animal attacks shark attacks analysis

source /home/coolhand/html/datavis/data_trove/data/wild/animal_attacks/shark_attacks_analysis.csv 8 rows 9 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

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dataset summary · medium confidence anthropic:claude-opus-4-7

This is a tiny 8-row table that appears to hold Vega-Lite chart specifications rather than tidy observational data, with 9 columns mixing JSON-like fields (data, encoding, config, mark, $schema, autosize) and chart dimensions. The most informative columns are the chart metadata: every row uses the same `$schema` (vega-lite v2) and `mark` value of 'bar', while `height` (719) and `width` (761) are constants — so the dataset really describes 8 near-identical bar-chart specs. Worth a closer look: the `encoding` and `autosize`/`config` columns, which carry the only real variation (3 distinct encodings across `year`, `sex`, and a count aggregate), and the high null rates (62–87%) on those spec fields, which suggest each row only fills in one facet of the chart definition. Treat this as a chart-spec catalog, not a data table — joins or aggregations on `data` won't be meaningful since that column embeds long serialized arrays.

citing: row_count · column_count · columns · kinds

Charts the summary said to look at first

mark · Confirms every spec uses the same 'bar' mark — a constant signal across rows.

Show data table

Top values for mark (1 unique shown, of 1 total).
value	count	share
bar	1	12.5%

encoding · Shows the three distinct encoding fragments (year, sex, count) that drive what each chart actually plots.

Show data table

Top values for encoding (3 unique shown, of 3 total).
value	count	share
{'field': 'year', 'type': 'ordinal', 'scale': {'type': 'linear', 'zero': True}}	1	12.5%
{'field': '*', 'type': 'quantitative', 'aggregate': 'count', 'scale': {'type': 'linear', 'zero': True}}	1	12.5%
{'field': 'sex', 'type': 'nominal', 'scale': {'type': 'linear', 'zero': True}}	1	12.5%

autosize · Splits the few non-null rows between 'fit' and 'padding' sizing strategies.

Show data table

Top values for autosize (2 unique shown, of 2 total).
value	count	share
fit	1	12.5%
padding	1	12.5%

config · Reveals the two config values present ('#ffffff' and '20'), useful for spotting styling vs padding settings.

Show data table

Top values for config (2 unique shown, of 2 total).
value	count	share
#ffffff	1	12.5%
20	1	12.5%

index · Sanity check on row ordering — values 0–7 confirm this is just a row index, not a measure.

Show data table

Histogram bins for index (median: 3.5).
bin	count
0 – 1.4	2
1.4 – 2.8	1
2.8 – 4.2	2
4.2 – 5.6	1
5.6 – 7	2

Schema

9 columns

Per-column summary. Click column name to jump to its detail.
				Alerts
index	numeric	0.0%	8
$schema	categorical	0.0%	1	imbalance
width	numeric	0.0%	1	constant
height	numeric	0.0%	1	constant
autosize	categorical	75.0%	2	long_tail null_rate
mark	categorical	87.5%	1	long_tail null_rate imbalance
encoding	categorical	62.5%	3	long_tail null_rate
data	categorical	87.5%	1	long_tail null_rate imbalance
config	categorical	75.0%	2	long_tail null_rate

index

numeric identifier

This column is a sequential row index running 0 through 7 across all 8 rows, with no nulls and no duplicates. The perfectly symmetric distribution (skew 0.0, mean and median both 3.5) and unit cardinality match a positional counter rather than a meaningful feature. Treatment: drop before modelling; it is just a row number. high · anthropic:claude-opus-4-7

n: 8
nulls: 0 (0.0%)
unique: 8
min: 0
max: 7
mean: 3.5
median: 3.5
std: 2.449
q1: 1.75
q3: 5.25
iqr: 3.5
skew: 0
kurtosis: -1.238
n_outliers: 0
outlier_rate: 0
zero_rate: 0.125

$schema

categorical metadata imbalance

This column is a constant `$schema` reference pointing to `https://vega.github.io/schema/vega-lite/v2.json` across all 8 rows. With cardinality 1 and entropy 0, it carries no information for analysis — it's metadata declaring the Vega-Lite spec version. Treatment: Drop; constant schema declaration with no analytic value. high · anthropic:claude-opus-4-7

n: 8
nulls: 0 (0.0%)
unique: 1
top_value: https://vega.github.io/schema/vega-lite/v2.json
top_rate: 1
cardinality: 1
entropy: 0
entropy_ratio: 0

width

numeric metadata constant

This column records a width measurement, but every one of the 8 rows holds the identical value 761.0. With zero variance, no nulls, and only one unique value, it carries no information for analysis or modelling. Treatment: Drop; constant column with no signal. high · anthropic:claude-opus-4-7

n: 8
nulls: 0 (0.0%)
unique: 1
min: 761
max: 761
mean: 761
median: 761
std: 0
q1: 761
q3: 761
iqr: 0
skew: 0
kurtosis: 0
n_outliers: 0
outlier_rate: 0
zero_rate: 0

height

numeric metadata constant

The column 'height' is a numeric field, but every one of the 8 rows holds the identical value 719.0, with std 0.0 and a single unique value. It carries no information for modelling and is flagged as constant. Treatment: Drop; constant column with zero variance. high · anthropic:claude-opus-4-7

n: 8
nulls: 0 (0.0%)
unique: 1
min: 719
max: 719
mean: 719
median: 719
std: 0
q1: 719
q3: 719
iqr: 0
skew: 0
kurtosis: 0
n_outliers: 0
outlier_rate: 0
zero_rate: 0

autosize

categorical metadata long_tail null_rate

A categorical configuration flag, likely controlling auto-sizing behaviour with values like 'fit' and 'padding'. With only 8 rows and a 75% null rate, just 2 non-null observations remain — each value appears exactly once, so the reported entropy of 1.0 and top_rate of 0.5 are essentially noise from a tiny sample. Treatment: Defer modelling decisions until more non-null data accrues; treat as a sparse config flag for now. low · anthropic:claude-opus-4-7

n: 8
nulls: 6 (75.0%)
unique: 2
top_value: fit
top_rate: 0.5
cardinality: 2
entropy: 1
entropy_ratio: 1

mark

categorical feature long_tail null_rate imbalance

The `mark` column appears to be a categorical flag, but it is almost entirely unobserved: 7 of 8 rows are null (null_rate 0.875) and the single non-null value is "bar". With cardinality 1 and entropy 0, this column carries no discriminative information in the sample. Treatment: Drop; effectively constant with 87.5% nulls. high · anthropic:claude-opus-4-7

n: 8
nulls: 7 (87.5%)
unique: 1
top_value: bar
top_rate: 1
cardinality: 1
entropy: 0
entropy_ratio: 0

encoding

categorical metadata long_tail null_rate

This column holds Vega-Lite-style encoding specifications stored as stringified dicts (field/type/scale objects), so each row describes a chart channel definition rather than a tidy value. With 8 rows, 62.5% nulls, and only 3 distinct non-null specs (each appearing once), there is no repetition signal and entropy_ratio is 1.0. The values mix different fields ('year', '*', 'sex') and types (ordinal, quantitative, nominal), suggesting this is metadata extracted from chart specs rather than a modelling feature. Treatment: Parse the JSON-like strings and explode into separate field/type/scale columns instead of using as-is. high · anthropic:claude-opus-4-7

n: 8
nulls: 5 (62.5%)
unique: 3
top_value: {'field': 'year', 'type': 'ordinal', 'scale': {'type': 'linear', 'zero': True}}
top_rate: 0.3333
cardinality: 3
entropy: 1.585
entropy_ratio: 1

data

categorical other long_tail null_rate imbalance

This column holds a single nested payload per row — each non-null cell is a giant serialized list of {year, sex} dicts spanning years from 1703 to 2019, with sex overwhelmingly 'M' and a small 'F' tail. Across n=8 rows, 7 are null (null_rate 0.875) and the one populated cell is unique, so cardinality=1, entropy=0, and top_rate=1.0 are artifacts of that single observation rather than signal. The structure looks like an aggregation accidentally stuffed into one cell — a flattening or groupby bug — not a usable feature. Treatment: Drop as-is and re-ingest by exploding the nested list into rows with proper year and sex columns. high · anthropic:claude-opus-4-7

n: 8
nulls: 7 (87.5%)
unique: 1
top_value: [{'year': 2019, 'sex': 'M'}, {'year': 2018, 'sex': 'M'}, {'year': 2018, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2017, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2017, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2017, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 2010, 'sex': 'M'}, {'year': 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{'year': 1934, 'sex': 'F'}, {'year': 1934, 'sex': 'F'}, {'year': 2015, 'sex': 'F'}, {'year': 1931, 'sex': 'F'}, {'year': 1929, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 1924, 'sex': 'F'}, {'year': 1922, 'sex': 'F'}, {'year': 1922, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 1920, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2018, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 1912, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 1880, 'sex': 'F'}, {'year': 1880, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2014, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2013, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2018, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2018, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2012, 'sex': 'F'}, {'year': 2011, 'sex': 'F'}, {'year': 2011, 'sex': 'F'}, {'year': 2017, 'sex': 'F'}, {'year': 2011, 'sex': 'F'}, {'year': 2017, 'sex': 'F'}, {'year': 2011, 'sex': 'F'}, {'year': 2017, 'sex': 'F'}, {'year': 2011, 'sex': 'F'}, {'year': 2017, 'sex': 'F'}, {'year': 2011, 'sex': 'F'}]
top_rate: 1
cardinality: 1
entropy: 0
entropy_ratio: 0

config

categorical metadata long_tail null_rate

A categorical 'config' field with only 2 of 8 rows populated (null_rate 0.75) and two distinct values, '#ffffff' and '20', that look like unrelated config payloads (a hex color and a number-as-string). With n=8 and entropy_ratio 1.0, the non-null values are perfectly split, so no dominant pattern can be inferred. The mixed value semantics suggest this column stores heterogeneous setting values rather than a single typed attribute. Treatment: Investigate upstream schema; likely split by config key or drop given 75% nulls and mixed value types. low · anthropic:claude-opus-4-7

n: 8
nulls: 6 (75.0%)
unique: 2
top_value: #ffffff
top_rate: 0.5
cardinality: 2
entropy: 1
entropy_ratio: 1