Overview

Dataset statistics

Number of variables7
Number of observations114
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory59.2 B

Variable types

Text2
Categorical5

Dataset

Description남동구의 현수막지정게시대 정보 제공
Author인천광역시남동구도시관리공단
URLhttps://www.data.go.kr/data/15001951/fileData.do

Alerts

부착일수 has constant value ""Constant
규격(cm) is highly overall correlated with 부착금액 and 1 other fieldsHigh correlation
부착금액 is highly overall correlated with 규격(cm) and 1 other fieldsHigh correlation
면수 is highly overall correlated with 부착금액 and 1 other fieldsHigh correlation
번호 has unique valuesUnique
게시대명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:04:27.553918
Analysis finished2023-12-12 00:04:27.997959
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T09:04:28.216867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1491228
Min length4

Characters and Unicode

Total characters587
Distinct characters17
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)100.0%

Sample

1st rowA-103
2nd rowA-106
3rd rowA-107
4th rowA-109
5th rowA-110
ValueCountFrequency (%)
a-103 1
 
0.9%
c-130 1
 
0.9%
c-118 1
 
0.9%
c-116 1
 
0.9%
c-115 1
 
0.9%
c-108 1
 
0.9%
b-저단15 1
 
0.9%
b-저단14 1
 
0.9%
b-저단09 1
 
0.9%
b-저단07 1
 
0.9%
Other values (104) 104
91.2%
2023-12-12T09:04:28.632162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 114
19.4%
1 110
18.7%
3 43
 
7.3%
A 40
 
6.8%
0 40
 
6.8%
B 39
 
6.6%
C 35
 
6.0%
2 26
 
4.4%
4 21
 
3.6%
7 20
 
3.4%
Other values (7) 99
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321
54.7%
Uppercase Letter 122
 
20.8%
Dash Punctuation 114
 
19.4%
Other Letter 30
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 110
34.3%
3 43
 
13.4%
0 40
 
12.5%
2 26
 
8.1%
4 21
 
6.5%
7 20
 
6.2%
5 20
 
6.2%
6 18
 
5.6%
8 13
 
4.0%
9 10
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 40
32.8%
B 39
32.0%
C 35
28.7%
S 8
 
6.6%
Other Letter
ValueCountFrequency (%)
저 15
50.0%
단 15
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 435
74.1%
Latin 122
 
20.8%
Hangul 30
 
5.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 114
26.2%
1 110
25.3%
3 43
 
9.9%
0 40
 
9.2%
2 26
 
6.0%
4 21
 
4.8%
7 20
 
4.6%
5 20
 
4.6%
6 18
 
4.1%
8 13
 
3.0%
Latin
ValueCountFrequency (%)
A 40
32.8%
B 39
32.0%
C 35
28.7%
S 8
 
6.6%
Hangul
ValueCountFrequency (%)
저 15
50.0%
단 15
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 557
94.9%
Hangul 30
 
5.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 114
20.5%
1 110
19.7%
3 43
 
7.7%
A 40
 
7.2%
0 40
 
7.2%
B 39
 
7.0%
C 35
 
6.3%
2 26
 
4.7%
4 21
 
3.8%
7 20
 
3.6%
Other values (5) 69
12.4%
Hangul
ValueCountFrequency (%)
저 15
50.0%
단 15
50.0%

게시대명칭
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T09:04:28.896158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.4824561
Min length5

Characters and Unicode

Total characters1081
Distinct characters192
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

Unique114 ?
Unique (%)100.0%

Sample

1st row공단본부사거리
2nd row2블록동보산업옆
3rd row4블록삼성공업옆
4th row37블록극동가스켓앞
5th row22블록화성보일러앞
ValueCountFrequency (%)
공단본부사거리 1
 
0.9%
효성다이아몬드a 1
 
0.9%
55블럭오공본드앞 1
 
0.9%
102블록원진산업옆 1
 
0.9%
74블록국제파이프옆 1
 
0.9%
동보아파트삼거리 1
 
0.9%
저단만의골종가집앞삼거리-2 1
 
0.9%
저단만의골종가집앞삼거리-1 1
 
0.9%
저단삼익아파트놀이터옹벽앞 1
 
0.9%
저단구월동cgv건너편 1
 
0.9%
Other values (104) 104
91.2%
2023-12-12T09:04:29.294782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 39
 
3.6%
앞 35
 
3.2%
리 34
 
3.1%
구 34
 
3.1%
거 30
 
2.8%
블 25
 
2.3%
사 25
 
2.3%
동 24
 
2.2%
1 23
 
2.1%
록 21
 
1.9%
Other values (182) 791
73.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 928
85.8%
Decimal Number 91
 
8.4%
Uppercase Letter 21
 
1.9%
Open Punctuation 20
 
1.9%
Close Punctuation 12
 
1.1%
Dash Punctuation 7
 
0.6%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 39
 
4.2%
앞 35
 
3.8%
리 34
 
3.7%
구 34
 
3.7%
거 30
 
3.2%
블 25
 
2.7%
사 25
 
2.7%
동 24
 
2.6%
록 21
 
2.3%
입 21
 
2.3%
Other values (157) 640
69.0%
Decimal Number
ValueCountFrequency (%)
1 23
25.3%
2 19
20.9%
7 11
12.1%
0 9
 
9.9%
5 8
 
8.8%
3 7
 
7.7%
4 6
 
6.6%
9 4
 
4.4%
8 3
 
3.3%
6 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
A 5
23.8%
B 5
23.8%
K 3
14.3%
I 2
 
9.5%
S 2
 
9.5%
G 1
 
4.8%
V 1
 
4.8%
C 1
 
4.8%
T 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 12
60.0%
[ 8
40.0%
Close Punctuation
ValueCountFrequency (%)
] 8
66.7%
) 4
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 928
85.8%
Common 132
 
12.2%
Latin 21
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 39
 
4.2%
앞 35
 
3.8%
리 34
 
3.7%
구 34
 
3.7%
거 30
 
3.2%
블 25
 
2.7%
사 25
 
2.7%
동 24
 
2.6%
록 21
 
2.3%
입 21
 
2.3%
Other values (157) 640
69.0%
Common
ValueCountFrequency (%)
1 23
17.4%
2 19
14.4%
( 12
9.1%
7 11
8.3%
0 9
 
6.8%
[ 8
 
6.1%
] 8
 
6.1%
5 8
 
6.1%
- 7
 
5.3%
3 7
 
5.3%
Other values (6) 20
15.2%
Latin
ValueCountFrequency (%)
A 5
23.8%
B 5
23.8%
K 3
14.3%
I 2
 
9.5%
S 2
 
9.5%
G 1
 
4.8%
V 1
 
4.8%
C 1
 
4.8%
T 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 928
85.8%
ASCII 153
 
14.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 39
 
4.2%
앞 35
 
3.8%
리 34
 
3.7%
구 34
 
3.7%
거 30
 
3.2%
블 25
 
2.7%
사 25
 
2.7%
동 24
 
2.6%
록 21
 
2.3%
입 21
 
2.3%
Other values (157) 640
69.0%
ASCII
ValueCountFrequency (%)
1 23
15.0%
2 19
12.4%
( 12
 
7.8%
7 11
 
7.2%
0 9
 
5.9%
[ 8
 
5.2%
] 8
 
5.2%
5 8
 
5.2%
- 7
 
4.6%
3 7
 
4.6%
Other values (15) 41
26.8%

행정동
Categorical

Distinct9
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
논현동
31 
고잔동
24 
만수동
16 
구월동
12 
남촌도림동
11 
Other values (4)
20 

Length

Max length5
Median length3
Mean length3.3596491
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고잔동
2nd row남촌도림동
3rd row고잔동
4th row고잔동
5th row고잔동

Common Values

ValueCountFrequency (%)
논현동 31
27.2%
고잔동 24
21.1%
만수동 16
14.0%
구월동 12
 
10.5%
남촌도림동 11
 
9.6%
장수서창 7
 
6.1%
간석동 5
 
4.4%
장수서창동 4
 
3.5%
남촌도림 4
 
3.5%

Length

2023-12-12T09:04:29.440603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:04:29.568197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
논현동 31
27.2%
고잔동 24
21.1%
만수동 16
14.0%
구월동 12
 
10.5%
남촌도림동 11
 
9.6%
장수서창 7
 
6.1%
간석동 5
 
4.4%
장수서창동 4
 
3.5%
남촌도림 4
 
3.5%

부착일수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
10
114 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 114
100.0%

Length

2023-12-12T09:04:29.699793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:04:29.786856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 114
100.0%

부착금액
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
28,850
91 
21,040
23 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28,850
2nd row28,850
3rd row28,850
4th row28,850
5th row28,850

Common Values

ValueCountFrequency (%)
28,850 91
79.8%
21,040 23
 
20.2%

Length

2023-12-12T09:04:29.927876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:04:30.047169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28,850 91
79.8%
21,040 23
 
20.2%

규격(cm)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
645x70
91 
430x50
23 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row645x70
2nd row645x70
3rd row645x70
4th row645x70
5th row645x70

Common Values

ValueCountFrequency (%)
645x70 91
79.8%
430x50 23
 
20.2%

Length

2023-12-12T09:04:30.185488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:04:30.300062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
645x70 91
79.8%
430x50 23
 
20.2%

면수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
6
78 
4
19 
2
16 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 78
68.4%
4 19
 
16.7%
2 16
 
14.0%
1 1
 
0.9%

Length

2023-12-12T09:04:30.415334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:04:30.521009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 78
68.4%
4 19
 
16.7%
2 16
 
14.0%
1 1
 
0.9%

Correlations

2023-12-12T09:04:30.937167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동부착금액규격(cm)면수
행정동1.0000.2870.2870.248
부착금액0.2871.0000.9990.964
규격(cm)0.2870.9991.0000.964
면수0.2480.9640.9641.000
2023-12-12T09:04:31.033069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격(cm)행정동면수부착금액
규격(cm)1.0000.2760.8220.973
행정동0.2761.0000.1550.276
면수0.8220.1551.0000.822
부착금액0.9730.2760.8221.000
2023-12-12T09:04:31.153186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동부착금액규격(cm)면수
행정동1.0000.2760.2760.155
부착금액0.2761.0000.9730.822
규격(cm)0.2760.9731.0000.822
면수0.1550.8220.8221.000

Missing values

2023-12-12T09:04:27.845781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:04:27.948219image/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

번호게시대명칭행정동부착일수부착금액규격(cm)면수
0A-103공단본부사거리고잔동1028,850645x706
1A-1062블록동보산업옆남촌도림동1028,850645x706
2A-1074블록삼성공업옆고잔동1028,850645x706
3A-10937블록극동가스켓앞고잔동1028,850645x706
4A-11022블록화성보일러앞고잔동1028,850645x706
5A-11130블록중소기업청앞고잔동1028,850645x706
6A-11251블록한신보일러앞고잔동1028,850645x706
7A-11390블록만승전기앞고잔동1028,850645x706
8A-11786블록규수방가구옆고잔동1028,850645x706
9A-122남동경찰서건너편A구월동1028,850645x706
번호게시대명칭행정동부착일수부착금액규격(cm)면수
104C-312신송천초교입구사거리논현동1028,850645x706
105C-313신한누리학교사거리코너논현동1028,850645x706
106C-314신영지웰아파트905동앞논현동1028,850645x706
107CS-1[소형]남동고등학교옆논현동1021,040430x502
108CS-2[소형]논현동은봉로351번길입구논현동1021,040430x502
109CS-3[소형]논현중학교건너편논현동1021,040430x502
110C-저단01소래포구역앞논현동1021,040430x502
111C-저단04호구포역하부논현동1021,040430x504
112C-저단12논현역공영주차장앞논현동1021,040430x504
113C-저단13논현경찰서건너편시민게시판옆논현동1021,040430x504