HW1.ipynb

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    "# 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",
    "---"
   ]
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    "# 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"
   ]
  },
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   "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. "
   ]
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   "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.  "
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salaries.csv\n",
      "Teams.csv\n",
      "   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\n"
     ]
    },
    {
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       "      <th>L</th>\n",
       "      <th>...</th>\n",
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       "      <td>88</td>\n",
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       "      <td>Oakland Athletics</td>\n",
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       "      <td>778355.0</td>\n",
       "      <td>97</td>\n",
       "      <td>95</td>\n",
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       "      <td>155</td>\n",
       "      <td>77.0</td>\n",
       "      <td>93</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
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       "      <td>170.0</td>\n",
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       "      <td>98</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
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       "      <td>1075518.0</td>\n",
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       "      <td>87</td>\n",
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       "      <td>81.0</td>\n",
       "      <td>63</td>\n",
       "      <td>98</td>\n",
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       "      <td>495599.0</td>\n",
       "      <td>97</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>69</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>526999.0</td>\n",
       "      <td>94</td>\n",
       "      <td>96</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
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       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>54</td>\n",
       "      <td>108</td>\n",
       "      <td>...</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>306763.0</td>\n",
       "      <td>94</td>\n",
       "      <td>96</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
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       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>83</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>842259.0</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
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       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1304052.0</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
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       "      <td>AL</td>\n",
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       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>68</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1735489.0</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
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       "      <td>OAK</td>\n",
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       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1294941.0</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
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       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>1984</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1353281.0</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>1985</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>79.0</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1334599.0</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>1986</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>83.0</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1314646.0</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1987</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1678921.0</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1988</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>104</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2287335.0</td>\n",
       "      <td>97</td>\n",
       "      <td>96</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>1989</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>99</td>\n",
       "      <td>63</td>\n",
       "      <td>...</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2667225.0</td>\n",
       "      <td>97</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>1990</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>103</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2900217.0</td>\n",
       "      <td>96</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083</th>\n",
       "      <td>1991</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2713493.0</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2106</th>\n",
       "      <td>1992</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>96</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2494160.0</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>1993</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>68</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2035025.0</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>1994</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51</td>\n",
       "      <td>63</td>\n",
       "      <td>...</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1242692.0</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>1995</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>72.0</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1174310.0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>1996</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1148380.0</td>\n",
       "      <td>101</td>\n",
       "      <td>102</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>1997</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1264218.0</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>1998</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1232343.0</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>1999</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1434610.0</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>2000</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>81.0</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0.970</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1603744.0</td>\n",
       "      <td>96</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>2001</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>102</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2133277.0</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>2002</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>103</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2169811.0</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>2003</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>96</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>2216596.0</td>\n",
       "      <td>99</td>\n",
       "      <td>98</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>2004</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>91</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Network Associates Coliseum</td>\n",
       "      <td>2201516.0</td>\n",
       "      <td>99</td>\n",
       "      <td>98</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>2005</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>McAfee Coliseum</td>\n",
       "      <td>2109118.0</td>\n",
       "      <td>101</td>\n",
       "      <td>100</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>2006</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>93</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>McAfee Coliseum</td>\n",
       "      <td>1976625.0</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>2007</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>McAfee Coliseum</td>\n",
       "      <td>1921844.0</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>2008</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>161</td>\n",
       "      <td>81.0</td>\n",
       "      <td>75</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>McAfee Coliseum</td>\n",
       "      <td>1665256.0</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>2009</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland-Alameda Country Coliseum</td>\n",
       "      <td>1408783.0</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2636</th>\n",
       "      <td>2010</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland-Alameda Country Coliseum</td>\n",
       "      <td>1418391.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>2011</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>Oakland-Alameda Country Coliseum</td>\n",
       "      <td>1476791.0</td>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2695</th>\n",
       "      <td>2012</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>94</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>O.co Coliseum</td>\n",
       "      <td>1679013.0</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725</th>\n",
       "      <td>2013</td>\n",
       "      <td>AL</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>81.0</td>\n",
       "      <td>96</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.983</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>O.co Coliseum</td>\n",
       "      <td>1809302.0</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "      <td>OAK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      yearID lgID teamID franchID divID  Rank    G  Ghome    W    L  \\\n",
       "1502    1968   AL    OAK      OAK   NaN     6  163   83.0   82   80   \n",
       "1524    1969   AL    OAK      OAK     W     2  162   81.0   88   74   \n",
       "1548    1970   AL    OAK      OAK     W     2  162   81.0   89   73   \n",
       "1571    1971   AL    OAK      OAK     W     1  161   81.0  101   60   \n",
       "1595    1972   AL    OAK      OAK     W     1  155   77.0   93   62   \n",
       "1619    1973   AL    OAK      OAK     W     1  162   81.0   94   68   \n",
       "1643    1974   AL    OAK      OAK     W     1  162   81.0   90   72   \n",
       "1667    1975   AL    OAK      OAK     W     1  162   81.0   98   64   \n",
       "1692    1976   AL    OAK      OAK     W     2  161   81.0   87   74   \n",
       "1722    1977   AL    OAK      OAK     W     7  161   81.0   63   98   \n",
       "1747    1978   AL    OAK      OAK     W     6  162   80.0   69   93   \n",
       "1774    1979   AL    OAK      OAK     W     7  162   81.0   54  108   \n",
       "1795    1980   AL    OAK      OAK     W     2  162   81.0   83   79   \n",
       "1820    1981   AL    OAK      OAK     W     1  109   56.0   64   45   \n",
       "1850    1982   AL    OAK      OAK     W     5  162   81.0   68   94   \n",
       "1875    1983   AL    OAK      OAK     W     4  162   81.0   74   88   \n",
       "1901    1984   AL    OAK      OAK     W     4  162   81.0   77   85   \n",
       "1928    1985   AL    OAK      OAK     W     4  162   79.0   77   85   \n",
       "1953    1986   AL    OAK      OAK     W     3  162   83.0   76   86   \n",
       "1978    1987   AL    OAK      OAK     W     3  162   81.0   81   81   \n",
       "2002    1988   AL    OAK      OAK     W     1  162   81.0  104   58   \n",
       "2028    1989   AL    OAK      OAK     W     1  162   81.0   99   63   \n",
       "2054    1990   AL    OAK      OAK     W     1  162   81.0  103   59   \n",
       "2083    1991   AL    OAK      OAK     W     4  162   81.0   84   78   \n",
       "2106    1992   AL    OAK      OAK     W     1  162   81.0   96   66   \n",
       "2138    1993   AL    OAK      OAK     W     7  162   81.0   68   94   \n",
       "2164    1994   AL    OAK      OAK     W     2  114   56.0   51   63   \n",
       "2194    1995   AL    OAK      OAK     W     4  144   72.0   67   77   \n",
       "2221    1996   AL    OAK      OAK     W     3  162   81.0   78   84   \n",
       "2250    1997   AL    OAK      OAK     W     4  162   81.0   65   97   \n",
       "2278    1998   AL    OAK      OAK     W     4  162   81.0   74   88   \n",
       "2306    1999   AL    OAK      OAK     W     2  162   81.0   87   75   \n",
       "2335    2000   AL    OAK      OAK     W     1  161   81.0   91   70   \n",
       "2366    2001   AL    OAK      OAK     W     2  162   81.0  102   60   \n",
       "2395    2002   AL    OAK      OAK     W     1  162   81.0  103   59   \n",
       "2425    2003   AL    OAK      OAK     W     1  162   81.0   96   66   \n",
       "2456    2004   AL    OAK      OAK     W     2  162   81.0   91   71   \n",
       "2486    2005   AL    OAK      OAK     W     2  162   81.0   88   74   \n",
       "2515    2006   AL    OAK      OAK     W     1  162   81.0   93   69   \n",
       "2547    2007   AL    OAK      OAK     W     3  162   81.0   76   86   \n",
       "2577    2008   AL    OAK      OAK     W     3  161   81.0   75   86   \n",
       "2608    2009   AL    OAK      OAK     W     4  162   81.0   75   87   \n",
       "2636    2010   AL    OAK      OAK     W     2  162   81.0   81   81   \n",
       "2667    2011   AL    OAK      OAK     W     3  162   81.0   74   88   \n",
       "2695    2012   AL    OAK      OAK     W     1  162   81.0   94   68   \n",
       "2725    2013   AL    OAK      OAK     W     1  162   81.0   96   66   \n",
       "\n",
       "         ...          DP     FP               name  \\\n",
       "1502     ...       136.0  0.970  Oakland Athletics   \n",
       "1524     ...       162.0  0.970  Oakland Athletics   \n",
       "1548     ...       152.0  0.970  Oakland Athletics   \n",
       "1571     ...       157.0  0.980  Oakland Athletics   \n",
       "1595     ...       146.0  0.970  Oakland Athletics   \n",
       "1619     ...       170.0  0.970  Oakland Athletics   \n",
       "1643     ...       154.0  0.970  Oakland Athletics   \n",
       "1667     ...       140.0  0.970  Oakland Athletics   \n",
       "1692     ...       130.0  0.970  Oakland Athletics   \n",
       "1722     ...       136.0  0.970  Oakland Athletics   \n",
       "1747     ...       145.0  0.970  Oakland Athletics   \n",
       "1774     ...       137.0  0.970  Oakland Athletics   \n",
       "1795     ...       115.0  0.970  Oakland Athletics   \n",
       "1820     ...        74.0  0.980  Oakland Athletics   \n",
       "1850     ...       140.0  0.970  Oakland Athletics   \n",
       "1875     ...       157.0  0.970  Oakland Athletics   \n",
       "1901     ...       159.0  0.970  Oakland Athletics   \n",
       "1928     ...       137.0  0.970  Oakland Athletics   \n",
       "1953     ...       120.0  0.970  Oakland Athletics   \n",
       "1978     ...       122.0  0.970  Oakland Athletics   \n",
       "2002     ...       151.0  0.980  Oakland Athletics   \n",
       "2028     ...       159.0  0.970  Oakland Athletics   \n",
       "2054     ...       152.0  0.980  Oakland Athletics   \n",
       "2083     ...       150.0  0.980  Oakland Athletics   \n",
       "2106     ...       158.0  0.970  Oakland Athletics   \n",
       "2138     ...       161.0  0.980  Oakland Athletics   \n",
       "2164     ...       105.0  0.970  Oakland Athletics   \n",
       "2194     ...       151.0  0.980  Oakland Athletics   \n",
       "2221     ...       195.0  0.980  Oakland Athletics   \n",
       "2250     ...       170.0  0.980  Oakland Athletics   \n",
       "2278     ...       155.0  0.970  Oakland Athletics   \n",
       "2306     ...       162.0  0.980  Oakland Athletics   \n",
       "2335     ...       164.0  0.970  Oakland Athletics   \n",
       "2366     ...       151.0  0.980  Oakland Athletics   \n",
       "2395     ...       144.0  0.980  Oakland Athletics   \n",
       "2425     ...       145.0  0.980  Oakland Athletics   \n",
       "2456     ...       172.0  0.980  Oakland Athletics   \n",
       "2486     ...       166.0  0.980  Oakland Athletics   \n",
       "2515     ...       173.0  0.980  Oakland Athletics   \n",
       "2547     ...       151.0  0.980  Oakland Athletics   \n",
       "2577     ...       169.0  0.980  Oakland Athletics   \n",
       "2608     ...       153.0  0.980  Oakland Athletics   \n",
       "2636     ...       146.0  0.980  Oakland Athletics   \n",
       "2667     ...       145.0  0.980  Oakland Athletics   \n",
       "2695     ...       136.0  0.980  Oakland Athletics   \n",
       "2725     ...       112.0  0.983  Oakland Athletics   \n",
       "\n",
       "                                  park  attendance  BPF  PPF  teamIDBR  \\\n",
       "1502                  Oakland Coliseum    837466.0   94   94       OAK   \n",
       "1524                  Oakland Coliseum    778232.0   95   95       OAK   \n",
       "1548                  Oakland Coliseum    778355.0   97   95       OAK   \n",
       "1571                  Oakland Coliseum    914993.0   98   97       OAK   \n",
       "1595                  Oakland Coliseum    921323.0   95   93       OAK   \n",
       "1619                  Oakland Coliseum   1000763.0   95   93       OAK   \n",
       "1643                  Oakland Coliseum    845693.0   94   92       OAK   \n",
       "1667                  Oakland Coliseum   1075518.0   98   96       OAK   \n",
       "1692                  Oakland Coliseum    780593.0   95   95       OAK   \n",
       "1722                  Oakland Coliseum    495599.0   97   98       OAK   \n",
       "1747                  Oakland Coliseum    526999.0   94   96       OAK   \n",
       "1774                  Oakland Coliseum    306763.0   94   96       OAK   \n",
       "1795                  Oakland Coliseum    842259.0   93   93       OAK   \n",
       "1820                  Oakland Coliseum   1304052.0   95   95       OAK   \n",
       "1850                  Oakland Coliseum   1735489.0   94   95       OAK   \n",
       "1875                  Oakland Coliseum   1294941.0   93   94       OAK   \n",
       "1901                  Oakland Coliseum   1353281.0   93   93       OAK   \n",
       "1928                  Oakland Coliseum   1334599.0   92   92       OAK   \n",
       "1953                  Oakland Coliseum   1314646.0   92   92       OAK   \n",
       "1978                  Oakland Coliseum   1678921.0   93   93       OAK   \n",
       "2002                  Oakland Coliseum   2287335.0   97   96       OAK   \n",
       "2028                  Oakland Coliseum   2667225.0   97   95       OAK   \n",
       "2054                  Oakland Coliseum   2900217.0   96   95       OAK   \n",
       "2083                  Oakland Coliseum   2713493.0   94   93       OAK   \n",
       "2106                  Oakland Coliseum   2494160.0   94   95       OAK   \n",
       "2138                  Oakland Coliseum   2035025.0   94   94       OAK   \n",
       "2164                  Oakland Coliseum   1242692.0   91   92       OAK   \n",
       "2194                  Oakland Coliseum   1174310.0   91   91       OAK   \n",
       "2221                  Oakland Coliseum   1148380.0  101  102       OAK   \n",
       "2250                  Oakland Coliseum   1264218.0   96   97       OAK   \n",
       "2278                  Oakland Coliseum   1232343.0   96   97       OAK   \n",
       "2306                  Oakland Coliseum   1434610.0   94   94       OAK   \n",
       "2335                  Oakland Coliseum   1603744.0   96   95       OAK   \n",
       "2366                  Oakland Coliseum   2133277.0   99   97       OAK   \n",
       "2395                  Oakland Coliseum   2169811.0   98   97       OAK   \n",
       "2425                  Oakland Coliseum   2216596.0   99   98       OAK   \n",
       "2456       Network Associates Coliseum   2201516.0   99   98       OAK   \n",
       "2486                   McAfee Coliseum   2109118.0  101  100       OAK   \n",
       "2515                   McAfee Coliseum   1976625.0   97   97       OAK   \n",
       "2547                   McAfee Coliseum   1921844.0   94   94       OAK   \n",
       "2577                   McAfee Coliseum   1665256.0   95   95       OAK   \n",
       "2608  Oakland-Alameda Country Coliseum   1408783.0   99   99       OAK   \n",
       "2636  Oakland-Alameda Country Coliseum   1418391.0  100  100       OAK   \n",
       "2667  Oakland-Alameda Country Coliseum   1476791.0   97   98       OAK   \n",
       "2695                     O.co Coliseum   1679013.0   97   97       OAK   \n",
       "2725                     O.co Coliseum   1809302.0   95   93       OAK   \n",
       "\n",
       "      teamIDlahman45  teamIDretro  \n",
       "1502             OAK          OAK  \n",
       "1524             OAK          OAK  \n",
       "1548             OAK          OAK  \n",
       "1571             OAK          OAK  \n",
       "1595             OAK          OAK  \n",
       "1619             OAK          OAK  \n",
       "1643             OAK          OAK  \n",
       "1667             OAK          OAK  \n",
       "1692             OAK          OAK  \n",
       "1722             OAK          OAK  \n",
       "1747             OAK          OAK  \n",
       "1774             OAK          OAK  \n",
       "1795             OAK          OAK  \n",
       "1820             OAK          OAK  \n",
       "1850             OAK          OAK  \n",
       "1875             OAK          OAK  \n",
       "1901             OAK          OAK  \n",
       "1928             OAK          OAK  \n",
       "1953             OAK          OAK  \n",
       "1978             OAK          OAK  \n",
       "2002             OAK          OAK  \n",
       "2028             OAK          OAK  \n",
       "2054             OAK          OAK  \n",
       "2083             OAK          OAK  \n",
       "2106             OAK          OAK  \n",
       "2138             OAK          OAK  \n",
       "2164             OAK          OAK  \n",
       "2194             OAK          OAK  \n",
       "2221             OAK          OAK  \n",
       "2250             OAK          OAK  \n",
       "2278             OAK          OAK  \n",
       "2306             OAK          OAK  \n",
       "2335             OAK          OAK  \n",
       "2366             OAK          OAK  \n",
       "2395             OAK          OAK  \n",
       "2425             OAK          OAK  \n",
       "2456             OAK          OAK  \n",
       "2486             OAK          OAK  \n",
       "2515             OAK          OAK  \n",
       "2547             OAK          OAK  \n",
       "2577             OAK          OAK  \n",
       "2608             OAK          OAK  \n",
       "2636             OAK          OAK  \n",
       "2667             OAK          OAK  \n",
       "2695             OAK          OAK  \n",
       "2725             OAK          OAK  \n",
       "\n",
       "[46 rows x 48 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import zipfile \n",
    "import os\n",
    "import shutil \n",
    "import urllib.request  \n",
    "from io import BytesIO\n",
    "from zipfile import ZipFile \n",
    "import pandas as pd\n",
    "from pandas import DataFrame \n",
    "\n",
    "#download the files \n",
    "#url = urllib.request.urlopen(\"http://seanlahman.com/files/database/lahman-csv_2014-02-14.zip\")\n",
    "\n",
    "filestodownload = [\"Salaries.csv\",\"Teams.csv\"]    \n",
    "mylocaldir = r\"C:\\Users\\Celin\\.ssh\\Testing\\ML_CS109\\content\\HW1_data\"\n",
    "test=True\n",
    "if not (test) : \n",
    "    with ZipFile(BytesIO(url.read())) as hw1_zipfiles:\n",
    "        for containedfile in hw1_zipfiles.namelist():\n",
    "                if containedfile in filestodownload:\n",
    "                    source = hw1_zipfiles.open(containedfile)\n",
    "                    target = open(os.path.join(mylocaldir, containedfile), \"wb\")\n",
    "                    with source, target:\n",
    "                        shutil.copyfileobj(source, target)\n",
    "            \n",
    "        \n",
    "            \n",
    "if  (test):\n",
    "    for dirs,subdirs,files in os.walk(mylocaldir):\n",
    "        for file in files:\n",
    "            if(file in [\"Salaries.csv\",\"Teams.csv\"]  ) :\n",
    "                print(file)\n",
    "            else :\n",
    "                print(\"in else\")\n",
    "     \n",
    "    \"\"\"\n",
    "readfilestodf(mylocaldir,[\"Salaries.csv\"])      \n",
    "\n",
    "def readfilestodf(localdirs,filesarray = []):\n",
    "    for files in filesarray:\n",
    "           salaries_pd=pd.read_csv(os.path.join(localdirs,files))\n",
    "        print(salaries_pd)\n",
    "                            \n",
    "    \"\"\"    \n",
    "      \n",
    "salaries_pd=pd.read_csv(os.path.join(mylocaldir,\"Salaries.csv\"))    \n",
    "print(salaries_pd.head())\n",
    "\n",
    "\n",
    "teams_pd=pd.read_csv(os.path.join(mylocaldir,\"Teams.csv\"))    \n",
    "teams_pd.head()    \n",
    "\n",
    "teams_pd[teams_pd.name.str.contains(\"Oakland\")]"
   ]
  },
  {
   "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",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Salaries.grouped\n",
    "\n",
    "grouped_salaries_pd = salaries_pd.groupby([\"yearID\",\"teamID\"])\n",
    "grouped_salaries_pd=grouped_salaries_pd.salary.sum().to_frame().reset_index()\n",
    "#grouped_salaries_pd = grouped_salaries_pd.to_frame()\n",
    "grouped_salaries_pd.head()\n",
    "#type(grouped_salaries_pd)"
   ]
  },
  {
   "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",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'teams_pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-94b2a51bfa9e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#your\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mteams_w_pd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mteams_pd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'yearID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'teamID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'W'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#print(type((teams_w_pd.head())))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mteams_pd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mteamID\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mresult_pd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrouped_salaries_pd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mteams_w_pd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'inner'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'yearID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'teamID'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'teams_pd' is not defined"
     ]
    }
   ],
   "source": [
    "#your \n",
    "teams_w_pd = teams_pd[['yearID','teamID','W']]\n",
    "#print(type((teams_w_pd.head())))\n",
    "\n",
    "result_pd = pd.merge(grouped_salaries_pd,teams_w_pd,how='inner',on = ['yearID','teamID'] )\n",
    "#print(result_pd.describe())\n",
    "print(result_pd.head())\n"
   ]
  },
  {
   "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",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013}\n"
     ]
    },
    {
     "data": {
      "image/png": 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UCaN9FZOZdVhePam/BNwgaUfgz8CZJMnqZklnA08BJ+cUW48xfswQJwQz67Jc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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ccb780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rYuqsoy0zM1ur6RJEM3W0ZWa2IZouQTRTR1tmZhui6RJEM3W0ZWa2IZqukrqt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\nMzPL5QRhZma5/j9SxcOf+LIIHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x990a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iVWrXDL2FE4GZVbTmrhma78pt7poBcDLoJi4aMrOKVsldM/QWTgRmVtEquWuG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x996c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XsrR5JUP79+X8MSMZN7qh4vMwK4UThFmFTJ/XxEVTn2DlqjUANDWv5KKpTwCU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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x992ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PksYMs2rmBGFmZYkIpkyZwuTJk1myZAmPPvoof//73zn77LOB9ON/0003sf322/Ob3/wm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\n/x8OjAvIe8gx9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x996c9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Mhv2AVOwUwELgpJLEUVF/dOFhjWX/abPpqFAe2zpqGL+d+v4CIrLe8PdXH3rb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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9987438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7ZrNpXWPHMHfW9H7eYWb2fO5wYEPS16nAvd3MbCScfGzIZs+c4mRjZiPiajcz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x979c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oq11tx0NMmJn1XKVDTOQ66FxEvA1s3mHaElIvIjMzKwD/stjMrME5EZiZNTgn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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ba6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6iKQdJd0jaXH6d4c8YrPa4kkHzXovrxrEfwB3RcQewAeBp4CpwNyIGAvMTR+b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\nQZiZWSYnCDMzy+QEYWZmmZwgzMws0/8HiZVJKmK+D58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x955d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N9LWtqakpmpubqx2GmVldkTQ3Ipq6ms9PFpuZ5ZwTgZlZzjkRmJnlnBOBmVnOORGYmeWc\nE4GZWc5l+hyBmZl1353zWyr6HIMTgZlZDSmMfloY+K4w+imQWTJw05CZWQ2pxuinTgRmZjWkGqOf\nOhGYmdWQaox+6kRgZlZDqjH6qW8Wm5nVkGqMfupEYGZWYyaNH1HRYa+dCMysbJXu326V4URgZmWp\nRv92qwzfLDazslSjf7tVhhOBmZWlGv3brTKcCMysLNXo326V4URgZmWpRv92qwzfLDazslSjf7tV\nhhOB5VqW3SEbsatlT/u3N2KdNAInAsutLLtDuqvl27lOapfvEVhuZdkd0l0t3851UrucCCy3suwO\n6a6Wb+c6qV1OBJZbWXaHdFfLt3Od1C4nAsutLLtDuqvl27lOapdvFltuZdkd0l0t3851UrsUEdmt\nXBoMXAfsAwRwKjAB+DywIp3t4oi4p7P1NDU1RXNzc2Zxmpk1IklzI6Kpq/myviL4HnBvRHxa0tbA\nO0gSwVURcUXGn21mZmXILBFIGgQcBpwMEBFvAm9KyuojzcxsC2R5s3g3kuafH0maL+k6SQPSaWdL\nekzS9ZJ2KLWwpNMkNUtqXrFiRalZzMysF2SZCPoCBwDXRsR44HXgQuBaYHdgHLAMuLLUwhExLSKa\nIqJpyJAhGYZpZpZvWSaCF4EXI+Kh9P3twAERsTwiNkbEJuCHwEEZxmBmZl3ILBFExEvAC5IKnYQP\nB/4iaVjRbMcAC7KKwczMupZ1r6GzgZvSHkPPAqcA/yVpHEl30sXA6RnHYGZV4tFG60OmiSAiHgXa\n92E9McvPNLPa4NFG64eHmDCzTHi00frhRGBmmfBoo/XDicDMMuHRRuuHE4GZZcKjjdYPjz5qZpnw\naKP1w4nAzDLT0392b5XhRGBmnfKzAI3PicDMOuRnAfLBN4vNrEN+FiAfnAjMrEN+FiAfnAjMrEN+\nFiAfnAjMrEN+FiAffLPYzDrkZwHywYnAzDrlZwEan5uGzMxyzonAzCznnAjMzHLOicDMLOfKSgSS\nzpE0UIn/kTRP0hFZB2dmZtkr94rg1Ih4FTgC2IHk/w5PySwqMzOrmHITgdLf/wz8OCKeKCozM7M6\nVm4imCvpPpJEMEfS9sCm7MIyM7NKKfeBss8B44BnI2KtpHcCp2QXlpmZVUpZiSAiNklaDrxHkp9G\nNjNrIGUd1CV9GzgO+AtQGJw8gAcyisvMzCqk3LP7ScDYiHgjy2DMzKzyyr1Z/CzQL8tAzMysOsq9\nIlgLPCrpV8Dmq4KImJxJVGZmVjHlJoK70p9ukTQYuA7Yh+SewqnAQuBWYBSwGDg2Il7p7rrNzKx3\nlNtraMYWrv97wL0R8WlJWwPvAC4GfhURUyRdCFwIXLCF6zczsx7qNBFImhURx0p6nOSMvo2I2K+T\nZQcBhwEnp/O+CbwpaSLwwXS2GcD9OBGYmVVNV1cE8yUdBBwDrO/muncDVgA/krQ/MBc4BxgaEcvS\neV4ChpZaWNJpwGkAI0eO7OZHm5lZubrqNfRO4LvAw8ANwOnAvsBrEbGki2X7AgcA10bEeOB1kmag\nzSIiKHGlkU6bFhFNEdE0ZMiQLjfEzMy2TKeJICLOi4h/At4NXASsJhlaYoGkv3Sx7heBFyPiofT9\n7SSJYbmkYQDp75d7EL+ZmfVQuc8R9AcGAoPSn6XAQ50tEBEvAS9IGpsWHU7yZPJdwElp2UnAT7sZ\ns5mZ9aKubhZPA/YGXiM58P8B+M9udPc8G7gp7TH0LMnVxFbALEmfA5YAx25h7GZm1gu6ulk8EtgG\nWAS0kDT3tJa78oh4FGgqMenwctdhZmbZ6jQRRMSRkkRyVfBPwJeBfSStBv4YEZdVIEYzM8tQlw+U\npT17FkhqBf6W/nwcOAhwIjAzq3Nd3SOYTHIl8E8kzxH8If25Hng88+jMzCxzXV0RjAJuA75Y9BCY\nmZk1kK7uEXypUoGYmVl1+N9Omhl3zm9h6pyFLG1dx/DB/Tl/wlgmjR9R7bCsQpwIzHLuzvktXHTH\n46xbn/wX2pbWdVx0R3IL0MkgH8p9stjMGtTUOQs3J4GCdes3MnXOwipFZJXmRGCWc0tb13Wr3BqP\nE4FZzg0f3L9b5dZ4nAjMcu78CWPp369Pm7L+/fpw/oSxHSxhjcY3i81yrnBD2L2G8suJwMyYNH6E\nD/w55qYhM7OccyIwM8s5JwIzs5xzIjAzyzknAjOznHMiMDPLOScCM7OccyIwM8s5JwIzs5xzIjAz\nyzknAjOznHMiMDPLOScCM7OccyIwM8u5TIehlrQYeA3YCGyIiCZJlwOfB1aks10cEfdkGYdZue6c\n3+Jx+WtUZ/vG+61nKvH/CD4UESvblV0VEVdU4LPNynbn/BYuuuPxzf/IvaV1HRfd8TiADypV1tm+\nAbzfeshNQ2apqXMWbj6YFKxbv5GpcxZWKSIr6GzfeL/1XNaJIIBfSpor6bSi8rMlPSbpekk7lFpQ\n0mmSmiU1r1ixotQsZr1qaeu6bpVb5XS2b7zfei7rRHBoRIwDjgLOlHQYcC2wOzAOWAZcWWrBiJgW\nEU0R0TRkyJCMwzSD4YP7d6vcKqezfeP91nOZJoKIaEl/vwzMBg6KiOURsTEiNgE/BA7KMgazcp0/\nYSz9+/VpU9a/Xx/OnzC2ShFZQWf7xvut5zK7WSxpALBVRLyWvj4C+JqkYRGxLJ3tGGBBVjGYdUfh\nxqJ7n9SecvaN99uWU0Rks2Jpd5KrAEgSzs0R8Q1JPyZpFgpgMXB6UWIoqampKZqbmzOJ08ysUUma\nGxFNXc2X2RVBRDwL7F+i/MSsPtMsC43eR73Rt8+6VonnCMzqVqM/W9Do22fl8XMEZp1o9D7qjb59\nVh4nArNONHof9UbfPiuPE4FZJxq9j3qjb5+Vx4nArBON3ke90bfPyuObxWadaPRnCxp9+6w8mT1H\n0Jv8HEFlFXcnHNS/HxK8snY9fSQ2RjCiBg4WHpK4MXhfZavqzxFYfWrfnbB13frN0zamJw3V7mLo\nIYkbg7uu1g7fI7A2SnUnLKWaXQw9JHFj8L6qHb4isDa6022wWl0Mt6TLo7tD1h53Xa0dviKwNrrT\nbbBaXQw9JHFj8L6qHU4E1kap7oSlVLOLoYckbgzeV7XDTUPWRvvuhLXYa8hDEjcGd12tHe4+apYB\nd4u0WuDuo2ZV4m6RVm98j8Csl7lbpNUbJwKzXuZukVZvnAjMepm7RVq9cSIw62XuFmn1xjeLzXqZ\nu0VavXEiMMvApPEjfOC3uuFE0CDcb70+eb9ZLXAiaADut16fvN+sVvhmcQNwv/X65P1mtcJXBA3A\n/dbrU6n9s/aZZp5oeZIbd17Kdtttx4ABA9htt93Yc889qxCh5YUTQQMYPrg/LSUOKu63XttK7bc3\nXnyCV/90G19d+ifGjh3Lxo0bGTNmDNdcc02VorQ8cNNQA3C/9fpUar8NO/wUxr3vA+y88840Nzfz\nvve9j29/+9tVitDywomgAUwaP4JvfXJfRgzuj4ARg/vzrU/u6xuONa7Ufpvyqf245yczWbRoEdOn\nT2fx4sWMGTOGadOmsWHDhmqHbA0q02GoJS0GXgM2AhsioknSjsCtwChgMXBsRLzS2Xo8DLXlza23\n3soVV1zBI488QnNzM+eddx4rV65k6tSpHHnkkUiqdohWB8odhroSiaApIlYWlX0HWB0RUyRdCOwQ\nERd0th4nAsuj5557jt122w2AiOCuu+7iK1/5CiNHjuSKK65g//33r3KEVuvKTQTVaBqaCMxIX88A\nJlUhBrOaV0gCAJKYOHEiCxYsYNKkSRxxxBGceuqptLS0VDFCaxRZJ4IAfilprqTT0rKhEbEsff0S\nMLTUgpJOk9QsqXnFihUZh2lWH/r168eZZ57JU089xbve9S72228/LrvsMtasWVPt0KyOZZ0IDo2I\nccBRwJmSDiueGEm7VMm2qYiYFhFNEdE0ZMiQjMM0qy+DBg1iypQpzJs3j6effprDDz+82iFZHcv0\nOYKIaEl/vyxpNnAQsFzSsIhYJmkY8HKWMZg1sl133ZWbbrqJ9evXVzsUq2OZXRFIGiBp+8Jr4Ahg\nAXAXcFI620nAT7OKwSwv+vXrV+0QrI5leUUwFJiddnPrC9wcEfdKegSYJelzwBLg2AxjsBzK64ie\ned1u67nMrggi4tmI2D/92TsivpGWr4qIwyNidER8JCJWZxWD5U9hRM+W1nUEb43oeef8xu5d09F2\nX3fvI0ycOJHRo0ezxx57cM455/Dmm29uXu7cc89lxIgRbNq0aXPZ9OnTOeusswDYtGkTJ510Eqee\neipZdjW36vKTxdZQ8jqiZ6ntXvvmBr74+ROZNGkSixYt4qmnnmLNmjVccsklQHKQnz17Nrvssgu/\n/e1v37bOiOCMM85g/fr1XHfddX6IrYE5EVhDyetIrKW27+9L/sx6+nLKKacA0KdPH6666iquv/56\n1q5dy/3338/ee+/NF77wBWbOnPm25SdPnsyqVau44YYb2GorHyoamfeuNZSORlxt9JFYS23f+pXP\ns+Oue7UpGzhwICNHjuTpp59m5syZnHDCCRxzzDH8/Oc/b9Pz6Oabb2bevHnccsst9O3rQYobnROB\nNZS8jsRaarv79dmKpl136HCZe+65h0mTJjFw4EAOPvhg5syZs3naAQccwJIlS3j44Yczi9lqhxOB\nNZS8jsRaarv//ZgP0PpC23sjr776Ks8//zzPPfccra2t7LvvvowaNYoHH3ywTfPQXnvtxaxZszju\nuON44oknKrw1VmmZDjrXWzzonFn3RQQHHnggkydP5rOf/SwbN27kjDPOYODAgSxbtoyjjz6aE044\nAYDXX3+d3XbbjcWLFzNr1iyam5u5+uqrufvuu5k8eTK//e1vGTlyZJW3yLqrlgedM7MKkMTs2bO5\n7bbbGD16NGPGjGHbbbfl0ksv5d577+VjH/vY5nkHDBjAoYceyt13391mHUcffTSXXnopRx55JKtW\nrar0JliF+IrAzKxB+YrAzMzK4kRgZpZzTgRmZjnnRGBmlnNOBGZmOdewz457SN7KcV2b1beGTASF\nIXkLozEWhuQFfIDqZa5rs/rXkE1DeR2KuBpc12b1ryETQV6HIq4G17VZ/WvIRJDXoYirwXVtVv8a\nMhHkdSjianBdm9W/hrxZXLhJ6Z4s2XNdm9U/DzpnZtagPOicmZmVxYnAzCznnAjMzHLOicDMLOec\nCMzMcq4ueg1JWgEs6cEqdgJW9lI4leB4s1Vv8UL9xex4s1VuvLtGxJCuZqqLRNBTkprL6UJVKxxv\ntuotXqi/mB1vtno7XjcNmZnlnBOBmVnO5SURTKt2AN3keLNVb/FC/cXseLPVq/Hm4h6BmZl1LC9X\nBGZm1gEnAjOznGu4RCDpekkvS1pQVHa5pBZJj6Y//1zNGAsk7SLpN5L+IukJSeek5TtK+oWkRenv\nHaoda0EnMddqHW8r6WFJf07j/Y+0vCbruJN4a7J+CyT1kTRf0s/S9zVZvwUl4q3Z+pW0WNLjaVzN\naVmv1m/D3SOQdBiwBrghIvZJyy4H1kTEFdWMrT1Jw4BhETFP0vbAXGAScDKwOiKmSLoQ2CEiLqhi\nqJt1EvOx1GYdCxgQEWsk9QMeBM4BPkkN1nEn8R5JDdZvgaQvAU3AwIj4uKTvUIP1W1Ai3sup0fqV\ntBhoioiVRWW9Wr8Nd0UQEQ8Aq6sdRzkiYllEzEtfvwY8CYwAJgIz0tlmkBxoa0InMdekSKxJ3/ZL\nf4IareNO4q1ZknYGPgZcV1Rck/ULHcZbb3q1fhsuEXTibEmPpU1HNXWZCiBpFDAeeAgYGhHL0kkv\nAUOrFFan2sUMNVrHaTPAo8DLwC8ioqbruIN4oUbrF/gu8BVgU1FZzdYvpeOF2q3fAH4paa6k09Ky\nXq3fvCSCa4HdgXHAMuDK6obTlqTtgJ8A50bEq8XTImm7q7kzwhIx12wdR8TGiBgH7AwcJGmfdtNr\nqo47iLcm61fSx4GXI2JuR/PUUv12Em9N1m/q0PT7cBRwZtr8vVlv1G8uEkFELE//uDYBPwQOqnZM\nBWk78E+AmyLijrR4edoWX2iTf7la8ZVSKuZaruOCiGgFfkPS3l7TdQxt463h+j0E+ETajn0L8GFJ\nN1K79VuxBsXaAAAEG0lEQVQy3hquXyKiJf39MjCbJLZerd9cJIJChaWOARZ0NG8lpTcG/wd4MiL+\ns2jSXcBJ6euTgJ9WOraOdBRzDdfxEEmD09f9gY8Cf6VG67ijeGu1fiPioojYOSJGAccDv46Iz1Cj\n9dtRvLVav5IGpJ0ykDQAOIIktl6t3749WbgWSZoJfBDYSdKLwGXAByWNI7l8WgycXrUA2zoEOBF4\nPG0TBrgYmALMkvQ5kuG3j61SfKV0FPMJNVrHw4AZkvqQnPjMioifSfojtVnHHcX74xqt347U8ne4\nlO/UaP0OBWYn51/0BW6OiHslPUIv1m/DdR81M7PuyUXTkJmZdcyJwMws55wIzMxyzonAzCznnAjM\nzHLOicBqjqRLlIy8+Vg64uLBXcw/XdKnM4jjDEmf7cb8oySFpK8Xle0kab2kq9uvszhuSfdLakpf\n31N4lsCsEhruOQKrb5LeB3wcOCAi3pC0E7B1L39G34jY0NV8EfHfW7D650gGNPtq+v5fgCe6s86I\nqJkhkC0ffEVgtWYYsDIi3gCIiJURsRRA0qWSHpG0QNK09CnnNjqaJz3j/q6S8dwvkfRcOlQGkgYW\nvy9a1+WSzita/ttK/lfAU5Le30H8a4EnC2f3wHHArFLr7IiS8ed3Sl9/Kd2WBZLOTctGSXpS0g/T\nK6f70qeQkTRZyf+KeEzSLZ19jlmBE4HVmvuAXdKD7TWSPlA07eqIODD9PxP9Sa4c2utsnq0joiki\n/gO4n+TMHZKhBu6IiPVdxNY3Ig4CziV5Yr0jtwDHS9oF2Ags7WK9JUl6L3AKcDDwj8DnJY1PJ48G\nfhARewOtwKfS8guB8RGxH3DGlnyu5Y8TgdWUdCz+9wKnASuAWyWdnE7+kKSHJD0OfBjYu8QqOpvn\n1qLX15EcZEl//6iM8AqDAs4FRnUy370kYwQd3+4zu+tQYHZEvJ7Wyx1A4UrkuYgoDPFRHM9jwE2S\nPgN02fxlBk4EVoPSUSDvj4jLgLOAT0naFrgG+HRE7EsyQuS2xcuVMc/rRZ/xe2CUpA8CfSKinEHG\n3kh/b6ST+2sR8SbJwfnLwO1lrHdLvFH0ujiejwE/AA4AHpHk+4DWJScCqymSxkoaXVQ0jmRQrcIB\nfaWS/4VQqpdQOfMUuwG4mfKuBrrrSuCCiOjJf8v7HTBJ0jvSkSePSctKkrQVsEtE/Aa4ABgEbNeD\nz7ec8NmC1ZrtgO+n3Sc3AE8Dp0VEq6QfkgzB+xLwSPsFy5mnnZuArwMzezH+QixPUNRbaAvXMU/S\ndODhtOi6iJiv5D/DldIHuFHSIEDAf6X/08CsUx591HIr7cM/MSJOrHYsZtXkKwLLJUnfJ/nXf+6z\nb7nnKwIzs5zzzWIzs5xzIjAzyzknAjOznHMiMDPLOScCM7Oc+//r22teojYipgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c2e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ilqfzvAKMyHqxpJMktUhqWbFiRWUiNjMrYVnbmm6V15KKJ4iIeBK4ELgTuB14DFhXNE8A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c195f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P5Pxp4xg5aAACRg4awPnTxvWao90iOjUV2ZHKerciqpiWAt+RtAOwAfg8sAgYFhErs2VW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\nzCyXE4SZmeVygjA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9cb0cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DbfxM0mbAauDjEbFS0jclzQQCeAD4aEmxmZkZJSWIiNinYNqRZcRiZmbF/EtqMzMr5ARh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\ny4E3RMTyiFgTES8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c9a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v7kYxWdfwCCOz2ucAYZ1Wq/fhNrOEU0xmZpbJAcLMzDLlEiAk9ZF0m6QFkp6W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9cbe8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M6sichW1Vv/SrE4WZWZ3oGNneMWixY2Q7UNFk4aonM7M6kdfIdicKM7M6kdfIdicKM7M6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99f8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7LB8+PqxR+qStoZXltobMzHquaG0NmZlZQTkRmJmVnBOBmVnJORGYmZVcS9Ya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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a874e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HDKW9wsG82q6yfV3ezGrDVxAF0lkX396xjmBjXfxNC9rzzlq3+tpV1l1tzYrJ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\nkQOEmZlV5ABhZmYV/X/HiM1icx7C2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae4d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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peoCIiJXAs5Lar/gaBzwK3AScmpWdCtxY7bqZmdlGRV0H8TfAlZK2Bn4PnE4K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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x986c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0puFzgAURMQlYkIbNzKwk9VTFNA2YnV7PBqaXGIuZWdMrK0EE8EtJiySdmsrG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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x955d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJdv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\nAWIExfvuTEU/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9cb20b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4bcc668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n5ysQg1XBQFQ2m1n+KpEIjmRdsRDArcAx2etjgF9UIAargg2tbDazylAqnclp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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaefda20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fo3fk1ujxm9WC71Bmq2j0jtwaPX6zWvA9iweByxXNrB75nsVV4nJFM2t0vkYw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\nnswAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b1bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rls3M+p8yAsTGwH7AFRExFnibiuKk7Mqi8NImIq6MiNaIaB02bFjNM2tm1l+V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\nmJlZIQcIMzMr5ABhZmaF/j/ErGoEKZdXUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99c1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bb04zs2bkBNFLfHOamTUbVzGZmVmuQhKEpP6SbpX0nKRnJf21pG0k3S9pZvZ3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\nuZwgzMwslxOEmZnlcoIwM7Nc/x/mTMDUtCKScAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9789748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gdw1Zt1x0Nmt8TgTWLRedzRpf3ncoGyzpJkmPSnpE0gcknS+pXdLc7HFUnjHU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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x995ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JUtPq1asrFLKZWd+TR4LYATgIuCIixgHrKConRUQAkfXmiLgyIhojonGPPfYo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\nZpmcIMzMLJMThJmZZXKCMDOzTP8H9Dq7uMkuePEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d4e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qzcz6nmokiP7APsClETEWeIei6qSsZJFbtImIyyKiNSJahw4dWvZgzcz6qmok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\nzCyXE4SZmeVygjAzs1xOEGZmluv/A++FQSlP4YWgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9cfd470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mNuQ1o2hoIjAr+zwLmLS+FzBpQjvnT96V9lFtCGgf1cb5k3d1RbGZWQ2KiPxm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\nZiXnRGBmVnL/HxLSYD+Bx3k0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x997beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "\n",
    "years_plot = np.array(set(result_pd[\"yearID\"])).tolist()\n",
    "print(years_plot)\n",
    "#year=2000\n",
    "for year in years_plot:\n",
    "    oaks_pd = result_pd[ (result_pd.yearID==year)]\n",
    "    plt.title(\"Salary  Vs Number of Wins for year \" + str(year))\n",
    "    plt.xlabel(\"Salary in Millions \")\n",
    "    plt.ylabel(\"Wins\")\n",
    "#print(oaks_pd)\n",
    "    plt.scatter(oaks_pd.salary/1e6,oaks_pd.W)\n",
    "#xytext= (oaks_pd['salary'][oaks_pd['teamID']==\"OAK\"]/1e6,oaks_pd['W'][oaks_pd['teamID'] == \"OAK\"])\n",
    "    plt.annotate(\"OAK\",xy= (oaks_pd['salary'][oaks_pd['teamID']==\"OAK\"]/1e6,oaks_pd['W'][oaks_pd['teamID'] == \"OAK\"]), xytext = (20, -20), \n",
    "             textcoords = 'offset points',arrowprops=dict(arrowstyle = '->'))\n",
    "    plt.show()\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "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",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here"
   ]
  },
  {
   "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",
    "---\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",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>"
      ],
      "text/plain": [
       "    Country  Region\n",
       "0   Algeria  AFRICA\n",
       "1    Angola  AFRICA\n",
       "2     Benin  AFRICA\n",
       "3  Botswana  AFRICA\n",
       "4   Burkina  AFRICA"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "import zipfile \n",
    "import os\n",
    "import shutil \n",
    "import urllib.request  \n",
    "from io import BytesIO\n",
    "from zipfile import ZipFile \n",
    "import pandas as pd\n",
    "from pandas import DataFrame \n",
    "import requests \n",
    "url = urllib.request.urlopen(\"https://raw.githubusercontent.com/buzyjess/2014_data/master/countries.csv\")\n",
    "\n",
    "countries_pd=pd.read_csv(url)\n",
    "countries_pd.head()\n"
   ]
  },
  {
   "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",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita</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>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",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abkhazia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1173.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>1548.0</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>1925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akrotiri and Dhekelia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>667.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7476.0</td>\n",
       "      <td>7977.0</td>\n",
       "      <td>8644.0</td>\n",
       "      <td>8994.0</td>\n",
       "      <td>9374.0</td>\n",
       "      <td>9640.0</td>\n",
       "      <td>9811.0</td>\n",
       "      <td>9961.0</td>\n",
       "      <td>10160.0</td>\n",
       "      <td>10620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>716.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12088.0</td>\n",
       "      <td>12289.0</td>\n",
       "      <td>12314.0</td>\n",
       "      <td>12285.0</td>\n",
       "      <td>12494.0</td>\n",
       "      <td>12606.0</td>\n",
       "      <td>12779.0</td>\n",
       "      <td>12893.0</td>\n",
       "      <td>13179.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          GDP per capita   1800   1801   1802   1803   1804   1805   1806  \\\n",
       "0               Abkhazia    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1            Afghanistan  603.0  603.0  603.0  603.0  603.0  603.0  603.0   \n",
       "2  Akrotiri and Dhekelia    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3                Albania  667.0  667.0  668.0  668.0  668.0  668.0  668.0   \n",
       "4                Algeria  716.0  716.0  717.0  718.0  719.0  720.0  721.0   \n",
       "\n",
       "    1807   1808   ...        2006     2007     2008     2009     2010  \\\n",
       "0    NaN    NaN   ...         NaN      NaN      NaN      NaN      NaN   \n",
       "1  603.0  603.0   ...      1173.0   1298.0   1311.0   1548.0   1637.0   \n",
       "2    NaN    NaN   ...         NaN      NaN      NaN      NaN      NaN   \n",
       "3  668.0  668.0   ...      7476.0   7977.0   8644.0   8994.0   9374.0   \n",
       "4  722.0  723.0   ...     12088.0  12289.0  12314.0  12285.0  12494.0   \n",
       "\n",
       "      2011     2012     2013     2014     2015  \n",
       "0      NaN      NaN      NaN      NaN      NaN  \n",
       "1   1695.0   1893.0   1884.0   1877.0   1925.0  \n",
       "2      NaN      NaN      NaN      NaN      NaN  \n",
       "3   9640.0   9811.0   9961.0  10160.0  10620.0  \n",
       "4  12606.0  12779.0  12893.0  13179.0  13434.0  \n",
       "\n",
       "[5 rows x 217 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#downloaded the file manually .URL download didnt work \n",
    "\n",
    "file_dir = r\"/home/shared/gitrepo/ML_CS109/HW1_data\"\n",
    "\n",
    "income_pd = pd.read_excel(os.path.join(file_dir,\"indicator gapminder gdp_per_capita_ppp.xlsx\"))\n",
    "\n",
    "#remove NaN from Countries\n",
    "income_pd=income_pd.drop(income_pd[income_pd[\"GDP per capita\"].isnull()].index)\n",
    "#print(income_pd.tail())\n",
    "income_pd.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "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",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Countries</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Aruba</th>\n",
       "      <th>Australia</th>\n",
       "      <th>...</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>South Sudan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>603</td>\n",
       "      <td>667</td>\n",
       "      <td>716</td>\n",
       "      <td>1197</td>\n",
       "      <td>618</td>\n",
       "      <td>757</td>\n",
       "      <td>1507</td>\n",
       "      <td>514</td>\n",
       "      <td>833</td>\n",
       "      <td>815</td>\n",
       "      <td>...</td>\n",
       "      <td>1758</td>\n",
       "      <td>502</td>\n",
       "      <td>585</td>\n",
       "      <td>682</td>\n",
       "      <td>1220</td>\n",
       "      <td>861</td>\n",
       "      <td>877</td>\n",
       "      <td>663</td>\n",
       "      <td>869</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>603</td>\n",
       "      <td>667</td>\n",
       "      <td>716</td>\n",
       "      <td>1199</td>\n",
       "      <td>620</td>\n",
       "      <td>757</td>\n",
       "      <td>1508</td>\n",
       "      <td>514</td>\n",
       "      <td>833</td>\n",
       "      <td>816</td>\n",
       "      <td>...</td>\n",
       "      <td>1758</td>\n",
       "      <td>502</td>\n",
       "      <td>586</td>\n",
       "      <td>682</td>\n",
       "      <td>1221</td>\n",
       "      <td>861</td>\n",
       "      <td>879</td>\n",
       "      <td>665</td>\n",
       "      <td>870</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>603</td>\n",
       "      <td>668</td>\n",
       "      <td>717</td>\n",
       "      <td>1201</td>\n",
       "      <td>623</td>\n",
       "      <td>757</td>\n",
       "      <td>1508</td>\n",
       "      <td>514</td>\n",
       "      <td>833</td>\n",
       "      <td>818</td>\n",
       "      <td>...</td>\n",
       "      <td>1758</td>\n",
       "      <td>502</td>\n",
       "      <td>586</td>\n",
       "      <td>682</td>\n",
       "      <td>1221</td>\n",
       "      <td>861</td>\n",
       "      <td>882</td>\n",
       "      <td>667</td>\n",
       "      <td>871</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>603</td>\n",
       "      <td>668</td>\n",
       "      <td>718</td>\n",
       "      <td>1204</td>\n",
       "      <td>626</td>\n",
       "      <td>757</td>\n",
       "      <td>1508</td>\n",
       "      <td>514</td>\n",
       "      <td>833</td>\n",
       "      <td>820</td>\n",
       "      <td>...</td>\n",
       "      <td>1759</td>\n",
       "      <td>502</td>\n",
       "      <td>586</td>\n",
       "      <td>682</td>\n",
       "      <td>1221</td>\n",
       "      <td>861</td>\n",
       "      <td>884</td>\n",
       "      <td>668</td>\n",
       "      <td>872</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>603</td>\n",
       "      <td>668</td>\n",
       "      <td>719</td>\n",
       "      <td>1206</td>\n",
       "      <td>628</td>\n",
       "      <td>757</td>\n",
       "      <td>1508</td>\n",
       "      <td>514</td>\n",
       "      <td>833</td>\n",
       "      <td>822</td>\n",
       "      <td>...</td>\n",
       "      <td>1759</td>\n",
       "      <td>503</td>\n",
       "      <td>586</td>\n",
       "      <td>682</td>\n",
       "      <td>1221</td>\n",
       "      <td>861</td>\n",
       "      <td>887</td>\n",
       "      <td>670</td>\n",
       "      <td>873</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Countries Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda  \\\n",
       "1800              603     667     716    1197    618                 757   \n",
       "1801              603     667     716    1199    620                 757   \n",
       "1802              603     668     717    1201    623                 757   \n",
       "1803              603     668     718    1204    626                 757   \n",
       "1804              603     668     719    1206    628                 757   \n",
       "\n",
       "Countries Argentina Armenia Aruba Australia     ...     Uruguay Uzbekistan  \\\n",
       "1800           1507     514   833       815     ...        1758        502   \n",
       "1801           1508     514   833       816     ...        1758        502   \n",
       "1802           1508     514   833       818     ...        1758        502   \n",
       "1803           1508     514   833       820     ...        1759        502   \n",
       "1804           1508     514   833       822     ...        1759        503   \n",
       "\n",
       "Countries Vanuatu Venezuela West Bank and Gaza Vietnam Yemen Zambia Zimbabwe  \\\n",
       "1800          585       682               1220     861   877    663      869   \n",
       "1801          586       682               1221     861   879    665      870   \n",
       "1802          586       682               1221     861   882    667      871   \n",
       "1803          586       682               1221     861   884    668      872   \n",
       "1804          586       682               1221     861   887    670      873   \n",
       "\n",
       "Countries South Sudan  \n",
       "1800              507  \n",
       "1801              507  \n",
       "1802              507  \n",
       "1803              507  \n",
       "1804              508  \n",
       "\n",
       "[5 rows x 203 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "income_pd_tr= income_pd.transpose()\n",
    "header = income_pd_tr.iloc[0]\n",
    "header.name=\"Countries\"\n",
    "\n",
    "\n",
    "#header=header.drop(header[header.isnull()].index)\n",
    "#print(header[header.isnull()])\n",
    "#print(header.head())\n",
    "\n",
    "income_pd_tr=income_pd_tr.iloc[1:]\n",
    "#income_pd_tr.shape\n",
    "\n",
    "rows = np.array(income_pd_tr.index)\n",
    "\n",
    "rows =  rows[0:]\n",
    "\n",
    "#print(header.head())\n",
    "#print(incomde_pd_tr2.head())\n",
    "#print(rows)\n",
    "\n",
    "\n",
    "income_pd_tr.set_axis(0,rows)\n",
    "income_pd_tr.set_axis(1,header)\n",
    "income_pd_tr.head()\n",
    "\n",
    "\n",
    "#income_pd_tr[:].isnull()\n",
    "\n",
    "#Copy the Countries with onl NaN rows. \n",
    "NanCountries=income_pd_tr.loc[:,income_pd_tr.apply(lambda x: sum(x.isnull()),axis=0)==216]\n",
    "#NanCountries.head()\n",
    "\n",
    "\n",
    "income_clean = income_pd_tr.drop(NanCountries.columns,axis =1)\n",
    "income_clean.head()\n",
    "\n"
   ]
  },
  {
   "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",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2000 2001 2002 2003 2004 2005 2006 2007 2008 2009]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ghu1VZfpqYMZwC0qaB8wD2GOPPcYQZkREjEVHiUDSacBTgKuBDaXYQLtE8ALbt0t6ArBE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\n8x0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83603ac6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EGRERY9FRIpB0MvB44HJgQyk20C4RPNv2LZIeAyyRdG3rRNuW5OEWLEljEUB/f/+w80RE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83600a0748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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khZJ+Lmm5pJdI+kSJ/SequkB52HoHImkPlT74Vb0D4aSyjt9Jek7LvjurrHeZ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\nBBERDZdEEBHRcEkEEREN97/P5ZeZuJ0TmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8360204c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835c0f1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0SkknlNh/qKoLlEetdzCS9lDpg1/VOxBOLuv4taQXtuy7c8p6l0k6oZRPKPvy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\nRDRcEkFERMP9LwOIp8odmvBiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83601f3208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E7a93WMbAhZI2AF+0vRCYbntVmb4amD7UgpLmAfMAZs6cOYowIyJiNLpKBJJOBZ4EXAFs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\nCCIiWu7/AXVIoGcKl/R5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835bfe0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BRETPjOZVlU8D9izLHCIJ26e2WcbAxZI2AF+yvQCYZntFmb4SmDbUgpLmAnMBZsyYMYow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\nkggiIhouiSAiouGSCCIiGi6JICKi4f4/vSejlqBl1L4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835bde0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LlMesdyiS9lbpg1/VOxBOLev4maQX1/bduWW9yyV9pJRPKvvy2rLMXw23nRg/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\nC38ooMYMHm2hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835bcd3f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7fvFdKqm3ZZ9+osTwa0mTB5h/rqT5kn4uaamkl0v6ZIn9J6q6QHnEegciaS+V\nPvhVvQPh1LKO30p6Qcu+O7esd4mkT5bycWVfXluWeddg24nRI4kgOqaqO+QXA9dIehLwauAw2wcB\n64DXlVknAJfZPtD2L1qW3xHYwfbNg2zii7afbvsAYFuq/lz6bFe28zbgm60L2f4V1SP+/2T7INs3\nAeeWdR1I1TXDCR1UcQrVE+LHAH1f+C+m6vztmWVdnyzzfht4v+2nAtcAH2lZz0O2e4GvUnUn8Hbg\nAOB4Sbu02XeDmQD8usTwM+Atg8z3eOCFVInxdOAS208BHqDqXny43k7VT9xTgNcC8yVtU6YdVOrx\nFODVkh5Xyna3fUBZ5tSN2GaMsPHdDiDGhG0lXVWGf07Vp9Js4BDginLgvi0bOhJbR9X53nC9QNL7\nqPrenwQsBn5Qpp0JVR/xknZUy5uhBnFAOQKfCGwP/GcH2/++7fXAdS1H3EcAp7r0K2P7z6redzDR\n9k/LPPOB77Wsp69PrGuAxS7dL0u6mapjsucw+L4bzEPAD8vwlcCRg8z3Y9trJV1D9XKnn7TEsleb\nbQzkOcDJALZvkLQU2LdMW2j7HgBJ1wF7Uv3O9pF0MvAj4KKN2GaMsCSC6MQD5cj1r8ppm/m2PzDA\n/P/Tel2gT7kmsEbSPv1bBeUo88tU56tvlTQX2KZ18f6raxPzt4CZtq9W1Vvk4W3mB3iwNaQO5m+3\nnvX91rme6n9uqH03mLXecEFvHYP/7z4IYHu9pNZl1g+xzMZqrds6qjd3/UXSgcCLgLcCx1H12x+j\nWE4NxcZaCLxS0mPgr++k3bOD5f4V+FI5TYSk7VXdNdT3pX+nqnc6vLLfcq8u8z+H6iUt9/Sbfi/V\nK0L77AAsK+fF2512GcoC4E191xgkTSrb/ouk55Z53gD8dLAVDGBj9103/Jyy/yTtC0yl6lRtQKru\nQHqU7XOAD1N1/x2jXFoEsVFsXyfpw8BFkh5F1UPj24GlbRb9CtWpmiskrS3Lfcb23ZK+TtVL43Kq\nbshb/Y+k3wJbMfAR5neBr0t6B1US+WeqN8StKp87DLBMW7Z/IukgYJGkh4ALqS54zwK+WhLEzcCb\nhrHOjd133fBl4CvlVNPDwPG2H9Tg71/fnerNcH0HmcNp9USX5PbRGPUkXQq81/aibscSsSXKqaGI\niIZLiyAiouHSIoiIaLgkgoiIhksiiIhouCSCiIiGSyKIiGi4/wVgC5vrx6YjEQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835bd7e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gl7LN/pKwfWqHbQxcJGkd8Hnbi4CZtleU5SuBmcNtKGk+MB9gp512GkOYERExFl0lAklf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\nES2XRBAR0XJJBBERLfe/90yhDMnTkWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835bd8ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\nplt.subplot(1,2,2) \\n\\n\\n\\ny=plt.hist((np.array(year_data))  ,log=True,align=\\'mid\\',bins=50)\\n\\nplt.xlabel(\"Per Capita income in millions\")\\nplt.ylabel(\"Frequency\")\\nplt.title(\" differrent Countries for \" + str(selection_year))\\nplt.axis([0,1300,0,90])\\nplt.show()\\n\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "years = np.arange(2000,2010)\n",
    "print(years)\n",
    "i=0\n",
    "for year in years:\n",
    "    i=i+1\n",
    "    selection_year=year\n",
    "    year_data=(income_clean.loc[[selection_year]]/100).transpose()\n",
    "    year_data.index = list(range(len(year_data)))\n",
    "\n",
    "\n",
    "#plot the graph\n",
    "\n",
    "#y=plt.hist(np.array(year_data) ,bins=20,log=True,align='right',alpha=0.50,facecolor=\"b\")\n",
    "    #f, axarr =plt.subplot(5,5)\n",
    "    #print(ax)\n",
    "    #plt.subplot(5,5,i) \n",
    "\n",
    "    plt.hist(np.array(year_data) ,bins=20,align='right',alpha=0.50,facecolor=\"b\")\n",
    "\n",
    "    plt.xlabel(\"Per Capita income in millions\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.title(\" differrent Countries for \" + str(selection_year))\n",
    "    plt.axis([0,1350,0,90])\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\"\"\"\n",
    "plt.subplot(1,2,2) \n",
    "\n",
    "\n",
    "\n",
    "y=plt.hist((np.array(year_data))  ,log=True,align='mid',bins=50)\n",
    "\n",
    "plt.xlabel(\"Per Capita income in millions\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\" differrent Countries for \" + str(selection_year))\n",
    "plt.axis([0,1300,0,90])\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "#print(y)\n",
    "#plt.hist()\n",
    "\n",
    "#your code here"
   ]
  },
  {
   "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",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         PCI               Countries         Region\n",
      "0     1695.0             Afghanistan           ASIA\n",
      "1     9640.0                 Albania         EUROPE\n",
      "2    12606.0                 Algeria         AFRICA\n",
      "3    41958.0                 Andorra         EUROPE\n",
      "4     7094.0                  Angola         AFRICA\n",
      "5    19988.0     Antigua and Barbuda  NORTH AMERICA\n",
      "6    16972.0               Argentina  SOUTH AMERICA\n",
      "7     6812.0                 Armenia         EUROPE\n",
      "9    41706.0               Australia        OCEANIA\n",
      "10   44029.0                 Austria         EUROPE\n",
      "11   15754.0              Azerbaijan         EUROPE\n",
      "12   22689.0                 Bahamas  NORTH AMERICA\n",
      "13   40083.0                 Bahrain           ASIA\n",
      "14    2589.0              Bangladesh           ASIA\n",
      "15   15336.0                Barbados  NORTH AMERICA\n",
      "16   16603.0                 Belarus         EUROPE\n",
      "17   40946.0                 Belgium         EUROPE\n",
      "18    8178.0                  Belize  NORTH AMERICA\n",
      "19    1643.0                   Benin         AFRICA\n",
      "21    6909.0                  Bhutan           ASIA\n",
      "22    5462.0                 Bolivia  SOUTH AMERICA\n",
      "23    9248.0  Bosnia and Herzegovina         EUROPE\n",
      "24   14341.0                Botswana         AFRICA\n",
      "25   15101.0                  Brazil  SOUTH AMERICA\n",
      "26   71991.0                  Brunei           ASIA\n",
      "27   15278.0                Bulgaria         EUROPE\n",
      "29     731.0                 Burundi         AFRICA\n",
      "30    2646.0                Cambodia           ASIA\n",
      "31    2610.0                Cameroon         AFRICA\n",
      "32   41567.0                  Canada  NORTH AMERICA\n",
      "..       ...                     ...            ...\n",
      "169   3524.0                   Sudan         AFRICA\n",
      "170  14942.0                Suriname  SOUTH AMERICA\n",
      "171   5846.0               Swaziland         AFRICA\n",
      "172  43709.0                  Sweden         EUROPE\n",
      "173  54551.0             Switzerland         EUROPE\n",
      "174   6358.0                   Syria           ASIA\n",
      "176   2212.0              Tajikistan           ASIA\n",
      "177   2243.0                Tanzania         AFRICA\n",
      "178  12798.0                Thailand           ASIA\n",
      "180   1273.0                    Togo         AFRICA\n",
      "181   5023.0                   Tonga        OCEANIA\n",
      "182  28743.0     Trinidad and Tobago  NORTH AMERICA\n",
      "183  10235.0                 Tunisia         AFRICA\n",
      "184  17908.0                  Turkey           ASIA\n",
      "185  11361.0            Turkmenistan           ASIA\n",
      "187   3488.0                  Tuvalu        OCEANIA\n",
      "188   1607.0                  Uganda         AFRICA\n",
      "189   8282.0                 Ukraine         EUROPE\n",
      "190  56192.0    United Arab Emirates           ASIA\n",
      "191  36549.0          United Kingdom         EUROPE\n",
      "192  49781.0           United States  NORTH AMERICA\n",
      "193  17916.0                 Uruguay  SOUTH AMERICA\n",
      "194   4412.0              Uzbekistan           ASIA\n",
      "195   2916.0                 Vanuatu        OCEANIA\n",
      "196  16960.0               Venezuela  SOUTH AMERICA\n",
      "198   4717.0                 Vietnam           ASIA\n",
      "199   3761.0                   Yemen           ASIA\n",
      "200   3557.0                  Zambia         AFRICA\n",
      "201   1626.0                Zimbabwe         AFRICA\n",
      "202   3505.0             South Sudan         AFRICA\n",
      "\n",
      "[175 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\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",
    "\n",
    "year=2010\n",
    "def mergeByYear(year):\n",
    "    year_sp=income_clean.loc[[year]].transpose()\n",
    "    year_sp['Countries']= income_clean.loc[[year]].transpose().index\n",
    "##year_sp.reindex_axis(['PGDP',\"Countries\",\"Country\",\"Region\"], axis=\"columns\")\n",
    "\n",
    "#print year_sp.count() --203\n",
    "\n",
    "    merged_pd = year_sp.merge(countries_pd,left_on=year_sp.Countries, right_on=countries_pd.Country,how='left')\n",
    "\n",
    "    merged_pd_Region_Nan = merged_pd[merged_pd.Region.isnull()]\n",
    "#merged_pd_Region_Nan.count() -- 28 \n",
    "#total number of countries in Countries_pd =194. The numbers dont add because some of the countries which have Nan \n",
    "#for Per Capita is dropped\n",
    "\n",
    "    merged_pd = merged_pd[~merged_pd.Region.isnull()]\n",
    "    merged_pd.reindex()\n",
    "    merged_pd=merged_pd.loc[:,[year,'Countries','Region']]\n",
    "    merged_pd=merged_pd.rename(columns={year:\"PCI\"})\n",
    "    merged_pd.PCI=merged_pd.PCI.astype(float)\n",
    "    return merged_pd\n",
    "\n",
    "print(mergeByYear(2011))\n",
    "\n",
    "\n",
    "\n",
    "                          \n",
    "\n"
   ]
  },
  {
   "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",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAADDCakAAAAAGE5IBQAAAMBwQioAAAAAhhNSAQAAADDc8aMLAABg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\n61W1fZK/pFVVSUOhqq5J8g7aur6v273vRcDmSZa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835def6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "\n",
    "year=2000\n",
    "merged_year = mergeByYear(year)\n",
    "regions = merged_year.Region.unique()\n",
    "#there was error when ploating South America data\n",
    "#regions=np.delete(regions,4)\n",
    "count =np.count_nonzero(regions)\n",
    "#print(merged_year.loc[merged_year[\"Region\"]==\"EUROPE\"])\n",
    "plt.close()\n",
    "data = []\n",
    "ylabel= []\n",
    "fig, ax1 = plt.subplots(figsize=(20, 10))\n",
    "for region in regions:\n",
    "    op=merged_year.loc[merged_year[\"Region\"]==region]\n",
    "    #print(op.loc[:,[2012]].dtypes)\n",
    "    #print(op.loc[:,[2012]])\n",
    "    \n",
    "    data.append(op['PCI'].tolist())\n",
    "    ylabel.append(region)\n",
    "#plt.boxplot(data)\n",
    "bp = plt.boxplot(data, notch=0, vert=1, whis=1.5)\n",
    "plt.xlabel('Distribution')\n",
    "plt.ylabel('Value')\n",
    "plt.setp(bp['fliers'],  marker='+')\n",
    "xtickNames = plt.setp(ax1, xticklabels=ylabel)\n",
    "plt.setp(xtickNames, rotation=45, fontsize=8)\n",
    "#plt.setp(xtickNames, rotation=45, fontsize=8)\n",
    "\n",
    "plt.title(\"Distribution for the year \" +str(year) + \" across differrent regions\" )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#grouped_income_pd=merged_year.groupby('Region')\n",
    "#grouped_income_pd[2011].sum().to_frame().reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion for Problem 2\n",
    "\n",
    "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
    "\n",
    "---\n"
   ]
  },
  {
   "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",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here"
   ]
  },
  {
   "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",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average income of differrent Regions\n",
      "_____________________________________\n",
      " \n",
      "          Region           PCI\n",
      "1           ASIA  23500.432432\n",
      "5  SOUTH AMERICA  13015.750000\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "year=2012\n",
    "mergedbyYear_3b= mergeByYear(year)\n",
    "\n",
    "mergedbyYear_3b = mergedbyYear_3b.groupby(\"Region\")\n",
    "income_3b_mean = mergedbyYear_3b.PCI.mean().to_frame().reset_index()\n",
    "print('Average income of differrent Regions')\n",
    "print('_____________________________________')\n",
    "print(' ')\n",
    "print(income_3b_mean.loc[(income_3b_mean.Region==\"ASIA\") | (income_3b_mean.Region==\"SOUTH AMERICA\")])\n",
    "\n",
    "\n"
   ]
  },
  {
   "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",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of countries with PCI value > $10,000 is 0.5371428571428571\n",
      " \n",
      "Regions with higher PCI\n",
      "_______________________\n",
      " \n",
      "Region\n",
      "AFRICA           10\n",
      "ASIA             21\n",
      "EUROPE           37\n",
      "NORTH AMERICA    13\n",
      "OCEANIA           4\n",
      "SOUTH AMERICA     9\n",
      "Name: Countries, dtype: int64\n",
      "Regions with higher PCI\n",
      "_______________________\n",
      " \n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "\n",
    "\n",
    "data_3c=mergeByYear(2012)\n",
    "countries_PCI=data_3c.loc[data_3c.PCI>10000]\n",
    "per_countries_PCI= (len(countries_PCI))/len(data_3c)\n",
    "print(\"Proportion of countries with PCI value > $10,000 is \" + str(per_countries_PCI))\n",
    "print(\" \")\n",
    "print(\"Regions with higher PCI\")\n",
    "print(\"_______________________\")\n",
    "print(\" \")\n",
    "print((countries_PCI.groupby(\"Region\").count().Countries))\n",
    "\n",
    "\n",
    "print(\"Regions with higher PCI\")\n",
    "print(\"_______________________\")\n",
    "print(\" \")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "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",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion for Problem 3\n",
    "\n",
    "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
    "\n",
    "---\n"
   ]
  }
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