Overview

Dataset statistics

Number of variables5
Number of observations164
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory40.8 B

Variable types

Text4
Categorical1

Dataset

Description대구광역시_수성구 모범음식점 현황_20191227
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15054680&dataSetDetailId=150546801f04b2b748ae0&provdMethod=FILE

Alerts

소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2024-04-20 18:33:37.071715
Analysis finished2024-04-20 18:33:38.146777
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

Distinct163
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-21T03:33:38.888754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length13
Mean length5.9512195
Min length2

Characters and Unicode

Total characters976
Distinct characters273
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

Unique162 ?
Unique (%)98.8%

Sample

1st row예전애 서울칡냉면
2nd row뉴욕 바닷가재 대구본점(New York Lobster)
3rd row도토리흑돼지
4th row금산삼계탕본점
5th row서태후중화요리전문점
ValueCountFrequency (%)
삼수장어 2
 
1.1%
폴인샤브시지점 1
 
0.5%
본죽 1
 
0.5%
수성구청점 1
 
0.5%
본죽중동교점 1
 
0.5%
신안동갈비 1
 
0.5%
자성화맛집코다리네대구점 1
 
0.5%
편대장영화식당대구범어점 1
 
0.5%
복어잡는사람들(황금점 1
 
0.5%
유자 1
 
0.5%
Other values (171) 171
94.0%
2024-04-21T03:33:40.227551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 30
 
3.1%
식 23
 
2.4%
18
 
1.8%
당 17
 
1.7%
한 17
 
1.7%
지 16
 
1.6%
가 16
 
1.6%
어 15
 
1.5%
리 15
 
1.5%
대 14
 
1.4%
Other values (263) 795
81.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 923
94.6%
Space Separator 18
 
1.8%
Lowercase Letter 11
 
1.1%
Uppercase Letter 10
 
1.0%
Open Punctuation 6
 
0.6%
Close Punctuation 6
 
0.6%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 30
 
3.3%
식 23
 
2.5%
당 17
 
1.8%
한 17
 
1.8%
지 16
 
1.7%
가 16
 
1.7%
어 15
 
1.6%
리 15
 
1.6%
대 14
 
1.5%
수 14
 
1.5%
Other values (242) 746
80.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
18.2%
o 2
18.2%
r 2
18.2%
t 1
9.1%
s 1
9.1%
b 1
9.1%
k 1
9.1%
w 1
9.1%
Uppercase Letter
ValueCountFrequency (%)
H 2
20.0%
I 2
20.0%
B 1
10.0%
A 1
10.0%
C 1
10.0%
L 1
10.0%
Y 1
10.0%
N 1
10.0%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 923
94.6%
Common 32
 
3.3%
Latin 21
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 30
 
3.3%
식 23
 
2.5%
당 17
 
1.8%
한 17
 
1.8%
지 16
 
1.7%
가 16
 
1.7%
어 15
 
1.6%
리 15
 
1.6%
대 14
 
1.5%
수 14
 
1.5%
Other values (242) 746
80.8%
Latin
ValueCountFrequency (%)
e 2
 
9.5%
o 2
 
9.5%
r 2
 
9.5%
H 2
 
9.5%
I 2
 
9.5%
B 1
 
4.8%
A 1
 
4.8%
C 1
 
4.8%
t 1
 
4.8%
s 1
 
4.8%
Other values (6) 6
28.6%
Common
ValueCountFrequency (%)
18
56.2%
( 6
 
18.8%
) 6
 
18.8%
& 1
 
3.1%
. 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 923
94.6%
ASCII 53
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 30
 
3.3%
식 23
 
2.5%
당 17
 
1.8%
한 17
 
1.8%
지 16
 
1.7%
가 16
 
1.7%
어 15
 
1.6%
리 15
 
1.6%
대 14
 
1.5%
수 14
 
1.5%
Other values (242) 746
80.8%
ASCII
ValueCountFrequency (%)
18
34.0%
( 6
 
11.3%
) 6
 
11.3%
e 2
 
3.8%
o 2
 
3.8%
r 2
 
3.8%
H 2
 
3.8%
I 2
 
3.8%
& 1
 
1.9%
B 1
 
1.9%
Other values (11) 11
20.8%

소재지(도로명)
Text

UNIQUE 

Distinct164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-21T03:33:41.453836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length40
Mean length25.335366
Min length20

Characters and Unicode

Total characters4155
Distinct characters105
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

Unique164 ?
Unique (%)100.0%

Sample

1st row대구광역시 수성구 고산로18길 15 (신매동)
2nd row대구광역시 수성구 상록로 12 (범어동)
3rd row대구광역시 수성구 수성로 282 (중동)
4th row대구광역시 수성구 들안로 49 (상동)
5th row대구광역시 수성구 용학로25길 46 (두산동)
ValueCountFrequency (%)
대구광역시 164
19.4%
수성구 164
19.4%
들안로 37
 
4.4%
두산동 33
 
3.9%
범어동 23
 
2.7%
동대구로 18
 
2.1%
상동 18
 
2.1%
달구벌대로 18
 
2.1%
지산동 16
 
1.9%
용학로 12
 
1.4%
Other values (232) 343
40.5%
2024-04-21T03:33:42.705894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
682
16.4%
구 375
 
9.0%
대 210
 
5.1%
동 197
 
4.7%
수 188
 
4.5%
성 184
 
4.4%
) 172
 
4.1%
( 172
 
4.1%
시 166
 
4.0%
광 165
 
4.0%
Other values (95) 1644
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2473
59.5%
Space Separator 682
 
16.4%
Decimal Number 582
 
14.0%
Close Punctuation 172
 
4.1%
Open Punctuation 172
 
4.1%
Other Punctuation 48
 
1.2%
Dash Punctuation 21
 
0.5%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 375
15.2%
대 210
 
8.5%
동 197
 
8.0%
수 188
 
7.6%
성 184
 
7.4%
시 166
 
6.7%
광 165
 
6.7%
역 164
 
6.6%
로 162
 
6.6%
산 55
 
2.2%
Other values (76) 607
24.5%
Decimal Number
ValueCountFrequency (%)
1 135
23.2%
2 99
17.0%
3 65
11.2%
0 49
 
8.4%
4 49
 
8.4%
6 44
 
7.6%
7 36
 
6.2%
9 35
 
6.0%
5 35
 
6.0%
8 35
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
L 1
20.0%
S 1
20.0%
B 1
20.0%
Space Separator
ValueCountFrequency (%)
682
100.0%
Close Punctuation
ValueCountFrequency (%)
) 172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 172
100.0%
Other Punctuation
ValueCountFrequency (%)
, 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2473
59.5%
Common 1677
40.4%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 375
15.2%
대 210
 
8.5%
동 197
 
8.0%
수 188
 
7.6%
성 184
 
7.4%
시 166
 
6.7%
광 165
 
6.7%
역 164
 
6.6%
로 162
 
6.6%
산 55
 
2.2%
Other values (76) 607
24.5%
Common
ValueCountFrequency (%)
682
40.7%
) 172
 
10.3%
( 172
 
10.3%
1 135
 
8.1%
2 99
 
5.9%
3 65
 
3.9%
0 49
 
2.9%
4 49
 
2.9%
, 48
 
2.9%
6 44
 
2.6%
Other values (5) 162
 
9.7%
Latin
ValueCountFrequency (%)
A 2
40.0%
L 1
20.0%
S 1
20.0%
B 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2473
59.5%
ASCII 1682
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
682
40.5%
) 172
 
10.2%
( 172
 
10.2%
1 135
 
8.0%
2 99
 
5.9%
3 65
 
3.9%
0 49
 
2.9%
4 49
 
2.9%
, 48
 
2.9%
6 44
 
2.6%
Other values (9) 167
 
9.9%
Hangul
ValueCountFrequency (%)
구 375
15.2%
대 210
 
8.5%
동 197
 
8.0%
수 188
 
7.6%
성 184
 
7.4%
시 166
 
6.7%
광 165
 
6.7%
역 164
 
6.6%
로 162
 
6.6%
산 55
 
2.2%
Other values (76) 607
24.5%

소재지전화번호
Text

Distinct163
Distinct (%)100.0%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-04-21T03:33:43.565600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1956
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

Unique163 ?
Unique (%)100.0%

Sample

1st row053-791-4288
2nd row053-742-0996
3rd row053-766-9322
4th row053-761-9332
5th row053-783-0791
ValueCountFrequency (%)
053-791-4288 1
 
0.6%
053-762-6633 1
 
0.6%
053-744-6288 1
 
0.6%
053-794-8558 1
 
0.6%
053-764-6288 1
 
0.6%
053-783-2234 1
 
0.6%
053-763-4831 1
 
0.6%
053-744-2655 1
 
0.6%
053-762-0707 1
 
0.6%
053-768-7670 1
 
0.6%
Other values (153) 153
93.9%
2024-04-21T03:33:44.627324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 326
16.7%
5 277
14.2%
0 260
13.3%
3 259
13.2%
7 231
11.8%
6 150
7.7%
4 94
 
4.8%
8 92
 
4.7%
1 90
 
4.6%
2 90
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1630
83.3%
Dash Punctuation 326
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 277
17.0%
0 260
16.0%
3 259
15.9%
7 231
14.2%
6 150
9.2%
4 94
 
5.8%
8 92
 
5.6%
1 90
 
5.5%
2 90
 
5.5%
9 87
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 326
16.7%
5 277
14.2%
0 260
13.3%
3 259
13.2%
7 231
11.8%
6 150
7.7%
4 94
 
4.8%
8 92
 
4.7%
1 90
 
4.6%
2 90
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 326
16.7%
5 277
14.2%
0 260
13.3%
3 259
13.2%
7 231
11.8%
6 150
7.7%
4 94
 
4.8%
8 92
 
4.7%
1 90
 
4.6%
2 90
 
4.6%

업태명
Categorical

Distinct13
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
한식
68 
식육(숯불구이)
37 
경양식
11 
일식
11 
회집
9 
Other values (8)
28 

Length

Max length15
Median length2
Mean length3.8658537
Min length2

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st row한식
2nd row경양식
3rd row식육(숯불구이)
4th row한식
5th row중국식

Common Values

ValueCountFrequency (%)
한식 68
41.5%
식육(숯불구이) 37
22.6%
경양식 11
 
6.7%
일식 11
 
6.7%
회집 9
 
5.5%
복어취급 8
 
4.9%
기타 6
 
3.7%
중국식 4
 
2.4%
분식 4
 
2.4%
외국음식전문점(인도,태국등) 3
 
1.8%
Other values (3) 3
 
1.8%

Length

2024-04-21T03:33:44.867821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 68
41.5%
식육(숯불구이 37
22.6%
경양식 11
 
6.7%
일식 11
 
6.7%
회집 9
 
5.5%
복어취급 8
 
4.9%
기타 6
 
3.7%
중국식 4
 
2.4%
분식 4
 
2.4%
외국음식전문점(인도,태국등 3
 
1.8%
Other values (3) 3
 
1.8%

주된음식
Text

Distinct81
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-21T03:33:45.657571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.6036585
Min length1

Characters and Unicode

Total characters591
Distinct characters121
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)34.1%

Sample

1st row냉면
2nd row바닷가재
3rd row갈비살
4th row삼계탕
5th row중화요리
ValueCountFrequency (%)
갈비살 20
 
12.0%
한정식 13
 
7.8%
회 8
 
4.8%
돼지갈비 7
 
4.2%
삼계탕 6
 
3.6%
복어매운탕 6
 
3.6%
감자탕 5
 
3.0%
중화요리 4
 
2.4%
샤브샤브 4
 
2.4%
낙지볶음 4
 
2.4%
Other values (71) 90
53.9%
2024-04-21T03:33:46.912470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 32
 
5.4%
갈 31
 
5.2%
탕 23
 
3.9%
살 22
 
3.7%
식 17
 
2.9%
회 17
 
2.9%
한 16
 
2.7%
정 16
 
2.7%
지 15
 
2.5%
어 15
 
2.5%
Other values (111) 387
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 573
97.0%
Other Punctuation 15
 
2.5%
Space Separator 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 32
 
5.6%
갈 31
 
5.4%
탕 23
 
4.0%
살 22
 
3.8%
식 17
 
3.0%
회 17
 
3.0%
한 16
 
2.8%
정 16
 
2.8%
지 15
 
2.6%
어 15
 
2.6%
Other values (109) 369
64.4%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 573
97.0%
Common 18
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 32
 
5.6%
갈 31
 
5.4%
탕 23
 
4.0%
살 22
 
3.8%
식 17
 
3.0%
회 17
 
3.0%
한 16
 
2.8%
정 16
 
2.8%
지 15
 
2.6%
어 15
 
2.6%
Other values (109) 369
64.4%
Common
ValueCountFrequency (%)
, 15
83.3%
3
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 573
97.0%
ASCII 18
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 32
 
5.6%
갈 31
 
5.4%
탕 23
 
4.0%
살 22
 
3.8%
식 17
 
3.0%
회 17
 
3.0%
한 16
 
2.8%
정 16
 
2.8%
지 15
 
2.6%
어 15
 
2.6%
Other values (109) 369
64.4%
ASCII
ValueCountFrequency (%)
, 15
83.3%
3
 
16.7%

Correlations

2024-04-21T03:33:47.165827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명주된음식
업태명1.0000.971
주된음식0.9711.000

Missing values

2024-04-21T03:33:37.715405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T03:33:38.026724image/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

업소명소재지(도로명)소재지전화번호업태명주된음식
0예전애 서울칡냉면대구광역시 수성구 고산로18길 15 (신매동)053-791-4288한식냉면
1뉴욕 바닷가재 대구본점(New York Lobster)대구광역시 수성구 상록로 12 (범어동)053-742-0996경양식바닷가재
2도토리흑돼지대구광역시 수성구 수성로 282 (중동)053-766-9322식육(숯불구이)갈비살
3금산삼계탕본점대구광역시 수성구 들안로 49 (상동)053-761-9332한식삼계탕
4서태후중화요리전문점대구광역시 수성구 용학로25길 46 (두산동)053-783-0791중국식중화요리
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