├── images ├── ice.JPG ├── hail.JPG ├── ntsb.jpg ├── crash-2.jpg ├── crash3.jpg ├── crash4.jpg ├── analytics.jpg ├── bird_strike.png ├── data4safety.png ├── engine_surge.jpg ├── hudson-plane.jpg ├── pandas_help.jpeg ├── aeropython_logo.png ├── no-landing-gear.png ├── phase_of_flight.jpg ├── travel_by_plane.jpg ├── crash-boston-logan.jpg ├── scared_to_flight.jpg ├── commercial-pilots-1.jpg └── pydata-logo-warsaw-2017.png ├── styles ├── FluxBold.ttf └── style.css ├── flight_safety ├── __init__.py ├── get_data.py ├── utils.py └── queries.py ├── environment.yml ├── data ├── API_IS.AIR.DPRT_DS2_en_csv_v2 │ ├── Metadata_Indicator_API_IS.AIR.DPRT_DS2_en_csv_v2.csv │ ├── Metadata_Country_API_IS.AIR.DPRT_DS2_en_csv_v2.csv │ └── API_IS.AIR.DPRT_DS2_en_csv_v2.csv ├── readme.md └── annual_passengers_carried_data.csv ├── utils └── export_access2csv.sh ├── LICENSE └── README.md /images/ice.JPG: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | pd.set_option('max_columns', 100) 4 | pd.set_option('max_rows', 100) -------------------------------------------------------------------------------- /images/analytics.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AeroPython/pydata_warsaw2017_flight_safety/master/images/analytics.jpg -------------------------------------------------------------------------------- /images/bird_strike.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AeroPython/pydata_warsaw2017_flight_safety/master/images/bird_strike.png -------------------------------------------------------------------------------- /images/data4safety.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/AeroPython/pydata_warsaw2017_flight_safety/master/images/commercial-pilots-1.jpg -------------------------------------------------------------------------------- /images/pydata-logo-warsaw-2017.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AeroPython/pydata_warsaw2017_flight_safety/master/images/pydata-logo-warsaw-2017.png -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: flight-safety 2 | channels: 3 | - damianavila82 4 | - conda-forge 5 | - anaconda-fusion 6 | - defaults 7 | dependencies: 8 | - python=3.6 9 | - bokeh 10 | - ipython 11 | - ipywidgets 12 | - jupyter 13 | - matplotlib 14 | - numpy 15 | - pandas 16 | - scikit-learn 17 | - scipy 18 | - tqdm 19 | - cartopy 20 | - rise 21 | -------------------------------------------------------------------------------- /data/API_IS.AIR.DPRT_DS2_en_csv_v2/Metadata_Indicator_API_IS.AIR.DPRT_DS2_en_csv_v2.csv: -------------------------------------------------------------------------------- 1 | "INDICATOR_CODE","INDICATOR_NAME","SOURCE_NOTE","SOURCE_ORGANIZATION", 2 | "IS.AIR.DPRT","Air transport, registered carrier departures worldwide","Registered carrier departures worldwide are domestic takeoffs and takeoffs abroad of air carriers registered in the country.","International Civil Aviation Organization, Civil Aviation Statistics of the World and ICAO staff estimates.", 3 | -------------------------------------------------------------------------------- /data/readme.md: -------------------------------------------------------------------------------- 1 | # Getting the data 2 | 3 | As the database is too big to be uploaded to the repository it is stored separately [here](https://www.dropbox.com/s/n9inalri0dvff1j/avall.db?dl=1). It can be placed in the `data` folder manually or downloaded using the `get_data.py` python script. 4 | 5 | The data can also be obtained directly from NTSB website (https://app.ntsb.gov/avdata/) in `mdb` format. In order to convert it to a SQLite database the script `export_access2csv.sh` in `utils` folder can be used: 6 | 7 | ``` 8 | $ bash utils/export_access2csv.sh data/raw/avall.mdb data 9 | ``` 10 | 11 | * avall.db -> NTSB aviation data from the new NTSB accident database system (eADMS) 12 | -------------------------------------------------------------------------------- /utils/export_access2csv.sh: -------------------------------------------------------------------------------- 1 | fullfilename=$1 2 | destfolder=$2 3 | filename=$(basename "$fullfilename") 4 | dbname="${filename%.*}" 5 | 6 | mkdir $destfolder/$dbname 7 | 8 | echo .mode csv > $destfolder/$dbname/export_data.sql 9 | 10 | for table in $(mdb-tables $fullfilename); do 11 | echo "Export table "$table 12 | mdb-export $fullfilename $table > $destfolder/$dbname/$table.csv 13 | echo .import $destfolder/$dbname/$table.csv $table >> $destfolder/$dbname/export_data.sql 14 | done 15 | 16 | echo "Export schema "$dbname 17 | mdb-schema $fullfilename > $destfolder/$dbname/schema.sql sqlite 18 | 19 | sqlite3 $destfolder/$dbname.db < $destfolder/$dbname/schema.sql 20 | sqlite3 $destfolder/$dbname.db < $destfolder/$dbname/export_data.sql 21 | 22 | rm -r $destfolder/$dbname 23 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Jesús Martos, Álex Sáez 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /flight_safety/get_data.py: -------------------------------------------------------------------------------- 1 | import urllib.request 2 | import os 3 | from tqdm import tqdm 4 | 5 | 6 | def download_data(url, destination_folder="../data", name='avall.db', force=False): 7 | 8 | path = os.path.join(destination_folder, name) 9 | 10 | if not os.path.isfile(path) or force: 11 | print('Downloading...') 12 | with TqdmUpTo(unit='B', unit_scale=True, miniters=1) as t: 13 | urllib.request.urlretrieve(url, path, reporthook=t.update_to) 14 | else: 15 | print("Data has already been downloaded") 16 | 17 | 18 | class TqdmUpTo(tqdm): 19 | """Provides `update_to(n)` which uses `tqdm.update(delta_n)`.""" 20 | def update_to(self, b=1, bsize=1, tsize=None): 21 | """ 22 | b : int, optional 23 | Number of blocks transferred so far [default: 1]. 24 | bsize : int, optional 25 | Size of each block (in tqdm units) [default: 1]. 26 | tsize : int, optional 27 | Total size (in tqdm units). If [default: None] remains unchanged. 28 | """ 29 | if tsize is not None: 30 | self.total = tsize 31 | self.update(b * bsize - self.n) # will also set self.n = b * bsize 32 | 33 | 34 | 35 | if __name__ == '__main__': 36 | 37 | URL = "https://www.dropbox.com/s/n9inalri0dvff1j/avall.db?dl=1" 38 | download_data(URL, force=True) 39 | -------------------------------------------------------------------------------- /flight_safety/utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | Util functions 3 | """ 4 | 5 | import numpy as np 6 | 7 | 8 | def convert_lat(string): 9 | try: 10 | degs = float(string[0:2]) 11 | mins = float(string[2:4]) 12 | secs = float(string[4:6]) 13 | last = string[6].lower() 14 | if last == 's': 15 | factor = -1.0 16 | elif last == 'n': 17 | factor = 1.0 18 | else: 19 | raise ValueError("invalid hemisphere") 20 | return factor * (degs + mins / 60 + secs / 3600) 21 | except ValueError: 22 | return np.nan 23 | 24 | 25 | def convert_lon(string): 26 | try: 27 | degs = float(string[0:3]) 28 | mins = float(string[3:5]) 29 | secs = float(string[5:7]) 30 | last = string[7].lower() 31 | if last == 'w': 32 | factor = -1.0 33 | elif last == 'e': 34 | factor = 1.0 35 | else: 36 | raise ValueError("invalid direction") 37 | return factor * (degs + mins / 60 + secs / 3600) 38 | except ValueError: 39 | return np.nan 40 | 41 | 42 | def rename_categories(old_categories, codes_meaning): 43 | new_categories = [] 44 | for cat in old_categories: 45 | if str(int(cat)) in codes_meaning.index: 46 | new_categories.append(codes_meaning.loc[str(int(cat)), 'meaning']) 47 | else: 48 | new_categories.append(cat) 49 | return new_categories 50 | -------------------------------------------------------------------------------- /data/annual_passengers_carried_data.csv: -------------------------------------------------------------------------------- 1 | Series Name,Series Code,Country Name,Country Code,1960 [YR1960],1961 [YR1961],1962 [YR1962],1963 [YR1963],1964 [YR1964],1965 [YR1965],1966 [YR1966],1967 [YR1967],1968 [YR1968],1969 [YR1969],1970 [YR1970],1971 [YR1971],1972 [YR1972],1973 [YR1973],1974 [YR1974],1975 [YR1975],1976 [YR1976],1977 [YR1977],1978 [YR1978],1979 [YR1979],1980 [YR1980],1981 [YR1981],1982 [YR1982],1983 [YR1983],1984 [YR1984],1985 [YR1985],1986 [YR1986],1987 [YR1987],1988 [YR1988],1989 [YR1989],1990 [YR1990],1991 [YR1991],1992 [YR1992],1993 [YR1993],1994 [YR1994],1995 [YR1995],1996 [YR1996],1997 [YR1997],1998 [YR1998],1999 [YR1999],2000 [YR2000],2001 [YR2001],2002 [YR2002],2003 [YR2003],2004 [YR2004],2005 [YR2005],2006 [YR2006],2007 [YR2007],2008 [YR2008],2009 [YR2009],2010 [YR2010],2011 [YR2011],2012 [YR2012],2013 [YR2013],2014 [YR2014],2015 [YR2015] 2 | "Air transport, passengers carried",IS.AIR.PSGR,World,WLD,..,..,..,..,..,..,..,..,..,..,310441392,331604904,..,401571800,421145200,432276500,471773396,513269292,576090004,648400600,641872888,640619400,654482108,685101596,732410288,783198104,842594296,904838104,953896012,983208800,1024976616,1133228204,1145436692,1142265216,1233233404,1302891640.1,1390963704,1455104192,1466961780,1562256300,1674064712,1655230214,1627404873,1665309283,1888695284,1970142144,2072995909,2209136496,2208218737,2249518122,2628231062.6889,2786918945.85518,2894035636.71704,3050796410.97472,3213664822.0802,.. 3 | ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 4 | ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 5 | ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 6 | Data from database: World Development Indicators,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 7 | Last Updated: 02/17/2016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | ![PyData_logo](./images/pydata-logo-warsaw-2017.png) 3 | 4 | ## Friday Oct. 20, 2017 - 15:00–15:30 in Track 3 5 | # [Analysing flight safety data with Python](https://pydata.org/warsaw2017/schedule/presentation/51/) 6 | 7 | ### JESÚS MARTOS CARRIZO, ALEJANDRO SÁEZ MOLLEJO 8 | #### Audience level: Novice 9 | 10 | ## Description 11 | 12 | Is aviation the safest means of transport? Is flying now safer than in the previous decades? Which are the main accident causes? During this talk we will try to answer these and other questions using the capabilities of libraries such as pandas, cartopy or Bokeh. 13 | 14 | ## Abstract 15 | 16 | A continuous improvement in flight safety is a common goal for all public agencies and private companies. Many analysis are published every year but, would it be possible to reproduce those same results? During this talk a real open database from the American National Transportation Safety Board (NTSB) will be used in order to show to the audience the potential of Python to read, clean and manipulate data. Among other questions, we will try to identify the most common causes of aviation accidents and their consequences. During the talk the importance of open data will be highlighted. 17 | 18 | --- 19 | 20 | ### Getting the data 21 | 22 | As the database is too big to be uploaded to the repository it is stored separately [here](https://www.dropbox.com/s/n9inalri0dvff1j/avall.db?dl=1). It can be placed in the `data` folder manually or downloaded using `flight_safety/get_data.py` python script. 23 | 24 | The data can also be obtained directly from NTSB website (https://app.ntsb.gov/avdata/) in `mdb` format. In order to convert it to a SQLite database the script `export_access2csv.sh` in `utils` folder can be used: 25 | 26 | ``` 27 | $ bash utils/export_access2csv.sh data/raw/avall.mdb data 28 | ``` 29 | 30 | ### Requirements 31 | 32 | It is recommended to use Anaconda/miniconda (https://www.anaconda.com/download) to install the dependencies. After conda is installed, the environment.yml can be used: 33 | 34 | ``` 35 | $ conda env create -f environment.yml 36 | ``` 37 | 38 | -------------------------------------------------------------------------------- /styles/style.css: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 112 | 128 | -------------------------------------------------------------------------------- /flight_safety/queries.py: -------------------------------------------------------------------------------- 1 | """ 2 | Queries to retrieve data from database 3 | """ 4 | 5 | import pandas as pd 6 | 7 | from .utils import convert_lat, convert_lon, rename_categories 8 | 9 | DATE_FORMAT = '%m/%d/%y %H:%M:%S' 10 | TIME_FORMAT = '%H%M' 11 | 12 | FAR_PARTS = "'121 ', '125 '" 13 | 14 | EVENTS_COLUMNS = ( 15 | 'ev_id', 16 | 'ntsb_no', 17 | 'ev_type', 18 | 'ev_date', 19 | 'ev_time', 20 | 'ev_tmzn', 21 | 'ev_city', 22 | 'ev_state', 23 | 'ev_country', 24 | 'ev_site_zipcode', 25 | 'mid_air', 26 | 'on_ground_collision', 27 | 'latitude', 28 | 'longitude', 29 | 'latlong_acq', 30 | 'apt_name', 31 | 'ev_nr_apt_id', 32 | 'ev_nr_apt_loc', 33 | 'apt_dist', 34 | 'apt_dir', 35 | 'apt_elev', 36 | 'wx_brief_comp', 37 | 'wx_src_iic', 38 | 'wx_obs_time', 39 | 'wx_obs_dir', 40 | 'wx_obs_fac_id', 41 | 'wx_obs_elev', 42 | 'wx_obs_dist', 43 | 'wx_obs_tmzn', 44 | 'light_cond', 45 | 'sky_cond_nonceil', 46 | 'sky_nonceil_ht', 47 | 'sky_ceil_ht', 48 | 'sky_cond_ceil', 49 | 'vis_rvr', 50 | 'vis_rvv', 51 | 'vis_sm', 52 | 'wx_temp', 53 | 'wx_dew_pt', 54 | 'wind_dir_deg', 55 | 'wind_dir_ind', 56 | 'wind_vel_kts', 57 | 'wind_vel_ind', 58 | 'gust_ind', 59 | 'gust_kts', 60 | 'altimeter', 61 | 'wx_dens_alt', 62 | 'wx_int_precip', 63 | 'metar', 64 | 'ev_highest_injury', 65 | 'inj_f_grnd', 66 | 'inj_m_grnd', 67 | 'inj_s_grnd', 68 | 'inj_tot_f', 69 | 'inj_tot_m', 70 | 'inj_tot_n', 71 | 'inj_tot_s', 72 | 'inj_tot_t', 73 | 'invest_agy', 74 | 'ntsb_docket', 75 | 'ntsb_notf_from', 76 | 'ntsb_notf_date', 77 | 'ntsb_notf_tm', 78 | 'fiche_number', 79 | 'wx_cond_basic', 80 | 'faa_dist_office' 81 | ) 82 | 83 | EVENTS_NUMERIC = ( 84 | "apt_elev", 85 | "apt_dir", 86 | "gust_kts", 87 | "inj_f_grnd", 88 | "inj_m_grnd", 89 | "inj_s_grnd", 90 | "inj_tot_f", 91 | "inj_tot_m", 92 | "inj_tot_n", 93 | "inj_tot_s", 94 | "inj_tot_t", 95 | "ntsb_notf_tm", 96 | "vis_rvv", 97 | "wind_dir_deg", 98 | "wx_obs_dist", 99 | "wx_obs_time", 100 | "wx_dew_pt", 101 | "wx_obs_dir", 102 | "wx_temp", 103 | "altimeter", 104 | "apt_dist", 105 | "vis_rvr", 106 | "vis_sm", 107 | "ntsb_docket", 108 | "sky_ceil_ht", 109 | "sky_nonceil_ht", 110 | "wx_obs_elev", 111 | "wx_dens_alt" 112 | ) 113 | 114 | EVENTS_CATEGORICAL = ( 115 | "ev_highest_injury", 116 | "ev_nr_apt_loc", 117 | "ev_state", 118 | "ev_tmzn", 119 | "ev_type", 120 | "gust_ind", 121 | "invest_agy", 122 | "latlong_acq", 123 | "light_cond", 124 | "mid_air", 125 | "on_ground_collision", 126 | "sky_cond_ceil", 127 | "sky_cond_nonceil", 128 | "wind_dir_ind", 129 | "wind_vel_ind", 130 | "wx_brief_comp", 131 | "wx_cond_basic", 132 | "wx_int_precip", 133 | "wx_src_iic" 134 | ) 135 | 136 | AIRCRAFT_COLUMNS = ( 137 | "ev_id", 138 | "Aircraft_Key", 139 | "regis_no", 140 | "ntsb_no", 141 | "acft_missing", 142 | "far_part", 143 | "flt_plan_filed", 144 | "flight_plan_activated", 145 | "damage", 146 | "acft_fire", 147 | "acft_expl", 148 | "acft_make", 149 | "acft_model", 150 | "acft_series", 151 | "acft_serial_no", 152 | "cert_max_gr_wt", 153 | "acft_category", 154 | "acft_reg_cls", 155 | "homebuilt", 156 | "date_last_insp", 157 | "afm_hrs", 158 | "afm_hrs_last_insp", 159 | "commercial_space_flight", 160 | "unmanned", 161 | "ifr_equipped_cert", 162 | "elt_mounted_aircraft", 163 | "elt_connected_antenna", 164 | "afm_hrs_since", 165 | "air_medical", 166 | "certs_held", 167 | "dest_apt_id", 168 | "dest_country", 169 | "dest_same_local", 170 | "dest_state", 171 | "dprt_apt_id", 172 | "dprt_country", 173 | "dprt_pt_same_ev", 174 | "dprt_state", 175 | "dprt_timezn", 176 | "elt_aided_loc_ev", 177 | "elt_install", 178 | "elt_oper", 179 | "elt_type", 180 | "evacuation", 181 | "fixed_retractable", 182 | "oper_addr_same", 183 | "oper_cert", 184 | "oper_code", 185 | "oper_country", 186 | "oper_dom_int", 187 | "oper_individual_name", 188 | "oper_pax_cargo", 189 | "oper_same", 190 | "oper_sched", 191 | "oper_state", 192 | "oprtng_cert", 193 | "owner_country", 194 | "owner_state", 195 | "report_to_icao", 196 | "second_pilot", 197 | "site_seeing", 198 | "type_fly", 199 | "type_last_insp", 200 | "dest_city", 201 | "dprt_city", 202 | "med_type_flight", 203 | "oper_cert_num", 204 | "oper_city", 205 | "oper_dba", 206 | "oper_name", 207 | "oper_street", 208 | "oper_zip", 209 | "owner_acft", 210 | "owner_city", 211 | "owner_street", 212 | "owner_zip", 213 | "rwy_num", 214 | "fuel_on_board", 215 | "elt_manufacturer", 216 | "elt_model", 217 | "elt_reason_other", 218 | "cc_seats", 219 | "fc_seats", 220 | "pax_seats", 221 | "phase_flt_spec", 222 | "rwy_len", 223 | "rwy_width", 224 | "acft_year", 225 | "dprt_time", 226 | "total_seats", 227 | "num_eng" 228 | ) 229 | 230 | AIRCRAFT_CATEGORICAL = ( 231 | "commercial_space_flight", 232 | "unmanned", 233 | "ifr_equipped_cert", 234 | "elt_mounted_aircraft", 235 | "elt_connected_antenna", 236 | "acft_category", 237 | "acft_expl", 238 | "acft_fire", 239 | "acft_missing", 240 | "flt_plan_filed", 241 | "homebuilt", 242 | "afm_hrs_since", 243 | "air_medical", 244 | "certs_held", 245 | "damage", 246 | "dest_apt_id", 247 | "dest_country", 248 | "dest_same_local", 249 | "dest_state", 250 | "dprt_apt_id", 251 | "dprt_country", 252 | "dprt_pt_same_ev", 253 | "dprt_state", 254 | "dprt_timezn", 255 | "elt_aided_loc_ev", 256 | "elt_install", 257 | "elt_oper", 258 | "elt_type", 259 | "evacuation", 260 | "far_part", 261 | "fixed_retractable", 262 | "flight_plan_activated", 263 | "oper_addr_same", 264 | "oper_cert", 265 | "oper_code", 266 | "oper_country", 267 | "oper_dom_int", 268 | "oper_individual_name", 269 | "oper_pax_cargo", 270 | "oper_same", 271 | "oper_sched", 272 | "oper_state", 273 | "oprtng_cert", 274 | "owner_country", 275 | "owner_state", 276 | "report_to_icao", 277 | "second_pilot", 278 | "site_seeing", 279 | "type_fly", 280 | "type_last_insp", 281 | "acft_make", 282 | "acft_model", 283 | "acft_series", 284 | "dest_city", 285 | "dprt_city", 286 | "med_type_flight", 287 | "oper_cert_num", 288 | "oper_city", 289 | "oper_dba", 290 | "oper_name", 291 | "oper_street", 292 | "oper_zip", 293 | "owner_acft", 294 | "owner_city", 295 | "owner_street", 296 | "owner_zip", 297 | "rwy_num", 298 | "fuel_on_board", 299 | "elt_manufacturer", 300 | "elt_model", 301 | "elt_reason_other" 302 | ) 303 | 304 | AIRCRAFT_NUMERIC = ( 305 | "Aircraft_Key", 306 | "cc_seats", 307 | "cert_max_gr_wt", 308 | "fc_seats", 309 | "pax_seats", 310 | "rwy_len", 311 | "rwy_width", 312 | "acft_year", 313 | "afm_hrs", 314 | "afm_hrs_last_insp", 315 | "dprt_time", 316 | "total_seats", 317 | "num_eng", 318 | "phase_flt_spec", 319 | ) 320 | 321 | OCCURRENCES_COLUMNS = ( 322 | "ev_id", 323 | "Aircraft_Key", 324 | "Occurrence_No", 325 | "Occurrence_Code", 326 | "Phase_of_Flight", 327 | "Altitude" 328 | ) 329 | 330 | OCCURENCES_NUMERIC = ( 331 | "Aircraft_Key", 332 | "Occurrence_No", 333 | "Altitude", 334 | "Phase_of_Flight" 335 | ) 336 | 337 | OCCURRENCE_CATEGORICAL = ( 338 | "Occurrence_Code", 339 | "Phase_of_Flight", 340 | ) 341 | 342 | FLIGHT_TIME_COLS = ( 343 | "ev_id", 344 | "Aircraft_Key", 345 | "crew_no", 346 | "flight_type", 347 | "flight_craft", 348 | "flight_hours" 349 | ) 350 | 351 | FLIGHT_TIME_NUMERIC = ( 352 | "Aircraft_Key", 353 | "crew_no", 354 | "flight_hours" 355 | ) 356 | 357 | FLIGHT_TIME_CATEGORICAL = ( 358 | "flight_type", 359 | "flight_craft" 360 | ) 361 | 362 | SEQ_OF_EVETNS_COLUMNS = ( 363 | "ev_id", 364 | "Aircraft_Key", 365 | "Occurrence_No", 366 | "seq_event_no", 367 | "group_code", 368 | "Subj_Code", 369 | "Cause_Factor", 370 | "Modifier_Code", 371 | "Person_Code", 372 | ) 373 | 374 | SEQ_OF_EVENTS_NUMERIC = ( 375 | "Aircraft_Key", 376 | "Occurrence_No", 377 | "seq_event_no" 378 | ) 379 | 380 | SEQ_OF_EVENTS_CATEGORICAL = ( 381 | "group_code", 382 | "Subj_Code", 383 | "Cause_Factor", 384 | "Modifier_Code", 385 | "Person_Code", 386 | ) 387 | 388 | FLIGHT_CREW_COLS = ( 389 | 'ev_id', 390 | 'Aircraft_Key', 391 | 'crew_no', 392 | 'crew_category', 393 | 'crew_age', 394 | 'crew_sex' 395 | ) 396 | 397 | FLIGHT_CREW_NUMERIC = ( 398 | 'crew_no', 399 | 'crew_age' 400 | ) 401 | 402 | FLIGHT_CREW_CATEGORICAL = ( 403 | 'crew_category', 404 | 'crew_sex' 405 | ) 406 | 407 | # "SELECT * FROM Occurrences WHERE ev_id IN (SELECT ev_id FROM aircraft WHERE ev_id IN (SELECT ev_id FROM events WHERE ev_type='ACC') AND far_part IN ('121 ', '125 ', '129 ', '135 '))" 408 | 409 | 410 | def get_codes_meaning(con, table, column): 411 | query = ( 412 | "select distinct code_iaids, meaning from eADMSPUB_DataDictionary " 413 | f"where \"Table\"='{table}' and \"Column\"='{column}'" 414 | ) 415 | return pd.read_sql(query, con, index_col='code_iaids') 416 | 417 | 418 | def get_events_accidents(con): 419 | query = ("SELECT {cols} FROM events " 420 | "WHERE ev_type='ACC' AND ev_date IS NOT NULL " 421 | "AND ev_id IN (SELECT ev_id FROM aircraft WHERE " 422 | "far_part IN ({far_parts}))".format( 423 | cols=", ".join(EVENTS_COLUMNS), 424 | far_parts=FAR_PARTS 425 | ) 426 | ) 427 | events = pd.read_sql_query(query, con, 428 | index_col='ev_id', 429 | parse_dates={'ev_date': DATE_FORMAT, 430 | 'ev_time': TIME_FORMAT, 431 | 'ntsb_notf_date': DATE_FORMAT, 432 | 'ntsb_notf_tm': TIME_FORMAT 433 | } 434 | ) 435 | 436 | for c in EVENTS_NUMERIC: 437 | events[c] = pd.to_numeric(events[c], errors='coerce') 438 | 439 | for c in EVENTS_CATEGORICAL: 440 | events[c] = events[c].astype('category') 441 | 442 | events['latitude'] = events['latitude'].apply(convert_lat) 443 | events['longitude'] = events['longitude'].apply(convert_lon) 444 | 445 | return events 446 | 447 | 448 | def get_events_all(con): 449 | query = ("SELECT {cols} FROM events " 450 | .format( 451 | cols=", ".join(EVENTS_COLUMNS) 452 | ) 453 | ) 454 | events = pd.read_sql_query(query, con, 455 | index_col='ev_id', 456 | parse_dates={'ev_date': DATE_FORMAT, 457 | 'ev_time': TIME_FORMAT, 458 | 'ntsb_notf_date': DATE_FORMAT, 459 | 'ntsb_notf_tm': TIME_FORMAT 460 | } 461 | ) 462 | 463 | for c in EVENTS_NUMERIC: 464 | events[c] = pd.to_numeric(events[c], errors='coerce') 465 | 466 | for c in EVENTS_CATEGORICAL: 467 | events[c] = events[c].astype('category') 468 | 469 | events['latitude'] = events['latitude'].apply(convert_lat) 470 | events['longitude'] = events['longitude'].apply(convert_lon) 471 | 472 | return events 473 | 474 | 475 | def get_aircrafts_accidents(con): 476 | ac_columns = ", ".join(AIRCRAFT_COLUMNS) 477 | 478 | query = ( 479 | f"SELECT {ac_columns} FROM aircraft WHERE ev_id IN " 480 | "(SELECT ev_id FROM events WHERE ev_type='ACC' AND " 481 | f"ev_date IS NOT NULL) AND far_part IN ({FAR_PARTS})" 482 | ) 483 | 484 | aircrafts = pd.read_sql(query, con, 485 | parse_dates={'date_last_insp': DATE_FORMAT} 486 | ) 487 | 488 | for c in AIRCRAFT_NUMERIC: 489 | aircrafts[c] = pd.to_numeric(aircrafts[c], errors='coerce') 490 | 491 | # phase_flt_spec is parsed as numeric and this is used to get phases of 492 | # flight with less detail (ie. "Takeoff - roll/run" -> "Takeoff) 493 | aircrafts['phase_flt_spec_gross'] = ((aircrafts.phase_flt_spec // 10) * 10) 494 | 495 | new_categorical_cols = ['phase_flt_spec_gross', 'phase_flt_spec'] 496 | for c in list(AIRCRAFT_CATEGORICAL) + new_categorical_cols: 497 | aircrafts[c] = aircrafts[c].astype('category') 498 | 499 | PHASE_FLT_SPEC_DICT = get_codes_meaning(con, 'aircraft', 'phase_flt_spec') 500 | 501 | # Change codes for names (ie. 570 to Landing) 502 | cats = rename_categories(aircrafts['phase_flt_spec_gross'].cat.categories, 503 | PHASE_FLT_SPEC_DICT) 504 | aircrafts['phase_flt_spec_gross'].cat.rename_categories(cats, inplace=True) 505 | 506 | cats = rename_categories(aircrafts['phase_flt_spec'].cat.categories, 507 | PHASE_FLT_SPEC_DICT) 508 | aircrafts['phase_flt_spec'].cat.rename_categories(cats, inplace=True) 509 | 510 | return aircrafts 511 | 512 | 513 | def get_aircrafts_all(con): 514 | ac_columns = ", ".join(AIRCRAFT_COLUMNS) 515 | 516 | query = ( 517 | f"SELECT {ac_columns} FROM aircraft " 518 | ) 519 | 520 | aircrafts = pd.read_sql(query, con, 521 | parse_dates={'date_last_insp': DATE_FORMAT} 522 | ) 523 | 524 | for c in AIRCRAFT_NUMERIC: 525 | aircrafts[c] = pd.to_numeric(aircrafts[c], errors='coerce') 526 | 527 | # phase_flt_spec is parsed as numeric and this is used to get phases of 528 | # flight with less detail (ie. "Takeoff - roll/run" -> "Takeoff) 529 | aircrafts['phase_flt_spec_gross'] = ((aircrafts.phase_flt_spec // 10) * 10) 530 | 531 | new_categorical_cols = ['phase_flt_spec_gross', 'phase_flt_spec'] 532 | for c in list(AIRCRAFT_CATEGORICAL) + new_categorical_cols: 533 | aircrafts[c] = aircrafts[c].astype('category') 534 | 535 | PHASE_FLT_SPEC_DICT = get_codes_meaning(con, 'aircraft', 'phase_flt_spec') 536 | 537 | # Change codes for names (ie. 570 to Landing) 538 | cats = rename_categories(aircrafts['phase_flt_spec_gross'].cat.categories, 539 | PHASE_FLT_SPEC_DICT) 540 | aircrafts['phase_flt_spec_gross'].cat.rename_categories(cats, inplace=True) 541 | 542 | cats = rename_categories(aircrafts['phase_flt_spec'].cat.categories, 543 | PHASE_FLT_SPEC_DICT) 544 | aircrafts['phase_flt_spec'].cat.rename_categories(cats, inplace=True) 545 | 546 | return aircrafts 547 | 548 | 549 | def get_occurrences_accidents(con): 550 | occurrence_cols = ", ".join(OCCURRENCES_COLUMNS) 551 | 552 | query = ( 553 | f"SELECT {occurrence_cols} FROM Occurrences WHERE ev_id IN " 554 | "(SELECT ev_id FROM events WHERE ev_type='ACC' AND " 555 | "ev_date IS NOT NULL) AND ev_id IN (SELECT ev_id FROM aircraft " 556 | f"WHERE far_part in ({FAR_PARTS}))" 557 | ) 558 | 559 | occurrences = pd.read_sql(query, con) 560 | 561 | for c in OCCURENCES_NUMERIC: 562 | occurrences[c] = pd.to_numeric(occurrences[c], errors='coerce') 563 | 564 | # phase_flt_spec is parsed as numeric and this is used to get phases of 565 | # flight with less detail (ie. "Takeoff - roll/run" -> "Takeoff) 566 | occurrences['phase_flt_spec_gross'] = ((occurrences.Phase_of_Flight // 10) * 10) 567 | 568 | for c in list(OCCURRENCE_CATEGORICAL) + ['phase_flt_spec_gross']: 569 | occurrences[c] = occurrences[c].astype('category') 570 | 571 | PHASE_FLT_SPEC = get_codes_meaning(con, 'Occurrences', 'Phase_of_Flight') 572 | OCCU_CODE_SPEC = get_codes_meaning(con, 'Occurrences', 'Occurrence_Code') 573 | 574 | cats = rename_categories(occurrences['Phase_of_Flight'].cat.categories, 575 | PHASE_FLT_SPEC) 576 | occurrences['Phase_of_Flight'].cat.rename_categories(cats, inplace=True) 577 | 578 | cats = rename_categories(occurrences['phase_flt_spec_gross'].cat.categories, 579 | PHASE_FLT_SPEC) 580 | occurrences['phase_flt_spec_gross'].cat.rename_categories(cats, inplace=True) 581 | 582 | cats = rename_categories(occurrences['Occurrence_Code'].cat.categories, 583 | OCCU_CODE_SPEC) 584 | occurrences['Occurrence_Code'].cat.rename_categories(cats, inplace=True) 585 | 586 | return occurrences 587 | 588 | 589 | def get_flight_time_accidents(con): 590 | flight_time_cols = ", ".join(FLIGHT_TIME_COLS) 591 | 592 | query = ( 593 | f"SELECT {flight_time_cols} FROM flight_time WHERE ev_id IN " 594 | "(SELECT ev_id FROM events WHERE ev_type='ACC' AND " 595 | "ev_date IS NOT NULL) AND ev_id IN (SELECT ev_id FROM aircraft WHERE " 596 | f"far_part in ({FAR_PARTS}))" 597 | ) 598 | 599 | flight_time = pd.read_sql(query, con) 600 | 601 | for c in FLIGHT_TIME_NUMERIC: 602 | flight_time[c] = pd.to_numeric(flight_time[c], errors='coerce') 603 | 604 | for c in FLIGHT_TIME_CATEGORICAL: 605 | flight_time[c] = flight_time[c].astype('category') 606 | 607 | return flight_time 608 | 609 | 610 | def get_seq_of_events_accidents(con): 611 | seq_of_events_cols = ", ".join(SEQ_OF_EVETNS_COLUMNS) 612 | 613 | query = ( 614 | f"SELECT {seq_of_events_cols} FROM seq_of_events WHERE ev_id IN " 615 | "(SELECT ev_id FROM events WHERE ev_type='ACC' AND " 616 | "ev_date IS NOT NULL) AND ev_id IN (SELECT ev_id FROM aircraft WHERE " 617 | f"far_part IN ({FAR_PARTS}))" 618 | ) 619 | 620 | seq_of_events = pd.read_sql(query, con) 621 | 622 | # DROP GROUP_CODE = 0 because it is not codified 623 | # seq_of_events = seq_of_events[seq_of_events.group_code != 0] 624 | 625 | for c in SEQ_OF_EVENTS_NUMERIC: 626 | seq_of_events[c] = pd.to_numeric(seq_of_events[c], errors='coerce') 627 | 628 | for c in SEQ_OF_EVENTS_CATEGORICAL: 629 | seq_of_events[c] = seq_of_events[c].astype('category') 630 | 631 | return seq_of_events 632 | 633 | 634 | def get_flight_crew_accidents(con): 635 | 636 | flight_crew_cols = ', '.join(FLIGHT_CREW_COLS) 637 | 638 | query = ( 639 | f"SELECT {flight_crew_cols} FROM Flight_Crew WHERE ev_id IN " 640 | "(SELECT ev_id FROM events WHERE ev_type='ACC' AND " 641 | "ev_date IS NOT NULL) AND ev_id IN (SELECT ev_id FROM aircraft " 642 | f"WHERE far_part in ({FAR_PARTS}))" 643 | ) 644 | flight_crew = pd.read_sql_query(query, con) 645 | 646 | for c in FLIGHT_CREW_NUMERIC: 647 | flight_crew[c] = pd.to_numeric(flight_crew[c], errors='coerce') 648 | 649 | for c in FLIGHT_CREW_CATEGORICAL: 650 | flight_crew[c] = flight_crew[c].astype('category') 651 | 652 | return flight_crew 653 | -------------------------------------------------------------------------------- /data/API_IS.AIR.DPRT_DS2_en_csv_v2/Metadata_Country_API_IS.AIR.DPRT_DS2_en_csv_v2.csv: -------------------------------------------------------------------------------- 1 | "Country Code","Region","IncomeGroup","SpecialNotes","TableName", 2 | "ABW","Latin America & Caribbean","High income","SNA data for 2000-2011 are updated from official government statistics; 1994-1999 from UN databases. Base year has changed from 1995 to 2000.","Aruba", 3 | "AFG","South Asia","Low income","Fiscal year end: March 20; reporting period for national accounts data is calendar year, estimated to insure consistency between national accounts and fiscal data. National accounts data are sourced from the IMF and differ from the Central Statistics Organization numbers due to exclusion of the opium economy.","Afghanistan", 4 | "AGO","Sub-Saharan Africa","Lower middle income","","Angola", 5 | "ALB","Europe & Central Asia","Upper middle income","","Albania", 6 | "AND","Europe & Central Asia","High income","WB-3 code changed from ADO to AND to align with ISO code.","Andorra", 7 | "ARB","","","Arab World aggregate. Arab World is composed of members of the League of Arab States.","Arab World", 8 | "ARE","Middle East & North Africa","High income","","United Arab Emirates", 9 | "ARG","Latin America & Caribbean","Upper middle income","National Institute of Statistics and Census revised national accounts from 2004-2015. Argentina, which was temporarily unclassified in July 2016 pending release of revised national accounts statistics, is classified as upper middle income for FY17 as of September 29, 2016. 10 | 11 | The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. An alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. In the case of Argentina, the World Bank has found that during 2012-2015 there were two exchange rates (official and parallel) and parallel exchange rate (blue chip swap rate) was used in around 20% of the transactions. Based on this information an alternative conversion factor has been calculated using a weighted average method for this period.","Argentina", 12 | "ARM","Europe & Central Asia","Lower middle income","","Armenia", 13 | "ASM","East Asia & Pacific","Upper middle income","","American Samoa", 14 | "ATG","Latin America & Caribbean","High income","","Antigua and Barbuda", 15 | "AUS","East Asia & Pacific","High income","Fiscal year end: June 30; reporting period for national accounts data: FY. Value added current series updated by the Australian Bureau of Statistics; data revised from 1990 onward; Australia reports using SNA 2008.","Australia", 16 | "AUT","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 13.7603 Austrian schilling. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Austria", 17 | "AZE","Europe & Central Asia","Upper middle income","","Azerbaijan", 18 | "BDI","Sub-Saharan Africa","Low income","","Burundi", 19 | "BEL","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 40.3399 Belgian franc. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Belgium", 20 | "BEN","Sub-Saharan Africa","Low income","","Benin", 21 | "BFA","Sub-Saharan Africa","Low income","","Burkina Faso", 22 | "BGD","South Asia","Lower middle income","Fiscal year end: June 30; reporting period for national accounts data: FY.","Bangladesh", 23 | "BGR","Europe & Central Asia","Upper middle income","The new reference year for chain linked series is 2010.","Bulgaria", 24 | "BHR","Middle East & North Africa","High income","","Bahrain", 25 | "BHS","Latin America & Caribbean","High income","","Bahamas, The", 26 | "BIH","Europe & Central Asia","Upper middle income","","Bosnia and Herzegovina", 27 | "BLR","Europe & Central Asia","Upper middle income","In July 2016 a new Belarusian ruble was introduced, at a rate of 1 new ruble = 10,000 old rubles. Local currency values in this database are in new rubels.","Belarus", 28 | "BLZ","Latin America & Caribbean","Upper middle income","","Belize", 29 | "BMU","North America","High income","","Bermuda", 30 | "BOL","Latin America & Caribbean","Lower middle income","","Bolivia", 31 | "BRA","Latin America & Caribbean","Upper middle income","","Brazil", 32 | "BRB","Latin America & Caribbean","High income","","Barbados", 33 | "BRN","East Asia & Pacific","High income","National accounts revision is based on data from the Asian Development Bank and the IMF. Data have been revised from 2010-2015; the new base year is 2010.","Brunei Darussalam", 34 | "BTN","South Asia","Lower middle income","","Bhutan", 35 | "BWA","Sub-Saharan Africa","Upper middle income","Fiscal year end: March 31; reporting period for national accounts data: CY. Based on official government statistics, national accounts data have been revised from 2006 onward; the new base year is 2006. Data before 2006 were reported on a fiscal year basis.","Botswana", 36 | "CAF","Sub-Saharan Africa","Low income","The source of national accounts has changed to the International Monetary Fund. The base year has reverted back to 1985; the new reference year is 2005.","Central African Republic", 37 | "CAN","North America","High income","Fiscal year end: March 31; reporting period for national accounts data: CY.","Canada", 38 | "CEB","","","Central Europe and the Baltics aggregate.","Central Europe and the Baltics", 39 | "CHE","Europe & Central Asia","High income","","Switzerland", 40 | "CHI","Europe & Central Asia","High income","","Channel Islands", 41 | "CHL","Latin America & Caribbean","High income","","Chile", 42 | "CHN","East Asia & Pacific","Upper middle income","On 1 July 1997 China resumed its exercise of sovereignty over Hong Kong; and on 20 December 1999 China resumed its exercise of sovereignty over Macao. Unless otherwise noted, data for China do not include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China. National accounts have been revised from 2010-2015 based on the National Bureau of Statistics data and World Bank estimates. The new base year is 2010.","China", 43 | "CIV","Sub-Saharan Africa","Lower middle income","","Côte d'Ivoire", 44 | "CMR","Sub-Saharan Africa","Lower middle income","","Cameroon", 45 | "COD","Sub-Saharan Africa","Low income","WB-3 code changed from ZAR to COD to align with ISO code.","Congo, Dem. Rep.", 46 | "COG","Sub-Saharan Africa","Lower middle income","","Congo, Rep.", 47 | "COL","Latin America & Caribbean","Upper middle income","","Colombia", 48 | "COM","Sub-Saharan Africa","Low income","","Comoros", 49 | "CPV","Sub-Saharan Africa","Lower middle income","Cabo Verde is the name for the country previously listed as Cape Verde. Value added is measured in basic prices.","Cabo Verde", 50 | "CRI","Latin America & Caribbean","Upper middle income","National accounts are revised from 1991 to 2015 using SNA 2008 based on official government data. The new reference year is 2012.","Costa Rica", 51 | "CSS","","","Caribbean small states aggregate.","Caribbean small states", 52 | "CUB","Latin America & Caribbean","Upper middle income","Based on official government statistics, the new reference year is 2005.","Cuba", 53 | "CUW","Latin America & Caribbean","High income","","Curaçao", 54 | "CYM","Latin America & Caribbean","High income","","Cayman Islands", 55 | "CYP","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2008: 1 euro = 0.585274 Cyprus pounds. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries.","Cyprus", 56 | "CZE","Europe & Central Asia","High income","","Czech Republic", 57 | "DEU","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 1.95583 German mark. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Germany", 58 | "DJI","Middle East & North Africa","Lower middle income","","Djibouti", 59 | "DMA","Latin America & Caribbean","Upper middle income","","Dominica", 60 | "DNK","Europe & Central Asia","High income","","Denmark", 61 | "DOM","Latin America & Caribbean","Upper middle income","","Dominican Republic", 62 | "DZA","Middle East & North Africa","Upper middle income","The new base year is 1999.","Algeria", 63 | "EAP","","","East Asia and Pacific regional aggregate (does not include high-income economies).","East Asia & Pacific (excluding high income)", 64 | "EAR","","","Early-dividend countries are mostly lower-middle-income countries further along the fertility transition. Fertility rates have fallen below four births per woman and the working-age share of the population is likely rising considerably.","Early-demographic dividend", 65 | "EAS","","","East Asia and Pacific regional aggregate (includes all income levels).","East Asia & Pacific", 66 | "ECA","","","Europe and Central Asia regional aggregate (does not include high-income economies).","Europe & Central Asia (excluding high income)", 67 | "ECS","","","Europe and Central Asia regional aggregate (includes all income levels).","Europe & Central Asia", 68 | "ECU","Latin America & Caribbean","Upper middle income","","Ecuador", 69 | "EGY","Middle East & North Africa","Lower middle income","Fiscal year end: June 30; reporting period for national accounts data: FY.","Egypt, Arab Rep.", 70 | "EMU","","","Euro area aggregate.","Euro area", 71 | "ERI","Sub-Saharan Africa","Low income","","Eritrea", 72 | "ESP","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 166.386 Spanish peseta. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Spain", 73 | "EST","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2011: 1 euro = 15.6466 Estonian kroon. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries.","Estonia", 74 | "ETH","Sub-Saharan Africa","Low income","Fiscal year end: July 7; reporting period for national accounts data: FY.","Ethiopia", 75 | "EUU","","","European Union aggregate.","European Union", 76 | "FCS","","","Fragile and conflict affected situations aggregate. Countries with fragile situations are primarily International Development Association-eligible countries and nonmember or inactive countries and territories with a 3.2 or lower harmonized average of the World Bank's Country Policy and Institutional Assessment rating and the corresponding rating by a regional development bank, or that have had a UN or regional peacebuilding and political mission (for example by the African Union, European Union, or Organization of American States) or peacekeeping mission (for example, by the African Union, European Union, North Atlantic Treaty Organization, or Organization of American States) during the last three years. The group excludes IBRD countries (for which the CPIA scores are not publically disclosed); unless there is the presence of a peace-keeping or political/peacebuilding mission. This definition is pursuant to an agreement between the World Bank and other multilateral development banks at the start of the International Development Association 15 round in 2007. The list of countries and territories with fragile situations is imperfect and used here to reflect a complex concept. The World Bank continues to work with partners and client countries to refine the concept.","Fragile and conflict affected situations", 77 | "FIN","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 5.94573 Finnish markka. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Finland", 78 | "FJI","East Asia & Pacific","Upper middle income","National accounts have been revised from 2011 to 2015 based on the Fiji Bureau of Statistics and World Bank estimates. The new base year is 2011.","Fiji", 79 | "FRA","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 6.55957 French franc. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","France", 80 | "FRO","Europe & Central Asia","High income","","Faroe Islands", 81 | "FSM","East Asia & Pacific","Lower middle income","Fiscal year ends on September 30; reporting period for national accounts data: FY. Based on the Pacific and Virgin Islands Training Initiative, national accounts data have been revised from 2009 onward.","Micronesia, Fed. Sts.", 82 | "GAB","Sub-Saharan Africa","Upper middle income","","Gabon", 83 | "GBR","Europe & Central Asia","High income","","United Kingdom", 84 | "GEO","Europe & Central Asia","Lower middle income","Value added data in constant prices before 2007 have been temporarily removed until revised series become available. Constant price expenditure estimates before 2011 have been deleted.","Georgia", 85 | "GHA","Sub-Saharan Africa","Lower middle income","","Ghana", 86 | "GIB","Europe & Central Asia","High income","","Gibraltar", 87 | "GIN","Sub-Saharan Africa","Low income","","Guinea", 88 | "GMB","Sub-Saharan Africa","Low income","Fiscal year end: June 30; reporting period for national accounts data: CY.","Gambia, The", 89 | "GNB","Sub-Saharan Africa","Low income","","Guinea-Bissau", 90 | "GNQ","Sub-Saharan Africa","Upper middle income","","Equatorial Guinea", 91 | "GRC","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 340.75 Greek drachma. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Greece", 92 | "GRD","Latin America & Caribbean","Upper middle income","","Grenada", 93 | "GRL","Europe & Central Asia","High income","","Greenland", 94 | "GTM","Latin America & Caribbean","Lower middle income","","Guatemala", 95 | "GUM","East Asia & Pacific","High income","","Guam", 96 | "GUY","Latin America & Caribbean","Upper middle income","","Guyana", 97 | "HIC","","","High income group aggregate. High-income economies are those in which 2015 GNI per capita was $12,476 or more.","High income", 98 | "HKG","East Asia & Pacific","High income","On 1 July 1997 China resumed its exercise of sovereignty over Hong Kong. Unless otherwise noted, data for China do not include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China. Agriculture value added includes mining and quarrying.","Hong Kong SAR, China", 99 | "HND","Latin America & Caribbean","Lower middle income","","Honduras", 100 | "HPC","","","Heavily indebted poor countries aggregate.","Heavily indebted poor countries (HIPC)", 101 | "HRV","Europe & Central Asia","Upper middle income","","Croatia", 102 | "HTI","Latin America & Caribbean","Low income","Fiscal year end: September 30; reporting period for national accounts data: FY.","Haiti", 103 | "HUN","Europe & Central Asia","High income","","Hungary", 104 | "IBD","","","IBRD only group aggregate.","IBRD only", 105 | "IBT","","","IDA and IBRD total group aggregate (includes IDA only, IDA blend, and IBRD only).","IDA & IBRD total", 106 | "IDA","","","IDA total group aggregate (includes IDA only and IDA blend).","IDA total", 107 | "IDB","","","IDA blend group aggregate.","IDA blend", 108 | "IDN","East Asia & Pacific","Lower middle income","Fiscal year end: March 31; reporting period for national accounts data: CY. Data for Indonesia include Timor-Leste through 1999 unless otherwise noted. Statistics Indonesia revised national accounts based on SNA2008. The new base year is 2010. Price valuation is in basic prices.","Indonesia", 109 | "IDX","","","IDA only group aggregate.","IDA only", 110 | "IMN","Europe & Central Asia","High income","WB-3 code changed from IMY to IMN to align with ISO code.","Isle of Man", 111 | "IND","South Asia","Lower middle income","Fiscal year end: March 31; reporting period for national accounts data: FY. Based on official government statistics; the new base year is 2011/12. India reports using SNA 2008.","India", 112 | "IRL","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 0.787564 Irish pound. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Ireland", 113 | "IRN","Middle East & North Africa","Upper middle income","Fiscal year end: March 20; reporting period for national accounts data: FY. Based on data from the Central Bank of Iran, the new base year is 2004/05.","Iran, Islamic Rep.", 114 | "IRQ","Middle East & North Africa","Upper middle income","Based on official government statistics, the new base year is 2007.","Iraq", 115 | "ISL","Europe & Central Asia","High income","","Iceland", 116 | "ISR","Middle East & North Africa","High income","","Israel", 117 | "ITA","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 1936.27 Italian lira. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Italy", 118 | "JAM","Latin America & Caribbean","Upper middle income","","Jamaica", 119 | "JOR","Middle East & North Africa","Lower middle income","","Jordan", 120 | "JPN","East Asia & Pacific","High income","Fiscal year end: March 31; reporting period for national accounts data: CY.","Japan", 121 | "KAZ","Europe & Central Asia","Upper middle income","","Kazakhstan", 122 | "KEN","Sub-Saharan Africa","Lower middle income","Fiscal year end: June 30; reporting period for national accounts data: CY.","Kenya", 123 | "KGZ","Europe & Central Asia","Lower middle income","","Kyrgyz Republic", 124 | "KHM","East Asia & Pacific","Lower middle income","","Cambodia", 125 | "KIR","East Asia & Pacific","Lower middle income","Based on IMF and World Bank data, GDP in current and constant prices have been revised from 2000 onward. Value added components are calculated using shares from the Asian Development Bank.","Kiribati", 126 | "KNA","Latin America & Caribbean","High income","","St. Kitts and Nevis", 127 | "KOR","East Asia & Pacific","High income","The new base year is 2010. GDP data are available from 1970 onward while components are revised from 2000 onward only. Historical data in constant prices are linked to preserve growth rates.","Korea, Rep.", 128 | "KWT","Middle East & North Africa","High income","Fiscal year end: June 30; reporting period for national accounts data: CY.","Kuwait", 129 | "LAC","","","Latin America and Caribbean regional aggregate (does not include high-income economies).","Latin America & Caribbean (excluding high income)", 130 | "LAO","East Asia & Pacific","Lower middle income","","Lao PDR", 131 | "LBN","Middle East & North Africa","Upper middle income","The new reference year is 2010.","Lebanon", 132 | "LBR","Sub-Saharan Africa","Low income","National accounts local currency data have been revised to be reported in U.S. dollars instead of Liberian dollars.","Liberia", 133 | "LBY","Middle East & North Africa","Upper middle income","Official statistics for Libya are not available; national accounts data are based on World Bank estimates.","Libya", 134 | "LCA","Latin America & Caribbean","Upper middle income","","St. Lucia", 135 | "LCN","","","Latin America and Caribbean regional aggregate (includes all income levels).","Latin America & Caribbean", 136 | "LDC","","","Least developed countries (UN classification) aggregate.","Least developed countries: UN classification", 137 | "LIC","","","Low income group aggregate. Low-income economies are those in which 2015 GNI per capita was $1,025 or less.","Low income", 138 | "LIE","Europe & Central Asia","High income","National accounts from 2013 to 2014 are revised according to the SNA 2008.","Liechtenstein", 139 | "LKA","South Asia","Lower middle income","Bureau of Census and Statistics revised national accounts from 2010-2015. Value added is in basic prices; prior to 2010 in producer prices.","Sri Lanka", 140 | "LMC","","","Lower middle income group aggregate. Lower-middle-income economies are those in which 2015 GNI per capita was between $1,026 and $4,035.","Lower middle income", 141 | "LMY","","","Low and middle income group aggregate (all developing economies). Low- and middle-income economies are those in which 2015 GNI per capita was $12,475 or less.","Low & middle income", 142 | "LSO","Sub-Saharan Africa","Lower middle income","Fiscal year end: March 31; reporting period for national accounts data: CY. The new base year is 2012.","Lesotho", 143 | "LTE","","","Late-dividend countries are mostly upper-middle-income countries where fertility rates are typically above replacement levels of 2.1 births per woman, but fertility continues to decline.","Late-demographic dividend", 144 | "LTU","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2015: 1 euro = 3.45280 Lithuanian litas. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries. Based on data from EUROSTAT, the new reference year is 2010.","Lithuania", 145 | "LUX","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 40.3399 Luxembourg franc. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Luxembourg", 146 | "LVA","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2014: 1 euro = 0.702804 Latvian lats. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries. Based on data from EUROSTAT, the new reference year is 2010.","Latvia", 147 | "MAC","East Asia & Pacific","High income","On 20 December 1999 China resumed its exercise of sovereignty over Macao. Unless otherwise noted, data for China do not include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China.","Macao SAR, China", 148 | "MAF","Latin America & Caribbean","High income","","St. Martin (French part)", 149 | "MAR","Middle East & North Africa","Lower middle income","","Morocco", 150 | "MCO","Europe & Central Asia","High income","","Monaco", 151 | "MDA","Europe & Central Asia","Lower middle income","Due to data reliability issues estimates prior to 1995 have been deleted. The new base year is 2000.","Moldova", 152 | "MDG","Sub-Saharan Africa","Low income","","Madagascar", 153 | "MDV","South Asia","Upper middle income","","Maldives", 154 | "MEA","","","Middle East and North Africa regional aggregate (includes all income levels).","Middle East & North Africa", 155 | "MEX","Latin America & Caribbean","Upper middle income","","Mexico", 156 | "MHL","East Asia & Pacific","Upper middle income","Fiscal year ends on September 30; reporting period for national accounts data: FY.","Marshall Islands", 157 | "MIC","","","Middle income group aggregate. Middle-income economies are those in which 2015 GNI per capita was between $1,026 and $12,475.","Middle income", 158 | "MKD","Europe & Central Asia","Upper middle income","","Macedonia, FYR", 159 | "MLI","Sub-Saharan Africa","Low income","The new base year is 1999.","Mali", 160 | "MLT","Middle East & North Africa","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2008: 1 euro = 0.4293 Maltese lira. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries. National accounts data source from 1995 to 2015 is Eurostat; prior to 1995 is UN. The new reference year is 2010.","Malta", 161 | "MMR","East Asia & Pacific","Lower middle income","Fiscal year end: March 31; reporting period for national accounts data: FY. Based on the government’s Planning Department and the IMF national accounts have been revised from 2010 to 2015. The new base year is 2010/11.","Myanmar", 162 | "MNA","","","Middle East and North Africa regional aggregate (does not include high-income economies).","Middle East & North Africa (excluding high income)", 163 | "MNE","Europe & Central Asia","Upper middle income","Montenegro declared independence from Serbia and Montenegro on June 3, 2006. Where available, data for each country are shown separately. However, for Serbia, some indicators continue to include data for Montenegro through 2005.","Montenegro", 164 | "MNG","East Asia & Pacific","Lower middle income","","Mongolia", 165 | "MNP","East Asia & Pacific","High income","","Northern Mariana Islands", 166 | "MOZ","Sub-Saharan Africa","Low income","","Mozambique", 167 | "MRT","Sub-Saharan Africa","Lower middle income","Based on official statistics from the Ministry of Economic Affairs and Development; the base year has been returned to 2004.","Mauritania", 168 | "MUS","Sub-Saharan Africa","Upper middle income","Authorities made significant revisions to the national accounts from 2006 to 2015.","Mauritius", 169 | "MWI","Sub-Saharan Africa","Low income","Fiscal year end: March 31; reporting period for national accounts data: CY. The new base year is 2010.","Malawi", 170 | "MYS","East Asia & Pacific","Upper middle income","","Malaysia", 171 | "NAC","","","North America regional aggregate. There are no economies in North America classified as low or middle income.","North America", 172 | "NAM","Sub-Saharan Africa","Upper middle income","Fiscal year end: March 31; reporting period for national accounts data: CY.","Namibia", 173 | "NCL","East Asia & Pacific","High income","","New Caledonia", 174 | "NER","Sub-Saharan Africa","Low income","","Niger", 175 | "NGA","Sub-Saharan Africa","Lower middle income","","Nigeria", 176 | "NIC","Latin America & Caribbean","Lower middle income","","Nicaragua", 177 | "NLD","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 2.20371 Netherlands guilder. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Netherlands", 178 | "NOR","Europe & Central Asia","High income","","Norway", 179 | "NPL","South Asia","Low income","Fiscal year end: July 14; reporting period for national accounts data: FY.","Nepal", 180 | "NRU","East Asia & Pacific","Upper middle income","","Nauru", 181 | "NZL","East Asia & Pacific","High income","Fiscal year end: March 31; reporting period for national accounts data: CY.","New Zealand", 182 | "OED","","","OECD members aggregate.","OECD members", 183 | "OMN","Middle East & North Africa","High income","National Center for Statistics and Information revised national accounts from 2011 to 2015 based on the SNA 2008. Value added is in basic prices; and before 2011 in producer prices.","Oman", 184 | "OSS","","","Other small states aggregate.","Other small states", 185 | "PAK","South Asia","Lower middle income","Fiscal year end: June 30; reporting period for national accounts data: FY.","Pakistan", 186 | "PAN","Latin America & Caribbean","Upper middle income","Based on government statistics national accounts have been revised from 2014.","Panama", 187 | "PER","Latin America & Caribbean","Upper middle income","","Peru", 188 | "PHL","East Asia & Pacific","Lower middle income","","Philippines", 189 | "PLW","East Asia & Pacific","High income","Fiscal year ends on September 30; reporting period for national accounts data: FY. National accounts data are revised based on IMF reports.","Palau", 190 | "PNG","East Asia & Pacific","Lower middle income","","Papua New Guinea", 191 | "POL","Europe & Central Asia","High income","","Poland", 192 | "PRE","","","Pre-dividend countries are mostly low-income countries, lagging in key human development indicators and with current fertility levels above four births per woman. They face very rapid population growth.","Pre-demographic dividend", 193 | "PRI","Latin America & Caribbean","High income","Fiscal year end: June 30; reporting period for national accounts data: FY.","Puerto Rico", 194 | "PRK","East Asia & Pacific","Low income","","Korea, Dem. People's Rep.", 195 | "PRT","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate was adopted by the EU Council on January 1, 1999: 1 euro = 200.482 Portuguese escudo. Please note that historical data before 1999 are not actual euros and are not comparable or suitable for aggregation across countries.","Portugal", 196 | "PRY","Latin America & Caribbean","Upper middle income","","Paraguay", 197 | "PSE","Middle East & North Africa","Lower middle income","WB-3 code changed from WBG to PSE to align with ISO code. National accounts local currency data are reported in U.S. dollars.","West Bank and Gaza", 198 | "PSS","","","Pacific island small states aggregate.","Pacific island small states", 199 | "PST","","","Post-dividend countries are mostly high-income countries where fertility has transitioned below replacement levels.","Post-demographic dividend", 200 | "PYF","East Asia & Pacific","High income","","French Polynesia", 201 | "QAT","Middle East & North Africa","High income","","Qatar", 202 | "ROU","Europe & Central Asia","Upper middle income","WB-3 code changed from ROM to ROU to align with ISO code.","Romania", 203 | "RUS","Europe & Central Asia","Upper middle income","The new base year is 2011.","Russian Federation", 204 | "RWA","Sub-Saharan Africa","Low income","","Rwanda", 205 | "SAS","","","South Asia regional aggregate. There are no economies in South Asia classified as high income.","South Asia", 206 | "SAU","Middle East & North Africa","High income","Based on data from the Saudi Central Department of Statistics and Information under the authority of the Ministry of Economy and Planning.","Saudi Arabia", 207 | "SDN","Sub-Saharan Africa","Lower middle income","Demographic data (total population, life expectancy, fertility, child mortality, migration), education series, and HIV prevalence are reported separately for Sudan and South Sudan; see specific notes for other series. National accounts data exclude South Sudan after July 9, 2011. Other data reported for Sudan generally include South Sudan to 2011 unless otherwise noted. External debt data and land-related data (including population density, but excluding surface area after 2010) for all years include South Sudan.","Sudan", 208 | "SEN","Sub-Saharan Africa","Low income","","Senegal", 209 | "SGP","East Asia & Pacific","High income","Fiscal year end: March 31; reporting period for national accounts data: CY. Country reports using a blend of SNA 1993 and SNA 2008.","Singapore", 210 | "SLB","East Asia & Pacific","Lower middle income","","Solomon Islands", 211 | "SLE","Sub-Saharan Africa","Low income","Fiscal year end: June 30; reporting period for national accounts data: CY.","Sierra Leone", 212 | "SLV","Latin America & Caribbean","Lower middle income","","El Salvador", 213 | "SMR","Europe & Central Asia","High income","","San Marino", 214 | "SOM","Sub-Saharan Africa","Low income","","Somalia", 215 | "SRB","Europe & Central Asia","Upper middle income","Montenegro declared independence from Serbia and Montenegro on June 3, 2006. Where available, data for each country are shown separately. However, for Serbia, some indicators, such as those series for which data appear only for Serbia and not Montenegro--e.g., aid, environment, external debt, balance of payments, various social indicators excluding population--continue to include data for Montenegro through 2005. Moreover, data from 1999 onward for Serbia for most indicators exclude data for Kosovo, 1999 being the year when Kosovo became a territory under international administration pursuant to UN Security Council Resolution 1244 (1999); any exceptions are noted. Kosovo became a World Bank member on June 29, 2009; available data are shown separately for Kosovo. In 2011, the Statistical Office of Serbia improved the methodology of national accounts data for 2003 onward. Specifically, the classification of sectors was revised. The new reference year for chain linked series is 2010.","Serbia", 216 | "SSA","","","Sub-Saharan Africa regional aggregate (does not include high-income economies).","Sub-Saharan Africa (excluding high income)", 217 | "SSD","Sub-Saharan Africa","Low income","South Sudan declared its independence on July 9, 2011. Data are shown separately for South Sudan where available.","South Sudan", 218 | "SSF","","","Sub-Saharan Africa regional aggregate (includes all income levels).","Sub-Saharan Africa", 219 | "SST","","","Small states aggregate. Includes 41 members of the Small States Forum. (Does not include the high-income countries Bahrain, Brunei Darussalam, Cyprus, Estonia, Iceland, Malta, Qatar, and San Marino.)","Small states", 220 | "STP","Sub-Saharan Africa","Lower middle income","The new base year is 2008.","São Tomé and Principe", 221 | "SUR","Latin America & Caribbean","Upper middle income","","Suriname", 222 | "SVK","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2009: 1 euro = 30.126 Slovak koruna. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries.","Slovak Republic", 223 | "SVN","Europe & Central Asia","High income","A simple multiplier is used to convert the national currencies of EMU members to euros. The following irrevocable euro conversion rate entered into force on January 1, 2007: 1 euro = 239.64 Slovenian tolar. Please note that historical data are not actual euros and are not comparable or suitable for aggregation across countries.","Slovenia", 224 | "SWE","Europe & Central Asia","High income","Fiscal year end: June 30; reporting period for national accounts data: CY.","Sweden", 225 | "SWZ","Sub-Saharan Africa","Lower middle income","Fiscal year end: March 31; reporting period for national accounts data: CY. Authorities revised national accounts from 1999 to 2015.","Swaziland", 226 | "SXM","Latin America & Caribbean","High income","","Sint Maarten (Dutch part)", 227 | "SYC","Sub-Saharan Africa","High income","","Seychelles", 228 | "SYR","Middle East & North Africa","Lower middle income","","Syrian Arab Republic", 229 | "TCA","Latin America & Caribbean","High income","","Turks and Caicos Islands", 230 | "TCD","Sub-Saharan Africa","Low income","","Chad", 231 | "TEA","","","East Asia & Pacific (IDA & IBRD countries) aggregate.","East Asia & Pacific (IDA & IBRD)", 232 | "TEC","","","Europe & Central Asia (IDA & IBRD countries) aggregate.","Europe & Central Asia (IDA & IBRD)", 233 | "TGO","Sub-Saharan Africa","Low income","","Togo", 234 | "THA","East Asia & Pacific","Upper middle income","Fiscal year end: September 30; reporting period for national accounts data: CY.","Thailand", 235 | "TJK","Europe & Central Asia","Lower middle income","","Tajikistan", 236 | "TKM","Europe & Central Asia","Upper middle income","","Turkmenistan", 237 | "TLA","","","Latin America & the Caribbean (IDA & IBRD countries) aggregate.","Latin America & Caribbean (IDA & IBRD)", 238 | "TLS","East Asia & Pacific","Lower middle income","WB-3 code changed from TMP to TLS to align with ISO code.","Timor-Leste", 239 | "TMN","","","Middle East & North Africa (IDA & IBRD countries) aggregate.","Middle East & North Africa (IDA & IBRD)", 240 | "TON","East Asia & Pacific","Upper middle income","","Tonga", 241 | "TSA","","","South Asia (IDA & IBRD countries) aggregate.","South Asia (IDA & IBRD)", 242 | "TSS","","","Sub-Saharan Africa (IDA & IBRD countries) aggregate.","Sub-Saharan Africa (IDA & IBRD)", 243 | "TTO","Latin America & Caribbean","High income","","Trinidad and Tobago", 244 | "TUN","Middle East & North Africa","Lower middle income","","Tunisia", 245 | "TUR","Europe & Central Asia","Upper middle income","","Turkey", 246 | "TUV","East Asia & Pacific","Upper middle income","Value added is measured at producer prices up to 1999 and at basic prices from 2000 onward.","Tuvalu", 247 | "TZA","Sub-Saharan Africa","Low income","Tanzania reports using a blend of SNA 1993 and SNA 2008.","Tanzania", 248 | "UGA","Sub-Saharan Africa","Low income","Fiscal year end: June 30; reporting period for national accounts data: FY.","Uganda", 249 | "UKR","Europe & Central Asia","Lower middle income","The new base year is 2010.","Ukraine", 250 | "UMC","","","Upper middle income group aggregate. Upper-middle-income economies are those in which 2015 GNI per capita was between $4,036 and $12,475.","Upper middle income", 251 | "URY","Latin America & Caribbean","High income","","Uruguay", 252 | "USA","North America","High income","Fiscal year end: September 30; reporting period for national accounts data: CY.","United States", 253 | "UZB","Europe & Central Asia","Lower middle income","","Uzbekistan", 254 | "VCT","Latin America & Caribbean","Upper middle income","","St. Vincent and the Grenadines", 255 | "VEN","Latin America & Caribbean","Upper middle income","","Venezuela, RB", 256 | "VGB","Latin America & Caribbean","High income","","British Virgin Islands", 257 | "VIR","Latin America & Caribbean","High income","","Virgin Islands (U.S.)", 258 | "VNM","East Asia & Pacific","Lower middle income","","Vietnam", 259 | "VUT","East Asia & Pacific","Lower middle income","Based on official government statistics, value added is measured at producer prices through 1997 and at basic prices from 1998 onward.","Vanuatu", 260 | "WLD","","","World aggregate.","World", 261 | "WSM","East Asia & Pacific","Upper middle income","Fiscal year ends on June 30; reporting period for national accounts data: FY. Data are revised from Samoa Bureau of Statistics and Central Bank of Samoa. The base year is 2008/09. Other methodological changes include increased reliance on summary data from the country’s Value Added Goods and Services Tax system, incorporation of more recent benchmarks, and use of improved data sources.","Samoa", 262 | "XKX","Europe & Central Asia","Lower middle income","WB-3 code changed from KSV to XKX to align with ISO provisional code.","Kosovo", 263 | "YEM","Middle East & North Africa","Lower middle income","Based on official government statistics and International Monetary Fund data, national accounts data have been revised for 1990 onward. The base year has reverted to 1990.","Yemen, Rep.", 264 | "ZAF","Sub-Saharan Africa","Upper middle income","Fiscal year end: March 31; reporting period for national accounts data: CY.","South Africa", 265 | "ZMB","Sub-Saharan Africa","Lower middle income","The base year is 2010. National accounts data were rebased to reflect the January 1, 2013, introduction of the new Zambian kwacha at a rate of 1,000 old kwacha = 1 new kwacha. Zambia reports using SNA 2008.","Zambia", 266 | "ZWE","Sub-Saharan Africa","Low income","Fiscal year end: June 30; reporting period for national accounts data: CY. As of January 2009, multiple hard currencies, such as rand, pound sterling, euro and U.S. dollar are in use. Data are reported in U.S. dollars, the most-used currency.","Zimbabwe", 267 | -------------------------------------------------------------------------------- /data/API_IS.AIR.DPRT_DS2_en_csv_v2/API_IS.AIR.DPRT_DS2_en_csv_v2.csv: -------------------------------------------------------------------------------- 1 | "Data Source","World Development Indicators", 2 | 3 | "Last Updated Date","2017-10-18", 4 | 5 | "Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016", 6 | "Aruba","ABW","Air transport, registered carrier departures worldwide","IS.AIR.DPRT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 7 | "Afghanistan","AFG","Air transport, registered carrier departures worldwide","IS.AIR.DPRT","","","","","","","","","","","4000","4700","4800","3700","4300","4700","4500","4900","4800","4900","4200","4200","5400","5700","6200","6400","5800","6000","5900","5200","5300","5300","5300","5100","7000","7300","7500","4600","1900","3400","3409","","","","","","","","","","21677","25021","17775","21696","25920","23532.128","22770.566", 8 | "Angola","AGO","Air transport, registered carrier departures worldwide","IS.AIR.DPRT","","","","","","","","","","","","","","","","","2600","4800","5000","11900","12800","15000","13900","22400","10700","13100","11100","11100","11000","6800","6900","7000","6900","5600","7600","7300","7700","7300","7400","5100","4399","4354","4343","4633","4849","4948","4965","5277","3361","3236","17060","12282","13072","14496","13716","13116","13042.376", 9 | "Albania","ALB","Air transport, registered carrier departures worldwide","IS.AIR.DPRT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","600","600","1400","1900","1100","2700","3885","3974","3762","3800","4104","4309","4481","4930","5058","5140","9412","10373.2109418273","10309.1791376912","11196","1992","","306", 10 | "Andorra","AND","Air transport, registered carrier departures 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