# EN.ATM.CO2E.PP.GD.KD CO2 emissions (kg per 2011 PPP $ of GDP)
# EN.CLC.GHGR.MT.CE GHG net emissions/removals by LUCF (Mt of CO2 equivalent)
# SP.POP.TOTL Population, total
# NY.GDP.MKTP.CD GDP (current US$)
# NY.GDP.MKTP.KD GDP (constant 2010 US$)
# NY.GDP.MKTP.PP.CD GDP, PPP (current international $)
# NY.GDP.MKTP.PP.KD GDP, PPP (constant 2011 international $)
# NY.GDP.PCAP.CD GDP per capita (current US$)
# NY.GDP.PCAP.KD GDP per capita (constant 2010 US$)
# NY.GDP.PCAP.PP.CD GDP per capita, PPP (current international $)
# NY.GDP.PCAP.PP.KD GDP per capita, PPP (constant 2011 international $)
wb.get_countries().show()
wb.get_regions().show()
wb.get_series('SP.POP.TOTL', id_or_value='id')
wb.get_series('SP.POP.TOTL').reset_index()
# looks simple - so I need:
# - GCA country to WB code conversion
# - then I can just join everything and I should have all available years, so should be able to do ASOF over countries
# country mapping
root = 'D:\\projects\\fakta-o-klimatu\\work\\111-emise-svet-srovnani\\data'
path_gca = root + '\\global-carbon-atlas\\export_20190819_2250.csv'
country_map = pd.read_csv(root + '\\country_mapping.csv')[['wb', 'gca']]
country_map = pd.merge(country_map,
wb.get_countries()['name'].rename('wb').reset_index())
country_map.show_csv()
def test_one_region():
reg = get_regions('AFR')
assert 'id' not in reg.columns
assert reg.index == ['AFR']
assert_numeric_or_string(reg)
def test_two_regions():
reg = get_regions(['AFR', 'ANR'])
assert 'id' not in reg.columns
assert set(reg.index) == set(['AFR', 'ANR'])
assert_numeric_or_string(reg)
def test_all_regions():
reg = get_regions()
assert 'id' not in reg.columns
assert len(reg.index) > 30
assert_numeric_or_string(reg)
def test_region_language():
reg = get_regions(['ECS'], language='fr')
assert reg.name[0] == 'Europe et Asie centrale'