Overview

Dataset statistics

Number of variables13
Number of observations2721
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory297.7 KiB
Average record size in memory112.0 B

Variable types

Categorical5
Numeric7
Text1

Dataset

Description통영시 도시정보시스템의 석축옹벽에 대하여 지형지물부호,관리번호,행정읍면동,도엽번호,관리기관,도로구간번호,재질,연장,폭원 등 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15062756/fileData.do

Alerts

관리기관 has constant value ""Constant
재질 is highly overall correlated with 지형지물부호High correlation
지형지물부호 is highly overall correlated with 재질High correlation
관리번호 is highly overall correlated with 도로구간번호High correlation
도로구간번호 is highly overall correlated with 관리번호High correlation
재질 is highly imbalanced (56.9%)Imbalance
대장초기화여부 is highly imbalanced (98.4%)Imbalance
폭원 is highly skewed (γ1 = 44.12724321)Skewed
구분ID has unique valuesUnique
높이-최소 has 46 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-12 22:42:49.599279
Analysis finished2023-12-12 22:42:56.742362
Duration7.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
옹벽
2019 
석축
702 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row석축
2nd row옹벽
3rd row옹벽
4th row석축
5th row옹벽

Common Values

ValueCountFrequency (%)
옹벽 2019
74.2%
석축 702
 
25.8%

Length

2023-12-13T07:42:56.814521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:42:56.913826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
옹벽 2019
74.2%
석축 702
 
25.8%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2715
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13394423
Minimum1
Maximum2.02301 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:42:57.032341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile148
Q1734
median1502
Q32193
95-th percentile201029
Maximum2.02301 × 109
Range2.02301 × 109
Interquartile range (IQR)1459

Descriptive statistics

Standard deviation1.6394243 × 108
Coefficient of variation (CV)12.239604
Kurtosis146.44455
Mean13394423
Median Absolute Deviation (MAD)724
Skewness12.179362
Sum3.6446225 × 1010
Variance2.6877122 × 1016
MonotonicityNot monotonic
2023-12-13T07:42:57.279041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2613 2
 
0.1%
2001 2
 
0.1%
2004 2
 
0.1%
2003 2
 
0.1%
2002 2
 
0.1%
332 2
 
0.1%
1324 1
 
< 0.1%
181109 1
 
< 0.1%
1971 1
 
< 0.1%
450001 1
 
< 0.1%
Other values (2705) 2705
99.4%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2023010006 1
< 0.1%
2023010005 1
< 0.1%
2023010004 1
< 0.1%
2023010003 1
< 0.1%
2023010002 1
< 0.1%
2023010001 1
< 0.1%
2022080005 1
< 0.1%
2022080004 1
< 0.1%
2022080003 1
< 0.1%
2022080002 1
< 0.1%

행정읍면동
Categorical

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
사량면
920 
용남면
401 
광도면
329 
산양읍
324 
욕지면
280 
Other values (9)
467 

Length

Max length3
Median length3
Mean length2.999265
Min length2

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row용남면
2nd row용남면
3rd row용남면
4th row용남면
5th row사량면

Common Values

ValueCountFrequency (%)
사량면 920
33.8%
용남면 401
14.7%
광도면 329
 
12.1%
산양읍 324
 
11.9%
욕지면 280
 
10.3%
도산면 236
 
8.7%
한산면 208
 
7.6%
서호동 14
 
0.5%
미수동 2
 
0.1%
당동 2
 
0.1%
Other values (4) 5
 
0.2%

Length

2023-12-13T07:42:57.509560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사량면 920
33.8%
용남면 401
14.7%
광도면 329
 
12.1%
산양읍 324
 
11.9%
욕지면 280
 
10.3%
도산면 236
 
8.7%
한산면 208
 
7.6%
서호동 14
 
0.5%
미수동 2
 
0.1%
당동 2
 
0.1%
Other values (4) 5
 
0.2%

도엽번호
Text

Distinct989
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
2023-12-13T07:42:57.759773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters27210
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

Unique371 ?
Unique (%)13.6%

Sample

1st row348021454D
2nd row348021454B
3rd row348021454B
4th row348021454D
5th row348021464D
ValueCountFrequency (%)
348021860b 31
 
1.1%
348021906a 25
 
0.9%
348021454d 22
 
0.8%
348020484b 19
 
0.7%
348021914b 16
 
0.6%
348021906d 15
 
0.6%
348020484a 12
 
0.4%
348060651b 12
 
0.4%
348011592d 12
 
0.4%
348021905b 12
 
0.4%
Other values (979) 2545
93.5%
2023-12-13T07:42:58.146822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 4061
14.9%
0 3733
13.7%
8 3611
13.3%
3 3598
13.2%
2 3231
11.9%
1 3044
11.2%
9 1061
 
3.9%
5 987
 
3.6%
B 740
 
2.7%
6 723
 
2.7%
Other values (4) 2421
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24489
90.0%
Uppercase Letter 2721
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 4061
16.6%
0 3733
15.2%
8 3611
14.7%
3 3598
14.7%
2 3231
13.2%
1 3044
12.4%
9 1061
 
4.3%
5 987
 
4.0%
6 723
 
3.0%
7 440
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
B 740
27.2%
A 695
25.5%
D 688
25.3%
C 598
22.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24489
90.0%
Latin 2721
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 4061
16.6%
0 3733
15.2%
8 3611
14.7%
3 3598
14.7%
2 3231
13.2%
1 3044
12.4%
9 1061
 
4.3%
5 987
 
4.0%
6 723
 
3.0%
7 440
 
1.8%
Latin
ValueCountFrequency (%)
B 740
27.2%
A 695
25.5%
D 688
25.3%
C 598
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 4061
14.9%
0 3733
13.7%
8 3611
13.3%
3 3598
13.2%
2 3231
11.9%
1 3044
11.2%
9 1061
 
3.9%
5 987
 
3.6%
B 740
 
2.7%
6 723
 
2.7%
Other values (4) 2421
8.9%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
통영시
2721 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 2721
100.0%

Length

2023-12-13T07:42:58.295281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:42:58.411373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 2721
100.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1009
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13400120
Minimum0
Maximum2.02301 × 109
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:42:58.537204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1027
Q15925
median9054
Q310538
95-th percentile201001
Maximum2.02301 × 109
Range2.02301 × 109
Interquartile range (IQR)4613

Descriptive statistics

Standard deviation1.6394197 × 108
Coefficient of variation (CV)12.234365
Kurtosis146.44455
Mean13400120
Median Absolute Deviation (MAD)1828
Skewness12.179363
Sum3.6461727 × 1010
Variance2.6876969 × 1016
MonotonicityNot monotonic
2023-12-13T07:42:58.736120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201001 51
 
1.9%
9046 28
 
1.0%
8869 28
 
1.0%
9106 24
 
0.9%
9489 23
 
0.8%
9740 20
 
0.7%
10640 20
 
0.7%
10654 18
 
0.7%
10750 18
 
0.7%
2426 18
 
0.7%
Other values (999) 2473
90.9%
ValueCountFrequency (%)
0 2
 
0.1%
35 1
 
< 0.1%
45 5
0.2%
75 2
 
0.1%
78 1
 
< 0.1%
82 6
0.2%
83 1
 
< 0.1%
84 2
 
0.1%
88 2
 
0.1%
95 4
0.1%
ValueCountFrequency (%)
2023010002 2
 
0.1%
2023010001 4
0.1%
2022080001 5
0.2%
2021120008 1
 
< 0.1%
2021120005 1
 
< 0.1%
2021120001 5
0.2%
450027 1
 
< 0.1%
450026 1
 
< 0.1%
450016 1
 
< 0.1%
450012 1
 
< 0.1%

재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
철근콘크리트
1941 
석축
520 
화강석
 
104
무근콘크리트
 
88
조경석
 
59
Other values (3)
 
9

Length

Max length6
Median length6
Mean length5.0481441
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row석축
2nd row철근콘크리트
3rd row조경석
4th row석축
5th row철근콘크리트

Common Values

ValueCountFrequency (%)
철근콘크리트 1941
71.3%
석축 520
 
19.1%
화강석 104
 
3.8%
무근콘크리트 88
 
3.2%
조경석 59
 
2.2%
화학재질 7
 
0.3%
<NA> 1
 
< 0.1%
돌 1
 
< 0.1%

Length

2023-12-13T07:42:58.901415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:42:59.064311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근콘크리트 1941
71.3%
석축 520
 
19.1%
화강석 104
 
3.8%
무근콘크리트 88
 
3.2%
조경석 59
 
2.2%
화학재질 7
 
0.3%
na 1
 
< 0.1%
돌 1
 
< 0.1%

연장
Real number (ℝ)

Distinct2445
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.898739
Minimum1.03
Maximum2075.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:42:59.225859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile8.91
Q123.87
median48.94
Q393.63
95-th percentile279.69
Maximum2075.17
Range2074.14
Interquartile range (IQR)69.76

Descriptive statistics

Standard deviation130.08979
Coefficient of variation (CV)1.5322935
Kurtosis78.946053
Mean84.898739
Median Absolute Deviation (MAD)29.43
Skewness6.9303418
Sum231009.47
Variance16923.353
MonotonicityNot monotonic
2023-12-13T07:42:59.385206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.12 4
 
0.1%
16.36 3
 
0.1%
24.33 3
 
0.1%
35.52 3
 
0.1%
31.03 3
 
0.1%
42.05 3
 
0.1%
31.22 3
 
0.1%
14.27 3
 
0.1%
11.86 3
 
0.1%
45.18 3
 
0.1%
Other values (2435) 2690
98.9%
ValueCountFrequency (%)
1.03 1
< 0.1%
1.3 1
< 0.1%
1.84 1
< 0.1%
2.48 1
< 0.1%
2.62 1
< 0.1%
2.68 1
< 0.1%
2.8 1
< 0.1%
3.28 1
< 0.1%
3.32 1
< 0.1%
3.38 1
< 0.1%
ValueCountFrequency (%)
2075.17 2
0.1%
2060.79 1
< 0.1%
1617.79 1
< 0.1%
1348.33 1
< 0.1%
1262.79 1
< 0.1%
1111.74 1
< 0.1%
1060.32 1
< 0.1%
1020.93 1
< 0.1%
954.13 1
< 0.1%
886.53 1
< 0.1%

폭원
Real number (ℝ)

SKEWED 

Distinct27
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31433297
Minimum0
Maximum15
Zeros10
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:42:59.545208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.3
median0.3
Q30.3
95-th percentile0.35
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29830786
Coefficient of variation (CV)0.94901869
Kurtosis2162.0908
Mean0.31433297
Median Absolute Deviation (MAD)0
Skewness44.127243
Sum855.3
Variance0.088987579
MonotonicityNot monotonic
2023-12-13T07:42:59.693605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.3 2424
89.1%
0.2 62
 
2.3%
0.4 32
 
1.2%
0.5 30
 
1.1%
0.6 29
 
1.1%
0.15 28
 
1.0%
0.1 22
 
0.8%
0.35 15
 
0.6%
1.0 13
 
0.5%
0.25 12
 
0.4%
Other values (17) 54
 
2.0%
ValueCountFrequency (%)
0.0 10
 
0.4%
0.1 22
 
0.8%
0.13 1
 
< 0.1%
0.15 28
 
1.0%
0.16 5
 
0.2%
0.18 1
 
< 0.1%
0.2 62
 
2.3%
0.22 6
 
0.2%
0.25 12
 
0.4%
0.3 2424
89.1%
ValueCountFrequency (%)
15.0 1
 
< 0.1%
2.0 1
 
< 0.1%
1.6 1
 
< 0.1%
1.5 1
 
< 0.1%
1.2 1
 
< 0.1%
1.1 1
 
< 0.1%
1.0 13
0.5%
0.94 1
 
< 0.1%
0.9 6
0.2%
0.8 5
 
0.2%

높이-최대
Real number (ℝ)

Distinct108
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7245314
Minimum0
Maximum10.8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:42:59.847549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11
median1.2
Q32.3
95-th percentile4
Maximum10.8
Range10.8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.1643657
Coefficient of variation (CV)0.675178
Kurtosis4.5130447
Mean1.7245314
Median Absolute Deviation (MAD)0.4
Skewness1.750452
Sum4692.45
Variance1.3557474
MonotonicityNot monotonic
2023-12-13T07:43:00.030040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 436
 
16.0%
1.1 296
 
10.9%
1.2 113
 
4.2%
0.8 110
 
4.0%
2.0 102
 
3.7%
3.0 95
 
3.5%
1.5 92
 
3.4%
2.5 75
 
2.8%
0.9 73
 
2.7%
1.8 72
 
2.6%
Other values (98) 1257
46.2%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.2 15
 
0.6%
0.23 2
 
0.1%
0.25 2
 
0.1%
0.3 33
1.2%
0.35 4
 
0.1%
0.4 36
1.3%
0.45 3
 
0.1%
0.5 70
2.6%
0.55 12
 
0.4%
ValueCountFrequency (%)
10.8 1
 
< 0.1%
9.8 1
 
< 0.1%
8.2 1
 
< 0.1%
8.0 1
 
< 0.1%
7.0 4
0.1%
6.8 1
 
< 0.1%
6.6 1
 
< 0.1%
6.2 2
 
0.1%
6.1 3
0.1%
6.0 7
0.3%

높이-최소
Real number (ℝ)

ZEROS 

Distinct71
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6692172
Minimum0
Maximum6
Zeros46
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:43:00.229443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.5
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.74031031
Coefficient of variation (CV)1.1062332
Kurtosis11.93797
Mean0.6692172
Median Absolute Deviation (MAD)0.3
Skewness2.9003546
Sum1820.94
Variance0.54805935
MonotonicityNot monotonic
2023-12-13T07:43:00.414826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 557
20.5%
0.5 339
12.5%
0.1 309
11.4%
0.3 245
9.0%
1.0 201
 
7.4%
0.4 124
 
4.6%
1.1 123
 
4.5%
0.6 106
 
3.9%
0.8 90
 
3.3%
0.7 55
 
2.0%
Other values (61) 572
21.0%
ValueCountFrequency (%)
0.0 46
 
1.7%
0.05 4
 
0.1%
0.1 309
11.4%
0.15 9
 
0.3%
0.16 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 557
20.5%
0.23 4
 
0.1%
0.25 14
 
0.5%
0.26 2
 
0.1%
ValueCountFrequency (%)
6.0 3
 
0.1%
5.6 1
 
< 0.1%
5.5 4
 
0.1%
5.0 9
0.3%
4.0 10
0.4%
3.7 2
 
0.1%
3.5 6
0.2%
3.4 2
 
0.1%
3.2 5
0.2%
3.1 1
 
< 0.1%

대장초기화여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
1
2717 
0
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2717
99.9%
0 4
 
0.1%

Length

2023-12-13T07:43:00.575413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:43:00.699523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2717
99.9%
0 4
 
0.1%

구분ID
Real number (ℝ)

UNIQUE 

Distinct2721
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1360
Minimum0
Maximum2720
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2023-12-13T07:43:00.823384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile136
Q1680
median1360
Q32040
95-th percentile2584
Maximum2720
Range2720
Interquartile range (IQR)1360

Descriptive statistics

Standard deviation785.62937
Coefficient of variation (CV)0.57766865
Kurtosis-1.2
Mean1360
Median Absolute Deviation (MAD)680
Skewness0
Sum3700560
Variance617213.5
MonotonicityStrictly increasing
2023-12-13T07:43:00.970819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1817 1
 
< 0.1%
1809 1
 
< 0.1%
1810 1
 
< 0.1%
1811 1
 
< 0.1%
1812 1
 
< 0.1%
1813 1
 
< 0.1%
1814 1
 
< 0.1%
1815 1
 
< 0.1%
1816 1
 
< 0.1%
Other values (2711) 2711
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
2720 1
< 0.1%
2719 1
< 0.1%
2718 1
< 0.1%
2717 1
< 0.1%
2716 1
< 0.1%
2715 1
< 0.1%
2714 1
< 0.1%
2713 1
< 0.1%
2712 1
< 0.1%
2711 1
< 0.1%

Interactions

2023-12-13T07:42:55.283617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:50.321464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.159535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.971299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.801023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.631250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.474728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.424697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:50.417591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.285980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.086646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.936968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.769614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.606987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.581239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:50.532486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.401418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.219294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.073460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.924772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.725985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.694265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:50.631940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.501530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.341512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.181534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.026177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.851112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.798031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:50.860679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.609270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.470218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.299178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.120252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.973349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.915223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:50.978492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.705297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.584645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.407888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.222749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.084012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:56.014743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.073071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:51.833455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:52.692487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:53.527718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:54.351086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:42:55.185168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:43:01.080133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지형지물부호관리번호행정읍면동도로구간번호재질연장폭원높이-최대높이-최소대장초기화여부구분ID
지형지물부호1.0000.0000.2530.0000.8450.0650.0220.2540.1060.0000.099
관리번호0.0001.0000.1170.9990.2170.0000.0000.1620.0000.0000.147
행정읍면동0.2530.1171.0000.1170.4110.0000.1670.3160.2080.0000.531
도로구간번호0.0000.9990.1171.0000.2170.0000.0000.1620.0000.0000.147
재질0.8450.2170.4110.2171.0000.0000.1360.2410.1530.0000.183
연장0.0650.0000.0000.0000.0001.0000.0000.1010.2690.0000.044
폭원0.0220.0000.1670.0000.1360.0001.0000.0000.0000.0000.000
높이-최대0.2540.1620.3160.1620.2410.1010.0001.0000.6680.3510.097
높이-최소0.1060.0000.2080.0000.1530.2690.0000.6681.0000.0500.130
대장초기화여부0.0000.0000.0000.0000.0000.0000.0000.3510.0501.0000.087
구분ID0.0990.1470.5310.1470.1830.0440.0000.0970.1300.0871.000
2023-12-13T07:43:01.237869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재질대장초기화여부행정읍면동지형지물부호
재질1.0000.0000.1640.914
대장초기화여부0.0001.0000.0000.000
행정읍면동0.1640.0001.0000.197
지형지물부호0.9140.0000.1971.000
2023-12-13T07:43:01.370334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도로구간번호연장폭원높이-최대높이-최소구분ID지형지물부호행정읍면동재질대장초기화여부
관리번호1.0000.955-0.054-0.010-0.240-0.1210.0220.0000.0910.2320.000
도로구간번호0.9551.000-0.0600.005-0.247-0.1480.0530.0000.0910.2320.000
연장-0.054-0.0601.000-0.0280.0230.0380.0860.0650.0000.0000.000
폭원-0.0100.005-0.0281.0000.0720.0030.0480.0490.1220.1080.000
높이-최대-0.240-0.2470.0230.0721.0000.281-0.0070.2550.1340.1290.351
높이-최소-0.121-0.1480.0380.0030.2811.000-0.0530.1070.0850.0770.071
구분ID0.0220.0530.0860.048-0.007-0.0531.0000.0760.2460.0930.067
지형지물부호0.0000.0000.0650.0490.2550.1070.0761.0000.1970.9140.000
행정읍면동0.0910.0910.0000.1220.1340.0850.2460.1971.0000.1640.000
재질0.2320.2320.0000.1080.1290.0770.0930.9140.1641.0000.000
대장초기화여부0.0000.0000.0000.0000.3510.0710.0670.0000.0000.0001.000

Missing values

2023-12-13T07:42:56.469597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:42:56.669178image/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.

Sample

지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호재질연장폭원높이-최대높이-최소대장초기화여부구분ID
0석축1647용남면348021454D통영시7094석축37.610.33.00.210
1옹벽2612용남면348021454B통영시12146철근콘크리트55.640.36.00.811
2옹벽2592용남면348021454B통영시12143조경석10.660.32.70.012
3석축1646용남면348021454D통영시7094석축15.390.33.00.213
4옹벽8사량면348021464D통영시9322철근콘크리트122.650.31.050.3514
5옹벽957용남면348021454D통영시7094철근콘크리트33.160.31.00.115
6옹벽964용남면348021454D통영시7051철근콘크리트4.50.31.40.216
7석축965용남면348021454D통영시7093석축17.380.32.60.317
8옹벽956용남면348021454D통영시7094철근콘크리트35.870.31.00.118
9석축961용남면348021454D통영시7052석축15.00.31.70.719
지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호재질연장폭원높이-최대높이-최소대장초기화여부구분ID
2711석축67사량면348021491A통영시3704석축28.350.31.10.212711
2712석축60사량면348021300B통영시3181석축70.320.31.650.312712
2713옹벽53사량면348021398A통영시368철근콘크리트18.680.30.70.112713
2714석축66사량면348021491A통영시3704석축9.720.31.750.6512714
2715석축62사량면348021491A통영시3187석축19.00.31.60.212715
2716옹벽1763욕지면348061143B통영시9764철근콘크리트114.230.30.850.8512716
2717옹벽1795욕지면348061133D통영시9734철근콘크리트30.220.30.50.512717
2718석축1794욕지면348061133D통영시9734석축29.940.31.21.212718
2719옹벽1762욕지면348061143B통영시9764철근콘크리트50.260.30.850.8512719
2720옹벽1761욕지면348061143B통영시9764철근콘크리트47.040.30.850.8512720