Overview

Dataset statistics

Number of variables44
Number of observations299
Missing cells2717
Missing cells (%)20.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.6 KiB
Average record size in memory375.4 B

Variable types

Categorical21
Text7
DateTime4
Unsupported6
Numeric5
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
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-18272/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (54.6%)Imbalance
위생업태명 is highly imbalanced (52.4%)Imbalance
남성종사자수 is highly imbalanced (61.3%)Imbalance
여성종사자수 is highly imbalanced (57.8%)Imbalance
총인원 is highly imbalanced (82.2%)Imbalance
보증액 is highly imbalanced (74.9%)Imbalance
월세액 is highly imbalanced (73.5%)Imbalance
시설총규모 is highly imbalanced (78.6%)Imbalance
인허가취소일자 has 299 (100.0%) missing valuesMissing
폐업일자 has 34 (11.4%) missing valuesMissing
휴업시작일자 has 299 (100.0%) missing valuesMissing
휴업종료일자 has 299 (100.0%) missing valuesMissing
재개업일자 has 299 (100.0%) missing valuesMissing
전화번호 has 79 (26.4%) missing valuesMissing
소재지면적 has 18 (6.0%) missing valuesMissing
소재지우편번호 has 9 (3.0%) missing valuesMissing
지번주소 has 9 (3.0%) missing valuesMissing
도로명주소 has 143 (47.8%) missing valuesMissing
도로명우편번호 has 145 (48.5%) missing valuesMissing
좌표정보(X) has 10 (3.3%) missing valuesMissing
좌표정보(Y) has 10 (3.3%) missing valuesMissing
공장생산직종업원수 has 144 (48.2%) missing valuesMissing
다중이용업소여부 has 26 (8.7%) missing valuesMissing
전통업소지정번호 has 299 (100.0%) missing valuesMissing
전통업소주된음식 has 299 (100.0%) missing valuesMissing
홈페이지 has 296 (99.0%) 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
소재지면적 has 7 (2.3%) zerosZeros
공장생산직종업원수 has 116 (38.8%) zerosZeros

Reproduction

Analysis started2024-05-11 01:32:46.212960
Analysis finished2024-05-11 01:32:48.148036
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
3200000
299 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 299
100.0%

Length

2024-05-11T01:32:48.378196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:32:48.795886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 299
100.0%

관리번호
Text

UNIQUE 

Distinct299
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-05-11T01:32:49.346019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters6578
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

Unique299 ?
Unique (%)100.0%

Sample

1st row3200000-106-1899-00627
2nd row3200000-106-1970-00340
3rd row3200000-106-1973-00354
4th row3200000-106-1978-00341
5th row3200000-106-1982-00379
ValueCountFrequency (%)
3200000-106-1899-00627 1
 
0.3%
3200000-106-2012-00012 1
 
0.3%
3200000-106-2012-00010 1
 
0.3%
3200000-106-2012-00009 1
 
0.3%
3200000-106-2012-00008 1
 
0.3%
3200000-106-2012-00007 1
 
0.3%
3200000-106-2012-00006 1
 
0.3%
3200000-106-2012-00005 1
 
0.3%
3200000-106-2012-00004 1
 
0.3%
3200000-106-2012-00003 1
 
0.3%
Other values (289) 289
96.7%
2024-05-11T01:32:50.531023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3102
47.2%
- 897
 
13.6%
2 624
 
9.5%
1 623
 
9.5%
3 412
 
6.3%
6 381
 
5.8%
9 206
 
3.1%
7 104
 
1.6%
8 80
 
1.2%
4 75
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5681
86.4%
Dash Punctuation 897
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3102
54.6%
2 624
 
11.0%
1 623
 
11.0%
3 412
 
7.3%
6 381
 
6.7%
9 206
 
3.6%
7 104
 
1.8%
8 80
 
1.4%
4 75
 
1.3%
5 74
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3102
47.2%
- 897
 
13.6%
2 624
 
9.5%
1 623
 
9.5%
3 412
 
6.3%
6 381
 
5.8%
9 206
 
3.1%
7 104
 
1.6%
8 80
 
1.2%
4 75
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3102
47.2%
- 897
 
13.6%
2 624
 
9.5%
1 623
 
9.5%
3 412
 
6.3%
6 381
 
5.8%
9 206
 
3.1%
7 104
 
1.6%
8 80
 
1.2%
4 75
 
1.1%
Distinct279
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum1970-03-19 00:00:00
Maximum2024-04-16 00:00:00
2024-05-11T01:32:51.124141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:32:51.887616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing299
Missing (%)100.0%
Memory size2.8 KiB
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
3
265 
1
34 

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 (%)
3 265
88.6%
1 34
 
11.4%

Length

2024-05-11T01:32:52.593050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:32:53.061738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 265
88.6%
1 34
 
11.4%

영업상태명
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
폐업
265 
영업/정상
34 

Length

Max length5
Median length2
Mean length2.3411371
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 265
88.6%
영업/정상 34
 
11.4%

Length

2024-05-11T01:32:53.653467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:32:54.224641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 265
88.6%
영업/정상 34
 
11.4%
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2
265 
1
34 

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 (%)
2 265
88.6%
1 34
 
11.4%

Length

2024-05-11T01:32:54.536593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:32:55.078010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 265
88.6%
1 34
 
11.4%
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
폐업
265 
영업
34 

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 265
88.6%
영업 34
 
11.4%

Length

2024-05-11T01:32:55.676574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:32:56.175158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 265
88.6%
영업 34
 
11.4%

폐업일자
Date

MISSING 

Distinct241
Distinct (%)90.9%
Missing34
Missing (%)11.4%
Memory size2.5 KiB
Minimum1997-10-10 00:00:00
Maximum2024-03-04 00:00:00
2024-05-11T01:32:56.713895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:32:57.309824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing299
Missing (%)100.0%
Memory size2.8 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing299
Missing (%)100.0%
Memory size2.8 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing299
Missing (%)100.0%
Memory size2.8 KiB

전화번호
Text

MISSING 

Distinct189
Distinct (%)85.9%
Missing79
Missing (%)26.4%
Memory size2.5 KiB
2024-05-11T01:32:58.213896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.1818182
Min length2

Characters and Unicode

Total characters2020
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)83.2%

Sample

1st row02 8735247
2nd row02 8558422
3rd row02 8899922
4th row02 8548839
5th row02
ValueCountFrequency (%)
02 192
46.9%
070 4
 
1.0%
8749295 3
 
0.7%
876 2
 
0.5%
8866368 2
 
0.5%
878 2
 
0.5%
872 2
 
0.5%
889 2
 
0.5%
8713644 2
 
0.5%
8691159 2
 
0.5%
Other values (194) 196
47.9%
2024-05-11T01:32:59.778523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 345
17.1%
8 314
15.5%
2 312
15.4%
216
10.7%
7 153
7.6%
5 133
 
6.6%
6 125
 
6.2%
3 116
 
5.7%
1 105
 
5.2%
9 102
 
5.0%
Other values (2) 99
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1803
89.3%
Space Separator 216
 
10.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 345
19.1%
8 314
17.4%
2 312
17.3%
7 153
8.5%
5 133
 
7.4%
6 125
 
6.9%
3 116
 
6.4%
1 105
 
5.8%
9 102
 
5.7%
4 98
 
5.4%
Space Separator
ValueCountFrequency (%)
216
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 345
17.1%
8 314
15.5%
2 312
15.4%
216
10.7%
7 153
7.6%
5 133
 
6.6%
6 125
 
6.2%
3 116
 
5.7%
1 105
 
5.2%
9 102
 
5.0%
Other values (2) 99
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 345
17.1%
8 314
15.5%
2 312
15.4%
216
10.7%
7 153
7.6%
5 133
 
6.6%
6 125
 
6.2%
3 116
 
5.7%
1 105
 
5.2%
9 102
 
5.0%
Other values (2) 99
 
4.9%

소재지면적
Real number (ℝ)

MISSING  ZEROS 

Distinct238
Distinct (%)84.7%
Missing18
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean65.053665
Minimum0
Maximum386.26
Zeros7
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-11T01:33:00.475580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.5
Q125.45
median48
Q396.31
95-th percentile159.86
Maximum386.26
Range386.26
Interquartile range (IQR)70.86

Descriptive statistics

Standard deviation53.651277
Coefficient of variation (CV)0.82472335
Kurtosis5.369701
Mean65.053665
Median Absolute Deviation (MAD)27
Skewness1.77865
Sum18280.08
Variance2878.4595
MonotonicityNot monotonic
2024-05-11T01:33:01.202513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
2.3%
33.0 5
 
1.7%
100.0 5
 
1.7%
25.0 5
 
1.7%
20.0 4
 
1.3%
60.0 3
 
1.0%
13.2 2
 
0.7%
16.5 2
 
0.7%
57.0 2
 
0.7%
39.18 2
 
0.7%
Other values (228) 244
81.6%
(Missing) 18
 
6.0%
ValueCountFrequency (%)
0.0 7
2.3%
5.02 1
 
0.3%
5.5 1
 
0.3%
6.0 1
 
0.3%
9.36 1
 
0.3%
9.9 1
 
0.3%
10.92 1
 
0.3%
11.44 1
 
0.3%
11.5 1
 
0.3%
12.48 1
 
0.3%
ValueCountFrequency (%)
386.26 1
0.3%
294.32 1
0.3%
264.0 1
0.3%
215.28 1
0.3%
209.28 1
0.3%
200.22 1
0.3%
198.0 1
0.3%
188.96 1
0.3%
180.0 1
0.3%
177.58 1
0.3%

소재지우편번호
Text

MISSING 

Distinct101
Distinct (%)34.8%
Missing9
Missing (%)3.0%
Memory size2.5 KiB
2024-05-11T01:33:02.144626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0758621
Min length6

Characters and Unicode

Total characters1762
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

Unique37 ?
Unique (%)12.8%

Sample

1st row151899
2nd row151880
3rd row151835
4th row151883
5th row151876
ValueCountFrequency (%)
151849 13
 
4.5%
151848 8
 
2.8%
151015 8
 
2.8%
151843 7
 
2.4%
151876 7
 
2.4%
151869 7
 
2.4%
151855 7
 
2.4%
151832 7
 
2.4%
151913 7
 
2.4%
151830 7
 
2.4%
Other values (91) 212
73.1%
2024-05-11T01:33:03.578214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 630
35.8%
5 353
20.0%
8 282
16.0%
9 103
 
5.8%
0 92
 
5.2%
4 72
 
4.1%
3 69
 
3.9%
7 53
 
3.0%
2 52
 
3.0%
6 34
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1740
98.8%
Dash Punctuation 22
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 630
36.2%
5 353
20.3%
8 282
16.2%
9 103
 
5.9%
0 92
 
5.3%
4 72
 
4.1%
3 69
 
4.0%
7 53
 
3.0%
2 52
 
3.0%
6 34
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1762
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 630
35.8%
5 353
20.0%
8 282
16.0%
9 103
 
5.8%
0 92
 
5.2%
4 72
 
4.1%
3 69
 
3.9%
7 53
 
3.0%
2 52
 
3.0%
6 34
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 630
35.8%
5 353
20.0%
8 282
16.0%
9 103
 
5.8%
0 92
 
5.2%
4 72
 
4.1%
3 69
 
3.9%
7 53
 
3.0%
2 52
 
3.0%
6 34
 
1.9%

지번주소
Text

MISSING 

Distinct280
Distinct (%)96.6%
Missing9
Missing (%)3.0%
Memory size2.5 KiB
2024-05-11T01:33:04.500907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length24.417241
Min length17

Characters and Unicode

Total characters7081
Distinct characters88
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

Unique270 ?
Unique (%)93.1%

Sample

1st row서울특별시 관악구 신림동 1576-16번지
2nd row서울특별시 관악구 신림동 626-8
3rd row서울특별시 관악구 봉천동 1603-9번지 10
4th row서울특별시 관악구 신림동 637-14번지
5th row서울특별시 관악구 신림동 541-0번지
ValueCountFrequency (%)
서울특별시 290
22.9%
관악구 289
22.8%
봉천동 142
 
11.2%
신림동 138
 
10.9%
지하1층 14
 
1.1%
1층 12
 
0.9%
남현동 9
 
0.7%
지상1층 7
 
0.6%
지하 6
 
0.5%
지층 5
 
0.4%
Other values (326) 355
28.0%
2024-05-11T01:33:06.485946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1231
 
17.4%
1 383
 
5.4%
295
 
4.2%
292
 
4.1%
291
 
4.1%
291
 
4.1%
290
 
4.1%
290
 
4.1%
290
 
4.1%
290
 
4.1%
Other values (78) 3138
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3970
56.1%
Decimal Number 1568
 
22.1%
Space Separator 1231
 
17.4%
Dash Punctuation 282
 
4.0%
Open Punctuation 10
 
0.1%
Close Punctuation 10
 
0.1%
Other Punctuation 7
 
0.1%
Uppercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
295
 
7.4%
292
 
7.4%
291
 
7.3%
291
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
Other values (60) 1061
26.7%
Decimal Number
ValueCountFrequency (%)
1 383
24.4%
6 176
11.2%
2 156
9.9%
5 150
 
9.6%
4 144
 
9.2%
9 129
 
8.2%
3 125
 
8.0%
0 124
 
7.9%
7 100
 
6.4%
8 81
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
1231
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 282
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3970
56.1%
Common 3109
43.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
295
 
7.4%
292
 
7.4%
291
 
7.3%
291
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
Other values (60) 1061
26.7%
Common
ValueCountFrequency (%)
1231
39.6%
1 383
 
12.3%
- 282
 
9.1%
6 176
 
5.7%
2 156
 
5.0%
5 150
 
4.8%
4 144
 
4.6%
9 129
 
4.1%
3 125
 
4.0%
0 124
 
4.0%
Other values (6) 209
 
6.7%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3970
56.1%
ASCII 3111
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1231
39.6%
1 383
 
12.3%
- 282
 
9.1%
6 176
 
5.7%
2 156
 
5.0%
5 150
 
4.8%
4 144
 
4.6%
9 129
 
4.1%
3 125
 
4.0%
0 124
 
4.0%
Other values (8) 211
 
6.8%
Hangul
ValueCountFrequency (%)
295
 
7.4%
292
 
7.4%
291
 
7.3%
291
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
290
 
7.3%
Other values (60) 1061
26.7%

도로명주소
Text

MISSING 

Distinct152
Distinct (%)97.4%
Missing143
Missing (%)47.8%
Memory size2.5 KiB
2024-05-11T01:33:07.793495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length43
Mean length29.307692
Min length21

Characters and Unicode

Total characters4572
Distinct characters134
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

Unique148 ?
Unique (%)94.9%

Sample

1st row서울특별시 관악구 난곡로34나길 6 (신림동)
2nd row서울특별시 관악구 남부순환로226길 23-10 (봉천동,10)
3rd row서울특별시 관악구 은천로 9 (봉천동)
4th row서울특별시 관악구 봉천로 571 (봉천동,지하1층)
5th row서울특별시 관악구 원신2길 34 (신림동, 신림시장)
ValueCountFrequency (%)
서울특별시 156
17.1%
관악구 155
17.0%
봉천동 75
 
8.2%
신림동 63
 
6.9%
1층 29
 
3.2%
남부순환로 13
 
1.4%
2층 10
 
1.1%
지하1층 9
 
1.0%
은천로 9
 
1.0%
봉천로 9
 
1.0%
Other values (265) 385
42.2%
2024-05-11T01:33:09.434905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
757
 
16.6%
1 192
 
4.2%
171
 
3.7%
170
 
3.7%
164
 
3.6%
161
 
3.5%
158
 
3.5%
) 158
 
3.5%
( 158
 
3.5%
158
 
3.5%
Other values (124) 2325
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2669
58.4%
Space Separator 757
 
16.6%
Decimal Number 690
 
15.1%
Close Punctuation 158
 
3.5%
Open Punctuation 158
 
3.5%
Other Punctuation 116
 
2.5%
Dash Punctuation 22
 
0.5%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
171
 
6.4%
170
 
6.4%
164
 
6.1%
161
 
6.0%
158
 
5.9%
158
 
5.9%
157
 
5.9%
157
 
5.9%
156
 
5.8%
128
 
4.8%
Other values (107) 1089
40.8%
Decimal Number
ValueCountFrequency (%)
1 192
27.8%
2 114
16.5%
3 76
 
11.0%
0 57
 
8.3%
5 55
 
8.0%
4 54
 
7.8%
6 50
 
7.2%
7 32
 
4.6%
8 31
 
4.5%
9 29
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
757
100.0%
Close Punctuation
ValueCountFrequency (%)
) 158
100.0%
Open Punctuation
ValueCountFrequency (%)
( 158
100.0%
Other Punctuation
ValueCountFrequency (%)
, 116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2669
58.4%
Common 1901
41.6%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
171
 
6.4%
170
 
6.4%
164
 
6.1%
161
 
6.0%
158
 
5.9%
158
 
5.9%
157
 
5.9%
157
 
5.9%
156
 
5.8%
128
 
4.8%
Other values (107) 1089
40.8%
Common
ValueCountFrequency (%)
757
39.8%
1 192
 
10.1%
) 158
 
8.3%
( 158
 
8.3%
, 116
 
6.1%
2 114
 
6.0%
3 76
 
4.0%
0 57
 
3.0%
5 55
 
2.9%
4 54
 
2.8%
Other values (5) 164
 
8.6%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2669
58.4%
ASCII 1903
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
757
39.8%
1 192
 
10.1%
) 158
 
8.3%
( 158
 
8.3%
, 116
 
6.1%
2 114
 
6.0%
3 76
 
4.0%
0 57
 
3.0%
5 55
 
2.9%
4 54
 
2.8%
Other values (7) 166
 
8.7%
Hangul
ValueCountFrequency (%)
171
 
6.4%
170
 
6.4%
164
 
6.1%
161
 
6.0%
158
 
5.9%
158
 
5.9%
157
 
5.9%
157
 
5.9%
156
 
5.8%
128
 
4.8%
Other values (107) 1089
40.8%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct85
Distinct (%)55.2%
Missing145
Missing (%)48.5%
Infinite0
Infinite (%)0.0%
Mean8765.4351
Minimum7621
Maximum8865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-11T01:33:10.062672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7621
5-th percentile8707
Q18739
median8769
Q38796.75
95-th percentile8856.35
Maximum8865
Range1244
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation103.19465
Coefficient of variation (CV)0.011772907
Kurtosis99.858544
Mean8765.4351
Median Absolute Deviation (MAD)30
Skewness-8.9590437
Sum1349877
Variance10649.136
MonotonicityNot monotonic
2024-05-11T01:33:10.644509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8719 5
 
1.7%
8739 5
 
1.7%
8750 5
 
1.7%
8782 5
 
1.7%
8793 4
 
1.3%
8784 4
 
1.3%
8789 4
 
1.3%
8788 3
 
1.0%
8727 3
 
1.0%
8742 3
 
1.0%
Other values (75) 113
37.8%
(Missing) 145
48.5%
ValueCountFrequency (%)
7621 1
 
0.3%
8700 1
 
0.3%
8701 1
 
0.3%
8702 3
1.0%
8703 1
 
0.3%
8707 2
0.7%
8708 1
 
0.3%
8709 1
 
0.3%
8711 1
 
0.3%
8713 1
 
0.3%
ValueCountFrequency (%)
8865 2
0.7%
8863 1
 
0.3%
8861 2
0.7%
8857 3
1.0%
8856 1
 
0.3%
8854 1
 
0.3%
8849 1
 
0.3%
8848 1
 
0.3%
8846 3
1.0%
8845 2
0.7%
Distinct281
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-05-11T01:33:11.462352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22
Mean length6.1036789
Min length2

Characters and Unicode

Total characters1825
Distinct characters374
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique264 ?
Unique (%)88.3%

Sample

1st row(주)나눔공동체
2nd row성운유과
3rd row관악식품공업사
4th row광명제과
5th row청미식품
ValueCountFrequency (%)
주식회사 5
 
1.4%
커피 4
 
1.2%
coffee 3
 
0.9%
신선식품 3
 
0.9%
풍년식품 2
 
0.6%
천연식품 2
 
0.6%
우일식품 2
 
0.6%
주)나눔공동체 2
 
0.6%
한국가시오갈피재배협회 2
 
0.6%
동현통상 2
 
0.6%
Other values (310) 320
92.2%
2024-05-11T01:33:12.998793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
 
4.7%
81
 
4.4%
48
 
2.6%
41
 
2.2%
( 40
 
2.2%
) 40
 
2.2%
37
 
2.0%
35
 
1.9%
27
 
1.5%
27
 
1.5%
Other values (364) 1363
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1528
83.7%
Lowercase Letter 113
 
6.2%
Space Separator 48
 
2.6%
Uppercase Letter 47
 
2.6%
Open Punctuation 40
 
2.2%
Close Punctuation 40
 
2.2%
Other Punctuation 8
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
86
 
5.6%
81
 
5.3%
41
 
2.7%
37
 
2.4%
35
 
2.3%
27
 
1.8%
27
 
1.8%
26
 
1.7%
25
 
1.6%
25
 
1.6%
Other values (319) 1118
73.2%
Lowercase Letter
ValueCountFrequency (%)
e 16
14.2%
o 14
12.4%
r 9
 
8.0%
n 9
 
8.0%
s 7
 
6.2%
a 7
 
6.2%
f 7
 
6.2%
i 6
 
5.3%
y 5
 
4.4%
p 5
 
4.4%
Other values (10) 28
24.8%
Uppercase Letter
ValueCountFrequency (%)
F 7
14.9%
C 6
12.8%
O 3
 
6.4%
M 3
 
6.4%
S 3
 
6.4%
E 3
 
6.4%
R 3
 
6.4%
A 3
 
6.4%
B 3
 
6.4%
G 2
 
4.3%
Other values (9) 11
23.4%
Other Punctuation
ValueCountFrequency (%)
. 7
87.5%
& 1
 
12.5%
Space Separator
ValueCountFrequency (%)
48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1526
83.6%
Latin 160
 
8.8%
Common 137
 
7.5%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
86
 
5.6%
81
 
5.3%
41
 
2.7%
37
 
2.4%
35
 
2.3%
27
 
1.8%
27
 
1.8%
26
 
1.7%
25
 
1.6%
25
 
1.6%
Other values (318) 1116
73.1%
Latin
ValueCountFrequency (%)
e 16
 
10.0%
o 14
 
8.8%
r 9
 
5.6%
n 9
 
5.6%
s 7
 
4.4%
F 7
 
4.4%
a 7
 
4.4%
f 7
 
4.4%
C 6
 
3.8%
i 6
 
3.8%
Other values (29) 72
45.0%
Common
ValueCountFrequency (%)
48
35.0%
( 40
29.2%
) 40
29.2%
. 7
 
5.1%
- 1
 
0.7%
& 1
 
0.7%
Han
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1526
83.6%
ASCII 297
 
16.3%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
86
 
5.6%
81
 
5.3%
41
 
2.7%
37
 
2.4%
35
 
2.3%
27
 
1.8%
27
 
1.8%
26
 
1.7%
25
 
1.6%
25
 
1.6%
Other values (318) 1116
73.1%
ASCII
ValueCountFrequency (%)
48
16.2%
( 40
 
13.5%
) 40
 
13.5%
e 16
 
5.4%
o 14
 
4.7%
r 9
 
3.0%
n 9
 
3.0%
s 7
 
2.4%
F 7
 
2.4%
a 7
 
2.4%
Other values (35) 100
33.7%
CJK
ValueCountFrequency (%)
2
100.0%
Distinct249
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum1999-02-22 00:00:00
Maximum2024-04-26 15:17:37
2024-05-11T01:33:13.418707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:33:13.920235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
I
222 
U
77 

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 222
74.2%
U 77
 
25.8%

Length

2024-05-11T01:33:14.507453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:14.926299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 222
74.2%
u 77
 
25.8%
Distinct56
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 22:08:00
2024-05-11T01:33:15.595101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:33:16.263373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
식품제조가공업
243 
기타 식품제조가공업
55 
도시락제조업
 
1

Length

Max length10
Median length7
Mean length7.548495
Min length6

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 243
81.3%
기타 식품제조가공업 55
 
18.4%
도시락제조업 1
 
0.3%

Length

2024-05-11T01:33:16.952157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:17.309067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 298
84.2%
기타 55
 
15.5%
도시락제조업 1
 
0.3%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct260
Distinct (%)90.0%
Missing10
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean194372.55
Minimum183117.28
Maximum198315.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-11T01:33:17.659805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183117.28
5-th percentile191879.28
Q1193108.16
median194303.13
Q3195880.85
95-th percentile196874.02
Maximum198315.17
Range15197.893
Interquartile range (IQR)2772.6956

Descriptive statistics

Standard deviation1749.1378
Coefficient of variation (CV)0.0089988931
Kurtosis4.758225
Mean194372.55
Median Absolute Deviation (MAD)1376.7758
Skewness-0.77697892
Sum56173666
Variance3059482.9
MonotonicityNot monotonic
2024-05-11T01:33:18.404407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193957.944323928 3
 
1.0%
192875.641473476 3
 
1.0%
192096.349629652 3
 
1.0%
192907.998435918 3
 
1.0%
194765.424854219 2
 
0.7%
193392.187793429 2
 
0.7%
192174.723909669 2
 
0.7%
193578.742465284 2
 
0.7%
193524.803927514 2
 
0.7%
195042.018575645 2
 
0.7%
Other values (250) 265
88.6%
(Missing) 10
 
3.3%
ValueCountFrequency (%)
183117.281621173 1
0.3%
191084.937997691 1
0.3%
191128.308770063 1
0.3%
191190.030869923 1
0.3%
191214.160659729 1
0.3%
191250.724263753 1
0.3%
191439.696081567 2
0.7%
191508.163450457 1
0.3%
191562.399029785 1
0.3%
191599.370605007 1
0.3%
ValueCountFrequency (%)
198315.174254041 1
0.3%
198210.417913572 1
0.3%
198059.477138778 1
0.3%
197875.712608802 1
0.3%
197853.70704131 1
0.3%
197842.730350319 1
0.3%
197793.560496642 1
0.3%
197754.665850664 1
0.3%
197546.64110951 1
0.3%
197267.105289725 1
0.3%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct260
Distinct (%)90.0%
Missing10
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean442078.53
Minimum439023.17
Maximum451398.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-11T01:33:18.851741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439023.17
5-th percentile440760.04
Q1441606.82
median442147.68
Q3442590.3
95-th percentile442995.9
Maximum451398.8
Range12375.638
Interquartile range (IQR)983.47934

Descriptive statistics

Standard deviation908.7548
Coefficient of variation (CV)0.0020556411
Kurtosis37.86165
Mean442078.53
Median Absolute Deviation (MAD)475.79076
Skewness3.2846325
Sum1.277607 × 108
Variance825835.29
MonotonicityNot monotonic
2024-05-11T01:33:19.366077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
441437.774867372 3
 
1.0%
440760.03645246 3
 
1.0%
442339.213626523 3
 
1.0%
442460.358193313 3
 
1.0%
441929.168470765 2
 
0.7%
442795.997366942 2
 
0.7%
441573.738036061 2
 
0.7%
443547.049696825 2
 
0.7%
442138.97718266 2
 
0.7%
442021.083674931 2
 
0.7%
Other values (250) 265
88.6%
(Missing) 10
 
3.3%
ValueCountFrequency (%)
439023.167125842 1
0.3%
439825.822160271 1
0.3%
439843.900681742 1
0.3%
439852.868058712 1
0.3%
439931.980156728 1
0.3%
440358.537758338 1
0.3%
440380.585173363 1
0.3%
440444.706886832 1
0.3%
440498.447701298 1
0.3%
440685.11304384 1
0.3%
ValueCountFrequency (%)
451398.804774616 1
0.3%
443547.049696825 2
0.7%
443341.379446435 1
0.3%
443221.321009043 1
0.3%
443170.874650406 1
0.3%
443149.226765425 1
0.3%
443091.453335206 1
0.3%
443087.261787918 1
0.3%
443073.871803044 1
0.3%
443070.18671087 2
0.7%

위생업태명
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
식품제조가공업
238 
기타 식품제조가공업
34 
<NA>
26 
도시락제조업
 
1

Length

Max length10
Median length7
Mean length7.0769231
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 238
79.6%
기타 식품제조가공업 34
 
11.4%
<NA> 26
 
8.7%
도시락제조업 1
 
0.3%

Length

2024-05-11T01:33:19.704978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:20.024726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 272
81.7%
기타 34
 
10.2%
na 26
 
7.8%
도시락제조업 1
 
0.3%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
248 
0
30 
1
 
15
2
 
5
7
 
1

Length

Max length4
Median length4
Mean length3.4882943
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1
2nd row0
3rd row7
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 248
82.9%
0 30
 
10.0%
1 15
 
5.0%
2 5
 
1.7%
7 1
 
0.3%

Length

2024-05-11T01:33:20.370633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:20.719348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 248
82.9%
0 30
 
10.0%
1 15
 
5.0%
2 5
 
1.7%
7 1
 
0.3%

여성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
248 
0
37 
1
 
10
2
 
4

Length

Max length4
Median length4
Mean length3.4882943
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 248
82.9%
0 37
 
12.4%
1 10
 
3.3%
2 4
 
1.3%

Length

2024-05-11T01:33:21.103144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:21.422011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 248
82.9%
0 37
 
12.4%
1 10
 
3.3%
2 4
 
1.3%
Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
224 
주택가주변
55 
기타
 
18
아파트지역
 
2

Length

Max length5
Median length4
Mean length4.0702341
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row주택가주변
3rd row주택가주변
4th row주택가주변
5th row주택가주변

Common Values

ValueCountFrequency (%)
<NA> 224
74.9%
주택가주변 55
 
18.4%
기타 18
 
6.0%
아파트지역 2
 
0.7%

Length

2024-05-11T01:33:21.780545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:22.162562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 224
74.9%
주택가주변 55
 
18.4%
기타 18
 
6.0%
아파트지역 2
 
0.7%

등급구분명
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
224 
기타
75 

Length

Max length4
Median length4
Mean length3.4983278
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 224
74.9%
기타 75
 
25.1%

Length

2024-05-11T01:33:22.560371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:22.882868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 224
74.9%
기타 75
 
25.1%
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
178 
상수도전용
121 

Length

Max length5
Median length4
Mean length4.4046823
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 178
59.5%
상수도전용 121
40.5%

Length

2024-05-11T01:33:23.163352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:23.419061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 178
59.5%
상수도전용 121
40.5%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
291 
0
 
8

Length

Max length4
Median length4
Mean length3.9197324
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> 291
97.3%
0 8
 
2.7%

Length

2024-05-11T01:33:23.747654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:24.186560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 291
97.3%
0 8
 
2.7%
Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
148 
0
142 
1
 
8
2
 
1

Length

Max length4
Median length1
Mean length2.4849498
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 148
49.5%
0 142
47.5%
1 8
 
2.7%
2 1
 
0.3%

Length

2024-05-11T01:33:24.574734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:24.971473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 148
49.5%
0 142
47.5%
1 8
 
2.7%
2 1
 
0.3%
Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
148 
0
141 
1
 
9
6
 
1

Length

Max length4
Median length1
Mean length2.4849498
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 148
49.5%
0 141
47.2%
1 9
 
3.0%
6 1
 
0.3%

Length

2024-05-11T01:33:25.389050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:26.066021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 148
49.5%
0 141
47.2%
1 9
 
3.0%
6 1
 
0.3%
Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
149 
<NA>
148 
1
 
1
9
 
1

Length

Max length4
Median length1
Mean length2.4849498
Min length1

Unique

Unique2 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 149
49.8%
<NA> 148
49.5%
1 1
 
0.3%
9 1
 
0.3%

Length

2024-05-11T01:33:26.524063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:26.965840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 149
49.8%
na 148
49.5%
1 1
 
0.3%
9 1
 
0.3%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)6.5%
Missing144
Missing (%)48.2%
Infinite0
Infinite (%)0.0%
Mean0.78064516
Minimum0
Maximum11
Zeros116
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-11T01:33:27.404379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.3
Maximum11
Range11
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.8385164
Coefficient of variation (CV)2.3551243
Kurtosis11.256923
Mean0.78064516
Median Absolute Deviation (MAD)0
Skewness3.1483658
Sum121
Variance3.3801424
MonotonicityNot monotonic
2024-05-11T01:33:28.078700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 116
38.8%
1 14
 
4.7%
4 8
 
2.7%
2 8
 
2.7%
5 3
 
1.0%
6 2
 
0.7%
8 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
3 1
 
0.3%
(Missing) 144
48.2%
ValueCountFrequency (%)
0 116
38.8%
1 14
 
4.7%
2 8
 
2.7%
3 1
 
0.3%
4 8
 
2.7%
5 3
 
1.0%
6 2
 
0.7%
8 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
ValueCountFrequency (%)
11 1
 
0.3%
10 1
 
0.3%
8 1
 
0.3%
6 2
 
0.7%
5 3
 
1.0%
4 8
 
2.7%
3 1
 
0.3%
2 8
 
2.7%
1 14
 
4.7%
0 116
38.8%
Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
152 
임대
105 
자가
42 

Length

Max length4
Median length4
Mean length3.0167224
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 152
50.8%
임대 105
35.1%
자가 42
 
14.0%

Length

2024-05-11T01:33:28.789389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:29.254682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 152
50.8%
임대 105
35.1%
자가 42
 
14.0%

보증액
Categorical

IMBALANCE 

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
260 
0
35 
30000000
 
1
20000000
 
1
5000000
 
1

Length

Max length8
Median length4
Mean length3.6989967
Min length1

Unique

Unique4 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 260
87.0%
0 35
 
11.7%
30000000 1
 
0.3%
20000000 1
 
0.3%
5000000 1
 
0.3%
10000000 1
 
0.3%

Length

2024-05-11T01:33:29.690082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:30.007026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 260
87.0%
0 35
 
11.7%
30000000 1
 
0.3%
20000000 1
 
0.3%
5000000 1
 
0.3%
10000000 1
 
0.3%

월세액
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
261 
0
35 
1700000
 
1
1000000
 
1
500000
 
1

Length

Max length7
Median length4
Mean length3.6755853
Min length1

Unique

Unique3 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 261
87.3%
0 35
 
11.7%
1700000 1
 
0.3%
1000000 1
 
0.3%
500000 1
 
0.3%

Length

2024-05-11T01:33:30.490243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:30.942110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 261
87.3%
0 35
 
11.7%
1700000 1
 
0.3%
1000000 1
 
0.3%
500000 1
 
0.3%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing26
Missing (%)8.7%
Memory size730.0 B
False
273 
(Missing)
 
26
ValueCountFrequency (%)
False 273
91.3%
(Missing) 26
 
8.7%
2024-05-11T01:33:31.244274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Categorical

IMBALANCE 

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0.0
269 
<NA>
 
26
4.0
 
1
49.0
 
1
4.6
 
1

Length

Max length4
Median length3
Mean length3.090301
Min length3

Unique

Unique4 ?
Unique (%)1.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 269
90.0%
<NA> 26
 
8.7%
4.0 1
 
0.3%
49.0 1
 
0.3%
4.6 1
 
0.3%
2.0 1
 
0.3%

Length

2024-05-11T01:33:31.669489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:33:32.192585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 269
90.0%
na 26
 
8.7%
4.0 1
 
0.3%
49.0 1
 
0.3%
4.6 1
 
0.3%
2.0 1
 
0.3%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing299
Missing (%)100.0%
Memory size2.8 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing299
Missing (%)100.0%
Memory size2.8 KiB

홈페이지
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing296
Missing (%)99.0%
Memory size2.5 KiB
2024-05-11T01:33:32.697104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length17.666667
Min length13

Characters and Unicode

Total characters53
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowwww.nanumfood.co.kr
2nd rowwww.starcollege.or.kr
3rd rowwww.coftea.kr
ValueCountFrequency (%)
www.nanumfood.co.kr 1
33.3%
www.starcollege.or.kr 1
33.3%
www.coftea.kr 1
33.3%
2024-05-11T01:33:33.843216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 9
17.0%
. 8
15.1%
o 6
11.3%
r 5
9.4%
a 3
 
5.7%
e 3
 
5.7%
c 3
 
5.7%
k 3
 
5.7%
t 2
 
3.8%
n 2
 
3.8%
Other values (7) 9
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45
84.9%
Other Punctuation 8
 
15.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 9
20.0%
o 6
13.3%
r 5
11.1%
a 3
 
6.7%
e 3
 
6.7%
c 3
 
6.7%
k 3
 
6.7%
t 2
 
4.4%
n 2
 
4.4%
l 2
 
4.4%
Other values (6) 7
15.6%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45
84.9%
Common 8
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 9
20.0%
o 6
13.3%
r 5
11.1%
a 3
 
6.7%
e 3
 
6.7%
c 3
 
6.7%
k 3
 
6.7%
t 2
 
4.4%
n 2
 
4.4%
l 2
 
4.4%
Other values (6) 7
15.6%
Common
ValueCountFrequency (%)
. 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 9
17.0%
. 8
15.1%
o 6
11.3%
r 5
9.4%
a 3
 
5.7%
e 3
 
5.7%
c 3
 
5.7%
k 3
 
5.7%
t 2
 
3.8%
n 2
 
3.8%
Other values (7) 9
17.0%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
032000003200000-106-1899-0062719991102<NA>3폐업2폐업20031103<NA><NA><NA>02 8735247164.8151899서울특별시 관악구 신림동 1576-16번지<NA><NA>(주)나눔공동체2003-02-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업192949.350677442285.117237식품제조가공업12기타기타상수도전용<NA>0008임대<NA><NA>N0.0<NA><NA><NA>
132000003200000-106-1970-0034019700319<NA>3폐업2폐업20210713<NA><NA><NA>02 8558422103.2151880서울특별시 관악구 신림동 626-8서울특별시 관악구 난곡로34나길 6 (신림동)8854성운유과2021-07-13 10:13:49U2021-07-15 02:40:00.0식품제조가공업192926.353934441148.437816식품제조가공업00주택가주변기타상수도전용00006자가00N0.0<NA><NA><NA>
232000003200000-106-1973-0035419730113<NA>3폐업2폐업20161226<NA><NA><NA>02 8899922209.28151835서울특별시 관악구 봉천동 1603-9번지 10서울특별시 관악구 남부순환로226길 23-10 (봉천동,10)8788관악식품공업사2016-03-14 17:01:07I2018-08-31 23:59:59.0식품제조가공업195880.854038441905.294942식품제조가공업71주택가주변기타상수도전용<NA>0004자가<NA><NA>N0.0<NA><NA><NA>
332000003200000-106-1978-0034119780616<NA>3폐업2폐업20011121<NA><NA><NA>02 85488390.0151883서울특별시 관악구 신림동 637-14번지<NA><NA>광명제과2000-02-11 00:00:00I2018-08-31 23:59:59.0식품제조가공업192866.607104440902.89498식품제조가공업<NA><NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
432000003200000-106-1982-0037919821029<NA>3폐업2폐업19990304<NA><NA><NA>0232.16151876서울특별시 관악구 신림동 541-0번지<NA><NA>청미식품2001-09-29 00:00:00I2018-08-31 23:59:59.0식품제조가공업192096.34963442339.213627식품제조가공업00주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
532000003200000-106-1985-0034619850725<NA>3폐업2폐업19980907<NA><NA><NA>02 8617094121.34151903서울특별시 관악구 신림동 1666-22번지<NA><NA>봉황식품2001-09-29 00:00:00I2018-08-31 23:59:59.0식품제조가공업191084.937998442045.986216식품제조가공업00기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
632000003200000-106-1991-0038119910516<NA>3폐업2폐업20050119<NA><NA><NA>02 875343488.59151812서울특별시 관악구 봉천동 1686-1번지<NA><NA>서울식품2000-04-22 00:00:00I2018-08-31 23:59:59.0식품제조가공업196644.195463441763.558535식품제조가공업00주택가주변기타상수도전용<NA>00010임대<NA><NA>N0.0<NA><NA><NA>
732000003200000-106-1993-0034219930820<NA>3폐업2폐업20040205<NA><NA><NA>02 8864351105.15151928서울특별시 관악구 신림동 1456-13번지<NA><NA>낙원식품2003-04-23 00:00:00I2018-08-31 23:59:59.0식품제조가공업193232.947218442795.087153식품제조가공업<NA><NA>주택가주변기타상수도전용<NA>0001자가<NA><NA>N0.0<NA><NA><NA>
832000003200000-106-1993-0037519930319<NA>3폐업2폐업20030530<NA><NA><NA>02 886732270.44151848서울특별시 관악구 봉천동 1610-19번지<NA><NA>제일당면2003-04-23 00:00:00I2018-08-31 23:59:59.0식품제조가공업195936.056801441749.640411식품제조가공업<NA><NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
932000003200000-106-1993-0038719930906<NA>3폐업2폐업20110511<NA><NA><NA>02 8783311131.06151913서울특별시 관악구 봉천동 954-1번지<NA><NA>재주식품2010-08-17 16:05:21I2018-08-31 23:59:59.0식품제조가공업194434.469386442757.652061식품제조가공업<NA><NA>주택가주변기타상수도전용<NA>0005<NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
28932000003200000-106-2022-000032022-06-20<NA>1영업/정상1영업<NA><NA><NA><NA>02 876 284230.7151-828서울특별시 관악구 봉천동 635-665서울특별시 관악구 국회단지길 67, 1층 (봉천동)8713함께사는세상2024-04-26 15:13:18U2023-12-03 22:08:00.0기타 식품제조가공업194741.979689442942.674207<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29032000003200000-106-2022-000042022-07-22<NA>1영업/정상1영업<NA><NA><NA><NA>070 5151003395.12151-725서울특별시 관악구 봉천동 31-1 관악프라자서울특별시 관악구 관악로 212, 관악프라자 1층 (봉천동)8737사회적협동조합 스윗2024-04-26 15:13:52U2023-12-03 22:08:00.0기타 식품제조가공업195963.335275442381.53601<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29132000003200000-106-2022-000052022-08-23<NA>1영업/정상1영업<NA><NA><NA><NA><NA>36.3151-803서울특별시 관악구 봉천동 7-270 성지빌딩서울특별시 관악구 관악로 249-1, 성지빌딩 4층 401호 (봉천동)8727김치찜에 진심2024-04-26 15:14:34U2023-12-03 22:08:00.0기타 식품제조가공업196102.926116442732.953916<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29232000003200000-106-2022-000062022-11-03<NA>3폐업2폐업2024-03-04<NA><NA><NA><NA>71.02151-871서울특별시 관악구 신림동 497-29서울특별시 관악구 신사로 128, 3층 (신림동)8702헤이프커피로스터스2024-03-05 13:49:24U2023-12-03 00:07:00.0기타 식품제조가공업192508.083211442926.925963<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29332000003200000-106-2023-000012023-03-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>40.53151-812서울특별시 관악구 봉천동 1688-127서울특별시 관악구 남부순환로 1947, B1층 (봉천동)8801로스터리 안밀2024-04-26 15:15:07U2023-12-03 22:08:00.0기타 식품제조가공업196869.322042441606.821757<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29432000003200000-106-2023-000022023-06-07<NA>1영업/정상1영업<NA><NA><NA><NA><NA>119.28151-832서울특별시 관악구 봉천동 1634-9서울특별시 관악구 봉천로 616, 5층 (봉천동)8793티포인트스튜디오 커피로스터스2024-04-26 15:15:50U2023-12-03 22:08:00.0기타 식품제조가공업196874.021008441472.976437<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29532000003200000-106-2023-000032023-08-16<NA>3폐업2폐업2024-01-05<NA><NA><NA><NA>60.0151-895서울특별시 관악구 신림동 1524-5서울특별시 관악구 신림로23길 28, 2층 일부호 (신림동)8812발루토2024-01-05 10:37:52U2023-12-01 00:07:00.0기타 식품제조가공업194153.458583440847.046527<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29632000003200000-106-2024-000012024-03-08<NA>1영업/정상1영업<NA><NA><NA><NA>02 844958545.0151-867서울특별시 관악구 신림동 1633-8서울특별시 관악구 문성로 244, 1층 1,2호 (신림동)8843르방팡2024-04-26 15:16:27U2023-12-03 22:08:00.0기타 식품제조가공업193783.25434441813.770397<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29732000003200000-106-2024-000022024-03-14<NA>1영업/정상1영업<NA><NA><NA><NA><NA>16.5151-769서울특별시 관악구 봉천동 1698-1 보라매삼성아파트서울특별시 관악구 보라매로 62, 지하1층 10호 (봉천동, 보라매삼성아파트)8709제이에스푸드2024-04-26 15:17:05U2023-12-03 22:08:00.0기타 식품제조가공업193578.742465443547.049697<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29832000003200000-106-2024-000032024-04-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>25.0151-893서울특별시 관악구 신림동 485-24서울특별시 관악구 남부순환로161길 30, 1층 (신림동)8763하프스텝2024-04-26 15:17:37U2023-12-03 22:08:00.0기타 식품제조가공업192907.998436442460.358193<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>