animal attacks shark attacks analysis

Overview

Source: /home/coolhand/html/datavis/data_trove/data/wild/animal_attacks/shark_attacks_analysis.csv

Saturn profiled 8 rows across 9 columns. The stats below are deterministic and machine-readable; the prose is a language-model interpretation of those stats (opt-in, added after the fact, never sees raw rows).

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.

$schema categorical metadata

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.

width numeric metadata

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.

height numeric metadata

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.

autosize categorical metadata

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.

mark categorical feature

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.

encoding categorical metadata

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.

data categorical other

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.

config categorical metadata

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.