Overview

Dataset statistics

Number of variables5
Number of observations22
Missing cells9
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory47.0 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description대구 지역 외국인전용유흥음식점업 현황_2017.3월
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15054186&dataSetDetailId=150541861ac627c0e9b89_201704200915&provdMethod=FILE

Alerts

연번 is highly overall correlated with 구군High correlation
구군 is highly overall correlated with 연번High correlation
전화번호 has 9 (40.9%) missing valuesMissing
연번 has unique valuesUnique
업체명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-10 18:18:34.365787
Analysis finished2023-12-10 18:18:36.864248
Duration2.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T03:18:36.973306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2023-12-11T03:18:37.160903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

구군
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size308.0 B
달서구
5 
동구
4 
남구
4 
북구
4 
수성구
3 
Other values (2)
2 

Length

Max length3
Median length2
Mean length2.4090909
Min length2

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row동구
2nd row동구
3rd row동구
4th row동구
5th row서구

Common Values

ValueCountFrequency (%)
달서구 5
22.7%
동구 4
18.2%
남구 4
18.2%
북구 4
18.2%
수성구 3
13.6%
서구 1
 
4.5%
달성군 1
 
4.5%

Length

2023-12-11T03:18:37.365248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:18:37.584762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달서구 5
22.7%
동구 4
18.2%
남구 4
18.2%
북구 4
18.2%
수성구 3
13.6%
서구 1
 
4.5%
달성군 1
 
4.5%

업체명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-11T03:18:37.936667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length6.5
Mean length4.2272727
Min length1

Characters and Unicode

Total characters93
Distinct characters68
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

Unique22 ?
Unique (%)100.0%

Sample

1st row뉴캐슬
2nd row모스크바
3rd rowBMB노래주점
4th row아레나
5th row런던회관
ValueCountFrequency (%)
뉴캐슬 1
 
4.2%
모스크바 1
 
4.2%
뉴욕 1
 
4.2%
카우보이드림 1
 
4.2%
마닐라 1
 
4.2%
하이힐클럽 1
 
4.2%
제니스 1
 
4.2%
포에버 1
 
4.2%
2.4(투게더 1
 
4.2%
포르쉐 1
 
4.2%
Other values (14) 14
58.3%
2023-12-11T03:18:38.537302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
클 4
 
4.3%
럽 4
 
4.3%
뉴 3
 
3.2%
스 3
 
3.2%
나 3
 
3.2%
아 3
 
3.2%
맨 2
 
2.2%
2
 
2.2%
이 2
 
2.2%
캐 2
 
2.2%
Other values (58) 65
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
89.2%
Uppercase Letter 3
 
3.2%
Space Separator 2
 
2.2%
Decimal Number 2
 
2.2%
Open Punctuation 1
 
1.1%
Other Punctuation 1
 
1.1%
Close Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
클 4
 
4.8%
럽 4
 
4.8%
뉴 3
 
3.6%
스 3
 
3.6%
나 3
 
3.6%
아 3
 
3.6%
맨 2
 
2.4%
이 2
 
2.4%
캐 2
 
2.4%
포 2
 
2.4%
Other values (50) 55
66.3%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
M 1
33.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
89.2%
Common 7
 
7.5%
Latin 3
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
클 4
 
4.8%
럽 4
 
4.8%
뉴 3
 
3.6%
스 3
 
3.6%
나 3
 
3.6%
아 3
 
3.6%
맨 2
 
2.4%
이 2
 
2.4%
캐 2
 
2.4%
포 2
 
2.4%
Other values (50) 55
66.3%
Common
ValueCountFrequency (%)
2
28.6%
( 1
14.3%
2 1
14.3%
. 1
14.3%
4 1
14.3%
) 1
14.3%
Latin
ValueCountFrequency (%)
B 2
66.7%
M 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
89.2%
ASCII 10
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
클 4
 
4.8%
럽 4
 
4.8%
뉴 3
 
3.6%
스 3
 
3.6%
나 3
 
3.6%
아 3
 
3.6%
맨 2
 
2.4%
이 2
 
2.4%
캐 2
 
2.4%
포 2
 
2.4%
Other values (50) 55
66.3%
ASCII
ValueCountFrequency (%)
2
20.0%
B 2
20.0%
( 1
10.0%
2 1
10.0%
. 1
10.0%
4 1
10.0%
) 1
10.0%
M 1
10.0%

소재지
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-11T03:18:38.944609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length23.954545
Min length16

Characters and Unicode

Total characters527
Distinct characters71
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row대구광역시 동구 동대구로 487 (신천동)
2nd row대구광역시 동구 동부로22길 69 (신천동)
3rd row대구광역시 동구 아양로 344 (입석동)
4th row대구광역시 동구 아양로 46 (신암동)
5th row대구광역시 서구 달서로 89 (비산동)
ValueCountFrequency (%)
대구광역시 22
 
20.8%
달서구 5
 
4.7%
남구 4
 
3.8%
동구 4
 
3.8%
북구 4
 
3.8%
수성구 3
 
2.8%
동천동 3
 
2.8%
이천로 2
 
1.9%
와룡로 2
 
1.9%
동천로24길 2
 
1.9%
Other values (53) 55
51.9%
2023-12-11T03:18:39.610816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
16.1%
구 46
 
8.7%
동 34
 
6.5%
대 26
 
4.9%
광 22
 
4.2%
역 22
 
4.2%
시 22
 
4.2%
로 21
 
4.0%
( 20
 
3.8%
) 20
 
3.8%
Other values (61) 209
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 316
60.0%
Space Separator 85
 
16.1%
Decimal Number 75
 
14.2%
Open Punctuation 20
 
3.8%
Close Punctuation 20
 
3.8%
Other Punctuation 6
 
1.1%
Dash Punctuation 5
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 46
14.6%
동 34
 
10.8%
대 26
 
8.2%
광 22
 
7.0%
역 22
 
7.0%
시 22
 
7.0%
로 21
 
6.6%
천 11
 
3.5%
달 8
 
2.5%
서 7
 
2.2%
Other values (46) 97
30.7%
Decimal Number
ValueCountFrequency (%)
1 19
25.3%
2 11
14.7%
3 11
14.7%
4 7
 
9.3%
8 6
 
8.0%
6 5
 
6.7%
9 5
 
6.7%
0 4
 
5.3%
5 4
 
5.3%
7 3
 
4.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 316
60.0%
Common 211
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 46
14.6%
동 34
 
10.8%
대 26
 
8.2%
광 22
 
7.0%
역 22
 
7.0%
시 22
 
7.0%
로 21
 
6.6%
천 11
 
3.5%
달 8
 
2.5%
서 7
 
2.2%
Other values (46) 97
30.7%
Common
ValueCountFrequency (%)
85
40.3%
( 20
 
9.5%
) 20
 
9.5%
1 19
 
9.0%
2 11
 
5.2%
3 11
 
5.2%
4 7
 
3.3%
8 6
 
2.8%
, 6
 
2.8%
- 5
 
2.4%
Other values (5) 21
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 316
60.0%
ASCII 211
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
40.3%
( 20
 
9.5%
) 20
 
9.5%
1 19
 
9.0%
2 11
 
5.2%
3 11
 
5.2%
4 7
 
3.3%
8 6
 
2.8%
, 6
 
2.8%
- 5
 
2.4%
Other values (5) 21
 
10.0%
Hangul
ValueCountFrequency (%)
구 46
14.6%
동 34
 
10.8%
대 26
 
8.2%
광 22
 
7.0%
역 22
 
7.0%
시 22
 
7.0%
로 21
 
6.6%
천 11
 
3.5%
달 8
 
2.5%
서 7
 
2.2%
Other values (46) 97
30.7%

전화번호
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2023-12-11T03:18:39.937244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters156
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

Unique13 ?
Unique (%)100.0%

Sample

1st row053-754-3119
2nd row053-741-4202
3rd row053-982-2258
4th row053-955-5571
5th row053-472-5372
ValueCountFrequency (%)
053-754-3119 1
 
7.7%
053-741-4202 1
 
7.7%
053-982-2258 1
 
7.7%
053-955-5571 1
 
7.7%
053-472-5372 1
 
7.7%
053-475-5909 1
 
7.7%
053-624-7666 1
 
7.7%
053-323-1199 1
 
7.7%
053-943-2833 1
 
7.7%
053-585-0085 1
 
7.7%
Other values (3) 3
23.1%
2023-12-11T03:18:40.509630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 27
17.3%
- 26
16.7%
3 22
14.1%
0 21
13.5%
4 10
 
6.4%
1 10
 
6.4%
2 10
 
6.4%
9 9
 
5.8%
7 8
 
5.1%
6 8
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
83.3%
Dash Punctuation 26
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 27
20.8%
3 22
16.9%
0 21
16.2%
4 10
 
7.7%
1 10
 
7.7%
2 10
 
7.7%
9 9
 
6.9%
7 8
 
6.2%
6 8
 
6.2%
8 5
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 27
17.3%
- 26
16.7%
3 22
14.1%
0 21
13.5%
4 10
 
6.4%
1 10
 
6.4%
2 10
 
6.4%
9 9
 
5.8%
7 8
 
5.1%
6 8
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 27
17.3%
- 26
16.7%
3 22
14.1%
0 21
13.5%
4 10
 
6.4%
1 10
 
6.4%
2 10
 
6.4%
9 9
 
5.8%
7 8
 
5.1%
6 8
 
5.1%

Interactions

2023-12-11T03:18:36.364119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T03:18:40.737307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구군업체명소재지전화번호
연번1.0000.8871.0001.0001.000
구군0.8871.0001.0001.0001.000
업체명1.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000
2023-12-11T03:18:40.905932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구군
연번1.0000.637
구군0.6371.000

Missing values

2023-12-11T03:18:36.592099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T03:18:36.792931image/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

연번구군업체명소재지전화번호
01동구뉴캐슬대구광역시 동구 동대구로 487 (신천동)053-754-3119
12동구모스크바대구광역시 동구 동부로22길 69 (신천동)053-741-4202
23동구BMB노래주점대구광역시 동구 아양로 344 (입석동)053-982-2258
34동구아레나대구광역시 동구 아양로 46 (신암동)053-955-5571
45서구런던회관대구광역시 서구 달서로 89 (비산동)<NA>
56남구킹대구광역시 남구 삼정길 86-2 (봉덕동)053-472-5372
67남구모나코대구광역시 남구 이천로 115 (이천동,지하1층)053-475-5909
78남구엔젤대구광역시 남구 현충로 236 (대명동)053-624-7666
89남구쥴리아나도쿄대구광역시 남구 이천로 13(봉덕동)<NA>
910북구아테네 클럽대구광역시 북구 동천로24길 17-13 (동천동)053-323-1199
연번구군업체명소재지전화번호
1213북구제우스대구광역시 북구 동천로 3-14, 3층 3호 (동천동)<NA>
1314수성구외인구단대구광역시 수성구 청수로 92<NA>
1415수성구포르쉐대구광역시 수성구 동대구로11(두산동)<NA>
1516수성구2.4(투게더 포에버)대구광역시 수성구 청수로25길 23(황금동)<NA>
1617달서구제니스대구광역시 달서구 달구벌대로251길 18, 지하층(이곡동)053-585-0085
1718달서구하이힐클럽대구광역시 달서구 월곡로 180, 지하층(상인동)053-631-0740
1819달서구마닐라대구광역시 달서구 와룡로 39, 3층(본동)053-566-1441
1920달서구카우보이드림대구광역시 달서구 상화북로 180(상인동)053-639-0015
2021달서구뉴욕대구광역시 달서구 와룡로 101, 3층(본리동)<NA>
2122달성군뉴캐슬클럽대구광역시 달성군 현풍면 현풍중앙로 56-1<NA>