The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<multi_retailer_diamonds: int64, avg_spread_pct: double, median_spread_pct: double, total_spread_usd: int64>
to
{'multi_retailer_diamonds': Value('int64'), 'avg_spread_pct': Value('float64'), 'median_spread_pct': Value('float64')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<multi_retailer_diamonds: int64, avg_spread_pct: double, median_spread_pct: double, total_spread_usd: int64>
to
{'multi_retailer_diamonds': Value('int64'), 'avg_spread_pct': Value('float64'), 'median_spread_pct': Value('float64')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Caratlytics Diamond Price Index
A monthly, openly licensed index of retail diamond prices: the median USD per carat for natural and lab-grown diamonds, computed from a systematic 1 in 10 sample of live listings across more than 100 online retailers. Release 2026-08; series from 2025-12.
Latest reading (2026-08): natural 2184.28 USD per carat, lab-grown 464.15 USD per carat, natural to lab ratio 4.7 to 1. Observed in the release month: about 33,819,218 active listings, 13,999,549 certified diamonds, 4,645,741 diamonds matched across retailers, 127 retailers.
Author and publisher: Carat Hunter (https://carathunter.com), the cross-retailer diamond price comparison service. The index is released under the Caratlytics name at https://caratlytics.com, which also holds the methodology (https://caratlytics.com/methodology/) and the index method notes (https://caratlytics.com/price-index/). Releases are immutable; corrections are published as a new release with a changelog entry.
Files
| File | Columns |
|---|---|
diamond_price_index.csv |
month, segment (natural, lab_grown, overall), median_usd_per_carat, q1_usd_per_carat, q3_usd_per_carat, listings_observed |
diamond_price_index_by_shape.csv |
month, shape, segment, median_usd_per_carat, listings_observed |
diamond_price_index_by_lab.csv |
month, lab, segment, median_usd_per_carat, listings_observed |
diamond_price_index_lab_vs_natural.csv |
month, natural, lab_grown, natural_over_lab_ratio, natural_premium_pct |
The four CSVs are the latest vintage (release 2026-08). Every release recomputes the whole window from the live sample, so earlier months can be restated slightly as listings are added or delisted. The releases as first published are kept as vintages:
| File | What it is |
|---|---|
caratlytics-index-2026-05.json |
Release 2026-05 as first published: full document (series, by shape, by lab, natural vs lab-grown, cross-retailer spread, counts) |
caratlytics-index-2026-06.json |
Release 2026-06 as first published: full document (series, by shape, by lab, natural vs lab-grown, cross-retailer spread, counts) |
caratlytics-index-2026-07.json |
Release 2026-07 as first published: full document (series, by shape, by lab, natural vs lab-grown, cross-retailer spread, counts) |
caratlytics-index-2026-08.json |
Release 2026-08 as first published: full document (series, by shape, by lab, natural vs lab-grown, cross-retailer spread, counts) |
caratlytics-index-2026-08.json |
the full release document (segments, shapes, laboratories, cross-retailer spread, counts) |
Method, in brief
- Sample: a systematic 1 in 10 sample of listings by listing identifier, each contributing its most recent observed price in the calendar month.
- Unit: USD per carat under pinned conversion rates, so the series moves when retailers reprice, never on currency fluctuations.
- Filters: carat 0.25 to 10, price 100 to 100,000 USD per carat, positive prices only.
- Segments: natural and lab-grown from the certified origin, plus the overall market. Cells with fewer than 150 sampled stones in a month are withheld.
Update cadence
Monthly. A new release is published on the second day of each month for the month just closed, and this dataset is refreshed the same day.
License and attribution
CC BY 4.0. Cite as: Carat Hunter (2026). Caratlytics Diamond Price Index, release 2026-08. caratlytics.com. Attribution to Carat Hunter and Caratlytics is required.
Entities
- Caratlytics: https://caratlytics.com/
- Carat Hunter (publisher): https://www.wikidata.org/wiki/Q139620291
Packaged 2026-09-13.
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