Overview

Dataset statistics

Number of variables44
Number of observations174
Missing cells1975
Missing cells (%)25.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.8 KiB
Average record size in memory375.8 B

Variable types

Categorical18
Text8
DateTime4
Unsupported7
Numeric6
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-17979/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
건물소유구분명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
영업장주변구분명 is highly imbalanced (76.5%)Imbalance
등급구분명 is highly imbalanced (87.4%)Imbalance
총인원 is highly imbalanced (70.6%)Imbalance
본사종업원수 is highly imbalanced (66.0%)Imbalance
공장사무직종업원수 is highly imbalanced (66.0%)Imbalance
공장판매직종업원수 is highly imbalanced (66.0%)Imbalance
공장생산직종업원수 is highly imbalanced (66.0%)Imbalance
보증액 is highly imbalanced (66.0%)Imbalance
월세액 is highly imbalanced (66.0%)Imbalance
인허가취소일자 has 174 (100.0%) missing valuesMissing
폐업일자 has 51 (29.3%) missing valuesMissing
휴업시작일자 has 174 (100.0%) missing valuesMissing
휴업종료일자 has 174 (100.0%) missing valuesMissing
재개업일자 has 174 (100.0%) missing valuesMissing
전화번호 has 74 (42.5%) missing valuesMissing
도로명주소 has 54 (31.0%) missing valuesMissing
도로명우편번호 has 54 (31.0%) missing valuesMissing
좌표정보(X) has 17 (9.8%) missing valuesMissing
좌표정보(Y) has 17 (9.8%) missing valuesMissing
남성종사자수 has 109 (62.6%) missing valuesMissing
여성종사자수 has 108 (62.1%) missing valuesMissing
건물소유구분명 has 172 (98.9%) missing valuesMissing
다중이용업소여부 has 50 (28.7%) missing valuesMissing
시설총규모 has 50 (28.7%) missing valuesMissing
전통업소지정번호 has 174 (100.0%) missing valuesMissing
전통업소주된음식 has 174 (100.0%) missing valuesMissing
홈페이지 has 174 (100.0%) missing valuesMissing
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
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
남성종사자수 has 49 (28.2%) zerosZeros
여성종사자수 has 36 (20.7%) zerosZeros

Reproduction

Analysis started2024-05-11 08:44:28.807381
Analysis finished2024-05-11 08:44:30.883608
Duration2.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3020000
174 

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 174
100.0%

Length

2024-05-11T08:44:31.179474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:31.585500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000 174
100.0%

관리번호
Text

UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T08:44:32.156671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3828
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

Unique174 ?
Unique (%)100.0%

Sample

1st row3020000-120-2003-00002
2nd row3020000-120-2003-00003
3rd row3020000-120-2003-00004
4th row3020000-120-2003-00005
5th row3020000-120-2003-00006
ValueCountFrequency (%)
3020000-120-2003-00002 1
 
0.6%
3020000-120-2017-00005 1
 
0.6%
3020000-120-2017-00007 1
 
0.6%
3020000-120-2016-00008 1
 
0.6%
3020000-120-2016-00009 1
 
0.6%
3020000-120-2016-00010 1
 
0.6%
3020000-120-2017-00001 1
 
0.6%
3020000-120-2017-00002 1
 
0.6%
3020000-120-2017-00003 1
 
0.6%
3020000-120-2017-00004 1
 
0.6%
Other values (164) 164
94.3%
2024-05-11T08:44:33.627903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1964
51.3%
2 600
 
15.7%
- 522
 
13.6%
1 306
 
8.0%
3 258
 
6.7%
4 41
 
1.1%
6 35
 
0.9%
5 30
 
0.8%
7 26
 
0.7%
9 24
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3306
86.4%
Dash Punctuation 522
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1964
59.4%
2 600
 
18.1%
1 306
 
9.3%
3 258
 
7.8%
4 41
 
1.2%
6 35
 
1.1%
5 30
 
0.9%
7 26
 
0.8%
9 24
 
0.7%
8 22
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1964
51.3%
2 600
 
15.7%
- 522
 
13.6%
1 306
 
8.0%
3 258
 
6.7%
4 41
 
1.1%
6 35
 
0.9%
5 30
 
0.8%
7 26
 
0.7%
9 24
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1964
51.3%
2 600
 
15.7%
- 522
 
13.6%
1 306
 
8.0%
3 258
 
6.7%
4 41
 
1.1%
6 35
 
0.9%
5 30
 
0.8%
7 26
 
0.7%
9 24
 
0.6%
Distinct139
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2003-10-15 00:00:00
Maximum2024-05-02 00:00:00
2024-05-11T08:44:34.351861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:44:35.144810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3
123 
1
51 

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 (%)
3 123
70.7%
1 51
29.3%

Length

2024-05-11T08:44:35.952156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:36.432255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 123
70.7%
1 51
29.3%

영업상태명
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
폐업
123 
영업/정상
51 

Length

Max length5
Median length2
Mean length2.8793103
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 123
70.7%
영업/정상 51
29.3%

Length

2024-05-11T08:44:36.823697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:37.140431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 123
70.7%
영업/정상 51
29.3%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
123 
1
51 

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 (%)
2 123
70.7%
1 51
29.3%

Length

2024-05-11T08:44:37.498878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:37.906793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 123
70.7%
1 51
29.3%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
폐업
123 
영업
51 

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 123
70.7%
영업 51
29.3%

Length

2024-05-11T08:44:38.312624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:38.645833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 123
70.7%
영업 51
29.3%

폐업일자
Date

MISSING 

Distinct110
Distinct (%)89.4%
Missing51
Missing (%)29.3%
Memory size1.5 KiB
Minimum2004-01-27 00:00:00
Maximum2024-05-07 00:00:00
2024-05-11T08:44:38.990363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:44:39.445055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB

전화번호
Text

MISSING 

Distinct92
Distinct (%)92.0%
Missing74
Missing (%)42.5%
Memory size1.5 KiB
2024-05-11T08:44:40.065366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.71
Min length8

Characters and Unicode

Total characters1071
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

Unique86 ?
Unique (%)86.0%

Sample

1st row0232712065
2nd row02 7125111
3rd row02 7997781
4th row0237805306
5th row0232737635
ValueCountFrequency (%)
02 65
34.2%
0233920455 4
 
2.1%
031 3
 
1.6%
20276080 2
 
1.1%
5738215 2
 
1.1%
6100 2
 
1.1%
7993097 2
 
1.1%
0220014195 2
 
1.1%
15448272 2
 
1.1%
34007696 2
 
1.1%
Other values (104) 104
54.7%
2024-05-11T08:44:41.198058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 188
17.6%
2 170
15.9%
121
11.3%
7 105
9.8%
1 87
8.1%
5 81
7.6%
9 73
 
6.8%
3 68
 
6.3%
6 62
 
5.8%
4 61
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 950
88.7%
Space Separator 121
 
11.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 188
19.8%
2 170
17.9%
7 105
11.1%
1 87
9.2%
5 81
8.5%
9 73
 
7.7%
3 68
 
7.2%
6 62
 
6.5%
4 61
 
6.4%
8 55
 
5.8%
Space Separator
ValueCountFrequency (%)
121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 188
17.6%
2 170
15.9%
121
11.3%
7 105
9.8%
1 87
8.1%
5 81
7.6%
9 73
 
6.8%
3 68
 
6.3%
6 62
 
5.8%
4 61
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 188
17.6%
2 170
15.9%
121
11.3%
7 105
9.8%
1 87
8.1%
5 81
7.6%
9 73
 
6.8%
3 68
 
6.3%
6 62
 
5.8%
4 61
 
5.7%
Distinct140
Distinct (%)80.9%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-05-11T08:44:42.206773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.8843931
Min length4

Characters and Unicode

Total characters1018
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

Unique114 ?
Unique (%)65.9%

Sample

1st row16.50
2nd row459.00
3rd row5.58
4th row18.00
5th row182.50
ValueCountFrequency (%)
644.60 4
 
2.3%
784.97 4
 
2.3%
796.40 3
 
1.7%
35.83 3
 
1.7%
247.50 3
 
1.7%
120.70 2
 
1.2%
874.02 2
 
1.2%
157.74 2
 
1.2%
145.20 2
 
1.2%
927.30 2
 
1.2%
Other values (130) 146
84.4%
2024-05-11T08:44:43.612640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 196
19.3%
. 173
17.0%
1 103
10.1%
2 83
8.2%
4 78
 
7.7%
5 74
 
7.3%
8 67
 
6.6%
7 62
 
6.1%
9 62
 
6.1%
6 58
 
5.7%
Other values (2) 62
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 837
82.2%
Other Punctuation 181
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 196
23.4%
1 103
12.3%
2 83
9.9%
4 78
 
9.3%
5 74
 
8.8%
8 67
 
8.0%
7 62
 
7.4%
9 62
 
7.4%
6 58
 
6.9%
3 54
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 173
95.6%
, 8
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1018
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 196
19.3%
. 173
17.0%
1 103
10.1%
2 83
8.2%
4 78
 
7.7%
5 74
 
7.3%
8 67
 
6.6%
7 62
 
6.1%
9 62
 
6.1%
6 58
 
5.7%
Other values (2) 62
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 196
19.3%
. 173
17.0%
1 103
10.1%
2 83
8.2%
4 78
 
7.7%
5 74
 
7.3%
8 67
 
6.6%
7 62
 
6.1%
9 62
 
6.1%
6 58
 
5.7%
Other values (2) 62
 
6.1%
Distinct75
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T08:44:44.480056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2298851
Min length6

Characters and Unicode

Total characters1084
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

Unique33 ?
Unique (%)19.0%

Sample

1st row140712
2nd row140850
3rd row140850
4th row140012
5th row140880
ValueCountFrequency (%)
140883 11
 
6.3%
140893 8
 
4.6%
140833 8
 
4.6%
140132 7
 
4.0%
140872 6
 
3.4%
140-883 5
 
2.9%
140850 5
 
2.9%
140-780 5
 
2.9%
140823 5
 
2.9%
140-884 4
 
2.3%
Other values (65) 110
63.2%
2024-05-11T08:44:45.968150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 223
20.6%
1 215
19.8%
4 197
18.2%
8 173
16.0%
3 80
 
7.4%
7 55
 
5.1%
2 43
 
4.0%
- 40
 
3.7%
5 24
 
2.2%
9 23
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1044
96.3%
Dash Punctuation 40
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223
21.4%
1 215
20.6%
4 197
18.9%
8 173
16.6%
3 80
 
7.7%
7 55
 
5.3%
2 43
 
4.1%
5 24
 
2.3%
9 23
 
2.2%
6 11
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223
20.6%
1 215
19.8%
4 197
18.2%
8 173
16.0%
3 80
 
7.4%
7 55
 
5.1%
2 43
 
4.0%
- 40
 
3.7%
5 24
 
2.2%
9 23
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223
20.6%
1 215
19.8%
4 197
18.2%
8 173
16.0%
3 80
 
7.4%
7 55
 
5.1%
2 43
 
4.0%
- 40
 
3.7%
5 24
 
2.2%
9 23
 
2.1%
Distinct151
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T08:44:46.820816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length35.5
Mean length27.982759
Min length17

Characters and Unicode

Total characters4869
Distinct characters155
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)76.4%

Sample

1st row서울특별시 용산구 한강로3가 16-49번지 삼구빌딩 지하1층
2nd row서울특별시 용산구 원효로4가 114-38 현대자동차 10층
3rd row서울특별시 용산구 원효로4가 113-58번지 (지상4층)
4th row서울특별시 용산구 한강로2가 191번지 (지하1층)
5th row서울특별시 용산구 한강로3가 40-1번지 (지상1층)
ValueCountFrequency (%)
서울특별시 174
20.2%
용산구 174
20.2%
한강로3가 36
 
4.2%
한남동 25
 
2.9%
지하1층 20
 
2.3%
지상1층 18
 
2.1%
한강로2가 16
 
1.9%
이촌동 14
 
1.6%
이태원동 13
 
1.5%
용산동2가 10
 
1.2%
Other values (203) 360
41.9%
2024-05-11T08:44:48.317809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
810
 
16.6%
204
 
4.2%
202
 
4.1%
188
 
3.9%
185
 
3.8%
183
 
3.8%
182
 
3.7%
177
 
3.6%
175
 
3.6%
175
 
3.6%
Other values (145) 2388
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2919
60.0%
Decimal Number 877
 
18.0%
Space Separator 810
 
16.6%
Dash Punctuation 135
 
2.8%
Close Punctuation 56
 
1.2%
Open Punctuation 56
 
1.2%
Other Punctuation 10
 
0.2%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
204
 
7.0%
202
 
6.9%
188
 
6.4%
185
 
6.3%
183
 
6.3%
182
 
6.2%
177
 
6.1%
175
 
6.0%
175
 
6.0%
126
 
4.3%
Other values (124) 1122
38.4%
Decimal Number
ValueCountFrequency (%)
1 174
19.8%
2 133
15.2%
3 131
14.9%
0 87
9.9%
4 74
8.4%
5 69
 
7.9%
6 61
 
7.0%
9 55
 
6.3%
7 55
 
6.3%
8 38
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B 2
33.3%
H 1
16.7%
Y 1
16.7%
C 1
16.7%
E 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
. 2
 
20.0%
Space Separator
ValueCountFrequency (%)
810
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2919
60.0%
Common 1944
39.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
204
 
7.0%
202
 
6.9%
188
 
6.4%
185
 
6.3%
183
 
6.3%
182
 
6.2%
177
 
6.1%
175
 
6.0%
175
 
6.0%
126
 
4.3%
Other values (124) 1122
38.4%
Common
ValueCountFrequency (%)
810
41.7%
1 174
 
9.0%
- 135
 
6.9%
2 133
 
6.8%
3 131
 
6.7%
0 87
 
4.5%
4 74
 
3.8%
5 69
 
3.5%
6 61
 
3.1%
) 56
 
2.9%
Other values (6) 214
 
11.0%
Latin
ValueCountFrequency (%)
B 2
33.3%
H 1
16.7%
Y 1
16.7%
C 1
16.7%
E 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2919
60.0%
ASCII 1950
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
810
41.5%
1 174
 
8.9%
- 135
 
6.9%
2 133
 
6.8%
3 131
 
6.7%
0 87
 
4.5%
4 74
 
3.8%
5 69
 
3.5%
6 61
 
3.1%
) 56
 
2.9%
Other values (11) 220
 
11.3%
Hangul
ValueCountFrequency (%)
204
 
7.0%
202
 
6.9%
188
 
6.4%
185
 
6.3%
183
 
6.3%
182
 
6.2%
177
 
6.1%
175
 
6.0%
175
 
6.0%
126
 
4.3%
Other values (124) 1122
38.4%

도로명주소
Text

MISSING 

Distinct105
Distinct (%)87.5%
Missing54
Missing (%)31.0%
Memory size1.5 KiB
2024-05-11T08:44:49.562144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length42
Mean length34.916667
Min length22

Characters and Unicode

Total characters4190
Distinct characters179
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)75.8%

Sample

1st row서울특별시 용산구 원효로 74, 10층 (원효로4가, 현대자동차)
2nd row서울특별시 용산구 원효로 86 (원효로4가,(지상4층))
3rd row서울특별시 용산구 소월로 322 (한남동, 747-7 지하1층)
4th row서울특별시 용산구 청파로 263 (청파동3가,신광초.중.고등학교)
5th row서울특별시 용산구 백범로90다길 13 (문배동,(지상4층))
ValueCountFrequency (%)
서울특별시 120
 
15.5%
용산구 120
 
15.5%
한강로3가 27
 
3.5%
지하1층 23
 
3.0%
한강대로 17
 
2.2%
한남동 17
 
2.2%
1층 15
 
1.9%
서빙고로 11
 
1.4%
24 11
 
1.4%
100 11
 
1.4%
Other values (201) 401
51.9%
2024-05-11T08:44:51.201314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
653
 
15.6%
168
 
4.0%
147
 
3.5%
146
 
3.5%
144
 
3.4%
1 136
 
3.2%
( 134
 
3.2%
) 134
 
3.2%
128
 
3.1%
128
 
3.1%
Other values (169) 2272
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2483
59.3%
Space Separator 653
 
15.6%
Decimal Number 632
 
15.1%
Open Punctuation 134
 
3.2%
Close Punctuation 134
 
3.2%
Other Punctuation 125
 
3.0%
Dash Punctuation 19
 
0.5%
Uppercase Letter 10
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
168
 
6.8%
147
 
5.9%
146
 
5.9%
144
 
5.8%
128
 
5.2%
128
 
5.2%
121
 
4.9%
121
 
4.9%
121
 
4.9%
101
 
4.1%
Other values (146) 1158
46.6%
Decimal Number
ValueCountFrequency (%)
1 136
21.5%
2 108
17.1%
3 96
15.2%
4 70
11.1%
0 64
10.1%
5 48
 
7.6%
7 42
 
6.6%
6 30
 
4.7%
8 23
 
3.6%
9 15
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 4
40.0%
H 1
 
10.0%
Y 1
 
10.0%
I 1
 
10.0%
C 1
 
10.0%
K 1
 
10.0%
E 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 123
98.4%
. 2
 
1.6%
Space Separator
ValueCountFrequency (%)
653
100.0%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2483
59.3%
Common 1697
40.5%
Latin 10
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
168
 
6.8%
147
 
5.9%
146
 
5.9%
144
 
5.8%
128
 
5.2%
128
 
5.2%
121
 
4.9%
121
 
4.9%
121
 
4.9%
101
 
4.1%
Other values (146) 1158
46.6%
Common
ValueCountFrequency (%)
653
38.5%
1 136
 
8.0%
( 134
 
7.9%
) 134
 
7.9%
, 123
 
7.2%
2 108
 
6.4%
3 96
 
5.7%
4 70
 
4.1%
0 64
 
3.8%
5 48
 
2.8%
Other values (6) 131
 
7.7%
Latin
ValueCountFrequency (%)
B 4
40.0%
H 1
 
10.0%
Y 1
 
10.0%
I 1
 
10.0%
C 1
 
10.0%
K 1
 
10.0%
E 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2483
59.3%
ASCII 1707
40.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
653
38.3%
1 136
 
8.0%
( 134
 
7.9%
) 134
 
7.9%
, 123
 
7.2%
2 108
 
6.3%
3 96
 
5.6%
4 70
 
4.1%
0 64
 
3.7%
5 48
 
2.8%
Other values (13) 141
 
8.3%
Hangul
ValueCountFrequency (%)
168
 
6.8%
147
 
5.9%
146
 
5.9%
144
 
5.8%
128
 
5.2%
128
 
5.2%
121
 
4.9%
121
 
4.9%
121
 
4.9%
101
 
4.1%
Other values (146) 1158
46.6%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct44
Distinct (%)36.7%
Missing54
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean4371.6167
Minimum4300
Maximum4427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T08:44:51.806926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4300
5-th percentile4310
Q14347
median4377
Q34390.5
95-th percentile4426
Maximum4427
Range127
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation34.917474
Coefficient of variation (CV)0.0079873137
Kurtosis-0.6530368
Mean4371.6167
Median Absolute Deviation (MAD)24
Skewness-0.3607141
Sum524594
Variance1219.23
MonotonicityNot monotonic
2024-05-11T08:44:52.535234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4388 9
 
5.2%
4386 7
 
4.0%
4377 6
 
3.4%
4353 6
 
3.4%
4310 6
 
3.4%
4426 6
 
3.4%
4419 5
 
2.9%
4363 4
 
2.3%
4401 4
 
2.3%
4334 4
 
2.3%
Other values (34) 63
36.2%
(Missing) 54
31.0%
ValueCountFrequency (%)
4300 1
 
0.6%
4301 3
1.7%
4303 1
 
0.6%
4310 6
3.4%
4313 1
 
0.6%
4314 1
 
0.6%
4322 2
 
1.1%
4323 1
 
0.6%
4324 1
 
0.6%
4331 1
 
0.6%
ValueCountFrequency (%)
4427 2
 
1.1%
4426 6
3.4%
4420 3
1.7%
4419 5
2.9%
4417 3
1.7%
4404 2
 
1.1%
4401 4
2.3%
4396 1
 
0.6%
4393 1
 
0.6%
4392 3
1.7%
Distinct173
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T08:44:53.183518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.011494
Min length2

Characters and Unicode

Total characters2438
Distinct characters259
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

Unique172 ?
Unique (%)98.9%

Sample

1st row(주)현대푸드시스템현대홈쇼핑점
2nd row(주)현대그린푸드현대엠엔소프트점
3rd row(주)현대그린푸드현대자동차원효로서비스센터점
4th row신세계푸드 국제상사점
5th row아라코(주)서울철도차량지점
ValueCountFrequency (%)
주)엘에스씨푸드 5
 
2.0%
본우리집밥 5
 
2.0%
주식회사 5
 
2.0%
주)동원홈푸드 4
 
1.6%
용산공업고등학교 3
 
1.2%
참푸드시스템 3
 
1.2%
주)아워홈 3
 
1.2%
주)신세계푸드시스템 3
 
1.2%
서울정수기능대학 2
 
0.8%
구내식당 2
 
0.8%
Other values (205) 220
86.3%
2024-05-11T08:44:54.231288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 124
 
5.1%
( 123
 
5.0%
123
 
5.0%
81
 
3.3%
79
 
3.2%
75
 
3.1%
66
 
2.7%
66
 
2.7%
51
 
2.1%
49
 
2.0%
Other values (249) 1601
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2076
85.2%
Close Punctuation 124
 
5.1%
Open Punctuation 123
 
5.0%
Space Separator 81
 
3.3%
Uppercase Letter 29
 
1.2%
Dash Punctuation 4
 
0.2%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
123
 
5.9%
79
 
3.8%
75
 
3.6%
66
 
3.2%
66
 
3.2%
51
 
2.5%
49
 
2.4%
47
 
2.3%
42
 
2.0%
36
 
1.7%
Other values (232) 1442
69.5%
Uppercase Letter
ValueCountFrequency (%)
K 5
17.2%
S 5
17.2%
I 4
13.8%
B 3
10.3%
L 3
10.3%
C 2
 
6.9%
H 2
 
6.9%
G 1
 
3.4%
N 1
 
3.4%
Y 1
 
3.4%
Other values (2) 2
 
6.9%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2076
85.2%
Common 333
 
13.7%
Latin 29
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
123
 
5.9%
79
 
3.8%
75
 
3.6%
66
 
3.2%
66
 
3.2%
51
 
2.5%
49
 
2.4%
47
 
2.3%
42
 
2.0%
36
 
1.7%
Other values (232) 1442
69.5%
Latin
ValueCountFrequency (%)
K 5
17.2%
S 5
17.2%
I 4
13.8%
B 3
10.3%
L 3
10.3%
C 2
 
6.9%
H 2
 
6.9%
G 1
 
3.4%
N 1
 
3.4%
Y 1
 
3.4%
Other values (2) 2
 
6.9%
Common
ValueCountFrequency (%)
) 124
37.2%
( 123
36.9%
81
24.3%
- 4
 
1.2%
2 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2076
85.2%
ASCII 362
 
14.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 124
34.3%
( 123
34.0%
81
22.4%
K 5
 
1.4%
S 5
 
1.4%
- 4
 
1.1%
I 4
 
1.1%
B 3
 
0.8%
L 3
 
0.8%
C 2
 
0.6%
Other values (7) 8
 
2.2%
Hangul
ValueCountFrequency (%)
123
 
5.9%
79
 
3.8%
75
 
3.6%
66
 
3.2%
66
 
3.2%
51
 
2.5%
49
 
2.4%
47
 
2.3%
42
 
2.0%
36
 
1.7%
Other values (232) 1442
69.5%
Distinct169
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2003-10-15 00:00:00
Maximum2024-05-07 15:30:48
2024-05-11T08:44:54.702618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:44:55.192225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
I
106 
U
68 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 106
60.9%
U 68
39.1%

Length

2024-05-11T08:44:55.740985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:56.183458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 106
60.9%
u 68
39.1%
Distinct71
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T08:44:56.571228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:44:57.121349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
위탁급식영업
174 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위탁급식영업 174
100.0%

Length

2024-05-11T08:44:57.647666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:44:58.042583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁급식영업 174
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct77
Distinct (%)49.0%
Missing17
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean197853.05
Minimum195670.19
Maximum201064.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T08:44:58.698536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum195670.19
5-th percentile196274.25
Q1196831.46
median197373.84
Q3198818.59
95-th percentile200550.11
Maximum201064.18
Range5393.9904
Interquartile range (IQR)1987.1267

Descriptive statistics

Standard deviation1349.4832
Coefficient of variation (CV)0.006820634
Kurtosis-0.21209941
Mean197853.05
Median Absolute Deviation (MAD)593.3519
Skewness0.91945058
Sum31062928
Variance1821104.9
MonotonicityNot monotonic
2024-05-11T08:44:59.270800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196831.461759469 10
 
5.7%
197007.922668946 9
 
5.2%
197761.410710916 9
 
5.2%
198168.762047391 7
 
4.0%
196762.077394917 6
 
3.4%
196610.663839509 4
 
2.3%
199062.254914329 4
 
2.3%
200358.6724039 4
 
2.3%
197138.823553 4
 
2.3%
197373.839856311 4
 
2.3%
Other values (67) 96
55.2%
(Missing) 17
 
9.8%
ValueCountFrequency (%)
195670.19398129 2
1.1%
195808.172728202 1
 
0.6%
196060.559012563 1
 
0.6%
196116.002569515 1
 
0.6%
196116.309022386 1
 
0.6%
196145.20051733 1
 
0.6%
196147.046126753 1
 
0.6%
196306.051443697 2
1.1%
196610.663839509 4
2.3%
196701.958402155 2
1.1%
ValueCountFrequency (%)
201064.184415519 2
1.1%
201059.460247346 1
 
0.6%
200977.746562332 2
1.1%
200930.259032306 1
 
0.6%
200550.113887155 3
1.7%
200460.554296253 1
 
0.6%
200441.911481979 1
 
0.6%
200358.6724039 4
2.3%
200310.979917902 1
 
0.6%
200107.397089349 1
 
0.6%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct77
Distinct (%)49.0%
Missing17
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean448068.48
Minimum446131.33
Maximum450168.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T08:44:59.975634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum446131.33
5-th percentile446131.33
Q1447413.49
median448016.24
Q3448913.67
95-th percentile449783.99
Maximum450168.32
Range4036.9891
Interquartile range (IQR)1500.1837

Descriptive statistics

Standard deviation1035.1545
Coefficient of variation (CV)0.0023102595
Kurtosis-0.75621357
Mean448068.48
Median Absolute Deviation (MAD)684.98278
Skewness0.081209794
Sum70346751
Variance1071544.7
MonotonicityNot monotonic
2024-05-11T08:45:00.628782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449320.370380461 10
 
5.7%
446929.732199319 9
 
5.2%
446131.334535482 9
 
5.2%
449206.873601966 7
 
4.0%
447480.039577359 6
 
3.4%
447742.348636253 4
 
2.3%
447812.90979317 4
 
2.3%
448657.143586243 4
 
2.3%
447436.953717 4
 
2.3%
450014.537949042 4
 
2.3%
Other values (67) 96
55.2%
(Missing) 17
 
9.8%
ValueCountFrequency (%)
446131.334535482 9
5.2%
446491.618834731 1
 
0.6%
446526.164539271 1
 
0.6%
446573.556869158 3
 
1.7%
446837.982163744 2
 
1.1%
446873.431044381 1
 
0.6%
446925.923870883 1
 
0.6%
446929.732199319 9
5.2%
446931.975248096 2
 
1.1%
447077.126289262 2
 
1.1%
ValueCountFrequency (%)
450168.3235982 1
 
0.6%
450014.537949042 4
 
2.3%
450012.796232665 1
 
0.6%
449844.319841559 1
 
0.6%
449839.909004623 1
 
0.6%
449770.016319402 2
 
1.1%
449711.539621321 3
 
1.7%
449491.153524325 2
 
1.1%
449385.004852507 1
 
0.6%
449320.370380461 10
5.7%

위생업태명
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
위탁급식영업
124 
<NA>
50 

Length

Max length6
Median length6
Mean length5.4252874
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위탁급식영업 124
71.3%
<NA> 50
28.7%

Length

2024-05-11T08:45:01.187941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:01.728760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁급식영업 124
71.3%
na 50
28.7%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)13.8%
Missing109
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean1.0923077
Minimum0
Maximum13
Zeros49
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T08:45:02.150484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.8
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4731248
Coefficient of variation (CV)2.2641283
Kurtosis8.497148
Mean1.0923077
Median Absolute Deviation (MAD)0
Skewness2.7683334
Sum71
Variance6.1163462
MonotonicityNot monotonic
2024-05-11T08:45:02.530821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 49
28.2%
7 3
 
1.7%
2 3
 
1.7%
1 3
 
1.7%
6 2
 
1.1%
4 2
 
1.1%
5 1
 
0.6%
3 1
 
0.6%
13 1
 
0.6%
(Missing) 109
62.6%
ValueCountFrequency (%)
0 49
28.2%
1 3
 
1.7%
2 3
 
1.7%
3 1
 
0.6%
4 2
 
1.1%
5 1
 
0.6%
6 2
 
1.1%
7 3
 
1.7%
13 1
 
0.6%
ValueCountFrequency (%)
13 1
 
0.6%
7 3
 
1.7%
6 2
 
1.1%
5 1
 
0.6%
4 2
 
1.1%
3 1
 
0.6%
2 3
 
1.7%
1 3
 
1.7%
0 49
28.2%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)19.7%
Missing108
Missing (%)62.1%
Infinite0
Infinite (%)0.0%
Mean3.3181818
Minimum0
Maximum23
Zeros36
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T08:45:02.971218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7883162
Coefficient of variation (CV)1.4430542
Kurtosis3.8888078
Mean3.3181818
Median Absolute Deviation (MAD)0
Skewness1.7870711
Sum219
Variance22.927972
MonotonicityNot monotonic
2024-05-11T08:45:03.470932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 36
 
20.7%
10 5
 
2.9%
4 4
 
2.3%
3 3
 
1.7%
7 3
 
1.7%
9 3
 
1.7%
2 3
 
1.7%
5 2
 
1.1%
6 2
 
1.1%
8 2
 
1.1%
Other values (3) 3
 
1.7%
(Missing) 108
62.1%
ValueCountFrequency (%)
0 36
20.7%
2 3
 
1.7%
3 3
 
1.7%
4 4
 
2.3%
5 2
 
1.1%
6 2
 
1.1%
7 3
 
1.7%
8 2
 
1.1%
9 3
 
1.7%
10 5
 
2.9%
ValueCountFrequency (%)
23 1
 
0.6%
18 1
 
0.6%
11 1
 
0.6%
10 5
2.9%
9 3
1.7%
8 2
 
1.1%
7 3
1.7%
6 2
 
1.1%
5 2
 
1.1%
4 4
2.3%

영업장주변구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
164 
기타
 
6
주택가주변
 
4

Length

Max length5
Median length4
Mean length3.954023
Min length2

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> 164
94.3%
기타 6
 
3.4%
주택가주변 4
 
2.3%

Length

2024-05-11T08:45:04.026453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:04.512244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 164
94.3%
기타 6
 
3.4%
주택가주변 4
 
2.3%

등급구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
171 
자율
 
3

Length

Max length4
Median length4
Mean length3.9655172
Min length2

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> 171
98.3%
자율 3
 
1.7%

Length

2024-05-11T08:45:05.019718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:05.574856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 171
98.3%
자율 3
 
1.7%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
132 
상수도전용
42 

Length

Max length5
Median length4
Mean length4.2413793
Min length4

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> 132
75.9%
상수도전용 42
 
24.1%

Length

2024-05-11T08:45:06.024399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:06.366535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 132
75.9%
상수도전용 42
 
24.1%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
165 
0
 
9

Length

Max length4
Median length4
Mean length3.8448276
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 165
94.8%
0 9
 
5.2%

Length

2024-05-11T08:45:06.674054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:07.011203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 165
94.8%
0 9
 
5.2%

본사종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
163 
0
 
11

Length

Max length4
Median length4
Mean length3.8103448
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 163
93.7%
0 11
 
6.3%

Length

2024-05-11T08:45:07.450625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:07.873585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 163
93.7%
0 11
 
6.3%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
163 
0
 
11

Length

Max length4
Median length4
Mean length3.8103448
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 163
93.7%
0 11
 
6.3%

Length

2024-05-11T08:45:08.281404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:08.755820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 163
93.7%
0 11
 
6.3%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
163 
0
 
11

Length

Max length4
Median length4
Mean length3.8103448
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 163
93.7%
0 11
 
6.3%

Length

2024-05-11T08:45:09.167955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:09.535371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 163
93.7%
0 11
 
6.3%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
163 
0
 
11

Length

Max length4
Median length4
Mean length3.8103448
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 163
93.7%
0 11
 
6.3%

Length

2024-05-11T08:45:09.963737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:10.366460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 163
93.7%
0 11
 
6.3%

건물소유구분명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing172
Missing (%)98.9%
Memory size1.5 KiB
2024-05-11T08:45:10.577310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4
Distinct characters2
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

Unique0 ?
Unique (%)0.0%

Sample

1st row자가
2nd row자가
ValueCountFrequency (%)
자가 2
100.0%
2024-05-11T08:45:11.252974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
163 
0
 
11

Length

Max length4
Median length4
Mean length3.8103448
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 163
93.7%
0 11
 
6.3%

Length

2024-05-11T08:45:11.668494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:12.017369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 163
93.7%
0 11
 
6.3%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
163 
0
 
11

Length

Max length4
Median length4
Mean length3.8103448
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 163
93.7%
0 11
 
6.3%

Length

2024-05-11T08:45:12.681762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:45:13.181831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 163
93.7%
0 11
 
6.3%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.8%
Missing50
Missing (%)28.7%
Memory size480.0 B
False
124 
(Missing)
50 
ValueCountFrequency (%)
False 124
71.3%
(Missing) 50
28.7%
2024-05-11T08:45:13.647091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING 

Distinct102
Distinct (%)82.3%
Missing50
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean338.24008
Minimum3.3
Maximum1892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T08:45:14.002144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile18.165
Q198.28
median176.1
Q3596.3375
95-th percentile1005.555
Maximum1892
Range1888.7
Interquartile range (IQR)498.0575

Descriptive statistics

Standard deviation358.87359
Coefficient of variation (CV)1.0610025
Kurtosis2.3751134
Mean338.24008
Median Absolute Deviation (MAD)106.825
Skewness1.5697964
Sum41941.77
Variance128790.25
MonotonicityNot monotonic
2024-05-11T08:45:14.555227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784.97 4
 
2.3%
644.6 4
 
2.3%
120.7 2
 
1.1%
1341.0 2
 
1.1%
796.4 2
 
1.1%
289.6 2
 
1.1%
927.3 2
 
1.1%
94.05 2
 
1.1%
214.33 2
 
1.1%
174.0 2
 
1.1%
Other values (92) 100
57.5%
(Missing) 50
28.7%
ValueCountFrequency (%)
3.3 1
0.6%
5.58 1
0.6%
10.0 1
0.6%
13.2 1
0.6%
16.5 2
1.1%
18.0 1
0.6%
19.1 1
0.6%
23.0 1
0.6%
25.4 1
0.6%
32.0 1
0.6%
ValueCountFrequency (%)
1892.0 1
0.6%
1341.0 2
1.1%
1112.69 1
0.6%
1087.0 1
0.6%
1035.12 1
0.6%
1008.0 1
0.6%
991.7 1
0.6%
961.2 1
0.6%
955.33 1
0.6%
927.4 1
0.6%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing174
Missing (%)100.0%
Memory size1.7 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
030200003020000-120-2003-0000220031015<NA>3폐업2폐업20071119<NA><NA><NA><NA>16.50140712서울특별시 용산구 한강로3가 16-49번지 삼구빌딩 지하1층<NA><NA>(주)현대푸드시스템현대홈쇼핑점2007-04-09 00:00:00I2018-08-31 23:59:59.0위탁급식영업196060.559013447732.807254위탁급식영업00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N16.5<NA><NA><NA>
130200003020000-120-2003-0000320031015<NA>3폐업2폐업20210927<NA><NA><NA>0232712065459.00140850서울특별시 용산구 원효로4가 114-38 현대자동차 10층서울특별시 용산구 원효로 74, 10층 (원효로4가, 현대자동차)4365(주)현대그린푸드현대엠엔소프트점2021-09-27 13:58:13U2021-09-29 02:40:00.0위탁급식영업195670.193981447749.370373위탁급식영업00<NA><NA><NA>00000<NA>00N459.0<NA><NA><NA>
230200003020000-120-2003-0000420031015<NA>3폐업2폐업20170120<NA><NA><NA>02 71251115.58140850서울특별시 용산구 원효로4가 113-58번지 (지상4층)서울특별시 용산구 원효로 86 (원효로4가,(지상4층))4365(주)현대그린푸드현대자동차원효로서비스센터점2010-07-20 14:03:32I2018-08-31 23:59:59.0위탁급식영업195808.172728447753.654114위탁급식영업00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N5.58<NA><NA><NA>
330200003020000-120-2003-0000520031015<NA>3폐업2폐업20080924<NA><NA><NA>02 799778118.00140012서울특별시 용산구 한강로2가 191번지 (지하1층)<NA><NA>신세계푸드 국제상사점2006-03-24 00:00:00I2018-08-31 23:59:59.0위탁급식영업197099.381482447328.548832위탁급식영업00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N18.0<NA><NA><NA>
430200003020000-120-2003-0000620031015<NA>3폐업2폐업20050331<NA><NA><NA>0237805306182.50140880서울특별시 용산구 한강로3가 40-1번지 (지상1층)<NA><NA>아라코(주)서울철도차량지점2003-10-15 00:00:00I2018-08-31 23:59:59.0위탁급식영업196145.200517447480.626892위탁급식영업00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N182.5<NA><NA><NA>
530200003020000-120-2003-0000720031015<NA>3폐업2폐업20150930<NA><NA><NA><NA>54.20140893서울특별시 용산구 한남동 747-7번지 지하1층서울특별시 용산구 소월로 322 (한남동, 747-7 지하1층)4347삼성웰스토리(주)하얏트호텔2013-12-31 16:16:46I2018-08-31 23:59:59.0위탁급식영업199737.097414448587.753315위탁급식영업710<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N54.2<NA><NA><NA>
630200003020000-120-2003-0000820031015<NA>3폐업2폐업20040127<NA><NA><NA>023273763574.00140830서울특별시 용산구 서계동 259-3번지 배문중고등학교<NA><NA>씨제이푸드시스템(주) 배문중고등학교점2003-12-08 00:00:00I2018-08-31 23:59:59.0위탁급식영업196780.487957449770.016319위탁급식영업00<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N74.0<NA><NA><NA>
730200003020000-120-2003-0000920031015<NA>1영업/정상1영업<NA><NA><NA><NA>02 7024136237.00140133서울특별시 용산구 청파동3가 100번지 신광초.중.고등학교서울특별시 용산구 청파로 263 (청파동3가,신광초.중.고등학교)4313삼호단체급식 신광초중고등학교점2003-10-15 00:00:00I2018-08-31 23:59:59.0위탁급식영업197276.781534448973.970262위탁급식영업710<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N237.0<NA><NA><NA>
830200003020000-120-2003-0001020031017<NA>3폐업2폐업20050812<NA><NA><NA><NA>217.76140823서울특별시 용산구 보광동 168번지 (지상1,2층)<NA><NA>삼성애버랜드(주)오산2003-10-17 00:00:00I2018-08-31 23:59:59.0위탁급식영업<NA><NA>위탁급식영업00<NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N217.76<NA><NA><NA>
930200003020000-120-2003-0001120031017<NA>3폐업2폐업20060731<NA><NA><NA>02793 9400252.50140883서울특별시 용산구 한강로3가 65번지 (지하1층)<NA><NA>삼성애버랜드(주)용산공고2003-10-17 00:00:00I2018-08-31 23:59:59.0위탁급식영업<NA><NA>위탁급식영업00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N252.5<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
16430200003020000-120-2023-000092023-06-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>145.20140-821서울특별시 용산구 동자동 43-205 한국철도공사서울특별시 용산구 청파로 378, 서울역 4층 (동자동)4301(주)후레쉬케터링 서울역점2023-06-01 15:38:13I2022-12-06 00:03:00.0위탁급식영업197373.839856450014.537949<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16530200003020000-120-2023-000102023-06-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>68.60140-884서울특별시 용산구 한남동 4-13 서울독일학교서울특별시 용산구 독서당로 123-6, 서울독일학교 1층 (한남동)4419유한책임회사 맥시멈케터링2023-06-30 14:07:54I2022-12-07 00:02:00.0위탁급식영업200977.746562448332.300522<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16630200003020000-120-2023-000112023-07-14<NA>1영업/정상1영업<NA><NA><NA><NA><NA>836.55140-877서울특별시 용산구 한강로3가 40-969 서울드래곤시티서울특별시 용산구 청파로20길 95, 서울드래곤시티 지하1층 (한강로3가)4372(주)동원홈푸드 서울드래곤시티2023-07-14 15:53:26I2022-12-06 23:06:00.0위탁급식영업196610.66384447742.348636<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16730200003020000-120-2023-000122023-12-15<NA>1영업/정상1영업<NA><NA><NA><NA><NA>305.19140-780서울특별시 용산구 한강로3가 40-999 용산역서울특별시 용산구 한강대로23길 55, 이마트 지하2층 (한강로3가)4377(주)엘에스씨푸드이마트용산점2023-12-15 14:06:23I2022-11-01 23:07:00.0위탁급식영업196762.077395447480.039577<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16830200003020000-120-2024-000012024-02-27<NA>1영업/정상1영업<NA><NA><NA><NA><NA>303.20140-860서울특별시 용산구 이태원동 22-34서울특별시 용산구 장문로 9, 1층 (이태원동)4392주식회사 비케이푸드2024-02-27 17:04:06I2023-12-01 22:09:00.0위탁급식영업199305.125668447556.568723<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16930200003020000-120-2024-000022024-02-29<NA>1영업/정상1영업<NA><NA><NA><NA>0269057777640.00140-883서울특별시 용산구 한강로3가 65-209 용산공업고등학교서울특별시 용산구 서빙고로 24, 용산공업고등학교 지하1층 (한강로3가)4388제이제이케터링(주)용산철도고등학교2024-02-29 11:12:49I2023-12-03 00:02:00.0위탁급식영업197007.922669446929.732199<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17030200003020000-120-2024-000032024-03-11<NA>1영업/정상1영업<NA><NA><NA><NA>02 79320401087.00140-853서울특별시 용산구 이촌동 300-302 중경고등학교서울특별시 용산구 이촌로84길 34, 중경고등학교 1층 (이촌동)4426세종에프앤에스(주) 중경고2024-03-11 10:46:11I2023-12-02 23:03:00.0위탁급식영업197761.410711446131.334535<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17130200003020000-120-2024-000042024-03-11<NA>1영업/정상1영업<NA><NA><NA><NA><NA>344.58140-013서울특별시 용산구 한강로3가 98서울특별시 용산구 서빙고로 17, 101동 3층 (한강로3가)4387삼성웰스토리(주)용산센트럴파크2024-03-11 13:41:15I2023-12-02 23:03:00.0위탁급식영업197056.728931447077.126289<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17230200003020000-120-2024-000052024-03-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>140.20140-881서울특별시 용산구 한강로3가 40-708서울특별시 용산구 한강대로23길 25, 지하2층 (한강로3가)4378(주)동원홈푸드 나인트리호텔 용산2024-03-12 10:04:04I2023-12-02 23:04:00.0위탁급식영업196785.404802447267.982864<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17330200003020000-120-2024-000062024-05-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>105.18140-861서울특별시 용산구 이태원동 427서울특별시 용산구 녹사평대로40길 1, 1층 (이태원동)4345프로뱅크 천우모터스점2024-05-02 16:02:32I2023-12-05 00:08:00.0위탁급식영업198818.588488448180.77044<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>