Overview

Dataset statistics

Number of variables47
Number of observations173
Missing cells2417
Missing cells (%)29.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.7 KiB
Average record size in memory406.8 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_0000084658&dataSetDetailId=DDI_0000084715&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
보증액 is highly imbalanced (59.5%)Imbalance
월세액 is highly imbalanced (59.5%)Imbalance
인허가취소일자 has 173 (100.0%) missing valuesMissing
폐업일자 has 62 (35.8%) missing valuesMissing
휴업시작일자 has 173 (100.0%) missing valuesMissing
휴업종료일자 has 173 (100.0%) missing valuesMissing
재개업일자 has 173 (100.0%) missing valuesMissing
소재지전화 has 45 (26.0%) missing valuesMissing
소재지면적 has 17 (9.8%) missing valuesMissing
소재지우편번호 has 3 (1.7%) missing valuesMissing
도로명전체주소 has 56 (32.4%) missing valuesMissing
도로명우편번호 has 57 (32.9%) missing valuesMissing
좌표정보(X) has 9 (5.2%) missing valuesMissing
좌표정보(Y) has 9 (5.2%) missing valuesMissing
남성종사자수 has 173 (100.0%) missing valuesMissing
여성종사자수 has 173 (100.0%) missing valuesMissing
영업장주변구분명 has 173 (100.0%) missing valuesMissing
등급구분명 has 173 (100.0%) missing valuesMissing
총종업원수 has 173 (100.0%) missing valuesMissing
공장판매직종업원수 has 41 (23.7%) missing valuesMissing
공장생산직종업원수 has 42 (24.3%) missing valuesMissing
전통업소지정번호 has 173 (100.0%) missing valuesMissing
전통업소주된음식 has 173 (100.0%) missing valuesMissing
홈페이지 has 173 (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 114 (65.9%) zerosZeros
공장생산직종업원수 has 117 (67.6%) zerosZeros
시설총규모 has 168 (97.1%) zerosZeros

Reproduction

Analysis started2024-04-17 14:52:22.676105
Analysis finished2024-04-17 14:52:23.200567
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87
Minimum1
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:23.253924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.6
Q144
median87
Q3130
95-th percentile164.4
Maximum173
Range172
Interquartile range (IQR)86

Descriptive statistics

Standard deviation50.084928
Coefficient of variation (CV)0.57568883
Kurtosis-1.2
Mean87
Median Absolute Deviation (MAD)43
Skewness0
Sum15051
Variance2508.5
MonotonicityStrictly increasing
2024-04-17T23:52:23.357857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (163) 163
94.2%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
식품운반업
173 

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 173
100.0%

Length

2024-04-17T23:52:23.459501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:23.544295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품운반업 173
100.0%

개방서비스ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
07_22_09_P
173 

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 173
100.0%

Length

2024-04-17T23:52:23.623884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:23.699043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
07_22_09_p 173
100.0%

개방자치단체코드
Real number (ℝ)

Distinct8
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3448901.7
Minimum3410000
Maximum3480000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:23.764566image/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 deviation20129.137
Coefficient of variation (CV)0.0058363903
Kurtosis-0.86247984
Mean3448901.7
Median Absolute Deviation (MAD)20000
Skewness-0.2806502
Sum5.9666 × 108
Variance4.0518215 × 108
MonotonicityIncreasing
2024-04-17T23:52:23.866109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3450000 58
33.5%
3470000 28
16.2%
3420000 26
15.0%
3480000 17
 
9.8%
3460000 16
 
9.2%
3430000 12
 
6.9%
3410000 8
 
4.6%
3440000 8
 
4.6%
ValueCountFrequency (%)
3410000 8
 
4.6%
3420000 26
15.0%
3430000 12
 
6.9%
3440000 8
 
4.6%
3450000 58
33.5%
3460000 16
 
9.2%
3470000 28
16.2%
3480000 17
 
9.8%
ValueCountFrequency (%)
3480000 17
 
9.8%
3470000 28
16.2%
3460000 16
 
9.2%
3450000 58
33.5%
3440000 8
 
4.6%
3430000 12
 
6.9%
3420000 26
15.0%
3410000 8
 
4.6%

관리번호
Text

UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-17T23:52:24.030585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3806
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

Unique173 ?
Unique (%)100.0%

Sample

1st row3410000-117-2011-00001
2nd row3410000-117-2014-00001
3rd row3410000-117-2010-00001
4th row3410000-117-2008-00002
5th row3410000-117-2008-00001
ValueCountFrequency (%)
3410000-117-2011-00001 1
 
0.6%
3450000-117-2003-00005 1
 
0.6%
3450000-117-1993-00001 1
 
0.6%
3460000-117-2014-00001 1
 
0.6%
3460000-117-2004-00001 1
 
0.6%
3460000-117-2015-00001 1
 
0.6%
3460000-117-2018-00001 1
 
0.6%
3460000-117-2015-00002 1
 
0.6%
3460000-117-2019-00001 1
 
0.6%
3460000-117-2011-00001 1
 
0.6%
Other values (163) 163
94.2%
2024-04-17T23:52:24.297601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1649
43.3%
1 527
 
13.8%
- 519
 
13.6%
2 240
 
6.3%
7 228
 
6.0%
3 225
 
5.9%
4 219
 
5.8%
5 84
 
2.2%
8 53
 
1.4%
6 44
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3287
86.4%
Dash Punctuation 519
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1649
50.2%
1 527
 
16.0%
2 240
 
7.3%
7 228
 
6.9%
3 225
 
6.8%
4 219
 
6.7%
5 84
 
2.6%
8 53
 
1.6%
6 44
 
1.3%
9 18
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1649
43.3%
1 527
 
13.8%
- 519
 
13.6%
2 240
 
6.3%
7 228
 
6.0%
3 225
 
5.9%
4 219
 
5.8%
5 84
 
2.2%
8 53
 
1.4%
6 44
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1649
43.3%
1 527
 
13.8%
- 519
 
13.6%
2 240
 
6.3%
7 228
 
6.0%
3 225
 
5.9%
4 219
 
5.8%
5 84
 
2.2%
8 53
 
1.4%
6 44
 
1.2%

인허가일자
Real number (ℝ)

Distinct161
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20103286
Minimum19930205
Maximum20200218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:24.418948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19930205
5-th percentile20031019
Q120060428
median20081226
Q320150915
95-th percentile20190205
Maximum20200218
Range270013
Interquartile range (IQR)90487

Descriptive statistics

Standard deviation55520.477
Coefficient of variation (CV)0.0027617613
Kurtosis-0.96358609
Mean20103286
Median Absolute Deviation (MAD)40895
Skewness0.12013917
Sum3.4778685 × 109
Variance3.0825234 × 109
MonotonicityNot monotonic
2024-04-17T23:52:24.536707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140113 4
 
2.3%
20031021 3
 
1.7%
20190628 2
 
1.2%
20181105 2
 
1.2%
20181217 2
 
1.2%
20060508 2
 
1.2%
20150203 2
 
1.2%
20150915 2
 
1.2%
20100210 2
 
1.2%
20040506 1
 
0.6%
Other values (151) 151
87.3%
ValueCountFrequency (%)
19930205 1
 
0.6%
20030320 1
 
0.6%
20030522 1
 
0.6%
20030721 1
 
0.6%
20030731 1
 
0.6%
20030806 1
 
0.6%
20030906 1
 
0.6%
20031006 1
 
0.6%
20031015 1
 
0.6%
20031021 3
1.7%
ValueCountFrequency (%)
20200218 1
0.6%
20191226 1
0.6%
20191021 1
0.6%
20191001 1
0.6%
20190725 1
0.6%
20190628 2
1.2%
20190613 1
0.6%
20190321 1
0.6%
20190128 1
0.6%
20190115 1
0.6%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

영업상태구분코드
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3
111 
1
62 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 111
64.2%
1 62
35.8%

Length

2024-04-17T23:52:24.638394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:24.714958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 111
64.2%
1 62
35.8%

영업상태명
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
폐업
111 
영업/정상
62 

Length

Max length5
Median length2
Mean length3.0751445
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 111
64.2%
영업/정상 62
35.8%

Length

2024-04-17T23:52:24.800398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:24.883088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 111
64.2%
영업/정상 62
35.8%

상세영업상태코드
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
111 
1
62 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 111
64.2%
1 62
35.8%

Length

2024-04-17T23:52:24.980367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:25.070551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 111
64.2%
1 62
35.8%

상세영업상태명
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
폐업
111 
영업
62 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 111
64.2%
영업 62
35.8%

Length

2024-04-17T23:52:25.153321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:25.230842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 111
64.2%
영업 62
35.8%

폐업일자
Real number (ℝ)

MISSING 

Distinct106
Distinct (%)95.5%
Missing62
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean20125383
Minimum20040603
Maximum20200210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:25.321525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040603
5-th percentile20051112
Q120080906
median20120302
Q320161222
95-th percentile20191127
Maximum20200210
Range159607
Interquartile range (IQR)80315

Descriptive statistics

Standard deviation47430.047
Coefficient of variation (CV)0.0023567276
Kurtosis-1.3011976
Mean20125383
Median Absolute Deviation (MAD)40503
Skewness-0.064975091
Sum2.2339176 × 109
Variance2.2496093 × 109
MonotonicityNot monotonic
2024-04-17T23:52:25.438873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20161102 2
 
1.2%
20100706 2
 
1.2%
20181031 2
 
1.2%
20161122 2
 
1.2%
20080909 2
 
1.2%
20140804 1
 
0.6%
20061208 1
 
0.6%
20110902 1
 
0.6%
20061220 1
 
0.6%
20100331 1
 
0.6%
Other values (96) 96
55.5%
(Missing) 62
35.8%
ValueCountFrequency (%)
20040603 1
0.6%
20041126 1
0.6%
20041201 1
0.6%
20041229 1
0.6%
20050324 1
0.6%
20051017 1
0.6%
20051207 1
0.6%
20060109 1
0.6%
20060124 1
0.6%
20060328 1
0.6%
ValueCountFrequency (%)
20200210 1
0.6%
20200206 1
0.6%
20191218 1
0.6%
20191211 1
0.6%
20191206 1
0.6%
20191129 1
0.6%
20191125 1
0.6%
20190729 1
0.6%
20190625 1
0.6%
20190531 1
0.6%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

소재지전화
Text

MISSING 

Distinct117
Distinct (%)91.4%
Missing45
Missing (%)26.0%
Memory size1.5 KiB
2024-04-17T23:52:25.692237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.578125
Min length7

Characters and Unicode

Total characters1354
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 (%)84.4%

Sample

1st row053 4223753
2nd row053 4262669
3rd row053 250 2024
4th row053 9549460
5th row053 5875605
ValueCountFrequency (%)
053 93
33.9%
070 4
 
1.5%
7301 3
 
1.1%
5252113 3
 
1.1%
294 3
 
1.1%
954 3
 
1.1%
552 2
 
0.7%
956 2
 
0.7%
583 2
 
0.7%
6323 2
 
0.7%
Other values (147) 157
57.3%
2024-04-17T23:52:26.039078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 220
16.2%
0 219
16.2%
3 208
15.4%
146
10.8%
2 107
7.9%
1 90
6.6%
6 84
 
6.2%
8 78
 
5.8%
4 76
 
5.6%
9 65
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1208
89.2%
Space Separator 146
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 220
18.2%
0 219
18.1%
3 208
17.2%
2 107
8.9%
1 90
7.5%
6 84
 
7.0%
8 78
 
6.5%
4 76
 
6.3%
9 65
 
5.4%
7 61
 
5.0%
Space Separator
ValueCountFrequency (%)
146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 220
16.2%
0 219
16.2%
3 208
15.4%
146
10.8%
2 107
7.9%
1 90
6.6%
6 84
 
6.2%
8 78
 
5.8%
4 76
 
5.6%
9 65
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 220
16.2%
0 219
16.2%
3 208
15.4%
146
10.8%
2 107
7.9%
1 90
6.6%
6 84
 
6.2%
8 78
 
5.8%
4 76
 
5.6%
9 65
 
4.8%

소재지면적
Text

MISSING 

Distinct122
Distinct (%)78.2%
Missing17
Missing (%)9.8%
Memory size1.5 KiB
2024-04-17T23:52:26.303008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0641026
Min length3

Characters and Unicode

Total characters790
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

Unique108 ?
Unique (%)69.2%

Sample

1st row84.20
2nd row26.00
3rd row27.69
4th row66.00
5th row19.95
ValueCountFrequency (%)
10.00 7
 
4.5%
49.50 5
 
3.2%
6.60 5
 
3.2%
20.00 4
 
2.6%
9.90 4
 
2.6%
3.30 3
 
1.9%
50.00 3
 
1.9%
58.95 3
 
1.9%
30.00 3
 
1.9%
16.50 3
 
1.9%
Other values (112) 116
74.4%
2024-04-17T23:52:26.655432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 182
23.0%
. 156
19.7%
1 63
 
8.0%
2 58
 
7.3%
6 57
 
7.2%
3 51
 
6.5%
4 50
 
6.3%
5 50
 
6.3%
9 47
 
5.9%
8 45
 
5.7%
Other values (2) 31
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 632
80.0%
Other Punctuation 158
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 182
28.8%
1 63
 
10.0%
2 58
 
9.2%
6 57
 
9.0%
3 51
 
8.1%
4 50
 
7.9%
5 50
 
7.9%
9 47
 
7.4%
8 45
 
7.1%
7 29
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 156
98.7%
, 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 790
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 182
23.0%
. 156
19.7%
1 63
 
8.0%
2 58
 
7.3%
6 57
 
7.2%
3 51
 
6.5%
4 50
 
6.3%
5 50
 
6.3%
9 47
 
5.9%
8 45
 
5.7%
Other values (2) 31
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 182
23.0%
. 156
19.7%
1 63
 
8.0%
2 58
 
7.3%
6 57
 
7.2%
3 51
 
6.5%
4 50
 
6.3%
5 50
 
6.3%
9 47
 
5.9%
8 45
 
5.7%
Other values (2) 31
 
3.9%

소재지우편번호
Real number (ℝ)

MISSING 

Distinct111
Distinct (%)65.3%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean704187.21
Minimum700082
Maximum711891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:26.794652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700082
5-th percentile701144.5
Q1702802
median702903
Q3704944
95-th percentile711822.1
Maximum711891
Range11809
Interquartile range (IQR)2142

Descriptive statistics

Standard deviation2806.9208
Coefficient of variation (CV)0.0039860434
Kurtosis1.9980581
Mean704187.21
Median Absolute Deviation (MAD)1070
Skewness1.5336909
Sum1.1971182 × 108
Variance7878804.3
MonotonicityNot monotonic
2024-04-17T23:52:26.907731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702903 12
 
6.9%
702825 6
 
3.5%
704830 4
 
2.3%
711821 4
 
2.3%
702300 4
 
2.3%
702845 3
 
1.7%
702867 3
 
1.7%
704932 3
 
1.7%
701870 3
 
1.7%
705840 3
 
1.7%
Other values (101) 125
72.3%
ValueCountFrequency (%)
700082 1
 
0.6%
700192 1
 
0.6%
700810 1
 
0.6%
700823 1
 
0.6%
700826 1
 
0.6%
700845 2
1.2%
700847 1
 
0.6%
701140 1
 
0.6%
701150 1
 
0.6%
701804 3
1.7%
ValueCountFrequency (%)
711891 1
 
0.6%
711874 1
 
0.6%
711852 1
 
0.6%
711842 1
 
0.6%
711833 2
1.2%
711832 2
1.2%
711823 1
 
0.6%
711821 4
2.3%
711814 1
 
0.6%
706852 2
1.2%

소재지전체주소
Text

Distinct151
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-17T23:52:27.205649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length23.843931
Min length18

Characters and Unicode

Total characters4125
Distinct characters142
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

Unique134 ?
Unique (%)77.5%

Sample

1st row대구광역시 중구 동인동3가 0271-0212번지
2nd row대구광역시 중구 계산동2가 0200번지 지하3층
3rd row대구광역시 중구 봉산동 0222-0037번지 지상1층
4th row대구광역시 중구 남산동 2110-0009번지 지상1층
5th row대구광역시 중구 동인동3가 0240-0006번지 지상1층
ValueCountFrequency (%)
대구광역시 173
22.1%
북구 58
 
7.4%
달서구 27
 
3.5%
동구 26
 
3.3%
달성군 17
 
2.2%
수성구 16
 
2.0%
팔달동 14
 
1.8%
서구 12
 
1.5%
230-3번지 10
 
1.3%
남구 9
 
1.2%
Other values (268) 420
53.7%
2024-04-17T23:52:27.616545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
789
19.1%
구 336
 
8.1%
지 203
 
4.9%
동 194
 
4.7%
대 185
 
4.5%
역 174
 
4.2%
번 173
 
4.2%
광 173
 
4.2%
시 173
 
4.2%
1 156
 
3.8%
Other values (132) 1569
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2335
56.6%
Decimal Number 837
 
20.3%
Space Separator 789
 
19.1%
Dash Punctuation 138
 
3.3%
Close Punctuation 9
 
0.2%
Open Punctuation 9
 
0.2%
Other Punctuation 6
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 336
14.4%
지 203
 
8.7%
동 194
 
8.3%
대 185
 
7.9%
역 174
 
7.5%
번 173
 
7.4%
광 173
 
7.4%
시 173
 
7.4%
북 59
 
2.5%
달 58
 
2.5%
Other values (114) 607
26.0%
Decimal Number
ValueCountFrequency (%)
1 156
18.6%
2 109
13.0%
0 95
11.4%
3 88
10.5%
4 81
9.7%
6 71
8.5%
7 61
 
7.3%
8 59
 
7.0%
5 59
 
7.0%
9 58
 
6.9%
Other Punctuation
ValueCountFrequency (%)
, 4
66.7%
. 2
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
789
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 138
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2335
56.6%
Common 1788
43.3%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 336
14.4%
지 203
 
8.7%
동 194
 
8.3%
대 185
 
7.9%
역 174
 
7.5%
번 173
 
7.4%
광 173
 
7.4%
시 173
 
7.4%
북 59
 
2.5%
달 58
 
2.5%
Other values (114) 607
26.0%
Common
ValueCountFrequency (%)
789
44.1%
1 156
 
8.7%
- 138
 
7.7%
2 109
 
6.1%
0 95
 
5.3%
3 88
 
4.9%
4 81
 
4.5%
6 71
 
4.0%
7 61
 
3.4%
8 59
 
3.3%
Other values (6) 141
 
7.9%
Latin
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2335
56.6%
ASCII 1790
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
789
44.1%
1 156
 
8.7%
- 138
 
7.7%
2 109
 
6.1%
0 95
 
5.3%
3 88
 
4.9%
4 81
 
4.5%
6 71
 
4.0%
7 61
 
3.4%
8 59
 
3.3%
Other values (8) 143
 
8.0%
Hangul
ValueCountFrequency (%)
구 336
14.4%
지 203
 
8.7%
동 194
 
8.3%
대 185
 
7.9%
역 174
 
7.5%
번 173
 
7.4%
광 173
 
7.4%
시 173
 
7.4%
북 59
 
2.5%
달 58
 
2.5%
Other values (114) 607
26.0%

도로명전체주소
Text

MISSING 

Distinct102
Distinct (%)87.2%
Missing56
Missing (%)32.4%
Memory size1.5 KiB
2024-04-17T23:52:27.921075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length27.410256
Min length20

Characters and Unicode

Total characters3207
Distinct characters169
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

Unique90 ?
Unique (%)76.9%

Sample

1st row대구광역시 중구 국채보상로143길 65 (동인동3가)
2nd row대구광역시 중구 달구벌대로 2077 (계산동2가, 지하3층)
3rd row대구광역시 중구 봉산문화1길 5, 1층 (봉산동)
4th row대구광역시 중구 동덕로38길 93-1, 1층 (동인동3가)
5th row대구광역시 중구 중앙대로77길 43 (종로2가)
ValueCountFrequency (%)
대구광역시 117
 
17.6%
북구 30
 
4.5%
1층 26
 
3.9%
동구 22
 
3.3%
달서구 21
 
3.2%
달성군 13
 
2.0%
서구 10
 
1.5%
2층 9
 
1.4%
15 9
 
1.4%
남구 8
 
1.2%
Other values (265) 399
60.1%
2024-04-17T23:52:28.321365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
547
 
17.1%
구 233
 
7.3%
동 148
 
4.6%
대 139
 
4.3%
1 124
 
3.9%
시 120
 
3.7%
역 118
 
3.7%
광 117
 
3.6%
로 106
 
3.3%
( 105
 
3.3%
Other values (159) 1450
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1841
57.4%
Space Separator 547
 
17.1%
Decimal Number 514
 
16.0%
Open Punctuation 105
 
3.3%
Close Punctuation 105
 
3.3%
Other Punctuation 65
 
2.0%
Dash Punctuation 28
 
0.9%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 233
 
12.7%
동 148
 
8.0%
대 139
 
7.6%
시 120
 
6.5%
역 118
 
6.4%
광 117
 
6.4%
로 106
 
5.8%
길 74
 
4.0%
층 54
 
2.9%
달 47
 
2.6%
Other values (142) 685
37.2%
Decimal Number
ValueCountFrequency (%)
1 124
24.1%
3 82
16.0%
2 74
14.4%
4 42
 
8.2%
5 38
 
7.4%
6 36
 
7.0%
0 36
 
7.0%
7 32
 
6.2%
8 29
 
5.6%
9 21
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
547
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Other Punctuation
ValueCountFrequency (%)
, 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1841
57.4%
Common 1364
42.5%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 233
 
12.7%
동 148
 
8.0%
대 139
 
7.6%
시 120
 
6.5%
역 118
 
6.4%
광 117
 
6.4%
로 106
 
5.8%
길 74
 
4.0%
층 54
 
2.9%
달 47
 
2.6%
Other values (142) 685
37.2%
Common
ValueCountFrequency (%)
547
40.1%
1 124
 
9.1%
( 105
 
7.7%
) 105
 
7.7%
3 82
 
6.0%
2 74
 
5.4%
, 65
 
4.8%
4 42
 
3.1%
5 38
 
2.8%
6 36
 
2.6%
Other values (5) 146
 
10.7%
Latin
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1841
57.4%
ASCII 1366
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
547
40.0%
1 124
 
9.1%
( 105
 
7.7%
) 105
 
7.7%
3 82
 
6.0%
2 74
 
5.4%
, 65
 
4.8%
4 42
 
3.1%
5 38
 
2.8%
6 36
 
2.6%
Other values (7) 148
 
10.8%
Hangul
ValueCountFrequency (%)
구 233
 
12.7%
동 148
 
8.0%
대 139
 
7.6%
시 120
 
6.5%
역 118
 
6.4%
광 117
 
6.4%
로 106
 
5.8%
길 74
 
4.0%
층 54
 
2.9%
달 47
 
2.6%
Other values (142) 685
37.2%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct87
Distinct (%)75.0%
Missing57
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean41962.336
Minimum41042
Maximum43010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:28.438009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41042
5-th percentile41082
Q141475.75
median41754
Q342666.25
95-th percentile42957
Maximum43010
Range1968
Interquartile range (IQR)1190.5

Descriptive statistics

Standard deviation651.59493
Coefficient of variation (CV)0.01552809
Kurtosis-1.4469995
Mean41962.336
Median Absolute Deviation (MAD)583
Skewness0.21922235
Sum4867631
Variance424575.95
MonotonicityNot monotonic
2024-04-17T23:52:28.556456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41490 8
 
4.6%
42500 3
 
1.7%
42722 3
 
1.7%
42904 3
 
1.7%
41754 3
 
1.7%
41082 3
 
1.7%
41750 2
 
1.2%
41504 2
 
1.2%
42788 2
 
1.2%
42712 2
 
1.2%
Other values (77) 85
49.1%
(Missing) 57
32.9%
ValueCountFrequency (%)
41042 1
 
0.6%
41046 1
 
0.6%
41051 1
 
0.6%
41055 1
 
0.6%
41078 1
 
0.6%
41082 3
1.7%
41085 1
 
0.6%
41124 1
 
0.6%
41129 2
1.2%
41143 1
 
0.6%
ValueCountFrequency (%)
43010 1
 
0.6%
43003 1
 
0.6%
42974 1
 
0.6%
42968 2
1.2%
42957 2
1.2%
42946 2
1.2%
42922 1
 
0.6%
42904 3
1.7%
42819 1
 
0.6%
42815 1
 
0.6%

사업장명
Text

Distinct160
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-17T23:52:28.778540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.4566474
Min length2

Characters and Unicode

Total characters1117
Distinct characters199
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

Unique148 ?
Unique (%)85.5%

Sample

1st row(주) 전진통운
2nd row(주)코리아택배물류
3rd row남양유업 동성로대리점
4th row대구우체국
5th row박통식품
ValueCountFrequency (%)
주식회사 6
 
3.2%
베스트바이 3
 
1.6%
개인용달 3
 
1.6%
주)정우운수 2
 
1.1%
개별화물 2
 
1.1%
주)성진냉동 2
 
1.1%
주)능원통운 2
 
1.1%
주 2
 
1.1%
주)일성화물 2
 
1.1%
박통식품 2
 
1.1%
Other values (160) 164
86.3%
2024-04-17T23:52:29.113636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 80
 
7.2%
) 70
 
6.3%
( 69
 
6.2%
스 35
 
3.1%
식 33
 
3.0%
드 27
 
2.4%
물 26
 
2.3%
대 25
 
2.2%
푸 25
 
2.2%
통 22
 
2.0%
Other values (189) 705
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 956
85.6%
Close Punctuation 70
 
6.3%
Open Punctuation 69
 
6.2%
Space Separator 17
 
1.5%
Uppercase Letter 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 80
 
8.4%
스 35
 
3.7%
식 33
 
3.5%
드 27
 
2.8%
물 26
 
2.7%
대 25
 
2.6%
푸 25
 
2.6%
통 22
 
2.3%
성 22
 
2.3%
류 20
 
2.1%
Other values (181) 641
67.1%
Uppercase Letter
ValueCountFrequency (%)
S 1
20.0%
G 1
20.0%
T 1
20.0%
C 1
20.0%
F 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 956
85.6%
Common 156
 
14.0%
Latin 5
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 80
 
8.4%
스 35
 
3.7%
식 33
 
3.5%
드 27
 
2.8%
물 26
 
2.7%
대 25
 
2.6%
푸 25
 
2.6%
통 22
 
2.3%
성 22
 
2.3%
류 20
 
2.1%
Other values (181) 641
67.1%
Latin
ValueCountFrequency (%)
S 1
20.0%
G 1
20.0%
T 1
20.0%
C 1
20.0%
F 1
20.0%
Common
ValueCountFrequency (%)
) 70
44.9%
( 69
44.2%
17
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 956
85.6%
ASCII 161
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 80
 
8.4%
스 35
 
3.7%
식 33
 
3.5%
드 27
 
2.8%
물 26
 
2.7%
대 25
 
2.6%
푸 25
 
2.6%
통 22
 
2.3%
성 22
 
2.3%
류 20
 
2.1%
Other values (181) 641
67.1%
ASCII
ValueCountFrequency (%)
) 70
43.5%
( 69
42.9%
17
 
10.6%
S 1
 
0.6%
G 1
 
0.6%
T 1
 
0.6%
C 1
 
0.6%
F 1
 
0.6%

최종수정시점
Real number (ℝ)

Distinct167
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0129739 × 1013
Minimum2.0031121 × 1013
Maximum2.0200311 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:29.444486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0031121 × 1013
5-th percentile2.0040931 × 1013
Q12.008053 × 1013
median2.014032 × 1013
Q32.0181031 × 1013
95-th percentile2.0191208 × 1013
Maximum2.0200311 × 1013
Range1.6919018 × 1011
Interquartile range (IQR)1.0050095 × 1011

Descriptive statistics

Standard deviation5.4567289 × 1010
Coefficient of variation (CV)0.0027107797
Kurtosis-1.3635541
Mean2.0129739 × 1013
Median Absolute Deviation (MAD)4.9807957 × 1010
Skewness-0.33397172
Sum3.4824449 × 1015
Variance2.977589 × 1021
MonotonicityNot monotonic
2024-04-17T23:52:29.556546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20041011000000 6
 
3.5%
20161122170313 2
 
1.2%
20120201161419 1
 
0.6%
20070115000000 1
 
0.6%
20180206143523 1
 
0.6%
20190517154812 1
 
0.6%
20191205095957 1
 
0.6%
20191105164905 1
 
0.6%
20120110104710 1
 
0.6%
20031215000000 1
 
0.6%
Other values (157) 157
90.8%
ValueCountFrequency (%)
20031121000000 1
 
0.6%
20031215000000 1
 
0.6%
20040107000000 1
 
0.6%
20040114000000 1
 
0.6%
20040204000000 1
 
0.6%
20040212000000 1
 
0.6%
20040401000000 1
 
0.6%
20040506000000 1
 
0.6%
20040811000000 1
 
0.6%
20041011000000 6
3.5%
ValueCountFrequency (%)
20200311183326 1
0.6%
20200302165640 1
0.6%
20200211094523 1
0.6%
20200210153155 1
0.6%
20200116124213 1
0.6%
20200115143614 1
0.6%
20191218164033 1
0.6%
20191218144316 1
0.6%
20191211143011 1
0.6%
20191206162256 1
0.6%

데이터갱신구분
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
I
134 
U
39 

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 134
77.5%
U 39
 
22.5%

Length

2024-04-17T23:52:29.668444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:29.746904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 134
77.5%
u 39
 
22.5%

데이터갱신일자
Date

Distinct41
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2018-08-31 23:59:59
Maximum2020-03-13 02:40:00
2024-04-17T23:52:29.827481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:52:29.936521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
식품운반업
173 

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 173
100.0%

Length

2024-04-17T23:52:30.058182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:30.139285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품운반업 173
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct133
Distinct (%)81.1%
Missing9
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean341531.86
Minimum327299.69
Maximum355567.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:30.232798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum327299.69
5-th percentile330167.27
Q1338507.87
median340249.41
Q3345353.01
95-th percentile352779.33
Maximum355567.69
Range28268.001
Interquartile range (IQR)6845.1406

Descriptive statistics

Standard deviation5996.8855
Coefficient of variation (CV)0.017558788
Kurtosis0.18288723
Mean341531.86
Median Absolute Deviation (MAD)3340.707
Skewness0.11829476
Sum56011224
Variance35962636
MonotonicityNot monotonic
2024-04-17T23:52:30.361214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
338985.366314 10
 
5.8%
339388.690364 3
 
1.7%
337329.213273 3
 
1.7%
340946.597285 3
 
1.7%
327735.044524 3
 
1.7%
329271.879011 2
 
1.2%
339120.646244 2
 
1.2%
338415.345471 2
 
1.2%
333920.60853 2
 
1.2%
340411.771764 2
 
1.2%
Other values (123) 132
76.3%
(Missing) 9
 
5.2%
ValueCountFrequency (%)
327299.692057 1
 
0.6%
327735.044524 3
1.7%
329271.879011 2
1.2%
329604.038183 1
 
0.6%
329859.122079 1
 
0.6%
330128.30921 1
 
0.6%
330388.015281 1
 
0.6%
330706.421358 1
 
0.6%
333070.51248 1
 
0.6%
333920.60853 2
1.2%
ValueCountFrequency (%)
355567.693374 2
1.2%
355474.652805 1
0.6%
355444.760056 1
0.6%
354790.108191 1
0.6%
354215.082384 1
0.6%
353414.507288 1
0.6%
352803.154449 2
1.2%
352644.320881 1
0.6%
351788.563589 1
0.6%
350504.394278 1
0.6%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct133
Distinct (%)81.1%
Missing9
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean264338.2
Minimum240636.85
Maximum273735.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:30.470521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum240636.85
5-th percentile256183.37
Q1261903.27
median264733.99
Q3267354.45
95-th percentile271678.57
Maximum273735.13
Range33098.279
Interquartile range (IQR)5451.1805

Descriptive statistics

Standard deviation4860.5313
Coefficient of variation (CV)0.018387548
Kurtosis4.3932027
Mean264338.2
Median Absolute Deviation (MAD)2633.1965
Skewness-1.322366
Sum43351465
Variance23624765
MonotonicityNot monotonic
2024-04-17T23:52:30.603810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267354.446312 10
 
5.8%
269664.126149 3
 
1.7%
260416.780233 3
 
1.7%
259953.127592 3
 
1.7%
263479.562399 3
 
1.7%
254619.354156 2
 
1.2%
269054.376386 2
 
1.2%
258340.404616 2
 
1.2%
261405.687893 2
 
1.2%
272600.063881 2
 
1.2%
Other values (123) 132
76.3%
(Missing) 9
 
5.2%
ValueCountFrequency (%)
240636.852024 1
0.6%
244838.382091 1
0.6%
248561.77396 1
0.6%
254017.803261 1
0.6%
254171.232318 1
0.6%
254619.354156 2
1.2%
255771.102947 1
0.6%
256102.203744 1
0.6%
256643.317146 2
1.2%
257765.775409 1
0.6%
ValueCountFrequency (%)
273735.131409 1
0.6%
273355.790439 1
0.6%
273296.193994 1
0.6%
272745.678782 1
0.6%
272600.063881 2
1.2%
272092.19703 1
0.6%
271937.692097 1
0.6%
271825.291142 1
0.6%
270847.157898 2
1.2%
270477.697377 1
0.6%

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
식품운반업
173 

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 173
100.0%

Length

2024-04-17T23:52:30.704257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:30.777977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품운반업 173
100.0%

남성종사자수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

여성종사자수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

영업장주변구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

등급구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

급수시설구분명
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
122 
상수도전용
51 

Length

Max length5
Median length4
Mean length4.2947977
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 122
70.5%
상수도전용 51
29.5%

Length

2024-04-17T23:52:30.854452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:30.953527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 122
70.5%
상수도전용 51
29.5%

총종업원수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

본사종업원수
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
129 
<NA>
42 
3
 
2

Length

Max length4
Median length1
Mean length1.7283237
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 129
74.6%
<NA> 42
 
24.3%
3 2
 
1.2%

Length

2024-04-17T23:52:31.059293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:31.152842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 129
74.6%
na 42
 
24.3%
3 2
 
1.2%

공장사무직종업원수
Categorical

Distinct6
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
111 
<NA>
41 
1
13 
2
 
5
3
 
2

Length

Max length4
Median length1
Mean length1.7109827
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 111
64.2%
<NA> 41
 
23.7%
1 13
 
7.5%
2 5
 
2.9%
3 2
 
1.2%
5 1
 
0.6%

Length

2024-04-17T23:52:31.248112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:31.342156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
64.2%
na 41
 
23.7%
1 13
 
7.5%
2 5
 
2.9%
3 2
 
1.2%
5 1
 
0.6%

공장판매직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)5.3%
Missing41
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean0.31818182
Minimum0
Maximum7
Zeros114
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:31.421549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.45
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0212863
Coefficient of variation (CV)3.2097569
Kurtosis19.081663
Mean0.31818182
Median Absolute Deviation (MAD)0
Skewness4.1280662
Sum42
Variance1.0430257
MonotonicityNot monotonic
2024-04-17T23:52:31.507743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 114
65.9%
1 9
 
5.2%
3 3
 
1.7%
2 2
 
1.2%
4 2
 
1.2%
5 1
 
0.6%
7 1
 
0.6%
(Missing) 41
 
23.7%
ValueCountFrequency (%)
0 114
65.9%
1 9
 
5.2%
2 2
 
1.2%
3 3
 
1.7%
4 2
 
1.2%
5 1
 
0.6%
7 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
5 1
 
0.6%
4 2
 
1.2%
3 3
 
1.7%
2 2
 
1.2%
1 9
 
5.2%
0 114
65.9%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)4.6%
Missing42
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean0.26717557
Minimum0
Maximum7
Zeros117
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:31.591766image/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 deviation0.97526665
Coefficient of variation (CV)3.6502837
Kurtosis25.079015
Mean0.26717557
Median Absolute Deviation (MAD)0
Skewness4.7443873
Sum35
Variance0.95114504
MonotonicityNot monotonic
2024-04-17T23:52:31.674816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 117
67.6%
2 5
 
2.9%
1 5
 
2.9%
5 2
 
1.2%
7 1
 
0.6%
3 1
 
0.6%
(Missing) 42
 
24.3%
ValueCountFrequency (%)
0 117
67.6%
1 5
 
2.9%
2 5
 
2.9%
3 1
 
0.6%
5 2
 
1.2%
7 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
5 2
 
1.2%
3 1
 
0.6%
2 5
 
2.9%
1 5
 
2.9%
0 117
67.6%

건물소유구분명
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
74 
자가
56 
임대
43 

Length

Max length4
Median length2
Mean length2.8554913
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 74
42.8%
자가 56
32.4%
임대 43
24.9%

Length

2024-04-17T23:52:31.781416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:31.869774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 74
42.8%
자가 56
32.4%
임대 43
24.9%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
159 
0
 
14

Length

Max length4
Median length4
Mean length3.7572254
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> 159
91.9%
0 14
 
8.1%

Length

2024-04-17T23:52:31.960911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:32.045887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 159
91.9%
0 14
 
8.1%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
159 
0
 
14

Length

Max length4
Median length4
Mean length3.7572254
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> 159
91.9%
0 14
 
8.1%

Length

2024-04-17T23:52:32.134824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:52:32.230574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 159
91.9%
0 14
 
8.1%

다중이용업소여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size305.0 B
False
173 
ValueCountFrequency (%)
False 173
100.0%
2024-04-17T23:52:32.307185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88589595
Minimum0
Maximum134.13
Zeros168
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-17T23:52:32.395649image/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 deviation10.226665
Coefficient of variation (CV)11.543866
Kurtosis170.44285
Mean0.88589595
Median Absolute Deviation (MAD)0
Skewness13.013787
Sum153.26
Variance104.58467
MonotonicityNot monotonic
2024-04-17T23:52:32.497121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 168
97.1%
10.37 1
 
0.6%
3.3 1
 
0.6%
134.13 1
 
0.6%
3.0 1
 
0.6%
2.46 1
 
0.6%
ValueCountFrequency (%)
0.0 168
97.1%
2.46 1
 
0.6%
3.0 1
 
0.6%
3.3 1
 
0.6%
10.37 1
 
0.6%
134.13 1
 
0.6%
ValueCountFrequency (%)
134.13 1
 
0.6%
10.37 1
 
0.6%
3.3 1
 
0.6%
3.0 1
 
0.6%
2.46 1
 
0.6%
0.0 168
97.1%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing173
Missing (%)100.0%
Memory size1.6 KiB

Sample

번호개방서비스명개방서비스ID개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
01식품운반업07_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>
12식품운반업07_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>
23식품운반업07_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>
34식품운반업07_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>
45식품운반업07_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>
56식품운반업07_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>
67식품운반업07_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>
78식품운반업07_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>
89식품운반업07_22_09_P34200003420000-117-2016-0000320161207<NA>1영업/정상1영업<NA><NA><NA><NA>053 284 005910.37701835대구광역시 동구 용계동 740-21번지대구광역시 동구 반야월로2길 36, 1층 (용계동)41129소연푸드20161216151814I2018-08-31 23:59:59.0식품운반업352803.154449264946.079267식품운반업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N10.37<NA><NA><NA>
910식품운반업07_22_09_P34200003420000-117-2016-0000220161202<NA>1영업/정상1영업<NA><NA><NA><NA>053 284 005916.16701835대구광역시 동구 용계동 740-21번지대구광역시 동구 반야월로2길 36 (용계동)41129소향장터20161208115357I2018-08-31 23:59:59.0식품운반업352803.154449264946.079267식품운반업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
번호개방서비스명개방서비스ID개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
163164식품운반업07_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>
164165식품운반업07_22_09_P34800003480000-117-2018-0000120180620<NA>3폐업2폐업20190729<NA><NA><NA>053 634 513314.00<NA>대구광역시 달성군 옥포읍 강림리 1157번지대구광역시 달성군 옥포읍 돌미로2길 24, 1층42974영은식자재20190729163938U2019-07-31 02:40:00.0식품운반업329859.122079254017.803261식품운반업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
165166식품운반업07_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>
166167식품운반업07_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>
167168식품운반업07_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>
168169식품운반업07_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>
169170식품운반업07_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>
170171식품운반업07_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>
171172식품운반업07_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>
172173식품운반업07_22_09_P34800003480000-117-2003-0000120031101<NA>1영업/정상1영업<NA><NA><NA><NA>053 635728049.50711832대구광역시 달성군 화원읍 명곡리 198-4번지대구광역시 달성군 화원읍 성화로 1142946(주)하나물류시스템20150415141724I2018-08-31 23:59:59.0식품운반업334750.422674256643.317146식품운반업<NA><NA><NA><NA><NA><NA>0000자가00N0.0<NA><NA><NA>