Overview

Dataset statistics

Number of variables8
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows53
Duplicate rows (%)23.1%
Total size in memory14.9 KiB
Average record size in memory66.6 B

Variable types

Categorical3
Text3
Numeric2

Dataset

Description부산광역시 서구에 주소지를 둔 환경오염 배출시설에 대한 정보(오염 배출 물질의 구분, 오염배출사업장명, 오염배출사업장소재지주소, 대표 업종)
URLhttps://www.data.go.kr/data/3045747/fileData.do

Alerts

구군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 53 (23.1%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
업종 is highly overall correlated with 위도High correlation

Reproduction

Analysis started2023-12-12 23:03:46.053445
Analysis finished2023-12-12 23:03:46.960657
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
부산 서구
229 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산 서구
2nd row부산 서구
3rd row부산 서구
4th row부산 서구
5th row부산 서구

Common Values

ValueCountFrequency (%)
부산 서구 229
100.0%

Length

2023-12-13T08:03:47.023976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:03:47.122492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 229
50.0%
서구 229
50.0%

업체명
Text

Distinct157
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T08:03:47.368064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length7.5327511
Min length3

Characters and Unicode

Total characters1725
Distinct characters233
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)38.9%

Sample

1st row고신대학교 복음병원
2nd row㈜동양/건재부문 부산공장
3rd row삼육부산병원
4th row㈜사조대림 부산공장
5th row부산대학교병원
ValueCountFrequency (%)
부산공장 7
 
2.4%
주식회사 5
 
1.7%
동원산업㈜ 4
 
1.4%
사조씨푸드㈜ 4
 
1.4%
극동유화㈜ 4
 
1.4%
어업회사법인 3
 
1.0%
㈜사조대림 3
 
1.0%
㈜디시에너지 3
 
1.0%
감천항물류센터 3
 
1.0%
수협중앙회 3
 
1.0%
Other values (176) 253
86.6%
2023-12-13T08:03:47.815381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 160
 
9.3%
63
 
3.7%
산 63
 
3.7%
스 43
 
2.5%
푸 41
 
2.4%
드 41
 
2.4%
장 41
 
2.4%
부 38
 
2.2%
동 35
 
2.0%
이 35
 
2.0%
Other values (223) 1165
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1450
84.1%
Other Symbol 160
 
9.3%
Space Separator 63
 
3.7%
Decimal Number 19
 
1.1%
Uppercase Letter 15
 
0.9%
Close Punctuation 6
 
0.3%
Open Punctuation 6
 
0.3%
Other Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 63
 
4.3%
스 43
 
3.0%
푸 41
 
2.8%
드 41
 
2.8%
장 41
 
2.8%
부 38
 
2.6%
동 35
 
2.4%
이 35
 
2.4%
수 30
 
2.1%
대 30
 
2.1%
Other values (204) 1053
72.6%
Uppercase Letter
ValueCountFrequency (%)
F 4
26.7%
G 4
26.7%
N 3
20.0%
U 1
 
6.7%
M 1
 
6.7%
T 1
 
6.7%
A 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 7
36.8%
0 5
26.3%
3 3
15.8%
1 2
 
10.5%
8 1
 
5.3%
5 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
& 4
66.7%
/ 2
33.3%
Other Symbol
ValueCountFrequency (%)
㈜ 160
100.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1610
93.3%
Common 100
 
5.8%
Latin 15
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 160
 
9.9%
산 63
 
3.9%
스 43
 
2.7%
푸 41
 
2.5%
드 41
 
2.5%
장 41
 
2.5%
부 38
 
2.4%
동 35
 
2.2%
이 35
 
2.2%
수 30
 
1.9%
Other values (205) 1083
67.3%
Common
ValueCountFrequency (%)
63
63.0%
2 7
 
7.0%
) 6
 
6.0%
( 6
 
6.0%
0 5
 
5.0%
& 4
 
4.0%
3 3
 
3.0%
1 2
 
2.0%
/ 2
 
2.0%
8 1
 
1.0%
Latin
ValueCountFrequency (%)
F 4
26.7%
G 4
26.7%
N 3
20.0%
U 1
 
6.7%
M 1
 
6.7%
T 1
 
6.7%
A 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1450
84.1%
None 160
 
9.3%
ASCII 115
 
6.7%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 160
100.0%
ASCII
ValueCountFrequency (%)
63
54.8%
2 7
 
6.1%
) 6
 
5.2%
( 6
 
5.2%
0 5
 
4.3%
F 4
 
3.5%
& 4
 
3.5%
G 4
 
3.5%
N 3
 
2.6%
3 3
 
2.6%
Other values (8) 10
 
8.7%
Hangul
ValueCountFrequency (%)
산 63
 
4.3%
스 43
 
3.0%
푸 41
 
2.8%
드 41
 
2.8%
장 41
 
2.8%
부 38
 
2.6%
동 35
 
2.4%
이 35
 
2.4%
수 30
 
2.1%
대 30
 
2.1%
Other values (204) 1053
72.6%

도로명주소
Text

Distinct151
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T08:03:48.106542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length30.829694
Min length21

Characters and Unicode

Total characters7060
Distinct characters100
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)34.9%

Sample

1st row부산광역시 서구 감천로 262 (암남동)
2nd row부산광역시 서구 원양로 268 (암남동)
3rd row부산광역시 서구 대티로 170 (서대신동2가)
4th row부산광역시 서구 충무대로 170 (남부민동)
5th row부산광역시 서구 구덕로 179 (아미동1가)
ValueCountFrequency (%)
부산광역시 229
15.9%
서구 229
15.9%
암남동 148
 
10.3%
원양로 142
 
9.9%
1 83
 
5.8%
수산가공선진화단지 81
 
5.6%
b동 55
 
3.8%
충무대로 30
 
2.1%
남부민동 30
 
2.1%
a동 27
 
1.9%
Other values (183) 385
26.8%
2023-12-13T08:03:48.503773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1213
 
17.2%
동 330
 
4.7%
산 312
 
4.4%
부 264
 
3.7%
구 244
 
3.5%
서 240
 
3.4%
시 236
 
3.3%
광 229
 
3.2%
역 229
 
3.2%
( 227
 
3.2%
Other values (90) 3536
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4183
59.2%
Space Separator 1213
 
17.2%
Decimal Number 889
 
12.6%
Open Punctuation 227
 
3.2%
Close Punctuation 227
 
3.2%
Other Punctuation 220
 
3.1%
Uppercase Letter 82
 
1.2%
Other Symbol 9
 
0.1%
Dash Punctuation 9
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 330
 
7.9%
산 312
 
7.5%
부 264
 
6.3%
구 244
 
5.8%
서 240
 
5.7%
시 236
 
5.6%
광 229
 
5.5%
역 229
 
5.5%
로 225
 
5.4%
남 190
 
4.5%
Other values (70) 1684
40.3%
Decimal Number
ValueCountFrequency (%)
1 225
25.3%
2 143
16.1%
0 130
14.6%
3 79
 
8.9%
4 70
 
7.9%
5 62
 
7.0%
7 58
 
6.5%
6 51
 
5.7%
8 37
 
4.2%
9 34
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 206
93.6%
· 14
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
B 55
67.1%
A 27
32.9%
Space Separator
ValueCountFrequency (%)
1213
100.0%
Open Punctuation
ValueCountFrequency (%)
( 227
100.0%
Close Punctuation
ValueCountFrequency (%)
) 227
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4192
59.4%
Common 2786
39.5%
Latin 82
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 330
 
7.9%
산 312
 
7.4%
부 264
 
6.3%
구 244
 
5.8%
서 240
 
5.7%
시 236
 
5.6%
광 229
 
5.5%
역 229
 
5.5%
로 225
 
5.4%
남 190
 
4.5%
Other values (71) 1693
40.4%
Common
ValueCountFrequency (%)
1213
43.5%
( 227
 
8.1%
) 227
 
8.1%
1 225
 
8.1%
, 206
 
7.4%
2 143
 
5.1%
0 130
 
4.7%
3 79
 
2.8%
4 70
 
2.5%
5 62
 
2.2%
Other values (7) 204
 
7.3%
Latin
ValueCountFrequency (%)
B 55
67.1%
A 27
32.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4183
59.2%
ASCII 2854
40.4%
None 23
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1213
42.5%
( 227
 
8.0%
) 227
 
8.0%
1 225
 
7.9%
, 206
 
7.2%
2 143
 
5.0%
0 130
 
4.6%
3 79
 
2.8%
4 70
 
2.5%
5 62
 
2.2%
Other values (8) 272
 
9.5%
Hangul
ValueCountFrequency (%)
동 330
 
7.9%
산 312
 
7.5%
부 264
 
6.3%
구 244
 
5.8%
서 240
 
5.7%
시 236
 
5.6%
광 229
 
5.5%
역 229
 
5.5%
로 225
 
5.4%
남 190
 
4.5%
Other values (70) 1684
40.3%
None
ValueCountFrequency (%)
· 14
60.9%
㈜ 9
39.1%

업종
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
기타 수산동물 가공 및 저장 처리업(10219)
123 
안경 및 렌즈 소매업
15 
자동차 세차업(95213)
14 
냉장 및 냉동 창고업(52102)
 
11
주유소 운영업
 
10
Other values (29)
56 

Length

Max length32
Median length26
Mean length20.550218
Min length2

Unique

Unique18 ?
Unique (%)7.9%

Sample

1st row종합병원
2nd row레미콘 제조업
3rd row종합병원
4th row육류 기타 가공 및 저장 처리업(가금류 제외)
5th row종합병원

Common Values

ValueCountFrequency (%)
기타 수산동물 가공 및 저장 처리업(10219) 123
53.7%
안경 및 렌즈 소매업 15
 
6.6%
자동차 세차업(95213) 14
 
6.1%
냉장 및 냉동 창고업(52102) 11
 
4.8%
주유소 운영업 10
 
4.4%
기타 수산동물 가공 및 저장 처리업 5
 
2.2%
신선, 냉동 및 기타 수산물 도매업(46315) 5
 
2.2%
종합병원 5
 
2.2%
종합병원(86101) 4
 
1.7%
폐수 처리업(37012) 4
 
1.7%
Other values (24) 33
 
14.4%

Length

2023-12-13T08:03:48.723101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 173
15.9%
기타 141
13.0%
수산동물 132
12.1%
가공 131
12.0%
저장 131
12.0%
처리업(10219 123
11.3%
냉동 17
 
1.6%
자동차 16
 
1.5%
안경 15
 
1.4%
렌즈 15
 
1.4%
Other values (69) 194
17.8%

전화번호
Text

Distinct146
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T08:03:48.942328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.008734
Min length12

Characters and Unicode

Total characters2750
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

Unique80 ?
Unique (%)34.9%

Sample

1st row051-990-6059
2nd row051-250-5590
3rd row051-600-7695
4th row051-250-2165
5th row051-240-7130
ValueCountFrequency (%)
051-000-0000 12
 
5.2%
051-243-1225 4
 
1.7%
051-250-2165 3
 
1.3%
051-253-3113 3
 
1.3%
051-257-0349 3
 
1.3%
051-231-7928 3
 
1.3%
051-256-5467 3
 
1.3%
051-717-3386 2
 
0.9%
051-257-2406 2
 
0.9%
051-266-6002 2
 
0.9%
Other values (136) 192
83.8%
2023-12-13T08:03:49.315738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 501
18.2%
- 458
16.7%
5 403
14.7%
1 398
14.5%
2 300
10.9%
3 157
 
5.7%
4 137
 
5.0%
6 121
 
4.4%
7 109
 
4.0%
8 91
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2292
83.3%
Dash Punctuation 458
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 501
21.9%
5 403
17.6%
1 398
17.4%
2 300
13.1%
3 157
 
6.8%
4 137
 
6.0%
6 121
 
5.3%
7 109
 
4.8%
8 91
 
4.0%
9 75
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 501
18.2%
- 458
16.7%
5 403
14.7%
1 398
14.5%
2 300
10.9%
3 157
 
5.7%
4 137
 
5.0%
6 121
 
4.4%
7 109
 
4.0%
8 91
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 501
18.2%
- 458
16.7%
5 403
14.7%
1 398
14.5%
2 300
10.9%
3 157
 
5.7%
4 137
 
5.0%
6 121
 
4.4%
7 109
 
4.0%
8 91
 
3.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.073178
Minimum35.055428
Maximum35.120998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T08:03:49.481757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.055428
5-th percentile35.055428
Q135.055428
median35.064232
Q335.08636
95-th percentile35.111418
Maximum35.120998
Range0.06556944
Interquartile range (IQR)0.03093231

Descriptive statistics

Standard deviation0.019717907
Coefficient of variation (CV)0.00056219335
Kurtosis-0.53325047
Mean35.073178
Median Absolute Deviation (MAD)0.00880391
Skewness0.8601127
Sum8031.7577
Variance0.00038879586
MonotonicityNot monotonic
2023-12-13T08:03:49.634054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.05542807 81
35.4%
35.0588383 6
 
2.6%
35.06255421 5
 
2.2%
35.06423198 5
 
2.2%
35.06208908 5
 
2.2%
35.06577571 4
 
1.7%
35.07433185 4
 
1.7%
35.12050966 4
 
1.7%
35.08108175 3
 
1.3%
35.06644223 3
 
1.3%
Other values (80) 109
47.6%
ValueCountFrequency (%)
35.05542807 81
35.4%
35.0588383 6
 
2.6%
35.0613489 3
 
1.3%
35.06175958 1
 
0.4%
35.06179106 1
 
0.4%
35.06208908 5
 
2.2%
35.06233514 2
 
0.9%
35.06240149 2
 
0.9%
35.06255421 5
 
2.2%
35.06291734 2
 
0.9%
ValueCountFrequency (%)
35.12099751 1
 
0.4%
35.12050966 4
1.7%
35.11466023 2
0.9%
35.11350352 1
 
0.4%
35.11220544 1
 
0.4%
35.11206559 2
0.9%
35.11145859 1
 
0.4%
35.11135774 1
 
0.4%
35.11088361 2
0.9%
35.11065187 1
 
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.01466
Minimum129.00559
Maximum129.02637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T08:03:49.781485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.00559
5-th percentile129.00875
Q1129.01122
median129.01122
Q3129.01922
95-th percentile129.02538
Maximum129.02637
Range0.0207771
Interquartile range (IQR)0.0080004

Descriptive statistics

Standard deviation0.0058970435
Coefficient of variation (CV)4.5708322 × 10-5
Kurtosis-0.81101698
Mean129.01466
Median Absolute Deviation (MAD)0.0014269
Skewness0.80336834
Sum29544.357
Variance3.4775123 × 10-5
MonotonicityNot monotonic
2023-12-13T08:03:49.919103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0112229 81
35.4%
129.009796 6
 
2.6%
129.0117055 5
 
2.2%
129.0126472 5
 
2.2%
129.0091125 5
 
2.2%
129.0123703 4
 
1.7%
129.005667 4
 
1.7%
129.0187528 4
 
1.7%
129.0145115 3
 
1.3%
129.0102735 3
 
1.3%
Other values (80) 109
47.6%
ValueCountFrequency (%)
129.0055892 1
 
0.4%
129.005667 4
1.7%
129.0062939 1
 
0.4%
129.0064996 1
 
0.4%
129.0071629 1
 
0.4%
129.0072367 1
 
0.4%
129.0075631 2
 
0.9%
129.0087474 2
 
0.9%
129.0089744 2
 
0.9%
129.0091125 5
2.2%
ValueCountFrequency (%)
129.0263663 2
0.9%
129.0261046 1
 
0.4%
129.0256419 1
 
0.4%
129.0256348 1
 
0.4%
129.0256017 1
 
0.4%
129.0255484 1
 
0.4%
129.0254295 2
0.9%
129.025394 3
1.3%
129.0253612 2
0.9%
129.0252826 1
 
0.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-08-11
229 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-11
2nd row2023-08-11
3rd row2023-08-11
4th row2023-08-11
5th row2023-08-11

Common Values

ValueCountFrequency (%)
2023-08-11 229
100.0%

Length

2023-12-13T08:03:50.057021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:03:50.156291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-11 229
100.0%

Interactions

2023-12-13T08:03:46.542331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:03:46.352944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:03:46.643446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:03:46.438633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:03:50.212688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종위도경도
업종1.0000.8770.847
위도0.8771.0000.939
경도0.8470.9391.000
2023-12-13T08:03:50.316276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업종
위도1.0000.6110.519
경도0.6111.0000.473
업종0.5190.4731.000

Missing values

2023-12-13T08:03:46.754654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:03:46.900784image/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.

Sample

구군명업체명도로명주소업종전화번호위도경도데이터기준일자
0부산 서구고신대학교 복음병원부산광역시 서구 감천로 262 (암남동)종합병원051-990-605935.081082129.0145112023-08-11
1부산 서구㈜동양/건재부문 부산공장부산광역시 서구 원양로 268 (암남동)레미콘 제조업051-250-559035.074332129.0056672023-08-11
2부산 서구삼육부산병원부산광역시 서구 대티로 170 (서대신동2가)종합병원051-600-769535.112066129.010982023-08-11
3부산 서구㈜사조대림 부산공장부산광역시 서구 충무대로 170 (남부민동)육류 기타 가공 및 저장 처리업(가금류 제외)051-250-216535.085831129.0253942023-08-11
4부산 서구부산대학교병원부산광역시 서구 구덕로 179 (아미동1가)종합병원051-240-713035.101053129.0192232023-08-11
5부산 서구동아대학교병원부산광역시 서구 대신공원로 26 (동대신동3가)종합병원051-240-237235.12051129.0187532023-08-11
6부산 서구수협중앙회 감천항물류센터부산광역시 서구 원양로 119 (암남동)기타 수산동물 가공 및 저장 처리업051-253-311335.063586129.0123432023-08-11
7부산 서구참손푸드㈜부산광역시 서구 원양로 171 (암남동)기타 수산동물 가공 및 저장 처리업051-250-707635.066442129.0102742023-08-11
8부산 서구동원산업㈜ 부산공장부산광역시 서구 원양로 147, 지하2층 (암남동)기타 수산동물 가공 및 저장 처리업051-250-066135.065776129.012372023-08-11
9부산 서구성진수산㈜부산광역시 서구 원양로 87 (암남동)기타 수산동물 가공 및 저장 처리업051-231-792835.062554129.0117062023-08-11
구군명업체명도로명주소업종전화번호위도경도데이터기준일자
219부산 서구우리안경점부산광역시 서구 대영로 72-1 (동대신동1가)안경 및 렌즈 소매업051-242-263735.110384129.0191532023-08-11
220부산 서구대학병원안경부산광역시 서구 구덕로 188 (토성동3가)안경 및 렌즈 소매업051-256-106735.101326129.0200162023-08-11
221부산 서구대성안경원부산광역시 서구 충무대로 252-4 (남부민동)안경 및 렌즈 소매업051-254-365235.093179129.0241042023-08-11
222부산 서구알프스안경점부산광역시 서구 대영로 49 (서대신동2가)안경 및 렌즈 소매업051-248-330935.10988129.0168432023-08-11
223부산 서구세진안경점부산광역시 서구 구덕로118번길 28 (충무동1가)안경 및 렌즈 소매업051-255-462635.097394129.0244862023-08-11
224부산 서구아이리스안경원부산광역시 서구 구덕로 121 (토성동4가)안경 및 렌즈 소매업051-254-100835.096444129.0228292023-08-11
225부산 서구갤러리안경 서대신점부산광역시 서구 대영로 47 (서대신동2가)안경 및 렌즈 소매업051-242-734035.109819129.0166342023-08-11
226부산 서구대학안경부산광역시 서구 까치고개로 210-1 (아미동1가)안경 및 렌즈 소매업051-256-563035.09966129.0187562023-08-11
227부산 서구이지아이안경원부산광역시 서구 구덕로 309, 1층 (서대신동2가)안경 및 렌즈 소매업051-231-300935.111358129.0169272023-08-11
228부산 서구으뜸50안경 부산동대신역점부산광역시 서구 구덕로 299(서대신동2가)안경 및 렌즈 소매업051-000-000035.110652129.0172592023-08-11

Duplicate rows

Most frequently occurring

구군명업체명도로명주소업종전화번호위도경도데이터기준일자# duplicates
0부산 서구㈜다해푸드부산광역시 서구 원양로 1, B동 507호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-266-600235.055428129.0112232023-08-112
1부산 서구㈜대풍수산식품부산광역시 서구 원양로 1, B동 308호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-246-911035.055428129.0112232023-08-112
2부산 서구㈜더이룸부산광역시 서구 원양로 1, B동 604호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-231-399635.055428129.0112232023-08-112
3부산 서구㈜두이부산광역시 서구 원양로 71, 11층(암남동, 사조씨푸드㈜ 물류센타)기타 수산동물 가공 및 저장 처리업(10219)051-000-000035.062089129.0091122023-08-112
4부산 서구㈜락피쉬부산광역시 서구 원양로 1, B동 503호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-717-338635.055428129.0112232023-08-112
5부산 서구㈜명진에스에프부산광역시 서구 원양로 1, A동 303호(암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-261-387735.055428129.0112232023-08-112
6부산 서구㈜바다를품다부산광역시 서구 원양로 1, B동 406호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-261-335535.055428129.0112232023-08-112
7부산 서구㈜백화부산광역시 서구 원양로 1, B동 201호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-243-883335.055428129.0112232023-08-112
8부산 서구㈜보림로지스틱스부산광역시 서구 원양로 73 (암남동)기타 수산동물 가공 및 저장 처리업(10219)051-231-150035.062335129.0098722023-08-112
9부산 서구㈜부광F&G부산광역시 서구 원양로 1, B동 601·602호 (암남동, 수산가공선진화단지)기타 수산동물 가공 및 저장 처리업(10219)051-254-301035.055428129.0112232023-08-112