Overview

Dataset statistics

Number of variables9
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory80.2 B

Variable types

Numeric5
Text3
DateTime1

Dataset

Description부산광역시_강서구_공동주택현황_20210318
Author부산광역시 강서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15026135

Alerts

연번 is highly overall correlated with 층수High correlation
동수 is highly overall correlated with 층수High correlation
층수 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연면적 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 연면적High correlation
연번 has unique valuesUnique
단지명 has unique valuesUnique
위치(새주소) has unique valuesUnique
연면적 has unique valuesUnique
세대수 has unique valuesUnique
관리사무소연락처 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:31:57.806787
Analysis finished2023-12-10 16:32:02.819960
Duration5.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T01:32:02.931085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2023-12-11T01:32:03.162355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

단지명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T01:32:03.464751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length11.853659
Min length6

Characters and Unicode

Total characters486
Distinct characters122
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

Unique41 ?
Unique (%)100.0%

Sample

1st row퀸덤 1차 에디슨타운
2nd row퀸덤 1차 링컨타운
3rd row퀸덤 1차 아인슈타인
4th row명지 극동스타클래스
5th row명지 두산위브포세이돈
ValueCountFrequency (%)
명지 25
24.8%
1차 7
 
6.9%
2차 4
 
4.0%
부산신호사랑으로부영 4
 
4.0%
금강펜테리움 4
 
4.0%
지사 4
 
4.0%
삼정그린코아 3
 
3.0%
센트럴파크 3
 
3.0%
퀸덤 3
 
3.0%
더샵 2
 
2.0%
Other values (38) 42
41.6%
2023-12-11T01:32:03.947414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62
 
12.8%
지 34
 
7.0%
명 28
 
5.8%
차 14
 
2.9%
스 14
 
2.9%
부 9
 
1.9%
사 8
 
1.6%
움 7
 
1.4%
호 7
 
1.4%
루 7
 
1.4%
Other values (112) 296
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 384
79.0%
Space Separator 62
 
12.8%
Decimal Number 20
 
4.1%
Uppercase Letter 9
 
1.9%
Close Punctuation 5
 
1.0%
Open Punctuation 5
 
1.0%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 34
 
8.9%
명 28
 
7.3%
차 14
 
3.6%
스 14
 
3.6%
부 9
 
2.3%
사 8
 
2.1%
움 7
 
1.8%
호 7
 
1.8%
루 7
 
1.8%
크 7
 
1.8%
Other values (99) 249
64.8%
Decimal Number
ValueCountFrequency (%)
1 7
35.0%
2 6
30.0%
3 5
25.0%
5 1
 
5.0%
4 1
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 4
44.4%
C 3
33.3%
D 1
 
11.1%
B 1
 
11.1%
Space Separator
ValueCountFrequency (%)
62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 384
79.0%
Common 92
 
18.9%
Latin 10
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 34
 
8.9%
명 28
 
7.3%
차 14
 
3.6%
스 14
 
3.6%
부 9
 
2.3%
사 8
 
2.1%
움 7
 
1.8%
호 7
 
1.8%
루 7
 
1.8%
크 7
 
1.8%
Other values (99) 249
64.8%
Common
ValueCountFrequency (%)
62
67.4%
1 7
 
7.6%
2 6
 
6.5%
) 5
 
5.4%
3 5
 
5.4%
( 5
 
5.4%
5 1
 
1.1%
4 1
 
1.1%
Latin
ValueCountFrequency (%)
S 4
40.0%
C 3
30.0%
e 1
 
10.0%
D 1
 
10.0%
B 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 384
79.0%
ASCII 102
 
21.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62
60.8%
1 7
 
6.9%
2 6
 
5.9%
) 5
 
4.9%
3 5
 
4.9%
( 5
 
4.9%
S 4
 
3.9%
C 3
 
2.9%
5 1
 
1.0%
e 1
 
1.0%
Other values (3) 3
 
2.9%
Hangul
ValueCountFrequency (%)
지 34
 
8.9%
명 28
 
7.3%
차 14
 
3.6%
스 14
 
3.6%
부 9
 
2.3%
사 8
 
2.1%
움 7
 
1.8%
호 7
 
1.8%
루 7
 
1.8%
크 7
 
1.8%
Other values (99) 249
64.8%

위치(새주소)
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T01:32:04.251454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length42
Mean length39.390244
Min length32

Characters and Unicode

Total characters1615
Distinct characters138
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

Unique41 ?
Unique (%)100.0%

Sample

1st row부산광역시 강서구 명지오션시티10로 17 (명지동, 퀸덤1차아파트)
2nd row부산광역시 강서구 명지오션시티10로 16 (명지동, 퀸덤1차 링컨타운)
3rd row부산광역시 강서구 명지오션시티11로 51 (명지동, 퀸덤1차 아인슈타인타운)
4th row부산광역시 강서구 명지오션시티2로 71 (명지동, 극동스타클래스아파트)
5th row부산광역시 강서구 명지오션시티11로 22 (명지동, 명지두산위브포세이돈)
ValueCountFrequency (%)
부산광역시 41
 
15.3%
강서구 41
 
15.3%
명지동 28
 
10.4%
명지국제5로 11
 
4.1%
신호동 5
 
1.9%
명지국제7로 4
 
1.5%
더 3
 
1.1%
명지오션시티11로 3
 
1.1%
명지오션시티10로 3
 
1.1%
명지 3
 
1.1%
Other values (108) 126
47.0%
2023-12-11T01:32:04.717744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
246
 
15.2%
지 82
 
5.1%
명 72
 
4.5%
산 57
 
3.5%
시 57
 
3.5%
1 53
 
3.3%
부 51
 
3.2%
로 45
 
2.8%
강 45
 
2.8%
( 44
 
2.7%
Other values (128) 863
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1055
65.3%
Space Separator 246
 
15.2%
Decimal Number 173
 
10.7%
Open Punctuation 44
 
2.7%
Close Punctuation 44
 
2.7%
Other Punctuation 42
 
2.6%
Uppercase Letter 8
 
0.5%
Dash Punctuation 2
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 82
 
7.8%
명 72
 
6.8%
산 57
 
5.4%
시 57
 
5.4%
부 51
 
4.8%
로 45
 
4.3%
강 45
 
4.3%
동 42
 
4.0%
서 41
 
3.9%
역 41
 
3.9%
Other values (109) 522
49.5%
Decimal Number
ValueCountFrequency (%)
1 53
30.6%
2 25
14.5%
5 21
 
12.1%
0 18
 
10.4%
3 18
 
10.4%
7 11
 
6.4%
9 9
 
5.2%
4 8
 
4.6%
6 6
 
3.5%
8 4
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
S 4
50.0%
C 3
37.5%
D 1
 
12.5%
Space Separator
ValueCountFrequency (%)
246
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1055
65.3%
Common 551
34.1%
Latin 9
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 82
 
7.8%
명 72
 
6.8%
산 57
 
5.4%
시 57
 
5.4%
부 51
 
4.8%
로 45
 
4.3%
강 45
 
4.3%
동 42
 
4.0%
서 41
 
3.9%
역 41
 
3.9%
Other values (109) 522
49.5%
Common
ValueCountFrequency (%)
246
44.6%
1 53
 
9.6%
( 44
 
8.0%
) 44
 
8.0%
, 42
 
7.6%
2 25
 
4.5%
5 21
 
3.8%
0 18
 
3.3%
3 18
 
3.3%
7 11
 
2.0%
Other values (5) 29
 
5.3%
Latin
ValueCountFrequency (%)
S 4
44.4%
C 3
33.3%
e 1
 
11.1%
D 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1055
65.3%
ASCII 560
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246
43.9%
1 53
 
9.5%
( 44
 
7.9%
) 44
 
7.9%
, 42
 
7.5%
2 25
 
4.5%
5 21
 
3.8%
0 18
 
3.2%
3 18
 
3.2%
7 11
 
2.0%
Other values (9) 38
 
6.8%
Hangul
ValueCountFrequency (%)
지 82
 
7.8%
명 72
 
6.8%
산 57
 
5.4%
시 57
 
5.4%
부 51
 
4.8%
로 45
 
4.3%
강 45
 
4.3%
동 42
 
4.0%
서 41
 
3.9%
역 41
 
3.9%
Other values (109) 522
49.5%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.341463
Minimum4
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T01:32:04.915600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q110
median13
Q316
95-th percentile21
Maximum29
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.4018967
Coefficient of variation (CV)0.40489536
Kurtosis1.5030682
Mean13.341463
Median Absolute Deviation (MAD)3
Skewness0.82210483
Sum547
Variance29.180488
MonotonicityNot monotonic
2023-12-11T01:32:05.061663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
16 6
14.6%
11 4
9.8%
12 4
9.8%
17 4
9.8%
8 4
9.8%
13 4
9.8%
14 3
7.3%
6 2
 
4.9%
9 2
 
4.9%
4 2
 
4.9%
Other values (6) 6
14.6%
ValueCountFrequency (%)
4 2
 
4.9%
6 2
 
4.9%
8 4
9.8%
9 2
 
4.9%
10 1
 
2.4%
11 4
9.8%
12 4
9.8%
13 4
9.8%
14 3
7.3%
16 6
14.6%
ValueCountFrequency (%)
29 1
 
2.4%
28 1
 
2.4%
21 1
 
2.4%
20 1
 
2.4%
19 1
 
2.4%
17 4
9.8%
16 6
14.6%
14 3
7.3%
13 4
9.8%
12 4
9.8%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.731707
Minimum4
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T01:32:05.198442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q115
median20
Q325
95-th percentile30
Maximum34
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8191803
Coefficient of variation (CV)0.34559504
Kurtosis-0.3998767
Mean19.731707
Median Absolute Deviation (MAD)5
Skewness-0.035398758
Sum809
Variance46.50122
MonotonicityNot monotonic
2023-12-11T01:32:05.323427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20 14
34.1%
15 7
17.1%
10 4
 
9.8%
25 4
 
9.8%
29 4
 
9.8%
11 2
 
4.9%
30 2
 
4.9%
21 1
 
2.4%
4 1
 
2.4%
27 1
 
2.4%
ValueCountFrequency (%)
4 1
 
2.4%
10 4
 
9.8%
11 2
 
4.9%
15 7
17.1%
20 14
34.1%
21 1
 
2.4%
25 4
 
9.8%
27 1
 
2.4%
29 4
 
9.8%
30 2
 
4.9%
ValueCountFrequency (%)
34 1
 
2.4%
30 2
 
4.9%
29 4
 
9.8%
27 1
 
2.4%
25 4
 
9.8%
21 1
 
2.4%
20 14
34.1%
15 7
17.1%
11 2
 
4.9%
10 4
 
9.8%

연면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140860.98
Minimum42063
Maximum291067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T01:32:05.473766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42063
5-th percentile74795
Q197108
median125631
Q3187292
95-th percentile244670
Maximum291067
Range249004
Interquartile range (IQR)90184

Descriptive statistics

Standard deviation57411.312
Coefficient of variation (CV)0.40757429
Kurtosis0.1922994
Mean140860.98
Median Absolute Deviation (MAD)36256
Skewness0.652904
Sum5775300
Variance3.2960588 × 109
MonotonicityNot monotonic
2023-12-11T01:32:05.903125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
125631 1
 
2.4%
75729 1
 
2.4%
98539 1
 
2.4%
124866 1
 
2.4%
108526 1
 
2.4%
167134 1
 
2.4%
147792 1
 
2.4%
192716 1
 
2.4%
185079 1
 
2.4%
176136 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
42063 1
2.4%
45851 1
2.4%
74795 1
2.4%
75729 1
2.4%
76032 1
2.4%
90654 1
2.4%
92862 1
2.4%
93784 1
2.4%
94806 1
2.4%
96473 1
2.4%
ValueCountFrequency (%)
291067 1
2.4%
274224 1
2.4%
244670 1
2.4%
202705 1
2.4%
199392 1
2.4%
193776 1
2.4%
192716 1
2.4%
192500 1
2.4%
190251 1
2.4%
190077 1
2.4%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean914.68293
Minimum222
Maximum1664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T01:32:06.067498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum222
5-th percentile375
Q1652
median961
Q31124
95-th percentile1515
Maximum1664
Range1442
Interquartile range (IQR)472

Descriptive statistics

Standard deviation365.74427
Coefficient of variation (CV)0.39985908
Kurtosis-0.71541325
Mean914.68293
Median Absolute Deviation (MAD)290
Skewness-0.039012343
Sum37502
Variance133768.87
MonotonicityNot monotonic
2023-12-11T01:32:06.210494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
652 1
 
2.4%
961 1
 
2.4%
694 1
 
2.4%
1013 1
 
2.4%
750 1
 
2.4%
1201 1
 
2.4%
1064 1
 
2.4%
1388 1
 
2.4%
1278 1
 
2.4%
1109 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
222 1
2.4%
252 1
2.4%
375 1
2.4%
377 1
2.4%
414 1
2.4%
431 1
2.4%
480 1
2.4%
600 1
2.4%
610 1
2.4%
642 1
2.4%
ValueCountFrequency (%)
1664 1
2.4%
1530 1
2.4%
1515 1
2.4%
1406 1
2.4%
1388 1
2.4%
1278 1
2.4%
1277 1
2.4%
1256 1
2.4%
1210 1
2.4%
1201 1
2.4%

사용승인일
Date

Distinct36
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
Minimum2005-11-07 00:00:00
Maximum2020-07-28 00:00:00
2023-12-11T01:32:06.338571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:06.451129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

관리사무소연락처
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-11T01:32:06.642576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters492
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

Unique41 ?
Unique (%)100.0%

Sample

1st row051-271-0541
2nd row051-271-7170
3rd row051-271-7781
4th row051-271-2142
5th row051-271-3787
ValueCountFrequency (%)
051-271-0541 1
 
2.4%
051-292-2846 1
 
2.4%
051-271-2766 1
 
2.4%
051-972-0555 1
 
2.4%
051-201-5923 1
 
2.4%
051-201-5976 1
 
2.4%
051-971-2563 1
 
2.4%
051-832-6111 1
 
2.4%
051-831-4560 1
 
2.4%
051-972-6996 1
 
2.4%
Other values (31) 31
75.6%
2023-12-11T01:32:06.932147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 89
18.1%
- 82
16.7%
2 68
13.8%
0 65
13.2%
5 62
12.6%
7 37
7.5%
6 21
 
4.3%
9 20
 
4.1%
8 18
 
3.7%
4 15
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 410
83.3%
Dash Punctuation 82
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 89
21.7%
2 68
16.6%
0 65
15.9%
5 62
15.1%
7 37
9.0%
6 21
 
5.1%
9 20
 
4.9%
8 18
 
4.4%
4 15
 
3.7%
3 15
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 89
18.1%
- 82
16.7%
2 68
13.8%
0 65
13.2%
5 62
12.6%
7 37
7.5%
6 21
 
4.3%
9 20
 
4.1%
8 18
 
3.7%
4 15
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 89
18.1%
- 82
16.7%
2 68
13.8%
0 65
13.2%
5 62
12.6%
7 37
7.5%
6 21
 
4.3%
9 20
 
4.1%
8 18
 
3.7%
4 15
 
3.0%

Interactions

2023-12-11T01:32:01.679637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:58.597732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.558951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.228055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.982751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:01.845526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:58.876275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.692214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.397525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:01.126053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:02.010529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.074676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.828551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.534556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:01.249979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:02.143345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.216761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.964173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.662007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:01.393775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:02.291074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:59.388760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.093281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:00.822904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:32:01.519272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:32:07.019171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단지명위치(새주소)동수층수연면적세대수사용승인일관리사무소연락처
연번1.0001.0001.0000.1850.7190.2530.3890.9811.000
단지명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위치(새주소)1.0001.0001.0001.0001.0001.0001.0001.0001.000
동수0.1851.0001.0001.0000.1750.4950.4540.9161.000
층수0.7191.0001.0000.1751.0000.6860.4930.8661.000
연면적0.2531.0001.0000.4950.6861.0000.6500.9631.000
세대수0.3891.0001.0000.4540.4930.6501.0000.0001.000
사용승인일0.9811.0001.0000.9160.8660.9630.0001.0001.000
관리사무소연락처1.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-11T01:32:07.120724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수층수연면적세대수
연번1.000-0.3450.5840.1220.224
동수-0.3451.000-0.6000.3850.409
층수0.584-0.6001.0000.1630.308
연면적0.1220.3850.1631.0000.790
세대수0.2240.4090.3080.7901.000

Missing values

2023-12-11T01:32:02.522679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:32:02.724649image/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)동수층수연면적세대수사용승인일관리사무소연락처
01퀸덤 1차 에디슨타운부산광역시 강서구 명지오션시티10로 17 (명지동, 퀸덤1차아파트)11151256316522009-05-01051-271-0541
12퀸덤 1차 링컨타운부산광역시 강서구 명지오션시티10로 16 (명지동, 퀸덤1차 링컨타운)171519939211022009-05-01051-271-7170
23퀸덤 1차 아인슈타인부산광역시 강서구 명지오션시티11로 51 (명지동, 퀸덤1차 아인슈타인타운)171520270511122009-05-01051-271-7781
34명지 극동스타클래스부산광역시 강서구 명지오션시티2로 71 (명지동, 극동스타클래스아파트)211518729211242008-11-25051-271-2142
45명지 두산위브포세이돈부산광역시 강서구 명지오션시티11로 22 (명지동, 명지두산위브포세이돈)161519250012562013-03-15051-271-3787
56명지 엘크루솔마레(B3)부산광역시 강서구 명지오션시티12로 10 (명지동, 엘크루솔마레)1311747954802014-11-21051-271-3999
67명지 한신휴플러스부산광역시 강서구 명지오션시티1로 155 (명지동, 한신 휴플러스)29111269108412014-08-13051-271-2750
78명지 엘크루블루오션(C2)부산광역시 강서구 명지오션시티6로 33 (명지동, 엘크루 블루오션(C2))17101126644142012-06-29051-271-2262
89명지 엘크루블루오션(C3)부산광역시 강서구 명지오션시티12로 92 (명지동, 엘크루 블루오션(C3))14101076983752012-06-29051-271-2263
910명지 엘크루블루오션(C4)부산광역시 강서구 명지오션시티12로 120 (명지동, 엘크루 블루오션(C4))1310760322522012-06-29051-271-2264
연번단지명위치(새주소)동수층수연면적세대수사용승인일관리사무소연락처
3132지사 휴먼시아부산광역시 강서구 과학산단2로20번길 35 (지사동, 부산지사휴먼시아)820757299612011-10-07051-832-5121
3233명지 금강펜테리움 센트럴파크 3차부산광역시 강서구 명지국제5로 110 (명지동, 명지3차금강펜테리움센트럴파크)1220989998702018-11-19051-201-4521
3334명지 스위트팰리스부산광역시 강서구 명지국제5로 165 (명지동, 명지 스위트팰리스)12201244249082018-11-21051-201-0062
3435명지 중흥S클래스 더테라스부산광역시 강서구 명지국제13로38번길 11 (명지동, 중흥S클래스 더 테라스)164458512222019-01-25051-292-0573
3536명지 더 에듀 팰리스 부영부산광역시 강서구 명지국제7로 110 (명지동, 더 에듀 팰리스 부영)162024467012102019-01-28051-271-3577
3637명지 (대림)e편한세상부산광역시 강서구 명지국제2로 80 (명지동, e편한세상)427964733772019-02-25051-201-1231
3738명지 삼정그린코아 더 베스트부산광역시 강서구 명지국제3로 97 (명지동, 삼정그린코아 더 베스트)4301618874312019-09-17051-206-2920
3839화전 우방아이유쉘부산광역시 강서구 화전산단4로 74(화전동, 우방아이유쉘)162019025115152020-02-21051-974-1246
3940더샵 명지퍼스트월드 2단지부산광역시 강서구 명지국제7로 37(명지동, 더샵 명지퍼스트월드 2단지)113027422414062020-07-28051-293-2224
4041더샵 명지퍼스트월드3단지부산광역시 강서구 명지국제2로 41(명지동, 더샵 명지퍼스트월드 3단지)93429106715302020-07-28051-710-0802