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The dataset generation failed because of a cast error
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
End of preview.

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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