Overview

Dataset statistics

Number of variables4
Number of observations295
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory34.4 B

Variable types

Text2
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15250/F/1/datasetView.do

Alerts

관리ID has unique valuesUnique

Reproduction

Analysis started2023-12-11 10:22:15.701515
Analysis finished2023-12-11 10:22:16.437519
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리ID
Text

UNIQUE 

Distinct295
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-11T19:22:16.615232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2950
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)100.0%

Sample

1st rowVML5000090
2nd rowVMK1000020
3rd rowVMQ5000010
4th rowVML5000020
5th rowVMK1000090
ValueCountFrequency (%)
vml5000090 1
 
0.3%
vmr0000010 1
 
0.3%
vma9000910 1
 
0.3%
vma9000890 1
 
0.3%
vma9000870 1
 
0.3%
vma9000700 1
 
0.3%
vml1000050 1
 
0.3%
vml1000010 1
 
0.3%
vmk5000110 1
 
0.3%
vmk5000010 1
 
0.3%
Other values (285) 285
96.6%
2023-12-11T19:22:17.035238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1318
44.7%
V 295
 
10.0%
M 295
 
10.0%
1 176
 
6.0%
9 161
 
5.5%
5 107
 
3.6%
A 89
 
3.0%
2 71
 
2.4%
3 57
 
1.9%
4 50
 
1.7%
Other values (17) 331
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2043
69.3%
Uppercase Letter 907
30.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 295
32.5%
M 295
32.5%
A 89
 
9.8%
O 49
 
5.4%
L 29
 
3.2%
K 25
 
2.8%
R 24
 
2.6%
D 22
 
2.4%
G 16
 
1.8%
T 16
 
1.8%
Other values (7) 47
 
5.2%
Decimal Number
ValueCountFrequency (%)
0 1318
64.5%
1 176
 
8.6%
9 161
 
7.9%
5 107
 
5.2%
2 71
 
3.5%
3 57
 
2.8%
4 50
 
2.4%
6 38
 
1.9%
7 35
 
1.7%
8 30
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2043
69.3%
Latin 907
30.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 295
32.5%
M 295
32.5%
A 89
 
9.8%
O 49
 
5.4%
L 29
 
3.2%
K 25
 
2.8%
R 24
 
2.6%
D 22
 
2.4%
G 16
 
1.8%
T 16
 
1.8%
Other values (7) 47
 
5.2%
Common
ValueCountFrequency (%)
0 1318
64.5%
1 176
 
8.6%
9 161
 
7.9%
5 107
 
5.2%
2 71
 
3.5%
3 57
 
2.8%
4 50
 
2.4%
6 38
 
1.9%
7 35
 
1.7%
8 30
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1318
44.7%
V 295
 
10.0%
M 295
 
10.0%
1 176
 
6.0%
9 161
 
5.5%
5 107
 
3.6%
A 89
 
3.0%
2 71
 
2.4%
3 57
 
1.9%
4 50
 
1.7%
Other values (17) 331
 
11.2%

설치위치
Text

Distinct294
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-11T19:22:17.330027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length19.389831
Min length5

Characters and Unicode

Total characters5720
Distinct characters316
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)99.3%

Sample

1st row[올림픽대로] 한남~동호
2nd row[강변북로] 동작대교→한강대교 (WB) (VMS교체)
3rd row[강남순환]시흥대교
4th row[올림픽대로] 방화~가양(#4032)
5th row[강변북로] 잠실대교→청담대교 (WB) (VMS 교체)
ValueCountFrequency (%)
68
 
7.8%
앞 57
 
6.6%
올림픽대로 26
 
3.0%
강변북로 21
 
2.4%
vms교체 19
 
2.2%
wb 16
 
1.8%
eb 16
 
1.8%
건너편 10
 
1.2%
내부순환 9
 
1.0%
남산-강남 8
 
0.9%
Other values (521) 617
71.2%
2023-12-11T19:22:17.937058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
573
 
10.0%
] 282
 
4.9%
[ 282
 
4.9%
로 201
 
3.5%
( 189
 
3.3%
) 189
 
3.3%
교 152
 
2.7%
대 122
 
2.1%
0 121
 
2.1%
앞 109
 
1.9%
Other values (306) 3500
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3226
56.4%
Space Separator 573
 
10.0%
Close Punctuation 471
 
8.2%
Open Punctuation 471
 
8.2%
Decimal Number 433
 
7.6%
Uppercase Letter 348
 
6.1%
Math Symbol 91
 
1.6%
Other Punctuation 80
 
1.4%
Dash Punctuation 18
 
0.3%
Lowercase Letter 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 201
 
6.2%
교 152
 
4.7%
대 122
 
3.8%
앞 109
 
3.4%
동 109
 
3.4%
강 63
 
2.0%
부 58
 
1.8%
남 55
 
1.7%
성 51
 
1.6%
소 45
 
1.4%
Other values (263) 2261
70.1%
Uppercase Letter
ValueCountFrequency (%)
B 77
22.1%
S 43
12.4%
C 40
11.5%
M 34
9.8%
I 26
 
7.5%
E 22
 
6.3%
V 22
 
6.3%
W 21
 
6.0%
N 16
 
4.6%
J 12
 
3.4%
Other values (8) 35
10.1%
Decimal Number
ValueCountFrequency (%)
0 121
27.9%
1 89
20.6%
2 56
12.9%
4 45
 
10.4%
3 38
 
8.8%
7 34
 
7.9%
6 21
 
4.8%
5 12
 
2.8%
8 9
 
2.1%
9 8
 
1.8%
Other Punctuation
ValueCountFrequency (%)
# 34
42.5%
, 27
33.8%
: 13
 
16.2%
* 5
 
6.2%
& 1
 
1.2%
Close Punctuation
ValueCountFrequency (%)
] 282
59.9%
) 189
40.1%
Open Punctuation
ValueCountFrequency (%)
[ 282
59.9%
( 189
40.1%
Math Symbol
ValueCountFrequency (%)
→ 59
64.8%
~ 32
35.2%
Lowercase Letter
ValueCountFrequency (%)
m 8
88.9%
k 1
 
11.1%
Space Separator
ValueCountFrequency (%)
573
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3226
56.4%
Common 2137
37.4%
Latin 357
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 201
 
6.2%
교 152
 
4.7%
대 122
 
3.8%
앞 109
 
3.4%
동 109
 
3.4%
강 63
 
2.0%
부 58
 
1.8%
남 55
 
1.7%
성 51
 
1.6%
소 45
 
1.4%
Other values (263) 2261
70.1%
Common
ValueCountFrequency (%)
573
26.8%
] 282
13.2%
[ 282
13.2%
( 189
 
8.8%
) 189
 
8.8%
0 121
 
5.7%
1 89
 
4.2%
→ 59
 
2.8%
2 56
 
2.6%
4 45
 
2.1%
Other values (13) 252
11.8%
Latin
ValueCountFrequency (%)
B 77
21.6%
S 43
12.0%
C 40
11.2%
M 34
9.5%
I 26
 
7.3%
E 22
 
6.2%
V 22
 
6.2%
W 21
 
5.9%
N 16
 
4.5%
J 12
 
3.4%
Other values (10) 44
12.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3226
56.4%
ASCII 2435
42.6%
Arrows 59
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
573
23.5%
] 282
11.6%
[ 282
11.6%
( 189
 
7.8%
) 189
 
7.8%
0 121
 
5.0%
1 89
 
3.7%
B 77
 
3.2%
2 56
 
2.3%
4 45
 
1.8%
Other values (32) 532
21.8%
Hangul
ValueCountFrequency (%)
로 201
 
6.2%
교 152
 
4.7%
대 122
 
3.8%
앞 109
 
3.4%
동 109
 
3.4%
강 63
 
2.0%
부 58
 
1.8%
남 55
 
1.7%
성 51
 
1.6%
소 45
 
1.4%
Other values (263) 2261
70.1%
Arrows
ValueCountFrequency (%)
→ 59
100.0%

XPOINT
Real number (ℝ)

Distinct293
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.0103
Minimum126.78408
Maximum127.15379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-11T19:22:18.153247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.78408
5-th percentile126.8734
Q1126.94931
median127.02986
Q3127.06802
95-th percentile127.11775
Maximum127.15379
Range0.36971
Interquartile range (IQR)0.118705

Descriptive statistics

Standard deviation0.077830247
Coefficient of variation (CV)0.00061278691
Kurtosis-0.50274016
Mean127.0103
Median Absolute Deviation (MAD)0.0491
Skewness-0.56800695
Sum37468.038
Variance0.0060575474
MonotonicityNot monotonic
2023-12-11T19:22:18.339928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.89112 3
 
1.0%
127.02238 1
 
0.3%
126.87403 1
 
0.3%
127.05673 1
 
0.3%
127.04802 1
 
0.3%
127.12632 1
 
0.3%
127.12328 1
 
0.3%
127.07643 1
 
0.3%
126.99534 1
 
0.3%
126.86985 1
 
0.3%
Other values (283) 283
95.9%
ValueCountFrequency (%)
126.78408 1
0.3%
126.80436 1
0.3%
126.82401 1
0.3%
126.82901 1
0.3%
126.83343 1
0.3%
126.8357 1
0.3%
126.83633 1
0.3%
126.8476 1
0.3%
126.84784 1
0.3%
126.84819 1
0.3%
ValueCountFrequency (%)
127.15379 1
0.3%
127.13863 1
0.3%
127.13407 1
0.3%
127.13233 1
0.3%
127.13036 1
0.3%
127.13003 1
0.3%
127.12656 1
0.3%
127.12632 1
0.3%
127.12564 1
0.3%
127.12548 1
0.3%

YPOINT
Real number (ℝ)

Distinct291
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.550883
Minimum37.43843
Maximum37.7073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-11T19:22:18.516865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.43843
5-th percentile37.476869
Q137.517185
median37.54169
Q337.58149
95-th percentile37.648371
Maximum37.7073
Range0.26887
Interquartile range (IQR)0.064305

Descriptive statistics

Standard deviation0.04908252
Coefficient of variation (CV)0.0013070936
Kurtosis0.10361688
Mean37.550883
Median Absolute Deviation (MAD)0.0285
Skewness0.53852736
Sum11077.51
Variance0.0024090938
MonotonicityNot monotonic
2023-12-11T19:22:18.700171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.53638 3
 
1.0%
37.51543 2
 
0.7%
37.53767 2
 
0.7%
37.52912 1
 
0.3%
37.56989 1
 
0.3%
37.46234 1
 
0.3%
37.46252 1
 
0.3%
37.48397 1
 
0.3%
37.64606 1
 
0.3%
37.50873 1
 
0.3%
Other values (281) 281
95.3%
ValueCountFrequency (%)
37.43843 1
0.3%
37.44574 1
0.3%
37.45173 1
0.3%
37.46138 1
0.3%
37.46234 1
0.3%
37.46252 1
0.3%
37.46586 1
0.3%
37.46615 1
0.3%
37.46879 1
0.3%
37.46909 1
0.3%
ValueCountFrequency (%)
37.7073 1
0.3%
37.68151 1
0.3%
37.67961 1
0.3%
37.67112 1
0.3%
37.66859 1
0.3%
37.66787 1
0.3%
37.66326 1
0.3%
37.66244 1
0.3%
37.66116 1
0.3%
37.65825 1
0.3%

Interactions

2023-12-11T19:22:16.089895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:22:15.914816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:22:16.176810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:22:16.003678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:22:18.810066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
XPOINTYPOINT
XPOINT1.0000.586
YPOINT0.5861.000
2023-12-11T19:22:18.885742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
XPOINTYPOINT
XPOINT1.0000.050
YPOINT0.0501.000

Missing values

2023-12-11T19:22:16.312926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:22:16.406147image/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

관리ID설치위치XPOINTYPOINT
0VML5000090[올림픽대로] 한남~동호127.0223837.52912
1VMK1000020[강변북로] 동작대교→한강대교 (WB) (VMS교체)126.9155637.51673
2VMQ5000010[강남순환]시흥대교126.8958337.44574
3VML5000020[올림픽대로] 방화~가양(#4032)126.8290137.58042
4VMK1000090[강변북로] 잠실대교→청담대교 (WB) (VMS 교체)127.0668637.53158
5VMA9000370[ 능동로 ] [1*10] 민중병원건너편(SB)127.0724437.54397
6VMO9000230[경부고속도로] 주공아파트 325동 맞은편 (7010)127.0174637.50633
7VMO9000240[강남대로] 성보빌딩 앞(7009)127.0209337.5124
8VMO9000250[도산대로] 한국제과기술학원 앞127.0219437.51727
9VMO9000260[압구정로] 현대9차 상가 2동 앞127.0251237.52557
관리ID설치위치XPOINTYPOINT
285VMA9000460[강동대로] 올림픽공원 건너편, 함흥냉면 앞(NB)#7001127.1300337.52336
286VML5000100[올림픽대로]성수-영동(#4037)127.0448737.52996
287VMT5000010[남산-강남] 계성초교:1호,3호,소파소월126.9884137.56311
288VMT5000020[남산-강남] 명동역:1호,소파소월126.9851137.56067
289VMT5000030[남산-강남] 극동빌딩:1호,3호,소파소월126.9915137.56115
290VMT5000040[남산-강남] 3호터널북단:1호,3호,소파소월126.9832337.55761
291VMT1000010[남산-도심] 단국대:1호,소파소월127.0057837.53623
292VMT5000050[남산-강남] 2호터널북단:2호127.0023637.55518
293VML1000070[올림픽대로] 성수~동호(#4036)127.0304637.53414
294VML5000110[올림픽대로] 청담~잠실127.0821437.51658