Overview

Dataset statistics

Number of variables12
Number of observations1254
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.8 KiB
Average record size in memory101.1 B

Variable types

Categorical3
Text3
Numeric5
DateTime1

Dataset

Description제주특별자치도개발공사가 진행하는 주거 취약계층 주거 안정화를 위한 임대주택 정보로 주소, 단지명, 공급유형, 세대수, 공급형태, 전유면적, 공용면적, 공급면적, 매입일자 등이 포함된 데이터 입니다.
URLhttps://www.data.go.kr/data/15029692/fileData.do

Alerts

광역시 has constant value ""Constant
전유면적(제곱미터) is highly overall correlated with 공급면적(제곱미터)High correlation
공용면적(제곱미터) is highly overall correlated with 공급유형High correlation
공급면적(제곱미터) is highly overall correlated with 전유면적(제곱미터)High correlation
공급유형 is highly overall correlated with 공용면적(제곱미터)High correlation
공급유형 is highly imbalanced (54.9%)Imbalance
공용면적(제곱미터) has 83 (6.6%) zerosZeros

Reproduction

Analysis started2023-12-12 12:52:18.084407
Analysis finished2023-12-12 12:52:21.623155
Duration3.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

광역시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
제주특별자치도
1254 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 1254
100.0%

Length

2023-12-12T21:52:21.701081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:52:21.798630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 1254
100.0%

시군구
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
제주시
769 
서귀포시
413 
제주시
 
72

Length

Max length4
Median length3
Mean length3.3867624
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 769
61.3%
서귀포시 413
32.9%
제주시 72
 
5.7%

Length

2023-12-12T21:52:21.906053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:52:22.015695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 841
67.1%
서귀포시 413
32.9%

단지명
Text

Distinct120
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2023-12-12T21:52:22.323057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.6786284
Min length3

Characters and Unicode

Total characters7121
Distinct characters167
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row탐라하이츠
2nd row탐라하이츠
3rd row탐라하이츠
4th row탐라하이츠
5th row탐라하이츠
ValueCountFrequency (%)
정실뜨래별 80
 
5.0%
이도스타빌리지 48
 
3.0%
킹스톤 48
 
3.0%
파크뷰 41
 
2.6%
a동 39
 
2.4%
아뜨네오피스텔 39
 
2.4%
대원에버그린빌 33
 
2.1%
꿈의숲 31
 
1.9%
b동 29
 
1.8%
희건주택 28
 
1.7%
Other values (115) 1191
74.1%
2023-12-12T21:52:22.830788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 530
 
7.4%
동 421
 
5.9%
353
 
5.0%
스 295
 
4.1%
1 204
 
2.9%
이 179
 
2.5%
0 152
 
2.1%
아 152
 
2.1%
주 143
 
2.0%
정 143
 
2.0%
Other values (157) 4549
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5956
83.6%
Decimal Number 596
 
8.4%
Space Separator 353
 
5.0%
Uppercase Letter 216
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 530
 
8.9%
동 421
 
7.1%
스 295
 
5.0%
이 179
 
3.0%
아 152
 
2.6%
주 143
 
2.4%
정 143
 
2.4%
우 133
 
2.2%
하 130
 
2.2%
택 125
 
2.1%
Other values (140) 3705
62.2%
Decimal Number
ValueCountFrequency (%)
1 204
34.2%
0 152
25.5%
2 71
 
11.9%
5 61
 
10.2%
3 39
 
6.5%
4 34
 
5.7%
6 11
 
1.8%
9 8
 
1.3%
7 8
 
1.3%
8 8
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
B 80
37.0%
A 67
31.0%
C 29
 
13.4%
S 16
 
7.4%
K 16
 
7.4%
E 8
 
3.7%
Space Separator
ValueCountFrequency (%)
353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5956
83.6%
Common 949
 
13.3%
Latin 216
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 530
 
8.9%
동 421
 
7.1%
스 295
 
5.0%
이 179
 
3.0%
아 152
 
2.6%
주 143
 
2.4%
정 143
 
2.4%
우 133
 
2.2%
하 130
 
2.2%
택 125
 
2.1%
Other values (140) 3705
62.2%
Common
ValueCountFrequency (%)
353
37.2%
1 204
21.5%
0 152
16.0%
2 71
 
7.5%
5 61
 
6.4%
3 39
 
4.1%
4 34
 
3.6%
6 11
 
1.2%
9 8
 
0.8%
7 8
 
0.8%
Latin
ValueCountFrequency (%)
B 80
37.0%
A 67
31.0%
C 29
 
13.4%
S 16
 
7.4%
K 16
 
7.4%
E 8
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5956
83.6%
ASCII 1165
 
16.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 530
 
8.9%
동 421
 
7.1%
스 295
 
5.0%
이 179
 
3.0%
아 152
 
2.6%
주 143
 
2.4%
정 143
 
2.4%
우 133
 
2.2%
하 130
 
2.2%
택 125
 
2.1%
Other values (140) 3705
62.2%
ASCII
ValueCountFrequency (%)
353
30.3%
1 204
17.5%
0 152
13.0%
B 80
 
6.9%
2 71
 
6.1%
A 67
 
5.8%
5 61
 
5.2%
3 39
 
3.3%
4 34
 
2.9%
C 29
 
2.5%
Other values (7) 75
 
6.4%

도로명주소
Text

Distinct107
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2023-12-12T21:52:23.154485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length12.69059
Min length7

Characters and Unicode

Total characters15914
Distinct characters113
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

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시 우정로8길 6
2nd row제주시 우정로8길 6
3rd row제주시 우정로8길 6
4th row제주시 우정로8길 6
5th row제주시 우정로8길 6
ValueCountFrequency (%)
제주시 708
 
20.6%
서귀포시 346
 
10.0%
아연로212 80
 
2.3%
대정읍 78
 
2.3%
애월읍 63
 
1.8%
신효로 48
 
1.4%
16 48
 
1.4%
서귀동328-2 41
 
1.2%
삼도이동 39
 
1.1%
819-6 39
 
1.1%
Other values (168) 1954
56.7%
2023-12-12T21:52:23.583714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2190
 
13.8%
시 1054
 
6.6%
1 1031
 
6.5%
주 736
 
4.6%
로 736
 
4.6%
제 708
 
4.4%
2 638
 
4.0%
길 612
 
3.8%
- 541
 
3.4%
서 495
 
3.1%
Other values (103) 7173
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9004
56.6%
Decimal Number 4179
26.3%
Space Separator 2190
 
13.8%
Dash Punctuation 541
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1054
 
11.7%
주 736
 
8.2%
로 736
 
8.2%
제 708
 
7.9%
길 612
 
6.8%
서 495
 
5.5%
귀 387
 
4.3%
포 346
 
3.8%
동 340
 
3.8%
읍 224
 
2.5%
Other values (91) 3366
37.4%
Decimal Number
ValueCountFrequency (%)
1 1031
24.7%
2 638
15.3%
3 417
10.0%
6 401
 
9.6%
8 388
 
9.3%
5 334
 
8.0%
4 310
 
7.4%
0 234
 
5.6%
9 230
 
5.5%
7 196
 
4.7%
Space Separator
ValueCountFrequency (%)
2190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 541
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9004
56.6%
Common 6910
43.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1054
 
11.7%
주 736
 
8.2%
로 736
 
8.2%
제 708
 
7.9%
길 612
 
6.8%
서 495
 
5.5%
귀 387
 
4.3%
포 346
 
3.8%
동 340
 
3.8%
읍 224
 
2.5%
Other values (91) 3366
37.4%
Common
ValueCountFrequency (%)
2190
31.7%
1 1031
14.9%
2 638
 
9.2%
- 541
 
7.8%
3 417
 
6.0%
6 401
 
5.8%
8 388
 
5.6%
5 334
 
4.8%
4 310
 
4.5%
0 234
 
3.4%
Other values (2) 426
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9004
56.6%
ASCII 6910
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2190
31.7%
1 1031
14.9%
2 638
 
9.2%
- 541
 
7.8%
3 417
 
6.0%
6 401
 
5.8%
8 388
 
5.6%
5 334
 
4.8%
4 310
 
4.5%
0 234
 
3.4%
Other values (2) 426
 
6.2%
Hangul
ValueCountFrequency (%)
시 1054
 
11.7%
주 736
 
8.2%
로 736
 
8.2%
제 708
 
7.9%
길 612
 
6.8%
서 495
 
5.5%
귀 387
 
4.3%
포 346
 
3.8%
동 340
 
3.8%
읍 224
 
2.5%
Other values (91) 3366
37.4%

공급유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
일반매입
1006 
청년매입
191 
신혼부부
 
47
공공전세
 
10

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반매입 1006
80.2%
청년매입 191
 
15.2%
신혼부부 47
 
3.7%
공공전세 10
 
0.8%

Length

2023-12-12T21:52:23.749970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:52:23.879993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반매입 1006
80.2%
청년매입 191
 
15.2%
신혼부부 47
 
3.7%
공공전세 10
 
0.8%

세대수
Real number (ℝ)

Distinct23
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.694577
Minimum3
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-12T21:52:24.056590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q18
median12
Q316
95-th percentile39
Maximum41
Range38
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.1588806
Coefficient of variation (CV)0.62328303
Kurtosis1.5978796
Mean14.694577
Median Absolute Deviation (MAD)4
Skewness1.5075173
Sum18427
Variance83.885094
MonotonicityNot monotonic
2023-12-12T21:52:24.169906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 226
18.0%
10 140
 
11.2%
16 128
 
10.2%
13 78
 
6.2%
12 72
 
5.7%
6 60
 
4.8%
11 55
 
4.4%
17 51
 
4.1%
24 48
 
3.8%
14 42
 
3.3%
Other values (13) 354
28.2%
ValueCountFrequency (%)
3 15
 
1.2%
4 20
 
1.6%
5 20
 
1.6%
6 60
 
4.8%
7 28
 
2.2%
8 226
18.0%
9 36
 
2.9%
10 140
11.2%
11 55
 
4.4%
12 72
 
5.7%
ValueCountFrequency (%)
41 41
 
3.3%
39 39
 
3.1%
33 33
 
2.6%
28 28
 
2.2%
26 23
 
1.8%
24 48
 
3.8%
23 23
 
1.8%
18 18
 
1.4%
17 51
 
4.1%
16 128
10.2%

공급형태
Text

Distinct86
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2023-12-12T21:52:24.483453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0063796
Min length3

Characters and Unicode

Total characters3770
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)2.2%

Sample

1st row201
2nd row202
3rd row301
4th row302
5th row401
ValueCountFrequency (%)
201 105
 
8.4%
202 103
 
8.2%
301 102
 
8.1%
302 101
 
8.1%
203 71
 
5.7%
303 69
 
5.5%
401 60
 
4.8%
402 55
 
4.4%
204 49
 
3.9%
304 48
 
3.8%
Other values (76) 491
39.2%
2023-12-12T21:52:24.942650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1254
33.3%
2 733
19.4%
3 578
15.3%
1 479
 
12.7%
4 326
 
8.6%
5 231
 
6.1%
6 87
 
2.3%
7 38
 
1.0%
8 30
 
0.8%
9 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3769
> 99.9%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1254
33.3%
2 733
19.4%
3 578
15.3%
1 479
 
12.7%
4 326
 
8.6%
5 231
 
6.1%
6 87
 
2.3%
7 38
 
1.0%
8 30
 
0.8%
9 13
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3769
> 99.9%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1254
33.3%
2 733
19.4%
3 578
15.3%
1 479
 
12.7%
4 326
 
8.6%
5 231
 
6.1%
6 87
 
2.3%
7 38
 
1.0%
8 30
 
0.8%
9 13
 
0.3%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1254
33.3%
2 733
19.4%
3 578
15.3%
1 479
 
12.7%
4 326
 
8.6%
5 231
 
6.1%
6 87
 
2.3%
7 38
 
1.0%
8 30
 
0.8%
9 13
 
0.3%

전유면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.283892
Minimum14
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-12T21:52:25.134735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile21
Q127
median35
Q350
95-th percentile75
Maximum138
Range124
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.315737
Coefficient of variation (CV)0.4298427
Kurtosis2.4443877
Mean40.283892
Median Absolute Deviation (MAD)10
Skewness1.3704249
Sum50516
Variance299.83474
MonotonicityNot monotonic
2023-12-12T21:52:25.345555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 71
 
5.7%
50 70
 
5.6%
24 64
 
5.1%
31 57
 
4.5%
35 54
 
4.3%
30 50
 
4.0%
27 48
 
3.8%
25 43
 
3.4%
19 42
 
3.3%
29 41
 
3.3%
Other values (76) 714
56.9%
ValueCountFrequency (%)
14 3
 
0.2%
16 3
 
0.2%
17 1
 
0.1%
18 6
 
0.5%
19 42
3.3%
20 5
 
0.4%
21 17
 
1.4%
22 30
2.4%
23 32
2.6%
24 64
5.1%
ValueCountFrequency (%)
138 1
0.1%
118 1
0.1%
113 1
0.1%
111 2
0.2%
107 1
0.1%
106 1
0.1%
104 2
0.2%
102 1
0.1%
100 1
0.1%
98 1
0.1%

공용면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.108453
Minimum0
Maximum49
Zeros83
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-12T21:52:25.500419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median9
Q315
95-th percentile27
Maximum49
Range49
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.848488
Coefficient of variation (CV)0.70653294
Kurtosis1.4068277
Mean11.108453
Median Absolute Deviation (MAD)4
Skewness1.1398904
Sum13930
Variance61.598763
MonotonicityNot monotonic
2023-12-12T21:52:25.658737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
6 134
 
10.7%
7 125
 
10.0%
4 103
 
8.2%
11 89
 
7.1%
14 86
 
6.9%
0 83
 
6.6%
8 70
 
5.6%
5 60
 
4.8%
9 53
 
4.2%
21 47
 
3.7%
Other values (25) 404
32.2%
ValueCountFrequency (%)
0 83
6.6%
2 13
 
1.0%
3 13
 
1.0%
4 103
8.2%
5 60
4.8%
6 134
10.7%
7 125
10.0%
8 70
5.6%
9 53
 
4.2%
10 37
 
3.0%
ValueCountFrequency (%)
49 2
 
0.2%
45 1
 
0.1%
36 2
 
0.2%
35 2
 
0.2%
32 41
3.3%
30 5
 
0.4%
29 6
 
0.5%
28 1
 
0.1%
27 4
 
0.3%
26 12
 
1.0%

공급면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.526316
Minimum19
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-12T21:52:25.842887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile27
Q138
median49
Q362
95-th percentile86
Maximum138
Range119
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.611297
Coefficient of variation (CV)0.36119985
Kurtosis2.1720403
Mean51.526316
Median Absolute Deviation (MAD)12
Skewness1.2142299
Sum64614
Variance346.38039
MonotonicityNot monotonic
2023-12-12T21:52:26.016160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 63
 
5.0%
46 63
 
5.0%
37 58
 
4.6%
54 51
 
4.1%
44 49
 
3.9%
67 44
 
3.5%
50 39
 
3.1%
51 39
 
3.1%
39 37
 
3.0%
40 36
 
2.9%
Other values (80) 775
61.8%
ValueCountFrequency (%)
19 3
 
0.2%
20 1
 
0.1%
22 6
 
0.5%
23 18
1.4%
24 7
 
0.6%
25 5
 
0.4%
26 16
1.3%
27 16
1.3%
28 8
0.6%
29 5
 
0.4%
ValueCountFrequency (%)
138 2
0.2%
125 1
0.1%
123 1
0.1%
122 1
0.1%
121 1
0.1%
120 1
0.1%
118 1
0.1%
117 2
0.2%
116 2
0.2%
114 1
0.1%

매입일자
Date

Distinct43
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
Minimum2007-01-12 00:00:00
Maximum2023-06-30 00:00:00
2023-12-12T21:52:26.169188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:26.300215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)

공급연도
Real number (ℝ)

Distinct11
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.4713
Minimum2007
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2023-12-12T21:52:26.409503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12017
median2020
Q32022
95-th percentile2023
Maximum2023
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.7047264
Coefficient of variation (CV)0.0023308364
Kurtosis0.73628168
Mean2018.4713
Median Absolute Deviation (MAD)2
Skewness-1.391544
Sum2531163
Variance22.134451
MonotonicityNot monotonic
2023-12-12T21:52:26.525743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2021 269
21.5%
2022 194
15.5%
2020 193
15.4%
2019 131
10.4%
2023 127
10.1%
2015 96
 
7.7%
2007 75
 
6.0%
2014 50
 
4.0%
2008 48
 
3.8%
2017 36
 
2.9%
ValueCountFrequency (%)
2007 75
 
6.0%
2008 48
 
3.8%
2009 35
 
2.8%
2014 50
 
4.0%
2015 96
 
7.7%
2017 36
 
2.9%
2019 131
10.4%
2020 193
15.4%
2021 269
21.5%
2022 194
15.5%
ValueCountFrequency (%)
2023 127
10.1%
2022 194
15.5%
2021 269
21.5%
2020 193
15.4%
2019 131
10.4%
2017 36
 
2.9%
2015 96
 
7.7%
2014 50
 
4.0%
2009 35
 
2.8%
2008 48
 
3.8%

Interactions

2023-12-12T21:52:20.664680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:18.631213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.055708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.762989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.135653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.799222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:18.720462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.140269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.836970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.225313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.942844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:18.809714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.221695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.909452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.308729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:21.054753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:18.887100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.586398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.977965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.390436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:21.225833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:18.969787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:19.672429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.056894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:52:20.526483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:52:26.621849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구공급유형세대수공급형태전유면적(제곱미터)공용면적(제곱미터)공급면적(제곱미터)매입일자공급연도
시군구1.0000.1830.5000.2620.2890.4840.2980.9330.525
공급유형0.1831.0000.7050.5860.4040.7860.6070.8150.308
세대수0.5000.7051.0000.6420.4730.6310.5330.9410.549
공급형태0.2620.5860.6421.0000.7210.6010.5920.0000.000
전유면적(제곱미터)0.2890.4040.4730.7211.0000.5300.9380.7570.486
공용면적(제곱미터)0.4840.7860.6310.6010.5301.0000.6490.8410.553
공급면적(제곱미터)0.2980.6070.5330.5920.9380.6491.0000.7700.416
매입일자0.9330.8150.9410.0000.7570.8410.7701.0001.000
공급연도0.5250.3080.5490.0000.4860.5530.4161.0001.000
2023-12-12T21:52:27.070483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급유형시군구
공급유형1.0000.173
시군구0.1731.000
2023-12-12T21:52:27.182930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수전유면적(제곱미터)공용면적(제곱미터)공급면적(제곱미터)공급연도시군구공급유형
세대수1.000-0.4030.416-0.1680.1640.3660.379
전유면적(제곱미터)-0.4031.000-0.0170.854-0.4130.1800.251
공용면적(제곱미터)0.416-0.0171.0000.4400.1470.2440.641
공급면적(제곱미터)-0.1680.8540.4401.000-0.3240.1860.410
공급연도0.164-0.4130.147-0.3241.0000.4050.227
시군구0.3660.1800.2440.1860.4051.0000.173
공급유형0.3790.2510.6410.4100.2270.1731.000

Missing values

2023-12-12T21:52:21.376293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:52:21.545331image/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

광역시시군구단지명도로명주소공급유형세대수공급형태전유면적(제곱미터)공용면적(제곱미터)공급면적(제곱미터)매입일자공급연도
0제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8201757822007-01-122007
1제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8202757822007-01-122007
2제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8301757822007-01-122007
3제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8302757822007-01-122007
4제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8401757822007-01-122007
5제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8402757822007-01-122007
6제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8501757822007-01-122007
7제주특별자치도제주시탐라하이츠제주시 우정로8길 6일반매입8502757822007-01-122007
8제주특별자치도제주시남양빌 나동제주시 신성마을1길 28일반매입8101606662007-01-122007
9제주특별자치도제주시남양빌 나동제주시 신성마을1길 28일반매입8102606662007-01-122007
광역시시군구단지명도로명주소공급유형세대수공급형태전유면적(제곱미터)공용면적(제곱미터)공급면적(제곱미터)매입일자공급연도
1244제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입185101925442023-06-302023
1245제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186011925442023-06-302023
1246제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186021925442023-06-302023
1247제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186031925442023-06-302023
1248제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186041925442023-06-302023
1249제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186051925442023-06-302023
1250제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186061925442023-06-302023
1251제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186071925442023-06-302023
1252제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186081925442023-06-302023
1253제주특별자치도서귀포시제주더그레이튼서귀포시 서문로 4청년매입186101925442023-06-302023