Overview

Dataset statistics

Number of variables12
Number of observations200
Missing cells200
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.1 KiB
Average record size in memory102.7 B

Variable types

DateTime1
Numeric5
Categorical3
Text2
Unsupported1

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTSCTNDDSTST0000001

Alerts

생성일시 has constant value ""Constant
도로구간지역명 is highly overall correlated with 도로구간ID and 1 other fieldsHigh correlation
도로전광표지판표출명 is highly overall correlated with 도로구간ID and 4 other fieldsHigh correlation
도로구간ID is highly overall correlated with 도로구간지역명 and 1 other fieldsHigh correlation
차량평균속도값 is highly overall correlated with 평균도로점유율 and 1 other fieldsHigh correlation
평균도로점유율 is highly overall correlated with 차량평균속도값 and 1 other fieldsHigh correlation
도로등급명 is highly overall correlated with 도로전광표지판표출명High correlation
도로등급명 is highly imbalanced (53.6%)Imbalance
도로구간유형명 has 200 (100.0%) missing valuesMissing
도로구간ID has unique valuesUnique
도로구간유형명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
차량평균속도값 has 115 (57.5%) zerosZeros
평균도로점유율 has 116 (58.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:21:22.497222
Analysis finished2023-12-10 06:21:28.447080
Duration5.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생성일시
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2019-12-22 15:00:00
Maximum2019-12-22 15:00:00
2023-12-10T15:21:28.530917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:28.702533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

도로구간ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7998057 × 109
Minimum1.6110509 × 109
Maximum9.6800726 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:28.961248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6110509 × 109
5-th percentile1.64 × 109
Q11.6510291 × 109
median8.710085 × 109
Q39.6800196 × 109
95-th percentile9.6800726 × 109
Maximum9.6800726 × 109
Range8.0690217 × 109
Interquartile range (IQR)8.0289906 × 109

Descriptive statistics

Standard deviation3.8806922 × 109
Coefficient of variation (CV)0.66910728
Kurtosis-1.980422
Mean5.7998057 × 109
Median Absolute Deviation (MAD)9.6998759 × 108
Skewness-0.12147361
Sum1.1599611 × 1012
Variance1.5059772 × 1019
MonotonicityNot monotonic
2023-12-10T15:21:29.247533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9680007800 1
 
0.5%
1700000005 1
 
0.5%
1700000033 1
 
0.5%
9680072510 1
 
0.5%
9680072592 1
 
0.5%
9680007900 1
 
0.5%
9680050600 1
 
0.5%
9680053800 1
 
0.5%
9680007400 1
 
0.5%
9680072532 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
1611050900 1
0.5%
1631000500 1
0.5%
1631000600 1
0.5%
1631001200 1
0.5%
1631002600 1
0.5%
1640000007 1
0.5%
1640000008 1
0.5%
1640000014 1
0.5%
1640000019 1
0.5%
1640000028 1
0.5%
ValueCountFrequency (%)
9680072596 1
0.5%
9680072595 1
0.5%
9680072594 1
0.5%
9680072593 1
0.5%
9680072592 1
0.5%
9680072591 1
0.5%
9680072590 1
0.5%
9680072589 1
0.5%
9680072579 1
0.5%
9680072563 1
0.5%

도로등급명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
특별/광역시도
162 
고속국도
23 
일반국도
 
13
지방도
 
2

Length

Max length7
Median length7
Mean length6.42
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
특별/광역시도 162
81.0%
고속국도 23
 
11.5%
일반국도 13
 
6.5%
지방도 2
 
1.0%

Length

2023-12-10T15:21:29.528346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:21:29.724540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특별/광역시도 162
81.0%
고속국도 23
 
11.5%
일반국도 13
 
6.5%
지방도 2
 
1.0%

도로구간지역명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
송도
93 
청라
79 
영종
18 
미단
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송도 93
46.5%
청라 79
39.5%
영종 18
 
9.0%
미단 10
 
5.0%

Length

2023-12-10T15:21:30.206272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:21:30.396836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
송도 93
46.5%
청라 79
39.5%
영종 18
 
9.0%
미단 10
 
5.0%

차량평균속도값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9238.465
Minimum0
Maximum911270
Zeros115
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:30.657666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3735.5
95-th percentile4171.3
Maximum911270
Range911270
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation82014.679
Coefficient of variation (CV)8.8775223
Kurtosis101.719
Mean9238.465
Median Absolute Deviation (MAD)0
Skewness10.040935
Sum1847693
Variance6.7264076 × 109
MonotonicityNot monotonic
2023-12-10T15:21:30.985929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
57.5%
4929 3
 
1.5%
3508 2
 
1.0%
1324 2
 
1.0%
2315 2
 
1.0%
126 2
 
1.0%
1741 2
 
1.0%
249 1
 
0.5%
100 1
 
0.5%
911270 1
 
0.5%
Other values (69) 69
34.5%
ValueCountFrequency (%)
0 115
57.5%
93 1
 
0.5%
98 1
 
0.5%
100 1
 
0.5%
104 1
 
0.5%
105 1
 
0.5%
125 1
 
0.5%
126 2
 
1.0%
171 1
 
0.5%
177 1
 
0.5%
ValueCountFrequency (%)
911270 1
 
0.5%
718568 1
 
0.5%
92099 1
 
0.5%
8219 1
 
0.5%
5134 1
 
0.5%
5124 1
 
0.5%
4929 3
1.5%
4367 1
 
0.5%
4161 1
 
0.5%
3508 2
1.0%

평균속도값
Real number (ℝ)

Distinct65
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.03
Minimum17
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:31.271796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile31.85
Q141.75
median51
Q359.25
95-th percentile93
Maximum105
Range88
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation17.204332
Coefficient of variation (CV)0.3244264
Kurtosis1.241008
Mean53.03
Median Absolute Deviation (MAD)9
Skewness0.96521647
Sum10606
Variance295.98905
MonotonicityNot monotonic
2023-12-10T15:21:31.595525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 11
 
5.5%
60 10
 
5.0%
58 10
 
5.0%
53 9
 
4.5%
49 7
 
3.5%
40 7
 
3.5%
44 7
 
3.5%
46 7
 
3.5%
59 7
 
3.5%
48 7
 
3.5%
Other values (55) 118
59.0%
ValueCountFrequency (%)
17 2
 
1.0%
19 1
 
0.5%
21 1
 
0.5%
22 1
 
0.5%
25 1
 
0.5%
27 1
 
0.5%
28 1
 
0.5%
29 2
 
1.0%
32 6
3.0%
33 1
 
0.5%
ValueCountFrequency (%)
105 2
1.0%
100 1
 
0.5%
99 1
 
0.5%
98 2
1.0%
97 1
 
0.5%
96 1
 
0.5%
95 1
 
0.5%
93 3
1.5%
92 1
 
0.5%
91 1
 
0.5%

평균도로점유율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88
Minimum0
Maximum8
Zeros116
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:31.822332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5353784
Coefficient of variation (CV)1.7447482
Kurtosis9.2296859
Mean0.88
Median Absolute Deviation (MAD)0
Skewness2.7952623
Sum176
Variance2.3573869
MonotonicityNot monotonic
2023-12-10T15:21:32.171362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 116
58.0%
1 43
 
21.5%
2 21
 
10.5%
3 11
 
5.5%
8 4
 
2.0%
4 2
 
1.0%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
0 116
58.0%
1 43
 
21.5%
2 21
 
10.5%
3 11
 
5.5%
4 2
 
1.0%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
8 4
 
2.0%
ValueCountFrequency (%)
8 4
 
2.0%
7 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
4 2
 
1.0%
3 11
 
5.5%
2 21
 
10.5%
1 43
 
21.5%
0 116
58.0%

차량평균통행시간값
Real number (ℝ)

Distinct107
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.395
Minimum3
Maximum544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:21:32.417205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q131.5
median54.5
Q385.5
95-th percentile192.55
Maximum544
Range541
Interquartile range (IQR)54

Descriptive statistics

Standard deviation66.771334
Coefficient of variation (CV)0.97626046
Kurtosis14.073179
Mean68.395
Median Absolute Deviation (MAD)28
Skewness2.9051932
Sum13679
Variance4458.411
MonotonicityNot monotonic
2023-12-10T15:21:32.690555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 10
 
5.0%
40 7
 
3.5%
11 6
 
3.0%
8 5
 
2.5%
63 5
 
2.5%
50 4
 
2.0%
42 4
 
2.0%
58 4
 
2.0%
9 4
 
2.0%
32 4
 
2.0%
Other values (97) 147
73.5%
ValueCountFrequency (%)
3 3
 
1.5%
4 2
 
1.0%
6 10
5.0%
7 3
 
1.5%
8 5
2.5%
9 4
 
2.0%
11 6
3.0%
13 1
 
0.5%
16 1
 
0.5%
17 2
 
1.0%
ValueCountFrequency (%)
544 1
0.5%
326 1
0.5%
312 1
0.5%
260 1
0.5%
251 1
0.5%
230 1
0.5%
223 1
0.5%
216 1
0.5%
204 1
0.5%
203 1
0.5%

도로시작지점명
Text

Distinct104
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:21:33.120194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.43
Min length1

Characters and Unicode

Total characters1286
Distinct characters181
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 row영종대교 동단(하부도로)
2nd row복지시설부지뒤편
3rd row자연대로11
4th rowG타워앞사거리
5th row미추홀공원북측사거리
ValueCountFrequency (%)
없음 20
 
9.5%
영종대교 7
 
3.3%
외암도사거리 6
 
2.9%
북인천ic입구 4
 
1.9%
송도3교교차로 4
 
1.9%
사리골사거리 4
 
1.9%
아암대로1 3
 
1.4%
동단(하부도로 3
 
1.4%
인천대입구역사거리 3
 
1.4%
하우스토리 3
 
1.4%
Other values (97) 153
72.9%
2023-12-10T15:21:34.007752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 68
 
5.3%
로 63
 
4.9%
거 58
 
4.5%
사 56
 
4.4%
대 41
 
3.2%
도 32
 
2.5%
인 29
 
2.3%
종 27
 
2.1%
영 23
 
1.8%
천 23
 
1.8%
Other values (171) 866
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1129
87.8%
Decimal Number 62
 
4.8%
Uppercase Letter 45
 
3.5%
Close Punctuation 17
 
1.3%
Open Punctuation 17
 
1.3%
Space Separator 10
 
0.8%
Dash Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 68
 
6.0%
로 63
 
5.6%
거 58
 
5.1%
사 56
 
5.0%
대 41
 
3.6%
도 32
 
2.8%
인 29
 
2.6%
종 27
 
2.4%
영 23
 
2.0%
천 23
 
2.0%
Other values (150) 709
62.8%
Decimal Number
ValueCountFrequency (%)
1 22
35.5%
3 10
16.1%
2 9
14.5%
4 7
 
11.3%
0 5
 
8.1%
9 4
 
6.5%
7 2
 
3.2%
5 2
 
3.2%
6 1
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
C 19
42.2%
I 15
33.3%
G 6
 
13.3%
S 1
 
2.2%
D 1
 
2.2%
B 1
 
2.2%
K 1
 
2.2%
J 1
 
2.2%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1129
87.8%
Common 112
 
8.7%
Latin 45
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 68
 
6.0%
로 63
 
5.6%
거 58
 
5.1%
사 56
 
5.0%
대 41
 
3.6%
도 32
 
2.8%
인 29
 
2.6%
종 27
 
2.4%
영 23
 
2.0%
천 23
 
2.0%
Other values (150) 709
62.8%
Common
ValueCountFrequency (%)
1 22
19.6%
) 17
15.2%
( 17
15.2%
10
8.9%
3 10
8.9%
2 9
8.0%
4 7
 
6.2%
- 6
 
5.4%
0 5
 
4.5%
9 4
 
3.6%
Other values (3) 5
 
4.5%
Latin
ValueCountFrequency (%)
C 19
42.2%
I 15
33.3%
G 6
 
13.3%
S 1
 
2.2%
D 1
 
2.2%
B 1
 
2.2%
K 1
 
2.2%
J 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1129
87.8%
ASCII 157
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 68
 
6.0%
로 63
 
5.6%
거 58
 
5.1%
사 56
 
5.0%
대 41
 
3.6%
도 32
 
2.8%
인 29
 
2.6%
종 27
 
2.4%
영 23
 
2.0%
천 23
 
2.0%
Other values (150) 709
62.8%
ASCII
ValueCountFrequency (%)
1 22
14.0%
C 19
12.1%
) 17
10.8%
( 17
10.8%
I 15
9.6%
10
 
6.4%
3 10
 
6.4%
2 9
 
5.7%
4 7
 
4.5%
- 6
 
3.8%
Other values (11) 25
15.9%

도로종료지점명
Text

Distinct108
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:21:34.457420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.48
Min length1

Characters and Unicode

Total characters1296
Distinct characters178
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

Unique54 ?
Unique (%)27.0%

Sample

1st row북인천요금소
2nd row사파이어로종점
3rd row영종순환로9
4th row송도3교북측
5th row프리미엄아울렛앞
ValueCountFrequency (%)
없음 20
 
9.5%
영종대교 7
 
3.3%
선학역사거리 5
 
2.4%
청학사거리 4
 
1.9%
북인천ic입구 4
 
1.9%
연수사거리 3
 
1.4%
미추홀대로1 3
 
1.4%
사리골사거리 3
 
1.4%
인천대입구역사거리 3
 
1.4%
외암도사거리 3
 
1.4%
Other values (102) 156
73.9%
2023-12-10T15:21:35.274832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 68
 
5.2%
로 60
 
4.6%
거 58
 
4.5%
사 55
 
4.2%
대 42
 
3.2%
종 33
 
2.5%
인 29
 
2.2%
영 28
 
2.2%
도 26
 
2.0%
동 23
 
1.8%
Other values (168) 874
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1137
87.7%
Decimal Number 67
 
5.2%
Uppercase Letter 42
 
3.2%
Close Punctuation 17
 
1.3%
Open Punctuation 17
 
1.3%
Space Separator 11
 
0.8%
Dash Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 68
 
6.0%
로 60
 
5.3%
거 58
 
5.1%
사 55
 
4.8%
대 42
 
3.7%
종 33
 
2.9%
인 29
 
2.6%
영 28
 
2.5%
도 26
 
2.3%
동 23
 
2.0%
Other values (148) 715
62.9%
Decimal Number
ValueCountFrequency (%)
1 23
34.3%
2 12
17.9%
3 9
 
13.4%
4 7
 
10.4%
0 5
 
7.5%
9 3
 
4.5%
5 3
 
4.5%
6 2
 
3.0%
7 2
 
3.0%
8 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 19
45.2%
I 15
35.7%
G 4
 
9.5%
S 2
 
4.8%
J 1
 
2.4%
B 1
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1137
87.7%
Common 117
 
9.0%
Latin 42
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 68
 
6.0%
로 60
 
5.3%
거 58
 
5.1%
사 55
 
4.8%
대 42
 
3.7%
종 33
 
2.9%
인 29
 
2.6%
영 28
 
2.5%
도 26
 
2.3%
동 23
 
2.0%
Other values (148) 715
62.9%
Common
ValueCountFrequency (%)
1 23
19.7%
) 17
14.5%
( 17
14.5%
2 12
10.3%
11
9.4%
3 9
 
7.7%
4 7
 
6.0%
- 5
 
4.3%
0 5
 
4.3%
9 3
 
2.6%
Other values (4) 8
 
6.8%
Latin
ValueCountFrequency (%)
C 19
45.2%
I 15
35.7%
G 4
 
9.5%
S 2
 
4.8%
J 1
 
2.4%
B 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1137
87.7%
ASCII 159
 
12.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 68
 
6.0%
로 60
 
5.3%
거 58
 
5.1%
사 55
 
4.8%
대 42
 
3.7%
종 33
 
2.9%
인 29
 
2.6%
영 28
 
2.5%
도 26
 
2.3%
동 23
 
2.0%
Other values (148) 715
62.9%
ASCII
ValueCountFrequency (%)
1 23
14.5%
C 19
11.9%
) 17
10.7%
( 17
10.7%
I 15
9.4%
2 12
7.5%
11
6.9%
3 9
 
5.7%
4 7
 
4.4%
- 5
 
3.1%
Other values (10) 24
15.1%

도로전광표지판표출명
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
121 
경명로
18 
경인고속도로
 
7
인천국제공항고속도로
 
6
중봉로
 
5
Other values (34)
43 

Length

Max length15
Median length4
Mean length4.86
Min length3

Unique

Unique29 ?
Unique (%)14.5%

Sample

1st row인천국제공항고속도로
2nd row사파이어로 상3
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 121
60.5%
경명로 18
 
9.0%
경인고속도로 7
 
3.5%
인천국제공항고속도로 6
 
3.0%
중봉로 5
 
2.5%
인천국제공항고속도로 영종대교 4
 
2.0%
중봉대로 3
 
1.5%
봉수대길 3
 
1.5%
청라로 2
 
1.0%
청라루비로 하2 2
 
1.0%
Other values (29) 29
 
14.5%

Length

2023-12-10T15:21:35.622789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 121
51.5%
경명로 18
 
7.7%
인천국제공항고속도로 10
 
4.3%
경인고속도로 7
 
3.0%
국제대로 6
 
2.6%
중봉로 5
 
2.1%
중봉대로 5
 
2.1%
사파이어로 5
 
2.1%
상1 5
 
2.1%
청라루비로 5
 
2.1%
Other values (23) 48
 
20.4%

도로구간유형명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

Interactions

2023-12-10T15:21:27.178685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.449476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.430448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.250449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:26.251057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:27.324729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.641587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.595322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.604829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:26.500254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:27.463939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:23.846730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.756991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.725162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:26.665310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:27.595697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.012689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.903994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.857214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:26.835829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:27.790745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:24.265565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:25.069741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:26.058390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:21:27.034417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:21:35.900572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간ID도로등급명도로구간지역명차량평균속도값평균속도값평균도로점유율차량평균통행시간값도로전광표지판표출명
도로구간ID1.0000.2780.7120.0000.3410.7480.1011.000
도로등급명0.2781.0000.4590.0000.5780.2040.5201.000
도로구간지역명0.7120.4591.0000.0000.3850.5220.000NaN
차량평균속도값0.0000.0000.0001.0000.0000.7480.000NaN
평균속도값0.3410.5780.3850.0001.0000.3570.5210.000
평균도로점유율0.7480.2040.5220.7480.3571.0000.0001.000
차량평균통행시간값0.1010.5200.0000.0000.5210.0001.0000.000
도로전광표지판표출명1.0001.000NaNNaN0.0001.0000.0001.000
2023-12-10T15:21:36.098432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간지역명도로등급명도로전광표지판표출명
도로구간지역명1.0000.1921.000
도로등급명0.1921.0000.730
도로전광표지판표출명1.0000.7301.000
2023-12-10T15:21:36.253146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간ID차량평균속도값평균속도값평균도로점유율차량평균통행시간값도로등급명도로구간지역명도로전광표지판표출명
도로구간ID1.0000.0990.0440.131-0.0960.2580.7500.730
차량평균속도값0.0991.0000.0400.958-0.2600.0000.0001.000
평균속도값0.0440.0401.000-0.020-0.1420.3830.2360.000
평균도로점유율0.1310.958-0.0201.000-0.2590.1290.3570.739
차량평균통행시간값-0.096-0.260-0.142-0.2591.0000.3810.0000.000
도로등급명0.2580.0000.3830.1290.3811.0000.1920.730
도로구간지역명0.7500.0000.2360.3570.0000.1921.0001.000
도로전광표지판표출명0.7301.0000.0000.7390.0000.7301.0001.000

Missing values

2023-12-10T15:21:28.012211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:21:28.321837image/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

생성일시도로구간ID도로등급명도로구간지역명차량평균속도값평균속도값평균도로점유율차량평균통행시간값도로시작지점명도로종료지점명도로전광표지판표출명도로구간유형명
02019-12-22 15:00:009680007800고속국도청라0590101영종대교 동단(하부도로)북인천요금소인천국제공항고속도로<NA>
12019-12-22 15:00:008710088500특별/광역시도청라33441245복지시설부지뒤편사파이어로종점사파이어로 상3<NA>
22019-12-22 15:00:009680072579특별/광역시도미단27348170자연대로11영종순환로9<NA><NA>
32019-12-22 15:00:001700000017특별/광역시도송도35086236G타워앞사거리송도3교북측<NA><NA>
42019-12-22 15:00:001640000059특별/광역시도송도174661259미추홀공원북측사거리프리미엄아울렛앞<NA><NA>
52019-12-22 15:00:001700000028특별/광역시도송도13245327인천타워대로-3인천타워대로-7<NA><NA>
62019-12-22 15:00:008710085300특별/광역시도청라86748132청라고삼거리담지로종점담지로 하6<NA>
72019-12-22 15:00:009680072591특별/광역시도미단105272114미단중앙로13영종순환로4<NA><NA>
82019-12-22 15:00:001631002600일반국도송도0320144인하대병원거리제2경인고속도로시점<NA><NA>
92019-12-22 15:00:009610006600고속국도청라0990216영종대교 서단(상하부도로 합류)영종대교 동단(하부도로)인천국제공항고속도로 영종대교<NA>
생성일시도로구간ID도로등급명도로구간지역명차량평균속도값평균속도값평균도로점유율차량평균통행시간값도로시작지점명도로종료지점명도로전광표지판표출명도로구간유형명
1902019-12-22 15:00:009680019600특별/광역시도청라060059북인천IC입구없음청라로<NA>
1912019-12-22 15:00:001700000008특별/광역시도송도24245836프리미엄아울렛앞센트럴파크역<NA><NA>
1922019-12-22 15:00:008710086600특별/광역시도청라180245178중흥S클래스13블록담지로종점국제대로 상1<NA>
1932019-12-22 15:00:009680072529특별/광역시도영종2315282162하늘대로11하늘대로종점<NA><NA>
1942019-12-22 15:00:001651031200특별/광역시도송도049050번영로제2사거리연수사거리<NA><NA>
1952019-12-22 15:00:008710086500특별/광역시도청라219347274담지로종점중흥S클래스13블록국제대로 하1<NA>
1962019-12-22 15:00:009680004800고속국도청라055033서인천IC동측(본선)서인천IC북측(연결로)경인고속도로<NA>
1972019-12-22 15:00:001651094500지방도송도0340156없음고잔요금소<NA><NA>
1982019-12-22 15:00:009680072590특별/광역시도미단10436373미단중앙로13영종순환로9<NA><NA>
1992019-12-22 15:00:009680053200특별/광역시도청라040033북인천IC입구없음경명로<NA>