Overview

Dataset statistics

Number of variables30
Number of observations500
Missing cells3542
Missing cells (%)23.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.6 KiB
Average record size in memory255.3 B

Variable types

Text10
Categorical9
Numeric10
Unsupported1

Dataset

Description샘플 데이터
Author국토교통부(open.eais.go.kr)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=28

Alerts

새주소_지상지하_코드 has constant value ""Constant
대지_구분_코드 is highly imbalanced (91.6%)Imbalance
지역_코드 is highly imbalanced (74.0%)Imbalance
구역_코드 is highly imbalanced (89.7%)Imbalance
지역_코드_명 is highly imbalanced (71.4%)Imbalance
관리_상위_건축물대장_PK has 145 (29.0%) missing valuesMissing
도로명_대지_위치 has 44 (8.8%) missing valuesMissing
건물_명 has 191 (38.2%) missing valuesMissing
특수지_명 has 493 (98.6%) missing valuesMissing
블록 has 498 (99.6%) missing valuesMissing
로트 has 500 (100.0%) missing valuesMissing
새주소_도로_코드 has 56 (11.2%) missing valuesMissing
새주소_법정동_코드 has 51 (10.2%) missing valuesMissing
새주소_본_번 has 47 (9.4%) missing valuesMissing
새주소_부_번 has 60 (12.0%) missing valuesMissing
지구_코드 has 486 (97.2%) missing valuesMissing
지구_코드_명 has 489 (97.8%) missing valuesMissing
구역_코드_명 has 482 (96.4%) missing valuesMissing
관리_건축물대장_PK has unique valuesUnique
로트 is an unsupported type, check if it needs cleaning or further analysisUnsupported
지 has 198 (39.6%) zerosZeros
외필지_수 has 416 (83.2%) zerosZeros
새주소_부_번 has 343 (68.6%) zerosZeros

Reproduction

Analysis started2023-12-10 15:00:53.430118
Analysis finished2023-12-10 15:00:55.406831
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_건축물대장_PK
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:00:55.854580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.142
Min length8

Characters and Unicode

Total characters6071
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

Unique500 ?
Unique (%)100.0%

Sample

1st row42170-15299
2nd row42780-16898
3rd row41290-15582
4th row46130-45115
5th row11530-92777
ValueCountFrequency (%)
42170-15299 1
 
0.2%
43750-16573 1
 
0.2%
26380-100209080 1
 
0.2%
41830-100237640 1
 
0.2%
41281-99751 1
 
0.2%
26350-42859 1
 
0.2%
47150-25016 1
 
0.2%
11230-94423 1
 
0.2%
26380-14355 1
 
0.2%
11305-16099 1
 
0.2%
Other values (490) 490
98.0%
2023-12-11T00:00:56.801389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1108
18.3%
0 969
16.0%
2 614
10.1%
4 611
10.1%
- 500
8.2%
3 490
8.1%
7 383
 
6.3%
8 379
 
6.2%
5 369
 
6.1%
6 356
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5571
91.8%
Dash Punctuation 500
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1108
19.9%
0 969
17.4%
2 614
11.0%
4 611
11.0%
3 490
8.8%
7 383
 
6.9%
8 379
 
6.8%
5 369
 
6.6%
6 356
 
6.4%
9 292
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1108
18.3%
0 969
16.0%
2 614
10.1%
4 611
10.1%
- 500
8.2%
3 490
8.1%
7 383
 
6.3%
8 379
 
6.2%
5 369
 
6.1%
6 356
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1108
18.3%
0 969
16.0%
2 614
10.1%
4 611
10.1%
- 500
8.2%
3 490
8.1%
7 383
 
6.3%
8 379
 
6.2%
5 369
 
6.1%
6 356
 
5.9%

관리_상위_건축물대장_PK
Text

MISSING 

Distinct354
Distinct (%)99.7%
Missing145
Missing (%)29.0%
Memory size4.0 KiB
2023-12-11T00:00:57.355542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length11.769014
Min length8

Characters and Unicode

Total characters4178
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

Unique353 ?
Unique (%)99.4%

Sample

1st row41360-15246
2nd row48127-1000047876
3rd row41465-182449
4th row44810-3125
5th row46150-26011
ValueCountFrequency (%)
41135-126007 2
 
0.6%
26200-20678 1
 
0.3%
41210-10651 1
 
0.3%
41271-944 1
 
0.3%
41210-12678 1
 
0.3%
11380-28980 1
 
0.3%
11710-1271 1
 
0.3%
11740-100199823 1
 
0.3%
41570-100180393 1
 
0.3%
48330-100181086 1
 
0.3%
Other values (344) 344
96.9%
2023-12-11T00:00:58.224024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 837
20.0%
0 657
15.7%
2 441
10.6%
4 384
9.2%
- 355
8.5%
3 331
 
7.9%
7 270
 
6.5%
6 244
 
5.8%
8 234
 
5.6%
5 226
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3823
91.5%
Dash Punctuation 355
 
8.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 837
21.9%
0 657
17.2%
2 441
11.5%
4 384
10.0%
3 331
 
8.7%
7 270
 
7.1%
6 244
 
6.4%
8 234
 
6.1%
5 226
 
5.9%
9 199
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 837
20.0%
0 657
15.7%
2 441
10.6%
4 384
9.2%
- 355
8.5%
3 331
 
7.9%
7 270
 
6.5%
6 244
 
5.8%
8 234
 
5.6%
5 226
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 837
20.0%
0 657
15.7%
2 441
10.6%
4 384
9.2%
- 355
8.5%
3 331
 
7.9%
7 270
 
6.5%
6 244
 
5.8%
8 234
 
5.6%
5 226
 
5.4%

대장_구분_코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
328 
1
172 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 328
65.6%
1 172
34.4%

Length

2023-12-11T00:00:58.573032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:58.833663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 328
65.6%
1 172
34.4%

대장_구분_코드_명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
집합
331 
일반
169 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반
2nd row일반
3rd row일반
4th row집합
5th row일반

Common Values

ValueCountFrequency (%)
집합 331
66.2%
일반 169
33.8%

Length

2023-12-11T00:00:59.096150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:59.404793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집합 331
66.2%
일반 169
33.8%

대장_종류_코드
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4
334 
2
143 
3
 
14
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row2
5th row4

Common Values

ValueCountFrequency (%)
4 334
66.8%
2 143
28.6%
3 14
 
2.8%
1 9
 
1.8%

Length

2023-12-11T00:00:59.658763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:59.883063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 334
66.8%
2 143
28.6%
3 14
 
2.8%
1 9
 
1.8%

대장_종류_코드_명
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
전유부
330 
일반건축물
148 
총괄표제부
 
16
표제부
 
6

Length

Max length5
Median length3
Mean length3.656
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반건축물
2nd row일반건축물
3rd row전유부
4th row일반건축물
5th row일반건축물

Common Values

ValueCountFrequency (%)
전유부 330
66.0%
일반건축물 148
29.6%
총괄표제부 16
 
3.2%
표제부 6
 
1.2%

Length

2023-12-11T00:01:00.209280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:00.573760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전유부 330
66.0%
일반건축물 148
29.6%
총괄표제부 16
 
3.2%
표제부 6
 
1.2%

대지_위치
Text

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:01:01.103498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length28
Mean length21.414
Min length15

Characters and Unicode

Total characters10707
Distinct characters248
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique494 ?
Unique (%)98.8%

Sample

1st row경상남도 창녕군 창녕읍 말흘리 272-2번지
2nd row제주특별자치도 제주시 조천읍 신촌리 1795번지
3rd row경상북도 칠곡군 북삼읍 인평리 271-1번지
4th row대구광역시 달성군 논공읍 남리 573-2번지
5th row서울특별시 양천구 목동 911번지
ValueCountFrequency (%)
경기도 118
 
5.3%
서울특별시 70
 
3.1%
경상남도 45
 
2.0%
부산광역시 31
 
1.4%
대구광역시 30
 
1.3%
전라남도 29
 
1.3%
경상북도 25
 
1.1%
충청남도 24
 
1.1%
광주광역시 23
 
1.0%
강원도 21
 
0.9%
Other values (1238) 1807
81.3%
2023-12-11T00:01:02.091348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1725
 
16.1%
지 518
 
4.8%
번 494
 
4.6%
동 436
 
4.1%
시 433
 
4.0%
1 397
 
3.7%
구 335
 
3.1%
도 322
 
3.0%
- 299
 
2.8%
2 241
 
2.3%
Other values (238) 5507
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6750
63.0%
Decimal Number 1926
 
18.0%
Space Separator 1725
 
16.1%
Dash Punctuation 299
 
2.8%
Uppercase Letter 3
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 518
 
7.7%
번 494
 
7.3%
동 436
 
6.5%
시 433
 
6.4%
구 335
 
5.0%
도 322
 
4.8%
경 194
 
2.9%
광 168
 
2.5%
남 164
 
2.4%
서 143
 
2.1%
Other values (223) 3543
52.5%
Decimal Number
ValueCountFrequency (%)
1 397
20.6%
2 241
12.5%
3 210
10.9%
4 178
9.2%
5 167
8.7%
7 158
 
8.2%
8 152
 
7.9%
6 147
 
7.6%
0 138
 
7.2%
9 138
 
7.2%
Space Separator
ValueCountFrequency (%)
1725
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 299
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6750
63.0%
Common 3954
36.9%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 518
 
7.7%
번 494
 
7.3%
동 436
 
6.5%
시 433
 
6.4%
구 335
 
5.0%
도 322
 
4.8%
경 194
 
2.9%
광 168
 
2.5%
남 164
 
2.4%
서 143
 
2.1%
Other values (223) 3543
52.5%
Common
ValueCountFrequency (%)
1725
43.6%
1 397
 
10.0%
- 299
 
7.6%
2 241
 
6.1%
3 210
 
5.3%
4 178
 
4.5%
5 167
 
4.2%
7 158
 
4.0%
8 152
 
3.8%
6 147
 
3.7%
Other values (4) 280
 
7.1%
Latin
ValueCountFrequency (%)
A 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6750
63.0%
ASCII 3957
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1725
43.6%
1 397
 
10.0%
- 299
 
7.6%
2 241
 
6.1%
3 210
 
5.3%
4 178
 
4.5%
5 167
 
4.2%
7 158
 
4.0%
8 152
 
3.8%
6 147
 
3.7%
Other values (5) 283
 
7.2%
Hangul
ValueCountFrequency (%)
지 518
 
7.7%
번 494
 
7.3%
동 436
 
6.5%
시 433
 
6.4%
구 335
 
5.0%
도 322
 
4.8%
경 194
 
2.9%
광 168
 
2.5%
남 164
 
2.4%
서 143
 
2.1%
Other values (223) 3543
52.5%

도로명_대지_위치
Text

MISSING 

Distinct454
Distinct (%)99.6%
Missing44
Missing (%)8.8%
Memory size4.0 KiB
2023-12-11T00:01:02.733596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length18.785088
Min length14

Characters and Unicode

Total characters8566
Distinct characters285
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique452 ?
Unique (%)99.1%

Sample

1st row세종특별자치시 달빛1로 39
2nd row전라남도 강진군 신흥길 64-21
3rd row경기도 평택시 지산로 115
4th row대구광역시 중구 달구벌대로 1950
5th row경기도 고양시 일산동구 강송로 119
ValueCountFrequency (%)
경기도 118
 
6.2%
서울특별시 75
 
4.0%
부산광역시 34
 
1.8%
경상북도 27
 
1.4%
인천광역시 27
 
1.4%
경상남도 27
 
1.4%
대구광역시 21
 
1.1%
전라남도 17
 
0.9%
전라북도 16
 
0.8%
대전광역시 15
 
0.8%
Other values (913) 1520
80.1%
2023-12-11T00:01:03.715360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1451
 
16.9%
시 425
 
5.0%
로 378
 
4.4%
1 368
 
4.3%
구 310
 
3.6%
도 278
 
3.2%
길 270
 
3.2%
2 235
 
2.7%
3 184
 
2.1%
경 184
 
2.1%
Other values (275) 4483
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5358
62.5%
Decimal Number 1649
 
19.3%
Space Separator 1451
 
16.9%
Dash Punctuation 108
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 425
 
7.9%
로 378
 
7.1%
구 310
 
5.8%
도 278
 
5.2%
길 270
 
5.0%
경 184
 
3.4%
광 151
 
2.8%
서 134
 
2.5%
기 129
 
2.4%
남 128
 
2.4%
Other values (263) 2971
55.4%
Decimal Number
ValueCountFrequency (%)
1 368
22.3%
2 235
14.3%
3 184
11.2%
5 172
10.4%
6 128
 
7.8%
4 122
 
7.4%
9 114
 
6.9%
0 112
 
6.8%
8 108
 
6.5%
7 106
 
6.4%
Space Separator
ValueCountFrequency (%)
1451
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5358
62.5%
Common 3208
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 425
 
7.9%
로 378
 
7.1%
구 310
 
5.8%
도 278
 
5.2%
길 270
 
5.0%
경 184
 
3.4%
광 151
 
2.8%
서 134
 
2.5%
기 129
 
2.4%
남 128
 
2.4%
Other values (263) 2971
55.4%
Common
ValueCountFrequency (%)
1451
45.2%
1 368
 
11.5%
2 235
 
7.3%
3 184
 
5.7%
5 172
 
5.4%
6 128
 
4.0%
4 122
 
3.8%
9 114
 
3.6%
0 112
 
3.5%
8 108
 
3.4%
Other values (2) 214
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5358
62.5%
ASCII 3208
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1451
45.2%
1 368
 
11.5%
2 235
 
7.3%
3 184
 
5.7%
5 172
 
5.4%
6 128
 
4.0%
4 122
 
3.8%
9 114
 
3.6%
0 112
 
3.5%
8 108
 
3.4%
Other values (2) 214
 
6.7%
Hangul
ValueCountFrequency (%)
시 425
 
7.9%
로 378
 
7.1%
구 310
 
5.8%
도 278
 
5.2%
길 270
 
5.0%
경 184
 
3.4%
광 151
 
2.8%
서 134
 
2.5%
기 129
 
2.4%
남 128
 
2.4%
Other values (263) 2971
55.4%

건물_명
Text

MISSING 

Distinct292
Distinct (%)94.5%
Missing191
Missing (%)38.2%
Memory size4.0 KiB
2023-12-11T00:01:04.393943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length18
Mean length7.618123
Min length1

Characters and Unicode

Total characters2354
Distinct characters344
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique285 ?
Unique (%)92.2%

Sample

1st row하소주공아파트2단지
2nd row미영리치타운103
3rd rowPico빌라
4th row효삼빌라
5th row강남 지웰홈스
ValueCountFrequency (%)
아파트 14
 
3.3%
현대아파트 9
 
2.1%
주공아파트 6
 
1.4%
2단지 4
 
1.0%
롯데캐슬 3
 
0.7%
무지개아파트 2
 
0.5%
은평뉴타운 2
 
0.5%
a동 2
 
0.5%
3차 2
 
0.5%
동.식물관련시설 2
 
0.5%
Other values (365) 373
89.0%
2023-12-11T00:01:05.338481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 161
 
6.8%
트 147
 
6.2%
파 142
 
6.0%
110
 
4.7%
동 50
 
2.1%
지 44
 
1.9%
스 43
 
1.8%
빌 41
 
1.7%
대 40
 
1.7%
주 36
 
1.5%
Other values (334) 1540
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2117
89.9%
Space Separator 110
 
4.7%
Decimal Number 78
 
3.3%
Uppercase Letter 18
 
0.8%
Open Punctuation 8
 
0.3%
Close Punctuation 8
 
0.3%
Other Punctuation 5
 
0.2%
Dash Punctuation 5
 
0.2%
Lowercase Letter 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 161
 
7.6%
트 147
 
6.9%
파 142
 
6.7%
동 50
 
2.4%
지 44
 
2.1%
스 43
 
2.0%
빌 41
 
1.9%
대 40
 
1.9%
주 36
 
1.7%
산 32
 
1.5%
Other values (307) 1381
65.2%
Decimal Number
ValueCountFrequency (%)
1 30
38.5%
2 21
26.9%
3 10
 
12.8%
0 5
 
6.4%
5 4
 
5.1%
6 3
 
3.8%
8 2
 
2.6%
4 2
 
2.6%
9 1
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
T 3
16.7%
A 3
16.7%
Y 2
11.1%
C 2
11.1%
B 2
11.1%
P 2
11.1%
S 2
11.1%
I 1
 
5.6%
M 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
o 1
20.0%
c 1
20.0%
i 1
20.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2117
89.9%
Common 214
 
9.1%
Latin 23
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 161
 
7.6%
트 147
 
6.9%
파 142
 
6.7%
동 50
 
2.4%
지 44
 
2.1%
스 43
 
2.0%
빌 41
 
1.9%
대 40
 
1.9%
주 36
 
1.7%
산 32
 
1.5%
Other values (307) 1381
65.2%
Common
ValueCountFrequency (%)
110
51.4%
1 30
 
14.0%
2 21
 
9.8%
3 10
 
4.7%
( 8
 
3.7%
) 8
 
3.7%
. 5
 
2.3%
- 5
 
2.3%
0 5
 
2.3%
5 4
 
1.9%
Other values (4) 8
 
3.7%
Latin
ValueCountFrequency (%)
T 3
13.0%
A 3
13.0%
Y 2
8.7%
C 2
8.7%
e 2
8.7%
B 2
8.7%
P 2
8.7%
S 2
8.7%
I 1
 
4.3%
o 1
 
4.3%
Other values (3) 3
13.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2117
89.9%
ASCII 237
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 161
 
7.6%
트 147
 
6.9%
파 142
 
6.7%
동 50
 
2.4%
지 44
 
2.1%
스 43
 
2.0%
빌 41
 
1.9%
대 40
 
1.9%
주 36
 
1.7%
산 32
 
1.5%
Other values (307) 1381
65.2%
ASCII
ValueCountFrequency (%)
110
46.4%
1 30
 
12.7%
2 21
 
8.9%
3 10
 
4.2%
( 8
 
3.4%
) 8
 
3.4%
. 5
 
2.1%
- 5
 
2.1%
0 5
 
2.1%
5 4
 
1.7%
Other values (17) 31
 
13.1%

시군구_코드
Real number (ℝ)

Distinct192
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34726.838
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:05.687820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11320
Q127140
median41220
Q345132.5
95-th percentile48311
Maximum50130
Range39020
Interquartile range (IQR)17992.5

Descriptive statistics

Standard deviation12908.726
Coefficient of variation (CV)0.37172189
Kurtosis-0.8237033
Mean34726.838
Median Absolute Deviation (MAD)6909
Skewness-0.73991469
Sum17363419
Variance1.666352 × 108
MonotonicityNot monotonic
2023-12-11T00:01:06.176053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11740 9
 
1.8%
30170 9
 
1.8%
28260 8
 
1.6%
28237 8
 
1.6%
27230 7
 
1.4%
50110 7
 
1.4%
41281 6
 
1.2%
41480 6
 
1.2%
41113 6
 
1.2%
11380 6
 
1.2%
Other values (182) 428
85.6%
ValueCountFrequency (%)
11110 3
0.6%
11140 3
0.6%
11200 2
0.4%
11215 4
0.8%
11230 3
0.6%
11260 3
0.6%
11290 4
0.8%
11305 2
0.4%
11320 3
0.6%
11350 3
0.6%
ValueCountFrequency (%)
50130 4
0.8%
50110 7
1.4%
48890 1
 
0.2%
48880 1
 
0.2%
48860 1
 
0.2%
48850 3
0.6%
48820 1
 
0.2%
48740 2
 
0.4%
48330 5
1.0%
48310 2
 
0.4%

법정동_코드
Real number (ℝ)

Distinct140
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16068.15
Minimum10100
Maximum44029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:06.489044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110500
median11200
Q325021
95-th percentile35078.6
Maximum44029
Range33929
Interquartile range (IQR)14521

Descriptive statistics

Standard deviation8924.4767
Coefficient of variation (CV)0.55541407
Kurtosis0.60169157
Mean16068.15
Median Absolute Deviation (MAD)1000
Skewness1.4265921
Sum8034075
Variance79646284
MonotonicityNot monotonic
2023-12-11T00:01:06.816582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10200 36
 
7.2%
10100 31
 
6.2%
10300 28
 
5.6%
10400 28
 
5.6%
10600 26
 
5.2%
10500 24
 
4.8%
10700 23
 
4.6%
10800 17
 
3.4%
10900 14
 
2.8%
11100 11
 
2.2%
Other values (130) 262
52.4%
ValueCountFrequency (%)
10100 31
6.2%
10200 36
7.2%
10300 28
5.6%
10400 28
5.6%
10500 24
4.8%
10600 26
5.2%
10700 23
4.6%
10800 17
3.4%
10900 14
 
2.8%
11000 10
 
2.0%
ValueCountFrequency (%)
44029 1
0.2%
44024 1
0.2%
43022 1
0.2%
43021 1
0.2%
42030 1
0.2%
41023 1
0.2%
40031 1
0.2%
40027 1
0.2%
40024 1
0.2%
38025 1
0.2%

대지_구분_코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
492 
2
 
5
1
 
3

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 (%)
0 492
98.4%
2 5
 
1.0%
1 3
 
0.6%

Length

2023-12-11T00:01:07.178101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:07.442775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 492
98.4%
2 5
 
1.0%
1 3
 
0.6%

번
Real number (ℝ)

Distinct411
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean585.736
Minimum0
Maximum3905
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:07.783098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1191.25
median474.5
Q3844.25
95-th percentile1528.6
Maximum3905
Range3905
Interquartile range (IQR)653

Descriptive statistics

Standard deviation520.10675
Coefficient of variation (CV)0.88795421
Kurtosis7.7621915
Mean585.736
Median Absolute Deviation (MAD)317.5
Skewness1.9884942
Sum292868
Variance270511.03
MonotonicityNot monotonic
2023-12-11T00:01:08.103622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 5
 
1.0%
115 3
 
0.6%
150 3
 
0.6%
144 3
 
0.6%
21 3
 
0.6%
792 3
 
0.6%
218 3
 
0.6%
52 3
 
0.6%
135 3
 
0.6%
722 2
 
0.4%
Other values (401) 469
93.8%
ValueCountFrequency (%)
0 2
0.4%
1 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
10 2
0.4%
11 2
0.4%
12 2
0.4%
16 1
0.2%
ValueCountFrequency (%)
3905 1
0.2%
3771 1
0.2%
3605 1
0.2%
2747 1
0.2%
2102 1
0.2%
1889 1
0.2%
1850 1
0.2%
1795 1
0.2%
1787 1
0.2%
1772 1
0.2%

지
Real number (ℝ)

ZEROS 

Distinct68
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.304
Minimum0
Maximum766
Zeros198
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:08.650847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile55.2
Maximum766
Range766
Interquartile range (IQR)7

Descriptive statistics

Standard deviation58.346622
Coefficient of variation (CV)3.8125079
Kurtosis79.965357
Mean15.304
Median Absolute Deviation (MAD)1
Skewness8.1037449
Sum7652
Variance3404.3282
MonotonicityNot monotonic
2023-12-11T00:01:09.026340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 198
39.6%
1 66
 
13.2%
2 34
 
6.8%
3 29
 
5.8%
4 16
 
3.2%
5 12
 
2.4%
6 12
 
2.4%
9 10
 
2.0%
10 9
 
1.8%
7 9
 
1.8%
Other values (58) 105
21.0%
ValueCountFrequency (%)
0 198
39.6%
1 66
 
13.2%
2 34
 
6.8%
3 29
 
5.8%
4 16
 
3.2%
5 12
 
2.4%
6 12
 
2.4%
7 9
 
1.8%
8 3
 
0.6%
9 10
 
2.0%
ValueCountFrequency (%)
766 1
0.2%
488 1
0.2%
480 1
0.2%
469 1
0.2%
293 1
0.2%
275 1
0.2%
249 1
0.2%
235 1
0.2%
155 1
0.2%
154 1
0.2%

특수지_명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing493
Missing (%)98.6%
Memory size4.0 KiB
2023-12-11T00:01:09.459129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length10.142857
Min length4

Characters and Unicode

Total characters71
Distinct characters40
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row39브럭
2nd row죽전택지개발지구 31-1블럭
3rd row동탄2신도시
4th row구월보금자리지구
5th row57블럭 1롯트
ValueCountFrequency (%)
39브럭 1
10.0%
죽전택지개발지구 1
10.0%
31-1블럭 1
10.0%
동탄2신도시 1
10.0%
구월보금자리지구 1
10.0%
57블럭 1
10.0%
1롯트 1
10.0%
동백택지개발지구 1
10.0%
c10-2블럭 1
10.0%
경북도청이전신도시사업지구내 1
10.0%
2023-12-11T00:01:10.143130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 6
 
8.5%
구 5
 
7.0%
1 4
 
5.6%
럭 4
 
5.6%
도 3
 
4.2%
블 3
 
4.2%
3
 
4.2%
3 2
 
2.8%
신 2
 
2.8%
2 2
 
2.8%
Other values (30) 37
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
74.6%
Decimal Number 12
 
16.9%
Space Separator 3
 
4.2%
Dash Punctuation 2
 
2.8%
Uppercase Letter 1
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 6
 
11.3%
구 5
 
9.4%
럭 4
 
7.5%
도 3
 
5.7%
블 3
 
5.7%
신 2
 
3.8%
동 2
 
3.8%
시 2
 
3.8%
발 2
 
3.8%
택 2
 
3.8%
Other values (20) 22
41.5%
Decimal Number
ValueCountFrequency (%)
1 4
33.3%
3 2
16.7%
2 2
16.7%
0 1
 
8.3%
5 1
 
8.3%
7 1
 
8.3%
9 1
 
8.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
74.6%
Common 17
 
23.9%
Latin 1
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 6
 
11.3%
구 5
 
9.4%
럭 4
 
7.5%
도 3
 
5.7%
블 3
 
5.7%
신 2
 
3.8%
동 2
 
3.8%
시 2
 
3.8%
발 2
 
3.8%
택 2
 
3.8%
Other values (20) 22
41.5%
Common
ValueCountFrequency (%)
1 4
23.5%
3
17.6%
3 2
11.8%
2 2
11.8%
- 2
11.8%
0 1
 
5.9%
5 1
 
5.9%
7 1
 
5.9%
9 1
 
5.9%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
74.6%
ASCII 18
 
25.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 6
 
11.3%
구 5
 
9.4%
럭 4
 
7.5%
도 3
 
5.7%
블 3
 
5.7%
신 2
 
3.8%
동 2
 
3.8%
시 2
 
3.8%
발 2
 
3.8%
택 2
 
3.8%
Other values (20) 22
41.5%
ASCII
ValueCountFrequency (%)
1 4
22.2%
3
16.7%
3 2
11.1%
2 2
11.1%
- 2
11.1%
0 1
 
5.6%
C 1
 
5.6%
5 1
 
5.6%
7 1
 
5.6%
9 1
 
5.6%

블록
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing498
Missing (%)99.6%
Memory size4.0 KiB
2023-12-11T00:01:10.429401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters8
Distinct characters8
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowA27블록
2nd row근상5
ValueCountFrequency (%)
a27블록 1
50.0%
근상5 1
50.0%
2023-12-11T00:01:11.060049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1
12.5%
2 1
12.5%
7 1
12.5%
블 1
12.5%
록 1
12.5%
근 1
12.5%
상 1
12.5%
5 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4
50.0%
Decimal Number 3
37.5%
Uppercase Letter 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
블 1
25.0%
록 1
25.0%
근 1
25.0%
상 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
7 1
33.3%
5 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
50.0%
Common 3
37.5%
Latin 1
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
블 1
25.0%
록 1
25.0%
근 1
25.0%
상 1
25.0%
Common
ValueCountFrequency (%)
2 1
33.3%
7 1
33.3%
5 1
33.3%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
50.0%
Hangul 4
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1
25.0%
2 1
25.0%
7 1
25.0%
5 1
25.0%
Hangul
ValueCountFrequency (%)
블 1
25.0%
록 1
25.0%
근 1
25.0%
상 1
25.0%

로트
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

외필지_수
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.338
Minimum0
Maximum17
Zeros416
Zeros (%)83.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:11.316349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2453344
Coefficient of variation (CV)3.6844212
Kurtosis89.852747
Mean0.338
Median Absolute Deviation (MAD)0
Skewness8.2395347
Sum169
Variance1.5508577
MonotonicityNot monotonic
2023-12-11T00:01:11.567840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 416
83.2%
1 52
 
10.4%
2 17
 
3.4%
3 8
 
1.6%
4 2
 
0.4%
7 1
 
0.2%
13 1
 
0.2%
6 1
 
0.2%
8 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
0 416
83.2%
1 52
 
10.4%
2 17
 
3.4%
3 8
 
1.6%
4 2
 
0.4%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
13 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
17 1
 
0.2%
13 1
 
0.2%
8 1
 
0.2%
7 1
 
0.2%
6 1
 
0.2%
4 2
 
0.4%
3 8
 
1.6%
2 17
 
3.4%
1 52
 
10.4%
0 416
83.2%

새주소_도로_코드
Real number (ℝ)

MISSING 

Distinct437
Distinct (%)98.4%
Missing56
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean3.4850239 × 1011
Minimum1.111031 × 1011
Maximum5.0130335 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:12.371257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111031 × 1011
5-th percentile1.1305412 × 1011
Q12.8010397 × 1011
median4.1220319 × 1011
Q34.4225424 × 1011
95-th percentile4.8250334 × 1011
Maximum5.0130335 × 1011
Range3.9020025 × 1011
Interquartile range (IQR)1.6215027 × 1011

Descriptive statistics

Standard deviation1.2518875 × 1011
Coefficient of variation (CV)0.3592192
Kurtosis-0.65144454
Mean3.4850239 × 1011
Median Absolute Deviation (MAD)6.9031596 × 1010
Skewness-0.81125125
Sum1.5473506 × 1014
Variance1.5672224 × 1022
MonotonicityNot monotonic
2023-12-11T00:01:12.737532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416104433275 2
 
0.4%
412853193017 2
 
0.4%
292004289101 2
 
0.4%
411503181049 2
 
0.4%
291704286341 2
 
0.4%
411114322072 2
 
0.4%
113504130048 2
 
0.4%
467204661088 1
 
0.2%
415504424652 1
 
0.2%
311404310248 1
 
0.2%
Other values (427) 427
85.4%
(Missing) 56
 
11.2%
ValueCountFrequency (%)
111103100021 1
0.2%
111403005009 1
0.2%
111403101004 1
0.2%
111404103109 1
0.2%
111703102005 1
0.2%
111704106098 1
0.2%
111704106144 1
0.2%
111704106471 1
0.2%
112003005011 1
0.2%
112154112293 1
0.2%
ValueCountFrequency (%)
501303350194 1
0.2%
501303350174 1
0.2%
501303350034 1
0.2%
501104848475 1
0.2%
501104848129 1
0.2%
501103349041 1
0.2%
488804841361 1
0.2%
488504832184 1
0.2%
488503344002 1
0.2%
488404829253 1
0.2%

새주소_법정동_코드
Real number (ℝ)

MISSING 

Distinct100
Distinct (%)22.3%
Missing51
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean15685.555
Minimum10101
Maximum42001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:13.137555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile10101
Q110401
median11001
Q314501
95-th percentile35001
Maximum42001
Range31900
Interquartile range (IQR)4100

Descriptive statistics

Standard deviation8761.6649
Coefficient of variation (CV)0.55858178
Kurtosis0.62845565
Mean15685.555
Median Absolute Deviation (MAD)800
Skewness1.4706273
Sum7042814
Variance76766772
MonotonicityNot monotonic
2023-12-11T00:01:13.526615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10101 46
 
9.2%
10401 25
 
5.0%
10201 25
 
5.0%
25001 24
 
4.8%
10701 19
 
3.8%
10301 18
 
3.6%
10501 17
 
3.4%
11101 16
 
3.2%
10601 14
 
2.8%
10801 13
 
2.6%
Other values (90) 232
46.4%
(Missing) 51
 
10.2%
ValueCountFrequency (%)
10101 46
9.2%
10201 25
5.0%
10202 7
 
1.4%
10301 18
 
3.6%
10302 9
 
1.8%
10303 1
 
0.2%
10401 25
5.0%
10402 2
 
0.4%
10404 1
 
0.2%
10501 17
 
3.4%
ValueCountFrequency (%)
42001 1
 
0.2%
41001 1
 
0.2%
40001 1
 
0.2%
39001 4
0.8%
38002 1
 
0.2%
38001 2
 
0.4%
37005 1
 
0.2%
37001 6
1.2%
36001 3
0.6%
35002 2
 
0.4%

새주소_지상지하_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

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 (%)
0 500
100.0%

Length

2023-12-11T00:01:13.894057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:14.148249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

새주소_본_번
Real number (ℝ)

MISSING 

Distinct198
Distinct (%)43.7%
Missing47
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean168.14349
Minimum0
Maximum7740
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:14.381292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q120
median47
Q3121
95-th percentile531
Maximum7740
Range7740
Interquartile range (IQR)101

Descriptive statistics

Standard deviation582.22721
Coefficient of variation (CV)3.4626807
Kurtosis119.76151
Mean168.14349
Median Absolute Deviation (MAD)34
Skewness10.148228
Sum76169
Variance338988.52
MonotonicityNot monotonic
2023-12-11T00:01:14.693672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 10
 
2.0%
15 10
 
2.0%
13 10
 
2.0%
23 9
 
1.8%
14 9
 
1.8%
5 9
 
1.8%
19 8
 
1.6%
11 8
 
1.6%
25 8
 
1.6%
10 8
 
1.6%
Other values (188) 364
72.8%
(Missing) 47
 
9.4%
ValueCountFrequency (%)
0 1
 
0.2%
1 2
 
0.4%
2 5
1.0%
3 2
 
0.4%
4 3
 
0.6%
5 9
1.8%
6 4
0.8%
7 3
 
0.6%
8 2
 
0.4%
9 5
1.0%
ValueCountFrequency (%)
7740 1
0.2%
7372 1
0.2%
4236 1
0.2%
2066 1
0.2%
1784 1
0.2%
1720 1
0.2%
1684 1
0.2%
1456 1
0.2%
1325 1
0.2%
1220 1
0.2%

새주소_부_번
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)7.0%
Missing60
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean2.7227273
Minimum0
Maximum97
Zeros343
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:15.063647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.1442717
Coefficient of variation (CV)3.3584971
Kurtosis56.362098
Mean2.7227273
Median Absolute Deviation (MAD)0
Skewness6.5901816
Sum1198
Variance83.617706
MonotonicityNot monotonic
2023-12-11T00:01:15.380715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 343
68.6%
1 14
 
2.8%
3 9
 
1.8%
2 8
 
1.6%
5 6
 
1.2%
12 5
 
1.0%
7 5
 
1.0%
9 4
 
0.8%
11 4
 
0.8%
13 4
 
0.8%
Other values (21) 38
 
7.6%
(Missing) 60
 
12.0%
ValueCountFrequency (%)
0 343
68.6%
1 14
 
2.8%
2 8
 
1.6%
3 9
 
1.8%
4 1
 
0.2%
5 6
 
1.2%
6 2
 
0.4%
7 5
 
1.0%
8 4
 
0.8%
9 4
 
0.8%
ValueCountFrequency (%)
97 1
 
0.2%
95 1
 
0.2%
70 1
 
0.2%
42 1
 
0.2%
39 1
 
0.2%
32 1
 
0.2%
30 1
 
0.2%
27 4
0.8%
22 1
 
0.2%
21 1
 
0.2%

지역_코드
Categorical

IMBALANCE 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
427 
1020
 
16
1022
 
8
0230
 
7
1021
 
5
Other values (17)
 
37

Length

Max length6
Median length4
Mean length4.032
Min length4

Unique

Unique8 ?
Unique (%)1.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0240

Common Values

ValueCountFrequency (%)
<NA> 427
85.4%
1020 16
 
3.2%
1022 8
 
1.6%
0230 7
 
1.4%
1021 5
 
1.0%
0240 5
 
1.0%
1330 5
 
1.0%
0300 4
 
0.8%
UQB100 4
 
0.8%
1120 3
 
0.6%
Other values (12) 16
 
3.2%

Length

2023-12-11T00:01:15.708555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 427
85.4%
1020 16
 
3.2%
1022 8
 
1.6%
0230 7
 
1.4%
1021 5
 
1.0%
0240 5
 
1.0%
1330 5
 
1.0%
0300 4
 
0.8%
uqb100 4
 
0.8%
1120 3
 
0.6%
Other values (12) 16
 
3.2%

지구_코드
Text

MISSING 

Distinct10
Distinct (%)71.4%
Missing486
Missing (%)97.2%
Memory size4.0 KiB
2023-12-11T00:01:16.032264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.7142857
Min length3

Characters and Unicode

Total characters52
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)50.0%

Sample

1st row980
2nd row260
3rd row101
4th row103
5th row210
ValueCountFrequency (%)
980 3
21.4%
112 2
14.3%
uqm110 2
14.3%
260 1
 
7.1%
101 1
 
7.1%
103 1
 
7.1%
210 1
 
7.1%
uqi100 1
 
7.1%
1021 1
 
7.1%
160 1
 
7.1%
2023-12-11T00:01:16.717143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16
30.8%
0 13
25.0%
2 5
 
9.6%
9 3
 
5.8%
8 3
 
5.8%
U 3
 
5.8%
Q 3
 
5.8%
M 2
 
3.8%
6 2
 
3.8%
3 1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43
82.7%
Uppercase Letter 9
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
37.2%
0 13
30.2%
2 5
 
11.6%
9 3
 
7.0%
8 3
 
7.0%
6 2
 
4.7%
3 1
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
U 3
33.3%
Q 3
33.3%
M 2
22.2%
I 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 43
82.7%
Latin 9
 
17.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
37.2%
0 13
30.2%
2 5
 
11.6%
9 3
 
7.0%
8 3
 
7.0%
6 2
 
4.7%
3 1
 
2.3%
Latin
ValueCountFrequency (%)
U 3
33.3%
Q 3
33.3%
M 2
22.2%
I 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
30.8%
0 13
25.0%
2 5
 
9.6%
9 3
 
5.8%
8 3
 
5.8%
U 3
 
5.8%
Q 3
 
5.8%
M 2
 
3.8%
6 2
 
3.8%
3 1
 
1.9%

구역_코드
Categorical

IMBALANCE 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
482 
301
 
4
070
 
3
170
 
3
UQQ320
 
2
Other values (4)
 
6

Length

Max length6
Median length4
Mean length3.994
Min length3

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 482
96.4%
301 4
 
0.8%
070 3
 
0.6%
170 3
 
0.6%
UQQ320 2
 
0.4%
UEA110 2
 
0.4%
980 2
 
0.4%
UQQ300 1
 
0.2%
050 1
 
0.2%

Length

2023-12-11T00:01:16.994564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:17.262835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 482
96.4%
301 4
 
0.8%
070 3
 
0.6%
170 3
 
0.6%
uqq320 2
 
0.4%
uea110 2
 
0.4%
980 2
 
0.4%
uqq300 1
 
0.2%
050 1
 
0.2%

지역_코드_명
Categorical

IMBALANCE 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
421 
일반주거지역
 
16
제2종일반주거지역
 
14
농림지역
 
7
계획관리지역
 
7
Other values (13)
 
35

Length

Max length15
Median length4
Mean length4.356
Min length4

Unique

Unique5 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 421
84.2%
일반주거지역 16
 
3.2%
제2종일반주거지역 14
 
2.8%
농림지역 7
 
1.4%
계획관리지역 7
 
1.4%
관리지역 6
 
1.2%
일반상업지역 5
 
1.0%
준농림지역 5
 
1.0%
제1종일반주거지역 4
 
0.8%
기타지역 3
 
0.6%
Other values (8) 12
 
2.4%

Length

2023-12-11T00:01:17.607916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 421
84.2%
일반주거지역 16
 
3.2%
제2종일반주거지역 14
 
2.8%
농림지역 7
 
1.4%
계획관리지역 7
 
1.4%
관리지역 6
 
1.2%
일반상업지역 5
 
1.0%
준농림지역 5
 
1.0%
제1종일반주거지역 4
 
0.8%
자연녹지지역 3
 
0.6%
Other values (8) 12
 
2.4%

지구_코드_명
Text

MISSING 

Distinct9
Distinct (%)81.8%
Missing489
Missing (%)97.8%
Memory size4.0 KiB
2023-12-11T00:01:17.932138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.0909091
Min length4

Characters and Unicode

Total characters67
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)63.6%

Sample

1st row고도지구
2nd row준보전임지
3rd row중심지미관지구
4th row최고고도지구
5th row택지개발지구
ValueCountFrequency (%)
중심지미관지구 2
18.2%
최고고도지구 2
18.2%
고도지구 1
9.1%
준보전임지 1
9.1%
택지개발지구 1
9.1%
주차장정비지구 1
9.1%
방화지구 1
9.1%
역사문화미관지구 1
9.1%
용도지구미지정 1
9.1%
2023-12-11T00:01:18.546751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 15
22.4%
구 10
14.9%
고 5
 
7.5%
도 4
 
6.0%
미 4
 
6.0%
관 3
 
4.5%
화 2
 
3.0%
심 2
 
3.0%
중 2
 
3.0%
최 2
 
3.0%
Other values (17) 18
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 15
22.4%
구 10
14.9%
고 5
 
7.5%
도 4
 
6.0%
미 4
 
6.0%
관 3
 
4.5%
화 2
 
3.0%
심 2
 
3.0%
중 2
 
3.0%
최 2
 
3.0%
Other values (17) 18
26.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 15
22.4%
구 10
14.9%
고 5
 
7.5%
도 4
 
6.0%
미 4
 
6.0%
관 3
 
4.5%
화 2
 
3.0%
심 2
 
3.0%
중 2
 
3.0%
최 2
 
3.0%
Other values (17) 18
26.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 15
22.4%
구 10
14.9%
고 5
 
7.5%
도 4
 
6.0%
미 4
 
6.0%
관 3
 
4.5%
화 2
 
3.0%
심 2
 
3.0%
중 2
 
3.0%
최 2
 
3.0%
Other values (17) 18
26.9%

구역_코드_명
Text

MISSING 

Distinct9
Distinct (%)50.0%
Missing482
Missing (%)96.4%
Memory size4.0 KiB
2023-12-11T00:01:18.864179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length8.1111111
Min length6

Characters and Unicode

Total characters146
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)22.2%

Sample

1st row용도구역미지정
2nd row개발제한구역
3rd row제한 보호구역
4th row용도구역미지정
5th row농업진흥구역
ValueCountFrequency (%)
용도구역미지정 4
21.1%
개발제한구역 3
15.8%
농업진흥구역 3
15.8%
제2종지구단위계획구역 2
10.5%
제1종지구단위계획구역 2
10.5%
제한 1
 
5.3%
보호구역 1
 
5.3%
농업보호구역 1
 
5.3%
전술항공작전기지의비행안전제6구역 1
 
5.3%
가축사육제한구역 1
 
5.3%
2023-12-11T00:01:19.452354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 22
 
15.1%
역 18
 
12.3%
제 10
 
6.8%
지 9
 
6.2%
한 5
 
3.4%
용 4
 
2.7%
업 4
 
2.7%
도 4
 
2.7%
계 4
 
2.7%
위 4
 
2.7%
Other values (30) 62
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 140
95.9%
Decimal Number 5
 
3.4%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 22
 
15.7%
역 18
 
12.9%
제 10
 
7.1%
지 9
 
6.4%
한 5
 
3.6%
용 4
 
2.9%
업 4
 
2.9%
도 4
 
2.9%
계 4
 
2.9%
위 4
 
2.9%
Other values (26) 56
40.0%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
6 1
20.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 140
95.9%
Common 6
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 22
 
15.7%
역 18
 
12.9%
제 10
 
7.1%
지 9
 
6.4%
한 5
 
3.6%
용 4
 
2.9%
업 4
 
2.9%
도 4
 
2.9%
계 4
 
2.9%
위 4
 
2.9%
Other values (26) 56
40.0%
Common
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
6 1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 140
95.9%
ASCII 6
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 22
 
15.7%
역 18
 
12.9%
제 10
 
7.1%
지 9
 
6.4%
한 5
 
3.6%
용 4
 
2.9%
업 4
 
2.9%
도 4
 
2.9%
계 4
 
2.9%
위 4
 
2.9%
Other values (26) 56
40.0%
ASCII
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
6 1
16.7%
1
16.7%

생성_일자
Real number (ℝ)

Distinct300
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20125811
Minimum20090317
Maximum20160601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:19.749570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090317
5-th percentile20090320
Q120110420
median20121171
Q320150112
95-th percentile20160407
Maximum20160601
Range70284
Interquartile range (IQR)39691.75

Descriptive statistics

Standard deviation22263.793
Coefficient of variation (CV)0.0011062309
Kurtosis-1.2287904
Mean20125811
Median Absolute Deviation (MAD)19557.5
Skewness-0.0042091679
Sum1.0062905 × 1010
Variance4.9567648 × 108
MonotonicityNot monotonic
2023-12-11T00:01:20.081450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110420 23
 
4.6%
20090320 21
 
4.2%
20090319 16
 
3.2%
20111117 14
 
2.8%
20110415 13
 
2.6%
20111021 8
 
1.6%
20111123 7
 
1.4%
20110417 6
 
1.2%
20111125 5
 
1.0%
20111124 5
 
1.0%
Other values (290) 382
76.4%
ValueCountFrequency (%)
20090317 3
 
0.6%
20090319 16
3.2%
20090320 21
4.2%
20090321 2
 
0.4%
20090323 2
 
0.4%
20090512 1
 
0.2%
20090523 1
 
0.2%
20090703 1
 
0.2%
20090707 1
 
0.2%
20090725 1
 
0.2%
ValueCountFrequency (%)
20160601 1
0.2%
20160531 1
0.2%
20160528 1
0.2%
20160526 2
0.4%
20160524 2
0.4%
20160521 1
0.2%
20160519 1
0.2%
20160517 1
0.2%
20160514 2
0.4%
20160513 1
0.2%

Sample

관리_건축물대장_PK관리_상위_건축물대장_PK대장_구분_코드대장_구분_코드_명대장_종류_코드대장_종류_코드_명대지_위치도로명_대지_위치건물_명시군구_코드법정동_코드대지_구분_코드번지특수지_명블록로트외필지_수새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드새주소_본_번새주소_부_번지역_코드지구_코드구역_코드지역_코드_명지구_코드_명구역_코드_명생성_일자
042170-1529941360-152461일반2일반건축물경상남도 창녕군 창녕읍 말흘리 272-2번지세종특별자치시 달빛1로 39하소주공아파트2단지41133137000562<NA><NA><NA>0468304685591<NA>0415<NA><NA><NA><NA><NA><NA>20160325
142780-1689848127-10000478762일반4일반건축물제주특별자치도 제주시 조천읍 신촌리 1795번지전라남도 강진군 신흥길 64-21미영리치타운103412201470001662<NA><NA><NA>1112904121667109010170<NA><NA><NA><NA><NA><NA>20110617
241290-15582<NA>1일반2전유부경상북도 칠곡군 북삼읍 인평리 271-1번지경기도 평택시 지산로 115<NA>4128511900012533<NA><NA><NA>141199318601110402073720<NA><NA><NA><NA><NA><NA>20140703
346130-4511541465-1824492집합2일반건축물대구광역시 달성군 논공읍 남리 573-2번지대구광역시 중구 달구벌대로 1950Pico빌라42110105000961<NA><NA><NA>1112304115269107010147<NA><NA><NA><NA><NA><NA>20141101
411530-9277744810-31252일반4일반건축물서울특별시 양천구 목동 911번지<NA>효삼빌라4373025022005<NA><NA><NA>04888048413612500105220240<NA><NA>일반상업지역<NA><NA>20090319
541480-10027256246150-260112일반4일반건축물인천광역시 남구 숭의동 159-1번지경기도 고양시 일산동구 강송로 119<NA>48125102000380<NA><NA><NA>0<NA>106050303<NA><NA><NA>자연녹지지역<NA><NA>20091006
641117-10029386811710-12522일반4전유부서울특별시 영등포구 대림동 1101-1번지경기도 오산시 경기대로 24-41<NA>3017025024039211<NA><NA><NA>1281704253680110010151<NA><NA><NA><NA><NA><NA>20150606
711560-10020730248240-1001761722집합4전유부대구광역시 달서구 진천동 602번지광주광역시 북구 일곡마을로 50<NA>4146110400010874<NA><NA><NA>0272903147036<NA>0300<NA><NA><NA><NA><NA><NA>20140711
841390-3670611110-220422집합4전유부부산광역시 동래구 온천동 707번지경기도 안산시 상록구 반월로 47강남 지웰홈스457901070009843<NA><NA><NA>0441314547736123010280<NA><NA><NA><NA><NA><NA>20120821
911380-10025881041197-1623162집합4전유부인천광역시 남구 주안동 1557-81번지전라북도 군산시 축동로 42<NA>5011031026016501<NA><NA><NA>04136031970181080101291020<NA><NA><NA><NA><NA>20110420
관리_건축물대장_PK관리_상위_건축물대장_PK대장_구분_코드대장_구분_코드_명대장_종류_코드대장_종류_코드_명대지_위치도로명_대지_위치건물_명시군구_코드법정동_코드대지_구분_코드번지특수지_명블록로트외필지_수새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드새주소_본_번새주소_부_번지역_코드지구_코드구역_코드지역_코드_명지구_코드_명구역_코드_명생성_일자
49048250-258411320-169682집합4전유부서울특별시 강서구 방화동 831번지서울특별시 중랑구 신내역로1길 85더시티세븐42110250340280<NA><NA><NA>0113054124109256020160<NA><NA><NA><NA><NA><NA>20111125
49141150-3844811170-45851일반4일반건축물경상북도 구미시 형곡동 151-10번지제주특별자치도 제주시 인다1길 20<NA>26500108000218144<NA>근상5<NA>04511132660153800103213<NA><NA><NA><NA><NA><NA>20101110
49245800-1796311530-151572집합2전유부서울특별시 영등포구 영등포동 645-70번지경기도 군포시 수리산로203번안길 41한양빌라4372013000045115<NA><NA><NA>17421504460085112010150<NA><NA><NA>제2종일반주거지역<NA><NA>20090317
49311290-10020857511200-1002091352집합4전유부경기도 수원시 팔달구 지동 401-10번지제주특별자치도 제주시 부룡수길 61-1광명 두산위브 트레지움4119510300035550경북도청이전신도시사업지구내<NA><NA>0<NA>104010410<NA><NA><NA><NA><NA><NA>20141025
49441150-7450642150-221982집합4전유부울산광역시 울주군 온양읍 대안리 417번지경기도 수원시 영통구 에듀타운로 101익수 수 아파트414101030001210<NA><NA><NA>04111331750421320109781022<NA><NA>제2종일반주거지역<NA><NA>20151007
49541830-10018672226260-69562집합4전유부경기도 오산시 원동 813-8번지경상북도 포항시 남구 구룡포길 108-2동양시멘트2826011300017721<NA><NA><NA>0291103162072107010250<NA><NA><NA><NA><NA><NA>20110308
49641281-4486428237-26672일반1전유부경상북도 성주군 수륜면 남은리 836번지서울특별시 강서구 초록마을로26길 35-1<NA>451901250007920<NA><NA><NA>0412204364220256010370<NA><NA><NA><NA><NA><NA>20160206
49746910-356928237-8381집합4전유부서울특별시 양천구 신정동 323-9번지경기도 성남시 분당구 판교원로 186<NA>4128111500073913<NA><NA><NA>0116204160364320010190<NA><NA><NA><NA><NA><NA>20150513
49841150-80182<NA>1집합4전유부경상남도 남해군 남해읍 아산리 526번지<NA>효성프라자4418012000040<NA><NA><NA>02914031600581100104013<NA><NA><NA><NA><NA><NA>20140623
49941360-94466<NA>2집합4전유부경기도 안성시 공도읍 진사리 36-1번지서울특별시 영등포구 신길로43길 3-4<NA>412201040003670<NA><NA><NA>0<NA>36001090<NA><NA><NA><NA><NA><NA>20160315