Doing some stats.ipynb

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   "metadata": {
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   "source": [
    "##  <span style=\"color:#a6ca6d\">HDRdata API demo: </span> <span style=\"color:#3972A0\">First Statistical analysis and graphs with <span style=\"color:#00acee\"> &#9733; Python &#9733; </span>\n",
    "\n",
    "*Version May 30, 2015* <br>\n",
    "\n",
    "*Credit **Thomas Roca** - Human Development Report Office - UNDP, AFD*<br>\n",
    "\n",
    "For more information about HDROdata API see UNDP - HDRO web site: http://hdr.undp.org/en/content/developers-data-api <br>\n",
    "For more information about Highcharts: http://www.highcharts.com/\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">1. Load and tabulate:  </span> <span style=\"color:#3972A0\">from API + json class</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API: http://ec2-52-1-168-42.compute-1.amazonaws.com/version/1/indicator_id/136906,137006,137506,141706,69206,306,69706/year/2013?structure=iyc\n",
      "                    cname  Year  HDI women  HDI men    HDI    GNI  Life exp  \\\n",
      "AFG           Afghanistan  2013      0.330    0.549  0.468   1904      60.9   \n",
      "ALB               Albania  2013      0.694    0.725  0.716   9225      77.4   \n",
      "ARE  United Arab Emirates  2013      0.801    0.835  0.827  58068      76.8   \n",
      "ARG             Argentina  2013      0.806    0.805  0.808  17297      76.3   \n",
      "ARM               Armenia  2013      0.725    0.729  0.730   7952      74.6   \n",
      "\n",
      "     Population  EySchooling         HDIcat  HDIclass  \\\n",
      "AFG     30551.7          9.3        Low HDI         4   \n",
      "ALB      3173.3         10.8       High HDI         2   \n",
      "ARE      9346.1         13.3  Very high HDI         1   \n",
      "ARG     41446.2         16.4  Very high HDI         1   \n",
      "ARM      2976.6         12.3       High HDI         2   \n",
      "\n",
      "                                             Region  \n",
      "AFG                                      South Asia  \n",
      "ALB       Europe & Central Asia (all income levels)  \n",
      "ARE  Middle East & North Africa (all income levels)  \n",
      "ARG   Latin America & Caribbean (all income levels)  \n",
      "ARM       Europe & Central Asia (all income levels)  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series, DataFrame, concat\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "#=============================================================================================================\n",
    "indicator_id1=str(136906)   # NB. HDI women\n",
    "indicator_id2=str(137006)   # NB. HDI men\n",
    "indicator_id3=str(137506)   # NB. HDI\n",
    "indicator_id4=str(141706)   # NB. GNI\n",
    "indicator_id5=str(69206)    # NB. Life expectancy\n",
    "indicator_id6=str(306)      # NB. Population\n",
    "indicator_id7=str(69706)    # NB. Expected Years of Schooling  \n",
    "Year=str(2013)\n",
    "#=============================================================================================================\n",
    "\n",
    "#------------------------- prepare the API request -------------------------\n",
    "indicator_id=indicator_id1+','+indicator_id2+','+indicator_id3+','+indicator_id4+','+indicator_id5+','+indicator_id6+','+indicator_id7\n",
    "\n",
    "url=\"http://ec2-52-1-168-42.compute-1.amazonaws.com/version/1/indicator_id/\"+indicator_id+\"/year/\"+Year+\"?structure=iyc\"\n",
    "print('API:',url)\n",
    "\n",
    "#------------------------- Load the data with Pandas -----------------------\n",
    "# load dataframe:\n",
    "df=pd.read_json(url)\n",
    "\n",
    "#------------------------- Parsing the data ----------------------------- \n",
    "\n",
    "# Parse the data: each serie saved (looping for 1 to 7 on var1 = Series(df['indicator_value'][indicator_id1][Year]))\n",
    "for v in range(1,8):\n",
    "    vars()['var'+str(v)]=Series(df['indicator_value'][vars()['indicator_id'+str(v)]][Year])\n",
    "\n",
    "cname=Series(df[\"country_name\"])\n",
    "\n",
    "# Generate a table with data merged and joind by country iso. (To have a matrix with no missing values)\n",
    "table = concat([var1,var2,var3,var4,var5,var6,var7], axis=1, join='inner')\n",
    "# Convert into float as a whole, replace X=[float(i) for i in X] i.e. for each column\n",
    "\n",
    "table = table.astype(np.float)\n",
    "table = concat([table, cname], axis=1, join='inner') # to have table without missing value\n",
    "\n",
    "#genrate a column with the year \n",
    "year=(table[0]/table[0])*int(Year)  # NB. Not smart trick to generate a column with a constant \n",
    "table = concat([table,year], axis=1, join='inner') #insert that column\n",
    "\n",
    "# set columns name:\n",
    "table.columns = ['HDI women','HDI men','HDI','GNI','Life exp','Population','EySchooling','cname','Year'] \n",
    "\n",
    "#change columns order:\n",
    "table = table[['cname','Year','HDI women','HDI men','HDI','GNI','Life exp','Population','EySchooling']]\n",
    "\n",
    "#------------------------------- Add information further information on countries ---------------------------------------\n",
    "data = {'HDIclass': [4,4,2,1,1,1,2,2,1,1,2,4,1,4,4,3,2,1,2,2,2,2,3,2,2,1,3,3,4,1,1,1,2,4,4,4,3,2,4,3,2,1,1,1,1,4,2,1,2,2,2,3,4,1,1,4,1,2,1,3,1,2,3,4,4,4,3,1,2,3,3,1,3,1,4,1,3,3,1,3,1,1,1,2,2,1,2,4,3,3,3,2,1,1,3,2,4,2,2,1,2,4,1,1,1,3,3,4,3,2,2,4,1,4,2,3,4,4,2,4,2,3,4,4,3,1,1,4,1,2,4,2,2,3,2,4,1,1,3,1,2,2,4,1,4,4,1,4,4,3,4,2,4,3,2,1,1,1,4,2,3,4,4,2,3,3,3,2,2,2,2,4,4,2,2,1,3,2,2,3,3,3,4,3,3,4],\n",
    "'HDIcat': ['Low HDI','Low HDI','High HDI','Very high HDI','Very high HDI','Very high HDI','High HDI','High HDI','Very high HDI','Very high HDI','High HDI','Low HDI','Very high HDI','Low HDI','Low HDI','Medium HDI','High HDI','Very high HDI','High HDI','High HDI','High HDI','High HDI','Medium HDI','High HDI','High HDI','Very high HDI','Medium HDI','Medium HDI','Low HDI','Very high HDI','Very high HDI','Very high HDI','High HDI','Low HDI','Low HDI','Low HDI','Medium HDI','High HDI','Low HDI','Medium HDI','High HDI','Very high HDI','Very high HDI','Very high HDI','Very high HDI','Low HDI','High HDI','Very high HDI','High HDI','High HDI','High HDI','Medium HDI','Low HDI','Very high HDI','Very high HDI','Low HDI','Very high HDI','High HDI','Very high HDI','Medium HDI','Very high HDI','High HDI','Medium HDI','Low HDI','Low HDI','Low HDI','Medium HDI','Very high HDI','High HDI','Medium HDI','Medium HDI','Very high HDI','Medium HDI','Very high HDI','Low HDI','Very high HDI','Medium HDI','Medium HDI','Very high HDI','Medium HDI','Very high HDI','Very high HDI','Very high HDI','High HDI','High HDI','Very high HDI','High HDI','Low HDI','Medium HDI','Medium HDI','Medium HDI','High HDI','Very high HDI','Very high HDI','Medium HDI','High HDI','Low HDI','High HDI','High HDI','Very high HDI','High HDI','Low HDI','Very high HDI','Very high HDI','Very high HDI','Medium HDI','Medium HDI','Low HDI','Medium HDI','High HDI','High HDI','Low HDI','Very high HDI','Low HDI','High HDI','Medium HDI','Low HDI','Low HDI','High HDI','Low HDI','High HDI','Medium HDI','Low HDI','Low HDI','Medium HDI','Very high HDI','Very high HDI','Low HDI','Very high HDI','High HDI','Low HDI','High HDI','High HDI','Medium HDI','High HDI','Low HDI','Very high HDI','Very high HDI','Medium HDI','Very high HDI','High HDI','High HDI','Low HDI','Very high HDI','Low HDI','Low HDI','Very high HDI','Low HDI','Low HDI','Medium HDI','Low HDI','High HDI','Low HDI','Medium HDI','High HDI','Very high HDI','Very high HDI','Very high HDI','Low HDI','High HDI','Medium HDI','Low HDI','Low HDI','High HDI','Medium HDI','Medium HDI','Medium HDI','High HDI','High HDI','High HDI','High HDI','Low HDI','Low HDI','High HDI','High HDI','Very high HDI','Medium HDI','High HDI','High HDI','Medium HDI','Medium HDI','Medium HDI','Low HDI','Medium HDI','Medium HDI','Low HDI'],\n",
    "'Region': ['South Asia','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Middle East & North Africa (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','South Asia','Europe & Central Asia (all income levels)','Middle East & North Africa (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','South Asia','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','North America','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Latin America & Caribbean (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Middle East & North Africa (all income levels)','Latin America & Caribbean (all income levels)','Middle East & North Africa (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','East Asia & Pacific (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','East Asia & Pacific (all income levels)','South Asia','Europe & Central Asia (all income levels)','Middle East & North Africa (all income levels)','Europe & Central Asia (all income levels)','Middle East & North Africa (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Middle East & North Africa (all income levels)','East Asia & Pacific (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','Middle East & North Africa (all income levels)','East Asia & Pacific (all income levels)','Middle East & North Africa (all income levels)','Sub-Saharan Africa (all income levels)','Middle East & North Africa (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','South Asia','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Middle East & North Africa (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','South Asia','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Middle East & North Africa (all income levels)','East Asia & Pacific (all income levels)','Europe & Central Asia (all income levels)','East Asia & Pacific (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','East Asia & Pacific (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','South Asia','East Asia & Pacific (all income levels)','Middle East & North Africa (all income levels)','South Asia','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Middle East & North Africa (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Middle East & North Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','Sub-Saharan Africa (all income levels)','Latin America & Caribbean (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Latin America & Caribbean (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Middle East & North Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','East Asia & Pacific (all income levels)','Europe & Central Asia (all income levels)','Europe & Central Asia (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','Latin America & Caribbean (all income levels)','Middle East & North Africa (all income levels)','Europe & Central Asia (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','North America','Europe & Central Asia (all income levels)','Latin America & Caribbean (all income levels)','Latin America & Caribbean (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','East Asia & Pacific (all income levels)','Middle East & North Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)','Sub-Saharan Africa (all income levels)']}\n",
    "\n",
    "tablePlus=DataFrame(data, index= ['AFG','AGO','ALB','AND','ARE','ARG','ARM','ATG','AUS','AUT','AZE','BDI','BEL','BEN','BFA','BGD','BGR','BHR','BHS','BIH','BLR','BLZ','BOL','BRA','BRB','BRN','BTN','BWA','CAF','CAN','CHE','CHL','CHN','CIV','CMR','COD','COG','COL','COM','CPV','CRI','CUB','CYP','CZE','DEU','DJI','DMA','DNK','DOM','DZA','ECU','EGY','ERI','ESP','EST','ETH','FIN','FJI','FRA','GAB','GBR','GEO','GHA','GIN','GMB','GNB','GNQ','GRC','GRD','GTM','GUY','HKG','HND','HRV','HTI','HUN','IDN','IND','IRL','IRQ','ISL','ISR','ITA','JAM','JOR','JPN','KAZ','KEN','KGZ','KHM','KIR','KNA','KOR','KWT','LAO','LBN','LBR','LBY','LCA','LIE','LKA','LSO','LTU','LUX','LVA','MAR','MDA','MDG','MDV','MEX','MKD','MLI','MLT','MMR','MNE','MNG','MOZ','MRT','MUS','MWI','MYS','NAM','NER','NGA','NIC','NLD','NOR','NPL','NZL','OMN','PAK','PAN','PER','PHL','PLW','PNG','POL','PRT','PRY','QAT','ROU','RUS','RWA','SAU','SDN','SEN','SGP','SLB','SLE','SLV','SOM','SRB','SSD','STP','SUR','SVK','SVN','SWE','SWZ','SYC','SYR','TCD','TGO','THA','TJK','TKM','TLS','TON','TTO','TUN','TUR','TZA','UGA','UKR','URY','USA','UZB','VCT','VEN','VNM','VUT','WSM','YEM','ZAF','ZMB','ZWE'])\n",
    "\n",
    "#join tablePlus to original table\n",
    "table = concat([table, tablePlus], axis=1, join='inner')\n",
    "\n",
    "#Show the first lines of the table\n",
    "print(table.head())\n",
    "\n",
    "#Save the dataset\n",
    "table.to_csv(\"table.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">1.2 Load and tabulate:  </span> <span style=\"color:#3972A0\">Other types of tables</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Using Tabulate format -----------\n",
      "---------- HTML format -----------\n",
      "---------- LaTeX format -----------\n"
     ]
    }
   ],
   "source": [
    "#!pip install tabulate\n",
    "from tabulate import tabulate\n",
    "\n",
    "#generate a smaller table\n",
    "table2= table[['cname','Year','HDI women','HDI men','HDI','GNI','Life exp','EySchooling']]\n",
    "\n",
    "#output using tabulate\n",
    "print('---------- Using Tabulate format -----------')\n",
    "#print (tabulate(table2, headers=['iso','cname','Year','HDI women','HDI men','HDI','GNI','Life exp'],  tablefmt=\"grid\"))\n",
    "print('---------- HTML format -----------')\n",
    "\n",
    "#in HTML format\n",
    "#print (tabulate(table, headers=['iso','cname','Year','HDI women','HDI men','HDI','GNI','Life exp','Population','EySchool'], tablefmt=\"html\"))\n",
    "print('---------- LaTeX format -----------')\n",
    "\n",
    "#or in LaTeX\n",
    "#print (tabulate(table, headers=['iso','cname','Year','HDI women','HDI men','HDI','GNI','Life exp','Population','EySchool'], tablefmt=\"latex\"))\n",
    "#-------------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">2. Compute group averages:  </span> <span style=\"color:#3972A0\">Region and HDI class</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- By HDI Class--------\n",
      "               Year  HDI women   HDI men       HDI           GNI   Life exp  \\\n",
      "HDIcat                                                                        \n",
      "High HDI       2013   0.726778  0.756583  0.747806  13936.805556  73.841667   \n",
      "Low HDI        2013   0.413312  0.489313  0.458469   2046.000000  58.028125   \n",
      "Medium HDI     2013   0.600375  0.651312  0.630938   6223.437500  68.900000   \n",
      "Very high HDI  2013   0.853894  0.873660  0.866809  39306.425532  79.387234   \n",
      "\n",
      "                 Population  EySchooling  HDIclass  \n",
      "HDIcat                                              \n",
      "High HDI       60080.397222    13.725000         2  \n",
      "Low HDI        30851.046875     9.565625         4  \n",
      "Medium HDI     65757.237500    11.703125         3  \n",
      "Very high HDI  25311.285106    16.006383         1  \n",
      "----- By Region--------\n",
      "                                                Year  HDI women   HDI men  \\\n",
      "Region                                                                      \n",
      "East Asia & Pacific (all income levels)         2013   0.727700  0.765000   \n",
      "Europe & Central Asia (all income levels)       2013   0.806818  0.822455   \n",
      "Latin America & Caribbean (all income levels)   2013   0.709773  0.732227   \n",
      "Middle East & North Africa (all income levels)  2013   0.696353  0.772765   \n",
      "North America                                   2013   0.902000  0.910500   \n",
      "\n",
      "                                                     HDI           GNI  \\\n",
      "Region                                                                   \n",
      "East Asia & Pacific (all income levels)         0.751800  22201.200000   \n",
      "Europe & Central Asia (all income levels)       0.817159  25897.863636   \n",
      "Latin America & Caribbean (all income levels)   0.723955  12487.772727   \n",
      "Middle East & North Africa (all income levels)  0.746529  30433.823529   \n",
      "North America                                   0.908000  47097.500000   \n",
      "\n",
      "                                                 Life exp     Population  \\\n",
      "Region                                                                     \n",
      "East Asia & Pacific (all income levels)         74.980000  103757.385000   \n",
      "Europe & Central Asia (all income levels)       76.334091   19988.268182   \n",
      "Latin America & Caribbean (all income levels)   74.277273   16975.022727   \n",
      "Middle East & North Africa (all income levels)  74.617647   18637.876471   \n",
      "North America                                   80.200000  177616.200000   \n",
      "\n",
      "                                                EySchooling  \n",
      "Region                                                       \n",
      "East Asia & Pacific (all income levels)           14.075000  \n",
      "Europe & Central Asia (all income levels)         15.134091  \n",
      "Latin America & Caribbean (all income levels)     13.059091  \n",
      "Middle East & North Africa (all income levels)    13.470588  \n",
      "North America                                     16.200000  \n"
     ]
    }
   ],
   "source": [
    "#compute mean by HDI categories\n",
    "tableHDIcat = table.groupby('HDIcat').mean()\n",
    "tableRegion = table.groupby('Region').mean()\n",
    "\n",
    "#get rid of HDIclass column\n",
    "tableRegion = tableRegion.drop(['HDIclass'], axis=1)\n",
    "\n",
    "\n",
    "#Show the data\n",
    "print('----- By HDI Class--------')\n",
    "print(tableHDIcat.head())\n",
    "\n",
    "print('----- By Region--------')\n",
    "print(tableRegion.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">3. Drawing Charts: </span> <span style=\"color:#3972A0\">bar graph using <span style=\"color:#00acee\"> &#9733; Highchart &#9733; </span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"BarGraphHDIregion.html\" scrolling=\"no\" frameborder=\"0\" width=\"90%\" height=\"410px\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "#-----------------Prepare the data for Highcart-------------------------\n",
    "\n",
    "# Records each columns as an array to be ploted - Set the number of decimals to 3\n",
    "HDI_women_HDIcat=str(np.around(tableHDIcat['HDI women'], decimals=3).tolist())\n",
    "HDI_men_HDIcat=str(np.around(tableHDIcat['HDI men'], decimals=3).tolist())\n",
    "\n",
    "#store index in a list\n",
    "HDIcat=str(tableHDIcat.index.tolist())\n",
    "region=str(tableRegion.index.tolist())\n",
    "\n",
    "#Store data as string list to be displayed in Highchart - Set the number of decimals to 3\n",
    "HDI_women_Region=str(np.around(tableRegion['HDI women'], decimals=3).tolist())\n",
    "HDI_men_Region=str(np.around(tableRegion['HDI men'], decimals=3).tolist())\n",
    "\n",
    "\n",
    "#----------------------   write html code -----------------------------\n",
    "\n",
    "htmlStart=''' \n",
    "<script type='text/javascript'>//<![CDATA[ \n",
    "var dataValues =''' \n",
    "\n",
    "html1=''' \n",
    "; \n",
    "var dataValues2 = \n",
    "''' \n",
    "\n",
    "html2=''' \n",
    ";\n",
    "var categories = \n",
    "'''\n",
    "\n",
    "html3='''\n",
    ";\n",
    "// Define the variables\n",
    "var title=\"Human Development Index by gender, 2013\";\n",
    "var c1=\"rgba(165,170,217,1)\";\n",
    "var c2=\"rgba(126,86,134,.9)\";\n",
    "var serie1=\"Men\";\n",
    "var serie2=\"Women\";\n",
    "</script>\n",
    "'''\n",
    "\n",
    "htmlEND='''\n",
    "<html><head>\n",
    "  <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\">\n",
    "  <title>Highcharts with variable Column - by ThomasRoca</title>\n",
    "  <script type='text/javascript' src='//ajax.googleapis.com/ajax/libs/jquery/1.7.2/jquery.min.js'></script>\n",
    "\n",
    "<script type='text/javascript'>\n",
    "$(function () {\n",
    "    $('#container').highcharts({\n",
    "    chart: {type: 'column'},\n",
    "    plotOptions: {column: {grouping:false,shadow:false,borderWidth: 0} },\n",
    "title: { text:title },\n",
    "xAxis: {categories: categories },\n",
    "series: [{ name: serie1, color: c1, data: dataValues,pointPadding: 0.3 }, \n",
    "          {name: serie2, color: c2, data: dataValues2,pointPadding: 0.4 }\n",
    "     ]\n",
    "    });\n",
    "});  \n",
    "//]]>  \n",
    "\n",
    "</script></head>\n",
    "<body><script src=\"http://code.highcharts.com/highcharts.js\"></script>\n",
    "<div id=\"container\" style=\"height: 400px\"></div>\n",
    "</body></html>\n",
    "''' \n",
    "#------------------------------------------------------------------------------- End of HTML code\n",
    "\n",
    "#open an handle\n",
    "f = open('BarGraphHDIregion.html','w')\n",
    "#store the html file content\n",
    "file_content=(htmlStart+HDI_men_Region+html1+HDI_women_Region+html2+region+html3+htmlEND)\n",
    "# htmlStart+HDI_men_HDIcat+html1+HDI_women_HDIcat+html2+HDIcat+html3+htmlEND)\n",
    "#write the content within the handle\n",
    "f.write(file_content)\n",
    "f.close()\n",
    "\n",
    "#generate an html file\n",
    "filename='BarGraphHDIregion.html'\n",
    "#Embed the html file (NB. it probably exists smarter way to do all this)\n",
    "HTML('''<iframe src=\"BarGraphHDIregion.html\" scrolling=\"no\" frameborder=\"0\" width=\"90%\" height=\"410px\"></iframe>''')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\"> Or by HDI categories ...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"BarGraphHDIclass.html\" scrolling=\"no\" frameborder=\"0\" width=\"90%\" height=\"410px\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#open an handle\n",
    "f = open('BarGraphHDIclass.html','w')\n",
    "#store the html file content\n",
    "file_content=(htmlStart+HDI_men_HDIcat+html1+HDI_women_HDIcat+html2+HDIcat+html3+htmlEND)\n",
    "#write the content within the handle\n",
    "f.write(file_content)\n",
    "f.close()\n",
    "\n",
    "#generate an html file\n",
    "filename='BarGraphHDIclass.html'\n",
    "#Embed the html file (NB. it probably exists smarter way to do all this)\n",
    "HTML('''<iframe src=\"BarGraphHDIclass.html\" scrolling=\"no\" frameborder=\"0\" width=\"90%\" height=\"410px\"></iframe>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#<span style=\"color:#a6ca6d\">4. Some statistics: </span> <span style=\"color:#3972A0\">with python </span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">4.1 Descriptive statistics: </span> <span style=\"color:#3972A0\">with describe() </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td> 147</td>\n",
       "      <td>  2013.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td> 2013.000</td>\n",
       "      <td> 2013.0000</td>\n",
       "      <td>  2013.000</td>\n",
       "      <td>  2013.000</td>\n",
       "      <td>    2013.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI women</th>\n",
       "      <td> 147</td>\n",
       "      <td>     0.671667</td>\n",
       "      <td>      0.172830</td>\n",
       "      <td>    0.275</td>\n",
       "      <td>    0.5370</td>\n",
       "      <td>     0.704</td>\n",
       "      <td>     0.812</td>\n",
       "      <td>       0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI men</th>\n",
       "      <td> 147</td>\n",
       "      <td>     0.712918</td>\n",
       "      <td>      0.150502</td>\n",
       "      <td>    0.369</td>\n",
       "      <td>    0.6030</td>\n",
       "      <td>     0.740</td>\n",
       "      <td>     0.830</td>\n",
       "      <td>       0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI</th>\n",
       "      <td> 147</td>\n",
       "      <td>     0.697429</td>\n",
       "      <td>      0.159910</td>\n",
       "      <td>    0.337</td>\n",
       "      <td>    0.5785</td>\n",
       "      <td>     0.724</td>\n",
       "      <td>     0.828</td>\n",
       "      <td>       0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNI</th>\n",
       "      <td> 147</td>\n",
       "      <td> 17780.605442</td>\n",
       "      <td>  19105.738620</td>\n",
       "      <td>  444.000</td>\n",
       "      <td> 4202.0000</td>\n",
       "      <td> 11337.000</td>\n",
       "      <td> 24596.500</td>\n",
       "      <td>  119029.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life exp</th>\n",
       "      <td> 147</td>\n",
       "      <td>    71.096599</td>\n",
       "      <td>      8.959234</td>\n",
       "      <td>   45.600</td>\n",
       "      <td>   66.4500</td>\n",
       "      <td>    73.500</td>\n",
       "      <td>    77.800</td>\n",
       "      <td>      83.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td> 147</td>\n",
       "      <td> 43836.665306</td>\n",
       "      <td> 157208.675542</td>\n",
       "      <td>  105.300</td>\n",
       "      <td> 3877.0500</td>\n",
       "      <td>  9954.900</td>\n",
       "      <td> 29325.550</td>\n",
       "      <td> 1385566.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EySchooling</th>\n",
       "      <td> 147</td>\n",
       "      <td>    13.108844</td>\n",
       "      <td>      2.822098</td>\n",
       "      <td>    5.400</td>\n",
       "      <td>   11.1500</td>\n",
       "      <td>    13.200</td>\n",
       "      <td>    15.450</td>\n",
       "      <td>      19.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDIclass</th>\n",
       "      <td> 147</td>\n",
       "      <td>     2.333333</td>\n",
       "      <td>      1.142776</td>\n",
       "      <td>    1.000</td>\n",
       "      <td>    1.0000</td>\n",
       "      <td>     2.000</td>\n",
       "      <td>     3.000</td>\n",
       "      <td>       4.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count          mean            std       min        25%  \\\n",
       "Year           147   2013.000000       0.000000  2013.000  2013.0000   \n",
       "HDI women      147      0.671667       0.172830     0.275     0.5370   \n",
       "HDI men        147      0.712918       0.150502     0.369     0.6030   \n",
       "HDI            147      0.697429       0.159910     0.337     0.5785   \n",
       "GNI            147  17780.605442   19105.738620   444.000  4202.0000   \n",
       "Life exp       147     71.096599       8.959234    45.600    66.4500   \n",
       "Population     147  43836.665306  157208.675542   105.300  3877.0500   \n",
       "EySchooling    147     13.108844       2.822098     5.400    11.1500   \n",
       "HDIclass       147      2.333333       1.142776     1.000     1.0000   \n",
       "\n",
       "                   50%        75%          max  \n",
       "Year          2013.000   2013.000     2013.000  \n",
       "HDI women        0.704      0.812        0.940  \n",
       "HDI men          0.740      0.830        0.944  \n",
       "HDI              0.724      0.828        0.944  \n",
       "GNI          11337.000  24596.500   119029.000  \n",
       "Life exp        73.500     77.800       83.600  \n",
       "Population    9954.900  29325.550  1385566.500  \n",
       "EySchooling     13.200     15.450       19.900  \n",
       "HDIclass         2.000      3.000        4.000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Desciptive statistics\n",
    "table.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('NER', 'NOR') ('COD', 'AUS')\n"
     ]
    }
   ],
   "source": [
    "#values min and max\n",
    "minmaxvar1=var1.idxmin(), var1.idxmax()\n",
    "minmaxvar2=var2.idxmin(), var2.idxmax()\n",
    "print(minmaxvar1,minmaxvar2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#hitogram (value count), min and max value (nb on series)\n",
    "#var1.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">4.2 Descriptive statistics: </span> <span style=\"color:#3972A0\">Ploting correlation Matrix with <span style=\"color:#00acee\"> &#9733; Seaborn &#9733; </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nxQxU0EREBEr7xuo1QAfDMLZcWH/EMIweQLRpmksu2tfjQSEqaCIiUqpM03QD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\nggJNRESs8Af0dUIgMIll/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x976db90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RJEnVshKXJKmmTOKSJNWUSVySpJoyiUuSVFMmcUmSasokLklSTZnEJUmqKdcT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\nJEk1ZRKXJKmmTOKSJNXU/wdFmWl4dCHBogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x981aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "table3=table[['HDI women','HDI men','HDI','GNI','Life exp','EySchooling']]\n",
    "\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.set_context(rc={\"figure.figsize\": (8, 8)})\n",
    "sns.corrplot(table3)\n",
    "\n",
    "#Another display\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "#customized color\n",
    "cmap = sns.blend_palette([\"#a6ca6d\", \"#e5f6fc\",\"#70c9e1\",\"#2057a0\"], as_cmap=True)\n",
    "\n",
    "sns.corrplot(table3, annot=False, diag_names=False, cmap=cmap)\n",
    "ax.grid(False);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">4.2 Descriptive statistics: </span> <span style=\"color:#3972A0\">Visualizing our dataset with seaborn heat map </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                HDI women   HDI men       HDI\n",
      "Region                                                                       \n",
      "East Asia & Pacific (all income levels)          0.727700  0.765000  0.751800\n",
      "Europe & Central Asia (all income levels)        0.806818  0.822455  0.817159\n",
      "Latin America & Caribbean (all income levels)    0.709773  0.732227  0.723955\n",
      "Middle East & North Africa (all income levels)   0.696353  0.772765  0.746529\n",
      "North America                                    0.902000  0.910500  0.908000\n",
      "South Asia                                       0.533000  0.626429  0.591000\n",
      "Sub-Saharan Africa (all income levels)           0.448371  0.510257  0.484571\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5edbe30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KxsW/rD0DrN3G+TEzs36sVuveT1+qWsUR0ry7YV1sUwzELxUqSgtLejK/vpdPw+ATkt4F\niIiHgABWAI7Oc/1qpOpe0WHAvyTtHhGXA7fmbTYpbPMmMFdz5yJiB1IVcWHg6oiAVGG8DXgeeDxv\n+i9SJREmr7Q25gi+ChwTEcNJwanldxoRc+e+zR8RB5KqZAeQKo27AYdHxOLAA037eh34bkRsDYwF\nBrRofvrCOf43cFxEjAdmB95r1Z9ONB/7fMA7kt4EkHRKRMwPjJX0St72XuAkUnX5aUkTIuI94AVJ\n/42Id0mBb35gISY957d20I8VgMERMahxjBExL6n6ujPwEfCbDtpsfI9IGh0R55OqtwNIFe+GV0ih\n0szMpmnVKTlWseLYkQ9JQ6kAqxaWTyy8/ndErJhfr8en4etLETFzrm4NAkaTqkHfzxXHvYFrmvY3\nkE+/6eNIlaMxxQ0kfQLckoMaABHxLeBA4A1SOPpm3sdJwB15s1bz4yaQhn+bj+sE4FJJOwN30fF3\nuiPwa0lDJW2Sj/NrETEfqbq2T66YrcKk8y8PBR6QtFM+B61+u8fnCirA6cAPJe0KPN1JfzrSfOyv\nA3Pl4EtEnA4sDswREQvlbYrfZWdzC99k8nN+ZwfbPkOqtK5PmjpwjaS3gN+RqodbAVeQhsk7+h4b\nQ/ezS/oGqRJ5ZmEfcwOvddJfMzObBrji2Hvq5KHqpmWbkio550TEP0jDhcXhz4Y9gbNyyPmEdGFC\no5p4NWlY+2pJT0bE4cC5+aromUnz+IpOBS7K8xWnB74NHBYRK0h6urDdocBpETEq9+VtYBtJ9Yg4\nGLgxIqYjVeZ2ARal9bD7X4CTI2JM0/qrgVMi4ihSgOmogjWcFB4BkDQ+In6fz8lTwL0R8Z/cxkOk\nKmQduA44MyK2B94FPomIATkUN4wCvgL8mTR38uqIeKepP+1ecDLJsefztB9wQ0RMAB6V9HBE7An8\nX0RMJJ3TXYEVmz/foq3mc75zB304HxiR51/OAZwNIOn9iHicVGV9H6CL7/E54IcRsR0pRB9X2M8q\nwBFtnhczM7PS1er1nriAtLoi4oukytJXy+5LVeWrqreX9N2y+1IVEbEc8F1Je3WyWX3QDx/uqy5Z\nH3noR2lq8cpHP1hyT6ynPfGTNQFY9ZiHSu6J9bRHTxoEvTievMYPHu5WGHv4hDX6rO7Yn4aqu2Pa\nTs/dJOlBYIaI+HzZfamQA5i0+mhmZtMoD1VXiKS/MeX3VLQmkg4ouw9VImm/svtgZmZTh958ckye\nRnUOsBLpws49JL1QWL8DaVrdBOA3LW65NwlXHM3MzMz6ry2BgfmWe0cy+X2nTwY2JF3ke1hEzNlZ\nYw6OZmZmZiWqdfOnC2sDNwNIeghYrWn9k6RbB86cm+t0+p6Do5mZmVmJenmO4xyk+zA3TMjD1w1/\nJd255WngOknFbSfj4GhmZmZWql6tOY4lPZCjYTpJEwEiYiXSLQ0XIz3yd8GI2LazxhwczczMzPqv\nUaRw2Lh93pOFde8B44GPcph8nRZPvCua5q+qNjMzMytTL99S51pg4/wgEoDdImIYMJukEfmxuPdF\nxMekx+Ve3FljDo5mZmZmJerN4CipDuzbtPjZwvrzSU9La4uDo5mZmVmJ+vge3t3iOY5mZmZm1hZX\nHM3MzMxK1JtPjulpDo5mZmZmJapObHRwNDMzMytVhQqODo5mZmZmpapQcPTFMWZmZmbWFlcczczM\nzEpUq1DJ0cHRzMzMrESe42hmZmZmbalQbvQcRzMzMzNrT61er5fdBzNrzf9ymplNPXqtMLjRzx7t\n1n/vbz9y1T4rWnqo2szMzKxEfnKMmfWIPS9S2V2wHjZitwDgO+eOLrkn1tOu2Hc5AHa+YEzJPbGe\nduleX+rV9qsTGz3H0czMzMza5IqjmZmZWZkqVHJ0cDQzMzMrkW8AbmZmZmZtqdC1MZ7jaGZmZmbt\nccXRzMzMrEQVKjg6OJqZmZmVqUpD1Q6OZmZmZiXyDcDNzMzMrC3ViY2+OMbMzMzM2uSKo5mZmVmZ\nKlRydHA0MzMzK1GFcqODo5mZmVmZqnRxjOc4mpmZmVlbXHE0MzMzK1GFCo4OjmZmZmZlqlBudHA0\nMzMzK5PnOAIRMSQiRk7B9vvnP4dGxJ5T8LntImJcRCz8WfrZSbtT1I8O2vhpRPw5Iu6OiLk62Gb5\niLg+Iu6IiIcj4vgpaH/liDguv361xfrjI2Lvz3wA7fdj6Yj4SSfrj4+IvSNivebfieIxlC0ivhgR\nD/RQW3+LiIEdrFshIn7QE/sxMzPrS715cUx9Crc/FkDSLZJGTMHn9gROB/aawv116jP0YxIRMTOw\nu6TVgBeBDVpsMxcwEjhY0gbAmsCK7YY9SU9I+nF+2+p8T+l38FmdDJzSyfoO+9F0DP1JZ8f8NLBU\nRCzRh/0xM7OpVK2bP32pN4eqWx5LRGwL7AcMIP3PdStgH2CeiDgbeBhYFjgP+B3wD2BJ4GFJ+zW1\ntTgwF/AL4C8RcaKkCRFxMfAxsBgwY25nc2BRYAtJL0bET4F1gOmB0yRdExF3Aa8B85AC3dKSjoqI\nY4EtSOfrXEkX5M9/BZgXeELS7sW+SRofEQ9GxIXAwsAtLU7HFsCfJL2QPzMxInYGPo6I6YALgEXy\n5/+fpOPysc2T93sy8G1Jw4CZcjXvC8CT+VzVgK0iYjtgFuAgSY9ExLeAQ4AJwH35GBcBzgFmyvs7\nVtIfI+JJ4C5gpfx9bSFpbOE7CGA6SW/n952el2YRMQTYW9KwiHgOuA+I/D1sk7+/i/J3NxA4APhL\nXrZ44fu7Kn9/jwMrAOOAe4GhpN+RrwHvk36vliL9pelYSXd30K/1gBPzOXoB2Bu4Ejhd0j0RsRrp\nLzvbAud31GZEbA0cAXwC/BvYXlIduArYHziss/NjZmbTgOqMVJdyO56lgc0kDQZGA0MlnQS8LWn/\nFtvuDqwBbBoRCzStHw5cJOk94AFS0IAUcF6SNBQYA3xR0mbA74HNI2KTvGwwqRJ4TETMmT93haSN\nSYGBiFgF+HruwxrA0hExR+7v14DVgTWbh8rz5+YE1gYuBmaPiPma+r8w8FJxgaT3JX1CCoAPSPo6\nMIgUrhvH9idJawPvFj46E3CEpHWAeSNi87zti5I2zOfqvIiYGzge2CAf/+cjYiNSWDs1H9NepFAD\nMHs+J0OAl4FNmo5hPeDJfMyztzgvn6N9i5OC11rA/LmNffIxrAVsn8/FXsBr+RxsBJwYEfPm431I\n0kakwPl+7svo3M89gDckrQdsCZzdSV9GAFsVjnvXvGyXvH43UrDfs5M2a7nPv8jn+npgjrzuKWDI\nFJwbMzPrp2q17v30pTIujnkDuCQixpEqi/c3rS+eguclvQ8QEa+QwhH5/fTAjsCLOSTNQ6pGXZU3\neTT/+S4pPAK8k9tYAfhKRNyZl88AfDG/VlN/liFVO+ukqtH3ImIAsEBEXEGqbM1GqqAWXQlsCnwE\n3A28QhpSv6qwzd+BVYsfylXURUiVs9UjYn1gLKna1tDcR4B/SPpnfn0/KQhCqrohaXRELESqjM0P\n3JSKhcwOLAGMIgXo4aQAVvzdeCz/+U8K30E2L6k6CPAhXZ+Xzrwp6eWmfS0D3JSP4Xng9Ig4C7g9\nLxsXEaNJVWmY9HsfnV8Xv/fBETEoL58+IuZpVEsbImJ+YCHg6nyOZgZuBS4ETs7hex3gQOAsYJ2m\nNufNr+vAocBREXEQ6ffwD3ndK6RzZ2ZmVhl9WnHMVb3jgW+TKjXj+TQotsrMnc3R25RUXdpA0iaS\nBgELRsSKLbZtbvsZ4E5J6wMbAleThiMBJrbYdtWIqEXEgIi4lVR1+4Kk7wDHkIJF8z4GAuQwdyGp\nWvlc0zbXA19vzHXLgfQ0UsDZFXhX0o552ayFz7U6L4vkYAgp1DyVX6+R216RFFRfIoWyjfLxnwk8\nBJwAXCppZ9LQdPF3o7Pv4XXSUDC0d14602o/Y0iVRyJiiYj4bV42OC+bHViRTyu3nfX1GWBkPu5N\ngWtIobLZm8C/gG/mbU8C7sh/ebiaNNx9raSJuS/NbTaCaI1UHT0+Vy5rpKkZAHOTzp2ZmU3jat38\npy/19sUxX4uIR/LPw8CCpMrWg8A9wAdAYyhzdERclj9XL7TRkT2Ay5qW/ZpUdSy20dxOXdJ1wLiI\nuAf4MzBR0rhWxyDpCeDm3O97gctJQWuJiLibT0Nn85DsrsBlEXEvsDypOnpycQNJ/yENfY7I1c8H\ngMcknQv8iRQq7ybNPXy2MOxbPD+N128BZ0TE/cDfJDXmVC4eEX8CziXNJXyTFETviYgHScPwz+bj\nOCXvbyM6roY1fyd3kYaP6eK8dPSddvZ910nzB5fI8xcvAU4lDRHPm8/tnaRg9kYH/W1ua9nc1ijS\neZpsn3nZwcCNETEK2Bd4Oq+/iDQk/Zv8vqM2Gz8PA9dHxO2k3//r8ucGkaumZmY2bavSUHWtXu+r\nC2+tv4qI/wfsIckVtDZFxOXAMZL+3slm9T0vajUrwapsxG5pFsl3zh3dxZZWNVfsuxwAO18wpost\nrWou3etL0IuXsGx31tPdCmNXHbBCn8VHP6vaesIRpLl81oY8beD5LkKjmZlNI6pUcfSTY6zbJD0D\nHFl2P6pC0lN8OgfVzMysMhwczczMzErkRw6amZmZWb/jiqOZmZlZiSpUcHTF0czMzMza44qjmZmZ\nWYn6+ibe3eHgaGZmZlaiKg1VOziamZmZlahKwdFzHM3MzMysLa44mpmZmZWoQgVHB0czMzOzMvkG\n4GZmZmbW7zg4mpmZmVlbPFRtZmZmVqIKjVQ7OJqZmZmVycHRzMzMzNriJ8eYmZmZWVuqVHH0xTFm\nZmZm1pZavV4vuw9m1pr/5TQzm3r0Wl1w+IXPdOu/9xcOX7bPapYeqjYzMzMrUZWGqh0czaZi+1/2\nXNldsB529k5LAzD0F4+X3BPrabcc8WUAtjnzqZJ7Yj3t9weu2Ls7qFBy9BxHMzMzM2uLK45mZmZm\nJapOvdHB0czMzKxUFRqpdnA0MzMzK1Nv3gA8IqYDzgFWAj4C9pD0Ql63IPC7wuZfBr4v6YKO2nNw\nNDMzM+u/tgQGSlorIgYBp+ZlSHoNWB8gIr4K/BgY0VljvjjGzMzMrES1Wvd+urA2cDOApIeA1Zo3\niIgacAawr6RO7ynZVsUxIpYH5qEwf1PSPe181szMzMw61stzHOcAxhbeT4iI6SRNLCzbHHhaUpf3\ngOsyOEbE2bnBF5n0SRbrt9dfMzMzM+tIL18bMxaYvfC+OTQC7AD8qp3G2qk4fg0ISePb65+ZmZmZ\nTSVGkQqAV0fEmsCTLbZZTdID7TTWTnB8Ec+FNDMzM+sdvTtWfS2wcUSMyu93i4hhwGySRkTE/MB7\n7TbWTnB8BxgdEfcDH+ZldUm7T0mvzczMzGxyvRkb88Uu+zYtfraw/g1g1Xbbayc43px/GvMba0w6\n19HMzMzMPqMq3QC8yyFoSRcDfyFdlTMP8LikS3q5X2ZmZmbThF6+HU+P6jI4RsROwB+AxYHFgGsj\nYnhvd8zMzMzMpi7tDFUfDqwh6S2AiDgRuBu4sDc7ZmZmZjYt6M1HDva0dq6Wnq4RGgEkvQlM6L0u\nmZmZmU07qjRU3U7F8cmI+BWpwlgDhgNP9GqvzMzMzKYR1ak3tldx3BP4GPgNcFF+vV9vdsrMzMzM\npj5dVhwlfQAc8Vl3EBFDgDuAYZKuLCx/EviLpN0i4veStmn63D7AgpJ+1LT8AWB7YDfgFUnnt9GH\nvwF/B4qP2DlM0qNTcBxfAFaWdH3T8lmAK4HPAY9K2rPFZ48HNgHWkjQhL3sQ2E7SP9rY94zAjpIu\nzG11edwR8Thwn6QDCst+DnwdOLD4rPGIGAnsLOmTrvrSSf9GALu0ejh6/h3YW9KwiHhV0kJN6yf7\n/suSf1eWkfRxN9u5GBgp6ZYW62YGzpW0a3f2YWZm/USF7sfTYXCMiMckrRIRzc8zhHQD8OmnYD/P\nkMLelbntFYFZyPeD/IyhYUruJVkHNu5mGNgQCOD6puXrAeMlfSUiXoqIuSW90+LzXwSOAk4s9Kld\nCwN7kKYLdPm5iFib9EihDSJiNknj8qptgZUkvV/cXtKwKehLK98FrmwVGluYbJupJTRmPXWP0npH\nbUkaHxHvGNFpAAAfG0lEQVT3R8TOki7tof2ZmVlFVSg3dhwcJa2S/+zu4wbrpDmRy0TEHJLGAjsC\nvwW+ANCoQkXEOqSHbL8D/Bd4IK8/CRgK/BOYr3kHEfFTYB1geuA0Sde06MdkX0tEzEGqlM1Fqhie\nLem8iNgP2JlUoXwEOAQ4Epg5IkY1VR0fAH4VEecAN3YQGuvAL4A9IuJ6SY8X+jCANAVg8UL/r4qI\nu4DXSPfOfAlYLiKOyx/bIiK+BcwLHNdcBSWFzKvz+doFODsifpCP8YaI+BnwI+Aj4ALgx6RQvBjw\na2AA8AEp7C8EnJr7Nh+wb/F5lhFRI32fX87vtyVNZRiQj3urFudjEhHxiqSF8zE/BqxAum/otyT9\nIyKOBbYg/b6eK+mCiDgM+Dbp9+QeSUfmauySuZ/zAmcD2wDLkKqhD0XEgcCw3LffSTqzgz59ATgf\nmBkYD+yVj2VuSSfkKuvjwErAPh20WYuIZUjf7yekqSHfkfQv4CrSjfUdHM3MpnEVyo1t3cfxhxHx\ng8LPcRFxeERsNoX7+j2wdX69OnA/n56rRmXmXGB7SRuTwlItIlYDBktajRTmZm/q3ybAFyUNBjYA\njomIOVvs/9aIuDP/3JaXLUn6H/1QUjA9NC/fFdhf0lrAmNzPnwK/bRHSNgNeBr5JCpBLR8RMLfY/\njhQ+Lo6IgXlZDdgbeE3S2sBGwIkRMW8+J1fkc3ESMFrSj/Nn/iVpI1Klb5LHCOUwvA5wI3BxY72k\nE4BXga+RHh05o6R1JV1e6MspwEn5uE8HVgGWIw3rbwT8nDRFoGhp4L3GEHx+v1n+Pkbn89puFa8O\nPJSP+TZgWESsQhpeXyP/LBMRKwDfAr6a+7p0/n2sAx9I2oT0+7appG8CPwO2j4gvAdsBawPrAlvm\nYNescS7OkLQ+KTj/DLgsfx7S930dsFQXbW4EPJj//CEwJ4Ckd4H5ImKS32czM7OpWTtXVS9JCgMj\nSf9D3QYYC6wTEetJ6mr+YyMcjgTOjYgXgXs72HYBSc/n16NI/1NeGvgzgKT/RMRTTZ9ZAfhKRNxZ\nOKbFSEO1Ra2Gql8HvhsRW+djGpCX7wYcHhGLkyqKtcLP/0TEYsCxpGrbBsD/y22sC/y7+eAk3RsR\nt5MqfA3LArfn9eMiYjTpnAMo/1ncb530JB9IFclZmnazA+kvBI2Au1BEbCDpjqa2xOSWyceLpOvy\nMa4DHBcR40mhvflB6PPlfjS8AVwSEePysT3AlHks//lPUrVzGeDhPAz+Cel72RZ4sBBW7wWWz68b\n81bfBf5aeD0T6XdlMdKcW0iV5qUoPLOzYEXg6Ij4PumcfSzp3Yh4LJ+TXUh/0fhyizaXLrRzIfB9\nUnXxPeDowrpGRfk/XZwTMzPrx6o0VN3OMPSywBBJZ0g6nVQ5mU/SlqRKUFskvQTMChxEqty0Ok0v\nR8Sy+fUa+c/RwBoRUYuIWUkVsKJngDtzZWhD0hDtiy3abrW/Q4EHJO0EXFPYZk9gH0lDSFW3tUj3\nrmw+XwNIQ7h1UjD4e17+QYt9NRxDulBmqfx+DDAYIFefViRVW+HTi3kmFvbd1a/XcOAbkjbJlbeD\ngP0L6xvVv1ZzV8eQz3tE7BARB5Aqjz/MF3I8zeTn4DVSWCJXeo8nDSHvSRrindJ/HZqrk88Aq+bv\nf0BE3EoKeoMiYvo8VL4uk4e/yYI+KSz/VdL6+fflEib/C0bDGOD7ebu9Sb8fkKY2HALMJOnZ3L/O\n2twCuDdXbK8hhciGuUhB28zMpmG1bv7Tl9oJjnPxaSUOYEZgtvy6nd4WLxK4ElgkVxWLyxt/7g1c\nmqtyi5IuwnkCuIk013Akk1a36rkyNi4i7iFVJicWLgYpKg5V3xkRW5KGGvfPc+sOBj7Jw8hPAfdG\nxJ/y/h7My7aIiMZQJfk4ziJVvO4HHibdtuioDs4Dkj4iVTTnyMsuAOaNiHuBO4HjJTWHideBgXlu\nYvNFF/97HRGr5n2MKaz/P2DtiFiESc93cxt14HvAUbl6Owy4PP9cnc/v0qQLdf5H0gvAAhExnaT3\nSJXiB4F7SAF64cI+Julvq2NoXp6//5tzu/cCl0t6kjRHcBTwEPCSpD+02M8kr/Pn/hQR90XEI6Tw\n3lwZbnzucOCH+XfjUvK9S/PV6MuTpgHQQZsvF9r6M3BC/l3aCzgDICLmAt7Ndy0wM7NpWJVuAF6r\n1zuffhYRB5EudriOVF3blPQ/v4HA6pJ26O1O2tQtIo4EnimEN+tCvgDrXUlXdLJZff/LnuurLlkf\nOXunNJNh6C8e72JLq5pbjvgyANuc2Tyjyqru9weuCL14Dcv3rnqxW3f0OHm7JfosPnZZcZR0Bmny\n/7+BvwHbSDoHuIHJL5SwadOvgG/lYWPrQr6P41pdhEYzM5tG1Lr505fauTgG0ry7+YCfkC6O+ask\nl0IMAEkfki7KsTZIGk+6hZGZmVn/ujgmP21kU9KtdGYAdo+I03q7Y2ZmZmbThApNcmzn4pihwE7A\nh/nih41IVwWbmZmZWTdVaai6neA4oen9jC2WmZmZmVk/105wvBr4HTBPRBxCuiXKyF7tlZmZmdk0\nokIj1Z1fHBMRQbpZ9+PAP0jPlj6NdMNlMzMzM+umvr6Jd3d0WHGMiONJj7YTaWj6SNKNqM8BvtgH\nfTMzMzPr9/pLxXEX0pNCPkd6tvL3gQWBb0m6pQ/6ZmZmZmZTkc6C41hJrwCvRMTqpCHr70nyhTFm\nZmZmPaRK93HsLDhOLLx+EzhMUrceiWNmZmZmk6pQbmz7yTEfOjSamZmZ9YIKlRw7C47LR8RL+fXn\nCq8B6pKW6MV+mZmZmdlUprPguEyf9cLMzMxsGlWdemMnwVHS3/qwH2ZmZmbTpAqNVLc9x9HMzMzM\nekGVgmM7jxw0MzMzM3PF0czMzKxMVXrkoIOjmZmZWYmqNFRdq9d9e0azqZT/5TQzm3r0Wrz78XX/\n6NZ/74/bfNE+i56uOJqZmZmVqUIlRwdHs6nYzLvcXnYXrIeNv2QjAAbudFvJPbGe9vFlGwMw487+\nbvubjy7duOwuTDUcHM3MzMxKVKGCo4OjmZmZWZkqlBsdHM3MzMzKVKWKo28AbmZmZmZtccXRzMzM\nrES+AbiZmZmZtaVKQ9UOjmZmZmYlqlJw9BxHMzMzM2uLg6OZmZmZtcVD1WZmZmYlqlVorNrB0czM\nzKxE1YmNHqo2MzMzsza54mhmZmZWogqNVDs4mpmZmZXJwdHMzMzM2uInx5iZmZlZW6pUcfTFMWZm\nZmbWFgdHMzMzM2uLg6OVLiKGRMS7EbFIYdnPImKXKWhjr4iYIbc1so3tz4mIRz9rnztp9/sRsXpP\nt2tmZv1XrVbr1k9fcnC0qcVHwEWF9/Up/PxRwPTtbBgRswBrA6MjYr0p3E+nJP1c0iM92aaZmfVv\ntVr3fvqSL46xqUEduAOoRcT+ks4uroyIw4BvA/8F7pF0ZEQcD6wFzAr8FlgIGAmcDiwdETcCCwDX\nSfpR0/62A24HbgYOAO7O+3kqv14JeAZ4DViXFGo3zfu6EJgnt3OQpKcj4u/AGGA0MDfwO+AeUhBe\nFBiY9zMa+DUwJ/A54GxJ533ms2ZmZtbHXHG0qUHj70v7AYdExJKNFRGxIvAt4KuS1iKFws1IYfOv\nktaWdA7wKrB9bmsmYAtgMCmwNduDFAD/BKwSEQvn5bMBv5W0bv7sKEnrkYLf8sDRwO2SNgD2Bs7N\nn1sEGCbp0MI+9gFezH3eHhgELAmMlDQUGAoUtzczs2lUrZs/fcnB0aYakt4Gvgtcwqe/mwE8KGlC\nfn8vKcQBPNtBU09L+kTSeFKV8n8i4kv586cCNwATgX0LmzTmPb5LqhACvEMKoysAu0fEncAFpOoi\nwJuS3mnqwzLAg/m4npd0OvA6sGVEXAYcAwzooP9mZjYNqdJQtYOjTVUkXQ8I2JVUVXwGGBQR00dE\njTR03AiMEwsfncincxw7mx+5B3C0pE0kbQJsSAqDjRDX2WefAX4paX3S0PnlLfrRMAZYHSAiloiI\n35IqjA9I2gm4hmo9197MzHpJrZv/9CUHR5sa1Jk0sH0XGA8g6WngKmAU8BDwkqQ/FD7XcC9wY4vl\n/3sdEQNJw8ZXNpZJ+ifwOLAtnYfGOnASsF2uON4EPN1if4335wNLRMRdpArqqcB1wP552cHAJ4XA\namZmNtWr1etTevGqmfWR+sy73F52H6yHjb9kIwAG7nRbyT2xnvbxZRsDMOPO/m77m48u3Rh6cZTo\n7Lte7VYY23/IQn1WdvRV1WZmZmZlqtDEJQdHMzMzsxJVKDc6OJqZmZn1VxExHXAO6R7FHwF7SHqh\nsH510jz8GunWdjtK+qij9nxxjJmZmVmJevmRg1sCA/N9hY8khUQA8t1KLgB2lTSY9GCMxTprzMHR\nzMzMrES9fAPwtUmBEEkPAasV1i0DvAUcmu/4MY+kju6RDDg4mpmZmZWql28APgcwtvB+Qh6+BpiP\n9PjeM4GNgA0jYv3OGnNwNDMzMytRLw9VjwVmL7yfTlLjwRVvAc8r+S+pMrlacwNFDo5mZmZm/dco\nYFOAiFgTeLKw7kVgtohYMr8fzKcPt2jJV1WbmZmZlaiXb8dzLbBxRIzK73eLiGHAbJJGRMRw4Ip8\nocwoSTd11piDo5mZmVmZejE5SqoD+zYtfraw/k5gULvtOTiamZmZlahKNwD3HEczMzMza4srjmZm\nZmYlauPK6KmGg6OZmZlZiSqUGx0czczMzMpUodzoOY5mZmZm1h5XHM3MzMxK5KFqMzMzM2tLrUKD\n1Q6OZmZmZmWqTm6kVq/Xy+6DmbXmfznNzKYevRbvLnnozW79936XQfP1WfR0xdHMzMysRBUqODo4\nmk3Nbnl2XNldsB42dJnZADj1tpdL7on1tMM2/jwAF97/Rsk9sZ42fK35e7V93wDczMzMzNpSodzo\n4GhmZmZWpgrlRt8A3MzMzMza44qjmZmZWYk8VG1mZmZmbfENwM3MzMysPdXJjZ7jaGZmZmbtccXR\nzMzMrESe42hmZmZmbalQbnRwNDMzMytTlZ4c4zmOZmZmZtYWVxzNzMzMSlSdeqODo5mZmVmpKjRS\n7eBoZmZmVqYqBUfPcTQzMzOztrjiaGZmZlaq6pQcHRzNzMzMSlSloWoHRzMzM7MSVSg3Ojha9UXE\nkcCGwABgInC4pEensI25ga9LGhkRFwMjJd3SyfYzAX8DTpF0SifbLQj8QNL+U9IfMzObdvgG4GZ9\nJCKWAzaXtLGkIcAhwG8+Q1MrA9/Mr+ttbL8NMBLYNSI6/Dde0msOjWZm1l+44mhV9x6waETsDtwi\n6YmIWAMgIlYBzgAmAB8CewLTk6qJX83bPABsDxwDrBQRe+Z2946II4A5gX0lPdK03+HAwcACwKbA\nDRExP3AladRhJmCf3L+Rkr4aEdsC+5Eqo3VgK0lv9fgZMTOzSqlQwdEVR6s2SS+TKoVrA/dHxBjg\nG3n1CGD/XIk8BziN1tXEOnAicIekEXnZnyVtCJwJ7FrcOCKWBmaV9BRwEdCoKK4BvAlskpfN2rS/\npYHNJA0GRgNDP9tRm5lZf1Lr5k9fcsXRKi0ilgTGShqe338FuCki7gQWlvRk3vRe4Gctmqg1/dnw\nl/zna8AsTev2AGaNiJvy59bK/bgRWAr4I/AJKYwW230DuCQixgHLAvdPybGamVn/5IqjWd9ZCTgr\nIgbk988B75CGp/8dESvm5esBIg1ZLxAR00XEXMDief0E2vj3Ie/n28A6kjaR9HVSIN0PGAK8Imko\ncBLwE3LFMSLmAI7Pn90TGN/O/szMzKYm/h+XVZqka0nVxEci4j7gZuB7ksaSAtpZEXEPcCBwiKTX\ngNuAR4ALSEET4AVgxYg4OL+vF/4sDjdvThrGfrew7GJgR1Iw3SNXO39BCo4A9dyfUcCDwD3AB8DC\n3T8DZmZWfdUZrK7V6+1cQGpmJajf8uy4svtgPWzoMrMBcOptL5fcE+tph238eQAuvP+NkntiPW34\nWvNDLya060eP7VYY+8Zyc/RZevQcRzMzM7MSeY6jmZmZmfU7rjiamZmZlahCBUcHRzMzM7MyVemR\ngw6OZmZmZiWqTmz0HEczMzMza5MrjmZmZmYlqtBItYOjmZmZWakqlBwdHM3MzMxKVJ3Y6OBoZmZm\nVqoKFRx9cYyZmZmZtccVRzMzM7MSVajg6OBoZmZmVibfANzMzMzM2lKh3Og5jmZmZmbWHlcczczM\nzEpUoYKjg6OZmZlZmao0VO3gaGZmZlaq6iRHz3E0MzMzs7bU6vV62X0ws9b8L6eZ2dSj18qCd7/0\nQbf+e7/e4rP0WcnSQ9VmZmZmJfIcRzPrEX9/v+weWE9bbNb0531/H19uR6zHrbPYzAA8/tonJffE\netqXFxzQq+3XPMfRzMzMzPobVxzNzMzMSuShajMzMzNrS4Vyo4OjmZmZWZlccTQzMzOz9vRicoyI\n6YBzgJWAj4A9JL1QWH8IMBx4Iy/aW9KzHbXn4GhmZmbWf20JDJS0VkQMAk7NyxpWBXaS9Fg7jfmq\najMzM7MS1br504W1gZsBJD0ErNa0/ivA0RFxb0Qc2VVjDo5mZmZmJarVuvfThTmAsYX3E/LwdcNI\nYG9gA2CdiNiss8YcHM3MzMxK1MsVx7HA7IX300maWHh/uqS3JX0C3ACs0lljDo5mZmZm/dcoYFOA\niFgTeLKxIiLmBJ6OiFkjokaqOv65s8Z8cYyZmZlZiWq9ez+ea4GNI2JUfr9bRAwDZpM0IiKOBu4k\nXXF9u6SbO2vMwdHMzMysRL2ZGyXVgX2bFj9bWH85cHm77Tk4mpmZmZWoQvf/9hxHMzMzM2uPK45m\nZmZmZarQMwcdHM3MzMxKVJ3Y6OBoZmZmVqoKFRw9x9HMzMzM2tOvK475mYsbAgOAicDhkh7tYNsv\nAiMlfbWLNncBdiZVlgcCP5J0Wyfbvyppoc92BJ9NRHwOeB7YWdI1edn0wO2kc7GZpPfy8gWBH0ja\nvxv7WxrYTdLRHaw/HngFeAbYR9KwwrqVgW9K+vFn3X9Pafd3oM22/gYsI+njFutWALaWdEJ392Nm\nZtVXoYJj/w2OEbEcsLmktfP7lYFLgC93o805gWOBL0n6b0QsDDwMfKGTj9U/6/66YTfgdGB/4Jq8\n7PPA7JImebi5pNfydt1xMrB7J+s7PAeSngCe6Ob+p0adHfPTEXFERCwh6cW+7JSZmU19evkG4D2q\n3wZH4D1g0YjYHbhF0hMRsTpARNwF7CXp2YjYB1gQuBiYPyL+mN9fL+nEpjY/IlUZ94uIGyS9EBFL\n5jZXAE4FpgfmA/aV9AAwY0T8FlgUeAvYFlgIOAeYCVgYOFbSHyPiaUDAx8DhwLkttnkSuAtYiRRO\ntpD0v4eX50cG7QgMBv4YEctL+itwHrB0RJxHqv6tDcwC7AFcJOmrEfEN4Aekv/w8CuwDbAPsR6pU\n1oGtJL1V2F+Qnnv5dn7/U+ArwLzAE5I6C5RExBBgb0nDIuI54D4ggNfyvmcELsrnbyBwAPCXvGzx\nfL5Pk3RV/l4fB1YAxgH3AkOBuYCvAe/n87AUaZrGsZLu7qBf6wEnAhOAF0gPgL+S9EzPeyJiNdJf\nIrYFzu+ozYjYGjgC+AT4N7B9vhnrVaTAflhn58fMzPq/CuXG/jvHUdLLwDdJAen+iBgDbJ5XF6tB\nxdezkULXWsAmEbFSU5sfkp7juDRwUx6ObASj5YDDJG0E/JxU9Wu0eZSkwcCcpIeHB3CqpK8Be/Fp\nxW9W4IQ8lLtsB9vMDlwhaQjwMrBJ06FvCDwl6U1SuGp8bl9gtKR98vu/SloH+BD+N5R9JrCppNWB\n54BF8rFulvs/mhTEitYjP/cyImYH3s59Xh1YMw+bt2txUvBaC5g/t7EP8GJetj0wKJ+P13I1eSPg\nxIiYl/RdPpS/gxmB93NfRud+7gG8IWk9YEvg7E76MoIUkoeQzvOuedkuef1uwAXAnp20Wct9/kU+\nf9cDc+R1TwFDpuDcmJlZP1Xr5k9f6rcVx1wJHCtpeH7/FVLYu7Np02J4fkLSf/L2DwPLRMRJpPD3\nFPBTYBZJB+ZtlgZujoj7SNWk4yJiPCncvZfbfFvSP/LrV0lVvleBYyJiOCnsFL8HFbbtaJvH8p//\nJFUki/YEFo+Im0gVupXzXM/m361nm97PB7yTAyeSTsnH+AZwSUSMI4XZ+5s+Ny+pOggphC4QEVeQ\nKn6zkSqV7XozB/7isS0D3JT79DxwekScRZqviaRxETEaWDJ/rjGH9V1SYAR4J7e1AjA4Igbl5dNH\nxDyNamlDRMxPqgpfnQqqzAzcClwInBwRcwPrAAcCZwHrNLU5b35dBw4FjoqIg4AxwB/yuldI587M\nzKwy+m3FkTSUe1ZENILLc6QAMYEUcBqVsFULn/lSRMwaETOQKltPS9pc0vqSDiINGV8WEbPl7f8B\nvEkaWj4d+KGkXYGn+fTctprrdgJwqaSdScPOxe9hYhvbtJw/FxHz5X6vIWkTSRsC/0eqkhU/Uyvs\np+F1YK4cioiIMyJiXeB44NukQDqeyX9nXicNBUOqfn5B0neAY0iBa0r+MtTquMaQKo9ExBJ52H8M\naSi+UeVcEXipkzYaniFd/LI+sClp/uc7LbZ7E/gX6aKd9YGTgDvyEPPVpOHuayVNzH1pbrMRRGuk\n6ujxuXJZA7bK6+YmnTszM5vWVajk2G+Do6RrSXPcHskVwZuB7+X5gGcA50TEzaRz0Agbb5PmsY0C\nrpb0TFObj5KGc++JiFHA3cAISc+SHhB+dcT/b+9OQy2f4ziOv2/2BmEsCd1IvrI/kbIMM1kiWyFL\n8UgzUXgma57YHpCkCYknxlJE2YpkFhTKOrZv44FpouySIQzHg9/v5HY7y597nf+ce9+v2+nc+z//\n3//87vn95vaZ7+//PyfWUJZ39+zTtW74uCMiVlOWWntVnprs0z1e18XAkzXgdD1AWaaemLJvZ1q7\nTm1zOfB8RLxat62pr8UbwBrglx6/1ypKWAV4E9iv9vkJyrmB3YDemXY/tf+DHru/HnMV5eKmOylL\nxAtrP1dSgtk3DNY91oH1WK8Dn097reCf1+Iq4IU6zpdR/jMAZfn/bOCh+nO/Y3ZvbwHPRcTLlHNn\nn63tjqJWTSVJ89vEDL9G2tdOp42LfjWXRMQzwKWZaQWtoYhYAVyfmesH7NZZv3FUPdKoTC4o96+t\n/7XdjmjWHTu5HQDvffVHyz3RbDtij63gf6ztffjtphmFsUN23XJk6XHOVhw1UldTzuVTAxFxKPDZ\nkNAoSdJmZ85eHKPRqUv617Tdj3GRmWspF1tJkjRWb8djcJQkSWrRGOVGg6MkSVKbxumTYzzHUZIk\nSY1YcZQkSWrR+NQbDY6SJEntGqPkaHCUJElqkec4SpIkac6x4ihJktSi8ak3GhwlSZJaNUYr1QZH\nSZKkNo1RbjQ4SpIktcmLYyRJkjTnWHGUJElq0RgVHK04SpIkqRkrjpIkSS2y4ihJkqQ5x4qjJElS\niybG6A15JjqdTtt9kNSb/zglafPxv6W79Rtn9vd+csHokqfBUZIkSY14jqMkSZIaMThKkiSpEYOj\nJEmSGjE4SpIkqRGDoyRJkhoxOEqSJKkR3wBc0kARcQKwLDMvnLLtduATYDXwAfA25T3OtgFWZOby\nut/nwAGZ+ftoe62ZcMzV1Wcu3AZ8CtyD82DeseIoaZheb/bamXL7KDMXZ+YJwHHAqRFx+oC22vw5\n5uoaNJ7Og3nIiqOkYXp9IsFEr+2ZuSki7gYuAZ6b/nhEHA7cnJlnRMQFwLWZeXhEHFPbXA08AuxA\n+ft0Q2aujIi1lErXYZRKx1fAIuA34DRgAfAgsEt9qisz88OIWAe8BkRtc05m/vUfX4f5ZNbGHCAi\nVgHvAYcAPwOvAqcAOwEnAxuB+4D9KQWNGzJzdUR8AKyijHsHOCszf5rJL6Z/rdEnkjSZB5obrDhK\namJJRKzs3oAL6V9R+BrYtdcDmfk+MBkRWwOnAn9GxO7AmcBTwI3Ai5l5PHAeJQwCbA88kpmLKJWN\n1+s+WwMHA9cBL2fmEmAZcG9tty8lhBwN7AYc+Z9fgflnVsa86gBvZuaJlCXNjZl5MvAxcDxwKfBN\nHdOzgeW13Q7Ao7Wi9QVlzmj0ps+Fi/rsN2weaA6w4iipiVd6nOPUrxIxCWwYcKwXgSXA3pTq4kmU\nMHg9cAXwMEBmfhkRP9VgCfBOvf+REjgAfgC2pVSyFkfE+XX7zvX+28z8on6/gRJa1MxsjjkMH7/j\nIuKoun2LiFhYv3+33m+o+2r0es2FXprMA405K46SZk1EbANcCTw+YLengWuA94GXKGFxXWZuolx8\nsageay/KUuZ3td2gc6Y+Be7KzMXA+cCKPm0aLbupuYZjDsPH77E6fqcBTwLfN2inzcS/mAcac1Yc\nJQ3TvSCi13aAg+ry1V/AVpQrK1+Zts9UbwAHALdn5tqI2AfoVjBuBR6KiHOB7YClmflnRAwKDx3g\nFuDBiFgK7Ajc1Of5DSHNzPaYD3uu+4EH6rmQOwLLM7MTEf2eX6PTby7A7M4DjYmJTsexlSRJ0nAu\nVUuSJKkRg6MkSZIaMThKkiSpEYOjJEmSGjE4SpIkqRGDoyRJkhoxOEqSJKkRg6MkSZIa+RtUPf0e\nBHXkZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a46b30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tableRegionPop=tableRegion[['HDI women','HDI men','HDI']]\n",
    "print(tableRegionPop)\n",
    "\n",
    "# Define the color set\n",
    "color = sns.blend_palette([\"#e0f4ff\",\"#055cba\"], as_cmap=True)\n",
    "\n",
    "# Define figure size\n",
    "sns.set_context(rc={\"figure.figsize\": (8, 6)}) \n",
    "\n",
    "sns.heatmap(tableRegionPop,cmap=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <span style=\"color:#a6ca6d\">4.3 Econometrics: </span> <span style=\"color:#3972A0\">Linear models with quantitative data <span style=\"color:#00acee\"> &#9733; Seaborn &#9733; </span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Scatter plots & <span style=\"color:#00acee\">linear regression adjustment curve</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9aa3c50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9uJ1d3WTzxVcgYYduxEBvQOK9wd/FjcCZiAFn3LFdVRbf3I6MggPallWg3dX6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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9aa3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#Drawing a scatter plot\n",
    "sns.lmplot('HDI women','Life exp', table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J+G9kSzr3vUktTuff92QGejLuzrER50deXWAXY757G6lLwhHBM6zelCFjoy4bNYHo8kNJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9845170>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#with different options (color, etc.)\n",
    "sns.lmplot('HDI women','Life exp', table, scatter_kws={\"marker\": \".\", \"color\":\"#30b3d5\"}, line_kws={\"linewidth\": 1, \"color\": \"#83c9db\"});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Scatter plot & marginal distributions ...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hDmKS1GdseXdXvevTm8ojQ9uYRyvc7mxJ6m+Gd+85N/84pzwy9CKyLnBwQJgk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\n+f9csVlKnFw5ogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a27110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('GNI','Life exp', table, color=\"#f48c0c\", size=7);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Scatter plot by categories ...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9d84cd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D4EjDqMlQ2IEamFzhvKKQtK/xNbSGKyDhG1X+aM+INhQqqTMCwbJCaeWqfPHK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\nDgYDIiIicjAYEBERkeP/A2E2I4NjQgkhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d84d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using HDI categories (with option marker):\n",
    "\n",
    "#On one single graph\n",
    "sns.lmplot('HDI women','HDI men', table, hue=\"HDIcat\", markers=[\"x\", \"o\", \".\", \"*\"], size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1tmAXzeaEb6/VeBudQmYRkSr5rTBmMTBYVsWyiDS9ZCY1BwuYI4x+5fJ0IAmc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ruNZZzRvVttu9Xda+82k9HerS2j3VtPxhndvW3v3X4Z7d568tm7tWW0d+vJOKN6t3WDzJIkSZIk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\nSZIkaWD/BaeZr/l6hgVgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9af5dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Or on different graphs\n",
    "sns.lmplot(\"HDI women\", \"HDI men\", table, col=\"HDIcat\", hue=\"HDIcat\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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684B+YCwHDyZsdJxGv+/b6YD+Q4B7OTC+oNHxctdyHYMSiTxfN2r2qLmhJNmH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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x995dc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Or without regression lines\n",
    "sns.lmplot('HDI women','HDI men', table, hue=\"HDIcat\", palette=\"Set2\", fit_reg=False, size=6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">2 variables regression: taking a look at the residuals ...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nAXCcQI8d3Lf/PhfiYYucXucqhqqbSYA0hvOlNWxlafnCm150GWy8k966CWId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\nxEEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x972ca30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set size\n",
    "sns.set_context(rc={\"figure.figsize\": (8, 6)}) \n",
    "\n",
    "#ploting the residuals\n",
    "sns.residplot('HDI women','HDI men', table,  color=\"#db0171\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Scatter plots & <span style=\"color:#00acee\">quadratic or non linear regression adjustment curve</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PkCSpn44hnebsHKdFajcxUJKkgZKHAV5FOi1wN3AvaX6TvZsdsAiQJA2FPAxwGdML1j0E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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x619c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqHDI=(table['HDI'] * table['HDI'])\n",
    "tableNew = concat([table, sqHDI], axis=1, join='inner')\n",
    "tableNew.columns = ['cname','Year','HDI women','HDI men','HDI','GNI','Life exp','Population','EySchooling','HDIcat','HDIclass','Region','sqHDI']\n",
    "\n",
    "\n",
    "#Set size\n",
    "sns.set_context(rc={\"figure.figsize\": (8, 6)}) \n",
    "sns.residplot('HDI','sqHDI',tableNew, color=\"#51d0ff\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##  <span style=\"color:#a6ca6d\">4.3 Econometrics: </span> <span style=\"color:#3972A0\">OLS with <span style=\"color:#00acee\"> &#9733; statsmodels &#9733; </span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    HDI   R-squared:                       0.997\n",
      "Model:                            OLS   Adj. R-squared:                  0.996\n",
      "Method:                 Least Squares   F-statistic:                 1.032e+04\n",
      "Date:                Fri, 05 Jun 2015   Prob (F-statistic):          6.64e-175\n",
      "Time:                        10:44:02   Log-Likelihood:                 257.33\n",
      "No. Observations:                 147   AIC:                            -506.7\n",
      "Df Residuals:                     143   BIC:                            -494.7\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------\n",
      "GNI          1.974e-06   2.25e-07      8.757      0.000      1.53e-06  2.42e-06\n",
      "Life exp        0.0057      0.000     12.107      0.000         0.005     0.007\n",
      "EySchooling     0.0252      0.002     12.105      0.000         0.021     0.029\n",
      "LogPop         -0.0076      0.002     -4.254      0.000        -0.011    -0.004\n",
      "==============================================================================\n",
      "Omnibus:                        9.383   Durbin-Watson:                   2.150\n",
      "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.663\n",
      "Skew:                          -0.625   Prob(JB):                      0.00797\n",
      "Kurtosis:                       3.113   Cond. No.                     1.60e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.6e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Parameters:  GNI            0.000002\n",
      "Life exp       0.005667\n",
      "EySchooling    0.025226\n",
      "LogPop        -0.007566\n",
      "dtype: float64\n",
      "R2:  0.996549006332\n",
      "Standard errors:  GNI            2.254489e-07\n",
      "Life exp       4.680360e-04\n",
      "EySchooling    2.083836e-03\n",
      "LogPop         1.778528e-03\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "\n",
    "#Take a look at the table we have\n",
    "#print(table.head())\n",
    "\n",
    "#Add the log of Population\n",
    "LogPop=np.log(table['Population'])\n",
    "LogGNI=np.log(table['GNI'])\n",
    "tableReg = concat([table, LogPop, LogGNI], axis=1, join='inner')\n",
    "tableReg.columns = ['cname','Year','HDI women','HDI men','HDI','GNI','Life exp','Population','EySchooling','HDIcat','HDIclass','Region', 'LogPop', 'LogGNI'] \n",
    "\n",
    "# Define the model:\n",
    "y=tableReg['HDI']\n",
    "X=tableReg[['GNI','Life exp','EySchooling','LogPop']]\n",
    "\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())\n",
    "\n",
    "#Print only a few information\n",
    "print('Parameters: ', results.params)\n",
    "print('R2: ', results.rsquared)\n",
    "print('Standard errors: ', results.bse)\n",
    "#print('Predicted values: ', results.predict())\n",
    "#print('Residuals: ', results.resid)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Take a look at the residuals ...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x99295d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9989670>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(results.predict())\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Playing with xkcd package..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mU3nkkUfwwQ9+ELVaDa1WC1/60peQyWSQyWQwGAzwhCc8QUVfUX4KbIl8Ca4U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\nGAsSnPQZY4yxIMFJnzHGGAsS/wdZQVKXaLJuiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x67cfdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    plt.hist(results.predict())\n",
    "    plt.ylabel('Count')\n",
    "    plt.xlabel('residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Influence plots ...</span>\n",
    "\n",
    "Influence plots show the (externally) studentized residuals vs. the leverage of each observation as measured by the hat matrix.\n",
    "Externally studentized residuals are residuals that are scaled by their standard deviation where:\n",
    "\n",
    "$var(\\hat{\\epsilon}_i)=\\hat{\\sigma}^2_i(1-h_{ii})$\n",
    "\n",
    "with\n",
    "\n",
    "$\\hat{\\sigma}^2_i=\\frac{1}{n - p - 1 \\;\\;}\\sum_{j}^{n} \\forall  \\ j \\neq i$\n",
    "\n",
    "\\(n\\) is the number of observations and \\(p\\) is the number of regressors. \\(h_{ii}\\) is the \\(i\\)-th diagonal element of the hat matrix\n",
    "$H=X(X^{\\;\\prime}X)^{-1}X^{\\;\\prime}$\n",
    "\n",
    "The influence of each point can be visualized by the criterion keyword argument. Options are Cook's distance and DFFITS, two measures of influence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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og7ExWF4DiePhpMjAOpnHOTijFh44fv/ksrxsAG8WEREZEiVrw9JDz8K2qP99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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9947870>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,7),dpi=150) #facecolor='#eeeeee'\n",
    "fig = sm.graphics.influence_plot(results, ax=ax, criterion=\"cooks\", plot_alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">Oberve the difference when using Log(GNI) ...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    HDI   R-squared:                       0.996\n",
      "Model:                            OLS   Adj. R-squared:                  0.996\n",
      "Method:                 Least Squares   F-statistic:                     8767.\n",
      "Date:                Fri, 05 Jun 2015   Prob (F-statistic):          7.52e-170\n",
      "Time:                        10:44:33   Log-Likelihood:                 245.36\n",
      "No. Observations:                 147   AIC:                            -482.7\n",
      "Df Residuals:                     143   BIC:                            -470.8\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------\n",
      "Life exp        0.0018      0.001      2.386      0.018         0.000     0.003\n",
      "EySchooling     0.0247      0.002     10.510      0.000         0.020     0.029\n",
      "LogPop         -0.0143      0.002     -7.675      0.000        -0.018    -0.011\n",
      "LogGNI          0.0412      0.006      6.609      0.000         0.029     0.053\n",
      "==============================================================================\n",
      "Omnibus:                        4.842   Durbin-Watson:                   1.798\n",
      "Prob(Omnibus):                  0.089   Jarque-Bera (JB):                3.721\n",
      "Skew:                          -0.268   Prob(JB):                        0.156\n",
      "Kurtosis:                       2.434   Cond. No.                         123.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "y=tableReg['HDI']\n",
    "X=tableReg[['Life exp','EySchooling','LogPop', 'LogGNI']]\n",
    "\n",
    "model = sm.OLS(y, X)\n",
    "results2 = model.fit()\n",
    "print(results2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NhP0FfWuqoesVh8cYOH1B3847NBlz2+vws3/BCzFmyV4ts3u9xqpBYGslTHwn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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9929e70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,7),dpi=150) #facecolor='#eeeeee'\n",
    "fig = sm.graphics.influence_plot(results2, ax=ax, criterion=\"cooks\", plot_alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##  <span style=\"color:#a6ca6d\">5. Econometrics: </span> <span style=\"color:#3972A0\">Panel data with<span style=\"color:#00acee\"> &#9733; Pandas &#9733; </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">5.1 From HDRdata API to Panel data</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API: http://ec2-52-1-168-42.compute-1.amazonaws.com/version/1/indicator_id/137506,306,141706,69206/year/2011,2012,2013?structure=icy\n",
      "               HDI      Pop   GNI  LifeExpc\n",
      "major minor                                \n",
      "AFG   2011   0.458  29105.5  1698      60.1\n",
      "      2012   0.466  29824.5  1882      60.5\n",
      "      2013   0.468  30551.7  1904      60.9\n",
      "AGO   2011   0.521  20180.5  6271      51.1\n",
      "      2012   0.524  20820.5  6259      51.5\n"
     ]
    }
   ],
   "source": [
    "from pandas import Series, DataFrame, concat\n",
    "import json\n",
    "import numpy as np\n",
    "randn = np.random.randn\n",
    "from pandas import *\n",
    "import pandas as pd\n",
    "\n",
    "#=============================================================================================================\n",
    "indicator_id1=str(137506)   # NB. HDI\n",
    "indicator_id2=str(306)      # NB. Population\n",
    "indicator_id3=str(141706)   # NB. GNI\n",
    "indicator_id4=str(69206)    # NB. Life expectancy\n",
    "indicator_id7=str(69706)    # NB. Expected Years of Schooling  \n",
    "\n",
    "year=['2011','2012','2013']\n",
    "list_ID=indicator_id1,indicator_id2,indicator_id3, indicator_id4 #Needs to have at least two indicator\n",
    "#=============================================================================================================\n",
    "yearlist= ','.join(year)\n",
    "ID_l= ','.join(list_ID)\n",
    "\n",
    "list_iso=['AFG','ALB','DZA','AND','AGO','ATG','AZE','ARG','AUS','AUT','BHS','BHR','BGD','ARM','BRB','BEL','BTN','BOL','BIH','BWA','BVT','BRA','BLZ','IOT','SLB','VGB','BRN','BGR','MMR','BDI','BLR','KHM','CMR','CAN','CPV','CYM','CAF','LKA','TCD','CHL','CHN','TWN','CXR','CCK','COL','COM','MYT','COG','COD','COK','CRI','HRV','CUB','CYP','CZE','BEN','DNK','DMA','DOM','ECU','SLV','GNQ','ETH','ERI','EST','FRO','FLK','SGS','FJI','FIN','ALA','FRA','GUF','PYF','ATF','DJI','GAB','GEO','GMB','PSE','DEU','GHA','GIB','KIR','GRC','GRL','GRD','GLP','GUM','GTM','GIN','GUY','HTI','HMD','VAT','HND','HKG','HUN','ISL','IND','IDN','IRN','IRQ','IRL','ISR','ITA','CIV','JAM','JPN','KAZ','JOR','KEN','PRK','KOR','KWT','KGZ','LAO','LBN','LSO','LVA','LBR','LBY','LIE','LTU','LUX','MAC','MDG','MWI','MYS','MDV','MLI','MLT','MTQ','MRT','MUS','MEX','MCO','MNG','MDA','MNE','MSR','MAR','MOZ','OMN','NAM','NRU','NPL','NLD','CUW','ABW','SXM','BES','NCL','VUT','NZL','NIC','NER','NGA','NIU','NFK','NOR','MNP','UMI','FSM','MHL','PLW','PAK','PAN','PNG','PRY','PER','PHL','PCN','POL','PRT','GNB','TLS','PRI','QAT','REU','ROU','RUS','RWA','BLM','SHN','KNA','AIA','LCA','MAF','SPM','VCT','SMR','STP','SAU','SEN','SRB','SYC','SLE','SGP','SVK','VNM','SVN','SOM','ZAF','ZWE','ESP','SSD','SDN','ESH','SUR','SJM','SWZ','SWE','CHE','SYR','TJK','THA','TGO','TKL','TON','TTO','ARE','TUN','TUR','TKM','TCA','TUV','UGA','UKR','MKD','EGY','GBR','GGY','JEY','IMN','TZA','USA','VIR','BFA','URY','UZB','VEN','WLF','WSM','YEM','ZMB','PSE']\n",
    "\n",
    "l=len(list_iso) #record the lenght of the iso list\n",
    "ly=len(year)    #record the lenght of the year list\n",
    "lID=len(list_ID)\n",
    "\n",
    "#------------------------- prepare the API request -------------------------\n",
    "#Structure icy\n",
    "url=\"http://ec2-52-1-168-42.compute-1.amazonaws.com/version/1/indicator_id/\"+ID_l+\"/year/\"+yearlist+\"?structure=icy\"\n",
    "print('API:',url)\n",
    "\n",
    "#------------------------- Load the data with Pandas -----------------------\n",
    "# load dataframe:\n",
    "df=pd.read_json(url)\n",
    "\n",
    "#load each serie : time serie by indicator and country (e.g. (indicator_X[AFG][2010,2011,2012,2013])\n",
    "for ID in range (1,lID+1):\n",
    "    for i in range (0,l):\n",
    "        try:\n",
    "            vars()['valueV'+str(ID)+'_'+str(i)]=np.array(Series(df['indicator_value'][vars()['indicator_id'+str(ID)]][list_iso[i]]).astype(np.float))\n",
    "        except KeyError:\n",
    "            vars()['valueV'+str(ID)+'_'+str(i)]=np.nan * np.ones(shape=(1,ly))  #fill a j long list with NaN     \n",
    "\n",
    "            \n",
    "#------------------------- Write the data  -----------------------\n",
    "# writing first iteration ex. dataV1_0=valueV1_0, dataV2_0=valueV2_0, etc...\n",
    "for ID in range (0,lID):\n",
    "    ID=ID+1\n",
    "    vars()['dataV'+str(ID)+'_0']=vars()['valueV'+str(ID)+'_0']\n",
    "\n",
    "# Stack the observations (country on contry) by indicators\n",
    "for ID in range (1,lID+1):\n",
    "    for i in range (1,l):\n",
    "        a=i-1\n",
    "        vars()['dataV'+str(ID)+'_'+str(i)]=np.vstack((vars()['dataV'+str(ID)+'_'+str(a)],vars()['valueV'+str(ID)+'_'+str(i)]))\n",
    "\n",
    "#------------------------- Set the panel  -----------------------\n",
    "#set each indicator as a panel\n",
    "for ID in range (1,lID+1):\n",
    "    n=ID-1\n",
    "    vars()['p'+str(ID)] = Panel(np.array([vars()['dataV'+str(ID)+'_'+str(l-1)]]), items=['Var'+str(ID)],major_axis=[list_iso],minor_axis=[year]).to_frame()\n",
    "\n",
    "# Concatenate all indicators: [p1,p2.....p(LiD)]\n",
    "#first iteration:\n",
    "if lID>1: dataset=pandas.concat([p1,p2], axis=1, join='inner') #1st iteration p1,p2\n",
    "#all other iteration:\n",
    "for ID in range (3,lID+1):\n",
    "    dataset=pandas.concat([dataset,vars()['p'+str(ID)]], axis=1, join='inner')                \n",
    "\n",
    "#show the results\n",
    "#print(dataset.head())\n",
    "\n",
    "#set colomuns names\n",
    "dataset.columns=['HDI','Pop','GNI','LifeExpc']\n",
    "\n",
    "#show the results\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               HDI      Pop   GNI  LifeExpc     LogPop    LogGNI\n",
      "major minor                                                     \n",
      "AFG   2011   0.458  29105.5  1698      60.1  10.278682  7.437206\n",
      "      2012   0.466  29824.5  1882      60.5  10.303085  7.540090\n",
      "      2013   0.468  30551.7  1904      60.9  10.327176  7.551712\n",
      "AGO   2011   0.521  20180.5  6271      51.1   9.912472  8.743691\n",
      "      2012   0.524  20820.5  6259      51.5   9.943693  8.741776\n"
     ]
    }
   ],
   "source": [
    "#Add the log of Population\n",
    "LogPop=np.log(dataset['Pop'])\n",
    "LogGNI=np.log(dataset['GNI'])\n",
    "\n",
    "dataset2 = concat([dataset, LogPop, LogGNI], axis=1, join='inner')\n",
    "dataset2.columns = ['HDI','Pop','GNI','LifeExpc','LogPop','LogGNI'] \n",
    "print(dataset2.head())\n",
    "dataset2.to_csv(\"Panel.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">5.2 Panel data analysis with Pandas</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.stats.plm import PanelOLS\n",
    "from pandas.stats.ols import OLS, MovingOLS\n",
    "from pandas.stats.plm import PanelOLS, MovingPanelOLS, NonPooledPanelOLS\n",
    "from pandas.core.api import Series\n",
    "\n",
    "#set the model\n",
    "y=dataset2['HDI']\n",
    "X=dataset2[['LogPop','LogGNI','LifeExpc']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">5.2.1 Pooled OLS </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-------------------------Summary of Regression Analysis-------------------------\n",
       "\n",
       "Formula: Y ~ <LogPop> + <LogGNI> + <LifeExpc> + <intercept>\n",
       "\n",
       "Number of Observations:         558\n",
       "Number of Degrees of Freedom:   4\n",
       "\n",
       "R-squared:         0.9493\n",
       "Adj R-squared:     0.9490\n",
       "\n",
       "Rmse:              0.0352\n",
       "\n",
       "F-stat (3, 554):  3457.5571, p-value:     0.0000\n",
       "\n",
       "Degrees of Freedom: model 3, resid 554\n",
       "\n",
       "-----------------------Summary of Estimated Coefficients------------------------\n",
       "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
       "--------------------------------------------------------------------------------\n",
       "        LogPop    -0.0001     0.0007      -0.08     0.9341    -0.0015     0.0014\n",
       "        LogGNI     0.0792     0.0021      38.33     0.0000     0.0752     0.0833\n",
       "      LifeExpc     0.0072     0.0003      25.39     0.0000     0.0067     0.0078\n",
       "     intercept    -0.5436     0.0145     -37.57     0.0000    -0.5720    -0.5153\n",
       "---------------------------------End of Summary---------------------------------"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Equivalent to Pooled OLS regression in Stata ?\n",
    "reg = PanelOLS(y=y,x=X)\n",
    "reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison with Stata output for pooled ols: (*NB. same dataset*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Stata Output](http://www.stats4dev.com/Ipython/output1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">5.2.2 Pannel data analysis: fixed effect </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-------------------------Summary of Regression Analysis-------------------------\n",
       "\n",
       "Formula: Y ~ <LogPop> + <LogGNI> + <LifeExpc>\n",
       "\n",
       "Number of Observations:         558\n",
       "Number of Degrees of Freedom:   189\n",
       "\n",
       "R-squared:         0.7905\n",
       "Adj R-squared:     0.6837\n",
       "\n",
       "Rmse:              0.0019\n",
       "\n",
       "F-stat (3, 369):     7.4048, p-value:     0.0001\n",
       "\n",
       "Degrees of Freedom: model 188, resid 369\n",
       "\n",
       "-----------------------Summary of Estimated Coefficients------------------------\n",
       "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
       "--------------------------------------------------------------------------------\n",
       "        LogPop     0.0263     0.0080       3.28     0.0012     0.0106     0.0420\n",
       "        LogGNI     0.0516     0.0020      25.36     0.0000     0.0476     0.0556\n",
       "      LifeExpc     0.0046     0.0005       9.64     0.0000     0.0036     0.0055\n",
       "---------------------------------End of Summary---------------------------------"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#with time fixed effects (eq Stata xtreg y = x, fe)\n",
    "regTime  = PanelOLS(y=y,x=X,time_effects=True)\n",
    "regTime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison with Stata output for pooled xtreg, fe: (*NB. same dataset*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Stata Output](http://www.stats4dev.com/Ipython/output2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#3972A0\">5.2.3 Pannel data analysis: random effect </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-------------------------Summary of Regression Analysis-------------------------\n",
       "\n",
       "Formula: Y ~ <LogPop> + <LogGNI> + <LifeExpc> + <intercept>\n",
       "\n",
       "Number of Observations:         558\n",
       "Number of Degrees of Freedom:   4\n",
       "\n",
       "R-squared:         0.9493\n",
       "Adj R-squared:     0.9490\n",
       "\n",
       "Rmse:              0.0352\n",
       "\n",
       "F-stat (3, 554):  3457.5571, p-value:     0.0000\n",
       "\n",
       "Degrees of Freedom: model 3, resid 554\n",
       "\n",
       "-----------------------Summary of Estimated Coefficients------------------------\n",
       "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
       "--------------------------------------------------------------------------------\n",
       "        LogPop    -0.0001     0.0007      -0.08     0.9341    -0.0015     0.0014\n",
       "        LogGNI     0.0792     0.0021      38.33     0.0000     0.0752     0.0833\n",
       "      LifeExpc     0.0072     0.0003      25.39     0.0000     0.0067     0.0078\n",
       "     intercept    -0.5436     0.0145     -37.57     0.0000    -0.5720    -0.5153\n",
       "---------------------------------End of Summary---------------------------------"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random effects (eq Stata xtreg y = x, re)\n",
    "regNoTime  = PanelOLS(y=y,x=X,time_effects=False)\n",
    "regNoTime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison with Stata output for xtreg, re: (*nb. same dataset*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Stata Output](http://www.stats4dev.com/Ipython/output3.png)"
   ]
  }
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