Overview

Dataset statistics

Number of variables7
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory61.8 B

Variable types

Numeric2
Categorical4
Text1

Dataset

Description부산광역시 사상구 관내 공동주택 미분양 현황에 대한 데이터로 사업주체, 대지위치, 아파트명, 전용면적, 미분양세대수 등을 제공합니다.
Author부산광역시 사상구
URLhttps://www.data.go.kr/data/15025631/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위치 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
사업주체 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
아파트명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 사업주체 and 2 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:24:59.927340
Analysis finished2024-04-29 22:25:02.632928
Duration2.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-30T07:25:02.726139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q19.5
median18
Q326.5
95-th percentile33.3
Maximum35
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.246951
Coefficient of variation (CV)0.56927504
Kurtosis-1.2
Mean18
Median Absolute Deviation (MAD)9
Skewness0
Sum630
Variance105
MonotonicityStrictly increasing
2024-04-30T07:25:02.850937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
27 1
 
2.9%
28 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
35 1
2.9%
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%

사업주체
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
㈜무궁화신탁
17 
㈜코리아신탁
8 
㈜신승주택
4 
㈜ 린
3 
㈜경보센트리안
2 

Length

Max length7
Median length6
Mean length5.6285714
Min length3

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row㈜ 린
2nd row㈜ 린
3rd row㈜ 린
4th row㈜경보센트리안
5th row㈜경보센트리안

Common Values

ValueCountFrequency (%)
㈜무궁화신탁 17
48.6%
㈜코리아신탁 8
22.9%
㈜신승주택 4
 
11.4%
㈜ 린 3
 
8.6%
㈜경보센트리안 2
 
5.7%
월드컵㈜ 1
 
2.9%

Length

2024-04-30T07:25:02.977288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:25:03.096963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
㈜무궁화신탁 17
44.7%
㈜코리아신탁 8
21.1%
㈜신승주택 4
 
10.5%
㈜ 3
 
7.9%
린 3
 
7.9%
㈜경보센트리안 2
 
5.3%
월드컵㈜ 1
 
2.6%

위치
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
모라동 1375-1번지
8 
괘법동 527-2번지
8 
괘법동 564-8번지
5 
감전동 118-35번지
4 
괘법동 480-9번지
4 
Other values (3)
6 

Length

Max length15
Median length12
Mean length11.857143
Min length11

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row괘법동 545-11번지 일원
2nd row괘법동 545-11번지 일원
3rd row괘법동 545-11번지 일원
4th row괘법동 525-56번지
5th row괘법동 525-56번지

Common Values

ValueCountFrequency (%)
모라동 1375-1번지 8
22.9%
괘법동 527-2번지 8
22.9%
괘법동 564-8번지 5
14.3%
감전동 118-35번지 4
11.4%
괘법동 480-9번지 4
11.4%
괘법동 545-11번지 일원 3
 
8.6%
괘법동 525-56번지 2
 
5.7%
모라동 산91-16번지 일원 1
 
2.9%

Length

2024-04-30T07:25:03.222921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:25:03.346681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
괘법동 22
29.7%
모라동 9
12.2%
1375-1번지 8
 
10.8%
527-2번지 8
 
10.8%
564-8번지 5
 
6.8%
감전동 4
 
5.4%
118-35번지 4
 
5.4%
480-9번지 4
 
5.4%
일원 4
 
5.4%
545-11번지 3
 
4.1%
Other values (2) 3
 
4.1%

아파트명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
모라역 베스티움 더 시티
8 
보해썬시티리버파크
8 
사상역 포르투나 더 테라스
5 
감전동 엘크루센트로
4 
사상역 경보센트리안 3차
4 
Other values (3)
6 

Length

Max length14
Median length13
Mean length11
Min length6

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row네오리더스 사상
2nd row네오리더스 사상
3rd row네오리더스 사상
4th row경보센트리안
5th row경보센트리안

Common Values

ValueCountFrequency (%)
모라역 베스티움 더 시티 8
22.9%
보해썬시티리버파크 8
22.9%
사상역 포르투나 더 테라스 5
14.3%
감전동 엘크루센트로 4
11.4%
사상역 경보센트리안 3차 4
11.4%
네오리더스 사상 3
 
8.6%
경보센트리안 2
 
5.7%
구남역모라동원로얄듀크 1
 
2.9%

Length

2024-04-30T07:25:03.487438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:25:03.624527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
더 13
14.6%
사상역 9
10.1%
모라역 8
9.0%
베스티움 8
9.0%
시티 8
9.0%
보해썬시티리버파크 8
9.0%
경보센트리안 6
 
6.7%
포르투나 5
 
5.6%
테라스 5
 
5.6%
감전동 4
 
4.5%
Other values (5) 15
16.9%

전용면적(제곱미터)
Text

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-04-30T07:25:03.804115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.9142857
Min length3

Characters and Unicode

Total characters172
Distinct characters19
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)94.3%

Sample

1st row34A
2nd row34A1
3rd row34A2
4th row63.52
5th row47.94
ValueCountFrequency (%)
70a 2
 
5.0%
52a 1
 
2.5%
49d-1 1
 
2.5%
49d-2 1
 
2.5%
75b 1
 
2.5%
75a 1
 
2.5%
70b 1
 
2.5%
55a 1
 
2.5%
52b 1
 
2.5%
34a1 1
 
2.5%
Other values (29) 29
72.5%
2024-04-30T07:25:04.130764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 18
10.5%
4 17
 
9.9%
2 16
 
9.3%
A 13
 
7.6%
9 13
 
7.6%
7 12
 
7.0%
. 11
 
6.4%
1 11
 
6.4%
8 9
 
5.2%
3 9
 
5.2%
Other values (9) 43
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
70.3%
Uppercase Letter 24
 
14.0%
Other Punctuation 11
 
6.4%
Dash Punctuation 6
 
3.5%
Space Separator 5
 
2.9%
Other Letter 5
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 18
14.9%
4 17
14.0%
2 16
13.2%
9 13
10.7%
7 12
9.9%
1 11
9.1%
8 9
7.4%
3 9
7.4%
0 8
6.6%
6 8
6.6%
Uppercase Letter
ValueCountFrequency (%)
A 13
54.2%
B 7
29.2%
C 2
 
8.3%
D 2
 
8.3%
Other Letter
ValueCountFrequency (%)
가 3
60.0%
나 2
40.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 143
83.1%
Latin 24
 
14.0%
Hangul 5
 
2.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 18
12.6%
4 17
11.9%
2 16
11.2%
9 13
9.1%
7 12
8.4%
. 11
7.7%
1 11
7.7%
8 9
6.3%
3 9
6.3%
0 8
5.6%
Other values (3) 19
13.3%
Latin
ValueCountFrequency (%)
A 13
54.2%
B 7
29.2%
C 2
 
8.3%
D 2
 
8.3%
Hangul
ValueCountFrequency (%)
가 3
60.0%
나 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167
97.1%
Hangul 5
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 18
10.8%
4 17
10.2%
2 16
9.6%
A 13
 
7.8%
9 13
 
7.8%
7 12
 
7.2%
. 11
 
6.6%
1 11
 
6.6%
8 9
 
5.4%
3 9
 
5.4%
Other values (7) 38
22.8%
Hangul
ValueCountFrequency (%)
가 3
60.0%
나 2
40.0%

미분양세대수
Real number (ℝ)

Distinct17
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.942857
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-30T07:25:04.249514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q313.5
95-th percentile28
Maximum54
Range53
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation11.517105
Coefficient of variation (CV)1.0524769
Kurtosis5.7191413
Mean10.942857
Median Absolute Deviation (MAD)8
Skewness2.1365161
Sum383
Variance132.6437
MonotonicityNot monotonic
2024-04-30T07:25:04.356724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 7
20.0%
1 4
11.4%
11 3
8.6%
12 3
8.6%
21 2
 
5.7%
4 2
 
5.7%
22 2
 
5.7%
14 2
 
5.7%
13 2
 
5.7%
18 1
 
2.9%
Other values (7) 7
20.0%
ValueCountFrequency (%)
1 4
11.4%
2 7
20.0%
3 1
 
2.9%
4 2
 
5.7%
6 1
 
2.9%
7 1
 
2.9%
8 1
 
2.9%
10 1
 
2.9%
11 3
8.6%
12 3
8.6%
ValueCountFrequency (%)
54 1
 
2.9%
42 1
 
2.9%
22 2
5.7%
21 2
5.7%
18 1
 
2.9%
14 2
5.7%
13 2
5.7%
12 3
8.6%
11 3
8.6%
10 1
 
2.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-04-20
35 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-20
2nd row2024-04-20
3rd row2024-04-20
4th row2024-04-20
5th row2024-04-20

Common Values

ValueCountFrequency (%)
2024-04-20 35
100.0%

Length

2024-04-30T07:25:04.484122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:25:04.575843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-20 35
100.0%

Interactions

2024-04-30T07:25:02.133412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:25:01.832043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:25:02.255170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:25:02.036886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:25:04.634741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업주체위치아파트명전용면적(제곱미터)미분양세대수
연번1.0000.8040.8640.8640.8980.454
사업주체0.8041.0001.0001.0001.0000.000
위치0.8641.0001.0001.0000.9390.000
아파트명0.8641.0001.0001.0000.9390.000
전용면적(제곱미터)0.8981.0000.9390.9391.0000.000
미분양세대수0.4540.0000.0000.0000.0001.000
2024-04-30T07:25:04.724461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치사업주체아파트명
위치1.0000.9651.000
사업주체0.9651.0000.965
아파트명1.0000.9651.000
2024-04-30T07:25:04.802350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번미분양세대수사업주체위치아파트명
연번1.0000.3480.5570.6420.642
미분양세대수0.3481.0000.0000.0000.000
사업주체0.5570.0001.0000.9650.965
위치0.6420.0000.9651.0001.000
아파트명0.6420.0000.9651.0001.000

Missing values

2024-04-30T07:25:02.398126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:25:02.546587image/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

연번사업주체위치아파트명전용면적(제곱미터)미분양세대수데이터기준일자
01㈜ 린괘법동 545-11번지 일원네오리더스 사상34A182024-04-20
12㈜ 린괘법동 545-11번지 일원네오리더스 사상34A1122024-04-20
23㈜ 린괘법동 545-11번지 일원네오리더스 사상34A2102024-04-20
34㈜경보센트리안괘법동 525-56번지경보센트리안63.5212024-04-20
45㈜경보센트리안괘법동 525-56번지경보센트리안47.9442024-04-20
56㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스가 22.399242024-04-20
67㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스가1 21.524512024-04-20
78㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스가2 21.533812024-04-20
89㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스나 18.5856112024-04-20
910㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스나1 18.005822024-04-20
연번사업주체위치아파트명전용면적(제곱미터)미분양세대수데이터기준일자
2526㈜신승주택괘법동 480-9번지사상역 경보센트리안 3차69.306722024-04-20
2627㈜신승주택괘법동 480-9번지사상역 경보센트리안 3차64.212582024-04-20
2728㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크52A142024-04-20
2829㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크52B12024-04-20
2930㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크55A112024-04-20
3031㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크70A422024-04-20
3132㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크70B222024-04-20
3233㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크75A542024-04-20
3334㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크75B212024-04-20
3435㈜무궁화신탁괘법동 527-2번지보해썬시티리버파크75C212024-04-20