        98025    233   \n",
       "2015   81164    436          1649    272391     11217        20186     80   \n",
       "\n",
       "       day  \n",
       "year        \n",
       "2006    24  \n",
       "2007   512  \n",
       "2009    78  \n",
       "2011    72  \n",
       "2012   819  \n",
       "2013  6111  \n",
       "2014   579  \n",
       "2015   193  "
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg = data.groupby('year').sum()\n",
    "dg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1214</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278</td>\n",
       "      <td>291</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>29504</td>\n",
       "      <td>538</td>\n",
       "      <td>22</td>\n",
       "      <td>139</td>\n",
       "      <td>8319</td>\n",
       "      <td>10635</td>\n",
       "      <td>289</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>48148</td>\n",
       "      <td>724</td>\n",
       "      <td>39</td>\n",
       "      <td>151</td>\n",
       "      <td>8850</td>\n",
       "      <td>11206</td>\n",
       "      <td>328</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>51037</td>\n",
       "      <td>752</td>\n",
       "      <td>123</td>\n",
       "      <td>179</td>\n",
       "      <td>9182</td>\n",
       "      <td>11867</td>\n",
       "      <td>378</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>514757</td>\n",
       "      <td>6685</td>\n",
       "      <td>2902</td>\n",
       "      <td>59690</td>\n",
       "      <td>21497</td>\n",
       "      <td>44365</td>\n",
       "      <td>700</td>\n",
       "      <td>1505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>577897</td>\n",
       "      <td>7622</td>\n",
       "      <td>3473</td>\n",
       "      <td>102955</td>\n",
       "      <td>45856</td>\n",
       "      <td>84727</td>\n",
       "      <td>3158</td>\n",
       "      <td>7616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>635661</td>\n",
       "      <td>8394</td>\n",
       "      <td>5689</td>\n",
       "      <td>119619</td>\n",
       "      <td>57122</td>\n",
       "      <td>182752</td>\n",
       "      <td>3391</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>716825</td>\n",
       "      <td>8830</td>\n",
       "      <td>7338</td>\n",
       "      <td>392010</td>\n",
       "      <td>68339</td>\n",
       "      <td>202938</td>\n",
       "      <td>3471</td>\n",
       "      <td>8388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       click  reply  followed_num  fans_num  post_num  comment_num  month  \\\n",
       "year                                                                        \n",
       "2006    1214     24             0         2       278          291      8   \n",
       "2007   29504    538            22       139      8319        10635    289   \n",
       "2009   48148    724            39       151      8850        11206    328   \n",
       "2011   51037    752           123       179      9182        11867    378   \n",
       "2012  514757   6685          2902     59690     21497        44365    700   \n",
       "2013  577897   7622          3473    102955     45856        84727   3158   \n",
       "2014  635661   8394          5689    119619     57122       182752   3391   \n",
       "2015  716825   8830          7338    392010     68339       202938   3471   \n",
       "\n",
       "       day  \n",
       "year        \n",
       "2006    24  \n",
       "2007   536  \n",
       "2009   614  \n",
       "2011   686  \n",
       "2012  1505  \n",
       "2013  7616  \n",
       "2014  8195  \n",
       "2015  8388  "
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgs = dg.cumsum()\n",
    "dgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def getDate(dat):\n",
    "    dat_date_str = map(lambda x: str(x) +'-01-01', dat.index)\n",
    "    dat_date = pd.to_datetime(dat_date_str)\n",
    "    return dat_date\n",
    "\n",
    "dg.date = getDate(dg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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p2W85PZ6SAkeOeP5cTx5LS8s5aPsirJ/juRP++9/T4dn9exq+ZQtvdOtGVERE9nB85EjG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\nFfD/AfBXW8ajJ9C/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af6c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "plt.plot(dg.date, dg.click, 'r-o', label = '$Yearly\\:Number\\:of\\:Clicks$')\n",
    "plt.plot(dg.date, dg.reply, 'g-s', label = '$Yearly\\:Number\\:of\\:Replies$')\n",
    "plt.plot(dg.date, dg.fans_num, 'b->', label = '$Yearly\\:Number\\:of\\:Fans$')\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend(loc=4,numpoints=1,fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2006      1214\n",
       "2007     28290\n",
       "2009     18644\n",
       "2011      2889\n",
       "2012    463720\n",
       "2013     63140\n",
       "2014     57764\n",
       "2015     81164\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('year')['click'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2006     1214.000000\n",
       "2007      832.058824\n",
       "2009     3107.333333\n",
       "2011      481.500000\n",
       "2012    14052.121212\n",
       "2013      207.697368\n",
       "2014      916.888889\n",
       "2015     4058.200000\n",
       "Name: click, dtype: float64"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('year')['click'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 常用的统计分析方法\n",
    "- t检验\n",
    "- 卡方检验\n",
    "- 相关\n",
    "- 回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# T-test\n",
    "\n",
    "http://statsmodels.sourceforge.net/devel/stats.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# RQ: 转载的文章的点击量是否显著地小于原创的文章？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "repost = []\n",
    "for i in df.title:\n",
    "    if u'转载' in i.decode('utf8'):\n",
    "        repost.append(1)\n",
    "    else:\n",
    "        repost.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df['repost'] = repost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repost</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536495</td>\n",
       "      <td>6208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180330</td>\n",
       "      <td>2622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         click  reply\n",
       "repost               \n",
       "0       536495   6208\n",
       "1       180330   2622"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('repost').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    194675\n",
       "2     82779\n",
       "3     45304\n",
       "5     27026\n",
       "6     24026\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['click'][df['repost']==0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     88244\n",
       "4     38132\n",
       "13     4990\n",
       "16     3720\n",
       "18     3421\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['click'][df['repost']==1][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.9514887561591618, pvalue=0.0032417014839700789)"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.ttest_ind(data.click, data.repost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.9514887561591618, 0.0032417014839700789, 932.0)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.ttest_ind(data.click, data.reply)\n",
    "# test statistic, pvalue and degrees of freedom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A chi-squared test\n",
    "\n",
    "https://en.wikipedia.org/wiki/Chi-squared_test\n",
    "\n",
    "- also referred to as χ² test (or chi-square test), is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true. \n",
    "- A chi-squared test can then be used to reject the null hypothesis that the data are independent.\n",
    "- Test statistics that follow a chi-squared distribution arise from an assumption of independent normally distributed data, which is valid in many cases due to the central limit theorem. \n",
    "- Chi-squared tests are often constructed from a sum of squared errors, or through the sample variance.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Suppose there is a city of 1 million residents with four neighborhoods: A, B, C, and D. \n",
    "\n",
    "- A random sample of 650 residents of the city is taken and their occupation is recorded as \"blue collar\", \"white collar\", or \"no collar\". \n",
    "\n",
    "- The null hypothesis is that each person's neighborhood of residence is independent of the person's occupational classification. The data are tabulated as:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "\n",
    "|               |  A           |    B          |C              | D  | Total|\n",
    "| -------------|:-------------:|:-------------:|:-------------:|-----:|-----:|\n",
    "| White collar| 90 | 60 | 104 |95 | 349|\n",
    "| Blue collar| 30 | 50 | 51 | 20| 151|\n",
    "| No coloar| 30 | 40 | 45 | 35|150|\n",
    "| Total | 150 | 150| 200| 150| 650|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let us take the sample living in neighborhood A, 150/650, to estimate what proportion of the whole 1 million people live in neighborhood A. \n",
    "- Similarly we take 349/650 to estimate what proportion of the 1 million people are white-collar workers. \n",
    "- By the assumption of independence under the hypothesis we should \"expect\" the number of white-collar workers in neighborhood A to be\n",
    "\n",
    "$\n",
    "\\frac{150}{650} \\frac{349}{650} 650 = 80.54\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Then in that \"cell\" of the table, we have\n",
    "\n",
    "$\\frac{(\\text{observed}-\\text{expected})^2}{\\text{expected}} = \\frac{(90-80.54)^2}{80.54}$.\n",
    "\n",
    "The sum of these quantities over all of the cells is the test statistic. \n",
    "\n",
    "Under the null hypothesis, it has approximately a chi-square distribution whose number of degrees of freedom are\n",
    "\n",
    "$ (\\text{number of rows}-1)(\\text{number of columns}-1) = (3-1)(4-1) = 6. $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If the test statistic is improbably large according to that chi-square distribution, then one rejects the null hypothesis of independence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./img/600px-Chi-square_distributionCDF-English.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# scipy.stats.chisquare(f_obs, f_exp=None, ddof=0, axis=0)[source]\n",
    "- Calculates a one-way chi square test.\n",
    "- The chi square test tests the null hypothesis that the categorical data has the given frequencies.\n",
    "\n",
    "Parameters:\t\n",
    "- f_obs : array_like Observed frequencies in each category.\n",
    "- f_exp : array_like, optional Expected frequencies in each category. By default the categories are assumed to be equally likely.\n",
    "- ddof : int, optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=3.5, pvalue=0.62338762774958223)"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chisquare\n",
    "chisquare([16, 18, 16, 14, 12, 12], f_exp=[16, 16, 16, 16, 16, 8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/scipy_org_logo.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0471507774946 0.0471507774946\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chisqprob, chi2\n",
    "# p_value = chi2.sf(chi_statistic, df)\n",
    "print chisqprob(3.94,1), 1 - chi2.cdf(3.94,1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.96396571]\n",
      " [ 0.96396571  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "print np.corrcoef(data.click, data.reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>followed_num</th>\n",
       "      <th>fans_num</th>\n",
       "      <th>post_num</th>\n",
       "      <th>comment_num</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>click</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963966</td>\n",
       "      <td>0.143595</td>\n",
       "      <td>0.158116</td>\n",
       "      <td>0.097502</td>\n",
       "      <td>0.085615</td>\n",
       "      <td>0.038788</td>\n",
       "      <td>-0.024827</td>\n",
       "      <td>0.048361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reply</th>\n",
       "      <td>0.963966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199270</td>\n",
       "      <td>0.159387</td>\n",
       "      <td>0.090342</td>\n",
       "      <td>0.123341</td>\n",
       "      <td>0.040165</td>\n",
       "      <td>-0.041208</td>\n",
       "      <td>0.058738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>followed_num</th>\n",
       "      <td>0.143595</td>\n",
       "      <td>0.199270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407656</td>\n",
       "      <td>0.211677</td>\n",
       "      <td>0.499612</td>\n",
       "      <td>-0.036037</td>\n",
       "      <td>0.051187</td>\n",
       "      <td>-0.020604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fans_num</th>\n",
       "      <td>0.158116</td>\n",
       "      <td>0.159387</td>\n",
       "      <td>0.407656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341724</td>\n",
       "      <td>0.145387</td>\n",
       "      <td>-0.084243</td>\n",
       "      <td>0.102301</td>\n",
       "      <td>-0.045883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>post_num</th>\n",
       "      <td>0.097502</td>\n",
       "      <td>0.090342</td>\n",
       "      <td>0.211677</td>\n",
       "      <td>0.341724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.514695</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>-0.011786</td>\n",
       "      <td>-0.033254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comment_num</th>\n",
       "      <td>0.085615</td>\n",
       "      <td>0.123341</td>\n",
       "      <td>0.499612</td>\n",
       "      <td>0.145387</td>\n",
       "      <td>0.514695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.118703</td>\n",
       "      <td>0.069160</td>\n",
       "      <td>-0.119840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.038788</td>\n",
       "      <td>0.040165</td>\n",
       "      <td>-0.036037</td>\n",
       "      <td>-0.084243</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>-0.118703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.236920</td>\n",
       "      <td>0.535354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>-0.024827</td>\n",
       "      <td>-0.041208</td>\n",
       "      <td>0.051187</td>\n",
       "      <td>0.102301</td>\n",
       "      <td>-0.011786</td>\n",
       "      <td>0.069160</td>\n",
       "      <td>-0.236920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0.048361</td>\n",
       "      <td>0.058738</td>\n",
       "      <td>-0.020604</td>\n",
       "      <td>-0.045883</td>\n",
       "      <td>-0.033254</td>\n",
       "      <td>-0.119840</td>\n",
       "      <td>0.535354</td>\n",
       "      <td>-0.046699</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 click     reply  followed_num  fans_num  post_num  \\\n",
       "click         1.000000  0.963966      0.143595  0.158116  0.097502   \n",
       "reply         0.963966  1.000000      0.199270  0.159387  0.090342   \n",
       "followed_num  0.143595  0.199270      1.000000  0.407656  0.211677   \n",
       "fans_num      0.158116  0.159387      0.407656  1.000000  0.341724   \n",
       "post_num      0.097502  0.090342      0.211677  0.341724  1.000000   \n",
       "comment_num   0.085615  0.123341      0.499612  0.145387  0.514695   \n",
       "month         0.038788  0.040165     -0.036037 -0.084243 -0.070024   \n",
       "year         -0.024827 -0.041208      0.051187  0.102301 -0.011786   \n",
       "day           0.048361  0.058738     -0.020604 -0.045883 -0.033254   \n",
       "\n",
       "              comment_num     month      year       day  \n",
       "click            0.085615  0.038788 -0.024827  0.048361  \n",
       "reply            0.123341  0.040165 -0.041208  0.058738  \n",
       "followed_num     0.499612 -0.036037  0.051187 -0.020604  \n",
       "fans_num         0.145387 -0.084243  0.102301 -0.045883  \n",
       "post_num         0.514695 -0.070024 -0.011786 -0.033254  \n",
       "comment_num      1.000000 -0.118703  0.069160 -0.119840  \n",
       "month           -0.118703  1.000000 -0.236920  0.535354  \n",
       "year             0.069160 -0.236920  1.000000 -0.046699  \n",
       "day             -0.119840  0.535354 -0.046699  1.000000  "
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P4e6jRrn/4Aepe72pU93POSfEe8cd7l98kbrXFpGMiX53pvX3c16NaeBOxZFH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159a7350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(df.click, df.reply, 'r-o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l8cz/2P6Uvcx/Wu6VSLNLXZBx9zvN7JCc5GOAB939EQAzux44DRg0s/2AzwBL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a58d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.click, df.reply, 'gs')\n",
    "plt.xlabel('$Clicks$', fontsize = 20)\n",
    "plt.ylabel('$Replies$', fontsize = 20)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.title('$Allowmetric\\,Law$', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "dat = sm.datasets.get_rdataset(\"Guerry\", \"HistData\").data\n",
    "# Fit regression model (using the natural log of one of the regressors)\n",
    "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有些使用windows的同学无法运行上述代码：\n",
    "\n",
    "## 在spyder中打开terminal\n",
    "# 输入: pip install -U patsy\n",
    "\n",
    "https://groups.google.com/forum/#!topic/pystatsmodels/KcSzNqDxv-Q\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Lottery   R-squared:                       0.348\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "Method:                 Least Squares   F-statistic:                     22.20\n",
      "Date:                Sat, 16 Apr 2016   Prob (F-statistic):           1.90e-08\n",
      "Time:                        23:39:42   Log-Likelihood:                -379.82\n",
      "No. Observations:                  86   AIC:                             765.6\n",
      "Df Residuals:                      83   BIC:                             773.0\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         246.4341     35.233      6.995      0.000       176.358   316.510\n",
      "Literacy           -0.4889      0.128     -3.832      0.000        -0.743    -0.235\n",
      "np.log(Pop1831)   -31.3114      5.977     -5.239      0.000       -43.199   -19.424\n",
      "==============================================================================\n",
      "Omnibus:                        3.713   Durbin-Watson:                   2.019\n",
      "Prob(Omnibus):                  0.156   Jarque-Bera (JB):                3.394\n",
      "Skew:                          -0.487   Prob(JB):                        0.183\n",
      "Kurtosis:                       3.003   Cond. No.                         702.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Inspect the results\n",
    "print results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "reg = smf.ols('reply ~ click + followed_num', data=data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  reply   R-squared:                       0.933\n",
      "Model:                            OLS   Adj. R-squared:                  0.933\n",
      "Method:                 Least Squares   F-statistic:                     3231.\n",
      "Date:                Sun, 17 Apr 2016   Prob (F-statistic):          4.30e-273\n",
      "Time:                        02:04:27   Log-Likelihood:                -2354.7\n",
      "No. Observations:                 467   AIC:                             4715.\n",
      "Df Residuals:                     464   BIC:                             4728.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept       -1.4024      1.766     -0.794      0.428        -4.873     2.068\n",
      "click            0.0125      0.000     78.660      0.000         0.012     0.013\n",
      "followed_num     0.0749      0.015      5.117      0.000         0.046     0.104\n",
      "==============================================================================\n",
      "Omnibus:                      374.515   Durbin-Watson:                   1.938\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            97373.297\n",
      "Skew:                          -2.416   Prob(JB):                         0.00\n",
      "Kurtosis:                      73.575   Cond. No.                     1.14e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.14e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      np.log(reply + 1)   R-squared:                       0.606\n",
      "Model:                            OLS   Adj. R-squared:                  0.603\n",
      "Method:                 Least Squares   F-statistic:                     236.9\n",
      "Date:                Sun, 17 Apr 2016   Prob (F-statistic):           4.03e-93\n",
      "Time:                        14:22:55   Log-Likelihood:                -596.73\n",
      "No. Observations:                 467   AIC:                             1201.\n",
      "Df Residuals:                     463   BIC:                             1218.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   -2.6009      0.189    -13.778      0.000        -2.972    -2.230\n",
      "np.log(click + 1)            0.6872      0.029     24.083      0.000         0.631     0.743\n",
      "np.log(followed_num + 1)     0.0118      0.034      0.347      0.729        -0.055     0.079\n",
      "month                        0.0172      0.013      1.275      0.203        -0.009     0.044\n",
      "==============================================================================\n",
      "Omnibus:                       26.408   Durbin-Watson:                   1.904\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               44.572\n",
      "Skew:                          -0.389   Prob(JB):                     2.10e-10\n",
      "Kurtosis:                       4.299   Cond. No.                         44.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg1 = smf.ols('np.log(reply+1) ~ np.log(click+1) +np.log(followed_num+1)+month', data=data).fit()\n",
    "print reg1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RfAMs2CHkgd5fn8IbTg9f99cGmNGguPjhhx/KJZdcIsccc4w8/PDDsn///ga3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2AAAg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\nAAqbO4Qq90AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c52ae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "fig = sm.graphics.plot_partregress_grid(reg1, fig = fig)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "moore = sm.datasets.get_rdataset(\"Moore\", \"car\",\n",
    "                                 cache=True) # load data\n",
    "data = moore.data\n",
    "data = data.rename(columns={\"partner.status\" :\n",
    "                             \"partner_status\"}) # make name pythonic\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>partner_status</th>\n",
       "      <th>conformity</th>\n",
       "      <th>fcategory</th>\n",
       "      <th>fscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>low</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>high</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>low</td>\n",
       "      <td>7</td>\n",
       "      <td>low</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low</td>\n",
       "      <td>10</td>\n",
       "      <td>low</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  partner_status  conformity fcategory  fscore\n",
       "0            low           8       low      37\n",
       "1            low           4      high      57\n",
       "2            low           8      high      65\n",
       "3            low           7       low      20\n",
       "4            low          10       low      36"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)',\n",
    "                 data=data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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KbdhJudPnKUUgX/GobJVaIaipU2ewfXvySJUKtm8/gbq6r9Gv30j1bIlI33L3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\npVwq4Cx8RcQqSS7Nn5FiU+KebPkkWb81s+8B9wH70hvd/T9L1qpBmjN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2CsFyEEkNBkNh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\n3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PeTtvkdTb6xAVkKSzoDXyzDG6GpyT2uAG4slLlHbG4bU2huueUXXHbZnbS1VVJSspsb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\nZYwxxhhjjDFZZJksY4wxxhhjjMmi/w/J9odyfbhuVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d170d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "fig = sm.graphics.plot_partregress_grid(moore_lm, fig = fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              sum_sq    df          F  \\\n",
      "C(fcategory, Sum)                          11.614700   2.0   0.276958   \n",
      "C(partner_status, Sum)                    212.213778   1.0  10.120692   \n",
      "C(fcategory, Sum):C(partner_status, Sum)  175.488928   2.0   4.184623   \n",
      "Residual                                  817.763961  39.0        NaN   \n",
      "\n",
      "                                            PR(>F)  \n",
      "C(fcategory, Sum)                         0.759564  \n",
      "C(partner_status, Sum)                    0.002874  \n",
      "C(fcategory, Sum):C(partner_status, Sum)  0.022572  \n",
      "Residual                                       NaN  \n"
     ]
    }
   ],
   "source": [
    "table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame\n",
    "print table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# 参考文献\n",
    "- scipy-lectures http://www.scipy-lectures.org/index.html"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
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