Overview

Dataset statistics

Number of variables47
Number of observations187
Missing cells2618
Missing cells (%)29.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory406.7 B

Variable types

Numeric12
Categorical15
Text6
Unsupported12
DateTime1
Boolean1

Dataset

Description6270000_대구광역시_07_22_09_P_식품운반업_3월
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=DMI_0000088932&dataSetDetailId=DDI_0000088959&provdMethod=FILE

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
본사종업원수 is highly imbalanced (51.8%)Imbalance
보증액 is highly imbalanced (61.6%)Imbalance
월세액 is highly imbalanced (61.6%)Imbalance
인허가취소일자 has 187 (100.0%) missing valuesMissing
폐업일자 has 67 (35.8%) missing valuesMissing
휴업시작일자 has 187 (100.0%) missing valuesMissing
휴업종료일자 has 187 (100.0%) missing valuesMissing
재개업일자 has 187 (100.0%) missing valuesMissing
소재지전화 has 54 (28.9%) missing valuesMissing
소재지면적 has 20 (10.7%) missing valuesMissing
소재지우편번호 has 3 (1.6%) missing valuesMissing
도로명전체주소 has 56 (29.9%) missing valuesMissing
도로명우편번호 has 57 (30.5%) missing valuesMissing
좌표정보(X) has 9 (4.8%) missing valuesMissing
좌표정보(Y) has 9 (4.8%) missing valuesMissing
남성종사자수 has 187 (100.0%) missing valuesMissing
여성종사자수 has 187 (100.0%) missing valuesMissing
영업장주변구분명 has 187 (100.0%) missing valuesMissing
등급구분명 has 187 (100.0%) missing valuesMissing
총종업원수 has 187 (100.0%) missing valuesMissing
공장판매직종업원수 has 49 (26.2%) missing valuesMissing
공장생산직종업원수 has 50 (26.7%) missing valuesMissing
전통업소지정번호 has 187 (100.0%) missing valuesMissing
전통업소주된음식 has 187 (100.0%) missing valuesMissing
홈페이지 has 187 (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
공장판매직종업원수 has 120 (64.2%) zerosZeros
공장생산직종업원수 has 123 (65.8%) zerosZeros
시설총규모 has 182 (97.3%) zerosZeros

Reproduction

Analysis started2024-04-18 03:42:30.028968
Analysis finished2024-04-18 03:42:30.708554
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94
Minimum1
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:30.779819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.3
Q147.5
median94
Q3140.5
95-th percentile177.7
Maximum187
Range186
Interquartile range (IQR)93

Descriptive statistics

Standard deviation54.126395
Coefficient of variation (CV)0.57581272
Kurtosis-1.2
Mean94
Median Absolute Deviation (MAD)47
Skewness0
Sum17578
Variance2929.6667
MonotonicityStrictly increasing
2024-04-18T12:42:30.976666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
130 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
Other values (177) 177
94.7%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
식품운반업
187 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품운반업 187
100.0%

Length

2024-04-18T12:42:31.127778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:31.241289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품운반업 187
100.0%

개방서비스ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
07_22_09_P
187 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07_22_09_P
2nd row07_22_09_P
3rd row07_22_09_P
4th row07_22_09_P
5th row07_22_09_P

Common Values

ValueCountFrequency (%)
07_22_09_P 187
100.0%

Length

2024-04-18T12:42:31.353785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:31.481920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
07_22_09_p 187
100.0%

개방자치단체코드
Real number (ℝ)

Distinct8
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3448930.5
Minimum3410000
Maximum3480000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:31.592721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3410000
5-th percentile3420000
Q13430000
median3450000
Q33470000
95-th percentile3480000
Maximum3480000
Range70000
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation19917.315
Coefficient of variation (CV)0.0057749251
Kurtosis-0.85876514
Mean3448930.5
Median Absolute Deviation (MAD)20000
Skewness-0.29233338
Sum6.4495 × 108
Variance3.9669944 × 108
MonotonicityIncreasing
2024-04-18T12:42:31.727205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3450000 62
33.2%
3470000 31
16.6%
3420000 28
15.0%
3460000 19
 
10.2%
3480000 17
 
9.1%
3430000 14
 
7.5%
3410000 8
 
4.3%
3440000 8
 
4.3%
ValueCountFrequency (%)
3410000 8
 
4.3%
3420000 28
15.0%
3430000 14
 
7.5%
3440000 8
 
4.3%
3450000 62
33.2%
3460000 19
 
10.2%
3470000 31
16.6%
3480000 17
 
9.1%
ValueCountFrequency (%)
3480000 17
 
9.1%
3470000 31
16.6%
3460000 19
 
10.2%
3450000 62
33.2%
3440000 8
 
4.3%
3430000 14
 
7.5%
3420000 28
15.0%
3410000 8
 
4.3%

관리번호
Text

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T12:42:31.936787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4114
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

Unique187 ?
Unique (%)100.0%

Sample

1st row3410000-117-2004-00001
2nd row3410000-117-2005-00001
3rd row3410000-117-2005-00002
4th row3410000-117-2008-00001
5th row3410000-117-2008-00002
ValueCountFrequency (%)
3410000-117-2004-00001 1
 
0.5%
3460000-117-2014-00002 1
 
0.5%
3460000-117-2019-00001 1
 
0.5%
3450000-117-2006-00003 1
 
0.5%
3460000-117-2015-00002 1
 
0.5%
3460000-117-2008-00001 1
 
0.5%
3460000-117-2020-00001 1
 
0.5%
3460000-117-2020-00002 1
 
0.5%
3460000-117-2020-00003 1
 
0.5%
3460000-117-2011-00001 1
 
0.5%
Other values (177) 177
94.7%
2024-04-18T12:42:33.343571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1785
43.4%
1 564
 
13.7%
- 561
 
13.6%
2 272
 
6.6%
7 245
 
6.0%
3 244
 
5.9%
4 234
 
5.7%
5 89
 
2.2%
8 53
 
1.3%
6 47
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3553
86.4%
Dash Punctuation 561
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1785
50.2%
1 564
 
15.9%
2 272
 
7.7%
7 245
 
6.9%
3 244
 
6.9%
4 234
 
6.6%
5 89
 
2.5%
8 53
 
1.5%
6 47
 
1.3%
9 20
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1785
43.4%
1 564
 
13.7%
- 561
 
13.6%
2 272
 
6.6%
7 245
 
6.0%
3 244
 
5.9%
4 234
 
5.7%
5 89
 
2.2%
8 53
 
1.3%
6 47
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1785
43.4%
1 564
 
13.7%
- 561
 
13.6%
2 272
 
6.6%
7 245
 
6.0%
3 244
 
5.9%
4 234
 
5.7%
5 89
 
2.2%
8 53
 
1.3%
6 47
 
1.1%

인허가일자
Real number (ℝ)

Distinct172
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20110270
Minimum19930205
Maximum20210126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:33.505681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19930205
5-th percentile20031021
Q120060618
median20100210
Q320171116
95-th percentile20200618
Maximum20210126
Range279921
Interquartile range (IQR)110498

Descriptive statistics

Standard deviation58909.494
Coefficient of variation (CV)0.0029293239
Kurtosis-1.1036586
Mean20110270
Median Absolute Deviation (MAD)49906
Skewness0.050526971
Sum3.7606204 × 109
Variance3.4703285 × 109
MonotonicityNot monotonic
2024-04-18T12:42:33.663718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140113 4
 
2.1%
20150203 3
 
1.6%
20031021 3
 
1.6%
20060508 2
 
1.1%
20100210 2
 
1.1%
20181217 2
 
1.1%
20150915 2
 
1.1%
20181105 2
 
1.1%
20190725 2
 
1.1%
20191001 2
 
1.1%
Other values (162) 163
87.2%
ValueCountFrequency (%)
19930205 1
 
0.5%
20030320 1
 
0.5%
20030522 1
 
0.5%
20030721 1
 
0.5%
20030731 1
 
0.5%
20030806 1
 
0.5%
20030906 1
 
0.5%
20031006 1
 
0.5%
20031015 1
 
0.5%
20031021 3
1.6%
ValueCountFrequency (%)
20210126 1
0.5%
20201210 1
0.5%
20201207 1
0.5%
20201127 1
0.5%
20201126 1
0.5%
20201124 1
0.5%
20201007 1
0.5%
20201005 1
0.5%
20200929 1
0.5%
20200709 1
0.5%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

영업상태구분코드
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3
120 
1
67 

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 (%)
3 120
64.2%
1 67
35.8%

Length

2024-04-18T12:42:33.824409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:33.956618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 120
64.2%
1 67
35.8%

영업상태명
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
폐업
120 
영업/정상
67 

Length

Max length5
Median length2
Mean length3.0748663
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 120
64.2%
영업/정상 67
35.8%

Length

2024-04-18T12:42:34.092885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:34.219154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 120
64.2%
영업/정상 67
35.8%

상세영업상태코드
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2
120 
1
67 

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 (%)
2 120
64.2%
1 67
35.8%

Length

2024-04-18T12:42:34.349560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:34.481511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 120
64.2%
1 67
35.8%

상세영업상태명
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
폐업
120 
영업
67 

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 120
64.2%
영업 67
35.8%

Length

2024-04-18T12:42:34.606667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:34.718817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 120
64.2%
영업 67
35.8%

폐업일자
Real number (ℝ)

MISSING 

Distinct114
Distinct (%)95.0%
Missing67
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean20131193
Minimum20040603
Maximum20210219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:34.839998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040603
5-th percentile20051198
Q120080985
median20140214
Q320180286
95-th percentile20200517
Maximum20210219
Range169616
Interquartile range (IQR)99301.75

Descriptive statistics

Standard deviation50004.44
Coefficient of variation (CV)0.0024839283
Kurtosis-1.3116125
Mean20131193
Median Absolute Deviation (MAD)49197.5
Skewness-0.11904115
Sum2.4157432 × 109
Variance2.500444 × 109
MonotonicityNot monotonic
2024-04-18T12:42:35.014877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20161102 2
 
1.1%
20100706 2
 
1.1%
20210219 2
 
1.1%
20080909 2
 
1.1%
20181031 2
 
1.1%
20161122 2
 
1.1%
20090114 1
 
0.5%
20151027 1
 
0.5%
20110902 1
 
0.5%
20070125 1
 
0.5%
Other values (104) 104
55.6%
(Missing) 67
35.8%
ValueCountFrequency (%)
20040603 1
0.5%
20041126 1
0.5%
20041201 1
0.5%
20041229 1
0.5%
20050324 1
0.5%
20051017 1
0.5%
20051207 1
0.5%
20060109 1
0.5%
20060124 1
0.5%
20060328 1
0.5%
ValueCountFrequency (%)
20210219 2
1.1%
20201229 1
0.5%
20201214 1
0.5%
20200707 1
0.5%
20200611 1
0.5%
20200512 1
0.5%
20200501 1
0.5%
20200413 1
0.5%
20200210 1
0.5%
20200206 1
0.5%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

소재지전화
Text

MISSING 

Distinct119
Distinct (%)89.5%
Missing54
Missing (%)28.9%
Memory size1.6 KiB
2024-04-18T12:42:35.300097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.609023
Min length7

Characters and Unicode

Total characters1411
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

Unique108 ?
Unique (%)81.2%

Sample

1st row053 5875605
2nd row053 9549460
3rd row053 250 2024
4th row053 4262669
5th row053 4223753
ValueCountFrequency (%)
053 97
33.9%
070 4
 
1.4%
5252113 4
 
1.4%
954 3
 
1.0%
7301 3
 
1.0%
294 3
 
1.0%
650 2
 
0.7%
4828 2
 
0.7%
583 2
 
0.7%
956 2
 
0.7%
Other values (150) 164
57.3%
2024-04-18T12:42:35.705763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 232
16.4%
0 230
16.3%
3 216
15.3%
153
10.8%
2 110
7.8%
1 91
 
6.4%
6 88
 
6.2%
8 83
 
5.9%
4 79
 
5.6%
9 65
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1258
89.2%
Space Separator 153
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 232
18.4%
0 230
18.3%
3 216
17.2%
2 110
8.7%
1 91
 
7.2%
6 88
 
7.0%
8 83
 
6.6%
4 79
 
6.3%
9 65
 
5.2%
7 64
 
5.1%
Space Separator
ValueCountFrequency (%)
153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 232
16.4%
0 230
16.3%
3 216
15.3%
153
10.8%
2 110
7.8%
1 91
 
6.4%
6 88
 
6.2%
8 83
 
5.9%
4 79
 
5.6%
9 65
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 232
16.4%
0 230
16.3%
3 216
15.3%
153
10.8%
2 110
7.8%
1 91
 
6.4%
6 88
 
6.2%
8 83
 
5.9%
4 79
 
5.6%
9 65
 
4.6%

소재지면적
Text

MISSING 

Distinct125
Distinct (%)74.9%
Missing20
Missing (%)10.7%
Memory size1.6 KiB
2024-04-18T12:42:36.024032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0239521
Min length3

Characters and Unicode

Total characters839
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

Unique110 ?
Unique (%)65.9%

Sample

1st row66.04
2nd row65.60
3rd row26.44
4th row19.95
5th row66.00
ValueCountFrequency (%)
10.00 8
 
4.8%
9.90 6
 
3.6%
49.50 6
 
3.6%
6.60 5
 
3.0%
20.00 5
 
3.0%
3.30 4
 
2.4%
16.50 3
 
1.8%
30.00 3
 
1.8%
50.00 3
 
1.8%
00 3
 
1.8%
Other values (115) 121
72.5%
2024-04-18T12:42:36.462471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 198
23.6%
. 167
19.9%
1 65
 
7.7%
2 61
 
7.3%
6 58
 
6.9%
4 56
 
6.7%
3 55
 
6.6%
9 52
 
6.2%
5 51
 
6.1%
8 46
 
5.5%
Other values (2) 30
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 670
79.9%
Other Punctuation 169
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198
29.6%
1 65
 
9.7%
2 61
 
9.1%
6 58
 
8.7%
4 56
 
8.4%
3 55
 
8.2%
9 52
 
7.8%
5 51
 
7.6%
8 46
 
6.9%
7 28
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 167
98.8%
, 2
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 839
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 198
23.6%
. 167
19.9%
1 65
 
7.7%
2 61
 
7.3%
6 58
 
6.9%
4 56
 
6.7%
3 55
 
6.6%
9 52
 
6.2%
5 51
 
6.1%
8 46
 
5.5%
Other values (2) 30
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 198
23.6%
. 167
19.9%
1 65
 
7.7%
2 61
 
7.3%
6 58
 
6.9%
4 56
 
6.7%
3 55
 
6.6%
9 52
 
6.2%
5 51
 
6.1%
8 46
 
5.5%
Other values (2) 30
 
3.6%

소재지우편번호
Real number (ℝ)

MISSING 

Distinct118
Distinct (%)64.1%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean704168.67
Minimum700082
Maximum711891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:36.610738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700082
5-th percentile701166.5
Q1702666
median702903
Q3704944
95-th percentile711821
Maximum711891
Range11809
Interquartile range (IQR)2278

Descriptive statistics

Standard deviation2741.6583
Coefficient of variation (CV)0.0038934681
Kurtosis2.1290741
Mean704168.67
Median Absolute Deviation (MAD)1079
Skewness1.5354948
Sum1.2956704 × 108
Variance7516690.1
MonotonicityNot monotonic
2024-04-18T12:42:36.763589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702903 11
 
5.9%
704830 7
 
3.7%
702300 6
 
3.2%
702825 4
 
2.1%
711821 4
 
2.1%
701804 4
 
2.1%
705840 3
 
1.6%
702835 3
 
1.6%
702865 3
 
1.6%
704932 3
 
1.6%
Other values (108) 136
72.7%
ValueCountFrequency (%)
700082 1
0.5%
700192 1
0.5%
700810 1
0.5%
700823 1
0.5%
700826 1
0.5%
700845 2
1.1%
700847 1
0.5%
701140 1
0.5%
701150 1
0.5%
701260 1
0.5%
ValueCountFrequency (%)
711891 1
 
0.5%
711874 1
 
0.5%
711852 1
 
0.5%
711842 1
 
0.5%
711833 2
1.1%
711832 2
1.1%
711823 1
 
0.5%
711821 4
2.1%
711814 1
 
0.5%
706852 2
1.1%

소재지전체주소
Text

Distinct165
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T12:42:37.178969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length33
Mean length23.695187
Min length17

Characters and Unicode

Total characters4431
Distinct characters158
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

Unique150 ?
Unique (%)80.2%

Sample

1st row대구광역시 중구 동인동4가 0420번지 (1층)
2nd row대구광역시 중구 종로2가 0038번지
3rd row대구광역시 중구 대봉동 55-50번지 (65,66호)
4th row대구광역시 중구 동인동3가 0240-0006번지 지상1층
5th row대구광역시 중구 남산동 2110-0009번지 지상1층
ValueCountFrequency (%)
대구광역시 187
22.0%
북구 62
 
7.3%
달서구 30
 
3.5%
동구 28
 
3.3%
수성구 19
 
2.2%
달성군 17
 
2.0%
서구 14
 
1.6%
팔달동 14
 
1.6%
남구 9
 
1.1%
230-3번지 9
 
1.1%
Other values (298) 460
54.2%
2024-04-18T12:42:37.720280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
854
19.3%
구 364
 
8.2%
동 212
 
4.8%
대 199
 
4.5%
지 197
 
4.4%
역 188
 
4.2%
광 187
 
4.2%
시 187
 
4.2%
1 167
 
3.8%
번 166
 
3.7%
Other values (148) 1710
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2501
56.4%
Decimal Number 904
 
20.4%
Space Separator 854
 
19.3%
Dash Punctuation 145
 
3.3%
Open Punctuation 9
 
0.2%
Close Punctuation 9
 
0.2%
Other Punctuation 6
 
0.1%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 364
14.6%
동 212
 
8.5%
대 199
 
8.0%
지 197
 
7.9%
역 188
 
7.5%
광 187
 
7.5%
시 187
 
7.5%
번 166
 
6.6%
북 63
 
2.5%
달 61
 
2.4%
Other values (130) 677
27.1%
Decimal Number
ValueCountFrequency (%)
1 167
18.5%
2 113
12.5%
0 105
11.6%
3 94
10.4%
4 84
9.3%
6 75
8.3%
8 74
8.2%
9 65
 
7.2%
7 64
 
7.1%
5 63
 
7.0%
Other Punctuation
ValueCountFrequency (%)
, 4
66.7%
. 2
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
C 1
33.3%
Space Separator
ValueCountFrequency (%)
854
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2501
56.4%
Common 1927
43.5%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 364
14.6%
동 212
 
8.5%
대 199
 
8.0%
지 197
 
7.9%
역 188
 
7.5%
광 187
 
7.5%
시 187
 
7.5%
번 166
 
6.6%
북 63
 
2.5%
달 61
 
2.4%
Other values (130) 677
27.1%
Common
ValueCountFrequency (%)
854
44.3%
1 167
 
8.7%
- 145
 
7.5%
2 113
 
5.9%
0 105
 
5.4%
3 94
 
4.9%
4 84
 
4.4%
6 75
 
3.9%
8 74
 
3.8%
9 65
 
3.4%
Other values (6) 151
 
7.8%
Latin
ValueCountFrequency (%)
A 2
66.7%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2501
56.4%
ASCII 1930
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
854
44.2%
1 167
 
8.7%
- 145
 
7.5%
2 113
 
5.9%
0 105
 
5.4%
3 94
 
4.9%
4 84
 
4.4%
6 75
 
3.9%
8 74
 
3.8%
9 65
 
3.4%
Other values (8) 154
 
8.0%
Hangul
ValueCountFrequency (%)
구 364
14.6%
동 212
 
8.5%
대 199
 
8.0%
지 197
 
7.9%
역 188
 
7.5%
광 187
 
7.5%
시 187
 
7.5%
번 166
 
6.6%
북 63
 
2.5%
달 61
 
2.4%
Other values (130) 677
27.1%

도로명전체주소
Text

MISSING 

Distinct116
Distinct (%)88.5%
Missing56
Missing (%)29.9%
Memory size1.6 KiB
2024-04-18T12:42:38.060906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length41
Mean length28.374046
Min length20

Characters and Unicode

Total characters3717
Distinct characters189
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

Unique104 ?
Unique (%)79.4%

Sample

1st row대구광역시 중구 중앙대로77길 43 (종로2가)
2nd row대구광역시 중구 동덕로38길 93-1, 1층 (동인동3가)
3rd row대구광역시 중구 봉산문화1길 5, 1층 (봉산동)
4th row대구광역시 중구 국채보상로143길 65 (동인동3가)
5th row대구광역시 중구 달구벌대로 2077 (계산동2가, 지하3층)
ValueCountFrequency (%)
대구광역시 131
 
17.1%
북구 34
 
4.4%
1층 29
 
3.8%
달서구 24
 
3.1%
동구 24
 
3.1%
2층 14
 
1.8%
달성군 13
 
1.7%
서구 12
 
1.6%
수성구 11
 
1.4%
3층 9
 
1.2%
Other values (308) 467
60.8%
2024-04-18T12:42:38.885729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
637
 
17.1%
구 263
 
7.1%
동 175
 
4.7%
대 158
 
4.3%
1 134
 
3.6%
시 134
 
3.6%
역 132
 
3.6%
광 131
 
3.5%
로 119
 
3.2%
) 119
 
3.2%
Other values (179) 1715
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2121
57.1%
Space Separator 637
 
17.1%
Decimal Number 600
 
16.1%
Close Punctuation 119
 
3.2%
Open Punctuation 119
 
3.2%
Other Punctuation 87
 
2.3%
Dash Punctuation 30
 
0.8%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 263
 
12.4%
동 175
 
8.3%
대 158
 
7.4%
시 134
 
6.3%
역 132
 
6.2%
광 131
 
6.2%
로 119
 
5.6%
길 79
 
3.7%
층 67
 
3.2%
서 54
 
2.5%
Other values (162) 809
38.1%
Decimal Number
ValueCountFrequency (%)
1 134
22.3%
2 95
15.8%
3 93
15.5%
4 52
 
8.7%
0 45
 
7.5%
5 44
 
7.3%
6 42
 
7.0%
8 35
 
5.8%
7 34
 
5.7%
9 26
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
75.0%
C 1
 
25.0%
Space Separator
ValueCountFrequency (%)
637
100.0%
Close Punctuation
ValueCountFrequency (%)
) 119
100.0%
Open Punctuation
ValueCountFrequency (%)
( 119
100.0%
Other Punctuation
ValueCountFrequency (%)
, 87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2121
57.1%
Common 1592
42.8%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 263
 
12.4%
동 175
 
8.3%
대 158
 
7.4%
시 134
 
6.3%
역 132
 
6.2%
광 131
 
6.2%
로 119
 
5.6%
길 79
 
3.7%
층 67
 
3.2%
서 54
 
2.5%
Other values (162) 809
38.1%
Common
ValueCountFrequency (%)
637
40.0%
1 134
 
8.4%
) 119
 
7.5%
( 119
 
7.5%
2 95
 
6.0%
3 93
 
5.8%
, 87
 
5.5%
4 52
 
3.3%
0 45
 
2.8%
5 44
 
2.8%
Other values (5) 167
 
10.5%
Latin
ValueCountFrequency (%)
A 3
75.0%
C 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2121
57.1%
ASCII 1596
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
637
39.9%
1 134
 
8.4%
) 119
 
7.5%
( 119
 
7.5%
2 95
 
6.0%
3 93
 
5.8%
, 87
 
5.5%
4 52
 
3.3%
0 45
 
2.8%
5 44
 
2.8%
Other values (7) 171
 
10.7%
Hangul
ValueCountFrequency (%)
구 263
 
12.4%
동 175
 
8.3%
대 158
 
7.4%
시 134
 
6.3%
역 132
 
6.2%
광 131
 
6.2%
로 119
 
5.6%
길 79
 
3.7%
층 67
 
3.2%
서 54
 
2.5%
Other values (162) 809
38.1%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct96
Distinct (%)73.8%
Missing57
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean41953.592
Minimum41042
Maximum43010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:39.090528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41042
5-th percentile41082
Q141469.75
median41754.5
Q342645
95-th percentile42952.05
Maximum43010
Range1968
Interquartile range (IQR)1175.25

Descriptive statistics

Standard deviation640.75463
Coefficient of variation (CV)0.015272938
Kurtosis-1.412166
Mean41953.592
Median Absolute Deviation (MAD)562.5
Skewness0.22855327
Sum5453967
Variance410566.49
MonotonicityNot monotonic
2024-04-18T12:42:39.303670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41490 7
 
3.7%
42722 6
 
3.2%
42904 3
 
1.6%
42500 3
 
1.6%
41163 3
 
1.6%
41082 3
 
1.6%
41754 3
 
1.6%
41491 3
 
1.6%
42957 2
 
1.1%
42712 2
 
1.1%
Other values (86) 95
50.8%
(Missing) 57
30.5%
ValueCountFrequency (%)
41042 1
 
0.5%
41046 1
 
0.5%
41051 1
 
0.5%
41055 1
 
0.5%
41065 1
 
0.5%
41078 1
 
0.5%
41082 3
1.6%
41085 1
 
0.5%
41124 1
 
0.5%
41129 2
1.1%
ValueCountFrequency (%)
43010 1
 
0.5%
43003 1
 
0.5%
42974 1
 
0.5%
42968 2
1.1%
42957 2
1.1%
42946 2
1.1%
42922 1
 
0.5%
42904 3
1.6%
42819 1
 
0.5%
42815 1
 
0.5%

사업장명
Text

Distinct169
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T12:42:39.681851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.5026738
Min length2

Characters and Unicode

Total characters1216
Distinct characters203
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)82.4%

Sample

1st row사옹원
2nd row(주)일성화물
3rd row아진식품
4th row박통식품
5th row대구우체국
ValueCountFrequency (%)
주식회사 6
 
2.9%
개인용달 4
 
1.9%
개별화물 3
 
1.5%
주)정우운수 3
 
1.5%
베스트바이 3
 
1.5%
주식회사그린로직스 2
 
1.0%
주 2
 
1.0%
주)센트랄푸드시스템 2
 
1.0%
주)성진냉동 2
 
1.0%
가야푸드 2
 
1.0%
Other values (170) 177
85.9%
2024-04-18T12:42:40.175340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 89
 
7.3%
) 77
 
6.3%
( 76
 
6.2%
스 38
 
3.1%
식 35
 
2.9%
물 34
 
2.8%
드 28
 
2.3%
푸 26
 
2.1%
대 26
 
2.1%
류 26
 
2.1%
Other values (193) 761
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1036
85.2%
Close Punctuation 77
 
6.3%
Open Punctuation 76
 
6.2%
Space Separator 19
 
1.6%
Uppercase Letter 8
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 89
 
8.6%
스 38
 
3.7%
식 35
 
3.4%
물 34
 
3.3%
드 28
 
2.7%
푸 26
 
2.5%
대 26
 
2.5%
류 26
 
2.5%
성 23
 
2.2%
통 22
 
2.1%
Other values (183) 689
66.5%
Uppercase Letter
ValueCountFrequency (%)
G 2
25.0%
M 1
12.5%
D 1
12.5%
F 1
12.5%
C 1
12.5%
T 1
12.5%
S 1
12.5%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1036
85.2%
Common 172
 
14.1%
Latin 8
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 89
 
8.6%
스 38
 
3.7%
식 35
 
3.4%
물 34
 
3.3%
드 28
 
2.7%
푸 26
 
2.5%
대 26
 
2.5%
류 26
 
2.5%
성 23
 
2.2%
통 22
 
2.1%
Other values (183) 689
66.5%
Latin
ValueCountFrequency (%)
G 2
25.0%
M 1
12.5%
D 1
12.5%
F 1
12.5%
C 1
12.5%
T 1
12.5%
S 1
12.5%
Common
ValueCountFrequency (%)
) 77
44.8%
( 76
44.2%
19
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1036
85.2%
ASCII 180
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 89
 
8.6%
스 38
 
3.7%
식 35
 
3.4%
물 34
 
3.3%
드 28
 
2.7%
푸 26
 
2.5%
대 26
 
2.5%
류 26
 
2.5%
성 23
 
2.2%
통 22
 
2.1%
Other values (183) 689
66.5%
ASCII
ValueCountFrequency (%)
) 77
42.8%
( 76
42.2%
19
 
10.6%
G 2
 
1.1%
M 1
 
0.6%
D 1
 
0.6%
F 1
 
0.6%
C 1
 
0.6%
T 1
 
0.6%
S 1
 
0.6%

최종수정시점
Real number (ℝ)

Distinct181
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0139072 × 1013
Minimum2.0031121 × 1013
Maximum2.0210304 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:40.336055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0031121 × 1013
5-th percentile2.0041011 × 1013
Q12.0085667 × 1013
median2.0160224 × 1013
Q32.0190815 × 1013
95-th percentile2.0201206 × 1013
Maximum2.0210304 × 1013
Range1.7918311 × 1011
Interquartile range (IQR)1.0514807 × 1011

Descriptive statistics

Standard deviation5.7923106 × 1010
Coefficient of variation (CV)0.0028761556
Kurtosis-1.3077492
Mean2.0139072 × 1013
Median Absolute Deviation (MAD)4.0294008 × 1010
Skewness-0.4674893
Sum3.7660065 × 1015
Variance3.3550862 × 1021
MonotonicityNot monotonic
2024-04-18T12:42:40.483757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20041011000000 6
 
3.2%
20161122170313 2
 
1.1%
20040204000000 1
 
0.5%
20031215000000 1
 
0.5%
20200423152629 1
 
0.5%
20201005150455 1
 
0.5%
20201211094457 1
 
0.5%
20200413152039 1
 
0.5%
20200210153155 1
 
0.5%
20141210093840 1
 
0.5%
Other values (171) 171
91.4%
ValueCountFrequency (%)
20031121000000 1
 
0.5%
20031215000000 1
 
0.5%
20040107000000 1
 
0.5%
20040114000000 1
 
0.5%
20040204000000 1
 
0.5%
20040212000000 1
 
0.5%
20040401000000 1
 
0.5%
20040506000000 1
 
0.5%
20040811000000 1
 
0.5%
20041011000000 6
3.2%
ValueCountFrequency (%)
20210304113806 1
0.5%
20210304105146 1
0.5%
20210202152424 1
0.5%
20210118145459 1
0.5%
20201229095743 1
0.5%
20201214150255 1
0.5%
20201211135839 1
0.5%
20201211094457 1
0.5%
20201209174705 1
0.5%
20201208175252 1
0.5%

데이터갱신구분
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
I
129 
U
58 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 129
69.0%
U 58
31.0%

Length

2024-04-18T12:42:40.638417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:40.742242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 129
69.0%
u 58
31.0%

데이터갱신일자
Date

Distinct66
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2018-08-31 23:59:59
Maximum2021-03-06 02:40:00
2024-04-18T12:42:40.856111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T12:42:40.999163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
식품운반업
187 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품운반업 187
100.0%

Length

2024-04-18T12:42:41.143823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:41.249020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품운반업 187
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct144
Distinct (%)80.9%
Missing9
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean341645.31
Minimum327299.69
Maximum355567.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:41.368673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum327299.69
5-th percentile330349.06
Q1338520.02
median340249.41
Q3345553.54
95-th percentile352803.15
Maximum355567.69
Range28268.001
Interquartile range (IQR)7033.5148

Descriptive statistics

Standard deviation5940.4742
Coefficient of variation (CV)0.01738784
Kurtosis0.18379919
Mean341645.31
Median Absolute Deviation (MAD)3330.2686
Skewness0.13419497
Sum60812866
Variance35289234
MonotonicityNot monotonic
2024-04-18T12:42:41.510703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
338985.366314 10
 
5.3%
337329.213273 5
 
2.7%
340946.597285 3
 
1.6%
327735.044524 3
 
1.6%
339388.690364 3
 
1.6%
333920.60853 2
 
1.1%
352803.154449 2
 
1.1%
338167.933836 2
 
1.1%
329271.879011 2
 
1.1%
334750.422674 2
 
1.1%
Other values (134) 144
77.0%
(Missing) 9
 
4.8%
ValueCountFrequency (%)
327299.692057 1
 
0.5%
327735.044524 3
1.6%
329271.879011 2
1.1%
329604.038183 1
 
0.5%
329859.122079 1
 
0.5%
330128.30921 1
 
0.5%
330388.015281 1
 
0.5%
330706.421358 1
 
0.5%
333070.51248 1
 
0.5%
333920.60853 2
1.1%
ValueCountFrequency (%)
355567.693374 2
1.1%
355474.652805 1
0.5%
355444.760056 1
0.5%
354790.108191 1
0.5%
354215.082384 1
0.5%
353971.781376 1
0.5%
353414.507288 1
0.5%
352803.154449 2
1.1%
352644.320881 1
0.5%
351788.563589 1
0.5%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct144
Distinct (%)80.9%
Missing9
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean264345.07
Minimum240636.85
Maximum273735.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:41.653444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum240636.85
5-th percentile256562.15
Q1261818.39
median264718.49
Q3267354.45
95-th percentile271842.15
Maximum273735.13
Range33098.279
Interquartile range (IQR)5536.0599

Descriptive statistics

Standard deviation4801.4697
Coefficient of variation (CV)0.018163644
Kurtosis4.225247
Mean264345.07
Median Absolute Deviation (MAD)2685.3762
Skewness-1.2462751
Sum47053422
Variance23054111
MonotonicityNot monotonic
2024-04-18T12:42:41.798568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267354.446312 10
 
5.3%
260416.780233 5
 
2.7%
259953.127592 3
 
1.6%
263479.562399 3
 
1.6%
269664.126149 3
 
1.6%
261405.687893 2
 
1.1%
264946.079267 2
 
1.1%
265608.87802 2
 
1.1%
254619.354156 2
 
1.1%
256643.317146 2
 
1.1%
Other values (134) 144
77.0%
(Missing) 9
 
4.8%
ValueCountFrequency (%)
240636.852024 1
0.5%
244838.382091 1
0.5%
248561.77396 1
0.5%
254017.803261 1
0.5%
254171.232318 1
0.5%
254619.354156 2
1.1%
255771.102947 1
0.5%
256102.203744 1
0.5%
256643.317146 2
1.1%
257765.775409 1
0.5%
ValueCountFrequency (%)
273735.131409 1
0.5%
273355.790439 1
0.5%
273296.193994 1
0.5%
272745.678782 1
0.5%
272600.063881 2
1.1%
272219.113572 1
0.5%
272092.19703 1
0.5%
271937.692097 1
0.5%
271825.291142 1
0.5%
270847.157898 2
1.1%

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
식품운반업
187 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품운반업 187
100.0%

Length

2024-04-18T12:42:41.944623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:42.055166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품운반업 187
100.0%

남성종사자수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

여성종사자수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

영업장주변구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

등급구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

급수시설구분명
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
134 
상수도전용
53 

Length

Max length5
Median length4
Mean length4.2834225
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 134
71.7%
상수도전용 53
 
28.3%

Length

2024-04-18T12:42:42.168169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:42.278183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 134
71.7%
상수도전용 53
 
28.3%

총종업원수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

본사종업원수
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
134 
<NA>
50 
3
 
2
1
 
1

Length

Max length4
Median length1
Mean length1.802139
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 134
71.7%
<NA> 50
 
26.7%
3 2
 
1.1%
1 1
 
0.5%

Length

2024-04-18T12:42:42.391268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:42.511505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 134
71.7%
na 50
 
26.7%
3 2
 
1.1%
1 1
 
0.5%

공장사무직종업원수
Categorical

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
116 
<NA>
49 
1
14 
2
 
5
3
 
2

Length

Max length4
Median length1
Mean length1.7860963
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 116
62.0%
<NA> 49
26.2%
1 14
 
7.5%
2 5
 
2.7%
3 2
 
1.1%
5 1
 
0.5%

Length

2024-04-18T12:42:42.636521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:42.780224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
62.0%
na 49
26.2%
1 14
 
7.5%
2 5
 
2.7%
3 2
 
1.1%
5 1
 
0.5%

공장판매직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)5.1%
Missing49
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean0.30434783
Minimum0
Maximum7
Zeros120
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:42.890666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.15
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0007931
Coefficient of variation (CV)3.2883201
Kurtosis20.09778
Mean0.30434783
Median Absolute Deviation (MAD)0
Skewness4.2326083
Sum42
Variance1.0015868
MonotonicityNot monotonic
2024-04-18T12:42:43.004990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 120
64.2%
1 9
 
4.8%
3 3
 
1.6%
2 2
 
1.1%
4 2
 
1.1%
7 1
 
0.5%
5 1
 
0.5%
(Missing) 49
26.2%
ValueCountFrequency (%)
0 120
64.2%
1 9
 
4.8%
2 2
 
1.1%
3 3
 
1.6%
4 2
 
1.1%
5 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
7 1
 
0.5%
5 1
 
0.5%
4 2
 
1.1%
3 3
 
1.6%
2 2
 
1.1%
1 9
 
4.8%
0 120
64.2%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)4.4%
Missing50
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean0.25547445
Minimum0
Maximum7
Zeros123
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:43.115206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95508852
Coefficient of variation (CV)3.7384893
Kurtosis26.372461
Mean0.25547445
Median Absolute Deviation (MAD)0
Skewness4.8616728
Sum35
Variance0.91219407
MonotonicityNot monotonic
2024-04-18T12:42:43.226317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 123
65.8%
2 5
 
2.7%
1 5
 
2.7%
5 2
 
1.1%
7 1
 
0.5%
3 1
 
0.5%
(Missing) 50
26.7%
ValueCountFrequency (%)
0 123
65.8%
1 5
 
2.7%
2 5
 
2.7%
3 1
 
0.5%
5 2
 
1.1%
7 1
 
0.5%
ValueCountFrequency (%)
7 1
 
0.5%
5 2
 
1.1%
3 1
 
0.5%
2 5
 
2.7%
1 5
 
2.7%
0 123
65.8%

건물소유구분명
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
82 
자가
62 
임대
43 

Length

Max length4
Median length2
Mean length2.8770053
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 82
43.9%
자가 62
33.2%
임대 43
23.0%

Length

2024-04-18T12:42:43.373863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:43.501738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 82
43.9%
자가 62
33.2%
임대 43
23.0%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
173 
0
 
14

Length

Max length4
Median length4
Mean length3.7754011
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 173
92.5%
0 14
 
7.5%

Length

2024-04-18T12:42:43.624060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:43.735049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 173
92.5%
0 14
 
7.5%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
173 
0
 
14

Length

Max length4
Median length4
Mean length3.7754011
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 173
92.5%
0 14
 
7.5%

Length

2024-04-18T12:42:43.853786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:42:43.960984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 173
92.5%
0 14
 
7.5%

다중이용업소여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size319.0 B
False
187 
ValueCountFrequency (%)
False 187
100.0%
2024-04-18T12:42:44.043229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81957219
Minimum0
Maximum134.13
Zeros182
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T12:42:44.148570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum134.13
Range134.13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.8370399
Coefficient of variation (CV)12.002652
Kurtosis184.23788
Mean0.81957219
Median Absolute Deviation (MAD)0
Skewness13.530156
Sum153.26
Variance96.767354
MonotonicityNot monotonic
2024-04-18T12:42:44.269102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 182
97.3%
134.13 1
 
0.5%
10.37 1
 
0.5%
3.3 1
 
0.5%
3.0 1
 
0.5%
2.46 1
 
0.5%
ValueCountFrequency (%)
0.0 182
97.3%
2.46 1
 
0.5%
3.0 1
 
0.5%
3.3 1
 
0.5%
10.37 1
 
0.5%
134.13 1
 
0.5%
ValueCountFrequency (%)
134.13 1
 
0.5%
10.37 1
 
0.5%
3.3 1
 
0.5%
3.0 1
 
0.5%
2.46 1
 
0.5%
0.0 182
97.3%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing187
Missing (%)100.0%
Memory size1.8 KiB

Sample

번호개방서비스명개방서비스ID개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
01식품운반업07_22_09_P34100003410000-117-2004-0000120040114<NA>3폐업2폐업20080225<NA><NA><NA><NA>66.04700847대구광역시 중구 동인동4가 0420번지 (1층)<NA><NA>사옹원20040204000000I2018-08-31 23:59:59.0식품운반업345414.421341264120.775769식품운반업<NA><NA><NA><NA><NA><NA>0110자가<NA><NA>N0.0<NA><NA><NA>
12식품운반업07_22_09_P34100003410000-117-2005-0000120050324<NA>3폐업2폐업20150916<NA><NA><NA>053 587560565.60700192대구광역시 중구 종로2가 0038번지대구광역시 중구 중앙대로77길 43 (종로2가)41934(주)일성화물20120504134608I2018-08-31 23:59:59.0식품운반업343638.059396264281.159118식품운반업<NA><NA><NA><NA>상수도전용<NA>0200임대<NA><NA>N0.0<NA><NA><NA>
23식품운반업07_22_09_P34100003410000-117-2005-0000220050719<NA>3폐업2폐업20051017<NA><NA><NA><NA>26.44700810대구광역시 중구 대봉동 55-50번지 (65,66호)<NA><NA>아진식품20050719000000I2018-08-31 23:59:59.0식품운반업344625.726336263421.522163식품운반업<NA><NA><NA><NA><NA><NA>0110임대<NA><NA>N0.0<NA><NA><NA>
34식품운반업07_22_09_P34100003410000-117-2008-0000120080805<NA>3폐업2폐업20140306<NA><NA><NA>053 954946019.95700845대구광역시 중구 동인동3가 0240-0006번지 지상1층대구광역시 중구 동덕로38길 93-1, 1층 (동인동3가)41906박통식품20131206104127I2018-08-31 23:59:59.0식품운반업345292.345402264638.147502식품운반업<NA><NA><NA><NA>상수도전용<NA>0030<NA><NA><NA>N0.0<NA><NA><NA>
45식품운반업07_22_09_P34100003410000-117-2008-0000220081112<NA>3폐업2폐업20111230<NA><NA><NA>053 250 202466.00700826대구광역시 중구 남산동 2110-0009번지 지상1층<NA><NA>대구우체국20100709163030I2018-08-31 23:59:59.0식품운반업343570.613158263072.964308식품운반업<NA><NA><NA><NA>상수도전용<NA>0110<NA><NA><NA>N0.0<NA><NA><NA>
56식품운반업07_22_09_P34100003410000-117-2010-0000120100210<NA>3폐업2폐업20140319<NA><NA><NA>053 426266927.69700823대구광역시 중구 봉산동 0222-0037번지 지상1층대구광역시 중구 봉산문화1길 5, 1층 (봉산동)41959남양유업 동성로대리점20120201161346I2018-08-31 23:59:59.0식품운반업344241.713047263665.761646식품운반업<NA><NA><NA><NA><NA><NA>0010<NA><NA><NA>N0.0<NA><NA><NA>
67식품운반업07_22_09_P34100003410000-117-2011-0000120110920<NA>1영업/정상1영업<NA><NA><NA><NA>053 422375384.20700845대구광역시 중구 동인동3가 0271-0212번지대구광역시 중구 국채보상로143길 65 (동인동3가)41905(주) 전진통운20120201161419I2018-08-31 23:59:59.0식품운반업345197.150819264705.746035식품운반업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
78식품운반업07_22_09_P34100003410000-117-2014-0000120140313<NA>3폐업2폐업20160223<NA><NA><NA><NA>26.00700082대구광역시 중구 계산동2가 0200번지 지하3층대구광역시 중구 달구벌대로 2077 (계산동2가, 지하3층)41936(주)코리아택배물류20160224112705I2018-08-31 23:59:59.0식품운반업343588.735555264119.01075식품운반업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
89식품운반업07_22_09_P34200003420000-117-2008-0000220080327<NA>1영업/정상1영업<NA><NA><NA><NA>053 957 04246.60701817대구광역시 동구 신암동 460-9번지대구광역시 동구 신암로20길 57 (신암동)41202현지상회20171229170526I2018-08-31 23:59:59.0식품운반업345599.909137265378.669775식품운반업<NA><NA><NA><NA>상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
910식품운반업07_22_09_P34200003420000-117-2009-0000220090529<NA>1영업/정상1영업<NA><NA><NA><NA>954 632384.67701816대구광역시 동구 신암동 364-8번지대구광역시 동구 동북로 440 (신암동)41220(주)전진운수20090529152930I2018-08-31 23:59:59.0식품운반업347483.915581266103.178136식품운반업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
번호개방서비스명개방서비스ID개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
177178식품운반업07_22_09_P34800003480000-117-2017-0000120171211<NA>1영업/정상1영업<NA><NA><NA><NA>070 412299956.60711821대구광역시 달성군 하빈면 동곡리 627번지 1층대구광역시 달성군 하빈면 하빈남로 171-1, 1층42904(주)대구유통20171211101032I2018-08-31 23:59:59.0식품운반업327735.044524263479.562399식품운반업<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA>
178179식품운반업07_22_09_P34800003480000-117-2004-0000220040107<NA>3폐업2폐업20110621<NA><NA><NA>053 616336341.85711842대구광역시 달성군 옥포면 강림리 580번지<NA><NA>(주)굿모닝로지스20040107000000I2018-08-31 23:59:59.0식품운반업329604.038183254171.232318식품운반업<NA><NA><NA><NA><NA><NA>0000자가<NA><NA>N0.0<NA><NA><NA>
179180식품운반업07_22_09_P34800003480000-117-2006-0000120060808<NA>3폐업2폐업20110808<NA><NA><NA>053 582164735.00711821대구광역시 달성군 하빈면 현내리 572번지<NA><NA>창성산업주식회사20071121132429I2018-08-31 23:59:59.0식품운반업330388.015281268137.397927식품운반업<NA><NA><NA><NA>상수도전용<NA>0000자가<NA><NA>N0.0<NA><NA><NA>
180181식품운반업07_22_09_P34800003480000-117-2008-0000120080404<NA>3폐업2폐업20080414<NA><NA><NA>053 3121656<NA>711852대구광역시 달성군 논공읍 북리 824-35번지 외 1필지 111호<NA><NA>남경식품20080404093827I2018-08-31 23:59:59.0식품운반업330706.421358248561.77396식품운반업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
181182식품운반업07_22_09_P34800003480000-117-2008-0000220080516<NA>3폐업2폐업20180528<NA><NA><NA>053 635728049.50711832대구광역시 달성군 화원읍 명곡리 198-4번지대구광역시 달성군 화원읍 성화로 1142946(주)하나통운20180528111732I2018-08-31 23:59:59.0식품운반업334750.422674256643.317146식품운반업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
182183식품운반업07_22_09_P34800003480000-117-2008-0000320081127<NA>3폐업2폐업20170510<NA><NA><NA>053 614300750.00711874대구광역시 달성군 현풍면 원교리 94번지대구광역시 달성군 현풍면 비슬로130길 743003달성우체국20170510164444I2018-08-31 23:59:59.0식품운반업330128.30921244838.382091식품운반업<NA><NA><NA><NA>상수도전용<NA>0000자가<NA><NA>N0.0<NA><NA><NA>
183184식품운반업07_22_09_P34800003480000-117-2013-0000120130117<NA>3폐업2폐업20140804<NA><NA><NA>070 44178456256.00711833대구광역시 달성군 화원읍 설화리 616-1번지 . 1층대구광역시 달성군 화원읍 옥터길 10, 1층42957싱싱푸드20130214095247I2018-08-31 23:59:59.0식품운반업334264.122637256102.203744식품운반업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
184185식품운반업07_22_09_P34800003480000-117-2014-0000120140113<NA>3폐업2폐업20161202<NA><NA><NA>070 4122999570.00711821대구광역시 달성군 하빈면 동곡리 627번지대구광역시 달성군 하빈면 하빈남로 171-1, 1층42904주식회사 광림유통20160624152311I2018-08-31 23:59:59.0식품운반업327735.044524263479.562399식품운반업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
185186식품운반업07_22_09_P34800003480000-117-2016-0000120160930<NA>3폐업2폐업20191211<NA><NA><NA><NA>70.00711821대구광역시 달성군 하빈면 동곡리 627번지대구광역시 달성군 하빈면 하빈남로 171-142904(주)달성푸드20191211143011U2019-12-13 02:40:00.0식품운반업327735.044524263479.562399식품운반업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
186187식품운반업07_22_09_P34800003480000-117-2019-0000220190628<NA>1영업/정상1영업<NA><NA><NA><NA>053 313 303520.00<NA>대구광역시 달성군 옥포읍 강림리 528-4번지대구광역시 달성군 옥포읍 시저로2길 16-142968백제농산20190807172026U2019-08-09 02:40:00.0식품운반업329271.879011254619.354156식품운반업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>