Overview

Dataset statistics

Number of variables19
Number of observations2778
Missing cells4746
Missing cells (%)9.0%
Duplicate rows7
Duplicate rows (%)0.3%
Total size in memory417.9 KiB
Average record size in memory154.0 B

Variable types

Categorical3
Text10
DateTime4
Numeric2

Dataset

Description서울특별시 영등포구 건축허가 및 사용승인 현황 자료입니다.건축구분,대지위치, 연면적,대지면적, 건축면적, 연면적, 허가일, 착공처리일, 사용승인일, 주용도 등이 포함되어 있습니다.
Author서울특별시 영등포구
URLhttps://www.data.go.kr/data/15127661/fileData.do

Alerts

Dataset has 7 (0.3%) duplicate rowsDuplicates
구조 is highly imbalanced (67.8%)Imbalance
증축연면적(제곱미터) has 2328 (83.8%) missing valuesMissing
허가일 has 153 (5.5%) missing valuesMissing
착공처리일 has 528 (19.0%) missing valuesMissing
착공예정일 has 529 (19.0%) missing valuesMissing
실제착공일 has 609 (21.9%) missing valuesMissing
최대지하층수 has 208 (7.5%) missing valuesMissing
부속용도 has 365 (13.1%) missing valuesMissing
최대지하층수 has 757 (27.2%) zerosZeros

Reproduction

Analysis started2024-04-21 02:39:52.714944
Analysis finished2024-04-21 02:39:55.997660
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
신축
1749 
증축
460 
용도변경
312 
대수선
243 
발코니구조변경
 
12
Other values (2)
 
2

Length

Max length9
Median length2
Mean length2.3362131
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
신축 1749
63.0%
증축 460
 
16.6%
용도변경 312
 
11.2%
대수선 243
 
8.7%
발코니구조변경 12
 
0.4%
개축 1
 
< 0.1%
가설건축물축조허가 1
 
< 0.1%

Length

2024-04-21T11:39:56.065316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:39:56.174453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 1749
63.0%
증축 460
 
16.6%
용도변경 312
 
11.2%
대수선 243
 
8.7%
발코니구조변경 12
 
0.4%
개축 1
 
< 0.1%
가설건축물축조허가 1
 
< 0.1%
Distinct2484
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
2024-04-21T11:39:56.457638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length37
Mean length22.160187
Min length17

Characters and Unicode

Total characters61561
Distinct characters61
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2312 ?
Unique (%)83.2%

Sample

1st row서울특별시 영등포구 당산동2가 1
2nd row서울특별시 영등포구 양평동4가 195-1
3rd row서울특별시 영등포구 영등포동3가 9-13
4th row서울특별시 영등포구 신길동 4114
5th row서울특별시 영등포구 신길동 4765 외3필지
ValueCountFrequency (%)
서울특별시 2778
23.5%
영등포구 2778
23.5%
신길동 690
 
5.8%
대림동 586
 
5.0%
외1필지 446
 
3.8%
여의도동 197
 
1.7%
도림동 124
 
1.0%
외2필지 114
 
1.0%
당산동1가 97
 
0.8%
양평동4가 96
 
0.8%
Other values (2312) 3921
33.2%
2024-04-21T11:39:56.877264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9049
 
14.7%
3183
 
5.2%
3183
 
5.2%
3183
 
5.2%
1 2928
 
4.8%
2783
 
4.5%
2778
 
4.5%
2778
 
4.5%
2778
 
4.5%
2778
 
4.5%
Other values (51) 26140
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37208
60.4%
Decimal Number 13062
 
21.2%
Space Separator 9049
 
14.7%
Dash Punctuation 2241
 
3.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3183
 
8.6%
3183
 
8.6%
3183
 
8.6%
2783
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
Other values (38) 8208
22.1%
Decimal Number
ValueCountFrequency (%)
1 2928
22.4%
2 1806
13.8%
3 1550
11.9%
4 1231
9.4%
5 1081
 
8.3%
6 1028
 
7.9%
0 907
 
6.9%
7 861
 
6.6%
8 851
 
6.5%
9 819
 
6.3%
Space Separator
ValueCountFrequency (%)
9049
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2241
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37208
60.4%
Common 24353
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3183
 
8.6%
3183
 
8.6%
3183
 
8.6%
2783
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
Other values (38) 8208
22.1%
Common
ValueCountFrequency (%)
9049
37.2%
1 2928
 
12.0%
- 2241
 
9.2%
2 1806
 
7.4%
3 1550
 
6.4%
4 1231
 
5.1%
5 1081
 
4.4%
6 1028
 
4.2%
0 907
 
3.7%
7 861
 
3.5%
Other values (3) 1671
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37208
60.4%
ASCII 24353
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9049
37.2%
1 2928
 
12.0%
- 2241
 
9.2%
2 1806
 
7.4%
3 1550
 
6.4%
4 1231
 
5.1%
5 1081
 
4.4%
6 1028
 
4.2%
0 907
 
3.7%
7 861
 
3.5%
Other values (3) 1671
 
6.9%
Hangul
ValueCountFrequency (%)
3183
 
8.6%
3183
 
8.6%
3183
 
8.6%
2783
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
2778
 
7.5%
Other values (38) 8208
22.1%
Distinct2096
Distinct (%)75.6%
Missing4
Missing (%)0.1%
Memory size21.8 KiB
2024-04-21T11:39:57.142797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.4048306
Min length2

Characters and Unicode

Total characters17767
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1749 ?
Unique (%)63.0%

Sample

1st row2400.10
2nd row645.50
3rd row2490.00
4th row158.00
5th row534.00
ValueCountFrequency (%)
132.00 28
 
1.0%
139.00 14
 
0.5%
122.00 12
 
0.4%
33,058 11
 
0.4%
3176.00 10
 
0.4%
46465.00 10
 
0.4%
33058.00 10
 
0.4%
145.00 9
 
0.3%
132 9
 
0.3%
331.00 9
 
0.3%
Other values (2078) 2652
95.6%
2024-04-21T11:39:57.535638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3040
17.1%
. 2401
13.5%
1 1960
11.0%
1900
10.7%
2 1437
8.1%
3 1320
7.4%
4 1040
 
5.9%
6 1008
 
5.7%
5 946
 
5.3%
7 867
 
4.9%
Other values (3) 1848
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13272
74.7%
Other Punctuation 2595
 
14.6%
Space Separator 1900
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3040
22.9%
1 1960
14.8%
2 1437
10.8%
3 1320
9.9%
4 1040
 
7.8%
6 1008
 
7.6%
5 946
 
7.1%
7 867
 
6.5%
8 848
 
6.4%
9 806
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 2401
92.5%
, 194
 
7.5%
Space Separator
ValueCountFrequency (%)
1900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17767
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3040
17.1%
. 2401
13.5%
1 1960
11.0%
1900
10.7%
2 1437
8.1%
3 1320
7.4%
4 1040
 
5.9%
6 1008
 
5.7%
5 946
 
5.3%
7 867
 
4.9%
Other values (3) 1848
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3040
17.1%
. 2401
13.5%
1 1960
11.0%
1900
10.7%
2 1437
8.1%
3 1320
7.4%
4 1040
 
5.9%
6 1008
 
5.7%
5 946
 
5.3%
7 867
 
4.9%
Other values (3) 1848
10.4%
Distinct2507
Distinct (%)90.3%
Missing1
Missing (%)< 0.1%
Memory size21.8 KiB
2024-04-21T11:39:57.867472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.474973
Min length2

Characters and Unicode

Total characters17981
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2310 ?
Unique (%)83.2%

Sample

1st row1439.60
2nd row312.52
3rd row1494.85
4th row66.50
5th row269.55
ValueCountFrequency (%)
24766.00 10
 
0.4%
15,569.50 10
 
0.4%
15569.50 10
 
0.4%
5,378.75 6
 
0.2%
5,015.68 6
 
0.2%
388.23 6
 
0.2%
2845.86 5
 
0.2%
10592.40 5
 
0.2%
29416.48 4
 
0.1%
562.88 4
 
0.1%
Other values (2431) 2711
97.6%
2024-04-21T11:39:58.309474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2752
15.3%
1 1987
11.1%
1899
10.6%
2 1444
8.0%
5 1301
7.2%
6 1292
7.2%
8 1279
7.1%
3 1217
6.8%
9 1191
6.6%
0 1188
6.6%
Other values (3) 2431
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13198
73.4%
Other Punctuation 2884
 
16.0%
Space Separator 1899
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1987
15.1%
2 1444
10.9%
5 1301
9.9%
6 1292
9.8%
8 1279
9.7%
3 1217
9.2%
9 1191
9.0%
0 1188
9.0%
4 1162
8.8%
7 1137
8.6%
Other Punctuation
ValueCountFrequency (%)
. 2752
95.4%
, 132
 
4.6%
Space Separator
ValueCountFrequency (%)
1899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2752
15.3%
1 1987
11.1%
1899
10.6%
2 1444
8.0%
5 1301
7.2%
6 1292
7.2%
8 1279
7.1%
3 1217
6.8%
9 1191
6.6%
0 1188
6.6%
Other values (3) 2431
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2752
15.3%
1 1987
11.1%
1899
10.6%
2 1444
8.0%
5 1301
7.2%
6 1292
7.2%
8 1279
7.1%
3 1217
6.8%
9 1191
6.6%
0 1188
6.6%
Other values (3) 2431
13.5%
Distinct2578
Distinct (%)92.8%
Missing1
Missing (%)< 0.1%
Memory size21.8 KiB
2024-04-21T11:39:58.640440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.235866
Min length3

Characters and Unicode

Total characters20094
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2437 ?
Unique (%)87.8%

Sample

1st row3038.64
2nd row2739.67
3rd row29025.36
4th row199.50
5th row1112.79
ValueCountFrequency (%)
629047.23 10
 
0.4%
203,903.56 7
 
0.3%
99,807.63 6
 
0.2%
161207.32 5
 
0.2%
5,406.22 4
 
0.1%
99140.74 4
 
0.1%
168,506.51 4
 
0.1%
299.76 4
 
0.1%
145109.25 4
 
0.1%
8,889.11 4
 
0.1%
Other values (2547) 2725
98.1%
2024-04-21T11:39:59.120194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2762
13.7%
2 1899
9.5%
1899
9.5%
1 1782
8.9%
9 1728
8.6%
3 1592
7.9%
4 1525
7.6%
6 1405
7.0%
5 1379
6.9%
8 1362
6.8%
Other values (3) 2761
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15117
75.2%
Other Punctuation 3078
 
15.3%
Space Separator 1899
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1899
12.6%
1 1782
11.8%
9 1728
11.4%
3 1592
10.5%
4 1525
10.1%
6 1405
9.3%
5 1379
9.1%
8 1362
9.0%
7 1290
8.5%
0 1155
7.6%
Other Punctuation
ValueCountFrequency (%)
. 2762
89.7%
, 316
 
10.3%
Space Separator
ValueCountFrequency (%)
1899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20094
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2762
13.7%
2 1899
9.5%
1899
9.5%
1 1782
8.9%
9 1728
8.6%
3 1592
7.9%
4 1525
7.6%
6 1405
7.0%
5 1379
6.9%
8 1362
6.8%
Other values (3) 2761
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2762
13.7%
2 1899
9.5%
1899
9.5%
1 1782
8.9%
9 1728
8.6%
3 1592
7.9%
4 1525
7.6%
6 1405
7.0%
5 1379
6.9%
8 1362
6.8%
Other values (3) 2761
13.7%
Distinct438
Distinct (%)97.3%
Missing2328
Missing (%)83.8%
Memory size21.8 KiB
2024-04-21T11:39:59.492688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.4111111
Min length1

Characters and Unicode

Total characters2435
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique428 ?
Unique (%)95.1%

Sample

1st row222.05
2nd row7.08
3rd row175.23
4th row49.24
5th row51.48
ValueCountFrequency (%)
18 3
 
0.7%
54.89 3
 
0.7%
0 2
 
0.4%
31.2 2
 
0.4%
28 2
 
0.4%
19.32 2
 
0.4%
22.81 2
 
0.4%
29.84 2
 
0.4%
42.68 2
 
0.4%
22.4 2
 
0.4%
Other values (427) 428
95.1%
2024-04-21T11:39:59.980016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 439
18.0%
2 267
11.0%
1 247
10.1%
4 221
9.1%
3 197
8.1%
6 183
7.5%
9 159
 
6.5%
8 158
 
6.5%
7 153
 
6.3%
5 148
 
6.1%
Other values (6) 263
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1857
76.3%
Other Punctuation 456
 
18.7%
Space Separator 114
 
4.7%
Dash Punctuation 6
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 267
14.4%
1 247
13.3%
4 221
11.9%
3 197
10.6%
6 183
9.9%
9 159
8.6%
8 158
8.5%
7 153
8.2%
5 148
8.0%
0 124
6.7%
Other Punctuation
ValueCountFrequency (%)
. 439
96.3%
, 17
 
3.7%
Space Separator
ValueCountFrequency (%)
114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 439
18.0%
2 267
11.0%
1 247
10.1%
4 221
9.1%
3 197
8.1%
6 183
7.5%
9 159
 
6.5%
8 158
 
6.5%
7 153
 
6.3%
5 148
 
6.1%
Other values (6) 263
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 439
18.0%
2 267
11.0%
1 247
10.1%
4 221
9.1%
3 197
8.1%
6 183
7.5%
9 159
 
6.5%
8 158
 
6.5%
7 153
 
6.3%
5 148
 
6.1%
Other values (6) 263
10.8%
Distinct1485
Distinct (%)53.6%
Missing5
Missing (%)0.2%
Memory size21.8 KiB
2024-04-21T11:40:00.323413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.8204111
Min length2

Characters and Unicode

Total characters16140
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1058 ?
Unique (%)38.2%

Sample

1st row59.98
2nd row48.42
3rd row60.03
4th row42.09
5th row50.48
ValueCountFrequency (%)
59.98 37
 
1.3%
59.89 33
 
1.2%
59.94 31
 
1.1%
59.93 29
 
1.0%
59.97 28
 
1.0%
59.96 27
 
1.0%
59.95 27
 
1.0%
59.99 26
 
0.9%
59.80 24
 
0.9%
59.78 23
 
0.8%
Other values (1269) 2488
89.7%
2024-04-21T11:40:00.965075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2781
17.2%
. 2769
17.2%
9 2058
12.8%
1899
11.8%
4 1176
7.3%
8 1079
 
6.7%
7 932
 
5.8%
3 791
 
4.9%
6 762
 
4.7%
2 679
 
4.2%
Other values (3) 1214
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11471
71.1%
Other Punctuation 2770
 
17.2%
Space Separator 1899
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2781
24.2%
9 2058
17.9%
4 1176
10.3%
8 1079
 
9.4%
7 932
 
8.1%
3 791
 
6.9%
6 762
 
6.6%
2 679
 
5.9%
1 657
 
5.7%
0 556
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 2769
> 99.9%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2781
17.2%
. 2769
17.2%
9 2058
12.8%
1899
11.8%
4 1176
7.3%
8 1079
 
6.7%
7 932
 
5.8%
3 791
 
4.9%
6 762
 
4.7%
2 679
 
4.2%
Other values (3) 1214
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2781
17.2%
. 2769
17.2%
9 2058
12.8%
1899
11.8%
4 1176
7.3%
8 1079
 
6.7%
7 932
 
5.8%
3 791
 
4.9%
6 762
 
4.7%
2 679
 
4.2%
Other values (3) 1214
7.5%
Distinct2418
Distinct (%)87.2%
Missing5
Missing (%)0.2%
Memory size21.8 KiB
2024-04-21T11:40:01.292102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.7547782
Min length3

Characters and Unicode

Total characters18731
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2182 ?
Unique (%)78.7%

Sample

1st row126.60
2nd row299.94
3rd row736.88
4th row84.18
5th row159.87
ValueCountFrequency (%)
199.99 11
 
0.4%
791.67 10
 
0.4%
199.91 7
 
0.3%
549.59 6
 
0.2%
926.12 6
 
0.2%
199.93 6
 
0.2%
399.98 6
 
0.2%
199.82 6
 
0.2%
299.98 5
 
0.2%
199.85 5
 
0.2%
Other values (2314) 2705
97.5%
2024-04-21T11:40:01.728090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2765
14.8%
1 2254
12.0%
9 2253
12.0%
1899
10.1%
2 1526
8.1%
3 1301
6.9%
4 1275
6.8%
8 1245
6.6%
7 1179
6.3%
5 1139
6.1%
Other values (3) 1895
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14064
75.1%
Other Punctuation 2768
 
14.8%
Space Separator 1899
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2254
16.0%
9 2253
16.0%
2 1526
10.9%
3 1301
9.3%
4 1275
9.1%
8 1245
8.9%
7 1179
8.4%
5 1139
8.1%
6 1123
8.0%
0 769
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 2765
99.9%
, 3
 
0.1%
Space Separator
ValueCountFrequency (%)
1899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2765
14.8%
1 2254
12.0%
9 2253
12.0%
1899
10.1%
2 1526
8.1%
3 1301
6.9%
4 1275
6.8%
8 1245
6.6%
7 1179
6.3%
5 1139
6.1%
Other values (3) 1895
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2765
14.8%
1 2254
12.0%
9 2253
12.0%
1899
10.1%
2 1526
8.1%
3 1301
6.9%
4 1275
6.8%
8 1245
6.6%
7 1179
6.3%
5 1139
6.1%
Other values (3) 1895
10.1%

구조
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
철근콘크리트구조
2187 
벽돌구조
 
172
<NA>
 
130
철골철근콘크리트구조
 
90
일반철골구조
 
88
Other values (13)
 
111

Length

Max length12
Median length8
Mean length7.4892009
Min length4

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 2187
78.7%
벽돌구조 172
 
6.2%
<NA> 130
 
4.7%
철골철근콘크리트구조 90
 
3.2%
일반철골구조 88
 
3.2%
경량철골구조 38
 
1.4%
철골콘크리트구조 24
 
0.9%
일반목구조 14
 
0.5%
블록구조 13
 
0.5%
강파이프구조 6
 
0.2%
Other values (8) 16
 
0.6%

Length

2024-04-21T11:40:01.877745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 2187
78.7%
벽돌구조 172
 
6.2%
na 130
 
4.7%
철골철근콘크리트구조 90
 
3.2%
일반철골구조 88
 
3.2%
경량철골구조 38
 
1.4%
철골콘크리트구조 24
 
0.9%
일반목구조 14
 
0.5%
블록구조 13
 
0.5%
강파이프구조 6
 
0.2%
Other values (8) 16
 
0.6%

허가일
Date

MISSING 

Distinct1501
Distinct (%)57.2%
Missing153
Missing (%)5.5%
Memory size21.8 KiB
Minimum1988-10-15 00:00:00
Maximum2024-04-12 00:00:00
2024-04-21T11:40:02.015051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:02.162555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공처리일
Date

MISSING 

Distinct1348
Distinct (%)59.9%
Missing528
Missing (%)19.0%
Memory size21.8 KiB
Minimum1996-09-18 00:00:00
Maximum2024-03-13 00:00:00
2024-04-21T11:40:02.284189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:02.401749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공예정일
Text

MISSING 

Distinct1396
Distinct (%)62.1%
Missing529
Missing (%)19.0%
Memory size21.8 KiB
2024-04-21T11:40:02.684042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9991107
Min length8

Characters and Unicode

Total characters22488
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

Unique860 ?
Unique (%)38.2%

Sample

1st row2024-01-05
2nd row2023-11-08
3rd row2023-10-04
4th row2023-08-16
5th row2023-11-01
ValueCountFrequency (%)
2015-07-01 7
 
0.3%
2017-04-05 7
 
0.3%
2015-11-16 7
 
0.3%
2017-04-03 7
 
0.3%
2016-08-16 6
 
0.3%
2019-04-15 6
 
0.3%
2016-11-07 6
 
0.3%
2017-09-13 6
 
0.3%
2017-01-09 6
 
0.3%
2017-06-26 6
 
0.3%
Other values (1386) 2185
97.2%
2024-04-21T11:40:03.103123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5280
23.5%
- 4498
20.0%
2 4221
18.8%
1 3762
16.7%
7 772
 
3.4%
5 770
 
3.4%
6 744
 
3.3%
8 673
 
3.0%
9 653
 
2.9%
3 569
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17990
80.0%
Dash Punctuation 4498
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5280
29.3%
2 4221
23.5%
1 3762
20.9%
7 772
 
4.3%
5 770
 
4.3%
6 744
 
4.1%
8 673
 
3.7%
9 653
 
3.6%
3 569
 
3.2%
4 546
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 4498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5280
23.5%
- 4498
20.0%
2 4221
18.8%
1 3762
16.7%
7 772
 
3.4%
5 770
 
3.4%
6 744
 
3.3%
8 673
 
3.0%
9 653
 
2.9%
3 569
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5280
23.5%
- 4498
20.0%
2 4221
18.8%
1 3762
16.7%
7 772
 
3.4%
5 770
 
3.4%
6 744
 
3.3%
8 673
 
3.0%
9 653
 
2.9%
3 569
 
2.5%

실제착공일
Date

MISSING 

Distinct1368
Distinct (%)63.1%
Missing609
Missing (%)21.9%
Memory size21.8 KiB
Minimum1990-07-05 00:00:00
Maximum2024-03-05 00:00:00
2024-04-21T11:40:03.237027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:03.361243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1474
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
Minimum2015-01-02 00:00:00
Maximum2024-04-16 00:00:00
2024-04-21T11:40:03.483198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:03.592973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Real number (ℝ)

Distinct40
Distinct (%)1.4%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.9913514
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2024-04-21T11:40:03.706848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q38
95-th percentile17
Maximum69
Range68
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.3202515
Coefficient of variation (CV)1.0470439
Kurtosis26.273135
Mean6.9913514
Median Absolute Deviation (MAD)2
Skewness4.5164136
Sum19401
Variance53.586082
MonotonicityNot monotonic
2024-04-21T11:40:03.824234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 493
17.7%
3 487
17.5%
4 424
15.3%
6 220
7.9%
7 185
 
6.7%
2 131
 
4.7%
8 124
 
4.5%
9 121
 
4.4%
1 97
 
3.5%
10 92
 
3.3%
Other values (30) 401
14.4%
ValueCountFrequency (%)
1 97
 
3.5%
2 131
 
4.7%
3 487
17.5%
4 424
15.3%
5 493
17.7%
6 220
7.9%
7 185
 
6.7%
8 124
 
4.5%
9 121
 
4.4%
10 92
 
3.3%
ValueCountFrequency (%)
69 4
 
0.1%
60 7
0.3%
56 1
 
< 0.1%
55 5
0.2%
53 5
0.2%
50 12
0.4%
41 1
 
< 0.1%
40 3
 
0.1%
39 7
0.3%
38 2
 
0.1%

최대지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.4%
Missing208
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean1.263035
Minimum0
Maximum8
Zeros757
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2024-04-21T11:40:03.929770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5338643
Coefficient of variation (CV)1.2144274
Kurtosis4.8052206
Mean1.263035
Median Absolute Deviation (MAD)1
Skewness2.1587078
Sum3246
Variance2.3527397
MonotonicityNot monotonic
2024-04-21T11:40:04.026861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1281
46.1%
0 757
27.2%
2 186
 
6.7%
3 106
 
3.8%
4 92
 
3.3%
7 51
 
1.8%
5 50
 
1.8%
6 38
 
1.4%
8 9
 
0.3%
(Missing) 208
 
7.5%
ValueCountFrequency (%)
0 757
27.2%
1 1281
46.1%
2 186
 
6.7%
3 106
 
3.8%
4 92
 
3.3%
5 50
 
1.8%
6 38
 
1.4%
7 51
 
1.8%
8 9
 
0.3%
ValueCountFrequency (%)
8 9
 
0.3%
7 51
 
1.8%
6 38
 
1.4%
5 50
 
1.8%
4 92
 
3.3%
3 106
 
3.8%
2 186
 
6.7%
1 1281
46.1%
0 757
27.2%
Distinct858
Distinct (%)31.0%
Missing7
Missing (%)0.3%
Memory size21.8 KiB
2024-04-21T11:40:04.358634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9047275
Min length1

Characters and Unicode

Total characters10820
Distinct characters12
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

Unique414 ?
Unique (%)14.9%

Sample

1st row7.6
2nd row48.2
3rd row66.41
4th row9
5th row12.2
ValueCountFrequency (%)
0 82
 
3.0%
10.35 66
 
2.4%
9 32
 
1.2%
10.5 25
 
0.9%
14.4 22
 
0.8%
10.25 21
 
0.8%
13.15 18
 
0.6%
15.5 18
 
0.6%
10.3 18
 
0.6%
10.2 17
 
0.6%
Other values (848) 2452
88.5%
2024-04-21T11:40:04.827645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2407
22.2%
1 1887
17.4%
5 1201
11.1%
2 966
8.9%
3 830
 
7.7%
4 752
 
7.0%
9 610
 
5.6%
7 554
 
5.1%
8 547
 
5.1%
0 542
 
5.0%
Other values (2) 524
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8411
77.7%
Other Punctuation 2409
 
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1887
22.4%
5 1201
14.3%
2 966
11.5%
3 830
9.9%
4 752
 
8.9%
9 610
 
7.3%
7 554
 
6.6%
8 547
 
6.5%
0 542
 
6.4%
6 522
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 2407
99.9%
, 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2407
22.2%
1 1887
17.4%
5 1201
11.1%
2 966
8.9%
3 830
 
7.7%
4 752
 
7.0%
9 610
 
5.6%
7 554
 
5.1%
8 547
 
5.1%
0 542
 
5.0%
Other values (2) 524
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2407
22.2%
1 1887
17.4%
5 1201
11.1%
2 966
8.9%
3 830
 
7.7%
4 752
 
7.0%
9 610
 
5.6%
7 554
 
5.1%
8 547
 
5.1%
0 542
 
5.0%
Other values (2) 524
 
4.8%

주용도
Categorical

Distinct24
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
단독주택
836 
공동주택
523 
업무시설
509 
제2종근린생활시설
405 
제1종근린생활시설
185 
Other values (19)
320 

Length

Max length10
Median length4
Mean length5.0863931
Min length2

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row운동시설
2nd row업무시설
3rd row판매시설
4th row제2종근린생활시설
5th row제2종근린생활시설

Common Values

ValueCountFrequency (%)
단독주택 836
30.1%
공동주택 523
18.8%
업무시설 509
18.3%
제2종근린생활시설 405
14.6%
제1종근린생활시설 185
 
6.7%
숙박시설 62
 
2.2%
공장 58
 
2.1%
의료시설 36
 
1.3%
종교시설 36
 
1.3%
판매시설 34
 
1.2%
Other values (14) 94
 
3.4%

Length

2024-04-21T11:40:04.970875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택 836
30.1%
공동주택 523
18.8%
업무시설 509
18.3%
제2종근린생활시설 405
14.6%
제1종근린생활시설 185
 
6.7%
숙박시설 62
 
2.2%
공장 58
 
2.1%
의료시설 36
 
1.3%
종교시설 36
 
1.3%
판매시설 34
 
1.2%
Other values (14) 94
 
3.4%

부속용도
Text

MISSING 

Distinct988
Distinct (%)40.9%
Missing365
Missing (%)13.1%
Memory size21.8 KiB
2024-04-21T11:40:05.161464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length43
Mean length10.50145
Min length2

Characters and Unicode

Total characters25340
Distinct characters200
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique776 ?
Unique (%)32.2%

Sample

1st row운동시설,근린생활시설,업무시설
2nd row오피스텔
3rd row문화및집회시설,근린생활시설,업무시설
4th row근린생활시설
5th row사무소
ValueCountFrequency (%)
다중주택 353
 
10.5%
254
 
7.6%
근린생활시설 225
 
6.7%
다세대주택 207
 
6.2%
오피스텔 191
 
5.7%
다가구주택 184
 
5.5%
사무소 90
 
2.7%
업무시설 56
 
1.7%
소매점 45
 
1.3%
일반음식점 38
 
1.1%
Other values (768) 1714
51.1%
2024-04-21T11:40:05.503315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1544
 
6.1%
1528
 
6.0%
1455
 
5.7%
1289
 
5.1%
1266
 
5.0%
971
 
3.8%
953
 
3.8%
912
 
3.6%
, 793
 
3.1%
792
 
3.1%
Other values (190) 13837
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20997
82.9%
Other Punctuation 983
 
3.9%
Space Separator 953
 
3.8%
Open Punctuation 812
 
3.2%
Close Punctuation 806
 
3.2%
Decimal Number 721
 
2.8%
Dash Punctuation 56
 
0.2%
Math Symbol 8
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1544
 
7.4%
1528
 
7.3%
1455
 
6.9%
1289
 
6.1%
1266
 
6.0%
971
 
4.6%
912
 
4.3%
792
 
3.8%
740
 
3.5%
582
 
2.8%
Other values (163) 9918
47.2%
Decimal Number
ValueCountFrequency (%)
2 311
43.1%
1 245
34.0%
8 36
 
5.0%
4 30
 
4.2%
6 30
 
4.2%
5 17
 
2.4%
0 15
 
2.1%
9 14
 
1.9%
3 13
 
1.8%
7 10
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 793
80.7%
/ 162
 
16.5%
. 25
 
2.5%
: 2
 
0.2%
& 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
P 1
33.3%
G 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 728
89.7%
[ 84
 
10.3%
Close Punctuation
ValueCountFrequency (%)
) 725
90.0%
] 81
 
10.0%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
= 1
 
12.5%
Space Separator
ValueCountFrequency (%)
953
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20997
82.9%
Common 4340
 
17.1%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1544
 
7.4%
1528
 
7.3%
1455
 
6.9%
1289
 
6.1%
1266
 
6.0%
971
 
4.6%
912
 
4.3%
792
 
3.8%
740
 
3.5%
582
 
2.8%
Other values (163) 9918
47.2%
Common
ValueCountFrequency (%)
953
22.0%
, 793
18.3%
( 728
16.8%
) 725
16.7%
2 311
 
7.2%
1 245
 
5.6%
/ 162
 
3.7%
[ 84
 
1.9%
] 81
 
1.9%
- 56
 
1.3%
Other values (14) 202
 
4.7%
Latin
ValueCountFrequency (%)
L 1
33.3%
P 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20996
82.9%
ASCII 4343
 
17.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1544
 
7.4%
1528
 
7.3%
1455
 
6.9%
1289
 
6.1%
1266
 
6.0%
971
 
4.6%
912
 
4.3%
792
 
3.8%
740
 
3.5%
582
 
2.8%
Other values (162) 9917
47.2%
ASCII
ValueCountFrequency (%)
953
21.9%
, 793
18.3%
( 728
16.8%
) 725
16.7%
2 311
 
7.2%
1 245
 
5.6%
/ 162
 
3.7%
[ 84
 
1.9%
] 81
 
1.9%
- 56
 
1.3%
Other values (17) 205
 
4.7%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

Interactions

2024-04-21T11:39:55.193862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:39:54.951109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:39:55.281129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:39:55.104915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:40:05.584352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분구조최대지상층수최대지하층수주용도
건축구분1.0000.5010.5220.4770.773
구조0.5011.0000.4740.5150.489
최대지상층수0.5220.4741.0000.7770.583
최대지하층수0.4770.5150.7771.0000.660
주용도0.7730.4890.5830.6601.000
2024-04-21T11:40:05.671837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구조주용도건축구분
구조1.0000.1630.253
주용도0.1631.0000.467
건축구분0.2530.4671.000
2024-04-21T11:40:05.754332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최대지상층수최대지하층수건축구분구조주용도
최대지상층수1.0000.4030.3080.2100.263
최대지하층수0.4031.0000.2760.2400.324
건축구분0.3080.2761.0000.2530.467
구조0.2100.2400.2531.0000.163
주용도0.2630.3240.4670.1631.000

Missing values

2024-04-21T11:39:55.414330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:39:55.634025image/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.
2024-04-21T11:39:55.854138image/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

건축구분대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일착공예정일실제착공일사용승인일최대지상층수최대지하층수최고높이(미터)주용도부속용도
0용도변경서울특별시 영등포구 당산동2가 12400.101439.603038.64<NA>59.98126.60철근콘크리트구조2024-04-12<NA><NA><NA>2024-04-162<NA>7.6운동시설운동시설,근린생활시설,업무시설
1용도변경서울특별시 영등포구 양평동4가 195-1645.50312.522739.67<NA>48.42299.94철근콘크리트구조2024-01-15<NA><NA><NA>2024-01-1913248.2업무시설오피스텔
2용도변경서울특별시 영등포구 영등포동3가 9-132490.001494.8529025.36<NA>60.03736.88철골철근콘크리트구조2024-01-11<NA><NA><NA>2024-01-163<NA>66.41판매시설문화및집회시설,근린생활시설,업무시설
3용도변경서울특별시 영등포구 신길동 4114158.0066.50199.50<NA>42.0984.18<NA>2023-12-282024-01-082024-01-052024-01-052024-02-16219제2종근린생활시설근린생활시설
4용도변경서울특별시 영등포구 신길동 4765 외3필지534.00269.551112.79<NA>50.48159.87철근콘크리트구조2023-12-08<NA><NA><NA>2024-01-154112.2제2종근린생활시설사무소
5용도변경서울특별시 영등포구 양평동1가 13-8 외1필지380.60222.812106.20<NA>58.54397.02철근콘크리트구조2023-11-03<NA><NA><NA>2023-11-099239.5자동차관련시설근린생활시설
6대수선서울특별시 영등포구 양평동3가 70-12162.601215.382230.68<NA>56.20103.15일반철골구조2023-10-312023-11-062023-11-082023-11-082024-01-102<NA>18.2판매시설<NA>
7증축서울특별시 영등포구 문래동3가 82-4463.80242.46716.84222.0552.28154.56일반철골구조2023-09-192023-09-272023-10-042023-10-042023-11-073<NA>12.45제1종근린생활시설소매점
8용도변경서울특별시 영등포구 여의도동 23-83176.001487.4343669.26<NA>46.83762.48철골철근콘크리트구조2023-09-07<NA><NA><NA>2023-10-2021790업무시설업무시설,근린생활시설
9대수선서울특별시 영등포구 여의도동 2246465.0024766.00629047.23<NA>53.30791.67철골철근콘크리트구조2023-08-092023-08-192023-08-162023-08-162023-09-07697317.7업무시설판매시설, 숙박시설, 근린생활시설, 문화및집회시설, 교육연구시설
건축구분대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일착공예정일실제착공일사용승인일최대지상층수최대지하층수최고높이(미터)주용도부속용도
2768증축서울특별시 영등포구 도림동 97-3174.2294277.430.853.95159.22벽돌구조<NA><NA><NA><NA>2015-01-164<NA>9단독주택다가구주택
2769증축서울특별시 영등포구 영등포동 645-399053.55162.02162.0259.5123.8556벽돌구조<NA><NA><NA><NA>2015-01-16319.1단독주택다가구주택
2770증축서울특별시 영등포구 신길동 186-31689.8153.92171.081860.03135.29벽돌구조<NA><NA><NA><NA>2015-01-16318.9단독주택다가구용
2771증축서울특별시 영등포구 신길동 186-192116.4971.73322.0444.7661.58216.95경량철골구조<NA><NA><NA><NA>2015-01-164111.4단독주택다가구용
2772증축서울특별시 영등포구 도림동 253-7511569.16310.7311.3360.14212.81벽돌구조<NA><NA><NA><NA>2015-01-16410제1종근린생활시설주택
2773증축서울특별시 영등포구 도림동 147-364527.3590.5263.1760.7778201.1556벽돌구조<NA><NA><NA><NA>2015-01-164<NA>10.6단독주택다가구(2가구) 제1종근린생활시설
2774증축서울특별시 영등포구 영등포동 571-497.1169.4209.13209.1371.47155.42벽돌구조<NA><NA><NA><NA>2015-01-16219.5단독주택다가구주택
2775증축서울특별시 영등포구 영등포동 580-9966.2248.6769.5739.8273.5105.06벽돌구조<NA><NA><NA><NA>2015-01-162<NA>5.8단독주택<NA>
2776증축서울특별시 영등포구 신길동 190-7010159.07206.35206.3558.4851144.4059벽돌구조<NA><NA><NA><NA>2015-01-16319.5단독주택<NA>
2777증축서울특별시 영등포구 신길동 186-317102.7461.16262.8826.3259.5289198.3648경량철골구조<NA><NA><NA><NA>2015-01-164110.3단독주택다가구주택

Duplicate rows

Most frequently occurring

건축구분대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일착공예정일실제착공일사용승인일최대지상층수최대지하층수최고높이(미터)주용도부속용도# duplicates
6증축서울특별시 영등포구 신길동 3939289170.71632.5154.8959.07218.86경량철골구조<NA><NA><NA><NA>2015-01-165<NA>13.4공동주택다세대주택3
0대수선서울특별시 영등포구 문래동6가 51,550.40926.319,719.43<NA>59.7465399.8678철근콘크리트구조2015-07-062015-07-102015-07-112015-07-122015-08-058335.15제2종근린생활시설업무시설2
1신축서울특별시 영등포구 문래동6가 24-3375.2278.84278.84<NA>74.3274.32벽돌구조<NA><NA><NA><NA>2015-01-05103.1단독주택<NA>2
2용도변경서울특별시 영등포구 당산동4가 80 외1필지12,811.305,378.7599,807.63<NA>41.9844499.0971<NA>2015-07-28<NA><NA><NA>2015-07-3119478.4공장<NA>2
3용도변경서울특별시 영등포구 문래동3가 55-2030073.4011983.01196644.23<NA>39.85399.39철근콘크리트구조2018-02-14<NA><NA><NA>2018-03-2220379.4공장아파트형공장2
4용도변경서울특별시 영등포구 문래동6가 5-21,385.10795.898,889.11<NA>57.46399.74철근콘크리트구조2015-04-09<NA><NA><NA>2015-04-148334.5업무시설<NA>2
5용도변경서울특별시 영등포구 문래동6가 5-21,385.10795.898,889.11<NA>57.4608399.735철근콘크리트구조2014-12-29<NA><NA><NA>2015-01-088334.5업무시설근린생활시설2