Overview

Dataset statistics

Number of variables15
Number of observations181
Missing cells38
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.6 KiB
Average record size in memory133.7 B

Variable types

Numeric10
Categorical4
DateTime1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 우회도로 및 구간정보를 가지고 있으며, 도로대장의 중앙분리대 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091957

Alerts

관리기관 has constant value ""Constant
비고 is highly overall correlated with 식별번호 and 7 other fieldsHigh correlation
이력코드 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
식별번호 is highly overall correlated with 이력코드 and 1 other fieldsHigh correlation
관리번호 is highly overall correlated with 노선번호 and 2 other fieldsHigh correlation
노선번호 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
구간번호 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
위치_시점 is highly overall correlated with 위치_종점High correlation
위치_종점 is highly overall correlated with 위치_시점High correlation
종류 is highly overall correlated with 연장 and 1 other fieldsHigh correlation
연장 is highly overall correlated with 종류High correlation
높이 is highly overall correlated with 이력코드 and 1 other fieldsHigh correlation
도로종류 is highly overall correlated with 비고High correlation
도로종류 is highly imbalanced (79.0%)Imbalance
이력코드 is highly imbalanced (79.0%)Imbalance
종류 has 36 (19.9%) missing valuesMissing
설치일자 has 2 (1.1%) missing valuesMissing
식별번호 has unique valuesUnique
위치_시점 has unique valuesUnique
관리번호 has 14 (7.7%) zerosZeros
분리대폭 has 11 (6.1%) zerosZeros
높이 has 2 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-11 00:31:59.037165
Analysis finished2023-12-11 00:32:08.694413
Duration9.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91
Minimum1
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:08.754631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q146
median91
Q3136
95-th percentile172
Maximum181
Range180
Interquartile range (IQR)90

Descriptive statistics

Standard deviation52.394338
Coefficient of variation (CV)0.57576196
Kurtosis-1.2
Mean91
Median Absolute Deviation (MAD)45
Skewness0
Sum16471
Variance2745.1667
MonotonicityStrictly increasing
2023-12-11T09:32:08.870937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
115 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
122 1
 
0.6%
123 1
 
0.6%
124 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
181 1
0.6%
180 1
0.6%
179 1
0.6%
178 1
0.6%
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7598572.2
Minimum0
Maximum10770019
Zeros14
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:08.991529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1670001
median10080017
Q310200012
95-th percentile10770010
Maximum10770019
Range10770019
Interquartile range (IQR)9530011

Descriptive statistics

Standard deviation4475338.1
Coefficient of variation (CV)0.58897092
Kurtosis-0.7854736
Mean7598572.2
Median Absolute Deviation (MAD)119997
Skewness-1.0992181
Sum1.3753416 × 109
Variance2.0028651 × 1013
MonotonicityNot monotonic
2023-12-11T09:32:09.124278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
7.7%
670001 1
 
0.6%
10770008 1
 
0.6%
10770000 1
 
0.6%
10770001 1
 
0.6%
10770002 1
 
0.6%
10770003 1
 
0.6%
10770004 1
 
0.6%
10770005 1
 
0.6%
10770006 1
 
0.6%
Other values (158) 158
87.3%
ValueCountFrequency (%)
0 14
7.7%
1 1
 
0.6%
2 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
10770019 1
0.6%
10770018 1
0.6%
10770017 1
0.6%
10770016 1
0.6%
10770015 1
0.6%
10770014 1
0.6%
10770013 1
0.6%
10770012 1
0.6%
10770011 1
0.6%
10770010 1
0.6%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1683
181 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 181
100.0%

Length

2023-12-11T09:32:09.250395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:32:09.363539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 181
100.0%

도로종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1504
175 
1507
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1507
2nd row1507
3rd row1507
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 175
96.7%
1507 6
 
3.3%

Length

2023-12-11T09:32:09.462004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:32:09.558134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 175
96.7%
1507 6
 
3.3%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean851.24862
Minimum30
Maximum1084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:09.641196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11004
median1009
Q31028
95-th percentile1080
Maximum1084
Range1054
Interquartile range (IQR)24

Descriptive statistics

Standard deviation376.52117
Coefficient of variation (CV)0.44231633
Kurtosis0.73518427
Mean851.24862
Median Absolute Deviation (MAD)11
Skewness-1.6415796
Sum154076
Variance141768.19
MonotonicityNot monotonic
2023-12-11T09:32:09.741676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
58 27
14.9%
1004 25
13.8%
1008 23
12.7%
1020 20
11.0%
1077 20
11.0%
1009 15
8.3%
1042 12
6.6%
1018 8
 
4.4%
1084 8
 
4.4%
1028 6
 
3.3%
Other values (9) 17
9.4%
ValueCountFrequency (%)
30 1
 
0.6%
58 27
14.9%
60 3
 
1.7%
67 2
 
1.1%
1001 1
 
0.6%
1004 25
13.8%
1008 23
12.7%
1009 15
8.3%
1011 5
 
2.8%
1018 8
 
4.4%
ValueCountFrequency (%)
1084 8
 
4.4%
1080 2
 
1.1%
1077 20
11.0%
1042 12
6.6%
1041 1
 
0.6%
1040 1
 
0.6%
1028 6
 
3.3%
1022 1
 
0.6%
1020 20
11.0%
1018 8
 
4.4%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3149171
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:09.827118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q37
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7919027
Coefficient of variation (CV)0.64703507
Kurtosis-0.42611679
Mean4.3149171
Median Absolute Deviation (MAD)2
Skewness0.73822711
Sum781
Variance7.7947207
MonotonicityNot monotonic
2023-12-11T09:32:09.921786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 70
38.7%
7 49
27.1%
1 29
16.0%
2 14
 
7.7%
10 10
 
5.5%
4 3
 
1.7%
9 2
 
1.1%
11 2
 
1.1%
6 1
 
0.6%
13 1
 
0.6%
ValueCountFrequency (%)
1 29
16.0%
2 14
 
7.7%
3 70
38.7%
4 3
 
1.7%
6 1
 
0.6%
7 49
27.1%
9 2
 
1.1%
10 10
 
5.5%
11 2
 
1.1%
13 1
 
0.6%
ValueCountFrequency (%)
13 1
 
0.6%
11 2
 
1.1%
10 10
 
5.5%
9 2
 
1.1%
7 49
27.1%
6 1
 
0.6%
4 3
 
1.7%
3 70
38.7%
2 14
 
7.7%
1 29
16.0%

이력코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
96.7%
1 6
 
3.3%

Length

2023-12-11T09:32:10.039679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:32:10.123729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175
96.7%
1 6
 
3.3%

위치_시점
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3777017
Minimum0
Maximum19.423
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:10.217127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q12.571
median5.11
Q39.376
95-th percentile16.514
Maximum19.423
Range19.423
Interquartile range (IQR)6.805

Descriptive statistics

Standard deviation4.870454
Coefficient of variation (CV)0.76366914
Kurtosis-0.037518425
Mean6.3777017
Median Absolute Deviation (MAD)3.38
Skewness0.78070137
Sum1154.364
Variance23.721322
MonotonicityNot monotonic
2023-12-11T09:32:10.351648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.577 1
 
0.6%
4.043 1
 
0.6%
5.062 1
 
0.6%
5.125 1
 
0.6%
5.915 1
 
0.6%
6.035 1
 
0.6%
6.064 1
 
0.6%
7.442 1
 
0.6%
7.465 1
 
0.6%
7.816 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
0.0 1
0.6%
0.007 1
0.6%
0.021 1
0.6%
0.022 1
0.6%
0.027 1
0.6%
0.06 1
0.6%
0.072 1
0.6%
0.107 1
0.6%
0.108 1
0.6%
0.12 1
0.6%
ValueCountFrequency (%)
19.423 1
0.6%
19.372 1
0.6%
19.035 1
0.6%
18.815 1
0.6%
18.711 1
0.6%
17.067 1
0.6%
17.047 1
0.6%
16.76 1
0.6%
16.713 1
0.6%
16.514 1
0.6%

위치_종점
Real number (ℝ)

HIGH CORRELATION 

Distinct180
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7673149
Minimum0.022
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:10.482358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.022
5-th percentile0.728
Q12.735
median5.454
Q39.745
95-th percentile16.6
Maximum20
Range19.978
Interquartile range (IQR)7.01

Descriptive statistics

Standard deviation4.8626903
Coefficient of variation (CV)0.71855534
Kurtosis-0.093254132
Mean6.7673149
Median Absolute Deviation (MAD)3.254
Skewness0.75259078
Sum1224.884
Variance23.645757
MonotonicityNot monotonic
2023-12-11T09:32:10.619332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.543 2
 
1.1%
1.775 1
 
0.6%
2.865 1
 
0.6%
5.915 1
 
0.6%
5.958 1
 
0.6%
6.064 1
 
0.6%
7.387 1
 
0.6%
7.465 1
 
0.6%
7.816 1
 
0.6%
7.859 1
 
0.6%
Other values (170) 170
93.9%
ValueCountFrequency (%)
0.022 1
0.6%
0.08 1
0.6%
0.238 1
0.6%
0.31 1
0.6%
0.377 1
0.6%
0.4 1
0.6%
0.492 1
0.6%
0.65 1
0.6%
0.652 1
0.6%
0.728 1
0.6%
ValueCountFrequency (%)
20.0 1
0.6%
19.423 1
0.6%
19.098 1
0.6%
19.035 1
0.6%
18.815 1
0.6%
18.711 1
0.6%
17.067 1
0.6%
16.823 1
0.6%
16.737 1
0.6%
16.6 1
0.6%

종류
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)4.1%
Missing36
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean3134.6414
Minimum3100
Maximum3199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:10.773744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3100
5-th percentile3100
Q13101
median3101
Q33199
95-th percentile3199
Maximum3199
Range99
Interquartile range (IQR)98

Descriptive statistics

Standard deviation46.177297
Coefficient of variation (CV)0.014731285
Kurtosis-1.5422233
Mean3134.6414
Median Absolute Deviation (MAD)1
Skewness0.68737059
Sum454523
Variance2132.3427
MonotonicityNot monotonic
2023-12-11T09:32:10.866541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3101 66
36.5%
3199 49
27.1%
3100 14
 
7.7%
3105 8
 
4.4%
3108 6
 
3.3%
3109 2
 
1.1%
(Missing) 36
19.9%
ValueCountFrequency (%)
3100 14
 
7.7%
3101 66
36.5%
3105 8
 
4.4%
3108 6
 
3.3%
3109 2
 
1.1%
3199 49
27.1%
ValueCountFrequency (%)
3199 49
27.1%
3109 2
 
1.1%
3108 6
 
3.3%
3105 8
 
4.4%
3101 66
36.5%
3100 14
 
7.7%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.43646
Minimum8
Maximum2717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:10.972622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile20
Q165
median180
Q3509
95-th percentile1392
Maximum2717
Range2709
Interquartile range (IQR)444

Descriptive statistics

Standard deviation509.78598
Coefficient of variation (CV)1.3400029
Kurtosis7.0292917
Mean380.43646
Median Absolute Deviation (MAD)137
Skewness2.4575099
Sum68859
Variance259881.75
MonotonicityNot monotonic
2023-12-11T09:32:11.093094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 5
 
2.8%
40 4
 
2.2%
86 3
 
1.7%
91 3
 
1.7%
10 3
 
1.7%
100 3
 
1.7%
26 3
 
1.7%
29 3
 
1.7%
64 3
 
1.7%
43 3
 
1.7%
Other values (137) 148
81.8%
ValueCountFrequency (%)
8 2
 
1.1%
10 3
1.7%
12 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
17 1
 
0.6%
20 5
2.8%
24 1
 
0.6%
25 1
 
0.6%
26 3
1.7%
ValueCountFrequency (%)
2717 1
0.6%
2700 1
0.6%
2640 1
0.6%
2382 1
0.6%
1805 1
0.6%
1644 1
0.6%
1555 1
0.6%
1421 1
0.6%
1415 1
0.6%
1392 1
0.6%

분리대폭
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.41547
Minimum0
Maximum900
Zeros11
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:11.196502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median50
Q3150
95-th percentile200
Maximum900
Range900
Interquartile range (IQR)120

Descriptive statistics

Standard deviation100.83248
Coefficient of variation (CV)1.079398
Kurtosis24.609453
Mean93.41547
Median Absolute Deviation (MAD)48
Skewness3.6690079
Sum16908.2
Variance10167.188
MonotonicityNot monotonic
2023-12-11T09:32:11.303130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
50.0 55
30.4%
200.0 27
14.9%
150.0 26
14.4%
2.0 15
 
8.3%
0.0 11
 
6.1%
120.0 11
 
6.1%
20.0 7
 
3.9%
30.0 7
 
3.9%
60.0 5
 
2.8%
0.6 2
 
1.1%
Other values (13) 15
 
8.3%
ValueCountFrequency (%)
0.0 11
6.1%
0.5 1
 
0.6%
0.6 2
 
1.1%
1.0 1
 
0.6%
1.3 1
 
0.6%
2.0 15
8.3%
4.2 1
 
0.6%
20.0 7
3.9%
30.0 7
3.9%
40.0 1
 
0.6%
ValueCountFrequency (%)
900.0 1
 
0.6%
600.0 1
 
0.6%
300.0 1
 
0.6%
200.0 27
14.9%
190.0 2
 
1.1%
180.0 1
 
0.6%
170.0 1
 
0.6%
150.0 26
14.4%
120.0 11
6.1%
100.0 2
 
1.1%

높이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.995028
Minimum0
Maximum150
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:32:11.398543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q170
median80
Q390
95-th percentile140
Maximum150
Range150
Interquartile range (IQR)20

Descriptive statistics

Standard deviation36.05867
Coefficient of variation (CV)0.45076139
Kurtosis0.59817891
Mean79.995028
Median Absolute Deviation (MAD)10
Skewness-0.61178714
Sum14479.1
Variance1300.2277
MonotonicityNot monotonic
2023-12-11T09:32:11.496850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
80.0 48
26.5%
70.0 28
15.5%
77.5 28
15.5%
120.0 26
14.4%
140.0 11
 
6.1%
0.5 9
 
5.0%
90.0 8
 
4.4%
0.6 6
 
3.3%
100.0 5
 
2.8%
20.0 3
 
1.7%
Other values (6) 9
 
5.0%
ValueCountFrequency (%)
0.0 2
 
1.1%
0.5 9
 
5.0%
0.6 6
 
3.3%
0.7 1
 
0.6%
7.8 1
 
0.6%
20.0 3
 
1.7%
30.0 1
 
0.6%
70.0 28
15.5%
72.5 1
 
0.6%
77.5 28
15.5%
ValueCountFrequency (%)
150.0 3
 
1.7%
140.0 11
 
6.1%
120.0 26
14.4%
100.0 5
 
2.8%
90.0 8
 
4.4%
80.0 48
26.5%
77.5 28
15.5%
72.5 1
 
0.6%
70.0 28
15.5%
30.0 1
 
0.6%

설치일자
Date

MISSING 

Distinct8
Distinct (%)4.5%
Missing2
Missing (%)1.1%
Memory size1.5 KiB
Minimum1900-01-01 00:00:00
Maximum2016-03-31 00:00:00
2023-12-11T09:32:11.590893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:11.951585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

비고
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
105 
차선규제봉
35 
공사중
14 
가드레일
11 
콘크리트보형,중앙분리대봉
 
3
Other values (10)
13 

Length

Max length13
Median length4
Mean length4.6906077
Min length3

Unique

Unique7 ?
Unique (%)3.9%

Sample

1st row<NA>
2nd row차선규제봉 12개
3rd row차선규제봉
4th row차선규제봉
5th row차선규제봉

Common Values

ValueCountFrequency (%)
<NA> 105
58.0%
차선규제봉 35
 
19.3%
공사중 14
 
7.7%
가드레일 11
 
6.1%
콘크리트보형,중앙분리대봉 3
 
1.7%
차선규제봉(10EA) 2
 
1.1%
차선규제봉(5EA) 2
 
1.1%
연석형(화단) 2
 
1.1%
차선규제봉 12개 1
 
0.6%
차선규제봉(22EA) 1
 
0.6%
Other values (5) 5
 
2.8%

Length

2023-12-11T09:32:12.063449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 105
57.7%
차선규제봉 36
 
19.8%
공사중 14
 
7.7%
가드레일 11
 
6.0%
콘크리트보형,중앙분리대봉 3
 
1.6%
차선규제봉(10ea 2
 
1.1%
차선규제봉(5ea 2
 
1.1%
연석형(화단 2
 
1.1%
12개 1
 
0.5%
차선규제봉(22ea 1
 
0.5%
Other values (5) 5
 
2.7%

Interactions

2023-12-11T09:32:07.383489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:59.607882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.631205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.400224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.257771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.413315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.329143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.169437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.885771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.650297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.456115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:59.714415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.715308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.483199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.356040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.510550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.417776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.244220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.969728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.717969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.528558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:59.811823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.795258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.574783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.439535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.593024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.495045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.319651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.031193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.789037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.610395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:59.911362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.865979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.657624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.538209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.677079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.570047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.381776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.092257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.858551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.683043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:59.995272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.944604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.745930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.613432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.764399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.642495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.440649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.155030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.931887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.773518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.118113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.030006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.833525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.708282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.849048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.729860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.522130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.237958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.010874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:08.084874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.237910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.114093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.923507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.814260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.933870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.828034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.611569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.328035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.088010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:08.151992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.330712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.186298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.992197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.181775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.030609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.915591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.693424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.409758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.159608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:08.215957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.435166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.258799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.064853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.259620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.126199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.996970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.760178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.486437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.240382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:08.279907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:00.527603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:01.324869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:02.144928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:03.331188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:04.222232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.077832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:05.818071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:06.560616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:07.312047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:32:12.155261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호도로종류노선번호구간번호이력코드위치_시점위치_종점종류연장분리대폭높이설치일자비고
식별번호1.0000.9190.2990.9330.7220.6730.8160.8090.0000.0000.6030.7730.7880.874
관리번호0.9191.0000.4090.9410.6910.0070.5500.4930.0000.0820.6550.4900.9890.957
도로종류0.2990.4091.0000.5160.3070.0000.2490.1220.0000.0000.0000.5800.0000.781
노선번호0.9330.9410.5161.0000.6100.0000.7540.7030.0000.1730.8020.5230.9830.781
구간번호0.7220.6910.3070.6101.0000.2630.4490.4300.3250.0000.4270.5660.8100.863
이력코드0.6730.0070.0000.0000.2631.0000.2180.3280.1550.0270.0000.6751.000NaN
위치_시점0.8160.5500.2490.7540.4490.2181.0000.9950.4630.0000.3740.5220.3990.632
위치_종점0.8090.4930.1220.7030.4300.3280.9951.0000.5130.1420.4200.5430.4590.465
종류0.0000.0000.0000.0000.3250.1550.4630.5131.0000.6150.5120.7600.2601.000
연장0.0000.0820.0000.1730.0000.0270.0000.1420.6151.0000.0000.0000.0000.408
분리대폭0.6030.6550.0000.8020.4270.0000.3740.4200.5120.0001.0000.6800.6030.209
높이0.7730.4900.5800.5230.5660.6750.5220.5430.7600.0000.6801.0000.8160.945
설치일자0.7880.9890.0000.9830.8101.0000.3990.4590.2600.0000.6030.8161.000NaN
비고0.8740.9570.7810.7810.863NaN0.6320.4651.0000.4080.2090.945NaN1.000
2023-12-11T09:32:12.305989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고이력코드도로종류
비고1.0001.0000.576
이력코드1.0001.0000.000
도로종류0.5760.0001.000
2023-12-11T09:32:12.407904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점종류연장분리대폭높이도로종류이력코드비고
식별번호1.000-0.2000.1680.168-0.052-0.051-0.0350.1290.009-0.4110.2900.5120.623
관리번호-0.2001.0000.603-0.670-0.102-0.1160.079-0.149-0.2550.2390.2680.0000.760
노선번호0.1680.6031.000-0.420-0.457-0.4640.0250.004-0.404-0.0700.3450.0000.576
구간번호0.168-0.670-0.4201.0000.2450.258-0.1870.1790.129-0.0040.3100.2670.616
위치_시점-0.052-0.102-0.4570.2451.0000.9940.187-0.1470.1350.1840.1860.1630.322
위치_종점-0.051-0.116-0.4640.2580.9941.0000.149-0.0710.1440.2020.0900.2460.209
종류-0.0350.0790.025-0.1870.1870.1491.000-0.517-0.328-0.2570.0000.0750.915
연장0.129-0.1490.0040.179-0.147-0.071-0.5171.0000.2660.0030.0000.0220.196
분리대폭0.009-0.255-0.4040.1290.1350.144-0.3280.2661.000-0.0840.0000.0000.091
높이-0.4110.239-0.070-0.0040.1840.202-0.2570.003-0.0841.0000.1270.5100.629
도로종류0.2900.2680.3450.3100.1860.0900.0000.0000.0000.1271.0000.0000.576
이력코드0.5120.0000.0000.2670.1630.2460.0750.0220.0000.5100.0001.0001.000
비고0.6230.7600.5760.6160.3220.2090.9150.1960.0910.6290.5761.0001.000

Missing values

2023-12-11T09:32:08.388540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:32:08.540524image/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.
2023-12-11T09:32:08.645068image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점종류연장분리대폭높이설치일자비고
016700011683150767301.5771.775319919820.070.01900-01-01<NA>
126700021683150767909.869.93199400.0120.01900-01-01차선규제봉 12개
2330000116831507307011.0411.1043199640.0120.01900-01-01차선규제봉
3410200018168315041020408.4928.54331995140.070.01900-01-01차선규제봉
4510200019168315041020408.58.5433199430.070.01900-01-01차선규제봉
5610200001168315041020300.1070.823101713200.080.01900-01-01<NA>
6710200002168315041020300.821.4063105586200.080.01900-01-01<NA>
7810200003168315041020301.4061.623101214200.080.01900-01-01<NA>
8910200004168315041020301.621.8393105219200.080.01900-01-01<NA>
91010200005168315041020302.1282.6083105480200.080.01900-01-01<NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점종류연장분리대폭높이설치일자비고
17117201683150410841000.9141.598<NA>6842.00.520121219<NA>
17217301683150410841000.5510.745<NA>1942.00.520121219<NA>
17317401683150410841000.140.377<NA>2372.00.520121219<NA>
17417501683150410401300.0720.4<NA>3280.572.52012.03.31<NA>
17517610420011168315041042113.1543.66231015082.00.620160331<NA>
17617710420010168315041042112.843.11631012762.00.620160331<NA>
17717810420009168315041042112.562.6631011002.00.620160331<NA>
17817910420008168315041042112.312.49631011862.00.620160331<NA>
17918010420007168315041042111.2042.15331019492.00.620160331<NA>
18018110420006168315041042110.3040.7331014262.00.620160331<NA>