Overview

Dataset statistics

Number of variables12
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory101.1 B

Variable types

Numeric4
Text6
DateTime2

Dataset

Description부산광역시사하구_공동주택현황_20230711
Author부산광역시 사하구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15102380

Alerts

연번 is highly overall correlated with 층High correlation
층 is highly overall correlated with 연번High correlation
동 is highly overall correlated with 세대수 High correlation
세대수 is highly overall correlated with 동High correlation
연번 has unique valuesUnique
도로명주소 has unique valuesUnique
연락처 has unique valuesUnique
팩스번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:09:21.166640
Analysis finished2023-12-10 17:09:25.360578
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.5
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:09:25.497798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q130.75
median60.5
Q390.25
95-th percentile114.05
Maximum120
Range119
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation34.785054
Coefficient of variation (CV)0.57495957
Kurtosis-1.2
Mean60.5
Median Absolute Deviation (MAD)30
Skewness0
Sum7260
Variance1210
MonotonicityStrictly increasing
2023-12-11T02:09:25.784920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
62 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%

단지명
Text

Distinct116
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:09:26.253635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.45
Min length4

Characters and Unicode

Total characters894
Distinct characters171
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)94.2%

Sample

1st row신동양아파트
2nd row협진태양아파트
3rd row괴정자유1차아파트
4th row국제아파트
5th row코오롱아파트
ValueCountFrequency (%)
현대아파트 3
 
2.1%
아파트 3
 
2.1%
장림역 3
 
2.1%
사하역 2
 
1.4%
하단 2
 
1.4%
괴정 2
 
1.4%
구평 2
 
1.4%
경동메르빌 2
 
1.4%
우림그린맨션 2
 
1.4%
비스타동원 1
 
0.7%
Other values (123) 123
84.8%
2023-12-11T02:09:26.842202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 70
 
7.8%
트 69
 
7.7%
아 66
 
7.4%
대 27
 
3.0%
25
 
2.8%
신 23
 
2.6%
스 20
 
2.2%
림 16
 
1.8%
동 15
 
1.7%
다 14
 
1.6%
Other values (161) 549
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 817
91.4%
Space Separator 25
 
2.8%
Decimal Number 25
 
2.8%
Uppercase Letter 14
 
1.6%
Open Punctuation 4
 
0.4%
Close Punctuation 4
 
0.4%
Other Punctuation 3
 
0.3%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 70
 
8.6%
트 69
 
8.4%
아 66
 
8.1%
대 27
 
3.3%
신 23
 
2.8%
스 20
 
2.4%
림 16
 
2.0%
동 15
 
1.8%
다 14
 
1.7%
차 14
 
1.7%
Other values (140) 483
59.1%
Uppercase Letter
ValueCountFrequency (%)
S 3
21.4%
I 2
14.3%
W 2
14.3%
K 2
14.3%
D 1
 
7.1%
L 1
 
7.1%
H 1
 
7.1%
V 1
 
7.1%
E 1
 
7.1%
Decimal Number
ValueCountFrequency (%)
2 10
40.0%
1 7
28.0%
3 4
 
16.0%
5 2
 
8.0%
0 1
 
4.0%
4 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
: 1
33.3%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 817
91.4%
Common 61
 
6.8%
Latin 16
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 70
 
8.6%
트 69
 
8.4%
아 66
 
8.1%
대 27
 
3.3%
신 23
 
2.8%
스 20
 
2.4%
림 16
 
2.0%
동 15
 
1.8%
다 14
 
1.7%
차 14
 
1.7%
Other values (140) 483
59.1%
Common
ValueCountFrequency (%)
25
41.0%
2 10
 
16.4%
1 7
 
11.5%
( 4
 
6.6%
) 4
 
6.6%
3 4
 
6.6%
, 2
 
3.3%
5 2
 
3.3%
0 1
 
1.6%
4 1
 
1.6%
Latin
ValueCountFrequency (%)
S 3
18.8%
I 2
12.5%
W 2
12.5%
e 2
12.5%
K 2
12.5%
D 1
 
6.2%
L 1
 
6.2%
H 1
 
6.2%
V 1
 
6.2%
E 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 817
91.4%
ASCII 77
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 70
 
8.6%
트 69
 
8.4%
아 66
 
8.1%
대 27
 
3.3%
신 23
 
2.8%
스 20
 
2.4%
림 16
 
2.0%
동 15
 
1.8%
다 14
 
1.7%
차 14
 
1.7%
Other values (140) 483
59.1%
ASCII
ValueCountFrequency (%)
25
32.5%
2 10
 
13.0%
1 7
 
9.1%
( 4
 
5.2%
) 4
 
5.2%
3 4
 
5.2%
S 3
 
3.9%
I 2
 
2.6%
W 2
 
2.6%
e 2
 
2.6%
Other values (11) 14
18.2%

위치
Text

Distinct119
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:09:27.279479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length14
Mean length9.6666667
Min length6

Characters and Unicode

Total characters1160
Distinct characters38
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

Unique118 ?
Unique (%)98.3%

Sample

1st row신동양아파트
2nd row괴정1동 530-13
3rd row괴정1동 740
4th row괴정1동 491-9
5th row다대1동 37
ValueCountFrequency (%)
장림2동 17
 
6.7%
다대1동 16
 
6.3%
당리동 12
 
4.7%
괴정동 12
 
4.7%
괴정1동 8
 
3.2%
다대2동 8
 
3.2%
감천1동 7
 
2.8%
구평동 6
 
2.4%
신평2동 6
 
2.4%
장림동 5
 
2.0%
Other values (137) 156
61.7%
2023-12-11T02:09:28.140826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 143
 
12.3%
133
 
11.5%
동 124
 
10.7%
2 98
 
8.4%
- 71
 
6.1%
4 47
 
4.1%
5 44
 
3.8%
6 40
 
3.4%
7 40
 
3.4%
3 40
 
3.4%
Other values (28) 380
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 554
47.8%
Other Letter 387
33.4%
Space Separator 133
 
11.5%
Dash Punctuation 71
 
6.1%
Open Punctuation 5
 
0.4%
Close Punctuation 5
 
0.4%
Other Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 124
32.0%
다 27
 
7.0%
대 27
 
7.0%
정 25
 
6.5%
괴 25
 
6.5%
림 22
 
5.7%
장 22
 
5.7%
평 20
 
5.2%
신 15
 
3.9%
단 13
 
3.4%
Other values (13) 67
17.3%
Decimal Number
ValueCountFrequency (%)
1 143
25.8%
2 98
17.7%
4 47
 
8.5%
5 44
 
7.9%
6 40
 
7.2%
7 40
 
7.2%
3 40
 
7.2%
0 38
 
6.9%
8 33
 
6.0%
9 31
 
5.6%
Space Separator
ValueCountFrequency (%)
133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 773
66.6%
Hangul 387
33.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 124
32.0%
다 27
 
7.0%
대 27
 
7.0%
정 25
 
6.5%
괴 25
 
6.5%
림 22
 
5.7%
장 22
 
5.7%
평 20
 
5.2%
신 15
 
3.9%
단 13
 
3.4%
Other values (13) 67
17.3%
Common
ValueCountFrequency (%)
1 143
18.5%
133
17.2%
2 98
12.7%
- 71
9.2%
4 47
 
6.1%
5 44
 
5.7%
6 40
 
5.2%
7 40
 
5.2%
3 40
 
5.2%
0 38
 
4.9%
Other values (5) 79
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 773
66.6%
Hangul 387
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 143
18.5%
133
17.2%
2 98
12.7%
- 71
9.2%
4 47
 
6.1%
5 44
 
5.7%
6 40
 
5.2%
7 40
 
5.2%
3 40
 
5.2%
0 38
 
4.9%
Other values (5) 79
10.2%
Hangul
ValueCountFrequency (%)
동 124
32.0%
다 27
 
7.0%
대 27
 
7.0%
정 25
 
6.5%
괴 25
 
6.5%
림 22
 
5.7%
장 22
 
5.7%
평 20
 
5.2%
신 15
 
3.9%
단 13
 
3.4%
Other values (13) 67
17.3%

우편번호
Text

Distinct94
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:09:28.596069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.65
Min length5

Characters and Unicode

Total characters798
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)64.2%

Sample

1st row604-855
2nd row604-812
3rd row604-764
4th row604-812
5th row604-766
ValueCountFrequency (%)
604-812 4
 
3.3%
604-754 4
 
3.3%
604-815 3
 
2.5%
604-832 3
 
2.5%
49465 3
 
2.5%
604-840 3
 
2.5%
49459 3
 
2.5%
604-782 2
 
1.7%
604-753 2
 
1.7%
604-828 2
 
1.7%
Other values (84) 91
75.8%
2023-12-11T02:09:29.506487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 157
19.7%
0 129
16.2%
6 124
15.5%
- 99
12.4%
7 81
10.2%
8 53
 
6.6%
5 38
 
4.8%
2 32
 
4.0%
3 32
 
4.0%
9 29
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 699
87.6%
Dash Punctuation 99
 
12.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 157
22.5%
0 129
18.5%
6 124
17.7%
7 81
11.6%
8 53
 
7.6%
5 38
 
5.4%
2 32
 
4.6%
3 32
 
4.6%
9 29
 
4.1%
1 24
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 157
19.7%
0 129
16.2%
6 124
15.5%
- 99
12.4%
7 81
10.2%
8 53
 
6.6%
5 38
 
4.8%
2 32
 
4.0%
3 32
 
4.0%
9 29
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 157
19.7%
0 129
16.2%
6 124
15.5%
- 99
12.4%
7 81
10.2%
8 53
 
6.6%
5 38
 
4.8%
2 32
 
4.0%
3 32
 
4.0%
9 29
 
3.6%

도로명주소
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:09:30.090569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length39
Mean length34.058333
Min length27

Characters and Unicode

Total characters4087
Distinct characters194
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st row부산광역시 사하구 낙동대로 265 (괴정동, 신동양아파트)
2nd row부산광역시 사하구 괴정로270번길 34 (괴정동, 협진아파트)
3rd row부산광역시 사하구 낙동대로 263 (괴정동, 자유1차아파트)
4th row부산광역시 사하구 사하로141번길 20 (괴정동, 국제아파트)
5th row부산광역시 사하구 다송로 36 (다대동, 코오롱아파트)
ValueCountFrequency (%)
부산광역시 120
 
16.9%
사하구 120
 
16.9%
다대동 22
 
3.1%
괴정동 21
 
3.0%
장림동 19
 
2.7%
신평동 11
 
1.5%
다대로 10
 
1.4%
당리동 10
 
1.4%
하단동 9
 
1.3%
감천동 8
 
1.1%
Other values (276) 361
50.8%
2023-12-11T02:09:30.938462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
666
 
16.3%
하 155
 
3.8%
동 147
 
3.6%
사 138
 
3.4%
산 133
 
3.3%
구 131
 
3.2%
역 125
 
3.1%
부 123
 
3.0%
시 123
 
3.0%
( 122
 
3.0%
Other values (184) 2224
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2614
64.0%
Space Separator 666
 
16.3%
Decimal Number 425
 
10.4%
Open Punctuation 122
 
3.0%
Close Punctuation 122
 
3.0%
Other Punctuation 121
 
3.0%
Uppercase Letter 10
 
0.2%
Dash Punctuation 5
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
하 155
 
5.9%
동 147
 
5.6%
사 138
 
5.3%
산 133
 
5.1%
구 131
 
5.0%
역 125
 
4.8%
부 123
 
4.7%
시 123
 
4.7%
광 120
 
4.6%
로 116
 
4.4%
Other values (161) 1303
49.8%
Decimal Number
ValueCountFrequency (%)
1 83
19.5%
2 68
16.0%
3 55
12.9%
0 44
10.4%
7 44
10.4%
4 43
10.1%
5 27
 
6.4%
8 21
 
4.9%
6 21
 
4.9%
9 19
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
S 3
30.0%
K 2
20.0%
W 1
 
10.0%
D 1
 
10.0%
I 1
 
10.0%
L 1
 
10.0%
H 1
 
10.0%
Space Separator
ValueCountFrequency (%)
666
100.0%
Open Punctuation
ValueCountFrequency (%)
( 122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 122
100.0%
Other Punctuation
ValueCountFrequency (%)
, 121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2614
64.0%
Common 1461
35.7%
Latin 12
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
하 155
 
5.9%
동 147
 
5.6%
사 138
 
5.3%
산 133
 
5.1%
구 131
 
5.0%
역 125
 
4.8%
부 123
 
4.7%
시 123
 
4.7%
광 120
 
4.6%
로 116
 
4.4%
Other values (161) 1303
49.8%
Common
ValueCountFrequency (%)
666
45.6%
( 122
 
8.4%
) 122
 
8.4%
, 121
 
8.3%
1 83
 
5.7%
2 68
 
4.7%
3 55
 
3.8%
0 44
 
3.0%
7 44
 
3.0%
4 43
 
2.9%
Other values (5) 93
 
6.4%
Latin
ValueCountFrequency (%)
S 3
25.0%
K 2
16.7%
e 2
16.7%
W 1
 
8.3%
D 1
 
8.3%
I 1
 
8.3%
L 1
 
8.3%
H 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2614
64.0%
ASCII 1473
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
666
45.2%
( 122
 
8.3%
) 122
 
8.3%
, 121
 
8.2%
1 83
 
5.6%
2 68
 
4.6%
3 55
 
3.7%
0 44
 
3.0%
7 44
 
3.0%
4 43
 
2.9%
Other values (13) 105
 
7.1%
Hangul
ValueCountFrequency (%)
하 155
 
5.9%
동 147
 
5.6%
사 138
 
5.3%
산 133
 
5.1%
구 131
 
5.0%
역 125
 
4.8%
부 123
 
4.7%
시 123
 
4.7%
광 120
 
4.6%
로 116
 
4.4%
Other values (161) 1303
49.8%

사업승인일
Date

Distinct109
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum1980-01-19 00:00:00
Maximum2019-10-11 00:00:00
2023-12-11T02:09:31.184854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:31.443044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용검사일
Date

Distinct114
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum1980-12-31 00:00:00
Maximum2022-12-28 00:00:00
2023-12-11T02:09:31.744552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:32.022754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

층
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.55
Minimum4
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:09:32.278473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q115
median21.5
Q325
95-th percentile29
Maximum38
Range34
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.5694096
Coefficient of variation (CV)0.3196793
Kurtosis0.2109098
Mean20.55
Median Absolute Deviation (MAD)3.5
Skewness-0.2686826
Sum2466
Variance43.157143
MonotonicityNot monotonic
2023-12-11T02:09:32.561608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
15 29
24.2%
25 25
20.8%
20 11
 
9.2%
22 9
 
7.5%
29 8
 
6.7%
24 6
 
5.0%
6 4
 
3.3%
21 4
 
3.3%
23 3
 
2.5%
19 3
 
2.5%
Other values (12) 18
15.0%
ValueCountFrequency (%)
4 1
 
0.8%
5 2
 
1.7%
6 4
 
3.3%
11 1
 
0.8%
12 1
 
0.8%
13 2
 
1.7%
15 29
24.2%
17 1
 
0.8%
18 1
 
0.8%
19 3
 
2.5%
ValueCountFrequency (%)
38 1
 
0.8%
35 2
 
1.7%
32 2
 
1.7%
29 8
 
6.7%
28 2
 
1.7%
26 2
 
1.7%
25 25
20.8%
24 6
 
5.0%
23 3
 
2.5%
22 9
 
7.5%

동
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5416667
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:09:32.786470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile15.1
Maximum49
Range48
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.2977865
Coefficient of variation (CV)1.1364427
Kurtosis21.86805
Mean5.5416667
Median Absolute Deviation (MAD)2
Skewness3.927432
Sum665
Variance39.662115
MonotonicityNot monotonic
2023-12-11T02:09:33.002840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 23
19.2%
2 19
15.8%
4 18
15.0%
5 11
9.2%
3 10
8.3%
6 7
 
5.8%
7 6
 
5.0%
9 5
 
4.2%
12 3
 
2.5%
11 3
 
2.5%
Other values (8) 15
12.5%
ValueCountFrequency (%)
1 23
19.2%
2 19
15.8%
3 10
8.3%
4 18
15.0%
5 11
9.2%
6 7
 
5.8%
7 6
 
5.0%
8 3
 
2.5%
9 5
 
4.2%
10 3
 
2.5%
ValueCountFrequency (%)
49 1
 
0.8%
35 1
 
0.8%
18 2
 
1.7%
17 2
 
1.7%
15 1
 
0.8%
13 2
 
1.7%
12 3
2.5%
11 3
2.5%
10 3
2.5%
9 5
4.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.325
Minimum150
Maximum3462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:09:33.232060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile164.9
Q1255.75
median386.5
Q3635.5
95-th percentile1922.65
Maximum3462
Range3312
Interquartile range (IQR)379.75

Descriptive statistics

Standard deviation599.6953
Coefficient of variation (CV)1.0193266
Kurtosis7.7518725
Mean588.325
Median Absolute Deviation (MAD)157
Skewness2.6491072
Sum70599
Variance359634.46
MonotonicityNot monotonic
2023-12-11T02:09:33.484215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298 5
 
4.2%
299 4
 
3.3%
176 3
 
2.5%
152 2
 
1.7%
312 2
 
1.7%
546 2
 
1.7%
937 2
 
1.7%
270 2
 
1.7%
990 2
 
1.7%
432 1
 
0.8%
Other values (95) 95
79.2%
ValueCountFrequency (%)
150 1
 
0.8%
152 2
1.7%
154 1
 
0.8%
162 1
 
0.8%
163 1
 
0.8%
165 1
 
0.8%
176 3
2.5%
182 1
 
0.8%
183 1
 
0.8%
187 1
 
0.8%
ValueCountFrequency (%)
3462 1
0.8%
2980 1
0.8%
2960 1
0.8%
2181 1
0.8%
2107 1
0.8%
1973 1
0.8%
1920 1
0.8%
1828 1
0.8%
1746 1
0.8%
1669 1
0.8%

연락처
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:09:33.917028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st row051-993-0488
2nd row051-202-7908
3rd row051-202-0977
4th row051-293-0847
5th row051-263-2643
ValueCountFrequency (%)
051-993-0488 1
 
0.8%
051-202-7908 1
 
0.8%
051-201-0653 1
 
0.8%
051-265-4488 1
 
0.8%
051-263-0496 1
 
0.8%
051-204-2329 1
 
0.8%
051-203-6894 1
 
0.8%
051-293-6997 1
 
0.8%
051-262-1917 1
 
0.8%
051-292-3116 1
 
0.8%
Other values (110) 110
91.7%
2023-12-11T02:09:34.470489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 240
16.7%
0 219
15.2%
1 182
12.6%
2 181
12.6%
5 171
11.9%
6 116
8.1%
9 89
 
6.2%
4 79
 
5.5%
3 64
 
4.4%
8 57
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
83.3%
Dash Punctuation 240
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 219
18.2%
1 182
15.2%
2 181
15.1%
5 171
14.2%
6 116
9.7%
9 89
7.4%
4 79
 
6.6%
3 64
 
5.3%
8 57
 
4.8%
7 42
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 240
16.7%
0 219
15.2%
1 182
12.6%
2 181
12.6%
5 171
11.9%
6 116
8.1%
9 89
 
6.2%
4 79
 
5.5%
3 64
 
4.4%
8 57
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 240
16.7%
0 219
15.2%
1 182
12.6%
2 181
12.6%
5 171
11.9%
6 116
8.1%
9 89
 
6.2%
4 79
 
5.5%
3 64
 
4.4%
8 57
 
4.0%

팩스번호
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:09:34.882313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st row051-993-0479
2nd row051-202-7908
3rd row051-202-0967
4th row051-294-0848
5th row051-263-2645
ValueCountFrequency (%)
051-993-0479 1
 
0.8%
051-202-7908 1
 
0.8%
051-201-0654 1
 
0.8%
051-919-4488 1
 
0.8%
051-900-6158 1
 
0.8%
051-919-2329 1
 
0.8%
051-203-6895 1
 
0.8%
051-293-6998 1
 
0.8%
051-262-1918 1
 
0.8%
051-292-3117 1
 
0.8%
Other values (110) 110
91.7%
2023-12-11T02:09:35.442595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 240
16.7%
0 207
14.4%
5 181
12.6%
1 180
12.5%
2 167
11.6%
6 112
7.8%
9 108
7.5%
4 79
 
5.5%
3 65
 
4.5%
8 52
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
83.3%
Dash Punctuation 240
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207
17.2%
5 181
15.1%
1 180
15.0%
2 167
13.9%
6 112
9.3%
9 108
9.0%
4 79
 
6.6%
3 65
 
5.4%
8 52
 
4.3%
7 49
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 240
16.7%
0 207
14.4%
5 181
12.6%
1 180
12.5%
2 167
11.6%
6 112
7.8%
9 108
7.5%
4 79
 
5.5%
3 65
 
4.5%
8 52
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 240
16.7%
0 207
14.4%
5 181
12.6%
1 180
12.5%
2 167
11.6%
6 112
7.8%
9 108
7.5%
4 79
 
5.5%
3 65
 
4.5%
8 52
 
3.6%

Interactions

2023-12-11T02:09:24.114919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:21.967052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:22.579297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:23.513348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:24.290268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:22.122717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:22.730490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:23.675838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:24.448119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:22.281401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:22.897973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:23.832086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:24.601784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:22.424348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:23.373791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:23.982649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:09:35.573034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번우편번호층동세대수
연번1.0000.9200.6320.2840.104
우편번호0.9201.0000.9050.9880.984
층0.6320.9051.0000.3420.701
동0.2840.9880.3421.0000.892
세대수0.1040.9840.7010.8921.000
2023-12-11T02:09:35.707716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번층동세대수
연번1.0000.629-0.048-0.023
층0.6291.0000.1390.339
동-0.0480.1391.0000.804
세대수-0.0230.3390.8041.000

Missing values

2023-12-11T02:09:24.915262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:09:25.247850image/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

연번단지명위치우편번호도로명주소사업승인일사용검사일층동세대수연락처팩스번호
01신동양아파트신동양아파트604-855부산광역시 사하구 낙동대로 265 (괴정동, 신동양아파트)1980-01-191980-12-31133274051-993-0488051-993-0479
12협진태양아파트괴정1동 530-13604-812부산광역시 사하구 괴정로270번길 34 (괴정동, 협진아파트)1980-02-181981-03-1159300051-202-7908051-202-7908
23괴정자유1차아파트괴정1동 740604-764부산광역시 사하구 낙동대로 263 (괴정동, 자유1차아파트)1981-11-031982-09-04126312051-202-0977051-202-0967
34국제아파트괴정1동 491-9604-812부산광역시 사하구 사하로141번길 20 (괴정동, 국제아파트)1984-11-091985-06-05112176051-293-0847051-294-0848
45코오롱아파트다대1동 37604-766부산광역시 사하구 다송로 36 (다대동, 코오롱아파트)1986-12-011988-08-0559520051-263-2643051-263-2645
56다해아파트다대2동 80-14604-758부산광역시 사하구 다송로 7 (다대동, 다대다해아파트)1988-05-141989-02-1866389051-261-2327051-261-2327
67남영임대1,2차하단2동 479, 하단2동 475, 476-1604-760부산광역시 사하구 낙동대로575번길 10 (하단동, 남영임대1,2차아파트)1987-08-151989-04-15157519051-203-4441051-203-4443
78신평삼익아파트신평1동 7604-832부산광역시 사하구 장평로 311 (신평동, 신평삼익아파트)1987-10-301989-05-2267312051-201-4690051-201-4690
89우신타워괴정1동 614-9604-812부산광역시 사하구 사하로141번나길 35-4 (괴정동, 우신타워)1987-12-151989-10-12152250051-206-2550051-206-2550
910신괴정화신맨션괴정3동 398-23604-706부산광역시 사하구 오작로 68 (괴정동, 신괴정화신맨션)1988-06-021990-03-31153270051-204-4930051-204-4928
연번단지명위치우편번호도로명주소사업승인일사용검사일층동세대수연락처팩스번호
110111괴정어반푸르지오괴정동 950-249381부산광역시 사하구 사하로 189(괴정동, 어반푸르지오아파트)2015-12-312019-02-15231152051-294-9596051-294-9597
111112장림 리버팰리스장림동 43549469부산광역시 사하구 다대로 328 (장림동, 리버팰리스)2016-04-182019-05-17283154051-265-7088051-263-7088
112113신동아파밀리에괴정동 472-2749360부산광역시 사하구 사하로142번길 38 (괴정동, 신동아파밀리에)2016-10-132020-06-02194150051-204-0142051-202-0142
113114장림역 베스티움2차장림동 115349465부산광역시 사하구 장림번영로 76 (장림동, 장림역베스티움2차)2017-06-092020-06-04294221051-266-8061051-266-8063
114115사하역 비스타동원괴정동 128449343부산광역시 사하구 낙동대로 280(괴정동, 사하역비스타동원)2016-10-272021-01-26324513051-271-5501051-271-5502
115116부산사하중흥S클래스구평동 51349459부산광역시 사하구 서포로30번길 42(구평동, 부산사하중흥S-클래스)2016-05-162021-07-05296665051-266-5999051-266-6999
116117코오롱하늘채신평동 38349420부산광역시 사하구 신산북로41(신평동, 코오롱하늘채)2018-03-292021-12-312911969051-294-9111051-294-9110
117118한신더휴포레스티지괴정동 216-1049319부산광역시 사하구 사리로 106 (괴정동, 한신더휴포레스티지)2018-02-262021-12-292911835051-294-9606051-294-9607
118119하단 롯데캐슬하단동 121949410부산광역시 사하구 낙동대로 413 (하단동, 하단 롯데캐슬)2019-10-112022-09-30201356051-294-9860051-294-9861
119120힐스테이스 사하역괴정동 120849406부산광역시 사하구 괴정로 166 (괴정동, 힐스테이트 사하역)2013-05-022022-12-2838121314051-292-4445051-292-4443