data trove bfro bigfoot sightings full scrape

This column is a numeric row identifier: all 5,411 values are unique, there are no nulls, and the zero rate is 0.0, consistent with a primary key or surrogate ID. The IDs are not sequential (range 60–79,711 with a mean of ~23,288 and median of ~16,598), suggesting they originate from a larger parent table or were assigned non-contiguously. Mild positive skew (0.91) indicates more records cluster in lower ID ranges, but the near-zero kurtosis (−0.15) and absence of outliers confirm a broadly spread, roughly uniform-ish distribution rather than a tightly clustered one. Treatment: Retain as row key for joins/lookups; exclude from any predictive model as a feature. high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

5,411

min

60

max

79,711

mean

2.329e+04

median

16,598

std

2.138e+04

q1

4898

q3

3.636e+04

iqr

31,464

skew

0.9109

kurtosis

-0.151

n_outliers

0

outlier_rate

0

zero_rate

0

This column contains US state names, with all 50 states likely represented plus Washington D.C. and possibly territories (53 unique values, 0 nulls across 5,411 rows). Washington dominates at 11.7% (631 records), roughly 1.5× California's share (431), suggesting a dataset with geographic bias toward the Pacific Northwest. Entropy ratio of 0.877 indicates reasonably broad distribution across states, though concentration in a handful of large/coastal states is apparent. Treatment: One-hot encode for tree models or ordinal-encode by region grouping; investigate the Washington overrepresentation relative to population before modelling. high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

53

top_value

Washington

top_rate

0.1166

cardinality

53

entropy

5.025

entropy_ratio

0.8773

This column contains US state abbreviations (plus possibly DC and territories, explaining the 53 distinct values vs. 50 states). Washington ('wa') is notably over-represented at 11.7% of 5,411 rows — roughly 1.5× California ('ca') and nearly 2× Ohio ('oh') — suggesting a geographic skew toward the Pacific Northwest rather than a nationally representative sample. Entropy ratio of 0.877 indicates reasonably broad distribution across states, but the top-heavy concentration in 'wa' is worth flagging. Treatment: One-hot encode or target-encode for modelling; investigate why 'wa' is over-represented relative to population share before training. high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

53

top_value

wa

top_rate

0.1166

cardinality

53

entropy

5.025

entropy_ratio

0.8773

This column contains US county names, functioning as a categorical geographic label with 1,022 unique values across 5,411 rows. The duplicate rate is high at 81.1%, which is expected for a county field where many records share the same geography. Notably, 338 rows (6.2%) have empty strings rather than nulls, masking true missingness since null_rate reports 0.0. The top counties — Pierce, Jefferson, Lewis, Washington, Snohomish — suggest a Pacific Northwest-heavy dataset, but the presence of Humboldt and Polk hints at multi-state coverage. Treatment: Replace empty strings with NaN, then encode as categorical (ordinal or target-encode) for modelling or use as a group-by key for geographic aggregation. high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

1,022

len_min

0

len_max

23

len_mean

6.621

len_median

7

len_p95

10

word_mean

1

word_median

1

n_empty

338

n_duplicates

4,389

duplicate_rate

0.8111

vocab_size

1,020

readability_flesch_mean

16.9

emoji_rate

0

url_rate

0

one_word_rate

1

allcaps_rate

0

boilerplate_rate

0

This column contains unique URLs pointing to individual report pages on bfro.net (the Bigfoot Field Researchers Organization database), all following the pattern `https://www.bfro.net/gdb/show_report.asp?id=`. Every one of the 5,411 rows holds a distinct URL (duplicate_rate: 0.0, n_unique: 5411), with lengths tightly clustered between 46 and 49 characters (len_mean: 48.56, len_median: 49.0), reflecting only variation in the numeric report ID. This column is effectively a primary or foreign key into the BFRO report database — not a content feature — and carries no modelling signal on its own. Treatment: Retain as a row identifier or use to left-join additional scraped report metadata; drop from any feature matrix. high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

5,411

len_min

46

len_max

49

len_mean

48.56

len_median

49

len_p95

49

word_mean

1

word_median

1

n_empty

0

n_duplicates

0

duplicate_rate

0

vocab_size

5,411

readability_flesch_mean

-301.8

emoji_rate

0

url_rate

1

one_word_rate

1

allcaps_rate

0

boilerplate_rate

0

This column represents calendar month names, but with a cardinality of 32 instead of the expected 12, there are clearly duplicate or variant entries beyond the standard month labels — likely encoding errors, alternate spellings, or appended year/year-month combinations. The distribution is notably skewed toward summer and early-autumn months (August 634, October 632, July 618), with winter months dramatically underrepresented (December 233, January 228), suggesting seasonal bias in data collection. The entropy_ratio of 0.761 across 32 unique values rather than 12 is a strong flag that this field is dirty and needs normalisation before use. Treatment: Audit and collapse the 32 distinct values down to 12 canonical month names, then encode as an ordered cyclic feature. medium · anthropic:default

n

5,411

nulls

160 (3.0%)

unique

32

top_value

August

top_rate

0.1207

cardinality

32

entropy

3.807

entropy_ratio

0.7614

This column represents calendar years for records in the dataset, spanning 1870 to 2025 with 99 distinct values. The distribution is left-skewed (skew = -0.974) with a mean of ~1998 and IQR of 22 years (1987–2009), meaning the bulk of records cluster in the late 20th to early 21st century while a thin tail extends back to 1870. The 49 outliers (0.9%) likely correspond to those historically distant records, and analysts should verify whether pre-20th-century entries are genuine or data quality issues. Treatment: Treat as an ordinal temporal feature; investigate the 49 outlier records (pre-~1960s) for validity before modelling. high · anthropic:default

n

5,411

nulls

57 (1.1%)

unique

99

min

1,870

max

2,025

mean

1998

median

2,001

std

15.79

q1

1,987

q3

2,009

iqr

22

skew

-0.9738

kurtosis

1.997

n_outliers

49

outlier_rate

0.009152

zero_rate

0

This column is a three-level ordinal or nominal classification label applied to all 5,411 rows with no nulls. The distribution is nearly balanced between 'Class B' (2,722; 50.3%) and 'Class A' (2,655; 49.1%), but 'Class C' is severely underrepresented with only 34 instances (~0.6%), which would surprise any analyst expecting a balanced multi-class target and will require oversampling or class-weight adjustments if used as a target variable. Treatment: Use as classification target; apply class-weighting or oversampling to address severe 'Class C' imbalance (34 of 5411 rows). high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

3

top_value

Class B

top_rate

0.503

cardinality

3

entropy

1.049

entropy_ratio

0.6616

This column contains short free-text descriptions of reported sightings — most likely UFO or wildlife sighting reports, inferred from the high-frequency terms 'sighting', 'near', and 'possible'. With 5,407 unique values out of 5,411 rows and zero nulls, entries are nearly all distinct; the 4 duplicates (duplicate_rate 0.00074) are negligible. Mean length of ~67 characters and ~10 words per entry suggests structured-but-natural one-line summaries rather than long narratives. Flesch readability of 55.7 indicates plain, accessible prose with a vocabulary of 7,169 unique tokens across the corpus. Treatment: Tokenize and embed (e.g., TF-IDF or sentence transformer) before modelling; near-uniqueness makes direct encoding unusable. high · anthropic:default

n

5,411

nulls

0 (0.0%)

unique

5,407

len_min

10

len_max

221

len_mean

67.04

len_median

65

len_p95

101.5

word_mean

10.62

word_median

10

n_empty

0

n_duplicates

4

duplicate_rate

0.0007392

vocab_size

7,169

readability_flesch_mean

55.71

emoji_rate

0

url_rate

0

one_word_rate

0

allcaps_rate

0

boilerplate_rate

0.0001848