Overview

Dataset statistics

Number of variables11
Number of observations5659
Missing cells49
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory530.7 KiB
Average record size in memory96.0 B

Variable types

Numeric7
Text2
Categorical1
DateTime1

Dataset

Description대구광역시_도로 표준링크 현황_20191127
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15049952&dataSetDetailId=150499521fb641857375d&provdMethod=FILE

Alerts

표준링크ID is highly overall correlated with 출발표준노드 and 2 other fieldsHigh correlation
출발표준노드 is highly overall correlated with 표준링크ID and 1 other fieldsHigh correlation
도착표준노드 is highly overall correlated with 표준링크ID and 1 other fieldsHigh correlation
도로등급코드 is highly overall correlated with 통행제한차량유형코드High correlation
통행제한차량유형코드 is highly overall correlated with 표준링크ID and 1 other fieldsHigh correlation
표준링크ID has unique valuesUnique
도로유형코드 has 5393 (95.3%) zerosZeros
연결로유형코드 has 5505 (97.3%) zerosZeros

Reproduction

Analysis started2023-12-10 18:55:18.291753
Analysis finished2023-12-10 18:55:29.079982
Duration10.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준링크ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5659
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5678574 × 109
Minimum1.5000001 × 109
Maximum3.9101591 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:29.526139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5000001 × 109
5-th percentile1.5000324 × 109
Q11.530004 × 109
median1.5500006 × 109
Q31.5600814 × 109
95-th percentile1.5705948 × 109
Maximum3.9101591 × 109
Range2.410159 × 109
Interquartile range (IQR)30077400

Descriptive statistics

Standard deviation2.3118577 × 108
Coefficient of variation (CV)0.14745331
Kurtosis77.493233
Mean1.5678574 × 109
Median Absolute Deviation (MAD)10105098
Skewness8.8616296
Sum8.8725051 × 1012
Variance5.3446861 × 1016
MonotonicityNot monotonic
2023-12-11T03:55:29.746532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500010800 1
 
< 0.1%
1510045502 1
 
< 0.1%
1510019500 1
 
< 0.1%
1510024900 1
 
< 0.1%
1510024800 1
 
< 0.1%
1540061200 1
 
< 0.1%
1540061100 1
 
< 0.1%
1540057700 1
 
< 0.1%
1570596300 1
 
< 0.1%
1570596200 1
 
< 0.1%
Other values (5649) 5649
99.8%
ValueCountFrequency (%)
1500000100 1
< 0.1%
1500000200 1
< 0.1%
1500000500 1
< 0.1%
1500000600 1
< 0.1%
1500000700 1
< 0.1%
1500000800 1
< 0.1%
1500001100 1
< 0.1%
1500001200 1
< 0.1%
1500001500 1
< 0.1%
1500001600 1
< 0.1%
ValueCountFrequency (%)
3910159100 1
< 0.1%
3910159000 1
< 0.1%
3910158600 1
< 0.1%
3910158400 1
< 0.1%
3910044801 1
< 0.1%
3910044701 1
< 0.1%
3690485800 1
< 0.1%
3690167600 1
< 0.1%
3690167500 1
< 0.1%
3690036500 1
< 0.1%

출발표준노드
Real number (ℝ)

HIGH CORRELATION 

Distinct1847
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5787941 × 109
Minimum1.5000001 × 109
Maximum3.9101367 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:30.146636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5000001 × 109
5-th percentile1.5000081 × 109
Q11.5300012 × 109
median1.5500001 × 109
Q31.5600223 × 109
95-th percentile1.5701976 × 109
Maximum3.9101367 × 109
Range2.4101366 × 109
Interquartile range (IQR)30021100

Descriptive statistics

Standard deviation2.770315 × 108
Coefficient of variation (CV)0.17547032
Kurtosis53.258083
Mean1.5787941 × 109
Median Absolute Deviation (MAD)10083000
Skewness7.3969999
Sum8.9343957 × 1012
Variance7.6746454 × 1016
MonotonicityNot monotonic
2023-12-11T03:55:30.408616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1570005700 6
 
0.1%
1510009600 5
 
0.1%
1500000500 5
 
0.1%
1540004700 5
 
0.1%
1510003300 5
 
0.1%
1550007500 5
 
0.1%
1540008100 5
 
0.1%
1550000200 5
 
0.1%
1540002300 5
 
0.1%
1510008600 5
 
0.1%
Other values (1837) 5608
99.1%
ValueCountFrequency (%)
1500000100 2
 
< 0.1%
1500000200 4
0.1%
1500000300 4
0.1%
1500000400 4
0.1%
1500000500 5
0.1%
1500000600 4
0.1%
1500000700 4
0.1%
1500000800 5
0.1%
1500000900 2
 
< 0.1%
1500001000 4
0.1%
ValueCountFrequency (%)
3910136700 1
< 0.1%
3910071500 2
< 0.1%
3910071200 2
< 0.1%
3910071100 2
< 0.1%
3910020300 1
< 0.1%
3910020200 2
< 0.1%
3910019000 1
< 0.1%
3910015900 1
< 0.1%
3690163300 2
< 0.1%
3690163200 2
< 0.1%

도착표준노드
Real number (ℝ)

HIGH CORRELATION 

Distinct1848
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5784318 × 109
Minimum1.5000001 × 109
Maximum3.9101367 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:30.664787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5000001 × 109
5-th percentile1.5000081 × 109
Q11.5300012 × 109
median1.5500001 × 109
Q31.5600222 × 109
95-th percentile1.5701975 × 109
Maximum3.9101367 × 109
Range2.4101366 × 109
Interquartile range (IQR)30021050

Descriptive statistics

Standard deviation2.7572529 × 108
Coefficient of variation (CV)0.17468306
Kurtosis53.859983
Mean1.5784318 × 109
Median Absolute Deviation (MAD)10083000
Skewness7.4369234
Sum8.9323455 × 1012
Variance7.6024437 × 1016
MonotonicityNot monotonic
2023-12-11T03:55:30.886676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1570005700 6
 
0.1%
1540011800 5
 
0.1%
1540010500 5
 
0.1%
1530003900 5
 
0.1%
1540004700 5
 
0.1%
1500000800 5
 
0.1%
1540002300 5
 
0.1%
1510003300 5
 
0.1%
1540013300 5
 
0.1%
1570014700 5
 
0.1%
Other values (1838) 5608
99.1%
ValueCountFrequency (%)
1500000100 2
 
< 0.1%
1500000200 4
0.1%
1500000300 4
0.1%
1500000400 4
0.1%
1500000500 5
0.1%
1500000600 4
0.1%
1500000700 4
0.1%
1500000800 5
0.1%
1500000900 2
 
< 0.1%
1500001000 4
0.1%
ValueCountFrequency (%)
3910136700 1
< 0.1%
3910071500 2
< 0.1%
3910071200 2
< 0.1%
3910071100 2
< 0.1%
3910020300 1
< 0.1%
3910020200 2
< 0.1%
3910019000 1
< 0.1%
3910015900 1
< 0.1%
3690163300 2
< 0.1%
3690163200 2
< 0.1%

지도상거리
Real number (ℝ)

Distinct4675
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.3344
Minimum14.21
Maximum15214.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:31.137708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.21
5-th percentile100.445
Q1209.095
median324.77
Q3544.81
95-th percentile1297.114
Maximum15214.69
Range15200.48
Interquartile range (IQR)335.715

Descriptive statistics

Standard deviation770.36124
Coefficient of variation (CV)1.5396927
Kurtosis102.08497
Mean500.3344
Median Absolute Deviation (MAD)141.38
Skewness8.2368333
Sum2831392.4
Variance593456.45
MonotonicityNot monotonic
2023-12-11T03:55:31.388621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
271.86 6
 
0.1%
253.65 5
 
0.1%
293.18 4
 
0.1%
206.18 4
 
0.1%
160.67 4
 
0.1%
300.42 4
 
0.1%
283.68 4
 
0.1%
276.42 4
 
0.1%
150.36 4
 
0.1%
712.23 4
 
0.1%
Other values (4665) 5616
99.2%
ValueCountFrequency (%)
14.21 2
< 0.1%
14.52 2
< 0.1%
15.54 2
< 0.1%
16.89 1
< 0.1%
16.98 1
< 0.1%
17.42 2
< 0.1%
17.43 2
< 0.1%
18.25 2
< 0.1%
18.52 2
< 0.1%
18.77 2
< 0.1%
ValueCountFrequency (%)
15214.69 1
< 0.1%
15213.04 1
< 0.1%
14275.94 1
< 0.1%
9947.19 1
< 0.1%
9944.51 1
< 0.1%
8471.08 1
< 0.1%
8470.5 1
< 0.1%
8446.99 1
< 0.1%
8313.35 1
< 0.1%
7983.66 1
< 0.1%

도로등급코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.1631
Minimum101
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:31.567842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1104
median104
Q3104
95-th percentile107
Maximum107
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4183601
Coefficient of variation (CV)0.013616723
Kurtosis0.8466078
Mean104.1631
Median Absolute Deviation (MAD)0
Skewness0.51620631
Sum589459
Variance2.0117455
MonotonicityNot monotonic
2023-12-11T03:55:31.764584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
104 3793
67.0%
107 856
 
15.1%
103 504
 
8.9%
101 271
 
4.8%
102 195
 
3.4%
106 22
 
0.4%
105 18
 
0.3%
ValueCountFrequency (%)
101 271
 
4.8%
102 195
 
3.4%
103 504
 
8.9%
104 3793
67.0%
105 18
 
0.3%
106 22
 
0.4%
107 856
 
15.1%
ValueCountFrequency (%)
107 856
 
15.1%
106 22
 
0.4%
105 18
 
0.3%
104 3793
67.0%
103 504
 
8.9%
102 195
 
3.4%
101 271
 
4.8%

도로유형코드
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12970489
Minimum0
Maximum5
Zeros5393
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:31.956221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63183996
Coefficient of variation (CV)4.8713656
Kurtosis27.65659
Mean0.12970489
Median Absolute Deviation (MAD)0
Skewness5.2139583
Sum734
Variance0.39922174
MonotonicityNot monotonic
2023-12-11T03:55:32.156170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5393
95.3%
3 120
 
2.1%
2 46
 
0.8%
1 46
 
0.8%
4 34
 
0.6%
5 20
 
0.4%
ValueCountFrequency (%)
0 5393
95.3%
1 46
 
0.8%
2 46
 
0.8%
3 120
 
2.1%
4 34
 
0.6%
5 20
 
0.4%
ValueCountFrequency (%)
5 20
 
0.4%
4 34
 
0.6%
3 120
 
2.1%
2 46
 
0.8%
1 46
 
0.8%
0 5393
95.3%

도로번호
Text

Distinct60
Distinct (%)1.1%
Missing49
Missing (%)0.9%
Memory size44.3 KiB
2023-12-11T03:55:32.418481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.4087344
Min length1

Characters and Unicode

Total characters7903
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11
ValueCountFrequency (%)
0 2044
36.4%
990
17.6%
5 174
 
3.1%
4 174
 
3.1%
50 126
 
2.2%
13 125
 
2.2%
11 112
 
2.0%
33 108
 
1.9%
20 104
 
1.9%
28 101
 
1.8%
Other values (50) 1552
27.7%
2023-12-11T03:55:32.927226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2524
31.9%
- 990
 
12.5%
5 862
 
10.9%
1 762
 
9.6%
3 701
 
8.9%
4 549
 
6.9%
2 535
 
6.8%
7 363
 
4.6%
8 257
 
3.3%
6 244
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6913
87.5%
Dash Punctuation 990
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2524
36.5%
5 862
 
12.5%
1 762
 
11.0%
3 701
 
10.1%
4 549
 
7.9%
2 535
 
7.7%
7 363
 
5.3%
8 257
 
3.7%
6 244
 
3.5%
9 116
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7903
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2524
31.9%
- 990
 
12.5%
5 862
 
10.9%
1 762
 
9.6%
3 701
 
8.9%
4 549
 
6.9%
2 535
 
6.8%
7 363
 
4.6%
8 257
 
3.3%
6 244
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2524
31.9%
- 990
 
12.5%
5 862
 
10.9%
1 762
 
9.6%
3 701
 
8.9%
4 549
 
6.9%
2 535
 
6.8%
7 363
 
4.6%
8 257
 
3.3%
6 244
 
3.1%

도로명
Text

Distinct664
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
2023-12-11T03:55:33.360796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.4873653
Min length1

Characters and Unicode

Total characters25394
Distinct characters282
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row신천대로
2nd row신천대로
3rd row신천대로
4th row신천대로
5th row신천대로
ValueCountFrequency (%)
329
 
5.8%
일반국도4호선 168
 
3.0%
일반국도5호선 158
 
2.8%
신천동로 119
 
2.1%
신천대로 111
 
2.0%
국채보상로 84
 
1.5%
일반국도25호선 82
 
1.4%
달구벌대로 62
 
1.1%
일반국도30호선 60
 
1.1%
호곡로 60
 
1.1%
Other values (654) 4426
78.2%
2023-12-11T03:55:34.044655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 3805
 
15.0%
길 1298
 
5.1%
도 823
 
3.2%
국 812
 
3.2%
산 685
 
2.7%
호 662
 
2.6%
대 656
 
2.6%
선 559
 
2.2%
동 557
 
2.2%
단 514
 
2.0%
Other values (272) 15023
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22997
90.6%
Decimal Number 2052
 
8.1%
Dash Punctuation 343
 
1.4%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 3805
 
16.5%
길 1298
 
5.6%
도 823
 
3.6%
국 812
 
3.5%
산 685
 
3.0%
호 662
 
2.9%
대 656
 
2.9%
선 559
 
2.4%
동 557
 
2.4%
단 514
 
2.2%
Other values (260) 12626
54.9%
Decimal Number
ValueCountFrequency (%)
2 394
19.2%
4 366
17.8%
3 332
16.2%
5 330
16.1%
1 270
13.2%
0 148
 
7.2%
9 74
 
3.6%
6 66
 
3.2%
7 46
 
2.2%
8 26
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 343
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22997
90.6%
Common 2395
 
9.4%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 3805
 
16.5%
길 1298
 
5.6%
도 823
 
3.6%
국 812
 
3.5%
산 685
 
3.0%
호 662
 
2.9%
대 656
 
2.9%
선 559
 
2.4%
동 557
 
2.4%
단 514
 
2.2%
Other values (260) 12626
54.9%
Common
ValueCountFrequency (%)
2 394
16.5%
4 366
15.3%
- 343
14.3%
3 332
13.9%
5 330
13.8%
1 270
11.3%
0 148
 
6.2%
9 74
 
3.1%
6 66
 
2.8%
7 46
 
1.9%
Latin
ValueCountFrequency (%)
E 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22997
90.6%
ASCII 2397
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 3805
 
16.5%
길 1298
 
5.6%
도 823
 
3.6%
국 812
 
3.5%
산 685
 
3.0%
호 662
 
2.9%
대 656
 
2.9%
선 559
 
2.4%
동 557
 
2.4%
단 514
 
2.2%
Other values (260) 12626
54.9%
ASCII
ValueCountFrequency (%)
2 394
16.4%
4 366
15.3%
- 343
14.3%
3 332
13.9%
5 330
13.8%
1 270
11.3%
0 148
 
6.2%
9 74
 
3.1%
6 66
 
2.8%
7 46
 
1.9%
Other values (2) 28
 
1.2%

연결로유형코드
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7769924
Minimum0
Maximum108
Zeros5505
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size49.9 KiB
2023-12-11T03:55:34.243586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.608186
Coefficient of variation (CV)5.9806379
Kurtosis31.867104
Mean2.7769924
Median Absolute Deviation (MAD)0
Skewness5.8171058
Sum15715
Variance275.83184
MonotonicityNot monotonic
2023-12-11T03:55:34.445478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5505
97.3%
101 111
 
2.0%
108 13
 
0.2%
103 12
 
0.2%
104 11
 
0.2%
102 5
 
0.1%
105 2
 
< 0.1%
ValueCountFrequency (%)
0 5505
97.3%
101 111
 
2.0%
102 5
 
0.1%
103 12
 
0.2%
104 11
 
0.2%
105 2
 
< 0.1%
108 13
 
0.2%
ValueCountFrequency (%)
108 13
 
0.2%
105 2
 
< 0.1%
104 11
 
0.2%
103 12
 
0.2%
102 5
 
0.1%
101 111
 
2.0%
0 5505
97.3%

통행제한차량유형코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
<NA>
2912 
0
2551 
5
 
184
4
 
12

Length

Max length4
Median length4
Mean length2.5437356
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
<NA> 2912
51.5%
0 2551
45.1%
5 184
 
3.3%
4 12
 
0.2%

Length

2023-12-11T03:55:34.797975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:55:34.979183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2912
51.5%
0 2551
45.1%
5 184
 
3.3%
4 12
 
0.2%

적용시작일자
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
Minimum2011-03-24 00:00:00
Maximum2018-10-29 00:00:00
2023-12-11T03:55:35.175081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:35.383196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-11T03:55:27.810553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:21.596803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:22.777013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:23.902658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.934011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.903878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.880969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.957412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:21.771834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:22.925255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.048253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.065638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.081683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.001834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:28.087268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:21.945701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:23.085365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.207151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.173134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.218216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.116584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:28.226441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:22.107335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:23.240162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.366344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.306589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.352997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.238355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:28.368696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:22.270794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:23.411910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.515991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.465201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.502561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.381072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:28.506451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:22.416804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:23.576627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.658765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.613234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.616471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.530171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:28.618663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:22.586308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:23.744782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:24.788752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:25.761394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:26.739184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:55:27.670901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T03:55:35.542360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준링크ID출발표준노드도착표준노드지도상거리도로등급코드도로유형코드도로번호연결로유형코드통행제한차량유형코드적용시작일자
표준링크ID1.0000.9520.9580.2380.2550.1580.5100.000NaN0.114
출발표준노드0.9521.0000.9370.3380.2550.1220.5020.0070.0000.138
도착표준노드0.9580.9371.0000.3360.2570.1240.5040.0070.0000.137
지도상거리0.2380.3380.3361.0000.2510.0930.5990.0000.0730.171
도로등급코드0.2550.2550.2570.2511.0000.3050.9800.4930.6710.708
도로유형코드0.1580.1220.1240.0930.3051.0000.5840.4680.2440.116
도로번호0.5100.5020.5040.5990.9800.5841.0000.6840.8390.972
연결로유형코드0.0000.0070.0070.0000.4930.4680.6841.0000.0410.000
통행제한차량유형코드NaN0.0000.0000.0730.6710.2440.8390.0411.000NaN
적용시작일자0.1140.1380.1370.1710.7080.1160.9720.000NaN1.000
2023-12-11T03:55:35.809322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준링크ID출발표준노드도착표준노드지도상거리도로등급코드도로유형코드연결로유형코드통행제한차량유형코드
표준링크ID1.0000.9680.9690.0750.2360.0110.0161.000
출발표준노드0.9681.0000.9470.0790.2390.0090.0160.000
도착표준노드0.9690.9471.0000.0780.2390.0080.0160.000
지도상거리0.0750.0790.0781.000-0.0510.0110.0710.055
도로등급코드0.2360.2390.239-0.0511.000-0.137-0.2450.644
도로유형코드0.0110.0090.0080.011-0.1371.0000.0840.189
연결로유형코드0.0160.0160.0160.071-0.2450.0841.0000.067
통행제한차량유형코드1.0000.0000.0000.0550.6440.1890.0671.000

Missing values

2023-12-11T03:55:28.775354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T03:55:28.972378image/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

표준링크ID출발표준노드도착표준노드지도상거리도로등급코드도로유형코드도로번호도로명연결로유형코드통행제한차량유형코드적용시작일자
01500010800154001040015000031002372.53102211신천대로052011-03-24
1154002360015400104001540009800237.45102011신천대로052011-03-24
2154002350015400098001540010400235.38102011신천대로052011-03-24
3150000490015000019001500000200276.49102011신천대로052011-03-24
4150000500015000002001500001900274.03102011신천대로052011-03-24
5154005710015400201001540019400186.31104033호곡로002011-03-24
6154005700015400194001540020100186.31104033호곡로002011-03-24
7154006370015400219001540022600426.89104033호곡로002011-03-24
8154006380015400226001540021900416.96104033호곡로002011-03-24
9154006250015400214001540021900239.07104033호곡로002011-03-24
표준링크ID출발표준노드도착표준노드지도상거리도로등급코드도로유형코드도로번호도로명연결로유형코드통행제한차량유형코드적용시작일자
5649157057790015701918001570192200251.081070-국가산단서로84길0<NA>2018-10-29
5650157057800015701923001570191600393.611070-국가산단대로33길0<NA>2018-10-29
5651157057810015701923001570192000159.361070-국가산단서로50길0<NA>2018-10-29
5652157057820015701920001570192300159.311070-국가산단서로50길0<NA>2018-10-29
5653157057830015701924001570192100258.81070-국가산단서로60길0<NA>2018-10-29
5654157057840015701921001570192400258.771070-국가산단서로60길0<NA>2018-10-29
5655157057850015701922001570192400663.131070-국가산단대로49길0<NA>2018-10-29
5656157057860015701920001570192600544.011070-국가산단서로0<NA>2018-10-29
56571570227501367012580036702396004714.871070<NA>사문진로0<NA>2018-10-29
5658157022750236702396001570014900524.571073<NA>사문진로0<NA>2018-10-29