Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Categorical2
Text2

Dataset

Description서울특별시 은평구의 필지별 용도지역에 대한 데이터로 필지별 지번, 지목, 용도지역 등의 항목에 대하여 제공합니다. 2022년 3월 기준입니다.
Author서울특별시 은평구
URLhttps://www.data.go.kr/data/15100363/fileData.do

Alerts

지목 is highly imbalanced (70.5%)Imbalance

Reproduction

Analysis started2023-12-12 18:24:43.438958
Analysis finished2023-12-12 18:24:43.975042
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동면
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
불광동
1706 
응암동
1552 
갈현동
1230 
역촌동
988 
신사동
920 
Other values (6)
3604 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 응암동
2nd row 증산동
3rd row 갈현동
4th row 역촌동
5th row 응암동

Common Values

ValueCountFrequency (%)
불광동 1706
17.1%
응암동 1552
15.5%
갈현동 1230
12.3%
역촌동 988
9.9%
신사동 920
9.2%
대조동 877
8.8%
녹번동 761
7.6%
수색동 561
 
5.6%
구산동 534
 
5.3%
진관동 464
 
4.6%

Length

2023-12-13T03:24:44.061112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
불광동 1706
17.1%
응암동 1552
15.5%
갈현동 1230
12.3%
역촌동 988
9.9%
신사동 920
9.2%
대조동 877
8.8%
녹번동 761
7.6%
수색동 561
 
5.6%
구산동 534
 
5.3%
진관동 464
 
4.6%

지번
Text

Distinct9440
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:24:44.464618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.5668
Min length3

Characters and Unicode

Total characters75668
Distinct characters31
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

Unique8933 ?
Unique (%)89.3%

Sample

1st row 597-41도
2nd row 239-4도
3rd row 515-36대
4th row 14-14대
5th row 578-22 대
ValueCountFrequency (%)
대 836
 
7.3%
도 405
 
3.5%
임 88
 
0.8%
전 41
 
0.4%
공 29
 
0.3%
구 20
 
0.2%
종 19
 
0.2%
답 17
 
0.1%
학 10
 
0.1%
산 8
 
0.1%
Other values (9431) 10031
87.2%
2023-12-13T03:24:44.992506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11504
15.2%
- 9789
12.9%
1 8098
10.7%
대 7583
10.0%
2 6577
8.7%
3 5451
7.2%
4 4665
 
6.2%
5 3674
 
4.9%
8 3334
 
4.4%
7 3251
 
4.3%
Other values (21) 11742
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44046
58.2%
Space Separator 11504
 
15.2%
Other Letter 10329
 
13.7%
Dash Punctuation 9789
 
12.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 7583
73.4%
도 1568
 
15.2%
임 377
 
3.6%
산 329
 
3.2%
전 157
 
1.5%
구 79
 
0.8%
답 46
 
0.4%
공 41
 
0.4%
잡 33
 
0.3%
천 33
 
0.3%
Other values (9) 83
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 8098
18.4%
2 6577
14.9%
3 5451
12.4%
4 4665
10.6%
5 3674
8.3%
8 3334
7.6%
7 3251
7.4%
9 3024
 
6.9%
6 3008
 
6.8%
0 2964
 
6.7%
Space Separator
ValueCountFrequency (%)
11504
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9789
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65339
86.3%
Hangul 10329
 
13.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 7583
73.4%
도 1568
 
15.2%
임 377
 
3.6%
산 329
 
3.2%
전 157
 
1.5%
구 79
 
0.8%
답 46
 
0.4%
공 41
 
0.4%
잡 33
 
0.3%
천 33
 
0.3%
Other values (9) 83
 
0.8%
Common
ValueCountFrequency (%)
11504
17.6%
- 9789
15.0%
1 8098
12.4%
2 6577
10.1%
3 5451
8.3%
4 4665
7.1%
5 3674
 
5.6%
8 3334
 
5.1%
7 3251
 
5.0%
9 3024
 
4.6%
Other values (2) 5972
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65339
86.3%
Hangul 10329
 
13.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11504
17.6%
- 9789
15.0%
1 8098
12.4%
2 6577
10.1%
3 5451
8.3%
4 4665
7.1%
5 3674
 
5.6%
8 3334
 
5.1%
7 3251
 
5.0%
9 3024
 
4.6%
Other values (2) 5972
9.1%
Hangul
ValueCountFrequency (%)
대 7583
73.4%
도 1568
 
15.2%
임 377
 
3.6%
산 329
 
3.2%
전 157
 
1.5%
구 79
 
0.8%
답 46
 
0.4%
공 41
 
0.4%
잡 33
 
0.3%
천 33
 
0.3%
Other values (9) 83
 
0.8%

지목
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대
7581 
도
1569 
임
 
376
전
 
157
구
 
79
Other values (14)
 
238

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row 도
2nd row 도
3rd row 대
4th row 대
5th row 대

Common Values

ValueCountFrequency (%)
대 7581
75.8%
도 1569
 
15.7%
임 376
 
3.8%
전 157
 
1.6%
구 79
 
0.8%
답 46
 
0.5%
공 41
 
0.4%
잡 34
 
0.3%
천 33
 
0.3%
종 30
 
0.3%
Other values (9) 54
 
0.5%

Length

2023-12-13T03:24:45.134725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대 7581
75.8%
도 1569
 
15.7%
임 376
 
3.8%
전 157
 
1.6%
구 79
 
0.8%
답 46
 
0.5%
공 41
 
0.4%
잡 34
 
0.3%
천 33
 
0.3%
종 30
 
0.3%
Other values (8) 53
 
0.5%

용도지역
Text

Distinct134
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:24:45.364044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length63
Mean length23.3864
Min length17

Characters and Unicode

Total characters233864
Distinct characters44
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

Unique62 ?
Unique (%)0.6%

Sample

1st row 도시지역(1)|제2종일반주거지역(1)
2nd row 도시지역(1)|준주거지역(1)
3rd row 제2종일반주거지역(7층이하)(1)|도시지역(1)
4th row 도시지역(1)|제3종일반주거지역(1)
5th row 제3종일반주거지역(1)|도시지역(1)
ValueCountFrequency (%)
제2종일반주거지역(7층이하)(1)|도시지역(1 1996
19.9%
도시지역(1)|제2종일반주거지역(7층이하)(1 1979
19.7%
제2종일반주거지역(1)|도시지역(1 782
 
7.8%
도시지역(1)|제2종일반주거지역(1 754
 
7.5%
도시지역(1)|제3종일반주거지역(1 749
 
7.5%
제3종일반주거지역(1)|도시지역(1 733
 
7.3%
도시지역(1)|제1종일반주거지역(1 517
 
5.1%
제1종일반주거지역(1)|도시지역(1 495
 
4.9%
준주거지역(1)|도시지역(1 331
 
3.3%
도시지역(1)|준주거지역(1 321
 
3.2%
Other values (129) 1382
13.8%
2023-12-13T03:24:45.739924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 24534
 
10.5%
( 24534
 
10.5%
지 20973
 
9.0%
1 20858
 
8.9%
역 20359
 
8.7%
| 10346
 
4.4%
10039
 
4.3%
시 10013
 
4.3%
도 10013
 
4.3%
주 9312
 
4.0%
Other values (34) 72883
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131295
56.1%
Decimal Number 33116
 
14.2%
Close Punctuation 24534
 
10.5%
Open Punctuation 24534
 
10.5%
Math Symbol 10346
 
4.4%
Space Separator 10039
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 20973
16.0%
역 20359
15.5%
시 10013
7.6%
도 10013
7.6%
주 9312
7.1%
거 9312
7.1%
일 8831
6.7%
반 8831
6.7%
제 8595
6.5%
종 8595
6.5%
Other values (26) 16461
12.5%
Decimal Number
ValueCountFrequency (%)
1 20858
63.0%
2 6518
 
19.7%
7 4175
 
12.6%
3 1565
 
4.7%
Close Punctuation
ValueCountFrequency (%)
) 24534
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24534
100.0%
Math Symbol
ValueCountFrequency (%)
| 10346
100.0%
Space Separator
ValueCountFrequency (%)
10039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131295
56.1%
Common 102569
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 20973
16.0%
역 20359
15.5%
시 10013
7.6%
도 10013
7.6%
주 9312
7.1%
거 9312
7.1%
일 8831
6.7%
반 8831
6.7%
제 8595
6.5%
종 8595
6.5%
Other values (26) 16461
12.5%
Common
ValueCountFrequency (%)
) 24534
23.9%
( 24534
23.9%
1 20858
20.3%
| 10346
10.1%
10039
9.8%
2 6518
 
6.4%
7 4175
 
4.1%
3 1565
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131295
56.1%
ASCII 102569
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 24534
23.9%
( 24534
23.9%
1 20858
20.3%
| 10346
10.1%
10039
9.8%
2 6518
 
6.4%
7 4175
 
4.1%
3 1565
 
1.5%
Hangul
ValueCountFrequency (%)
지 20973
16.0%
역 20359
15.5%
시 10013
7.6%
도 10013
7.6%
주 9312
7.1%
거 9312
7.1%
일 8831
6.7%
반 8831
6.7%
제 8595
6.5%
종 8595
6.5%
Other values (26) 16461
12.5%

Correlations

2023-12-13T03:24:45.825448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동면지목
읍면동면1.0000.426
지목0.4261.000
2023-12-13T03:24:45.937434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동면지목
읍면동면1.0000.170
지목0.1701.000
2023-12-13T03:24:46.039107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동면지목
읍면동면1.0000.170
지목0.1701.000

Missing values

2023-12-13T03:24:43.806242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:24:43.916602image/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

읍면동면지번지목용도지역
31430응암동597-41도도도시지역(1)|제2종일반주거지역(1)
43712증산동239-4도도도시지역(1)|준주거지역(1)
18779갈현동515-36대대제2종일반주거지역(7층이하)(1)|도시지역(1)
34094역촌동14-14대대도시지역(1)|제3종일반주거지역(1)
30575응암동578-22 대대제3종일반주거지역(1)|도시지역(1)
21427구산동342-1 대대도시지역(1)|제2종일반주거지역(7층이하)(1)
1758수색동346-9대대도시지역(1)|제2종일반주거지역(7층이하)(1)
5822녹번동282-3 공공도시지역(1)|제2종일반주거지역(1)
41650신사동349-5대대도시지역(1)|제1종일반주거지역(1)
28337응암동123-8대대도시지역(1)|준주거지역(1)
읍면동면지번지목용도지역
132수색동16-3대대제2종일반주거지역(7층이하)(1)|도시지역(1)
42379증산동164-3대대제3종일반주거지역(1)|도시지역(1)
20889구산동210-28대대제2종일반주거지역(7층이하)(1)|도시지역(1)
23194대조동70-9대대제2종일반주거지역(2)|도시지역(1)|제2종일반주거지역(7층이하)(2)
31654응암동602-36대대제3종일반주거지역(1)|도시지역(1)
26342응암동32-1도도제2종일반주거지역(7층이하)(2)|제3종일반주거지역(2)|도시지역(1)
6622불광동37-2 전전도시지역(1)|자연녹지지역(1)
8385불광동244-113대대제2종일반주거지역(1)|도시지역(1)
42608증산동176-6대대도시지역(1)|제2종일반주거지역(1)
31312응암동594-97대대도시지역(1)|제2종일반주거지역(7층이하)(1)