Overview

Dataset statistics

Number of variables19
Number of observations5055
Missing cells3332
Missing cells (%)3.5%
Duplicate rows12
Duplicate rows (%)0.2%
Total size in memory790.0 KiB
Average record size in memory160.0 B

Variable types

Categorical8
Numeric8
Text3

Dataset

Description경상남도 사천시 공간정보시스템 의 데이터베이스 테이블 중 교통표지판 테이블에 내용입니다. 실제 교통표지판과 차이가 있습니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15063662

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
Dataset has 12 (0.2%) duplicate rowsDuplicates
공사번호 is highly overall correlated with 관리번호 and 8 other fieldsHigh correlation
법정읍면동 is highly overall correlated with 공사번호High correlation
관리번호 is highly overall correlated with 도로구간번호 and 2 other fieldsHigh correlation
행정읍면동코드 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
높이 is highly overall correlated with 지주형식High correlation
위도 is highly overall correlated with 행정읍면동코드 and 3 other fieldsHigh correlation
경도 is highly overall correlated with X좌표 and 1 other fieldsHigh correlation
X좌표 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 행정읍면동코드 and 3 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 관리번호 and 4 other fieldsHigh correlation
지주형식 is highly overall correlated with 높이High correlation
법정읍면동 is highly imbalanced (92.5%)Imbalance
공사번호 is highly imbalanced (87.0%)Imbalance
규격 has 1770 (35.0%) missing valuesMissing
기재사항 has 1562 (30.9%) missing valuesMissing
높이 has 1563 (30.9%) zerosZeros

Reproduction

Analysis started2023-12-11 00:27:51.165273
Analysis finished2023-12-11 00:27:58.700447
Duration7.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
교통표지
5055 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교통표지
2nd row교통표지
3rd row교통표지
4th row교통표지
5th row교통표지

Common Values

ValueCountFrequency (%)
교통표지 5055
100.0%

Length

2023-12-11T09:27:58.751878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:27:58.826720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교통표지 5055
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct4939
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483308.14
Minimum1
Maximum999208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:27:58.920527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile745.4
Q1173943.5
median373002
Q3800297.5
95-th percentile990275.3
Maximum999208
Range999207
Interquartile range (IQR)626354

Descriptive statistics

Standard deviation351854.34
Coefficient of variation (CV)0.72801243
Kurtosis-1.648235
Mean483308.14
Median Absolute Deviation (MAD)344006
Skewness0.09118887
Sum2.4431227 × 109
Variance1.2380147 × 1011
MonotonicityNot monotonic
2023-12-11T09:27:59.046884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 2
 
< 0.1%
16 2
 
< 0.1%
672010 2
 
< 0.1%
672008 2
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
19 2
 
< 0.1%
13 2
 
< 0.1%
14 2
 
< 0.1%
675011 2
 
< 0.1%
Other values (4929) 5035
99.6%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 1
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
999208 1
< 0.1%
999207 1
< 0.1%
999206 1
< 0.1%
999205 1
< 0.1%
999204 1
< 0.1%
999203 1
< 0.1%
999202 1
< 0.1%
999201 1
< 0.1%
999200 1
< 0.1%
999199 1
< 0.1%

행정읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48240372
Minimum48240109
Maximum48240595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:27:59.153985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48240109
5-th percentile48240250
Q148240320
median48240350
Q348240370
95-th percentile48240570
Maximum48240595
Range486
Interquartile range (IQR)50

Descriptive statistics

Standard deviation105.33696
Coefficient of variation (CV)2.1835852 × 10-6
Kurtosis-0.0033909362
Mean48240372
Median Absolute Deviation (MAD)30
Skewness0.57939133
Sum2.4385508 × 1011
Variance11095.876
MonotonicityNot monotonic
2023-12-11T09:27:59.258764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
48240330 664
13.1%
48240250 581
11.5%
48240370 566
11.2%
48240320 504
10.0%
48240350 461
9.1%
48240360 434
8.6%
48240310 330
6.5%
48240510 269
 
5.3%
48240340 250
 
4.9%
48240550 220
 
4.4%
Other values (10) 776
15.4%
ValueCountFrequency (%)
48240109 30
 
0.6%
48240111 14
 
0.3%
48240116 25
 
0.5%
48240125 6
 
0.1%
48240126 19
 
0.4%
48240127 19
 
0.4%
48240250 581
11.5%
48240310 330
6.5%
48240320 504
10.0%
48240330 664
13.1%
ValueCountFrequency (%)
48240595 205
 
4.1%
48240570 180
 
3.6%
48240550 220
 
4.4%
48240530 107
 
2.1%
48240520 171
 
3.4%
48240510 269
5.3%
48240370 566
11.2%
48240360 434
8.6%
48240350 461
9.1%
48240340 250
4.9%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
용현면
664 
사천읍
581 
서포면
566 
사남면
504 
곤양면
461 
Other values (10)
2279 

Length

Max length7
Median length3
Mean length3.1105836
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사남면
2nd row남양동
3rd row남양동
4th row남양동
5th row남양동

Common Values

ValueCountFrequency (%)
용현면 664
13.1%
사천읍 581
11.5%
서포면 566
11.2%
사남면 504
10.0%
곤양면 461
9.1%
곤명면 434
8.6%
정동면 330
6.5%
동서동 269
 
5.3%
축동면 250
 
4.9%
벌용동 220
 
4.4%
Other values (5) 776
15.4%

Length

2023-12-11T09:27:59.372335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용현면 664
12.8%
사천읍 581
11.2%
서포면 566
11.0%
사남면 504
9.8%
곤양면 461
8.9%
곤명면 434
8.4%
정동면 330
 
6.4%
동서동 269
 
5.2%
축동면 250
 
4.8%
벌용동 220
 
4.3%
Other values (6) 889
17.2%

법정읍면동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
없음
4942 
용강동
 
30
향촌동
 
25
죽림동
 
19
송포동
 
19
Other values (2)
 
20

Length

Max length3
Median length2
Mean length2.0223541
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row없음
2nd row없음
3rd row없음
4th row없음
5th row없음

Common Values

ValueCountFrequency (%)
없음 4942
97.8%
용강동 30
 
0.6%
향촌동 25
 
0.5%
죽림동 19
 
0.4%
송포동 19
 
0.4%
봉남동 14
 
0.3%
대포동 6
 
0.1%

Length

2023-12-11T09:27:59.486285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:27:59.588568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 4942
97.8%
용강동 30
 
0.6%
향촌동 25
 
0.5%
죽림동 19
 
0.4%
송포동 19
 
0.4%
봉남동 14
 
0.3%
대포동 6
 
0.1%

도엽번호
Text

Distinct1213
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
2023-12-11T09:27:59.808897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9998022
Min length9

Characters and Unicode

Total characters50549
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique342 ?
Unique (%)6.8%

Sample

1st row358132205B
2nd row348010285A
3rd row348010274B
4th row348010274B
5th row348010275C
ValueCountFrequency (%)
358132293c 44
 
0.9%
358132293a 39
 
0.8%
358132283a 33
 
0.7%
358131728d 29
 
0.6%
358131739a 26
 
0.5%
348010708c 26
 
0.5%
358131738a 25
 
0.5%
357162083a 25
 
0.5%
348010745b 25
 
0.5%
348010731d 24
 
0.5%
Other values (1203) 4759
94.1%
2023-12-11T09:28:00.174193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 8942
17.7%
1 8301
16.4%
8 5418
10.7%
5 5013
9.9%
0 4140
8.2%
7 3797
7.5%
2 3179
 
6.3%
4 2877
 
5.7%
6 2574
 
5.1%
A 1331
 
2.6%
Other values (4) 4977
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45495
90.0%
Uppercase Letter 5054
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8942
19.7%
1 8301
18.2%
8 5418
11.9%
5 5013
11.0%
0 4140
9.1%
7 3797
8.3%
2 3179
 
7.0%
4 2877
 
6.3%
6 2574
 
5.7%
9 1254
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
A 1331
26.3%
D 1279
25.3%
B 1230
24.3%
C 1214
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45495
90.0%
Latin 5054
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8942
19.7%
1 8301
18.2%
8 5418
11.9%
5 5013
11.0%
0 4140
9.1%
7 3797
8.3%
2 3179
 
7.0%
4 2877
 
6.3%
6 2574
 
5.7%
9 1254
 
2.8%
Latin
ValueCountFrequency (%)
A 1331
26.3%
D 1279
25.3%
B 1230
24.3%
C 1214
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8942
17.7%
1 8301
16.4%
8 5418
10.7%
5 5013
9.9%
0 4140
8.2%
7 3797
7.5%
2 3179
 
6.3%
4 2877
 
5.7%
6 2574
 
5.1%
A 1331
 
2.6%
Other values (4) 4977
9.8%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
사천시
5055 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사천시
2nd row사천시
3rd row사천시
4th row사천시
5th row사천시

Common Values

ValueCountFrequency (%)
사천시 5055
100.0%

Length

2023-12-11T09:28:00.297414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:28:00.376891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 5055
100.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1750
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388478.64
Minimum3
Maximum999030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:28:00.472234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile1194
Q1204422
median352228
Q3475022
95-th percentile900295
Maximum999030
Range999027
Interquartile range (IQR)270600

Descriptive statistics

Standard deviation249538.56
Coefficient of variation (CV)0.64234821
Kurtosis0.4130651
Mean388478.64
Median Absolute Deviation (MAD)125876
Skewness0.87313148
Sum1.9637595 × 109
Variance6.2269494 × 1010
MonotonicityNot monotonic
2023-12-11T09:28:00.599225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999002 37
 
0.7%
478404 33
 
0.7%
900295 32
 
0.6%
491901 31
 
0.6%
999004 31
 
0.6%
18 30
 
0.6%
471501 25
 
0.5%
21 25
 
0.5%
434702 25
 
0.5%
479910 23
 
0.5%
Other values (1740) 4763
94.2%
ValueCountFrequency (%)
3 2
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
17 11
 
0.2%
18 30
0.6%
20 5
 
0.1%
21 25
0.5%
24 6
 
0.1%
25 6
 
0.1%
ValueCountFrequency (%)
999030 6
 
0.1%
999029 21
0.4%
999027 3
 
0.1%
999026 1
 
< 0.1%
999025 1
 
< 0.1%
999024 4
 
0.1%
999022 12
0.2%
999021 16
0.3%
999020 2
 
< 0.1%
999019 3
 
0.1%

공사번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
4691 
RD20110005
 
126
RD20110009
 
99
2001000006
 
32
RD20110013
 
20
Other values (18)
 
87

Length

Max length10
Median length4
Mean length4.4320475
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4691
92.8%
RD20110005 126
 
2.5%
RD20110009 99
 
2.0%
2001000006 32
 
0.6%
RD20110013 20
 
0.4%
RD20110002 13
 
0.3%
RD20110014 11
 
0.2%
RD20110007 8
 
0.2%
RD20110022 7
 
0.1%
RD20110004 6
 
0.1%
Other values (13) 42
 
0.8%

Length

2023-12-11T09:28:00.724263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4691
92.8%
rd20110005 126
 
2.5%
rd20110009 99
 
2.0%
2001000006 32
 
0.6%
rd20110013 20
 
0.4%
rd20110002 13
 
0.3%
rd20110014 11
 
0.2%
rd20110007 8
 
0.2%
rd20110022 7
 
0.1%
rd20110004 6
 
0.1%
Other values (13) 42
 
0.8%

위치구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
미분류
1599 
우
1521 
좌
1442 
기타
418 
중앙
 
75

Length

Max length3
Median length1
Mean length1.7301682
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우
2nd row우
3rd row좌
4th row우
5th row우

Common Values

ValueCountFrequency (%)
미분류 1599
31.6%
우 1521
30.1%
좌 1442
28.5%
기타 418
 
8.3%
중앙 75
 
1.5%

Length

2023-12-11T09:28:00.851408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:28:00.950376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 1599
31.6%
우 1521
30.1%
좌 1442
28.5%
기타 418
 
8.3%
중앙 75
 
1.5%

표지판구분
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
교통안전(주의)
1950 
교통안전(지시)
1814 
교통안전(규제)
1167 
교통안전(보조)
 
90
교통안전(기타)
 
33

Length

Max length8
Median length8
Mean length7.9988131
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row교통안전(지시)
2nd row교통안전(규제)
3rd row교통안전(주의)
4th row교통안전(주의)
5th row교통안전(주의)

Common Values

ValueCountFrequency (%)
교통안전(주의) 1950
38.6%
교통안전(지시) 1814
35.9%
교통안전(규제) 1167
23.1%
교통안전(보조) 90
 
1.8%
교통안전(기타) 33
 
0.7%
기타 1
 
< 0.1%

Length

2023-12-11T09:28:01.049332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:28:01.135231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교통안전(주의 1950
38.6%
교통안전(지시 1814
35.9%
교통안전(규제 1167
23.1%
교통안전(보조 90
 
1.8%
교통안전(기타 33
 
0.7%
기타 1
 
< 0.1%

지주형식
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
단주식
2706 
미분류
1496 
기타
336 
가로등 병설식
 
205
현수식
 
196
Other values (3)
 
116

Length

Max length7
Median length3
Mean length3.120277
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단주식
2nd row현수식
3rd row단주식
4th row가로등 병설식
5th row단주식

Common Values

ValueCountFrequency (%)
단주식 2706
53.5%
미분류 1496
29.6%
기타 336
 
6.6%
가로등 병설식 205
 
4.1%
현수식 196
 
3.9%
편지식 85
 
1.7%
신호등 병설식 28
 
0.6%
한전주 병설식 3
 
0.1%

Length

2023-12-11T09:28:01.229379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:28:01.340613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단주식 2706
51.1%
미분류 1496
28.3%
기타 336
 
6.4%
병설식 236
 
4.5%
가로등 205
 
3.9%
현수식 196
 
3.7%
편지식 85
 
1.6%
신호등 28
 
0.5%
한전주 3
 
0.1%

높이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8866291
Minimum0
Maximum8
Zeros1563
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:28:01.444914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32.55
95-th percentile5.5
Maximum8
Range8
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation1.655871
Coefficient of variation (CV)0.8776876
Kurtosis0.72050717
Mean1.8866291
Median Absolute Deviation (MAD)0.9
Skewness0.83232018
Sum9536.91
Variance2.7419086
MonotonicityNot monotonic
2023-12-11T09:28:01.562677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1563
30.9%
2.0 333
 
6.6%
2.5 251
 
5.0%
2.1 202
 
4.0%
5.5 180
 
3.6%
3.0 171
 
3.4%
5.0 153
 
3.0%
2.6 109
 
2.2%
1.9 108
 
2.1%
2.7 100
 
2.0%
Other values (203) 1885
37.3%
ValueCountFrequency (%)
0.0 1563
30.9%
0.24 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 2
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
0.72 1
 
< 0.1%
0.77 1
 
< 0.1%
0.8 3
 
0.1%
0.87 1
 
< 0.1%
ValueCountFrequency (%)
8.0 15
 
0.3%
7.0 29
 
0.6%
6.5 40
 
0.8%
6.0 49
 
1.0%
5.5 180
3.6%
5.4 3
 
0.1%
5.3 1
 
< 0.1%
5.2 1
 
< 0.1%
5.1 1
 
< 0.1%
5.0 153
3.0%

규격
Text

MISSING 

Distinct124
Distinct (%)3.8%
Missing1770
Missing (%)35.0%
Memory size39.6 KiB
2023-12-11T09:28:01.765101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.4365297
Min length3

Characters and Unicode

Total characters24429
Distinct characters24
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)1.4%

Sample

1st row0.60X0.68
2nd rowφ600
3rd row0.84X0.74
4th row0.84X0.74
5th row0.84X0.74
ValueCountFrequency (%)
0.84x0.74 1074
32.7%
0.6x0.68 577
17.6%
φ600 411
 
12.5%
600 129
 
3.9%
900 92
 
2.8%
0.8x0.8 88
 
2.7%
삼각0.9 84
 
2.6%
0.6+0.2x0.6 82
 
2.5%
0.84x0.7 79
 
2.4%
0.6x0.2x0.6 69
 
2.1%
Other values (101) 600
18.3%
2023-12-11T09:28:02.054302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6544
26.8%
. 5218
21.4%
6 2351
 
9.6%
4 2275
 
9.3%
8 2135
 
8.7%
X 2080
 
8.5%
7 1221
 
5.0%
x 380
 
1.6%
9 348
 
1.4%
2 330
 
1.4%
Other values (14) 1547
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15648
64.1%
Other Punctuation 5218
 
21.4%
Uppercase Letter 2360
 
9.7%
Lowercase Letter 605
 
2.5%
Other Letter 403
 
1.6%
Math Symbol 195
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6544
41.8%
6 2351
 
15.0%
4 2275
 
14.5%
8 2135
 
13.6%
7 1221
 
7.8%
9 348
 
2.2%
2 330
 
2.1%
1 284
 
1.8%
5 110
 
0.7%
3 50
 
0.3%
Other Letter
ValueCountFrequency (%)
각 193
47.9%
삼 172
42.7%
오 19
 
4.7%
역 17
 
4.2%
팔 1
 
0.2%
형 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
X 2080
88.1%
Φ 204
 
8.6%
Ø 76
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
x 380
62.8%
φ 225
37.2%
Math Symbol
ValueCountFrequency (%)
× 113
57.9%
+ 82
42.1%
Other Punctuation
ValueCountFrequency (%)
. 5218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21061
86.2%
Latin 2536
 
10.4%
Greek 429
 
1.8%
Hangul 403
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6544
31.1%
. 5218
24.8%
6 2351
 
11.2%
4 2275
 
10.8%
8 2135
 
10.1%
7 1221
 
5.8%
9 348
 
1.7%
2 330
 
1.6%
1 284
 
1.3%
× 113
 
0.5%
Other values (3) 242
 
1.1%
Hangul
ValueCountFrequency (%)
각 193
47.9%
삼 172
42.7%
오 19
 
4.7%
역 17
 
4.2%
팔 1
 
0.2%
형 1
 
0.2%
Latin
ValueCountFrequency (%)
X 2080
82.0%
x 380
 
15.0%
Ø 76
 
3.0%
Greek
ValueCountFrequency (%)
φ 225
52.4%
Φ 204
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23408
95.8%
None 618
 
2.5%
Hangul 403
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6544
28.0%
. 5218
22.3%
6 2351
 
10.0%
4 2275
 
9.7%
8 2135
 
9.1%
X 2080
 
8.9%
7 1221
 
5.2%
x 380
 
1.6%
9 348
 
1.5%
2 330
 
1.4%
Other values (4) 526
 
2.2%
None
ValueCountFrequency (%)
φ 225
36.4%
Φ 204
33.0%
× 113
18.3%
Ø 76
 
12.3%
Hangul
ValueCountFrequency (%)
각 193
47.9%
삼 172
42.7%
오 19
 
4.7%
역 17
 
4.2%
팔 1
 
0.2%
형 1
 
0.2%

기재사항
Text

MISSING 

Distinct287
Distinct (%)8.2%
Missing1562
Missing (%)30.9%
Memory size39.6 KiB
2023-12-11T09:28:02.263041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length4.8688806
Min length2

Characters and Unicode

Total characters17007
Distinct characters206
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)3.7%

Sample

1st row횡단보도
2nd row제한속도60
3rd rowㅓ형 교차로
4th row├자형교차로
5th row횡단보도
ValueCountFrequency (%)
횡단보도 950
26.7%
과속방지턱 276
 
7.8%
주차금지 161
 
4.5%
양보 120
 
3.4%
ㅓ자형교차로 115
 
3.2%
천천히 81
 
2.3%
├자형교차로 76
 
2.1%
우측면통행 76
 
2.1%
직진및우회전 66
 
1.9%
┼자형교차로 60
 
1.7%
Other values (275) 1580
44.4%
2023-12-11T09:28:02.586419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 1547
 
9.1%
보 1185
 
7.0%
단 991
 
5.8%
횡 966
 
5.7%
로 878
 
5.2%
차 680
 
4.0%
속 628
 
3.7%
지 617
 
3.6%
교 440
 
2.6%
자 432
 
2.5%
Other values (196) 8643
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15967
93.9%
Decimal Number 594
 
3.5%
Other Symbol 143
 
0.8%
Uppercase Letter 101
 
0.6%
Space Separator 68
 
0.4%
Other Punctuation 61
 
0.4%
Lowercase Letter 27
 
0.2%
Open Punctuation 16
 
0.1%
Close Punctuation 16
 
0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1547
 
9.7%
보 1185
 
7.4%
단 991
 
6.2%
횡 966
 
6.0%
로 878
 
5.5%
차 680
 
4.3%
속 628
 
3.9%
지 617
 
3.9%
교 440
 
2.8%
자 432
 
2.7%
Other values (161) 7603
47.6%
Decimal Number
ValueCountFrequency (%)
0 282
47.5%
6 125
21.0%
8 52
 
8.8%
3 47
 
7.9%
5 28
 
4.7%
4 27
 
4.5%
1 17
 
2.9%
2 9
 
1.5%
7 4
 
0.7%
9 3
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
T 35
34.7%
L 14
 
13.9%
O 14
 
13.9%
W 14
 
13.9%
S 14
 
13.9%
Y 4
 
4.0%
M 2
 
2.0%
P 2
 
2.0%
A 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 25
41.0%
, 16
26.2%
/ 13
21.3%
% 6
 
9.8%
' 1
 
1.6%
Other Symbol
ValueCountFrequency (%)
├ 76
53.1%
┼ 60
42.0%
㎞ 7
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
t 14
51.9%
m 11
40.7%
k 2
 
7.4%
Space Separator
ValueCountFrequency (%)
68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15962
93.9%
Common 912
 
5.4%
Latin 128
 
0.8%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1547
 
9.7%
보 1185
 
7.4%
단 991
 
6.2%
횡 966
 
6.1%
로 878
 
5.5%
차 680
 
4.3%
속 628
 
3.9%
지 617
 
3.9%
교 440
 
2.8%
자 432
 
2.7%
Other values (160) 7598
47.6%
Common
ValueCountFrequency (%)
0 282
30.9%
6 125
13.7%
├ 76
 
8.3%
68
 
7.5%
┼ 60
 
6.6%
8 52
 
5.7%
3 47
 
5.2%
5 28
 
3.1%
4 27
 
3.0%
. 25
 
2.7%
Other values (13) 122
13.4%
Latin
ValueCountFrequency (%)
T 35
27.3%
t 14
 
10.9%
L 14
 
10.9%
O 14
 
10.9%
W 14
 
10.9%
S 14
 
10.9%
m 11
 
8.6%
Y 4
 
3.1%
M 2
 
1.6%
k 2
 
1.6%
Other values (2) 4
 
3.1%
Han
ValueCountFrequency (%)
十 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15720
92.4%
ASCII 897
 
5.3%
Compat Jamo 242
 
1.4%
Box Drawing 136
 
0.8%
CJK Compat 7
 
< 0.1%
CJK 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 1547
 
9.8%
보 1185
 
7.5%
단 991
 
6.3%
횡 966
 
6.1%
로 878
 
5.6%
차 680
 
4.3%
속 628
 
4.0%
지 617
 
3.9%
교 440
 
2.8%
자 432
 
2.7%
Other values (157) 7356
46.8%
ASCII
ValueCountFrequency (%)
0 282
31.4%
6 125
13.9%
68
 
7.6%
8 52
 
5.8%
3 47
 
5.2%
T 35
 
3.9%
5 28
 
3.1%
4 27
 
3.0%
. 25
 
2.8%
1 17
 
1.9%
Other values (22) 191
21.3%
Compat Jamo
ValueCountFrequency (%)
ㅓ 127
52.5%
ㅜ 59
24.4%
ㅏ 56
23.1%
Box Drawing
ValueCountFrequency (%)
├ 76
55.9%
┼ 60
44.1%
CJK Compat
ValueCountFrequency (%)
㎞ 7
100.0%
CJK
ValueCountFrequency (%)
十 5
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct4783
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.030898
Minimum34.921065
Maximum35.157349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:28:02.736130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.921065
5-th percentile34.931071
Q134.985013
median35.042066
Q335.079132
95-th percentile35.117805
Maximum35.157349
Range0.236284
Interquartile range (IQR)0.094119

Descriptive statistics

Standard deviation0.060792505
Coefficient of variation (CV)0.0017353967
Kurtosis-1.0392223
Mean35.030898
Median Absolute Deviation (MAD)0.041253
Skewness-0.23202044
Sum177081.19
Variance0.0036957287
MonotonicityNot monotonic
2023-12-11T09:28:03.145496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.032309 6
 
0.1%
35.061507 6
 
0.1%
34.969528 5
 
0.1%
35.024719 4
 
0.1%
35.004349 3
 
0.1%
35.030337 3
 
0.1%
34.931063 3
 
0.1%
34.946474 3
 
0.1%
34.945766 3
 
0.1%
35.062251 3
 
0.1%
Other values (4773) 5016
99.2%
ValueCountFrequency (%)
34.921065 2
< 0.1%
34.921666 1
< 0.1%
34.921826 1
< 0.1%
34.922052 1
< 0.1%
34.922092 1
< 0.1%
34.922152 1
< 0.1%
34.922205 1
< 0.1%
34.922232 1
< 0.1%
34.922339 1
< 0.1%
34.922346 1
< 0.1%
ValueCountFrequency (%)
35.157349 1
< 0.1%
35.156723 1
< 0.1%
35.15638 1
< 0.1%
35.156304 1
< 0.1%
35.155524 1
< 0.1%
35.155087 1
< 0.1%
35.15498 1
< 0.1%
35.154384 1
< 0.1%
35.154304 1
< 0.1%
35.154172 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct4766
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.04602
Minimum127.89673
Maximum128.16874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:28:03.275840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.89673
5-th percentile127.93875
Q1128.00375
median128.06142
Q3128.08522
95-th percentile128.12276
Maximum128.16874
Range0.272015
Interquartile range (IQR)0.0814695

Descriptive statistics

Standard deviation0.05616359
Coefficient of variation (CV)0.00043862034
Kurtosis-0.40577748
Mean128.04602
Median Absolute Deviation (MAD)0.026002
Skewness-0.63514738
Sum647272.65
Variance0.0031543488
MonotonicityNot monotonic
2023-12-11T09:28:03.405966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.069928 6
 
0.1%
128.061398 6
 
0.1%
128.043522 6
 
0.1%
128.061318 4
 
0.1%
128.056287 4
 
0.1%
128.05557 3
 
0.1%
128.097451 3
 
0.1%
128.049663 3
 
0.1%
127.937574 3
 
0.1%
128.05718 3
 
0.1%
Other values (4756) 5014
99.2%
ValueCountFrequency (%)
127.896729 1
< 0.1%
127.897922 1
< 0.1%
127.897985 1
< 0.1%
127.898082 1
< 0.1%
127.89857 1
< 0.1%
127.899047 1
< 0.1%
127.899309 1
< 0.1%
127.899706 1
< 0.1%
127.901232 1
< 0.1%
127.901525 1
< 0.1%
ValueCountFrequency (%)
128.168744 1
< 0.1%
128.168596 1
< 0.1%
128.168431 1
< 0.1%
128.167931 1
< 0.1%
128.167168 1
< 0.1%
128.166514 1
< 0.1%
128.16646 1
< 0.1%
128.166404 1
< 0.1%
128.16617 1
< 0.1%
128.165599 1
< 0.1%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct4864
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112945.63
Minimum99466.826
Maximum124143.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:28:03.551456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99466.826
5-th percentile103224.91
Q1109073.08
median114332.27
Q3116524.3
95-th percentile119925.91
Maximum124143.62
Range24676.794
Interquartile range (IQR)7451.22

Descriptive statistics

Standard deviation5106.2113
Coefficient of variation (CV)0.045209463
Kurtosis-0.4085457
Mean112945.63
Median Absolute Deviation (MAD)2433.299
Skewness-0.62624547
Sum5.7094016 × 108
Variance26073394
MonotonicityNot monotonic
2023-12-11T09:28:03.683380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112717.776 6
 
0.1%
115157.752 6
 
0.1%
114283.684 5
 
0.1%
113946.413 3
 
0.1%
117685.853 3
 
0.1%
116613.241 3
 
0.1%
117634.839 3
 
0.1%
116432.436 3
 
0.1%
116859.265 3
 
0.1%
115622.182 3
 
0.1%
Other values (4854) 5017
99.2%
ValueCountFrequency (%)
99466.826 1
< 0.1%
99496.566 1
< 0.1%
99502.166 1
< 0.1%
99511.208 1
< 0.1%
99598.083 1
< 0.1%
99633.573 1
< 0.1%
99700.75 1
< 0.1%
99736.878 1
< 0.1%
99795.8 1
< 0.1%
99822.315 1
< 0.1%
ValueCountFrequency (%)
124143.62 1
< 0.1%
124130.634 1
< 0.1%
124115.502 1
< 0.1%
124070.32 1
< 0.1%
124001.549 1
< 0.1%
123937.308 1
< 0.1%
123932.206 1
< 0.1%
123910.833 1
< 0.1%
123895.993 1
< 0.1%
123859.222 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct4866
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270940.27
Minimum258717.22
Maximum285027.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-11T09:28:03.808545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum258717.22
5-th percentile259838.44
Q1265903.07
median272153.28
Q3276262.84
95-th percentile280656.3
Maximum285027.9
Range26310.682
Interquartile range (IQR)10359.766

Descriptive statistics

Standard deviation6759.1646
Coefficient of variation (CV)0.024947066
Kurtosis-1.0310977
Mean270940.27
Median Absolute Deviation (MAD)4587.58
Skewness-0.2308412
Sum1.369603 × 109
Variance45686306
MonotonicityNot monotonic
2023-12-11T09:28:03.927050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
271097.529 6
 
0.1%
274314.187 6
 
0.1%
264116.722 5
 
0.1%
276208.881 3
 
0.1%
276357.226 3
 
0.1%
261458.61 3
 
0.1%
261538.899 3
 
0.1%
262182.778 3
 
0.1%
276039.09 3
 
0.1%
274391.775 3
 
0.1%
Other values (4856) 5017
99.2%
ValueCountFrequency (%)
258717.219 2
< 0.1%
258783.354 1
< 0.1%
258847.137 1
< 0.1%
258872.218 1
< 0.1%
258876.215 1
< 0.1%
258880.322 1
< 0.1%
258888.899 1
< 0.1%
258888.958 1
< 0.1%
258900.557 1
< 0.1%
258902.054 1
< 0.1%
ValueCountFrequency (%)
285027.901 1
< 0.1%
284961.053 1
< 0.1%
284940.405 1
< 0.1%
284931.878 1
< 0.1%
284828.588 1
< 0.1%
284780.203 1
< 0.1%
284768.294 1
< 0.1%
284718.92 1
< 0.1%
284710.186 1
< 0.1%
284695.608 1
< 0.1%

Interactions

2023-12-11T09:27:57.644501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:52.647630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.274204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.018745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.751648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.516598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.185911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.782295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.718264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:52.717880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.356654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.114681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.847459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.606520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.258899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.853624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.790153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:52.790464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.459047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.211681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.946607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.692543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.331894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.942926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.865453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:52.864686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.552853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.304536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.035856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.769572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.414080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.022706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.943049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:52.965346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.664894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.399371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.126018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.859904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.495888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.111038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:58.011955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.042529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.751356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.484345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.219223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.949068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.570732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.181625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:58.086065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.118588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.831201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.565171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.320711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.044364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.641454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.253784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:58.160994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.197571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:53.930266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:54.647531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:55.418464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.116127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:56.710355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:27:57.570472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:28:04.018122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동코드행정읍면동법정읍면동도로구간번호공사번호위치구분표지판구분지주형식높이위도경도X좌표Y좌표
관리번호1.0000.7180.8570.2310.8421.0000.7720.2950.5230.6330.8230.7870.7860.822
행정읍면동코드0.7181.0001.0000.8320.6520.8640.5480.3670.5760.5470.7840.6830.6860.785
행정읍면동0.8571.0001.0000.6960.7420.9510.7990.3670.6550.6270.9000.8270.8290.901
법정읍면동0.2310.8320.6961.0000.321NaN0.3730.1560.2420.2640.3330.1990.2040.333
도로구간번호0.8420.6520.7420.3211.0000.9370.8330.3680.5250.6290.6250.6890.6880.626
공사번호1.0000.8640.951NaN0.9371.0000.0000.5940.5460.7270.9010.8380.8490.893
위치구분0.7720.5480.7990.3730.8330.0001.0000.2240.6720.8350.6220.6750.6730.628
표지판구분0.2950.3670.3670.1560.3680.5940.2241.0000.3170.3540.1850.2430.2390.187
지주형식0.5230.5760.6550.2420.5250.5460.6720.3171.0000.7820.4920.4940.4950.497
높이0.6330.5470.6270.2640.6290.7270.8350.3540.7821.0000.6060.6240.6240.609
위도0.8230.7840.9000.3330.6250.9010.6220.1850.4920.6061.0000.6870.6981.000
경도0.7870.6830.8270.1990.6890.8380.6750.2430.4940.6240.6871.0001.0000.687
X좌표0.7860.6860.8290.2040.6880.8490.6730.2390.4950.6240.6981.0001.0000.697
Y좌표0.8220.7850.9010.3330.6260.8930.6280.1870.4970.6091.0000.6870.6971.000
2023-12-11T09:28:04.157949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사번호표지판구분법정읍면동지주형식행정읍면동위치구분
공사번호1.0000.3501.0000.3000.7520.000
표지판구분0.3501.0000.0930.1820.1800.153
법정읍면동1.0000.0931.0000.1320.4050.250
지주형식0.3000.1820.1321.0000.3510.496
행정읍면동0.7520.1800.4050.3511.0000.473
위치구분0.0000.1530.2500.4960.4731.000
2023-12-11T09:28:04.261938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동코드도로구간번호높이위도경도X좌표Y좌표행정읍면동법정읍면동공사번호위치구분표지판구분지주형식
관리번호1.0000.2630.5170.069-0.145-0.358-0.365-0.1420.5310.1180.9730.4270.1600.282
행정읍면동코드0.2631.0000.061-0.185-0.528-0.357-0.369-0.5240.9990.4460.6460.4260.1450.370
도로구간번호0.5170.0611.000-0.1880.049-0.398-0.3990.0510.3850.1690.7410.4930.2030.283
높이0.069-0.185-0.1881.000-0.3030.3760.373-0.3050.2890.1460.3750.4960.1880.524
위도-0.145-0.5280.049-0.3031.000-0.160-0.1451.0000.6120.1750.6460.3060.0980.261
경도-0.358-0.357-0.3980.376-0.1601.0001.000-0.1660.4860.1010.6080.3440.1300.262
X좌표-0.365-0.369-0.3990.373-0.1451.0001.000-0.1510.4890.1040.6240.3430.1280.263
Y좌표-0.142-0.5240.051-0.3051.000-0.166-0.1511.0000.6150.1750.6290.3100.0990.264
행정읍면동0.5310.9990.3850.2890.6120.4860.4890.6151.0000.4050.7520.4730.1800.351
법정읍면동0.1180.4460.1690.1460.1750.1010.1040.1750.4051.0001.0000.2500.0930.132
공사번호0.9730.6460.7410.3750.6460.6080.6240.6290.7521.0001.0000.0000.3500.300
위치구분0.4270.4260.4930.4960.3060.3440.3430.3100.4730.2500.0001.0000.1530.496
표지판구분0.1600.1450.2030.1880.0980.1300.1280.0990.1800.0930.3500.1531.0000.182
지주형식0.2820.3700.2830.5240.2610.2620.2630.2640.3510.1320.3000.4960.1821.000

Missing values

2023-12-11T09:27:58.279424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:27:58.466836image/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.
2023-12-11T09:27:58.641731image/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

지형지물부호관리번호행정읍면동코드행정읍면동법정읍면동도엽번호관리기관도로구간번호공사번호위치구분표지판구분지주형식높이규격기재사항위도경도X좌표Y좌표
0교통표지30500148240320사남면없음358132205B사천시112045<NA>우교통안전(지시)단주식2.590.60X0.68횡단보도35.049796128.072733115401.547273012.429
1교통표지28500948240595남양동없음348010285A사천시100801<NA>우교통안전(규제)현수식6.5φ600제한속도6034.959803128.071104115160.088263029.468
2교통표지27500148240595남양동없음348010274B사천시100813<NA>좌교통안전(주의)단주식3.030.84X0.74ㅓ형 교차로34.96394128.068316114909.719263490.91
3교통표지27500348240595남양동없음348010274B사천시100810<NA>우교통안전(주의)가로등 병설식3.550.84X0.74├자형교차로34.963571128.068665114941.148263449.582
4교통표지28500548240595남양동없음348010275C사천시100801<NA>우교통안전(주의)단주식1.90.84X0.74횡단보도34.961361128.070097115069.648263203.188
5교통표지28500248240595남양동없음348010274B사천시100807<NA>좌교통안전(주의)단주식2.60.84X0.74횡단보도34.962506128.069238114992.405263331.01
6교통표지28500648240595남양동없음348010275C사천시100801<NA>우교통안전(주의)단주식2.450.84X0.74과속방지턱34.960781128.070471115103.229263138.545
7교통표지28500448240595남양동없음348010274D사천시100801<NA>우교통안전(지시)단주식1.980.60X0.68횡단보도34.961897128.069748115038.359263262.932
8교통표지28500348240595남양동없음348010274D사천시100804<NA>좌교통안전(지시)단주식1.90.60X0.68횡단보도34.96203128.069547115020.126263277.901
9교통표지28500148240595남양동없음348010274B사천시100807<NA>우교통안전(주의)가로등 병설식2.70.84X0.74├자형교차로34.963078128.068977114969.173263394.649
지형지물부호관리번호행정읍면동코드행정읍면동법정읍면동도엽번호관리기관도로구간번호공사번호위치구분표지판구분지주형식높이규격기재사항위도경도X좌표Y좌표
5045교통표지19415848240350곤양면없음357161966B사천시194022<NA>우교통안전(지시)단주식2.1오각0.6X0.2X0.6횡단보도35.067636127.927951102213.222275124.197
5046교통표지19415948240350곤양면없음357161977B사천시194026<NA>우교통안전(지시)단주식2.0오각0.6X0.2X0.6횡단보도35.064359127.932859102657.098274755.724
5047교통표지19416048240370서포면없음358132181B사천시194049<NA>좌교통안전(지시)단주식2.5오각0.6X0.2X0.6횡단보도35.009322128.002627108960.429268583.695
5048교통표지19416148240370서포면없음358132181B사천시194049<NA>중앙교통안전(지시)단주식1.3Ø600직진및우회전35.009338128.002553108953.619268585.564
5049교통표지19416248240370서포면없음358132181A사천시194015<NA>중앙교통안전(지시)단주식1.3Ø600직진및우회전35.009597128.002452108944.749268614.398
5050교통표지19416348240370서포면없음358132181B사천시194044<NA>좌교통안전(지시)단주식2.7오각0.6X0.2X0.6횡단보도35.009636128.002516108950.607268618.583
5051교통표지19416448240370서포면없음358132181D사천시194049<NA>우교통안전(지시)단주식1.0오각0.6X0.2X0.6횡단보도35.005685128.003976109079.487268178.959
5052교통표지19416548240370서포면없음358132181A사천시194049<NA>우교통안전(지시)단주식2.0오각0.6X0.2X0.6횡단보도35.00945128.00225108926.144268598.243
5053교통표지19416648240370서포면없음358132181A사천시194044<NA>중앙교통안전(지시)단주식1.3Ø600직진및우회전35.009761128.002213108923.094268632.831
5054교통표지19416748240370서포면없음358132181A사천시194044<NA>우교통안전(지시)단주식2.1오각0.6X0.2X0.6횡단보도35.009835128.002128108915.449268641.016

Duplicate rows

Most frequently occurring

지형지물부호관리번호행정읍면동코드행정읍면동법정읍면동도엽번호관리기관도로구간번호공사번호위치구분표지판구분지주형식높이규격기재사항위도경도X좌표Y좌표# duplicates
0교통표지22000548240330용현면없음358132120C사천시390107<NA>우교통안전(주의)단주식2.00.84X0.74횡단보도35.040046128.046942113038.155271952.9222
1교통표지65500448240350곤양면없음357162055D사천시491402<NA>좌교통안전(주의)단주식1.980.84X0.74횡단보도35.070144127.973206106344.176275358.9932
2교통표지66100848240350곤양면없음357162061B사천시230310<NA>우교통안전(주의)단주식2.160.84X0.74과속방지턱35.067879127.952898104489.168275126.9482
3교통표지66500748240350곤양면없음357162065B사천시491402<NA>우교통안전(주의)단주식2.250.84X0.74횡단보도35.069532127.973339106355.662275290.9772
4교통표지66501148240350곤양면없음357162065D사천시491402<NA>우교통안전(주의)단주식2.00.84X0.74좌우로굽은도로35.065037127.973067106325.674274792.4692
5교통표지67200448240350곤양면없음357162072A사천시410213<NA>우교통안전(주의)단주식2.20.84X0.74위험35.064216127.957279104884.526274716.2872
6교통표지67200648240350곤양면없음357162072A사천시410213<NA>우교통안전(주의)단주식2.240.84X0.74위험35.063296127.957286104884.064274614.2522
7교통표지67200848240350곤양면없음357162072A사천시410213<NA>좌교통안전(주의)단주식2.240.84X0.74위험35.06298127.957179104873.964274579.3172
8교통표지67201048240350곤양면없음357162072A사천시410213<NA>우교통안전(주의)단주식2.090.84X0.74위험35.062546127.957269104881.689274531.0492
9교통표지67500948240350곤양면없음357162075B사천시230139<NA>우교통안전(주의)단주식2.030.84X0.74ㅓ자형교차로35.062778127.973052106321.7274541.8362