data trove ufo sightings analysis

source /home/coolhand/html/datavis/data_trove/data/quirky/ufo_by_state.json 58 rows 2 columns profiled 2026-06-22 raw JSON static .html .ipynb Report Notebook

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dataset summary · high confidence anthropic:default

This dataset contains UFO sighting counts aggregated by U.S. state, covering all 58 rows with no missing values. The count distribution is heavily right-skewed (skew ~2.93) with high kurtosis and 4 outlier states that far exceed the norm — the max of 16,197 sightings dwarfs the median of 1,510, suggesting a handful of states dominate UFO reports. The state column has one entry per state, so the interesting story is entirely in how unevenly sightings are distributed across states. Look closely at the top states to see which ones are driving the bulk of reported sightings.

citing: row_count · column_count · stats.max · stats.median · stats.mean · stats.skew · stats.kurtosis · n_outliers · outlier_rate · top_value

Charts the summary said to look at first

count · Look for the extreme right tail — a few states have sighting counts many times higher than the median of 1,510.

Show data table

Histogram bins for count (median: 1510.5).
bin	count
1 – 2315	38
2315 – 4628	13
4628 – 6942	4
6942 – 9256	2
9256 – 1.157e+04	0
1.157e+04 – 1.388e+04	0
1.388e+04 – 1.62e+04	1

state · Compare sighting counts across all 58 states to identify which handful of states dominate UFO reports.

Show data table

Top values for state (20 unique shown, of 58 total).
value	count	share
CA	1	1.7%
FL	1	1.7%
WA	1	1.7%
TX	1	1.7%
NY	1	1.7%
PA	1	1.7%
AZ	1	1.7%
OH	1	1.7%
IL	1	1.7%
NC	1	1.7%
MI	1	1.7%
OR	1	1.7%
CO	1	1.7%
NJ	1	1.7%
MO	1	1.7%
GA	1	1.7%
IN	1	1.7%
MA	1	1.7%
VA	1	1.7%
WI	1	1.7%

count · Ranking states by count makes the outlier states immediately visible against the rest of the distribution.

Show data table

Histogram bins for count (median: 1510.5).
bin	count
1 – 2315	38
2315 – 4628	13
4628 – 6942	4
6942 – 9256	2
9256 – 1.157e+04	0
1.157e+04 – 1.388e+04	0
1.388e+04 – 1.62e+04	1

Schema

2 columns

Per-column summary. Click column name to jump to its detail.
				Alerts
state	categorical	0.0%	58	long_tail
count	numeric	0.0%	55	high_skew outliers

state

categorical identifier long_tail

This column contains US state abbreviations, with exactly 58 unique values across 58 rows — meaning every row has a distinct state code and the dataset contains one record per state (plus potentially DC and a territory or two beyond the standard 50). Entropy ratio of 1.0 and a top_rate of 0.0172 (1/58) confirm perfectly uniform distribution with zero repetition, making this effectively a lookup key rather than a grouping variable. The long_tail alert is technically correct but misleading — there is no tail, just perfect cardinality. Treatment: Use as a join key or index; do not one-hot encode or use as a categorical feature without aggregating additional rows per state first. high · anthropic:default

n: 58
nulls: 0 (0.0%)
unique: 58
top_value: CA
top_rate: 0.01724
cardinality: 58
entropy: 5.858
entropy_ratio: 1

count

numeric feature high_skew outliers

This column appears to be an event or item count, likely representing frequency or volume of some activity across 58 records. The distribution is severely right-skewed (skew = 2.93, kurtosis = 11.75) with a min of 1 and a max of 16,197 against a median of only 1,510.5, indicating a handful of dominant observations pulling the mean (2,274.7) well above the median. Four outliers (≈6.9% of rows) are driving the extreme tail, and the standard deviation (2,642.8) exceeds the mean, confirming high dispersion. Treatment: Log-transform before modelling to reduce skew; investigate the 4 outliers (max 16,197) for data-quality issues before including them. medium · anthropic:default

n: 58
nulls: 0 (0.0%)
unique: 55
min: 1
max: 16,197
mean: 2275
median: 1510
std: 2643
q1: 648.8
q3: 2,789
iqr: 2140
skew: 2.925
kurtosis: 11.75
n_outliers: 4
outlier_rate: 0.06897
zero_rate: 0