Source: /home/coolhand/html/datavis/data_trove/data/urban/food_deserts/snap_gap_states.json
Saturn profiled 20 rows across 6 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).
!pip install saturn-dissect
import subprocess
subprocess.run([
"saturn", "analyze", "/home/coolhand/html/datavis/data_trove/data/urban/food_deserts/snap_gap_states.json",
"--findings", "data-trove-snap-participation-benefits.json",
"--llm", "anthropic:default",
])This dataset tracks SNAP (food stamp) program enrollment across 20 U.S. states, capturing estimated eligible populations, actual participants, and the resulting coverage gap. Two things stand out immediately: first, the enrollment rate and gap percentage are constant across all states (67% enrolled, 33% gap), suggesting these are summary-level figures rather than state-specific calculations — they should not be used for cross-state comparison. Second, the three population-count columns (eligible, participants, gap) are all heavily right-skewed with 2 outliers each, pointing to a small number of very large states — likely California and/or Florida — that dwarf the rest and will dominate any totals-based analysis.
citing: snap_enrollment_rate.stats.mean · snap_gap_pct.stats.mean · snap_eligible_est.stats.max · snap_eligible_est.stats.median · snap_eligible_est.n_outliers · snap_gap.stats.max · snap_gap.stats.median · snap_participants_est.n_outliers · snap_eligible_est.stats.skew
saturn.schema() · 6 columns
| column | kind | n | null% | unique | alerts |
|---|---|---|---|---|---|
| state_name | categorical | 20 | 0.0% | 20 | long_tail |
| snap_eligible_est | numeric | 20 | 0.0% | 20 | high_skew outliers |
| snap_participants_est | numeric | 20 | 0.0% | 20 | high_skew outliers |
| snap_gap | numeric | 20 | 0.0% | 20 | high_skew outliers |
| snap_gap_pct | numeric | 20 | 0.0% | 1 | constant |
| snap_enrollment_rate | numeric | 20 | 0.0% | 1 | constant |
| bin | count |
|---|---|
| 7.523e+04 – 9.972e+05 | 16 |
| 9.972e+05 – 1.919e+06 | 2 |
| 1.919e+06 – 2.841e+06 | 1 |
| 2.841e+06 – 3.763e+06 | 0 |
| 3.763e+06 – 4.685e+06 | 1 |
| bin | count |
|---|---|
| 2.482e+04 – 3.291e+05 | 16 |
| 3.291e+05 – 6.333e+05 | 2 |
| 6.333e+05 – 9.376e+05 | 1 |
| 9.376e+05 – 1.242e+06 | 0 |
| 1.242e+06 – 1.546e+06 | 1 |
| bin | count |
|---|---|
| 5.04e+04 – 6.681e+05 | 16 |
| 6.681e+05 – 1.286e+06 | 2 |
| 1.286e+06 – 1.904e+06 | 1 |
| 1.904e+06 – 2.521e+06 | 0 |
| 2.521e+06 – 3.139e+06 | 1 |
| value | count | share |
|---|---|---|
| Alabama | 1 | 5.0% |
| Alaska | 1 | 5.0% |
| Arizona | 1 | 5.0% |
| Arkansas | 1 | 5.0% |
| California | 1 | 5.0% |
| Colorado | 1 | 5.0% |
| Connecticut | 1 | 5.0% |
| Delaware | 1 | 5.0% |
| District of Columbia | 1 | 5.0% |
| Florida | 1 | 5.0% |
| Georgia | 1 | 5.0% |
| Hawaii | 1 | 5.0% |
| Idaho | 1 | 5.0% |
| Illinois | 1 | 5.0% |
| Indiana | 1 | 5.0% |
| Iowa | 1 | 5.0% |
| Kansas | 1 | 5.0% |
| Kentucky | 1 | 5.0% |
| Louisiana | 1 | 5.0% |
| Maine | 1 | 5.0% |
| column | kind | null % |
|---|---|---|
| state_name | categorical | 0.0% |
| snap_eligible_est | numeric | 0.0% |
| snap_participants_est | numeric | 0.0% |
| snap_gap | numeric | 0.0% |
| snap_gap_pct | numeric | 0.0% |
| snap_enrollment_rate | numeric | 0.0% |
| snap_eligible_est | snap_participants_est | snap_gap | snap_gap_pct | snap_enrollment_rate | |
|---|---|---|---|---|---|
| snap_eligible_est | +1.00 | +1.00 | +1.00 | +nan | +nan |
| snap_participants_est | +1.00 | +1.00 | +1.00 | +nan | +nan |
| snap_gap | +1.00 | +1.00 | +1.00 | +nan | +nan |
| snap_gap_pct | +nan | +nan | +nan | +nan | +nan |
| snap_enrollment_rate | +nan | +nan | +nan | +nan | +nan |
This column contains US state (and territory) names, with exactly 20 unique values across 20 rows — every row holds a distinct state name, yielding a perfect entropy ratio of 1.0 and a top_rate of 0.05. The dataset appears to be a small, deduplicated lookup or summary table with one row per geographic unit rather than a transactional dataset. The 'long_tail' alert is a statistical artifact of the perfectly uniform distribution, not a genuine skew concern.
Treatment: Use as a join key or display label; consider mapping to standard FIPS codes if merging with other geographic datasets.
saturn.columns["state_name"].stats
| stat | value |
|---|---|
| n | 20 |
| nulls | 0 (0.0%) |
| unique | 20 |
| top_value | Alabama |
| top_rate | 0.05 |
| cardinality | 20 |
| entropy | 4.322 |
| entropy_ratio | 1 |
| alert: long_tail | 20 singleton categories |
state_name.
| value | count | share |
|---|---|---|
| Alabama | 1 | 5.0% |
| Alaska | 1 | 5.0% |
| Arizona | 1 | 5.0% |
| Arkansas | 1 | 5.0% |
| California | 1 | 5.0% |
| Colorado | 1 | 5.0% |
| Connecticut | 1 | 5.0% |
| Delaware | 1 | 5.0% |
| District of Columbia | 1 | 5.0% |
| Florida | 1 | 5.0% |
| Georgia | 1 | 5.0% |
| Hawaii | 1 | 5.0% |
| Idaho | 1 | 5.0% |
| Illinois | 1 | 5.0% |
| Indiana | 1 | 5.0% |
| Iowa | 1 | 5.0% |
| Kansas | 1 | 5.0% |
| Kentucky | 1 | 5.0% |
| Louisiana | 1 | 5.0% |
| Maine | 1 | 5.0% |
This column represents estimated counts of SNAP-eligible individuals, likely at a geographic unit level (e.g., county or district). Values span a wide range from 75,227 to 4,685,272, with a median of 507,917 well below the mean of 857,172.75 — a classic signature of population-size data. The distribution is heavily right-skewed (skew = 2.42, kurtosis = 5.58) with 2 outliers (10% of rows), almost certainly large urban jurisdictions pulling the tail sharply upward.
Treatment: Log-transform before regression or clustering to reduce right-skew and dampen outlier influence.
saturn.columns["snap_eligible_est"].stats
| stat | value |
|---|---|
| n | 20 |
| nulls | 0 (0.0%) |
| unique | 20 |
| min | 75,227 |
| max | 4.685e+06 |
| mean | 8.572e+05 |
| median | 507,917 |
| std | 1.103e+06 |
| q1 | 1.855e+05 |
| q3 | 8.607e+05 |
| iqr | 6.753e+05 |
| skew | 2.421 |
| kurtosis | 5.577 |
| n_outliers | 2 |
| outlier_rate | 0.1 |
| zero_rate | 0 |
| alert: high_skew | skew=+2.42 |
| alert: outliers | 10.0% rows beyond 1.5 IQR |
snap_eligible_est. Vertical dash marks the median.
| bin | count |
|---|---|
| 7.523e+04 – 9.972e+05 | 16 |
| 9.972e+05 – 1.919e+06 | 2 |
| 1.919e+06 – 2.841e+06 | 1 |
| 2.841e+06 – 3.763e+06 | 0 |
| 3.763e+06 – 4.685e+06 | 1 |
This column represents estimated SNAP (Supplemental Nutrition Assistance Program) participant counts, likely at the state or large geographic-unit level given the scale of values ranging from 50,403 to 3,139,134. With only 20 rows and 20 unique values, each record appears to represent a distinct entity (e.g., a U.S. state or territory). The distribution is heavily right-skewed (skew = 2.42, kurtosis = 5.58) with 2 outliers (10% of the dataset), suggesting a small number of very large states—likely California or Texas—dominate the upper tail, while the median (340,304) sits well below the mean (574,306).
Treatment: Log-transform before regression or clustering to reduce right skew and outlier influence.
saturn.columns["snap_participants_est"].stats
| stat | value |
|---|---|
| n | 20 |
| nulls | 0 (0.0%) |
| unique | 20 |
| min | 50,403 |
| max | 3.139e+06 |
| mean | 5.743e+05 |
| median | 340,304 |
| std | 7.39e+05 |
| q1 | 1.243e+05 |
| q3 | 5.767e+05 |
| iqr | 4.524e+05 |
| skew | 2.421 |
| kurtosis | 5.577 |
| n_outliers | 2 |
| outlier_rate | 0.1 |
| zero_rate | 0 |
| alert: high_skew | skew=+2.42 |
| alert: outliers | 10.0% rows beyond 1.5 IQR |
snap_participants_est. Vertical dash marks the median.
| bin | count |
|---|---|
| 5.04e+04 – 6.681e+05 | 16 |
| 6.681e+05 – 1.286e+06 | 2 |
| 1.286e+06 – 1.904e+06 | 1 |
| 1.904e+06 – 2.521e+06 | 0 |
| 2.521e+06 – 3.139e+06 | 1 |
snap_gap is a numeric column likely representing some form of gap or interval measurement (e.g., a time delta, distance, or capacity gap between snapshots). With only 20 rows and 20 unique values, every observation is distinct. The distribution is heavily right-skewed (skew = 2.42, kurtosis = 5.58): the median is 167,613 while the mean is pulled to 282,866 by a long upper tail, and 2 outliers (10% of rows) push up to a maximum of 1,546,138 — roughly 25× the minimum of 24,824. The IQR of 222,841 spans a wide range relative to Q1 of 61,204, signalling high dispersion.
Treatment: log-transform before regression or modelling to reduce skew; investigate the 2 outliers (values near 1,546,138) for validity before inclusion.
saturn.columns["snap_gap"].stats
| stat | value |
|---|---|
| n | 20 |
| nulls | 0 (0.0%) |
| unique | 20 |
| min | 24,824 |
| max | 1.546e+06 |
| mean | 2.829e+05 |
| median | 167,613 |
| std | 3.64e+05 |
| q1 | 6.12e+04 |
| q3 | 2.84e+05 |
| iqr | 222,841 |
| skew | 2.421 |
| kurtosis | 5.577 |
| n_outliers | 2 |
| outlier_rate | 0.1 |
| zero_rate | 0 |
| alert: high_skew | skew=+2.42 |
| alert: outliers | 10.0% rows beyond 1.5 IQR |
snap_gap. Vertical dash marks the median.
| bin | count |
|---|---|
| 2.482e+04 – 3.291e+05 | 16 |
| 3.291e+05 – 6.333e+05 | 2 |
| 6.333e+05 – 9.376e+05 | 1 |
| 9.376e+05 – 1.242e+06 | 0 |
| 1.242e+06 – 1.546e+06 | 1 |
This column appears to represent a snapshot gap percentage, likely a configuration or policy parameter defining some threshold or interval (e.g., 33%). Across all 20 rows the value is identically 33.0 with zero variance, zero nulls, and a single unique value — making it a degenerate constant with no discriminative power whatsoever.
Treatment: Drop before modelling; constant column provides zero information gain and will cause issues in variance-sensitive methods.
saturn.columns["snap_gap_pct"].stats
| stat | value |
|---|---|
| n | 20 |
| nulls | 0 (0.0%) |
| unique | 1 |
| min | 33 |
| max | 33 |
| mean | 33 |
| median | 33 |
| std | 0 |
| q1 | 33 |
| q3 | 33 |
| iqr | 0 |
| skew | 0 |
| kurtosis | 0 |
| n_outliers | 0 |
| outlier_rate | 0 |
| zero_rate | 0 |
| alert: constant | only one distinct value |
snap_gap_pct. Vertical dash marks the median.
| bin | count |
|---|---|
| 32.5 – 32.7 | 0 |
| 32.7 – 32.9 | 0 |
| 32.9 – 33.1 | 20 |
| 33.1 – 33.3 | 0 |
| 33.3 – 33.5 | 0 |
This column represents a SNAP (Supplemental Nutrition Assistance Program) enrollment rate, likely expressed as a percentage. Every single one of the 20 rows holds the identical value of 67.0, with zero variance, zero IQR, and a standard deviation of 0.0 — the column is perfectly constant. This is a strong signal that the value was hardcoded, imputed with a single figure, or the dataset captures a single geographic/temporal stratum where this rate was applied uniformly. It carries no predictive or descriptive information across rows.
Treatment: Drop before modelling — zero-variance constant column provides no information and will cause issues with many estimators.
saturn.columns["snap_enrollment_rate"].stats
| stat | value |
|---|---|
| n | 20 |
| nulls | 0 (0.0%) |
| unique | 1 |
| min | 67 |
| max | 67 |
| mean | 67 |
| median | 67 |
| std | 0 |
| q1 | 67 |
| q3 | 67 |
| iqr | 0 |
| skew | 0 |
| kurtosis | 0 |
| n_outliers | 0 |
| outlier_rate | 0 |
| zero_rate | 0 |
| alert: constant | only one distinct value |
snap_enrollment_rate. Vertical dash marks the median.
| bin | count |
|---|---|
| 66.5 – 66.7 | 0 |
| 66.7 – 66.9 | 0 |
| 66.9 – 67.1 | 20 |
| 67.1 – 67.3 | 0 |
| 67.3 – 67.5 | 0 |
click to copy
@misc{saturn-data-trove-snap-participation-benefits-2026,
author = {Steuber, Luke},
title = {Saturn reading: data trove snap participation benefits},
year ={2026},
howpublished = {\url{https://dr.eamer.dev/saturn/view/data-trove-snap-participation-benefits}},
note = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:default},
}
Steuber, L. (2026). Saturn reading: data trove snap participation benefits. Source: /home/coolhand/html/datavis/data_trove/data/urban/food_deserts/snap_gap_states.json. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:default). Retrieved from https://dr.eamer.dev/saturn/view/data-trove-snap-participation-benefits