Overview

Dataset statistics

Number of variables7
Number of observations81
Missing cells89
Missing cells (%)15.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory60.6 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description부산광역시 남구 음식물류폐기물 다량배출사업장 정보데이터를 사업장규모,주소,연락처에 대한 항목을 제공합니다.
Author부산광역시 남구
URLhttps://www.data.go.kr/data/15034287/fileData.do

Alerts

구분 is highly imbalanced (62.9%)Imbalance
전화번호 has 8 (9.9%) missing valuesMissing
규모(면적-제곱미터) has 75 (92.6%) missing valuesMissing
규모(명) has 6 (7.4%) missing valuesMissing
연번 has unique valuesUnique
사업장명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:01:23.594538
Analysis finished2023-12-12 02:01:25.749143
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T11:01:25.825029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q121
median41
Q361
95-th percentile77
Maximum81
Range80
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.526581
Coefficient of variation (CV)0.57381904
Kurtosis-1.2
Mean41
Median Absolute Deviation (MAD)20
Skewness0
Sum3321
Variance553.5
MonotonicityStrictly increasing
2023-12-12T11:01:26.010432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
62 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (71) 71
87.7%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%

구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size780.0 B
집단
70 
일반
 
7
대규모
 
3
관광숙박업
 
1

Length

Max length5
Median length2
Mean length2.0740741
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row대규모
2nd row대규모
3rd row대규모
4th row일반
5th row일반

Common Values

ValueCountFrequency (%)
집단 70
86.4%
일반 7
 
8.6%
대규모 3
 
3.7%
관광숙박업 1
 
1.2%

Length

2023-12-12T11:01:26.219159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:01:26.350095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집단 70
86.4%
일반 7
 
8.6%
대규모 3
 
3.7%
관광숙박업 1
 
1.2%

사업장명
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T11:01:26.613907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length9.8765432
Min length4

Characters and Unicode

Total characters800
Distinct characters189
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)100.0%

Sample

1st rowGS하이츠자이상가 운영위원회
2nd row㈜용호시장
3rd row㈜케이디리빙 리마크빌
4th row본가 대연점
5th row오륙도가원
ValueCountFrequency (%)
부경대학교 7
 
5.2%
요양병원 3
 
2.2%
㈜아워홈 3
 
2.2%
위드센터 3
 
2.2%
씨제이프레시웨이㈜ 2
 
1.5%
장원에프엔에스 2
 
1.5%
용당캠퍼스 2
 
1.5%
㈜동원홈푸드 2
 
1.5%
부경대 2
 
1.5%
㈜파로스식품 2
 
1.5%
Other values (106) 107
79.3%
2023-12-12T11:01:27.111427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
 
7.6%
학 39
 
4.9%
교 38
 
4.8%
부 27
 
3.4%
대 22
 
2.8%
원 21
 
2.6%
등 21
 
2.6%
㈜ 21
 
2.6%
산 19
 
2.4%
초 18
 
2.2%
Other values (179) 513
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 705
88.1%
Space Separator 61
 
7.6%
Other Symbol 21
 
2.6%
Uppercase Letter 9
 
1.1%
Decimal Number 2
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 39
 
5.5%
교 38
 
5.4%
부 27
 
3.8%
대 22
 
3.1%
원 21
 
3.0%
등 21
 
3.0%
산 19
 
2.7%
초 18
 
2.6%
점 15
 
2.1%
스 14
 
2.0%
Other values (167) 471
66.8%
Uppercase Letter
ValueCountFrequency (%)
G 3
33.3%
P 2
22.2%
S 1
 
11.1%
B 1
 
11.1%
H 1
 
11.1%
L 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
7 1
50.0%
Space Separator
ValueCountFrequency (%)
61
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 726
90.8%
Common 65
 
8.1%
Latin 9
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 39
 
5.4%
교 38
 
5.2%
부 27
 
3.7%
대 22
 
3.0%
원 21
 
2.9%
등 21
 
2.9%
㈜ 21
 
2.9%
산 19
 
2.6%
초 18
 
2.5%
점 15
 
2.1%
Other values (168) 485
66.8%
Latin
ValueCountFrequency (%)
G 3
33.3%
P 2
22.2%
S 1
 
11.1%
B 1
 
11.1%
H 1
 
11.1%
L 1
 
11.1%
Common
ValueCountFrequency (%)
61
93.8%
( 1
 
1.5%
2 1
 
1.5%
7 1
 
1.5%
) 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 705
88.1%
ASCII 74
 
9.2%
None 21
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
82.4%
G 3
 
4.1%
P 2
 
2.7%
( 1
 
1.4%
S 1
 
1.4%
B 1
 
1.4%
H 1
 
1.4%
L 1
 
1.4%
2 1
 
1.4%
7 1
 
1.4%
Hangul
ValueCountFrequency (%)
학 39
 
5.5%
교 38
 
5.4%
부 27
 
3.8%
대 22
 
3.1%
원 21
 
3.0%
등 21
 
3.0%
산 19
 
2.7%
초 18
 
2.6%
점 15
 
2.1%
스 14
 
2.0%
Other values (167) 471
66.8%
None
ValueCountFrequency (%)
㈜ 21
100.0%

소재지주소
Text

Distinct78
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T11:01:27.405952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length24.506173
Min length16

Characters and Unicode

Total characters1985
Distinct characters110
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)95.1%

Sample

1st row부산광역시 남구 신선로 566 (용호동)
2nd row부산광역시 남구 동명로152번길 93 (용호동)
3rd row부산광역시 남구 수영로 324 (대연동)
4th row부산광역시 남구 신선로468(대연동)
5th row부산광역시 남구 백운포로 41 (용호동)
ValueCountFrequency (%)
남구 83
20.2%
부산광역시 81
19.7%
대연동 24
 
5.8%
용호동 16
 
3.9%
45 9
 
2.2%
용소로 8
 
1.9%
문현동 8
 
1.9%
신선로 8
 
1.9%
6
 
1.5%
용당동 6
 
1.5%
Other values (127) 162
39.4%
2023-12-12T11:01:27.883421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
331
 
16.7%
남 89
 
4.5%
동 87
 
4.4%
구 84
 
4.2%
부 82
 
4.1%
산 82
 
4.1%
광 81
 
4.1%
역 81
 
4.1%
시 81
 
4.1%
로 81
 
4.1%
Other values (100) 906
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1183
59.6%
Space Separator 331
 
16.7%
Decimal Number 296
 
14.9%
Open Punctuation 75
 
3.8%
Close Punctuation 75
 
3.8%
Other Punctuation 13
 
0.7%
Dash Punctuation 12
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 89
 
7.5%
동 87
 
7.4%
구 84
 
7.1%
부 82
 
6.9%
산 82
 
6.9%
광 81
 
6.8%
역 81
 
6.8%
시 81
 
6.8%
로 81
 
6.8%
용 40
 
3.4%
Other values (85) 395
33.4%
Decimal Number
ValueCountFrequency (%)
1 60
20.3%
5 43
14.5%
6 36
12.2%
4 34
11.5%
2 31
10.5%
3 30
10.1%
9 22
 
7.4%
0 17
 
5.7%
8 13
 
4.4%
7 10
 
3.4%
Space Separator
ValueCountFrequency (%)
331
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1183
59.6%
Common 802
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 89
 
7.5%
동 87
 
7.4%
구 84
 
7.1%
부 82
 
6.9%
산 82
 
6.9%
광 81
 
6.8%
역 81
 
6.8%
시 81
 
6.8%
로 81
 
6.8%
용 40
 
3.4%
Other values (85) 395
33.4%
Common
ValueCountFrequency (%)
331
41.3%
( 75
 
9.4%
) 75
 
9.4%
1 60
 
7.5%
5 43
 
5.4%
6 36
 
4.5%
4 34
 
4.2%
2 31
 
3.9%
3 30
 
3.7%
9 22
 
2.7%
Other values (5) 65
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1183
59.6%
ASCII 802
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
331
41.3%
( 75
 
9.4%
) 75
 
9.4%
1 60
 
7.5%
5 43
 
5.4%
6 36
 
4.5%
4 34
 
4.2%
2 31
 
3.9%
3 30
 
3.7%
9 22
 
2.7%
Other values (5) 65
 
8.1%
Hangul
ValueCountFrequency (%)
남 89
 
7.5%
동 87
 
7.4%
구 84
 
7.1%
부 82
 
6.9%
산 82
 
6.9%
광 81
 
6.8%
역 81
 
6.8%
시 81
 
6.8%
로 81
 
6.8%
용 40
 
3.4%
Other values (85) 395
33.4%

전화번호
Text

MISSING 

Distinct71
Distinct (%)97.3%
Missing8
Missing (%)9.9%
Memory size780.0 B
2023-12-12T11:01:28.171975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.109589
Min length12

Characters and Unicode

Total characters884
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

Unique69 ?
Unique (%)94.5%

Sample

1st row051-626-3631
2nd row051-623-3818
3rd row051-626-8604
4th row051-628-7880
5th row051-635-0707
ValueCountFrequency (%)
051-554-2042 2
 
2.7%
070-7126-4600 2
 
2.7%
070-8672-1192 1
 
1.4%
051-647-7007 1
 
1.4%
051-750-9790 1
 
1.4%
051-626-8305 1
 
1.4%
051-610-7490 1
 
1.4%
051-623-4265 1
 
1.4%
051-638-7212 1
 
1.4%
051-626-3631 1
 
1.4%
Other values (61) 61
83.6%
2023-12-12T11:01:28.626811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 150
17.0%
- 146
16.5%
1 115
13.0%
5 98
11.1%
6 95
10.7%
7 66
7.5%
2 62
7.0%
4 42
 
4.8%
3 38
 
4.3%
8 38
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 738
83.5%
Dash Punctuation 146
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
20.3%
1 115
15.6%
5 98
13.3%
6 95
12.9%
7 66
8.9%
2 62
8.4%
4 42
 
5.7%
3 38
 
5.1%
8 38
 
5.1%
9 34
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 884
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150
17.0%
- 146
16.5%
1 115
13.0%
5 98
11.1%
6 95
10.7%
7 66
7.5%
2 62
7.0%
4 42
 
4.8%
3 38
 
4.3%
8 38
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
17.0%
- 146
16.5%
1 115
13.0%
5 98
11.1%
6 95
10.7%
7 66
7.5%
2 62
7.0%
4 42
 
4.8%
3 38
 
4.3%
8 38
 
4.3%

규모(면적-제곱미터)
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)100.0%
Missing75
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean1791.9617
Minimum607.59
Maximum4802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T11:01:28.848588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum607.59
5-th percentile624.1325
Q1755.32
median1373.71
Q31877.605
95-th percentile4081.75
Maximum4802
Range4194.41
Interquartile range (IQR)1122.285

Descriptive statistics

Standard deviation1572.2137
Coefficient of variation (CV)0.87737018
Kurtosis3.6042298
Mean1791.9617
Median Absolute Deviation (MAD)623.62
Skewness1.8308804
Sum10751.77
Variance2471856
MonotonicityNot monotonic
2023-12-12T11:01:28.993888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1000.0 1
 
1.2%
1921.0 1
 
1.2%
4802.0 1
 
1.2%
673.76 1
 
1.2%
607.59 1
 
1.2%
1747.42 1
 
1.2%
(Missing) 75
92.6%
ValueCountFrequency (%)
607.59 1
1.2%
673.76 1
1.2%
1000.0 1
1.2%
1747.42 1
1.2%
1921.0 1
1.2%
4802.0 1
1.2%
ValueCountFrequency (%)
4802.0 1
1.2%
1921.0 1
1.2%
1747.42 1
1.2%
1000.0 1
1.2%
673.76 1
1.2%
607.59 1
1.2%

규모(명)
Real number (ℝ)

MISSING 

Distinct53
Distinct (%)70.7%
Missing6
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean542.69453
Minimum40
Maximum1800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T11:01:29.161381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile100
Q1177.5
median440
Q3720
95-th percentile1430
Maximum1800
Range1760
Interquartile range (IQR)542.5

Descriptive statistics

Standard deviation445.1443
Coefficient of variation (CV)0.82024836
Kurtosis0.45192165
Mean542.69453
Median Absolute Deviation (MAD)270
Skewness1.1146614
Sum40702.09
Variance198153.45
MonotonicityNot monotonic
2023-12-12T11:01:29.342393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 6
 
7.4%
150.0 3
 
3.7%
500.0 3
 
3.7%
450.0 3
 
3.7%
550.0 2
 
2.5%
140.0 2
 
2.5%
120.0 2
 
2.5%
200.0 2
 
2.5%
1200.0 2
 
2.5%
650.0 2
 
2.5%
Other values (43) 48
59.3%
(Missing) 6
 
7.4%
ValueCountFrequency (%)
40.0 1
 
1.2%
45.0 1
 
1.2%
98.0 1
 
1.2%
100.0 6
7.4%
120.0 2
 
2.5%
121.0 1
 
1.2%
140.0 2
 
2.5%
150.0 3
3.7%
160.0 1
 
1.2%
170.0 1
 
1.2%
ValueCountFrequency (%)
1800.0 2
2.5%
1500.0 2
2.5%
1400.0 1
1.2%
1270.0 1
1.2%
1250.0 1
1.2%
1245.78 1
1.2%
1200.0 2
2.5%
1155.0 1
1.2%
1123.0 1
1.2%
1094.0 1
1.2%

Interactions

2023-12-12T11:01:25.037654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:24.343203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:24.709490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:25.179554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:24.480458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:24.817607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:25.277637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:24.584961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:01:24.913266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:01:29.485643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분사업장명소재지주소전화번호규모(면적-제곱미터)규모(명)
연번1.0000.6791.0000.9681.000NaN0.084
구분0.6791.0001.0001.0001.0000.0000.000
사업장명1.0001.0001.0001.0001.0001.0001.000
소재지주소0.9681.0001.0001.0000.9951.0000.318
전화번호1.0001.0001.0000.9951.0001.0000.762
규모(면적-제곱미터)NaN0.0001.0001.0001.0001.000NaN
규모(명)0.0840.0001.0000.3180.762NaN1.000
2023-12-12T11:01:29.650746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번규모(면적-제곱미터)규모(명)구분
연번1.000-0.314-0.1270.488
규모(면적-제곱미터)-0.3141.000NaN0.000
규모(명)-0.127NaN1.0000.000
구분0.4880.0000.0001.000

Missing values

2023-12-12T11:01:25.422199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:01:25.539762image/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.
2023-12-12T11:01:25.679814image/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

연번구분사업장명소재지주소전화번호규모(면적-제곱미터)규모(명)
01대규모GS하이츠자이상가 운영위원회부산광역시 남구 신선로 566 (용호동)051-626-36311000.0<NA>
12대규모㈜용호시장부산광역시 남구 동명로152번길 93 (용호동)051-623-38181921.0<NA>
23대규모㈜케이디리빙 리마크빌부산광역시 남구 수영로 324 (대연동)051-626-86044802.0<NA>
34일반본가 대연점부산광역시 남구 신선로468(대연동)051-628-7880673.76<NA>
45일반오륙도가원부산광역시 남구 백운포로 41 (용호동)051-635-0707607.59<NA>
56일반쿠우쿠우 경성대점부산광역시 남구 수영로 305, 스파크 3층(대연동)051-622-62741747.42<NA>
67일반이가한우한돈부산광역시 남구 분포로 66-17 (용호동)051-622-9100<NA>835.83
78일반㈜대웅식품 호포갈비부산광역시 남구 남구 우암로 359 (문현동)051-639-9696<NA>1245.78
89일반㈜미옹 진주냉면부산광역시 남구 남구 유엔로 214 (대연동)051-623-2777<NA>622.48
910집단BH 병원부산광역시 남구 수영로 165 (대연동)051-633-6665<NA>350.0
연번구분사업장명소재지주소전화번호규모(면적-제곱미터)규모(명)
7172집단㈜파로스식품 동명대 생활관부산광역시 남구 동명로 92번길 56-10, 동명대학교 기숙사(용당동)051-554-2042<NA>700.0
7273집단㈜풀무원푸드앤컬처 한국거래소 부산점부산광역시 남구 문현금융로40, 62층 구내식당 (문현동)051-662-2562<NA>300.0
7374집단㈜호성식품 시민여객점부산광역시 남구 백운포로 40051-831-3880<NA>230.0
7475집단파크사이드 재활의학병원부산광역시 남구 유엔평화로125번길 11-10 (대연동)051-629-8000<NA>100.0
7576집단한얼고등학교부산광역시 남구 고동골로 69번가길 54051-673-5983<NA>500.0
7677집단해연중학교부산광역시 남구 진남로 110번길 25 (대연동)051-636-0233<NA>670.0
7778집단효메디 요양병원부산광역시 남구 용호로 95 (용호동)051-624-6600<NA>200.0
7879집단롯데캐슬레전드유치원부산광역시 남구 수영로 135(대연동)051-714-6757<NA>1500.0
7980일반희성식품부산광역시 남구 수영로325번길 45051-625-7404<NA>45.0
8081관광숙박업주식회사 에이서비스코리아부산광역시 남구 전포대로 133(문현동)051-791-5912<NA>100.0