Overview

Dataset statistics

Number of variables3
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory982.0 B
Average record size in memory28.9 B

Variable types

Numeric1
Text2

Dataset

Description서울특별시 용산구 체육도장업 현황에 대한 데이터로 연번 체육도장업 상호명, 체육도장업 시설 주소에 대한 데이터 항목을 제공합니다
URLhttps://www.data.go.kr/data/3077843/fileData.do

Alerts

연번 has unique valuesUnique
상호 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:58:01.257563
Analysis finished2023-12-12 00:58:01.594089
Duration0.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.5
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T09:58:01.642964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.65
Q19.25
median17.5
Q325.75
95-th percentile32.35
Maximum34
Range33
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation9.9582462
Coefficient of variation (CV)0.56904264
Kurtosis-1.2
Mean17.5
Median Absolute Deviation (MAD)8.5
Skewness0
Sum595
Variance99.166667
MonotonicityStrictly increasing
2023-12-12T09:58:01.745097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 1
 
2.9%
27 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
28 1
 
2.9%
19 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%
25 1
2.9%

상호
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T09:58:01.925617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length13
Mean length8.2058824
Min length4

Characters and Unicode

Total characters279
Distinct characters109
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row청우태권도장
2nd row한국참피온
3rd row신동아체육관
4th row용산 제2체육관
5th row경희체육관
ValueCountFrequency (%)
태권도 5
 
7.8%
복싱 4
 
6.2%
경희대 2
 
3.1%
용산 2
 
3.1%
짐 2
 
3.1%
복싱짐 1
 
1.6%
보광점 1
 
1.6%
합기도 1
 
1.6%
천지관 1
 
1.6%
eclc 1
 
1.6%
Other values (44) 44
68.8%
2023-12-12T09:58:02.218917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
10.8%
도 17
 
6.1%
태 14
 
5.0%
권 13
 
4.7%
싱 10
 
3.6%
복 10
 
3.6%
관 8
 
2.9%
체 6
 
2.2%
육 6
 
2.2%
대 6
 
2.2%
Other values (99) 159
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 208
74.6%
Space Separator 30
 
10.8%
Lowercase Letter 24
 
8.6%
Uppercase Letter 14
 
5.0%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 17
 
8.2%
태 14
 
6.7%
권 13
 
6.2%
싱 10
 
4.8%
복 10
 
4.8%
관 8
 
3.8%
체 6
 
2.9%
육 6
 
2.9%
대 6
 
2.9%
짐 5
 
2.4%
Other values (73) 113
54.3%
Lowercase Letter
ValueCountFrequency (%)
i 3
12.5%
t 3
12.5%
y 3
12.5%
e 3
12.5%
n 2
8.3%
g 2
8.3%
c 2
8.3%
v 1
 
4.2%
r 1
 
4.2%
m 1
 
4.2%
Other values (3) 3
12.5%
Uppercase Letter
ValueCountFrequency (%)
C 3
21.4%
M 2
14.3%
A 2
14.3%
L 2
14.3%
E 1
 
7.1%
G 1
 
7.1%
B 1
 
7.1%
S 1
 
7.1%
W 1
 
7.1%
Space Separator
ValueCountFrequency (%)
30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
74.6%
Latin 38
 
13.6%
Common 33
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 17
 
8.2%
태 14
 
6.7%
권 13
 
6.2%
싱 10
 
4.8%
복 10
 
4.8%
관 8
 
3.8%
체 6
 
2.9%
육 6
 
2.9%
대 6
 
2.9%
짐 5
 
2.4%
Other values (73) 113
54.3%
Latin
ValueCountFrequency (%)
i 3
 
7.9%
t 3
 
7.9%
C 3
 
7.9%
y 3
 
7.9%
e 3
 
7.9%
M 2
 
5.3%
A 2
 
5.3%
n 2
 
5.3%
L 2
 
5.3%
g 2
 
5.3%
Other values (12) 13
34.2%
Common
ValueCountFrequency (%)
30
90.9%
) 1
 
3.0%
( 1
 
3.0%
2 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
74.6%
ASCII 71
 
25.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
42.3%
i 3
 
4.2%
t 3
 
4.2%
C 3
 
4.2%
y 3
 
4.2%
e 3
 
4.2%
M 2
 
2.8%
A 2
 
2.8%
n 2
 
2.8%
L 2
 
2.8%
Other values (16) 18
25.4%
Hangul
ValueCountFrequency (%)
도 17
 
8.2%
태 14
 
6.7%
권 13
 
6.2%
싱 10
 
4.8%
복 10
 
4.8%
관 8
 
3.8%
체 6
 
2.9%
육 6
 
2.9%
대 6
 
2.9%
짐 5
 
2.4%
Other values (73) 113
54.3%

소재지
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T09:58:02.422354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length31
Mean length24.176471
Min length19

Characters and Unicode

Total characters822
Distinct characters85
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

Unique34 ?
Unique (%)100.0%

Sample

1st row서울특별시 용산구 청파동2가 90-39
2nd row서울특별시 용산구 용문동 42-27
3rd row서울특별시 용산구 서빙고동 241-94 95
4th row서울특별시 용산구 동빙고동 1-9 지상2층
5th row서울특별시 용산구 이촌동 193-4
ValueCountFrequency (%)
서울특별시 34
20.9%
용산구 34
20.9%
이촌동 4
 
2.5%
2층 3
 
1.8%
효창동 3
 
1.8%
지하1층 2
 
1.2%
보광동 2
 
1.2%
3층 2
 
1.2%
갈월동 2
 
1.2%
동빙고동 2
 
1.2%
Other values (71) 75
46.0%
2023-12-12T09:58:02.740195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
161
19.6%
용 37
 
4.5%
산 37
 
4.5%
1 37
 
4.5%
서 36
 
4.4%
시 35
 
4.3%
특 34
 
4.1%
별 34
 
4.1%
구 34
 
4.1%
울 34
 
4.1%
Other values (75) 343
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 457
55.6%
Decimal Number 169
 
20.6%
Space Separator 161
 
19.6%
Dash Punctuation 30
 
3.6%
Uppercase Letter 2
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 37
 
8.1%
산 37
 
8.1%
서 36
 
7.9%
시 35
 
7.7%
특 34
 
7.4%
별 34
 
7.4%
구 34
 
7.4%
울 34
 
7.4%
동 30
 
6.6%
층 13
 
2.8%
Other values (58) 133
29.1%
Decimal Number
ValueCountFrequency (%)
1 37
21.9%
2 28
16.6%
3 23
13.6%
5 18
10.7%
4 14
 
8.3%
0 13
 
7.7%
9 12
 
7.1%
6 11
 
6.5%
7 8
 
4.7%
8 5
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
161
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 457
55.6%
Common 363
44.2%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 37
 
8.1%
산 37
 
8.1%
서 36
 
7.9%
시 35
 
7.7%
특 34
 
7.4%
별 34
 
7.4%
구 34
 
7.4%
울 34
 
7.4%
동 30
 
6.6%
층 13
 
2.8%
Other values (58) 133
29.1%
Common
ValueCountFrequency (%)
161
44.4%
1 37
 
10.2%
- 30
 
8.3%
2 28
 
7.7%
3 23
 
6.3%
5 18
 
5.0%
4 14
 
3.9%
0 13
 
3.6%
9 12
 
3.3%
6 11
 
3.0%
Other values (5) 16
 
4.4%
Latin
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 457
55.6%
ASCII 365
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
161
44.1%
1 37
 
10.1%
- 30
 
8.2%
2 28
 
7.7%
3 23
 
6.3%
5 18
 
4.9%
4 14
 
3.8%
0 13
 
3.6%
9 12
 
3.3%
6 11
 
3.0%
Other values (7) 18
 
4.9%
Hangul
ValueCountFrequency (%)
용 37
 
8.1%
산 37
 
8.1%
서 36
 
7.9%
시 35
 
7.7%
특 34
 
7.4%
별 34
 
7.4%
구 34
 
7.4%
울 34
 
7.4%
동 30
 
6.6%
층 13
 
2.8%
Other values (58) 133
29.1%

Interactions

2023-12-12T09:58:01.411134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:58:02.822111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호소재지
연번1.0001.0001.000
상호1.0001.0001.000
소재지1.0001.0001.000

Missing values

2023-12-12T09:58:01.517067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:58:01.572093image/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

연번상호소재지
01청우태권도장서울특별시 용산구 청파동2가 90-39
12한국참피온서울특별시 용산구 용문동 42-27
23신동아체육관서울특별시 용산구 서빙고동 241-94 95
34용산 제2체육관서울특별시 용산구 동빙고동 1-9 지상2층
45경희체육관서울특별시 용산구 이촌동 193-4
56태비원도체육관서울특별시 용산구 이촌동 301-154 지상3층
67정무 태권도서울특별시 용산구 효창동 5-31
78조인주복싱교실서울특별시 용산구 원효로2가 91-5
89경희대 효 태권도서울특별시 용산구 도원동 28 삼성래미안상가 4층 401,409호
910레거시 복싱짐(Legacy Boxing Gym)서울특별시 용산구 한남동 621-1
연번상호소재지
2425비엔에스 복싱 동자점서울특별시 용산구 동자동 19-33 여명빌딩
2526샷건 복싱 짐서울특별시 용산구 한강로2가 84-7
2627비앤에스 복싱짐 용산역점서울특별시 용산구 한강로3가 40-171 삼정빌딩 지하1층
2728풍산체육관서울특별시 용산구 갈월동 69-65
2829ECLC Activity Center서울특별시 용산구 동빙고동 7-14 지하1층
2930합기도 천지관서울특별시 용산구 효창동 5-530 지하층
3031한남권투 보광점서울특별시 용산구 보광동 218 신호찜질사우나
3132코리아 유도 짐서울특별시 용산구 이태원동 562
3233WS복싱클럽서울특별시 용산구 서계동 266-73 힐타워 8층
3334효창복싱서울특별시 용산구 원효로2가 3-15 2층