Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells10104
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

Text7
Categorical3
Numeric4

Dataset

Description관리_층별_개요_PK,관리_건축물대장_PK,관리_주_건축물대장_PK,주_부속_구분_코드,주_부속_일련번호,층_구분_코드,층_번호,층_번호_명,구조_코드,기타_구조,주_용도_코드,기타_용도,면적
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15390/S/1/datasetView.do

Alerts

주_부속_구분_코드 is highly imbalanced (92.0%)Imbalance
구조_코드 is highly imbalanced (65.1%)Imbalance
관리_주_건축물대장_PK has 9984 (99.8%) missing valuesMissing
주_부속_일련번호 has 472 (4.7%) zerosZeros

Reproduction

Analysis started2024-05-11 07:12:47.995527
Analysis finished2024-05-11 07:12:58.874149
Duration10.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_층별_개요_PK
Text

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:12:59.284624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length11
Mean length12.3962
Min length8

Characters and Unicode

Total characters123962
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9998 ?
Unique (%)> 99.9%

Sample

1st row11140-100146679
2nd row11110-30895
3rd row11200-84058
4th row11110-40128
5th row11260-5902
ValueCountFrequency (%)
11200-100120006 2
 
< 0.1%
11170-96142 1
 
< 0.1%
11140-80288 1
 
< 0.1%
11620-73032 1
 
< 0.1%
11110-37167 1
 
< 0.1%
11230-100172909 1
 
< 0.1%
11200-49517 1
 
< 0.1%
11200-61083 1
 
< 0.1%
11215-72305 1
 
< 0.1%
11110-38264 1
 
< 0.1%
Other values (9989) 9989
99.9%
2024-05-11T07:13:00.323646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 38522
31.1%
0 23729
19.1%
- 10000
 
8.1%
2 8597
 
6.9%
4 7252
 
5.9%
3 7027
 
5.7%
5 6864
 
5.5%
6 6083
 
4.9%
7 6070
 
4.9%
9 4918
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113962
91.9%
Dash Punctuation 10000
 
8.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38522
33.8%
0 23729
20.8%
2 8597
 
7.5%
4 7252
 
6.4%
3 7027
 
6.2%
5 6864
 
6.0%
6 6083
 
5.3%
7 6070
 
5.3%
9 4918
 
4.3%
8 4900
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123962
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38522
31.1%
0 23729
19.1%
- 10000
 
8.1%
2 8597
 
6.9%
4 7252
 
5.9%
3 7027
 
5.7%
5 6864
 
5.5%
6 6083
 
4.9%
7 6070
 
4.9%
9 4918
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38522
31.1%
0 23729
19.1%
- 10000
 
8.1%
2 8597
 
6.9%
4 7252
 
5.9%
3 7027
 
5.7%
5 6864
 
5.5%
6 6083
 
4.9%
7 6070
 
4.9%
9 4918
 
4.0%

관리_건축물대장_PK
Text

Distinct6680
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:13:00.770275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length12.0128
Min length9

Characters and Unicode

Total characters120128
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4622 ?
Unique (%)46.2%

Sample

1st row11140-100229802
2nd row11110-10344
3rd row11200-23769
4th row11110-13057
5th row11260-2300
ValueCountFrequency (%)
11650-3256 16
 
0.2%
11170-3645 16
 
0.2%
11215-15013 15
 
0.1%
11110-14616 15
 
0.1%
11740-1742 11
 
0.1%
11710-2403 11
 
0.1%
11545-100262238 10
 
0.1%
11710-100190602 10
 
0.1%
11200-100241938 10
 
0.1%
11110-100194239 10
 
0.1%
Other values (6670) 9876
98.8%
2024-05-11T07:13:01.679524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 38274
31.9%
0 22907
19.1%
2 10497
 
8.7%
- 10000
 
8.3%
5 6274
 
5.2%
4 6184
 
5.1%
3 5902
 
4.9%
7 5447
 
4.5%
6 5393
 
4.5%
8 4658
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110128
91.7%
Dash Punctuation 10000
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38274
34.8%
0 22907
20.8%
2 10497
 
9.5%
5 6274
 
5.7%
4 6184
 
5.6%
3 5902
 
5.4%
7 5447
 
4.9%
6 5393
 
4.9%
8 4658
 
4.2%
9 4592
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120128
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38274
31.9%
0 22907
19.1%
2 10497
 
8.7%
- 10000
 
8.3%
5 6274
 
5.2%
4 6184
 
5.1%
3 5902
 
4.9%
7 5447
 
4.5%
6 5393
 
4.5%
8 4658
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38274
31.9%
0 22907
19.1%
2 10497
 
8.7%
- 10000
 
8.3%
5 6274
 
5.2%
4 6184
 
5.1%
3 5902
 
4.9%
7 5447
 
4.5%
6 5393
 
4.5%
8 4658
 
3.9%

관리_주_건축물대장_PK
Text

MISSING 

Distinct14
Distinct (%)87.5%
Missing9984
Missing (%)99.8%
Memory size156.2 KiB
2024-05-11T07:13:01.979898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length13.375
Min length10

Characters and Unicode

Total characters214
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)75.0%

Sample

1st row11170-100191936
2nd row11110-100192457
3rd row11440-13743
4th row11110-11274
5th row11170-100191936
ValueCountFrequency (%)
11170-100191936 2
12.5%
11170-100218443 2
12.5%
11110-100192457 1
 
6.2%
11440-13743 1
 
6.2%
11110-11274 1
 
6.2%
11110-10460 1
 
6.2%
11290-6502 1
 
6.2%
11110-1007 1
 
6.2%
11170-100190716 1
 
6.2%
11170-100193572 1
 
6.2%
Other values (4) 4
25.0%
2024-05-11T07:13:02.666707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 82
38.3%
0 42
19.6%
- 16
 
7.5%
7 13
 
6.1%
3 11
 
5.1%
2 11
 
5.1%
4 11
 
5.1%
9 8
 
3.7%
8 7
 
3.3%
5 7
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 198
92.5%
Dash Punctuation 16
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 82
41.4%
0 42
21.2%
7 13
 
6.6%
3 11
 
5.6%
2 11
 
5.6%
4 11
 
5.6%
9 8
 
4.0%
8 7
 
3.5%
5 7
 
3.5%
6 6
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 214
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 82
38.3%
0 42
19.6%
- 16
 
7.5%
7 13
 
6.1%
3 11
 
5.1%
2 11
 
5.1%
4 11
 
5.1%
9 8
 
3.7%
8 7
 
3.3%
5 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 82
38.3%
0 42
19.6%
- 16
 
7.5%
7 13
 
6.1%
3 11
 
5.1%
2 11
 
5.1%
4 11
 
5.1%
9 8
 
3.7%
8 7
 
3.3%
5 7
 
3.3%

주_부속_구분_코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주건축물
9832 
부속건축물
 
163
<NA>
 
5

Length

Max length5
Median length4
Mean length4.0163
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주건축물 9832
98.3%
부속건축물 163
 
1.6%
<NA> 5
 
0.1%

Length

2024-05-11T07:13:03.084229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:13:03.378850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주건축물 9832
98.3%
부속건축물 163
 
1.6%
na 5
 
< 0.1%

주_부속_일련번호
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8588
Minimum0
Maximum127
Zeros472
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:13:03.750407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum127
Range127
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6492176
Coefficient of variation (CV)2.501193
Kurtosis157.95603
Mean1.8588
Median Absolute Deviation (MAD)0
Skewness10.013638
Sum18588
Variance21.615224
MonotonicityNot monotonic
2024-05-11T07:13:04.095701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8365
83.7%
0 472
 
4.7%
2 332
 
3.3%
3 159
 
1.6%
4 104
 
1.0%
5 79
 
0.8%
6 62
 
0.6%
7 43
 
0.4%
8 43
 
0.4%
13 35
 
0.4%
Other values (44) 306
 
3.1%
ValueCountFrequency (%)
0 472
 
4.7%
1 8365
83.7%
2 332
 
3.3%
3 159
 
1.6%
4 104
 
1.0%
5 79
 
0.8%
6 62
 
0.6%
7 43
 
0.4%
8 43
 
0.4%
9 33
 
0.3%
ValueCountFrequency (%)
127 2
< 0.1%
68 1
< 0.1%
66 1
< 0.1%
64 2
< 0.1%
62 1
< 0.1%
58 2
< 0.1%
57 1
< 0.1%
54 1
< 0.1%
52 1
< 0.1%
49 2
< 0.1%

층_구분_코드
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지상
7548 
지하
1837 
옥탑
 
615

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지상
2nd row지상
3rd row옥탑
4th row지하
5th row지하

Common Values

ValueCountFrequency (%)
지상 7548
75.5%
지하 1837
 
18.4%
옥탑 615
 
6.2%

Length

2024-05-11T07:13:04.414970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:13:04.727004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 7548
75.5%
지하 1837
 
18.4%
옥탑 615
 
6.2%

층_번호
Real number (ℝ)

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9057
Minimum0
Maximum49
Zeros80
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:13:05.045998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile10
Maximum49
Range49
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.7418188
Coefficient of variation (CV)1.2877512
Kurtosis21.965864
Mean2.9057
Median Absolute Deviation (MAD)1
Skewness3.9528513
Sum29057
Variance14.001208
MonotonicityNot monotonic
2024-05-11T07:13:05.360074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 4753
47.5%
2 1902
19.0%
3 1076
 
10.8%
4 698
 
7.0%
5 390
 
3.9%
6 191
 
1.9%
7 155
 
1.6%
8 109
 
1.1%
9 96
 
1.0%
0 80
 
0.8%
Other values (30) 550
 
5.5%
ValueCountFrequency (%)
0 80
 
0.8%
1 4753
47.5%
2 1902
19.0%
3 1076
 
10.8%
4 698
 
7.0%
5 390
 
3.9%
6 191
 
1.9%
7 155
 
1.6%
8 109
 
1.1%
9 96
 
1.0%
ValueCountFrequency (%)
49 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
43 1
< 0.1%
40 2
< 0.1%
36 2
< 0.1%
33 2
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
30 2
< 0.1%

층_번호_명
Text

Distinct138
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T07:13:05.772290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.2834283
Min length1

Characters and Unicode

Total characters22832
Distinct characters49
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.5%

Sample

1st row1층
2nd row2층
3rd row옥탑
4th row지하층
5th row지1층
ValueCountFrequency (%)
1층 2683
26.6%
2층 1702
16.9%
3층 988
 
9.8%
4층 660
 
6.5%
지1층 601
 
6.0%
5층 362
 
3.6%
지1 348
 
3.5%
지하층 260
 
2.6%
옥탑 243
 
2.4%
지층 198
 
2.0%
Other values (124) 2037
20.2%
2024-05-11T07:13:06.476258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
층 9238
40.5%
1 4585
20.1%
2 2074
 
9.1%
지 1855
 
8.1%
3 1165
 
5.1%
4 772
 
3.4%
옥 610
 
2.7%
탑 609
 
2.7%
하 468
 
2.0%
5 434
 
1.9%
Other values (39) 1022
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12880
56.4%
Decimal Number 9794
42.9%
Space Separator 83
 
0.4%
Close Punctuation 32
 
0.1%
Open Punctuation 31
 
0.1%
Other Punctuation 6
 
< 0.1%
Uppercase Letter 5
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 9238
71.7%
지 1855
 
14.4%
옥 610
 
4.7%
탑 609
 
4.7%
하 468
 
3.6%
내 23
 
0.2%
상 19
 
0.1%
동 13
 
0.1%
구 5
 
< 0.1%
이 5
 
< 0.1%
Other values (18) 35
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 4585
46.8%
2 2074
21.2%
3 1165
 
11.9%
4 772
 
7.9%
5 434
 
4.4%
6 219
 
2.2%
7 183
 
1.9%
8 136
 
1.4%
9 123
 
1.3%
0 103
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
T 1
20.0%
I 1
20.0%
P 1
20.0%
C 1
20.0%
D 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 4
66.7%
/ 2
33.3%
Space Separator
ValueCountFrequency (%)
83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12878
56.4%
Common 9947
43.6%
Latin 5
 
< 0.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 9238
71.7%
지 1855
 
14.4%
옥 610
 
4.7%
탑 609
 
4.7%
하 468
 
3.6%
내 23
 
0.2%
상 19
 
0.1%
동 13
 
0.1%
구 5
 
< 0.1%
이 5
 
< 0.1%
Other values (17) 33
 
0.3%
Common
ValueCountFrequency (%)
1 4585
46.1%
2 2074
20.9%
3 1165
 
11.7%
4 772
 
7.8%
5 434
 
4.4%
6 219
 
2.2%
7 183
 
1.8%
8 136
 
1.4%
9 123
 
1.2%
0 103
 
1.0%
Other values (6) 153
 
1.5%
Latin
ValueCountFrequency (%)
T 1
20.0%
I 1
20.0%
P 1
20.0%
C 1
20.0%
D 1
20.0%
Han
ValueCountFrequency (%)
內 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12878
56.4%
ASCII 9952
43.6%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
층 9238
71.7%
지 1855
 
14.4%
옥 610
 
4.7%
탑 609
 
4.7%
하 468
 
3.6%
내 23
 
0.2%
상 19
 
0.1%
동 13
 
0.1%
구 5
 
< 0.1%
이 5
 
< 0.1%
Other values (17) 33
 
0.3%
ASCII
ValueCountFrequency (%)
1 4585
46.1%
2 2074
20.8%
3 1165
 
11.7%
4 772
 
7.8%
5 434
 
4.4%
6 219
 
2.2%
7 183
 
1.8%
8 136
 
1.4%
9 123
 
1.2%
0 103
 
1.0%
Other values (11) 158
 
1.6%
CJK
ValueCountFrequency (%)
內 2
100.0%

구조_코드
Categorical

IMBALANCE 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
철근콘크리트구조
6589 
벽돌구조
2283 
일반목구조
 
475
철골철근콘크리트구조
 
248
블록구조
 
149
Other values (15)
 
256

Length

Max length12
Median length8
Mean length6.8886
Min length3

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 6589
65.9%
벽돌구조 2283
 
22.8%
일반목구조 475
 
4.8%
철골철근콘크리트구조 248
 
2.5%
블록구조 149
 
1.5%
일반철골구조 117
 
1.2%
경량철골구조 76
 
0.8%
철골콘크리트구조 21
 
0.2%
<NA> 8
 
0.1%
기타조적구조 8
 
0.1%
Other values (10) 26
 
0.3%

Length

2024-05-11T07:13:06.764111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 6589
65.9%
벽돌구조 2283
 
22.8%
일반목구조 475
 
4.8%
철골철근콘크리트구조 248
 
2.5%
블록구조 149
 
1.5%
일반철골구조 117
 
1.2%
경량철골구조 76
 
0.8%
철골콘크리트구조 21
 
0.2%
na 8
 
0.1%
기타조적구조 8
 
0.1%
Other values (10) 26
 
0.3%

기타_구조
Text

Distinct331
Distinct (%)3.3%
Missing64
Missing (%)0.6%
Memory size156.2 KiB
2024-05-11T07:13:07.226618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length6.4632649
Min length2

Characters and Unicode

Total characters64219
Distinct characters121
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)2.0%

Sample

1st row철근콘크리트구조
2nd row철근콘크리트조,세멘벽돌조
3rd row연와조
4th row철근콘크리트조
5th row연와조
ValueCountFrequency (%)
철근콘크리트구조 3080
30.5%
철근콘크리트조 2387
23.7%
연와조 1816
18.0%
철근콘크리트 486
 
4.8%
목조 437
 
4.3%
철골철근콘크리트구조 191
 
1.9%
세멘벽돌조 168
 
1.7%
철근콘크리트벽식구조 166
 
1.6%
조적조 118
 
1.2%
철근콘크리트라멘조 79
 
0.8%
Other values (282) 1158
 
11.5%
2024-05-11T07:13:08.186277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
조 9865
15.4%
철 7289
11.4%
트 6824
10.6%
크 6783
10.6%
리 6778
10.6%
근 6763
10.5%
콘 6761
10.5%
구 3726
 
5.8%
와 2007
 
3.1%
연 1994
 
3.1%
Other values (111) 5429
8.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63510
98.9%
Other Punctuation 347
 
0.5%
Space Separator 151
 
0.2%
Uppercase Letter 84
 
0.1%
Open Punctuation 43
 
0.1%
Close Punctuation 43
 
0.1%
Decimal Number 28
 
< 0.1%
Other Symbol 7
 
< 0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
조 9865
15.5%
철 7289
11.5%
트 6824
10.7%
크 6783
10.7%
리 6778
10.7%
근 6763
10.6%
콘 6761
10.6%
구 3726
 
5.9%
와 2007
 
3.2%
연 1994
 
3.1%
Other values (87) 4720
7.4%
Decimal Number
ValueCountFrequency (%)
4 5
17.9%
5 5
17.9%
2 3
10.7%
8 3
10.7%
7 3
10.7%
3 2
 
7.1%
6 2
 
7.1%
1 2
 
7.1%
9 2
 
7.1%
0 1
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 42
50.0%
R 38
45.2%
P 2
 
2.4%
S 2
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 259
74.6%
. 52
 
15.0%
/ 36
 
10.4%
Math Symbol
ValueCountFrequency (%)
+ 4
66.7%
> 1
 
16.7%
< 1
 
16.7%
Space Separator
ValueCountFrequency (%)
151
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63510
98.9%
Common 625
 
1.0%
Latin 84
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
조 9865
15.5%
철 7289
11.5%
트 6824
10.7%
크 6783
10.7%
리 6778
10.7%
근 6763
10.6%
콘 6761
10.6%
구 3726
 
5.9%
와 2007
 
3.2%
연 1994
 
3.1%
Other values (87) 4720
7.4%
Common
ValueCountFrequency (%)
, 259
41.4%
151
24.2%
. 52
 
8.3%
( 43
 
6.9%
) 43
 
6.9%
/ 36
 
5.8%
㎡ 7
 
1.1%
4 5
 
0.8%
5 5
 
0.8%
+ 4
 
0.6%
Other values (10) 20
 
3.2%
Latin
ValueCountFrequency (%)
C 42
50.0%
R 38
45.2%
P 2
 
2.4%
S 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63510
98.9%
ASCII 702
 
1.1%
CJK Compat 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
조 9865
15.5%
철 7289
11.5%
트 6824
10.7%
크 6783
10.7%
리 6778
10.7%
근 6763
10.6%
콘 6761
10.6%
구 3726
 
5.9%
와 2007
 
3.2%
연 1994
 
3.1%
Other values (87) 4720
7.4%
ASCII
ValueCountFrequency (%)
, 259
36.9%
151
21.5%
. 52
 
7.4%
( 43
 
6.1%
) 43
 
6.1%
C 42
 
6.0%
R 38
 
5.4%
/ 36
 
5.1%
4 5
 
0.7%
5 5
 
0.7%
Other values (13) 28
 
4.0%
CJK Compat
ValueCountFrequency (%)
㎡ 7
100.0%

주_용도_코드
Text

Distinct184
Distinct (%)1.8%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T07:13:08.818321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length4.6450871
Min length2

Characters and Unicode

Total characters46423
Distinct characters189
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.4%

Sample

1st row소매점
2nd row기타제2종근린생활시설
3rd row다가구주택
4th row기타위락시설
5th row다가구주택
ValueCountFrequency (%)
단독주택 1819
18.2%
다가구주택 1243
12.4%
아파트 914
 
9.1%
다세대주택 879
 
8.8%
사무소 781
 
7.8%
소매점 631
 
6.3%
기타제1종근린생활시설 393
 
3.9%
일반음식점 382
 
3.8%
기타제2종근린생활시설 280
 
2.8%
오피스텔 223
 
2.2%
Other values (178) 2460
24.6%
2024-05-11T07:13:09.888094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 4246
 
9.1%
택 4173
 
9.0%
다 2239
 
4.8%
단 1852
 
4.0%
독 1846
 
4.0%
소 1713
 
3.7%
시 1455
 
3.1%
설 1353
 
2.9%
구 1348
 
2.9%
기 1330
 
2.9%
Other values (179) 24868
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45677
98.4%
Decimal Number 675
 
1.5%
Close Punctuation 29
 
0.1%
Open Punctuation 29
 
0.1%
Space Separator 11
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 4246
 
9.3%
택 4173
 
9.1%
다 2239
 
4.9%
단 1852
 
4.1%
독 1846
 
4.0%
소 1713
 
3.8%
시 1455
 
3.2%
설 1353
 
3.0%
구 1348
 
3.0%
기 1330
 
2.9%
Other values (173) 24122
52.8%
Decimal Number
ValueCountFrequency (%)
1 395
58.5%
2 280
41.5%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45677
98.4%
Common 746
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 4246
 
9.3%
택 4173
 
9.1%
다 2239
 
4.9%
단 1852
 
4.1%
독 1846
 
4.0%
소 1713
 
3.8%
시 1455
 
3.2%
설 1353
 
3.0%
구 1348
 
3.0%
기 1330
 
2.9%
Other values (173) 24122
52.8%
Common
ValueCountFrequency (%)
1 395
52.9%
2 280
37.5%
) 29
 
3.9%
( 29
 
3.9%
11
 
1.5%
. 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45672
98.4%
ASCII 746
 
1.6%
Compat Jamo 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 4246
 
9.3%
택 4173
 
9.1%
다 2239
 
4.9%
단 1852
 
4.1%
독 1846
 
4.0%
소 1713
 
3.8%
시 1455
 
3.2%
설 1353
 
3.0%
구 1348
 
3.0%
기 1330
 
2.9%
Other values (172) 24117
52.8%
ASCII
ValueCountFrequency (%)
1 395
52.9%
2 280
37.5%
) 29
 
3.9%
( 29
 
3.9%
11
 
1.5%
. 2
 
0.3%
Compat Jamo
ValueCountFrequency (%)
ㆍ 5
100.0%

기타_용도
Text

Distinct1701
Distinct (%)17.1%
Missing49
Missing (%)0.5%
Memory size156.2 KiB
2024-05-11T07:13:10.376798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length46
Mean length7.1085318
Min length1

Characters and Unicode

Total characters70737
Distinct characters384
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1182 ?
Unique (%)11.9%

Sample

1st row제1종근린생활시설(소매점)
2nd row영업용
3rd row연면적제외
4th row위락시설(유흥주점)
5th row다가구주택
ValueCountFrequency (%)
주택 1371
 
13.1%
아파트 471
 
4.5%
주차장 293
 
2.8%
근린생활시설 244
 
2.3%
제2종근린생활시설(사무소 233
 
2.2%
단독주택 226
 
2.2%
주택(1가구 208
 
2.0%
소매점 202
 
1.9%
사무소 182
 
1.7%
사무실 158
 
1.5%
Other values (1636) 6846
65.6%
2024-05-11T07:13:11.363002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 4404
 
6.2%
( 4201
 
5.9%
) 4195
 
5.9%
택 3886
 
5.5%
시 2525
 
3.6%
설 2305
 
3.3%
생 1807
 
2.6%
활 1785
 
2.5%
근 1642
 
2.3%
실 1640
 
2.3%
Other values (374) 42347
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57012
80.6%
Open Punctuation 4208
 
5.9%
Close Punctuation 4202
 
5.9%
Decimal Number 3224
 
4.6%
Other Punctuation 1010
 
1.4%
Space Separator 485
 
0.7%
Uppercase Letter 303
 
0.4%
Dash Punctuation 157
 
0.2%
Other Symbol 84
 
0.1%
Lowercase Letter 37
 
0.1%
Other values (3) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 4404
 
7.7%
택 3886
 
6.8%
시 2525
 
4.4%
설 2305
 
4.0%
생 1807
 
3.2%
활 1785
 
3.1%
근 1642
 
2.9%
실 1640
 
2.9%
린 1636
 
2.9%
제 1622
 
2.8%
Other values (313) 33760
59.2%
Uppercase Letter
ValueCountFrequency (%)
E 107
35.3%
V 59
19.5%
L 50
16.5%
F 15
 
5.0%
M 14
 
4.6%
D 13
 
4.3%
T 8
 
2.6%
I 6
 
2.0%
R 5
 
1.7%
P 5
 
1.7%
Other values (9) 21
 
6.9%
Decimal Number
ValueCountFrequency (%)
2 1348
41.8%
1 1068
33.1%
4 221
 
6.9%
3 219
 
6.8%
5 85
 
2.6%
6 69
 
2.1%
8 67
 
2.1%
0 61
 
1.9%
7 46
 
1.4%
9 40
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
e 10
27.0%
l 7
18.9%
v 5
13.5%
m 5
13.5%
r 2
 
5.4%
p 2
 
5.4%
i 2
 
5.4%
t 2
 
5.4%
h 1
 
2.7%
a 1
 
2.7%
Other Symbol
ValueCountFrequency (%)
㎡ 72
85.7%
┓ 5
 
6.0%
┛ 4
 
4.8%
┃ 1
 
1.2%
┌ 1
 
1.2%
┘ 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 730
72.3%
. 142
 
14.1%
/ 94
 
9.3%
: 43
 
4.3%
& 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 2
33.3%
< 2
33.3%
> 2
33.3%
Open Punctuation
ValueCountFrequency (%)
( 4201
99.8%
[ 7
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 4195
99.8%
] 7
 
0.2%
Space Separator
ValueCountFrequency (%)
485
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 157
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Number
ValueCountFrequency (%)
² 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57012
80.6%
Common 13385
 
18.9%
Latin 340
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 4404
 
7.7%
택 3886
 
6.8%
시 2525
 
4.4%
설 2305
 
4.0%
생 1807
 
3.2%
활 1785
 
3.1%
근 1642
 
2.9%
실 1640
 
2.9%
린 1636
 
2.9%
제 1622
 
2.8%
Other values (313) 33760
59.2%
Common
ValueCountFrequency (%)
( 4201
31.4%
) 4195
31.3%
2 1348
 
10.1%
1 1068
 
8.0%
, 730
 
5.5%
485
 
3.6%
4 221
 
1.7%
3 219
 
1.6%
- 157
 
1.2%
. 142
 
1.1%
Other values (22) 619
 
4.6%
Latin
ValueCountFrequency (%)
E 107
31.5%
V 59
17.4%
L 50
14.7%
F 15
 
4.4%
M 14
 
4.1%
D 13
 
3.8%
e 10
 
2.9%
T 8
 
2.4%
l 7
 
2.1%
I 6
 
1.8%
Other values (19) 51
15.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57012
80.6%
ASCII 13638
 
19.3%
CJK Compat 72
 
0.1%
Box Drawing 12
 
< 0.1%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 4404
 
7.7%
택 3886
 
6.8%
시 2525
 
4.4%
설 2305
 
4.0%
생 1807
 
3.2%
활 1785
 
3.1%
근 1642
 
2.9%
실 1640
 
2.9%
린 1636
 
2.9%
제 1622
 
2.8%
Other values (313) 33760
59.2%
ASCII
ValueCountFrequency (%)
( 4201
30.8%
) 4195
30.8%
2 1348
 
9.9%
1 1068
 
7.8%
, 730
 
5.4%
485
 
3.6%
4 221
 
1.6%
3 219
 
1.6%
- 157
 
1.2%
. 142
 
1.0%
Other values (44) 872
 
6.4%
CJK Compat
ValueCountFrequency (%)
㎡ 72
100.0%
Box Drawing
ValueCountFrequency (%)
┓ 5
41.7%
┛ 4
33.3%
┃ 1
 
8.3%
┌ 1
 
8.3%
┘ 1
 
8.3%
None
ValueCountFrequency (%)
² 3
100.0%

면적
Real number (ℝ)

Distinct6930
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.64212
Minimum0
Maximum21207.4
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:13:11.871869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.56
Q142.24
median80.98
Q3164.3075
95-th percentile770.163
Maximum21207.4
Range21207.4
Interquartile range (IQR)122.0675

Descriptive statistics

Standard deviation572.22139
Coefficient of variation (CV)2.7037217
Kurtosis359.98419
Mean211.64212
Median Absolute Deviation (MAD)50.48
Skewness14.591628
Sum2116421.2
Variance327437.32
MonotonicityNot monotonic
2024-05-11T07:13:12.279839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
 
0.2%
33.06 22
 
0.2%
26.45 18
 
0.2%
42.98 18
 
0.2%
296.6 17
 
0.2%
430.7943 16
 
0.2%
39.67 16
 
0.2%
16.53 15
 
0.1%
13.22 14
 
0.1%
9.36 14
 
0.1%
Other values (6920) 9825
98.2%
ValueCountFrequency (%)
0.0 25
0.2%
0.64 2
 
< 0.1%
0.68 1
 
< 0.1%
0.8 1
 
< 0.1%
0.83 1
 
< 0.1%
0.95 1
 
< 0.1%
1.0 1
 
< 0.1%
1.2 1
 
< 0.1%
1.21 2
 
< 0.1%
1.38 1
 
< 0.1%
ValueCountFrequency (%)
21207.4 1
< 0.1%
15804.27 1
< 0.1%
15051.68 1
< 0.1%
13194.36 1
< 0.1%
10858.84 1
< 0.1%
10134.595 1
< 0.1%
9420.34 1
< 0.1%
8963.92 1
< 0.1%
8358.318 1
< 0.1%
6313.53 1
< 0.1%

작업_일자
Real number (ℝ)

Distinct210
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20217146
Minimum20200108
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:13:12.579419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200108
5-th percentile20200428
Q120200428
median20220329
Q320230808
95-th percentile20240330
Maximum20240510
Range40402
Interquartile range (IQR)30380

Descriptive statistics

Standard deviation14380.691
Coefficient of variation (CV)0.00071131162
Kurtosis-1.4420005
Mean20217146
Median Absolute Deviation (MAD)10699
Skewness0.13280967
Sum2.0217146 × 1011
Variance2.0680427 × 108
MonotonicityNot monotonic
2024-05-11T07:13:13.080956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200428 2161
21.6%
20211029 1086
 
10.9%
20231028 889
 
8.9%
20230908 370
 
3.7%
20220831 264
 
2.6%
20220823 243
 
2.4%
20240330 231
 
2.3%
20240510 211
 
2.1%
20200306 167
 
1.7%
20240227 159
 
1.6%
Other values (200) 4219
42.2%
ValueCountFrequency (%)
20200108 46
 
0.5%
20200110 1
 
< 0.1%
20200117 5
 
0.1%
20200121 7
 
0.1%
20200129 6
 
0.1%
20200213 11
 
0.1%
20200218 8
 
0.1%
20200304 54
 
0.5%
20200306 167
1.7%
20200310 9
 
0.1%
ValueCountFrequency (%)
20240510 211
2.1%
20240507 89
 
0.9%
20240425 113
1.1%
20240420 6
 
0.1%
20240417 12
 
0.1%
20240411 3
 
< 0.1%
20240406 8
 
0.1%
20240402 6
 
0.1%
20240330 231
2.3%
20240327 86
 
0.9%

Interactions

2024-05-11T07:12:55.929485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:52.433539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:53.630139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:54.743686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:56.228243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:52.773929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:53.903522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:55.087157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:56.534288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:53.043612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:54.165735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:55.357122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:56.823037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:53.320179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:54.425393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:12:55.617260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T07:13:13.381675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리_주_건축물대장_PK주_부속_구분_코드주_부속_일련번호층_구분_코드층_번호구조_코드면적작업_일자
관리_주_건축물대장_PK1.000NaNNaN0.600NaN1.000NaN1.000
주_부속_구분_코드NaN1.0000.0000.0370.0550.1620.1610.043
주_부속_일련번호NaN0.0001.0000.0540.2250.2190.0740.092
층_구분_코드0.6000.0370.0541.0000.2480.2020.1660.056
층_번호NaN0.0550.2250.2481.0000.2300.0460.186
구조_코드1.0000.1620.2190.2020.2301.0000.1990.206
면적NaN0.1610.0740.1660.0460.1991.0000.042
작업_일자1.0000.0430.0920.0560.1860.2060.0421.000
2024-05-11T07:13:13.657129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_구분_코드주_부속_구분_코드구조_코드
층_구분_코드1.0000.0610.108
주_부속_구분_코드0.0611.0000.144
구조_코드0.1080.1441.000
2024-05-11T07:13:13.983268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주_부속_일련번호층_번호면적작업_일자주_부속_구분_코드층_구분_코드구조_코드
주_부속_일련번호1.0000.1810.2170.0840.0000.0360.098
층_번호0.1811.0000.4850.0990.0420.1520.088
면적0.2170.4851.0000.1180.1610.0730.079
작업_일자0.0840.0990.1181.0000.0300.0230.096
주_부속_구분_코드0.0000.0420.1610.0301.0000.0610.144
층_구분_코드0.0360.1520.0730.0230.0611.0000.108
구조_코드0.0980.0880.0790.0960.1440.1081.000

Missing values

2024-05-11T07:12:57.287449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T07:12:57.925399image/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.
2024-05-11T07:12:58.579509image/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

관리_층별_개요_PK관리_건축물대장_PK관리_주_건축물대장_PK주_부속_구분_코드주_부속_일련번호층_구분_코드층_번호층_번호_명구조_코드기타_구조주_용도_코드기타_용도면적작업_일자
4683511140-10014667911140-100229802<NA>주건축물1지상11층철근콘크리트구조철근콘크리트구조소매점제1종근린생활시설(소매점)47.4120200709
1954811110-3089511110-10344<NA>주건축물1지상22층철근콘크리트구조철근콘크리트조,세멘벽돌조기타제2종근린생활시설영업용280.9920200428
5642411200-8405811200-23769<NA>주건축물1옥탑1옥탑벽돌구조연와조다가구주택연면적제외7.6820200417
3021711110-4012811110-13057<NA>주건축물1지하1지하층철근콘크리트구조철근콘크리트조기타위락시설위락시설(유흥주점)66.9420200428
5760211260-590211260-2300<NA>주건축물1지하1지1층벽돌구조연와조다가구주택다가구주택94.220231028
3739311620-10839911620-25780<NA>주건축물1지상33층벽돌구조연와조단독주택주택(2가구)161.3720240327
2806311110-2974211110-9991<NA>주건축물1지상44층철근콘크리트구조철근콘크리트조다가구주택다가구주택(1가구)81.0920210119
2829711110-3418811110-11343<NA>주건축물1지상33층철근콘크리트구조철근콘크리트조다세대주택주택(2세대)96.4320200428
2486311680-2587111680-3300<NA>주건축물1지상44층철근콘크리트구조철근콘크리트조학원제2종근린생활시설(학원)171.4920200306
3999711440-5028011440-16029<NA>주건축물1옥탑0옥탑벽돌구조조적조다가구주택물탱크실(연면적제외)12.220230908
관리_층별_개요_PK관리_건축물대장_PK관리_주_건축물대장_PK주_부속_구분_코드주_부속_일련번호층_구분_코드층_번호층_번호_명구조_코드기타_구조주_용도_코드기타_용도면적작업_일자
3492411710-981911710-1831<NA>주건축물7지상1010철근콘크리트구조철근콘크리트조아파트아파트1048.6220211029
4219511140-766911140-2958<NA>주건축물1지상11층철근콘크리트구조철근콘크리트일반음식점근린생활시설(대중음식점)92.220200108
4377811110-10011780211110-100187471<NA>주건축물1지하1지1층철근콘크리트구조철근콘크리트구조단독주택단독주택90.2320200428
1819411530-10017142611530-100265429<NA>주건축물2지상22층철근콘크리트구조철근콘크리트구조오피스텔업무시설(오피스텔-3호)138.3120211029
3484411110-4648511110-14916<NA>주건축물1옥탑1옥탑층철근콘크리트구조철근콘크리트구조기타제1종근린생활시설계단실(연면적제외)17.2620200428
6234711170-4168211170-11167<NA>주건축물0옥탑2옥탑2층철근콘크리트구조철근콘크리트조연립주택ELEV기계실(연면적제외)9.3620200428
6199511260-507511260-2077<NA>주건축물1지상22층벽돌구조연와조다가구주택주택(1가구)57.1720231028
5047911260-10445411260-27743<NA>주건축물1지하1지층철근콘크리트구조철근콘크리트조기타제1종근린생활시설근린생활시설73.0320220503
2586411500-10019580811500-100321152<NA>주건축물1지상77층철근콘크리트구조철근콘크리트구조의원제1종근린생활시설(의원)397.3120240425
3829811140-10014937511140-100232251<NA>주건축물1지상44층철근콘크리트구조철근콘크리트구조오피스텔업무시설(오피스텔(주거형))119.2520220702