{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gapminder Data Analysis\n",
    "\n",
    "The dataset used for this notebook was obtained from [gapminder.org](http://www.gapminder.org/data/) with the following information:\n",
    "\n",
    "* Aged 15+ Employment Rate (%)\n",
    "* Life Expectancy (years)\n",
    "* GDP/capita (US$, inflation adjusted)\n",
    "* Primary school completion (% of boys)\n",
    "* Primary school completion (% of girls)\n",
    "\n",
    "###### Summary \n",
    "* 1) Import and clean data\n",
    "    * import data \n",
    "    * worldwide data\n",
    "    * canadian data\n",
    "   \n",
    "* 2) Explore Data\n",
    "    * summary statistics canada vs worldwide\n",
    "        * employment rate \n",
    "        * life expectancy \n",
    "        * GDP\n",
    "        * male school completion\n",
    "        * female school completion \n",
    "    * correlations canada vs worldwide\n",
    "        * life expectancy and GDP \n",
    "        * gender and school completion \n",
    "    * compare canada and worldwide data \n",
    "        * standardizing the data point\n",
    "        * time series graphs\n",
    "  \n",
    "* 3) Conclusions\n",
    "\n",
    "# 1) Import and Clean Data\n",
    "\n",
    "###### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "\n",
    "# Read worldwide data into Pandas DataFrames with index column as country \n",
    "employment = pd.read_csv('employment_above_15.csv', index_col='Country')\n",
    "female_completion = pd.read_csv('female_completion_rate.csv', index_col='Country')\n",
    "male_completion = pd.read_csv('male_completion_rate.csv', index_col='Country')\n",
    "life_expectancy = pd.read_csv('life_expectancy.csv', index_col='Country')\n",
    "gdp = pd.read_csv('gdp_per_capita.csv', index_col='Country')\n",
    "\n",
    "# Data specific to index Canada:\n",
    "# Use .loc to look up values in pd series by index and .iloc for position \n",
    "employment_ca = employment.loc['Canada'].round(2)\n",
    "# Drop NAN from the male/female data series \n",
    "female_completion_ca = female_completion.loc['Canada'].dropna().round(2)\n",
    "male_completion_ca = male_completion.loc['Canada'].dropna().round(2)\n",
    "life_expectancy_ca = life_expectancy.loc['Canada'].round(2)\n",
    "gdp_ca = gdp.loc['Canada'].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Worldwide Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Afghanistan' 'Albania' 'Algeria' 'Angola' 'Argentina' 'Armenia'\n",
      " 'Australia' 'Austria' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Bangladesh'\n",
      " 'Barbados' 'Belarus' 'Belgium' 'Belize' 'Benin' 'Bhutan' 'Bolivia'\n",
      " 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei' 'Bulgaria'\n",
      " 'Burkina Faso' 'Burundi' 'Cambodia' 'Cameroon' 'Canada' 'Cape Verde'\n",
      " 'Central African Rep.' 'Chad' 'Chile' 'China' 'Colombia' 'Comoros'\n",
      " 'Congo, Rep.' 'Congo, Dem. Rep.' 'Costa Rica' \"Cote d'Ivoire\" 'Croatia'\n",
      " 'Cuba' 'Cyprus' 'Czech Rep.' 'Denmark' 'Dominican Rep.' 'Timor-Leste'\n",
      " 'Ecuador' 'Egypt' 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Estonia'\n",
      " 'Ethiopia' 'Fiji' 'Finland' 'France' 'Gabon' 'Gambia' 'Georgia' 'Germany'\n",
      " 'Ghana' 'Greece' 'Guadeloupe' 'Guatemala' 'Guinea' 'Guinea-Bissau'\n",
      " 'Guyana' 'Haiti' 'Honduras' 'Hong Kong, China' 'Hungary' 'Iceland' 'India'\n",
      " 'Indonesia' 'Iran' 'Iraq' 'Ireland' 'Israel' 'Italy' 'Jamaica' 'Japan'\n",
      " 'Jordan' 'Kazakhstan' 'Kenya' 'Korea, Dem. Rep.' 'Korea, Rep.' 'Kuwait'\n",
      " 'Kyrgyzstan' 'Laos' 'Latvia' 'Lebanon' 'Lesotho' 'Liberia' 'Libya'\n",
      " 'Lithuania' 'Luxembourg' 'Macao, China' 'Madagascar' 'Malawi' 'Malaysia'\n",
      " 'Maldives' 'Mali' 'Malta' 'Martinique' 'Mauritania' 'Mauritius' 'Mexico'\n",
      " 'Mongolia' 'Morocco' 'Mozambique' 'Myanmar' 'Namibia' 'Nepal'\n",
      " 'Netherlands' 'Netherlands Antilles' 'New Zealand' 'Nicaragua' 'Niger'\n",
      " 'Nigeria' 'Norway' 'Oman' 'Pakistan' 'Panama' 'Papua New Guinea'\n",
      " 'Paraguay' 'Peru' 'Philippines' 'Poland' 'Portugal' 'Puerto Rico' 'Qatar'\n",
      " 'Moldova' 'Reunion' 'Romania' 'Russia' 'Rwanda' 'Saudi Arabia' 'Senegal'\n",
      " 'Serbia and Montenegro' 'Sierra Leone' 'Singapore' 'Slovak Republic'\n",
      " 'Slovenia' 'Solomon Islands' 'Somalia' 'South Africa' 'Spain' 'Sri Lanka'\n",
      " 'Sudan' 'Suriname' 'Swaziland' 'Sweden' 'Switzerland' 'Syria' 'Taiwan'\n",
      " 'Tajikistan' 'Tanzania' 'Thailand' 'Macedonia, FYR' 'Togo'\n",
      " 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Turkmenistan' 'Uganda' 'Ukraine'\n",
      " 'United Arab Emirates' 'United Kingdom' 'United States' 'Uruguay'\n",
      " 'Uzbekistan' 'Venezuela' 'Vietnam' 'West Bank and Gaza' 'Yemen, Rep.'\n",
      " 'Zambia' 'Zimbabwe']\n",
      "Number of countries in dataset:  178\n"
     ]
    }
   ],
   "source": [
    "# Print all countries avaliable in worldwide data \n",
    "print employment.index.values\n",
    "print \"Number of countries in dataset: \",len(employment.index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this data analysis, we will be using data from 178 countries to represent \"worldwide\" trends which should be sufficent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Canadian Data\n",
    "\n",
    "When we were cleaning the data, we removed all the NAN values from male and female school completion rates which left us with only 4 data points for Canada. All floating point data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1991' '1992' '1993' '1994' '1995' '1996' '1997' '1998' '1999' '2000'\n",
      " '2001' '2002' '2003' '2004' '2005' '2006' '2007']\n",
      "Employment data points for Canada: 17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print employment_ca.index.values\n",
    "print \"Employment data points for Canada:\", len(employment_ca.index.values)\n",
    "employment_ca.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 94.94  96.47  97.74  95.51]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print female_completion_ca.values\n",
    "female_completion_ca.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 94.18  95.54  97.33  95.98]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print male_completion_ca.values\n",
    "male_completion_ca.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 39.    39.    39.    39.    39.01  39.01  39.01  39.01  39.01  39.01\n",
      "  39.01  39.02  39.02  39.02  39.02  39.02  39.02  39.03  39.03  39.03\n",
      "  39.03  39.03  39.03  39.03  39.04  39.04  39.04  39.04  39.04  39.04\n",
      "  39.04  39.05  39.17  39.3   39.43  39.55  39.68  39.81  39.94  40.06\n",
      "  40.19  40.32  40.39  40.47  40.54  40.61  40.69  40.76  40.84  40.91\n",
      "  40.99  41.06  41.11  41.17  41.22  41.27  41.33  41.38  41.43  41.49\n",
      "  41.54  41.6   41.69  41.79  41.88  41.98  42.08  42.17  42.27  42.36\n",
      "  42.46  42.56  42.77  42.99  43.21  43.43  43.64  43.86  44.08  44.3\n",
      "  44.51  44.73  44.77  44.82  44.86  44.91  44.95  45.    45.04  45.09\n",
      "  45.13  45.17  45.52  45.87  46.21  46.56  46.9   47.25  47.6   47.94\n",
      "  48.29  48.63  49.02  49.41  49.8   50.18  50.57  50.96  51.35  51.74\n",
      "  52.12  52.51  52.96  53.41  53.86  54.31  54.77  55.22  47.17  56.12\n",
      "  56.57  57.02  57.02  57.02  58.83  59.31  57.99  58.68  58.57  57.97\n",
      "  58.95  60.36  61.43  62.36  62.77  62.49  62.72  61.35  63.31  63.74\n",
      "  64.01  63.76  64.7   64.61  65.35  66.33  66.53  66.78  67.33  67.65\n",
      "  68.28  68.55  68.75  69.13  69.99  70.05  70.04  69.96  70.62  70.66\n",
      "  71.04  71.27  71.3   71.31  71.69  71.79  71.92  72.13  72.29  72.45\n",
      "  72.65  72.98  72.88  73.11  73.19  73.49  73.92  74.21  74.54  74.9\n",
      "  75.14  77.4   77.6   77.7   77.8   77.9   78.    78.3   78.7   78.9   79.1\n",
      "  79.3   79.5   79.7   79.9   80.1   80.3   80.5   80.6   80.7   80.9   81.1\n",
      "  81.3   81.4   81.5 ]\n",
      "Life expectancy data points for Canada: 205\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print life_expectancy_ca.values\n",
    "print \"Life expectancy data points for Canada:\", len(life_expectancy_ca.index.values)\n",
    "life_expectancy_ca.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  9374.88   9479.82   9967.37  10290.36  10774.65  11283.61  11804.67\n",
      "  11932.17  12362.93  12837.47  12986.31  13320.19  13823.29  14537.59\n",
      "  14806.31  14793.71  15358.41  15703.9   16161.77  16610.17  16751.34\n",
      "  17124.39  16435.47  16713.7   17516.24  18183.73  18437.71  18971.41\n",
      "  19659.67  19817.99  19561.88  18893.8   18826.66  19057.23  19781.71\n",
      "  20169.54  20276.45  20911.11  21580.32  22586.44  23559.5   23738.9\n",
      "  24214.93  24425.88  24936.83  25437.65  25943.74  26229.74  26101.76\n",
      "  25069.87  25575.22  25933.29]\n",
      "GDP data points for Canada: 52\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print gdp_ca.values\n",
    "print \"GDP data points for Canada:\", len(gdp_ca.index.values)\n",
    "gdp_ca.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Data \n",
    "\n",
    "#### Summary Statistics Canada vs Worldwide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Employment CA: Mean: 60.1470588235\n",
      "Standard deviation: 1.91086290948\n",
      "Max: 63.5\n",
      "Sum: 1022.5\n",
      "None\n",
      "Female school completion rates CA: Mean: 96.165\n",
      "Standard deviation: 1.22519386221\n",
      "Max: 97.74\n",
      "Sum: 384.66\n",
      "None\n",
      "Male school completion rates CA: Mean: 95.7575\n",
      "Standard deviation: 1.2984702538\n",
      "Max: 97.33\n",
      "Sum: 383.03\n",
      "None\n",
      "Life expectancy CA: Mean: 54.486195122\n",
      "Standard deviation: 14.7185819983\n",
      "Max: 81.5\n",
      "Sum: 11169.67\n",
      "None\n",
      "GDP CA: Mean: 18089.1092308\n",
      "Standard deviation: 5057.09836739\n",
      "Max: 26229.74\n",
      "Sum: 940633.68\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' \\nprint \"Employment worldwide:\", sum_stats(employment)\\nprint \"Female school completion rates worldwide:\", sum_stats(female_completion)\\nprint \"Male school completion rates worldwide:\", sum_stats(male_completion)\\nprint \"Life expectancy worldwide:\", sum_stats(life_expectancy)\\nprint \"GDP worldwide:\", sum_stats(gdp)\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum_stats(index):\n",
    "    sum_mean = index.mean()\n",
    "    sum_std = index.std()\n",
    "    sum_max = index.max()\n",
    "    sum_sum = index.sum()\n",
    "    print \"Mean:\", sum_mean\n",
    "    print \"Standard deviation:\", sum_std\n",
    "    print \"Max:\", sum_max\n",
    "    print \"Sum:\", sum_sum\n",
    "\n",
    "# Canada \n",
    "print \"Employment CA:\", sum_stats(employment_ca)\n",
    "print \"Female school completion rates CA:\", sum_stats(female_completion_ca)\n",
    "print \"Male school completion rates CA:\", sum_stats(male_completion_ca)\n",
    "print \"Life expectancy CA:\", sum_stats(life_expectancy_ca)\n",
    "print \"GDP CA:\", sum_stats(gdp_ca)\n",
    "\n",
    "# Worldwide \n",
    "''' \n",
    "print \"Employment worldwide:\", sum_stats(employment)\n",
    "print \"Female school completion rates worldwide:\", sum_stats(female_completion)\n",
    "print \"Male school completion rates worldwide:\", sum_stats(male_completion)\n",
    "print \"Life expectancy worldwide:\", sum_stats(life_expectancy)\n",
    "print \"GDP worldwide:\", sum_stats(gdp)\n",
    "''' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Worldwide: The countries with the highest and lowest employment rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instead of using data from all 178 countries, we will sample the first 20 countries\n",
    "\n",
    "countries = ['Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda',\n",
    "             'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan',\n",
    "             'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n",
    "             'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia']\n",
    "\n",
    "employment_values = [\n",
    "    55.70000076,  51.40000153,  50.5       ,  75.69999695,\n",
    "    58.40000153,  40.09999847,  61.5       ,  57.09999847,\n",
    "    60.90000153,  66.59999847,  60.40000153,  68.09999847,\n",
    "    66.90000153,  53.40000153,  48.59999847,  56.79999924,\n",
    "    71.59999847,  58.40000153,  70.40000153,  41.20000076,\n",
    "]\n",
    "\n",
    "life_expectancy_values = [74.7,  75. ,  83.4,  57.6,  74.6,  75.4,  72.3,  81.5,  80.2,\n",
    "                          70.3,  72.1,  76.4,  68.1,  75.2,  69.8,  79.4,  70.8,  62.7,\n",
    "                          67.3,  70.6]\n",
    "\n",
    "gdp_values = [ 1681.61390973,   2155.48523109,  21495.80508273,    562.98768478,\n",
    "              13495.1274663 ,   9388.68852258,   1424.19056199,  24765.54890176,\n",
    "              27036.48733192,   1945.63754911,  21721.61840978,  13373.21993972,\n",
    "                483.97086804,   9783.98417323,   2253.46411147,  25034.66692293,\n",
    "               3680.91642923,    366.04496652,   1175.92638695,   1132.21387981]\n",
    "\n",
    "female_completion = np.array([\n",
    "    97.35583,  104.62379,  103.02998,   95.14321,  103.69019,\n",
    "    98.49185,  100.88828,   95.43974,   92.11484,   91.54804,\n",
    "    95.98029,   98.22902,   96.12179,  119.28105,   97.84627,\n",
    "    29.07386,   38.41644,   90.70509,   51.7478 ,   95.45072\n",
    "])\n",
    "\n",
    "male_completion = np.array([\n",
    "     95.47622,  100.66476,   99.7926 ,   91.48936,  103.22096,\n",
    "     97.80458,  103.81398,   88.11736,   93.55611,   87.76347,\n",
    "    102.45714,   98.73953,   92.22388,  115.3892 ,   98.70502,\n",
    "     37.00692,   45.39401,   91.22084,   62.42028,   90.66958\n",
    "])\n",
    "\n",
    "\n",
    "employment = pd.Series(employment_values, index=countries)\n",
    "life_expectancy = pd.Series(life_expectancy_values, index=countries)\n",
    "gdp_completion = pd.Series(gdp_values, index=countries)\n",
    "female_completion = pd.Series(female_completion, index=countries)\n",
    "male_completion = pd.Series(male_completion, index=countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Angola', 75.699996949999999) ('Argentina', 40.099998470000003)\n",
      "('Andorra', 83.400000000000006) ('Angola', 57.600000000000001)\n",
      "('Austria', 27036.487331920001) ('Benin', 366.04496652)\n"
     ]
    }
   ],
   "source": [
    "def max_variable(variable):\n",
    "    max_country = variable.argmax()           # argmax returns index of maximum value \n",
    "    max_value = variable.loc[max_country]     # get index value\n",
    "    return (max_country, max_value)\n",
    "\n",
    "def min_variable(variable):\n",
    "    min_country = variable.argmin()\n",
    "    min_value = variable.loc[min_country]\n",
    "    return (min_country, min_value)\n",
    "\n",
    "print max_variable(employment), min_variable(employment)\n",
    "print max_variable(life_expectancy), min_variable(life_expectancy)\n",
    "print max_variable(gdp_completion), min_variable(gdp_completion)\n",
    "# print max_variable(female_completion), min_variable(female_completion)\n",
    "# print max_variable(male_completion), min_variable(male_completion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender and School Completion \n",
    "The overall school completion rate in each country and how it relates to gender.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Barbados', 119.28104999999999) ('Belgium', 29.07386)\n",
      "('Barbados', 115.3892) ('Belgium', 37.006920000000001)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Albania                 96.42\n",
       "Algeria                102.64\n",
       "Andorra                101.41\n",
       "Angola                  93.32\n",
       "Antigua and Barbuda    103.46\n",
       "Argentina               98.15\n",
       "Armenia                102.35\n",
       "Australia               91.78\n",
       "Austria                 92.84\n",
       "Azerbaijan              89.66\n",
       "Bahamas                 99.22\n",
       "Bahrain                 98.48\n",
       "Bangladesh              94.17\n",
       "Barbados               117.34\n",
       "Belarus                 98.28\n",
       "Belgium                 33.04\n",
       "Belize                  41.91\n",
       "Benin                   90.96\n",
       "Bhutan                  57.08\n",
       "Bolivia                 93.06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print max_variable(female_completion), min_variable(female_completion)\n",
    "print max_variable(male_completion), min_variable(male_completion)\n",
    "\n",
    "# Calculate the overall completion rate assuming 50/50 gender ratio \n",
    "def overall_completion_rate(female_completion, male_completion):\n",
    "    return (female_completion + male_completion)/2\n",
    "\n",
    "overall_completion_rate(female_completion, male_completion).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing Data \n",
    "\n",
    "We can standardize data for each country by comparing the single data point to the rest of the data points (first 20 countries). Specifically, we look at how a variable in Canada compares to other countries. We can convert each data point to the number of standard deviations from the mean. A positive value represents that a data point is above the mean, and a negative value represents that data point below the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function takes in values array and standardizes the values\n",
    "def standardize_data(values):\n",
    "    standardized_values = (values - values.mean()) / values.std()\n",
    "    return standardized_values  # Outputs a numpy array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albania               -0.311559\n",
      "Algeria               -0.760370\n",
      "Andorra               -0.854307\n",
      "Angola                 1.775936\n",
      "Antigua and Barbuda   -0.029747\n",
      "Argentina             -1.939805\n",
      "Armenia                0.293815\n",
      "Australia             -0.165434\n",
      "Austria                0.231190\n",
      "Azerbaijan             0.826126\n",
      "Bahamas                0.179003\n",
      "Bahrain                0.982688\n",
      "Bangladesh             0.857439\n",
      "Barbados              -0.551620\n",
      "Belarus               -1.052619\n",
      "Belgium               -0.196746\n",
      "Belize                 1.348000\n",
      "Benin                 -0.029747\n",
      "Bhutan                 1.222750\n",
      "Bolivia               -1.824992\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print standardize_data(employment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albania                0.334440\n",
      "Algeria                0.654398\n",
      "Andorra                0.584233\n",
      "Angola                 0.237034\n",
      "Antigua and Barbuda    0.613298\n",
      "Argentina              0.384451\n",
      "Armenia                0.489949\n",
      "Australia              0.250088\n",
      "Austria                0.103715\n",
      "Azerbaijan             0.078763\n",
      "Bahamas                0.273884\n",
      "Bahrain                0.372880\n",
      "Bangladesh             0.280114\n",
      "Barbados               1.299655\n",
      "Belarus                0.356030\n",
      "Belgium               -2.671540\n",
      "Belize                -2.260251\n",
      "Benin                  0.041654\n",
      "Bhutan                -1.673364\n",
      "Bolivia                0.250571\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print standardize_data(female_completion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albania                0.291654\n",
      "Algeria                0.558076\n",
      "Andorra                0.513292\n",
      "Angola                 0.086936\n",
      "Antigua and Barbuda    0.689332\n",
      "Argentina              0.411211\n",
      "Armenia                0.719782\n",
      "Australia             -0.086210\n",
      "Austria                0.193060\n",
      "Azerbaijan            -0.104382\n",
      "Bahamas                0.650111\n",
      "Bahrain                0.459219\n",
      "Bangladesh             0.124652\n",
      "Barbados               1.314148\n",
      "Belarus                0.457447\n",
      "Belgium               -2.710635\n",
      "Belize                -2.279974\n",
      "Benin                  0.073148\n",
      "Bhutan                -1.405707\n",
      "Bolivia                0.044842\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print standardize_data(male_completion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albania                0.294496\n",
      "Algeria                0.342774\n",
      "Andorra                1.694561\n",
      "Angola                -2.457355\n",
      "Antigua and Barbuda    0.278404\n",
      "Argentina              0.407145\n",
      "Armenia               -0.091728\n",
      "Australia              1.388800\n",
      "Austria                1.179595\n",
      "Azerbaijan            -0.413582\n",
      "Bahamas               -0.123914\n",
      "Bahrain                0.568072\n",
      "Bangladesh            -0.767622\n",
      "Barbados               0.374960\n",
      "Belarus               -0.494046\n",
      "Belgium                1.050853\n",
      "Belize                -0.333119\n",
      "Benin                 -1.636627\n",
      "Bhutan                -0.896363\n",
      "Bolivia               -0.365304\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print standardize_data(life_expectancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print standardize_data(gdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Variables Over Time\n",
    "\n",
    "Panda series were used to create a plot of each variable over time for Canada. The variables include employment rates, female school completion rate, male school completion rate, life expectancy, and gdp. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c289c90>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ff/jPYrLW7ODhC3vRuWntWMcRqXSSplgc3aY+/drW59kvV1OiQXpJ5a05G3ju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\nCxERKZeKhYiIlEvFQkREyqViISIi5fr/GUIIfv/CVwgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b929250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "employment_ca.plot()\n",
    "plt.ylabel('Employment rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ba700d0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lzKVTVd5ZsJX7PoqlbqXSzB7RjS4Nq7odK+CFhQQxdkA0qelZPP5FHFlZdlOvgpZnUVHV\nXUBF/jdES0WnLa/5NgCvA/OAuUA8npP95wziwnspABf6U++CPx2qOlFVY1Q1Jjzc7mFt/O9MagYP\nf7aKf/ywiRtb12LmQ12oW7m027GMo0F4WV7o04zFW48wZfEOt+MEnDyLioiMBD7Dc2OuasCnIvKI\nNwtX1fdVta2q9gCOAlucZYYA/YDpOcyayG/3auoA+7xZpzH+tOvIGfr961d+WHeAP/duytgB0ZQK\ns+tPCpuB7etyTbPqjJ67ifX7TrodJ6B4c/jrPqCjqr6gqi8AnfCcgM+TiFRzHiPwFJFzeyZXAxtV\nNTGHWVcAjUWkvoiEAQOB2d6s0xh/Wbj5EH3GLeLgqRQ+GtKBB3o0sPMnhZSI8PotrahQOpSR01aT\nkp7pdqSA4U1RESD7N5LJhQ9PXchMEVkPzAGGq+oxp30g5x36EpFaIvI9gHNifwTwA7ABmKGq67xc\npzE+paq8t3Abgz9YTq2KpZg9vBvdG9uh1sKucpkwxtzWmi1Jp/n79xvcjhMwvOlS/AGwTERmOa9v\nBt73ZuGq2j2H9nsv0LYPz8n8c6+/B773Zj3G+EtyWgZPfbmGb9fs5/pWNfnHra0oHebNfxtTGPSI\nCmdI1/pMWbyDKy6rRs8m1dyOVOx5c6L+TWAwnnMix4DBqjrW38GMcdueo8n0+9evfLd2P0/3uozx\ng9pYQSmCnurVhMtqlOPJL9Zw+HSq23GKvRyLioiUdx4rAzuBT4FPgF1OmzHF1uKth+kzfhH7jp/l\ng3vb89AVDe38SRFVMjSYsQOjOZmSztNfrkHVuhn7U257Kp87jyuB2GzTudfGFDuqyuRftnPX+8uo\nVq4Es0d04wo7ZFLkXVajPM/0uoz5G5P4bNlut+MUaznuy6vqDc5j/YKLY4x7UtIzeWbmGr6O20ev\n5jV4o39rypaww13Fxb1dIvlp8yH++t16OjWoQqNqZd2OVCx5c53KfG/ajCnK9h4/y63v/co38ft4\n4too/nVHWysoxUxQkPCG09Fi5LTVpGXYTb38IbdzKiWdcydVRaSSiFR2pkigVkEFNMbflmw7Qp9x\ni9h1OJnJd8cw4srGBAXZ+ZPiqFr5krzWryXr9p1kzLxNbscplnL7U2wYMApPAVmVrf0knhGEjSnS\nVJWPft3JK99tILJKaSbeHUPDcDskUtxd27wGgzpEMPHn7VweFW5jtvmY5NUTQkQeUdVxBZTnksTE\nxGhsrPUhMHlLSc/kz7MSmLkqkaubVuetAa0pVzLU7VimgCSnZXDDPxdxNj2TuSN7UKF04H73IrJS\nVWN8tTxvrqifIiLPichEJ0BjEbnBVwGMKWj7T5xlwIQlzFyVyMirGjPxrnZWUAJM6bAQxg6M5tCp\nVJ6dtda6GfuQV0UFz022ujivE4G/+i2RMX60YudR+oxbxNak00y4qx2PXRNl508CVKs6FXnsmii+\nW7ufmav2uh2n2PCmqDRU1dFAOoCqnsX7sb+MKRRUlU+W7mLQxKWUKxnK18O7cl3zGm7HMi578PKG\ndKhfmRe/SWDXkTNuxykWvCkqaSJSCud+JiLSELCxDkyRkZqRyZ++WsvzXyfQvXFVvh7elcbVy7kd\nyxQCwUHCWwOiCQoSHpseR0amdTO+VN4UlRfx3GSrroh8BswHnvJrKmN85ODJFAZOXMq0FXsY0bMR\nk+9pT4VSdv7E/E/tiqX4W9+WrNp9nPELtrodp8jL8+ouVZ0nIqvw3EdFgJGqetjvyYy5RCt3HePB\nT1dyJjWDd+9oyx9a1nQ7kimkbmxdiwUbk/jn/C10bxxOu3qV3I5UZOV28WPbcxNQD9iP5+6LEU6b\nMYXWtOW7GThxCaVCg5n1cFcrKCZPf7mpObUqluKx6XGcTs3IewZzQbntqYzJ5T0FrvRxFmMuWVpG\nFn+Zs47Plu2me+OqjBvUhoqlw9yOZYqA8iVDGTsgmv4TlvDS7HW8cVtrtyMVSbkNKNmzIIMYc6mS\nTqXw8KeriN11jAcvb8iT1zUh2LoLm3yIiazMiJ6N+Of/baVnk2pc38r2cPMrz3MqIlISeBjohmcP\n5RfgPVVN8XM2Y7wWt+c4D36ykuNn0xg3qA19WtvwdObiPHJVYxZuOcyfvlpDm4iK1KpYyu1IRYo3\nvb8+BpoD44DxQDM8N+syplCYEbuH/hOWEBIsfPVQVyso5pKEBgfx9oBoMrKUx2fEk5VlV9vnhzdF\npYmq3qeqC5xpKBDl72DG5CU9M4sXv0ngqS/X0D6yEnNGdKNZrfJuxzLFQGTVMrzUpzlLth9h0i/b\n3Y5TpHhTVFaLSKdzL0SkI7DYf5GMydvh06ncMXkZHy3Zxf3d6vPR4A5UKmMn5I3v3BZThz+0qMEb\nP24iYe8Jt+MUGd4UlY7AryKyU0R2AkuAy0VkrYis8Ws6Yy5gbeIJbhy3iPg9xxk7IJrnbmhGSLA3\nP8rGeE9EeLVvSyqXCWPktNWcTct0O1KR4M2t7Xr5PYUxXpq1OpFnZq6lSpkwZj7UhRa1K7gdyRRj\nlcqE8Wb/aO6YvIy/fb+ev97c0u1IhZ43V9TvEpFKQN3sn1fVVTnPZYxvZWRm8fd/b+T9RTvoWL8y\n79zRlqplS7gdywSAro2q8kD3+kz6ZQc9m1TjqqbV3Y5UqHnTpfgV4F5gG86gktjFj6YAHT2TxojP\nV/HrtiPc2yWSP1/flFA73GUK0BPXNWHR1iM89eUa5o7qQXg5+4MmJ978z+yPZ/j7K1S1pzNZQTEF\nYt2+E/QZt4jYXcf4x62teOnG5lZQTIErERLM2wOjOZ2awZNfxttNvXLhzf/OBKDixSxcREaKSIKI\nrBORUdnaHxGRTU776Bzm3el0BogTEbtHcACaHb+PW979lcws5Ythnbktpq7bkUwAi6pejmd7N+Wn\nTYf4eMkut+MUWt6cqP87nm7FCWS7j4qq3pjbTCLSAngA6IDnzpFzReQ7oA5wE9BKVVNFpFoui+lp\nIyIHnswsZfTcjUz4eTvtIyvxrzva2eEGUyjc3bkeP21K4tXvN9C5YRWi7L48v+NNUfkIeB1YC+Tn\nDjZNgaWqmgwgIguBvkAM8JqqpgKoalK+Epti7XhyGo9MXc0vWw5zV6d6PH9DM8JC7HCXKRxEhNG3\ntqbX2J95dOpqvhnRlRIhwW7HKlS8+d96WFX/6VxNv/Dc5MV8CUAPEakiIqWB3nh6kEUB3UVkmYgs\nFJH2OcyvwI8islJEhnq1NaZI23jgJDeOX8yy7Ud5rV9LXrm5hRUUU+iElyvB6FtbsfHAKd74YZPb\ncQodb/ZUVorI34HZ/PbwV65dilV1g4i8DswDTgPxQIazzkp4bvrVHpghIg3092e+uqrqPufw2DwR\n2aiqP5+/HqfgDAWIiIjwYnNMYfT92v088UU8ZUuEMG1YJ9pG2E2STOF1VdPq3Nkpgkm/7ODyqGp0\na1zV7UiFhuTVi0FEFlygWfPbA0xEXgUSgRvxHP76yWnfBnRS1UO5zPsScFpV38htHTExMRoba+f0\nixJVZfz/bWXMvM20jajIe3e2o1r5km7HMiZPZ9MyuWHcL5xOzWDuyB5FdpggEVmpqjG+Wl6exxay\ndSPOPnlVUM6dhBeRCKAfMBX4GucaFxGJAsKAw+fNV0ZEyp17DlyL53CaKUZUlX/8sIkx8zbTr01t\npg7tZAXFFBmlwoJ5e2Abjp5J49lZa62bsSPPoiIiFUTkTRGJdaYxIuLt2BgzRWQ9MAcYrqrHgClA\nA6c32TTgHlVVEaklIt8781UHFolIPLAc+E5V5+Z760yhpaq88u0G/vXTNm7vGMEbt7W2E56myGlR\nuwJPXNuEfycc4IvYRLfjFAreHP6aiWcv4SOn6S6gtar283O2fLPDX0VDVpbywuwEPl26m8FdI3nh\nhmaI2B0aTdGUlaXcMXkZ8YnH+f7R7kRWLeN2pHwp8MNfeK6mf1FVtzvTX4AGvgpgAktmlvL0zDV8\nunQ3D17e0AqKKfKCgoQx/VsTGhzEyOlxpGfm58qL4sebonJWRLqdeyEiXYGz/otkiquMzCz+OCOO\nL1YmMvKqxjzdq4kVFFMs1KpYilf7tiR+z3HGzd/idhxXedOl+CHgo2znUY7hGWDSGK+lZWQxctpq\n/p1wgKd6NeHhKxq5HckYn7q+VU0WbKrD+AVb6R4VTvvIym5HcoU3vb/iVLU10ArP0CptVDXe/9FM\ncZGSnslDn67k3wkHeP6GZlZQTLH10o3NqVOpNI9Nj+NkSrrbcVzhTe+vV0WkoqqeVNWTIlJJRP5a\nEOFM0Xc2LZMHPo5l/sYkXrm5Bfd1q+92JGP8pmyJEN4aEM3+Eym8+M06t+O4wptzKn9Q1ePnXjjd\ngnv7L5IpLs6kZjD4w+Us2nqY0be24q5O9dyOZIzftatXiUeubMSs1Xv5Jm6v23EKnDdFJVhE/jtE\nrIiUAmzIWJOrkynp3D1lOSt2HmPsgGj627D1JoCM6NmIthEVee7rBBKPJbsdp0B5U1Q+BeaLyH0i\nMgTPWF4f5TGPCWDHk9O4c/Iy4vccZ/ygNtwUXdvtSMYUqJDgIMYOaENWlvLHGfFkZgXO1fbenKgf\nDfwVz1D2zYFXnDZjfufI6VQGTVrGxv2neO/OdvyhZU23IxnjiogqpfnLTS1YvuMo7y3c5nacAuNN\nl2KcIVJsmBSTq6RTKdwxaRm7jyYz+Z4YekSFux3JGFfd0rY2CzYl8da8zXRvXJVWdS7qJrpFit2s\nwvjE/hNnGThhKXuPn+XDwR2soBiD56Zer97ckvByJRg1LY7ktAy3I/mdFRVzyfYcTab/hCUcOpXK\nJ/d1oHPDKm5HMqbQqFA6lDH9W7PjyBle+XaD23H8zoqKuSQ7Dp9hwIQlnDybwaf3d6RdvcC8itiY\n3HRpWJWhPRowdfluflh3wO04fpXjORURWYvnlr6/ewvPTbpa+S2VKRK2Jp3i9knLyMhSPn+gI81r\neXtHBGMCz+PXNGHx1sM8M3MNbepWLLb3DsrtRP0NBZbCFDkb9p/kzsnLCAoSpg3tRFT1cm5HMqZQ\nCwvxdDO+YdwvPP5FPB8N7kBQUPEbUDXHw1+quuvcBKQALZ3prNNmAtTaxBMMmrSU0OAgpltBMcZr\njaqV5bnrm/HLlsN8+OtOt+P4hTdjf/XHc/fF24D+wDIRudXfwUzhtHLXMW6fvJSyJUKYMawzDcLL\nuh3JmCLljo4RXN20Gq/N3cjGAyfdjuNz3pyo/zPQXlXvUdW7gQ7A8/6NZQqjZduPcPf7y6hSJozp\nwzoTUaW025GMKXJEhNduaUX5kqGMnBpHSnqm25F8ypuiEqSqSdleH/FyPlOMLNpymHs+WE6NCiWZ\nPqwztSuWcjuSMUVW1bIl+Mdtrdh08BSj525yO45PeVMc5orIDyJyr4jcC3wHfO/fWKYwWbAxiSEf\nrSCyShmmD+tM9WLaa8WYgtSzSTXu7RLJlMU7+HnzIbfj+Iw3Y389CUzAc5Ou1sBEVX3a38FM4TA3\n4QBDP4mlSfVyTH2gE1XL2gDVxvjKM3+4jKjqZXn8i3iOnklzO45PeHsYazGwAJjvPDcBYE78PoZ/\nvooWtSvw6f0dqVQmzO1IxhQrJUODGTugDSeS03l65hpUi/5oxvnp/XUr1vsrYHy5MpGR01bTrl4l\nPrmvIxVKhbodyZhiqVmt8jzVqwnz1h9k2oo9bse5ZN6MUnyu91cSgIiEA/8BvvRnMOOez5ft5s9f\nr6Vrw6pMujuGUmHBbkcyplgb0rU+P206xMtz1tOxfuUi3VXfen+Z3/hw8Q6enbWWK6LCmXyPFRRj\nCkJQkPDGba0pERrEyGlxpGVkuR3polnvL/NfExZu46U567mueXUm3BVDyVArKMYUlBoVSvJav5as\n3XuCsf/Z7Haci+Zt76+JXETvLxEZKSIJIrJOREZla39ERDY57Re8i6SI9HI+s1VEnvFuc8zFUFX+\nOX8Lf//3Rvq0rsX429sSFmI7o8YUtF4tajIgpi7vLtzGsu1H3I5zUcRfvQ1EpAUwDc8V+Gl47hz5\nEFAHz3ma61U1VUSqnXd4DREJBjYD1wCJwApgkKquz22dMTExGhsb6/NtKc5UlTd+3MQ7C7ZxS9s6\njL61FcEHE9O4AAAVzUlEQVTFcJA7Y4qKM6kZXP/PX0jLyOLfo3r4vZOMiKxU1RhfLc+b3l/9RGSL\niJwQkZMickpEvBmwpimwVFWTVTUDWAj0xVNYXlPVVIDzC4qjA7BVVberahqe4nSTtxtlvKOq/PW7\nDbyzYBuDOkTwDysoxriuTIkQxg5sw8FTqTz/dYLbcfLNm2Mco4EbVbWCqpZX1XKqWt6L+RKAHiJS\nRURKA72BukAU0F1ElonIQhFpf4F5awPZ+9YlOm3GR7KylOe/SeD9RTu4t0skr/ZtUSyH4TamKIqu\nW5FRVzVmdvw+vl691+04+eJNUTmoqvm+B6Yzz+vAPDyHvuKBDDzdmCsBnYAngRkicv5vswv9drvg\ncToRGSoisSISe+hQ8RnqwJ8ys5RnvlrDp0t3M+zyBrzYpxm//wqMMW56uGcj2kdW4vmvE9hzNNnt\nOF7Lsag4h736AbEiMl1EBp1rc9rzpKrvq2pbVe0BHAW24Nnr+Eo9lgNZQNXzZk3Es1dzTh1gXw7r\nmKiqMaoaEx4e7k2sgJaRmcXjM+KYEZvIo1c15plel1lBMaYQCg4S3uwfDcBj0+PIyCwa3Yxz21Pp\n40zlgWTg2mxtXt0VUkSqOY8RQD9gKvA1cKXTHgWEAYfPm3UF0FhE6otIGDAQmO3dJpmcpGVk8ei0\n1Xwdt48nr2vCH6+JsoJiTCFWt3JpXrm5BbG7jvHuT9vcjuOVHK+oV9XBPlj+TBGpAqQDw1X1mIhM\nAaaISAKeXmH3qKqKSC1gsqr2VtUMERkB/AAEA1NUdZ0P8gSs1IxMhn+2iv9sSOK565tyf/cGbkcy\nxnjh5ja1+b+NSYydv4XuUeFE163odqRc5dmlWEQ+Akaq6nHndSVgjKoOKYB8+WJdii/sbFomwz5d\nyc+bD/HKzS24q1M9tyMZY/LhxNl0er/9C6HBwnePdqdMCW9G2PJOgXcpBlqdKygAqnoMaOOrAMa/\nzqRmMOTDFfyy5RCjb2llBcWYIqhCqVDe7N+aXUeTeXlOrpfruc6rsb+cvRMARKQy3g1EaVx2MiWd\ne6YsZ/nOo7zVP5r+7evmPZMxplDq2KAKD1/RkOmxe5ibsN/tODnypjiMAX4VkXOjEt8G/M1/kYwv\nnEhO5+4py1i37yTjBrWhd8uabkcyxlyiUVdH8cuWwzzz1Vqi61aiRoXCdxdWb8b++hi4BTgIJAH9\nVPUTfwczF+/I6VQGTVrKhv2neO/OdlZQjCkmQoODGDsgmtT0LB7/Io6srMJ3Uy9vhmlpCGxT1fHA\nWuBqESnc3Q8CWNKpFAZNWsq2Q6eZdE8MVzer7nYkY4wPNQgvywt9mrF46xGmLN7hdpzf8eacykwg\nU0QaAZOB+sDnfk1lLsr+E2cZOGEpicfO8sHg9lweZReDGlMcDWxfl2uaVWf03E2s3+fNUIwFx5ui\nkuUMCNkPeFtVHwPseEohs+doMv0nLCHpVCofD+lAl4bnD1JgjCkuRITXb2lFhdKhjJy2mpT0TLcj\n/Zc3RSVdRAYBdwPfOm12w/JCZOfhMwyYsIQTyel8en9HYiIrux3JGONnlcuEMea21mxJOs3fv8/3\n8Ix+401RGQx0Bv6mqjtEpD7wqX9jGW9tTTpN/wlLOJueydShnQr91bbGGN/pERXOkK71+WjJLhZs\nvNBdRAqe327S5YZAu6J+44GT3Dl5GSB8/kBHoqqXczuSMaaApaRncvM7izl8OpW5o3pQtWyJfM3v\nxhX1phBK2HuCgROXEhIUxIxhnaygGBOgSoYGM3ZgNCdTMnj6yzW4vaNgRaUIWrX7GIMmLaVMWAgz\nhnWmQXhZtyMZY1x0WY3yPNPrMuZvTOKzZbtdzeJ1URGRMv4MYryzfMdR7pq8jMplwpjxYGciqpR2\nO5IxphC4t0skPaLC+et369madNq1HN5c/NhFRNYDG5zXrUXkX35PZn5n8dbD3DNlOTUqlGTGsM7U\nrljK7UjGmEIiKEh449ZWlA4LYeS01aRluHNTL2/2VN4CrgOOAKhqPNDDn6HM7y3YmMTgD1dQr0pp\npg/rTPXyhW/MH2OMu6qVL8lr/Vqybt9Jxszb5EoGrw5/qeqe85oKz5U2AeCHdQcY+kksUdXLMvWB\nTvnu3WGMCRzXNq/BoA4RTPx5O79uO/+muv7nTVHZIyJdABWRMBF5AudQmPG/OfH7ePizVTSvVYHP\n7u9EpTJhbkcyxhRyz9/QlPpVyvD4jHhOJKcX6Lq9KSoPAsOB2kAiEO28Nn42c2UiI6etpl1EJT69\nvyMVStlABsaYvJUOC+HtgW04dCqVZ2etLdBuxt4MfX9YVe9Q1eqqWk1V71TVIwURLpBNXb6bJ76M\np3PDKnw4pD1lfXj7UGNM8deyTgX+eG0U363dz8xVewtsvTn+phKRcUCO5U1VH/VLIsNHv+7kxdnr\n6NkknHfvbEfJ0GC3IxljiqBhPRry06ZDvPhNAu0jK1Gviv+vDMltTyUWWJnLZPxg4s/beHH2Oq5t\nVp337rKCYoy5eMFBwlsDogkKEh6bHkdGpv+7Gee4p6KqH/l97eY3xs3fwph5m7mhVU3eGhBNaLAN\neGCMuTS1K5bib31b8ujU1YxfsJVRV0f5dX15HqgXkXDgaaAZ8N+LI1T1Sj/mCiiqypgfNzN+wVb6\nta3NP25tTXCQuB3LGFNM3Ni6Fgs2JvHP+Vvo3jicdvUq+W1d3vwp/BmeLsT1gb8AO4EVfksUYFSV\nv323gfELtjKoQ13esIJijPGDv9zUnFoVSzFq+mpOpfivm7E3RaWKqr4PpKvqQlUdAnTyW6IAkpWl\nvPDNOiYv2sG9XSJ5tW9LgqygGGP8oHzJUMYOiGbvsbO8NHu939bj1Z0fncf9InK9iLQB6vgtUYDI\nzFL+9NVaPlm6i2E9GvBin2aIWEExxvhPTGRlRvRsxMxViXy3Zr9f1uHNxQ9/FZEKwOPAOKA88Jhf\n0gSIjMwsnvxyDbNW7+XRKxvx2DVRVlCMMQXikasas3DLYf701RraRPj+TrHeXPz4raqeUNUEVe2p\nqu1UdbY3CxeRkSKSICLrRGSU0/aSiOwVkThn6p3DvDtFZK3zmWJzO8f0zCxGTotj1uq9PHldE/54\nbRMrKMaYAhMaHMTbA6LJyFL+OCPO58v3pvdXfeARIDL751X1xjzmawE8AHQA0oC5IvKd8/ZbqvqG\nF/l6qmrBj4jmJ6kZmQz/bDX/2XCQ565vyv3dG7gdyRgTgCKrluGlPs15auYany/bm8NfXwPvA3OA\n/Fw50xRYqqrJACKyEOib74TFREp6JsM+WcnCzYd45abm3NU50u1IxpgAdltMHRZsSuI9Hy9X8hpo\nTESWqWrHfC9YpCnwDdAZOAvMx3OV/hHgXuCk8/pxVT12gfl3AMfwDBUzQVUn5rCeocBQgIiIiHa7\ndu3Kb1S/S07L4L4PY1m64wiv9WvJgPYRbkcyxhiOnUmjctkSK1U1xlfL9Kao3A40Bn4EUs+1q+qq\nPBcuch+eEY1PA+vxFJfXgMN4isUrQE2nm/L589ZS1X0iUg2YBzyiqj/ntr6YmBiNjS1cp19OpaQz\n+IMVrNp9jDH9W9O3jXWcM8YUHiLi06LizeGvlsBdwJX87/CXOq9z5Vzf8j6AiLwKJKrqwXPvi8gk\n4Nsc5t3nPCaJyCw852ZyLSqFzYnkdO7+YDnr9p5g3KC2XN+qptuRjDHGr7wpKn2BBqqalt+Fi0g1\npyhEAP2AziJSU1XPdZDuCyRcYL4yQJCqnnKeXwu8nN/1u+nomTTunLyMrUmneffOdlzTrLrbkYwx\nxu+8KSrxQEUg6SKWP1NEquC5gHK4qh4TkU9EJBrP3s5OYBh4DncBk1W1N1AdmOV0tQ0BPlfVuRex\nflccOpXKHZOXsutIMhPvbscVTaq5HckYYwqEN0WlOrBRRFbw23MquXYpdj7T/QJtd+Xw2X1Ab+f5\ndqC1F9kKnQMnUrh98lL2H0/hg3vb06VRVbcjGWNMgfGmqLzo9xTFROKxZG6ftIyjZ9L4+L4OtI+s\n7HYkY4wpUHkWFVVdKCL1gMaq+h8RKQ3YnaPOs/PwGe6YvIxTKel8en9Houv6fvgDY4wp7PIcpkVE\nHgC+BCY4TbXxXBBpHFuTTjNg4hKS0zL4/IFOVlCMMQHLm1GKhwNd8VysiKpuAezMs2PjgZMMnLiE\nzCyYNrQzLWpXcDuSMca4xpuikpq9O7GIhODpuRXwEvaeYODEpQQHCdOHdaJJjXJuRzLGGFd5U1QW\nisizQCkRuQb4As84YAFt9e5jDJq0lDJhIcwY1pmG4WXdjmSMMa7zpqg8AxwC1uK5puR74Dl/hirs\nlu84yp2Tl1G5TBjTh3WiXpUybkcyxphCIcfeXyISoaq7VTULmORMAW/x1sPc/1EstSqW5LP7O1Gj\nQkm3IxljTKGR257Kf3t4icjMAshS6C3YlMTgD1cQUbk004Z2toJijDHnye06ley3Iwz4u0n9uO4A\nwz9fRVT1cnxyX0cqlwlzO5IxxhQ6uRUVzeF5wPl2zT5GTYujRe0KfDSkAxVKhbodyRhjCqXcikpr\nETmJZ4+llPMc57Wqanm/pysEvlqVyBNfxNOuXiWm3NueciWtoBhjTE5yLCqqGvBDsUxbvps/zVpL\n5wZVmHxPDKXDvBkqzRhjApf9lszBx0t28sI367iiSTjv3dmOkqEBX2ONMSZPVlQuYNLP2/nb9xu4\npll1xt/ehhIhVlCMMcYbVlTOM27+FsbM28z1rWoydkA0ocHeXB9qjDEGrKj8l6oy5sfNjF+wlX5t\najP61laEWEExxph8saKCp6C8+v0GJv2yg4Ht6/Jq35YEBUneMxpjjPmNgC8qWVnKS3PW8fGSXdzT\nuR4v9mluBcUYYy5SQBeVzCzlz7PWMm3FHob2aMCf/nAZIlZQjDHmYgVsUcnIzOKpL9fw1eq9PHJl\nI/54TZQVFGOMuUQBWVTSM7MYNS2O79bu54lroxhxZWO3IxljTLEQcEUlNSOTEZ+vZt76g/y5d1Me\n6BHwY2UaY4zPBFRRSUnPZNgnK1m4+RAv39ScuztHuh3JGGOKlYApKslpGdz/USxLth/htX4tGdgh\nwu1IxhhT7AREUTmVks6QD1ewctcx3uzfmr5t6rgdyRhjiiW/XjIuIiNFJEFE1onIKKftJRHZKyJx\nztQ7h3l7icgmEdkqIs9cbIYTyenc+f5yVu8+zrhBba2gGGOMH/ltT0VEWgAPAB2ANGCuiHznvP2W\nqr6Ry7zBwDvANUAisEJEZqvq+vxkOHomjbveX8aWg6f51x1tubZ5jYvaFmOMMd7x555KU2Cpqiar\nagawEOjr5bwdgK2qul1V04BpwE35WfmhU6kMmriUrUmnmXh3OysoxhhTAPxZVBKAHiJSRURKA72B\nus57I0RkjYhMEZFKF5i3NrAn2+tEp80rB06kMGDiEnYfTeaDe9tzRZNqF7sNxhhj8sFvRUVVNwCv\nA/OAuUA8kAG8CzQEooH9wJgLzH6hS9v1QusRkaEiEisisYcOHSLxWDL9Jywh6WQqH9/XgS6Nqvpi\nc4wxxnjBryfqVfV9VW2rqj2Ao8AWVT2oqpmqmgVMwnOo63yJ/G+vBqAOsC+HdUxU1RhVjalQqQoD\nJizleHIan9zXgfaRlX29ScYYY3Lh795f1ZzHCKAfMFVEamb7SF88h8nOtwJoLCL1RSQMGAjMzmt9\n2w6dJjktg88f6ESbiAsdVTPGGONP/r5OZaaIVAHSgeGqekxEPhGRaDyHs3YCwwBEpBYwWVV7q2qG\niIwAfgCCgSmqus6bFU4b2pkmNcr5Y1uMMcbkQVQveKqiSGoZ3VbXxq1yO4YxxhQZIrJSVWN8tbxi\ndb/cEiHFanOMMabIsd/CxhhjfMaKijHGGJ+xomKMMcZnrKgYY4zxGSsqxhhjfMaKijHGGJ+xomKM\nMcZnrKgYY4zxmWJ1Rb2InAI2uZ3DT6oCh90O4Ue2fUWbbV/R1URVfTa2VXG7R/0mXw43UJiISGxx\n3Taw7SvqbPuKLhGJ9eXy7PCXMcYYn7GiYowxxmeKW1GZ6HYAPyrO2wa2fUWdbV/R5dNtK1Yn6o0x\nxriruO2pGGOMcVGhLioiMkVEkkQkIVtbaxFZIiJrRWSOiJR32sNE5AOnPV5Ersg2zyCnfY2IzBWR\nqi5sTsDx4fc3wPnu1onIaBc2JeCISF0RWSAiG5x/95FOe2URmSciW5zHSk67iMg/RWSr8121zbas\ne5zPbxGRe9zapkDh4+8uQkR+dJa1XkQi8wygqoV2AnoAbYGEbG0rgMud50OAV5znw4EPnOfVgJV4\nimYIkARUdd4bDbzk9rYFwuSj768KsBsId977CLjK7W0r7hNQE2jrPC8HbAaaOf9/nnHanwFed573\nBv4NCNAJWOa0Vwa2O4+VnOeV3N6+4jz56rtz3vsJuMZ5XhYondf6C/Weiqr+DBw9r7kJ8LPzfB5w\ni/O8GTDfmS8JOA7E4PmHEqCMiAhQHtjn3+QGfPb9NQA2q+oh53P/yTaP8RNV3a+qq5znp4ANQG3g\nJjyFHefxZuf5TcDH6rEUqCgiNYHrgHmqelRVj+H5znsV4KYEHF99dyLSDAhR1XnOsk6ranJe6y/U\nRSUHCcCNzvPbgLrO83jgJhEJEZH6QDugrqqmAw8Ba/EUk2bA+wUb2WSTr+8P2ApcJiKRIhKC5z9C\nXUyBcQ55tAGWAdVVdT94fnnh2asEzy+tPdlmS3Tacmo3BeASv7so4LiIfCUiq0XkHyISnNc6i2JR\nGQIMF5GVeHbt0pz2KXj+MWKBscCvQIaIhOIpKm2AWsAa4E8FHdr8V76+P+ev24eA6cAvwE4go4Az\nBywRKQvMBEap6sncPnqBNs2l3fiZD767EKA78ATQHs9Rg3vzWm+RG6ZFVTcC1wKISBRwvdOeATx2\n7nMi8iuwBYh23t/mtM/AczzRuOAivj9UdQ4wx2kfCmQWbOrA5PxBNhP4TFW/cpoPikhNVd3vHN5K\nctoT+e0eZB08RwYSgSvOa//Jn7mNz767UGC1qm53lvk1nnMuuR7pKXJ7KiJSzXkMAp4D3nNelxaR\nMs7za/D8lbse2As0E5FwZxHX4DnGaFxwEd9f9nkqAQ8Dk12IHlCc84/vAxtU9c1sb80GzvXgugf4\nJlv73U5Pok7ACecQyw/AtSJSyfn+rnXajJ/48LtbAVTK9rvzSmB9ngHc7qmQRy+GqcB+IB1PNb0P\nGImnN8Nm4DX+dwFnJJ4RijfgOZlbL9tyHnTa1+D5i7eK29sWCJMPv7+pzg/zemCg29sVCBPQDc8h\nkDVAnDP1xtMbbz6evcj5QGXn8wK8A2zDc/4yJtuyhuA5N7YVGOz2thX3ycff3TXOctYCHwJhea3f\nrqg3xhjjM0Xu8JcxxpjCy4qKMcYYn7GiYowxxmesqBhjjPEZKyrGGGN8xoqKMcYYn7GiYowxxmes\nqBhjjPGZ/web+kwMRXpD9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba4bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_completion_ca.plot()\n",
    "plt.ylabel('Female school completion rate')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10dca0610>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eTL+xczm3dgWmDOygQBEpJkLt/jKzFkCH4OVn7r4gv2ncfamZPQZMA/YD84EM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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11baff3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_completion_ca.plot()\n",
    "plt.ylabel('Male school completion rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11806cbd0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hiDRgW0Epl81fRElFNY9cdyxThoY/mqgxdTWEVF3+WkKmhCBykC35pVw2fyHl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\n/j/wxX0yFHXBfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2aefd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "life_expectancy_ca.plot()\n",
    "plt.ylabel('Life expectancy rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c2e0310>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qHlzjaVPkXF+Tz2vPxpRM/rNwC11ahvG7Li1+s01+gfLZuhQe+3wzEfVq8cjI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\noOM5+dz+zioWb07lzgtjueO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c273f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdp_ca.plot()\n",
    "plt.ylabel('GDP in the United States')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GDP and Life Expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import Data\n",
    "countries = ['Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda',\n",
    "             'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan',\n",
    "             'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n",
    "             'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia']\n",
    "\n",
    "life_expectancy_values = [74.7,  75. ,  83.4,  57.6,  74.6,  75.4,  72.3,  81.5,  80.2,\n",
    "                          70.3,  72.1,  76.4,  68.1,  75.2,  69.8,  79.4,  70.8,  62.7,\n",
    "                          67.3,  70.6]\n",
    "\n",
    "gdp_values = [ 1681.61390973,   2155.48523109,  21495.80508273,    562.98768478,\n",
    "              13495.1274663 ,   9388.68852258,   1424.19056199,  24765.54890176,\n",
    "              27036.48733192,   1945.63754911,  21721.61840978,  13373.21993972,\n",
    "                483.97086804,   9783.98417323,   2253.46411147,  25034.66692293,\n",
    "               3680.91642923,    366.04496652,   1175.92638695,   1132.21387981]\n",
    "\n",
    "life_expectancy = pd.Series(life_expectancy_values)\n",
    "gdp = pd.Series(gdp_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Life expectancy summary statistics \n",
    "print \"Mean life expectancy:\", life_expectancy.mean()\n",
    "print \"Standard deviation of the mean life expectancy:\", round(life_expectancy.std(),2)\n",
    "print \"Max life expectancy:\", round(life_expectancy.max(),2)\n",
    "print \"Sum life expectancy:\", round(life_expectancy.sum(),2)\n",
    "print\n",
    "\n",
    "# GDP summary statistics \n",
    "print \"Mean GDP:\", round(gdp.mean(),2)\n",
    "print \"Standard deviation of the mean GDP:\", round(gdp.std(),2)\n",
    "print \"Max GDP:\", round(gdp.max(),2)\n",
    "print \"Sum GDP:\", round(gdp.sum(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for the coorelation between two panda series where:\n",
    "* variable1 : The number of countries where both values are above or both are below the mean \n",
    "* variable2 : The number of countries where one value is above and one is below the mean \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def variable_correlation(variable1, variable2):\n",
    "    '''\n",
    "    Returns number of data points for when directions of variable1 \n",
    "    and variable2 relative to the mean are the same or different. Direction \n",
    "    here means whether each value is above or below its mean.\n",
    "    '''\n",
    "    # Data points in the same/different direction as boolean \n",
    "    both_above = (variable1 > variable1.mean()) & (variable2 > variable2.mean())\n",
    "    both_below = (variable1 < variable1.mean()) & (variable2 < variable2.mean())\n",
    "    \n",
    "    # If value is equal to the mean \n",
    "    is_same_direction = both_above | both_below \n",
    "    # Add up array of booleans to count number of Trues \n",
    "    num_same_direction = is_same_direction.sum() \n",
    "    \n",
    "    num_different_direction = len(variable1) - num_same_direction\n",
    "    \n",
    "    return (num_same_direction, num_different_direction)\n",
    "\n",
    "# Correlation between life expectancy and GDP\n",
    "variable_correlation(life_expectancy, gdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Positive correlation between GDP and Life Expectancy \n",
    "For our sample set, 17 pairs of data points are going in the same direction, and 3 pairs of data are going in the opposite direction. Since the first number is large and the second number is small, there is a positive correlation. If one number is large whereas the other number was small, then there would be a negative coorelation. If the two numbers were equal, then there would be no correlation."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [cats]",
   "language": "python",
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    "name": "ipython",
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