Overview

Dataset statistics

Number of variables7
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory65.5 B

Variable types

Numeric1
Text2
Categorical4

Dataset

Description인천광역시 부평구 벽보게시판 현황 데이터는 게시대명, 게시대 위치, 벽보 면수, 규격 등에 대한 데이터를 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15102528&srcSe=7661IVAWM27C61E190

Alerts

상단광고 has constant value ""Constant
벽보면수 has constant value ""Constant
부착일 has constant value ""Constant
규격 has constant value ""Constant
번호 has unique valuesUnique
게시대 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2024-01-28 06:31:39.133390
Analysis finished2024-01-28 06:31:39.509667
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-28T15:31:39.553873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2024-01-28T15:31:39.659463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

게시대
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-01-28T15:31:39.815675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Characters and Unicode

Total characters76
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row갈산1
2nd row갈산2
3rd row부개16
4th row부개10
5th row부개15
ValueCountFrequency (%)
갈산1 1
 
4.2%
갈산2 1
 
4.2%
청천5 1
 
4.2%
청천4 1
 
4.2%
청천3 1
 
4.2%
청천2 1
 
4.2%
청천1 1
 
4.2%
삼산10 1
 
4.2%
삼산1 1
 
4.2%
삼산2 1
 
4.2%
Other values (14) 14
58.3%
2024-01-28T15:31:40.054558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 9
11.8%
산 9
11.8%
1 8
10.5%
개 8
10.5%
천 6
 
7.9%
청 6
 
7.9%
3 4
 
5.3%
곡 4
 
5.3%
2 4
 
5.3%
5 3
 
3.9%
Other values (9) 15
19.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48
63.2%
Decimal Number 28
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
28.6%
3 4
14.3%
2 4
14.3%
5 3
 
10.7%
4 2
 
7.1%
0 2
 
7.1%
6 2
 
7.1%
8 1
 
3.6%
7 1
 
3.6%
9 1
 
3.6%
Other Letter
ValueCountFrequency (%)
부 9
18.8%
산 9
18.8%
개 8
16.7%
천 6
12.5%
청 6
12.5%
곡 4
8.3%
삼 3
 
6.2%
갈 2
 
4.2%
평 1
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48
63.2%
Common 28
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
28.6%
3 4
14.3%
2 4
14.3%
5 3
 
10.7%
4 2
 
7.1%
0 2
 
7.1%
6 2
 
7.1%
8 1
 
3.6%
7 1
 
3.6%
9 1
 
3.6%
Hangul
ValueCountFrequency (%)
부 9
18.8%
산 9
18.8%
개 8
16.7%
천 6
12.5%
청 6
12.5%
곡 4
8.3%
삼 3
 
6.2%
갈 2
 
4.2%
평 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48
63.2%
ASCII 28
36.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 9
18.8%
산 9
18.8%
개 8
16.7%
천 6
12.5%
청 6
12.5%
곡 4
8.3%
삼 3
 
6.2%
갈 2
 
4.2%
평 1
 
2.1%
ASCII
ValueCountFrequency (%)
1 8
28.6%
3 4
14.3%
2 4
14.3%
5 3
 
10.7%
4 2
 
7.1%
0 2
 
7.1%
6 2
 
7.1%
8 1
 
3.6%
7 1
 
3.6%
9 1
 
3.6%

위치
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-01-28T15:31:40.246779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length26.708333
Min length22

Characters and Unicode

Total characters641
Distinct characters105
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

Unique24 ?
Unique (%)100.0%

Sample

1st row(갈산2동)갈산주공2단지 정문 앞(주부토로 206)
2nd row(갈산2동)대동@ 103동 앞(주부토로 193)
3rd row(부개2동)휴먼시아 7단지 입구 앞(동수천로 118)
4th row(부개3동)부개주공 204동 앞(길주남로 143)
5th row(부개3동)미래생활고등학교 앞(수변로 165)
ValueCountFrequency (%)
부개3동)부개주공 4
 
4.4%
앞(안남로 3
 
3.3%
정문 3
 
3.3%
청천2동)대우아파트 2
 
2.2%
12 2
 
2.2%
165 2
 
2.2%
청천2동)금호아파트 2
 
2.2%
앞(부영로 2
 
2.2%
272 2
 
2.2%
앞(부개로 2
 
2.2%
Other values (62) 66
73.3%
2024-01-28T15:31:40.536330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
 
10.3%
( 48
 
7.5%
) 48
 
7.5%
동 39
 
6.1%
1 30
 
4.7%
2 29
 
4.5%
부 25
 
3.9%
로 24
 
3.7%
3 22
 
3.4%
개 14
 
2.2%
Other values (95) 296
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 350
54.6%
Decimal Number 126
 
19.7%
Space Separator 66
 
10.3%
Open Punctuation 48
 
7.5%
Close Punctuation 48
 
7.5%
Uppercase Letter 2
 
0.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
 
11.1%
부 25
 
7.1%
로 24
 
6.9%
개 14
 
4.0%
앞 14
 
4.0%
정 12
 
3.4%
산 12
 
3.4%
주 10
 
2.9%
아 10
 
2.9%
파 9
 
2.6%
Other values (79) 181
51.7%
Decimal Number
ValueCountFrequency (%)
1 30
23.8%
2 29
23.0%
3 22
17.5%
6 11
 
8.7%
5 10
 
7.9%
0 9
 
7.1%
4 6
 
4.8%
7 5
 
4.0%
8 2
 
1.6%
9 2
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
66
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 350
54.6%
Common 289
45.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
 
11.1%
부 25
 
7.1%
로 24
 
6.9%
개 14
 
4.0%
앞 14
 
4.0%
정 12
 
3.4%
산 12
 
3.4%
주 10
 
2.9%
아 10
 
2.9%
파 9
 
2.6%
Other values (79) 181
51.7%
Common
ValueCountFrequency (%)
66
22.8%
( 48
16.6%
) 48
16.6%
1 30
10.4%
2 29
10.0%
3 22
 
7.6%
6 11
 
3.8%
5 10
 
3.5%
0 9
 
3.1%
4 6
 
2.1%
Other values (4) 10
 
3.5%
Latin
ValueCountFrequency (%)
M 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 350
54.6%
ASCII 291
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66
22.7%
( 48
16.5%
) 48
16.5%
1 30
10.3%
2 29
10.0%
3 22
 
7.6%
6 11
 
3.8%
5 10
 
3.4%
0 9
 
3.1%
4 6
 
2.1%
Other values (6) 12
 
4.1%
Hangul
ValueCountFrequency (%)
동 39
 
11.1%
부 25
 
7.1%
로 24
 
6.9%
개 14
 
4.0%
앞 14
 
4.0%
정 12
 
3.4%
산 12
 
3.4%
주 10
 
2.9%
아 10
 
2.9%
파 9
 
2.6%
Other values (79) 181
51.7%

상단광고
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 24
100.0%

Length

2024-01-28T15:31:40.634001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:31:40.698991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 24
100.0%

벽보면수
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
12
24 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 24
100.0%

Length

2024-01-28T15:31:40.775436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:31:40.840535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12 24
100.0%

부착일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
15
24 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 24
100.0%

Length

2024-01-28T15:31:40.906929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:31:40.970677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 24
100.0%

규격
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
380 × 530
24 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row380 × 530
2nd row380 × 530
3rd row380 × 530
4th row380 × 530
5th row380 × 530

Common Values

ValueCountFrequency (%)
380 × 530 24
100.0%

Length

2024-01-28T15:31:41.036910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:31:41.097755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
380 24
33.3%
× 24
33.3%
530 24
33.3%

Interactions

2024-01-28T15:31:39.286302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T15:31:41.135020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호게시대위치
번호1.0001.0001.000
게시대1.0001.0001.000
위치1.0001.0001.000

Missing values

2024-01-28T15:31:39.395468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T15:31:39.477353image/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

번호게시대위치상단광고벽보면수부착일규격
01갈산1(갈산2동)갈산주공2단지 정문 앞(주부토로 206)11215380 × 530
12갈산2(갈산2동)대동@ 103동 앞(주부토로 193)11215380 × 530
23부개16(부개2동)휴먼시아 7단지 입구 앞(동수천로 118)11215380 × 530
34부개10(부개3동)부개주공 204동 앞(길주남로 143)11215380 × 530
45부개15(부개3동)미래생활고등학교 앞(수변로 165)11215380 × 530
56부개3(부개3동)부개주공 3단지 정문(부흥북로 144)11215380 × 530
67부개4(부개3동)부평기적의도서관(길주남로 166)11215380 × 530
78부개6(부개3동)부개주공 503동 앞(부개로 12)11215380 × 530
89부개7(부개3동)부개주공 603동 앞(부개로 12)11215380 × 530
910부개8(부개3동)부일초등학교 정문 앞(부흥북로 175)11215380 × 530
번호게시대위치상단광고벽보면수부착일규격
1415산곡5(산곡4동)우성아파트 101동 앞(부영로 165)11215380 × 530
1516삼산2(삼산1동)대보아파트 정문(후정동로 5)11215380 × 530
1617삼산1(삼산2동)부평역사박물관(굴포로 151)11215380 × 530
1718삼산10(삼산2동)진산중학교 삼거리(동선로 234번길 70)11215380 × 530
1819청천1(청천2동)광명아파트 버스정류장(평천로 153번길 13)11215380 × 530
1920청천2(청천2동)금호아파트 303동 앞(안남로 272)11215380 × 530
2021청천3(청천2동)금호아파트 버스정류장 앞(안남로 272)11215380 × 530
2122청천4(청천2동)대우아파트 115동 버스정류장(안남로 16)11215380 × 530
2223청천5(청천2동)대우아파트 118동 버스정류장(안남로 16)11215380 × 530
2324청천9(청천2동)GM자동차 정문(부평대로 233)11215380 × 530