Overview

Dataset statistics

Number of variables5
Number of observations133
Missing cells96
Missing cells (%)14.4%
Duplicate rows2
Duplicate rows (%)1.5%
Total size in memory5.3 KiB
Average record size in memory41.0 B

Variable types

Categorical1
Text3
DateTime1

Dataset

Description대구광역시 남구 관내 생활체육 시설 및 프로그램 등 현황에 대한 데이터로 (시설종류명, 체육시설명, 주소, 전화번호)를 제공합니다.
Author대구광역시 남구
URLhttps://www.data.go.kr/data/3072394/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 2 (1.5%) duplicate rowsDuplicates
상호 has 7 (5.3%) missing valuesMissing
주소 has 7 (5.3%) missing valuesMissing
전화번호 has 75 (56.4%) missing valuesMissing
데이터기준일자 has 7 (5.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 18:53:26.112618
Analysis finished2023-12-12 18:53:27.255490
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct10
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
체육도장업
34 
체력단련장업
31 
당구장업
27 
골프연습장업
11 
무도학원업
10 
Other values (5)
20 

Length

Max length10
Median length6
Mean length5.1954887
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
체육도장업 34
25.6%
체력단련장업 31
23.3%
당구장업 27
20.3%
골프연습장업 11
 
8.3%
무도학원업 10
 
7.5%
<NA> 8
 
6.0%
체육교습업 5
 
3.8%
가상체험 체육시설업 4
 
3.0%
수영장업 2
 
1.5%
인공암벽장업 1
 
0.8%

Length

2023-12-13T03:53:27.381927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:27.618201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육도장업 34
24.8%
체력단련장업 31
22.6%
당구장업 27
19.7%
골프연습장업 11
 
8.0%
무도학원업 10
 
7.3%
na 8
 
5.8%
체육교습업 5
 
3.6%
가상체험 4
 
2.9%
체육시설업 4
 
2.9%
수영장업 2
 
1.5%

상호
Text

MISSING 

Distinct124
Distinct (%)98.4%
Missing7
Missing (%)5.3%
Memory size1.2 KiB
2023-12-13T03:53:28.143050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length7.0555556
Min length2

Characters and Unicode

Total characters889
Distinct characters236
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

Unique123 ?
Unique (%)97.6%

Sample

1st row홈스파월드 수영장
2nd row광양커뮤니티(주) 대봉생활체육센터 수영장
3rd row상록수태권도
4th row리2태권도장
5th row용인대정진유도교실
ValueCountFrequency (%)
짐 4
 
2.4%
당구클럽 4
 
2.4%
태권도장 4
 
2.4%
tbc당구클럽 3
 
1.8%
앞산 2
 
1.2%
당구장 2
 
1.2%
tbc 2
 
1.2%
대명점 2
 
1.2%
무도학원 2
 
1.2%
휘트니스 2
 
1.2%
Other values (138) 143
84.1%
2023-12-13T03:53:28.975984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
 
4.9%
스 40
 
4.5%
도 40
 
4.5%
장 31
 
3.5%
구 31
 
3.5%
당 28
 
3.1%
권 26
 
2.9%
태 25
 
2.8%
클 20
 
2.2%
럽 17
 
1.9%
Other values (226) 587
66.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 799
89.9%
Space Separator 44
 
4.9%
Uppercase Letter 37
 
4.2%
Decimal Number 4
 
0.4%
Other Punctuation 2
 
0.2%
Close Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 40
 
5.0%
도 40
 
5.0%
장 31
 
3.9%
구 31
 
3.9%
당 28
 
3.5%
권 26
 
3.3%
태 25
 
3.1%
클 20
 
2.5%
럽 17
 
2.1%
대 16
 
2.0%
Other values (202) 525
65.7%
Uppercase Letter
ValueCountFrequency (%)
T 7
18.9%
C 6
16.2%
B 5
13.5%
R 3
8.1%
Y 3
8.1%
P 2
 
5.4%
M 2
 
5.4%
I 2
 
5.4%
G 1
 
2.7%
E 1
 
2.7%
Other values (5) 5
13.5%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
4 1
25.0%
5 1
25.0%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
' 1
50.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 799
89.9%
Common 52
 
5.8%
Latin 38
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 40
 
5.0%
도 40
 
5.0%
장 31
 
3.9%
구 31
 
3.9%
당 28
 
3.5%
권 26
 
3.3%
태 25
 
3.1%
클 20
 
2.5%
럽 17
 
2.1%
대 16
 
2.0%
Other values (202) 525
65.7%
Latin
ValueCountFrequency (%)
T 7
18.4%
C 6
15.8%
B 5
13.2%
R 3
7.9%
Y 3
7.9%
P 2
 
5.3%
M 2
 
5.3%
I 2
 
5.3%
G 1
 
2.6%
E 1
 
2.6%
Other values (6) 6
15.8%
Common
ValueCountFrequency (%)
44
84.6%
2 2
 
3.8%
) 1
 
1.9%
4 1
 
1.9%
# 1
 
1.9%
5 1
 
1.9%
( 1
 
1.9%
' 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 799
89.9%
ASCII 90
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
48.9%
T 7
 
7.8%
C 6
 
6.7%
B 5
 
5.6%
R 3
 
3.3%
Y 3
 
3.3%
P 2
 
2.2%
M 2
 
2.2%
2 2
 
2.2%
I 2
 
2.2%
Other values (14) 14
 
15.6%
Hangul
ValueCountFrequency (%)
스 40
 
5.0%
도 40
 
5.0%
장 31
 
3.9%
구 31
 
3.9%
당 28
 
3.5%
권 26
 
3.3%
태 25
 
3.1%
클 20
 
2.5%
럽 17
 
2.1%
대 16
 
2.0%
Other values (202) 525
65.7%

주소
Text

MISSING 

Distinct121
Distinct (%)96.0%
Missing7
Missing (%)5.3%
Memory size1.2 KiB
2023-12-13T03:53:29.544053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length46
Mean length26.428571
Min length21

Characters and Unicode

Total characters3330
Distinct characters99
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

Unique116 ?
Unique (%)92.1%

Sample

1st row대구광역시 남구 앞산순환로 651 (봉덕동, 홈스파월드)
2nd row대구광역시 남구 대봉로26길 33 (이천동)
3rd row대구광역시 남구 양지로 12, 3층 (대명동)
4th row대구광역시 남구 효성로 77 (봉덕동)
5th row대구광역시 남구 효성로 56 (봉덕동)
ValueCountFrequency (%)
대구광역시 126
17.7%
남구 126
17.7%
대명동 67
 
9.4%
봉덕동 39
 
5.5%
2층 28
 
3.9%
대명로 18
 
2.5%
현충로 13
 
1.8%
3층 10
 
1.4%
이천로 9
 
1.3%
앞산순환로 8
 
1.1%
Other values (182) 267
37.6%
2023-12-13T03:53:30.361989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
596
17.9%
구 253
 
7.6%
대 248
 
7.4%
( 133
 
4.0%
) 133
 
4.0%
동 133
 
4.0%
시 131
 
3.9%
역 128
 
3.8%
남 127
 
3.8%
광 127
 
3.8%
Other values (89) 1321
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1906
57.2%
Space Separator 596
 
17.9%
Decimal Number 464
 
13.9%
Open Punctuation 133
 
4.0%
Close Punctuation 133
 
4.0%
Other Punctuation 85
 
2.6%
Dash Punctuation 10
 
0.3%
Math Symbol 2
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 253
13.3%
대 248
13.0%
동 133
 
7.0%
시 131
 
6.9%
역 128
 
6.7%
남 127
 
6.7%
광 127
 
6.7%
로 114
 
6.0%
명 113
 
5.9%
층 70
 
3.7%
Other values (72) 462
24.2%
Decimal Number
ValueCountFrequency (%)
2 100
21.6%
1 95
20.5%
3 59
12.7%
6 50
10.8%
4 35
 
7.5%
5 34
 
7.3%
0 29
 
6.2%
8 22
 
4.7%
9 20
 
4.3%
7 20
 
4.3%
Space Separator
ValueCountFrequency (%)
596
100.0%
Open Punctuation
ValueCountFrequency (%)
( 133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Other Punctuation
ValueCountFrequency (%)
, 85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1906
57.2%
Common 1423
42.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 253
13.3%
대 248
13.0%
동 133
 
7.0%
시 131
 
6.9%
역 128
 
6.7%
남 127
 
6.7%
광 127
 
6.7%
로 114
 
6.0%
명 113
 
5.9%
층 70
 
3.7%
Other values (72) 462
24.2%
Common
ValueCountFrequency (%)
596
41.9%
( 133
 
9.3%
) 133
 
9.3%
2 100
 
7.0%
1 95
 
6.7%
, 85
 
6.0%
3 59
 
4.1%
6 50
 
3.5%
4 35
 
2.5%
5 34
 
2.4%
Other values (6) 103
 
7.2%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1906
57.2%
ASCII 1424
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
596
41.9%
( 133
 
9.3%
) 133
 
9.3%
2 100
 
7.0%
1 95
 
6.7%
, 85
 
6.0%
3 59
 
4.1%
6 50
 
3.5%
4 35
 
2.5%
5 34
 
2.4%
Other values (7) 104
 
7.3%
Hangul
ValueCountFrequency (%)
구 253
13.3%
대 248
13.0%
동 133
 
7.0%
시 131
 
6.9%
역 128
 
6.7%
남 127
 
6.7%
광 127
 
6.7%
로 114
 
6.0%
명 113
 
5.9%
층 70
 
3.7%
Other values (72) 462
24.2%

전화번호
Text

MISSING 

Distinct57
Distinct (%)98.3%
Missing75
Missing (%)56.4%
Memory size1.2 KiB
2023-12-13T03:53:30.745698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters696
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

Unique56 ?
Unique (%)96.6%

Sample

1st row053-470-1117
2nd row053-521-7666
3rd row053-472-5236
4th row053-473-3292
5th row053-626-8713
ValueCountFrequency (%)
053-470-1100 2
 
3.4%
053-473-7575 1
 
1.7%
053-470-1117 1
 
1.7%
053-472-2070 1
 
1.7%
053-626-0707 1
 
1.7%
053-65-70753 1
 
1.7%
053-624-1065 1
 
1.7%
053-628-0568 1
 
1.7%
053-761-3969 1
 
1.7%
053-655-1227 1
 
1.7%
Other values (47) 47
81.0%
2023-12-13T03:53:31.355520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 116
16.7%
5 110
15.8%
0 101
14.5%
3 90
12.9%
6 67
9.6%
7 50
7.2%
2 48
6.9%
4 45
 
6.5%
1 33
 
4.7%
8 21
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 580
83.3%
Dash Punctuation 116
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 110
19.0%
0 101
17.4%
3 90
15.5%
6 67
11.6%
7 50
8.6%
2 48
8.3%
4 45
7.8%
1 33
 
5.7%
8 21
 
3.6%
9 15
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 116
16.7%
5 110
15.8%
0 101
14.5%
3 90
12.9%
6 67
9.6%
7 50
7.2%
2 48
6.9%
4 45
 
6.5%
1 33
 
4.7%
8 21
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 116
16.7%
5 110
15.8%
0 101
14.5%
3 90
12.9%
6 67
9.6%
7 50
7.2%
2 48
6.9%
4 45
 
6.5%
1 33
 
4.7%
8 21
 
3.0%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)0.8%
Missing7
Missing (%)5.3%
Memory size1.2 KiB
Minimum2023-11-03 00:00:00
Maximum2023-11-03 00:00:00
2023-12-13T03:53:31.583137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:31.765998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2023-12-13T03:53:31.900261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종전화번호
업종1.0000.969
전화번호0.9691.000

Missing values

2023-12-13T03:53:26.759134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:53:26.920312image/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.
2023-12-13T03:53:27.129763image/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

업종상호주소전화번호데이터기준일자
0수영장업홈스파월드 수영장대구광역시 남구 앞산순환로 651 (봉덕동, 홈스파월드)053-470-11172023-11-03
1수영장업광양커뮤니티(주) 대봉생활체육센터 수영장대구광역시 남구 대봉로26길 33 (이천동)053-521-76662023-11-03
2체육도장업상록수태권도대구광역시 남구 양지로 12, 3층 (대명동)<NA>2023-11-03
3체육도장업리2태권도장대구광역시 남구 효성로 77 (봉덕동)053-472-52362023-11-03
4체육도장업용인대정진유도교실대구광역시 남구 효성로 56 (봉덕동)053-473-32922023-11-03
5체육도장업태권도승리5체육관대구광역시 남구 성당로34길 42 (대명동)053-626-87132023-11-03
6체육도장업태백태권도대구광역시 남구 대봉로 87 (봉덕동)<NA>2023-11-03
7체육도장업부부석사승리태권도스쿨대구광역시 남구 대명로20길 31 (대명동)053-621-35002023-11-03
8체육도장업리 태권도장대구광역시 남구 효성로 24 (봉덕동,3층)053-471-88042023-11-03
9체육도장업드림 태권도장대구광역시 남구 대봉로30길 15, 301동 2층 206호 (이천동, 대봉교역 태왕아너스)<NA>2023-11-03
업종상호주소전화번호데이터기준일자
123체육교습업대구 중구 리틀야구단대구광역시 남구 대명로53길 22-8, 지하 1층 (대명동)<NA>2023-11-03
124체육교습업팀맥스스포츠아카데미대구광역시 남구 앞산순환로 669 (봉덕동)<NA>2023-11-03
125인공암벽장업핸즈클라이밍대구광역시 남구 이천로 34, 3층 (봉덕동)<NA>2023-11-03
126<NA><NA><NA><NA><NA>
127<NA><NA><NA><NA><NA>
128<NA><NA><NA><NA><NA>
129<NA><NA><NA><NA><NA>
130<NA><NA><NA><NA><NA>
131<NA><NA><NA><NA><NA>
132<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

업종상호주소전화번호데이터기준일자# duplicates
1<NA><NA><NA><NA><NA>7
0당구장업TBC당구클럽대구광역시 남구 이천로 28, 4층 (봉덕동)<NA>2023-11-032