Overview

Dataset statistics

Number of variables6
Number of observations105
Missing cells5
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory51.3 B

Variable types

Numeric1
Text3
Categorical2

Dataset

Description인천광역시 미추홀구 현수막 지정게시대 현황에 대한 데이터로 현수막에 대한 고유번호, 동명, 게시대명, 위치, 면수 등의 정보를 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15118015&srcSe=7661IVAWM27C61E190

Alerts

고유번호 has 5 (4.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 09:02:37.552942
Analysis finished2024-01-28 09:02:38.073172
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53
Minimum1
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-28T18:02:38.156282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.2
Q127
median53
Q379
95-th percentile99.8
Maximum105
Range104
Interquartile range (IQR)52

Descriptive statistics

Standard deviation30.454885
Coefficient of variation (CV)0.57462047
Kurtosis-1.2
Mean53
Median Absolute Deviation (MAD)26
Skewness0
Sum5565
Variance927.5
MonotonicityStrictly increasing
2024-01-28T18:02:38.312968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
80 1
 
1.0%
78 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
Other values (95) 95
90.5%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
105 1
1.0%
104 1
1.0%
103 1
1.0%
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%

고유번호
Text

MISSING 

Distinct100
Distinct (%)100.0%
Missing5
Missing (%)4.8%
Memory size972.0 B
2024-01-28T18:02:38.604627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters400
Distinct characters15
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

Unique100 ?
Unique (%)100.0%

Sample

1st row01-C
2nd row02-C
3rd row03-C
4th row04-C
5th row05-C
ValueCountFrequency (%)
06-a 1
 
1.0%
09-a 1
 
1.0%
46-a 1
 
1.0%
20-a 1
 
1.0%
18-a 1
 
1.0%
25-a 1
 
1.0%
24-a 1
 
1.0%
23-a 1
 
1.0%
22-b 1
 
1.0%
22-a 1
 
1.0%
Other values (90) 90
90.0%
2024-01-28T18:02:38.987973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
25.0%
A 45
11.2%
C 38
 
9.5%
3 35
 
8.8%
2 35
 
8.8%
1 30
 
7.5%
0 24
 
6.0%
4 23
 
5.8%
6 13
 
3.2%
5 11
 
2.8%
Other values (5) 46
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200
50.0%
Dash Punctuation 100
25.0%
Uppercase Letter 100
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 35
17.5%
2 35
17.5%
1 30
15.0%
0 24
12.0%
4 23
11.5%
6 13
 
6.5%
5 11
 
5.5%
8 11
 
5.5%
7 10
 
5.0%
9 8
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 45
45.0%
C 38
38.0%
B 11
 
11.0%
D 6
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
75.0%
Latin 100
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 100
33.3%
3 35
 
11.7%
2 35
 
11.7%
1 30
 
10.0%
0 24
 
8.0%
4 23
 
7.7%
6 13
 
4.3%
5 11
 
3.7%
8 11
 
3.7%
7 10
 
3.3%
Latin
ValueCountFrequency (%)
A 45
45.0%
C 38
38.0%
B 11
 
11.0%
D 6
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
25.0%
A 45
11.2%
C 38
 
9.5%
3 35
 
8.8%
2 35
 
8.8%
1 30
 
7.5%
0 24
 
6.0%
4 23
 
5.8%
6 13
 
3.2%
5 11
 
2.8%
Other values (5) 46
11.5%

동명
Categorical

Distinct20
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size972.0 B
도화2.3동
18 
학익1동
12 
용현5동
10 
주안1동
6 
숭의2동
6 
Other values (15)
53 

Length

Max length6
Median length4
Mean length4.447619
Min length3

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row주안1동
2nd row주안8동
3rd row주안3동
4th row관교동
5th row도화1동

Common Values

ValueCountFrequency (%)
도화2.3동 18
17.1%
학익1동 12
11.4%
용현5동 10
 
9.5%
주안1동 6
 
5.7%
숭의2동 6
 
5.7%
용현2동 5
 
4.8%
주안6동 5
 
4.8%
용현1.4동 5
 
4.8%
도화1동 5
 
4.8%
관교동 5
 
4.8%
Other values (10) 28
26.7%

Length

2024-01-28T18:02:39.113973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도화2.3동 18
17.1%
학익1동 12
11.4%
용현5동 10
 
9.5%
주안1동 6
 
5.7%
숭의2동 6
 
5.7%
용현2동 5
 
4.8%
주안6동 5
 
4.8%
용현1.4동 5
 
4.8%
도화1동 5
 
4.8%
관교동 5
 
4.8%
Other values (10) 28
26.7%

게시대명
Text

Distinct103
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size972.0 B
2024-01-28T18:02:39.330582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9
Min length4

Characters and Unicode

Total characters945
Distinct characters181
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)96.2%

Sample

1st row주안남부역
2nd row신기사거리(A)
3rd row용일사거리(A)
4th row롯데백화점 사거리
5th row도화초등학교
ValueCountFrequency (%)
앞 9
 
5.8%
숭의역 3
 
1.9%
담장 3
 
1.9%
인하대역 3
 
1.9%
학익사거리 2
 
1.3%
앞(b 2
 
1.3%
보훈병원 2
 
1.3%
주안택시승강장 2
 
1.3%
옆 2
 
1.3%
성인천장례식장 2
 
1.3%
Other values (115) 126
80.8%
2024-01-28T18:02:39.668346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
 
5.4%
앞 37
 
3.9%
( 37
 
3.9%
) 37
 
3.9%
리 31
 
3.3%
거 30
 
3.2%
사 26
 
2.8%
구 26
 
2.8%
장 21
 
2.2%
역 21
 
2.2%
Other values (171) 628
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 732
77.5%
Uppercase Letter 60
 
6.3%
Space Separator 51
 
5.4%
Open Punctuation 37
 
3.9%
Close Punctuation 37
 
3.9%
Decimal Number 23
 
2.4%
Other Punctuation 2
 
0.2%
Dash Punctuation 2
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 37
 
5.1%
리 31
 
4.2%
거 30
 
4.1%
사 26
 
3.6%
구 26
 
3.6%
장 21
 
2.9%
역 21
 
2.9%
인 18
 
2.5%
도 17
 
2.3%
화 15
 
2.0%
Other values (145) 490
66.9%
Uppercase Letter
ValueCountFrequency (%)
A 19
31.7%
B 18
30.0%
K 5
 
8.3%
C 4
 
6.7%
S 4
 
6.7%
I 3
 
5.0%
G 2
 
3.3%
L 1
 
1.7%
V 1
 
1.7%
P 1
 
1.7%
Other values (2) 2
 
3.3%
Decimal Number
ValueCountFrequency (%)
2 8
34.8%
1 4
17.4%
3 3
 
13.0%
0 2
 
8.7%
9 2
 
8.7%
4 2
 
8.7%
6 1
 
4.3%
5 1
 
4.3%
Space Separator
ValueCountFrequency (%)
51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 732
77.5%
Common 152
 
16.1%
Latin 61
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 37
 
5.1%
리 31
 
4.2%
거 30
 
4.1%
사 26
 
3.6%
구 26
 
3.6%
장 21
 
2.9%
역 21
 
2.9%
인 18
 
2.5%
도 17
 
2.3%
화 15
 
2.0%
Other values (145) 490
66.9%
Common
ValueCountFrequency (%)
51
33.6%
( 37
24.3%
) 37
24.3%
2 8
 
5.3%
1 4
 
2.6%
3 3
 
2.0%
0 2
 
1.3%
9 2
 
1.3%
. 2
 
1.3%
- 2
 
1.3%
Other values (3) 4
 
2.6%
Latin
ValueCountFrequency (%)
A 19
31.1%
B 18
29.5%
K 5
 
8.2%
C 4
 
6.6%
S 4
 
6.6%
I 3
 
4.9%
G 2
 
3.3%
L 1
 
1.6%
V 1
 
1.6%
P 1
 
1.6%
Other values (3) 3
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 732
77.5%
ASCII 213
 
22.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
23.9%
( 37
17.4%
) 37
17.4%
A 19
 
8.9%
B 18
 
8.5%
2 8
 
3.8%
K 5
 
2.3%
C 4
 
1.9%
S 4
 
1.9%
1 4
 
1.9%
Other values (16) 26
12.2%
Hangul
ValueCountFrequency (%)
앞 37
 
5.1%
리 31
 
4.2%
거 30
 
4.1%
사 26
 
3.6%
구 26
 
3.6%
장 21
 
2.9%
역 21
 
2.9%
인 18
 
2.5%
도 17
 
2.3%
화 15
 
2.0%
Other values (145) 490
66.9%

위치
Text

Distinct85
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size972.0 B
2024-01-28T18:02:39.930380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length8.6190476
Min length5

Characters and Unicode

Total characters905
Distinct characters65
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)65.7%

Sample

1st row주안로 89
2nd row인주대로 394
3rd row인주대로 282
4th row관교동 16
5th row숙골로 4
ValueCountFrequency (%)
도화동 14
 
6.4%
용현동 11
 
5.0%
미추홀구 9
 
4.1%
매소홀로 8
 
3.7%
경원대로 8
 
3.7%
주안동 6
 
2.8%
소성로 5
 
2.3%
인주대로 4
 
1.8%
학익동 4
 
1.8%
95 4
 
1.8%
Other values (104) 145
66.5%
2024-01-28T18:02:40.297877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
113
 
12.5%
로 62
 
6.9%
1 55
 
6.1%
2 54
 
6.0%
동 43
 
4.8%
5 40
 
4.4%
3 38
 
4.2%
0 38
 
4.2%
4 29
 
3.2%
6 29
 
3.2%
Other values (55) 404
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 409
45.2%
Decimal Number 356
39.3%
Space Separator 113
 
12.5%
Dash Punctuation 27
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 62
 
15.2%
동 43
 
10.5%
홀 21
 
5.1%
대 17
 
4.2%
주 15
 
3.7%
미 14
 
3.4%
소 14
 
3.4%
경 14
 
3.4%
추 14
 
3.4%
화 14
 
3.4%
Other values (43) 181
44.3%
Decimal Number
ValueCountFrequency (%)
1 55
15.4%
2 54
15.2%
5 40
11.2%
3 38
10.7%
0 38
10.7%
4 29
8.1%
6 29
8.1%
8 26
7.3%
7 24
6.7%
9 23
6.5%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 496
54.8%
Hangul 409
45.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 62
 
15.2%
동 43
 
10.5%
홀 21
 
5.1%
대 17
 
4.2%
주 15
 
3.7%
미 14
 
3.4%
소 14
 
3.4%
경 14
 
3.4%
추 14
 
3.4%
화 14
 
3.4%
Other values (43) 181
44.3%
Common
ValueCountFrequency (%)
113
22.8%
1 55
11.1%
2 54
10.9%
5 40
 
8.1%
3 38
 
7.7%
0 38
 
7.7%
4 29
 
5.8%
6 29
 
5.8%
- 27
 
5.4%
8 26
 
5.2%
Other values (2) 47
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496
54.8%
Hangul 409
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
22.8%
1 55
11.1%
2 54
10.9%
5 40
 
8.1%
3 38
 
7.7%
0 38
 
7.7%
4 29
 
5.8%
6 29
 
5.8%
- 27
 
5.4%
8 26
 
5.2%
Other values (2) 47
9.5%
Hangul
ValueCountFrequency (%)
로 62
 
15.2%
동 43
 
10.5%
홀 21
 
5.1%
대 17
 
4.2%
주 15
 
3.7%
미 14
 
3.4%
소 14
 
3.4%
경 14
 
3.4%
추 14
 
3.4%
화 14
 
3.4%
Other values (43) 181
44.3%

면수
Categorical

Distinct5
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size972.0 B
6
53 
2
45 
3
 
3
7
 
3
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
6 53
50.5%
2 45
42.9%
3 3
 
2.9%
7 3
 
2.9%
4 1
 
1.0%

Length

2024-01-28T18:02:40.401066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T18:02:40.478215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 53
50.5%
2 45
42.9%
3 3
 
2.9%
7 3
 
2.9%
4 1
 
1.0%

Interactions

2024-01-28T18:02:37.823497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T18:02:40.536527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고유번호동명위치면수
연번1.0001.0000.7610.9940.816
고유번호1.0001.0001.0001.0001.000
동명0.7611.0001.0001.0000.535
위치0.9941.0001.0001.0000.878
면수0.8161.0000.5350.8781.000
2024-01-28T18:02:40.622891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명면수
동명1.0000.236
면수0.2361.000
2024-01-28T18:02:40.693259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명면수
연번1.0000.3260.462
동명0.3261.0000.236
면수0.4620.2361.000

Missing values

2024-01-28T18:02:37.915342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T18:02:38.028782image/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

연번고유번호동명게시대명위치면수
0101-C주안1동주안남부역주안로 892
1202-C주안8동신기사거리(A)인주대로 3942
2303-C주안3동용일사거리(A)인주대로 2822
3404-C관교동롯데백화점 사거리관교동 162
4505-C도화1동도화초등학교숙골로 42
5606-C학익1동학익사거리소성로 1462
6708-C도화2.3동제물포북부역경인로 1252
7809-C도화2.3동박문삼거리숙골로 712
8911-C주안1동시민공원 3번출구미추홀구 주안동 190-22
91012-C관교동롯데백화점주차장관교동 152
연번고유번호동명게시대명위치면수
959649-B용현5동SK뷰 109동 앞 B용현동 6756
969747-A학익1동함흥관 송도점 옆학익동 587-356
979848-A도화2.3동하늘꿈 교회 맞은편도화동 10246
989949-A용현5동SK스카이뷰 109동 앞 A용현동 6756
9910050-A주안4동주안캐슬 112동 건너편학익동 744-56
100101<NA>숭의2동구청본관앞A독정이로 952
101102<NA>숭의2동구청본관앞B독정이로 952
102103<NA>숭의2동구청본관 앞독정이로 952
103104<NA>숭의2동구청 민원실 앞독정이로 952
104105<NA>도화2.3동도화사거리도화동 10024