report_aux.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Data Exploration\n",
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Set up the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
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       "        font-style: oblique;\n",
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       "    }\n",
       "    div.cell{\n",
       "        width:800px;\n",
       "        margin-left:16% !important;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    h1 {\n",
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       "        font-size: 130%;\n",
       "        width:800px;\n",
       "        margin-left:auto;\n",
       "        margin-right:auto;\n",
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       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
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       "    .prompt{\n",
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       "        font-weight: 300;\n",
       "        font-size: 22pt;\n",
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       "        margin-bottom: .5em;\n",
       "        margin-top: 0.5em;\n",
       "        display: block;\n",
       "    }\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }  \n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
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       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "\n",
    "def css_styling():\n",
    "    styles = open(\"css/ipython2.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "\n",
    "css_styling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_palette(\"deep\", desat=.6)\n",
    "sns.set_context(rc={\"figure.figsize\": (11, 4)})\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Check-points\n",
    "\n",
    "1. [There is too much data to plot as a scatter plot](#Ok....if-I-classify-each-data-point-by-if-its-country-has-more-than-20,000-of-GDP-per-capta....-how-it-relates-to-the-Math-score)\n",
    "\n",
    "2. [Testing the code created](#I-am-going-to-put-all-this-work-inside-a-library)\n",
    "\n",
    "3. [What I have donw to the outliers](#Ok...there-are-2,289-datapoint-outside-the-border-of-my-box-plot-,-the-whiskers.-It-is-less-then-1%-of-my-data.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Open file and initiate variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import csv\n",
    "import zipfile\n",
    "import StringIO\n",
    "\n",
    "s_root = \"C:/Users/Vera\" #windows\n",
    "s_root = \"/Users/ucaiado\"#mac\n",
    "s_root = \"/home/uira\"#linux\n",
    "s_name = s_root + \"/Dropbox/NEUTRINO/ALGO/DATA/pisa2012.csv.zip\"\n",
    "s_dict = s_root + \"/Dropbox/NEUTRINO/ALGO/DATA/pisadict2012.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Create a function to read the file and read the file labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_labels = pd.read_csv(s_dict)\n",
    "df_labels.columns=[\"VAR\",\"DESCRIPTION\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VAR</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CNT</td>\n",
       "      <td>Country code 3-character</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUBNATIO</td>\n",
       "      <td>Adjudicated sub-region code 7-digit code (3-di...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STRATUM</td>\n",
       "      <td>Stratum ID 7-character (cnt + region ID + orig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OECD</td>\n",
       "      <td>OECD country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>National Centre 6-digit Code</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VAR                                        DESCRIPTION\n",
       "0       CNT                           Country code 3-character\n",
       "1  SUBNATIO  Adjudicated sub-region code 7-digit code (3-di...\n",
       "2   STRATUM  Stratum ID 7-character (cnt + region ID + orig...\n",
       "3      OECD                                       OECD country\n",
       "4        NC                       National Centre 6-digit Code"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import zipfile\n",
    "\n",
    "def _getFields(l_fields, row):\n",
    "    '''\n",
    "    Return a dictionary with the data into the row, filtering \n",
    "    the fields passed \n",
    "    '''\n",
    "    return {k: row.get(k, None) for k in l_fields}\n",
    "\n",
    "def getData(s_file, l_fields):\n",
    "    '''\n",
    "    Return a list of dictionaries with the data desired\n",
    "    '''\n",
    "    with open(s_file, 'rb') as f1:\n",
    "        zfile = zipfile.ZipFile(f1)\n",
    "        l_rtn = []\n",
    "        with zfile.open(zfile.filelist[0].filename) as f:\n",
    "            reader = csv.DictReader(f)\n",
    "            for idx, row in enumerate(reader):\n",
    "                #count lines processed\n",
    "                if idx%50000==0: print \"estou na linha {}\".format(idx)\n",
    "                #get data desired\n",
    "                l_rtn.append(_getFields(l_fields, row))\n",
    "        print \"estou na linha {}\".format(idx)\n",
    "        \n",
    "    return l_rtn    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estou na linha 0\n",
      "estou na linha 50000\n",
      "estou na linha 100000\n",
      "estou na linha 150000\n",
      "estou na linha 200000\n",
      "estou na linha 250000\n",
      "estou na linha 300000\n",
      "estou na linha 350000\n",
      "estou na linha 400000\n",
      "estou na linha 450000\n",
      "estou na linha 485489\n",
      "CPU times: user 2min 25s, sys: 1.09 s, total: 2min 26s\n",
      "Wall time: 2min 27s\n"
     ]
    }
   ],
   "source": [
    "l_test = list(df_labels.head(5).VAR)\n",
    "%time d_data = getData(s_name, l_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###let's see the countries names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_country =  pd.DataFrame(d_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNT</th>\n",
       "      <th>NC</th>\n",
       "      <th>OECD</th>\n",
       "      <th>STRATUM</th>\n",
       "      <th>SUBNATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CNT       NC      OECD  STRATUM SUBNATIO\n",
       "0  Albania  Albania  Non-OECD  ALB0006  0080000\n",
       "1  Albania  Albania  Non-OECD  ALB0006  0080000\n",
       "2  Albania  Albania  Non-OECD  ALB0006  0080000\n",
       "3  Albania  Albania  Non-OECD  ALB0006  0080000\n",
       "4  Albania  Albania  Non-OECD  ALB0006  0080000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b=[x.strip() for x in df_country.CNT.unique()]\n",
    "a=[x.strip() for x in df_country.NC.unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['United Kingdom (excl.Scotland)',\n",
       " 'United Kingdom (Scotland)',\n",
       " 'Republic of Korea',\n",
       " 'China (Shanghai)',\n",
       " 'Perm (Russian Federation)',\n",
       " 'Viet Nam']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for  x in a if x not in b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['United Kingdom',\n",
       " 'Korea',\n",
       " 'China-Shanghai',\n",
       " 'Perm(Russian Federation)',\n",
       " 'Florida (USA)',\n",
       " 'Connecticut (USA)',\n",
       " 'Massachusetts (USA)',\n",
       " 'Vietnam']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for  x in b if x not in a]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###I like more the CNT names. I will make just some changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estou na linha 0\n",
      "estou na linha 50000\n",
      "estou na linha 100000\n",
      "estou na linha 150000\n",
      "estou na linha 200000\n",
      "estou na linha 250000\n",
      "estou na linha 300000\n",
      "estou na linha 350000\n",
      "estou na linha 400000\n",
      "estou na linha 450000\n",
      "estou na linha 485489\n",
      "CPU times: user 2min 21s, sys: 794 ms, total: 2min 22s\n",
      "Wall time: 2min 22s\n"
     ]
    }
   ],
   "source": [
    "l_test = [\"CNT\",\"SUBJECT\"]\n",
    "%time d_data = getData(s_name, l_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df =  pd.DataFrame(d_data)\n",
    "b=[x.strip() for x in df.CNT.unique()]\n",
    "d_newNames = dict(zip(b,b))\n",
    "d_newNames[\"United Arab Emirates\"] = \"UAE\"\n",
    "d_newNames[\"United Kingdom\"] = \"UK\"\n",
    "d_newNames[\"Serbia\"] = \"Serbia\"\n",
    "d_newNames[\"Korea\"] = \"South Korea\"\n",
    "d_newNames[\"Chinese Taipei\"] = \"Taiwan\"\n",
    "d_newNames[\"Slovak Republic\"] = \"Slovakia\"\n",
    "d_newNames[\"Russian Federation\"] = \"Russia\"\n",
    "d_newNames[\"Perm(Russian Federation)\"] = \"Russia\"\n",
    "d_newNames[\"Hong Kong-China\"] = \"Hong Kong\"\n",
    "d_newNames[\"China-Shanghai\"] = \"China\"\n",
    "d_newNames[\"Macau\"] = \"Macau\"\n",
    "d_newNames[\"Connecticut (USA)\"] = \"USA\"\n",
    "d_newNames[\"Florida (USA)\"] = \"USA\"\n",
    "d_newNames[\"Massachusetts (USA)\"] = \"USA\"\n",
    "d_newNames[\"United States of America\"] = \"USA\"\n",
    "df.CNT1 = None\n",
    "df.CNT1 = df.CNT.apply(lambda x: d_newNames[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows: 485490\n",
      "Number of Countries in the dataset: 64\n"
     ]
    }
   ],
   "source": [
    "print \"Number of Rows: {}\".format(df.shape[0])\n",
    "print \"Number of Countries in the dataset: {}\".format(len(df.CNT.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Also, I would like to use Gini Index in my visualizations...maybe I could change the color of the countries by it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is how it would be done in R\n",
    "<code>\n",
    "    > repGINI = vars %>%\n",
    "    +         group_by(CNT, SUBJECT) %>%\n",
    "    +         summarise_each(funs(Gini( (./sum(.)) * SCORE)), \n",
    "    +                        contains('W_FSTR')) %>%\n",
    "    +         melt(id=c('CNT', 'SUBJECT'), variable.name='REPNAME', \n",
    "    +              value.name='GINI')\n",
    "</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Think better, I will calculate it using R.... I guess that it would be faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estou na linha 0\n",
      "estou na linha 50000\n",
      "estou na linha 100000\n",
      "estou na linha 150000\n",
      "estou na linha 200000\n",
      "estou na linha 250000\n",
      "estou na linha 300000\n",
      "estou na linha 350000\n",
      "estou na linha 400000\n",
      "estou na linha 450000\n",
      "estou na linha 485489\n",
      "CPU times: user 2min 21s, sys: 3.59 s, total: 2min 25s\n",
      "Wall time: 2min 25s\n"
     ]
    }
   ],
   "source": [
    "l_test = [\"CNT\",\"SUBNATIO\",\"STRATUM\",\"OECD\",\"NC\",\"SCHOOLID\",\"STIDSTD\",\"PV1MATH\",\"PV2MATH\",\"PV3MATH\",\"PV4MATH\",\"PV5MATH\",\"PV1READ\",\"PV2READ\",\"PV3READ\",\"PV4READ\",\"PV5READ\",\"PV1SCIE\",\"PV2SCIE\",\"PV3SCIE\",\"PV4SCIE\",\"PV5SCIE\",\"W_FSTUWT\"]\n",
    "%time d_data = getData(s_name, l_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df =  pd.DataFrame(d_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNT</th>\n",
       "      <th>NC</th>\n",
       "      <th>OECD</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>PV2MATH</th>\n",
       "      <th>PV2READ</th>\n",
       "      <th>PV2SCIE</th>\n",
       "      <th>PV3MATH</th>\n",
       "      <th>...</th>\n",
       "      <th>PV4READ</th>\n",
       "      <th>PV4SCIE</th>\n",
       "      <th>PV5MATH</th>\n",
       "      <th>PV5READ</th>\n",
       "      <th>PV5SCIE</th>\n",
       "      <th>SCHOOLID</th>\n",
       "      <th>STIDSTD</th>\n",
       "      <th>STRATUM</th>\n",
       "      <th>SUBNATIO</th>\n",
       "      <th>W_FSTUWT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
       "      <td>None</td>\n",
       "      <td>376.4683</td>\n",
       "      <td>254.342</td>\n",
       "      <td>408.84</td>\n",
       "      <td>344.5319</td>\n",
       "      <td>...</td>\n",
       "      <td>175.7053</td>\n",
       "      <td>367.8105</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>218.5981</td>\n",
       "      <td>392.9877</td>\n",
       "      <td>0000001</td>\n",
       "      <td>00001</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "      <td>8.9096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
       "      <td>None</td>\n",
       "      <td>464.3325</td>\n",
       "      <td>349.8975</td>\n",
       "      <td>471.5964</td>\n",
       "      <td>453.4273</td>\n",
       "      <td>...</td>\n",
       "      <td>369.7553</td>\n",
       "      <td>443.6218</td>\n",
       "      <td>476.0165</td>\n",
       "      <td>396.7618</td>\n",
       "      <td>454.8116</td>\n",
       "      <td>0000001</td>\n",
       "      <td>00002</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "      <td>8.9096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>None</td>\n",
       "      <td>481.0796</td>\n",
       "      <td>404.3872</td>\n",
       "      <td>428.7952</td>\n",
       "      <td>489.6479</td>\n",
       "      <td>...</td>\n",
       "      <td>431.3938</td>\n",
       "      <td>512.7191</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>0000001</td>\n",
       "      <td>00003</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "      <td>8.4871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>None</td>\n",
       "      <td>498.6836</td>\n",
       "      <td>481.4353</td>\n",
       "      <td>481.574</td>\n",
       "      <td>415.3373</td>\n",
       "      <td>...</td>\n",
       "      <td>425.0393</td>\n",
       "      <td>474.1141</td>\n",
       "      <td>454.2842</td>\n",
       "      <td>471.9036</td>\n",
       "      <td>426.5573</td>\n",
       "      <td>0000001</td>\n",
       "      <td>00004</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "      <td>8.4871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Non-OECD</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>None</td>\n",
       "      <td>328.1742</td>\n",
       "      <td>141.7883</td>\n",
       "      <td>275.774</td>\n",
       "      <td>403.7311</td>\n",
       "      <td>...</td>\n",
       "      <td>272.8495</td>\n",
       "      <td>403.5248</td>\n",
       "      <td>395.1628</td>\n",
       "      <td>260.1405</td>\n",
       "      <td>422.1746</td>\n",
       "      <td>0000001</td>\n",
       "      <td>00005</td>\n",
       "      <td>ALB0006</td>\n",
       "      <td>0080000</td>\n",
       "      <td>8.4871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CNT       NC      OECD   PV1MATH   PV1READ PV1SCIE    PV2MATH  \\\n",
       "0  Albania  Albania  Non-OECD  406.8469  249.5762     None  376.4683   \n",
       "1  Albania  Albania  Non-OECD  486.1427  406.2936     None  464.3325   \n",
       "2  Albania  Albania  Non-OECD  533.2684    401.21     None  481.0796   \n",
       "3  Albania  Albania  Non-OECD  412.2215   547.363     None  498.6836   \n",
       "4  Albania  Albania  Non-OECD  381.9209  311.7707     None  328.1742   \n",
       "\n",
       "    PV2READ   PV2SCIE   PV3MATH   ...      PV4READ   PV4SCIE   PV5MATH  \\\n",
       "0   254.342    408.84  344.5319   ...     175.7053  367.8105  381.9209   \n",
       "1  349.8975  471.5964  453.4273   ...     369.7553  443.6218  476.0165   \n",
       "2  404.3872  428.7952  489.6479   ...     431.3938  512.7191  533.2684   \n",
       "3  481.4353   481.574  415.3373   ...     425.0393  474.1141  454.2842   \n",
       "4  141.7883   275.774  403.7311   ...     272.8495  403.5248  395.1628   \n",
       "\n",
       "    PV5READ   PV5SCIE SCHOOLID STIDSTD  STRATUM SUBNATIO W_FSTUWT  \n",
       "0  218.5981  392.9877  0000001   00001  ALB0006  0080000   8.9096  \n",
       "1  396.7618  454.8116  0000001   00002  ALB0006  0080000   8.9096  \n",
       "2    401.21  499.6643  0000001   00003  ALB0006  0080000   8.4871  \n",
       "3  471.9036  426.5573  0000001   00004  ALB0006  0080000   8.4871  \n",
       "4  260.1405  422.1746  0000001   00005  ALB0006  0080000   8.4871  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "485490"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.ix[:,['CNT', 'SUBNATIO', 'STRATUM', 'OECD', 'NC','SCHOOLID', 'STIDSTD', 'W_FSTUWT']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "485490"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_aux = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.index = [df.CNT, df.SUBNATIO, df.STRATUM, df.OECD, df.NC, df.SCHOOLID, df.STIDSTD, df.W_FSTUWT]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.drop(['CNT', 'SUBNATIO', 'STRATUM', 'OECD', 'NC','SCHOOLID', 'STIDSTD', 'W_FSTUWT'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_aux = df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7282350,)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###Wait a minute... this Gini coefficient is about inequality in the Data...it is not economics. Let's classify the data in another way. I want the GDP per capita from this countries...in 2012\n",
    "\n",
    "I found a source about GDP in OCDE website...nice. The data is in dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_gdp = \"data/OCDE_GDP_percapta.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Source</th>\n",
       "      <th>Year of reference</th>\n",
       "      <th>Per capita GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>c</td>\n",
       "      <td>2010</td>\n",
       "      <td>9409.766550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>d</td>\n",
       "      <td>2010</td>\n",
       "      <td>46915.900070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>c</td>\n",
       "      <td>2010</td>\n",
       "      <td>14003.932110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>c</td>\n",
       "      <td>2010</td>\n",
       "      <td>4097.937464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>c</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country       Year Source  Year of reference  Per capita GDP\n",
       "96                Tunisia  PISA 2012      c               2010     9409.766550\n",
       "97   United Arab Emirates  PISA 2012      d               2010    46915.900070\n",
       "98                Uruguay  PISA 2012      c               2010    14003.932110\n",
       "99               Viet Nam  PISA 2012      c               2010     4097.937464\n",
       "100         Liechtenstein  PISA 2012      c               2010             NaN"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gdp = pd.read_csv(s_gdp, sep = \"\\t\")\n",
    "df_gdp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XdXW1/3ZDSqTDkRh8MhPG3ftHjWuSVwIAoGmkp6fpD38YEu4YwHEFfRgxLS1N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\nlG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12263c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_gdp[\"Per capita GDP\"].hist(bins = 15);\n",
    "ax.set_title(\"Distribution of GDP per capta\\n\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###Ok, I'm not sure if it is what I want. I would like to stratify people into social status. Let's see how is distributed the feature _ESCS_, that is about Index of economic, social and cultural status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estou na linha 0\n",
      "estou na linha 50000\n",
      "estou na linha 100000\n",
      "estou na linha 150000\n",
      "estou na linha 200000\n",
      "estou na linha 250000\n",
      "estou na linha 300000\n",
      "estou na linha 350000\n",
      "estou na linha 400000\n",
      "estou na linha 450000\n",
      "estou na linha 485489\n",
      "CPU times: user 2min 19s, sys: 599 ms, total: 2min 19s\n",
      "Wall time: 2min 19s\n"
     ]
    }
   ],
   "source": [
    "l_test = [\"CNT\",\"ESCS\",\"STIDSTD\",\"PV1MATH\",\"PV1READ\",\"PV1SCIE\",\"BELONG\", \"OUTHOURS\", \"ST28Q01\", \"ST57Q01\"]\n",
    "%time d_data = getData(s_name, l_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(d_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BELONG</th>\n",
       "      <th>CNT</th>\n",
       "      <th>ESCS</th>\n",
       "      <th>OUTHOURS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>ST28Q01</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>STIDSTD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.18</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
       "      <td>341.7009</td>\n",
       "      <td>0-10 books</td>\n",
       "      <td>NA</td>\n",
       "      <td>00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NA</td>\n",
       "      <td>8</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
       "      <td>548.9929</td>\n",
       "      <td>201-500 books</td>\n",
       "      <td>NA</td>\n",
       "      <td>00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NA</td>\n",
       "      <td>24</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>More than 500 books</td>\n",
       "      <td>NA</td>\n",
       "      <td>00003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NA</td>\n",
       "      <td>17</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>438.6796</td>\n",
       "      <td>11-25 books</td>\n",
       "      <td>10</td>\n",
       "      <td>00004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.63</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>361.5628</td>\n",
       "      <td>101-200 books</td>\n",
       "      <td>NA</td>\n",
       "      <td>00005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BELONG      CNT ESCS OUTHOURS   PV1MATH   PV1READ   PV1SCIE  \\\n",
       "0  -1.18  Albania   NA       NA  406.8469  249.5762  341.7009   \n",
       "1     NA  Albania   NA        8  486.1427  406.2936  548.9929   \n",
       "2     NA  Albania   NA       24  533.2684    401.21  499.6643   \n",
       "3     NA  Albania   NA       17  412.2215   547.363  438.6796   \n",
       "4   2.63  Albania   NA       NA  381.9209  311.7707  361.5628   \n",
       "\n",
       "               ST28Q01 ST57Q01 STIDSTD  \n",
       "0          0-10 books       NA   00001  \n",
       "1       201-500 books       NA   00002  \n",
       "2  More than 500 books      NA   00003  \n",
       "3         11-25 books       10   00004  \n",
       "4       101-200 books       NA   00005  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.ST57Q01[df.ST57Q01==\"NA\"]=None\n",
    "df.ST57Q01 = df.ST57Q01.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.ESCS[df.ESCS==\"NA\"]=None\n",
    "df.ESCS = df.ESCS.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rVlFXV0dWVhb9+vWjtLSU7t27k5OTw1//+lcWL17MxIkTKSgoYOHChSQkJDB6\n9Gi2bt1Kjx49Wm+kIiIi7UyTu9O3bdvGoUOH+PnPf869995LRUUFH3/8Mb179wYgPT2d8vJyNm3a\nhMfjwel04na7SUxMpLKykvXr15Oeng5AWloaa9euxbIsgsEgCQkJAKSmplJeXt6KwxQREWl/mtwS\nP/fcc/n5z39OZmYmn3/+Ob/4xS8a3O9yuaitrcWyLGJiYhostywLy7JwuVwN2vr9/vDu+WPLfT5f\nS41JRETkrNBkiF9++eUkJiaG/9+xY0e2bt0avt+yLGJjY3G73fj9/vByv99PTExMg+V+v5/Y2Fhc\nLleDtsf6aEp8fEyTbeTMaI5b3/dxjvftczfdSL5X4uLcbb4utfXzSzNCfNWqVVRWVlJQUMCePXvw\n+/3079+fdevW0adPH7xeL3379iU5OZl58+YRCASoq6tjx44ddOvWDY/Hg9frJTk5Ga/XS0pKCm63\nG6fTic/no3PnzpSVlTXrwLbq6toWGbScXHx8jOa4lX1f57imxmrrEuQ01dRYbboufV/X5fakOV+S\nmgzxu+++mwkTJjBs2DAAZs+eTceOHZk8eTLBYJCkpCQyMjJwOByMHDmS7OxsQqEQubm5REdHk5WV\nRV5eHtnZ2URHR1NUVATAtGnTGDduHPX19aSmppKcnHyGwxURETm7NBniUVFRzJ0794Tlxx+tfkxm\nZiaZmZkNlnXo0IH58+ef0LZXr16sWLHidGoVERGR4+hkLyIiIjalEBcREbEphbiIiIhNKcRFRERs\nSiEuIiJiUwpxERERm1KIi4iI2JRCXERExKYU4iIiIjalEBcREbEphbiIiIhNKcRFRERsSiEuIiJi\nU01exUxEvj8CgQA+X1WL97tzZ8v3KSKtTyEuYiM+XxVTnirG3fGiFu13j287nRK6tmifItL6FOIi\nNuPueBHnx13Son3W7t/bov2JyHdDv4mLiIjYlEJcRETEphTiIiIiNqUQFxERsalmhfhXX33FgAED\n+Oyzz6iqqiIrK4thw4YxdepUjDEArFy5krvuuot77rmHt99+G4DDhw/z0EMPMWzYMEaPHk1NTQ0A\nFRUV/OxnPyMrK4uFCxe2zshERETauSZDPBgMMmXKFM4991yMMcyePZvc3FxKSkowxrBmzRqqq6sp\nLi5m+fLlLFu2jKKiIgKBAKWlpXTv3p2SkhIGDx7M4sWLASgoKKCoqIjS0lI2btzI1q1bW32gIiIi\n7U2TIf4ZFXrFAAAR3UlEQVTEE0+QlZVFfHw8AB9//DG9e/cGID09nfLycjZt2oTH48HpdOJ2u0lM\nTKSyspL169eTnp4OQFpaGmvXrsWyLILBIAkJCQCkpqZSXl7eWuMTERFptxoN8VWrVhEXF0dqaioA\nxpjw7nMAl8tFbW0tlmURExPTYLllWViWhcvlatDW7/fjdrtP6ENEREROT6Mne1m1ahUOh4Py8nK2\nbdtGfn4++/btC99vWRaxsbG43W78fn94ud/vJyYmpsFyv99PbGwsLperQdtjfTRHfHxM043kjGiO\nW9+ZzPG+fe6mG8lZIS7O3ebv17Z+fmkixF944YXw/0eMGMG0adN44oknWLduHX369MHr9dK3b1+S\nk5OZN28egUCAuro6duzYQbdu3fB4PHi9XpKTk/F6vaSkpOB2u3E6nfh8Pjp37kxZWRk5OTnNKra6\nWlvsrSk+PkZz3MrOdI5raqwWrEbsrKbGatP3qz4vWl9zviSd1mlXHQ4H+fn5TJ48mWAwSFJSEhkZ\nGTgcDkaOHEl2djahUIjc3Fyio6PJysoiLy+P7OxsoqOjKSoqAmDatGmMGzeO+vp6UlNTSU5O/nYj\nFBEROYs1O8SLi4tP+v9jMjMzyczMbLCsQ4cOzJ8//4S2vXr1YsWKFadTp4iIiHyDTvYiIiJiUwpx\nERERm1KIi4iI2JRCXERExKYU4iIiIjalEBcREbEphbiIiIhNKcRFRERsSiEuIiJiUwpxERERm1KI\ni4iI2JRCXERExKYU4iIiIjalEBcREbEphbiIiIhNKcRFRERsSiEuIiJiUwpxERERm1KIi4iI2JRC\nXERExKaimmpQX1/PpEmT+Pzzz3E4HEybNo3o6Gjy8/OJiIiga9euFBQU4HA4WLlyJStWrCAqKoox\nY8YwcOBADh8+zPjx46mpqcHlclFYWEhcXBwVFRXMmjWLyMhI+vfvT05OzncxXhERkXajyS3xt956\ni4iICEpLSxk7dixPPfUUhYWF5ObmUlJSgjGGNWvWUF1dTXFxMcuXL2fZsmUUFRURCAQoLS2le/fu\nlJSUMHjwYBYvXgxAQUEBRUVFlJaWsnHjRrZu3drqgxUREWlPmgzxm2++menTpwPwxRdfcP7557Nl\nyxZ69+4NQHp6OuXl5WzatAmPx4PT6cTtdpOYmEhlZSXr168nPT0dgLS0NNauXYtlWQSDQRISEgBI\nTU2lvLy8tcYoIiLSLjXrN/HIyEjy8/OZOXMmd9xxB8aY8H0ul4va2losyyImJqbBcsuysCwLl8vV\noK3f78ftdp/Qh4iIiDRfk7+JH1NYWMjevXvJzMwkEAiEl1uWRWxsLG63G7/fH17u9/uJiYlpsNzv\n9xMbG4vL5WrQ9lgfTYmPj2myjZwZzXHrO5M53rfP3XQjOSvExbnb/P3a1s8vzQjx1157jT179vDA\nAw/QoUMHIiIiuPrqq1m3bh19+vTB6/XSt29fkpOTmTdvHoFAgLq6Onbs2EG3bt3weDx4vV6Sk5Px\ner2kpKTgdrtxOp34fD46d+5MWVlZsw5sq67W1nprio+P0Ry3sjOd45oaqwWrETurqbHa9P2qz4vW\n15wvSU2GeEZGBvn5+QwfPpwjR44wceJEfvjDHzJ58mSCwSBJSUlkZGTgcDgYOXIk2dnZhEIhcnNz\niY6OJisri7y8PLKzs4mOjqaoqAiAadOmMW7cOOrr60lNTSU5OfnMRywiInIWaTLEO3TowNNPP33C\n8uLi4hOWZWZmkpmZecLj58+ff0LbXr16sWLFitOpVURERI6jk72IiIjYlEJcRETEphTiIiIiNqUQ\nFxERsSmFuIiIiE0pxEVERGyq2WdsE5HTEwgE8PmqGizbt899Rids2bmzqulG0u6F6o+0yrqQkJBI\ndHR0i/crrUchLtJKfL4qpjxVjLvjRS3W5x7fdjoldG2x/sSe/LX7ee5lb4uuW9b+vUzPHUFSktYv\nO1GIi7Qid8eLOD/ukhbrr3b/3hbrS+ytpdctsSf9Ji4iImJTCnERERGbUoiLiIjYlEJcRETEphTi\nIiIiNqUQFxERsSmFuIiIiE0pxEVERGxKIS4iImJTCnERERGbUoiLiIjYVKPnTg8Gg/zmN7/hn//8\nJ4FAgDFjxpCUlER+fj4RERF07dqVgoICHA4HK1euZMWKFURFRTFmzBgGDhzI4cOHGT9+PDU1Nbhc\nLgoLC4mLi6OiooJZs2YRGRlJ//79ycnJ+a7GKyIi0m40uiX+pz/9ibi4OEpKSnjuueeYPn06hYWF\n5ObmUlJSgjGGNWvWUF1dTXFxMcuXL2fZsmUUFRURCAQoLS2le/fulJSUMHjwYBYvXgxAQUEBRUVF\nlJaWsnHjRrZu3fqdDFZERKQ9aTTEMzIyePjhhwEIhUJERUXx8ccf07t3bwDS09MpLy9n06ZNeDwe\nnE4nbrebxMREKisrWb9+Penp6QCkpaWxdu1aLMsiGAySkJAAQGpqKuXl5a05RhERkXap0RA/77zz\ncLlcWJbFI488wtixYwmFQuH7XS4XtbW1WJZFTExMg+WWZWFZFi6Xq0Fbv9+P2+0+oQ8RERE5PU1e\nT3z37t3k5OQwbNgwbr/9dubOnRu+z7IsYmNjcbvd+P3+8HK/309MTEyD5X6/n9jYWFwuV4O2x/po\njvj4mKYbyRnRHLecffvcTTcS+R6Ji3Of1meAPi/aXqMhvnfvXkaNGkVBQQE33HADAD169GDdunX0\n6dMHr9dL3759SU5OZt68eQQCAerq6tixYwfdunXD4/Hg9XpJTk7G6/WSkpKC2+3G6XTi8/no3Lkz\nZWVlzT6wrbpaW+ytKT4+RnPcgmpqrLYuQeS01NRYzf4M0OdF62vOl6RGQ3zJkiXU1tayaNEiFi1a\nBMDEiROZOXMmwWCQpKQkMjIycDgcjBw5kuzsbEKhELm5uURHR5OVlUVeXh7Z2dlER0dTVFQEwLRp\n0xg3bhz19fWkpqaSnJzcAsMVERE5uzQa4pMmTWLSpEknLC8uLj5hWWZmJpmZmQ2WdejQgfnz55/Q\ntlevXqxYseJ0axUREZHj6GQvIiIiNqUQFxERsSmFuIiIiE0pxEVERGyqyb8TF2nvAoEAPl9Vi/e7\nc2fL9ykicjyFuJz1fL4qpjxVjLvjRS3a7x7fdjoldG3RPkVEjqcQFwHcHS/i/LhLWrTP2v17W7Q/\nEZFv0m/iIiIiNqUQFxERsSmFuIiIiE0pxEVERGxKIS4iImJTCnERERGbUoiLiIjYlEJcRETEphTi\nIiIiNqUQFxERsSmFuIiIiE0pxEVERGxKIS4iImJTzQrxDRs2MGLECACqqqrIyspi2LBhTJ06FWMM\nACtXruSuu+7innvu4e233wbg8OHDPPTQQwwbNozRo0dTU1MDQEVFBT/72c/Iyspi4cKFrTAsERGR\n9q/JEP/tb3/LpEmTCAaDAMyePZvc3FxKSkowxrBmzRqqq6spLi5m+fLlLFu2jKKiIgKBAKWlpXTv\n3p2SkhIGDx7M4sWLASgoKKCoqIjS0lI2btzI1q1bW3eUIiIi7VCTIZ6YmMjChQvDW9wff/wxvXv3\nBiA9PZ3y8nI2bdqEx+PB6XTidrtJTEyksrKS9evXk56eDkBaWhpr167FsiyCwSAJCQkApKamUl5e\n3lrjExERabeimmowaNAgdu3aFb59LMwBXC4XtbW1WJZFTExMg+WWZWFZFi6Xq0Fbv9+P2+1u0Nbn\n87XIYKT9CwQC+HxVLdrnzp0t25+IyHelyRD/poiI/9t4tyyL2NhY3G43fr8/vNzv9xMTE9Ngud/v\nJzY2FpfL1aDtsT6aIz4+pulGcka+73P8ySefMOWpYtwdL2qxPvf4ttMpoWuL9SdiV3Fx7tP6DPi+\nf16cDU47xHv06MG6devo06cPXq+Xvn37kpyczLx58wgEAtTV1bFjxw66deuGx+PB6/WSnJyM1+sl\nJSUFt9uN0+nE5/PRuXNnysrKyMnJadZzV1fXnvYApfni42O+93NcU2Ph7ngR58dd0mJ91u7f22J9\nidhZTY3V7M8AO3xe2F1zviQ1O8QdDgcA+fn5TJ48mWAwSFJSEhkZGTgcDkaOHEl2djahUIjc3Fyi\no6PJysoiLy+P7OxsoqOjKSoqAmDatGmMGzeO+vp6UlNTSU5O/pZDFBEROXs1K8Q7d+7M8uXLAbj8\n8sspLi4+oU1mZiaZmZkNlnXo0IH58+ef0LZXr16sWLHi29QrIiIi/z+d7EVERMSmFOIiIiI2pRAX\nERGxKYW4iIiITSnERUREbEohLiIiYlMKcREREZtSiIuIiNiUQlxERMSmFOIiIiI2ddoXQBFpjta4\nZCjosqEirSVUf+S03l/79rmpqbGa1TYhIZHo6OhvW5o0QiEurcLnq2rxS4aCLhsq0lr8tft57mVv\ni79nrf17mZ47gqQkvW9bg0JcWk1LXzIUdNlQkdbUGu9ZaV36TVxERMSmFOIiIiI2pRAXERGxKYW4\niIiITSnERUREbEohLiIiYlMKcREREZtqs78TD4VCTJ06lU8++QSn08nMmTO57LLL2qqcs9qxs6ud\nzhmYmqIzq4kInP6Z4JpLZ4E7qs1C/I033iAYDLJ8+XI2bNhAYWEhzz77bFuVc1ZrjbOr6cxqIgKt\ncyY4nQXu/7RZiK9fv560tDQAevXqxebNm9uqFNtozfORt/SZmnRmNRE5RmeCaz1tFuKWZeF2u8O3\nIyMjCYVCRESc+mf6nTurmD+/qMVr6du3P9de62nxflvazp1VPP38q5zn7tii/X61Zyfx/9alRfv8\nunYfjhbtsfX6Va326le1qlZr/15b/WTXmnsM2izE3W43fr8/fLupAAe47rqr+f3v/7u1S/veuuEG\nDz/72ZC2LkNERL4n2uzodI/Hg9frBaCiooLu3bu3VSkiIiK25DDGmLZ4YmMMU6dOpbKyEoDZs2fT\npUvL7tIVERFpz9osxEVEROTM6GQvIiIiNqUQFxERsSmFuIiIiE3ZIsTr6+uZMWMGWVlZ3H333eGj\n2qV17Nixg5SUFAKBQFuX0u7U1tby4IMPMmLECIYOHUpFRUVbl9RuhEIhpkyZwtChQxkxYgQ7d+5s\n65LanWAwyPjx4xk2bBiZmZm8+eabbV1Su/bVV18xYMAAPvvss1O2sUWI/+EPf6C+vp7S0lIWLVrE\n//7v/7Z1Se2WZVnMmTOHc845p61LaZf+53/+h379+lFcXMzs2bOZPn16W5fUbhx/Kudx48ZRWFjY\n1iW1O3/605+Ii4ujpKSE5557jscff7ytS2q3gsEgU6ZM4dxzz220nS1CvKysjE6dOvHAAw8wefJk\nbrrpprYuqV0yxjBlyhRyc3MV4q3kvvvu45577gHgyJEjmucWpFM5t76MjAwefvhh4Oiej8jIyDau\nqP164oknyMrKIj4+vtF2bXbGtlN56aWX+P3vf99g2QUXXMA555zD0qVL+eCDD5gwYQIvvPBCG1XY\nPpxsni+99FJuu+02rrzyyjaqqn052RzPnj2bq6++murqah577DEmTpzYRtW1P9/mVM5yes477zzg\n6Fw/8sgjPProo21cUfu0atUq4uLiSE1NZenSpTT2l+C2+Dvx3NxcMjIyGDRoEACpqam8++67bVxV\n+zNo0CA6deoEwIYNG+jVqxfFxcVtXFX7U1lZya9//Wvy8vLCW45y5goLC+nVqxe33norAAMGDOCd\nd95p46ran927d5OTk8OwYcP46U9/2tbltEvDhw/H4Th6xvlt27bRpUsXnn32WS666MQrwX3vtsRP\n5rrrruOdd95h0KBBbNu2jUsvvbStS2qXVq9eHf7/f/zHf7Bs2bI2rKZ9+vTTT3nkkUeYP3++TjXc\nwjweD2+99Ra33nqrTuXcSvbu3cuoUaMoKCjghhtuaOty2q3j9zSPGDGC6dOnnzTAwSYhnpmZydSp\nU8O/JU6bNq2NK2r/jn0LlJb11FNPEQwGmTFjBgCxsbEsWrSojatqH370ox9RVlbG0KFDgaM/XUjL\nWrJkCbW1tSxatCi83j733HM6tqMN2WJ3uoiIiJxIR3yIiIjYlEJcRETEphTiIiIiNqUQFxERsSmF\nuIiIiE0pxEVERGxKIS4iImJTCnERERGb+n9a4RHjX3cESAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c0e2510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.ESCS.hist(bins = 20);\n",
    "ax.set_title(\"Distribution of Index of economic, social and cultural status\\n\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Curious.... how it is correlated with Math scores? And how the scores are distributed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.PV1MATH = df.PV1MATH.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RoPFcJzFdx2axYBqJe2iqimEanDhznvqmdvYeaWBWUR7H6s/S4wvhdNjwuh2p\n/WfpOnvIqC37TLd9ZJLIhMigdAnu5q6hramSe8HqGlrp9YcJhQcmMtOEUCSGoiiE9Vgq8emG0e82\nJvVNbZxubgclMYoLhiPYrRqRmM5vtu1PVUUmR2dyvIvIBrIhWkxJl9pgPNHP1X+/V//bQKIoYyTi\nuk4kGiOuG0OuU/udy2KaJoZpYtEUlH6X67pJXDf6SvBBQcEwTOJxgwu9QSLRWCpZJjd7T8RhnkKM\nBRmRiSnr5rVLuXn9Urq6xqfYo/+UW9WS0rRTc4NHNB+6KVFo0XC2M1VC73LYCPaNsJZXzh2wj6wg\n14PLYaO+qQ3TMLFaNRx2GxaLRiye2DVtpJlFMgwTVVEwMIdUsDns1tTzKYqCpqnouoGu6wM2XIvs\nZcjUohBTR1GBd0iXjLFY8+mfoPonooWlRayrWkhBrodTzW2p874gcf7XW0dPY+gG0XicXG+iF2Iw\nHOVDN62k1x/CHwxTd6qFAu/F9lTdvQFURQGLgmGaBEORtCOz/kxz+A+zaCyOoigomJimSSQaB6DX\nHybH42Bx+czU72bwJvBkTLJeNrnJGpkQU9jl1nxGWkKffAxd12k420Gu10kwFGVHZw+1Da3kuJ34\nAiHauxIjK1VV0PuSz8XPGIVcr4u4rvP8qwf7jleB379ykMqyEj754Q28+PohunqCGIYByij2BykM\nuyc2Gkvf/yqZjE82tfPy7uOJEW2/TeA1tU2pUWT/391EbnEQI2OkG6ZPYZLIxLRxuerCKy1s0A2D\nru5AagTk84fo6g6Q502cuGwYJphDk1A4EgMCGMbFJJJUf6aNR37xB1o7ei9eOMLPJlUF49IDtmGF\nIlG8umPA72a4LvtVS0qpqW2asC0OYuT0K30DZCkp9hCC0XW3TxZ36HriTC+Lpg2YxotE45iDs44y\nNAtZLYn1Lrtt6PdJwzQHJrERUgCLZkG57C3TM02I9k01Xk63LyjFIJPUdBuRSSIT08ZYVzImPyos\nFhWLpqKqibOYFUVhbklB6naqqqAqQ/+qKapCOBKl1x+64hj6s2gqVquGxaJeVae9YDg65HeT7ncn\nx79MXtMtkcnUophWLtX4d2FpESeb2tF1fUDBQ39dPf7USMSiaYTCsb4pwgS3y86yitmYJHohqoqC\nqqqJNS4SSU5VFBQV3A4bXT0B9DH60InrBujDr4GNVCymD5nqhPS/u+WVc6k93ZL6s6yTTQ7TbWpx\nwhOZYRgcAI7CAAAgAElEQVQ88MADNDQ0oKoq//Iv/4KmaXz1q19FVVUqKyt56KGHUBSFp59+mqee\negqLxcJnPvMZbrrppokOV0xBgz9s+6+NWTWNuK4PKHjof7tkI95wJIbTbiUYjqCqCi6HDQVYNH8W\nh+qaCUdjqKqCRVOJJasElUQ1mW6a2C0WZhfn0X5h8k3FGabJsfqzrK+qSNsyq7/x3uIgrox+marW\nqWbCE9kbb7xBKBTiySefZOfOnTz66KPE43G2bNnC6tWreeihh9i2bRsrV65k69atPPvss0QiEe64\n4w7Wr1+PzWab6JBFlurq8cNltkUl18Z0XUfXDdp8vRTkJcri+7dtAthZc4JQOIZuJDYWB0KRVAFH\nrz8MwN7Dp1OPbRgmhpJYLetfDp3rceBw2CibXcjBuotrTJOFoiQ+CM+0dA64PFn00e0LDtgvl26L\ng8isYGhkG+2niglPZA6HA5/Ph2ma+Hw+rFYrBw8eZPXq1QDceOON7NixA1VVqa6uxmq1YrVaKSsr\no66ujuXLl090yGKSS1cynxxlWS0aS+bPumQ1nS+QaPlkYg5YW/AFQvx2235MIN/rSjTi1c1UYcdI\nSuHT7ffyBcLohknDuc4095gEzEQhx69e2oPVomG3WfG6HbgcNs539hIKR3E6rKyvqpQqxUkqHpcR\n2biqrq4mGo2yceNGuru7+dGPfsTevXtT17vdbnw+H36/H6/XO+Byv3/yTcOIzEpXMj+SJr4DXWzb\nZLUkijLiug4oBMNR/MEI56+ggnA4hgn+YIRDk3A0Bn2/DZPUZum4rmPRVM539oCZWOcLhWMcqG2k\nakkpRUXeyz2kmGCRaOzyN5pCJjyR/fSnP6W6upp//Md/pLW1lbvvvpt4/GK5r9/vJycnB4/HQyAQ\nSF0eCATIycm57ONn21+qbIsXJk/M7V0+ak+3YO1r13T4RDM3r19KQYEn1fUeEmXuBQWexBTYYFpi\nJBdz630f2Bp3/dd1AGz93S7aOnunXQXYYLpu0usPpU6ZVvqaOFq0xO8VBr4nkpvA0/6+J5HJ8j4e\nD4qmTOnXN9iEJ7JQKITbnViDyMnJIR6Ps2zZMvbs2cOaNWvYvn0769atY8WKFTz66KNEo1EikQj1\n9fVUVlZe9vGTPeqyQVGRN6vihckVc1ePn1hcT00NAjz/8kE2bVjJkvmzBkwtdnX5OdXYNmBtJzkl\nuWT+LHbWnCQUjmK1aLz02iHWVS1kTnEerR3dwz7/dKLric4iiqJgmiZOhzVRjt+3NpZ8T2RLt/zJ\n9D4eqdEkpgs9wax7fZdzqdc/4YnsE5/4BP/0T//E5s2bicfj3H///VxzzTU8+OCDxGIxKioq2Lhx\nI4qicPfdd7N582YMw2DLli1S6DGNjKRVVEGuh4WlReysqQfAbtOoa2hhfVVFqlS8oMDDyzuP8++/\nemXA2g6QqkCcU5yHRU18QAfDUQ7WNXOwrpkcj5OZMxInMU+3JqyDmYBFVcnxOHjf+muYN6sw7bll\no5vSFePlardgZJsJT2Q5OTn827/925DLt27dOuSy22+/ndtvv30iwhKTyGi+1a+rWkhtQyuBYIRI\nNE4kGmBXzUk+uKEqUWXX7eeto6dTI7ZgKMruQ/XE4nrqFOWOYUrge/0hXA4ryxfNmZTVhRNNNwyi\nsfiQJJbstSgmj1BE1siEyJjBDXlr+goKhvtWX5DrYXH5LHbVnATAbrNw+MRZ5pTk09WT2Ljc7Qum\n1rkMw6THH4JLdIfvLxSOTtoy+YlmmolquGRZfkGuhxdeq+FkUzuQ+NIxuFv+cJvKk/cXY09VFAKh\noRvapzJJZGJS6r/utbOmnk0bVg572/VVFbzd0EJvIEwgFMEfjPB/XtiN3WZhVlEeLocdXyB0sVze\nHPkxF/5QhOdfPXS1L2fKCIajPP3HvbgcNgpyPJxtu4CiKDjsVg6faOauD60btnMKZM8aWjazaImq\n2OlEei2KSSW57pVMYk6HlfqmtstOXVk0jWAoOmBvVyQaJxyJ4nU7yPe6UhV3hpnYpJzqj3iJDrt6\nv31jIsEwEmuJTa2dGKaBaZqEwlF0PbEuk+yYP1jyfLa4nqgQlSbD48OiQiAUnVbvWxmRiUknue4F\niQR1Kcm2UZ1p1rkU5eKG5IVlJbx1tGHASMzrdlC9bB4XegK0X/DTGwjT4xubBr5TWer0acMERcEw\ndRRVYd6swrS37+rxs6vmJHUNranSfE1VcTqsExn2tOF02AlGwwRDETwuR6bDmRCSyMSkU5Dr4dol\nZSNaa0neRlEU1L7KQzPx+YrNaiEa0wlHYhw5cXbIfrALvUG2vVmb+vlSIzMxkG70HVTT98VARaGx\npZOtz+0aMGWY/KLR1R1IHVdjGiamasIVHzYjLsVmTUy09frDksiEyKRLdakfzKJpOB1WQuEYuV4n\nM/I8dHb70TQ1cbqyaaCPoGXPNJqJuWqD1xh1w0DvO5stWXYPDCjHD0diqIoCmkK+14XNJiOycWEk\nKhbbL/Qyuzgvw8FMDFkjExnX1eNPu1Yy3FpLfxWlxQB43U7WVS3k7267kVtvqU59SJqmiWmM+HBl\ncYUUBTQt8XGi6zrdvotNa5NfNAzTTP0Tiely7Ms4cTsT+23Ptk7SXp7jQEZkIqOutIqt//0Wlhax\nrmrhgA/F5ZVz2VlzMnEuk2SxcWe1aAT6KuVUVeG5Vw8OKMd3OmwYhonbZU8dMZIctYmxNbO4iJPn\nAnT2BC5/4ylCRmQiY9J1ghhJFVvyfsnqt+Q+pv6qlpRiGAaaqspSzASIxXQCwQihSJTkLzw5xXjX\nh9bxV7dU47Bb8fkTBTU9vhAv7z4uVYvjwOmwoqoK5zvHrtH1ZCcjMpGVfIEQoXBiLSBd9Vu3L5g6\nuXm6N/2dCMnfsK6bBMMRbFYtNdVYkOvhqZf20NUdwDBNFCUxgtt/vJEzLZ1ULSmT/WRjqLf7Am67\nRvsFP8FQBJfTnumQxp2MyMSoDbemNVoFuZ5E49k+o1szUYb5c8Lx+nMYppn2PDAxvuJxg66eAKFw\njJraJv7vn/bScLZjwDlu0ZhOPG7QGwjLfrIxphs6uR4HpglvN5zNdDgTQkZkYlSef+Ugb/Y16R2L\nzgyXq05M186o2xfE5bDi6huJaf32miVPMD7Z1I7LYSMQjMgSWQYogM2qUVPbSCQaH/Z2oVA0Ucko\nxkxhYREWp05zu5+m1gtUTYPBriQyMWJdPX72HWlI/TxW3c2Hu3+6QpDkZckWPB6XnTl9JcbJ64J9\nbaqM5F4nMeF0w6TbF8Rhs+J22VE1NVXkIcZfUYEXRYETZ4auH09FkshExqUbdaUrBCmfU8jhE834\nAqHUycWRaIzO7gA1tU2oqoJhGNPuCIvJSCGx9UFVVebNKqSrO4DZ1x6sP6fDRq7XlZkgp6juC104\nnWFyXFZaO3o539FDyYzcTIc1rmSNTIxYQa6HVe8oT/08mjWt4dbVXt59nK3P7WLrc7t4effxyz5O\nXNcJhWN9Pf4gFjeIxXWisTjhSEyS2CRhkij8iERjVM4rxmrRUNSLU4jJ2USX0yb7ycaYbujoepxZ\nhYnf6Rv7ai9zj+wnIzIxKpvevZLKvk3II/3wGW6v2KUOYkwWgvS/34K5xSwun8Ub+9+WSsQsEY3F\n+cMbRwhHB56PpakqDrs1dUinGDuFhUW4PV4KiwxOtfRy8MRZ/p+b41itU/fjfuq+MjFuRvPt+VLJ\nqtsXRNf1AcUa/aUrBFlfVcH+Yw2EI7FElwhJaJNaciZRU9XE5vQ+NmviiJ3X3nobkCNdxoOqqswt\nzuXU2S5qas+wevmCTIc0bmRqUWTEy7uP89yrBwmGo/gCYSD9VGXy5/7TkmrfJmdFAYdd+vVNZoqS\nWAez2ywDvnREY3F8gYsnDUgJ/lC6bnD81Dn+87ldfOenL1zRY8wtyUVTFbbtPk5cn7rT7jIiE+Mq\n3RQhXGwm63U7ies6G65blHaKKTktqes6i8tnsq5qIZAYiSX+ifUVecjIbLIKhaOpzdFJcd2gpzdE\nQb6sjfVnmibn2rrZd6yBmtozV3RAZveFLiLhxJfDSCjI8oUl1LzdyvY9x7hp7TWJL4JTjCQyMe6q\nlpRSPqeQPK+LglzPkG/eoXCMbbuPY9G0IWtoNbWN+IMRwtEY7QdOcL6zF5vVgmkm/qKaozjtWUy8\n5H+fSGToXjJTgUg0ht1mnfYFHz3+EDXHG9l3rJHWjp4h15fNHvk6YrLYA8Bms+Gw2BKjsj11rFg0\nmxmFU29NUhKZGFfpCj36j9ISpwqbqQM0+6+hbdt9nPYLPnT9YqKqPd2Kpqpy5EoWifZtlRgseVlF\nafG0XB+LxuIcPXmWfccaOdHYOuQ9nZ/jYtWycqqXlTEj3zvix00We/S3sCxM3elW3qg5xa23SCIT\nYsQuVeiRLOTo9gV57tWDQ+77wms11Bw/g6EPzViGKRtrs0koGsNMM/Vrs1jQNJW6hhbWV1VMixGZ\nYZqcbm5n39EGDp9oHtL1xG6zsGJRKdXLypg/t2jMup4sLp9J47kO3jzYwJrllVPunDJJZGJc6X3f\nutNVJg5XZg9Q19CKSfrOHDIayw6KkvhvNdz6ZSgSJRyNoakqu2pO8sENVRMc4cRp7/Kx/3gD+481\ncqE3OOA6RYFF5TNZtaycaypmX3WZfP81sv7KilzUNffyn8/t4BN/tY78vLwps14miUyMm5raJoLh\nKKFwDKfDxvpBZ4Yl3bx2KeVzEtMdC+YW09XjR9M0bFYL8Xh0osMWY+RyXziS62cuh4WTTe109fin\n1KgsGIpwsK6JfccSXf4Hm1WUx6plZVQtmUeOxzlmz9t/jay/4gIPwZhC0/kefvG7nfzdbTeQkzM1\nOn5IIhPjIjmt6HU7cToSJ9YOd5BiunW05ZVzqaltlKNYpjhFSfTLnCriuk7d6Vb2H2vk2KlzQ/pL\nelx2qpeWUb2sfNym99KtkSUVFBn0Bmtp7Qpy5GQL66slkQkxIpZB3elh4P6wdD0Vq5aUUrWklJd3\nH+dA7RkMY+rugZnuTEZ7hM/kYpomzecvsL+vZD4QGjiLYNFUrlk4h+pl5SwqL0kc9pohmqqydvkC\ntr15jBffOMqCebOYOQX6MEoiE+Mi3dpXTW1T6ueFpUV9e8IGrqP5AmF+/ae30DSVHLeTs+cvEJP+\niVOWacIta5dStaQs06GMWrcvyIHjjew/1pj2NOb5c4uoXlrGisVzcdptExfXMGtk/c2f6eHkOR8/\ne+Y1Pn7r9ZQUzcjq9TJJZGLc9G8xBbD1uV0A+AJhdhw4ydH6c+TnuAmGowTDUWxWC7puEApHMUyD\n8x3T56j26cwfDGfN+lgkGuPIybPsP9rIyTPnhxQjFeS6WXVNOdVLyyjMy8zrGW6NrL9ZM7wYipVT\nZ7v46bM7+MxHbszq/WWSyMS4Sm6ATvZV1HUDfzCMYZhc6AnS1RPA5bCBmegAgQmKdOqYVl7dW8e+\nY2cmbb9FwzQ51dSWKpkffMKC025lxeJSVi0rp2x2IUqGDwq91BpZfzOKTeKmwplznfz6LzV88sPv\nHtKBJVtIIhOjlu78sOEkCzl8gRDhSAxdN1JJKtlENjECM1NVbun2HImpKxCMAMqYHdQ6Vto6e9l3\nrIEDx8/Q7RtYMq8qCovnJ0rml1bMxmpJ3/h6MlMUheqlZQSDYU41d/J//7iXj7x/TVae2C2JTIzK\n868c5M2aeuDSHcuTo7ADtY19o7DEic39/44off+XKMMe/9jF5GQYJqFwFJcj8w2gA8EINXVn2H+s\nkabWriHXzynOZ9U1iZJ5j8uRgQgvbyRrZP2VFzuwaAr7jzdisyq8Z+1iPB5vVq2ZSSITI9bV42ff\nkYbUz8N9g06Ownp8QYLhKAoXy+f7JyxFVXA5bBQXeDnV3DEBr0BMRiZgGAaLy2dmZDQWj+vUnm5h\n37FGak+dQx80I5DjcVK9dB7Vy8qzosJvJGtk/TkddhaX5eALRHjzUANtHRfY/MHrs2qPmSQycdX6\nTzUmy+l1XU+130nX1FdVEpcbpklbl0862E9z11TO4YMbqkY1bX01TNPkTEsn+442cLAusXG/P6tF\n4x2ViZL5ynnFWTU6Geka2WA3XOfm1b11nDrXw54jjbxn/YpxiG58SCITI1aQ62HVO8oHTC3urKmn\nrqEl1bl+8KZnTVXxuOz0+EMogG6YqKqCRdOIxuKEw4kDMrNvVl6MpXyva9iTxMfShd4AB443UlPX\nRGv70C7zFaXFVC8rY/miuThsmZ/qnEgup513Vlfy+lt1/GlXLTEdNr5recaLV0ZCEpkYlU3vXkll\naTEAu2pOsqsvqTkd1tRUY3L/mNNhwzB0bDYLFaXF9AZC+PxhYnGdaCwxWjP6RmsyFpveXt9/ApfD\nRq7XBQw/bX0lwtEYR95uZt+xRuqb2oZcPyPfy6plZVQvKyM/x33Vz5fNcj1O1r5jHkfqz/PKnlr8\nwQh//d5VGd3EPRKSyMSoJacQ6xpaU5cl+ynCxf1ju2pOUtfQSjQaJ3+Wi1lFeRx+u4lwJDGNoyAJ\nTFwUjsTwuPS0DaZHyzAMTp5pY/+xRg6faCYWH1gy73baWLGolFXXlFM6syArRh0jNdpij8GioSAf\nvnkZz7/xNnuPnKa7189f37JyUjcZlkQmrpimaTgdtsT+L2Bx+awB36BPNrVzoTdINBpnz+HTaKqS\nWNhPltlnIGYxOVktGnbbxY+j5CkIo90o3drRw/5jDew/foZef2jAdaqqsHT+LKqvKeeGNYvovhAc\n5lGy22iLPQaz2Ww0tHSzbEEx0VicE2fa+eFT2/nkbe+kpLhoDCMdOxlJZD/+8Y955ZVXiMVi/O3f\n/i3V1dV89atfRVVVKisreeihh1AUhaeffpqnnnoKi8XCZz7zGW666aZMhCvS6N+CyuWwsrh8Jh/c\nsHLAbYKhyIDzlgZXgwmRpBsGVUvKWF9VASROTkh2grncepk/GKam9gz7jjZytu3CkOtLZxZQvayM\nlYtLUyXz2bjva6SutNgjnRuu8/LW0QaaWy/wi+f28Knb301ejmtMHnssTXgi2717NwcOHOBXv/oV\nwWCQn/70p/zpT39iy5YtrF69moceeoht27axcuVKtm7dyrPPPkskEuGOO+5g/fr12GwT17NMXNrg\nFlT9vz3X1DbhD0YyFZrIIoqSKPZIHq55qrmNA7WNaU8NT4rFdY6fOse+o43UNbQMqXjN9Tip7lv3\nKinMnjLyyUZVVVa/Yz6aYtLY0s2/PbmNv/vwjZPudzrhiWzHjh0sXryYz372s/j9fr785S/z61//\nmtWrVwNw4403smPHDlRVpbq6GqvVitVqpaysjLq6OpYvXz7RIYtLKMj1DKk2q1pSyltHTw8pu1cU\nJbGnTHY/i35MM9FgN/leqqltpKs7gNNhxet29rudSeO5TvYda+BQXROhviN+kmzWROVs9bJyKkqL\nJu16zni72jWydEpyNOYWz2PHwTP825Pb+Oh/qaa0JH/SbJweNpH95je/QVEUTNNM/TtJURRuvfXW\nK3rCrq4uWlpa+PGPf0xTUxN///d/P+Cx3W43Pp8Pv9+P1+sdcLnf77+i5xTjJ90xLC3tF+js9g/Y\n/KwqoGoK+V4X3b7QkMV3MX1pqsLCecWcam7j8InmAWuvToeNxeWz2H+skX3HGunsHvgZoAAL55VQ\nfU0Z71g4B/s0K5lP52rXyNKx2+04HDbeUVHC0frzbH1+L0vLCvir91w3KTZOD5vIdu/enUpgr7zy\nCjfffPOA6680keXn51NRUYHFYmH+/PnY7Xba2i6WxPr9fnJycvB4PAQCgdTlgUCAnJycyz5+UdHY\nzA1PlGyIt73LB0BRQSLWATFrA9cbOnv8nO/sGdpySlFSU0W6YaAo0pZKJOTluNlZU08srhMIRcjz\nusjzOlFVBZvVwva36obcZ3ZxHuurF7Lu2oUU5F5ZyXw2/N27EqWlc8YtucyeXUJxUQGv7a3jWEMn\nK8518r6KuePyXKMxbCL7zne+k/rzrbfeyre//e0xecJVq1bxy1/+knvuuYfz588TDoe5/vrr2bNn\nD2vWrGH79u2sW7eOFStW8OijjxKNRolEItTX11NZWXnZx29v941JnBOhqMg76eMdPG340U1rqDvZ\nAlzsvrBk/iwOn2gmEo2hxxONgAeX1ie/FPX4w9LBQwwQCEZw2K2YpkkkGqO1vZtwX5FQd+/FykKX\nw0bVknmsuqacuSX5KIqCHjWu6O9QNvzdG2ykiTcYjKKoYzu12F+e18W7qivZsf8ET77wFj29UW5a\ns2Tcni/pUq9/wtfIbrrpJvbu3cuHP/xhDMPgoYceYs6cOTz44IPEYjEqKirYuHEjiqJw9913s3nz\nZgzDYMuWLVLoMcHSTRvyAhx+e2D3hZvXLiUUjnD47WaisTiapmKo5oAu9omu94aMwsQQ4WiMts7e\nxHtk0BtEUxWWVsxm1bJyigu8aJo6abrjT1bjsUY2mAIsnZfD6dYgL75+iFA4xDurFmRszSwj5fdf\n+tKXhly2devWIZfdfvvt3H777RMRkriE5AnOcDGJwcVqMoADtWcI9h3HYsR0PG47s4vyqD19cdO0\nJDExnMFrpvNmFVC9rJyVi0txO+28vPs4f3nzGDB+7aumivFYI0snz+uiKieXt46f5ZW9J2g428ad\nGWo2PGwi27NnT2o6KBgMsnfv3lThB5CqMhRTV0GuB5fDRsPZRGf6OSX5AKnOHJqm0e0L0usP4Q9G\nMA0zNZ3o84c5FZWO9mLkVEXB7bLjdNjY/MHrUyOvdDMDk+ncsslmLPeRjcSNHi/b36rj9Lkedh9u\n4L3vXHn5O42xYRPZD37wg9SfS0pK+P73vz/g+nQjKDG1dPX4CYajFOQlFtNjcR2X08bZ84kinByP\nk99u208kGsc0zQFrYiYQjY7/t0IxddhtFnI8zsvfUEwqHpedG1ct4rW9tfz5zTo0i5V3r1k6oW2/\nLpnI8vLyJiwQMTbG4xiMVLWhrhMMJRKbrht09QRQlMRZYzJtKK6GokBM14nrOtcuKRvy/q0oLU41\n/F1eOVdGY5cwEWtk6SwuzeF0a4A/vHGE8x3dvP+dS1FVdULWzYZNZPfccw+/+c1vxvXJxdga62Mw\n+rehAlhcPpPGlsSpuYZuSPWhGDNupx2Py86tt1SzYG5x6vL+7+mFpUWsq1ooSewyJmqNbLBcj5Nr\nF+dy4O0WDtQ209R6gcWlubz7+uXjvm42bCJLdxiimLyGW0dIutK//P3bUBXketh95BQ79r2Nbpiy\nF0yMCUVJTE9VLSkbkMQGv6dPNrWzrmphJkLMKhO9RjbYTXl57Dl0ivOdvURjcVaviDCCLcBXZdhE\n1tHRwWOPPTbsHe+9995xCUiMnV01JznZ1A5c3QhtcBI0SXQSV1UVXTfS3sfrdhCJxojGpIOHuDTT\nhHmzCqUScYqwWjTWVS3kwPFGGs918vPf7eaTH95AceH4ZTM5xmWKGDwNWFFazMl+hwiOttIr3Vpb\nV4+ffUcaUmtmpmmiqYkF3f6d7RVFwR8IyzEtYsRqT7cMObJl8Hta1sZGJlNrZIOVF7vQoyGaO4I8\n9uRfuGPjKuYU543LmtmwiWzGjBky6soyg7vRpzsNdyRGstYWCEZSa2SKkvwnkdT6l+ELMRLBUDTt\n5YOntsXlZWqNLJ35s/PIzfFw9FQbW5/fy/KKGXxww7VjvmZ2RSOyN954g3e9611jGogYG/3/sl/J\nt9lTzW3U1DamTuntP5IryPWw6h3l7Nj3NuFIDKWvD1VynUzWVcWV0g2Dbl8w7XtUEtjoZHqNbLCi\nEnC53Ow9cpojpzpYu8I/cYns5z//+YCfOzs7eeaZZ3j66aeJRCK8/vrrYxqIGHuj/Tb78u7jvHX0\nNN29QVxOO163Y8htNr17JUV5Hn79p7fo8YUwTRO9r/WUFH+IK2WaDDnRWUwdpbMK0A2D/ccaeeKl\nfdy3OX9MD+gcdqIyPz/RxeHNN9/kC1/4Ahs2bOCxxx7jH/7hH9i2bduYBSDGV3IkdTldPX521pyg\nxxfCME38wTC6rqcdyS2YW8x118zHbtOI60a/Edl4vAIxHSgKshl6iiufM4PKeTPwBcL89JntBEJj\nd/DuJUdkTz31FFarlQ984AN84Qtf4OMf/zh/9Vd/NWZPLiaPbl+QUDhxUKGmqpiYfSO6siG37erx\nEwpHMNMXLAoxak67jTzv2H1Dn84mS7FHOvlOk2sXz+JAXQs//fWr3PmB6yjIz7/q4o9hE9kjjzzC\nzTffzJ133snq1asntN2ImHh5XlfqMEMAl8POvFmFQ2731At7eOtIYvpRTnoWY+UdlXOuqqJWXDSZ\nij0Gs9vt5DiczJrh5WxbD48/s4O//8gN5OflX9XjDpvItm/fzvPPP893vvMd2tvb2bhxI9Fo+soi\nkf0Kcj2sr1pITW0jAFVp2gS98FoNuw6ewjCN1LqYEBNprLvXTEWTrdgjnbUrc9lVc5Lznb289MYx\nNm9651U93iXXyO666y6effZZHn/8cQDi8TibNm3iiSeeuKonFZPTzWuX8rFb38XHbn3XkA+Irh4/\ndQ2JI1kUFBmhizF1qrk9NdIaTrruNZe7j5icVFVh7coF5Ljt1NSd5ejJs1f1eCMqv1+yZAkPPPAA\nX/7yl3nllVd49tlnufPOO6/qiUXmXGpqZrjpmm5fEF03cNithMJRVFXBYtGkw70YE4FAZNjye0i8\nZ7t9wbTXiYEm8xrZYGXFTo41Rnnmz2+xYG4RTseVHZ48bCLbu3cvwIAzyADy8vL4+Mc/fkVPJjLv\nSqZmXt59nJ01J/EHwygoOB1W8rwuWjt6xjtcMU2EYzF+s20/1y4pG/Ke7P+edTlsBPvWcaXTR3qT\neY1ssDyvi7JZCqfOXmDbrsNseveqK3qcYRPZXXfdRWFhIQsWLEh7vZxHln1Ge0Bh8ltwTW0joXAU\nrSUsZZ8AACAASURBVK+ySFMVwtGYbIAWY8Y0QdeNIe/Jwe/ZYDjKh25aSZ7XJUlsGNmwRtZffqFO\nc1svuw418O6178Dtso/6MYZNZI899hgvvvgiTU1NbNiwgQ984APDJjWRfeJ6+ma+yWnHXTUnqWto\nxSTRjipJNwx8gTCmibShEmPKGKYB9WCSxKYWi0VjwZwCahva2b6vjvffsGL0jzHcFe95z3t4z3ve\nQygU4tVXX+Vf//VfOX/+PLfccgvvf//7KS0tHe6uYpJKNmHdWXOCUDiG02GjprYpNZWTnMLp8QUJ\n9o3A7LbEW8RusxAMR+UMMjGuBk8XSuPg0cumNbIkr83A5bCy48AJrl08i+IZhaPaW6aYo5gfqq+v\n54EHHuDgwYMcP378igIeb+3tvkyHMGJFRd4Jj7erx8///u0b6LqBpqlomsZdH1oHwNbndqHrOh3d\nfvS4AX29FDWLysLSYprPdxEIJtYnJJ2JsVYyI4f7/9vGtNeN9d6xTPzdu1pFRSObLvy3/3gWpyP7\nuqS09USpa+ygtNjLf/uv64f0Y7zU679s1WJzczN//OMf+dOf/kQsFmPjxo1873vfu/qoRcYEwzFC\n4SgmJg6bdcj1CgqKqmAYJgqJfHbizPlE9lIUWRsT46Lzgp9TzW0DDtdMklHYyGXbGllSwQyDhpZu\nznX4CYajozqMc9hE9pOf/IQ//vGPmKbJxo0b+Z//838yb968sYhXZFyi0a9hmAT1KDtr6tm0YWVq\nCsfpsGEYRl+He4VYPLGeZtFUMKQvlRgfsjdxetM0lcXlMzn0djN7j57hQ8VFI77vJVtUlZSUMG/e\nPF5//fUB3e4VReGXv/zl1UUtMsblsOELJObQTdPkrSOnmVuSR9WS0gHnmW3bfZy3jpxO/Rwf4WK8\nEKNl0VTK58xIOxoTo5ONa2RJuc7EZunDJ86x6abqEX+5GTaR/eIXvwASSWvwVJJ8c5oYV7Mu0P++\n/f9ckOuhpDCH1o5eFAAlUdL89B/24nLaWV+1MFX8MWtGjrShEuNKVRWWV85hXdVCSWJjJJv2kaWT\n57bS5QvR0t7D7OK8Ed1n2ES2du3aMQtMjN7V9JS71AZSgNbOXlRVAZOLjX8VCIWj1NQ2UrWklJra\nJt46ejrt4wsxVlwOK++/YYWsgY2hbF0jSyrujdHla6f9gm/EiezqeueLcXE1PeX631fXdRrOdqT2\njB2obUwlJ4/LjqoqaKqCqiooifEZcd3gTEsnh080Y7dZ0VQZfYvxE47EeXn35KyAFpmR3PLjD4x8\nenTYEdmmTZsIhUJpK9QURZHDNbNQIBghHI1dbDOV42JOcR6nz3YQCsdQVYVoTOfl3cfxByM47FYp\nsxfjKq4b7Dl8mrYuH5/9m5szHY6YBJJr8Xb70Irq4QybyB5++GE++clP8sgjjzBz5syrj06M2Eg2\ngQ63ftb/vpqmUT5nBsFwlLiuo6oqLoedUDiKPxjB5TDo6A5QtaSMuSV5vLz7OJqm4QuECYajBEMR\nDDOxjiEbocV4aj7fNWzpvRidbC72AP7/9u49PMr6TPz/+3meOSeThEDQADHBcAjIIQbQQrVQbD1U\n+WqtbLGtXmv30Oq62y5Wy9deQu0Jtdvtdq/qVfttd3sV+609rMuudrff/qotrkA9AEE0JEIkIUAg\n5zDnwzPP749n5mFyTiDJzCT367r0CjOTmU+Smbnn87nvz/2ht9fc35c/hlZVQwayxYsXs3XrVn76\n05/yz//8z5c+OjEm5unMZgVh/2D10p7DNDS1YtO0QfNn/b831TPxxT8eBsBp1+j2BcnzuABobGlj\naWUpmqah67rVV9HjsuMLRCSQiQknz6/xk+vFHsGIOfaSGaPP8w27IfqOO+7ghhtuuLRRiYs2WAL8\nN3tq2V/bCIDbZedQfTMVc2cO+CTbv80PwIKyEo63tKNpKh6XA5umWbcp8npYvnCedbCmokAgHAXF\nbOYqxESy2zSKvJ5MD2NKyPVij3hjJ5qmMqNg9M+HIYs9du3aRW9vL15v7v5Cppr0wy0B/MEInd1+\ndr98cNiE+SuvH2XXi/s53tJOZdls/vyO61hXvRBd19F13Vq6TB2sOXtmAbGYjh5PoCqKlOCLCaUq\n4Bykw4yYfgzDIBCKUlzgGVOvxSFnZHV1dTzzzDOsXbuWT3ziE3zwg5d2FLUYH5qm4XY5CIYjGAkD\nl8eBpmlDHsnSvwKysaWNddWVybyZ2W+xv0jULPxIGIZsghYTzkj7v7h0uZwj8wWj6AmDubMLR75x\nmiED2c6dOwmFQvz+97/nX/7lX9ixYwe33347d955J3Pnzr3kAYuxSy/kcNo1QpEYhRexHPPK60c5\ndPQkYC5PpgfB/bXH6eoNSM5CTApFIZmPvbiTgcVAuZwjO9vRA8Ciijlj+r5hc2Rut5tNmzaxadMm\n2tvbeemll3jooYfIy8vjxz/+8cWPVly09EKO2vqWEY+36F8BWVk2m/eaWq3rQ+EYDrvNOka+oems\nNAUWk0JRsPK0iysul03R4yRXc2SGYXDwWAeapnLVwrFNlkbsfp8SiUSIRCJEo1Hy8+UJNxmGK7GH\nvkEtdfvB3gxSt0sFq8aWNpwOG+FojETCIByJ8eIfD7OgrAR/MCI5MTEpbJpGodfN0sq53Lp+ZaaH\nIzKs6XQHgVCUZQtKcTvHNkMfNpB1dXXxX//1X7z00kt0d3dzxx138Mwzz8i+skkw2hZVxYX5o7pt\n+uwtHIkRicbBMJu1ppYnG5rOEo9LTkxMPEUBb56Lu25cLXvHxlku5sjihsKR906hqQobr1k05u8f\nMpD9xV/8BYcPH+ajH/0oDz30EKtXrx7XZsGdnZ3ceeed/OQnP0FVVbZt24aqqixcuJAdO3agKAq/\n/OUv+cUvfoHNZuP+++9nw4YN4/b42WywFlXphRz9GwIfOXbKakM1WNFH/7ZV5/0hCr3mwXu9vhC6\nrqNpGv5gxDqyRYiJZBjmB6qm050SyMZZruXIfIEATW0R4nqCTeuXMeey0R/fkjJkILv66qtxuVwc\nOXIEu93O4sWLKRjLSWfDiMVibN++HbfbjWEY7Ny5k61bt7JmzRp27NjByy+/zMqVK9m1axcvvPAC\nkUiEu+++m3Xr1uFwTO+kcP/ZV3VVGb5AiFA4BpjFGyNJ5cCcDjtul/mEj0QvfL8/GLFumzwkWohx\nl0gYA/ZBjvdJ0NNRLuXIDMOg7s16enxhNqxZzPWrl17U/QxZqH/w4EEqKyt5+OGHiUaj7Ny586IH\n299TTz3F3XffTUmJGXnr6upYs2YNAB/60IfYt28fR44coaamBrvdTn5+PuXl5TQ0NIzbGLJZqkAj\nJVXIMdhMzcx7pc+UB86a0+8vGI6iaSq9vhC+QIh11QtZVFEKQCQaR1VV8j1ObJrZ2cPtnt4fHMTE\nCUdjtHf5+LffvcVv9tRa+x13vbhfGglPE3WNZ+jsDbLwihJuvn7FRd/PkDOytrY2tm7dCsC6deu4\n/fbbL/pB0r3wwgsUFxdz3XXX8eyzz2IYRp8quby8PHw+H36/v89m7Ly8PPz+0XWAnwqGa1HVnzfP\nhSc5E9PSunX0v7+KuTPZ/fJBvHlu9ORSZMXcmbz4x8PW7CwYjjDD62HV0nKCkRgNJ1oHvT8hxkMi\nYdDtC7K39jguh41Cbx4w+BL5WEznmV2u5Mh6gjEaTpzF5dC4fcNy1EtIXQ0ZyOx2e5+vx2tJ74UX\nXkBRFPbt20d9fT3btm2ju7vbut7v91NQUEB+fj6BQMC6PBAIjGpps6QkN6bUKcONt/91JSVePlBd\nyYF3mgBYtayCa6+upL3H3+eyxQtKB79DDVz9qoGKCj3YbWbwU1UVDHMp8UxHL43NbRfOKxNiHDjs\nGtFYvzysYZ6oEYmal6eej8XF+ZQUj/31/NIfDvd5Pdz24cErInPtvWK0nA4Fl2vUBekZ0dXdS/2J\nXuw2lQc/82GWLCgbUyeP/ob8aSdqL9Fzzz1nfX3PPffw+OOP89RTT/HGG29wzTXX8Oqrr7J27VpW\nrFjBd7/7XaLRKJFIhMbGRhYuXDji/be3+yZk3BOhpMQ75vFeu+xKFpaZ+YTiwnza232DXjaUqvml\n7Ks9Tigcxe2yU1vXQtX8Ut569wTn/SEAenxBOrqnz+xXTJ5YTMdu0zAwSCTM/wyM5IZoxSpaWr5w\nHuhjfz139fr5U7IXKcCfahtZWDZ7wMzsYl57mTbawOvOm4Eni3NkvkCYw43NRGI6f3bzNcyeUUxn\nZ2DE7xvu5x8ykB0/fpyNGy+cD9TW1mb9ezzPI1MUhW3btvHYY48Ri8WorKzk5ptvRlEU7r33Xj71\nqU+RSCTYunXrtC/0SBlq4/NoVFeVcai+GbfLjq4neOvdEyytnIuuJ0gkDFRVQZcSfDFBDCCu6+S5\nzSM6orF4souMwrrqBaNeThe5qas3wP7a40RiOjeurWL1VRXjcr9DBrLf/va34/IAw9m1a9egX6ds\n3ryZzZs3T/g4phubptHR7SMajWMAfzp8nBmFeXJci5gUhgEKBk6ng0Kvx6qYvZScWMpozvITmXGy\ntZODdc0kEgZL5s/mmmXl43bfQwayefPmDXWVyGHFhfnMmV3EmXNmXlJVzFOhdT1BvsfJeX/2J4lF\n7guEojidDnp9QXNzPrC/9ji3rq++5PseS6HUVJRtxR6a3c57zR20nOvFpqlUV80h3zV+e5JhDC2q\nxNTQ1etn4RWzOXTU/GRkJGdgPn8YQ3aMiUmSMMAfiBCJxlBUhXyPi+Mt7UO2WRur6RjAUrJpQ3RX\nr5+zPXE6e4OUzMjnEx9Zyawi82+TP455PAlk08grrx/lUPLgTI/L0Wf2FYllxxNfTB9ul514wizu\n8IxiI78YnWzYEG0YBk2nOzna4iORMFi7spLb1q/Ebp+YkCOBbJro6vWzr/YYoXAMPZEwS56Rrh0i\nc2JxHZfDbi0tSj5raojFdA4dbebUuW5smsqdH1nJNSvH3j9xLCSQTWHpm0J7fEFC4RiGYUhBh8go\nJfk/fzCCzaYyb3YxWz52jQSxcZKpHJnD6aTXH+btY2cJRWIUeV0sKiuiav5lE/7YEsimqJf2HKah\nqRWbplk9GVX1wqZTITLFAJTkZymvx0UoGqXHF5RANk4ykSMLhoI44l7efPcUCcPguquv5EM1lWbL\nu0lY5pRANgX9Zk8t+5ObQlMnQFfMnZnhUQlxgQGoqkIsruMPRtj98kGqq8qHPK5IjN5k58h8gTBv\nv99Fr7+Hwnw3n7zlWhZcMbknGkggm2K6ev3mKc/J7FcoHMPtcnDeH5IjWkRWcdhsRKJxnA7zbehS\n+yuKyWUYBu+3tPPOsVPoCYNlC0q566Zrk11aJpcEsimmxxfEH4xYpfWKqrC4opSCfPP8MSnwENnC\nblNIxMxTFyLROO4MvAFORROdI3M4nYSjcd45fpau8yHsNpVFV8zgjg+vyEgQAwlkU0qqvD4YjgKg\n2VRcDjvrqisByPc4CYajyRMHzK75kUiMcFRK78Xks9lsBMIxVEVJHtorH7HGw0TmyIKhIMWOmbx+\npIVILM7CK0q49fqryPc4JyUXNhQJZFNE+lllmqpiGAZFXg9Oh7k/p7gwn+qqcuoaT6NpKgV5bs51\nnpf9YyJjwuEYmqpS6HWjaSq2IY4gEmMzUTmySDRGQ0sjB4414nTY2HzjalYvm5/8EJJZEsimGJum\n4XbZCYVj2DTV2pvzyutHaWxpw6apLK64nLXVC3j2l39ETmkRmRKKxvp82JJ9ZNnrXOd53nrnBJFo\nnCtKZ/CpW9dRXJiX6WFZJJBNEenNUr15bqrml1KztJwr583uO1vTNI63tLOkcg6advHn/whxqTRN\nwem0sWnDSoq8HoBxa1E1nY1XjszhdJIwDI6d7KC5tQdFgflzCvnUx9ZQmEVBDCSQZa2LOeE21Sx1\nX20jjS1tnG7rsfaQpTMMA38wgtspyXWROZpqLicWeT3U1rf06VgvZfgXbzxyZKFwiKrLZ/C71xtp\n6/IzszCPOzauoHRWQUZzYUORQJaFXnn96CW9qBtb2qyvU3vIFpSV8G7jGYKhKHoiwXMv7h/XMQsx\nVrG4TmXyQNjU8z31tZThX7zxyJG99/4pnv/dEeJ6gmtXXMmmDdU4JqhP4njI3pFNU+nLgHDpL+rz\n/iD/9zd/IhA0A5gQE0VRGHXOVVFA01SWVpZO7KDEmMT1BLX1Jzl5phOXw8anbl3LsoVzMz2sEUmS\nJIPau3zWEuJ4KS7M56oFcwlHYnR0+/AFIpz3hyWIiQk31sIhj8tJkddj5XdTpOgjM3yBMH984ygn\nz3RSkOfkLz6eG0EMZEaWMa+8fpT6E63E4nqf5cPRnHA7VP7sTFsPB+qaOHT0JP5g9hysJ0R/hgFV\n8y+3nsPT/TDM8TTWYg+H08m5Lj/vHD+LnjAou6yQ8svymVHgmcBRji8JZBmQWj6028x9M/2XD4d7\nUffPny24YjZ1jac5+v5ZWtt7BjxWodeNkTDwBcMDPjErybUgqcAXk82mqdQs7XvUvQSw8TGWYo9g\nKIhTL+Dwe63YbRr/a8NVXJVc7s3Goo6hSCDLUoO9qFMB0DAMQpEYv9//Lr997ciA22mqisftoLrq\nCj549QJ6fEEO1DVz8N3mPkuMqgob1lTx1jtN9PplBicmh6oqzJ9XwpXzJrex7HQx2mIPwzB460gj\nLcdOUuT1cN/Hr6O0pGgSRjj+JJBlQGr5sP5EKzC6nIBhGLSc7aL7fIBQODrI7AoWV1xOR48fh92G\noigcOtrM0ffP4HTYWVBWQqHXTc/5IInkNycSBrG4wdrqhfzhjaPWAYdCTKTlC+fy6dvWZXoY05ph\nGNTWn6TlXC+XzfTyV3dtsPqx5iIJZBmy8dolbFy3hK6u4TeAdnT7OFjXzMGjzXT1BgZc73TY8Lic\nuFx2NlxTxYt/PAxAry9IIBghHInhcTs5VN9CIqFbXYNVRSE/z0ljSxvrVy+iav7lNJ3p5Lw/JN0+\nxIRRVYVznedl4/MEGilHZnc4aGju4OTZHvLcdj7zsdU5HcRAAllGlRR7YZCTVYLhKG83tHCgronm\nM50Dv29GPssWzsMw4NjJc4A5q7ty3mwWlJVQ13iGcDSGoprNWIPhCAAzi/JxOR10nw8woyAPm6bS\n3nWeX/z3G+gJA01VJIiJCWOzqShc6Mt3MZv+xciGy5EFQ0F0xc3Jsz3MLs7nM7euoWRW7p9VKIEs\nS+h6goamsxysa6Ku8QxxvW+5fJ7bydVLrmDV0grmzC6yGnWmvxm88vpRjre0YwAuhx1VVQiFY5D8\nt03TzF6MTgfBUJRINIaeuBC50r8WYjw4HDZmFHis1QGn00Z1VfmwnTwkwF2a4XJkRxqaOXaylctm\nFvC5P9tAvsc1yaObGBLIMsgwDE6d6+ZgsmQ+EIr0ud6mqSytnEPN0goWV1xOrz8I0KfbdOrFnr6R\n2umwE43F8bgcuF0OFleU4nE5OHLsFL6AueQQjcfRNBU90XdKqCigoFh5NCEuhqLApg0rae04z8lW\nc1XB4bCR73ESCkc43tJu3Ta9avdSu9qIoZ0628Wxkx0U5Lv4q7vWT5kgBhLIMqLXH+LQ0WYON5zk\n9LmBJfPlc2ay6qoKViwqsw6q6/8CH2nPjTfPbTVjTd2mYu5M/v3lgyiYhxnG4gM3STvsNhIJA9VI\nENclmImLM6son7Md5zlU1wxAwjDQVLP/Ql3jGQywut6njHdXm+lqsBxZLKFw4N2TaKrClptqcj4n\n1p8EskkSjcV55/hpDr7bxLGT5wbkoooL86hZUs6qqyqYWTRwA3T6C3xf7XEO1Tdj0zTrU+tgG6n7\nlzcXeT3WmU+KomD0G4TDbrM2QbZ3+ZGDDsXF6uwJ0NHtx+DCqeSqajarjkTjOB02orE43jy3VbU7\n3l1upqv+ObJgKEhLh5lGuOPDK7jyijkZHN3EkEA2gRKGwYlT7Rx4t4m33ztFtN8hlm6nneWL5rFq\naQXlc2ehjuKAOl3XCYWjuF3mp9n0T60jdUdIBbva+mYSRgJVVVAU0HUDTVMpzHdx3h8mHI2RkHyZ\nuEgK9FmaNqzLFSLROG6XHW+eG13X2bRhpfWBazRdbcTI+ufI6o6dpK27h5ql5ayrqcrgyCaOBLIJ\n0NZ13iyZr2umxxfsc52qKCyquJyapeWs/8BientCI95f/xe422Uf8jTd9JxZ+r9TNl67xFpiDAQj\nhKMxVNXcRN3tC6LrCalcFJdksKePAuR7nGiaai0paskjXNJJq6rxFYnGOd7Sictp47b1KzM9nAkj\ngWycBEMRahtaOFjXxMnWrgHXl5YUsmppBVcvuQJvnrk+PZZjEdJf4P0rvvq/4H+zp5aGprNoaUuP\n6Yq8HtwOB109ATCgdFYR8YSOrifo8QWJD5I7E+KSKGaxR3npTE62dlrPzaFWDsTFS8+RNZ/zEdcT\nfHjNwilV3NGfBLJLENd1Gk6c5UBdE0cbzwwoX/fmubi66gpqkiXzl2o0vRhf2nOYfbXHAbO7eP+E\n+SuvH6W2vpnOngDOZBVZPKGjYFZJuhx2AnrE/LdNA0UhKh0/xBDMAg7zee+w24jpOno8MeiszO1w\ncLqtBwOoLJstFYkTJJUj8weDnG7zke928KE1SzM9rAklgWyMUiXzB95torb+JMFwtM/1Nk3lqgVz\nWXVVBQvLL7MqtcbbUL0YD9Y1WW8k/mAYj8ve5/r0opFINE6ex4lN03DYNE6d60ZPJLDbbBTmu3A6\n7PiCYQlkYkiqqljVr5FYHJumYnPYCUdjfW7nctgJRaPWXsbGljbp7jFBUjmy7hOnicZ7uH714gEV\nolONBLJR6jkf5NDRZg7UNdPWdX7A9fPnlbB6aTnLFs3D7XRkYITQ4wua/RKTZWKJhIGmaQPeLDRN\nw+1yEEoG4cqy2TS2tJm9GH1BDMMgEIrS0e1Hs6nmfjN98OVGVVWkMGSaUqBP5WsiYRAzdGYVuSkq\ncHOu8zyGYT5HNE2OPpxsp9t6AVh9VUVmBzIJJJANIxKN8c6x0xyoa6LxZNuA5ZKZhXnUXFXBqqXl\nWfHJssjrwemwEQ+am5wVxVz+TH3yTS8aMZc9y1hbvQCAxpY2NM1sIWQYBpHYhU/UqqJQWOChqMBD\n0+lOqyJNUcwXyYIrZvOffziEPxgdOCgxJZn7DQf/cJMKWqltJJqmYtM06wMTSEXiRAsEI3SfD1Fe\nOmPAdp6pSAJZPwnDoPFkGwfqmjjy3ili8b6dL1xOOysXl5kl83Nm9umykWnFhfmsWlrO3kPJHJnb\nMaC6caj8WirAuV12EgmDSDSOzXFhtrV80TxuXV/N9577/zh9rhsw38xOtpqNhgMhCWLTgaqYXV9i\n8TgOu82scE2brRsG+IMRCr0eFlWU9glcG69dIu2nJkFPdxcdPjMdsGzB1NszNhgJZEnnOs9zsK6J\ng3XN9Pr7lsSrqsLiilJWXVXOkivnWAdiZqNb11djoNDQ1IpN01hQVjLgNoO9iaQHOID9tcc53tJO\nXNdZXFHKretX8ps9tZzrNJcrFEUhFtc513Gesx0Dl1rF1KOqCqqqQMIMaAV5LgzMPqHnA+apCUbC\nIByJsbSyiNvWr6Sr10+PL2iV2UsAm3h6Qqe92/zAsKKqIrODmSTTOpAFghFq609yoK6JU8lZRrq5\nl81g1dJyqquuyKnS1dvWr2RddSX7ahs53tLG8ZZ2Kstms666csgiEej7JnPr+uo+l3f1+jlQ12yV\n5huGIfvNpglFAbfLYbVL6+oNoOsJOnv9uBwOVi+fz/5Dx0EBQ0mQSCQ42drJK68fJRiOWh+qhnsO\nivFTXDyL0Pu9FHndzCjIy/RwJsWkB7JYLMajjz7KmTNniEaj3H///VRWVrJt2zZUVWXhwoXs2LED\nRVH45S9/yS9+8QtsNhv3338/GzZsuOTHj8d1jp5o5eC7TRw90TqgUKEgz0XN0nJqllZw+azCS368\nTEot6/gCIfbXHue9plaqq8r7lD0P16Q1/Q2nxxccUIkmpj5VUSjyurE7bCyuKOW9JvMwWAMwEuaR\nQ63tPdYHp1R/zu7zQV47+J55nBDmTK6t8/ygz0ExvsLJPqqXzyrI9FAmzaQHshdffJHi4mK+/e1v\n09vby+23386SJUvYunUra9asYceOHbz88susXLmSXbt28cILLxCJRLj77rtZt24dDsfYKwINw+Bk\naxcH68yS+VCk7xuy3aaxbOFcVi01CxfUCSqZv1QXk1+I67p1lAv0bWk1WJPWirkz+zQaTinyesw9\nZvGIdGCcJjRVwQDOB8N4Ek48Lgd33FDDL/77DbrPX+hYc+JUO0vmX25VMKaaUsfjOoqqYGAQjepW\nEYg0A55Yrec6ALh8pgSyCXPzzTdz0003AZBIJLDZbNTV1bFmzRoAPvShD7F3715UVaWmpga73Y7d\nbqe8vJyGhgaWL18+6sfqPh/gYF0zB+qa6Oge2JC0smw2NUvLWb5oHq4s32cx1uMtUhWKh+rN7uNu\nlwNtiLZWKb5AmH9/+aDVjDi9KKS4MJ/rahaxt/YY/kAYDGkpPNW5XQ4ShkGey4HDYbcCUFlpcZ9A\npusJ3ms6h6KYMy8jucrhdNix2TTrYFeX0z7ic1Bcut5kW7yKubNHuOXUMemBzOMxk75+v58vfOEL\nfPGLX+TJJ5+0rs/Ly8Pn8+H3+/F6vX0u9/tH7o4djsQ4cuwUB+qaeD/tzKOUkhne5NJhec6sH1/s\n8RapAo5U4Qb0LXtOL8fXdR0wrCrH1w6+x4F3T+Bw2K3AufHaJWxct4Rf//ebNDSdpdc3cp9IkZtU\nxWzwa2AQi+k4HTHyPU721TbS2RPAbtOsil6H3YYvGGH2zAKzGEhVKLusmMXzSzly7BQelx2300E0\neXspvZ9YiuYEgsyaMfjhmlNRRoo9WltbefDBB/n0pz/Nbbfdxre//W3rOr/fT0FBAfn5+QQCojlo\nqAAAHTNJREFUAevyQCBAQcHwU+Vnn/8jB95tIhbrWzKf53Zy7cor+eCqhcyfNyurSuZLSkbxZNMY\nUClZXJxPSfHI31tS4mXxglLau3zmv/t9zydvu4aNXUvo6vHz/G/eAKCz108gFEl2/khQf6KVjeuW\nUFLspb3LR1uXjxkFeYQjMXMDtphyEoZBIq7jcTmIROPWoa9vv9dCkdfD7JkFnDnXbc7ADOjs8TOz\nKJ+ZRfksnl/KfZ+4DoCNXebKQeq5k/o6G4zqtZeDYsntEIsqL8M+hn6uuWzSf8qOjg4++9nPsmPH\nDj7wgQ8AsGTJEt544w2uueYaXn31VdauXcuKFSv47ne/SzQaJRKJ0NjYyMKFC4e97z/VNlpfq6rC\nkvmlrLqqgqr5pWbfQKCjI3vOPCop8dLe7hvVbauSn27B/ESLzqi/N91Q31PszadqfimH6psJhaJW\nsA8EI7gcNrq6/KADGoQj5p6xGQUeznWcH7zbuYJUNU4BkWjMOhTT7bTT4wvhdJhvG4qqmDkwwyAa\n09F13Ww/dbKNhuOtfWZd6c+7i3nejrexvPayxWgDrz8YIc/toGcUJ2vkkuF+/kkPZD/4wQ/w+Xw8\n/fTTPP300wB85Stf4Zvf/CaxWIzKykpuvvlmFEXh3nvv5VOf+hSJRIKtW7eOqtBj3mUzWH1VBSsX\nX0GexznRP86kmYzjLVJHvOx++SDBcNQqEllccbn1mK8fft+6zm7T+gQxRTFve67T3FcWCEWIps2O\nVSVZ7SYBLieY59UpkDBwOe04HHbcLvPvqesJPC6H9Rrr6gkMeT+DFSnJxuiJE9MNigunznvfaChG\n/2OCc9jZjl40IzsrDgeTrZ8KU4Ulqc3QqXOMunr9PP/fbxCL68lqyCjn/ReOVFeAFYvnca7zPLqe\noNcfspZ5FVXBpqnE44k+hy5qqjLg1AAx8WyaSjytI4cC5Lkd+NM6tNhtGkVeN6FILHmWmLnBPhiJ\ncbK1M9mrU6G4MA+7TbMaaKcXIw1WpDTWwqWJkK2vveGMdkZ237Yfc+W8mXz+kzdM8IgmV1bNyCbS\n5bMKc+7JmY1GM/uzJRsPhyMxK1jZ7RrnOs9blWmGYVa+RWNxs6Fsv3ZfanJZSmRGavlXUxXcLgef\n2bSWX//uLQCisTihcAxNU7muZhHVVWXsq22koamVzp6AdcpzXNfZcus1FHvzB8yyhtrecTGFS2Js\nUpvXp4spFcjE+BnqwMNVyyqsXOTVVeUAvPXuCQCWVs61NmEDVi4FzCXFVENiAFU19/epimrurBWT\nQgEum1WQ7MwRQFHM2XJq72Tqb+h02KmuKrc6cXT1+vv8bUPhGG6X2cuzuCgfdFkmzCbpxzdNBxLI\nxJjc9uGVLCwz96ek3rjSZ2+vvH6UfbXHCYWjyUBm4LBrBMOxPvk0h81OJBpD0UAxLuTNbJqKnkgM\nyKNpqophGH2WJUVftmSX+cE6sNg0FRTzXLA/v+M6TrZ2svvlQ0TjZtVpJBpj98sHreXDtdULBg1M\n/Y8AWr5wnlmROMhKSPr2jtRtr5w3e8BlEgDHX1HB1KzIHIoEMjFm/d940v9dXVXGofpm3C47CtDR\n4yeh9w0+igI2m4qBDT2RAMPct+Rw2IjG4lgHqqUxDMM810pRiev6gEBn01Rmzcino9vfJ/dzMdRk\nVUo2BE1NU9D10Y3DbtOI6Xqf356imA2evXkuHA4bV1eVU1vfQm19M+FoLDkrVoglLiz7Hm9pt473\nSUnfYO922a0jgEa7lzF1H0NdJsZXps5EzBQJZOKiDFd1ln50jMNmIxjve8SLWdhv8IEVV1LXeIbO\nXrPiLR7XMQyDGV4PXWmdIwBUzTxSxtATKEryzCvlwuPd+ZEaqqvKeWnPYfYdOoZhQMIYOLMbjUTC\nYLidhpO5tcBus6Ggo+uJETup9G+9BuY4K+bM5JO3XAOYPTN3v3zwwn0ZgMqo27Skfi9ul3PYIJT+\n/BhqmVpMHFsWn9AxESSQiTEbqdFwereQZQvnUtd4hlAkZp0yraoqkajOkso5zL1sBrtfPkQsrpMw\nEiiKgj5IlLCpKuHkMlie24GqmlV30Vgch12jq9cMfOuqK3mvqRVdT9B9PjiqANDfYIFKVRQUBQry\n3cTiOoFQ5JKD2UinazsdNgq9bq4oncnRxtNjPrhUTbaMuum6Zday76H6Zrp6AjgdNnPmqSjMKPAQ\niV3Y2D7Ycl+qcEPTNHRdp7a+ecgijWyoSpzusvmoqYkggUyMSXuXb8Sqs43XLrGO7zjd1kNpSRE9\n5wPWzEtRzCq5o41naGg6Syyum0tgyWKQ6CDdQlIbtM03Zw23y46uJ/DmubBpWp9xVFeVm62R3E4i\n0cG7jwxcvEyTrORzOuygQDQax+W0Y7dp+IJmn0mbpg2owhwtJfngqqowszCPjm4/arJy0G7TzA3I\nioLbbe4FuuHaJdxw7RL++3/e5p1jp9EThnXA5WBBV1HMnGLq91zk9ViByKw2teMPRqzbR2I666oX\njmq5zxcIW/mxfbWN1taMlIttpybGl82WO9uQxoMEMjHuUhVuqSXGYDjKJ25cTV1jq3UMyKKKUo63\ntKFpGk6HjUAwgqapeJJv5rF43DoSxGHXyPM4URSFaDxOMBRh/tyZdPQE0HWzo0R6M9pUDqbHFwQF\n/u9Lf+qz3w1gRmEemqqwtrqSfI+L3/7PEcKxOOFIDCNh4HY5uK5mEcFwlLrG0wRDUXr9IVRVwWG3\nEY3HzTxfwrD2wWmqgs2mmeNMbjkYjKIoeNzmZuLy0pl0+y4so8bicVRVxReKEk8YrEvLQ91y/QpO\nt/XQ1RuwGvMqmN3lL9w3lM+ZZXVfqa4qt6oOU9wuB8FQlKICj9WRfqRgU1yYz4KyEvYlK1bdLjuN\nLW109fqnbKunXNb/ZPipTgLZNHWxnRVKir0XVXVW5PVw2/qVvF9Zav07Vc7tdtoJR2I47BqRaNzc\nWJs2KyrId+EPRszlR90AxTzROxZPcD55mnfF3Fl9xlFb38KRY6ew2zSKC/LxByNm7itZ/OCwa1RX\nlbO0ci77ahuJxuNEo3E8LgdLK+dYy2G7XtyPTVOJxGIkkjOhWFzH5bATjelEE+ZsT1HMJVM9kcCm\naYMGMUUxc14JI0Gex8niilIaW9rwuJz4g2H8wQiKAoVeD8VFZkPr1Cwp9fe6qnIO/3PwmJnaUhU8\nLgeF+R7OdfZiGFBWWswDWzYO+Pv2ryB0Oe3YNHVM3ejXVi+gvukscOGNsscXtHooDvY4UpWYGakP\nKNOFBLJp6FJzGCNVnQ31Ztb/cZcvnGeV6quqQjgaN/M2YFUVRGNxriidSf2J1uSp1IaZR9PNIFbo\ndaNpKsFwlK5e/6DnrMV0ndVXVXDiVDuaprKoopR11ZXU1rfwk92v0ZZ8I1YVM2e18dolfWYx/mDE\nqrxMJAw0TWFp5Ryak90tfIGIOTYMnDbboDk+LblXK1XM8vEbaqxg7nHZCYYjqMnl02DI3LqQChbp\nnVbKS2ficTkIRaIoyV9SPKEzc4b5d4jFdev3MNTfbV9tI7X1zcmNzY4+s77hFBfmc3Vy2RbMTbcv\n/vEwdptG1fxS63kkVYmZlz1t0SeHBLJpZrxyGGMtux7scTdtWGmVc4PZQb0gz40vEEZPmIUfqVek\nN8+Nw26jxxe03sDB/OSZvozS1es3lxQHGQ/JN9r08cT1BEbCXJxLYKDrUV55/Sh33biG4sJ85swu\n4lxnr9kgF3MWVL3kCjZeu4Rnnn8FfzCSdqCk2UQ3Humbk0vlv1K5uqWVc7hynrkXb/nCedTWN6Og\n4HbbAYVwsvpw+cJ51u/KFwgRCsfo7PGbeUKl7yfusSwlNba04c1z4052f0j9nUYjfdn2xT8eti7v\n/zySAJZZ2XTCx2SQQCYmzGjezNLfgD0uBw67mTMLhqNWscLpth4qy2YnZy8OQMHhsFMxN49gOGr1\nhEwtJ6buK73331Bj0TTV7OCeyjmpCu+fauf9U200ne7kZGundX/5ngvFF2DOzoxUqb5iHhRrNtE1\nCASjqJqC22lWWLpddms58da0AonqqjIq5s6krrHVWmpdV7OA6kUXTvHuc8p3cn9bUYHHWhpM/W5G\n+lmH+92PhQQpkW0kkE0zmcphjKbLQ6pyLrXXCbByOOuqK1mall9L3edv9tTS0HSWhqZWQuEY3jwX\nYBaYbNqwkivLZ5vHz6RJLRmmHjsV9FRVQVNVen0hfv27t4jGdLx5LlwOO+Go2Tg3vXgi3+Ps20Uj\nuaJY6M0DFNzJjvGVZbNZWllKkdfT53edWjLUdZ3FFZdzz6a1AOb5cclOGcWF+SyuKGV/7XFz83hS\nOBKj0OuxloZHm/Mczd9/NPcluTCRTSSQTUOZymGMtstDegk9pJbfWgbk9bp6/RxvaUfTNKsbv8dl\nt/4NDGif9NKewzQ0tWLTNJYvnMc9m9bS4wtysK6Z90+10+sL4XY50DSVkC9EIpEwlwQNuKJ0Zp+g\nUV1VbuX43C47l80stGaBqUa75uncbTS2tPXJR6aWNlPl7PtqGzFQBpSzA9y2fiXhSJSDR5NLkC47\nbpfDDNTz+rYLu9i/Q8pY8qep+ykuzh/wYUFk1jRbWZRANl1l6tPzaLs8pL/Zglk9mJLKx6RL7Y8C\n8AVCgMKLfzxMe4+fa5ddCcBv9tSyP618/MixU4TCERqSlXjz55XQ0tppzQKdDrNvoYKCx+3kdFsP\nL+053GcZ74EtH6bHF7RmW+mzmVSgTXnr3RMUF3qoTjZb1pPBN6WhqZV11ZWDlrNvvHYJzcllztSS\nYGpmejEG+51fTP60uDB/yF6LQkyW6VWjKXLKUO2N0q9PFUSAuTR5xw01eFwOa4nxwDtNdPX66er1\nWwELzO7tkWiMA3Vm9V5nT4CGE2dZXHG5dZuapfOZ4fVQ5HXjzXMRjcaoazxtXZ96079y3uw+s8nB\nxtzR7aOz288v/9+b/J9f70kuGV54LLfLPmzOKlUxmLqNLOWJ4U2vKZnMyETW69/2KnVidVevn+qq\nsgHVkak2StC3VU+qc3swbHa1mDu7iLrGVuv6UDjKkso5VsPc2voWIslzuVQ1jN1m7nNzu+J489yj\nHvdb754gGo0nKx8Vmk538P6pNm5dX42B0mepc6iZEkz8krDkvaYOWVoUIgult7063tLO//n1nkFP\nJC4uzMfjctB0ugOARfO91pvxgrISDtQ1A+ZxJkVeD26X3aoIdLvsfZYIjxw7daHs/3yQGQUeVFVN\n5sQcXJ0s/Bhp3MWFHn75/97ss20g5bb1K1lXXWmNvb+hclYXu6F9JLIHTOQiCWQiJ6S3vYrrOk2n\nO5hZlIfWr89iV6+fYDhqdcYIBCN09fqprW+hvuks4UgMl9NOodfD8ZZ2qqvKaUi2zRoqMGmaau3L\n8ea58Ljs3HFDjVVoMZLqqnLefKfJCq4Vc2f1+d6hAsZQOavBCl/GkwQwkWskkIkpKT3f1OMLWm/8\niqIQicat/ozrqiuHnBEtKCvheEs7Nk2jYu4sawZYXVU+6iCW8ld3ref9U2aRyFi/N136zwLSlFcM\nTjZEC5GF0vM3/QNLei6nf55n1bIKq7ovVdmYWkocKgeUvpxXWTabddWVAyoSL8ZYA9hgOatLqVQU\nYqqSQCZyxmBtr1JfD3W71ObiVEDw5rmpriq3glN//ZfzGlvahs1hTbTBclZSkCFGMr3mYxLIRI5J\nf9MeqTQ/3XgWMUxUocVQJvJnEWIqkEAmpo3RdngfbsaTLacfSwAT4gIJZCKnTNRsKP1+h5rxyOnH\nIldIsYcQWWqiZkOD3a8EJyFyh7SoEjlhsNlQahY1WffbvyWWFFoIkR1kRibEGEihhcgF02xlUWZk\nIjdM1GzoYu53pGbGU0mq4bIQ2UxmZCJnTNRsSGZZg8uWCk1xMabXlExmZCKnTNRsaDrNskZjonKS\nYnLI0qIQQgiRQySQCSEGkApNkUskRybEJZjsdlWTSXKHuUs2RAshRmU6FENIABO5QJYWhbgIUgwh\nRPaQQCaEEFPMNFtZzO5Alkgk2L59O1u2bOGee+7h5MmTmR6SEIAUQwiRTbI6R/b73/+eWCzG888/\nz+HDh3niiSd45plnMj0sIQAphhDZbHpNybI6kB08eJDrr78egJUrV/LOO+9keERC9CUBTGSj6RXG\nsnxp0e/3k59/4Y1C0zQSiUQGRySEECLbZPWMLD8/n0AgYP07kUigqsPH3pIS70QPa1zl2ngh98ac\na+OF3Btzro0XcnPMozVrVv6U/vn6y+pAVlNTwx/+8AduueUWamtrWbx48Yjf097um4SRjY+SEm9O\njRdyb8y5Nl7IvTHn2nghd8c8Gh/70Arsqi3nfr6RDPfzZ3Ug++hHP8revXvZsmULADt37szwiIQQ\nIrttWFOV6SFMuqwOZIqi8Pjjj2d6GEIIIbJYVhd7CCGEECORQCaEECKnSSATQgiR0ySQCSGEyGkS\nyIQQQuQ0CWRCCCFymgQyIYQQOU0CmRBCiJwmgUwIIUROk0AmhBAip0kgE0IIkdMkkAkhhMhpEsiE\nEELkNAlkQgghcpoEMiGEEDlNApkQQoicJoFMCCFETpNAJoQQIqdJIBNCCJHTJJAJIYTIaRLIhBBC\n5DQJZEIIIXKaBDIhhBA5TQKZEEKInCaBTAghRE6TQCaEECKnSSATQgiR0ySQCSGEyGkSyIQQQuQ0\nCWRCCCFymgQyIYQQOU0CmRBCiJwmgUwIIUROk0AmhBAip0kgE0IIkdMkkAkhhMhpEsiEEELkNNtk\nPpjP5+Phhx8mEAgQi8XYtm0b1dXV1NbW8q1vfQtN0/jgBz/Igw8+CMD3v/999uzZg6ZpPProo6xY\nsWIyhyuEECIHTGog+8lPfsK6deu49957OXHiBA899BAvvPACO3bs4Pvf/z5lZWX89V//NUePHiWR\nSPDmm2/yq1/9itbWVv72b/+WX//615M5XCGEEDlgUgPZn//5n+NwOACIx+M4nU78fj+xWIyysjIA\nrrvuOvbt24fD4eCDH/wgAKWlpei6Tnd3NzNmzJjMIQshhMhyExbIfvWrX/HTn/60z2U7d+5k2bJl\ntLe388gjj/CVr3wFv99Pfn6+dZu8vDxaWlpwOp0UFRX1udzv90sgE0II0ceEBbLNmzezefPmAZc3\nNDTw0EMP8eUvf5nVq1fj9/sJBALW9X6/n4KCAux2e5/LA4EAXq93xMctKRn5Ntkk18YLuTfmXBsv\n5N6Yc228kJtjHo2p+nMNZ1KrFo8fP84XvvAFvvOd73D99dcDkJ+fj91up6WlBcMw2Lt3L6tXr6am\npobXXnsNwzA4c+YMiUSizwxNCCGEAFAMwzAm68EeeOABGhoamDNnDgAFBQU8/fTTHD58mG9961vo\nus51113HF7/4RcCsWnz11VdJJBI8+uij1NTUTNZQhRBC5IhJDWRCCCHEeJMN0UIIIXKaBDIhhBA5\nTQKZEEKInCaBTAghRE6bUoEsFApx//3385nPfIb77ruPjo6OTA9pWLqu841vfIO7776bu+66i1df\nfTXTQxq1xsZGVq9eTTQazfRQhuXz+fj85z/PPffcw5YtW6itrc30kIaUSCTYvn07W7Zs4Z577uHk\nyZOZHtKwYrEYDz/8MJ/+9KfZvHkzr7zySqaHNGqdnZ2sX7+eEydOZHooI3r22WfZsmULn/jEJ/j3\nf//3TA8nK02pQLZ7924qKyt57rnn+NjHPsaPf/zjTA9pWP/xH/+Bruv8/Oc/5+mnn+b999/P9JBG\nxe/38+STT+J0OjM9lBGl+nvu2rWLnTt38rWvfS3TQxrS73//e2KxGM8//zxf+tKXeOKJJzI9pGG9\n+OKLFBcX87Of/Ywf/ehHfP3rX8/0kEYlFouxfft23G53pocyotdff51Dhw7x/PPPs2vXLlpaWjI9\npKw0qb0WJ5rL5aKnpwcwP4nb7fYMj2h4e/fuZeHChXzuc5/DMAwee+yxTA9pRIZhsH37drZu3coD\nDzyQ6eGMaLD+ntnq4MGDVqOAlStX8s4772R4RMO7+eabuemmmwBzNqlpWoZHNDpPPfUUd999N88+\n+2ymhzKivXv3snjxYh544AH8fj+PPPJIpoeUlXI2kA3Wy3H79u388Ic/5NZbb6W3t5ef/exnGRrd\nQIONd8aMGTidTp599lnefPNN/vf//t8899xzGRrhQIONec6cOXzsYx+jqqoqQ6Ma2mj7e2ar/n1H\nNU0jkUigqtm5cOLxeABz3F/4whf4+7//+wyPaGQvvPACxcXFXHfddTz77LNk+zbarq4uWltbefbZ\nZ2lpaeH+++/nt7/9baaHlXWm1Iboxx57jOXLl/Nnf/ZnNDQ08PDDD/Of//mfmR7WkLZu3crNN9/M\njTfeCJid/1977bUMj2p4N954I5dddhkAhw8fZuXKlezatSvDoxpeen/P1IwnGz3xxBOsXLmSW265\nBYD169ezZ8+eDI9qeK2trTz44IN8+tOf5s4778z0cEb0mc98BkVRAKivr2f+/Pk888wzzJo1K8Mj\nG9x3vvMdiouLue+++wC4/fbb+dd//VeKi4szPLLskrMzssEEg0HrE21xcTF+vz/DIxreqlWr2LNn\nDzfeeCP19fVW665s9rvf/c76euPGjVmfh0z19/ze977H4sWLMz2cYdXU1PCHP/yBW265hdra2qwf\nb0dHB5/97GfZsWMHH/jABzI9nFFJX/G45557+NrXvpa1QQzM94if/vSn3HfffZw7d45QKCQngAxi\nSgWyrVu38thjj/Gzn/0MXdf55je/mekhDWvz5s189atf5ZOf/CQAjz/+eIZHNDapT7bZ7B//8R+J\nxWJ84xvfAC7098xGH/3oR9m7dy9btmwBzGXRbPaDH/wAn8/H008/bf1Of/SjH2V1HjLXbNiwgTff\nfJO77rqLRCLBjh07cuJ1N9mm1NKiEEKI6Sc7s8hCCCHEKEkgE0IIkdMkkAkhhMhpEsiEEELkNAlk\nQgghcpoEMiGEEDltSu0jE2KsTp06xc0338yCBQv6XL5582ZWrVrFt771LXp7e9F1nerqar7yla9Y\nzWZra2v5p3/6J7q7u0kkEqxevZpt27bhdDqJRqPs3LmTt956C0VRKCgo4Mtf/jLLly/PxI8pxJQm\n+8jEtHbq1CnuvffeQY8gueWWW6y2UYZh8Pjjj+Nyudi2bRv19fX85V/+Jc888wwrVqxA13W+/vWv\nEwwGeeqpp/jhD3/ImTNn+OpXvwqYDYH/7u/+jj179uRMc10hcoXMyIQYQmdnJ8FgEDC7mDz44IOc\nOXMGgB//+Mds2bKFFStWAGaD3y996Uvs37/f+t5YLEYsFsNut1NTU8MTTzyBrusSyIQYZzIjE9Pa\nUEuLTz31FO+++y5f+9rXmD17Ntdeey033HAD69evB2DTpk088sgjQzYhPn36NJ/73Oc4e/Ys11xz\nDWvXruXjH/94n+72QojxITMyMe3Nnj2b3bt3D7h80aJF3HTTTezbt499+/axbds2Nm3axKOPPoqi\nKMMeATJ37lxeeukl3n77bfbv38/u3bv5yU9+wu7du/F6vRP54wgx7UjVohCDaG5u5umnn8bj8fCR\nj3yE7du38/zzz/OrX/0KgGXLlnHkyJE+3+Pz+fj85z9PLBbjH/7hH2hra2PFihV87nOf49/+7d+Y\nPXs2+/bty8SPI8SUJoFMiEHMmDGD5557jj/96U/WZceOHWPp0qWAefL0z3/+c95++20AYrEYTz75\nJAUFBdjtdjo6OnjmmWeIx+MA9PT00NXVxaJFiyb/hxFiipMcmZjWhsqRrVmzhk2bNvHtb3+b1tZW\n7HY7V155Jdu3b7cOFt2/fz/f+973CIVCxONx1q5dyyOPPILD4SAQCPDkk0/y2muv4fF4sNvt/M3f\n/A0f+chHMvFjCjGlSSATQgiR02RpUQghRE6TQCaEECKnSSATQgiR0ySQCSGEyGkSyIQQQuQ0CWRC\nCCFymgQyIYQQOe3/BwbvJRPIDYIYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d1aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(\"ESCS\", \"PV1MATH\", data=df, kind=\"reg\",\n",
    " size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curious....the Math Score has a bell-nice-looking shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###There is too much data here.... I don't know if I can plot all this data using D3. Maybe I should group it in some way....Besides that, This chart is too cluttered...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Know what....let's see if I just classify each point by its country's GDP per capta. First, I need to identify the GDP for each country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what countries from the dataset does not appear in GDP dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_gdp = df_gdp.loc[df_gdp[\"Year\"]==\"PISA 2012\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l = [x for x in list(df.CNT) if x not in  list(df_gdp.country)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'China-Shanghai',\n",
       " 'Connecticut (USA)',\n",
       " 'Florida (USA)',\n",
       " 'Hong Kong-China',\n",
       " 'Macao-China',\n",
       " 'Massachusetts (USA)',\n",
       " 'Norway',\n",
       " 'Perm(Russian Federation)',\n",
       " 'United States of America',\n",
       " 'Vietnam'}"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, what is in GDP dataset that does not appear in my dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cyprus2, 3',\n",
       " 'Hong Kong - China',\n",
       " 'Macao - China',\n",
       " 'Norway1',\n",
       " 'OECD average',\n",
       " 'Shanghai - China',\n",
       " 'United States',\n",
       " 'Viet Nam'}"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [x for x in  list(df_gdp.country) if x not in   list(df.CNT)] \n",
    "set(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok...let's map what is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rename_contries(l_names):\n",
    "    \"\"\"\n",
    "    rename the name of each string in a list by names pre-selected\n",
    "    inside the function. Return a new list with the names modifieds\n",
    "    \"\"\"\n",
    "    set_names = set(l_names)\n",
    "    b=[x.strip() for x in set_names]\n",
    "    d_newNames = dict(zip(b,b))\n",
    "    l2 = [\"UAE\", \"UK\", \"Serbia\", \"South Korea\", \"Taiwan\", \"Slovakia\", \"Russia\", \n",
    "          \"Russia\", \"Hong Kong\",\"China\", \"Macau\", \"USA\", \"USA\",\"USA\", \"USA\",\n",
    "          \"Vietnam\", \"Macau\", \"Hong Kong\",\"China\", \"Norway\", \"USA\",\"Macau\"]\n",
    "    \n",
    "    l1 = [\"United Arab Emirates\",\"United Kingdom\",\"Serbia\",\"Korea\",\"Chinese Taipei\",\n",
    "          \"Slovak Republic\",\"Russian Federation\",\"Perm(Russian Federation)\",\n",
    "          \"Hong Kong-China\", \"China-Shanghai\",\"Macau\",\"Connecticut (USA)\",\"Florida (USA)\",\n",
    "          \"Massachusetts (USA)\", \"United States of America\", \"Viet Nam\", \"Macao - China\",\n",
    "         \"Hong Kong - China\", \"Shanghai - China\", \"Norway1\", 'United States', 'Macao-China']\n",
    "    for s1, s2 in zip(l1,l2):\n",
    "        if s1 in set_names:\n",
    "           d_newNames[s1] = s2\n",
    "    \n",
    "    l_rtn = [d_newNames[x] for x in l_names] \n",
    "    return l_rtn\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BELONG</th>\n",
       "      <th>CNT</th>\n",
       "      <th>ESCS</th>\n",
       "      <th>OUTHOURS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>ST28Q01</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>STIDSTD</th>\n",
       "      <th>ST57Q01_bk</th>\n",
       "      <th>ESCS_bk</th>\n",
       "      <th>ESCS_bk2</th>\n",
       "      <th>CNT1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.18</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
       "      <td>341.7009</td>\n",
       "      <td>0-10 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
       "      <td>548.9929</td>\n",
       "      <td>201-500 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>More than 500 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>438.6796</td>\n",
       "      <td>11-25 books</td>\n",
       "      <td>10</td>\n",
       "      <td>00004</td>\n",
       "      <td>(8, 10]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.63</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>361.5628</td>\n",
       "      <td>101-200 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BELONG      CNT  ESCS OUTHOURS   PV1MATH   PV1READ   PV1SCIE  \\\n",
       "0  -1.18  Albania   NaN       NA  406.8469  249.5762  341.7009   \n",
       "1     NA  Albania   NaN        8  486.1427  406.2936  548.9929   \n",
       "2     NA  Albania   NaN       24  533.2684    401.21  499.6643   \n",
       "3     NA  Albania   NaN       17  412.2215   547.363  438.6796   \n",
       "4   2.63  Albania   NaN       NA  381.9209  311.7707  361.5628   \n",
       "\n",
       "               ST28Q01  ST57Q01 STIDSTD ST57Q01_bk ESCS_bk ESCS_bk2  CNT1  \n",
       "0          0-10 books       NaN   00001        NaN     NaN      NaN  None  \n",
       "1       201-500 books       NaN   00002        NaN     NaN      NaN  None  \n",
       "2  More than 500 books      NaN   00003        NaN     NaN      NaN  None  \n",
       "3         11-25 books        10   00004    (8, 10]     NaN      NaN  None  \n",
       "4       101-200 books       NaN   00005        NaN     NaN      NaN  None  "
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CNT1\"] = None\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df[\"CNT1\"] = rename_contries(list(df.CNT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BELONG</th>\n",
       "      <th>CNT</th>\n",
       "      <th>ESCS</th>\n",
       "      <th>OUTHOURS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>ST28Q01</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>STIDSTD</th>\n",
       "      <th>ST57Q01_bk</th>\n",
       "      <th>ESCS_bk</th>\n",
       "      <th>ESCS_bk2</th>\n",
       "      <th>CNT1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.18</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
       "      <td>341.7009</td>\n",
       "      <td>0-10 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
       "      <td>548.9929</td>\n",
       "      <td>201-500 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>More than 500 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>438.6796</td>\n",
       "      <td>11-25 books</td>\n",
       "      <td>10</td>\n",
       "      <td>00004</td>\n",
       "      <td>(8, 10]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.63</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>361.5628</td>\n",
       "      <td>101-200 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BELONG      CNT  ESCS OUTHOURS   PV1MATH   PV1READ   PV1SCIE  \\\n",
       "0  -1.18  Albania   NaN       NA  406.8469  249.5762  341.7009   \n",
       "1     NA  Albania   NaN        8  486.1427  406.2936  548.9929   \n",
       "2     NA  Albania   NaN       24  533.2684    401.21  499.6643   \n",
       "3     NA  Albania   NaN       17  412.2215   547.363  438.6796   \n",
       "4   2.63  Albania   NaN       NA  381.9209  311.7707  361.5628   \n",
       "\n",
       "               ST28Q01  ST57Q01 STIDSTD ST57Q01_bk ESCS_bk ESCS_bk2     CNT1  \n",
       "0          0-10 books       NaN   00001        NaN     NaN      NaN  Albania  \n",
       "1       201-500 books       NaN   00002        NaN     NaN      NaN  Albania  \n",
       "2  More than 500 books      NaN   00003        NaN     NaN      NaN  Albania  \n",
       "3         11-25 books        10   00004    (8, 10]     NaN      NaN  Albania  \n",
       "4       101-200 books       NaN   00005        NaN     NaN      NaN  Albania  "
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Source</th>\n",
       "      <th>Year of reference</th>\n",
       "      <th>Per capita GDP</th>\n",
       "      <th>country1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Australia</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>b</td>\n",
       "      <td>2010</td>\n",
       "      <td>40801.34357</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Austria</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>b</td>\n",
       "      <td>2010</td>\n",
       "      <td>40410.79897</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>b</td>\n",
       "      <td>2010</td>\n",
       "      <td>37877.85105</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Canada</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>b</td>\n",
       "      <td>2009</td>\n",
       "      <td>40135.59065</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Chile</td>\n",
       "      <td>PISA 2012</td>\n",
       "      <td>b</td>\n",
       "      <td>2011</td>\n",
       "      <td>17311.55842</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country       Year Source  Year of reference  Per capita GDP   country1\n",
       "35  Australia  PISA 2012      b               2010     40801.34357  Australia\n",
       "36    Austria  PISA 2012      b               2010     40410.79897    Austria\n",
       "37    Belgium  PISA 2012      b               2010     37877.85105    Belgium\n",
       "38     Canada  PISA 2012      b               2009     40135.59065     Canada\n",
       "39      Chile  PISA 2012      b               2011     17311.55842      Chile"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gdp[\"country1\"] = None\n",
    "df_gdp[\"country1\"] = rename_contries(list(df_gdp.country))\n",
    "df_gdp.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_gdp.index = list(df_gdp.country1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cyprus2, 3', 'OECD average'}"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [x for x in  list(df_gdp.country1) if x not in   list(df[\"CNT1\"])] \n",
    "set(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [x for x in list(df[\"CNT1\"]) if x not in  list(df_gdp.country1)] \n",
    "set(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice...all cleaned. Let's insert the GDP per capta to each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"perCaptaGDP\"]=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BELONG</th>\n",
       "      <th>CNT</th>\n",
       "      <th>ESCS</th>\n",
       "      <th>OUTHOURS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>ST28Q01</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>STIDSTD</th>\n",
       "      <th>CNT1</th>\n",
       "      <th>perCaptaGDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.18</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
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       "      <td>8630.994455</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>More than 500 books</td>\n",
       "      <td>NA</td>\n",
       "      <td>00003</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>438.6796</td>\n",
       "      <td>11-25 books</td>\n",
       "      <td>10</td>\n",
       "      <td>00004</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.63</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>361.5628</td>\n",
       "      <td>101-200 books</td>\n",
       "      <td>NA</td>\n",
       "      <td>00005</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BELONG      CNT  ESCS OUTHOURS   PV1MATH   PV1READ   PV1SCIE  \\\n",
       "0  -1.18  Albania   NaN       NA  406.8469  249.5762  341.7009   \n",
       "1     NA  Albania   NaN        8  486.1427  406.2936  548.9929   \n",
       "2     NA  Albania   NaN       24  533.2684    401.21  499.6643   \n",
       "3     NA  Albania   NaN       17  412.2215   547.363  438.6796   \n",
       "4   2.63  Albania   NaN       NA  381.9209  311.7707  361.5628   \n",
       "\n",
       "               ST28Q01 ST57Q01 STIDSTD     CNT1  perCaptaGDP  \n",
       "0          0-10 books       NA   00001  Albania  8630.994455  \n",
       "1       201-500 books       NA   00002  Albania  8630.994455  \n",
       "2  More than 500 books      NA   00003  Albania  8630.994455  \n",
       "3         11-25 books       10   00004  Albania  8630.994455  \n",
       "4       101-200 books       NA   00005  Albania  8630.994455  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"perCaptaGDP\"]=None\n",
    "df[\"perCaptaGDP\"] = list(df_gdp.loc[df[\"CNT1\"]][\"Per capita GDP\"].values)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAACmEhXqAnB28Hq9amraE+oygrS22mWzna/o6OhQl2JaZvzcJCkxMYnPDQDCGAEV3aKp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\nBFQAAACYyv8HYyTn0ParVZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ddebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.perCaptaGDP.hist(bins = 15);\n",
    "ax.set_title(\"Distrubution of GDP per capta\\n\");\n",
    "ax.set_xlabel(\"GDP\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Ok....if I classify each data point by if its country has more than 20,000 of GDP per capta.... how it relates to the Math score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BELONG</th>\n",
       "      <th>CNT</th>\n",
       "      <th>ESCS</th>\n",
       "      <th>OUTHOURS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>ST28Q01</th>\n",
       "      <th>STIDSTD</th>\n",
       "      <th>CNT1</th>\n",
       "      <th>perCaptaGDP</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.18</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
       "      <td>341.7009</td>\n",
       "      <td>0-10 books</td>\n",
       "      <td>00001</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
       "      <td>548.9929</td>\n",
       "      <td>201-500 books</td>\n",
       "      <td>00002</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>More than 500 books</td>\n",
       "      <td>00003</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>438.6796</td>\n",
       "      <td>11-25 books</td>\n",
       "      <td>00004</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.63</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>361.5628</td>\n",
       "      <td>101-200 books</td>\n",
       "      <td>00005</td>\n",
       "      <td>Albania</td>\n",
       "      <td>8630.994455</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BELONG      CNT  ESCS OUTHOURS   PV1MATH   PV1READ   PV1SCIE  \\\n",
       "0  -1.18  Albania   NaN       NA  406.8469  249.5762  341.7009   \n",
       "1     NA  Albania   NaN        8  486.1427  406.2936  548.9929   \n",
       "2     NA  Albania   NaN       24  533.2684    401.21  499.6643   \n",
       "3     NA  Albania   NaN       17  412.2215   547.363  438.6796   \n",
       "4   2.63  Albania   NaN       NA  381.9209  311.7707  361.5628   \n",
       "\n",
       "               ST28Q01 STIDSTD     CNT1  perCaptaGDP   test  \n",
       "0          0-10 books    00001  Albania  8630.994455  False  \n",
       "1       201-500 books    00002  Albania  8630.994455  False  \n",
       "2  More than 500 books   00003  Albania  8630.994455  False  \n",
       "3         11-25 books    00004  Albania  8630.994455  False  \n",
       "4       101-200 books    00005  Albania  8630.994455  False  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"test\"]=None\n",
    "df[\"test\"] = df[\"perCaptaGDP\"]>20000\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y2SWSWdx0JIQCBCo3suo9ESGOHhIClCLqDbtWZPdx0wQzcPofTikQJu3JDK0xt/7nDdPK\neaW2iawt8XtMpFK5YNmZ7pDSXPgMunj6/X6i0ShKKaLRKB6Ph40bNzJv3jwA5s+fz9q1azEMg7lz\n5+LxePB4PFRUVFBbW8vMmTMHe8oajk8UB06YJN+dwrOjPkpnyqa9oIRsWxL8PjpGFcH+gzzx2hu0\npxyIZYhaeTiFERrCRWSE5Qqn0WUtIrqW3QKQx8yoh3/xGC3NLb/Phq7IfDprsm53E3tb4vgsg3TW\nIesoPJbB2MIA104azaiApf2dI5BBF8+5c+eSyWRYsGAB7e3t/OxnP+Ptt9/OvR4KhYhGo8RiMSKR\nSK/jsZj2Kw0FJ9rlU10WPmmSvNsHaCsq7aDwIxwHZ08bO9tjpGxFkzcfO78Yn52h0xvEFqbrejQE\nrmj2UESlANW1JAfMwXDVu64CW0nSsSQHHIUtFYUhHxG/he1IUlkH09SR9pHKoIvnz3/+c+bOnctd\nd91FfX09d9xxB7Zt516PxWLk5eURDoeJx+O54/F4nLy8vFO+f2np0CUvD+XYAzn+5gPtOKZBMOij\nOZomHstQlG8TDLkBFNuRvN+RwnskyqXVRRhbtzF7+w7qnCTtZhDbMSAeoz0Vo8VfgCWyJC0/Wcsi\nK0ykMFBCoLrFUfSwOrv8na6YqeOtzAFH0I6FkXQwDLCjaQrCbo6o3++6kAqLQmw/7NYXnTo6D2tQ\nxN1lqK+5kcygi2cymSQUcnvO5OXlYds206ZNY/369XzgAx9gzZo1XH755cyaNYuHHnqITCZDOp1m\n9+7dVFdXn/L9m5qiA/0RTkhpaWTIxj5X4/e1h7u9PUEslqauMUY665DIOLR0JpkyKo/2VIb1+9tJ\np23aO1PsfGsz/5Ldzu73W9kUmEBjSYR6GxwzRMQyQIKVzaK6YjsZ0+oK6ggwzC6h7LIyOdaiE8cf\nGiQkrpY7SBraE1yU76c85KF6VJhf/mlXzip/a4dn0NKWzodrbiQz6OL52c9+lq9+9assXrwY27a5\n++67mT59Ovfddx/ZbJbKykoWLFiAEII77riDxYsXI6Vk6dKlOlg0gJysunt1WZjXdjaTyti0JbJu\ncnkiQ2NHEk9bM960IhIKIlpbaU+keTheTGPxeA5580gpL3jcwE/a8uNxsthSIlAIlBtBF8LdBZRj\n6ETyZCggK6Esz8enLhnHlFERfr/xCHubY7k0pp7uC11QZHgjlFKDvhAaSLTl2T921Ed5Z18rIGiN\nZ1BKcf20cqaNdl0lWw938j/vHKAllsHvNWjqTBNKxJhJC/vIRyXjSNOiwTbxSBthmjT68nqnFwFI\niTebwitt0paXrDDdSPix550viNz/clSVBvhQVSnNsQytqSxt0RQBj0V1uRtIu3RCEdVl4V43o4j/\n3Fuk58M1N5LRO4w0OaSCusYoGVuilOL1umZqRkVyO4lG5ftp7EyyqyGJUhDDIkkhE7OdtNsO71v5\nOBb4ZYa05Tuxe9Iw3CUwAsNxwOs5f4UTckH/nhxsTfJ/Gw9jmQaRgIdE2vXZN8fSTCwJU10WZkd9\nlN1NUaJJh/yghVRKFxQZZpzHV61mMKkuC5POStJZB1D4PSZ+j9Fr50xJ2EdTLIvTFbeRUmE7MCob\nxU7bGNIhLNNkDQ/ZnCAem2IEtsdL3OMnbZ7nwtkHKQc6kjatsQzN0TTJjIOUisnleSycMYod9VH+\nb+Mhth6Ksq8lxpZDnexsiOHIYbXIG/Foy1MDuPvUr6gqJp7JIhAUh3v7l3c1xjjYliBgGaRsB4FE\nKHAcRVtLJ5Y3D8uxSZsWKdODa671sUQ1LPe23R1NP+853vy0u3SwM2UjFIR8Jqmsw+82H2F/S5wD\nrW6DOq/H3S+fSNvnoxtXcxZo8RzBHBvQmDIqQl1X8WE42ltI2TZy505Ua4ayiI942sFWoAwDfyZD\nIJFAePNImxZpj/9oatGpuFCsTtX1vxP4P6UCEwh6TDYfaifPbyGEIJqyUbjfsSkEYwuDGDpgNKzQ\n4jlC6Su6fqLeQs6TK5m4ayfjAhW0FExggj9MWyJFWbSFeUd2sMcKk1aCzAW6DD9teokodD9wgEPt\nCdK2n45kljEFfpRSKKWQUpJRirQtmVQSGpp5awYELZ4jlJP1Tu8Z1HDe24hc+xpmJs2NHKAqfx+H\nr72JUdu2s7ehjX2RMnaER9FpBVGGOVQfZ8iJZ0FGU1yUH2BXQ4ys7ZB1XLeG36No7Ezx4uZ6Pj5b\nl64bLgxjM2HkoGyb9F82IDe+h+qxW+ucvPfePZBOgRCYAqa1v8818fc5QpD9oVJkOk26qyzceZmc\nOYgkbWjoTNGZsskqN1Rmdx3P2A77mmO6dN0wQlueFzjdrSxS2QROPH3igsMnoK/e6e6e9KOV3+XE\nidQWjMPJZt0Sbx4vu5Nh3g/4OawcGqWHDn+euzvoRHk9w5FcBafcg9xLaVvhMRVmj6/BUXCoPY3H\nMpA64j5s0OJ5gZNrZREJ5AoOd3eePBmmIbhxWik739wEwOS5s9ze6T1aAKvNm3lx8odoGzeTurSF\n9HpoN4MkY35Gyw7eF36SwWCXcMKIEM6eqB4PuoJJUkHWPj5BywFSthz8rfmaAUOL5wjhWIsSQPzP\nU1R3CaU4sA05dVqvFsC1bSk6Xl9He1aQMiz2mYVkTROlDFpEEfi5QFKNBoGugiaqq+7osSIpgLBX\nl64bTmjxvMAR02dgbNmMyiZQUmIUFObEsZvupX3PXkJMm3Z8r/S9e3r/XGsrKhGjI1zK4WCYjGHS\nq/ePpjcKhHLwCgclIIVFd1jBAIpCXh1xH0Zo8bzAEZaFcdsi/If3kmqLH9ehEnp3qQRXKNm1C9XY\nAEIgSsvc95o0CZFK4bS2QFMjE+sb+e3Y+cQsHxnMoxXbtW72iRSCrIReTk+gLM/D6MIAq7bU62Zx\nwwQdbR8GCMvCd8lcjNlzThkoAlBSovbuhoYG7P372V53hJ3BMtT0WYhP3opIJXGEwSvjLiaTdQgk\nOhnTfhBsWwvnKRHYGNgY9Pyywl4TyzB0s7hhhLY8RwBi+gzEpo3IXbXugVAYEQzhzJjFqmiAqOFB\nFEygbks91+9+k51xWGdUUGsV0eYPkO7qJdSzVZCmL1RXC5Ded5n6zgw7G6JU6V5HwwYtniMG1cNP\n6UrgbhEmFsnHQCGEoO2djfyqJUmiw8OegghJZaKkJOkJIA0DUymU4yBNfdn0jeBE2zgVirZ4hmTG\n0c3ihgn6r2AEYG/ZTG2HgyirpEokEI4NqSQqEEGhMPx+ENCaccCRiK6SdCnTQ8b0IA0DDBMHuhqy\nacdnn5xAOAHSGYXqtbVTc6GjxXOY40jFi4ds2slHKMFOgiwUjXiuvYEaw6DuYIZOW0FrG0Fl4090\n4KRjJLxBEt4AtjC62mOAa1WN3C2Yp0Uf95Us4DENAh5T1/UcJmjxHObsaozRWViCeaQBlUzSGU+x\nSwimSgcDWNi6nV2dDsqxmbhxHasums2fxs4kZXncNkK5vkKa0+f4nUfgfo06yD580OI5AhCGgZg6\nDbX+Tffv+shB5EOvQ14esrMTNXkutLdDtJ2GEkUs4ifuDaKEefb9z0caPd3KorcZ+n5jgisnFWuf\n5zBBi+cwp3sPe/u+elo9IQIZh4m7N0EsirN/H6uqP0RHm6SNYpJjr6HeX0SnN+RWSBrO5eUGEtXz\n36NPBDaed9/BmHORWxBac0Gj/zqGASerqmQagoUzRpF2QDkS35GDvFwwGSeZoi5vNB1WgD3+Eg6H\nitk8qpJ9hWPdaLpeqvePPnO5BKaUbEmb2Fs2D+aMNAOEvv1d4JysqlL3fva69iw+O0Ngx0bIZoh6\nAtRFysEwaPPnkfR4OZQ3irgv6FqbJ+yZrjklXS3n+yKBRTEZdnVKpg3apDQDRZ/i+dxzzyGEQCmV\n+7cbIQS33HLLoExQc3KOq6rU2oJ6/jnExEmorZtR7e04exrACYNjdwmjS9WhWl4fM4vGYCExX/Co\ntamtzv5xih0EylAcII/3C0Zh1Ed1L/cLnD7F86233sqJ5iuvvMI111zT63UtnuchUqK2bkHV7oBX\nVoNtI8rLqWo/yC7zIqLKjZxH0nGqmvcDUJToIOnxc9KGbZqzI3cz8lBreZkUzdCcaM21PtECemHS\np3h+73vfyz2+5ZZbeOCBBwZlQpozo2dVJVl/BI4chqIiiMchHkdZJmZTAwvTB6gTYVCKqub9mEpS\nWzqRQ/lllMZaOJTvRVqeof44w49jrHjbUbzfkmRyebhX6xPNhYf2eV7g9KyqFOv4PygsdNOLwmGI\nxaCjExyJ2dlJjd0CjgOAIwz2F4ymwx8hkE3jyyRJCtEjr1P7Pc8JSvUSUFtBLJVFKmiJZdjfEtfL\n9wuUIRHPRx55hFdeeYVsNsvf/d3fMXfuXJYtW4ZhGFRXV3P//fcjhOCZZ57h6aefxrIsvvCFL3D1\n1VcPxXTPe7qrKpktnTjbt0KbW7eTceNgcg2sfa1rW2VXt0dhsGrqR+jwh7FND4fzy8la3qM5nUoe\nDRxp/+c54OiNyBIQ9BjU1nciBDR0enhh0xG9fL8A6VM8169fn/N5JhIJ3n777VzwCGDevHn9GvCt\nt97i3Xff5amnniKRSPDzn/+cl19+maVLlzJv3jzuv/9+Vq9ezezZs1mxYgXPPvss6XSaRYsWccUV\nV+D1evv3SUcAYup08AdBtrgHfAEIhaG+ATKZ3Hl1JRV0+MO0B/MxpZ2LIZmOjdMzv1ML57kh17JY\nEfaaRAIe4hmHSaWhXmXq9PL9wqJP8fzxj3+ce1xeXs6PfvSjXq+vWLGiXwOuXbuWmpoa/umf/olY\nLMY999zD//7v/+bEeP78+axduxbDMJg7dy4ejwePx0NFRQW1tbXMnDmzX+OOCGp3QGUlFOS7z1Mp\nWPkrtw5nDzKGwabRU8laHlKWl4xpYSjIWJ6jgqmtznOLUlg4mNIglrZJZSW7G+NUl+vdRhcqJxXP\ngoKCcz5ga2srR44c4ZFHHuHAgQP84z/+Y680qFAoRDQaJRaLEYlEeh2PxU5dRLa0dOju3oM5trJt\nMhvd5m2e6dNI/2UDoYYD2CEfIjIO5/BhkmvfhmSyV3qSIwzWV1xCZyCCY5jYwsA2DJTp7S2WWjjP\nPdJtC5dyIBL0krIdYrZi8qgIl08bhWWe+Z6VobzeRzp9iudnPvMZnnvuuXM+YGFhIZWVlViWxcSJ\nE/H5fDQ2NuZej8Vi5OXlEQ6HicfjuePxeJy8vLxTvn9TU/Scz/l0KC2NDNrYvXoSSQk//BHBGdNI\nxNOo3bsgmYKWZkgkXOHsChKBu2RPev0UJdpJePw0hkoQmCjtbxtYhMAWFnHlVvKPeE0iXpOJhX4+\nNKGQddvqAc4oeDSY11xf449k+rzV9bQGzyWXXHIJr732GgANDQ2kUik++MEPsn79egDWrFnDpZde\nyqxZs3jnnXfIZDJEo1F2795NdXX1gMzpQiOXGA+oXTtRBw8i6+vdFhzhPPB4XMG0PL2Es5vCRAf+\nbIa06UUZAgsHdD/xgUcIHAVt8QxKKSaUhPhITRmrttTzzr5W3tnXygubjuDo38UFQZ+WZ3NzMw8/\n/HCfP/jFL36xXwNeffXVvP3223zqU59CSsn999/PmDFjuO+++8hms1RWVrJgwQKEENxxxx0sXrwY\nKSVLly7VwaKeSInavg2am6C9ncy77yEnT4UD+yAWd6skdXQc92NVzfvZVTqJSc37iFteOgJ5Xf01\nJLrUwUAjyDpgS4Xfa7Jwxij2NMeJprIYXW4SHTy6cBiSVKV//dd/Pe7YiQJQt956K7feeutgTGnI\ncKTKNQQ73SWbmD4DXl6FSiYgGISmJpzOTvjTHwEFWRvs7Al/1lSShdtfobZ0AnHLT0NeGVF/2M3v\nlFKXoBsEfJYg6DXZ0xzHkYrmWBqBoDisjYMLiT7Fs6SkpN/Wpeb0cKTihU1HiKZcoTvd7XrCsjCu\nvQEnGoX2NqiogPojblRdyeOi68diKompFBE7yczDW1k76bKu3ZnG0eCSDhide5RbDNmR0BrPuDfO\nhhgtsQwZW9IST3Px+EJd7/MCoV9mxuuvv36u5zEi2dUYyy3ZhFJ07j9A7esbjisrdyKMWbMxa6ZC\nYZF7QDoa6q0LAAAgAElEQVTufyf4WUcY1JZOpLZ0Io4wcruLWoIFNEZKu3SyR1EQLZwDhHtj8lqC\noNdCALF0lppReYwpDFIc8lFTHtHJ8hcIfVqev/jFL3o9b2lp4Te/+Q3PPPMM6XQ6F/TRnD2qq6CH\nTKWQBzqQB7blysqd8PyuUnNMnYaorEI99hPw+k4YHOreTRT1hwCoLa1ECEXUF6YlVERjuHTAgoOa\nYxACU8DYohCfvmICe5rdbBJDQEnYi1QKQwvnBUOf4llYWAjAm2++yVNPPcUf//hHDMPg29/+Njfe\neOOgTXA4013lvXP/AWQqRZ6wqRZJZHscsXULYvacXucr20Zu2ohc/TL4A65/MpGAGbMR7X9GmeZx\nAlpXUkHUH8otMd4vGg1AcaIDJQRCKN3UbdBw2xIvnD4Kr2Xkfv/dbpuI36OX7BcQJ7U8n376aTwe\nDx/72Mf48pe/zJ133slf/dVfDeb8hjWmIbhp1kXUdh5BHuigWiQxRa+c9hzKtnGeXIl8ax001ENJ\nKcaMmchoJ7S1IbxeVCDg7ipynOPeRCJoDebT5s9DAO2BPPYWjydteThlIUrN2dNlUDoSDrYnmTO+\nIPf7P9OAoeb8oE/xfPDBB7nmmmu4/fbbmTdvXm5Pu+bcYhqCqVfMRh7YhmyPoxQYBYVuRL0HctNG\n5NrXoLUFOjshmUCWlkJpGSSTyETC7a1umNQVjgPctKSq5v3Ullby7tjppD1eOvwRhIKoL0jc68eQ\nUvcqGmh6+JQF0NSZyr1kGkKnJV2g9Cmea9as4YUXXuB73/seTU1NLFiwgEyP4hKac0d3WTmxdYv7\nfPqM4/ydau8eSKfcUnOJBGQz0NaKUT0ZGY4g6g9hH6lnVdWHcv7NXaWTWLj9Faqb9/J+0Wg6/REE\n0BIsQKFQCBzdr2hQMQSU5fmHehqac0CfJkdhYSFLlizh2Wef5bHHHgPAtm1uuukmVq5cOWgTHCkI\ny8KYPQdj9pwTBorEpEmunxOgy+IU86+GKdNg725Mr4+6iyp7+DcF+4rGsLrqchSK4kQ7+akoSY8f\nxzARwtCR9cGih1fENGB8UfC4Uxyp2FEfZUd9VO8wAjo6OvjDH/5w2ue/9NJLRKODu1X1tNZrU6ZM\n4etf/zqvvfYa//Iv/6Ij7YOAsm3kxvdyHTGNmbMxrrgSxo2H8RUYH12I+fFbUK+uhgPv4zQ3Q9pd\nGUgEdSUTOJJfzs7ySnaXTiSUSlCY6MDjZJHCQAoDodSJHayaAcD9nj2mOM6v2Z3vq7doHmXHjh1n\nlBL5xBNPkE6nB3BGx9Pnsv3tt98G6FXDE6CgoIA777xz4Gc2gulV+ANyHTHNRX+H0WNpLzdvRHV0\nQEcnSkl362VRBfuKxpDyePHbGYoTHcR8IeYc3IqhJDMOb+N/Z93I/uKxCOmgDEsXjB9E0llFxpa5\n545U/HF7A/ua4xSHfRhCb9EEePzxx9m+fTsTJkxg9erVgGvEfeMb3+DFF1/kV7/6FVJKbr75ZiZO\nnMiOHTv42te+xqOPPjpoc+xTPJcsWUJxcTGTJk064ev9reepOTW5wh9dWyVlexti6xZ3ST97Ti5l\nyfn1E7Bti9sVMxHHtLMs3P4Kq6sux92p7kbYCxIdGF3iumrqR5jcvI/WUD4xb1DvaR9kMgp+t/kw\nM8e6NVdf2HSEvc0xjrSnaIlnmFw+cgWzJ5/73Of43e9+x0svvcR//ud/Ul5ezr/927/x2muv8fvf\n/5577rmH2bNn8/zzz3PFFVcwZcqUQe+z1qd4Pvzww7z44oscOHCAD3/4w3zsYx/rU0g1A4OSEtXU\n6BYBcRw3XendDcinn4TGemhpgVgUR0FdsBz8pVQ17+fq3W+xYdwsGvNKACjrbObW936Xy/k0UQTt\nDKaSOIZH72kfDHLV5OFQWyKXnhRNZSkJ+2iLZ0llHVpiaSaUhHS+Zxd79+7lK1/5CgCJRIKamhru\nueceHnnkEX74wx/ykY98ZMjm1qd4XnfddVx33XUkk0leffVVfvjDH9LQ0MC1117LwoULGTdu3GDO\nc0Qhps9AdKcmpVPgDyA3b3SX6S//Hvbvy+VyOhisqr6KqMeHRLB24jxGdxwh6fWRtrwooCVUwCtV\nH2Rc+2EAWoP52KZF0E4RN1z/J9J2i4PoNfzAohT+Y7JWDCGoLg/THEsz5aII100tH/H5nkIIpJRU\nVlbyox/9iMLCQl544QUqKyv5zW9+w1133UVRURE333wzt912G0IInBPssBtITllVKRAIsHDhQhYu\nXMju3bv5+te/zkMPPcT27dsHY34jEmFZMGUqzvq3qMu7CDFuPJV79mC2NkO082iQx3GoK7iIqNcP\nwmBP0XhSHi+7SidwuGAUXjtDyvLRFizg1eor+OD+dwmlEjQHC5FA2vSCEm7+jBK69cYgUeQjZ1n2\n3GE0sSSshbOL8ePHs2HDBm6++Wa+9KUvYds2JSUlfP/732fq1Kl8/vOfJxQKceWVVxIMBpkzZw5f\n/vKX+fWvfz1ocxTqFBubDx48yEsvvcTLL79MNptlwYIFLFiwgPHjxw/WHM+I4VBJXtk26Yd+wItN\ngqjlBymJWLDw/fWYbW3Q1uruJLJtaksnsmHcTFqDBRzJLwfAQXCgcDRCStJeP0IpSuKtlEebueW9\nVayvuJiNY6bSGCnBtjx0bxvUVucgICS3zLqIz86vAvpXkrAbXUl+aOnT8nz00Ud56aWXUEqxYMEC\n/uM//uO8FcwLne5CH0o6oNyE+F1WAVGRwOjsQAqDfYE8VgcmcG1LC6ZztHpSd3FjN/Ed/HaG/EQ7\nbcF8kqYXQzp4pIPfzpC2vGwcM5WgnUKgyMV8tbU5OAhBns9kdGEod0jvMLpwOen2zPLycsaPH89r\nr73WK7dTCMGvfvWrQZnghcrpWhS5tKTWFtS2re7SuagYDrRA+WSkgrqC0aT8QQgEyZSWsXDN/2Am\nk0Dv4sZvTrgEv+360ya0HCRrGOwrrsArbfzZFN5shtJoC3XlkxBKInu2GdYMLEphCcnYwjyunVo+\n1LPRnAP6FM///u//Bsj1bu+J3ud+cs6kyHF3WhItzW5wCKCjnar63ewKlLIvXEbK9OB3bIqzcaJK\nUFc2kZrOd3PvYSrJtMY91DTto66kAoBPvfc76krG88aES0l6AwgU41sPc83uNzlYOJqYN6SFc1CR\n5GVSfPWGS/Fa+nsfDvQpnpdddtlgzmNY0bPIMRxNeq4uC5/aGlUKjhzGFIqFO9eweuwcMATF6TiG\nZSJNw/V3ngBTSWqa9uaeHyuoVc37yRgWzaF8OvxB7eIcTJQiJGyeeepP3PnpG7C8nqGekeYsGZIe\nRiMRKRUvvHeQzgNuulDtuNHcNGcsxvQZGFs2I6WEhga3YlIkAokEps/g2uYdZIpKiRaWIoUgItNU\nxRq6mradegtft6A6wmB72ST+67LbOJI/CmV5teU5yGQ9PqKZGDvf3MS0+ZcM9XQ0Z0mf4nnTTTeR\nTCZPWGVcCJHbMqU5nhMVuZW2TcdfNiLSKVCK9j27qY1WM/XKi3MVldSHrsKprYW1r0E4Ag0NmOkU\nC2UDdYZb/LhKtmEqedLxHWFQV1KBIwSiy7ysLZnEmxPmcCi/HNvj1UGiwUaYxIAWvEhdP3VY0Kd4\nLl++nH/4h3/gwQcfZNSoUYM5pwueExW5rX19AyqdwgBUcxNkszh/Pog8uAPjtkUYXdsu1ZYtyGwW\nkglobgTTwmw4TI0BzLoY/u8VSMTdBPkukQR3SW4qScaweOLSvybqDXKwYBRCCMKpKAcLR5O2fNja\n4hwahMBB0YIfWTFxqGczLDh48CCf+MQnmD59eu7YBz/4Qf75n//5uHOXLVvGjTfeyFVXXXXOxu9T\nPGtqali6dCm/+tWv+NGPfnTOBhwpHJuCUp1nUItNZyINdpaInaZKRpHt/lzLDbV1C6qzAzFzFmrH\nNrAsCARcsUulYctmaGtzhfeY3kS7Sidxw44/s2LeX7PlohpagwU4wkQJEHnlpC0PBkpvwxwqhADh\nJa80yJFYlllDPZ8hQClFdts2ZEcnnprJmMXFZ/2e1dXVp1VnQwhxzgPdJ/V53nLLLVx77bXndMCR\nQq5JG+52S2vGTG7csoXa1kZoP0KVimFVzDjhzwrDQAkBpgmBoPuHd/gQeL1uERCO700U9Yd4peqD\n7CybRHswn4zHh22YCMdBCgNlGEgh3D3WQqGjRYOP5RE4CMYWHl/PcySQ+t2LZLZtQ5gm6XXrCC36\nW6zRo8/pGI7j8M1vfpP6+nqampq45ppr+PKXvwy44r13716++tWv4vF4kFKyfPlyRo0axfLly/nL\nX/6ClJJPf/rTLFiw4JRj9SmeK1as4BOf+AT5+fnn7pONEPoqKef520VM27wR+cc/oLxjUY0NiHAY\naqYArsgaWzaTbWtjV9EEKMlS1bgb08m61mcsBvF4n+M2houxpIMlJUJKpOGB7rQY2eUnNboFdAC/\nAM0JCXgtasojIzIpXqXTZDZvRni9gHv5Zd5+B+vmT5zV+9bV1bFkyZLc87vuuos5c+Zw6623kk6n\n+fCHP5wTT4A33niDOXPm8JWvfIV33nmHaDRKbW0thw4d4te//jXpdJrbbruNK6+8kkjk5L+nPsVz\n27Zt/OQnP+Hyyy/nk5/8JFdeeeVZfchjaWlp4a//+q/55S9/iWEYLFu2DMMwqK6u5v7770cIwTPP\nPMPTTz+NZVl84Qtf4Oqrrz6ncxgo+iopJ6bPQBgm4iPXoP7wMrS0oByJ/J+nEYtuR1gW6ta/5aU/\nvEfH6Cwq42dXoJiFe9dhmiZ0tOfG6N5Z1L1sj6TiVLTsY0/JBEqizcSKxgESlIFAYTlZlFTYXh/u\ndkytnoOHwmcIQl6LOy6vGLl71wfgmquqquq1bI/FYvz2t7/lrbfeIhwO92odJITg1ltv5dFHH+Vz\nn/sckUiEu+66i507d7J169acCDuOw6FDh5gyZcpJx+7TAfbAAw/wpz/9iY985CP813/9F9dddx0/\n/vGPOXTo0Nl+XrLZLN/85jcJBAIopXjggQdYunQpK1euRCnF6tWraWpqYsWKFTz11FM8/vjjLF++\n/ILuoaSkg3z6SZzX1yCffw7efAPiMTh8ELn2NeSmjQDUtaaIFpdhGCaGx0M0nE9dxXRIp7tSk9wL\nsHtn0dwDm5l7YDMLt7/CtMa9XHxwK+F0nPxkB14ni99O43GyOIaJIcBwbC2cg40wsEyDvKDJK9uP\nkH3v3VyHgJGC8PnwXnyx685SCkwT3+UfPOfjPPvss+Tl5fGDH/yAz3zmM6R65EQrpfjjH//IpZde\nyi9/+Us++tGP8thjj1FZWclll13GihUr+MUvfsFHP/rR06oad1KfZyAQ4OMf/zgf//jHaWpq4oUX\nXuDuu+8mFArx+OOP9/sDfv/732fRokU88sgjgGvlzps3D4D58+ezdu1aDMNg7ty5eDwePB4PFRUV\n1NbWMnPmzH6PO1h0L79zy/aCQlDkrFHV0Q6ppNvILRyGdMpt8DbXzf1TUsLe3W6TNwVEo67geX3u\nMds+YaQd4Mbtf8JyUuwvGoslFRnLg22YGHYWy8niaN0cXLq+72RWsrM+TvxQE0nVxI004uly55yo\nZ1VPjvWfn+r885XAR2/AqqpEtrVhTZ6MmZd31u95bBDoiiuu4O6772br1q2MHj2aGTNm0NDQkDt3\n5syZ3Hvvvfz0pz9FSsnXvvY1pk6dyvr167n99ttJJBJcf/31hEKhEw3Xi9P+LaTTadLpNJlMhnC4\n/4Van332WYqKivjQhz7EI488glKqVy5pKBQiGo0Si8V6+RxCoRCxWOyU7z+UlV56jq2++HkyGzcB\n4J09i8zGTaRCPoRhYI8qI7VvD6ZpICwT/EHy5s7AXxqhsCjE+9v30Ozz4QD5pqQ62wrFhQipUM3N\nOPEEq6ZcfVyXTFNJV0SlQBoGppPparOhENLBVA6OEdL+zsFEAUJiAkgDWykOmWEO+bJMzSbwH96L\n75K5ff+4bRP75X8j27puxPt2Ev703+cE9EKrbOSprDxn7zV27FieeuqpXseqqqp4/vnnjzu3Z5X5\nE5WtW7Zs2RmPf1LxbG1t5cUXX+SFF16gra2NW265hZ/85Cdnlff57LPPIoTgjTfeYMeOHSxbtoy2\nrgsDXJ9FXl4e4XCYeI/gSDweJ+807lTnVUm68dXuv21J1OiJSE8Q2d6GihTCpGrsSB4YAqOqms5x\n1US7fv7q/Cy1u96BTIqqgzswMCC/ELV/P46C1ZUfZF/RGIoTHRgoov4QdSUVR3cSja5BKEXG40Ma\nhrvcNwRRb8SN4Gv1HDyUQgjl+sekpAML25GkklnipEm1xTFOcs3Kje/hHKzP+c9VvJ7Eq+swZs/R\nJemGmD7F87Of/SwbN27k+uuv5+677+bSSy89J3lSTzzxRO7xkiVL+Pa3v833v/991q9fzwc+8AHW\nrFnD5ZdfzqxZs3jooYfIZDKk02l2795NdXX1WY8/VBzbm51/+Eeo3eG+dsxSzERQk2iEA++7yfKZ\nDCQTOI7DqvEfYF/RGI7kl9MWLKSqeR89e9vWlVRQGm/lPY+fjOV1U54c6RY7tgz3XIX2ew4WAhQS\niUBiogAvNpUqhlFYiJh+4nQ1zflPn+J58cUX4/f72bx5Mx6Ph5qamtOy/M4UIQTLli3jvvvuI5vN\nUllZyYIFCxBCcMcdd7B48WKklCxduhRvV5rDhYqwLMTsOUcP9Hzc60RcCzERd9ttZG1ob6MufwxR\nf4jiRAdtoUJSHi8twXwmtB6iqnk/AFIYbLmopiudU6KECSiUaSGUxJAKx9RFKQYPARiYSAxLgOXH\nLBiNWTURY8bMU/ovT+Q/14J7ftBnJfk777yTGTNmcMkll7Bq1SqEEIPena4/nFfL9n6gbBt75QrU\nUyvdMnW2DVIBKlc13sDtzd4SzGdyw26urVuXCxhtLq/k/111J+1B90ZnmxYohdexMQBHSjJen+5X\nNBj0qM7vEVAQ8mIImDE6jwUzLzrtfM++AkZ62T609Hnba2xsZOnSpYAbwbr55psHbVIjGblpI/a6\nN6grrwIZYGLTfvaWumkTE1sO9MjtVExoPZQTTkcY7CydyEtTrsZnpxAqjCMMlOMuGEOpGAE7xZFI\nGQgtnIONAkI+i6DXPGP313ErFs15QZ/i6fF4ej2+0JfM5zPKtt3OmHv2kH1/P6silUT9EmUU8tvp\nNzC2/TAGKrd/fW+xK6bdKUrd+9z3FY2hLVyEND1EUjFaQ0VgmCgU0UAEFXOQpqX9nYPBMV+xVNAc\nTVFZGqaiWLcWHg70KZ6n6AunOUco28Z58gnkG2shlaTOU0jULMawszQH8+kMhGnP5FOSaCfqD7G3\neFyvgseOMFhddTn7isaghEHK48fjZMl2FaMznSwmCikE7aECXVFpsOj+8+kSUaOrx17IZ/GxmaNG\n7i6jc8i///u/s2XLFpqbm0mlUowdO5bi4mJ++MMfDsr4fYpnXV0d11xzTe55Y2Nj7rmu53nuUFu3\nIHfudP2bQkAgBD7ntAod97Q4D+ePojlURMbyAArDcbCkjaEUtmFiGyYoR1udQ0jYbxHwGOxpjo/M\n/e1Ksaurzm1lWZiCkO+s3u/ee+8F4LnnnmPv3r05N+Ng0ad4/v73vx/MeYxYZCYDWzZBRwd4PVQ1\nNLKrKp+oN0hhooOYN0RBogOJu3+9O6oORysrFSY62Tx6Ku3BPAzpYEqH0s5mbMMk7g93VZ3v+kdK\nXZZuUHFvgraSOErRHM8g5chc1b2yrZ5d9VEMw+DdfW18fO4YyvMD5+S9u1fKy5Yto729nfb2dj73\nuc/x4osv8uCDDwJw5ZVXsnbtWo4cOcI3v/lNUqkUfr+f73znO/3KXe9TPMeOHdvPj6E5XZRtI//0\nR3ffeiYNmQymz8fC3euou6gKop186r3fHefjPJb2YB6mdK1VU0r82RRN+RfhmIarmFIhlIMQhq5h\nPth0feEmDkUqiyECI/J3kLElOw5H8XRV+VLApvfbuX7muRHPboQQXH755fz93/8969evP+41cJf7\nS5YsYf78+axbt44f/OAH/OAHPzjjsS7MTbLDBLV1i1scpKzMtQYzaQgEMUNhajKtEGuBUICalv1H\nS8r1oLuyUnOokJTlBvQcwyDqCyKEgSVda0caAqUslCFws7al9n0OJkoSzqYIebxMOkkb6uHOsR6j\ngbqJTJzoVuo/Nm7T/Xznzp088sgjPPbYY0Dv4PiZoMVzgDjtYg7FJbBpI2Sz7n8ijbjmOmhrRTU3\nw+GDbhV54bgJ8z0wleSGHX9m5ZyPoxB4HBvHNEGCz86gDNPdYSQMN1ohZVfkwuh6rAV0UFCSsJ0h\nYkAqK5FS4Ug1okTUaxlMH1PAtsPtGEJgGYJLJhSdk/fuqzW63++nqakJgEOHDtHR0QFAZWUld955\nJxdffDE7d+5k48aN/RpXi+cA0Fcx5GMFVEyfgXh5FaqgwK0Sn3WLHqu/vAN5EXdbZWenWz3+BJan\nIwxenvJhDheNxlIOpp1GZCWmk8VQis6A6wN1RJfDs+etXwvnuaP7ez1RkE8pwCBjeQh6DPwekw3v\nt7GrMcZNsy4aUQI6f2oZFaVBOhJZJpaFifjPzU63Y1tsdD+eMWMGkUiEv/mbv6GysjLnirznnnv4\n1re+RSaTIZVK8Y1vfKN/4/a1w+hC5XzYYSQ3vofz+pqjxRykxPzQfIwTJDo7G/6C89vfuE/q62HH\nNpysTV3BGEBQVV/XZ7fM2tKJvDNuFvuKxrGnZDwJbwCPtAmmE0xs3EdruJAjeWUkPT6k6ektmHp/\n+7mhxy4ioLeAdj0WymGMSlIzbSJlXQESqRSXTig6q6i73mE0tGjL8xygbJv0XzYg2+In3Xd8oqW8\nMWs2bN+GU7sd2lpxfAFWVc4lKty78q7Ccblyc8fiCEFdyQTSHh9Jj4+MMAGJ6TjEfWGS3gA+aSMc\nE8fOkPIE3GW8YeD6PpUW0HPCMUmd3XR9t0qY+IrLcjdTzfBAi+dZ0r1ET2UTOPE0xpbNiE/eenwx\n5JopR5fyUsLLqzCuvQExbTpMmwbNTVA+irrIRURNP0aXf7NnuTmgVxFkJQyEgJTHba1hGALLlqQ8\nfqLBMOWxFlpDhch0gqwwaPD4XWNIKbcTh3RQukjI2dFz3dYVj+t1XLgH/T6TiuIQsXQWgIjfo3cZ\nXeBo8TxLcv2KIgGEYSDb2zBrd/QqPyemzzh6HqC2b0MlEzgdHYgnfgkTK11BbWgAMw9ChScc69h2\nw0nLT2XzfraMqnYtU8fGUg6OaSCFwcTWg0jDpC2QR8rrx2dn8ChJzONzy6OZ+td/7ulSzx4dSv0m\n1JTn8fHZF7Gr0S3oXT2Co+7DBf3XM0AcW8yh2xBRTY2oVNK1SDo7UNEohBpg8yZIJKhS7ezyFhH1\n+EFBJBXLJcbXlk5gX9EYBFCU6MBnp0l5/DiGhQJM6WBKSWG8jerGvbQG8iiIt+NPJ7DzR2GHLGzD\nwlQKaXbPSv8Bn3tUj38Vlmlx66XjMA0xIncWDVe0eJ4l3fUWVTaBkrLPeovd5zmNDe6yORDAKSii\nzglAa4qqdBrTEJiWh4V71lFXOAZs202MF+Bg8OaESziSX07S4+eAnWXG4R2MaT1E3ONDKMhYFgKo\nbtxDcbyddypmIYUgbfiQwiBjeMh4vJiOg1AKhdLaOaA4FJNlesRg7e5mrptarq3NYYQWz7Oku0K8\n//BeUl0Bo2NTknKBomnTMGpqUK/8CdvvZxXlRD2d4DjssgMsPLwBMxDAzKSpybZDexvYWVCKutKJ\n+Jwsnf4IacvL/9/enUfHVd0JHv/et1SVqkqlXbLlDdtabGPLjmwTbLMlxMFmSSYJnpjG0IGZ0wSa\nDIEAoaFjJ3TAENKdTnfcJ+5J+jABJiQEQg856SRNQkOwgZiA902yjVfZ2qVaVFWv3r3zx5NkyZaN\nka2SLN/POT6gklT3lZaf7rv3d38/5YedpVNZfngrbbmFlCQ6aA3moYDy9iP8ZuZi4v4gMX8IiSCU\njKEQiIyDkBJLgmP6unNA9S/0RyI4OcO775dQgZAZLjajJPHRmIIdDZ0kHcn1NWMxpDsqGrpd6PR3\n7RwQloV/bu2AvWj65ny6CvaGy+C//0/cDz4g3u5i1ZagGo8RdRzqrelUxxu9rpqBAEgXupN8Adpy\nIkSSUZJ2AAWUxpoxlCQ3GScaCFGYaCc3Gaclt5j2nAgZ00YKEwSkLR+uaaGUQpomrjAwXBfpZrzC\nyHon+CMQ3Wua9Amix3eKTCdFmROl0xDE/BY5GSjIsYgmHXYfaafq9V99aA6wNvLp79gQ69koksLg\nPyimM2ZibNxDsqAUf6lAGAaifBxq6fUYB7Zg+B3U+PGoZ/4PNHdXWlKKiub9rJs8D4AcJ4nfSVOU\naMdUiqU7XuvXhvjZj30GlOr+vfZ6FklhIBFYSuLLZEj4c5BCIYTw0pf0+udH0GfaedKXTJHnU5jC\nRyARxTAtAkmHtq0dFM2ahtq7t7cFNXS3o962VRc7Pg/p6UaW1KsgUWVjAIYQ+G3DO6qnFK7rEulo\npWrGJMzPfg5r7nzE+IleHyPTBLyjmCve/SVTm/YzpuMYU5o/IK97M8lUkuqmfVQ37cNUktrDW8jr\nipKTTmC4LqbrkJuKE8ikyU3FECgs6WKrTJ9ffh04z9zpvlaCVvwcM3JwTYs8lfZOxjppIm3NVEb0\nr9xooWeeQ6x3Q6ktjUJhBAJQUooh4LKKYnDSyBeeZ2r0KBQXI7dtxfjiTXDVJ+CtdV7hEARIF5+A\nv9zwi36zzIGS52c07mPxzj+yrbwKF0HASTHz6C4O5JWzo7wa2/VyDV0hcIVEdQdo7aM4fQCVQNQK\nEPErphBnAW3MGDcRa+YsZPfdCOiGbuczHTyHWM+GUvXWLew5nKGzoBiEIDdgU1Wcg/zeP8O7GyCV\nQqosAekAACAASURBVOVGcC/5OGzZBK+/5m0W5UYg2umtf/r8mF2JfpXkB2IqyQ07fs/0pnrA6330\nu2lXEsl0UdHyASnTh6sERwrKiPpz6QhGuvsaaR/qQzfXvPdnTB+YEkGCEtJMz/dhdXfLPDEHWK93\nnp/0dy0LhGVhz/kY19eofknSbHrPq6jU0OCViWttBRSyvByViHul6hIJQHn/tW2QfkgmP3TMnlt5\n8M7ARwMhTBSl8TYkUBhrJcdNsm1MNaZ0cXuObGonG2Q2gp2XS0UkRIIS9sytYnp3kNQN3UYHHTyz\n6MQk6czevZBOH/8A6XYHSxDFJdDU5G3muN3l6Gzb+2/aAcvyPj6TOePxJaI3nanm8HYO55djoJCG\nccI5d7151GuQgdMEwn4f5pgCpFJ6djkK6e/oMBKTp4DfD8Ggd4tumjB9BsbUSkg7yOnemXeRE0K5\nGdj4nteGUSkIh71b+kMHoavrtONUNO9nd+kU3h8/k5Tlw+ek2VN0EbUHNvPHqZegRJ9CIUoB0ruN\nVy4YF/qPyOn/kJyY8mkCQZ+BMARj8v1IpfQ59lHqQv/NGFZGzWzk4mtQ//lbrxVHQSGEI8g9dYjq\nagzTRBimdzY+k8H95++h2tthyxavxmcw6H2OaoWkF0D7Fg7p2VAylWRq0z72Fxw/2hkPBNlUPg1D\nSgJOCse0kYAv1UU4FacjlE/GPrsGXaPCALHTAGSfd4MXNH0W+C2T3BybcfkBpo3JY3JxiGljcvXJ\nolEo68HTcRwefvhhjhw5Qjqd5s4772Tq1Kk89NBDGIZBZWUlq1atQgjBz3/+c372s59hWRZ33nkn\nV111VbYvd0gJy8K6+VZkzWxkfR2qvh4+2Ieq24kK5GAsXIRYtrz3NIr5lXth107kf/sC6oO9qEMH\n4U/vQFsrcHLhkLqSKb3l7EylKEq09+amScBAURZrojG3GF8mTdyXg4XE9fmRQpesG0iOCWlJb9Q0\n6DnBDo4LrnQJ+gzaE2ne2dtCxlX6PPsolfXg+corr1BYWMhTTz1FR0cHn/3sZ5k+fTr33Xcf8+fP\nZ9WqVfz+979n9uzZPPPMM7z00kukUiluuukmFi5ciM/ny/Yln7XTteQQloX5sbkIwySzZTM4ae+0\nTyqJ3L0L9c/fg6AXDMXmjYiLZyFME1FRiXvkSHf1eQGmSX3hRKKBUG+A7FvOrqffUU9gzU3GuXrX\nH/mn/HIE0JoTASUpiTZxIH888sT0pZ4ivxdoMDWAkN/AyUiUAtvw1rBdV+EqsEzIuOAqaO9ykBgU\nCoP9LXHqGmM6gI5CWQ+eS5Ys4ZprrgFASollWWzfvp358+cDcMUVV7Bu3ToMw6C2thbbtrFtm0mT\nJrFr1y5mzZqV7Us+K2fakmNA7W0ow8AI54KUyPXrYNdO78hmPAZ5+RCLeuuffTeeBmAqedJJJFNJ\n7v2v/80fpl7K25Pn0mUHOFA0sfvMO5x0YLtf7cruRnKu663Vip5ilmp0Vanvfh1hv0E4YBFPZZBI\nQHh/T7pPavpMA4Ei7Sqkgowr6Uq7p31q7fyW9eMOwWCQUChELBbjnnvu4atf/SqyT3+eUChENBol\nFouRm5vb7/FYLJbtyz1rvXU8DaO33mfPLLQvcfFMjMpq8Pm92p7+AIyf4O2645WyI9kF7e3eJlFz\nEzQ1eh8fDIPfT0XzfnKTcSQM2Of9xJNIAD6ZYXL7YSpb9tMRjOD2FArp+aekdz1KgvROKwnVfd+q\n1PGq9D1dOXtv9c/j7i6Ck3o+OVLSkXBQSuG4kHYVGamwLQPbBMf1via2CTm2iVIKyxRMKgrpzaJR\nalg2jBoaGrj77ru5+eabuf7663nqqad63xeLxYhEIoTDYeLxeO/j8XicSCTyoc89nH1VBho7VRAi\nGfL362cUKAjhH+Bj1f/6MqnLPk6mrg6rsgJfTQ3xZ59DtrWR8VnISBhlWmSURJgmhm2h8koxy8fi\nHsiDPfUs3fU69YWn7/N+IikM9hRP8up8Gt236302SgQSQ3r/cpwuYv4Qqued/Vp6GH0CaJ8nOZ/W\nTk+acXtSDgT9Brk5Fk46hasgErSxLIO0k8EQBq6CySVByvKCpF3JZ2vH47MMmtKS6eURLPPcz1Uu\n9D5CwynrwbO5uZnbb7+dVatWcemllwIwffp0/vSnP3HJJZfwxhtvsGDBAmpqavje975HOp0mlUqx\nZ88eKisrP/T5R0IDuL5U+WSkHex3HC9VPhlxquucPM37BxB1UNd9HrVtK2q2i9q6Fblrh9dhE5A+\nPxgWbjyFZduQm4fZGaW6+YOBOzmegkKhFPgyKQwlcQGhJEqYgMJ2M1hSYrkOka4orhDEfaE+s84+\nejaaem5qzqce8acJ8N4rVSTbo1iAH4UVTyFzwxSHA0woDKKUorIsF9sUjM3LYdPelt62G+/stM95\nt0zdAG54ZT14/vCHPyQajbJmzRrWrFkDwCOPPMJjjz2G4zhMnTqVJUuWIITg1ltv5S/+4i+QUnLf\nffedl5tFZ3scr+9pFDVrNmLzJtzcXIjGvB7soTAEApj7kmSctLf+aFleK+NYzCttp+hNZTqRKwwO\n5Y2lINGGpVwkgrZgHj7XQQoD1zAJpbtwDZOCeBtCKZLpACBI+nJwe/rCo7wc1J4AJGX3re+5Dpyq\n/x+Gs3r+vnlIA+dzGnibQzk+k3TK9f7QIPAjGUMXKSOHWRPyMQ1BRiqOtHcR9JnsaOikJZamekwE\nQ0A06eiNo1FGtx4+R7I5C+i7e6+ki1y/jlDIT2zDe14ivc8HCC8XdOJF3qbSkcMnbSr1pDZ1BMLU\nF1/Uu1apFBQm2slJp0AoGnOLifuD5DhppjTvZ3fJJPYUX0TS8vcWFTEzaVDgmmb3tpHorgh1QkDq\nvaUfYAZ2Rrf36ngPe2F466+yuyq+aZ3Z8kBv5gC967YGEoWJwptRFIQsfJaJaYDPMinN9WN3thFr\nbiWMxAAqiWLNmElLMB+ArrTEbwssw6ApmuJIexfjCoIUh33npNXwifTMc3jpJPnzUL/ZaCYD27eD\nk4Bp072A5XqbOxw75uXQWJZX8Lj3ltpTXzyJaDAXS0qqmj+gJZhHZeNeJrQfweyuIQrw+4oF7C6b\nSlGiAwNFYaKd1kQ7nYFcHMuHUJJIMoovkybWfTsf9QW98/LK25lGABkHo3t8adnH10iFQHhtPVHC\n6v6cbj2Xa/SZ0dK9vioAJbClg51JEw+EzqDASffMVUqEAT6hELhkMMl4V4rfFhSGfRSHA8wen8ek\nohAKeHefH6ujA+W45OMwNWyx8HOX8PauZgBcqXj/gLc8UxT20RJPecWn9SmjUUkHz/Ncz7KA/2A9\nife2wKULEaaJ2rcXeeggtDTD9m1eULV9kE71fwLLBuliSElRop2L2g6fVLXp6vq3SNsBooEQEnAC\nQXIzSXxdsrfNhz+TpiTWym1bf87R3BK2llWyddwMUj4/rmFiuxkK460kfTkoBV1uAIFCCkhbAYSQ\n3VlOLqAwpcRF9Oaw9s5UDRPcDKbM9M5wTeUSSifo8gW85P5T3crLTPf7JBYK2/aRa4IlIGPbdKYc\npATLNEhnFDm2yeSSMNPG5JLOSF5+/zDthWPp6kwQtgRfXFaL7fMxbUwurlTsOholkXYJ2AaGEHxs\nYgHVZbkYhtDdMkchHTxHCWfzZtTRo3C0AZFfgJg+A3HkMDQ3Hy8uIrtzMl0XhKCi7RB1ySqigVyv\nTF4i2i+1CQDLwsxkvBzRsimQl0/NrIt4MTAO0dxEQkq6fDmEU5AxLA4UTuC6HX9gYscRYsEIh/LH\nkrL9hJwuKlv2czhvLMLN0J4ToSOU33sHL4WJIb1rFEphOWmkz9997r5b76knA8PNIIS3yZWfjDG1\n7QC7BDQFC3BN0wuyfdcze5IylcIA/Bb4DRhflkdLIk0m7WKbJinp5WbapkFB6Pga+97mOOMLcmiN\nOxjBINI2ePbdw3x9fCGuVPxqcwPRpEPANkk5koUVhfpY5iing+cooLZtRbb1b+1gCCDZhepKeEn0\npWXQ3uqVs/P5wDAwbZulDZuoD5VBMknF0fqTU5ukBJ8P07KoDgGV45BWkjk5abbk+HG7kuR2xpjQ\ndoji7jPz9cWTEAgMAWXxFlqD+WCaCNNi4b53aQgW887UWjKGhTRNhHQBAYbAL71NK9kdAIUQ3vpp\n91qmIQRSeOlTgXSK4lgrcxrrscrHUpCT4g0yRA0blIsjDO+233URBpiuxMYlhKLMdKguC2CPCeM2\ndNDS0YVQYBgCyxTMGpdHXk7/W+32hINtCnyWl8eZSGfYcaST9vYE0aSDIbzXbPgMrxOqDpyjmg6e\nI9TpjnSeCWGYGFd/Gjca9VKKaufB0QY4etQ7C59OQ3k5ZlMT1Ud2HZ+Znsi08AKb4VVvOnQAEnGM\nL36G/Pd3QkcHGdeluKvTK28nBFgWhoIp7Yc5kF9OsPMYYTJUux2MJ8HBoiLyhUvGhLRyvR17AAyk\nCUJKlGl191Y6PnsUKCzDIIhLaVcrY1sOEUomMHJDyDHlFNbO4bNNR/jVvjSCDAlMkhL8mSShTBp/\nJkW+KbkxtxM7FGFj2QyEUhQeO0yraxEkw1gDRPlkxuYH+rUKriwN80ef10APvBNFSin2HIuSb58n\nqVjaOaWD5wj0UY90iotnYnywGxU/6n18d2sHAbBje+/ziGkzUFXVqPo62LsXjh4Du3szyba9WWb3\nLT1CwJixXoqT43hHQYNBiESom3Qxse27KRlbRKGpqDvSSkvZeIo6m8ntilLhz+CaBr8smkhzpMir\ngJ/s4KriKPuSOWAIgm6KcDpBhz+PnoqkCsigsEwIZJJIJXGFgakkllDk4OKzApTn+KhobgNLMcfs\nwOcapBt3s+dYGVFXMNbOcDgtKMh0kXAFealOxjsd5AZ83DLZT870+cjpF7N3exOd+w9CJk0hUEUC\nkVGQ7GBS0bh+M0fTEPzlwkk8vf4DYimH1rj3r6G9iyapCPm9o5uA3hy6QOjgOQL1PdIJH95hUVgW\n4S/9JYn/est7u89MtW+OqZIuav06jPJx3gxxx3YvIFZVealMCvDZYNm4iS7qQ2Mg11sbNYNBCIW8\nXM5t21FT/Kh4DNPvZ/qEQvIaYkzy+6mItmGOrWG3r5CUGfZegxCk/AH2fNBO1eFd7DJyaTGDFBgG\nMpyHhVeRSEkXB1CuJOCm8RkuCWXgV5LL3WO4wRCqYCxJCS3BfCYmOqnqaiJgGWyVhUTr92JYNoVd\nLu0qhxKRYVL7IdpyIlSXBLmaFnzTr8CYPQcDuL5mLLs6G3AONlNPiDheWb58yxgw+Pksg/9x2WRe\n3XGMnQ1RisJ+LNMg1pWkdmIBRp9Zqr5lH/108BwlhGVhDBBc+6Y1yU0bjz9uGKhwGCZM9M7J5+V5\ngdHvxy0p5T9CU4gWlUEqRd3U2Sxt2Y55tAGiUSqSKeqKJxIVQDzGmLk1fLK2HIC6jgxyy2YOEiAd\n9xF00+CkSAs4qCyqBFQ67fjb9qD8YxATxvOHqJ+29gQZ6SJQBN0kJfEWYqF88t0M4+MtyKCPWyJx\nfjt5PFsPd6DChaj2Bi/lKJDjVZ4yLWhuwhA+gn6TPJJYxSUU2TaT6MQu6N9szTQE0xfORh7cTnVb\nI/WEMMJhqhZ7X6+dR70cyr7B0DS88+pN0RR946NxQpcAbfTTwXME6um4ea47LPZ73qJiRGcHqqQU\nSkph0mTvtj2RoN4uIFpWjVlWBkAMxZ74ZKp2boCOdszOTpZu+U/qS6dAIMDsUCup2+/k169tpSPt\ngq+UaCqNY/nIuJIcP/iUZKxK8R8TLyEaiqBsH8lgIQtmVFC9v4OtsQQ4DvlkmOqLI51OOqRFQcCg\nXfpJFJTy2oxpbDzUgeMqVOkYNueGqZ45mUULplHdFqPut+8TzTgUkCFm+Mn3u6gpleTbJtXjLIzu\nBmz9vibdqV6+bVuZ0f01kobZu3sOsOtotN/RysrSMLuORokmHZ3DeQHTJ4zOkXN92uOjbhid6fh9\nn5fqaV6Ju+4xwFsy2Nnh8merELP79FBGKkraG5mwZzMVxDHeWQeNjZBfgCgqJlg7m/fbYUOgjHZs\npGnRkpQklMBB4HPSLAwmqdq7mY1mIVgW9YUTSZWMZey4QiYWh2nqTNK1q458J+4F0KDgvbwJ7DnY\nQsq0UUJgGwKnpBSr+7qkVHxyeikrrqrk2MEmUisfob45ATk5TFZx9k2fhzm7lurLaj/SbfTOo1He\n/aAVo3sja6DTQa70mvnl5wcp6d5dzzZ9wmh46ZnnCDVUHRZPet4TxhCz51AtFXWbG+hMpJCNjRxK\nKNSEYpr8ZdSlYiyZNBVTKigshMIiMkePkulQ1I8ZR8YfIN7lkjACXGx3YaBQsXYqk01eqRDTpNUO\nkVJArBOxr4NEeBqXV5ViVBSh9u6lMmIgZszk7VfeI2n5vLxMJBGZpiGewMz1Znl+22B8QdD7g/Di\nC5jjxlF95B1IgXvJxzFzI4gpU8751xCON/Mb7gCmDR8dPLWTmIbguhkl7P6/L7EvlkHix0w0I2bM\nINbcxN4ZF1H5/56FjnZUNIrT1oIYNx2RTKIyEuwAAq+oRonI4BaEMWWaCreDupwwLSnDK82XTlIg\nMtDUiDmlmGnlBTB+bu91LCwxiTckEQgKcVAocsMGybwAAJOKvP5A6U2bvdxWnw8WLMJpbOQ3+dOI\nVU5HHOpkd3PXR6po1Pe2HPTuuTYwHTy1ARk7tlEZP4YSYVpUAJVMIlqaEWVjEOlmmFrhHf1sbQHL\nxJCKikQjbVYQlZ9Piy8CUuEqRSTHpjKSh9EKS7v2s7s1xVv5U/CbISBE5BS729MunU397sN0JNMo\nIC9gs/z6Wva1e0dMB9zVNgz2lEwmll/Uu+zwUSsamYbg+pqx1DXGTj2OdsHTwVM7rQqRYDdBOpUF\nShEJ2FQGDDAMRNkYFGAku6goDVGXk4MwLMRF45k8sZzKeCOGUkzdvw1xrB2OHcMEpitFVctG9pTN\nxwgHqFo8Z8DgZPlsrr/1Wna/vRmAqktrsHw208b0L03om12D8ce3j2+whcPeJthZMPXuufYhdPDU\nBtSzM097G0tVI3vDZYi5VVSV52PIUmR3LipFxZjJOGZJCdeKLvaGI4j51VSV52MaE5GbNuJu7URY\nFmpWDerYUcSsGnwVlVQLg/rii6hvTVJZap0ygM64Yu4AV9jnWk+omVo1/WLqtjfp225tSOngqQ2o\nb0Aygel9d/yN/sGq8LL5NL25AU78uBOf0zCgbAxm1TTUrNn8enMD0UOdwMnpQIO53p6NsJ4EeH3b\nrQ0lHTy1QekXrAKBARP0XanYXTQJFSpjSuwYpjies7q7MdZbTAPOfaV1fdutDTUdPLUBnVXLZOhX\npk1VXUJdWzPXniJRXdPOR7ocjDagM22ZfCp1fWaWpmkSLSplz5iK3sBZWRomN2AjdaV17TylpwDa\nsDhf0oF6ThLByL1GbXjo4KkN6GzP159JovlIX5fsu/QAZ7+ppY0uOnhqAzrblsnny8zydOqGeFNL\nO7/p4Kmd0tmerz+TmeXZVszXtOGif1K1YXO2O/pDTZ9x105nZPyUahekj1oxP9tGw9KDNnRGdPCU\nUvLNb36T3bt3Y9s2jz32GBMnThzuy9IuICN9U0sbPiM6z/PVV1/FcRyef/557r//fp544onhviTt\nHBIXz8TIL0BJiZLynFXM17RsGNEzz/fee4/LL78cgNmzZ7N165knaWsj39nu6GvacBrRP6mxWIxw\n+PgCvWmaSCkxjBE9YdY+gqGqmK9pQ21EB89wOEw8Hu99+0wC53D2VRnuni76teuxtewZ0cGztraW\n1157jaVLl7Jx40aqq6s/9HNGSwO482l8/dovvLF7xr+QjejguXjxYtatW8fy5csBWL169TBfkaZp\nmmdEB08hBN/61reG+zI0TdNOondeNE3TBkEHT03TtEHQwVPTNG0QdPDUNE0bBB08NU3TBkEHT03T\ntEHQwVPTNG0QdPDUNE0bBB08NU3TBkEHT03TtEHQwVPTNG0QdPDUNE0bBB08NU3TBkEHT03TtEHQ\nwVPTNG0QdPDUNE0bBB08NU3TBkEHT03TtEHQwVPTNG0QdPDUNE0bBB08NU3TBkEHT03TtEHQwVPT\nNG0QdPDUNE0bBB08NU3TBsHK5mDRaJQHHniAeDyO4zg89NBDzJkzh40bN/L4449jmiaLFi3i7rvv\nBuAHP/gBr7/+OqZp8vDDD1NTU5PNy9U0TTulrAbPp59+moULF3Lrrbeyb98+vva1r/HSSy+xatUq\nfvCDHzBhwgT+6q/+ih07diClZMOGDbzwwgs0NDTwla98hV/84hfZvFxN07RTymrw/NKXvoTP5wMg\nk8ng9/uJxWI4jsOECRMAuOyyy1i/fj0+n49FixYBMHbsWFzXpa2tjYKCgmxesqZp2oCGLHi+8MIL\n/OQnP+n32OrVq5k5cyZNTU08+OCDPPLII8RiMcLhcO/HhEIhDh48iN/vJz8/v9/jsVhMB09N00aE\nIQuey5YtY9myZSc9vmvXLr72ta/x9a9/nXnz5hGLxYjH473vj8ViRCIRbNvu93g8Hic3N/dDxy0p\n+fCPGSrDOfZwj69f+4U39oUuq7vt9fX13HPPPfz93/89l19+OQDhcBjbtjl48CBKKdatW8e8efOo\nra3lzTffRCnFkSNHkFL2m4lqmqYNJ6GUUtka7K677mLXrl2Ul5cDEIlEWLNmDZs2beLxxx/HdV0u\nu+wyvvrVrwLebvsbb7yBlJKHH36Y2trabF2qpmnaaWU1eGqapo0WOkle0zRtEHTw1DRNGwQdPDVN\n0wZBB09N07RBGFXBs6urizvvvJMVK1Zw22230dzcnLWxXdfl29/+NjfddBM33ngjb7zxRtbG7rFn\nzx7mzZtHOp3O6rjRaJQvf/nL3HLLLSxfvpyNGzcO+ZhSSlauXMny5cu55ZZbOHDgwJCP2ZfjODzw\nwAPcfPPNLFu2jD/84Q9ZHR+gpaWFK6+8kn379mV13LVr17J8+XK+8IUv8Mtf/jKrY48koyp4vvzy\ny0ydOpVnn32Wa6+9lh//+MdZG/vf//3fcV2Xn/70p6xZs4a9e/dmbWzwDhc8+eST+P3+rI4Lx2sW\nPPPMM6xevZpHH310yMd89dVXcRyH559/nvvvv58nnnhiyMfs65VXXqGwsJDnnnuOH/3oR/zd3/1d\nVsd3HIeVK1eSk5OT1XHfeecd3n//fZ5//nmeeeYZDh48mNXxR5Ksnm0faoFAgPb2dsCbDdm2nbWx\n161bR2VlJXfccQdKKb7xjW9kbWylFCtXruS+++7jrrvuytq4PQaqWTDU3nvvvd6DFrNnz2br1q1D\nPmZfS5Ys4ZprrgG8WbBpmlkd/zvf+Q433XQTa9euzeq469ato7q6mrvuuotYLMaDDz6Y1fFHkvM2\neA50dn7lypX867/+K9dddx0dHR0899xzWRu7oKAAv9/P2rVr2bBhA3/zN3/Ds88+m5Wxy8vLufba\na5k2bdo5H+9Mxh+oZsFQO7EmgmmaSCkxjOzcTAWDwd7ruOeee7j33nuzMi7ASy+9RGFhIZdddhlr\n164lm6nara2tNDQ0sHbtWg4ePMidd97Jb37zm6yNP6KoUeRv//Zv1c9+9jOllFI7d+5UN9xwQ9bG\nvvfee9Vvf/vb3rcXLVqUtbEXL16sVqxYoVasWKFmzZqlVqxYkbWxe+zcuVNdd9116o033sjKeKtX\nr1a//vWve9++4oorsjJuX0eOHFGf//zn1YsvvpjVcW+++ebe7/e8efPUsmXLVFNTU1bG/u53v6v+\n7d/+rfftz3zmM6qlpSUrY4805+3McyCJRKJ3NlJYWEgsFsva2HPnzuX111/n05/+NDt37uw9gpoN\nv/vd73r//5Of/GRW13rheM2C73//+1RXV2dlzNraWl577TWWLl3Kxo0bszZuj+bmZm6//XZWrVrF\npZdemtWx+97R3HLLLTz66KMUFxdnZey5c+fyk5/8hNtuu41jx47R1dV1wVY6G1XB87777uMb3/gG\nzz33HK7r8thjj2Vt7GXLlvHNb36TL37xiwB861vfytrYfQkhsj7mP/zDP+A4Dt/+9reB4zULhtLi\nxYtZt24dy5cvB7ylg2z64Q9/SDQaZc2aNb2v9Uc/+tGwbNhl01VXXcWGDRu48cYbkVKyatWqYfmZ\nGwn02XZN07RBGFWpSpqmadmig6emadog6OCpaZo2CDp4apqmDYIOnpqmaYOgg6emadogjKo8T214\nHTp0iCVLllBRUdHv8WXLljF37lwef/xxOjo6cF2XOXPm8Mgjj/QWtti4cSP/+I//SFtbG1JK5s2b\nx0MPPYTf7yedTrN69WreffddhBBEIhG+/vWvM2vWrOF4mZrmGe4jTtrocfDgQfWJT3xiwPctWbJE\nbdy4USmllJRSrVq1Sq1evVoppdSOHTvUokWL1KZNm5RSSmUyGbVq1Sr1wAMPKKWUWrt2rVq1alXv\nc/35z39WixYtUplMZghfjaadnp55alnR0tJCIpEAvFNQd999N0eOHAHgxz/+McuXL6empgbwinzc\nf//9vPXWW72f6zgOjuNg2za1tbU88cQTuK6b9WpGmtZDnzDSzplT3bZ/5zvfYdu2bTz66KOUlpby\n8Y9/nKuvvporr7wSgBtuuIEHH3ywt8TciQ4fPswdd9zB0aNHueSSS1iwYAGf+9zn+lVV0rRs0zNP\n7ZwqLS3l5ZdfPunxqqoqrrnmGtavX8/69et56KGHuOGGG3j44YcRQpy2rNq4ceP41a9+xebNm3nr\nrbd4+eWXefrpp3n55ZfJzc0dypejaaekd9u1Ibd//37WrFlDMBjkU5/6FCtXruT555/nhRdeAGDm\nzJls2bKl3+f0tPZwHIfvfve7NDY2UlNTwx133MGLL75IaWkp69evH46Xo2mADp5aFhQUFPDsiweq\nYwAAANxJREFUs8/y9ttv9z5WV1fHjBkzAK8S/U9/+lM2b94MeC0mnnzySSKRCLZt09zczL/8y7+Q\nyWQAaG9vp7W1laqqquy/GE3rptc8tXPmVGue8+fP54YbbuCpp56ioaEB27aZMmUKK1eupKysDIC3\n3nqL73//+3R1dZHJZFiwYAEPPvggPp+PeDzOk08+yZtvvkkwGMS2bf76r/+aT33qU8PxMjUN0MFT\n0zRtUPRtu6Zp2iDo4KlpmjYIOnhqmqYNgg6emqZpg6CDp6Zp2iDo4KlpmjYIOnhqmqYNwv8HqQFi\n0EQYMIsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19afeec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, hue=\"test\", palette=\"Set1\", size = 4)\n",
    "\n",
    "g = (g.map(plt.scatter, \"ESCS\", \"PV1MATH\", alpha=.5).add_legend())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it isn't good. Maybe if I plot in another way. Let's start by creating a boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5OBaLBQCQmpqKqqoqaDQamM1m6HQ66HQ6xMXFob6+HsOGDQtJDUSknKDzZHQ6XbtxKLqI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\nJBRDhoiEYsgQkVD/D6rWEATdB12DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df72510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, size = 4)\n",
    "g.map(sns.boxplot, \"test\", \"PV1MATH\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The box plot is showing more the scatter...I am going to classify data by their decil in relation to theirs GDPs and compare when I do the same in relation to the social indicator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BELONG         485490\n",
       "CNT            485490\n",
       "ESCS           473648\n",
       "OUTHOURS       485490\n",
       "PV1MATH        485490\n",
       "PV1READ        485490\n",
       "PV1SCIE        485490\n",
       "ST28Q01        485490\n",
       "ST57Q01        485490\n",
       "STIDSTD        485490\n",
       "CNT1           485490\n",
       "perCaptaGDP    485197\n",
       "gdp_bk         485197\n",
       "dtype: int64"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gdp_bk\"] = None\n",
    "df[\"gdp_bk\"]  = pd.qcut(df.perCaptaGDP, q = 5)\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some data missing in perCaptaGDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Liechtenstein'], dtype=object)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[pd.qcut(df.perCaptaGDP, q = 10).isnull()].CNT.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, it is correct...let's plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142d65410>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zz6BWq/HWW29hwIABigVIRERERM7B6qX2zZs3Izg4GIsWLUL79u1x4MABNG3aFMOGDYNW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c9e0590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\"gdp_bk\", \"PV1MATH\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Intresting...let's see regarding to the social score...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ucaiado/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BELONG        485490\n",
       "CNT           485490\n",
       "ESCS          473648\n",
       "OUTHOURS      485490\n",
       "PV1MATH       485490\n",
       "PV1READ       485490\n",
       "PV1SCIE       485490\n",
       "ST28Q01       485490\n",
       "ST57Q01       301367\n",
       "STIDSTD       485490\n",
       "ST57Q01_bk    290694\n",
       "ESCS_bk       473648\n",
       "dtype: int64"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ESCS[df.ESCS==\"NA\"]=None\n",
    "df.ESCS = df.ESCS.astype(float)\n",
    "df[\"ESCS_bk\"] = None\n",
    "df[\"ESCS_bk\"]  = pd.qcut(df.ESCS, q = 5)\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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STwAAAJiCxBMAAACmYAF5L1m9+jXt2LHNlLqmTp3stWsnJ9/itX3IAQBAaKHHEwAAAKag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\nAACAKUg8AQAAYIr/B6rXs4+T98rMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce2e850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(\"ESCS_bk\", \"PV1MATH\", data=df);\n",
    "ax.set_title(\"distribution of \");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is much more interesting...I could explore that. Let's see the box-plot of the other scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.PV1READ = df.PV1READ.astype(float)\n",
    "df.PV1SCIE = df.PV1SCIE.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lCgsL1b59ezPDBc6IN2/BVlVV1St76/arxC1YAID3mTq5KCYmRrGxsbLZbJo4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13c644a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(\"ESCS_bk\", \"PV1READ\", data=df);\n",
    "ax.set_title(\"distribution of \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JiouLU3l5ufv58vJyJSQkNHjcFi1iFBUV6bO4A011dZUkqXXr+LO8EqGEfIcP\nch0+yHV4IM9nmFp4Hjt2TKNHj9aMGTP04x//WJLUqVMnffTRR+revbsKCwvVs2dPJSUladGiRXI4\nHLLb7dq3b586dOjQ4LFLS0+b8RYCxsmTJyVJR4+W+TkSmIl8hw9yHT7IdXgItzx7K7RNLTxffPFF\nlZWVaenSpVq6dKkk6cknn9TTTz8tp9Opdu3aKS0tTRaLRSNHjlRWVpZcLpdyc3OZWFRLcfGeOu3E\nxM5+jAYAAKBxTC08p02bpmnTptV7Pj8/v95zGRkZysjIMCMsnzBrpvOCBbN16aWtfNKPxExnAADQ\ndLhzEQAAAEzBnYt8hJnOAAAAdTHiCQAAAFNQeAIAAMAUFJ4AAAAwBYUnAAAATEHhCQAAAFNQeAIA\nAMAUFJ4AAAAwBYUnAAAATEHhCQAAAFNQeAJAALBYLB7bABBKKDwBIADExMR6bANAKKHwBIAAcPp0\nucc2gOAUERHhsR3u+CQAIAAYhuGxjdDTrFkzj22EliuvvMpjO9xReAIAYKJBgzI8thFaMjNHemyH\nOwpPAABM1L//HR7bCC0HDnzlsR3uKDwBADDRhg3rPbYRWtauLfDYDncUnkAAu+ii5h7bAIIXBUl4\ncDjsHtvhjsITCGATJ0722EboiYyM9NhG6HG5qj22EVqqq6s9tsMdhScABACWXgkfnMkID3ynPeOT\nAALY2rVrPLYRevgjFT4cDofHNkLLLbfc6rEd7vjtFoQ4JQeEnoSEiz22EXri4uI8thFacnJGe2yH\nOwrPIBQXF++xjdCTnj7YYxuh56KLLvLYRugh1+GhuHiPx3a4o/AMQpymCR+JiZ3VvHmMmjePUWJi\nZ3+HA6AJxMTEemwjtHCplGcUnkGoVatWHtsIPcXFe1RRcVoVFaf5jxkIEZzJQDij8AxCN92U6rGN\n0MN/zOGDUbDwkZjYWR07dlLHjp04kxHC+AfDMwrPILRlS6HHNoDgxR+p8JKePpg8hzj+wfAsyt8B\n4NwdO3bMYxuhJz19sBYsmO1uI3TV/JGqaSO0kePwcP31N/g7hIBD4RmEWrVqpYMHD7jbCF0UI+GF\nfy6A0PLxx3+XJPXvf4efIwkcnGoPQpmZIz22EZo4JRc+EhM78w8GECKKi/eopOQfKin5B5NDa6Hw\nBAIcxQgABB8mh3pG4RmEVq58xWMboam4eA//LQMAQgKFZxBiclF4Wbt2Df8tA0CQYaUKz5hcFISY\nXBQ+aq5RtF0oAAAR4ElEQVQRqmlzyh0AggOTQz1jxDMIMbkofHCNEAAELyaH1seIZxBKTOwsi8Xi\nbgMAgMDD3+j6AnbE0+Vyafr06Ro+fLhycnJ04MABf4cUMDZsWC/DMGQYhjZsWO/vcOBDtRcfZiFi\nAECwC9jC891335XT6dSqVas0ZcoUzZs3z98hBYy1aws8thF6ahYf/n4bAIBgFLCF586dO3XzzTdL\nkrp166bPPvvMzxEBAADgQgRs4Wmz2RQXF+d+HBkZKZfL5ceIAkd6+j0e2wg9LMcBAAglATu5KC4u\nTuXl5e7HLpdLERHe6+TWrePNCCsgZGcP05/+VOBuI3S1bt1D69d3kSTdfHMPP0cDAMCFCdjCMzk5\nWe+9955+8pOfaNeuXerYsaO/Qwoor732mr9DgEnmzp3r7xAAAGgSFsMwDH8H4YlhGJo5c6ZKSkok\nnfnje8011/g5KgAAAJyvgC08AQAAEFoCdnIRAAAAQguFJwAAAExB4QkAAABTUHg2oYKCAvXt21fL\nly+vt238+PHKzMxUTk6OHnzwwXrbi4qKdM8992j48OFasGBBo/c7mzlz5mjVqlX1nnc6nZo6daqy\ns7OVkZGhTZs2ed1vxYoVuvXWWz0eJ5yVlpZq+vTpXrcvX75czz77rMdt8+fP1/DhwzVkyBD98Y9/\nbPR+3qxevVqDBw/WsGHD9P7779fbvmfPHqWmpionJ0c5OTlav/6/t1o9ceKEBgwYIIfDIUkaO3as\nkpKS3I9xhrd8nzhxQqNHj1Z2drYeeeQRVVZW1ntNQ9/jxuS7srJSDz30kLKzs/Xggw/qxIkTHl/3\n/VyePn1a48eP14gRI3Tffffp22+/1Y4dOzRw4EDl5uY29q2Hrdo537Rpk4YMGaLhw4fX+87W9v18\n/vWvf9WQIUOUkZGhV155pcH+du3apaFDhyozM1PPP/98ve2//vWv3d/h9PR09e7dW5L097//XUOH\nDtWwYcP0q1/9SpK0aNEi9e7dW5s3bz7n9x1uziXPc+bMcecgLS1Nw4adWdLwnXfe0eDBgzVkyBCt\nXLmywf527NjhztczzzxTb3t1dbVmz56tzMxMDRkyRIWFhZI81wlBmWcDTaagoMB49tlnPW674447\nvO5XXV1t9O3b1zh48KBhGIYxZcoU45133jnrfg05fvy4MWbMGKNfv37GqlWr6m1fs2aNMWfOHMMw\nDOPkyZNGnz59GtzvueeeM1auXHlesYSq6dOnGyUlJfWer6ysNHJzc43+/ft7/HkoKioyJkyYYBiG\nYdjtduP22283vvvuu7Pu582RI0eMO++803A4HEZZWZlx5513Gna7vc5rVq9ebbz88sv19i0sLDTS\n09ONG264oc4+ffv2rXeMcOct37/4xS+MN954wzAMw1i2bJnxu9/9rt5rPH2PzyXfL7/8svHcc88Z\nhmEYf/7zn43Zs2fXe42nXC5fvtxYunSpYRhnfj/V7Ldt2zbjkUceabBP/DfnDofD/T11OBzG4MGD\njWPHjtV5rad8VlVVGf379zfKysqM6upqY8CAAUZpaanX/tLT040DBw4YhmEYDzzwgLFnzx6vrx07\ndqyxZcsWwzAMIyMjw/jiiy8MwzCMzMxM45///KdhGIaRl5dnbN68+fw/gDBxLnmu4XQ6jYyMDPdn\n3bdvX+PUqVN1juHNoEGDjEOHDhmGYRg5OTn18rxmzRpj5syZhmEYxjfffGP87ne/M1wul9c6Idjy\nzIhnEzM8LBJw7Ngxfffddxo3bpyysrLqjUiVlpYqPj5eV111lSTp+uuv10cffaTjx483uF9DTp8+\nrYkTJyo9Pd1jTGlpaZo4caKkM4vzR0ZGNmo/nGGz2fTZZ5/p2muvrbfNbrfrnnvu0bhx4zx+hsnJ\nyXr66afdj6urqxUVFXXW/bzZvXu3kpOTZbVaFRcXp7Zt27qXIavx+eef6/3339eIESP05JNPum/O\nEBkZqeXLlyshIaHR/YWjhvJd+/a+qampKioqqrPd2/f/XPK9c+dOpaamSpJuvvnmen1InnM5atQo\njRs3TpL09ddf6+KLL5bk+fcU6qqd83379qlNmzaKj4+X1WrVDTfcoO3bt9d5vad8RkZG6i9/+Yvi\n4uJ04sQJuVwuWa1Wr/05HA5dffXVkqTevXtr69atHl+7YcMGXXzxxerVq5ckqVmzZjp58qQcDofs\ndnudm62Q64ada55r5Ofnq3fv3urQoYMkyWq16rvvvpPdbpdhGLJYLF77fP3113XllVeqvLxcNptN\nsbGxdbZv2bJFl19+ucaOHaunnnpKt912m06cOOGxTqgRTHmm8DRBVVWVxowZoxdeeEHPP/+85s6d\nW+dUWcuWLVVZWakvv/xS1dXVKiwsVGVlpZxOZ4P7NeSqq65SUlKS1+0xMTGKjY2VzWbTpEmT9Mgj\njzRqP5yxa9cur+vKJiQk6KabbvK6b3R0tBISEuR0OpWXl6dhw4apefPmZ93Pm/LycsXH//fOXTV5\nrS0pKUmPPfaYVqxYoauvvtp9Gq9Xr1665JJLzrnPcNNQvm02m/vzj42NVVlZWZ3t3r7/55Lv2rcQ\n9tSH5D2XERERGjVqlP7whz+oX79+jeoPdXNeO8eS5xx4y2dERIQ2bNiggQMHqkePHmrevLnH/r5/\nm2hveZbOnHKfMGGC+/GYMWM0btw4/fSnP9UPfvAD/fCHP2z8Gw1z55pnSXI4HHrttdc0evRo93P3\n3XefBg8erDvvvFN9+/atk8vvi4iI0K5du3TXXXepdevWuvzyy+tsLy0t1YEDB7Rs2TI98MADevzx\nx3XppZd6rBOCEYWnj4wdO1Y5OTmaPXu2WrVqpWHDhikiIkItW7ZUp06d9K9//cv9WovFogULFmjm\nzJkaO3asrrnmGrVo0eKs+9X217/+1X3dyeeff96oGA8fPqxRo0Zp4MCB+ulPf9ok7ztcnDx5Uq1a\ntZJ05nqdms/+b3/7W6P2P3XqlB544AF16NDhvK7dnTZtmnJycjRp0qR6t5ctLy+vN4J5++23q3Pn\nzpKkfv366R//+Mc59xnOGsp3XFycu9D39Nl7+h5/+eWX59T/2fo4m9///vdasWKFHnrooXPaL5zV\nzrmn71jN6HFj9O/fX5s3b5bD4dCbb77p8TXf78Nms3nM8xdffKGEhAT3yGhlZaVmz56t9evX6513\n3lGbNm308ssvNzq2cHc+eS4qKlL37t3dxeW///1vvfrqq9q0aZM2bdqk48eP6+23326w3+uuu06b\nNm1Sp06d9Otf/7rOtksuuUR9+vSRJKWkpOirr76SJI91QjCi8PSRZcuWKT8/X9OmTdPWrVs1adIk\nSWd+kPfu3at27drVef3mzZv10ksv6Te/+Y3279+vm266SUVFRWfdr8aAAQOUn5+v/Px8/ehHPzpr\nfMeOHdPo0aM1depU3XPPPRf4bsPPpZdequ+++06SdOONN7o/+1tuueWs+1ZWVuree+/VkCFDNH78\n+PPqf/bs2crPz9fixYuVlJSkHTt2yOFwqKysTPv27XOf/qlx//33a/fu3ZLO/NLs0qXLefUbrhrK\nd3JysvsfjsLCQt1444119vX0/W/fvv059Z+cnOyeYOCpD2+WLVvmLnRiYmLcl9Tg7GrnvF27dtq/\nf79OnTolh8Oh7du367rrrjvrMWw2m0aMGCGHwyGLxaLmzZvXOQ1eW1xcnKxWqw4ePCjDMLRlyxaP\ned66dav7sgvpzKVSVVVV7pHU1q1bu+PG2Z1Pnr+fg5rLG6Kjo93/YHobrTYMQ1lZWe4+Y2Ji6v1M\n3HDDDe7fKcXFxfrBD34gyXOdEIwoPE2Qmpqqdu3aadiwYbr//vs1efJkXXLJJfrwww+1dOlSSdLl\nl1+uoUOHaujQoUpKSlL37t118803e9xv8+bN9f5Dakjta00ee+wxHT58WMuWLVNZWZmWLl3qHr2x\n2+1e90Nd3bp1U3Fx8VlfV/szXLhwoXbv3q1Vq1bp0KFDeu2119yf/aFDh7zuN2fOnAb7atWqlUaO\nHKmsrCyNGjVKubm5io6O1hdffKFZs2ZJkmbNmqW5c+cqJydHu3btqlfwkuuGNZTv8ePH689//rMy\nMzP1ySefaMSIEZL+m29v3//azpbvzMxM7d27V1lZWfrjH//oPs26fPnyeitS1D7WkCFDtG7dOuXk\n5Gjy5MmaO3duvdfAs9o5t1qtysvL05gxY9yrUVx22WU6efKkx1Hkms83Li5Od999t0aMGKGsrCxF\nREQoPT1dR48e9biqwKxZszRlyhRlZGSoc+fO7suexowZo6qqKknSV1995R7tlM4ULlOmTNG9996r\nnJwcbd++Xffff3+Tfx6h6nzy/NVXX7mvtZSka665RoMGDdLw4cOVlZUlm82mQYMGecyzxWLRmDFj\n9MADDygnJ0clJSXuU/Y1ec7IyJBhGBo2bJhmzJjh/j3uqU4ISv6Y0RSqCgoKjGeeecbn/Rw/ftx4\n8cUXfd5PbUuWLGFW+/dMnz69wVmnTSU/P9/Yv3+/z/upjVnt9YVSvj/88ENmtTeCr3JeVVVlzJs3\nr8mP+32PPfaYUVhY6PN+gh15Nhcjnk1s3bp1HtfxbEqGYdS5qNnXVqxYoTfeeINRku+ZNGmS/vCH\nP/i8n9tuu01t2rTxeT81xo4dq+PHj5vWX7AIlXzv2LFDc+fO5fvcCL7KuWEYGjNmTJMft7ZFixbp\ngw8+IM+NQJ7NZTGMIJqDDwAAgKDFiCcAAABMQeEJAAAAU1B4AgAAwBQUngAAADBFlL8DAIBgcujQ\nIaWlpdVbBD4jI0M33HCD5syZo1OnTqm6ulrXXXednnzySffi3rt27dKvfvUrlZaWyuVy6cYbb1Re\nXp6aNWsmh8OhuXPnaseOHbJYLEpISNBjjz2mrl27NhjL+PHj9dZbb9Xbduutt2rFihXuxacBIBBQ\neALAObrssss83vrwJz/5iebNm6du3brJMAzNmjVLixcvVl5enoqLizVhwgS98MILSkpKUnV1tX7x\ni1/oqaee0oIFC7R8+XIZhuEuInfu3Knx48frb3/7G3ccAhAyONUOAE3k+PHjOn36tKQzdyiZMGGC\n7rjjDknSSy+9pOHDh7vvRhMZGakpU6bo9ttvd+/rdDrldDolnblN5rx581RdXd1gn+Xl5ZowYYLu\nvvtuTZw40X1P9xr/+te/NGDAAPctUwHAnxjxBIBzdOTIEQ0cOLDOcwsWLNDjjz+un/3sZ7rsssvU\no0cP3Xbbbbrlllsknbnn8t13311nn7i4OHfhOXLkSI0dO1Y9e/ZU9+7d1bNnTw0aNEjR0dENxvLt\nt99q8eLF6tq1qxYsWKAXXnhBjz76qCTp66+/1qxZszRv3jx3wQsA/kThCQDnyNup9muvvVYDBgzQ\n1q1btXXrVuXl5emuu+7SE088IYvFoobu13HllVdq3bp12r17t4qKivTmm29q+fLlevPNNxUfH+91\nv2uvvdZ9HWh6eroef/xx97ZHHnlEXbt21fXXX38B7xYAmg6n2gGgCezfv19Lly5VTEyM+vXrp+nT\np2vVqlX64x//KEnq0qWLPv300zr7lJWVady4cXI6nXrmmWd05MgRJSUlaezYsVqzZo0uu+wybd26\ntcF+a1//aRhGncfTpk3TgQMH9Le//a0J3ykAnD8KTwBoAi1atNCKFSv04Ycfup/bu3evOnfuLEm6\n9957tXLlSve1lk6nU/Pnz1dCQoKsVquOHTumF154QVVVVZKkkydP6sSJE7r22msb7Le4uFh79+6V\nJL3++uvq1auXe1tSUpJmzpypWbNmqaKioknfLwCcD+7VDgDnwNtySikpKbrrrru0cOFCHT58WFar\nVT/84Q81ffp0XX755ZKkoqIiLV68WBUVFaqqqlLPnj316KOPKjo6WuXl5Zo/f74++OADxcTEyGq1\n6uc//7n69evnNZavv/5a48aN01VXXaVDhw4pMTFR//u//6vmzZvXWU7p8ccf18UXX6y8vDyffjYA\ncDYUngAAADAFk4sAIIAdOHBAEydO9Lht9uzZ6tKli8kRAcD5Y8QTAAAApmByEQAAAExB4QkAAABT\nUHgCAADAFBSeAAAAMAWFJwAAAExB4QkAAABT/H+mj2FDZvt4OAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c685bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(\"ESCS_bk\", \"PV1SCIE\", data=df);\n",
    "ax.set_title(\"distribution of \");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice...it is consistent with all scores... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Ok, let me explore the others variables that I choose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ucaiado/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df.BELONG[df.BELONG==\"NA\"]=None\n",
    "df.BELONG = df.BELONG.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cef89d0>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AsBUBEwAAALYiYAIAAMBWBEwAAADYioAJAAAAWxEwAQAAYCsCJgAAAGxFwAQA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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143514190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.BELONG.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ucaiado/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df.OUTHOURS[df.OUTHOURS==\"NA\"]=None\n",
    "df.OUTHOURS = df.OUTHOURS.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BELONG         313399\n",
       "CNT            485490\n",
       "ESCS           473648\n",
       "OUTHOURS       308799\n",
       "PV1MATH        485490\n",
       "PV1READ        485490\n",
       "PV1SCIE        485490\n",
       "ST28Q01        485490\n",
       "ST57Q01        301367\n",
       "STIDSTD        485490\n",
       "CNT1           485490\n",
       "perCaptaGDP    485197\n",
       "gdp_bk         485197\n",
       "ESCS_bk        473648\n",
       "dtype: int64"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x145402d10>"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KVt/D0gEA7kTYBBbJ7Q9Mn7h+1ekSAACYxTWbAAAAsA1hEwAAALYhbAIAAMA2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1459b9c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ST57Q01.hist(bins = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    301367.000000\n",
       "mean          5.493770\n",
       "std           5.383815\n",
       "min           0.000000\n",
       "25%           2.000000\n",
       "50%           4.000000\n",
       "75%           7.000000\n",
       "max          30.000000\n",
       "Name: ST57Q01, dtype: float64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ST57Q01.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.cut?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"ST57Q01_bk\"] = None\n",
    "df[\"ST57Q01_bk\"]  =pd.cut(df.ST57Q01, bins = [0, 2,4,5,7,8,10,12, 15, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.qcut?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.PV1MATH = df.PV1MATH.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3JQn9ZViSEKh4XyHyXEmOiIiIGsBq43n9+nWkpKQAALp164bBgwc3emfZ2dkI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b662f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(\"ST57Q01_bk\", \"PV1MATH\", data=df);\n",
    "ax.set_title(\"distribution of \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "####Hmm....let me see the last data faceting by the social buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wcLBqOtCZdiQrNzdHNa0l2Tv96LFDTIMGDVTTWvr666/c0lr39MseyXBxdQYs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14b2c2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, size = 4, col_wrap =3, col=\"ESCS_bk\")\n",
    "g.map(sns.boxplot, \"ST57Q01_bk\", \"PV1MATH\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###would be nice if I could filter the data to see each bucket. Maybe by a barplot with the count on each bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PvvzyS1VWVqpbt26yWq2BGBsAAABasAaD6u7duzVt2jS1bdtWhmHoxIkTWr58uXr16hWI\n8QEAAKCFajCoPvPMM1q6dKl69uwpSSooKNDChQv12muv+X1wAAAAaLkavOr/7NmzvpAqSb169VJ5\neblfBwUAAAA0GFTbtm2r999/3/f6vffeU7t27fw6KAAAAKDBr/6feuopJSYmas6cOTIMQxaLRdnZ\n2YEYGwAAAFqwBmdUt2zZotatW+uDDz7QK6+8onbt2mnbtm2BGBsAAABasAaD6vr16/Xqq6+qTZs2\nuv766/XWW29p3bp1gRgbAAAAWrAGg2pFRUW1+6ZardZqDwAAAAAA/KHBc1SHDBmiCRMm6J577pFh\nGHr33Xd1++23B2JsAAAAaMEaDKozZ87UP/7xD+3YsUMhISGaMGGChgwZEoixAQAAoAVrMKhK0t13\n3627777b32MBAAAAfBo8RxUAAABoDgRVAAAAmFKzBNWTJ0/qtttu05dffqnDhw8rLi5OY8eO1YIF\nC2QYhiRpw4YNGjFihEaPHq0PP/xQklRWVqYpU6Zo7Nixevzxx1VcXCxJKigo0KhRoxQXF6fly5c3\nxyEBAACgiQU8qHq9Xs2fP1+tW7eWYRhavHixkpOTlZWVJcMwtGnTJhUVFSkzM1M5OTlas2aNMjIy\n5PF4lJ2drR49eigrK0vDhw/XqlWrJEmpqanKyMhQdna2du3apX379gX6sAAAANDEAh5UlyxZori4\nOHXo0EGStHfvXvXt21eSFB0drby8PO3evVuRkZGyWq2y2+3q2rWrDhw4oJ07dyo6OlqSNHDgQOXn\n58vlcsnr9apLly6SpKioKOXl5QX6sAAAANDEAhpU33jjDYWHhysqKkqSZBiG76t+SbLZbCotLZXL\n5VJYWFi15S6XSy6XSzabrVpbt9stu91eYx8AAAC4tDXq9lRN5Y033pDFYlFeXp7279+vlJQUlZSU\n+Na7XC45HA7Z7Xa53W7fcrfbrbCwsGrL3W63HA6HbDZbtbbn9gEAAIBLW0CD6rp163w/JyQkKC0t\nTUuWLNG2bdvUr18/5ebmqn///nI6nVq6dKk8Ho/Ky8t16NAhde/eXZGRkcrNzZXT6VRubq769Okj\nu90uq9WqwsJCde7cWVu3blVSUlK942jfvo1CQoIbPe6SEnvDjRAQ4eF2degQ1nDDi0CdzcOfdZao\ntZlQ65aDWrccTVnrgAbVH7NYLEpJSdG8efPk9XoVERGhmJgYWSwWjR8/XvHx8aqqqlJycrJCQ0MV\nFxenWbNmKT4+XqGhocrIyJAkpaWlacaMGaqsrFRUVJScTme9/ZaUnL2gcRYXuy76GNG0iotdKiry\nz6kd1Nk8/Fnnc/uHOVDrloNatxwXWuv6Qm2zBdXMzMxafz4nNjZWsbGx1Za1atVKy5Ytq9G2Z8+e\nWr9+fdMPEgAAAM2GG/4DAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAA\nAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJ\noAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoA\nAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABT\nIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlEIC2ZnX69WTTz6pb775Rh6PR5MnT1ZE\nRIRSUlIUFBSkbt26KTU1VRaLRRs2bND69esVEhKiyZMna9CgQSorK9PMmTNVXFwsm82m9PR0hYeH\nq6CgQIsWLVJwcLAGDBigpKSkQB4WAAAA/CCgM6pvv/22wsPDlZWVpT//+c966qmnlJ6eruTkZGVl\nZckwDG3atElFRUXKzMxUTk6O1qxZo4yMDHk8HmVnZ6tHjx7KysrS8OHDtWrVKklSamqqMjIylJ2d\nrV27dmnfvn2BPCwAAAD4QUCDakxMjKZOnSpJqqqqUkhIiPbu3au+fftKkqKjo5WXl6fdu3crMjJS\nVqtVdrtdXbt21YEDB7Rz505FR0dLkgYOHKj8/Hy5XC55vV516dJFkhQVFaW8vLxAHhYAAAD8IKBB\ntU2bNrLZbHK5XJo2bZp+//vfq6qqyrfeZrOptLRULpdLYWFh1Za7XC65XC7ZbLZqbd1ut+x2e419\nAAAA4NIW0HNUJenYsWNKSkrS2LFjde+99+q5557zrXO5XHI4HLLb7XK73b7lbrdbYWFh1Za73W45\nHA7ZbLZqbc/toz7t27dRSEhwo8dcUmJvuBECIjzcrg4dwhpueBGos3n4s84StTYTat1yUOuWoylr\nHdCgeuLECT3yyCNKTU3VLbfcIkm64YYbtG3bNvXr10+5ubnq37+/nE6nli5dKo/Ho/Lych06dEjd\nu3dXZGSkcnNz5XQ6lZubqz59+shut8tqtaqwsFCdO3fW1q1bG7yYqqTk7AWNu7jYddHHjKZVXOxS\nUZF/Zsyps3n4s87n9g9zoNYtB7VuOS601vWF2oAG1RdffFGlpaVasWKFVqxYIUmaM2eOnnnmGXm9\nXkVERCgmJkYWi0Xjx49XfHy8qqqqlJycrNDQUMXFxWnWrFmKj49XaGioMjIyJElpaWmaMWOGKisr\nFRUVJafTGcjDAgAAgB8ENKjOnTtXc+fOrbE8MzOzxrLY2FjFxsZWW9aqVSstW7asRtuePXtq/fr1\nTTdQAAAANDtu+A8AAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWC\nKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAA\nAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJ\noAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoAAABTIqgCAADAlAiqAAAAMCWCKgAAAEyJoAoA\nAABTIqgCAADAlAiqAAAAMCWCKgAAAEwppLkH0FSqqqq0YMECff7557JarXrmmWd07bXXNvewAAAA\ncJEumxnV999/X16vVzk5OZoxY4bS09Obe0gAAAD4CS6boLpz504NHDhQktSzZ0999tlnzTwiAAAA\n/BSXTVB1uVyy2+2+18HBwaqqqmrGEQEAAOCnuGzOUbXb7XK73b7XVVVVCgpquhxeevpkk+0LFycQ\nNThz8rTf+0D9AlWDojPUurkFqgbFJUUB6Qd1C1QNvj1eHJB+ULdvjxfL2YT7sxiGYTTh/prNu+++\nqw8++ECLFy9WQUGBVq5cqZdeeqm5hwUAAICLdNkEVcMwtGDBAh04cECStHjxYl133XXNPCoAAABc\nrMsmqAIAAODyctlcTAUAAIDLC0EVAAAApkRQBQAAgCkRVAEAAGBKBNVm8sYbb2jw4MFau3ZtjXWT\nJ09WXFycEhIS9Pjjj9dYn5+frwcffFBjxozRkiVLGr1dQxYtWqScnJway71er2bOnKmxY8cqNjZW\nmzdvrnO7devW6fbbb691Py1ZSUmJ5s+fX+f6tWvXKiMjo9Z1zz77rMaMGaORI0dq48aNjd6uLhs2\nbNCIESM0evRoffjhhzXW7927V9HR0UpISFBCQoL+/ve/+9YVFxdr6NCh8ng8kqRJkybJ6XT6XuMH\nddW7uLhYjzzyiMaOHavp06errKysRpv6PseNqXdZWZmmTJmisWPH6vHHH1dxce33lfxxLc+ePavJ\nkydr3Lhxevjhh/Xdd99px44dGj58uJKTkxt76C3W+TXfvHmzRo4cqTFjxtT4zJ7vx/X85z//qZEj\nRyo2NlavvPJKvf0VFBRo1KhRiouL0/Lly2usf+mll3yf4WHDhikqKkqS9L//+78aNWqURo8erRde\neEGStHTpUkVFRWnLli0XfNwt0YXUetGiRb46xMTEaPTo0ZKk9957TyNGjNDIkSOVnZ1db387duzw\n1ez555+vsb6yslILFy5UXFycRo4cqdzcXEm1Z4VLstYGmsUbb7xhZGRk1LrunnvuqXO7yspKY/Dg\nwUZhYaFhGIYxY8YM47333mtwu/qcPHnSmDhxojFkyBAjJyenxvrXX3/dWLRokWEYhnHq1Clj0KBB\n9W73xz/+0cjOzr6osVyu5s+fbxw4cKDG8rKyMiM5Odm46667av19yM/PN5KSkgzDMIzy8nLjzjvv\nNM6cOdPgdnU5fvy4ce+99xoej8coLS017r33XqO8vLxamw0bNhgvv/xyjW1zc3ONYcOGGb179662\nzeDBg2vso6Wrq95PP/208eabbxqGYRirV682/vKXv9RoU9vn+ELq/fLLLxt//OMfDcMwjL/97W/G\nwoULa7SprZZr1641VqxYYRjGD3+fzm336aefGtOnT6+3T/x/zT0ej+9z6vF4jBEjRhgnTpyo1ra2\nelZUVBh33XWXUVpaalRWVhpDhw41SkpK6uxv2LBhxpEjRwzDMIzHHnvM2Lt3b51tJ02aZGzdutUw\nDMOIjY01vvjiC8MwDCMuLs74/PPPDcMwjJSUFGPLli0X/wa0IBdS63O8Xq8RGxvre78HDx5snD59\nuto+6vLAAw8YR48eNQzDMBISEmrU+vXXXzcWLFhgGIZhfPvtt8Zf/vIXo6qqqs6scKnVmhnVZmTU\ncmewEydO6MyZM0pMTFR8fHyNGa+SkhKFhYWpc+fOkqRf/epX2rZtm06ePFnvdvU5e/aspk6dqmHD\nhtU6ppiYGE2dOlXSD0/8Cg4ObtR2+IHL5dJnn32m7t2711hXXl6uBx98UImJibW+h5GRkXrmmWd8\nrysrKxUSEtLgdnXZtWuXIiMjZbVaZbfb1bVrV9+9h8/Zs2ePPvzwQ40bN05z5szxPfEtODhYa9eu\nlcPhaHR/LVF99d65c6cGDhwoSYqOjlZ+fn619XV9/i+k3jt37lR0dLQkaeDAgTX6kGqv5YQJE5SY\nmChJ+vrrr9W2bVtJtf+dQnXn1/zQoUO69tprFRYWJqvVqt69e2v79u3V2tdWz+DgYP3jH/+Q3W5X\ncXGxqqqqZLVa6+zP4/GoS5cukqSoqCjl5eXV2vbdd99V27Ztdeutt0qSrrjiCp06dUoej0fl5eXV\nnuBIrRt2obU+JzMzU1FRUerWrZskyWq16syZMyovL5dhGLJYLHX2+dprr+maa66R2+2Wy+WSzWar\ntn7r1q3q1KmTJk2apHnz5umOO+5QcXFxrVnhnEup1gRVk6moqNDEiRO1cuVKLV++XIsXL6721V14\neLjKysr0n//8R5WVlcrNzVVZWZm8Xm+929Wnc+fOcjrrfuBZmzZtZLPZ5HK5NG3aNE2fPr1R2+EH\nBQUFdT58wuFwaMCAAXVuGxoaKofDIa/Xq5SUFI0ePVqtW7ducLu6uN1uhYWF+V6fq+v5nE6nZs2a\npXXr1qlLly6+rxVvvfVWtWvX7oL7bGnqq7fL5fK9/zabTaWlpdXW1/X5v5B6u1wu2e32OvuQ6q5l\nUFCQJkyYoFdffVVDhgxpVH+oXvPzayzVXoO66hkUFKR3331Xw4cP169//Wu1bt261v7Or3FdfZzz\n0ksvKSkpyfd64sSJSkxM1G9+8xtdffXV+vnPf974A8UF11qSPB6P1q9fr0ceecS37OGHH9aIESN0\n7733avDgwdXq+WNBQUEqKCjQfffdpw4dOqhTp07V1peUlOjIkSNavXq1HnvsMc2ePVtXXnllrVnh\nUkRQNYFJkyYpISFBCxcu1FVXXaXRo0crKChI4eHhuuGGG/Tll1/62losFi1ZskQLFizQpEmTdN11\n16l9+/YNbne+f/7zn75zZvbs2dOoMR47dkwTJkzQ8OHD9Zvf/KZJjrulOHXqlK666ipJP5xrdO69\n/+ijjxq1/enTp/XYY4+pW7duF3Xu8dy5c5WQkKBp06bJbrf7ZkilH4Lrj2dI77zzTt14442SpCFD\nhmjfvn0X3GdLVl+97Xa77x+D2t772j7H//nPfy6o/4b6aMhf//pXrVu3TlOmTLmg7Vqy82te22fs\n3Ox0Y9ytpM6zAAAIwUlEQVR1113asmWLPB6P3nrrrVrb/LgPl8tVa52/+OILORwO38xrWVmZFi5c\nqL///e967733dO211+rll19u9NhwcbXOz89Xv379fGH0m2++UVZWljZv3qzNmzfr5MmT+p//+Z96\n++3Vq5c2b96sG264ocbj4du1a6dBgwZJkvr27auvvvpKkmrNCpcigqoJrF69WpmZmZo7d67y8vI0\nbdo0ST/80h88eFARERHV2m/ZskVr1qzRn/70Jx0+fFgDBgxQfn5+g9udM3ToUGVmZiozM1O//OUv\nGxzfiRMn9Mgjj2jmzJl68MEHf+LRtjxXXnmlzpw5I0nq06eP772/7bbbGty2rKxMDz30kEaOHKnJ\nkydfVP8LFy5UZmamli1bJqfTqR07dsjj8ai0tFSHDh3yfRV1zqOPPqpdu3ZJ+uEP7E033XRR/bZU\n9dU7MjLS9w9Kbm6u+vTpU23b2j7/v/jFLy6o/8jISN/FFLX1UZfVq1f7glGbNm18p/igYefXPCIi\nQocPH9bp06fl8Xi0fft29erVq8F9uFwujRs3Th6PRxaLRa1bt672tfz57Ha7rFarCgsLZRiGtm7d\nWmud8/LyfKeBSD+culVRUeGbqe3QoYNv3Gici6n1j+tw7pSL0NBQ3z+ldc2IG4ah+Ph4X59t2rSp\n8XvRu3dv39+V/fv36+qrr5ZUe1a4FBFUTSY6OloREREaPXq0Hn30UT3xxBNq166dPvnkE61YsUKS\n1KlTJ40aNUqjRo2S0+lUv379NHDgwFq327JlS43/vupz/nkys2bN0rFjx7R69WqVlpZqxYoVvtmh\n8vLyOrdDdT179tT+/fsbbHf+e/jcc89p165dysnJ0dGjR7V+/Xrfe3/06NE6t1u0aFG9fV111VUa\nP3684uPjNWHCBCUnJys0NFRffPGF0tLSJElpaWlavHixEhISVFBQUCMgU+v61VfvyZMn629/+5vi\n4uL073//W+PGjZP0//Wu6/N/vobqHRcXp4MHDyo+Pl4bN270fe27du3aGnfsOH9fI0eO1DvvvKOE\nhAQ98cQTWrx4cY02qN35NbdarUpJSdHEiRN9d+vo2LGjTp06Vess9bn312636/7779e4ceMUHx+v\noKAgDRs2TEVFRbXedSEtLU0zZsxQbGysbrzxRt9pWBMnTlRFRYUk6auvvvLNpko/hJwZM2booYce\nUkJCgrZv365HH320yd+Py9nF1Pqrr77ynSsqSdddd50eeOABjRkzRvHx8XK5XHrggQdqrbXFYtHE\niRP12GOPKSEhQQcOHPCdQnCu1rGxsTIMQ6NHj1Zqaqrvb3ltWeGS1BxXcOGHq2qff/55v/dz8uRJ\n48UXX/R7P+f77//+b676/5H58+fXe1VuU8nMzDQOHz7s937Ox1X/NV1O9f7kk0+46r8R/FXziooK\nIz09vcn3+2OzZs0ycnNz/d7P5YBaBxYzqs3onXfeqfU+qk3JMIxqJ3D727p16/Tmm28yC/Mj06ZN\n06uvvur3fu644w5de+21fu/nnEmTJunkyZMB6+9ScbnUe8eOHVq8eDGf50bwV80Nw9DEiRObfL/n\nW7p0qT7++GPq3EjUOrAshnEJ3aMAAAAALQYzqgAAADAlgioAAABMiaAKAAAAUyKoAgAAwJRCmnsA\nAHA5O3r0qGJiYmrcuD82Nla9e/fWokWLdPr0aVVWVqpXr16aM2eO74bsBQUFeuGFF1RSUqKqqir1\n6dNHKSkpuuKKK+TxeLR48WLt2LFDFotFDodDs2bN0s0331zvWCZPnqy33367xrrbb79d69at890s\nHADMgKAKAH7WsWPHWh+Heffddys9PV09e/aUYRhKS0vTsmXLlJKSov379yspKUkrV66U0+lUZWWl\nnn76ac2bN09LlizR2rVrZRiGL3Tu3LlTkydP1kcffcRTpQBcNvjqHwCaycmTJ3X27FlJPzyBJikp\nSffcc48kac2aNRozZozviUPBwcGaMWOG7rzzTt+2Xq9XXq9X0g+PTk1PT1dlZWW9fbrdbiUlJen+\n++/X1KlT5XK5qq3/8ssvNXToUN9jdAGgOTGjCgB+dvz4cQ0fPrzasiVLlmj27Nn67W9/q44dO+rX\nv/617rjjDt12222Sfnhm9/33319tG7vd7guq48eP16RJk9S/f3/169dP/fv31wMPPKDQ0NB6x/Ld\nd99p2bJluvnmm7VkyRKtXLlSf/jDHyRJX3/9tdLS0pSenu4LyADQnAiqAOBndX313717dw0dOlR5\neXnKy8tTSkqK7rvvPj355JOyWCyq73ks11xzjd555x3t2rVL+fn5euutt7R27Vq99dZbCgsLq3O7\n7t27+85jHTZsmGbPnu1bN336dN1888361a9+9ROOFgCaDl/9A0AzOHz4sFasWKE2bdpoyJAhmj9/\nvnJycrRx40ZJ0k033aTdu3dX26a0tFSJiYnyer16/vnndfz4cTmdTk2aNEmvv/66OnbsqLy8vHr7\nPf/8VcMwqr2eO3eujhw5oo8++qgJjxQALh5BFQCaQfv27bVu3Tp98sknvmUHDx7UjTfeKEl66KGH\nlJ2d7TtX1Ov16tlnn5XD4ZDVatWJEye0cuVKVVRUSJJOnTql4uJide/evd5+9+/fr4MHD0qSXnvt\nNd16662+dU6nUwsWLFBaWpq+//77Jj1eALgYFqO+75YAAD9JXben6tu3r+677z4999xzOnbsmKxW\nq37+859r/vz56tSpkyQpPz9fy5Yt0/fff6+Kigr1799ff/jDHxQaGiq3261nn31WH3/8sdq0aSOr\n1arf/e53GjJkSJ1j+frrr5WYmKjOnTvr6NGjuv766/XUU0+pdevW1W5PNXv2bLVt21YpKSl+fW8A\noCEEVQAAAJgSF1MBwGXkyJEjmjp1aq3rFi5cqJtuuinAIwKAi8eMKgAAAEyJi6kAAABgSgRVAAAA\nmBJBFQAAAKZEUAUAAIApEVQBAABgSgRVAAAAmNL/AQW5V6T7GRtVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131b5fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"ESCS_bk\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###Own....I have forgotten that I bucketize all data by its deciles. Maybe by its histogram then\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DkrRjxw7t2rVLyWRSJSUlqqyslMPhUF1dnaqrq5VKpdTQ0CCXy6VgMKimpiZVV1fL5XIp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\nTgAAABj3fwFb6pqd3ixpuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129e6ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.ESCS.hist(bins = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.95 , -4.986, -4.022, -3.058, -2.094, -1.13 , -0.166,  0.798,\n",
       "        1.762,  2.726,  3.69 ])"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a_heights, a_bins = np.histogram(df.ESCS[~df['ESCS'].isnull()], bins = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"ESCS_bk2\"] = None\n",
    "df[\"ESCS_bk2\"] = pd.cut(df.ESCS, bins= a_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pHMLq+PHjm6IOAAAAoI4Gw2rHjh3rTGvVqpXy8vJ8UhAAAABwRoNhdd++fe5/V1VV6ZNP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\nAIwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1465e7990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"ESCS_bk2\", data=df);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kCgrynWmrxca2Up06dZxpO/iihdDubtuxsa3UoMGFzrQdyir37Lpe//4DtWLF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113e9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, size = 4, col_wrap =3, col=\"ESCS_bk2\")\n",
    "g.map(sns.boxplot, \"ST57Q01_bk\", \"PV1MATH\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Nice, I also would like to plot something about each country....the average time, maybe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_grouped = df.groupby(\"CNT1\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DJ3t7o9Fu4B6OxszgCQAH3/sI0848xZIKaDKl4MhHIbx35Lh5qk8oHMOJ7uFU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d9eb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"ST57Q01\", y=\"PV1MATH\", data=df_grouped);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###Then, I would allow the readers to mouse ouver each point to see more data and the name of each country. Maybe I could colored the points by the countries'continent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_continents = pd.read_csv(\"data/continents.csv\", sep = \"\\t\")\n",
    "df_continents.index=df_continents.CNT1.values\n",
    "df_continents.drop([\"CNT1\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CONTINENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CONTINENT\n",
       "Albania           Europe\n",
       "Argentina  South America\n",
       "Australia        Oceania\n",
       "Austria           Europe\n",
       "Belgium           Europe"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_grouped[\"continent\"] = None\n",
    "df_grouped[\"continent\"]  = df_continents.loc[df_grouped.index].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ESCS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>395.296185</td>\n",
       "      <td>5.026426</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>-0.626649</td>\n",
       "      <td>395.402241</td>\n",
       "      <td>3.681577</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>0.189072</td>\n",
       "      <td>492.842855</td>\n",
       "      <td>5.718562</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>0.108333</td>\n",
       "      <td>507.711753</td>\n",
       "      <td>4.601316</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.175149</td>\n",
       "      <td>519.868902</td>\n",
       "      <td>5.540794</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ESCS     PV1MATH   ST57Q01      continent\n",
       "CNT1                                                    \n",
       "Albania         NaN  395.296185  5.026426         Europe\n",
       "Argentina -0.626649  395.402241  3.681577  South America\n",
       "Australia  0.189072  492.842855  5.718562        Oceania\n",
       "Austria    0.108333  507.711753  4.601316         Europe\n",
       "Belgium    0.175149  519.868902  5.540794         Europe"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QAYCpU6eyYMECFi1aBICHhwdeXl6kpKQwcuRIypUrx+XLl8nOzgagcePGltfJ\nW1QlT0BAAG+99RZeXl6kp6dbVh5zFjcdQCMl49qWzI1aNrZq9RQWfM4SgqB5mOI8KpStQOfALnz+\nx2cAtKzWksBibrHWsWNH5s+fz759+yxdpatXr6ZSpUqEh4ezf/9+WrVqxcaNG/nzzz8JCQlh4MCB\n3H333fz222/s3buXbdu2cebMGebMmcP58+f5/PPPb2k3iKlTpzJz5kxCQkIsXarOpNAw7NGjBxkZ\nGQV+CAaDwek3+LU1Q81aeHXvTupV3aSF/UK+tiUDFNqyud1Wz+1O+FdoiJScbsHdaVn9XnLMOQSU\nK/4C/uXKlWP+/PlMnTqVixcvkpOTQ8OGDS3BNnHiRN566y3KlSvHzJkzadu2LbGxsWRmZnLlyhXG\njx9PzZo1eeutt4iOjsbf35+mTZty9mzumIOCdqHI+7Nnz548//zzVKtWjSZNmhRrBwl7KHQ0aUJC\nAk8++SSzZ8+mWrXrJ4kX1C9dkhx9dBnkjjA7+1PufMzCQsZ88kS+EZvmzMzcTT+vmlqSN4Lz2mOv\nfuxmbjSAxhlG68Gt12nvbtLS9nnamzPUqXmGzq/QlmGDBg2IiYnhvffe48033yzJmkoVR2lpOdq2\nUbbkjNc5RcS+rt9O+Cq9evXilVdeKalaHI755AnL9TlbMrRoafm5TLcHcOv2gOX21d2reYNcCnrM\nHtKT00lPTr/5gXZgqFlLQSgit6zQlmFcXBw9e/bkjjvuKMl6HMbtdLUVdUDL1a9haN6SMv0iLOco\nrGVT0q2ewt5bwscJnNpzCoAaYTVo0L2BzWsREbGVQluGv/zyC127diUmJoYdO3aUZE12V9CIxLxQ\nuLa1mLNkIdmjRpA9agQ5SxYW+TXM3+/O9/iNWjYl1eop7L2lJ6dbghDg1J5TDttCtLaS6i0QkZJV\naMtw2rRpZGRk8MUXX/D2228zadIkHnroIR5++GFq1qxZkjU6jIKWKyutw/g1ReF69h6YIyK2c8Nr\nht7e3jz44IMsWbKE5cuX4+vry8iRIxk8eHBJ1WcXBV2bA65vLZ45bdXXcJag8fH3oUZYDcvtGmE1\n8PG3zSLAjuJGvQUi4vxuGIZXMxqNGI1GMjMzXWI5tjKDh+A+cw7uM+cU2gLIW64sz+0G2q28hr3c\nLKwbdG9Ay6da0vKplrpeKOIgdu3aRfPmzTlz5ozlvtdee41169bd8jlWrlxJdnY2u3btIiYm5qbH\nx8bG0rt3wrgWAAAgAElEQVR37yLVeyMLFy4scDUdW7nhCjTnz5/n448/ZuPGjVy4cIFevXrx1ltv\nFTjvsDS6dhUWWyxX5sitwZu9t9LeGrxaYX//IsX15+lLmLJNVKh1h1UaGp6enowZM4Z33nkHuP0l\n3hYsWHDL4ZaRkcGePXu48847LfvUWsuQISXbQCg0DAcPHszevXvp3LkzI0eOpHnz5i7RIryRq8MB\ncrvOSvsQ/hstFnCjx0sjzV8Ua9v3wQEObz8KQM3QarSMDivW71mDwcB9992H2Wxm2bJllr1k87z9\n9tt8/PHHuLu707x5c1544QXmzp3Ljz/+yOXLl3nwwQc5d+4cMTExREdHc/ToUZ588klSUlLo0KED\nw4cPz3e+Tz75hNatW3P//fezdOlSSxg++OCDtGjRgoSEBIKDg6lcuTLff/89np6eLFy4kIyMDMaN\nG8fFixcBGD9+PHfeeSft27cnODiYevXqcenSJbp3706LFi0YM2YMp0+fJjMzk4kTJ1KvXj3GjRtH\nWloaZ8+eJTIykoiIiCJ/bnCDbtK7776be++9l/3797NhwwZSUx17BYiSYqhZC9Omj4s0grS0KOoI\n2tKgtH/5kZJz+WKGJQgBTu47w/k/LhbrnHkLik2aNIn//ve/HDt2zPJYQkICmzZtYuXKlaxYsYI/\n/viDrVu3YjAYqFevHitWrGDAgAFUqVKF2bNnYzabyczM5K233uL9999n6dKl173e6tWr6dOnD61a\nteLXX3+1LNuWnp7Ogw8+yLJly/j+++8JCwtj6dKlZGVlcfjwYebPn0+rVq147733mDJlCrGxsQCc\nOXOG2bNnM2bMGMtrrFixgtq1a7NixQrmzJnD3r17OXbsGD169GDJkiUsXryY//73v8X63OAGYbhn\nzx5CQkIYNWoUmZmZTJs2rdgvVhq4+kAKV3//ItZSUPvPWp1vFSpUYOzYsbz44ouYTCYAjhw5QtOm\nTSlTpgwA99xzD4cOHQIgKCiowPPUr1/fspOFu3v+jsTExEQOHz7M9OnTGTJkCG5ubixfvtzy+N/+\n9jcA/Pz8qFevnuVno9HIoUOHWLt2LVFRUUyYMIFLly4BULFixevmtufVDRAYGMjjjz9O5cqV+eKL\nLxg1ahTz588nKyurOB8XcIMwPHv2LDExMbRr146XXnqJvXv3FvvFREQkl3cFbxp2qme5HdiiFpUC\nK1rt/O3bt6du3bqsW7cOg8FAcHAw+/btIycnB7PZzPfff0/durl7J7q5/RUFbm5ulgC9UZft6tWr\nGTFiBIsXL7a0ztauXXtLwRQcHMwTTzxBXFwcr732muUa5dV15AkJCWH//v0AHD9+nFGjRvHOO+/Q\nrFkzZs6cyT/+8Y9b/1BuoNAw9PDwyPez51ULR7syZ54SYQ2u/v5FrKlxtwb8Y1x7Or/Yjnv6Ny32\n+QwGQ74AGzt2LF5eXgDceeeddOvWjYiICPr27UutWrXo1KmT5Xl5mjdvzpNPPnnd/VfLzMzko48+\nonv3v8ZQVK9enUaNGvHpp5/eMEQNBgNDhw7lk08+ISoqimHDhhEcHFzosf379+f48eNERUXx4osv\n8sQTT9C+fXvef/99Bg8ezJYtW/Dx8Sl267DQXSt69erFBx98UOhte7P3Kva3MoDEGVbbh6LVaY8B\nNKX587QH1Wk92rXC+RU6mvTw4cOW3ZAht9s077b2M9RoQld//yJSuhQahps2bSrJOkREROym0DC0\n9+a9IiIiJeWWl2OT0kM7L4iI5HfD5djEPtwuHQXA5Bdk9XNr5wURkeupZehgvPfMwu+zx/H77HG8\n98yy6rk1YV5EpGAKQwfidukoZX9fb7ld9vf1llaiiMjtWLRoEW3atCEzM7PQY0p6ZwhHpjB0IZow\nL+KAzh+G5F+h4CnfRbZ+/Xp69OjBRx99VOgxQ4YMITQ01Kqv66x0zdCBmPyCMAb3tLQOjcE9rX7d\nUDsviDiQb2bBzytyf67bATpOt8oCpbt27SIoKIh+/foxatQoevfuzbJly/jwww9xc3OjSZMmjB8/\nntGjR/PAAw9w9913W30XCGejMHQwGWEjMdZ7BLDNABpQCIo4hLSkv4IQ4MiXcHY/VC1+Sy1vN4m6\ndevi6enJvn37WLduHbGxsTRp0oTly5eTk5NjWTYtbxeIzp07k5SURHR0tMJQ7M9WISgiDqSgFqCh\n+Feu/vzzT7Zv386FCxeIi4sjNTWVpUuXMm3aNN5++21OnDhBs2bNuHolzsqVK/Puu+/y2Wef4evr\na5VdIJyNwlBExB58AuDuQfDj27m37+wBAU2Kfdr169fTp08fRo0aBcCVK1fo2LEjvr6+TJ48GU9P\nTwYPHsyPP/5oeU7eLhARERF8++23fPXVV8Wuw9koDEVE7KX5MGjwEORkQYVAq5xyzZo1zJw503Lb\ny8uLLl26ULlyZSIjI/Hx8aFatWo0bdqU+Ph4DAYD7du35+WXX+aLL76gXr16ll0grt69qLQrdNcK\nR+foq9iDc6y2D6rT2lSndTlDndq1wvlpaoWIiLg8haGIiLg8haGIiLg8haGIiLg8haGIiLg8haGI\niLg8hWEpoM16RSTPoUOHeOqpp4iOjqZPnz7MnTu3SOdZuXIl2dnZt3Tsrl27iImJKfCxhx56iClT\nphSphhuJiYmx6ko5CkMnl7NkIdmjRpA9agQ5SxbauxwRuU1m83HM5iNYY8r3pUuXiImJYdy4cbz3\n3nusWrWK3377jRUrVtz8yddYsGABJpPplo41FLK4+A8//ECDBg349ttvSU9Pv+0abmT27NlWXRRA\nK9A4sYI263Xr2l0LcYs4CbNpKWbzp7k3DC2AZwsNlluxefNmWrVqRZ06dQBwc3NjxowZltCYNWsW\nP/zwAyaTiSeeeIKuXbsSFRVFo0aNOHToEGlpabzxxhvs2LGDc+fOERMTQ3R0NDNnzsTT05NHH32U\nsmXL8v7775OdnY3BYGDevHmFBvmaNWvo2rUr1atX54MPPmDAgAGcOHGCESNGUL16dU6ePMkDDzzA\noUOH+OWXXwgPD2fEiBEkJCTwyiuvYDabqVixIlOnTuXAgQO89tprljreeOMNNm3axKlTpxg/fjzZ\n2dl4eXkxe/Zszp07x4wZM8jJyeHChQvExsZy99133/Czs3nLMCUlhXbt2nHkyBF++eUX7r//fqKi\nooiKiuKTTz4BYNWqVTzyyCP069ePrVu32rokERG7M5tT/gpCAPN3wOFinTM5OZlatfJ/GS5Xrhwe\nHh589dVXnDx5kvfff593332X+fPnk5qau7JP06ZNeeedd2jdujUbN26kb9++VKlShdmzZ2M2m8nM\nzGTZsmU89NBD/PHHHyxcuJD333+fkJAQvv766wIDPC0tjR9++IF27drRu3dvli9fbnnsxIkTTJ06\nlQULFvDGG28wZswYVq9ezZo1awCYMGECkyZNIi4ujrZt27Jo0SIMBkO+OvLMmDGDoUOHsmLFCqKj\no/n11185fPgwL774Iv/973958skniY+Pv+lnZ9OWYVZWFhMnTsTb2xuz2cyBAwcYNGgQAwcOtByT\nnJxMXFwc8fHxGI1GIiIiaN2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13fffc3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = sns.lmplot(x=\"ST57Q01\", y=\"PV1MATH\",hue = \"continent\", palette=\"Set1\",fit_reg=False,\n",
    "           data=df_grouped)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###I am not sure about the trend line....It can be painful to redraw it when the data is filtered out. One last chart before start to reshape the data to use in my visualization....I would like to use my personal data (about the time I commited to the Udacity course) as an introduction. It would be a nice introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l_wk = [\"SEGUNDA-FEIRA\", \"TERCA-FEIRA\",\"QUARTA-FEIRA\",\"QUINTA-FEIRA\",\n",
    " \"SEXTA-FEIRA\",\"SABADO\", \"DOMINGO\"]\n",
    "l_lab = [u\"JANEIRO\", u\"FEVEREIRO\", u\"MARAO\", u\"ABRIL\", u\"MAIO\", u\"JUNHO\",\n",
    " u\"JULHO\", u\"AGOSTO\", u\"SETEMBRO\", u\"OUTUBRO\", u\"NOVEMBRO\", u\"DEZEMBRO\" ]\n",
    "d_convert = dict(zip(l_lab, range(1,13)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ucaiado/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:7: RuntimeWarning: Argument <type 'str'> is not an unicode object. Passing an encoded string will likely have unexpected results.\n",
      "/Users/ucaiado/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:8: RuntimeWarning: Argument <type 'str'> is not an unicode object. Passing an encoded string will likely have unexpected results.\n"
     ]
    }
   ],
   "source": [
    "l_data=[]\n",
    "with open(\"data/DailyTimeDetail_DataScience.csv\") as fo:\n",
    "    b_test  = True\n",
    "    i_year=2014\n",
    "    for idx, row in enumerate(fo):\n",
    "        l = row.strip().split(\",\")\n",
    "        if unidecode(l[0][1:]) in l_wk:\n",
    "            i_m = d_convert[unidecode(l[1].split()[-1][:-1])]\n",
    "            i_d = int(l[1].split()[0])\n",
    "            if i_m==1 and b_test: \n",
    "                i_year+=1\n",
    "                b_test=False\n",
    "            i_y = i_year\n",
    "            dt_last = datetime.datetime(i_y, i_m, i_d)\n",
    "        if \"Total\" in l:\n",
    "            d1 = datetime.datetime.strptime(l[1],'%H:%M')\n",
    "            dt1 = datetime.timedelta(hours = d1.hour, minutes=d1.minute, seconds=d1.second)\n",
    "            i_min = d1.hour*60 + d1.minute\n",
    "            l_data.append([dt_last, dt1,i_min, l[1]]) \n",
    "            \n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MINT</th>\n",
       "      <th>ORG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-10-04</td>\n",
       "      <td>04:17:00</td>\n",
       "      <td>257</td>\n",
       "      <td>4:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-10-05</td>\n",
       "      <td>00:24:00</td>\n",
       "      <td>24</td>\n",
       "      <td>0:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-10-06</td>\n",
       "      <td>01:15:00</td>\n",
       "      <td>75</td>\n",
       "      <td>1:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-10-10</td>\n",
       "      <td>01:06:00</td>\n",
       "      <td>66</td>\n",
       "      <td>1:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-10-12</td>\n",
       "      <td>01:24:00</td>\n",
       "      <td>84</td>\n",
       "      <td>1:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE     TIME  MINT   ORG\n",
       "0 2014-10-04 04:17:00   257  4:17\n",
       "1 2014-10-05 00:24:00    24  0:24\n",
       "2 2014-10-06 01:15:00    75  1:15\n",
       "3 2014-10-10 01:06:00    66  1:06\n",
       "4 2014-10-12 01:24:00    84  1:24"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_timeCommited = pd.DataFrame(l_data)\n",
    "df_timeCommited.columns = [\"DATE\",\"TIME\", \"MINT\", \"ORG\"]\n",
    "df_timeCommited.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "455.6666666666667"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_timeCommited.TIME.sum()).total_seconds()/60**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GRAND TOTAL,455:42'"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kw = lambda x: x.isocalendar()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_timeCommited[\"WK_IDX\"] = list(df_timeCommited['DATE'].map(kw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ORG</th>\n",
       "      <th>WK_IDX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-10-04</td>\n",
       "      <td>04:17:00</td>\n",
       "      <td>4:17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-10-05</td>\n",
       "      <td>00:24:00</td>\n",
       "      <td>0:24</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-10-06</td>\n",
       "      <td>01:15:00</td>\n",
       "      <td>1:15</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-10-10</td>\n",
       "      <td>01:06:00</td>\n",
       "      <td>1:06</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-10-12</td>\n",
       "      <td>01:24:00</td>\n",
       "      <td>1:24</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE     TIME   ORG  WK_IDX\n",
       "0 2014-10-04 04:17:00  4:17      40\n",
       "1 2014-10-05 00:24:00  0:24      40\n",
       "2 2014-10-06 01:15:00  1:15      41\n",
       "3 2014-10-10 01:06:00  1:06      41\n",
       "4 2014-10-12 01:24:00  1:24      41"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_timeCommited.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_t = df_timeCommited.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_t.index = df_timeCommited.DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ORG</th>\n",
       "      <th>WK_IDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>2014-10-04</td>\n",
       "      <td>04:17:00</td>\n",
       "      <td>4:17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05</th>\n",
       "      <td>2014-10-05</td>\n",
       "      <td>00:24:00</td>\n",
       "      <td>0:24</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-06</th>\n",
       "      <td>2014-10-06</td>\n",
       "      <td>01:15:00</td>\n",
       "      <td>1:15</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-10</th>\n",
       "      <td>2014-10-10</td>\n",
       "      <td>01:06:00</td>\n",
       "      <td>1:06</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-12</th>\n",
       "      <td>2014-10-12</td>\n",
       "      <td>01:24:00</td>\n",
       "      <td>1:24</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DATE     TIME   ORG  WK_IDX\n",
       "DATE                                        \n",
       "2014-10-04 2014-10-04 04:17:00  4:17      40\n",
       "2014-10-05 2014-10-05 00:24:00  0:24      40\n",
       "2014-10-06 2014-10-06 01:15:00  1:15      41\n",
       "2014-10-10 2014-10-10 01:06:00  1:06      41\n",
       "2014-10-12 2014-10-12 01:24:00  1:24      41"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_wk = df_t.resample(\"W\", how=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_wk.fillna(0, inplace=True)\n",
    "df_wk/=60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#When I started the NanoDegree\n",
    "df_wk = df_wk.loc[df_wk.index>datetime.datetime(2014,11,10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BAABgKQIoAAAALEUABQAAgKUIoAAAALCU31yIHgAAoCwo7HaxBZlyu9iLRQAFAAC4ANwu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\n+v8A1ojWh9rvyvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13a016590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_wk.plot(kind = \"bar\")\n",
    "lgd=plt.legend()\n",
    "lgd.set_visible(False)\n",
    "lf_ticks=plt.xticks()\n",
    "labels = [item.get_text() for item in ax.get_xticklabels()]\n",
    "labels = [x[:10] for x in labels]\n",
    "i_labelidx=np.arange(0,len(lf_ticks[0]),max(1,len(lf_ticks[0])/10))\n",
    "l_labels=[labels[i] for i in i_labelidx]\n",
    "plt.xticks(lf_ticks[0][np.arange(0,len(lf_ticks[0]),max(1,len(lf_ticks[0])/10))],l_labels);\n",
    "ax.set_ylabel(\"Hours Commited\\n\");\n",
    "ax.set_title(\"Time Commited to Udacity\\n\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of weeks studied\n",
    "df_wk.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MINT    10.64625\n",
       "dtype: float64"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average time studied\n",
    "df_wk.sum()*1./df_wk.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Nice, I guess taht I already have enought ideas to start to make my project....well...I mean, before I need to make one last BIG thing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Reshape the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###I need to organize the data in a way that I can filter out what the reader want. As I will allow filter just by the Social index buckets....then, it is one of my levels.....also, I will like to plot the scatter by country....then, I also need a column with the weight of each row and a index by country...all data also need to be identifyed by continent....and finally....all data that will be uses in the boxplot. I would like to let the loading time of visualization fast.... then, I believe that I need to summarize the data before use it.....Let's remove columns and rows that will not be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GGjlW1VZVGUXLUpIjy3DWTMYSz0lguZiveufMAd/tAo0cLNfo8VqQKsebzpnf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\nHxpWc98/ln3ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a11e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, size = 4)\n",
    "g.map(sns.boxplot, \"ST57Q01_bk\", \"PV1MATH\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3bt1CQkICEhMTsWHDBqxcuRKFhYX4+uuv0aZNG3z55ZcICAjARx99VCsnRERERESWo8Jk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\nRGQ2TEaJiIiIyGz+H1XOBmHMdqRqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1239b6d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.boxplot(by=\"ST57Q01_bk\", column=\"PV1MATH\", sym=\"k.\", return_type = \"dict\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.boxplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = x[\"PV1MATH\"][\"whiskers\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path(array([[   1.    ,  371.7168],\n",
       "       [   1.    ,  161.6373]]), None)"
      ]
     },
     "execution_count": 698,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.get_path()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BELONG</th>\n",
       "      <th>CNT</th>\n",
       "      <th>ESCS</th>\n",
       "      <th>OUTHOURS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>PV1READ</th>\n",
       "      <th>PV1SCIE</th>\n",
       "      <th>ST28Q01</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>STIDSTD</th>\n",
       "      <th>ST57Q01_bk</th>\n",
       "      <th>ESCS_bk</th>\n",
       "      <th>ESCS_bk2</th>\n",
       "      <th>CNT1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.18</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>406.8469</td>\n",
       "      <td>249.5762</td>\n",
       "      <td>341.7009</td>\n",
       "      <td>0-10 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>486.1427</td>\n",
       "      <td>406.2936</td>\n",
       "      <td>548.9929</td>\n",
       "      <td>201-500 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>533.2684</td>\n",
       "      <td>401.21</td>\n",
       "      <td>499.6643</td>\n",
       "      <td>More than 500 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>412.2215</td>\n",
       "      <td>547.363</td>\n",
       "      <td>438.6796</td>\n",
       "      <td>11-25 books</td>\n",
       "      <td>10</td>\n",
       "      <td>00004</td>\n",
       "      <td>(8, 10]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.63</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>381.9209</td>\n",
       "      <td>311.7707</td>\n",
       "      <td>361.5628</td>\n",
       "      <td>101-200 books</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BELONG      CNT  ESCS OUTHOURS   PV1MATH   PV1READ   PV1SCIE  \\\n",
       "0  -1.18  Albania   NaN       NA  406.8469  249.5762  341.7009   \n",
       "1     NA  Albania   NaN        8  486.1427  406.2936  548.9929   \n",
       "2     NA  Albania   NaN       24  533.2684    401.21  499.6643   \n",
       "3     NA  Albania   NaN       17  412.2215   547.363  438.6796   \n",
       "4   2.63  Albania   NaN       NA  381.9209  311.7707  361.5628   \n",
       "\n",
       "               ST28Q01  ST57Q01 STIDSTD ST57Q01_bk ESCS_bk ESCS_bk2     CNT1  \n",
       "0          0-10 books       NaN   00001        NaN     NaN      NaN  Albania  \n",
       "1       201-500 books       NaN   00002        NaN     NaN      NaN  Albania  \n",
       "2  More than 500 books      NaN   00003        NaN     NaN      NaN  Albania  \n",
       "3         11-25 books        10   00004    (8, 10]     NaN      NaN  Albania  \n",
       "4       101-200 books       NaN   00005        NaN     NaN      NaN  Albania  "
      ]
     },
     "execution_count": 700,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()\n",
    "df2.drop([\"BELONG\", \"OUTHOURS\", \"PV1READ\", \"PV1SCIE\", \"ST28Q01\", \"STIDSTD\"], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(485490, 8)"
      ]
     },
     "execution_count": 702,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2.drop([\"CNT\"], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2.drop(df2[df2.isnull().sum(axis=1)>0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(287265, 7)"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ESCS</th>\n",
       "      <th>PV1MATH</th>\n",
       "      <th>ST57Q01</th>\n",
       "      <th>ST57Q01_bk</th>\n",
       "      <th>ESCS_bk</th>\n",
       "      <th>ESCS_bk2</th>\n",
       "      <th>CNT1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4748</th>\n",
       "      <td>0.25</td>\n",
       "      <td>223.0955</td>\n",
       "      <td>1</td>\n",
       "      <td>(0, 2]</td>\n",
       "      <td>(0.13, 0.78]</td>\n",
       "      <td>(-0.441, 0.936]</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4753</th>\n",
       "      <td>-0.95</td>\n",
       "      <td>379.5840</td>\n",
       "      <td>1</td>\n",
       "      <td>(0, 2]</td>\n",
       "      <td>(-1.21, -0.51]</td>\n",
       "      <td>(-1.819, -0.441]</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4754</th>\n",
       "      <td>0.40</td>\n",
       "      <td>384.3356</td>\n",
       "      <td>5</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(0.13, 0.78]</td>\n",
       "      <td>(-0.441, 0.936]</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755</th>\n",
       "      <td>0.74</td>\n",
       "      <td>405.9121</td>\n",
       "      <td>20</td>\n",
       "      <td>(15, 30]</td>\n",
       "      <td>(0.13, 0.78]</td>\n",
       "      <td>(-0.441, 0.936]</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4756</th>\n",
       "      <td>0.38</td>\n",
       "      <td>473.9913</td>\n",
       "      <td>4</td>\n",
       "      <td>(2, 4]</td>\n",
       "      <td>(0.13, 0.78]</td>\n",
       "      <td>(-0.441, 0.936]</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ESCS   PV1MATH  ST57Q01 ST57Q01_bk         ESCS_bk          ESCS_bk2  \\\n",
       "4748  0.25  223.0955        1     (0, 2]    (0.13, 0.78]   (-0.441, 0.936]   \n",
       "4753 -0.95  379.5840        1     (0, 2]  (-1.21, -0.51]  (-1.819, -0.441]   \n",
       "4754  0.40  384.3356        5     (4, 5]    (0.13, 0.78]   (-0.441, 0.936]   \n",
       "4755  0.74  405.9121       20   (15, 30]    (0.13, 0.78]   (-0.441, 0.936]   \n",
       "4756  0.38  473.9913        4     (2, 4]    (0.13, 0.78]   (-0.441, 0.936]   \n",
       "\n",
       "     CNT1  \n",
       "4748  UAE  \n",
       "4753  UAE  \n",
       "4754  UAE  \n",
       "4755  UAE  \n",
       "4756  UAE  "
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VXbnyi2TsqXhyorSWLVs5O4U6ydp4BgUFYdWqVTXuz8zMrHFfbGwsYmNj5UiLiMjpKpfK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112749590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(\"ST57Q01_bk\", \"PV1MATH\", data=df);\n",
    "ax.set_title(\"distribution of \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ydTY/j/a3JQm9JViSEKi6rgBpdpIjIiKiJjBbeN66dQtJSUkAgF69emH48OHN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e7eb390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(\"ST57Q01_bk\", \"PV1MATH\", data=df2);\n",
    "ax.set_title(\"distribution of \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kCgrynWmrxca2Up06dZxpO/iihdDubtuxsa3UoMGFzrQdyir37Lpe//4DtWLF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1332c2250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df2, size = 4, col_wrap =3, col=\"ESCS_bk2\")\n",
    "g.map(sns.boxplot, \"ST57Q01_bk\", \"PV1MATH\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AkJ6ejoULF8JsNiM4OBiRkZFQKBSYMGEC4uLiYLVakZKSApVKhdjYWKSmpiIu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d8b8110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"ESCS_bk2\", data=df2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(287265, 7)"
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###It seems that excluding all those points weren't a problem. Ok, let's organize all I have done so far.\n",
    "###I am going to put all this work inside a library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import csv\n",
    "import zipfile\n",
    "import StringIO\n",
    "import dataset\n",
    "\n",
    "s_root = \"C:/Users/Vera\" #windows\n",
    "s_root = \"/Users/ucaiado\"#mac\n",
    "# s_root = \"/home/uira\"#linux\n",
    "s_name = s_root + \"/Dropbox/NEUTRINO/ALGO/DATA/pisa2012.csv.zip\"\n",
    "s_dict = s_root + \"/Dropbox/NEUTRINO/ALGO/DATA/pisadict2012.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estou na linha 0\n",
      "estou na linha 50000\n",
      "estou na linha 100000\n",
      "estou na linha 150000\n",
      "estou na linha 200000\n",
      "estou na linha 250000\n",
      "estou na linha 300000\n",
      "estou na linha 350000\n",
      "estou na linha 400000\n",
      "estou na linha 450000\n",
      "estou na linha 485489"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "dataset.py:130: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df.ESCS[df.ESCS==\"NA\"]=None\n",
      "dataset.py:132: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df.ST57Q01[df.ST57Q01==\"NA\"]=None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 2min 25s, sys: 2.24 s, total: 2min 28s\n",
      "Wall time: 2min 29s\n"
     ]
    }
   ],
   "source": [
    "reload(dataset)\n",
    "%time o_data = dataset.loadPISA(s_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "self = o_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V5OoMeCa6OV+9ejXQXFDftFZnUKvc7baqX75X/RTYdetvp+lM1tHA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\nSZYkSZIkVcgkS5IkSZIq9P8BsNa9kY/Xv/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10373cd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(self.df, size = 4, col_wrap =3, col=\"ESCS_bk2\")\n",
    "g.map(sns.boxplot, \"ST57Q01_bk\", \"PV1MATH\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A/FtpaZCvQ+jUQkODFBYW3Ob+5Ne9c80t4K+8Xrju27dPAwYMUI8ePSRJt956qz799FP1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b9b8a2650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"ESCS_bk2\", data=self.df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lDZvyDKuqPjQtUhaZW34ga/dunJs3w6Vv3zK3d/LkSerVq0eNGjUAqFevnvW9bdu2sWDB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\nUkRExIRCUkRExIRCUkRExIRCUkRExMQ7kt4HfodQVbsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ba2513250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lmplot(x=\"ST57Q01\", y=\"PV1MATH\",hue = \"continent\", palette=\"Set1\",fit_reg=False,\n",
    "           data=self.df_avgByCountry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Nice, all data cleaning done. Finally, let's do the hard work. I need to reshape the data to use in my visualization. Maybe I could create how would be the data after each filter applyied.Then, I could keep those returns in a separate file. All the work of my visualization would be select the data given the filters applyed. \n",
    "\n",
    "###I would need to recalculate the average scores by country. To do that, I could keep a column with counts in order to calculate this average correctly. \n",
    "\n",
    "###My main concern is about the outliers to be ploted in the boxplot....let see the number of outliers that I would need to show in the chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_deciles(data):\n",
    "    data = data[\"PV1MATH\"]\n",
    "    median = np.median(data)\n",
    "\n",
    "\n",
    "    try:\n",
    "        upper_quartile = np.percentile(data, 75)\n",
    "        lower_quartile = np.percentile(data, 25)        \n",
    "        iqr = upper_quartile - lower_quartile\n",
    "        upper_whisker = data[data<=upper_quartile+1.5*iqr].max()\n",
    "        lower_whisker = data[data>=lower_quartile-1.5*iqr].min()\n",
    "        lower_outliers = data[data>upper_whisker].count()\n",
    "        upper_outliers = data[data<lower_whisker].count()\n",
    "    except:\n",
    "        upper_quartile = None\n",
    "        lower_quartile = None\n",
    "        upper_whisker = None\n",
    "        lower_whisker = None\n",
    "        lower_outliers = None\n",
    "        upper_outliers = None\n",
    "\n",
    "    \n",
    "    \n",
    "    count = data.count()\n",
    "\n",
    "    return pd.Series(dict(median= median,\n",
    "                          upper_outliers=upper_outliers,\n",
    "                          upper_whisker=upper_whisker,\n",
    "                          upper_quartile = upper_quartile,\n",
    "                          lower_quartile=lower_quartile,\n",
    "                          lower_whisker=lower_whisker,\n",
    "                          lower_outliers=lower_outliers,\n",
    "                          count = count\n",
    "                         ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_rtn = self.df.ix[:,[\"ESCS_bk2\",\"ST57Q01_bk\",\"PV1MATH\"]].groupby([\"ESCS_bk2\", \"ST57Q01_bk\"]).apply(get_deciles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rtn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of outliers overall: 2289.0\n"
     ]
    }
   ],
   "source": [
    "print \"number of outliers overall: {}\".format(df_rtn.lower_outliers.sum() + df_rtn.upper_outliers.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###Ok...there are 2,289 datapoint outside the border of my box-plot , the whiskers. It is less then 1% of my data.\n",
    "\n",
    "###It is less then 1% of my data. I know that I can't just ignore them. Know what, I will keep them to calculate the means and etc...but I will exclude them from the boxplot in my visualization. It will keep things more simple.\n",
    "\n",
    "###...I am thinking.... I can't just group the data...I need to group all possible combinations..... If I am goint to filter out the data using the ESCS histogram, I will have 28 different combinations...thus, with 9 classes ST57Q01, the file should have 252 rows.\n",
    "\n",
    "### As there are 62 countries in the database, the file about the average by country should present 1,736 rows (maybe less)....\n",
    "\n",
    "###Ok, let's make these files. First, let me check if I counted it correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-5.95, -4.573](-5.95, -4.573]:         29\n",
      "(-5.95, -4.573](-4.573, -3.196]:         2260\n",
      "(-5.95, -4.573](-3.196, -1.819]:         26962\n",
      "(-5.95, -4.573](-1.819, -0.441]:         117584\n",
      "(-5.95, -4.573](-0.441, 0.936]:         241573\n",
      "(-5.95, -4.573](0.936, 2.313]:         286674\n",
      "(-5.95, -4.573](2.313, 3.69]:         287077\n",
      "(-4.573, -3.196](-4.573, -3.196]:         2231\n",
      "(-4.573, -3.196](-3.196, -1.819]:         26933\n",
      "(-4.573, -3.196](-1.819, -0.441]:         117555\n",
      "(-4.573, -3.196](-0.441, 0.936]:         241544\n",
      "(-4.573, -3.196](0.936, 2.313]:         286645\n",
      "(-4.573, -3.196](2.313, 3.69]:         287048\n",
      "(-3.196, -1.819](-3.196, -1.819]:         24702\n",
      "(-3.196, -1.819](-1.819, -0.441]:         115324\n",
      "(-3.196, -1.819](-0.441, 0.936]:         239313\n",
      "(-3.196, -1.819](0.936, 2.313]:         284414\n",
      "(-3.196, -1.819](2.313, 3.69]:         284817\n",
      "(-1.819, -0.441](-1.819, -0.441]:         90622\n",
      "(-1.819, -0.441](-0.441, 0.936]:         214611\n",
      "(-1.819, -0.441](0.936, 2.313]:         259712\n",
      "(-1.819, -0.441](2.313, 3.69]:         260115\n",
      "(-0.441, 0.936](-0.441, 0.936]:         123989\n",
      "(-0.441, 0.936](0.936, 2.313]:         169090\n",
      "(-0.441, 0.936](2.313, 3.69]:         169493\n",
      "(0.936, 2.313](0.936, 2.313]:         45101\n",
      "(0.936, 2.313](2.313, 3.69]:         45504\n",
      "(2.313, 3.69](2.313, 3.69]:         403\n",
      "\n",
      "Total of Combinations:\t28\n"
     ]
    }
   ],
   "source": [
    "l_comb_aux = self.df.ESCS_bk2.values.categories\n",
    "\n",
    "i=0\n",
    "for idx, o_lower in enumerate(l_comb_aux):\n",
    "    for o_upper in l_comb_aux[idx:]:\n",
    "        #filter out the data\n",
    "        df_aux = self.df.ix[(self.df.ESCS_bk2>=o_lower) & (self.df.ESCS_bk2<=o_upper),:]\n",
    "        #print the index and the count\n",
    "        s_index_name = \"{}{}\".format(o_lower, o_upper)\n",
    "        print \"{}:         {}\".format(s_index_name, df_aux.shape[0])\n",
    "        i+=1\n",
    "print \"\\nTotal of Combinations:\\t{}\".format(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###nice, now, I need to create a new table agregating all the filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n",
      "(-5.95, -4.573](-5.95, -4.573]:         29\n",
      "(-5.95, -4.573](-4.573, -3.196]:         2260\n",
      "(-5.95, -4.573](-3.196, -1.819]:         26962\n",
      "(-5.95, -4.573](-1.819, -0.441]:         117584\n",
      "(-5.95, -4.573](-0.441, 0.936]:         241573\n",
      "(-5.95, -4.573](0.936, 2.313]:         286674\n",
      "(-5.95, -4.573](2.313, 3.69]:         287077\n",
      "(-4.573, -3.196](-4.573, -3.196]:         2231\n",
      "(-4.573, -3.196](-3.196, -1.819]:         26933\n",
      "(-4.573, -3.196](-1.819, -0.441]:         117555\n",
      "(-4.573, -3.196](-0.441, 0.936]:         241544\n",
      "(-4.573, -3.196](0.936, 2.313]:         286645\n",
      "(-4.573, -3.196](2.313, 3.69]:         287048\n",
      "(-3.196, -1.819](-3.196, -1.819]:         24702\n",
      "(-3.196, -1.819](-1.819, -0.441]:         115324\n",
      "(-3.196, -1.819](-0.441, 0.936]:         239313\n",
      "(-3.196, -1.819](0.936, 2.313]:         284414\n",
      "(-3.196, -1.819](2.313, 3.69]:         284817\n",
      "(-1.819, -0.441](-1.819, -0.441]:         90622\n",
      "(-1.819, -0.441](-0.441, 0.936]:         214611\n",
      "(-1.819, -0.441](0.936, 2.313]:         259712\n",
      "(-1.819, -0.441](2.313, 3.69]:         260115\n",
      "(-0.441, 0.936](-0.441, 0.936]:         123989\n",
      "(-0.441, 0.936](0.936, 2.313]:         169090\n",
      "(-0.441, 0.936](2.313, 3.69]:         169493\n",
      "(0.936, 2.313](0.936, 2.313]:         45101\n",
      "(0.936, 2.313](2.313, 3.69]:         45504\n",
      "(2.313, 3.69](2.313, 3.69]:         403\n",
      "\n",
      "Total of Combinations:\t28\n",
      "\n",
      "Number of rows in the datafrane: 252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ucaiado/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "l_comb_aux = self.df.ESCS_bk2.values.categories\n",
    "\n",
    "i=0\n",
    "for idx, o_lower in enumerate(l_comb_aux):\n",
    "    for o_upper in l_comb_aux[idx:]:\n",
    "        #filter out the data\n",
    "        df_aux = self.df.ix[(self.df.ESCS_bk2>=o_lower) & (self.df.ESCS_bk2<=o_upper),:]\n",
    "        df_aux2 = df_aux.groupby([\"ST57Q01_bk\"]).apply(get_deciles)\n",
    "        df_aux2.reset_index(inplace=True)\n",
    "        df_aux2.index = [s_index_name]*df_aux2.shape[0]\n",
    "        #merge if with last run\n",
    "        if i==0:\n",
    "            print \"here\"\n",
    "            df_rtn = df_aux2.copy()\n",
    "        else:\n",
    "            df_rtn = pd.concat([df_rtn, df_aux2])\n",
    "        #print the index and the count\n",
    "        s_index_name = \"{}{}\".format(o_lower, o_upper)\n",
    "        print \"{}:         {}\".format(s_index_name, df_aux.shape[0])\n",
    "        i+=1\n",
    "print \"\\nTotal of Combinations:\\t{}\".format(i)\n",
    "print \"\\nNumber of rows in the datafrane: {}\".format(df_rtn.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###As I predicted. I still need to export this data...maybe I should create a Json....I believe that It would be easier to work in D3.\n",
    "\n",
    "###Well, before I worry about that, I need to create the last file, about the countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n",
      "(-5.95, -4.573](-5.95, -4.573]:         29\n",
      "(-5.95, -4.573](-4.573, -3.196]:         2260\n",
      "(-5.95, -4.573](-3.196, -1.819]:         26962\n",
      "(-5.95, -4.573](-1.819, -0.441]:         117584\n",
      "(-5.95, -4.573](-0.441, 0.936]:         241573\n",
      "(-5.95, -4.573](0.936, 2.313]:         286674\n",
      "(-5.95, -4.573](2.313, 3.69]:         287077\n",
      "(-4.573, -3.196](-4.573, -3.196]:         2231\n",
      "(-4.573, -3.196](-3.196, -1.819]:         26933\n",
      "(-4.573, -3.196](-1.819, -0.441]:         117555\n",
      "(-4.573, -3.196](-0.441, 0.936]:         241544\n",
      "(-4.573, -3.196](0.936, 2.313]:         286645\n",
      "(-4.573, -3.196](2.313, 3.69]:         287048\n",
      "(-3.196, -1.819](-3.196, -1.819]:         24702\n",
      "(-3.196, -1.819](-1.819, -0.441]:         115324\n",
      "(-3.196, -1.819](-0.441, 0.936]:         239313\n",
      "(-3.196, -1.819](0.936, 2.313]:         284414\n",
      "(-3.196, -1.819](2.313, 3.69]:         284817\n",
      "(-1.819, -0.441](-1.819, -0.441]:         90622\n",
      "(-1.819, -0.441](-0.441, 0.936]:         214611\n",
      "(-1.819, -0.441](0.936, 2.313]:         259712\n",
      "(-1.819, -0.441](2.313, 3.69]:         260115\n",
      "(-0.441, 0.936](-0.441, 0.936]:         123989\n",
      "(-0.441, 0.936](0.936, 2.313]:         169090\n",
      "(-0.441, 0.936](2.313, 3.69]:         169493\n",
      "(0.936, 2.313](0.936, 2.313]:         45101\n",
      "(0.936, 2.313](2.313, 3.69]:         45504\n",
      "(2.313, 3.69](2.313, 3.69]:         403\n",
      "\n",
      "Total of Combinations:\t28\n",
      "\n",
      "Number of rows in the datafrane: 1624\n"
     ]
    }
   ],
   "source": [
    "l_comb_aux = self.df.ESCS_bk2.values.categories\n",
    "\n",
    "i=0\n",
    "for idx, o_lower in enumerate(l_comb_aux):\n",
    "    for o_upper in l_comb_aux[idx:]:\n",
    "        #filter out the data\n",
    "        df_aux = self.df.ix[(self.df.ESCS_bk2>=o_lower) & (self.df.ESCS_bk2<=o_upper),:]\n",
    "        df_aux2 = df_aux.groupby([\"continent\", \"CNT1\"]).mean().ix[:,[\"PV1MATH\",\"ST57Q01\"]]\n",
    "        df_aux2.reset_index(inplace=True)\n",
    "        df_aux2.index = [s_index_name]*df_aux2.shape[0]\n",
    "        #merge if with last run\n",
    "        if i==0:\n",
    "            print \"here\"\n",
    "            df_rtn2 = df_aux2.copy()\n",
    "        else:\n",
    "            df_rtn2 = pd.concat([df_rtn2, df_aux2])\n",
    "        #print the index and the count\n",
    "        s_index_name = \"{}{}\".format(o_lower, o_upper)\n",
    "        print \"{}:         {}\".format(s_index_name, df_aux.shape[0])\n",
    "        i+=1\n",
    "print \"\\nTotal of Combinations:\\t{}\".format(i)\n",
    "print \"\\nNumber of rows in the datafrane: {}\".format(df_rtn2.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Finally, I can just create the files and start to work in the visualizations =))). for now, I will keep the data in CSV format. Meybe it will be enought. Let's try that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_rtn.reset_index(inplace=True)\n",
    "df_rtn2.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_rtn2.to_csv(\"data/countries_agregated.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_rtn.to_csv(\"data/boxplot_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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