median rents

saturn notebook · generated 2026-05-01 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/cache/median_rents.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_rents.parquet",
    "--findings", "median_rents.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset contains 3,222 rows of U.S. county-level median gross rent figures, keyed by county name and FIPS code. The standout issue is the median_gross_rent column: while the median is a plausible $817.50 and the IQR runs $718 to $978, the minimum is -666,666,666, dragging the mean to roughly -2.07M and producing extreme skew (-17.87) and kurtosis (317.2). That sentinel-style negative value and the 235 flagged outliers (7.3%) should be cleaned or filtered before any analysis. The fips column is well-behaved and unique per row, and county_name is essentially an identifier (3,222 unique values), so neither needs deep inspection beyond confirming coverage.

citing: median_gross_rent.stats.median · median_gross_rent.stats.mean · median_gross_rent.stats.min · median_gross_rent.stats.max · median_gross_rent.stats.q1 · median_gross_rent.stats.q3 · median_gross_rent.stats.skew · median_gross_rent.stats.kurtosis · median_gross_rent.stats.n_outliers · median_gross_rent.stats.outlier_rate · fips.stats.min · fips.stats.max · row_count · column_count

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_gross_rent	numeric	3,222	0.0%	984	high_skew outliers

Fig 1.

median_gross_rent · Expect an extreme left tail from the -666,666,666 sentinel; filter it out to see the real distribution centered near $817.

Show data table

Histogram bins for median_gross_rent (median: 817.5).
bin	count
-6.667e+08 – -6.5e+08	10
-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 – -5.667e+08	0
-5.667e+08 – -5.5e+08	0
-5.5e+08 – -5.333e+08	0
-5.333e+08 – -5.167e+08	0
-5.167e+08 – -5e+08	0
-5e+08 – -4.833e+08	0
-4.833e+08 – -4.667e+08	0
-4.667e+08 – -4.5e+08	0
-4.5e+08 – -4.333e+08	0
-4.333e+08 – -4.167e+08	0
-4.167e+08 – -4e+08	0
-4e+08 – -3.833e+08	0
-3.833e+08 – -3.667e+08	0
-3.667e+08 – -3.5e+08	0
-3.5e+08 – -3.333e+08	0
-3.333e+08 – -3.167e+08	0
-3.167e+08 – -3e+08	0
-3e+08 – -2.833e+08	0
-2.833e+08 – -2.667e+08	0
-2.667e+08 – -2.5e+08	0
-2.5e+08 – -2.333e+08	0
-2.333e+08 – -2.167e+08	0
-2.167e+08 – -2e+08	0
-2e+08 – -1.833e+08	0
-1.833e+08 – -1.667e+08	0
-1.667e+08 – -1.5e+08	0
-1.5e+08 – -1.333e+08	0
-1.333e+08 – -1.167e+08	0
-1.167e+08 – -1e+08	0
-1e+08 – -8.333e+07	0
-8.333e+07 – -6.666e+07	0
-6.666e+07 – -5e+07	0
-5e+07 – -3.333e+07	0
-3.333e+07 – -1.666e+07	0
-1.666e+07 – 2805	3212

Fig 2.

fips · Should spread fairly evenly across state FIPS prefixes, confirming nationwide county coverage.

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; useful as a sanity check that no rows are truncated or malformed.

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_gross_rent	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_gross_rent
fips	+1.00	-0.06
median_gross_rent	-0.06	+1.00

fips numeric identifier

This is the county-level FIPS code: an integer geographic identifier where every one of the 3222 rows is unique and non-null. The range (1001 to 72153) and distribution (mean 31377, median 30022, low skew 0.16) are consistent with the standard 5-digit state+county FIPS scheme covering US states and territories. There is nothing anomalous here — it behaves as a clean primary key rather than a numeric feature.

Treatment: Treat as a categorical key for joins; do not use 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 fully-qualified US county names (e.g. 'X County, Texas'), with the word 'county,' appearing in 2999 of 3222 rows and state names like Texas (256), Virginia (189) and Georgia (159) dominating the remaining tokens. Every one of the 3222 values is unique with zero nulls or duplicates, so it functions as a row identifier rather than a categorical feature. String lengths are tight (16-59 chars, median 24) and there is no boilerplate, URL or emoji noise.

Treatment: Split into county and state fields, or use as a join key against county-level reference data.

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_gross_rent numeric feature

Median gross rent in dollars, with a healthy interquartile range of 718 to 978 around a median of 817.5. The minimum of -666666666 is clearly a sentinel for missing data, dragging the mean to -2068220 and producing extreme skew (-17.87) and kurtosis (317.20). 235 outliers (7.3%) flag this contamination even though null_rate is 0.

Treatment: Replace the -666666666 sentinel with NA before any modelling or aggregation.

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

Out[17]:

saturn.columns["median_gross_rent"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	984
min	-6.667e+08
max	2,805
mean	-2.068e+06
median	817.5
std	3.709e+07
q1	718
q3	978
iqr	260
skew	-17.87
kurtosis	317.2
n_outliers	235
outlier_rate	0.07294
zero_rate	0
alert: high_skew	skew=-17.87
alert: outliers	7.3% rows beyond 1.5 IQR

Fig 8.

Distribution of median_gross_rent. Vertical dash marks the median.

Show data table

Histogram bins for median_gross_rent (median: 817.5).
bin	count
-6.667e+08 – -6.5e+08	10
-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 – -5.667e+08	0
-5.667e+08 – -5.5e+08	0
-5.5e+08 – -5.333e+08	0
-5.333e+08 – -5.167e+08	0
-5.167e+08 – -5e+08	0
-5e+08 – -4.833e+08	0
-4.833e+08 – -4.667e+08	0
-4.667e+08 – -4.5e+08	0
-4.5e+08 – -4.333e+08	0
-4.333e+08 – -4.167e+08	0
-4.167e+08 – -4e+08	0
-4e+08 – -3.833e+08	0
-3.833e+08 – -3.667e+08	0
-3.667e+08 – -3.5e+08	0
-3.5e+08 – -3.333e+08	0
-3.333e+08 – -3.167e+08	0
-3.167e+08 – -3e+08	0
-3e+08 – -2.833e+08	0
-2.833e+08 – -2.667e+08	0
-2.667e+08 – -2.5e+08	0
-2.5e+08 – -2.333e+08	0
-2.333e+08 – -2.167e+08	0
-2.167e+08 – -2e+08	0
-2e+08 – -1.833e+08	0
-1.833e+08 – -1.667e+08	0
-1.667e+08 – -1.5e+08	0
-1.5e+08 – -1.333e+08	0
-1.333e+08 – -1.167e+08	0
-1.167e+08 – -1e+08	0
-1e+08 – -8.333e+07	0
-8.333e+07 – -6.666e+07	0
-6.666e+07 – -5e+07	0
-5e+07 – -3.333e+07	0
-3.333e+07 – -1.666e+07	0
-1.666e+07 – 2805	3212

How to cite

click to copy

BibTeX

@misc{saturn-median-rents-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: median rents},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/median_rents}},
  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 rents. Source: /home/coolhand/html/datavis/data_trove/cache/median_rents.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_rents