diff --git a/README.md b/README.md
index 88b27af..5c92f87 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,5 @@
+Open ipython notebook education.ipynb
+
 # American Time Use Analysis
 
 ## Description
diff --git a/education.ipynb b/education.ipynb
new file mode 100644
index 0000000..9ce006c
--- /dev/null
+++ b/education.ipynb
@@ -0,0 +1,1777 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:0b5de4328fc3f4fb5e713ff4290f78cebed77d25895a6836c31ada85af97c79c"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "ANALYSIS OBJECTIVES"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This analysis will look at how different population groups spend their time in terms of getting additional education. I will be looking specifically at:\n",
+      "- Education to get a degree\n",
+      "- Education for personal interest"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "import seaborn \n",
+      "import re"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1183
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "plt.rc('figure', figsize=(6,6))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1184
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Data Loading"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "respondent = pd.read_csv(\"atusdata/atusresp_2013/atusresp_2013.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1185
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "demographics = pd.read_csv(\"atusdata/atusrost_2013/atusrost_2013.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1186
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "activities = pd.read_csv(\"atusdata/atusact_2013/atusact_2013.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1187
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "respondent.info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 11385 entries, 0 to 11384\n",
+        "Columns: 175 entries, TUCASEID to TXTONHH\n",
+        "dtypes: float64(1), int64(172), object(2)\n",
+        "memory usage: 15.3+ MB\n"
+       ]
+      }
+     ],
+     "prompt_number": 1188
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "demographics.info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 30831 entries, 0 to 30830\n",
+        "Data columns (total 8 columns):\n",
+        "TUCASEID    30831 non-null int64\n",
+        "TULINENO    30831 non-null int64\n",
+        "TEAGE       30831 non-null int64\n",
+        "TERRP       30831 non-null int64\n",
+        "TESEX       30831 non-null int64\n",
+        "TXAGE       30831 non-null int64\n",
+        "TXRRP       30831 non-null int64\n",
+        "TXSEX       30831 non-null int64\n",
+        "dtypes: int64(8)\n",
+        "memory usage: 2.1 MB\n"
+       ]
+      }
+     ],
+     "prompt_number": 1189
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "activities.info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 215576 entries, 0 to 215575\n",
+        "Data columns (total 31 columns):\n",
+        "TUCASEID        215576 non-null int64\n",
+        "TUACTIVITY_N    215576 non-null int64\n",
+        "TEWHERE         215576 non-null int64\n",
+        "TRTCCTOT_LN     215576 non-null int64\n",
+        "TRTCC_LN        215576 non-null int64\n",
+        "TRTCOC_LN       215576 non-null int64\n",
+        "TRTEC_LN        215576 non-null int64\n",
+        "TRTHH_LN        215576 non-null int64\n",
+        "TRTNOHH_LN      215576 non-null int64\n",
+        "TRTOHH_LN       215576 non-null int64\n",
+        "TRTONHH_LN      215576 non-null int64\n",
+        "TRTO_LN         215576 non-null int64\n",
+        "TRWBELIG        215576 non-null int64\n",
+        "TUACTDUR        215576 non-null int64\n",
+        "TUACTDUR24      215576 non-null int64\n",
+        "TUCC5           215576 non-null int64\n",
+        "TUCC5B          215576 non-null int64\n",
+        "TUCC7           215576 non-null int64\n",
+        "TUCC8           215576 non-null int64\n",
+        "TUCUMDUR        215576 non-null int64\n",
+        "TUCUMDUR24      215576 non-null int64\n",
+        "TUDURSTOP       215576 non-null int64\n",
+        "TUEC24          215576 non-null int64\n",
+        "TUSTARTTIM      215576 non-null object\n",
+        "TUSTOPTIME      215576 non-null object\n",
+        "TUTIER1CODE     215576 non-null int64\n",
+        "TUTIER2CODE     215576 non-null int64\n",
+        "TUTIER3CODE     215576 non-null int64\n",
+        "TRCODE          215576 non-null int64\n",
+        "TRTIER2         215576 non-null int64\n",
+        "TXWHERE         215576 non-null int64\n",
+        "dtypes: int64(29), object(2)\n",
+        "memory usage: 52.6+ MB\n"
+       ]
+      }
+     ],
+     "prompt_number": 1190
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Data Reduction"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Columns to use in Analysis:\n",
+      "\n",
+      "- TUCASEID:  Identifies each Household\n",
+      "- TULINENO: Identifies each person in the household.  Its value is 1 for the person who answered  the survey\n",
+      "\n",
+      "\n",
+      "From Roster Dataset:\n",
+      "- TEAGE: Respondent Age\n",
+      "- TESEX: Gender\n",
+      "    - 1 Male\n",
+      "    - 2 Female\n",
+      "\n",
+      "From Respondent Dataset:\n",
+      "\n",
+      "- TEHRUSL1: How many hours do you work a week?\n",
+      "- TEIO1COW: Individual class of worker (government, private, Self employed,etc)\n",
+      "   Valid Entries: \n",
+      "   - 1 Government federal\n",
+      "   - 2 Government, state\n",
+      "   - 3 Government, local\n",
+      "   - 4 Private, for profit\n",
+      "   - 5 Private, nonprofit\n",
+      "   - 6 Self-employed, incorporated\n",
+      "   - 7 Self-employed, unincorporated 8 Without pay\n",
+      "\n",
+      "From Activities Dataset:\n",
+      "- TRCODE: 6 digit code\n",
+      "- TUACTDUR: Duration of activity in minutes\n",
+      "\n",
+      "Activity Codes of interest\n",
+      "\n",
+      "- 060101: Taking classes for a degree\n",
+      "- 060102:Taking Classes for personal experience"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "resp_reduced = respondent[['TUCASEID',\n",
+      "                           'TUFINLWGT',\n",
+      "                           'TEHRUSL1',\n",
+      "                           'TEIO1COW',\n",
+      "                           ]]\n",
+      "\n",
+      "for invalid in range (-1,-10,-1):\n",
+      "    resp_reduced.replace(invalid, np.nan,inplace = True)\n",
+      "resp_reduced.head()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TUFINLWGT</th>\n",
+        "      <th>TEHRUSL1</th>\n",
+        "      <th>TEIO1COW</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 20130101130004</td>\n",
+        "      <td> 11899905.662034</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 20130101130112</td>\n",
+        "      <td>  4447638.009513</td>\n",
+        "      <td> 40</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 20130101130123</td>\n",
+        "      <td> 10377056.507734</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 20130101130611</td>\n",
+        "      <td>  7731257.992805</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 20130101130616</td>\n",
+        "      <td>  4725269.227067</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1193,
+       "text": [
+        "         TUCASEID        TUFINLWGT  TEHRUSL1  TEIO1COW\n",
+        "0  20130101130004  11899905.662034       NaN       NaN\n",
+        "1  20130101130112   4447638.009513        40         2\n",
+        "2  20130101130123  10377056.507734       NaN       NaN\n",
+        "3  20130101130611   7731257.992805       NaN         4\n",
+        "4  20130101130616   4725269.227067        12         5"
+       ]
+      }
+     ],
+     "prompt_number": 1193
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dem_reduced = demographics[['TUCASEID','TULINENO','TEAGE','TESEX']]\n",
+      "#The person that responds the survey is always TULINENO = 1 but the \n",
+      "#file has demographics for all people in the household. \n",
+      "\n",
+      "dem_reduced = dem_reduced[dem_reduced['TULINENO'] == 1]\n",
+      "del dem_reduced['TULINENO']\n",
+      "for invalid in range (-1,-10,-1):\n",
+      "    dem_reduced.replace(invalid, np.nan,inplace = True)\n",
+      "dem_reduced.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TEAGE</th>\n",
+        "      <th>TESEX</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0 </th>\n",
+        "      <td> 20130101130004</td>\n",
+        "      <td> 22</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td> 20130101130112</td>\n",
+        "      <td> 39</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td> 20130101130123</td>\n",
+        "      <td> 47</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>12</th>\n",
+        "      <td> 20130101130611</td>\n",
+        "      <td> 50</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td> 20130101130616</td>\n",
+        "      <td> 45</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1194,
+       "text": [
+        "          TUCASEID  TEAGE  TESEX\n",
+        "0   20130101130004     22      2\n",
+        "7   20130101130112     39      1\n",
+        "10  20130101130123     47      2\n",
+        "12  20130101130611     50      2\n",
+        "14  20130101130616     45      2"
+       ]
+      }
+     ],
+     "prompt_number": 1194
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "codes_desired = [60101, 60102]\n",
+      "act_reduced = activities[activities['TRCODE'].isin(codes_desired)]\n",
+      "act_reduced = act_reduced[['TUCASEID', 'TRCODE', 'TUACTDUR']]\n",
+      "act_reduced.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TRCODE</th>\n",
+        "      <th>TUACTDUR</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>964 </th>\n",
+        "      <td> 20130101131401</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 140</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>966 </th>\n",
+        "      <td> 20130101131401</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 200</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1212</th>\n",
+        "      <td> 20130101131579</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 255</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1381</th>\n",
+        "      <td> 20130101131694</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 160</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1772</th>\n",
+        "      <td> 20130101131999</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 200</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1195,
+       "text": [
+        "            TUCASEID  TRCODE  TUACTDUR\n",
+        "964   20130101131401   60101       140\n",
+        "966   20130101131401   60101       200\n",
+        "1212  20130101131579   60101       255\n",
+        "1381  20130101131694   60101       160\n",
+        "1772  20130101131999   60101       200"
+       ]
+      }
+     ],
+     "prompt_number": 1195
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Data Merging"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Merge demographics with respondents by TUCASEID"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "resp_dem = pd.merge(dem_reduced, resp_reduced, on=['TUCASEID'])\n",
+      "resp_dem.sort_index(inplace=True, by='TUCASEID')\n",
+      "resp_dem.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TEAGE</th>\n",
+        "      <th>TESEX</th>\n",
+        "      <th>TUFINLWGT</th>\n",
+        "      <th>TEHRUSL1</th>\n",
+        "      <th>TEIO1COW</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 20130101130004</td>\n",
+        "      <td> 22</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 11899905.662034</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 20130101130112</td>\n",
+        "      <td> 39</td>\n",
+        "      <td> 1</td>\n",
+        "      <td>  4447638.009513</td>\n",
+        "      <td> 40</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 20130101130123</td>\n",
+        "      <td> 47</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 10377056.507734</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 20130101130611</td>\n",
+        "      <td> 50</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  7731257.992805</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 20130101130616</td>\n",
+        "      <td> 45</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  4725269.227067</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1196,
+       "text": [
+        "         TUCASEID  TEAGE  TESEX        TUFINLWGT  TEHRUSL1  TEIO1COW\n",
+        "0  20130101130004     22      2  11899905.662034       NaN       NaN\n",
+        "1  20130101130112     39      1   4447638.009513        40         2\n",
+        "2  20130101130123     47      2  10377056.507734       NaN       NaN\n",
+        "3  20130101130611     50      2   7731257.992805       NaN         4\n",
+        "4  20130101130616     45      2   4725269.227067        12         5"
+       ]
+      }
+     ],
+     "prompt_number": 1196
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Merging respondent/demographics with Activities by TUCASEID"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "all_data = pd.merge(resp_dem, act_reduced, on=['TUCASEID'])\n",
+      "all_data['weighted_minutes'] = all_data['TUFINLWGT'] * all_data['TUACTDUR']\n",
+      "all_data.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TEAGE</th>\n",
+        "      <th>TESEX</th>\n",
+        "      <th>TUFINLWGT</th>\n",
+        "      <th>TEHRUSL1</th>\n",
+        "      <th>TEIO1COW</th>\n",
+        "      <th>TRCODE</th>\n",
+        "      <th>TUACTDUR</th>\n",
+        "      <th>weighted_minutes</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 20130101131401</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 17284603.689761</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 140</td>\n",
+        "      <td> 2.419845e+09</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 20130101131401</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 17284603.689761</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 200</td>\n",
+        "      <td> 3.456921e+09</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 20130101131579</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 23220400.569550</td>\n",
+        "      <td> 25</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 255</td>\n",
+        "      <td> 5.921202e+09</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 20130101131694</td>\n",
+        "      <td> 43</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  7791711.857311</td>\n",
+        "      <td> 36</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 160</td>\n",
+        "      <td> 1.246674e+09</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 20130101131999</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 13186606.269365</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 200</td>\n",
+        "      <td> 2.637321e+09</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1197,
+       "text": [
+        "         TUCASEID  TEAGE  TESEX        TUFINLWGT  TEHRUSL1  TEIO1COW  TRCODE  \\\n",
+        "0  20130101131401     16      1  17284603.689761       NaN       NaN   60101   \n",
+        "1  20130101131401     16      1  17284603.689761       NaN       NaN   60101   \n",
+        "2  20130101131579     17      2  23220400.569550        25         4   60101   \n",
+        "3  20130101131694     43      2   7791711.857311        36         4   60101   \n",
+        "4  20130101131999     15      1  13186606.269365       NaN       NaN   60101   \n",
+        "\n",
+        "   TUACTDUR  weighted_minutes  \n",
+        "0       140      2.419845e+09  \n",
+        "1       200      3.456921e+09  \n",
+        "2       255      5.921202e+09  \n",
+        "3       160      1.246674e+09  \n",
+        "4       200      2.637321e+09  "
+       ]
+      }
+     ],
+     "prompt_number": 1197
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Data Histograms"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def print_hist(data, key, title, axis_labels, xticks):\n",
+      "   ax = data[key].plot(kind='hist', alpha=0.5, title=title, xticks=xticks)\n",
+      "   if axis_labels:\n",
+      "      ax.set_xticklabels(axis_labels, rotation=45) "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1210
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print_hist(all_data,'TEAGE', \"Histogram for Age\", None, None)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x13672f0b8>"
+       ]
+      }
+     ],
+     "prompt_number": 1211
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "\n",
+      "This data sample is heavily skewed towards younger people (Most respondents are younger than 20)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print_hist(all_data,'TESEX', \"Histogram for Gender\", ['M', 'F'], [1,2])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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EXwTuALbPzL0ycy9ge+B+4MI6hpMk1WNjh4lmZ+aRzQsysy8iPgXcV+1YkqQ6bWzP4Mnh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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x135dc3668>"
+       ]
+      }
+     ],
+     "prompt_number": 1213
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "There is a much larger number of females in the sample"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print_hist(all_data,'TEHRUSL1', \"Histogram for how many hours people work a week\", None, None)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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O1hURPcDFwKHABPAWqnnXsZoaansi8F3gBXUtS6GmfwK21k/vAz7Y6boi4j3A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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x13c089cc0>"
+       ]
+      }
+     ],
+     "prompt_number": 1214
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Most people in this sample work less than 50 hours a week (which is expected)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "axis_labels=['Fed Gov', \n",
+      "             'State Gov',\n",
+      "             'Local Gov', \n",
+      "             'Priv/Profit',\n",
+      "             'Priv/NonProfit',\n",
+      "             'Self/Inc',\n",
+      "             'Self/NotInc',\n",
+      "             'No Pay']\n",
+      "print_hist(all_data,'TEIO1COW', \"Histogram for employee types\", axis_labels, None)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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DeixwurvPSh+K8m4ccZBNZ0fDgOuAvwLfA9YHRrn7dxtawBoxs9HA64HfA+8H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\nsihIREQki4JERESyKEhERCTL/wduU6hCGVlmxQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1513019e8>"
+       ]
+      }
+     ],
+     "prompt_number": 1217
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Most people in this sample work for the private sector (For Profit and Non Profit)"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "2013 Analysis. This part of the report analyzes data for year 2013"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.rc('figure', figsize=(10,6))\n",
+      "xlabel = \"Education Type\"\n",
+      "ylabel = \"Average Hours\"\n",
+      "axis_labels = ['Degree','Personal']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1221
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def prepare_data(data, key):\n",
+      "    data = data.groupby(['TRCODE', key]).sum()\n",
+      "    data.columns = ['weight_sum', 'weight_min_sum']\n",
+      "    data['weighted_avg'] = (data['weight_min_sum'] / \n",
+      "                            data['weight_sum']/60).round(decimals=1)\n",
+      "    ax = data.weighted_avg.unstack(level=1).plot(kind='bar')\n",
+      "    return data, ax\n",
+      "\n",
+      "def plot_bars(ax,\n",
+      "              legend_labels,\n",
+      "              axis_labels,\n",
+      "              xlabel,\n",
+      "              ylabel,\n",
+      "              title):\n",
+      "    patches, labels = ax.get_legend_handles_labels()\n",
+      "    ax.legend(patches, legend_labels, loc='best')\n",
+      "    ax.set_xlabel(xlabel)\n",
+      "    ax.set_ylabel(ylabel)\n",
+      "    ax.set_title(title)\n",
+      "    ax.set_xticklabels(axis_labels, rotation=45) "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1222
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Year 2013 - Differences in Education by Gender"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sum_data=all_data[['TESEX','TUFINLWGT','TRCODE','weighted_minutes']]\n",
+      "sum_data, ax = prepare_data(sum_data,'TESEX')\n",
+      "title = \"Average Hours Spent in Education by Gender and Education Type\"\n",
+      "legend_labels=['Male','Female']\n",
+      "plot_bars(ax, legend_labels, axis_labels, xlabel, ylabel, title )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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6l3chkiRJK4KBJtzfXGMdkiRJKwTnfEmSJNXI8CVJklSjZn5Ym4hYH9gYuAFY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\nJrgtSvLHAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x137167b70>"
+       ]
+      }
+     ],
+     "prompt_number": 1223
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "There is no significant different among the genders in terms of the time spent acquiring additional education. In general, people study more to get a degree than for personal development"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Year 2013 - Differences in Education by Employer Type"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sum_data=all_data[['TRCODE','TEIO1COW','TUFINLWGT','weighted_minutes']]\n",
+      "sum_data, ax = prepare_data(sum_data,'TEIO1COW')\n",
+      "title = \"Average Hours Spent in Education by Employer Type\"\n",
+      "legend_labels=['Fed Gov',\n",
+      "               'State Gov',\n",
+      "               'Local Gov',\n",
+      "               'Priv/Profit',\n",
+      "               'Priv/NonProfit',\n",
+      "               'Self/Inc',\n",
+      "               'Self/NotInc',\n",
+      "               'No Pay']\n",
+      "plot_bars(ax, legend_labels, axis_labels, xlabel, ylabel, title )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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kli5dygknnEKHDh1WlZE5Knl5eWyxxXe4//57CGFrfvCD/WstOxfysgfJ1acQ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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x133b548d0>"
+       ]
+      }
+     ],
+     "prompt_number": 1224
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "In 2013:\n",
+      "- Employees in the Federal Government only pursued education for personal development, while employees in State and Local government pursued both types of education\n",
+      "- Entrepreneurs who are incorporated pursued education for a degree. Entrepreneurs not incorporated did not pursue any additional education\n",
+      "- Employees in the private sector in for profit organizations pursued both types of education vs. those in the nonprofit sector who only pursued education towards a degree\n"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Year 2013 - Differences in Education by Age"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sum_data = all_data[['TRCODE','TEAGE','TUFINLWGT','weighted_minutes']]\n",
+      "sum_data['bin']= pd.cut(sum_data['TEAGE'],[20,35,50,65,80,95],retbins=True)[0]\n",
+      "del sum_data['TEAGE']\n",
+      "sum_data, ax = prepare_data(sum_data,'bin')\n",
+      "title = \"Average Hours Spent in Education by Age Group\"\n",
+      "legend_labels=['20-34', '35-49', '50-64', '65-79', '80-94']\n",
+      "plot_bars(ax, legend_labels, axis_labels, xlabel, ylabel, title )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Hsdhi/VlzzbX51Kc+u9DqbsvwJUmS6mLkyBVhuxMX2vFGrFUeswODBg3irLPO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\n9P8BjDZOyx4hSZwAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x171a727f0>"
+       ]
+      }
+     ],
+     "prompt_number": 1230
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "People younger than 65 pursue both education towards a degree as well as for personal development. It is interesting to notice that older people in this group spend as much time as the younger ones studying towards a degree. For the 65+ group, education for personal development is the exclusive priority."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Year 2013 - Differences in Education by how many hours people work"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sum_data = all_data[['TRCODE','TEHRUSL1','TUFINLWGT','weighted_minutes']]\n",
+      "sum_data['bin']= pd.cut(sum_data['TEHRUSL1'],[20,40,60,80,100],retbins=True)[0]\n",
+      "del sum_data['TEHRUSL1']\n",
+      "sum_data, ax = prepare_data(sum_data,'bin')\n",
+      "legend_labels=['20-39', '40-59', '60-79','80-99']\n",
+      "title = \"Average Hours Spent in Education by Number of Hours spent at work and by Education Type\"\n",
+      "plot_bars(ax, legend_labels, axis_labels, xlabel, ylabel, title )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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PPPE4Z5xxKgMGDGDllVfhq1/9OoMGDeKKK37KzTf/jqampdh5513YbrvtF3k9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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x12c6d5c18>"
+       ]
+      }
+     ],
+     "prompt_number": 1231
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "In this sample, people who work 40 to 59 hours spend about the same time studying as the people who work less than 40 hours. It is interesting to see people who are working between 80 and 99 hours also finding the time to pursue education towards a degree."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Trend Analysis - Years 2003 - 2013"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Data Loading"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trespondent = pd.read_csv(\"atusdata/atusresp_0313.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1157
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tdemographics = pd.read_csv(\"atusdata/atusrost_0313.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1158
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tactivities = pd.read_csv(\"atusdata/atusact_0313.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1159
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Data Reduction"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tresp_reduced = trespondent[['TUCASEID',\n",
+      "                           'TUFNWGTP',\n",
+      "                           'TEHRUSL1',\n",
+      "                           'TEIO1COW',\n",
+      "                           ]]\n",
+      "\n",
+      "for invalid in range (-1,-10,-1):\n",
+      "    tresp_reduced.replace(invalid, np.nan,inplace = True)\n",
+      "tresp_reduced.head()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TUFNWGTP</th>\n",
+        "      <th>TEHRUSL1</th>\n",
+        "      <th>TEIO1COW</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 20030100013280</td>\n",
+        "      <td> 8155462.672158</td>\n",
+        "      <td> 30</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 20030100013344</td>\n",
+        "      <td> 1735322.527819</td>\n",
+        "      <td> 30</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 20030100013352</td>\n",
+        "      <td> 3830527.482672</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 20030100013848</td>\n",
+        "      <td> 6622022.995205</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 20030100014165</td>\n",
+        "      <td> 3068387.344956</td>\n",
+        "      <td> 80</td>\n",
+        "      <td>  6</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1232,
+       "text": [
+        "         TUCASEID        TUFNWGTP  TEHRUSL1  TEIO1COW\n",
+        "0  20030100013280  8155462.672158        30         3\n",
+        "1  20030100013344  1735322.527819        30         4\n",
+        "2  20030100013352  3830527.482672        12         5\n",
+        "3  20030100013848  6622022.995205       NaN       NaN\n",
+        "4  20030100014165  3068387.344956        80         6"
+       ]
+      }
+     ],
+     "prompt_number": 1232
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tdem_reduced = tdemographics[['TUCASEID','TULINENO','TEAGE','TESEX']]\n",
+      "#The person that responds the survey is always TULINENO = 1 but the \n",
+      "#file has demographics for all people in the household. \n",
+      "\n",
+      "tdem_reduced = tdem_reduced[tdem_reduced['TULINENO'] == 1]\n",
+      "del tdem_reduced['TULINENO']\n",
+      "for invalid in range (-1,-10,-1):\n",
+      "    tdem_reduced.replace(invalid, np.nan,inplace = True)\n",
+      "tdem_reduced.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TEAGE</th>\n",
+        "      <th>TESEX</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0 </th>\n",
+        "      <td> 20030100013280</td>\n",
+        "      <td> 60</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3 </th>\n",
+        "      <td> 20030100013344</td>\n",
+        "      <td> 41</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td> 20030100013352</td>\n",
+        "      <td> 26</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9 </th>\n",
+        "      <td> 20030100013848</td>\n",
+        "      <td> 36</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td> 20030100014165</td>\n",
+        "      <td> 51</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1233,
+       "text": [
+        "          TUCASEID  TEAGE  TESEX\n",
+        "0   20030100013280     60      1\n",
+        "3   20030100013344     41      2\n",
+        "7   20030100013352     26      2\n",
+        "9   20030100013848     36      2\n",
+        "13  20030100014165     51      1"
+       ]
+      }
+     ],
+     "prompt_number": 1233
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "codes_desired = [60101, 60102]\n",
+      "tact_reduced = tactivities[tactivities['TRCODEP'].isin(codes_desired)]\n",
+      "tact_reduced = tact_reduced[['TUCASEID', 'TRCODEP', 'TUACTDUR']]\n",
+      "tact_reduced.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TRCODEP</th>\n",
+        "      <th>TUACTDUR</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>804 </th>\n",
+        "      <td> 20030101030611</td>\n",
+        "      <td> 60102</td>\n",
+        "      <td>  80</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>950 </th>\n",
+        "      <td> 20030101030813</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td>  90</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>952 </th>\n",
+        "      <td> 20030101030813</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 160</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>954 </th>\n",
+        "      <td> 20030101030813</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td>  90</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1167</th>\n",
+        "      <td> 20030101030962</td>\n",
+        "      <td> 60101</td>\n",
+        "      <td> 315</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1234,
+       "text": [
+        "            TUCASEID  TRCODEP  TUACTDUR\n",
+        "804   20030101030611    60102        80\n",
+        "950   20030101030813    60101        90\n",
+        "952   20030101030813    60101       160\n",
+        "954   20030101030813    60101        90\n",
+        "1167  20030101030962    60101       315"
+       ]
+      }
+     ],
+     "prompt_number": 1234
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Data Merging"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tresp_dem = pd.merge(tdem_reduced, tresp_reduced, on=['TUCASEID'])\n",
+      "tresp_dem.sort_index(inplace=True, by='TUCASEID')\n",
+      "tresp_dem.head()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>TUCASEID</th>\n",
+        "      <th>TEAGE</th>\n",
+        "      <th>TESEX</th>\n",
+        "      <th>TUFNWGTP</th>\n",
+        "      <th>TEHRUSL1</th>\n",
+        "      <th>TEIO1COW</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 20030100013280</td>\n",
+        "      <td> 60</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 8155462.672158</td>\n",
+        "      <td> 30</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 20030100013344</td>\n",
+        "      <td> 41</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 1735322.527819</td>\n",
+        "      <td> 30</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 20030100013352</td>\n",
+        "      <td> 26</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3830527.482672</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 20030100013848</td>\n",
+        "      <td> 36</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 6622022.995205</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 20030100014165</td>\n",
+        "      <td> 51</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 3068387.344956</td>\n",
+        "      <td> 80</td>\n",
+        "      <td>  6</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1162,
+       "text": [
+        "         TUCASEID  TEAGE  TESEX        TUFNWGTP  TEHRUSL1  TEIO1COW\n",
+        "0  20030100013280     60      1  8155462.672158        30         3\n",
+        "1  20030100013344     41      2  1735322.527819        30         4\n",
+        "2  20030100013352     26      2  3830527.482672        12         5\n",
+        "3  20030100013848     36      2  6622022.995205       NaN       NaN\n",
+        "4  20030100014165     51      1  3068387.344956        80         6"
+       ]
+      }
+     ],
+     "prompt_number": 1162
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tall_data = pd.merge(tresp_dem, tact_reduced, on=['TUCASEID'])\n",
+      "tall_data['weighted_minutes'] = tall_data['TUFNWGTP'] * tall_data['TUACTDUR']\n",
+      "tall_data['year'] = tall_data['TUCASEID']//10000000000\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1235
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Exploring how trends have changed during 2003 and 2013"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def prepare_multi_year_data(data, key):\n",
+      "    data= tsum_data.groupby(['year','TRCODEP',key]).sum()\n",
+      "    data.columns = ['weight_sum', 'weight_min_sum']\n",
+      "    data['weighted_avg'] = (data['weight_min_sum'] / \n",
+      "                            data['weight_sum']/60).round(decimals=1)\n",
+      "    del data['weight_min_sum']\n",
+      "    del data['weight_sum']\n",
+      "    data.reset_index(inplace=True)\n",
+      "    data.set_index(['year',key],inplace=True)\n",
+      "    data1 = data [data['TRCODEP'] == 60101]['weighted_avg'].unstack(level=1)\n",
+      "    data2 = data [data['TRCODEP'] == 60102]['weighted_avg'].unstack(level=1)\n",
+      "    return data1, data2\n",
+      "    \n",
+      " \n",
+      "def prepare_plot_area():\n",
+      "    fig = plt.figure(figsize=(16,8))\n",
+      "    ax1 = fig.add_subplot(211)\n",
+      "    ax2 = fig.add_subplot(212)\n",
+      "    fig.subplots_adjust(hspace=.5)\n",
+      "    return ax1, ax2\n",
+      "\n",
+      "    \n",
+      "def plot_lines(data,\n",
+      "               ax,\n",
+      "              legend_labels,\n",
+      "              xlabel,\n",
+      "              ylabel,\n",
+      "              title):\n",
+      "    x_labels= data.index\n",
+      "    ax.set_title(title)\n",
+      "    ax.set_ylabel(ylabel)\n",
+      "    ax.set_xlabel(xlabel)\n",
+      "    ax.set_xticks(np.arange(0,12))\n",
+      "    ax.set_xticklabels(x_labels)\n",
+      "    ax.plot(data)\n",
+      "    patches, labels = ax1.get_legend_handles_labels()\n",
+      "    legend_labels = legend_labels\n",
+      "    ax.legend(legend_labels, loc='best')   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1244
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Trends in Education by Gender - Years 2003 -2013"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ax1, ax2 = prepare_plot_area()\n",
+      "tsum_data = tall_data[['year','TESEX','TUFNWGTP','TRCODEP','weighted_minutes']]\n",
+      "data1, data2 = prepare_multi_year_data(tsum_data,'TESEX')\n",
+      "legend_labels = ['M','F']\n",
+      "\n",
+      "plot_lines(data1,\n",
+      "           ax1,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education towards a degree by Gender. Years 2003 - 2013')\n",
+      "plot_lines(data2,\n",
+      "           ax2,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education for Personal Devleopment by Gender. Years 2003 - 2013')\n",
+      "       "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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xsM+PPRStys/+nJl7VDYqqQYMupLU/mxPVi0ZaqX2MNhM7kv6/PtHwKtYP+hK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\n8/Atmfj7tf9Vp7qxhuKaUpeUIz46hPtnDeW3D09m7sRU6hrsLPswl+8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AT1/cTllVPf91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qaJypommls7Tnt9TmYKgwZ4SXS72VhWDcCUogHcOq+QQTl9ZiRL2ETb+aKlrYOHHl/PgepGvnrj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\nbvx/C5u3VDwvNVwAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x126071dd8>"
+       ]
+      }
+     ],
+     "prompt_number": 1240
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Time spent acquiring education towards a degree seem to follow similar trends for males and females with males spending on average more time than females in this pursuit.  There is an interesting drop in males pursuing education for personal development in the year 2008 (financial crisis)\n"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Trends in Education by Employer Type Years 2003 - 2013"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ax1, ax2 = prepare_plot_area()\n",
+      "tsum_data = tall_data[['year','TEIO1COW','TUFNWGTP','TRCODEP','weighted_minutes']]\n",
+      "data1, data2 = prepare_multi_year_data(tsum_data,'TEIO1COW')\n",
+      "legend_labels=['Fed Gov', 'State Gov', 'Local Gov', 'Priv/Profit', 'Priv/NonProfit', 'Self/Inc','Self/NotInc','No Pay']\n",
+      "\n",
+      "plot_lines(data1,\n",
+      "           ax1,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education towards a degree by Employer Type. Years 2003 - 2013')\n",
+      "plot_lines(data2,\n",
+      "           ax2,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education for Personal Development by Employer Type. Years 2003 - 2013')\n",
+      "       "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ddc6+5EzmfJeIh5sL425pj5+3u7NDqpR2zRow/q7ONPCvx9JVe5j9bSL5BUXO\nDksIIYQQolbydHVhdOemuFstLN6bQlJOnrNDEsLpHJnEZgNvaa37AA8Bnymlam5XYg2SkZXH1MUJ\nFBQU88DA1kQE+zg7pPPSyObDc8O70LyRP2sSU3jzi3iOZ8kXrhBCCCHEhQj39eS2pqEUFNv5ZMdh\nsqWDQFziHLawk1LKHbBqrU+aj9cAg7XWh86wS41ZYcqZ8gqKGD9jFdv3Z3B3v9YMua6Fs0O6YAWF\nRUxbtIGV6w/SIMCTF0Z2o0m4v7PDEkIIIYSolb7ZkcS3O5JQQT482rUFLlaZsiVqnRq/sNM9QDtg\ntFIqHPADks62w6W+GljJSsTb92fQPTqUq6NDqrRNnLHi2p29WhDo7c7Xv+/myal/8ODANnRoUbcX\nJZCV7RxP2tjxpI2rh7Sz40kbO560seOVtHE3f292BXiTmJ7Fx/F76B9pc3ZodYocy45ns1XNnG5H\nDu/9CPBTSv0OLATu0VoXO7C+KhcXF8uLL46v8nJfe+0l1qz587Tnpy/4hsVzXyUtdjYbfpjCxImv\nkJ2dVeX1VyeLxUL/7lGMGhSN3W5n2uIEfliznxp0aychhBBCiFrBarFwa9NQguu5szolg7gjJ5wd\nkhBO4bCeWK11IXCXo8qvDo5aVddisZxW9rKf/2Lpl3OJvu5BXn2oF37e7ixa9Dmff/4J99//sEPi\nqE5dWgZjC/BkylcbWbRyJ4fTsxneR+HqItOkhRBCCCEqy8PFyl0twng/8QBL96Ziq+dOhE89Z4cl\nRLVy6H1iq1LalwvJjF1XpWX6dumK7dahZ3z9TL2F69b9xYcfzsLd3R1/f3+eeeZFvL29ee+9N9m6\nNZHCwgJGjnyQ7t2v5s03XyM1NZX09CNcdVWP0oS0bNn7kjP56OPPCGnZmyfu6lG6EvFtt91x1jrn\nzfuQ5s1bcOON/UlPP8K//vUoH330SVU0jUM0DvXl+RFdmbo4gVUJSaQdy2X04Lb4eLo5OzQhhBBC\niFojqJ47Q5uFsmD7YT7bmcToNhH4utWa03ohLpp0g50nu93Om29O5PXX32b69Nl06NCZBQs+4o8/\n/svx48f58MMFTJ36Adu2bSU1NYXo6La8++40Zs+ez7Jli08rr2Ql4rysowzr342IYB8OHz7E2LEP\nMmbMA4wadR9AhXUOGDCIH374DoAff/yefv0GVmtbXIhAXw+eHtaJLsqGPpDBhAWxJKVnOzssIYQQ\nQoha5TKdg8BuAAAgAElEQVR/b/o0CuJEQSGf70yisFimaolLR625ZGO7dehZe02rS0ZGBt7e3jRo\nYCxO1KFDRz744H38/f2Jjm4HgK+vL/fd9xDZ2Vls3ZpIXNx6vLy8yc8vOKWs/IIipi3exLHMPFo0\njcDfLQeA8PCGTJv2Afn5+QwbdstpdbZv34HZs2cQFdWEoqIikpOT+e23X5gyZUY1tsSF83Bz4aFB\n0Sz9YzfLV+9jwsfrGTUomjZN6js7NCGEEEKIWuPq0EAO5eSx6WgWy/enMigqxNkhCVEtpCf2PAUE\nBJCdnU16+hEA4uPXExnZmKioJmzbtgWArKwsnnhiHCtWLMfHx5cXXniVoUOHkZd3srQcu93OvBXb\n2JN0gu7RoYy5fwQLFnxUWi7A+vXrsFgsp9W5YUMckZGNAejXbyAzZkyhSZOmeHvXnvvJWi0WBvdo\nxv39W1NQWMR7izbyW9xBZ4clhBBCCFFrWCwWhkSFEObpztq0E6xJPe7skISoFrWmJ9YZLBYL69at\n4b77hpc+9+KLE3jqqWd59tl/YbFY8PPz49lnX8LPz5/Y2LWMGnUfRUVF3HvvAwQHh/Dyy8+h9VZC\nQ8NQqhVHjqQBsHZrKhtSTtK8oT8j+rbEzdXKqFH/5LXXXqKwsJDc3FyCg4OZMOENgArrBOjZsxdT\nprzDG2+8V+3tUxWuiA7FFujJ9MUJfPrTdpKO5DC0V3NcrHJ9RQghhBDiXNxdrNzZIpz3E/ezfH8q\nIZ7uRPl6OjssIRzKUoNudWK/VO7LFLstlRlLNxPk58HzI7qWLuTkaDX53ldHMnKZsjiBQ2nZRDep\nz0M3ReNVr3ZeY6nJ7VxXSBs7nrRx9ZB2djxpY8eTNna8yrTxrhM5zNOH8HJ1YXSbCPzdZeHM8yXH\nsuPZbL5VcvsX6e6qZvuSM5nzXSIebi6Mu6V9tSWwNV2DAE/G39mZds2C2LznKK99EktqRq6zwxJC\nCCGEqBWa+Xnxj0gbWYVFfLYziYLiYmeHJITDSBJbjUpWIi4oKOaBga2JCK49c1irg6eHK+OGtOOG\nrhEkpecwYUEs2w9kODssIYQQQoha4YpgfzoF+XIwO49le1PPeLtIIWo7SWKrSdmViG+5thkdW9ic\nHVKNZLVaGHp9C0b0VeTmFfLWF/GsSkhydlhCCCGEEDWexWLhpqhgGnl7EJeeyeoU6QwQdZMksdWg\n/ErEfbtFOjukGu+aDg157PYO1HN3Ye73W/ly5U6K5WqiEEIIIcRZuVmtDGseho+rCysOHGHXiRxn\nhyRElZMkthosX72XNYkppSsRWyxVMp+5zmvVOJBnh3chpL4XK9bs5/2vN3Eyv9DZYQkhhBBC1Gj+\n7m4Max6GxQJf7EriaF6Bs0MSokpJEutgsdtSWfLHHoL8PBgzuC1urtLk5yO0vhfPDe9Mq8aBxO84\nwqRP4zh64uS5dxRCCCGEuIQ19vVkQGQwOYXFfLbjMPlFstCTqDtq5z1MqklcXCwvvPAMTZo0xWKx\nkJeXxw039GXIkNtLt1mz5k9SUpIZOPDm0/YvWYk4KXY+t49+gDtu73fWss5my5bNvPrq8/Ts2YvD\nhw/x/POvkJ5+hJ07d3DllVdX2XuuibzrufHobe35/Oft/GfDYV5dEMvYIe1oGu7n7NCEEEIIIWqs\nmGB/DufksTbtOIv3pjC0aaiMCBR1giSxZ2GxWOjSJYaXXnoNgIKCAu64Ywh9+vTDx8dYWbhbtysq\n3LdkJeKcE+l0aN2M4ABPOnfuyssvv35KWX379sPb+9yrFK9d+ye33jr0lKR3/fp17N+/r84nsQCu\nLlbu6qMIC/Jm4W87eOPzOEb2a0VMqxBnhyaEEEIIUWP1j7SRkpvHpqNZhHsd45qw+s4OSYiLVmuS\n2NW/7WL3ttQqLbNpy2C6X9fsjK/b7fZTlibPzs7GarXyyCOjCA9vyIkTx+nVqw8HDx6gQYMGZGZm\ncs8995OVncvtQ28lvPs/aeF3hN5X9j6t7OzsbFxcXLBaXRgz5gHq1w8iM/MEb745mYkTXyEp6RBF\nRcXcfvswwsPD+f77b3Fzc8NmC2HatHf55JNFfPrpfPLy8mjbtv0lkchaLBZ6d40gpL4ns5ZtYday\nLSSn5zDgyii5qiiEEEIIUQFXq4U7mofx/pYD/HQwnVBPD1SAt7PDEuKinHOCplIqSCnV2/x5vFLq\nS6VUa8eHVjPExcUyduyD/POfD/PKK8/zyCNP4unpSe/efZg8eQYuLi4A9OnTj99++xm73c5r73+B\ne33Fle0akp+xh86du2K3289YlsVioXfvPrz33vt8883XBAbWZ+bMuUyePIMPP5xJeHhDbryxP7ff\nPowePa4FwGq1ctdd93DDDTdeEglsWe2aNWD8XZ1p4F+Ppav2MPvbRPILipwdlhBCCCFEjeTr5sqd\nzcNwsVj49+5kjpzMd3ZIQlyUyvTEfgF8q5SyA7cAk4FZQA9HBlZe9+uanbXX1FE6depSOgS4xOef\nf0xkZNQpz/n6+nLZZYpZn60g7q/f6NzzDm6/Noq311lwc3M7Y1klSsrbt28vXbp0A8DLy4smTZpw\n6NBBgNNuWF2+p/hS0sjmw3PDuzB9ySbWJKaQlpHL2MFt8ffxcHZoQgghhBA1TiOfegyKCuarPSl8\nsiOJUa0j8HCRBUdF7VSZIzdQaz0NuAlYoLX+GPBybFg1X8nw1bJJ5GXtr2XZ0kW4Wop5+t5ebNyw\nrjQhrWx5jRs3YePGeABycrLZtWsnYWENK9zHarVSXHzprjTn5+3Ok0M7ckWbUHYfPsGrH8eyPyXT\n2WEJIYQQQtRInRr4cWVIAGkn81m0O5niS7QzRNR+lUliLUqpzsAgYLlSqgO1aC7txbBYLOeca1my\nzb7kTFZud6EgK4VhQ2/Bz9udP//8H1dccVWlywK46abBnDhxnFGj7mPs2Ie4994HCAwMLC3DrBWL\nxUKzZs1Zteq//Prrzxf1PmszN1cr9/VvxeAeTTl6Io+Jn8axYccRZ4clhBBCCFEj9Y1oQFNfT7Zm\nZLPy8FFnhyPEBbGcaziqUup64FngG631ZKXUauA5rfVvVRyLPS2tdvaiZWTl8eqCWDIy8xgzpC0d\nW9icHVKFbDZfamsbV0bstlTmLE+koLCYW3s2p09MhFMWfKrr7VwTSBs7nrRx9ZB2djxpY8eTNna8\nqm7j7IIiZiTu51h+IXc2D6N14LnvlHEpkGPZ8Ww23yo5Oa9Mj+pdWuvrSh5orbtXRcV1RX5BEdMW\nb+JYZh63Xtusxiawl4IuLYOxBXgy5auNLFq5k8Pp2Qzvo3CV+R5CVFpSdgprk+PI2ZXNkKiBuLu4\nOzskIYQQVczbzYU7W4Qza+sBFu1O5uHWEYR4yroiovaozNl9tFLK1+GR1EJ2u525329lT9IJukeH\n0rdbpLNDuuQ1DvXl+RFdaRzqy6qEJN5ZuIGs3AJnhyVEjXY87wS/7v+dSWsnM2HNO/y0byWbU7ZR\nWFzo7NCEEEI4SJiXB0OahJBfbOfTHUnkFsqdHkTtUZme2GJgv1JKA7nmc/ayvbOXquWr97J2ayrN\nG/ozom9LuVdpDRHo68HTwzoxZ3ki63UaExbE8s9b2xEWJPdEE6LEycI8NqZtZm1yHPrYTuzYsVqs\ntG3QmpjQTvRUXTl+LM/ZYQohhHCgdvV9ScrO47/Jx/j37mSGtwjHKuezohaoTBL7rwqeu+SXMovd\nlsqSP/YQ5OfBmMFtcXOVIas1iYebCw8PimbpH7tZvnofEz5ez6hB0bRpUt/ZoQnhNEXFRWw7toO1\nyXEkpG0hv9gYpdDErzExoR3pFNweH3fjYo+7qzsgSawQ4uwKCotJPZqD3W6X5KeW6t0oiKTcPLYf\nz+Gng+n0jWjg7JCEOKfKJLF2JGk9xb7kTOYsT8TDzYVxt7THz1vmjNVEVouFwT2aEVbfm3krtvLe\noo3c0bsF13Vq5OzQhKg2drud/ZkHWZscx/qUjWQWZAFg8wwiJrQTXUM6YfMKcnKUQojaaOveo8z9\nfivpJ/Lw9HChcYgvjUN9iQr1IyrUF1ugpyS2tYDVYuH2pqHMSDzA78nHCPfyoF2QzCQUNVtlktiX\n+TuJdQPaAX8AvzsqqJosIyuPqYsTKCgsZsyQtkQEy2puNd0V0aHYAjyZ9nUCn/60naQjOQzt1RwX\nq/Sei7rrSO5R1iXHsy4ljpScNAB83Ly5plF3uoZ0IsrPOat3CyFqv7z8Ir76zy5+jTuI1WKha+sQ\nDiRnovdnsG1/Rul2ktjWHp6uLtzVIpyZiQdYvDeFBp7uhHvJQk+i5jpnEqu1vrbsY6VUE2CyowKq\nSeLiYnnhhWdo0qQpFouFkydPUhzQhuL6nUtXIl6z5k9SUpIZOPDmM5bz1FOPMnTonTz99GN88ski\ngoNDAJg5cxpRUU248cb+5xXXmDEPkJeXh6enJwAuLi48++zLNGhQueEfL744nsOHD9Kv301YrVYG\nDryZZcu+pl+/gbi61s1bADdv5M/zw7swZXECv8YdJOVYDg/dFI1Xvbr5fsWlKasgm/jUBNYmx7P7\n+F4A3KyudA5uT0xoJ1rVvwwXq4tzgxRC1Go7Dx5nzneJpB7LJSzIi/v6tyamXUPS0jLJzStkf0om\ne5Mz2Zds/C+Jbe0R7OnOrU1D+HRnEp/uOMzo1pF4u8nfDFEznfcZvNZ6j1KqpSOCqWksFgtdusTw\n0kuvYbfbmbFkI1/OfJLbHuxZuhJxt25XnLWM5ORkQkJCAXB39+D1119m8uQZpeVfaFzPP/8KkZGN\nAVi69Cu++OITxo59tFL7r1+/juXLfz7luU8/nX/eyXRt0yDAk/F3duaDb7aQsCud1z6J5Z+3tic4\nwNPZoQlxwQqKCticvo21yXFsSd9Gkb0ICxZUYHO6hnaigy0aT9d6zg5TCFHLFRQWs3TVbn5Ysx/s\n0Ccmgpuvbop7mSTH08MVFRmIigwsfU4S29qldaAP14fX59fDR/liVxL3qIa4yOcgaqBzJrFKqXll\nHlqAVsCmylaglAoG1gPXa623n3eEpmOHfiYnI/FCd6+QV0BrAhv2PuPrdrsdu90YSb189V7Wbj6A\nu5sr61dM4cWERpw4cZxevfpw8OABGjRoQGZmJvfccz/5+fncc88dLFiwkNWr/6B796sB6NSpC2Bn\n8eJFDBly2yl1ffHFp/z220+4uLjSvn1HHn54LB999AHJyUkcO3aU5ORkxo17jJiYy0uiK933+PHj\neHl5ER+/nhkzpuLu7s7AgTdTv359PvxwFu7u7vj7+/POO2/x9tuTyM7O4plnHqdHj57s27eXRo0i\nSE9P56WXnuX119+q0jauaTw9XBk3pB2LVu7kp3UHmLAgljGD23JZRICzQxOi0ortxezK2MPa5Hji\n0xLILTwJQEOfMGJCO9ElpAMBHv5OjlIIUVfsS85kzneJHErLxhZQj5H9Wlf676YktrVPz/D6JOXk\nkZiRzYoDR+gfaXN2SEKcpjI9sf/l74zJDiwCfqlM4UopN+ADIPuCoqsB4uJiuXvkvRw6koO7uytP\n/etpli7+nN69+3D11deyYsVyAPr06ceoUSO55577WbXqd6688mpcXV2Jj1/PgAGD2LRpIwCPP/40\n998/gssv715ax65dO1m58hdmzZpnDg1+ktWrV2GxWHB3d+ftt6eybt0aFi78jJiYy7Hb7bz66ovU\nq1cPq9VKZGQUo0aNY+vWLRQUFPDhhwuw2+3cdtsgZs78iAYNGvDllwuZMWMGTzzxNH/8sZKJE9/h\n+++/xWKx0L//TSxY8BEvv/y6U9q4ulmtFoZe34KwIC8+/Wk7b30Rz4i+LbmqXZizQxPirJKyU1ib\nHMe65HiO5RknewEe/lwVfjldQzvS0EeOYSFE1SksKub7P/fx7eq9FBXb6dmxIbf2bEY994ubiiOJ\nbc1mtVi4tWkoMxMPsDolg3AvDzo18HN2WEKcojJzYucrpdoC15rbJ2it8ytZ/lvATOCZC47QFNiw\n91l7TR2lZesOZNr+QXOLhfF3dSYi2Ieliz8nMjLqlO18fX257DLFxo0b+OGH5YwZ8ygnT57EarXg\n5uZWup2fnz/jxj3OhAkv0rZtewD2799LmzZtcXExhuS0b9+RPXt2AdCixWUABAeHkJ9v3O6i/HDi\nskqey8jIwNvbu3SebPv2HZg/f/Zp25f0NF+KrunQkOAAT2Ys3czc77eSlJ7NkGubyR/DWuBSOm6P\n550gNmUD65LjOJB1GIB6LvW4IqwrMaEdaR7QFKtFFikTQlStQ0eymbM8kX3JmQT6enDPP1oS3cRx\nK5lLYluzeLhYuatFGO8nHmDp3lRs9dyJ8JGpKaLmqMxw4ruAl4BlgBVYopSaoLX+6Bz73Q2kaa1/\nUko9gzEUuVbJyi1g+4EMGgSevhJxyXzWsifTAwbczKJFn5GXl09kZGNWrfovXbp0O63cK6+8mt9/\nX8mKFcsZNWocjRtHsXDhZxQVFWG1WtmwIZ6+ffuxc+d2ztxsFZ/El8QVEBBAdnY26elHCApqwIYN\ncTRp0uSM79VisVBcXISxAPWlo1VUfZ4d3oUpXyWwYs1+ko/mcP+A1hd9lVk4xubd6Xzx6w5CgrwZ\nN7its8NxmJOFJ9mYtoW1yXHoYzuxY8dqsdK2QWtiQjsRHdQKd5dL63dVCFE9iovt/LTuAF//vpvC\nomK6R4dyR68WeNWr/u+csyW2JUmtJLaOE1TPnaHNQlmw/TCf7UxidJsIfN3k/EjUDJU5Ep8AYrTW\n6QBKqQkYQ4zPmsQC9wB2pVQvoAOwQCl1k9Y65Uw72Gw1555UeQVFfLt6L/mFxYzo15obujctfc3N\nzYX69b2x2Xzx8/PE29sDm82X3r2v4Z13JvLwww9js/kSH7+WMWPGYLP5Ehjojaene+l7fOWVFxk4\ncCB+fp5069aRgQP7M27cAxQXF9OlSxeGDBnA9OnT8fPzxGbz5cQJL9zdXbHZfM36fU5rr/J1TJz4\nGi+99AwWiwV/f38mTZpEQIAvVqv1tNi7dYth/PjH+fjjj6uvkWsIm82XyY9ew8QF64jfcYS3F27k\nuXu7YQu88AWfatKxXBekHsthzrLN/LkpCasFesU0rnNtXFRcRELKVv7Yu5a1hzaQX1QAwGVBTbm6\ncQxXRHbGz6N6b+lV19q4ppJ2djxp48o5fCSLyYs2sHXvUQJ8PBh1S3uuaFu5aQrV2caRjQK5qszj\nnJMF7D50nJ0Hj7PzQAY7D2agD5ya2HrVc6VZwwCaNfKnRUQAzRsFEBrkjdVaexJbZxzHNpsvmRZY\nrA/z5b5UHu/WAtc6fotC+b6oHSznGpanlNqktW5b7rkErXW7ylailFoJPHiOhZ3saWmZlS3Soex2\nOx98s4W1W1PpHh3KyH6t6sT9FG02X2pKG9dEhUXFfP7zdv6z4TD+3u6MHdKOpuHnPwdE2rnqFBQW\n8+Pa/Sw3Lyi1aOTPsN6X0Tk6vE60sd1uZ3/mQdYmx7E+ZSOZBVkA2DyDiAntRNeQTti8HDd872zk\nOK4e0s6OJ218bna7nf/EH+LfK3eSX1BMZ2Xjrj4KPy/3Su1fE9u4oh7blKM5p4xjq009ts5sY7vd\nzr93J5NwNIsYmx+DokKcEkd1qInHcl1js/lWyS9YZXpiE5RSkzF6Xi3ASGBjVVReUy1fvZe1W1Np\n3sifEX1b1okEVpybq4uVu/oowoK8WfjbDt74PI6R/VoR06ruflnXZJt3p/PZz9tJOZaLn5cbw/sq\nrmgTWid+H4/kHmVdcjzrUuJIyUkDwMfNm2sadadrSCei/CLqxPsUQtR8R0+cZN73W9my9xje9Vy5\n+8aWdGsVUuu/g2QoctWxWCwMjgoh7WQBa9NOEOZVj27BsgK+cK7KJLH3Y8yJnYsxJ/Y3YNT5VKK1\n7nnekTlJ7LZUlvyxhyC/eoy5uS1urnV7yIQ4lcVioXfXCELqezJr2RZmLdtCUnoOA6+MqvV/0GuL\n9OMnWfjrDtZvT8NigV6dGzHo6iZOmY9VlbIKsolPTWBtcjy7j+8FwM3qSufg9sSEdqJV/ctwscpN\n5YUQ1cNut7N6czKf/7Kd3Lwi2jULYkTflgT6ejg7NIeRxPbCubtYubO5sdDT8v2phHi6E+V74dOu\nhLhYlVmdOAf4VzXE4nT7kjOZszwRDzcXxt3SDj/vyg2jEXVPu2YNGH9XZ6Z+lcCyVXtIPprDPTe2\nPOWm7qJqnWnocGRI7Z2bUlBUwKb0raxLjmdL+jaK7EVYsKACm9M1tBMdbNF4uspqj0KI6nU8K48F\nP2g27DyCh7sLd9/YkqvbhV2SF2slsa28QA83/q9ZKPP0IT43F3ryd6/dF5hF7XXGJFYptafMQzun\nLpNr11o3pQ7JyMpj6uIECgpPX4lYXJoa2Xx4bngXpi/ZxJrEFNIychk7uC3+PnX3KrWz1KWhw8X2\nYnZl7GFtcjzxaQnkFp4EoKFPGDGhnegS0oEADxmGJYRwjnXbUvnkR01WbgEtIwO49x+taBAgPWpl\nnTOxTclkb9Klmdg28/PiH5E2lu9P49MdSTzQqhFudXyhJ1Ezna0ntuwQ4O+Af1ALb5NTGfkFRUxb\nvIljmXncem0zOrawOTskUUP4ebvz5NCOzF+xjT+3JPPqx7GMG9KuVvcO1iR1aejw4axk1qXEsy45\nnmN5xklNgIc/V4VfTtfQjjT0qdwKn0II4QhZuQV8+pNm7dZU3F2t3NGrBdd1blSnEixHksT2b1cE\n+3M4+yRx6Zks3ZvKLU1q/xxqUfucMYnVWu8t+Vkpla+13lctEVUzu93O3O+3sifpBN2jQ+nbLdLZ\nIYkaxs3Vyn39WxEW5MXXv+9m4qdxPDCwtVzsuAh1Zejw8bwTxKZsYG1yHAezDgNQz6UeV4R1JSa0\nI80DmmK1yBVqIYRzbdx5hPkrtnE8O59m4X6M7N+a0Ppezg6r1rtUE1uLxcJNUcGknswnPj2TcC8P\nrgwNPPeOQlShS/6OxedaifiTT+azfv1aCgsLsVqtjB79CEq1PK2cuLhYli37mpdffp3Fi//NkiVf\nce+9D3Lddb146qlHefTRf/Hii+P54IN51fXWRBWyWCz07x5FaH0v5ixPZPriTdzaszl9YmQV2fNV\n24cOnyw8yca0LaxNjkMf24kdO1aLlbYNWhMT2onooFa4u9S+nmQhRN2Tm1fIF7/uYFVCEq4uFm65\nthl9YyJr1b1Ra5tLJbF1s1oZ1jyM97ccYMWBI4R6edDMTy6MiOpzSSex51qJeM+e3axe/TszZ84F\nYMeO7bz22kvMn//5Wcv9739X8sork2jatBnJycmEhIQ67D2I6tWlZTC2AE+mfLWRRSt3cjg9m+F9\nFK4u0tt2LrV56HBRcRHbju1gbXIcG9O2UFBcAEATv8bEhHakU3B7fNy9nRylqC3sdjtpGbkkH82l\nm48s7CUcY+veo8z9fivpJ/KIDPbhvv6taSTrfThFVSS2bVsE4+NuJbS+V41ZZNLf3Y1hzcOYow/y\nxa4kRrWOpL5Hzf+bXpHivDxO7tlNvpvCuBmLqOnOtrDTyjIPm5d7bNdaX+e4sE634kAam45mVVl5\nhUXFpO09ftaViH18fEhJSWH58mV063YFLVpcxocfLmDXrp1MmfI2drsdf39/nnnmhdJ9vvlmCdu3\nayZNepWXX36dP//8H927X31KuWPGPMBllyl2795FdnY2r776BqGhocyfP4dVq36nqKiQQYNu4aab\nBlfZ+xVVp3GoL8+P6MrUxQmsSkgi7Vguowe3xcezdn5xO1ptHTpst9vZn3mQtclxrE/ZSGaB8f1j\n8wwiJrQTXUM6YfMKcnKUoqYrSVhLVjjdZ/7LySsEwOvbLfTs2JDeXSPw85IV8cXFyyso4qv/7OLX\n9QexWiwM6B7FgCuj5GJrDXO+ie2Paw8AxuI0DQLqERbkTXiQN2FBXoQ18CY8yMspF4Ub+3oyIDKY\npftS+WzHYR5sFYF7LTnWinKyyU7YSFbcerI3b8Ken89hDw/8r+tF/T434uIjF31qsrP1xL58ltfs\nVR1IdSq228nMKaC42M4DA1ufcSVimy2YSZPeYfHiRcyb9yH16tXj/vsf5osvPmX8+Bdp3DiK5cuX\n8dlnH9O1azcABg68mZ9//oEnnxxPWFg48fHrGTBgEEeOpJWWa7FYaN06mnHjHmf27Bn88ssPxMRc\nzpo1f/LhhwsoKipi1qzp1dIW4sIE+nrw9LBOzFmeyHqdxoQFsfzz1naEBUlvXFm1cejwkdx01iXH\nszYljtScIwD4uHlzTaPudA3pRJSfDCEXFTtXwloipL4XbZsFEejjwV+JKXz35z5+XneAHh3C6RsT\nSX0/6Z0VF2bnweN89F0iKcdyCQvy4r7+rWkS5ufssEQlnS2xPZpdwI59RzmcnkNSejYJu9JJ2JV+\nyv7+3u5lklozwQ3yJsDH3aF/t2KC/Tmck8fatOMs3pvC0KY19+984YkTZG2IIytuPTlbE6GoCAC3\nkFC8Wrcmd+MGjq34juP/+Y3APjcS2Ks31nqyendNdLaFnf5TjXGc040RNm6MuPiFdPILinjj83hS\nkk6ccyXiQ4cO4u3tU9rTum3bVp54Yiz5+QW8/fZEAAoLC4mIqHgxqJMnT2K1WnBzO/3K2GWXKQCC\ng0M4ejSdAwf207p1GywWC66urowZ88jFvlXhYB5uLjw8KJqlf+xm+ep9TPh4PaMGRXOtrWb3MFaH\n2jZ0OKsgm/jUBNYmx7P7+F4A3KyudA5uT0xoJ1rVvwwXa80YviVqhvNNWBuH+BIV6ktkiC9e9f7+\n03vf4HYs+XU7K9bs45fYg6yMO8SVbcP4x+WRBAfK/DJROQWFxSxdtZsf1uwHO/SJieDmq5vWmGGn\n4sKVJLY2my9pacGlz2flFnD4SDZJ6dkkpedwOD2bpCM5bCs3JNkow4UwM6k1kltvwhp4YfP3rLL5\n0XJ9tfIAACAASURBVP0jbaTk5rHpaBbhXse4Jqx+lZRbFQrS08mKX09W3Hpyd2wHu9EX5xHZGJ9O\nnfHp1Bn3sHAsFgv1H76PnV8u49iK70lf+jUZv/xM/X/0w//a67C6y2iZmuSSmhN7visR79y5g2++\nWcIbb7yLq6srERER+Pj44eXlxXPPvUxISCgbNsRx/PjximojNnYNXbp0O0Ppp35pREZGsWTJV/w/\ne2ceHsdZpftfVe+7ulv7Ltlye7cl73YSZyWLncUEuAzrhYSBhACXYdiZgTAQhnmYfSaBOzBsYYBc\nkjjE2eyEhMSx48SWvNsty9Zura1Wq/elqu4frd3a1docvc/TT3dVV3V9Ovrqq+/9zjnvURQFSZL4\nyle+yD/8wz+jVr+r/kULDqIg8N7rlpDjMPHzF87xz0+c4GxjN6uL7CwrsKF6l9VOW0ihw3EpzinP\nOd5preKM5zySIiEg4LIvZXN2BesyVmNQL3rEFpE6wjoSdBoVN23IZ+f6XA6faeX5w/W8fuIyb5y8\nzJYVWezaVkRexmJI2yJGR32rn58+d5bmjiAZaXru27WSZQVpc92sRcwwzAYNywrSrvhfR+MSrb3e\n2j6vbYsnRH2rn0uXe4Ycq1YJZDmMvaHJxn6im+M0olFPbgFELQp8qFfoaX+Th2yDDlfa3EWnxVou\nE6iqxF95jGhdbXKnIGBYWoa5fAPm8go0GVc6slQ6HY5bbydt5/V4D+zHu/9FOp74HV37X8S5+y5s\n11yHsDg3nxd4V/0XxlMiHo6dO2+gvr6W++//GAaDAUVReOihL5CRkcX3vvdtJElCFEW+9rW/oaOj\n/YrfO3z4TT7xib/s3x7teoIgUFa2jC1btvPAA/chyzJ79rxvkcAuIGxbnU1GmoFH957ihUN1vHCo\nDrNBw/qydDYsy2BlsX3SD4SFhoUQOiwrMhe7a3m7tYqqjpOEExEA8sw5bM6uYGPWetJ0tjlu5SLm\nEjNJWMeCWiVy7dpcdqzO4ai7nX2H6nnrbBtvnW2jvCyd3duLF8NCFzEECUnm+cP1PHuoDklWuKE8\nj/ffsAS9dnHu8G6GTqOiKDspBjUYCUmmoztMSx/B7ewluF0hmjuCQ46dat6tRaPmI2U5/N9zTfz+\nUisPriwgXT873ktFUYg21BOoTHpcYy3JsneoVBhXrU56XNeXo7ZNbIFH1Btw3nk3aTfcRNdLL9D9\nygHaH/8V3hdfwHnXPVi2bkN4lzkq5hsERRk/vdXlcpUAK4H9QL7b7a6dgbYoHR3+GfjZJI6eb+fR\nvadxWvX8zcc3jijkdLUjGYoyczZeRPIh0d4T45V36qms7sAXiAGg16pYu8RJxbIM1pQ6MeiunknG\n8NDhmypmPnR4sn35cqCVd9qqeKe1Cm80GWaVprOxKaucTdnl5JlzZqqpCxbvhvFiMoS1ONuSMsI6\nGKPZWVEUTtR42He4rt97sqrEwe5tRUPy5RYxPq7GvtzcGeSn+85S3+rHbtHxiTuWs7pk7oTmrkYb\nzzfMlI0VRcHrj/aHIw/24PpD8SuOn0jebWVnD3+obSNDr+WBlfnoVTOziK/IMpGLNfgrjxGoPErC\nk8wRFjQajKvXYKnYgGntelSmiXuER7NzwtdN13P78L3+GkoigTYnF+fdezBXbFgks5NERoYlJd6N\ncUmsy+X6IPBNwAjsAKqAr7jd7l+nogGDMGMktr7Vzw8eP4YgCHzjoxtGFXK6mqEoCoJZR9wfQbN4\ns80o+gZAWVG4dLmHSncHx6rb6ehOev3UKpHVJQ4qlmWwvix9waoaz2Xo8EQe5r5oD0fbjvN2ayVN\ngeSKrF6lpzxzDZuzy1maVoooLN4Lo+Fqm5TOB8I6Esazs6IonKv3su9QXX+eW1m+jV3billT6phX\nkQ7zFVdTX5Zlhf3vNPLU65dISDLbV2fzoZvL5lxv4Gqy8XzFXNh4tLxbT0/kimOH5902yQkuxKOs\nzrHxkbLclNW7VRIJQu7zBCqPEqiqROpJLvKJBgOmteswV2zAtHotok43pd8fz85xjwfPs8/Qc+gg\nyDK6wiLS99yLcfWaxfF4gphNElsF7AT+7Ha7y10uVw7witvtXpmKBgzCjJDY7kCUv/vlUbr9UR66\nd82YQk5XMy74gvy8+jKiAFl6LbkmPXkmHXlGPdlG7SKxTSFGGgAVRaGxPUBldQfHqjv6Q3dEQcBV\nmMYGVwblZRnYLVMbdGcbw0OHP3Dj0lkNHR7tIRNJRDjRcYa3Wytxe2tQUBAFkVXO5WzOrmC1cwVa\n1cJcNJhtLORJ6XwlrCNhMnauafax71BdvyJpUZaFXduKqHBlpGyCeDViIfflwWj3hvjZc+e40OTr\nTdlYTsWy+TGnuVpsPJ8xn2w8Wt5tW1cISR7GK0RIs+opy7b2e20nm3crR6OEzp7GX3mM4InjyKEQ\nACqLBXN5BebyDRiWr0AcQUh1spionWNtrXie2Yv/nSOgKOiXliXJrGv5tNtwtWM2SexRt9u90eVy\nVbnd7vLefafcbveaVDRgEFJOYvuUiGt7lYhv31qU0t9fSAgnJA57A7g7emgJRUkM+r8vEtvUYiID\nYGtXKElo3R3UtgwILSzJs7JhWSYVy9LnpTLpXIQOj4TBNpZkiXNd1bzTVsWJjjPE5WT4U4m1iM3Z\n5VRkrsOsXSx9NFnMpwnTWFAUhfbucLKu4jwnrCNhKnZuaPPz3OF6jp5vRwFynEZ2bStiy8qsd52Y\n3ESwUPryaFAUhdeqmvn9qzXE4jIbXBl89FbXvKorvNBtvBCwEGw8PO+2oSPIqWYv0UAcRRrKN8bL\nu5VCIYKnemu4njqJEkumZ6kdjl5F4Y0YlpalPJR3snaONjXSufcpgserADCuXEX6nnvRl5SmtF1X\nE2aTxP4SOAp8Bvgw8CBgcLvdH01FAwYhpSRWURR+8sczvH2une2rs7lv14p3vZu/78aUFIWOcIzm\nYITmUJTmYHSR2KYQkx0Au3oiVF3o5Ji7HXdjd5/yO/kZZja4MtiwLIO8DNOc9t/5pjqcnm7m2KVz\nvN1aydG24wTiSc92hsHJ5uwKNmVVkGGcu/ywqwHzccK00AnrSJiOnVs8QZ5/q563zrQhyQrpNj13\nbC1ix5ocNOrFsboP87EvTxRdPRF+/vw5ztR5MenVfPg9y9iyImvezWcWso0XChaqjVtCUR4724Ac\nlbjVmUbEHx8379ZMHEfYQ3q0G2fcR5ZJRdHqJeRuKkdfXDKj/X+qdg5fuoRn75OEzp4BwLS+nPR7\n3osuvyDVTVzwmE0Sawa+BdwMiMCfgIfdbneq76SUkthn36zl6TdqWZpv48sfLF98oDP2jblIbKeP\nWKiFUPdZHBkFSOolCMLkhQx6QjFOXOjkWHUHZ+u6SPSuXGbaDWxYlkGFK4OSHOushg7OZeiwoij4\n4wG8kW48ES9dES9dkW6qfRdo8bcDYNaY2JC1jk1ZFRRbC+bd5G6hYq4nTFcjYR0JqbBzpy/MC0ca\neONECwlJJs2s5bbNhexcn4dOe3Wrok8Ec92XpwJFUTh0upX/ebmacFRi7RInH79t+bxNOVmINl5o\nWMg2Ptnl53cXW3HqNDy4sgDDoDBib3Mbl94+QWN1Ay3eCB6NFY/Whk9z5SK5QafuD0WeqXq307Vz\n6Pw5Op9+ksjFGhAELJu34LxrD9qsrJS072rArJHYWUTKSOyiEvHImOyNuUhsx4csxQh5TxPwVBIL\nXe7fr9KmYc3ajtmxHkGc2oQ6HE1w8qKHY9UdnLroIRqXALBbdFSUJQntTNainY3QYUmW8EZ9dEW8\neCPdvSQ1SVS7osl9cTlxxXkalYa1zpVszq5ghWMZKnFxop5qzOaE6d1CWEdCKu3cHYiy/51GXq1q\nJhqTMBs03LIxn5s25M+58M9cYqFN/n2BKL980c3xmk50WhV/cVMZ167NmdcLdAvNxgsRC93GLzV2\n8udWL8tsRj5oEQiNUMNVv2QplooNmMs3IKc5Jpx3q1aJZDsMA8JS6UmCm+0wTLq8YSrsrCgKwVMn\n8ex9imhDPYgi1h3X4Nx9NxrnYpTYbHpiG4E8oLt3V1rv54vAp9xu9/FUNIQUkdhUKxH/+te/4Nix\nt0kkEoiiyGc/+39wjZC0XVl5lGeeeYqHH36EJ5/8PU8//Qc++clPc+ONN/PVr36RD37wI3zta3/F\nr3/9BJmZydWYxx77d4qLS7j99t0jXrunp4cjRw5xyy238fzzz9LQUM9nPvPQlP+WVNyYi8Q2iVio\nhYCnkmDXKRQ5BggYrGWYHGsRpMt0NB0BRUKlNmPJ2obZuQFRNfXFlFhc4kxdF5XuDo7XdBKMJCf3\nM1GLNpWhw5FEdAgx9UaHElVftAeFkccgs8aEQ5+GQ2/H3vvu0Ntx6NJYVViCv/vKEKRFpA4zWc7h\n3UpYR8JM2DkQjvPy0UZeOdZEMJLAoFNxY0U+t2wqmFd5lLOFhTT5f+d8O79+yU0gHGd5YRqfvGMF\n6WmGuW7WuFhINl6oWMg2VhSFcEM9jzd4qNNbWV11iI1vv5qs4bp8RVKcaX0F6rTxa7iOVe82GpOG\nHCsIkGEzkN3vuR2/3m0q7azIMoHKY3ieeZpYy2UEtRrbzhtw3LEbte3dW5M+VSR2IjOCPwN/cLvd\newFcLtftwAeAfwceBbanoiGpQHcgyr89eZJ4Quahe9dMm8DW1l7i0KHXeeyx/wbgwoVqvv/97/CL\nX/zPmOf9+c+v8t3v/j2lpUtobW0lKysbAK1WxyOPPMy//MujAOOuqtbUVHPw4Ovccstt82YFViUI\nZBt1ZBt1bOjdNxqxbQnHONaZPOZqILYjeV1VGivmzG2YnOtRa5MDUkbGFrS2rfS0HybQeYzu5gP0\ntB7EkrkFS/pmRLV+0tfWalSUlyUVjBOSTHVjN8eqO6is7uDgyRYOnmxJSS3a4aHDH7vNNWro8Eih\nvklv6gBRDSXCI15HFERsWiultmIcejtOfdoQomrXp6Ebg/TrNXr8LJLY+Y7JENY1S5xXPWGdLZgN\nGu65tpRbNxfyWlUzL73dwHOH6znwTiPXrc/lts2FOKyTH4cWMXMIhOM8vt/N2+fa0apFPnRzGTdu\nyF9UnV7EgsVINVy3aXV0vfeTnC7fzpK1q9i4ZsWkarhC0uua0xtKDAPq3GPVuz150dOv6t6H0erd\npqenrgynIIpYNm7CXLEB/1uH8fxxL92vHMD3xp9Ju+kWHLfejsr87iv7mSpMxBN7wu12rxu2r9Lt\ndlf0vaeoLWN6Yp/4Uw3vnG8f/WQUeoIxEpKCUaee0AR+0/JMPnDj0lG/7+ho59Of/gSf/ORfsmXL\nNjIyMonH4zQ01POv//ojFEXBZrPx9a//LW73eZ555ik2bNjEo4/+G4WFRTz88CMcPvwmubl56HQ6\n9u59ElBYu7ace+/9AD/+8X9QVFTM7bfv5re/fZw//Wk/KpWadevKeeCBz/F//s+DXLxYw6c+9QAa\njYb6+jo+85mH+OAH97B27XoaGuqx2x18//v/QDwe45FHHqatrY14PM4Xv/gVVq8eKiA9m6t4V5PH\ndjSvqzm9Ar11KcKwWqNDlHMTYfwdRwh0vI0sRRBELZaMTVgytqLSTF8xN1W1aEcKHb5zRyFRITTp\nUF8Arai50oPa/zkNm9Y6rRDghbwiPd9R66vnj5deor6ngTXpK9mRu4WytNJxF9IWPaxTw2z05Vhc\n4o2TLbxwpJ6unigqUWDHmhzu2Fo4L1XQU435Pl6cqOnkFy+cxxeMsSTXyn27V5LtWFj/l/lu46sB\nC8HGE6nhGly6gp/UtCGj8OkVBeQaZz7Pu6/ebWtXqLfubZLcdvqurHfrtOlZkmvFVZBGWUEauemm\nlNa49R18Hc++PyJ1dyMaDNhvvR37zbcg6ud/xEWqMJue2G6Xy/UZ4NeACvgQ4HG5XCtICj3NCwTC\ncRKSgk4jTskDNRIyMjL5+7//R5588gl+/vP/Qq/X86lPPcBvf/s43/jGtykqKmbfvmf4zW9+xaZN\nWwC46649HDjwIl/+8jfIycmlquoYd955D6dOnQDgS1/6Gp/61MfZunXAgX3xYg2vvvoyP/7xz1Gp\nVHzzm1/m0KGDfPzj97F375Pcddcenn/+2f7jW1ou8+///hMyMjJ54IH7OHfuLKdPnyA3N5+HH/4B\nTU2NHDp08AoSO5tY6B7biXpdx4NKbSAt53qsmdsIdB6lp/0tetrexN9+BFN6BdbMbRP+rZEgCgJL\n82wszbPx/huWDKlFe7ymk+M1naPWoo0korQHu3j5ZDXvXKxDUodwrJZwOGXOyIc59NbYob45pqwh\nntN+oqqzY9IY5030wCImhkb/ZfZdeonTnnMA2PU2jrYd52jbcTKN6ezI3cLW7I2YtaZFD+sCg1aj\n4qYN+excn8vhM608f7ie109c5o2Tl9myIotd24rIy1j0Bsw2wtEEv33lAgdPtqBWCbzv+iXctrkw\nZQI1i1jEbGDUGq5mC9Zrr8NSsXFIDVcL8P5SeLymhccvXOazKwsxaWZW18Js0LCsII1lBUPDlYfX\nu73cGeRSSw9vn2vn7XNJx5lJr6YsP63//MIsM2rV1OakglpN2vU3Yt1+Dd2vvoL3hefx7H2K7pcP\n4LhjF7brb0TUvvtSPqaKicwmPgz8K/BDQAIOAB8D3gd8beaaNhQfuHHpqF7TmVIibm5uwmQy8/Wv\n/y0A58+f46//+nPEYnF+9KMfAJBIJCgoKBzx/EgkgigKaAYVX7ZabXz+81/ie9/7NmvWJB3cDQ11\nrFq1BpUqeROvW1dObe1FVq5cPeLv2mxpZGRkApCZmUUsFqWxsaGfGOfnF/CBD/xFCiyQWiwEYjuy\n13XZqF7XiUJU6bBm7cCcsZmg5zg9bYcIdLxNoPMoJsc6rFk70Ogc02q7IAgUZiWJwt3XlHCpvYMj\nNXWcaW7mQugSNecjPFEbRmeKIWjDxIkOtK8wuSIVBloiImk625BQ3+FEVTuN/N5FzC+0BdvZV7uf\nyvaTACxNK+HO0tvYunQNb9Wc4o2mI1Q2XeD/NR7j/4UuYoznEO0xEInJQ35nkbDOf6hVIteuzWXH\n6hyOutvZd6iet8628dbZNsrL0tm9vZiSHOtcN/NdgXN1Xfz38+fw9EQpzDRz/+6V5E8zBWoRi5gt\njFXD1bptB+aKDRjKlo1aw3Wl3cxNuQ5eudzFby+28IlleajmYPFGp1FRlG2hKHtA8yM93czp6naq\nG7v7X31Ogb5zluRZWdZLbEtzrWgnScJFrRbHrbeTtvN6vAf2493/Ih1P/I6u/S/i3H0XtmuuQ1Av\nPj/Hw7gWcrvdTcC9g/e5XC6D2+3+91Q25O2DtYhqAbvTiMWmn7AX5+j5dp5+oxanVc9De9aktJRO\nTc0F/vjHp/nhD/8JtVpNQUEBZrMVo9HIt771MFlZ2Rw/XonP5xvhbIWjR4+wceOWK77ZseNaXn/9\nVV54YR8PPvh5ioqK+d3vfoMkSYiiyPHjVdx++y5EUWSkcO+RTFNUVMK5c2e55pqdNDc38d///RP+\n5m/+LgVWmFnMB2KbKq/rRCCKGiwZmzA7Kwh6T9HTdpCgp4qg5zhG+yqsWdegNWSO+zt9qr7evvDe\nsUJ902FwMHFcUqGE9ShRC0pMj12bxo7lpazIycVhsE871HcRCwOecBfP177MkdZjKCgUWvK5Lf89\nWJQcWhpD/OL8Wc7VdlPfmksoOtAnfYCg92PMjODKS+eapctx5WUsEtYFBFEU2Lwii03LMzlR42Hf\n4TqqLnRSdaGTVSUOdm8rwlVon+tmXpWIxiX+8NpFXjnWhCgI3Lm9mDt3FE/Zs7OIRcwWEj09BI9X\n4a88RujcGZCSIkqarGwsGzZirtiArqh4wvP3G3IdtISinO0O8kJTJ7sLM8Y/aRYgCALZDiPZDiPX\nrcsFkvWaq5u6qW70caGxm7N1Xs7WeQFQiQLFOZakpzY/jbJ824TV4EW9Aeedd5N2w010vfQC3a8c\noP3xX+F98QWcd92DZeu2URcCFjEBEutyud4H/C1gIumsUQE6IKUFj158+vRAo9QiaQ4jaelG7M6+\nlwmbw4Bq0EBf3+rnp/vOotOo+Pz71qa8lM7OnTdQX1/L/fd/DIPBgKIoPPTQF8jIyOJ73/t2P+n8\n2tf+ho6O9itu3MOH3+QTn/hLIHlTDP7+C1/4EseOvQNAaelSbrzxZh544D4URWbt2nKuvfZ6Ojra\nuXSphiee+C0Wi2XQ+UOvIwgCd9/9Xn7wg+/y0EN/iSzLfOELf51SW8wmZovYzpTXdSIQRBVm53pM\njrWEus/R03qQkPc0Ie9pDDYXuvTNBERDMh81OlgsKfk+nqrvSKG+VrWN42cDvHSohbgERr2aaEyi\nXVZ4ujrGm/Y2NiyTqXCJs16LdhGzh+6Ijz9Wv8qR2moSYQOGRDk2OY92v8i/vnIZuDzk+MEe1qJs\nM5Ley9HOd6jqOMl5OUFNw37WR9ZwTe4Wlk4gd3YR8weCILC+LJ11S52cq/ey71AdZ2q7OFPbRVm+\njV3billT6lj8n6YINc0+frbvLG3eMDlOI/fvXrno+V7EvEbc4yFQdYxA5THCF6qh17GiKyzCXLEB\nc8UGtDm5UxojREHg/aXZPHa2kUNt3eQadVSkz8/7wWHVs3VlNltXJoVaA+E4Fxq7e4ltN7WX/Vxs\n7uEFGhCA/Exzf/jxsnwbNvPYeb8qs5mMe9+P/eZb6HpuH77XX6P1v/+Lrheew3n3HswVGxbJ7AiY\niLDTJeB+4K+A7wO3AgG32/2jVDbk7InLSn2tB68nRHdniO6uEInE0HA1QQCr3YDdaURn0bH3bCvB\nSILP3LWKTSsXiwiPh4UgCjAZTEU8Kkevwha5QMQ7zOvqLE+Z13U8OyuKQiAepCviHaLq2+FvpDPU\nhi8RJTLKbSkKyVBfhz4Nu27iob7DVYc/cONStq3KJhKT5rQW7VRxtfXlmYCsKHT5Iv3qjA0dPs5f\nbqXbJ6Mkrlwltlt0/eqMuU4jK5ZmYNWqRvWwBuJB3m6t5GDzEdpCydyh4bmzixgf860v1zT72Heo\nrl/JsyjLwq5tRVS4MhbswtZc2ziekNl78BIvHmkABd6zuYA915ZOOgRxPmOubfxuwGzZONbaQqDy\n2Jg1XDUZqfOaeiIx/vNsIwlZ4VPL8ykwz61y+lTsHIkluNjc0x9+fKmlh/ggDpNlN1BWkNYvFpUx\nTsRp3OPB8+wz9Bw6CLKMrrCI9D33Yly95qpYVJzNOrHH3G73BpfL9S3gqNvtftHlcr3pdrt3pKIB\ngzBEnVhRFPy+CF5PCG9nCK8nSLcnhNcTIhyJcx6FIJCPQA4CJotuwGubnvTcpjmNGIyaq+Ifngq8\nGx4yEyK2yNjxka2VKExzUuTIJ8ekT1mOrd1p5EJT07BQ3973Caj62rVmzMSxyBGsoojTmE5uxgay\nHKtI09kmFeo7kurwPdeWjBjqMpu1aKeLd0NfnigSkkxbV6hfbbHFE+KyJ6nCGIvLw45WUBki5DnN\nrMzLJS/d3F8cfrgg3kRtrCgKF311HGw+QlXHSRJyArWgYn3mond2Ipivfbmhzc9zh+s5er4dBchx\nGtm1rYgtK7Pm3aLWeJhLG9e3+vnpc2dp7giSkabnvl0rrxCXuRowX/vx1YSZrN0dbWxIKgpXHiN2\nuTcaZwo1XKeKal+QX1ZfxqJR89lVBVg0c5eekgo7xxMyda1JUnuhyceFpm7C0YEatnaLjrJ827gK\nyLG2VjzP7MX/zhFQFPRLy5Jk1rV8Wu2ba8wmiX0DuA9YC2wEvg2ccbvdo9emmRrGLLHTB1mWeezp\n0xy70MnKXCtbsq10d4XxekIE/dErjtfp1f2k1u40ktZLdCeTd3u14N32kOnLde3prKQtFKRDceAR\nsvGocmmPa0kM6vqpyLGVZIl/rHyUBn/TiLnMkAz1dQwpO9PnRU3uM6kHVH2jwSZ8rW8Q6bkAgMaQ\njS3rGgxpK8btu/GEzEtvN7DvUB2xhExZvo0P37KMwizLmOf1YXgtWl8gKdqQilq0qcC7rS9DUsl0\neHmAy54QHd4w8rD+plGLZDsMiIYgHXIdCZ0Xs1nhtuVb2FmwFY1q/Hydqdh40Ts7ecz3vtziCfL8\nW/W8daYNSVZIt+m5Y2sRO9bkpFSDYiYxFzZOSDLPH67n2UN1SLLCDeV5vP+GJei1V2fu+Hzvx1cD\nUmnjZA3XiwQqj+KvOkaiM5mXJWg0GFevwVKxAdPa9ZOu4TodvN7SxYtNHorMeu5z5aOeRaGnWLid\nUNdJQr5q9EYrojYPnbkQnakAMQVClrKs0NQRwN3YnQxDbuymJzRQ6348BeRoUyOde58ieLwKAOPK\nVaTvuRd9Sem02zYXmE0SuxN4CPgIcBAoA37qdrvHTbp0uVwq4L+AZYACfMbtdp8Z5fAJkdixlIhj\n0QTdXX2e26T31usJ0eMNM/zPnGje7dWEd8tDZiJ1XWeijq0kS/zX6V+TEOKYRUt/qO9gojoVVd9Y\nqJWetoOEus8CoNalY83agcmxGkG40is6WujwVBdtUlWLNpW4Wvuyoij4Q/GBIu2dwf7P3hEW6Ux6\ndb8nNcdpIjfdSIZdx/ngCfbXv0pPzI9BbeCWwp3szN+BXj3xenzTsfGid3biWCh9udMX5oUjDbxx\nooWEJJNm1nLb5kJ2rs9Dp5376IyxMNs2bu4M8rN9Z6lr9WO36PjEHctZXeKctevPBRZKP17ImK6N\nh9RwPV6F1CtKOriGq2n1WkTdzNdtHbF9isLvL7VysivA5gwr9xTPbJpgIu4n1HWaoPck8XAbAIKo\nQZET0K85IqA15qAzFSZJrbkQlXr6NZwVRaHNGx6igDy4Zu1oCsjhS5fw7H2S0NkklTKtLyf9nvei\nyy+YdptmE7NJYh9wu92PDdp2uN3uron8uMvluhu40+12399Lhr/odrvvGeXwcUns0fPtPLr3+RXo\nUQAAIABJREFUNE6rnr/5+MYJCzlJCRmfN9xPaiead9vnvbWnG0lzGNHOkdcpVbiaHzKjKgxPItc1\nVcR2puwcj3TS03aIYNdJQEalTcOatR2zYz2CqJ5U6PBUoSjKkFq0zR1BgFFr0c4UFnpfHp6vOpi0\n9oVxD8bwfNUcp4mcdBPWQekSkixxpLWSF+pepiviRavScmP+NdxUuBOjZvJF1FNl40Xv7NhYaH25\nOxBl/zuNvFrVTDQmYTZouGVjPjdtyE/pWJNKzJaNZVlh/zuNPPX6JRKSzPbV2Xzo5rJ5a5dUYqH1\n44WIqdh4rBqupvLyK2q4zjViksxPzjfREopyd1EmWzJTVx0CQJaihLrPE/KeJOLvzfdFxGBbism+\nFr2tDKfDwOX6c0QD9USCDck5pTLAFTT6jF4vbZLUpqqCxXAF5ObOYP93KlGgJMdKWUEyBDk32Ebo\nuaeJXKwBQcCyeQvOu/agzVoY+kCzSWLPuN3uVVO9gMvlUrndbsnlcn0cuN7tdn9ilEPHJLH1rX5+\n8PgxBEHgGx/dQEEK6qmNmHfb68mNjjCRXOh5t1fjQ2YiXtfpYCrEdl2+E100MWMiKIlYNz1thwl4\nKkGRENVmmsLL+c2bOoJRYdKhw9NBa1coSWjdHdS29PTvX5JnZcOyTCqWpZNpn/6q5XAslL6ckGTa\nvOF+j+pY+aqCAJlphl6CaiTXaRo1X3UwZEWmqv0k+2r30x7qRC2quS5vG+8pugGLdurjZKptvOid\nHQopESIWbMLutBMIaVBprDOqiJ5qBMJxXj7ayCvHmghGEhh0Km6syOeWTQVYjfOrjvRsjBft3hA/\ne+4cF5p8WI0aPnbbciqWzY+SIbOBhTImL1REEhGC6h5ssgO1OLZDZawarubyDePWcJ1reKNx/vNs\nI1FJ4j5XPsWWyS/CDoaiSER6LhHsOknY50ZRkvN7rSkfk30tRvvKId7V4X1ZluPEgs1EA/VEgw1E\ng00o8kAosEqbhs5UiL7XU6vWOVPyLBuugFzfGuhPHepTQC4xJMisOUZ20xnMSgzrjmtw7r4bjXN+\nR37MJol9EdACR4A+X7fidru/O9GLuFyuXwB7gPe53e4Doxw2KontDkT5u18epdsf5aF711BeNrMP\nBkVRCIfivUJSwUHhyWPn3aY5er236fMz7/Zqecikwus6HUyE2OpUIkVmPcVmAyUWA3kmHeoUPzCk\neIBL1a8ihE+iVUmE4xpiurWsXHkjqil43qaLrp4IVRc6OeZux93Y3R/Cn59hZoMrgw3LMsjLMKXk\nnphvfXmkfNUWT4j2UfNVjeQ4e4lqepKoZtmNk8oxVBSF055zPHvpJZoDLYiCyPacTdxWfBN2/fQF\nOGbSxu9G76yiyMSCzYT9F4n01PSPXQMQUGmsqLU2VFob6t5X32eVxpaS3KxUIxxN8FpVMy+93UBP\nKI5WLXLd+lxu21yIwzq3KqN9mMm+rCgKr1U18/tXa4jFZTa4Mvjora55R+RnGvNtTL5aEJPivN58\niAP1rxGIB8kwONmzdBdr01cNeZamuobrXONiT4ifu5sxqlV8dlUBNu3kPMWKohALXSboPUXIexo5\nkfRAq3UOTPY1GB1r0OgcI547foUJiViolWiggWiwnmigEVkK938vqk3ozIXoez21GkNWShYox1NA\ndshB8gOXKYh2snLdEpbedRuaGRTjmg5mk8R+p/fjQIB4ksQ+PJkLuVyuLJJEeIXb7Q6PcMiIDYnG\nJb7+nwe50NjN/961kntvLJvMZaeFI0eO8NnPfpZ9+/aRnZ2sDfXDv/8HMtLz2LD+OjrbAnS2+els\nD9DlCaHIQ/+Et07+np5ACxaLFY1OjUoFX/vat9i4cQ2qBSKIMZ8Q6mmmo+ktulqqkKUoIGDLWEF6\n/hZs6cvn1IshyQotgQh1viA13iAXugK0hwYWPDSiQEmaiTKHmWV2M6V2E/ppqPy2e0P89JnTHD7V\nglET5yPXhsk3XEBOhBFVOjILt5NZeB0a3fQjFqYCXyDK22daOXSqhePVHSSk5ECbk25i+5octq3J\noazAjjiLwg3ThaIo+AIxGtv9NLX5aWwP0NiW/Dw4l6UPZoOGgiwL+ZlmCrIs/Z8z7cZp/92n2s7z\nu1N/5IKnFgGBa4s2877Vu8g2LyzPj6IonO+s4eWLB3mrsZK4nEAtqtmSv55bllzLioyyBTPpGgmx\nSDc9ndX4PG78ngtIid5HnyBiTivG4liCIkvEwl6iES+xsJd4tIdRHoeoNSa0+jS0BjtavR2tIa33\n3Y5Ob0elMc6ZvaJxiQNH6nny1Ro6u8OoVQI3bSrk3hvKyEm/+hYlADq8Yf7tiSqOV3dgNmj4zHvX\ncl153oLus4uYH0hICf5Ue4gnzz6PN+zDoNGzNmsF7zSfQFZkVmSU8ZGCGzGdb8Jz+Ag9586DnHzO\nmkpLcG7binPrFgwF+Qu2P75S187vzjZRZDPy1a3L0ExAryYa8uBpqaSrpZJoKClWpdaYsOesx5lT\ngdFakHJ7KIpMJNhOwHsJv7eWgLeWeNTX/72o1mNOK8acVoLFXoLRVoA4jjd9IognJC40dnPmkocz\nlzycq+siNCiK1JIIUZauYeOOVaxdkUdBlmU+zblmh8QCuFwuM7AEOAUY3W53YCI/7nK5Pgrku93u\nH7hcLitwnCSJvdKdOYInVlEUfvLHM7x9rp3tq7O5b9f4qqypRGXlUb797W+wZMlS/uVfHgXgxz/+\nD4qKirn99t1Djh0p7/aJpx8jL2MtWY4k8b7cfp6ahiPs3PTxOcm7XYgrpXPtdZ0K+uzcE0tQHwhT\n6w9T7w/TGo71T01FINeko9hsoNiSfBknQGrHUh2WpSiBzmP0tB9GTgQRBDWm9Aqsmdvm1E7haCLl\ntWhnsi/PRL5qqnDJV8+zF1+kuvsiAOszVrOr5D3kmrNTeh2Y/fHiavDOKnKCaKCBsL+GSM9F4pGO\n/u9U2jQM1iXoLUvQW0oQVcm88eF2VhQJKeYnEesmEfMhxX3J99jAe1843HAIogaVxjaGN3fmQ5YT\nkszhM608f7ieNm8YQYAtK7PYtbWIvIy5WVSbidD4Q6db+Z+XqwlHJdYucfLx25bPuBbAfMZCnF/M\nR8iKzDutVTxXewBPpAuNqOH6/B3cXLSTktxsThw5zIlXn8TsbiSrq3ccmMEarnMJRVF4sq6Nyk4/\n5U4L7yvJGvGZKiVChLxnCXpPEgs2ASAIagxpyzHZ16C3lo4ogjkapi2gpShIsW4igYZk+HGgnkR0\nQEpIENRoTXkDYlGpVkCu7+Ls8YvUdEYJigNj0ngKyLOJ2fTE3gT8BFADO4ATwIfdbvdL4/24y+Uy\nAL8AsgEN8AO32/3sKIdfQWIHKxEv29zCic5T411yUijPXMN7l+4e9fuqqmPs3fskoLB2bTn33vuB\nIST2t799nD/9aT8qlZp168p54IHPDTn/kUce5sYbb2Hl8nK8nhD7X3qBk6eOceOOj3Lg1d/T7mkk\nFg+SZs1l27r/xf43/4Prt32I0tIltHdVc6nxJA9+5q+wp5tSkne7kB4yM53rOpMYzc7hhER9IEKd\nP0ydP0xzKII06PbL1Gt7Ca2eEovhivCZiaoOy3KcoOc4PW2HkOI+EERMjnVYs3aMGj4zW0hVLdpU\n9OXZyFdNFRr9l9l36SVOe84BsNLh4s7SWym05s/YNedqvFhIubOKopCIdhHxXyTcU0M0UN+fKyUI\nanSWYgyWJeitS0bNk5qsnRVFQU6ESMS6keI9A2R3EMkdHNo2FLMXsizLCkfd7ew7VE9TR3Ldu7ws\nnd3biynJsabkGhNFKvuyLxDlly+6OV7TiU6r4i9uKuPatTnzpk9OFIqiIMsKibhEIi4T731PJAZv\nj7SvdzsmkUjIJOIS8biMRqNCpRYxGDXoDZrku1HTv603ajEYNItRaKNAVmSOd5zmuUv76epuxR6C\nzbqlrFPno+4JEPd4kFovE25qTp4girTlmDiTCw0FRra5buCWouvRq+dHCH+qEJdl/ut8E03BKLsK\n0tmRbQeSC4ZhXzVB70nCPTX9wkt6SwlG+1qMacv7Fwoni5l49knxANFAA5FgA9FAA/Fw66BvZ0YB\nWYpGqXnxVU69dZoGwUaTMZtu9cBi8GgKyLOB2SSxbwN3A8+73e5yl8u1Cvit2+1em4oGDMIQEjtc\nifjllv1Utc8uia2sPMozzzzFl770VT71qY/zT//0Hzz77F6KiopZtmw5P/zh93jssZ+hUqn45je/\nzK5dd7N9+zX953//+9+hutqN1WpFFFVkZGTw4IOfR6/X8/TTT7Lnng/S1RHgi3/9ST7xoa9z/MRR\nmi83snrJbbxx7FesWnoTDlse0Jt321/ndmp5t/OdxC5Er+tImKidY5JMY7CX1AbCNAQixAeFpNu1\naootBtLVak4cb+Xk2fZJqQ4rskTQe4qetoO9q4ACRvsqrFnXoDVkTvfPnDamU4t2Mn15LvJVU4XW\nYDvP1e6nsv0kAEvTSriz9DaWppXM+LXnw3gxH72zshQlEqgj0pMkrlKsu/87jT4j6Wm1LkFvLkKY\nQMjYTNhZlmK9pLabRHwowU16d/2MFrIsqo393twryW4aosowKcKmKAonajzsO1zHpctJ8bdVJQ52\nbyvCVWhPwV87PlJl46Pn2/nVS24C4TjLC9P45B0rSE9Lvf5AqglmIj6wPfj8CQTipRwarWooyTX0\nkV1tL9kdvE+DVqdecAsEY0GRZRI+H4kuD3FPJ/HOTtovX6T98kU0viCWkIwuPsq9qdViWLkKS8VG\nTGvXIZiMHGmt5NmLL+KL9WDRmrmz5Fa25W5CnMcL/ZOFLxbnP880EkpIfKhARVbsDCHvWRQ5GdSp\nMWQn81ztq1Brp79ANhvPPjkRIRpsnBUFZDkSxntgP979L9IdE7jsLKF96QbqJCOXPaH+44YrIC/N\ns82Ysvpsktijbrd7o8vlqnK73eW9+0643e51qWjAIPST2JlQIp4K+kjsww8/wptvvsHjj/+CNWvW\nUVJSil6v5+TJE3zhC18C4Ikn/od4PM6HP/zx/vMfeeRhbr75VjZv3jrkdxOJBD/5yX/S0dGOwWDk\nyJFDPProz7DZbNx//0f55396jK9//Ut85Yv/kNJ6t/NhUjoSRvK66q1LsaRvmPde15EwVTtLssLl\nUJQ6f5jaQDIEOSwNDGpCQqbEYmBlhpVis55so25CCsiKIhPqPkdP60HikWQtNIPNhTXrGnSmvEm3\ncyYw2Vq0V4ZgTq++ao7TSE66iXSrfl7kjHjCXTxf+zJHWo+hoFBoyeeu0ttY7pi9HNH5NF7MpXdW\nURTi4bYBb2uwsX+yIah06C2lA97WKUw05sLOcxGyrCgK5+q97DtUx/mGJPEvy7exe3sxq0scM9qv\np2vjQDjObw5Uc+RsG1q1yHt3lnLt6mykhDKMTA4ijfOIYKpUAmqNqvclolEn3/u21RoVarWIZtAx\narUKjTb5PuQYrWrIPo1GRKVWkZ5upqmhi3A4TiQUJxKOEw4N+xyOEw7F+rdlafw/UhBA3+/d1Q4Q\n4Hnq7ZXjMRJdXSS6upIk1eMh4ekk3tXV/94nujQcCa0abXo6hvQs1E4nGkc6Gqcz+dnpJHtJPp2e\n4BXnRaUYrzT8mQP1rxGT4+Sasnnv0t2scC6b6T93VhALt1Pd4uZ3nky0xHmv6iXsWhUm+2qMjrUp\nX5SfizF5NhSQpUCArpdeoPuVAyixGJr0DHS3301r9jKqm3xcaBpZAbkv/HhZvg2bOTUpE7NJYvcC\nPwO+C9wAfBbY6na770xFAwZB6ejwz7oS8VgYTGIBfvCD73Lo0EEefPDzuFzL+eEPv8+jj/4UURT5\n5je/wu237+Laa6/vP/+RRx7mppvew5Yt24b87sGDf+bAgRd5+OEf4PV6+ehH389Pf/prsrNz+NGP\n/p5AwM+6deXs2fO+IedNt95t8ZJ0YvEEeoMGzRwXp79avK4jIRUDYF/osCeWwJppoLjMiV9U8McH\nHn59CsglFgPF5vEVkBVFIdJzAV/rG8RCyZAkvaU0SWbNRfNmtXu8WrTr800UOjR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YAAAg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\n7WjJeB5bRXDb2vy888EYL741xNk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JTcqeWEmSGiDG+D3gu8B1JInnHwB/A7wrxng2STJ7\nbXr4bwOXp/uvAK6ue6mtMcaTgYeBN8cYXwm8DjgxhNBZRF0kSWpmbctdAEmSVpDfA34CvB04Bjge\n+LcQwszv+9Ptu4G3hhDeAZwDzMybrQH3po+fAfaGEO4Evg18MsY4nnsNJElqcvbESpLUIDHG3cAO\n4MdAK/BkjPHMGOOZwNnA+emhdwKvAu4H/pgDr8f70teqAj8HfIpkiPHdIYQTC6iGJElNzSRWkqR8\n/Ag4MoTw+vTny4B/CCEcAZwIfDrGeAvwJpKEF6Ay8+QQwhnAbcDtMcaPAo8B64sqvCRJzcokVpKk\nHKRDf38F+EII4UHgvcBlMcbtJHNlHw0h3AWMAp0hhB6S4cS19PkPAncDj4QQHgCeAr5TfE0kSWou\nrk4sSZIkSSoNe2IlSZIkSaVhEitJkiRJKg2TWEmSJElSaZjESpIkSZJKwyRWkiRJklQaJrGSJEmS\npNIwiZUkSZIklcb/A5+PXAbiIlkWAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x133b49c18>"
+       ]
+      }
+     ],
+     "prompt_number": 1245
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Education towards a degree had its highest point with Federal Government employees in 2004. In latest years trends are very similar for all the different employer types except for incorporated enrepreneurs who have shown a marked decrease between 2012 and 2013. For Personal develeopment educaion, it is worth noting how the peak was for government employees at the local level in the year of the financial crisis: 2009.\n"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Trends by Age - Years 2003 - 2013"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ax1, ax2 = prepare_plot_area()\n",
+      "tsum_data = tall_data[['year','TRCODEP','TEAGE','TUFNWGTP','weighted_minutes']]\n",
+      "tsum_data['bin']= pd.cut(tsum_data['TEAGE'],[20,35,50,65,80,95],retbins=True)[0]\n",
+      "del tsum_data['TEAGE']\n",
+      "data1, data2 = prepare_multi_year_data(tsum_data,'bin')\n",
+      "legend_labels=['20-34', '35-49', '50-64', '65-79', '80-94']\n",
+      "\n",
+      "plot_lines(data1,\n",
+      "           ax1,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education towards a degree by Age Group. Years 2003 - 2013')\n",
+      "plot_lines(data2,\n",
+      "           ax2,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education for Personal Development by Age Group. Years 2003 - 2013')\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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1bNjwJfXqxaLX6xkwYBCdO3dz6HlER4fiynH2BLU5xiajkYw1q8j8aj1efn7U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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x138bc6208>"
+       ]
+      }
+     ],
+     "prompt_number": 1246
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Trends are similar throughout the years. Nothing significant is noticed."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Trends by Number of Hours Worked - Years 2003 - 2013"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ax1, ax2 = prepare_plot_area()\n",
+      "tsum_data = tall_data[['year','TRCODEP','TEHRUSL1','TUFNWGTP','weighted_minutes']]\n",
+      "tsum_data['bin']= pd.cut(tsum_data['TEHRUSL1'],[20,35,50,65,80,95],retbins=True)[0]\n",
+      "tsum_data.tail(50)\n",
+      "del tsum_data['TEHRUSL1']\n",
+      "data1, data2 = prepare_multi_year_data(tsum_data,'bin')\n",
+      "legend_labels=['20-39', '40-59', '60-79','80-99']\n",
+      "\n",
+      "plot_lines(data1,\n",
+      "           ax1,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education towards a degree by Hours Worked. Years 2003 - 2013')\n",
+      "plot_lines(data2,\n",
+      "           ax2,\n",
+      "           legend_labels,\n",
+      "           \"Years\",\n",
+      "           \"Average Hours\",\n",
+      "           'Trends in Education for Personal Development by Hours Worked. Years 2003 - 2013')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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sYsmSpW2mKyk5wMyZswCYOXM2TzzxKHl5I5g3r4jExERn2RMmsnnzJk48se20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PceGWBfWz/7jg4SjSUnfZzP66TWnwm2Pje1fg9Bnzv9sz99m4RdUYly2ep/AzgX+LXW+pBS6h3A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\nve0MsW9XN/t2dY96z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ox4Hyffvgx8CfjLzHOtAeQEImPk24+B54D3Zp5rDbCviM23hDxr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\nQliGhFghhBBCCCGEEJbx/wOraWLF3tLkLQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1371bdd68>"
+       ]
+      }
+     ],
+     "prompt_number": 1253
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "During the financial crisis people that worked less than 39 hours also allocated a significant amount of time to education towards personal development\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file