Overview

Dataset statistics

Number of variables8
Number of observations47
Missing cells51
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory67.8 B

Variable types

Text2
Categorical3
Boolean2
Unsupported1

Dataset

Description파일 다운로드
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-21825/F/1/datasetView.do

Alerts

뷰어여부 has constant value ""Constant
등록일시 is highly imbalanced (85.1%)Imbalance
수정일시 is highly imbalanced (85.1%)Imbalance
시스템명영문 has 4 (8.5%) missing valuesMissing
뷰어타입분류명 has 47 (100.0%) missing valuesMissing
시스템명한글 has unique valuesUnique
뷰어타입분류명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 09:46:38.174337
Analysis finished2023-12-11 09:46:38.736453
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시스템명한글
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T18:46:38.905743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.2340426
Min length2

Characters and Unicode

Total characters246
Distinct characters124
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

Unique47 ?
Unique (%)100.0%

Sample

1st row재난안전긴급대응
2nd row시설물원격전원 ON/OFF
3rd row대기오염
4th row강도신고접수
5th row비상벨
ValueCountFrequency (%)
vms 2
 
4.0%
재난안전긴급대응 1
 
2.0%
모바일 1
 
2.0%
불법주정차단속 1
 
2.0%
통합연계 1
 
2.0%
어린이보호구역 1
 
2.0%
학교안전 1
 
2.0%
공원방범 1
 
2.0%
생활방범 1
 
2.0%
도로방범 1
 
2.0%
Other values (39) 39
78.0%
2023-12-11T18:46:39.302559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 10
 
4.1%
S 6
 
2.4%
상 6
 
2.4%
감 6
 
2.4%
통 5
 
2.0%
원 5
 
2.0%
수 5
 
2.0%
방 5
 
2.0%
M 5
 
2.0%
교 4
 
1.6%
Other values (114) 189
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 208
84.6%
Uppercase Letter 24
 
9.8%
Space Separator 4
 
1.6%
Open Punctuation 3
 
1.2%
Close Punctuation 3
 
1.2%
Decimal Number 3
 
1.2%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 10
 
4.8%
상 6
 
2.9%
감 6
 
2.9%
통 5
 
2.4%
원 5
 
2.4%
수 5
 
2.4%
방 5
 
2.4%
교 4
 
1.9%
기 4
 
1.9%
범 4
 
1.9%
Other values (100) 154
74.0%
Uppercase Letter
ValueCountFrequency (%)
S 6
25.0%
M 5
20.8%
F 4
16.7%
N 3
12.5%
V 2
 
8.3%
O 2
 
8.3%
B 1
 
4.2%
I 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
84.6%
Latin 24
 
9.8%
Common 14
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 10
 
4.8%
상 6
 
2.9%
감 6
 
2.9%
통 5
 
2.4%
원 5
 
2.4%
수 5
 
2.4%
방 5
 
2.4%
교 4
 
1.9%
기 4
 
1.9%
범 4
 
1.9%
Other values (100) 154
74.0%
Latin
ValueCountFrequency (%)
S 6
25.0%
M 5
20.8%
F 4
16.7%
N 3
12.5%
V 2
 
8.3%
O 2
 
8.3%
B 1
 
4.2%
I 1
 
4.2%
Common
ValueCountFrequency (%)
4
28.6%
( 3
21.4%
) 3
21.4%
1 2
14.3%
9 1
 
7.1%
/ 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
84.6%
ASCII 38
 
15.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 10
 
4.8%
상 6
 
2.9%
감 6
 
2.9%
통 5
 
2.4%
원 5
 
2.4%
수 5
 
2.4%
방 5
 
2.4%
교 4
 
1.9%
기 4
 
1.9%
범 4
 
1.9%
Other values (100) 154
74.0%
ASCII
ValueCountFrequency (%)
S 6
15.8%
M 5
13.2%
F 4
10.5%
4
10.5%
( 3
7.9%
) 3
7.9%
N 3
7.9%
V 2
 
5.3%
O 2
 
5.3%
1 2
 
5.3%
Other values (4) 4
10.5%

시스템명영문
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing4
Missing (%)8.5%
Memory size508.0 B
2023-12-11T18:46:39.590317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length27
Mean length19
Min length3

Characters and Unicode

Total characters817
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st rowdisaster Safety urgency countermeasure
2nd rowNetwork Power ON/OFF
3rd rowAir Pollution
4th rowReport Robbery
5th rowEmergency Bell
ValueCountFrequency (%)
system 8
 
7.0%
of 6
 
5.2%
prevention 5
 
4.3%
watch 5
 
4.3%
crime 4
 
3.5%
management 4
 
3.5%
water 3
 
2.6%
network 3
 
2.6%
monitoring 2
 
1.7%
violation 2
 
1.7%
Other values (67) 73
63.5%
2023-12-11T18:46:40.035763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 87
 
10.6%
72
 
8.8%
n 61
 
7.5%
t 60
 
7.3%
i 60
 
7.3%
o 56
 
6.9%
a 53
 
6.5%
r 47
 
5.8%
s 32
 
3.9%
l 29
 
3.5%
Other values (36) 260
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 678
83.0%
Space Separator 72
 
8.8%
Uppercase Letter 66
 
8.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 87
12.8%
n 61
 
9.0%
t 60
 
8.8%
i 60
 
8.8%
o 56
 
8.3%
a 53
 
7.8%
r 47
 
6.9%
s 32
 
4.7%
l 29
 
4.3%
c 29
 
4.3%
Other values (14) 164
24.2%
Uppercase Letter
ValueCountFrequency (%)
W 10
15.2%
S 8
12.1%
P 7
10.6%
R 6
 
9.1%
F 4
 
6.1%
A 4
 
6.1%
O 3
 
4.5%
T 3
 
4.5%
C 3
 
4.5%
B 3
 
4.5%
Other values (10) 15
22.7%
Space Separator
ValueCountFrequency (%)
72
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 744
91.1%
Common 73
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 87
 
11.7%
n 61
 
8.2%
t 60
 
8.1%
i 60
 
8.1%
o 56
 
7.5%
a 53
 
7.1%
r 47
 
6.3%
s 32
 
4.3%
l 29
 
3.9%
c 29
 
3.9%
Other values (34) 230
30.9%
Common
ValueCountFrequency (%)
72
98.6%
/ 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 817
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 87
 
10.6%
72
 
8.8%
n 61
 
7.5%
t 60
 
7.3%
i 60
 
7.3%
o 56
 
6.9%
a 53
 
6.5%
r 47
 
5.8%
s 32
 
3.9%
l 29
 
3.5%
Other values (36) 260
31.8%

유서비스그룹분류명
Categorical

Distinct10
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
DP
10 
TR
9 
CP
7 
FT
5 
UC
5 
Other values (5)
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)4.3%

Sample

1st rowDP
2nd rowFT
3rd rowEN
4th rowCP
5th rowCP

Common Values

ValueCountFrequency (%)
DP 10
21.3%
TR 9
19.1%
CP 7
14.9%
FT 5
10.6%
UC 5
10.6%
SF 4
 
8.5%
EN 3
 
6.4%
UM 2
 
4.3%
AO 1
 
2.1%
SH 1
 
2.1%

Length

2023-12-11T18:46:40.522793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:46:40.675732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dp 10
21.3%
tr 9
19.1%
cp 7
14.9%
ft 5
10.6%
uc 5
10.6%
sf 4
 
8.5%
en 3
 
6.4%
um 2
 
4.3%
ao 1
 
2.1%
sh 1
 
2.1%

사용유형분류명
Boolean

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size179.0 B
True
36 
False
11 
ValueCountFrequency (%)
True 36
76.6%
False 11
 
23.4%
2023-12-11T18:46:40.806136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

등록일시
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
2016-01-26 10:50:35
46 
2016-05-04 16:43:22
 
1

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row2016-05-04 16:43:22
2nd row2016-01-26 10:50:35
3rd row2016-01-26 10:50:35
4th row2016-01-26 10:50:35
5th row2016-01-26 10:50:35

Common Values

ValueCountFrequency (%)
2016-01-26 10:50:35 46
97.9%
2016-05-04 16:43:22 1
 
2.1%

Length

2023-12-11T18:46:40.943864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:46:41.061508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-01-26 46
48.9%
10:50:35 46
48.9%
2016-05-04 1
 
1.1%
16:43:22 1
 
1.1%

수정일시
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
2016-01-26 10:50:35
46 
2016-05-04 16:43:22
 
1

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row2016-05-04 16:43:22
2nd row2016-01-26 10:50:35
3rd row2016-01-26 10:50:35
4th row2016-01-26 10:50:35
5th row2016-01-26 10:50:35

Common Values

ValueCountFrequency (%)
2016-01-26 10:50:35 46
97.9%
2016-05-04 16:43:22 1
 
2.1%

Length

2023-12-11T18:46:41.194137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:46:41.341280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-01-26 46
48.9%
10:50:35 46
48.9%
2016-05-04 1
 
1.1%
16:43:22 1
 
1.1%

뷰어타입분류명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing47
Missing (%)100.0%
Memory size555.0 B

뷰어여부
Boolean

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size179.0 B
False
47 
ValueCountFrequency (%)
False 47
100.0%
2023-12-11T18:46:41.436510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:46:41.495111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시스템명한글시스템명영문유서비스그룹분류명사용유형분류명등록일시수정일시
시스템명한글1.0001.0001.0001.0001.0001.000
시스템명영문1.0001.0001.0001.0001.0001.000
유서비스그룹분류명1.0001.0001.0000.0000.0000.000
사용유형분류명1.0001.0000.0001.0000.0000.000
등록일시1.0001.0000.0000.0001.0000.675
수정일시1.0001.0000.0000.0000.6751.000
2023-12-11T18:46:41.617773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일시사용유형분류명유서비스그룹분류명수정일시
등록일시1.0000.0000.0000.472
사용유형분류명0.0001.0000.0000.000
유서비스그룹분류명0.0000.0001.0000.000
수정일시0.4720.0000.0001.000
2023-12-11T18:46:41.743865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유서비스그룹분류명사용유형분류명등록일시수정일시
유서비스그룹분류명1.0000.0000.0000.000
사용유형분류명0.0001.0000.0000.000
등록일시0.0000.0001.0000.472
수정일시0.0000.0000.4721.000

Missing values

2023-12-11T18:46:38.547735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:46:38.682822image/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

시스템명한글시스템명영문유서비스그룹분류명사용유형분류명등록일시수정일시뷰어타입분류명뷰어여부
0재난안전긴급대응disaster Safety urgency countermeasureDPY2016-05-04 16:43:222016-05-04 16:43:22<NA>N
1시설물원격전원 ON/OFFNetwork Power ON/OFFFTY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
2대기오염Air PollutionENY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
3강도신고접수Report RobberyCPN2016-01-26 10:50:352016-01-26 10:50:35<NA>N
4비상벨Emergency BellCPN2016-01-26 10:50:352016-01-26 10:50:35<NA>N
5화재감시Fire WatchingDPY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
6지능형영상분석Image AnalysisUMY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
7산사태감시Landslide WatchDPN2016-01-26 10:50:352016-01-26 10:50:35<NA>N
8실종신고A Report of DisappearanceSFY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
9정전감시Black Out WatchDPY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
시스템명한글시스템명영문유서비스그룹분류명사용유형분류명등록일시수정일시뷰어타입분류명뷰어여부
37교통돌발공사<NA>SHY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
38119긴급지원<NA>DPN2016-01-26 10:50:352016-01-26 10:50:35<NA>N
39NMS(내부)network management systemFTY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
40센터시설물(FMS)facility management systemFTY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
41광선로관리(FNMS)fiber network management systemFTY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
42모니터링시스템monitoring systemUCY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
43이노뎁 VMS<NA>TRY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
44주행차량자동인식<NA>TRY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
45BISbus information systemTRY2016-01-26 10:50:352016-01-26 10:50:35<NA>N
46교통신호제어Traffic signal controlTRY2016-01-26 10:50:352016-01-26 10:50:35<NA>N