Overview

Dataset statistics

Number of variables4
Number of observations47
Missing cells9
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory35.8 B

Variable types

Numeric1
Text3

Dataset

Description서울특별시 용산구 헬스장현황(업종 헬스장상호 헬스장시설주소(지번) 헬스장시설전화번호)에 대한 데이터를 제공합니다
Author서울특별시 용산구
URLhttps://www.data.go.kr/data/3077842/fileData.do

Alerts

시설전화번호 has 9 (19.1%) missing valuesMissing
업종 has unique valuesUnique
상호 has unique valuesUnique
시설주소(지번) has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:56:22.793342
Analysis finished2023-12-12 09:56:23.344195
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Real number (ℝ)

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:56:23.425137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q112.5
median24
Q335.5
95-th percentile44.7
Maximum47
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.711309
Coefficient of variation (CV)0.57130455
Kurtosis-1.2
Mean24
Median Absolute Deviation (MAD)12
Skewness0
Sum1128
Variance188
MonotonicityStrictly increasing
2023-12-12T18:56:23.570207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 1
 
2.1%
2 1
 
2.1%
27 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%
38 1
2.1%

상호
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-12T18:56:23.870205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length14
Mean length8.9574468
Min length3

Characters and Unicode

Total characters421
Distinct characters140
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

Unique47 ?
Unique (%)100.0%

Sample

1st row우노 휘트니스클럽
2nd rowAK운동맞춤센터
3rd row웰니스짐
4th row그랜드남여헬스크럽
5th row동국스포츠
ValueCountFrequency (%)
스튜디오 3
 
4.0%
휘트니스클럽 2
 
2.7%
fit 2
 
2.7%
웰니스 2
 
2.7%
휘트니스 2
 
2.7%
gym 2
 
2.7%
크로스핏 2
 
2.7%
우노 1
 
1.3%
센티널 1
 
1.3%
유(u)pt 1
 
1.3%
Other values (57) 57
76.0%
2023-12-12T18:56:24.372793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 37
 
8.8%
28
 
6.7%
트 17
 
4.0%
니 13
 
3.1%
이 11
 
2.6%
휘 10
 
2.4%
i 9
 
2.1%
t 9
 
2.1%
레 8
 
1.9%
n 8
 
1.9%
Other values (130) 271
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 266
63.2%
Lowercase Letter 70
 
16.6%
Uppercase Letter 45
 
10.7%
Space Separator 28
 
6.7%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Dash Punctuation 2
 
0.5%
Other Punctuation 2
 
0.5%
Letter Number 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 37
 
13.9%
트 17
 
6.4%
니 13
 
4.9%
이 11
 
4.1%
휘 10
 
3.8%
레 8
 
3.0%
짐 7
 
2.6%
럽 6
 
2.3%
널 5
 
1.9%
닝 5
 
1.9%
Other values (85) 147
55.3%
Uppercase Letter
ValueCountFrequency (%)
E 5
 
11.1%
F 4
 
8.9%
C 4
 
8.9%
S 3
 
6.7%
B 3
 
6.7%
P 3
 
6.7%
T 3
 
6.7%
L 3
 
6.7%
U 2
 
4.4%
G 2
 
4.4%
Other values (10) 13
28.9%
Lowercase Letter
ValueCountFrequency (%)
i 9
12.9%
t 9
12.9%
n 8
11.4%
u 6
8.6%
o 6
8.6%
l 5
7.1%
e 5
7.1%
a 4
 
5.7%
s 4
 
5.7%
r 3
 
4.3%
Other values (8) 11
15.7%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 266
63.2%
Latin 116
27.6%
Common 39
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 37
 
13.9%
트 17
 
6.4%
니 13
 
4.9%
이 11
 
4.1%
휘 10
 
3.8%
레 8
 
3.0%
짐 7
 
2.6%
럽 6
 
2.3%
널 5
 
1.9%
닝 5
 
1.9%
Other values (85) 147
55.3%
Latin
ValueCountFrequency (%)
i 9
 
7.8%
t 9
 
7.8%
n 8
 
6.9%
u 6
 
5.2%
o 6
 
5.2%
l 5
 
4.3%
E 5
 
4.3%
e 5
 
4.3%
a 4
 
3.4%
s 4
 
3.4%
Other values (29) 55
47.4%
Common
ValueCountFrequency (%)
28
71.8%
( 3
 
7.7%
) 3
 
7.7%
- 2
 
5.1%
& 2
 
5.1%
1 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 266
63.2%
ASCII 154
36.6%
Number Forms 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 37
 
13.9%
트 17
 
6.4%
니 13
 
4.9%
이 11
 
4.1%
휘 10
 
3.8%
레 8
 
3.0%
짐 7
 
2.6%
럽 6
 
2.3%
널 5
 
1.9%
닝 5
 
1.9%
Other values (85) 147
55.3%
ASCII
ValueCountFrequency (%)
28
18.2%
i 9
 
5.8%
t 9
 
5.8%
n 8
 
5.2%
u 6
 
3.9%
o 6
 
3.9%
l 5
 
3.2%
E 5
 
3.2%
e 5
 
3.2%
a 4
 
2.6%
Other values (34) 69
44.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

시설주소(지번)
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-12T18:56:24.715081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length31
Mean length24.978723
Min length19

Characters and Unicode

Total characters1174
Distinct characters70
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

Unique47 ?
Unique (%)100.0%

Sample

1st row서울특별시 용산구 보광동 260-8번지 지상3층
2nd row서울특별시 용산구 용산동2가 23번지
3rd row서울특별시 용산구 이태원동 226-3번지 지하1층
4th row서울특별시 용산구 보광동 216-96번지
5th row서울특별시 용산구 원효로4가 142-1번지 2.3층
ValueCountFrequency (%)
서울특별시 47
22.2%
용산구 47
22.2%
한남동 14
 
6.6%
이태원동 6
 
2.8%
지하1층 6
 
2.8%
이촌동 5
 
2.4%
보광동 4
 
1.9%
남영동 3
 
1.4%
4층 3
 
1.4%
원효로1가 2
 
0.9%
Other values (71) 75
35.4%
2023-12-12T18:56:25.235998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210
17.9%
지 56
 
4.8%
1 51
 
4.3%
용 49
 
4.2%
산 49
 
4.2%
시 48
 
4.1%
번 47
 
4.0%
울 47
 
4.0%
서 47
 
4.0%
별 47
 
4.0%
Other values (60) 523
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 693
59.0%
Decimal Number 223
 
19.0%
Space Separator 210
 
17.9%
Dash Punctuation 43
 
3.7%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 56
 
8.1%
용 49
 
7.1%
산 49
 
7.1%
시 48
 
6.9%
번 47
 
6.8%
울 47
 
6.8%
서 47
 
6.8%
별 47
 
6.8%
구 47
 
6.8%
특 47
 
6.8%
Other values (43) 209
30.2%
Decimal Number
ValueCountFrequency (%)
1 51
22.9%
2 36
16.1%
3 28
12.6%
0 23
10.3%
6 22
9.9%
4 17
 
7.6%
7 15
 
6.7%
5 14
 
6.3%
9 9
 
4.0%
8 8
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 693
59.0%
Common 479
40.8%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 56
 
8.1%
용 49
 
7.1%
산 49
 
7.1%
시 48
 
6.9%
번 47
 
6.8%
울 47
 
6.8%
서 47
 
6.8%
별 47
 
6.8%
구 47
 
6.8%
특 47
 
6.8%
Other values (43) 209
30.2%
Common
ValueCountFrequency (%)
210
43.8%
1 51
 
10.6%
- 43
 
9.0%
2 36
 
7.5%
3 28
 
5.8%
0 23
 
4.8%
6 22
 
4.6%
4 17
 
3.5%
7 15
 
3.1%
5 14
 
2.9%
Other values (5) 20
 
4.2%
Latin
ValueCountFrequency (%)
S 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 693
59.0%
ASCII 481
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
43.7%
1 51
 
10.6%
- 43
 
8.9%
2 36
 
7.5%
3 28
 
5.8%
0 23
 
4.8%
6 22
 
4.6%
4 17
 
3.5%
7 15
 
3.1%
5 14
 
2.9%
Other values (7) 22
 
4.6%
Hangul
ValueCountFrequency (%)
지 56
 
8.1%
용 49
 
7.1%
산 49
 
7.1%
시 48
 
6.9%
번 47
 
6.8%
울 47
 
6.8%
서 47
 
6.8%
별 47
 
6.8%
구 47
 
6.8%
특 47
 
6.8%
Other values (43) 209
30.2%

시설전화번호
Text

MISSING 

Distinct38
Distinct (%)100.0%
Missing9
Missing (%)19.1%
Memory size508.0 B
2023-12-12T18:56:25.487234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.052632
Min length8

Characters and Unicode

Total characters420
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 (%)100.0%

Sample

1st row02-790-6776
2nd row02-777-3579
3rd row02-795-9966
4th row02-795-5511
5th row02-715-5588
ValueCountFrequency (%)
02-778-7496 1
 
2.6%
02-749-9690 1
 
2.6%
02-714-2011 1
 
2.6%
02-792-1031 1
 
2.6%
02-701-0011 1
 
2.6%
02-703-8378 1
 
2.6%
02-790-7175 1
 
2.6%
02-790-0801 1
 
2.6%
02-796-7978 1
 
2.6%
02-6431-0802 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T18:56:25.930352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 76
18.1%
- 74
17.6%
7 60
14.3%
2 51
12.1%
9 44
10.5%
1 25
 
6.0%
3 23
 
5.5%
6 20
 
4.8%
5 19
 
4.5%
4 16
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 346
82.4%
Dash Punctuation 74
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76
22.0%
7 60
17.3%
2 51
14.7%
9 44
12.7%
1 25
 
7.2%
3 23
 
6.6%
6 20
 
5.8%
5 19
 
5.5%
4 16
 
4.6%
8 12
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 420
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76
18.1%
- 74
17.6%
7 60
14.3%
2 51
12.1%
9 44
10.5%
1 25
 
6.0%
3 23
 
5.5%
6 20
 
4.8%
5 19
 
4.5%
4 16
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76
18.1%
- 74
17.6%
7 60
14.3%
2 51
12.1%
9 44
10.5%
1 25
 
6.0%
3 23
 
5.5%
6 20
 
4.8%
5 19
 
4.5%
4 16
 
3.8%

Interactions

2023-12-12T18:56:23.067881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:56:26.028853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종상호시설주소(지번)시설전화번호
업종1.0001.0001.0001.000
상호1.0001.0001.0001.000
시설주소(지번)1.0001.0001.0001.000
시설전화번호1.0001.0001.0001.000

Missing values

2023-12-12T18:56:23.209120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:56:23.304830image/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

업종상호시설주소(지번)시설전화번호
01우노 휘트니스클럽서울특별시 용산구 보광동 260-8번지 지상3층02-790-6776
12AK운동맞춤센터서울특별시 용산구 용산동2가 23번지02-777-3579
23웰니스짐서울특별시 용산구 이태원동 226-3번지 지하1층02-795-9966
34그랜드남여헬스크럽서울특별시 용산구 보광동 216-96번지02-795-5511
45동국스포츠서울특별시 용산구 원효로4가 142-1번지 2.3층02-715-5588
56한강헬스서울특별시 용산구 이촌동 300-15번지02-795-9333
67그램 휘트니스서울특별시 용산구 한남동 657-201번지02-794-6010
78J헬스클럽서울특별시 용산구 한남동 631-5번지 4층02-796-5176
89B&B 휘트니스서울특별시 용산구 이태원동 21-1번지 태광빌딩 207호02-794-5600
910스카이 휘트니스클럽서울특별시 용산구 남영동 127-1번지 3층02-797-3993
업종상호시설주소(지번)시설전화번호
3738레브트레이닝서울특별시 용산구 한남동 1-307번지02-796-9600
3839스포벡서울특별시 용산구 갈월동 101-45번지<NA>
3940와가짐서울특별시 용산구 이촌동 300-301번지070-4794-6591
4041시크모어 웰니스 스튜디오서울특별시 용산구 한남동 722-3번지 은성빌딩 2층27921710
4142패스트앤슬로우 컨설팅그룹서울특별시 용산구 갈월동 69-109번지<NA>
4243서울체대입시서울특별시 용산구 남영동 17-1번지02-797-6530
4344칼라퍼스널트레이닝서울특별시 용산구 원효로1가 39-10번지02-711-4013
4445랜스앤제이 퍼스널 트레이닝 스튜디오서울특별시 용산구 한남동 258번지02-749-7498
4546유즈웰(USEWELL)서울특별시 용산구 한남동 72-1번지02-749-2260
4647제이 스튜디오서울특별시 용산구 보광동 217-21번지02-792-8281