median income

source /home/coolhand/html/datavis/data_trove/cache/median_income.parquet 3,222 rows 3 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 covering U.S. counties, with three columns: a county name, a FIPS code, and median household income. The income column is the headline issue — it has a minimum of -666,666,666 and a mean of roughly -144,603 against a median of 60,458, indicating a sentinel value (likely a missing-data placeholder) that is dragging the distribution into nonsense. About 5.8% of records (188 rows) are flagged as outliers and skew is extreme (-56.7), so any analysis should filter these sentinels before computing summary stats. County names are essentially unique row labels, while FIPS codes look clean and well-distributed across the expected national range.

citing: row_count · column_count · median_household_income.stats.min · median_household_income.stats.max · median_household_income.stats.mean · median_household_income.stats.median · median_household_income.stats.skew · median_household_income.stats.n_outliers · median_household_income.stats.outlier_rate · fips.stats.min · fips.stats.max · county_name.top_words

Charts the summary said to look at first

median_household_income · Look for the long left tail caused by sentinel values like -666,666,666 that distort the mean.

Show data table

Histogram bins for median_household_income (median: 60458.5).
bin	count
-6.667e+08 – -6.5e+08	1
-6.5e+08 – -6.333e+08	0
-6.333e+08 – -6.167e+08	0
-6.167e+08 – -6e+08	0
-6e+08 – -5.833e+08	0
-5.833e+08 – -5.666e+08	0
-5.666e+08 – -5.5e+08	0
-5.5e+08 – -5.333e+08	0
-5.333e+08 – -5.166e+08	0
-5.166e+08 – -5e+08	0
-5e+08 – -4.833e+08	0
-4.833e+08 – -4.666e+08	0
-4.666e+08 – -4.499e+08	0
-4.499e+08 – -4.333e+08	0
-4.333e+08 – -4.166e+08	0
-4.166e+08 – -3.999e+08	0
-3.999e+08 – -3.833e+08	0
-3.833e+08 – -3.666e+08	0
-3.666e+08 – -3.499e+08	0
-3.499e+08 – -3.332e+08	0
-3.332e+08 – -3.166e+08	0
-3.166e+08 – -2.999e+08	0
-2.999e+08 – -2.832e+08	0
-2.832e+08 – -2.666e+08	0
-2.666e+08 – -2.499e+08	0
-2.499e+08 – -2.332e+08	0
-2.332e+08 – -2.166e+08	0
-2.166e+08 – -1.999e+08	0
-1.999e+08 – -1.832e+08	0
-1.832e+08 – -1.665e+08	0
-1.665e+08 – -1.499e+08	0
-1.499e+08 – -1.332e+08	0
-1.332e+08 – -1.165e+08	0
-1.165e+08 – -9.986e+07	0
-9.986e+07 – -8.318e+07	0
-8.318e+07 – -6.651e+07	0
-6.651e+07 – -4.984e+07	0
-4.984e+07 – -3.317e+07	0
-3.317e+07 – -1.65e+07	0
-1.65e+07 – 1.705e+05	3221

fips · FIPS codes spread evenly from 1,001 to 72,153, confirming national county coverage with no outliers.

Show data table

Histogram bins for fips (median: 30022.0).
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

county_name · County name lengths cluster tightly around 24 characters, reflecting the consistent 'X County, State' format.

Show data table

Character-length distribution for county_name (mean: 24.324022346368714).
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

Schema

3 columns

Per-column summary. Click column name to jump to its detail.
				Alerts
fips	numeric	0.0%	3,222
county_name	text	0.0%	3,222	near_unique
median_household_income	numeric	0.0%	3,099	high_skew outliers

fips

numeric identifier

This column is a FIPS county/area code—every one of the 3222 rows is unique with no nulls, and the values span 1001 to 72153, the canonical FIPS range covering U.S. states and territories. The distribution is nearly symmetric (skew 0.157, kurtosis -0.631) with no outliers, consistent with a structured geographic identifier rather than a measured quantity. Treat it as a key, not a numeric feature. Treatment: Use as a categorical join key on county-level data; do not feed as a numeric feature. 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 holds full US county identifiers (e.g., 'X County, '), with all 3222 rows unique and zero nulls. The token 'county,' appears 2999 times, suggesting ~223 rows use a different suffix (likely 'Parish' in Louisiana, 'Borough'/'Census Area' in Alaska, or independent cities). State-name frequencies match expected US distribution, with Texas (256) leading. Treatment: Use as a join key after splitting into county and state components; do not treat as a feature. 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

median_household_income

numeric feature high_skew outliers

County-level median household income in dollars, with 3099 distinct values across 3222 rows and no nulls. The minimum of -666666666 is a clear sentinel for missing data, dragging the mean to -144603 even though the median is 60458.5 and Q1-Q3 sit between 51814.75 and 70376.25. This sentinel produces the extreme skew (-56.74) and kurtosis (3216.99), and 188 outliers (5.83%) are flagged. Treatment: Replace the -666666666 sentinel with null, then consider a log or robust scaler before modelling. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 3,099
min: -6.667e+08
max: 170,463
mean: -1.446e+05
median: 6.046e+04
std: 1.175e+07
q1: 5.181e+04
q3: 7.038e+04
iqr: 1.856e+04
skew: -56.74
kurtosis: 3217
n_outliers: 188
outlier_rate: 0.05835
zero_rate: 0