Overview

Dataset statistics

Number of variables48
Number of observations74
Missing cells1176
Missing cells (%)33.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.1 KiB
Average record size in memory416.8 B

Variable types

Numeric12
Categorical16
Text6
Unsupported13
Boolean1

Dataset

Description2021-05-01
Author지방행정인허가공개데이터
URLhttps://bigdata.busan.go.kr/data/bigDataDetailView.do?menuCode=M00000000007&hdfs_file_sn=20230901050101123105

Alerts

개방서비스명 has constant value ""Constant
개방서비스id has constant value ""Constant
업태구분명 has constant value ""Constant
위생업태명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
인허가취소일자 has 74 (100.0%) missing valuesMissing
폐업일자 has 37 (50.0%) missing valuesMissing
휴업시작일자 has 74 (100.0%) missing valuesMissing
휴업종료일자 has 74 (100.0%) missing valuesMissing
재개업일자 has 74 (100.0%) missing valuesMissing
소재지전화 has 17 (23.0%) missing valuesMissing
소재지면적 has 11 (14.9%) missing valuesMissing
소재지우편번호 has 15 (20.3%) missing valuesMissing
도로명전체주소 has 20 (27.0%) missing valuesMissing
도로명우편번호 has 22 (29.7%) missing valuesMissing
좌표정보(x) has 17 (23.0%) missing valuesMissing
좌표정보(y) has 17 (23.0%) missing valuesMissing
남성종사자수 has 74 (100.0%) missing valuesMissing
여성종사자수 has 74 (100.0%) missing valuesMissing
영업장주변구분명 has 74 (100.0%) missing valuesMissing
등급구분명 has 74 (100.0%) missing valuesMissing
총종업원수 has 74 (100.0%) missing valuesMissing
공장판매직종업원수 has 29 (39.2%) missing valuesMissing
공장생산직종업원수 has 29 (39.2%) missing valuesMissing
전통업소지정번호 has 74 (100.0%) missing valuesMissing
전통업소주된음식 has 74 (100.0%) missing valuesMissing
홈페이지 has 74 (100.0%) missing valuesMissing
Unnamed: 47 has 74 (100.0%) missing valuesMissing
번호 has unique valuesUnique
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
남성종사자수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
여성종사자수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
영업장주변구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
등급구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총종업원수 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
Unnamed: 47 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공장판매직종업원수 has 38 (51.4%) zerosZeros
공장생산직종업원수 has 34 (45.9%) zerosZeros
시설총규모 has 58 (78.4%) zerosZeros

Reproduction

Analysis started2024-04-17 10:44:10.099468
Analysis finished2024-04-17 10:44:10.637838
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.5
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:10.697022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q119.25
median37.5
Q355.75
95-th percentile70.35
Maximum74
Range73
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation21.505813
Coefficient of variation (CV)0.57348835
Kurtosis-1.2
Mean37.5
Median Absolute Deviation (MAD)18.5
Skewness0
Sum2775
Variance462.5
MonotonicityStrictly increasing
2024-04-17T19:44:10.824739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
57 1
 
1.4%
55 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
74 1
1.4%
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
식품첨가물제조업
74 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품첨가물제조업 74
100.0%

Length

2024-04-17T19:44:10.952157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:11.047423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품첨가물제조업 74
100.0%

개방서비스id
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
07_22_12_P
74 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07_22_12_P
2nd row07_22_12_P
3rd row07_22_12_P
4th row07_22_12_P
5th row07_22_12_P

Common Values

ValueCountFrequency (%)
07_22_12_P 74
100.0%

Length

2024-04-17T19:44:11.139880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:11.225757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
07_22_12_p 74
100.0%

개방자치단체코드
Real number (ℝ)

Distinct16
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3349864.9
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:11.310481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3266500
Q13340000
median3350000
Q33387500
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)47500

Descriptive statistics

Standard deviation38374.265
Coefficient of variation (CV)0.011455467
Kurtosis0.48100038
Mean3349864.9
Median Absolute Deviation (MAD)20000
Skewness-0.89080598
Sum2.4789 × 108
Variance1.4725842 × 109
MonotonicityNot monotonic
2024-04-17T19:44:11.419630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3360000 14
18.9%
3390000 12
16.2%
3350000 11
14.9%
3340000 10
13.5%
3400000 7
9.5%
3330000 4
 
5.4%
3250000 2
 
2.7%
3370000 2
 
2.7%
3260000 2
 
2.7%
3280000 2
 
2.7%
Other values (6) 8
10.8%
ValueCountFrequency (%)
3250000 2
 
2.7%
3260000 2
 
2.7%
3270000 1
 
1.4%
3280000 2
 
2.7%
3290000 1
 
1.4%
3300000 2
 
2.7%
3310000 2
 
2.7%
3320000 1
 
1.4%
3330000 4
 
5.4%
3340000 10
13.5%
ValueCountFrequency (%)
3400000 7
9.5%
3390000 12
16.2%
3380000 1
 
1.4%
3370000 2
 
2.7%
3360000 14
18.9%
3350000 11
14.9%
3340000 10
13.5%
3330000 4
 
5.4%
3320000 1
 
1.4%
3310000 2
 
2.7%

관리번호
Text

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-04-17T19:44:11.600128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1628
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

Unique74 ?
Unique (%)100.0%

Sample

1st row3280000-108-2014-00001
2nd row3300000-108-2020-00001
3rd row3310000-108-2021-00001
4th row3340000-108-2011-00008
5th row3340000-108-2011-00009
ValueCountFrequency (%)
3280000-108-2014-00001 1
 
1.4%
3340000-108-2011-00001 1
 
1.4%
3340000-108-2011-00007 1
 
1.4%
3340000-108-2011-00003 1
 
1.4%
3330000-108-2011-00001 1
 
1.4%
3330000-108-2011-00004 1
 
1.4%
3330000-108-2011-00003 1
 
1.4%
3330000-108-2011-00002 1
 
1.4%
3320000-108-2017-00001 1
 
1.4%
3310000-108-2019-00001 1
 
1.4%
Other values (64) 64
86.5%
2024-04-17T19:44:11.896468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 756
46.4%
- 222
 
13.6%
1 219
 
13.5%
3 147
 
9.0%
2 109
 
6.7%
8 84
 
5.2%
4 24
 
1.5%
9 20
 
1.2%
6 19
 
1.2%
5 19
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1406
86.4%
Dash Punctuation 222
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 756
53.8%
1 219
 
15.6%
3 147
 
10.5%
2 109
 
7.8%
8 84
 
6.0%
4 24
 
1.7%
9 20
 
1.4%
6 19
 
1.4%
5 19
 
1.4%
7 9
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1628
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 756
46.4%
- 222
 
13.6%
1 219
 
13.5%
3 147
 
9.0%
2 109
 
6.7%
8 84
 
5.2%
4 24
 
1.5%
9 20
 
1.2%
6 19
 
1.2%
5 19
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 756
46.4%
- 222
 
13.6%
1 219
 
13.5%
3 147
 
9.0%
2 109
 
6.7%
8 84
 
5.2%
4 24
 
1.5%
9 20
 
1.2%
6 19
 
1.2%
5 19
 
1.2%

인허가일자
Real number (ℝ)

Distinct68
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20102337
Minimum19971101
Maximum20210317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:12.062884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19971101
5-th percentile19976951
Q120030983
median20115914
Q320180971
95-th percentile20204322
Maximum20210317
Range239216
Interquartile range (IQR)149988.25

Descriptive statistics

Standard deviation81383.76
Coefficient of variation (CV)0.0040484726
Kurtosis-1.4320813
Mean20102337
Median Absolute Deviation (MAD)75057.5
Skewness-0.21590316
Sum1.4875729 × 109
Variance6.6233165 × 109
MonotonicityNot monotonic
2024-04-17T19:44:12.199976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19971101 4
 
5.4%
20070614 2
 
2.7%
20030909 2
 
2.7%
20051208 2
 
2.7%
20140424 1
 
1.4%
20191018 1
 
1.4%
20031205 1
 
1.4%
20030410 1
 
1.4%
20020412 1
 
1.4%
19990528 1
 
1.4%
Other values (58) 58
78.4%
ValueCountFrequency (%)
19971101 4
5.4%
19980101 1
 
1.4%
19980109 1
 
1.4%
19980515 1
 
1.4%
19980907 1
 
1.4%
19981126 1
 
1.4%
19990426 1
 
1.4%
19990528 1
 
1.4%
19991026 1
 
1.4%
20000202 1
 
1.4%
ValueCountFrequency (%)
20210317 1
1.4%
20210312 1
1.4%
20210125 1
1.4%
20210106 1
1.4%
20201207 1
1.4%
20200811 1
1.4%
20200701 1
1.4%
20200615 1
1.4%
20200508 1
1.4%
20200304 1
1.4%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

영업상태구분코드
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
1
37 
3
37 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 37
50.0%
3 37
50.0%

Length

2024-04-17T19:44:12.316005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:12.404710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 37
50.0%
3 37
50.0%

영업상태명
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
영업/정상
37 
폐업
37 

Length

Max length5
Median length3.5
Mean length3.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row영업/정상
3rd row영업/정상
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 37
50.0%
폐업 37
50.0%

Length

2024-04-17T19:44:12.791177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:12.918499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 37
50.0%
폐업 37
50.0%

상세영업상태코드
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
1
37 
2
37 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 37
50.0%
2 37
50.0%

Length

2024-04-17T19:44:13.038126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:13.147808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 37
50.0%
2 37
50.0%

상세영업상태명
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
영업
37 
폐업
37 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업 37
50.0%
폐업 37
50.0%

Length

2024-04-17T19:44:13.243481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:13.330821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 37
50.0%
폐업 37
50.0%

폐업일자
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)81.1%
Missing37
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean20115878
Minimum20040622
Maximum20201106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:13.428937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040622
5-th percentile20041231
Q120060602
median20101126
Q320170904
95-th percentile20200937
Maximum20201106
Range160484
Interquartile range (IQR)110302

Descriptive statistics

Standard deviation60813.648
Coefficient of variation (CV)0.0030231665
Kurtosis-1.6961612
Mean20115878
Median Absolute Deviation (MAD)50722
Skewness0.20980727
Sum7.4428748 × 108
Variance3.6982998 × 109
MonotonicityNot monotonic
2024-04-17T19:44:13.548320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20060612 4
 
5.4%
20060602 4
 
5.4%
20041231 2
 
2.7%
20200914 1
 
1.4%
20201027 1
 
1.4%
20200316 1
 
1.4%
20060525 1
 
1.4%
20070104 1
 
1.4%
20101126 1
 
1.4%
20190225 1
 
1.4%
Other values (20) 20
27.0%
(Missing) 37
50.0%
ValueCountFrequency (%)
20040622 1
 
1.4%
20041231 2
2.7%
20050315 1
 
1.4%
20050404 1
 
1.4%
20050722 1
 
1.4%
20060525 1
 
1.4%
20060602 4
5.4%
20060612 4
5.4%
20070104 1
 
1.4%
20080711 1
 
1.4%
ValueCountFrequency (%)
20201106 1
1.4%
20201027 1
1.4%
20200914 1
1.4%
20200907 1
1.4%
20200820 1
1.4%
20200316 1
1.4%
20190225 1
1.4%
20180905 1
1.4%
20180123 1
1.4%
20170904 1
1.4%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

소재지전화
Text

MISSING 

Distinct53
Distinct (%)93.0%
Missing17
Missing (%)23.0%
Memory size724.0 B
2024-04-17T19:44:13.772737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11
Min length9

Characters and Unicode

Total characters627
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

Unique49 ?
Unique (%)86.0%

Sample

1st row051 724 8219
2nd row051 611 2257
3rd row0512624294
4th row0512621141
5th row0512619560
ValueCountFrequency (%)
051 26
 
22.8%
0512621321 2
 
1.8%
832 2
 
1.8%
051304 2
 
1.8%
8668 2
 
1.8%
831 2
 
1.8%
527 2
 
1.8%
722 2
 
1.8%
971 2
 
1.8%
9780 2
 
1.8%
Other values (68) 70
61.4%
2024-04-17T19:44:14.109441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
16.9%
5 97
15.5%
1 97
15.5%
2 64
10.2%
57
9.1%
3 50
8.0%
8 43
6.9%
6 39
 
6.2%
7 29
 
4.6%
9 23
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 570
90.9%
Space Separator 57
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
18.6%
5 97
17.0%
1 97
17.0%
2 64
11.2%
3 50
8.8%
8 43
7.5%
6 39
 
6.8%
7 29
 
5.1%
9 23
 
4.0%
4 22
 
3.9%
Space Separator
ValueCountFrequency (%)
57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 627
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
16.9%
5 97
15.5%
1 97
15.5%
2 64
10.2%
57
9.1%
3 50
8.0%
8 43
6.9%
6 39
 
6.2%
7 29
 
4.6%
9 23
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
16.9%
5 97
15.5%
1 97
15.5%
2 64
10.2%
57
9.1%
3 50
8.0%
8 43
6.9%
6 39
 
6.2%
7 29
 
4.6%
9 23
 
3.7%

소재지면적
Text

MISSING 

Distinct59
Distinct (%)93.7%
Missing11
Missing (%)14.9%
Memory size724.0 B
2024-04-17T19:44:14.328000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.4285714
Min length3

Characters and Unicode

Total characters342
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

Unique55 ?
Unique (%)87.3%

Sample

1st row334.69
2nd row10.80
3rd row14.00
4th row234.90
5th row108.00
ValueCountFrequency (%)
2.00 2
 
3.2%
00 2
 
3.2%
14.00 2
 
3.2%
198.32 2
 
3.2%
8.00 1
 
1.6%
11.75 1
 
1.6%
334.69 1
 
1.6%
32.40 1
 
1.6%
147.12 1
 
1.6%
31.35 1
 
1.6%
Other values (49) 49
77.8%
2024-04-17T19:44:14.663622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66
19.3%
. 63
18.4%
1 34
9.9%
2 31
9.1%
4 30
8.8%
6 27
7.9%
3 25
 
7.3%
7 18
 
5.3%
8 17
 
5.0%
9 15
 
4.4%
Other values (2) 16
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 277
81.0%
Other Punctuation 65
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66
23.8%
1 34
12.3%
2 31
11.2%
4 30
10.8%
6 27
9.7%
3 25
 
9.0%
7 18
 
6.5%
8 17
 
6.1%
9 15
 
5.4%
5 14
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 63
96.9%
, 2
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66
19.3%
. 63
18.4%
1 34
9.9%
2 31
9.1%
4 30
8.8%
6 27
7.9%
3 25
 
7.3%
7 18
 
5.3%
8 17
 
5.0%
9 15
 
4.4%
Other values (2) 16
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66
19.3%
. 63
18.4%
1 34
9.9%
2 31
9.1%
4 30
8.8%
6 27
7.9%
3 25
 
7.3%
7 18
 
5.3%
8 17
 
5.0%
9 15
 
4.4%
Other values (2) 16
 
4.7%

소재지우편번호
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)71.2%
Missing15
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean613180.47
Minimum600016
Maximum619912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:14.797963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600016
5-th percentile604463.7
Q1609133
median614803
Q3618800.5
95-th percentile619906.6
Maximum619912
Range19896
Interquartile range (IQR)9667.5

Descriptive statistics

Standard deviation5956.524
Coefficient of variation (CV)0.0097141449
Kurtosis-0.97307932
Mean613180.47
Median Absolute Deviation (MAD)4017
Skewness-0.5474006
Sum36177648
Variance35480178
MonotonicityNot monotonic
2024-04-17T19:44:14.934546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
618820 4
 
5.4%
609420 3
 
4.1%
619912 3
 
4.1%
618800 3
 
4.1%
617814 3
 
4.1%
609845 2
 
2.7%
604843 2
 
2.7%
618807 2
 
2.7%
609809 2
 
2.7%
604845 2
 
2.7%
Other values (32) 33
44.6%
(Missing) 15
20.3%
ValueCountFrequency (%)
600016 1
1.4%
600082 1
1.4%
601050 1
1.4%
604843 2
2.7%
604845 2
2.7%
604846 1
1.4%
604849 1
1.4%
606791 1
1.4%
606805 1
1.4%
607802 1
1.4%
ValueCountFrequency (%)
619912 3
4.1%
619906 1
 
1.4%
618820 4
5.4%
618819 1
 
1.4%
618817 1
 
1.4%
618809 1
 
1.4%
618807 2
2.7%
618802 1
 
1.4%
618801 1
 
1.4%
618800 3
4.1%

소재지전체주소
Text

Distinct68
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-04-17T19:44:15.258647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length34
Mean length22.202703
Min length8

Characters and Unicode

Total characters1643
Distinct characters136
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

Unique64 ?
Unique (%)86.5%

Sample

1st row부산광역시 영도구 동삼동 201 외 2필지(199, 200)
2nd row부산광역시 동래구 명륜동 6-9 세명빌딩
3rd row부산광역시 남구 용호동 516-10 용호시장
4th row부산광역시 사하구 장림동 392-1번지
5th row부산광역시 사하구 장림동 457-7번지
ValueCountFrequency (%)
부산광역시 74
22.7%
강서구 14
 
4.3%
사상구 12
 
3.7%
금정구 11
 
3.4%
사하구 10
 
3.1%
기장군 7
 
2.1%
송정동 6
 
1.8%
장림동 5
 
1.5%
강동동 5
 
1.5%
외 4
 
1.2%
Other values (144) 178
54.6%
2024-04-17T19:44:15.695669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
257
 
15.6%
산 84
 
5.1%
부 80
 
4.9%
광 78
 
4.7%
시 75
 
4.6%
역 74
 
4.5%
1 72
 
4.4%
동 71
 
4.3%
구 67
 
4.1%
- 50
 
3.0%
Other values (126) 735
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1010
61.5%
Decimal Number 311
 
18.9%
Space Separator 257
 
15.6%
Dash Punctuation 50
 
3.0%
Other Punctuation 6
 
0.4%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 84
 
8.3%
부 80
 
7.9%
광 78
 
7.7%
시 75
 
7.4%
역 74
 
7.3%
동 71
 
7.0%
구 67
 
6.6%
지 49
 
4.9%
번 44
 
4.4%
사 24
 
2.4%
Other values (109) 364
36.0%
Decimal Number
ValueCountFrequency (%)
1 72
23.2%
3 36
11.6%
2 34
10.9%
0 31
10.0%
6 29
9.3%
7 27
 
8.7%
4 26
 
8.4%
9 23
 
7.4%
5 19
 
6.1%
8 14
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
C 1
33.3%
Space Separator
ValueCountFrequency (%)
257
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1010
61.5%
Common 630
38.3%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 84
 
8.3%
부 80
 
7.9%
광 78
 
7.7%
시 75
 
7.4%
역 74
 
7.3%
동 71
 
7.0%
구 67
 
6.6%
지 49
 
4.9%
번 44
 
4.4%
사 24
 
2.4%
Other values (109) 364
36.0%
Common
ValueCountFrequency (%)
257
40.8%
1 72
 
11.4%
- 50
 
7.9%
3 36
 
5.7%
2 34
 
5.4%
0 31
 
4.9%
6 29
 
4.6%
7 27
 
4.3%
4 26
 
4.1%
9 23
 
3.7%
Other values (5) 45
 
7.1%
Latin
ValueCountFrequency (%)
B 2
66.7%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1010
61.5%
ASCII 633
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
257
40.6%
1 72
 
11.4%
- 50
 
7.9%
3 36
 
5.7%
2 34
 
5.4%
0 31
 
4.9%
6 29
 
4.6%
7 27
 
4.3%
4 26
 
4.1%
9 23
 
3.6%
Other values (7) 48
 
7.6%
Hangul
ValueCountFrequency (%)
산 84
 
8.3%
부 80
 
7.9%
광 78
 
7.7%
시 75
 
7.4%
역 74
 
7.3%
동 71
 
7.0%
구 67
 
6.6%
지 49
 
4.9%
번 44
 
4.4%
사 24
 
2.4%
Other values (109) 364
36.0%

도로명전체주소
Text

MISSING 

Distinct52
Distinct (%)96.3%
Missing20
Missing (%)27.0%
Memory size724.0 B
2024-04-17T19:44:15.968010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length39
Mean length31.777778
Min length23

Characters and Unicode

Total characters1716
Distinct characters140
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

Unique50 ?
Unique (%)92.6%

Sample

1st row부산광역시 영도구 해양로 241 (동삼동)
2nd row부산광역시 동래구 시실로 20, 세명빌딩 3층 (명륜동)
3rd row부산광역시 남구 동명로152번길 93, 용호시장 3(일부)층 (용호동)
4th row부산광역시 사하구 다대로300번길 34 (장림동)
5th row부산광역시 사하구 다대로354번안길 80, 1층 (장림동)
ValueCountFrequency (%)
부산광역시 54
 
16.7%
강서구 14
 
4.3%
금정구 10
 
3.1%
1층 7
 
2.2%
사상구 7
 
2.2%
기장군 7
 
2.2%
2층 5
 
1.5%
사하구 5
 
1.5%
강동동 5
 
1.5%
송정동 5
 
1.5%
Other values (163) 205
63.3%
2024-04-17T19:44:16.384089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270
 
15.7%
산 80
 
4.7%
동 77
 
4.5%
부 61
 
3.6%
1 60
 
3.5%
광 58
 
3.4%
시 57
 
3.3%
역 54
 
3.1%
) 52
 
3.0%
( 52
 
3.0%
Other values (130) 895
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 991
57.8%
Decimal Number 292
 
17.0%
Space Separator 270
 
15.7%
Close Punctuation 52
 
3.0%
Open Punctuation 52
 
3.0%
Other Punctuation 39
 
2.3%
Dash Punctuation 14
 
0.8%
Uppercase Letter 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 80
 
8.1%
동 77
 
7.8%
부 61
 
6.2%
광 58
 
5.9%
시 57
 
5.8%
역 54
 
5.4%
구 48
 
4.8%
로 47
 
4.7%
길 31
 
3.1%
번 26
 
2.6%
Other values (111) 452
45.6%
Decimal Number
ValueCountFrequency (%)
1 60
20.5%
2 47
16.1%
3 38
13.0%
5 35
12.0%
0 29
9.9%
4 27
9.2%
6 21
 
7.2%
9 12
 
4.1%
7 12
 
4.1%
8 11
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 3
50.0%
A 1
 
16.7%
D 1
 
16.7%
C 1
 
16.7%
Space Separator
ValueCountFrequency (%)
270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Other Punctuation
ValueCountFrequency (%)
, 39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 991
57.8%
Common 719
41.9%
Latin 6
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 80
 
8.1%
동 77
 
7.8%
부 61
 
6.2%
광 58
 
5.9%
시 57
 
5.8%
역 54
 
5.4%
구 48
 
4.8%
로 47
 
4.7%
길 31
 
3.1%
번 26
 
2.6%
Other values (111) 452
45.6%
Common
ValueCountFrequency (%)
270
37.6%
1 60
 
8.3%
) 52
 
7.2%
( 52
 
7.2%
2 47
 
6.5%
, 39
 
5.4%
3 38
 
5.3%
5 35
 
4.9%
0 29
 
4.0%
4 27
 
3.8%
Other values (5) 70
 
9.7%
Latin
ValueCountFrequency (%)
B 3
50.0%
A 1
 
16.7%
D 1
 
16.7%
C 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 991
57.8%
ASCII 725
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
270
37.2%
1 60
 
8.3%
) 52
 
7.2%
( 52
 
7.2%
2 47
 
6.5%
, 39
 
5.4%
3 38
 
5.2%
5 35
 
4.8%
0 29
 
4.0%
4 27
 
3.7%
Other values (9) 76
 
10.5%
Hangul
ValueCountFrequency (%)
산 80
 
8.1%
동 77
 
7.8%
부 61
 
6.2%
광 58
 
5.9%
시 57
 
5.8%
역 54
 
5.4%
구 48
 
4.8%
로 47
 
4.7%
길 31
 
3.1%
번 26
 
2.6%
Other values (111) 452
45.6%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)80.8%
Missing22
Missing (%)29.7%
Infinite0
Infinite (%)0.0%
Mean47149.462
Minimum46018
Maximum49469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:16.513969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46018
5-th percentile46036.3
Q146252
median46736
Q347724.5
95-th percentile49464.8
Maximum49469
Range3451
Interquartile range (IQR)1472.5

Descriptive statistics

Standard deviation1135.0099
Coefficient of variation (CV)0.024072595
Kurtosis-0.35217341
Mean47149.462
Median Absolute Deviation (MAD)499
Skewness1.0486377
Sum2451772
Variance1288247.4
MonotonicityNot monotonic
2024-04-17T19:44:16.636922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
46048 3
 
4.1%
46237 3
 
4.1%
46757 3
 
4.1%
46720 2
 
2.7%
49467 2
 
2.7%
46330 2
 
2.7%
46705 2
 
2.7%
48942 1
 
1.4%
46990 1
 
1.4%
46020 1
 
1.4%
Other values (32) 32
43.2%
(Missing) 22
29.7%
ValueCountFrequency (%)
46018 1
 
1.4%
46020 1
 
1.4%
46022 1
 
1.4%
46048 3
4.1%
46080 1
 
1.4%
46214 1
 
1.4%
46226 1
 
1.4%
46227 1
 
1.4%
46237 3
4.1%
46257 1
 
1.4%
ValueCountFrequency (%)
49469 1
1.4%
49467 2
2.7%
49463 1
1.4%
49315 1
1.4%
49112 1
1.4%
49013 1
1.4%
48963 1
1.4%
48942 1
1.4%
48587 1
1.4%
48582 1
1.4%

사업장명
Text

Distinct68
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-04-17T19:44:16.883077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length11
Mean length7.6081081
Min length3

Characters and Unicode

Total characters563
Distinct characters156
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

Unique62 ?
Unique (%)83.8%

Sample

1st row미창석유공업(주)
2nd row잡으리
3rd row(주)광명생약 제2공장
4th row유림기업
5th row화인캡
ValueCountFrequency (%)
성림식품 2
 
2.3%
주)광명생약 2
 
2.3%
주)엔씨시스템 2
 
2.3%
금정산성 2
 
2.3%
제2공장 2
 
2.3%
주식회사 2
 
2.3%
주)한정성 2
 
2.3%
주)젤텍 2
 
2.3%
연개산업 2
 
2.3%
주)세진바이오텍 1
 
1.1%
Other values (68) 68
78.2%
2024-04-17T19:44:17.244189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 43
 
7.6%
주 43
 
7.6%
) 43
 
7.6%
이 23
 
4.1%
13
 
2.3%
제 13
 
2.3%
업 12
 
2.1%
산 11
 
2.0%
식 10
 
1.8%
오 10
 
1.8%
Other values (146) 342
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 432
76.7%
Open Punctuation 43
 
7.6%
Close Punctuation 43
 
7.6%
Space Separator 13
 
2.3%
Uppercase Letter 13
 
2.3%
Lowercase Letter 12
 
2.1%
Other Punctuation 5
 
0.9%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 43
 
10.0%
이 23
 
5.3%
제 13
 
3.0%
업 12
 
2.8%
산 11
 
2.5%
식 10
 
2.3%
오 10
 
2.3%
공 9
 
2.1%
프 9
 
2.1%
바 8
 
1.9%
Other values (122) 284
65.7%
Uppercase Letter
ValueCountFrequency (%)
C 3
23.1%
F 2
15.4%
O 1
 
7.7%
J 1
 
7.7%
S 1
 
7.7%
E 1
 
7.7%
G 1
 
7.7%
L 1
 
7.7%
T 1
 
7.7%
I 1
 
7.7%
Lowercase Letter
ValueCountFrequency (%)
n 3
25.0%
t 2
16.7%
a 2
16.7%
l 1
 
8.3%
o 1
 
8.3%
i 1
 
8.3%
r 1
 
8.3%
e 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
& 2
40.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 432
76.7%
Common 106
 
18.8%
Latin 25
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 43
 
10.0%
이 23
 
5.3%
제 13
 
3.0%
업 12
 
2.8%
산 11
 
2.5%
식 10
 
2.3%
오 10
 
2.3%
공 9
 
2.1%
프 9
 
2.1%
바 8
 
1.9%
Other values (122) 284
65.7%
Latin
ValueCountFrequency (%)
C 3
 
12.0%
n 3
 
12.0%
F 2
 
8.0%
t 2
 
8.0%
a 2
 
8.0%
O 1
 
4.0%
J 1
 
4.0%
S 1
 
4.0%
E 1
 
4.0%
G 1
 
4.0%
Other values (8) 8
32.0%
Common
ValueCountFrequency (%)
( 43
40.6%
) 43
40.6%
13
 
12.3%
. 3
 
2.8%
2 2
 
1.9%
& 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 432
76.7%
ASCII 131
 
23.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 43
32.8%
) 43
32.8%
13
 
9.9%
C 3
 
2.3%
. 3
 
2.3%
n 3
 
2.3%
2 2
 
1.5%
F 2
 
1.5%
t 2
 
1.5%
& 2
 
1.5%
Other values (14) 15
 
11.5%
Hangul
ValueCountFrequency (%)
주 43
 
10.0%
이 23
 
5.3%
제 13
 
3.0%
업 12
 
2.8%
산 11
 
2.5%
식 10
 
2.3%
오 10
 
2.3%
공 9
 
2.1%
프 9
 
2.1%
바 8
 
1.9%
Other values (122) 284
65.7%

최종수정시점
Real number (ℝ)

Distinct61
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0151376 × 1013
Minimum2.0060818 × 1013
Maximum2.0210323 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:17.392389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0060818 × 1013
5-th percentile2.0060818 × 1013
Q12.0103652 × 1013
median2.0170703 × 1013
Q32.020068 × 1013
95-th percentile2.0210155 × 1013
Maximum2.0210323 × 1013
Range1.4950494 × 1011
Interquartile range (IQR)9.7027689 × 1010

Descriptive statistics

Standard deviation5.4373613 × 1010
Coefficient of variation (CV)0.0026982581
Kurtosis-1.0724863
Mean2.0151376 × 1013
Median Absolute Deviation (MAD)3.0453516 × 1010
Skewness-0.66781371
Sum1.4912018 × 1015
Variance2.9564898 × 1021
MonotonicityNot monotonic
2024-04-17T19:44:17.530352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20060818222941 14
 
18.9%
20210125111352 1
 
1.4%
20101014230409 1
 
1.4%
20180113184843 1
 
1.4%
20200812143315 1
 
1.4%
20210323164947 1
 
1.4%
20190105163746 1
 
1.4%
20130830110846 1
 
1.4%
20180123142112 1
 
1.4%
20201214191811 1
 
1.4%
Other values (51) 51
68.9%
ValueCountFrequency (%)
20060818222941 14
18.9%
20080104090354 1
 
1.4%
20090916070405 1
 
1.4%
20101014230409 1
 
1.4%
20101117230408 1
 
1.4%
20101201230409 1
 
1.4%
20111004160055 1
 
1.4%
20111103174455 1
 
1.4%
20130404172234 1
 
1.4%
20130404172315 1
 
1.4%
ValueCountFrequency (%)
20210323164947 1
1.4%
20210315174952 1
1.4%
20210315143701 1
1.4%
20210208161531 1
1.4%
20210126094313 1
1.4%
20210125111352 1
1.4%
20210120094326 1
1.4%
20210107161558 1
1.4%
20210105154759 1
1.4%
20201214191811 1
1.4%

데이터갱신구분
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
I
55 
U
19 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 55
74.3%
U 19
 
25.7%

Length

2024-04-17T19:44:17.650212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:17.737872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 55
74.3%
u 19
 
25.7%

데이터갱신일자
Categorical

Distinct32
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size724.0 B
2018-08-31 23:59:59.0
43 
2021-01-27 02:40:00.0
 
1
2021-03-17 00:22:59.0
 
1
2021-02-10 02:40:00.0
 
1
2020-06-17 00:23:27.0
 
1
Other values (27)
27 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique31 ?
Unique (%)41.9%

Sample

1st row2021-01-27 02:40:00.0
2nd row2021-01-22 02:40:00.0
3rd row2021-03-17 00:22:59.0
4th row2018-08-31 23:59:59.0
5th row2018-08-31 23:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 23:59:59.0 43
58.1%
2021-01-27 02:40:00.0 1
 
1.4%
2021-03-17 00:22:59.0 1
 
1.4%
2021-02-10 02:40:00.0 1
 
1.4%
2020-06-17 00:23:27.0 1
 
1.4%
2020-09-10 02:40:00.0 1
 
1.4%
2020-07-03 00:23:16.0 1
 
1.4%
2021-01-07 02:40:00.0 1
 
1.4%
2021-03-17 02:40:00.0 1
 
1.4%
2019-01-13 02:40:00.0 1
 
1.4%
Other values (22) 22
29.7%

Length

2024-04-17T19:44:17.839476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23:59:59.0 44
29.7%
2018-08-31 43
29.1%
02:40:00.0 17
 
11.5%
2021-03-17 2
 
1.4%
2020-09-16 2
 
1.4%
2019-01-08 1
 
0.7%
00:23:05.0 1
 
0.7%
2019-05-29 1
 
0.7%
02:20:57.0 1
 
0.7%
2020-12-09 1
 
0.7%
Other values (35) 35
23.6%

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
식품첨가물제조업
74 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품첨가물제조업 74
100.0%

Length

2024-04-17T19:44:17.944978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:18.029022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품첨가물제조업 74
100.0%

좌표정보(x)
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)91.2%
Missing17
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean384362.43
Minimum367423.88
Maximum402792.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:18.123661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum367423.88
5-th percentile368716.08
Q1379240.57
median382570.4
Q3390593.56
95-th percentile402204.48
Maximum402792.32
Range35368.438
Interquartile range (IQR)11352.99

Descriptive statistics

Standard deviation9383.4901
Coefficient of variation (CV)0.02441313
Kurtosis-0.51884285
Mean384362.43
Median Absolute Deviation (MAD)6556.8548
Skewness0.22246527
Sum21908659
Variance88049886
MonotonicityNot monotonic
2024-04-17T19:44:18.250300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
402792.323019555 3
 
4.1%
380993.864832345 2
 
2.7%
380101.664479822 2
 
2.7%
377845.877883642 2
 
2.7%
390593.563077685 1
 
1.4%
380863.37663821 1
 
1.4%
380065.02615957 1
 
1.4%
379971.604498203 1
 
1.4%
400570.661477722 1
 
1.4%
397395.93170902 1
 
1.4%
Other values (42) 42
56.8%
(Missing) 17
23.0%
ValueCountFrequency (%)
367423.884531247 1
1.4%
368673.20669772 1
1.4%
368677.63451413 1
1.4%
368725.685782611 1
1.4%
368967.073191522 1
1.4%
369710.36638299 1
1.4%
374246.169434343 1
1.4%
374676.507507246 1
1.4%
375850.302781565 1
1.4%
376065.302166244 1
1.4%
ValueCountFrequency (%)
402792.323019555 3
4.1%
402057.519947617 1
 
1.4%
400570.661477722 1
 
1.4%
398817.266532354 1
 
1.4%
397395.93170902 1
 
1.4%
392944.843535371 1
 
1.4%
392880.268663129 1
 
1.4%
392683.520016537 1
 
1.4%
392543.912546981 1
 
1.4%
392212.158032624 1
 
1.4%

좌표정보(y)
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)91.2%
Missing17
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean188388.4
Minimum176518.95
Maximum205160.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:18.385666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum176518.95
5-th percentile177219.2
Q1180232.68
median188262.83
Q3194161.22
95-th percentile200398.49
Maximum205160.1
Range28641.15
Interquartile range (IQR)13928.542

Descriptive statistics

Standard deviation8252.5926
Coefficient of variation (CV)0.043806267
Kurtosis-1.0128347
Mean188388.4
Median Absolute Deviation (MAD)7114.7072
Skewness0.17479757
Sum10738139
Variance68105285
MonotonicityNot monotonic
2024-04-17T19:44:18.500441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199422.531912758 3
 
4.1%
188262.830603753 2
 
2.7%
177863.674839855 2
 
2.7%
188223.814408098 2
 
2.7%
177284.095401433 1
 
1.4%
188417.856470315 1
 
1.4%
188181.272583295 1
 
1.4%
185510.082021673 1
 
1.4%
205160.097214495 1
 
1.4%
204302.342855919 1
 
1.4%
Other values (42) 42
56.8%
(Missing) 17
23.0%
ValueCountFrequency (%)
176518.946863378 1
1.4%
176940.729568457 1
1.4%
176959.637648592 1
1.4%
177284.095401433 1
1.4%
177691.456345521 1
1.4%
177701.032082067 1
1.4%
177863.674839855 2
2.7%
177993.247645975 1
1.4%
178008.059360826 1
1.4%
178327.13723327 1
1.4%
ValueCountFrequency (%)
205160.097214495 1
 
1.4%
204890.501363464 1
 
1.4%
204302.342855919 1
 
1.4%
199422.531912758 3
4.1%
198598.098045752 1
 
1.4%
197852.093229957 1
 
1.4%
197796.975145421 1
 
1.4%
197037.594569589 1
 
1.4%
196634.386535169 1
 
1.4%
196607.007863462 1
 
1.4%

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
식품첨가물제조업
74 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품첨가물제조업 74
100.0%

Length

2024-04-17T19:44:18.620748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:18.706243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품첨가물제조업 74
100.0%

남성종사자수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

여성종사자수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

영업장주변구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

등급구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

급수시설구분명
Categorical

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
51 
상수도전용
22 
지하수전용
 
1

Length

Max length5
Median length4
Mean length4.3108108
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 51
68.9%
상수도전용 22
29.7%
지하수전용 1
 
1.4%

Length

2024-04-17T19:44:18.795226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:18.888659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 51
68.9%
상수도전용 22
29.7%
지하수전용 1
 
1.4%

총종업원수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

본사종업원수
Categorical

Distinct4
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
0
40 
<NA>
31 
2
 
2
40
 
1

Length

Max length4
Median length1
Mean length2.2702703
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 40
54.1%
<NA> 31
41.9%
2 2
 
2.7%
40 1
 
1.4%

Length

2024-04-17T19:44:18.980533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:19.080356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40
54.1%
na 31
41.9%
2 2
 
2.7%
40 1
 
1.4%

공장사무직종업원수
Categorical

Distinct5
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size724.0 B
0
35 
<NA>
30 
2
5 
5
 
2
1
 
2

Length

Max length4
Median length1
Mean length2.2162162
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 35
47.3%
<NA> 30
40.5%
2 5
 
6.8%
5 2
 
2.7%
1 2
 
2.7%

Length

2024-04-17T19:44:19.197611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:19.299673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
47.3%
na 30
40.5%
2 5
 
6.8%
5 2
 
2.7%
1 2
 
2.7%

공장판매직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)13.3%
Missing29
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean0.46666667
Minimum0
Maximum6
Zeros38
Zeros (%)51.4%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:19.382979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.8
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2898203
Coefficient of variation (CV)2.7639006
Kurtosis8.9994573
Mean0.46666667
Median Absolute Deviation (MAD)0
Skewness3.0364756
Sum21
Variance1.6636364
MonotonicityNot monotonic
2024-04-17T19:44:19.474088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 38
51.4%
1 2
 
2.7%
4 2
 
2.7%
2 1
 
1.4%
6 1
 
1.4%
3 1
 
1.4%
(Missing) 29
39.2%
ValueCountFrequency (%)
0 38
51.4%
1 2
 
2.7%
2 1
 
1.4%
3 1
 
1.4%
4 2
 
2.7%
6 1
 
1.4%
ValueCountFrequency (%)
6 1
 
1.4%
4 2
 
2.7%
3 1
 
1.4%
2 1
 
1.4%
1 2
 
2.7%
0 38
51.4%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)17.8%
Missing29
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean1.5333333
Minimum0
Maximum34
Zeros34
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:19.573321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.8
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.2249402
Coefficient of variation (CV)3.4075697
Kurtosis35.661443
Mean1.5333333
Median Absolute Deviation (MAD)0
Skewness5.7281619
Sum69
Variance27.3
MonotonicityNot monotonic
2024-04-17T19:44:19.668348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 34
45.9%
2 3
 
4.1%
4 2
 
2.7%
6 2
 
2.7%
34 1
 
1.4%
3 1
 
1.4%
1 1
 
1.4%
5 1
 
1.4%
(Missing) 29
39.2%
ValueCountFrequency (%)
0 34
45.9%
1 1
 
1.4%
2 3
 
4.1%
3 1
 
1.4%
4 2
 
2.7%
5 1
 
1.4%
6 2
 
2.7%
34 1
 
1.4%
ValueCountFrequency (%)
34 1
 
1.4%
6 2
 
2.7%
5 1
 
1.4%
4 2
 
2.7%
3 1
 
1.4%
2 3
 
4.1%
1 1
 
1.4%
0 34
45.9%

건물소유구분명
Categorical

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
55 
임대
11 
자가
8 

Length

Max length4
Median length4
Mean length3.4864865
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> 55
74.3%
임대 11
 
14.9%
자가 8
 
10.8%

Length

2024-04-17T19:44:19.793530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:19.902119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 55
74.3%
임대 11
 
14.9%
자가 8
 
10.8%

보증액
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
53 
0
21 

Length

Max length4
Median length4
Mean length3.1486486
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
71.6%
0 21
 
28.4%

Length

2024-04-17T19:44:20.323090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:20.414093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
71.6%
0 21
 
28.4%

월세액
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
53 
0
21 

Length

Max length4
Median length4
Mean length3.1486486
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
71.6%
0 21
 
28.4%

Length

2024-04-17T19:44:20.510446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:44:20.605920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
71.6%
0 21
 
28.4%

다중이용업소여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size206.0 B
False
74 
ValueCountFrequency (%)
False 74
100.0%
2024-04-17T19:44:20.686094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.860135
Minimum0
Maximum2931.31
Zeros58
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size798.0 B
2024-04-17T19:44:20.777170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile67.569
Maximum2931.31
Range2931.31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation340.41801
Coefficient of variation (CV)7.1127674
Kurtosis73.428929
Mean47.860135
Median Absolute Deviation (MAD)0
Skewness8.5538206
Sum3541.65
Variance115884.42
MonotonicityNot monotonic
2024-04-17T19:44:20.888080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 58
78.4%
23.88 2
 
2.7%
29.18 1
 
1.4%
11.9 1
 
1.4%
20.62 1
 
1.4%
30.49 1
 
1.4%
5.66 1
 
1.4%
88.52 1
 
1.4%
90.5 1
 
1.4%
85.34 1
 
1.4%
Other values (6) 6
 
8.1%
ValueCountFrequency (%)
0.0 58
78.4%
5.66 1
 
1.4%
11.9 1
 
1.4%
15.0 1
 
1.4%
20.62 1
 
1.4%
21.47 1
 
1.4%
23.88 2
 
2.7%
29.18 1
 
1.4%
30.49 1
 
1.4%
52.1 1
 
1.4%
ValueCountFrequency (%)
2931.31 1
1.4%
90.5 1
1.4%
88.52 1
1.4%
85.34 1
1.4%
58.0 1
1.4%
53.8 1
1.4%
52.1 1
1.4%
30.49 1
1.4%
29.18 1
1.4%
23.88 2
2.7%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

Unnamed: 47
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing74
Missing (%)100.0%
Memory size798.0 B

Sample

번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지Unnamed: 47
01식품첨가물제조업07_22_12_P32800003280000-108-2014-0000120140424<NA>1영업/정상1영업<NA><NA><NA><NA><NA>334.69606805부산광역시 영도구 동삼동 201 외 2필지(199, 200)부산광역시 영도구 해양로 241 (동삼동)49013미창석유공업(주)20210125111352U2021-01-27 02:40:00.0식품첨가물제조업389127.25598178327.137233식품첨가물제조업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
12식품첨가물제조업07_22_12_P33000003300000-108-2020-0000120200304<NA>1영업/정상1영업<NA><NA><NA><NA>051 724 821910.80607802부산광역시 동래구 명륜동 6-9 세명빌딩부산광역시 동래구 시실로 20, 세명빌딩 3층 (명륜동)47744잡으리20210120094326U2021-01-22 02:40:00.0식품첨가물제조업389864.091785192842.545374식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
23식품첨가물제조업07_22_12_P33100003310000-108-2021-0000120210312<NA>1영업/정상1영업<NA><NA><NA><NA>051 611 225714.00608846부산광역시 남구 용호동 516-10 용호시장부산광역시 남구 동명로152번길 93, 용호시장 3(일부)층 (용호동)48587(주)광명생약 제2공장20210315174952I2021-03-17 00:22:59.0식품첨가물제조업392543.912547181589.698263식품첨가물제조업<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
34식품첨가물제조업07_22_12_P33400003340000-108-2011-0000820030509<NA>1영업/정상1영업<NA><NA><NA><NA>0512624294234.90604845부산광역시 사하구 장림동 392-1번지부산광역시 사하구 다대로300번길 34 (장림동)49467유림기업20161228131325I2018-08-31 23:59:59.0식품첨가물제조업380027.405764176959.637649식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00N0.0<NA><NA><NA><NA>
45식품첨가물제조업07_22_12_P33400003340000-108-2011-0000920030909<NA>1영업/정상1영업<NA><NA><NA><NA>0512621141108.00604846부산광역시 사하구 장림동 457-7번지부산광역시 사하구 다대로354번안길 80, 1층 (장림동)49469화인캡20160715173750I2018-08-31 23:59:59.0식품첨가물제조업379991.322409176518.946863식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
56식품첨가물제조업07_22_12_P33400003340000-108-2011-0000620040519<NA>1영업/정상1영업<NA><NA><NA><NA>051261956071.40604845부산광역시 사하구 장림동 386번지부산광역시 사하구 장림번영로104번길 120 (장림동)49467삼진 그린푸드 주식회사20150820130828I2018-08-31 23:59:59.0식품첨가물제조업380129.577906176940.729568식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
67식품첨가물제조업07_22_12_P33400003340000-108-2011-0000420050805<NA>1영업/정상1영업<NA><NA><NA><NA>0512002380566.31604843부산광역시 사하구 장림동 1037부산광역시 사하구 다대로 210 (장림동)49463씨제이제일제당주식회사20210208161531U2021-02-10 02:40:00.0식품첨가물제조업380101.66448177863.67484식품첨가물제조업<NA><NA><NA><NA><NA><NA>0000<NA>00N0.0<NA><NA><NA><NA>
78식품첨가물제조업07_22_12_P33400003340000-108-2017-0000120170327<NA>1영업/정상1영업<NA><NA><NA><NA>051 220766720.40604849부산광역시 사하구 하단동 840번지부산광역시 사하구 낙동대로550번길 37, 21동 305호 (하단동, 동아대학교 창업보육센터)49315(주)아미노피아20170327160152I2018-08-31 23:59:59.0식품첨가물제조업379240.573215181413.478007식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
89식품첨가물제조업07_22_12_P33500003350000-108-2020-0000220200615<NA>1영업/정상1영업<NA><NA><NA><NA>051 465 541922.62609845부산광역시 금정구 회동동 153-14번지부산광역시 금정구 개좌로 191, B동 2층 (회동동)46259프로메디칼20200615154637I2020-06-17 00:23:27.0식품첨가물제조업392944.843535194161.22231식품첨가물제조업<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
910식품첨가물제조업07_22_12_P33500003350000-108-2020-0000120200508<NA>1영업/정상1영업<NA><NA><NA><NA>051 527 0369116.20609845부산광역시 금정구 회동동 127부산광역시 금정구 동대로 20, D동 3층 (회동동)46257(주)라이프에이드20200908162627U2020-09-10 02:40:00.0식품첨가물제조업392683.520017193035.032324식품첨가물제조업<NA><NA><NA><NA>상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지Unnamed: 47
6465식품첨가물제조업07_22_12_P33700003370000-108-2011-0000119980907<NA>3폐업2폐업20070104<NA><NA><NA>0518506116.00<NA>부산광역시 연제구<NA><NA>대상(주)20080104090354I2018-08-31 23:59:59.0식품첨가물제조업<NA><NA>식품첨가물제조업<NA><NA><NA><NA><NA><NA>0000<NA>00N0.0<NA><NA><NA><NA>
6566식품첨가물제조업07_22_12_P33900003390000-108-2011-0000920051208<NA>3폐업2폐업20200914<NA><NA><NA>051304 3588198.32617814부산광역시 사상구 덕포동 366-3부산광역시 사상구 사상로 374-38 (덕포동)<NA>성림식품20200914092610U2020-09-16 02:40:00.0식품첨가물제조업380993.864832188262.830604식품첨가물제조업<NA><NA><NA><NA><NA><NA>2246<NA><NA><NA>N23.88<NA><NA><NA><NA>
6667식품첨가물제조업07_22_12_P33900003390000-108-2011-0000320051208<NA>3폐업2폐업20190225<NA><NA><NA>051304 3588198.32617814부산광역시 사상구 덕포동 366-3번지부산광역시 사상구 사상로 374-38 (덕포동)<NA>성림식품20190225094710U2019-02-27 02:40:00.0식품첨가물제조업380993.864832188262.830604식품첨가물제조업<NA><NA><NA><NA><NA><NA>2246<NA><NA><NA>N23.88<NA><NA><NA><NA>
6768식품첨가물제조업07_22_12_P33900003390000-108-2011-0000720091110<NA>3폐업2폐업20101126<NA><NA><NA>0519996323788.64617060부산광역시 사상구 괘법동 산 1-1번지 신라대학교 마린바이오센터내 공장동<NA><NA>신라바이오텍20101201230409I2018-08-31 23:59:59.0식품첨가물제조업382570.401135187570.045156식품첨가물제조업<NA><NA><NA><NA><NA><NA>0104<NA><NA><NA>N0.0<NA><NA><NA><NA>
6869식품첨가물제조업07_22_12_P33900003390000-108-2011-0000820091223<NA>3폐업2폐업20101117<NA><NA><NA>0513147726114.48617829부산광역시 사상구 엄궁동 138번지<NA><NA>(주)지엔티라이프20101117230408I2018-08-31 23:59:59.0식품첨가물제조업379966.182259182179.154483식품첨가물제조업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
6970식품첨가물제조업07_22_12_P33900003390000-108-2011-0000419980109<NA>3폐업2폐업20060602<NA><NA><NA>028207192<NA><NA>부산광역시 사상구<NA><NA>(주)농심부산공장20060818222941I2018-08-31 23:59:59.0식품첨가물제조업<NA><NA>식품첨가물제조업<NA><NA><NA><NA><NA><NA>0000<NA>00N0.0<NA><NA><NA><NA>
7071식품첨가물제조업07_22_12_P33900003390000-108-2011-0000519980515<NA>3폐업2폐업20060612<NA><NA><NA>0518090759<NA><NA>부산광역시 사상구<NA><NA>동산식품20060818222941I2018-08-31 23:59:59.0식품첨가물제조업<NA><NA>식품첨가물제조업<NA><NA><NA><NA><NA><NA>0000<NA>00N0.0<NA><NA><NA><NA>
7172식품첨가물제조업07_22_12_P33900003390000-108-2011-0000620040624<NA>3폐업2폐업20050315<NA><NA><NA>0513105847.00617805부산광역시 사상구 감전동 951-1 번지<NA><NA>(주)금양20060818222941I2018-08-31 23:59:59.0식품첨가물제조업<NA><NA>식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
7273식품첨가물제조업07_22_12_P34000003400000-108-2011-0000120111101<NA>3폐업2폐업20161207<NA><NA><NA>051 722 6674302.00619912부산광역시 기장군 일광면 횡계리 27번지부산광역시 기장군 일광면 횡계길 7, 302호 (해양생물산업육성센터)46048(주)제이케이바이오켐20111103174455I2018-08-31 23:59:59.0식품첨가물제조업402792.32302199422.531913식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA><NA>
7374식품첨가물제조업07_22_12_P34000003400000-108-2012-0000120120727<NA>3폐업2폐업20160906<NA><NA><NA>051 722 0275407.17619912부산광역시 기장군 일광면 횡계리 27번지 (재)부산테크노파크 해양생물산업육성센터 304,305호,B동101호부산광역시 기장군 일광면 횡계길 7 ((재)부산테크노파크 해양생물산업육성센터 304,305호,B동101호)46048(주)글루칸20130605171421I2018-08-31 23:59:59.0식품첨가물제조업402792.32302199422.531913식품첨가물제조업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA><NA>