housing_units · saturn notebook

Overview

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

Saturn profiled 3,222 rows across 6 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/housing_units.parquet",
    "--findings", "housing_units.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset covers 3,222 U.S. counties with housing-unit counts (owner-occupied, renter-occupied, total) plus a FIPS code, county name, and the percent of renters. The three count columns are extremely right-skewed (skew between 9.5 and 15.8, kurtosis above 140) with 13–14% of rows flagged as outliers — a handful of huge urban counties (max total_housing_units of about 3.36M vs a median of roughly 10,021) dominate the distribution. The pct_renter field is far better behaved, centered near 26% with a much tighter spread, making it the most useful comparable metric across counties. Start by inspecting the long tail of total_housing_units, then use pct_renter to compare counties on a normalized basis.

citing: owner_occupied.stats · renter_occupied.stats · total_housing_units.stats · pct_renter.stats · fips.stats · county_name.top_words · row_count

Out[4]:

saturn.schema() · 6 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
total_housing_units	numeric	3,222	0.0%	3,074	high_skew outliers
owner_occupied	numeric	3,222	0.0%	3,001	high_skew outliers
renter_occupied	numeric	3,222	0.0%	2,709	high_skew outliers
pct_renter	numeric	3,222	0.0%	1,925

Fig 1.

total_housing_units · Heavily right-skewed: most counties cluster near the median of ~10K units while a few exceed 3M.

Show data table

Histogram bins for total_housing_units (median: 10021.0).
bin	count
32 – 8.411e+04	2907
8.411e+04 – 1.682e+05	153
1.682e+05 – 2.523e+05	62
2.523e+05 – 3.363e+05	38
3.363e+05 – 4.204e+05	22
4.204e+05 – 5.045e+05	6
5.045e+05 – 5.886e+05	11
5.886e+05 – 6.726e+05	5
6.726e+05 – 7.567e+05	5
7.567e+05 – 8.408e+05	3
8.408e+05 – 9.249e+05	1
9.249e+05 – 1.009e+06	3
1.009e+06 – 1.093e+06	1
1.093e+06 – 1.177e+06	1
1.177e+06 – 1.261e+06	0
1.261e+06 – 1.345e+06	0
1.345e+06 – 1.429e+06	0
1.429e+06 – 1.513e+06	0
1.513e+06 – 1.597e+06	0
1.597e+06 – 1.682e+06	1
1.682e+06 – 1.766e+06	1
1.766e+06 – 1.85e+06	0
1.85e+06 – 1.934e+06	0
1.934e+06 – 2.018e+06	0
2.018e+06 – 2.102e+06	1
2.102e+06 – 2.186e+06	0
2.186e+06 – 2.27e+06	0
2.27e+06 – 2.354e+06	0
2.354e+06 – 2.438e+06	0
2.438e+06 – 2.522e+06	0
2.522e+06 – 2.606e+06	0
2.606e+06 – 2.69e+06	0
2.69e+06 – 2.775e+06	0
2.775e+06 – 2.859e+06	0
2.859e+06 – 2.943e+06	0
2.943e+06 – 3.027e+06	0
3.027e+06 – 3.111e+06	0
3.111e+06 – 3.195e+06	0
3.195e+06 – 3.279e+06	0
3.279e+06 – 3.363e+06	1

Fig 2.

pct_renter · A relatively well-behaved distribution centered around 26%, useful for cross-county comparison.

Show data table

Histogram bins for pct_renter (median: 26.07).
bin	count
3.01 – 5.435	1
5.435 – 7.859	3
7.859 – 10.28	9
10.28 – 12.71	26
12.71 – 15.13	63
15.13 – 17.56	156
17.56 – 19.98	316
19.98 – 22.41	371
22.41 – 24.83	450
24.83 – 27.26	419
27.26 – 29.68	357
29.68 – 32.11	301
32.11 – 34.53	203
34.53 – 36.96	169
36.96 – 39.38	115
39.38 – 41.81	75
41.81 – 44.23	56
44.23 – 46.66	45
46.66 – 49.08	25
49.08 – 51.5	15
51.5 – 53.93	11
53.93 – 56.35	10
56.35 – 58.78	8
58.78 – 61.2	4
61.2 – 63.63	4
63.63 – 66.05	1
66.05 – 68.48	1
68.48 – 70.9	3
70.9 – 73.33	1
73.33 – 75.75	1
75.75 – 78.18	0
78.18 – 80.6	1
80.6 – 83.03	0
83.03 – 85.45	1
85.45 – 87.88	0
87.88 – 90.3	0
90.3 – 92.73	0
92.73 – 95.15	0
95.15 – 97.58	0
97.58 – 100	1

Fig 3.

owner_occupied · Long-tailed counts with ~429 outlier counties pulling the mean far above the median.

Show data table

Histogram bins for owner_occupied (median: 7325.5).
bin	count
0 – 3.88e+04	2761
3.88e+04 – 7.761e+04	225
7.761e+04 – 1.164e+05	78
1.164e+05 – 1.552e+05	52
1.552e+05 – 1.94e+05	36
1.94e+05 – 2.328e+05	20
2.328e+05 – 2.716e+05	10
2.716e+05 – 3.104e+05	10
3.104e+05 – 3.492e+05	6
3.492e+05 – 3.88e+05	6
3.88e+05 – 4.268e+05	3
4.268e+05 – 4.656e+05	3
4.656e+05 – 5.045e+05	4
5.045e+05 – 5.433e+05	2
5.433e+05 – 5.821e+05	0
5.821e+05 – 6.209e+05	1
6.209e+05 – 6.597e+05	1
6.597e+05 – 6.985e+05	0
6.985e+05 – 7.373e+05	0
7.373e+05 – 7.761e+05	0
7.761e+05 – 8.149e+05	0
8.149e+05 – 8.537e+05	0
8.537e+05 – 8.925e+05	0
8.925e+05 – 9.313e+05	1
9.313e+05 – 9.701e+05	0
9.701e+05 – 1.009e+06	0
1.009e+06 – 1.048e+06	0
1.048e+06 – 1.087e+06	1
1.087e+06 – 1.125e+06	0
1.125e+06 – 1.164e+06	0
1.164e+06 – 1.203e+06	1
1.203e+06 – 1.242e+06	0
1.242e+06 – 1.281e+06	0
1.281e+06 – 1.319e+06	0
1.319e+06 – 1.358e+06	0
1.358e+06 – 1.397e+06	0
1.397e+06 – 1.436e+06	0
1.436e+06 – 1.475e+06	0
1.475e+06 – 1.513e+06	0
1.513e+06 – 1.552e+06	1

Fig 4.

renter_occupied · The most extreme skew in the dataset (skew ~15.8) — watch for a small number of dense urban counties.

Show data table

Histogram bins for renter_occupied (median: 2579.5).
bin	count
28 – 4.53e+04	3019
4.53e+04 – 9.057e+04	109
9.057e+04 – 1.358e+05	38
1.358e+05 – 1.811e+05	17
1.811e+05 – 2.264e+05	11
2.264e+05 – 2.717e+05	9
2.717e+05 – 3.169e+05	5
3.169e+05 – 3.622e+05	0
3.622e+05 – 4.075e+05	2
4.075e+05 – 4.528e+05	2
4.528e+05 – 4.98e+05	3
4.98e+05 – 5.433e+05	1
5.433e+05 – 5.886e+05	1
5.886e+05 – 6.338e+05	1
6.338e+05 – 6.791e+05	0
6.791e+05 – 7.244e+05	1
7.244e+05 – 7.697e+05	1
7.697e+05 – 8.149e+05	0
8.149e+05 – 8.602e+05	0
8.602e+05 – 9.055e+05	1
9.055e+05 – 9.508e+05	0
9.508e+05 – 9.96e+05	0
9.96e+05 – 1.041e+06	0
1.041e+06 – 1.087e+06	0
1.087e+06 – 1.132e+06	0
1.132e+06 – 1.177e+06	0
1.177e+06 – 1.222e+06	0
1.222e+06 – 1.268e+06	0
1.268e+06 – 1.313e+06	0
1.313e+06 – 1.358e+06	0
1.358e+06 – 1.403e+06	0
1.403e+06 – 1.449e+06	0
1.449e+06 – 1.494e+06	0
1.494e+06 – 1.539e+06	0
1.539e+06 – 1.585e+06	0
1.585e+06 – 1.63e+06	0
1.63e+06 – 1.675e+06	0
1.675e+06 – 1.72e+06	0
1.72e+06 – 1.766e+06	0
1.766e+06 – 1.811e+06	1

Fig 5.

fips · Roughly uniform across the FIPS range, confirming nationwide county coverage with no clustering.

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 6.

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%
total_housing_units	numeric	0.0%
owner_occupied	numeric	0.0%
renter_occupied	numeric	0.0%
pct_renter	numeric	0.0%

Fig 7.

Pearson correlation across numeric columns (sampled, bounded).

Show data table

Pearson correlation across 5 numeric columns (values clipped to 2 decimals).
	fips	total_housing_units	owner_occupied	renter_occupied	pct_renter
fips	+1.00	-0.06	-0.06	-0.06	-0.10
total_housing_units	-0.06	+1.00	+0.99	+0.99	+0.19
owner_occupied	-0.06	+0.99	+1.00	+0.96	+0.16
renter_occupied	-0.06	+0.99	+0.96	+1.00	+0.22
pct_renter	-0.10	+0.19	+0.16	+0.22	+1.00

fips numeric identifier

This column is the FIPS code for U.S. counties — every one of 3,222 rows is unique with no nulls, matching the count of U.S. counties. Values span 1001 to 72153, consistent with state-prefixed county FIPS identifiers, and the distribution is essentially uniform across the code space (skew 0.157, kurtosis -0.63, no outliers).

Treatment: Treat as a categorical key; left-join on this code to county-level reference data rather than using as a numeric feature.

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

Out[13]:

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 8.

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, State'), with 3222 rows all unique and zero nulls. The token 'county,' appears 2999 times, so roughly 223 rows use a different administrative suffix (parish, borough, census area). Texas (256), Virginia (189), and Georgia (159) lead the state distribution, consistent with the real US county count.

Treatment: use as a join key after splitting into county and state components.

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

Out[16]:

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 9.

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

total_housing_units numeric feature

Counts of total housing units per record, almost certainly at a county or similar geographic level given 3,222 rows with 3,074 unique values and no nulls. The distribution is severely right-skewed (skew 12.05, kurtosis 240.5) with a median of 10,021 but a max of 3,363,093, and 443 rows (13.7%) flagged as outliers well above the Q3 of 25,939. The mean of 39,402 sits far above the median, confirming a long heavy tail driven by a few very large geographies.

Treatment: log-transform before modelling to tame the heavy right tail.

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

Out[19]:

saturn.columns["total_housing_units"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	3,074
min	32
max	3.363e+06
mean	3.94e+04
median	10,021
std	1.201e+05
q1	4211
q3	25,939
iqr	2.173e+04
skew	12.05
kurtosis	240.5
n_outliers	443
outlier_rate	0.1375
zero_rate	0
alert: high_skew	skew=+12.05
alert: outliers	13.7% rows beyond 1.5 IQR

Fig 10.

Distribution of total_housing_units. Vertical dash marks the median.

Show data table

Histogram bins for total_housing_units (median: 10021.0).
bin	count
32 – 8.411e+04	2907
8.411e+04 – 1.682e+05	153
1.682e+05 – 2.523e+05	62
2.523e+05 – 3.363e+05	38
3.363e+05 – 4.204e+05	22
4.204e+05 – 5.045e+05	6
5.045e+05 – 5.886e+05	11
5.886e+05 – 6.726e+05	5
6.726e+05 – 7.567e+05	5
7.567e+05 – 8.408e+05	3
8.408e+05 – 9.249e+05	1
9.249e+05 – 1.009e+06	3
1.009e+06 – 1.093e+06	1
1.093e+06 – 1.177e+06	1
1.177e+06 – 1.261e+06	0
1.261e+06 – 1.345e+06	0
1.345e+06 – 1.429e+06	0
1.429e+06 – 1.513e+06	0
1.513e+06 – 1.597e+06	0
1.597e+06 – 1.682e+06	1
1.682e+06 – 1.766e+06	1
1.766e+06 – 1.85e+06	0
1.85e+06 – 1.934e+06	0
1.934e+06 – 2.018e+06	0
2.018e+06 – 2.102e+06	1
2.102e+06 – 2.186e+06	0
2.186e+06 – 2.27e+06	0
2.27e+06 – 2.354e+06	0
2.354e+06 – 2.438e+06	0
2.438e+06 – 2.522e+06	0
2.522e+06 – 2.606e+06	0
2.606e+06 – 2.69e+06	0
2.69e+06 – 2.775e+06	0
2.775e+06 – 2.859e+06	0
2.859e+06 – 2.943e+06	0
2.943e+06 – 3.027e+06	0
3.027e+06 – 3.111e+06	0
3.111e+06 – 3.195e+06	0
3.195e+06 – 3.279e+06	0
3.279e+06 – 3.363e+06	1

owner_occupied numeric feature

This appears to be a count of owner-occupied housing units per geographic area, with 3001 unique values across 3222 rows and effectively no zeros (zero_rate 0.0003) or nulls. The distribution is severely right-skewed (skew 9.52, kurtosis 146.9): the median is 7325.5 but the mean is 25551.7 and the max reaches 1,552,164, producing 429 outliers (13.3% outlier rate). The interquartile range (3147.75 to 18863.5) is dwarfed by the standard deviation of 67553, indicating a long tail of large jurisdictions.

Treatment: Log-transform before modelling to tame the heavy right tail.

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

Out[22]:

saturn.columns["owner_occupied"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	3,001
min	0
max	1.552e+06
mean	2.555e+04
median	7326
std	6.755e+04
q1	3148
q3	1.886e+04
iqr	1.572e+04
skew	9.516
kurtosis	146.9
n_outliers	429
outlier_rate	0.1331
zero_rate	0.0003104
alert: high_skew	skew=+9.52
alert: outliers	13.3% rows beyond 1.5 IQR

Fig 11.

Distribution of owner_occupied. Vertical dash marks the median.

Show data table

Histogram bins for owner_occupied (median: 7325.5).
bin	count
0 – 3.88e+04	2761
3.88e+04 – 7.761e+04	225
7.761e+04 – 1.164e+05	78
1.164e+05 – 1.552e+05	52
1.552e+05 – 1.94e+05	36
1.94e+05 – 2.328e+05	20
2.328e+05 – 2.716e+05	10
2.716e+05 – 3.104e+05	10
3.104e+05 – 3.492e+05	6
3.492e+05 – 3.88e+05	6
3.88e+05 – 4.268e+05	3
4.268e+05 – 4.656e+05	3
4.656e+05 – 5.045e+05	4
5.045e+05 – 5.433e+05	2
5.433e+05 – 5.821e+05	0
5.821e+05 – 6.209e+05	1
6.209e+05 – 6.597e+05	1
6.597e+05 – 6.985e+05	0
6.985e+05 – 7.373e+05	0
7.373e+05 – 7.761e+05	0
7.761e+05 – 8.149e+05	0
8.149e+05 – 8.537e+05	0
8.537e+05 – 8.925e+05	0
8.925e+05 – 9.313e+05	1
9.313e+05 – 9.701e+05	0
9.701e+05 – 1.009e+06	0
1.009e+06 – 1.048e+06	0
1.048e+06 – 1.087e+06	1
1.087e+06 – 1.125e+06	0
1.125e+06 – 1.164e+06	0
1.164e+06 – 1.203e+06	1
1.203e+06 – 1.242e+06	0
1.242e+06 – 1.281e+06	0
1.281e+06 – 1.319e+06	0
1.319e+06 – 1.358e+06	0
1.358e+06 – 1.397e+06	0
1.397e+06 – 1.436e+06	0
1.436e+06 – 1.475e+06	0
1.475e+06 – 1.513e+06	0
1.513e+06 – 1.552e+06	1

renter_occupied numeric feature

Counts of renter-occupied housing units per record, ranging from 28 to 1,810,929 with a median of 2,579.5 — consistent with a geographic rollup (likely county or similar). The distribution is extremely right-skewed (skew 15.82, kurtosis 398.15) and 13.9% of rows fall outside the IQR fences, reflecting a few very large metros dominating a long tail of small areas. No nulls or zeros, and 2,709 unique values across 3,222 rows.

Treatment: log-transform before modelling to tame the skew and heavy outliers.

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

Out[25]:

saturn.columns["renter_occupied"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	2,709
min	28
max	1.811e+06
mean	1.385e+04
median	2580
std	5.535e+04
q1	1004
q3	7396
iqr	6,392
skew	15.82
kurtosis	398.2
n_outliers	449
outlier_rate	0.1394
zero_rate	0
alert: high_skew	skew=+15.82
alert: outliers	13.9% rows beyond 1.5 IQR

Fig 12.

Distribution of renter_occupied. Vertical dash marks the median.

Show data table

Histogram bins for renter_occupied (median: 2579.5).
bin	count
28 – 4.53e+04	3019
4.53e+04 – 9.057e+04	109
9.057e+04 – 1.358e+05	38
1.358e+05 – 1.811e+05	17
1.811e+05 – 2.264e+05	11
2.264e+05 – 2.717e+05	9
2.717e+05 – 3.169e+05	5
3.169e+05 – 3.622e+05	0
3.622e+05 – 4.075e+05	2
4.075e+05 – 4.528e+05	2
4.528e+05 – 4.98e+05	3
4.98e+05 – 5.433e+05	1
5.433e+05 – 5.886e+05	1
5.886e+05 – 6.338e+05	1
6.338e+05 – 6.791e+05	0
6.791e+05 – 7.244e+05	1
7.244e+05 – 7.697e+05	1
7.697e+05 – 8.149e+05	0
8.149e+05 – 8.602e+05	0
8.602e+05 – 9.055e+05	1
9.055e+05 – 9.508e+05	0
9.508e+05 – 9.96e+05	0
9.96e+05 – 1.041e+06	0
1.041e+06 – 1.087e+06	0
1.087e+06 – 1.132e+06	0
1.132e+06 – 1.177e+06	0
1.177e+06 – 1.222e+06	0
1.222e+06 – 1.268e+06	0
1.268e+06 – 1.313e+06	0
1.313e+06 – 1.358e+06	0
1.358e+06 – 1.403e+06	0
1.403e+06 – 1.449e+06	0
1.449e+06 – 1.494e+06	0
1.494e+06 – 1.539e+06	0
1.539e+06 – 1.585e+06	0
1.585e+06 – 1.63e+06	0
1.63e+06 – 1.675e+06	0
1.675e+06 – 1.72e+06	0
1.72e+06 – 1.766e+06	0
1.766e+06 – 1.811e+06	1

pct_renter numeric feature

This is a numeric feature representing the percentage of renters per record, ranging from 3.01 to 100.0 with a mean of 27.35 and median of 26.07. The distribution is right-skewed (skew 1.32, kurtosis 4.41) with 88 outliers (2.7%) on the high end, suggesting a small set of records — likely dense urban areas — with renter shares far above the typical 21.64–31.66 IQR. No nulls or zeros, and 1925 unique values across 3222 rows indicate well-populated continuous data.

Treatment: Use as-is or apply a mild transform (e.g., log or winsorize) before regression to dampen the right-skew.

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

Out[28]:

saturn.columns["pct_renter"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	1,925
min	3.01
max	100
mean	27.35
median	26.07
std	8.564
q1	21.64
q3	31.66
iqr	10.02
skew	1.317
kurtosis	4.412
n_outliers	88
outlier_rate	0.02731
zero_rate	0

Fig 13.

Distribution of pct_renter. Vertical dash marks the median.

Show data table

Histogram bins for pct_renter (median: 26.07).
bin	count
3.01 – 5.435	1
5.435 – 7.859	3
7.859 – 10.28	9
10.28 – 12.71	26
12.71 – 15.13	63
15.13 – 17.56	156
17.56 – 19.98	316
19.98 – 22.41	371
22.41 – 24.83	450
24.83 – 27.26	419
27.26 – 29.68	357
29.68 – 32.11	301
32.11 – 34.53	203
34.53 – 36.96	169
36.96 – 39.38	115
39.38 – 41.81	75
41.81 – 44.23	56
44.23 – 46.66	45
46.66 – 49.08	25
49.08 – 51.5	15
51.5 – 53.93	11
53.93 – 56.35	10
56.35 – 58.78	8
58.78 – 61.2	4
61.2 – 63.63	4
63.63 – 66.05	1
66.05 – 68.48	1
68.48 – 70.9	3
70.9 – 73.33	1
73.33 – 75.75	1
75.75 – 78.18	0
78.18 – 80.6	1
80.6 – 83.03	0
83.03 – 85.45	1
85.45 – 87.88	0
87.88 – 90.3	0
90.3 – 92.73	0
92.73 – 95.15	0
95.15 – 97.58	0
97.58 – 100	1

Overview

Summary confidence: high

fips numeric identifier

county_name text identifier

total_housing_units numeric feature

owner_occupied numeric feature

renter_occupied numeric feature

pct_renter numeric feature

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