saturn

/home/coolhand/html/datavis/data_trove/data/geographic/nationwide/2020_election.csv 3,152 rows sample n=3,152 seed 42 2026-05-01T17:50:52+00:00

Overview

Source/home/coolhand/html/datavis/data_trove/data/geographic/nationwide/2020_election.csv
Total rows3,152
Profiled sample3,152
Columns10
Generated2026-05-01T17:50:52+00:00

Insights opt-in

Model-generated narrative. These are opinions, not facts — the stats below are what saturn measured. Generated by: anthropic:claude-opus-4-7.

Dataset high anthropic:claude-opus-4-7

This dataset covers 3,152 U.S. counties from a 2020 election results file, with 10 columns spanning vote totals, party percentages, and geographic identifiers. Vote-count fields (total_votes, votes_dem, votes_gop, diff) are extremely right-skewed with high kurtosis and 12-16% outlier rates, reflecting a few massive-population counties dominating the raw counts — worth inspecting on a log scale. The percentage fields tell a cleaner story: per_gop has a median of 0.68 versus per_dem's 0.30, and per_point_diff is negatively skewed, indicating Republican margins in most counties despite Democrats winning the national popular vote. State coverage is led by Texas (254 counties), Georgia (159), and Virginia (133), so any state-level aggregation should account for that imbalance.

Errors during insight pass (10)
  • column:state_name:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoK8qyPAss7L3NVHu2'}
  • column:county_fips:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoKcco1PLPK7kbSdEk'}
  • column:county_name:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoLKXc5UvWkr6vsvxq'}
  • column:votes_gop:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoM5BCGXvfarg4GqWG'}
  • column:votes_dem:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoMagnGkRFhVCe1g9W'}
  • column:total_votes:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoN2ibW8DX8vEYw8VP'}
  • column:diff:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoNRHkH9jwsaGLfbkw'}
  • column:per_gop:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoNvJVRdAgjDSs6hA1'}
  • column:per_dem:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoPPpjpw3u7rLdUu4A'}
  • column:per_point_diff:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacGoPtLhBcxKka162HaK'}

Numeric correlation

state_name categorical

rows3,152
null0 (0.0%)
unique51
top_valueTexas
top_rate0.081
cardinality51
entropy5.279
entropy_ratio0.931
Top values (rank 1–20)
  1. Texas — 254
  2. Georgia — 159
  3. Virginia — 133
  4. Kentucky — 120
  5. Missouri — 115
  6. Kansas — 105
  7. Illinois — 102
  8. North Carolina — 100
  9. Iowa — 99
  10. Tennessee — 95
  11. Nebraska — 93
  12. Indiana — 92
  13. Ohio — 88
  14. Minnesota — 87
  15. Michigan — 83
  16. Mississippi — 82
  17. Oklahoma — 77
  18. Arkansas — 75
  19. Wisconsin — 72
  20. Alabama — 67

county_fips numeric

rows3,152
null0 (0.0%)
unique3,152
min1,001
max56,045
mean30,300
median29,166
std15,209
q118,162
q345,076
iqr26,913
skew-0.078
kurtosis-1.104
n_outliers0
outlier_rate0.000
zero_rate0.000

county_name text

95th-percentile length under 20 chars 40.1% duplicate strings
rows3,152
null0 (0.0%)
unique1,887
len_min10
len_max27
len_mean13.904
len_median14.000
len_p9517.000
word_mean2.063
word_median2.000
n_empty0
n_duplicates1,265
duplicate_rate0.401
vocab_size1,880
readability_flesch_mean35.121
emoji_rate0.000
url_rate0.000
one_word_rate0.000
allcaps_rate0.000
boilerplate_rate0.000
Sample values (first 10)
  1. Bibb County
  2. Custer County
  3. Rusk County
  4. Fannin County
  5. Brown County
  6. Dane County
  7. Pennington County
  8. Augusta County
  9. Franklin County
  10. Santa Cruz County

votes_gop numeric

skew=+8.68 12.3% rows beyond 1.5 IQR
rows3,152
null0 (0.0%)
unique2,965
min60.000
max1,145,530
mean23,543
median8,123
std54,040
q13,662
q320,509
iqr16,847
skew8.684
kurtosis122.745
n_outliers387
outlier_rate0.123
zero_rate0.000

votes_dem numeric

skew=+14.13 14.8% rows beyond 1.5 IQR
rows3,152
null0 (0.0%)
unique2,762
min4.000
max3,028,885
mean25,782
median3,690
std96,930
q11,320
q311,944
iqr10,625
skew14.131
kurtosis340.069
n_outliers468
outlier_rate0.148
zero_rate0.000

total_votes numeric

skew=+11.88 13.9% rows beyond 1.5 IQR
rows3,152
null0 (0.0%)
unique3,049
min66.000
max4,263,443
mean50,264
median12,336
std149,379
q15,415
q333,304
iqr27,890
skew11.884
kurtosis245.246
n_outliers437
outlier_rate0.139
zero_rate0.000

diff numeric

skew=-17.70 15.8% rows beyond 1.5 IQR
rows3,152
null0 (0.0%)
unique2,906
min-1,883,355
max119,005
mean-2,239
median2,984
std55,868
q1816.750
q37,286
iqr6,470
skew-17.696
kurtosis485.391
n_outliers499
outlier_rate0.158
zero_rate0.000

per_gop numeric

rows3,152
null0 (0.0%)
unique3,151
min0.054
max0.962
mean0.648
median0.682
std0.162
q10.554
q30.774
iqr0.220
skew-0.792
kurtosis0.151
n_outliers44
outlier_rate0.014
zero_rate0.000

per_dem numeric

rows3,152
null0 (0.0%)
unique3,151
min0.031
max0.921
mean0.334
median0.300
std0.160
q10.210
q30.426
iqr0.216
skew0.818
kurtosis0.215
n_outliers46
outlier_rate0.015
zero_rate0.000

per_point_diff numeric

rows3,152
null0 (0.0%)
unique3,152
min-0.868
max0.931
mean0.314
median0.380
std0.321
q10.129
q30.564
iqr0.435
skew-0.801
kurtosis0.178
n_outliers46
outlier_rate0.015
zero_rate0.000