Overview

Dataset statistics

Number of variables44
Number of observations2345
Missing cells37348
Missing cells (%)36.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory865.8 KiB
Average record size in memory378.1 B

Variable types

Categorical13
Text7
DateTime4
Unsupported10
Numeric9
Boolean1

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author용산구
URLhttps://data.seoul.go.kr/dataList/OA-18079/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
남성종사자수 is highly imbalanced (65.2%)Imbalance
여성종사자수 is highly imbalanced (65.2%)Imbalance
급수시설구분명 is highly imbalanced (86.1%)Imbalance
총인원 is highly imbalanced (65.4%)Imbalance
공장생산직종업원수 is highly imbalanced (62.5%)Imbalance
인허가취소일자 has 2345 (100.0%) missing valuesMissing
폐업일자 has 807 (34.4%) missing valuesMissing
휴업시작일자 has 2345 (100.0%) missing valuesMissing
휴업종료일자 has 2345 (100.0%) missing valuesMissing
재개업일자 has 2345 (100.0%) missing valuesMissing
전화번호 has 1267 (54.0%) missing valuesMissing
소재지면적 has 688 (29.3%) missing valuesMissing
도로명주소 has 383 (16.3%) missing valuesMissing
도로명우편번호 has 389 (16.6%) missing valuesMissing
업태구분명 has 2345 (100.0%) missing valuesMissing
영업장주변구분명 has 2345 (100.0%) missing valuesMissing
등급구분명 has 2345 (100.0%) missing valuesMissing
본사종업원수 has 1753 (74.8%) missing valuesMissing
공장사무직종업원수 has 1660 (70.8%) missing valuesMissing
공장판매직종업원수 has 1609 (68.6%) missing valuesMissing
보증액 has 1898 (80.9%) missing valuesMissing
월세액 has 1894 (80.8%) missing valuesMissing
다중이용업소여부 has 758 (32.3%) missing valuesMissing
시설총규모 has 758 (32.3%) missing valuesMissing
전통업소지정번호 has 2345 (100.0%) missing valuesMissing
전통업소주된음식 has 2345 (100.0%) missing valuesMissing
홈페이지 has 2345 (100.0%) missing valuesMissing
도로명우편번호 is highly skewed (γ1 = 30.59629376)Skewed
본사종업원수 is highly skewed (γ1 = 24.17805566)Skewed
보증액 is highly skewed (γ1 = 20.96617155)Skewed
시설총규모 is highly skewed (γ1 = 23.96578264)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
영업장주변구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
등급구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported
본사종업원수 has 572 (24.4%) zerosZeros
공장사무직종업원수 has 474 (20.2%) zerosZeros
공장판매직종업원수 has 295 (12.6%) zerosZeros
보증액 has 343 (14.6%) zerosZeros
월세액 has 344 (14.7%) zerosZeros
시설총규모 has 1541 (65.7%) zerosZeros

Reproduction

Analysis started2024-05-11 05:22:12.287200
Analysis finished2024-05-11 05:22:15.493313
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3020000
2345 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000
2nd row3020000
3rd row3020000
4th row3020000
5th row3020000

Common Values

ValueCountFrequency (%)
3020000 2345
100.0%

Length

2024-05-11T05:22:15.611437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:15.942078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000 2345
100.0%

관리번호
Text

UNIQUE 

Distinct2345
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2024-05-11T05:22:16.349436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters51590
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

Unique2345 ?
Unique (%)100.0%

Sample

1st row3020000-134-2004-00001
2nd row3020000-134-2004-00002
3rd row3020000-134-2004-00004
4th row3020000-134-2004-00005
5th row3020000-134-2004-00006
ValueCountFrequency (%)
3020000-134-2004-00001 1
 
< 0.1%
3020000-134-2020-00069 1
 
< 0.1%
3020000-134-2020-00054 1
 
< 0.1%
3020000-134-2020-00055 1
 
< 0.1%
3020000-134-2020-00062 1
 
< 0.1%
3020000-134-2020-00056 1
 
< 0.1%
3020000-134-2020-00057 1
 
< 0.1%
3020000-134-2020-00058 1
 
< 0.1%
3020000-134-2020-00059 1
 
< 0.1%
3020000-134-2020-00060 1
 
< 0.1%
Other values (2335) 2335
99.6%
2024-05-11T05:22:17.482129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21884
42.4%
- 7035
 
13.6%
2 6390
 
12.4%
3 5502
 
10.7%
1 4661
 
9.0%
4 3160
 
6.1%
5 628
 
1.2%
6 628
 
1.2%
9 578
 
1.1%
7 570
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44555
86.4%
Dash Punctuation 7035
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21884
49.1%
2 6390
 
14.3%
3 5502
 
12.3%
1 4661
 
10.5%
4 3160
 
7.1%
5 628
 
1.4%
6 628
 
1.4%
9 578
 
1.3%
7 570
 
1.3%
8 554
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 7035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21884
42.4%
- 7035
 
13.6%
2 6390
 
12.4%
3 5502
 
10.7%
1 4661
 
9.0%
4 3160
 
6.1%
5 628
 
1.2%
6 628
 
1.2%
9 578
 
1.1%
7 570
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21884
42.4%
- 7035
 
13.6%
2 6390
 
12.4%
3 5502
 
10.7%
1 4661
 
9.0%
4 3160
 
6.1%
5 628
 
1.2%
6 628
 
1.2%
9 578
 
1.1%
7 570
 
1.1%
Distinct1653
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
Minimum2004-02-28 00:00:00
Maximum2024-05-03 00:00:00
2024-05-11T05:22:18.195777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:22:18.802909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
1538 
1
807 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1538
65.6%
1 807
34.4%

Length

2024-05-11T05:22:19.369001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:19.710194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1538
65.6%
1 807
34.4%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
폐업
1538 
영업/정상
807 

Length

Max length5
Median length2
Mean length3.0324094
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1538
65.6%
영업/정상 807
34.4%

Length

2024-05-11T05:22:20.053056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:20.505396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1538
65.6%
영업/정상 807
34.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2
1538 
1
807 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 1538
65.6%
1 807
34.4%

Length

2024-05-11T05:22:21.024209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:21.372737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1538
65.6%
1 807
34.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
폐업
1538 
영업
807 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1538
65.6%
영업 807
34.4%

Length

2024-05-11T05:22:21.788691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:22.200315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1538
65.6%
영업 807
34.4%

폐업일자
Date

MISSING 

Distinct1101
Distinct (%)71.6%
Missing807
Missing (%)34.4%
Memory size18.4 KiB
Minimum2004-06-18 00:00:00
Maximum2024-05-07 00:00:00
2024-05-11T05:22:22.658275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:22:23.245131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

전화번호
Text

MISSING 

Distinct1030
Distinct (%)95.5%
Missing1267
Missing (%)54.0%
Memory size18.4 KiB
2024-05-11T05:22:24.214675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.684601
Min length7

Characters and Unicode

Total characters11518
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

Unique1000 ?
Unique (%)92.8%

Sample

1st row02 7924371
2nd row02 7902220
3rd row027772 591
4th row02 7114166
5th row02 7166321
ValueCountFrequency (%)
02 726
33.5%
070 56
 
2.6%
790 17
 
0.8%
749 16
 
0.7%
0232848342 13
 
0.6%
792 12
 
0.6%
719 11
 
0.5%
796 11
 
0.5%
704 10
 
0.5%
797 10
 
0.5%
Other values (1132) 1283
59.3%
2024-05-11T05:22:25.748736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1990
17.3%
2 1698
14.7%
1459
12.7%
7 1422
12.3%
1 893
7.8%
9 767
 
6.7%
3 743
 
6.5%
5 686
 
6.0%
4 663
 
5.8%
8 646
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10059
87.3%
Space Separator 1459
 
12.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1990
19.8%
2 1698
16.9%
7 1422
14.1%
1 893
8.9%
9 767
 
7.6%
3 743
 
7.4%
5 686
 
6.8%
4 663
 
6.6%
8 646
 
6.4%
6 551
 
5.5%
Space Separator
ValueCountFrequency (%)
1459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11518
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1990
17.3%
2 1698
14.7%
1459
12.7%
7 1422
12.3%
1 893
7.8%
9 767
 
6.7%
3 743
 
6.5%
5 686
 
6.0%
4 663
 
5.8%
8 646
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1990
17.3%
2 1698
14.7%
1459
12.7%
7 1422
12.3%
1 893
7.8%
9 767
 
6.7%
3 743
 
6.5%
5 686
 
6.0%
4 663
 
5.8%
8 646
 
5.6%

소재지면적
Text

MISSING 

Distinct566
Distinct (%)34.2%
Missing688
Missing (%)29.3%
Memory size18.4 KiB
2024-05-11T05:22:26.736816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.7459264
Min length3

Characters and Unicode

Total characters7864
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

Unique432 ?
Unique (%)26.1%

Sample

1st row59.50
2nd row103.80
3rd row75.36
4th row66.00
5th row160.23
ValueCountFrequency (%)
3.30 250
 
15.1%
10.00 159
 
9.6%
00 69
 
4.2%
33.00 48
 
2.9%
6.60 42
 
2.5%
3.00 38
 
2.3%
5.00 37
 
2.2%
15.00 30
 
1.8%
30.00 20
 
1.2%
20.00 20
 
1.2%
Other values (556) 944
57.0%
2024-05-11T05:22:28.309392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2505
31.9%
. 1657
21.1%
3 927
 
11.8%
1 625
 
7.9%
2 392
 
5.0%
6 371
 
4.7%
5 367
 
4.7%
4 304
 
3.9%
9 285
 
3.6%
8 242
 
3.1%
Other values (2) 189
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6204
78.9%
Other Punctuation 1660
 
21.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2505
40.4%
3 927
 
14.9%
1 625
 
10.1%
2 392
 
6.3%
6 371
 
6.0%
5 367
 
5.9%
4 304
 
4.9%
9 285
 
4.6%
8 242
 
3.9%
7 186
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 1657
99.8%
, 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 7864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2505
31.9%
. 1657
21.1%
3 927
 
11.8%
1 625
 
7.9%
2 392
 
5.0%
6 371
 
4.7%
5 367
 
4.7%
4 304
 
3.9%
9 285
 
3.6%
8 242
 
3.1%
Other values (2) 189
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2505
31.9%
. 1657
21.1%
3 927
 
11.8%
1 625
 
7.9%
2 392
 
5.0%
6 371
 
4.7%
5 367
 
4.7%
4 304
 
3.9%
9 285
 
3.6%
8 242
 
3.1%
Other values (2) 189
 
2.4%
Distinct287
Distinct (%)12.2%
Missing1
Missing (%)< 0.1%
Memory size18.4 KiB
2024-05-11T05:22:29.472770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2056314
Min length6

Characters and Unicode

Total characters14546
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

Unique65 ?
Unique (%)2.8%

Sample

1st row140897
2nd row140160
3rd row140889
4th row140801
5th row140883
ValueCountFrequency (%)
140832 80
 
3.4%
140879 73
 
3.1%
140871 48
 
2.0%
140780 44
 
1.9%
140823 42
 
1.8%
140847 40
 
1.7%
140893 38
 
1.6%
140887 36
 
1.5%
140012 36
 
1.5%
140863 35
 
1.5%
Other values (277) 1872
79.9%
2024-05-11T05:22:31.929718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3294
22.6%
1 3029
20.8%
4 2694
18.5%
8 1779
12.2%
7 836
 
5.7%
2 635
 
4.4%
9 601
 
4.1%
3 599
 
4.1%
- 482
 
3.3%
6 316
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14064
96.7%
Dash Punctuation 482
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3294
23.4%
1 3029
21.5%
4 2694
19.2%
8 1779
12.6%
7 836
 
5.9%
2 635
 
4.5%
9 601
 
4.3%
3 599
 
4.3%
6 316
 
2.2%
5 281
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14546
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3294
22.6%
1 3029
20.8%
4 2694
18.5%
8 1779
12.2%
7 836
 
5.7%
2 635
 
4.4%
9 601
 
4.1%
3 599
 
4.1%
- 482
 
3.3%
6 316
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14546
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3294
22.6%
1 3029
20.8%
4 2694
18.5%
8 1779
12.2%
7 836
 
5.7%
2 635
 
4.4%
9 601
 
4.1%
3 599
 
4.1%
- 482
 
3.3%
6 316
 
2.2%
Distinct1425
Distinct (%)60.8%
Missing1
Missing (%)< 0.1%
Memory size18.4 KiB
2024-05-11T05:22:33.049275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length43
Mean length27.357082
Min length16

Characters and Unicode

Total characters64125
Distinct characters396
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

Unique1073 ?
Unique (%)45.8%

Sample

1st row서울특별시 용산구 효창동 *-***번지 ***호
2nd row서울특별시 용산구 남영동 **-*번지
3rd row서울특별시 용산구 한남동 ***-*번지
4th row서울특별시 용산구 갈월동 **-*번지 *층
5th row서울특별시 용산구 한강로*가 **-***번지 바울빌딩 *층
ValueCountFrequency (%)
서울특별시 2344
19.5%
용산구 2341
19.5%
번지 1250
10.4%
1076
 
8.9%
한강로*가 594
 
4.9%
434
 
3.6%
379
 
3.1%
원효로*가 208
 
1.7%
한남동 207
 
1.7%
이촌동 162
 
1.3%
Other values (790) 3037
25.2%
2024-05-11T05:22:36.081255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 12321
19.2%
11076
17.3%
2759
 
4.3%
2699
 
4.2%
2446
 
3.8%
2392
 
3.7%
2376
 
3.7%
2371
 
3.7%
2350
 
3.7%
2344
 
3.7%
Other values (386) 20991
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38118
59.4%
Other Punctuation 12363
 
19.3%
Space Separator 11076
 
17.3%
Dash Punctuation 1995
 
3.1%
Decimal Number 215
 
0.3%
Uppercase Letter 140
 
0.2%
Open Punctuation 95
 
0.1%
Close Punctuation 95
 
0.1%
Lowercase Letter 26
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2759
 
7.2%
2699
 
7.1%
2446
 
6.4%
2392
 
6.3%
2376
 
6.2%
2371
 
6.2%
2350
 
6.2%
2344
 
6.1%
1784
 
4.7%
1512
 
4.0%
Other values (338) 15085
39.6%
Uppercase Letter
ValueCountFrequency (%)
B 29
20.7%
S 17
12.1%
G 16
11.4%
A 16
11.4%
L 11
 
7.9%
C 9
 
6.4%
D 8
 
5.7%
E 6
 
4.3%
I 6
 
4.3%
T 5
 
3.6%
Other values (10) 17
12.1%
Decimal Number
ValueCountFrequency (%)
1 46
21.4%
2 35
16.3%
3 28
13.0%
8 19
8.8%
9 16
 
7.4%
4 16
 
7.4%
6 16
 
7.4%
0 14
 
6.5%
5 14
 
6.5%
7 11
 
5.1%
Other Punctuation
ValueCountFrequency (%)
* 12321
99.7%
, 33
 
0.3%
@ 3
 
< 0.1%
/ 2
 
< 0.1%
. 2
 
< 0.1%
& 1
 
< 0.1%
: 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 13
50.0%
c 6
23.1%
k 4
 
15.4%
t 1
 
3.8%
b 1
 
3.8%
d 1
 
3.8%
Space Separator
ValueCountFrequency (%)
11076
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1995
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38118
59.4%
Common 25841
40.3%
Latin 166
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2759
 
7.2%
2699
 
7.1%
2446
 
6.4%
2392
 
6.3%
2376
 
6.2%
2371
 
6.2%
2350
 
6.2%
2344
 
6.1%
1784
 
4.7%
1512
 
4.0%
Other values (338) 15085
39.6%
Latin
ValueCountFrequency (%)
B 29
17.5%
S 17
10.2%
G 16
9.6%
A 16
9.6%
e 13
 
7.8%
L 11
 
6.6%
C 9
 
5.4%
D 8
 
4.8%
E 6
 
3.6%
c 6
 
3.6%
Other values (16) 35
21.1%
Common
ValueCountFrequency (%)
* 12321
47.7%
11076
42.9%
- 1995
 
7.7%
( 95
 
0.4%
) 95
 
0.4%
1 46
 
0.2%
2 35
 
0.1%
, 33
 
0.1%
3 28
 
0.1%
8 19
 
0.1%
Other values (12) 98
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38117
59.4%
ASCII 26007
40.6%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 12321
47.4%
11076
42.6%
- 1995
 
7.7%
( 95
 
0.4%
) 95
 
0.4%
1 46
 
0.2%
2 35
 
0.1%
, 33
 
0.1%
B 29
 
0.1%
3 28
 
0.1%
Other values (38) 254
 
1.0%
Hangul
ValueCountFrequency (%)
2759
 
7.2%
2699
 
7.1%
2446
 
6.4%
2392
 
6.3%
2376
 
6.2%
2371
 
6.2%
2350
 
6.2%
2344
 
6.1%
1784
 
4.7%
1512
 
4.0%
Other values (337) 15084
39.6%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct1529
Distinct (%)77.9%
Missing383
Missing (%)16.3%
Memory size18.4 KiB
2024-05-11T05:22:37.178152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length49
Mean length36.738532
Min length22

Characters and Unicode

Total characters72081
Distinct characters402
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

Unique1294 ?
Unique (%)66.0%

Sample

1st row서울특별시 용산구 백범로**길 ** (효창동,***호)
2nd row서울특별시 용산구 두텁바위로 * (남영동)
3rd row서울특별시 용산구 서빙고로 ** (한강로*가,바울빌딩 *층)
4th row서울특별시 용산구 이태원로 *** (이태원동)
5th row서울특별시 용산구 청파로 ***, 지*층 (한강로*가)
ValueCountFrequency (%)
1986
14.6%
서울특별시 1962
14.4%
용산구 1959
14.4%
964
 
7.1%
760
 
5.6%
한강로*가 440
 
3.2%
268
 
2.0%
한강대로 174
 
1.3%
원효로*가 172
 
1.3%
청파로 170
 
1.3%
Other values (1032) 4733
34.8%
2024-05-11T05:22:38.989800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 12708
17.6%
11642
 
16.2%
2688
 
3.7%
, 2442
 
3.4%
2314
 
3.2%
2282
 
3.2%
2148
 
3.0%
) 2024
 
2.8%
( 2024
 
2.8%
2015
 
2.8%
Other values (392) 29794
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40211
55.8%
Other Punctuation 15156
 
21.0%
Space Separator 11642
 
16.2%
Close Punctuation 2024
 
2.8%
Open Punctuation 2024
 
2.8%
Dash Punctuation 425
 
0.6%
Decimal Number 318
 
0.4%
Uppercase Letter 235
 
0.3%
Lowercase Letter 43
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2688
 
6.7%
2314
 
5.8%
2282
 
5.7%
2148
 
5.3%
2015
 
5.0%
1991
 
5.0%
1984
 
4.9%
1969
 
4.9%
1962
 
4.9%
1779
 
4.4%
Other values (341) 19079
47.4%
Uppercase Letter
ValueCountFrequency (%)
B 90
38.3%
A 38
16.2%
C 16
 
6.8%
G 14
 
6.0%
S 14
 
6.0%
L 11
 
4.7%
E 11
 
4.7%
D 6
 
2.6%
I 6
 
2.6%
T 5
 
2.1%
Other values (11) 24
 
10.2%
Decimal Number
ValueCountFrequency (%)
1 73
23.0%
2 46
14.5%
0 44
13.8%
3 44
13.8%
5 29
 
9.1%
4 25
 
7.9%
7 18
 
5.7%
6 17
 
5.3%
8 12
 
3.8%
9 10
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
e 15
34.9%
c 7
16.3%
l 5
 
11.6%
a 5
 
11.6%
b 5
 
11.6%
k 3
 
7.0%
p 1
 
2.3%
m 1
 
2.3%
d 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
* 12708
83.8%
, 2442
 
16.1%
@ 3
 
< 0.1%
& 1
 
< 0.1%
: 1
 
< 0.1%
. 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
11642
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2024
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 425
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40211
55.8%
Common 31592
43.8%
Latin 278
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2688
 
6.7%
2314
 
5.8%
2282
 
5.7%
2148
 
5.3%
2015
 
5.0%
1991
 
5.0%
1984
 
4.9%
1969
 
4.9%
1962
 
4.9%
1779
 
4.4%
Other values (341) 19079
47.4%
Latin
ValueCountFrequency (%)
B 90
32.4%
A 38
13.7%
C 16
 
5.8%
e 15
 
5.4%
G 14
 
5.0%
S 14
 
5.0%
L 11
 
4.0%
E 11
 
4.0%
c 7
 
2.5%
D 6
 
2.2%
Other values (20) 56
20.1%
Common
ValueCountFrequency (%)
* 12708
40.2%
11642
36.9%
, 2442
 
7.7%
) 2024
 
6.4%
( 2024
 
6.4%
- 425
 
1.3%
1 73
 
0.2%
2 46
 
0.1%
0 44
 
0.1%
3 44
 
0.1%
Other values (11) 120
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40210
55.8%
ASCII 31870
44.2%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 12708
39.9%
11642
36.5%
, 2442
 
7.7%
) 2024
 
6.4%
( 2024
 
6.4%
- 425
 
1.3%
B 90
 
0.3%
1 73
 
0.2%
2 46
 
0.1%
0 44
 
0.1%
Other values (41) 352
 
1.1%
Hangul
ValueCountFrequency (%)
2688
 
6.7%
2314
 
5.8%
2282
 
5.7%
2148
 
5.3%
2015
 
5.0%
1991
 
5.0%
1984
 
4.9%
1969
 
4.9%
1962
 
4.9%
1779
 
4.4%
Other values (340) 19078
47.4%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING  SKEWED 

Distinct132
Distinct (%)6.7%
Missing389
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean4365.386
Minimum4156
Maximum7264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:39.617000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4156
5-th percentile4309
Q14336
median4370
Q34383
95-th percentile4421
Maximum7264
Range3108
Interquartile range (IQR)47

Descriptive statistics

Standard deviation74.137329
Coefficient of variation (CV)0.016982995
Kurtosis1196.1621
Mean4365.386
Median Absolute Deviation (MAD)22
Skewness30.596294
Sum8538695
Variance5496.3435
MonotonicityNot monotonic
2024-05-11T05:22:40.217542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4373 96
 
4.1%
4376 57
 
2.4%
4382 55
 
2.3%
4315 50
 
2.1%
4366 49
 
2.1%
4371 49
 
2.1%
4378 48
 
2.0%
4377 41
 
1.7%
4356 40
 
1.7%
4370 38
 
1.6%
Other values (122) 1433
61.1%
(Missing) 389
 
16.6%
ValueCountFrequency (%)
4156 1
 
< 0.1%
4300 11
0.5%
4301 9
0.4%
4302 4
 
0.2%
4303 7
0.3%
4304 16
0.7%
4305 8
0.3%
4306 3
 
0.1%
4307 12
0.5%
4308 12
0.5%
ValueCountFrequency (%)
7264 1
 
< 0.1%
4637 1
 
< 0.1%
4428 13
0.6%
4427 21
0.9%
4426 20
0.9%
4425 5
 
0.2%
4424 3
 
0.1%
4423 17
0.7%
4422 11
0.5%
4421 7
 
0.3%
Distinct2236
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2024-05-11T05:22:41.225780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length7.3479744
Min length1

Characters and Unicode

Total characters17231
Distinct characters700
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

Unique2160 ?
Unique (%)92.1%

Sample

1st row마이다스
2nd row상록물산(주)
3rd row(주)뷰티어드벤처
4th row비앤에치코스메틱
5th row(주)에이스아이엔씨
ValueCountFrequency (%)
주식회사 170
 
5.9%
gs25 32
 
1.1%
세븐일레븐 14
 
0.5%
용산점 14
 
0.5%
아모레카운셀러 11
 
0.4%
하이리빙 10
 
0.3%
한남점 7
 
0.2%
주)하이리빙 7
 
0.2%
인셀덤 6
 
0.2%
정관장 6
 
0.2%
Other values (2415) 2614
90.4%
2024-05-11T05:22:42.653509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
791
 
4.6%
655
 
3.8%
) 613
 
3.6%
( 613
 
3.6%
546
 
3.2%
513
 
3.0%
346
 
2.0%
281
 
1.6%
280
 
1.6%
274
 
1.6%
Other values (690) 12319
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14258
82.7%
Close Punctuation 614
 
3.6%
Open Punctuation 614
 
3.6%
Space Separator 546
 
3.2%
Uppercase Letter 523
 
3.0%
Lowercase Letter 425
 
2.5%
Decimal Number 212
 
1.2%
Other Punctuation 23
 
0.1%
Dash Punctuation 16
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
791
 
5.5%
655
 
4.6%
513
 
3.6%
346
 
2.4%
281
 
2.0%
280
 
2.0%
274
 
1.9%
223
 
1.6%
218
 
1.5%
202
 
1.4%
Other values (619) 10475
73.5%
Uppercase Letter
ValueCountFrequency (%)
S 90
17.2%
G 64
 
12.2%
C 31
 
5.9%
E 30
 
5.7%
N 24
 
4.6%
O 24
 
4.6%
A 23
 
4.4%
M 22
 
4.2%
T 20
 
3.8%
H 19
 
3.6%
Other values (16) 176
33.7%
Lowercase Letter
ValueCountFrequency (%)
e 55
12.9%
o 51
12.0%
n 34
 
8.0%
a 31
 
7.3%
t 30
 
7.1%
i 28
 
6.6%
l 24
 
5.6%
r 22
 
5.2%
s 21
 
4.9%
m 19
 
4.5%
Other values (15) 110
25.9%
Decimal Number
ValueCountFrequency (%)
2 83
39.2%
5 66
31.1%
1 17
 
8.0%
4 14
 
6.6%
0 10
 
4.7%
3 9
 
4.2%
9 4
 
1.9%
7 4
 
1.9%
6 3
 
1.4%
8 2
 
0.9%
Other Punctuation
ValueCountFrequency (%)
& 10
43.5%
. 9
39.1%
? 3
 
13.0%
, 1
 
4.3%
Close Punctuation
ValueCountFrequency (%)
) 613
99.8%
] 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 613
99.8%
[ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
546
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14258
82.7%
Common 2025
 
11.8%
Latin 948
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
791
 
5.5%
655
 
4.6%
513
 
3.6%
346
 
2.4%
281
 
2.0%
280
 
2.0%
274
 
1.9%
223
 
1.6%
218
 
1.5%
202
 
1.4%
Other values (619) 10475
73.5%
Latin
ValueCountFrequency (%)
S 90
 
9.5%
G 64
 
6.8%
e 55
 
5.8%
o 51
 
5.4%
n 34
 
3.6%
a 31
 
3.3%
C 31
 
3.3%
t 30
 
3.2%
E 30
 
3.2%
i 28
 
3.0%
Other values (41) 504
53.2%
Common
ValueCountFrequency (%)
) 613
30.3%
( 613
30.3%
546
27.0%
2 83
 
4.1%
5 66
 
3.3%
1 17
 
0.8%
- 16
 
0.8%
4 14
 
0.7%
& 10
 
0.5%
0 10
 
0.5%
Other values (10) 37
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14258
82.7%
ASCII 2973
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
791
 
5.5%
655
 
4.6%
513
 
3.6%
346
 
2.4%
281
 
2.0%
280
 
2.0%
274
 
1.9%
223
 
1.6%
218
 
1.5%
202
 
1.4%
Other values (619) 10475
73.5%
ASCII
ValueCountFrequency (%)
) 613
20.6%
( 613
20.6%
546
18.4%
S 90
 
3.0%
2 83
 
2.8%
5 66
 
2.2%
G 64
 
2.2%
e 55
 
1.8%
o 51
 
1.7%
n 34
 
1.1%
Other values (61) 758
25.5%
Distinct2276
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
Minimum2004-06-15 00:00:00
Maximum2024-05-07 17:12:23
2024-05-11T05:22:43.064342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:22:43.536757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
I
1356 
U
989 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowI
5th rowU

Common Values

ValueCountFrequency (%)
I 1356
57.8%
U 989
42.2%

Length

2024-05-11T05:22:44.314239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:44.872135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1356
57.8%
u 989
42.2%
Distinct846
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T05:22:45.428211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:22:46.041061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

좌표정보(X)
Real number (ℝ)

Distinct1226
Distinct (%)52.6%
Missing16
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean197605.65
Minimum190555.77
Maximum201244.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:46.720608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum190555.77
5-th percentile195992.15
Q1196659.03
median197197.3
Q3198422.71
95-th percentile200287.53
Maximum201244.31
Range10688.538
Interquartile range (IQR)1763.6821

Descriptive statistics

Standard deviation1356.9272
Coefficient of variation (CV)0.0068668444
Kurtosis-0.15396391
Mean197605.65
Median Absolute Deviation (MAD)695.94442
Skewness0.73071714
Sum4.6022355 × 108
Variance1841251.5
MonotonicityNot monotonic
2024-05-11T05:22:47.349884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196762.077394917 46
 
2.0%
196839.641743356 29
 
1.2%
196060.559012563 27
 
1.2%
196118.749575977 24
 
1.0%
197115.958077971 22
 
0.9%
197287.50506674 20
 
0.9%
196861.350875235 17
 
0.7%
196087.977652228 17
 
0.7%
196005.836412851 16
 
0.7%
196370.746398773 16
 
0.7%
Other values (1216) 2095
89.3%
(Missing) 16
 
0.7%
ValueCountFrequency (%)
190555.768991595 1
 
< 0.1%
195086.35935132 2
 
0.1%
195266.362355276 3
 
0.1%
195299.311260071 1
 
< 0.1%
195385.722601944 4
 
0.2%
195454.875715409 1
 
< 0.1%
195529.949270409 1
 
< 0.1%
195544.606275448 5
0.2%
195547.140326252 10
0.4%
195556.853873045 1
 
< 0.1%
ValueCountFrequency (%)
201244.307236051 1
< 0.1%
201171.398073119 1
< 0.1%
201023.711563219 1
< 0.1%
200962.552550576 1
< 0.1%
200909.310822627 1
< 0.1%
200887.091124451 2
0.1%
200846.604536733 2
0.1%
200837.136549128 1
< 0.1%
200824.55933234 2
0.1%
200793.804471719 1
< 0.1%

좌표정보(Y)
Real number (ℝ)

Distinct1226
Distinct (%)52.6%
Missing16
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean448127.3
Minimum446088.42
Maximum450296.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:47.934176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum446088.42
5-th percentile446591.33
Q1447552.77
median448008.98
Q3448717.2
95-th percentile449760.78
Maximum450296.59
Range4208.1729
Interquartile range (IQR)1164.4292

Descriptive statistics

Standard deviation910.50493
Coefficient of variation (CV)0.0020317997
Kurtosis-0.3841635
Mean448127.3
Median Absolute Deviation (MAD)561.3248
Skewness0.20501793
Sum1.0436885 × 109
Variance829019.24
MonotonicityNot monotonic
2024-05-11T05:22:48.517551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447480.039577359 46
 
2.0%
447863.232025192 29
 
1.2%
447732.807254308 27
 
1.2%
447760.157527455 24
 
1.0%
449203.316855129 22
 
0.9%
448662.153501017 20
 
0.9%
447226.720509901 17
 
0.7%
447749.621213224 17
 
0.7%
447699.758814725 16
 
0.7%
447841.698787531 16
 
0.7%
Other values (1216) 2095
89.3%
(Missing) 16
 
0.7%
ValueCountFrequency (%)
446088.416295863 1
 
< 0.1%
446103.473050838 2
 
0.1%
446105.680627721 1
 
< 0.1%
446112.132597741 1
 
< 0.1%
446114.155238838 8
0.3%
446185.244544796 1
 
< 0.1%
446202.36502252 1
 
< 0.1%
446202.695152684 1
 
< 0.1%
446207.612235932 8
0.3%
446246.110967125 1
 
< 0.1%
ValueCountFrequency (%)
450296.589217562 7
0.3%
450290.442701448 1
 
< 0.1%
450288.561776513 2
 
0.1%
450252.400952479 1
 
< 0.1%
450247.06018934 1
 
< 0.1%
450208.807869693 1
 
< 0.1%
450198.033078924 1
 
< 0.1%
450173.41042678 1
 
< 0.1%
450171.953371765 1
 
< 0.1%
450168.3235982 1
 
< 0.1%

위생업태명
Categorical

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
758 
영업장판매
620 
전자상거래(통신판매업)
452 
통신판매
278 
방문판매
123 
Other values (5)
114 

Length

Max length14
Median length12
Mean length5.9113006
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row통신판매
2nd row영업장판매
3rd row통신판매
4th row영업장판매
5th row전자상거래(통신판매업)

Common Values

ValueCountFrequency (%)
<NA> 758
32.3%
영업장판매 620
26.4%
전자상거래(통신판매업) 452
19.3%
통신판매 278
 
11.9%
방문판매 123
 
5.2%
다단계판매 81
 
3.5%
전화권유판매 11
 
0.5%
기타 건강기능식품일반판매업 11
 
0.5%
도매업(유통) 10
 
0.4%
자동판매기판매 1
 
< 0.1%

Length

2024-05-11T05:22:49.176058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:49.648977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 758
32.2%
영업장판매 620
26.3%
전자상거래(통신판매업 452
19.2%
통신판매 278
 
11.8%
방문판매 123
 
5.2%
다단계판매 81
 
3.4%
전화권유판매 11
 
0.5%
기타 11
 
0.5%
건강기능식품일반판매업 11
 
0.5%
도매업(유통 10
 
0.4%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
2192 
0
 
153

Length

Max length4
Median length4
Mean length3.8042644
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2192
93.5%
0 153
 
6.5%

Length

2024-05-11T05:22:50.248440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:51.010833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2192
93.5%
0 153
 
6.5%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
2192 
0
 
153

Length

Max length4
Median length4
Mean length3.8042644
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2192
93.5%
0 153
 
6.5%

Length

2024-05-11T05:22:51.479316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:51.886513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2192
93.5%
0 153
 
6.5%

영업장주변구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

등급구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

급수시설구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
2299 
상수도전용
 
46

Length

Max length5
Median length4
Mean length4.0196162
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2299
98.0%
상수도전용 46
 
2.0%

Length

2024-05-11T05:22:52.227766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:52.529095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2299
98.0%
상수도전용 46
 
2.0%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
2193 
0
 
152

Length

Max length4
Median length4
Mean length3.8055437
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2193
93.5%
0 152
 
6.5%

Length

2024-05-11T05:22:52.905187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:53.216572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2193
93.5%
0 152
 
6.5%

본사종업원수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct14
Distinct (%)2.4%
Missing1753
Missing (%)74.8%
Infinite0
Infinite (%)0.0%
Mean5.6081081
Minimum0
Maximum3000
Zeros572
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:53.496917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3000
Range3000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation123.54671
Coefficient of variation (CV)22.030015
Kurtosis586.83023
Mean5.6081081
Median Absolute Deviation (MAD)0
Skewness24.178056
Sum3320
Variance15263.789
MonotonicityNot monotonic
2024-05-11T05:22:53.884451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 572
 
24.4%
1 4
 
0.2%
4 3
 
0.1%
5 2
 
0.1%
2 2
 
0.1%
6 1
 
< 0.1%
3000 1
 
< 0.1%
9 1
 
< 0.1%
194 1
 
< 0.1%
15 1
 
< 0.1%
Other values (4) 4
 
0.2%
(Missing) 1753
74.8%
ValueCountFrequency (%)
0 572
24.4%
1 4
 
0.2%
2 2
 
0.1%
3 1
 
< 0.1%
4 3
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
3000 1
 
< 0.1%
194 1
 
< 0.1%
26 1
 
< 0.1%
24 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
5 2
0.1%
4 3
0.1%

공장사무직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)2.0%
Missing1660
Missing (%)70.8%
Infinite0
Infinite (%)0.0%
Mean0.81459854
Minimum0
Maximum36
Zeros474
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:54.281643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum36
Range36
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2958945
Coefficient of variation (CV)2.8184368
Kurtosis110.35422
Mean0.81459854
Median Absolute Deviation (MAD)0
Skewness8.6193859
Sum558
Variance5.2711316
MonotonicityNot monotonic
2024-05-11T05:22:54.806701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 474
 
20.2%
1 105
 
4.5%
2 33
 
1.4%
3 26
 
1.1%
4 22
 
0.9%
5 7
 
0.3%
6 7
 
0.3%
8 4
 
0.2%
10 2
 
0.1%
7 1
 
< 0.1%
Other values (4) 4
 
0.2%
(Missing) 1660
70.8%
ValueCountFrequency (%)
0 474
20.2%
1 105
 
4.5%
2 33
 
1.4%
3 26
 
1.1%
4 22
 
0.9%
5 7
 
0.3%
6 7
 
0.3%
7 1
 
< 0.1%
8 4
 
0.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
28 1
 
< 0.1%
12 1
 
< 0.1%
10 2
 
0.1%
9 1
 
< 0.1%
8 4
 
0.2%
7 1
 
< 0.1%
6 7
 
0.3%
5 7
 
0.3%
4 22
0.9%

공장판매직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)3.4%
Missing1609
Missing (%)68.6%
Infinite0
Infinite (%)0.0%
Mean2.6942935
Minimum0
Maximum330
Zeros295
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:55.168912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum330
Range330
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.759211
Coefficient of variation (CV)5.4779522
Kurtosis339.79193
Mean2.6942935
Median Absolute Deviation (MAD)1
Skewness16.642756
Sum1983
Variance217.83431
MonotonicityNot monotonic
2024-05-11T05:22:55.575350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 295
 
12.6%
1 249
 
10.6%
2 66
 
2.8%
3 41
 
1.7%
4 24
 
1.0%
5 16
 
0.7%
10 9
 
0.4%
6 6
 
0.3%
7 5
 
0.2%
8 4
 
0.2%
Other values (15) 21
 
0.9%
(Missing) 1609
68.6%
ValueCountFrequency (%)
0 295
12.6%
1 249
10.6%
2 66
 
2.8%
3 41
 
1.7%
4 24
 
1.0%
5 16
 
0.7%
6 6
 
0.3%
7 5
 
0.2%
8 4
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
330 1
< 0.1%
110 1
< 0.1%
109 1
< 0.1%
82 2
0.1%
73 1
< 0.1%
60 1
< 0.1%
50 1
< 0.1%
40 1
< 0.1%
25 1
< 0.1%
22 1
< 0.1%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
1757 
0
570 
1
 
11
2
 
6
3
 
1

Length

Max length4
Median length4
Mean length3.2477612
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
<NA> 1757
74.9%
0 570
 
24.3%
1 11
 
0.5%
2 6
 
0.3%
3 1
 
< 0.1%

Length

2024-05-11T05:22:56.056647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:56.408674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1757
74.9%
0 570
 
24.3%
1 11
 
0.5%
2 6
 
0.3%
3 1
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
<NA>
1098 
임대
714 
자가
533 

Length

Max length4
Median length2
Mean length2.9364606
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자가
2nd row임대
3rd row임대
4th row임대
5th row임대

Common Values

ValueCountFrequency (%)
<NA> 1098
46.8%
임대 714
30.4%
자가 533
22.7%

Length

2024-05-11T05:22:56.866756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:22:57.188610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1098
46.8%
임대 714
30.4%
자가 533
22.7%

보증액
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct38
Distinct (%)8.5%
Missing1898
Missing (%)80.9%
Infinite0
Infinite (%)0.0%
Mean33218469
Minimum0
Maximum1.164737 × 1010
Zeros343
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:57.542594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33500000
Maximum1.164737 × 1010
Range1.164737 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5215374 × 108
Coefficient of variation (CV)16.62189
Kurtosis441.84223
Mean33218469
Median Absolute Deviation (MAD)0
Skewness20.966172
Sum1.4848656 × 1010
Variance3.0487375 × 1017
MonotonicityNot monotonic
2024-05-11T05:22:57.945897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 343
 
14.6%
10000000 30
 
1.3%
20000000 8
 
0.3%
5000000 7
 
0.3%
50000000 7
 
0.3%
30000000 6
 
0.3%
25000000 4
 
0.2%
1000000 3
 
0.1%
2000000 3
 
0.1%
40000000 3
 
0.1%
Other values (28) 33
 
1.4%
(Missing) 1898
80.9%
ValueCountFrequency (%)
0 343
14.6%
700000 1
 
< 0.1%
1000000 3
 
0.1%
1500000 1
 
< 0.1%
2000000 3
 
0.1%
3000000 2
 
0.1%
3960000 1
 
< 0.1%
4000000 1
 
< 0.1%
4500000 1
 
< 0.1%
5000000 7
 
0.3%
ValueCountFrequency (%)
11647369600 1
 
< 0.1%
800000000 1
 
< 0.1%
200000000 1
 
< 0.1%
190986160 1
 
< 0.1%
150000000 1
 
< 0.1%
100000000 1
 
< 0.1%
75000000 1
 
< 0.1%
70000000 1
 
< 0.1%
51000000 1
 
< 0.1%
50000000 7
0.3%

월세액
Real number (ℝ)

MISSING  ZEROS 

Distinct63
Distinct (%)14.0%
Missing1894
Missing (%)80.8%
Infinite0
Infinite (%)0.0%
Mean609393.44
Minimum0
Maximum85661220
Zeros344
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:58.425242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2125000
Maximum85661220
Range85661220
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4288356.9
Coefficient of variation (CV)7.0370907
Kurtosis346.7132
Mean609393.44
Median Absolute Deviation (MAD)0
Skewness17.75103
Sum2.7483644 × 108
Variance1.8390005 × 1013
MonotonicityNot monotonic
2024-05-11T05:22:58.865924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 344
 
14.7%
1000000 13
 
0.6%
500000 8
 
0.3%
1500000 7
 
0.3%
1200000 6
 
0.3%
600000 5
 
0.2%
800000 3
 
0.1%
750000 2
 
0.1%
250000 2
 
0.1%
2500000 2
 
0.1%
Other values (53) 59
 
2.5%
(Missing) 1894
80.8%
ValueCountFrequency (%)
0 344
14.7%
100 1
 
< 0.1%
1050 1
 
< 0.1%
100000 1
 
< 0.1%
150000 1
 
< 0.1%
200000 1
 
< 0.1%
220000 1
 
< 0.1%
250000 2
 
0.1%
280000 1
 
< 0.1%
300000 2
 
0.1%
ValueCountFrequency (%)
85661220 1
< 0.1%
19713500 1
< 0.1%
15000000 1
< 0.1%
10000000 1
< 0.1%
9270570 1
< 0.1%
6000000 1
< 0.1%
5000000 1
< 0.1%
4800000 1
< 0.1%
4000000 1
< 0.1%
3900000 1
< 0.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing758
Missing (%)32.3%
Memory size4.7 KiB
False
1587 
(Missing)
758 
ValueCountFrequency (%)
False 1587
67.7%
(Missing) 758
32.3%
2024-05-11T05:22:59.217841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct27
Distinct (%)1.7%
Missing758
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean1.2254064
Minimum0
Maximum487
Zeros1541
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-05-11T05:22:59.572312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum487
Range487
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.989386
Coefficient of variation (CV)12.232175
Kurtosis716.50383
Mean1.2254064
Median Absolute Deviation (MAD)0
Skewness23.965783
Sum1944.72
Variance224.68168
MonotonicityNot monotonic
2024-05-11T05:23:00.025471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 1541
65.7%
3.3 18
 
0.8%
66.0 2
 
0.1%
33.0 2
 
0.1%
10.0 2
 
0.1%
30.0 1
 
< 0.1%
23.1 1
 
< 0.1%
487.0 1
 
< 0.1%
8.74 1
 
< 0.1%
65.52 1
 
< 0.1%
Other values (17) 17
 
0.7%
(Missing) 758
32.3%
ValueCountFrequency (%)
0.0 1541
65.7%
2.0 1
 
< 0.1%
3.3 18
 
0.8%
8.74 1
 
< 0.1%
10.0 2
 
0.1%
16.0 1
 
< 0.1%
16.2 1
 
< 0.1%
16.52 1
 
< 0.1%
16.97 1
 
< 0.1%
23.1 1
 
< 0.1%
ValueCountFrequency (%)
487.0 1
< 0.1%
165.0 1
< 0.1%
152.37 1
< 0.1%
105.49 1
< 0.1%
102.92 1
< 0.1%
95.36 1
< 0.1%
86.31 1
< 0.1%
72.65 1
< 0.1%
68.44 1
< 0.1%
66.0 2
0.1%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2345
Missing (%)100.0%
Memory size20.7 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
030200003020000-134-2004-0000120040403<NA>3폐업2폐업20190607<NA><NA><NA><NA>59.50140897서울특별시 용산구 효창동 *-***번지 ***호서울특별시 용산구 백범로**길 ** (효창동,***호)4317마이다스2019-06-07 09:39:30U2019-06-09 02:40:00.0<NA>196792.407533448864.263014통신판매<NA><NA><NA><NA><NA><NA>0010자가00N0.0<NA><NA><NA>
130200003020000-134-2004-0000220040414<NA>3폐업2폐업20190821<NA><NA><NA>02 7924371103.80140160서울특별시 용산구 남영동 **-*번지서울특별시 용산구 두텁바위로 * (남영동)4352상록물산(주)2019-08-21 10:16:27U2019-08-23 02:40:00.0<NA>197518.842759449200.07576영업장판매<NA><NA><NA><NA>상수도전용<NA>6521임대1000000015000000N0.0<NA><NA><NA>
230200003020000-134-2004-0000420040414<NA>3폐업2폐업20190624<NA><NA><NA>02 790222075.36140889서울특별시 용산구 한남동 ***-*번지<NA><NA>(주)뷰티어드벤처2019-06-24 10:48:24U2019-06-26 02:40:00.0<NA>200562.054329447604.291099통신판매<NA><NA><NA><NA>상수도전용<NA>0400임대100000001000000N0.0<NA><NA><NA>
330200003020000-134-2004-0000520040414<NA>3폐업2폐업20061130<NA><NA><NA>027772 59166.00140801서울특별시 용산구 갈월동 **-*번지 *층<NA><NA>비앤에치코스메틱2004-10-07 00:00:00I2018-08-31 23:59:59.0<NA>197478.77035449308.544294영업장판매<NA><NA><NA><NA><NA><NA>0020임대10000000N0.0<NA><NA><NA>
430200003020000-134-2004-0000620040414<NA>3폐업2폐업20190624<NA><NA><NA>02 7114166160.23140883서울특별시 용산구 한강로*가 **-***번지 바울빌딩 *층서울특별시 용산구 서빙고로 ** (한강로*가,바울빌딩 *층)4388(주)에이스아이엔씨2019-06-24 10:49:46U2019-06-26 02:40:00.0<NA>197138.708778446873.375211전자상거래(통신판매업)<NA><NA><NA><NA>상수도전용<NA>0532임대10000000800000N0.0<NA><NA><NA>
530200003020000-134-2004-0000720040416<NA>3폐업2폐업20051212<NA><NA><NA><NA>132.00140901서울특별시 용산구 후암동 ***-**번지 대도빌딩 ***호<NA><NA>로드마들2005-08-26 00:00:00I2018-08-31 23:59:59.0<NA>198101.445233449672.909205방문판매<NA><NA><NA><NA>상수도전용<NA>0160임대5000000250000N0.0<NA><NA><NA>
630200003020000-134-2004-0000820040510<NA>3폐업2폐업20190422<NA><NA><NA>02 71663213.30140866서울특별시 용산구 이태원동 ***-*번지 ***호서울특별시 용산구 이태원로 *** (이태원동)4351(주)천일무역2019-04-22 10:04:12U2019-04-24 02:40:00.0<NA>199041.320145448036.150297영업장판매<NA><NA><NA><NA>상수도전용<NA>0210임대105000001050N0.0<NA><NA><NA>
730200003020000-134-2004-0000920040512<NA>3폐업2폐업20121210<NA><NA><NA>02 709511413,103.44140871서울특별시 용산구 한강로*가 ***번지<NA><NA>(주)아모레퍼시픽2008-05-07 10:50:37I2018-08-31 23:59:59.0<NA>197138.823553447436.953717영업장판매<NA><NA><NA><NA>상수도전용<NA>3000000자가00N0.0<NA><NA><NA>
830200003020000-134-2004-0001020040519<NA>3폐업2폐업20180320<NA><NA><NA><NA>5.00140873서울특별시 용산구 한강로*가 **-**번지서울특별시 용산구 청파로 ***, 지*층 (한강로*가)4371정관장 농협유통2018-03-20 13:06:52I2018-08-31 23:59:59.0<NA>196813.258497447881.970772영업장판매<NA><NA><NA><NA>상수도전용<NA>0010자가00N0.0<NA><NA><NA>
930200003020000-134-2004-0001120040520<NA>3폐업2폐업20200205<NA><NA><NA>02 7117001321.40140133서울특별시 용산구 청파동*가 **-**번지 복조빌딩 *층서울특별시 용산구 청파로**길 ** (청파동*가,복조빌딩 *층)4313오휘 남영점2020-02-05 10:02:38U2020-02-07 02:40:00.0<NA>197231.028951449114.142891방문판매<NA><NA><NA><NA>상수도전용<NA>00730임대380600003108000N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
233530200003020000-134-2024-000362024-03-29<NA>3폐업2폐업2024-04-30<NA><NA><NA><NA>10.00140-910서울특별시 용산구 한남동 *** 한남동 하이페리온서울특별시 용산구 서빙고로 ***, ***동 B*층 ***-*호 (한남동, 한남동 하이페리온)4416비타믹스 주식회사2024-05-01 04:15:08U2023-12-05 00:03:00.0<NA>200357.541925447271.660262<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
233630200003020000-134-2024-000372024-04-08<NA>1영업/정상1영업<NA><NA><NA><NA><NA>34.71140-832서울특별시 용산구 용문동 **-*서울특별시 용산구 새창로**길 **, *층 (용문동)4356호주면세점 용산점2024-04-08 14:12:06I2023-12-03 23:00:00.0<NA>196339.86793448357.106062<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
233730200003020000-134-2024-000382024-04-09<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.30140-132서울특별시 용산구 청파동*가 *-** 준쉐르빌서울특별시 용산구 청파로**나길 **-*, ***호 (청파동*가, 준쉐르빌)4307포켓폰(pocket phone)2024-04-09 11:20:35I2023-12-03 23:01:00.0<NA>197024.437554449390.130344<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
233830200003020000-134-2024-000392024-04-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>15.00140-013서울특별시 용산구 한강로*가 **서울특별시 용산구 서빙고로 **, ***동 ***호 (한강로*가)4387대민2024-04-15 17:53:29I2023-12-03 23:07:00.0<NA>197056.728931447077.126289<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
233930200003020000-134-2024-000402024-04-17<NA>1영업/정상1영업<NA><NA><NA><NA><NA>29.70140-897서울특별시 용산구 효창동 *-*** 트윈스빌라*차서울특별시 용산구 효창원로**길 **, ***동 ***호 (효창동, 트윈스빌라*차)4319다비드서2024-04-18 13:59:05I2023-12-03 22:00:00.0<NA>196410.253993448911.426918<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
234030200003020000-134-2024-000412024-04-19<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.22140-160서울특별시 용산구 남영동 **-*서울특별시 용산구 한강대로 ***, *층 ***호 (남영동)4352억중2024-04-19 12:11:30I2023-12-03 22:01:00.0<NA>197532.655905448979.128029<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
234130200003020000-134-2024-000422024-04-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.30140-899서울특별시 용산구 후암동 *-**서울특별시 용산구 후암로 **, B층 **호 (후암동)4327산뜻한아트2024-04-24 14:52:04I2023-12-03 22:06:00.0<NA>197904.654329449981.028322<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
234230200003020000-134-2024-000432024-04-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>101.01140-841서울특별시 용산구 용산동*가 ** 남산헬스서울특별시 용산구 신흥로 **, 남산헬스 *층 (용산동*가)4339에코앤?2024-04-25 10:01:05I2023-12-03 22:07:00.0<NA>198767.690949449149.320575<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
234330200003020000-134-2024-000442024-04-29<NA>1영업/정상1영업<NA><NA><NA><NA><NA>5.00140-910서울특별시 용산구 한남동 *** 한남동 하이페리온서울특별시 용산구 서빙고로 ***, ***동 B*층 ***, ***호 (한남동, 한남동 하이페리온)4416비타믹스 주식회사2024-04-29 16:36:07I2023-12-05 00:01:00.0<NA>200357.541925447271.660262<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
234430200003020000-134-2024-000452024-05-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.30140-849서울특별시 용산구 원효로*가 ***-*서울특별시 용산구 원효로**길 **, *층 (원효로*가)4362주식회사 제이앤디럭2024-05-03 15:33:28I2023-12-05 00:08:00.0<NA>195998.212637447915.987784<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>