Overview

Dataset statistics

Number of variables13
Number of observations203
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.3 KiB
Average record size in memory107.7 B

Variable types

Numeric1
Categorical9
Text3

Dataset

Description시흥시 현수막 게시대 현황입니다.(용도구분, 게시대번호, 게시대명칭, 지번주소, 부착일수, 부착그액, 규격, 면수 정보가 있습니다.)
Author공공데이터포털
URLhttps://www.data.go.kr/data/15117032/fileData.do

Alerts

광역시도명 has constant value ""Constant
시군구명 has constant value ""Constant
부착일수 has constant value ""Constant
데이터기준일 has constant value ""Constant
용도구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
부착금액 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 행정동명 and 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 연번High correlation
규격 is highly imbalanced (71.7%)Imbalance
연번 has unique valuesUnique
게시대번호 has unique valuesUnique
게시대명칭 has unique valuesUnique

Reproduction

Analysis started2024-04-17 10:51:51.678451
Analysis finished2024-04-17 10:51:52.361147
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct203
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102
Minimum1
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-17T19:51:52.424807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.1
Q151.5
median102
Q3152.5
95-th percentile192.9
Maximum203
Range202
Interquartile range (IQR)101

Descriptive statistics

Standard deviation58.745213
Coefficient of variation (CV)0.57593346
Kurtosis-1.2
Mean102
Median Absolute Deviation (MAD)51
Skewness0
Sum20706
Variance3451
MonotonicityStrictly increasing
2024-04-17T19:51:52.565663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
141 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
Other values (193) 193
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%

광역시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
경기도
203 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 203
100.0%

Length

2024-04-17T19:51:52.673573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:52.747496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 203
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
시흥시
203 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시흥시 203
100.0%

Length

2024-04-17T19:51:52.822371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:52.895423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시흥시 203
100.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
정왕동
95 
대야동
17 
목감동
12 
신천동
12 
월곶동
12 
Other values (9)
55 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정왕동 95
46.8%
대야동 17
 
8.4%
목감동 12
 
5.9%
신천동 12
 
5.9%
월곶동 12
 
5.9%
연성동 10
 
4.9%
은행동 9
 
4.4%
능곡동 9
 
4.4%
신현동 8
 
3.9%
군자동 6
 
3.0%
Other values (4) 13
 
6.4%

Length

2024-04-17T19:51:52.975199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정왕동 95
46.8%
대야동 17
 
8.4%
목감동 12
 
5.9%
신천동 12
 
5.9%
월곶동 12
 
5.9%
연성동 10
 
4.9%
은행동 9
 
4.4%
능곡동 9
 
4.4%
신현동 8
 
3.9%
군자동 6
 
3.0%
Other values (4) 13
 
6.4%

용도구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상업용
157 
행정용
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상업용 157
77.3%
행정용 46
 
22.7%

Length

2024-04-17T19:51:53.064476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:53.141518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상업용 157
77.3%
행정용 46
 
22.7%

게시대번호
Text

UNIQUE 

Distinct203
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-17T19:51:53.428696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1330049
Min length5

Characters and Unicode

Total characters1042
Distinct characters20
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

Unique203 ?
Unique (%)100.0%

Sample

1st rowE-001
2nd rowE-003
3rd rowE-005
4th rowE-0051
5th rowE-006
ValueCountFrequency (%)
e-001 1
 
0.5%
w-010 1
 
0.5%
y-0251 1
 
0.5%
s-012 1
 
0.5%
s-013 1
 
0.5%
w-001 1
 
0.5%
w-002 1
 
0.5%
w-003 1
 
0.5%
w-004 1
 
0.5%
w-005 1
 
0.5%
Other values (193) 193
95.1%
2024-04-17T19:51:53.849512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 272
26.1%
- 203
19.5%
1 105
 
10.1%
J 80
 
7.7%
2 56
 
5.4%
H 46
 
4.4%
5 42
 
4.0%
3 40
 
3.8%
4 38
 
3.6%
6 33
 
3.2%
Other values (10) 127
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 636
61.0%
Dash Punctuation 203
 
19.5%
Uppercase Letter 203
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 272
42.8%
1 105
 
16.5%
2 56
 
8.8%
5 42
 
6.6%
3 40
 
6.3%
4 38
 
6.0%
6 33
 
5.2%
7 20
 
3.1%
9 17
 
2.7%
8 13
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
J 80
39.4%
H 46
22.7%
E 21
 
10.3%
Y 17
 
8.4%
M 13
 
6.4%
W 9
 
4.4%
S 9
 
4.4%
G 4
 
2.0%
P 4
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 839
80.5%
Latin 203
 
19.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 272
32.4%
- 203
24.2%
1 105
 
12.5%
2 56
 
6.7%
5 42
 
5.0%
3 40
 
4.8%
4 38
 
4.5%
6 33
 
3.9%
7 20
 
2.4%
9 17
 
2.0%
Latin
ValueCountFrequency (%)
J 80
39.4%
H 46
22.7%
E 21
 
10.3%
Y 17
 
8.4%
M 13
 
6.4%
W 9
 
4.4%
S 9
 
4.4%
G 4
 
2.0%
P 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 272
26.1%
- 203
19.5%
1 105
 
10.1%
J 80
 
7.7%
2 56
 
5.4%
H 46
 
4.4%
5 42
 
4.0%
3 40
 
3.8%
4 38
 
3.6%
6 33
 
3.2%
Other values (10) 127
12.2%

게시대명칭
Text

UNIQUE 

Distinct203
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-17T19:51:54.077736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length12.758621
Min length4

Characters and Unicode

Total characters2590
Distinct characters216
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

Unique203 ?
Unique (%)100.0%

Sample

1st row대야동 여우고개삼거리
2nd row대야동 대야동주민센터 앞
3rd row대야동 한신(아)삼거리(1)
4th row대야동 한신(아)삼거리(2)
5th row대야동 포도탑(1)
ValueCountFrequency (%)
정왕동 78
 
15.0%
앞 18
 
3.5%
대야동 15
 
2.9%
입구 15
 
2.9%
사거리 13
 
2.5%
삼거리 11
 
2.1%
월곶동 11
 
2.1%
앞(1 9
 
1.7%
은행동 8
 
1.5%
삼거리(2 7
 
1.3%
Other values (233) 334
64.4%
2024-04-17T19:51:54.412978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
320
 
12.4%
동 190
 
7.3%
) 105
 
4.1%
( 105
 
4.1%
정 96
 
3.7%
왕 92
 
3.6%
리 82
 
3.2%
거 74
 
2.9%
구 61
 
2.4%
사 59
 
2.3%
Other values (206) 1406
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1913
73.9%
Space Separator 320
 
12.4%
Decimal Number 130
 
5.0%
Close Punctuation 105
 
4.1%
Open Punctuation 105
 
4.1%
Uppercase Letter 14
 
0.5%
Dash Punctuation 2
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 190
 
9.9%
정 96
 
5.0%
왕 92
 
4.8%
리 82
 
4.3%
거 74
 
3.9%
구 61
 
3.2%
사 59
 
3.1%
앞 51
 
2.7%
교 40
 
2.1%
입 34
 
1.8%
Other values (183) 1134
59.3%
Decimal Number
ValueCountFrequency (%)
2 48
36.9%
1 47
36.2%
3 12
 
9.2%
7 5
 
3.8%
5 5
 
3.8%
6 5
 
3.8%
0 3
 
2.3%
4 3
 
2.3%
8 1
 
0.8%
9 1
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S 4
28.6%
K 3
21.4%
G 2
14.3%
L 1
 
7.1%
I 1
 
7.1%
T 1
 
7.1%
P 1
 
7.1%
A 1
 
7.1%
Space Separator
ValueCountFrequency (%)
320
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1913
73.9%
Common 662
 
25.6%
Latin 15
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 190
 
9.9%
정 96
 
5.0%
왕 92
 
4.8%
리 82
 
4.3%
거 74
 
3.9%
구 61
 
3.2%
사 59
 
3.1%
앞 51
 
2.7%
교 40
 
2.1%
입 34
 
1.8%
Other values (183) 1134
59.3%
Common
ValueCountFrequency (%)
320
48.3%
) 105
 
15.9%
( 105
 
15.9%
2 48
 
7.3%
1 47
 
7.1%
3 12
 
1.8%
7 5
 
0.8%
5 5
 
0.8%
6 5
 
0.8%
0 3
 
0.5%
Other values (4) 7
 
1.1%
Latin
ValueCountFrequency (%)
S 4
26.7%
K 3
20.0%
G 2
13.3%
L 1
 
6.7%
I 1
 
6.7%
e 1
 
6.7%
T 1
 
6.7%
P 1
 
6.7%
A 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1913
73.9%
ASCII 677
 
26.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
320
47.3%
) 105
 
15.5%
( 105
 
15.5%
2 48
 
7.1%
1 47
 
6.9%
3 12
 
1.8%
7 5
 
0.7%
5 5
 
0.7%
6 5
 
0.7%
S 4
 
0.6%
Other values (13) 21
 
3.1%
Hangul
ValueCountFrequency (%)
동 190
 
9.9%
정 96
 
5.0%
왕 92
 
4.8%
리 82
 
4.3%
거 74
 
3.9%
구 61
 
3.2%
사 59
 
3.1%
앞 51
 
2.7%
교 40
 
2.1%
입 34
 
1.8%
Other values (183) 1134
59.3%

지번주소
Text

Distinct132
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-17T19:51:54.687728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length16.458128
Min length14

Characters and Unicode

Total characters3341
Distinct characters58
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

Unique85 ?
Unique (%)41.9%

Sample

1st row경기도 시흥시 대야동 91-3
2nd row경기도 시흥시 대야동 911
3rd row경기도 시흥시 대야동 215
4th row경기도 시흥시 대야동 215
5th row경기도 시흥시 대야동 278-2
ValueCountFrequency (%)
경기도 203
25.2%
시흥시 203
25.2%
정왕동 90
 
11.2%
대야동 16
 
2.0%
신천동 12
 
1.5%
은행동 10
 
1.2%
조남동 8
 
1.0%
월곶동 8
 
1.0%
하중동 8
 
1.0%
1630 7
 
0.9%
Other values (146) 240
29.8%
2024-04-17T19:51:55.035435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
602
18.0%
시 406
12.2%
경 203
 
6.1%
도 203
 
6.1%
흥 203
 
6.1%
동 203
 
6.1%
기 203
 
6.1%
1 169
 
5.1%
3 98
 
2.9%
왕 94
 
2.8%
Other values (48) 957
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1837
55.0%
Decimal Number 813
24.3%
Space Separator 602
 
18.0%
Dash Punctuation 89
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 406
22.1%
경 203
11.1%
도 203
11.1%
흥 203
11.1%
동 203
11.1%
기 203
11.1%
왕 94
 
5.1%
정 92
 
5.0%
산 17
 
0.9%
대 16
 
0.9%
Other values (36) 197
10.7%
Decimal Number
ValueCountFrequency (%)
1 169
20.8%
3 98
12.1%
9 93
11.4%
2 81
10.0%
8 73
9.0%
7 70
8.6%
6 66
 
8.1%
0 59
 
7.3%
4 54
 
6.6%
5 50
 
6.2%
Space Separator
ValueCountFrequency (%)
602
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1837
55.0%
Common 1504
45.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 406
22.1%
경 203
11.1%
도 203
11.1%
흥 203
11.1%
동 203
11.1%
기 203
11.1%
왕 94
 
5.1%
정 92
 
5.0%
산 17
 
0.9%
대 16
 
0.9%
Other values (36) 197
10.7%
Common
ValueCountFrequency (%)
602
40.0%
1 169
 
11.2%
3 98
 
6.5%
9 93
 
6.2%
- 89
 
5.9%
2 81
 
5.4%
8 73
 
4.9%
7 70
 
4.7%
6 66
 
4.4%
0 59
 
3.9%
Other values (2) 104
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1837
55.0%
ASCII 1504
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
602
40.0%
1 169
 
11.2%
3 98
 
6.5%
9 93
 
6.2%
- 89
 
5.9%
2 81
 
5.4%
8 73
 
4.9%
7 70
 
4.7%
6 66
 
4.4%
0 59
 
3.9%
Other values (2) 104
 
6.9%
Hangul
ValueCountFrequency (%)
시 406
22.1%
경 203
11.1%
도 203
11.1%
흥 203
11.1%
동 203
11.1%
기 203
11.1%
왕 94
 
5.1%
정 92
 
5.0%
산 17
 
0.9%
대 16
 
0.9%
Other values (36) 197
10.7%

부착일수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
7
203 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 203
100.0%

Length

2024-04-17T19:51:55.145160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:55.238892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 203
100.0%

부착금액
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
14900
157 
무료
46 

Length

Max length5
Median length5
Mean length4.320197
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14900
2nd row14900
3rd row14900
4th row14900
5th row14900

Common Values

ValueCountFrequency (%)
14900 157
77.3%
무료 46
 
22.7%

Length

2024-04-17T19:51:55.339369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:55.429668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
14900 157
77.3%
무료 46
 
22.7%

규격
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
700X70
193 
700X50
 
10

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row700X70
2nd row700X70
3rd row700X70
4th row700X70
5th row700X70

Common Values

ValueCountFrequency (%)
700X70 193
95.1%
700X50 10
 
4.9%

Length

2024-04-17T19:51:55.510433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:55.605622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
700x70 193
95.1%
700x50 10
 
4.9%

면수
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
6
117 
7
41 
4
41 
3
 
3
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row6
2nd row7
3rd row4
4th row4
5th row7

Common Values

ValueCountFrequency (%)
6 117
57.6%
7 41
 
20.2%
4 41
 
20.2%
3 3
 
1.5%
5 1
 
0.5%

Length

2024-04-17T19:51:55.706508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:55.790401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 117
57.6%
7 41
 
20.2%
4 41
 
20.2%
3 3
 
1.5%
5 1
 
0.5%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-07-24
203 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-24
2nd row2023-07-24
3rd row2023-07-24
4th row2023-07-24
5th row2023-07-24

Common Values

ValueCountFrequency (%)
2023-07-24 203
100.0%

Length

2024-04-17T19:51:55.885404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:51:55.962205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-24 203
100.0%

Interactions

2024-04-17T19:51:52.061924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T19:51:56.012667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동명용도구분부착금액규격면수
연번1.0000.8270.9960.9960.3980.646
행정동명0.8271.0000.2300.2300.2800.279
용도구분0.9960.2301.0001.0000.5720.291
부착금액0.9960.2301.0001.0000.5720.291
규격0.3980.2800.5720.5721.0000.356
면수0.6460.2790.2910.2910.3561.000
2024-04-17T19:51:56.095903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명용도구분규격부착금액면수
행정동명1.0000.1730.2110.1730.145
용도구분0.1731.0000.3880.9860.353
규격0.2110.3881.0000.3880.431
부착금액0.1730.9860.3881.0000.353
면수0.1450.3530.4310.3531.000
2024-04-17T19:51:56.173995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동명용도구분부착금액규격면수
연번1.0000.5210.9240.9240.2990.314
행정동명0.5211.0000.1730.1730.2110.145
용도구분0.9240.1731.0000.9860.3880.353
부착금액0.9240.1730.9861.0000.3880.353
규격0.2990.2110.3880.3881.0000.431
면수0.3140.1450.3530.3530.4311.000

Missing values

2024-04-17T19:51:52.164126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T19:51:52.307923image/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

연번광역시도명시군구명행정동명용도구분게시대번호게시대명칭지번주소부착일수부착금액규격면수데이터기준일
01경기도시흥시대야동상업용E-001대야동 여우고개삼거리경기도 시흥시 대야동 91-3714900700X7062023-07-24
12경기도시흥시대야동상업용E-003대야동 대야동주민센터 앞경기도 시흥시 대야동 911714900700X7072023-07-24
23경기도시흥시대야동상업용E-005대야동 한신(아)삼거리(1)경기도 시흥시 대야동 215714900700X7042023-07-24
34경기도시흥시대야동상업용E-0051대야동 한신(아)삼거리(2)경기도 시흥시 대야동 215714900700X7042023-07-24
45경기도시흥시대야동상업용E-006대야동 포도탑(1)경기도 시흥시 대야동 278-2714900700X7072023-07-24
56경기도시흥시대야동상업용E-011대야동 포도탑(2)경기도 시흥시 대야동 278-2714900700X7072023-07-24
67경기도시흥시대야동상업용E-0111대야동 뱅뱅 삼거리경기도 시흥시 대야동 293-13714900700X7062023-07-24
78경기도시흥시대야동상업용E-012대야동 은계중 건너경기도 시흥시 대야동 691714900700X7062023-07-24
89경기도시흥시대야동상업용E-015대야동 대우(아) 310동 건너(1)경기도 시흥시 대야동 604714900700X7042023-07-24
910경기도시흥시대야동상업용E-016대야동 대우(아) 310동 건너(2)경기도 시흥시 대야동 604714900700X7042023-07-24
연번광역시도명시군구명행정동명용도구분게시대번호게시대명칭지번주소부착일수부착금액규격면수데이터기준일
193194경기도시흥시하중동행정용H-026농업기술센터앞경기도 시흥시 하중동 270-17무료700X7062023-07-24
194195경기도시흥시하중동행정용H-031하중동 국민체육센터경기도 시흥시 하중동 8977무료700X5042023-07-24
195196경기도시흥시능곡동행정용H-023능곡동노인복지관경기도 시흥시 능곡동 7847무료700X7062023-07-24
196197경기도시흥시능곡동행정용H-025능곡동사무소경기도 시흥시 능곡동 8037무료700X7062023-07-24
197198경기도시흥시능곡동행정용H-053신안아파트 녹지경기도 시흥시 능곡동 8037무료700X7042023-07-24
198199경기도시흥시장곡동행정용H-027장곡동사무소경기도 시흥시 장곡동 900-17무료700X7062023-07-24
199200경기도시흥시장곡동행정용H-017경찰서앞경기도 시흥시 장곡동 3407무료700X7062023-07-24
200201경기도시흥시월곶동행정용H-028월곶동사무소경기도 시흥시 월곶동 9957무료700X7062023-07-24
201202경기도시흥시월곶동행정용H-050월곶동 풍림아파트경기도 시흥시 월곶동 1010-17무료700X7042023-07-24
202203경기도시흥시월곶동행정용H-033월곶동 월곶상곡교차로삼거리경기도 시흥시 월곶동 942-307무료700X5042023-07-24