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nationwide 2016 election

source /home/coolhand/html/datavis/data_trove/data/geographic/nationwide/2016_election.csv 3,141 rows 11 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 3,141 rows and 11 columns covering 2016 U.S. presidential election results at the county level, including total votes, Democratic and Republican vote counts and shares, and county/state identifiers. Vote-count columns (total_votes, votes_dem, votes_gop) are extremely right-skewed with high kurtosis and many outliers, reflecting a few very populous counties dominating the totals — worth a log-scale or filtered view. The per_gop and per_dem share columns tell a clearer story: per_gop has a mean of about 0.64 versus per_dem at 0.32, indicating Republican margins were larger across most counties. State coverage is broad (51 categories) with Texas (254 counties) and Georgia (159) most represented, so any state-level aggregation should account for that imbalance.

citing: row_count · column_count · total_votes · votes_dem · votes_gop · per_dem · per_gop · state_abbr · county_name

per_gop · Distribution of Republican vote share across counties — note the left-skewed shape with a median near 0.67.
Show data table
bincount
0.04122 – 0.064011
0.06401 – 0.08682
0.0868 – 0.10965
0.1096 – 0.13242
0.1324 – 0.15526
0.1552 – 0.177911
0.1779 – 0.20077
0.2007 – 0.223517
0.2235 – 0.246317
0.2463 – 0.269117
0.2691 – 0.291923
0.2919 – 0.314732
0.3147 – 0.337534
0.3375 – 0.360230
0.3602 – 0.38343
0.383 – 0.405841
0.4058 – 0.428664
0.4286 – 0.451471
0.4514 – 0.474263
0.4742 – 0.49789
0.497 – 0.519878
0.5198 – 0.5425117
0.5425 – 0.5653116
0.5653 – 0.5881147
0.5881 – 0.6109147
0.6109 – 0.6337156
0.6337 – 0.6565165
0.6565 – 0.6793193
0.6793 – 0.7021190
0.7021 – 0.7249215
0.7249 – 0.7476223
0.7476 – 0.7704213
0.7704 – 0.7932187
0.7932 – 0.816142
0.816 – 0.8388113
0.8388 – 0.861674
0.8616 – 0.884449
0.8844 – 0.907229
0.9072 – 0.92999
0.9299 – 0.95273
per_dem · Democratic vote share by county; compare against per_gop to see the partisan tilt of most counties.
Show data table
bincount
0.03145 – 0.053878
0.05387 – 0.076316
0.0763 – 0.0987252
0.09872 – 0.121174
0.1211 – 0.1436116
0.1436 – 0.166146
0.166 – 0.1884203
0.1884 – 0.2109226
0.2109 – 0.2333240
0.2333 – 0.2557218
0.2557 – 0.2781200
0.2781 – 0.3006205
0.3006 – 0.323153
0.323 – 0.3454147
0.3454 – 0.3678153
0.3678 – 0.3903152
0.3903 – 0.4127106
0.4127 – 0.4351111
0.4351 – 0.457577
0.4575 – 0.4878
0.48 – 0.502456
0.5024 – 0.524872
0.5248 – 0.547245
0.5472 – 0.569742
0.5697 – 0.592136
0.5921 – 0.614536
0.6145 – 0.636934
0.6369 – 0.659425
0.6594 – 0.681830
0.6818 – 0.704216
0.7042 – 0.726612
0.7266 – 0.749112
0.7491 – 0.771514
0.7715 – 0.79399
0.7939 – 0.81636
0.8163 – 0.83884
0.8388 – 0.86124
0.8612 – 0.88364
0.8836 – 0.9062
0.906 – 0.92851
total_votes · Total votes per county is heavily right-skewed (skew ~8.9); a handful of large counties dwarf the rest.
Show data table
bincount
64 – 6.636e+042699
6.636e+04 – 1.327e+05192
1.327e+05 – 1.99e+0577
1.99e+05 – 2.653e+0570
2.653e+05 – 3.316e+0533
3.316e+05 – 3.979e+0520
3.979e+05 – 4.642e+0512
4.642e+05 – 5.305e+054
5.305e+05 – 5.968e+057
5.968e+05 – 6.631e+059
6.631e+05 – 7.294e+054
7.294e+05 – 7.957e+055
7.957e+05 – 8.62e+051
8.62e+05 – 9.283e+051
9.283e+05 – 9.946e+051
9.946e+05 – 1.061e+061
1.061e+06 – 1.127e+061
1.127e+06 – 1.193e+060
1.193e+06 – 1.26e+061
1.26e+06 – 1.326e+061
1.326e+06 – 1.392e+060
1.392e+06 – 1.459e+060
1.459e+06 – 1.525e+060
1.525e+06 – 1.591e+060
1.591e+06 – 1.658e+060
1.658e+06 – 1.724e+060
1.724e+06 – 1.79e+060
1.79e+06 – 1.856e+060
1.856e+06 – 1.923e+060
1.923e+06 – 1.989e+060
1.989e+06 – 2.055e+061
2.055e+06 – 2.122e+060
2.122e+06 – 2.188e+060
2.188e+06 – 2.254e+060
2.254e+06 – 2.321e+060
2.321e+06 – 2.387e+060
2.387e+06 – 2.453e+060
2.453e+06 – 2.519e+060
2.519e+06 – 2.586e+060
2.586e+06 – 2.652e+061
state_abbr · Counties per state — Texas and Georgia dominate the row counts, which matters for any aggregation.
Show data table
valuecountshare
TX2548.1%
GA1595.1%
VA1334.2%
KY1203.8%
MO1153.7%
KS1053.3%
IL1023.2%
NC1003.2%
IA993.2%
TN953.0%
NE933.0%
IN922.9%
OH882.8%
MN872.8%
MI832.6%
MS822.6%
OK772.5%
AR752.4%
WI722.3%
AL672.1%
votes_gop · Republican vote totals per county; check the long tail of outlier counties versus the median of ~7,268.
Show data table
bincount
57 – 1.556e+042260
1.556e+04 – 3.107e+04381
3.107e+04 – 4.657e+04153
4.657e+04 – 6.208e+04100
6.208e+04 – 7.759e+0454
7.759e+04 – 9.309e+0432
9.309e+04 – 1.086e+0527
1.086e+05 – 1.241e+0523
1.241e+05 – 1.396e+0547
1.396e+05 – 1.551e+0513
1.551e+05 – 1.706e+0514
1.706e+05 – 1.861e+054
1.861e+05 – 2.016e+058
2.016e+05 – 2.171e+052
2.171e+05 – 2.326e+052
2.326e+05 – 2.481e+052
2.481e+05 – 2.637e+054
2.637e+05 – 2.792e+053
2.792e+05 – 2.947e+051
2.947e+05 – 3.102e+051
3.102e+05 – 3.257e+051
3.257e+05 – 3.412e+052
3.412e+05 – 3.567e+051
3.567e+05 – 3.722e+050
3.722e+05 – 3.877e+051
3.877e+05 – 4.032e+050
4.032e+05 – 4.187e+050
4.187e+05 – 4.342e+050
4.342e+05 – 4.497e+051
4.497e+05 – 4.652e+050
4.652e+05 – 4.807e+051
4.807e+05 – 4.962e+050
4.962e+05 – 5.117e+050
5.117e+05 – 5.273e+050
5.273e+05 – 5.428e+050
5.428e+05 – 5.583e+051
5.583e+05 – 5.738e+050
5.738e+05 – 5.893e+050
5.893e+05 – 6.048e+051
6.048e+05 – 6.203e+051

Schema

11 columns
Alerts
numeric 0.0% 3,141
votes_dem numeric 0.0% 2,688
high_skew outliers
votes_gop numeric 0.0% 2,901
high_skew outliers
total_votes numeric 0.0% 2,966
high_skew outliers
per_dem numeric 0.0% 3,112
per_gop numeric 0.0% 3,112
diff text 0.0% 2,738
one_word allcaps short_text
per_point_diff text 0.0% 2,555
one_word allcaps short_text
state_abbr categorical 0.0% 51
county_name text 0.0% 1,848
short_text duplicates
combined_fips numeric 0.0% 3,141

numeric identifier
This unnamed numeric column runs from 0 to 3140 with exactly 3141 unique values across 3141 rows, mean and median both 1570, and zero skew — the hallmarks of a row index rather than a measured feature. There are no nulls and no outliers, and the only zero is the single index-0 row (zero_rate ≈ 0.00032). Treatment: Drop before modelling; it is a sequential row index. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
3,141
min
0
max
3,140
mean
1,570
median
1,570
std
906.9
q1
785
q3
2,355
iqr
1,570
skew
0
kurtosis
-1.2
n_outliers
0
outlier_rate
0
zero_rate
0.0003184

votes_dem

numeric feature high_skew outliers
Counts of Democratic votes per row (likely a US county or precinct), ranging from 4 to 1,893,770 with a median of 3,194 but a mean of 20,734. The distribution is extremely right-skewed (skew 11.65, kurtosis 224.4), and 468 rows (14.9%) flag as outliers — consistent with a few large urban jurisdictions dwarfing the rest. No nulls or zeros, and 2,688 unique values across 3,141 rows. Treatment: Log-transform before regression or convert to a share/per-capita rate to tame the skew. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
2,688
min
4
max
1.894e+06
mean
2.073e+04
median
3,194
std
7.2e+04
q1
1,175
q3
10,047
iqr
8,872
skew
11.65
kurtosis
224.4
n_outliers
468
outlier_rate
0.149
zero_rate
0

votes_gop

numeric feature high_skew outliers
Per-county GOP vote totals across 3,141 rows, almost all distinct (2,901 unique) and never null or zero. The distribution is heavily right-skewed (skew 5.78, kurtosis 51.78) with a median of 7,268 but a max of 620,285, and 394 rows (12.5%) flagged as outliers — consistent with a few very populous counties dwarfing the rest. Treatment: Log-transform or normalize by county population before modelling. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
2,901
min
57
max
620,285
mean
2.065e+04
median
7,268
std
4.163e+04
q1
3,241
q3
18,130
iqr
14,889
skew
5.78
kurtosis
51.78
n_outliers
394
outlier_rate
0.1254
zero_rate
0

total_votes

numeric feature high_skew outliers
Per-row vote totals across 3,141 records, almost all distinct (2,966 unique) with no nulls or zeros. The distribution is severely right-skewed (skew 8.89, kurtosis 136.17): the median is 11,144 yet the mean is 43,636 and the max reaches 2,652,072, far above Q3 of 29,799. About 14% of rows (442) flag as outliers, consistent with a few very high-vote jurisdictions dominating the tail. Treatment: log-transform before regression or aggregation to tame the heavy right tail. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
2,966
min
64
max
2.652e+06
mean
4.364e+04
median
11,144
std
1.146e+05
q1
4,870
q3
29,799
iqr
24,929
skew
8.894
kurtosis
136.2
n_outliers
442
outlier_rate
0.1407
zero_rate
0

per_dem

numeric feature
Values are continuous proportions bounded between 0.031 and 0.928 with mean 0.318 and median 0.286, consistent with a per-unit Democratic vote share across 3,141 rows (matching the U.S. county count). Distribution is right-skewed (skew 0.94) with 76 outliers (2.4%) on the upper tail, reflecting a minority of heavily Democratic units. Near-unique values (3,112/3,141) and zero null/zero rates indicate a clean, fully-populated feature. Treatment: Use as-is as a bounded proportion; consider a logit transform if feeding a linear model due to right skew. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
3,112
min
0.03145
max
0.9285
mean
0.3176
median
0.2864
std
0.153
q1
0.2054
q3
0.3982
iqr
0.1929
skew
0.9422
kurtosis
0.6859
n_outliers
76
outlier_rate
0.0242
zero_rate
0

per_gop

numeric feature
Likely the Republican (GOP) vote share per geographic unit (e.g., county), bounded between 0.041 and 0.953 with no nulls and 3112 unique values across 3141 rows. The distribution is left-skewed (skew -0.82) with a median of 0.665 above the mean of 0.635, indicating most units lean Republican while a tail of low-GOP units pulls the mean down. 63 outliers (2.0%) sit outside the IQR fence, consistent with strongly Democratic enclaves. Treatment: Use as-is as a proportion feature; consider a logit transform if feeding a linear model. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
3,112
min
0.04122
max
0.9527
mean
0.6351
median
0.6654
std
0.1561
q1
0.5458
q3
0.7503
iqr
0.2045
skew
-0.8193
kurtosis
0.376
n_outliers
63
outlier_rate
0.02006
zero_rate
0

diff

text feature one_word allcaps short_text
Despite being typed as text, `diff` is a single-token numeric field stored as comma-formatted strings (one_word_rate 1.0, len_mean ~4.9, max length 9). All 3,141 rows are populated with 2,738 unique values and 403 duplicates (12.8%); the value '37,410' appears 29 times, far above any other, suggesting either a sentinel or a heavily repeated magnitude. The allcaps and short_text alerts are artefacts of digits-only content rather than real prose. Treatment: strip commas and cast to numeric before modelling, and investigate the spike at 37,410. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
2,738
len_min
1
len_max
9
len_mean
4.935
len_median
5
len_p95
6
word_mean
1
word_median
1
n_empty
0
n_duplicates
403
duplicate_rate
0.1283
vocab_size
2,738
readability_flesch_mean
121.2
emoji_rate
0
url_rate
0
one_word_rate
1
allcaps_rate
0.9924
boilerplate_rate
0

per_point_diff

text feature one_word allcaps short_text
This column stores a per-point differential as a percentage string (e.g. '15.17%', '63.21%'), with lengths tightly bound between 5 and 6 characters and exactly one token per cell. Despite 2555 unique values across 3141 rows, the duplicate rate is 18.7% and '15.17%' alone appears 31 times — far more than any other value, which is worth checking. The values are stored as text with a trailing '%', not as numbers. Treatment: Strip the '%' and cast to float before any numeric modelling. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
2,555
len_min
5
len_max
6
len_mean
5.896
len_median
6
len_p95
6
word_mean
1
word_median
1
n_empty
0
n_duplicates
586
duplicate_rate
0.1866
vocab_size
2,555
readability_flesch_mean
121.2
emoji_rate
0
url_rate
0
one_word_rate
1
allcaps_rate
1
boilerplate_rate
0

state_abbr

categorical foreign_key
This column holds US state abbreviations, with 51 unique values across 3141 rows and no nulls — consistent with one row per US county (50 states plus DC). The distribution tracks county counts rather than population: TX leads at 254 (8.1%), followed by GA (159), VA (133), and KY (120). Entropy ratio of 0.93 indicates a fairly even spread across states. Treatment: Use as a categorical grouping key or left-join to a state-level reference table. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
51
top_value
TX
top_rate
0.08087
cardinality
51
entropy
5.275
entropy_ratio
0.9299

county_name

text feature short_text duplicates
This column holds US county names — 3,006 of 3,141 rows contain the word 'county', with 'parish' (64) and 'city' (43) covering Louisiana and Virginia equivalents. Names repeat heavily across states: 1,293 duplicates (41.2%) leave only 1,848 unique values, with 'Washington County' (30), 'Jefferson County' (25), and 'Franklin County' (24) leading. One oddity: 'Alaska' appears 29 times as a bare state name, breaking the county/parish/city pattern. Treatment: Pair with a state column before joining or grouping; the name alone is not unique. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
1,848
len_min
6
len_max
27
len_mean
13.87
len_median
14
len_p95
17
word_mean
2.054
word_median
2
n_empty
0
n_duplicates
1,293
duplicate_rate
0.4117
vocab_size
1,840
readability_flesch_mean
38.38
emoji_rate
0
url_rate
0
one_word_rate
0.009233
allcaps_rate
0
boilerplate_rate
0

combined_fips

numeric identifier
This is almost certainly the 5-digit combined state+county FIPS code (state*1000 + county), with all 3141 values unique and no nulls — matching the count of US counties. The range 1001 to 56045 spans Alabama (01) through Wyoming (56), and the near-zero skew reflects roughly uniform numeric county codes across states rather than a meaningful distribution. Treatment: treat as a categorical key; left-join on this code rather than using as a numeric feature. high · anthropic:claude-opus-4-7
n
3,141
nulls
0 (0.0%)
unique
3,141
min
1,001
max
56,045
mean
3.039e+04
median
29,177
std
1.516e+04
q1
18,179
q3
45,081
iqr
26,902
skew
-0.08027
kurtosis
-1.098
n_outliers
0
outlier_rate
0
zero_rate
0