median income

saturn notebook · generated 2026-05-01 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/cache/median_income.parquet

Saturn profiled 3,222 rows across 3 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).

[2]:

!pip install saturn-dissect
import subprocess
subprocess.run([
    "saturn", "analyze", "/home/coolhand/html/datavis/data_trove/cache/median_income.parquet",
    "--findings", "median_income.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

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

Out[4]:

saturn.schema() · 3 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
median_household_income	numeric	3,222	0.0%	3,099	high_skew outliers

Fig 1.

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

Fig 2.

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

Fig 3.

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

Fig 4.

Per-column null rate across the corpus. Columns are ordered by input position.

Show data table

Per-column null rate across the corpus.
column	kind	null %
fips	numeric	0.0%
county_name	text	0.0%
median_household_income	numeric	0.0%

Fig 5.

Pearson correlation across numeric columns (sampled, bounded).

Show data table

Pearson correlation across 2 numeric columns (values clipped to 2 decimals).
	fips	median_household_income
fips	+1.00	-0.07
median_household_income	-0.07	+1.00

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.

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

Out[11]:

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

Fig 6.

Distribution of fips. Vertical dash marks the median.

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 text identifier

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.

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

Out[14]:

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

Fig 7.

Character-length distribution for county_name.

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

median_household_income numeric feature

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.

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

Out[17]:

saturn.columns["median_household_income"].stats

stat	value
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
alert: high_skew	skew=-56.74
alert: outliers	5.8% rows beyond 1.5 IQR

Fig 8.

Distribution of median_household_income. Vertical dash marks the median.

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

How to cite

click to copy

BibTeX

@misc{saturn-median-income-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: median income},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/median_income}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}

APA

Steuber, L. (2026). Saturn reading: median income. Source: /home/coolhand/html/datavis/data_trove/cache/median_income.parquet. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/median_income