Overview

Dataset statistics

Number of variables8
Number of observations219
Missing cells127
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 KiB
Average record size in memory67.6 B

Variable types

Numeric3
Categorical1
Text3
DateTime1

Dataset

Description대구광역시_북구_식육포장처리업현황_20190830
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15030565&dataSetDetailId=150305651aca5d6eaa4b4_201908301745&provdMethod=FILE

Alerts

업종구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
전화번호 has 126 (57.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 18:11:38.497312
Analysis finished2023-12-10 18:11:40.740235
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct219
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110
Minimum1
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T03:11:40.895536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.9
Q155.5
median110
Q3164.5
95-th percentile208.1
Maximum219
Range218
Interquartile range (IQR)109

Descriptive statistics

Standard deviation63.364028
Coefficient of variation (CV)0.57603661
Kurtosis-1.2
Mean110
Median Absolute Deviation (MAD)55
Skewness0
Sum24090
Variance4015
MonotonicityStrictly increasing
2023-12-11T03:11:41.167962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
152 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
143 1
 
0.5%
144 1
 
0.5%
145 1
 
0.5%
146 1
 
0.5%
147 1
 
0.5%
148 1
 
0.5%
Other values (209) 209
95.4%
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 (%)
219 1
0.5%
218 1
0.5%
217 1
0.5%
216 1
0.5%
215 1
0.5%
214 1
0.5%
213 1
0.5%
212 1
0.5%
211 1
0.5%
210 1
0.5%

업종구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
식육포장처리업
219 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식육포장처리업 219
100.0%

Length

2023-12-11T03:11:41.374280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:11:41.530115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식육포장처리업 219
100.0%

업소명
Text

Distinct212
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T03:11:41.858462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length5.6894977
Min length2

Characters and Unicode

Total characters1246
Distinct characters208
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)93.6%

Sample

1st row지유식품
2nd row(주)진명에프엔씨
3rd row(주)하나랑
4th row(주)해원식품
5th row(주)G.M식품
ValueCountFrequency (%)
주식회사 13
 
5.1%
푸드 3
 
1.2%
뉴태평 2
 
0.8%
에이스식품 2
 
0.8%
대가축산 2
 
0.8%
농업회사법인 2
 
0.8%
닭고기 2
 
0.8%
금미식품 2
 
0.8%
갑이식품 2
 
0.8%
명자상회 2
 
0.8%
Other values (222) 224
87.5%
2023-12-11T03:11:42.456521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 54
 
4.3%
축 47
 
3.8%
산 47
 
3.8%
주 45
 
3.6%
드 43
 
3.5%
푸 42
 
3.4%
품 38
 
3.0%
37
 
3.0%
) 32
 
2.6%
( 30
 
2.4%
Other values (198) 831
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1081
86.8%
Uppercase Letter 43
 
3.5%
Space Separator 37
 
3.0%
Close Punctuation 32
 
2.6%
Open Punctuation 30
 
2.4%
Lowercase Letter 17
 
1.4%
Other Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 54
 
5.0%
축 47
 
4.3%
산 47
 
4.3%
주 45
 
4.2%
드 43
 
4.0%
푸 42
 
3.9%
품 38
 
3.5%
우 24
 
2.2%
유 23
 
2.1%
통 21
 
1.9%
Other values (173) 697
64.5%
Uppercase Letter
ValueCountFrequency (%)
D 6
14.0%
F 6
14.0%
J 5
11.6%
H 4
9.3%
S 4
9.3%
G 4
9.3%
C 3
7.0%
O 2
 
4.7%
N 2
 
4.7%
K 2
 
4.7%
Other values (3) 5
11.6%
Lowercase Letter
ValueCountFrequency (%)
o 4
23.5%
f 3
17.6%
p 3
17.6%
a 2
11.8%
c 2
11.8%
d 2
11.8%
y 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
& 4
66.7%
. 2
33.3%
Space Separator
ValueCountFrequency (%)
37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1081
86.8%
Common 105
 
8.4%
Latin 60
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 54
 
5.0%
축 47
 
4.3%
산 47
 
4.3%
주 45
 
4.2%
드 43
 
4.0%
푸 42
 
3.9%
품 38
 
3.5%
우 24
 
2.2%
유 23
 
2.1%
통 21
 
1.9%
Other values (173) 697
64.5%
Latin
ValueCountFrequency (%)
D 6
 
10.0%
F 6
 
10.0%
J 5
 
8.3%
H 4
 
6.7%
o 4
 
6.7%
S 4
 
6.7%
G 4
 
6.7%
C 3
 
5.0%
f 3
 
5.0%
p 3
 
5.0%
Other values (10) 18
30.0%
Common
ValueCountFrequency (%)
37
35.2%
) 32
30.5%
( 30
28.6%
& 4
 
3.8%
. 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1081
86.8%
ASCII 165
 
13.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 54
 
5.0%
축 47
 
4.3%
산 47
 
4.3%
주 45
 
4.2%
드 43
 
4.0%
푸 42
 
3.9%
품 38
 
3.5%
우 24
 
2.2%
유 23
 
2.1%
통 21
 
1.9%
Other values (173) 697
64.5%
ASCII
ValueCountFrequency (%)
37
22.4%
) 32
19.4%
( 30
18.2%
D 6
 
3.6%
F 6
 
3.6%
J 5
 
3.0%
H 4
 
2.4%
& 4
 
2.4%
o 4
 
2.4%
S 4
 
2.4%
Other values (15) 33
20.0%

전화번호
Text

MISSING 

Distinct90
Distinct (%)96.8%
Missing126
Missing (%)57.5%
Memory size1.8 KiB
2023-12-11T03:11:42.954315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.043011
Min length12

Characters and Unicode

Total characters1120
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

Unique87 ?
Unique (%)93.5%

Sample

1st row053-982-3995
2nd row053-356-4841
3rd row053-352-3660
4th row053-383-1475
5th row053-767-9676
ValueCountFrequency (%)
053-427-1152 2
 
2.2%
053-426-4203 2
 
2.2%
053-381-3834 2
 
2.2%
053-939-1490 1
 
1.1%
053-322-6733 1
 
1.1%
053-323-9602 1
 
1.1%
053-321-1363 1
 
1.1%
053-382-5447 1
 
1.1%
053-353-0263 1
 
1.1%
053-311-8338 1
 
1.1%
Other values (80) 80
86.0%
2023-12-11T03:11:43.756721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 195
17.4%
- 186
16.6%
5 154
13.8%
0 147
13.1%
2 95
8.5%
9 76
 
6.8%
1 66
 
5.9%
4 56
 
5.0%
6 54
 
4.8%
8 51
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 934
83.4%
Dash Punctuation 186
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 195
20.9%
5 154
16.5%
0 147
15.7%
2 95
10.2%
9 76
 
8.1%
1 66
 
7.1%
4 56
 
6.0%
6 54
 
5.8%
8 51
 
5.5%
7 40
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 195
17.4%
- 186
16.6%
5 154
13.8%
0 147
13.1%
2 95
8.5%
9 76
 
6.8%
1 66
 
5.9%
4 56
 
5.0%
6 54
 
4.8%
8 51
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 195
17.4%
- 186
16.6%
5 154
13.8%
0 147
13.1%
2 95
8.5%
9 76
 
6.8%
1 66
 
5.9%
4 56
 
5.0%
6 54
 
4.8%
8 51
 
4.6%

소재지도로명주소
Text

Distinct214
Distinct (%)98.2%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2023-12-11T03:11:44.262233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length35
Mean length25.490826
Min length21

Characters and Unicode

Total characters5557
Distinct characters115
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

Unique210 ?
Unique (%)96.3%

Sample

1st row대구광역시 북구 팔달북로3길 10 (노원동3가)
2nd row대구광역시 북구 모산골길 10-24 (검단동)
3rd row대구광역시 북구 호국로51길 39-4 (서변동)
4th row대구광역시 북구 팔달북로18길 47-4 (노원동3가)
5th row대구광역시 북구 노원로47길 18 (침산동)
ValueCountFrequency (%)
대구광역시 218
 
19.5%
북구 218
 
19.5%
검단동 20
 
1.8%
칠성동1가 19
 
1.7%
구암동 17
 
1.5%
국우동 16
 
1.4%
서변동 15
 
1.3%
침산동 15
 
1.3%
팔달동 14
 
1.2%
동변동 13
 
1.2%
Other values (338) 555
49.6%
2023-12-11T03:11:45.066637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
902
 
16.2%
구 469
 
8.4%
동 274
 
4.9%
시 235
 
4.2%
대 233
 
4.2%
북 231
 
4.2%
( 219
 
3.9%
) 219
 
3.9%
광 218
 
3.9%
역 218
 
3.9%
Other values (105) 2339
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3204
57.7%
Space Separator 902
 
16.2%
Decimal Number 889
 
16.0%
Open Punctuation 219
 
3.9%
Close Punctuation 219
 
3.9%
Dash Punctuation 92
 
1.7%
Other Punctuation 27
 
0.5%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 469
14.6%
동 274
 
8.6%
시 235
 
7.3%
대 233
 
7.3%
북 231
 
7.2%
광 218
 
6.8%
역 218
 
6.8%
로 196
 
6.1%
길 171
 
5.3%
단 50
 
1.6%
Other values (88) 909
28.4%
Decimal Number
ValueCountFrequency (%)
1 216
24.3%
2 131
14.7%
3 130
14.6%
5 78
 
8.8%
4 76
 
8.5%
7 60
 
6.7%
9 52
 
5.8%
0 52
 
5.8%
8 51
 
5.7%
6 43
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 4
80.0%
B 1
 
20.0%
Space Separator
ValueCountFrequency (%)
902
100.0%
Open Punctuation
ValueCountFrequency (%)
( 219
100.0%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Other Punctuation
ValueCountFrequency (%)
, 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3204
57.7%
Common 2348
42.3%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 469
14.6%
동 274
 
8.6%
시 235
 
7.3%
대 233
 
7.3%
북 231
 
7.2%
광 218
 
6.8%
역 218
 
6.8%
로 196
 
6.1%
길 171
 
5.3%
단 50
 
1.6%
Other values (88) 909
28.4%
Common
ValueCountFrequency (%)
902
38.4%
( 219
 
9.3%
) 219
 
9.3%
1 216
 
9.2%
2 131
 
5.6%
3 130
 
5.5%
- 92
 
3.9%
5 78
 
3.3%
4 76
 
3.2%
7 60
 
2.6%
Other values (5) 225
 
9.6%
Latin
ValueCountFrequency (%)
A 4
80.0%
B 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3204
57.7%
ASCII 2353
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
902
38.3%
( 219
 
9.3%
) 219
 
9.3%
1 216
 
9.2%
2 131
 
5.6%
3 130
 
5.5%
- 92
 
3.9%
5 78
 
3.3%
4 76
 
3.2%
7 60
 
2.5%
Other values (7) 230
 
9.8%
Hangul
ValueCountFrequency (%)
구 469
14.6%
동 274
 
8.6%
시 235
 
7.3%
대 233
 
7.3%
북 231
 
7.2%
광 218
 
6.8%
역 218
 
6.8%
로 196
 
6.1%
길 171
 
5.3%
단 50
 
1.6%
Other values (88) 909
28.4%

위도
Real number (ℝ)

Distinct133
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.915583
Minimum35.874029
Maximum35.962009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T03:11:45.389598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.874029
5-th percentile35.879346
Q135.899766
median35.914443
Q335.927281
95-th percentile35.94729
Maximum35.962009
Range0.08798041
Interquartile range (IQR)0.02751415

Descriptive statistics

Standard deviation0.020093609
Coefficient of variation (CV)0.00055946772
Kurtosis-0.72172123
Mean35.915583
Median Absolute Deviation (MAD)0.01382736
Skewness0.0092571276
Sum7865.5126
Variance0.00040375313
MonotonicityNot monotonic
2023-12-11T03:11:45.706660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.90685628 30
 
13.7%
35.92411853 18
 
8.2%
35.92276446 8
 
3.7%
35.94246934 7
 
3.2%
35.94904142 5
 
2.3%
35.89142518 4
 
1.8%
35.89813382 3
 
1.4%
35.91247013 3
 
1.4%
35.93835709 2
 
0.9%
35.94728965 2
 
0.9%
Other values (123) 137
62.6%
ValueCountFrequency (%)
35.87402888 1
0.5%
35.87429327 1
0.5%
35.87453522 1
0.5%
35.87460809 1
0.5%
35.8746473 1
0.5%
35.87539296 1
0.5%
35.87819755 2
0.9%
35.8786997 1
0.5%
35.87912837 1
0.5%
35.87923574 1
0.5%
ValueCountFrequency (%)
35.96200929 1
 
0.5%
35.95603287 1
 
0.5%
35.95315684 1
 
0.5%
35.94960959 1
 
0.5%
35.94923131 1
 
0.5%
35.94904142 5
2.3%
35.94728965 2
 
0.9%
35.94690873 1
 
0.5%
35.94607098 1
 
0.5%
35.94599838 2
 
0.9%

경도
Real number (ℝ)

Distinct133
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58479
Minimum128.52602
Maximum128.62922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T03:11:45.969937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.52602
5-th percentile128.54045
Q1128.56426
median128.59443
Q3128.60562
95-th percentile128.62107
Maximum128.62922
Range0.1031968
Interquartile range (IQR)0.0413623

Descriptive statistics

Standard deviation0.027110444
Coefficient of variation (CV)0.00021083709
Kurtosis-1.150315
Mean128.58479
Median Absolute Deviation (MAD)0.0245775
Skewness-0.18194742
Sum28160.07
Variance0.0007349762
MonotonicityNot monotonic
2023-12-11T03:11:46.807488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.6210707 30
 
13.7%
128.5944321 18
 
8.2%
128.5953331 8
 
3.7%
128.571015 7
 
3.2%
128.5667357 5
 
2.3%
128.5631286 4
 
1.8%
128.548055 3
 
1.4%
128.6120179 3
 
1.4%
128.5449411 2
 
0.9%
128.5757738 2
 
0.9%
Other values (123) 137
62.6%
ValueCountFrequency (%)
128.5260207 1
0.5%
128.5359976 2
0.9%
128.5367373 1
0.5%
128.5390746 1
0.5%
128.5391082 1
0.5%
128.539279 1
0.5%
128.5393222 1
0.5%
128.5397245 1
0.5%
128.5398193 1
0.5%
128.540451 2
0.9%
ValueCountFrequency (%)
128.6292175 1
 
0.5%
128.6267929 1
 
0.5%
128.6261488 1
 
0.5%
128.6258091 1
 
0.5%
128.6256331 1
 
0.5%
128.6253679 1
 
0.5%
128.6251818 1
 
0.5%
128.6215798 1
 
0.5%
128.6210707 30
13.7%
128.6190377 1
 
0.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2019-08-30 00:00:00
Maximum2019-08-30 00:00:00
2023-12-11T03:11:47.243757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:47.510863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T03:11:39.818732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.007321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.424385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.938455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.148127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.554344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:40.071168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.283937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:11:39.697105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T03:11:47.640658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전화번호위도경도
연번1.0000.8240.7720.773
전화번호0.8241.0000.9590.943
위도0.7720.9591.0000.882
경도0.7730.9430.8821.000
2023-12-11T03:11:47.884353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.034-0.008
위도0.0341.000-0.297
경도-0.008-0.2971.000

Missing values

2023-12-11T03:11:40.269587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T03:11:40.479456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T03:11:40.656349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번업종구분업소명전화번호소재지도로명주소위도경도데이터기준일자
01식육포장처리업지유식품<NA>대구광역시 북구 팔달북로3길 10 (노원동3가)35.891173128.5633232019-08-30
12식육포장처리업(주)진명에프엔씨<NA>대구광역시 북구 모산골길 10-24 (검단동)35.907662128.6256332019-08-30
23식육포장처리업(주)하나랑053-982-3995대구광역시 북구 호국로51길 39-4 (서변동)35.907505128.6258092019-08-30
34식육포장처리업(주)해원식품053-356-4841대구광역시 북구 팔달북로18길 47-4 (노원동3가)35.924119128.5944322019-08-30
45식육포장처리업(주)G.M식품053-352-3660대구광역시 북구 노원로47길 18 (침산동)35.924119128.5944322019-08-30
56식육포장처리업(주)가나안식품053-383-1475대구광역시 북구 검단동로 27-11 (검단동)35.924119128.5944322019-08-30
67식육포장처리업선정식품053-767-9676대구광역시 북구 구암로60길 12-13 (구암동)35.924119128.5944322019-08-30
78식육포장처리업성호식품<NA>대구광역시 북구 3공단로 113-1 (노원동3가)35.924119128.5944322019-08-30
89식육포장처리업(주)대일푸드<NA>대구광역시 북구 검단로46길 12 (검단동)35.924119128.5944322019-08-30
910식육포장처리업(주)호박꽃식품053-422-4212대구광역시 북구 칠성시장로3길 26-14, 1,2,3층 (칠성동1가)35.924119128.5944322019-08-30
연번업종구분업소명전화번호소재지도로명주소위도경도데이터기준일자
209210식육포장처리업고령식육점<NA>대구광역시 북구 검단로 237-12 (검단동)35.922764128.5953332019-08-30
210211식육포장처리업경동축산053-217-5583대구광역시 북구 동암로38길 3-18, 102호 (구암동)35.914443128.6292182019-08-30
211212식육포장처리업세명축산<NA>대구광역시 북구 호국로57길 31-11 (서변동)35.942469128.5710152019-08-30
212213식육포장처리업(주)정우씨앤에프053-311-6682대구광역시 북구 학남로17길 8-19, 103호 (국우동)35.942469128.5710152019-08-30
213214식육포장처리업부자축산053-201-0245대구광역시 북구 구리로38길 15-9 (국우동)35.942469128.5710152019-08-30
214215식육포장처리업명성육가공<NA>대구광역시 북구 태전로13길 28, 101호 (태전동)35.942469128.5710152019-08-30
215216식육포장처리업대지축산농원053-955-3744대구광역시 북구 연암로22길 1-1 (산격동)35.942469128.5710152019-08-30
216217식육포장처리업서가한우축산<NA>대구광역시 북구 한강로8길 20, 동림프라자 108,109호 (금호동)35.942469128.5710152019-08-30
217218식육포장처리업농업회사법인(주)성진한우053-944-2563대구광역시 북구 도남길 59-1 (국우동)35.942469128.5710152019-08-30
218219식육포장처리업피그패밀리<NA>대구광역시 북구 구암로50길 16 (구암동)35.934172128.5670542019-08-30