MCC Project 2 Notebook-Analyzing MovieLens.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study 2 : Analyzing data from MovieLens\n",
    "\n",
    "TEAM Member: Matt Curcio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Desired outcome of the case study.**\n",
    "* In this case study we will look at the MovieLens 1M Data Set. It contains data about users and how the rate movies.\n",
    "* The idea is to *analyze* the data set, make *conjectures*, support or refute those conjectures with *data*, and *tell a story* about the data!\n",
    "    \n",
    "**Required Readings:** \n",
    "* Chapter 2 (only the section on the MovieLens 1M Data Set), Chapter 5, Chapter 6 Pg 171-172,\n",
    "and Chapter 8 of the book [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)  (available from the WPI library as an e-book).\n",
    "* If you want your code to be really compact then you might want to also look into the pivot_table method of Panda's DataFrame, though there are many other ways to complete the case study!\n",
    "\n",
    "**Case study assumptions:**\n",
    "* You have access to a ipython notebook installation, python 2.7\n",
    "\n",
    "**Required Python libraries:**\n",
    "* Pandas (pandas.pydata.org)\n",
    "* Matplotlib (matplotlib.org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Problem 1: Importing the MovieLens data set and merging it into a single Pandas DataFrame\n",
    "\n",
    "* Download the 1 million ratings data set from http://grouplens.org/datasets/movielens/ (though if you are feeling adventerous you can download the 10 million ratings file instead)\n",
    "* Merge all of the data into a single Pandas DataFrame\n",
    "* Store the data into an HDF5 file.\n",
    "\n",
    "###Report some basic details of the data you collected.  For example:\n",
    "\n",
    "*  How many movies have an average rating over 4.5 overall?\n",
    "*  How many movies have an average rating over 4.5 among men?  How about women?\n",
    "*  How many movies have an *median* rating over 4.5 among men over age 30?  How about women over age 30?\n",
    "*  What are the ten most popular movies?\n",
    "    * Choose what you consider to be a reasonable defintion of \"popular\".\n",
    "    * Be perpared to defend this choice.\n",
    "*  Make some conjectures about how easy various groups are to please? Support your answers with data!  \n",
    "    *  For example, one might conjecture that people between the ages of 1 and 10 are the easiest to please since they are all young children.  This conjecture may or may not be true, but how would you support or disprove either conclusion with with data?\n",
    "    *  Be sure to come up with your own conjectures and support them with data!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages \n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib.pyplot import * \n",
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "import matplotlib.pylab as py\n",
    "import matplotlib.pyplot as plt\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "# defining function to read and save data to file\n",
    "def save_hdf5(filename, data):\n",
    "    data.to_hdf(filename, 'table', append=True)\n",
    "\n",
    "def read_hdf5(filename):\n",
    "    return pd.read_hdf(filename, 'table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read users data\n",
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
    "users = pd.read_table('users.dat', sep='::', header=None, names=unames)\n",
    "# read ratings data\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_table('ratings.dat', sep='::', header=None, names=rnames)\n",
    "\n",
    "# read movies data\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('movies.dat', sep='::', header=None, names=mnames)\n",
    "# merge data\n",
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "# save in HDF5 format\n",
    "save_hdf5('movielens_1M.h5', data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data\n",
    "data = read_hdf5('movielens_1M.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>  1</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978300760</td>\n",
       "      <td> F</td>\n",
       "      <td>  1</td>\n",
       "      <td> 10</td>\n",
       "      <td> 48067</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>  2</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978298413</td>\n",
       "      <td> M</td>\n",
       "      <td> 56</td>\n",
       "      <td> 16</td>\n",
       "      <td> 70072</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 12</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978220179</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td> 12</td>\n",
       "      <td> 32793</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 15</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978199279</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  7</td>\n",
       "      <td> 22903</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 17</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978158471</td>\n",
       "      <td> M</td>\n",
       "      <td> 50</td>\n",
       "      <td>  1</td>\n",
       "      <td> 95350</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
       "0        1      1193       5  978300760      F    1          10  48067   \n",
       "1        2      1193       5  978298413      M   56          16  70072   \n",
       "2       12      1193       4  978220179      M   25          12  32793   \n",
       "3       15      1193       4  978199279      M   25           7  22903   \n",
       "4       17      1193       5  978158471      M   50           1  95350   \n",
       "\n",
       "                                    title genres  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "1  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "2  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "3  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "4  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies with overall mean rating over 4.5: 21\n"
     ]
    }
   ],
   "source": [
    "# average rating overall\n",
    "mean_ratings = data.pivot_table('rating', rows='title', aggfunc='mean')\n",
    "good_reviews_idx = mean_ratings.index[mean_ratings > 4.5]\n",
    "good_ratings = mean_ratings.ix[good_reviews_idx]\n",
    "print 'Movies with overall mean rating over 4.5:', len(good_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# average rating by gender\n",
    "mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='mean')\n",
    "\n",
    "good_reviews_idx_m = mean_ratings.index[mean_ratings['M'] > 4.5]\n",
    "good_reviews_idx_f = mean_ratings.index[mean_ratings['F'] > 4.5]\n",
    "good_ratings_m = mean_ratings.ix[good_reviews_idx_m]\n",
    "good_ratings_f = mean_ratings.ix[good_reviews_idx_f]\n",
    "print 'Movies with male mean rating over 4.5:', len(good_ratings_m)\n",
    "print 'Movies with female mean rating over 4.5:', len(good_ratings_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies with 31+ y.o. male median rating over 4.5: 86\n",
      "Movies with 31+ y.o. female median rating over 4.5: 149\n"
     ]
    }
   ],
   "source": [
    "data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# median rating by gender and age > 30\n",
    "over_30 = data[(data['age'] > 30)]\n",
    "median_ratings = over_30.pivot_table('rating', rows='title', cols='gender', aggfunc='median')\n",
    "good_reviews_idx_m = median_ratings.index[median_ratings['M'] > 4.5]\n",
    "good_reviews_idx_f = median_ratings.index[median_ratings['F'] > 4.5]\n",
    "good_ratings_m = median_ratings.ix[good_reviews_idx_m]\n",
    "good_ratings_f = median_ratings.ix[good_reviews_idx_f]\n",
    "print 'Movies with 31+ y.o. male median rating over 4.5:', len(good_ratings_m)\n",
    "print 'Movies with 31+ y.o. female median rating over 4.5:', len(good_ratings_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "American Beauty (1999)                                   6856\n",
       "Star Wars: Episode IV - A New Hope (1977)                5982\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    5980\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        5766\n",
       "Jurassic Park (1993)                                     5344\n",
       "Saving Private Ryan (1998)                               5306\n",
       "Terminator 2: Judgment Day (1991)                        5298\n",
       "Matrix, The (1999)                                       5180\n",
       "Back to the Future (1985)                                5166\n",
       "Silence of the Lambs, The (1991)                         5156\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# top 10 most reviewed movies\n",
    "top_10 = data.groupby('title').size().order(ascending=False)[:10]\n",
    "top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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toNVqTY9v3boVXl5eaNGiBWbPnm3Wwonux8GDSo9//nwGP1k5KUF6erqIiOTk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\n+BMRWSGGPxGRFWL4ExFZof8HqS76x5VzIRQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3e36257d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qVKkjYozVN5z8tZSbmzAInJ+fUAG88YbUETHG6hNO/lrM2RlQKIQbwuTmAm+/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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3e062e810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how easy each group is pleased\n",
    "\n",
    "rate=list(data['rating'].groupby(data['age']).mean())\n",
    "x=[1,2,3,4,5,6,7]\n",
    "title(\"Average ratings versus Age\")\n",
    "xticks([1,2,3,4,5,6,7],[\"Under18\",\"18-24\",\"25-34\",\"35-44\",\"45-49\",\"50-55\",\"56+\"])\n",
    "plot(x,rate)\n",
    "show()\n",
    "\n",
    "rate_sd=Series(data['rating'].groupby(data['age']).std())\n",
    "x=[1,2,3,4,5,6,7]\n",
    "title(\"Standared Deviation of ratings versus Age\")\n",
    "xticks([1,2,3,4,5,6,7],[\"Under18\",\"18-24\",\"25-34\",\"35-44\",\"45-49\",\"50-55\",\"56+\"])\n",
    "plot(x,rate_sd)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd3debcb250>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd3debd7590>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3debd7bd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd3debcbed0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3debca8d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd3debcb890>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd3debd1c10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3debd1e90>]}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1a9d6t61cuVIOh0NLliyRJCUlJamyslI2m833RBaLX9ADwcBikXhrIlh1l53dtlAaGhrU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\nQ4kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3e062e290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot for the age \n",
    "age=dict(list(data.groupby('age')))\n",
    "counts=list(data.groupby(\"age\").size())\n",
    "#len(subset_age)\n",
    "# subplot(241)\n",
    "age1=list(age[1]['rating'])\n",
    "title(\"rating <18\")\n",
    "xticks([1],[counts[0]])\n",
    "boxplot(age1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2238.,     0.,  2983.,     0.,     0.,  6380.,     0.,  8808.,\n",
       "           0.,  6802.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QETEpBYCIiEkpAERETEoBICJiUgoAERGTUgCIiJiUAkBExKQUACIiJqUAEBEx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3db0f9a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#len(subset_age)\n",
    "age1=list(age[1]['rating'])\n",
    "title(\"Rating < 18\")\n",
    "hist(age1,bins=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 13063.,      0.,  22073.,      0.,      0.,  47601.,      0.,\n",
       "        60241.,      0.,  40558.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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62hlQOIQChYNI6FM4hJ6RvnbqDmkREbFQOIiIiIXCQURELBQOIiJioXAQERELhYOIiFgo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a5316d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age18=list(age[18]['rating'])\n",
    "title(\"18 < Rating < 24\")\n",
    "hist(age18,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  23898.,       0.,   44817.,       0.,       0.,  104287.,\n",
       "             0.,  136824.,       0.,   85730.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MRqPcd999kpqaKqmpqcpdTiIiGzZskNjYWImPjxer1aqMHz9+XJKSkiQ2NlZWrlypjF+6\ndEkeffRRMRqNkp6eLg6HQ5m3bds2MRqNYjQaZceOHdet8Sa7QER+ALxLyq942wvN1ZXHLY1Gg3G+\nC0R3PI1GA8DXv6d8rRjg7esm3+lNRESqMDCIiEgVBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEBGR\nKgwMIiJShYFBRESqMDCIiEgVBgYREanCwCAiIlUYGEREpAoDg4iIVGFgEBGRKgwMIiJShYFBRESq\nMDCIiEgVBgYREanCwCAiIlUYGEREpEqArwsgulMFB4fD42n3aQ1BQWHo6mrzaQ1059CIiPi6iNHQ\naDQY57tAdyiNRgPA1z+b/vH7wV74F29fN294SmrFihXQarVITk5Wxtra2pCVlYW4uDhkZ2ejo6ND\nmVdaWgqTyYSEhATU1NQo4ydOnEBycjJMJhNWrVqljF++fBn5+fkwmUyYNWsWGhoalHkVFRWIi4tD\nXFwcdu7cOeIdIyKiMSY3cPjwYTl58qQkJSUpY88//7y88sorIiJSVlYma9asERGR06dPS0pKinR3\nd4vD4ZDY2Fjp6+sTEZGZM2eKzWYTEZH58+fLgQMHRERky5YtUlJSIiIiFotF8vPzRUSktbVVpk2b\nJu3t7dLe3q48Hs5NdoHIZwAIID6e/OP3g73wL9724oZHGLNnz0ZYWNigserqahQVFQEAioqKsG/f\nPgBAVVUVCgoKEBgYiJiYGBiNRthsNrS0tMDj8cBsNgMAli1bpqxz7bby8vJw8OBBAMCbb76J7Oxs\nhIaGIjQ0FFlZWbBarWMSkERE5J0R3yXldruh1WoBAFqtFm63GwDQ3NwMg8GgLGcwGOByuYaM6/V6\nuFwuAIDL5UJ0dDQAICAgACEhIWhtbb3utoiIyHdGdZeURqO5ejHLt9atW6c8zsjIQEZGhs9qISLy\nN7W1taitrR31dkYcGFqtFufOnYNOp0NLSwsiIyMB9B85NDU1Kcs5nU4YDAbo9Xo4nc4h4wPrNDY2\nIioqCj09Pejs7ERERAT0ev2gnWtqasLcuXOvW9O1gUFERIN99Q/p9evXe7WdEZ+Sys3NRUVFBYD+\nO5kWLVqkjFssFnR3d8PhcMBut8NsNkOn0yE4OBg2mw0igsrKSixcuHDItvbu3YvMzEwAQHZ2Nmpq\natDR0YH29na89dZbmDdvnlc7SEREY+RGV8SXLl0qU6ZMkcDAQDEYDLJt2zZpbW2VzMxMMZlMkpWV\nNejupQ0bNkhsbKzEx8eL1WpVxo8fPy5JSUkSGxsrK1euVMYvXbokjz76qBiNRklPTxeHw6HM27Zt\nmxiNRjEajbJjx47r1niTXSDyGfDOIAV74V+87QXfuEd0i/DNatdUwV74lVvyxj0iIqIBDAwiIlKF\ngUFERKowMIiISBUGBhERqcLAICIiVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIiISBUG\nBhERqcLAICIiVRgYRESkCgODiIhUYWAQEZEqDAwiIlIlwNcFEBF9nQQHh8Pjafd1GV7h//QmukX4\nf6yvqYK9+LKKcdwLnpIiIiJVGBhERKQKA4OIiFTxOjBKS0sxY8YMJCcn47HHHsPly5fR1taGrKws\nxMXFITs7Gx0dHYOWN5lMSEhIQE1NjTJ+4sQJJCcnw2QyYdWqVcr45cuXkZ+fD5PJhFmzZqGhocHb\nUomIaAx4FRhnz57FH/7wB5w8eRIffPABent7YbFYUFZWhqysLJw5cwaZmZkoKysDANTX12P37t2o\nr6+H1WrFU089pVxwKSkpQXl5Oex2O+x2O6xWKwCgvLwcERERsNvtWL16NdasWTNGu0xERN7wKjCC\ng4MRGBiICxcuoKenBxcuXEBUVBSqq6tRVFQEACgqKsK+ffsAAFVVVSgoKEBgYCBiYmJgNBphs9nQ\n0tICj8cDs9kMAFi2bJmyzrXbysvLw8GDB0e9s0RE5D2vAiM8PBw/+clPcN999yEqKgqhoaHIysqC\n2+2GVqsFAGi1WrjdbgBAc3MzDAaDsr7BYIDL5Royrtfr4XK5AAAulwvR0dEAgICAAISEhKCtrc27\nvSQiolHz6o17n3zyCX7961/j7NmzCAkJwaOPPorXX3990DIajebq/ca33rp165THGRkZyMjIuC3P\nS0Q0PtRenUbHq8A4fvw4/u7v/g4REREAgEceeQRHjhyBTqfDuXPnoNPp0NLSgsjISAD9Rw5NTU3K\n+k6nEwaDAXq9Hk6nc8j4wDqNjY2IiopCT08POjs7ER4ePmw91wYGERF9VcbVacB6r7bi1SmphIQE\nHD16FBcvXoSI4O2330ZiYiIWLFiAiooKAEBFRQUWLVoEAMjNzYXFYkF3dzccDgfsdjvMZjN0Oh2C\ng4Nhs9kgIqisrMTChQuVdQa2tXfvXmRmZnq1g0RENDa8OsJISUnBsmXL8NBDD2HChAl44IEH8KMf\n/QgejwdLlixBeXk5YmJisGfPHgBAYmIilixZgsTERAQEBGDr1q3K6aqtW7di+fLluHjxInJycvDw\nww8DAIqLi1FYWAiTyYSIiAhYLJYx2mUiIvIGP0uK6BYZz58ZNOZVsBdfVjGOe8F3ehMRkSoMDCIi\nUoWBQUREqjAwiIhIFQYGERGpwsAgIiJVGBhERKQKA4OIiFRhYBARkSoMDCIiUoWBQUREqjAwiIhI\nFQYGERGpwsAgIiJVGBhERKQKA4OIiFRhYBARkSoMDCIiUoWBQUREqjAwiIhIFQYGERGpwsAgIiJV\nGBhERKSK14HR0dGBxYsXY/r06UhMTITNZkNbWxuysrIQFxeH7OxsdHR0KMuXlpbCZDIhISEBNTU1\nyviJEyeQnJwMk8mEVatWKeOXL19Gfn4+TCYTZs2ahYaGBm9LJSKiMeB1YKxatQo5OTn46KOP8D//\n8z9ISEhAWVkZsrKycObMGWRmZqKsrAwAUF9fj927d6O+vh5WqxVPPfUURAQAUFJSgvLyctjtdtjt\ndlitVgBAeXk5IiIiYLfbsXr1aqxZs2YMdpeIiLwmXujo6JCpU6cOGY+Pj5dz586JiEhLS4vEx8eL\niMjGjRulrKxMWW7evHly5MgRaW5uloSEBGV8165d8uMf/1hZ5ujRoyIicuXKFbnnnnuGrcXLXSC6\n5QAIID6e/OP3g7340njuhVdHGA6HA/feey8ef/xxPPDAA3jyySdx/vx5uN1uaLVaAIBWq4Xb7QYA\nNDc3w2AwKOsbDAa4XK4h43q9Hi6XCwDgcrkQHR0NAAgICEBISAja2tq8KZeIiMZAgDcr9fT04OTJ\nk9i8eTNmzpyJZ599Vjn9NECj0UCj0YxJkTezbt065XFGRgYyMjJuy/MSEY0PtVen0fEqMAwGAwwG\nA2bOnAkAWLx4MUpLS6HT6XDu3DnodDq0tLQgMjISQP+RQ1NTk7K+0+mEwWCAXq+H0+kcMj6wTmNj\nI6KiotDT04POzk6Eh4cPW8+1gUFERF+VcXUasN6rrXh1Skqn0yE6OhpnzpwBALz99tuYMWMGFixY\ngIqKCgBARUUFFi1aBADIzc2FxWJBd3c3HA4H7HY7zGYzdDodgoODYbPZICKorKzEwoULlXUGtrV3\n715kZmZ6tYNERDQ2NFcvwoxYXV0dnnjiCXR3dyM2Nhbbt29Hb28vlixZgsbGRsTExGDPnj0IDQ0F\nAGzcuBHbtm1DQEAANm3ahHnz5gHov612+fLluHjxInJycvDaa68B6L+ttrCwEKdOnUJERAQsFgti\nYmKG7oBGAy93geiW6j8l6+ufTf/4/WAvrqliHPfC68DwFwwM8lfj+YVhzKtgL76sYhz3gu/0JiIi\nVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKowMIiISBUGBhERqcLAICIiVRgYRESkCgODiIhU\nYWAQEZEqDAwiIlKFgUFERKowMIiISBUGBhERqcLAICIiVRgYRESkCgODiIhUCfB1ATR2goPD4fG0\n+7SGoKAwdHW1+bQGIro1NOIP/xV9FDQa//jH7v5gPP9z+TsRvx/XVMFefFnFOO4FT0kREZEqDAwi\nIlJlVIHR29uLtLQ0LFiwAADQ1taGrKwsxMXFITs7Gx0dHcqypaWlMJlMSEhIQE1NjTJ+4sQJJCcn\nw2QyYdWqVcr45cuXkZ+fD5PJhFmzZqGhoWE0pRIR0SiNKjA2bdqExMTEq+fkgLKyMmRlZeHMmTPI\nzMxEWVkZAKC+vh67d+9GfX09rFYrnnrqKeX8WUlJCcrLy2G322G322G1WgEA5eXliIiIgN1ux+rV\nq7FmzZrRlEpERKPkdWA4nU7s378fTzzxhPLiX11djaKiIgBAUVER9u3bBwCoqqpCQUEBAgMDERMT\nA6PRCJvNhpaWFng8HpjNZgDAsmXLlHWu3VZeXh4OHjzo/V4SEdGoeR0Yq1evxquvvooJE77chNvt\nhlarBQBotVq43W4AQHNzMwwGg7KcwWCAy+UaMq7X6+FyuQAALpcL0dHRAICAgACEhISgrY23axIR\n+YpX78N44403EBkZibS0NNTW1g67jEajUU5V3Wrr1q1THmdkZCAjI+O2PC8R0fhQe3UaHa8C4733\n3kN1dTX279+PS5cuoaurC4WFhdBqtTh37hx0Oh1aWloQGRkJoP/IoampSVnf6XTCYDBAr9fD6XQO\nGR9Yp7GxEVFRUejp6UFnZyfCw8OHrefawCAioq/KuDoNWO/VVrw6JbVx40Y0NTXB4XDAYrFg7ty5\nqKysRG5uLioqKgAAFRUVWLRoEQAgNzcXFosF3d3dcDgcsNvtMJvN0Ol0CA4Ohs1mg4igsrISCxcu\nVNYZ2NbevXuRmZnp1Q4SEdHYGJOPBhk49fSv//qvWLJkCcrLyxETE4M9e/YAABITE7FkyRIkJiYi\nICAAW7duVdbZunUrli9fjosXLyInJwcPP/wwAKC4uBiFhYUwmUyIiIiAxWIZi1KJiMhL/GiQO8h4\n/siBOxG/H9dUwV58WcU47gXf6U1ERKowMIiISBUGBhERqcLAICIiVRgYRESkCgODiIhUYWAQEZEq\nDAwiIlKFgUFERKowMIiISBUGBhERqcLAICIiVRgYRESkCgODiIhUYWAQEZEqDAwiIlKFgUFERKow\nMIiISBUGBhERqcLAICIiVQJ8XcBYMBof9Onzv/jiM1ixosinNRAR3Wp3RGB88snvffjsr+PkyTqs\nWOHDEoiIboM7IjAAXx5hHALg9OHzExHdHl5dw2hqasKcOXMwY8YMJCUl4bXXXgMAtLW1ISsrC3Fx\nccjOzkZHR4eyTmlpKUwmExISElBTU6OMnzhxAsnJyTCZTFi1apUyfvnyZeTn58NkMmHWrFloaGjw\ndh+JiGgMeBUYgYGB+NWvfoXTp0/j6NGj2LJlCz766COUlZUhKysLZ86cQWZmJsrKygAA9fX12L17\nN+rr62G1WvHUU09BRAAAJSUlKC8vh91uh91uh9VqBQCUl5cjIiICdrsdq1evxpo1a8Zol4mIyBte\nBYZOp0NqaioAYPLkyZg+fTpcLheqq6tRVNR/8beoqAj79u0DAFRVVaGgoACBgYGIiYmB0WiEzWZD\nS0sLPB4PzGYzAGDZsmXKOtduKy8vDwcPHhzdnhIR0aiM+rbas2fP4tSpU0hPT4fb7YZWqwUAaLVa\nuN1uAEBzczMMBoOyjsFggMvlGjKu1+vhcrkAAC6XC9HR0QCAgIAAhISEoK2tbbTlEhGRl0Z10fuL\nL75AXl4eNm3ahKCgoEHzNBoNNBrNqIpTb901jzOuTkRE1K/26jQ6XgfGlStXkJeXh8LCQixatAhA\n/1HFuXPnoNPp0NLSgsjISAD9Rw5NTU3Kuk6nEwaDAXq9Hk6nc8j4wDqNjY2IiopCT08POjs7ER4e\nfp1q1nm7G0REXwMZGPyH9HqvtuLVKSkRQXFxMRITE/Hss88q47m5uaioqAAAVFRUKEGSm5sLi8WC\n7u5uOBwO2O12mM1m6HQ6BAcHw2azQURQWVmJhQsXDtnW3r17kZmZ6dUOEhHRGBEvvPvuu6LRaCQl\nJUVSU1MlNTVVDhw4IK2trZKZmSkmk0mysrKkvb1dWWfDhg0SGxsr8fHxYrValfHjx49LUlKSxMbG\nysqVK5U+KPw0AAAGv0lEQVTxS5cuyaOPPipGo1HS09PF4XAMWwsAAcSH0y/k6adXe9PGMef7XvTX\nQP34/fgSe/Gl8dwLzdUdGLf6r5P4chd+iaefdmLz5l/6sIZ+vu8FAGgwzn+kxgy/H9dUwV58WcU4\n7gU/fJCIiFRhYBARkSoMDCIiUoWBQUREqjAwiIhIFQYGERGpwsAgIiJVGBhERKQKA4OIiFRhYBAR\nkSoMDCIiUoWBQUREqjAwiIhIFQYGERGpwsAgIiJVGBhERKQKA4OIiFRhYBARkSoMDCIiUoWBQURE\nqjAwiIhIFQYGERGp4veBYbVakZCQAJPJhFdeecXX5RARfW35dWD09vbin//5n2G1WlFfX49du3bh\no48+8nVZI1ZbW+vrElSq9XUBqoyffo4P46eftb4uQJXx08+R8+vAOHbsGIxGI2JiYhAYGIilS5ei\nqqrK12WN2Pj5Aar1dQGqjJ9+jg/jp5+1vi5AlfHTz5Hz68BwuVyIjo5WvjYYDHC5XD6siIjo6yvA\n1wXciEajUbVccPCCW1zJ9XV3f4K77srx2fMTEd024seOHDki8+bNU77euHGjlJWVDVomNjZWAHDi\nxIkTJ5VTbGysV6/JGhER+Kmenh7Ex8fj4MGDiIqKgtlsxq5duzB9+nRfl0ZE9LXj16ekAgICsHnz\nZsybNw+9vb0oLi5mWBAR+YhfH2EQEZH/8Ou7pAasWLECWq0WycnJ113mmWeegclkQkpKCk6dOnUb\nq+t3sxpra2sREhKCtLQ0pKWl4eWXX77NFfZramrCnDlzMGPGDCQlJeG1114bdjlf91NNnf7Q00uX\nLiE9PR2pqalITEzET3/602GX83U/1dTpD/0E+t9/lZaWhgULhr+Zxde9HHCjOv2llzExMbj//vuR\nlpYGs9k87DIj6qfXV6Rvo8OHD8vJkyclKSlp2Pn//d//LfPnzxcRkaNHj0p6evrtLE9Ebl7jX/7y\nF1mwYMFtrmqolpYWOXXqlIiIeDweiYuLk/r6+kHL+EM/1dTpLz09f/68iIhcuXJF0tPT5d133x00\n3x/6KXLzOv2ln7/4xS/kscceG7YWf+mlyI3r9JdexsTESGtr63Xnj7Sf4+IIY/bs2QgLC7vu/Orq\nahQVFQEA0tPT0dHRAbfbfbvKA3DzGgFA/ODsn06nQ2pqKgBg8uTJmD59Opqbmwct4w/9VFMn4B89\nnTRpEgCgu7sbvb29CA8PHzTfH/qppk7A9/10Op3Yv38/nnjiiWFr8Zde3qxOwPe9HHCjOkbaz3ER\nGDcz3Bv8nE6nDysaSqPR4L333kNKSgpycnJQX1/v65Jw9uxZnDp1Cunp6YPG/a2f16vTX3ra19eH\n1NRUaLVazJkzB4mJiYPm+0s/b1anP/Rz9erVePXVVzFhwvAvTf7Sy5vV6Q+9HKjje9/7Hh566CH8\n4Q9/GDJ/pP28IwIDGJqiat/0d7s88MADaGpqQl1dHVauXIlFixb5tJ4vvvgCixcvxqZNmzB58uQh\n8/2lnzeq0196OmHCBLz//vtwOp04fPjwsB8N4Q/9vFmdvu7nG2+8gcjISKSlpd3wr2Jf91JNnb7u\n5YC//e1vOHXqFA4cOIAtW7bg3XffHbLMSPp5RwSGXq9HU1OT8rXT6YRer/dhRUMFBQUppwTmz5+P\nK1euoK2tzSe1XLlyBXl5efjhD3847A+yv/TzZnX6U08BICQkBN///vdx/PjxQeP+0s8B16vT1/18\n7733UF1djalTp6KgoADvvPMOli1bNmgZf+ilmjp93csBU6ZMAQDce++9+MEPfoBjx44Nmj/Sft4R\ngZGbm4udO3cCAI4ePYrQ0FBotVofVzWY2+1WkvzYsWMQkWHPId9qIoLi4mIkJibi2WefHXYZf+in\nmjr9oaeff/45Ojo6AAAXL17EW2+9hbS0tEHL+EM/1dTp635u3LgRTU1NcDgcsFgsmDt3rtK3Af7Q\nSzV1+rqXAHDhwgV4PB4AwPnz51FTUzPkLs6R9tOv37g3oKCgAIcOHcLnn3+O6OhorF+/HleuXAEA\n/PjHP0ZOTg72798Po9GIb33rW9i+fbvf1bh371785je/QUBAACZNmgSLxXLbawT6D1Fff/115VY7\noP8XoLGxUanVH/qppk5/6GlLSwuKiorQ19eHvr4+FBYWIjMzE7/73e+UOv2hn2rq9Id+Xmvg1Ii/\n9fKrhqvTH3rpdrvxgx/8AED/p2b84z/+I7Kzs0fVT75xj4iIVLkjTkkREdGtx8AgIiJVGBhERKQK\nA4OIiFRhYBARkSoMDCIiUoWBQUREqjAwiIhIlf8HtDmDi25rl2QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3dc2ad3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age25=list(age[25]['rating'])\n",
    "title(\"25 < Rating < 34\")\n",
    "hist(age25,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  9067.,      0.,  20253.,      0.,      0.,  52990.,      0.,\n",
       "        71983.,      0.,  44710.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LrVeuXInX68Xn8+Hz+airqwPA6/USExODz+ejrKyM8vLyi7jLIiIynGHDweVy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\nyTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3dd1050d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age35=list(age[35]['rating'])\n",
    "title(\"35 < Rating <44\")\n",
    "hist(age35,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3409.,      0.,   8437.,      0.,      0.,  22311.,      0.,\n",
       "        30334.,      0.,  19142.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EsHv3bgDi4uJYvXo1cXFxBAQEUFRU5D3NVFRUxLp16+js7CQtLY2lS5cCkJubS1ZWFk6n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\nTwu1k00AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3db1ad990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age45=list(age[45]['rating'])\n",
    "title(\"45 < Rating < 49\")\n",
    "hist(age45,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2948.,      0.,   5993.,      0.,      0.,  18465.,      0.,\n",
       "        26484.,      0.,  18600.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mzMDj8QCwfv16tm7dyqFDh3A4HNxxxx28+OKLAKSkpLB8+XJSUlIICwtj8+bNn3x3Cmze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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a16cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age50=list(age[50]['rating'])\n",
    "title(\"50 < Rating < 55\")\n",
    "hist(age50,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1551.,      0.,   3001.,      0.,      0.,   9163.,      0.,\n",
       "        14297.,      0.,  10768.]),\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zg0FERC70x0+aV6xYEdN2+n0qqaGhgSeeeIK6ujouv/xya7y4uJja2lq6urpobm4mFArh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\nsdcEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38ef99a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age56=list(age[56]['rating'])\n",
    "title(\"Rating > 56\")\n",
    "hist(age56,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jYmNjsW/fPhM8BdJYjDHEx8fD3d0d/fv3R79+/fCvf/3L0tUy2BdffAEPDw/07NkTjo6O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\nhBDSDFCwJ4SQZuD/A6ZWe6ffh7e8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3de87a210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ISgIqKvTugsXeEnjwABg+3ODLNEZP9J09WxUWe0aVtDTj2DgA0KaNKKZ28qTe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3e0483ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rate=list(data['rating'].groupby(data['occupation']).mean())\n",
    "x=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]\n",
    "\n",
    "title(\"Average ratings versus Occupation\")\n",
    "xticks(x,x)\n",
    "#xticks(x,[\"other or not specified\",\"academic/educator\",\"artist\",\n",
    "#          \"clerical/admin\",\"college/grad student\",\"customer service\",\n",
    "#         \"doctor/health care\",\"executive/managerial\",\"farmer\",\"homemaker\",\n",
    "#          \"K-12 student\",\"lawyer\",\"programmer\",\"retired\",\"sales/marketing\",\n",
    "#          \"scientist\",\"self-employed\",\"technician/engineer\",\n",
    "#          \"tradesman/craftsman\",\"unemployed\",\"writer\"])\n",
    "plot(x,rate)\n",
    "show()\n",
    "title(\"Standarded Deviation of Rating versus the Occupation\")\n",
    "sd=rate=list(data['rating'].groupby(data['occupation']).std())\n",
    "xticks(x,x)\n",
    "plot(x,sd,color='r')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RnWN3DRHRCMWQJyJyYAx5IiIHxpAnInJgDHkiIgfGkCcicmAMeSIiB8aQJyJyYAx5IiIH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\nGEOeiMiBMeSJiBwYQ56IyIH9P0OFaS5Vo0kFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38d3db550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define \"popular\" and justify\n",
    "#using boxplot to get shape of movie counting\n",
    "counts=list(data.groupby(\"movie_id\").size())\n",
    "title(\"Counts of movie being rated \")\n",
    "xticks([1],[\"counts\"])\n",
    "boxplot(counts)\n",
    "show()\n",
    "#get the baisic idea of the spread of these data\n",
    "#I m intested in the movies which were rated larger than the upper inner fence which is 3rd quantile + 1.5 IQR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes.AxesSubplot object at 0x7fd3a16f4990>]], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OEDfjDgGFw2H4fD4MDg5icHAQjzzyCIqLi1FUVISKigrU1dXpHwMFgPz8fFRU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\nO35hVk108AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38d488750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rK5N/+Id/0Nuys7PF7/eLiMjg4KBkZ2eLiEhTU5M0NTXp+23YsEGOHj0qfr9f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38d488f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y3QAOHDiAxsZGAEBjYyP2798PAGhtbUVDQwMyMjLgdrvh9XoRDAYRiUQwMDCA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\nTo2VK9lj9fOf/xwHDhzAHXf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd395ae8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y3QAOHDiAxsZGAEBjYyP2798PAGhtbUVDQwMyMjLgdrvh9XoRDAYRiUQwMDCA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\nlrBgEBGRJf8PKuU/6oGR//kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38c794a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HN4PBqESmQJrNQ6vPwP5OVl8TkdBUUm1tLTo6OgAAHR0dWLVqVaS9s7MToVAI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\nFgYiIorCwkBERFFYGIiIKMr/AzZVjDp1I6qyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3883ca590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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792PcuHE4deoUcnJykJyc7HNc/Vv/iIjoagVVGMaNGwcAuP7667Fw4UIcOnQI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38d67ec10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tW7bIjh07RESkrq5Otm7dKiIinZ2dkp+fL36/XzwejzgcDgkEAiIiUlhYKG63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\nQEREIVgYiIgoBAsDERGFYGEgIqIQ/w9KLwMwyaKh/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd395337050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_under_18 = data[(data['age'] == 1)]\n",
    "age_18_24 = data[(data['age'] == 18)]\n",
    "age_25_34 = data[(data['age'] == 25)]\n",
    "age_35_44 = data[(data['age'] == 25)]\n",
    "age_45_49 = data[(data['age'] == 45)]\n",
    "age_50_55 = data[(data['age'] == 50)]\n",
    "age_56_older = data[(data['age'] == 56)]\n",
    "\n",
    "age_under_18.hist(column='rating', bins=5)\n",
    "age_18_24.hist(column='rating', bins=5)\n",
    "age_25_34.hist(column='rating', bins=5)\n",
    "age_35_44.hist(column='rating', bins=5)\n",
    "age_45_49.hist(column='rating', bins=5)\n",
    "age_50_55.hist(column='rating', bins=5)\n",
    "age_56_older.hist(column='rating', bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2: Expand our investigation to histograms\n",
    "\n",
    "**An obvious issue with any inferences drawn from Problem 1 is that we did not consider how many times a movie was rated.** \n",
    "* Plot a histogram of the ratings of all movies.\n",
    "* Plot a histogram of the *number* of ratings each movie recieved.\n",
    "* Plot a histogram of the *average rating* for each movie.\n",
    "* Plot a histogram of the *average rating* for movies which are rated more than 100 times.\n",
    "    * What do you observe about the tails of the histogram where you use all the movies versus the one where you only use movies rated more than 100 times?\n",
    "    * Which highly rated movies would you trust are actually good?  Those rated more than 100 times or those rated less than 100 times?\n",
    "* Make some conjectures about the distribution of ratings? Support your answers with data!  \n",
    "    *  For example, what age range do you think has more extreme ratings?  Do you think children are more or less likely to rate a movie 1 or 5?\n",
    "    *  Be sure to come up with your own conjectures and support them with data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f506992b110>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nwoULOH/+PE6dOoW6ujp5/JkzZ5zaee/atQtXX301kpKS7P4SajSaX67aEScv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5079a47c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#good movies can be trusted\n",
    "\n",
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# rating histogram\n",
    "data.hist(column='rating', bins=5)\n",
    "plt.title('Distribution of ratings')\n",
    "plt.ylabel('count of ratings')\n",
    "plt.xlabel('rating')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f506c88ca50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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44oknrJmJiIgUVKPrUH744QekpKSgpKREmxcVFdWgwWqLPRQiotqz6mfKT5w4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\nKuJmumgs3tsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f50674733d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# count of ratings per movie histogram\n",
    "ratings_count = data.groupby('title').size()\n",
    "ratings_count.hist(bins=20)\n",
    "plt.title('Distribution of number of ratings')\n",
    "plt.ylabel('count of movies')\n",
    "plt.xlabel('count of ratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f506714bed0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y8vJw8+ZN5OfnC37v3LljlgTAmq+93NhjZsi+iiSw5g0vV/9frXlZ0lBdTqfT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f506c885910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# average rating per movie histogram \n",
    "mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='median')\n",
    "mean_ratings.hist(bins=5)\n",
    "plt.title('Distribution Of Median Movie Ratings')\n",
    "plt.ylabel('Movie Count')\n",
    "plt.xlabel('Rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5066fb0110>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b5Lwtx6tklhwPSqb0/XN0Bs8sBw8eDE9PT511w4cPh5NTzc0iIyORnZ0NANiy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\nY8wIniwZY4wxI/4frGk7DwHs50MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5067331f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# at least 1000 ratings\n",
    "ratings_by_title = data.groupby('title').size()\n",
    "atleast_100_idx = ratings_by_title.index[ratings_by_title > 1000]\n",
    "mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='mean')\n",
    "mean_ratings = mean_ratings.ix[atleast_100_idx]\n",
    "mean_ratings.hist(bins=10)\n",
    "plt.title('Distribution Of Mean Movie Ratings, #Ratings > 1000')\n",
    "plt.ylabel('Movie Count')\n",
    "plt.xlabel('Rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Mystery': 40178, 'Drama': 354529, 'Sci-Fi': 157294, 'Fantasy': 36301, 'Horror': 76386, 'Film-Noir': 18261, 'Crime': 79541, 'Romance': 147523, \"Children's\": 72186, 'Musical': 41533, 'Animation': 43293, 'Adventure': 133953, 'Action': 257457, 'Comedy': 356580, 'Documentary': 7910, 'War': 68527, 'Thriller': 189680, 'Western': 20683}\n"
     ]
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "ratings_by_genres = data.groupby('genres').size()\n",
    "dictionary = {}\n",
    "for idx in ratings_by_genres.iteritems():\n",
    "    genres = idx[0].split('|')\n",
    "    for g in genres:\n",
    "        if g in dictionary.keys():\n",
    "            dictionary[g] += idx[1]\n",
    "        else:\n",
    "            dictionary[g] = idx[1]\n",
    "print dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd3dbb666d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd3dbb5da10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3dbb66090>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd3dbb72390>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3dbb72d50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd3dbb66d10>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd3dbb5d0d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3dbb5d350>]}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38e4b9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## conjecture about occupation\n",
    "#occupation  versus age\n",
    "occ=dict(list(data.groupby('occupation')))\n",
    "counts=list(data.groupby(\"occupation\").size())\n",
    "#print occ\n",
    "\n",
    "occ0=list(occ[0]['rating'])\n",
    "title(\"Other or Not Specified\") # \n",
    "xticks([1],[counts[0]])\n",
    "boxplot(occ0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd3954e0350>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd3b6a9a690>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3b6a9acd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd3954e0fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3954eb9d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd3954e0990>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd3b6a8dd10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3b6a8df90>]}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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83PNArELBORhMK0QG03vBhXPOq1DWrVun8vJyHT58WDt27NDMmTM9wluS0tLS3K8VFhYq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38e483850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ1=list(occ[1]['rating'])\n",
    "title(\"Academic/Educator\") # \n",
    "xticks([1],[counts[1]])\n",
    "boxplot(occ1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd3b6a2d250>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd3b6ab6590>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3b6ab6bd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd3b6a2ded0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3b6a348d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd3b6a2d890>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd3b6aa8c10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3b6aa8e90>]}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ogSOWDagHHgwGtWnTJuXm5vYa3n6/P3SCuro6BYPBsPAGYhnPQoGpIs7Ajx07pgULFmjG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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd396a8b490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ2=list(occ[2]['rating'])\n",
    "title(\"Artist\")\n",
    "xticks([1],[counts[2]])\n",
    "boxplot(occ2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd3db1760d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd3db164410>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3db164a50>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd3db176d50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3dae4a750>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd3db176710>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd3db148ad0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3db148d50>]}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3dadd6ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ3=list(occ[3]['rating'])\n",
    "title(\"Clerical/Admin\") \n",
    "xticks([1],[counts[3]])\n",
    "boxplot(occ3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd38c911fd0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd38c911350>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38c911990>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd3dcd4bc90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3dcd56690>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd3dcd4b650>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd38c9209d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38c920c50>]}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p64KP+10ul1wuV6sLRTfVws9SWzKS1P7T/P/J+LQU/nk8Hr95eyG/q1CeeuoprV27VkFB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\nigAHAIsiwAHAoghwALAoAhwALOr/ARWQ3T5R/Ew8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3dae77410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ4=list(occ[4]['rating'])\n",
    "title(\"College/Grad Student\") \n",
    "xticks([1],[counts[4]])\n",
    "boxplot(occ4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd399ef7d90>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd399ef7110>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd399ef7750>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd38bb6ca50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38bb7a450>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd38bb6c410>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd399ee0790>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd399ee0a10>]}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R4ABgUgQ4AJhUpwHe0NCgpKQk2Ww2JSQkKDMzs02Ny+VSUFCQ7Ha77Ha71q5d2yvNAgD+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\nzbiCzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd396b2f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ5=list(occ[5]['rating'])\n",
    "title(\"Customer Service\") \n",
    "xticks([1],[counts[5]])\n",
    "boxplot(occ5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39ab2bd10>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39ab2b090>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39ab2b6d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39ab319d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38d10b3d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39ab31390>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39ab17710>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39ab17990>]}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LedCpdaW2Q1d6L7hwuJ0sAHRBBDgAWBQBDgAWRYADgEUR4ABgUQQ4AFgUAQ4AFkWAA4BF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3dc92f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ6=list(occ[6]['rating'])\n",
    "title(\"Doctor/Health Care\") \n",
    "xticks([1],[counts[6]])\n",
    "boxplot(occ6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39b8e6c50>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39b8daf90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b8e6610>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39b8f6910>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b8fb310>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39b8f62d0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39b8da650>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b8da8d0>]}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v52fmzU21SFLqSZf7XS6XXC5XmwtFD3WK76X2ZEpSxw/z38G4WoqWud3uFvP2ZC2uQnno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\n/wCRScuMY5RtxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38d130090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ7=list(occ[7]['rating'])\n",
    "title(\"Executive/Managerial\") \n",
    "xticks([1],[counts[7]])\n",
    "boxplot(occ7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39a661050>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39a661450>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a661dd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39ad465d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3db37fdd0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39ad46b50>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39bc95410>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39bc95dd0>]}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5pkUCACIlDXViV1eX1q1bp+rqaiUnJ4eNLVy4UG1tbXK5XDpy5IjKysp05syZiH1U3dJs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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a510290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ8=list(occ[8]['rating'])\n",
    "title(\"Farmer\") \n",
    "xticks([1],[counts[8]])\n",
    "boxplot(occ8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd38d0b2710>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd38d0b2bd0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38d0b29d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd38d098ed0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a8ec050>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd38d098dd0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39a8fd690>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a8fdd10>]}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xsf1dAezANE2ZptmtsYZ1Z/p24ZVXXlF4eLg2bdrkfWzNmjVyuVzKy8uTJCUnJ6u0tFQJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39ab52750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ9=list(occ[9]['rating'])\n",
    "title(\"Homemaker\") \n",
    "xticks([1],[counts[9]])\n",
    "boxplot(occ9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39aa93250>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39aa80590>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39aa80bd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39aa93ed0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd3db6a68d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39aa93890>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd38ce73c10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38ce73e90>]}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39ab0b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ10=list(occ[10]['rating'])\n",
    "title(\"K-12 student\") \n",
    "xticks([1],[counts[10]])\n",
    "boxplot(occ10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd399f240d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd399f3f410>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd399f3fa50>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd399f24d50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd399f35750>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd399f24710>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd399f12a90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd399f12d10>]}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38ce73a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ11=list(occ[11]['rating'])\n",
    "title(\"Lawyer\") #\n",
    "xticks([1],[counts[11]])\n",
    "boxplot(occ11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39bbdff10>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39bbdf290>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39bbdf8d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39bbf0bd0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a7935d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39bbf0590>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39bbcc910>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39bbccb90>]}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39bae7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ12=list(occ[12]['rating'])\n",
    "title(\"Programmer\") # \n",
    "xticks([1],[counts[12]])\n",
    "boxplot(occ12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39a4dfd90>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39a4df110>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a4df750>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39a4c5a50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a4ef450>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39a4c5410>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39b42c790>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b42ca10>]}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lyhRdunQpZBzhDABD64Z64A0NDaqrq1NeXl7Q+5Zlqbq6Wk6nU16vV2fPno1qkQCAUIn9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\nKAIcAAxFgAOAoQhwADDU/wEMWkY+ag9FUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39ae9a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ13=list(occ[13]['rating'])\n",
    "title(\"Retired\") # \n",
    "xticks([1],[counts[13]])\n",
    "boxplot(occ13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39b0c9bd0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39b0e9f10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b0c9590>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39b0da890>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b0ea290>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39b0da250>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39b0e95d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b0e9850>]}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RCQkJio+P1wMPPNBijMvlUmhoqGw2m2w2m7KysrqkWADAt3x+K33//v31zjvvyGKx6PTp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\nADCp/wNC9vXuP8dCGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39b42c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ14=list(occ[14]['rating'])\n",
    "title(\"Sales/marketing\") \n",
    "xticks([1],[counts[14]])\n",
    "boxplot(occ14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39aded950>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39ade2c90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39aded310>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39adf7610>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39adf7fd0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39adedf90>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39ade2350>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39ade25d0>]}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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30y16AKMwhYJejwt5EM6YQgGACESAA4ChCHAAMBQBDgCGIsDR63EvFJiKKRQACGNMoQBA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38e3a3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ15=list(occ[15]['rating'])\n",
    "title(\"Scientist\") # \n",
    "xticks([1],[counts[15]])\n",
    "boxplot(occ15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39baa08d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39ba97c10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39baa0290>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39a263590>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39a263f50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39baa0f10>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39ba972d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39ba97550>]}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38a9c5c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ16=list(occ[16]['rating'])\n",
    "title(\"Self-Employed\") \n",
    "xticks([1],[counts[16]])\n",
    "boxplot(occ16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39bc8b750>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd38fb6ca90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39bc8b110>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39bc98410>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39bc98dd0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39bc8bd90>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd38fb6c150>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd38fb6c3d0>]}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a2674d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ17=list(occ[17]['rating'])\n",
    "title(\"Technician/Engineer\") \n",
    "xticks([1],[counts[17]])\n",
    "boxplot(occ17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39b1af5d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39b1a5910>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b1a5f50>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd39b1bc290>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b1bcc50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39b1afc10>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39b191f90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b1a5250>]}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEKCAYAAAALoA6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOo72v5bP6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a249d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ18=list(occ[18]['rating'])\n",
    "title(\"Tradesman/Craftsman\")\n",
    "xticks([1],[counts[18]])\n",
    "boxplot(occ18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fd39b4b6450>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd39b4b5790>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b4b5dd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd395cee110>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd395ceead0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd39b4b6a90>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fd39b4a3e10>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd39b4b50d0>]}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a241950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ19=list(occ[19]['rating'])\n",
    "title(\"Unemployed\")\n",
    "xticks([1],[counts[19]])\n",
    "boxplot(occ19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39a745c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ20=list(occ[20]['rating'])\n",
    "title(\"Writer\")\n",
    "xticks([1],[counts[20]])\n",
    "boxplot(occ20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*------------------------\n",
    "\n",
    "## Problem 3: Correlation:  Men versus women\n",
    "\n",
    "Let look more closely at the relationship between the pieces of data we have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Make a scatter plot of men versus women and their mean rating for every movie.\n",
    "* Make a scatter plot of men versus women and their mean rating for movies rated more than 200 times.\n",
    "* Compute the *correlation coefficent* between the ratings of men and women.\n",
    "    * What do you observe?\n",
    "    * Are the ratings similiar or not? Support your answer with data!\n",
    "* Conjecture under what circumstances the rating given by one gender can be used to predict the rating given by the other gender.\n",
    "    * For example, are men and women more similar when they are younger or older?\n",
    "    * Be sure to come up with your own conjectures and support them with data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7f50670c2f50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LIumdTrtrY2OjTZ482WbNmvXp4QFs7mZmkOKPL0IG6ZaYmOg0Lykp28/LMMi0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\n4ZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5066e4ad10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# scatter plot of average rating by gender\n",
    "mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='median')\n",
    "mean_ratings.plot(x='M', y='F', kind='scatter')\n",
    "\n",
    "#py.savefig('figures/scatter.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fd3daecf1d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LnEVBQQGJiYkMGTLEmQ/qkMiisYxIGy1HxfTr14977rmn9TodLSN+o1P+iog4\nSHvuIiIOUnMXEXGQmruIiIPU3EVEHKTmLiLiIDV3EREHqbmLiDhIzV1ExEH/H9Lh+k2Du63AAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3daeba6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# scatter plot of average rating by gender for movies with 201+ ratings\n",
    "ratings_by_title = data.groupby('title').size()\n",
    "atleast_200_idx = ratings_by_title.index[ratings_by_title > 200]\n",
    "mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='median')\n",
    "mean_ratings = mean_ratings.ix[atleast_200_idx]\n",
    "mean_ratings.plot(x='M', y='F', kind='scatter')\n",
    "\n",
    "#py.savefig('figures/scatter_cleaned.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td> 1.000000</td>\n",
       "      <td> 0.708826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td> 0.708826</td>\n",
       "      <td> 1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender         F         M\n",
       "gender                    \n",
       "F       1.000000  0.708826\n",
       "M       0.708826  1.000000\n",
       "\n",
       "[2 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "# correlation coefficient\n",
    "mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='median')\n",
    "mean_ratings.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd38eeaef50>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d2gEYmU7tAIxIp3YANRLP9JiIqKgo1K5dGxMmTFDWNW/eHJMmTQIA5ObmKt+ae3l5KR9c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39b5cbe50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data = read_hdf5('movielens_1M.h5')\n",
    "\n",
    "ratings_by_id = data.groupby('movie_id').size()\n",
    "atleast_200_idx = ratings_by_id.index[ratings_by_id > 200]\n",
    "pivoted = data[data.movie_id.isin(atleast_200_idx)].pivot_table('rating', rows='title', cols='gender')\n",
    "pivoted['diff'] = pivoted.M - pivoted.F\n",
    "#pivoted.reset_index('movie_id', inplace=True)\n",
    "disagreements = pivoted['diff']\n",
    "top_male = disagreements.order()[-10:]\n",
    "top_female = disagreements.order()[:10]\n",
    "top_disagreements = pd.concat([top_male, top_female])\n",
    "top_disagreements.order().plot(kind='barh', figsize=[9, 15])\n",
    "plt.title('Ratings by (Males - Females)')\n",
    "plt.ylabel('Genre')\n",
    "plt.xlabel('Average Rating Difference')\n",
    "\n",
    "#py.savefig('figures/genres_diff.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 4: Open Ended Question:  Business Intelligence\n",
    "\n",
    "* Do any of your conjectures in Problems 1, 2, and 3 provide insights that a movie company might be interested in?\n",
    "* Propose a business question that you think this data can answer.\n",
    "* Suppose you are a Data Sciencetist at a movie company.  Convince your boss that your conjecture is correct!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//ZSf7zPJOHLkSPLy8ujWrVulpcU+evQoYW3DeGXYK7QZ04Yx+8Y8scILYGBk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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38f336ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#which age group is main costomer?\n",
    "age_size=data['user_id'].groupby(data['age']).size()\n",
    "x=[\"Under18\",\"18-24\",\"25-34\",\"35-44\",\"45-49\",\"50-55\",\"56+\"]\n",
    "colors=['yellow', 'lightgreen', 'green', 'lightgreen','gold','lightseagreen','orchid']\n",
    "title(\"Age Group By Percentage\")\n",
    "pie(age_size, labels=x, colors=colors, autopct='%1.1f', shadow=True, startangle=90)\n",
    "\n",
    "show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get subset\n",
    "data25_34=data[data['age']==25]\n",
    "data18_24=data[data['age']==18]\n",
    "data35_44=data[data['age']==35]\n",
    "\n",
    "data18_44=data25_34.append(data18_24)\n",
    "data18_44=data18_44.append(data35_44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PpdRUTp86xaK1X/Ba/ddwbu7MkiVLsiXTUlDIQlEcZQR/f38OHz6Mra0tjo6O\n2mQ9z4v99LzptlnHVqxYQXBwMM7Ozjg4OGhzM7wMly9fZvbs2dSpU4fmzZuzePFiYmOfkJKSQa9e\naXh4/GsZkiT46CMVlypcIJjglx5bV9xUbphF3NC5nWxiQmTRi1NmWS7LqE1k0sdkkPZBGuF24Xyy\n+xPsne1xbu7Mt99+S0xMTGmLqVBGUKbjKhSIqKgo1q9fz4YNG4iOjiYzM5P0HH4AWZYxMYGff1Yj\n53gk2bVLYtdGY3Yl/4Jcgs8rD3jAO7xD2pHfySXUc9AbNYqv/v6bCcUoW1khEOgMJI8FclqoMoG/\nweSqCZl/ZdKmXRvGvDuGHj16YGhoWOKyKpQNlDcOhXxRqVTs3buXDh060KBBAxYuXMjt27dJTk7O\npTQA1Go1SUmCvNJ6vPWWwKRyMkvJHZG3OLHGGiPJCM6d06mdZGnJ3RdXK/ckAf0liWRPcisN0HhB\nG0HS/yWR5pvGYf3D+MzyoXK1ygwZPoSgoKCSFVihTKAoDoVc3Lt3j88++4zq1aszePBgjh07Rmpq\nKmlpaS9sq1YLAgLgxIns5Xp6MGOm4HDFvUQRVUyS542L7ApHjujWyMKC+8UjTplikiwTbylpXjle\nhCHgCgmDEkgemcyOuzto/2Z7Gjk3YsOGDdrc7wqvPoriUAA0s68CAgK08/sXLFhAdHQ0CQk6r4IA\n4PPPJXIuvG7YELp2lZltNK0IJC44TVVumIT9qVMblZUVj4pJnrLCAWCrUJM4rBCLHS1A7akmeWwy\n1xyvMeHLCVSrWY2Jkydy/fr1IpdVoWyhKI7/OBkZGWzfvh17e3u6du3K/v37SUtLIzU19aX6FUJi\n4sTcl9fIUSpi9e/xA3lPPy4OnHBC/Ug3NSAsLXn0CofsiAWGAMkd0QTPKiwy0AAS+yeSOCyR1cGr\ncXRzpHXH1hw8eLBMBMZUKHoUxfEfJTY2lgULFlC9enVGjx7NtWvXSEpKKrIfukql5tYtNTlyOWFi\nAh9NEfgZrSGRxCIZ60XYYIMkJNAlT7uZGbHPrJN51RgtyyRXk6F1EXZaCTI6ZpDqm8ops1P0fbcv\njV0a4+/vj7qMhnBRKByK4viP8eDBA8aOHUutWrX4/PPPiYmJITGxeG7gQsDmzZDTctG6NTg0gTn6\ns4pl3JxISDjpOcHhwwVvZG5O3CsaVHI3sA9ByjvFdDPXB1whaUQSV+2vMmzSMOo1rMeWLVu0C1MV\nyjeK4vjksjLQAAAgAElEQVSP8OTJE6ZMmUK9evW0jsyScGZKksykSXKudBUffqjisl4YQZTMrBx3\nVVOMzkcUvIG5Oa+iq/ceMAJIelNAcefikgF7SByeyO0Wtxn7+Vhq1avFmjVrCjTRQqHsoiiOV5yk\npCTmzZtH7dq1+frrr0lJSSnRH21+U3SrVYN335VYZDwXNcVvxnDGGfmeDn6OMhwht7AIYKgsk24j\ngVsJDiwBtpD4diIPuzzko5UfYVPfhm3btikmrHKKojheUTIyMpg5cya1atVi/vz5JCUlvbTDu7Co\n1YLAQHgad1FL374C0yopfMVXxS5DPeqRqUqHmwUMJGJm9sqZVb4DgmRBmncpOqzrQNKAJB51fsTo\nmaOxd7bniK5TpRVKHUVxvGIIIfjtt99wdHRk2bJlpKWlkVzQFKjFzPz52afo6unB9BmCIxV/L/a1\nHXro0VivMezfX7AG5uaIZ7LylXduApOAhL5CE4e9tKkLSe8k8af9n/Qa0ou2HdsSHh5e2lIpFBBF\ncbxCXLx4kS5dujB16lSWLVvG3bt3adWqFaamJRuVNj+EkJgwIfsl17AhdO0m84nRx8U+vrvKHYNz\nBQzy+DRC7qsQnUkNDJRl0uwkaFza0jyDBDhA8qhkAioG0KJtCwZ6D+TBgwelLZnCC1AUxyvAw4cP\nGTNmDJ06daJ3796EhYXRrVs3LCwsOHjwIGPHjn1uePaSQqVSc/u2mvXrs5ePGqXiicH9Yl/b4Ywz\nBv88LFhlSQJjY3QPj1j2+FKSuKovyBxYRtdUVADhIUgZm4L/bX9s7W1ZvXq14v8owyiKo5yze/du\nnJycMDQ05OrVq4wfPx59/X9zcuvp6bFo0SLWrVtX7MmbCoIQsHUr/P33v2XGxvDRR8W/tqMBDUjL\nSIICPtHKJibcKjZpSoZLwKdCkDBIlP3sO4aadSCJgxKZsmQKrs1dFfNVGaXcKQ49PT1cXV1xcnKi\nb9++xbYGoazz+PFjBg8ezKxZs9izZw/Lli3Dyir/JcCDBw8mICCAatWqZUsAVRrkNUW3dWtwbAJz\n9GcW27j66GOrZwf79hWovmRuXsJRtYqWDKCfJJHSBChPWWKtIck7iYs1L+LRxoMPPvyApKcpcRXK\nBuVOcRgbGxMaGkp4eDjm5uZ8++23pS1SibN3716cnJyoXr06oaGheHh4FKidq6srFy9epEmTJqXq\n91Cr1SQnw+zZ2fOJTP5QxWW9cM5yttjGbqZyp8LZguUFkSwsynWE3LmyzD9GEqJPaUtSCGQQTQWp\no1NZe2wt9RrU47AuCzgVipVypziepWXLltqAahcuXKBFixY4OzvTt29fnjx5AoCXlxeTJ0+mWbNm\n2Nvbc+7cOfr06UODBg345JN/s9L16dMHd3d3HB0ds2XiMzU1ZdasWbi4uNCyZUsePtTYyB88eECf\nPn1wcXHBxcWFM2fOALBlyxY8PDxwdXVlzJgxRWqnjY+PZ8SIEYwfP56tW7eydOlSnc1P1apV49Sp\nUwwcOLBU/R5qtZrTpwVHjz4rG4wYIbHI+LNiW9vhjDNGN+8VrLKVFeXVTRsELFWrSRyqLt+/clNI\n6Z3Cow6P6DWwF+9PeL/UppUr/Eu5vaRUKhUHDx7E0dERgGHDhrF48WLCwsJo0qQJc+fOBTTZ8CpW\nrMi5c+cYO3YsvXv3Zs2aNURERODn50dsbCwA33//PcHBwZw7d44VK1Zoy5OTk2nZsiUXLlygbdu2\nWqUyYcIE2rdvz4ULFwgNDaVx48ZcuXKFXbt2ERgYSGhoKLIss3Xr1iI536NHj+Lk5ISenh7h4eF4\neXkVui8DAwO+++47Fi9eXOp+j/nzJZ7qeAD69BGYVU3lS5YUy3iNaUxKWjzZBs0HlaVluYyQm4LG\nRJXsAdQobWmKCFtIGZmC33E/HFwdiIjQIQqADmSZwh0dHXFxceGrr75SAjXmQblTHCkpKbi6ulKj\nRg2ioqIYM2YMcXFxxMXF0aZNGwDeeecdTjyTEKJXr14AODo64ujoiLW1NQYGBrz++utERWms2MuX\nL9e+VURFRfHXX38Bmptsjx49AGjatKk2B/OxY8cYO3YsoFFO5ubmHDlyhJCQENzd3XF1deXo0aPc\nLOiCs3xITk5mwoQJDBs2jNWrV7N27VrMzMxeqs8sxo4dy/79+7G0tKRCqcVlyj5FV08PZsxQc6Ti\nvmJZ22GIIXXkOnDw4AvrltcIuR/pycSaSdCttCUpYowhuU8yNxvdpHnr5ny19Ksin3mVZQqPiIjg\n0KFD7Nu3T/sQ+iyv2uJQXSl3isPIyIjQ0FBu3bqFoaEhe/bsyZV7O+cTQpYzWJblbI5hWZbJzMzk\njz/+4MiRI5w5c4YLFy7g6uqqfR1+doZSVv38xgGN0goNDSU0NJSrV68ye/bsQp/rmTNncHFxISYm\nhvDwcLp1K/o7QZs2bQgLC8POzq5UTFcqlZp//lHzjHWQBg2gRw89PjGaWixjugt35MDTL65oZkZs\nOUuPehTwU6tJLK4AhqWNBMJFkPJOCrNXzqZtx7bFtu6jatWqrF27lq+//hoAPz8/evXqRceOHenc\nuTNJSUl06tSJpk2b4uTkxC+//AJAZGQkjRo1wsfHh4YNGzJkyBAOHjyIp6cnDRo04NzTbJRBQUG0\natUKNzc3PD09+fNP3XLGlCblTnFkYWRkxIoVK5g5cyZmZmZYWVkREBAAwObNmwtsyhFCEB8fj5WV\nlXZKa5a/4nl07NiR1atXAxqzWXx8PB07duSHH37g0dPcDzExMdy+fbtQ5/ftt9/Sq1cv5s+fz5Yt\nW6hUqVKh+ikIderUITg4mK5du5aK01wI2L4dnv3djByp4onBA3axq8jHcxEumPx158UVzc2JL0dv\nHHHAICDZC6hcurIUO5U0M6+CCMLR1ZGQkJBiGaZevXqoVCqtbzM0NJQff/yRY8eOYWhoiL+/PyEh\nIRw9epQPP/xQ2+769et89NFHXL16lWvXrrFz505OnTrFkiVLmD9/PgD29vacPHmS8+fPM3fuXGbM\nmFEs51AclDvF8ezbhYuLC7a2tuzatYuNGzcyZcoUnJ2dCQ8Pz/NJX5KkXG8nkiTRtWtXMjMzady4\nMdOnT6dly5Z5jvds++XLl3Ps2DGcnJxwd3fnypUr2Nvb87///Y8uXbrg7OxMly5duH9ftwSkGRkZ\njBs3jmXLlhEQEEC/fv10al9YjI2N2b17N7NmzSoVv4ckyXz44b9TdI2MYMoUwSajtUW+tqMJTUhJ\njoUXhWIxN6dsBGspGGP19EiuIkO70pakhNCDDK8MottE07ZTW/w2+hXbUFm/+86dO2NpaQloJnhM\nnz4dZ2dnOnfuzN27d7UKpl69ejg4OCBJEg4ODnTq1AnQmMuzzN1PnjyhX79+NGnShMmTJ3Pp0qVi\nk7+okYTi+SkzPHr0iP79+2NqasrWrVuxsLAoFTn27dvHgAEDijSxU0GQZYnmzWHBgn/HnD5Nj7Tz\njfkqY0WRjfMFX3CQg6irVILdu3NXiIuDefPg3j24e5cNajXDC9j3NTRP/VncAD4HJuSo9wea2FEZ\nQJWn+y/Dz4C3DEmTgKJxgZUvHoLxj8a80/8dVixdUWifnZmZWbZ0yTdu3KB58+ZER0fj5+dHSEgI\nK1euBDSmq/3797N161b09PSoV68ex48fR61W07NnTy4+TRzm4+PDm2++yVtvvUVkZKT22PDhw3F3\nd2f8+PHcunULLy+vl/aJlhTl7o3jVSUiIoLmzZvTqlUr9uzZU2pKA6Bbt26EhIRgY2NTom8farXg\nzBnBs8FSJ3+o4oreRc6Q23yYTjpjGctIRjKc4axjXa46ccQxlamMZCQ++LCf/XSjG22ltpDfojJ/\nf7Czg6++An19PgQK6gptCIQ+3UIAYyDnMoonwPvAr0AEvHSglYfAcCCpG/9NpQFQDZKHJ7PxyEZa\nd2hNdHT0S3f56NEjxowZg6+vb57H4+PjqVatGnp6ehw7doxbt3SLMxAfH0/NmjUB2JAzVWYZR1Ec\nZYCjR4/SoUMH5s2bx/z589ErA3b1Bg0aEB4eTsuWLUvc77Fgwb9TdKtWhZEjJRYbf55rbYcBBixl\nKd/xHetZTyihXCR7elh//LHDju/4jmUsYzWrccABZ+GMnJZP9NtKlTRKxcwMMjKoTOGidRwG6gM2\nOcq3AW8BtZ/uVylE31kIYLgsk1ZLhmYv0dGrgBEk908mVAqliVsT7cxIXciateno6Ejnzp3p2rUr\nc54mk8lp6h4yZAjBwcE4OTmxefNm7O3ttcfyMonn/Hvq1KlMnz4dNzc3VCpVrjZlGcVUVcps3ryZ\njz76iF27dtGuXdkzTqtUKmbMmME333xTYmEf9PRkataETZvUT2WAkSNl7CO7MJW8o+imksoHfMA0\nplGXutryX/iFG9zgAz7gLnf5mI/ZzGaucpWxjIUDB8DAIHtnajVMngz//AOPH7MDGFiI83gXcAfG\n5SjPMlFdAhKAicDQQvQPsBEYryeROEVA+ZoAVqzI52XMAs049PshmjX7r2vUokdRHKWEEIIFCxaw\ndu1afv/9dxo3LkvxrnOzY8cORowYUaK5PUxN4e23YfBg+Osv8PWFb9P8sMSSecwjhhhUqEgiiUQS\n6UUvxjAmWx9q1ExmMv/wD8kkM4c5eODBfe7jjTeq2TOhffvsA2/erPFzjB8PPXpQMzmZq+hmBUoH\nagGXgao5jo0HzgNHgGSgJbAXsNOhf4DbaKKkJ70FNNGx8X+Bq2C8zxj/Xf506dKltKV5pVBMVaWA\nEAJfX19++OEHAgMDy7zSABg0aBCnTp0q0SCJiYnw++9w65bG5fBmD5nZRh/nMj+lkcZ2thNOOBfI\nnm9jK1uxxZYf+IF1rGM5y0l+OlfKCCP444/cA1+6BE/f/mRzcyqhcXrrwj6gKbmVBmhMV10AIzSz\nZtsCYTr2rwYGyTLpr0uK0siPRpDcN5n/G/B//PTTT6UtzSuFojhKGCEEH3/8MefOneOPP/7QOsfK\nAy4uLkRERODk5FQifg9ZlnnwQOLkSc3+iJFq4is+5CY3SUJjNksiCXPMscCCFrTgWo5b/CUu0e7p\n/NRa1KI61bUr0o0wwjQij4wbNjZw/rzmbyMjooDXdZR9OzA4n2O9gQBAheaN4yy651daKUlE6Aky\nBisGg+fyGqQMTMF7pHexTtf9r6EojhJmwYIF7Nu3j3379mFubl7a4uhM1apVOXXqFIMHDy72leZq\ntZrMTPj1V81+1tqOYMMArnOdvvRlJCMZz3jSSCOEEGyxzdaHDTacR6MEYoghiii2spXxjCeGGBJj\n/oG9e+GXXzQbgLc3XLsGI0agvn+f1oAuyy+T0DjG+z5T9u3TDaAR0BVwAjyAUeimOK4CM7JybOi/\nsLpCTUh5O4Vxk8exffv20pbmlUDxcZQg33zzDUuXLuXkyZPUqFH+o899++23TJ48uUT8HjNnwtM1\nVAwbJpFypxJfqBfxGZ9xhzvUpjZv8AaDGMQvaBRAL3oRRxxf8AUPeIBA8DZv04lO2n7/T/o/4hbO\ngObN8xxXb/583j50iE3FfoYFIxNwlSUu2wvU/UtbmnLGAzDabsTOTTvp2bNnaUtTrlHeOEqILVu2\nsHDhQg4fPvxKKA2A9957j4MHD5ZIkMSFCyWeBiymShVBfIXHPOABfvjRhCZMYxqDni696/X0H4AF\nFsxnPutZz/d8n01pALjKrmRbOJIDtZVVmYqQO0+WuWUgoX6rtCUph1hDSr8UBg0dxJHnfOcKL0ZR\nHCXAnj17mDJlCgcPHqRu3bqlLU6R4unpSXh4OA0aNChWv4cQEr6+msu1fn1wcYHFxp/ziEdEEUWN\nQsYPd1O5YRKWv+tbWFryqNQiB2fnPPCFWk2CdznPsVGa1NZE2O3drzenTxcg0KVCniiXXzFz5MgR\nRo0axW+//ZZtgdCrhI2NTbEHSVSr1dy5o+bbbzUuCFmGpMwU3uVd3uM9zCmcv8gZZ9QPn/NOYWZG\nbCmn2gVIBd6SJFLc+XfloELhqAtJPZLo0qOLktO8kCiKoxg5c+YMgwcP5ocffqBp06alLU6xYmRk\nxK5du/jkk0+KNUzJjh1w/z4sWADffAMZFROpT/1C92eDDZJAMwU3L8pIhNzpsky0qQTdS1uSVwQ7\nSOyQSOfunbXRrBUKjqI4iolLly7Ru3dv/Pz8aNu2bWmLUyJIksTUqVP58ccfMTMzK5bQKbL8bxRd\nW1vo2VPmU6Pphe5PQqKJnlP+iZ3MzUkpdO9FwwlgrVCTOEwxURUpTSDWLpZuvbuRnp5e2tKUK5TL\nsBhISEigb9++LFq0iO7d/3uPiF27diU4OJjatWtjZGRUpH2r1WpSUmDGDE1cn3ffVZNQ8SHbKfw0\nS3dVU4zO55OK1MyMNJWq0H2/LAnAQAmS25D3akKFlyKjbQZX4q/w3vvvlbYo5QpFcRQxQghGjRpF\nu3bteOedd0pbnFIjK0iip6dnkfs91Go1584JDh58urZjqmCr0XriiS9Uf844I9/Lx1xhbo6qFBWH\nr55MQiUZOpSaCK82MiT3TGbX77v4ZtU3pS1NuUFRHEXMqlWruHbtGitWFF3+iPKKubk5Bw4c4P33\n3y8Wv8eiRZopui1bgrMrzDYoXAa113mdDFU65JULwdwckZ5OaSRi3Qv8oFaTNOwVTQNbVjCE5LeS\nmTJzCidOnChtacoFiuIoQo4fP86cOXPYvXs3huUsV3VxIcsyCxcuZMOGDUWuPJ6dojtpkoo/5Uuc\n4pTO/eihR2M9e02k3JxUrAiSxOOXFVZHHqOJmJv0BlB6qVn+O1SGlB4pvDXoLZ5kxfRXyBdFcRQR\niYmJ9Oo1iMePE2nZsgvvvfdeucnmVRIMGDCAwMBArK2tiyxIolqt5u5dNatXQ5UqMGq0xJfG88gs\ncNqlf2mmaobBudA8j0nGxuQR0arYEMC7ejIpNWRoUYID/9exg4S6CYx4b0RpS1LmURRHETF+/Eek\np78B3CM6ei7r10fy+uv2WFrWwdvbu1zlEy4unJ2duXjxIi4uLkXm9xACdu2Cy5ehVy9BpeppLJYW\n6dyPE04YRD3M85hkYoJuud1eju3AUQSpiomqxElrn8b+k/vZsWNHaYtSpikyxXH//n0GDRqEra0t\n7u7u9OjRg3Xr1uUbE2bUqFFcvXoVgLp16xITE5OrzqeffsqXX35ZJPL98ccf+Pj4FElfOdm7dy+7\nd+8nNXU5YAUMRaU6AEQTF/clO3Yk4ujYDFPTWvTt+xZBQUHFIkdR8+TJE/r164e9vT2NGzfmzJns\n6VtjY2Pp06cPzs7OeHh4FEg5Vq1alZMnTzJkyJAiC5IoyzJTpsioVDBtuprjBoe4iW5vew1pSGpG\nIjx4kOuYZG7+NJ5u8XMHeA9I7CU0cdcVShYDSH4zmVHjRnHnzp3SlqbMUiSKQwhBnz596NChA3//\n/TfBwcEsWLCAB3n8CLNYt24djRo1AnKnWcwiv/LCzHIprrSM0dHReHuPJjl5I7mN0aZAf1Sqn4EY\nkpLW8MsvFfHw6ICRkTXduvUo0zFzJk6cSPfu3bly5Qrh4eG5Vr7Pnz8fNzc3wsLC2LRpExMnTixQ\nv/r6+qxZs4avvvqqSPwearWa1FTNFF1bW+jVS2aO0TSd+tBHH1s9O9i/P9cxydKSuy8t5YsRwNuy\nTPprEjiXwIA5+RlYDKzKUX4W+Br4Bjj0nPZqYA2a3LjlmVqQ6pZK/yH9UauVt768KBLFcezYMQwM\nDBg9erS2zMnJiTZt2pCYmEj//v2xt7fH29tbe9zLy4vzWTkPnmHevHk0bNiQNm3acO3aNe0N38vL\ni0mTJtGsWTNWrFhBSEgIXl5euLu707VrV+7fv6+tN23aNDw8PGjYsCEBAQEAGBgYYGlpCWic2K6u\nrri6uuLm5kZiYmKhz33ECF+SkwcDL0r7agj0RKXaBsSSmrqFQ4dq0KnTWxgYVKFduw74+/uXmQs1\nLi6OkydP8u677wJQoUIFLCyyK8YrV67Q/mn2vIYNGxIZGanTKtzRo0dz8OBBrKys0Nd/ufjgarWa\nkBDB/v3g46Mm0fAR23S8gzVXNaPC2eBc5cLSkvwfgYqO1ZLEeVmQPqSUAla7At45ym6iyWI1Fngf\naPWc9md4ZdaaZLbKJPxWOFu2bCltUcokRaI4IiIi8gypIYQgNDSU5cuXc/nyZW7cuEFgYCCQ9xtA\nSEgIO3fuJCwsjN9//51z585pj0mSREZGBufOncPX1xdfX19+/PFHgoOD8fHxYebMmdp6KpWKs2fP\nsmzZMubOnQtAy5YtWbp0KQBffvklq1atIjQ0lICAgEIvUgsICODw4VOkp3+mY0t9oDMq1XfAYzIy\n/AkIcOCtt97DwKAyHh6t2bRpU6kqkZs3b1K1alV8fHxwc3Nj1KhRucKnOzs7azOrBQUFcevWLf75\n5x+dxskKkmhnZ1ckpqslSyRSUmDqVMFWQ93WdjjhhNHNe7nKSyJC7t/AFCFI7C/A4IXVi4fXyJ23\n/BzQGsgKApDfVxQH/AW4FY9oJY4eJHVMYuJHE0lISChtacocRaI4nmcGat68OTVr1kSSJFxcXIiM\njMyznhCCkydP0rdvXwwNDTEzM6NXr17Z6gwcOBCAq1evcunSJTp16oSrqyvz5s3LZo/s21eTQsfN\nzS3P8Tw9PZk0aRIrV64kNja2UKEx1Go1o0ZNIjl5AfAy5hY9oA1q9UqEeIBKdYjg4FYMHz4DfX1L\nnJ2bsWrVKjIyMl5iDN3JzMzk/PnzjBs3jvPnz2NiYsLChQuz1Zk2bRpPnjzB1dWVr7/+GldX10J9\nlrVr1yY4OJju3bsXgdNcM0W3RQtwbSrxiUHBw5E0pjEpqXGafOPPICwsiH7JN6LnoQIGyDJpjYCG\nxTZM4YgBbgHrgA1onDB5cQBNPtzisQiXDjaQVieNOZ/NKW1JyhxFojgcHBwICQnJ89izUy/19PTI\nzMx/qqQkSTybVypnjqmsJ1IhBA4ODoSGhhIaGkp4eDj7n7FNZ42Z33gff/wx69evJyUlBU9PT65d\n0zWjNGzduo2oKJn8E4QWBglwR61ehBBRqNWBXLzYHV/fr6hY0ZKGDV344osvSiRxUu3atalduzbN\nmjUDoF+/frlMi2ZmZnz//feEhoayadMmHj16xOuv65pkVYORkRE7d+5k9uzZLxWmRKVSc++eYNUq\n+OADFX/JlwkgoGAyYISNXCd33Cpzc2INiu81YJEs87c+qMpiYiY1mtC8o9Aoht151LmG5k2kBhpH\nzStEilcKa9au4e+//y5tUcoURaI4OnToQFpaGuvWrdOWhYeHczIrWXQBkCSJtm3b8vPPP5OamkpC\nQgK//fZbtjpZiqRhw4Y8evRIO8snIyODy5cvF3is69ev4+DgwNSpU2nWrJnOiiM5OZkPPphOUtJX\nFN+MZglwRIi5qNV/I8QF/vprEDNmbMbEpAr16jkwe/bsYlusVL16dWxsbPjzzz8BOHz4MA4ODtnq\nxMXFaYPDrVu3jnbt2r3UG4MkSUyZMgV/f3/MzMyQ5cJ9tkIIdu/WTJAa/Z7EVzqs7Wgm3JEDc+Rp\nKMYIueHA52o1CUPU/5qDyhLmQNaciFpoLsuczy1RaJTHMuBHNH6Rn0pKwGLGDNJbpPOerxLL6lmK\n7K7n7+/P4cOHsbW1xdHRkZkzZ1KjRg2dZjO5uroycOBAnJ2d6d69O81zpPPM6svAwIAffviBjz/+\nGBcXF1xdXfNNypLX+MuXL6dJkyY4OztjYGBAt27ddDhTWLp0BampLQBPndq9HHYIMQ21OgK4RmTk\naObP/x0rq+rUqtWAyZMnaycIFBUrV65kyJAhODs7Ex4ezvTp0/n222/59ltN9uzLly/TpEkTGjVq\nxIEDB1i+fHmRjPvGG28QEhJCnTp1Cj3rSjNFV6JbN0Gl6ukskr4oUDsX4YLJXznsMWZmxRIhNw1N\njo1UV6BOMQxQFDQC7czmaDR2tZxfSSdgMvAB0A+oR/aE6+UcVXMVZy+c5dixY6UtSplByTmuI0+e\nPMHGxo7ExJNoflWlzQNgD3p6m1GpzlGlSk3eeqsL06ZNK/fZBuPj4+nfvz+nT58ulINSlmVcXATj\nxgnefx9Wpa3ndZ5vSksggb70JfP3XzQRFAFu3KDChAlkJCUV5jTyZaoss9oIEj8sI+HSfwAigRQ0\npqf2gBOwB7iP5o2oCxrFEA/8CgzJ0UckEAi8XRIClyCh4BHrwZnjZ15c9z+Aojh0ZM6cz1m06Dqp\nqX6lLUoexAK/oqe3BZXqBBYW1ejVy4uPP/44l5mpvKBWq5k1axbLly8vtG9n6lSIjJQJ/LUym1N2\nvbD+ENmbu5MHQ48emoLoaCRvb9RpaYUaPy8C0Tyop4wFrIusW4XiQgXGq405tvdYLkvIf5Gy8JxT\nbkhNTWXZsm9ITZ1a2qLkgxUwDJXqIJpV60vYti1eu2r9rbf6ZZviXB6QZZn58+fj5+dX6Om6S5ZA\nr15qkgyj2cKL5+U3k9yRTjzjnzMzQ2RkFFmE3CRggCSR4omiNMoLepDSLIVZc2eVtiRlAkVx6MC2\nbdtQq12BxqUtSgEwBQY8s2p9NXv26NO8eXuMjKzp3r0HR48eLW0hC0z//v05deoU1tbWOkceliRN\nSJKPPxZsN9xAHHHPre+icsH06u1/CypWBFkm70hWuvOBLPPEQoLORdShQokgXAUBgQFK3DkUxVFg\nhBB8/vlSEhMnl7YohcAQ6IVKtR3NqvXNHDxoTceOfalYsQpeXh3Zs2dPmVm1nh/Ozs5EREToHCRR\npVJz/74gKAiaur94bYcTTqTGR8Mz6UQlIyMdo1/lzQFgm1CT9E7Z/qwV8sAA0t3TmfM/ZV2HojgK\nyOHDh4mOBo1lujyjD3RBpfoeeEx6uj8nT9rTp89IDAwq06JFazZv3lxmlUiVKlU4ceIE3t7eOpmu\nhMprq0kAACAASURBVBD89BO80VXF33pXOEn+U8UrUQkLyRJO/ZvbQzI15Xa+LQpGLBpfcnJHNFZF\nhXKHqqmK3379jdjY2NIWpVRRFEcBWbJkDYmJvrxaS2OzVq1/jRAPUakOcu5cS955Zxr6+pa4uDRn\n1apVz120WRro6+uzevVqli5dqtN0XVmWmTdPZsQIWGo8/7lrO5rKbnD8uHZfMjd/acUxWk8muZqs\nCeGhUD4xggp2Ff7zYdcVxVEAHj9+zPHjR4CBpS3KS5AKeAAuaHw0Oc01EpCEWr0WIaqhVtciLMwE\nX98vMTCwoFEjFxYtWlQiq9YLyqhRozh06FCBgySq/5+9846v6X7j+Pucm3mTmAklZimqmWLGiiqx\n1R4lQWvPWDWKmB20RpWqH0URJYhVLSVG7RGbGrUJYie5Weec3x/n5spest2313nl3HvWcyP3fM73\n+T5DlomMhH/+EShaIopvha+T3ddVcsX6wg3Da6FQIRJXsUo7G4CdioLO6KLK84RVDWPh0oU5bUaO\nkuXCIYoiPXv2NLyOiYnBzs4u2T4dqdG3b18uX76c7uP27duX4WvOmDGDSOklorYEGosKQGvAF9gH\nRKV0aC7CAggEzqDmKwdCkqU4GgJBwGUgEFm+gaKc4erVLowfvzJe1vrLlylPMmcH7u7unDt3jkqV\nKqVp3kOWZc6cUahZS+ag2V5ucCPJ/ZxxJvpZCOhddm9TITcY+BwIa6UkXyTQSN6hgloENCOlivIL\nWS4cVlZWXLx4kYiICAB2795NqVKlMtwfY+nSpYn6QmQ1+47sg1Ygd9IhNf4PseYfiGVmgPZjEM0R\nLMzQaEuC0BAYjppJlRt9oLFunSjUFOAiSeyTVFrPByjKeGT5InCFW7f6MmvWDgoVKo69fSVGjRqV\nYu+VrKZUqVKcOHGCli1bpnnSfN06+PhjAV/LpCfKi1Mcc8zhpFpmPaMVchWgpygSVVrIP5Vj33U0\nIDlILPt1WU5bkmNki6uqRYsW7NixAwA/Pz+6detmqDuVsMufg4MDd+7cISwsjJYtW+Li4oKjoyMb\nNqjV1Tw8PAwFFf/880/c3NxwcXGhSRM1tvH48eO4u7tTrVo16tata6i1lFHu37/P5YuXwQE1Y7YG\nyC1k5D4SjFXgS1B6RiN5PkSoexDNB4sQCnYBsQiYa9BoiyCYVAN6oXa5SfoJN3uQUV1VxVHTghOG\nFQuoqWnOQAsgqfpfZYARSNIp4DYPHoxi/vzjvPdeWYoVe58BAwZw+3Z2NlpVsbS0xM/PjylTpqRp\n3kMQRE6eFAi3DOE3fku8HQFX0QX0jbYyWiH3f8BRUSGyhzHPNj8R5RDFitUrEhVifVfIFuHo0qUL\n69atIzIykvPnz1OrVi3DtoQjj9gKuX/++Sf29vacOXOG8+fP4+npadguCAJPnjyhX79+bNq0iTNn\nzhiE5cMPP+TgwYOcPn2aqVOnMmHChLeyfdu2bWgqa8AkmR3MUYu/OYPyiYL0WQyKjwwTgb4yUuvn\nKA2D0DiuQbQbDCYVwVRE1FojmldGLeozCziCOgrISkRUV9U94ACqqy0u1VAr1p0FhgKfpnK+4kB/\nJOkg8IAnT6awbNl/lCtXmcKFy+Ll5ZUht2JGEQSB0aNHExAQkGqRREmSCQlRKFVKYZ3FCl6QuFik\nm+SG9qzeHWFjk+4KuTcBHyC0vaL+nRjJPxSHsIgwrl27ltOW5AjZIhyOjo7cunULPz8/WsaWcUgB\nQRBwcnJi9+7djBs3jn/++YcCBQoYtiuKwtGjR2nQoAFly5YFMHT3i+2T7ejoyMiRI986Wcd/qz/h\nZTMwIawBbFEri9YHqUMM8mC9oAxVkDuEIX98FbH6FsRSk8HCHTQmCJYWaCxLAY2BMcBWIOMdCpOm\nINASSNjtzoY37qzmQDRqQ4a0UATwJiZmF/CEFy++Y+3al1StWg0bG3s6dsy+rPUmTZpw+vTpVIsk\nKorCuXNQwl5JMrfDCSeU2I6GBQvyOh0VcmWgiygS+YGQN/JFjaQPAZT3FXYlLMH/jpBtUVVt2rRh\n9OjR8dxUoLYkjZszEDsX8sEHHxAUFISjoyNfffUV06dPj3e+5OZIJk2aROPGjTl//jzbtm0znC8j\nREdHc+jgIVKpi5c+BNRS1RWAWiC3kpG/kGAcMBqU7pFITe8juAeiqTAPweZTEG0QzE3QaG1BrIXa\nHOFX1JFDWgkBw1O1DrV5tGuCfR7xZo7juH49qXmQ1LABuiBJW4BnhIYuJiDAhJo1PfRZ662yvNJo\nxYoVOXfuHPXr18fGxibZ/URR5M4dhWtc4QAH4m0rQxmQFbh4Md0Vcr8XBK6YKsR0eTddGe8CujI6\nNm3PL/Xj00e2CUefPn3w9fVNVGyvXLlyhgZBp0+f5uZNNT/34cOHWFhY8NlnnzF69GiCgoIMxwiC\nQO3atTlw4IChw19sQs6rV68oWbIkAL/++utb2Xz69GlMi5iq1TuyA0ugNOAKSlMFqWcMyigFJoDy\nuYTU8ik0PI6m6gqEol+AprTe7VUA0awq0BmYA5yGRJWVHgIfo85x1EKNDGsMLNEvoE7qO+r3GQFk\nRqy6JWrW+jreZK3b8fHHn2JubkujRp+wdevWLEk4tLGx4Y8//mDo0KHJjjxkWUaSRERN4twOAQEn\njZPa2KlAAaKktLkSLwK+isLrrkryLk4jeZ/ycOTQkWzvzpkbyPI/69iRgb29PUOGDDG8F/t+hw4d\nWLVqFQ4ODtSqVYvKldXemefPn2fMmDGIooipqSk///xzvPPa2tryyy+/0L59e2RZpnjx4vz111+M\nHTsWb29vZsyYQcuWLeONTNIbyRUYGEhkqcyriJphTIBi+gWQYm9uMvBKQX7yGkIuo3l8FSV4E/JT\nCSQQzSwQlOJIER+izl98jRpuG7fWU9wGNYP1S1Zhhpq13hT4H1FRhzhw4Hf27++DRiPh5vYRQ4YM\noHv37hlu4pQQNelvJtWqVcPb25uwJEqjy7KMTgc6IvlamMUkZbJhW3WpOudO/4nOywspDYmQ0UBH\nQUDnoKjBFEbyL9ZgWtSUY8eOUa/eu5XVaSyrngIenh7sL7gf8mJF8o3ANcAUxI9EhGAR6YkEOgXB\nzARBKISsk1F9Z4WA5UCDHDJWAU4gihtQlLUIwmscHavQv39v+vbti4lJ5jzfnDt3Dk9PT168eJGs\nC9PEBH6O+R8VqADAVa4yUjOasD/8oXlzJFlOcZg+URRZYAGho3NJjw0jWYrpblOmtpzK+PFp722f\nHzAKRwoUea8Iz7s8z5ibP6e5jfqAvxkYFOf9KNT57j3qusbSBOmBBK8UMBPRmNigRJdBjnZEdWk1\nRp3hz667oAKcRxD8EYTVKMojKlf+gD59PmPo0KHproybkJCQEFq3bs2FCxcIDY0fdKDRiEiSjL2l\nLat1apSehEQLWhC1/Gfo14/7MTGUTObcxwEPQNcftf+2kfzPWWihtGDH5h05bUm2YhSOZLh79y5l\nypSBiqhzHDaok9qFUIWkELmzR3RcngN+xBeOWNag1kwqq389H+iA2iwiBDSPNSjBID+TQAHRzBJB\nLoEUURVwQ71FuqOqU1Zy1SAisnyL8uXL07NnR0aNGhUv0i49REdHM3z4cFatWpXIdRUbDt6b3njh\nBcAIjQ9nO1ZG2LmTg69eJdkwWAdUFgTu1lTUgDQj+Q8ZtXJPeJzlIRS+UphnwWmNPswfGIUjGfbs\n2UPvZs3oKEk8FEUeAU8UhaeKwktFQYcamm8mCpiYCGAqEGMO4ZYSMVrU0hI5LTYpCcceVId8M9Tg\nrOWowVoJn5QVVDF5AoSA+ESEYAE5RIYIBcHMFFG0RdJVAsUZqI86SsmK8q93gI36DoeXsLcvQ9eu\nrRk7dizFihVL99mWLVvGsGHDkqy/ZWEm4Be1iUIUYg1rWPX+QWIiXrP6wQO6JXGuwRqRVVYQOtJY\niypPIJFYBHTqTyFcQAwVIUxACVNQwhWUCFn9vmhA0GgQNKYIgiXIBZCj7hEeGpru0bAoinz22Wf8\n9puagBoTE0OJEiWoXbs227ZtS9e5zp49y4MHD2jePHueWtLkPA4ICKB9+/ZcvnzZMHmdHH379mXk\nyJFvXRbk9u3bHD58mG7d1K/pqVOnWLVqFfPnz3+r86aVGzdu0MTMjB/CwyGJaJoY1PtyiKzwNEoh\nJApCwtSg18eCkKzYWACmccQm2hx0OSE29YCdqMnsxYD3SLrwr4A64rIGyoMcN1orEpSQaKSQhwhP\nghEfHUJ+vBDltQymIhpNQeTI8iiSI1AHtXPR28Q2lwF8kCQfIJj79wOYN281339fBju7knTo4Mm4\nceMMuT2p8fnnn/PRRx/RsmVLXr9+bYiOEQSBiCiFr0y/ZGH0EpxwwuyuP1KF97j74EGi8+wFVsgy\n4d5v8dGMZBwJw00/oRCIYSJCmAihqCKgk1EiFFUETEAw0SCIZghoQbFBjiqKItkhURz1Kao0UA41\n0qE8xFigxMQvzGNjU4Xr16/j4OCQLrPjlmOysLB4q3JMQUFBnDp1Kl3CERMTk+H5wzSNOLp06YJO\np6NatWr4+vpm6ELpZd++fXz//ffpVt7MYuSQIbz3009kZpNYg9jol6dx1h8JAsF6sXmsKDxLIDZm\nooAmuZFNAVTBSSg2KY04EjIPGEjmZDhL+mvrRymaxyYowTLycxkEAdFUC5I9cuRHQA1Ut1ctMj6P\n8ow3vdYPUqhQcdq08WDcuHFpeoC5f/8+zZs35+bNm4Z5D1EUkWUZX3ypQx1a0hL5o8oMu3iRuXGO\nfQl8ADxphBqwZuTtiCHe039KIiDr1FEvEqAR4ouAXBApqgjIseGI9kApVBF4H9VHmzlu1gIFPmX5\n8p506NAhXcfZ2NgwfPhwXF1d6dChA15eXjg4OHDw4EG2bt1K5cqVOXz4MLa2tsiyTOXKlTl69Ch7\n9+5l2rRpaDQaChUqxO7du6lQoQIRERHY29szYcIEWrRowdChQ7l48SLR0dH4+vrSpk0bVqxYwaZN\nmwgLC0OSJMqWLUv79u1p27YtAJ999hldunShTZs2KdqeqtyEhoZy7NgxDhw4gKenJ76+vuzbtw9f\nX1/s7Oy4cOECbm5urF6t9nL28PDghx9+oFq1alhbWzNo0CD++OMPSpQowfTp0/nyyy+5d+8e8+bN\no3Xr1ty6dQsvLy+Dr3nhwoXUqVOHcePGceXKFVxdXfH29sbFxcUgJM+ePaNPnz7cvHkTrVbLL7/8\ngqOjI76+vty5c4ebN29y584dRowYwdChQ9P1nxnL3WvXqJX6bunCBLDTL4lQlGRHNs+Ap7JCiH5k\n8zQsvtgE82Zk80ovNrHPLDKgnSVgYi3GFxuAW8Ar1IepMmReWYzYrHlb9aUhfFgBXivIT8Ig5Cri\nk+sQvBU5RIJoEMzMEbFF0lVBTU6sj5p7kloijZq1LknewGtevPiDNWvWsGpVNayti9CsmTtffvkl\n1atXT/Joe3t7jh8/Tu/evdm+fTuhoaGGvJI55jPZHPkHFTUVuXL3bqIKuQM1GsIKK9DQ6KKKRwBq\nVJ8F0IlEowHhpYhyVYEoRf0j1aAKgAyYCKo7SDR/IwKRtshKQhEojyoCpSHGJNFIILuIjCzF/fv3\nM3Rsly5dmDZtGq1ateL8+fN8/vnnHDx4EEEQ6NGjB2vWrGH48OH8/fffuLi4ULRoUaZPn86uXbso\nUaIEr169wtTUlOnTp3Pq1CkWLFgAwIQJE2jcuDHLly/nxYsX1KpVi08+UZvQBQUFcf78eQoVKsSB\nAweYO3cubdu25eXLlxw5csTgOkuJVIVjy5YtNGvWjDJlymBnZ2dI1jtz5gyXLl2iRIkS1K1bl8OH\nD+Pu7h5vmBUeHk7jxo357rvvaN++PZMnT2bv3r1cvHgRb29vWrduTfHixdm9ezfm5uZcu3aN7t27\nc+LECb799lvmzJljGHHs27fPcN4pU6bg5uZGQEAAgYGBeHl5GRIEr169SmBgIK9evaJy5coMGjQI\nTTpKRcTy6OFDiqf7qMwnQQpHfJIRm3tAT9REtGdARJRCq2cSIvBKELATRS7KMlcURa2Opa+7aHEu\nhZFN3ACBwqijmvS60WKz5vWZ8/HcXjpQQiKRQu4jPHmA+Gg/8uPvUcIUBFMNoqYQUkQFkJ1QJ+Wb\noN48EhKbtd4F0BEauovNm9fi798QS0sbGjWqzpgxo/Hw8Ih3lIWFBWvXruWHH35g8uTJhnmP0Mho\npjGVGnINrr5aE69CbgCwVZHyv4tKQXXtJJgLiF3EcBEhVEQJAyVMRtEp6khABsJAWGGqFwErkFQR\nUHiKmhzaDigM0leoNdLKQLSIEp0zIpARIiNLcPduYhdmWkipHFOfPn1o27Ytw4cPZ/ny5fTu3RuA\nunXr4u3tTefOnWnfvj2gls+J6zzatWsX27ZtY86cOXobI7lz5w6CINCkSRNDiaYGDRowaNAgQkJC\n8Pf3p2PHjmnKoUpVOPz8/PDx8QGgU6dO+Pn50apVK2rWrGnI0HZxceHWrVu4u7vHO9bMzMxQnNDR\n0RELCws0Gg0ODg6GjO+oqCiGDBnC2bNn0Wg0hqJhKXnQDh06xKZNaqp/o0aNePr0Ka9fv0YQBFq2\nbImpqSlFixalWLFiPHr0yGBnengUEpIrhCMjlELtthHLp6glCxtDIrGZiqoJw1FFJsk5G1HkoSgQ\nrEBInDmbSN4ECMSKTbQF6CySEZsi+vWkxCY2a740KChvRikxoDyTkJ48hZCnaB6dRn60HOWFDKKA\naGoNMaWRoz5CdXc1Qs18F/UnbYsktQWi0OkC+euvdfzxR1vMzMxwd3fGx2eYYVguCAKjRo3CycmJ\nDh06EBYWhqIo/MM/uCiuWGBBiGk0REfzGLXecVhz/WfMKyioIdnJiUCYBiFMQAnVTwrrFIjUfxdN\nBP2ksDkC1ihSQeRIO2TsUCfJSqK6gMqhhiOGA21RIs4nIQJLUPvCTAb+A37UH5cXKc7t29czfHRs\nOab9+/fz5MmbR5NSpUpRvHhx9u7dy4kTJ/Dz8wNg8eLFHD9+nB07duDm5maoFp6QTZs28cEHH8R7\n79ixY4laLnt5efHbb7/x+++/s2LFijTZnKJwPHv2jMDAQC5cuIAgCEiSZLg5m5u/8WtoNJok24vG\n7comiiJm+uqioiga9p87dy4lSpTgt99+Q5KkNEcmJCcsZnEqmCZnV1p4/vo1RTN0ZO7iFmpbptTc\nbimObGQ5cQUT3rjRQvRutKdxxOaRKBIsCAY32rP0ik1B3szZFCWVrPlLaB7/myhrHqU4siFrvgHQ\nEEnyRM1a/4eDB2Oz1mWqV1ez1rt160aTJk0ICgqiadOmBAcHEx4ezkrTZURFR/HUzBwlOppeokjk\ne0CNHHRRKUAkSUYGEQ6aMI0aGZRQBATARETQmCAIFqoIxBRCjooVgRK8EYHyqCJgC1HqJdM+EriV\nwra+qG7IksBrYH36PnuuoihPnmS8/06fPn0oXLgwH330UTzPCsAXX3xBjx498Pb2Nnhzbty4Qc2a\nNalZsyY7d+7k3r17FChQgNevXxuO8/T0ZMGCBfz444+A6p5ydXVN8r7Zq1cvatSoQcmSJalSpUqa\nbE5ROPz9/fHy8mLx4sWG9zw8PDhw4EAKR6WPV69eUaqU6nJYtWoVkv5p2MbGJt4vIi7169dnzZo1\nfPXVV+zbtw87OztsbGwytTb+q4gIMpYlkHsIBTqipmhkRbmtVMUmCWLr7SY1ZxMsiuq8DbFiI/NS\nIbHYmOmj0SwkYqwAK5BsJHWoVQiwADk6Ap7eRnxyFyH4b6QnXxuy5kWxCJKuIpLkDMwnJqYwx4/v\npWfPMXh5DcLJqQoDBvTh5MmTdO/enYMHD/JKPwf3QjFjJXBQUIjwykRnikyyIiCEC4hhIoTqw0PD\n9OGhUagDK42IqDFFECxAsUaJKYwcbYdEMVQRsEedxNKLgFI4AyKQXv5ELV9zD/gW+DLB9kn6bXao\nf0F9UbtO5qXhWyyFefo0/XkcqZVjAmjdujW9e/c2uKkAxo4dy7Vr11AUhU8++QQnJydKly7NN998\ng6urKxMmTGDSpEmMGDECJycnZFnm/fffZ+vWrYnOD1CsWDGqVq1Ku3bt0mx7isKxbt06xo0bF++9\nDh06sHjxYipWrJjqyZPqtZFwfdCgQYZ6Vc2aNTN0cHN2dkaj0eDi4kKvXr1wdXU1HOPr60ufPn1w\ndnbGysqKlStXGs6Z0c6CcYmKikKS5TzdQiEaNZ+vB6l31chOTFG7eCTpBkyH2ISEqlFpwXo32mMl\nNkBA5lVcsdEoaDQymIlEF1QIU2KQ5Mdg8RiiDoOkPhTJGgHRxAY5ypozZ64wYMAABgwYSaVKFSlX\nrhw3btwgIiKCsMhohgBh7ZT4Jb/ifQ7Um34SkUFCmF4E4uYI6PQ5AiJgIiKKZnoRsEGOLowSU0wv\nAu9h8OdRQV0kG5CSHBDmIBIwBFgJDEAN7WuDWoEgFj9U5+lS4F/UcO1/gaQDGHI3FkRFpb+F9KtX\nrxK917BhQxo2fBOed/bsWVxcXKhUqZLhvY0bNyY6rnDhwhw/fjzeewnr+wF4e3vj7R1/Ui48PJxr\n164ZUh/SgjEBMAnCw8MpWrAgugy6uXIaBfBG9fDMTWVfX9RnvFFZbFN2IKO6z6JQcxaDUQvFB+uX\nx6gRwsFAsCAQoig8h3jl0s1Rp2AE1N9jDBAt6J/MY78pZqgP8DEgRInq03uUPkIoSm+ISILoYg1I\nFqBYEt8fV4Q3IWhZnYWfXcQ2CmsB/I7aPhOIl3O/ENUV1hp1bDwfVWTyooP4AaVLH+HOnaQ6ZqaM\nj48P5cqVY/jw4YDqYipTpgxLly7lm2++YdasWfTq1csQLZUa+/fvx8zMjDp16qRp/7///psvvviC\nkSNHMmzYsDTbbRSOJIiMjMRGqyUqC0p9J0espyJKv0SiPoRG6X/Gvo6Os090MssV1K/he/pzK6hT\nxs/01/kINf9gvf48AurQs7l+XUK9Ycb9aVgXBGJEEUkUiREEJFFEEgQkQUAWBHVf/bqsv168df2i\nxFlkRXnzWr9O7HuKYliPHZHEvpfkAiAIIIqJf4qiOiLVryOKCHHW0WjirSuA8ugRQlg4llgSTrj6\nGxJir2Ma/xNlhe9HgzpMMxEQTOL8NBUQTISkkzZzGCVURn4mq78LCdVGcxCsVSUVC4hIIRKEK6q4\n6v/ARHsNgnku/ECpoEQqFJIK8fRhSLqP3bhxI+vXr+f3339HlmVq1qyJubk5hw4dAsDd3Z158+ZR\ns2bNNJ3P19cXGxsbRo1K+6NgRhIBjd0CkkAURaJNTChubp7opme42RH/RqckuPklvPERGy4Xe3NT\nFPVGmPCGF/dml/Aml9xNT3/DE+Ksi6LIY40GQf96g0Zj2BYYe4M0MVFvkKJIpEZDQJx9lDjblNj3\nY4+Le4NN4oab7D7ZcVwqrsqE9/VE93mdDrZswXz3PjS37mGiaKgntsBFduYHi68ZMULhxx/NCAsz\nQ6AoijIT6MKbr5KE2jDrGWoW5HP961dxlteoT9mhqGMj1ZclmIQhaHQIYgQI6mOEQgwQgyLFQIyM\nIssosvLmD1JEFReNoGZCxwqKmbqOGarwmCkoZgqKifoTE/37sUtKr2PX05qfeQm4juqdAjXK9j4o\nLWT9b0hWn4R2og7/igEhILeW3jzt5CXuQdGjGauEWqdOHUPU6sWLF3FwcCA4OJgXL15gaWlpaL3s\n4eFBaGgotra2rFixgvfee48FCxawZMkSTExM+Oijj/j6669ZsmQJGo2G1atXs3DhQipVqsTAgQO5\nc+cOAPPmzcPd3R1fX19u3LjBzZs3KVOmDJUrV+b27dtpzoEzjjiSQFEUTMzMkL/8EszNs+cGmYab\nnpEs4u5d8PfH6tBpop4+oqRoTyPFg7pKXSpQAQGBMSajMK1xllmzJGQZBg+G69dNiImxQBAKoCjT\nULNnstPdJBNfpF7ol5f65XWcJSzOEo5gEo6gCUcQI+OIVDQQg6JIIEsoiqx2QIwVqdihqQb9yEdQ\nxckUBDNBL1CgRMvIj2QEBwHFVFE9VxqgCskL1G9Ab9TOxekRqdzALXC44MD5E+czdPj777/P/v37\n2blzJ4qicP/+ferUqUOBAgUYM2YMgiCwZcsWbG1t+f3339m1axfLli3D3t6eW7duYWpqyqtXryhQ\noABTp07FxsaGkSNHAtC9e3cGDx5M3bp1uXPnDs2aNePSpUv4+vqyY8cO/vnnH8zNzfH19eXvv/+O\nlwP36NGjZHPgjCOOJBAEAQtra8Jr1ICCBXPaHCOZjSzDsWMIAVuwPnedyIjXOGmc8ZC6UpvaFJXj\n+9lvcIPzmtMs0/e4EkVYvBg2bIhh5UqZqKgnaDTjiIycgKJMBj4n+ZnzzEREnSNJ/9OuEqMuaUcG\n5RVEP4VoVaQUXqAYRCp2JBWmf70V5VA11BFYEILpe4h3FINIyUoUqvNTRomOVgXqJ+KLlAb1DpVQ\npGJHUmaoIylTUExlVaTSMoJK+PptRSoKrK0yHrfo7u7O4cOHOXz4MCNHjuT+/fscPnyYggULYm9v\nz65du2jSpAkAkiQZ8tKcnJzo3r07n376KZ9++iYEJu5Y4O+//zaMWgBev35NWFgYgiDQpk0bQ1pF\nenPgjMKRDFobGzV72Cgc+YOICAgIwPzvfYg372GqaKgn1Ke+/CmuuGIuJR9DN8vSl1YtROzt4895\ndeoE7u7hDBhgiihG0bnzp2zf/h3Pnk1CEL5CUQagPkLnB0TelAtICztR2w9LwGSU6PFI0bEtivsD\nR1BTKAXUyfNlIMd+12RQXkPMM3UxiNQLVHF6iermi3X5JeXu04GgzhYqQjQo+pGUIuldfXJikdJg\nmDsSTPXrcUXKVAEzkOOKlAnwGMyLZjwGs27duhw6dIjz58/j6OhI6dKlmTNnDgULFsTDw8MgtE5M\nsgAAIABJREFUJAnZsWMHBw4cYNu2bcycOZPz5xOPeBRF4dixY/Hy22JJ2E45PTlwRuFIBitra0KS\naDNqJA+RjAvKXXGnIhURlNRdgwc5SLBwjwQRjAbs7WHLlmiGD4/G39+frl27Uq9ePcaNm87jx9MQ\nhC9RlCHkzfyEt6E5iRuTxG1THBt+mxQiasRZQdLbfzdjI6lQiHkOMU8h8gUKz1F4RfyRVKxQxcZW\nhxncfQqPsPj47UYcs2fPpmLFigiCQOHChXnx4gWXLl1iyZIl/Pjjjxw9epTatWsTHR3NtWvX+PDD\nD7lz5w4eHh7UrVuXdevWERoaio2NTbww36ZNm7JgwQJGjx4NqOG9zs7OGbY1FqNwJEORokW5/eJF\nTpthJD0kcEFFRLzCOQUXVFr4UTubPn0EbGySnwoURfjxR9i8OZylS9eyZ88edu3aws2bNxkyZCz3\n789CFEciyz6k/YndSPYg8qaAWtrK8ccSK1ImJmNp1DDjYcQODg48ffqUHj16GN5zcnIiPDwcOzs7\n/P39GTZsGC9fviQmJgYfHx8qVapEz549efnyJYqiMHz4cAoWLEjr1q3p2LEjW7ZsYeHChSxYsIDB\ngwfj7OxMTEwMDRs2ZNGiRUDKeXapYZwcT4Z+gwezFFR/hJHcS0SEPgoqEM3Ne2hkDfXF+tSX66ku\nqLdI4/yN39hu9ytr1iqkNVrx3j3o319AUSyYMWMGPj4+7Nq1iwEDfLh16zaiOARZHoOhdLCRPI+1\ndQ8WLfKkZ8+eOW1KtpGXYheylRouLmj1hRjzNKGhMGUKeHtDr15wKZkkpStXoHFjyMRyMlnGvXsw\ndy5WHXti2rwNZX/5k67/1WaePJctBDBGHk1tar+VaEQRxQbLVQwdlnbRAChVCrZtU6hUScekSZP4\n+OOPcXFx4ebNS+zfv5OKFf8EyiCKI1BjUY3kdTSam5QuXTqnzchWjK6qZHBycsJkbmp513mAH3+E\nWrVg6lS1Kq5Ol3gfSYJffoGaNd/klOQmDC6orVifu5YpLqjUmC18R+lyMnWTajCeCqII8+ZBQEA4\nixb9Q5UqVVi7di3Nmzfn33/PcuLECfr0GciFC+8jil7I8kTUMiJG8h4y4eHncHJyymlDshWjqyoZ\nQkNDKWxnR8z27WrORV4kNBT69YO1a1Pez99fTQb891+oXRvi1MrJMSIiYOtWzHftzXQXVGo84Qle\n5l2Yv0AhTomgDHH3LgwYoLquevfuzffff2+oAH3+/Hl69erH6dNn0Wg6I0mTebvWukayn6vY2jbl\nyZNbOW1ItmJ0VSWDtbU1RUuUUL/5eZXgYDWc+NtvVQGZM0e9IcflyRM4dAj0rSNzNAkxjgvKrHlb\nyi75I9NdUGlhutkU3OuKby0aAKVLw5YtCpUr6/j1119xcnLikt5d6OjoyKlTR7hyJYiaNW8CDmg0\n3Uk+2shI7iMIZ+dqOW1EtmMUjhRwdHSE//7LaTMyjiTBtWuqKPzyC1hYJB59/PQT9O2rCkbc8ifZ\ngSzD0aMI48Zh06Ijpj37UH3HAwY+7cpa1rJCXkEvpRcf8AFCNhVlusAFrgqXGTAgcWfFjGJiAnPn\nwhdf6Lh+/To1atRg0aJFhkQttbf0fm7dukyDBk8QhGpoNO2BjGUiG8k+TEyCqF/fNafNyHaMcxwp\n4O7qSuD160gff5zTpmQMOzt1iW3O0rBhYuG4ehWmT1fXX76E48fVO11GnPtpISkXlKYe9aS2VKNa\niol42cF3ltPo1F7Ezi7zC1y2bw/VqikMHqxj7NixbN26ldWrV2Nrq0ZYlS1blr17d/PgwQM+/7wf\nf/1VG1FsgCTNANwy3R4jb49WG4SbW/I1nfIrRuFIAVcXF6z27CFx1fw8QpEiUKyY6m4rXRpOnYJy\n5eLvE1dIvv0W6tTJfNG4dw82bMDq8GmiQh5RQixpqAVVkYoIUu6o0fUnf/LC5AndP8u6a5Qrp7qu\nRo8OY+/evVSuXJn169fTuHFjwz4lS5Zk587thISE0Ldvf7ZsaYgo1kSSZqImzuVG5gP/Qy0b2Re1\nGXFc1gDf6bfbAIuBvD6hLBMVdRpX13dvxGGcHE+Bu3fvUsnZmYgNG/LuBPn16+rcRnS0muY8dizs\n3atua9Mm/r6xwtGgwdtdU5bh+HF9It41InSxUVAe1KIWtrkwh0FGpqO2JZ8PiqBly+y55oYNsGgR\nWFpaMmjQIGbNmpVkaYhXr17Rv/9A1q/fhiA46gWkIbmnpvoFoBtwArUORzPgZ9RmU7EcAaqiZoP/\nidoJ5mi2Wpn5nMDe3ot79y6nvms+wygcqVDewYFb/fuDo2NOm5K70bugzHbtxSSeC6qe6oLK5f0U\nf+ZnDtmvZ8VKJVufEf77D4YOVaOuypcvT0BAAB988EGS+4aHhzNkyBBWrdqIolRElmcBTcl5AfFH\nFYP/6V/PQG2JNSaZ/Z8Djqhlc/MuGs0khg2L4ocfvs1pU7KddE+OBwQEIIoi//6bdOSHh4cHp06d\nemvD4rJly5Z4FR6zk27t2mGaRIExI8D9+2oUVCc1CqrMkj/oFjcKShpDHerketEII4ztlhsY4ZO9\nogHw/vuwebNCxYo6Ll++jIuLC8uWLSOp5zmtVsvy5csJDX3EgAG1MTXtiig6AlvJyu7hqeMAHEQt\n7x4O7CBlUViG2h0wb6PVbqN9+9Y5bUaOkG7h8PPzo1WrVvj5+SW5PbP6fsdl8+bNhhDGtJJSZcf0\n0LFdO8yPHMmdiXHZjaKoiXjjxqtRUD16q1FQIV1ZwxpW5kAUVGbwtTiTyh+CWw7NP5uZwYIF0K+f\nQnh4OMOGDaNdu3a8SKZWmoWFBT/99BPh4U8YObI5Zma9EcXKwAZypvt4FeBL1NFPc8CV5G8tgcBy\nIK8/pd9GUe6nuUVrfiNdwhEaGsqxY8dYuHAhv//+OwA6nY6uXbtStWpV2rdvj06nQ1EUlixZwtix\nYw3HrlixwtBRavXq1dSqVQtXV1cGDBiArG8Jam1tzVdffYWLiwt16tTh8ePHHD58mG3btjFmzBiq\nVavGf//9F29UExISQvny5Q3XaNOmDY0bN6ZJkyaEh4fTp08fatWqRbVq1di6dWu6f0Gurq6Yx8RA\nfig/khEiImD9esy+GIj2kzbYjJtF85NFGK8bxTa2MVuaTUta5sp5i7Rwl7ucND3CsGE5ccONT9eu\natS0oujYuXMnlStX5uDBg8nub2JiwuzZs9HpnjBxYlcsLAYhCO+jTkRnzoNT2ukDnAT2oxZyrJzE\nPudQJ863AoWzz7QsYTvNmrVIttFRfiddwrFlyxaaNWtGmTJlsLOz4/Tp0yxevBhra2suXbrE1KlT\nOXXqFIIg0KFDBzZv3mw4dv369XTr1o3Lly+zfv16Dh8+TFBQEKIosmbNGkD14dapU4czZ87QoEED\nli5diru7O23atGHOnDmcPn2a999/P8VRTVBQEBs3biQwMJAZM2bQuHFjjh07xt69exkzZozaYyMd\nCILAZ126YBI7ofwucP8+zJuXwAVVi3nyD3nKBZUWZpr70qSJhrLpK4yaZXzwAQQEKFSsGM3jx4/x\n9PTkq6++SnEELYoi06ZNIyzsEbNmDcDKahSCUBbVJRSVTZY/1v+8A2wGuifYfgdoD6wGKmaTTVmH\njc1WunZtk/qO+ZR0CYefnx+d9NViO3XqxNq1azl48KChHLCjo6OhZoutrS3vv/8+x44d4+nTp1y5\ncgV3d3f27NnDqVOnqF69Oq6uruzdu5ebN28CaiORlvqQFjc3N27FecpP6xx+kyZNKFRILV29a9cu\nvvnmG1xdXWnUqBGRkZHczUAmeB8vL8z27FGjhfIjsS6o8eOxadEJ0x69cdt+jwEhXfK0Cyo1jnOc\n28J/fP555iX7ZQZmZvDTTwp9+qgj+rlz51KjRg3D9yQ5RFFk3LhxhIYGM3/+OAoWnIwglEZtrReR\n4rFvT0fgI9RG44tQy5Qv0S8A01AnxQeiurJqZrE9WckdJOkkzZo1y2lDcow053E8e/aMwMBALly4\ngCAISJKEIAhUq1Yt2Zt6165dWb9+PVWqVKF9+/aG9729vZk1a1ai/U1NTQ3roijGe8qKO8IwMTEx\nuLciEpTQsLKyivd606ZNyUappBVnZ2eKFS7MrXPnwMXlrc6VazBEQQXqo6BE6mnqUV8amSsS8bKD\nudqv6fmZQKFCuXP+qmdPte7kiBHhnDt3DkdHR5YsWcJnn6WeaDJ06FCGDh3K//73P778cno2dCVM\nqqpy3MZN/+NN1FXexsTkF3r27JHoXvMukeYRh7+/P15eXty6dYubN29y584dypcvT7Vq1VirTyK7\ncOEC586dMxzTrl07AgIC8PPzo2vXrgA0btwYf39/njx5AqiCdOfOnRSvnbCrVbly5Th58qTBruTw\n9PRkwYIFhtdBQUFp/biJGODtjcWePRk+PldgcEF5YRrHBTVX/p4tBDA2H7mgUsMffyLNX9KxY+4U\njVgqV4aAAKhUCcLCwujXrx9du3aN931IiS+++IKnT2+zdu1P2NktBEoiCF+jdrMzkn4iMTX9Hz4+\ng3LakBwlzcKxbt062rVrF++9Dh06cPPmTUJDQ6latSpTpkyhevXqhu2FChWiatWq3Llzx/D+hx9+\nyIwZM2jatCnOzs40bdqU4GC1L0HcUUXceYyuXbsye/Zs3NzcuHnzJqNHj2bx4sVUq1aNp0+fGvZL\nOPcxadIkoqOjcXJywsHBgSlTpqT392PA28sLDh6EZ88yfI5sJ1kXVGfWxnFBVaJSvnJBpUYMMfym\n/YUhQxSSyLfLdZibw+LFMt7e6jxgQEAAVapU4fjx42k+R7du3Xj06D82b15ByZK/AiURxSmo7iMj\naWcjjo4OVK6c1OT/u4MxATAd9B86lBWPHxM1cGBOm5I8sS6o3YGY/HcPjSxQT1Of+nkkES87+J45\nXK6wk6VL5RwtBpwRLl6E0aMFIiIULC0tGT9+PBMmTEh3dM9ff/1l7EqYAWxs6rJy5ehED9HvGkbh\nSAcPHjygQtWqRCxfrtaByi08eADr12N1KLYWVAkaKR64K+75bkL7bXnBC7pbtOe72QoODjltTcaI\niIBhw0SuXZPRarU4ODjg7++foS50Bw4coG/foVy9eg1R7IcsjwPey3yj8wVnKFKkNY8e3cQkPW0h\n8yFG4UgnuWbUcewYQkAA1mevE6F7iZPGCQ/Jg9rUzrM5FdnBaBMfLGqdZ8aM3BVJlRGWLYPVq0Gj\n0aDVavn111/p0KFDhs71pivhJWNXwmTQarsxYYIzEyeOy2lTchyjcKSTHBt1RETAtm1qLSijCypD\nXOc6Q8z68usKKFEip63JHM6fhzFjBCIjFbRaLZ06deKnn37KcMTP+fPn6d27P6dOnTF2JYzHOQoU\naMr9+9extrbOaWNyHKNwZIBsG3UkckG9RyOlkdEFlUH6WPSgeuuHDBqUv/JxwsNh6FCR//6TsbS0\nxNbWli1btrxVue9///2X3r37ceTICTSatkiSL0lng78bWFm1w9e3AaNH+6T5mKdPn/LJJ58AEBwc\njEajwc7Ojlu3blGyZEkuXryY6jmWLFmClZUVPXr0oFevXrRu3ZoOHTrg4eHB999/j1sO1cl5tx11\nGWTK+PGsqloVunTJ/FGH3gVldfY6kQYXVGfVBSUbXVAZZR/7eCTex8srpy3JfLRaWLZMZskSWLdO\nx927d6lbty5Tp05l1KhRiGL6G33GdiW8ffs2vXt/wb591RDFpkjSNNTKtu8SRzA3P8ngwWtT3zUO\nRYsWNaQATJ06FRsbG0aOHMnt27dp1apVqsdLkkT//m9yYeJGjaa3JqAsyxn6O0gOY+vYDFCyZEm8\nevbETF+v662IiIANGzDrOxBt49bYjJtJsxOFmaAbyVa2MkeaQytaGect3gIZmZ+s5vDFFwL52cvQ\nv7/aotbMTECn0zFt2jQ8PDx4+PBhisdFRERQq1YtXFxcqFq1KuPHjzdsi+1KuG/fTqyt9wFOCEIV\nIHMrYOdeFKysRvL99zOwtLR8uzPpnTuKoiBJEv369cPBwQFPT09DIrOHhwc+Pj7UqFGD+fPnM3Xq\nVL7//vsUz7tr1y7c3d1xc3Ojc+fOhIWFAWq+27hx43Bzc0sx3y0jGIUjg0wZPx7xr7/g8ePUd07I\nw4cwbx7aTj0xbd6W0j9vp9uN2ES8LYyVxlKHOlhgkfmGv4OsYhWiVTitW+d/r6yLC2zcqFCunEho\naChHjx7lww8/ZPv27ckeY2FhQWBgIGfOnOHcuXMEBgbyzz//xNvnww8/5O+/d+Pj48NHH5khCA3R\naD5GbdCUn/GnZMkIvLx6ZupZr127xpAhQ7hw4QKFChVi48aNgDqSiI6O5sSJE4wcOdLwXnKEhIQw\nc+ZMQyknNzc3fvjhB8Nxtra2nDp1is6dO2eq/UbhyCAlS5ZkrI8P2gUL0lZy/fhxhPET1ES87t7x\nEvFWySvfyUS87CCKKDZarmbECIV3JYLS2hp+/VWmQweIjo7m5cuXdOnShQEDBqDT6ZI8RqtVy5BE\nRUUhSRJFErhg7ezsqF69OgULFqRXL29evHhA584lEMWmaDTuwD5ytidIVvASS8uR/PLL3Ex18wCU\nL1/eUNcvYV2+Ll26xNs3uWloRVE4evQoly5dwt3dHVdXV1atWhWvEkfCc2UWRuF4CyaOH0/xZ88g\nqVIkERHg749prAvqyxk0O1GI8XFcUK1pbXRBZTHfCF9T9n2F2rVz2pLsZ8gQtWuwqanqulq9ejUO\nDg5cuHAh0b6yLOPi4kLx4sVp1KgRVatWTfHcBQoUYO3aNbx+/QgvrypoNG0RxerAX+QXAbGwGE2n\nTi3w8PDI9HObm7+JgtRoNEjSm/DwhBFxqc1lNGnShKCgIIKCgrh48SJLly5N9lyZhVE43gIzMzPW\nrViB5ZIl8PKl6oKaP/+NC2rxNrrnIxfUt3xLe9rThz6G937mZ7zx5nM+ZzKTCSU0yWNDCWUKU/DG\nm1704hLpa8yVER7xiCNm+xkxIu9liGcWbm6waZNCqVICYWFh/Pfffzg5OWFnZ8c333xj2E8URc6c\nOcO9e/c4cOAAP//8MyYmJmzatCnF8+feroRvy26srXfx44+zs+VqKQW3JrdNEARq167NoUOHuHHj\nBqDWM7t27VqW2BgXo3C8JTVr1qRr+/ZoO/ZUXVDb7uZbF1RzmvNtgs5t1anOr/zKMpZRilKsJenI\nkx/5kVrUYiUrWcYyylAmy+2dbjaF+g1EKub99g9vhbU1rFol07at+lpRFCIiIpg5cyaHDh2Kt2/B\nggVp3rw5c+bMoVmzZmluZ5D7uhK+Da/RavuyevUvFChQINPOmrAWX3LbUjouIba2tqxYsYJu3brh\n7OyMu7t7sm29MxNjHkcm8OzZM1wqu9A9pDvNyN81+oMJZgITWM7yRNsOcpADHGAiE+O9H0oo/eiX\nrKhkBec5zxjzYaxeDbZGbyCg1rlasAD++09AktRRhpmZGStXrjT0sdHpdDg4ONCyZUtev35Nq1at\n4mWj+/r6YmNjw6hRo1K8lizL+Pr6Mnv2YiIjrVCUGUBX8kIGgLn5INq3j2Tt2mU5bUquxTjiyASK\nFCnC5j83s9RyKY/JQJRVPmEnO6lFrUTvBxNMQQryLd/Sj37MYQ4RWdxY6DvtdLp0EY2iEYeQELXD\noL+/QsmSak8dnU6Hl5cXlSpVwtnZmWrVqgEwf/58/v33X3bv3g2oCWylS5dm7ty5zJgxgzJlyhAa\nmrRbEpLqSjgaQSiD2pMju7oSZoRArKy2sWhRyiGw7zpG4cgk3NzcGDl+JN9qv0XOc0Pzt2c1qzHB\nhE/4JNE2CYlrXKMtbfmFX7DAIktHHzvYwSvNE7p2fff+H9JCwYKwerVs6EkWERGBTqcjMjKSsmXL\nsnbtWgRBoFKlSjRp0gSA9957j7t37/Ly5UueP3/OnTt30lR6I35XwvEULOibjV0J08sDLC17sHbt\n/wxdRI0kjVE4MpFxE8ZhUcWCdZp1OW1KtvInf3KUo3zFV0lut9P/q0IVABrSkGtkzQSejMz/tAsZ\nMBDeMl8r32FnFz/tqHp18PQEExN14vzq1avs2rWLli1bUr58eTZu3MigQYPYunVrplx/6NChvHhx\nj19+mUmRIt+hNpX6AQjPlPO/HVFYWXVizJiBeHp65rQxuR6jcGQiGo2GdQHr2F5gO/vZn9PmZAvH\nOc461jGTmZiRdFekIhShGMW4i9rv/RSnKEe5LLFnMYuxKRqJ8bufmMqV1SaQwcEQHQ2BgdCtG6xf\nr1C8uDoBqygKkZGRODg40Lp1axYvXkybNm0y1Y6kuxLOIie7Epqbj8Dd3Y4pUybkmA15CePkeBZw\n+vRpmtRvwvTw6VQl5Xj4vMR0pnOWs7zkJYUpTC96sZa1xBCDDTYAVKUqPvgQQghzmMM3qCGf17nO\nHOYQTTT22DOWsViTufU/Qgmli0Vbps+U0bvqjSTg2DH46SeQJGjRAj77DGIHFFeuwM6d6rqlpSWy\nLDNx4kQmTZqUpTZt2bKFwYPHcP/+Q0RxJLI8AiicpdeMiyAsp1Sp77hw4XimRlHlZ4zCkUVs376d\nPp37MF83nxLkkxreuZwJ4jiiq51k9uy832sjpzh4EKZOFQABSVIbRQ0ZMoQZM2ZgamqapdfOma6E\nJ7CyasGJEwf48MMPs/ha+QejqyqLaNWqFV/N+oqJ2om8zsEh+LvCXe5y2vQYQ4caReNtqF9fdV0V\nLarmD4SHh7No0SLc3NwMSWZZhaenJzdvXmL//p1UrPgnUAZRHAGkXKQx49xHq+3Ib7/9YhSNdGIU\njixk2IhhtPJqxVTtVKKJzmlz8jXTLSbj6SlSJuvzCvM9RYqAn59M48aqMyI0NJRLly7h7OzMqlWr\n0pwUmFEaNGjAv/+e5fjx/VSt+g9QAVEcAPo5sswhBK22CV99NeSd7x+eEYyuqixGkiQ+bf4p0kGJ\nMRFj8kUGeW7jKEeZZjkePz811NRI5rFvH8yY8cZ1ZW1tjaenJ8uWLaNgNv2yL1y4QK9e/Th1KgiN\npksmdCV8hZXVxwwc6Mns2TMzy8x3CuOII4vRaDSs27yO4PLBrDZdndPm5EvmWX2Dl5dgFI0swMMD\n/PwUChdWXVehoaHs3LmTKlWqcORI9pRUd3Bw4OTJw1y5coaaNW8CDmg0XYGMlNbQodW2pkuXWnz3\n3YxMtvTdwTjiyCaCg4Op5VyLtk/b0l5qn9Pm5BvWs57fi/yMn5+CWdLRwFnOt9+q0UqFCsFyfSWW\nadPgrt6zEhqq1ouKU7QUUHMqvv4anj8HQYBWrSBOdY9chSzDjBlqCG8slpaWjB07lkmTJqHRaLLN\nljddCQ8jik2QpOmkrSthFFptO5o3L8L69SszvVT6u4RROLKR27dv07huYxo8bkDP6J5Gt9VbEkUU\nnbSt8BkTTRZUvk4z586pyYZff/1GOOKyeLEqHD0T9AJ69kxdKlYEnU7t4Dd9OpQtmz12Z4S//1Y/\nJ4jIsoyNjQ2VKlVi06ZNlMnmCaaHDx/y+ed9+fPPQESxAZI0A0iuB7eEpWUP6tYN548//LM8Qiy/\nY5TcbKRs2bIcOnWIE2VPsMhs0TtZmiQzmcc8ipeUaNgwZ+1wcgIbm6S3KYo6T/Dxx4m3FSmCoXKv\npSWUKaPWk8rNfPIJrF2rjq4EQeD169ecPXuWjz76iN8zo5VyOihRogR//LGdx49v06aNlb4rYSMS\ndyWMwtKyGy4uIWzb9rtRNDIBo3BkM8WLF+fgiYPcq3qP2RazkTCGj2aE5zwn0PxPRvjk7l4b585B\n4cJgb5/yfsHBcP06pNI/KVdQvDhs2CBTr57qrIiJiSE0NJTPP/+cHj16pFj8MCuwtbVl0yZ/fVfC\nkgm6Eoaj1balXr0o9u7dhoVF3uyFk9swCkcOUKhQIfYc2oNUU2Ka5TSicnW10NzJdBNfatYUc/2N\ndu9eaNw45X10OpgyRe3Yl1fqa4miOo8zbpy6rtFoCAsLIyAggCpVqnDy5MlstyluV8Jevaqi0XyK\nIJSgeXNb/vjD3ygamYhROHIIrVbL9t3bKfZJMSZoJxCeKwq95Q3+5V8uac4xcFDuHq1JkpqJ3ahR\n8vvExMDkydCkCdSrl322ZRaenrB6NRQooCCKarHE+/fv06BBA7755htkOfvdsVqtlkmTJvHBByXo\n3r0169evxORdaTifTRiFIwcxMzNj3eZ1uHR0YYx2DC95mdMm5Qm+tpzKp5+KvPdeTluSMqdOqRPd\nyfUEURT47jsoVw46dsxW0zKVEiXA31+O19ddp9Mxa9Ys6tWrx/3797PVnjNnzlC3bl0GDBjA6tWr\njdFTWYDxN5rDaDQalq5YSsv+LfHR+nCb2zltUq5mL3sJER/So0fuCSyYPl11M927B507vykUGBiY\neFI8JER17wBcuKBGKQUFQd++6nL8ePbanlmIIsycqTB6tLouiiKvX7/m1KlTVK1alS1btmSLHbt2\n7aJp06bMmzeP4cOHZ8s130WM4bi5iP8t/R9fjviSgeEDk2yI9K4jI9NZ25oefXV8+qnxzza3cv8+\nDB4s8vq1giyr/09arZZu3bqxYMECtFptpl9TURRmz57N3Llz2bBhA/Xyot8vD2EUjlzG2bNnadei\nHU5PnRgUOSjZHhfvIstZzu7iq1m9RiEb882MZABJgokTBY4de3N7sbKyws7OjoCAAJydnTPtWi9f\nvqRXr148fPiQDRs2ULp06Uw7t5GkMbqqchnOzs4EXQpC+FhguNVwHvAgp03KFUQQwSbLtQwfYRSN\nvIBGA998ozBihJoVL4oiYWFh3L59G3d3d+bOnZspxRLPnTtH9erVsbe358CBA0bRyCaMwpELKViw\nIJt2bGLA9AEMtRzKP/yT0yblON8IX1OhokKtWjltiZH00LYtrFwJVlaqeCiKQnh4OJNAi3DkAAAV\nNklEQVQnT6Zx48Y8evQoQ+dVFIWlS5fSuHFjfH19WbhwIWY5VXPmHcToqsrlHD16lE6tO1H3VV2+\niPoCE969sMJggull3o2ffoIKFXLaGiMZISYGxo0TOHXqze3G3NwcS0tL/Pz8aNasWZrP9fz5c/r1\n68e1a9fw8/Mz9tLIAYwjjlxO7dq1OXPlDM9rP2e0djRPeJLTJmU7082m4OGhMYpGHsbEBObMURg6\n9I3rKjIykhcvXtChQweGDBlCREREquc5dOgQrq6ulCxZkqNHj6ZLNHx8fJg/f77htaenJ3379jW8\nHjVqFHPnzk3fB3tHMQpHHqBo0aLsDNxJp3Gd6G/Znx3CDhTejYHiGc5wQ7hKv365O9nPSNpo3x6W\nLQOtFkN+RXh4OCtXrsTJyYlLly4leVxERAQTJ06kQ4cO/Pjjj8yfPz/dmeD16tXj8OHDAMiyzNOn\nT+Nd78iRI9StWzfV80iS8W/RKBx5BFEUmThpIvuO7mPvh3sZYzWG+2RvYlVOMEc7g27dRIoUyWlL\njGQW5cvDpk0yTk5vHn5CQ0O5fv06NWrUYNGiRfEmzvfv34+TkxNXr14lKCiI1q1bZ+i6derUMfQQ\nuXjxIg4ODtjY2PDixQsiIyO5fPkyf/31FzVr1sTR0ZH+/fsbjvXw8MDHx4caNWqwYMGCDH7y/INR\nOPIYTk5OHD93nG6TuzHUcii/a37Pt4USt7GN1ybP6NIl9yT7GckcTE1h7lyFgQPfuK5iJ86//PJL\nmjdvzvXr1+nbty89evRg9uzZbNiwgRIlSmT4miVLlsTExIS7d+9y5MgR6tSpQ82aNTly5AgnT57E\n0dGRoUOHcvz4cc6fP49Op2P79u2AWgk4OjqaEydO4OPjk1m/hjyLUTjyIBqNhlFjR3Hi/Amu1LjC\nIKtBXOBCTpuVqcjILNf+xKBBCsbadPmXzp3VBlcWFm9cV6GhoQQGBlKpUiXMzMy4ePEibdu2zZTr\nubu7c/jwYQ4fPkydOnWoU6cOhw8fNrip9uzZQ+3atXFycmLv3r3xXFldunTJFBvyA0bhyMNUqFCB\nvYf3MvV/U/l/e3ceFdV5N3D8e2dENkUIiA0WBRcwMoyCigQFqwKCtta9BhO3gPE0sUZrGhtPQvLm\n2PY9phq3c2o8iaYYEcSgTV0aFxTBLSiKIosmUNxAFNmUsM19/5g4rxg1DoKA+X3OmTNz78y992GY\nO795nvs8v+cvDn9hmdUySilt6WI1iTWsoZNTDSEhLV0S0dx69oTERANeXsbmKSsrK1xdXdm/fz9r\n167Fzs6uyY41ZMgQUlNTOXv2LN7e3vj7+5sCSUBAAK+//jrbtm0jIyODqKioBhfsbW1tm6wcbZ0E\nDjNs374djUZDTs6j5zr++OOPqaqqMi2PGTOG8vLyZimToihMnTqV7PxsPF714FXrV9mh7GjTzVeV\nVLLHagdvLlCR/HQ/D1VV0Lt3ezp2tCIyMpLs7GyGPyqtcCMFBATw73//G0dHRxRFwcHBgdLSUo4d\nO0ZAQABg7IxSWVnJ1q1bm/z4zwo5Lc0QGxvLr3/9a2JjYx/5upUrV3Lnzv+nSd+5c2eT/mp6EDs7\nO1asWcGhE4dI800jyjaKAxxok7MMLtX+D3q9Qv/+LV0S0dxqauDLLzXMnm1Nly4vk5d3mdWrVzdb\nGnSdTsfNmzfxvyeVr16vp1OnTjg6OhIVFYVOpyMsLIzBMtr0oWQA4GOqrKxEp9ORnJzMqFGjyMrK\nor6+nrfffpv//Oc/aDQaoqKiUFWVRYsW4enpSefOndm/fz9ubm6cOnWK5557juXLl7NhwwYAIiMj\nmT9/Pvn5+YSHhxMYGMiRI0fo2rUrO3bsaPTEM6qqsm/fPt5Z8A6l+aW8cvsVAglsE3Oc55PPXMtZ\nrF8Pkj3i2VVXB3v2wObNNuj1A1m2bA3e3t5m7UOr1aLX66mtraVdu3ZMnz6dBQsWoLTmKSF/cObM\nGa5evUp4eHhLF6VRfn7DkBtpx44dhIWF0a1bNzp37sypU6c4fvw4BQUFnDlzBo1Gw61bt3BwcGD5\n8uUcPHiQ537oQ3r3g3zy5Ek2btzIiRMnMBgMDB48mGHDhmFvb8/FixeJi4vjk08+4Xe/+x3btm1j\n2rRpjSqroiiEhIQQfDaYXbt2sWThEmKvxjK9cjr++LfqALLUKprwMC2urm23qU08XH29MZX8pk02\n9OrlzbZtK3jxxRcbtS8bGxvS09MBKC4uJiIigvLyct5///0mLHHzSE9P5+TJk2YFjrq6ulYzIZU0\nVT2m2NhYJk+eDMDkyZPZvHkz+/fv57XXXjP1BnFwcHjo9qqqkpKSwoQJE7C2tsbW1pYJEyZw+PBh\nFEXB3d0dvV4PwIABA8jPz3/iMiuKwpgxY0jPTuevMX9lk/sm5nWYxwlOtMoBhEc4whWlgFmzJGg8\nawwG4zS6UVG2HDrUn5iYnSQlHWt00Lhf586d+eSTT1izZg1gHDA4a9Ys9Ho9vr6+HDx4EDAO3lu0\naBHe3t7069ePtWvXAuDm5kZJSQkAaWlppusr77//PjNmzCAoKAg3Nze+/PJLFi1ahF6vJzw8nLq6\nOsD4o/BXv/oVAwcOJCwsjMLCQsA4/mPx4sUMHjwYT09PUlJSqK2t5b333iMuLg4fHx/i4+P55ptv\nCAgIwNfXlyFDhpCbmwvAxo0bGTt2LCNHjiQ4OJgZM2Y0mNtk2rRp/Otf/2qS99AcrSN8tXIlJSUk\nJSVx7tw5FEWhvr4eRVHw8/MzK8OnoigNXq+qqqk2YmlpaVqv1WobXFx/UoqiMG7cOMaOHUtCQgLv\nLnqXzbc2M6NyBj74NNlxntRKm/9l5gwFO7vWF9RE49TWGie0SkjoQMeOrvzjHx8TEhLSLM1J7u7u\n1NfXc/36dWJiYtBqtWRkZJCTk0NoaCi5ubl89tlnP2olAB5Znry8PJKSksjMzMTf35/ExEQ++ugj\nJkyYwM6dOxk9ejTz5s3jq6++wtHRkbi4OJYsWcKnn35q+r44fvw4u3fv5oMPPmDv3r18+OGHnDx5\n0jSYsKKigsOHD6PVao3NzO+8Q0JCAmCsnZw9exZ7e3uSk5NZsWIFv/3tbykrK+Po0aPExMQ0+Xv5\nU6TG8RgSEhKYPn06+fn55OXlUVBQYKohrFu3zpSC4O6HsGPHjj/qRaUoCoGBgWzfvp2qqipu377N\n9u3bCQwMbJL00o9Do9EwZcoUzued561/vMXq51ezsMNCDnKQOuqeShkeJpZY6q0rGD9egsazoLwc\nNm/W8vLL1hw9OpiPP44jLS2T0NDQp3INIjU1lZdffhkAT09PunfvTm5urlmtBGA8b8PDw9Fqteh0\nOgwGA6NGjQLA29ub/Px8cnNzyczMJDg4GB8fH5YuXdpgutwJEyYA4Ovra2pJUFW1wXlfWlrKpEmT\n8Pb2ZuHChQ3Gj4SGhmJvbw9AUFAQFy5c4MaNG8TGxjJp0qQWmRpXAsdj2LJlC+PHj2+wbuLEiVy7\ndo1u3bqh1+vp37+/qbfVnDlzCAsLY+TIkQ228fHxYebMmfj5+eHv709UVJRpQpv7T6bmPLm0Wi3T\npk0jtyCXxZ8u5sCAA0RYR7Ch3YYWSaJYQw2xNp/xhz+oWFg89cOLJnTpEqxaZckrr1jx/fcT+frr\noyQlHWP06NHNHjC+++47tFotzs7OAA/9Qfag9e3atcNgMPZAvD/Z4t107RqNBot7PqAajYa6ujpU\nVcXLy4v09HTS09PJyMhgz549ptfdbU3QarWmpq37vfvuu4wcOZKzZ8/y1VdfNWhxuH/GxOnTpxMT\nE8PGjRuZPXv2g9+MZiZNVY/hwIEDP1o3b9480+O///3vDZ574403eOONN0zLeXl5pscLFiz4UcoC\nNzc3MjIyTMt//OMfn7jMj6Ndu3ZMmTLFWAs5f561K9YS9UUU/TX9+c3t3+CDD5qn8NtiOctxcTUQ\nGNjshxLNwGCAU6dgxw5bsrIUXnvtddaunf9E6UHMVVxczNy5c03nZWBgIF988QXDhw8nNzeXgoIC\n+vTpQ0hICOvWrWP48OFotVpThxY3NzfS0tIICwtj27Ztpv0+TmuAp6cnxcXFHDt2DH9/f2pra7lw\n4QJ9+/Z96DZ2dnZUVFSYlsvLy3FxcQEw9bp8mJkzZzJo0CBcXFzo06fPT5avOUiNQwDQt29f1q5f\ny+Xrl5n60VQ2uG9gtu1stipbqaDip3fQSCWUcMjya95800Ab6EUp7lFYCJ9/ruWVV2z45z97EhGx\njIKCIpYu/dtTCRpVVVX4+Pig0+kICQkhLCyM9957D4Df//73GAwG9Ho9U6dO5fPPP8fCwoLIyMgH\nthJER0czf/58Bg0aRLt27Uy1I0VRGtSUHtQyYGFhQUJCAm+//Tb9+/fHx8fHlEzxfne3Hz58OOfP\nnzddHP/Tn/7En//8Z3x9fU3XUB90fABnZ2f69u3LrFmzmuBdbBwZxyEeSFVVjh49yuqPVrNr9y6C\nlCDGVI3BE88m7c67wGIe9kOyiI6WnlRtQXU1HD4MX3/dkYsXDURETOPVV+fi49N6Olk86+7cuYNe\nryc9PZ2OHTu2SBmkqUo8kKIoBAQEEPBlANevX+fT9Z/yt9V/w1BpILAmkMDawCcOItlkk6U5x+dz\nm7DgoskZDJCZCQcOWHLwoMLAgb4sWjSfsWPHNnqQqmicffv2ERkZycKFC1ssaIDUOIQZVFXl9OnT\nxMfGE78pnu/Lv2dozVCCaoN4gRfMvh4y0/olAsZdZ86ctpcW5VlXXw9nzkBKiiUpKRqcnJx56aVZ\nzJw5G1cZ0v+zJ4FDNIqqqpw7d46tW7ayddNWbt24xdC6oQTWBKJDhxbtI7ffy15W2f6FuHjjbHCi\n5dXWQno6pKRYkZoKrq6uTJkyg0mTJuPh4dHSxROtiAQO0SSysrJIiE8g/p/xFF4rZKhhKEOqh6BH\nT3vaN3itAQOTbX7DzLl3aORkbqKJFBdDWhqkp9ty4kQdHh69mDJlJhMnTsTd3b2liydaKQkcosld\nvHiRhK0JbNu0jayLWfS16ou+Qo+v6osnnnzGZyQ9H0tMjIr20RUT0cSqquD0aTh5sj3p6ZbcumVg\nxIhhhIWNJzw8nK5du7Z0EUUbIIFDNKvy8nKSk5PZu2sv+3bt43LhZWqVSl4MMDB5MvTujQSPZnTn\nDmRnQ2amhjNnOpCVVYWvrzfh4RMIDR2Fj48PWvkHCDNJ4BBPVVFR0Q/5gi5y6NA+CgoK0ems8PCo\nxMPDgIcHdO6MjOloBIPBOHL7/HnIzrYiK8uCy5e/R6/3JCBgBMHBowgKCqJDhw4tXVTRxkngEC3q\n5s2bpKamcuLEMU6cOER6+lkMhlr69GlPjx636d27Hg8P6NJFgsm9amqMQSI/H/LztXz7rS2ZmdXY\n29vx4osvMmRIMP7+/vTr18+UMkOIpiKBQ7Qqqqpy5coVTp06RVraCVMwqampxs3Nkq5da+na9Q6/\n/KVxoicXF3iWvxdra+HaNcjLg/x8hYKCDuTnw5UrVXTv3gUvL2/0ej98fHzx9/enS5cuLV1k8TMg\ngUO0CYWFheTk5JCTk0N2diZZWelcuHCRS5eK6dLFCldXDc8//z2OjjU4OYGjIzg5GZu9rK1buvQP\nV1Nj7Nl0/ToUFRnvr1+3oajIgitX6rhxowoXFye8vPqi1w/C27s/Op0OT0/PBqn4hXiaJHCINq22\ntpa8vDxycnL49ttvuXQpn0uXvuXq1UtcvVrItWslaLUKzs6WODlpcHCox8amDhubGmxtDdjaYrp1\n6GC8t7YGjcZ40f7u/d3Hd5fB+KV/7626uuFyVZUxvXhZGVRUWFBR0Z7yci3l5QqlpfWUltZQXV3P\nL37xHK6uLnTr5k737h706NELd3d3evTogaura4OMrEK0BhI4xDNNVVXKysq4cuUKV69epaioiLKy\nMsrKyrh16yalpTcoLb1JWdktysvLKC+voLLyDvX1Burr63+4v/tYpa6uHoPBOJeCpaUFVlYWWFq2\nx8qq/Q/3VlhbW2FlZYWNjS2dO/8CZ+euODl1wcnJqcHN0dGRTp06tch8CkI8CQkcQgghzCI/dYQQ\nQphFAocQrVhhYSFTp06lV69eDBw4kDFjxnDhwoWncmw3NzdKSkqeyrFE2yJp1YVopVRVZfz48cya\nNYstW7YAkJGRQVFREb1792724z+NucFF2yQ1DiFaqaSkJNq3b8+cOXNM6/R6PUOHDuWtt97C29sb\nvV5PfHw8AAcPHmTYsGGMGzeOnj17snjxYmJiYvDz80Ov1/Pdd98BxmlWJ02ahJ+fH35+fhw5cgQw\nDsYMDQ1Fp9MRFRWFqho7AURHR7Ny5UpTGZYsWcKqVaue4jshWh1VCNEqrVy5Ul2wYMGP1ickJKgh\nISGqwWBQi4qK1G7duqnXrl1Tk5KSVHt7e7WwsFCtrq5WXVxc1OjoaNO+3nzzTVVVVfWll15SU1JS\nVFVV1f/+97/qCy+8oKqqqs6bN0/98MMPVVVV1Z07d6qKoqg3b95U8/PzVV9fX1VVVbW+vl7t2bOn\nWlJS0tx/vmjFpKlKiFbqYU1FqampREREoCgKzs7ODBs2jG+++QY7OzsGDRpkGj3eq1cvRo0aBYBO\npyMpKQkwziKXlZVl2l9FRQW3b9/m8OHDJCYmAjB69GgcHBwA6N69O46Ojpw+fZrCwkJ8fX1Nz4mf\nJwkcQrRSXl5eJCQkPPA59b5e9HeDzL2jyTUajWlZo9FQV1dn2vb48eMPzGF1/37vioyMZMOGDRQV\nFTF79mzz/xjxTJFrHEK0UiNGjKC6upr169eb1mVkZGBvb09cXBwGg4Hi4mKSk5Px8/N76Jf+/UJD\nQxtcozhz5gwAQUFBbN68GYDdu3dz69Yt02vGjx/Pnj17SEtLM9VixM+XBA4hWrHExET27dtHr169\n0Ol0LFmyhIiICPR6Pf369WPkyJEsW7YMZ2dnFEV5aPPWvc+tWrWKtLQ0+vXrh5eXF+vWrQMgOjqa\n5ORkdDodiYmJdO/e3bS9hYUFI0aMYMqUKdLbSsjIcSHETzMYDAwYMICEhAR69uzZ0sURLUxqHEKI\nRzp//jy9e/cmODhYgoYApMYhhBDCTFLjEEIIYRYJHEIIIcwigUMIIYRZJHAIIYQwiwQOIYQQZpHA\nIYQQwiwSOIQQQphFAocQQgizSOAQQghhFgkcQgghzCKBQwghhFkkcAghhDCLBA4hhBBmkcAhhBDC\nLBI4hBBCmEUChxBCCLP8HwQ11jqc3v7zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3a8344ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#what they like\n",
    "genres=data18_44.groupby('genres')\n",
    "size=genres.size()\n",
    "\n",
    "ratings_by_genres = data18_44.groupby('genres').size()\n",
    "dictionary = {}\n",
    "for idx in ratings_by_genres.iteritems():\n",
    "    genres = idx[0].split('|')\n",
    "    for g in genres:\n",
    "        if g in dictionary.keys():\n",
    "            dictionary[g] += idx[1]\n",
    "        else:\n",
    "            dictionary[g] = idx[1]\n",
    "#print dictionary\n",
    "\n",
    "movies_genres=dictionary.keys()\n",
    "counts=[]\n",
    "for x in movies_genres:\n",
    "     counts.append(dictionary[x])\n",
    "\n",
    "title(\"What Attributes Both M&F 18-44 Like? \")\n",
    "pie(counts,labels=movies_genres,autopct='%1.1f')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating of 18:  3.50757344608 average rating of 25:  3.54523506153 average rating of 35 :  3.61816153525\n",
      "average of all people : 3.58156445303\n",
      "std of rating of 18:  1.16597047289 std of rating of 25:  1.12717465647 std rating of 35:  1.07810068422\n",
      "std of all:  1.11710184537\n",
      "1.12717465647 1.16597047289 1.07810068422\n"
     ]
    }
   ],
   "source": [
    "#what feature do they have\n",
    "\n",
    "average_rating25=data25_34['rating'].mean()\n",
    "average_rating18=data18_24['rating'].mean()\n",
    "average_rating35=data35_44['rating'].mean()\n",
    "\n",
    "print \"average rating of 18: \",average_rating18,\"average rating of 25: \",average_rating25,\"average rating of 35 : \",average_rating35\n",
    "\n",
    "average_all=data['rating'].mean()\n",
    "print \"average of all people :\",average_all\n",
    "\n",
    "std_rating25=data25_34['rating'].std()\n",
    "std_rating18=data18_24['rating'].std()\n",
    "std_rating35=data35_44['rating'].std()\n",
    "\n",
    "print \"std of rating of 18: \",std_rating18,\"std of rating of 25: \",std_rating25,\"std rating of 35: \",std_rating35\n",
    "\n",
    "std_all=data['rating'].std()\n",
    "print \"std of all: \",std_all\n",
    "\n",
    "\n",
    "genre18_24=data18_24[data18_24['rating']>4].groupby('genres').size().order(ascending=False)[:20]\n",
    "genre25_34=data25_34[data25_34['rating']>4].groupby('genres').size().order(ascending=False)[:20]\n",
    "genre35_44=data35_44[data35_44['rating']>4].groupby('genres').size().order(ascending=False)[:20]\n",
    "genre_all=data[data['rating']>4].groupby('genres').size().order(ascending=False)[:20]\n",
    "\n",
    "common_genre=list(set(genre18_24.index)&set(genre25_34.index)&set(genre35_44.index))\n",
    "common_genre\n",
    "\n",
    "\n",
    "print std_rating25,std_rating18,std_rating35\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Is my answer reliable?\n",
    "#what are they opinion of drama\n",
    "\n",
    "drama=data18_44[data18_44['genres']=='Drama']\n",
    "drama_rating=drama['rating'].mean()\n",
    "drama_std=drama['rating'].std()\n",
    "drama_size=drama['rating'].size\n",
    "drama=['Drama',drama_rating,drama_std,drama_size]\n",
    "\n",
    "drama=data18_44[data18_44['genres']=='Comedy']\n",
    "drama_rating=drama['rating'].mean()\n",
    "drama_std=drama['rating'].std()\n",
    "drama_size=drama['rating'].size\n",
    "comedy=[ 'Comedy',drama_rating,drama_std,drama_size]\n",
    "\n",
    "drama=data18_44[data18_44['genres']=='Action']\n",
    "drama_rating=drama['rating'].mean()\n",
    "drama_std=drama['rating'].std()\n",
    "drama_size=drama['rating'].size\n",
    "action=[ 'Action',drama_rating,drama_std,drama_size]\n",
    "\n",
    "drama=data18_44[data18_44['genres']=='Thriller']\n",
    "drama_rating=drama['rating'].mean()\n",
    "drama_std=drama['rating'].std()\n",
    "drama_size=drama['rating'].size\n",
    "thriller=['Thriller',drama_rating,drama_std,drama_size]\n",
    "\n",
    "drama=data18_44[data18_44['genres']=='Drama|Thriller']\n",
    "drama_rating=drama['rating'].mean()\n",
    "drama_std=drama['rating'].std()\n",
    "drama_size=drama['rating'].size\n",
    "drama_triller=[ 'Drama|Thriller',drama_rating,drama_std,drama_size]\n",
    "\n",
    "Cdrama=data18_44[data18_44['genres']=='Comedy|Drama']\n",
    "Cdrama_rating=Cdrama['rating'].mean()\n",
    "Cdrama_std=Cdrama['rating'].std()\n",
    "Cdrama_size=Cdrama['rating'].size\n",
    "comedy_drama=['Comedy|Drama',Cdrama_rating,Cdrama_std,Cdrama_size]\n",
    "\n",
    "crdrama=data18_44[data18_44['genres']=='Action|Drama']\n",
    "crdrama_rating=crdrama['rating'].mean()\n",
    "crdrama_std=crdrama['rating'].std()\n",
    "crdrama_size=crdrama['rating'].size\n",
    "action_drama=['Action|Drama',crdrama_rating,crdrama_std,crdrama_size]\n",
    "\n",
    "drdrama=data18_44[data18_44['genres']=='Drama|Romance']\n",
    "drdrama_rating=drdrama['rating'].mean()\n",
    "drdrama_std=drdrama['rating'].std()\n",
    "drdrama_size=drdrama['rating'].size\n",
    "drama_romance=['Drama|Romance',drdrama_rating,drdrama_std,drdrama_size]\n",
    "\n",
    "dwdrama=data18_44[data18_44['genres']=='Drama|Sci-Fi']\n",
    "dwdrama_rating=dwdrama['rating'].mean()\n",
    "dwdrama_std=dwdrama['rating'].std()\n",
    "dwdrama_size=dwdrama['rating'].size\n",
    "drama_scifi=['Drama|Sci-Fi',dwdrama_rating,dwdrama_std,dwdrama_size]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------+----------------+---------------+-----------------------+\n",
      "|     Genres     | Average Rating |      Std      | Counts of being Rated |\n",
      "+----------------+----------------+---------------+-----------------------+\n",
      "|     Drama      | 3.75687847442  | 1.05649744408 |         14211         |\n",
      "|     Comedy     | 3.44904566423  | 1.14508351247 |         94516         |\n",
      "|     Action     | 3.31930005147  | 1.06459731007 |          9715         |\n",
      "|    Thriller    | 3.52119736465  | 1.09489336941 |         13964         |\n",
      "| Drama|Thriller | 3.75687847442  | 1.05649744408 |         14211         |\n",
      "|  Comedy|Drama  |  3.708386277   | 1.07112695873 |         33054         |\n",
      "|  Action|Drama  | 3.54734802616  | 1.13900039474 |          8258         |\n",
      "| Drama|Romance  | 3.56855003404  | 1.07525673792 |         22035         |\n",
      "|  Drama|Sci-Fi  | 3.80330544787  | 1.01039358382 |          4901         |\n",
      "+----------------+----------------+---------------+-----------------------+\n"
     ]
    }
   ],
   "source": [
    "result_table=PrettyTable([\"Genres\",\"Average Rating\",\"Std\",\"Counts of being Rated\"])\n",
    "\n",
    "result_table.add_row(['Drama',drama_rating,drama_std,drama_size])\n",
    "result_table.add_row(comedy)\n",
    "result_table.add_row(action)\n",
    "result_table.add_row(thriller)\n",
    "result_table.add_row(drama_triller)\n",
    "result_table.add_row(comedy_drama)\n",
    "result_table.add_row(action_drama)\n",
    "result_table.add_row(drama_romance)\n",
    "result_table.add_row(drama_scifi)\n",
    "\n",
    "print result_table"
   ]
  },
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   "source": [
    "* -----------------\n",
    "# Done\n",
    "\n",
    "All set! \n",
    "\n",
    "** What do you need to submit?**\n",
    "\n",
    "* **Notebook File**: Save this IPython notebook, and find the notebook file in your folder (for example, \"filename.ipynb\"). This is the file you need to submit. Please make sure all the plotted tables and figures are in the notebook. If you used \"ipython notebook --pylab=inline\" to open the notebook, all the figures and tables should have shown up in the notebook.\n",
    "\n",
    "\n",
    "* **PPT Slides**: please prepare PPT slides (for 10 minutes' talk) to present about the case study . We will ask two teams which are randomly selected to present their case studies in class for this case study. \n",
    "\n",
    "* ** Report**: please prepare a report (less than 10 pages) to report what you found in the data.\n",
    "    * What is the relationship between this topic and Business Intelligence?\n",
    "    * How did you analyse the data?\n",
    "    * What did you find in the data? \n",
    "    * What conjectures did you make and how did you support or disprove them using data?\n",
    "    * Did you find anything suprising in the data?\n",
    "    * What business decision do you think this data could help answer?  Why?\n",
    "\n",
    "     (please include figures or tables in the report, but no source code)\n",
    "\n",
    "Please compress all the files in a zipped file.\n"
   ]
  }
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