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healthcare data county health rankings 20260121

source /home/coolhand/html/datavis/data_trove/cache/healthcare_data/county_health_rankings_20260121.parquet 3,222 rows 5 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

Reading

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

This dataset covers 3,222 U.S. counties (one row per FIPS code) with population totals and uninsured counts and rates. Both total_pop and uninsured_pop are extremely right-skewed (skew 13.4 and 17.8) with hundreds of outliers, indicating a handful of very large counties dominate the raw counts — analysts should work in per-capita or log space. The uninsured_rate is the more comparable metric: median 0.12 with about 17.5% of counties reporting zero, and a long tail reaching 3.7 that warrants a data-quality check. The county_name field shows Texas, Virginia, and Georgia contributing the most counties, useful context for any state-level rollups.

citing: row_count · column_count · columns.total_pop.stats.skew · columns.total_pop.stats.median · columns.total_pop.stats.max · columns.total_pop.stats.n_outliers · columns.uninsured_pop.stats.skew · columns.uninsured_pop.stats.zero_rate · columns.uninsured_rate.stats.median · columns.uninsured_rate.stats.max · columns.uninsured_rate.stats.zero_rate · columns.county_name.top_words

total_pop · Look for the extreme right skew — most counties are small but a few exceed a million residents.
Show data table
bincount
47 – 2.467e+052942
2.467e+05 – 4.934e+05137
4.934e+05 – 7.4e+0556
7.4e+05 – 9.867e+0539
9.867e+05 – 1.233e+0613
1.233e+06 – 1.48e+069
1.48e+06 – 1.727e+067
1.727e+06 – 1.973e+063
1.973e+06 – 2.22e+063
2.22e+06 – 2.467e+064
2.467e+06 – 2.713e+063
2.713e+06 – 2.96e+060
2.96e+06 – 3.207e+062
3.207e+06 – 3.453e+060
3.453e+06 – 3.7e+060
3.7e+06 – 3.947e+060
3.947e+06 – 4.193e+060
4.193e+06 – 4.44e+061
4.44e+06 – 4.687e+060
4.687e+06 – 4.933e+061
4.933e+06 – 5.18e+060
5.18e+06 – 5.427e+061
5.427e+06 – 5.673e+060
5.673e+06 – 5.92e+060
5.92e+06 – 6.167e+060
6.167e+06 – 6.413e+060
6.413e+06 – 6.66e+060
6.66e+06 – 6.907e+060
6.907e+06 – 7.153e+060
7.153e+06 – 7.4e+060
7.4e+06 – 7.647e+060
7.647e+06 – 7.893e+060
7.893e+06 – 8.14e+060
8.14e+06 – 8.387e+060
8.387e+06 – 8.633e+060
8.633e+06 – 8.88e+060
8.88e+06 – 9.127e+060
9.127e+06 – 9.373e+060
9.373e+06 – 9.62e+060
9.62e+06 – 9.867e+061
uninsured_rate · Check the distribution around the 0.12 median and the tail of high-rate outliers up to 3.7.
Show data table
bincount
0 – 0.09251403
0.0925 – 0.185704
0.185 – 0.2775403
0.2775 – 0.37213
0.37 – 0.4625158
0.4625 – 0.555101
0.555 – 0.647565
0.6475 – 0.7443
0.74 – 0.832527
0.8325 – 0.92523
0.925 – 1.0189
1.018 – 1.1115
1.11 – 1.20214
1.202 – 1.2955
1.295 – 1.3877
1.387 – 1.487
1.48 – 1.5735
1.573 – 1.6652
1.665 – 1.7584
1.758 – 1.851
1.85 – 1.9421
1.942 – 2.0351
2.035 – 2.1272
2.127 – 2.222
2.22 – 2.3121
2.312 – 2.4050
2.405 – 2.4980
2.498 – 2.591
2.59 – 2.6830
2.683 – 2.7751
2.775 – 2.8680
2.868 – 2.961
2.96 – 3.0521
3.052 – 3.1450
3.145 – 3.2371
3.237 – 3.330
3.33 – 3.4220
3.422 – 3.5150
3.515 – 3.6070
3.607 – 3.71
uninsured_pop · Note how raw uninsured counts concentrate near zero with a long tail driven by populous counties.
Show data table
bincount
0 – 522.93022
522.9 – 1046124
1046 – 156932
1569 – 209216
2092 – 26147
2614 – 31375
3137 – 36605
3660 – 41832
4183 – 47060
4706 – 52291
5229 – 57522
5752 – 62741
6274 – 67970
6797 – 73200
7320 – 78430
7843 – 83661
8366 – 88891
8889 – 94120
9412 – 99350
9935 – 1.046e+040
1.046e+04 – 1.098e+040
1.098e+04 – 1.15e+042
1.15e+04 – 1.203e+040
1.203e+04 – 1.255e+040
1.255e+04 – 1.307e+040
1.307e+04 – 1.359e+040
1.359e+04 – 1.412e+040
1.412e+04 – 1.464e+040
1.464e+04 – 1.516e+040
1.516e+04 – 1.569e+040
1.569e+04 – 1.621e+040
1.621e+04 – 1.673e+040
1.673e+04 – 1.725e+040
1.725e+04 – 1.778e+040
1.778e+04 – 1.83e+040
1.83e+04 – 1.882e+040
1.882e+04 – 1.935e+040
1.935e+04 – 1.987e+040
1.987e+04 – 2.039e+040
2.039e+04 – 2.092e+041
county_name · Top words reveal which states contribute the most counties, led by Texas, Virginia, and Georgia.
Show data table
charscount
16 – 1726
17 – 1872
18 – 19121
19 – 20190
20 – 21264
21 – 22407
22 – 24420
24 – 25363
25 – 26320
26 – 27240
27 – 28231
28 – 29152
29 – 30139
30 – 31165
31 – 3241
32 – 3328
33 – 3416
34 – 3510
35 – 365
36 – 380
38 – 391
39 – 401
40 – 410
41 – 421
42 – 431
43 – 440
44 – 452
45 – 460
46 – 471
47 – 481
48 – 490
49 – 500
50 – 510
51 – 530
53 – 542
54 – 551
55 – 560
56 – 570
57 – 580
58 – 591

Schema

5 columns
Alerts
fips text 0.0% 3,222
near_unique one_word allcaps short_text
county_name text 0.0% 3,222
near_unique
total_pop numeric 0.0% 3,141
high_skew outliers
uninsured_pop numeric 0.0% 584
high_skew outliers
uninsured_rate numeric 0.0% 152
high_skew outliers

fips

text identifier near_unique one_word allcaps short_text
This column is a 5-character FIPS code identifying U.S. counties, with every one of the 3222 rows holding a unique value (n_unique equals n) and zero nulls. Lengths are uniformly 5 (min, median, max all 5), values are single tokens (one_word_rate 1.0), and the leading samples like 01001, 01003, 01005 match Alabama county FIPS prefixes. It functions as a primary key rather than a feature. Treatment: Use as a join key to county-level reference tables; do not feed into a model as a feature. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
3,222
len_min
5
len_max
5
len_mean
5
len_median
5
len_p95
5
word_mean
1
word_median
1
n_empty
0
n_duplicates
0
duplicate_rate
0
vocab_size
3,222
readability_flesch_mean
121.2
emoji_rate
0
url_rate
0
one_word_rate
1
allcaps_rate
1
boilerplate_rate
0

county_name

text identifier near_unique
This column holds U.S. county identifiers, likely formatted as 'County Name County, State' given that 'county,' appears in 2,999 of 3,222 rows and state names like Texas (256), Virginia (189), and Georgia (159) dominate the top tokens. Every one of the 3,222 rows is unique with zero nulls and zero duplicates, consistent with a canonical roster of U.S. counties. String lengths cluster tightly (min 16, median 24, max 59) and average 3.25 words, so formatting is highly regular. Treatment: Use as a join key to state/county references; do not feed raw into a model. high · anthropic:claude-opus-4-7
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

total_pop

numeric feature high_skew outliers
Likely a county- or region-level total population count: 3222 rows with 3141 unique values, no nulls, integer-scale magnitudes from 47 up to 9,866,623. The distribution is extremely right-skewed (skew 13.38, kurtosis 298.69) with median 25,328 far below mean 102,232, and 14.06% of rows flagged as outliers. The std of 326,933 dwarfs the IQR of 54,579, consistent with a few massive metros pulling the tail. Treatment: log-transform before regression to tame the heavy right tail. high · anthropic:claude-opus-4-7
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

uninsured_pop

numeric feature high_skew outliers
Counts of uninsured people per record, likely aggregated to a geographic unit (3222 rows hints at US counties). The distribution is brutally right-skewed: median is 36 but the mean is 159.9 and the max hits 20915, with skew 17.8 and kurtosis 462.9. Roughly 17.2% of rows are zero and 11.4% flag as outliers, so a handful of large jurisdictions dominate the totals. Treatment: Apply a log1p transform and consider normalising by population before modelling. high · anthropic:claude-opus-4-7
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

uninsured_rate

numeric feature high_skew outliers
This looks like a per-record uninsured rate, ranging from 0.0 to 3.7 with a median of 0.12 and IQR of 0.21. The distribution is severely right-skewed (skew 4.10, kurtosis 27.70) with 230 outliers (7.14%) and 17.54% exact zeros, and the max of 3.7 is implausible if this is meant to be a proportion bounded at 1. Treatment: Validate the >1 values against the expected [0,1] range, then log- or logit-transform after winsorising before modelling. high · anthropic:claude-opus-4-7
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