Overview

Dataset statistics

Number of variables12
Number of observations402
Missing cells367
Missing cells (%)7.6%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory39.4 KiB
Average record size in memory100.3 B

Variable types

Text2
Categorical5
Numeric4
DateTime1

Dataset

Description안전하고 쾌적한 정주환경을 조성하기 위하여 특례법에 따라 시행된 2020년 시행한 빈집 실태조사 용역으로 파악된 남구 빈집의 현황 자료임.
URLhttps://www.data.go.kr/data/15060675/fileData.do

Alerts

확인일자 has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
용도지구 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
용도지역1 is highly overall correlated with 용도지구High correlation
건축면적 is highly overall correlated with 연면적 and 2 other fieldsHigh correlation
연면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
대지면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
주택유형 is highly imbalanced (61.3%)Imbalance
건축구조 is highly imbalanced (51.3%)Imbalance
전용면적 has 363 (90.3%) missing valuesMissing
건축면적 has 34 (8.5%) zerosZeros

Reproduction

Analysis started2023-12-12 02:53:49.945752
Analysis finished2023-12-12 02:53:52.945727
Duration3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지
Text

Distinct396
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-12T11:53:53.352087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.975124
Min length16

Characters and Unicode

Total characters7628
Distinct characters31
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

Unique390 ?
Unique (%)97.0%

Sample

1st row부산광역시 남구 용호동 559-11
2nd row부산광역시 남구 용호동 555-42
3rd row부산광역시 남구 감만동 238-7
4th row부산광역시 남구 용호동 525-3
5th row부산광역시 남구 용호동 533-67
ValueCountFrequency (%)
부산광역시 402
25.0%
남구 402
25.0%
문현동 165
10.3%
우암동 112
 
7.0%
감만동 45
 
2.8%
대연동 42
 
2.6%
용호동 29
 
1.8%
용당동 9
 
0.6%
333-4 2
 
0.1%
285-12 2
 
0.1%
Other values (394) 398
24.8%
2023-12-12T11:53:54.023552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1206
15.8%
산 404
 
5.3%
부 402
 
5.3%
구 402
 
5.3%
남 402
 
5.3%
동 402
 
5.3%
시 402
 
5.3%
역 402
 
5.3%
광 402
 
5.3%
1 397
 
5.2%
Other values (21) 2807
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4022
52.7%
Decimal Number 2006
26.3%
Space Separator 1206
 
15.8%
Dash Punctuation 394
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 404
10.0%
부 402
10.0%
구 402
10.0%
남 402
10.0%
동 402
10.0%
시 402
10.0%
역 402
10.0%
광 402
10.0%
현 165
 
4.1%
문 165
 
4.1%
Other values (9) 474
11.8%
Decimal Number
ValueCountFrequency (%)
1 397
19.8%
3 234
11.7%
2 221
11.0%
9 201
10.0%
8 198
9.9%
7 179
8.9%
4 178
8.9%
5 174
8.7%
6 139
 
6.9%
0 85
 
4.2%
Space Separator
ValueCountFrequency (%)
1206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4022
52.7%
Common 3606
47.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 404
10.0%
부 402
10.0%
구 402
10.0%
남 402
10.0%
동 402
10.0%
시 402
10.0%
역 402
10.0%
광 402
10.0%
현 165
 
4.1%
문 165
 
4.1%
Other values (9) 474
11.8%
Common
ValueCountFrequency (%)
1206
33.4%
1 397
 
11.0%
- 394
 
10.9%
3 234
 
6.5%
2 221
 
6.1%
9 201
 
5.6%
8 198
 
5.5%
7 179
 
5.0%
4 178
 
4.9%
5 174
 
4.8%
Other values (2) 224
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4022
52.7%
ASCII 3606
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1206
33.4%
1 397
 
11.0%
- 394
 
10.9%
3 234
 
6.5%
2 221
 
6.1%
9 201
 
5.6%
8 198
 
5.5%
7 179
 
5.0%
4 178
 
4.9%
5 174
 
4.8%
Other values (2) 224
 
6.2%
Hangul
ValueCountFrequency (%)
산 404
10.0%
부 402
10.0%
구 402
10.0%
남 402
10.0%
동 402
10.0%
시 402
10.0%
역 402
10.0%
광 402
10.0%
현 165
 
4.1%
문 165
 
4.1%
Other values (9) 474
11.8%

도로명
Text

Distinct389
Distinct (%)97.7%
Missing4
Missing (%)1.0%
Memory size3.3 KiB
2023-12-12T11:53:54.364608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length11.751256
Min length6

Characters and Unicode

Total characters4677
Distinct characters65
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

Unique380 ?
Unique (%)95.5%

Sample

1st row용호로 261
2nd row용호로216번가길 71-1
3rd row양지골로 21-4
4th row용주로 62-23
5th row용호로216번길 6
ValueCountFrequency (%)
동항로27번길 13
 
1.6%
지게골로 11
 
1.4%
지게골로108번길 8
 
1.0%
고동골로69번길 8
 
1.0%
유엔로38번길 8
 
1.0%
지게골로168번길 7
 
0.9%
4 7
 
0.9%
자유평화로59번길 7
 
0.9%
문현금융로18번길 7
 
0.9%
전포대로67번가길 7
 
0.9%
Other values (426) 713
89.6%
2023-12-12T11:53:54.945509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 398
 
8.5%
398
 
8.5%
1 370
 
7.9%
번 360
 
7.7%
길 323
 
6.9%
2 267
 
5.7%
- 259
 
5.5%
3 188
 
4.0%
6 176
 
3.8%
7 157
 
3.4%
Other values (55) 1781
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2196
47.0%
Decimal Number 1824
39.0%
Space Separator 398
 
8.5%
Dash Punctuation 259
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 398
18.1%
번 360
16.4%
길 323
14.7%
동 76
 
3.5%
골 69
 
3.1%
포 60
 
2.7%
영 59
 
2.7%
암 59
 
2.7%
우 59
 
2.7%
대 51
 
2.3%
Other values (43) 682
31.1%
Decimal Number
ValueCountFrequency (%)
1 370
20.3%
2 267
14.6%
3 188
10.3%
6 176
9.6%
7 157
8.6%
8 154
8.4%
4 136
 
7.5%
5 127
 
7.0%
9 127
 
7.0%
0 122
 
6.7%
Space Separator
ValueCountFrequency (%)
398
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 259
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2481
53.0%
Hangul 2196
47.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 398
18.1%
번 360
16.4%
길 323
14.7%
동 76
 
3.5%
골 69
 
3.1%
포 60
 
2.7%
영 59
 
2.7%
암 59
 
2.7%
우 59
 
2.7%
대 51
 
2.3%
Other values (43) 682
31.1%
Common
ValueCountFrequency (%)
398
16.0%
1 370
14.9%
2 267
10.8%
- 259
10.4%
3 188
7.6%
6 176
7.1%
7 157
 
6.3%
8 154
 
6.2%
4 136
 
5.5%
5 127
 
5.1%
Other values (2) 249
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2481
53.0%
Hangul 2196
47.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 398
18.1%
번 360
16.4%
길 323
14.7%
동 76
 
3.5%
골 69
 
3.1%
포 60
 
2.7%
영 59
 
2.7%
암 59
 
2.7%
우 59
 
2.7%
대 51
 
2.3%
Other values (43) 682
31.1%
ASCII
ValueCountFrequency (%)
398
16.0%
1 370
14.9%
2 267
10.8%
- 259
10.4%
3 188
7.6%
6 176
7.1%
7 157
 
6.3%
8 154
 
6.2%
4 136
 
5.5%
5 127
 
5.1%
Other values (2) 249
10.0%

주택유형
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
단독주택
339 
다세대
 
25
다가구주택
 
24
연립
 
10
아파트
 
4

Length

Max length5
Median length4
Mean length3.9378109
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단독주택 339
84.3%
다세대 25
 
6.2%
다가구주택 24
 
6.0%
연립 10
 
2.5%
아파트 4
 
1.0%

Length

2023-12-12T11:53:55.168782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:53:55.351726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 339
84.3%
다세대 25
 
6.2%
다가구주택 24
 
6.0%
연립 10
 
2.5%
아파트 4
 
1.0%

건축연수
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
30년 초과 40년 이하
267 
40년 초과
126 
20년 초과 30년 이하
 
8
20년 이하
 
1

Length

Max length13
Median length13
Mean length10.788557
Min length6

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row30년 초과 40년 이하
2nd row30년 초과 40년 이하
3rd row30년 초과 40년 이하
4th row40년 초과
5th row40년 초과

Common Values

ValueCountFrequency (%)
30년 초과 40년 이하 267
66.4%
40년 초과 126
31.3%
20년 초과 30년 이하 8
 
2.0%
20년 이하 1
 
0.2%

Length

2023-12-12T11:53:55.513232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:53:55.668237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초과 401
29.6%
40년 393
29.0%
이하 276
20.4%
30년 275
20.3%
20년 9
 
0.7%

건축구조
Categorical

IMBALANCE 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
블록구조
274 
벽돌구조
43 
일반목구조
35 
시멘트블럭조
 
25
철근콘크리트구조
 
18
Other values (5)
 
7

Length

Max length17
Median length4
Mean length4.4179104
Min length2

Unique

Unique4 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
블록구조 274
68.2%
벽돌구조 43
 
10.7%
일반목구조 35
 
8.7%
시멘트블럭조 25
 
6.2%
철근콘크리트구조 18
 
4.5%
목조 3
 
0.7%
시멘트벽돌조 1
 
0.2%
<NA> 1
 
0.2%
기타조적구조 1
 
0.2%
철근콘크리트조(RC조, RS조) 1
 
0.2%

Length

2023-12-12T11:53:56.201939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:53:56.378621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
블록구조 274
68.0%
벽돌구조 43
 
10.7%
일반목구조 35
 
8.7%
시멘트블럭조 25
 
6.2%
철근콘크리트구조 18
 
4.5%
목조 3
 
0.7%
시멘트벽돌조 1
 
0.2%
na 1
 
0.2%
기타조적구조 1
 
0.2%
철근콘크리트조(rc조 1
 
0.2%

전용면적
Real number (ℝ)

MISSING 

Distinct38
Distinct (%)97.4%
Missing363
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean45.260641
Minimum22.67
Maximum71.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T11:53:56.569136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.67
5-th percentile29.81
Q135.605
median42.24
Q354.37
95-th percentile69.703
Maximum71.5
Range48.83
Interquartile range (IQR)18.765

Descriptive statistics

Standard deviation12.553966
Coefficient of variation (CV)0.27737049
Kurtosis-0.44247246
Mean45.260641
Median Absolute Deviation (MAD)7.46
Skewness0.60842478
Sum1765.165
Variance157.60206
MonotonicityNot monotonic
2023-12-12T11:53:56.744941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
33.06 2
 
0.5%
47.24 1
 
0.2%
69.91 1
 
0.2%
38.45 1
 
0.2%
28.55 1
 
0.2%
29.95 1
 
0.2%
57.66 1
 
0.2%
71.5 1
 
0.2%
69.68 1
 
0.2%
50.48 1
 
0.2%
Other values (28) 28
 
7.0%
(Missing) 363
90.3%
ValueCountFrequency (%)
22.67 1
0.2%
28.55 1
0.2%
29.95 1
0.2%
32.2 1
0.2%
33.06 2
0.5%
34.78 1
0.2%
35.15 1
0.2%
35.16 1
0.2%
35.24 1
0.2%
35.97 1
0.2%
ValueCountFrequency (%)
71.5 1
0.2%
69.91 1
0.2%
69.68 1
0.2%
69.36 1
0.2%
60.66 1
0.2%
59.57 1
0.2%
58.64 1
0.2%
57.75 1
0.2%
57.66 1
0.2%
54.74 1
0.2%

건축면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct311
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.172933
Minimum0
Maximum4199.07
Zeros34
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T11:53:56.911489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.8175
median29.75
Q356.2
95-th percentile258.89
Maximum4199.07
Range4199.07
Interquartile range (IQR)38.3825

Descriptive statistics

Standard deviation267.57997
Coefficient of variation (CV)3.337535
Kurtosis147.56048
Mean80.172933
Median Absolute Deviation (MAD)16.8
Skewness10.748919
Sum32229.519
Variance71599.04
MonotonicityNot monotonic
2023-12-12T11:53:57.075239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 34
 
8.5%
49.59 8
 
2.0%
13.22 5
 
1.2%
26.18 5
 
1.2%
27.27 5
 
1.2%
29.75 5
 
1.2%
39.67 3
 
0.7%
11.04 3
 
0.7%
21.6 3
 
0.7%
33.06 3
 
0.7%
Other values (301) 328
81.6%
ValueCountFrequency (%)
0.0 34
8.5%
4.43 1
 
0.2%
5.75 1
 
0.2%
6.61 1
 
0.2%
7.8 1
 
0.2%
8.41 1
 
0.2%
9.2 2
 
0.5%
9.24 1
 
0.2%
9.4 1
 
0.2%
9.6 1
 
0.2%
ValueCountFrequency (%)
4199.07 1
0.2%
1895.2 1
0.2%
1418.34 1
0.2%
989.52 2
0.5%
926.501 1
0.2%
842.23 2
0.5%
757.82 1
0.2%
656.4 1
0.2%
607.91 1
0.2%
526.62 1
0.2%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct348
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.92047
Minimum0
Maximum21642
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T11:53:57.248574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.012
Q124.0625
median40.6
Q366.7575
95-th percentile687.34
Maximum21642
Range21642
Interquartile range (IQR)42.695

Descriptive statistics

Standard deviation1438.0022
Coefficient of variation (CV)6.0187484
Kurtosis161.05198
Mean238.92047
Median Absolute Deviation (MAD)20.24
Skewness12.147869
Sum96046.028
Variance2067850.3
MonotonicityNot monotonic
2023-12-12T11:53:57.445016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.59 7
 
1.7%
29.75 5
 
1.2%
13.22 5
 
1.2%
26.18 5
 
1.2%
27.27 5
 
1.2%
21.6 4
 
1.0%
39.67 3
 
0.7%
12.0 3
 
0.7%
33.0 2
 
0.5%
687.34 2
 
0.5%
Other values (338) 361
89.8%
ValueCountFrequency (%)
0.0 1
0.2%
4.43 1
0.2%
5.75 1
0.2%
6.61 1
0.2%
7.8 1
0.2%
9.2 2
0.5%
9.4 1
0.2%
9.6 1
0.2%
9.89 1
0.2%
9.9 1
0.2%
ValueCountFrequency (%)
21642.0 1
0.2%
16009.463 1
0.2%
8772.83 1
0.2%
3039.555 1
0.2%
2836.42 1
0.2%
1979.04 2
0.5%
1752.25 2
0.5%
1603.98 1
0.2%
1515.63 1
0.2%
1483.92 1
0.2%

대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.74157
Minimum0
Maximum19932
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T11:53:57.632584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.045
Q125.575
median58.5
Q3129.75
95-th percentile521.925
Maximum19932
Range19932
Interquartile range (IQR)104.175

Descriptive statistics

Standard deviation1345.8278
Coefficient of variation (CV)5.5442824
Kurtosis167.3785
Mean242.74157
Median Absolute Deviation (MAD)40.5
Skewness12.482036
Sum97582.11
Variance1811252.4
MonotonicityNot monotonic
2023-12-12T11:53:57.832067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.0 11
 
2.7%
30.0 10
 
2.5%
26.0 9
 
2.2%
13.0 8
 
2.0%
43.0 6
 
1.5%
56.0 6
 
1.5%
83.0 6
 
1.5%
21.0 5
 
1.2%
99.0 5
 
1.2%
25.0 5
 
1.2%
Other values (229) 331
82.3%
ValueCountFrequency (%)
0.0 1
 
0.2%
1.0 1
 
0.2%
1.8 1
 
0.2%
6.1 1
 
0.2%
6.3 1
 
0.2%
8.0 2
0.5%
10.0 3
0.7%
10.2 1
 
0.2%
10.6 1
 
0.2%
10.7 1
 
0.2%
ValueCountFrequency (%)
19932.0 1
0.2%
16481.8 1
0.2%
5623.0 1
0.2%
3339.0 1
0.2%
2710.0 1
0.2%
2230.0 2
0.5%
1611.0 1
0.2%
1559.0 2
0.5%
1457.0 1
0.2%
1241.0 1
0.2%

용도지역1
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
제2종일반주거지역
179 
제3종일반주거지역
133 
일반상업지역
74 
준주거지역
 
7
제1종일반주거지역
 
6
Other values (2)
 
3

Length

Max length9
Median length9
Mean length8.3532338
Min length5

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row제2종일반주거지역
2nd row제2종일반주거지역
3rd row준공업지역
4th row제3종일반주거지역
5th row준주거지역

Common Values

ValueCountFrequency (%)
제2종일반주거지역 179
44.5%
제3종일반주거지역 133
33.1%
일반상업지역 74
18.4%
준주거지역 7
 
1.7%
제1종일반주거지역 6
 
1.5%
자연녹지지역 2
 
0.5%
준공업지역 1
 
0.2%

Length

2023-12-12T11:53:58.026735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:53:58.212174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종일반주거지역 179
44.5%
제3종일반주거지역 133
33.1%
일반상업지역 74
18.4%
준주거지역 7
 
1.7%
제1종일반주거지역 6
 
1.5%
자연녹지지역 2
 
0.5%
준공업지역 1
 
0.2%

용도지구
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
296 
방화지구
74 
기타지구
31 
시가지경관지구(중심지미관지구)
 
1

Length

Max length16
Median length4
Mean length4.0298507
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 296
73.6%
방화지구 74
 
18.4%
기타지구 31
 
7.7%
시가지경관지구(중심지미관지구) 1
 
0.2%

Length

2023-12-12T11:53:58.416422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:53:58.585086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 296
73.6%
방화지구 74
 
18.4%
기타지구 31
 
7.7%
시가지경관지구(중심지미관지구 1
 
0.2%

확인일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum2022-08-22 00:00:00
Maximum2022-08-22 00:00:00
2023-12-12T11:53:58.730885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:58.908846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T11:53:51.956232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:50.628109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.020964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.445734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:52.057494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:50.734602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.124570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.564430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:52.187724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:50.826136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.229306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.676076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:52.316698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:50.908602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.330501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:53:51.823822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:53:59.047264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택유형건축연수건축구조전용면적건축면적연면적대지면적용도지역1용도지구
주택유형1.0000.1110.5090.5100.6080.8400.7610.2670.000
건축연수0.1111.0000.3090.0000.0000.2210.0450.0410.000
건축구조0.5090.3091.0000.2730.2880.3630.2420.0000.429
전용면적0.5100.0000.2731.0000.0000.0000.2210.000NaN
건축면적0.6080.0000.2880.0001.0000.8940.8860.000NaN
연면적0.8400.2210.3630.0000.8941.0000.9740.110NaN
대지면적0.7610.0450.2420.2210.8860.9741.0000.489NaN
용도지역10.2670.0410.0000.0000.0000.1100.4891.0001.000
용도지구0.0000.0000.429NaNNaNNaNNaN1.0001.000
2023-12-12T11:53:59.257083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도지구용도지역1건축연수건축구조주택유형
용도지구1.0000.9950.0000.3130.000
용도지역10.9951.0000.0270.0000.173
건축연수0.0000.0271.0000.2010.091
건축구조0.3130.0000.2011.0000.323
주택유형0.0000.1730.0910.3231.000
2023-12-12T11:53:59.427838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용면적건축면적연면적대지면적주택유형건축연수건축구조용도지역1용도지구
전용면적1.0000.4710.4870.4800.2270.0000.0860.000NaN
건축면적0.4711.0000.7640.7440.4670.0000.1460.0001.000
연면적0.4870.7641.0000.7740.4720.1810.2180.0691.000
대지면적0.4800.7440.7741.0000.3820.0360.1410.3401.000
주택유형0.2270.4670.4720.3821.0000.0910.3230.1730.000
건축연수0.0000.0000.1810.0360.0911.0000.2010.0270.000
건축구조0.0860.1460.2180.1410.3230.2011.0000.0000.313
용도지역10.0000.0000.0690.3400.1730.0270.0001.0000.995
용도지구NaN1.0001.0001.0000.0000.0000.3130.9951.000

Missing values

2023-12-12T11:53:52.503850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:53:52.703101image/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.
2023-12-12T11:53:52.846888image/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

소재지도로명주택유형건축연수건축구조전용면적건축면적연면적대지면적용도지역1용도지구확인일자
0부산광역시 남구 용호동 559-11용호로 261단독주택30년 초과 40년 이하블록구조<NA>81.65132.23165.3제2종일반주거지역<NA>2022-08-22
1부산광역시 남구 용호동 555-42용호로216번가길 71-1다세대30년 초과 40년 이하블록구조42.2484.5213.56143.9제2종일반주거지역<NA>2022-08-22
2부산광역시 남구 감만동 238-7양지골로 21-4단독주택30년 초과 40년 이하블록구조<NA>51.4951.4956.0준공업지역<NA>2022-08-22
3부산광역시 남구 용호동 525-3용주로 62-23단독주택40년 초과벽돌구조<NA>71.871.8121.2제3종일반주거지역<NA>2022-08-22
4부산광역시 남구 용호동 533-67용호로216번길 6단독주택40년 초과블록구조<NA>32.3632.3662.9준주거지역<NA>2022-08-22
5부산광역시 남구 용당동 179-6신선로 311-12단독주택30년 초과 40년 이하블록구조<NA>62.2162.21382.0제2종일반주거지역<NA>2022-08-22
6부산광역시 남구 용호동 529-15동명로158번길 127단독주택40년 초과철근콘크리트구조<NA>78.68131.08133.2제2종일반주거지역<NA>2022-08-22
7부산광역시 남구 용호동 536-18용호로198번길 37아파트30년 초과 40년 이하철근콘크리트구조44.594199.0721642.016481.8제2종일반주거지역<NA>2022-08-22
8부산광역시 남구 용호동 532-43용호로 203-3단독주택40년 초과블록구조<NA>69.4269.42119.2준주거지역<NA>2022-08-22
9부산광역시 남구 감만동 205-129양지골로 114-12단독주택20년 초과 30년 이하벽돌구조<NA>67.59193.36116.0제2종일반주거지역<NA>2022-08-22
소재지도로명주택유형건축연수건축구조전용면적건축면적연면적대지면적용도지역1용도지구확인일자
392부산광역시 남구 문현동 3-32고동골로69번길 136다가구주택30년 초과 40년 이하벽돌구조<NA>65.28142.63118.0제3종일반주거지역<NA>2022-08-22
393부산광역시 남구 문현동 616-18황령대로90번가길 16단독주택30년 초과 40년 이하블록구조<NA>20.0520.0521.0제3종일반주거지역기타지구2022-08-22
394부산광역시 남구 문현동 382-3전포대로 118-2다세대40년 초과철근콘크리트구조45.87111.56558.8796.0일반상업지역방화지구2022-08-22
395부산광역시 남구 문현동 3-59고동골로69번길 143단독주택30년 초과 40년 이하블록구조<NA>65.3365.33134.0제3종일반주거지역<NA>2022-08-22
396부산광역시 남구 문현동 3-7고동골로69번길 146단독주택40년 초과블록구조<NA>45.8545.85123.0제3종일반주거지역<NA>2022-08-22
397부산광역시 남구 문현동 623-67전포대로92번나길 69-15단독주택30년 초과 40년 이하일반목구조<NA>19.8319.8373.0일반상업지역방화지구2022-08-22
398부산광역시 남구 대연동 231-23황령대로353번길 33단독주택40년 초과블록구조<NA>34.4534.4556.0제2종일반주거지역<NA>2022-08-22
399부산광역시 남구 대연동 231-25황령대로353번길 33-3단독주택40년 초과블록구조<NA>35.0735.0750.0제2종일반주거지역<NA>2022-08-22
400부산광역시 남구 문현동 623-17전포대로92번나길 73-6단독주택30년 초과 40년 이하일반목구조<NA>16.5316.5333.0제2종일반주거지역<NA>2022-08-22
401부산광역시 남구 문현동 623-95황령대로90번길 40-5단독주택30년 초과 40년 이하시멘트블럭조<NA>0.039.3686.0제2종일반주거지역<NA>2022-08-22

Duplicate rows

Most frequently occurring

소재지도로명주택유형건축연수건축구조전용면적건축면적연면적대지면적용도지역1용도지구확인일자# duplicates
0부산광역시 남구 대연동 1722-6유엔평화로17번길 90연립30년 초과 40년 이하철근콘크리트구조33.06355.571051.64523.0제2종일반주거지역<NA>2022-08-222