Overview

Dataset statistics

Number of variables9
Number of observations480
Missing cells295
Missing cells (%)6.8%
Duplicate rows22
Duplicate rows (%)4.6%
Total size in memory36.2 KiB
Average record size in memory77.3 B

Variable types

Text2
Categorical2
Numeric5

Dataset

Description부산광역시해운대구_빈집정비계획대상_20220622
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15060364

Alerts

Dataset has 22 (4.6%) duplicate rowsDuplicates
건축년도 is highly overall correlated with 건축구조High correlation
건축면적 is highly overall correlated with 연면적 and 1 other fieldsHigh correlation
연면적 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
대지면적 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
주택유형 is highly overall correlated with 대지면적 and 1 other fieldsHigh correlation
건축구조 is highly overall correlated with 건축년도 and 2 other fieldsHigh correlation
전용면적 has 293 (61.0%) missing valuesMissing
건축면적 has 22 (4.6%) zerosZeros

Reproduction

Analysis started2023-12-10 17:32:43.579048
Analysis finished2023-12-10 17:32:50.227383
Duration6.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지
Text

Distinct387
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-11T02:32:50.919364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length20.791667
Min length17

Characters and Unicode

Total characters9980
Distinct characters30
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

Unique370 ?
Unique (%)77.1%

Sample

1st row부산광역시 해운대구 중동 1500-9
2nd row부산광역시 해운대구 중동 1499-16
3rd row부산광역시 해운대구 중동 1499-4
4th row부산광역시 해운대구 중동 1493-21
5th row부산광역시 해운대구 중동 1506-8
ValueCountFrequency (%)
부산광역시 480
25.0%
해운대구 480
25.0%
반송동 200
10.4%
재송동 98
 
5.1%
반여동 80
 
4.2%
1030 58
 
3.0%
우동 55
 
2.9%
중동 39
 
2.0%
1026-1 15
 
0.8%
송정동 6
 
0.3%
Other values (386) 409
21.3%
2023-12-11T02:32:52.328692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1440
 
14.4%
1 544
 
5.5%
대 482
 
4.8%
부 480
 
4.8%
운 480
 
4.8%
산 480
 
4.8%
동 480
 
4.8%
구 480
 
4.8%
해 480
 
4.8%
시 480
 
4.8%
Other values (20) 4154
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5666
56.8%
Decimal Number 2473
24.8%
Space Separator 1440
 
14.4%
Dash Punctuation 401
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 482
8.5%
부 480
8.5%
운 480
8.5%
산 480
8.5%
동 480
8.5%
구 480
8.5%
해 480
8.5%
시 480
8.5%
역 480
8.5%
광 480
8.5%
Other values (8) 864
15.2%
Decimal Number
ValueCountFrequency (%)
1 544
22.0%
0 351
14.2%
2 349
14.1%
5 235
9.5%
3 215
 
8.7%
4 198
 
8.0%
6 168
 
6.8%
9 163
 
6.6%
7 139
 
5.6%
8 111
 
4.5%
Space Separator
ValueCountFrequency (%)
1440
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5666
56.8%
Common 4314
43.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 482
8.5%
부 480
8.5%
운 480
8.5%
산 480
8.5%
동 480
8.5%
구 480
8.5%
해 480
8.5%
시 480
8.5%
역 480
8.5%
광 480
8.5%
Other values (8) 864
15.2%
Common
ValueCountFrequency (%)
1440
33.4%
1 544
 
12.6%
- 401
 
9.3%
0 351
 
8.1%
2 349
 
8.1%
5 235
 
5.4%
3 215
 
5.0%
4 198
 
4.6%
6 168
 
3.9%
9 163
 
3.8%
Other values (2) 250
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5666
56.8%
ASCII 4314
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1440
33.4%
1 544
 
12.6%
- 401
 
9.3%
0 351
 
8.1%
2 349
 
8.1%
5 235
 
5.4%
3 215
 
5.0%
4 198
 
4.6%
6 168
 
3.9%
9 163
 
3.8%
Other values (2) 250
 
5.8%
Hangul
ValueCountFrequency (%)
대 482
8.5%
부 480
8.5%
운 480
8.5%
산 480
8.5%
동 480
8.5%
구 480
8.5%
해 480
8.5%
시 480
8.5%
역 480
8.5%
광 480
8.5%
Other values (8) 864
15.2%

도로명
Text

Distinct384
Distinct (%)80.3%
Missing2
Missing (%)0.4%
Memory size3.9 KiB
2023-12-11T02:32:52.970225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.253138
Min length6

Characters and Unicode

Total characters6335
Distinct characters50
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

Unique367 ?
Unique (%)76.8%

Sample

1st row달맞이길117번가길 97
2nd row달맞이길117번나길 15-17
3rd row달맞이길117번나길 15-19
4th row달맞이길117번다길 130-1
5th row달맞이길117번다길 17
ValueCountFrequency (%)
윗반송로51번길 81
 
8.5%
해운대로61번길 68
 
7.1%
62-7 58
 
6.1%
아랫반송로70번길 17
 
1.8%
82 16
 
1.7%
재반로165번길 15
 
1.6%
윗반송로31번길 14
 
1.5%
아랫반송로60번길 9
 
0.9%
재반로270번길 9
 
0.9%
재반로225번길 9
 
0.9%
Other values (458) 660
69.0%
2023-12-11T02:32:53.916717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 665
 
10.5%
478
 
7.5%
로 460
 
7.3%
길 456
 
7.2%
번 438
 
6.9%
2 422
 
6.7%
- 385
 
6.1%
6 299
 
4.7%
반 283
 
4.5%
5 260
 
4.1%
Other values (40) 2189
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2781
43.9%
Decimal Number 2691
42.5%
Space Separator 478
 
7.5%
Dash Punctuation 385
 
6.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 460
16.5%
길 456
16.4%
번 438
15.7%
반 283
10.2%
송 210
7.6%
해 117
 
4.2%
운 109
 
3.9%
대 106
 
3.8%
윗 99
 
3.6%
재 80
 
2.9%
Other values (28) 423
15.2%
Decimal Number
ValueCountFrequency (%)
1 665
24.7%
2 422
15.7%
6 299
11.1%
5 260
 
9.7%
7 233
 
8.7%
3 228
 
8.5%
9 154
 
5.7%
0 154
 
5.7%
8 145
 
5.4%
4 131
 
4.9%
Space Separator
ValueCountFrequency (%)
478
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3554
56.1%
Hangul 2781
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 460
16.5%
길 456
16.4%
번 438
15.7%
반 283
10.2%
송 210
7.6%
해 117
 
4.2%
운 109
 
3.9%
대 106
 
3.8%
윗 99
 
3.6%
재 80
 
2.9%
Other values (28) 423
15.2%
Common
ValueCountFrequency (%)
1 665
18.7%
478
13.4%
2 422
11.9%
- 385
10.8%
6 299
8.4%
5 260
 
7.3%
7 233
 
6.6%
3 228
 
6.4%
9 154
 
4.3%
0 154
 
4.3%
Other values (2) 276
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3554
56.1%
Hangul 2781
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 665
18.7%
478
13.4%
2 422
11.9%
- 385
10.8%
6 299
8.4%
5 260
 
7.3%
7 233
 
6.6%
3 228
 
6.4%
9 154
 
4.3%
0 154
 
4.3%
Other values (2) 276
7.8%
Hangul
ValueCountFrequency (%)
로 460
16.5%
길 456
16.4%
번 438
15.7%
반 283
10.2%
송 210
7.6%
해 117
 
4.2%
운 109
 
3.9%
대 106
 
3.8%
윗 99
 
3.6%
재 80
 
2.9%
Other values (28) 423
15.2%

주택유형
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
단독주택
259 
다세대
93 
아파트
87 
다가구주택
34 
연립
 
7

Length

Max length5
Median length4
Mean length3.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아파트
2nd row다세대
3rd row다세대
4th row단독주택
5th row아파트

Common Values

ValueCountFrequency (%)
단독주택 259
54.0%
다세대 93
 
19.4%
아파트 87
 
18.1%
다가구주택 34
 
7.1%
연립 7
 
1.5%

Length

2023-12-11T02:32:54.250287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:32:54.532542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 259
54.0%
다세대 93
 
19.4%
아파트 87
 
18.1%
다가구주택 34
 
7.1%
연립 7
 
1.5%

건축년도
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1982.35
Minimum1938
Maximum2004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T02:32:54.826040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1938
5-th percentile1970
Q11977
median1980
Q31991
95-th percentile1996
Maximum2004
Range66
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.80444
Coefficient of variation (CV)0.0044414155
Kurtosis1.938789
Mean1982.35
Median Absolute Deviation (MAD)5
Skewness-0.31873453
Sum951528
Variance77.518163
MonotonicityNot monotonic
2023-12-11T02:32:55.166779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1980 82
17.1%
1977 45
 
9.4%
1985 33
 
6.9%
1994 28
 
5.8%
1981 25
 
5.2%
1978 24
 
5.0%
1995 23
 
4.8%
1974 20
 
4.2%
1979 18
 
3.8%
1973 17
 
3.5%
Other values (30) 165
34.4%
ValueCountFrequency (%)
1938 1
 
0.2%
1941 1
 
0.2%
1948 1
 
0.2%
1953 2
 
0.4%
1968 2
 
0.4%
1969 10
2.1%
1970 8
1.7%
1971 7
1.5%
1972 3
 
0.6%
1973 17
3.5%
ValueCountFrequency (%)
2004 1
 
0.2%
2002 1
 
0.2%
2001 2
 
0.4%
2000 1
 
0.2%
1999 2
 
0.4%
1998 2
 
0.4%
1997 8
 
1.7%
1996 15
3.1%
1995 23
4.8%
1994 28
5.8%

건축구조
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
블록구조
218 
철근콘크리트구조
128 
기타조적구조
69 
벽돌구조
41 
시멘트블럭조
 
19
Other values (3)
 
5

Length

Max length8
Median length4
Mean length5.4395833
Min length2

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row철근콘크리트구조
2nd row철근콘크리트구조
3rd row철근콘크리트구조
4th row벽돌구조
5th row철근콘크리트구조

Common Values

ValueCountFrequency (%)
블록구조 218
45.4%
철근콘크리트구조 128
26.7%
기타조적구조 69
 
14.4%
벽돌구조 41
 
8.5%
시멘트블럭조 19
 
4.0%
일반목구조 3
 
0.6%
목조 1
 
0.2%
시멘트벽돌조 1
 
0.2%

Length

2023-12-11T02:32:55.530383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:32:55.857210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
블록구조 218
45.4%
철근콘크리트구조 128
26.7%
기타조적구조 69
 
14.4%
벽돌구조 41
 
8.5%
시멘트블럭조 19
 
4.0%
일반목구조 3
 
0.6%
목조 1
 
0.2%
시멘트벽돌조 1
 
0.2%

전용면적
Real number (ℝ)

MISSING 

Distinct96
Distinct (%)51.3%
Missing293
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean44.697765
Minimum20.12
Maximum234.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T02:32:56.187589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.12
5-th percentile30.62
Q140.13
median40.13
Q346.185
95-th percentile62.38
Maximum234.51
Range214.39
Interquartile range (IQR)6.055

Descriptive statistics

Standard deviation18.844769
Coefficient of variation (CV)0.42160429
Kurtosis59.863714
Mean44.697765
Median Absolute Deviation (MAD)2.92
Skewness6.7690958
Sum8358.482
Variance355.12533
MonotonicityNot monotonic
2023-12-11T02:32:56.512486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.13 58
 
12.1%
43.9 9
 
1.9%
46.94 6
 
1.2%
55.745 4
 
0.8%
32.69 3
 
0.6%
46.56 3
 
0.6%
36.85 2
 
0.4%
42.12 2
 
0.4%
62.38 2
 
0.4%
41.485 2
 
0.4%
Other values (86) 96
 
20.0%
(Missing) 293
61.0%
ValueCountFrequency (%)
20.12 1
 
0.2%
25.2 1
 
0.2%
25.22 1
 
0.2%
25.86 2
0.4%
27.0 2
0.4%
27.34 1
 
0.2%
30.05 2
0.4%
31.95 1
 
0.2%
32.69 3
0.6%
34.87 1
 
0.2%
ValueCountFrequency (%)
234.51 1
0.2%
141.6 1
0.2%
115.24 1
0.2%
106.23 1
0.2%
76.17 1
0.2%
66.24 1
0.2%
65.16 1
0.2%
65.05 1
0.2%
64.1 1
0.2%
62.38 2
0.4%

건축면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct337
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.77316
Minimum0
Maximum4123.64
Zeros22
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T02:32:56.863136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.508
Q131
median55.87
Q3109.62
95-th percentile459.17
Maximum4123.64
Range4123.64
Interquartile range (IQR)78.62

Descriptive statistics

Standard deviation254.30382
Coefficient of variation (CV)1.8458155
Kurtosis130.12526
Mean137.77316
Median Absolute Deviation (MAD)31.48
Skewness9.1685952
Sum66131.115
Variance64670.435
MonotonicityNot monotonic
2023-12-11T02:32:57.215536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
459.17 40
 
8.3%
0.0 22
 
4.6%
367.34 12
 
2.5%
411.24 7
 
1.5%
275.5 6
 
1.2%
42.98 6
 
1.2%
253.42 5
 
1.0%
49.59 5
 
1.0%
435.57 3
 
0.6%
326.68 3
 
0.6%
Other values (327) 371
77.3%
ValueCountFrequency (%)
0.0 22
4.6%
12.495 1
 
0.2%
17.52 1
 
0.2%
18.56 1
 
0.2%
18.63 1
 
0.2%
18.68 1
 
0.2%
18.84 1
 
0.2%
19.2 1
 
0.2%
19.84 1
 
0.2%
20.0 1
 
0.2%
ValueCountFrequency (%)
4123.64 1
 
0.2%
1867.98 1
 
0.2%
1071.27 1
 
0.2%
827.0 2
 
0.4%
703.03 2
 
0.4%
564.46 1
 
0.2%
529.02 1
 
0.2%
459.17 40
8.3%
435.57 3
 
0.6%
411.24 7
 
1.5%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct369
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.77176
Minimum12.495
Maximum22903.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T02:32:58.205931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.495
5-th percentile23.736
Q142.53
median92.815
Q3444.6625
95-th percentile2339.49
Maximum22903.16
Range22890.665
Interquartile range (IQR)402.1325

Descriptive statistics

Standard deviation1386.9227
Coefficient of variation (CV)2.3516261
Kurtosis143.73913
Mean589.77176
Median Absolute Deviation (MAD)66.845
Skewness9.7319352
Sum283090.44
Variance1923554.5
MonotonicityNot monotonic
2023-12-11T02:32:58.561460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2339.49 40
 
8.3%
1880.34 12
 
2.5%
1421.14 6
 
1.2%
42.98 6
 
1.2%
1652.72 5
 
1.0%
2229.42 4
 
0.8%
49.59 4
 
0.8%
458.08 3
 
0.6%
222.74 3
 
0.6%
2273.72 3
 
0.6%
Other values (359) 394
82.1%
ValueCountFrequency (%)
12.495 1
0.2%
17.52 1
0.2%
18.56 1
0.2%
18.63 1
0.2%
18.68 1
0.2%
18.84 1
0.2%
19.2 1
0.2%
20.0 1
0.2%
20.36 1
0.2%
20.94 1
0.2%
ValueCountFrequency (%)
22903.16 1
 
0.2%
10011.9 1
 
0.2%
4460.46 1
 
0.2%
3455.71 2
 
0.4%
3454.93 1
 
0.2%
3282.26 2
 
0.4%
3147.84 1
 
0.2%
2339.49 40
8.3%
2273.72 3
 
0.6%
2229.42 4
 
0.8%

대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct163
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1431.5239
Minimum0
Maximum32359
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T02:32:58.836267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q146
median106
Q3251.125
95-th percentile7662.7
Maximum32359
Range32359
Interquartile range (IQR)205.125

Descriptive statistics

Standard deviation3135.7296
Coefficient of variation (CV)2.1904836
Kurtosis19.65541
Mean1431.5239
Median Absolute Deviation (MAD)66
Skewness3.2515306
Sum687131.47
Variance9832800.1
MonotonicityNot monotonic
2023-12-11T02:32:59.159964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7662.7 58
 
12.1%
36.0 34
 
7.1%
46.0 22
 
4.6%
50.0 21
 
4.4%
33.0 17
 
3.5%
7156.7 15
 
3.1%
116.0 10
 
2.1%
109.0 9
 
1.9%
132.0 9
 
1.9%
34.0 8
 
1.7%
Other values (153) 277
57.7%
ValueCountFrequency (%)
0.0 1
 
0.2%
13.0 2
 
0.4%
18.0 1
 
0.2%
25.0 1
 
0.2%
27.0 2
 
0.4%
28.0 1
 
0.2%
29.0 3
0.6%
30.0 7
1.5%
31.0 5
1.0%
32.0 4
0.8%
ValueCountFrequency (%)
32359.0 1
 
0.2%
9218.0 4
 
0.8%
7710.0 1
 
0.2%
7662.7 58
12.1%
7156.7 15
 
3.1%
7116.0 1
 
0.2%
2000.0 1
 
0.2%
1956.8 2
 
0.4%
976.0 1
 
0.2%
726.9 1
 
0.2%

Interactions

2023-12-11T02:32:48.173996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:44.337474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:45.335962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:46.220488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:47.160981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:48.411989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:44.556608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:45.523381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:46.427114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:47.354992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:48.627277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:44.729756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:45.673245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:46.607292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:47.504921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:48.866869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:44.928280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:45.850013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:46.800581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:47.704916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:49.100373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:45.136381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:46.023315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:46.975755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:47.929655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:32:59.393615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택유형건축년도건축구조전용면적건축면적연면적대지면적
주택유형1.0000.6640.7150.7300.7370.4750.690
건축년도0.6641.0000.8300.1990.3420.2890.661
건축구조0.7150.8301.0000.2030.4080.5980.640
전용면적0.7300.1990.2031.0000.3920.4450.631
건축면적0.7370.3420.4080.3921.0000.9640.843
연면적0.4750.2890.5980.4450.9641.0000.804
대지면적0.6900.6610.6400.6310.8430.8041.000
2023-12-11T02:32:59.666824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구조주택유형
건축구조1.0000.543
주택유형0.5431.000
2023-12-11T02:32:59.876004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축년도전용면적건축면적연면적대지면적주택유형건축구조
건축년도1.0000.0570.3110.4580.0780.4610.611
전용면적0.0571.0000.1410.0780.0740.4060.131
건축면적0.3110.1411.0000.9000.8590.3600.264
연면적0.4580.0780.9001.0000.8030.4050.300
대지면적0.0780.0740.8590.8031.0000.6670.511
주택유형0.4610.4060.3600.4050.6671.0000.543
건축구조0.6110.1310.2640.3000.5110.5431.000

Missing values

2023-12-11T02:32:49.379023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:32:49.845168image/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.
2023-12-11T02:32:50.113065image/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

소재지도로명주택유형건축년도건축구조전용면적건축면적연면적대지면적
0부산광역시 해운대구 중동 1500-9달맞이길117번가길 97아파트1991철근콘크리트구조234.51529.023147.84726.9
1부산광역시 해운대구 중동 1499-16달맞이길117번나길 15-17다세대1992철근콘크리트구조27.0150.67634.8318.4
2부산광역시 해운대구 중동 1499-4달맞이길117번나길 15-19다세대1992철근콘크리트구조27.0150.67634.8318.3
3부산광역시 해운대구 중동 1493-21달맞이길117번다길 130-1단독주택1985벽돌구조<NA>96.87157.90.0
4부산광역시 해운대구 중동 1506-8달맞이길117번다길 17아파트1991철근콘크리트구조141.6564.463454.93233.9
5부산광역시 해운대구 중동 1405-10해운대해변로 302아파트1975철근콘크리트구조32.69253.421652.72430.0
6부산광역시 해운대구 중동 1405-10해운대해변로 302아파트1975철근콘크리트구조30.05253.421652.72430.0
7부산광역시 해운대구 중동 1405-10해운대해변로 302아파트1975철근콘크리트구조32.69253.421652.72430.0
8부산광역시 해운대구 중동 1405-10해운대해변로 302아파트1975철근콘크리트구조30.05253.421652.72430.0
9부산광역시 해운대구 중동 1405-10해운대해변로 302아파트1975철근콘크리트구조32.69253.421652.72430.0
소재지도로명주택유형건축년도건축구조전용면적건축면적연면적대지면적
470부산광역시 해운대구 반송동 7-59윗반송로51번길 226다가구주택1977블록구조<NA>68.6126.1369.0
471부산광역시 해운대구 반송동 40-5윗반송로51번길 217-16단독주택1977블록구조<NA>29.4829.4838.0
472부산광역시 해운대구 반송동 54-6윗반송로51번길 236-16단독주택1977블록구조<NA>33.057.9948.0
473부산광역시 해운대구 반송동 40-9윗반송로51번길 217-18단독주택1980블록구조<NA>24.7648.5436.0
474부산광역시 해운대구 반송동 6-155윗반송로51번길 223-19다가구주택1994기타조적구조<NA>31.0293.0640.0
475부산광역시 해운대구 반송동 6-141윗반송로51번길 223-18단독주택1980블록구조<NA>24.8649.7237.0
476부산광역시 해운대구 반송동 6-146윗반송로51번길 223-28단독주택1978블록구조<NA>21.021.037.0
477부산광역시 해운대구 반송동 7-7윗반송로51번길 248-6단독주택1971블록구조<NA>33.7733.7729.0
478부산광역시 해운대구 반송동 566운봉길186번길 28-4단독주택1941일반목구조<NA>39.6739.67215.0
479부산광역시 해운대구 반송동 549-1운봉길186번길 56단독주택1981기타조적구조<NA>179.42179.42976.0

Duplicate rows

Most frequently occurring

소재지도로명주택유형건축년도건축구조전용면적건축면적연면적대지면적# duplicates
17부산광역시 해운대구 재송동 1030해운대로61번길 62-7아파트1980철근콘크리트구조40.13459.172339.497662.740
16부산광역시 해운대구 재송동 1030해운대로61번길 62-7아파트1980철근콘크리트구조40.13367.341880.347662.712
15부산광역시 해운대구 재송동 1030해운대로61번길 62-7아파트1980철근콘크리트구조40.13275.51421.147662.76
12부산광역시 해운대구 재송동 1026-1재반로165번길 82아파트1981철근콘크리트구조43.9411.242229.427156.74
7부산광역시 해운대구 반여동 1576-5재반로270번길 57-108다세대1996벽돌구조46.5656.24222.7463.03
11부산광역시 해운대구 재송동 1026-1재반로165번길 82아파트1981철근콘크리트구조43.9411.242099.517156.73
13부산광역시 해운대구 재송동 1026-1재반로165번길 82아파트1981철근콘크리트구조46.94326.681681.337156.73
14부산광역시 해운대구 재송동 1026-1재반로165번길 82아파트1981철근콘크리트구조46.94435.572273.727156.73
20부산광역시 해운대구 중동 1405-10해운대해변로 302아파트1975철근콘크리트구조32.69253.421652.72430.03
0부산광역시 해운대구 반송동 250-1884아랫반송로70번길 37-21다세대1995기타조적구조36.8583.16332.6493.02