Overview

Dataset statistics

Number of variables6
Number of observations43
Missing cells3
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory52.0 B

Variable types

Numeric1
Text4
DateTime1

Dataset

Description전라남도 광양시 헬스장 정보(업체명,주소,연락처 등)에 대한 데이터를 전 국민에게 무료로 인터넷을 통하여 제공함*연락처 항목 중 공란 데이터는 개인휴대전화번호를 사용하는 업체 또는 데이터 미집계 사유임.
Author전라남도 광양시
URLhttps://www.data.go.kr/data/15077386/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연락처 has 3 (7.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 09:43:03.907756
Analysis finished2024-03-14 09:43:05.498677
Duration1.59 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.0 B
2024-03-14T18:43:05.674179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q111.5
median22
Q332.5
95-th percentile40.9
Maximum43
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.57075176
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)11
Skewness0
Sum946
Variance157.66667
MonotonicityStrictly increasing
2024-03-14T18:43:05.906641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 1
 
2.3%
2 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%
34 1
2.3%

업체명
Text

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size472.0 B
2024-03-14T18:43:06.701098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length6.5348837
Min length2

Characters and Unicode

Total characters281
Distinct characters112
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

Unique39 ?
Unique (%)90.7%

Sample

1st row발리 휘트니스
2nd row광양휘트니스클럽
3rd row비엔비헬스클럽
4th rowGold Gym
5th row빅토리헬스
ValueCountFrequency (%)
휘트니스 5
 
8.1%
제일헬스 2
 
3.2%
힘피트니스 2
 
3.2%
gym 2
 
3.2%
프라우드짐 2
 
3.2%
미학 1
 
1.6%
비케이짐 1
 
1.6%
sporec 1
 
1.6%
크로스핏예스 1
 
1.6%
탠핏휘트니스 1
 
1.6%
Other values (44) 44
71.0%
2024-03-14T18:43:07.675303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 32
 
11.4%
19
 
6.8%
니 15
 
5.3%
트 15
 
5.3%
휘 12
 
4.3%
짐 10
 
3.6%
헬 8
 
2.8%
라 5
 
1.8%
핏 4
 
1.4%
G 4
 
1.4%
Other values (102) 157
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
77.9%
Uppercase Letter 23
 
8.2%
Space Separator 19
 
6.8%
Lowercase Letter 11
 
3.9%
Other Punctuation 4
 
1.4%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 32
 
14.6%
니 15
 
6.8%
트 15
 
6.8%
휘 12
 
5.5%
짐 10
 
4.6%
헬 8
 
3.7%
라 5
 
2.3%
핏 4
 
1.8%
럽 4
 
1.8%
클 4
 
1.8%
Other values (76) 110
50.2%
Uppercase Letter
ValueCountFrequency (%)
G 4
17.4%
B 2
8.7%
M 2
8.7%
P 2
8.7%
E 2
8.7%
J 2
8.7%
S 2
8.7%
Y 1
 
4.3%
T 1
 
4.3%
H 1
 
4.3%
Other values (4) 4
17.4%
Lowercase Letter
ValueCountFrequency (%)
y 3
27.3%
d 2
18.2%
m 2
18.2%
o 2
18.2%
b 1
 
9.1%
l 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
. 2
50.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 219
77.9%
Latin 34
 
12.1%
Common 28
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 32
 
14.6%
니 15
 
6.8%
트 15
 
6.8%
휘 12
 
5.5%
짐 10
 
4.6%
헬 8
 
3.7%
라 5
 
2.3%
핏 4
 
1.8%
럽 4
 
1.8%
클 4
 
1.8%
Other values (76) 110
50.2%
Latin
ValueCountFrequency (%)
G 4
 
11.8%
y 3
 
8.8%
B 2
 
5.9%
M 2
 
5.9%
P 2
 
5.9%
E 2
 
5.9%
J 2
 
5.9%
d 2
 
5.9%
S 2
 
5.9%
m 2
 
5.9%
Other values (10) 11
32.4%
Common
ValueCountFrequency (%)
19
67.9%
& 2
 
7.1%
) 2
 
7.1%
( 2
 
7.1%
. 2
 
7.1%
- 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
77.9%
ASCII 62
 
22.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 32
 
14.6%
니 15
 
6.8%
트 15
 
6.8%
휘 12
 
5.5%
짐 10
 
4.6%
헬 8
 
3.7%
라 5
 
2.3%
핏 4
 
1.8%
럽 4
 
1.8%
클 4
 
1.8%
Other values (76) 110
50.2%
ASCII
ValueCountFrequency (%)
19
30.6%
G 4
 
6.5%
y 3
 
4.8%
B 2
 
3.2%
M 2
 
3.2%
& 2
 
3.2%
) 2
 
3.2%
( 2
 
3.2%
P 2
 
3.2%
E 2
 
3.2%
Other values (16) 22
35.5%

지번주소
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size472.0 B
2024-03-14T18:43:08.565664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length29
Mean length22.116279
Min length16

Characters and Unicode

Total characters951
Distinct characters59
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

Unique41 ?
Unique (%)95.3%

Sample

1st row전라남도 광양시 광영동 775-4 목우아파트
2nd row전라남도 광양시 중동 1349 2층
3rd row전라남도 광양시 광영동 788-7 2,3층
4th row전라남도 광양시 광양읍 칠성리 937-10
5th row전라남도 광양시 광영동 677-10
ValueCountFrequency (%)
전라남도 43
21.5%
광양시 43
21.5%
중동 21
 
10.5%
광양읍 11
 
5.5%
광영동 7
 
3.5%
마동 3
 
1.5%
3층 3
 
1.5%
칠성리 3
 
1.5%
인동리 2
 
1.0%
덕례리 2
 
1.0%
Other values (57) 62
31.0%
2024-03-14T18:43:09.838907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
199
20.9%
광 63
 
6.6%
양 56
 
5.9%
1 52
 
5.5%
라 44
 
4.6%
시 44
 
4.6%
전 43
 
4.5%
도 43
 
4.5%
남 43
 
4.5%
- 37
 
3.9%
Other values (49) 327
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 487
51.2%
Decimal Number 222
23.3%
Space Separator 199
20.9%
Dash Punctuation 37
 
3.9%
Other Punctuation 4
 
0.4%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 63
12.9%
양 56
11.5%
라 44
9.0%
시 44
9.0%
전 43
8.8%
도 43
8.8%
남 43
8.8%
동 35
7.2%
중 21
 
4.3%
리 12
 
2.5%
Other values (33) 83
17.0%
Decimal Number
ValueCountFrequency (%)
1 52
23.4%
3 25
11.3%
6 24
10.8%
7 22
9.9%
2 20
 
9.0%
4 19
 
8.6%
0 17
 
7.7%
8 16
 
7.2%
5 16
 
7.2%
9 11
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
. 1
 
25.0%
Space Separator
ValueCountFrequency (%)
199
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 487
51.2%
Common 464
48.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 63
12.9%
양 56
11.5%
라 44
9.0%
시 44
9.0%
전 43
8.8%
도 43
8.8%
남 43
8.8%
동 35
7.2%
중 21
 
4.3%
리 12
 
2.5%
Other values (33) 83
17.0%
Common
ValueCountFrequency (%)
199
42.9%
1 52
 
11.2%
- 37
 
8.0%
3 25
 
5.4%
6 24
 
5.2%
7 22
 
4.7%
2 20
 
4.3%
4 19
 
4.1%
0 17
 
3.7%
8 16
 
3.4%
Other values (6) 33
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 487
51.2%
ASCII 464
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199
42.9%
1 52
 
11.2%
- 37
 
8.0%
3 25
 
5.4%
6 24
 
5.2%
7 22
 
4.7%
2 20
 
4.3%
4 19
 
4.1%
0 17
 
3.7%
8 16
 
3.4%
Other values (6) 33
 
7.1%
Hangul
ValueCountFrequency (%)
광 63
12.9%
양 56
11.5%
라 44
9.0%
시 44
9.0%
전 43
8.8%
도 43
8.8%
남 43
8.8%
동 35
7.2%
중 21
 
4.3%
리 12
 
2.5%
Other values (33) 83
17.0%

도로명주소
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size472.0 B
2024-03-14T18:43:10.860180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length35
Mean length25.651163
Min length19

Characters and Unicode

Total characters1103
Distinct characters85
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)95.3%

Sample

1st row전라남도 광양시 광포로 72, 목우아파트 (광영동)
2nd row전라남도 광양시 불로로 123, 2층 (중동)
3rd row전라남도 광양시 광영로 84, 2,3층 (광영동)
4th row전라남도 광양시 광양읍 서북3길 24, 2층
5th row전라남도 광양시 광영시장길 1 (광영동)
ValueCountFrequency (%)
전라남도 43
17.0%
광양시 43
17.0%
중동 21
 
8.3%
광양읍 11
 
4.3%
3층 11
 
4.3%
2층 8
 
3.2%
광영동 7
 
2.8%
진등1길 4
 
1.6%
공영로 4
 
1.6%
인덕로 3
 
1.2%
Other values (80) 98
38.7%
2024-03-14T18:43:12.143770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210
19.0%
광 72
 
6.5%
양 56
 
5.1%
시 47
 
4.3%
라 44
 
4.0%
전 43
 
3.9%
남 43
 
3.9%
도 43
 
3.9%
1 40
 
3.6%
동 39
 
3.5%
Other values (75) 466
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 620
56.2%
Space Separator 210
 
19.0%
Decimal Number 169
 
15.3%
Other Punctuation 33
 
3.0%
Close Punctuation 32
 
2.9%
Open Punctuation 32
 
2.9%
Dash Punctuation 4
 
0.4%
Lowercase Letter 2
 
0.2%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 72
11.6%
양 56
 
9.0%
시 47
 
7.6%
라 44
 
7.1%
전 43
 
6.9%
남 43
 
6.9%
도 43
 
6.9%
동 39
 
6.3%
로 30
 
4.8%
중 27
 
4.4%
Other values (57) 176
28.4%
Decimal Number
ValueCountFrequency (%)
1 40
23.7%
3 33
19.5%
2 32
18.9%
0 15
 
8.9%
4 11
 
6.5%
6 11
 
6.5%
5 9
 
5.3%
7 8
 
4.7%
9 7
 
4.1%
8 3
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
j 1
50.0%
h 1
50.0%
Space Separator
ValueCountFrequency (%)
210
100.0%
Other Punctuation
ValueCountFrequency (%)
, 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 620
56.2%
Common 481
43.6%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 72
11.6%
양 56
 
9.0%
시 47
 
7.6%
라 44
 
7.1%
전 43
 
6.9%
남 43
 
6.9%
도 43
 
6.9%
동 39
 
6.3%
로 30
 
4.8%
중 27
 
4.4%
Other values (57) 176
28.4%
Common
ValueCountFrequency (%)
210
43.7%
1 40
 
8.3%
, 33
 
6.9%
3 33
 
6.9%
) 32
 
6.7%
2 32
 
6.7%
( 32
 
6.7%
0 15
 
3.1%
4 11
 
2.3%
6 11
 
2.3%
Other values (6) 32
 
6.7%
Latin
ValueCountFrequency (%)
j 1
50.0%
h 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 620
56.2%
ASCII 483
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
43.5%
1 40
 
8.3%
, 33
 
6.8%
3 33
 
6.8%
) 32
 
6.6%
2 32
 
6.6%
( 32
 
6.6%
0 15
 
3.1%
4 11
 
2.3%
6 11
 
2.3%
Other values (8) 34
 
7.0%
Hangul
ValueCountFrequency (%)
광 72
11.6%
양 56
 
9.0%
시 47
 
7.6%
라 44
 
7.1%
전 43
 
6.9%
남 43
 
6.9%
도 43
 
6.9%
동 39
 
6.3%
로 30
 
4.8%
중 27
 
4.4%
Other values (57) 176
28.4%

연락처
Text

MISSING 

Distinct39
Distinct (%)97.5%
Missing3
Missing (%)7.0%
Memory size472.0 B
2024-03-14T18:43:12.973125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.35
Min length12

Characters and Unicode

Total characters494
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

Unique38 ?
Unique (%)95.0%

Sample

1st row061-793-3692
2nd row061-791-8800
3rd row061-794-0880
4th row061-913-1331
5th row061-772-1105
ValueCountFrequency (%)
061-763-8588 2
 
5.0%
061-792-4535 1
 
2.5%
061-793-3692 1
 
2.5%
061-762-1200 1
 
2.5%
061-795-2755 1
 
2.5%
061-795-6270 1
 
2.5%
061-795-1005 1
 
2.5%
061-794-9009 1
 
2.5%
061-792-3000 1
 
2.5%
061-794-8900 1
 
2.5%
Other values (29) 29
72.5%
2024-03-14T18:43:14.038297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 80
16.2%
- 80
16.2%
1 66
13.4%
7 55
11.1%
6 53
10.7%
9 45
9.1%
5 33
6.7%
3 28
 
5.7%
8 24
 
4.9%
2 17
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 414
83.8%
Dash Punctuation 80
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80
19.3%
1 66
15.9%
7 55
13.3%
6 53
12.8%
9 45
10.9%
5 33
8.0%
3 28
 
6.8%
8 24
 
5.8%
2 17
 
4.1%
4 13
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80
16.2%
- 80
16.2%
1 66
13.4%
7 55
11.1%
6 53
10.7%
9 45
9.1%
5 33
6.7%
3 28
 
5.7%
8 24
 
4.9%
2 17
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80
16.2%
- 80
16.2%
1 66
13.4%
7 55
11.1%
6 53
10.7%
9 45
9.1%
5 33
6.7%
3 28
 
5.7%
8 24
 
4.9%
2 17
 
3.4%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size472.0 B
Minimum2024-01-05 00:00:00
Maximum2024-01-05 00:00:00
2024-03-14T18:43:14.229158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:14.382924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T18:43:04.501836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:43:14.530887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업체명지번주소도로명주소연락처
연번1.0000.8730.9360.9360.945
업체명0.8731.0000.9850.9921.000
지번주소0.9360.9851.0000.9950.994
도로명주소0.9360.9920.9951.0000.997
연락처0.9451.0000.9940.9971.000

Missing values

2024-03-14T18:43:05.011744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:43:05.362184image/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

연번업체명지번주소도로명주소연락처데이터기준일
01발리 휘트니스전라남도 광양시 광영동 775-4 목우아파트전라남도 광양시 광포로 72, 목우아파트 (광영동)061-793-36922024-01-05
12광양휘트니스클럽전라남도 광양시 중동 1349 2층전라남도 광양시 불로로 123, 2층 (중동)061-791-88002024-01-05
23비엔비헬스클럽전라남도 광양시 광영동 788-7 2,3층전라남도 광양시 광영로 84, 2,3층 (광영동)061-794-08802024-01-05
34Gold Gym전라남도 광양시 광양읍 칠성리 937-10전라남도 광양시 광양읍 서북3길 24, 2층061-913-13312024-01-05
45빅토리헬스전라남도 광양시 광영동 677-10전라남도 광양시 광영시장길 1 (광영동)061-772-11052024-01-05
56라이프헬스클럽전라남도 광양시 중동 1641-2전라남도 광양시 공영로 67 (중동)061-793-66112024-01-05
67제일헬스전라남도 광양시 광양읍 인동리 261-3 3층전라남도 광양시 광양읍 숲샘길 129, 3층061-763-85882024-01-05
78현대헬스&휘트니스전라남도 광양시 광영동 759-2 3층전라남도 광양시 광영로 144, 3층 (광영동)061-793-73572024-01-05
89프라임헬스전라남도 광양시 중동 1418 무등파크맨션 상가동 301.302호전라남도 광양시 중마로 230, 상가동 301,302호 (중동, 무등파크맨션)061-793-11652024-01-05
910건화베스파전라남도 광양시 광양읍 구산리 769-1전라남도 광양시 광양읍 인덕로 1121061-762-98112024-01-05
연번업체명지번주소도로명주소연락처데이터기준일
3334머슬팩토리(근육공장)전라남도 광양시 광양읍 덕례리 340-2전라남도 광양시 광양읍 인덕로 922, 3층061-762-12002024-01-05
3435프라우드짐전라남도 광양시 광영동 775-3 목우아파트전라남도 광양시 금영로 156, 목우아파트 상가동 3층 (광영동)061-817-17172024-01-05
3536운동의 미학전라남도 광양시 중동 1566-2전라남도 광양시 진등1길 12, 3층 (중동)061-795-09772024-01-05
3637힘피트니스전라남도 광양시 중동 1674 광양시티프라자전라남도 광양시 광장로 125, 광양시티프라자 205동 (중동)0507-1334-52092024-01-05
3738워너핏전라남도 광양시 광양읍 덕례리 1728-1전라남도 광양시 광양읍 대림오성로 112, 3층0507-1312-04962024-01-05
3839맛짐휘트니스전라남도 광양시 중동 1419-2전라남도 광양시 중마로 227, 2층 (중동)0507-1303-84902024-01-05
3940프라우드짐전라남도 광양시 광양읍 용강리 869-10전라남도 광양시 광양읍 용강로 39, 2층061-761-76112024-01-05
4041더드림PT전라남도 광양시 중동 1557-12전라남도 광양시 진등1길 31, 1층 (중동)061-794-89002024-01-05
4142탠핏휘트니스전라남도 광양시 중동 1318-3전라남도 광양시 중동로 63, 2층 (중동)0507-1369-22982024-01-05
4243다짐휘트니스전라남도 광양시 광양읍 인동리 261-3전라남도 광양시 광양읍 숲샘길 129, 3층0507-1318-09602024-01-05