Overview

Dataset statistics

Number of variables13
Number of observations200
Missing cells400
Missing cells (%)15.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory111.7 B

Variable types

Categorical5
Numeric4
Unsupported2
Text2

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTSCTN1HHSTS0000001

Alerts

도로전광표지판표출명 is highly overall correlated with 도로구간ID and 4 other fieldsHigh correlation
도로구간지역명 is highly overall correlated with 도로구간ID and 1 other fieldsHigh correlation
도로구간ID is highly overall correlated with 도로구간지역명 and 1 other fieldsHigh correlation
평균교통량값 is highly overall correlated with 도로전광표지판표출명High correlation
도로등급명 is highly overall correlated with 도로전광표지판표출명High correlation
평균도로점유율 is highly overall correlated with 도로전광표지판표출명High correlation
도로등급명 is highly imbalanced (56.7%)Imbalance
도로구간유형명 has 200 (100.0%) missing valuesMissing
도로파티션구분자여부 has 200 (100.0%) missing valuesMissing
도로구간유형명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
도로파티션구분자여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
평균교통량값 has 113 (56.5%) zerosZeros

Reproduction

Analysis started2023-12-10 06:12:33.112050
Analysis finished2023-12-10 06:12:46.053384
Duration12.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생성일시
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2019-12-22 17:00:00
116 
2019-12-22 19:00:00
52 
2019-12-22 21:00:00
32 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-12-22 19:00:00
2nd row2019-12-22 19:00:00
3rd row2019-12-22 19:00:00
4th row2019-12-22 19:00:00
5th row2019-12-22 19:00:00

Common Values

ValueCountFrequency (%)
2019-12-22 17:00:00 116
58.0%
2019-12-22 19:00:00 52
26.0%
2019-12-22 21:00:00 32
 
16.0%

Length

2023-12-10T15:12:46.303170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:12:46.941561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-22 200
50.0%
17:00:00 116
29.0%
19:00:00 52
 
13.0%
21:00:00 32
 
8.0%

도로구간ID
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.715095 × 109
Minimum1.6110509 × 109
Maximum9.6800726 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:12:47.477378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6110509 × 109
5-th percentile1.64 × 109
Q11.6510295 × 109
median8.7100846 × 109
Q39.6800113 × 109
95-th percentile9.6800726 × 109
Maximum9.6800726 × 109
Range8.0690217 × 109
Interquartile range (IQR)8.0289818 × 109

Descriptive statistics

Standard deviation3.8776224 × 109
Coefficient of variation (CV)0.67848782
Kurtosis-1.9883427
Mean5.715095 × 109
Median Absolute Deviation (MAD)9.6998794 × 108
Skewness-0.080705736
Sum1.143019 × 1012
Variance1.5035955 × 1019
MonotonicityNot monotonic
2023-12-10T15:12:48.029544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9680011300 2
 
1.0%
1700000003 2
 
1.0%
1651045200 2
 
1.0%
1641000700 2
 
1.0%
1641035900 2
 
1.0%
9680072538 2
 
1.0%
1641017100 2
 
1.0%
9670029500 2
 
1.0%
1651028100 2
 
1.0%
1700000031 2
 
1.0%
Other values (158) 180
90.0%
ValueCountFrequency (%)
1611050900 1
0.5%
1631000600 1
0.5%
1631001200 1
0.5%
1631002600 2
1.0%
1640000008 1
0.5%
1640000014 1
0.5%
1640000019 1
0.5%
1640000028 1
0.5%
1640000029 2
1.0%
1640000054 1
0.5%
ValueCountFrequency (%)
9680072596 1
0.5%
9680072595 2
1.0%
9680072591 2
1.0%
9680072590 1
0.5%
9680072588 1
0.5%
9680072587 1
0.5%
9680072585 1
0.5%
9680072579 2
1.0%
9680072569 1
0.5%
9680072554 1
0.5%

도로구간유형명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing200
Missing (%)100.0%
Memory size1.9 KiB

도로등급명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
특별/광역시도
166 
고속국도
20 
일반국도
 
12
지방도
 
2

Length

Max length7
Median length7
Mean length6.48
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고속국도
2nd row특별/광역시도
3rd row고속국도
4th row특별/광역시도
5th row특별/광역시도

Common Values

ValueCountFrequency (%)
특별/광역시도 166
83.0%
고속국도 20
 
10.0%
일반국도 12
 
6.0%
지방도 2
 
1.0%

Length

2023-12-10T15:12:48.372009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:12:48.634917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특별/광역시도 166
83.0%
고속국도 20
 
10.0%
일반국도 12
 
6.0%
지방도 2
 
1.0%

도로구간지역명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
송도
95 
청라
77 
영종
16 
미단
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청라
2nd row송도
3rd row청라
4th row송도
5th row청라

Common Values

ValueCountFrequency (%)
송도 95
47.5%
청라 77
38.5%
영종 16
 
8.0%
미단 12
 
6.0%

Length

2023-12-10T15:12:48.872653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:12:49.054172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
송도 95
47.5%
청라 77
38.5%
영종 16
 
8.0%
미단 12
 
6.0%

평균교통량값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.315
Minimum0
Maximum544
Zeros113
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:12:49.242496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323
95-th percentile147.3
Maximum544
Range544
Interquartile range (IQR)23

Descriptive statistics

Standard deviation71.996271
Coefficient of variation (CV)2.4559533
Kurtosis23.148608
Mean29.315
Median Absolute Deviation (MAD)0
Skewness4.4137481
Sum5863
Variance5183.4631
MonotonicityNot monotonic
2023-12-10T15:12:49.503714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 113
56.5%
23 8
 
4.0%
19 8
 
4.0%
12 7
 
3.5%
21 6
 
3.0%
15 3
 
1.5%
16 3
 
1.5%
38 3
 
1.5%
191 3
 
1.5%
72 2
 
1.0%
Other values (38) 44
 
22.0%
ValueCountFrequency (%)
0 113
56.5%
11 1
 
0.5%
12 7
 
3.5%
13 2
 
1.0%
15 3
 
1.5%
16 3
 
1.5%
17 1
 
0.5%
18 1
 
0.5%
19 8
 
4.0%
20 1
 
0.5%
ValueCountFrequency (%)
544 1
 
0.5%
475 1
 
0.5%
342 1
 
0.5%
339 1
 
0.5%
263 1
 
0.5%
206 1
 
0.5%
197 1
 
0.5%
191 3
1.5%
145 1
 
0.5%
141 1
 
0.5%

차량평균속도값
Real number (ℝ)

Distinct53
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.185
Minimum18
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:12:49.854885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27.9
Q142
median51
Q359
95-th percentile77.45
Maximum99
Range81
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.169421
Coefficient of variation (CV)0.29636458
Kurtosis1.2226725
Mean51.185
Median Absolute Deviation (MAD)9
Skewness0.66036499
Sum10237
Variance230.11133
MonotonicityNot monotonic
2023-12-10T15:12:50.128462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 16
 
8.0%
47 13
 
6.5%
53 10
 
5.0%
44 10
 
5.0%
56 9
 
4.5%
55 7
 
3.5%
59 6
 
3.0%
38 6
 
3.0%
35 6
 
3.0%
52 6
 
3.0%
Other values (43) 111
55.5%
ValueCountFrequency (%)
18 1
 
0.5%
20 1
 
0.5%
22 3
1.5%
23 3
1.5%
25 1
 
0.5%
26 1
 
0.5%
28 1
 
0.5%
29 2
1.0%
31 4
2.0%
32 4
2.0%
ValueCountFrequency (%)
99 2
1.0%
97 1
 
0.5%
96 1
 
0.5%
91 2
1.0%
88 2
1.0%
86 2
1.0%
77 2
1.0%
74 3
1.5%
73 1
 
0.5%
72 1
 
0.5%

평균도로점유율
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
114 
1
43 
2
30 
3
12 
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 114
57.0%
1 43
 
21.5%
2 30
 
15.0%
3 12
 
6.0%
7 1
 
0.5%

Length

2023-12-10T15:12:50.395148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:12:50.606688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 114
57.0%
1 43
 
21.5%
2 30
 
15.0%
3 12
 
6.0%
7 1
 
0.5%

차량평균통행시간값
Real number (ℝ)

Distinct98
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6
Minimum4
Maximum571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:12:50.859183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q131.5
median53
Q385
95-th percentile216
Maximum571
Range567
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation68.892021
Coefficient of variation (CV)0.98982789
Kurtosis14.546481
Mean69.6
Median Absolute Deviation (MAD)26
Skewness2.9275677
Sum13920
Variance4746.1106
MonotonicityNot monotonic
2023-12-10T15:12:51.238487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 11
 
5.5%
6 7
 
3.5%
7 6
 
3.0%
39 6
 
3.0%
65 5
 
2.5%
11 5
 
2.5%
63 4
 
2.0%
41 4
 
2.0%
53 4
 
2.0%
4 4
 
2.0%
Other values (88) 144
72.0%
ValueCountFrequency (%)
4 4
 
2.0%
6 7
3.5%
7 6
3.0%
8 11
5.5%
9 2
 
1.0%
11 5
2.5%
14 1
 
0.5%
16 1
 
0.5%
17 1
 
0.5%
18 2
 
1.0%
ValueCountFrequency (%)
571 1
0.5%
306 1
0.5%
260 2
1.0%
255 1
0.5%
248 1
0.5%
242 1
0.5%
230 1
0.5%
220 1
0.5%
216 2
1.0%
210 1
0.5%

도로파티션구분자여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing200
Missing (%)100.0%
Memory size1.9 KiB

도로시작지점명
Text

Distinct93
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:12:51.702620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.73
Min length1

Characters and Unicode

Total characters1346
Distinct characters172
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)17.5%

Sample

1st row북인천요금소
2nd rowKDB산업은행앞
3rd row서인천IC남측(본선)
4th row선학역사거리
5th row사파이어로시점
ValueCountFrequency (%)
없음 14
 
6.7%
외암도사거리 7
 
3.3%
영종대교 7
 
3.3%
사리골사거리 5
 
2.4%
외암삼삼거리 4
 
1.9%
청라고삼거리 4
 
1.9%
g타워앞사거리 4
 
1.9%
송도3교교차로 4
 
1.9%
동단(하부도로 4
 
1.9%
원인제역삼거리 4
 
1.9%
Other values (85) 153
72.9%
2023-12-10T15:12:52.333895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 81
 
6.0%
거 69
 
5.1%
로 67
 
5.0%
사 61
 
4.5%
대 40
 
3.0%
도 31
 
2.3%
인 31
 
2.3%
종 27
 
2.0%
영 23
 
1.7%
1 23
 
1.7%
Other values (162) 893
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1190
88.4%
Decimal Number 65
 
4.8%
Uppercase Letter 43
 
3.2%
Close Punctuation 16
 
1.2%
Open Punctuation 16
 
1.2%
Space Separator 10
 
0.7%
Dash Punctuation 6
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 81
 
6.8%
거 69
 
5.8%
로 67
 
5.6%
사 61
 
5.1%
대 40
 
3.4%
도 31
 
2.6%
인 31
 
2.6%
종 27
 
2.3%
영 23
 
1.9%
천 23
 
1.9%
Other values (142) 737
61.9%
Decimal Number
ValueCountFrequency (%)
1 23
35.4%
3 13
20.0%
2 7
 
10.8%
0 6
 
9.2%
4 6
 
9.2%
5 4
 
6.2%
7 4
 
6.2%
9 2
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 16
37.2%
I 12
27.9%
G 7
16.3%
K 2
 
4.7%
D 2
 
4.7%
B 2
 
4.7%
J 1
 
2.3%
S 1
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1190
88.4%
Common 113
 
8.4%
Latin 43
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 81
 
6.8%
거 69
 
5.8%
로 67
 
5.6%
사 61
 
5.1%
대 40
 
3.4%
도 31
 
2.6%
인 31
 
2.6%
종 27
 
2.3%
영 23
 
1.9%
천 23
 
1.9%
Other values (142) 737
61.9%
Common
ValueCountFrequency (%)
1 23
20.4%
) 16
14.2%
( 16
14.2%
3 13
11.5%
10
8.8%
2 7
 
6.2%
0 6
 
5.3%
4 6
 
5.3%
- 6
 
5.3%
5 4
 
3.5%
Other values (2) 6
 
5.3%
Latin
ValueCountFrequency (%)
C 16
37.2%
I 12
27.9%
G 7
16.3%
K 2
 
4.7%
D 2
 
4.7%
B 2
 
4.7%
J 1
 
2.3%
S 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1190
88.4%
ASCII 156
 
11.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 81
 
6.8%
거 69
 
5.8%
로 67
 
5.6%
사 61
 
5.1%
대 40
 
3.4%
도 31
 
2.6%
인 31
 
2.6%
종 27
 
2.3%
영 23
 
1.9%
천 23
 
1.9%
Other values (142) 737
61.9%
ASCII
ValueCountFrequency (%)
1 23
14.7%
) 16
10.3%
C 16
10.3%
( 16
10.3%
3 13
8.3%
I 12
 
7.7%
10
 
6.4%
2 7
 
4.5%
G 7
 
4.5%
0 6
 
3.8%
Other values (10) 30
19.2%

도로종료지점명
Text

Distinct98
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:12:52.731300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length6.4
Min length1

Characters and Unicode

Total characters1280
Distinct characters167
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)23.0%

Sample

1st row북인천IC입구
2nd row인천대입구역사거리
3rd row서인천IC남측(연결로)
4th row문학경기장 입구
5th row골드클래스
ValueCountFrequency (%)
없음 21
 
10.0%
선학역사거리 6
 
2.9%
영종대교 6
 
2.9%
영종순환로4 4
 
1.9%
인천타워대로-7 4
 
1.9%
서인천ic남측(본선 4
 
1.9%
인천대입구역사거리 4
 
1.9%
동춘역사거리 3
 
1.4%
담지로종점 3
 
1.4%
동단(하부도로 3
 
1.4%
Other values (92) 151
72.2%
2023-12-10T15:12:53.759907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 69
 
5.4%
로 63
 
4.9%
거 59
 
4.6%
사 57
 
4.5%
대 43
 
3.4%
종 36
 
2.8%
영 31
 
2.4%
인 31
 
2.4%
천 26
 
2.0%
도 23
 
1.8%
Other values (157) 842
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1132
88.4%
Decimal Number 65
 
5.1%
Uppercase Letter 36
 
2.8%
Close Punctuation 16
 
1.2%
Open Punctuation 16
 
1.2%
Space Separator 9
 
0.7%
Dash Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 69
 
6.1%
로 63
 
5.6%
거 59
 
5.2%
사 57
 
5.0%
대 43
 
3.8%
종 36
 
3.2%
영 31
 
2.7%
인 31
 
2.7%
천 26
 
2.3%
도 23
 
2.0%
Other values (138) 694
61.3%
Decimal Number
ValueCountFrequency (%)
1 21
32.3%
2 13
20.0%
3 8
 
12.3%
4 6
 
9.2%
7 6
 
9.2%
5 3
 
4.6%
0 3
 
4.6%
9 3
 
4.6%
6 1
 
1.5%
8 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 16
44.4%
I 14
38.9%
G 3
 
8.3%
S 2
 
5.6%
B 1
 
2.8%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1132
88.4%
Common 112
 
8.8%
Latin 36
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 69
 
6.1%
로 63
 
5.6%
거 59
 
5.2%
사 57
 
5.0%
대 43
 
3.8%
종 36
 
3.2%
영 31
 
2.7%
인 31
 
2.7%
천 26
 
2.3%
도 23
 
2.0%
Other values (138) 694
61.3%
Common
ValueCountFrequency (%)
1 21
18.8%
) 16
14.3%
( 16
14.3%
2 13
11.6%
9
8.0%
3 8
 
7.1%
4 6
 
5.4%
- 6
 
5.4%
7 6
 
5.4%
5 3
 
2.7%
Other values (4) 8
 
7.1%
Latin
ValueCountFrequency (%)
C 16
44.4%
I 14
38.9%
G 3
 
8.3%
S 2
 
5.6%
B 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1132
88.4%
ASCII 148
 
11.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 69
 
6.1%
로 63
 
5.6%
거 59
 
5.2%
사 57
 
5.0%
대 43
 
3.8%
종 36
 
3.2%
영 31
 
2.7%
인 31
 
2.7%
천 26
 
2.3%
도 23
 
2.0%
Other values (138) 694
61.3%
ASCII
ValueCountFrequency (%)
1 21
14.2%
) 16
10.8%
( 16
10.8%
C 16
10.8%
I 14
9.5%
2 13
8.8%
9
 
6.1%
3 8
 
5.4%
4 6
 
4.1%
- 6
 
4.1%
Other values (9) 23
15.5%

도로전광표지판표출명
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
123 
경명로
17 
경인고속도로
 
8
인천국제공항고속도로
 
5
중봉로
 
5
Other values (32)
42 

Length

Max length15
Median length4
Mean length4.85
Min length3

Unique

Unique25 ?
Unique (%)12.5%

Sample

1st row인천국제공항고속도로
2nd row<NA>
3rd row경인고속도로
4th row<NA>
5th row사파이어로 상1

Common Values

ValueCountFrequency (%)
<NA> 123
61.5%
경명로 17
 
8.5%
경인고속도로 8
 
4.0%
인천국제공항고속도로 5
 
2.5%
중봉로 5
 
2.5%
인천국제공항고속도로 영종대교 4
 
2.0%
중봉대로 3
 
1.5%
청라루비로 하2 2
 
1.0%
청라루비로 상2 2
 
1.0%
사파이어로 상3 2
 
1.0%
Other values (27) 29
 
14.5%

Length

2023-12-10T15:12:53.984650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 123
52.1%
경명로 17
 
7.2%
인천국제공항고속도로 9
 
3.8%
경인고속도로 8
 
3.4%
사파이어로 6
 
2.5%
청라루비로 6
 
2.5%
상1 5
 
2.1%
하1 5
 
2.1%
담지로 5
 
2.1%
국제대로 5
 
2.1%
Other values (23) 47
 
19.9%

Interactions

2023-12-10T15:12:41.601179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:36.800520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:38.162096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:39.855367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:42.313319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:37.077413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:38.588852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:40.280738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:43.420913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:37.469617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:38.933872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:40.734945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:43.898008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:37.884734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:39.419702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:12:41.180625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:12:54.157697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성일시도로구간ID도로등급명도로구간지역명평균교통량값차량평균속도값평균도로점유율차량평균통행시간값도로시작지점명도로종료지점명도로전광표지판표출명
생성일시1.0000.2570.0000.1310.2380.0000.1300.0000.0000.0000.000
도로구간ID0.2571.0000.3060.7160.2460.5290.5160.0000.9970.9921.000
도로등급명0.0000.3061.0000.4940.0000.5140.2180.3800.9060.8971.000
도로구간지역명0.1310.7160.4941.0000.3650.5190.4410.1260.9990.997NaN
평균교통량값0.2380.2460.0000.3651.0000.4600.4940.0000.9050.8990.990
차량평균속도값0.0000.5290.5140.5190.4601.0000.3080.4890.9330.9330.645
평균도로점유율0.1300.5160.2180.4410.4940.3081.0000.2470.9700.9700.966
차량평균통행시간값0.0000.0000.3800.1260.0000.4890.2471.0000.9470.8620.705
도로시작지점명0.0000.9970.9060.9990.9050.9330.9700.9471.0000.9900.984
도로종료지점명0.0000.9920.8970.9970.8990.9330.9700.8620.9901.0000.978
도로전광표지판표출명0.0001.0001.000NaN0.9900.6450.9660.7050.9840.9781.000
2023-12-10T15:12:54.457783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성일시도로전광표지판표출명도로등급명도로구간지역명평균도로점유율
생성일시1.0000.0000.0000.1230.097
도로전광표지판표출명0.0001.0000.7391.0000.636
도로등급명0.0000.7391.0000.2100.179
도로구간지역명0.1231.0000.2101.0000.372
평균도로점유율0.0970.6360.1790.3721.000
2023-12-10T15:12:54.694400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간ID평균교통량값차량평균속도값차량평균통행시간값생성일시도로등급명도로구간지역명평균도로점유율도로전광표지판표출명
도로구간ID1.0000.1370.004-0.0010.0850.2820.7550.4510.739
평균교통량값0.1371.000-0.123-0.1160.1530.0000.1680.3290.666
차량평균속도값0.004-0.1231.000-0.0830.0000.3270.3310.1300.198
차량평균통행시간값-0.001-0.116-0.0831.0000.0000.2680.0850.1590.308
생성일시0.0850.1530.0000.0001.0000.0000.1230.0970.000
도로등급명0.2820.0000.3270.2680.0001.0000.2100.1790.739
도로구간지역명0.7550.1680.3310.0850.1230.2101.0000.3721.000
평균도로점유율0.4510.3290.1300.1590.0970.1790.3721.0000.636
도로전광표지판표출명0.7390.6660.1980.3080.0000.7391.0000.6361.000

Missing values

2023-12-10T15:12:44.276768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:12:45.498753image/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

생성일시도로구간ID도로구간유형명도로등급명도로구간지역명평균교통량값차량평균속도값평균도로점유율차량평균통행시간값도로파티션구분자여부도로시작지점명도로종료지점명도로전광표지판표출명
02019-12-22 19:00:009680008000<NA>고속국도청라060042<NA>북인천요금소북인천IC입구인천국제공항고속도로
12019-12-22 19:00:001640000054<NA>특별/광역시도송도2247236<NA>KDB산업은행앞인천대입구역사거리<NA>
22019-12-22 19:00:009680004300<NA>고속국도청라057016<NA>서인천IC남측(본선)서인천IC남측(연결로)경인고속도로
32019-12-22 19:00:001641043000<NA>특별/광역시도송도047094<NA>선학역사거리문학경기장 입구<NA>
42019-12-22 19:00:008710088200<NA>특별/광역시도청라1938186<NA>사파이어로시점골드클래스사파이어로 상1
52019-12-22 19:00:001641000303<NA>일반국도송도042095<NA>송도3교교차로없음<NA>
62019-12-22 19:00:001631001200<NA>일반국도송도0390120<NA>제2경인고속도로시점인하대병원거리<NA>
72019-12-22 19:00:009680072517<NA>특별/광역시도영종2164143<NA>영종대로14영종대로13<NA>
82019-12-22 19:00:009680072533<NA>특별/광역시도영종1277142<NA>영종대로19영종대로20<NA>
92019-12-22 19:00:008710084700<NA>특별/광역시도청라2723353<NA>호반베르디움한일베라체청라루비로 상1
생성일시도로구간ID도로구간유형명도로등급명도로구간지역명평균교통량값차량평균속도값평균도로점유율차량평균통행시간값도로파티션구분자여부도로시작지점명도로종료지점명도로전광표지판표출명
1902019-12-22 17:00:001640000014<NA>특별/광역시도송도7256221<NA>롯데마트송도점앞인천대입구역사거리<NA>
1912019-12-22 17:00:009610006500<NA>고속국도청라0860192<NA>영종대교 동단(하부도로)영종대교 서단(상하부도로 합류)인천국제공항고속도로 영종대교
1922019-12-22 17:00:001641000200<NA>일반국도송도0500100<NA>외암도사거리없음<NA>
1932019-12-22 17:00:001651027500<NA>특별/광역시도송도0550100<NA>호구포길사거리-<NA>
1942019-12-22 17:00:001631000600<NA>고속국도송도0680242<NA>제2경인고속도로시점문학IC동측(본선)<NA>
1952019-12-22 17:00:008710091200<NA>특별/광역시도청라12372112<NA>국제대로시점첨단동로종점첨단동로 상4
1962019-12-22 17:00:009680034100<NA>특별/광역시도청라044038<NA>북항고가차도서인천선착장입구중봉로
1972019-12-22 17:00:001641019200<NA>특별/광역시도송도052060<NA>신연수역사거리원인제역삼거리<NA>
1982019-12-22 17:00:009680018600<NA>특별/광역시도청라053034<NA>공촌사거리없음경명로
1992019-12-22 17:00:008710093600<NA>특별/광역시도청라24451129<NA>하우스토리복지시설부지뒤편경제로지하차로 하