Overview

Dataset statistics

Number of variables9
Number of observations562
Missing cells33
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.8 KiB
Average record size in memory76.2 B

Variable types

Numeric4
Categorical1
Text3
DateTime1

Dataset

Description부산광역시부산진구_공동주택현황_20230724
Author부산광역시 부산진구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15007226

Alerts

연번 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
층수 is highly overall correlated with 세대수 High correlation
동수 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 연번High correlation
단지명 has 30 (5.3%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:30:14.376949
Analysis finished2023-12-10 16:30:17.352298
Duration2.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct562
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.5
Minimum1
Maximum562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T01:30:17.417700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.05
Q1141.25
median281.5
Q3421.75
95-th percentile533.95
Maximum562
Range561
Interquartile range (IQR)280.5

Descriptive statistics

Standard deviation162.3797
Coefficient of variation (CV)0.5768373
Kurtosis-1.2
Mean281.5
Median Absolute Deviation (MAD)140.5
Skewness0
Sum158203
Variance26367.167
MonotonicityStrictly increasing
2023-12-11T01:30:17.559323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
379 1
 
0.2%
373 1
 
0.2%
374 1
 
0.2%
375 1
 
0.2%
376 1
 
0.2%
377 1
 
0.2%
378 1
 
0.2%
380 1
 
0.2%
371 1
 
0.2%
Other values (552) 552
98.2%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
562 1
0.2%
561 1
0.2%
560 1
0.2%
559 1
0.2%
558 1
0.2%
557 1
0.2%
556 1
0.2%
555 1
0.2%
554 1
0.2%
553 1
0.2%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
도시형 생활주택(임의관리)
231 
공공주택(의무관리)
127 
공동주택(임의관리)
111 
주상복합(임의관리)
93 

Length

Max length14
Median length10
Mean length11.644128
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공공주택(의무관리)
2nd row공공주택(의무관리)
3rd row공공주택(의무관리)
4th row공공주택(의무관리)
5th row공공주택(의무관리)

Common Values

ValueCountFrequency (%)
도시형 생활주택(임의관리) 231
41.1%
공공주택(의무관리) 127
22.6%
공동주택(임의관리) 111
19.8%
주상복합(임의관리) 93
16.5%

Length

2023-12-11T01:30:17.702512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:30:17.813043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시형 231
29.1%
생활주택(임의관리 231
29.1%
공공주택(의무관리 127
16.0%
공동주택(임의관리 111
14.0%
주상복합(임의관리 93
11.7%

단지명
Text

MISSING 

Distinct504
Distinct (%)94.7%
Missing30
Missing (%)5.3%
Memory size4.5 KiB
2023-12-11T01:30:18.055105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length5.9022556
Min length2

Characters and Unicode

Total characters3140
Distinct characters331
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)90.2%

Sample

1st row가야KT e편한세상
2nd row가야동원로얄듀크
3rd row가야반도보라빌
4th row가야벽산
5th row가야삼정그린코아
ValueCountFrequency (%)
수목하우스 7
 
1.2%
대동레미안 5
 
0.8%
서면 4
 
0.7%
더 3
 
0.5%
초읍 3
 
0.5%
동원아파트 3
 
0.5%
은하수빌 2
 
0.3%
노블레스 2
 
0.3%
스카이빌 2
 
0.3%
보광맨션 2
 
0.3%
Other values (533) 559
94.4%
2023-12-11T01:30:18.445701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 122
 
3.9%
트 113
 
3.6%
아 107
 
3.4%
파 103
 
3.3%
빌 91
 
2.9%
동 75
 
2.4%
이 68
 
2.2%
60
 
1.9%
리 54
 
1.7%
우 51
 
1.6%
Other values (321) 2296
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2935
93.5%
Space Separator 60
 
1.9%
Decimal Number 59
 
1.9%
Uppercase Letter 46
 
1.5%
Lowercase Letter 13
 
0.4%
Close Punctuation 11
 
0.4%
Open Punctuation 11
 
0.4%
Other Punctuation 3
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 122
 
4.2%
트 113
 
3.9%
아 107
 
3.6%
파 103
 
3.5%
빌 91
 
3.1%
동 75
 
2.6%
이 68
 
2.3%
리 54
 
1.8%
우 51
 
1.7%
원 48
 
1.6%
Other values (280) 2103
71.7%
Uppercase Letter
ValueCountFrequency (%)
B 5
 
10.9%
H 4
 
8.7%
T 4
 
8.7%
E 3
 
6.5%
A 3
 
6.5%
K 3
 
6.5%
L 3
 
6.5%
S 3
 
6.5%
D 3
 
6.5%
I 2
 
4.3%
Other values (10) 13
28.3%
Decimal Number
ValueCountFrequency (%)
2 21
35.6%
1 17
28.8%
3 7
 
11.9%
5 4
 
6.8%
0 3
 
5.1%
4 3
 
5.1%
6 3
 
5.1%
8 1
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
e 4
30.8%
o 2
15.4%
n 2
15.4%
d 1
 
7.7%
l 1
 
7.7%
s 1
 
7.7%
u 1
 
7.7%
i 1
 
7.7%
Space Separator
ValueCountFrequency (%)
60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2934
93.4%
Common 146
 
4.6%
Latin 59
 
1.9%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 122
 
4.2%
트 113
 
3.9%
아 107
 
3.6%
파 103
 
3.5%
빌 91
 
3.1%
동 75
 
2.6%
이 68
 
2.3%
리 54
 
1.8%
우 51
 
1.7%
원 48
 
1.6%
Other values (279) 2102
71.6%
Latin
ValueCountFrequency (%)
B 5
 
8.5%
H 4
 
6.8%
e 4
 
6.8%
T 4
 
6.8%
E 3
 
5.1%
A 3
 
5.1%
K 3
 
5.1%
L 3
 
5.1%
S 3
 
5.1%
D 3
 
5.1%
Other values (18) 24
40.7%
Common
ValueCountFrequency (%)
60
41.1%
2 21
 
14.4%
1 17
 
11.6%
) 11
 
7.5%
( 11
 
7.5%
3 7
 
4.8%
5 4
 
2.7%
0 3
 
2.1%
, 3
 
2.1%
4 3
 
2.1%
Other values (3) 6
 
4.1%
Han
ValueCountFrequency (%)
秀 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2934
93.4%
ASCII 205
 
6.5%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 122
 
4.2%
트 113
 
3.9%
아 107
 
3.6%
파 103
 
3.5%
빌 91
 
3.1%
동 75
 
2.6%
이 68
 
2.3%
리 54
 
1.8%
우 51
 
1.7%
원 48
 
1.6%
Other values (279) 2102
71.6%
ASCII
ValueCountFrequency (%)
60
29.3%
2 21
 
10.2%
1 17
 
8.3%
) 11
 
5.4%
( 11
 
5.4%
3 7
 
3.4%
B 5
 
2.4%
5 4
 
2.0%
H 4
 
2.0%
e 4
 
2.0%
Other values (31) 61
29.8%
CJK
ValueCountFrequency (%)
秀 1
100.0%

소재지 지번주소
Text

Distinct556
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-11T01:30:18.754619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length19
Mean length11.286477
Min length5

Characters and Unicode

Total characters6343
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)97.9%

Sample

1st row가야동 698
2nd row가야동 128-1
3rd row가야동 588
4th row가야동 669-9
5th row가야동 361-85
ValueCountFrequency (%)
부산진구 224
 
19.9%
개금동 54
 
4.8%
전포동 39
 
3.5%
양정동 37
 
3.3%
범천동 28
 
2.5%
부암동 26
 
2.3%
외1필지 26
 
2.3%
부전동 24
 
2.1%
당감동 16
 
1.4%
연지동 15
 
1.3%
Other values (579) 638
56.6%
2023-12-11T01:30:19.209170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
565
 
8.9%
동 562
 
8.9%
1 512
 
8.1%
- 501
 
7.9%
2 339
 
5.3%
부 324
 
5.1%
3 282
 
4.4%
4 270
 
4.3%
5 240
 
3.8%
산 228
 
3.6%
Other values (27) 2520
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2703
42.6%
Decimal Number 2563
40.4%
Space Separator 565
 
8.9%
Dash Punctuation 501
 
7.9%
Other Punctuation 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 562
20.8%
부 324
12.0%
산 228
 
8.4%
구 224
 
8.3%
진 224
 
8.3%
전 152
 
5.6%
포 91
 
3.4%
양 87
 
3.2%
정 87
 
3.2%
개 81
 
3.0%
Other values (14) 643
23.8%
Decimal Number
ValueCountFrequency (%)
1 512
20.0%
2 339
13.2%
3 282
11.0%
4 270
10.5%
5 240
9.4%
6 225
8.8%
7 223
8.7%
8 170
 
6.6%
9 158
 
6.2%
0 144
 
5.6%
Space Separator
ValueCountFrequency (%)
565
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 501
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3640
57.4%
Hangul 2703
42.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 562
20.8%
부 324
12.0%
산 228
 
8.4%
구 224
 
8.3%
진 224
 
8.3%
전 152
 
5.6%
포 91
 
3.4%
양 87
 
3.2%
정 87
 
3.2%
개 81
 
3.0%
Other values (14) 643
23.8%
Common
ValueCountFrequency (%)
565
15.5%
1 512
14.1%
- 501
13.8%
2 339
9.3%
3 282
7.7%
4 270
7.4%
5 240
6.6%
6 225
 
6.2%
7 223
 
6.1%
8 170
 
4.7%
Other values (3) 313
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3640
57.4%
Hangul 2703
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
565
15.5%
1 512
14.1%
- 501
13.8%
2 339
9.3%
3 282
7.7%
4 270
7.4%
5 240
6.6%
6 225
 
6.2%
7 223
 
6.1%
8 170
 
4.7%
Other values (3) 313
8.6%
Hangul
ValueCountFrequency (%)
동 562
20.8%
부 324
12.0%
산 228
 
8.4%
구 224
 
8.3%
진 224
 
8.3%
전 152
 
5.6%
포 91
 
3.4%
양 87
 
3.2%
정 87
 
3.2%
개 81
 
3.0%
Other values (14) 643
23.8%

소재지 도로명주소
Text

Distinct552
Distinct (%)98.4%
Missing1
Missing (%)0.2%
Memory size4.5 KiB
2023-12-11T01:30:19.474876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length9.8609626
Min length4

Characters and Unicode

Total characters5532
Distinct characters88
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique543 ?
Unique (%)96.8%

Sample

1st row가야대로 569
2nd row가야대로 531
3rd row가야공원로 41
4th row엄광로 122
5th row엄광로238번길 5
ValueCountFrequency (%)
가야대로 17
 
1.8%
동평로 13
 
1.4%
복지로21번길 9
 
0.9%
11 9
 
0.9%
전포대로 9
 
0.9%
신천대로 9
 
0.9%
신암로 9
 
0.9%
시민공원로19번길 7
 
0.7%
중앙대로 7
 
0.7%
10 7
 
0.7%
Other values (555) 855
89.9%
2023-12-11T01:30:19.927802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 558
 
10.1%
1 458
 
8.3%
390
 
7.0%
번 370
 
6.7%
길 368
 
6.7%
2 265
 
4.8%
5 235
 
4.2%
3 220
 
4.0%
6 213
 
3.9%
7 206
 
3.7%
Other values (78) 2249
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2773
50.1%
Decimal Number 2260
40.9%
Space Separator 390
 
7.0%
Dash Punctuation 109
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 558
20.1%
번 370
13.3%
길 368
13.3%
대 202
 
7.3%
가 91
 
3.3%
야 81
 
2.9%
전 72
 
2.6%
동 68
 
2.5%
지 60
 
2.2%
천 52
 
1.9%
Other values (66) 851
30.7%
Decimal Number
ValueCountFrequency (%)
1 458
20.3%
2 265
11.7%
5 235
10.4%
3 220
9.7%
6 213
9.4%
7 206
9.1%
4 187
8.3%
8 171
 
7.6%
9 167
 
7.4%
0 138
 
6.1%
Space Separator
ValueCountFrequency (%)
390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2773
50.1%
Common 2759
49.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 558
20.1%
번 370
13.3%
길 368
13.3%
대 202
 
7.3%
가 91
 
3.3%
야 81
 
2.9%
전 72
 
2.6%
동 68
 
2.5%
지 60
 
2.2%
천 52
 
1.9%
Other values (66) 851
30.7%
Common
ValueCountFrequency (%)
1 458
16.6%
390
14.1%
2 265
9.6%
5 235
8.5%
3 220
8.0%
6 213
7.7%
7 206
7.5%
4 187
6.8%
8 171
 
6.2%
9 167
 
6.1%
Other values (2) 247
9.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2773
50.1%
ASCII 2759
49.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 558
20.1%
번 370
13.3%
길 368
13.3%
대 202
 
7.3%
가 91
 
3.3%
야 81
 
2.9%
전 72
 
2.6%
동 68
 
2.5%
지 60
 
2.2%
천 52
 
1.9%
Other values (66) 851
30.7%
ASCII
ValueCountFrequency (%)
1 458
16.6%
390
14.1%
2 265
9.6%
5 235
8.5%
3 220
8.0%
6 213
7.7%
7 206
7.5%
4 187
6.8%
8 171
 
6.2%
9 167
 
6.1%
Other values (2) 247
9.0%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.078292
Minimum2
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T01:30:20.104844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q16
median10
Q315
95-th percentile29.95
Maximum147
Range145
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.347833
Coefficient of variation (CV)0.79122203
Kurtosis50.349253
Mean13.078292
Median Absolute Deviation (MAD)5
Skewness4.7131505
Sum7350
Variance107.07764
MonotonicityNot monotonic
2023-12-11T01:30:20.261090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5 98
17.4%
15 80
14.2%
6 67
11.9%
7 46
 
8.2%
10 32
 
5.7%
25 28
 
5.0%
20 26
 
4.6%
8 22
 
3.9%
9 20
 
3.6%
14 20
 
3.6%
Other values (29) 123
21.9%
ValueCountFrequency (%)
2 1
 
0.2%
3 5
 
0.9%
4 5
 
0.9%
5 98
17.4%
6 67
11.9%
7 46
8.2%
8 22
 
3.9%
9 20
 
3.6%
10 32
 
5.7%
11 4
 
0.7%
ValueCountFrequency (%)
147 1
 
0.2%
58 2
 
0.4%
43 2
 
0.4%
40 1
 
0.2%
39 2
 
0.4%
38 1
 
0.2%
37 5
0.9%
36 2
 
0.4%
35 4
0.7%
33 1
 
0.2%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0818505
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T01:30:20.425371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.75
95-th percentile8
Maximum24
Range23
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation2.894532
Coefficient of variation (CV)1.3903649
Kurtosis19.103612
Mean2.0818505
Median Absolute Deviation (MAD)0
Skewness4.0360922
Sum1170
Variance8.3783153
MonotonicityNot monotonic
2023-12-11T01:30:20.548022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 421
74.9%
2 41
 
7.3%
3 28
 
5.0%
5 17
 
3.0%
4 14
 
2.5%
8 9
 
1.6%
7 7
 
1.2%
13 4
 
0.7%
6 4
 
0.7%
9 4
 
0.7%
Other values (9) 13
 
2.3%
ValueCountFrequency (%)
1 421
74.9%
2 41
 
7.3%
3 28
 
5.0%
4 14
 
2.5%
5 17
 
3.0%
6 4
 
0.7%
7 7
 
1.2%
8 9
 
1.6%
9 4
 
0.7%
10 1
 
0.2%
ValueCountFrequency (%)
24 1
 
0.2%
21 1
 
0.2%
20 1
 
0.2%
18 2
0.4%
17 2
0.4%
16 1
 
0.2%
15 1
 
0.2%
13 4
0.7%
11 3
0.5%
10 1
 
0.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct221
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.25979
Minimum8
Maximum2716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T01:30:20.695016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile20
Q124
median45
Q3159.75
95-th percentile751
Maximum2716
Range2708
Interquartile range (IQR)135.75

Descriptive statistics

Standard deviation321.41163
Coefficient of variation (CV)1.8877718
Kurtosis22.126735
Mean170.25979
Median Absolute Deviation (MAD)25
Skewness4.1537069
Sum95686
Variance103305.44
MonotonicityNot monotonic
2023-12-11T01:30:20.883686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 91
 
16.2%
24 49
 
8.7%
28 33
 
5.9%
40 15
 
2.7%
29 11
 
2.0%
36 9
 
1.6%
50 8
 
1.4%
48 8
 
1.4%
60 7
 
1.2%
23 6
 
1.1%
Other values (211) 325
57.8%
ValueCountFrequency (%)
8 2
 
0.4%
9 2
 
0.4%
12 1
 
0.2%
16 1
 
0.2%
18 1
 
0.2%
20 91
16.2%
21 5
 
0.9%
22 4
 
0.7%
23 6
 
1.1%
24 49
8.7%
ValueCountFrequency (%)
2716 1
0.2%
2544 1
0.2%
2510 1
0.2%
1901 1
0.2%
1812 1
0.2%
1772 1
0.2%
1733 1
0.2%
1424 1
0.2%
1395 1
0.2%
1360 1
0.2%

준공일
Date

Distinct507
Distinct (%)90.5%
Missing2
Missing (%)0.4%
Memory size4.5 KiB
Minimum1974-08-28 00:00:00
Maximum2022-05-20 00:00:00
2023-12-11T01:30:21.068711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:21.216598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T01:30:16.622382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:14.953643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:15.767061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.196503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.727550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:15.075719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:15.875931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.287407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.837039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:15.200132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:15.995623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.412857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.939862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:15.318309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.103475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:30:16.534622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:30:21.358407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분층수동수세대수
연번1.0000.9690.6610.4940.530
구분0.9691.0000.4740.4690.776
층수0.6610.4741.0000.3700.650
동수0.4940.4690.3701.0000.805
세대수0.5300.7760.6500.8051.000
2023-12-11T01:30:21.497221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번층수동수세대수구분
연번1.000-0.164-0.640-0.5410.906
층수-0.1641.0000.2480.6160.404
동수-0.6400.2481.0000.6340.297
세대수-0.5410.6160.6341.0000.440
구분0.9060.4040.2970.4401.000

Missing values

2023-12-11T01:30:17.080857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:30:17.208973image/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.
2023-12-11T01:30:17.304669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번구분단지명소재지 지번주소소재지 도로명주소층수동수세대수준공일
01공공주택(의무관리)가야KT e편한세상가야동 698가야대로 5692952992006-09-28
12공공주택(의무관리)가야동원로얄듀크가야동 128-1가야대로 5312822982013-05-01
23공공주택(의무관리)가야반도보라빌가야동 588가야공원로 41251110482005-02-02
34공공주택(의무관리)가야벽산가야동 669-9엄광로 122152117721992-06-26
45공공주택(의무관리)가야삼정그린코아가야동 361-85엄광로238번길 52233862000-07-08
56공공주택(의무관리)가야유림아파트가야동 50-1가야대로 679번길 1552523001996-02-17
67공공주택(의무관리)가야화신타운가야동 55가야대로679번길 1662532991999-12-28
78공공주택(의무관리)가야센트레빌가야동 699가야공원로 63522122020-06-30
89공공주택(의무관리)개금대동아파트개금동 566-51진사로89번길 731532461999-04-22
910공공주택(의무관리)개금롯데캐슬아파트개금동 796개금온정로 103054892010-11-12
연번구분단지명소재지 지번주소소재지 도로명주소층수동수세대수준공일
552553주상복합(임의관리)세원秀아파트초읍동173-8성지로130151982018-04-27
553554주상복합(임의관리)TK 골드뷰범천동842-4범일로154번길61151302014-11-25
554555주상복합(임의관리)<NA>가야동617-8냉정로26151201983-05-07
555556주상복합(임의관리)가남아파트 1호동가야동450-2대학로64-151281982-04-13
556557주상복합(임의관리)반도맨숀초읍동252-1성지로93번길3041361981-03-21
557558주상복합(임의관리)대동레미안 더 오션부전동415-1부전로111번길15121752017-03-16
558559주상복합(임의관리)<NA>부전동410-5가야대로755번길55-451201976-05-08
559560주상복합(임의관리)산정아파트양정동138-10연수로47-261321975-07-05
560561주상복합(임의관리)골든엠파이어당감동880-4신천대로191151272014-12-10
561562주상복합(임의관리)대동레미안 센트럴시티5범천동 849-2범일로192번길 102011872019-11-05