Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells350
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory164.0 B

Variable types

Categorical1
Text3
Numeric4
Unsupported11

Dataset

Description제17대 대통령선거 개표 결과에 대한 정보로, 전국 시도, 구시군, 읍면동, 투표구별 제17대 대통령선거의 개표 결과 데이터를 조회하실 수 있습니다.
URLhttps://www.data.go.kr/data/15104365/fileData.do

Alerts

선거인수 is highly overall correlated with 투표수 and 2 other fieldsHigh correlation
투표수 is highly overall correlated with 선거인수 and 2 other fieldsHigh correlation
무효투표수 is highly overall correlated with 시도명High correlation
기권수 is highly overall correlated with 선거인수 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 선거인수 and 3 other fieldsHigh correlation
투표구명 has 334 (3.3%) missing valuesMissing
선거인수 is highly skewed (γ1 = 92.5633566)Skewed
투표수 is highly skewed (γ1 = 92.56031726)Skewed
무효투표수 is highly skewed (γ1 = 95.95972686)Skewed
기권수 is highly skewed (γ1 = 92.55181794)Skewed
후보자별 득표수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported
무효투표수 has 125 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-12 14:08:21.852204
Analysis finished2023-12-12 14:08:25.778035
Duration3.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1823 
서울특별시
1602 
경상북도
818 
전라남도
716 
경상남도
714 
Other values (13)
4327 

Length

Max length7
Median length5
Mean length4.1941
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row경상북도
2nd row경기도
3rd row경상남도
4th row인천광역시
5th row경상남도

Common Values

ValueCountFrequency (%)
경기도 1823
18.2%
서울특별시 1602
16.0%
경상북도 818
8.2%
전라남도 716
 
7.2%
경상남도 714
 
7.1%
부산광역시 657
 
6.6%
전라북도 541
 
5.4%
충청남도 535
 
5.3%
강원도 511
 
5.1%
인천광역시 432
 
4.3%
Other values (8) 1651
16.5%

Length

2023-12-12T23:08:25.860616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1823
18.2%
서울특별시 1602
16.0%
경상북도 818
8.2%
전라남도 716
 
7.2%
경상남도 714
 
7.1%
부산광역시 657
 
6.6%
전라북도 541
 
5.4%
충청남도 535
 
5.3%
강원도 511
 
5.1%
인천광역시 432
 
4.3%
Other values (8) 1651
16.5%

구시군명
Text

Distinct226
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-12T23:08:26.191261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.2851285
Min length2

Characters and Unicode

Total characters32848
Distinct characters139
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울진군
2nd row이천시
3rd row진주시
4th row서구
5th row김해시
ValueCountFrequency (%)
서구 236
 
2.4%
남구 230
 
2.3%
동구 225
 
2.3%
북구 218
 
2.2%
중구 204
 
2.0%
강서구 107
 
1.1%
제주시 103
 
1.0%
천안시 94
 
0.9%
여수시 91
 
0.9%
노원구 90
 
0.9%
Other values (216) 8401
84.0%
2023-12-12T23:08:26.663024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 5020
 
15.3%
시 4380
 
13.3%
군 2135
 
6.5%
주 1046
 
3.2%
산 934
 
2.8%
천 929
 
2.8%
동 814
 
2.5%
양 813
 
2.5%
남 761
 
2.3%
성 735
 
2.2%
Other values (129) 15281
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32848
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5020
 
15.3%
시 4380
 
13.3%
군 2135
 
6.5%
주 1046
 
3.2%
산 934
 
2.8%
천 929
 
2.8%
동 814
 
2.5%
양 813
 
2.5%
남 761
 
2.3%
성 735
 
2.2%
Other values (129) 15281
46.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32848
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5020
 
15.3%
시 4380
 
13.3%
군 2135
 
6.5%
주 1046
 
3.2%
산 934
 
2.8%
천 929
 
2.8%
동 814
 
2.5%
양 813
 
2.5%
남 761
 
2.3%
성 735
 
2.2%
Other values (129) 15281
46.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32848
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 5020
 
15.3%
시 4380
 
13.3%
군 2135
 
6.5%
주 1046
 
3.2%
산 934
 
2.8%
천 929
 
2.8%
동 814
 
2.5%
양 813
 
2.5%
남 761
 
2.3%
성 735
 
2.2%
Other values (129) 15281
46.5%

읍면동명
Text

Distinct3103
Distinct (%)31.1%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T23:08:27.018568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length3
Mean length3.6253879
Min length1

Characters and Unicode

Total characters36214
Distinct characters329
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509 ?
Unique (%)5.1%

Sample

1st row평해읍
2nd row관고동
3rd row대평면
4th row검단2동
5th row계
ValueCountFrequency (%)
계 144
 
1.4%
부재자투표 139
 
1.4%
중앙동 54
 
0.5%
잘못 40
 
0.4%
투입·구분된 40
 
0.4%
투표지 40
 
0.4%
남면 37
 
0.4%
북면 31
 
0.3%
서면 27
 
0.3%
동면 22
 
0.2%
Other values (3095) 9495
94.3%
2023-12-12T23:08:27.454222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6793
 
18.8%
면 2380
 
6.6%
제 1854
 
5.1%
2 1352
 
3.7%
1 1299
 
3.6%
읍 883
 
2.4%
산 697
 
1.9%
3 601
 
1.7%
신 494
 
1.4%
천 463
 
1.3%
Other values (319) 19398
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32209
88.9%
Decimal Number 3817
 
10.5%
Other Punctuation 108
 
0.3%
Space Separator 80
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6793
 
21.1%
면 2380
 
7.4%
제 1854
 
5.8%
읍 883
 
2.7%
산 697
 
2.2%
신 494
 
1.5%
천 463
 
1.4%
성 431
 
1.3%
부 378
 
1.2%
남 354
 
1.1%
Other values (306) 17482
54.3%
Decimal Number
ValueCountFrequency (%)
2 1352
35.4%
1 1299
34.0%
3 601
15.7%
4 276
 
7.2%
5 100
 
2.6%
6 80
 
2.1%
7 44
 
1.2%
9 28
 
0.7%
8 25
 
0.7%
0 12
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 65
60.2%
· 43
39.8%
Space Separator
ValueCountFrequency (%)
80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32209
88.9%
Common 4005
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6793
 
21.1%
면 2380
 
7.4%
제 1854
 
5.8%
읍 883
 
2.7%
산 697
 
2.2%
신 494
 
1.5%
천 463
 
1.4%
성 431
 
1.3%
부 378
 
1.2%
남 354
 
1.1%
Other values (306) 17482
54.3%
Common
ValueCountFrequency (%)
2 1352
33.8%
1 1299
32.4%
3 601
15.0%
4 276
 
6.9%
5 100
 
2.5%
6 80
 
2.0%
80
 
2.0%
. 65
 
1.6%
7 44
 
1.1%
· 43
 
1.1%
Other values (3) 65
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32209
88.9%
ASCII 3962
 
10.9%
None 43
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6793
 
21.1%
면 2380
 
7.4%
제 1854
 
5.8%
읍 883
 
2.7%
산 697
 
2.2%
신 494
 
1.5%
천 463
 
1.4%
성 431
 
1.3%
부 378
 
1.2%
남 354
 
1.1%
Other values (306) 17482
54.3%
ASCII
ValueCountFrequency (%)
2 1352
34.1%
1 1299
32.8%
3 601
15.2%
4 276
 
7.0%
5 100
 
2.5%
6 80
 
2.0%
80
 
2.0%
. 65
 
1.6%
7 44
 
1.1%
9 28
 
0.7%
Other values (2) 37
 
0.9%
None
ValueCountFrequency (%)
· 43
100.0%

투표구명
Text

MISSING 

Distinct7286
Distinct (%)75.4%
Missing334
Missing (%)3.3%
Memory size156.2 KiB
2023-12-12T23:08:27.649948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.6391475
Min length2

Characters and Unicode

Total characters54508
Distinct characters320
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7088 ?
Unique (%)73.3%

Sample

1st row평해읍제2투
2nd row소계
3rd row소계
4th row검단2동제2투
5th row농암면제1투
ValueCountFrequency (%)
소계 2087
 
21.6%
중앙동제1투 12
 
0.1%
남면제2투 9
 
0.1%
중앙동제2투 8
 
0.1%
북면제1투 8
 
0.1%
서면제1투 7
 
0.1%
북면제2투 7
 
0.1%
남면제1투 7
 
0.1%
남면제3투 7
 
0.1%
송정동제2투 6
 
0.1%
Other values (7276) 7508
77.7%
2023-12-12T23:08:27.962399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 8884
16.3%
투 7579
13.9%
동 5458
 
10.0%
1 2998
 
5.5%
2 2939
 
5.4%
소 2375
 
4.4%
계 2241
 
4.1%
3 1877
 
3.4%
면 1658
 
3.0%
4 1180
 
2.2%
Other values (310) 17319
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43896
80.5%
Decimal Number 10563
 
19.4%
Other Punctuation 49
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 8884
20.2%
투 7579
17.3%
동 5458
 
12.4%
소 2375
 
5.4%
계 2241
 
5.1%
면 1658
 
3.8%
읍 757
 
1.7%
산 519
 
1.2%
신 397
 
0.9%
천 374
 
0.9%
Other values (298) 13654
31.1%
Decimal Number
ValueCountFrequency (%)
1 2998
28.4%
2 2939
27.8%
3 1877
17.8%
4 1180
 
11.2%
5 687
 
6.5%
6 391
 
3.7%
7 230
 
2.2%
8 127
 
1.2%
9 94
 
0.9%
0 40
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 45
91.8%
· 4
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43896
80.5%
Common 10612
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 8884
20.2%
투 7579
17.3%
동 5458
 
12.4%
소 2375
 
5.4%
계 2241
 
5.1%
면 1658
 
3.8%
읍 757
 
1.7%
산 519
 
1.2%
신 397
 
0.9%
천 374
 
0.9%
Other values (298) 13654
31.1%
Common
ValueCountFrequency (%)
1 2998
28.3%
2 2939
27.7%
3 1877
17.7%
4 1180
 
11.1%
5 687
 
6.5%
6 391
 
3.7%
7 230
 
2.2%
8 127
 
1.2%
9 94
 
0.9%
. 45
 
0.4%
Other values (2) 44
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43896
80.5%
ASCII 10608
 
19.5%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 8884
20.2%
투 7579
17.3%
동 5458
 
12.4%
소 2375
 
5.4%
계 2241
 
5.1%
면 1658
 
3.8%
읍 757
 
1.7%
산 519
 
1.2%
신 397
 
0.9%
천 374
 
0.9%
Other values (298) 13654
31.1%
ASCII
ValueCountFrequency (%)
1 2998
28.3%
2 2939
27.7%
3 1877
17.7%
4 1180
 
11.1%
5 687
 
6.5%
6 391
 
3.7%
7 230
 
2.2%
8 127
 
1.2%
9 94
 
0.9%
. 45
 
0.4%
None
ValueCountFrequency (%)
· 4
100.0%

선거인수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5279
Distinct (%)52.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12239.243
Minimum0
Maximum37653518
Zeros40
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:08:28.095748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile700
Q12097.5
median3227
Q34169
95-th percentile18500.4
Maximum37653518
Range37653518
Interquartile range (IQR)2071.5

Descriptive statistics

Standard deviation387594.3
Coefficient of variation (CV)31.66816
Kurtosis8917.8554
Mean12239.243
Median Absolute Deviation (MAD)1033
Skewness92.563357
Sum1.2238019 × 108
Variance1.5022934 × 1011
MonotonicityNot monotonic
2023-12-12T23:08:28.231274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40
 
0.4%
3199 10
 
0.1%
3664 9
 
0.1%
3229 9
 
0.1%
2663 9
 
0.1%
4083 8
 
0.1%
3032 8
 
0.1%
3283 8
 
0.1%
3050 8
 
0.1%
3514 8
 
0.1%
Other values (5269) 9882
98.8%
ValueCountFrequency (%)
0 40
0.4%
46 1
 
< 0.1%
51 1
 
< 0.1%
57 1
 
< 0.1%
69 1
 
< 0.1%
72 1
 
< 0.1%
73 2
 
< 0.1%
78 1
 
< 0.1%
88 1
 
< 0.1%
98 1
 
< 0.1%
ValueCountFrequency (%)
37653518 1
< 0.1%
8051696 1
< 0.1%
2005874 1
< 0.1%
1896866 1
< 0.1%
1500831 1
< 0.1%
1164655 1
< 0.1%
1098977 1
< 0.1%
1031333 1
< 0.1%
806423 1
< 0.1%
483567 1
< 0.1%

투표수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4294
Distinct (%)42.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7707.8669
Minimum1
Maximum23732854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:08:28.461512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile484
Q11327.5
median1982
Q32636.5
95-th percentile11293.3
Maximum23732854
Range23732853
Interquartile range (IQR)1309

Descriptive statistics

Standard deviation244298.56
Coefficient of variation (CV)31.694704
Kurtosis8917.8134
Mean7707.8669
Median Absolute Deviation (MAD)655
Skewness92.560317
Sum77070961
Variance5.9681787 × 1010
MonotonicityNot monotonic
2023-12-12T23:08:28.602012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1683 11
 
0.1%
1477 11
 
0.1%
2186 11
 
0.1%
2010 11
 
0.1%
2088 11
 
0.1%
1415 10
 
0.1%
2082 10
 
0.1%
1980 10
 
0.1%
2193 10
 
0.1%
2170 9
 
0.1%
Other values (4284) 9895
99.0%
ValueCountFrequency (%)
1 3
< 0.1%
2 1
 
< 0.1%
3 6
0.1%
4 3
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
10 3
< 0.1%
13 1
 
< 0.1%
14 2
 
< 0.1%
ValueCountFrequency (%)
23732854 1
< 0.1%
5066022 1
< 0.1%
1267969 1
< 0.1%
1210220 1
< 0.1%
971461 1
< 0.1%
728895 1
< 0.1%
680264 1
< 0.1%
663338 1
< 0.1%
521216 1
< 0.1%
313620 1
< 0.1%

후보자별 득표수
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 8
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 9
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 10
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 11
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 12
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 13
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 14
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 15
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

Unnamed: 16
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

무효투표수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct255
Distinct (%)2.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean36.830583
Minimum0
Maximum119974
Zeros125
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:08:28.770223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q316
95-th percentile48
Maximum119974
Range119974
Interquartile range (IQR)11

Descriptive statistics

Standard deviation1217.116
Coefficient of variation (CV)33.046341
Kurtosis9436.4285
Mean36.830583
Median Absolute Deviation (MAD)4
Skewness95.959727
Sum368269
Variance1481371.4
MonotonicityNot monotonic
2023-12-12T23:08:28.940539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 833
 
8.3%
5 830
 
8.3%
6 777
 
7.8%
7 686
 
6.9%
3 681
 
6.8%
8 555
 
5.5%
2 521
 
5.2%
9 510
 
5.1%
10 392
 
3.9%
11 298
 
3.0%
Other values (245) 3916
39.2%
ValueCountFrequency (%)
0 125
 
1.2%
1 243
 
2.4%
2 521
5.2%
3 681
6.8%
4 833
8.3%
5 830
8.3%
6 777
7.8%
7 686
6.9%
8 555
5.5%
9 510
5.1%
ValueCountFrequency (%)
119974 1
< 0.1%
14653 1
< 0.1%
8610 1
< 0.1%
5392 1
< 0.1%
4863 1
< 0.1%
4291 1
< 0.1%
2630 1
< 0.1%
2589 1
< 0.1%
2316 1
< 0.1%
1786 1
< 0.1%

기권수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3402
Distinct (%)34.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4531.3761
Minimum-124
Maximum13920664
Zeros0
Zeros (%)0.0%
Negative40
Negative (%)0.4%
Memory size166.0 KiB
2023-12-12T23:08:29.105573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-124
5-th percentile196
Q1707
median1164
Q31619
95-th percentile7018.6
Maximum13920664
Range13920788
Interquartile range (IQR)912

Descriptive statistics

Standard deviation143304.96
Coefficient of variation (CV)31.625042
Kurtosis8915.6424
Mean4531.3761
Median Absolute Deviation (MAD)456
Skewness92.551818
Sum45309230
Variance2.0536312 × 1010
MonotonicityNot monotonic
2023-12-12T23:08:29.280522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
688 14
 
0.1%
1333 14
 
0.1%
1076 13
 
0.1%
1067 13
 
0.1%
1114 12
 
0.1%
1150 12
 
0.1%
1378 12
 
0.1%
1318 12
 
0.1%
1112 12
 
0.1%
1387 12
 
0.1%
Other values (3392) 9873
98.7%
ValueCountFrequency (%)
-124 1
< 0.1%
-99 1
< 0.1%
-95 1
< 0.1%
-82 1
< 0.1%
-65 1
< 0.1%
-56 1
< 0.1%
-55 1
< 0.1%
-44 1
< 0.1%
-42 2
< 0.1%
-39 1
< 0.1%
ValueCountFrequency (%)
13920664 1
< 0.1%
2985674 1
< 0.1%
795654 1
< 0.1%
628897 1
< 0.1%
529370 1
< 0.1%
435760 1
< 0.1%
418713 1
< 0.1%
367995 1
< 0.1%
285207 1
< 0.1%
171606 1
< 0.1%

Interactions

2023-12-12T23:08:24.337743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:22.981496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.426872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.908395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:24.455935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.081745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.534500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:24.019747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:24.574688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.201824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.671222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:24.117533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:24.720234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.318741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:23.801275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:24.221070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:08:29.391031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명선거인수투표수무효투표수기권수
시도명1.0000.8520.8520.8520.852
선거인수0.8521.0001.0001.0001.000
투표수0.8521.0001.0001.0001.000
무효투표수0.8521.0001.0001.0001.000
기권수0.8521.0001.0001.0001.000
2023-12-12T23:08:29.487705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선거인수투표수무효투표수기권수시도명
선거인수1.0000.9740.2890.9360.706
투표수0.9741.0000.3070.8500.706
무효투표수0.2890.3071.0000.2500.706
기권수0.9360.8500.2501.0000.706
시도명0.7060.7060.7060.7061.000

Missing values

2023-12-12T23:08:24.908259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:08:25.437350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T23:08:25.668084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명구시군명읍면동명투표구명선거인수투표수후보자별 득표수Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16무효투표수기권수
15757경상북도울진군평해읍평해읍제2투1532107755855183192011104105819455
9116경기도이천시관고동소계77444247693246111632315415115934231163497
16079경상남도진주시대평면소계1057782874473811605001797739275
5290인천광역시서구검단2동검단2동제2투3950254763313174181812801347253891403
16389경상남도김해시계<NA>328602196486330109492386195951215410988057138449451954301056132116
15260경상북도문경시농암면농암면제1투1644123678839469241613203121026408
10975충청북도제천시고암.모산동소계65434103770198215623220215129114082212440
14018전라남도강진군강진읍강진읍제3투1759107178813721323043004910647688
16052경상남도진주시내동면내동면투표소1507106695648333370202223104323441
11220충청북도음성군부재자투표<NA>14941404292573833684576328613752990
시도명구시군명읍면동명투표구명선거인수투표수후보자별 득표수Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16무효투표수기권수
10639강원도철원군김화읍김화읍제3투12258531794743818461600838458372
13821전라남도고흥군고흥읍고흥읍제1투702455397222146010094514247
12026충청남도부여군내산면소계166210832712432961253713423106617579
9953강원도원주시단구동단구동제6투2997187833690894816841512335187171119
8865경기도하남시덕풍1동덕풍1동제4투4103263462713734992054610354262861469
9830강원도춘천시약사명동약사명동제1투28331744301926471510132102323173951089
1317서울특별시서대문구남가좌제2동남가좌제2동제6투32362296516126746111742170225422897940
2439서울특별시강남구대치제3동대치제3동제3투3738219540713464321370401254219411543
5198인천광역시계양구계양1동계양1동제3투465527657971211971026801500359275781890
12887전라북도김제시금구면금구면제2투7855464593917470201155442239