Overview

Dataset statistics

Number of variables11
Number of observations241
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory94.5 B

Variable types

Categorical4
Numeric6
Text1

Dataset

Description도시정보시스템의 도로 미끄럼방지시설에 대한 관리번호, 행정읍면동, 도엽번호, 포장재질, 경도, 위도 등에 대한 정보
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15062767

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
관리번호 is highly overall correlated with 도로구간번호High correlation
도로구간번호 is highly overall correlated with 관리번호High correlation
경도 is highly overall correlated with 행정읍면동High correlation
위도 is highly overall correlated with 행정읍면동High correlation
행정읍면동 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
포장재질 is highly imbalanced (57.4%)Imbalance
위도 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:07:47.125164
Analysis finished2024-04-17 19:07:50.505851
Duration3.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
미끄럼방지시설
241 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미끄럼방지시설 241
100.0%

Length

2024-04-18T04:07:50.554785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:07:50.635842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미끄럼방지시설 241
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92364993
Minimum1
Maximum2.02302 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-18T04:07:50.739088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q169
median135
Q3320
95-th percentile300002
Maximum2.02302 × 109
Range2.02302 × 109
Interquartile range (IQR)251

Descriptive statistics

Standard deviation4.2307834 × 108
Coefficient of variation (CV)4.5805053
Kurtosis17.339365
Mean92364993
Median Absolute Deviation (MAD)74
Skewness4.3812707
Sum2.2259963 × 1010
Variance1.7899528 × 1017
MonotonicityNot monotonic
2024-04-18T04:07:50.853700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181001 2
 
0.8%
305 2
 
0.8%
19 1
 
0.4%
402 1
 
0.4%
98 1
 
0.4%
109 1
 
0.4%
107 1
 
0.4%
106 1
 
0.4%
105 1
 
0.4%
108 1
 
0.4%
Other values (229) 229
95.0%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
11 1
0.4%
12 1
0.4%
ValueCountFrequency (%)
2023020010 1
0.4%
2023020009 1
0.4%
2023020008 1
0.4%
2023020007 1
0.4%
2023020006 1
0.4%
2023020005 1
0.4%
2023020004 1
0.4%
2023020003 1
0.4%
2023020002 1
0.4%
2023020001 1
0.4%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
도산면
51 
광도면
45 
산양읍
32 
용남면
29 
미수동
15 
Other values (10)
69 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row미수동
2nd row미수동
3rd row미수동
4th row미수동
5th row봉평동

Common Values

ValueCountFrequency (%)
도산면 51
21.2%
광도면 45
18.7%
산양읍 32
13.3%
용남면 29
12.0%
미수동 15
 
6.2%
한산면 10
 
4.1%
북신동 9
 
3.7%
무전동 9
 
3.7%
도천동 9
 
3.7%
정량동 8
 
3.3%
Other values (5) 24
10.0%

Length

2024-04-18T04:07:50.955612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도산면 51
21.2%
광도면 45
18.7%
산양읍 32
13.3%
용남면 29
12.0%
미수동 15
 
6.2%
한산면 10
 
4.1%
북신동 9
 
3.7%
무전동 9
 
3.7%
도천동 9
 
3.7%
정량동 8
 
3.3%
Other values (5) 24
10.0%

도엽번호
Text

Distinct144
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-04-18T04:07:51.131191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2410
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

Unique84 ?
Unique (%)34.9%

Sample

1st row348021932C
2nd row348021932B
3rd row348021932B
4th row348021943A
5th row348021943B
ValueCountFrequency (%)
348022334c 8
 
3.3%
348021314c 7
 
2.9%
348021512b 6
 
2.5%
348020893b 5
 
2.1%
348021443d 4
 
1.7%
348012027a 4
 
1.7%
348022465c 4
 
1.7%
348021941c 4
 
1.7%
348021443b 3
 
1.2%
348021497b 3
 
1.2%
Other values (134) 193
80.1%
2024-04-18T04:07:51.407460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 386
16.0%
3 365
15.1%
0 338
14.0%
2 316
13.1%
8 302
12.5%
1 229
9.5%
9 102
 
4.2%
C 69
 
2.9%
B 61
 
2.5%
A 56
 
2.3%
Other values (4) 186
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2169
90.0%
Uppercase Letter 241
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 386
17.8%
3 365
16.8%
0 338
15.6%
2 316
14.6%
8 302
13.9%
1 229
10.6%
9 102
 
4.7%
5 44
 
2.0%
7 44
 
2.0%
6 43
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 69
28.6%
B 61
25.3%
A 56
23.2%
D 55
22.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2169
90.0%
Latin 241
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 386
17.8%
3 365
16.8%
0 338
15.6%
2 316
14.6%
8 302
13.9%
1 229
10.6%
9 102
 
4.7%
5 44
 
2.0%
7 44
 
2.0%
6 43
 
2.0%
Latin
ValueCountFrequency (%)
C 69
28.6%
B 61
25.3%
A 56
23.2%
D 55
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 386
16.0%
3 365
15.1%
0 338
14.0%
2 316
13.1%
8 302
12.5%
1 229
9.5%
9 102
 
4.2%
C 69
 
2.9%
B 61
 
2.5%
A 56
 
2.3%
Other values (4) 186
7.7%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
통영시
241 

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 241
100.0%

Length

2024-04-18T04:07:51.511292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:07:51.588737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 241
100.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92369681
Minimum83
Maximum2.02302 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-18T04:07:51.663430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile1037
Q16185
median8415
Q311054
95-th percentile300001
Maximum2.02302 × 109
Range2.0230199 × 109
Interquartile range (IQR)4869

Descriptive statistics

Standard deviation4.2307731 × 108
Coefficient of variation (CV)4.5802617
Kurtosis17.339365
Mean92369681
Median Absolute Deviation (MAD)2311
Skewness4.3812707
Sum2.2261093 × 1010
Variance1.7899441 × 1017
MonotonicityNot monotonic
2024-04-18T04:07:51.777076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201101 7
 
2.9%
8388 6
 
2.5%
8774 5
 
2.1%
8593 5
 
2.1%
201103 4
 
1.7%
8130 4
 
1.7%
5233 4
 
1.7%
7016 3
 
1.2%
1037 3
 
1.2%
9073 3
 
1.2%
Other values (150) 197
81.7%
ValueCountFrequency (%)
83 1
 
0.4%
84 2
0.8%
275 2
0.8%
584 2
0.8%
644 1
 
0.4%
663 1
 
0.4%
752 1
 
0.4%
839 1
 
0.4%
1037 3
1.2%
1136 2
0.8%
ValueCountFrequency (%)
2023020024 1
 
0.4%
2023020020 1
 
0.4%
2023020014 1
 
0.4%
2023020012 2
0.8%
2023020009 1
 
0.4%
2023020006 2
0.8%
2023020005 2
0.8%
2022080001 1
 
0.4%
300001 3
1.2%
210101 2
0.8%

포장재질
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
아스팔트콘크리트
202 
기타
 
19
콘크리트
 
17
알루미늄
 
3

Length

Max length8
Median length8
Mean length7.1950207
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아스팔트콘크리트
2nd row기타
3rd row기타
4th row아스팔트콘크리트
5th row아스팔트콘크리트

Common Values

ValueCountFrequency (%)
아스팔트콘크리트 202
83.8%
기타 19
 
7.9%
콘크리트 17
 
7.1%
알루미늄 3
 
1.2%

Length

2024-04-18T04:07:51.892660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:07:51.966109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아스팔트콘크리트 202
83.8%
기타 19
 
7.9%
콘크리트 17
 
7.1%
알루미늄 3
 
1.2%

폭원
Real number (ℝ)

Distinct157
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.73278
Minimum2.4
Maximum281.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-18T04:07:52.045913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile2.79
Q13.13
median4.06
Q36.51
95-th percentile80.25
Maximum281.36
Range278.96
Interquartile range (IQR)3.38

Descriptive statistics

Standard deviation35.28484
Coefficient of variation (CV)2.5693879
Kurtosis24.874333
Mean13.73278
Median Absolute Deviation (MAD)1.26
Skewness4.7512447
Sum3309.6
Variance1245.0199
MonotonicityNot monotonic
2024-04-18T04:07:52.141283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.4 10
 
4.1%
3.0 10
 
4.1%
2.9 5
 
2.1%
3.23 5
 
2.1%
6.0 5
 
2.1%
5.8 4
 
1.7%
2.8 4
 
1.7%
2.74 4
 
1.7%
3.13 4
 
1.7%
3.15 3
 
1.2%
Other values (147) 187
77.6%
ValueCountFrequency (%)
2.4 1
 
0.4%
2.59 2
0.8%
2.7 2
0.8%
2.72 1
 
0.4%
2.74 4
1.7%
2.78 2
0.8%
2.79 1
 
0.4%
2.8 4
1.7%
2.84 3
1.2%
2.87 1
 
0.4%
ValueCountFrequency (%)
281.36 1
0.4%
208.86 1
0.4%
205.12 1
0.4%
183.41 1
0.4%
172.24 1
0.4%
146.13 1
0.4%
122.3 1
0.4%
103.88 1
0.4%
103.83 1
0.4%
95.65 1
0.4%

연장
Real number (ℝ)

Distinct232
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.841826
Minimum1.33
Maximum802.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-18T04:07:52.468354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile2.83
Q120.95
median62.9
Q3129.09
95-th percentile301.22
Maximum802.8
Range801.47
Interquartile range (IQR)108.14

Descriptive statistics

Standard deviation119.73656
Coefficient of variation (CV)1.2237769
Kurtosis11.763464
Mean97.841826
Median Absolute Deviation (MAD)49.6
Skewness2.9230671
Sum23579.88
Variance14336.844
MonotonicityNot monotonic
2024-04-18T04:07:52.576672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.83 3
 
1.2%
87.9 2
 
0.8%
66.17 2
 
0.8%
802.8 2
 
0.8%
235.93 2
 
0.8%
100.23 2
 
0.8%
20.95 2
 
0.8%
7.82 2
 
0.8%
66.81 1
 
0.4%
86.3 1
 
0.4%
Other values (222) 222
92.1%
ValueCountFrequency (%)
1.33 1
0.4%
1.35 1
0.4%
1.37 1
0.4%
1.9 1
0.4%
2.0 1
0.4%
2.01 1
0.4%
2.22 1
0.4%
2.64 1
0.4%
2.7 1
0.4%
2.79 1
0.4%
ValueCountFrequency (%)
802.8 2
0.8%
617.4 1
0.4%
595.59 1
0.4%
488.79 1
0.4%
473.1 1
0.4%
380.0 1
0.4%
357.06 1
0.4%
343.98 1
0.4%
330.61 1
0.4%
326.1 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct240
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.40213
Minimum128.22652
Maximum128.55007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-18T04:07:52.685063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.22652
5-th percentile128.34028
Q1128.36583
median128.41247
Q3128.42854
95-th percentile128.4644
Maximum128.55007
Range0.323552
Interquartile range (IQR)0.0627155

Descriptive statistics

Standard deviation0.049248822
Coefficient of variation (CV)0.00038355145
Kurtosis3.063025
Mean128.40213
Median Absolute Deviation (MAD)0.021645
Skewness-0.92706439
Sum30944.913
Variance0.0024254465
MonotonicityNot monotonic
2024-04-18T04:07:52.792153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.36003 2
 
0.8%
128.40742 1
 
0.4%
128.413264 1
 
0.4%
128.357657 1
 
0.4%
128.358234 1
 
0.4%
128.358497 1
 
0.4%
128.358671 1
 
0.4%
128.358828 1
 
0.4%
128.358846 1
 
0.4%
128.359033 1
 
0.4%
Other values (230) 230
95.4%
ValueCountFrequency (%)
128.226522 1
0.4%
128.227559 1
0.4%
128.2298 1
0.4%
128.230236 1
0.4%
128.230868 1
0.4%
128.231259 1
0.4%
128.231703 1
0.4%
128.304455 1
0.4%
128.304809 1
0.4%
128.30881 1
0.4%
ValueCountFrequency (%)
128.550074 1
0.4%
128.511851 1
0.4%
128.510325 1
0.4%
128.508725 1
0.4%
128.502965 1
0.4%
128.502419 1
0.4%
128.501343 1
0.4%
128.500951 1
0.4%
128.498843 1
0.4%
128.498104 1
0.4%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct241
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.864325
Minimum34.75311
Maximum34.962558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-18T04:07:52.909033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.75311
5-th percentile34.781211
Q134.833944
median34.865756
Q334.894447
95-th percentile34.947677
Maximum34.962558
Range0.209448
Interquartile range (IQR)0.060503

Descriptive statistics

Standard deviation0.048872864
Coefficient of variation (CV)0.0014018015
Kurtosis-0.46518593
Mean34.864325
Median Absolute Deviation (MAD)0.031623
Skewness-0.14581992
Sum8402.3023
Variance0.0023885568
MonotonicityNot monotonic
2024-04-18T04:07:53.012874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.83216 1
 
0.4%
34.947677 1
 
0.4%
34.912071 1
 
0.4%
34.912813 1
 
0.4%
34.930246 1
 
0.4%
34.913688 1
 
0.4%
34.915113 1
 
0.4%
34.916355 1
 
0.4%
34.917715 1
 
0.4%
34.914036 1
 
0.4%
Other values (231) 231
95.9%
ValueCountFrequency (%)
34.75311 1
0.4%
34.765347 1
0.4%
34.765926 1
0.4%
34.766332 1
0.4%
34.766855 1
0.4%
34.767214 1
0.4%
34.767469 1
0.4%
34.768132 1
0.4%
34.768751 1
0.4%
34.780718 1
0.4%
ValueCountFrequency (%)
34.962558 1
0.4%
34.959751 1
0.4%
34.959697 1
0.4%
34.958931 1
0.4%
34.958626 1
0.4%
34.958389 1
0.4%
34.95752 1
0.4%
34.957458 1
0.4%
34.951692 1
0.4%
34.950442 1
0.4%

Interactions

2024-04-18T04:07:49.845146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:47.422835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.099557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.545399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.953014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.435995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.915461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:47.495111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.172667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.617958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.029807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.507582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.999127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:47.570230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.252553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.700608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.108285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.580323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:50.079303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:47.635242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.318788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.758491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.190155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.641674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:50.149328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:47.938236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.393016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.830248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.281622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.716415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:50.214303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.024007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.478202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:48.893581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.369537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:07:49.781517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:07:53.086726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동도로구간번호포장재질폭원연장경도위도
관리번호1.0000.3440.9970.0000.0000.0000.3160.511
행정읍면동0.3441.0000.3440.7190.0000.2580.8990.884
도로구간번호0.9970.3441.0000.0000.0000.0000.3160.511
포장재질0.0000.7190.0001.0000.0000.2270.3510.588
폭원0.0000.0000.0000.0001.0000.0000.0000.000
연장0.0000.2580.0000.2270.0001.0000.2160.259
경도0.3160.8990.3160.3510.0000.2161.0000.688
위도0.5110.8840.5110.5880.0000.2590.6881.000
2024-04-18T04:07:53.169587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정읍면동포장재질
행정읍면동1.0000.489
포장재질0.4891.000
2024-04-18T04:07:53.235538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도로구간번호폭원연장경도위도행정읍면동포장재질
관리번호1.0000.863-0.077-0.341-0.246-0.2920.3050.000
도로구간번호0.8631.000-0.259-0.139-0.390-0.2570.3050.000
폭원-0.077-0.2591.000-0.2230.0250.2540.0000.000
연장-0.341-0.139-0.2231.0000.0500.1050.1050.144
경도-0.246-0.3900.0250.0501.000-0.2360.6470.229
위도-0.292-0.2570.2540.105-0.2361.0000.5740.388
행정읍면동0.3050.3050.0000.1050.6470.5741.0000.489
포장재질0.0000.0000.0000.1440.2290.3880.4891.000

Missing values

2024-04-18T04:07:50.314980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:07:50.452769image/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

지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호포장재질폭원연장경도위도
0미끄럼방지시설19미수동348021932C통영시2089아스팔트콘크리트7.0587.9128.4074234.83216
1미끄럼방지시설300003미수동348021932B통영시300001기타3.8953.61128.40828934.833974
2미끄럼방지시설300002미수동348021932B통영시300001기타4.0550.6128.40831534.833944
3미끄럼방지시설21미수동348021943A통영시1970아스팔트콘크리트5.87281.35128.41132134.828033
4미끄럼방지시설22봉평동348021943B통영시1970아스팔트콘크리트6.1150.27128.41438834.827787
5미끄럼방지시설412봉평동348021944C통영시7767아스팔트콘크리트2.9840.67128.41701134.826194
6미끄럼방지시설127산양읍348022464B통영시8774아스팔트콘크리트3.7193.87128.41909234.768132
7미끄럼방지시설128산양읍348022465C통영시8774아스팔트콘크리트3.0525.18128.42151834.767469
8미끄럼방지시설130산양읍348022465C통영시8774아스팔트콘크리트2.724.97128.42189834.767214
9미끄럼방지시설129산양읍348022465C통영시8774아스팔트콘크리트2.8424.59128.4222734.766855
지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호포장재질폭원연장경도위도
231미끄럼방지시설2023020010도산면348021314C통영시2023020012아스팔트콘크리트6.582.0128.36582634.891277
232미끄럼방지시설2023020009도산면348021314C통영시2023020009아스팔트콘크리트6.682.01128.36575634.891322
233미끄럼방지시설2023020008도산면348021314C통영시2023020005아스팔트콘크리트6.672.22128.365534.891495
234미끄럼방지시설2023020007도산면348021314C통영시2023020005아스팔트콘크리트6.621.9128.36543534.891533
235미끄럼방지시설2023020006도산면348021324B통영시2023020024아스팔트콘크리트3.3732.11128.36791334.889973
236미끄럼방지시설2023020005도산면348021314C통영시2023020012아스팔트콘크리트3.464.25128.36685834.890581
237미끄럼방지시설2023020004도산면348021314C통영시2023020020아스팔트콘크리트3.3812.25128.36740934.890219
238미끄럼방지시설2023020003도산면348021314D통영시2023020014아스팔트콘크리트3.36132.78128.36821334.890013
239미끄럼방지시설2023020002도산면348021314D통영시2023020006아스팔트콘크리트2.84152.03128.36734434.891477
240미끄럼방지시설2023020001도산면348021314C통영시2023020006아스팔트콘크리트2.8796.29128.36617234.891781