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Prez at a glance
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#<span style=\"color:#285c8c\"><big>★ Programmatic access to data ★</span><big> <span style=\"color:#00acee;\"><i>[hmmm... WTH?]<b><big><big></b></span> </span>\n",
"\n",
"★ Thomas Roca, PhD, AFD Research Direction ★<br>\n",
"*Version sept 22, 2015*"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"####1/\tData access through API:\n",
"\t1.1\tAutomate data collection, processing and visualization\t\t\t\t\t\t\t\t\t\n",
"\t1.2\tExample, HDX, World Bank Data, Quandl, etc.\n",
" 1.3 The Json format\n",
" \n",
"#####2/\tThe \"At a glance\" project: \t\t\t\t\t\t\t\t\t\t\n",
"\t2.1\tTemplate proposed by our economists (sept 2014)\t\t\t\t\t\t\t\t\t\n",
"\t2.2\tTowards automation: A first prototype\t\t\t\t\t\t\t\t\t\n",
"\t2.3\tLet's talk a little bit about tech\t\t\t\t\t\t\t\t\t\n",
"#####3/\tReactions and Actions \t\t\t\t\t\t\t\t\t\n",
"\t3.1 Indicator validation\t\t\t\t\t\t\t\t\t\n",
"\t3.2 Emebding country risk data, talyor made services (app, graph, maps, etc.) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#<span style=\"color:#00acee;\">1. Programmatic access to data: What's an API ?\n",
"- Automatic data collection tool\n",
"- E.g. API Twitter, Facebook, etc. but also... World Bank, UN data, Quanld, etc.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style=\"margin-top:0px; margin-left:0px;\"> \n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------------\n",
"## <span style=\"color:#285c8c;\">Example UN/HDX, UNDP/HDR data and Quandl"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=\"https://data.hdx.rwlabs.org/ebola\" scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"550px\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML('''<iframe src=\"https://data.hdx.rwlabs.org/ebola\" scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"550px\"></iframe>''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#97b518;\">A. An example of request on HDX API "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------\n",
"API answer: https://data.hdx.rwlabs.org/api/action/datastore_search?resource_id=a02903a9-022b-4047-bbb5-45127b591c85\n",
"--------------------------------\n",
"Cumulative Cases of Ebola = 28331.0\n",
"--------------------------------\n",
"Open Ebola Treatment Centers = 24.0\n"
]
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"url=\"https://data.hdx.rwlabs.org/api/action/datastore_search?resource_id=a02903a9-022b-4047-bbb5-45127b591c85\"\n",
"\n",
"print('--------------------------------')\n",
"print(\"API answer:\", url) #Show url\n",
"print('--------------------------------')\n",
"\n",
"df=pd.read_json(url)\n",
"\n",
"title=df['result']['records'][0]['title']\n",
"value=df['result']['records'][0]['value']\n",
"print(title,'=', value)\n",
"print('--------------------------------')\n",
"title2=df['result']['records'][4]['title']\n",
"value2=df['result']['records'][4]['value']\n",
"print(title2,'=', value2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#97b518;\">B. An example of request on the World Bank data API"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GDP per capita (current US$)\n",
" NY.GDP.PCAP.CD\n",
"country year \n",
"France 2006 36544.508534\n",
" 2005 34879.726329\n",
" 2004 33874.742548\n",
" 2003 29691.181584\n",
" 2002 24275.242603\n",
" 2001 22527.317751\n",
" 2000 22465.641841\n",
" 1999 24799.296010\n",
" 1998 25101.368737\n",
" 1997 24359.425062\n",
" 1996 27015.258959\n",
" 1995 27037.972132\n",
" 1994 23625.528997\n",
" 1993 22503.260851\n",
" 1992 23937.056918\n",
" 1991 21782.416204\n",
" 1990 21795.237825\n",
" 1989 17704.958983\n",
" 1988 17696.511150\n",
" 1987 16324.393559\n",
" 1986 13557.147215\n",
" 1985 9775.339435\n",
" 1984 9432.292357\n",
" 1983 10005.151672\n",
" 1982 10505.735472\n",
" 1981 11110.559768\n",
" 1980 12712.601399\n"
]
}
],
"source": [
"from pandas.io import wb\n",
"\n",
"#indicator to request (GDPpc)\n",
"ind = ['NY.GDP.PCAP.CD']\n",
"\n",
"#request\n",
"dataset = wb.download(indicator=ind, country='FRA', start=2006, end=1980)\n",
"\n",
"#indicator label\n",
"print(\"GDP per capita (current US$)\")\n",
"print(dataset)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#97b518;\">C. From data request to dataset construction (ex. with Quandl)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Attempt downloading....PER.. Indicator: NID_NGDP\n",
"Attempt downloading....PER.. Indicator: PPPGDP\n",
"Attempt downloading....PER.. Indicator: BCA_NGDPD\n",
"Attempt downloading....PER.. Indicator: BCA\n",
"Attempt downloading....PER.. Indicator: LE\n",
"Attempt downloading....PER.. Indicator: LE... failed\n",
"Attempt downloading....PER.. Indicator: LUR\n",
"***These indicators were not available for PER :\n",
"['LE']\n",
"Attempt downloading....BOL.. Indicator: NID_NGDP\n",
"Attempt downloading....BOL.. Indicator: PPPGDP\n",
"Attempt downloading....BOL.. Indicator: BCA_NGDPD\n",
"Attempt downloading....BOL.. Indicator: BCA\n",
"Attempt downloading....BOL.. Indicator: LE\n",
"Attempt downloading....BOL.. Indicator: LE... failed\n",
"Attempt downloading....BOL.. Indicator: LUR\n",
"***These indicators were not available for BOL :\n",
"['LE']\n",
"Attempt downloading....DZA.. Indicator: NID_NGDP\n",
"Attempt downloading....DZA.. Indicator: PPPGDP\n",
"Attempt downloading....DZA.. Indicator: BCA_NGDPD\n",
"Attempt downloading....DZA.. Indicator: BCA\n",
"Attempt downloading....DZA.. Indicator: LE\n",
"Attempt downloading....DZA.. Indicator: LE... failed\n",
"Attempt downloading....DZA.. Indicator: LUR\n",
"***These indicators were not available for DZA :\n",
"['LE']\n",
" LUR BCA BCA_NGDPD PPPGDP NID_NGDP LE Income_class Region\n",
"DZA 1980 15.789 0.242 0.571 85.869 32.586 NaN UMC MEA\n",
" 1981 15.385 -0.209 -0.472 96.704 30.830 NaN UMC MEA\n",
" 1982 15.000 -0.436 -0.973 109.277 31.091 NaN UMC MEA\n",
" 1983 14.286 -0.085 -0.179 119.724 31.329 NaN UMC MEA\n",
" 1984 16.536 0.074 0.144 130.916 29.229 NaN UMC MEA\n",
" 1985 16.901 1.015 1.660 142.671 27.682 NaN UMC MEA\n",
" 1986 18.356 -2.230 -3.623 145.256 27.919 NaN UMC MEA\n",
" 1987 20.056 0.141 0.223 147.921 25.041 NaN UMC MEA\n",
" 1988 21.801 -1.900 -3.678 150.190 23.504 NaN UMC MEA\n",
" 1989 20.679 -1.033 -1.965 163.520 25.449 NaN UMC MEA\n",
" 1990 19.757 1.350 2.181 171.690 24.097 NaN UMC MEA\n",
" 1991 20.263 2.390 5.121 175.276 29.412 NaN UMC MEA\n",
" 1992 21.368 1.290 2.621 182.140 27.600 NaN UMC MEA\n",
" 1993 23.152 0.810 1.589 182.553 24.060 NaN UMC MEA\n",
" 1994 24.362 -1.839 -4.334 184.760 29.485 NaN UMC MEA\n",
" 1995 28.105 -2.237 -5.318 195.871 30.170 NaN UMC MEA\n",
" 1996 27.986 1.248 2.658 207.025 26.473 NaN UMC MEA\n",
" 1997 27.961 3.450 7.161 212.886 22.440 NaN UMC MEA\n",
" 1998 28.021 -0.910 -1.888 226.165 27.146 NaN UMC MEA\n",
" 1999 29.293 0.020 0.041 236.974 28.717 NaN UMC MEA\n",
" 2000 29.496 9.142 16.699 251.576 25.014 NaN UMC MEA\n",
" 2001 27.306 7.060 12.896 265.029 26.754 NaN UMC MEA\n",
" 2002 25.664 4.359 7.680 284.167 30.804 NaN UMC MEA\n",
" 2003 23.716 8.808 12.979 310.701 30.337 NaN UMC MEA\n",
" 2004 17.656 11.116 13.028 332.973 33.305 NaN UMC MEA\n",
" 2005 15.265 21.183 20.526 363.963 31.334 NaN UMC MEA\n",
" 2006 12.512 28.950 24.738 381.466 29.837 NaN UMC MEA\n",
" 2007 13.793 30.600 22.671 404.825 34.294 NaN UMC MEA\n",
" 2008 11.333 34.449 20.146 422.508 37.349 NaN UMC MEA\n",
" 2009 10.167 0.411 0.300 432.666 46.959 NaN UMC MEA\n",
" 2010 9.961 12.157 7.541 453.789 42.328 NaN UMC MEA\n",
" 2011 9.971 19.802 9.931 476.254 38.309 NaN UMC MEA\n",
" 2012 11.000 12.290 5.914 500.814 40.958 NaN UMC MEA\n",
" 2013 9.829 0.835 0.400 522.314 44.917 NaN UMC MEA\n",
" 2014 10.600 -9.289 -4.339 551.809 44.545 NaN UMC MEA\n",
" 2015 11.759 -29.358 -15.686 571.210 50.959 NaN UMC MEA\n",
" 2016 11.887 -26.010 -13.169 602.200 50.842 NaN UMC MEA\n",
" 2017 11.930 -24.587 -11.917 639.266 51.351 NaN UMC MEA\n",
" 2018 12.046 -20.727 -9.645 678.688 51.340 NaN UMC MEA\n",
" 2019 12.314 -17.036 -7.641 717.463 51.285 NaN UMC MEA\n",
" 2020 12.650 -16.259 -7.000 757.768 51.497 NaN UMC MEA\n"
]
}
],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame, concat\n",
"import numpy as np\n",
"import os, sys\n",
"import Quandl\n",
"from datetime import datetime\n",
"\n",
"#https://www.quandl.com/data/ODA/documentation/documentation\n",
"APIKey=\"DWys3k7zkj4oyByx6Z48\" # optional, to allow more than 50 request per day\n",
"isolist=['PER', 'BOL', 'DZA']\n",
"indicator_list=['NID_NGDP','PPPGDP','BCA_NGDPD','BCA','LE','LUR']\n",
"source='\"data_source\":{\"source\":\"IMF, WEO\", \"provider\":\"Quandl\", \"Download_date\":\"'+datetime.now().strftime('%Y-%m-%d')+'\" }'\n",
"\n",
"\n",
"for ISO in isolist:\n",
" \n",
" #empty list to start with\n",
" isolistPlus=[] \n",
"\n",
" #Add the meta for countries after the last list\n",
" with open (\"data/json meta country.json.txt\", \"r\") as meta:\n",
" for line in meta :\n",
" if line.startswith('\"'+ISO+'\"') :\n",
" metacountry=line\n",
"\n",
" #This dictionnary associate a country name to an iso3 code \n",
" country_dict = {\"Arab World\":\"ARB\", \"Central Europe and the Baltics\":\"CEB\", \"Caribbean small states\":\"CSS\", \"East Asia & Pacific\":\"EAP\", \"East Asia & Pacific (all income levels)\":\"EAS\", \"Europe & Central Asia\":\"ECA\", \"Europe & Central Asia (all income levels)\":\"ECS\", \"Euro area\":\"EMU\", \"European Union\":\"EUU\", \"Fragile and conflict affected situations\":\"FCS\", \"High income\":\"HIC\", \"Heavily indebted poor countries (HIPC)\":\"HPC\", \"Latin America & Caribbean\":\"LAC\", \"Latin America & Caribbean (all income levels)\":\"LCN\", \"Least developed countries: UN classification\":\"LDC\", \"Low income\":\"LIC\", \"Lower middle income\":\"LMC\", \"Low & middle income\":\"LMY\", \"Middle East & North Africa (all income levels)\":\"MEA\", \"Middle income\":\"MIC\", \"Middle East & North Africa\":\"MNA\", \"North America\":\"NAC\", \"High income: nonOECD\":\"NOC\", \"High income: OECD\":\"OEC\", \"OECD members\":\"OED\", \"Other small states\":\"OSS\", \"Pacific island small states\":\"PSS\", \"South Asia\":\"SAS\", \"Sub-Saharan Africa\":\"SSA\", \"Sub-Saharan Africa (all income levels)\":\"SSF\", \"Small states\":\"SST\", \"Upper middle income\":\"UMC\", \"World\":\"WLD\",\"Cote d'Ivoire\":\"CIV\",\"Curacao\":\"CUW\",\"Sao Tome and Principe\":\"STP\",\"American Samoa\":\"ASM\",\"Australia\":\"AUS\",\"Brunei Darussalam\":\"BRN\",\"China\":\"CHN\",\"Fiji\":\"FJI\",\"Micronesia, Fed. Sts.\":\"FSM\",\"Guam\":\"GUM\",\"Hong Kong SAR, China\":\"HKG\",\"Indonesia\":\"IDN\",\"Japan\":\"JPN\",\"Cambodia\":\"KHM\",\"Kiribati\":\"KIR\",\"Korea, Rep.\":\"KOR\",\"Lao PDR\":\"LAO\",\"Macao SAR, China\":\"MAC\",\"Marshall Islands\":\"MHL\",\"Myanmar\":\"MMR\",\"Mongolia\":\"MNG\",\"Northern Mariana Islands\":\"MNP\",\"Malaysia\":\"MYS\",\"New Caledonia\":\"NCL\",\"New Zealand\":\"NZL\",\"Philippines\":\"PHL\",\"Palau\":\"PLW\",\"Papua New Guinea\":\"PNG\",\"Korea, Dem. Rep.\":\"PRK\",\"French Polynesia\":\"PYF\",\"Singapore\":\"SGP\",\"Solomon Islands\":\"SLB\",\"Thailand\":\"THA\",\"Timor-Leste\":\"TLS\",\"Tonga\":\"TON\",\"Tuvalu\":\"TUV\",\"Taiwan, China\":\"TWN\",\"Vietnam\":\"VNM\",\"Vanuatu\":\"VUT\",\"Samoa\":\"WSM\",\"Albania\":\"ALB\",\"Andorra\":\"AND\",\"Armenia\":\"ARM\",\"Austria\":\"AUT\",\"Azerbaijan\":\"AZE\",\"Belgium\":\"BEL\",\"Bulgaria\":\"BGR\",\"Bosnia and Herzegovina\":\"BIH\",\"Belarus\":\"BLR\",\"Switzerland\":\"CHE\",\"Channel Islands\":\"CHI\",\"Cyprus\":\"CYP\",\"Czech Republic\":\"CZE\",\"Germany\":\"DEU\",\"Denmark\":\"DNK\",\"Spain\":\"ESP\",\"Estonia\":\"EST\",\"Finland\":\"FIN\",\"France\":\"FRA\",\"Faeroe Islands\":\"FRO\",\"United Kingdom\":\"GBR\",\"Georgia\":\"GEO\",\"Greece\":\"GRC\",\"Greenland\":\"GRL\",\"Croatia\":\"HRV\",\"Hungary\":\"HUN\",\"Isle of Man\":\"IMN\",\"Ireland\":\"IRL\",\"Iceland\":\"ISL\",\"Italy\":\"ITA\",\"Kazakhstan\":\"KAZ\",\"Kyrgyz Republic\":\"KGZ\",\"Liechtenstein\":\"LIE\",\"Lithuania\":\"LTU\",\"Luxembourg\":\"LUX\",\"Latvia\":\"LVA\",\"Monaco\":\"MCO\",\"Moldova\":\"MDA\",\"Macedonia, FYR\":\"MKD\",\"Montenegro\":\"MNE\",\"Netherlands\":\"NLD\",\"Norway\":\"NOR\",\"Poland\":\"POL\",\"Portugal\":\"PRT\",\"Romania\":\"ROU\",\"Russian Federation\":\"RUS\",\"San Marino\":\"SMR\",\"Serbia\":\"SRB\",\"Slovak Republic\":\"SVK\",\"Slovenia\":\"SVN\",\"Sweden\":\"SWE\",\"Tajikistan\":\"TJK\",\"Turkmenistan\":\"TKM\",\"Turkey\":\"TUR\",\"Ukraine\":\"UKR\",\"Uzbekistan\":\"UZB\",\"Aruba\":\"ABW\",\"Argentina\":\"ARG\",\"Antigua and Barbuda\":\"ATG\",\"Bahamas, The\":\"BHS\",\"Belize\":\"BLZ\",\"Bolivia\":\"BOL\",\"Brazil\":\"BRA\",\"Barbados\":\"BRB\",\"Chile\":\"CHL\",\"Colombia\":\"COL\",\"Costa Rica\":\"CRI\",\"Cuba\":\"CUB\",\"Curaçao\":\"CUW\",\"Cayman Islands\":\"CYM\",\"Dominica\":\"DMA\",\"Dominican Republic\":\"DOM\",\"Ecuador\":\"ECU\",\"Grenada\":\"GRD\",\"Guatemala\":\"GTM\",\"Guyana\":\"GUY\",\"Honduras\":\"HND\",\"Haiti\":\"HTI\",\"Jamaica\":\"JAM\",\"St. Kitts and Nevis\":\"KNA\",\"St. Lucia\":\"LCA\",\"St. Martin (French part)\":\"MAF\",\"Mexico\":\"MEX\",\"Nicaragua\":\"NIC\",\"Panama\":\"PAN\",\"Peru\":\"PER\",\"Puerto Rico\":\"PRI\",\"Paraguay\":\"PRY\",\"El Salvador\":\"SLV\",\"Suriname\":\"SUR\",\"Sint Maarten (Dutch part)\":\"SXM\",\"Turks and Caicos Islands\":\"TCA\",\"Trinidad and Tobago\":\"TTO\",\"Uruguay\":\"URY\",\"St. Vincent and the Grenadines\":\"VCT\",\"Venezuela, RB\":\"VEN\",\"Virgin Islands (U.S.)\":\"VIR\",\"United Arab Emirates\":\"ARE\",\"Bahrain\":\"BHR\",\"Djibouti\":\"DJI\",\"Algeria\":\"DZA\",\"Egypt, Arab Rep.\":\"EGY\",\"Iran, Islamic Rep.\":\"IRN\",\"Iraq\":\"IRQ\",\"Israel\":\"ISR\",\"Jordan\":\"JOR\",\"Kuwait\":\"KWT\",\"Lebanon\":\"LBN\",\"Libya\":\"LBY\",\"Morocco\":\"MAR\",\"Malta\":\"MLT\",\"Oman\":\"OMN\",\"West Bank and Gaza\":\"PSE\",\"Qatar\":\"QAT\",\"Saudi Arabia\":\"SAU\",\"Syrian Arab Republic\":\"SYR\",\"Tunisia\":\"TUN\",\"Yemen, Rep.\":\"YEM\",\"Bermuda\":\"BMU\",\"Canada\":\"CAN\",\"United States\":\"USA\",\"Afghanistan\":\"AFG\",\"Bangladesh\":\"BGD\",\"Bhutan\":\"BTN\",\"India\":\"IND\",\"Sri Lanka\":\"LKA\",\"Maldives\":\"MDV\",\"Nepal\":\"NPL\",\"Pakistan\":\"PAK\",\"Angola\":\"AGO\",\"Burundi\":\"BDI\",\"Benin\":\"BEN\",\"Burkina Faso\":\"BFA\",\"Botswana\":\"BWA\",\"Central African Republic\":\"CAF\",\"Côte d'Ivoire\":\"CIV\",\"Cameroon\":\"CMR\",\"Congo, Dem. Rep.\":\"COD\",\"Congo, Rep.\":\"COG\",\"Comoros\":\"COM\",\"Cabo Verde\":\"CPV\",\"Eritrea\":\"ERI\",\"Ethiopia\":\"ETH\",\"Gabon\":\"GAB\",\"Ghana\":\"GHA\",\"Guinea\":\"GIN\",\"Gambia, The\":\"GMB\",\"Guinea-Bissau\":\"GNB\",\"Equatorial Guinea\":\"GNQ\",\"Kenya\":\"KEN\",\"Liberia\":\"LBR\",\"Lesotho\":\"LSO\",\"Madagascar\":\"MDG\",\"Mali\":\"MLI\",\"Mozambique\":\"MOZ\",\"Mauritania\":\"MRT\",\"Mauritius\":\"MUS\",\"Malawi\":\"MWI\",\"Namibia\":\"NAM\",\"Niger\":\"NER\",\"Nigeria\":\"NGA\",\"Rwanda\":\"RWA\",\"Sudan\":\"SDN\",\"Senegal\":\"SEN\",\"Sierra Leone\":\"SLE\",\"Somalia\":\"SOM\",\"South Sudan\":\"SSD\",\"São Tomé and Principe\":\"STP\",\"Swaziland\":\"SWZ\",\"Seychelles\":\"SYC\",\"Chad\":\"TCD\",\"Togo\":\"TGO\",\"Tanzania\":\"TZA\",\"Uganda\":\"UGA\",\"South Africa\":\"ZAF\",\"Zambia\":\"ZMB\",\"Zimbabwe\":\"ZWE\"}\n",
"\n",
" #Region dictionnary that affect to a country iso3 its income group, region group, and worl:\n",
" region_dict= {\"ASM\":\"UMC,EAS,WLD\",\"AUS\":\"OEC,EAS,WLD\",\"BRN\":\"NOC,EAS,WLD\",\"CHN\":\"UMC,EAS,WLD\",\"FJI\":\"UMC,EAS,WLD\",\"FSM\":\"LMC,EAS,WLD\",\"GUM\":\"NOC,EAS,WLD\",\"HKG\":\"NOC,EAS,WLD\",\"IDN\":\"LMC,EAS,WLD\",\"JPN\":\"OEC,EAS,WLD\",\"KHM\":\"LIC,EAS,WLD\",\"KIR\":\"LMC,EAS,WLD\",\"KOR\":\"OEC,EAS,WLD\",\"LAO\":\"LMC,EAS,WLD\",\"MAC\":\"NOC,EAS,WLD\",\"MHL\":\"UMC,EAS,WLD\",\"MMR\":\"LMC,EAS,WLD\",\"MNG\":\"UMC,EAS,WLD\",\"MNP\":\"NOC,EAS,WLD\",\"MYS\":\"UMC,EAS,WLD\",\"NCL\":\"NOC,EAS,WLD\",\"NZL\":\"OEC,EAS,WLD\",\"PHL\":\"LMC,EAS,WLD\",\"PLW\":\"UMC,EAS,WLD\",\"PNG\":\"LMC,EAS,WLD\",\"PRK\":\"LIC,EAS,WLD\",\"PYF\":\"NOC,EAS,WLD\",\"SGP\":\"NOC,EAS,WLD\",\"SLB\":\"LMC,EAS,WLD\",\"THA\":\"UMC,EAS,WLD\",\"TLS\":\"LMC,EAS,WLD\",\"TON\":\"UMC,EAS,WLD\",\"TUV\":\"UMC,EAS,WLD\",\"TWN\":\"NOC,EAS,WLD\",\"VNM\":\"LMC,EAS,WLD\",\"VUT\":\"LMC,EAS,WLD\",\"WSM\":\"LMC,EAS,WLD\",\"\":\"LMC,ECS,WLD\",\"ALB\":\"UMC,ECS,WLD\",\"AND\":\"NOC,ECS,WLD\",\"ARM\":\"LMC,ECS,WLD\",\"AUT\":\"OEC,ECS,WLD\",\"AZE\":\"UMC,ECS,WLD\",\"BEL\":\"OEC,ECS,WLD\",\"BGR\":\"UMC,ECS,WLD\",\"BIH\":\"UMC,ECS,WLD\",\"BLR\":\"UMC,ECS,WLD\",\"CHE\":\"OEC,ECS,WLD\",\"CHI\":\"NOC,ECS,WLD\",\"CYP\":\"NOC,ECS,WLD\",\"CZE\":\"OEC,ECS,WLD\",\"DEU\":\"OEC,ECS,WLD\",\"DNK\":\"OEC,ECS,WLD\",\"ESP\":\"OEC,ECS,WLD\",\"EST\":\"OEC,ECS,WLD\",\"FIN\":\"OEC,ECS,WLD\",\"FRA\":\"OEC,ECS,WLD\",\"FRO\":\"NOC,ECS,WLD\",\"GBR\":\"OEC,ECS,WLD\",\"GEO\":\"LMC,ECS,WLD\",\"GRC\":\"OEC,ECS,WLD\",\"GRL\":\"NOC,ECS,WLD\",\"HRV\":\"NOC,ECS,WLD\",\"HUN\":\"OEC,ECS,WLD\",\"IMN\":\"NOC,ECS,WLD\",\"IRL\":\"OEC,ECS,WLD\",\"ISL\":\"OEC,ECS,WLD\",\"ITA\":\"OEC,ECS,WLD\",\"KAZ\":\"UMC,ECS,WLD\",\"KGZ\":\"LMC,ECS,WLD\",\"LIE\":\"NOC,ECS,WLD\",\"LTU\":\"NOC,ECS,WLD\",\"LUX\":\"OEC,ECS,WLD\",\"LVA\":\"NOC,ECS,WLD\",\"MCO\":\"NOC,ECS,WLD\",\"MDA\":\"LMC,ECS,WLD\",\"MKD\":\"UMC,ECS,WLD\",\"MNE\":\"UMC,ECS,WLD\",\"NLD\":\"OEC,ECS,WLD\",\"NOR\":\"OEC,ECS,WLD\",\"POL\":\"OEC,ECS,WLD\",\"PRT\":\"OEC,ECS,WLD\",\"ROU\":\"UMC,ECS,WLD\",\"RUS\":\"NOC,ECS,WLD\",\"SMR\":\"NOC,ECS,WLD\",\"SRB\":\"UMC,ECS,WLD\",\"SVK\":\"OEC,ECS,WLD\",\"SVN\":\"OEC,ECS,WLD\",\"SWE\":\"OEC,ECS,WLD\",\"TJK\":\"LMC,ECS,WLD\",\"TKM\":\"UMC,ECS,WLD\",\"TUR\":\"UMC,ECS,WLD\",\"UKR\":\"LMC,ECS,WLD\",\"UZB\":\"LMC,ECS,WLD\",\"ABW\":\"NOC,LCN,WLD\",\"ARG\":\"NOC,LCN,WLD\",\"ATG\":\"NOC,LCN,WLD\",\"BHS\":\"NOC,LCN,WLD\",\"BLZ\":\"UMC,LCN,WLD\",\"BOL\":\"LMC,LCN,WLD\",\"BRA\":\"UMC,LCN,WLD\",\"BRB\":\"NOC,LCN,WLD\",\"CHL\":\"OEC,LCN,WLD\",\"COL\":\"UMC,LCN,WLD\",\"CRI\":\"UMC,LCN,WLD\",\"CUB\":\"UMC,LCN,WLD\",\"CUW\":\"NOC,LCN,WLD\",\"CYM\":\"NOC,LCN,WLD\",\"DMA\":\"UMC,LCN,WLD\",\"DOM\":\"UMC,LCN,WLD\",\"ECU\":\"UMC,LCN,WLD\",\"GRD\":\"UMC,LCN,WLD\",\"GTM\":\"LMC,LCN,WLD\",\"GUY\":\"LMC,LCN,WLD\",\"HND\":\"LMC,LCN,WLD\",\"HTI\":\"LIC,LCN,WLD\",\"JAM\":\"UMC,LCN,WLD\",\"KNA\":\"NOC,LCN,WLD\",\"LCA\":\"UMC,LCN,WLD\",\"MAF\":\"NOC,LCN,WLD\",\"MEX\":\"UMC,LCN,WLD\",\"NIC\":\"LMC,LCN,WLD\",\"PAN\":\"UMC,LCN,WLD\",\"PER\":\"UMC,LCN,WLD\",\"PRI\":\"NOC,LCN,WLD\",\"PRY\":\"UMC,LCN,WLD\",\"SLV\":\"LMC,LCN,WLD\",\"SUR\":\"UMC,LCN,WLD\",\"SXM\":\"NOC,LCN,WLD\",\"TCA\":\"NOC,LCN,WLD\",\"TTO\":\"NOC,LCN,WLD\",\"URY\":\"NOC,LCN,WLD\",\"VCT\":\"UMC,LCN,WLD\",\"VEN\":\"NOC,LCN,WLD\",\"VIR\":\"NOC,LCN,WLD\",\"ARE\":\"NOC,MEA,WLD\",\"BHR\":\"NOC,MEA,WLD\",\"DJI\":\"LMC,MEA,WLD\",\"DZA\":\"UMC,MEA,WLD\",\"EGY\":\"LMC,MEA,WLD\",\"IRN\":\"UMC,MEA,WLD\",\"IRQ\":\"UMC,MEA,WLD\",\"ISR\":\"OEC,MEA,WLD\",\"JOR\":\"UMC,MEA,WLD\",\"KWT\":\"NOC,MEA,WLD\",\"LBN\":\"UMC,MEA,WLD\",\"LBY\":\"UMC,MEA,WLD\",\"MAR\":\"LMC,MEA,WLD\",\"MLT\":\"NOC,MEA,WLD\",\"OMN\":\"NOC,MEA,WLD\",\"PSE\":\"LMC,MEA,WLD\",\"QAT\":\"NOC,MEA,WLD\",\"SAU\":\"NOC,MEA,WLD\",\"SYR\":\"LMC,MEA,WLD\",\"TUN\":\"UMC,MEA,WLD\",\"YEM\":\"LMC,MEA,WLD\",\"BMU\":\"NOC,NAC,WLD\",\"CAN\":\"OEC,NAC,WLD\",\"USA\":\"OEC,NAC,WLD\",\"AFG\":\"LIC,SAS,WLD\",\"BGD\":\"LMC,SAS,WLD\",\"BTN\":\"LMC,SAS,WLD\",\"IND\":\"LMC,SAS,WLD\",\"LKA\":\"LMC,SAS,WLD\",\"MDV\":\"UMC,SAS,WLD\",\"NPL\":\"LIC,SAS,WLD\",\"PAK\":\"LMC,SAS,WLD\",\"AGO\":\"UMC,SSF,WLD\",\"BDI\":\"LIC,SSF,WLD\",\"BEN\":\"LIC,SSF,WLD\",\"BFA\":\"LIC,SSF,WLD\",\"BWA\":\"UMC,SSF,WLD\",\"CAF\":\"LIC,SSF,WLD\",\"CIV\":\"LMC,SSF,WLD\",\"CMR\":\"LMC,SSF,WLD\",\"COD\":\"LIC,SSF,WLD\",\"COG\":\"LMC,SSF,WLD\",\"COM\":\"LIC,SSF,WLD\",\"CPV\":\"LMC,SSF,WLD\",\"ERI\":\"LIC,SSF,WLD\",\"ETH\":\"LIC,SSF,WLD\",\"GAB\":\"UMC,SSF,WLD\",\"GHA\":\"LMC,SSF,WLD\",\"GIN\":\"LIC,SSF,WLD\",\"GMB\":\"LIC,SSF,WLD\",\"GNB\":\"LIC,SSF,WLD\",\"GNQ\":\"NOC,SSF,WLD\",\"KEN\":\"LMC,SSF,WLD\",\"LBR\":\"LIC,SSF,WLD\",\"LSO\":\"LMC,SSF,WLD\",\"MDG\":\"LIC,SSF,WLD\",\"MLI\":\"LIC,SSF,WLD\",\"MOZ\":\"LIC,SSF,WLD\",\"MRT\":\"LMC,SSF,WLD\",\"MUS\":\"UMC,SSF,WLD\",\"MWI\":\"LIC,SSF,WLD\",\"NAM\":\"UMC,SSF,WLD\",\"NER\":\"LIC,SSF,WLD\",\"NGA\":\"LMC,SSF,WLD\",\"RWA\":\"LIC,SSF,WLD\",\"SDN\":\"LMC,SSF,WLD\",\"SEN\":\"LMC,SSF,WLD\",\"SLE\":\"LIC,SSF,WLD\",\"SOM\":\"LIC,SSF,WLD\",\"SSD\":\"LIC,SSF,WLD\",\"STP\":\"LMC,SSF,WLD\",\"SWZ\":\"LMC,SSF,WLD\",\"SYC\":\"NOC,SSF,WLD\",\"TCD\":\"LIC,SSF,WLD\",\"TGO\":\"LIC,SSF,WLD\",\"TZA\":\"LIC,SSF,WLD\",\"UGA\":\"LIC,SSF,WLD\",\"ZAF\":\"UMC,SSF,WLD\",\"ZMB\":\"LMC,SSF,WLD\",\"ZWE\":\"LIC,SSF,WLD\"}\n",
" \n",
" dataset_Final=DataFrame()\n",
" failed_list=[]\n",
" \n",
" for ind in indicator_list:\n",
" \n",
" try:\n",
" print('Attempt downloading....'+ISO+'.. Indicator: '+ind)\n",
" #======================================< Query >======================================================================\n",
" data = Quandl.get(\"ODA/\"+ISO+\"_\"+ind, authtoken=APIKey, trim_start=\"1980-12-31\", trim_end=\"2050-12-31\")\n",
" #======================================< Query >======================================================================\n",
"\n",
" iso3=[]\n",
" year=[] \n",
" data.columns=[ind]\n",
" year=data.index.get_level_values('Date').year\n",
" for item in year:\n",
" iso3.append(ISO)\n",
"\n",
" data = data.set_index([iso3, year])\n",
" #identify data that are not empty\n",
" Yrange=np.where(data[ind].loc[iso3].notnull())[0]\n",
" #merge indicators\n",
" dataset_Final=pd.concat([data,dataset_Final], axis=1, join='outer')\n",
" #Set index name\n",
" dataset_Final.index.name = ['iso3', 'year']\n",
" \n",
" except:\n",
" print('Attempt downloading....'+ISO+'.. Indicator: '+ind+'... failed')\n",
" failed_list.append(ind)\n",
" continue\n",
"\n",
"#============================================================================================================================\n",
" print('***These indicators were not available for '+ISO+' :')\n",
" print(failed_list) \n",
" \n",
" #Add empty columns for the indicators that were not available\n",
" for fail_ind in failed_list:\n",
" dataset_Final[fail_ind]= np.nan\n",
" \n",
" #browse the region dictionnary\n",
" for iso3,region_iso in region_dict.items(): \n",
" if ISO==iso3:\n",
" isolistPlus.append(ISO)\n",
" for item in region_iso.split(','):\n",
" isolistPlus.append(str(item))\n",
" \n",
" #save income class et Region in a separate variable\n",
" dataset_Final['Income_class']= isolistPlus[1]\n",
" dataset_Final['Region']= isolistPlus[2]\n",
" \n",
"print(dataset_Final)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#97b518;\">D. Database: the JSON format <small>***(JavaScript Object Notation)***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Json datasets are text embeded data. It uses keys to sort the data and is readable by JavaScript and can be integrated into HTML pages. Here is an example of the data structure.\n",
"\n",
"```javascript\n",
"{\n",
" \"indicator_value\": {\n",
" \"103606\": {\n",
" \"AFG\": {\n",
" \"2005\": \"0.366\",\n",
" \"2006\": \"0.376\",\n",
" \"2007\": \"0.383\",\n",
" \"2009\": \"0.404\",\n",
" \"2010\": \"0.426\",\n",
" \"2011\": \"0.428\",\n",
" \"2012\": \"0.443\",\n",
" \"2013\": \"0.445\"\n",
" },\n",
" \"ALB\": {\n",
" \"2005\": \"0.64\",\n",
" \"2006\": \"0.65\",\n",
" \"2007\": \"0.659\",\n",
" \"2009\": \"0.669\",\n",
" \"2010\": \"0.675\",\n",
" \"2011\": \"0.681\",\n",
" \"2012\": \"0.681\",\n",
" \"2013\": \"0.683\"\n",
" }\n",
" }\n",
" },\n",
" \"country_name\": {\n",
" \"AFG\": \"Afghanistan\",\n",
" \"ALB\": \"Albania\"\n",
" },\n",
" \"indicator_name\": {\n",
" \"103606\": \"HDI: Income index\"\n",
" }\n",
"}```"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<div style=\"font-size:16px\"> The following request:<span style=\"color:#1174d5;\"> json.indicator_value[\"103606\"].AFG[\"2005\"]</span> will return the value: <span style=\"color:#1174d5;\">0.366</span>\n",
"\n",
"Then, it becomes pretty easy to automatically generate a graph, a table, a map, etc."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"100%\" height=\"485\" src=\"//jsfiddle.net/ThomasRoca/0nxmbs05/embedded/\" allowfullscreen=\"allowfullscreen\" scrolling:\"no\" frameborder=\"0\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML('''<iframe width=\"100%\" height=\"485\" src=\"//jsfiddle.net/ThomasRoca/0nxmbs05/embedded/\" allowfullscreen=\"allowfullscreen\" scrolling:\"no\" frameborder=\"0\"></iframe>''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#<span style=\"color:#00acee;\">2. The Project \"At a glance\" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Main Idea:\n",
"- Automate data collection, process and layout/dataviz (table, chart, map)\n",
" \n",
"###Advantages:\n",
"- Time saving and efficiency, the value added being nested in the analysis rather than in the data collection and process...\n",
"- Scale economy:\n",
" - The process (and the data) can be mobilize for different applications:\n",
" - country dashboards; \n",
" - Research/analytics\n",
" - Dataviz, etc.\n",
"\n",
"###Constrains:\n",
"- Standardisation\n",
"- Data accessible through Open Data /API \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----------\n",
"## <span style=\"color:#97b518;\">B. Technical side"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data journey: *API (World Bank, Quandl, UN etc) -> JSON (python, R, etc.) -> output (HTML, Web)*\n",
"\n",
"\n",
"\n",
"\n",
"####JSON sur mesure, avec metadata, disponibilité temporel des données, etc.:\n",
"\n",
" We choose not to connect directly application to external database (using for e.g. Ajax)\n",
" We prefered keeping control on the database and host it locally\n",
" The facilitate request we designed the json such as => [Indicator_id][\"ISO3\"+year] e.g. [\"GDP\"][\"USA2015\"]\n",
" This allows easily getting rid of automation issues.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data usage:\n",
"\n",
"Example (JavaScript):\n",
"Display the last available data for Population i Peru (Indicator id in the WDI: SP.POP.TOTL): \n",
"```javascript\n",
" $.getJSON(\"http://afd.countrydashboards.com/glance/data/WB_WDI_PER.json\", function (json) { // link to the dataset\n",
"\n",
" var Pop_date= json[\"SP.POP.TOTL\"][\"YearMax\"]; // Get the value of YearMax\n",
"\n",
" if(Pop_date.charAt){$('#Pop_date').text('no data');} else { // test whether a data exist\n",
"\n",
" var value=\"PER\"+Pop_date; // test whether a data exist\n",
" var Pop = json[\"SP.POP.TOTL\"][value]; // test wether a data exist\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style=\"color:#97b518;\">C. Présentation du prototype"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=\"http://afd.countrydashboards.com/glance/\" scrolling=\"no\" frameborder=\"0\" width=1000px\" height=\"800px\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML('''<iframe src=\"http://afd.countrydashboards.com/glance/\" scrolling=\"no\" frameborder=\"0\" width=1000px\" height=\"800px\"></iframe>''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}