Overview

Dataset statistics

Number of variables18
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory145.9 B

Variable types

Text2
Categorical3
Boolean13

Dataset

Description부산광역시 북구의 U옥외광고물통합관리시스템의 윈도우관리정보에 대한 데이터로 윈도우ID,윈도우명,등급 등의 항목을 제공합니다.
Author부산광역시 북구
URLhttps://www.data.go.kr/data/15050084/fileData.do

Alerts

조회구분 has constant value ""Constant
가용여부 has constant value ""Constant
수정일자 is highly overall correlated with 생성일자High correlation
생성일자 is highly overall correlated with 수정일자High correlation
등록구분 is highly overall correlated with 저장구분 and 1 other fieldsHigh correlation
저장구분 is highly overall correlated with 등록구분 and 1 other fieldsHigh correlation
삭제구분 is highly overall correlated with 등록구분 and 1 other fieldsHigh correlation
최초구분 is highly overall correlated with 이전구분 and 3 other fieldsHigh correlation
이전구분 is highly overall correlated with 최초구분 and 3 other fieldsHigh correlation
다음구분 is highly overall correlated with 최초구분 and 3 other fieldsHigh correlation
마지막구분 is highly overall correlated with 최초구분 and 3 other fieldsHigh correlation
정렬구분 is highly overall correlated with 최초구분 and 3 other fieldsHigh correlation
윈도우등급 is highly imbalanced (80.9%)Imbalance
최초구분 is highly imbalanced (80.9%)Imbalance
이전구분 is highly imbalanced (80.9%)Imbalance
다음구분 is highly imbalanced (80.9%)Imbalance
마지막구분 is highly imbalanced (80.9%)Imbalance
정렬구분 is highly imbalanced (73.9%)Imbalance
화일구분 is highly imbalanced (56.9%)Imbalance
생성일자 is highly imbalanced (56.0%)Imbalance
수정일자 is highly imbalanced (56.0%)Imbalance
윈도우ID has unique valuesUnique

Reproduction

Analysis started2024-03-14 10:43:30.221898
Analysis finished2024-03-14 10:43:33.522954
Duration3.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

윈도우ID
Text

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size672.0 B
2024-03-14T19:43:34.355658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length9.2205882
Min length9

Characters and Unicode

Total characters627
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)100.0%

Sample

1st rowW_JUSOCONV
2nd rowW_STS4400
3rd rowW_STS6700
4th rowW_STS6600
5th rowW_STS6500
ValueCountFrequency (%)
w_jusoconv 1
 
1.5%
w_sts1040 1
 
1.5%
w_sts9000 1
 
1.5%
w_sts1012 1
 
1.5%
w_sts1011 1
 
1.5%
w_sts4100 1
 
1.5%
w_sts7003 1
 
1.5%
w_sts4400 1
 
1.5%
w_sts7004 1
 
1.5%
w_sts1900 1
 
1.5%
Other values (58) 58
85.3%
2024-03-14T19:43:35.576774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 132
21.1%
0 127
20.3%
_ 72
11.5%
W 70
11.2%
T 67
10.7%
1 45
 
7.2%
5 19
 
3.0%
2 16
 
2.6%
6 13
 
2.1%
4 12
 
1.9%
Other values (18) 54
8.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 295
47.0%
Decimal Number 260
41.5%
Connector Punctuation 72
 
11.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 132
44.7%
W 70
23.7%
T 67
22.7%
N 3
 
1.0%
I 3
 
1.0%
E 3
 
1.0%
V 2
 
0.7%
D 2
 
0.7%
L 2
 
0.7%
A 2
 
0.7%
Other values (7) 9
 
3.1%
Decimal Number
ValueCountFrequency (%)
0 127
48.8%
1 45
 
17.3%
5 19
 
7.3%
2 16
 
6.2%
6 13
 
5.0%
4 12
 
4.6%
7 11
 
4.2%
3 11
 
4.2%
9 5
 
1.9%
8 1
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 332
53.0%
Latin 295
47.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 132
44.7%
W 70
23.7%
T 67
22.7%
N 3
 
1.0%
I 3
 
1.0%
E 3
 
1.0%
V 2
 
0.7%
D 2
 
0.7%
L 2
 
0.7%
A 2
 
0.7%
Other values (7) 9
 
3.1%
Common
ValueCountFrequency (%)
0 127
38.3%
_ 72
21.7%
1 45
 
13.6%
5 19
 
5.7%
2 16
 
4.8%
6 13
 
3.9%
4 12
 
3.6%
7 11
 
3.3%
3 11
 
3.3%
9 5
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 132
21.1%
0 127
20.3%
_ 72
11.5%
W 70
11.2%
T 67
10.7%
1 45
 
7.2%
5 19
 
3.0%
2 16
 
2.6%
6 13
 
2.1%
4 12
 
1.9%
Other values (18) 54
8.6%

윈도우명
Text

Distinct67
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size672.0 B
2024-03-14T19:43:36.431350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length9.7941176
Min length5

Characters and Unicode

Total characters666
Distinct characters134
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)97.1%

Sample

1st row새주소 변환관리
2nd row전수조사허가처리(검증)
3rd row건물사진 없는 자료 현황
4th row간판사진 없는 자료 현황
5th row정비상태(폐업,철거) 현황
ValueCountFrequency (%)
현황 8
 
9.2%
전수조사허가처리(검증 2
 
2.3%
자료 2
 
2.3%
허가.신고 2
 
2.3%
적법·불법 2
 
2.3%
없는 2
 
2.3%
계고장발행(자진정비 1
 
1.1%
건물통합관리(새주소도로명별 1
 
1.1%
허가/전수정보관리 1
 
1.1%
전수/허가정보관리 1
 
1.1%
Other values (65) 65
74.7%
2024-03-14T19:43:37.475883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 26
 
3.9%
리 26
 
3.9%
고 24
 
3.6%
관 22
 
3.3%
황 21
 
3.2%
현 21
 
3.2%
조 20
 
3.0%
물 20
 
3.0%
수 19
 
2.9%
19
 
2.9%
Other values (124) 448
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 600
90.1%
Space Separator 19
 
2.9%
Close Punctuation 15
 
2.3%
Open Punctuation 15
 
2.3%
Other Punctuation 7
 
1.1%
Uppercase Letter 6
 
0.9%
Connector Punctuation 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 26
 
4.3%
리 26
 
4.3%
고 24
 
4.0%
관 22
 
3.7%
황 21
 
3.5%
현 21
 
3.5%
조 20
 
3.3%
물 20
 
3.3%
수 19
 
3.2%
전 17
 
2.8%
Other values (113) 384
64.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
28.6%
· 2
28.6%
. 2
28.6%
, 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 2
33.3%
D 2
33.3%
A 2
33.3%
Space Separator
ValueCountFrequency (%)
19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 600
90.1%
Common 60
 
9.0%
Latin 6
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 26
 
4.3%
리 26
 
4.3%
고 24
 
4.0%
관 22
 
3.7%
황 21
 
3.5%
현 21
 
3.5%
조 20
 
3.3%
물 20
 
3.3%
수 19
 
3.2%
전 17
 
2.8%
Other values (113) 384
64.0%
Common
ValueCountFrequency (%)
19
31.7%
) 15
25.0%
( 15
25.0%
_ 4
 
6.7%
/ 2
 
3.3%
· 2
 
3.3%
. 2
 
3.3%
, 1
 
1.7%
Latin
ValueCountFrequency (%)
P 2
33.3%
D 2
33.3%
A 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 600
90.1%
ASCII 64
 
9.6%
None 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 26
 
4.3%
리 26
 
4.3%
고 24
 
4.0%
관 22
 
3.7%
황 21
 
3.5%
현 21
 
3.5%
조 20
 
3.3%
물 20
 
3.3%
수 19
 
3.2%
전 17
 
2.8%
Other values (113) 384
64.0%
ASCII
ValueCountFrequency (%)
19
29.7%
) 15
23.4%
( 15
23.4%
_ 4
 
6.2%
P 2
 
3.1%
D 2
 
3.1%
A 2
 
3.1%
/ 2
 
3.1%
. 2
 
3.1%
, 1
 
1.6%
None
ValueCountFrequency (%)
· 2
100.0%

윈도우등급
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size672.0 B
B
66 
A
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
B 66
97.1%
A 2
 
2.9%

Length

2024-03-14T19:43:37.843925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:43:38.162760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 66
97.1%
a 2
 
2.9%

조회구분
Boolean

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
68 
ValueCountFrequency (%)
True 68
100.0%
2024-03-14T19:43:38.426112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

등록구분
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
False
49 
True
19 
ValueCountFrequency (%)
False 49
72.1%
True 19
 
27.9%
2024-03-14T19:43:38.691666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

저장구분
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
False
43 
True
25 
ValueCountFrequency (%)
False 43
63.2%
True 25
36.8%
2024-03-14T19:43:38.968750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

삭제구분
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
False
44 
True
24 
ValueCountFrequency (%)
False 44
64.7%
True 24
35.3%
2024-03-14T19:43:39.244659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최초구분
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
66 
False
 
2
ValueCountFrequency (%)
True 66
97.1%
False 2
 
2.9%
2024-03-14T19:43:39.527229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

이전구분
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
66 
False
 
2
ValueCountFrequency (%)
True 66
97.1%
False 2
 
2.9%
2024-03-14T19:43:39.796366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

다음구분
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
66 
False
 
2
ValueCountFrequency (%)
True 66
97.1%
False 2
 
2.9%
2024-03-14T19:43:40.067449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

마지막구분
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
66 
False
 
2
ValueCountFrequency (%)
True 66
97.1%
False 2
 
2.9%
2024-03-14T19:43:40.337224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

정렬구분
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
65 
False
 
3
ValueCountFrequency (%)
True 65
95.6%
False 3
 
4.4%
2024-03-14T19:43:40.607691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

필터구분
Boolean

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
52 
False
16 
ValueCountFrequency (%)
True 52
76.5%
False 16
 
23.5%
2024-03-14T19:43:40.884214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

화일구분
Boolean

IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
62 
False
 
6
ValueCountFrequency (%)
True 62
91.2%
False 6
 
8.8%
2024-03-14T19:43:41.161370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

인쇄구분
Boolean

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
37 
False
31 
ValueCountFrequency (%)
True 37
54.4%
False 31
45.6%
2024-03-14T19:43:41.441896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

가용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size196.0 B
True
68 
ValueCountFrequency (%)
True 68
100.0%
2024-03-14T19:43:41.709976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

생성일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size672.0 B
2010-10-18
52 
2010-12-27
13 
2011-03-31
 
1
2011-08-30
 
1
2013-05-07
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique3 ?
Unique (%)4.4%

Sample

1st row2011-03-31
2nd row2011-08-30
3rd row2010-12-27
4th row2010-12-27
5th row2010-12-27

Common Values

ValueCountFrequency (%)
2010-10-18 52
76.5%
2010-12-27 13
 
19.1%
2011-03-31 1
 
1.5%
2011-08-30 1
 
1.5%
2013-05-07 1
 
1.5%

Length

2024-03-14T19:43:42.039890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:43:42.369764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2010-10-18 52
76.5%
2010-12-27 13
 
19.1%
2011-03-31 1
 
1.5%
2011-08-30 1
 
1.5%
2013-05-07 1
 
1.5%

수정일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size672.0 B
2010-10-18
52 
2010-12-27
13 
2011-03-31
 
1
2011-08-30
 
1
2013-05-07
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique3 ?
Unique (%)4.4%

Sample

1st row2011-03-31
2nd row2011-08-30
3rd row2010-12-27
4th row2010-12-27
5th row2010-12-27

Common Values

ValueCountFrequency (%)
2010-10-18 52
76.5%
2010-12-27 13
 
19.1%
2011-03-31 1
 
1.5%
2011-08-30 1
 
1.5%
2013-05-07 1
 
1.5%

Length

2024-03-14T19:43:42.752896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:43:43.089380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2010-10-18 52
76.5%
2010-12-27 13
 
19.1%
2011-03-31 1
 
1.5%
2011-08-30 1
 
1.5%
2013-05-07 1
 
1.5%

Correlations

2024-03-14T19:43:43.348611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
윈도우ID윈도우명윈도우등급등록구분저장구분삭제구분최초구분이전구분다음구분마지막구분정렬구분필터구분화일구분인쇄구분생성일자수정일자
윈도우ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
윈도우명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.000
윈도우등급1.0001.0001.0000.0000.0000.0000.3000.3000.3000.3000.1970.0000.0000.0000.0000.000
등록구분1.0001.0000.0001.0000.9400.9540.0000.0000.0000.0000.0000.4380.0000.5450.1640.164
저장구분1.0001.0000.0000.9401.0000.9540.0000.0000.0000.0000.0000.2260.0000.6140.2390.239
삭제구분1.0001.0000.0000.9540.9541.0000.0000.0000.0000.0000.0000.3880.0000.5740.1680.168
최초구분1.0001.0000.3000.0000.0000.0001.0000.9160.9160.9160.8000.2700.5760.0000.0000.000
이전구분1.0001.0000.3000.0000.0000.0000.9161.0000.9160.9160.8000.2700.5760.0000.0000.000
다음구분1.0001.0000.3000.0000.0000.0000.9160.9161.0000.9160.8000.2700.5760.0000.0000.000
마지막구분1.0001.0000.3000.0000.0000.0000.9160.9160.9161.0000.8000.2700.5760.0000.0000.000
정렬구분1.0001.0000.1970.0000.0000.0000.8000.8000.8000.8001.0000.4250.4390.0000.0000.000
필터구분1.0001.0000.0000.4380.2260.3880.2700.2700.2700.2700.4251.0000.5360.6100.1600.160
화일구분1.0001.0000.0000.0000.0000.0000.5760.5760.5760.5760.4390.5361.0000.4010.2860.286
인쇄구분1.0001.0000.0000.5450.6140.5740.0000.0000.0000.0000.0000.6100.4011.0000.0000.000
생성일자1.0000.0000.0000.1640.2390.1680.0000.0000.0000.0000.0000.1600.2860.0001.0001.000
수정일자1.0000.0000.0000.1640.2390.1680.0000.0000.0000.0000.0000.1600.2860.0001.0001.000
2024-03-14T19:43:43.734326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이전구분정렬구분저장구분다음구분화일구분수정일자인쇄구분윈도우등급생성일자필터구분최초구분마지막구분등록구분삭제구분
이전구분1.0000.5900.0000.7380.3900.0000.0000.1930.0000.1740.7380.7380.0000.000
정렬구분0.5901.0000.0000.5900.2890.0000.0000.1260.0000.2790.5900.5900.0000.000
저장구분0.0000.0001.0000.0000.0000.2830.4210.0000.2830.1440.0000.0000.7790.806
다음구분0.7380.5900.0001.0000.3900.0000.0000.1930.0000.1740.7380.7380.0000.000
화일구분0.3900.2890.0000.3901.0000.3400.2630.0000.3400.3600.3900.3900.0000.000
수정일자0.0000.0000.2830.0000.3401.0000.0000.0001.0000.1880.0000.0000.1940.198
인쇄구분0.0000.0000.4210.0000.2630.0001.0000.0000.0000.4170.0000.0000.3670.389
윈도우등급0.1930.1260.0000.1930.0000.0000.0001.0000.0000.0000.1930.1930.0000.000
생성일자0.0000.0000.2830.0000.3401.0000.0000.0001.0000.1880.0000.0000.1940.198
필터구분0.1740.2790.1440.1740.3600.1880.4170.0000.1881.0000.1740.1740.2890.253
최초구분0.7380.5900.0000.7380.3900.0000.0000.1930.0000.1741.0000.7380.0000.000
마지막구분0.7380.5900.0000.7380.3900.0000.0000.1930.0000.1740.7381.0000.0000.000
등록구분0.0000.0000.7790.0000.0000.1940.3670.0000.1940.2890.0000.0001.0000.806
삭제구분0.0000.0000.8060.0000.0000.1980.3890.0000.1980.2530.0000.0000.8061.000
2024-03-14T19:43:44.097106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
윈도우등급등록구분저장구분삭제구분최초구분이전구분다음구분마지막구분정렬구분필터구분화일구분인쇄구분생성일자수정일자
윈도우등급1.0000.0000.0000.0000.1930.1930.1930.1930.1260.0000.0000.0000.0000.000
등록구분0.0001.0000.7790.8060.0000.0000.0000.0000.0000.2890.0000.3670.1940.194
저장구분0.0000.7791.0000.8060.0000.0000.0000.0000.0000.1440.0000.4210.2830.283
삭제구분0.0000.8060.8061.0000.0000.0000.0000.0000.0000.2530.0000.3890.1980.198
최초구분0.1930.0000.0000.0001.0000.7380.7380.7380.5900.1740.3900.0000.0000.000
이전구분0.1930.0000.0000.0000.7381.0000.7380.7380.5900.1740.3900.0000.0000.000
다음구분0.1930.0000.0000.0000.7380.7381.0000.7380.5900.1740.3900.0000.0000.000
마지막구분0.1930.0000.0000.0000.7380.7380.7381.0000.5900.1740.3900.0000.0000.000
정렬구분0.1260.0000.0000.0000.5900.5900.5900.5901.0000.2790.2890.0000.0000.000
필터구분0.0000.2890.1440.2530.1740.1740.1740.1740.2791.0000.3600.4170.1880.188
화일구분0.0000.0000.0000.0000.3900.3900.3900.3900.2890.3601.0000.2630.3400.340
인쇄구분0.0000.3670.4210.3890.0000.0000.0000.0000.0000.4170.2631.0000.0000.000
생성일자0.0000.1940.2830.1980.0000.0000.0000.0000.0000.1880.3400.0001.0001.000
수정일자0.0000.1940.2830.1980.0000.0000.0000.0000.0000.1880.3400.0001.0001.000

Missing values

2024-03-14T19:43:32.554253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:43:33.256802image/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

윈도우ID윈도우명윈도우등급조회구분등록구분저장구분삭제구분최초구분이전구분다음구분마지막구분정렬구분필터구분화일구분인쇄구분가용여부생성일자수정일자
0W_JUSOCONV새주소 변환관리BYNNNYYYYYYNNY2011-03-312011-03-31
1W_STS4400전수조사허가처리(검증)BYNYYYYYYYYYNY2011-08-302011-08-30
2W_STS6700건물사진 없는 자료 현황BYNNNYYYYYYYNY2010-12-272010-12-27
3W_STS6600간판사진 없는 자료 현황BYNNNYYYYYYYNY2010-12-272010-12-27
4W_STS6500정비상태(폐업,철거) 현황BYNNNYYYYYYYNY2010-12-272010-12-27
5W_STS0014새주소권역별설정하기BYYYYYYYYYYYNY2010-12-272010-12-27
6W_STS3600허가.신고 기간만료(연장대상)자료조회BYNNNYYYYYYYYY2010-12-272010-12-27
7W_STS3700허가.신고 기간만료(연장대상)출력내역BYNNNYYYYYYYYY2010-12-272010-12-27
8W_STS1900광고물정비내역BYNNNYYYYYYYYY2010-12-272010-12-27
9W_STS1045자진정비안내발행(유동광고물)BYNNNYYYYYYYYY2010-12-272010-12-27
윈도우ID윈도우명윈도우등급조회구분등록구분저장구분삭제구분최초구분이전구분다음구분마지막구분정렬구분필터구분화일구분인쇄구분가용여부생성일자수정일자
58W_STS1010전수조사내역BYYYYYYYYYNYNY2010-10-182010-10-18
59W_STS0003사용자권한관리BYYYYYYYYYNNNY2010-10-182010-10-18
60W_STS2010허가정보수신BYNNNYYYYYNYNY2010-10-182010-10-18
61W_STS0109시도그룹주소관리BYYYYYYYYYYYNY2010-10-182010-10-18
62W_STS7001적법·불법 현황BYNNNYYYYYYYYY2010-10-182010-10-18
63W_STS1070간판사진유무관리BYNNNYYYYYYYYY2010-10-182010-10-18
64W_STS1050재조사관리BYYYYYYYYYYYYY2010-10-182010-10-18
65W_STS1060건물사진관리BYNNNYYYYYYYYY2010-10-182010-10-18
66W_STS1090허가매칭여부관리BYNNNYYYYYYYYY2010-10-182010-10-18
67W_STS0020재조사관리(특화지역)BYYYYYYYYYYYYY2013-05-072013-05-07