Facebook_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Term 1 and 2 Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Facebook dataset having 99K lines of rows having different column names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial steps:\n",
    "\n",
    "##### 1) Load packages to Jupyter note book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_profiling\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set()\n",
    "\n",
    "from subprocess import check_output\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) Load dataset into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb_df=pd.read_csv(\"rawdata/facebook_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) Display datafrom dataFrame object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>age</th>\n",
       "      <th>dob_day</th>\n",
       "      <th>dob_year</th>\n",
       "      <th>dob_month</th>\n",
       "      <th>gender</th>\n",
       "      <th>tenure</th>\n",
       "      <th>friend_count</th>\n",
       "      <th>friendships_initiated</th>\n",
       "      <th>likes</th>\n",
       "      <th>likes_received</th>\n",
       "      <th>mobile_likes</th>\n",
       "      <th>mobile_likes_received</th>\n",
       "      <th>www_likes</th>\n",
       "      <th>www_likes_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2094382</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>1999</td>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>266.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1192601</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>11</td>\n",
       "      <td>female</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2083884</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>1999</td>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1203168</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>female</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1733186</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userid  age  dob_day  dob_year  dob_month  gender  tenure  friend_count  \\\n",
       "0  2094382   14       19      1999         11    male   266.0             0   \n",
       "1  1192601   14        2      1999         11  female     6.0             0   \n",
       "2  2083884   14       16      1999         11    male    13.0             0   \n",
       "3  1203168   14       25      1999         12  female    93.0             0   \n",
       "4  1733186   14        4      1999         12    male    82.0             0   \n",
       "\n",
       "   friendships_initiated  likes  likes_received  mobile_likes  \\\n",
       "0                      0      0               0             0   \n",
       "1                      0      0               0             0   \n",
       "2                      0      0               0             0   \n",
       "3                      0      0               0             0   \n",
       "4                      0      0               0             0   \n",
       "\n",
       "   mobile_likes_received  www_likes  www_likes_received  \n",
       "0                      0          0                   0  \n",
       "1                      0          0                   0  \n",
       "2                      0          0                   0  \n",
       "3                      0          0                   0  \n",
       "4                      0          0                   0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) Display list of columns and datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userid                     int64\n",
       "age                        int64\n",
       "dob_day                    int64\n",
       "dob_year                   int64\n",
       "dob_month                  int64\n",
       "gender                    object\n",
       "tenure                   float64\n",
       "friend_count               int64\n",
       "friendships_initiated      int64\n",
       "likes                      int64\n",
       "likes_received             int64\n",
       "mobile_likes               int64\n",
       "mobile_likes_received      int64\n",
       "www_likes                  int64\n",
       "www_likes_received         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99003 entries, 0 to 99002\n",
      "Data columns (total 15 columns):\n",
      "userid                   99003 non-null int64\n",
      "age                      99003 non-null int64\n",
      "dob_day                  99003 non-null int64\n",
      "dob_year                 99003 non-null int64\n",
      "dob_month                99003 non-null int64\n",
      "gender                   98828 non-null object\n",
      "tenure                   99001 non-null float64\n",
      "friend_count             99003 non-null int64\n",
      "friendships_initiated    99003 non-null int64\n",
      "likes                    99003 non-null int64\n",
      "likes_received           99003 non-null int64\n",
      "mobile_likes             99003 non-null int64\n",
      "mobile_likes_received    99003 non-null int64\n",
      "www_likes                99003 non-null int64\n",
      "www_likes_received       99003 non-null int64\n",
      "dtypes: float64(1), int64(13), object(1)\n",
      "memory usage: 11.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fb_df.info()      #display no.of values each feild holding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>age</th>\n",
       "      <th>dob_day</th>\n",
       "      <th>dob_year</th>\n",
       "      <th>dob_month</th>\n",
       "      <th>tenure</th>\n",
       "      <th>friend_count</th>\n",
       "      <th>friendships_initiated</th>\n",
       "      <th>likes</th>\n",
       "      <th>likes_received</th>\n",
       "      <th>mobile_likes</th>\n",
       "      <th>mobile_likes_received</th>\n",
       "      <th>www_likes</th>\n",
       "      <th>www_likes_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.900300e+04</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99001.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "      <td>99003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.597045e+06</td>\n",
       "      <td>37.280224</td>\n",
       "      <td>14.530408</td>\n",
       "      <td>1975.719776</td>\n",
       "      <td>6.283365</td>\n",
       "      <td>537.887375</td>\n",
       "      <td>196.350787</td>\n",
       "      <td>107.452471</td>\n",
       "      <td>156.078785</td>\n",
       "      <td>142.689363</td>\n",
       "      <td>106.116300</td>\n",
       "      <td>84.120491</td>\n",
       "      <td>49.962425</td>\n",
       "      <td>58.568831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.440592e+05</td>\n",
       "      <td>22.589748</td>\n",
       "      <td>9.015606</td>\n",
       "      <td>22.589748</td>\n",
       "      <td>3.529672</td>\n",
       "      <td>457.649874</td>\n",
       "      <td>387.304229</td>\n",
       "      <td>188.786951</td>\n",
       "      <td>572.280681</td>\n",
       "      <td>1387.919613</td>\n",
       "      <td>445.252985</td>\n",
       "      <td>839.889444</td>\n",
       "      <td>285.560152</td>\n",
       "      <td>601.416348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000008e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.298806e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.596148e+06</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.895744e+06</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>675.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.193542e+06</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3139.000000</td>\n",
       "      <td>4923.000000</td>\n",
       "      <td>4144.000000</td>\n",
       "      <td>25111.000000</td>\n",
       "      <td>261197.000000</td>\n",
       "      <td>25111.000000</td>\n",
       "      <td>138561.000000</td>\n",
       "      <td>14865.000000</td>\n",
       "      <td>129953.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userid           age       dob_day      dob_year     dob_month  \\\n",
       "count  9.900300e+04  99003.000000  99003.000000  99003.000000  99003.000000   \n",
       "mean   1.597045e+06     37.280224     14.530408   1975.719776      6.283365   \n",
       "std    3.440592e+05     22.589748      9.015606     22.589748      3.529672   \n",
       "min    1.000008e+06     13.000000      1.000000   1900.000000      1.000000   \n",
       "25%    1.298806e+06     20.000000      7.000000   1963.000000      3.000000   \n",
       "50%    1.596148e+06     28.000000     14.000000   1985.000000      6.000000   \n",
       "75%    1.895744e+06     50.000000     22.000000   1993.000000      9.000000   \n",
       "max    2.193542e+06    113.000000     31.000000   2000.000000     12.000000   \n",
       "\n",
       "             tenure  friend_count  friendships_initiated         likes  \\\n",
       "count  99001.000000  99003.000000           99003.000000  99003.000000   \n",
       "mean     537.887375    196.350787             107.452471    156.078785   \n",
       "std      457.649874    387.304229             188.786951    572.280681   \n",
       "min        0.000000      0.000000               0.000000      0.000000   \n",
       "25%      226.000000     31.000000              17.000000      1.000000   \n",
       "50%      412.000000     82.000000              46.000000     11.000000   \n",
       "75%      675.000000    206.000000             117.000000     81.000000   \n",
       "max     3139.000000   4923.000000            4144.000000  25111.000000   \n",
       "\n",
       "       likes_received  mobile_likes  mobile_likes_received     www_likes  \\\n",
       "count    99003.000000  99003.000000           99003.000000  99003.000000   \n",
       "mean       142.689363    106.116300              84.120491     49.962425   \n",
       "std       1387.919613    445.252985             839.889444    285.560152   \n",
       "min          0.000000      0.000000               0.000000      0.000000   \n",
       "25%          1.000000      0.000000               0.000000      0.000000   \n",
       "50%          8.000000      4.000000               4.000000      0.000000   \n",
       "75%         59.000000     46.000000              33.000000      7.000000   \n",
       "max     261197.000000  25111.000000          138561.000000  14865.000000   \n",
       "\n",
       "       www_likes_received  \n",
       "count        99003.000000  \n",
       "mean            58.568831  \n",
       "std            601.416348  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              2.000000  \n",
       "75%             20.000000  \n",
       "max         129953.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userid', 'age', 'dob_day', 'dob_year', 'dob_month', 'gender', 'tenure',\n",
       "       'friend_count', 'friendships_initiated', 'likes', 'likes_received',\n",
       "       'mobile_likes', 'mobile_likes_received', 'www_likes',\n",
       "       'www_likes_received'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userid                     0\n",
       "age                        0\n",
       "dob_day                    0\n",
       "dob_year                   0\n",
       "dob_month                  0\n",
       "gender                   175\n",
       "tenure                     2\n",
       "friend_count               0\n",
       "friendships_initiated      0\n",
       "likes                      0\n",
       "likes_received             0\n",
       "mobile_likes               0\n",
       "mobile_likes_received      0\n",
       "www_likes                  0\n",
       "www_likes_received         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DOB fields into single date field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>age</th>\n",
       "      <th>dob_day</th>\n",
       "      <th>dob_year</th>\n",
       "      <th>dob_month</th>\n",
       "      <th>gender</th>\n",
       "      <th>tenure</th>\n",
       "      <th>friend_count</th>\n",
       "      <th>friendships_initiated</th>\n",
       "      <th>likes</th>\n",
       "      <th>likes_received</th>\n",
       "      <th>mobile_likes</th>\n",
       "      <th>mobile_likes_received</th>\n",
       "      <th>www_likes</th>\n",
       "      <th>www_likes_received</th>\n",
       "      <th>dateofbirth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2094382</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>1999</td>\n",
       "      <td>11</td>\n",
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       "    </tr>\n",
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       "      <td>1192601</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2083884</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>1999</td>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>1999</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-1999-12</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1733186</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>82.0</td>\n",
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       "      <td>4-1999-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userid  age  dob_day  dob_year  dob_month  gender  tenure  friend_count  \\\n",
       "0  2094382   14       19      1999         11    male   266.0             0   \n",
       "1  1192601   14        2      1999         11  female     6.0             0   \n",
       "2  2083884   14       16      1999         11    male    13.0             0   \n",
       "3  1203168   14       25      1999         12  female    93.0             0   \n",
       "4  1733186   14        4      1999         12    male    82.0             0   \n",
       "\n",
       "   friendships_initiated  likes  likes_received  mobile_likes  \\\n",
       "0                      0      0               0             0   \n",
       "1                      0      0               0             0   \n",
       "2                      0      0               0             0   \n",
       "3                      0      0               0             0   \n",
       "4                      0      0               0             0   \n",
       "\n",
       "   mobile_likes_received  www_likes  www_likes_received dateofbirth  \n",
       "0                      0          0                   0  19-1999-11  \n",
       "1                      0          0                   0   2-1999-11  \n",
       "2                      0          0                   0  16-1999-11  \n",
       "3                      0          0                   0  25-1999-12  \n",
       "4                      0          0                   0   4-1999-12  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df['dateofbirth'] = fb_df[fb_df.columns[2:5]].apply(\n",
    "lambda x: '-'.join(x.astype(int).astype(str)), axis=1\n",
    ")\n",
    "\n",
    "fb_df.head()\n",
    "#fb_df[fb_df.columns[2:5]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userid                            int64\n",
       "age                               int64\n",
       "dob_day                           int64\n",
       "dob_year                          int64\n",
       "dob_month                         int64\n",
       "gender                           object\n",
       "tenure                          float64\n",
       "friend_count                      int64\n",
       "friendships_initiated             int64\n",
       "likes                             int64\n",
       "likes_received                    int64\n",
       "mobile_likes                      int64\n",
       "mobile_likes_received             int64\n",
       "www_likes                         int64\n",
       "www_likes_received                int64\n",
       "dateofbirth                      object\n",
       "dob                      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "fb_df['dob']=fb_df['dateofbirth'].apply(\n",
    "lambda x: (datetime.strptime(x,'%d-%Y-%m'))\n",
    ")  \n",
    "\n",
    "#fb_df['dateofbirth']=pd.to_datetime(fb_df['dateofbirth'])\n",
    "fb_df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>www_likes</th>\n",
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       "      <td>1203168</td>\n",
       "      <td>14</td>\n",
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       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>female</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-1999-12</td>\n",
       "      <td>1999-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1733186</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1999</td>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4-1999-12</td>\n",
       "      <td>1999-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userid  age  dob_day  dob_year  dob_month  gender  tenure  friend_count  \\\n",
       "0  2094382   14       19      1999         11    male   266.0             0   \n",
       "1  1192601   14        2      1999         11  female     6.0             0   \n",
       "2  2083884   14       16      1999         11    male    13.0             0   \n",
       "3  1203168   14       25      1999         12  female    93.0             0   \n",
       "4  1733186   14        4      1999         12    male    82.0             0   \n",
       "\n",
       "   friendships_initiated  likes  likes_received  mobile_likes  \\\n",
       "0                      0      0               0             0   \n",
       "1                      0      0               0             0   \n",
       "2                      0      0               0             0   \n",
       "3                      0      0               0             0   \n",
       "4                      0      0               0             0   \n",
       "\n",
       "   mobile_likes_received  www_likes  www_likes_received dateofbirth        dob  \n",
       "0                      0          0                   0  19-1999-11 1999-11-19  \n",
       "1                      0          0                   0   2-1999-11 1999-11-02  \n",
       "2                      0          0                   0  16-1999-11 1999-11-16  \n",
       "3                      0          0                   0  25-1999-12 1999-12-25  \n",
       "4                      0          0                   0   4-1999-12 1999-12-04  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
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       "      <th>gender</th>\n",
       "      <th>tenure</th>\n",
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       "      <th>0</th>\n",
       "      <td>2094382</td>\n",
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       "      <th>1</th>\n",
       "      <td>1192601</td>\n",
       "      <td>14</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2083884</td>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1999-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1203168</td>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1733186</td>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userid  age  gender  tenure  friend_count  friendships_initiated  likes  \\\n",
       "0  2094382   14    male   266.0             0                      0      0   \n",
       "1  1192601   14  female     6.0             0                      0      0   \n",
       "2  2083884   14    male    13.0             0                      0      0   \n",
       "3  1203168   14  female    93.0             0                      0      0   \n",
       "4  1733186   14    male    82.0             0                      0      0   \n",
       "\n",
       "   likes_received  mobile_likes  mobile_likes_received  www_likes  \\\n",
       "0               0             0                      0          0   \n",
       "1               0             0                      0          0   \n",
       "2               0             0                      0          0   \n",
       "3               0             0                      0          0   \n",
       "4               0             0                      0          0   \n",
       "\n",
       "   www_likes_received        dob  \n",
       "0                   0 1999-11-19  \n",
       "1                   0 1999-11-02  \n",
       "2                   0 1999-11-16  \n",
       "3                   0 1999-12-25  \n",
       "4                   0 1999-12-04  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_cleandf=fb_df\n",
    "fb_cleandf.drop(columns=['dateofbirth','dob_day','dob_month','dob_year'], inplace=True)\n",
    "fb_cleandf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre profilling for facebook dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb_profile=fb_df.profile_report(title=\"Facebook User Report\")\n",
    "fb_profile.to_file(output_file=\"rawdata\\Facebook_PredataProfiling_Report.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get counts for gender distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(fb_cleandf['gender']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      58574\n",
       "female    40254\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_cleandf['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gender</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>1483075</td>\n",
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       "      <th>15272</th>\n",
       "      <td>2083169</td>\n",
       "      <td>56</td>\n",
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       "      <td>1968.0</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1957-05-11</td>\n",
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       "    <tr>\n",
       "      <th>15425</th>\n",
       "      <td>1326030</td>\n",
       "      <td>105</td>\n",
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       "      <td>2160.0</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>37</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1908-08-01</td>\n",
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       "    <tr>\n",
       "      <th>15861</th>\n",
       "      <td>1071932</td>\n",
       "      <td>74</td>\n",
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       "      <td>2213.0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1939-08-21</td>\n",
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       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>1297309</td>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1934-04-10</td>\n",
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       "      <th>16960</th>\n",
       "      <td>1425233</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1950-03-05</td>\n",
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       "    <tr>\n",
       "      <th>17334</th>\n",
       "      <td>1156871</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1822.0</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
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       "      <td>1955-02-02</td>\n",
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       "      <th>17368</th>\n",
       "      <td>1119045</td>\n",
       "      <td>23</td>\n",
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       "      <td>186.0</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
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       "      <td>0</td>\n",
       "      <td>1990-12-09</td>\n",
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       "      <th>17653</th>\n",
       "      <td>1587920</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1059.0</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>402</td>\n",
       "      <td>45</td>\n",
       "      <td>402</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1971-11-22</td>\n",
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       "    <tr>\n",
       "      <th>18256</th>\n",
       "      <td>2192387</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1930-10-29</td>\n",
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       "    <tr>\n",
       "      <th>18764</th>\n",
       "      <td>1021360</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2132.0</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1936-09-01</td>\n",
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       "    <tr>\n",
       "      <th>20674</th>\n",
       "      <td>2069184</td>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1957-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20705</th>\n",
       "      <td>2037907</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1936-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21547</th>\n",
       "      <td>1227386</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2092.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1941-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22110</th>\n",
       "      <td>1721035</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>47</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>1985-07-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22643</th>\n",
       "      <td>1622476</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2228.0</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1923-08-12</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>84817</th>\n",
       "      <td>1414203</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2298.0</td>\n",
       "      <td>332</td>\n",
       "      <td>207</td>\n",
       "      <td>193</td>\n",
       "      <td>751</td>\n",
       "      <td>26</td>\n",
       "      <td>452</td>\n",
       "      <td>167</td>\n",
       "      <td>299</td>\n",
       "      <td>1958-12-21</td>\n",
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       "    <tr>\n",
       "      <th>84993</th>\n",
       "      <td>2187174</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>327</td>\n",
       "      <td>257</td>\n",
       "      <td>678</td>\n",
       "      <td>341</td>\n",
       "      <td>576</td>\n",
       "      <td>84</td>\n",
       "      <td>102</td>\n",
       "      <td>257</td>\n",
       "      <td>1912-06-13</td>\n",
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       "    <tr>\n",
       "      <th>85780</th>\n",
       "      <td>1046190</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>357</td>\n",
       "      <td>101</td>\n",
       "      <td>137</td>\n",
       "      <td>243</td>\n",
       "      <td>10</td>\n",
       "      <td>89</td>\n",
       "      <td>127</td>\n",
       "      <td>154</td>\n",
       "      <td>1910-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85884</th>\n",
       "      <td>1266665</td>\n",
       "      <td>89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2420.0</td>\n",
       "      <td>339</td>\n",
       "      <td>249</td>\n",
       "      <td>247</td>\n",
       "      <td>100</td>\n",
       "      <td>127</td>\n",
       "      <td>41</td>\n",
       "      <td>120</td>\n",
       "      <td>59</td>\n",
       "      <td>1924-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86861</th>\n",
       "      <td>1524489</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.0</td>\n",
       "      <td>374</td>\n",
       "      <td>194</td>\n",
       "      <td>254</td>\n",
       "      <td>184</td>\n",
       "      <td>252</td>\n",
       "      <td>166</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1994-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87501</th>\n",
       "      <td>1664073</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1849.0</td>\n",
       "      <td>402</td>\n",
       "      <td>210</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1943-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88521</th>\n",
       "      <td>1148954</td>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>411</td>\n",
       "      <td>230</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1908-05-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88699</th>\n",
       "      <td>1215516</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>430</td>\n",
       "      <td>241</td>\n",
       "      <td>119</td>\n",
       "      <td>27</td>\n",
       "      <td>88</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>1919-06-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89037</th>\n",
       "      <td>1015943</td>\n",
       "      <td>96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2181.0</td>\n",
       "      <td>437</td>\n",
       "      <td>68</td>\n",
       "      <td>1010</td>\n",
       "      <td>1934</td>\n",
       "      <td>570</td>\n",
       "      <td>1108</td>\n",
       "      <td>440</td>\n",
       "      <td>826</td>\n",
       "      <td>1917-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89159</th>\n",
       "      <td>2118190</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>965.0</td>\n",
       "      <td>474</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1990-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89371</th>\n",
       "      <td>1495304</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>451</td>\n",
       "      <td>360</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>1910-09-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89457</th>\n",
       "      <td>1936456</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1509.0</td>\n",
       "      <td>470</td>\n",
       "      <td>360</td>\n",
       "      <td>31</td>\n",
       "      <td>126</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>1940-05-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89871</th>\n",
       "      <td>1547232</td>\n",
       "      <td>113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1252.0</td>\n",
       "      <td>441</td>\n",
       "      <td>309</td>\n",
       "      <td>385</td>\n",
       "      <td>313</td>\n",
       "      <td>169</td>\n",
       "      <td>97</td>\n",
       "      <td>216</td>\n",
       "      <td>216</td>\n",
       "      <td>1900-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90398</th>\n",
       "      <td>1020076</td>\n",
       "      <td>110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>503</td>\n",
       "      <td>167</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1903-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90599</th>\n",
       "      <td>2061175</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2147.0</td>\n",
       "      <td>512</td>\n",
       "      <td>320</td>\n",
       "      <td>99</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>1913-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90835</th>\n",
       "      <td>2002208</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>493</td>\n",
       "      <td>291</td>\n",
       "      <td>357</td>\n",
       "      <td>886</td>\n",
       "      <td>85</td>\n",
       "      <td>601</td>\n",
       "      <td>272</td>\n",
       "      <td>285</td>\n",
       "      <td>1980-02-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91040</th>\n",
       "      <td>1744132</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>515</td>\n",
       "      <td>292</td>\n",
       "      <td>3372</td>\n",
       "      <td>3168</td>\n",
       "      <td>2348</td>\n",
       "      <td>1812</td>\n",
       "      <td>1024</td>\n",
       "      <td>1356</td>\n",
       "      <td>1977-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92580</th>\n",
       "      <td>1413660</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>599</td>\n",
       "      <td>234</td>\n",
       "      <td>130</td>\n",
       "      <td>81</td>\n",
       "      <td>129</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1905-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92722</th>\n",
       "      <td>1226051</td>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2244.0</td>\n",
       "      <td>621</td>\n",
       "      <td>266</td>\n",
       "      <td>257</td>\n",
       "      <td>210</td>\n",
       "      <td>156</td>\n",
       "      <td>150</td>\n",
       "      <td>101</td>\n",
       "      <td>60</td>\n",
       "      <td>1957-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92959</th>\n",
       "      <td>2105175</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>608</td>\n",
       "      <td>403</td>\n",
       "      <td>971</td>\n",
       "      <td>338</td>\n",
       "      <td>971</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>1923-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93002</th>\n",
       "      <td>2034467</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1651.0</td>\n",
       "      <td>634</td>\n",
       "      <td>454</td>\n",
       "      <td>1362</td>\n",
       "      <td>1886</td>\n",
       "      <td>757</td>\n",
       "      <td>722</td>\n",
       "      <td>605</td>\n",
       "      <td>1164</td>\n",
       "      <td>1905-07-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93475</th>\n",
       "      <td>1108752</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>706</td>\n",
       "      <td>178</td>\n",
       "      <td>69</td>\n",
       "      <td>435</td>\n",
       "      <td>2</td>\n",
       "      <td>113</td>\n",
       "      <td>67</td>\n",
       "      <td>322</td>\n",
       "      <td>1905-05-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93735</th>\n",
       "      <td>1294174</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2238.0</td>\n",
       "      <td>689</td>\n",
       "      <td>487</td>\n",
       "      <td>330</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>313</td>\n",
       "      <td>31</td>\n",
       "      <td>1905-07-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94316</th>\n",
       "      <td>2131204</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2363.0</td>\n",
       "      <td>749</td>\n",
       "      <td>564</td>\n",
       "      <td>27</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>1910-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94756</th>\n",
       "      <td>1012669</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2228.0</td>\n",
       "      <td>830</td>\n",
       "      <td>383</td>\n",
       "      <td>413</td>\n",
       "      <td>506</td>\n",
       "      <td>0</td>\n",
       "      <td>243</td>\n",
       "      <td>413</td>\n",
       "      <td>263</td>\n",
       "      <td>1918-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94778</th>\n",
       "      <td>1748557</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>790</td>\n",
       "      <td>435</td>\n",
       "      <td>422</td>\n",
       "      <td>425</td>\n",
       "      <td>410</td>\n",
       "      <td>318</td>\n",
       "      <td>12</td>\n",
       "      <td>107</td>\n",
       "      <td>1945-04-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95317</th>\n",
       "      <td>2173780</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>863</td>\n",
       "      <td>389</td>\n",
       "      <td>49</td>\n",
       "      <td>781</td>\n",
       "      <td>24</td>\n",
       "      <td>327</td>\n",
       "      <td>25</td>\n",
       "      <td>454</td>\n",
       "      <td>1963-09-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95479</th>\n",
       "      <td>1442490</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>967</td>\n",
       "      <td>537</td>\n",
       "      <td>150</td>\n",
       "      <td>1311</td>\n",
       "      <td>76</td>\n",
       "      <td>671</td>\n",
       "      <td>74</td>\n",
       "      <td>640</td>\n",
       "      <td>1905-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97530</th>\n",
       "      <td>2119521</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1609</td>\n",
       "      <td>348</td>\n",
       "      <td>313</td>\n",
       "      <td>834</td>\n",
       "      <td>179</td>\n",
       "      <td>696</td>\n",
       "      <td>134</td>\n",
       "      <td>138</td>\n",
       "      <td>1939-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98216</th>\n",
       "      <td>1966857</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2389.0</td>\n",
       "      <td>2548</td>\n",
       "      <td>1130</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>50</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1911-12-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userid  age gender  tenure  friend_count  friendships_initiated  \\\n",
       "3224   1781137   25    NaN   203.0             1                      1   \n",
       "4123   1110111   31    NaN   227.0             2                      2   \n",
       "5920   2000185   25    NaN   239.0             4                      4   \n",
       "6432   1137054   59    NaN  2076.0             4                      0   \n",
       "6870   1783336   62    NaN  2047.0             5                      0   \n",
       "6886   1280036   67    NaN  2056.0             5                      2   \n",
       "7130   2185523  108    NaN  1517.0             5                      2   \n",
       "7242   1117499   90    NaN  1960.0             5                      4   \n",
       "7555   1098347   27    NaN  1150.0             7                      2   \n",
       "10093  1020617   85    NaN  2222.0             9                      1   \n",
       "10238  1388497   70    NaN  2009.0             9                      4   \n",
       "13801  1714206  108    NaN  2039.0            15                     13   \n",
       "13806  1379012   25    NaN   185.0            15                     12   \n",
       "14741  1483075   76    NaN  2135.0            17                     12   \n",
       "14850  2034827   67    NaN  2416.0            16                      5   \n",
       "15272  2083169   56    NaN  1968.0            16                      6   \n",
       "15425  1326030  105    NaN  2160.0            16                      7   \n",
       "15861  1071932   74    NaN  2213.0            18                      6   \n",
       "16718  1297309   79    NaN  2066.0            19                      5   \n",
       "16960  1425233   63    NaN  1401.0            19                      3   \n",
       "17334  1156871   58    NaN  1822.0            19                     10   \n",
       "17368  1119045   23    NaN   186.0            19                     12   \n",
       "17653  1587920   42    NaN  1059.0            19                     10   \n",
       "18256  2192387   83    NaN  2235.0            21                      8   \n",
       "18764  1021360   77    NaN  2132.0            22                      5   \n",
       "20674  2069184   56    NaN  1494.0            25                      9   \n",
       "20705  2037907   77    NaN  2230.0            25                      4   \n",
       "21547  1227386   72    NaN  2092.0            26                      0   \n",
       "22110  1721035   28    NaN  1864.0            27                     16   \n",
       "22643  1622476   90    NaN  2228.0            28                     24   \n",
       "...        ...  ...    ...     ...           ...                    ...   \n",
       "84817  1414203   55    NaN  2298.0           332                    207   \n",
       "84993  2187174  101    NaN  2337.0           327                    257   \n",
       "85780  1046190  103    NaN  2127.0           357                    101   \n",
       "85884  1266665   89    NaN  2420.0           339                    249   \n",
       "86861  1524489   19    NaN   237.0           374                    194   \n",
       "87501  1664073   70    NaN  1849.0           402                    210   \n",
       "88521  1148954  105    NaN  2025.0           411                    230   \n",
       "88699  1215516   94    NaN  2214.0           430                    241   \n",
       "89037  1015943   96    NaN  2181.0           437                     68   \n",
       "89159  2118190   23    NaN   965.0           474                    335   \n",
       "89371  1495304  103    NaN  2112.0           451                    360   \n",
       "89457  1936456   73    NaN  1509.0           470                    360   \n",
       "89871  1547232  113    NaN  1252.0           441                    309   \n",
       "90398  1020076  110    NaN  2219.0           503                    167   \n",
       "90599  2061175  100    NaN  2147.0           512                    320   \n",
       "90835  2002208   33    NaN  2016.0           493                    291   \n",
       "91040  1744132   36    NaN  2130.0           515                    292   \n",
       "92580  1413660  108    NaN  1988.0           599                    234   \n",
       "92722  1226051   56    NaN  2244.0           621                    266   \n",
       "92959  2105175   90    NaN  2288.0           608                    403   \n",
       "93002  2034467  108    NaN  1651.0           634                    454   \n",
       "93475  1108752  108    NaN  2041.0           706                    178   \n",
       "93735  1294174  108    NaN  2238.0           689                    487   \n",
       "94316  2131204  103    NaN  2363.0           749                    564   \n",
       "94756  1012669   95    NaN  2228.0           830                    383   \n",
       "94778  1748557   68    NaN  1862.0           790                    435   \n",
       "95317  2173780   50    NaN  2129.0           863                    389   \n",
       "95479  1442490  108    NaN  1332.0           967                    537   \n",
       "97530  2119521   74    NaN  1998.0          1609                    348   \n",
       "98216  1966857  102    NaN  2389.0          2548                   1130   \n",
       "\n",
       "       likes  likes_received  mobile_likes  mobile_likes_received  www_likes  \\\n",
       "3224       1               0             1                      0          0   \n",
       "4123       0               0             0                      0          0   \n",
       "5920       0               0             0                      0          0   \n",
       "6432      23               5            23                      4          0   \n",
       "6870       0               1             0                      1          0   \n",
       "6886       0               0             0                      0          0   \n",
       "7130       6              15             0                      4          6   \n",
       "7242      33              12             2                      0         31   \n",
       "7555       0               0             0                      0          0   \n",
       "10093      1               1             0                      0          1   \n",
       "10238      2               1             0                      0          2   \n",
       "13801      0               0             0                      0          0   \n",
       "13806      1               0             0                      0          1   \n",
       "14741      1               2             0                      1          1   \n",
       "14850      2               2             0                      0          2   \n",
       "15272     18               3             1                      2         17   \n",
       "15425     65              22            37                     16         28   \n",
       "15861      1               5             0                      1          1   \n",
       "16718      0               1             0                      0          0   \n",
       "16960      3               1             2                      1          1   \n",
       "17334     17              32            17                     28          0   \n",
       "17368     22               0            22                      0          0   \n",
       "17653    402              45           402                     13          0   \n",
       "18256     35              12            34                      8          1   \n",
       "18764      0               0             0                      0          0   \n",
       "20674      0               1             0                      1          0   \n",
       "20705      0               0             0                      0          0   \n",
       "21547      0               0             0                      0          0   \n",
       "22110     16              47            13                     29          3   \n",
       "22643      0               0             0                      0          0   \n",
       "...      ...             ...           ...                    ...        ...   \n",
       "84817    193             751            26                    452        167   \n",
       "84993    678             341           576                     84        102   \n",
       "85780    137             243            10                     89        127   \n",
       "85884    247             100           127                     41        120   \n",
       "86861    254             184           252                    166          2   \n",
       "87501     32               6            32                      4          0   \n",
       "88521     40               2             0                      2         40   \n",
       "88699    119              27            88                     19         31   \n",
       "89037   1010            1934           570                   1108        440   \n",
       "89159      0               4             0                      2          0   \n",
       "89371     13              16             5                      3          8   \n",
       "89457     31             126            30                     22          1   \n",
       "89871    385             313           169                     97        216   \n",
       "90398     26               4            25                      2          1   \n",
       "90599     99              73             2                     27         97   \n",
       "90835    357             886            85                    601        272   \n",
       "91040   3372            3168          2348                   1812       1024   \n",
       "92580    130              81           129                     62          1   \n",
       "92722    257             210           156                    150        101   \n",
       "92959    971             338           971                    179          0   \n",
       "93002   1362            1886           757                    722        605   \n",
       "93475     69             435             2                    113         67   \n",
       "93735    330              41            17                     10        313   \n",
       "94316     27             144             0                     85         27   \n",
       "94756    413             506             0                    243        413   \n",
       "94778    422             425           410                    318         12   \n",
       "95317     49             781            24                    327         25   \n",
       "95479    150            1311            76                    671         74   \n",
       "97530    313             834           179                    696        134   \n",
       "98216     50              29            50                     22          0   \n",
       "\n",
       "       www_likes_received        dob  \n",
       "3224                    0 1988-01-03  \n",
       "4123                    0 1982-01-12  \n",
       "5920                    0 1988-02-15  \n",
       "6432                    1 1954-02-18  \n",
       "6870                    0 1951-09-05  \n",
       "6886                    0 1946-07-02  \n",
       "7130                   11 1905-06-06  \n",
       "7242                   12 1923-11-02  \n",
       "7555                    0 1986-04-19  \n",
       "10093                   1 1928-08-07  \n",
       "10238                   1 1943-11-18  \n",
       "13801                   0 1905-01-01  \n",
       "13806                   0 1988-07-13  \n",
       "14741                   1 1937-02-18  \n",
       "14850                   2 1946-08-15  \n",
       "15272                   1 1957-05-11  \n",
       "15425                   6 1908-08-01  \n",
       "15861                   4 1939-08-21  \n",
       "16718                   1 1934-04-10  \n",
       "16960                   0 1950-03-05  \n",
       "17334                   4 1955-02-02  \n",
       "17368                   0 1990-12-09  \n",
       "17653                  32 1971-11-22  \n",
       "18256                   4 1930-10-29  \n",
       "18764                   0 1936-09-01  \n",
       "20674                   0 1957-09-07  \n",
       "20705                   0 1936-04-05  \n",
       "21547                   0 1941-09-07  \n",
       "22110                  18 1985-07-17  \n",
       "22643                   0 1923-08-12  \n",
       "...                   ...        ...  \n",
       "84817                 299 1958-12-21  \n",
       "84993                 257 1912-06-13  \n",
       "85780                 154 1910-06-30  \n",
       "85884                  59 1924-03-26  \n",
       "86861                  18 1994-04-28  \n",
       "87501                   2 1943-01-14  \n",
       "88521                   0 1908-05-02  \n",
       "88699                   8 1919-06-12  \n",
       "89037                 826 1917-10-01  \n",
       "89159                   2 1990-03-24  \n",
       "89371                  13 1910-09-09  \n",
       "89457                 104 1940-05-25  \n",
       "89871                 216 1900-11-01  \n",
       "90398                   2 1903-03-02  \n",
       "90599                  46 1913-02-22  \n",
       "90835                 285 1980-02-02  \n",
       "91040                1356 1977-02-17  \n",
       "92580                  19 1905-12-06  \n",
       "92722                  60 1957-08-28  \n",
       "92959                 159 1923-02-19  \n",
       "93002                1164 1905-07-08  \n",
       "93475                 322 1905-05-25  \n",
       "93735                  31 1905-07-28  \n",
       "94316                  59 1910-01-05  \n",
       "94756                 263 1918-12-29  \n",
       "94778                 107 1945-04-27  \n",
       "95317                 454 1963-09-11  \n",
       "95479                 640 1905-07-01  \n",
       "97530                 138 1939-09-26  \n",
       "98216                   7 1911-12-06  \n",
       "\n",
       "[177 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_cleandf[fb_cleandf.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling missed gender field values with \"Not specified\" value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb_cleandf['gender']=fb_cleandf['gender'].fillna('Not specified')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19040528b70>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as plot\n",
    "fb_cleandf['gender'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling missed tenure field values (2 records) with total mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(fb_cleandf['tenure']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb_cleandf['tenure']= fb_cleandf['tenure'].fillna(int(fb_cleandf['tenure'].mean()))\n",
    "#int(fb_cleandf['tenure'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(fb_cleandf['tenure']).sum()  #Varify null count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post profilling for facebook dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb_profile=fb_df.profile_report(title=\"Facebook User Report\")\n",
    "fb_profile.to_file(output_file=\"rawdata\\Facebook_PostdataProfiling_Report.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping data and finding insights from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99003 entries, 0 to 99002\n",
      "Data columns (total 13 columns):\n",
      "userid                   99003 non-null int64\n",
      "age                      99003 non-null int64\n",
      "gender                   99003 non-null object\n",
      "tenure                   99003 non-null float64\n",
      "friend_count             99003 non-null int64\n",
      "friendships_initiated    99003 non-null int64\n",
      "likes                    99003 non-null int64\n",
      "likes_received           99003 non-null int64\n",
      "mobile_likes             99003 non-null int64\n",
      "mobile_likes_received    99003 non-null int64\n",
      "www_likes                99003 non-null int64\n",
      "www_likes_received       99003 non-null int64\n",
      "dob                      99003 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(10), object(1)\n",
      "memory usage: 9.8+ MB\n"
     ]
    }
   ],
   "source": [
    "fb_cleandf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userid                   0\n",
       "age                      0\n",
       "gender                   0\n",
       "tenure                   0\n",
       "friend_count             0\n",
       "friendships_initiated    0\n",
       "likes                    0\n",
       "likes_received           0\n",
       "mobile_likes             0\n",
       "mobile_likes_received    0\n",
       "www_likes                0\n",
       "www_likes_received       0\n",
       "dob                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_cleandf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>tenure</th>\n",
       "      <th>friend_count</th>\n",
       "      <th>friendships_initiated</th>\n",
       "      <th>likes</th>\n",
       "      <th>likes_received</th>\n",
       "      <th>mobile_likes</th>\n",
       "      <th>mobile_likes_received</th>\n",
       "      <th>www_likes</th>\n",
       "      <th>www_likes_received</th>\n",
       "      <th>dob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2094382</th>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>266.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-11-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192601</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083884</th>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203168</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733186</th>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  gender  tenure  friend_count  friendships_initiated  likes  \\\n",
       "userid                                                                     \n",
       "2094382   14    male   266.0             0                      0      0   \n",
       "1192601   14  female     6.0             0                      0      0   \n",
       "2083884   14    male    13.0             0                      0      0   \n",
       "1203168   14  female    93.0             0                      0      0   \n",
       "1733186   14    male    82.0             0                      0      0   \n",
       "\n",
       "         likes_received  mobile_likes  mobile_likes_received  www_likes  \\\n",
       "userid                                                                    \n",
       "2094382               0             0                      0          0   \n",
       "1192601               0             0                      0          0   \n",
       "2083884               0             0                      0          0   \n",
       "1203168               0             0                      0          0   \n",
       "1733186               0             0                      0          0   \n",
       "\n",
       "         www_likes_received        dob  \n",
       "userid                                  \n",
       "2094382                   0 1999-11-19  \n",
       "1192601                   0 1999-11-02  \n",
       "2083884                   0 1999-11-16  \n",
       "1203168                   0 1999-12-25  \n",
       "1733186                   0 1999-12-04  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set userid as index of data frame\n",
    "\n",
    "fb_cleandf.set_index('userid', inplace=True)\n",
    "fb_cleandf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1904104fc50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb_cleandf['age'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Histogram plot we can say \"most of the users are in 10-30 age range.<br/>\n",
    "- we can say young generation are more active in social media."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which age range people having high likes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19047858780>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fb_cleandf.plot(x = 'age', y = 'likes', ax = ax)\n",
    "\n",
    "\n",
    "#fb_cleandf.plot(x = 'age', y = 'likes', ax = ax, secondary_y = True) \n",
    "\n",
    "#fig,ax = plt.subplots()\n",
    "#fb_cleandf.plot(x='age',y='likes_received',ax=ax,secondary_y=True)\n",
    "\n",
    "#fb_cleandf.plot(x='age',y='likes_received')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the plot we can see 20-25 age range people having highest likes their posts on social media\n",
    "- age range from 65 to end having less than total mean likes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19044a86a58>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "fb_cleandf.plot(x='age',y='likes_received',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing likes Vs likes_received in same graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19001fa9c50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fb_cleandf.plot(x = 'age', y = 'likes', ax = ax)\n",
    "fb_cleandf.plot(x = 'age', y = 'likes_received', ax = ax, secondary_y = True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Users likes ration is greater than likes_received counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing mob_likes vs mob_likes_received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1900647dbe0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "fb_cleandf.plot(x='age', y='mobile_likes',ax=ax)\n",
    "fb_cleandf.plot(x='age', y='mobile_likes_received',ax=ax,secondary_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if you observe above plot graph, we can both likes vs likes_received and mobile_likes vs mobile_likes_received are same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'tenure', 'friend_count', 'friendships_initiated',\n",
       "       'likes', 'likes_received', 'mobile_likes', 'mobile_likes_received',\n",
       "       'www_likes', 'www_likes_received', 'dob'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_cleandf.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting graph for www_likes Vs www_likes_received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1900653a4e0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VyeHDB9Hp+hYtg8GA1dr5B3pp6Rmio2PO+VxjBWHRgYjRCUYVQx3FcymO6lmwYBHZ2Vk88MDd2O02Zs+ex7Jl16DT6bjpppU8+OC/YbcrLF68lJkzZ5OQkNjjHlzx9PRi0qQpdHR04OPjA8A999zPH/7wLD/84UrsdjsPPfQIUVHR3HTTCvLycrjjjpsJD48gMTHprD0mJ6dSX19HU1NTlzhdd66//iays09z1123EBwcQnh4BJ6enj2WJLi+/rvvvp2XX/4nbm5uZGVl8sQTT/f7/RvtnHNMzyXEgMf0NL/3OPa6UjymXoNt8g3OgvGJiUH8v5VTnee1ddg4VVDLlKTgc/5lJRAIBL3x/vvvoNfr+MEPbun1nN27d6IoCvPnL6SpqYl/+7c7ePnl151u1nOxY8dWjh79jh/96D/7db4Y0zPKccTmFHvXmFx3i27/yXJe2ZDJA9eOZ86EgccpBILRwPke1dPWZmH16nt6PHbffasvyVq9G2+8mSee+AnLl1/fYy0dQHx8As8883NeeulvgPpa+ytydrudzz77hF/84plznzyGGNMWXdNbj6I0VeM+8Urs01c6Lbposx+/vLczC/b9rdls2FtIgJ8Hv3lgDl4eY/rvA4FAMIoYCxbd2E5GUXpLRulq4VXWtuLlYaCuqZ0v9hScr90JBAKBYBgY20LXz2SUirpWUqIDmDshnK/3F1JRO/A+cgKBQCC4MIxpoVN6aAFm0Ou6CJ2iKFTWtRIa4M3NS5IwGPS8szm7+60EAoFAcJEypoUO+9nTC7w8DFhtdqw29VhTawetbTbMgd4E+nty3bx4jmRXcSy3+oJsWSAQCAQDY4wL3dmuS0eiicOqq6hrBSA0wBuAy2fEEBbozdubTjvFUCAQCAQXL2Nb6JSzXZdenmqLHccEg8paVejMgarQubvpue3yFMpqWtiUUYxAIBAILm7GttD10ALMy0MVOkfmpcOiM5s6a14mJ4UwOSmYT3flUd/Udp42KxAIBILB0O+CMG2G0G7gGlmW8yVJegB4BLWTaQawWpbldkmSpgLrASOwHXhQlmWrJEmxwBtAKCADd8iy3CRJUgDwJpAIVAIrZVkuG76X2DNqsbiife8idO4Ooeu06AL9PfFw79qk9bbvpfDE+n18sC2He5cPb6GsQCAQCIaPfll0kiTNRi0kTNUepwJrgXnAZO0+P9JOfwN4WJblVNS24fdr6y8CL8qyPA5VGJ/U1n+FOjk2DXgJOPdkx+HAtaTgHDE6sxafcyUsyIcrZ8aw61gZuWcaRnavAoFAIBg0/XVd3o8qZI7W223AQ7IsN8iyrADHgFhJkuIAb1mW92rnvQqskCTJHVgEfOC6rn2/HNWiA3gbWKad3yuSJAVIkhTv+rV169aB9eZyHc3jGqPrwXUZ2oPQAVwzLx6TrwdvfpOFffR0mBEIBIJRRb+ETpbl+2RZ3uHyuECW5W8AJEkyAw8DnwCRQKnLpaVANBACNMiybO22jus12vEGwHyOLa0B8ly/1q1b935/XosT1/6W9p4turYOG/VN7c5ElO54e7qxYmkSeaUN7D424t5WgUAgEAyCISWjSJIUBWwGXpZleat2P1fTRgfYe1hHW3ec44rO5VhvPA8kuH6tXr16Rd+XdKXLYENXofPsjNFVdist6Ik5E8JJijLywbYcWixiYKtAIBBcbAy6O7Gkjtj9GviLLMt/0JaLgQiX08JR3Z0VgEmSJIMsyzbtHIcbtEQ7r1iSJDfAH+izGluW5Tqg+wz56J7O7RW7a/cTm1NtXV2XjtKC0F4sOgC9Tsftl6fyq9cy+Gx3HrdcljKgbQgEAoFgZBmURSdJkj+wEXjCReSQZbkAsEiS5BhH/ENggyzLHcAOwDGE6S5gg/b9l9pjtOM7tPNHFtdGzi4xOjeD3tkGzFla0IdFB5AQYWThlAg2ZRRTWt08ItsVCAQCweAYrOvyPiAMeFSSpCPa1y+1Y3cAf5IkKRPwA/6irT8EPCBJ0knUcRCO8cFPAnMkSTqhnePI3hxZ7D27LnWoVp2lTRU6H083/Lz7zI0B4KZFSXi4G3h702lG0egjgUAguOQZkOtSluV47ds/aV89nfMdMKuH9QJgSQ/rNcB1A9nHsGDvubwANKFrt1Lf3HsiSneMvh5cvyCBdzaf5rvsaqamhAznbgUCgeCipIca68uBPwLewLuyLD+hnTcsNdaSJHkALwMzgFbgdlmWM/va45jtjKI4xU3XpWAc1MxLh+uyr0SU7lw2PYqIYB/e3pxFh9V27gsEAoHgEqaHGmtv4B/A9UAaMFOSpGXa6cNVY/0I0Kytr0EtV+uTMSt0Ttelm3tXNyaqRdfSZqW63nLO+JwrbgY9t1+eSmWdhY0HioZztwKBQDCibNiwIbx7fbJmVfVF9xrrWcBpWZbztHKxN1BrqYezxtq5LsvydsCsWYW9Muisy4sVbST8OWmzetIC6N090ekU4mIC+ewP1wNwx/IJg37+JWZ/lsyKG/T1AoFAcCF47rnneqpFfhp4qrdrZFm+D0CSJMdSb7XUQ66xliTJUWPd270Ke9vnqBO62tpmrNZzj8+x1TQCoOjdUTo6KCiq5eHnt3PrZcmcLq7nYFYlAGtvm0ZaXOCA9lBZ18rPXtrHDMnMA9cNXjQFAoFgpHFz0xMY6MvatWtXrFmzJqPb4e5lXOeiv7XUQ6mx7u1evTLqhK6/KA53pcEdbF2rGTw9Ohs4DyRG58Ac4M2y2bF8tjufJdOiSI05l/UvEAgEF5Zly5aVLVu2LH+It+mtlno4a6wd98rpdq9eGcMxOtVS1rm5o9i7djRxFI27GXQE+nsO6vbfnxtHkNGTt77Jwm4X5QaCgWG3KzS0tF/obQgEA2UfIEmSlCxJkgG4HbWWejhrrJ3rkiQtACyyLPfqtoQxbNE5E1AMHj0ko6hvS4jJG72+u/XcPzzdDaxcmszfPznB9qNnWDI1akjbFYw+mi0dVNa1UlVnobKulcp6i/a4lap6Cza7wv3XjmfuhIH1KxcILhSyLFskSbob+BDwQhUlR6LJHcBLWjnCIbrWWL8mSdITqHG227T1J4FXtRrrOu16gBeAddp6G6po9snYFTqtvEDn5t5jHR1AkHFw1pyDmeNC+fZQCR9ty2XmuFB8vc5deC4YPXRYbVTVW6hyCphD0FqprLPQ2tZ7b9TYUD/SJTPTU87V31wguPC41Fgjy/JmYEoP5wxLjbUsyxZg1UD2N3aFzlE75+bRtXicTqEz+ngM6Sl0Oh23X57C068e4OMdedxxReqQ7ie4uLArCvVN7ap4aVaYwyKrrLdQ19jWJWLuZtATYvLCHOCNj6cbBeVNXcQuIcLIjHFm0lPNhAb6nP8XJBCMUsas0DmSUXQGd1CULtMMrDb148l/iEIHEBvmz5JpUWw5VMLiKZFEh/oN+Z6C80eLxaqJmGqFqdaYap1V1Vuw2jp/b3RAgL8nZpMXaXGBmAO8ncJmDvCmobmdg1mVHJQrKK1uQQekRJtIl0JJTzUTbPK6YK9TIBjNjHqhs9sVnn71AB7uesbFBiLFBJAcbcLgsOIMmpi5CJ0jCcDoOzyuxhsXJrL/ZDlvbcpi7W3T0OkGF/cTDD9Wm53qeovTnVilWWeV9er3zd1GL3l7umEO8CLK7MvU5BDMAV6EaEIWbPTC3a0zv0tRFPJKGzkoV3BQrqSirhWdDsbFBvK99Gimp5oJ8Buae1wgEJybUS90oApXU0sH+aWNfLGnAINex5XmM1wNVDfbMEEX92Vjsyp0w2HRAfh5u3PTokT+uTGLg3IlM8aFDst9BedGURTqm9s7Y2QuFlllfSu1Dd3dizqCjaoVlhBhxBzghdmkCllIgNc546x2RSGnpJ6MzEoOZVVQ3dCGQa8jLT6Q78+NY2pKyJBd4gKBYGCMeqHT63XMHBfK1sMlPPvgPEqqmpAL69DllgBwNL+BhV7w3FsH1ce51bRrBef9mVrQXxZPjWLL4TO8++1pJiUF4+luOPdFgn7R2mbtFiNzEbR6Cx3dGggE+HkQEuCNFBOoCpmLizHAz3PAmbY2u52sonrVcsuqpL6pHTeDnokJQdywMJGpKSEiEUkguICMeqEDmD0+jE0ZxZzMr2H+pAgmJgTTHlZM23boUFTBcderf9efzK91XvfO5tMY9Drc3fS8uiGTRVMiuWJmzKBESq/XcccVKTz71mG+2lfI9QsShufFjQGsNjs1DRaX9HtLl7hZU2vXgn9vTwMhJm8ign2ZlBisxci8nO5Fj2H4I8Nqs5NZUEuGXMmhrEqaWjvwcNMzKSmYGVIok5OC8fYcE/+9BIKLnjHxPzExwkiIyYt9p8qZP0krzteSUTpQP/TW/GAi/7HuOyYnBXM0Rx1wXlVv4c8fHHXe56PtuXy0PZdbLkvmihkxA/7LX4oNZFZaKF/uLWD+pHBCTAPvujIaURSFhpaOzoxFlxhZZZ2FmkYLriP+DHodwSYvzCYvZkhmZ4zMYZX5ermNSBy0w2rjRF4tB+UKjmRX0Wyx4uVhYEpyCOmpZiYlBnfpqiMQCC4OxoTQ6XQ6ZqWF8dW+Qhpb2tXYm1Y757DoHMko4+MCOZlfi9Vm5weLE0mMNPHXj451SQN/99ts3v02m/AgH1YsSSI1NqDfrqmVS5M5crqK977N5qEbJw3vC72IsbRbXVyKnUkfVVoiSHtHV/eiydeDkAAvUmJMhJjCu8TKAv0H7l4cLG0dNo7lVHMwq5LvsquwtNvw8XRjWkoI6VIoExICcXcT4iYQXMyMCaEDnJbUQbmSJdOinMknHY63QHvc1mFzpozb7QppcYF8f04sH27L5YU1Cyksb+KtTVmUVDZTVtPCCx8dA9QCXyk2kHGxAaTEBPQa3wsyerF8bhz/2pHHqfwa0uKDRviVnx9sdjs1DW3OGrLutWWNLWf3EzWbvAkN9GZCQpDTGgvRLLMLGcNsbbNyNKeaDLmCY7nVtHfY8fN2Z1ZaGDMkM+PiAnEzjN3ueQLBpcaYEbqYUD8ign3Yf6pcEzrNdemw6DShq6yzOK+xtKtrGbI6ycCmCd8z987GarOzKaOY97ZkA1BY0URhRRPfZBShA6JD/ZBiApBiA5Fiuwrf1bNj2XG0lLc2neape2Zi0F/8H5qKotDY2tFjjKyyrpWahjbsLv5Fg15HkNGTEJM301LMLkkfarzMz9v9oiqzaLZ0cOR0FQflSo7n1WC12TH5ejB/UgQzpFBSY0yXxM9JIBCcTb+F7nyPSx+2V6jhcF9+ujOP2sY2fHpxXVbUtTqvsXSo55ypalYft1mdqeFuBj1Xz45l8dRINuwrYOP+ItqtduLD/UmJDqC4sont351h08FiAKLNvk6LLzUmgFu/l8JfPzrGlkMlXD4jZrhf7qBo67B1sci6C1pbR9cOMkYfd8wB3iRFmZgzwUsTMW/MJi8CjZ4XvTA0tLRz5HQVGZkVnCqoxWZXCDJ6snRaFDPGmUmKMqG/iMRYIBAMjn4JnTYu/SXOHpe+GCgCvpAkaZksyxtQxew+WZb3SpL0MuoE2r/ROS79HUmSnkRt2PlTOselL5ck6Yeo49JvYQSYlRbKJzvzyMisYJHB4brsbtGpQufj6YalTTunj/l23p5u3LQoiaXTovnXjlx2HSulvLaVa+bG8fBNkyiubCKzsA65sJYdR8+wWRO+qBB1QOxbm04zPj6IyJD+DYwdCna7Qk2jpUsT4SqX3osNzV275Xu46zXh8mZcXGCXejKzyfuSTLyoa2rjUFYlB+VKMgtrURR1FNOVM2NIl0JJiPC/qCxNgUAwdPpr0TnGpf9Te+wclw4gSZJjXPpJzh6X/rQkSetRx6Xf4LK+DVXolmvHQB2X/r+SJLlr4xh6RLMCuwx5W7duXfiSJUv6fBERwb7Ehvqx/1Q5i8ar4mV1ui7Vx7WNbfh6uRHo74mlvfemu90J9Pfknu+nceXMGD7YmsP7W3PYfKiYGxcmsnxuHNfOi8dqs5Nf2ohcVEtmYR0lmqX4xPp9RIb4IsUGMC42kNSYAEy+Ay8qVhSFZq1l1dn9Fy1UN6gd8R3odap70RzgzZSk4E4R08TN3+fici8OlpoGCxmy2noru7geBYgI9mH53HgoajNsAAAgAElEQVRmSGZiQv1GxesUCAQ90y+hu0Dj0vsapLcG+IXrwrp16ziX0AHMGh/GB1tzaIm3oFN02Bwj+ZROUTMHeFNU0UR5bWsvd+mdaLMfa1ZM4VRBLe9tyeblL06x8UARK5cmMyEhiORoE8nRJpbPVWuxnn3zEDlnGmhqaWf3sTK2HFIL2SOCfdSWZbEBSDEBmLRWUe0djo74nfExV0FzxBUd+Pu4E2LyJj7Cn5lpoV2SPoL8PUdtUkVFXSsH5QoyMivJK20A1J/N9QsSSB8X6rSoBQLB6GewySjnY1x6XzyPahU6Wb169Qzg/XNcx6xxoXywNYfSqkbC0WN3PL3LTLqQAG/yyxq7WD8DJS0ukCdXzeDAqQo+3JbDH949woSEIFYsSSI2zB9Q43w/XjmFx/9vL6FBPvzktmnklzWy70Q5O4+VsuVwCVsOl/T5PB5uerWOzORFakyAM0ZmDvAm2OQ1poqWS6ubVcsts4LCiiYA4sP9+cHiRGZIoYQFiYkAAsFYZLCfgudjXHqvyLJchzqIz5Xons7tTkiAN0mRRsqrmzGjx6Y4LLpOoauqG7gl1xN6nY7Z48OYnmpmy+ESPtuVx9OvHGBKcgjzJ0WgKAqVda20WKxkF9fzwHNbMeh1/RbYqckhzEwLZVxs4KAnoV/KKIpCcWWzarnJlc6koeQoE7dclkx6qlpMLhAIxjaDFTrnuHQgD3Vc+j9kWS6QJMkiSdJ8WZZ34TIuXZIkx7j0t+h5XPpv6DoufcSYNT6Mlt0WbB467A7XpUtT5/yyxiHdv7eBm96ebjRbrBzJruJIdpXzfF8vN2eX/MVT1VE+Zi0NP8jo5XQv2ux2CsvVXp1yYS1yUa3zPmGB3qqbU5vQEGQcnSNfFEUhv6yRg1rMrbxWnQggxQSw9IpUpqeax6ToCwSC3hmU0J2ncekjxsxxoezZrWBHj01zXdrtPSeeKMrZ1tVAB266u6kDNyNDfJmcFIxer2PXsVJa22zodTqumRdPfLg/z751GG9PN5ZMjepxLwa9noQIIwkRRq6eHYvdrlBU0URmYS1yYR0ZmZVs/04NkYYGOIRPTXC5lIXPrijknmkgI1Mdd1PdYEGv05EWF8BVs2OZnmLGOIjkHYFAMDbQ9fRBfomyANhRW9uMtY9yAAe7XnqWWFs+Lzcu5VHTlzTPWc1/f3m2y/Le5Wm8/MUpQLWadDpdjwM3A7Xi6O5jXcwB3hh9PXqsxyosb+SDrTkcz6shxORFTUMbOh38+v7Zg5ow7RA+1dqrI6uozmkpmgO8kGICneJ3sffZtNsVTher4n0wq4K6pnbcDDrGxweRLpmZlmIe1ukSAsFYxc1NT2CgL8BCYOcF3s6IMGaFLvPtP+Jdl8P6piX8xPQFH+mvZltV33PivD3dmBAf6Gwi7Ej6COo2cHOgnMir4f0t2c4ECk8PA3/7f4sHfT8HdkWhuEJ1dWYW1nYRvhCTl5bRqRaxXwyxLKvNjlxYx0G5gkNZlTS0dODupmdSYjDpkpkpSSH4eI2d5BqB4HwwFoRuzH5qmHzdaK3TO2N0dfWd1pyft7tz9MstlyXz7rdqm69f3D1jUJbWuZiQEERa/Ez2nihj/eenaGu3ce9vv+Xpe2YRHeo36PvqdTpiw/yJDfPnipkx2BWFkspmVfQK6/guu5pdx9QmNMFGry6uzhCT13mpLeuw2jmZX8NBuZLDpytptljxdDcwJTmYdCmUSYlBeHmM2V9TgUAwDIzdTxC7HTs6Z9Zlekog351UD/3x4fm88uUp9pwoP2/dP/Q6HfMmRjA12czDz29HAX7+j/0smBTBDQsThiXGptfpiAn1IybUjytmqMJ3pqrZafEdzalm93FV+IKMnk5X57hYtWxhuISvvcPGsdwaDmZV8F12Fa1tNrw93ZiaHMIMycyEhKBhmRl3KWK12UdtbaNg9CFJ0p3A49rDDbIsPzZcbSAlSfIAXgZmAK3A7bIsZw5mn2NW6GzWDuxKZx1dVW0z6s9FrW8L9FeF5Uxl83ndl4+XG2tWTOb599U5eHtPlrH/VDlXzIxh2ey4YXXd6XU6os1+RJv9+F56NIomfJmFdchFdRzPq2bPCVX4Av09ndaeFBtA6ACFz9LumAhQybGcato6bPh6uZEuhTJDCmV8/NieCNDaZuWNjTKHsqp4+t5ZhF4ErmSBoC8kSfJBTTZMRU0k3KX1QH6e4WkD+QjQLMtymiRJi1Brp+cMZq9jV+hsNmx0dkYpr+4qaI4U9cLyoZUaDIbJSSFMTgomq6iOR2+ZyuZDxXyxp4BtR85w3fx4lkyLGhFR0Ol0RJn9iHIRvtLqFuRCtWXZyfxa9p4oByDAz0NtV6aJnyNRx5UWi5XvsqvIkCs4nldDh9WO0deDuRPDSZfMSDEBY1rcHOSXNfD3T06ofVYV2HW0lBsXJV7obQnGGBs2bAhfs2ZNfLflOq1uuScMqM1AfIFmwB3oYJjaQGrrPweQZXm7JElmSZJiZVkuHOhrG3VCpwVVz4k1xBu7TxD/uPlqCl/4iEdWTMI4/Urn8VuvTuPWq9NGapvn5NcPLXB+P2dqv2rhR4TQUCNT0sIHfX1cTCDXLU0Zxh2NPsxmf2ZO6rmkRCA4Xzz33HM9dZZ6Gniqp/NlWW7ULLNMoAVVuNoZvjaQvbWUFELX36zLgpJampuaWPvLb/hVIPztg8PsfLOVxVMjWXX1OArKGnn61QNdrvnt6jkjkozSG+9vyWbDvkKeuGsGiZFGFEXhuJahWVzZTEKEkZVLk5BiA8/bnlxRFIXy2lb2nyrn4x15PZ4zf2I4V8+OJTLEVzROdqGhpZ1/fHGKoznVTEsJ4d++n4aftzt7TpTx0mcn+ent0y7Yz1UwtnBkXa5du3bFmjVrMrod7s2aQ5KkycA9QBxQjxp/u5LhawPZW0vJATPqhK6/2G02FJ3B6bo0aO9niEmNzfn7XPgarWvmxbP7eBlvfpPFz+5KR6/TMSkxmAnxQew+Xsa/duTy7FuHmZocws1Lks7LqB8HNQ0WDmrjbk4Xqf8XwgK9iQnzx9/bndY2K3JRHbuOl7HreBlGH3dStXl8UmwgkcE+Y1b4ThXU8n+fnaC5tYM7rkjlsulRzvdieqoZTw8Du46XCaETnFeWLVtWtmzZsvwBXHIVsFmW5QoASZJeBR5j+NpAOlpK5nS714AZs0Kn2G0YDAZsivoBo9epfyi4azEjr4tg1pq3pxsrliax/vNT7D5WxoLJ6u+PXq9jweQIZqaFsimjiC/3FvDky/tYNCWS6xckEOA3Mi2wqupaneNucs6oEwGizL5cOz+eGdpEAFfxcvTydMzjyyysIyOzAlD/kHCdwB4Z4ntRDDltau2goKwRN4Nu2IXGZrfzyc58vtidT1iQDz9eMcXZ4NuBp7uBmVIoGZkV3HFFKp5jNPtUcEnwHfA7SZJ8UV2X16K6L28ejjaQkiQ51ndKkrQAsAwmPgdjWehsNgxuXs46On03q/piGSo6Z0I4Ww6X8MG2HKanmrtkXXq6G1g+N55FUyL5bHc+Ww6VsOdEGVfPiuWqWbHDMrmgrKbFOe6mQEvMiQvz56ZFiaRLZiKCe7cidTodoYE+hAb6sGhKpCp89RbkArVzi1xYS4ZcCai1i6rwqcktkeaRFz6HqOWXNWj/NlJVbwFUy/7ZB+cOm9VZXW9h3WcnyC6uZ8GkCFXEevkdmz8pnJ3HSjmcVcmcCYOPjwoEI4ksyxslSZoGHERNQtkP/Bb4F8PTBvIFYJ223oYqmoNi7Aqd3Ybe4Oq67Or6NegvjmxAvU7H7Zen8qvXMvhsdx63XHZ2Yoe/jwe3X57K5enRfLgtl0935bP1cAnXLUhg0ZTIAWU2KopCSVUzB+VKMuQKSrTyiqRIIyuXJjNdMg869V2n0xEa4E1ogDcLp0QCqpWY6WxSXcfBLFX4fL3cnA2qpdgAokP9hiR8Ta0dXQStwEXUuuwRmDsxnJsWJQ6byB2UK3l1wylsdoUHrh1/TvFKiQkg2OjFruNlQugEFzWyLD8LPNtt+TvU4dzdzy0AlvSwXgNc18O6BVg1HPscs0KnU2w0t9mddXQO12WHbVCxzhElIcLIwikRbMooZtGUyF6tqNBAH/79holceaae97fk8MbGLL7JKObmxUlMTw3p9YNbURQKy5vIkNWmyWU1LeiAlGgTt12eQnqqecSaQocEeLMgwNvplq2qa9WsPbWI/ZCL8KVqrs5x5xC+xpb2LoKWX9ZIdUOnqIUGeJMQYWTJtCiq6i0cOFVOs8VKWlwgt1yWfJY7cbC0d9h4d0s2Ww6VEB/uz4PXT+hXMpPaPCCcz/fkU9vYJqYxCARDZEwKXVuHDZ2i0NxuB3TYFJ3TdXksp5rlc+Mv6P564qZFSRzIrOTtTaf58copfVobSZEmfnr7NL7Lrub9rdn877+OkRxlYuXSZJKjTYDaBzPvTIPTcquqVycCSLEBXDEjmumpZudU8/NJiDb9fP4kVfiq6y3IRWp8L6uwjsOn1bFEPp6q8EWZffF0N2DXxLqgrIHqhjbn/UIDvEmMNHLZ9Cjiwv2JC/fH18ud7OJ63t1ympySBqLMvtx/7QQmJQYNmxV3pqqZv39ynOLKZq6aFcMPFicNyLKeNzGcz3bns/dEGcvmxA3LngSCscqYFLrm1g70Ojt2rf2XHZ3TdZlVXH8ht9YrRl8Prl+QwDubT/NddjVTU0L6PF+n0zE1JYRJSUHsPFrKxzvy+M0bB/H3cScu3J+SymZqG9sw6NWJANfMi2daSgj+PhfXuJtgkxfzTBHMmxhBQ3M7h09XsvFAEaXVLWfN9XMwKy2UhZMjSYjwx8era/ZseU0Lr36ZycGsSkx+Hty9bBwLJkWg1w+PwCmKwo6jpby1KQtPdwNrVkxhclLwgO8TFuRDcpSJXcfLuHp27JjNUBUIhoMxKXRNrR0Y6HRb2tE7ywtAbVc12EbCiqKM2IfSZdOj2HakhLc3ZzEhIRB3t/4lzJgDvJmQoJYkNLZ0cDy3BoCVS5NZNCXiLDG4GKhvbqegrKGL+7G2sdNSCwv0Ji7cH5OvJ20dVlrbbBRWNFFe08L+UxUcy60mJbqzZVmQvyef7ylg6+ES3Ax6bliQwFWzYoc16ajFYuX1rzPZf6qCtLhA7r92/JAyYOdNDOf1r2UKyhuJDzcO2z4FgrHGmBO6E/k1nMqvZSYKik79kLMpemeMDuBIdhVzxg88CeB4bjUvf3mKZ+6dPSKz0twMem6/PJU/vHuEjQeKenWxWm12TubXclCu4PDpKppaO/Bw1zNDMpMSE0B+aSP7Tpbzya482q02rpo5vB/4A+WcohbkQ2pMAHFh/sSHq9MYeuv5WdvYhlykTmfILKzjaE71Wec8dON4JicFD2vCUe6ZBv7+yXFqGtr4weJEls2OG7KVODMtlLc2nWb3sTIhdALBEBhzQvfRthzyShuZE2AnKtQfmrq6LgH2n6wYsNBZbXbe2nSa+qZ2hskL1iMTEoKYnmrms935zJ0Q7kwSae+wcSKvhgy5kiPZVbS2WfHyMDA1OYR0yczExOAuNVnXzIvjo225fLwjjy2HSrhhYQILJkeMeLZpfVNbF0HryfU4QzKTFGVyitpAyiQC/T2ZMz6cWWlh7D1RxitfZmKzdy0deeHDY3h6GEiJNqkWX0wAceH+g+q7aVcUvt5fyEfbcgnw8+C/7pjujIMOFV8vd6amhLD3ZDkrL0sWfUEFgkEyJKEbyRENQ9lXXzQ0q3Pm9CiEhfhDLtjQd6mjO5ZbTbOlAzeDDqutf4Nptx05Q1lNCwF+HiPuCrzlsmSOvlTNm99kMWdCOAflCr7LqaatXZ0IMD01hHQplAnxQb0OhI0I9uVHN00iu7ie97Zk89pXMhsPFLFiSTJTkoOHxf1a5yJqjnq1uqb2c153JLuK2sY2ahraqG9uJynSRJDRs997OpVfw7tbsiksbyIuzJ+VlyWTFqcWf9c3tztLGeTCOj7YqjZd8HRXhU/SOrfE90P46pvbefnzkxzPqyFdMnP3snH4DvPPfv7EcDIyVVfstBTzsN5bIBgrDFrozsOIhhHBkWaux46Hh/qhZFd0GFxclza7wiG5kiB/LyrqWnu8jystFiuf7FR7PUaNcBuu1jYrOSX1WG12Dp+u4vDpKvx93JkzPox0ycy42IGNu0mONvH4ndM5fLqK97fm8JcPj5IaE8DKpckkRvbfXVbb2Na1+Lq8kXpN1HRAeLAP4+ICiQ/zp81q5+t9hbR12LhxUSJXz4pFr9dR29hG7pkGcs7Uk1tSz7YjJXyTUQSo0xKSIk0kRhlJilStve4z60oqm3h/aw5Hc6oJNnpy/7XjmT0+rEsZgsnXg1lpYcxKCwOgobmdrCK1lEEuquPDbbkAeLjrSYkyOTu3JEQYu7yvJ/JqeOnzk7S2WbnrKonFUyNHJDY7ISEIo487u4+VCaETCAbJUCy6ER3RIMtyxxD21iNt7bbOzevs6A3qy3e16Iw+7nh6GNh/qpxAf89+Cd0Xe/OdE8kjQwY/Ebw3mlo7OHJaHXdzMr8Gq03B29ON1ja1EfgffjR/SG4tnU7H9FQzk5OC2fHdGT7ZmcevXs9g5rhQfrA48azar9rGtrOKr+ubu4ra+LhA4sKNxIf7ExPqh7enG1abnU935fPFnnzMAd48et1UEiI6xTTQ35N0yUy6pH6gW212iiubyClxiF+Ds6DcoFeHyCZFmgg2eXEst5rMwlq8PNS2aZenR/crWcfo68GMcaHMGBcKqM2Ws7R5fHJhLR9t14TPTU9ytInkKBOnCmo5XVxPZIgvj906lWjz8P/MHbgZ9MweH863h4ppau0YkdivQDDaGbTQnYcRDb0279TcnQGua+vWrQtfsmRJn3uua+5McDCggE4VBxt6Z4yuoaWD5VMi+XJvAanRAT3ex5Wq+la+OVBMSrRJ+/AbnukGDc3tHDqtNk3OLKjFZlcINnpy2fRoZkihJEYZycis4O+fnGDnsVKWTB36mBc3g56l06OZMyGcr/cX8tX+Qg5kVhBi8mJSUjDV9ZauoqZTXaDj44OI12rUYsP8esxYrahr5aVPT5BzpoEFkyK4/YqUc2a2uhn0xIcbiQ838r30aOf74rD6TubXsvlQcZdrosy+2O0K2SUNJET4Dzh71ujTVfgaW9rJKqpHLqxl1/FSTubXOs/19jRwKKuS5tYOEiNNvbqJh8r8SeF8k1HE/lPlXDb9wo1sEgguVYbiuhzpEQ19sQb4hevCunXrOJfQ7TzaqcF6nYLFqm7Jrui6ZF3OHh/GF3sKkIt6nVDh5KNtueh0ak/K08X1RA3BoqttbONQlto0WS6qQ1EgNNCbK2fFMEMKJT7cv4t7bOa4UL49VMJH23KZOS50SPEhRVFc3I+NFJQ30t6hvidV9Ra2HCoBYMa4UFKitUSRUP9+ZWvuOVHGP7+W0el0PHj9BKfbcDAYfT2YlBREfXMbNZobOizIh2nJITS2tpNT0uB0P+p0EG32IynKRFKkkcRII+FBA5ua4O/jQbpkxq4o7Dqu/v7MGR+Gv48HcmEtn+zIQwHc3fQkRRqdnVsSI439Lv84FzGhfkSbfdl9vEwInUAwCIbiuhzpEQ198Tyq+9PJ6tWrZwA9DQ508sWeAu07BQN2ztSpFp69WzKKl7uBqBBfSqqae7hLJ3mlDew9Wc7yuXFON+JALbqq+lYOyqrlll1Sr93Dl2vmxpMumYkJ9ev1g1mn03H75Sk8/eoBPt6Rxx1XpPbrOR2ilu/ieiwoa6ChpUO7L0QG+zJ/YjhxWixs38lyThXUklNSz6TEIJIiTedMn29ts/LGRpk9J8pJiTZx/7XjCTENrk+mY9/Hcqt5b0sOZ6qaSY4y8aObJpEc1TXLsam1g9wzDeSeqSfnTAP7Tpaz9bAq1L5ebiRGmkjSYn0JEcZeSxVA7aLz9qbTbP/uDImRRlZfNwGzS6/PZksHWVpii1xYx6c78/gE1RpVhU9NbkmKNJ4VU+wvOp2OeRMjeG9LNqXVzX020hYIBGczFKEb0RENfT2xNtq9u7nV55+6VS6xNoeoOevoXFyXAM+9c5gJCcF9Cp2iKLy7+TRGH3e+PyeON7/J6nfGZXlti9p6K7OC/DJ1IkBMqB83LkwgXQod0Fy52DB/lkyLYsuhEhZPiSQ6tKtFqSgKNQ1a9mN5Z61ao6uohfgyKTGYuHB/4sONxIT6nWWpLZoSiVxYy3tbcnjly0xnhmZvbbOyS+r5v09PUNPQxg0LE1g+N25IpQsFZY28tyWbUwW1hAZ689ANE0mXzD0+t5+3O5OTgp0dSeyKQml1Czkl9U7xO76jGgXVfRAZ4ktipNFp+UVoI4OKK5v4+ycnKK1q5vtz4rhhYcJZsVBfL3empZidiSItlg7V1am1Lftsdz6f7srHzaAjMdKEFBPAuNgAkqJMAxK+ORPCeH9rNruPl/GDxUmDfh8FgrHIUGJ0Iz2iYVjZfrQz5NcpdJ0twFxdl5V1FnZ81/d8v8Onq8gqrueHV0l4e7pRUtXcZ8alOhFAHXdTXNkEQEKEPyuWJDFdMhM2hMnlNy5MZP/Jct7alMU9y9POamjsSJTR63REhvgwOSmY+HAjcVqiSH9nnkmxgTxxVzoZciUfbs3h+fe/Iy0ukBVLk5wFzXa7whd78vlkZz5BRs8h15VV11v4aHsOe06U4+ftzu2Xp7BkWtSAkm/0Oh1RIb5EhfiySJua0GKxklfWoIlfA4eyKtmhuba9PQ20tnUmLg3E3eqj1b45WrS1WKycLtYsvqJaPt+Tz2e7wc2gIyHC6MzqTI4y9flzCPDzZGJCMHtOlHHjosSLYnafQHCpoFOU/tWJXQIsAHbU1jZjtXYN8Vltdta+uNuZROFJB78Lepui6Kv5/dFQHvH/Cht6/rfxSgCizb5U1LbS3u0+v109h9BAH6w2O0+u34der+OX985Cp9Px0B+3sXhKFLddro7RURSFooom56DS0uoWQE3nn5FqZrpkHrIbr7rBQn6pGk/rdMuqqKLm60wScWQ/DtZ91h2rzc7WwyV8ukvNOJ0zPozFUyP51/ZcsorrmTM+jDuvlPp0C/ZFi8XKF3vz+eaAmmxyxcxols+JH/T9zoWiKJTXtnIsp5q3N58+63hYkA/JkUYSNasvyuw7KAu1ta1T+DIL6ygoa8SuKBj0OhIijZrFF6gKXzeret/JctZ9eoK1t04lLT5o0K9VIHDFzU1PYKAvwEJg5wXezogwJjqjfJddTX1zO75ebjRbrE7rrbVDS0ZBh97FdVlS1cz/u2Uqf3jnSI/323K4hPLaVv7z5skY9Hoq61pp77ATGeJD7pkGDmrjbirqWtHpQIoJ4LLp6kSAwYxcURSF6nqLM0nEYa25WmpRZl/n7Lif3DaNxCHEhPqDm0HP5TNimDcxgg37CvhiTwF7T5YDcNvlKVwxI2ZQ9+0uoHMnhHHjosQh/VHQH3Q6HU0tHWw8UIhBr+OmxYksmRpFQVkjOWfqySlp4FhuNbuOq70MPN0NJET4kxRlUt2ekSaMvuduiO3t6cbkpBAmJ6kWX2ubleySerWOr7CODXsL+WJPAQa9jvgIf2fnluRoE9NSQvD2NLD7eJkQOoFgAIwJodt2pIRAf08iQ3w5kVfTWUrQqiaQ2NHjgdV5vqJASpQJH083WtqsXe7VYung0515pMUFMjkpGLuisO2I6uZ87SsZUGu80uICWTYnlmkp5n59AHY+t0KVlsafryWJ5Jc10myxOu8dGeLLtJQQzVozEhPqi7ubAbmwlmffOoxcVMc4rRPISKPX47SUHXyyIw+rzd7vWjZQX/dBuZIPtuVQUdtKWlwgK5cmExc+sNlwrW1Wdh4rZeHkiH6XFtjtCl/uLeDjHXkEGT15/M50Z7H8uLhA53vp+NnklKhxvpySer7aV+hsMWYO8CIp0uQUv5hQv3O6WL093ZiUGMykxGDn/nNK6pG1Ivav9rkIX7g/rW02dh0v4+YlSRdkjJJAcCky6oWusq6VE3k1XDs/3tng1xGja2hV4zA2RY9B39VNKRfVsWRaFF/u7XQJdljtfL67gGaLlSnJIbz5TRYHsyqdHUBSo00snBLJlOSQfhX2KopCpVPUGpytslxFLSrEl+mp5rNErSek2EBmpYXy5d4C5k8KH3ErKK+0gf/79AQVta1cMy+O6+YnUFbdwgfbcnh/Sw7fHizmxkWJzJkQ3mdMKbuknve+zSa7RC3CXrNiMpMSB96GzGa387dPjnM8t4aG5vZ+JW3UNbXx0mcnOVVQy6y0UO66alyv7lGdToc5wBtzgLdz8nd7h42C8kZnUXtmYa3TsnV30xMf7q+Jn5HESNM5LXpvTzcmJgYzURO+tnZbF4vPwY//uouECCPjYtUJ7CnRAQPqCSoQjCVG/f+M7d+dAZ2aNbjrmOp2crT7qm/RhK6b6xLUAazdC4CffHm/8/t3Np/Gw03PpMRgjmRX4e6m57/uTO/XnqrrLfxzo0xOSX1XUTP7ki6ZnR1Fos29i1pvrFyazJHTVbz3bTYP3ThpQNf2F7ui8PW+Qj7anovR14Of3D4NKVa1eqJD/VizYgqn8mt4b2sO6z8/xcb9RaxYmsyEhK7utvLaFj7cmkOGXInJ14NVV0tDaiz9zqZsjufWEBrozaaMYq6cGdPnfL2jOdW8/MVJ2tpt3L1sHAsnRwxYXD3cDaREq0IDnaUbDosv50w9mw4W8dV+9Y+rYKOnWt6gZXnGhvn3WWju6WFgQkKQ872ztFt56I/bATWhZeOBIjbsK0Sng/hwf6SYQKfwjVQ8UyC41BjV/xOsNjs7jpYyOTGYIKMXjS2q5eWw6JrbVHHrXkcH6odgakzPnVGSo0xcOTOGSYnBeHoYePrVA/j180PFZrez7rMTFFU0MTstzJksEm32G5bOGkFGL8EYoEwAACAASURBVJbPjeNfO/I4lV8z7LGc2sY21n+uWkAzJDN3XT2uR+s1LT6IJ1cFsv9UOR9ty+UP7x5hQkIQK5YkEWT04tNd6tQEg0HH9QsSuGpWzKBnAAJsPljM5kPFXDUrhoWTI3ly/T6+2lfIiqXJZ51rtdn5cFsOX+8vItrsy4O3TxxQSUdf6HQ6goxeBBm9mKl1V+mw2imsaCRXs/pySho4kFkBqGIVF+bfpbavrwbWXh5uXL8ggU935rH6ugn4eruTW1JPZqHaskwVVVX4YsP8NYsvkNRo01mlL1abnezieo7n1VBa3czdy8ZddIN3BYLhYFQL3ZHTVTQ0t7N4WhRt7TZnFqUjRucYvGrr1tQZ1JZVvfW5NAd4M10yo9fptBqtZhZPiUJRFOy1xRiCek/E+GJPAdnF9dx/7XjmThj4zLv+cPXsWG3K9WmeumfmsI3eOZxVySsbMmm39s8C0ut0zBkfTnpqKFsOFfPRjlyeeuWA8/iiKZHcsDBhSMNJQf2j5K1NWUxNDmHFkmT0eh2zJ4Sx+VAxV86KxeQSIy2vbWHdJyfIL2tk6fQoblmaPKJJO+DommIiKdLEFai/G3VNbeSUaEXt3RpYm/w8SO6jgfXcieF8sjOPPSfKWD43nrT4IOcfNO0dNnLONKgTGgrr2HywhK/3F6EDYsL8CPL3osOq/l8orGhy9n+NNvfemEAguNQZ1UK37UgJQUZPJicGO6cWgNr+C9TYHPRs0fXG9+fE8eXeAjzc9dx1lUR1vYX2DjtRZl+sBYewbHwB39t+j94/5Kxrc0rq+XRnPnPGh42YyAG4uxm49Xsp/PWjY2w5VMLlg8yAdNDWYePdb7PZeriEuDB/Hrhu/IC6cxgMOvx9PTTB7fyDwtfLDY8hWrFqUfdxYsx+PHDdeGe3luvmJ7DvZDkb9hZw6/fUko+9J8p4/WsZvU7Hj26c5GwefSEI8Ou9gXWuZvW5NrCODvVzET8jKdEmdh8v4/tz4roIlIe7gbS4QOdYoobmdj7fk8+mjGIKy5soLG/qso/QQG9uWJjArLQwUZsnGDCSJF2L2o7RF9goy/J/alNs/gh4A+/KsvyEdu4FG+E2aoWuoraFE/m13LAgAb1e5+wEAq4WXWdT5+4xut64eUkSOp1qmXm4GUiLVz9QIoN9sZ9RR/Vgt511XWublf/77ASB/p7ceWX/WnUNlk925tHU2kFsqB8f78hjttabcTAUljey7tMTlFa3cPXsWG5alDigYu1TBbW8tyWbgrJGYsP8uGVpMqGBPvxrRy5f7Stk+3dnuHZePEunRw/YdVvf3M6f3z+Kl4eBR26e3MX1GR7kw7wJ4Ww5XMLSaVF8saeAncdKSY42sfraCQSbvAb0XCNNjw2s/z975x0eR31u/89sb9pV7714bcu994YxNh2CgUAgkF7vJb+Em15vbsJNQi7pIQRCEgjF9GYwGBv3XiXba6v3Lq20vc3vj9kd7UorWW4QjM7z6FlpdrZqd8687/c957h8cruzpqWfHRWtw0ysf//CcVbPyZMNrEMhkfr2ASpquqmo7aG6uZ+QKKLVKFEpBHlNGCDZrOWnn5k/Hug6jvOC1WotBv4MzAfagXetVus64GFgOdAIvG61WtfZbLaNSGT2gUS4XbZEt+1oK4IAS6ZJ1puR9TlAJrVguHUZnUdXXphEZZRD/VCERJGblxXj9Qd5+0Cj3G4CGGipRQugGk4q/3r7NF12D9+8Y9YlD2bdfLBJ1tgBfOOPu7hv/XQsRg0WkwaDVnXWNlVIFHnnQBPPba3CqFfz9dtmDBsmGQ3NXU6e21LF0epuks1aPnPtpJjpy89cO5k1c/PYsLWap9+t4p2DTdy8vHjMlYXPH+R3zx9jwO3jW3fOkpPWo3Hd4kJ2VrTx7b/sQQCuXVTIDUsKL3mK+oXCHwhx8HQH+092sHZ+vjw9GgqJNHc5qW6W1tUOne6UMwnjoSAjgXULpNbtv945gzfquh/dO5f8jHOTboxjHENwE1LF1gRgtVpvA8qAMzabrTa87QlgvdVqPcEHGOF2WRJdIBhix7EWppekygfAfmc00YVblwy2LpXhbaORHMBn/ncLJr0ajy9WX/ezJw7yQ0sdWiV0OYKkR3X29p1sZ2dFG9ctKhxxwOViYvmM7BinFH8gxC+fOiz/rVIqZNKzGKUfs1GDxaQl0agBAZ7bWk1rt4sZpancc/VEzGOsCO0OLy/tqGXb0RZ0GiW3rJCy4eKtg+VnJPD122ZQUdvNhi3V/OWVE7y1r5FbVw4mgsdDSBR57I2T1Lb086Wbpsr2Y9GQDKB75L8/fe0kFk3JGrbf+43GDgfvHmpi9exccobk2HX0unjvSAvbj7XicPtRqxRcs7BQvl4RzuDLSNKTmqjjULi1ORLq2yWDgWh88cYp8pDMOMYRjY0bN2bed999hUM294W9heOhFPBZrdZXgHzgNaCS+FFt2SNsv6gRbiPhsrMAG8uO7oYTtP7z+2Te8QMMRdPpeuuvOCq2U/j1v5/3gwc9TuofvBuAom89jaAcD8gcxzjG8eHBqlWraG5uHrr5xzab7Ufx9rdarY8Ai4AVgAN4BdgKTLTZbHeF97kS+Drw38ADNpttaXh7GfAqcAWwx2az5YW3qwCHzWbTWa1WH2CIkKDVam0GZp/POt1lV9H19jppbB/gxW01KJUKtGolOo2STfsHW4ylqja+aobv/HkP1YE6bjLUsUDr5bqvv0yKWRczuBKN/AwTfQNeHvzKYpQKBSFR5Au/2kogKFKsauc/zdIE5/ZDLUwqTCYUEvnlU4epax/gx/fOHZbUfanR0+/hp/84QJ/DN+w6i0lDv8OHiDQdmZ6kp63HJV+/Zm4eRr0au8OL3enD7vTR7/DR5/TKOXUjISPZwIRcS7hi1A6pHrWjZtj5A0HeOdjEa7vq8fgCLJ6axU1Li2Wh9e6KNh557QRLpmVx77qJw1qwpxv7ePiVSvqdPtavKOHKuXn8c9Npth9t4eefW0Bq4qUV0Q+F0+Pn9d31vHOgiUBQet/uvXoikwuS2Xa0hW3HWrA7fCQlaMlNM3G8phuLUcPnry+n3+WjoraHytoeegekxmNWioHyomSmFKVQlmvhO3/ZQ3G2ma9+bBp2h5eXd9bJkUQg5QeuX1FCR5+bmihHl8hanV6rpCjLHCNqH08x/2gh4nV5//33r7/vvvsODLl6tFDONuAdm83WCWC1Wl8E1gPRQwqRqLYm3p8It/iv8Xxu9O+OrBTjMLG0w+3nVEMvPf3e4cMo4uAwypVzctl0oJGefi9DMX9SBhu2VnOqoY/ywmS67B4CQZG71kzAV9kEbvCjwh8+oG3cK4W3furqSe87yYGkqfvPW6bz8ycOytKKJVOz2FXRht8f4rrFhRRkJrDzeNuwNtih052U5looy7GwfEYOOalGeaLR7Q1gd/rYebw1pkWakWwgPVGP3enlWE03/U4f8RoGOo1ysGVqkogw0RRunxq1TC5IZmpxCtuOtLD1SDP7TrRz5dw8ynIt/G3jSSbmJ3L3VdYYkguFxHAkTi1piXq+e/dsuaV57cICdhxr4bXdddyzbtJFfpfjwx8IsvlgM6/vrsPlCcgzvRPzEzlk6+TxjadAhKklKay4SpKm/O6F49JrEaUTJBEwaFVMLkySyW3oEM2C8gze2tfI4xtPsu3oYGcoK8XAl26cIrdH0xL1lIclCKIo0tHrpiqc3FDdbOe13XXy/yoj2SAL2i/EwHocHy6sW7eubd26dXXncJPXkBJpEoEBYB3wHPAtq9VaCtQCdwCP2Wy2eqvV6rnUEW4j4bIkungYcPnl6bLI4ElIDA+jIMTk0eWnJ8QlOoVCQKtRsu9EO+WFybSE8+ryMhLIHggStIFPlEx3EwwaXtpey9yJ6SyeeumkBGdDQWYCX7ppCg9tOAZAglHNjz81l6ffreKVnXXyfgkGNfesnUhigpYzTXaqmvo4WdfLnkrJzkqvVVKSIxGfVq3kQDgoNi1Rx/oVpXGz4UIhkQG3H7vDS3+4KuwLV4j9Th92h4+mDgeVTp8cXBsNQSB88BVjCLUkx8Kh053y2mJIhCfesmFr7GNhuZSaEG2HlWzWsXyGlNl39YKCS3rSEQqJ7K5s48XtNfT0e5lanILD7ae2tR+AUw19mI0arllYQHlhMq3dLn77/LGY+0hP0rNyZg5TilMoykoYkWTc3oD8OY2QnAB89ZZpTC8Z2UJNEAQykg1kJBtYPFU6yfb4AtS3DcjkV1HTza4hBtbRovZz8W8dx+UJm82212q1/gIp8UANvI00RXkKeB7QIZHVc+Gb3MkHFOH2kSG6fpdP/uIPHUYJokApiBDe3jMQv3X5zLtVAGw/1spdV1llostOMSAekNpFflHFvpMd7DvZQVKClrvXWj9wIe60klQ+sWYCT2w6zcY9DSyfns3nrpvM1363k1D4NN5i1JKepCcnzURRlpk1c/NkL86qpj6qmuzsO9lBRdSAByB7MtqdvmHCb4VCkCu3s8HrD8pkaHf46Hd66XNIf7d1OzndZJf3HRpJFI2OPjePvn5yWLt0ckESmw828dL2Wj53fXnMbfyBIM++W01ehknOqztXRIZfnttaRVOnk4LMBBaWZ8YSdLaZzBQDaqWC/ac6eW1X7Ou4d91EZlnTMJ5lKtcfCLLlUDPPbqmW/38At60q5YrZueclF9BpVOFsvCEG1mFNX02Lnbf2DTewjgTWjsXAehyXH2w222PAY0M2bwamx9n3KDAvzvZ6pHW+odt7gOsvxvP8yBCdw+WTJ//itS5BIkARholqI1hQniFXOD98bB+ZyQaSErTotUocPZK+yScOrj8tnpp11oPW+4VVs3I502Rn74l2vvXwHiwmDQoFfGxpCUqFwCs76/jhY/tZMTObG5cWY9KrEQSB9EQ9CXo1rd0uuSWbk2ZkQl4iLZ1Odh5rZcshieRTLTrKci2U5iZSlmMhO804ZhGyVq2UDZOjEQiG+L9nj6JUCHzj9hl4fEEef/OUbKQdwYzSVHRaJXaHj/YeF7aG3hjNWAR7TrSz50Q7OalGzEYNapVCNvsG5JQAi0mLUXd2GQZI5tYbtlRxqqEPvVZFYWaClKcXRXIWk4a6tgGqW/rRqBRY85OwO7x4fEFmlqXyhRvKz+prGgiG2HG8lZe218ZMEUcwpTjlopFNjIH15HMzsI6Q3/lEUo1jHJcCHwmiE0WRAZef1ETp5Uby6IJRrUuQiO5A2IMwHm5YUsQty0v4xh930drtksNUg/1d4JeqQH/UW7ppfwNTipLfF0nBWPCpqyeyN3xgsjt8MVqqhVMyeWlHLVsON7Onsp0blhSxbEY2O461ygL0BeUZ3DwkGy4QDNHQ7qCqqY8zTXYq63rZHT4ZMGhVlORY5LW+omzzmNPMQfq/Pfn2aU7W9/KpqyfJ1cb9t8/ke3/dK+83pSiZW1aUDPOr9AdCDLgGq8SGjgFe2i6J+jOSDdS39dM9pEX96Osn5d+VCiGmKjSH1xKllqkWnz/I5kNN1LT0y7dxewPUtcWO9AMk6DUsLM9kSlEyJTkWnt58Bo8vyNJpWdy91jrqGlgoJLL3RDsv76iNsaWbVJDE7VeUYTZq+Prvd7KropX1K4Z7e14sDDWwBmngaaiBdSBsYJ1s1oatz6TA2oKzGFiPYxyXCh8JovP6JW+/yMl5pHUZ7YwibQ9RHXXQGgoBab1n2fSsmIX/p57bwo2AqFTj80tv6YNfXswvnzrMQxuOcv/HZ1KUNVzr9X6irUfyeIxG9PRkgkHDXWusrJyZw9Obz/BU+AekAYr1K0vjvgaVUkFxtpnibDNr5oWjh/rc0jpfs52qJjsvbpMqJqVCID/DRGlOImW5FspyLaNmqm3a38h7R1q4ekGBLPzfebyVJzadxqRX84k1E+jsc/P67nq+/+helk3P5oYlg96ZapVCNlgGmFGWitcX5M29DSwsz6CurR+tRslXbp5KUaaZh1+p5HhNN9cuKsSkV2N3eukPt0+77B5qWuwMuPxjNIuTcMfqMuZMTJefkz8Q5C+vnODg6U6uWVjAzcuKR6waRVHk0OkuXtpeQ3O4TQ6QnqjntlWlzChLlW87tTiZ3RVtfGxZiTw09H7gXA2s8zOiY4vMpJh1H3hrfxyXPy6I6C6lz9mFPK+hiNh/RdpocutSGHRGgfCQigifunoSj71xctj9RA5w8yZlyEQ3qSAJc18lKKHNa8AnKpk3KZ2kBC33f3wmDzx5kF8/c4T/umMWeemmYfd5qSGKYtjg+TRqpYKv3DwVlyfAY2+c5GdPHOQXX1gYM3Lv9QfxB2LlAyqlYszGx4IgkJ5kID1pcNDB4fZT3SwR35kmO1ujDIzTEnUxxJeVKrU7D5/p5Nl3q5htTePm5cW4vQGe2GRjd2U71rxEPnd9udwaWzo9m9d21rHlcDO7K9tYOy+fq+blx81nWzs/n417G/jDixWYjRq+dccsOdz1s9dN5pt/3k1j+wD/uX7YEgNeX5CNe+tjhnjOhp3H28hKMZJo0uL2Bvjd88c41dDH7atKWTMvP+5tRFGksq6HF96riakOtRol1y8uZPXsvGGV0aKpWRyt7uZkfe85OdhcbJzVwLqlf5iBdUnUkEtBZsI5Vf3jGMdYcN5E9z74nF009Iftv+SKLmzqPLUkja2nPXJlF6n0Fk3JjEt0LZ1OMpIMTMwfdO24aVkxWZU76ahJQETAjzSMcu+6oER2t8/k508e4ldPH+Zbd846JzPkC4XT4+fvG09xwNbJpIIkPnPtZJkcXt1VS2efh5/8/QAPfH4BDk+A57dWs/9UB2ajhruvsrJwSibvHW7m5Z11/PDRfayclcMNS4rOWWdl0quZXprK9FLJ6DoQDFHfPkBVk0R8lbXd7K6UJvwMWhUatULW/n1y7UQa2gf488uVdPa5uXFJEdcuKoypWswGDXdcOYEr5uTywns1vBLWkt2wpIil07Nj1q2qmgeHWj55lTUmwdykV3PNwgKe21qNraFXbpU63H5+9/wxzkQNxERQkm3m6oUFTMxPYsDl42dPHEKpEPj2nbM43dTHi9tqefCZI+SmGWnqdKIQBD4zikvL6cY+XthWw+nGWPnSsunZ3LSseMTBnhmlKRi0KnZVtH6gRBcP8Qysmzud4QlPifwODTGwjpY3pCXqx6u+cVwQLqSiu9Q+ZyMirNuIWfh6+OGHM1esWBF3/0hFJwyp6LyBcExPmOgi20f6Th2r6WZ6WSoP/OuQvO2PLx7nK6oztAYSyVb24g8Po3zx1+9RlJXAsunZLJ+RzUvba/nuI3v54o1TyE83odVIQnatWnlJvsS2hl4eee0EdocknL5qfn7MYMj/u3UG3/7LHhxuP195SDKU0agVXLeokLXzB6uhNfPyWTAlk5e31/LuoSb2VLZxw5IiVszMOe/BB5Vy8Kz/qnC7s6PPTVWTnQOnOjgaNRzyH78ZNLu5ck4ey2fmjNiay0gy8MUbp7Cmxc6Gd6v456bTbDrQxC3LS5g1IZVtR1v4x1s20hP1dPS52VXRxswJsQkGq2fn8vaBRv73X4e5YUkRL++oHfY4uWlGPnPt5BivyD6Hl189fYRQSOS/Pj6T1EQ9qYl65k7MYMPWKt45IA0rqdXSax+K+rYBXthWw/GaWD2sNS+Rj68uO6svpVoldRJ2VbbxCW/g3zptXKVUUBDOYYwxsA6v9dW09LOzoo13w0NOCQZ1zJBLxMB6HOMYKy7k03Kpfc5Gw31ILVMZDz/8MCMTXThwVV6jkwgtM9UENe5hrcuv/X5n3Ps5XtPNgNNHVdSZvdPhIiWpn4PBAgpVXfjEwbe0tnWA2lZbzH386aWKmL8FQBMmPZ1aiU6jkklw8EclObxo4+0j/a7XKNFqlKiVCl7bXcfru+pJS9Lznbtmx11bSzZr5Ty9CH7+uYVxJ+XMBg13XSWt3z21+Qz/eucMWw43c/sVZUwNywsuBIIgkJFkINGo5Z2DTWjVSu69eiJ/HrKmGDHRTk/UU5o7OOQSaXdGUJJt4Zt3zuJoVTcbtlbxhxePy9dNLU7hizeW8+beBl7ZWUd92wAFmQn09HuorO2horZHnugcSnLfvGMmE/ISh52YOD1+fv3MEQZcfu7/+MyYoZj2Hhf7T3UgCDAxP4nqFjvf+6u0nnj94kKcngAvba/hgC1WsJ9q0XHryvj6xJGwaEoWW4+0cNDWKa9pflhgNmiYUZrKjHDVLxtYh/P6alr6OVIlmVcLAuSkmigNO7mU5JjJSDaMxwyNY0RcCNGpkKqxFQz6nLkhZq1eQAogU4xxOzCmvJyHkKo/GZ///OfnABvi7Ty8ogtrxxKktangkNZleWGy3EqLRk+/d5iGK0NpRyGIzFkwE/PJM6gUOuYVpPPpayaz+WATz26RtHfZqUamFCXLVmTXLy5Eq1bi8QXx+IJ4/QH5d48vKI3J+8Lb/EE5IPNc4PYG+NsbJ2OIUatWcqy6OybdIII/v1zBLStKwqSqkslXrVIgCFJL6Ru3z+BIVRfPvFvF/z17lKnFKdx+RekFt2RDoshfXztBQ9sAV87N46nNZ1ApBW5bVcaqWTkEQyL1bQOcabJzpqmP41GCZqNOmu4sy7VQmmOhKMuMRq1kRlkq5UXJfOHBrbLrh1Ih0OfwyRXSjx/fT3aqUdZEDkV6kp7vfGL2iAJprz/Ib547Rmu3i/tunU5x9uBJxZmmPn6z4RgatYIff2oeuWkm7A4vr4TXE7ccHuYriFaj5NqFBayZm3dWucFQlOSYSU/Ss6ui9UNHdEMRMbDOSzexYkYOIJ1QRFd9e092sPWI5BZl1KkoyjbLmX3FWRYMuvGqbxwSLuSTcKl9zkZE2E17qAfbiJXggMuHRqWQtUcReYEoSAeSiLwg0rr81DUT4xIdwDsHY/PAcpRS2kHehIm4j/sICWp8/hBqlYK18/NZPiObV3fV8fb+Rnr6PcwsS+VYdTcHbJ18846ZY86JC4kiPn+YFGVCHCTHrYebsYXXdVLMOqaVpMRc7/IEqKztGfUxzjTZ+fkTh4ZtVwjCsCrTbNTQ0evmeE13TLstxazlmkWFcrWpC1ei2nAlqgtXnkPPvp9/r5qDpzvRapS8vb+RjGQDX1s/XSYklVKQ1mxyLKydny/bWEnTnZK0IaKHUyoEuTUW0fhdMTuXPZVtHKnqkiuDCEYiOZBamSORXCAY4k8vVVDdZOeLN06RLbYAjlZ18ceXKkhO0PL122bIAz+hUUY2F5RncOvK0vNOXBcEgUVTMnlpey1ddneMDORygFGnZmpxitxFCIkibd2usLRBGnZ5eUctItKZdFaqkeJsM6U5UtszO3Xsus5xXF64EKK71D5nFw0DLj8JBjVddknrppTlBbFrdJEhlY5ed5x7iY9sVS8+Ucl3Hz/J/xiDdLtCHOnuwh1eJ9FrVdy6spTl07N55t0qOTuspcvJg88c4b8+PnNM+XQKQQgTRey/zOUJ8MTbkv1VWa6Fz143edgBrrXbyYYt1QAkJWi5eVkxC8szCYZCuL1BfvjYPuxRAuTVs3MpyEwYRqbeyN9hws1NM9Fpd8dUm939Xv7xZmy7Nh40aoVMfNHvd+S+JuRZOGDrpKK2R65EI/tHyFKnkaq2BeUZqJQKHG6/NODS3Mexqm6Z5EDK6BsLJhcm8bnry0nQq/nV00d4ZWcdi6dmDVvzikQFHavu5u6rrMyJir7ZebyVv71xirx0E1+7dTpmo4YBl4+NexrYfKiJUEgkK8XAgMsfU1mfabRzoq6HBZMzz1sisKhcIrrdFW1ct7jovO7jwwKFIJCdaiQ71cjSsKON2xugtrVfJr8jZ7rYcUxaNYkYWBdnW+S257iB9UcD501074PP2UXDroo2ks2DZ8mRNbq9p6QKICQqYrbHm64bCdnKXlqDiSycmAqN4Au/pV95aBu5aSZKw4Lp0hwLX/3YVCrrenh6cxUtXU4a2h1844+7ePDLi89reKCq2c5fXqmkp9/LjUuLuHZh7DSi3enjlR21vHekBY1awceWF3PlnDxZKqBQKFGrpHTun/79AIunZVHdbGd3ZRsrZ+WMqR3Z0efmd88di9F5AXx8dRmlORY83kFijFeJHq0eHhqaaNKw/1QHHm9wzJo1lVJArVLg9p57izcaWrWSI2e6mFqcwvqVJfzk8QNs3FvPzctK5H1EUeSpd86wp7Kdm5cVs2JmjnzdW/saeObdKiYVJPGVmyVj8Ze217BpfyNef5DSHAteX5CGDgcpZh2fWDOBuRPTOVHXy3Nbq/nrayd5c28Dt6woYWrxyH6VIyE1UY81L5FdFW1cu6jwIzetqNeqmFyYzOQhBtYRTV91i503dtfLa9OygXV40GXcwPryxGWXR9fb6yQQpQPz+YN84cH3Yna8Wn+Yq/QV/GfPXQCUqxv5XMIWfmW/msZgKrOtaRy0jR5qKUHkfxKf5bgvD9eka7mi9iGedc5np9c6YtyPRq2gNMdCS5czJj7nP2+ZRqpFh0IhoFQI4UtF1O+DlwBv7KnnlR11JJu1fO76ckpzBif5vP4gm/Y18MbeBgKBEMtnZHP94qJRjXgf33iSncfb+OrHpvHo6yfQaZR89+45owaunqzv5Y/hQY8v3DiFyQVJ/PCxfTR1SqQ3vSSFW1eNvH7X2OHgh4/tAyAjSc/Xbp0eY7gsiiI+fyhMlIEoohwky6ZOBwdtnXKMzaXExPxEtGplzFTo9YsL5TXQ13fX0dPvxahT8YUbprD3RDs7jg/qLbVqJRW13SgVCq5eWMBVc/Ni9IkhUXLmeeG9Gjr63EzIS2T9ihJKcoZPaY6G7cda+Nsbp/jOXbNjPhfjkOD1Balr6x90dGm20x9ex9eoFRRlDkobinMsY/Jq/TAjEtMDLEUqXC47XPZEB/CpB96N+fta/SGuMJzka92SGfYkdTNfTKMP+AAAIABJREFUSNjMr+3rqA+mITB8QiYeEgQ3P03awAvOuVT4c/lB4os84VjMfl/J2W98EaHXSmtegiAMGzJRKqT2TjRZqsKXCoWAUpAunZ6ArN2KHs5YNj0LnUYl3z5yH+8daZHJZfXsXLJSDCiVCry+oOyoEkFGkp4blxaTYFDLt69p6ZdNsjOTDXzppilo1Er5+UQTe/TvXn+QU/V9VNR2U1nbQ3u47Zli1qJRK2VbNo1KgS8QYmJ+IpnJBnloIRolOWauWVjIb587Nuy6Swm1SkFGkkGaoo0zbatWKth3qkP+H2jVSu5YXUZRljlmrVSlVMSt2NzeAF/73Q4WTc3i7qus7+tr+zBCFEW67R6qWuyyo0tDu0M2sE616GTiuxwNrD8KRHfZjyXFI3IFIQKhwQNEMFpewMgk94N75vCTxwdzCbPDgyjNwSRykjQgQka6BUWzwAOfX0BQFAkGRUIhkWBIJCRKl70DXk439mFr6KOpM74JTHG2maJMMxnJekQRgmG/w/p2ySkj1aJj7qR0+b6PV3fLB32QDvzF2ZZhjx352+8PDl4Xvowgejhj29FWNGoFosiw/SIYOqAzFO29bh5+pXLE69t6XPzg0X2j3sfZMNSzMpK/d6qhj1MN8bMjQyFR0tKVpcprp4umZNLY4aCx46Ka8wDSSUdOmpFQSCTVosPjC+B0++m2e2Iq1aEfWa8/yN82nop7f4PrlSo5ZFinUeILhNh6uBmVQsBkUIeHgmL3iR4O0mmUaNTDh4Q+ChAEQdY9RhtYN7Q7ZFH76cY+2SdWrZJ0gFLL0zJuYP0hwGVPdF7/8DUbpSASRKA0x0JVsx2TUfJCVJyljosmOZAGUQBag4mk9A2ABdrtAUKiSCAkYtKrMehUcQ8ec6OGF/yBII+9cUr+IgHUtPRT09KPXqsiJ80oa/dyUo189ZZppIen+Bo7HDy7pYr2XjepFh23rChh7sT081qbCQRD/PCxfQSCIX76mflsPtjMs1uquGJWLutXltLv8vHHFys43djHlXPyuGFJERBLlqGQiMsb4Ed/249Rp+Ibt8+krq2fJ98+IydsRzAh18KVc/Nibh85EThe0x1jlhyNiEu+oJDI6t1Dw8f0R0J+hklOp/AHQjR3OghFkfeuijbp/6ZVyScqQ5/3+SIYEuXHXjM3P64EQBRF/IGQvK7p8Qbosnt4/r3qmGp11axcFAph2ICQ1xeUdaNw9pOQaMiaTnWsRjN62larVjFM4xkzIDQ4aavTKD+0610atVLWakbQ0++hpqVfzuzbfLCZt/ZJcqGkBG1M1VeQYTpnecg4Lh0ue6KLRzIKQoREQbaDMhm04CUmfHUsKNL10xfS4xR1ZApS1RApqr7zlz3y45v0KkwGDSa9mgSDmgS9GpNBE76Utq2dl49Jp2bzodgDk9sbiBGoN3c5+eMLx0k266io7SYQFDHqVNy+qpSVs3IvyB1epVRwx+oJPPjMETbtb+TqBQV09rnZuLcBrz/I0apu+l0+PnfdZBaUjx4me+eVE3jy7dMMuH0sn5FDXnoCP/1H7InCvVdPIiPZgD8Qoqqpj4qwYDtSTSUY1JQXJVNemMyUouRhBtCiKPLi9uHOJWqVYphfZ7Sn57f+vJvcdJM8LALSycY3/7ybtEQ937pzVsyJQnWznf/550EAvnBDOUVZZvaf6uC5rdVne0tHxGNvnKS910V2ipGsVANZyUa0GsklR6OWqitzeLkyPyOBWRPS6Oxz89L2GvZUtrPtaAvXLCrg+sWFcX1IQyGRb/xxJwUZCXz55ql4w1pMd/T07NBJWm8Qrz92WChG0xlFpmOFSqmIJcpoMoxLlEOmayOazvD+EU3nB4GIgXVkwjaS3BEtao+kn0gG5gmyh2dJzriB9QeJy57oNGolt64slYXbIMkLIpICgPoOF1gGW5djxcw0L5Wtkh+iRpC+/H4x9qATEkX6XX5CIug0Slq6nAy4/Djd5+aCD1IFVN/uoKFD+onA6w9yptmOCJTmSnEo57uGUF6UzKwJaby6q46F5ZnccWUZWw43y1XT9z85Z0xJDMtnZPPWvgae31pDY7uDF7bVDNvn23/ZE/O3UiFQlmvhY8uLmVKUQl6GacRWWjAU4tfPHOVkfW/M9tw0I519sUNAv/ziIlIsUtU+4PLR0edm2YzYgFW1Ssm1iwp5YtNpTtQNGiM3dzp4aMNREk0aBlx+3trXyIoZ2TEkd/daKzXN/fLgyVgx1HwgxawlM8VIVoqBNIsetVqBSqHA7QvQ2eumKMvMZ68r56p5+Tz/Xg0btlTzzoEmblxSxKKpmTHVk0IhsLA8k7f2NeLyBDAbNRctG3E0TafXP3xgKJpYPb4gbm+A3gFvzIBRvJZ4PMTTdMaXnsS2ZXVqVVwyjafpHCuikzuunCMZWNsdXmnIJTzlue1Ii2z/ZjFqYnR9hVnnFls1jvPHZU90QAzJgURooSiim1qaBp1nb11GQ0GIUG8LrSFpsV+D5GIWkRdkJBuYXpLC6cY+6tsGcLj9+AJBJuQmsmRaFhPzk0ix6HC6/bKeasDlY8Dlj0sKQEzKdjQCQZGDts64k6LWvERmlKWSlWKMqibVo3ps3raqlGOPdPPsliqyh0xMRqY+zwaVUsHKmTls2FpNffsABq0Kl1d6j8xGzbDg0GklKXzuuslj0hQ2dTqGrenNm5ROVbOdpk4nE/ISqWmxo1AIfOvOWTLJgRSSClAch6yXTsvmjT31vLi9hsmFSXTbPTz4zBFUSgXf+sRs3trbwJbDzfJ9LJmaxaIpmTyzpYr6tgGKsszcsbqM4mwzbT0uSczeZGfvyfZhFWY8dPd76e73jirs73f5yEoxclc4oui592r428ZTvLmvgY8tL2FmVHTPoimZbNzbwJ4T7ayZm3fWxx8rRtJ0ni9EUSQQFGMnayOVpTeaPAOx5BpVfXb3e2Ju7xvD+x2BrOlUj0KUcdZBh++jwqhXM2tCGrMmxBpYR8sbIuvBCkFyfynOGXR0SR83sL4k+EgQ3VAoCMkDKACtvV55+1iRpuiHUIDmgFTRqQXpIB6p6Np7XJzRqvjSjVPQ61Scqu/jZH0PJ+t7ZfG2UadiYn4SkwqTmFSQJA9FGHUqOR37+sWF5Gck8PsXjsc8/r3rJqJWKySSdPkZcPtp6nTEtDkBbI19smNKNFRKgQSDZoRWqgaLUc2+k1IbprwomTtWl/Grp4/wm+eO8b2755x18b2ippsNUVVPhORAcvyfUpxCeVEybk+Apzaf4Vh1Nw88eYjbryiTNVBDUd1s5+l3z1DdPLh2N3tCGu29bvad7KAgM4F7103i3UNNBEMiX7pxKoWZsYRW09KPIEBh1nCTZLVKwbWLCvnHmzZ2Hm/j9d11+PwhPnHVBJ7ZfEY+QIHUwjx0upNfPHWYpAQtn712MvPLM+TqICvFSHqSnokFScwvz6Cm2c6uynbae1yjvm8RRH8GIjDp1fKkKkhrdRnJBtQqBa3dLn7/wnH0WiVfvmkqkwuTyUkzUZCZwK6K1otKdBcbgiCgVgmoVRoSDGfffywIhUSZHIdWmfFkKt6YCjXAgMtHZ1/s7cc6oB4ZEopXZZZkSx2Xli4nzV1O6tsHaOt1yeYGJr1aljWUhqu+f2eD7g8LLnt5gT8Q5PO/itXR3W3cRr6qm5/abwIgQ9HHdxJf4XHHUg77xuYmMVNTyz2m7fyv/Vpagsks0p7mNuMevt97C/1i7Le1vDCJtQsKyE4xkmjS0Ofwcaq+lxNh4uvpH64B+8btM3hq8xmaw5q0zGQD1y8ppLXLxZv7GhCAdQsKWDs/f8T2h88f5ER9L0fOdHHkTKesFYoHs0GNPxgas+B6Yn4iqYn6MFFK64/+YIjalpFbeHddZWXptKxhbVVRlCrSZ7dU0WWXbNJuXVlKRrKBUEjk8Jku3trXEBOxA9Jkak1LP2mJOj62vIQ5E9N5ZnMVbx9o5I7VZayek0dVk52OPhcalRKNWsHvX6ggEAzxo3vnSmthKgVqlQKNSolarSAUEvna73bIJJNq0dFt98St9dUqqWqdY03H7vTS0eems89DZ6+Lzj4P3f2emJacSikQCEp/TylKRqtRYnf6aOpw4Bll3WvlzBxmW9MoybHg8wfD6fZOOeW+tdspu/5EI9Wio9/pwxcIcc+6icyxpo/7P46AkCjiDRNevHas2xtgwOXH7vDR5/RKlw4vdqdvTNX6SMhKMfDfn55PS1ds1RcZPIoYWEeCaktzLBfdwPqjIC+47IkOoKF9gH+9c0bWid1jfI8sVR8/t98ASNXZ9xJf4h+OJRz0FY/pwa7VH2KVrpL7e+8giJJVhpPcoNvPt3pvxy2OLDDVa5VkJhvJTjGQlSqtx7R0OXn+vfjtygiuW1TITcuk59Zld7Nhi5Qdl2zWcuvK0jFNWkbH4VQ32znTbKel0yl5AwqQl27CHwjJX7JkszaGhJdNz2L/qQ6ZDHUa5agH6KGYNSGNRJOGhHiDOeF26ruHmnhtdz1eXxCNWoFWrZRNuYfCpFdz/eJCOTLonQON/OudM1w5J4+blxXzzJYqtsYxTn4/IACLp2VRlmMhPUlPWqKexAQtm/Y18uyWKv7wtWUxZ+ohUWTfiXb+8uqJs9738hnZzJqQRmmORb4Prz9IW7eL+vYBnthkkwl1KBJNGrLC64BZKYOfQ4tR86FpmUmtztAQa7ogHv/wai26mvPKl7Fm6REyGyvUKkVMC3PogI0s94iaYI1ph0YlkRj16rjr6U6Pn9qWQVF7TUu/3BWJGFhLUVdmJuQljjkYOR4+CkT3kTi9y89IwBPVOlMIohzNAxAcYuo8ErRqJV5/ELNBTZaij/aghSDSByw3SQNu8Imjf+Dc3iC1rf3yOs9Y8equOl7dVcfa+flMLkjiU1dPYtWsHJ565wx/frmSzQebuGP1hJgg0aGIxOFkRKV/u8KO8FXN9mHJ2UMHBNQqJd++czZ/ePE47b3uEUluanEKkwqShq2NHjrdGbNWdzb4/CF8/vj/k3mT0lm/opRksxZBEDhypounNp9hZlkq8yal86PH99PR42LtvHyWzcjGHwjR1OngkVdPMHtCGgvKM/AFQvgDIVyeAC3dTk7U9cStrs8HIiCGRNmDMQKnxy+3tiJwefy8uK2Wdw83YTaouXVVKQvLM+l3Sd6dx2u62XZ0UPD+3pEW3osSwM8sS2Xp9Gwm5EoJ3cumZ+Nw+3ljTz1v72+U/49r5ubhdPtp6Xaxq6It5v+n16rC5GeQJkHD06BpFv15+25GEAyFYghlpDaiN44DztAp0EjVdaHDK8lmXcz62lDTcVnIP0R7qFErL0gsLoqi/FocHkk64vYFcHulqjHy4/EFcXkDeLwBRFEkI1lPU6cTfyCE0xOgoqaHihppLXfxlEw+fe3k835OHwV8JIgOiJlSVBIiiIKblxXzwraawYRxYfQvT+Ssb/WcPLKP9lITkMaMv3BDOdMdvXgOCzHTnCANWRyr7h52X3qt8rx8Gd/c28CbexsAydljRlkqeekmDp/p4ieP72fJtCxuXl4yZtsig07NpMIk2dZqUkES1y4qpLVbSoDeUzmo7dt8sGmYOXJZroVUi47dle2UZJv5/PXlpCbq5Urqitm53LG6jAeePERHn5sHPr8QZdiJZcDlk9cXTzf0DZNWjIZ9JzvYd7IDpUKIOegdPtMlr6XNnpBGfqaJbrsHQWAwxVopcLS6m85eNx197jHbh6Un6ZlVlkZ7rytmvQ6ks/zsFCM5aUZy00zkpBkpyx1uv+X0BDDoVAiCgChKgvUNW6oYcPtZNTOXm5YVyQM5FqNGTua+Z91EfH7pJKmyroc39zbK+r7o1wxSS3fN3DyuXlDA6tm5/ODRfbi8ATbtb+SmZcV8Yo1VTnGPtEBbup20djmpqOlh5/HhyR1ajZLkBC3JCVqSEnQkJmgIhYir4xu67nUurT2tOpZotBolJr2GFEvsNl3cCilWqqAfxT3mXBHRNzrdfty+aFIK/+4LE5Q3KP8efZ3HG8DljW8IEPd90CgxhKOyDFoVBp2a6SUpslG8XqtCr5GIeHJB0gW/vguF1Wr9FZBqs9nusVqtM4C/AmZgG/AFm80WsFqt+cATQDpgA+602WyOcDDAk0Ax0AncarPZ2qxWqwZ4FJiDFAF3h81mG+6cMAZ8ZIguGgpChBAwhp3Lg2JswvjZUJauIVnpZKdXCjk36FSIfb5w6Grsl+pYdTdfuKEchSDwx6jQVbc3iEIQKMg0Uds6IG9fOk2y3Drd2Ce7oIyE6ub+mMEMgO3HWtl+rJWCzARuWVFCqlmHxaQZcULO4fbzp5cqOFnfy5q5eXxseTG1rQP0Oby0dbvOaocm5cPZMenV3HWVlWSLjsraHp7YdFrOqhMEgY8tL+GBJw+x5VAza+fnYzFqMBvUnKrvZcexVo7XdKNRKVg8NYuF5Zm8sad+WJwOSMLcqxcUoNMocbj91LcPxJBxNA6e7uTg6eGTqJEhm5Ewe0Ja3Nt19LrZtL+RjGQ9BZkJ1LcNYNKr+e5ds0lLHKx8vP4gj752gtONfXxseawdnMvjx6BT09Th4J+bbJxpslOSbeZrt84YtRoHSSpjzU/Cmp/EzctKCIkiTR0ODp3ujKnGa1r6h4XWRvDithpeDE/1luZYosTpUiU1kkbO6wvKa4KjoSAjgbRE/cjj/kOmFrVR27Rq5QVXj/EQCIbiEpPHG66afIFw9RQmKU8ghswikoixVJEalQJdFBHptSrMRoP8u06rkghMq0SviZCWMry/9HvEcu/DAqvVegXwSeD18KYngM/YbLY9Vqv1UeCzSIb/fwT+aLPZnrZard8Hvg98E/gpsN1ms11jtVrvAn6DlGjzH4DTZrNNslqty5AySBecz3P8SBBdMBRLYEpBRK0eHGOPxPWMNnUpkaNEiIFuyQ2hJSidSem1KkS/d8S25cOvVPKZaybzf19dwtd+N9gCD4kita0D5KaZuHVVCVOKRk7rDgRDtPe4+P4YrbLq2wZ48OkjMdtSLTqSE7RYTFosJg1Od0DO3dOoFdS09vMfv92B1yeRcHGOmRuWFlGYaebhVypxh1uOkwuTMBs1MQTjcPv50d/2xzzeinDLUKlRMCEvkSnFyby+u44l07I4XtPNW3sbaOhwYDaouXFpEStn5lDT0s/PnjgYcz/fuH0GpTkWXt1Vx+u763ny7dMIAkwvSY1LhiCtORrCBxxd+AASSaVYNCVTDm2Nh3gkF0FhVgJJJi0mg1qWjew43oo1LxGTQY1apeSx109S29rPTUtjB5tCIZHmTiftPS5+EDazXlieyZyJabT1uKhr6x8yOh+1nhQzGTi4bawtvHiIDPfMm5QeOz4/JMk+UiWpFAr6HF56B7z0DHjo6HXT2u2ircclV2317QP0DnjIDK//JZq0ZKVK7dCkBO05VVfBUEgmmZjWXpik5ErKE11FDZJUpIoai7ONUiEMI50Us04ingg5RaqsKFKKrrAi/qMfVmzcuDHzvvvuKxyyuS+c/xkXVqs1Gfgf4GfAdKvVWgDobTZbRCT7OPBjq9X6V6Sg7hujtr+HRHTXhK8DeAr4g9VqVYe3/wDAZrNts1qtaVarNd9mszWc62u77IZRxrJjyz9/AIhk3/XfAAQ9TuofvJvk1feQOP+6Yfu7647T+vRPscxZR+KS9TgqttP91iPkf/UvqMwSOXW88lvctcdJWX03xslLPjQL++MYxzjGAbBq1Sqam4cNb/3YZrP9aKTbWK3WDcCfgTxgBfAw8EubzbYkfH0pUlzbcmC/zWbLDW9XAS6bzaaxWq1ewGiz2QLh65qAecAW4BqbzVYV3r4D+C+bzbbrXF/bZVfRxZu6bOly8r2/7pX//s+EDgSVmpeePczGvQ1o8PPLZPjbK8d499nhZ38T1c18MSGAfe+rNO/ehEHw4hXV3PTj7YDAFbNymd7aQoG3h46XHuJ//15FS3BQCzZ0HUmrUXLV3DzaelxyG02vVfKb/1g6pjPCAZeP//3XYbrtHr4ernbiIRAM8ejrJ2M8NMeKSDXkCwRHnHoEyEkz4vEGYkyV18zNw5qfSLfdQ2efh2PVXTGG0xGkJeqwmLTDtH8RCEi2S2mJOnlyMfLz33+PtRO7blEh/S4fe0+0n9Mk6L8zJuQlUppjIcGgDk+paqJ+H130PxSiKPLdR/ZiMqi5a42Vdw81xQy0DEVOqpGVs3LISDaEK6OR15+Grk2dS45gPKhVCikgNctMYoJWbvvpwy0/Q7hKN2hVaNQfnCXY5YLI1OX999+//r777jsw5OrRqrnPAI02m22z1Wq9J7xZQexqhwCE4mwnvD2yTzRGuo0QdZtzwmVHdPEwNLpGIYi4/bAxPNQRGSBRjvD17AtJ7iDvuMspVHVRqm5HL/gpVbVTFcjkaHUXJUEXhOc/DEKs68fQ9tKVc/K4cakkFbh2keTy4fYG+dwvt3Lf+ulMKxm5hQmQYNDwjdtn8MCTh/i/Z4/wXx+fNWx9p88hOWwIMObYoWi4wgc3i0lDWa5lxDDaJJMWp0oRQ3Sb9jeyaX/jWR+js88zzK4rgpWzcjDp1ARCIfoGfNS1DrDjWJscmDkUr+6qO/uLCiMyPXspUZiZgNmoiRlEsuYlYmvsI9UiBa6KIrh9ARyyM44fu9MnD82cbuyTJTHxoFIqZImGTqtCpZQyDFVKAZVSulQqFagU0mVbjwt64B9vnsKoVzMh14LLG4yboNHc5eSJTafjPq5GrYhZX9JpVCQatdK6k9zWG9La08SSlCBIqRVD9YAdvW78gZD82pPN2mFyCKNFIvpxgru4WLduXdu6devqzuEmtwFZVqv1CJAMmJAONdFu5ZlAC9ABWKxWq9JmswXD+0TOtJrD+zWFK70EoBtoCu9XPeS+zhkfCaIbWpEow8MoEchTlyOcLPQGJQG4U9Ty+4E1PJT8TwC+at7EYV8BrwzMRmMcHJkfSnRD8dquOgxaFWvn55ObZuLRb67kN88d41h1Nw9tOEp+uolv3zV7VB+8RJOW+2+fyQNPHuLBZ47w/26bjtMToLJWGjuOHLzMBjULyjPITjXGaPVy0oysX1GKVq3gZH0vJ+p7qW3pjyFlnUaJzx8cNXG9YhS7qgvBlnNIJBgKQZCGIurahg/zzJqQJhPJ2aBVK0kwqGPE2Gvm5knkEnaSQYDfPS+51qQl6ujs87B+ZQlpFj1Pvyvl8i2emsn6FaWYDGo++4stLCjPZFpJKiCtQ7m9wcGqyRdkybQs2ntcMS4oIE19dva55am9QDBE74D3nENnq0dIhRjrCcBPPz1fNsi+EBRlmYf5pgaCIXntTyJBJy3dLrYfbY15bkadStKhJocJMFW6TLHoPpJRQx8EbDbblZHfwxXdCpvNdq/Vaq2wWq2LbTbbTuAuYKPNZvNbrdbtSOT4L+BuYGP45m+E//5Z+Prt4f0j23dYrdYlgOd81ufgI0J0zV2xZ6yKIabOIgIhcWRTZy8aPKKaRIWLhYVq6IfnnXPRCX5W6yuYYmlEHXVb3VmIDiT/TX8wxHWLChEEgfvWT6eqyc7PnjhIQ4eDLz74HvetnyYfEIdCFEV8gSAzy1J552CTHCEkCJCfnsAVs3IpyTWTnmjgZH2PTHLTSlJweQJUNdt5aMNRQJIpJOg15GckxOj7LlYLMNGkwaTX4PT435ckcFEkLskBI5LctYsKCQZDbNzbwPqVJVwxK1cW4YqiyE8eP4DT4+eWFSWolApCokhnr5ufRLVQI9VpxOINpMoO4J9v2egZ8CCK0onO9qMtuH2BEXWC8dDR60anUZKfbhqc7Iua7osMRGjUSoLBEMGQJKwOBAcF1i/vkNIeJhUkhT1WJX/VYEgcc5X7wrYa0pP0Q1qpgyYAFzKQoVIqyE41kp1qBNLk7SFRpG/AG5ZBuGQCPFrVxfZjg048apWCzOQoPWDYlCEjyXBByR7jOCfcCTxitVrNwCHgt+HtXwL+brVavwc0AB8Pb/8+8LjVaq1EapXeGd7+O+Dh8HYvEmmeFy7KMMql0FCcx9MY0RnlNxuOyjoxhSDwTfNLtAYTedyxXN7nwaQn2OKZzGvuWXHv/NuWl2kPWtjnLeazCVvlNHKL4OQ6w2HmagerpRedc9jqlQScOalGmqOCTCOInP1fNS+PtfPy5Ym6fpePXz9zNGbf1bOlg27PgIfDZ7rOKSZlHPERrWNcv7KEVIueP4XlH9csLJDG7n1h7ZM3EFO5nosjTKS1Z9CqcHr89Dmkk6Bl07Ni2nzyuLluyESfRoVSKfDOgSZe3lFLSBS5ZmEB6+bnn1fe2c7jrTz6+km+decsJuRJ8piIiHnAHfZNdflwuP1UNduHreOlmMOBsZ6RRf96rRKTXo1JP+ilmmDQYIpaX0zQa+SIKr02fmbjWOFw+wf1gF1O2nqky2jrNkGAtER9WAxviGmHftRt0cadUcaAS6ihuGioiapSinPMKAZCMc4oIK3TjSYv6A0ZSFQ4yVZKayatQekgYReNPOFcEkN0NxkPUBtIo9+QG5fkYPDs/619jXJ440gYLTwzL90kR9NExsWvmpdHepKBV3fW0ufwIQjwtVunoxQEXt1VR2efh37X2T36THo1X7l5KtmpRgJBad1k+9EWKut6R73dhUKvVVKWm0h1s33YAfWuq6y4PH5ON9qpqOk+76GHaLF+dAUGw+NzIs8pcpuJ+UmyrGFheSYgsjtKanH/7TOkCJYhETD1bQP8+PH9fOXmqbK7/Vixdn4+8yal8/S7Vby0vZZdFW184soJTCkefT13KGZb03hi02l2VbTJRCcIglwRpke1JBdPzeKTaydS09LPc1urONXQhyDAHVdOYO7EdJyeAI4wKQ6Ehf8RE4BIGkefw0tTp4MBl3/Ez5tCkFLQE/SDRBhjMj6kYkwIyzgiMOnVlOUmUpabGHM8Ywz9AAAgAElEQVS/Xn+Q9h5JDN/W7aIl3A6N5DhGYDFp4hJgounDY4s2jtFxQUR3KTUUNpttxFG/cBUY86l++OGHM1esWCH/HTFp9fgGpwYzkg1UNdlRWMSYmB5pfwHlKM4o9pCRbHUf2cpeOoMJ+FAPuV6PiiBGhXTG/v8sG9nnLeY1YRZ28eyW7HnpJtbNz8fjD1IT9reLJ8795Fori6cON0YGKYLml08dZk9lO4kmLX0OH1fMyuW2K0qpbxvg9y8cx+MLMqkgibREfXiSUUdaop7mTmeMoB2kM+UHnjx01ud+seH2BuO6yYDUArxYWDwlEwTJamx/VGBmZrJBjkyKfCKiiTFauxfRIUbjZEMfk+IkMDg90ufQeJ4VRLJZx5dunEJFbTdPbjrNr589ymxrGh+/ooxks+7sdwDoNCpmW9PYf6qdO1aXjckjsTjbzP0fn0llbQ8btlbzyKsneHNvA7esKGFKUfKYyEAURXz+kPS+ugeJ0CET5OC25i4nA66+UTMbI+unJpkMNTIJxlSTBjXlhcnMmzSYLBEMhejq88jrgC3hanB3ZVvM/1mvVUrElzzoS5udYiQ1UfehTU7/qOJCK7qHge8iaSgAsoFo6/pWIBdIBfojOomo7TG3Cbc4+5Ga86NN19wH/DDmiTz8MCtWrOC3zx/nWFVn3LWPSESKZAEW++UMIbBCd5L93mKagsPPkntDBhIEN3mqbpqDww9iGgJ4RDVGfBzyFtAVMrNSV8l0TQNvu6ew1TMZ/yhvd2OHY0yGvn9/08bf37SRkSS5TyiVAkqF9KNSKgZTocN5b7bGXj73y60x9zGSyPqDhEIQ0GuVo7bERsKauXlML02lMDOBjl43f3q5go44coZoFGWZWTs/n6xUIwpBoP1v+0CEH31qHj6/NB6vVAjhSUgvfQ4fVU123tgzvNqLhlajZNO+BlbOzBkWZeTyREx5LywAdUpRCj/59Hze3NfAa7vqqKjp4frFhVw5N29M62MRwfyRqi7mTcoY02MKgsCU4hQmFyWz70Q7L2yr4f+ePcrE/ERuWVFKcfboYbxC2HNSq9GPeZAlFBJxeqJJ0M+AO7ZijJBka5cLh9s/4jqjIEjv+1AjcZNeTV56ApMLkjEZ1ARDoty67bZLZFhZ18POKIMBlVLyjJUrwLAgPiPZMB6k+m+K8ya690FDMRoeQqoKZXz+85+fA2ww6FSsnJkTY9L6j3AV8OWbpvCHFyvCps6xB4TIcMp95o0861zAPl9pzPV9ISMKAVKVDg7ESThQC0H6RekLrBREXnfPZI+3lBsMB7nWcISF2ipeds3mqD8/zksehFGnYlJBEhnJBoLByDBBiK1D1kri6dLioakzfuv0g8aM0lRmW9PYXdnGibpeEgxqmZwB/vsz89ld0cYbe+pZv7KE2RPSeOz1kzHhs0qFQCgkxpUz6LUqbl1Zwmu76mKkDxHUtvbHdZn56kPbcHoCpJi1/OKLi0hK0JKUoKW9xzWsmpw7MZ2MZAOvhaUN1y8uZOGUTL73yF5e3lHLPesmxuwfqeguxpqQWqXgukWFLJicwVPvnGHD1mp2HG/lrjVWJkZ5H/oDIRxuKdF+IHzZ7/KhVAjsPN42ZqKLQCEILCjPZM7EdLYebubVXXX89B8HmG1N4+ZlxWQNCeq9ECgUkczEsfm2ghRNNZQUI23V6CqyrcdFVZNElCONKWhUCkwGNRajlmSzTr5ftzdAczhPThorkCAAKRad3P7MTjWSmSxdmvQXJ919HOeHC/nGXWoNxYgIW9IMFRjlAtgHvKxbJWWZgdSmiBDdH16UWnND5QUwKDFQCyHuNO0iz9PNi665hFCgE3xMVQ8eSFsCsSaqCkKohFDY63Jw6rI7lMBjjhWUqtq42bCfTyW8xxl/Bi+65satCrVqJYkmLT0DXs4027E7zj69+UFganEK96ybiE6jZMOWqmEkHMEVs3O5ZUUJB20d/PW1kzHXHanqiqkso0nuof9YwuHTnbyxp54F5Rn4/SF+/Ph+PN4gC8szuGFJEelJ0v/X5fHzxKbT7IkSxSclaOl3+vj7m/HbnGaDesRsvkhFKQL7T3VQmmPB4wvGGA5EMG9SuvyZ+vJNU5ltldbdVs7KYfPBJq6cm0dO6uCB/0IrushBfOhPYWYCTZ0OWrtd/OKpw4A0vahUCpdscEmlVLB6Th6Lp2bx1r4G3trfyOHTXSydnsX1i4vOGsx7qaBRK0kOpxOMBSFRxOUJxGmlSlVddGs1FBIZbXhPBLrsHrrsHo7XxB7CEgxqWQcYsUfLSjHK6RvjuLQ4b6K71BqK831eSoXAn16q4Dt3zUajjj8dpwinF0QjKCo45c+iVNVOCIFlOhvZyl4edyxniqaRKZrBgZCIx2UEamLTxfVC7NOvCmTyy/5rWKit4hr9Yb5hfo3d3jJed8/EKQ5+Ib3+4IjDK5caJTlmRFEyBI5gzsR0rpqXh98fkg+gADctK8Ji1LC7so29JwcJRiEIsqB7xcwc7rxyAq3dzmEkNxrMBjXfe2SvLPI/VtXNHm87syakcePSInLTTIC05nP4TBd/e+OkTE4LyjMQEGjvdY0qY5hWmkpGkh6LUct7R5qH6cqKs800dTriGiPPmpDGsunZPLThqExy1ywskEkOJJeWbUdb+NNLFdy2qpSOXjdKpZTYoFQIaNQKvL4gA24fTneAPoeXQ6c72XG8FVGUPEJd3sAwQhtNiqDXKrEYNfIJQyAYIhiEG5YUYQ4Pd5h0KulSr8akV53X1Obwx1Vx49JiVs3K5dVddWw93MzuijaunJvHuvn5chLDvysUghB+P9RkJo8t3jxSJcvrjUNaqdGTqzFtV9dwA4CCjAR+eO/cS/HSxhGFiyUvuAeJ6O6xWq3TgUeQ5AWHgHttNps3PKjydyR5QQPwcZvN1hseaHkcKCGsobDZbHXn8TSWANv3V7Twk7/tZ/mMbD65diK7K9t4JLz2deWcPN4+0Mgvkv7FLs8EXnLPkW/8PcuLNARS8KNilqaWV1yzuN5wCGdIS3MwOYbo7uv5BGIUUSYIbn6atIFT/iwmqlvpCCbwP+H08qHQC17W6o+xVHvq/7f33tFxnOfd9jVl+6ISlSQa27AXiaoURVKiCk3Llm3JsmXLcSIndhL7E3Ms2dHrJuck+fxFiV+XJLZsR/brT7bjWLJlNarLEqlCikXsHFIEWECCBAiiL7ZMef+Y2cUuKgmiEIvnOmcPdmdngQeDwfzmvp/7+d3EbJUXokvYFNVSfe3GkgXVBfzZrXNpOBfhxa3H2Xe0BZ9HYeXicm66ooJidy6lvqmTf/zlNuIJC48qZ1TPVZXloMhShkAOxfTiMGuXT+cXGy+s40Y44Bm0QGEoStyO6IZlYxjWoDcVvW3bBuPe9fM4erqD2lNtGZ0ohovPo5AXdqoOQwFPz9dgz+tw2qN3884z5yI89tIh9tWdo6IkzD03a8zqp2XQaNDY2s2Tb9Tyzv4zhPwq66+p5sbLp42IqE5UbNumO2b2pFLTIsaCsI+rF5SN6/gmw/KCrDN1bmnp4jcvH2LjO8dZUFPIvn6cO/614DFej87j6e7LU9sezPsjDUY+z0eX8GDeUzwbWcq+xHTuDb/GFCXzgnjfuc9kvJ4id/DN/D+wJz6dRd56Oiw/X2/9+KCDLZHb+EhwG/O9Jzlj5vJkZDn7E9MYbP5urAgHPKm2KR2R+IBpvonE7Ol5qaId/URrnw7cKxaVcex0x7jNaS6aMYXbrq2mZmrOiFT02bbNdr2J37xymJaOGNctLueO1TPJvYD5rovh2OkOnnj9CHvrzlGY6+P262Zw7cKyCdV+ZrIwGYROeeihh8Z7DCNFJfAX0WiC6tIcnnvnGE2t/RdsrAvs4n2jlMNGz3TiCt8huvGxKTaXSuUsi70neLZ7GVvjM7kxkJnCer57ScbrfDnCdf5D1BuFTFVbUbB4Mbp40MF22X62x2dw3JjCXM8pVvkPUqWcpd4ozEhnjgdxwyISM+iIJIhdgHPHQNy7fh7LtRJK8gOptX5DIUsSU4tDaJUFLJ45ZcjPXbOgjGsXlrHvqHNj4/XI3PvBeWzXmwgHPCyfW8Jbe0/T1Bqlv0DtRGPniAp6UZ7/vDqpV5Xm8OCnL2Pt8goKc0fOvkqSJKYWhVi1dCqmZfP6e6d4471TBH0qlaU5oz4vlB/2cc3CMuZMz6P2VDuv7TzJdr2JghwfZYVBMS91CSHLEoGAF+DnONm2rCMrLQEGWoMFIGE7VZe9IicTOdV49dXoAr6U+yLLfbW8E5vNUaOIarWncCJH6qbD7imR9uDMAyaXD6iShYqJkZaOXLGwjPwcn1MkIEvusgAZ05rJyycXEzy2mVsCu/hq3tNsis3l+e7FdNujN6E/v7qAW6+sZHZFPt0xg0jU8VqMRBNEoo7zxa9e6t/U90L5r2fPf44OHCeY2RX5HDzWwms7z8/z8u19p3k77X4knrD4yVNOyrqzO5GqjLxY1i6fTjxh8sauBuZVFXDgWAu3XlnJx1bPwDBs9tad48lNtYOmRedU5HP9knKOne5k854GfvPyYe67c8mA+18Mfq/Kx9fMYsXCMh578RC/fEFn565DfHyRTGl5CerUeaPyc5PMqy7k61UFbNebeOL1I/zwiT3Mmp7Hnatn9lngLRCMFlkndOc6olwxr4RpRSH21p3jf17LNMZNup+Y/SwvSL73vlHKCaOQNf79bInNIixlOuzfn/csj3as4pjpFCB4JOfOPb3xakCKp8SwsiTMXTfOpqUjxtYDZ9i8+wzN7b1d++fzbnwG6wPvcb3vIMu9tTzXvZS3Y7OxkCnI8Y2oT2RB2McCd7FvstozyZlzER78yTt9PpNebAKwsKaQuOs0P5K8vL1+UDeY8SIn6KGyJIeNW45RPiXIFz68gPt+sJnntx7n+a3nfyPs9yr81zMHkGUJjyrT6haQWLbNiTOdVJSGRyyys4045tljFDe+z5dKjhBNHMITb4ftcNxfRenHv37R6/qGQpIkls8tYensIjbvbuCPm+v4fx/bwdJZRXxs1QymuQVGAsFokXVzdF/99zdoaY/16yoCToXkvxb+mqcil/FKdGFq+305G0mg8J8dNwNwmbeOPwtv4icda/iz8CZ8Uk8aqtUKEpai/E/XVWyJz2ae5yRfyHmFl7sXsNZNc/5T64dptIZXADBVOcdHg+8y23OGU0Y+v49ckZFmFYw91ywozbD5Ol/SrcPSWa4Vs3rZNH75gk57V5xv/fkVbDvYyBOv16LIEgtqCinK83PNgjKK8vxIkoQk0fMV56uc2u5+466z2I21WI1HMBuPYDUfB8v9+b4QSulsrKJZbGoI8of9JoGAnztXz+LaRWXnLa62bWPZNrbtLOq2bBvLwt1mY7nbbdt23gfs1H6Or+YL755gm+tGUz4lyG0rqikI+5zP2nbP/jaZz5Pfc4Cf2fPZnnFafV6T2m67P2+wn5n6XdJ/37Tf3x7sZ/Q6TrZt88Frq1m5eOoFn0ujxWSYo8u6iC4/7GN/3cBejMmorb91dHJaPd978SpuM3dwR8F+fEbmXMtPO9bwoeAO7g6/TWW0mSOGs+g2YfccTr80/PmeU2Yh/95xM4s9x7k9uJ0v5r7Ernglf4xcTrOVM/Q3EIw4wxE5oF+RA9imN7FN71ls/OAjPRG0admp9Purg7Qr8pKgUm2mWm2iSj1LtdpErtyTKWgxgxwxKqk1Sqg1Sjht5mM3pJ/3Ch2RBI8+d4BHn7uw9PJI0tAcSaWZJyqSBF5VwaPKeFQZryrjURW8quI898ip90tGoMWR4MLIOqG7d/18PIo8YPfkZCuevqlLKTXXBo7wvR6dx0eU3g13wSsZ/LjjRj4Y2MmNgX1chzOXFSczdQmOJZTPo9CetiB68cwp5Icdt436ps4BbK8kdieq2N82ndX+/dwc2MOCvHpei87npe5FxLi01ycJRhqbYrmDarUp9ShXWjP8WRuMPPbEKzniCluLJVKCw+X262oozg/0ES6PKuP1yO52V8RUZ95dFNhcumSd0HlUmZlT8wYWOjdq6xPR2TKynCk4b8dm85FQj9CdNvMoU9rIlyNYyDzVfTknzEI+G94EkOpsAD1CF4ubfdwpBiuW6Y2BwsvRRWyNzeS24A5uCuzlSt8Rnoks4934TOxLYDnCWFBZGmbO9HyK8gN4PTK/dF1PAj6VFYvKeHmbM6dXWRJm7fIKdhxquiQ9Pc8XvxSnUjlLtRupVatNKcNwcG7EWr3lnFancihWTGD6XAK5eXgUmfmqzEJZwjCdnoWJhEXCtIgnLBKGScKwiBsWCcN5HTecBq7pDWbBcT8xzIuvuh0rZElKtf3xexX30fe5b5D3/F6FoH9kFtMLLh2yTujA6cQ8EMn0ZG9nFAspVXWZJIaHs2aYIsVp3HogPo2yQBv5ck9F3c54DfNip7jKdySjVY9fHtm1Z+12kF91Xcem6Fw+GtrKp8JvsdLQeSJyBUeNkhH9WZcix890cvxMZ5/t3TEjJXIAxxs7xyQNF/SprFhUTtmUIG/vO837g3RhH4jkonQJm1KljWqliSq1iRr1LKVKK+lLzmK2ip4o50iihCNGKceMokyT8KZmhnDO65cZU3OxLJuAT+3TOzEpciG/mpqfMi0L07SHvWg/iQQZguPzKgTc5ycaOzL8SWdNz+PahWUEBhAmv1fF65FFRCUYkKwUuqqygeexkqnL/kyd5X7+fd+LV6UKTGqNEq62DpMvZxa6NJl9ndtzpN5VlSPDcbOI77ev4zJvHR8Kbufvcp9nW6yGpyKX0WaPnKGuYHAiMYOXtg3eR3AgglIsNadWrTZRpZwl0OvGqMPyU5sooTZRwhGjhJNmYZ/WUiPB+TjaDKejRG9yQ160iny0ynwWz5wy5JpB27Z57/BZnnijlvfr2zBNmztXz8wwrBYIzpesFLpkO57+SC0v6G8dndQ3TZN+cWm3/bRaQQrkzDVSHsnAtCV+2bmSP895A4APBneyJTaT9vPoRXeh2Ehsj89gT7yCtYG9rPHvZ6m/ns6aG7Dm3sy3H9s19DcRjAkyFmVKKzVpBSOlSl9xOWuGqTWcaK02UUKjlcvFuORIgNerYFs28SEa7A6Xojw/VWU5VJbmUFWaw7SiEHlh73m1ChoKSZJYNqeYJbOKeHNvA09uquNffrOThTMKuWPVTCpLRVGW4PzJSqHr6B44bZgTcHLv/TVe7a/DeI7U465ylfd9Wq1Qn4jOKxkkUPvM+92f9yyPdq7mqHFh3aTLpwQHXB6RThwPz3Uv453YbD4U2M6y2hc4d3gzy7yXsTNezaVgJzaRyA15M4qGhkNY6s6YV6tUmzOWpgBYtmMMnqyGrE2Unldz3gvBhmF3LiifEqS6zBGvqcUhzrZFedZtd7RwRiGfumkOpQUjfwPXH7IssXLxVK6aV8qrO07y7NtH+fbP3+WqBaV8ZOWMlB+rQDAYWSl0C6oL+fMPzOXnz/U1DZbtgReMK/2kLnPlHqG71t+z+DxPiqQuTl7MjMXiACeMQgJSnC/lvMDjkSt5OzbnvMd/PiKXzjkrzC+6VrEpdoaPBrfy2fAmViZ0fh+5ot8msoL+uVCRk7GYppxzRc0Rt+R8bjqGLXPMKEoJW51RQrc9Np6TvVFkibtumEVBjp+CHB/5YS95Ye+Q/porF5fzyrZ6ntxcxzd+tpUPXF3JB66uOq8O5SOB16Nw61WVrFxSznPvHOPlbfW8e6CRNcum8cEV1WPm4SmYmGSl0AED9qOKxuLg728dXf8RXUk/aSaAfyh4nGYzRJ1RwnJfHdATP1m2s6j8P7tu4jOhTXwi9A4VyjmeiFwxql0Kjhil/Gv7eq72vc/6wHt8OfdZtsRm8Wz3sgzLMsHwyJUibgrSEbYKtRmv1Ddq6rY81BklHLVKORwv5rhRlGEHN9IU5flZOGMKBTk+6k61p6pNC3J8lBYEOHSijYIcH1/86KLU/HU0bqRccc4HRZa5+cpKrphXym9fPcxTbx7l7X2nuXvtHJbMKhq13603Ib+HO1fPYu3lFfxxcx2v7Khn054G1l1Zyc1XVuD3ToxLmmXbJBIWMbcqNm6YblVsz/N4WoVsPGG6lbLO+bb28gpyQ0Lcz5esc0ZpaenCMCwOHmvJ6KGWpFI5y5fznuORjhvYn5ie2v7x4Nss9NbzzdY7M/b/fuEv+/1h9UYBxUpHn7RUEsuGr7TcjYHM+sB73BTYS12imEc7V43KvF1v/FKcW/y7ud5/EAOFF7oX8Xp03ri0A5qIKJhUqOeoVnrWrRUo/UfabVaAIwk3DWmUcsrMz2jjdKnh8yjEEiY+r8KtV1ZyyzAEYv/Rc/zqpUM0NEdYNruIT66dTVHe2N9MNTR38fvXa9l+qIncoIfbVtSwaunUC54ntG07tRwjuQzDEZj052ZqWUbyeTzhilGaMMUS6Us4ekQr/fv17p5xIQR8Cvd/Yhk15X2L4IbDxTijaJr2LSDZquVZXde/omnaWuC7QAD4ra7rX3f3XQr8DKeF2xvAF3RdNzRNqwQew2nhpuO0auvUNC0f+BUwA6eV+8d1XT89nN8xK4UuGjP40ZN72Xm47zqqarWRv8t9nh913MjBxLTU9juCW1jmPcrXWu9KbZOw+V7h/596vS1Wk4revt9+C8eMImapZ/ib3Jf7HZBhy5wwC6lNlJAnd7PcV0ebFeDRzlVjtiSgWG7n9uA2FnrraTJz+ENkOfsS0xHzd+nYFMhdGSnI6co51H6KkwDOmLlORaRbPNJshZlIx/Om5RXkhb3UNbSzXXcE4kPX1XD9kgsTCMO0eGHrcZ5+6yjYcNuKam65snJEilHAWc7QJ7pJE430SOjg8Rbe2ttzDcwLeVk6uygjIkpGS04klSlCiYR10UsmLoaw228wL+QlJ+glN9TTjzDkVwklew/6VVRFdpZ62DamaacWtg+X4QqdK2jfBtbgTAs/jyNk/x+wCjgBPAt8T9f1jZqm7QU+p+v6O5qm/RewTdf1H2ma9gzwmK7r/61p2jeAsK7rX9U07d+Bel3Xv6Np2j3AB3Vdv6vvSM7jdxzOhy51dhxq6lfkIG3B+CCmzkmCUqaJ8o54j9AVyF3UUopuTOVwohQJ2J+YxoeCO1L7q5JFjXqWmrTOB3lyN3+X+zx1RhG/6ryOJiuH0bxINlm5/LTzBuZ6TvKR4Db+Kuc1DibK+UPkCk6bk9M93oNBpdqcSkFWq03kyf23dLJsiXqz0C0accRtIqeBgz6VghwfRXl+5lUVsGJROc+/c4zHXjzES++e4GOrZnK5VtzvmjQn6rFSkUncMFkys4iSgiC/fP4gT7xeyxOv17JiURmzpuX1EhirJ1WXHh0lI6Ne+8YT5nk3vu2Ptq74gKYRQKo3oaJIeFWFgFdFVSRkWSJhWESixqhVq6YjSc7fxLZtWjtjNLdHMU3Xh/M8v8e1C8v43AfnX/RYNm7cWLZhw4bqXptbdV0fyLW9AfiyrutxAE3TDgBzgMO6rte52x4D7tQ0bT8Q0HU96XX3C+Dbmqb9DLgeuD1t++vAV4H17nsAvwH+Q9M0j67rF7xIOeuErqAgxPrrZ7H++ln9vt9dt5uGX7/AP/7tSgKVC1Lbm19upX1HHU//24dT2+KNx6n/6f+kXn/zr1YQPV5I65uP89dXQentzr4nf/42sj/MimU3cuaJHfgr5hE9eZjSj36ZyPvbiRzZgdmR2QC2Rj3L1/OfRPYF8Vcvwl8xF//0ufjKapCU0bH3sk2D9h0vMP+N3zLX+wy5l99CwfV3oQSyt1Tbtm2MltNETx4idvIQ0ZOHiJ85Cnb/FzFJ9eKbNht/xTznMU1jli/A6jEd9dhy0zU1F/X5ZcAHVs4cmcEIxo2HH374d/1s/jbwUH/767qeaoyladpsnBTmD3EEMEkDMB2YOsD2IqBd13Wj13bSP+OmONuBYmDgO5gBuCihG8387HDH9PtXD/H05ro+dkZJ5npO8tc58JX/eIujRk8V5W2BI6z2x7nty3/s+f3UU/xNWhr8m4+8zXGziO8UQNe+zdy2aQYAX81tosmKsW3XVu7NgXePdLDAY/KJn5zC+VuVM1VpYYHnJPM99czw9Jj5WrEIEX0LEX0L4LT6Sa/QO2oUEx3hCr2QtJ51gV2sePd5Gra+wnPdS3krNmdUFiSPNT4SVKbK+52vYXng9kZdlrenzN9tz2Q2KrAT4Jj7EFwMXo+MmlbVOZiByfm6myRTlmNFwKcgIWEmOxVY9kVFnCPJB66u4o7Vw7/RSKYuH3jggTs3bNjQ29x3yB5cmqYtwElRPgAYOFFdEgmwABkygtSBtuNuT+6TjpT23gUxbKFzBe1mnBs6G3he07RP0is/q2naOl3XN+KIWXp+9i+BHwH/CfxnWn72Gzhh67DYuv/MgCIHPRZg5+OMsjzN0itJell4vtxFqxVy1tGlLS+I2l5kycaH4ZovS5wyCzllFvJSdBFBKcpcTwOfDL3Vp2rPK5lUq03MUBtRJKf1R4O75qrOKB4Rs94u28/jkat4MzaHjwbf5c7QVq7zHeL3keUcMi6d9iFDIWFTIrdRrZ51rbOaKOtlndUbx9G/NNPRfwLNr01E4gmL+PCuT6NGwKcyvTiEIjvpSsuyiSV65u1iaUUphmkP2IViJCjK81OY6yfgVRyfTterM+BV8PvUlPWZ856S8drnVUasd+G6detOr1u37uiFfEbTtBXAE8AG9xq+CkjvKVaGE4HVD7C9EcjTNE3Rdd1090lGbCfd/eo1TVOBHIbjc8fFRXSjnZ8dELcaJ2OC6ZFHHilbvXo1d6yZxd1rJb720y39flYZwBnFsmVkyfEdTF74ruxH6KDH3Pl63wGe6l6ORzKJp7Xo6bad1KNfihOz+6YhI7afHfEadsRrmKq08NW8pzPe90gWXZaXLstH1PZi2DJX+H4quy0AABnCSURBVI6w0u8YGbeYwbQopISGYVb5NZgF/EfHTSzynOD24Db+Nvdl9sSn82RkOWetkanoGkkCSess5WyqNU1QHnztW4ORR61RKhz9sxBFltIMnFV8XhlsiKWKVJLFJlYfc+rumMHhYfiTXih5IS+Fuf40X06FNcumM2v68HpVXkpomlYBPAncpev6q+7mLc5b2iygDrgbeFTX9WOapkU1TVuh6/qbwD3ARl3XE5qmbQLuAn4NfAbY6H6v59zX/+y+v2k483NwEUI3BvnZwdgAfCt9wyOPPMLq1avZU9tMLOZ8u3lVBRw4ltmbLhXR0bdNj/O+1acEvy5RTE1aurE2UUyZ0sYK/2FejC7Gi0GcdKFzor6AnKBtiBvBU2YBf99yF58JbWa+9yT74tPYn5jGHM9pNM8pSqQOErZMrVFKixUkanvJlbqZ6TnD5b6jzs+zPBx1o71ao4TjxhTi593GR2JPopL9bdNY7T/AzYHdPJj3FH+KzuPF7kXEGJ+1OhIWZUpbWluas5Qpg1+YTFvihDnFLfUvpdYoJmL3v55SMPExLZvO7gSdgzghjTdtXXHa0owIVEVmxtS8rBA64H7AD3xX07Tkth8Dn8WJ8vw4YvW4+96ngJ9qmpYL7AB+4G7/G+D/aJr2deA48El3+zeAX2iatg8nhfqp4Q70ootRRjE/Oxjfw4n+Unz+859fDvzucH0re953otveIgcgp0yd+3pdgiOEJk5lXpJYr8PUZjnmyX4pwTW+w3gks1fq0hGZZKueoei2ffykcw3rAru4JbCHkBzj0Y7VdNo+ZqqNzPfWM99zEs3j3CucMXPZGauhwcwDJCrVs8xQG1kXeA9Z6rng1xnF1CYcJ46hKgVNFF6JLuTd2AzWB3eyNrAv1Q5oa3zWqKf3QlI0IwVZqZ7FP8AaxSQxW+WoUZxy9L8wgRcIhofP47TyCflVgn4PQV/Pc+dr8uHJ3O5Tx8xJZizQdf0+4L4B3l7Sz/67gCv72X4M+tZ76bp+DvjQxY3S4WKLUUYzPzsgbrlr70nS6QBGWknwysXlbNrdkLFTT+qyd5se57UiWSRsmO0ZeF1ii+Us+G42w6zx70eVrF6pSzeiO0+hA7CRea57GfVGIZ8Ov8n9ec/w885VHDLKOWSU8yRXUCS3M99zkvneelb6D6JKFlHbg54o50/ReRw1ipmidDJDbaRGbWKF7xBr/E7LmiYzJ81bcWDT4HY7yG+6VrA5qvHR4LvcHX6blYZjJ1brdlK/WGQspiotGQUjxUrHkJ/rsPyp8Y+mo79AMBixhEksYdLSMXCRU3/4vc7iJtu2wXZMJapKw3ztM8tT+9i2Y8IdS5h0RBK0d8Vp64rR3pVwvzoRYntnnLZInI6uBA9++jJmTsuKCHHUuJhilNHOzw6L9Ev32uUVLKgp5Md/TGVZ0xqvylQqZ/lwcBsSMNPTCMC94T/RagUz5ufmupGU5HZzbnUjup1pLXzSU5fR1BzdhadUdieq+G57HveGX+OLOS/yRORK3ozNASTOWrm8Ecvljdg8vCSY4zntCJ+nniXh44Djsbk/MY2nIpdxyixgmtpCjdrIDLWRBZ56rvIdAaDT8qWKW1LVhmkp2xNmEd/vuJVl3qN8OLCd+3JfYHusmqe7L7vgea4cqTvDOqtSPZsqwolYTrFOzFaRsVCwUsUkI+3oLxCMJ9F+TLaPnGrnL77zaj97nz8P//dOfvzl1Rf1PbKdi4noRjs/OyzS86DfenRrn/flNLeLu0NvEpZjNKQtnJ6unGOK3H90kYwGW92IrsEsoMvyEpLjGanL4UR06Zw28/lu+3ruCW3i46EtVKrN/K7rqgy/xDge9iYq2JuoAGymKq3M9zgpzpv8e7klsIdOy8eBxDT2Jabxm65ridheSuR2ZqiNzPA0UqM2ssjrNC1NuMbDdSnj4WK6bR874zXsjVdwY2AvN/r3sch7gleiC3ile2Fm48/UMTKZrpxLRWpVahNTFKetkWHLtFgh2q2AK2g2YSmaqi4dbUd/gSAbuX7JxKmUHi8uphhlVPOzo0VSrK71HaJcbeMnHWvYl6jgGt8hPhF6h++03UabHUq9fjc2g1w5guY5jeGKWTKiy5MjbI5p3BLYQ7XaxJ54BQDd1sUJHThi+dPOG7g1sItbA7spU1p5tGP1ABd/iVNmAafMAl6OLiIoxdA8p1jgOck8z0mu8NVi2RJ1RjH7E9PYn5jOO/FZgJSKtmaojdR4GrnBv4+bpL0AnDLyU8K3NTaTLbFZ3BbYwbrAbq72vs/T3ZdRa5RQpZyl2tNEtXKW6WozHvdmosUM0moH6TL8yFj4JCOVokw6+m81Zo67o79AMJF5eVs9d689/+4ok5Gsc0YZimTV5U2BveyPT3V9H3v4h4InqDcKmK46hSwVanOq2i9p4BzDQ8TykC93sSU2i1sCe7jSV5sSugQKpi0NK3WZjo3Exu6lnDQK+VR4c2rebqi5sogbie2M1yBhUak0s8AtaLktuJPb2EmLGUyJ3sFEObsTldDtFOFUucUtNWojl3nrWOE/BDiLqxNuVFmgRPhMuMcWL24r1BuFHE6UI0tOCrJI7kjZnzmO/sVsic3iiFEy6o7+AkG2kb6cIuBTU+vu1l1dNd5Du+SZdEKX3kX8D5ErSM75JCO9LstLmxVkOo7QpZe035vzJzosP81WmKCc4Hq/nrHgu0JN2nxJdNvei4ro0tmdqORM2we4N+dPfDHnRX4fuYLNMY3zma+ykTlmFnOsu5jnupeRK0WY5znJAu9JLvcdZYX/MIYtc9goZX98OvsT03jfKON9o5Qpcic1aiPX+N5nlucMoUHWrHklM8Pxpc0KUJso4ZXoxHD0FwgudQZaTrHrSDOP/v0N4zSqicGkE7oqN8LYFNVotHoqlZLVe7viVfw2cjXfd7sW/LRjDZ8ObSYgJzicKKXRzGVKWnPNq309NmI3B/YAcF/u8/ilBNf5D3HcmEKzlcM5K0yrFRx2leAZK5/vtn+Ae0KbuTO0lQq1md91XX3BUVG7HWRLfDZb4rNRMN0ilZMs9R5lXqiBj/Fun8/EbJXDidLUXOYUuZMF3pMD/oyDiXLejs2mNlEyJi2JBILJSjjg4Z5btKF3nORMKqGTsFnmdbwLN3ZnTiN6cCKza/2HM5qylimtBGTnDupgYiq74lXEbJXbg9vQPA08072MT4Teyfhe3rQ1eHeH3049N22JVitEsxXmnBWi2XQEMPm83Q4Mul4tanv5Weea1LxdudLKf3Wsps0OXeCRsCmR2x2XEXdB9kDu/eCkbGd7zjDbcwZw1iAeN6akSv1PmQVonlPcGXKKf+Z6GlKVqsnKyeQ83BkzT1huCQQjQFVpDjOm5VI+RdxMDkXW9aP7yg/f4MDRvgvFAZZ7j3BP+E0ANpy7J+OC+2DeH4d03jhfzpkhCt1Kw13xSsJSlJAcIyTFCEnRAb0YTVui2Qo7QmjmZAhisxWmy/aRTFcu8hzn0+E3SdgKP+9cxZFB5u38Upwq5SxV6tlUmX8yDdlteThmFnHUKKbT8qNIFmVyK3M9pwZsNPp6dC474jUcM6b0SUfOUU9xR2grpW5n9jNmLkEpTo7s+I92WV7q3KrOZitM3PaQI3UTQyVi+ei2vUTcR7ftFelOgeA8qCwNUxD2UZDjIz/Hl/k8x0fQpw5omH0xjVcnCpNG6Hwk+Fr+k6nI5b5zn8l4P72T+FORy1J95f659UP8r/ynADhmTOGN6Dx8UoJV/gOpi/lgxGx1wC7kI8mhRBlPRy4jZqsUKF2UKW1MVVqoVJopdY2OLRvOmPnUGUUcN4uI2yohKUaN2shMT2Pq2PTn6F+itKfW7NWoTSiSTafl42DCKeg5mJhKxPYBzoLwa32H+EDgPQJSnIOJqZwwp7DYc4JydUgz9Ay6LU9K9CK2K4RWUgh9GaKYLpTdtlcsJhcIXLweuY8QJr9Wlecyf1YxZLHQTZrU5c2B3eTJ3RxKlDFDbcx4T+rlQjZV6RHK9DmmI4lStsVnUKmc7SNyD7V+lOt8OmsD+3gnNgsJmzlqAw+13YGEhRcDv2TgkxLuw8Cf9twnJXpe47zOlbuZqrQMWgSSZI7nNF/Oe27QfWQJytVWytVWruX9Pu+fNAo4kJjKMaOYqO0hanuI2So5cpRWK8Rr0fm8El1IQIox13OK+Z6TzPOcSjWj7bR8nLPCNJthTGSitoeQHGe+9xTzXcObTsvHKbOATstHUI4TlOLkyZF+U6dnzTCnzXwibmFPUIpTIrcTUGMEpXifzg+9idoq3ZYjkD1i6aXb6iWQto+I5U2LJn1CJAUTitygh4TpNMY1jL7d0uMJizMt3Zxp6ft/Nq+6gH/5UvHYDHScmARCZ/NA7jOp5QJzXGuvjwTfpcHMJ2J5KeklWskLN8A9oU2p57lyN1/IeYl5nkxbMXDmz44ZRYBToPJGVEvN7dnIxPASs71pK9pt+quazJUi2Ej9elPKWJQrrdSojczxnGaJ9/h5H4XzYZrawjS1/7RvEstmwNRrWI4RlmNUqpmdNDotX6onXFiOsaO7mvfiVcRsT0pQkl2/ky4uNWojRUonRUon7a711654JXVGCfWu9ZeCSVCKE5RiBFzRDEpxApIjhEE57gpkjIAUp0jucF7L8SGj7Jitpolfb6HsJ6K0nAhSwsYrGQSlGA1mwYTuRi6YOHzv/1mZem7bTq8807RJmFZK/BKm1c82m9xw9q9fzXqhk7BTIpfOatcDcijSqwvTBbA3Xgxkqec+ao7nNH4pgYSVMc9UpTSx2n+Axd7jRG0PcVtNzeel83DbemwkpsgdVKrNVKtNVKrNqQt0h+Vnd7yCY4Yzv9ZmBbk1sGvQMbZbftqsIH4pQYHchSpl+mdbtkSX7aPT8mMgp+YW0yOnwXq9DUTvxqefCL2TKuCJ2wpxW3WjRyeCjNkeDhvl5MtdTFNayJWjLPUeZ2kvYT+YKOdAYhp6opxGM49omnAOhYKZihKT4pcUxAK5iyKlgyK5gyKlg6nShaVb0zmSKMlMvVrpc5CuUKYJqlhbKLhYJElCVSRUBXzncT6pavZnL7JO6Gqox1DbUxfN/myqRoN/KHg843WysOV7hY9h2VKGCCYJSzGgf2PYB/Ke7Xd7xPKyO1FBp+WnSOlgqfcYN/r3EpT7Lk7fFa/ksc4VKUd/Lwkq1WYStkKn7UfGpkxpTRlF58vd5EjRVOFIb3bGqjhj5XHazOO0mU+TmTvAhdnGi0FQjjPPc5LLvXWpis3eeCWTPfEKbCS8bjo3KMcokLrwkSBuK8jY/R6/9OrO3jSbjh+nIpko2CiYKJKN6n4dK0xkCuQupkotjqj283dKJ2HLGXORfSJKK22uMm1uMmInF/OLilaBoDdZJ3TrpTcwcpuG3nEM6e8iPVyCcpyrXWPmoVjiPc6SwpFJb3ZYTl+3ArmLoBSjRm3CJyXwYuCTDLySkZpr9EmGuz2RISqvR+cSt1Xme05mpEgv9x2l3fLz885VtFihtEu1UxfrkxLkyxEK5U4K5S4K5U4KlC6mKi0DztOlr3UcS1rMILsSVXS7EWrcjVCjtkrc9mAhpdKpwVS6NeaKYIyQG2HmSN2UeS6sCtiw5dQcY0961ZuxLbOqtSeidEzJhUgKspOsq7p884ffYvcZiVvcxdvvJ0rZFa9MXYyTi7oFgolGzFb5SccNyK4htoKNByNtfjKWSsf2np8MSPFB086GLfeKEvuPJvurdI0JkZxwTCsK8dBfXIEiy5NieUHWRXQV0hn+kFhCi/cIBUqEWZ4zzPKcocUMuuvQBIKJiU8y+FLui322RyyvM7dJT6VszPbQbgWIufOfcVtFwuneIWOnukc4rZFsFMlCxkLFJCjHCcsxiqWOlHAOlpUwbSlTJNMqW3tHlL2jSaellRDJsebk2S4sC5Tsn54DsjCie++HXyHRcprdiUrW+A9wOFGamh9Kr/4TCMaCZjOEgYJpy6n5TAVHVJReouNsd0RnLNZeDkRyWUaMHtEEUqKYNO1Ozp32/D5J4ex5b6g5UcuW6LY9GfOOA0WOvcU0anuFy84I4CwvuB5ERDdxeJWruF35NWsUp6oyvQhCiJxgrJnST0XtpY5fMvArYyO0smQTkuKEuHADdMt2lvUMFDlmpF6ttPdtL922R7juTCKyTuhOU0y75Sd3gMpBgUCQHcgSznzkMEQSkt6zwTTxE6472UrWCd0XpV9jCJETCARDoEg2U5QupjD2UXfMVpGwkbGR3Mdw1qgC/KjjRg4mpo3sALMMcVsiEAgEY4xTBW6iSu7c5kVMNf51zisjN7As5ZKI6DRNuxv4OuABvqfr+n+M85AEAoFAMAQT5do97kKnado04J+Ay3FsQt7SNO01Xdf3j+/IBAKBYGKQ3n3lQlGlYuD6C/7cRLp2j7vQAWuBV3VdPwegadrjwB3APwz0AU3T8oH89G2PPPJI2erVq0dxmAKBQJC9bNy4sWzDhg3VvTa36ro+kNnrBV+7x4tLYY5uKpBuWNgATB/iMxuAuvTHI4888rvRGZ5AIBBkPw8//PDv6HVdxbnWDsRwrt3jwqUQ0cmQ0T5JAqwB9k3yPeAX6Rs+//nPLwd+l/Opf8Mwhvq4QCAQCKCne8EDDzxw54YNG7b1enuw1h3DuXaPC5eC0NXjrMhPUgZul84BcEPp3n+AS/JOQiAQCCYC69atO71u3bqjF/CRC752jxeXgtC9DDykaVox0AV8DPir8R2SQCAQCIZgwly7x32OTtf1k8DXgNeA94Bf67q+dXxHJRAIBILBmEjX7qwzdW5p6RJzdAKBQHCeTIY2PeMe0QkEAoFAMJoIoRMIBAJBViOETiAQCARZzaVQdTlSeACUydIyVyAQCEaAtGumZzzHMZpkk9DNBcjNDYz3OAQCgWAiMhengjLryCahe8z9ehBIjOdABAKBYALhwRG5x4bacaKSTcsLBAKBQCDog5jQEggEAkFWI4ROIBAIBFmNEDqBQCAQZDVC6AQCgUCQ1QihEwgEAkFWI4ROIBAIBFmNEDqBQCAQZDVC6AQCgUCQ1QihEwgEAkFWI4ROIBAIBFmNEDqBQCAQZDXZZOo8omia9i3g4+7LZ3Vd/4qmaWuB7wIB4Le6rn993AY4xmia9q9Aka7rn9U0bSnwMyAXeAP4gq7rxrgOcIzQNO024FtACHhR1/X7Jut5oWnap4EH3ZcbdV2/fzKdG5qm5QJvAR/Udf3oQOfBZDomlyoiousH94S9GVgGLAUu1zTtk8CjwIeBecAVmqatG79Rjh2apt0I/FnapseAL+q6PgeQgL8cl4GNMZqmzQB+DNwOLAYuc8+BSXdeaJoWBH4ArAKWACvd/5tJcW5omnYVsBmY474OMPB5MCmOyaWMELr+aQC+rOt6XNf1BHAA54Q+rOt6nXs39hhw53gOcizQNK0Q+Cfgn93XVUBA1/V33F1+wSQ4Di4fwblTr3fPi7uACJPwvAAUnOtHCKfNiwenPdZkOTf+Evhb4JT7+kr6OQ8m+f/LJYNIXfaDruv7ks81TZuNk8L8IY4AJmkApo/x0MaDR4CvARXu66lMzuMAMAuIa5r2FFAJPAPsYxIeD13XOzRN+wZO/8cI8DoQZ5IcC13XPwegaVpy00D/F5P5/+WSQUR0g6Bp2gLgJeABoBZIb94nAdZ4jGus0DTtc8AJXddfSdssM8mOQxoqsBa4F7gGuAqYwSQ8HpqmLQb+AqjCuZibOOn+SXcsXAb6v5jM/y+XDCKiGwBN01YATwAbdF3/b03TVgHlabuU0ZO2yFbuAso1TXsPKATCOP+0k+04JDkNvKzrehOApml/wElDmWn7TJbjcQvwiq7rjQCapv0CuJ/Je27U0//vPtB2wRgiIrp+0DStAngSuFvX9f92N29x3tJmaZqmAHcDG8drjGOBrus36bq+UNf1pcA3gad0Xf9zIOreCADcQ5YfhzSeAW7RNC3fPQfWAY8zyc4Ll13AWk3TQpqmScBtOOnLyXpu9Ht90HX9GJP3mFwyCKHrn/sBP/BdTdPecyOaz7qPJ4D9OHMTj4/XAMeZTwH/W9O0gzhR3g/GeTxjgq7rW4B/wam22w8cA37EJDwvdF1/EfgNsB3YjVOM8h0m77kRZeDzYFIek0sJybbtofcSCAQCgWCCIiI6gUAgEGQ1QugEAoFAkNUIoRMIBAJBViOETiAQCARZjRA6gUAgEGQ1QugEAoFAkNUIoRMIBAJBViMswASCi0TTNBn438DVQA6On+HngEPAz4GZQDOOhdheXdcf0jRtHvB9YApOJ4Af6Lr+6DgMXyDIekREJxBcPFfhGBtfo+v6fOD/AH+P44CxT9f1eTiemNcCaJqm4rhm/L2u65fj9HS7X9O0q8dj8AJBtiOcUQSCEUBz+rXcgBO9rQY6gMuAy3RdP+Lu8wPgHPA/wA4cm6gkecC/6Lr+ozEctkAwKRCpS4HgItE0bT1OGvLfgD/iCNinAQMnjZkk2eVAAdpcs+zk9ygF2sZkwALBJEOkLgWCi+cm4Gk3GtsG3I4jZs/i9K5D07QpOB3KbUAHujVN+7T7XgWwF7h87IcuEGQ/InUpEFwkmqbNxXHyV93Hi8DHgCXAz3A6kzfjRHfP6Lr+sKZpS3CiwEIc5//v67r+43EYvkCQ9QihEwhGCU3T/gbYqev625qm+YBNwLd0XRf9yASCMUTM0QkEo8d+4IduI04v8DshcgLB2CMiOoFAIBBkNaIYRSAQCARZjRA6gUAgEGQ1QugEAoFAkNUIoRMIBAJBViOETiAQCARZzf8FcQIkKKBeXb8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "fb_cleandf.plot(x='age', y='www_likes', ax=ax)\n",
    "fb_cleandf.plot(x='age', y='www_likes_received',ax=ax, secondary_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above 3 Graphs we can strongly say that,\n",
    "- likes, mobile_likes and www_likes are giving same plot visuals but counts are higher in likes thans mob and followed by www.\n",
    "- likes_received,mobile_likes_received and www_likes_received are higher than actual respective likes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding which age people are more likely having friends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x190064fe208>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking data spread across years\n",
    "fig,ax=plt.subplots()\n",
    "fb_cleandf.plot(x='age', y='dob', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(fb_cleandf['age'], fb_cleandf['friend_count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the above plot, x-axis represents 'age' and y-axis represents friends count.\n",
    "- seems age range 10 t0 30 are most likely having high number of friends count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Friends count difference in male Vs female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_df= fb_cleandf[['age','friend_count']][fb_cleandf['gender']=='male']\n",
    "\n",
    "female_df=fb_cleandf[['age','friend_count']][fb_cleandf['gender']=='female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>friend_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2094382</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083884</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733186</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524765</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136133</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  friend_count\n",
       "userid                    \n",
       "2094382   14             0\n",
       "2083884   14             0\n",
       "1733186   14             0\n",
       "1524765   14             0\n",
       "1136133   13             0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>friend_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1192601</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203168</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680361</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932519</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751722</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  friend_count\n",
       "userid                    \n",
       "1192601   14             0\n",
       "1203168   14             0\n",
       "1680361   13             0\n",
       "1932519   13             0\n",
       "1751722   13             0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count plot for male Friends count')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='age', data=male_df).set_title(\"Count plot for male Friends count\")\n",
    "\n",
    "#fig,ax=plt.subplots()\n",
    "#male_df.plot(x='age', y='friend_count', ax=ax)\n",
    "#female_df.plot(x='age', y='friend_count', ax=ax, secondary_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count plot for female Friends count')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='age', data=female_df).set_title(\"Count plot for female Friends count\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From two graphs there is slight differences(in middle) between both genders friends count.\n",
    "- Male members are having high number of friends compares to female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age distribution using FacetGrid')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 751.35x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "as_fig = sns.FacetGrid(fb_cleandf,hue='gender',aspect=3)\n",
    "\n",
    "as_fig.map(sns.kdeplot,'age',shade=True)\n",
    "\n",
    "oldest = fb_cleandf['age'].max()\n",
    "\n",
    "as_fig.set(xlim=(0,oldest))\n",
    "\n",
    "as_fig.add_legend()\n",
    "plt.title('Age distribution using FacetGrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above FaceGrid plot, we can see\n",
    "- male gender having high number of friends count in age range 15 - 35 years\n",
    "- similarly, female are having high number than male in 45 - 80 range.\n",
    "- we can also observer, gender not specified most are belongs to age > 45."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>tenure</th>\n",
       "      <th>friend_count</th>\n",
       "      <th>friendships_initiated</th>\n",
       "      <th>likes</th>\n",
       "      <th>likes_received</th>\n",
       "      <th>mobile_likes</th>\n",
       "      <th>mobile_likes_received</th>\n",
       "      <th>www_likes</th>\n",
       "      <th>www_likes_received</th>\n",
       "      <th>dob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2094382</th>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>266.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-11-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192601</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083884</th>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203168</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733186</th>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  gender  tenure  friend_count  friendships_initiated  likes  \\\n",
       "userid                                                                     \n",
       "2094382   14    male   266.0             0                      0      0   \n",
       "1192601   14  female     6.0             0                      0      0   \n",
       "2083884   14    male    13.0             0                      0      0   \n",
       "1203168   14  female    93.0             0                      0      0   \n",
       "1733186   14    male    82.0             0                      0      0   \n",
       "\n",
       "         likes_received  mobile_likes  mobile_likes_received  www_likes  \\\n",
       "userid                                                                    \n",
       "2094382               0             0                      0          0   \n",
       "1192601               0             0                      0          0   \n",
       "2083884               0             0                      0          0   \n",
       "1203168               0             0                      0          0   \n",
       "1733186               0             0                      0          0   \n",
       "\n",
       "         www_likes_received        dob  \n",
       "userid                                  \n",
       "2094382                   0 1999-11-19  \n",
       "1192601                   0 1999-11-02  \n",
       "2083884                   0 1999-11-16  \n",
       "1203168                   0 1999-12-25  \n",
       "1733186                   0 1999-12-04  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_cleandf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing age and tenure of users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19047141710>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "fb_cleandf.plot(x='age', y='tenure',ax=ax)\n",
    "#fb_cleandf.plot(x='age', y='mobile_likes_received',ax=ax,secondary_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_df= fb_cleandf[['age','tenure']][fb_cleandf['gender']=='male']\n",
    "\n",
    "female_df=fb_cleandf[['age','tenure']][fb_cleandf['gender']=='female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1904798c0b8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xx6rRGcyKQXdfJTBnSphD+7d0aPj8SBVHzzZjslgGP29Xbr8imZmpoZelz4OjyAZCRkbmG4W6V0dBRRt55W0UVLShHdKxzcVZyeN3ZxMd6niqqN5g5I1tRTbFbVEh3vzmjmnjqkWQJImc4hY+3ldGm1pLdmood65K45Hnv7IaitFo7ezj8yNVHCloshoGhQKWz4hmzcL4cTUdulx8865IRkbmvwpJkqht6bHKWlQ0qIcpe8aG+XD/6jSHYwNgbrjz0qYCCqs6rGOpMf78bG0m7q7je/VVN3WzYXcJJXVdRId6c/81kxFiAggK8kahMFdJj4Sqs4+tR6s4XNBko/SaGOnLXVcKxEy6vLUN40E2EDIyMpcdrd5IUXUH+WUq8srb6Og21ybEh/uwPDua8oYuKhrU+Hq5cuuyJOaMw7ff26/n7xvOUN08WO09QwjhB9dOHlFmYyTUvTo2HajgYF4DXh4u3L1SYFFmhFWGW6lU4O3hYtdAtHX1s/VoFYfyG20Mg7eHCzcvSWR+ZviYuk1fN7KBkJGRuSy0dfWTX242CEXVHegNJtxcnUiPCyRzQRAZiUEUVLTx8b5yevv1LM+OYs2ChGEd10aiq0fLX949haprUFZ7UVYEd16ZMmZ3uPMxGE3sOVXHlsOV6PQmVsyM5rr5cXZ7Onh7uNCj0Vk/t6v72Xq0moN5ZsFqT3dna9e5RVkRrF2SeNllNiaKbCBkZGQuCSaTREWD2iJroaKutReAEH93FmdFkJUUTEq0Py7OSmqau3n507OU1XeRFOnHnVemOOx6ae3s4w9vnqB/SKxi1ewYblqcOKGGO/nlKjbsKaO5XUNGQhC3XZE0albRwAqiXd3PtmNmwyBJkBLtT1F1h9U4/P6uGSRGfn3d4SaCbCBkZGQuGpp+PWcr28krM4vf9fTpUSoUJEf5ccvSJLKSgggL9LS6izT9Bj7eV8ae3Dq8PVz4/tVpzMsIc8j1Ut/awxPnCejdtDiBVXNiJ+S6aWzrZcOeUs5WtDMp0JNHbs4kMzF4zP0MRhOldd089u+jSBLMnjyJ6uZuiqoHYx8/uG7yt844gGwgZGRkLgBJkmhq15BX1kZ+uYqS2i5MkoSXuzOZiUFkJQUzJT4Qr/NcM5IkcexcMx/uK6O7V8eS6ZHcuChh2Hb2qGhQ8+f/nLQZu+sqgSVTIyaUIqrp1/PZoSr25tbh6qLktmVJLJsRNaZbqqNby6ZDlVQ2mmMd89LDrVXZA8xMDWXdlSn4ek6ku/LXj2wgZGRkxoXeYOJclXmVkF/WRktnHwBRIV6smhNDVmIwCRG+I7p36lp7WL+rhJLaTuLDfXnk5kziwsauPC6qaudv5wnoPXjdFGalTax2wGSSOJDXwKYDFfT26Vk0NYIbFiZYq6NHoqtHy7Zj1Xx1psEm+Hwgb7BJpo+nC7+7awaTvmEd4saLbCBkZGTGpKtXR3652SAUVrfTpzXi7KRkclwAV82KJjMxmCC/0dt39mkNbDlcyZc5dXi4OXHPSoGFWRFjuoNyS1p5aVOBzdjDazPJShrb/TMSxdUdbNhTSm1LDynR/tx+RfKYUtpdvTq2H6tm3+l6jEaJeelhzM2K4G/rT9ls99i66aRE+0/42r5JyAZCRkZmGJIkUdPcQ16ZirxyldWNEuDjxuLp0aRE+JIWG4Cb69hpowMFZh/sKaWzR8eirAhuWpyAzyhuF0mSOFzQxJtf2MpqPHrHNISYgAnfl6qzj4/2lXFSbCXI140frUknWwgZdQWi7tWx/Xg1+3Lr0RtNzJsSRlZSMG9tL+ZQwaA76cqZ0dy6LOmSVEKrNTq83J0ve/c52UDIyMgAoNUZKawyd1fLL2+js0eHAkiI8OWGhfFkJQUTHepNaKivwx3lGtt6ee/LEgqrOoid5MOPb8wgMWLkYK1Jkthzso4Ne0ptxp+8N9shN9Ro97btWDU7jtegVMCahfGsnBWD6yhieGqNjh3Ha9ibW4feYGLO5DCmJQez80QNhzcPSoIrAAnGpcPkCJIkUVzTyc4TNeSXt9k0KrpcyAZCRua/GFVnH3nl5kY6xdWdGIwm3F2dSI8PJCspmIyEoDF98vbQ6ox8fqSKnSdqcHZWjpl2ajSZ2Hqkms8OVVrHFMD/3D+biOCJC9dJksSxwmY+2V9OR7eWOZMnsXZJIoG+I7vDujU6dpyoYe+penQGI7MnT2JacghHzzbx8mbbXhHr/7SSwrJW/vfdU6NWU48Hg9HEyeIWdp6opbq5G083Z66dF0d2auhFOf54kA2EjMx/EUaTifJ6c21Cflkb9SpzbcKkAA+WTY8kKzGI5Gj/cReWDSBJErklrWzYU0q7WktUiDe9/Xq+PFnLtfPjhklc6A0mPt5fxu6TddYxbw8Xnrwn+4Ib5FQ2qnl/dwnl9Wriwnz40fXpJEWNvHrp6dOz80QNu0/VodMZmTV5EtOSg8kpauGV8wzDQJzBz9vNWvTWo7kwA9GnNXAgr4EvT9bSrtbi4+nCTYsTWDY96mvTaZINhIzMd5yePj1nK9vILzOL3/X2G3BSKkiJ9mdhZjiZScGEBV54tk1zu4b3dpdwtqKdUH8PEiJ8qWxU4+nmzP3XTLYxDlq9kXd3ihw5O6jQOinAg0fXTR+Xsqo9Onu0bPyqnMMFTfh6uY5ZW9HTp2dXTg27T9ah1RmZmRbK9JQQcopb+Ndn52y2XbMwnmvnxdm4kgYMRPcEVxDt6n52n6zjq7x6+rRGAnzcuH15MouyIuR+EDIyMhcXSZJoaNOYdY7KVJTVqzFJEt4eLkxNCiYzKZgpcYEOS1iMhU5vZNvRarYfr8bZSUl6QiD1rb1UNKiZmhTMPSsF/Cwv/T6tgVe3nCOvvM26f0KEL4/cnHXB8hN6g5FdObVsPVqN0Whi1ZwYrpkbN+Lsu7dfz84Ttew+WUu/zsjMVLNhOCkONwzx4b786rapdo/l6e48pmCfPWqau9l5ooYTRS0YTRKh/h7cuiyWeelhE17BXWwc+g0RBOEPwC2Wj9tEUfyNIAhvAQuAXsv4n0RR/FQQhOXAc4AH8KEoio9bjjEVeB3wBQ4APxRF0XDxbkVG5r8XvcGIWNNJXpk5njCgRxQT6s3Vc821CfHhI9cmTJQzpSre312CqqufyXEBmEwSZyva8XBz5r7VacxLD0OhUNCt0fHixgLK6rus+2YmBvHgdVMccp8YjCZMJsluUFmSJE6Xqvhwbymtnf1MSw7mlmVJ1hoETb+Bfafr2He6nuUzolmUFc6unFq+PFlLn9ZIthDCdCGEXLGVf285N+z4f/r+rFHlxZUKBV7u9gX77F3r2cp2dp6osarMRgZ7sXpuLDPTQi97ltJYjPmTsbzwrwSmYQ7W7xAE4QYgG1gkimLjkG09gDeBxUAtsE0QhFWiKG4H1gP3i6J4TBCEN4AHgFcu9g3JyPy30NGtNfdNKFNRWNWBVm/E1VlJWmwAV8+JJTMxaNRg7IXQ2tnHht2lnClTER7kydwpYeSXq+jtN5CeEMi9K1MJ9HWno1vL3z84TWObxrrv3CmTuPuqVIdSZE2SxPFzzWw6UE6QnwePrZtu831daw8bdpdSVN1BRLAXv7x1KlPiAwGzq+nLk7XsP2123USHelPT0s2vX6miT2tgRkoIM4QQcktVvLqlcNi5Ha3TAHNh3FDBvvPRG0wcL2xmZ04N9RZNqrgwH66ZF8fU5OBvrKqrIyuIRuCXoijqAARBKAJiLP+9KQhCJPAp8CdgFlAqimKlZdv1wM2CIBQCHqIoHrMc823L9rKBkJFxEJMkUd3UbalNaKO6yZxqGujrxrz0MLKSgkiNCRg1dfNC0RuMfPClyEe7S1AqFCzKiqChrZej55pwd3Xi3lWpLMwMp7Wrn0deOIh6SOD2ihlR3LI00WHJ7aKqdj7aV051czeh/h7cuCjB+l1Pn57NByvYd7oeTzdn1q1IYcm0CJyUSpo7NOw4XsPhgkYMRonwIE+iQ1yoa+2ltqWHtNgAjEYTp0payS1pHdZ7YvbkSaxbkTIul9dIkt+9/Xr2n65n96k6unrMBiQl2p9r5sUyJS7wG9U9zh5jGghRFK1rLkEQkjG7mhYCS4CHgC5gK3Af0IPZoAzQCEQBESOMT5igIMcbh4yXkJBvbgOPy438LAax9yz+3wenWZodRWZSyCU5p6ZfT15pKzmFzeQUNdPZrUWhgNTYQO6+Oo2Zk8OIDfO5LC+a3OIW/vVpPo2qXuakhxHg487unBr0BhOZScE8fOs0+nQGHvjbfkxDJChuXZHCbSsEh/3q1U1q3t5ayMmiZpRKBTctTeK2KwXcXZ0xGk1sP1rFezuK0WgNrJ4Xz+1XpeLr5UpZXScb9xZzJL8BkwSTAj3x93ajvrWHxjYNoQEemCTJRkRvqHHw93bjiftmkzLOQryQEB8C/TxobtdYf0ea2nrZcrCCL49XW1Vmp6eGcssVKUxJCBrX8SVJoryui8hQ78uezeTw2QRBmAJsA34tiqII3DDkuxeBu4FPsH3mCsAEKEcYnzBtbT02v4QXi5AQH4eLgL7ryM9iEHvPwmA0sTunBiQT4WPITIyHlg6NtbtacU0nRpOEh5szGQmBZCUGk54QaFOFrFL1XLRz26Otq58P9pRyqqSVSYGerFuZyv5TtRw724Sri5I7r0whNsyH+/7y5bB9X//NUpRKBR3tvXaObEtnj5bNBys4mN+IJJm7yN27MpXYMB+6u/o4VtnOB3tKqVf1khYbwO3Lk4kM9uJMYSNfHKvmnMWnH+Djho+HC23qfprbB11bLR19I5573YoUlk6LRKlUOPQ7r+k3cLyomYzkUIK9XXB1UtDR3c/xvHp2nKjhZHGLzfYRwV78ZE06gMN/U3qDiWPnmtiVU0u9qpf7r0ljXvrEC+WUSsW4J9aOBqnnAxuBR0RR/EAQhAwgRRTFjZZNFIAeqAOG3kEY0DDKuIzMtxa9pQ9xp6Ub2kQxGE2U13dZA8wD/vrwIE9WZEeTmRhEUpTfZc9sMRhN7DxRw+dHqkCCq+fE0tOn4/2dxeZ+B1F+zMsI5+3txcP2jQj24ic3ZjgUFO/XGdhxvIYdJ2rQ6U24uii5YWECy7OjrC6jD/eUcaZMRYi/Oz+9MYOspGBOl7by1hdFVhkQH08XfDxd6ezWWjvUOcLzP12An4PFgPWtPezNrWff6XoAAo9W8X8/mkdZfRddPbphKrMA89PDuG5BvMPX09OnZ19uHXty61H36gj0dePulQKzJ09y+BgXC0eC1NHAZuBWURT3WoYVwD8EQdiL2a30A+Ad4Lh5FyEJqATuAN4URbFaEIR+QRDmi6J4GLgL2H7xb0dG5vIx0Ki+s2fk4ORI9PTpKbBUMJ+taEejNdcmpMb4s2RqJJlJQV+rEmhhVTvrd5XQ1K5hWnIwqTEB7DhRQ0e3FldnJfHhvoi1nZTUdQ3bd87kSdyzcuwgtNFk4mBeI5sPVaLuNT/DKfGB3H2VQIi/B31aA1uPVLArpxZnZyVrlySydFokOcUt/P7149bVgaebM94eLnT2aulWjb1SAbPx9fNyRazpZCwTZjSZOFPaxt7cOhv3FICHmwv3P7PP7n7LZ0Rx5axogv0cK/hrbtewK6eWwwWN6AwmAnzcuOvKFBZkRuDi/PVkNzmygvgV4A48JwjCwNi/gKeBw4ALsFEUxQ0AgiDci3m14Q58gdntBLAOeE0QBF8gF3jh4tyCzHcNSZJG9KmP9t3lRq83+5Y7e8aerUqSRH1rL2fKVGw6UGEd9/VyZXpKCFlJQUyOC7T6mLU6I69vLUTdq+MXt069KNebW9JKSW0nt12RbHsfBiMH8xsJD/IiLNCTD/eWcqKohVB/D+5dlUp+eZuNNpLOYEKs7bR+dnd1ol9nxEmp4LYrklk2PXLUn5EkSeSVtfHx/jLrasnbw4Xbr0hmzpRJSMDB/AY2flWBulfH/IwwVs+N40ypit+/dsxqkN1cnfB0c6ajW4tG61jGvFKh4KYlCazIjqa+tZc/vZ1DXpmKhVkRw7bt1ug4kNfA/tP1tKnt/4zrW4e7966bH8cVM6JGFSMc+ixK67rYeaKGM6UqJMDf25Wb58axKCt8zIC+SZLo7NZesmw1R4LUDwNtPGnUAAAgAElEQVQPj/D1y3a23wNk2RnPw5zlJCMzIpsOlFNQ0c4f7p1pM97Vq+MfH+eRHOXHHctTbL7T6oz87rVj/PzmLKJGyVe/2GitKwitXcOl0xsprum0yFqohr1kXJyVPPPg3GEz7eZ2DS99WkB9ay8zhAsPfpskic8PV/HZoUpiJnnbjJ8obGbjVxW0qfvxcHPGJEkYjZK5/7KbMx/uLaVPa7R73KtmRXP0XDPqXh0BPm48tCbd2jVN1dmHCQg9Ty6jslHNR3vLbAzM3CmTuPWKZHw9XSmr6+K93SVUN3WTGOHL91alUt7QxZ/fOWk1Ai7OStxcnOjp06PV2b82e8wQQrj9imTryzRmkjdBvm6cLrU1EFVNavacquN4YQsG4/hCpb+6bSqT4wLH3M5oMnFKbGXniRqri8zPy5Wr58SyeGqENRNNpzdS09xDnaqHqUnBNlXmzR0a3v6imJK6Tv7fzxZekj7XciX1d4SObvNL6lLNJC6Uc1XtlNZ2smZhwojbnC5tZeuRaibH2WaRqHt1/G3DaRpUvTS1a1i7ONEmlfNHz30FwJNvnuC13yyhqb0PF2flsJfT+Ty9/hR6g4knzzNGjqI3mF9OBqNEb78Bbw8XOrq1Vp2jwqp2dAazTz3AZ/DncuOiBEIDPPj3Z+d45bOz/OTGDGt84UyZitc+L6TP8jK8ek4sYJ7NPvdhHllJQVw1K8bhbBatzsgb2wo5KbYC4KRUIEkSYk0nH+4rs6bKgrnKOTMxiCVTI/niWLVNUdtQ/vzgPLYdqmDniVoA0mIDePD6Kfh6umI0mdh5opbNBytJjw/kZ2szAbPB2HigguOFzdbjBPu5c/dVAukJQbSr+3l1yzmOFTbj7+3KDQvj6ezV8fLms9ZYj5NSgdEkoTeYrGNDcVIqcFIqrK6/AUL83Vm3QiAz0TZ7SKFQMDUphIP5DWj69eSXt7Ent47yerVDz3Yoq+bEsP1YjU0DIXv0aQ0czG/ky5xa2tTmYkZfTxdWWQxDW1c/x4uaqWxQU9GopqZ5cIXyH0TefGwZJpPErpxaNh+sQKFQcPdVwiUxDiAbiG887ep+nJyUYwbRfvnPwwC8+diyy3FZ40KSJN7/soTGNg3LpkfZVQdVdfXx5jaz9v/8IZka3Rodf/vgNKrOPq6ZF8vWI9WcrWxneop5Zr3pQLnNcf76Xi41zT3Mzwjn7qsERqPUjv98LHr69Ow+Wct18+PR6QdfRG99UURbVz81LeY/6GA/dxZmRpCZFERZXZc50AvcfkUyK2ZGA6DRGvjPDpE3thVx/zVpfH64ii2Hq4gI9kKrM+Lp7mzNnXd3daJfb2TL4Sr25tazem4sS6dFjlrz0NbVzz8+zrMK8gFo9SZe+CTfRuoCIMjXnZuXJlLX2ssLG/OHHcvNxYnH78mmXd3Pcxtyabe83K6ZF8uaBQkolQoaVL28+UURFQ1qXJ2VrFkYT2+/nq1Hqthzqg6D0fzyVChgRXY0NyxMQKGALYcr+eJYNSYTTLU0AfrsUBUmybI95hTI81++zk5KDEYTrs5KfL1c6e3X26x2nJ2UXD0nhqvnxI74nOIjfNiTa+In/zg44nMcjWvmx3PDgjir4RtJsK9d3c/uU3V8dabBavzBLDMSF+ZDXpmKzYcqx1wR1bf28Nb2Yioa1CRF+XH/6jRCHYxVDTzP8SAbiG8wkiTxq5eP4Ovpwj9+tvDrvpwJU1jdYfU1F9d0MCvNNhtD3avjpU0F9PYbcHd1sr78e/r0/G3DGVo6+nhkbSbJ0f7sy63nZHEL01NCaFD1svVINTDoBx+Y/V07L+6S3MuHe0o5fLaJlo4+GzfJ6VIVKVF+rF2SSFZiEBHBXhiMJv69pZDcEvPs/br5cVbjALBkaiSafgOf7C+3mVn7ebnSoOqlp0/PS5sKeOHhBXi6u/CTG9L583/MstIf7i1jV45ZIXVBRviwDKfSuk7+8XHeMPdQo6qXhiEGw9lJwcrZsSRF+vH29qJhAfcgXzd+fcd0fDxc+HBvKQfyzOVMHm7OPHDNZKYmB2MySWw/Vs2nByutLpm7rhIoru7g8yNV9PYPvhBjQr25Z1UqcWE+nBRb+WhvKW1qLb6eLnh5uHCmTDXsmQ99rbm5OhER5EVVkxqlEhIn+aLW6Gjt7LfZJy02gOkpIWj69cMK4Qb8/ntz6zhR1MJEuGJ6FMtnRlHW2MMf3jxBnaU6Wq3R0dmj5UypiuzUUNq6+tmZU8Oxc812j1PRoKaiwfEVyx/fykGhgJuXJHLVrJgxs8Q6urXkFLdwoqiZPp2BV3+7wvGbRDYQ32gG3AK+XqOrW05kZnA52XOyDh9PFwxGE0XVtgZCbzDyyIuHrJ+zU0NxczX7l/++4TRN7RoeXptJmsWvO0MI4XhRC2qNjsdfPw7AXVemsGFPmc05Jcszae3s41xVO4uzJtbQfoCG1h725dRy2KI+eqzQ9g/+lqVJrJwdY/3c0a3l/97PpdmSex/s527XvTYlLpBPsF0FDWTKRIZ4Ud/ay7mqDmamhhIZ4s33rk7lX5+dIyrECzdXJ/6zQ2TH8RrWLIxnVtoklAoFB/MbeOuL4amnYPuynRIfyJqF8Ww/VsNWywpngAHZigAfN3Mv6PdPW10iCRF+/OBa88y1sa2XN7aZVw3OTubnG+jrxmeHKq16UGCOG1y/IJ4rZ5qDw8+8l2uTAaXW6G2qrs8nKdIPf29XToqtVDaaX6i+nq6UN6hxdlKiUMDAn4GbixPVTd0UVXfg5urE3PQw3Pw80OqNHC9sZs+pOmpbJlY7csfyZAJ83Dl6ronHXzuO0SQRH+5rraT+cG8ZH+41/y7+Z6do9xh+3q4kR/mTX66yWYU6QkSwFw9cM9kaa5MkiZaOPs5WtlNY1U5pXRer58bi5uLEiaJmxJpOJMyTjjULHU+1HUA2EBNArOkgv7yN6+bHO6QnM1Fe32rWh7lvddqo27VaXkJB37D4Q365CicnJXllKq6ZF0dtSw9FVbZpguerZi7ICKe3X8+zH5yxumuGShhkp4ZyIK+RR14wGxVPN2dSYwOsM9eVs2PYd7qef205x09uyODZD8+gN5hYMjVyXNd+uqSV3n4Dda095JW32RRcAUxLDiY7NZTXPjf/jPRDgpnlDV08/W6ujeH+0/cH8zMO5DUg1nTg7KTkYP5QgYFBQgM8ePKemfz8xUMUlLcx09IsZlbaJCob1ew8Ucv3r05j9dw4Nn1VwatbCtl2pBq1Rke3A30JHlqTTke3lr/8x7afsrurEz+8Pp3Wzj483ZxZv0tkb2699fsFGeE8sm4Gne29bD9ezacHKnFzUXLlzGh25ZhjEu3nBePTYgO4e6WAh6sz63eVcCDP8RKoJdMi0eqMHD3XNOy7zh4dvp4uwwyLVm9EoYBFWRGsWRiP3mDipU0F1pXcRFi3IoW2rn62Hq1G3Ws+7/LsKJbOjOXPbx53SKjvvtVptHb2seVw1bBCOkd54p5s+nVGcopbOFfZxrnKDqvhHmDAQIFZ72lFdjQz00InJNQoGwgHKaxqJ8TfgxB/D46ea+ZAXgMFFW38+IYMJl0ELX17DATixmqmPjCjig//5shiFFV38I+Pzb5sJ6WCJdMiOVncwpkyFcctHb7uvTqV06WDLoUQf3ciQ7x47sMz1Kt6ePC6Kby65Rz/3nKO2pYeblyUQOp5Mgg/vSmDv39wxvr5lqVJiDUdlNV18ciLh1AqFPzqNsfSRNUaHQXlbXy8v9yalw/mIOKiaZGsnh3D0bNNbD5USXSoN4YhwdCBVNfDBY28sc22j/JdVwnWoPKXObXD2mna439/MAelQsHk+EAKKttssqTWLkmkuqmbd3eJ/O7OGfzx+zP56nQ97+4qceg+gWGd0QD+fP9s9uTW8f8+zkOyXGtLp3ny4exkrphemBlOS7uGZ9eforxBzbTkYOZMCRvWUGeA712dypzJYezNrePjfeUOr3ZvXpJIvaqX/afrR9zGYDSh1gyfgacnBDJn8iQ27C4dlzGyx8rZMZTWdvLel4PPVoj2R6ztZOeJWmug3hHO/72YCP/77imqm7qHuc2GolQomCGEsCI7Gl8vFz49WMlrWwsJDfDgjcevHNf5ZAMxAu3qfl7cWMAP10xhUoAnr31eSLYQyrorU+jXGaz510+9k8P9qyczLeXiavGUWPzbjmQnVAwYiIjx9ezV6oycKmlh7pSwCbtfcktaqWrqthFS0/QbeHNbIV7uzmi0BmamhhLg40ZarPnlPiCp/OyQFzvA9JQQnv8oj5rmHn58YwZTk4L54lg1dS09fHGsmr25ddx+Xg7/8x/l2WStaHVGq/YNQEq0H6mxo2vrfH6kivwyFRUNart/eGqNngOn67l3yIteozXw1cHBeob2rn5e+CR/mA89ItiLRVnmoPuuEzV8sNfWFTYSuWIr2amhZCQEcrK4hT2n6sgtaeWBa6cQ4OPGD69P509v5/DSpgJuWZY0LuNwPj+/JYsejZ7/ez/XZjY+YByC/dx56IZ0YkJ92Hmilk8PVuDqrOS2K5JpUPXYNQ6xk3x45JYsqpvU/PKfhx3ulXDnlSlUNqj5eH/52BuPwNmKds5WtE94/6HsOF4zbGxo7OlyU9U0tkzH2iWJaLR6/nf9qTG3HQvZQIxAT5+e6uZudp+sY92KFIwmCa2lMKpPayQ0wIOH1qTzz81neXFTAavnxnLDwoSLprf/xjaz6+L+ayaPuW2VJY86fpxN3b88WcumAxVEhXgTM2n8qw+TJPHR3jJaOvtIjw8kJdofgA/2mAOPGQlBFFS0cUW2WZcxMsR+b+GBbJShs7GpScHoDUarrzgtNoCi6g7eOk/WQWcwEejrxvz0cD4/UkVHj9ZGWrqiQc3p0lamJZsNuFZvpKi6g8MFg66dTw9UEB/uw9VzY9l2tNo6fu28OGv2EcCbXxQREWS+hxNFLTarjPOzggYI9nPHSalkx/EaPtrnmHEA8wx/SlwAYZbzvb+7FF8vV1xdzMFoXy9XHlqTzl/ePTXi7N0Rls+IYvuxaopHqCjOSAjigWsn063R8fR7pyivVzM1JYRAb1c+GGEl9PDaTEIDPPj7htM2GVSjERHsRewkH9ZfgKGTMWPv9+wXt2SRkTg+kUCQDcSIhFhy6I+cbeSmxebZ8YCfuU9rwMPNmWB/D35353Te+7KUbUerqWhQW/PBL5SBrIzzc7ftMeBiGloE5QgnisyB1qZ2zYQMRHF1By2dfSgU5pfsb+6YxplSFYcKGslODaWupYf4cF8SI8zFU4cLhvuR775KGBbMS08wB6SH+rLPlzgYSlZSsPVFfuTs4Iv/iXuy+Z93TvLixgLuWJ7M2cp2iqo7huXQP/+T+XT26PjT2zk245+fF7g9erbJmmo51DiMRn55G5sPVrDlsPlYri5Ku4HJJdMi2X+6Hh9PF2sM4VxVh1WADsxxAy9384qyXtXLX9698Bni7lPmXtDny1UrgOsXxHP13Fh2n6zj04MVODuZe00UV7XbrNIGWD4jipWzY3hrezHnKh2bwWcLIZwUW2k4L7tK5sLx83Jl7ZJE5kyZxLnKDrYerebea9PHdQzZQIyAh0XfpadPb81YGXix9OkM+HqZ4w4uzmYN/MRIX97dWcKf3srhoRvSrS/FiTDeP5SBl5an+3B3lCRJPP76cZyUSqanBDM9JYToUG8aVL3W1LzzA7COciCvAS93Z66eE8vH+8s5XtTMht2leLo5k5UYxMniFh641rwCamrX2PhxB7CX6XGuoh2TJDncEnPfkCDqQNorwDPv5Vr//f7uUkL83VmcFYFao7NJbzRJDDMO5xM9yYcHrknjyTdOOHRNQxkwDsCIWSsDvvbRAszt6n50eiObD1ay48Rw18eFcL4L6Oe3ZBHk587/vX+asvounJ2U6A0mu4Y61N+D+6+ZzFdn6vnVy0fGdd6BTD2Zi4eHmzPXzDUX3p0UW/nDmzk0qHoJDfCQDcTFJMTfg54+PXtPmf94BwxEv9aAx5DsJUmSWJgZQUyoD//8tIC/rs/ljhUpLJk6/tTKPq3Bmr5576rUC74HhULB1XNi+TKnli2WQqxgP3drCqK7q5M1FXM8qDU6TomtLJ0eyfLsKHafqrN25bp3VSonilrw83JlZmooBqOJv64/ZXXRjYUE/Pj5A3heoPb9+RW1dyxPISzIkz++mWN1WcFgkaE90mIDaO3sI8DHjZ12/NEXSpCv+7AsFDBnZ52vL/Tq58O7nl0qnvsoz+bzaJIToYEeDvm7Y0K9yUgMsnHjyVw8nJ0ULJsexeKpEeQUtfDIi4fHLRVyPt+sBqjfMEL8zWmjda099PTprdIKGq3RGqxsbtfw4+cP8NG+MiJDvHjy3plMjgvk3Z3mCllHX4oDFNcMztAWZIbz5clath6puqBah/kZ4fzx+7N46r5ZrJoTY1OR2q8zcuRsE2fKVNb7c4QjBWZ3y+KsCFycnWxSbJOj/CioaGPJtEicnZRs2FM6ao67PaYmBY9LsnkkpiUH89ObMogO9ebVz8/xzHu56AzGMQPXAPPSw/j5LVmouvrJL1NZayAuJvaMA2A1Dk4XuYf0WIwlT2KPsQLCUSHe3LYsiZqWHtk4XETmZ4RZk1jmTJ7E/ddMZldOLb9/7TibD1VesHEAeQUxKqEB5j8WNxcntHojeqMJSZLo1xpwtxiIktpO+nVGdhyvoay+ix9eN4WHb840yyYcqqSmuYef3Jg+rBxerdHh4qTEw82ZutYeTomtXDc/ju1DZql6vYmN+8vRGUxUN3WzeFoEXxytJj7cl7S4AJIj/a1By6H09OnJKW4ZtoKJCvHm5iVJzEgJHaZb/8In+bi5OJGREMi0lBCyRol9SJLEgbwGkiL9iAzxNktMNHdbn9XeU/XW1NbcklYbF5CjqDrHv6qxx+lSFadLVfzxezP541s51sriT4coqo6EEOPv0HaXCk83Z+ZMmWRTh3AxGNA0skfLRXruQ6lr7XE4e0tmdJZOjyQ50o/NBys5XNBEaow/7d1ajhU2DyveHMpEpxmygRiFEIuOe2qMP3nlbbSrtfTrjEiAh5vZxVSv6sXVWck9K1P5zy6RP76Vw/3XTOb6BfEkRPjy6pZz/Ontk1ZZggGe/yiPiCBPHrh2CqfEVj47VMnSaZGUDakuza9oQ2cwMS89jKNnmzhlKfQpreti+/EanJQKwoMGDY/BaMLZSclJsYV3d4pMTQomwGd4FfbJ4pZhL4nESF8qG7o5KbZyUmzFSakgIymYjLgApiaH2Bxn54lamto13Lc6DZMk8eYXRegMJkL9PWjp7GNPbh1Tk4LZdqTKGgQdL+XjkB9whD++NXqMwR4jVSNfLjRaw0U3DoBl0mD+2Xu5O+PirJxQTwuZy8++3HqbCVdxzegpt3OmTGJhZgR7c+tG1IkaDdlAjMJAJlN6QhB55W10dGut2RsDLqa61h4igr2Ymx5GXLgPr2w+yz8+zmP13FjWLIznyXtn8vKnZ3lhY75V2MwkSdS19FhjGgP/H5oS6OXuzImiZny9XPn+1WkkRvhac91/e+cMNP16Cqs7bPK0f/C3/WQkBFFvaUGps+PeMkkSJ4qbCfBxs5FCuG1ZMg2qXlq7+jhX2UFlo5ozJa2cKWnl3V0lJET4Mi05mOhQH2saXXZqKHtPmZuoRAZ78fg92fzoWbOyqj1NnW8D8zPC7GZbfZcY6noYqpMk890hxN+dp+6bjZtFpPCNbYXMEELHfRzZQIzCgIFwchpcoPX2m62wh+uAgeglw5KWGR7kxeN3Z/P+7hK2Ha2mtLaTB69P53d3TWf9rhK2HqmmskHN9QsSMJokmts1GE2D0sUHh1R99vYbOCW2smy6uU/u0IDlsx+e5qE1GdyyNAl/L1eb5XtBxWA+/m9fPUZcmA8LM8NJjQ0gLNCTsrquYVIIAAfzGzmQ18B9q9O4cVEijW295Fd1sOdEDaqufruiYtuOVrHjeC0KYM3CeDZ99fW5Y+5ZKfDOjuEZUXevFNh8sNLhtNTvunGQ+W4yIFYJlv7a0yPpUGspbe+kqKqDdrWWvAlM2mQDMQoBPm44KRW0DvHLDqRHerg5o9boUPfqiAoZrD9wdXHi3lVpCNEBvLOzmD++dYIHrp3M965OIzHSj/W7SqwZH0aThKqz3zqjs+dDnJkaiiRJHCpoIiXKjweuncL/+ySP5z/K466rUqi0VFbesCiBfp2BI2ebrBLRYK68dKT6ciBldUBJNTzIi8zUMK6cHklZfRdHzzax/4ytbMHQlNJ/fjrxYq2LgT3jAGb5ZUeNg4zMt5GbFiew0TI5c3ZScjCvgY/3lQ3L4psI39ksJq3eaFX0nChKpYJgP3daO/utLqXtx8wvRQ83J+otdQRDDcQAc9PDePKemfh6uvL8h3lsOlDBgoxwfnfXdJvtGtp6hxVuLRrS3So5yp/yejXN7RrmZ4YT5OfOb++cweT4AN7ZIVploqcnB3PzkiRmp028sfmK7OhhjWgUCgXJUf7cvTKVaUNiKN8WNn2NQWYZmcvBxiErd3dXJ2paeuwaBxc7CS1j8Z01ED969iv+/J8LrzQN8fegtbPPmm44ENj1cDVnHwFEjSAhEWHxy8/PCGfrkSr+/sFp/L3drE1RAF7cWGB1Ww0wLz1s8IMCDhU04ObiZFX09HBz5uG1mSyZNqhQ6uPpSmFVu1VRE+CxddN54p5sVs2OIdB3dMlwgM2HKvlgTyn55W3062x90zXN3TbCepeboc9MRua/HX9vV5vJ3Oq5scxPD6N3FM2rpdPGp2gM33EXU2Wj+oKb3If4e1DZqB52DA83Z+pbe/D2cLHbIW0ANxcnvr86DSHGnze2FfGLl8xFWYkRvtZMnfNfvENz48vruzhR1EJ2agjuroM/LielkruuTLFW4D69/hSqrn6b7CSj0URKtD/x4b6sXZLIfc/sG/N+d+XUWo1MoK8bCzLCSYsNsKv+OXB/j66bxlNvn7T7/cXi2xr0lpG5mCzMDOfulQJHCppsdMnGqi9ZkR3N8hnRo25jj+/sCiLJ0jx93yhywY4Q4u9Bb78BzXnZHs5OCupae4kK8RrRAOUUt/DPTQUAhAXZ1kGMlsa5d0hq6PpdJfTrjCzICB+23dDzNnf0YTRJNppKhiFprEPTGG9fbquIOhLtai1bDlfxzPunh0lAuLk68Ytbs9DqjcN6OsjIyFwa7l2VyosbC4aJVg4Q4OPGlTOj+d2dM/DxHJTeiQrxstYqjYfvrIH4gUUD6ELVIQeqqQcqmaMtnZwKKtqpV/USaSf+AOZisi2HKzlV0kq/zkCFpRXm+XLV9hhqPAbUTCsa1ZwsbqG6qXuYS2ooA8J9gE2/gt+9esz67xXZ0Tx43ZQxr2M0tDojz31olmNomYBUh6O4OH9nf0VlZMbNfc/sI/885WBfTxeWTY/ksXXT+dtD87jtimS0BqPNpO6t7cW8ZJmsjofvrIspeIhkgN5gmvCLJuQ86YHIEC9qW3rY+FU5Wp1xxPhDVVO3NYjd0a2lskmNv7ertfnPzNRQckbpKjUpwMNGI+njfbb6+J5uzvic59q6bVmSTcrrQGpsU7vGKvnxlKWzmRDjb/e8gb5udtNgvw483JyG9VSWkZExV0YvzIpgdlooQkyAtc2AJElUN3UP67US7OfOPSvHr+3mkIEQBOEPwC2Wj9tEUfyNIAjLgecAD+BDURQft2w7FXgd8AUOAD8URdEgCEIMsB4IBURgnSiKE2sM6yCzJ0/ieGEzmw9WcPPSpDG3V3X1ca6mE08XJRFB5p6/5xuIAavcZUmdHGkFcWhIK8mObi2Vjd3Eh/vS2GY2GjcvSSQ+3HfEHgFDW1g+tCadyXEBtHb2o+rqo7Wzn9auPgrOm0mcL2fwxrYizlW226TPerg5YzJJbDlUafe87WotQb7uLJsRic4Inx2YeOOWC0U2DjIyw/n5LVmkxQbg7DQ46VV19XG8sJkjZ5ts+qEM8Of7Z1vlgcbDmHtYDMGVwDTM9fk7BEG4HXgGWAzUAtsEQVgliuJ2zEbgflEUjwmC8AbwAPAK8DLwsiiKHwiC8ATwBPDouK94HKxbkcLxwma2H69xyEB8eqDSpvdtsJ87EcG2KwRVZx9+3q7WWoPI4OErCJ3eyLHCZmsgul7VS3O7hnnpYTS2aXBxVhLo5052aohdA5EY6Wszi2/u0JCdGkpsmItN+9G/vHvSWg397I/n8/KnBcNiG+fXVvz6lbHlmNvU/cNWLDIyMl8/bz62zPrvBlUvr2w+a6PAMFI6jquL0wjfjI4jfpdG4JeiKOpEUdQDRUAKUCqKYqUoigbMRuFmQRBiAQ9RFAcc3m9bxl2ARcAnQ8cndMXjYGi7TkdaHrZ29pEc7c+Pb8jghoVmLaXz3S3NHX02hWgf7yvjr+tP0TZEtiK3pJU+rYFr58cBcMaSpRQf7kNTu4awQE+UCgWBQxRQh5KZGMytywYN2savKtBZ6joGiupqmrspt8Q11iyIJ8DHjX6d0a543wC/uCWLu1cKYz4HGRmZbyZdvTpe+CSf7/91L4+/ftxqHLJTQ4kK8bJpmfvQmsHeD0bTxIrmxlxBiKJoTVERBCEZs6vpRcyGY4BGIAqIGGE8GFBbjMnQ8QkTFORY97SbliaxcV8Zn3xVwa/vyh512/ZuLdOFUFYuSLAZ/+s7ORzOt9/8fKC62N7MfCBAPtB3oEWtJb+8DU93Z9o0+hH7TQ8oiA4tn//f93KZkx7OtkMVPPfzxbw5JIshK3USODsPa+/o4+lKt2bQmEVH+LPvzNg9BbKSg8n7GmseZGRk7PPzFw/ZfP7eNVPQaPVs2leGk1LBHVel8smeEhZNi6JlyOS2tLEHFyeFw+/NARx2SgmCMAXYBvwaMGBeRQygAEyYVySSA+NYxidMW1sPphEki4eyYnokG/eVceBMPfeOMoOzFKwAABtsSURBVHvWG4y0q/sJDfSktdU2Hczfy/6LfCyGiuEBrLe81DX9Bn79wsEx9x/a1rGmqZsai2TGg0/vsdnOVSFx4NTwZjZDjQPAz//x1YjnundVKm9bri8zIYiOrn5qWi5piEhGRuYCeWuref4+Ky2UW5clU9moRmcwkREfwIsbB7OW/u/dk0SEeDEnI2KkQ9nFodQeQRDmA3uAx0RRfAeoA4Ym5ocBDaOMtwB+giAMOMLCLeOXnKG+t+aOkVtrtlms7aRAz2HfnR+oHmCgdzKY4xXhQZ6snBUDwK9um8pPbsyY0DWPl9+/dtz6cp8It12RzKSAwXt8d6coGwcZmW8gbzy6lBsWxg8blySzi/ydHeb3wKtbztmo9jopFTx6x/Rh+43FmAZCEIRoYDNwhyiKH1iGj5u/EpIsL/07gO2iKFYD/RaDAnCXZVwPHARutYzfDWwf99VOkHUrzIudN7YWjbiNqsucUjoeA7Fqdqz133deKdDYprH2Cp4cF2jjQrplaRI/vN5ce3Dd/DhuXGTrxroQVs6OuaD9P9hTyrMfnhl7QxkZma+V+57Zx6cHBzMQgywSOjnFLfz1vVxrluWAjLuri5LIYC+mxAfiN4riw0g4soL4FeAOPCcIwhlBEM4A91r+2wgUAsUMBqDXAc8LglAMeAMvWMYfAn4gCEIhsBB4fNxXO0EWTzUvq8rqu0YU8BtwB53f+Q0Gi+XOZ2g70ehQb7zcbT12x4dkEH20r4xP9pszg1bNjuWaeXHDjnf+/o6yY4xeyb+7cwYxoaP7HodWYMvIyHzzSYzwpadv9H4eOr2JsEBPa8HteHEkSP0w8PAIX2fZ2T4P+P/t3Xl4VNeZ5/FvlUBCAkkIIYHYhNlewAuLWWLHWxLHHs84naQdT57YTuLp2EzazvTMPJ2eJzNx2r2nO5m2E2fi9EwntLsf2rHTeDyd4LjjjtfY2JDExgQDx2BAGJBBCBCSQEst88etVSpZqlKpiqv6fZ5Hj+se3Vv3cH1Vb51z7nnPugzlLcB1WdYvL1KfF95/tIPFcwZPEjt5xstjNK12Eqfa0y/mtOrMAeLAseTqb7948xhzGqbg3vVWeIpGo5zpSg4SBQOBRBD66WstrMiQfO73PnUZX9/0elrZsuY69racHjSAM1JN9VXMaZw8bJfRwLUeROTCNtJVF+MrUf6v//sb7r/7iqzOUTJ5DO79pPfI11B5g052nKe+ZlLGReKDQywc/+qu95g/s5pLFkzjp9taEsEBvNbDvpTlQ//gMysTr7dsPTRoTWjIvKbBwtk1OQcHgNb2c9zzwEujeAcRGQ+Ui+l9rFrsLYRzurM349NP7R091NdmbikMpf1sL62nzrHrwCn6+tMfynrs2X1pcy/+8V+TOaE+kWGQCbyJLwOlLsoDcPOVzYP2EZHSc+mCeu64YQl/8oV1XLNicDLPfCiZABEMBhJrIsSbXKlOdvQwPSVAhMIRXtv93rCP0vb2DU4HMadhMmcHZD890pb88E8dZMrWwIARFxxFSnMR8Zff//RKbr6ymTNdvfxgyx5eerN1yH3XLM1+Leq4kgkQAHff7GV4/V7K2gaRaJS+/jAd3X1pAeJn2w/zf368m5ZhmmWZBn+bZ1Szblnu/1NyERnl6nkicuG74mJvMbG/fnwHX9/0Olu2trzvZ9S9n7wkbUZ1tkoqQCyZmxycjn/zv+uvnue/xhbxqZ5cTl9/mM5zffz0Ne/JoExjEnGNdZUZn0Z6Zdd7XLcy+9WbCuWWaxdw0wdG92isiBReaq64TFYsrE/b/u6Tu/i9bw8/KXcogdGu21wE84GDI51JPdBfbPo1+4908JmPLOaja+fyO3/53LDHBAMBfUMXEV9rrKvkB/fdAHARcGgkx5RUCwLgjtikuR8+u2/Q7zLlRqqrrtC3bREpSSUXIOamjBnE13SIW7GofuDubPjYcm65duGQifWysX75jFG/h4hILi5qqsn6mHG7otxQAoFAYiGhn207TOPUSk6c8dJsdMdmJa6xBn7lvCed/urRN5hQFkzLa5KrbQPWZhARGUt11RUEAxAKR/ndHAarS64FAd76CQD/sv0wE1PWT9ix30txfcUlMxNla6yB2hyzuYqIFNPpzt7EI/e7Dp7K+viSDBCpCfnaB6TkBmg7kyyLkkx8JSLiN/2hCB3dffzDv2Sf8bkkAwSQSMvdk2Gi22MpA9i/dm0Z9xERGe9KMkBEo1ENGIuIDKPkBqn3HTkzKGOqiIgMVnItiOdeP1rsKoiI+ELJBYj2s8kB6EVzaotYExGRC1vJBYjU7KsHjiYX3BhNQisRkfGoJMYg9racZueBdlYtns7pzuQqb6n5lR5OyfAqIiIlEiC27TnOizuODbt2s4iIJJVEF1Pj1EoA7rhhSZFrIiLiHyURIF7Z5eVQrywviQaTiEhelESAiK/1/Ldbdhe5JiIi/jHir9RmVgNsBW52zh0ys78DrgLiiy3/sXPuSTO7HngAqAQed87dFzt+JfB9oAZ4Cfiic25MkhxFo1Hu37idD62azYdWzxmLU4iIFNWMukqiURLZqMfCiFoQZrYeeBlI7cRfA1zjnFsZ+3nSzCqBjcDHgWXAWjO7Kbb/JuBLzrklQAC4O1//iIGiwJG2bja/+A6d5/qG3V9ExG+Onz4/psEBRt7FdDdwL3AMwMyqgHnARjPbaWZ/bGZBYB2wzzl3MNY62ATcambNQKVz7rXY+z0C3JrHf0dG53vD/GTrocT20KtLi4jIQCPqYnLO3QVgZvGimcBzwD1AB7AF+ALQBbSmHNoKzAFmDVGes/r6KUP+LpyyVvULbyRTa2hVaRGRkcvpsR7n3AHgk/FtM/sO8DlgM+mfwwEggtdSyVSes/b2LiKRzB/5qau/BYMBCCs0iIhkK6enmMzsUjO7JaUoAPQDR4CmlPKZeN1SQ5WPufqaSYU4jYjIuJPrY64B4FtmVmdmE4ENwJPANsDMbJGZlQG3AU8751qAHjP7YOz4zwJPj7LuI9Lafq4QpxERGXdyChDOuZ3A14FXgN3ADufcD51zPcCdwBOx8r143U4AtwMPmtleYArw0OiqPjIbfmt5IU4jIjLuZDUG4Zybn/L6YeDhDPs8C6zIUP4m3lNOBXW0rXv4nUREZJBxP5P6qVdbil0FERFfGvcBQkREcjMuA0Q0qsdaRURSffVza7I+xrfpTftDEcqCybnRJ8+c59Gf76OnL8TZc/1FrJmIyIXlzpuWsvOddhbMm5bVcb5tQXzzh2/w8s7WxGS5A61n2bH/JOd6Q3T3KECIiMQ98vRefvLKwayP822AqK4qZ+NP93D/xu3s2H+SiollAHzuxqVsuFmPtoqIjJZvu5i+9NuX8Ms9J3jixXd4aPPORHlvf5gOZXAVEUlTN6Ui62N824IIBAKsWdrIn961ns/emEgiyIM/2sHbh88UsWYiIhee0129WR/j2wARN6EsyIdWzeZrn/dG6EPhKC/sKEiaJxGRcc23AWLgo6zVVRMB+NR1C4tRHRGRcce3AaK3Pz1beHyQunxCkEsuyu5RLhERGcy3AWLgUqLxANHbH6ajW4PUIiKjNW4CxMQJQQJ4LYt3T3QVp1IiIuOIbwNE1/lQ2nYgEKC8vIy+/nCRaiQiMr74NkAMbEGA183UdV6zqEVE8sHHASI9EPSHwpzt7mPrrveKVCMRkfHFtwHi7IAWxFsHTxepJiIi45NvA0RbR0/a9uRK32YNERG5IPk2QLS0nk3bnlY9qUg1EREZn3wbIAAOtp7l0HteoJhaXV7k2oiIXLjWWEPWx/g6QPzp3/+Kf3zmbQBa288VuTYiIheGO29aysavfJgb1s5NlH386gVZv4+vAwR4M6cBJsVmUouIlIrrVs1O254xrQqAlYunA/Dc60cTv6vI4TPS9wHiSFs34C0gJCJSKqoqJrBuaWNaWW9fiMapldRUlRMKRwiFI0McPTIjevTHzGqArcDNzrlDZnY98ABQCTzunLsvtt9K4PtADfAS8EXnXMjM5gGbgEbAAbc75/KWDyMSifLPL2e/nJ6IiF9ddVkThwekFTrfF2Zpcx1AWsqhT1x9UU7nGLYFYWbrgZeBJbHtSmAj8HFgGbDWzG6K7b4J+JJzbgkQAO6OlT8MPOycWwr8CvhaTrUdwutvt/GbA+35fEsRkQva+uUzOHCsI7EdAHr7wiycVQvAM798N/G7D6+ek9M5RtLFdDdwLxBfhWcdsM85d9A5F8ILCreaWTNQ6Zx7LbbfI7HyicA1wObU8pxqO4Qtrx7i6MnufL6liMgFq2HqJObPrObN/ckvxvEVchbMqgFg2+7jid9NqZyY03mG7WJyzt0FYJZY1nMW0JqySysw533KpwNnY8EktTxvDh9X9lYRKR3XXT6XqXWTEw/pxE2cEGTV8iaCgfT9GxqqczpPLtOPgySDFXgtm0gW5cTK82JOw2S6e0Kc7sx+vVURET+6eN5UduwZnHeueUY1Z053s31PsvXwbz/QTFtbJ8FggPr6KVmdJ5enmI4ATSnbM/G6n4YqPwHUmln8Gasmkt1Vo1ZWFuTGdfPy9XYiIhe0pvoq5jZO4fDxzkG/i3cvPf7c/kTZtStn5XyuXALENsDMbFHsQ/824GnnXAvQY2YfjO332Vh5P/AL4NOx8s8BT+dc4wyuXZH7BRAR8ZO1SxsJBAIZu9YXzKqhPxRJ61GZXpt7GqKsA4Rzrge4E3gC2A3sJTkAfTvwoJntBaYAD8XK7wE2mNlu4GrgvpxrnEFFuSbJiUhpWLdsBgCHTwxuQSycVcuuAU90BgKBQfuN1IjHIJxz81NePwusyLDPm3hPOQ0sbwGuy6mGw4hGBg5viIiMT031VcyaPplIJDpoaeXaKeVMq6ngn15Idi9dv2Z0zwP5fib14RNdo54tKCLiB1dcPBOA46fP0def/rm3oKmGvv4I2/ecGLR/rnwfIAA2fPOFYldBRGTMrVvmpdZoyTBAvXB2LW/sb0srm9uY3VNLA42LACEiUgoa67xkfJkGqBfOqmH77hNpZRPKRvcRrwAhIuIzmR5xbZhamZZy6JoVTYP2yZYChIiIj0SjUQ4f70prHcxtnMJbB08RTnloZ/WS7BcIGkgBQkTER0539tJ1vp/qqmR+pYWzatiWMnsavDGJ0fJtgJikuQ8iUoLiA9SpCwBNn1rJnpbTaftNnpRbgr5Uvg0QU6sril0FEZGCO3y8iwDQea4vUXbqbA/RlClh8aedRsu3AaKqIpc8gyIi/nb4eCczplXR3RNKlLUc72R2w+TE9qUL6vNyLt8GCE2gFpFSdPh4J/NmJOc3NEydxDtHzyYWCgJYNGf04w/g4wDR2xceficRkXGk63w/7Wd7aapPthY6uryuptQEfY1TK/NyPv8GiP7Q8DuJiIwj78YGqFOfYOoLRbioqSYxB2LxnNpRJehL5dsAMTAPiYjIeNcSm0E9cUL6R/f6lEHpVYtHP/8hzrcBYuBSeyIi493hE53UVVdw+L1kqo0AsLS5LrGdr/EH8HGA6A+pBSEipeXw8S6aZ1SnpdRYMncqO99JbjfPyG396Ux8GyBEREpNa3s382ZM4cSZ84my9ctn8NSrLYC3eNrA7qfRUIAQEfGJaBTmDWghXG4NiS73D62cndfzKUCIiPhI6hyI5fPr0sZjF8/N3/gDKECIiPjG5EkTqKpIPuJ6xcUz+U3K+MOiPCToS6UAISLiE3Mbp7D/6JnE9uolDfxs+7uJ7eqq8ryeTwFCRMQn5s2oTgxIg5fVOj5gfcmCaXk/nwKEiIhPNM+oZt+RDgBWLprOsfZzid+tsfxkcE01qpSoZvY80Aj0x4r+I7AQuA+YCHzLOffd2L7XAw8AlcDjzrn7RnNuEZFSkzpA/dvXLmBvyhoQi/M4QS4u5wBhZgFgCdDsnAvFymYDjwGXA73A1lgQOQhsBK4F3gWeMrObnHNPj7L+IiIloyElCd+Muir+4WcusT1zWlXezzeaFoTF/vuMmdUDfwt0As85504BmNlm4FPAi8A+59zBWPkm4FZAAUJEZISee/1o4nVZMMD+WHdTZcWEvCXoSzWaAFEHPAv8J7zupBeAx4HWlH1agXXArAzlc0ZxbhGRkvOj5/cnXnf0Juc//NY1C2hoyF+KjbicA4Rz7lXg1fi2mf0Ab4zhz1J2CwARvMHwaIZyERHJUlN9FVt3HElsX9Q4hba2zvc9JhgMUF8/5X33GXRMTrUDzOwqM/tISlEAOAQ0pZTNBI4BR4YoFxGRLC1rrmP3oeQA9fyZ+W89wOi6mKYCf2JmV+J1MX0euAPYZGYNQDdwC7AB2AmYmS3CG7C+DW/QWkREslRTVZ42HlE+sWxMzpNzC8I5twV4CngD+DWw0Tn3CvBV4HlgB/Coc267c64HuBN4AtgN7AU2j67qIiKl6XRXcnnRSxfUj9l5AtFodPi9LizzgYNf+LNnOHH6/HD7ioiMOzZ3Ku5dL+XGPZ+4hDVLh58klzIGcRHecMDwx+ReRRERKYb3TidnUC+eO3XMzqMAISLiMx1dfYnXtZPzm6AvlQKEiIhPVVWMKlvSsBQgRER86oa1c8f0/RUgRER8arU1jOn7K0CIiPjUrOmTx/T9FSBERHwqOAYJ+tLef0zfXURExsRYTpCLU4AQEfGhj1w+e8zPoQAhIuJDNrduzM+hACEi4kMV5WOToC+VAoSIiM+M9QS5OAUIERGfuWHd2E6Qi1OAEBHxmasubRp+pzxQgBAR8ZlpNZMKch4FCBERyUgBQkTERy5bOPYT5OIUIEREfOSjY5zBNZUChIiIjyybN/YT5OIUIEREfCQYHNsEfWnnKtiZRERkVCZPKswEuTgFCBERnxjrFeQGUoAQEfGJ61aNfQbXVAVtr5jZbcB9wETgW8657xby/CIiflZdVV7Q8xWsBWFms4E/B64CVgIbzGx5oc4vIiLZKWQX0/XAc865U865bmAz8KkCnl9ERLJQyC6mWUBrynYrsK6A5xe5YP2PO9dytruPN95uY+HsWuqqKwhHIBKJEI5EiUSinOsNcayti2Mnu2k92c2KxQ3MrK8iEokm9gmnvY6klUdiP6FwhP5QhJ37T9I8s5oocPpsL719IfpCkWHr2jitivkzawhFIoTDEULhaOy/sdeR1LL07b5QhEgkmpdr9sgf3kBlxQT2HzlD68luKiaWUR77KQsGKCsLUBYMEgwECIUjtJ05RygcpbJ8Ar39IfrDUUIh71r0h8Kx/0boC4U53xviXE+I872h5Ouefs7FXsdVV02kdkoFR050ATChLEAo7P37Ll04nbKyAN3n++nrDxMIBOjtD3Oup5/u8yFC4eGvdarb/81SGhqq83LtRqqQASIIpN4ZASC7K5TiG797Zd5utFQNDdW0tXXm/X39SNciqVDXYnUB0yjk6kK5LyJ9Ibr7QjTVTqKpdvjkdTNrK/Jeh0Jfi9GcKxgMUF8/Jbtjcj5b9o4AqTlqZwLHCnh+ERHJQiFbED8H/sjMGoBu4BZgQwHPLyIiWShYC8I5dxT4KvA8sAN41Dm3vVDnFxGR7BR0HoRz7lHg0UKeU0REcqOZ1CIikpEChIiIZKQAISIiGRU2d2x+lMHY5kQvZL71C52uRZKuRZKuRZJfrkVKPctGekwgGs3/ZLMxdhXwi2JXQkTEp64GXh7Jjn4MEBXAWrxUHeEi10VExC/K8CYr/xLoHckBfgwQIiJSABqkFhGRjBQgREQkIwUIERHJSAFCREQyUoAQEZGMFCBERCQjBQgREclIAUJERDJSgBARkYwUIEREJCMFCBERyciP6b7zxszuB/59bPMp59x/M7PrgQeASuBx59x9RatgEZjZ/wSmO+fuNLOVwPeBGuAl4IvOuVBRK1gAZvYx4H5gMvCMc+4/l+p9YWZ3AP89tvm0c+7LpXZfmFkNsBW42Tl3aKh7YTxel5JtQcT+J98ArAJWApeb2WeAjcDHgWXAWjO7qXi1LCwz+wjw+ZSiTcCXnHNLgABwd1EqVkBmtgD4G+ATwGXA6tg9UHL3hZlVAQ8B1wIrgKtjfzclc1+Y2Xq81NhLYtuVDH0vjLvrUrIBAi9d+O875/qcc/3AHrybYJ9z7mAs8m8Cbi1mJQvFzKYBfw78RWy7Gah0zr0W2+URSuNafBLvW+GR2H3xaeAcpXlflOF9RkwGJsZ++imt++Ju4F7gWGx7HRnuhfH691KyXUzOubfir81sMV5X03fwAkdcKzCnwFUrlv8NfBWYG9ueRWlei0VAn5n9GJgHbAHeogSvhXOu08y+BuzFC5IvAn2U0LVwzt0FYGbxoqH+Lsbl30sptyAAMLOLgX8F/gA4AKQukBEAIsWoVyGZ2V3Au865Z1OKg5TgtcD70nQ98AXgCmA9sIASvBZmdhnwO0Az3gdgGK9btuSuRYqh/i7G5d9LybYgAMzsg8ATwH9xzj1mZtfirbgUN5Nk03I8+zTQZGY7gGnAFLybvRSvxXvAz51zbQBm9iReV0Hq6oWlci1uBJ51zp0AMLNHgC9TmvdF3BEy//uHKve1km1BmNlc4P8BtznnHosVb/N+ZYvMrAy4DXi6WHUsFOfcR51zlzjnVgJ/CPzYOfcfgJ5YEAX4LCVwLfC6lG40s6mxe+AmYDMleF8AbwLXm9lkMwsAH8PrZirF+yIu42eEc66FcXhdSjZA4H0TmgQ8YGY7Yt+e74z9PAHsxut73VysCl4AbgceNLO9eK2Kh4pcnzHnnNsGfAPvyZXdQAvwPUrwvnDOPQP8EPg1sBNvkPovKcH7Is4518PQ98K4uy5ak1pERDIq5RaEiIi8DwUIERHJSAFCREQyUoAQEZGMFCBERCQjBQgREclIAUJERDIq6VQbIiNlZkHgQeADQDVerp27gLeBvwMWAu14qTp2Oef+yMyWAd8G6vEyoz7knNtYhOqL5EQtCJGRWY+XsO4K59xy4O+Br+DNln3LObcML2fTlQBmNgFvhu1XnHOX462p8GUz+0AxKi+SC82kFhkh83I+fxivtXAd0AmsBlY7596J7fMQcAr4EfA6XiqGuFrgG8657xWw2iI5UxeTyAiY2b/D6y76a+Cf8T747wBCeN1NcfGsr2VARywBYvw9ZgAdBamwSB6oi0lkZD4K/CT27f9XeEuSlgFP4a0dgZnV461IFwUccD62pnM8e/Au4PLCV10kN+piEhkBM1uKl9l0QuznGeAWvLWav4+3El07Xmtii3Pum2a2Aq/VMQ0vE+q3nXN/U4Tqi+REAUJkFMzsHuAN59yrZlYB/AK43znn+7UARDQGITI6u4HvxBaPKQf+ScFBxgu1IEREJCMNUouISEYKECIikpEChIiIZKQAISIiGSlAiIhIRv8fiwm0Hls0OcgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "male_df.plot(x='age', y='tenure',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x190470eaba8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "female_df.plot(x='age', y='tenure',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above graphs, we can see male users dominating slightly female,\n",
    "and both genders plot distrubution looking similar respect to age ranges \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "\n",
    "- Male users are more, compared with female users counts in all areas.\n",
    "- Gender \"not specified\" users are mostly falling under above age 45.\n",
    "- The likes ratio irrespective of mobile, www and likes are same between both genders. but likes received are different in each medium.\n",
    "- The tenure ration is bit higher for male than female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}