Overview

Dataset statistics

Number of variables13
Number of observations200
Missing cells522
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory111.7 B

Variable types

DateTime1
Numeric5
Unsupported2
Categorical2
Text3

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTSCTN15MMST0000001

Alerts

도로구간ID is highly overall correlated with 도로구간지역명High correlation
평균교통량값 is highly overall correlated with 평균도로점유율High correlation
평균도로점유율 is highly overall correlated with 평균교통량값High correlation
도로구간지역명 is highly overall correlated with 도로구간IDHigh correlation
도로등급명 is highly imbalanced (57.7%)Imbalance
도로구간유형명 has 200 (100.0%) missing valuesMissing
도로파티션구분자여부 has 200 (100.0%) missing valuesMissing
도로전광표지판표출명 has 122 (61.0%) 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 107 (53.5%) zerosZeros
평균도로점유율 has 115 (57.5%) zerosZeros

Reproduction

Analysis started2023-12-10 06:29:52.029184
Analysis finished2023-12-10 06:29:58.475328
Duration6.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생성일시
Date

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2019-12-09 15:45:00
Maximum2019-12-09 18:15:00
2023-12-10T15:29:58.560649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:58.743674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

도로구간ID
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.026343 × 109
Minimum1.6110509 × 109
Maximum9.6800726 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:29:58.977131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6110509 × 109
5-th percentile1.6400001 × 109
Q11.6510296 × 109
median8.7100854 × 109
Q39.6800331 × 109
95-th percentile9.6800726 × 109
Maximum9.6800726 × 109
Range8.0690217 × 109
Interquartile range (IQR)8.0290035 × 109

Descriptive statistics

Standard deviation3.8539998 × 109
Coefficient of variation (CV)0.63952546
Kurtosis-1.9332227
Mean6.026343 × 109
Median Absolute Deviation (MAD)9.6998712 × 108
Skewness-0.24213619
Sum1.2052686 × 1012
Variance1.4853314 × 1019
MonotonicityNot monotonic
2023-12-10T15:29:59.250104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1651000400 2
 
1.0%
8710085100 2
 
1.0%
9680072545 2
 
1.0%
9680072585 2
 
1.0%
9680004700 2
 
1.0%
9610006800 1
 
0.5%
9670029400 1
 
0.5%
9680011300 1
 
0.5%
1651028100 1
 
0.5%
1700000031 1
 
0.5%
Other values (185) 185
92.5%
ValueCountFrequency (%)
1611050900 1
0.5%
1631000500 1
0.5%
1631001200 1
0.5%
1631002600 1
0.5%
1640000007 1
0.5%
1640000008 1
0.5%
1640000019 1
0.5%
1640000028 1
0.5%
1640000029 1
0.5%
1640000054 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%
9680072588 1
0.5%
9680072587 1
0.5%

도로구간유형명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

도로등급명
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
특별/광역시도
166 
고속국도
21 
일반국도
 
12
지방도
 
1

Length

Max length7
Median length7
Mean length6.485
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
특별/광역시도 166
83.0%
고속국도 21
 
10.5%
일반국도 12
 
6.0%
지방도 1
 
0.5%

Length

2023-12-10T15:29:59.508818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:29:59.709297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특별/광역시도 166
83.0%
고속국도 21
 
10.5%
일반국도 12
 
6.0%
지방도 1
 
0.5%

도로구간지역명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
송도
87 
청라
78 
영종
20 
미단
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송도 87
43.5%
청라 78
39.0%
영종 20
 
10.0%
미단 15
 
7.5%

Length

2023-12-10T15:29:59.887708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:30:00.073238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
송도 87
43.5%
청라 78
39.0%
영종 20
 
10.0%
미단 15
 
7.5%

평균교통량값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.26
Minimum0
Maximum3136
Zeros107
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:30:00.256250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile27.05
Maximum3136
Range3136
Interquartile range (IQR)6

Descriptive statistics

Standard deviation221.57282
Coefficient of variation (CV)10.936467
Kurtosis199.45215
Mean20.26
Median Absolute Deviation (MAD)0
Skewness14.113345
Sum4052
Variance49094.515
MonotonicityNot monotonic
2023-12-10T15:30:00.473811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 107
53.5%
3 30
 
15.0%
7 10
 
5.0%
6 6
 
3.0%
5 5
 
2.5%
4 5
 
2.5%
29 4
 
2.0%
10 4
 
2.0%
8 4
 
2.0%
15 4
 
2.0%
Other values (17) 21
 
10.5%
ValueCountFrequency (%)
0 107
53.5%
3 30
 
15.0%
4 5
 
2.5%
5 5
 
2.5%
6 6
 
3.0%
7 10
 
5.0%
8 4
 
2.0%
9 3
 
1.5%
10 4
 
2.0%
11 1
 
0.5%
ValueCountFrequency (%)
3136 1
 
0.5%
45 1
 
0.5%
42 1
 
0.5%
34 1
 
0.5%
31 1
 
0.5%
29 4
2.0%
28 1
 
0.5%
27 1
 
0.5%
26 1
 
0.5%
24 1
 
0.5%

차량평균속도값
Real number (ℝ)

Distinct63
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.625
Minimum8
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:30:00.724443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile30.9
Q142
median50
Q360
95-th percentile82.05
Maximum100
Range92
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.819471
Coefficient of variation (CV)0.30643043
Kurtosis1.1345189
Mean51.625
Median Absolute Deviation (MAD)9
Skewness0.4264601
Sum10325
Variance250.25565
MonotonicityNot monotonic
2023-12-10T15:30:01.002411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 14
 
7.0%
60 14
 
7.0%
46 8
 
4.0%
51 8
 
4.0%
50 8
 
4.0%
58 7
 
3.5%
41 7
 
3.5%
55 7
 
3.5%
56 7
 
3.5%
39 7
 
3.5%
Other values (53) 113
56.5%
ValueCountFrequency (%)
8 1
 
0.5%
9 1
 
0.5%
11 1
 
0.5%
17 1
 
0.5%
18 2
1.0%
22 1
 
0.5%
26 1
 
0.5%
29 2
1.0%
31 1
 
0.5%
32 4
2.0%
ValueCountFrequency (%)
100 1
0.5%
99 1
0.5%
97 1
0.5%
96 1
0.5%
90 1
0.5%
89 1
0.5%
86 2
1.0%
83 2
1.0%
82 1
0.5%
81 1
0.5%

평균도로점유율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.445
Minimum0
Maximum144
Zeros115
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:30:01.186578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum144
Range144
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10.569558
Coefficient of variation (CV)7.3145729
Kurtosis169.45064
Mean1.445
Median Absolute Deviation (MAD)0
Skewness12.738693
Sum289
Variance111.71555
MonotonicityNot monotonic
2023-12-10T15:30:01.351754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 115
57.5%
1 66
33.0%
2 14
 
7.0%
3 3
 
1.5%
144 1
 
0.5%
42 1
 
0.5%
ValueCountFrequency (%)
0 115
57.5%
1 66
33.0%
2 14
 
7.0%
3 3
 
1.5%
42 1
 
0.5%
144 1
 
0.5%
ValueCountFrequency (%)
144 1
 
0.5%
42 1
 
0.5%
3 3
 
1.5%
2 14
 
7.0%
1 66
33.0%
0 115
57.5%

차량평균통행시간값
Real number (ℝ)

Distinct106
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.365
Minimum3
Maximum585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:30:01.566843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.95
Q129
median55
Q381
95-th percentile192
Maximum585
Range582
Interquartile range (IQR)52

Descriptive statistics

Standard deviation77.20632
Coefficient of variation (CV)1.1130443
Kurtosis20.049839
Mean69.365
Median Absolute Deviation (MAD)26
Skewness3.8243355
Sum13873
Variance5960.8159
MonotonicityNot monotonic
2023-12-10T15:30:01.834131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 8
 
4.0%
8 8
 
4.0%
9 6
 
3.0%
38 5
 
2.5%
6 5
 
2.5%
68 5
 
2.5%
73 4
 
2.0%
5 4
 
2.0%
7 4
 
2.0%
67 3
 
1.5%
Other values (96) 148
74.0%
ValueCountFrequency (%)
3 1
 
0.5%
5 4
2.0%
6 5
2.5%
7 4
2.0%
8 8
4.0%
9 6
3.0%
11 1
 
0.5%
13 1
 
0.5%
14 2
 
1.0%
15 2
 
1.0%
ValueCountFrequency (%)
585 1
0.5%
577 1
0.5%
369 1
0.5%
326 1
0.5%
306 1
0.5%
276 1
0.5%
271 1
0.5%
216 1
0.5%
196 1
0.5%
192 2
1.0%

도로파티션구분자여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

도로시작지점명
Text

Distinct104
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:30:02.224024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.27
Min length1

Characters and Unicode

Total characters1254
Distinct characters176
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

Unique51 ?
Unique (%)25.5%

Sample

1st row영종대교 동단(상부도로)
2nd row안말사거리
3rd row한일베라체
4th row롯데캐슬
5th row하늘교서측
ValueCountFrequency (%)
없음 21
 
10.1%
외암도사거리 6
 
2.9%
영종대교 5
 
2.4%
사리골사거리 4
 
1.9%
영종순환로4 4
 
1.9%
북인천ic입구 4
 
1.9%
영종순환로9 3
 
1.4%
프리미엄아울렛앞 3
 
1.4%
g타워앞사거리 3
 
1.4%
아암대로1 3
 
1.4%
Other values (97) 151
72.9%
2023-12-10T15:30:03.204676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 69
 
5.5%
로 65
 
5.2%
거 57
 
4.5%
사 54
 
4.3%
대 38
 
3.0%
종 32
 
2.6%
영 28
 
2.2%
인 27
 
2.2%
천 23
 
1.8%
도 22
 
1.8%
Other values (166) 839
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1097
87.5%
Decimal Number 63
 
5.0%
Uppercase Letter 50
 
4.0%
Open Punctuation 16
 
1.3%
Close Punctuation 16
 
1.3%
Space Separator 7
 
0.6%
Dash Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 69
 
6.3%
로 65
 
5.9%
거 57
 
5.2%
사 54
 
4.9%
대 38
 
3.5%
종 32
 
2.9%
영 28
 
2.6%
인 27
 
2.5%
천 23
 
2.1%
도 22
 
2.0%
Other values (146) 682
62.2%
Decimal Number
ValueCountFrequency (%)
1 21
33.3%
3 10
15.9%
2 8
 
12.7%
4 8
 
12.7%
9 6
 
9.5%
0 4
 
6.3%
5 4
 
6.3%
7 2
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 20
40.0%
I 16
32.0%
G 6
 
12.0%
B 2
 
4.0%
K 2
 
4.0%
D 2
 
4.0%
S 1
 
2.0%
J 1
 
2.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1097
87.5%
Common 107
 
8.5%
Latin 50
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 69
 
6.3%
로 65
 
5.9%
거 57
 
5.2%
사 54
 
4.9%
대 38
 
3.5%
종 32
 
2.9%
영 28
 
2.6%
인 27
 
2.5%
천 23
 
2.1%
도 22
 
2.0%
Other values (146) 682
62.2%
Common
ValueCountFrequency (%)
1 21
19.6%
( 16
15.0%
) 16
15.0%
3 10
9.3%
2 8
 
7.5%
4 8
 
7.5%
7
 
6.5%
9 6
 
5.6%
- 5
 
4.7%
0 4
 
3.7%
Other values (2) 6
 
5.6%
Latin
ValueCountFrequency (%)
C 20
40.0%
I 16
32.0%
G 6
 
12.0%
B 2
 
4.0%
K 2
 
4.0%
D 2
 
4.0%
S 1
 
2.0%
J 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1097
87.5%
ASCII 157
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 69
 
6.3%
로 65
 
5.9%
거 57
 
5.2%
사 54
 
4.9%
대 38
 
3.5%
종 32
 
2.9%
영 28
 
2.6%
인 27
 
2.5%
천 23
 
2.1%
도 22
 
2.0%
Other values (146) 682
62.2%
ASCII
ValueCountFrequency (%)
1 21
13.4%
C 20
12.7%
( 16
10.2%
) 16
10.2%
I 16
10.2%
3 10
 
6.4%
2 8
 
5.1%
4 8
 
5.1%
7
 
4.5%
9 6
 
3.8%
Other values (10) 29
18.5%

도로종료지점명
Text

Distinct106
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:30:03.634050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length6.415
Min length1

Characters and Unicode

Total characters1283
Distinct characters173
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

Unique52 ?
Unique (%)26.0%

Sample

1st row영종대교 서단(상하부도로 합류)
2nd row고잔사거리
3rd row골드클래스
4th row담지로시점
5th row하늘교동측
ValueCountFrequency (%)
없음 15
 
7.2%
선학역사거리 6
 
2.9%
영종대교 5
 
2.4%
사리골사거리 4
 
1.9%
영종순환로4 4
 
1.9%
청학사거리 4
 
1.9%
외암삼삼거리 4
 
1.9%
청라고삼거리 4
 
1.9%
북인천ic입구 4
 
1.9%
프리미엄아울렛앞 3
 
1.4%
Other values (100) 155
74.5%
2023-12-10T15:30:04.296524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 72
 
5.6%
로 65
 
5.1%
거 61
 
4.8%
사 56
 
4.4%
대 41
 
3.2%
종 33
 
2.6%
영 29
 
2.3%
인 27
 
2.1%
1 26
 
2.0%
도 22
 
1.7%
Other values (163) 851
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1127
87.8%
Decimal Number 72
 
5.6%
Uppercase Letter 42
 
3.3%
Close Punctuation 15
 
1.2%
Open Punctuation 15
 
1.2%
Space Separator 8
 
0.6%
Dash Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 72
 
6.4%
로 65
 
5.8%
거 61
 
5.4%
사 56
 
5.0%
대 41
 
3.6%
종 33
 
2.9%
영 29
 
2.6%
인 27
 
2.4%
도 22
 
2.0%
천 21
 
1.9%
Other values (143) 700
62.1%
Decimal Number
ValueCountFrequency (%)
1 26
36.1%
2 12
16.7%
3 9
 
12.5%
4 7
 
9.7%
7 6
 
8.3%
0 4
 
5.6%
5 3
 
4.2%
9 3
 
4.2%
6 1
 
1.4%
8 1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
C 19
45.2%
I 15
35.7%
G 4
 
9.5%
S 2
 
4.8%
B 1
 
2.4%
J 1
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1127
87.8%
Common 114
 
8.9%
Latin 42
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 72
 
6.4%
로 65
 
5.8%
거 61
 
5.4%
사 56
 
5.0%
대 41
 
3.6%
종 33
 
2.9%
영 29
 
2.6%
인 27
 
2.4%
도 22
 
2.0%
천 21
 
1.9%
Other values (143) 700
62.1%
Common
ValueCountFrequency (%)
1 26
22.8%
) 15
13.2%
( 15
13.2%
2 12
10.5%
3 9
 
7.9%
8
 
7.0%
4 7
 
6.1%
7 6
 
5.3%
0 4
 
3.5%
- 4
 
3.5%
Other values (4) 8
 
7.0%
Latin
ValueCountFrequency (%)
C 19
45.2%
I 15
35.7%
G 4
 
9.5%
S 2
 
4.8%
B 1
 
2.4%
J 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1127
87.8%
ASCII 156
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 72
 
6.4%
로 65
 
5.8%
거 61
 
5.4%
사 56
 
5.0%
대 41
 
3.6%
종 33
 
2.9%
영 29
 
2.6%
인 27
 
2.4%
도 22
 
2.0%
천 21
 
1.9%
Other values (143) 700
62.1%
ASCII
ValueCountFrequency (%)
1 26
16.7%
C 19
12.2%
) 15
9.6%
( 15
9.6%
I 15
9.6%
2 12
7.7%
3 9
 
5.8%
8
 
5.1%
4 7
 
4.5%
7 6
 
3.8%
Other values (10) 24
15.4%

도로전광표지판표출명
Text

MISSING 

Distinct39
Distinct (%)50.0%
Missing122
Missing (%)61.0%
Memory size1.7 KiB
2023-12-10T15:30:04.585811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length6.0512821
Min length3

Characters and Unicode

Total characters472
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)35.9%

Sample

1st row인천국제공항고속도로 영종대교
2nd row청중로지하차로 하
3rd row청중로 상1
4th row인천국제공항고속도로
5th row경명로
ValueCountFrequency (%)
경명로 16
 
14.0%
인천국제공항고속도로 8
 
7.0%
경인고속도로 8
 
7.0%
상1 7
 
6.1%
국제대로 6
 
5.3%
사파이어로 6
 
5.3%
하1 6
 
5.3%
청라루비로 6
 
5.3%
중봉로 5
 
4.4%
하2 5
 
4.4%
Other values (21) 41
36.0%
2023-12-10T15:30:05.100086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 78
 
16.5%
36
 
7.6%
경 27
 
5.7%
하 19
 
4.0%
대 17
 
3.6%
제 17
 
3.6%
상 17
 
3.6%
도 16
 
3.4%
속 16
 
3.4%
고 16
 
3.4%
Other values (33) 213
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
85.6%
Space Separator 36
 
7.6%
Decimal Number 32
 
6.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 78
19.3%
경 27
 
6.7%
하 19
 
4.7%
대 17
 
4.2%
제 17
 
4.2%
상 17
 
4.2%
도 16
 
4.0%
속 16
 
4.0%
고 16
 
4.0%
명 16
 
4.0%
Other values (26) 165
40.8%
Decimal Number
ValueCountFrequency (%)
1 13
40.6%
2 9
28.1%
3 4
 
12.5%
6 2
 
6.2%
4 2
 
6.2%
5 2
 
6.2%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
85.6%
Common 68
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 78
19.3%
경 27
 
6.7%
하 19
 
4.7%
대 17
 
4.2%
제 17
 
4.2%
상 17
 
4.2%
도 16
 
4.0%
속 16
 
4.0%
고 16
 
4.0%
명 16
 
4.0%
Other values (26) 165
40.8%
Common
ValueCountFrequency (%)
36
52.9%
1 13
 
19.1%
2 9
 
13.2%
3 4
 
5.9%
6 2
 
2.9%
4 2
 
2.9%
5 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
85.6%
ASCII 68
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 78
19.3%
경 27
 
6.7%
하 19
 
4.7%
대 17
 
4.2%
제 17
 
4.2%
상 17
 
4.2%
도 16
 
4.0%
속 16
 
4.0%
고 16
 
4.0%
명 16
 
4.0%
Other values (26) 165
40.8%
ASCII
ValueCountFrequency (%)
36
52.9%
1 13
 
19.1%
2 9
 
13.2%
3 4
 
5.9%
6 2
 
2.9%
4 2
 
2.9%
5 2
 
2.9%

Interactions

2023-12-10T15:29:57.126167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:53.840023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.688813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.490834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.301832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.303997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.037509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.866916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.644902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.486901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.452206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.206793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.022129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.775080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.640903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.591923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.352280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.147683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.901163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.786669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.742051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.521665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.315347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.134686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.958066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:30:05.260037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성일시도로구간ID도로등급명도로구간지역명평균교통량값차량평균속도값평균도로점유율차량평균통행시간값도로전광표지판표출명
생성일시1.0000.0000.0000.1000.0000.2310.0000.0000.354
도로구간ID0.0001.0000.2720.7250.0000.3610.0000.1721.000
도로등급명0.0000.2721.0000.4630.0000.4910.0000.5741.000
도로구간지역명0.1000.7250.4631.0000.0000.2410.0000.113NaN
평균교통량값0.0000.0000.0000.0001.0000.0000.0000.000NaN
차량평균속도값0.2310.3610.4910.2410.0001.0000.0000.5600.723
평균도로점유율0.0000.0000.0000.0000.0000.0001.0000.000NaN
차량평균통행시간값0.0000.1720.5740.1130.0000.5600.0001.0000.644
도로전광표지판표출명0.3541.0001.000NaNNaN0.723NaN0.6441.000
2023-12-10T15:30:05.466836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로등급명도로구간지역명
도로등급명1.0000.194
도로구간지역명0.1941.000
2023-12-10T15:30:05.604990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간ID평균교통량값차량평균속도값평균도로점유율차량평균통행시간값도로등급명도로구간지역명
도로구간ID1.0000.188-0.0150.205-0.0260.2510.766
평균교통량값0.1881.000-0.0140.859-0.1220.0000.000
차량평균속도값-0.015-0.0141.000-0.101-0.1910.3100.143
평균도로점유율0.2050.859-0.1011.000-0.1330.0000.000
차량평균통행시간값-0.026-0.122-0.191-0.1331.0000.2830.049
도로등급명0.2510.0000.3100.0000.2831.0000.194
도로구간지역명0.7660.0000.1430.0000.0490.1941.000

Missing values

2023-12-10T15:29:57.986572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:29:58.348862image/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
02019-12-09 18:15:009610006800<NA>고속국도청라0890185<NA>영종대교 동단(상부도로)영종대교 서단(상하부도로 합류)인천국제공항고속도로 영종대교
12019-12-09 18:15:001651024800<NA>특별/광역시도송도043084<NA>안말사거리고잔사거리<NA>
22019-12-09 18:15:008710094600<NA>특별/광역시도청라1082172<NA>한일베라체골드클래스청중로지하차로 하
32019-12-09 18:15:008710086000<NA>특별/광역시도청라750173<NA>롯데캐슬담지로시점청중로 상1
42019-12-09 18:15:009680072505<NA>특별/광역시도영종343164<NA>하늘교서측하늘교동측<NA>
52019-12-09 18:15:001641012100<NA>특별/광역시도송도058038<NA>동춘역사거리연수사거리<NA>
62019-12-09 18:15:009680072553<NA>특별/광역시도영종344166<NA>영종대로14하늘달빛로종점<NA>
72019-12-09 18:15:001641054500<NA>고속국도송도0960124<NA>송도IC연수JC<NA>
82019-12-09 18:15:001700000033<NA>특별/광역시도송도146416<NA>호수1교북측송도3교북측<NA>
92019-12-09 18:15:009680072510<NA>특별/광역시도영종1240131<NA>하늘대로2하늘대로1<NA>
생성일시도로구간ID도로구간유형명도로등급명도로구간지역명평균교통량값차량평균속도값평균도로점유율차량평균통행시간값도로파티션구분자여부도로시작지점명도로종료지점명도로전광표지판표출명
1902019-12-09 17:45:009680037600<NA>특별/광역시도청라060046<NA>없음서인천선착장입구중봉로
1912019-12-09 17:45:001651001000<NA>일반국도송도0580108<NA>외암삼삼거리외암도사거리<NA>
1922019-12-09 17:45:008710084800<NA>특별/광역시도청라641231<NA>한일베라체호반베르디움청라루비로 하1
1932019-12-09 17:45:001641002700<NA>특별/광역시도송도037064<NA>외암도사거리동막역사거리<NA>
1942019-12-09 17:45:009680072596<NA>특별/광역시도미단334286<NA>영종순환로1영종순환로4<NA>
1952019-12-09 17:45:001641007800<NA>특별/광역시도송도060039<NA>동막역사거리외암도사거리<NA>
1962019-12-09 17:45:001700000042<NA>특별/광역시도송도04109<NA>아암대로1청학사거리<NA>
1972019-12-09 17:45:001641014100<NA>특별/광역시도송도055045<NA>연수사거리번영로제2사거리<NA>
1982019-12-09 17:45:008710084400<NA>특별/광역시도청라732164<NA>한라비발디하우스토리청라루비로 하2
1992019-12-09 17:45:009680072502<NA>특별/광역시도영종766054<NA>영종대로10영종대로11<NA>