Source: /home/coolhand/datasets/us-inequality-atlas/healthcare/healthcare_desert_merged.csv
Saturn profiled 3,222 rows across 10 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/datasets/us-inequality-atlas/healthcare/healthcare_desert_merged.csv",
"--findings", "healthcare-healthcare_desert_merged.json",
"--llm", "anthropic:claude-opus-4-7",
])This dataset profiles 3,222 U.S. counties (one row per county, keyed by FIPS) with population, uninsured counts and rates, poverty rate, a hospital closure risk score, and rural/urban flags. Population and uninsured figures are extremely right-skewed (total_pop skew 13.4, uninsured_pop skew 17.8), so a handful of large counties will dominate any raw totals — analysis should likely use rates or log scales. The hospital_closure_risk_score collapses to just 3 distinct values (with ~29% scoring 0), and risk_category is heavily imbalanced with 84% of counties labeled 'Low' and the rest 'Moderate', which is worth examining first. About 69% of counties are flagged Rural, so rural/urban comparisons of uninsured and poverty rates should be a productive next cut.
citing: total_pop · uninsured_pop · uninsured_rate · poverty_rate · hospital_closure_risk_score · risk_category · rural_category
saturn.schema() · 10 columns
| column | kind | n | null% | unique | alerts |
|---|---|---|---|---|---|
| fips | numeric | 3,222 | 0.0% | 3,222 | |
| county_name | text | 3,222 | 0.0% | 3,222 | near_unique |
| total_pop | numeric | 3,222 | 0.0% | 3,141 | high_skew outliers |
| uninsured_pop | numeric | 3,222 | 0.0% | 584 | high_skew outliers |
| uninsured_rate | numeric | 3,222 | 0.0% | 152 | high_skew outliers |
| poverty_rate | numeric | 3,222 | 0.0% | 1,719 | high_skew |
| rural | categorical | 3,222 | 0.0% | 2 | |
| rural_category | categorical | 3,222 | 0.0% | 2 | |
| hospital_closure_risk_score | numeric | 3,222 | 0.0% | 3 | |
| risk_category | categorical | 3,222 | 0.0% | 2 |
| value | count | share |
|---|---|---|
| Low | 2719 | 84.4% |
| Moderate | 503 | 15.6% |
| bin | count |
|---|---|
| 0 – 1.25 | 929 |
| 1.25 – 2.5 | 0 |
| 2.5 – 3.75 | 0 |
| 3.75 – 5 | 0 |
| 5 – 6.25 | 0 |
| 6.25 – 7.5 | 0 |
| 7.5 – 8.75 | 0 |
| 8.75 – 10 | 0 |
| 10 – 11.25 | 0 |
| 11.25 – 12.5 | 0 |
| 12.5 – 13.75 | 0 |
| 13.75 – 15 | 0 |
| 15 – 16.25 | 0 |
| 16.25 – 17.5 | 0 |
| 17.5 – 18.75 | 0 |
| 18.75 – 20 | 0 |
| 20 – 21.25 | 0 |
| 21.25 – 22.5 | 0 |
| 22.5 – 23.75 | 0 |
| 23.75 – 25 | 0 |
| 25 – 26.25 | 1790 |
| 26.25 – 27.5 | 0 |
| 27.5 – 28.75 | 0 |
| 28.75 – 30 | 0 |
| 30 – 31.25 | 0 |
| 31.25 – 32.5 | 0 |
| 32.5 – 33.75 | 0 |
| 33.75 – 35 | 0 |
| 35 – 36.25 | 0 |
| 36.25 – 37.5 | 0 |
| 37.5 – 38.75 | 0 |
| 38.75 – 40 | 0 |
| 40 – 41.25 | 0 |
| 41.25 – 42.5 | 0 |
| 42.5 – 43.75 | 0 |
| 43.75 – 45 | 0 |
| 45 – 46.25 | 0 |
| 46.25 – 47.5 | 0 |
| 47.5 – 48.75 | 0 |
| 48.75 – 50 | 503 |
| value | count | share |
|---|---|---|
| Rural | 2212 | 68.7% |
| Urban/Suburban | 1010 | 31.3% |
| bin | count |
|---|---|
| 0 – 0.0925 | 1403 |
| 0.0925 – 0.185 | 704 |
| 0.185 – 0.2775 | 403 |
| 0.2775 – 0.37 | 213 |
| 0.37 – 0.4625 | 158 |
| 0.4625 – 0.555 | 101 |
| 0.555 – 0.6475 | 65 |
| 0.6475 – 0.74 | 43 |
| 0.74 – 0.8325 | 27 |
| 0.8325 – 0.925 | 23 |
| 0.925 – 1.018 | 9 |
| 1.018 – 1.11 | 15 |
| 1.11 – 1.202 | 14 |
| 1.202 – 1.295 | 5 |
| 1.295 – 1.387 | 7 |
| 1.387 – 1.48 | 7 |
| 1.48 – 1.573 | 5 |
| 1.573 – 1.665 | 2 |
| 1.665 – 1.758 | 4 |
| 1.758 – 1.85 | 1 |
| 1.85 – 1.942 | 1 |
| 1.942 – 2.035 | 1 |
| 2.035 – 2.127 | 2 |
| 2.127 – 2.22 | 2 |
| 2.22 – 2.312 | 1 |
| 2.312 – 2.405 | 0 |
| 2.405 – 2.498 | 0 |
| 2.498 – 2.59 | 1 |
| 2.59 – 2.683 | 0 |
| 2.683 – 2.775 | 1 |
| 2.775 – 2.868 | 0 |
| 2.868 – 2.96 | 1 |
| 2.96 – 3.052 | 1 |
| 3.052 – 3.145 | 0 |
| 3.145 – 3.237 | 1 |
| 3.237 – 3.33 | 0 |
| 3.33 – 3.422 | 0 |
| 3.422 – 3.515 | 0 |
| 3.515 – 3.607 | 0 |
| 3.607 – 3.7 | 1 |
| bin | count |
|---|---|
| 1.6 – 3.218 | 7 |
| 3.218 – 4.836 | 34 |
| 4.836 – 6.454 | 106 |
| 6.454 – 8.072 | 246 |
| 8.072 – 9.69 | 320 |
| 9.69 – 11.31 | 354 |
| 11.31 – 12.93 | 393 |
| 12.93 – 14.54 | 364 |
| 14.54 – 16.16 | 306 |
| 16.16 – 17.78 | 262 |
| 17.78 – 19.4 | 192 |
| 19.4 – 21.02 | 149 |
| 21.02 – 22.63 | 123 |
| 22.63 – 24.25 | 91 |
| 24.25 – 25.87 | 52 |
| 25.87 – 27.49 | 44 |
| 27.49 – 29.11 | 34 |
| 29.11 – 30.72 | 23 |
| 30.72 – 32.34 | 18 |
| 32.34 – 33.96 | 14 |
| 33.96 – 35.58 | 6 |
| 35.58 – 37.2 | 8 |
| 37.2 – 38.81 | 3 |
| 38.81 – 40.43 | 8 |
| 40.43 – 42.05 | 5 |
| 42.05 – 43.67 | 9 |
| 43.67 – 45.29 | 4 |
| 45.29 – 46.9 | 11 |
| 46.9 – 48.52 | 7 |
| 48.52 – 50.14 | 8 |
| 50.14 – 51.76 | 2 |
| 51.76 – 53.38 | 6 |
| 53.38 – 54.99 | 5 |
| 54.99 – 56.61 | 5 |
| 56.61 – 58.23 | 1 |
| 58.23 – 59.85 | 0 |
| 59.85 – 61.47 | 0 |
| 61.47 – 63.08 | 0 |
| 63.08 – 64.7 | 1 |
| 64.7 – 66.32 | 1 |
| column | kind | null % |
|---|---|---|
| fips | numeric | 0.0% |
| county_name | text | 0.0% |
| total_pop | numeric | 0.0% |
| uninsured_pop | numeric | 0.0% |
| uninsured_rate | numeric | 0.0% |
| poverty_rate | numeric | 0.0% |
| rural | categorical | 0.0% |
| rural_category | categorical | 0.0% |
| hospital_closure_risk_score | numeric | 0.0% |
| risk_category | categorical | 0.0% |
| fips | total_pop | uninsured_pop | uninsured_rate | poverty_rate | hospital_closure_risk_score | |
|---|---|---|---|---|---|---|
| fips | +1.00 | -0.07 | -0.02 | +0.01 | +0.16 | +0.01 |
| total_pop | -0.07 | +1.00 | +0.81 | -0.05 | -0.11 | -0.31 |
| uninsured_pop | -0.02 | +0.81 | +1.00 | +0.12 | -0.09 | -0.27 |
| uninsured_rate | +0.01 | -0.05 | +0.12 | +1.00 | -0.04 | +0.05 |
| poverty_rate | +0.16 | -0.11 | -0.09 | -0.04 | +1.00 | +0.58 |
| hospital_closure_risk_score | +0.01 | -0.31 | -0.27 | +0.05 | +0.58 | +1.00 |
This is the FIPS county code: 3222 rows with 3222 unique values, no nulls, and a min of 1001 / max of 72153 consistent with the U.S. county FIPS scheme (state prefix * 1000 + county). Distribution is near-uniform across that range (skew 0.16, kurtosis -0.63, no outliers), confirming it indexes geography rather than measuring anything. Treat it as a categorical key, not a quantity, despite the numeric dtype.
Treatment: Cast to zero-padded string and left-join on this county FIPS code; do not use as a numeric feature.
saturn.columns["fips"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 3,222 |
| min | 1,001 |
| max | 72,153 |
| mean | 3.138e+04 |
| median | 30,022 |
| std | 1.63e+04 |
| q1 | 1.903e+04 |
| q3 | 4.61e+04 |
| iqr | 27,075 |
| skew | 0.1574 |
| kurtosis | -0.6314 |
| n_outliers | 0 |
| outlier_rate | 0 |
| zero_rate | 0 |
fips. Vertical dash marks the median.
| bin | count |
|---|---|
| 1001 – 2780 | 97 |
| 2780 – 4559 | 15 |
| 4559 – 6337 | 133 |
| 6337 – 8116 | 59 |
| 8116 – 9895 | 14 |
| 9895 – 1.167e+04 | 4 |
| 1.167e+04 – 1.345e+04 | 226 |
| 1.345e+04 – 1.523e+04 | 5 |
| 1.523e+04 – 1.701e+04 | 49 |
| 1.701e+04 – 1.879e+04 | 189 |
| 1.879e+04 – 2.057e+04 | 204 |
| 2.057e+04 – 2.235e+04 | 184 |
| 2.235e+04 – 2.413e+04 | 39 |
| 2.413e+04 – 2.59e+04 | 15 |
| 2.59e+04 – 2.768e+04 | 170 |
| 2.768e+04 – 2.946e+04 | 196 |
| 2.946e+04 – 3.124e+04 | 150 |
| 3.124e+04 – 3.302e+04 | 27 |
| 3.302e+04 – 3.48e+04 | 21 |
| 3.48e+04 – 3.658e+04 | 95 |
| 3.658e+04 – 3.836e+04 | 153 |
| 3.836e+04 – 4.013e+04 | 155 |
| 4.013e+04 – 4.191e+04 | 46 |
| 4.191e+04 – 4.369e+04 | 67 |
| 4.369e+04 – 4.547e+04 | 51 |
| 4.547e+04 – 4.725e+04 | 161 |
| 4.725e+04 – 4.903e+04 | 268 |
| 4.903e+04 – 5.081e+04 | 29 |
| 5.081e+04 – 5.259e+04 | 133 |
| 5.259e+04 – 5.436e+04 | 94 |
| 5.436e+04 – 5.614e+04 | 95 |
| 5.614e+04 – 5.792e+04 | 0 |
| 5.792e+04 – 5.97e+04 | 0 |
| 5.97e+04 – 6.148e+04 | 0 |
| 6.148e+04 – 6.326e+04 | 0 |
| 6.326e+04 – 6.504e+04 | 0 |
| 6.504e+04 – 6.682e+04 | 0 |
| 6.682e+04 – 6.86e+04 | 0 |
| 6.86e+04 – 7.037e+04 | 0 |
| 7.037e+04 – 7.215e+04 | 78 |
This column holds fully-qualified US county names (e.g. 'X County, State'), with all 3222 values unique and no nulls. The token 'county,' appears 2999 times, confirming a 'County,
' format, while the remaining ~223 rows likely use alternate suffixes like Parish or Borough. Texas (256), Virginia (189), and Georgia (159) lead the state distribution, consistent with national county counts. Treatment: Use as a join key after splitting into county and state components.
saturn.columns["county_name"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 3,222 |
| len_min | 16 |
| len_max | 59 |
| len_mean | 24.32 |
| len_median | 24 |
| len_p95 | 31 |
| word_mean | 3.248 |
| word_median | 3 |
| n_empty | 0 |
| n_duplicates | 0 |
| duplicate_rate | 0 |
| vocab_size | 1,990 |
| readability_flesch_mean | 10.28 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: near_unique | 100.0% of rows are unique strings |
county_name.
| chars | count |
|---|---|
| 16 – 17 | 26 |
| 17 – 18 | 72 |
| 18 – 19 | 121 |
| 19 – 20 | 190 |
| 20 – 21 | 264 |
| 21 – 22 | 407 |
| 22 – 24 | 420 |
| 24 – 25 | 363 |
| 25 – 26 | 320 |
| 26 – 27 | 240 |
| 27 – 28 | 231 |
| 28 – 29 | 152 |
| 29 – 30 | 139 |
| 30 – 31 | 165 |
| 31 – 32 | 41 |
| 32 – 33 | 28 |
| 33 – 34 | 16 |
| 34 – 35 | 10 |
| 35 – 36 | 5 |
| 36 – 38 | 0 |
| 38 – 39 | 1 |
| 39 – 40 | 1 |
| 40 – 41 | 0 |
| 41 – 42 | 1 |
| 42 – 43 | 1 |
| 43 – 44 | 0 |
| 44 – 45 | 2 |
| 45 – 46 | 0 |
| 46 – 47 | 1 |
| 47 – 48 | 1 |
| 48 – 49 | 0 |
| 49 – 50 | 0 |
| 50 – 51 | 0 |
| 51 – 53 | 0 |
| 53 – 54 | 2 |
| 54 – 55 | 1 |
| 55 – 56 | 0 |
| 56 – 57 | 0 |
| 57 – 58 | 0 |
| 58 – 59 | 1 |
This is almost certainly a population count per geographic unit (likely US counties given n=3222), with values ranging from 47 to 9,866,623 and a median of 25,328. The distribution is severely right-skewed (skew 13.38, kurtosis 298.69) with the mean (102,232) nearly four times the median and 453 outliers (14.06%) — the standard deviation of 326,934 dwarfs the IQR of 54,579. No nulls or zeros, and 3,141 of 3,222 values are unique.
Treatment: Log-transform before any modelling or distance-based analysis to tame the extreme right skew.
saturn.columns["total_pop"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 3,141 |
| min | 47 |
| max | 9.867e+06 |
| mean | 1.022e+05 |
| median | 25,328 |
| std | 3.269e+05 |
| q1 | 1.061e+04 |
| q3 | 65,190 |
| iqr | 5.458e+04 |
| skew | 13.38 |
| kurtosis | 298.7 |
| n_outliers | 453 |
| outlier_rate | 0.1406 |
| zero_rate | 0 |
| alert: high_skew | skew=+13.38 |
| alert: outliers | 14.1% rows beyond 1.5 IQR |
total_pop. Vertical dash marks the median.
| bin | count |
|---|---|
| 47 – 2.467e+05 | 2942 |
| 2.467e+05 – 4.934e+05 | 137 |
| 4.934e+05 – 7.4e+05 | 56 |
| 7.4e+05 – 9.867e+05 | 39 |
| 9.867e+05 – 1.233e+06 | 13 |
| 1.233e+06 – 1.48e+06 | 9 |
| 1.48e+06 – 1.727e+06 | 7 |
| 1.727e+06 – 1.973e+06 | 3 |
| 1.973e+06 – 2.22e+06 | 3 |
| 2.22e+06 – 2.467e+06 | 4 |
| 2.467e+06 – 2.713e+06 | 3 |
| 2.713e+06 – 2.96e+06 | 0 |
| 2.96e+06 – 3.207e+06 | 2 |
| 3.207e+06 – 3.453e+06 | 0 |
| 3.453e+06 – 3.7e+06 | 0 |
| 3.7e+06 – 3.947e+06 | 0 |
| 3.947e+06 – 4.193e+06 | 0 |
| 4.193e+06 – 4.44e+06 | 1 |
| 4.44e+06 – 4.687e+06 | 0 |
| 4.687e+06 – 4.933e+06 | 1 |
| 4.933e+06 – 5.18e+06 | 0 |
| 5.18e+06 – 5.427e+06 | 1 |
| 5.427e+06 – 5.673e+06 | 0 |
| 5.673e+06 – 5.92e+06 | 0 |
| 5.92e+06 – 6.167e+06 | 0 |
| 6.167e+06 – 6.413e+06 | 0 |
| 6.413e+06 – 6.66e+06 | 0 |
| 6.66e+06 – 6.907e+06 | 0 |
| 6.907e+06 – 7.153e+06 | 0 |
| 7.153e+06 – 7.4e+06 | 0 |
| 7.4e+06 – 7.647e+06 | 0 |
| 7.647e+06 – 7.893e+06 | 0 |
| 7.893e+06 – 8.14e+06 | 0 |
| 8.14e+06 – 8.387e+06 | 0 |
| 8.387e+06 – 8.633e+06 | 0 |
| 8.633e+06 – 8.88e+06 | 0 |
| 8.88e+06 – 9.127e+06 | 0 |
| 9.127e+06 – 9.373e+06 | 0 |
| 9.373e+06 – 9.62e+06 | 0 |
| 9.62e+06 – 9.867e+06 | 1 |
Counts of uninsured residents per record, with values ranging from 0 to 20,915 across 3,222 rows and no nulls. The distribution is severely right-skewed (skew 17.81, kurtosis 462.87): the median is 36 while the mean is 159.95, and 17.2% of rows are zero. 368 outliers (11.4%) sit far above the Q3 of 120, consistent with a few very large populations dominating the tail.
Treatment: Apply a log1p transform before modelling to tame the heavy right tail.
saturn.columns["uninsured_pop"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 584 |
| min | 0 |
| max | 20,915 |
| mean | 159.9 |
| median | 36 |
| std | 627.2 |
| q1 | 7 |
| q3 | 120 |
| iqr | 113 |
| skew | 17.81 |
| kurtosis | 462.9 |
| n_outliers | 368 |
| outlier_rate | 0.1142 |
| zero_rate | 0.1723 |
| alert: high_skew | skew=+17.81 |
| alert: outliers | 11.4% rows beyond 1.5 IQR |
uninsured_pop. Vertical dash marks the median.
| bin | count |
|---|---|
| 0 – 522.9 | 3022 |
| 522.9 – 1046 | 124 |
| 1046 – 1569 | 32 |
| 1569 – 2092 | 16 |
| 2092 – 2614 | 7 |
| 2614 – 3137 | 5 |
| 3137 – 3660 | 5 |
| 3660 – 4183 | 2 |
| 4183 – 4706 | 0 |
| 4706 – 5229 | 1 |
| 5229 – 5752 | 2 |
| 5752 – 6274 | 1 |
| 6274 – 6797 | 0 |
| 6797 – 7320 | 0 |
| 7320 – 7843 | 0 |
| 7843 – 8366 | 1 |
| 8366 – 8889 | 1 |
| 8889 – 9412 | 0 |
| 9412 – 9935 | 0 |
| 9935 – 1.046e+04 | 0 |
| 1.046e+04 – 1.098e+04 | 0 |
| 1.098e+04 – 1.15e+04 | 2 |
| 1.15e+04 – 1.203e+04 | 0 |
| 1.203e+04 – 1.255e+04 | 0 |
| 1.255e+04 – 1.307e+04 | 0 |
| 1.307e+04 – 1.359e+04 | 0 |
| 1.359e+04 – 1.412e+04 | 0 |
| 1.412e+04 – 1.464e+04 | 0 |
| 1.464e+04 – 1.516e+04 | 0 |
| 1.516e+04 – 1.569e+04 | 0 |
| 1.569e+04 – 1.621e+04 | 0 |
| 1.621e+04 – 1.673e+04 | 0 |
| 1.673e+04 – 1.725e+04 | 0 |
| 1.725e+04 – 1.778e+04 | 0 |
| 1.778e+04 – 1.83e+04 | 0 |
| 1.83e+04 – 1.882e+04 | 0 |
| 1.882e+04 – 1.935e+04 | 0 |
| 1.935e+04 – 1.987e+04 | 0 |
| 1.987e+04 – 2.039e+04 | 0 |
| 2.039e+04 – 2.092e+04 | 1 |
This appears to be an uninsured rate per record, expressed as a proportion ranging from 0.0 to 3.7 with a median of 0.12. The maximum of 3.7 is suspicious for a rate that should cap at 1.0, and the distribution is severely right-skewed (skew 4.10, kurtosis 27.70) with 230 outliers (7.1%) and 17.5% exact zeros.
Treatment: Investigate values >1.0 for unit errors, then log-transform or winsorize before modelling.
saturn.columns["uninsured_rate"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 152 |
| min | 0 |
| max | 3.7 |
| mean | 0.2002 |
| median | 0.12 |
| std | 0.2829 |
| q1 | 0.04 |
| q3 | 0.25 |
| iqr | 0.21 |
| skew | 4.095 |
| kurtosis | 27.7 |
| n_outliers | 230 |
| outlier_rate | 0.07138 |
| zero_rate | 0.1754 |
| alert: high_skew | skew=+4.10 |
| alert: outliers | 7.1% rows beyond 1.5 IQR |
uninsured_rate. Vertical dash marks the median.
| bin | count |
|---|---|
| 0 – 0.0925 | 1403 |
| 0.0925 – 0.185 | 704 |
| 0.185 – 0.2775 | 403 |
| 0.2775 – 0.37 | 213 |
| 0.37 – 0.4625 | 158 |
| 0.4625 – 0.555 | 101 |
| 0.555 – 0.6475 | 65 |
| 0.6475 – 0.74 | 43 |
| 0.74 – 0.8325 | 27 |
| 0.8325 – 0.925 | 23 |
| 0.925 – 1.018 | 9 |
| 1.018 – 1.11 | 15 |
| 1.11 – 1.202 | 14 |
| 1.202 – 1.295 | 5 |
| 1.295 – 1.387 | 7 |
| 1.387 – 1.48 | 7 |
| 1.48 – 1.573 | 5 |
| 1.573 – 1.665 | 2 |
| 1.665 – 1.758 | 4 |
| 1.758 – 1.85 | 1 |
| 1.85 – 1.942 | 1 |
| 1.942 – 2.035 | 1 |
| 2.035 – 2.127 | 2 |
| 2.127 – 2.22 | 2 |
| 2.22 – 2.312 | 1 |
| 2.312 – 2.405 | 0 |
| 2.405 – 2.498 | 0 |
| 2.498 – 2.59 | 1 |
| 2.59 – 2.683 | 0 |
| 2.683 – 2.775 | 1 |
| 2.775 – 2.868 | 0 |
| 2.868 – 2.96 | 1 |
| 2.96 – 3.052 | 1 |
| 3.052 – 3.145 | 0 |
| 3.145 – 3.237 | 1 |
| 3.237 – 3.33 | 0 |
| 3.33 – 3.422 | 0 |
| 3.422 – 3.515 | 0 |
| 3.515 – 3.607 | 0 |
| 3.607 – 3.7 | 1 |
This is a numeric poverty rate (likely percentage of population in poverty) across 3222 rows with no nulls and 1719 unique values. The distribution is right-skewed (skew 2.10, kurtosis 6.89) with a median of 13.55 and mean 15.10, ranging from 1.6 to 66.32; 137 outliers (4.25%) sit in the upper tail. The high skew alert means a long tail of high-poverty units pulls the mean above the median.
Treatment: Consider a log or sqrt transform before regression to tame the right skew.
saturn.columns["poverty_rate"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 1,719 |
| min | 1.6 |
| max | 66.32 |
| mean | 15.1 |
| median | 13.55 |
| std | 7.706 |
| q1 | 10.16 |
| q3 | 17.91 |
| iqr | 7.75 |
| skew | 2.096 |
| kurtosis | 6.891 |
| n_outliers | 137 |
| outlier_rate | 0.04252 |
| zero_rate | 0 |
| alert: high_skew | skew=+2.10 |
poverty_rate. Vertical dash marks the median.
| bin | count |
|---|---|
| 1.6 – 3.218 | 7 |
| 3.218 – 4.836 | 34 |
| 4.836 – 6.454 | 106 |
| 6.454 – 8.072 | 246 |
| 8.072 – 9.69 | 320 |
| 9.69 – 11.31 | 354 |
| 11.31 – 12.93 | 393 |
| 12.93 – 14.54 | 364 |
| 14.54 – 16.16 | 306 |
| 16.16 – 17.78 | 262 |
| 17.78 – 19.4 | 192 |
| 19.4 – 21.02 | 149 |
| 21.02 – 22.63 | 123 |
| 22.63 – 24.25 | 91 |
| 24.25 – 25.87 | 52 |
| 25.87 – 27.49 | 44 |
| 27.49 – 29.11 | 34 |
| 29.11 – 30.72 | 23 |
| 30.72 – 32.34 | 18 |
| 32.34 – 33.96 | 14 |
| 33.96 – 35.58 | 6 |
| 35.58 – 37.2 | 8 |
| 37.2 – 38.81 | 3 |
| 38.81 – 40.43 | 8 |
| 40.43 – 42.05 | 5 |
| 42.05 – 43.67 | 9 |
| 43.67 – 45.29 | 4 |
| 45.29 – 46.9 | 11 |
| 46.9 – 48.52 | 7 |
| 48.52 – 50.14 | 8 |
| 50.14 – 51.76 | 2 |
| 51.76 – 53.38 | 6 |
| 53.38 – 54.99 | 5 |
| 54.99 – 56.61 | 5 |
| 56.61 – 58.23 | 1 |
| 58.23 – 59.85 | 0 |
| 59.85 – 61.47 | 0 |
| 61.47 – 63.08 | 0 |
| 63.08 – 64.7 | 1 |
| 64.7 – 66.32 | 1 |
Binary flag indicating whether a record is rural, stored as the strings "True"/"False" rather than booleans. The split is imbalanced toward rural at 68.7% (2212 of 3222) versus 1010 non-rural, with no nulls. Entropy ratio of 0.897 confirms a meaningful but skewed distribution.
Treatment: Cast string "True"/"False" to a 0/1 boolean and use directly as a feature.
saturn.columns["rural"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 2 |
| top_value | True |
| top_rate | 0.6865 |
| cardinality | 2 |
| entropy | 0.8971 |
| entropy_ratio | 0.8971 |
rural.
| value | count | share |
|---|---|---|
| True | 2212 | 68.7% |
| False | 1010 | 31.3% |
Binary categorical flag splitting records into 'Rural' (2212, 68.7%) and 'Urban/Suburban' (1010), with no nulls across 3222 rows. The split is moderately imbalanced but entropy ratio of 0.90 indicates both classes are well represented. Clean two-level partition suitable as a stratifier or feature.
Treatment: One-hot or binary-encode for modelling; consider stratifying splits on this flag.
saturn.columns["rural_category"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 2 |
| top_value | Rural |
| top_rate | 0.6865 |
| cardinality | 2 |
| entropy | 0.8971 |
| entropy_ratio | 0.8971 |
rural_category.
| value | count | share |
|---|---|---|
| Rural | 2212 | 68.7% |
| Urban/Suburban | 1010 | 31.3% |
Despite being typed as numeric, hospital_closure_risk_score takes only 3 distinct values across 3222 rows, spanning 0 to 50 with a median of 25 and roughly 28.8% zeros. This is effectively an ordinal risk band (likely 0/25/50) masquerading as a continuous score, so the reported mean of 21.69 and std of 16.34 reflect category mix rather than a smooth distribution.
Treatment: Treat as an ordinal categorical (low/medium/high) rather than a continuous numeric.
saturn.columns["hospital_closure_risk_score"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 3 |
| min | 0 |
| max | 50 |
| mean | 21.69 |
| median | 25 |
| std | 16.34 |
| q1 | 0 |
| q3 | 25 |
| iqr | 25 |
| skew | 0.1414 |
| kurtosis | -0.6949 |
| n_outliers | 0 |
| outlier_rate | 0 |
| zero_rate | 0.2883 |
hospital_closure_risk_score. Vertical dash marks the median.
| bin | count |
|---|---|
| 0 – 1.25 | 929 |
| 1.25 – 2.5 | 0 |
| 2.5 – 3.75 | 0 |
| 3.75 – 5 | 0 |
| 5 – 6.25 | 0 |
| 6.25 – 7.5 | 0 |
| 7.5 – 8.75 | 0 |
| 8.75 – 10 | 0 |
| 10 – 11.25 | 0 |
| 11.25 – 12.5 | 0 |
| 12.5 – 13.75 | 0 |
| 13.75 – 15 | 0 |
| 15 – 16.25 | 0 |
| 16.25 – 17.5 | 0 |
| 17.5 – 18.75 | 0 |
| 18.75 – 20 | 0 |
| 20 – 21.25 | 0 |
| 21.25 – 22.5 | 0 |
| 22.5 – 23.75 | 0 |
| 23.75 – 25 | 0 |
| 25 – 26.25 | 1790 |
| 26.25 – 27.5 | 0 |
| 27.5 – 28.75 | 0 |
| 28.75 – 30 | 0 |
| 30 – 31.25 | 0 |
| 31.25 – 32.5 | 0 |
| 32.5 – 33.75 | 0 |
| 33.75 – 35 | 0 |
| 35 – 36.25 | 0 |
| 36.25 – 37.5 | 0 |
| 37.5 – 38.75 | 0 |
| 38.75 – 40 | 0 |
| 40 – 41.25 | 0 |
| 41.25 – 42.5 | 0 |
| 42.5 – 43.75 | 0 |
| 43.75 – 45 | 0 |
| 45 – 46.25 | 0 |
| 46.25 – 47.5 | 0 |
| 47.5 – 48.75 | 0 |
| 48.75 – 50 | 503 |
Binary risk classification flagging records as either Low or Moderate, with no nulls across 3,222 rows. The distribution is heavily imbalanced: 84.4% fall into Low (2,719) versus only 503 Moderate, and no High tier appears at all. Entropy ratio of 0.62 confirms the skew.
Treatment: Treat as binary target; account for class imbalance via stratified sampling or class weighting.
saturn.columns["risk_category"].stats
| stat | value |
|---|---|
| n | 3,222 |
| nulls | 0 (0.0%) |
| unique | 2 |
| top_value | Low |
| top_rate | 0.8439 |
| cardinality | 2 |
| entropy | 0.6249 |
| entropy_ratio | 0.6249 |
risk_category.
| value | count | share |
|---|---|---|
| Low | 2719 | 84.4% |
| Moderate | 503 | 15.6% |
click to copy
@misc{saturn-healthcare-healthcare-desert-merged-2026,
author = {Steuber, Luke},
title = {Saturn reading: healthcare healthcare desert merged},
year ={2026},
howpublished = {\url{https://dr.eamer.dev/saturn/view/healthcare-healthcare_desert_merged}},
note = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}
Steuber, L. (2026). Saturn reading: healthcare healthcare desert merged. Source: /home/coolhand/datasets/us-inequality-atlas/healthcare/healthcare_desert_merged.csv. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/healthcare-healthcare_desert_merged