Overview

Dataset statistics

Number of variables9
Number of observations369
Missing cells381
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.5 KiB
Average record size in memory76.4 B

Variable types

Categorical1
Text2
Numeric4
DateTime2

Dataset

Description부산광역시동래구_공동주택현황_20221017
Author부산광역시 동래구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3079706

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 (3.8%) missing valuesMissing
철거일 has 367 (99.5%) missing valuesMissing
동수 has 6 (1.6%) zerosZeros

Reproduction

Analysis started2023-12-10 17:05:10.471023
Analysis finished2023-12-10 17:05:14.423585
Duration3.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군명
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
부산광역시 동래구
199 
부산광역시 동래구
156 
부산광역시 동래구
 
14

Length

Max length11
Median length11
Mean length10.116531
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
53.9%
부산광역시 동래구 156
42.3%
부산광역시 동래구 14
 
3.8%

Length

2023-12-11T02:05:14.567625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:05:14.763939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 369
50.0%
동래구 369
50.0%

단지명
Text

MISSING 

Distinct340
Distinct (%)95.8%
Missing14
Missing (%)3.8%
Memory size3.0 KiB
2023-12-11T02:05:15.191815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length6.1915493
Min length2

Characters and Unicode

Total characters2198
Distinct characters293
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

Unique326 ?
Unique (%)91.8%

Sample

1st row수안아파트
2nd row신사직아파트
3rd row사직시영(76)
4th row동남아파트
5th row세창아파트
ValueCountFrequency (%)
동래 5
 
1.2%
유아빌 4
 
1.0%
현대아파트 3
 
0.7%
더 3
 
0.7%
3차 3
 
0.7%
온천동 3
 
0.7%
삼정그린코아 2
 
0.5%
힐스테이트 2
 
0.5%
경동리인타워 2
 
0.5%
온천천 2
 
0.5%
Other values (366) 385
93.0%
2023-12-11T02:05:15.930972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 161
 
7.3%
트 137
 
6.2%
파 132
 
6.0%
동 81
 
3.7%
61
 
2.8%
빌 55
 
2.5%
스 53
 
2.4%
이 45
 
2.0%
차 37
 
1.7%
래 29
 
1.3%
Other values (283) 1407
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2023
92.0%
Decimal Number 71
 
3.2%
Space Separator 61
 
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 161
 
8.0%
트 137
 
6.8%
파 132
 
6.5%
동 81
 
4.0%
빌 55
 
2.7%
스 53
 
2.6%
이 45
 
2.2%
차 37
 
1.8%
래 29
 
1.4%
리 28
 
1.4%
Other values (250) 1265
62.5%
Uppercase Letter
ValueCountFrequency (%)
K 5
17.2%
S 5
17.2%
E 3
10.3%
I 2
 
6.9%
T 2
 
6.9%
A 2
 
6.9%
B 2
 
6.9%
H 2
 
6.9%
N 1
 
3.4%
W 1
 
3.4%
Other values (4) 4
13.8%
Decimal Number
ValueCountFrequency (%)
2 29
40.8%
1 22
31.0%
0 8
 
11.3%
3 7
 
9.9%
7 2
 
2.8%
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 (%)
61
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 2024
92.1%
Common 141
 
6.4%
Latin 33
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 161
 
8.0%
트 137
 
6.8%
파 132
 
6.5%
동 81
 
4.0%
빌 55
 
2.7%
스 53
 
2.6%
이 45
 
2.2%
차 37
 
1.8%
래 29
 
1.4%
리 28
 
1.4%
Other values (251) 1266
62.5%
Latin
ValueCountFrequency (%)
K 5
15.2%
S 5
15.2%
E 3
9.1%
I 2
 
6.1%
T 2
 
6.1%
A 2
 
6.1%
B 2
 
6.1%
H 2
 
6.1%
e 2
 
6.1%
N 1
 
3.0%
Other values (7) 7
21.2%
Common
ValueCountFrequency (%)
61
43.3%
2 29
20.6%
1 22
 
15.6%
0 8
 
5.7%
3 7
 
5.0%
7 2
 
1.4%
) 2
 
1.4%
: 2
 
1.4%
( 2
 
1.4%
, 1
 
0.7%
Other values (5) 5
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2023
92.0%
ASCII 172
 
7.8%
Number Forms 2
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 161
 
8.0%
트 137
 
6.8%
파 132
 
6.5%
동 81
 
4.0%
빌 55
 
2.7%
스 53
 
2.6%
이 45
 
2.2%
차 37
 
1.8%
래 29
 
1.4%
리 28
 
1.4%
Other values (250) 1265
62.5%
ASCII
ValueCountFrequency (%)
61
35.5%
2 29
16.9%
1 22
 
12.8%
0 8
 
4.7%
3 7
 
4.1%
K 5
 
2.9%
S 5
 
2.9%
E 3
 
1.7%
I 2
 
1.2%
7 2
 
1.2%
Other values (20) 28
16.3%
Number Forms
ValueCountFrequency (%)
Ⅶ 1
50.0%
Ⅱ 1
50.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지
Text

Distinct365
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-11T02:05:16.402725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length32.105691
Min length2

Characters and Unicode

Total characters11847
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

Unique361 ?
Unique (%)97.8%

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 365
 
17.4%
동래구 358
 
17.1%
온천동 108
 
5.2%
명장동 40
 
1.9%
안락동 39
 
1.9%
사직동 38
 
1.8%
수안동 34
 
1.6%
낙민동 19
 
0.9%
명륜동 17
 
0.8%
금강로 11
 
0.5%
Other values (715) 1063
50.8%
2023-12-11T02:05:17.121574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1723
 
14.5%
동 794
 
6.7%
시 414
 
3.5%
래 399
 
3.4%
산 380
 
3.2%
로 378
 
3.2%
부 373
 
3.1%
광 369
 
3.1%
역 366
 
3.1%
) 362
 
3.1%
Other values (275) 6289
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7543
63.7%
Space Separator 1723
 
14.5%
Decimal Number 1468
 
12.4%
Close Punctuation 362
 
3.1%
Open Punctuation 362
 
3.1%
Other Punctuation 300
 
2.5%
Dash Punctuation 59
 
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 794
 
10.5%
시 414
 
5.5%
래 399
 
5.3%
산 380
 
5.0%
로 378
 
5.0%
부 373
 
4.9%
광 369
 
4.9%
역 366
 
4.9%
구 360
 
4.8%
길 238
 
3.2%
Other values (243) 3472
46.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
13.0%
I 3
13.0%
K 3
13.0%
V 2
8.7%
E 2
8.7%
W 2
8.7%
T 2
8.7%
P 2
8.7%
A 2
8.7%
O 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 325
22.1%
2 210
14.3%
3 202
13.8%
4 131
8.9%
5 114
 
7.8%
0 109
 
7.4%
7 101
 
6.9%
6 99
 
6.7%
8 92
 
6.3%
9 85
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
k 2
40.0%
c 2
40.0%
s 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 299
99.7%
· 1
 
0.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅶ 1
50.0%
Space Separator
ValueCountFrequency (%)
1723
100.0%
Close Punctuation
ValueCountFrequency (%)
) 362
100.0%
Open Punctuation
ValueCountFrequency (%)
( 362
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7543
63.7%
Common 4274
36.1%
Latin 30
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 794
 
10.5%
시 414
 
5.5%
래 399
 
5.3%
산 380
 
5.0%
로 378
 
5.0%
부 373
 
4.9%
광 369
 
4.9%
역 366
 
4.9%
구 360
 
4.8%
길 238
 
3.2%
Other values (243) 3472
46.0%
Common
ValueCountFrequency (%)
1723
40.3%
) 362
 
8.5%
( 362
 
8.5%
1 325
 
7.6%
, 299
 
7.0%
2 210
 
4.9%
3 202
 
4.7%
4 131
 
3.1%
5 114
 
2.7%
0 109
 
2.6%
Other values (6) 437
 
10.2%
Latin
ValueCountFrequency (%)
S 3
10.0%
I 3
10.0%
K 3
10.0%
V 2
 
6.7%
E 2
 
6.7%
W 2
 
6.7%
T 2
 
6.7%
P 2
 
6.7%
A 2
 
6.7%
k 2
 
6.7%
Other values (6) 7
23.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7543
63.7%
ASCII 4301
36.3%
Number Forms 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1723
40.1%
) 362
 
8.4%
( 362
 
8.4%
1 325
 
7.6%
, 299
 
7.0%
2 210
 
4.9%
3 202
 
4.7%
4 131
 
3.0%
5 114
 
2.7%
0 109
 
2.5%
Other values (19) 464
 
10.8%
Hangul
ValueCountFrequency (%)
동 794
 
10.5%
시 414
 
5.5%
래 399
 
5.3%
산 380
 
5.0%
로 378
 
5.0%
부 373
 
4.9%
광 369
 
4.9%
역 366
 
4.9%
구 360
 
4.8%
길 238
 
3.2%
Other values (243) 3472
46.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅶ 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.634146
Minimum2
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-12-11T02:05:17.327368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median10
Q315
95-th percentile30.6
Maximum52
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.2908337
Coefficient of variation (CV)0.73537487
Kurtosis2.3344869
Mean12.634146
Median Absolute Deviation (MAD)5
Skewness1.5024518
Sum4662
Variance86.319592
MonotonicityNot monotonic
2023-12-11T02:05:17.569032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5 86
23.3%
15 40
 
10.8%
6 29
 
7.9%
10 22
 
6.0%
7 22
 
6.0%
20 18
 
4.9%
14 14
 
3.8%
12 12
 
3.3%
25 12
 
3.3%
8 12
 
3.3%
Other values (29) 102
27.6%
ValueCountFrequency (%)
2 2
 
0.5%
3 12
 
3.3%
4 7
 
1.9%
5 86
23.3%
6 29
 
7.9%
7 22
 
6.0%
8 12
 
3.3%
9 8
 
2.2%
10 22
 
6.0%
11 8
 
2.2%
ValueCountFrequency (%)
52 1
0.3%
49 2
0.5%
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 2
0.5%

동수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.601626
Minimum0
Maximum32
Zeros6
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-12-11T02:05:17.756738image/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 deviation3.5883413
Coefficient of variation (CV)1.3792687
Kurtosis25.31081
Mean2.601626
Median Absolute Deviation (MAD)0
Skewness4.3481424
Sum960
Variance12.876193
MonotonicityNot monotonic
2023-12-11T02:05:17.923739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 222
60.2%
2 35
 
9.5%
4 29
 
7.9%
3 27
 
7.3%
6 10
 
2.7%
5 9
 
2.4%
9 9
 
2.4%
8 8
 
2.2%
0 6
 
1.6%
7 3
 
0.8%
Other values (10) 11
 
3.0%
ValueCountFrequency (%)
0 6
 
1.6%
1 222
60.2%
2 35
 
9.5%
3 27
 
7.3%
4 29
 
7.9%
5 9
 
2.4%
6 10
 
2.7%
7 3
 
0.8%
8 8
 
2.2%
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 

Distinct174
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.10569
Minimum3
Maximum3853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-12-11T02:05:18.119741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q120
median50
Q3176
95-th percentile754.8
Maximum3853
Range3850
Interquartile range (IQR)156

Descriptive statistics

Standard deviation366.47224
Coefficient of variation (CV)1.9905536
Kurtosis38.74294
Mean184.10569
Median Absolute Deviation (MAD)40
Skewness5.2252656
Sum67935
Variance134301.9
MonotonicityNot monotonic
2023-12-11T02:05:18.324221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 26
 
7.0%
20 21
 
5.7%
24 16
 
4.3%
40 11
 
3.0%
48 10
 
2.7%
16 9
 
2.4%
50 8
 
2.2%
80 8
 
2.2%
28 8
 
2.2%
7 7
 
1.9%
Other values (164) 245
66.4%
ValueCountFrequency (%)
3 1
 
0.3%
4 1
 
0.3%
5 5
 
1.4%
6 1
 
0.3%
7 7
 
1.9%
8 26
7.0%
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 

Distinct365
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22679.305
Minimum326.6832
Maximum591401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-12-11T02:05:18.883984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum326.6832
5-th percentile515.5372
Q11425.5
median4354
Q313145
95-th percentile110579.2
Maximum591401
Range591074.32
Interquartile range (IQR)11719.5

Descriptive statistics

Standard deviation57452.72
Coefficient of variation (CV)2.5332663
Kurtosis39.438755
Mean22679.305
Median Absolute Deviation (MAD)3544.25
Skewness5.467208
Sum8368663.6
Variance3.300815 × 109
MonotonicityNot monotonic
2023-12-11T02:05:19.086299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90576.0 2
 
0.5%
2716.0 2
 
0.5%
2749.0 2
 
0.5%
999.04 2
 
0.5%
8596.0 1
 
0.3%
512.562 1
 
0.3%
594.496 1
 
0.3%
438.93 1
 
0.3%
760.49 1
 
0.3%
921.7 1
 
0.3%
Other values (355) 355
96.2%
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

Distinct351
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum1976-01-27 00:00:00
Maximum2022-05-26 00:00:00
2023-12-11T02:05:19.268188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:19.469293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

철거일
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing367
Missing (%)99.5%
Memory size3.0 KiB
Minimum2013-09-13 00:00:00
Maximum2013-09-16 00:00:00
2023-12-11T02:05:19.668621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:19.821828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-11T02:05:13.139029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:11.173070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:11.781344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:12.442531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:13.311396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:11.303780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:11.932295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:12.606695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:13.474739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:11.477514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:12.078417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:12.770519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:13.698886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:11.627877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:12.287187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:12.970621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:05:19.945469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구군명층수동수세대수연면적철거일
구군명1.0000.4290.2030.0000.000NaN
층수0.4291.0000.4900.6170.607NaN
동수0.2030.4901.0000.9000.8770.000
세대수0.0000.6170.9001.0000.9910.000
연면적0.0000.6070.8770.9911.0000.000
철거일NaNNaN0.0000.0000.0001.000
2023-12-11T02:05:20.106022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층수동수세대수연면적구군명
층수1.0000.2010.4940.5250.194
동수0.2011.0000.7510.6540.095
세대수0.4940.7511.0000.8830.000
연면적0.5250.6540.8831.0000.000
구군명0.1940.0950.0000.0001.000

Missing values

2023-12-11T02:05:13.934291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:05:14.179741image/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.
2023-12-11T02:05:14.338197image/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
359부산광역시 동래구낙민동 아델리아 주상복합부산광역시 동래구 충렬대로 238번가길 49-5(낙민동)151285648.02020-02-03<NA>
360부산광역시 동래구명장블리체부산광역시 명장로 106번길 69(명장동)81201891.02020-07-09<NA>
361부산광역시 동래구동래행복주택부산광역시 동래구 반송로 164(낙민동)25339520122.02019-12-13<NA>
362부산광역시 동래구쌍용더플래티넘사직아시아드아파트부산광역시 동래구 아시아드대로 134번길 14419914130687.02020-02-28<NA>
363부산광역시 동래구e편한세상 동래아시아드부산광역시 동래구 아시아드대로 202(온천동)31443958447.02020-06-26<NA>
364부산광역시 동래구온천천 경동리인타워 2차부산광역시 동래구 수안로 8번길 38(수안동)45117629197.02020-09-09<NA>
365부산광역시 동래구동래롯데캐슬퀸부산광역시 동래구 온천천로 16534621035684.02020-12-24<NA>
366부산광역시 동래구동래 3차 SK VIEW부산광역시 온천장로 65번길 9399999201649.02021-12-06<NA>
367부산광역시 동래구동래 래미안 아이파크부산광역시 금정마을로 15035323853591401.02021-12-29<NA>
368부산광역시 동래구힐스테이트 명륜 트라디움부산광역시 동래로 57번길 94428874151490.02022-05-26<NA>