Overview

Dataset statistics

Number of variables9
Number of observations1403
Missing cells89
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory102.9 KiB
Average record size in memory75.1 B

Variable types

Text3
Numeric3
Categorical2
Boolean1

Dataset

Description부산광역시_한국도로공사연계구간정보_20230828
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15121235

Alerts

브이디에스(VDS)시작기점 is highly overall correlated with 브이디에스(VDS)종료기점High correlation
브이디에스(VDS)종료기점 is highly overall correlated with 브이디에스(VDS)시작기점High correlation
생성일시 is highly imbalanced (77.4%)Imbalance
설정여부 is highly imbalanced (90.0%)Imbalance
브이디에스(VDS_ID) has 79 (5.6%) missing valuesMissing
구간(ID) has unique valuesUnique
브이디에스(VDS)시작기점 has 38 (2.7%) zerosZeros
브이디에스(VDS)종료기점 has 38 (2.7%) zerosZeros

Reproduction

Analysis started2023-12-10 16:58:43.814238
Analysis finished2023-12-10 16:58:45.861764
Duration2.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구간(ID)
Text

UNIQUE 

Distinct1403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2023-12-11T01:58:46.201634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9978617
Min length7

Characters and Unicode

Total characters14027
Distinct characters18
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

Unique1403 ?
Unique (%)100.0%

Sample

1st row0301CZS010
2nd row0301CZS030
3rd row0301CZS060
4th row0301CZS050
5th row0301CZS040
ValueCountFrequency (%)
0301czs010 1
 
0.1%
0200cze030 1
 
0.1%
0150czs150 1
 
0.1%
0150cze160 1
 
0.1%
0150cze140 1
 
0.1%
0150czs140 1
 
0.1%
0150cze150 1
 
0.1%
0150cze130 1
 
0.1%
0150czs130 1
 
0.1%
0150cze190 1
 
0.1%
Other values (1393) 1393
99.3%
2023-12-11T01:58:46.905926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5076
36.2%
1 1476
 
10.5%
C 1356
 
9.7%
Z 1356
 
9.7%
5 944
 
6.7%
2 862
 
6.1%
E 680
 
4.8%
S 676
 
4.8%
3 572
 
4.1%
4 350
 
2.5%
Other values (8) 679
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9955
71.0%
Uppercase Letter 4072
29.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5076
51.0%
1 1476
 
14.8%
5 944
 
9.5%
2 862
 
8.7%
3 572
 
5.7%
4 350
 
3.5%
6 259
 
2.6%
7 206
 
2.1%
8 120
 
1.2%
9 90
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 1356
33.3%
Z 1356
33.3%
E 680
16.7%
S 676
16.6%
I 1
 
< 0.1%
U 1
 
< 0.1%
D 1
 
< 0.1%
M 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9955
71.0%
Latin 4072
29.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5076
51.0%
1 1476
 
14.8%
5 944
 
9.5%
2 862
 
8.7%
3 572
 
5.7%
4 350
 
3.5%
6 259
 
2.6%
7 206
 
2.1%
8 120
 
1.2%
9 90
 
0.9%
Latin
ValueCountFrequency (%)
C 1356
33.3%
Z 1356
33.3%
E 680
16.7%
S 676
16.6%
I 1
 
< 0.1%
U 1
 
< 0.1%
D 1
 
< 0.1%
M 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5076
36.2%
1 1476
 
10.5%
C 1356
 
9.7%
Z 1356
 
9.7%
5 944
 
6.7%
2 862
 
6.1%
E 680
 
4.8%
S 676
 
4.8%
3 572
 
4.1%
4 350
 
2.5%
Other values (8) 679
 
4.8%

브이디에스(VDS_ID)
Text

MISSING 

Distinct1291
Distinct (%)97.5%
Missing79
Missing (%)5.6%
Memory size11.1 KiB
2023-12-11T01:58:47.200625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters15888
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

Unique1259 ?
Unique (%)95.1%

Sample

1st row0301VDS00100
2nd row0301VDS01400
3rd row0301VDS04700
4th row0301VDS03700
5th row0301VDS02400
ValueCountFrequency (%)
0600vde00052 3
 
0.2%
0600vde00047 2
 
0.2%
0201vde00800 2
 
0.2%
0500vds01400 2
 
0.2%
0100vds21300 2
 
0.2%
0150vde00050 2
 
0.2%
0600vde00016 2
 
0.2%
0600vde00018 2
 
0.2%
0600vde00100 2
 
0.2%
0600vde00025 2
 
0.2%
Other values (1281) 1303
98.4%
2023-12-11T01:58:47.645904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6536
41.1%
1 1705
 
10.7%
V 1324
 
8.3%
D 1324
 
8.3%
5 876
 
5.5%
2 748
 
4.7%
E 662
 
4.2%
S 662
 
4.2%
3 588
 
3.7%
4 402
 
2.5%
Other values (4) 1061
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11916
75.0%
Uppercase Letter 3972
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6536
54.9%
1 1705
 
14.3%
5 876
 
7.4%
2 748
 
6.3%
3 588
 
4.9%
4 402
 
3.4%
7 353
 
3.0%
6 315
 
2.6%
8 213
 
1.8%
9 180
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
V 1324
33.3%
D 1324
33.3%
E 662
16.7%
S 662
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11916
75.0%
Latin 3972
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6536
54.9%
1 1705
 
14.3%
5 876
 
7.4%
2 748
 
6.3%
3 588
 
4.9%
4 402
 
3.4%
7 353
 
3.0%
6 315
 
2.6%
8 213
 
1.8%
9 180
 
1.5%
Latin
ValueCountFrequency (%)
V 1324
33.3%
D 1324
33.3%
E 662
16.7%
S 662
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6536
41.1%
1 1705
 
10.7%
V 1324
 
8.3%
D 1324
 
8.3%
5 876
 
5.5%
2 748
 
4.7%
E 662
 
4.2%
S 662
 
4.2%
3 588
 
3.7%
4 402
 
2.5%
Other values (4) 1061
 
6.7%

브이디에스(VDS)시작기점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct691
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.41015
Minimum-1
Maximum425.5
Zeros38
Zeros (%)2.7%
Negative12
Negative (%)0.9%
Memory size12.5 KiB
2023-12-11T01:58:47.864744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.124
Q117.46
median56.27
Q3146.48
95-th percentile339.9
Maximum425.5
Range426.5
Interquartile range (IQR)129.02

Descriptive statistics

Standard deviation108.45727
Coefficient of variation (CV)1.091008
Kurtosis0.55679065
Mean99.41015
Median Absolute Deviation (MAD)46.59
Skewness1.2686088
Sum139472.44
Variance11762.979
MonotonicityNot monotonic
2023-12-11T01:58:48.055497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 38
 
2.7%
-1.0 12
 
0.9%
5.34 6
 
0.4%
61.77 6
 
0.4%
4.4 6
 
0.4%
56.27 5
 
0.4%
7.82 5
 
0.4%
16.9 5
 
0.4%
3.95 5
 
0.4%
38.26 4
 
0.3%
Other values (681) 1311
93.4%
ValueCountFrequency (%)
-1.0 12
 
0.9%
0.0 38
2.7%
0.02 2
 
0.1%
0.05 1
 
0.1%
0.2 1
 
0.1%
0.26 2
 
0.1%
0.36 3
 
0.2%
0.38 1
 
0.1%
0.42 1
 
0.1%
0.51 2
 
0.1%
ValueCountFrequency (%)
425.5 1
0.1%
423.3 1
0.1%
423.0 1
0.1%
421.7 2
0.1%
420.32 2
0.1%
418.54 2
0.1%
416.05 2
0.1%
408.43 1
0.1%
408.04 1
0.1%
406.94 2
0.1%

브이디에스(VDS)종료기점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct695
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.437384
Minimum-1
Maximum425.5
Zeros38
Zeros (%)2.7%
Negative12
Negative (%)0.9%
Memory size12.5 KiB
2023-12-11T01:58:48.240976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.124
Q117.355
median56.27
Q3146.54
95-th percentile339.91
Maximum425.5
Range426.5
Interquartile range (IQR)129.185

Descriptive statistics

Standard deviation108.5534
Coefficient of variation (CV)1.0916759
Kurtosis0.55758136
Mean99.437384
Median Absolute Deviation (MAD)46.59
Skewness1.2694192
Sum139510.65
Variance11783.841
MonotonicityNot monotonic
2023-12-11T01:58:48.435110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 38
 
2.7%
-1.0 12
 
0.9%
4.4 8
 
0.6%
61.77 6
 
0.4%
5.34 6
 
0.4%
3.95 5
 
0.4%
29.6 5
 
0.4%
16.9 5
 
0.4%
7.82 5
 
0.4%
10.3 5
 
0.4%
Other values (685) 1308
93.2%
ValueCountFrequency (%)
-1.0 12
 
0.9%
0.0 38
2.7%
0.02 2
 
0.1%
0.05 1
 
0.1%
0.2 1
 
0.1%
0.26 2
 
0.1%
0.36 3
 
0.2%
0.38 1
 
0.1%
0.42 1
 
0.1%
0.51 2
 
0.1%
ValueCountFrequency (%)
425.5 1
0.1%
423.7 1
0.1%
423.0 1
0.1%
421.7 2
0.1%
420.32 2
0.1%
418.54 2
0.1%
416.05 2
0.1%
408.43 1
0.1%
408.04 1
0.1%
406.94 2
0.1%

구간거리
Real number (ℝ)

Distinct527
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6626.8953
Minimum-1
Maximum30790
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.3%
Memory size12.5 KiB
2023-12-11T01:58:48.602768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile6.12
Q12330
median5140
Q39720
95-th percentile17950
Maximum30790
Range30791
Interquartile range (IQR)7390

Descriptive statistics

Standard deviation5684.0514
Coefficient of variation (CV)0.85772464
Kurtosis1.367506
Mean6626.8953
Median Absolute Deviation (MAD)3340
Skewness1.1813321
Sum9297534.1
Variance32308440
MonotonicityNot monotonic
2023-12-11T01:58:48.773210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000.0 10
 
0.7%
600.0 10
 
0.7%
3180.0 8
 
0.6%
5300.0 8
 
0.6%
2700.0 8
 
0.6%
1700.0 8
 
0.6%
4100.0 8
 
0.6%
2500.0 8
 
0.6%
200.0 8
 
0.6%
6000.0 8
 
0.6%
Other values (517) 1319
94.0%
ValueCountFrequency (%)
-1.0 4
0.3%
1.0 5
0.4%
1.5 2
 
0.1%
1.6 2
 
0.1%
1.7 1
 
0.1%
1.8 3
0.2%
1.9 2
 
0.1%
2.0 1
 
0.1%
2.2 1
 
0.1%
2.3 1
 
0.1%
ValueCountFrequency (%)
30790.0 2
0.1%
30250.0 2
0.1%
28150.0 2
0.1%
26610.0 2
0.1%
26520.0 2
0.1%
25980.0 2
0.1%
24800.0 2
0.1%
24330.0 2
0.1%
24170.0 2
0.1%
23720.0 2
0.1%

구간명칭
Text

Distinct1315
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2023-12-11T01:58:49.199641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length9.6493229
Min length1

Characters and Unicode

Total characters13538
Distinct characters229
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1235 ?
Unique (%)88.0%

Sample

1st row면천IC-당진JC
2nd row예산수덕IC-고덕IC
3rd row마곡사IC-유구IC
4th row유구IC-신양IC
5th row신양IC-예산수덕IC
ValueCountFrequency (%)
5
 
0.4%
고성ic-동고성ic 3
 
0.2%
연화산ic-고성ic 3
 
0.2%
남춘천ic-동산tg 3
 
0.2%
덕소삼패ic-남양주tg 3
 
0.2%
남양주tg-덕소삼패ic 3
 
0.2%
학산ic-서영암tg 2
 
0.1%
남경주ic-동경주ic 2
 
0.1%
서이천ic-마장jc 2
 
0.1%
마장jc-서이천ic 2
 
0.1%
Other values (1305) 1375
98.0%
2023-12-11T01:58:49.794074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2410
17.8%
I 1771
 
13.1%
- 1395
 
10.3%
J 639
 
4.7%
서 317
 
2.3%
산 314
 
2.3%
주 290
 
2.1%
남 271
 
2.0%
천 260
 
1.9%
동 236
 
1.7%
Other values (219) 5635
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7004
51.7%
Uppercase Letter 5107
37.7%
Dash Punctuation 1395
 
10.3%
Decimal Number 20
 
0.1%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 317
 
4.5%
산 314
 
4.5%
주 290
 
4.1%
남 271
 
3.9%
천 260
 
3.7%
동 236
 
3.4%
양 167
 
2.4%
성 159
 
2.3%
북 154
 
2.2%
안 140
 
2.0%
Other values (206) 4696
67.0%
Uppercase Letter
ValueCountFrequency (%)
C 2410
47.2%
I 1771
34.7%
J 639
 
12.5%
T 148
 
2.9%
G 139
 
2.7%
Decimal Number
ValueCountFrequency (%)
8 8
40.0%
1 6
30.0%
4 2
 
10.0%
3 2
 
10.0%
2 2
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 1395
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7004
51.7%
Latin 5107
37.7%
Common 1427
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 317
 
4.5%
산 314
 
4.5%
주 290
 
4.1%
남 271
 
3.9%
천 260
 
3.7%
동 236
 
3.4%
양 167
 
2.4%
성 159
 
2.3%
북 154
 
2.2%
안 140
 
2.0%
Other values (206) 4696
67.0%
Common
ValueCountFrequency (%)
- 1395
97.8%
8 8
 
0.6%
1 6
 
0.4%
( 6
 
0.4%
) 6
 
0.4%
4 2
 
0.1%
3 2
 
0.1%
2 2
 
0.1%
Latin
ValueCountFrequency (%)
C 2410
47.2%
I 1771
34.7%
J 639
 
12.5%
T 148
 
2.9%
G 139
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7004
51.7%
ASCII 6534
48.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2410
36.9%
I 1771
27.1%
- 1395
21.3%
J 639
 
9.8%
T 148
 
2.3%
G 139
 
2.1%
8 8
 
0.1%
1 6
 
0.1%
( 6
 
0.1%
) 6
 
0.1%
Other values (3) 6
 
0.1%
Hangul
ValueCountFrequency (%)
서 317
 
4.5%
산 314
 
4.5%
주 290
 
4.1%
남 271
 
3.9%
천 260
 
3.7%
동 236
 
3.4%
양 167
 
2.4%
성 159
 
2.3%
북 154
 
2.2%
안 140
 
2.0%
Other values (206) 4696
67.0%

생성일시
Categorical

IMBALANCE 

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2017-04-05
1229 
2007-10-26
 
76
2012-07-16
 
24
2011-02-16
 
21
2012-02-17
 
21
Other values (9)
 
32

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row2017-04-05
2nd row2017-04-05
3rd row2017-04-05
4th row2017-04-05
5th row2017-04-05

Common Values

ValueCountFrequency (%)
2017-04-05 1229
87.6%
2007-10-26 76
 
5.4%
2012-07-16 24
 
1.7%
2011-02-16 21
 
1.5%
2012-02-17 21
 
1.5%
2013-01-25 13
 
0.9%
2012-02-10 5
 
0.4%
2012-09-02 3
 
0.2%
2019-03-20 3
 
0.2%
2013-12-11 2
 
0.1%
Other values (4) 6
 
0.4%

Length

2023-12-11T01:58:49.950750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-04-05 1229
87.6%
2007-10-26 76
 
5.4%
2012-07-16 24
 
1.7%
2011-02-16 21
 
1.5%
2012-02-17 21
 
1.5%
2013-01-25 13
 
0.9%
2012-02-10 5
 
0.4%
2012-09-02 3
 
0.2%
2019-03-20 3
 
0.2%
2013-12-11 2
 
0.1%
Other values (4) 6
 
0.4%

연계일
Categorical

Distinct26
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2010-10-26
806 
2011-10-13
193 
2012-05-21
94 
2007-11-01
81 
2016-11-30
 
28
Other values (21)
201 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-10-26
2nd row2010-10-26
3rd row2010-10-26
4th row2010-10-26
5th row2010-10-26

Common Values

ValueCountFrequency (%)
2010-10-26 806
57.4%
2011-10-13 193
 
13.8%
2012-05-21 94
 
6.7%
2007-11-01 81
 
5.8%
2016-11-30 28
 
2.0%
2011-12-26 26
 
1.9%
2012-06-20 26
 
1.9%
2012-02-24 21
 
1.5%
2013-04-17 20
 
1.4%
2016-12-30 20
 
1.4%
Other values (16) 88
 
6.3%

Length

2023-12-11T01:58:50.099541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010-10-26 806
57.4%
2011-10-13 193
 
13.8%
2012-05-21 94
 
6.7%
2007-11-01 81
 
5.8%
2016-11-30 28
 
2.0%
2011-12-26 26
 
1.9%
2012-06-20 26
 
1.9%
2012-02-24 21
 
1.5%
2013-04-17 20
 
1.4%
2016-12-30 20
 
1.4%
Other values (16) 88
 
6.3%

설정여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.7%
Memory size2.9 KiB
False
1375 
True
 
18
(Missing)
 
10
ValueCountFrequency (%)
False 1375
98.0%
True 18
 
1.3%
(Missing) 10
 
0.7%
2023-12-11T01:58:50.237870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-11T01:58:45.103551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:44.376157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:44.686759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:45.239779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:44.469566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:44.841507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:45.363113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:44.575819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:58:44.970625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:58:50.329654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
브이디에스(VDS)시작기점브이디에스(VDS)종료기점구간거리생성일시연계일설정여부
브이디에스(VDS)시작기점1.0000.9890.4040.1940.4510.104
브이디에스(VDS)종료기점0.9891.0000.4050.2130.4510.104
구간거리0.4040.4051.0000.5080.5030.026
생성일시0.1940.2130.5081.0000.8150.507
연계일0.4510.4510.5030.8151.0000.571
설정여부0.1040.1040.0260.5070.5711.000
2023-12-11T01:58:50.464247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설정여부연계일생성일시
설정여부1.0000.4530.396
연계일0.4531.0000.410
생성일시0.3960.4101.000
2023-12-11T01:58:50.924296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
브이디에스(VDS)시작기점브이디에스(VDS)종료기점구간거리생성일시연계일설정여부
브이디에스(VDS)시작기점1.0000.9790.2820.0790.1790.080
브이디에스(VDS)종료기점0.9791.0000.2820.0870.1800.080
구간거리0.2820.2821.0000.2320.2060.020
생성일시0.0790.0870.2321.0000.4100.396
연계일0.1790.1800.2060.4101.0000.453
설정여부0.0800.0800.0200.3960.4531.000

Missing values

2023-12-11T01:58:45.500067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:58:45.656528image/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.
2023-12-11T01:58:45.786557image/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

구간(ID)브이디에스(VDS_ID)브이디에스(VDS)시작기점브이디에스(VDS)종료기점구간거리구간명칭생성일시연계일설정여부
00301CZS0100301VDS001004.040.04040.0면천IC-당진JC2017-04-052010-10-26N
10301CZS0300301VDS0140024.3812.711680.0예산수덕IC-고덕IC2017-04-052010-10-26N
20301CZS0600301VDS0470056.5647.429140.0마곡사IC-유구IC2017-04-052010-10-26N
30301CZS0500301VDS0370047.4236.8910530.0유구IC-신양IC2017-04-052010-10-26N
40301CZS0400301VDS0240036.8924.3812510.0신양IC-예산수덕IC2017-04-052010-10-26N
50301CZS0900301VDS0630065.963.022880.0공주IC-공주JC2017-04-052010-10-26N
60301CZS0800301VDS0590063.0260.42620.0공주JC-서공주JC2017-04-052010-10-26N
70301CZS0700301VDS0550060.456.563840.0서공주JC-마곡사IC2017-04-052010-10-26N
81510CZE0101510VDE001001.522.18660.0동서천IC-동서천JC2017-04-052010-10-26N
90301CZS1200301VDS0830091.8685.456410.0유성JC-남세종IC2017-04-052010-10-26N
구간(ID)브이디에스(VDS_ID)브이디에스(VDS)시작기점브이디에스(VDS)종료기점구간거리구간명칭생성일시연계일설정여부
13930105CZE1200105VDE016009.515.15600.0대청IC-산본IC2017-04-052017-01-13N
13940105CZE130<NA>15.115.7600.0산본IC-진례JC2017-04-052017-01-13N
13950105CZS1000105VDS001005.10.05100.0진해IC-남문대교2017-04-052017-01-13N
13960105CZS1100105VDS008009.55.14400.0대청IC-진해IC2017-04-052017-01-13N
13970105CZS1200105VDS0170015.19.55600.0산본IC-대청IC2017-04-052017-01-13N
13980105CZS130<NA>15.715.1600.0진례JC-산본IC2017-04-052017-01-13N
13990100CZE2550100VDE12200144.2145.741540.0진례JC-냉정JC2017-04-052017-01-13N
14000100CZS2550100VDS12200145.74144.21540.0냉정JC-진례JC2017-04-052017-01-13N
14010300CZS2900300VDS14000194.0190.772040.0청주영덕선종점-영덕IC2017-04-052017-01-13N
14020300CZE2900300VDE14000190.77194.02040.0영덕IC-청주영덕선종점2017-04-052017-01-13N