Overview

Dataset statistics

Number of variables4
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory34.9 B

Variable types

Numeric1
Categorical1
Text2

Dataset

Description경기도 고양시 덕양구, 일산동구, 일산서구 내에 설치된 시민 벽보게시판 현황 데이터로 각 게시판의 주소, 위치 설명 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/3079141/fileData.do

Alerts

번호 is highly overall correlated with 동High correlation
동 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique
위치설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:42:26.186041
Analysis finished2023-12-12 17:42:26.747710
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.5
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T02:42:26.848981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q117.75
median34.5
Q351.25
95-th percentile64.65
Maximum68
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.5731513
Kurtosis-1.2
Mean34.5
Median Absolute Deviation (MAD)17
Skewness0
Sum2346
Variance391
MonotonicityStrictly increasing
2023-12-13T02:42:27.067838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
45 1
 
1.5%
51 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
44 1
 
1.5%
36 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
68 1
1.5%
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%

동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
화정동
11 
주엽동
10 
행신동
7 
마두1동
5 
대화동
5 
Other values (12)
30 

Length

Max length4
Median length3
Mean length3.2647059
Min length3

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row성사동
2nd row토당동
3rd row화정동
4th row화정동
5th row화정동

Common Values

ValueCountFrequency (%)
화정동 11
16.2%
주엽동 10
14.7%
행신동 7
10.3%
마두1동 5
7.4%
대화동 5
7.4%
백석1동 5
7.4%
일산동 4
 
5.9%
탄현동 3
 
4.4%
중산동 3
 
4.4%
마두2동 3
 
4.4%
Other values (7) 12
17.6%

Length

2023-12-13T02:42:27.222619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화정동 11
16.2%
주엽동 10
14.7%
행신동 7
10.3%
마두1동 5
7.4%
대화동 5
7.4%
백석1동 5
7.4%
일산동 4
 
5.9%
마두2동 3
 
4.4%
중산동 3
 
4.4%
탄현동 3
 
4.4%
Other values (7) 12
17.6%

번지
Text

Distinct66
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-13T02:42:27.495030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length7.1764706
Min length4

Characters and Unicode

Total characters488
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)94.1%

Sample

1st row성사동 723
2nd row토당동 392-1
3rd row화정동 858
4th row화정동 869
5th row화정동 922
ValueCountFrequency (%)
화정동 11
 
9.9%
마두동 8
 
7.2%
행신동 7
 
6.3%
백석동 7
 
6.3%
중산동 3
 
2.7%
토당동 2
 
1.8%
921 2
 
1.8%
890 2
 
1.8%
장항동 2
 
1.8%
성사동 2
 
1.8%
Other values (65) 65
58.6%
2023-12-13T02:42:27.891434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 68
 
13.9%
1 48
 
9.8%
43
 
8.8%
2 32
 
6.6%
9 28
 
5.7%
8 27
 
5.5%
7 20
 
4.1%
3 18
 
3.7%
0 17
 
3.5%
화 16
 
3.3%
Other values (30) 171
35.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 232
47.5%
Other Letter 205
42.0%
Space Separator 43
 
8.8%
Dash Punctuation 6
 
1.2%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 68
33.2%
화 16
 
7.8%
정 12
 
5.9%
엽 10
 
4.9%
주 10
 
4.9%
마 8
 
3.9%
두 8
 
3.9%
산 8
 
3.9%
백 7
 
3.4%
석 7
 
3.4%
Other values (16) 51
24.9%
Decimal Number
ValueCountFrequency (%)
1 48
20.7%
2 32
13.8%
9 28
12.1%
8 27
11.6%
7 20
8.6%
3 18
 
7.8%
0 17
 
7.3%
5 16
 
6.9%
4 14
 
6.0%
6 12
 
5.2%
Space Separator
ValueCountFrequency (%)
43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 283
58.0%
Hangul 205
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 68
33.2%
화 16
 
7.8%
정 12
 
5.9%
엽 10
 
4.9%
주 10
 
4.9%
마 8
 
3.9%
두 8
 
3.9%
산 8
 
3.9%
백 7
 
3.4%
석 7
 
3.4%
Other values (16) 51
24.9%
Common
ValueCountFrequency (%)
1 48
17.0%
43
15.2%
2 32
11.3%
9 28
9.9%
8 27
9.5%
7 20
7.1%
3 18
 
6.4%
0 17
 
6.0%
5 16
 
5.7%
4 14
 
4.9%
Other values (4) 20
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
58.0%
Hangul 205
42.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 68
33.2%
화 16
 
7.8%
정 12
 
5.9%
엽 10
 
4.9%
주 10
 
4.9%
마 8
 
3.9%
두 8
 
3.9%
산 8
 
3.9%
백 7
 
3.4%
석 7
 
3.4%
Other values (16) 51
24.9%
ASCII
ValueCountFrequency (%)
1 48
17.0%
43
15.2%
2 32
11.3%
9 28
9.9%
8 27
9.5%
7 20
7.1%
3 18
 
6.4%
0 17
 
6.0%
5 16
 
5.7%
4 14
 
4.9%
Other values (4) 20
7.1%

위치설명
Text

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-13T02:42:28.093865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length14.147059
Min length5

Characters and Unicode

Total characters962
Distinct characters174
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row태영아파트 204동앞
2nd row능곡중학교삼거리(삼화맨션가동 앞)
3rd row달빛마을 101동 앞
4th row화수중학교앞 공원
5th row연세가정의원앞
ValueCountFrequency (%)
앞 33
 
16.8%
옆 10
 
5.1%
육교 6
 
3.0%
사거리 5
 
2.5%
밑 5
 
2.5%
사이 4
 
2.0%
맞은편 3
 
1.5%
버스정류장 3
 
1.5%
개나리공원 2
 
1.0%
6번출구 2
 
1.0%
Other values (115) 124
62.9%
2023-12-13T02:42:28.412100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
 
13.5%
앞 44
 
4.6%
0 32
 
3.3%
동 31
 
3.2%
마 29
 
3.0%
1 23
 
2.4%
교 20
 
2.1%
을 20
 
2.1%
원 20
 
2.1%
) 19
 
2.0%
Other values (164) 594
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 678
70.5%
Space Separator 130
 
13.5%
Decimal Number 106
 
11.0%
Close Punctuation 19
 
2.0%
Open Punctuation 19
 
2.0%
Other Punctuation 6
 
0.6%
Uppercase Letter 3
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 44
 
6.5%
동 31
 
4.6%
마 29
 
4.3%
교 20
 
2.9%
을 20
 
2.9%
원 20
 
2.9%
공 14
 
2.1%
정 13
 
1.9%
리 13
 
1.9%
구 13
 
1.9%
Other values (147) 461
68.0%
Decimal Number
ValueCountFrequency (%)
0 32
30.2%
1 23
21.7%
2 13
12.3%
5 8
 
7.5%
4 7
 
6.6%
8 5
 
4.7%
6 5
 
4.7%
3 5
 
4.7%
9 4
 
3.8%
7 4
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
S 2
66.7%
B 1
33.3%
Space Separator
ValueCountFrequency (%)
130
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 678
70.5%
Common 281
29.2%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 44
 
6.5%
동 31
 
4.6%
마 29
 
4.3%
교 20
 
2.9%
을 20
 
2.9%
원 20
 
2.9%
공 14
 
2.1%
정 13
 
1.9%
리 13
 
1.9%
구 13
 
1.9%
Other values (147) 461
68.0%
Common
ValueCountFrequency (%)
130
46.3%
0 32
 
11.4%
1 23
 
8.2%
) 19
 
6.8%
( 19
 
6.8%
2 13
 
4.6%
5 8
 
2.8%
4 7
 
2.5%
, 6
 
2.1%
8 5
 
1.8%
Other values (5) 19
 
6.8%
Latin
ValueCountFrequency (%)
S 2
66.7%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 678
70.5%
ASCII 284
29.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
45.8%
0 32
 
11.3%
1 23
 
8.1%
) 19
 
6.7%
( 19
 
6.7%
2 13
 
4.6%
5 8
 
2.8%
4 7
 
2.5%
, 6
 
2.1%
8 5
 
1.8%
Other values (7) 22
 
7.7%
Hangul
ValueCountFrequency (%)
앞 44
 
6.5%
동 31
 
4.6%
마 29
 
4.3%
교 20
 
2.9%
을 20
 
2.9%
원 20
 
2.9%
공 14
 
2.1%
정 13
 
1.9%
리 13
 
1.9%
구 13
 
1.9%
Other values (147) 461
68.0%

Interactions

2023-12-13T02:42:26.439194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:42:28.506011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동번지위치설명
번호1.0000.9321.0001.000
동0.9321.0001.0001.000
번지1.0001.0001.0001.000
위치설명1.0001.0001.0001.000
2023-12-13T02:42:28.592475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동
번호1.0000.681
동0.6811.000

Missing values

2023-12-13T02:42:26.589469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:42:26.702827image/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

번호동번지위치설명
01성사동성사동 723태영아파트 204동앞
12토당동토당동 392-1능곡중학교삼거리(삼화맨션가동 앞)
23화정동화정동 858달빛마을 101동 앞
34화정동화정동 869화수중학교앞 공원
45화정동화정동 922연세가정의원앞
56화정동화정동 941화정중학교 앞
67화정동화정동 950고양경찰서 맞은편
78화정동화정동 951별빛마을 1001동 앞(버스정류장 옆)
89행신동행신동 691무원마을 1002동 옆(버스정류장)
910행신동행신동 736무원마을 10단지상가 맞은편
번호동번지위치설명
5859대화동대화동2218종합운동장 사거리(하이투모로 앞)
5960대화동대화동2204대화역 6번출구 뒤
6061대화동대화동2097대진고등학교 앞
6162대화동대화동2214농협하나로마트 건너편(성저마을405동 앞)
6263탄현동탄현동1485탄현지구대 뒤(일산동중학교 앞)
6364탄현동탄현동1477SBS 제작센터 앞(탄현마을503동 앞)
6465탄현동탄현동1481탄현마을405동 앞 삼거리(홀트일산복지타운 인근)
6566일산동일산동1558한뫼공원 앞 삼거리(한뫼공원과 중산공원 사이)
6667덕이동덕이동1534하이파크시티3단지 앞 버스정류장(피프틴자전거 옆)
6768덕이동덕이동374-2하이파크시티5단지 앞 한별어린이공원(버스정류장 옆)