Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory742.2 KiB
Average record size in memory76.0 B

Variable types

Categorical2
Text2
Numeric4

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15276/F/1/datasetView.do

Alerts

ROUTE_ID is highly overall correlated with ROUTE_NAMEHigh correlation
ROUTE_NAME is highly overall correlated with ROUTE_IDHigh correlation
HOUR_CD has 433 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-11 06:49:54.635095
Analysis finished2023-12-11 06:49:57.492451
Duration2.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_NAME
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한강교량
2217 
올림픽대로
1680 
강변북로
1634 
내부순환로
1172 
동부간선로
925 
Other values (5)
2372 

Length

Max length6
Median length5
Mean length4.6529
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서부간선로
2nd row경부고속도로
3rd row올림픽대로
4th row한강교량
5th row분당수서로

Common Values

ValueCountFrequency (%)
한강교량 2217
22.2%
올림픽대로 1680
16.8%
강변북로 1634
16.3%
내부순환로 1172
11.7%
동부간선로 925
9.2%
강남순환로 889
8.9%
북부간선로 415
 
4.2%
분당수서로 382
 
3.8%
경부고속도로 380
 
3.8%
서부간선로 306
 
3.1%

Length

2023-12-11T15:49:57.562389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:49:57.679539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강교량 2217
22.2%
올림픽대로 1680
16.8%
강변북로 1634
16.3%
내부순환로 1172
11.7%
동부간선로 925
9.2%
강남순환로 889
8.9%
북부간선로 415
 
4.2%
분당수서로 382
 
3.8%
경부고속도로 380
 
3.8%
서부간선로 306
 
3.1%

ROUTE_ID
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LHD3000010
1112 
LHU3000010
1105 
LLR3000010
860 
LKR3000010
828 
LLL3000010
820 
Other values (14)
5275 

Length

Max length11
Median length10
Mean length10.0889
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLSN3000010
2nd rowLFD3000010
3rd rowLLL3000010
4th rowLHU3000010
5th rowLDI3000020

Common Values

ValueCountFrequency (%)
LHD3000010 1112
11.1%
LHU3000010 1105
11.1%
LLR3000010 860
 
8.6%
LKR3000010 828
 
8.3%
LLL3000010 820
 
8.2%
LKL3000010 806
 
8.1%
LRI3000010 615
 
6.2%
LRO3000010 557
 
5.6%
LDO3000010 502
 
5.0%
LQRW3000010 445
 
4.5%
Other values (9) 2350
23.5%

Length

2023-12-11T15:49:57.814068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lhd3000010 1112
11.1%
lhu3000010 1105
11.1%
llr3000010 860
 
8.6%
lkr3000010 828
 
8.3%
lll3000010 820
 
8.2%
lkl3000010 806
 
8.1%
lri3000010 615
 
6.2%
lro3000010 557
 
5.6%
ldo3000010 502
 
5.0%
lqrw3000010 445
 
4.5%
Other values (9) 2350
23.5%

ROAD_NAME
Text

Distinct268
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:49:58.008875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length43
Mean length14.5205
Min length5

Characters and Unicode

Total characters145205
Distinct characters147
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row오목교-목동교
2nd row잠원IC→반포IC
3rd row반포대교남단→동작대교남단
4th row반포대교남단→반포대교북단
5th row청담대교남단→탄천1교
ValueCountFrequency (%)
가상구간 798
 
4.9%
진입 431
 
2.7%
올림픽대로(wb 395
 
2.4%
올림픽대로(eb 294
 
1.8%
출구부 230
 
1.4%
봉천터널 227
 
1.4%
입구부 224
 
1.4%
관악터널 223
 
1.4%
중간부 209
 
1.3%
서초터널 207
 
1.3%
Other values (306) 12972
80.0%
2023-12-11T15:49:58.405123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 12515
 
8.6%
대 10493
 
7.2%
→ 9694
 
6.7%
단 9052
 
6.2%
6210
 
4.3%
남 5650
 
3.9%
북 4619
 
3.2%
동 2634
 
1.8%
구 2375
 
1.6%
C 2270
 
1.6%
Other values (137) 79693
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113575
78.2%
Math Symbol 9694
 
6.7%
Uppercase Letter 8130
 
5.6%
Space Separator 6210
 
4.3%
Open Punctuation 2988
 
2.1%
Close Punctuation 2988
 
2.1%
Other Punctuation 919
 
0.6%
Decimal Number 395
 
0.3%
Dash Punctuation 306
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 12515
 
11.0%
대 10493
 
9.2%
단 9052
 
8.0%
남 5650
 
5.0%
북 4619
 
4.1%
동 2634
 
2.3%
구 2375
 
2.1%
성 2184
 
1.9%
프 2079
 
1.8%
램 2079
 
1.8%
Other values (112) 59895
52.7%
Uppercase Letter
ValueCountFrequency (%)
C 2270
27.9%
B 1334
16.4%
I 1263
15.5%
J 1007
12.4%
W 745
 
9.2%
E 479
 
5.9%
O 187
 
2.3%
M 187
 
2.3%
F 187
 
2.3%
R 187
 
2.3%
Other values (4) 284
 
3.5%
Other Punctuation
ValueCountFrequency (%)
" 694
75.5%
, 187
 
20.3%
/ 38
 
4.1%
Open Punctuation
ValueCountFrequency (%)
( 2062
69.0%
[ 926
31.0%
Close Punctuation
ValueCountFrequency (%)
) 2062
69.0%
] 926
31.0%
Math Symbol
ValueCountFrequency (%)
→ 9694
100.0%
Space Separator
ValueCountFrequency (%)
6210
100.0%
Decimal Number
ValueCountFrequency (%)
1 395
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113575
78.2%
Common 23500
 
16.2%
Latin 8130
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 12515
 
11.0%
대 10493
 
9.2%
단 9052
 
8.0%
남 5650
 
5.0%
북 4619
 
4.1%
동 2634
 
2.3%
구 2375
 
2.1%
성 2184
 
1.9%
프 2079
 
1.8%
램 2079
 
1.8%
Other values (112) 59895
52.7%
Latin
ValueCountFrequency (%)
C 2270
27.9%
B 1334
16.4%
I 1263
15.5%
J 1007
12.4%
W 745
 
9.2%
E 479
 
5.9%
O 187
 
2.3%
M 187
 
2.3%
F 187
 
2.3%
R 187
 
2.3%
Other values (4) 284
 
3.5%
Common
ValueCountFrequency (%)
→ 9694
41.3%
6210
26.4%
( 2062
 
8.8%
) 2062
 
8.8%
] 926
 
3.9%
[ 926
 
3.9%
" 694
 
3.0%
1 395
 
1.7%
- 306
 
1.3%
, 187
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113575
78.2%
ASCII 21936
 
15.1%
Arrows 9694
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 12515
 
11.0%
대 10493
 
9.2%
단 9052
 
8.0%
남 5650
 
5.0%
북 4619
 
4.1%
동 2634
 
2.3%
구 2375
 
2.1%
성 2184
 
1.9%
프 2079
 
1.8%
램 2079
 
1.8%
Other values (112) 59895
52.7%
Arrows
ValueCountFrequency (%)
→ 9694
100.0%
ASCII
ValueCountFrequency (%)
6210
28.3%
C 2270
 
10.3%
( 2062
 
9.4%
) 2062
 
9.4%
B 1334
 
6.1%
I 1263
 
5.8%
J 1007
 
4.6%
] 926
 
4.2%
[ 926
 
4.2%
W 745
 
3.4%
Other values (14) 3131
14.3%

LINK_ID
Text

Distinct274
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:49:58.685349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.0889
Min length10

Characters and Unicode

Total characters100889
Distinct characters25
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowLSN2000170
2nd rowLFD2000020
3rd rowLLL2000090
4th rowLHU2000110
5th rowLDI2000110
ValueCountFrequency (%)
lko2000050 54
 
0.5%
lro2000090 53
 
0.5%
lqrw2000010 50
 
0.5%
lhu2000090 50
 
0.5%
ldi2100110 49
 
0.5%
lhu2000210 48
 
0.5%
lhd2000210 48
 
0.5%
lsn2000140 48
 
0.5%
lbl2000040 47
 
0.5%
lkr2100010 47
 
0.5%
Other values (264) 9506
95.1%
2023-12-11T15:49:59.168229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44206
43.8%
L 13218
 
13.1%
2 11993
 
11.9%
1 7128
 
7.1%
R 3508
 
3.5%
D 2655
 
2.6%
H 2333
 
2.3%
K 1634
 
1.6%
O 1575
 
1.6%
I 1532
 
1.5%
Other values (15) 11107
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
69.4%
Uppercase Letter 30889
30.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 13218
42.8%
R 3508
 
11.4%
D 2655
 
8.6%
H 2333
 
7.6%
K 1634
 
5.3%
O 1575
 
5.1%
I 1532
 
5.0%
U 1249
 
4.0%
Q 889
 
2.9%
W 445
 
1.4%
Other values (5) 1851
 
6.0%
Decimal Number
ValueCountFrequency (%)
0 44206
63.2%
2 11993
 
17.1%
1 7128
 
10.2%
5 1455
 
2.1%
3 1148
 
1.6%
4 935
 
1.3%
8 867
 
1.2%
6 773
 
1.1%
7 766
 
1.1%
9 729
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
69.4%
Latin 30889
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 13218
42.8%
R 3508
 
11.4%
D 2655
 
8.6%
H 2333
 
7.6%
K 1634
 
5.3%
O 1575
 
5.1%
I 1532
 
5.0%
U 1249
 
4.0%
Q 889
 
2.9%
W 445
 
1.4%
Other values (5) 1851
 
6.0%
Common
ValueCountFrequency (%)
0 44206
63.2%
2 11993
 
17.1%
1 7128
 
10.2%
5 1455
 
2.1%
3 1148
 
1.6%
4 935
 
1.3%
8 867
 
1.2%
6 773
 
1.1%
7 766
 
1.1%
9 729
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44206
43.8%
L 13218
 
13.1%
2 11993
 
11.9%
1 7128
 
7.1%
R 3508
 
3.5%
D 2655
 
2.6%
H 2333
 
2.3%
K 1634
 
1.6%
O 1575
 
1.6%
I 1532
 
1.5%
Other values (15) 11107
 
11.0%

DATE_CD
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20170997
Minimum20170930
Maximum20171009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:49:59.323965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170930
5-th percentile20170930
Q120171002
median20171004
Q320171007
95-th percentile20171009
Maximum20171009
Range79
Interquartile range (IQR)5

Descriptive statistics

Standard deviation22.932981
Coefficient of variation (CV)1.1369285 × 10-6
Kurtosis4.6496394
Mean20170997
Median Absolute Deviation (MAD)2
Skewness-2.5552668
Sum2.0170997 × 1011
Variance525.92163
MonotonicityNot monotonic
2023-12-11T15:49:59.452103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20171005 1034
10.3%
20170930 1031
10.3%
20171004 1007
10.1%
20171001 1006
10.1%
20171003 1004
10.0%
20171008 1003
10.0%
20171006 989
9.9%
20171002 979
9.8%
20171007 975
9.8%
20171009 972
9.7%
ValueCountFrequency (%)
20170930 1031
10.3%
20171001 1006
10.1%
20171002 979
9.8%
20171003 1004
10.0%
20171004 1007
10.1%
20171005 1034
10.3%
20171006 989
9.9%
20171007 975
9.8%
20171008 1003
10.0%
20171009 972
9.7%
ValueCountFrequency (%)
20171009 972
9.7%
20171008 1003
10.0%
20171007 975
9.8%
20171006 989
9.9%
20171005 1034
10.3%
20171004 1007
10.1%
20171003 1004
10.0%
20171002 979
9.8%
20171001 1006
10.1%
20170930 1031
10.3%

HOUR_CD
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5013
Minimum0
Maximum23
Zeros433
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:49:59.595485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation6.9930314
Coefficient of variation (CV)0.60802096
Kurtosis-1.2138568
Mean11.5013
Median Absolute Deviation (MAD)6
Skewness0.010613922
Sum115013
Variance48.902489
MonotonicityNot monotonic
2023-12-11T15:49:59.750456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 465
 
4.7%
9 451
 
4.5%
22 444
 
4.4%
3 444
 
4.4%
13 435
 
4.3%
2 435
 
4.3%
0 433
 
4.3%
15 425
 
4.2%
16 424
 
4.2%
21 418
 
4.2%
Other values (14) 5626
56.3%
ValueCountFrequency (%)
0 433
4.3%
1 412
4.1%
2 435
4.3%
3 444
4.4%
4 409
4.1%
5 414
4.1%
6 398
4.0%
7 397
4.0%
8 408
4.1%
9 451
4.5%
ValueCountFrequency (%)
23 465
4.7%
22 444
4.4%
21 418
4.2%
20 400
4.0%
19 401
4.0%
18 418
4.2%
17 362
3.6%
16 424
4.2%
15 425
4.2%
14 390
3.9%

SPD
Real number (ℝ)

Distinct5093
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.691932
Minimum6.43
Maximum160.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:49:59.938667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.43
5-th percentile36.32
Q163.01
median75.67
Q385.24
95-th percentile97.0905
Maximum160.44
Range154.01
Interquartile range (IQR)22.23

Descriptive statistics

Standard deviation18.338633
Coefficient of variation (CV)0.2522788
Kurtosis0.93127589
Mean72.691932
Median Absolute Deviation (MAD)10.885
Skewness-0.74908592
Sum726919.32
Variance336.30547
MonotonicityNot monotonic
2023-12-11T15:50:00.129429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.0 14
 
0.1%
80.23 9
 
0.1%
73.45 8
 
0.1%
80.2 8
 
0.1%
78.8 8
 
0.1%
76.42 8
 
0.1%
79.45 8
 
0.1%
80.14 7
 
0.1%
82.17 7
 
0.1%
76.12 7
 
0.1%
Other values (5083) 9916
99.2%
ValueCountFrequency (%)
6.43 1
< 0.1%
6.73 1
< 0.1%
7.48 1
< 0.1%
8.88 1
< 0.1%
9.13 1
< 0.1%
9.51 1
< 0.1%
9.97 1
< 0.1%
10.0 1
< 0.1%
10.56 1
< 0.1%
10.7 1
< 0.1%
ValueCountFrequency (%)
160.44 1
< 0.1%
155.41 1
< 0.1%
154.14 1
< 0.1%
151.3 1
< 0.1%
150.63 1
< 0.1%
148.82 1
< 0.1%
145.71 1
< 0.1%
141.77 1
< 0.1%
139.6 1
< 0.1%
138.88 1
< 0.1%

VOL
Real number (ℝ)

Distinct4703
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2558.1503
Minimum56
Maximum8476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:50:00.339387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile485.95
Q11213.75
median2218
Q33616.25
95-th percentile5713.1
Maximum8476
Range8420
Interquartile range (IQR)2402.5

Descriptive statistics

Standard deviation1650.392
Coefficient of variation (CV)0.64515052
Kurtosis-0.1503982
Mean2558.1503
Median Absolute Deviation (MAD)1136.5
Skewness0.75243122
Sum25581503
Variance2723793.7
MonotonicityNot monotonic
2023-12-11T15:50:00.511566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1614 9
 
0.1%
642 9
 
0.1%
660 9
 
0.1%
1442 9
 
0.1%
911 8
 
0.1%
1270 8
 
0.1%
1094 8
 
0.1%
1034 8
 
0.1%
1125 8
 
0.1%
2786 8
 
0.1%
Other values (4693) 9916
99.2%
ValueCountFrequency (%)
56 1
< 0.1%
61 1
< 0.1%
64 1
< 0.1%
65 1
< 0.1%
79 2
< 0.1%
84 1
< 0.1%
89 1
< 0.1%
91 1
< 0.1%
95 1
< 0.1%
96 2
< 0.1%
ValueCountFrequency (%)
8476 1
< 0.1%
8263 1
< 0.1%
8187 1
< 0.1%
8117 1
< 0.1%
8066 1
< 0.1%
8047 1
< 0.1%
8029 1
< 0.1%
8021 1
< 0.1%
7952 1
< 0.1%
7865 1
< 0.1%

Interactions

2023-12-11T15:49:56.908993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:55.605616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.088131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.479673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:57.031866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:55.747610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.204889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.598853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:57.107861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:55.853201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.293548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.711869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:57.214734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:55.980948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.392912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:49:56.815079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:50:00.622061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_NAMEROUTE_IDDATE_CDHOUR_CDSPDVOL
ROUTE_NAME1.0001.0000.0380.0000.5080.601
ROUTE_ID1.0001.0000.0220.0000.4490.532
DATE_CD0.0380.0221.0000.0000.0790.061
HOUR_CD0.0000.0000.0001.0000.4720.565
SPD0.5080.4490.0790.4721.0000.446
VOL0.6010.5320.0610.5650.4461.000
2023-12-11T15:50:00.756284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_IDROUTE_NAME
ROUTE_ID1.0001.000
ROUTE_NAME1.0001.000
2023-12-11T15:50:01.143302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DATE_CDHOUR_CDSPDVOLROUTE_NAMEROUTE_ID
DATE_CD1.0000.0080.097-0.0300.0180.016
HOUR_CD0.0081.000-0.3380.4000.0000.000
SPD0.097-0.3381.000-0.3450.1770.185
VOL-0.0300.400-0.3451.0000.2230.230
ROUTE_NAME0.0180.0000.1770.2231.0001.000
ROUTE_ID0.0160.0000.1850.2301.0001.000

Missing values

2023-12-11T15:49:57.336683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:49:57.443199image/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

ROUTE_NAMEROUTE_IDROAD_NAMELINK_IDDATE_CDHOUR_CDSPDVOL
58962서부간선로LSN3000010오목교-목동교LSN200017020171008570.251537
27463경부고속도로LFD3000010잠원IC→반포ICLFD2000020201710041817.073672
63127올림픽대로LLL3000010반포대교남단→동작대교남단LLL200009020171009875.853437
2368한강교량LHU3000010반포대교남단→반포대교북단LHU2000110201709301661.613169
39963분당수서로LDI3000020청담대교남단→탄천1교LDI2000110201710062177.273511
14051동부간선로LDO3000010창동교→상계교LDO200012020171002587.341205
50398올림픽대로LLR3000010방화대교남단→가양대교남단LLR2000020201710070102.51144
37109올림픽대로LLL3000010잠실대교남단→청담대교남단LLL2000150201710051574.593992
37738올림픽대로LLR3000010잠실철교 남단→올림픽대교남단LLR2000170201710052075.223152
4495올림픽대로LLR3000010방화대교남단→가양대교남단LLR200002020170930790.924109
ROUTE_NAMEROUTE_IDROAD_NAMELINK_IDDATE_CDHOUR_CDSPDVOL
18339강남순환로LQRE3000010봉천터널 중간부→봉천터널 출구부LQRE2000070201710022199.231245
51920내부순환로LRO3000010성동JC→사근램프LRO2000010201710071084.371780
50456올림픽대로LLR3000010성산대교남단→양화대교남단LLR2000040201710071085.244025
29493강변북로LKL3000010청담대교북단→영동대교북단LKL200009020171004874.83378
49658강변북로LKR3000010청담대교북단→잠실대교북단LKR200018020171007497.31028
18327강남순환로LQRE3000010봉천터널 중간부→봉천터널 출구부LQRE200007020171002995.971969
34194경부고속도로LFU3000010반포IC→한남ICLFU200003020171005487.641242
44571강남순환로LQRE3000010봉천터널 중간부→봉천터널 출구부LQRE2000070201710062197.681366
21074경부고속도로LFU3000010반포IC→한남ICLFU200003020171003486.52889
20889경부고속도로LFD3000010잠원IC→반포ICLFD2000020201710031115.123439