Overview

Dataset statistics

Number of variables7
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory62.6 B

Variable types

Categorical2
Text2
Numeric2
DateTime1

Dataset

Description도로노선정보,설치구간,설치세부위치,도로방향,방음벽최대높이(m),방음벽길이(m),수집일
Author서울시설공단
URLhttps://data.seoul.go.kr/dataList/OA-20498/S/1/datasetView.do

Alerts

도로노선정보 has constant value ""Constant
도로방향 has constant value ""Constant
수집일 has constant value ""Constant
설치세부위치 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:35:45.653576
Analysis finished2023-12-11 04:35:46.739473
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로노선정보
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
올림픽대로
29 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row올림픽대로
2nd row올림픽대로
3rd row올림픽대로
4th row올림픽대로
5th row올림픽대로

Common Values

ValueCountFrequency (%)
올림픽대로 29
100.0%

Length

2023-12-11T13:35:46.836408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:35:46.962043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
올림픽대로 29
100.0%

설치구간
Text

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-11T13:35:47.129798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.137931
Min length4

Characters and Unicode

Total characters120
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st row방화대교
2nd row방화대교
3rd row가양대교
4th row가양대교
5th row가양대교
ValueCountFrequency (%)
가양대교 4
13.8%
천호대교 3
10.3%
방화대교 2
 
6.9%
염창교시점 2
 
6.9%
동작대교 2
 
6.9%
반포대교 2
 
6.9%
한남대교 2
 
6.9%
영동대교 2
 
6.9%
잠실대교 2
 
6.9%
올림픽대교 2
 
6.9%
Other values (5) 6
20.7%
2023-12-11T13:35:47.509084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 29
24.2%
대 24
20.0%
동 5
 
4.2%
가 4
 
3.3%
염 4
 
3.3%
양 4
 
3.3%
창 4
 
3.3%
호 3
 
2.5%
천 3
 
2.5%
한 3
 
2.5%
Other values (21) 37
30.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 29
24.2%
대 24
20.0%
동 5
 
4.2%
가 4
 
3.3%
염 4
 
3.3%
양 4
 
3.3%
창 4
 
3.3%
호 3
 
2.5%
천 3
 
2.5%
한 3
 
2.5%
Other values (21) 37
30.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 29
24.2%
대 24
20.0%
동 5
 
4.2%
가 4
 
3.3%
염 4
 
3.3%
양 4
 
3.3%
창 4
 
3.3%
호 3
 
2.5%
천 3
 
2.5%
한 3
 
2.5%
Other values (21) 37
30.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 29
24.2%
대 24
20.0%
동 5
 
4.2%
가 4
 
3.3%
염 4
 
3.3%
양 4
 
3.3%
창 4
 
3.3%
호 3
 
2.5%
천 3
 
2.5%
한 3
 
2.5%
Other values (21) 37
30.8%

설치세부위치
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-11T13:35:47.777267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length12.241379
Min length6

Characters and Unicode

Total characters355
Distinct characters80
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row950M 지난 우측
2nd row방화대교 1.7KM 지난 우측
3rd row궁산램교 지난 우측
4th row가양대교 전 우측
5th row가양대교 지난 우측
ValueCountFrequency (%)
우측 24
24.0%
지난 15
15.0%
전 6
 
6.0%
앞 6
 
6.0%
천호대교 3
 
3.0%
1.0km 2
 
2.0%
잠실대교 2
 
2.0%
ic상 2
 
2.0%
한남대교 2
 
2.0%
반포대교 2
 
2.0%
Other values (31) 36
36.0%
2023-12-11T13:35:48.284239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
20.0%
측 24
 
6.8%
우 24
 
6.8%
교 24
 
6.8%
대 18
 
5.1%
지 15
 
4.2%
난 15
 
4.2%
0 12
 
3.4%
M 8
 
2.3%
동 7
 
2.0%
Other values (70) 137
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 238
67.0%
Space Separator 71
 
20.0%
Decimal Number 25
 
7.0%
Uppercase Letter 17
 
4.8%
Other Punctuation 3
 
0.8%
Lowercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
측 24
 
10.1%
우 24
 
10.1%
교 24
 
10.1%
대 18
 
7.6%
지 15
 
6.3%
난 15
 
6.3%
동 7
 
2.9%
전 6
 
2.5%
앞 6
 
2.5%
아 6
 
2.5%
Other values (55) 93
39.1%
Decimal Number
ValueCountFrequency (%)
0 12
48.0%
1 4
 
16.0%
5 3
 
12.0%
3 2
 
8.0%
6 1
 
4.0%
9 1
 
4.0%
7 1
 
4.0%
2 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
M 8
47.1%
I 3
 
17.6%
K 3
 
17.6%
C 3
 
17.6%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 238
67.0%
Common 99
27.9%
Latin 18
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
측 24
 
10.1%
우 24
 
10.1%
교 24
 
10.1%
대 18
 
7.6%
지 15
 
6.3%
난 15
 
6.3%
동 7
 
2.9%
전 6
 
2.5%
앞 6
 
2.5%
아 6
 
2.5%
Other values (55) 93
39.1%
Common
ValueCountFrequency (%)
71
71.7%
0 12
 
12.1%
1 4
 
4.0%
. 3
 
3.0%
5 3
 
3.0%
3 2
 
2.0%
6 1
 
1.0%
9 1
 
1.0%
7 1
 
1.0%
2 1
 
1.0%
Latin
ValueCountFrequency (%)
M 8
44.4%
I 3
 
16.7%
K 3
 
16.7%
C 3
 
16.7%
m 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 238
67.0%
ASCII 117
33.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
60.7%
0 12
 
10.3%
M 8
 
6.8%
1 4
 
3.4%
I 3
 
2.6%
K 3
 
2.6%
C 3
 
2.6%
. 3
 
2.6%
5 3
 
2.6%
3 2
 
1.7%
Other values (5) 5
 
4.3%
Hangul
ValueCountFrequency (%)
측 24
 
10.1%
우 24
 
10.1%
교 24
 
10.1%
대 18
 
7.6%
지 15
 
6.3%
난 15
 
6.3%
동 7
 
2.9%
전 6
 
2.5%
앞 6
 
2.5%
아 6
 
2.5%
Other values (55) 93
39.1%

도로방향
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
강일
29 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강일
2nd row강일
3rd row강일
4th row강일
5th row강일

Common Values

ValueCountFrequency (%)
강일 29
100.0%

Length

2023-12-11T13:35:48.466145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:35:48.623804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강일 29
100.0%

방음벽최대높이(m)
Real number (ℝ)

Distinct7
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0689655
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T13:35:48.743876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q36
95-th percentile6.6
Maximum10
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3805029
Coefficient of variation (CV)0.2723441
Kurtosis4.8400176
Mean5.0689655
Median Absolute Deviation (MAD)1
Skewness1.4536494
Sum147
Variance1.9057882
MonotonicityNot monotonic
2023-12-11T13:35:48.896450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5.0 10
34.5%
6.0 7
24.1%
4.0 5
17.2%
3.0 3
 
10.3%
4.5 2
 
6.9%
10.0 1
 
3.4%
7.0 1
 
3.4%
ValueCountFrequency (%)
3.0 3
 
10.3%
4.0 5
17.2%
4.5 2
 
6.9%
5.0 10
34.5%
6.0 7
24.1%
7.0 1
 
3.4%
10.0 1
 
3.4%
ValueCountFrequency (%)
10.0 1
 
3.4%
7.0 1
 
3.4%
6.0 7
24.1%
5.0 10
34.5%
4.5 2
 
6.9%
4.0 5
17.2%
3.0 3
 
10.3%

방음벽길이(m)
Real number (ℝ)

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.24138
Minimum56
Maximum1504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T13:35:49.064025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile144.4
Q1220
median374
Q3886
95-th percentile1075.2
Maximum1504
Range1448
Interquartile range (IQR)666

Descriptive statistics

Standard deviation380.05579
Coefficient of variation (CV)0.72496336
Kurtosis-0.24190006
Mean524.24138
Median Absolute Deviation (MAD)220
Skewness0.82956684
Sum15203
Variance144442.4
MonotonicityNot monotonic
2023-12-11T13:35:49.553235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
154 2
 
6.9%
56 1
 
3.4%
534 1
 
3.4%
460 1
 
3.4%
290 1
 
3.4%
200 1
 
3.4%
269 1
 
3.4%
752 1
 
3.4%
308 1
 
3.4%
374 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
56 1
3.4%
138 1
3.4%
154 2
6.9%
176 1
3.4%
192 1
3.4%
200 1
3.4%
220 1
3.4%
234 1
3.4%
269 1
3.4%
270 1
3.4%
ValueCountFrequency (%)
1504 1
3.4%
1080 1
3.4%
1068 1
3.4%
1054 1
3.4%
956 1
3.4%
926 1
3.4%
908 1
3.4%
886 1
3.4%
752 1
3.4%
692 1
3.4%

수집일
Date

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2022-12-02 09:19:37
Maximum2022-12-02 09:19:37
2023-12-11T13:35:49.691979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:35:49.797084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T13:35:46.196239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:35:45.931090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:35:46.320521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:35:46.071088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T13:35:49.873098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치구간설치세부위치방음벽최대높이(m)방음벽길이(m)
설치구간1.0001.0000.9100.359
설치세부위치1.0001.0001.0001.000
방음벽최대높이(m)0.9101.0001.0000.000
방음벽길이(m)0.3591.0000.0001.000
2023-12-11T13:35:49.983714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방음벽최대높이(m)방음벽길이(m)
방음벽최대높이(m)1.0000.223
방음벽길이(m)0.2231.000

Missing values

2023-12-11T13:35:46.483108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:35:46.661562image/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

도로노선정보설치구간설치세부위치도로방향방음벽최대높이(m)방음벽길이(m)수집일
0올림픽대로방화대교950M 지난 우측강일3.0562022-12-02 09:19:37.0
1올림픽대로방화대교방화대교 1.7KM 지난 우측강일5.02202022-12-02 09:19:37.0
2올림픽대로가양대교궁산램교 지난 우측강일6.09082022-12-02 09:19:37.0
3올림픽대로가양대교가양대교 전 우측강일6.06362022-12-02 09:19:37.0
4올림픽대로가양대교가양대교 지난 우측강일6.08862022-12-02 09:19:37.0
5올림픽대로염창교시점염창교 300M 전 우측강일4.51922022-12-02 09:19:37.0
6올림픽대로염창교시점염창교 전 IC 우측강일10.02702022-12-02 09:19:37.0
7올림픽대로동작대교노량대교 우측강일3.09262022-12-02 09:19:37.0
8올림픽대로동작대교동작대교 1.0Km 지난 우측강일5.06922022-12-02 09:19:37.0
9올림픽대로반포대교반포대교 지난 우측강일5.09562022-12-02 09:19:37.0
도로노선정보설치구간설치세부위치도로방향방음벽최대높이(m)방음벽길이(m)수집일
19올림픽대로올림픽대교올림픽대교 지난 우측강일6.010542022-12-02 09:19:37.0
20올림픽대로천호대교천호대교 전 우측 풍납동 한가람아파트 앞강일5.03742022-12-02 09:19:37.0
21올림픽대로천호대교천호대교 300M 우측강일4.03082022-12-02 09:19:37.0
22올림픽대로천호대교천호대교 600M 우측 진입램프강일4.07522022-12-02 09:19:37.0
23올림픽대로강동대교고덕천교 전 우측강일3.01542022-12-02 09:19:37.0
24올림픽대로염창교끝염창동 동아 1차 아파트 앞강일4.52692022-12-02 09:19:37.0
25올림픽대로염창교끝강변성원아파트 앞강일6.02002022-12-02 09:19:37.0
26올림픽대로한강철교여의교 한국개나리 아파트 앞강일7.02902022-12-02 09:19:37.0
27올림픽대로올림픽대교아산병원 앞강일6.04602022-12-02 09:19:37.0
28올림픽대로가양대교가양동 염강초등학교 앞강일6.01542022-12-02 09:19:37.0