Overview

Dataset statistics

Number of variables7
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory61.4 B

Variable types

Text2
Numeric3
Categorical1
DateTime1

Dataset

Description부산광역시 중구 관내 공동주택 현황에 관한 데이터로 단지명, 소재지, 동수, 층수, 세대수, 연면적, 준공일자 등의 항목을 제공합니다.
Author부산광역시 중구
URLhttps://www.data.go.kr/data/3072711/fileData.do

Alerts

세대수 is highly overall correlated with 연면적(제곱미터) and 1 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 세대수High correlation
동수 is highly overall correlated with 세대수High correlation
동수 is highly imbalanced (60.4%)Imbalance
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:19:17.453674
Analysis finished2023-12-12 09:19:19.631079
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단지명
Text

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-12T18:19:19.933392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length15
Mean length6.7916667
Min length3

Characters and Unicode

Total characters652
Distinct characters177
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)97.9%

Sample

1st row신창아파트
2nd row신생아파트
3rd row영주APT3블럭
4th row영주APT2블럭
5th row영주APT9블럭
ValueCountFrequency (%)
로하스 2
 
1.9%
포세이돈 1
 
0.9%
한웅베어스타운 1
 
0.9%
예그린아파트 1
 
0.9%
수목하우스다동 1
 
0.9%
미소지움주상복합아파트 1
 
0.9%
씨앤리진2차 1
 
0.9%
삼성팰리스 1
 
0.9%
보수허브센티움 1
 
0.9%
새미빌 1
 
0.9%
Other values (95) 95
89.6%
2023-12-12T18:19:20.391773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 34
 
5.2%
파 34
 
5.2%
트 32
 
4.9%
스 21
 
3.2%
동 20
 
3.1%
빌 14
 
2.1%
리 11
 
1.7%
이 11
 
1.7%
수 10
 
1.5%
( 10
 
1.5%
Other values (167) 455
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 569
87.3%
Decimal Number 29
 
4.4%
Uppercase Letter 18
 
2.8%
Open Punctuation 10
 
1.5%
Close Punctuation 10
 
1.5%
Space Separator 10
 
1.5%
Other Punctuation 6
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 34
 
6.0%
파 34
 
6.0%
트 32
 
5.6%
스 21
 
3.7%
동 20
 
3.5%
빌 14
 
2.5%
리 11
 
1.9%
이 11
 
1.9%
수 10
 
1.8%
하 10
 
1.8%
Other values (148) 372
65.4%
Decimal Number
ValueCountFrequency (%)
2 8
27.6%
1 8
27.6%
3 5
17.2%
9 2
 
6.9%
0 2
 
6.9%
4 2
 
6.9%
6 1
 
3.4%
5 1
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
T 5
27.8%
P 5
27.8%
A 5
27.8%
M 1
 
5.6%
C 1
 
5.6%
F 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
, 2
33.3%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 569
87.3%
Common 65
 
10.0%
Latin 18
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 34
 
6.0%
파 34
 
6.0%
트 32
 
5.6%
스 21
 
3.7%
동 20
 
3.5%
빌 14
 
2.5%
리 11
 
1.9%
이 11
 
1.9%
수 10
 
1.8%
하 10
 
1.8%
Other values (148) 372
65.4%
Common
ValueCountFrequency (%)
( 10
15.4%
) 10
15.4%
10
15.4%
2 8
12.3%
1 8
12.3%
3 5
7.7%
. 4
 
6.2%
9 2
 
3.1%
0 2
 
3.1%
, 2
 
3.1%
Other values (3) 4
 
6.2%
Latin
ValueCountFrequency (%)
T 5
27.8%
P 5
27.8%
A 5
27.8%
M 1
 
5.6%
C 1
 
5.6%
F 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 569
87.3%
ASCII 83
 
12.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 34
 
6.0%
파 34
 
6.0%
트 32
 
5.6%
스 21
 
3.7%
동 20
 
3.5%
빌 14
 
2.5%
리 11
 
1.9%
이 11
 
1.9%
수 10
 
1.8%
하 10
 
1.8%
Other values (148) 372
65.4%
ASCII
ValueCountFrequency (%)
( 10
12.0%
) 10
12.0%
10
12.0%
2 8
9.6%
1 8
9.6%
3 5
 
6.0%
T 5
 
6.0%
P 5
 
6.0%
A 5
 
6.0%
. 4
 
4.8%
Other values (9) 13
15.7%

소재지
Text

UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-12T18:19:20.721949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length19.239583
Min length14

Characters and Unicode

Total characters1847
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)100.0%

Sample

1st row부산광역시 중구 창선동1가 9-1외1
2nd row부산광역시 중구 신창동2가 22
3rd row부산광역시 중구 영주동 73-1
4th row부산광역시 중구 영주동 72-4
5th row부산광역시 중구 영주동 93-4
ValueCountFrequency (%)
부산광역시 96
24.6%
중구 96
24.6%
영주동 24
 
6.1%
보수동2가 14
 
3.6%
부평동4가 11
 
2.8%
보수동3가 10
 
2.6%
대청동4가 7
 
1.8%
보수동1가 4
 
1.0%
대청동1가 3
 
0.8%
중앙동4가 3
 
0.8%
Other values (113) 123
31.5%
2023-12-12T18:19:21.252513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
16.1%
부 111
 
6.0%
중 100
 
5.4%
광 99
 
5.4%
산 98
 
5.3%
구 97
 
5.3%
역 96
 
5.2%
시 96
 
5.2%
동 96
 
5.2%
1 84
 
4.5%
Other values (35) 672
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1060
57.4%
Decimal Number 391
 
21.2%
Space Separator 298
 
16.1%
Dash Punctuation 80
 
4.3%
Close Punctuation 7
 
0.4%
Open Punctuation 7
 
0.4%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 111
10.5%
중 100
9.4%
광 99
9.3%
산 98
9.2%
구 97
9.2%
역 96
9.1%
시 96
9.1%
동 96
9.1%
가 70
6.6%
보 29
 
2.7%
Other values (20) 168
15.8%
Decimal Number
ValueCountFrequency (%)
1 84
21.5%
2 63
16.1%
4 52
13.3%
3 36
9.2%
5 35
9.0%
7 31
 
7.9%
8 25
 
6.4%
6 24
 
6.1%
9 22
 
5.6%
0 19
 
4.9%
Space Separator
ValueCountFrequency (%)
298
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1060
57.4%
Common 787
42.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 111
10.5%
중 100
9.4%
광 99
9.3%
산 98
9.2%
구 97
9.2%
역 96
9.1%
시 96
9.1%
동 96
9.1%
가 70
6.6%
보 29
 
2.7%
Other values (20) 168
15.8%
Common
ValueCountFrequency (%)
298
37.9%
1 84
 
10.7%
- 80
 
10.2%
2 63
 
8.0%
4 52
 
6.6%
3 36
 
4.6%
5 35
 
4.4%
7 31
 
3.9%
8 25
 
3.2%
6 24
 
3.0%
Other values (5) 59
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1060
57.4%
ASCII 787
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
37.9%
1 84
 
10.7%
- 80
 
10.2%
2 63
 
8.0%
4 52
 
6.6%
3 36
 
4.6%
5 35
 
4.4%
7 31
 
3.9%
8 25
 
3.2%
6 24
 
3.0%
Other values (5) 59
 
7.5%
Hangul
ValueCountFrequency (%)
부 111
10.5%
중 100
9.4%
광 99
9.3%
산 98
9.2%
구 97
9.2%
역 96
9.1%
시 96
9.1%
동 96
9.1%
가 70
6.6%
보 29
 
2.7%
Other values (20) 168
15.8%

층수
Real number (ℝ)

Distinct18
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.614583
Minimum3
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T18:19:21.396554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median9
Q315
95-th percentile20
Maximum33
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.3140723
Coefficient of variation (CV)0.5006388
Kurtosis1.9507877
Mean10.614583
Median Absolute Deviation (MAD)4
Skewness1.0787396
Sum1019
Variance28.239364
MonotonicityNot monotonic
2023-12-12T18:19:21.519106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 13
13.5%
15 12
12.5%
9 10
10.4%
8 9
9.4%
6 8
8.3%
20 7
7.3%
10 7
7.3%
4 5
 
5.2%
14 5
 
5.2%
11 5
 
5.2%
Other values (8) 15
15.6%
ValueCountFrequency (%)
3 1
 
1.0%
4 5
 
5.2%
5 13
13.5%
6 8
8.3%
7 3
 
3.1%
8 9
9.4%
9 10
10.4%
10 7
7.3%
11 5
 
5.2%
12 3
 
3.1%
ValueCountFrequency (%)
33 1
 
1.0%
21 1
 
1.0%
20 7
7.3%
18 1
 
1.0%
16 4
 
4.2%
15 12
12.5%
14 5
5.2%
13 1
 
1.0%
12 3
 
3.1%
11 5
5.2%

동수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
1
80 
2
9 
4
 
3
3
 
3
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row4
4th row4
5th row2

Common Values

ValueCountFrequency (%)
1 80
83.3%
2 9
 
9.4%
4 3
 
3.1%
3 3
 
3.1%
5 1
 
1.0%

Length

2023-12-12T18:19:21.657226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:19:21.762356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 80
83.3%
2 9
 
9.4%
4 3
 
3.1%
3 3
 
3.1%
5 1
 
1.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.25
Minimum3
Maximum406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T18:19:21.901095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10.5
Q120
median41
Q374
95-th percentile206.75
Maximum406
Range403
Interquartile range (IQR)54

Descriptive statistics

Standard deviation72.274477
Coefficient of variation (CV)1.1248946
Kurtosis7.6878143
Mean64.25
Median Absolute Deviation (MAD)22
Skewness2.5961364
Sum6168
Variance5223.6
MonotonicityNot monotonic
2023-12-12T18:19:22.072147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 7
 
7.3%
16 5
 
5.2%
24 4
 
4.2%
18 4
 
4.2%
60 4
 
4.2%
47 3
 
3.1%
28 3
 
3.1%
8 3
 
3.1%
42 2
 
2.1%
30 2
 
2.1%
Other values (49) 59
61.5%
ValueCountFrequency (%)
3 1
 
1.0%
8 3
3.1%
9 1
 
1.0%
11 1
 
1.0%
12 1
 
1.0%
14 2
 
2.1%
16 5
5.2%
17 1
 
1.0%
18 4
4.2%
19 1
 
1.0%
ValueCountFrequency (%)
406 1
1.0%
328 1
1.0%
322 1
1.0%
268 1
1.0%
215 1
1.0%
204 1
1.0%
192 1
1.0%
167 1
1.0%
147 1
1.0%
140 1
1.0%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5952.5668
Minimum464.11
Maximum36367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T18:19:22.259400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum464.11
5-th percentile729.5
Q11396.75
median3182
Q37574
95-th percentile19318
Maximum36367
Range35902.89
Interquartile range (IQR)6177.25

Descriptive statistics

Standard deviation6856.1003
Coefficient of variation (CV)1.1517889
Kurtosis6.2182641
Mean5952.5668
Median Absolute Deviation (MAD)2205.5
Skewness2.2912527
Sum571446.41
Variance47006111
MonotonicityNot monotonic
2023-12-12T18:19:22.453498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901.0 3
 
3.1%
1785.0 2
 
2.1%
2452.0 2
 
2.1%
716.0 1
 
1.0%
5389.0 1
 
1.0%
995.0 1
 
1.0%
1205.0 1
 
1.0%
999.0 1
 
1.0%
8171.0 1
 
1.0%
771.0 1
 
1.0%
Other values (82) 82
85.4%
ValueCountFrequency (%)
464.11 1
1.0%
643.0 1
1.0%
662.0 1
1.0%
685.0 1
1.0%
716.0 1
1.0%
734.0 1
1.0%
771.0 1
1.0%
841.0 1
1.0%
879.0 1
1.0%
897.0 1
1.0%
ValueCountFrequency (%)
36367.0 1
1.0%
34055.0 1
1.0%
24625.0 1
1.0%
23408.0 1
1.0%
20191.0 1
1.0%
19027.0 1
1.0%
17612.0 1
1.0%
14764.0 1
1.0%
14558.7121 1
1.0%
14390.0 1
1.0%

준공일자
Date

Distinct90
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size900.0 B
Minimum1960-11-01 00:00:00
Maximum2022-12-22 00:00:00
2023-12-12T18:19:22.646652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:22.858584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T18:19:19.099319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:18.317101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:18.683441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:19.201277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:18.432691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:18.834199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:19.303090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:18.553249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:18.959161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:19:22.975723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단지명소재지층수동수세대수연면적(제곱미터)준공일자
단지명1.0001.0000.9751.0001.0001.0000.998
소재지1.0001.0001.0001.0001.0001.0001.000
층수0.9751.0001.0000.3230.4290.7390.997
동수1.0001.0000.3231.0000.9620.4420.000
세대수1.0001.0000.4290.9621.0000.7940.000
연면적(제곱미터)1.0001.0000.7390.4420.7941.0000.000
준공일자0.9981.0000.9970.0000.0000.0001.000
2023-12-12T18:19:23.094174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층수세대수연면적(제곱미터)동수
층수1.0000.1520.4450.155
세대수0.1521.0000.7750.711
연면적(제곱미터)0.4450.7751.0000.285
동수0.1550.7110.2851.000

Missing values

2023-12-12T18:19:19.439982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:19:19.569832image/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

단지명소재지층수동수세대수연면적(제곱미터)준공일자
0신창아파트부산광역시 중구 창선동1가 9-1외141241785.01960-11-01
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