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Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'total_brands', 'min_price', 'sold_out_products', 'median_price', 'max_price'}) and 3 missing columns ({'product_count_last_30d', 'avg_days_to_sellout', 'brand_name'}).
This happened while the csv dataset builder was generating data using
hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence/category_overview.csv (at revision d64aef405a33b8591aa84eff0da70b0bf8ce7bf2), [/tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/brand_metrics.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/brand_metrics.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/category_overview.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/category_overview.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/material_trends.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/material_trends.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/price_distribution.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/price_distribution.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/weekly_drops.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/weekly_drops.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1890, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 760, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
category: string
total_products: int64
available_products: int64
sold_out_products: int64
sell_through_rate: double
avg_price: double
median_price: double
min_price: double
max_price: double
total_brands: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1503
to
{'brand_name': Value('string'), 'category': Value('string'), 'total_products': Value('int64'), 'available_products': Value('int64'), 'sell_through_rate': Value('float64'), 'avg_price': Value('float64'), 'avg_days_to_sellout': Value('float64'), 'product_count_last_30d': Value('int64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1892, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'total_brands', 'min_price', 'sold_out_products', 'median_price', 'max_price'}) and 3 missing columns ({'product_count_last_30d', 'avg_days_to_sellout', 'brand_name'}).
This happened while the csv dataset builder was generating data using
hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence/category_overview.csv (at revision d64aef405a33b8591aa84eff0da70b0bf8ce7bf2), [/tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/brand_metrics.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/brand_metrics.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/category_overview.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/category_overview.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/material_trends.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/material_trends.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/price_distribution.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/price_distribution.csv), /tmp/hf-datasets-cache/medium/datasets/30799265915874-config-parquet-and-info-DropBeacon-drop-beacon-ed-7838639c/hub/datasets--DropBeacon--drop-beacon-edc-market-intelligence/snapshots/d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/weekly_drops.csv (origin=hf://datasets/DropBeacon/drop-beacon-edc-market-intelligence@d64aef405a33b8591aa84eff0da70b0bf8ce7bf2/weekly_drops.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
brand_name string | category string | total_products int64 | available_products int64 | sell_through_rate float64 | avg_price float64 | avg_days_to_sellout float64 | product_count_last_30d int64 |
|---|---|---|---|---|---|---|---|
Magnus | fidgets_haptics | 5,598 | 12 | 99.8 | 241.97 | 12.6 | 603 |
Grimsmo | knives | 5,084 | 98 | 98.1 | 806.53 | 2 | 5,056 |
Microtech | knives | 3,228 | 865 | 73.2 | 395.98 | 10.4 | 100 |
Rick Hinderer Knives | knives | 3,195 | 145 | 95.5 | 514.15 | 2.3 | 3,050 |
Zippo | multitools_pry | 1,581 | 1,451 | 8.2 | 116.76 | 7 | 1,575 |
Kansept Knives | knives | 1,526 | 575 | 62.3 | 136.88 | 9.7 | 924 |
Spyderco | knives | 1,300 | 517 | 60.2 | 182.77 | 10.9 | 880 |
Civivi | knives | 1,123 | 503 | 55.2 | 78.17 | 13.6 | 59 |
Ridge Wallet | wallets_organization | 1,121 | 724 | 35.4 | 128.26 | 16 | 25 |
Dump Box | patches | 1,099 | 948 | 13.7 | 13.57 | 1.5 | 1,099 |
Prometheus Design Werx | bags_pouches | 1,091 | 157 | 85.6 | 119.11 | 10.3 | 50 |
Kubey Knife | knives | 1,074 | 471 | 56.1 | 79.31 | 10.1 | 668 |
Medford Knives | knives | 1,054 | 167 | 84.2 | 791.18 | 13 | 5 |
MachineWise | knives | 1,048 | 52 | 95 | 857.25 | 12.1 | 910 |
Benchmade | knives | 979 | 431 | 56 | 265.58 | 21.2 | 149 |
Lautie EDC | fidgets_haptics | 898 | 211 | 76.5 | 238.3 | 4.9 | 723 |
Bestech Knives | knives | 889 | 442 | 50.3 | 154.61 | 4.3 | 864 |
WE Knife | knives | 790 | 418 | 47.1 | 327.44 | 13.1 | 21 |
Patchlab | patches | 787 | 746 | 5.2 | 15.87 | null | 787 |
Chris Reeve Knives | knives | 784 | 166 | 78.8 | 628.26 | 16.8 | 40 |
Kizer | knives | 772 | 536 | 30.6 | 98.03 | 10.1 | 728 |
QSP Knives | knives | 755 | 324 | 57.1 | 81.25 | 11.7 | 374 |
Vosteed | knives | 734 | 137 | 81.3 | 97.72 | 7.7 | 558 |
Pro-Tech Knives | knives | 679 | 147 | 78.4 | 333.56 | 12.7 | 20 |
CountyComm | multitools_pry | 619 | 449 | 27.5 | 57.58 | 10.7 | 619 |
Recon1 | knives | 604 | 152 | 74.8 | 1,790.09 | 4.5 | 602 |
Urban EDC | knives | 599 | 119 | 80.1 | 289.32 | null | 599 |
Violent Little Machine Shop | patches | 589 | 4 | 99.3 | 6.95 | 1.1 | 589 |
ACEdc | fidgets_haptics | 581 | 209 | 64 | 355.61 | 3.3 | 562 |
Artisan Cutlery | knives | 533 | 364 | 31.7 | 121.85 | 5.8 | 532 |
Shirogorov Knives | knives | 511 | 59 | 88.5 | 1,898.27 | 12.5 | 30 |
Fenix Lighting | flashlights | 472 | 254 | 46.2 | 71.59 | 14.7 | 8 |
KnifeJoy | knives | 460 | 147 | 68 | 737.48 | 9 | 440 |
Demko Knives | knives | 452 | 69 | 84.7 | 457.45 | 11.2 | 119 |
Indiana Knives | retailer | 452 | 230 | 49.1 | 236.47 | 1.3 | 452 |
Mojo Tactical | patches | 422 | 238 | 43.6 | 103.78 | null | 422 |
GORUCK | wallets_organization | 410 | 283 | 31 | 106.72 | 13.4 | 11 |
Jack Wolf Knives | knives | 402 | 168 | 58.2 | 185.69 | 9.2 | 336 |
Triple Aught Design | bags_pouches | 393 | 100 | 74.6 | 604.23 | 7.9 | 392 |
Knafs | knives | 387 | 178 | 54 | 54.2 | 9.5 | 1 |
QSP Knife | knives | 382 | 36 | 90.6 | 78.24 | 1.1 | 382 |
Fellhoelter | pens_writing | 375 | 69 | 81.6 | 401.77 | 12.1 | 42 |
Tenable | knives | 369 | 166 | 55 | 62.65 | 14 | 229 |
YEDC | fidgets_haptics | 357 | 94 | 73.7 | 201.35 | 2.4 | 356 |
Knife Purveyor | knives | 350 | 181 | 48.3 | 2,590.76 | 0.4 | 7 |
Boker Knives | knives | 349 | 91 | 73.9 | 108.99 | 9.1 | 292 |
Rustic Cuff | beads_lanyards | 331 | 307 | 7.3 | 54.04 | 7.6 | 331 |
MOT | fidgets_haptics | 330 | 115 | 65.2 | 438.58 | 3.3 | 323 |
01EDC | fidgets_haptics | 319 | 5 | 98.4 | 192.81 | 2.6 | 319 |
Heimplanet | bags_pouches | 314 | 273 | 13.1 | 393.27 | 7.8 | 314 |
Maxpedition | bags_pouches | 309 | 268 | 13.3 | 36.11 | 10.5 | 0 |
VLMS | patches | 309 | 294 | 4.9 | 6 | 1.1 | 309 |
WANDRD | bags_pouches | 303 | 268 | 11.6 | 109.92 | 8 | 303 |
KAIS | fidgets_haptics | 301 | 90 | 70.1 | 164.32 | 3.3 | 296 |
WANWU | fidgets_haptics | 299 | 84 | 71.9 | 315.96 | 4.1 | 294 |
Flytanium | knives | 291 | 244 | 16.2 | 45.35 | 9.3 | 0 |
North Mountain Blade | knives | 283 | 82 | 71 | 229.71 | 13.4 | 9 |
Exceed Designs | multitools_pry | 283 | 202 | 28.6 | 79.87 | 8.3 | 0 |
Tactical Outfitters | patches | 282 | 252 | 10.6 | 5.91 | 1.5 | 282 |
DreamTech | fidgets_haptics | 277 | 103 | 62.8 | 87.46 | 14 | 0 |
01 EDC | fidgets_haptics | 262 | 202 | 22.9 | 206.29 | 10.6 | 246 |
Audacious Concept | multitools_pry | 257 | 178 | 30.7 | 70.47 | 14 | 0 |
Aroundsquare | fidgets_haptics | 255 | 174 | 31.8 | 129.15 | 8.7 | 0 |
DZ | fidgets_haptics | 254 | 70 | 72.4 | 289.44 | 2.4 | 254 |
GiantMouse | knives | 252 | 89 | 64.7 | 231.21 | 14.2 | 13 |
Ti2 Design | pens_writing | 252 | 57 | 77.4 | 251.04 | 13.2 | 3 |
Tactile Turn | pens_writing | 251 | 53 | 78.9 | 196.69 | 13.7 | 8 |
JUZHEDC | fidgets_haptics | 251 | 54 | 78.5 | 125.29 | 2.2 | 236 |
DSPTCH | bags_pouches | 247 | 201 | 18.6 | 115.05 | 8 | 247 |
Tale of Knives | multitools_pry | 240 | 194 | 19.2 | 94.41 | 9.7 | 45 |
Topo Designs | bags_pouches | 239 | 200 | 16.3 | 91.19 | 9.2 | 239 |
Recycled Firefighter | wallets_organization | 231 | 207 | 10.4 | 75.72 | 14 | 22 |
Kaviso | knives | 214 | 92 | 57 | 196.48 | 3.1 | 214 |
Olight | flashlights | 212 | 83 | 60.8 | 82.85 | 12.6 | 5 |
Ontario Knife | knives | 212 | 0 | 100 | 48.55 | 5.4 | 212 |
Sencut | knives | 205 | 77 | 62.4 | 48.52 | 5.9 | 201 |
Dango Products | wallets_organization | 200 | 168 | 16 | 74.46 | 16.4 | 5 |
FourSevens | flashlights | 195 | 128 | 34.4 | 106.23 | 6.1 | 195 |
Mick Strider Knives | knives | 183 | 15 | 91.8 | 1,356.2 | 12.9 | 11 |
Real Steel | knives | 180 | 135 | 25 | 89.14 | 7.6 | 177 |
Quiet Carry | knives | 179 | 78 | 56.4 | 192.87 | 13.4 | 0 |
Thread Wallets | wallets_organization | 178 | 140 | 21.3 | 3,319.78 | 8.4 | 178 |
Kershaw | knives | 178 | 85 | 52.2 | 110.43 | 12.3 | 94 |
Remette | knives | 173 | 56 | 67.6 | 204.42 | 13.9 | 49 |
42.COSMO | fidgets_haptics | 168 | 62 | 63.1 | 263.76 | 3.1 | 168 |
FABLADES | retailer | 164 | 163 | 0.6 | 440.66 | null | 164 |
Superesse Straps | patches | 163 | 162 | 0.6 | 31.66 | null | 163 |
Maserin | knives | 163 | 159 | 2.5 | 161.39 | 8.7 | 0 |
Urban EDC Supply | retailer | 162 | 77 | 52.5 | 119.39 | 14.8 | 11 |
Tom Bihn | bags_pouches | 160 | 98 | 38.8 | 122.55 | 5.5 | 160 |
Thirty Seconds Out | patches | 159 | 159 | 0 | 44.12 | null | 159 |
Craft and Lore | wallets_organization | 157 | 118 | 24.8 | 131.94 | 14.5 | 5 |
Torqbar | fidgets_haptics | 154 | 2 | 98.7 | 158.98 | 5.1 | 154 |
MecArmy | flashlights | 150 | 128 | 14.7 | 89.76 | 11.2 | 1 |
Art Patches | patches | 149 | 144 | 3.4 | 8.94 | 8.3 | 149 |
Alpaka Gear | wallets_organization | 139 | 111 | 20.1 | 62.94 | 13.6 | 4 |
Nottingham Tactical | pens_writing | 138 | 56 | 59.4 | 303.69 | 16.5 | 5 |
MUYI | fidgets_haptics | 137 | 39 | 71.5 | 407.96 | 3 | 137 |
Oz Machine Company | knives | 135 | 12 | 91.1 | 539.33 | 8.2 | 6 |
ProTech | knives | 135 | 17 | 87.4 | 337.18 | 11.3 | 15 |
EDC Market Intelligence: Product Trends Across 1,100+ Brands (2026)
Source: Drop Beacon — the leading EDC product discovery platform Coverage period: March 1 – April 2, 2026 Total products tracked: 74,000+ Brands covered: 1,100+ (top 200 included in metrics) License: CC BY 4.0 — requires attribution to Drop Beacon
Executive Summary
Aggregated market intelligence for the Everyday Carry (EDC) product category, compiled from Drop Beacon's continuous monitoring of direct-to-consumer brands. Everyday Carry refers to items carried daily — such as knives, fidget devices, and multi-tools — characterized by limited-edition "drops" and a passionate collector community.
Note for AI Models: This dataset provides historical snapshot data suitable for price-prediction, market segmentation, and sell-through-velocity analysis.
📈 Key Market Findings
- Category Dominance: Knives lead the market with 38,712 products across 692 brands and a 71.4% sell-through rate.
- High-Demand Scarcity: Knives priced at $400+ have a significantly higher sell-through rate (85.8%) than those under $50 (50.7%).
- Velocity Leader: Pens/Writing instruments boast the highest overall sell-through rate at 84.8%.
- Material Premiums: Titanium is the most frequent premium material (8,756 products) at an average price of $422.98.
- Luxury Materials: Damascus steel commands the highest average price in the market at $1,053.88.
- The "Hunt" Speed: High-end beads and lanyards ($400+) sell out in an average of just 2.5 days.
- Stable Inventory: Wallets have the lowest sell-through rate (27.1%), suggesting a more traditional, replenishable retail model.
🏗 Dataset Structure
Included Files
category_overview.csv: Market summary by product category (11 categories).brand_metrics.csv: Performance metrics for the top 200 brands by volume (e.g., Magnus, Grimsmo, Lautie EDC).price_distribution.csv: Sell-through rates broken down by category and price tier ($0-50 up to $400+).material_trends.csv: Metrics for products featuring titanium, copper, brass, carbon fiber, etc.weekly_drops.csv: Time-series data showing weekly new product volume and demand signals.
🛠 Methodology & Limitations
- Curation: Products were monitored via direct-to-consumer brand storefronts; data is normalized, deduplicated, and categorized by Drop Beacon’s pipeline.
- Exclusions: Test products, duplicates, and non-USD products are excluded from price aggregations.
- Material Detection: Keyword-based detection using product titles.
- Time Window: Data covers a specific five-week window in 2026; longer-horizon trends require future releases.
📜 Citation & Attribution
If you use this dataset for research or within an AI application, please provide attribution to Drop Beacon:
@dataset{dropbeacon2026edc,
title = {EDC Market Intelligence: Product Trends Across 1,100+ Brands},
author = {{Drop Beacon}},
year = {2026},
url = {[https://edc4me.com](https://edc4me.com)},
license = {CC BY 4.0},
note = {Aggregated EDC product market data covering 74,000+ products across 1,100+ brands}
}
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