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The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Multiple files found in ZIP file. Only one file per ZIP: ['docProps/app.xml', 'docProps/core.xml', 'xl/theme/theme1.xml', 'xl/worksheets/sheet1.xml', 'xl/worksheets/sheet2.xml', 'xl/styles.xml', '_rels/.rels', 'xl/workbook.xml', 'xl/_rels/workbook.xml.rels', '[Content_Types].xml']
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 196, in _generate_tables
csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
return function(*args, download_config=download_config, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1279, in xpandas_read_csv
return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 620, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
self._engine = self._make_engine(f, self.engine)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1880, in _make_engine
self.handles = get_handle(
~~~~~~~~~~^
f,
^^
...<6 lines>...
storage_options=self.options.get("storage_options", None),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/io/common.py", line 805, in get_handle
raise ValueError(
...<2 lines>...
)
ValueError: Multiple files found in ZIP file. Only one file per ZIP: ['docProps/app.xml', 'docProps/core.xml', 'xl/theme/theme1.xml', 'xl/worksheets/sheet1.xml', 'xl/worksheets/sheet2.xml', 'xl/styles.xml', '_rels/.rels', 'xl/workbook.xml', 'xl/_rels/workbook.xml.rels', '[Content_Types].xml']
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
country string | country_code_x null | region string | income_level string | capital_city string | latitude float64 | longitude float64 | country_code_y string | inflation_year float64 | inflation_annual_pct float64 | cost_living_index float64 | rent_index float64 | groceries_index float64 | restaurants_index float64 | purchasing_power_index float64 | bread_price_usd float64 | transport_oneway_usd float64 | transport_monthly_pass_usd float64 | rent_monthly_usd float64 | utilities_monthly_usd float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Aruba | null | Latin America & Caribbean | High income | Oranjestad | 12.5167 | -70.0167 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Afghanistan | null | Middle East, North Africa, Afghanistan & Pakistan | Low income | Kabul | 34.5228 | 69.1761 | AFG | 2,024 | -6.601186 | 38.2 | 8.2 | 36.8 | 36.9 | 48.5 | 0.88 | 1.05 | 12.8 | 85 | 20 |
Angola | null | Sub-Saharan Africa | Lower middle income | Luanda | -8.81155 | 13.242 | AGO | 2,024 | 28.240495 | null | null | null | null | null | null | null | null | null | null |
Albania | null | Europe & Central Asia | Upper middle income | Tirane | 41.3317 | 19.8172 | ALB | 2,024 | 2.215874 | null | null | null | null | null | null | null | null | null | null |
Andorra | null | Europe & Central Asia | High income | Andorra la Vella | 42.5075 | 1.5218 | null | null | null | null | null | null | null | null | null | null | null | null | null |
United Arab Emirates | null | Middle East, North Africa, Afghanistan & Pakistan | High income | Abu Dhabi | 24.4764 | 54.3705 | ARE | 2,024 | 1.663365 | 72.4 | 34.2 | 68.5 | 70.8 | 96.2 | null | null | null | 1,100 | null |
Argentina | null | Latin America & Caribbean | Upper middle income | Buenos Aires | -34.6118 | -58.4173 | ARG | 2,024 | 219.883929 | null | null | null | null | null | null | null | null | null | null |
Armenia | null | Europe & Central Asia | Upper middle income | Yerevan | 40.1596 | 44.509 | ARM | 2,024 | 0.269512 | null | null | null | null | null | null | null | null | null | null |
American Samoa | null | East Asia & Pacific | High income | Pago Pago | -14.2846 | -170.691 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Antigua and Barbuda | null | Latin America & Caribbean | High income | Saint John's | 17.1175 | -61.8456 | ATG | 2,024 | 6.198867 | null | null | null | null | null | null | null | null | null | null |
Australia | null | East Asia & Pacific | High income | Canberra | -35.282 | 149.129 | AUS | 2,024 | 3.166567 | 69.5 | 32.8 | 65.4 | 68.9 | 95.6 | 3.25 | null | null | 1,450 | 140 |
Austria | null | Europe & Central Asia | High income | Vienna | 48.2201 | 16.3798 | AUT | 2,024 | 2.937916 | null | null | null | null | null | null | null | null | null | null |
Azerbaijan | null | Europe & Central Asia | Upper middle income | Baku | 40.3834 | 49.8932 | AZE | 2,024 | 2.212172 | null | null | null | null | null | 0.46 | null | null | null | null |
Burundi | null | Sub-Saharan Africa | Low income | Bujumbura | -3.3784 | 29.3639 | BDI | 2,024 | 20.212493 | null | null | null | null | null | null | null | null | null | null |
Belgium | null | Europe & Central Asia | High income | Brussels | 50.8371 | 4.36761 | BEL | 2,024 | 3.143491 | null | null | null | null | null | null | null | null | null | null |
Benin | null | Sub-Saharan Africa | Lower middle income | Porto-Novo | 6.4779 | 2.6323 | BEN | 2,024 | 1.160931 | null | null | null | null | null | null | null | null | null | null |
Burkina Faso | null | Sub-Saharan Africa | Low income | Ouagadougou | 12.3605 | -1.53395 | BFA | 2,024 | 4.190817 | null | null | null | null | null | null | null | null | null | null |
Bangladesh | null | South Asia | Lower middle income | Dhaka | 23.7055 | 90.4113 | BGD | 2,024 | 10.465748 | 39.5 | 8.8 | 38.1 | 38.2 | 50.8 | 0.95 | 1.18 | 14.5 | 100 | 25 |
Bulgaria | null | Europe & Central Asia | High income | Sofia | 42.7105 | 23.3238 | BGR | 2,024 | 2.446519 | null | null | null | null | null | null | null | null | null | null |
Bahrain | null | Middle East, North Africa, Afghanistan & Pakistan | High income | Manama | 26.1921 | 50.5354 | BHR | 2,024 | 0.919635 | 65.2 | 26.1 | 61.8 | 63.5 | 86.2 | null | null | null | 600 | null |
Bahamas, The | null | Latin America & Caribbean | High income | Nassau | 25.0661 | -77.339 | BHS | 2,024 | 0.409162 | null | null | null | null | null | null | null | null | null | null |
Bosnia and Herzegovina | null | Europe & Central Asia | Upper middle income | Sarajevo | 43.8607 | 18.4214 | BIH | 2,023 | 6.105901 | null | null | null | null | null | null | null | null | null | null |
Belarus | null | Europe & Central Asia | Upper middle income | Minsk | 53.9678 | 27.5766 | BLR | 2,024 | 5.785319 | null | null | null | null | null | null | null | null | null | null |
Belize | null | Latin America & Caribbean | Upper middle income | Belmopan | 17.2534 | -88.7713 | BLZ | 2,024 | 3.28956 | null | null | null | null | null | null | null | null | null | null |
Bermuda | null | North America | High income | Hamilton | 32.3293 | -64.706 | null | null | null | null | null | null | null | null | 8.61 | null | null | null | null |
Bolivia | null | Latin America & Caribbean | Lower middle income | La Paz | -13.9908 | -66.1936 | BOL | 2,024 | 5.099766 | null | null | null | null | null | null | null | null | null | null |
Brazil | null | Latin America & Caribbean | Upper middle income | Brasilia | -15.7801 | -47.9292 | BRA | 2,024 | 4.367464 | 51.4 | 15.9 | 49.2 | 50.1 | 67.5 | 1.85 | 2.85 | 52.4 | 270 | 70 |
Barbados | null | Latin America & Caribbean | High income | Bridgetown | 13.0935 | -59.6105 | BRB | 2,024 | 1.446437 | 75.2 | 41.8 | 70.8 | 73.2 | 88.5 | null | null | null | 1,200 | null |
Brunei Darussalam | null | East Asia & Pacific | High income | Bandar Seri Begawan | 4.94199 | 114.946 | BRN | 2,024 | -0.388674 | null | null | null | null | null | null | null | null | null | null |
Bhutan | null | South Asia | Lower middle income | Thimphu | 27.5768 | 89.6177 | BTN | 2,024 | 2.761316 | null | null | null | null | null | null | null | null | null | null |
Botswana | null | Sub-Saharan Africa | Upper middle income | Gaborone | -24.6544 | 25.9201 | BWA | 2,024 | 2.818351 | null | null | null | null | null | null | null | null | null | null |
Central African Republic | null | Sub-Saharan Africa | Low income | Bangui | 5.63056 | 21.6407 | CAF | 2,024 | 1.477336 | null | null | null | null | null | null | null | null | null | null |
Canada | null | North America | High income | Ottawa | 45.4215 | -75.6919 | CAN | 2,024 | 2.381584 | 65.4 | 28.4 | 61.5 | 64.2 | 92.1 | 3.18 | null | null | 1,200 | 145 |
Switzerland | null | Europe & Central Asia | High income | Bern | 46.948 | 7.44821 | CHE | 2,024 | 1.06234 | 82.3 | 50.1 | 75.2 | 88.4 | 120.5 | 3.81 | 6.36 | 149.7 | 1,850 | 220 |
Channel Islands | null | Europe & Central Asia | High income | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chile | null | Latin America & Caribbean | High income | Santiago | -33.475 | -70.6475 | CHL | 2,024 | 4.297639 | null | null | null | null | null | null | null | null | null | null |
China | null | East Asia & Pacific | Upper middle income | Beijing | 40.0495 | 116.286 | CHN | 2,024 | 0.218129 | 49.8 | 14.8 | 47.8 | 48.5 | 64.2 | 1.65 | 2.45 | 42.8 | 540 | 80 |
Cote d'Ivoire | null | Sub-Saharan Africa | Lower middle income | Yamoussoukro | 5.332 | -4.0305 | CIV | 2,024 | 3.45053 | null | null | null | null | null | null | null | null | null | null |
Cameroon | null | Sub-Saharan Africa | Lower middle income | Yaounde | 3.8721 | 11.5174 | CMR | 2,024 | 4.533313 | null | null | null | null | null | null | null | null | null | null |
Congo, Dem. Rep. | null | Sub-Saharan Africa | Low income | Kinshasa | -4.325 | 15.3222 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Congo, Rep. | null | Sub-Saharan Africa | Lower middle income | Brazzaville | -4.2767 | 15.2662 | COG | 2,024 | 3.091436 | null | null | null | null | null | null | null | null | null | null |
Colombia | null | Latin America & Caribbean | Upper middle income | Bogota | 4.60987 | -74.082 | COL | 2,024 | 6.609086 | null | null | null | null | null | null | null | null | null | null |
Comoros | null | Sub-Saharan Africa | Lower middle income | Moroni | -11.6986 | 43.2418 | COM | 2,024 | 5.051926 | null | null | null | null | null | null | null | null | null | null |
Cabo Verde | null | Sub-Saharan Africa | Upper middle income | Praia | 14.9218 | -23.5087 | CPV | 2,024 | 1.04817 | null | null | null | null | null | null | null | null | null | null |
Costa Rica | null | Latin America & Caribbean | High income | San Jose | 9.63701 | -84.0089 | CRI | 2,024 | -0.412853 | null | null | null | null | null | null | null | null | null | null |
Cuba | null | Latin America & Caribbean | Upper middle income | Havana | 23.1333 | -82.3667 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Curacao | null | Latin America & Caribbean | High income | Willemstad | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Cayman Islands | null | Latin America & Caribbean | High income | George Town | 19.3022 | -81.3857 | null | null | null | 94.3 | 60.2 | 85.6 | 92.1 | 115.8 | 6.36 | null | null | 2,500 | null |
Cyprus | null | Europe & Central Asia | High income | Nicosia | 35.1676 | 33.3736 | CYP | 2,024 | 1.80023 | null | null | null | null | null | null | null | null | null | null |
Czechia | null | Europe & Central Asia | High income | Prague | 50.0878 | 14.4205 | CZE | 2,024 | 2.435312 | null | null | null | null | null | null | null | null | null | null |
Germany | null | Europe & Central Asia | High income | Berlin | 52.5235 | 13.4115 | DEU | 2,024 | 2.256498 | 64.1 | 26.5 | 59.7 | 62.8 | 91.3 | 2.72 | 4.85 | 112.3 | 850 | 170 |
Djibouti | null | Middle East, North Africa, Afghanistan & Pakistan | Lower middle income | Djibouti | 11.5806 | 43.1425 | DJI | 2,024 | 2.112012 | null | null | null | null | null | 9.53 | null | null | null | null |
Dominica | null | Latin America & Caribbean | Upper middle income | Roseau | 15.2976 | -61.39 | DMA | 2,024 | 2.736208 | null | null | null | null | null | null | null | null | null | null |
Denmark | null | Europe & Central Asia | High income | Copenhagen | 55.6763 | 12.5681 | DNK | 2,024 | 1.3722 | 72.1 | 36.4 | 67.2 | 75.3 | 102.8 | 3.48 | 5.42 | 128.5 | null | null |
Dominican Republic | null | Latin America & Caribbean | Upper middle income | Santo Domingo | 18.479 | -69.8908 | DOM | 2,024 | 3.302233 | null | null | null | null | null | null | null | null | null | null |
Algeria | null | Middle East, North Africa, Afghanistan & Pakistan | Upper middle income | Algiers | 36.7397 | 3.05097 | DZA | 2,024 | 4.046115 | null | null | null | null | null | null | null | null | null | null |
Ecuador | null | Latin America & Caribbean | Upper middle income | Quito | -0.229498 | -78.5243 | ECU | 2,024 | 1.547325 | null | null | null | null | null | null | null | null | null | null |
Egypt, Arab Rep. | null | Middle East, North Africa, Afghanistan & Pakistan | Lower middle income | Cairo | 30.0982 | 31.2461 | EGY | 2,024 | 28.27059 | null | null | null | null | null | null | null | null | null | null |
Eritrea | null | Sub-Saharan Africa | Low income | Asmara | 15.3315 | 38.9183 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Spain | null | Europe & Central Asia | High income | Madrid | 40.4167 | -3.70327 | ESP | 2,024 | 2.747148 | 60.8 | 22.4 | 56.8 | 58.9 | 82.1 | 2.58 | 4.08 | 92.6 | 480 | 95 |
Estonia | null | Europe & Central Asia | High income | Tallinn | 59.4392 | 24.7586 | EST | 2,024 | 3.519412 | null | null | null | null | null | null | null | null | null | null |
Ethiopia | null | Sub-Saharan Africa | Low income | Addis Ababa | 9.02274 | 38.7468 | ETH | 2,024 | 21.037746 | null | null | null | null | null | null | null | null | null | null |
Finland | null | Europe & Central Asia | High income | Helsinki | 60.1608 | 24.9525 | FIN | 2,024 | 1.565689 | null | null | null | null | null | null | 5.12 | 118.6 | null | null |
Fiji | null | East Asia & Pacific | Upper middle income | Suva | -18.1149 | 178.399 | FJI | 2,024 | 3.890842 | null | null | null | null | null | null | null | null | null | null |
France | null | Europe & Central Asia | High income | Paris | 48.8566 | 2.35097 | FRA | 2,024 | 1.999049 | 64.8 | 27.9 | 60.8 | 63.5 | 89.7 | 2.78 | 4.62 | 108.4 | 738 | 155 |
Faroe Islands | null | Europe & Central Asia | High income | Torshavn | 61.8926 | -6.91181 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Micronesia, Fed. Sts. | null | East Asia & Pacific | Upper middle income | Palikir | 6.91771 | 158.185 | FSM | 2,022 | 5.408744 | null | null | null | null | null | null | null | null | null | null |
Gabon | null | Sub-Saharan Africa | Upper middle income | Libreville | 0.38832 | 9.45162 | GAB | 2,024 | 1.17312 | null | null | null | null | null | null | null | null | null | null |
United Kingdom | null | Europe & Central Asia | High income | London | 51.5002 | -0.126236 | GBR | 2,024 | 3.271573 | 67.8 | 30.5 | 63.2 | 66.7 | 90.2 | 3.12 | 4.48 | 105.2 | 1,235 | 160 |
Georgia | null | Europe & Central Asia | Upper middle income | Tbilisi | 41.71 | 44.793 | GEO | 2,024 | 1.109718 | null | null | null | null | null | null | null | null | null | null |
Ghana | null | Sub-Saharan Africa | Lower middle income | Accra | 5.57045 | -0.20795 | GHA | 2,024 | 22.848328 | null | null | null | null | null | null | null | null | null | null |
Gibraltar | null | Europe & Central Asia | High income | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Guinea | null | Sub-Saharan Africa | Lower middle income | Conakry | 9.51667 | -13.7 | GIN | 2,024 | 8.123139 | null | null | null | null | null | null | null | null | null | null |
Gambia, The | null | Sub-Saharan Africa | Low income | Banjul | 13.4495 | -16.5885 | GMB | 2,024 | 11.561053 | null | null | null | null | null | null | null | null | null | null |
Guinea-Bissau | null | Sub-Saharan Africa | Low income | Bissau | 11.8037 | -15.1804 | GNB | 2,024 | 3.765558 | null | null | null | null | null | null | null | null | null | null |
Equatorial Guinea | null | Sub-Saharan Africa | Upper middle income | Malabo | 3.7523 | 8.7741 | GNQ | 2,024 | 2.921402 | null | null | null | null | null | null | null | null | null | null |
Greece | null | Europe & Central Asia | High income | Athens | 37.9792 | 23.7166 | GRC | 2,024 | 2.741019 | 57.1 | 19.2 | 53.8 | 55.1 | 76.4 | 2.38 | 3.62 | 78.2 | 380 | 75 |
Grenada | null | Latin America & Caribbean | Upper middle income | Saint George's | 12.0653 | -61.7449 | GRD | 2,024 | 1.086154 | null | null | null | null | null | null | null | null | null | null |
Greenland | null | Europe & Central Asia | High income | Nuuk | 64.1836 | -51.7214 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Guatemala | null | Latin America & Caribbean | Upper middle income | Guatemala City | 14.6248 | -90.5328 | GTM | 2,024 | 2.869928 | null | null | null | null | null | null | null | null | null | null |
Guam | null | East Asia & Pacific | High income | Agana | 13.4443 | 144.794 | null | null | null | null | null | null | null | null | null | null | null | null | null |
Guyana | null | Latin America & Caribbean | High income | Georgetown | 6.80461 | -58.1548 | GUY | 2,024 | 2.903795 | null | null | null | null | null | null | null | null | null | null |
Hong Kong SAR, China | null | East Asia & Pacific | High income | null | 22.3964 | 114.109 | HKG | 2,024 | 1.729721 | null | null | null | null | null | null | null | null | null | null |
Honduras | null | Latin America & Caribbean | Lower middle income | Tegucigalpa | 15.1333 | -87.4667 | HND | 2,024 | 4.606211 | null | null | null | null | null | null | null | null | null | null |
Croatia | null | Europe & Central Asia | High income | Zagreb | 45.8069 | 15.9614 | HRV | 2,024 | 2.972005 | null | null | null | null | null | null | null | null | null | null |
Haiti | null | Latin America & Caribbean | Lower middle income | Port-au-Prince | 18.5392 | -72.3288 | HTI | 2,024 | 26.949056 | null | null | null | null | null | null | null | null | null | null |
Hungary | null | Europe & Central Asia | High income | Budapest | 47.4984 | 19.0408 | HUN | 2,024 | 3.703704 | null | null | null | null | null | null | null | null | null | null |
Indonesia | null | East Asia & Pacific | Upper middle income | Jakarta | -6.19752 | 106.83 | IDN | 2,024 | 2.181513 | 47.2 | 12.8 | 45.1 | 45.8 | 60.8 | 1.45 | 2.05 | 32.4 | 305 | 55 |
Isle of Man | null | Europe & Central Asia | High income | Douglas | 54.1509 | -4.47928 | null | null | null | null | null | null | null | null | null | null | null | null | null |
India | null | South Asia | Lower middle income | New Delhi | 28.6353 | 77.225 | IND | 2,024 | 4.953036 | 48.5 | 13.5 | 46.2 | 47.1 | 62.5 | 1.52 | 2.18 | 35.2 | 164 | 45 |
Ireland | null | Europe & Central Asia | High income | Dublin | 53.3441 | -6.26749 | IRL | 2,024 | 2.11345 | 68.9 | 40.2 | 64.7 | 70.1 | 92.4 | null | null | null | null | null |
Iran, Islamic Rep. | null | Middle East, North Africa, Afghanistan & Pakistan | Upper middle income | Tehran | 35.6878 | 51.4447 | IRN | 2,024 | 32.455871 | null | null | null | null | null | null | null | null | null | null |
Iraq | null | Middle East, North Africa, Afghanistan & Pakistan | Upper middle income | Baghdad | 33.3302 | 44.394 | IRQ | 2,024 | 2.611696 | null | null | null | null | null | null | null | null | null | null |
Iceland | null | Europe & Central Asia | High income | Reykjavik | 64.1353 | -21.8952 | ISL | 2,024 | 5.856838 | 78.2 | 45.6 | 72.3 | 82.1 | 98.7 | 4.26 | 7.94 | 131.85 | null | null |
Israel | null | Middle East, North Africa, Afghanistan & Pakistan | High income | null | 31.7717 | 35.2035 | ISR | 2,024 | 3.074809 | null | null | null | null | null | null | null | null | null | null |
Italy | null | Europe & Central Asia | High income | Rome | 41.8955 | 12.4823 | ITA | 2,024 | 0.982373 | 62.5 | 23.7 | 58.2 | 60.5 | 83.6 | 2.65 | 4.25 | 98.5 | 550 | 110 |
Jamaica | null | Latin America & Caribbean | Upper middle income | Kingston | 17.9927 | -76.792 | JAM | 2,024 | 5.411944 | null | null | null | null | null | null | null | null | null | null |
Jordan | null | Middle East, North Africa, Afghanistan & Pakistan | Upper middle income | Amman | 31.9497 | 35.9263 | JOR | 2,024 | 1.556596 | null | null | null | null | null | null | null | null | null | null |
Japan | null | East Asia & Pacific | High income | Tokyo | 35.67 | 139.77 | JPN | 2,024 | 2.738537 | 63.2 | 24.8 | 58.9 | 61.2 | 85.4 | 2.85 | null | null | 750 | 130 |
Kazakhstan | null | Europe & Central Asia | Upper middle income | Astana | 51.1879 | 71.4382 | KAZ | 2,024 | 8.690822 | null | null | null | null | null | 0.44 | null | null | null | null |
End of preview.
This dataset is a one-stop, meticulously curated resource designed for macroeconomists, data scientists, policy analysts, and business strategists. It bridges the gap between official macroeconomic statistics and real-world consumer expenses by combining real-time inflation data from the World Bank with crowd-sourced, granular cost-of-living indicators across all 194 recognized countries.
What does this dataset include? The dataset is structured to provide a 360-degree view of economic affordability. It is divided into three core layers:
Official Macroeconomic Data: Annual inflation rates (consumer prices, annual %) sourced directly from the World Bank API (indicator FP.CPI.TOTL.ZG), covering the latest available years (2020–2024), alongside country metadata including geographic region, income level (e.g., High-income, Lower-middle-income), and capital city coordinates.
Comparative Living Cost Indices: Standardized benchmark indices (relative to New York City = 100) that allow for easy cross-country comparison. These include the overall Cost of Living Index, Rent Index, Groceries Index, Restaurants Index, and the Purchasing Power Index.
Everyday Essential Goods & Services (in USD): To give a tangible sense of daily financial burden, the dataset includes average market prices for:
Food: Price of a 500g white loaf of bread.
Housing: Average monthly rent for a 1-bedroom apartment in the city center.
Transportation: Cost of a one-way local transport ticket and a standard monthly transport pass.
Utilities: Average monthly cost of basic utilities (electricity, heating, cooling, water, garbage) for a standard 85m² apartment.
Data Sources & Methodology:
Inflation & Country List: World Bank Open Data API (retrieved in real-time).
Cost Indices & Specific Prices: Aggregated from reputable international cost-of-living databases including Numbeo, Compare the Market AU analyses, Sputnik global surveys, and various national statistical offices.
Standardization: All monetary values are normalized and presented in US Dollars (USD) to ensure direct comparability across borders.
Potential Use Cases:
Machine learning models predicting inflation trends or housing affordability.
Exploratory data analysis (EDA) for academic papers on global economic disparity.
Dashboard creation for tracking "cost of living crises" or "purchasing power parity (PPP)" anomalies.
International relocation and expatriate compensation benchmarking.
Important Caveats (Read Me):
Time Variance: While the indices (Numbeo) represent a recent "point-in-time" snapshot, the World Bank inflation data is lagged by a few months (official annual releases). Users should treat the price data as indicative of the latest available period (2025–2026).
Data Availability: Not every country has complete reporting for every metric (especially small island nations or conflict zones). Missing values are left as NaN and clearly indicated in the data quality summary.
Crowd-sourced Nature: The price data (bread, rent, etc.) are averages derived from consumer contributions and may not perfectly represent rural versus urban extremes.
This dataset serves as a robust foundation for analyzing the global economic landscape, answering questions like "Where does my dollar go the furthest?" and "How does inflation impact everyday purchasing power?"
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