Overview

Dataset statistics

Number of variables9
Number of observations378
Missing cells390
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.2 KiB
Average record size in memory76.3 B

Variable types

Categorical1
Text2
Numeric4
DateTime2

Dataset

Description부산광역시 동래구 공동주택 현황에 대한 데이터로 구군명, 단지명, 소재지, 층수, 동수, 세대수, 연면적, 준공일자, 철거일 등의 항목을 제공합니다.
Author부산광역시 동래구
URLhttps://www.data.go.kr/data/3079706/fileData.do

Alerts

층수 is highly overall correlated with 연면적 High correlation
동수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
연면적 is highly overall correlated with 층수 and 2 other fieldsHigh correlation
단지명 has 14 (3.7%) missing valuesMissing
철거일 has 376 (99.5%) missing valuesMissing
동수 has 6 (1.6%) zerosZeros

Reproduction

Analysis started2023-12-12 20:11:31.692020
Analysis finished2023-12-12 20:11:34.430729
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군명
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
부산광역시 동래구
199 
부산광역시 동래구
165 
부산광역시 동래구
 
14

Length

Max length11
Median length11
Mean length10.089947
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 부산광역시 동래구
2nd row 부산광역시 동래구
3rd row 부산광역시 동래구
4th row 부산광역시 동래구
5th row 부산광역시 동래구

Common Values

ValueCountFrequency (%)
부산광역시 동래구 199
52.6%
부산광역시 동래구 165
43.7%
부산광역시 동래구 14
 
3.7%

Length

2023-12-13T05:11:34.513757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:11:34.650671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 378
50.0%
동래구 378
50.0%

단지명
Text

MISSING 

Distinct349
Distinct (%)95.9%
Missing14
Missing (%)3.7%
Memory size3.1 KiB
2023-12-13T05:11:34.932749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length6.1895604
Min length2

Characters and Unicode

Total characters2253
Distinct characters294
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique335 ?
Unique (%)92.0%

Sample

1st row수안아파트
2nd row신사직아파트
3rd row사직시영(76)
4th row동남아파트
5th row세창아파트
ValueCountFrequency (%)
동래 5
 
1.2%
더 4
 
0.9%
유아빌 4
 
0.9%
현대아파트 3
 
0.7%
3차 3
 
0.7%
명장 3
 
0.7%
온천동 3
 
0.7%
명륜 2
 
0.5%
2차 2
 
0.5%
대가하이츠아파트 2
 
0.5%
Other values (376) 395
92.7%
2023-12-13T05:11:35.377589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 163
 
7.2%
트 138
 
6.1%
파 134
 
5.9%
동 82
 
3.6%
64
 
2.8%
스 55
 
2.4%
빌 55
 
2.4%
이 47
 
2.1%
차 37
 
1.6%
리 31
 
1.4%
Other values (284) 1447
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2073
92.0%
Decimal Number 73
 
3.2%
Space Separator 64
 
2.8%
Uppercase Letter 29
 
1.3%
Other Punctuation 4
 
0.2%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Lowercase Letter 2
 
0.1%
Letter Number 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 163
 
7.9%
트 138
 
6.7%
파 134
 
6.5%
동 82
 
4.0%
스 55
 
2.7%
빌 55
 
2.7%
이 47
 
2.3%
차 37
 
1.8%
리 31
 
1.5%
래 30
 
1.4%
Other values (251) 1301
62.8%
Uppercase Letter
ValueCountFrequency (%)
S 5
17.2%
K 5
17.2%
E 3
10.3%
A 2
 
6.9%
B 2
 
6.9%
T 2
 
6.9%
I 2
 
6.9%
H 2
 
6.9%
W 1
 
3.4%
V 1
 
3.4%
Other values (4) 4
13.8%
Decimal Number
ValueCountFrequency (%)
2 29
39.7%
1 23
31.5%
3 8
 
11.0%
0 8
 
11.0%
7 2
 
2.7%
8 1
 
1.4%
4 1
 
1.4%
6 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
: 2
50.0%
, 1
25.0%
/ 1
25.0%
Letter Number
ValueCountFrequency (%)
Ⅶ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2074
92.1%
Common 146
 
6.5%
Latin 33
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 163
 
7.9%
트 138
 
6.7%
파 134
 
6.5%
동 82
 
4.0%
스 55
 
2.7%
빌 55
 
2.7%
이 47
 
2.3%
차 37
 
1.8%
리 31
 
1.5%
래 30
 
1.4%
Other values (252) 1302
62.8%
Latin
ValueCountFrequency (%)
S 5
15.2%
K 5
15.2%
E 3
9.1%
A 2
 
6.1%
B 2
 
6.1%
T 2
 
6.1%
I 2
 
6.1%
H 2
 
6.1%
e 2
 
6.1%
W 1
 
3.0%
Other values (7) 7
21.2%
Common
ValueCountFrequency (%)
64
43.8%
2 29
19.9%
1 23
 
15.8%
3 8
 
5.5%
0 8
 
5.5%
7 2
 
1.4%
: 2
 
1.4%
) 2
 
1.4%
( 2
 
1.4%
- 1
 
0.7%
Other values (5) 5
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2073
92.0%
ASCII 177
 
7.9%
Number Forms 2
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 163
 
7.9%
트 138
 
6.7%
파 134
 
6.5%
동 82
 
4.0%
스 55
 
2.7%
빌 55
 
2.7%
이 47
 
2.3%
차 37
 
1.8%
리 31
 
1.5%
래 30
 
1.4%
Other values (251) 1301
62.8%
ASCII
ValueCountFrequency (%)
64
36.2%
2 29
16.4%
1 23
 
13.0%
3 8
 
4.5%
0 8
 
4.5%
S 5
 
2.8%
K 5
 
2.8%
E 3
 
1.7%
7 2
 
1.1%
A 2
 
1.1%
Other values (20) 28
15.8%
Number Forms
ValueCountFrequency (%)
Ⅶ 1
50.0%
Ⅱ 1
50.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지
Text

Distinct374
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-13T05:11:35.645087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length31.854497
Min length2

Characters and Unicode

Total characters12041
Distinct characters285
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)97.9%

Sample

1st row부산광역시 동래구 명륜로45번길 31 (수안동, 수안아파트)
2nd row부산광역시 동래구 여고로135번길 27 (사직동, 신사직아파트)
3rd row부산광역시 동래구 사직북로66(사직동, 시영아파트)
4th row부산광역시 동래구 여고북로 141 (온천동, 동남온천맨션)
5th row부산광역시 동래구 온천천로 83 (명륜동, 세창아파트)
ValueCountFrequency (%)
부산광역시 373
 
17.5%
동래구 367
 
17.2%
온천동 108
 
5.1%
명장동 40
 
1.9%
안락동 39
 
1.8%
사직동 38
 
1.8%
수안동 34
 
1.6%
낙민동 19
 
0.9%
명륜동 17
 
0.8%
금강로 11
 
0.5%
Other values (726) 1090
51.0%
2023-12-13T05:11:36.064528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1758
 
14.6%
동 803
 
6.7%
시 423
 
3.5%
래 408
 
3.4%
산 389
 
3.2%
로 387
 
3.2%
부 382
 
3.2%
광 377
 
3.1%
역 375
 
3.1%
구 369
 
3.1%
Other values (275) 6370
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7662
63.6%
Space Separator 1758
 
14.6%
Decimal Number 1507
 
12.5%
Close Punctuation 362
 
3.0%
Open Punctuation 362
 
3.0%
Other Punctuation 300
 
2.5%
Dash Punctuation 60
 
0.5%
Uppercase Letter 23
 
0.2%
Lowercase Letter 5
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 803
 
10.5%
시 423
 
5.5%
래 408
 
5.3%
산 389
 
5.1%
로 387
 
5.1%
부 382
 
5.0%
광 377
 
4.9%
역 375
 
4.9%
구 369
 
4.8%
길 246
 
3.2%
Other values (243) 3503
45.7%
Uppercase Letter
ValueCountFrequency (%)
S 3
13.0%
K 3
13.0%
I 3
13.0%
T 2
8.7%
P 2
8.7%
A 2
8.7%
V 2
8.7%
W 2
8.7%
E 2
8.7%
O 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 333
22.1%
2 216
14.3%
3 206
13.7%
4 131
 
8.7%
5 120
 
8.0%
0 111
 
7.4%
7 104
 
6.9%
6 102
 
6.8%
8 95
 
6.3%
9 89
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
c 2
40.0%
k 2
40.0%
s 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 299
99.7%
· 1
 
0.3%
Letter Number
ValueCountFrequency (%)
Ⅶ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
1758
100.0%
Close Punctuation
ValueCountFrequency (%)
) 362
100.0%
Open Punctuation
ValueCountFrequency (%)
( 362
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7662
63.6%
Common 4349
36.1%
Latin 30
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 803
 
10.5%
시 423
 
5.5%
래 408
 
5.3%
산 389
 
5.1%
로 387
 
5.1%
부 382
 
5.0%
광 377
 
4.9%
역 375
 
4.9%
구 369
 
4.8%
길 246
 
3.2%
Other values (243) 3503
45.7%
Common
ValueCountFrequency (%)
1758
40.4%
) 362
 
8.3%
( 362
 
8.3%
1 333
 
7.7%
, 299
 
6.9%
2 216
 
5.0%
3 206
 
4.7%
4 131
 
3.0%
5 120
 
2.8%
0 111
 
2.6%
Other values (6) 451
 
10.4%
Latin
ValueCountFrequency (%)
S 3
10.0%
K 3
10.0%
I 3
10.0%
T 2
 
6.7%
P 2
 
6.7%
A 2
 
6.7%
V 2
 
6.7%
c 2
 
6.7%
W 2
 
6.7%
E 2
 
6.7%
Other values (6) 7
23.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7662
63.6%
ASCII 4376
36.3%
Number Forms 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1758
40.2%
) 362
 
8.3%
( 362
 
8.3%
1 333
 
7.6%
, 299
 
6.8%
2 216
 
4.9%
3 206
 
4.7%
4 131
 
3.0%
5 120
 
2.7%
0 111
 
2.5%
Other values (19) 478
 
10.9%
Hangul
ValueCountFrequency (%)
동 803
 
10.5%
시 423
 
5.5%
래 408
 
5.3%
산 389
 
5.1%
로 387
 
5.1%
부 382
 
5.0%
광 377
 
4.9%
역 375
 
4.9%
구 369
 
4.8%
길 246
 
3.2%
Other values (243) 3503
45.7%
Number Forms
ValueCountFrequency (%)
Ⅶ 1
50.0%
Ⅱ 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.756614
Minimum2
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T05:11:36.208634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median10
Q315
95-th percentile32
Maximum52
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.4500794
Coefficient of variation (CV)0.74079843
Kurtosis2.4433413
Mean12.756614
Median Absolute Deviation (MAD)5
Skewness1.5348109
Sum4822
Variance89.304001
MonotonicityNot monotonic
2023-12-13T05:11:36.349697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5 87
23.0%
15 42
 
11.1%
6 30
 
7.9%
10 22
 
5.8%
7 22
 
5.8%
20 18
 
4.8%
14 15
 
4.0%
12 12
 
3.2%
25 12
 
3.2%
8 12
 
3.2%
Other values (29) 106
28.0%
ValueCountFrequency (%)
2 2
 
0.5%
3 12
 
3.2%
4 7
 
1.9%
5 87
23.0%
6 30
 
7.9%
7 22
 
5.8%
8 12
 
3.2%
9 9
 
2.4%
10 22
 
5.8%
11 8
 
2.1%
ValueCountFrequency (%)
52 1
 
0.3%
49 3
0.8%
45 1
 
0.3%
42 1
 
0.3%
41 2
0.5%
40 2
0.5%
39 1
 
0.3%
38 1
 
0.3%
35 2
0.5%
34 3
0.8%

동수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5714286
Minimum0
Maximum32
Zeros6
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T05:11:36.474520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile9
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.5520276
Coefficient of variation (CV)1.3813441
Kurtosis25.923818
Mean2.5714286
Median Absolute Deviation (MAD)0
Skewness4.3992026
Sum972
Variance12.6169
MonotonicityNot monotonic
2023-12-13T05:11:36.603878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 229
60.6%
2 36
 
9.5%
4 29
 
7.7%
3 28
 
7.4%
6 10
 
2.6%
5 9
 
2.4%
9 9
 
2.4%
8 8
 
2.1%
0 6
 
1.6%
7 3
 
0.8%
Other values (10) 11
 
2.9%
ValueCountFrequency (%)
0 6
 
1.6%
1 229
60.6%
2 36
 
9.5%
3 28
 
7.4%
4 29
 
7.7%
5 9
 
2.4%
6 10
 
2.6%
7 3
 
0.8%
8 8
 
2.1%
9 9
 
2.4%
ValueCountFrequency (%)
32 1
0.3%
29 1
0.3%
23 1
0.3%
21 1
0.3%
18 1
0.3%
17 1
0.3%
16 1
0.3%
13 1
0.3%
11 1
0.3%
10 2
0.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.47619
Minimum3
Maximum3853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T05:11:36.757236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q120
median50
Q3175.5
95-th percentile746.7
Maximum3853
Range3850
Interquartile range (IQR)155.5

Descriptive statistics

Standard deviation363.30157
Coefficient of variation (CV)1.9909533
Kurtosis39.272469
Mean182.47619
Median Absolute Deviation (MAD)40
Skewness5.2507864
Sum68976
Variance131988.03
MonotonicityNot monotonic
2023-12-13T05:11:36.918624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 26
 
6.9%
20 25
 
6.6%
24 16
 
4.2%
40 12
 
3.2%
48 11
 
2.9%
16 9
 
2.4%
50 8
 
2.1%
28 8
 
2.1%
80 8
 
2.1%
7 7
 
1.9%
Other values (167) 248
65.6%
ValueCountFrequency (%)
3 1
 
0.3%
4 1
 
0.3%
5 5
 
1.3%
6 1
 
0.3%
7 7
 
1.9%
8 26
6.9%
9 3
 
0.8%
10 4
 
1.1%
11 4
 
1.1%
12 6
 
1.6%
ValueCountFrequency (%)
3853 1
0.3%
2947 1
0.3%
2058 1
0.3%
1898 1
0.3%
1536 1
0.3%
1420 1
0.3%
1312 1
0.3%
1149 1
0.3%
1064 1
0.3%
1000 1
0.3%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct374
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22655.583
Minimum326.6832
Maximum591401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T05:11:37.058698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum326.6832
5-th percentile518.8843
Q11424.375
median4338
Q313080.25
95-th percentile111021.6
Maximum591401
Range591074.32
Interquartile range (IQR)11655.875

Descriptive statistics

Standard deviation57077.568
Coefficient of variation (CV)2.5193599
Kurtosis39.542314
Mean22655.583
Median Absolute Deviation (MAD)3527.625
Skewness5.456174
Sum8563810.4
Variance3.2578487 × 109
MonotonicityNot monotonic
2023-12-13T05:11:37.292062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2716.0 2
 
0.5%
2749.0 2
 
0.5%
90576.0 2
 
0.5%
999.04 2
 
0.5%
8596.0 1
 
0.3%
760.49 1
 
0.3%
617.4 1
 
0.3%
1600.19 1
 
0.3%
712.88 1
 
0.3%
532.12 1
 
0.3%
Other values (364) 364
96.3%
ValueCountFrequency (%)
326.6832 1
0.3%
333.02 1
0.3%
345.605 1
0.3%
390.434 1
0.3%
392.8357 1
0.3%
397.03 1
0.3%
397.37 1
0.3%
400.37 1
0.3%
431.11 1
0.3%
432.5475 1
0.3%
ValueCountFrequency (%)
591401.0 1
0.3%
462126.0 1
0.3%
342170.0 1
0.3%
288837.0 1
0.3%
228198.0 1
0.3%
228075.0 1
0.3%
215852.0 1
0.3%
214662.0 1
0.3%
201649.0 1
0.3%
169920.6158 1
0.3%

준공일자
Date

Distinct360
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1976-01-27 00:00:00
Maximum2023-06-30 00:00:00
2023-12-13T05:11:37.452748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:37.600528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

철거일
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing376
Missing (%)99.5%
Memory size3.1 KiB
Minimum2013-09-13 00:00:00
Maximum2013-09-16 00:00:00
2023-12-13T05:11:37.727918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:37.847877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-13T05:11:33.297952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.191635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.553185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.911549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:33.394891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.288370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.642060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:33.015906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:33.485826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.378319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.721655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:33.109254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:33.584519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.471739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:32.823232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:33.200024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:11:37.956247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구군명층수동수세대수연면적철거일
구군명1.0000.4270.2270.0000.000NaN
층수0.4271.0000.4830.6170.617NaN
동수0.2270.4831.0000.9000.8760.000
세대수0.0000.6170.9001.0000.9900.000
연면적0.0000.6170.8760.9901.0000.000
철거일NaNNaN0.0000.0000.0001.000
2023-12-13T05:11:38.084083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층수동수세대수연면적구군명
층수1.0000.2030.4980.5320.197
동수0.2031.0000.7510.6550.105
세대수0.4980.7511.0000.8840.000
연면적0.5320.6550.8841.0000.000
구군명0.1970.1050.0000.0001.000

Missing values

2023-12-13T05:11:33.715600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:11:34.245222image/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.
2023-12-13T05:11:34.367133image/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

구군명단지명소재지층수동수세대수연면적준공일자철거일
0부산광역시 동래구수안아파트부산광역시 동래구 명륜로45번길 31 (수안동, 수안아파트)521578596.01976-01-27<NA>
1부산광역시 동래구신사직아파트부산광역시 동래구 여고로135번길 27 (사직동, 신사직아파트)531529008.01976-12-02<NA>
2부산광역시 동래구사직시영(76)부산광역시 동래구 사직북로66(사직동, 시영아파트)5211000214662.01977-02-042013-09-13
3부산광역시 동래구동남아파트부산광역시 동래구 여고북로 141 (온천동, 동남온천맨션)521009376.01978-03-11<NA>
4부산광역시 동래구세창아파트부산광역시 동래구 온천천로 83 (명륜동, 세창아파트)52503976.01978-03-15<NA>
5부산광역시 동래구대진아파트부산광역시 동래구 충렬대로218번길 54 (수안동, 대진아파트)541209147.01978-09-08<NA>
6부산광역시 동래구온천삼익아파트부산광역시 동래구 금강로 28 (온천동, 온천삼익아파트)12343241455.01978-12-11<NA>
7부산광역시 동래구명보아파트부산광역시 동래구 충렬대로86번길 62 (온천동, 명보APT)52505464.01979-02-27<NA>
8부산광역시 동래구해바라기1차부산광역시 동래구 충렬대로202번길 14 (수안동, 해바라기아파트)9321629147.01979-06-22<NA>
9부산광역시 동래구사직삼익아파트부산광역시 동래구 사직북로48번길 25 (사직동, 삼익아파트)12430029034.01979-06-25<NA>
구군명단지명소재지층수동수세대수연면적준공일자철거일
368부산광역시 동래구엘이즈엔 13부산광역시 동래구 여고북로 123번길 33131202307.92021-05-10<NA>
369부산광역시 동래구더 리치부산광역시 동래구 안락로 17번길 991201276.452021-09-15<NA>
370부산광역시 동래구수안다솜시티부산광역시 동래구 명륜로 98번길 29141484499.12021-09-17<NA>
371부산광역시 동래구명장 아이뷰파크부산광역시 동래구 명안로 78번길 25151644795.32021-12-20<NA>
372부산광역시 동래구오페라하우스부산광역시 동래구 명안로 17번길 165120658.02022-11-23<NA>
373부산광역시 동래구동래 3차 SK VIEW부산광역시 온천장로 65번길 9399999201649.02021-12-06<NA>
374부산광역시 동래구동래 래미안 아이파크부산광역시 금정마을로 15035323853591401.02021-12-29<NA>
375부산광역시 동래구힐스테이트 명륜 트라디움부산광역시 동래로 57번길 94428874151490.02022-05-26<NA>
376부산광역시 동래구동래더샵부산광역시 동래구 온천장로 38493603120124.02023-01-19<NA>
377부산광역시 동래구더샵온천헤리티지아파트부산공역시 동래구 온천장로 119번길 5634220656888.212023-06-30<NA>