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county health rankings

source /home/coolhand/html/datavis/data_trove/cache/county_health_rankings.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 contains 3,222 rows of US county-level health data, with each row identified by a unique county name and FIPS code, plus three numeric measures: total population, uninsured population, and uninsured rate. The population fields are extremely right-skewed — total_pop ranges from 47 to nearly 9.87 million with a median of 25,328, and uninsured_pop shows similar skew (median 36, max 20,915), so a few large counties dominate. The uninsured_rate is the most analytically interesting field: it has a median of 0.12 but stretches up to 3.7, with about 17% of counties reporting zero, suggesting either small/edge cases or data quality issues worth investigating. Start by examining the distribution of uninsured_rate and how it relates to total_pop.

citing: row_count · columns.total_pop.stats · columns.uninsured_pop.stats · columns.uninsured_rate.stats · columns.county_name.top_words

total_pop · Heavy right skew — most counties are small but a handful exceed several 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 · Look for the cluster near zero (17% of counties) and the long tail extending past 1.0, which warrants a data-quality check.
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 · Highly skewed counts of uninsured residents; the median is just 36 but a few counties report over 20,000.
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
fips · FIPS codes are roughly uniform across the 1,001–72,153 range, confirming nationwide coverage.
Show data table
bincount
1001 – 278097
2780 – 455915
4559 – 6337133
6337 – 811659
8116 – 989514
9895 – 1.167e+044
1.167e+04 – 1.345e+04226
1.345e+04 – 1.523e+045
1.523e+04 – 1.701e+0449
1.701e+04 – 1.879e+04189
1.879e+04 – 2.057e+04204
2.057e+04 – 2.235e+04184
2.235e+04 – 2.413e+0439
2.413e+04 – 2.59e+0415
2.59e+04 – 2.768e+04170
2.768e+04 – 2.946e+04196
2.946e+04 – 3.124e+04150
3.124e+04 – 3.302e+0427
3.302e+04 – 3.48e+0421
3.48e+04 – 3.658e+0495
3.658e+04 – 3.836e+04153
3.836e+04 – 4.013e+04155
4.013e+04 – 4.191e+0446
4.191e+04 – 4.369e+0467
4.369e+04 – 4.547e+0451
4.547e+04 – 4.725e+04161
4.725e+04 – 4.903e+04268
4.903e+04 – 5.081e+0429
5.081e+04 – 5.259e+04133
5.259e+04 – 5.436e+0494
5.436e+04 – 5.614e+0495
5.614e+04 – 5.792e+040
5.792e+04 – 5.97e+040
5.97e+04 – 6.148e+040
6.148e+04 – 6.326e+040
6.326e+04 – 6.504e+040
6.504e+04 – 6.682e+040
6.682e+04 – 6.86e+040
6.86e+04 – 7.037e+040
7.037e+04 – 7.215e+0478

Schema

5 columns
Alerts
fips numeric 0.0% 3,222
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

numeric identifier
This is the U.S. county FIPS code: every one of the 3222 rows is unique with no nulls, and the value range (1001 to 72153) matches the standard 5-digit state+county encoding. The distribution is near-symmetric (skew 0.16) with no statistical outliers, consistent with an identifier rather than a measured quantity. Treatment: Treat as a categorical key; left-join on this to bring in county-level attributes rather than feeding it into a model as numeric. high · anthropic:claude-opus-4-7
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

county_name

text identifier near_unique
This column lists US county names paired with their state (e.g., 'County, Texas'), with all 3222 values unique and no nulls. The token 'county,' appears 2999 times, suggesting ~223 rows use a different suffix (likely 'Parish' in Louisiana or 'Borough/Census Area' in Alaska). State frequencies match expectations, with Texas (256) leading — consistent with Texas having the most counties nationally. Treatment: Split into county and state fields, then left-join on this key to geographic reference tables. 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
This is a population count, almost certainly per geographic unit (likely US counties given n=3222), with values 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 453 outliers (14.06%), reflecting a few massive metros dwarfing thousands of small areas. Mean (102,232) sits far above the median, confirming the heavy tail. Treatment: log-transform before any modelling or distance-based analysis. 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
Likely a county- or tract-level count of uninsured residents, with 3222 rows and 584 unique values. The distribution is extremely right-skewed (skew 17.8, kurtosis 462.9): median is 36 while the max hits 20915 and the mean is 159.9, and 17.2% of rows are zero. About 11.4% of values (368) flag as outliers, consistent with a few very populous areas dominating. Treatment: Log1p-transform before modelling to tame the heavy tail and zero inflation. 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
Likely a per-record uninsured rate, expressed as a fraction (median 0.12, q3 0.25) but with a long tail reaching 3.7, which is implausible for a true rate and suggests mixed units or data entry errors. The distribution is severely right-skewed (skew 4.10, kurtosis 27.70) with 230 outliers (7.1%) and 17.5% exact zeros. No nulls across 3222 rows and only 152 unique values, hinting at rounded or binned source data. Treatment: Validate units and cap or winsorize the >1.0 tail before log-transforming for modelling. medium · 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