Overview

Dataset statistics

Number of variables7
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory59.9 B

Variable types

Text2
Categorical4
DateTime1

Dataset

Description서울특별시 서초구 개발행위 허가정보에 대한 데이터로 공작물설치, 형질변경, 토지분할 등에 관한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15036822/fileData.do

Alerts

개발행위목적 is highly overall correlated with 지목 and 1 other fieldsHigh correlation
허가구분 is highly overall correlated with 개발행위목적High correlation
지목 is highly overall correlated with 개발행위목적High correlation
지목 is highly imbalanced (53.8%)Imbalance

Reproduction

Analysis started2023-12-12 16:37:34.790810
Analysis finished2023-12-12 16:37:35.289782
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대지위치
Text

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T01:37:35.416343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.4117647
Min length6

Characters and Unicode

Total characters320
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)94.1%

Sample

1st row서초동 1466-11
2nd row서초동 1342-2
3rd row반포동 94-10
4th row양재동 9-4
5th row양재동 378-4
ValueCountFrequency (%)
서초동 11
 
15.1%
반포동 7
 
9.6%
양재동 6
 
8.2%
우면동 3
 
4.1%
내곡동 3
 
4.1%
방배동 2
 
2.7%
1650 2
 
2.7%
63 2
 
2.7%
산 1
 
1.4%
176-6,14 1
 
1.4%
Other values (35) 35
47.9%
2023-12-13T01:37:35.811176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
12.2%
동 34
 
10.6%
- 27
 
8.4%
1 25
 
7.8%
3 24
 
7.5%
2 21
 
6.6%
6 18
 
5.6%
4 17
 
5.3%
서 11
 
3.4%
0 11
 
3.4%
Other values (21) 93
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146
45.6%
Other Letter 107
33.4%
Space Separator 39
 
12.2%
Dash Punctuation 27
 
8.4%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 34
31.8%
서 11
 
10.3%
초 11
 
10.3%
반 7
 
6.5%
포 7
 
6.5%
양 6
 
5.6%
재 6
 
5.6%
곡 4
 
3.7%
외 3
 
2.8%
우 3
 
2.8%
Other values (8) 15
14.0%
Decimal Number
ValueCountFrequency (%)
1 25
17.1%
3 24
16.4%
2 21
14.4%
6 18
12.3%
4 17
11.6%
0 11
7.5%
7 9
 
6.2%
8 9
 
6.2%
5 7
 
4.8%
9 5
 
3.4%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 213
66.6%
Hangul 107
33.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 34
31.8%
서 11
 
10.3%
초 11
 
10.3%
반 7
 
6.5%
포 7
 
6.5%
양 6
 
5.6%
재 6
 
5.6%
곡 4
 
3.7%
외 3
 
2.8%
우 3
 
2.8%
Other values (8) 15
14.0%
Common
ValueCountFrequency (%)
39
18.3%
- 27
12.7%
1 25
11.7%
3 24
11.3%
2 21
9.9%
6 18
8.5%
4 17
8.0%
0 11
 
5.2%
7 9
 
4.2%
8 9
 
4.2%
Other values (3) 13
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213
66.6%
Hangul 107
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
18.3%
- 27
12.7%
1 25
11.7%
3 24
11.3%
2 21
9.9%
6 18
8.5%
4 17
8.0%
0 11
 
5.2%
7 9
 
4.2%
8 9
 
4.2%
Other values (3) 13
 
6.1%
Hangul
ValueCountFrequency (%)
동 34
31.8%
서 11
 
10.3%
초 11
 
10.3%
반 7
 
6.5%
포 7
 
6.5%
양 6
 
5.6%
재 6
 
5.6%
곡 4
 
3.7%
외 3
 
2.8%
우 3
 
2.8%
Other values (8) 15
14.0%

지목
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size404.0 B
대
27 
답
 
2
임야
 
2
도로
 
1
공원
 
1

Length

Max length6
Median length1
Mean length1.2647059
Min length1

Unique

Unique3 ?
Unique (%)8.8%

Sample

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

Common Values

ValueCountFrequency (%)
대 27
79.4%
답 2
 
5.9%
임야 2
 
5.9%
도로 1
 
2.9%
공원 1
 
2.9%
대,임야,전 1
 
2.9%

Length

2023-12-13T01:37:36.004323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:36.116379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대 27
79.4%
답 2
 
5.9%
임야 2
 
5.9%
도로 1
 
2.9%
공원 1
 
2.9%
대,임야,전 1
 
2.9%

허가면적(제곱미터)
Text

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T01:37:36.285199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.2647059
Min length2

Characters and Unicode

Total characters111
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)94.1%

Sample

1st row55
2nd row46
3rd row33
4th row60
5th row102
ValueCountFrequency (%)
243.83 2
 
5.9%
55 1
 
2.9%
516 1
 
2.9%
1,262 1
 
2.9%
719 1
 
2.9%
78.5 1
 
2.9%
57.13 1
 
2.9%
19554 1
 
2.9%
2030 1
 
2.9%
1683 1
 
2.9%
Other values (23) 23
67.6%
2023-12-13T01:37:36.656060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 16
14.4%
1 14
12.6%
5 13
11.7%
2 12
10.8%
4 11
9.9%
0 9
8.1%
8 8
7.2%
9 8
7.2%
6 7
6.3%
7 7
6.3%
Other values (2) 6
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
94.6%
Other Punctuation 6
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 16
15.2%
1 14
13.3%
5 13
12.4%
2 12
11.4%
4 11
10.5%
0 9
8.6%
8 8
7.6%
9 8
7.6%
6 7
6.7%
7 7
6.7%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
, 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 111
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 16
14.4%
1 14
12.6%
5 13
11.7%
2 12
10.8%
4 11
9.9%
0 9
8.1%
8 8
7.2%
9 8
7.2%
6 7
6.3%
7 7
6.3%
Other values (2) 6
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 16
14.4%
1 14
12.6%
5 13
11.7%
2 12
10.8%
4 11
9.9%
0 9
8.1%
8 8
7.2%
9 8
7.2%
6 7
6.3%
7 7
6.3%
Other values (2) 6
 
5.4%

용도지역
Categorical

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size404.0 B
제2종일반주거지역
13 
자연녹지지역
7 
제3종일반주거지역
4 
제1종일반주거지역
4 
제1종전용주거지역
2 
Other values (4)
4 

Length

Max length20
Median length9
Mean length8.7647059
Min length5

Unique

Unique4 ?
Unique (%)11.8%

Sample

1st row제2종일반주거지역
2nd row제2종일반주거지역
3rd row제3종일반주거지역
4th row제2종일반주거지역
5th row제2종일반주거지역

Common Values

ValueCountFrequency (%)
제2종일반주거지역 13
38.2%
자연녹지지역 7
20.6%
제3종일반주거지역 4
 
11.8%
제1종일반주거지역 4
 
11.8%
제1종전용주거지역 2
 
5.9%
제2종일반주거지역(7층이하) 1
 
2.9%
준주거지역 1
 
2.9%
제2종전용주거지역 1
 
2.9%
제1종일반주거지역, 제2종일반주거지역 1
 
2.9%

Length

2023-12-13T01:37:36.809629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:37.013042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종일반주거지역 14
40.0%
자연녹지지역 7
20.0%
제1종일반주거지역 5
 
14.3%
제3종일반주거지역 4
 
11.4%
제1종전용주거지역 2
 
5.7%
제2종일반주거지역(7층이하 1
 
2.9%
준주거지역 1
 
2.9%
제2종전용주거지역 1
 
2.9%

허가구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
공작물 설치
26 
토지분할
6 
형질변경
 
2

Length

Max length6
Median length6
Mean length5.5294118
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공작물 설치
2nd row공작물 설치
3rd row공작물 설치
4th row공작물 설치
5th row공작물 설치

Common Values

ValueCountFrequency (%)
공작물 설치 26
76.5%
토지분할 6
 
17.6%
형질변경 2
 
5.9%

Length

2023-12-13T01:37:37.167684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:37.281736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공작물 26
43.3%
설치 26
43.3%
토지분할 6
 
10.0%
형질변경 2
 
3.3%

허가일
Date

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size404.0 B
Minimum2019-05-14 00:00:00
Maximum2023-06-27 00:00:00
2023-12-13T01:37:37.377747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:37:37.503978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

개발행위목적
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size404.0 B
태양광설치
24 
토지분할
6 
부지조성 후 건축물 신축
 
1
건축물 신축
 
1
가설휀스설치
 
1

Length

Max length17
Median length5
Mean length5.4705882
Min length4

Unique

Unique4 ?
Unique (%)11.8%

Sample

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

Common Values

ValueCountFrequency (%)
태양광설치 24
70.6%
토지분할 6
 
17.6%
부지조성 후 건축물 신축 1
 
2.9%
건축물 신축 1
 
2.9%
가설휀스설치 1
 
2.9%
변경허가(사업기간변경)태양광설치 1
 
2.9%

Length

2023-12-13T01:37:37.636742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:37.764437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태양광설치 24
63.2%
토지분할 6
 
15.8%
건축물 2
 
5.3%
신축 2
 
5.3%
부지조성 1
 
2.6%
후 1
 
2.6%
가설휀스설치 1
 
2.6%
변경허가(사업기간변경)태양광설치 1
 
2.6%

Correlations

2023-12-13T01:37:37.859819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지위치지목허가면적(제곱미터)용도지역허가구분허가일개발행위목적
대지위치1.0001.0001.0001.0001.0000.9710.000
지목1.0001.0001.0000.6550.7920.8580.888
허가면적(제곱미터)1.0001.0001.0001.0001.0000.9710.000
용도지역1.0000.6551.0001.0000.0000.8640.640
허가구분1.0000.7921.0000.0001.0000.5301.000
허가일0.9710.8580.9710.8640.5301.0000.856
개발행위목적0.0000.8880.0000.6401.0000.8561.000
2023-12-13T01:37:37.985820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개발행위목적허가구분지목용도지역
개발행위목적1.0000.9500.5280.350
허가구분0.9501.0000.4440.000
지목0.5280.4441.0000.364
용도지역0.3500.0000.3641.000
2023-12-13T01:37:38.365097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지목용도지역허가구분개발행위목적
지목1.0000.3640.4440.528
용도지역0.3641.0000.0000.350
허가구분0.4440.0001.0000.950
개발행위목적0.5280.3500.9501.000

Missing values

2023-12-13T01:37:35.134377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:37:35.244817image/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

대지위치지목허가면적(제곱미터)용도지역허가구분허가일개발행위목적
0서초동 1466-11대55제2종일반주거지역공작물 설치2019-05-14태양광설치
1서초동 1342-2대46제2종일반주거지역공작물 설치2019-11-13태양광설치
2반포동 94-10대33제3종일반주거지역공작물 설치2020-01-07태양광설치
3양재동 9-4대60제2종일반주거지역공작물 설치2020-01-17태양광설치
4양재동 378-4대102제2종일반주거지역공작물 설치2020-02-10태양광설치
5방배동 862-14 외1대101제2종일반주거지역(7층이하)공작물 설치2020-06-11태양광설치
6양재동 302-2대97제2종일반주거지역공작물 설치2020-07-16태양광설치
7서초동 1364-20대86제3종일반주거지역공작물 설치2020-07-22태양광설치
8양재동 242-3대79제2종일반주거지역공작물 설치2020-07-24태양광설치
9반포동 612-94도로522제2종일반주거지역토지분할2020-09-08토지분할
대지위치지목허가면적(제곱미터)용도지역허가구분허가일개발행위목적
24서초동 산52-16임야1683자연녹지지역토지분할2021-12-24토지분할
25방배동 623-4답2030자연녹지지역형질변경2022-01-27건축물 신축
26우면동 63대19554제3종일반주거지역공작물 설치2022-06-09태양광설치
27양재동 353-1대57.13제2종일반주거지역공작물 설치2022-07-01태양광설치
28서초동 1626-5대78.5제3종일반주거지역공작물 설치2022-07-01태양광설치
29내곡동 196-11대719제1종일반주거지역토지분할2022-07-14토지분할
30서초동 1650대243.83제2종일반주거지역공작물 설치2022-08-08태양광설치
31서초동 산 52-16임야1,262자연녹지지역토지분할2022-12-02토지분할
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