Lecture_One_Data_Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello, Python\n",
    "Welcome to our class: Intro to Python. Today we will learn the most basic skills for data exploration and data structure with python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data\n",
    "There are many websites with public datasets.\n",
    "\n",
    "Let's try the NYC open data for our first class.\n",
    "\n",
    "https://data.cityofnewyork.us/Education/2017-2018-NYC-School-Survey-Parent-Data/iiu6-juu8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_excel('2017_-_2018_NYC_School_Survey_Parent_Data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can you tell me what is wrong with this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Total Parent \n",
       "Response Rate</th>\n",
       "      <th>Total Teacher Response Rate</th>\n",
       "      <th>Total Student Response Rate</th>\n",
       "      <th>Collaborative Teachers Score</th>\n",
       "      <th>Effective School Leadership Score</th>\n",
       "      <th>Rigorous Instruction Score</th>\n",
       "      <th>Supportive Environment Score</th>\n",
       "      <th>Strong Family-Community Ties Score</th>\n",
       "      <th>Trust Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M015</td>\n",
       "      <td>P.S. 015 ROBERTO CLEMENTE</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.12</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M019</td>\n",
       "      <td>P.S. 019 ASHER LEVY</td>\n",
       "      <td>96.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.10</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M020</td>\n",
       "      <td>P.S. 020 ANNA SILVER</td>\n",
       "      <td>50.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M034</td>\n",
       "      <td>P.S. 034 FRANKLIN D. ROOSEVELT</td>\n",
       "      <td>29.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01M063</td>\n",
       "      <td>THE STAR ACADEMY - P.S.63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01M064</td>\n",
       "      <td>P.S. 064 ROBERT SIMON</td>\n",
       "      <td>40.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01M110</td>\n",
       "      <td>P.S. 110 FLORENCE NIGHTINGALE</td>\n",
       "      <td>65.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.68</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01M134</td>\n",
       "      <td>P.S. 134 HENRIETTA SZOLD</td>\n",
       "      <td>37.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01M140</td>\n",
       "      <td>P.S. 140 NATHAN STRAUS</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.11</td>\n",
       "      <td>3.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01M142</td>\n",
       "      <td>P.S. 142 AMALIA CASTRO</td>\n",
       "      <td>97.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.04</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>01M184</td>\n",
       "      <td>P.S. 184M SHUANG WEN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.38</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>01M188</td>\n",
       "      <td>P.S. 188 THE ISLAND SCHOOL</td>\n",
       "      <td>100.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01M292</td>\n",
       "      <td>ORCHARD COLLEGIATE ACADEMY</td>\n",
       "      <td>46.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.02</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01M301</td>\n",
       "      <td>TECHNOLOGY, ARTS, AND SCIENCES STUDIO</td>\n",
       "      <td>19.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>01M315</td>\n",
       "      <td>THE EAST VILLAGE COMMUNITY SCHOOL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.68</td>\n",
       "      <td>3.63</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>01M332</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD MIDDLE SCHOOL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>01M361</td>\n",
       "      <td>THE CHILDREN'S WORKSHOP SCHOOL</td>\n",
       "      <td>36.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.84</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>01M363</td>\n",
       "      <td>NEIGHBORHOOD SCHOOL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.92</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>01M364</td>\n",
       "      <td>EARTH SCHOOL</td>\n",
       "      <td>67.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.51</td>\n",
       "      <td>3.67</td>\n",
       "      <td>4.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01M378</td>\n",
       "      <td>SCHOOL FOR GLOBAL LEADERS</td>\n",
       "      <td>63.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.62</td>\n",
       "      <td>4.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.07</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>31.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3.92</td>\n",
       "      <td>4.30</td>\n",
       "      <td>4.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>63.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.68</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>23.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>01M515</td>\n",
       "      <td>LOWER EAST SIDE PREPARATORY HIGH SCHOOL</td>\n",
       "      <td>51.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>01M539</td>\n",
       "      <td>NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.08</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>01M650</td>\n",
       "      <td>CASCADES HIGH SCHOOL</td>\n",
       "      <td>12.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.02</td>\n",
       "      <td>3.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>01M696</td>\n",
       "      <td>BARD HIGH SCHOOL EARLY COLLEGE</td>\n",
       "      <td>51.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.83</td>\n",
       "      <td>4.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.06</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>01M839</td>\n",
       "      <td>TOMPKINS SQUARE MIDDLE SCHOOL</td>\n",
       "      <td>52.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.74</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>Z077</td>\n",
       "      <td>BROOKLYN'S DAILY DISCOVERY PRE-K CENTER AT 385...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2861</th>\n",
       "      <td>Z078</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT P.S. 91 RICHARD ARKWRIGHT</td>\n",
       "      <td>89.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2862</th>\n",
       "      <td>Z080</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 50-15 44TH STREET</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2863</th>\n",
       "      <td>Z087</td>\n",
       "      <td>ROSE HILL PRE-K CENTER AT 2512 MARION AVENUE</td>\n",
       "      <td>66.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2864</th>\n",
       "      <td>Z088</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 56-01 61ST STREET</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>Z089</td>\n",
       "      <td>DISTRICT 2 PRE-K CENTER AT 10 READE STREET</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2866</th>\n",
       "      <td>Z090</td>\n",
       "      <td>DISTRICT 13 PRE-K CENTER AT 25 DOCK STREET</td>\n",
       "      <td>60.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2867</th>\n",
       "      <td>Z091</td>\n",
       "      <td>ROSE HILL PRE-K CENTER AT 3605 SEDGWICK AVENUE</td>\n",
       "      <td>87.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2868</th>\n",
       "      <td>Z092</td>\n",
       "      <td>ROSE HILL PRE-K CENTER AT 3560 WEBSTER AVENUE</td>\n",
       "      <td>65.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2869</th>\n",
       "      <td>Z093</td>\n",
       "      <td>THE LITTLE BROOKLYN PRE-K CENTER AT 305-307 TH...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2870</th>\n",
       "      <td>Z094</td>\n",
       "      <td>DISTRICT 20 PRE-K CENTER AT 2165 71ST STREET</td>\n",
       "      <td>94.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2871</th>\n",
       "      <td>Z095</td>\n",
       "      <td>BROOKLYN'S DAILY DISCOVERY PRE-K CENTER AT 10 ...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>Z097</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 107-17 NORTHERN BOULEVARD</td>\n",
       "      <td>93.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>Z098</td>\n",
       "      <td>ROSE HILL PRE-K CENTER AT 5500 BROADWAY</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>Z099</td>\n",
       "      <td>DISTRICT 20 PRE-K CENTER AT 550 59TH STREET</td>\n",
       "      <td>89.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>Z100</td>\n",
       "      <td>DISTRICT 25 PRE-K CENTER AT 14-45 143 STREET</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>Z102</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 106-02 NORTHERN BOULEVARD</td>\n",
       "      <td>89.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>Z104</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 41-15 104 STREET</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2878</th>\n",
       "      <td>Z105</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 109-10 47 AVENUE</td>\n",
       "      <td>78.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>Z106</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 80-55 CORNISH AVENUE</td>\n",
       "      <td>96.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>Z107</td>\n",
       "      <td>ROSE HILL PRE-K CENTER AT 2490-2500 WEBSTER AV...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2881</th>\n",
       "      <td>Z108</td>\n",
       "      <td>THE LITTLE BROOKLYN PRE-K CENTER AT 219 25TH S...</td>\n",
       "      <td>92.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>Z112</td>\n",
       "      <td>DISTRICT 20 PRE-K CENTER AT 1423 62ND STREET</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2883</th>\n",
       "      <td>Z114</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 70-24 47TH AVENUE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2884</th>\n",
       "      <td>Z115</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 104-14 ROOSEVELT AVENUE</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2885</th>\n",
       "      <td>Z116</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 55-41 98 PLACE</td>\n",
       "      <td>87.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2886</th>\n",
       "      <td>Z117</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 57-06 99TH STREET</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2887</th>\n",
       "      <td>Z118</td>\n",
       "      <td>DISTRICT 25 PRE-K CENTER AT 123-07 22ND AVE</td>\n",
       "      <td>88.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2888</th>\n",
       "      <td>Z119</td>\n",
       "      <td>DISTRICT 30 PRE-K CENTER AT 27-35 JACKSON AVENUE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2889</th>\n",
       "      <td>Z120</td>\n",
       "      <td>MOSAIC PRE-K CENTER AT 50-51 98TH STREET</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2890 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DBN                                        School Name  \\\n",
       "0        NaN                                                NaN   \n",
       "1     01M015                          P.S. 015 ROBERTO CLEMENTE   \n",
       "2     01M019                                P.S. 019 ASHER LEVY   \n",
       "3     01M020                               P.S. 020 ANNA SILVER   \n",
       "4     01M034                     P.S. 034 FRANKLIN D. ROOSEVELT   \n",
       "5     01M063                          THE STAR ACADEMY - P.S.63   \n",
       "6     01M064                              P.S. 064 ROBERT SIMON   \n",
       "7     01M110                      P.S. 110 FLORENCE NIGHTINGALE   \n",
       "8     01M134                           P.S. 134 HENRIETTA SZOLD   \n",
       "9     01M140                             P.S. 140 NATHAN STRAUS   \n",
       "10    01M142                             P.S. 142 AMALIA CASTRO   \n",
       "11    01M184                               P.S. 184M SHUANG WEN   \n",
       "12    01M188                         P.S. 188 THE ISLAND SCHOOL   \n",
       "13    01M292                         ORCHARD COLLEGIATE ACADEMY   \n",
       "14    01M301              TECHNOLOGY, ARTS, AND SCIENCES STUDIO   \n",
       "15    01M315                  THE EAST VILLAGE COMMUNITY SCHOOL   \n",
       "16    01M332              UNIVERSITY NEIGHBORHOOD MIDDLE SCHOOL   \n",
       "17    01M361                     THE CHILDREN'S WORKSHOP SCHOOL   \n",
       "18    01M363                                NEIGHBORHOOD SCHOOL   \n",
       "19    01M364                                       EARTH SCHOOL   \n",
       "20    01M378                          SCHOOL FOR GLOBAL LEADERS   \n",
       "21    01M448                UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "22    01M450                         EAST SIDE COMMUNITY SCHOOL   \n",
       "23    01M458                          FORSYTH SATELLITE ACADEMY   \n",
       "24    01M509                            MARTA VALLE HIGH SCHOOL   \n",
       "25    01M515            LOWER EAST SIDE PREPARATORY HIGH SCHOOL   \n",
       "26    01M539  NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...   \n",
       "27    01M650                               CASCADES HIGH SCHOOL   \n",
       "28    01M696                     BARD HIGH SCHOOL EARLY COLLEGE   \n",
       "29    01M839                      TOMPKINS SQUARE MIDDLE SCHOOL   \n",
       "...      ...                                                ...   \n",
       "2860    Z077  BROOKLYN'S DAILY DISCOVERY PRE-K CENTER AT 385...   \n",
       "2861    Z078   MOSAIC PRE-K CENTER AT P.S. 91 RICHARD ARKWRIGHT   \n",
       "2862    Z080           MOSAIC PRE-K CENTER AT 50-15 44TH STREET   \n",
       "2863    Z087       ROSE HILL PRE-K CENTER AT 2512 MARION AVENUE   \n",
       "2864    Z088           MOSAIC PRE-K CENTER AT 56-01 61ST STREET   \n",
       "2865    Z089         DISTRICT 2 PRE-K CENTER AT 10 READE STREET   \n",
       "2866    Z090         DISTRICT 13 PRE-K CENTER AT 25 DOCK STREET   \n",
       "2867    Z091     ROSE HILL PRE-K CENTER AT 3605 SEDGWICK AVENUE   \n",
       "2868    Z092      ROSE HILL PRE-K CENTER AT 3560 WEBSTER AVENUE   \n",
       "2869    Z093  THE LITTLE BROOKLYN PRE-K CENTER AT 305-307 TH...   \n",
       "2870    Z094       DISTRICT 20 PRE-K CENTER AT 2165 71ST STREET   \n",
       "2871    Z095  BROOKLYN'S DAILY DISCOVERY PRE-K CENTER AT 10 ...   \n",
       "2872    Z097   MOSAIC PRE-K CENTER AT 107-17 NORTHERN BOULEVARD   \n",
       "2873    Z098            ROSE HILL PRE-K CENTER AT 5500 BROADWAY   \n",
       "2874    Z099        DISTRICT 20 PRE-K CENTER AT 550 59TH STREET   \n",
       "2875    Z100       DISTRICT 25 PRE-K CENTER AT 14-45 143 STREET   \n",
       "2876    Z102   MOSAIC PRE-K CENTER AT 106-02 NORTHERN BOULEVARD   \n",
       "2877    Z104            MOSAIC PRE-K CENTER AT 41-15 104 STREET   \n",
       "2878    Z105            MOSAIC PRE-K CENTER AT 109-10 47 AVENUE   \n",
       "2879    Z106        MOSAIC PRE-K CENTER AT 80-55 CORNISH AVENUE   \n",
       "2880    Z107  ROSE HILL PRE-K CENTER AT 2490-2500 WEBSTER AV...   \n",
       "2881    Z108  THE LITTLE BROOKLYN PRE-K CENTER AT 219 25TH S...   \n",
       "2882    Z112       DISTRICT 20 PRE-K CENTER AT 1423 62ND STREET   \n",
       "2883    Z114           MOSAIC PRE-K CENTER AT 70-24 47TH AVENUE   \n",
       "2884    Z115     MOSAIC PRE-K CENTER AT 104-14 ROOSEVELT AVENUE   \n",
       "2885    Z116              MOSAIC PRE-K CENTER AT 55-41 98 PLACE   \n",
       "2886    Z117           MOSAIC PRE-K CENTER AT 57-06 99TH STREET   \n",
       "2887    Z118        DISTRICT 25 PRE-K CENTER AT 123-07 22ND AVE   \n",
       "2888    Z119   DISTRICT 30 PRE-K CENTER AT 27-35 JACKSON AVENUE   \n",
       "2889    Z120           MOSAIC PRE-K CENTER AT 50-51 98TH STREET   \n",
       "\n",
       "      Total Parent \\nResponse Rate  Total Teacher Response Rate  \\\n",
       "0                              NaN                          NaN   \n",
       "1                             75.0                        100.0   \n",
       "2                             96.0                         95.0   \n",
       "3                             50.0                         89.0   \n",
       "4                             29.0                         78.0   \n",
       "5                             63.0                        100.0   \n",
       "6                             40.0                         96.0   \n",
       "7                             65.0                         92.0   \n",
       "8                             37.0                         89.0   \n",
       "9                             46.0                         50.0   \n",
       "10                            97.0                         95.0   \n",
       "11                            65.0                         92.0   \n",
       "12                           100.0                         86.0   \n",
       "13                            46.0                        100.0   \n",
       "14                            19.0                         54.0   \n",
       "15                            65.0                         93.0   \n",
       "16                            81.0                        100.0   \n",
       "17                            36.0                         65.0   \n",
       "18                            65.0                         96.0   \n",
       "19                            67.0                         93.0   \n",
       "20                            63.0                         67.0   \n",
       "21                            31.0                        100.0   \n",
       "22                            63.0                         92.0   \n",
       "23                            23.0                        100.0   \n",
       "24                            21.0                         33.0   \n",
       "25                            51.0                         97.0   \n",
       "26                            63.0                         95.0   \n",
       "27                            12.0                        100.0   \n",
       "28                            51.0                         78.0   \n",
       "29                            52.0                         75.0   \n",
       "...                            ...                          ...   \n",
       "2860                         100.0                        100.0   \n",
       "2861                          89.0                        100.0   \n",
       "2862                          77.0                         88.0   \n",
       "2863                          66.0                         92.0   \n",
       "2864                         100.0                        100.0   \n",
       "2865                         100.0                        100.0   \n",
       "2866                          60.0                         83.0   \n",
       "2867                          87.0                        100.0   \n",
       "2868                          65.0                        100.0   \n",
       "2869                          80.0                        100.0   \n",
       "2870                          94.0                         77.0   \n",
       "2871                         100.0                        100.0   \n",
       "2872                          93.0                        100.0   \n",
       "2873                          75.0                        100.0   \n",
       "2874                          89.0                        100.0   \n",
       "2875                          70.0                        100.0   \n",
       "2876                          89.0                         75.0   \n",
       "2877                         100.0                        100.0   \n",
       "2878                          78.0                        100.0   \n",
       "2879                          96.0                        100.0   \n",
       "2880                          71.0                        100.0   \n",
       "2881                          92.0                        100.0   \n",
       "2882                          98.0                        100.0   \n",
       "2883                         100.0                        100.0   \n",
       "2884                          94.0                        100.0   \n",
       "2885                          87.0                        100.0   \n",
       "2886                          94.0                        100.0   \n",
       "2887                          88.0                         92.0   \n",
       "2888                         100.0                         60.0   \n",
       "2889                          90.0                         60.0   \n",
       "\n",
       "      Total Student Response Rate  Collaborative Teachers Score  \\\n",
       "0                             NaN                           NaN   \n",
       "1                             NaN                          4.12   \n",
       "2                             NaN                          4.27   \n",
       "3                             NaN                          2.76   \n",
       "4                            92.0                          2.40   \n",
       "5                             NaN                          4.53   \n",
       "6                             NaN                          4.44   \n",
       "7                             NaN                          3.68   \n",
       "8                             NaN                          3.52   \n",
       "9                            79.0                          3.25   \n",
       "10                            NaN                          3.81   \n",
       "11                           99.0                          2.96   \n",
       "12                           82.0                          4.25   \n",
       "13                           88.0                          4.02   \n",
       "14                           84.0                           NaN   \n",
       "15                            NaN                          3.68   \n",
       "16                           93.0                           NaN   \n",
       "17                            NaN                          3.79   \n",
       "18                            NaN                          3.92   \n",
       "19                            NaN                          4.51   \n",
       "20                           92.0                          3.20   \n",
       "21                           81.0                          3.92   \n",
       "22                           88.0                          4.57   \n",
       "23                           50.0                           NaN   \n",
       "24                           83.0                          2.95   \n",
       "25                           85.0                          4.65   \n",
       "26                           82.0                          3.19   \n",
       "27                           43.0                          3.25   \n",
       "28                           91.0                          4.25   \n",
       "29                           93.0                          4.44   \n",
       "...                           ...                           ...   \n",
       "2860                          NaN                           NaN   \n",
       "2861                          NaN                           NaN   \n",
       "2862                          NaN                           NaN   \n",
       "2863                          NaN                           NaN   \n",
       "2864                          NaN                           NaN   \n",
       "2865                          NaN                           NaN   \n",
       "2866                          NaN                           NaN   \n",
       "2867                          NaN                           NaN   \n",
       "2868                          NaN                           NaN   \n",
       "2869                          NaN                           NaN   \n",
       "2870                          NaN                           NaN   \n",
       "2871                          NaN                           NaN   \n",
       "2872                          NaN                           NaN   \n",
       "2873                          NaN                           NaN   \n",
       "2874                          NaN                           NaN   \n",
       "2875                          NaN                           NaN   \n",
       "2876                          NaN                           NaN   \n",
       "2877                          NaN                           NaN   \n",
       "2878                          NaN                           NaN   \n",
       "2879                          NaN                           NaN   \n",
       "2880                          NaN                           NaN   \n",
       "2881                          NaN                           NaN   \n",
       "2882                          NaN                           NaN   \n",
       "2883                          NaN                           NaN   \n",
       "2884                          NaN                           NaN   \n",
       "2885                          NaN                           NaN   \n",
       "2886                          NaN                           NaN   \n",
       "2887                          NaN                           NaN   \n",
       "2888                          NaN                           NaN   \n",
       "2889                          NaN                           NaN   \n",
       "\n",
       "      Effective School Leadership Score  Rigorous Instruction Score  \\\n",
       "0                                   NaN                         NaN   \n",
       "1                                  4.09                        3.91   \n",
       "2                                  4.25                        4.64   \n",
       "3                                  3.04                        1.94   \n",
       "4                                  2.45                        2.04   \n",
       "5                                  4.07                        4.14   \n",
       "6                                  3.94                        3.54   \n",
       "7                                  3.21                        2.51   \n",
       "8                                  3.61                        3.49   \n",
       "9                                  4.11                        3.41   \n",
       "10                                 3.91                        3.53   \n",
       "11                                 2.75                        2.98   \n",
       "12                                 4.99                        3.87   \n",
       "13                                 3.67                        3.57   \n",
       "14                                  NaN                         NaN   \n",
       "15                                 3.63                        4.07   \n",
       "16                                  NaN                         NaN   \n",
       "17                                 3.84                        3.77   \n",
       "18                                 4.31                        3.92   \n",
       "19                                 3.67                        4.05   \n",
       "20                                 3.62                        4.78   \n",
       "21                                 4.30                        4.10   \n",
       "22                                 4.20                        4.37   \n",
       "23                                  NaN                         NaN   \n",
       "24                                 3.62                        1.99   \n",
       "25                                 4.27                        4.50   \n",
       "26                                 3.74                        3.89   \n",
       "27                                 3.03                        3.00   \n",
       "28                                 3.83                        4.09   \n",
       "29                                 3.74                        4.25   \n",
       "...                                 ...                         ...   \n",
       "2860                                NaN                         NaN   \n",
       "2861                                NaN                         NaN   \n",
       "2862                                NaN                         NaN   \n",
       "2863                                NaN                         NaN   \n",
       "2864                                NaN                         NaN   \n",
       "2865                                NaN                         NaN   \n",
       "2866                                NaN                         NaN   \n",
       "2867                                NaN                         NaN   \n",
       "2868                                NaN                         NaN   \n",
       "2869                                NaN                         NaN   \n",
       "2870                                NaN                         NaN   \n",
       "2871                                NaN                         NaN   \n",
       "2872                                NaN                         NaN   \n",
       "2873                                NaN                         NaN   \n",
       "2874                                NaN                         NaN   \n",
       "2875                                NaN                         NaN   \n",
       "2876                                NaN                         NaN   \n",
       "2877                                NaN                         NaN   \n",
       "2878                                NaN                         NaN   \n",
       "2879                                NaN                         NaN   \n",
       "2880                                NaN                         NaN   \n",
       "2881                                NaN                         NaN   \n",
       "2882                                NaN                         NaN   \n",
       "2883                                NaN                         NaN   \n",
       "2884                                NaN                         NaN   \n",
       "2885                                NaN                         NaN   \n",
       "2886                                NaN                         NaN   \n",
       "2887                                NaN                         NaN   \n",
       "2888                                NaN                         NaN   \n",
       "2889                                NaN                         NaN   \n",
       "\n",
       "      Supportive Environment Score  Strong Family-Community Ties Score  \\\n",
       "0                              NaN                                 NaN   \n",
       "1                              NaN                                3.80   \n",
       "2                              NaN                                4.10   \n",
       "3                              NaN                                3.64   \n",
       "4                              NaN                                3.33   \n",
       "5                              NaN                                4.01   \n",
       "6                              NaN                                4.01   \n",
       "7                              NaN                                3.79   \n",
       "8                              NaN                                3.30   \n",
       "9                              NaN                                3.12   \n",
       "10                             NaN                                4.04   \n",
       "11                             NaN                                3.38   \n",
       "12                             NaN                                4.99   \n",
       "13                             NaN                                3.50   \n",
       "14                             NaN                                 NaN   \n",
       "15                             NaN                                4.69   \n",
       "16                             NaN                                 NaN   \n",
       "17                             NaN                                4.29   \n",
       "18                             NaN                                4.31   \n",
       "19                             NaN                                4.69   \n",
       "20                             NaN                                4.07   \n",
       "21                             NaN                                4.01   \n",
       "22                             NaN                                4.68   \n",
       "23                             NaN                                 NaN   \n",
       "24                             NaN                                 NaN   \n",
       "25                             NaN                                4.07   \n",
       "26                             NaN                                4.08   \n",
       "27                             NaN                                3.02   \n",
       "28                             NaN                                4.06   \n",
       "29                             NaN                                3.98   \n",
       "...                            ...                                 ...   \n",
       "2860                           NaN                                 NaN   \n",
       "2861                           NaN                                 NaN   \n",
       "2862                           NaN                                 NaN   \n",
       "2863                           NaN                                 NaN   \n",
       "2864                           NaN                                 NaN   \n",
       "2865                           NaN                                 NaN   \n",
       "2866                           NaN                                 NaN   \n",
       "2867                           NaN                                 NaN   \n",
       "2868                           NaN                                 NaN   \n",
       "2869                           NaN                                 NaN   \n",
       "2870                           NaN                                 NaN   \n",
       "2871                           NaN                                 NaN   \n",
       "2872                           NaN                                 NaN   \n",
       "2873                           NaN                                 NaN   \n",
       "2874                           NaN                                 NaN   \n",
       "2875                           NaN                                 NaN   \n",
       "2876                           NaN                                 NaN   \n",
       "2877                           NaN                                 NaN   \n",
       "2878                           NaN                                 NaN   \n",
       "2879                           NaN                                 NaN   \n",
       "2880                           NaN                                 NaN   \n",
       "2881                           NaN                                 NaN   \n",
       "2882                           NaN                                 NaN   \n",
       "2883                           NaN                                 NaN   \n",
       "2884                           NaN                                 NaN   \n",
       "2885                           NaN                                 NaN   \n",
       "2886                           NaN                                 NaN   \n",
       "2887                           NaN                                 NaN   \n",
       "2888                           NaN                                 NaN   \n",
       "2889                           NaN                                 NaN   \n",
       "\n",
       "      Trust Score  \n",
       "0             NaN  \n",
       "1            3.99  \n",
       "2            3.55  \n",
       "3            3.12  \n",
       "4            2.32  \n",
       "5            3.95  \n",
       "6            3.91  \n",
       "7            3.55  \n",
       "8            3.67  \n",
       "9            3.26  \n",
       "10           3.68  \n",
       "11           2.91  \n",
       "12           4.99  \n",
       "13           4.07  \n",
       "14            NaN  \n",
       "15           4.08  \n",
       "16           4.16  \n",
       "17           3.31  \n",
       "18           3.85  \n",
       "19           4.08  \n",
       "20           3.68  \n",
       "21           4.18  \n",
       "22           4.31  \n",
       "23           4.64  \n",
       "24            NaN  \n",
       "25           4.29  \n",
       "26           3.08  \n",
       "27           3.66  \n",
       "28           3.83  \n",
       "29           3.85  \n",
       "...           ...  \n",
       "2860          NaN  \n",
       "2861          NaN  \n",
       "2862          NaN  \n",
       "2863          NaN  \n",
       "2864          NaN  \n",
       "2865          NaN  \n",
       "2866          NaN  \n",
       "2867          NaN  \n",
       "2868          NaN  \n",
       "2869          NaN  \n",
       "2870          NaN  \n",
       "2871          NaN  \n",
       "2872          NaN  \n",
       "2873          NaN  \n",
       "2874          NaN  \n",
       "2875          NaN  \n",
       "2876          NaN  \n",
       "2877          NaN  \n",
       "2878          NaN  \n",
       "2879          NaN  \n",
       "2880          NaN  \n",
       "2881          NaN  \n",
       "2882          NaN  \n",
       "2883          NaN  \n",
       "2884          NaN  \n",
       "2885          NaN  \n",
       "2886          NaN  \n",
       "2887          NaN  \n",
       "2888          NaN  \n",
       "2889          NaN  \n",
       "\n",
       "[2890 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to Pandas\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/index.html\n",
    "\n",
    "'pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Parent \n",
       "Response Rate</th>\n",
       "      <th>Total Teacher Response Rate</th>\n",
       "      <th>Total Student Response Rate</th>\n",
       "      <th>Collaborative Teachers Score</th>\n",
       "      <th>Effective School Leadership Score</th>\n",
       "      <th>Rigorous Instruction Score</th>\n",
       "      <th>Supportive Environment Score</th>\n",
       "      <th>Strong Family-Community Ties Score</th>\n",
       "      <th>Trust Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>1113.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1492.000000</td>\n",
       "      <td>1731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55.455867</td>\n",
       "      <td>78.984770</td>\n",
       "      <td>81.066487</td>\n",
       "      <td>3.750836</td>\n",
       "      <td>3.593481</td>\n",
       "      <td>3.661765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.626434</td>\n",
       "      <td>3.615540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.964061</td>\n",
       "      <td>27.384518</td>\n",
       "      <td>20.578571</td>\n",
       "      <td>0.670557</td>\n",
       "      <td>0.627194</td>\n",
       "      <td>0.705729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.657868</td>\n",
       "      <td>0.635907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.257500</td>\n",
       "      <td>3.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>3.650000</td>\n",
       "      <td>3.690000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.720000</td>\n",
       "      <td>3.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>4.290000</td>\n",
       "      <td>4.030000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Parent \\nResponse Rate  Total Teacher Response Rate  \\\n",
       "count                   2889.000000                  2889.000000   \n",
       "mean                      55.455867                    78.984770   \n",
       "std                       25.964061                    27.384518   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                       34.000000                    68.000000   \n",
       "50%                       55.000000                    90.000000   \n",
       "75%                       76.000000                   100.000000   \n",
       "max                      100.000000                   100.000000   \n",
       "\n",
       "       Total Student Response Rate  Collaborative Teachers Score  \\\n",
       "count                  1113.000000                   1508.000000   \n",
       "mean                     81.066487                      3.750836   \n",
       "std                      20.578571                      0.670557   \n",
       "min                       0.000000                      1.000000   \n",
       "25%                      75.000000                      3.330000   \n",
       "50%                      88.000000                      3.780000   \n",
       "75%                      95.000000                      4.290000   \n",
       "max                     100.000000                      4.990000   \n",
       "\n",
       "       Effective School Leadership Score  Rigorous Instruction Score  \\\n",
       "count                        1508.000000                 1507.000000   \n",
       "mean                            3.593481                    3.661765   \n",
       "std                             0.627194                    0.705729   \n",
       "min                             1.040000                    1.460000   \n",
       "25%                             3.220000                    3.180000   \n",
       "50%                             3.650000                    3.690000   \n",
       "75%                             4.030000                    4.180000   \n",
       "max                             4.990000                    4.990000   \n",
       "\n",
       "       Supportive Environment Score  Strong Family-Community Ties Score  \\\n",
       "count                           0.0                         1492.000000   \n",
       "mean                            NaN                            3.626434   \n",
       "std                             NaN                            0.657868   \n",
       "min                             NaN                            1.200000   \n",
       "25%                             NaN                            3.257500   \n",
       "50%                             NaN                            3.720000   \n",
       "75%                             NaN                            4.100000   \n",
       "max                             NaN                            4.990000   \n",
       "\n",
       "       Trust Score  \n",
       "count  1731.000000  \n",
       "mean      3.615540  \n",
       "std       0.635907  \n",
       "min       1.100000  \n",
       "25%       3.235000  \n",
       "50%       3.690000  \n",
       "75%       4.070000  \n",
       "max       4.990000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "1. Delete a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=['Supportive Environment Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Total Parent \n",
       "Response Rate</th>\n",
       "      <th>Total Teacher Response Rate</th>\n",
       "      <th>Total Student Response Rate</th>\n",
       "      <th>Collaborative Teachers Score</th>\n",
       "      <th>Effective School Leadership Score</th>\n",
       "      <th>Rigorous Instruction Score</th>\n",
       "      <th>Strong Family-Community Ties Score</th>\n",
       "      <th>Trust Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M015</td>\n",
       "      <td>P.S. 015 ROBERTO CLEMENTE</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.12</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M019</td>\n",
       "      <td>P.S. 019 ASHER LEVY</td>\n",
       "      <td>96.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.10</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M020</td>\n",
       "      <td>P.S. 020 ANNA SILVER</td>\n",
       "      <td>50.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M034</td>\n",
       "      <td>P.S. 034 FRANKLIN D. ROOSEVELT</td>\n",
       "      <td>29.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01M063</td>\n",
       "      <td>THE STAR ACADEMY - P.S.63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.14</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01M064</td>\n",
       "      <td>P.S. 064 ROBERT SIMON</td>\n",
       "      <td>40.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.54</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01M110</td>\n",
       "      <td>P.S. 110 FLORENCE NIGHTINGALE</td>\n",
       "      <td>65.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.68</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.51</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01M134</td>\n",
       "      <td>P.S. 134 HENRIETTA SZOLD</td>\n",
       "      <td>37.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.49</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01M140</td>\n",
       "      <td>P.S. 140 NATHAN STRAUS</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.11</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DBN                     School Name  Total Parent \\nResponse Rate  \\\n",
       "0     NaN                             NaN                           NaN   \n",
       "1  01M015       P.S. 015 ROBERTO CLEMENTE                          75.0   \n",
       "2  01M019             P.S. 019 ASHER LEVY                          96.0   \n",
       "3  01M020            P.S. 020 ANNA SILVER                          50.0   \n",
       "4  01M034  P.S. 034 FRANKLIN D. ROOSEVELT                          29.0   \n",
       "5  01M063       THE STAR ACADEMY - P.S.63                          63.0   \n",
       "6  01M064           P.S. 064 ROBERT SIMON                          40.0   \n",
       "7  01M110   P.S. 110 FLORENCE NIGHTINGALE                          65.0   \n",
       "8  01M134        P.S. 134 HENRIETTA SZOLD                          37.0   \n",
       "9  01M140          P.S. 140 NATHAN STRAUS                          46.0   \n",
       "\n",
       "   Total Teacher Response Rate  Total Student Response Rate  \\\n",
       "0                          NaN                          NaN   \n",
       "1                        100.0                          NaN   \n",
       "2                         95.0                          NaN   \n",
       "3                         89.0                          NaN   \n",
       "4                         78.0                         92.0   \n",
       "5                        100.0                          NaN   \n",
       "6                         96.0                          NaN   \n",
       "7                         92.0                          NaN   \n",
       "8                         89.0                          NaN   \n",
       "9                         50.0                         79.0   \n",
       "\n",
       "   Collaborative Teachers Score  Effective School Leadership Score  \\\n",
       "0                           NaN                                NaN   \n",
       "1                          4.12                               4.09   \n",
       "2                          4.27                               4.25   \n",
       "3                          2.76                               3.04   \n",
       "4                          2.40                               2.45   \n",
       "5                          4.53                               4.07   \n",
       "6                          4.44                               3.94   \n",
       "7                          3.68                               3.21   \n",
       "8                          3.52                               3.61   \n",
       "9                          3.25                               4.11   \n",
       "\n",
       "   Rigorous Instruction Score  Strong Family-Community Ties Score  Trust Score  \n",
       "0                         NaN                                 NaN          NaN  \n",
       "1                        3.91                                3.80         3.99  \n",
       "2                        4.64                                4.10         3.55  \n",
       "3                        1.94                                3.64         3.12  \n",
       "4                        2.04                                3.33         2.32  \n",
       "5                        4.14                                4.01         3.95  \n",
       "6                        3.54                                4.01         3.91  \n",
       "7                        2.51                                3.79         3.55  \n",
       "8                        3.49                                3.30         3.67  \n",
       "9                        3.41                                3.12         3.26  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. delete a row\n",
    "### delete the row with 'NaN' in the column 'DBN '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna(subset=['DBN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Parent \n",
       "Response Rate</th>\n",
       "      <th>Total Teacher Response Rate</th>\n",
       "      <th>Total Student Response Rate</th>\n",
       "      <th>Collaborative Teachers Score</th>\n",
       "      <th>Effective School Leadership Score</th>\n",
       "      <th>Rigorous Instruction Score</th>\n",
       "      <th>Strong Family-Community Ties Score</th>\n",
       "      <th>Trust Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>1113.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "      <td>1492.000000</td>\n",
       "      <td>1731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55.455867</td>\n",
       "      <td>78.984770</td>\n",
       "      <td>81.066487</td>\n",
       "      <td>3.750836</td>\n",
       "      <td>3.593481</td>\n",
       "      <td>3.661765</td>\n",
       "      <td>3.626434</td>\n",
       "      <td>3.615540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.964061</td>\n",
       "      <td>27.384518</td>\n",
       "      <td>20.578571</td>\n",
       "      <td>0.670557</td>\n",
       "      <td>0.627194</td>\n",
       "      <td>0.705729</td>\n",
       "      <td>0.657868</td>\n",
       "      <td>0.635907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>3.257500</td>\n",
       "      <td>3.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>3.650000</td>\n",
       "      <td>3.690000</td>\n",
       "      <td>3.720000</td>\n",
       "      <td>3.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>4.290000</td>\n",
       "      <td>4.030000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>4.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Parent \\nResponse Rate  Total Teacher Response Rate  \\\n",
       "count                   2889.000000                  2889.000000   \n",
       "mean                      55.455867                    78.984770   \n",
       "std                       25.964061                    27.384518   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                       34.000000                    68.000000   \n",
       "50%                       55.000000                    90.000000   \n",
       "75%                       76.000000                   100.000000   \n",
       "max                      100.000000                   100.000000   \n",
       "\n",
       "       Total Student Response Rate  Collaborative Teachers Score  \\\n",
       "count                  1113.000000                   1508.000000   \n",
       "mean                     81.066487                      3.750836   \n",
       "std                      20.578571                      0.670557   \n",
       "min                       0.000000                      1.000000   \n",
       "25%                      75.000000                      3.330000   \n",
       "50%                      88.000000                      3.780000   \n",
       "75%                      95.000000                      4.290000   \n",
       "max                     100.000000                      4.990000   \n",
       "\n",
       "       Effective School Leadership Score  Rigorous Instruction Score  \\\n",
       "count                        1508.000000                 1507.000000   \n",
       "mean                            3.593481                    3.661765   \n",
       "std                             0.627194                    0.705729   \n",
       "min                             1.040000                    1.460000   \n",
       "25%                             3.220000                    3.180000   \n",
       "50%                             3.650000                    3.690000   \n",
       "75%                             4.030000                    4.180000   \n",
       "max                             4.990000                    4.990000   \n",
       "\n",
       "       Strong Family-Community Ties Score  Trust Score  \n",
       "count                         1492.000000  1731.000000  \n",
       "mean                             3.626434     3.615540  \n",
       "std                              0.657868     0.635907  \n",
       "min                              1.200000     1.100000  \n",
       "25%                              3.257500     3.235000  \n",
       "50%                              3.720000     3.690000  \n",
       "75%                              4.100000     4.070000  \n",
       "max                              4.990000     4.990000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We dont replace missing data with 0, why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find the outliers\n",
    "1. draw boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1115cf080>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(grid=False, rot=45, fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This graph doesnt make sense because the data are in different scale, and they should not be in the same graph.\n",
    "\n",
    "Let's look at the first five columns only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e6364e0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data1 = data[data.columns[1:5]]\n",
    "new_data1.boxplot(grid=False, rot=45, fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10eb9ef60>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAHCCAYAAADM5+93AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsnXl4VOX1xz9nkkAgYRUE2a1Li9BqlaqoVbH+UFsVq7Y0bnVpERfUVsWFWldUVKrWXQtqq6Rad6sWquJCtVasSy24VcEFlB0hCoTk/P447yQ3Y4AIl9y5k/N5nnnC3LkzvHfunft937OKquI4juM4cZFJegCO4zhOYeHC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLFSnPQAkqBLly7ar1+/pIfhOI6TKl555ZUFqtp1Xfu1SGHp168f06dPT3oYjuM4qUJEZjdlPzeFOY7jOLHiwuI4juPESmqFRUSOFhFt5DEy6bE5juO0ZArBx7IX8GXk+ftJDcRxHMcpDGF5WVWXJz0Ix3Ecx0itKcxxHMfJTwpBWP4nIqtF5G0ROT7pwTiO47R00mwKmwucB/wLKAIqgJtFpK2qXp27s4iMAEYA9OnTpznH6TiO06KQQup5LyL3AHsDXVW1dk37DRo0SJNOkBSRWD+vkM7j18W/y3jx7zNeCun7FJFXVHXQuvYrBFNYlPuAzkC/hMexTlR1nY++Z/21Sfu19B9uU7+jpn6fLR3/PuOlJf7WC01YsqTj23ccxylACk1YDgEWAE2qZ+M4juPET2qd9yJyP+a4fwNz3g8Pj1PW5l9xHMdxNi6pFRbgbeBYoDcgwAzgKFX9U6KjchzHaeGkVlhU9Vzg3KTH4TiO4zSk0HwsjuM4TsK4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6suLA4juM4seLC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6suLA4juM4seLC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6sFCc9AMdZG9teOIWlX1bH9nn9zn4sls/p0KaE188fGstnOU6h4cLi5DVLv6xm1uU/SnoYXyEugXKcQsRNYY7jOE6suLA4juM4sVIQpjAR6Qm8DZQB7VR1ecJDcpy8JB99Vu6vKjwKQliAK4HlmLA4jrMG8tFn5f6qwiP1wiIi3wf2BS7FBMZxHGejk4+rP8iPFWCqhUVEioDrgIuAJQkPx3GcFkQ+rv4gP1aAqRYWYCRQCtwAHJ7wWACfxTiO46RWWERkE+Bi4AhVrRaRde0/AhgB0KdPn402Lp/FOI7T0klzuPFY4CVVfbwpO6vqrao6SFUHde3adSMPzXEcp+WSyhWLiAwAjgV2F5GOYXPb8LeDiNSo6pfJjM5xHKdlk0phAbYCSoAXG3ntY2AC8ItmHZHjOI4DpFdYpgFDcrbtC5wF/BB4v9lH5DiO4wApFRZVXQA8E90mIv3CP5/3zHvHcZzkSLPz3nEcx8lDCkZYVPUOVRVfrTiO4yRLwQiL4ziOkx+4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLGSygRJx3HWj3b9z+bbd56d9DAa0K4/QP5VBHfWHxcWx2lBLJt5ed61dfCWDoWHC4uT1+TjDBt8lu34tbk2XFicvCYfZ9jgs2zHr8214cISMz6LcRynpePCEjM+i3Ecp6Xj4caO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6suLA4juM4seLC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6seAdJx2lh5Fs30Q5tSpIeghMzLiyO04KIs212v7Mfy8s23M1Jvok05IdQu7BsBPxic5zCx0V6zbiwxIxfbI7jtHRcWJy8x1eAjpMuXFicvMZXgI6TPjzc2HEcx4kVFxbHcRwnVlIrLCJyqIi8ICILRWSFiLwtIr8RkVZJj81xHKclk2YfyybAVOBKYAmwI3AB0B04OblhOY7jtGxSKyyqekvOpqki0h44SURGqaomMS7HcZyWTmpNYWtgIeCmMMdxnARJ7Yoli4gUAa2B7YFTgJt8teI4jpMcqRcWoAoTFoA/Amc2tpOIjABGAPTp06d5RuY4jtMCKQRT2C7A94HTgWHA9Y3tpKq3quogVR3UtWvX5hyf4zhOiyL1KxZV/Xf45zQRWQDcKSLjVfV/SY7LcRynpVIIK5YoWZHZPNFROI7jtGAKTVh2DX8/SHQUjuM4LZjUmsJE5G/Ak8B/gRpMVE4H7nEzmOM4TnKkVliAl4GjgX7AauB94Bzg5uSG5DiO46RWWFT1POC8pMfhOI7jNKTQfCyO4zhOwriwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6suLA4juM4seLC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6suLA4juM4seLC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4juPEiguL4ziOEysuLI7jOE6suLA4juM4seLC4jiO48SKC4vjOI4TKy4sjuM4Tqy4sDiO4zix4sLiOI7jxIoLi+M4jhMrLiyO4zhOrKRWWETkJyLyiIh8IiLLReQVEalIelyO4zgtneKkB7AB/Br4APgVsAD4ITBJRLqo6nWJjsxxHKcFk9oVC3CAqh6mqveq6tOqegZQiQlOqhk1ahSlpaXMHrc/paWljBo1KukhOY7jNJnUCouqLmhk86vAps09ljgZNWoUN9xwA6tXrwZg9erV3HDDDS4ujlOAVFZWMnDgQGZfcSADBw6ksrIy6SHFQmqFZQ3sAsxIehBNQUQafVx//fWoKjU1NQDU1NSgqlx//fVrfI+IJHw0Tktin332IZPJMHvc/mQyGfbZZ5+kh5RKKisrOfXUU6mqqgJVqqqqOPXUUwtCXApGWETkB8Aw4IY1vD5CRKaLyPT58+c37+AaQVUbfQAMHz6cAQMGkMlkGDBgAMOHD1/re7Lvc5w4WNsERkSYMmVK3TWnqkyZMsUnPevB6NGjWbVqlT0J39OqVasYPXp0gqOKhzQ77+sQkX7AJOBhVb2jsX1U9VbgVoBBgwbl9Z14ypQp3H///ey2225MmzaNQw45JOkhOS2ItU1URITS0lK6d+/O7Nmz6du3L59++ikrVqzwCc4aWJe4Ll26FIBZs2bVPV/be9LwPad+xSIinYEngA+BIxIeTiwsWbKEV199lerqal599VWWLFmS9JAcp45Vq1Yxa9YsVJVZs2bVz7qdRlmblWHw4MG0bt0agNatWzN48OC1vicNogIgaRloY4hIW+BJoBswWFXnNeV9gwYN0unTp2/Usa0vmUym0YtHRKitrU1gRIVDv7MfY9blP0p6GKkmO5MWEVS17i+kYyadT2S/y06dOrF06VI6dOjA4sWLgfz9LkXkFVUdtK79UrtiEZFi4C/AVsB+TRWVfKdTp05fa7vjJMHgwYOZM2dO3QzbWX8+//xzamtr+fzzz5MeSmykVliAG7GkyIuBziKyc+TROuGxrTdZ++r48eOpqqpi/PjxiEidHdZxkkZEeOGFF+jRowcvvPCCO+g3kC5dujT4WwikWViGhr/XAi/mPDZLalAbSk1NDdtttx1nnHEGZWVlnHHGGWy33XZ14ceOkzS5Zpp8NdukgSFDhtClSxcymQxdunRhyJAhSQ8pFlIrLKraT1VlDY9ZSY9vQ3j99de56qqrqKqq4qqrruL1119PekiO42wEnn32WY499liWLVvGsccey7PPPpv0kGIh1c779SWfnfdZs0JRURE1NTV1f8FnhhuKO+83nLSHweYTvXv3ZtGiRVRXV1NdXU1JSQklJSV07tyZjz76KOnhNUrBO+8LnWjmvePkE2VlZWt97jSNK664gqKiogbbioqKuOKKKxIaUXy4sOQhRUVFlJSUAFBSUvKVi89xkqSqqooTTjiBJUuWcMIJJ1hJEme9KC0tpWfPnmQyGXr27ElpaWnSQ4oFF5Y8pKamhvLycgDKy8t91eLkHTfddBMdO3bkpptuSnooqWXs2LGMGDGibsVXVlbGiBEjGDt2bMIj23AKoqRLoVFcXMzy5csBWL58OcXFxXXVjh3HKQxmzJjBvHnz6oSlqqqKW2+9lQULGivcni58xZKH1NbWcvnll1NVVcXll1/uGffrYF1FE7OP2eP2b9J+ztrp0qVLgwz8Qsq/aE6KiopYsmQJs2bNora2llmzZrFkyZKCMH27sOQhxcXFnH766ZSVlXH66adTXOwLy7WxtrpK6/Nw1s7ChQvp1q0bmUyGbt26sXDhwqSHlEpWr15NdXU1Bx54IPPnz+fAAw+kurq6IKwTLix5RufOnamurqZ79+5kMhm6d+9OdXU1nTt3TnpojkNxcTGtWrVi4cKF1NbWsnDhQlq1auWTn/Wkb9++TJ48ma5duzJ58mT69u2b9JBiwYUlz2jbti2lpaUNfrilpaW0bds26aE5Du3bt2fVqlV07twZEaFz586sWrWK9u3bJz20VDJ79mxWrlwJwMqVK5k9e3bCI4oHF5Y845NPPqGsrIyePXsiIvTs2ZOysjI++eSTpIfmOCxevJiioiI+++wzVJXPPvuMoqKiuqq8ztcn6q8qFFxY8oxWrVqxzz77UFZWhohQVlbGPvvsQ6tWrZIemuOQyWRYvXo13bp1Q0To1q0bq1evJpPxW8n6Ul5eTiaTqUsxKAT8asgzVq5cyT333NOgftA999xTt1x2nCSpqakhk8kwevRoli9fzujRo8lkMp5rtZ5sscUWLF++nNraWpYvX84WW2yR9JBiwYUlz2jdujXDhw9n4sSJtGvXjokTJzJ8+PC6LnOOkzQ77rgj5557LmVlZZx77rnsuOOOSQ8ptcybN4+nnnqKVatW8dRTTzFvXkG0lfIilPlGJpNhk002oby8nA8//JA+ffqwfPnyOme+4yRJ1g+QTdqNJu+2xHvJhlBeXk5VVRWdOnViyZIldOzYkcWLF1NWVlaXIJ1veBHKlNKzZ89Gk6Z69uyZ9NAcp45sTatCqW2VBLfddhtt2rRh8eLFqCqLFy+mTZs23HbbbUkPbYNxYckz5s+fz+rVqxskTa1evZr58+cnPbTUUllZycCBAykqKmLgwIFUVlYmPaRUU1RU1KDkUCFkiidBRUUFEyZMYMCAAWQyGQYMGMCECROoqKhIemgbjJvC8gwRYa+99uKzzz5j5syZ9O/fn27duvH000+7qWE9qKys5NRTT6WsrKzOtFhVVcW1115bED/g5kZEaN++PZ07d2b27Nn07duXRYsW8fnnn/v12QJwU1iKOe2003jzzTepqanhzTff5LTTTkt6SKll9OjRFBcXM3HiRFasWMHEiRMpLi5m9OjRSQ8ttdTW1jJx4kRWrlzJxIkT3fe3AYwaNYrS0lJEhNLSUkaNGpX0kOIh7jpLaXjssMMOmq8A2q5dO3366ad11apV+vTTT2u7du3UTpXzdQF0ypQpDbZNmTLFv8/1BNBMJqPdunVTQLt166aZTMa/z/Xg5JNPbvS7PPnkk5Me2hoBpmsT7rG+Yskzhg4dyrJlyzjkkEMoLS3lkEMOYdmyZQwdOjTpoTkOAwYMYPvtt68Li503bx7bb789AwYMSHhk6ePmm2+mTZs2tGnThkwmU/fvm2++OemhbTAuLHnG5MmTGTp0KEuWLKG2tpYlS5YwdOhQJk+enPTQUkmvXr046qijmDp1KtXV1UydOpWjjjqKXr16JT20VDJkyBBeeeWVukz7TCbDK6+8wpAhQxIeWfpYvXo15eXlDcy05eXlXt3Y2ThMnjyZ2tpaVJXa2loXlQ3giiuuoKamhmOPPZbWrVtz7LHHUlNTUxB9xZNg0qRJAHTt2pVMJkPXrl0bbHe+HsOGDWPIkCGUlJQwZMgQhg0blvSQYsGFxSloKioquPbaaxvUXvOIsPVn0aJFjBs3jrlz51JTU8PcuXMZN24cixYtSnpoqeS2225r0CKjEHJYwFsTOy2AiooKF5IYGThw4FqfO02jc+fOLFq0iAULFqCqdX8LofeSr1jyEE/oc/KV4uJijjjiiAY+qyOOOMIbfa0Hbdu2pWPHjvTu3RsRoXfv3nTs2LEgei+5sOQZlZWVjBkzhuuuu44VK1Zw3XXXMWbMGBcXJy8YOXIkixcvZujQobRq1YqhQ4eyePFiRo4cmfTQUsecOXOoqKhg7ty5qCpz586loqKCOXPmJD20DcaFJc8YO3YsEyZMaODQmzBhAmPHjk16aI7DLrvsQrt27Ro0p2rXrh277LJLwiNLHz169ODBBx/kiSeeYNWqVTzxxBM8+OCD9OjRI+mhbTAuLHnGzJkz2W233Rps22233Zg5c2ZCI3KcesaOHctDDz3EqlWrUFVWrVrFQw895BOf9SS3a2ShdJF0Yckz+vfvz7Rp0xpsmzZtGv37909oRI5Tj0984mPOnDmMGzeurqzLqFGjGDdunJvCnPgZM2YMxx13XAPn6HHHHceYMWOSHprj0L9/fy688MIGwSUXXnihT3zWg/79+9OrV68GdQF79epVGN9lU+q+FNojn2uFqapOmjRJBwwYoJlMRgcMGKCTJk1KekiOo6pW36q4uFjHjx+vVVVVOn78eC0uLs7r+lb5yqRJk3TzzTdvUBdw8803z+vfO02sFeZl8x3HaTIDBw7koIMO4qGHHqpr65B9/uabbyY9vNRRWVnJ2LFj677LMWPG5HXOVVPL5ruwOI7TZIqKilixYgUlJSV126qrqyktLaWmpibBkTnNgfdjcRwndjy4JF4KNRnahcVxnCbjwSXxUVlZyfHHH88777xDbW0t77zzDscff3xhiEtTHDGF9sh3573j5DMeXBIPnTt31qKiogaBEEVFRdq5c+ekh7ZGcOf9mnEfi+M4SSMiXHHFFZx55pl126688kpGjx5Nvt6XC97HIiJbisgtIvK6iNSIyDNJjykuCtXu6jhOQwq1UnRqhQUYAPwQeCc8CgIvQuk4LYPi4mIOP/zwBv6qww8/vCAqRadZWB5V1d6q+hPgv0kPJi68CKXjtAxGjhzJ0qVLqaiooFWrVlRUVLB06dKCqBSdWmlU1dqkx7Ax8FpMjtMyuO666wDqukYuWbKEE088sW57mknziqUg8TwBx2k5ZE3eqlpn+i4EWoywiMgIEZkuItPnz5+f9HDWiOcJOI6TdlJrCvu6qOqtwK1g4cYJD2eNZOsEjRo1qq5+0NixY/O6fpDjOE6UFiMsaaKiosKFxHGc1NJiTGGO4zhO8+DC4jiO48RKak1hItIWS5AE6Am0F5FDw/PHVfWLZEbmOI7TskmtsACbAn/J2ZZ9vjkwq1lH4ziO4wApFhZVnQVI0uNwHMdxGtIiqxuLyHxgdtLjaAJdgAVJD6KA8O8zXvz7jI+0fJd9VbXrunZqkcKSFkRkelNKVDtNw7/PePHvMz4K7bv0qDDHcRwnVlxYHMdxnFhxYclvbk16AAWGf5/x4t9nfBTUd+k+FsdxHCdWfMXiOI7jxIoLi+M4jhMrLiyO4zhOrLiwOI7jOLHiwuI4jlPAiMhXSl81ti1OXFicFoGIFDWyrcXUmosea0s67kJBRL5yr27KeRSRIg2hv9HfgKpqY58ZFx5u7BQ8IlKsqqtFpBTYBWgFzFLVtxIeWrMQOf4irPBsK1VdlvS4nKYROX8lQC9gBbBMVZeLSEZVa9fwviJVrQn/Ph/YDlgFvKWq52/UMbuwOIVM9ocnIu2AZ4CuQBugHLgcuKeQBSZ7cwnHfxuwFbASeBG4wAUmv8k5fw8BfYBS4DNglKq+2ITPuBebUE0B2gPbA/OBo1V15sYYd2rL5jtOUwii0gp4AlgCnIRVkf0ucA/QV0TOVtV5CQ5zoxFuSmXAS9hxT8Fmrb8Edg7H/nySY3TWTDh/bYBp2PV7HtAW2BX4h4gcgU2Oahp7v4j8BBgEHAE8G0xgh2C9q/YWkbfDb0Q0xlWGC4vTEvgGsAlwOvBy+LH2Dq9NL1RRifBLbJVyfHaGKiKfA1cCHeK+qTjxEDkvP8J6T52qqq+F1xQ4BiiLmLsEzH8S+ZhvYhOJN4KobAHcAlQCE7JmtLjPvzvvnYJDRHInTP2wH9j7QVQOB54CxqjqjSLSUUR2b+5xbiwaccoOBFZGROVwYBxwrqr+FTMNOnlC9vxFbvb9gO7AR+H1nwETgLNV9TYR6SQimwGZiKN+u+zHAdWqukhEtgZexq79Ear6hYiMFpEz4j4GFxanoAizvNUi0lVE/hQ2v4v9KL8jIkcCf8JE5bLw+pHAGSLSLYEhx0qwydeKSBcR2TZsXghkbzgV2PH/RlUvD2bCG0XkmISG7ESI+AQ7i8hJYfMSYDWwSER+CkzCJgVXhICM04DfEixQIvJn4HIRaQ88BwwQkXMxc9pTwHGqWiUi/YAdgE7hOogPVfWHPwrigc3YCD+wx4C3gC3DtinA50A1JipgE6tvYk79WwjBLGl9UB+MUwr8D3gwHONPw43pFswsck7kPduGm89pSY+/pT9yzt8rwKNYsElHzNk+PZzH0ZH3fCdcvxeF5/sCbwDfB0rDtt+H9/0r8n9shq163s/+RuJ8uI/FKRi03lG/L2YCOAb4MLx8KPBXYEdgdjALbAOcjQnRSaqqafU3RKKHMsDhwHuYqaQWuFdEvo8FLvwFuCa857vADZjYXJfMyB2o96eEFcgu2CrlTGCpqq4SkdOAi4F3gNtEpBM2KboGqAUuEpGLsGv5dWBa5Dq+CZtgnAjcIiLlmFh9D9hbVd+L/XhS+BtynEYJN9XHsJDaL4Fts2ITfpztgfuBnsC3gFeBT4GDVLU6GvefRkL00ETsJrJEVY+P3LC6AWMwcXkMC2Yow0Rl10I4/rQT8lRewAItFqnqgZHXOgL7A78DFmPnbj5QBQzBIsVeArYGngb2U9XqyPvbAQcABwOtsWv/LlV9Z6MciwuLU0iIyAHA9UBv4HBVrQzbi1V1dfj3lpi4fAB8HMSn7vW0EhJAZwGbAo+q6rBG9vkZMADLZ3gduDOsdFJ//IWAiIzDVirvYuLwfs7rHYFDgBLMjPUU5j/LAN2APwB7YObPxzUneTIyydqoK3MXFie1rCnrWET2Av6I/TgvVNVnwvaS6CxuXZ+T70RvDpHs7DaYz2Q74DDggayJbE3H6CuVZIiek4jTPgOMBi4FLgLGa0hijZg7G5yvnEnTZphvrRuWu/JC1MSb+3ejHZsLi5NGIjfS1pjpqzswA1ioqitF5EeYbflNYJyqPhvel0ofSi6R46+zzWt9PkMZ5uhtBZwC/G1d4uI0L5HzV4yFe3dR1Q8ir18MnAucA9yUKy7h32diq8/eWKTYs6r6XjB7Pgp0wSIeX2jua96FxUkd0rDMxRSsflJPLKR4KnCKqn4eEZf/YOLyXGKDjpHI8ZdjET/ZBNBKYLKqvhJem46ZTOrEJbFBO3XkXL93Y/6+XphZ63bgobB6uQQTlnOAG1V1eeQz7sMCUd7FTGF7AJOBa1T1SRHpDjwMdAJGELLum+0YXVicNBJWKs9izstrsB/YScA+mFNzvyAu+wE3AvOwzPPXEhpyrIRVyb+x6KE3gA5YiOn7mPlvStjnZSxS6BzgQV+x5Aci0hYL/52PCcJCIJuoOAG4KojLRcBZwBXApar6pYgcB1wGHAS8GrYdCozFfGyjVfX1YBabihWtHKyqXzbbAcYdv+wPfzTHA6uVNAcLl8xuawP8AgvJnIhV8QU4EHiAkOdSCI9wE/kv0DeybTgWVTQN2D5sa4vdtO5Nesz+aHD+fg7MBr4T2bYJtsp4G4tUzG6/Gnie+oXARZiJN3t9Z7cPwyZav428tzvQr7mPzzPvnbSyCbbM/wTqol2+xEwLT2IhmJ0AVPURVT1Y652jhUAf4DNVnS31JUDuwUxjWwE/CNu+wMyEFUkN1GmUzYESVX0DbAWuqguB47AIr6PD9oyq/grYXdWc7liSb2/qaz0WBV/bw9hq5zgRKQ/bPlXVWc16ZHhJFycFhKQxRCQTop7AzD81WFw+aiGUWXG5CeiL2a4boCk0BUkjTcqwXIc+UJcYWhL+/WfgH8Bh4fsqVtUVGqKJmm/UTpY1fO+vApuIyJ4AagEnrVV1AZar8gMR6RvZf7CI9FZbhkzDclnOFpFytYiwbNOvFcBy4IuwbyK4sDh5TbgxZh3V1wCniUhP4GMsX2WUWP0vVHVVeNvWmCM/9VWLI8ffRkSGSX39r3uBYhG5EkAtwTETZrTLgHmqWquR3BR1532zEzl/bUXkVyKyR8g3+h+WR3S8iAwEE5fwtk2wihELwqThDmyy9KMQRfYM8HeswsKJItI27NcZC+SYBbQO10IieEkXJ28JS/nVQVT+BSzCfCcLwva7gS2BK8Ps7hGgB1Ye/33MVp1aQvTQaqlvUrYMuAO7Ib2IhZT+KHxPWcfvN7Acln80/4idKMGMlT1/07AqB8uxcitvisjNWJkWROQWVX1GRHbAMuxnAF+IyJ3A7sCvgRe1Pl/leKyY6C+BI0XkNWyVvi1WSaH5HPWN4FFhTl4TZmh/xrLJj8ZaCtc1JgqzvQospLYEE5/3gB9kZ/FpNH9lCbPb5zHTx2+A17IrMxHpijV+OhALOf0E+56+BHbQSJ5LIoN3EKtd93esntcJwP+0YamVY4CRWFfHxZj4fAoMxgpM3o+d90qtz1+py2HCggAFSL+aAAAgAElEQVSGYBOKd7CEyhnNdHhrxIXFyRukkQzwSLLfXcBlWp+pHM06z2AO6m2w8NuXNcVlWqRhRvYPgZuxLOrnIw5c0fqWy4OwiLAizIRyWbjxpPL4C4kw8XkYK3aarYJQd/7CPgOwc9gHM5HdE/Y7GOtyumUI0lhj1nzwsdXkyyTKTWFOXiAiWwH7icj7as2nsnTDqri+F26kRapaExGVcqCjqn5EaIQUthel6aYqIptipcz/k3Nz+CbQWUNyp9SX/gBALSN7anhEPy9Vx592xDqSbg3M1obVgnthEWBvZkUliEP2fR2wPJV2wN2qel/YXoStvr8Evh0+t4GoiOVofaCqb2kjpYqSxJ33TuKIyM6Yf+SnmEkgyizMPj1CRDaLRjeFvz8DKoLJqI40OaqDXX0ScBXWhjbKO0BGRP4P6iLAMuEmUyoix4nIt3M/M03Hn3ZEZEesPtdYLBs+yhwsj2jfEPUVrde1CeZL2RbLS/lb9k3h/P0P+ALzoWwWtmsI0tgKM/9+dyMf3nrhwuIkioh8DyvL8gzwa1W9KGzP5mbUUl8K/xQR6RERly2whMiuqroiifFvKCKyC2aDnw/coaqP5uwyG4tuO1qsh0xWXAS7qRyFzYqdBAiToqewKgiXqeqksD3bf/4NLNjiZGBQRFzaYNd1Oyx593xVXS6RPKuwCh+JhdRfJiI7hZe+hWXp98dK5ecd7mNxEiOYASZhs7rRqrp4LfvejM3m38OSwLYMz4uAHdNo9hGRbwBPYDPVi0MOw1eqLQcH7wQsiOEOrN/GHsAFYZc9fYXS/IhIDywy7x+YMCwO2xv4QEIY8DSsM+SN2ETiIMzv8gHmpI+ad0uwtgYr1FoIVwC3YRFltVgn1A5Y2aK8LFHkPhYnSYowH8ITqro4x0SwA/aDWwLcrqojReRU7Ad5OzATMx8cFRzVaSz9vj2W5DlBVRdEBKVErJZUL+BtVb1dRKqx0NQfYzeXxdhqZk9tpJS60yx0BToDDwFLIoJSJlZhuBfWAfI1EdkVyz06A6v7NR+rPr1j9voFasQqFu8N9APmisgpqlopIm9inSW3xK79qRqphpxvuLA4SdId+3GugDr78bZYu9zvYdenYFnkI1X1WhH5PVbOYj42o9MURz99G5t5/hfqTFybY/049sK+m9nh2O8SkVexPJ0tMPv70+pNupJkcyx3ZEZktbE1MA5bUXYEVEQuDCbe/xORbbCQ8HJMaH4lIjcC/UXkCmBnLLLvTcz38ryIDFbV/2BVulOBm8KcRBGRCVg/+juwH+LB2JL/z1gC2DexfuxTVPWwRt6f2jwNsT70UzGTyFRshXYlZgZ5HngLE5iOwC6NzVB9pZIcERPXF8DlWLj7rzDf9SOY72UvLFT8ZFW9MfLejli/lZOw3JUOWKDKnVjp+6XB/zYJ8z/+kjwKJ14XLixOIuSYvS7FkhwFKyJ5j6pOjex7PjaL3wb4MK1CkksIlT4TS3KsxW5IT2Ptgv8U9tkGE537VPWkNAtpoREc7T/FwoWzpXYeACap6gNhn02xJMcvMTPuikj+yqZYle49gc8wUZmv9aWJCKvUt1X1Z81xTHHhpjAnEbI3R7WKrseLyOVArarObmT3DlhkzZxCuqmGKKBLsai472GO3CnasBzHp1gp9OXhPQVz/GkmCHytiNyLrVq+DSxT1WnR/VR1noisABarVZpu8BoWpvxgY5+PmdqWYwUrU7U6d2FxEiX7Y9GGbVmzHfYECzPeGZieb0lgcaBWePAfNFLbKxz/llgU0H+beWjOWojko9RiBVE/zr4WSWIVzLy5CdbK4SvkJDxGfWWbYqv0XsBfsv/nRjugmHFTmJOXiJUq2R64BGtWtZO2gNpXEVEtxvxLN2Nmsr3cl5L/REy8bbBkyYux6Mc9sHyruU34jF9hk6k9gaGq+vpGHPJGwVcszkbn64iB1Nf9eiRs+gwYkuKQ4q9FEJXemN1+O+ymtLuHFCfH1/neg6hsjtW2+waWo7UzZu76t4iMjfpQcv6fVljC72FYZNgeqvpWHMfQ3LiwOBsVqa/EmsE65q2MvPaVH2wwIawAKrFSGHekOaT264qBWNHNA7Cs+n8CZ6kXlEyMyPXbFjgEmKmq09eyv2D+si+wlfb7WE/7gcAFaxIVqGtW9yAWwDFXVZfGeCjNipvCnI1GxKzTDrgWy7+YDfxbVa8J+6xzNZPWmXrk+NtgPTbKsTIei9VK+jd67CLSHigDPg0z4FQef9rJuX6nYkEUE7BikWs8H2ESVQbsi/UPygAjI5F+BW3OBV+xOBuJ8OPJ3lSnYSad/2AZxQeJyF7AoWGWttYbZ1pvqlrf+fKfWLJjV0xYx4nI3aq6rLGbjKp+jjns677H5h67U3f+2mI5RZ8C52P9cHJbO9RdvxGH/rLw3mVYr5yDRGSmqk6POP4LVly8CKUTC8EEkO1Ln3VgZjAH/EJMRCqA/TA78k7AfZBe4VgTwfGe5RysAdeBWL+NN7GEyBNFpH32JrOmzyrkm09KOBibgJ+G9flZKSLfEZG9ReQAqBOgTBCYbIRXd1W9E8vMPxv7HZwrVgmZdZ33tOPC4sRFbzAfSfjRtMLaCV+Kxfe/FV6vwmornQLsISKjkhrwxiLY5MtE5GxgNZYw95KqfqyqB2AVaU8ERjZFXJxE6QG0CddvNxH5NfAs5pz/i4hcHfarW1mKyLXA70VkmKpWBxPY+Zjf7GyxNglgfekPFStGWlC4sDgbTPhhvCbWeChLG+AFLLN4cxGpK+0eHJh/xXIzcvuvFAo/xET1PMw2n20zjKoOx8xjJ2LJoR18ZZI8EilZH+EhYFMR+U/490XAb7Es+quAYSLSJyIq92Kr0ylYKX0AVPWP4b3bAReIyC+AqzGBWkmB4cLixEEN8AtVfUKs5DchouWS8BgIHC4Ne01UYaGYvcPqJvXkrDqeBo7HzIBHAqjqiuyxBnF5Actz2L+Zh+rkEKK/akWkRES+ISK9RKSTqr6D1ft6GSsaubuqXqeq/8T8Lkupnzicg0WE/QS4S1U/EpFWkQnF7Virgz7AhViv+l1U9ZPmPdqNjzvvnQ1GrQzL7CAqT4vIy6r6a7VyFtdhq5dLsXvvJFX9UKwX+LbAU2sLwUwDktMuGaxUjYjchwUtXCMit6vqMSFYoZWqrlLVw0TkIqzgppMQ4fytDtFfj2JVt7sB/xGRq1X1QRGZTuhTH67zb2DdS9/GWgiDmYOfy4Yjh2v8EqCziCwHDlDVP4rIy0AJME9VP23OY20uXFicOOmC/dCOFpHPVfWCcIMdF16/FPi5iLyHVexdBIyC9IZg5uQ5nITZ5FcDt6jqeyJyO1Zc8xoRISIurVV1par+NnyOhxQnRCR68TksGu83WGfH7wL3i8gvVXUCVgK/E1Zp+EAspPiISKBKR+DbIrIPlmn/a8zk+S4WtFIJ/FRVZzbvETY/LizOeiM5nQ5Vda6IjMXMA78ON9ILVHWRWK+JLzH79NtYVd+Xw4+6RFNYByyIYXam+y9MUFpjnQJPEJExWN7DxPCW8SJSo6q/0EiiKBReZFwK2R9bRZyioYRK1oSFCUiW3THT2AfAMcCpIvKWqj4mIidg+S53h9fPVdXfhRXOzcBmzXMoyePC4qwXWTEIobXdsU6PVar6gVjjImgoLgvD9hKsXMlkVf1nmKmnTlSgLmS0CPgDlq9wOOY3KsN8J78Lu/1eRCqxml83icj7qnppUuNuyYj1wPlGCAWO0g0rFvlp2G841nDurHD+OgLdVPVhEXkNK7nyXexaflVEvlTVp4HtRWRL4AtVnRM+uy2Ww/SptJQKCqrqD3806YHZlfsAmfC8Axbd9R6WBDkaK9sClmU/HjMtnB/5jE5YM6sVwOlJH9PXPP5+2M0luq0jFv1zcSP734IJzjcix34QUJz0sbTEB7aSvAq4rpHXRgBLwr8PwCYBZ4fnRcDpwPVAeeQ9mXA+X8f62P9fI5/7HWzisQD4VtLfQbN910kPwB/peGBdDRcAT2BOylZYZ7sXsZDapzBb8oRGxGURMD7ns24Mn9cp6WNr4vFvgZVGPyBne5twY7k1sq04/O0OfARc2sjnubg07/nbEUvM7RKel0XPJeZTeQ8z09YCp0Ze+zaWfX9JZFsm8u8fY1UlpgB7RrafFrbPBLZN+jtozoeHGztNQq3MyDFYpdarsHj8xcAJqnoxVhfpXswG/YdgKvsfJiAPYE7NTOSzzgK2UdXFzX4w60E4ll+p6qMhJHWT8FIN8Aawm4h8N+ybNXV8gbWd/Uryo7YEc0ieEMxf/8SqZmevt1HAwyJyVHheBYzFzudsYKKIdBSRwcBt2KrlAhG5XkT2UosOy17PD2KTq37A+SKyW/jMJ4Fbgf00haXvN4iklc0f6XpgAvIlNhN7HiiNvFaO+RbexXrYZ1cuPakveFqU9DGsxzEXR/5dgt0wbgB6hG29gflYFdvtI/v2x2arp4TnkvSxtLQHlqC7EjO/Rs1YXYHfY8J/TNhWDhwbrt9PMQf861i0WAnW0fE5LDdpl/Ce6MrlSGy18zesj0qLPede3dhZJ7nRXyIyFJiEzeJ+BLyo2V+RFV08CzgU+4EerGF2nvs5+U423yTyPNsZcAIwFPgTcJNaItzOWA7EAqxky6dYiGkt8D31FUqzIyKDMCF4HPiZ5jSKC6vOC4GRwAhVnRhWIe2wJMdarM7bk1h17jcxX+I4rNbdMFX9R9QhLyIzsbD754AjNacdcYshaWXzR34/CCsMzKfSg/pVyA8wx/wDQN+c95RjP8Q/E5nRpemB+YF+juUdgDl+K4Gtw/NrgbnAZUDPsK035mOajple7qTe35K6lVqaH5hJdiUmDo8TVhjhtegqYxPMKb+asHLJPV9YaPwCLKs+mxz5t7Dt+5H9+odr/mhg86S/gyQfvmJx1kgk+a8cq0T8CXC1qr4ZXt8HE5bHgTPUMvCz720DrFC15DFN0UoF6mazt2JO+5uwKsUfAz/R0F5WrNjgTzGz341qK5cSLFqolaouC/u1jBDTPCH4VJ7ERH8adrOfjkXu/SPsU3dNRlYuI7DJw8mRc7c/NmEoVdWrI//HFpgpbVcsGnI+ZibeHiv78mUzHGr+krSy+SM/H9T7RMqBGdgP9SgiPpXw+n6Y4/NeclYu0c9J4wPohUW9rcAc9NmIopLIPtmVy6UEn0uhHH8aH9iqoRbLISoP276P+UX+Buwa2Te6cukbrmPFzLdg2fW14TEqbIuuZLphK9Sa8Pmzge2S/g7y4eErFmeNBHvz7djS/wjgY7VM+S5YfkaJqi4XkR9is8KXgcM15fWPItE+tSLyL2ArLCFuvFqV2gb+FxG5BvMpPQycp6qLGv9kZ2MTzt2RwL2q+mXWpxIitR4CXgEu0sjKBQs9fh1z1mfDileLSA/MHDoamKJWOPQr5XdEZCfMlDZX65MiWzQuLE4DpL4da/bvC8Ajqnp5eP0nWC+VcuA14EJVnSUiBwEnY9EwqTJ7RYkcdyugGiuU2RErJliKJdfdGfaNissfsLyVA9R/VInQWL01ERHqi0d+RVyCmXca5jvZV1U/Cu/LmoG7Y7XBLgCuUNVzsv8XUOvnunE8j8WpI9ida0SkA3CZiPQF3gH2FZHjReRO4B7gLcw0NBj4SZgVPqSqe2skvj9tRESlHPgL5j+ao6rPYDeXFcAoEfk5WF8ZEekuIt9S1V8QRCXczJxmJldUwjbNXpOqOg3LlN8B+K2I/B+2UvkOVt5qq8i1Wxve/ylWQeFC4CwRuTTyf/l5XgOpvAE48RMJpW2FZRAPwCLB7sbqgJ2H2ZT3UdVfqurPgeVA79xZWxpXLEEcs6IyHVudPEnotaFWkXYEJi4nicjJIrIVFmJ8dtgnG6jgs9g8Yw3i8hAW2TUQM2XdCOwVJhjRBMh5WBHJC7AOkBdnP7P5jyQduCnMqSOISl8ss/4SVX05bC/Dwm9XqlUqLsGyjCcB96jqVQkNOVbCjeQabCV2OPBuEIsiTDdqRWQbLBpoWyy5bi4wWFNaSLMQiJrAonkqa9g3g600PseKQ96mqiPEyuH/A5tsnwQ8EyYa0eixTbGV68VY/buLN+qBpRgXFgeo+8E9DQwC3geGqOrCRvZrh914f4vN6nfWAgqlFZHngNmqemTO9ujNa3NMWNoDd4cbkIcUJ0DEOd8G6KqqHzbhPRms/ldXrMx9bfiMzpi/ZW3i0g1z6P9VVWdspMNKPS4sDlDn5NwfC5vtA/womA2i+7TBBGUoVnNpP7XS+alsUtVIRYFOWGHNaap6UiOZ9x2xcOtPcz4nlcdfKIQV5d3YSuQwmuBUz8nALw7vqW2iuKQuL6u5cR9LCyX8GOsIP7K/Y+XBq7CCe5vk7PMlZi64EvO1VIeZeipvquFG0lpEuobni7FSHD8XkR7BOd8K6oT3SKyfSnnO56Ty+AuF8P0vwlbbxU0JoIgKj6qujvhgFgG7Yc77G4A9Iz4XCfu7qKwDX7G0QCKhlKWYE7Mj5rCeF36U+2AzwH9h9Y4WNma7TvtMPYjri1hFgVGq+rGIfAu4H5t07a2qn4R9t8Gcu3OwXB3/4eQBkUi+TsB/gdtVdcwGfF42iCW7cqkFzgD+nuZrvblxYWlhRH447TCzTzes6B7AFcB9qvq2WKHJuzDBObIxf0shICJHYOGk92KNnT4LOTljsQCFuzBfykAsw3qQ5hQzdJqPiJBETVNFWATjtVgy6yG6AUmqOeIyA0uO3VNbakHJ9cCFpcBZw0qjNdaYawUWn/8ucAhWW+kx4EQsxHhv4I9Ys6q91fqopJbcFVbE8XsotkK7B+u5sjCEEp+I1YJaiuXtnBVExR31CRC54bfFovc+AC7HyqysFpFdMFPmsRoqJMTwf3UCOqv143GaiPe8L3xaAytyBGYLbKXyK+CfwVfyPyyb/jmtb771dxEZARyP5aykmjDTbQNspapvQJ243BfM53cDKiJnqeq7wK9EpJ2GgoRhfxeVZiSIiKhqldbnWW2B9UYZhoWFPyAid6rqCyJyC3CciDypG1BeJeJzWUx9czCnibjzvoAJfoEHRGS7HIdmP+zH+e8gKkdiFYrHqOpNItJBRPYK0TJ/VdX9NeUZ9ZG/NwOPishOkaggUdX7gOOwG9V5ItIPIEdUxEWl+Qgr61HAmSHIoi3m4+qORTAOxIJJ9gZeE5FzgU5YP5Qe4TPW+5p1J/36k8obhdNkvgd8E7hcRL4dWbHMxBzWe4nI4VjfkDGqelkQn2Owm2znnOiZ1P3QpL5MTSdMTB/DqtDeICI7R8SlCCvj8jhwAjBOrNhmHe5TaV5UdSXwGRbi/jus0dZMzElfrarzVfV4rOf8RVgY/PZYhePfhIlA6q7ZQsB9LAWOiByNFY1cDJymqv8RkfZYHaxtsEZHF6nq2DC72xLr8T0T62ef2gsk4kMpxXqpPAMMx0p6/BozE56sqv+MvGciFszQBfiB35iaHxHZGvhQVVeE56djvpSPsZL2r4btuT6zflgb7N9ik4gjVfVFD7RofnzFUqAEMwKqege2ItkEuFpEtglO+KOxfJWlwDyxjOJhYd9y7Iab2oKKYaWiwZy3OxY6+ltVrVHV+7EZ8ErgRrGy54RQ497Alao6JM3mv7QScoqmY62Bs2yOXad9gaNEZDOo85lJJL9kllo5/J9i1YqHh+0uKs2Mr1gKkJzY/hswodgR2BQr23KGqr4mIr0wIemBrVRmYLPCgzTFGfVZgrhOwGavC1V1/5ww1WHAacAuWM5OL6wo4c5pPu40I1aH7juq+oqItA7msOxrJ2DX8++xEvZfcc5Hrv3xmCl4P1Wtaq7xO4YLS4ESbqr/wGZ6V2GiMRKbCc6iXlzaYw7PrYH3sDpZtWmLforcUEqCKAp2XA9jNvfXVfUHYd8SDUUjRWRbzDa/Exa+ek4IXU21qKad4PN6HjPhHpwVGBE5CbgOE5fLQt7RZpjZ8q6wjxBqgAE/zJrUnGZE86CNpT/if2A9JhYBh+ZsHwn8DysJv80a3pvZ2OOL+VjbY9Fcg8LzDsDVWEWBXlgm/RdYgEL2PSU5n1Hc2L/9kdg5LQZ+gU2M7gDaRF47ERONiVhByBewgIzsRPmbWDOvHZI+jpb68DyWwqUcu+HOASsgqapfqurNwck5GvO5nKOq/46+UdPnsO6J2dO7iMh52I1oBiYQH4vIadiNqkJEqlT1Gs0x9WlkdaYpWqkVCpJT2FFt1Xg3NiG4zXaRkeEavlFEVmKZ9ntiK/AtNagKNnHaQ1VTn3uVVtwUViBEIqCyfzPYzXWmqv447NNGrQ/41sCzWImS+1X11ASHHgtiZVjOw8xeb2A3lpURE1lfzISyOTBBVa8J7/OIoYSR+tp1rbGSLCvVElSzCZIHYeJyHzBSrRgqIvJNLLLvTU2h+baQ8YiXlJNN/ovcHLPntAjzrXwvODLJ/iCBb2HFF0/Hwm5Ti9R3+XsIqxel2Hfw3bCLhhvObOBkbDZ7TFjZ4KKSLNmkU7HadU9ivpGnROTesIr5Auv0+EvgUKy6dFsAVX1bVd/Q+ix5F5U8wVcsKSYy0yvDKrD2Cy9dpar/DaGb52Dhl69g9ZU2w7KZ31FrL5zaKsWR1Ugx1ovjUMy/ciwWSjxaLY8hg13rNSLSGyvd8hFwhAtLckTOXxFQieUO/RELJDkSmAfsFlbZ2ZXLTdhq+1CN9Mpx8gsXlpQiDasUv4gVjfwI86vsit00/xqyxw8EzsTMDJ9jmct7BT9DKk1BOaI6BnhZVR8Mrw3HVmOrgDNV9cWwvQvm9K0FPg/fXyqPv1AI5q+9sWoP16vqMyGqa3/MdLkY2CUiLocBFcD/pdAX2GJwYUkx4Uf5CGb2qlDV+SLyMHAA8CXwc7UaWNn9d8KKSc5Ms006R1RfABZipVqu0/ps7QqsyOYK4HzM33Qv8JKqjo5+ThLH0NKJJN4+iOURfYblD1WF10uwMPAbsOjGXYO41OW2+PnLX9zHkm52wjKMzwqici/WuGso8AQwUUQOiPghXlLV/4abclEaRQXqKs+WAn/Dot5GAteo6oqIz6kSy64vwcT3OazH+Zjo5zT32Fs6UZ9gWCmeD8wFBmChw4TXq4EpWHvgDsC7Yq2iV0b28fOXp/iKJcWItQ7eHUsCPBcrb3+oqr4kIvtiBRXnYzXCKpMbafyIyPexrPqjNNT6EpFdMad9R+BqVa0SkT2wKrjtsVIt3k8lYYJJ6/vAC6q6LEQpPoiFFl+iqg9H9i3GTLmHAcPT6AtsibiwpIQ1OdilvpbVFMzENSps74g182oLfKaqezbXWJsDsfbJt2E3nWrMcT8GeB9z/k5T1d0beV8qAxUKCRH5AxZg8TPgcVVdLqHFA+YrvDxE+WX3rztnfv7SgSdIpoCIo7oN5tRsDXyqqk9mfSVYRd7NIm/7Lub4rMA6RKaWNdjSn8NuQn/F/ChlWKn/yVguy1QRGaKqU6Nv8ptS8qjqL0Rkc8w5nxGRv6rqDBE5GBOXs0SkVlUfCfvXRN7r5y8FuLDkOdn4/IijugwTlk1FpBK4KYTUPg0cLCLXYH0rjsOcnu9lEybTaJPOSZ7bGTPtLVLVT0VkMBZN9AnwlqrODO/5HvAOFq7qJEiu2VFESlV1har+QESewWp+kSMu92JVIRar6vPJjNzZENwUlgJChMwTWPTXKdjNtSMW6fQEcARWAv9mYC+gFPgPVoCvOsWikq0ikE2e6435Sh4HblTVZ3L2b4XViboBs9f/MI3HXWgEn8oJqjo+PC+NRO89g60wTwUeCz6XbYGzsH4qvkJJIb5iSQebYj3qLwFmhKSybcJrU4Al4Qb8C6AP0AZ4O+UhxdHkuZsx4RyBOeIPBcaLyGhVfSrs3wW4GPg2tqL7QSQj28UlWY4FrhSR7qp6Zojey4rLUOCf2LlDRB5X1dcxZ737VFKKhxungy5YOOaCcLM9DJvBj1HVa4EOIvITLIpzlqrO1JSWucjmN4TjbIP11FiNOXT/qqqXY21olwG/E5G9wlszmAC/BgwOK7ViF5W84BFgLFZK53cAEXFZhQVhbAHcjp3vBtdBMkN2NgRfseQRa8kCnwe8DWwrIn2wMNsxWLtWsITIY4FXsZ4qQLri/EVkB6Czqv49ssr4HRZC/TFW9wwAVX1ERGqwagK/E5HTVfUpETkcK2Coac7TSTORlWa0KOqHInIbJv4jw0una32flFZYPksnrFyL13BLOS4seYBYTa+lqrqqMXFR1bnBOZ+9uZ6vqpeJsTXWt2I2VmAxdQTH/K+xsvb7quqU8NJZWPn/w4G9ReQ9DYU0VfWxMKn9NXCXiBygqtPD54nPdJufMCGoEZFy4KIQrThDRCYFcbkl7HpCSHA9D/gGZtqsVNWsOczNXynHnfcJE4ThPswMcEOuuEjDVroTsV7152D1wfpgzvxiYMcQPZVKn0LwGZ0HHAIcqKp/C9vbYUUjd8GOfbKG7o/h9UOAIcCpfjNKnmC+fDk8bYMVB30fS25cKFYE9DBsxV2KNfL6ENjJV5iFgwtLwogVUXwKy0O5HusVkisu0X/fgN1kBwL/xsxEw7UA2umK9de4BBhGQ3EpB/6Clas5lhxxibw/1cefVnKuz12B32Bl7hdjEYujqI/SWxAmC12xnKxF2GqlJq2BJs5XcWFJkIg9ugwradEPa6nbmLhEs487Y6G3HwGLgy27IH6UTRCX7TFxmdKYuDjNSyTPqBhboQzCmnEND6+XYN09z8UKoO6nqgsb+RyfFBQQLiwJ8zXFpQTYEosOmx/5jFSav9bEOsTlz8APsT4dLyQ3Sicnz+g+rEV0KTBLVfeO7FeEVYA4BzN9HaiqC5IYs9M8eLhxwmRzNdTKhf8Y69/9K+A4sWqudeYwzBT0CDA+5zMKRlTAOgNi5pSHgUfECmqi1sO8AhPel5IboRMmMxomO/diIZPFdVwAACAASURBVPEPY+avISKSdd5nQ4YrgUuxOm6XJTRsp5nwFUszsraVxRpWLtcAN4eclF2BW7GcjkEtwQwUWbkciM1yJ+e87uaTBAnRfDsBpwFjVfWVsO1OYFuCmGRNtGHlsjfwpJ+3wsaFpZkQke0x+/M9qrp0Dfvkiktf4Fqs9tf1YbcdIsl/qfOpRI6xSaIQxOVCrL3yYFX1lUoeEFbQz2HFTmcAe6r1p89W1r4xvDYJuCz3WvVJQWHjwtJMiMgdwFHACVgUzOdr2C8qLg9gta86YiGZaReVrKO3HOtd/i/g9mDiWtv7BmAmsAvSeNyFiogMxOq29QJ+AjwYCY3viNVs2xarZ3e2C0nLwYWlGRGRP2HJYKcDdzVRXJ7E8lQGa4qbVEnDdsIvYiXvHwauVSvr0dTPSeXxp501rTBCHtY/sOTcU6MrShHpANyDhRQf7tn0LQcXlmYgejMUkUmYk76p4tIaqNYUF5TMEhy9j2NVmo8DPgpi2SoqLoUW5ZZ2ItdiG6x1QbGq/j3y+jbANMwkdoaGjp7htXLgi3D9rqlkkVNguLA0EzniUgkcxLrFJZp1n/qbrYh8C/Mdnaz1VYn3xswoAkwF/uw3n/wjCMQLWP5UOVaX7pdYte3qiLj8FxOXl3Len/rr12k6Hm68EQlRMABEVxqqWkF92PARItK+sfdHf4hp/FFmw00jLAt/dxaRnUTkaqzs/1ZY+fSzsZBqJ0+Q+tbX52IJuQdjk6IizAe4S1hxzgB2A74F/FHq2zoA6bx+nfXHhWUjEVYoNSLSRkQOE5GzReTHweFJyEx+lHWIS5oJZq4yEdk/bFqMJdIdga1chgKHqupe1N+U+icyWKcBkRyUrCAUAw+o6lRVfQyrqD0PCy3eOSIue2O1wd5OYNhOnuDVjTcC8tV2wh2w5lNdgZdE5A5VvUVVfyoi92BVi2tF5M+quiTBoW8MTgHGisgxqnqniFwJPIR9Hx+rVb0tATbB2gkvTnCsDnU+lWz03gXYOfk+VpsuO2maIyLDsACMO4CjReQltSZd+0U+xyPBWiDuY9lIhBnfo1iJi1Mx2/PuWCn4bwDjVfWWsO9dWMXXQ1T1wWRGvHEQ6+x4PtZX5XhVvT3n9TZY18fx2Ap6d78ZJUekTEsb4HWgBDsvmwCvqOoeYb+sQ39TzCS2Axa5+FpSY3fyBzeFbTzaY76Dh4H/qGqNqk7F/AgfAD8Xkf4AqnoE1hXx0aQGGwdRn1J4LqEm1PnAH4BbReSoyOsdsOTHG7Frcc9s8mQzDtsJRMq0FGNi/x9spfJ9rKnatiLyMDQoRTQPC774Y9jfcVxYNgYhK7k9Vpbls0hNJcKMbhwWtrll9j2qeoHWV4lNJRGf0hgR2TQb3aWqi7D+G7cBfxCRI8JbSoAvsRnv7pHkT1+xJEAICW6N5Z5cD6wC5qjqh8BdmFlzzxxxKVbVuap6vE8KnCypvYnlE9H4/EhY5SwRmQKMFpHnVPUTESlRq/H1ApY0tkXuZ6U5TyVwPDa77SEi54cVC6q6WEQuxsT2ZhGpVtV7ROSS8J3U2fYTG7kD0ANoi1Uq/ijrvFfVZSJyH6DA70XkQVX9ce758kmBA75i2WDCjE1FpCisNjpHXp6IzcovFZGeYUYuwABgJfBJAkPeqKjqNVixzL2Bi4OPJSu+c7Ew67ZApYj8SCPFNP2m1PyE67EOVf0Ay696FvihiJwVee0L4H6scdcwERnXnGN10oOvWDaASPRMO+xm2h/oLCKPA9er6n0isiXWs/2xkLfRDcsFmIOZgFJLbtRP9rmqjg4mkQPC9gtU9bPsbljZ+5nA5K98qNNsSH3ttgxQFjZ/qaozROSS8PwXIrJKVa8GExcReRDrifNkAsN2UoBHhW0gYvW8pgPzsV7fK4CTsZbBo1X1MRE5GOvXvi9WU+kt4KdhBZPKkMzITakMs71vDXwG/EtVHwj7XIm1n30duBi7eV0BPK+q50U/J4ljaMlEorraATdj568EE/zRqvpRCC45D/gecGNWXHI+x8+f8xVcWDYQETkNMw3sr6ozw7beWE0swQRkRmT758Dn2eibNP4oIyGp5ZiYKtbTvBy7Qf1OVc8I+/4WK3m/DTAX+BTYKY3HXWhEJkXLgKex1fTu2Hkcpqr/FJFvU18RYZKqXpTUeJ304MKygYjI9cBeqrpNeN5aVVcGEfkv8CdVPSm81piTP5UE88kELFv+GFV9S0T6YP3NLwWuVtXRYd/vYOGrgrUMaHI/Fidegk9FQgTYOVj+1MGq+m54/btYTtEArE3Dx2LlWX6HtRX+mfpNw1kH7rzfcGYAXSM5KSuDuHwE3II5QDtlcwSyb0qjqOQ4ekuwm88bqvoWQAhLvQELLT4jZGajqm+o6t2qepeLSvMTrr++IvIdNbLX3maYn/Wj7L6q+ipwBlAFXBZW1TOwaL+KsFKV3P/DcaK4sDSRtcTnv4uZgY4Rkc3AxCW81grzvSz5//bOO9yuqtriv5FAQgIJ9VGlCyiEXgy9I02MUqUIyEPEBgSQEvC95wMCBAQsVAFRBBFEpEgAQXmhE6qIFalKkN6TQDLeH3Ptm51DLvXmlHvn7/v4uGeX8+2Tc/aea6055xidGEjqlGDg8pBasHzGycQspK4t9Qbhgf4IRVBS04QMKcdkUGkSktYCLiUKJc6vqvQKrxFikkPKsf0BbN9LlMQvT/yGsf14meVMN0BKkhmRgeVdqG60cjNVzX97StpZ0soADl+K8wjZloPKskG1/LM28MfecCOWzz+YsKMdWTbfCuwiafmSyK8eTI8RD635y+uODqqdiqR1CCuCqoR4Y8JgreICol9lFLwj4E8gClEay5Hzu0zekyw37gZJawDbSzrV9jMl0XkX0VG/CPCgpF/YPs72/0gysDewj6TniZHeC4RnxXT5lU6iVv0lYonk30RuBeBYYB3gN5K2rhUpDCMqwP7WimtOQNKSxIDnXOBo2y837Jftv0g6GPhh+X6PIwYEiwGbA+Ntv97kS096ARlYumc1QjByFkknEiO+p4AjiH+3gwlF16G2D7f9HUnjiG76TxDS4We7g+2EYZr0PVEZtDBwk+1KEv1V4t/hJEK1+TJgKrAqsUZ/WgsuOQlWIppwzyMqEaejNsi5hKjqO5noTTEwhZit7AedOyhKWkdWhb0LkvYDzgC+TSQ677F9Xtm3BPGw3QK4xPYR3bxHxyeqJa0JVI6Ax1Y9KLX9QwgjqOFEUv8hwiXy7d7w+TsRSacC29he5n0evxzRyDuIKAv/XqcPipLWkYHlPZD0DaaNvL9o+8JaH8eixPr0ZkQZ7dHdvlGHUvus6xBKAc8S1UEPzeDYQcDkKpDkQ6l1SBpN9Fat2N33UJa/5gDms/1o48wkBwXJhyWT9w3UE/blxvo+sE/ZPULSQtXNV0qKjyXsdUdK2rclF92DNFa/1T7rbcAXiPX34yQtNYNzJtaCijKotJQ7gRUkfbq776F8t0sD48r32Zioz6CSfCgyx1JD08uUHAo8Kelnts9XyN6fSagWH+9pqr1PlhzMo8R6dsfS8PlHEUrE/QmHwPG2f6eQp/kVcKqkA23/o3oANfTp5FS4tfyB6Ko/QtLTth+EdzTp9icsoR8DnsuKr6SnyBlLQdPbCd9FLG8NIJKZ2D6b0AAbCRxe7wew/ZjtMe5gP4pqhqGQabkX2IZI1n8SuIhQKl7K9o3ACEL642RJy7bsopNusf0I4X+zPDBK0iplV9eMHFiSmIX+iSjESJIeIWcshdL8NSvR3PcM8BXgkRIsVDqWTy9lxd8nPOpPcjjo1d+nI5cPah3VpxElp58jTJ7eLuv1OwGWdLTtmySNIPSl/kpUzyVtQu33eo6keYCDgCXK93hDyYVtTBSfDAb2r77/nGkmPUGfDSy1pHT9ZpqfGKEfB/ytvr92s55RHsA/AB4nJEx6CwKWIqrfniijWmwfUT7zfkQC/wbbv5e0OmlH23aU3+sstt+2fYKkR4GvEt/dU4Rn0D8IKZcNs3ov6Wn6ZFVYKRVeD7i/Xt0kaTghZbGm7Xsaq2lK7mGK7Ykl13BlJyeo6w+TshQ4VdLtwPO2ty3bK9dLJP2R+DfbrWGtPqu/WkB3waA2GNoWWNj22ZLmJQZNyxLLYfcQ3+XU/P6SnqbP5VgkfQq4gsiXbNOw+2Eikbm/pAGuyZQotLD2JiRM+tm+vKrzb+Ll9xjl4VPJ1CxYS9xeDiwvaTcAh2dMP4UX+ktE011joj4fSi2gfH+DJZ1V+51Wmm47E26dKsc+b/sW2+fZPsf2vZ6m/ZXfX9Kj9KnAUoLKDURy/jDbJ5TtVZnlm4RY3/qEc96AcvP2I0Z6uwH1h3BHPlRrD5/+RL7odkkLl92/JiyTvyppd+jSh1oEmBt4ohXXnHTLCoRs0K7QFWy2Ai4mVCLOebeTsxIsmRn0maWwUsV1OeGRcqTtF7s5bl5CDXZR4BZC+n51YA8iJzW8E4NJRa2keBDh7vhFYCvgDmDXkltZnfDfWJxIzj9N/BtMITw6Ovbz9zbK7/pi4Bnbu0saQBSevAGcl4EjaQV9KbAsDdwIHGr70lpOYS6iSWwY8HhJSg8lrHS3IZLZjxIBaXt3sJ1wRSkpvo8QiXyBSOZuTHzGHR1d2MsQ2lHbEw+pR4ADMtHbGsqsul9jTqz8vRPwc2A927eVmfbkFl5u0sfpS4FlY+AaQubiprKtcsZbh5C2gJihHEY8TAcCywH/IkaEHWsnXEfF9RLY0mHOVemifYMQH/x8tX0G53b85+8kJM3umsKwwrpgERfHx7JtTmIJ8+/ANx2eOEnSMvpMjsX274AHge9JGilpDLH8syah8PpZYpayL3Fzvm379ZLknFCCSkclOiV9quSVGpmbmKk8U+WXbJ9FlFCvBFwsaZHyHv1r75cyLU1E4flznaSNapt/CPxF0umSdgRwSOJfC2wHzFnO7TP3dtJ+9IkZS23Z6xOEP8XaZdfPgItsX1s79ifARoRH+6udukZdqriuBu62fWTDvvOANWyvVF53LZ1Iuh7YFLgd2Mn2v5p75UlFKYv/C1EafKjtWyUtQCxR7kpYO9xJSAldD9wM3Gb7ay254CQp9InA0khJTk9yg0JvGeVdCMxje8uWXFwPImle28+X5ZOlbf+hbF+FmK2dafvAhnN+Uf5cknhQHZb5lOYiaTbbE8vfixPf1dNEifztZfa8EKHl9h2iYm8IIcvyCrGU+a+G5t8kaRp9arpc6yS/pwoq1bayJLQskawf37KL7EFKUBFwCvCAwv8col/nv4B9Fb4dlH6WpQj1gZ8SifwtiTxT0iQkrQaMlzRfKZJ4nJhhLwx8jyiFx/bTtm8nKvp2ISrDhgBrATuUYzKoJC2hT85YKmodyoOAlYExwGzA2qX6qaNGfEUZYFbg9apbvmxfjsifrECMZu8oI949gP8hqt5eBeYD3rQ9TGFZ+3VgLdvPNvmj9FlKXmVr26Mbti9BzFyeAg6yPW4G565IyO58Chhh+58z/YKTZAb0uhlLrdlxur/f5fgliKbJ04DJwDq1ktpOCiqrA5cRysT3S9qz9DjgsBLeD/gzcIWk4bafBk4lHkLjyr5LgUoFdyuiHDk9z5uI7Qdsjy4zyMtLXhDbjxEOnR8DTpG0XnVOpf5QljrHEpWMizT94pOk0KsCSymF9YwCyrsEmQHAA0QfwBalT2WWTsorSNoA+B0x67iS6Jw/G9hDodiM7X8A/0ksg1XBZbLtB23vZ3tP24cDy0m6kGiIHJmlqy1jMWIJ7FJJH4d3BJdTq+BSBkLVvXwX8CKhC5YkLaHXLIVVTXul+W80sAwwAbjF9o/e49yu3oxOa/6TtDaRZD8JONH2S2X7RUR123K2X60dvxTwI+ATwOds31lbElwZ+BLR47KbizlUMvNpXHYtA6GVCZO1gcBnbP+97FuCEEt9klCRuLF2zkmEkvEnSyBKkqbTawILdHWUjyfEEh8jBPi2JRKb3+5tpbOS5ifUBBYiNMzeVlEjLkso44ANbP+pdk4ljX8WEUCG2X647OtHLKO8YPuZJn+cPoumyexUg6MBtieXJa4VieAygHcGl38A59vep/ZeWwNP5aAgaSW9JrCUB+ZoQprkiyWvgKTTCe2kjYH/q5bKOil/0h2l4/qrwP7AnbZ3rO1bBvg90fzZr/z/8Sq4SloSOIRoBu2YGVpvo9ZjNQfRjzIXsZR5iu0H3yO4LAT82zUzutZ8iiSZnl4TWAAkXQO8Znvn8noXonT2KIfhUVd/QG9BoWv2JUKG5jbb25ftVwKbA/8GZidKUR8nci9/tX1l7T06avmvt6EQjryFEPl8lhBAXZKQH7pFoX6wEnA+IYS6g+0/187P7y9pKzo2ea8Gb3lJsxE35qTyejfCq/3oKqgAZ0jaoukXOxOx/Qox0j0BWFfSxSWoDAO2IPoaliHsaccDJwJfqRcz5EOp+Wh6yZW5iQHAF2xvR8gK3Qr8VtJG5ft5ENgLmBc4uv5e+f0l7UbHBhZPMzm6SNKgMhO5Bxgh6Uhi6eBo28eXU1YmVIznb80VzzxqweV4onx4W2K0O872M7ZftH267V2JIPOZ7qrnkplPyalMlTRQ0grE7GQwsQSG7fHEDPQGYKykDUvweAjYgLA6SJK2paOXwiStBNxNBJATy7LQZcBmwGm2DyrHrUAYHr0GbNVbR3gl5/IlIndyW5VzqSWD63bCuXzSAmoVeEOIwovFiSKTtwivnydrxw4DjiOKLD5v+/ravvz+kral0wPLUKJ0dl5Cnfh1YF1gFDGyO4toFPt4OWW4e4GfyrvRkHMZZ3unsr3LvyNpDbWqr/7AGUQF3lnEb3Y3YpC0t+2naucMI/Jik2xv3ILLTpIPTEcElrJkUzc5qqTerZCFvx3Yx/b5Zd9Q4ECimmYKcD8wppR09no/kRJc9gYOBf5ie9MWX1JSUMgHbQfsSDg8/qZsPxDYh7B+3q8huCwFPJYDg6RTaOvAImke4JVa8+JgYFnb99eO6UeM+lYkkp+P1vZNF0R680ylkRJcvkkk70fkQ6k9kHQGoYDwJLBZUUSo9h1Q9j0BfNkNWl8560w6hbZN3iu0r+4klguqAHIacK+k0yRtCVButGuIpPSK5dhZy9tMF0T6SlCBroT+KcBnS6K4bb/rPsYBhNvjEsDXS/8KALZPI3KBiwCXS/qP+okZVJJOoW1nLKU8+BDbx9SayBYnGh2/RVTR3Av8d2kkOwdYD1jF9qTWXXn7kc1zraG7GUYZ+PyKKAk/kVgSm1jbfyQhubNXBpOkE2nLwFLJkpS/BwGXEyO5q0ryfWmirPYoYBARYO4glhFOsn1Oa648SYKaTMtAQjF6QSIX+FwZJA0AriIKS74LnNsQXKrqsVz+SjqOWVp9AXVqN1H9RppCKO1+HHhL0nW2HwEekXQJkaTflGgQhGkVYEnSEkou7+1SUnwl0Uk/O+H1c7CksQ6Hx8+U/QcBUyX92Pab0FWYogwqSSfSNuvuJVG/taRhpSRziKQjHV7siwETiRzLpzXNf2KK7ZOJUuO9gJ8QpcZJ0hLK4GhKKTS5mRgY7Wx7IeBNoi9ld0kLlt/2dsBfCZO5rervlcuXSafSNkthpYnxEmJZ6wyi0fHvwPa2nytLYncTuZVvAmPdjctjXygpTtqH0qi7uO2ryut+hBf9msCetidIuoyYed8FbA8cCVxYZi4DCQHVQ/tSgUnSe2mbGYvtPxI+7J8lXPAeI8pknys5lzeJG/UNwvt7S00z9urX8F4ZVJKmoDBZuxtYvNZfNZX4/d5egso5xG93hEMgdSzRwLqLpI/ZnmR7ZK15Mkk6mrYILNXNZPuXgImE/ATCKY+SsB9QCy6vE6W0I8p6dq5DJ01H0vrA9cCZRPK9a+bsMJcbrbAv2IRYoq0UiX9LCE+eRJixUTsvZyxJx9PywFKTuagKCUYChxN6X98qywwUrav6zGUosEveiEkrkLQuESB+AIyy/eYMZs6TiP6qJYH7amXw/YlA8xXCEjtJehUtrQqrlWTOAZwk6VHCXteSniVuWiSNcXizvyVpvrI8tigNDZBJ0gyKxMoVRLA4pGyrnDtXJdQhLimH/5lwehwp6RCiEGUH4HrbZ5dzMyeY9CpaNmMpSfeqJPNuYNmyazCA7Z8C3yCqZkZKGi5pAeAaSYfanpxr0kmLmJ3wtplL0v7QtVy7OmHdsFFt9vJPwmxuU0LG5WrCw/5/qzfLoJL0NlpaFVaWvy4mPNv3BB4tzWNdIzhJexD5lJeI/MvbwEpVA2WStAJJKxKGW6sB3ybkhx4ALgRG2n6j1uQ4mGjoXYvID57ZVwRRk75JqwPLXMQN+WPboxv21YPLtkSpJsCxeVMm7UDJ/40igsZiwPcJRem3q4KS7uR0+pIgatL3aFmOpZRmLkBUfj1Wtg0oTWOU4DEnoW58taRrqhu06mxu0aUnCQBFo+5YIrgMJmbck2Fa4Kj9ZqcLMBlUkt5M02Ys7zJyGw+8Znuj8rpyO+xHVIfNYvs7TbnIJPkQlJnLUcSy2Mm2zyjbU+cr6ZM0JXlfa2TsL2m2krCv+AGwgqTTIcqKy/ZlCImLoc24xiT5sNh+EDiGUI04uJbQn1o1TSZJX2Kmz1hqfSpDgJ8RXhOLEbpeFxHujscRjod/As4lvCq2I+r918xlr6RVNOZC3i03Usu5rAL8yPaYJl1mkrQVMyWwNCbWS1XM3cBzhClXf0IvaVZC2uKW8np/wofin0SFzRdLriUTnUnLKDp1+9v+bnn9XsHlZOBFQnyyPcT4kqSJ9HjyXuFBv5mk02y/VjbvDQjYF3ikzGDmIAy7FijHXQBcUJrPngNeLctnWf2VNJ1a864Ia4aDJc1j+6iqf2pGwaUk9L9CJPIr6fsMLkmfokdzLJJWI8yMBhL1+hVLA6/b/mu5Kb9AJOaPsn2BpDkkVT4qj9p+pRKXzKCSNBtN76dyJDCc6J86TNIxEFVd3TXn2n6k5Ff6ZVBJ+iI9ElgUDCBGdmNtfxsYImmhcsjLRG4FSSOIXMso2yeU8w4GNik3dL0kMytqkqZTgsYgosdqQ+BGYB+iq34PSSfWjutW+SF/v0lfpadmLP1LNddrwOqSliB6U44qZcN3AK9KuoKwGT6U8PoGWA7YEhiaeZSkjfgsMD9whO3v2b4GGEEEmV0aZi4tF3NNknbiI98QkjYBripd9OcCzwMPlf8OsT3V9nXA/xGVXjcQkhZTJa0CnE0sM5zyUa8lSXqQIUQO8p/Q1V81gVgamwAcLuk4yLLiJGnkIwUWScOAS4GnbL9EWKwOIqq+5ibE+gCwvS8ReBYDxku6jVgSE7BJCkombcZ9wBzAp6HLtqEKLscDLwDbSPpW2Z+5lCQpfKRyY0kHAl8jSoRFGB69DjxMKBO/BWxh+9naOZ8m/FQGEoHoohJUsvoraTrdVXcVOaGTgS2AL9seW9t3MLA+MJUYKG1p+7kmXXKStD0ftdx4MjAb0X9yMPA7QtridaKO/7+A6yVtXt14ZVnsuvqbpPZX0gpqzbuzE53zswJP2z7W9suSziMCxzmSjgZuIopQPkcYdN1M9FutQdgNJ0nCh5ixSJofmGj7lfJ6HLAyse68qe0ny/b+hKHR0cTMZXNP869PyfukpTRI2t9HWDJMIUrjbwd2sv2spLWBLxG9WG8Ss5S/AesSM+9fAdvavrMFHyNJ2pIPlGORtDExSltG06yEP1nex8AWVYVMWV64lDA0mgUYK2mBDCpJq6nK2svgZx3C5XETwn9+O8JK+EpJC9q+nVjWXbP8f3dgLYfN8P7EzPyxpn+IJGlj3veMRdL6REXX+cBBtieW7TsQN+bpwHzAd4Hzq3XrEmi2L/uvtr13T3+IJPmgSJoN+CGx1PWE7X3K9v5EQ+RFREXY9rafbjh3A+AAIhBtYvuBJl56krQ972vGUoLKjURwGGl7Yq12/5e2HyLq/p8DRgJ7VRVepUnsl8DOwH/28PUnyYdlGWBtQjCyK3lfPFRuBXYlnE0vkbRYtb90469KLO9umEElSd7Je85YyhrzzUSFzGjbr1Q+E5KWI/Itj5dj5wGuIGYuJxPOkFMa3i8FJZOmMyPNLklrAKcSVY0jbf+kYf86RML+fNv717YPBvrVtPCSJKnxroGlJOpvJEZuCxb9pIG2J0lalZC4ONL28bUKm3mIhOY8wDnAD1LaImklDYKSsxD5wKllcDQcGEOUv59i++LaeQKGAQ/nYChJ3j/vtRQ2iVhrfgO4GKAElbWITvqzCJ/vLt0k2y8Q5ZizEF7g2TiWtIyaoOQcxO91LHA9MFrSnLbvIARRJwEji0AqEE2Ptv+QzbtJ8sF4P0thQ4lyy8OI5P0Y4Daijv8A2280HF9ZC89JWA5PSenwpJWUPpV7iAHSeMJIbkWi32oD2/+StC5hODeAMOk6t0WXmyQdz3sm70u/ynnACcCmREPYT4tEy6T6sZIWAXYrZZov12YxGVSSplNUt0U07b5JVHh92fYWRCHJRODmMnO5lfAHmotI6idJ8iF5X1VhteByEvAEkXOZTja8VM5cDewJ/Lt2bq5NJ02l1kvlMqhZnJitPFU77FpiCWwIcGgpSLmTKI3fr8mXnCS9ivfdIFkLLqcCwyVdWrZPUbg+/pyQxNg81V6TVlFmyFMlzS1pq7J5MtCl+FCS+VNtX03o2q1ZnW/74cypJMlH4wN13tt+mWnLYutJ+kUx8zofmBNY1fZb5cbN5a+k6ZSgMBgYB+xYDLt+BKwhaVQ5pq5L9zgh09Kv8X2adMlJ0uv4wCKUpY/lPKLa62CiO/lPwCq1oJKCkklTqfVWCdicaNY9lsgD3kUk5o8qhSTHlM77RYG1gJvyN5skPceHls0v1WIHACsAu5eSzgwqScsoweI3hFfK87b3q+1bgrAXHkWITk4BBhMmc2tUfS45006Sj85HvXiz4QAAAtNJREFU9WOZA3i9CPplUEmaRk2duEvJofweryQ0vH5NyLJMrIKFpIHA6kT5PEQy/5gcFCVJz/KRAkvXm+RIL2kikuYm1Ijvtv1EmT0fZ/vrpTrxOKK6a2fbV76P90uZoSTpQXoksCRJMynB4yrgSaL35CqmNTu+JGlRQhFiQ2AH2zfWzhWklXCSzEwysCQdR+lTWRX4LWGJ/QfCmOvp2jEfI9S416chuCRJMnP5QOXGSdIqJC0paQ/osmK4j/j9DiW66qfrO7H9FPBVQtPu55K2bu4VJ0nfJWcsSdtTRE/PBD4GnGz7hLJ9F6IH5UzgFuDrtp9oOHdh4HLgJdtbNvXCk6SPkoElaWuKrP21hM31BUXTq/GYjQkzuVuBr1XBRdLctl8sIpRvpn1DkjSHDCxJ2yLpPwj9ufsJ35/ny/Z+jUFC0qbAZcDvCdHJieXca22P7O68JEl6ngwsSdsiaQXgGmA/29fVDLvmJIRQ1yS66p+1/YKkzYBfELbBr5b/PmV7cos+QpL0ST6wpEuSNJG5CA26QRAaX5KWJ2yvh5d9LwM/kzTG9m8lrU6oEz9POEJm82OSNJmcsSRti6QFibzJk4Tz42zAIYQMy6+AS4DPA3sBo2yPKed1Nexm82OSNJ8MLElbI2llIjG/IKHtdTFwke1raseMBRYBVquk8ZMkaR25FJa0NbYfkLQeMD/Q3/Z91T5JsxKzl5eBtzOoJEl7kIElaXtsTwAmVK+r6q5i0/AJYEngupZdYJIk05FLYUlHUVM1HkAoFZ9EDJDWzQR9krQHOWNJOooSVFYiXEznIuRcNirVX5moT5I2ILXCkk5kMtGr8ktg85pzaQaVJGkDciks6UgkDbQ9qfydM5UkaSMysCQdTZrMJUn7kYElSZIk6VEyx5IkSZL0KBlYkiRJkh4lA0uSJEnSo2RgSZIkSXqUDCxJkiRJj5KBJUmSJOlR/h8NK1YK7gIXwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data2 = data[data.columns[6:10]]\n",
    "new_data2.boxplot(grid=False, rot=45, fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The x labels are still messy. We will learn other better packages for visualization in the later class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}