Overview

Dataset statistics

Number of variables7
Number of observations67
Missing cells51
Missing cells (%)10.9%
Duplicate rows1
Duplicate rows (%)1.5%
Total size in memory3.8 KiB
Average record size in memory58.0 B

Variable types

Text5
Categorical1
Boolean1

Dataset

Description경기도 양주시 도시계획정보시스템(UPIS) 용도구역 결정조서 현황으로 위치명, 지역명, 면적(기정), 면적(변경), 면적(변경후), 비고 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15115964/fileData.do

Alerts

Dataset has 1 (1.5%) duplicate rowsDuplicates
지역명 is highly overall correlated with 공간도형존재여부High correlation
공간도형존재여부 is highly overall correlated with 지역명High correlation
비고 has 34 (50.7%) missing valuesMissing
공간도형존재여부 has 17 (25.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:16:39.622438
Analysis finished2023-12-12 19:16:40.494343
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치명
Text

Distinct56
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-13T04:16:40.658229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length28
Mean length20.686567
Min length3

Characters and Unicode

Total characters1386
Distinct characters132
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

Unique50 ?
Unique (%)74.6%

Sample

1st row마전동 일원
2nd row유양동 68번지 일원
3rd row마전동 258-1번지 일원
4th row유양동 425-1번지 일원 외 8개소
5th row남방동 산2번지 일원 외 20개소
ValueCountFrequency (%)
일원 36
 
12.5%
장흥면 13
 
4.5%
양주시 13
 
4.5%
양주군 13
 
4.5%
남면 7
 
2.4%
마전동 7
 
2.4%
유양동 6
 
2.1%
6
 
2.1%
일부 6
 
2.1%
68번지 4
 
1.4%
Other values (129) 178
61.6%
2023-12-13T04:16:41.075732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
222
 
16.0%
양 56
 
4.0%
지 56
 
4.0%
일 56
 
4.0%
주 55
 
4.0%
, 52
 
3.8%
원 43
 
3.1%
면 40
 
2.9%
동 37
 
2.7%
역 35
 
2.5%
Other values (122) 734
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 943
68.0%
Space Separator 222
 
16.0%
Decimal Number 120
 
8.7%
Other Punctuation 62
 
4.5%
Dash Punctuation 19
 
1.4%
Close Punctuation 8
 
0.6%
Open Punctuation 8
 
0.6%
Uppercase Letter 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 56
 
5.9%
지 56
 
5.9%
일 56
 
5.9%
주 55
 
5.8%
원 43
 
4.6%
면 40
 
4.2%
동 37
 
3.9%
역 35
 
3.7%
시 34
 
3.6%
리 34
 
3.6%
Other values (103) 497
52.7%
Decimal Number
ValueCountFrequency (%)
2 22
18.3%
8 21
17.5%
1 18
15.0%
5 16
13.3%
3 13
10.8%
9 10
8.3%
0 7
 
5.8%
6 7
 
5.8%
4 5
 
4.2%
7 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 52
83.9%
/ 6
 
9.7%
. 4
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
G 2
50.0%
Space Separator
ValueCountFrequency (%)
222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 943
68.0%
Common 439
31.7%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 56
 
5.9%
지 56
 
5.9%
일 56
 
5.9%
주 55
 
5.8%
원 43
 
4.6%
면 40
 
4.2%
동 37
 
3.9%
역 35
 
3.7%
시 34
 
3.6%
리 34
 
3.6%
Other values (103) 497
52.7%
Common
ValueCountFrequency (%)
222
50.6%
, 52
 
11.8%
2 22
 
5.0%
8 21
 
4.8%
- 19
 
4.3%
1 18
 
4.1%
5 16
 
3.6%
3 13
 
3.0%
9 10
 
2.3%
) 8
 
1.8%
Other values (7) 38
 
8.7%
Latin
ValueCountFrequency (%)
B 2
50.0%
G 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 943
68.0%
ASCII 443
32.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
222
50.1%
, 52
 
11.7%
2 22
 
5.0%
8 21
 
4.7%
- 19
 
4.3%
1 18
 
4.1%
5 16
 
3.6%
3 13
 
2.9%
9 10
 
2.3%
) 8
 
1.8%
Other values (9) 42
 
9.5%
Hangul
ValueCountFrequency (%)
양 56
 
5.9%
지 56
 
5.9%
일 56
 
5.9%
주 55
 
5.8%
원 43
 
4.6%
면 40
 
4.2%
동 37
 
3.9%
역 35
 
3.7%
시 34
 
3.6%
리 34
 
3.6%
Other values (103) 497
52.7%

지역명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size668.0 B
양주시 개발제한구역
8 
개발행위허가 제한지역 지정
6 
의정부 개발제한구역
 
4
의정부 도시계획구역
 
3
개발행위허가 제한지역 해제
 
3
Other values (26)
43 

Length

Max length22
Median length19
Mean length13.164179
Min length8

Unique

Unique13 ?
Unique (%)19.4%

Sample

1st row양주시 개발제한구역
2nd row개발행위허가 제한지역
3rd row개발행위허가 제한지역 해제
4th row개발행위허가 제한지역 지정
5th row개발행위허가 제한지역 지정

Common Values

ValueCountFrequency (%)
양주시 개발제한구역 8
 
11.9%
개발행위허가 제한지역 지정 6
 
9.0%
의정부 개발제한구역 4
 
6.0%
의정부 도시계획구역 3
 
4.5%
개발행위허가 제한지역 해제 3
 
4.5%
시가화예정용지 개발행위허가제한지역 3
 
4.5%
개발행위허가 제한지역 3
 
4.5%
남면 도시계획구역 3
 
4.5%
광적 도시계획구역 3
 
4.5%
회천 도시계획구역 2
 
3.0%
Other values (21) 29
43.3%

Length

2023-12-13T04:16:41.230069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
개발행위허가제한지역 19
12.8%
제한지역 14
 
9.4%
도시계획구역 14
 
9.4%
개발제한구역 14
 
9.4%
개발행위허가 14
 
9.4%
양주시 11
 
7.4%
의정부 7
 
4.7%
지정 6
 
4.0%
도시자연공원구역 4
 
2.7%
해제 3
 
2.0%
Other values (25) 43
28.9%

면적(기정)
Text

Distinct39
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-13T04:16:41.438909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.3880597
Min length1

Characters and Unicode

Total characters428
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)52.2%

Sample

1st row76466760
2nd row72425
3rd row244532
4th row0
5th row0
ValueCountFrequency (%)
데이터 20
23.0%
미집계 20
23.0%
0 8
 
9.2%
31280000 2
 
2.3%
153770000 2
 
2.3%
3436000 1
 
1.1%
232940 1
 
1.1%
45353251 1
 
1.1%
78038966 1
 
1.1%
77712273 1
 
1.1%
Other values (30) 30
34.5%
2023-12-13T04:16:41.842516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 81
18.9%
3 31
 
7.2%
7 31
 
7.2%
4 28
 
6.5%
2 24
 
5.6%
8 23
 
5.4%
이 20
 
4.7%
데 20
 
4.7%
계 20
 
4.7%
집 20
 
4.7%
Other values (7) 130
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288
67.3%
Other Letter 120
28.0%
Space Separator 20
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 81
28.1%
3 31
 
10.8%
7 31
 
10.8%
4 28
 
9.7%
2 24
 
8.3%
8 23
 
8.0%
6 19
 
6.6%
1 18
 
6.2%
5 17
 
5.9%
9 16
 
5.6%
Other Letter
ValueCountFrequency (%)
이 20
16.7%
데 20
16.7%
계 20
16.7%
집 20
16.7%
미 20
16.7%
터 20
16.7%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308
72.0%
Hangul 120
 
28.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 81
26.3%
3 31
 
10.1%
7 31
 
10.1%
4 28
 
9.1%
2 24
 
7.8%
8 23
 
7.5%
20
 
6.5%
6 19
 
6.2%
1 18
 
5.8%
5 17
 
5.5%
Hangul
ValueCountFrequency (%)
이 20
16.7%
데 20
16.7%
계 20
16.7%
집 20
16.7%
미 20
16.7%
터 20
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308
72.0%
Hangul 120
 
28.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 81
26.3%
3 31
 
10.1%
7 31
 
10.1%
4 28
 
9.1%
2 24
 
7.8%
8 23
 
7.5%
20
 
6.5%
6 19
 
6.2%
1 18
 
5.8%
5 17
 
5.5%
Hangul
ValueCountFrequency (%)
이 20
16.7%
데 20
16.7%
계 20
16.7%
집 20
16.7%
미 20
16.7%
터 20
16.7%

면적(변경)
Text

Distinct55
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-13T04:16:42.097071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.2537313
Min length4

Characters and Unicode

Total characters419
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)70.1%

Sample

1st row217662
2nd row17372
3rd row244532
4th row37874
5th row10833
ValueCountFrequency (%)
미집계 8
 
10.7%
대이터 5
 
6.7%
데이터 3
 
4.0%
232940 2
 
2.7%
244532 2
 
2.7%
931949 2
 
2.7%
320000 2
 
2.7%
687206 2
 
2.7%
2626352 2
 
2.7%
496593 1
 
1.3%
Other values (46) 46
61.3%
2023-12-13T04:16:42.453211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 103
24.6%
2 46
11.0%
3 43
10.3%
8 28
 
6.7%
4 28
 
6.7%
1 25
 
6.0%
6 24
 
5.7%
7 24
 
5.7%
9 23
 
5.5%
5 19
 
4.5%
Other values (8) 56
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 363
86.6%
Other Letter 48
 
11.5%
Space Separator 8
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103
28.4%
2 46
12.7%
3 43
11.8%
8 28
 
7.7%
4 28
 
7.7%
1 25
 
6.9%
6 24
 
6.6%
7 24
 
6.6%
9 23
 
6.3%
5 19
 
5.2%
Other Letter
ValueCountFrequency (%)
이 8
16.7%
계 8
16.7%
집 8
16.7%
미 8
16.7%
터 8
16.7%
대 5
10.4%
데 3
 
6.2%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 371
88.5%
Hangul 48
 
11.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103
27.8%
2 46
12.4%
3 43
11.6%
8 28
 
7.5%
4 28
 
7.5%
1 25
 
6.7%
6 24
 
6.5%
7 24
 
6.5%
9 23
 
6.2%
5 19
 
5.1%
Hangul
ValueCountFrequency (%)
이 8
16.7%
계 8
16.7%
집 8
16.7%
미 8
16.7%
터 8
16.7%
대 5
10.4%
데 3
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371
88.5%
Hangul 48
 
11.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103
27.8%
2 46
12.4%
3 43
11.6%
8 28
 
7.5%
4 28
 
7.5%
1 25
 
6.7%
6 24
 
6.5%
7 24
 
6.5%
9 23
 
6.2%
5 19
 
5.1%
Hangul
ValueCountFrequency (%)
이 8
16.7%
계 8
16.7%
집 8
16.7%
미 8
16.7%
터 8
16.7%
대 5
10.4%
데 3
 
6.2%

면적(변경후)
Text

Distinct54
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-13T04:16:42.697745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.5223881
Min length1

Characters and Unicode

Total characters437
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)73.1%

Sample

1st row76249098
2nd row89847
3rd row0
4th row37874
5th row10833
ValueCountFrequency (%)
0 6
 
8.3%
미집계 5
 
6.9%
데이터 5
 
6.9%
31280000 3
 
4.2%
153450000 2
 
2.8%
5400000 2
 
2.8%
77215680 1
 
1.4%
4417000 1
 
1.4%
2626352 1
 
1.4%
108400 1
 
1.4%
Other values (45) 45
62.5%
2023-12-13T04:16:43.077698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 130
29.7%
2 40
 
9.2%
3 37
 
8.5%
7 36
 
8.2%
1 32
 
7.3%
4 32
 
7.3%
8 27
 
6.2%
6 27
 
6.2%
5 22
 
5.0%
9 19
 
4.3%
Other values (7) 35
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 402
92.0%
Other Letter 30
 
6.9%
Space Separator 5
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
32.3%
2 40
 
10.0%
3 37
 
9.2%
7 36
 
9.0%
1 32
 
8.0%
4 32
 
8.0%
8 27
 
6.7%
6 27
 
6.7%
5 22
 
5.5%
9 19
 
4.7%
Other Letter
ValueCountFrequency (%)
터 5
16.7%
미 5
16.7%
집 5
16.7%
계 5
16.7%
데 5
16.7%
이 5
16.7%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 407
93.1%
Hangul 30
 
6.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
31.9%
2 40
 
9.8%
3 37
 
9.1%
7 36
 
8.8%
1 32
 
7.9%
4 32
 
7.9%
8 27
 
6.6%
6 27
 
6.6%
5 22
 
5.4%
9 19
 
4.7%
Hangul
ValueCountFrequency (%)
터 5
16.7%
미 5
16.7%
집 5
16.7%
계 5
16.7%
데 5
16.7%
이 5
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 407
93.1%
Hangul 30
 
6.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
31.9%
2 40
 
9.8%
3 37
 
9.1%
7 36
 
8.8%
1 32
 
7.9%
4 32
 
7.9%
8 27
 
6.6%
6 27
 
6.6%
5 22
 
5.4%
9 19
 
4.7%
Hangul
ValueCountFrequency (%)
터 5
16.7%
미 5
16.7%
집 5
16.7%
계 5
16.7%
데 5
16.7%
이 5
16.7%

비고
Text

MISSING 

Distinct18
Distinct (%)54.5%
Missing34
Missing (%)50.7%
Memory size668.0 B
2023-12-13T04:16:43.299555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length10
Mean length15.545455
Min length3

Characters and Unicode

Total characters513
Distinct characters100
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

Unique17 ?
Unique (%)51.5%

Sample

1st row기정은 경기도고시제2017-21호(2017.2.24.)에 따름
2nd row단절토지
3rd row경계선관통대지 및 소규모토지
4th row기정은 경기도고시제2021-121호(2021.07.02.) 면적임
5th row단절토지 2개소 해제
ValueCountFrequency (%)
개발행위허가제한지역 16
25.8%
기정은 3
 
4.8%
따름 2
 
3.2%
양주군 2
 
3.2%
해제 2
 
3.2%
개발제한구역 2
 
3.2%
소규모토지 2
 
3.2%
경계선관통대지 2
 
3.2%
단절토지 2
 
3.2%
제척 1
 
1.6%
Other values (28) 28
45.2%
2023-12-13T04:16:43.657408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
 
5.7%
제 26
 
5.1%
개 22
 
4.3%
지 22
 
4.3%
2 22
 
4.3%
역 20
 
3.9%
한 19
 
3.7%
발 19
 
3.7%
가 18
 
3.5%
허 17
 
3.3%
Other values (90) 299
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 381
74.3%
Decimal Number 61
 
11.9%
Space Separator 29
 
5.7%
Other Punctuation 21
 
4.1%
Close Punctuation 8
 
1.6%
Open Punctuation 8
 
1.6%
Dash Punctuation 3
 
0.6%
Lowercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 26
 
6.8%
개 22
 
5.8%
지 22
 
5.8%
역 20
 
5.2%
한 19
 
5.0%
발 19
 
5.0%
가 18
 
4.7%
허 17
 
4.5%
위 17
 
4.5%
행 17
 
4.5%
Other values (72) 184
48.3%
Decimal Number
ValueCountFrequency (%)
2 22
36.1%
1 13
21.3%
0 13
21.3%
7 4
 
6.6%
5 3
 
4.9%
6 2
 
3.3%
8 1
 
1.6%
3 1
 
1.6%
9 1
 
1.6%
4 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 12
57.1%
, 9
42.9%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 381
74.3%
Common 130
 
25.3%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 26
 
6.8%
개 22
 
5.8%
지 22
 
5.8%
역 20
 
5.2%
한 19
 
5.0%
발 19
 
5.0%
가 18
 
4.7%
허 17
 
4.5%
위 17
 
4.5%
행 17
 
4.5%
Other values (72) 184
48.3%
Common
ValueCountFrequency (%)
29
22.3%
2 22
16.9%
1 13
10.0%
0 13
10.0%
. 12
9.2%
, 9
 
6.9%
) 8
 
6.2%
( 8
 
6.2%
7 4
 
3.1%
- 3
 
2.3%
Other values (6) 9
 
6.9%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 381
74.3%
ASCII 132
 
25.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29
22.0%
2 22
16.7%
1 13
9.8%
0 13
9.8%
. 12
9.1%
, 9
 
6.8%
) 8
 
6.1%
( 8
 
6.1%
7 4
 
3.0%
- 3
 
2.3%
Other values (8) 11
 
8.3%
Hangul
ValueCountFrequency (%)
제 26
 
6.8%
개 22
 
5.8%
지 22
 
5.8%
역 20
 
5.2%
한 19
 
5.0%
발 19
 
5.0%
가 18
 
4.7%
허 17
 
4.5%
위 17
 
4.5%
행 17
 
4.5%
Other values (72) 184
48.3%

공간도형존재여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)4.0%
Missing17
Missing (%)25.4%
Memory size266.0 B
False
29 
True
21 
(Missing)
17 
ValueCountFrequency (%)
False 29
43.3%
True 21
31.3%
(Missing) 17
25.4%
2023-12-13T04:16:43.773396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:16:43.845574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
위치명1.0000.9940.0000.9310.9481.0000.664
지역명0.9941.0000.0000.9720.9100.0000.894
면적(기정)0.0000.0001.0000.9540.0000.9740.462
면적(변경)0.9310.9720.9541.0000.9910.9740.774
면적(변경후)0.9480.9100.0000.9911.0000.9781.000
비고1.0000.0000.9740.9740.9781.0001.000
공간도형존재여부0.6640.8940.4620.7741.0001.0001.000
2023-12-13T04:16:43.956417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명공간도형존재여부
지역명1.0000.567
공간도형존재여부0.5671.000
2023-12-13T04:16:44.037396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명공간도형존재여부
지역명1.0000.567
공간도형존재여부0.5671.000

Missing values

2023-12-13T04:16:40.159971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:16:40.317683image/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.
2023-12-13T04:16:40.443959image/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

위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
0마전동 일원양주시 개발제한구역7646676021766276249098기정은 경기도고시제2017-21호(2017.2.24.)에 따름Y
1유양동 68번지 일원개발행위허가 제한지역724251737289847<NA><NA>
2마전동 258-1번지 일원개발행위허가 제한지역 해제2445322445320<NA><NA>
3유양동 425-1번지 일원 외 8개소개발행위허가 제한지역 지정03787437874단절토지<NA>
4남방동 산2번지 일원 외 20개소개발행위허가 제한지역 지정01083310833경계선관통대지 및 소규모토지<NA>
5유양동 68번지 일원양주시 개발제한구역762490985786576191233기정은 경기도고시제2021-121호(2021.07.02.) 면적임Y
6유양동 68번지 일원개발행위허가 제한지역 해제89847898470<NA><NA>
7남방동 210-9번지 일원 / 어둔동 268번지 일원개발행위허가 제한지역 일부해제37874862329251단절토지 2개소 해제N
8유양동 559-2번지 / 장흥면 교현리 299-3번지 / 장흥면 교현리 301번지 / 장흥면 삼상리 225-10번지 / 장흥면 삼상리 273-1번지 / 장흥면 삼상리 225-5번지개발행위허가 제한지역 일부해제1083332137620경계선관통대지 5개소, 소규모토지 1개소 해제N
9양주장흥 공공주택지구(지구단위계획구역) (장흥면 삼하리 일원)개발행위허가 제한지역 지정0962032962032<NA>N
위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
57남방동 52번지 일원양주시 개발제한구역7716458762073976543848<NA>Y
58마전동 94-1일원, 장흥면 울대리 386-11 일원양주시 개발제한구역765438487708876466760기정은 국토교통부고시제2016-258호(2016.05.12.)를 따름Y
59산북동 일원도시자연공원구역262635226263520<NA>N
60마전동 258-1번지 일원개발행위허가 제한지역 지정0381748381748<NA><NA>
61마전동 258-1번지 일원개발행위허가 제한지역3817489632391380<NA><NA>
62양주시 남면 상수리, 은현면 도하리 일원개발행위허가 제한지역0522326522326<NA><NA>
63양주시 남방동 52번지 일원양주역세권개발사업구역 개발행위허가제한지역6438406438400개발행위허가제한해제<NA>
64마전동 일원개발행위허가 제한지역 해제3913803913800<NA><NA>
65마전동 일원개발행위허가 제한지역 지정0244532244532<NA><NA>
66유양동 68번지 일원개발행위허가 제한지역 지정07247572475<NA><NA>

Duplicate rows

Most frequently occurring

위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부# duplicates
0양주시 관리지역 전역관리지역 개발행위허가제한지역데이터 미집계데이터 미집계데이터 미집계개발행위허가제한지역N2