Overview

Dataset statistics

Number of variables9
Number of observations383
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory76.3 B

Variable types

Numeric4
Text3
DateTime1
Categorical1

Dataset

Description부산광역시수영구_공동주택현황_20230701
Author부산광역시 수영구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15102128

Alerts

동수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
층수 is highly overall correlated with 의무 여부(의무_비의무_의무전환)High correlation
세대수 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
의무 여부(의무_비의무_의무전환) is highly overall correlated with 동수 and 2 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:47:07.605716
Analysis finished2023-12-10 16:47:10.384482
Duration2.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct383
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192
Minimum1
Maximum383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T01:47:10.473838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.1
Q196.5
median192
Q3287.5
95-th percentile363.9
Maximum383
Range382
Interquartile range (IQR)191

Descriptive statistics

Standard deviation110.70682
Coefficient of variation (CV)0.57659802
Kurtosis-1.2
Mean192
Median Absolute Deviation (MAD)96
Skewness0
Sum73536
Variance12256
MonotonicityStrictly increasing
2023-12-11T01:47:10.611745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
2 1
 
0.3%
263 1
 
0.3%
262 1
 
0.3%
261 1
 
0.3%
260 1
 
0.3%
259 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
256 1
 
0.3%
Other values (373) 373
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
383 1
0.3%
382 1
0.3%
381 1
0.3%
380 1
0.3%
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%
374 1
0.3%

단지명
Text

Distinct367
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-11T01:47:10.894466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.2219321
Min length1

Characters and Unicode

Total characters2383
Distinct characters294
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)93.5%

Sample

1st row수영아파트
2nd row광안아파트
3rd row민락아파트
4th row광명아파트
5th row망미아파트
ValueCountFrequency (%)
광안동 10
 
2.1%
광안 7
 
1.4%
봉황 5
 
1.0%
e편한세상 5
 
1.0%
오션테라스 4
 
0.8%
수목하우스 4
 
0.8%
세종 4
 
0.8%
부흥 3
 
0.6%
센텀뷰 3
 
0.6%
협성엠파이어 3
 
0.6%
Other values (413) 436
90.1%
2023-12-11T01:47:11.325520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111
 
4.7%
스 101
 
4.2%
이 77
 
3.2%
파 69
 
2.9%
트 68
 
2.9%
아 66
 
2.8%
빌 64
 
2.7%
동 56
 
2.3%
하 49
 
2.1%
광 49
 
2.1%
Other values (284) 1673
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2065
86.7%
Space Separator 111
 
4.7%
Decimal Number 82
 
3.4%
Uppercase Letter 70
 
2.9%
Lowercase Letter 17
 
0.7%
Other Punctuation 11
 
0.5%
Dash Punctuation 9
 
0.4%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Letter Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 101
 
4.9%
이 77
 
3.7%
파 69
 
3.3%
트 68
 
3.3%
아 66
 
3.2%
빌 64
 
3.1%
동 56
 
2.7%
하 49
 
2.4%
광 49
 
2.4%
안 46
 
2.2%
Other values (239) 1420
68.8%
Uppercase Letter
ValueCountFrequency (%)
B 8
11.4%
A 8
11.4%
S 7
10.0%
E 6
 
8.6%
K 5
 
7.1%
I 5
 
7.1%
V 4
 
5.7%
C 4
 
5.7%
H 3
 
4.3%
W 3
 
4.3%
Other values (11) 17
24.3%
Decimal Number
ValueCountFrequency (%)
1 22
26.8%
2 18
22.0%
5 10
12.2%
3 9
11.0%
4 7
 
8.5%
7 6
 
7.3%
0 6
 
7.3%
8 3
 
3.7%
6 1
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
e 10
58.8%
o 2
 
11.8%
l 1
 
5.9%
d 1
 
5.9%
s 1
 
5.9%
u 1
 
5.9%
n 1
 
5.9%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
66.7%
ⅴ 1
33.3%
Space Separator
ValueCountFrequency (%)
111
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2065
86.7%
Common 228
 
9.6%
Latin 90
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 101
 
4.9%
이 77
 
3.7%
파 69
 
3.3%
트 68
 
3.3%
아 66
 
3.2%
빌 64
 
3.1%
동 56
 
2.7%
하 49
 
2.4%
광 49
 
2.4%
안 46
 
2.2%
Other values (239) 1420
68.8%
Latin
ValueCountFrequency (%)
e 10
 
11.1%
B 8
 
8.9%
A 8
 
8.9%
S 7
 
7.8%
E 6
 
6.7%
K 5
 
5.6%
I 5
 
5.6%
V 4
 
4.4%
C 4
 
4.4%
H 3
 
3.3%
Other values (20) 30
33.3%
Common
ValueCountFrequency (%)
111
48.7%
1 22
 
9.6%
2 18
 
7.9%
, 11
 
4.8%
5 10
 
4.4%
3 9
 
3.9%
- 9
 
3.9%
( 7
 
3.1%
) 7
 
3.1%
4 7
 
3.1%
Other values (5) 17
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2065
86.7%
ASCII 315
 
13.2%
Number Forms 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111
35.2%
1 22
 
7.0%
2 18
 
5.7%
, 11
 
3.5%
e 10
 
3.2%
5 10
 
3.2%
3 9
 
2.9%
- 9
 
2.9%
B 8
 
2.5%
A 8
 
2.5%
Other values (33) 99
31.4%
Hangul
ValueCountFrequency (%)
스 101
 
4.9%
이 77
 
3.7%
파 69
 
3.3%
트 68
 
3.3%
아 66
 
3.2%
빌 64
 
3.1%
동 56
 
2.7%
하 49
 
2.4%
광 49
 
2.4%
안 46
 
2.2%
Other values (239) 1420
68.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
66.7%
ⅴ 1
33.3%

위치(새주소)
Text

Distinct377
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-11T01:47:11.642490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length39
Mean length31.028721
Min length16

Characters and Unicode

Total characters11884
Distinct characters271
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique371 ?
Unique (%)96.9%

Sample

1st row부산광역시 수영구 망미번영로38번길 30(광안동)
2nd row부산광역시 수영구 수영로660번길 37(광안동)
3rd row부산광역시 수영구 무학로63번길 91, 97, 115(민락동)
4th row부산광역시 수영구 광일로67번길 68(광안동)
5th row부산광역시 수영구 연수로 286(망미동)
ValueCountFrequency (%)
부산광역시 383
 
17.8%
수영구 382
 
17.7%
광안동 198
 
9.2%
남천동 34
 
1.6%
망미동 30
 
1.4%
민락동 26
 
1.2%
수영로 19
 
0.9%
광남로 15
 
0.7%
수영동 15
 
0.7%
수영로642번길 13
 
0.6%
Other values (654) 1040
48.3%
2023-12-11T01:47:12.119386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1859
 
15.6%
광 719
 
6.1%
수 572
 
4.8%
영 556
 
4.7%
로 399
 
3.4%
동 396
 
3.3%
부 395
 
3.3%
산 394
 
3.3%
시 391
 
3.3%
구 391
 
3.3%
Other values (261) 5812
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7456
62.7%
Space Separator 1859
 
15.6%
Decimal Number 1535
 
12.9%
Open Punctuation 349
 
2.9%
Close Punctuation 349
 
2.9%
Other Punctuation 264
 
2.2%
Dash Punctuation 41
 
0.3%
Uppercase Letter 16
 
0.1%
Lowercase Letter 13
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 719
 
9.6%
수 572
 
7.7%
영 556
 
7.5%
로 399
 
5.4%
동 396
 
5.3%
부 395
 
5.3%
산 394
 
5.3%
시 391
 
5.2%
구 391
 
5.2%
역 384
 
5.2%
Other values (228) 2859
38.3%
Decimal Number
ValueCountFrequency (%)
1 266
17.3%
6 194
12.6%
2 172
11.2%
4 160
10.4%
3 158
10.3%
5 152
9.9%
0 125
8.1%
8 118
7.7%
9 98
 
6.4%
7 92
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
H 3
18.8%
B 2
12.5%
V 2
12.5%
S 2
12.5%
P 2
12.5%
O 1
 
6.2%
K 1
 
6.2%
L 1
 
6.2%
A 1
 
6.2%
G 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
e 6
46.2%
o 2
 
15.4%
s 1
 
7.7%
u 1
 
7.7%
n 1
 
7.7%
d 1
 
7.7%
l 1
 
7.7%
Space Separator
ValueCountFrequency (%)
1859
100.0%
Open Punctuation
ValueCountFrequency (%)
( 349
100.0%
Close Punctuation
ValueCountFrequency (%)
) 349
100.0%
Other Punctuation
ValueCountFrequency (%)
, 264
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7456
62.7%
Common 4397
37.0%
Latin 31
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 719
 
9.6%
수 572
 
7.7%
영 556
 
7.5%
로 399
 
5.4%
동 396
 
5.3%
부 395
 
5.3%
산 394
 
5.3%
시 391
 
5.2%
구 391
 
5.2%
역 384
 
5.2%
Other values (228) 2859
38.3%
Latin
ValueCountFrequency (%)
e 6
19.4%
H 3
 
9.7%
B 2
 
6.5%
V 2
 
6.5%
Ⅱ 2
 
6.5%
S 2
 
6.5%
P 2
 
6.5%
o 2
 
6.5%
O 1
 
3.2%
K 1
 
3.2%
Other values (8) 8
25.8%
Common
ValueCountFrequency (%)
1859
42.3%
( 349
 
7.9%
) 349
 
7.9%
1 266
 
6.0%
, 264
 
6.0%
6 194
 
4.4%
2 172
 
3.9%
4 160
 
3.6%
3 158
 
3.6%
5 152
 
3.5%
Other values (5) 474
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7456
62.7%
ASCII 4426
37.2%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1859
42.0%
( 349
 
7.9%
) 349
 
7.9%
1 266
 
6.0%
, 264
 
6.0%
6 194
 
4.4%
2 172
 
3.9%
4 160
 
3.6%
3 158
 
3.6%
5 152
 
3.4%
Other values (22) 503
 
11.4%
Hangul
ValueCountFrequency (%)
광 719
 
9.6%
수 572
 
7.7%
영 556
 
7.5%
로 399
 
5.4%
동 396
 
5.3%
부 395
 
5.3%
산 394
 
5.3%
시 391
 
5.2%
구 391
 
5.2%
역 384
 
5.2%
Other values (228) 2859
38.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.843342
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T01:47:12.279117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum33
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4890338
Coefficient of variation (CV)1.3502832
Kurtosis67.615987
Mean1.843342
Median Absolute Deviation (MAD)0
Skewness6.6459245
Sum706
Variance6.1952893
MonotonicityNot monotonic
2023-12-11T01:47:12.408572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 293
76.5%
2 30
 
7.8%
4 17
 
4.4%
3 15
 
3.9%
5 6
 
1.6%
6 6
 
1.6%
7 4
 
1.0%
8 3
 
0.8%
11 2
 
0.5%
13 2
 
0.5%
Other values (4) 5
 
1.3%
ValueCountFrequency (%)
1 293
76.5%
2 30
 
7.8%
3 15
 
3.9%
4 17
 
4.4%
5 6
 
1.6%
6 6
 
1.6%
7 4
 
1.0%
8 3
 
0.8%
9 1
 
0.3%
10 2
 
0.5%
ValueCountFrequency (%)
33 1
 
0.3%
13 2
 
0.5%
12 1
 
0.3%
11 2
 
0.5%
10 2
 
0.5%
9 1
 
0.3%
8 3
0.8%
7 4
1.0%
6 6
1.6%
5 6
1.6%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9399478
Minimum2
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T01:47:12.544473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q314
95-th percentile26
Maximum43
Range41
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4319987
Coefficient of variation (CV)0.74768991
Kurtosis3.2454577
Mean9.9399478
Median Absolute Deviation (MAD)1
Skewness1.8098726
Sum3807
Variance55.234604
MonotonicityNot monotonic
2023-12-11T01:47:12.679095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 105
27.4%
6 76
19.8%
15 30
 
7.8%
7 29
 
7.6%
4 19
 
5.0%
10 15
 
3.9%
14 14
 
3.7%
20 9
 
2.3%
13 9
 
2.3%
19 8
 
2.1%
Other values (24) 69
18.0%
ValueCountFrequency (%)
2 4
 
1.0%
3 5
 
1.3%
4 19
 
5.0%
5 105
27.4%
6 76
19.8%
7 29
 
7.6%
8 5
 
1.3%
9 7
 
1.8%
10 15
 
3.9%
11 2
 
0.5%
ValueCountFrequency (%)
43 2
0.5%
36 2
0.5%
35 3
0.8%
34 1
 
0.3%
33 1
 
0.3%
31 1
 
0.3%
30 2
0.5%
29 1
 
0.3%
28 2
0.5%
27 1
 
0.3%

연면적
Text

Distinct361
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-11T01:47:12.967983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.4229765
Min length3

Characters and Unicode

Total characters1694
Distinct characters12
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

Unique345 ?
Unique (%)90.1%

Sample

1st row6019.82
2nd row4565.8
3rd row2739.6
4th row9570.57
5th row2590.09
ValueCountFrequency (%)
999 5
 
1.3%
657 3
 
0.8%
659 3
 
0.8%
1999 3
 
0.8%
843 2
 
0.5%
917 2
 
0.5%
819 2
 
0.5%
990 2
 
0.5%
651 2
 
0.5%
998 2
 
0.5%
Other values (351) 357
93.2%
2023-12-11T01:47:13.433378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 203
12.0%
9 189
11.2%
5 167
9.9%
4 167
9.9%
2 166
9.8%
8 164
9.7%
6 161
9.5%
3 139
8.2%
7 137
8.1%
0 124
7.3%
Other values (2) 77
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1617
95.5%
Other Punctuation 77
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 203
12.6%
9 189
11.7%
5 167
10.3%
4 167
10.3%
2 166
10.3%
8 164
10.1%
6 161
10.0%
3 139
8.6%
7 137
8.5%
0 124
7.7%
Other Punctuation
ValueCountFrequency (%)
. 76
98.7%
, 1
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 203
12.0%
9 189
11.2%
5 167
9.9%
4 167
9.9%
2 166
9.8%
8 164
9.7%
6 161
9.5%
3 139
8.2%
7 137
8.1%
0 124
7.3%
Other values (2) 77
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 203
12.0%
9 189
11.2%
5 167
9.9%
4 167
9.9%
2 166
9.8%
8 164
9.7%
6 161
9.5%
3 139
8.2%
7 137
8.1%
0 124
7.3%
Other values (2) 77
 
4.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.22193
Minimum20
Maximum3060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T01:47:13.595747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q122
median29
Q372.5
95-th percentile498.1
Maximum3060
Range3040
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation250.48719
Coefficient of variation (CV)2.2320698
Kurtosis54.025137
Mean112.22193
Median Absolute Deviation (MAD)9
Skewness6.0023211
Sum42981
Variance62743.833
MonotonicityNot monotonic
2023-12-11T01:47:13.735424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 66
 
17.2%
24 40
 
10.4%
22 22
 
5.7%
28 20
 
5.2%
25 15
 
3.9%
21 11
 
2.9%
26 9
 
2.3%
40 9
 
2.3%
48 8
 
2.1%
29 8
 
2.1%
Other values (111) 175
45.7%
ValueCountFrequency (%)
20 66
17.2%
21 11
 
2.9%
22 22
 
5.7%
23 6
 
1.6%
24 40
10.4%
25 15
 
3.9%
26 9
 
2.3%
28 20
 
5.2%
29 8
 
2.1%
30 6
 
1.6%
ValueCountFrequency (%)
3060 1
0.3%
1180 1
0.3%
1147 1
0.3%
1082 1
0.3%
1038 1
0.3%
1006 1
0.3%
990 1
0.3%
987 1
0.3%
975 1
0.3%
971 1
0.3%

준공일
Date

Distinct354
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1968-02-29 00:00:00
Maximum2023-02-10 00:00:00
2023-12-11T01:47:13.877685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:14.041512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

의무 여부(의무_비의무_의무전환)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
비의무
327 
의무
56 

Length

Max length3
Median length3
Mean length2.8537859
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비의무 327
85.4%
의무 56
 
14.6%

Length

2023-12-11T01:47:14.213640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:47:14.350486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비의무 327
85.4%
의무 56
 
14.6%

Interactions

2023-12-11T01:47:09.643630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.201849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.676256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.129014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.761513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.323401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.786544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.234305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.880271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.452646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.902667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.373691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.987986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:08.569499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.001116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:09.529908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:47:14.449200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수층수세대수의무 여부(의무_비의무_의무전환)
연번1.0000.2360.6310.2960.498
동수0.2361.0000.7150.9730.510
층수0.6310.7151.0000.7410.912
세대수0.2960.9730.7411.0000.602
의무 여부(의무_비의무_의무전환)0.4980.5100.9120.6021.000
2023-12-11T01:47:14.803149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수층수세대수의무 여부(의무_비의무_의무전환)
연번1.000-0.2320.419-0.2200.379
동수-0.2321.0000.2430.6310.616
층수0.4190.2431.0000.4870.744
세대수-0.2200.6310.4871.0000.723
의무 여부(의무_비의무_의무전환)0.3790.6160.7440.7231.000

Missing values

2023-12-11T01:47:10.184443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:47:10.333331image/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

연번단지명위치(새주소)동수층수연면적세대수준공일의무 여부(의무_비의무_의무전환)
01수영아파트부산광역시 수영구 망미번영로38번길 30(광안동)136019.821501968-02-29비의무
12광안아파트부산광역시 수영구 수영로660번길 37(광안동)544565.81221969-01-10비의무
23민락아파트부산광역시 수영구 무학로63번길 91, 97, 115(민락동)342739.6721969-02-15비의무
34광명아파트부산광역시 수영구 광일로67번길 68(광안동)459570.571821975-05-20비의무
45망미아파트부산광역시 수영구 연수로 286(망미동)152590.09401975-12-17비의무
56동원맨션부산광역시 수영구 광서로16번길 23(광안동)142302.22311976-09-09비의무
67망미오양아파트부산광역시 수영구 과정로67번길 47(망미동)356265.431201977-11-01비의무
78광일아파트부산광역시 수영구 광남로94번길 2(광안동)153317.95311978-01-14비의무
89우서아파트부산광역시 수영구 망미번영로38번길 35(광안동)151865.19201978-08-11비의무
910최신망미아파트부산광역시 수영구 연수로 276(망미동)153019.11401978-08-17비의무
연번단지명위치(새주소)동수층수연면적세대수준공일의무 여부(의무_비의무_의무전환)
373374광안 KCC스위첸 하버뷰부산광역시 수영구 민락수변로 4911834985.112942021-11-30의무
374375광안 에일린의 뜰부산광역시 수영구 호암로 45419374462252021-02-19의무
375376TIME76부산광역시 수영구 연수로364번길 2015658.87242022-06-22비의무
376377엘이즈G04부산광역시 수영구 연수로392번길 65161076.19212022-01-26비의무
377378광안에코하임센트럴뷰부산광역시 수영구 수영로 557-11203319.52292022-03-28비의무
378379경동리인부산광역시 수영구 광남로 12212015901.06962022-01-25비의무
379380광안스윗팰리스부산광역시 수영구 무학로9번길 101205432.99712022-03-02비의무
380381더샵 남천프레스티지부산광역시 수영구 수영로 389103542231.39752022-09-26의무
381382남천자이아파트부산광역시 수영구 남천동로 46736151270.169132023-01-19의무
382383케이디스페이스알파 552부산광역시 수영구 광남로 213번길 50171,450.78202023-02-10비의무