Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Text2
Categorical2
Numeric2

Dataset

Description한국교통안전공단에서는 2022년 1월부터 위험물질운송차량 1100대에 졸음운전방지장치를 설치하였고, 이 장치로 부터 졸음운전 과 전방주시태만이 발생한 도로의 위치를 노드링크로 데이터화 하였다.
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15104639/fileData.do

Reproduction

Analysis started2024-03-23 06:58:32.240862
Analysis finished2024-03-23 06:58:40.126579
Duration7.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

링크아이디
Text

Distinct8157
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T06:58:40.728564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length10.0008
Min length10

Characters and Unicode

Total characters100008
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

Unique6646 ?
Unique (%)66.5%

Sample

1st row2800322400
2nd row2840047900
3rd row3410042100
4th row3310021400
5th row3570097900
ValueCountFrequency (%)
3250161700 6
 
0.1%
3610348100 5
 
< 0.1%
3550061700 5
 
< 0.1%
3590027911 5
 
< 0.1%
2950192200 5
 
< 0.1%
3120065904 5
 
< 0.1%
2310799700 5
 
< 0.1%
1670002800 4
 
< 0.1%
2310237202 4
 
< 0.1%
1540001500 4
 
< 0.1%
Other values (8147) 9952
99.5%
2024-03-23T06:58:42.213615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35841
35.8%
2 11402
 
11.4%
1 11122
 
11.1%
3 11060
 
11.1%
5 5546
 
5.5%
4 5482
 
5.5%
6 5333
 
5.3%
8 5155
 
5.2%
9 4592
 
4.6%
7 4471
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100004
> 99.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35841
35.8%
2 11402
 
11.4%
1 11122
 
11.1%
3 11060
 
11.1%
5 5546
 
5.5%
4 5482
 
5.5%
6 5333
 
5.3%
8 5155
 
5.2%
9 4592
 
4.6%
7 4471
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35841
35.8%
2 11402
 
11.4%
1 11122
 
11.1%
3 11060
 
11.1%
5 5546
 
5.5%
4 5482
 
5.5%
6 5333
 
5.3%
8 5155
 
5.2%
9 4592
 
4.6%
7 4471
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35841
35.8%
2 11402
 
11.4%
1 11122
 
11.1%
3 11060
 
11.1%
5 5546
 
5.5%
4 5482
 
5.5%
6 5333
 
5.3%
8 5155
 
5.2%
9 4592
 
4.6%
7 4471
 
4.5%

기준년월
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-07-01
2051 
2023-08-01
1957 
2023-09-01
1956 
2023-10-01
1899 
2023-11-01
1735 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-01
2nd row2023-11-01
3rd row2023-07-01
4th row2023-11-01
5th row2023-08-01

Common Values

ValueCountFrequency (%)
2023-07-01 2051
20.5%
2023-08-01 1957
19.6%
2023-09-01 1956
19.6%
2023-10-01 1899
19.0%
2023-11-01 1735
17.3%
2023-12-01 402
 
4.0%

Length

2024-03-23T06:58:42.721666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:58:43.066229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-01 2051
20.5%
2023-08-01 1957
19.6%
2023-09-01 1956
19.6%
2023-10-01 1899
19.0%
2023-11-01 1735
17.3%
2023-12-01 402
 
4.0%

발생횟수
Real number (ℝ)

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7479
Minimum1
Maximum217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T06:58:43.547278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile9
Maximum217
Range216
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.1994668
Coefficient of variation (CV)2.2560744
Kurtosis316.17173
Mean2.7479
Median Absolute Deviation (MAD)0
Skewness14.082948
Sum27479
Variance38.433389
MonotonicityNot monotonic
2024-03-23T06:58:44.379280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5987
59.9%
2 1627
 
16.3%
3 759
 
7.6%
4 433
 
4.3%
5 262
 
2.6%
6 190
 
1.9%
7 130
 
1.3%
8 109
 
1.1%
9 70
 
0.7%
10 55
 
0.5%
Other values (57) 378
 
3.8%
ValueCountFrequency (%)
1 5987
59.9%
2 1627
 
16.3%
3 759
 
7.6%
4 433
 
4.3%
5 262
 
2.6%
6 190
 
1.9%
7 130
 
1.3%
8 109
 
1.1%
9 70
 
0.7%
10 55
 
0.5%
ValueCountFrequency (%)
217 1
 
< 0.1%
170 1
 
< 0.1%
151 1
 
< 0.1%
130 1
 
< 0.1%
127 1
 
< 0.1%
117 1
 
< 0.1%
100 1
 
< 0.1%
98 1
 
< 0.1%
88 1
 
< 0.1%
84 3
< 0.1%

발생등급코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
E15
6265 
E16
3623 
E18
 
100
E19
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE16
2nd rowE15
3rd rowE15
4th rowE15
5th rowE15

Common Values

ValueCountFrequency (%)
E15 6265
62.6%
E16 3623
36.2%
E18 100
 
1.0%
E19 12
 
0.1%

Length

2024-03-23T06:58:44.989082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:58:45.567611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e15 6265
62.6%
e16 3623
36.2%
e18 100
 
1.0%
e19 12
 
0.1%

도로명
Text

Distinct1430
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T06:58:46.356876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.4254
Min length3

Characters and Unicode

Total characters54254
Distinct characters372
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

Unique712 ?
Unique (%)7.1%

Sample

1st row중부로
2nd row두릉유리로
3rd row영광로
4th row순천완주고속도로
5th row대학길
ValueCountFrequency (%)
경부고속도로 497
 
5.0%
서해안고속도로 238
 
2.4%
남해고속도로 234
 
2.3%
수도권제1순환고속도로 212
 
2.1%
중앙고속도로 204
 
2.0%
중부내륙고속도로 198
 
2.0%
당진영덕고속도로 183
 
1.8%
순천완주고속도로 174
 
1.7%
영동고속도로 136
 
1.4%
울산포항고속도로 135
 
1.4%
Other values (1420) 7789
77.9%
2024-03-23T06:58:48.206123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 9750
 
18.0%
도 4559
 
8.4%
고 4076
 
7.5%
속 3996
 
7.4%
대 1638
 
3.0%
부 1261
 
2.3%
산 1018
 
1.9%
남 925
 
1.7%
경 856
 
1.6%
천 846
 
1.6%
Other values (362) 25329
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53075
97.8%
Decimal Number 1170
 
2.2%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9750
18.4%
도 4559
 
8.6%
고 4076
 
7.7%
속 3996
 
7.5%
대 1638
 
3.1%
부 1261
 
2.4%
산 1018
 
1.9%
남 925
 
1.7%
경 856
 
1.6%
천 846
 
1.6%
Other values (351) 24150
45.5%
Decimal Number
ValueCountFrequency (%)
1 417
35.6%
2 294
25.1%
3 129
 
11.0%
8 72
 
6.2%
7 57
 
4.9%
4 53
 
4.5%
9 46
 
3.9%
5 44
 
3.8%
0 36
 
3.1%
6 22
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53075
97.8%
Common 1179
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9750
18.4%
도 4559
 
8.6%
고 4076
 
7.7%
속 3996
 
7.5%
대 1638
 
3.1%
부 1261
 
2.4%
산 1018
 
1.9%
남 925
 
1.7%
경 856
 
1.6%
천 846
 
1.6%
Other values (351) 24150
45.5%
Common
ValueCountFrequency (%)
1 417
35.4%
2 294
24.9%
3 129
 
10.9%
8 72
 
6.1%
7 57
 
4.8%
4 53
 
4.5%
9 46
 
3.9%
5 44
 
3.7%
0 36
 
3.1%
6 22
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53075
97.8%
ASCII 1179
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 9750
18.4%
도 4559
 
8.6%
고 4076
 
7.7%
속 3996
 
7.5%
대 1638
 
3.1%
부 1261
 
2.4%
산 1018
 
1.9%
남 925
 
1.7%
경 856
 
1.6%
천 846
 
1.6%
Other values (351) 24150
45.5%
ASCII
ValueCountFrequency (%)
1 417
35.4%
2 294
24.9%
3 129
 
10.9%
8 72
 
6.1%
7 57
 
4.8%
4 53
 
4.5%
9 46
 
3.9%
5 44
 
3.7%
0 36
 
3.1%
6 22
 
1.9%

링크길이(미터)
Real number (ℝ)

Distinct8144
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1210.604
Minimum6.3449
Maximum16218.178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T06:58:48.775151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.3449
5-th percentile137.66981
Q1356.634
median664.89805
Q31315.6636
95-th percentile4197.6237
Maximum16218.178
Range16211.833
Interquartile range (IQR)959.02963

Descriptive statistics

Standard deviation1623.3254
Coefficient of variation (CV)1.3409219
Kurtosis17.100695
Mean1210.604
Median Absolute Deviation (MAD)385.17865
Skewness3.5877943
Sum12106040
Variance2635185.2
MonotonicityNot monotonic
2024-03-23T06:58:49.338504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1685.3793 6
 
0.1%
1074.0967 5
 
0.1%
1225.8616 5
 
0.1%
2133.4016 5
 
0.1%
1388.857 5
 
0.1%
245.2497 5
 
0.1%
2296.5538 5
 
0.1%
221.3306 5
 
0.1%
318.7339 5
 
0.1%
8652.0587 4
 
< 0.1%
Other values (8134) 9950
99.5%
ValueCountFrequency (%)
6.3449 1
< 0.1%
6.345 1
< 0.1%
18.0541 1
< 0.1%
18.9493 1
< 0.1%
19.249 1
< 0.1%
22.6499 1
< 0.1%
23.9841 1
< 0.1%
24.4059 1
< 0.1%
26.2934 1
< 0.1%
26.7811 1
< 0.1%
ValueCountFrequency (%)
16218.1781 1
 
< 0.1%
16208.892 3
< 0.1%
15598.6691 2
< 0.1%
14289.1418 2
< 0.1%
14088.0625 1
 
< 0.1%
14074.1498 2
< 0.1%
13699.0239 2
< 0.1%
12378.7661 1
 
< 0.1%
12217.4111 1
 
< 0.1%
12120.5248 4
< 0.1%

Interactions

2024-03-23T06:58:38.708465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:58:38.077874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:58:39.016078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:58:38.329261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:58:49.677008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월발생횟수발생등급코드링크길이(미터)
기준년월1.0000.0000.0520.020
발생횟수0.0001.0000.0000.242
발생등급코드0.0520.0001.0000.136
링크길이(미터)0.0200.2420.1361.000
2024-03-23T06:58:49.971325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생등급코드기준년월
발생등급코드1.0000.034
기준년월0.0341.000
2024-03-23T06:58:50.328854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생횟수링크길이(미터)기준년월발생등급코드
발생횟수1.0000.3270.0000.000
링크길이(미터)0.3271.0000.0100.082
기준년월0.0000.0101.0000.034
발생등급코드0.0000.0820.0341.000

Missing values

2024-03-23T06:58:39.467040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:58:39.844266image/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

링크아이디기준년월발생횟수발생등급코드도로명링크길이(미터)
3927028003224002023-08-016E16중부로1952.2354
7991028400479002023-11-011E15두릉유리로1243.9246
1775034100421002023-07-011E15영광로324.0795
9411033100214002023-11-011E15순천완주고속도로695.6672
3670335700979002023-08-011E15대학길225.6509
295715706085012023-07-011E16세천로3길456.3649
559837002364002023-07-011E15삼강로1035.2145
2708630700797032023-08-011E15새만금북로672.5729
7096739700178002023-10-011E15광주대구고속도로735.2974
2718330700319012023-08-011E16외항로425.4172
링크아이디기준년월발생횟수발생등급코드도로명링크길이(미터)
3865228003268012023-08-012E15중부로803.675
8407127100185002023-11-011E16중부고속도로522.5663
9394432900288312023-11-012E15광주대구고속도로874.2501
3547520703821002023-08-017E15세종포천고속도로857.8239
1876920100912012023-07-011E15권선로556.1769
7182227106841002023-10-014E15경부고속도로2082.5781
5739235007233002023-09-014E15영일만대로1963.441
9070630804221002023-11-011E16호남고속도로3244.4434
7046439801823002023-10-017E16합천대로3721.6764
7568028601706002023-10-011E16당진영덕고속도로767.7712