Overview

Dataset statistics

Number of variables7
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory62.9 B

Variable types

Numeric2
Categorical3
Text1
DateTime1

Dataset

Description부산광역시_사상구_미분양현황_20230420
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15025631

Alerts

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

Reproduction

Analysis started2023-12-10 17:21:04.670349
Analysis finished2023-12-10 17:21:09.204192
Duration4.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T02:21:09.347656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-11T02:21:09.656217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

사업주체
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
㈜무궁화신탁
9 
㈜코리아신탁
8 
㈜신승주택
4 
㈜ 린
3 
㈜경보센트리안
2 

Length

Max length7
Median length6
Mean length5.5185185
Min length3

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
㈜무궁화신탁 9
33.3%
㈜코리아신탁 8
29.6%
㈜신승주택 4
14.8%
㈜ 린 3
 
11.1%
㈜경보센트리안 2
 
7.4%
월드컵㈜ 1
 
3.7%

Length

2023-12-11T02:21:09.976724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:21:10.272502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
㈜무궁화신탁 9
30.0%
㈜코리아신탁 8
26.7%
㈜신승주택 4
13.3%
㈜ 3
 
10.0%
린 3
 
10.0%
㈜경보센트리안 2
 
6.7%
월드컵㈜ 1
 
3.3%

위치
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
모라동 1375-1번지
8 
괘법동 564-8번지
5 
감전동 118-35번지
4 
괘법동 480-9번지
4 
괘법동 545-11번지 일원
3 
Other values (2)
3 

Length

Max length15
Median length12
Mean length12.111111
Min length11

Unique

Unique1 ?
Unique (%)3.7%

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
29.6%
괘법동 564-8번지 5
18.5%
감전동 118-35번지 4
14.8%
괘법동 480-9번지 4
14.8%
괘법동 545-11번지 일원 3
 
11.1%
괘법동 525-56번지 2
 
7.4%
모라동 산91-16번지 일원 1
 
3.7%

Length

2023-12-11T02:21:10.600664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:21:10.893134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
괘법동 14
24.1%
모라동 9
15.5%
1375-1번지 8
13.8%
564-8번지 5
 
8.6%
감전동 4
 
6.9%
118-35번지 4
 
6.9%
480-9번지 4
 
6.9%
일원 4
 
6.9%
545-11번지 3
 
5.2%
525-56번지 2
 
3.4%

아파트명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
모라역 베스티움 더 시티
8 
사상역 포르투나 더 테라스
5 
감전동 엘크루센트로
4 
사상역 경보센트리안 3차
4 
네오리더스 사상
3 
Other values (2)
3 

Length

Max length14
Median length13
Mean length11.592593
Min length6

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
모라역 베스티움 더 시티 8
29.6%
사상역 포르투나 더 테라스 5
18.5%
감전동 엘크루센트로 4
14.8%
사상역 경보센트리안 3차 4
14.8%
네오리더스 사상 3
 
11.1%
경보센트리안 2
 
7.4%
구남역모라동원로얄듀크 1
 
3.7%

Length

2023-12-11T02:21:11.268826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:21:11.582388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
더 13
16.0%
사상역 9
11.1%
모라역 8
9.9%
베스티움 8
9.9%
시티 8
9.9%
경보센트리안 6
7.4%
포르투나 5
 
6.2%
테라스 5
 
6.2%
감전동 4
 
4.9%
엘크루센트로 4
 
4.9%
Other values (4) 11
13.6%

전용면적(제곱미터)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T02:21:12.025603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4814815
Min length3

Characters and Unicode

Total characters148
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

Unique27 ?
Unique (%)100.0%

Sample

1st row34A
2nd row34A1
3rd row34A2
4th row63.52
5th row47.94
ValueCountFrequency (%)
34a 1
 
3.1%
34a1 1
 
3.1%
69.3067 1
 
3.1%
74.3505 1
 
3.1%
80.7658 1
 
3.1%
78a 1
 
3.1%
70a 1
 
3.1%
69b 1
 
3.1%
69a 1
 
3.1%
49d-2 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T02:21:12.730619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 17
11.5%
2 14
 
9.5%
9 13
 
8.8%
5 11
 
7.4%
1 11
 
7.4%
. 11
 
7.4%
8 9
 
6.1%
3 9
 
6.1%
A 9
 
6.1%
6 8
 
5.4%
Other values (9) 36
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
70.9%
Uppercase Letter 16
 
10.8%
Other Punctuation 11
 
7.4%
Dash Punctuation 6
 
4.1%
Space Separator 5
 
3.4%
Other Letter 5
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 17
16.2%
2 14
13.3%
9 13
12.4%
5 11
10.5%
1 11
10.5%
8 9
8.6%
3 9
8.6%
6 8
7.6%
7 7
6.7%
0 6
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 9
56.2%
B 4
25.0%
D 2
 
12.5%
C 1
 
6.2%
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 127
85.8%
Latin 16
 
10.8%
Hangul 5
 
3.4%

Most frequent character per script

Common
ValueCountFrequency (%)
4 17
13.4%
2 14
11.0%
9 13
10.2%
5 11
8.7%
1 11
8.7%
. 11
8.7%
8 9
7.1%
3 9
7.1%
6 8
6.3%
7 7
5.5%
Other values (3) 17
13.4%
Latin
ValueCountFrequency (%)
A 9
56.2%
B 4
25.0%
D 2
 
12.5%
C 1
 
6.2%
Hangul
ValueCountFrequency (%)
가 3
60.0%
나 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143
96.6%
Hangul 5
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 17
11.9%
2 14
9.8%
9 13
9.1%
5 11
 
7.7%
1 11
 
7.7%
. 11
 
7.7%
8 9
 
6.3%
3 9
 
6.3%
A 9
 
6.3%
6 8
 
5.6%
Other values (7) 31
21.7%
Hangul
ValueCountFrequency (%)
가 3
60.0%
나 2
40.0%

미분양세대수
Real number (ℝ)

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2962963
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T02:21:13.009600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q312
95-th percentile16.8
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8822733
Coefficient of variation (CV)0.8061999
Kurtosis-0.27514411
Mean7.2962963
Median Absolute Deviation (MAD)4
Skewness0.72851354
Sum197
Variance34.60114
MonotonicityNot monotonic
2023-12-11T02:21:13.264687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 7
25.9%
12 3
11.1%
1 3
11.1%
4 2
 
7.4%
11 2
 
7.4%
13 2
 
7.4%
18 1
 
3.7%
10 1
 
3.7%
22 1
 
3.7%
6 1
 
3.7%
Other values (4) 4
14.8%
ValueCountFrequency (%)
1 3
11.1%
2 7
25.9%
3 1
 
3.7%
4 2
 
7.4%
6 1
 
3.7%
7 1
 
3.7%
8 1
 
3.7%
10 1
 
3.7%
11 2
 
7.4%
12 3
11.1%
ValueCountFrequency (%)
22 1
 
3.7%
18 1
 
3.7%
14 1
 
3.7%
13 2
7.4%
12 3
11.1%
11 2
7.4%
10 1
 
3.7%
8 1
 
3.7%
7 1
 
3.7%
6 1
 
3.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2023-04-20 00:00:00
Maximum2023-04-20 00:00:00
2023-12-11T02:21:13.511577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:21:13.787932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T02:21:08.374699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:21:07.964523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:21:08.573976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:21:08.196821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:21:14.034675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업주체위치아파트명전용면적(제곱미터)미분양세대수
연번1.0000.9030.9110.9111.0000.604
사업주체0.9031.0001.0001.0001.0000.000
위치0.9111.0001.0001.0001.0000.000
아파트명0.9111.0001.0001.0001.0000.000
전용면적(제곱미터)1.0001.0001.0001.0001.0001.000
미분양세대수0.6040.0000.0000.0001.0001.000
2023-12-11T02:21:14.341962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업주체아파트명위치
사업주체1.0000.9760.976
아파트명0.9761.0001.000
위치0.9761.0001.000
2023-12-11T02:21:14.593748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번미분양세대수사업주체위치아파트명
연번1.000-0.0350.6820.7090.709
미분양세대수-0.0351.0000.0000.0000.000
사업주체0.6820.0001.0000.9760.976
위치0.7090.0000.9761.0001.000
아파트명0.7090.0000.9761.0001.000

Missing values

2023-12-11T02:21:08.807070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:21:09.102895image/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 사상34A182023-04-20
12㈜ 린괘법동 545-11번지 일원네오리더스 사상34A1122023-04-20
23㈜ 린괘법동 545-11번지 일원네오리더스 사상34A2102023-04-20
34㈜경보센트리안괘법동 525-56번지경보센트리안63.5212023-04-20
45㈜경보센트리안괘법동 525-56번지경보센트리안47.9442023-04-20
56㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스가 22.399242023-04-20
67㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스가1 21.524512023-04-20
78㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스가2 21.533812023-04-20
89㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스나 18.5856112023-04-20
910㈜무궁화신탁괘법동 564-8번지사상역 포르투나 더 테라스나1 18.005822023-04-20
연번사업주체위치아파트명전용면적(제곱미터)미분양세대수데이터기준일자
1718㈜코리아신탁모라동 1375-1번지모라역 베스티움 더 시티49D-1142023-04-20
1819㈜코리아신탁모라동 1375-1번지모라역 베스티움 더 시티49D-2122023-04-20
1920㈜무궁화신탁감전동 118-35번지감전동 엘크루센트로69A132023-04-20
2021㈜무궁화신탁감전동 118-35번지감전동 엘크루센트로69B32023-04-20
2122㈜무궁화신탁감전동 118-35번지감전동 엘크루센트로70A132023-04-20
2223㈜무궁화신탁감전동 118-35번지감전동 엘크루센트로78A22023-04-20
2324㈜신승주택괘법동 480-9번지사상역 경보센트리안 3차80.765822023-04-20
2425㈜신승주택괘법동 480-9번지사상역 경보센트리안 3차74.350522023-04-20
2526㈜신승주택괘법동 480-9번지사상역 경보센트리안 3차69.306722023-04-20
2627㈜신승주택괘법동 480-9번지사상역 경보센트리안 3차64.212582023-04-20