Overview

Dataset statistics

Number of variables18
Number of observations839
Missing cells892
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.7 KiB
Average record size in memory152.2 B

Variable types

Categorical7
Numeric8
Text3

Dataset

Description지형지물부호, 관리번호, 행정읍면동, 도엽번호, 관리기관, 도로구간번호, 공사번호 , 위치구분, 표지종류, 지주형식 등을 기준으로 경상남도 사천시 공간정보시스템 상의 도로표지판 데이터 현황정보를 제공합니다.
Author경상남도 사천시
URLhttps://www.data.go.kr/data/15063663/fileData.do

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
공사번호 is highly overall correlated with 관리번호 and 8 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 관리번호 and 6 other fieldsHigh correlation
관리번호 is highly overall correlated with 행정읍면동 and 1 other fieldsHigh correlation
행정읍면동코드 is highly overall correlated with 경도 and 3 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 공사번호High correlation
높이 is highly overall correlated with 공사번호High correlation
위도 is highly overall correlated with Y좌표 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 행정읍면동코드 and 3 other fieldsHigh correlation
X좌표 is highly overall correlated with 행정읍면동코드 and 3 other fieldsHigh correlation
Y좌표 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
표지종류 is highly overall correlated with 지주형식High correlation
지주형식 is highly overall correlated with 표지종류High correlation
공사번호 is highly imbalanced (57.5%)Imbalance
규격 has 113 (13.5%) missing valuesMissing
설치위치 has 779 (92.8%) missing valuesMissing
높이 has 111 (13.2%) zerosZeros

Reproduction

Analysis started2023-12-12 02:13:10.322793
Analysis finished2023-12-12 02:13:19.626078
Duration9.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
도로표지
839 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로표지 839
100.0%

Length

2023-12-12T11:13:19.716029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:13:19.864744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로표지 839
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct823
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean415758.91
Minimum1
Maximum999027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:20.033782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.9
Q1100044.5
median263002
Q3800023.5
95-th percentile900080.1
Maximum999027
Range999026
Interquartile range (IQR)699979

Descriptive statistics

Standard deviation362096.77
Coefficient of variation (CV)0.87092966
Kurtosis-1.6392297
Mean415758.91
Median Absolute Deviation (MAD)262942
Skewness0.2670358
Sum3.4882173 × 108
Variance1.3111407 × 1011
MonotonicityIncreasing
2023-12-12T11:13:20.211718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.2%
10 2
 
0.2%
2 2
 
0.2%
16 2
 
0.2%
15 2
 
0.2%
13 2
 
0.2%
12 2
 
0.2%
11 2
 
0.2%
14 2
 
0.2%
9 2
 
0.2%
Other values (813) 819
97.6%
ValueCountFrequency (%)
1 2
0.2%
2 2
0.2%
3 2
0.2%
4 2
0.2%
5 2
0.2%
6 2
0.2%
7 2
0.2%
8 2
0.2%
9 2
0.2%
10 2
0.2%
ValueCountFrequency (%)
999027 1
0.1%
999026 1
0.1%
999025 1
0.1%
999024 1
0.1%
999023 1
0.1%
999022 1
0.1%
999021 1
0.1%
999020 1
0.1%
999019 1
0.1%
999018 1
0.1%

행정읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48240369
Minimum48240109
Maximum48240595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:20.392967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48240109
5-th percentile48240250
Q148240310
median48240340
Q348240370
95-th percentile48240595
Maximum48240595
Range486
Interquartile range (IQR)60

Descriptive statistics

Standard deviation102.33221
Coefficient of variation (CV)2.1212982 × 10-6
Kurtosis0.31949607
Mean48240369
Median Absolute Deviation (MAD)30
Skewness0.79639758
Sum4.047367 × 1010
Variance10471.88
MonotonicityNot monotonic
2023-12-12T11:13:20.564416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
48240310 111
13.2%
48240330 111
13.2%
48240360 92
11.0%
48240250 87
10.4%
48240370 76
9.1%
48240350 62
7.4%
48240340 57
6.8%
48240320 55
6.6%
48240510 51
6.1%
48240595 50
6.0%
Other values (9) 87
10.4%
ValueCountFrequency (%)
48240109 2
 
0.2%
48240111 1
 
0.1%
48240116 5
 
0.6%
48240124 2
 
0.2%
48240126 5
 
0.6%
48240250 87
10.4%
48240310 111
13.2%
48240320 55
6.6%
48240330 111
13.2%
48240340 57
6.8%
ValueCountFrequency (%)
48240595 50
6.0%
48240570 21
 
2.5%
48240550 21
 
2.5%
48240530 17
 
2.0%
48240520 13
 
1.5%
48240510 51
6.1%
48240370 76
9.1%
48240360 92
11.0%
48240350 62
7.4%
48240340 57
6.8%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
정동면
111 
용현면
111 
곤명면
92 
사천읍
87 
서포면
76 
Other values (10)
362 

Length

Max length7
Median length3
Mean length3.0917759
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정동면 111
13.2%
용현면 111
13.2%
곤명면 92
11.0%
사천읍 87
10.4%
서포면 76
9.1%
곤양면 62
7.4%
축동면 57
6.8%
사남면 55
6.6%
동서동 51
6.1%
남양동 50
6.0%
Other values (5) 87
10.4%

Length

2023-12-12T11:13:20.770987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정동면 111
13.0%
용현면 111
13.0%
곤명면 92
10.8%
사천읍 87
10.2%
서포면 76
8.9%
곤양면 62
7.3%
축동면 57
6.7%
사남면 55
6.4%
동서동 51
6.0%
남양동 50
5.9%
Other values (6) 102
11.9%

도엽번호
Text

Distinct399
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2023-12-12T11:13:21.093359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8390
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

Unique193 ?
Unique (%)23.0%

Sample

1st row348010739C
2nd row348010253B
3rd row348010739D
4th row348010253A
5th row348010749A
ValueCountFrequency (%)
358131709a 9
 
1.1%
358132243a 8
 
1.0%
358132291b 7
 
0.8%
358132242d 7
 
0.8%
357162083c 7
 
0.8%
348010263a 7
 
0.8%
348010736a 7
 
0.8%
358131775b 6
 
0.7%
357161989c 6
 
0.7%
358132291d 6
 
0.7%
Other values (389) 769
91.7%
2023-12-12T11:13:21.600791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1526
18.2%
1 1405
16.7%
8 928
11.1%
5 823
9.8%
0 626
7.5%
2 620
7.4%
7 552
 
6.6%
4 449
 
5.4%
6 407
 
4.9%
C 229
 
2.7%
Other values (4) 825
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7551
90.0%
Uppercase Letter 839
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1526
20.2%
1 1405
18.6%
8 928
12.3%
5 823
10.9%
0 626
8.3%
2 620
8.2%
7 552
 
7.3%
4 449
 
5.9%
6 407
 
5.4%
9 215
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
C 229
27.3%
A 225
26.8%
D 205
24.4%
B 180
21.5%

Most occurring scripts

ValueCountFrequency (%)
Common 7551
90.0%
Latin 839
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1526
20.2%
1 1405
18.6%
8 928
12.3%
5 823
10.9%
0 626
8.3%
2 620
8.2%
7 552
 
7.3%
4 449
 
5.9%
6 407
 
5.4%
9 215
 
2.8%
Latin
ValueCountFrequency (%)
C 229
27.3%
A 225
26.8%
D 205
24.4%
B 180
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1526
18.2%
1 1405
16.7%
8 928
11.1%
5 823
9.8%
0 626
7.5%
2 620
7.4%
7 552
 
6.6%
4 449
 
5.4%
6 407
 
4.9%
C 229
 
2.7%
Other values (4) 825
9.8%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
사천시
839 

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 839
100.0%

Length

2023-12-12T11:13:21.799989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:13:21.945056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 839
100.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct477
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410377.21
Minimum5
Maximum999029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:22.114921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1186
Q1230009
median352233
Q3478303
95-th percentile900084.7
Maximum999029
Range999024
Interquartile range (IQR)248294

Descriptive statistics

Standard deviation268783.93
Coefficient of variation (CV)0.65496798
Kurtosis-0.21239398
Mean410377.21
Median Absolute Deviation (MAD)125889
Skewness0.75195318
Sum3.4430648 × 108
Variance7.2244801 × 1010
MonotonicityNot monotonic
2023-12-12T11:13:22.283684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900025 15
 
1.8%
900022 11
 
1.3%
471801 11
 
1.3%
900033 11
 
1.3%
310527 8
 
1.0%
900029 8
 
1.0%
900059 8
 
1.0%
500701 7
 
0.8%
150113 7
 
0.8%
310550 7
 
0.8%
Other values (467) 746
88.9%
ValueCountFrequency (%)
5 1
 
0.1%
8 1
 
0.1%
15 1
 
0.1%
17 1
 
0.1%
18 2
0.2%
21 2
0.2%
24 1
 
0.1%
25 1
 
0.1%
1027 2
0.2%
1160 3
0.4%
ValueCountFrequency (%)
999029 1
 
0.1%
999028 2
 
0.2%
999027 1
 
0.1%
999021 1
 
0.1%
999019 1
 
0.1%
999014 1
 
0.1%
999013 4
0.5%
999004 5
0.6%
999003 4
0.5%
999002 6
0.7%

공사번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
<NA>
688 
RD20110005
110 
RD20110009
 
38
RD20110022
 
3

Length

Max length10
Median length4
Mean length5.079857
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd rowRD20110005
3rd row<NA>
4th rowRD20110005
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 688
82.0%
RD20110005 110
 
13.1%
RD20110009 38
 
4.5%
RD20110022 3
 
0.4%

Length

2023-12-12T11:13:22.483075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:13:22.670793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 688
82.0%
rd20110005 110
 
13.1%
rd20110009 38
 
4.5%
rd20110022 3
 
0.4%

위치구분
Categorical

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
좌
396 
우
368 
<NA>
 
31
기타
 
27
미분류
 
16

Length

Max length4
Median length1
Mean length1.18236
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
좌 396
47.2%
우 368
43.9%
<NA> 31
 
3.7%
기타 27
 
3.2%
미분류 16
 
1.9%
중앙 1
 
0.1%

Length

2023-12-12T11:13:22.817362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:13:22.977224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
좌 396
47.2%
우 368
43.9%
na 31
 
3.7%
기타 27
 
3.2%
미분류 16
 
1.9%
중앙 1
 
0.1%

표지종류
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
도로안내(방향)
460 
도로안내(이정)
210 
도로안내(노선)
58 
도로안내(기타)
 
44
도로안내(보조)
 
31
Other values (4)
 
36

Length

Max length8
Median length8
Mean length7.9129917
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도로안내(이정)
2nd row도로안내(방향)
3rd row도로안내(이정)
4th row도로안내(이정)
5th row도로안내(이정)

Common Values

ValueCountFrequency (%)
도로안내(방향) 460
54.8%
도로안내(이정) 210
25.0%
도로안내(노선) 58
 
6.9%
도로안내(기타) 44
 
5.2%
도로안내(보조) 31
 
3.7%
도로안내(경계) 20
 
2.4%
기타 7
 
0.8%
도로정보판 7
 
0.8%
미분류 2
 
0.2%

Length

2023-12-12T11:13:23.554222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:13:23.745343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로안내(방향 460
54.8%
도로안내(이정 210
25.0%
도로안내(노선 58
 
6.9%
도로안내(기타 44
 
5.2%
도로안내(보조 31
 
3.7%
도로안내(경계 20
 
2.4%
기타 7
 
0.8%
도로정보판 7
 
0.8%
미분류 2
 
0.2%

지주형식
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
편지식
459 
신호등 병설식
182 
단주식
106 
기타
58 
현수식
 
28

Length

Max length7
Median length3
Mean length3.8271752
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신호등 병설식
2nd row신호등 병설식
3rd row신호등 병설식
4th row기타
5th row신호등 병설식

Common Values

ValueCountFrequency (%)
편지식 459
54.7%
신호등 병설식 182
 
21.7%
단주식 106
 
12.6%
기타 58
 
6.9%
현수식 28
 
3.3%
가로등 병설식 6
 
0.7%

Length

2023-12-12T11:13:23.944239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:13:24.104776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
편지식 459
44.7%
병설식 188
18.3%
신호등 182
 
17.7%
단주식 106
 
10.3%
기타 58
 
5.6%
현수식 28
 
2.7%
가로등 6
 
0.6%

높이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6442193
Minimum0
Maximum8
Zeros111
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:24.269476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.9
median5.5
Q38
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation3.0041814
Coefficient of variation (CV)0.64686467
Kurtosis-1.5040675
Mean4.6442193
Median Absolute Deviation (MAD)2.5
Skewness-0.19995896
Sum3896.5
Variance9.0251058
MonotonicityNot monotonic
2023-12-12T11:13:24.429111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 290
34.6%
0.0 111
 
13.2%
6.0 81
 
9.7%
5.5 59
 
7.0%
1.9 34
 
4.1%
5.0 33
 
3.9%
2.0 32
 
3.8%
2.5 26
 
3.1%
3.0 17
 
2.0%
2.2 12
 
1.4%
Other values (49) 144
17.2%
ValueCountFrequency (%)
0.0 111
13.2%
0.6 1
 
0.1%
0.7 1
 
0.1%
0.86 1
 
0.1%
0.9 4
 
0.5%
1.0 5
 
0.6%
1.2 1
 
0.1%
1.3 4
 
0.5%
1.35 3
 
0.4%
1.36 1
 
0.1%
ValueCountFrequency (%)
8.0 290
34.6%
7.0 1
 
0.1%
6.5 3
 
0.4%
6.3 1
 
0.1%
6.0 81
 
9.7%
5.8 1
 
0.1%
5.5 59
 
7.0%
5.0 33
 
3.9%
4.5 1
 
0.1%
4.0 1
 
0.1%

규격
Text

MISSING 

Distinct116
Distinct (%)16.0%
Missing113
Missing (%)13.5%
Memory size6.7 KiB
2023-12-12T11:13:24.761414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.3498623
Min length4

Characters and Unicode

Total characters5336
Distinct characters15
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)9.4%

Sample

1st row3.6X2.2
2nd row3.6X2.2
3rd row3.6X2.2
4th row3.6X2.2
5th row3.6X2.2
ValueCountFrequency (%)
3.6x2.2 249
34.3%
4.45x2.2 53
 
7.3%
4.4x2.2 32
 
4.4%
3.60x2.20 30
 
4.1%
1.6x0.6 26
 
3.6%
1.85x1.35 24
 
3.3%
4.5x2.5 24
 
3.3%
5.0x2.5 20
 
2.8%
2.5x1.8 19
 
2.6%
0.24x0.44 16
 
2.2%
Other values (105) 233
32.1%
2023-12-12T11:13:25.285668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1445
27.1%
2 1014
19.0%
X 694
13.0%
6 379
 
7.1%
3 357
 
6.7%
0 350
 
6.6%
1 339
 
6.4%
5 315
 
5.9%
4 306
 
5.7%
8 68
 
1.3%
Other values (5) 69
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3165
59.3%
Other Punctuation 1471
27.6%
Uppercase Letter 699
 
13.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1014
32.0%
6 379
 
12.0%
3 357
 
11.3%
0 350
 
11.1%
1 339
 
10.7%
5 315
 
10.0%
4 306
 
9.7%
8 68
 
2.1%
7 31
 
1.0%
9 6
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1445
98.2%
* 26
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
X 694
99.3%
Φ 5
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
x 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4636
86.9%
Latin 695
 
13.0%
Greek 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1445
31.2%
2 1014
21.9%
6 379
 
8.2%
3 357
 
7.7%
0 350
 
7.5%
1 339
 
7.3%
5 315
 
6.8%
4 306
 
6.6%
8 68
 
1.5%
7 31
 
0.7%
Other values (2) 32
 
0.7%
Latin
ValueCountFrequency (%)
X 694
99.9%
x 1
 
0.1%
Greek
ValueCountFrequency (%)
Φ 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5331
99.9%
None 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1445
27.1%
2 1014
19.0%
X 694
13.0%
6 379
 
7.1%
3 357
 
6.7%
0 350
 
6.6%
1 339
 
6.4%
5 315
 
5.9%
4 306
 
5.7%
8 68
 
1.3%
Other values (4) 64
 
1.2%
None
ValueCountFrequency (%)
Φ 5
100.0%

설치위치
Text

MISSING 

Distinct51
Distinct (%)85.0%
Missing779
Missing (%)92.8%
Memory size6.7 KiB
2023-12-12T11:13:25.525384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.0166667
Min length5

Characters and Unicode

Total characters481
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)81.7%

Sample

1st row사천대교로
2nd row사천대교로
3rd row사천대교로
4th row사천대교로
5th row사천대교로
ValueCountFrequency (%)
사천대교로 9
 
14.8%
남문외리56-1 2
 
3.3%
송전리172-12 1
 
1.6%
송전리256-19 1
 
1.6%
서정리712-2 1
 
1.6%
신복리453-1전 1
 
1.6%
신복리458-7전 1
 
1.6%
선진리125-1철 1
 
1.6%
신복리466-1대 1
 
1.6%
송지리866-8답 1
 
1.6%
Other values (42) 42
68.9%
2023-12-12T11:13:26.014224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 49
 
10.2%
리 43
 
8.9%
1 39
 
8.1%
2 32
 
6.7%
4 23
 
4.8%
6 23
 
4.8%
3 20
 
4.2%
5 19
 
4.0%
9 16
 
3.3%
송 13
 
2.7%
Other values (40) 204
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 229
47.6%
Decimal Number 202
42.0%
Dash Punctuation 49
 
10.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 43
18.8%
송 13
 
5.7%
외 12
 
5.2%
대 12
 
5.2%
문 11
 
4.8%
남 11
 
4.8%
서 10
 
4.4%
정 10
 
4.4%
답 10
 
4.4%
사 10
 
4.4%
Other values (28) 87
38.0%
Decimal Number
ValueCountFrequency (%)
1 39
19.3%
2 32
15.8%
4 23
11.4%
6 23
11.4%
3 20
9.9%
5 19
9.4%
9 16
7.9%
7 13
 
6.4%
8 10
 
5.0%
0 7
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
52.4%
Hangul 229
47.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 43
18.8%
송 13
 
5.7%
외 12
 
5.2%
대 12
 
5.2%
문 11
 
4.8%
남 11
 
4.8%
서 10
 
4.4%
정 10
 
4.4%
답 10
 
4.4%
사 10
 
4.4%
Other values (28) 87
38.0%
Common
ValueCountFrequency (%)
- 49
19.4%
1 39
15.5%
2 32
12.7%
4 23
9.1%
6 23
9.1%
3 20
7.9%
5 19
 
7.5%
9 16
 
6.3%
7 13
 
5.2%
8 10
 
4.0%
Other values (2) 8
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
52.4%
Hangul 229
47.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 49
19.4%
1 39
15.5%
2 32
12.7%
4 23
9.1%
6 23
9.1%
3 20
7.9%
5 19
 
7.5%
9 16
 
6.3%
7 13
 
5.2%
8 10
 
4.0%
Other values (2) 8
 
3.2%
Hangul
ValueCountFrequency (%)
리 43
18.8%
송 13
 
5.7%
외 12
 
5.2%
대 12
 
5.2%
문 11
 
4.8%
남 11
 
4.8%
서 10
 
4.4%
정 10
 
4.4%
답 10
 
4.4%
사 10
 
4.4%
Other values (28) 87
38.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct809
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.035038
Minimum34.921723
Maximum35.14605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:26.209638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.921723
5-th percentile34.930796
Q134.99509
median35.045133
Q335.083307
95-th percentile35.118739
Maximum35.14605
Range0.224327
Interquartile range (IQR)0.088217

Descriptive statistics

Standard deviation0.059741703
Coefficient of variation (CV)0.0017051987
Kurtosis-0.89693495
Mean35.035038
Median Absolute Deviation (MAD)0.044427
Skewness-0.32492226
Sum29394.397
Variance0.003569071
MonotonicityNot monotonic
2023-12-12T11:13:26.475234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.031534 3
 
0.4%
35.025878 3
 
0.4%
34.98364 3
 
0.4%
34.981167 3
 
0.4%
34.968614 3
 
0.4%
34.998736 3
 
0.4%
35.029011 2
 
0.2%
35.028759 2
 
0.2%
34.990831 2
 
0.2%
35.028641 2
 
0.2%
Other values (799) 813
96.9%
ValueCountFrequency (%)
34.921723 1
0.1%
34.921803 1
0.1%
34.921874 1
0.1%
34.922151 1
0.1%
34.922265 1
0.1%
34.922381 1
0.1%
34.922481 1
0.1%
34.922542 1
0.1%
34.922684 1
0.1%
34.922689 1
0.1%
ValueCountFrequency (%)
35.14605 1
0.1%
35.143892 1
0.1%
35.14163 1
0.1%
35.141011 1
0.1%
35.14088 1
0.1%
35.140658 1
0.1%
35.140495 1
0.1%
35.140475 1
0.1%
35.14042 1
0.1%
35.138387 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct810
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.05224
Minimum127.9072
Maximum128.16919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:26.646384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.9072
5-th percentile127.93887
Q1128.00481
median128.06118
Q3128.09184
95-th percentile128.14412
Maximum128.16919
Range0.261984
Interquartile range (IQR)0.0870335

Descriptive statistics

Standard deviation0.061758877
Coefficient of variation (CV)0.00048229439
Kurtosis-0.57332048
Mean128.05224
Median Absolute Deviation (MAD)0.032249
Skewness-0.46853209
Sum107435.83
Variance0.0038141589
MonotonicityNot monotonic
2023-12-12T11:13:26.871667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.061972 3
 
0.4%
128.057657 3
 
0.4%
128.059741 3
 
0.4%
128.05686 3
 
0.4%
128.061463 3
 
0.4%
128.052996 3
 
0.4%
128.053451 2
 
0.2%
128.05655 2
 
0.2%
128.054531 2
 
0.2%
128.062346 2
 
0.2%
Other values (800) 813
96.9%
ValueCountFrequency (%)
127.907202 1
0.1%
127.915833 1
0.1%
127.919643 1
0.1%
127.919869 1
0.1%
127.920125 1
0.1%
127.92013 1
0.1%
127.920133 1
0.1%
127.920819 2
0.2%
127.92248 1
0.1%
127.922901 1
0.1%
ValueCountFrequency (%)
128.169186 1
0.1%
128.169079 1
0.1%
128.169054 1
0.1%
128.168284 1
0.1%
128.16774 1
0.1%
128.167021 1
0.1%
128.166312 1
0.1%
128.166205 1
0.1%
128.166052 1
0.1%
128.16525 1
0.1%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct811
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113517.16
Minimum100375.43
Maximum124183.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:27.044029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100375.43
5-th percentile103255.89
Q1109157.61
median114280.64
Q3117144.54
95-th percentile121882.21
Maximum124183.43
Range23808.005
Interquartile range (IQR)7986.924

Descriptive statistics

Standard deviation5620.907
Coefficient of variation (CV)0.049515924
Kurtosis-0.57916487
Mean113517.16
Median Absolute Deviation (MAD)3011.267
Skewness-0.45709168
Sum95240897
Variance31594596
MonotonicityNot monotonic
2023-12-12T11:13:27.228231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113547.018 3
 
0.4%
114191.164 3
 
0.4%
113954.142 3
 
0.4%
114354.194 3
 
0.4%
113884.026 3
 
0.4%
114335.125 3
 
0.4%
114261.343 2
 
0.2%
112835.272 2
 
0.2%
113674.083 2
 
0.2%
113584.722 2
 
0.2%
Other values (801) 813
96.9%
ValueCountFrequency (%)
100375.426 1
0.1%
101103.829 1
0.1%
101459.866 1
0.1%
101477.383 1
0.1%
101501.495 1
0.1%
101502.137 1
0.1%
101502.225 1
0.1%
101564.623 2
0.2%
101650.711 1
0.1%
101689.176 1
0.1%
ValueCountFrequency (%)
124183.431 1
0.1%
124174.239 1
0.1%
124171.483 1
0.1%
124102.218 1
0.1%
124053.291 1
0.1%
123987.991 1
0.1%
123899.993 1
0.1%
123877.975 1
0.1%
123868.162 1
0.1%
123782.021 1
0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct811
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271394.49
Minimum258835.83
Maximum283797.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T11:13:27.386109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum258835.83
5-th percentile259794.56
Q1266959.56
median272437.68
Q3276759.04
95-th percentile280755.14
Maximum283797.76
Range24961.927
Interquartile range (IQR)9799.481

Descriptive statistics

Standard deviation6640.4757
Coefficient of variation (CV)0.024467982
Kurtosis-0.89147279
Mean271394.49
Median Absolute Deviation (MAD)4896.51
Skewness-0.32140312
Sum2.2769998 × 108
Variance44095917
MonotonicityNot monotonic
2023-12-12T11:13:27.619550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267364.439 3
 
0.4%
270369.949 3
 
0.4%
265411.207 3
 
0.4%
270995.918 3
 
0.4%
265686.282 3
 
0.4%
264014.845 3
 
0.4%
270716.864 2
 
0.2%
270244.588 2
 
0.2%
267994.935 2
 
0.2%
266959.56 2
 
0.2%
Other values (801) 813
96.9%
ValueCountFrequency (%)
258835.832 1
0.1%
258844.839 1
0.1%
258852.655 1
0.1%
258880.242 1
0.1%
258892.78 1
0.1%
258907.43 1
0.1%
258918.879 1
0.1%
258923.654 1
0.1%
258939.255 1
0.1%
258939.797 1
0.1%
ValueCountFrequency (%)
283797.759 1
0.1%
283558.337 1
0.1%
283306.118 1
0.1%
283199.131 1
0.1%
283185.683 1
0.1%
283159.403 1
0.1%
283146.249 1
0.1%
283138.661 1
0.1%
283132.111 1
0.1%
282919.455 1
0.1%

Interactions

2023-12-12T11:13:17.908012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:11.308465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.304853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.128022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.907740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.021012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.054321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.985131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.046893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:11.404326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.405418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.236944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.006407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.119530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.178764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.088410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.167019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:11.556885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.533215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.352865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.122537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.226385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.278792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.206545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.273415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:11.710585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.632880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.449921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.237082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.331006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.384139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.316895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.419650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:11.850003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.743842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.546533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.336516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.470165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.521526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.467365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.579113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:11.965101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.836729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.632285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.429577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.596874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.653417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.585005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.710807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.078292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.930724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.720177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.525716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.788752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.761699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.703593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:18.835274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:12.203774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.029605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:13.817602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:14.647604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:15.947095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:16.878101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:13:17.805195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:13:27.782604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동코드행정읍면동도로구간번호공사번호위치구분표지종류지주형식높이설치위치위도경도X좌표Y좌표
관리번호1.0000.7720.8940.865NaN0.5330.4770.4180.7571.0000.8730.8510.8600.875
행정읍면동코드0.7721.0001.0000.6970.7410.4910.3810.4940.5011.0000.8140.6700.6790.811
행정읍면동0.8941.0001.0000.8370.9070.7230.5920.5690.7311.0000.9080.8460.8490.907
도로구간번호0.8650.6970.8371.0000.6440.5420.4900.5140.8321.0000.7690.8140.8160.764
공사번호NaN0.7410.9070.6441.0000.1040.3680.2570.685NaN0.7390.9420.9420.926
위치구분0.5330.4910.7230.5420.1041.0000.3940.4930.5320.5160.3110.3000.2880.304
표지종류0.4770.3810.5920.4900.3680.3941.0000.7800.5030.8510.3990.4240.4340.396
지주형식0.4180.4940.5690.5140.2570.4930.7801.0000.7100.9480.3870.4090.4320.383
높이0.7570.5010.7310.8320.6850.5320.5030.7101.0000.7880.6760.7250.7300.665
설치위치1.0001.0001.0001.000NaN0.5160.8510.9480.7881.0001.0001.0001.0001.000
위도0.8730.8140.9080.7690.7390.3110.3990.3870.6761.0001.0000.7940.7991.000
경도0.8510.6700.8460.8140.9420.3000.4240.4090.7251.0000.7941.0001.0000.795
X좌표0.8600.6790.8490.8160.9420.2880.4340.4320.7301.0000.7991.0001.0000.799
Y좌표0.8750.8110.9070.7640.9260.3040.3960.3830.6651.0001.0000.7950.7991.000
2023-12-12T11:13:27.967006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표지종류위치구분공사번호행정읍면동지주형식
표지종류1.0000.2400.2970.2860.525
위치구분0.2401.0000.1710.3940.203
공사번호0.2970.1711.0000.9500.245
행정읍면동0.2860.3940.9501.0000.306
지주형식0.5250.2030.2450.3061.000
2023-12-12T11:13:28.100880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동코드도로구간번호높이위도경도X좌표Y좌표행정읍면동공사번호위치구분표지종류지주형식
관리번호1.0000.1650.1030.3120.080-0.266-0.2720.0810.5991.0000.2490.2400.235
행정읍면동코드0.1651.0000.235-0.085-0.426-0.525-0.531-0.4220.9950.7850.3720.2040.208
도로구간번호0.1030.2351.000-0.345-0.202-0.392-0.394-0.2020.4990.6650.2540.2480.301
높이0.312-0.085-0.3451.0000.1800.1030.1010.1790.3740.6580.2500.2580.490
위도0.080-0.426-0.2020.1801.000-0.096-0.0841.0000.6290.6570.1330.1940.215
경도-0.266-0.525-0.3920.103-0.0961.0001.000-0.1030.5130.7050.1290.2080.229
X좌표-0.272-0.531-0.3940.101-0.0841.0001.000-0.0910.5170.7050.1230.2130.243
Y좌표0.081-0.422-0.2020.1791.000-0.103-0.0911.0000.6260.6660.1300.1920.213
행정읍면동0.5990.9950.4990.3740.6290.5130.5170.6261.0000.9500.3940.2860.306
공사번호1.0000.7850.6650.6580.6570.7050.7050.6660.9501.0000.1710.2970.245
위치구분0.2490.3720.2540.2500.1330.1290.1230.1300.3940.1711.0000.2400.203
표지종류0.2400.2040.2480.2580.1940.2080.2130.1920.2860.2970.2401.0000.525
지주형식0.2350.2080.3010.4900.2150.2290.2430.2130.3060.2450.2030.5251.000

Missing values

2023-12-12T11:13:19.012153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:13:19.321895image/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.
2023-12-12T11:13:19.541404image/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

지형지물부호관리번호행정읍면동코드행정읍면동도엽번호관리기관도로구간번호공사번호위치구분표지종류지주형식높이규격설치위치위도경도X좌표Y좌표
0도로표지148240570향촌동348010739C사천시302020<NA>미분류도로안내(이정)신호등 병설식5.03.6X2.2<NA>34.930974128.092269117064.189259813.355
1도로표지148240595남양동348010253B사천시900102RD20110005좌도로안내(방향)신호등 병설식0.0<NA><NA>34.971026128.06096114245.185264283.271
2도로표지248240570향촌동348010739D사천시302020<NA>미분류도로안내(이정)신호등 병설식5.03.6X2.2<NA>34.930545128.09255117089.4259765.527
3도로표지248240595남양동348010253A사천시900104RD20110005좌도로안내(이정)기타0.0<NA><NA>34.973002128.060324114189.179264503.031
4도로표지348240570향촌동348010749A사천시8<NA>미분류도로안내(이정)신호등 병설식5.03.6X2.2<NA>34.929177128.090962116942.9259615.104
5도로표지348240595남양동348010232A사천시900048RD20110005좌도로안내(이정)신호등 병설식0.0<NA><NA>34.983788128.056813113879.896265702.765
6도로표지448240570향촌동348010748B사천시5<NA>미분류도로안내(이정)신호등 병설식5.03.6X2.2<NA>34.928907128.089764116833.224259586.09
7도로표지448240595남양동348010232A사천시900051RD20110005좌도로안내(이정)신호등 병설식0.0<NA><NA>34.984806128.056485113850.987265815.939
8도로표지548240530동서금동348010748A사천시202003<NA>미분류도로안내(이정)신호등 병설식5.03.6X2.2<NA>34.929717128.085396116434.884259679.608
9도로표지548240595남양동348010222C사천시900057RD20110005우도로안내(방향)신호등 병설식0.0<NA><NA>34.987184128.055971113806.494266080.213
지형지물부호관리번호행정읍면동코드행정읍면동도엽번호관리기관도로구간번호공사번호위치구분표지종류지주형식높이규격설치위치위도경도X좌표Y좌표
829도로표지99901848240126(확인 불가)348010263A사천시999013<NA>기타도로안내(기타)기타8.01.2*1.0<NA>34.968614128.061972114335.125264014.845
830도로표지99901948240126(확인 불가)348010263A사천시999013<NA>기타도로안내(기타)기타7.01.5*1.2<NA>34.968614128.061972114335.125264014.845
831도로표지99902048240330용현면358132160C사천시999004<NA>기타도로안내(기타)기타1.00.6*1.8<NA>35.020917128.047216113042.842269830.411
832도로표지99902148240330용현면358132261C사천시999004<NA>기타도로안내(기타)단주식3.00.6*1.2<NA>35.016254128.050904113374.49269309.858
833도로표지99902248240330용현면358132261C사천시999004<NA>기타도로안내(기타)단주식3.00.9*0.6<NA>35.016237128.050433113331.497269308.397
834도로표지99902348240330용현면358132160A사천시999003<NA>기타도로안내(기타)신호등 병설식1.51.0*0.8<NA>35.023509128.045475112886.742270119.411
835도로표지99902448240330용현면358132160A사천시999003<NA>기타도로안내(기타)기타1.53.0*1.5<NA>35.023688128.045228112864.388270139.551
836도로표지99902548240109(확인 불가)348010718B사천시999019<NA>기타도로안내(기타)기타2.01.0*2.0<NA>34.943273128.089312116806.379261180.307
837도로표지99902648240116(확인 불가)348010739B사천시999002<NA>기타도로안내(기타)기타5.01.0*4.0<NA>34.933889128.093129117145.698260136.022
838도로표지99902748240126(확인 불가)348010263C사천시999014<NA>기타도로안내(기타)신호등 병설식2.01.0x1.0<NA>34.967072128.062375114370.368263843.443