manujeevanprakash/gist:6dbc72e6861af60aec87

## gistfile1.txt
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   "cells": [
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Homework 1. Exploratory Data Analysis\n",
      "\n",
      "Due: Thursday, September 18, 2014 11:59 PM\n",
      "\n",
      "<a href=https://raw.githubusercontent.com/cs109/2014/master/homework/HW1.ipynb download=HW1.ipynb> Download this assignment</a>\n",
      "\n",
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Introduction\n",
      "\n",
      "In this homework we ask you three questions that we expect you to answer using data. For each question we ask you to complete a series of tasks that should help guide you through the data analysis. Complete these tasks and then write a short (100 words or less) answer to the question.\n",
      "\n",
      "**Note**: We will briefly discuss this homework assignment on Thursday in class.\n",
      "\n",
      "#### Data\n",
      "For this assignment we will use two databases: \n",
      "\n",
      "1. The [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics) which contains the \"complete batting and pitching statistics from 1871 to 2013, plus fielding statistics, standings, team stats, managerial records, post-season data, and more. For more details on the latest release, please [read the documentation](http://seanlahman.com/files/database/readme2012.txt).\"\n",
      "\n",
      "2. [Gapminder](http://www.gapminder.org) is a great resource that contains over [500 data sets](http://www.gapminder.org/data/) related to world indicators such as income, GDP and life expectancy. \n",
      "\n",
      "\n",
      "#### Purpose\n",
      "\n",
      "In this assignment, you will learn how to: \n",
      "\n",
      "a. Load in CSV files from the web. \n",
      "\n",
      "b. Create functions in python. \n",
      "\n",
      "C. Create plots and summary statistics for exploratory data analysis such as histograms, boxplots and scatter plots. \n",
      "\n",
      "\n",
      "#### Useful libraries for this assignment \n",
      "\n",
      "* [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays\n",
      "* [pandas](http://pandas.pydata.org/), for data frames\n",
      "* [matplotlib](http://matplotlib.org/), for plotting\n",
      "\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# special IPython command to prepare the notebook for matplotlib\n",
      "%matplotlib inline \n",
      "\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import requests\n",
      "import StringIO"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 1\n",
      "\n",
      "In Lecture 1, we showed a plot that provided evidence that the 2002 and 2003 Oakland A's, a team that used data science, had a competitive advantage. Since, others teams have started using data science as well. Use exploratory data analysis to determine if the competitive advantage has since disappeared. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(a) \n",
      "Load in [these CSV files](http://seanlahman.com/files/database/lahman-csv_2014-02-14.zip) from the [Sean Lahman's Baseball Database](http://seanlahman.com/baseball-archive/statistics). For this assignment, we will use the 'Salaries.csv' and 'Teams.csv' tables. Read these tables into a pandas `DataFrame` and show the head of each table. \n",
      "\n",
      "**Hint** Use the [requests](http://docs.python-requests.org/en/latest/), [StringIO](http://docs.python.org/2/library/stringio.html) and [zipfile](https://docs.python.org/2/library/zipfile.html) modules to get from the web.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(b)\n",
      "\n",
      "Summarize the Salaries DataFrame to show the total salaries for each team for each year. Show the head of the new summarized DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "% cd C:\\Users\\Manu\\Dropbox\\My final projects\\2014-master\\homework\\data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "C:\\Users\\Manu\\Dropbox\\My final projects\\2014-master\\homework\\data\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salary_df = pd.read_csv('Salaries.csv')\n",
      "salary_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>yearID</th>\n",
        "      <th>teamID</th>\n",
        "      <th>lgID</th>\n",
        "      <th>playerID</th>\n",
        "      <th>salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> AL</td>\n",
        "      <td> murraed02</td>\n",
        "      <td> 1472819</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> AL</td>\n",
        "      <td>  lynnfr01</td>\n",
        "      <td> 1090000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> AL</td>\n",
        "      <td> ripkeca01</td>\n",
        "      <td>  800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> AL</td>\n",
        "      <td>  lacyle01</td>\n",
        "      <td>  725000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> AL</td>\n",
        "      <td> flanami01</td>\n",
        "      <td>  641667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   yearID teamID lgID   playerID   salary\n",
        "0    1985    BAL   AL  murraed02  1472819\n",
        "1    1985    BAL   AL   lynnfr01  1090000\n",
        "2    1985    BAL   AL  ripkeca01   800000\n",
        "3    1985    BAL   AL   lacyle01   725000\n",
        "4    1985    BAL   AL  flanami01   641667"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "teams_df = pd.read_csv('Teams.csv')\n",
      "teams_df = teams_df[['yearID', 'teamID', 'W']]\n",
      "teams_df.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>yearID</th>\n",
        "      <th>teamID</th>\n",
        "      <th>W</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1871</td>\n",
        "      <td> PH1</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1871</td>\n",
        "      <td> CH1</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1871</td>\n",
        "      <td> BS1</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1871</td>\n",
        "      <td> WS3</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1871</td>\n",
        "      <td> NY2</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "   yearID teamID   W\n",
        "0    1871    PH1  21\n",
        "1    1871    CH1  19\n",
        "2    1871    BS1  20\n",
        "3    1871    WS3  15\n",
        "4    1871    NY2  16"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salary_sum = salary_df.groupby(['yearID','teamID'], as_index=False).sum()\n",
      "salary_sum.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>yearID</th>\n",
        "      <th>teamID</th>\n",
        "      <th>salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1985</td>\n",
        "      <td> ATL</td>\n",
        "      <td> 14807000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> 11560712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BOS</td>\n",
        "      <td> 10897560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1985</td>\n",
        "      <td> CAL</td>\n",
        "      <td> 14427894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1985</td>\n",
        "      <td> CHA</td>\n",
        "      <td>  9846178</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   yearID teamID    salary\n",
        "0    1985    ATL  14807000\n",
        "1    1985    BAL  11560712\n",
        "2    1985    BOS  10897560\n",
        "3    1985    CAL  14427894\n",
        "4    1985    CHA   9846178"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(c)\n",
      "\n",
      "Merge the new summarized Salaries DataFrame and Teams DataFrame together to create a new DataFrame\n",
      "showing wins and total salaries for each team for each year year. Show the head of the new merged DataFrame.\n",
      "\n",
      "**Hint**: Merge the DataFrames using `teamID` and `yearID`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "combined_df = pd.merge(salary_sum, teams_df, on = ['yearID', 'teamID'] )\n",
      "combined_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>yearID</th>\n",
        "      <th>teamID</th>\n",
        "      <th>salary</th>\n",
        "      <th>W</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1985</td>\n",
        "      <td> ATL</td>\n",
        "      <td> 14807000</td>\n",
        "      <td> 66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BAL</td>\n",
        "      <td> 11560712</td>\n",
        "      <td> 83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1985</td>\n",
        "      <td> BOS</td>\n",
        "      <td> 10897560</td>\n",
        "      <td> 81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1985</td>\n",
        "      <td> CAL</td>\n",
        "      <td> 14427894</td>\n",
        "      <td> 90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1985</td>\n",
        "      <td> CHA</td>\n",
        "      <td>  9846178</td>\n",
        "      <td> 85</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "   yearID teamID    salary   W\n",
        "0    1985    ATL  14807000  66\n",
        "1    1985    BAL  11560712  83\n",
        "2    1985    BOS  10897560  81\n",
        "3    1985    CAL  14427894  90\n",
        "4    1985    CHA   9846178  85"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(d)\n",
      "\n",
      "How would you graphically display the relationship between total wins and total salaries for a given year? What kind of plot would be best? Choose a plot to show this relationship and specifically annotate the Oakland baseball team on the on the plot. Show this plot across multiple years. In which years can you detect a competitive advantage from the Oakland baseball team of using data science? When did this end? \n",
      "\n",
      "**Hints**: Use a `for` loop to consider multiple years. Use the `teamID` (three letter representation of the team name) to save space on the plot.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "team = 'OAK'\n",
      "uniq = [2000, 2001, 2002, 2003, 2004, 2005]\n",
      "\n",
      "for yr in uniq:\n",
      "    uniq_df = combined_df[combined_df['yearID'] == yr]\n",
      "    plt.scatter(uniq_df.salary/ 1000000.0, uniq_df.W, )\n",
      "    plt.xlabel('Total salary in millions')\n",
      "    plt.ylabel('Wins in %d' %yr)\n",
      "    plt.xlim(0, 150)\n",
      "    plt.ylim(40, 130)\n",
      "    plt.grid()\n",
      "    plt.annotate(team, xy = (uniq_df['salary'][uniq_df['teamID'] == team] / 1e6,  \n",
      "                 uniq_df['W'][uniq_df['teamID'] == team]), xytext = (-20, 20), textcoords = 'offset points', \n",
      "                 ha = 'right', va = 'bottom',bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
      "                 arrowprops = dict(arrowstyle = '->', facecolor = 'black' , connectionstyle = 'arc3,rad=0'))\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8u0hS1vKC2gQkw9obpnf10o2nXr6hoYFbb53I7NkzYn1fkUqhNgERkQqnNgER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\nARGRKqZCQESkiqkQEBGpYv8fiAXIY78nKr4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf801d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xb29eb00>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9jEVkYyoEyqxa6iV7M35Of6mWY5y20DKHlhfUT0ACFkovYxHZmNoEpF+MHn0S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\npwoTAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb016fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9ozYBqSg1zImERW0CGarGesl3jt1zdaZj91TjMc6j0DKHlhfUT6BqFN/DvGDB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb55fe10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xb791eb8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xce48b70>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(e):\n",
      "\n",
      "**For AC209 Students**: Fit a linear regression to the data from each year and obtain the residuals. Plot the residuals against time to detect patterns that support your answer in 1(d). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "teamName = 'OAK'\n",
      "years = [1999, 2000, 2001, 2002, 2003, 2004, 2005]\n",
      "\n",
      "resiData = pd.DataFrame()\n",
      "\n",
      "for yr in years:\n",
      "    df = combined_df[combined_df['yearID'] == yr]\n",
      "    X = df['salary'].values/1e6\n",
      "    X=sm.add_constant(X)\n",
      "    y = df['W'].values\n",
      "    \n",
      "    model = sm.OLS(y,X)\n",
      "    results = model.fit()\n",
      "    beta = results.params\n",
      "    yhat = beta[0]+beta[1] * X[:,1]\n",
      "    \n",
      "    resiData[yr] = results.resid\n",
      "    \n",
      "resiData.index = df['teamID']\n",
      "resiData = resiData.T\n",
      "resiData.index = resiData.index.format()\n",
      "\n",
      "resiData.plot(title= 'Residuals across the years', figsize=(15,8), color = map(lambda x: 'blue' if x =='OAK' else 'gray', df.teamID))  \n",
      "plt.xlabel('year')\n",
      "plt.ylabel('Residuals')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.text.Text at 0x175fa160>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IIYQQDUpCoXB4MjZf1JW0FWENaS+irqStCGtIexG2IKFQCCGEEEIIIZyYzCkUQgghhBBC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cP3582ccUoyvS0tLYsmWL9DyLjb6Yjq6uLvLz81GpVGRmZhIbG4uZmRldXV18+eWXUi/bWrw/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\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MoK2tbb1mHSuTE30nQPn5+dy8eZP09HQ6duyIjY2Nrm1FfZ67oUlWXfeRJAkTExPd64Va\nrUYmk2Fubo6ZmRkajQaVSkVFRQWmpqZ06NABCwsLLCwsMDU1xcTERPdv5ZepqSkymazG2x6+3ND7\nXL58mRkzZjR4XDSFJElcu3aNEydOYGZmxlNPPdWoHokPu3tX2+vwxx9h9Gjt0lJfX8PsO5QkibKy\nsmpJXuVM3sOX1Wr1Y2fxHv3eGJZwivehhrl//z5BQUHMmjWLgQMHAtr93AcPHmTVqlVGsQQ+Lw8O\nHdLOCJ48CXI5zJx5k5KS3Sxf7l9n30CNRsPevXtRKpUsWLCgyqqNh8fLrVu32LlzJ0uWLGnxCteP\nKigo0M1YDhs2jHHjxlWrppqXl8eGDRsICAhodO9EY58pDAoKws/Pj0mTJqFQKMjIyMDMzEwkhYbw\npCSFGo2mxtm3+s7SaTSaGhO2+s7SGcPyMkF4lCRJ3LlzR7fMNC8vDycnJwYOHIizszMdO3ascv/S\n0lJdgnjr1i3S0tIoKCjAxMSEnj174ujoSO/evblz5w6JiYn06tWLQYMG0a5du3onNSqVqsoH2NLS\n0ip/Z+bm5lUSvPomTpIkIZPJ9J7UVH7JZDIKCwvJzs6moKCAnj170rdvX7p166a3Y9TncTKZrEry\nLUkSKSkpREdHk5GRgZeXF15eXnTs2JGMjAxd24usrCwGDBigW2ramCJDrUF6ejrHjx+nuLiYyZMn\nM2jQIL0uYSwthZ9/1i4t7dBBmxwuXKiffYcajaZaUvfojF7l9+3atXtssvfoUk6xhLNtKi8vZ+PG\njXh4eCCXy6vcdvr0aZKSkvD39zdIW4/MTNi/X5sIRkSAn592WejMmVD5shMfH8/p06dZsWJFnfui\n1Wo1u3fvxszMjLlz51ZLGnJzc9m0aROzZ8/GxcWluX6sOuXm5hIeHs6lS5dwd3dn7Nixj03MJUli\ny5YtDBw4kLFjxzb6eK0lKbx+/Tr379/H09MTlUqlSwof15KiqKioyv9vQ1+/RFJYg9aQFEqSREVF\nRZNm6ZRKJebm5rUmbHUldO3atRNvmsITobCwULfMNDU1FWtra1xcXBg4cGCNTds1Gg2JiYmcO3eO\nO3fu6Iqd2NnZ0blz5zqTm8pCOHl5eeTm5nL//n06d+5Mr1696NOnD7169aJ9+/ZNSqYqZ7pa6u+4\nqKiI8+fPo1AokMlkuLu74+bmZvCqidnZ2URHR3Px4kUGDRqEXC6nT58+gDbhv379ui5JNDMz0yWI\n9vb29d6zY6yys7M5efIkd+7cwfevvmTNOfOl0TzYd3jpErz+OrzyyoMPvQ9TKpV1Lt8sKiqitLQU\nCwuLOpdvdurUySiLiQgtQ6PRsHPnTrp06cIzzzxT7XVPkiT27NlDhw4dmDVrVou8LqalaZdY792r\n7f85Y4Y2EZwxA2p6WTxx4gQ3b97ULX2tjUqlYvv27VhZWVWpKFpSUkJQUBDe3t4G20uZnZ3NmTNn\nSE5OxsvLC7lcXu2E68OioqK4dOkS/v7+zTIrZgweTgofplKpaN++fY0tKR51/Pjxas9RG5EU1qAl\nkkKVStWkwijl5eW0a9eu0clc5bJLkdA1nVi28+RRqVSkpqbqkkSNRqNLEB0cHB77obO4uJjg4GBe\nfPHFGmfH1Wo1d+7c0e0JTE9Pp0ePHro9gf379zeaAgBNJUkSt27dQqFQcOXKFQYMGKArTmPIpXgl\nJSWcO3eOs2fP0qNHD+RyeZWYJEkiKytLlyDeuXOHfv366ZLEykJE+tDcry35+fmEhISQlJTEuHHj\nGDVqVIskTJVLOLUnCArZt6+IK1eKGDmykCFDipDJHiR7KpWqzhm9Tp06YWlpafAlnIYk3ofq5+jR\no2RlZdX6OlxeXk5QUBCjRo1i1KhRzRJHUtKDHoLXr8Ps2dpE8KmntLPodalMXs3MzJgzZ06drzkV\nFRX8/PPP9O3bl+nTp3Py5Elu3rxJ//79eeqpp/T0U9VfRkYGYWFhpKWlMWbMGEaNGlXne1tOTg5B\nQUGsWLGiSlP3xjDmpNBQRFJYg7qSQo1GU68ll7UldkCjk7nKvXZi2aVxEG/GTzZJkrh3cnJX0gAA\nIABJREFU755umWlGRgYDBgzQJYkPb9p/dKxoNBoyMjJ0ewJv3bpFt27ddHsC+/fv/0T0viwvL+fi\nxYsoFAru37+vK07T1Df+plCr1Vy6dImoqChKS0sZM2YM7u7u1VrPlJeXk5qaqksS1Wq1LkF0dHRs\nUhLfXK8tpaWlhIWFER8fz8iRIxk3bpxeTjZULuGsrdVC5ZepqWmVpE4m60R8fCdOnuyMvX0nFi/u\njK9vJywsOoiTl/Ug3ofqFhsbS1RUFCtWrKjzdTU3N5eNGzeyYMECvRSekSRISHiQCObkPGgmP3Ei\nNGalqlKp1DWZr08FzrKyMrZs2YKTkxPh4eG4uroyb968Fv37SktLIywsjMzMTLy9vRk5cmS9Vlpo\nNBo2bdrEsGHDGDNmTJPjEElhdSIprIFMJpMOHjxYY6JXOYXblFk6Q6xVFwSh+ZWWllYpVtOlSxdd\ngmhjY0NmZqZuJjAtLQ0rKyvdTOCAAQNqXTrzJMjOzkahUJCQkECPHj3w8PBg6NChBluiWTmjGR0d\nzY0bN3B3d2f06NHVih9U3jc3N1eXIKalpdG7d29dktiSrSQeR6lUEhUVRVRUFEOGDGHixIn1Kqih\nUqkem+Q9muyVlJRgYWFR68xe5fc1zUiWlMC2bdqqpZaW2n2HCxaAWPEpNMX169fZu3cvAQEB9T7Z\ndO3aNfbv38+qVavo0qVLg4+p0UBMzINEUK1G10xeLq/aTL6xCgsLCQwMZOrUqbi6utZ5/5KSEjZv\n3oyFhQVLly5tkc+ikiRx48YNTp8+TX5+PuPGjcPd3b1Bxw4PD+fatWssW7ZML6+hIimsTiaTkZub\nW63lkEgKZTIpJiamxsROLLsUBKE+NBoNt2/fJikpiaSkJLKzs3XLQR0cHBgwYECraqDektRqNcnJ\nySgUCtLS0hgyZAienp7069fPYK+/9+/fJyYmhvj4eOzt7ZHL5djZ2dUYT2WLkMoksaSkBCcnJ5yd\nnXFycmqx/3u1Wo1CoeD06dPY2dnh5+dH9+7dKS8vr3VGr/KyUqmskuDV1FvP0tJSbytYNBo4ckS7\n7/DqVXjjDXj5ZW1zbkFoiHv37rFp0ybmz5+Pvb19gx4bFhbG1atX6114RqWCsLAHzeStrB70EHR3\nb56Ku3fv3mXbtm0sXryYfv361Xn/iooKZDJZsy8VlySJpKQkwsLCKC8vZ/z48QwbNqzBrxFZWVls\n2bKFVatWPfZkXGOIpLA6mUzGt99+y/Lly6vMpIuksBUUmhGMh1i2I9TX8ePHDbJ/o7UrLCzUFacx\nNTXVFacxVEJdXl5OfHw80dHRWFhYIJfLGTp0aJ0fdu7fv09KSgrXrl3jxo0bdO/eXTeLaGtrW21P\nXGNeWzQaDSUlJRQVFVFQUEBycjIXL16kXbt29OjRA7VarUv2TExM6pzR69SpExYWFgY9ERofr505\nPHgQXnwR1qwBZ2eDhWOUxPvQ45WUlBAYGIiPjw8eHh4NfrwkSfz666+Ym5tXKdLysPJyOHHiQTP5\n/v0fJIKDB+vjp6jb1atXOXz4cL37DDbneNFoNFy6dImwsDBMTEzw8fFh8ODBjdrzq1arCQoKYuTI\nkYwcOVJvMYqksDqZTMbRo0e5e/cuS5Ys0b2fiaRQJIVCA4g3Y6G+xFhpGkmSSEtL0xWncXBwwMPD\nA2dnZ4MUGdFoNCQnJxMVFUVOTg6jRo1i5MiR9VoCrFarSU9P180i3r9/HwcHB90sopWVVZXxUtmO\npK5KnCUlJbpCYqWlpchkMpycnLC1ta02u9faqqbeuQPffQc//QTjx2uXlo4fb5h+h8ZGvLZUp1ar\n2bZtG/369WPKlCmNfp6KigpdYjJ69GgAiovh6FFtIvj77zBsGLpm8g2cjNSbiIgIEhISCAgIqLb3\n+VHNMV7UajUJCQmEh4djYWGBj48PLi4uTTqhdPr0adLS0njxxRf1emJKJIXVVfYP3r17Nx06dODZ\nZ5/VVSYXSWErjFsQBOFJUV5ezoULF1AoFBQUFOiK0xiqh+Ddu3eJjo7mypUruLq6MmbMmAY1Vi4q\nKtLNIqakpOhm6yqTvYqKijpbLXTu3JnCwkJOnjxJXl4efn5+uLq6trntDsXFsHWrdvbQykqbHD7/\nvNh3KDwgSRIHDhygrKyMBQsWNPlvIDc3l++++4XOnRdx+nRPTpyAMWO0ieCzz4KBe74D2p/50KFD\nFBYW8sILL7TYiTKVSoVCoSA8PJzu3bvj4+ODvb19k3/nlctiX3nllUbt6ayNSAqrq/ydVFRU6HpB\nTpw4USSFIikUBEFoPbKysnTFaXr16qUrTmOIfnRFRUXExsYSGxtL3759GTNmDE5OTg36gFRZlbas\nrEyX7NW1hDM3N1dXZn7ChAl4enq2+QrVGg0cPqzdd3jtGrz5JqxaBXradiS0YuHh4Vy4cIGAgIAm\nzYhnZT1oJh8WpsHe/jqvvWbDwoUdH9tX09DUajW//PIL1tbWTJ8+vVmPVVFRQWxsLJGRkdjY2ODj\n44Otra1enlutVrNhwwbkcjnu7u56ec6HiaSwuod/J0VFRQQGBuLn54ebm1vrTgplMtlnwEygAkgB\nAiRJyv/rtveB5YAaeFOSpD8e83iRFAr1JpbtCPUlxkrzUqvVJCUloVAouHXrFkOHDsXT0xMbG5sW\nny1TqVQkJiYSFRWFRqNBLpczYsSIBiWq9RkvRUVFhIaGcvHiReRyOXK5vNUtC9WHuDjtzOHhw7B0\nqXbf4WP6ObdZ4rXlgcuXL3PkyBFWrlzZqBmmW7ceNJOPj4fp0x80kz9//gyXL18mICDAaKvIl5aW\nEhQUhFwur7EpfVPGS1lZGdHR0cTExGBvb4+Pjw99+vRpQsTVnTx5kszMTF544YVmee0WSWF1j/5O\nKgv8vPvuu3pLCg31F/MH8N+SJGlkMtmnwPvAezKZbCiwEBgK9AOOy2SygZIkaQwUpyAIgqAnpqam\nDBkyhCFDhlBQUMD58+cJDg6mXbt2eHh4MGLEiBYrTlN5THd3d1JTU4mKiuLkyZN4enoyatSoJi+H\nKisrIyIigtjYWNzc3Hj99def6HYmnp7aVha3b8O338Lo0dr+b2+9BWPHin2HT4qMjAwOHTrEiy++\n2KC/sWvXIDhYmwimpMCsWfD22zBlStVm8uPGjePu3bscPny4xsIzhmZhYcHixYvZuHEj3bp1w8nJ\nSS/PW1xcTGRkJHFxcQwcOJCAgAB69uypl+d+2O3bt4mLi+Nvf/ubUf5+nxTW1tbMnTuXd999V2/P\nafDlozKZ7DlgniRJS/6aJdRIkvR/f912FFgnSVLUI48RM4WCIAhtgCRJ3Lx5E4VCwdWrV3F0dMTD\nwwMnJ6cWL06Tk5NDdHQ0iYmJuLi4IJfLsbGxadBzqFQqzp49S3h4OM7Ozvj6+uqtTHtbUlQEW7bA\nV19B9+7a5HDevMY1CRdah4KCAoKCgpg2bRpDhw6t9b6SBBcuPEgEs7OrNpOvbUL/cYVnjFFqaip7\n9uzB39+/QfubH1VQUEBERATnz5/H1dWVcePGVetlpy8qlYoff/yRiRMnMmzYsGY5Bhj3TKG9vT0Z\nGRncuXOnSk9NDw8Pzp8/z40bN/jwww+xs7Pjk08+ITU1FUdHR2bMmMHhw4d191+yZAkuLi58+OGH\n9TpuSzSvN4aX3+XAjr++twEeTgDT0c4YCoIgCG2QTCbD3t4ee3t7ysrKuHDhAiEhIRw8eBB3d3c8\nPDya7QPOo3r06MHTTz/NpEmTiIuLY9euXXTt2pUxY8bUWbJdo9GQkJBASEgIvXv3ZtmyZVhbW7dI\n3K1Rp07w2mvwt7/BoUPafYfvvqvdd7hypbZAjdB2VFRUsHPnTry8vGpMCCUJzp59kAiqVNok8Pvv\nwdu7/s3kzc3NeeGFFwgKCsLa2rrBvQ9bir29PVOmTGHHjh2sXLmywSsJ8vLyOHPmDJcuXcLd3Z1X\nX32Vzp07N1O0WqdOnaJXr164uro263GMmUwmw9HRkR07dvD6668DkJiYqKsmXXmfR2dRY2JiiIyM\nxNvbu8b7GFqznYaVyWR/ymSyxMd8zXroPh8AFZIkba/lqYzzVIHQaoSEhBg6BKGVEGPFsDp06ICX\nlxerVq3ixRdfpKKigsDAQLZs2UJCQgJKpbJF4rCwsGDcuHGsWbOG0aNHExkZyTfffENkZCRlZWW6\n+4WEhCBJElevXuXHH38kLi6OuXPnsmjRIpEQ1pOpqbY6ZGioNhmIiwMHB/iv/4IbNwwdnf48ya8t\nkiSxb98+rK2tGT9+fJXb1Grt//2bb2r7B/r7a2cBd+2C69fhiy9g3Lj6J4SVunXrxnPPPUdwcDD5\n+fn6+2H0zN3dnaFDh7Jr1y5UKpXu+trGS3Z2Nr/99hsbNmzA0tKSN954g2nTpjV7Qnjr1i0SEhJ4\n5plnjC6ZaWlLlixh69atustbtmxh2bJlVWbyHp3Ve/fdd/nggw+qXGdss6HNNlMoSVKtTWdkMpk/\n8DQw+aGrbwN2D122/eu6avz9/XVnf7p27Yq7u7tuU27lH5O4LC4DxMfHG1U84rK4LC7X7/L06dMx\nMzMjLS2NxMREjhw5gkajwcXFhXnz5iGTyZr1+CYmJmRnZ+Pk5ISzszPR0dFs2rQJJycnVq1aRWZm\nJv/93/9NRUUFq1evZuDAgYSGhnL9+nWj+P21tsteXrBqVQjPPgvnzvkyahS4uoawYAG89prh42vK\n5UrGEk9LXj537hzdu3dn7ty5hIaGolSCWu1LcDDs2ROCtTW89JIvf/wBmZnax3t6Nv34Tk5OmJub\n8/HHH/Ppp59iZmZmFL+PRy+bmppiaWnJwYMH6dq1a5WE6+H7Z2Rk8MMPP5CVlcXixYuZMWMGUVFR\nxMTENHu848aNY9++ffTs2ZOzZ8+22N+LsZLL5Wzbto0rV67g4uLCrl27CA8P5x//+EeNj1m9ejXr\n16/nxIkTTJ48ucb71SYkJIT4+Hju378PaJcg65Ohqo9OB74AJkqSdO+h64cC24HR/FVoBnB+dAOh\n2FMoCILw5MnPzyc+Pp74+Hjat2+Pu7s7I0aMaNECLgUFBcTExBAXF4e5uTm+vr6MGDGixfc/PgmK\nimDTJu2+w169tPsO584V+w5bk/j4eE6fPs3ixSsJC+tIcLC2mfzQoQ+ayTs4NN/xJUli7969mJqa\n6pp9G6OKigo2b97MkCFD8PHxqXJbWloaYWFhZGZm4u3tzciRI1u8gvGRI0coLS1l7ty5LXI8Y95T\n6ODgQGBgIFFRURQXFzNhwgS+/PJLfv/9d8zMzLhx4wbr1q3D1ta2yp7Cyv2YW7duJTIykqVLl+Ls\n7GxUewoNlRQmA+ZA7l9XRUqS9Opft/0d7T5DFbBGkqRjj3m8SAoFQRCeUJIkkZqaikKhICkpCScn\nJzw8PHB0dGzRhtAymazN9xo0Bmo1HDig3Xd465Z2qeGKFWLfobFLTEzjf/83gYKCKYSFtWf0aG0i\nOGdOyzaTr6ioYOPGjXh4eDBmzJiWO3ADFRYWEhgYyNSpUxk6dCg3btwgLCyM+/fvM27cONzd3Q3S\nZiM1NZW9e/eyevVqLCwsWuSY9UkKP/rooyYfp74J2cMcHBwICgrC2dkZHx8fxo4dy8yZM1m4cCHm\n5ua1JoUqlYrBgwfz9ddfs2vXLpEU6oNICoWGCAkJ0S1NEITaiLHS+pSWlnLhwgUUCgXFxcW4u7vj\n7u7eIsVpxHhpeWfPavsdHjsGL72kTRCNtI5IFU/KWMnO1ibwO3dWEBYmMW6ciqVLLZk1Cx4q1Nji\n8vLyCAoK4vnnnzfawjOgbdnx888/k5mZiYODA+PHj2fYsGEGO/lUXl7ODz/8wIwZMxg4cGCLHdfY\nZwqDgoLw8/Nj0qRJKBQKMjIyMDMzqzMpNDExYcuWLXz11Ve4uroaXfVRsd5FEARBaLUsLCwYNWoU\nL7/8MosWLaKsrIwNGzawdetWEhMTW6w4jdAyRo2C7dtBodAuIx05EhYsgKiouh8rNI/0dG3vyUmT\nwMUFjhxRY2d3iqNHL3DihCX+/oZNCEFbeGbu3LlGX3imb9++zJ8/n2HDhrF69Wrc3NwMuhrhzz//\nxN7evkUTwtYkKCiIkydPVptBrS2hXbp0KWVlZRw9etToljOLpFBo856Es7OCfoix0rr16dOHGTNm\n8NZbb+Hp6cn58+f58ssvOXz4MBkZGXo/nhgvhtO/P/zrX5CaCuPHw+LFMHYs/PqrtpWBsWlrYyUl\nBT77DORycHOD2Fhtxdj0dDXPPrud+fM1+PqONHSYVTg6OuLt7c2uXbuM+mSRvb09/v7+Bt+nnJKS\nQnJyMtOmTTNoHMbM0dERT09P3eWaWlI8/L2JiQkff/wxeXl5LRdoPYnlo4IgCEKbdf/+fV1xGgsL\nC11xmpbaGyO0DLUa9u/X7ju8fRvWrIHly6FLF0NH1jZIEly8qO0fGBwMmZkPmsn7+mrbSEiSxOHD\nh8nPz2fRokUGT2oep7LwjImJCXPmzDG6mRpjUVZWxvfff8/s2bNxcnJq8eMb8/JRQxHLRwVBD1pL\niWPB8MRYaXu6du2Kr68va9asYcqUKaSnp7N+/XqCg4NJSUlp0gcPMV6Mh6mpNkE5cwZ27tQuJ3Vw\ngHfegbQ0Q0fXOsdKZTP599+HQYNg5ky4fx+++06beH//PUyZok0IQducOy0tjXnz5hllQgjaD9Cz\nZ88mKyuL6OhoQ4dTI0OPl2PHjuHi4mKQhFAwHFHYWRAEQWjzZDIZjo6OODo6UlpaSmJiIsePH6e0\ntFRXnKZr166GDlPQgzFjtInhzZvwzTfg4aFNXt56C0aPNnR0xk2thvBw7Wzgb79Bx44wbx7s2AGe\nnlDTxFpycjJnzpxh+fLldOjQoWWDbiAzMzMWLlxIYGAgvXv3xqE5e2K0QklJSaSmprJ69WpDhyK0\nMLF8VBAEQXhiZWRkoFAouHDhAn379sXDw4PBgwcbpPS70DwKCmDjRli/Hvr10yaHzz6rnV0UoKIC\nTp3SJoL792t/R3Pnar+GDq378VlZWWzZsoUXXngBOzu75g9YT65fv87evXtZuXKlOCH0l5KSEn74\n4Qfmzp1r0CqtYvlodaIlRQ1EUigIgiDok1Kp5MqVKygUCu7evcvw4cPx8PCgT58+hg5N0BOVCvbt\n0+47vHsX1q6FgADo3NnQkbW8khL44w9tInj4MAwZ8iARbMjEWXFxMYGBgUyaNIkRI0Y0X8DNJDIy\nkoSEBJYvX45Z5TrYJ1hwcDCWlpZMnz7doHGIpLA6sadQEPTA0GvzhdZDjJUnl5mZGcOHD2fZsmW8\n/PLLdOjQgR07dvDTTz9x9uxZSktLqz1GjJfWpV07eP55iIiAX37R7j90cIB334Vbt5r32MYwVgoK\ntMtAn39e2zz+22/B2xsuXNAuGX377YYlhCqVip07dzJixIhWmRACyOVyevXqxcGDB40qCTHEeLl0\n6RIZGRlMnjy5xY8tGAeRFAqCIAjCQ7p27cqkSZNYs2YNkydP5ubNm6xfv569e/dy/fp1o/rwKDSO\ntzfs3q1tpaBSgbu7tq1FbKyhI9Ove/e0S2efeQZsbbU9Hp95Bq5fh+PH4dVXwcam4c8rSRIHDhzA\nysqqVbfbkMlkzJo1i+zsbKKe4GaXxcXF/P777zz77LNixvQJJpaPCoIgCEIdSkpKSExMRKFQUF5e\njru7Ox4eHnQRPQ/ahIICCArS7jvs31+773DWrNa57/D2be0y2eBgiIuDqVO1y0Kfflp/LTpCQ0NJ\nTk7mpZdeahNJxP379wkKCuK5557D0dHR0OG0KEmS2LNnD926dWPKlCmGDgcQy0cfR+wprIFICgVB\nEARDkCSJjIwM4uLiuHjxIi4uLsjlcmwaM90iGB2VSlt184svtLNsa9eCvz906mToyGp3/fqDHoJJ\nSdr2EXPnahNCfbfkvHDhAsePH2flypV0MvZfTAPcuHGD4ODgJ67wTGJiImFhYbz88stGU2BLJIXV\niT2FgqAHxrCXQ2gdxFgR6iKTybCxsWHmzJl4eHjQu3dvdu3axaZNm7hy5QoajcbQIQpN0K4dzJ+v\n7XO4bRuEhoK9Pbz3HqSnN/559f3aUtlM/pNPtEtfx46Fa9fgo4+0RXS2bNFWWNV3Qpiens6RI0d4\n4YUX2lRCCODg4MD48ePZtWsXSqXSoLG01HtRYWEhx44dY86cOUaTEAqGI5JCQRAEQWgEc3Nzxo0b\nx5tvvsmoUaM4c+YM3377LdHR0ZSXlxs6PKGJvL1hzx5tA/fychgxApYsgXPnDBOPJGn3PP797zB4\nsHY5aG6uthfj7dvwww/amcHmWs2Zn5/P7t27mT17dputyjtmzBisra05cOBAm5+pkiSJgwcPMnLk\nSLHSoQHs7e3p3bs3JSUluusCAwOZOHEiEydO5OOPP65y/61bt+Ls7ExZWRkAmzdvxsTEhN27d7do\n3PUhlo8KgiAIgh5IkkR6ejpRUVHcuHEDd3d3xowZg5WVlaFDE/QgPx8CA7X7Dh0dtfsOZ84Ek2Y8\nva5Wa6ul7t2r/erQQdtMfu5cGDmy5mby+lZeXs6mTZtwc3PD29u7ZQ5qIEqlkk2bNjFs2DDGjh1r\n6HCajUKhICYmhpUrV2JqZJtnjXn5qL29PcXFxbz11lu8//77gDYp/OWXX/jpp58YPXo04eHhDB06\nlOzsbFxdXdmzZw8TJ04EYNKkSeTm5mJnZ8ehQ4fqfVyxp7AGIikUBEEQjFleXh4xMTHEx8fj5OSE\nXC7H1tbW0GEJeqBUahO0L76AvDz4r/+Cl14CS0v9Pf+pU9pj7NunbR/xcDP5lkoEK2k0Gnbt2kWn\nTp2YOXMmspYOwADy8/MJDAxkzpw5ODk5GTocvcvPz+enn35i2bJl9O7d29DhVGPMSaGDgwN/+9vf\n+Ne//sX169exsrLSJYWnTp3i888/Jzg4mIiICBYvXky3bt34z3/+A8DNmzdxcXEhKioKb29v0tLS\n6v37F3sKBUEPxD4xob7EWBEaorbx0q1bN6ZNm8batWvp168fwcHBBAUFcfHiRbHvsJUzM4OFCyE6\nGjZvhhMntPsO339fu4zzcep6bSkthf37tcllnz6wbh04O2v7ByoU8M9/gqtryyeEAH/++SdKpZKn\nn376iUgIAaysrJg3bx6//fYbeXl5LX785nwvqmwnMmbMGKNMCFsDLy8vfH19+fzzz6vd9tZbbyFJ\nEvPmzSMyMpLPPvtMd9vWrVuZOHEinp6eeHl58csvv7Rk2HUSSaEgCIIgNJP27dvj7e3NG2+8gbe3\nN9HR0Xz99ddERkbq9pgIrZNMBuPGaSt+RkVBSQkMHw5Ll2oTuboUFMDOnbBggXY28OuvYfRoSEjQ\nLhl95x0w9CTVuXPnSE5OZv78+Ua3xLC52dvb4+Pjw65du6ioqDB0OHpz7tw5ysrKGD9+vKFDabVk\nMhkff/wx33zzDffu3atym4mJCRs3bmTfvn188803WD60hGDr1q3Mnz8fgPnz57N169YWjbsuYvmo\nIAiCILSg27dvExUVxbVr13Bzc2PMmDF069bN0GEJenD/PmzYoE3wnJ21+w6feebBvsOcHDhwQLs0\n9PRp8PHRLgudPRt69jRs7I+6fv06e/fuJSAggB49ehg6HIOQJIn9+/ejUqmYN29eq58pzcvLY8OG\nDQQEBNCrVy9Dh1Oj+iwf/eijj5p8nA8//LDBj3FwcCAoKAg/Pz+WLFlCnz59GDJkCD///DOnTp3S\n3c/ExIRr167p+l6Gh4fj6+tLRkYGPXv2JD09nQEDBhAXF4ebm1udxxV7CmsgkkJBEAShtcvPzycm\nJgaFQoG9vT1yuRw7O7tW/8FT0O4L/PVX7b7DwkLtctOICG310ClTtIngM8/or5m8vt27d4/Nmzfz\n/PPPY29vb+hwDKqy8Iyrqyvjxo0zdDiNJkkSW7ZsYeDAgUZfQMfY9xRWJoUpKSl4enry9ttvc+rU\nqVqTwpdffpmNGzdWScazsrJYs2YN//73v+s8rthTKAh6IPaJCfUlxorQEE0dL1ZWVkyZMoW1a9di\nb2/Pvn37CAwMJDExEbVarZ8gBYMwM4NFi7TtLAID4cqVEF5/HTIytG0uFi0y3oSwpKSEHTt24Ofn\n98QnhABmZmYsXLiQqKgoUlJSWuSYzfFeFB0djUajQS6X6/25n1ROTk4sXLiQ9evX13q/srIydu/e\nzYYNGzh//rzu65tvvmH79u1G83ovkkJBEARBMCBzc3NGjx7N66+/jo+PD+fOnePrr78mPDyc0tJS\nQ4cnNIFMpl0i+uqrMGeO/pvJ65tarWb37t0MGjQIT09PQ4djNB4uPJObm2vocBosJyeH06dP8+yz\nz2LSnD1UnkD/8z//Q0lJSbUVHg9f3rdvH5aWlixbtgxra2vdV0BAACqVimPHjrV02I8llo8KgiAI\ngpHJyMggKiqKpKQkhg8fjlwup3v37oYOS2jDKqtSlpaWsmDBApE8PEZMTAznzp1jxYoVmJubGzqc\netFoNLq+i2PGjDF0OPVizMtHDUXsKayBSAoFQRCEJ0FBQQFnz54lLi4OOzs75HI5AwYMEPsOBb2L\niIggISGB5cuXt5qEp6VVJs5KpbLVFJ4JDw/n2rVrLFu2rFXECyIpfByxp1AQ9EDsExPqS4wVoSFa\nYrx06dKFyZMns3btWpydnTl06BA//fQTCQkJRrMPRaibsb+2XLlyhaioKBYtWiQSwlrIZDKeeeYZ\n8vLyCA8Pb7bj6Gu8ZGdnExERwbPPPttqEkLBcNoZOgBBEARBEGpnZmaGl5cXI0eOJDk5maioKI4f\nP86oUaMYOXIkHTt2NHSIQiuVkZHBwYMHWbx4MVZWVoYOx+i1a9eOhQsXsmHDBvr06YOzs7OhQ3os\njUbDvn378PPzo2vXroYOR2gFxPJRQRAEQWiF7t69S3R0NFeuXMHV1RW5XE5PY2sLwSLFAAAgAElE\nQVR2Jxi1wsJCAgMDmTp1Kq6uroYOp1W5efMme/bsYfny5Ua53/f06dOkpaXx4osvtrpZQrF8tDqx\np7AGIikUBEEQBK2ioiLOnj3LuXPnsLGxQS6X4+Dg0Oo+CAotS6lUsnnzZgYNGsSECRMMHU6rFBMT\nQ2xsLCtXrjSqZbd3795l27ZtvPzyy61y9lckhdWJPYWCoAfGvpdDMB5irAgNYSzjpVOnTkyaNIk1\na9YwePBgjh49yo8//ohCoUClUhk6PAHjGSuVJEli37599OzZEx8fH0OH02qNGjWKfv36sX//fr0m\nMU0ZL2q1mn379jFlypRWmRAKhiOSQkEQBEFoA8zMzPD09GT16tU89dRTXLx4ka+++orQ0FCKi4sN\nHZ5gRE6dOkVhYSGzZs0SM8pNUFl4Jj8/nzNnzhg6HABCQ0OxsrLCzc3N0KEIrYxYPioIgiAIbVRW\nVhZRUVFcvnyZIUOGIJfLsba2NnRYggGdP3+ekJAQVq5ciaWlpaHDaRMKCgoIDAxk1qxZuLi4GCyO\n27dvs2PHDl555RU6d+5ssDiaSiwfrU7sKayBSAoFQRAEof6Ki4uJjY0lNjaW3r17I5fLcXJyErNE\nT5i0tDR27drFSy+9JE4O6Fnl73bFihUGKTyjUqn48ccfmTBhAsOHD2/x4+uTSAqrE3sKBUEPjG0v\nh2C8xFgRGqI1jRdLS0smTpzImjVrGDZsGMePH+f777/n3LlzKJVKQ4fX5hnDWMnLy2PPnj0899xz\nIiFsBv3798fX15edO3dSXl7epOdqzHg5deoUvXr1YtiwYU06tlA7e3t7evfuTUlJie66wMBAJk6c\nyMSJE/n444+r3H/r1q04OztTWlqKv78/JiYmnD17Vnf7tWvXMDExjnTMOKIQBEEQBKHZtWvXDnd3\nd1555RWmT5/O1atX+eqrrzh16hRFRUWGDk9oJmVlZWzfvh0fHx+j7avXFnh5eWFra6v3wjN1uXXr\nFgkJCTzzzDNi9r8FaDQa1q9fX+U6ExMTAgMD+fLLL7l06RIA2dnZvPPOOwQFBWFhYQFA9+7d+cc/\n/tHiMdeHSAqFNs/X19fQIQithBgrQkO05vEik8lwdHRk8eLFBAQEUFxczHfffcf+/fvJzMw0dHht\njiHHikaj4ddff8XBwYHRo0cbLI4ngUwm4+mnn6awsJCwsLBGP09DxotSqWTfvn08/fTTYo9oC5DJ\nZLzzzjt8/vnn5OfnV7nNxcWFDz74gBUrViBJEm+++SbPP/88EydO1D32pZdeIiEhgdOnTxsi/FqJ\npFAQBEEQnmA9e/Zk5syZvPHGG3Tv3p1ffvmFrVu3kpSUJPb1tAFHjx4FYPr06QaO5MnQrl07FixY\nQGxsLElJSc1+vOPHj9OvXz+GDBnS7McStLy8vPD19eXzzz+vdttbb72FJEnMmzePyMhIPvvssyq3\nd+zYkb///e988MEHLRVuvYmkUGjzjGEvh9A6iLEiNERbGy8dO3bEx8eHNWvW4O7uTkhICN999x1n\nz56loqLC0OG1aoYaKzExMaSmpvL8888bzb6lJ0Hnzp2ZP38++/fvJycnp8GPr+94SU1N5fLly8yY\nMaPBxxAaTyaT8fHHH/PNN99w7969KreZmJiwceNG9u3bxzfffFNt9lYmk/HKK6+QlpamO2FjLNoZ\nOgBBEARBEIyHqakpI0aMYPjw4dy8eZOoqChCQkLw9PRk1KhRdOnSxdAhCvVw7do1wsLCWL58OR06\ndDB0OE8cOzs7Jk2apKtI2r59e70+f3l5Ofv372fmzJm6/WpPko8++qjJz/Hhhx82+rGurq7MnDmT\nTz/9tNos7dChQ3X3eRxzc3P++c9/8s9//pOdO3c2OgZ9Ey0pBEEQBEGoVW5uLlFRUSQmJuLi4oJc\nLsfGxsbQYQk1yMrKYsuWLSxcuJD+/fsbOpwn2sGDBykpKWHBggV6LQJz6NAh1Go1zz77rN6e01gY\nc0sKBwcHgoKC8PPzIyUlBU9PT95++21OnTrFqVOndPczMTHh2rVrODo66q4LCAjA1taWTz75BJVK\nxZAhQ1i1ahXvvfceGo2m1uOKlhSCIAiCIBhc9+7defrpp3nzzTfp3bs3u3btYvPmzVy5cqXODzNC\nyyouLmbHjh1MnTpVJIRGYMaMGRQVFTWp8MyjUlJSSE5OZtq0aXp7TqHhnJycWLhwYbVKpDV5OKlr\n164dH330Ef/3f//XXOE1mEgKhTavre37EZqPGCtCQzyJ48XCwoJx48bx5ptv4uXlRVhYGN9++y3R\n0dFi32EtWmqsqFQqdu3axfDhw3Fzc2uRYwq1a0zhmdrGS1lZGQcOHGD27NliWbAR+J//+R9KSkqq\nzQI/blZYJpNVuX7RokXY2NgYTRsRsadQEARBEIQGMTU1ZdiwYbi6unLr1i2ioqIIDQ3Fw8OD0aNH\nY2VlZegQnziSJHHw4EE6d+7MpEmTDB2O8JDKwjM7d+4kICCAnj17Nvq5jh07houLC05OTnqMUKiv\nGzduVLlsa2tLaWlptfup1epq123atKnKZZlMRmJion4DbAKxp1AQBEEQhCbLy8sjJiaG+Ph4nJyc\n8Pb2pl+/foYO64lx+vRprl69ir+/P2ZmZoYOR3iMc+fOERUVxcqVKxtVeCYpKYkjR46wevVqzM3N\nmyFC42DMewoNpSX2FIqkUBAEQRAEvSkvLycuLo7o6Gi6dOmCXC5n8ODBoiVCM7p48SJ//PEHK1eu\npHPnzoYOR6jFoUOHKCoqYuHChQ1aNlhSUsIPP/zA3Llzsbe3b74AjYBICqsThWYEQQ+exH0/QuOI\nsSI0hBgvj9e+fXu8vb158803kcvlREVF8c033xAZGUl5ebmhwzOI5hwrt2/f5vfff2fRokUiIWwF\nZsyYQUlJCadPn67xPo8bL0eOHGHo0KFtPiEUDEckhYIgCIIg6J2JiQlDhw5l+fLlzJs3j9u3b/PV\nV19x9OhR8vLyDB1em5Cfn8+uXbuYNWsWffr0MXQ4Qj2Ympoyf/58zp07x9WrV+v1mEuXLpGRkcHk\nyZObOTrhSSaWjwqCIAiC0CLy8/OJiYlBoVBgb2+Pt7c3tra2RlN9rzWpqKhg48aNjBgxgrFjxxo6\nHKGB0tPT2bFjR52FZ4qLi/n+++9ZuHAhdnZ2LRih4Yjlo9WJPYU1EEmhIAiCILRe5eXlxMfHEx0d\nTceOHZHL5QwZMgRTU1NDh9YqaDQadu/eTceOHZk1a5ZIqlupuLg4IiIiWLly5WPbS0iSxJ49e+jW\nrRtTpkwxQISGIZLC6trsnkKZTPaJTCY7L5PJ4mUy2QmZTGb30G3vy2SyZJlMdkUmk001RHxC2yL2\n/Qj1JcaK0BBivDRe+/btGTNmDK+//jrjx48nNjaWr7/+mvDwcMrKygwdnt7pe6wcP36c8vJynnnm\nGZEQtmKenp44ODjw22+/VfnAXzleLly4wL1790SLEaFFGGpP4b8kSXKTJMkd2Ad8CCCTyYYCC4Gh\nwHTgPzKZTOx7FARBEIQ2yMTEhMGDB+Pv78/ChQvJzMxk/fr1/P777+Tm5ho6PKMUFxfH1atXWbBg\ngZhZbQOmT59OWVkZoaGhVa4vLCzk2LFjzJkzh3btRFtxofkZfPmoTCZ7H7CSJOm9v77XSJL0f3/d\ndhRYJ0lS1COPEctHBUEQBKENKigo4OzZs8TFxWFnZ4e3tzf9+/cXM2JoG2cHBwcTEBBAjx49DB2O\noCdFRUVs2LCBGTNmMHjwYCRJYseOHfTt2/eJnCUUy0era7PLRwFkMtn/K5PJ0gB/4H//utoGSH/o\nbumA6HwrCIIgCE+ILl26MHnyZNasWYOTkxMHDx5kw4YNJCQkoFarDR2eweTk5BAcHMy8efNEQtjG\ndOrUifnz53Pw4EGys7OJj4+nsLCQCRMmGDo0oQabN29m+PDhWFpa0rdvX1599VXy8/Or3Mff3x8z\nMzPu3r1b5fp169axdOlS3eXbt28zePBg1q5d2yKx16TZkkKZTPanTCZLfMzXLABJkj6QJKk/sAn4\nqpanEqcKhCYR+36E+hJjRWgIMV6al7m5OaNGjeK1117D19eX+Ph41q9fT1hYGKWlpYYOr0GaOlZK\nS0vZvn07fn5+ODg46CcowajY2try1FNPsXPnTn788UfmzJkjlgcbqS+++IL33nuPL774goKCAqKi\norh58yZTpkxBqVQC2qqxwcHBDB06lJ9//rnK4x9e9XDz5k0mTJjAnDlz+Oqr2tKh5tdsi5QlSapv\nmaTtwO9/fX8beLjeru1f11Xj7++va+DZtWtX3N3d8fX1BR68+IrL4jJAfHy8UcUjLovL4rK4LC43\n/PLAgQPZu3cvoaGhRERE4OrqilqtxsrKyijiq+1ypcY8Xq1Wk56ezqBBgygoKCAkJMTgP4+43DyX\n8/PzqaioYMSIEfTu3dvg8Rj678UYFRQUsG7dOjZt2sTUqVMBGDBgALt378bBwYGff/6ZgIAAgoOD\ncXBw4N133+XTTz/lnXfe0T1H5TLQlJQU/Pz8CAgIYN26dXUeOyQkhPj4eO7fvw9AamqqXn82g+wp\nlMlkLpIkJf/1/RvAaEmSlv5VaGY7MBrtstHjgPOjGwjFnkJBEARBeHIVFRVx9uxZYmNj6devH3K5\nHAcHhza371CSJA4ePEhxcTELFy7ExMTE0CEJQrMz5j2FR48eZdasWZSXl1f7e/T396eiooLt27cz\nefJk/Pz8ePPNN7G2tiY8PBxPT08APvroI/744w9u3rzJ66+/znvvvVfncVtiT6Ghyhn9r0wmGwSo\ngRRgNYAkSZdkMtlu4BKgAl4V2Z8gCIIgCA/r1KkTkyZNYvz48SQkJHDkyBFMTU2Ry+UMGzaszVRr\njIqK4s6dOyxfvlwkhIJgBO7du0fPnj0f+/fYp08fFAoFaWlphISE8P3339O5c2emTZvG1q1bdUmh\nJElcuHABU1NTFixY0NI/Qo0M8gojSdLzkiQNlyTJXZKkeZIkZT102/8nSZKzJEmDJUk6Zoj4hLal\nNSxHEIyDGCtCQ4jxYnhmZmaMHDmSV199laeeeooLFy6wfv16QkNDKS4uNnR4Oo0ZK1evXiUyMpJF\nixZhbm6u/6AEoyVeW+omkzX9qzF69uzJvXv30Gg01W7LyMigR48ebNu2jWHDhjFw4EAA5s+fz/bt\n23WFsmQyGbNnzyYgIAA/Pz/S0tIa/XvQJ3HaSRAEQRCEVk0mk+Hs7MySJUtYunQp+fn5fPvttxw4\ncICsrKy6n8DI3L17lwMHDrBgwQKsrKwMHY4gGB1JavpXY3h7e9O+fXuCg4OrXF9UVMTRo0eZPHky\nW7duJTk5mb59+9K3b1/Wrl3LvXv3OHz48F+xaw/+xRdfMHPmTPz8/Lhz506Tfh/6YPA+hY0h9hQK\ngiAIglCb4uJiYmNjOXv2LH369EEul+Pk5GT0+w4LCwsJCgpiypQpuLq6GjocQWhxxrynEOCzzz7j\niy++YMuWLfj5+XH79m1effVVsrKy+OKLL3jqqaeIj4+nV69egDYJfPvttykrK+PXX39l3bp1pKSk\nsG3bNgBWrVrFmTNnCA0Nxdra+rHHbIk9hSIpFARBEAShzVKpVCQmJhIVFYUkScjlcoYPH46ZmZmh\nQ6tGqVTy/7d3/3FV1fm+x99fMENkI6CRWqPoVfGYP1IzwUDJSal0UqeIawMXR6NunUmb22Ny7MdB\nxpnbeKY8Op6pGW8m0hlJs5rSaei3Cvm7UjMdLEYSsfIXBp4Uf/C9f7DdAyGyt7D3Bvbr+Xish3t/\n91rf/V30acN7r+9aKycnR/369dOYMWP8PRzAL1p6KJSkF154Qf/xH/+h4uJihYeHa8qUKXrqqac0\nZ84cHT16VC+//HKd9bdt26bRo0fr0KFD+v3vf6/i4mLl5uZKqgmN06ZN044dO/TBBx8oKiqq3vsR\nChtAKIQnal++G7gUagWeoF5aF2ut9u/f77p4y/DhwzVixAiFhYV5/b3dqRVrrVavXq3g4GBNmTKl\nxR/RhPcE+mdLawiFvtaWrz4KAADgM8YY9e7dW71799bRo0e1efNm/eEPf1D//v0VFxenq6++2q/j\nW7dunSoqKpSRkUEgBOBzHCkEAAAB6bvvvtNHH32kbdu2qUuXLoqLi1Pfvn19Hsp27dqlDz74QPfe\ne686duzo0/cGWhqOFNbH9NEGEAoBAEBzOX/+vHbv3q3Nmzfr7NmziouL05AhQ3xy3uGBAwe0cuVK\nZWRkNHiRCSCQEArr80Uo5JYUaPO43w/cRa3AE9RL2xEcHKwhQ4bovvvu08SJE/XFF19o4cKFeu+9\n91RZWdnk/huqlfLycr388suaPHkygRAufLbAHzinEAAAQDXfusfExCgmJkbHjh3Tli1b9Oyzz6pf\nv36Ki4tTt27dmu29Tp8+rby8PCUkJKhv377N1i8AXA6mjwIAADTg1KlT+vjjj7V161ZFRkYqLi5O\n/fr1U1DQ5U+2qq6uVl5eniIiInT77bdzYRmgFqaP1sc5hQ0gFAIAAF86f/689u7dq02bNunUqVOK\ni4vT9ddfr/bt23vc19/+9jcdO3ZM99xzT5PCJdAWEQrr45xCoBkwNx/uolbgCeolsAQHB2vgwIG6\n9957NXnyZJWUlGjhwoV655139O23315y29q1snXrVv3jH//QXXfdRSDERfHZAn/gnEIAAAA3GWPU\no0cP9ejRQ+Xl5dqyZYv++Mc/qk+fPoqLi9M111zT4LbFxcUqKCjQ9OnTFRIS4sNRA8ClMX0UAACg\nCU6fPq1PPvlEW7ZsUXh4uOLi4tS/f/86RwKPHDminJwcpaamqkePHn4cLdCyteTpozExMTp8+LCC\ng4PVsWNH3XbbbfrP//xPTZgwQenp6ZoxY4Zr3XXr1ik9PV2lpaWSpKSkpHrruMsX00c5UggAANAE\nISEhio+P18iRI/X3v/9dmzZt0jvvvKORI0dq6NChOnfunPLy8jR+/HgCIdCKGWO0du1ajR07VocO\nHVJycrJ+/etfyxjT6AWj3FnHn5jMjjaPuflwF7UCT1Av+L6goCANGDBAM2bM0J133qmDBw9q0aJF\neuyxx3TddddpyJAh/h4iWgE+W1qH7t2767bbbtPu3bv9PZRmQSgEAABoZtdee63uuusu3X///Roy\nZIjGjh3r7yEBaAYXpnGWlpbqzTff1LBhw+q0t1acUwgAAACgRWjp5xQeO3ZM7dq1U6dOnTRx4kQ9\n/fTTuvXWW7Vt27Y6t6g5d+6cIiMjdeDAAUnSzTffrPT0dE2fPt3j9+WcQgAAAACoJTs7u8l9ZGVl\nebyNMUavv/56vSP/xhgtXry4TuBbv3690tLSmjxOXyEUos1bt26dkpKS/D0MtALUCjxBvcBd1Ao8\nQb007nICna+11KOdDeGcQgAAAABoAndCYEsOioRCtHl82wZ3USvwBPUCd1Er8AT10jpd7HYT329r\nybek4EIzAAAAAFqElnyhGX/xxYVmOFKINo/7/cBd1Ao8Qb3AXdQKPEG9wB8IhQAAAAAQwJg+CgAA\nAKBFYPpofUwfBQAAAAB4FaEQbR5z8+EuagWeoF7gLmoFnqBe4A+EQgAAAAAIYJxTCAAAAKBF4JzC\n+jinEAAAAADgVYRCtHnMzYe7qBV4gnqBu6gVeIJ6gT8QCgEAAACgETExMQoNDZXD4VDXrl3105/+\nVP/93/+tpKQkLV26VCtWrJDD4ZDD4VBoaKiCgoJcz8PDw/09/EvinEIAAAAALUJLPqewV69eWrp0\nqcaOHatDhw4pOTlZEydO1ObNm5Wenq7p06e71l2/fr3S0tJUWlra5PflnEIAAAAAaGG6d++u2267\nTbt375akeqGtpQbbhhAK0eYxNx/uolbgCeoF7qJW4AnqpWW7EPZKS0v15ptvatiwYX4eUfMgFAIA\nAABAI6y1mjx5siIjI5WYmKikpCTNmTNHUs1Uztasnb8HAHhbUlKSv4eAVoJagSeoF7iLWoEnqJfG\nZWdnN7mPrKwsj7cxxuj111/X2LFjm/z+LQ2hEAAAAECrcTmBDpfG9FG0eczNh7uoFXiCeoG7qBV4\ngnppnVrbhWW+j1AIAAAAAE1wsXMKW9N5htynEAAAAECL0JLvU+gv3KcQAAAAAOBVhEK0eczNh7uo\nFXiCeoG7qBV4gnqBPxAKAQAAACCAcU4hAAAAgBaBcwrr45xCAAAAAIBX+TUUGmMeMcZUG2OiarXN\nMcZ8boz5uzFmvD/Hh7aBuflwF7UCT1AvcBe1Ak9QL/AHv4VCY8wPJI2T9GWttgGSUiUNkHSrpGeN\nMRzNRJPs2LHD30NAK0GtwBPUC9xFrcAT1Av8wZ+Ba4GkR7/XNklSnrX2rLW2RNIXkm709cDQtpw4\nccLfQ0ArQa3AE9QL3EWtwBPUC/zBL6HQGDNJ0kFr7a7vvdRd0sFazw9KusZnAwMAAACAAOO1UGiM\neccY8+lFljskzZGUVXv1S3TF5YfQJCUlJf4eAloJagWeoF7gLmoFnqBeWrbCwkKNGjVKERER6ty5\nsxISErR9+3bl5OQoODhYDodDDodDvXv31vTp0/X555+7ti0pKVFQUJAmTJhQp8+0tDRlZ2f7elfq\nstb6dJE0UNI3kvY7l7OSSiRdLemXkn5Za918SSMv0odlYWFhYWFhYWFhYWl7S0v17bff2k6dOtmX\nXnrJVldX21OnTtm3337b7tq1y+bk5NjExERrrbXV1dW2uLjYPvjgg9bhcNjdu3dba63dv3+/NcbY\nLl262I0bN7r6TUtLs9nZ2Q2+byM/q2bJaO3kY9ba3aoJgJIkY8x+ScOttceNMW9IWmGMWaCaaaN9\nJW29SB/Ncj8OAAAAAC2HMcb6ewwN2bdvn4wxSk1NlSSFhIRo3LhxkqSPPvrIdS9BY4x69+6tP/zh\nDzpw4IDmzp2rl19+2dXPo48+qscff1zvv/++q+3Ctg3xdv5pCVf2dP0ErLV7JK2StEfS3yQ9aBv7\nCQEAAACAl8XGxio4OFjTpk1Tfn6+ysvLG93mxz/+sQoKCuq0PfDAA9q3b5/ee+89bw3VY34Phdba\n3tba47We/19rbR9rbX9r7Vv+HBsAAAAASJLD4VBhYaGMMcrMzFR0dLQmTZqkw4cPN7hNt27ddPz4\n8TptoaGhevzxx/XEE094e8hu8/n00YYYY16QNEHSYWvtIGfbEEl/lNRRNecd/sRaW2mMaS/pT5KG\nS6qWNMtau965TaqkxyQFS1prrf2lr/cF3uW8x2WupGjVHGleYq39vTEmStJKST1VUy93W2tPOLeZ\nI2m6pPOSZlpr33a2D5eUIylE0pvW2lm+3Rt4WzPXy28kpUuKtNY6fL0v8K7mqhVjTAdJqyX1drav\nsdbO8fX+wLua+bMlX1JXSVdI2izpf1trz/p2j+AtzVkrtfp8Q1KvC38zB5rmuChLVlbWZW3Xv39/\nLVu2TJJUVFSktLQ0Pfzww0pOTr7o+mVlZYqKiqrXPmPGDP3ud7/T2rVr3Xrfy/xsWaeaz5ZTzm7G\nWWuPXvQNmuvkxKYukhIlDZX0aa22bZISnY9/KulXzsf/Kmmp8/FVkrY7H3eW9KWkzs7nOZLG+nvf\nWJq9VrpKut75OExSkaR/kfTvkh51ts+W9Fvn4wGSdqjml22Mau5/aZyvbZV0o/Pxm5Ju9ff+sbTo\nernR2V+lv/eLpeXWiqQOksY417lC0gY+W9re0syfLWG1+l0tKc3f+8fS4molqFZ/P5b0Z0m7/L1v\nXvp52dZk8eLFdtCgQTYnJ8cmJCTUe33ChAk2JSXFWvvPC82cP3/eWmttTk6Ovf766+1PfvITO3fu\n3AbfQzVfJlzOZ8sHkoZZN37ufp8+eoG1tkDS9yfm9nW2S9K7ku50Pv4X1eykrLVHJJ0wxoxQzbey\nn1trjznXe6/WNmgjrLVfW2t3OB+flLRXNRcmukPScudqyyVNdj6eJCnPWnvWWluimv9ZRhpjukly\nWGsvXMwot9Y2aCOaq16c22+11n7tw+HDh5qrVqy1p6xz9oqtOdrzsbjnbpvTzJ8tJyXJGHOFpPaS\nLv5NPlqlZqqVGyXJGBMm6eeSfq1L39INXlBUVKQFCxaorKxMklRaWqq8vDzFx8fXWe/8+fPav3+/\nHnroIW3YsKHBo5Lp6ek6ffq08vPzZUyj/zk9/mxxcqtOWkwobMBnzhvdS1KKpB84H++UdIcxJtgY\n00s100ivlfS5pFhjTE9jTDvV/LB+8P1O0XYYY2JUc4R5i6SrrbXfOF/6Rv+8ym13SQdrbXZQNR/G\n328vE3+4tWlNrBcEkOaqFWNMhKQfqeZLSrRRzVEvxpi3nOufstbme3nI8JMm1Ep35+N5kp6W9J23\nx4r6HA6HtmzZopEjRyosLEzx8fEaPHiwnnnmGUnSpk2b5HA41KlTJ9188806efKktm3bpuuuu87V\nR+3wFxQUpF/96lduXbBGl1cvkrTcGPOJMeaSJzC2mHMKGzBd0u+NMU9KekPSGWf7C6o5WrhdNdNF\nN0o6b609YYx5QDXzbaud7f/D56OGTzi/LXtFNeeUVtb+n8xaa1vyJY3he02sF2opgDRXrTi/nMyT\ntMj57S3aoOaqF2ttsjHmSkkrjTEZ1trll9gOrVATa8UYY66X1Nta+3NnuISPde/eXStXrrzoaxkZ\nGcrIyLjk9jExMTp//nydtpSUFKWkpHg0Dg/+zv2JtfbQhdozxqRba1+82Iot+kihtbbIWptsrb1B\n0kuSip3t5621/8daO9RaO1lShKR9ztfWWmvjrLWjnG1F/ho/vMc5xeYVSS9aa//ibP7GGNPV+Xo3\nSRcuBVWmukeMr1XNtyhlzse128u8OW74RzPUC3URIJq5VpZIKrLW/t67o4a/NPdni7W2ytnfCG+O\nG77XTH+3xEm6wXmP7wJJ/Ywx7wuBwuPPFmvtIee/JyWtkHMa8sW06FBojEtmgg4AABVHSURBVLnK\n+W+QpCckPed83sEY09H5eJyks9bavzufRzv/jZT0gKTn/TB0eJGp+WptqaQ91tqFtV56Q9KFr2gy\nJP2lVvv/NMa0d0437ivpwrlhFcaYkc4+02ttgzaiuerFV+OF/zRnrRhjfi0pXDXn/qANaq56McZ0\ndP6Bd+Ho8kRJn/hiH+Abzfh3yx+ttddYa3tJSpC0z1o71jd7gRbA08+WYGNMF8n1pcSPJH3aUOcX\nrkzjd8aYPEljJHVRzTzZLNVcoelfnau8Yq19zLlujKR81UwRPShphrW21PnaCklDnNtkW2tX+WgX\n4CPGmATVXM1vl/459WaOav4YWyWph+pfqvcx1UxHPqeaaRt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       "text": [
        "<matplotlib.figure.Figure at 0x171f4470>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Discussion for Problem 1\n",
      "\n",
      "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
      "\n",
      "As seen from the  scatter plot. You can see that OAK team had clear advantage in the years 2002 and 2003. \n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 2\n",
      "\n",
      "Several media reports have demonstrated the income inequality has increased in the US during this last decade. Here we will look at global data. Use exploratory data analysis to determine if the gap between Africa/Latin America/Asia and Europe/NorthAmerica has increased, decreased or stayed the same during the last two decades. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(a)\n",
      "\n",
      "Using the list of countries by continent from [World Atlas](http://www.worldatlas.com/cntycont.htm) data, load in the `countries.csv` file into a pandas DataFrame and name this data set as `countries`.  This data set can be found on Github in the 2014_data repository [here](https://github.com/cs109/2014_data/blob/master/countries.csv). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd C:\\Users\\tk\\Dropbox\\My final projects\\2014-master\\homework"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "C:\\Users\\tk\\Dropbox\\My final projects\\2014-master\\homework\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_df = pd.read_csv('countries.csv')\n",
      "count_df.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Country</th>\n",
        "      <th>Region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  Algeria</td>\n",
        "      <td> AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   Angola</td>\n",
        "      <td> AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    Benin</td>\n",
        "      <td> AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Botswana</td>\n",
        "      <td> AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  Burkina</td>\n",
        "      <td> AFRICA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "    Country  Region\n",
        "0   Algeria  AFRICA\n",
        "1    Angola  AFRICA\n",
        "2     Benin  AFRICA\n",
        "3  Botswana  AFRICA\n",
        "4   Burkina  AFRICA"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the [data available on Gapminder](http://www.gapminder.org/data/), load in the [Income per person (GDP/capita, PPP$ inflation-adjusted)](https://spreadsheets.google.com/pub?key=phAwcNAVuyj1jiMAkmq1iMg&gid=0) as a pandas DataFrame and name this data set as `income`.\n",
      "\n",
      "**Hint**: Consider using the pandas function `pandas.read_excel()` to read in the .xlsx file directly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "complete_excel = pd.read_excel('gapminder.xlsx')\n",
      "complete_excel.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>gdp pc 2011 ppp</th>\n",
        "      <th>1800</th>\n",
        "      <th>1801</th>\n",
        "      <th>1802</th>\n",
        "      <th>1803</th>\n",
        "      <th>1804</th>\n",
        "      <th>1805</th>\n",
        "      <th>1806</th>\n",
        "      <th>1807</th>\n",
        "      <th>1808</th>\n",
        "      <th>...</th>\n",
        "      <th>2004</th>\n",
        "      <th>2005</th>\n",
        "      <th>2006</th>\n",
        "      <th>2007</th>\n",
        "      <th>2008</th>\n",
        "      <th>2009</th>\n",
        "      <th>2010</th>\n",
        "      <th>2011</th>\n",
        "      <th>2012</th>\n",
        "      <th>2013</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>         Afghanistan</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>  634.400014</td>\n",
        "      <td>...</td>\n",
        "      <td>  1081.472262</td>\n",
        "      <td>  1174.582145</td>\n",
        "      <td>  1193.282161</td>\n",
        "      <td>  1321.945588</td>\n",
        "      <td>  1323.495832</td>\n",
        "      <td>  1552.033398</td>\n",
        "      <td>  1632.338112</td>\n",
        "      <td>  1695.153436</td>\n",
        "      <td>  1885.356711</td>\n",
        "      <td>  1906.651322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>             Albania</td>\n",
        "      <td>  793.136557</td>\n",
        "      <td>  793.960291</td>\n",
        "      <td>  794.784880</td>\n",
        "      <td>  795.610326</td>\n",
        "      <td>  796.436629</td>\n",
        "      <td>  797.263790</td>\n",
        "      <td>  798.091810</td>\n",
        "      <td>  798.920691</td>\n",
        "      <td>  799.750432</td>\n",
        "      <td>...</td>\n",
        "      <td>  6748.155855</td>\n",
        "      <td>  7082.904782</td>\n",
        "      <td>  7456.309772</td>\n",
        "      <td>  7859.954708</td>\n",
        "      <td>  8397.549902</td>\n",
        "      <td>  8642.088613</td>\n",
        "      <td>  8900.061420</td>\n",
        "      <td>  9121.455958</td>\n",
        "      <td>  9329.213650</td>\n",
        "      <td>  9489.333939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>             Algeria</td>\n",
        "      <td> 1520.025973</td>\n",
        "      <td> 1519.988511</td>\n",
        "      <td> 1519.951050</td>\n",
        "      <td> 1519.913589</td>\n",
        "      <td> 1519.876130</td>\n",
        "      <td> 1519.838671</td>\n",
        "      <td> 1519.801214</td>\n",
        "      <td> 1519.763757</td>\n",
        "      <td> 1519.726301</td>\n",
        "      <td>...</td>\n",
        "      <td> 11487.607449</td>\n",
        "      <td> 11924.088001</td>\n",
        "      <td> 11946.446291</td>\n",
        "      <td> 12167.653542</td>\n",
        "      <td> 12225.056346</td>\n",
        "      <td> 12246.896810</td>\n",
        "      <td> 12498.666405</td>\n",
        "      <td> 12605.771553</td>\n",
        "      <td> 12751.716076</td>\n",
        "      <td> 12956.598567</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>              Angola</td>\n",
        "      <td>  650.000000</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>...</td>\n",
        "      <td>  3956.009823</td>\n",
        "      <td>  4613.317040</td>\n",
        "      <td>  5303.703090</td>\n",
        "      <td>  6341.613606</td>\n",
        "      <td>  7012.631357</td>\n",
        "      <td>  6980.112427</td>\n",
        "      <td>  7021.577189</td>\n",
        "      <td>  7094.084012</td>\n",
        "      <td>  7244.828429</td>\n",
        "      <td>  7319.344743</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Antigua and Barbuda</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>  771.878735</td>\n",
        "      <td>...</td>\n",
        "      <td> 20235.603121</td>\n",
        "      <td> 20902.150016</td>\n",
        "      <td> 23259.737514</td>\n",
        "      <td> 24604.989419</td>\n",
        "      <td> 24698.029639</td>\n",
        "      <td> 21820.445277</td>\n",
        "      <td> 19959.504800</td>\n",
        "      <td> 19367.790564</td>\n",
        "      <td> 19908.886232</td>\n",
        "      <td> 20002.068060</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 215 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "       gdp pc 2011 ppp         1800         1801         1802         1803  \\\n",
        "0          Afghanistan   634.400014   634.400014   634.400014   634.400014   \n",
        "1              Albania   793.136557   793.960291   794.784880   795.610326   \n",
        "2              Algeria  1520.025973  1519.988511  1519.951050  1519.913589   \n",
        "3               Angola   650.000000          NaN          NaN          NaN   \n",
        "4  Antigua and Barbuda   771.878735   771.878735   771.878735   771.878735   \n",
        "\n",
        "          1804         1805         1806         1807         1808  \\\n",
        "0   634.400014   634.400014   634.400014   634.400014   634.400014   \n",
        "1   796.436629   797.263790   798.091810   798.920691   799.750432   \n",
        "2  1519.876130  1519.838671  1519.801214  1519.763757  1519.726301   \n",
        "3          NaN          NaN          NaN          NaN          NaN   \n",
        "4   771.878735   771.878735   771.878735   771.878735   771.878735   \n",
        "\n",
        "      ...               2004          2005          2006          2007  \\\n",
        "0     ...        1081.472262   1174.582145   1193.282161   1321.945588   \n",
        "1     ...        6748.155855   7082.904782   7456.309772   7859.954708   \n",
        "2     ...       11487.607449  11924.088001  11946.446291  12167.653542   \n",
        "3     ...        3956.009823   4613.317040   5303.703090   6341.613606   \n",
        "4     ...       20235.603121  20902.150016  23259.737514  24604.989419   \n",
        "\n",
        "           2008          2009          2010          2011          2012  \\\n",
        "0   1323.495832   1552.033398   1632.338112   1695.153436   1885.356711   \n",
        "1   8397.549902   8642.088613   8900.061420   9121.455958   9329.213650   \n",
        "2  12225.056346  12246.896810  12498.666405  12605.771553  12751.716076   \n",
        "3   7012.631357   6980.112427   7021.577189   7094.084012   7244.828429   \n",
        "4  24698.029639  21820.445277  19959.504800  19367.790564  19908.886232   \n",
        "\n",
        "           2013  \n",
        "0   1906.651322  \n",
        "1   9489.333939  \n",
        "2  12956.598567  \n",
        "3   7319.344743  \n",
        "4  20002.068060  \n",
        "\n",
        "[5 rows x 215 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Transform the data set to have years as the rows and countries as the columns. Show the head of this data set when it is loaded. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "complete_excel.index = complete_excel[complete_excel.columns[0]]\n",
      "transfrom = complete_excel.drop(complete_excel.columns[0], axis = 1)\n",
      "transfrom.columns = map(lambda x: int(x), transfrom.columns)\n",
      "transfrom = transfrom.T\n",
      "#transfrom.head(5)#transfrom.index.unique()\n",
      "transfrom.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>gdp pc 2011 ppp</th>\n",
        "      <th>Afghanistan</th>\n",
        "      <th>Albania</th>\n",
        "      <th>Algeria</th>\n",
        "      <th>Angola</th>\n",
        "      <th>Antigua and Barbuda</th>\n",
        "      <th>Argentina</th>\n",
        "      <th>Armenia</th>\n",
        "      <th>Australia</th>\n",
        "      <th>Austria</th>\n",
        "      <th>Azerbaijan</th>\n",
        "      <th>...</th>\n",
        "      <th>Transnistria</th>\n",
        "      <th>USSR</th>\n",
        "      <th>Wallis et Futuna</th>\n",
        "      <th>West Germany</th>\n",
        "      <th>Western Sahara</th>\n",
        "      <th>Virgin Islands (U.S.)</th>\n",
        "      <th>Yemen Arab Republic (Former)</th>\n",
        "      <th>Yemen Democratic (Former)</th>\n",
        "      <th>Yugoslavia</th>\n",
        "      <th>\u00c5land</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1800</th>\n",
        "      <td> 634.400014</td>\n",
        "      <td> 793.136557</td>\n",
        "      <td> 1520.025973</td>\n",
        "      <td> 650</td>\n",
        "      <td> 771.878735</td>\n",
        "      <td> 948.545561</td>\n",
        "      <td> 540.718052</td>\n",
        "      <td> 792.125955</td>\n",
        "      <td> 1679.238994</td>\n",
        "      <td> 815.479659</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 580.030205</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>1801</th>\n",
        "      <td> 634.400014</td>\n",
        "      <td> 793.960291</td>\n",
        "      <td> 1519.988511</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 771.878735</td>\n",
        "      <td> 948.545561</td>\n",
        "      <td> 540.718052</td>\n",
        "      <td> 793.833777</td>\n",
        "      <td> 1685.753831</td>\n",
        "      <td> 815.563006</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 580.030205</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>1802</th>\n",
        "      <td> 634.400014</td>\n",
        "      <td> 794.784880</td>\n",
        "      <td> 1519.951050</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 771.878735</td>\n",
        "      <td> 948.545561</td>\n",
        "      <td> 540.718052</td>\n",
        "      <td> 795.545282</td>\n",
        "      <td> 1692.293943</td>\n",
        "      <td> 815.646361</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 580.030205</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>1803</th>\n",
        "      <td> 634.400014</td>\n",
        "      <td> 795.610326</td>\n",
        "      <td> 1519.913589</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 771.878735</td>\n",
        "      <td> 948.545561</td>\n",
        "      <td> 540.718052</td>\n",
        "      <td> 797.260476</td>\n",
        "      <td> 1698.859429</td>\n",
        "      <td> 815.729724</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 580.030205</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>1804</th>\n",
        "      <td> 634.400014</td>\n",
        "      <td> 796.436629</td>\n",
        "      <td> 1519.876130</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 771.878735</td>\n",
        "      <td> 948.545561</td>\n",
        "      <td> 540.718052</td>\n",
        "      <td> 798.979369</td>\n",
        "      <td> 1705.450386</td>\n",
        "      <td> 815.813096</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 580.030205</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>5 rows \u00d7 260 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "gdp pc 2011 ppp  Afghanistan     Albania      Algeria  Angola  \\\n",
        "1800              634.400014  793.136557  1520.025973     650   \n",
        "1801              634.400014  793.960291  1519.988511     NaN   \n",
        "1802              634.400014  794.784880  1519.951050     NaN   \n",
        "1803              634.400014  795.610326  1519.913589     NaN   \n",
        "1804              634.400014  796.436629  1519.876130     NaN   \n",
        "\n",
        "gdp pc 2011 ppp  Antigua and Barbuda   Argentina     Armenia   Australia  \\\n",
        "1800                      771.878735  948.545561  540.718052  792.125955   \n",
        "1801                      771.878735  948.545561  540.718052  793.833777   \n",
        "1802                      771.878735  948.545561  540.718052  795.545282   \n",
        "1803                      771.878735  948.545561  540.718052  797.260476   \n",
        "1804                      771.878735  948.545561  540.718052  798.979369   \n",
        "\n",
        "gdp pc 2011 ppp      Austria  Azerbaijan     ...      Transnistria  USSR  \\\n",
        "1800             1679.238994  815.479659     ...               NaN   NaN   \n",
        "1801             1685.753831  815.563006     ...               NaN   NaN   \n",
        "1802             1692.293943  815.646361     ...               NaN   NaN   \n",
        "1803             1698.859429  815.729724     ...               NaN   NaN   \n",
        "1804             1705.450386  815.813096     ...               NaN   NaN   \n",
        "\n",
        "gdp pc 2011 ppp  Wallis et Futuna  West Germany  Western Sahara  \\\n",
        "1800                   580.030205           NaN             NaN   \n",
        "1801                   580.030205           NaN             NaN   \n",
        "1802                   580.030205           NaN             NaN   \n",
        "1803                   580.030205           NaN             NaN   \n",
        "1804                   580.030205           NaN             NaN   \n",
        "\n",
        "gdp pc 2011 ppp  Virgin Islands (U.S.)  Yemen Arab Republic (Former)  \\\n",
        "1800                               NaN                           NaN   \n",
        "1801                               NaN                           NaN   \n",
        "1802                               NaN                           NaN   \n",
        "1803                               NaN                           NaN   \n",
        "1804                               NaN                           NaN   \n",
        "\n",
        "gdp pc 2011 ppp  Yemen Democratic (Former)  Yugoslavia  \u00c5land  \n",
        "1800                                   NaN         NaN    NaN  \n",
        "1801                                   NaN         NaN    NaN  \n",
        "1802                                   NaN         NaN    NaN  \n",
        "1803                                   NaN         NaN    NaN  \n",
        "1804                                   NaN         NaN    NaN  \n",
        "\n",
        "[5 rows x 260 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(b)\n",
      "\n",
      "Graphically display the distribution of income per person across all countries in the world for any given year (e.g. 2000).  What kind of plot would be best? "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years  = 2000\n",
      "plt.hist(transfrom.ix[years].dropna().values, bins = 20)\n",
      "plt.xlabel(\"Income per person\")\n",
      "plt.ylabel(\"Frequency\")\n",
      "plt.show()\n",
      "\n",
      "years = 2000\n",
      "plt.hist(np.log10(transfrom.ix[years].dropna().values), bins = 20)\n",
      "plt.title('Year: %i' % years)\n",
      "plt.xlabel('Income per person (log10 scale)')\n",
      "plt.ylabel('Frequency')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f2def0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f88030>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(c)\n",
      "\n",
      "Write a function to merge the `countries` and `income` data sets for any given year. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "mergeByYear\n",
      "\n",
      "Return a merged DataFrame containing the income, \n",
      "country name and region for a given year. \n",
      "\n",
      "Parameters\n",
      "----------\n",
      "year : int\n",
      "    The year of interest\n",
      "\n",
      "Returns\n",
      "-------\n",
      "a DataFrame\n",
      "   A pandas DataFrame with three columns titled \n",
      "   'Country', 'Region', and 'Income'. \n",
      "\n",
      "Example\n",
      "-------\n",
      ">>> mergeByYear(2010)\n",
      "\"\"\"\n",
      "#your code here\n",
      "\n",
      "\n",
      "def mergeByYear(yr):\n",
      "    data1 = pd.DataFrame(transfrom.ix[yr].values, columns = ['Income'])\n",
      "    data1['Country'] = transfrom.columns\n",
      "    combined = pd.merge(data1, count_df, on = ['Country'])\n",
      "    combined.Income = np.round(combined.Income, 2)\n",
      "    return combined\n",
      "\n",
      "mergeByYear(2000).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Income</th>\n",
        "      <th>Country</th>\n",
        "      <th>Region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   892.24</td>\n",
        "      <td>         Afghanistan</td>\n",
        "      <td>          ASIA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  5534.42</td>\n",
        "      <td>             Albania</td>\n",
        "      <td>        EUROPE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 10114.65</td>\n",
        "      <td>             Algeria</td>\n",
        "      <td>        AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  3340.65</td>\n",
        "      <td>              Angola</td>\n",
        "      <td>        AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 18263.75</td>\n",
        "      <td> Antigua and Barbuda</td>\n",
        "      <td> NORTH AMERICA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "     Income              Country         Region\n",
        "0    892.24          Afghanistan           ASIA\n",
        "1   5534.42              Albania         EUROPE\n",
        "2  10114.65              Algeria         AFRICA\n",
        "3   3340.65               Angola         AFRICA\n",
        "4  18263.75  Antigua and Barbuda  NORTH AMERICA"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(d) \n",
      "\n",
      "Use exploratory data analysis tools such as histograms and boxplots to explore the distribution of the income per person by region data set from 2(c) for a given year. Describe how these change through the recent years?\n",
      "\n",
      "**Hint**: Use a `for` loop to consider multiple years.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yearl = [1900,1990, 2003]\n",
      "for yr in yearl:\n",
      "    df = mergeByYear(yr)\n",
      "    df.boxplot('Income', by = 'Region',rot = 90 )\n",
      "    plt.title(\"Year:\" + str(yr))\n",
      "    plt.ylabel(\"Income per person\")\n",
      "    plt.xlabel(\"Region\")\n",
      "    plt.ylim(10**2, 10.5 **5)\n",
      "    plt.yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Tu7YwepVtnuqnT2Owc/rNaVY2/nc5fO7TGNo5/eY0Kxv/u8yP+zQG1i46gNyV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\njbgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x83297f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r1orF19tFuWFvQ77H97a3ve3torbbz5cuXUovWXpPnQJsCSwiGeQHQEQc1vOD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\nzMwsM2caZmaWmTMNMzPLzJmGmZll9v8BqT0i8L132pMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x85b4ab0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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27d9H0rWSfi3pvT37XwScQVJoVEin7ABy57WnmqUN6WxDGqFa6cxS0zgcOEnS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\n5NSmwPsi4vlFx2SjJWkOcGk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       "text": [
        "<matplotlib.figure.Figure at 0x86c9390>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 3\n",
      "\n",
      "In general, if group A has larger values than group B on average, does this mean the largest values are from group A? Discuss after completing each of the problems below. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(a)\n",
      "\n",
      "Assume you have two list of numbers, X and Y, with distribution approximately normal. X and Y have standard deviation equal to 1, but the average of X is different from the average of Y.  If the difference in the average of X and the average of Y is larger than 0, how does the proportion of X > a compare to the proportion of Y > a? "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Write a function that analytically calculates the ratio of these two proportions: Pr(X > a)/Pr(Y > a) as function of the difference in the average of X and the average of Y. \n",
      "\n",
      "**Hint**: Use the `scipy.stats` module for useful functions related to a normal random variable such as the probability density function, cumulative distribution function and survival function. \n",
      "\n",
      "**Update**: Assume Y is normally distributed with mean equal to 0. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show the curve for different values of a (a = 2,3,4 and 5)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats\n",
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "ratioNormals\n",
      "\n",
      "Return ratio of these two proportions: \n",
      "    Pr(X > a)/Pr(Y > a) as function of \n",
      "    the difference in the average of X \n",
      "    and the average of Y. \n",
      "\n",
      "Parameters\n",
      "----------\n",
      "diff : difference in the average of X \n",
      "    and the average of Y. \n",
      "a : cutoff value\n",
      "\n",
      "Returns\n",
      "-------\n",
      "Returns ratio of these two proportions: \n",
      "    Pr(X > a)/Pr(Y > a)\n",
      "    \n",
      "Example\n",
      "-------\n",
      ">>> ratioNormals(diff = 1, a = 2)\n",
      "\"\"\"\n",
      "#your code here\n",
      "def ratioNormals(diff, a):\n",
      "    X = scipy.stats.norm(loc=diff, scale=1)\n",
      "    Y = scipy.stats.norm(loc=0, scale=1) \n",
      "    return X.sf(a) / Y.sf(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# let diff range from 0 to 5 \n",
      "diffs = np.linspace(0, 5, 50)\n",
      "a_values = range(2,6)\n",
      "\n",
      "# Plot separate curves for \n",
      "# Pr(X > a) / Pr(Y > a) as a function of diff\n",
      "# for all given values of a\n",
      "plt.figure(figsize=(8,5));\n",
      "for a in a_values:\n",
      "    ratios = [ratioNormals(diff, a) for diff in diffs]\n",
      "    plt.plot(diffs, ratios)\n",
      "    \n",
      "# Labels\n",
      "plt.legend([\"a={}\".format(a) for a in a_values], loc=0);\n",
      "plt.xlabel('Diff');\n",
      "plt.ylabel('Pr(X>a) / Pr(Y>a)');\n",
      "plt.title('Ratio of Pr(X > a) to Pr(Y > a) as a Function of Diff');\n",
      "\n",
      "# Using a log scale so you can actually see the curves\n",
      "plt.yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fMGPGDIwxxMfHIyKsX7+eefPmNbntmjVrmDVrVqOvuVwud4WslALyq6t5NiOD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\ndtdSU0094zFrPTU17yUtzZSs9azXLBZTz3jMWs/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       "text": [
        "<matplotlib.figure.Figure at 0x98246f0>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(b)\n",
      "\n",
      "Now consider the distribution of income per person from two regions: Asia and South America. Estimate the average income per person across the countries in those two regions. Which region has the larger average of income per person across the countries in that region? \n",
      "\n",
      "**Update**: Use the year 2012. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "df_2012 = mergeByYear(2012)\n",
      "df_2012.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Income</th>\n",
        "      <th>Country</th>\n",
        "      <th>Region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1885.36</td>\n",
        "      <td>         Afghanistan</td>\n",
        "      <td>          ASIA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9329.21</td>\n",
        "      <td>             Albania</td>\n",
        "      <td>        EUROPE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12751.72</td>\n",
        "      <td>             Algeria</td>\n",
        "      <td>        AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  7244.83</td>\n",
        "      <td>              Angola</td>\n",
        "      <td>        AFRICA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 19908.89</td>\n",
        "      <td> Antigua and Barbuda</td>\n",
        "      <td> NORTH AMERICA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "     Income              Country         Region\n",
        "0   1885.36          Afghanistan           ASIA\n",
        "1   9329.21              Albania         EUROPE\n",
        "2  12751.72              Algeria         AFRICA\n",
        "3   7244.83               Angola         AFRICA\n",
        "4  19908.89  Antigua and Barbuda  NORTH AMERICA"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comby = df_2012.groupby('Region', as_index = False).mean()\n",
      "comby = comby.loc[(comby.Region == 'ASIA')| (comby.Region == 'SOUTH AMERICA')]\n",
      "comby.Income = np.round(comby.Income, 2)\n",
      "comby"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Region</th>\n",
        "      <th>Income</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>          ASIA</td>\n",
        "      <td> 22882.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> SOUTH AMERICA</td>\n",
        "      <td> 12964.08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "          Region    Income\n",
        "1           ASIA  22882.16\n",
        "5  SOUTH AMERICA  12964.08"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = mergeByYear(2012)\n",
      "df = df.loc[(df.Region == \"ASIA\") | (df.Region == \"SOUTH AMERICA\")]\n",
      "df.boxplot('Income', by = 'Region', rot = 90)\n",
      "plt.ylabel('Income per person (dollars)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x840d4f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ST0REfaba5xOxUtJ+Hv0m92zzknwiIqJxaXYb4O8fEVGnKTW7SXqupEsk/bTs\nbyfpX7odZMwcmdstIlakk2a3zwPvBR4q+9cBB9UWUfS9Y44Z7nUIESslfT7N6yT5PM32la2d0k71\ncH0hRUTETNdJ8vm1pC1bO5L2B35ZX0jR/4Z6HUDESsmM1s3r5CHTLYDPAS8G7gNuBV5ve6T26GqW\nAQf1yNxuEQFTHHBg++e29wKeCTzX9q4zIfFEnYZ7HUDESkmfT/NWOLebpHWBNwBzgNXKctq2fXi9\noUW/ytxuEbEinTS7XQFcQTXK7THK3G62T6k/vHql2S0ioj5TXc/nats71BDUqsCPgDtt/x9Js4Ez\ngc2AEeAA28vKuUcBbwIeBQ63fVEp3xE4GXgqcJ7td5TyNYBTgR2Ae4ADbd82RgxJPhERNZnq3G6n\nS/pbSRtKmt16dSGudwDXs3yG7COBi8sidZeUfSRtAxwIbAPsA5yo0vYHfBo41PZcYK6kfUr5ocA9\npfxjwHFdiDc6lPbz6De5Z5vXSfL5E/BhYBFwVXn9aCofKunZwCuAL1A14wHsC7Sa8k4BXlW29wPO\nsP1wGehwC7CLpA2BtW0vLued2vae9mudDew1lXgjIqK7OllM7ghgC9vdXL30Y8C7gWe0la1v++6y\nfTewftneiCrxtdwJbEz1oOudbeVLSznl3zsAbD8i6X5Js23f28XvEOPIMxPRb3LPNq+T5HMz8Mdu\nfaCkVwK/sn2NpKGxzrFtSY10xixYsIA5c+YAMGvWLObNm/f4jdiqimd/5faHh4dYuHD6xJP97Ge/\nmf0lS5awbNkyAEZGRphIJwMOzgWeD1wKPFiKJz3UWtKHgIOBR6gGCjwDOAfYCRiyfVdpUrvU9vMk\nHVk+8Njy/guAo4Hbyjlbl/KDgN1sv7Wcs9D2IkmrAb+0/awxYsmAgxpIw9hDvQ4jomPDw8OP/xKN\n7pnqgINzgQ8CP6Dq62n1+0yK7ffa3sT25sB84Lu2Dwa+AbSeEDmkfC6lfL6k1SVtDswFFtu+C/it\npF3KAISDga+3vad1rf2pBjBERMQ00dF6PmXo8lZl9wbbXZlYVNLuwBG29y0j6M4CNuXJQ63fSzXU\n+hHgHbYvLOWtodZrUg21Prwt3tOA7amGWs8fa1aG1Hzqkel1IgKm/pzPENXIsdZzMpsCh9j+XjeD\n7IUkn3ok+UQETL3Z7aPA3rZ3s70bsDfVaLWIcQz3OoCIldLqPI/mdJJ8VrN9Y2vH9k10NkouBlTm\ndouIFek0WiKjAAAPJUlEQVSk2e0kqmltvkz1QOjrgVVsv6n+8OqVZreIiPpMtc/nqcDbgF1L0eXA\nibYfHP9d/SHJJyKiPlPt81kV+Ljt19h+DXBCKYsYU9rPo9/knm1eJ8nnu1RDmVueBnynnnAiImIQ\ndNLstsT2vBWV9aM0u0VE1GeqzW6/Lw9zti72F3RxrreYeRYu7HUEETHddVLz2Qn4CvDLUrQh1eJs\nU1pWYTpIzacemdst+k3mdqvHRDWfFT6vY/uHkrYGnku18NuN3ZpeJyIiBlOnc7u9GNicKlkZwPap\n9YZWv9R86pHpdSICpljzkfRl4DnAEqqHTVv6PvlERERvdDJNzo7ANqkiROeGgaEexxDRufT5NK+T\n0W4/oRpkEANo9uyqGW1lXrDy75k9u7ffMyKa1clot2FgHrCYJ65kum+9odUvfT4r1lT/TfqJImae\nKfX5AAu7G05ERAy6FTa72R4e69VAbNGnMk9W9Jvcs80bt+Yj6QHKsOox2PYz6gkpIiJmuo6e85mp\n0uezYunziYjJmurcbhEREV2V5BNdl/bz6De5Z5vXePKRtImkSyX9VNJPJB1eymdLuljSTZIukjSr\n7T1HSbpZ0g2S9m4r31HSdeXY8W3la0g6s5QvkrRZs98yIiIm0nifj6QNgA1sL5G0FnAV8CrgjcBv\nbP+HpPcA69o+UtI2wOnATsDGVAvZzbVtSYuBt9teLOk84ATbF0g6DPhz24dJOhB4te35Y8SSPp8V\nSJ9PREzWtOrzsX2X7SVl+wHgZ1RJZV/glHLaKVQJCWA/4AzbD9seAW4BdpG0IbC27cXlvFPb3tN+\nrbOBver7RhERsbJ62ucjaQ6wPXAlsL7tu8uhu4H1y/ZGwJ1tb7uTKlmNLl9ayin/3gFg+xHgfkmZ\nwKUhaT+P6UjSpF5Rj54ln9LkdjbwDtu/az9W2sLSCBMRXWN73BdcOsGxqEMn0+t0naSnUCWe02yf\nW4rvlrSB7btKk9qvSvlSYJO2tz+bqsaztGyPLm+9Z1PgF5JWA9axfe9YsSxYsIA5c+YAMGvWLObN\nm/f47Latv+AHfb81Q/V0iSf72e/+/tA0i6c/95csWcKyZcsAGBkZYSK9GHAgqv6Ye2y/s638P0rZ\ncZKOBGaNGnCwM8sHHGxZBhxcCRxONenpt3nigINtbb9V0nzgVRlwMDkZcBCDIPdfPabVgANgV+D/\nAntIuqa89gGOBV4m6SZgz7KP7euBs4DrgfOBw9oyxmHAF4CbgVtsX1DKvwisJ+lm4B+AI5v5agHp\n84l+NNzrAAZOptcZ4O/ficn8RTg8iYW58pdn9JI0jD3U6zBmnIlqPkk+A/z9O5Fmt+g3s2fDfffV\n/znrrgv3jtmTHC1JPuNI8lmxJJ/oN7lnp4/p1ucTM1z6fKLf5J5tXpJPREQ0Ls1uA/z9O5EmjOg3\nuWenjzS7RUTEtJLkE12X9vPoN7lnm9eT6XUiIupiBA3MB+q2/42Vlz6fAf7+nUj7efSb3LPTx0R9\nPqn5xITyV2RE1CF9PjEh4erPu5V4DV966Uq/R0k80UPp82lekk9ERDQufT4D/P07kfbz6DdNLT6a\nud1WLH0+ETEwJvNHTP74aV6a3aLr0n4e/We41wEMnCSfiIhoXPp8Bvj7dyJ9PjEIcv/VI3O7RUTE\ntJLkE12XPp+YjiSN+4KJjkUdMtotVqiJ//+tu279nxGDbaIm9uHhYYaGhpoLJtLnM8jfvy5pP48I\nSJ9PRERMMzM6+UjaR9INkm6W9J5exzM4hnsdQMRKST9l82Zs8pG0KvBJYB9gG+AgSVv3NqpBsaTX\nAUSslCVLcs82bcYmH2Bn4BbbI7YfBr4C7NfjmGaMiUcOvTMjh6KvLFu2rNchDJyZnHw2Bu5o27+z\nlEUX2B73dfTRR497LCICZnbyyW+6HhkZGel1CBErJfds82bsUGtJLwQW2t6n7B8FPGb7uLZzZuaX\nj4iYJsYbaj2Tk89qwI3AXsAvgMXAQbZ/1tPAIiJi5s5wYPsRSW8HLgRWBb6YxBMRMT3M2JpPRERM\nXzN5wEFERMckPaXXMQySJJ+IGFiq/KWkL1I9jhENSfKJrpP0Ukmf6nUcEeOR9CJJJwC3AecClwOZ\nAaVBST7RFZJ2kPRhSbcB7wdu6HVMEaNJ+ndJNwFHU80DNQ/4te2Tbd/b2+gGy4wd7Rb1k/Rc4CDg\nQODXwFepBrEM9TKuiAm8GbgK+DRwvu2HMu1Tb2S0W0yapMeAbwFvt317KbvV9ua9jSxibOX5v5cB\n84E9qaZgfxmwSZkDMhqSZreYitcAfwQuk/QZSXsB+TMypi3bj9g+3/YhwFzg68APgDslnd7b6AZL\naj4xZZLWopox/CBgD+BU4Gu2L+ppYBEdkvQM4FW2T+11LIMiNZ+YMtsP2P4v268ENgGuAY7scVgR\nTyLpCElvHuPQ3wCzm45nkKXmE5Mmabz/swrA9j0NhhOxQpKuBl5o+6FR5asDV9netjeRDZ6Mdoup\nuJrxl64w8JwGY4noxGqjEw9AGfWW/soGJfnEpNme0+sYIlaSJG1g+65RheuTNcAalT6fmDRJm0ma\n1ba/p6QTJL2rNGNETDcfBr4taUjS2uW1B/Bt4CM9jm2gpM8nJk3SYqoRQr+QNA+4BPgQ8ALgIdtj\ndexG9JSkvwKOAp5fin4K/Lvt83sX1eBJ8olJk/Rj29uV7f+kWin2nyStAlybztuIGE/6fGIq2jto\n96L6axLbj6XvNqYjSZ+Y4LBtH95YMAMuySem4lJJXwV+CcwCvgsgaSPgwV4GFjGOq6gGFrT+Omo1\n/YgMOGhUkk9MxT9QTSq6AfCStiGsW5AH9mIasn3yeMckbdJgKAMvfT7RFZJ2oJpe5wDgVuBs2xM1\ncUT0hKQdqZ5Bu972T0vS+VdgH9ub9ja6wZGaT0xallSIfiPpA8BrqdbyOVbSuVQT5B4PpL+nQan5\nxKRlSYXoN5KuB3aw/acyPdQdwPNtj/Q2ssGTh0xjKrKkQvSbB23/CaCsXHpzEk9vpOYTU5YlFaJf\nSLofuKyt6KXA5WXbtvdtPqrBlOQTXVWaMvYH5tves9fxRLSTNDTBYdv+XlOxDLokn4gYGJLWsX3/\nOMc2s31b0zENqvT5RMQgGW5tSLpk1LFzmw1lsCX5RMSgyoPQPZTkExERjctDphExSJ4l6V1UjwS0\nbwM8q3dhDZ4MOIiIgSFpIWNPJiqq0W7H9CKuQZTkExERjUuzW0QMjLKeT/uSCu2ynk+DknwiYpD8\nPfAT4CzgF6Vs9No+0YAkn4gYJBsCf0O19MejwJnAV20v62lUAyhDrSNiYNj+je1P294DWACsA1wv\n6eDeRjZ4UvOJiIFTFpSbD7wMOJ9qee1oUEa7RcTAkPR+4BXAz4CvABfafri3UQ2mJJ+IGBhlAcRb\ngT+Mcdi2t2s4pIGVZreIGCTPmeBY/hJvUGo+ETHwJL2Uag2qt/U6lkGRmk9EDCRJO1CtvnsAVVPc\n2b2NaLAk+UTEwJD0XKqEcyDwa+CrVC1AQ72MaxCl2S0iBkYZcPAt4O22by9lt9revLeRDZ48ZBoR\ng+Q1wB+ByyR9RtJejD3PW9QsNZ+IGDiS1gL2o2qC2wM4Ffia7Yt6GtgASfKJiIEmaTawP9Votz17\nHc+gSPKJiIjGpc8nIiIal+QTERGNS/KJiIjG5SHTiBgYkh5g+Rxuo5fTtu1nNB/VYMqAg4gYSJKu\nsb19r+MYVGl2i4iIxiX5RERE49LnExEDQ9JrWd7Xs46k17C838e2z+lZcAMmfT4RMTAkndS+y6gF\n5Gy/sdmIBldqPhExSL5lO+v2TAOp+UTEwMgIt+kjAw4iIqJxqflExMCQ9Afg5+Mctu3tmoxnkKXP\nJyIGya3AK8kCcj2X5BMRg+Qh27f1OohIn09EDJYf9DqAqKTPJyIGhqQjRhUZ+DXwfdu39iCkgZWa\nT0QMkrWBtdpeawM7ARdIOqiXgQ2a1HwiYuBJmg1ckmeAmpOaT0QMPNv39jqGQZPkExEDT9IewH29\njmOQZKh1RAwMSdeNUbwu8EvgDQ2HM9DS5xMRA0PSnLZdl9e9th/oSUADLMknIgaOpD2B51Mln5/a\nvrTHIQ2cJJ+IGBiSNgbOAR4EflSKdwTWBF5te2mvYhs0ST4RMTAknQuca/vkUeVvAF5re7+eBDaA\nknwiYmBIusn2Vit7LLovQ60jYpBI0pNmtJa0Cvl92Kj8sCNikHwb+JyktVoFZfszwHk9i2oAJflE\nxCD5J+B+YETS1ZKuBkaA3wH/2MvABk36fCJi4Eh6GrAl1VDrn9v+Q49DGjip+UTEwJC0s6QNbf/B\n9o+BHYCvSDqhTC4aDUnyiYhB8lmqZ3yQtBtwLHAK8Fvgcz2Ma+BkbreIGCSrtM1gfSDwWdtnA2dL\nuraHcQ2c1HwiYpCsKukpZfsvgfZpdfLHeIPyw46IQXIG8D1JvwH+AFwOIGkusKyXgQ2ajHaLiIEi\n6UXABsBFtn9fyrYC1rJ9dU+DGyBJPhER0bj0+UREROOSfCIionFJPhER0bgkn4iGSHpU0jWSfizp\nnPbJLVfyOhtJ+mq344toUgYcRDRE0u9sr122Twaus/2R3kYV0Rup+UT0xhXAFgCStpB0vqQfSbpM\n0nPbyheVmtIHJP2ulM+RdF3Zfqqkk8o5V0saKuULSu3qfEk3STquN18zYmxJPhENk7QqsDfwk1L0\nOeD/2f4L4N3AiaX8eOBjtrcD7hjncm8DHi3nHAScImmNcuwFwAHAtsCBkjbu+peJmKTMcBDRnDUl\nXQNsTLWGzGdKv8+LgK+2LbC5evn3hcC+ZfsM4D/HuOauwAkAtm+UdBuwFdVSAZfYbtWWrgfmAEu7\n+5UiJifJJ6I5f7S9vaQ1gQuB/YDvAMtsbz+F6z5pWejiwbbtR4FVp/AZEV2VZreIhtn+I3A48EHg\nAeBWSfsDqLJdOXURsH/Znj/O5S4HXl/euxWwKXADYyek8ZJUROOSfCKa8/jQUttLgFuo+mReDxwq\naQlVP1Crqe0fgHeV8i2oln8efa0TgVUk/Rj4CnCI7YfL8dFDWTO0NaaNDLWOmKYkrVlqSUiaDxxo\n+9U9DiuiK9LnEzF97Sjpk1TNZfcBb+pxPBFdk5pPREQ0Ln0+ERHRuCSfiIhoXJJPREQ0LsknIiIa\nl+QTERGNS/KJiIjG/X9676yRQQtoiwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f31470>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Asia has a larger income compared to South America."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(c) \n",
      "\n",
      "Calculate the proportion of countries with income per person that is greater than 10,000 dollars. Which region has a larger proportion of countries with income per person greater than 10,000 dollars? If the answer here is different from the answer in 3(b), explain why in light of your answer to 3(a).\n",
      "\n",
      "**Update**: Use the year 2012. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "high_prop = df_2012[map(lambda x: x > 10000, df_2012.Income)]\n",
      "high = high_prop.groupby('Region')\n",
      "dft = df_2012.groupby('Region')\n",
      "length = [len(val.Income) for key, val in dft]\n",
      "length1 = [len(val.Income) for key, val in high]\n",
      "x = zip(length1, length)\n",
      "propotion = [float(i)/j for i, j in x]\n",
      "\n",
      "countries = [key for key, val in high]\n",
      "pd.DataFrame(propotion,countries, columns =['Proportion'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Proportion</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AFRICA</th>\n",
        "      <td> 0.204082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ASIA</th>\n",
        "      <td> 0.552632</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EUROPE</th>\n",
        "      <td> 0.767442</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NORTH AMERICA</th>\n",
        "      <td> 0.636364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>OCEANIA</th>\n",
        "      <td> 0.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SOUTH AMERICA</th>\n",
        "      <td> 0.666667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "               Proportion\n",
        "AFRICA           0.204082\n",
        "ASIA             0.552632\n",
        "EUROPE           0.767442\n",
        "NORTH AMERICA    0.636364\n",
        "OCEANIA          0.230769\n",
        "SOUTH AMERICA    0.666667"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "South America has a larger proportion of countries greater than 10,000 dollars compared to Asia. This is different from the answer in 3(b)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(d)\n",
      "\n",
      "**For AC209 Students**: Re-run this analysis in Problem 3 but compute the average income per person for each region, instead of the average of the reported incomes per person across countries in the region. Why are these two different? Hint: use this [data set](https://spreadsheets.google.com/pub?key=phAwcNAVuyj0XOoBL_n5tAQ&gid=0). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}