Overview

Dataset statistics

Number of variables7
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory63.3 B

Variable types

Numeric1
Text1
Categorical5

Dataset

Description서울특별시 서대문구 빈집 현황 정보입니다. 빈집이 위치한 자치구명, 해당동, 용도지역, 주택유형 등의 정보를 포함하고 있습니다.
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15109016/fileData.do

Alerts

시군구 has constant value ""Constant
연번 is highly overall correlated with 용도지역 and 1 other fieldsHigh correlation
용도지역 is highly overall correlated with 연번High correlation
등급판정결과 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2024-04-17 11:11:53.123700
Analysis finished2024-04-17 11:11:53.621925
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T20:11:53.665681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2024-04-17T20:11:53.767076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

소재지
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-04-17T20:11:53.925235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.3809524
Min length7

Characters and Unicode

Total characters197
Distinct characters29
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

Unique21 ?
Unique (%)100.0%

Sample

1st row남가좌동 5-327
2nd row남가좌동 5-330
3rd row대현동 67-9
4th row북가좌동 295-57
5th row북가좌동 345-37
ValueCountFrequency (%)
홍은동 4
 
9.5%
홍제동 4
 
9.5%
대현동 3
 
7.1%
북가좌동 2
 
4.8%
연희동 2
 
4.8%
남가좌동 2
 
4.8%
295-57 1
 
2.4%
382 1
 
2.4%
306-2 1
 
2.4%
53-99 1
 
2.4%
Other values (21) 21
50.0%
2024-04-17T20:11:54.216437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
 
10.7%
21
 
10.7%
- 20
 
10.2%
3 14
 
7.1%
1 11
 
5.6%
5 10
 
5.1%
2 10
 
5.1%
4 9
 
4.6%
9 8
 
4.1%
6 8
 
4.1%
Other values (19) 65
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
44.7%
Other Letter 68
34.5%
Space Separator 21
 
10.7%
Dash Punctuation 20
 
10.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
30.9%
홍 8
 
11.8%
현 4
 
5.9%
가 4
 
5.9%
제 4
 
5.9%
은 4
 
5.9%
좌 4
 
5.9%
대 4
 
5.9%
북 3
 
4.4%
희 2
 
2.9%
Other values (7) 10
14.7%
Decimal Number
ValueCountFrequency (%)
3 14
15.9%
1 11
12.5%
5 10
11.4%
2 10
11.4%
4 9
10.2%
9 8
9.1%
6 8
9.1%
0 7
8.0%
7 6
6.8%
8 5
 
5.7%
Space Separator
ValueCountFrequency (%)
21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129
65.5%
Hangul 68
34.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
30.9%
홍 8
 
11.8%
현 4
 
5.9%
가 4
 
5.9%
제 4
 
5.9%
은 4
 
5.9%
좌 4
 
5.9%
대 4
 
5.9%
북 3
 
4.4%
희 2
 
2.9%
Other values (7) 10
14.7%
Common
ValueCountFrequency (%)
21
16.3%
- 20
15.5%
3 14
10.9%
1 11
8.5%
5 10
7.8%
2 10
7.8%
4 9
7.0%
9 8
 
6.2%
6 8
 
6.2%
0 7
 
5.4%
Other values (2) 11
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129
65.5%
Hangul 68
34.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
30.9%
홍 8
 
11.8%
현 4
 
5.9%
가 4
 
5.9%
제 4
 
5.9%
은 4
 
5.9%
좌 4
 
5.9%
대 4
 
5.9%
북 3
 
4.4%
희 2
 
2.9%
Other values (7) 10
14.7%
ASCII
ValueCountFrequency (%)
21
16.3%
- 20
15.5%
3 14
10.9%
1 11
8.5%
5 10
7.8%
2 10
7.8%
4 9
7.0%
9 8
 
6.2%
6 8
 
6.2%
0 7
 
5.4%
Other values (2) 11
8.5%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
서대문구
21 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서대문구 21
100.0%

Length

2024-04-17T20:11:54.326081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:11:54.413017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서대문구 21
100.0%

동
Categorical

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Memory size300.0 B
홍은동
4 
홍제동
4 
대현동
3 
남가좌동
2 
북가좌동
2 
Other values (5)
6 

Length

Max length4
Median length3
Mean length3.2380952
Min length3

Unique

Unique4 ?
Unique (%)19.0%

Sample

1st row남가좌동
2nd row남가좌동
3rd row대현동
4th row북가좌동
5th row북가좌동

Common Values

ValueCountFrequency (%)
홍은동 4
19.0%
홍제동 4
19.0%
대현동 3
14.3%
남가좌동 2
9.5%
북가좌동 2
9.5%
연희동 2
9.5%
신촌동 1
 
4.8%
대신동 1
 
4.8%
북아현동 1
 
4.8%
창천동 1
 
4.8%

Length

2024-04-17T20:11:54.507953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:11:54.621071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍은동 4
19.0%
홍제동 4
19.0%
대현동 3
14.3%
남가좌동 2
9.5%
북가좌동 2
9.5%
연희동 2
9.5%
신촌동 1
 
4.8%
대신동 1
 
4.8%
북아현동 1
 
4.8%
창천동 1
 
4.8%

용도지역
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
제2종일반
9 
제1종일반
7 
준주거
3 
제3종일반
2 

Length

Max length5
Median length5
Mean length4.7142857
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제2종일반
2nd row제2종일반
3rd row준주거
4th row제2종일반
5th row제2종일반

Common Values

ValueCountFrequency (%)
제2종일반 9
42.9%
제1종일반 7
33.3%
준주거 3
 
14.3%
제3종일반 2
 
9.5%

Length

2024-04-17T20:11:54.757305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:11:54.874156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종일반 9
42.9%
제1종일반 7
33.3%
준주거 3
 
14.3%
제3종일반 2
 
9.5%

주택유형
Categorical

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
단독
15 
다가구
6 

Length

Max length3
Median length2
Mean length2.2857143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단독
2nd row단독
3rd row다가구
4th row단독
5th row다가구

Common Values

ValueCountFrequency (%)
단독 15
71.4%
다가구 6
 
28.6%

Length

2024-04-17T20:11:54.977440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:11:55.068403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독 15
71.4%
다가구 6
 
28.6%

등급판정결과
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
1등급
8 
4등급
5 
2등급
4 
3등급
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1등급
2nd row1등급
3rd row1등급
4th row1등급
5th row1등급

Common Values

ValueCountFrequency (%)
1등급 8
38.1%
4등급 5
23.8%
2등급 4
19.0%
3등급 4
19.0%

Length

2024-04-17T20:11:55.177314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:11:55.301381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1등급 8
38.1%
4등급 5
23.8%
2등급 4
19.0%
3등급 4
19.0%

Interactions

2024-04-17T20:11:53.389739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:11:55.371905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소재지동용도지역주택유형등급판정결과
연번1.0001.0000.8730.5290.0001.000
소재지1.0001.0001.0001.0001.0001.000
동0.8731.0001.0000.0000.0000.580
용도지역0.5291.0000.0001.0000.0000.054
주택유형0.0001.0000.0000.0001.0000.173
등급판정결과1.0001.0000.5800.0540.1731.000
2024-04-17T20:11:55.463465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도지역주택유형등급판정결과동
용도지역1.0000.0000.0000.000
주택유형0.0001.0000.0660.000
등급판정결과0.0000.0661.0000.274
동0.0000.0000.2741.000
2024-04-17T20:11:55.554308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동용도지역주택유형등급판정결과
연번1.0000.4630.5340.2160.622
동0.4631.0000.0000.0000.274
용도지역0.5340.0001.0000.0000.000
주택유형0.2160.0000.0001.0000.066
등급판정결과0.6220.2740.0000.0661.000

Missing values

2024-04-17T20:11:53.497587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:11:53.586542image/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

연번소재지시군구동용도지역주택유형등급판정결과
01남가좌동 5-327서대문구남가좌동제2종일반단독1등급
12남가좌동 5-330서대문구남가좌동제2종일반단독1등급
23대현동 67-9서대문구대현동준주거다가구1등급
34북가좌동 295-57서대문구북가좌동제2종일반단독1등급
45북가좌동 345-37서대문구북가좌동제2종일반다가구1등급
56신촌동 1-80서대문구신촌동제1종일반단독1등급
67연희동 446-123서대문구연희동제1종일반단독1등급
78연희동 82-4서대문구연희동제1종일반다가구1등급
89대신동 91-14서대문구대신동제1종일반단독2등급
910홍은동 265-369서대문구홍은동제1종일반단독2등급
연번소재지시군구동용도지역주택유형등급판정결과
1112홍제동 382서대문구홍제동제2종일반다가구2등급
1213대현동 62-34서대문구대현동제2종일반다가구3등급
1314홍은동 8-132서대문구홍은동제1종일반단독3등급
1415홍제동 8-35서대문구홍제동제1종일반다가구3등급
1516홍은동 207-11서대문구홍은동제2종일반단독3등급
1617대현동 110-49서대문구대현동제3종일반단독4등급
1718북아현동 1-605서대문구북아현동제2종일반단독4등급
1819창천동 53-99서대문구창천동제2종일반단독4등급
1920홍제동 306-2서대문구홍제동준주거단독4등급
2021홍제동 306-4서대문구홍제동준주거단독4등급