Overview

Dataset statistics

Number of variables15
Number of observations123
Missing cells14
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.0 KiB
Average record size in memory133.1 B

Variable types

Numeric11
Categorical4

Dataset

Description게시글번호,번호,시군구,분류명,제목,전체 건축물 수량,내진 확보 수량,내진 미확보 수량,내진율(건축물),내진 대상 수량,전체 건축물 면적,내진 대상 면적,내진 대상 확보 면적,내진 대상 미확보 면적,내진율(면적)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15614/S/1/datasetView.do

Alerts

내진율(면적) has constant value ""Constant
게시글번호 is highly overall correlated with 내진율(건축물) and 1 other fieldsHigh correlation
번호 is highly overall correlated with 내진 미확보 수량 and 3 other fieldsHigh correlation
전체 건축물 수량 is highly overall correlated with 내진 확보 수량 and 1 other fieldsHigh correlation
내진 확보 수량 is highly overall correlated with 전체 건축물 수량 and 3 other fieldsHigh correlation
내진 미확보 수량 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
내진율(건축물) is highly overall correlated with 게시글번호 and 7 other fieldsHigh correlation
내진 대상 수량 is highly overall correlated with 게시글번호 and 5 other fieldsHigh correlation
전체 건축물 면적 is highly overall correlated with 내진율(건축물) and 5 other fieldsHigh correlation
내진 대상 면적 is highly overall correlated with 내진율(건축물) and 5 other fieldsHigh correlation
내진 대상 확보 면적 is highly overall correlated with 내진율(건축물) and 5 other fieldsHigh correlation
내진 대상 미확보 면적 is highly overall correlated with 내진율(건축물) and 4 other fieldsHigh correlation
시군구 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
분류명 is highly overall correlated with 내진 확보 수량 and 4 other fieldsHigh correlation
제목 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
분류명 is highly imbalanced (76.6%)Imbalance
내진 대상 확보 면적 has 12 (9.8%) missing valuesMissing
내진 대상 미확보 면적 has 2 (1.6%) missing valuesMissing
게시글번호 has unique valuesUnique
내진 확보 수량 has 13 (10.6%) zerosZeros
내진 미확보 수량 has 2 (1.6%) zerosZeros
내진율(건축물) has 108 (87.8%) zerosZeros
내진 대상 수량 has 18 (14.6%) zerosZeros
전체 건축물 면적 has 18 (14.6%) zerosZeros
내진 대상 면적 has 18 (14.6%) zerosZeros
내진 대상 확보 면적 has 17 (13.8%) zerosZeros
내진 대상 미확보 면적 has 19 (15.4%) zerosZeros

Reproduction

Analysis started2024-05-11 05:47:17.481591
Analysis finished2024-05-11 05:48:03.091462
Duration45.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

게시글번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1452.8943
Minimum1
Maximum2625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:03.326515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile264
Q1853
median1484
Q32094
95-th percentile2542.1
Maximum2625
Range2624
Interquartile range (IQR)1241

Descriptive statistics

Standard deviation736.49856
Coefficient of variation (CV)0.50691819
Kurtosis-1.142618
Mean1452.8943
Median Absolute Deviation (MAD)621
Skewness-0.12301358
Sum178706
Variance542430.13
MonotonicityNot monotonic
2024-05-11T05:48:03.877958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 1
 
0.8%
2104 1
 
0.8%
2584 1
 
0.8%
2304 1
 
0.8%
2504 1
 
0.8%
2445 1
 
0.8%
1984 1
 
0.8%
2125 1
 
0.8%
2264 1
 
0.8%
2164 1
 
0.8%
Other values (113) 113
91.9%
ValueCountFrequency (%)
1 1
0.8%
160 1
0.8%
162 1
0.8%
181 1
0.8%
201 1
0.8%
242 1
0.8%
262 1
0.8%
282 1
0.8%
302 1
0.8%
322 1
0.8%
ValueCountFrequency (%)
2625 1
0.8%
2624 1
0.8%
2604 1
0.8%
2585 1
0.8%
2584 1
0.8%
2564 1
0.8%
2544 1
0.8%
2525 1
0.8%
2524 1
0.8%
2504 1
0.8%

번호
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.1626
Minimum1
Maximum757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:04.357787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q131
median31
Q3241.5
95-th percentile706
Maximum757
Range756
Interquartile range (IQR)210.5

Descriptive statistics

Standard deviation234.3124
Coefficient of variation (CV)1.3078198
Kurtosis0.38751432
Mean179.1626
Median Absolute Deviation (MAD)18
Skewness1.350688
Sum22037
Variance54902.301
MonotonicityNot monotonic
2024-05-11T05:48:04.947963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
31 45
36.6%
13 15
 
12.2%
32 9
 
7.3%
142 4
 
3.3%
141 3
 
2.4%
706 2
 
1.6%
506 2
 
1.6%
742 2
 
1.6%
442 2
 
1.6%
230 1
 
0.8%
Other values (38) 38
30.9%
ValueCountFrequency (%)
1 1
 
0.8%
6 1
 
0.8%
13 15
 
12.2%
27 1
 
0.8%
31 45
36.6%
32 9
 
7.3%
50 1
 
0.8%
59 1
 
0.8%
90 1
 
0.8%
135 1
 
0.8%
ValueCountFrequency (%)
757 1
0.8%
745 1
0.8%
742 2
1.6%
740 1
0.8%
739 1
0.8%
706 2
1.6%
700 1
0.8%
670 1
0.8%
645 1
0.8%
641 1
0.8%

시군구
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
서울특별시
72 
성동구
강동구
 
5
마포구
 
4
성북구
 
4
Other values (18)
30 

Length

Max length5
Median length5
Mean length4.195122
Min length2

Unique

Unique10 ?
Unique (%)8.1%

Sample

1st row서울특별시
2nd row서울특별시
3rd row성북구
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 72
58.5%
성동구 8
 
6.5%
강동구 5
 
4.1%
마포구 4
 
3.3%
성북구 4
 
3.3%
중량구 3
 
2.4%
양천구 3
 
2.4%
동대문구 3
 
2.4%
송파구 3
 
2.4%
구로구 2
 
1.6%
Other values (13) 16
 
13.0%

Length

2024-05-11T05:48:05.437824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 72
58.5%
성동구 8
 
6.5%
강동구 5
 
4.1%
마포구 4
 
3.3%
성북구 4
 
3.3%
중량구 3
 
2.4%
양천구 3
 
2.4%
동대문구 3
 
2.4%
송파구 3
 
2.4%
종로구 2
 
1.6%
Other values (13) 16
 
13.0%

분류명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
비주거용
116 
주거용
 
4
합 계
 
3

Length

Max length4
Median length4
Mean length3.9430894
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비주거용
2nd row비주거용
3rd row비주거용
4th row비주거용
5th row비주거용

Common Values

ValueCountFrequency (%)
비주거용 116
94.3%
주거용 4
 
3.3%
합 계 3
 
2.4%

Length

2024-05-11T05:48:05.996976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:48:06.364104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비주거용 116
92.1%
주거용 4
 
3.2%
3
 
2.4%
3
 
2.4%

제목
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
기 타
59 
기타용도
15 
제1종 근린생활시설
 
4
공장
 
4
숙박시설
 
4
Other values (21)
37 

Length

Max length13
Median length3
Mean length4.3414634
Min length2

Unique

Unique10 ?
Unique (%)8.1%

Sample

1st row기타용도
2nd row기타용도
3rd row노유자시설
4th row기 타
5th row기 타

Common Values

ValueCountFrequency (%)
기 타 59
48.0%
기타용도 15
 
12.2%
제1종 근린생활시설 4
 
3.3%
공장 4
 
3.3%
숙박시설 4
 
3.3%
위험물 저장 및 처리시설 4
 
3.3%
업무시설 3
 
2.4%
합 계 3
 
2.4%
방송통신시설 3
 
2.4%
의료시설 2
 
1.6%
Other values (16) 22
 
17.9%

Length

2024-05-11T05:48:06.756060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
59
27.2%
59
27.2%
기타용도 15
 
6.9%
7
 
3.2%
처리시설 5
 
2.3%
공장 4
 
1.8%
숙박시설 4
 
1.8%
위험물 4
 
1.8%
저장 4
 
1.8%
근린생활시설 4
 
1.8%
Other values (30) 52
24.0%

전체 건축물 수량
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8743.4309
Minimum1
Maximum623133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:07.237428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q174
median133
Q3244
95-th percentile17423.4
Maximum623133
Range623132
Interquartile range (IQR)170

Descriptive statistics

Standard deviation56544.187
Coefficient of variation (CV)6.467048
Kurtosis116.94546
Mean8743.4309
Median Absolute Deviation (MAD)73
Skewness10.695111
Sum1075442
Variance3.1972451 × 109
MonotonicityNot monotonic
2024-05-11T05:48:07.736921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131 6
 
4.9%
133 6
 
4.9%
128 5
 
4.1%
4 5
 
4.1%
1 5
 
4.1%
163 4
 
3.3%
2 3
 
2.4%
137 3
 
2.4%
31 3
 
2.4%
152 2
 
1.6%
Other values (69) 81
65.9%
ValueCountFrequency (%)
1 5
4.1%
2 3
2.4%
3 2
 
1.6%
4 5
4.1%
10 1
 
0.8%
16 1
 
0.8%
18 1
 
0.8%
20 1
 
0.8%
23 1
 
0.8%
30 1
 
0.8%
ValueCountFrequency (%)
623133 1
0.8%
64359 1
0.8%
29319 1
0.8%
27213 1
0.8%
25719 1
0.8%
20848 1
0.8%
17467 1
0.8%
17031 1
0.8%
16532 1
0.8%
16529 1
0.8%

내진 확보 수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1226.2195
Minimum0
Maximum91390
Zeros13
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:08.211405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median27
Q393.5
95-th percentile2511.8
Maximum91390
Range91390
Interquartile range (IQR)76.5

Descriptive statistics

Standard deviation8264.399
Coefficient of variation (CV)6.7397386
Kurtosis118.9049
Mean1226.2195
Median Absolute Deviation (MAD)26
Skewness10.819918
Sum150825
Variance68300290
MonotonicityNot monotonic
2024-05-11T05:48:08.818470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
10.6%
1 9
 
7.3%
24 7
 
5.7%
17 6
 
4.9%
18 5
 
4.1%
48 4
 
3.3%
21 3
 
2.4%
49 3
 
2.4%
27 3
 
2.4%
51 3
 
2.4%
Other values (55) 67
54.5%
ValueCountFrequency (%)
0 13
10.6%
1 9
7.3%
2 1
 
0.8%
4 2
 
1.6%
6 1
 
0.8%
8 1
 
0.8%
10 1
 
0.8%
16 2
 
1.6%
17 6
4.9%
18 5
 
4.1%
ValueCountFrequency (%)
91390 1
0.8%
5392 1
0.8%
5163 1
0.8%
3383 1
0.8%
3057 1
0.8%
3022 1
0.8%
2516 1
0.8%
2474 1
0.8%
2440 1
0.8%
2400 1
0.8%

내진 미확보 수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7517.2114
Minimum0
Maximum531743
Zeros2
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:09.293818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q142.5
median109
Q3147.5
95-th percentile15334.5
Maximum531743
Range531743
Interquartile range (IQR)105

Descriptive statistics

Standard deviation48309.301
Coefficient of variation (CV)6.4264923
Kurtosis116.32387
Mean7517.2114
Median Absolute Deviation (MAD)52
Skewness10.656533
Sum924617
Variance2.3337886 × 109
MonotonicityNot monotonic
2024-05-11T05:48:09.866593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 6
 
4.9%
1 5
 
4.1%
110 5
 
4.1%
114 5
 
4.1%
109 4
 
3.3%
111 4
 
3.3%
106 4
 
3.3%
104 4
 
3.3%
102 4
 
3.3%
3 4
 
3.3%
Other values (63) 78
63.4%
ValueCountFrequency (%)
0 2
 
1.6%
1 5
4.1%
2 2
 
1.6%
3 4
3.3%
4 2
 
1.6%
8 1
 
0.8%
12 2
 
1.6%
16 1
 
0.8%
22 1
 
0.8%
25 1
 
0.8%
ValueCountFrequency (%)
531743 1
0.8%
60976 1
0.8%
26262 1
0.8%
21821 1
0.8%
20556 1
0.8%
19277 1
0.8%
15358 1
0.8%
15123 1
0.8%
14429 1
0.8%
14410 1
0.8%

내진율(건축물)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6666667
Minimum0
Maximum15
Zeros108
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:10.375152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.5080378
Coefficient of variation (CV)2.7048227
Kurtosis3.7611244
Mean1.6666667
Median Absolute Deviation (MAD)0
Skewness2.3738462
Sum205
Variance20.322404
MonotonicityNot monotonic
2024-05-11T05:48:10.791929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 108
87.8%
14 5
 
4.1%
13 4
 
3.3%
15 4
 
3.3%
11 1
 
0.8%
12 1
 
0.8%
ValueCountFrequency (%)
0 108
87.8%
11 1
 
0.8%
12 1
 
0.8%
13 4
 
3.3%
14 5
 
4.1%
15 4
 
3.3%
ValueCountFrequency (%)
15 4
 
3.3%
14 5
 
4.1%
13 4
 
3.3%
12 1
 
0.8%
11 1
 
0.8%
0 108
87.8%

내진 대상 수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3548.935
Minimum0
Maximum300761
Zeros18
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:11.312871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median73
Q3108.5
95-th percentile5778.8
Maximum300761
Range300761
Interquartile range (IQR)104

Descriptive statistics

Standard deviation27451.989
Coefficient of variation (CV)7.7352753
Kurtosis115.24977
Mean3548.935
Median Absolute Deviation (MAD)43
Skewness10.596871
Sum436519
Variance7.536117 × 108
MonotonicityNot monotonic
2024-05-11T05:48:11.923004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
14.6%
109 6
 
4.9%
1 5
 
4.1%
89 5
 
4.1%
22 4
 
3.3%
69 4
 
3.3%
70 4
 
3.3%
87 3
 
2.4%
66 3
 
2.4%
88 3
 
2.4%
Other values (49) 68
55.3%
ValueCountFrequency (%)
0 18
14.6%
1 5
 
4.1%
2 3
 
2.4%
3 2
 
1.6%
4 3
 
2.4%
5 2
 
1.6%
15 1
 
0.8%
17 1
 
0.8%
18 1
 
0.8%
22 4
 
3.3%
ValueCountFrequency (%)
300761 1
0.8%
38887 1
0.8%
24089 1
0.8%
20896 1
0.8%
18688 1
0.8%
8621 1
0.8%
5780 1
0.8%
5768 1
0.8%
2897 1
0.8%
1860 1
0.8%

전체 건축물 면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6553.7967
Minimum0
Maximum605178
Zeros18
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:12.521917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median812
Q31050.5
95-th percentile10837.1
Maximum605178
Range605178
Interquartile range (IQR)1008.5

Descriptive statistics

Standard deviation54619.028
Coefficient of variation (CV)8.3339521
Kurtosis121.15991
Mean6553.7967
Median Absolute Deviation (MAD)531
Skewness10.969901
Sum806117
Variance2.9832382 × 109
MonotonicityNot monotonic
2024-05-11T05:48:13.186375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
14.6%
857 4
 
3.3%
813 4
 
3.3%
12 4
 
3.3%
1 3
 
2.4%
52 3
 
2.4%
839 3
 
2.4%
1048 3
 
2.4%
801 2
 
1.6%
1092 2
 
1.6%
Other values (69) 77
62.6%
ValueCountFrequency (%)
0 18
14.6%
1 3
 
2.4%
3 1
 
0.8%
8 1
 
0.8%
10 2
 
1.6%
11 1
 
0.8%
12 4
 
3.3%
36 1
 
0.8%
48 1
 
0.8%
51 1
 
0.8%
ValueCountFrequency (%)
605178 1
0.8%
32980 1
0.8%
24147 1
0.8%
22719 1
0.8%
18109 1
0.8%
15697 1
0.8%
11640 1
0.8%
3611 1
0.8%
3609 1
0.8%
2316 1
0.8%

내진 대상 면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6132.878
Minimum0
Maximum560050
Zeros18
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:13.682708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141.5
median804
Q31045
95-th percentile9281.8
Maximum560050
Range560050
Interquartile range (IQR)1003.5

Descriptive statistics

Standard deviation50549.558
Coefficient of variation (CV)8.2423877
Kurtosis121.06512
Mean6132.878
Median Absolute Deviation (MAD)523
Skewness10.963684
Sum754344
Variance2.5552579 × 109
MonotonicityNot monotonic
2024-05-11T05:48:14.181184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
14.6%
854 4
 
3.3%
11 4
 
3.3%
52 3
 
2.4%
837 3
 
2.4%
1045 3
 
2.4%
809 3
 
2.4%
1 3
 
2.4%
796 3
 
2.4%
848 2
 
1.6%
Other values (69) 77
62.6%
ValueCountFrequency (%)
0 18
14.6%
1 3
 
2.4%
2 1
 
0.8%
8 1
 
0.8%
9 1
 
0.8%
10 2
 
1.6%
11 4
 
3.3%
35 1
 
0.8%
48 1
 
0.8%
51 1
 
0.8%
ValueCountFrequency (%)
560050 1
0.8%
29683 1
0.8%
23896 1
0.8%
22498 1
0.8%
18084 1
0.8%
15124 1
0.8%
9912 1
0.8%
3610 1
0.8%
3608 1
0.8%
2219 1
0.8%

내진 대상 확보 면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)55.9%
Missing12
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean4360.955
Minimum0
Maximum360083
Zeros17
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:15.036156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median628
Q3838
95-th percentile6718.5
Maximum360083
Range360083
Interquartile range (IQR)778

Descriptive statistics

Standard deviation34176.718
Coefficient of variation (CV)7.8369803
Kurtosis109.58987
Mean4360.955
Median Absolute Deviation (MAD)276
Skewness10.438182
Sum484066
Variance1.1680481 × 109
MonotonicityNot monotonic
2024-05-11T05:48:15.609077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
13.8%
635 5
 
4.1%
41 5
 
4.1%
838 5
 
4.1%
868 4
 
3.3%
653 4
 
3.3%
628 3
 
2.4%
609 3
 
2.4%
618 3
 
2.4%
837 2
 
1.6%
Other values (52) 60
48.8%
(Missing) 12
 
9.8%
ValueCountFrequency (%)
0 17
13.8%
1 1
 
0.8%
9 1
 
0.8%
23 1
 
0.8%
34 1
 
0.8%
41 5
 
4.1%
45 1
 
0.8%
56 1
 
0.8%
64 1
 
0.8%
89 1
 
0.8%
ValueCountFrequency (%)
360083 1
0.8%
15811 1
0.8%
15712 1
0.8%
14202 1
0.8%
9239 1
0.8%
6987 1
0.8%
6450 1
0.8%
3416 2
1.6%
1600 1
0.8%
1592 2
1.6%

내진 대상 미확보 면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)51.2%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2661.6942
Minimum0
Maximum245095
Zeros19
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T05:48:16.582157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median178
Q3213
95-th percentile3906
Maximum245095
Range245095
Interquartile range (IQR)201

Descriptive statistics

Standard deviation22380.118
Coefficient of variation (CV)8.4082228
Kurtosis117.56389
Mean2661.6942
Median Absolute Deviation (MAD)48
Skewness10.779274
Sum322065
Variance5.0086968 × 108
MonotonicityNot monotonic
2024-05-11T05:48:17.679023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
15.4%
205 9
 
7.3%
213 8
 
6.5%
11 4
 
3.3%
224 4
 
3.3%
12 4
 
3.3%
170 3
 
2.4%
178 3
 
2.4%
1 3
 
2.4%
221 3
 
2.4%
Other values (52) 61
49.6%
ValueCountFrequency (%)
0 19
15.4%
1 3
 
2.4%
2 1
 
0.8%
8 1
 
0.8%
10 2
 
1.6%
11 4
 
3.3%
12 4
 
3.3%
21 1
 
0.8%
31 1
 
0.8%
48 1
 
0.8%
ValueCountFrequency (%)
245095 1
0.8%
25995 1
0.8%
8435 1
0.8%
6908 1
0.8%
6458 1
0.8%
5190 1
0.8%
3906 1
0.8%
2130 1
0.8%
1436 1
0.8%
1078 1
0.8%

내진율(면적)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
123 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123
100.0%

Length

2024-05-11T05:48:18.275296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:48:18.705076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 123
100.0%

Interactions

2024-05-11T05:47:57.068619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:18.574806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:21.117891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:23.443863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:27.229522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:31.167469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:36.208900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:41.538891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:45.983649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:49.677535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:53.079268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:57.360914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:18.811525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:21.349666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:23.670171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:27.488109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:31.430795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:36.600465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:41.888466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:46.277664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:49.991397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:53.414990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:57.746382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:19.068874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:21.624717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:24.182570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:27.795549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:31.700442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:37.278935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:42.291387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:46.670944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:50.316415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:53.781488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:58.177530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:19.322647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:21.808466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:24.497806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:28.071110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:32.010064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:37.741943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:42.670489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:47.016646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:50.612925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:54.168386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:58.570884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:19.556435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:21.961866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:24.849771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:28.438840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:32.301060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:38.364332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:43.082979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:47.383027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:50.911611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:54.603727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:58.855873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:19.802802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:22.123892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:25.270154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:28.779624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:32.597450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:39.118072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:43.579544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:47.629069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:51.209079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:54.951103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:59.178654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:20.056753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:22.313647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:25.609021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:29.305051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:32.994627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:39.751611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:44.004530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:47.966256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:51.605748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:55.233991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:59.537466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:20.265686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:22.481078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:25.919419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:29.782225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:33.438828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:40.164132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:44.464062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:48.305452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:51.884507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:55.679345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:59.960286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:20.506607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:22.691710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:26.195920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:30.153896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:34.338474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:40.450752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:44.826506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:48.594655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:52.138629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:56.000685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:48:00.334082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:20.745691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:22.888326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:26.632826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:30.423848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:34.888489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:40.712612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:45.163182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:48.937918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:52.486923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:56.282768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:48:00.714445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:20.925406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:23.163638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:26.988299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:30.807859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:35.753065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:41.121972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:45.695242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:49.373664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:52.802619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:47:56.761107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:48:19.059484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
게시글번호번호시군구분류명제목전체 건축물 수량내진 확보 수량내진 미확보 수량내진율(건축물)내진 대상 수량전체 건축물 면적내진 대상 면적내진 대상 확보 면적내진 대상 미확보 면적
게시글번호1.0000.1020.2700.0000.6930.0000.0000.0000.8630.0000.0000.0000.0000.000
번호0.1021.0000.9940.4220.9390.0000.0000.0000.0000.0000.0000.0000.0000.000
시군구0.2700.9941.0000.6480.9660.0000.0000.0000.0000.0000.0000.0000.0000.000
분류명0.0000.4220.6481.0001.0000.7310.3530.7310.0000.7310.3530.3530.3520.731
제목0.6930.9390.9661.0001.0000.5300.4960.5300.6530.5300.4960.4960.5190.523
전체 건축물 수량0.0000.0000.0000.7310.5301.0001.0001.0000.0001.0001.0001.0001.0001.000
내진 확보 수량0.0000.0000.0000.3530.4961.0001.0001.0000.0001.0000.6950.6950.6941.000
내진 미확보 수량0.0000.0000.0000.7310.5301.0001.0001.0000.0001.0001.0001.0001.0001.000
내진율(건축물)0.8630.0000.0000.0000.6530.0000.0000.0001.0000.0000.0000.0000.0000.000
내진 대상 수량0.0000.0000.0000.7310.5301.0001.0001.0000.0001.0001.0001.0001.0001.000
전체 건축물 면적0.0000.0000.0000.3530.4961.0000.6951.0000.0001.0001.0000.6950.6941.000
내진 대상 면적0.0000.0000.0000.3530.4961.0000.6951.0000.0001.0000.6951.0000.6941.000
내진 대상 확보 면적0.0000.0000.0000.3520.5191.0000.6941.0000.0001.0000.6940.6941.0001.000
내진 대상 미확보 면적0.0000.0000.0000.7310.5231.0001.0001.0000.0001.0001.0001.0001.0001.000
2024-05-11T05:48:19.754591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제목시군구분류명
제목1.0000.6730.899
시군구0.6731.0000.394
분류명0.8990.3941.000
2024-05-11T05:48:20.063875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
게시글번호번호전체 건축물 수량내진 확보 수량내진 미확보 수량내진율(건축물)내진 대상 수량전체 건축물 면적내진 대상 면적내진 대상 확보 면적내진 대상 미확보 면적시군구분류명제목
게시글번호1.0000.470-0.107-0.031-0.258-0.5520.5310.2490.2470.3320.2350.0880.0000.306
번호0.4701.000-0.494-0.376-0.570-0.5590.1250.0100.0100.069-0.0250.9080.2710.662
전체 건축물 수량-0.107-0.4941.0000.9350.9320.5000.4170.2660.2640.1400.2270.0000.3880.287
내진 확보 수량-0.031-0.3760.9351.0000.8110.5060.4020.2110.2100.0550.1670.0000.5600.352
내진 미확보 수량-0.258-0.5700.9320.8111.0000.5000.3200.2820.2800.1590.2830.0000.3880.287
내진율(건축물)-0.552-0.5590.5000.5060.5001.000-0.551-0.551-0.551-0.579-0.5490.0000.0000.363
내진 대상 수량0.5310.1250.4170.4020.320-0.5511.0000.8240.8220.7290.7890.0000.3880.287
전체 건축물 면적0.2490.0100.2660.2110.282-0.5510.8241.0001.0000.9410.9610.0000.5600.352
내진 대상 면적0.2470.0100.2640.2100.280-0.5510.8221.0001.0000.9410.9610.0000.5600.352
내진 대상 확보 면적0.3320.0690.1400.0550.159-0.5790.7290.9410.9411.0000.8510.0000.5590.371
내진 대상 미확보 면적0.235-0.0250.2270.1670.283-0.5490.7890.9610.9610.8511.0000.0000.3870.281
시군구0.0880.9080.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.3940.673
분류명0.0000.2710.3880.5600.3880.0000.3880.5600.5600.5590.3870.3941.0000.899
제목0.3060.6620.2870.3520.2870.3630.2870.3520.3520.3710.2810.6730.8991.000

Missing values

2024-05-11T05:48:01.514009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:48:02.353433image/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.
2024-05-11T05:48:02.881090image/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

게시글번호번호시군구분류명제목전체 건축물 수량내진 확보 수량내진 미확보 수량내진율(건축물)내진 대상 수량전체 건축물 면적내진 대상 면적내진 대상 확보 면적내진 대상 미확보 면적내진율(면적)
016013서울특별시비주거용기타용도1703119081512311000000
136213서울특별시비주거용기타용도1646723231414414000000
2503242성북구비주거용노유자시설1632214107513211256760
352331서울특별시비주거용기 타12816112062104810378372110
458331서울특별시비주거용기 타12816112062104810378372110
560531서울특별시비주거용기 타12817111070104810428382100
664331서울특별시비주거용기 타12917112070105110458382130
766331서울특별시비주거용기 타12817111069105010458382120
868331서울특별시비주거용기 타12817111069105010458382120
9703529금천구비주거용업무시설134656901244324312961350
게시글번호번호시군구분류명제목전체 건축물 수량내진 확보 수량내진 미확보 수량내진율(건축물)내진 대상 수량전체 건축물 면적내진 대상 면적내진 대상 확보 면적내진 대상 미확보 면적내진율(면적)
11328213서울특별시비주거용기타용도1647522261424914000000
11440213서울특별시비주거용기타용도1647824001407815000000
11544213서울특별시비주거용기타용도1642224741394815000000
11618113서울특별시비주거용기타용도1652921001442913000000
11724213서울특별시비주거용기타용도1650821581435013000000
11846213서울특별시비주거용기타용도1639925161388315000000
11932213서울특별시비주거용기타용도1646922691420014000000
12038213서울특별시비주거용기타용도1648623581412814000000
12142213서울특별시비주거용기타용도1645724401401715000000
12220113서울특별시비주거용기타용도1653221221441013000000