Overview

Dataset statistics

Number of variables6
Number of observations46
Missing cells16
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory51.9 B

Variable types

Numeric1
Categorical1
Text4

Dataset

Description서울특별시 강북구 헬스장_피트니스클럽정보(연번, 업종, 상호, 영업장소재지(도로명), 영업장 소재지(지번), 전화번호) 관련 정보입니다.
Author서울특별시 강북구
URLhttps://www.data.go.kr/data/15090669/fileData.do

Alerts

업종 has constant value ""Constant
영업장 소재지 (지번) has 5 (10.9%) missing valuesMissing
전화번호 has 11 (23.9%) missing valuesMissing
연번 has unique valuesUnique
영업장 소재지 (도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:32:50.331243
Analysis finished2023-12-13 00:32:50.894136
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:32:50.946889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q112.25
median23.5
Q334.75
95-th percentile43.75
Maximum46
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.57117522
Kurtosis-1.2
Mean23.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1081
Variance180.16667
MonotonicityStrictly increasing
2023-12-13T09:32:51.049434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1
 
2.2%
36 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
34 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
46 1
2.2%
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
체력단련장업
46 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체력단련장업 46
100.0%

Length

2023-12-13T09:32:51.146414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:32:51.211557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체력단련장업 46
100.0%

상 호
Text

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T09:32:51.367732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.0869565
Min length2

Characters and Unicode

Total characters326
Distinct characters127
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

Unique44 ?
Unique (%)95.7%

Sample

1st row가인
2nd row현대 휘트니스
3rd row람보
4th row서울헬스클럽
5th row라인짐
ValueCountFrequency (%)
휘트니스 10
 
13.9%
피트니스팩토리24 2
 
2.8%
수유점 2
 
2.8%
피트니스 2
 
2.8%
jfit 1
 
1.4%
fa 1
 
1.4%
제이짐 1
 
1.4%
이성현 1
 
1.4%
퍼스널 1
 
1.4%
트레이닝 1
 
1.4%
Other values (50) 50
69.4%
2023-12-13T09:32:51.658903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 31
 
9.5%
26
 
8.0%
트 21
 
6.4%
니 19
 
5.8%
휘 13
 
4.0%
짐 11
 
3.4%
이 7
 
2.1%
피 6
 
1.8%
점 5
 
1.5%
리 5
 
1.5%
Other values (117) 182
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
75.5%
Space Separator 26
 
8.0%
Uppercase Letter 22
 
6.7%
Lowercase Letter 20
 
6.1%
Decimal Number 7
 
2.1%
Close Punctuation 2
 
0.6%
Other Punctuation 1
 
0.3%
Dash Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 31
 
12.6%
트 21
 
8.5%
니 19
 
7.7%
휘 13
 
5.3%
짐 11
 
4.5%
이 7
 
2.8%
피 6
 
2.4%
점 5
 
2.0%
리 5
 
2.0%
클 4
 
1.6%
Other values (83) 124
50.4%
Uppercase Letter
ValueCountFrequency (%)
F 3
13.6%
M 2
 
9.1%
G 2
 
9.1%
Y 2
 
9.1%
N 2
 
9.1%
A 1
 
4.5%
J 1
 
4.5%
T 1
 
4.5%
P 1
 
4.5%
D 1
 
4.5%
Other values (6) 6
27.3%
Lowercase Letter
ValueCountFrequency (%)
l 3
15.0%
e 3
15.0%
o 3
15.0%
i 3
15.0%
t 2
10.0%
d 1
 
5.0%
f 1
 
5.0%
u 1
 
5.0%
g 1
 
5.0%
n 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
4 3
42.9%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 246
75.5%
Latin 42
 
12.9%
Common 38
 
11.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 31
 
12.6%
트 21
 
8.5%
니 19
 
7.7%
휘 13
 
5.3%
짐 11
 
4.5%
이 7
 
2.8%
피 6
 
2.4%
점 5
 
2.0%
리 5
 
2.0%
클 4
 
1.6%
Other values (83) 124
50.4%
Latin
ValueCountFrequency (%)
F 3
 
7.1%
l 3
 
7.1%
e 3
 
7.1%
o 3
 
7.1%
i 3
 
7.1%
t 2
 
4.8%
M 2
 
4.8%
G 2
 
4.8%
Y 2
 
4.8%
N 2
 
4.8%
Other values (17) 17
40.5%
Common
ValueCountFrequency (%)
26
68.4%
2 4
 
10.5%
4 3
 
7.9%
) 2
 
5.3%
. 1
 
2.6%
- 1
 
2.6%
( 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 246
75.5%
ASCII 80
 
24.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 31
 
12.6%
트 21
 
8.5%
니 19
 
7.7%
휘 13
 
5.3%
짐 11
 
4.5%
이 7
 
2.8%
피 6
 
2.4%
점 5
 
2.0%
리 5
 
2.0%
클 4
 
1.6%
Other values (83) 124
50.4%
ASCII
ValueCountFrequency (%)
26
32.5%
2 4
 
5.0%
F 3
 
3.8%
l 3
 
3.8%
4 3
 
3.8%
e 3
 
3.8%
o 3
 
3.8%
i 3
 
3.8%
t 2
 
2.5%
) 2
 
2.5%
Other values (24) 28
35.0%

영업장 소재지 (도로명)
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T09:32:51.884100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length35
Mean length29.521739
Min length23

Characters and Unicode

Total characters1358
Distinct characters103
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

Unique46 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 삼양로 424 (수유동)
2nd row서울특별시 강북구 삼양로 181, 지하,2,3층 (미아동)
3rd row서울특별시 강북구 도봉로77길 9 (수유동)
4th row서울특별시 강북구 도봉로 373 (수유동)
5th row서울특별시 강북구 한천로109길 53, 3,4,5층 (번동, 유림빌딩)
ValueCountFrequency (%)
서울특별시 46
16.6%
강북구 46
16.6%
미아동 19
 
6.9%
도봉로 16
 
5.8%
수유동 15
 
5.4%
삼양로 8
 
2.9%
3층 8
 
2.9%
번동 7
 
2.5%
4층 7
 
2.5%
솔샘로 3
 
1.1%
Other values (96) 102
36.8%
2023-12-13T09:32:52.216471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
235
 
17.3%
동 48
 
3.5%
서 47
 
3.5%
시 47
 
3.5%
구 46
 
3.4%
) 46
 
3.4%
( 46
 
3.4%
로 46
 
3.4%
울 46
 
3.4%
북 46
 
3.4%
Other values (93) 705
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
58.6%
Space Separator 235
 
17.3%
Decimal Number 187
 
13.8%
Close Punctuation 46
 
3.4%
Open Punctuation 46
 
3.4%
Other Punctuation 43
 
3.2%
Dash Punctuation 4
 
0.3%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 48
 
6.0%
서 47
 
5.9%
시 47
 
5.9%
구 46
 
5.8%
로 46
 
5.8%
울 46
 
5.8%
북 46
 
5.8%
강 46
 
5.8%
별 46
 
5.8%
특 46
 
5.8%
Other values (77) 332
41.7%
Decimal Number
ValueCountFrequency (%)
3 38
20.3%
1 37
19.8%
2 24
12.8%
4 22
11.8%
5 12
 
6.4%
8 12
 
6.4%
0 11
 
5.9%
7 11
 
5.9%
9 10
 
5.3%
6 10
 
5.3%
Space Separator
ValueCountFrequency (%)
235
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
58.6%
Common 561
41.3%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 48
 
6.0%
서 47
 
5.9%
시 47
 
5.9%
구 46
 
5.8%
로 46
 
5.8%
울 46
 
5.8%
북 46
 
5.8%
강 46
 
5.8%
별 46
 
5.8%
특 46
 
5.8%
Other values (77) 332
41.7%
Common
ValueCountFrequency (%)
235
41.9%
) 46
 
8.2%
( 46
 
8.2%
, 43
 
7.7%
3 38
 
6.8%
1 37
 
6.6%
2 24
 
4.3%
4 22
 
3.9%
5 12
 
2.1%
8 12
 
2.1%
Other values (5) 46
 
8.2%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
58.6%
ASCII 562
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
235
41.8%
) 46
 
8.2%
( 46
 
8.2%
, 43
 
7.7%
3 38
 
6.8%
1 37
 
6.6%
2 24
 
4.3%
4 22
 
3.9%
5 12
 
2.1%
8 12
 
2.1%
Other values (6) 47
 
8.4%
Hangul
ValueCountFrequency (%)
동 48
 
6.0%
서 47
 
5.9%
시 47
 
5.9%
구 46
 
5.8%
로 46
 
5.8%
울 46
 
5.8%
북 46
 
5.8%
강 46
 
5.8%
별 46
 
5.8%
특 46
 
5.8%
Other values (77) 332
41.7%

영업장 소재지 (지번)
Text

MISSING 

Distinct41
Distinct (%)100.0%
Missing5
Missing (%)10.9%
Memory size500.0 B
2023-12-13T09:32:52.412366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length22.609756
Min length17

Characters and Unicode

Total characters927
Distinct characters66
Distinct categories4 ?
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 (%)100.0%

Sample

1st row서울특별시 강북구 수유동 381-2
2nd row서울특별시 강북구 미아동 703-31
3rd row서울특별시 강북구 수유동 93-1
4th row서울특별시 강북구 수유동 174-5
5th row서울특별시 강북구 번동 165-4
ValueCountFrequency (%)
서울특별시 41
22.3%
강북구 41
22.3%
미아동 21
 
11.4%
수유동 14
 
7.6%
번동 6
 
3.3%
4층 2
 
1.1%
상가 1
 
0.5%
131-30 1
 
0.5%
322-1 1
 
0.5%
484-4 1
 
0.5%
Other values (55) 55
29.9%
2023-12-13T09:32:52.709045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
19.8%
동 43
 
4.6%
서 42
 
4.5%
시 42
 
4.5%
특 41
 
4.4%
별 41
 
4.4%
강 41
 
4.4%
북 41
 
4.4%
구 41
 
4.4%
울 41
 
4.4%
Other values (56) 370
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 527
56.9%
Space Separator 184
 
19.8%
Decimal Number 177
 
19.1%
Dash Punctuation 39
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 43
 
8.2%
서 42
 
8.0%
시 42
 
8.0%
특 41
 
7.8%
별 41
 
7.8%
강 41
 
7.8%
북 41
 
7.8%
구 41
 
7.8%
울 41
 
7.8%
아 23
 
4.4%
Other values (44) 131
24.9%
Decimal Number
ValueCountFrequency (%)
1 38
21.5%
3 25
14.1%
4 23
13.0%
8 19
10.7%
5 17
9.6%
2 14
 
7.9%
6 12
 
6.8%
0 11
 
6.2%
9 10
 
5.6%
7 8
 
4.5%
Space Separator
ValueCountFrequency (%)
184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 527
56.9%
Common 400
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 43
 
8.2%
서 42
 
8.0%
시 42
 
8.0%
특 41
 
7.8%
별 41
 
7.8%
강 41
 
7.8%
북 41
 
7.8%
구 41
 
7.8%
울 41
 
7.8%
아 23
 
4.4%
Other values (44) 131
24.9%
Common
ValueCountFrequency (%)
184
46.0%
- 39
 
9.8%
1 38
 
9.5%
3 25
 
6.2%
4 23
 
5.8%
8 19
 
4.8%
5 17
 
4.2%
2 14
 
3.5%
6 12
 
3.0%
0 11
 
2.8%
Other values (2) 18
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 527
56.9%
ASCII 400
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
46.0%
- 39
 
9.8%
1 38
 
9.5%
3 25
 
6.2%
4 23
 
5.8%
8 19
 
4.8%
5 17
 
4.2%
2 14
 
3.5%
6 12
 
3.0%
0 11
 
2.8%
Other values (2) 18
 
4.5%
Hangul
ValueCountFrequency (%)
동 43
 
8.2%
서 42
 
8.0%
시 42
 
8.0%
특 41
 
7.8%
별 41
 
7.8%
강 41
 
7.8%
북 41
 
7.8%
구 41
 
7.8%
울 41
 
7.8%
아 23
 
4.4%
Other values (44) 131
24.9%

전화번호
Text

MISSING 

Distinct35
Distinct (%)100.0%
Missing11
Missing (%)23.9%
Memory size500.0 B
2023-12-13T09:32:52.877975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.028571
Min length11

Characters and Unicode

Total characters386
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

Unique35 ?
Unique (%)100.0%

Sample

1st row02-999-2229
2nd row02-983-7600
3rd row02-903-7009
4th row02-989-6290
5th row02-984-2715
ValueCountFrequency (%)
02-999-2229 1
 
2.9%
02-989-2438 1
 
2.9%
02-900-9628 1
 
2.9%
02-945-0723 1
 
2.9%
02-982-2438 1
 
2.9%
02-945-8883 1
 
2.9%
02-923-8934 1
 
2.9%
02-991-1317 1
 
2.9%
02-981-2438 1
 
2.9%
02-990-9017 1
 
2.9%
Other values (25) 25
71.4%
2023-12-13T09:32:53.148631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 69
17.9%
9 60
15.5%
0 58
15.0%
2 56
14.5%
8 43
11.1%
7 22
 
5.7%
4 19
 
4.9%
6 16
 
4.1%
3 16
 
4.1%
1 14
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 317
82.1%
Dash Punctuation 69
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 60
18.9%
0 58
18.3%
2 56
17.7%
8 43
13.6%
7 22
 
6.9%
4 19
 
6.0%
6 16
 
5.0%
3 16
 
5.0%
1 14
 
4.4%
5 13
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 386
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 69
17.9%
9 60
15.5%
0 58
15.0%
2 56
14.5%
8 43
11.1%
7 22
 
5.7%
4 19
 
4.9%
6 16
 
4.1%
3 16
 
4.1%
1 14
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 69
17.9%
9 60
15.5%
0 58
15.0%
2 56
14.5%
8 43
11.1%
7 22
 
5.7%
4 19
 
4.9%
6 16
 
4.1%
3 16
 
4.1%
1 14
 
3.6%

Interactions

2023-12-13T09:32:50.624925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:32:53.222991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상 호영업장 소재지 (도로명)영업장 소재지 (지번)전화번호
연번1.0000.9311.0001.0001.000
상 호0.9311.0001.0001.0001.000
영업장 소재지 (도로명)1.0001.0001.0001.0001.000
영업장 소재지 (지번)1.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000

Missing values

2023-12-13T09:32:50.713992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:32:50.791540image/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.
2023-12-13T09:32:50.860343image/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

연번업종상 호영업장 소재지 (도로명)영업장 소재지 (지번)전화번호
01체력단련장업가인서울특별시 강북구 삼양로 424 (수유동)서울특별시 강북구 수유동 381-202-999-2229
12체력단련장업현대 휘트니스서울특별시 강북구 삼양로 181, 지하,2,3층 (미아동)서울특별시 강북구 미아동 703-31<NA>
23체력단련장업람보서울특별시 강북구 도봉로77길 9 (수유동)서울특별시 강북구 수유동 93-102-983-7600
34체력단련장업서울헬스클럽서울특별시 강북구 도봉로 373 (수유동)서울특별시 강북구 수유동 174-502-903-7009
45체력단련장업라인짐서울특별시 강북구 한천로109길 53, 3,4,5층 (번동, 유림빌딩)서울특별시 강북구 번동 165-402-989-6290
56체력단련장업국제헬스클럽서울특별시 강북구 도봉로 144 (미아동)서울특별시 강북구 미아동 131-602-984-2715
67체력단련장업업타운 휘트니스서울특별시 강북구 도봉로 41-1, 4층 (미아동)서울특별시 강북구 미아동 5802-982-2986
78체력단련장업M 휘트니스서울특별시 강북구 도봉로 167, 5층 (미아동)서울특별시 강북구 미아동 310-4 5층02-985-5757
89체력단련장업시너짐(SYNER GYM)서울특별시 강북구 한천로 1035 (수유동,우성빌딩지하1층)서울특별시 강북구 수유동 191-35 우성빌딩지하1층02-908-8862
910체력단련장업아크로짐서울특별시 강북구 솔샘로 223, 제일은행삼양동지점 5층 (미아동)서울특별시 강북구 미아동 838-4 제일은행삼양동지점02-984-8883
연번업종상 호영업장 소재지 (도로명)영업장 소재지 (지번)전화번호
3637체력단련장업더 퍼스트짐서울특별시 강북구 한천로131길 29, 3층 (번동)서울특별시 강북구 번동 416-123<NA>
3738체력단련장업알엔엘서울특별시 강북구 도봉로 331, 4층 (수유동)서울특별시 강북구 수유동 229-9<NA>
3839체력단련장업데일리P.T샵서울특별시 강북구 도봉로 98, 2층 (미아동)서울특별시 강북구 미아동 138-15<NA>
3940체력단련장업에스핏휘트니스 수유점서울특별시 강북구 도봉로67길 18, 수유시장 3층 (수유동)서울특별시 강북구 수유동 54-5 수유시장02-984-8100
4041체력단련장업라인짐2호점서울특별시 강북구 오현로32길 4-8, 동보프라자빌딩 3층 (번동)서울특별시 강북구 번동 306-38 동보프라자빌딩02-984-7878
4142체력단련장업웨일 미아점서울특별시 강북구 솔샘로 330, 4층 (미아동)서울특별시 강북구 미아동 65-502-988-7118
4243체력단련장업피트니스 독스 수유점서울특별시 강북구 덕릉로 82, 4층 (수유동, 제네스타워)서울특별시 강북구 수유동 48-1 제네스타워02-981-2438
4344체력단련장업Good Fit서울특별시 강북구 도봉로8길 12, 5층 501호 (미아동)서울특별시 강북구 미아동 860-241<NA>
4445체력단련장업부스터짐서울특별시 강북구 삼양로 162, 젬스톤프라자 지하1층 (미아동)서울특별시 강북구 미아동 682-11 젬스톤프라자02-986-9696
4546체력단련장업머슬엑스짐서울특별시 강북구 한천로 1127, 성은빌딩 지하1층 (수유동)서울특별시 강북구 수유동 263-7 성은빌딩<NA>