📊 Preventive chemotherapy for neglected tropical diseases #2588
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Login: Chart diff:No new or modified charts. Detailsdata-diff:- Dataset garden/climate/2024-05-20/climate_change_impacts
- Dataset garden/climate/2024-05-20/ghg_concentration
- Dataset garden/climate/2024-05-20/long_run_ghg_concentration
- Dataset garden/climate/2024-05-20/ocean_heat_content
- Dataset garden/climate/2024-05-20/ocean_ph_levels
- Dataset garden/climate/2024-05-20/sea_ice_index
- Dataset garden/climate/2024-05-20/sea_surface_temperature
- Dataset garden/climate/2024-05-20/snow_cover_extent
- Dataset garden/climate/2024-05-20/surface_temperature_analysis
= Dataset garden/democracy/2024-03-07/bmr
= Table population_regime_years
= Table num_countries_regime_years
= Table population_regime
= Table num_countries_regime
= Table bmr
= Dataset garden/democracy/2024-03-07/ert
= Table region_aggregates
= Table ert
- Dataset garden/democracy/2024-03-07/fh
= Dataset garden/democracy/2024-03-07/lexical_index
= Table region_aggregates
= Table lexical_index
= Dataset garden/emdat/2024-04-11/natural_disasters
= Table natural_disasters_yearly_deaths
= Table natural_disasters_decadal_deaths
= Table natural_disasters_yearly_impact
~ Column n_large_events (changed data)
~ Changed values: 1051 / 7170 (14.66%)
country year n_large_events - n_large_events +
Italy 2023 1 0
North America 2016 27 7
Oman 2010 1 0
Saint Vincent and the Grenadines 2021 1 0
Slovakia 2004 1 0
~ Column n_medium_events (changed data)
~ Changed values: 1148 / 7170 (16.01%)
country year n_medium_events - n_medium_events +
High-income countries 2010 32 29
Switzerland 2007 1 0
USSR 1989 2 3
Upper-middle-income countries 1987 32 26
World 2000 201 190
~ Column n_small_events (changed data)
~ Changed values: 1076 / 7170 (15.01%)
country year n_small_events - n_small_events +
Europe 1972 1 3
Italy 2002 1 3
Martinique 1980 0 1
Portugal 2004 1 2
World 1964 3 4
~ Column share_large_events (changed data)
~ Changed values: 1051 / 7170 (14.66%)
country year share_large_events - share_large_events +
Italy 2023 14.285714 0.00000
North America 2016 44.262295 11.47541
Oman 2010 100.000000 0.00000
Saint Vincent and the Grenadines 2021 50.000000 0.00000
Slovakia 2004 50.000000 0.00000
~ Column share_medium_events (changed data)
~ Changed values: 1148 / 7170 (16.01%)
country year share_medium_events - share_medium_events +
High-income countries 2010 32.989689 29.896908
Switzerland 2007 33.333332 0.000000
USSR 1989 40.000000 60.000000
Upper-middle-income countries 1987 46.376812 37.681160
World 2000 49.144253 46.454769
~ Column share_small_events (changed data)
~ Changed values: 1076 / 7170 (15.01%)
country year share_small_events - share_small_events +
Europe 1972 14.285714 42.857143
Italy 2002 14.285714 42.857143
Martinique 1980 0.000000 100.000000
Portugal 2004 50.000000 100.000000
World 1964 5.000000 6.666667
= Table natural_disasters_yearly
~ Column insured_damages (changed data)
~ Changed values: 14 / 37481 (0.04%)
country year type insured_damages - insured_damages +
Australia 2001 all_disasters_excluding_extreme_temperature 10652173 10652000
Bahamas 2017 all_disasters 397600 397000
Bahamas 2017 all_disasters_excluding_extreme_temperature 397600 397000
High-income countries 2001 wildfire 10652173 10652000
Oceania 2001 all_disasters_excluding_extreme_temperature 10652173 10652000
~ Column insured_damages_per_gdp (changed data)
~ Changed values: 8 / 37481 (0.02%)
country year type insured_damages_per_gdp - insured_damages_per_gdp +
Australia 2001 all_disasters_excluding_earthquakes 0.002808 0.002808
Australia 2001 all_disasters_excluding_extreme_temperature 0.002808 0.002808
Australia 2001 wildfire 0.002808 0.002808
Bahamas 2017 all_disasters_excluding_extreme_temperature 0.003217 0.003213
Bahamas 2017 extreme_weather 0.003217 0.003213
~ Column reconstruction_costs (changed data)
~ Changed values: 106 / 37481 (0.28%)
country year type reconstruction_costs - reconstruction_costs +
Asia 1999 earthquake 4935228928 35000000000
Asia 2022 all_disasters_excluding_earthquakes 1410065408 10000000000
Croatia 2020 earthquake 860065408 9450000000
Upper-middle-income countries 1999 all_disasters 4935228928 35000000000
World 2022 all_disasters 1499208266 10089143000
~ Column reconstruction_costs_per_gdp (changed data)
~ Changed values: 54 / 37481 (0.14%)
country year type reconstruction_costs_per_gdp - reconstruction_costs_per_gdp +
European Union (27) 2020 earthquake 0.005596 0.061486
Lower-middle-income countries 2010 earthquake 0.055383 0.218864
Upper-middle-income countries 1999 all_disasters 0.128486 0.911202
World 2010 all_disasters 0.011373 0.037166
World 2020 earthquake 0.001011 0.011104
~ Column total_damages (changed data)
~ Changed values: 672 / 37481 (1.79%)
country year type total_damages - total_damages +
Africa 1969 all_disasters_excluding_extreme_temperature 158316615 158315000
Asia 1971 drought 4000002 4002000
Indonesia 1973 all_disasters_excluding_extreme_temperature 34952121 34953000
South America 1997 extreme_weather 979591 979000
Zimbabwe 1982 drought 111786 111000
~ Column total_damages_per_gdp (changed data)
~ Changed values: 536 / 37481 (1.43%)
country year type total_damages_per_gdp - total_damages_per_gdp +
Guatemala 2019 all_disasters 0.017137 0.017137
Haiti 2017 drought 0.077557 0.077563
Peru 1998 all_disasters_excluding_earthquakes 0.019856 0.019857
South Africa 1995 all_disasters_excluding_extreme_temperature 0.004529 0.004528
Zimbabwe 1995 all_disasters_excluding_earthquakes 0.148236 0.148272
= Table natural_disasters_decadal_impact
~ Column n_large_events (changed data)
~ Changed values: 343 / 1612 (21.28%)
country year n_large_events - n_large_events +
Anguilla 1960 1 0
European Union (27) 1990 72 17
Hungary 1980 1 0
Taiwan 1990 5 2
Taiwan 2000 5 4
~ Column n_medium_events (changed data)
~ Changed values: 396 / 1612 (24.57%)
country year n_medium_events - n_medium_events +
Chile 1940 1 3
China 1960 2 1
Saudi Arabia 1980 0 1
South Korea 1990 10 11
South Korea 2010 11 14
~ Column n_small_events (changed data)
~ Changed values: 379 / 1612 (23.51%)
country year n_small_events - n_small_events +
High-income countries 2020 134 238
New Caledonia 1960 0 1
Saint Kitts and Nevis 2010 0 1
Saint Vincent and the Grenadines 1980 1 3
Sweden 2010 0 1
~ Column share_large_events (changed data)
~ Changed values: 343 / 1612 (21.28%)
country year share_large_events - share_large_events +
Anguilla 1960 100.000000 0.000000
European Union (27) 1990 28.015564 6.614786
Hungary 1980 50.000000 0.000000
Taiwan 1990 27.777779 11.111111
Taiwan 2000 15.151515 12.121212
~ Column share_medium_events (changed data)
~ Changed values: 396 / 1612 (24.57%)
country year share_medium_events - share_medium_events +
Chile 1940 20.000000 60.000000
China 1960 40.000000 20.000000
Saudi Arabia 1980 0.000000 50.000000
South Korea 1990 45.454544 50.000000
South Korea 2010 57.894737 73.684212
~ Column share_small_events (changed data)
~ Changed values: 379 / 1612 (23.51%)
country year share_small_events - share_small_events +
High-income countries 2020 29.257643 51.965065
New Caledonia 1960 0.000000 50.000000
Saint Kitts and Nevis 2010 0.000000 100.000000
Saint Vincent and the Grenadines 1980 25.000000 75.000000
Sweden 2010 0.000000 50.000000
= Table natural_disasters_decadal
~ Column reconstruction_costs (changed data)
~ Changed values: 93 / 42952 (0.22%)
country year type reconstruction_costs - reconstruction_costs +
Asia 1990 earthquake 493522880.0 3500000000
Chile 2010 earthquake 224906544.0 1083900000
China 2000 earthquake 141006544.0 1000000000
High-income countries 2010 all_disasters_excluding_extreme_temperature 453281632.0 1312275100
Pakistan 2020 all_disasters_excluding_earthquakes 282013088.0 2000000000
~ Column reconstruction_costs_per_gdp (changed data)
~ Changed values: 51 / 42952 (0.12%)
country year type reconstruction_costs_per_gdp - reconstruction_costs_per_gdp +
Chile 2010 earthquake 0.103593 0.499251
European Union (27) 2020 all_disasters_excluding_extreme_temperature 0.002798 0.030743
European Union (27) 2020 earthquake 0.001399 0.015371
Lower-middle-income countries 2000 all_disasters_excluding_extreme_temperature 0.004081 0.020832
Pakistan 2000 earthquake 0.075385 0.433134
~ Column total_damages (changed data)
~ Changed values: 45 / 42952 (0.10%)
country year type total_damages - total_damages +
Chad 1960 drought 225984.0000 225900
Chile 1960 drought 1765179.6250 1765100
Mauritania 1960 all_disasters_excluding_extreme_temperature 485524.1875 485500
Pakistan 1990 drought 989947.3750 989900
Yemen Arab Republic 1960 all_disasters 333333.3125 333300
~ Column total_damages_per_gdp (changed data)
~ Changed values: 58 / 42952 (0.14%)
country year type total_damages_per_gdp - total_damages_per_gdp +
Chad 1970 all_disasters_excluding_extreme_temperature 1.182782 1.182755
Gambia 1970 drought 0.026902 0.026668
Iraq 1970 all_disasters_excluding_earthquakes 0.003756 0.003758
Iraq 1970 all_disasters_excluding_extreme_temperature 0.003756 0.003758
Mauritania 1960 all_disasters_excluding_extreme_temperature 0.164550 0.164542
- Dataset garden/met_office_hadley_centre/2024-05-20/near_surface_temperature
+ Dataset garden/neglected_tropical_diseases/2024-05-02/lymphatic_filariasis
+ + Table lymphatic_filariasis
+ + Column current_status_of_mda
+ + Column number_of_ius_covered
+ + Column geographical_coverage__pct
+ + Column total_population_of_ius
+ + Column reported_number_of_people_treated
+ + Column programme__drug__coverage__pct
+ + Table lymphatic_filariasis_national
+ + Column national_coverage__pct
+ + Column population_requiring_pc_for_lf
+ + Column estimated_number_of_people_treated
+ Dataset garden/neglected_tropical_diseases/2024-05-02/schistosomiasis
+ + Table schistosomiasis
+ + Column population_requiring_pc_for_sch_annually
+ + Column sac_population_requiring_pc_for_sch_annually
+ + Column number_of_people_targeted
+ + Column reported_number_of_people_treated
+ + Column reported_number_of_sac_treated
+ + Column programme_coverage__pct
+ + Column national_coverage__pct
+ Dataset garden/neglected_tropical_diseases/2024-05-02/soil_transmitted_helminthiases
+ + Table soil_transmitted_helminthiases_national_pre_sac
+ + Column national_coverage__pre_sac__pct
+ + Column population_requiring_pc_for_sth__pre_sac
+ + Column estimated_number_of_pre_sac_treated
+ + Table soil_transmitted_helminthiases_national_sac
+ + Column national_coverage__sac__pct
+ + Column population_requiring_pc_for_sth__sac
+ + Column estimated_number_of_sac_treated
+ + Table soil_transmitted_helminthiases_pre_sac
+ + Column number_targeted
+ + Column reported_number_treated
+ + Column programme_coverage__pct
+ + Table soil_transmitted_helminthiases_sac
+ + Column number_targeted
+ + Column reported_number_treated
+ + Column programme_coverage__pct
- Dataset garden/neglected_tropical_diseases/2024-05-18/funding
= Dataset garden/oecd/2024-04-30/affordable_housing_database
= Table affordable_housing_database
~ Column point_in_time_1 (changed metadata)
- - - Data for Belgium only considers Brussels.
~ Column point_in_time_1_2_3 (changed metadata)
- - - Data for Belgium only considers Brussels.
~ Column point_in_time_2_3 (changed metadata)
- - - Data for Belgium only considers Brussels.
~ Column share (changed metadata)
- - description_key:
- - - Data for the United Kingdom only considers England.
~ Column type_of_strategy (changed metadata, changed data)
- - Defines whether the country has a Housing First strategy or housing-led strategy to adress homelessness at the national administrative level, other strategy, or no strategy at the national level at all.
? ^^^^ ^^^^^^^ ----------------------
+ + Defines whether the country has a Housing First strategy or housing-led strategy to adress homelessness at any administrative level, other strategy, or no strategy at all.
? ^ ^
- - - No national strategy
? ---------
+ + - No strategy
~ Changed values: 22 / 201 (10.95%)
country year type_of_strategy - type_of_strategy +
Austria 2023 No national strategy Housing First/housing-led strategy
Israel 2023 No national strategy No strategy
Malta 2023 No national strategy No strategy
Mexico 2023 No national strategy No strategy
Turkey 2023 No national strategy No strategy
- Dataset garden/research_development/2024-05-20/patents_wdi_unwpp
= Dataset garden/technology/2024-05-13/computer_memory_storage
= Table computer_memory_storage
~ Column ddrives (changed metadata, changed data)
- - description_short: This data is expressed in US dollars per terabyte (TB), adjusted for inflation.
- - - producer: U.S. Bureau of Labor Statistics
- - title: US consumer prices
- - description: |-
- - The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.
- - citation_full: U.S. Bureau of Labor Statistics
- - url_main: https://www.bls.gov/data/tools.htm
- - date_accessed: '2024-05-16'
- - date_published: '2024'
- - license:
- - name: Public domain
- - url: https://www.bls.gov/opub/copyright-information.htm
- - unit: constant 2020 US$ per terabyte
? ^^^^^ -----
+ + unit: current US$ per terabyte
? ^^^^
~ Changed values: 53 / 59 (89.83%)
country year ddrives - ddrives +
World 1981 5.265886e+08 1.850000e+08
World 2002 1.750592e+03 1.216670e+03
World 2003 1.055063e+03 7.499200e+02
World 2008 1.201961e+02 9.999000e+01
World 2013 4.072861e+01 3.666000e+01
~ Column flash (changed metadata, changed data)
- - description_short: This data is expressed in US dollars per terabyte (TB), adjusted for inflation.
- - - producer: U.S. Bureau of Labor Statistics
- - title: US consumer prices
- - description: |-
- - The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.
- - citation_full: U.S. Bureau of Labor Statistics
- - url_main: https://www.bls.gov/data/tools.htm
- - date_accessed: '2024-05-16'
- - date_published: '2024'
- - license:
- - name: Public domain
- - url: https://www.bls.gov/opub/copyright-information.htm
- - unit: constant 2020 US$ per terabyte
? ^^^^^ -----
+ + unit: current US$ per terabyte
? ^^^^
~ Changed values: 15 / 59 (25.42%)
country year flash - flash +
World 2004 262268.125000 191406.250000
World 2009 1950.484497 1616.819946
World 2011 1158.372559 1006.770020
World 2012 653.278870 579.530029
World 2017 152.898636 144.809998
~ Column memory (changed metadata, changed data)
- - description_short: This data is expressed in US dollars per terabyte (TB), adjusted for inflation.
- - - producer: U.S. Bureau of Labor Statistics
- - title: US consumer prices
- - description: |-
- - The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.
- - citation_full: U.S. Bureau of Labor Statistics
- - url_main: https://www.bls.gov/data/tools.htm
- - date_accessed: '2024-05-16'
- - date_published: '2024'
- - license:
- - name: Public domain
- - url: https://www.bls.gov/opub/copyright-information.htm
- - unit: constant 2020 US$ per terabyte
? ^^^^^ -----
+ + unit: current US$ per terabyte
? ^^^^
~ Changed values: 55 / 59 (93.22%)
country year memory - memory +
World 1979 2.390727e+10 6.704000e+09
World 1994 4.583433e+07 2.625000e+07
World 2001 2.157090e+05 1.475781e+05
World 2008 1.173614e+04 9.763180e+03
World 2013 4.067184e+03 3.660890e+03
~ Column ssd (changed metadata, changed data)
- - description_short: This data is expressed in US dollars per terabyte (TB), adjusted for inflation.
- - - producer: U.S. Bureau of Labor Statistics
- - title: US consumer prices
- - description: |-
- - The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.
- - citation_full: U.S. Bureau of Labor Statistics
- - url_main: https://www.bls.gov/data/tools.htm
- - date_accessed: '2024-05-16'
- - date_published: '2024'
- - license:
- - name: Public domain
- - url: https://www.bls.gov/opub/copyright-information.htm
- - unit: constant 2020 US$ per terabyte
? ^^^^^ -----
+ + unit: current US$ per terabyte
? ^^^^
~ Changed values: 10 / 59 (16.95%)
country year ssd - ssd +
World 2014 409.945923 374.980011
World 2015 272.944305 249.960007
World 2017 214.455078 203.110001
World 2022 39.796032 45.000000
World 2023 25.906467 30.500000
2024-05-21 10:14:20 [error ] Traceback (most recent call last):
File "/home/owid/etl/etl/datadiff.py", line 421, in cli
lines = future.result()
File "/usr/lib/python3.10/concurrent/futures/_base.py", line 458, in result
return self.__get_result()
File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/owid/etl/etl/datadiff.py", line 414, in func
differ.summary()
File "/home/owid/etl/etl/datadiff.py", line 252, in summary
self._diff_tables(self.ds_a, self.ds_b, table_name)
File "/home/owid/etl/etl/datadiff.py", line 121, in _diff_tables
table_b = future_b.result()
File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/owid/etl/lib/catalog/owid/catalog/datasets.py", line 153, in __getitem__
t = tables.Table.read(path)
File "/home/owid/etl/lib/catalog/owid/catalog/tables.py", line 171, in read
table = cls.read_feather(path)
File "/home/owid/etl/lib/catalog/owid/catalog/tables.py", line 354, in read_feather
cls._add_metadata(df, path)
File "/home/owid/etl/lib/catalog/owid/catalog/tables.py", line 327, in _add_metadata
metadata = cls._read_metadata(path)
File "/home/owid/etl/lib/catalog/owid/catalog/tables.py", line 390, in _read_metadata
with open(metadata_path, "r") as istream:
FileNotFoundError: [Errno 2] No such file or directory: 'data/garden/who/2024-01-03/gho/tobacco_tax_structure__uniform_excise_tax_applied_yes__uniform__no__tiered_varying_rates.meta.json'
= Dataset garden/who/2024-02-14/gho_suicides
= Table gho_suicides
= Table gho_suicides_ratio
- Dataset garden/who/2024-05-20/vehicles
⚠ Found errors, create an issue please
Legend: +New ~Modified -Removed =Identical Details
Hint: Run this locally with etl diff REMOTE data/ --include yourdataset --verbose --snippetAutomatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile are not included Edited: 2024-05-21 10:14:21 UTC |
lucasrodes
approved these changes
May 21, 2024
Comment on lines
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103
| tb_pre_sac.metadata.short_name = "soil_transmitted_helminthiases_pre_sac" | ||
| tb_sac.metadata.short_name = "soil_transmitted_helminthiases_sac" | ||
| tb_nat_sac.metadata.short_name = "soil_transmitted_helminthiases_national_sac" | ||
| tb_nat_pre_sac.metadata.short_name = "soil_transmitted_helminthiases_national_pre_sac" |
There was a problem hiding this comment.
Instead of assigning short_name directly, you can pass it as an argument of the function Table.format. E.g., for tb_sac:
tb_sac = tb_sac.format(["country", "year", "drug_combination"], short_name="soil_transmitted_helminthiases_sac")
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Thank you! I always forget this!
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