Overview

Dataset statistics

Number of variables3
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory26.6 B

Variable types

Numeric1
Text2

Dataset

Description서울특별시 용산구 헬스장 현황에 대한 데이터로 연번, 헬스장 상호명, 헬스장 소재지(지번)에 대한 데이터 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15074334/fileData.do

Alerts

연번 has unique valuesUnique
상호명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:15:47.439330
Analysis finished2023-12-11 23:15:47.894509
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T08:15:48.184324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.95
Q120.75
median40.5
Q360.25
95-th percentile76.05
Maximum80
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.2379
Coefficient of variation (CV)0.57377531
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20
Skewness0
Sum3240
Variance540
MonotonicityStrictly increasing
2023-12-12T08:15:48.329373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
42 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 (70) 70
87.5%
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 (%)
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%
71 1
1.2%

상호명
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-12T08:15:48.625741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length13
Mean length7.8375
Min length2

Characters and Unicode

Total characters627
Distinct characters176
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

Unique80 ?
Unique (%)100.0%

Sample

1st row우노 휘트니스클럽
2nd rowAK운동맞춤센터
3rd row웰니스짐
4th row동국스포츠
5th row리콥 웰니스센터
ValueCountFrequency (%)
fit 3
 
2.3%
한남 3
 
2.3%
웰니스 3
 
2.3%
스튜디오 3
 
2.3%
크로스핏 2
 
1.5%
휘트니스클럽 2
 
1.5%
리콥 2
 
1.5%
스포짐 2
 
1.5%
트레이닝 2
 
1.5%
짐나우 2
 
1.5%
Other values (104) 106
81.5%
2023-12-12T08:15:49.081730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
 
8.0%
스 47
 
7.5%
이 23
 
3.7%
트 23
 
3.7%
짐 23
 
3.7%
니 15
 
2.4%
레 12
 
1.9%
닝 10
 
1.6%
휘 9
 
1.4%
t 9
 
1.4%
Other values (166) 406
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 419
66.8%
Uppercase Letter 77
 
12.3%
Lowercase Letter 63
 
10.0%
Space Separator 50
 
8.0%
Decimal Number 6
 
1.0%
Open Punctuation 5
 
0.8%
Close Punctuation 5
 
0.8%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 47
 
11.2%
이 23
 
5.5%
트 23
 
5.5%
짐 23
 
5.5%
니 15
 
3.6%
레 12
 
2.9%
닝 10
 
2.4%
휘 9
 
2.1%
핏 8
 
1.9%
널 7
 
1.7%
Other values (118) 242
57.8%
Uppercase Letter
ValueCountFrequency (%)
E 8
 
10.4%
M 7
 
9.1%
F 6
 
7.8%
N 6
 
7.8%
C 5
 
6.5%
T 5
 
6.5%
S 4
 
5.2%
G 4
 
5.2%
I 3
 
3.9%
A 3
 
3.9%
Other values (13) 26
33.8%
Lowercase Letter
ValueCountFrequency (%)
t 9
14.3%
n 9
14.3%
i 7
11.1%
a 6
9.5%
e 5
7.9%
l 5
7.9%
o 5
7.9%
u 5
7.9%
s 4
6.3%
r 2
 
3.2%
Other values (6) 6
9.5%
Decimal Number
ValueCountFrequency (%)
4 2
33.3%
1 2
33.3%
5 1
16.7%
2 1
16.7%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 419
66.8%
Latin 140
 
22.3%
Common 68
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 47
 
11.2%
이 23
 
5.5%
트 23
 
5.5%
짐 23
 
5.5%
니 15
 
3.6%
레 12
 
2.9%
닝 10
 
2.4%
휘 9
 
2.1%
핏 8
 
1.9%
널 7
 
1.7%
Other values (118) 242
57.8%
Latin
ValueCountFrequency (%)
t 9
 
6.4%
n 9
 
6.4%
E 8
 
5.7%
i 7
 
5.0%
M 7
 
5.0%
a 6
 
4.3%
F 6
 
4.3%
N 6
 
4.3%
e 5
 
3.6%
l 5
 
3.6%
Other values (29) 72
51.4%
Common
ValueCountFrequency (%)
50
73.5%
( 5
 
7.4%
) 5
 
7.4%
4 2
 
2.9%
1 2
 
2.9%
5 1
 
1.5%
' 1
 
1.5%
2 1
 
1.5%
& 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 419
66.8%
ASCII 208
33.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
24.0%
t 9
 
4.3%
n 9
 
4.3%
E 8
 
3.8%
i 7
 
3.4%
M 7
 
3.4%
a 6
 
2.9%
F 6
 
2.9%
N 6
 
2.9%
e 5
 
2.4%
Other values (38) 95
45.7%
Hangul
ValueCountFrequency (%)
스 47
 
11.2%
이 23
 
5.5%
트 23
 
5.5%
짐 23
 
5.5%
니 15
 
3.6%
레 12
 
2.9%
닝 10
 
2.4%
휘 9
 
2.1%
핏 8
 
1.9%
널 7
 
1.7%
Other values (118) 242
57.8%

소재지(지번)
Text

Distinct77
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-12T08:15:49.408465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length31
Mean length24.7
Min length17

Characters and Unicode

Total characters1976
Distinct characters122
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

Unique75 ?
Unique (%)93.8%

Sample

1st row서울특별시 용산구 보광동 260-8 지상3층
2nd row서울특별시 용산구 용산동2가 23
3rd row서울특별시 용산구 이태원동 226-3 지하1층
4th row서울특별시 용산구 원효로4가 142-1 2.3층
5th row서울특별시 용산구 한남동 657-201
ValueCountFrequency (%)
서울특별시 80
20.8%
용산구 80
20.8%
한남동 26
 
6.8%
이태원동 8
 
2.1%
이촌동 7
 
1.8%
지하1층 6
 
1.6%
한강로3가 6
 
1.6%
원효로1가 5
 
1.3%
4층 5
 
1.3%
남영동 5
 
1.3%
Other values (134) 157
40.8%
2023-12-12T08:15:49.869554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
380
19.2%
1 91
 
4.6%
용 87
 
4.4%
산 86
 
4.4%
서 83
 
4.2%
시 83
 
4.2%
울 81
 
4.1%
구 80
 
4.0%
별 80
 
4.0%
특 80
 
4.0%
Other values (112) 845
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1107
56.0%
Decimal Number 393
 
19.9%
Space Separator 380
 
19.2%
Dash Punctuation 73
 
3.7%
Uppercase Letter 14
 
0.7%
Other Punctuation 5
 
0.3%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 87
 
7.9%
산 86
 
7.8%
서 83
 
7.5%
시 83
 
7.5%
울 81
 
7.3%
구 80
 
7.2%
별 80
 
7.2%
특 80
 
7.2%
동 72
 
6.5%
한 37
 
3.3%
Other values (85) 338
30.5%
Decimal Number
ValueCountFrequency (%)
1 91
23.2%
3 58
14.8%
2 51
13.0%
0 43
10.9%
4 36
 
9.2%
6 35
 
8.9%
7 28
 
7.1%
5 22
 
5.6%
8 18
 
4.6%
9 11
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
I 3
21.4%
B 2
14.3%
G 2
14.3%
A 1
 
7.1%
V 1
 
7.1%
S 1
 
7.1%
L 1
 
7.1%
T 1
 
7.1%
H 1
 
7.1%
E 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 2
40.0%
, 2
40.0%
: 1
20.0%
Space Separator
ValueCountFrequency (%)
380
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1107
56.0%
Common 855
43.3%
Latin 14
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 87
 
7.9%
산 86
 
7.8%
서 83
 
7.5%
시 83
 
7.5%
울 81
 
7.3%
구 80
 
7.2%
별 80
 
7.2%
특 80
 
7.2%
동 72
 
6.5%
한 37
 
3.3%
Other values (85) 338
30.5%
Common
ValueCountFrequency (%)
380
44.4%
1 91
 
10.6%
- 73
 
8.5%
3 58
 
6.8%
2 51
 
6.0%
0 43
 
5.0%
4 36
 
4.2%
6 35
 
4.1%
7 28
 
3.3%
5 22
 
2.6%
Other values (7) 38
 
4.4%
Latin
ValueCountFrequency (%)
I 3
21.4%
B 2
14.3%
G 2
14.3%
A 1
 
7.1%
V 1
 
7.1%
S 1
 
7.1%
L 1
 
7.1%
T 1
 
7.1%
H 1
 
7.1%
E 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1107
56.0%
ASCII 869
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
380
43.7%
1 91
 
10.5%
- 73
 
8.4%
3 58
 
6.7%
2 51
 
5.9%
0 43
 
4.9%
4 36
 
4.1%
6 35
 
4.0%
7 28
 
3.2%
5 22
 
2.5%
Other values (17) 52
 
6.0%
Hangul
ValueCountFrequency (%)
용 87
 
7.9%
산 86
 
7.8%
서 83
 
7.5%
시 83
 
7.5%
울 81
 
7.3%
구 80
 
7.2%
별 80
 
7.2%
특 80
 
7.2%
동 72
 
6.5%
한 37
 
3.3%
Other values (85) 338
30.5%

Interactions

2023-12-12T08:15:47.660381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:15:49.961357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명소재지(지번)
연번1.0001.0000.983
상호명1.0001.0001.000
소재지(지번)0.9831.0001.000

Missing values

2023-12-12T08:15:47.794421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:15:47.866394image/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

연번상호명소재지(지번)
01우노 휘트니스클럽서울특별시 용산구 보광동 260-8 지상3층
12AK운동맞춤센터서울특별시 용산구 용산동2가 23
23웰니스짐서울특별시 용산구 이태원동 226-3 지하1층
34동국스포츠서울특별시 용산구 원효로4가 142-1 2.3층
45리콥 웰니스센터서울특별시 용산구 한남동 657-201
56J헬스클럽서울특별시 용산구 한남동 631-5 4층
67스카이 휘트니스클럽서울특별시 용산구 남영동 127-1 2층,3층
78해밀톤 휘트니스센터서울특별시 용산구 이태원동 116-1 지하2층
89드래곤힐스파휘트니스클럽서울특별시 용산구 한강로3가 40-713 4층
910원짐서울특별시 용산구 한강로3가 16-85 GS한강에클라트
연번상호명소재지(지번)
7071이태원짐서울특별시 용산구 이태원동 183-1 동호프라자.외환은행
7172식스에이엠서울특별시 용산구 한남동 794-7 B1호
7273원(1)GYM서울특별시 용산구 한강로3가 16-85 지에스 한강에클라트 104호
7374크로스핏 남산서울특별시 용산구 남영동 114-15 17-17
7475바른핏서울특별시 용산구 원효로1가 133-3 리첸시아 용산 A동 210호
7576텐세그리티서울특별시 용산구 후암동 358-17 대원정사 본관 201호
7677MCT GYM 용산서울특별시 용산구 청파동3가 80-6 지층 B01호
7778드래곤짐서울특별시 용산구 한남동 107-4
7879상떼(Sante')서울특별시 용산구 이촌동 302-81
7980스카이짐 GDR골프아카데미 헬스서울특별시 용산구 갈월동 92 용산빌딩