Overview

Dataset statistics

Number of variables5
Number of observations157
Missing cells60
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory40.8 B

Variable types

Categorical2
Text3

Dataset

Description대구광역시 서구의 체육시설업 현황을 나타내는 데이터 입니다. 체육시설업, 체육도장업, 무도학원업 등 업종별 상호와 주소 등의 데이터를 포함하고 있습니다.
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15089141/fileData.do

Alerts

시설전화번호 has 60 (38.2%) missing valuesMissing
도로명주소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 17:04:54.517964
Analysis finished2024-03-14 17:04:55.454932
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct8
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
체육도장업
45 
당구장업
42 
가상체험 체육시설업
31 
체력단련장업
22 
무도학원업
11 
Other values (3)
6 

Length

Max length10
Median length6
Mean length5.866242
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row수영장업
2nd row체육도장업
3rd row체육도장업
4th row체육도장업
5th row체육도장업

Common Values

ValueCountFrequency (%)
체육도장업 45
28.7%
당구장업 42
26.8%
가상체험 체육시설업 31
19.7%
체력단련장업 22
14.0%
무도학원업 11
 
7.0%
체육교습업 3
 
1.9%
골프연습장업 2
 
1.3%
수영장업 1
 
0.6%

Length

2024-03-15T02:04:55.691184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:04:56.053393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육도장업 45
23.9%
당구장업 42
22.3%
가상체험 31
16.5%
체육시설업 31
16.5%
체력단련장업 22
11.7%
무도학원업 11
 
5.9%
체육교습업 3
 
1.6%
골프연습장업 2
 
1.1%
수영장업 1
 
0.5%

상호
Text

Distinct151
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-15T02:04:57.154217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.0318471
Min length2

Characters and Unicode

Total characters1104
Distinct characters245
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

Unique145 ?
Unique (%)92.4%

Sample

1st row서구청소년수련관
2nd row꿈키즈태권도체육관
3rd row천하무적태권도
4th row상원태권스쿨
5th row영남대 삼성태권도
ValueCountFrequency (%)
계명대 4
 
2.1%
태권도장 3
 
1.6%
주식회사 3
 
1.6%
tbc당구클럽 2
 
1.1%
서대구골프클럽 2
 
1.1%
휘트니스 2
 
1.1%
정부석 2
 
1.1%
스크린골프 2
 
1.1%
당구클럽 2
 
1.1%
짐 2
 
1.1%
Other values (160) 164
87.2%
2024-03-15T02:04:58.676267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 54
 
4.9%
장 50
 
4.5%
스 48
 
4.3%
구 48
 
4.3%
당 39
 
3.5%
태 38
 
3.4%
권 37
 
3.4%
34
 
3.1%
골 31
 
2.8%
프 31
 
2.8%
Other values (235) 694
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 997
90.3%
Uppercase Letter 54
 
4.9%
Space Separator 34
 
3.1%
Decimal Number 5
 
0.5%
Open Punctuation 4
 
0.4%
Close Punctuation 4
 
0.4%
Other Punctuation 4
 
0.4%
Dash Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 54
 
5.4%
장 50
 
5.0%
스 48
 
4.8%
구 48
 
4.8%
당 39
 
3.9%
태 38
 
3.8%
권 37
 
3.7%
골 31
 
3.1%
프 31
 
3.1%
클 24
 
2.4%
Other values (204) 597
59.9%
Uppercase Letter
ValueCountFrequency (%)
B 6
 
11.1%
T 6
 
11.1%
C 5
 
9.3%
S 5
 
9.3%
K 4
 
7.4%
M 3
 
5.6%
A 3
 
5.6%
J 3
 
5.6%
O 2
 
3.7%
Y 2
 
3.7%
Other values (10) 15
27.8%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
/ 1
25.0%
& 1
25.0%
Decimal Number
ValueCountFrequency (%)
4 2
40.0%
2 2
40.0%
8 1
20.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 997
90.3%
Latin 55
 
5.0%
Common 52
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 54
 
5.4%
장 50
 
5.0%
스 48
 
4.8%
구 48
 
4.8%
당 39
 
3.9%
태 38
 
3.8%
권 37
 
3.7%
골 31
 
3.1%
프 31
 
3.1%
클 24
 
2.4%
Other values (204) 597
59.9%
Latin
ValueCountFrequency (%)
B 6
 
10.9%
T 6
 
10.9%
C 5
 
9.1%
S 5
 
9.1%
K 4
 
7.3%
M 3
 
5.5%
A 3
 
5.5%
J 3
 
5.5%
O 2
 
3.6%
Y 2
 
3.6%
Other values (11) 16
29.1%
Common
ValueCountFrequency (%)
34
65.4%
( 4
 
7.7%
) 4
 
7.7%
. 2
 
3.8%
4 2
 
3.8%
2 2
 
3.8%
/ 1
 
1.9%
- 1
 
1.9%
& 1
 
1.9%
8 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 997
90.3%
ASCII 107
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 54
 
5.4%
장 50
 
5.0%
스 48
 
4.8%
구 48
 
4.8%
당 39
 
3.9%
태 38
 
3.8%
권 37
 
3.7%
골 31
 
3.1%
프 31
 
3.1%
클 24
 
2.4%
Other values (204) 597
59.9%
ASCII
ValueCountFrequency (%)
34
31.8%
B 6
 
5.6%
T 6
 
5.6%
C 5
 
4.7%
S 5
 
4.7%
( 4
 
3.7%
) 4
 
3.7%
K 4
 
3.7%
M 3
 
2.8%
A 3
 
2.8%
Other values (21) 33
30.8%

도로명주소
Text

UNIQUE 

Distinct157
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-15T02:04:59.743315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length40
Mean length19.006369
Min length6

Characters and Unicode

Total characters2984
Distinct characters89
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

Unique157 ?
Unique (%)100.0%

Sample

1st row대구광역시 서구 문화로 122
2nd row달구벌대로 361길 25(내당동)2층
3rd row통학로 39(내당동) 홍실2차 아파트 상가 2층 201호
4th row달서로9길 36(내당동)2층
5th row달구벌대로 1707(내당동,광장1차아파트) 2동2층 7~8호
ValueCountFrequency (%)
서구 41
 
9.0%
대구광역시 33
 
7.2%
국채보상로 23
 
5.0%
서대구로 16
 
3.5%
달서로 11
 
2.4%
달구벌대로 10
 
2.2%
북비산로 8
 
1.8%
3층 8
 
1.8%
2층 8
 
1.8%
4층 7
 
1.5%
Other values (227) 291
63.8%
2024-03-15T02:05:01.198858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
310
 
10.4%
2 154
 
5.2%
로 153
 
5.1%
동 140
 
4.7%
층 130
 
4.4%
) 124
 
4.2%
( 124
 
4.2%
3 122
 
4.1%
구 120
 
4.0%
1 116
 
3.9%
Other values (79) 1491
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1617
54.2%
Decimal Number 740
24.8%
Space Separator 310
 
10.4%
Close Punctuation 124
 
4.2%
Open Punctuation 124
 
4.2%
Other Punctuation 48
 
1.6%
Dash Punctuation 15
 
0.5%
Math Symbol 5
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 153
 
9.5%
동 140
 
8.7%
층 130
 
8.0%
구 120
 
7.4%
서 96
 
5.9%
대 86
 
5.3%
리 75
 
4.6%
평 54
 
3.3%
길 52
 
3.2%
상 52
 
3.2%
Other values (62) 659
40.8%
Decimal Number
ValueCountFrequency (%)
2 154
20.8%
3 122
16.5%
1 116
15.7%
4 71
9.6%
5 67
9.1%
6 56
 
7.6%
0 50
 
6.8%
7 42
 
5.7%
8 32
 
4.3%
9 30
 
4.1%
Space Separator
ValueCountFrequency (%)
310
100.0%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 124
100.0%
Other Punctuation
ValueCountFrequency (%)
, 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1617
54.2%
Common 1366
45.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 153
 
9.5%
동 140
 
8.7%
층 130
 
8.0%
구 120
 
7.4%
서 96
 
5.9%
대 86
 
5.3%
리 75
 
4.6%
평 54
 
3.3%
길 52
 
3.2%
상 52
 
3.2%
Other values (62) 659
40.8%
Common
ValueCountFrequency (%)
310
22.7%
2 154
11.3%
) 124
 
9.1%
( 124
 
9.1%
3 122
 
8.9%
1 116
 
8.5%
4 71
 
5.2%
5 67
 
4.9%
6 56
 
4.1%
0 50
 
3.7%
Other values (6) 172
12.6%
Latin
ValueCountFrequency (%)
M 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1617
54.2%
ASCII 1367
45.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
310
22.7%
2 154
11.3%
) 124
 
9.1%
( 124
 
9.1%
3 122
 
8.9%
1 116
 
8.5%
4 71
 
5.2%
5 67
 
4.9%
6 56
 
4.1%
0 50
 
3.7%
Other values (7) 173
12.7%
Hangul
ValueCountFrequency (%)
로 153
 
9.5%
동 140
 
8.7%
층 130
 
8.0%
구 120
 
7.4%
서 96
 
5.9%
대 86
 
5.3%
리 75
 
4.6%
평 54
 
3.3%
길 52
 
3.2%
상 52
 
3.2%
Other values (62) 659
40.8%

행정동
Categorical

Distinct25
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
상중이동
28 
평리4동
16 
내당4동
15 
평리3동
14 
비산7동
12 
Other values (20)
72 

Length

Max length6
Median length4
Mean length4.089172
Min length3

Unique

Unique7 ?
Unique (%)4.5%

Sample

1st row이현동
2nd row내당1동
3rd row내당1동
4th row내당2.3동
5th row내당4동

Common Values

ValueCountFrequency (%)
상중이동 28
17.8%
평리4동 16
10.2%
내당4동 15
9.6%
평리3동 14
 
8.9%
비산7동 12
 
7.6%
내당1동 9
 
5.7%
원대동 8
 
5.1%
비산2.3동 8
 
5.1%
비산4동 7
 
4.5%
비산6동 5
 
3.2%
Other values (15) 35
22.3%

Length

2024-03-15T02:05:01.635959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상중이동 28
17.7%
내당4동 16
10.1%
평리4동 16
10.1%
평리3동 14
 
8.9%
비산7동 12
 
7.6%
내당1동 9
 
5.7%
원대동 8
 
5.1%
비산2.3동 8
 
5.1%
비산4동 7
 
4.4%
비산5동 5
 
3.2%
Other values (15) 35
22.2%

시설전화번호
Text

MISSING 

Distinct92
Distinct (%)94.8%
Missing60
Missing (%)38.2%
Memory size1.4 KiB
2024-03-15T02:05:02.628494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.041237
Min length12

Characters and Unicode

Total characters1168
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

Unique87 ?
Unique (%)89.7%

Sample

1st row053-572-2451
2nd row053-551-5520
3rd row053-522-5003
4th row053-523-7898
5th row053-557-8915
ValueCountFrequency (%)
053-341-0707 2
 
2.1%
053-352-5566 2
 
2.1%
053-552-2001 2
 
2.1%
053-552-0202 2
 
2.1%
053-567-0500 2
 
2.1%
053-761-0504 1
 
1.0%
053-566-0453 1
 
1.0%
053-566-6591 1
 
1.0%
053-355-0754 1
 
1.0%
053-354-9613 1
 
1.0%
Other values (82) 82
84.5%
2024-03-15T02:05:04.067835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 262
22.4%
- 194
16.6%
0 182
15.6%
3 155
13.3%
2 79
 
6.8%
6 74
 
6.3%
7 70
 
6.0%
1 51
 
4.4%
4 36
 
3.1%
9 34
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 974
83.4%
Dash Punctuation 194
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 262
26.9%
0 182
18.7%
3 155
15.9%
2 79
 
8.1%
6 74
 
7.6%
7 70
 
7.2%
1 51
 
5.2%
4 36
 
3.7%
9 34
 
3.5%
8 31
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 262
22.4%
- 194
16.6%
0 182
15.6%
3 155
13.3%
2 79
 
6.8%
6 74
 
6.3%
7 70
 
6.0%
1 51
 
4.4%
4 36
 
3.1%
9 34
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 262
22.4%
- 194
16.6%
0 182
15.6%
3 155
13.3%
2 79
 
6.8%
6 74
 
6.3%
7 70
 
6.0%
1 51
 
4.4%
4 36
 
3.1%
9 34
 
2.9%

Correlations

2024-03-15T02:05:04.331676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종행정동시설전화번호
업종1.0000.7440.000
행정동0.7441.0000.000
시설전화번호0.0000.0001.000
2024-03-15T02:05:04.570936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동업종
행정동1.0000.385
업종0.3851.000
2024-03-15T02:05:04.809717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종행정동
업종1.0000.385
행정동0.3851.000

Missing values

2024-03-15T02:04:55.015607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:04:55.330091image/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

업종상호도로명주소행정동시설전화번호
0수영장업서구청소년수련관대구광역시 서구 문화로 122이현동053-572-2451
1체육도장업꿈키즈태권도체육관달구벌대로 361길 25(내당동)2층내당1동053-551-5520
2체육도장업천하무적태권도통학로 39(내당동) 홍실2차 아파트 상가 2층 201호내당1동053-522-5003
3체육도장업상원태권스쿨달서로9길 36(내당동)2층내당2.3동053-523-7898
4체육도장업영남대 삼성태권도달구벌대로 1707(내당동,광장1차아파트) 2동2층 7~8호내당4동053-557-8915
5체육도장업서구 무극태권도장서대구로7길 23(내당동)3층내당4동053-572-7711
6체육도장업아리솔비산태권도장북비산로65길 22-2(비산동)2층비산1동053-552-3334
7체육도장업한성태권도달서로26길 9(비산동)2층비산2.3동053-567-7788
8체육도장업필승태권도국채보상로 420-52(비산동)4층비산4동053-572-6568
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