Overview

Dataset statistics

Number of variables19
Number of observations868
Missing cells1505
Missing cells (%)9.1%
Duplicate rows2
Duplicate rows (%)0.2%
Total size in memory134.1 KiB
Average record size in memory158.2 B

Variable types

Categorical9
Text4
Numeric5
DateTime1

Dataset

Description경상남도 통영시 광도면 내에 있는 1994년 이후 준공된 상업용 건축물 현황자료로 대지위치, 도로명주소, 대지면적, 연면적 등 건축물에 관한 정보를 제공합니다.
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15127020

Alerts

통계용 용도 has constant value ""Constant
Dataset has 2 (0.2%) duplicate rowsDuplicates
대장구분 is highly overall correlated with 지상층수 and 3 other fieldsHigh correlation
기타용도지구 is highly overall correlated with 외필지수 and 7 other fieldsHigh correlation
용도지구 is highly overall correlated with 외필지수 and 8 other fieldsHigh correlation
용도구역 is highly overall correlated with 기타용도지역 and 1 other fieldsHigh correlation
지하층수 is highly overall correlated with 대장구분 and 2 other fieldsHigh correlation
외필지수 is highly overall correlated with 용도지구 and 1 other fieldsHigh correlation
대지면적(제곱미터) is highly overall correlated with 주용도 and 2 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 높이(미터) and 3 other fieldsHigh correlation
높이(미터) is highly overall correlated with 연면적(제곱미터) and 1 other fieldsHigh correlation
지상층수 is highly overall correlated with 연면적(제곱미터) and 2 other fieldsHigh correlation
주용도 is highly overall correlated with 대지면적(제곱미터)High correlation
용도지역 is highly overall correlated with 기타용도지역 and 2 other fieldsHigh correlation
기타용도지역 is highly overall correlated with 용도지역 and 3 other fieldsHigh correlation
대장구분 is highly imbalanced (53.8%)Imbalance
용도지구 is highly imbalanced (89.8%)Imbalance
기타용도지구 is highly imbalanced (91.2%)Imbalance
용도구역 is highly imbalanced (63.3%)Imbalance
도로명주소 has 21 (2.4%) missing valuesMissing
건물명 has 665 (76.6%) missing valuesMissing
동명 has 567 (65.3%) missing valuesMissing
대지면적(제곱미터) has 240 (27.6%) missing valuesMissing
외필지수 has 776 (89.4%) zerosZeros
대지면적(제곱미터) has 87 (10.0%) zerosZeros
높이(미터) has 33 (3.8%) zerosZeros

Reproduction

Analysis started2024-03-29 16:57:19.927343
Analysis finished2024-03-29 16:57:27.063281
Duration7.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대장구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
일반건축물
783 
집합건축물
85 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반건축물 783
90.2%
집합건축물 85
 
9.8%

Length

2024-03-30T01:57:27.125051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T01:57:27.221914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반건축물 783
90.2%
집합건축물 85
 
9.8%

대지위치
Text

Distinct639
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
2024-03-30T01:57:27.420318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length26
Mean length24.768433
Min length16

Characters and Unicode

Total characters21499
Distinct characters35
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

Unique538 ?
Unique (%)62.0%

Sample

1st row경상남도 통영시 광도면 안정리 1867
2nd row경상남도 통영시 광도면 덕포리 0356-0003
3rd row경상남도 통영시 광도면 노산리 0399-0001
4th row경상남도 통영시 광도면 죽림리 0789-0009
5th row경상남도 통영시 광도면 덕포리 0379-0001
ValueCountFrequency (%)
경상남도 868
20.0%
광도면 868
20.0%
통영시 868
20.0%
죽림리 503
11.6%
안정리 137
 
3.2%
노산리 89
 
2.1%
황리 80
 
1.8%
2050 65
 
1.5%
덕포리 21
 
0.5%
용호리 19
 
0.4%
Other values (631) 820
18.9%
2024-03-30T01:57:27.775993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3470
16.1%
0 2490
 
11.6%
도 1736
 
8.1%
경 868
 
4.0%
시 868
 
4.0%
면 868
 
4.0%
상 868
 
4.0%
광 868
 
4.0%
영 868
 
4.0%
통 868
 
4.0%
Other values (25) 7727
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11201
52.1%
Decimal Number 6156
28.6%
Space Separator 3470
 
16.1%
Dash Punctuation 672
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1736
15.5%
경 868
7.7%
시 868
7.7%
면 868
7.7%
상 868
7.7%
광 868
7.7%
영 868
7.7%
통 868
7.7%
남 868
7.7%
리 867
7.7%
Other values (13) 1654
14.8%
Decimal Number
ValueCountFrequency (%)
0 2490
40.4%
1 834
 
13.5%
5 670
 
10.9%
2 441
 
7.2%
7 416
 
6.8%
3 297
 
4.8%
8 286
 
4.6%
4 258
 
4.2%
9 236
 
3.8%
6 228
 
3.7%
Space Separator
ValueCountFrequency (%)
3470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11201
52.1%
Common 10298
47.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1736
15.5%
경 868
7.7%
시 868
7.7%
면 868
7.7%
상 868
7.7%
광 868
7.7%
영 868
7.7%
통 868
7.7%
남 868
7.7%
리 867
7.7%
Other values (13) 1654
14.8%
Common
ValueCountFrequency (%)
3470
33.7%
0 2490
24.2%
1 834
 
8.1%
- 672
 
6.5%
5 670
 
6.5%
2 441
 
4.3%
7 416
 
4.0%
3 297
 
2.9%
8 286
 
2.8%
4 258
 
2.5%
Other values (2) 464
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11201
52.1%
ASCII 10298
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3470
33.7%
0 2490
24.2%
1 834
 
8.1%
- 672
 
6.5%
5 670
 
6.5%
2 441
 
4.3%
7 416
 
4.0%
3 297
 
2.9%
8 286
 
2.8%
4 258
 
2.5%
Other values (2) 464
 
4.5%
Hangul
ValueCountFrequency (%)
도 1736
15.5%
경 868
7.7%
시 868
7.7%
면 868
7.7%
상 868
7.7%
광 868
7.7%
영 868
7.7%
통 868
7.7%
남 868
7.7%
리 867
7.7%
Other values (13) 1654
14.8%

외필지수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.8%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.17205543
Minimum0
Maximum10
Zeros776
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-30T01:57:27.879033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68287368
Coefficient of variation (CV)3.9689168
Kurtosis76.283078
Mean0.17205543
Median Absolute Deviation (MAD)0
Skewness7.3228188
Sum149
Variance0.46631646
MonotonicityNot monotonic
2024-03-30T01:57:27.969937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 776
89.4%
1 63
 
7.3%
3 12
 
1.4%
2 11
 
1.3%
7 2
 
0.2%
10 1
 
0.1%
4 1
 
0.1%
(Missing) 2
 
0.2%
ValueCountFrequency (%)
0 776
89.4%
1 63
 
7.3%
2 11
 
1.3%
3 12
 
1.4%
4 1
 
0.1%
7 2
 
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
10 1
 
0.1%
7 2
 
0.2%
4 1
 
0.1%
3 12
 
1.4%
2 11
 
1.3%
1 63
 
7.3%
0 776
89.4%

도로명주소
Text

MISSING 

Distinct632
Distinct (%)74.6%
Missing21
Missing (%)2.4%
Memory size6.9 KiB
2024-03-30T01:57:28.557201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length21.681228
Min length18

Characters and Unicode

Total characters18364
Distinct characters72
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

Unique535 ?
Unique (%)63.2%

Sample

1st row경상남도 통영시 광도면 안정1길 285
2nd row경상남도 통영시 광도면 덕포로 457
3rd row경상남도 통영시 광도면 노산길 91
4th row경상남도 통영시 광도면 죽림대밭길 185
5th row경상남도 통영시 광도면 안정로 362
ValueCountFrequency (%)
경상남도 847
20.0%
광도면 847
20.0%
통영시 847
20.0%
안정로 118
 
2.8%
죽림2로 109
 
2.6%
남해안대로 81
 
1.9%
770 65
 
1.5%
죽림5로 65
 
1.5%
죽림4로 62
 
1.5%
죽림해안로 59
 
1.4%
Other values (567) 1135
26.8%
2024-03-30T01:57:28.950075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3388
18.4%
도 1694
 
9.2%
남 928
 
5.1%
상 850
 
4.6%
경 847
 
4.6%
통 847
 
4.6%
영 847
 
4.6%
시 847
 
4.6%
광 847
 
4.6%
면 847
 
4.6%
Other values (62) 6422
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11343
61.8%
Space Separator 3388
 
18.4%
Decimal Number 3237
 
17.6%
Dash Punctuation 396
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1694
14.9%
남 928
8.2%
상 850
 
7.5%
경 847
 
7.5%
통 847
 
7.5%
영 847
 
7.5%
시 847
 
7.5%
광 847
 
7.5%
면 847
 
7.5%
로 646
 
5.7%
Other values (50) 2143
18.9%
Decimal Number
ValueCountFrequency (%)
1 616
19.0%
2 460
14.2%
5 366
11.3%
7 357
11.0%
3 343
10.6%
4 300
9.3%
0 233
 
7.2%
6 207
 
6.4%
8 187
 
5.8%
9 168
 
5.2%
Space Separator
ValueCountFrequency (%)
3388
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 396
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11343
61.8%
Common 7021
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1694
14.9%
남 928
8.2%
상 850
 
7.5%
경 847
 
7.5%
통 847
 
7.5%
영 847
 
7.5%
시 847
 
7.5%
광 847
 
7.5%
면 847
 
7.5%
로 646
 
5.7%
Other values (50) 2143
18.9%
Common
ValueCountFrequency (%)
3388
48.3%
1 616
 
8.8%
2 460
 
6.6%
- 396
 
5.6%
5 366
 
5.2%
7 357
 
5.1%
3 343
 
4.9%
4 300
 
4.3%
0 233
 
3.3%
6 207
 
2.9%
Other values (2) 355
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11343
61.8%
ASCII 7021
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3388
48.3%
1 616
 
8.8%
2 460
 
6.6%
- 396
 
5.6%
5 366
 
5.2%
7 357
 
5.1%
3 343
 
4.9%
4 300
 
4.3%
0 233
 
3.3%
6 207
 
2.9%
Other values (2) 355
 
5.1%
Hangul
ValueCountFrequency (%)
도 1694
14.9%
남 928
8.2%
상 850
 
7.5%
경 847
 
7.5%
통 847
 
7.5%
영 847
 
7.5%
시 847
 
7.5%
광 847
 
7.5%
면 847
 
7.5%
로 646
 
5.7%
Other values (50) 2143
18.9%

건물명
Text

MISSING 

Distinct102
Distinct (%)50.2%
Missing665
Missing (%)76.6%
Memory size6.9 KiB
2024-03-30T01:57:29.392526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length19
Mean length8.3054187
Min length2

Characters and Unicode

Total characters1686
Distinct characters218
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

Unique81 ?
Unique (%)39.9%

Sample

1st row가동(제1호)
2nd row가동(제1호)
3rd row나동(제2호)
4th row안정출장소
5th row국립수산물품질관리원 통영지원
ValueCountFrequency (%)
통영lng생산기지 62
 
21.4%
주영 10
 
3.4%
더 10
 
3.4%
팰리스 10
 
3.4%
통영 9
 
3.1%
이마트 6
 
2.1%
신세계 6
 
2.1%
통영종합버스터미널 5
 
1.7%
아파트 5
 
1.7%
5차아파트 5
 
1.7%
Other values (123) 162
55.9%
2024-03-30T01:57:29.918954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
영 116
 
6.9%
통 103
 
6.1%
87
 
5.2%
지 76
 
4.5%
산 66
 
3.9%
L 64
 
3.8%
생 64
 
3.8%
G 62
 
3.7%
기 62
 
3.7%
N 62
 
3.7%
Other values (208) 924
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1330
78.9%
Uppercase Letter 194
 
11.5%
Space Separator 87
 
5.2%
Decimal Number 45
 
2.7%
Open Punctuation 12
 
0.7%
Close Punctuation 12
 
0.7%
Dash Punctuation 3
 
0.2%
Other Punctuation 2
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 116
 
8.7%
통 103
 
7.7%
지 76
 
5.7%
산 66
 
5.0%
생 64
 
4.8%
기 62
 
4.7%
스 25
 
1.9%
동 25
 
1.9%
리 24
 
1.8%
빌 23
 
1.7%
Other values (185) 746
56.1%
Decimal Number
ValueCountFrequency (%)
2 14
31.1%
1 11
24.4%
5 6
13.3%
6 4
 
8.9%
3 3
 
6.7%
0 2
 
4.4%
9 2
 
4.4%
7 2
 
4.4%
4 1
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
L 64
33.0%
G 62
32.0%
N 62
32.0%
E 2
 
1.0%
T 1
 
0.5%
O 1
 
0.5%
M 1
 
0.5%
H 1
 
0.5%
Space Separator
ValueCountFrequency (%)
87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1330
78.9%
Latin 194
 
11.5%
Common 162
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 116
 
8.7%
통 103
 
7.7%
지 76
 
5.7%
산 66
 
5.0%
생 64
 
4.8%
기 62
 
4.7%
스 25
 
1.9%
동 25
 
1.9%
리 24
 
1.8%
빌 23
 
1.7%
Other values (185) 746
56.1%
Common
ValueCountFrequency (%)
87
53.7%
2 14
 
8.6%
( 12
 
7.4%
) 12
 
7.4%
1 11
 
6.8%
5 6
 
3.7%
6 4
 
2.5%
3 3
 
1.9%
- 3
 
1.9%
0 2
 
1.2%
Other values (5) 8
 
4.9%
Latin
ValueCountFrequency (%)
L 64
33.0%
G 62
32.0%
N 62
32.0%
E 2
 
1.0%
T 1
 
0.5%
O 1
 
0.5%
M 1
 
0.5%
H 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1330
78.9%
ASCII 356
 
21.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
영 116
 
8.7%
통 103
 
7.7%
지 76
 
5.7%
산 66
 
5.0%
생 64
 
4.8%
기 62
 
4.7%
스 25
 
1.9%
동 25
 
1.9%
리 24
 
1.8%
빌 23
 
1.7%
Other values (185) 746
56.1%
ASCII
ValueCountFrequency (%)
87
24.4%
L 64
18.0%
G 62
17.4%
N 62
17.4%
2 14
 
3.9%
( 12
 
3.4%
) 12
 
3.4%
1 11
 
3.1%
5 6
 
1.7%
6 4
 
1.1%
Other values (13) 22
 
6.2%

동명
Text

MISSING 

Distinct153
Distinct (%)50.8%
Missing567
Missing (%)65.3%
Memory size6.9 KiB
2024-03-30T01:57:30.191315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length4.0963455
Min length1

Characters and Unicode

Total characters1233
Distinct characters134
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

Unique134 ?
Unique (%)44.5%

Sample

1st row제1호
2nd row제2호
3rd row제2호동
4th row주1,2
5th row주3
ValueCountFrequency (%)
나동 53
 
17.3%
가동 48
 
15.7%
다동 13
 
4.2%
상가동 6
 
2.0%
제1호 6
 
2.0%
제2호 5
 
1.6%
2동 4
 
1.3%
1동 4
 
1.3%
제1동 4
 
1.3%
라동 4
 
1.3%
Other values (146) 159
52.0%
2024-03-30T01:57:30.682339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 267
21.7%
호 92
 
7.5%
제 90
 
7.3%
( 80
 
6.5%
) 80
 
6.5%
가 68
 
5.5%
나 58
 
4.7%
1 56
 
4.5%
2 54
 
4.4%
5 27
 
2.2%
Other values (124) 361
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 810
65.7%
Decimal Number 243
 
19.7%
Open Punctuation 80
 
6.5%
Close Punctuation 80
 
6.5%
Uppercase Letter 9
 
0.7%
Space Separator 5
 
0.4%
Dash Punctuation 4
 
0.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 267
33.0%
호 92
 
11.4%
제 90
 
11.1%
가 68
 
8.4%
나 58
 
7.2%
상 16
 
2.0%
주 16
 
2.0%
다 15
 
1.9%
부 11
 
1.4%
관 6
 
0.7%
Other values (103) 171
21.1%
Decimal Number
ValueCountFrequency (%)
1 56
23.0%
2 54
22.2%
5 27
11.1%
3 26
10.7%
4 22
 
9.1%
6 16
 
6.6%
0 15
 
6.2%
9 11
 
4.5%
7 8
 
3.3%
8 8
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
D 1
 
11.1%
E 1
 
11.1%
C 1
 
11.1%
F 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 810
65.7%
Common 414
33.6%
Latin 9
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 267
33.0%
호 92
 
11.4%
제 90
 
11.1%
가 68
 
8.4%
나 58
 
7.2%
상 16
 
2.0%
주 16
 
2.0%
다 15
 
1.9%
부 11
 
1.4%
관 6
 
0.7%
Other values (103) 171
21.1%
Common
ValueCountFrequency (%)
( 80
19.3%
) 80
19.3%
1 56
13.5%
2 54
13.0%
5 27
 
6.5%
3 26
 
6.3%
4 22
 
5.3%
6 16
 
3.9%
0 15
 
3.6%
9 11
 
2.7%
Other values (5) 27
 
6.5%
Latin
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
D 1
 
11.1%
E 1
 
11.1%
C 1
 
11.1%
F 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 810
65.7%
ASCII 423
34.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 267
33.0%
호 92
 
11.4%
제 90
 
11.1%
가 68
 
8.4%
나 58
 
7.2%
상 16
 
2.0%
주 16
 
2.0%
다 15
 
1.9%
부 11
 
1.4%
관 6
 
0.7%
Other values (103) 171
21.1%
ASCII
ValueCountFrequency (%)
( 80
18.9%
) 80
18.9%
1 56
13.2%
2 54
12.8%
5 27
 
6.4%
3 26
 
6.1%
4 22
 
5.2%
6 16
 
3.8%
0 15
 
3.5%
9 11
 
2.6%
Other values (11) 36
8.5%

주용도
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
제2종근린생활시설
383 
제1종근린생활시설
252 
위험물저장및처리시설
91 
업무시설
45 
자동차관련시설
42 
Other values (5)
55 

Length

Max length10
Median length9
Mean length8.4343318
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row제1종근린생활시설
2nd row제2종근린생활시설
3rd row제1종근린생활시설
4th row제2종근린생활시설
5th row근린생활시설

Common Values

ValueCountFrequency (%)
제2종근린생활시설 383
44.1%
제1종근린생활시설 252
29.0%
위험물저장및처리시설 91
 
10.5%
업무시설 45
 
5.2%
자동차관련시설 42
 
4.8%
숙박시설 36
 
4.1%
위락시설 8
 
0.9%
판매시설 5
 
0.6%
운수시설 5
 
0.6%
근린생활시설 1
 
0.1%

Length

2024-03-30T01:57:30.836018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T01:57:30.964227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종근린생활시설 383
44.1%
제1종근린생활시설 252
29.0%
위험물저장및처리시설 91
 
10.5%
업무시설 45
 
5.2%
자동차관련시설 42
 
4.8%
숙박시설 36
 
4.1%
위락시설 8
 
0.9%
판매시설 5
 
0.6%
운수시설 5
 
0.6%
근린생활시설 1
 
0.1%

통계용 용도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
상업용
868 

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 868
100.0%

Length

2024-03-30T01:57:31.108812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T01:57:31.231183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상업용 868
100.0%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct438
Distinct (%)69.7%
Missing240
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean854.66238
Minimum0
Maximum46199.4
Zeros87
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-30T01:57:31.363748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1278.25
median440.5
Q3750.75
95-th percentile2322.265
Maximum46199.4
Range46199.4
Interquartile range (IQR)472.5

Descriptive statistics

Standard deviation2374.5736
Coefficient of variation (CV)2.7783761
Kurtosis219.04311
Mean854.66238
Median Absolute Deviation (MAD)223.79
Skewness12.746154
Sum536727.98
Variance5638599.6
MonotonicityNot monotonic
2024-03-30T01:57:31.492393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
 
10.0%
279.0 8
 
0.9%
364.1 6
 
0.7%
363.9 6
 
0.7%
330.0 4
 
0.5%
552.0 4
 
0.5%
396.0 4
 
0.5%
998.0 4
 
0.5%
560.0 4
 
0.5%
857.8 3
 
0.3%
Other values (428) 498
57.4%
(Missing) 240
27.6%
ValueCountFrequency (%)
0.0 87
10.0%
16.0 1
 
0.1%
27.0 1
 
0.1%
63.0 1
 
0.1%
67.0 1
 
0.1%
86.0 1
 
0.1%
99.0 1
 
0.1%
100.0 1
 
0.1%
105.0 1
 
0.1%
108.0 1
 
0.1%
ValueCountFrequency (%)
46199.4 1
 
0.1%
14671.0 3
0.3%
10530.3 3
0.3%
10324.0 1
 
0.1%
9209.0 1
 
0.1%
8274.7 1
 
0.1%
7436.7 1
 
0.1%
6692.8 1
 
0.1%
5908.0 1
 
0.1%
5360.4 1
 
0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct780
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean641.31094
Minimum0.9
Maximum26465.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-30T01:57:31.619571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile14.1255
Q183.9
median259.08
Q3566.905
95-th percentile2698.9215
Maximum26465.28
Range26464.38
Interquartile range (IQR)483.005

Descriptive statistics

Standard deviation1418.5888
Coefficient of variation (CV)2.212014
Kurtosis135.1405
Mean641.31094
Median Absolute Deviation (MAD)199.08
Skewness9.0415253
Sum556657.9
Variance2012394.1
MonotonicityNot monotonic
2024-03-30T01:57:31.782803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 20
 
2.3%
27.0 6
 
0.7%
12.53 5
 
0.6%
22.91 5
 
0.6%
15.0 5
 
0.6%
12.0 4
 
0.5%
21.0 4
 
0.5%
24.0 4
 
0.5%
4.68 3
 
0.3%
270.0 3
 
0.3%
Other values (770) 809
93.2%
ValueCountFrequency (%)
0.9 1
 
0.1%
1.0 1
 
0.1%
1.7 1
 
0.1%
1.92 1
 
0.1%
2.58 1
 
0.1%
2.94 1
 
0.1%
4.68 3
0.3%
5.04 1
 
0.1%
6.38 1
 
0.1%
6.65 1
 
0.1%
ValueCountFrequency (%)
26465.28 1
0.1%
11152.2575 1
0.1%
10175.67 1
0.1%
8329.41 1
0.1%
7706.81 1
0.1%
7559.2704 1
0.1%
6199.4 1
0.1%
6013.6 1
0.1%
5955.37 1
0.1%
5522.12 1
0.1%

높이(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct323
Distinct (%)37.5%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean8.7331684
Minimum0
Maximum53.42
Zeros33
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-30T01:57:31.926237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q14.5
median6.3
Q39.7
95-th percentile24.3
Maximum53.42
Range53.42
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation7.1666829
Coefficient of variation (CV)0.82062804
Kurtosis4.3341796
Mean8.7331684
Median Absolute Deviation (MAD)2.3
Skewness1.9051152
Sum7519.258
Variance51.361343
MonotonicityNot monotonic
2024-03-30T01:57:32.109290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 33
 
3.8%
3.0 21
 
2.4%
5.0 18
 
2.1%
4.0 15
 
1.7%
4.5 15
 
1.7%
6.3 14
 
1.6%
5.4 13
 
1.5%
3.5 12
 
1.4%
5.2 12
 
1.4%
8.2 11
 
1.3%
Other values (313) 697
80.3%
ValueCountFrequency (%)
0.0 33
3.8%
1.7 1
 
0.1%
2.1 2
 
0.2%
2.2 1
 
0.1%
2.3 1
 
0.1%
2.4 6
 
0.7%
2.45 1
 
0.1%
2.5 6
 
0.7%
2.55 2
 
0.2%
2.6 9
 
1.0%
ValueCountFrequency (%)
53.42 1
0.1%
45.4 1
0.1%
39.85 1
0.1%
39.45 1
0.1%
34.15 1
0.1%
33.85 1
0.1%
33.7 1
0.1%
33.4 2
0.2%
33.2 1
0.1%
33.1 2
0.2%

지하층수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
515 
<NA>
289 
1
53 
2
 
11

Length

Max length4
Median length1
Mean length1.9988479
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 515
59.3%
<NA> 289
33.3%
1 53
 
6.1%
2 11
 
1.3%

Length

2024-03-30T01:57:32.233443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T01:57:32.339176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 515
59.3%
na 289
33.3%
1 53
 
6.1%
2 11
 
1.3%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)1.3%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2.0693642
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-30T01:57:32.444941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum14
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8658226
Coefficient of variation (CV)0.90164055
Kurtosis5.9391247
Mean2.0693642
Median Absolute Deviation (MAD)0
Skewness2.320688
Sum1790
Variance3.4812942
MonotonicityNot monotonic
2024-03-30T01:57:32.605334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 506
58.3%
2 181
 
20.9%
3 41
 
4.7%
4 37
 
4.3%
5 36
 
4.1%
6 21
 
2.4%
7 21
 
2.4%
8 11
 
1.3%
9 8
 
0.9%
12 2
 
0.2%
(Missing) 3
 
0.3%
ValueCountFrequency (%)
1 506
58.3%
2 181
 
20.9%
3 41
 
4.7%
4 37
 
4.3%
5 36
 
4.1%
6 21
 
2.4%
7 21
 
2.4%
8 11
 
1.3%
9 8
 
0.9%
12 2
 
0.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 2
 
0.2%
9 8
 
0.9%
8 11
 
1.3%
7 21
 
2.4%
6 21
 
2.4%
5 36
 
4.1%
4 37
 
4.3%
3 41
 
4.7%
2 181
20.9%

사용승인일
Date

Distinct651
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Minimum1994-01-01 00:00:00
Maximum2024-02-27 00:00:00
2024-03-30T01:57:32.757166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:32.907781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

용도지역
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
<NA>
257 
준주거지역
126 
일반상업지역
107 
제3종일반주거지역
56 
도시지역
51 
Other values (20)
271 

Length

Max length9
Median length8
Mean length5.3225806
Min length4

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row보전관리지역
2nd row<NA>
3rd row취락지역
4th row보전관리지역
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 257
29.6%
준주거지역 126
14.5%
일반상업지역 107
12.3%
제3종일반주거지역 56
 
6.5%
도시지역 51
 
5.9%
준공업지역 47
 
5.4%
보전관리지역 44
 
5.1%
자연녹지지역 36
 
4.1%
제2종일반주거지역 26
 
3.0%
농림지역 22
 
2.5%
Other values (15) 96
 
11.1%

Length

2024-03-30T01:57:33.025879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 257
29.6%
준주거지역 126
14.5%
일반상업지역 107
12.3%
제3종일반주거지역 56
 
6.5%
도시지역 51
 
5.9%
준공업지역 47
 
5.4%
보전관리지역 44
 
5.1%
자연녹지지역 36
 
4.1%
제2종일반주거지역 26
 
3.0%
농림지역 22
 
2.5%
Other values (15) 96
 
11.1%

기타용도지역
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
<NA>
310 
준주거지역
105 
일반상업지역
101 
준농림지역
50 
준공업지역
45 
Other values (34)
257 

Length

Max length10
Median length9
Mean length5.1002304
Min length2

Unique

Unique9 ?
Unique (%)1.0%

Sample

1st row준농림지역
2nd row진흥지역
3rd row취락지역
4th row진흥지역밖
5th row진흥지역

Common Values

ValueCountFrequency (%)
<NA> 310
35.7%
준주거지역 105
 
12.1%
일반상업지역 101
 
11.6%
준농림지역 50
 
5.8%
준공업지역 45
 
5.2%
제3종일반주거지역 44
 
5.1%
도시지역 26
 
3.0%
자연녹지지역 23
 
2.6%
준도시지역 19
 
2.2%
관리지역 19
 
2.2%
Other values (29) 126
14.5%

Length

2024-03-30T01:57:33.150679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 310
35.7%
준주거지역 105
 
12.1%
일반상업지역 101
 
11.6%
준농림지역 50
 
5.8%
준공업지역 45
 
5.2%
제3종일반주거지역 44
 
5.1%
도시지역 26
 
3.0%
자연녹지지역 23
 
2.6%
준도시지역 19
 
2.2%
관리지역 19
 
2.2%
Other values (30) 127
14.6%

용도지구
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
<NA>
835 
취락지구
 
19
자연취락지구
 
6
기타지구
 
3
주거개발진흥지구
 
2
Other values (3)
 
3

Length

Max length8
Median length4
Mean length4.0299539
Min length4

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 835
96.2%
취락지구 19
 
2.2%
자연취락지구 6
 
0.7%
기타지구 3
 
0.3%
주거개발진흥지구 2
 
0.2%
자연환경지구 1
 
0.1%
보전임지 1
 
0.1%
용도지구취락지구 1
 
0.1%

Length

2024-03-30T01:57:33.288479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T01:57:33.394984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 835
96.2%
취락지구 19
 
2.2%
자연취락지구 6
 
0.7%
기타지구 3
 
0.3%
주거개발진흥지구 2
 
0.2%
자연환경지구 1
 
0.1%
보전임지 1
 
0.1%
용도지구취락지구 1
 
0.1%

기타용도지구
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
<NA>
839 
취락지구
 
21
수산자원보전지구
 
2
주거개발진흥지구
 
2
취락
 
1
Other values (3)
 
3

Length

Max length8
Median length4
Mean length4.0230415
Min length2

Unique

Unique4 ?
Unique (%)0.5%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 839
96.7%
취락지구 21
 
2.4%
수산자원보전지구 2
 
0.2%
주거개발진흥지구 2
 
0.2%
취락 1
 
0.1%
자여환경보전지구 1
 
0.1%
보전임지 1
 
0.1%
자연취락지구 1
 
0.1%

Length

2024-03-30T01:57:33.506058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T01:57:33.606644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 839
96.7%
취락지구 21
 
2.4%
수산자원보전지구 2
 
0.2%
주거개발진흥지구 2
 
0.2%
취락 1
 
0.1%
자여환경보전지구 1
 
0.1%
보전임지 1
 
0.1%
자연취락지구 1
 
0.1%

용도구역
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
<NA>
647 
제1종지구단위계획구역
83 
도시계획구역
 
54
가축사육제한구역
 
28
기타구역
 
20
Other values (12)
 
36

Length

Max length14
Median length4
Mean length5.0610599
Min length4

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 647
74.5%
제1종지구단위계획구역 83
 
9.6%
도시계획구역 54
 
6.2%
가축사육제한구역 28
 
3.2%
기타구역 20
 
2.3%
상대보호구역 8
 
0.9%
수산자원보호구역 6
 
0.7%
지원시설구역 5
 
0.6%
문화재보존영향 검토대상구역 3
 
0.3%
농업보호구역 2
 
0.2%
Other values (7) 12
 
1.4%

Length

2024-03-30T01:57:33.723909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 647
74.3%
제1종지구단위계획구역 83
 
9.5%
도시계획구역 54
 
6.2%
가축사육제한구역 28
 
3.2%
기타구역 20
 
2.3%
상대보호구역 8
 
0.9%
수산자원보호구역 6
 
0.7%
지원시설구역 5
 
0.6%
검토대상구역 3
 
0.3%
문화재보존영향 3
 
0.3%
Other values (8) 14
 
1.6%

Interactions

2024-03-30T01:57:25.887841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:23.888649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.462914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.853389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.432663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.981514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.090592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.537400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.934674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.516454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:26.100306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.211222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.608954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.022325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.594373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:26.215738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.301744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.683616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.169223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.673449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:26.309188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.384109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:24.768240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.339103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T01:57:25.755409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T01:57:33.828463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장구분외필지수주용도대지면적(제곱미터)연면적(제곱미터)높이(미터)지하층수지상층수용도지역기타용도지역용도지구기타용도지구용도구역
대장구분1.0000.0000.3160.0000.4710.6020.3320.5020.4050.367NaNNaN0.478
외필지수0.0001.0000.0490.2520.0000.0000.0000.0000.6340.6180.0000.1200.661
주용도0.3160.0491.0000.8490.4290.6060.5010.5260.5740.6540.6460.6730.000
대지면적(제곱미터)0.0000.2520.8491.0000.5280.0000.1270.0000.0000.000NaNNaN0.380
연면적(제곱미터)0.4710.0000.4290.5281.0000.6990.7900.6490.0000.000NaNNaN0.000
높이(미터)0.6020.0000.6060.0000.6991.0000.5890.8870.3300.2660.2100.0000.082
지하층수0.3320.0000.5010.1270.7900.5891.0000.7260.3450.1571.0001.0000.304
지상층수0.5020.0000.5260.0000.6490.8870.7261.0000.3990.3070.0000.0000.000
용도지역0.4050.6340.5740.0000.0000.3300.3450.3991.0000.9920.9180.9830.907
기타용도지역0.3670.6180.6540.0000.0000.2660.1570.3070.9921.0000.7990.8930.894
용도지구NaN0.0000.646NaNNaN0.2101.0000.0000.9180.7991.0000.9481.000
기타용도지구NaN0.1200.673NaNNaN0.0001.0000.0000.9830.8930.9481.0000.000
용도구역0.4780.6610.0000.3800.0000.0820.3040.0000.9070.8941.0000.0001.000
2024-03-30T01:57:33.982395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도지역기타용도지역대장구분기타용도지구용도지구용도구역주용도지하층수
용도지역1.0000.8550.3150.7940.7770.4500.2550.186
기타용도지역0.8551.0000.2820.7630.6160.5260.2830.074
대장구분0.3150.2821.0001.0001.0000.3640.2410.531
기타용도지구0.7940.7631.0001.0000.8760.0000.4980.899
용도지구0.7770.6161.0000.8761.0001.0000.4650.910
용도구역0.4500.5260.3640.0001.0001.0000.0000.163
주용도0.2550.2830.2410.4980.4650.0001.0000.254
지하층수0.1860.0740.5310.8990.9100.1630.2541.000
2024-03-30T01:57:34.101373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외필지수대지면적(제곱미터)연면적(제곱미터)높이(미터)지상층수대장구분주용도지하층수용도지역기타용도지역용도지구기타용도지구용도구역
외필지수1.0000.113-0.0240.004-0.0180.0000.0000.0000.3110.3311.0001.0000.397
대지면적(제곱미터)0.1131.0000.4420.2740.1810.0000.5120.1200.0000.0001.0001.0000.216
연면적(제곱미터)-0.0240.4421.0000.8020.6910.3390.2410.4690.0000.0001.0001.0000.000
높이(미터)0.0040.2740.8021.0000.7960.4640.2250.4300.1250.0920.1130.0000.022
지상층수-0.0180.1810.6910.7961.0000.5010.2710.4290.1610.1140.0000.0000.000
대장구분0.0000.0000.3390.4640.5011.0000.2410.5310.3150.2821.0001.0000.364
주용도0.0000.5120.2410.2250.2710.2411.0000.2540.2550.2830.4650.4980.000
지하층수0.0000.1200.4690.4300.4290.5310.2541.0000.1860.0740.9100.8990.163
용도지역0.3110.0000.0000.1250.1610.3150.2550.1861.0000.8550.7770.7940.450
기타용도지역0.3310.0000.0000.0920.1140.2820.2830.0740.8551.0000.6160.7630.526
용도지구1.0001.0001.0000.1130.0001.0000.4650.9100.7770.6161.0000.8761.000
기타용도지구1.0001.0001.0000.0000.0001.0000.4980.8990.7940.7630.8761.0000.000
용도구역0.3970.2160.0000.0220.0000.3640.0000.1630.4500.5261.0000.0001.000

Missing values

2024-03-30T01:57:26.461740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T01:57:26.717224image/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.
2024-03-30T01:57:26.934652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

대장구분대지위치외필지수도로명주소건물명동명주용도통계용 용도대지면적(제곱미터)연면적(제곱미터)높이(미터)지하층수지상층수사용승인일용도지역기타용도지역용도지구기타용도지구용도구역
0일반건축물경상남도 통영시 광도면 안정리 18670경상남도 통영시 광도면 안정1길 285<NA><NA>제1종근린생활시설상업용526.0300.910.0<NA>21994보전관리지역준농림지역<NA><NA><NA>
1일반건축물경상남도 통영시 광도면 덕포리 0356-00030경상남도 통영시 광도면 덕포로 457<NA><NA>제2종근린생활시설상업용1786.0161.10.0021994-01-24<NA>진흥지역<NA><NA><NA>
2일반건축물경상남도 통영시 광도면 노산리 0399-00011경상남도 통영시 광도면 노산길 91<NA><NA>제1종근린생활시설상업용468.0331.177.4021994-03-14취락지역취락지역<NA><NA><NA>
3일반건축물경상남도 통영시 광도면 죽림리 0789-00090경상남도 통영시 광도면 죽림대밭길 185<NA>제1호제2종근린생활시설상업용512.0195.50.0021994-03-22보전관리지역진흥지역밖<NA><NA><NA>
4일반건축물경상남도 통영시 광도면 덕포리 0379-00010경상남도 통영시 광도면 안정로 362<NA><NA>근린생활시설상업용2317.0178.560.0011994-04-04<NA>진흥지역<NA><NA><NA>
5일반건축물경상남도 통영시 광도면 노산리 0391-00111경상남도 통영시 광도면 노산길 97<NA><NA>제2종근린생활시설상업용190.0311.2811.1031994-05-10<NA><NA><NA><NA><NA>
6일반건축물경상남도 통영시 광도면 용호리 03760경상남도 통영시 광도면 용호로 441<NA>제2호제2종근린생활시설상업용0.0124.80.0021994-05-14<NA><NA><NA><NA><NA>
7일반건축물경상남도 통영시 광도면 죽림리 0789-00090경상남도 통영시 광도면 죽림대밭길 185<NA><NA>제2종근린생활시설상업용512.0105.60.0011994-06-30보전관리지역준농림지역<NA><NA><NA>
8일반건축물경상남도 통영시 광도면 노산리 0391-00070경상남도 통영시 광도면 노산길 99<NA><NA>제1종근린생활시설상업용86.064.530.0021994-07-07도시지역준도시지역취락지구취락지구<NA>
9일반건축물경상남도 통영시 광도면 우동리 03430경상남도 통영시 광도면 전두2길 30<NA><NA>제1종근린생활시설상업용268.0101.140.0021994-09-16보전관리지역준농림<NA><NA><NA>
대장구분대지위치외필지수도로명주소건물명동명주용도통계용 용도대지면적(제곱미터)연면적(제곱미터)높이(미터)지하층수지상층수사용승인일용도지역기타용도지역용도지구기타용도지구용도구역
858집합건축물경상남도 통영시 광도면 죽림리 1573-00020경상남도 통영시 광도면 죽림5로 56주영 더 팰리스 5차아파트524동(상가-3)제2종근린생활시설상업용<NA>564.265<NA><NA>12018-02-14<NA><NA><NA><NA><NA>
859집합건축물경상남도 통영시 광도면 죽림리 1573-00020경상남도 통영시 광도면 죽림5로 56주영 더 팰리스 5차아파트522동(상가-1)제2종근린생활시설상업용<NA>621.6678<NA><NA>12018-02-14<NA><NA><NA><NA><NA>
860집합건축물경상남도 통영시 광도면 죽림리 1573-00020경상남도 통영시 광도면 죽림5로 56주영 더 팰리스 5차아파트525동(상가-4)제2종근린생활시설상업용<NA>500.8547<NA><NA>12018-02-14<NA><NA><NA><NA><NA>
861집합건축물경상남도 통영시 광도면 죽림리 1573-00020경상남도 통영시 광도면 죽림5로 56주영 더 팰리스 5차아파트523동(상가-2)제2종근린생활시설상업용<NA>482.6739<NA><NA>12018-02-14<NA><NA><NA><NA><NA>
862집합건축물경상남도 통영시 광도면 죽림리 16060경상남도 통영시 광도면 향교옆길 33-31통영코아루아파트 1단지상가동제2종근린생활시설상업용<NA>478.08188.1<NA>22018-08-24<NA><NA><NA><NA><NA>
863집합건축물경상남도 통영시 광도면 죽림리 1572-00670경상남도 통영시 광도면 죽림5로 55-9<NA><NA>제1종근린생활시설상업용1175.86199.439.85192019-01-30일반상업지역일반상업지역<NA><NA><NA>
864집합건축물경상남도 통영시 광도면 죽림리 1569-00310경상남도 통영시 광도면 죽림3로 43강남빌딩<NA>제1종근린생활시설상업용861.83144.9332.55172019-04-19준주거지역준주거지역<NA><NA><NA>
865집합건축물경상남도 통영시 광도면 죽림리 1584-00020경상남도 통영시 광도면 죽림1로 16웨스턴모나코<NA>업무시설상업용1626.65955.3745.41142019-12-31제3종일반주거지역제3종일반주거지역<NA><NA><NA>
866집합건축물경상남도 통영시 광도면 안정리 20630경상남도 통영시 광도면 벽방2길 76통영안정 LH 아파트상가동제2종근린생활시설상업용<NA>121.754.8<NA>12020-09-17<NA><NA><NA><NA><NA>
867집합건축물경상남도 통영시 광도면 죽림리 0496-00060경상남도 통영시 광도면 남해안대로 885-12<NA><NA>제2종근린생활시설상업용901.01392.3814.2232021-11-23자연녹지지역자연녹지지역<NA><NA><NA>

Duplicate rows

Most frequently occurring

대장구분대지위치외필지수도로명주소건물명동명주용도통계용 용도대지면적(제곱미터)연면적(제곱미터)높이(미터)지하층수지상층수사용승인일용도지역기타용도지역용도지구기타용도지구용도구역# duplicates
0일반건축물경상남도 통영시 광도면 죽림리 0256-00091경상남도 통영시 광도면 조암길 5<NA><NA>제2종근린생활시설상업용<NA>24.02.75<NA>12022-03-07자연녹지지역자연녹지지역<NA><NA>가축사육제한구역2
1일반건축물경상남도 통영시 광도면 죽림리 1566-00020경상남도 통영시 광도면 죽림4로 9신세계 통영 이마트<NA>판매시설상업용0.012.03.5012005-06-21<NA><NA><NA><NA><NA>2