Overview

Dataset statistics

Number of variables7
Number of observations44
Missing cells12
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory59.0 B

Variable types

Categorical1
Text6

Dataset

Description대전광역시 5개구 위생업소(식품위생업소, 건강기능식품업소, 공중위생업소, 의약업소) 현황에 대한 자료를 공개합니다.
URLhttps://www.data.go.kr/data/15062792/fileData.do

Alerts

동구 has 3 (6.8%) missing valuesMissing
중구 has 4 (9.1%) missing valuesMissing
서구 has 1 (2.3%) missing valuesMissing
유성구 has 3 (6.8%) missing valuesMissing
대덕구 has 1 (2.3%) missing valuesMissing
업종 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:05:22.174484
Analysis finished2023-12-12 13:05:23.051911
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
식품위생업소
20 
공중위생업소
13 
의약업소
9 
건강기능식품업소
 
2

Length

Max length8
Median length6
Mean length5.6818182
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식품위생업소
2nd row식품위생업소
3rd row식품위생업소
4th row식품위생업소
5th row식품위생업소

Common Values

ValueCountFrequency (%)
식품위생업소 20
45.5%
공중위생업소 13
29.5%
의약업소 9
20.5%
건강기능식품업소 2
 
4.5%

Length

2023-12-12T22:05:23.127410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:05:23.251779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품위생업소 20
45.5%
공중위생업소 13
29.5%
의약업소 9
20.5%
건강기능식품업소 2
 
4.5%

업종
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-12T22:05:23.506685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.1363636
Min length2

Characters and Unicode

Total characters270
Distinct characters90
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row일반음식점
2nd row유흥주점
3rd row단란주점
4th row제과점
5th row다방
ValueCountFrequency (%)
및 3
 
5.7%
가공업 2
 
3.8%
일반음식점 1
 
1.9%
화장분장 1
 
1.9%
일반숙박 1
 
1.9%
생활숙박 1
 
1.9%
종합(미용 1
 
1.9%
목욕장업 1
 
1.9%
이용업 1
 
1.9%
세탁업 1
 
1.9%
Other values (40) 40
75.5%
2023-12-12T22:05:23.848744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 26
 
9.6%
식 15
 
5.6%
기 11
 
4.1%
매 11
 
4.1%
품 11
 
4.1%
판 9
 
3.3%
9
 
3.3%
약 7
 
2.6%
제 6
 
2.2%
용 5
 
1.9%
Other values (80) 160
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 253
93.7%
Space Separator 9
 
3.3%
Open Punctuation 4
 
1.5%
Close Punctuation 4
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 26
 
10.3%
식 15
 
5.9%
기 11
 
4.3%
매 11
 
4.3%
품 11
 
4.3%
판 9
 
3.6%
약 7
 
2.8%
제 6
 
2.4%
용 5
 
2.0%
장 5
 
2.0%
Other values (77) 147
58.1%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 253
93.7%
Common 17
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 26
 
10.3%
식 15
 
5.9%
기 11
 
4.3%
매 11
 
4.3%
품 11
 
4.3%
판 9
 
3.6%
약 7
 
2.8%
제 6
 
2.4%
용 5
 
2.0%
장 5
 
2.0%
Other values (77) 147
58.1%
Common
ValueCountFrequency (%)
9
52.9%
( 4
23.5%
) 4
23.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 253
93.7%
ASCII 17
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 26
 
10.3%
식 15
 
5.9%
기 11
 
4.3%
매 11
 
4.3%
품 11
 
4.3%
판 9
 
3.6%
약 7
 
2.8%
제 6
 
2.4%
용 5
 
2.0%
장 5
 
2.0%
Other values (77) 147
58.1%
ASCII
ValueCountFrequency (%)
9
52.9%
( 4
23.5%
) 4
23.5%

동구
Text

MISSING 

Distinct40
Distinct (%)97.6%
Missing3
Missing (%)6.8%
Memory size484.0 B
2023-12-12T22:05:24.029464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.2195122
Min length3

Characters and Unicode

Total characters173
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)95.1%

Sample

1st row 2,805
2nd row 15
3rd row 17
4th row 81
5th row 72
ValueCountFrequency (%)
1 2
 
4.9%
6 1
 
2.4%
63 1
 
2.4%
2,805 1
 
2.4%
7 1
 
2.4%
101 1
 
2.4%
105 1
 
2.4%
71 1
 
2.4%
549 1
 
2.4%
69 1
 
2.4%
Other values (30) 30
73.2%
2023-12-12T22:05:24.335776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82
47.4%
1 25
 
14.5%
2 9
 
5.2%
3 9
 
5.2%
5 9
 
5.2%
4 8
 
4.6%
6 7
 
4.0%
8 7
 
4.0%
7 7
 
4.0%
9 6
 
3.5%
Other values (2) 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
52.0%
Space Separator 82
47.4%
Other Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25
27.8%
2 9
 
10.0%
3 9
 
10.0%
5 9
 
10.0%
4 8
 
8.9%
6 7
 
7.8%
8 7
 
7.8%
7 7
 
7.8%
9 6
 
6.7%
0 3
 
3.3%
Space Separator
ValueCountFrequency (%)
82
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
82
47.4%
1 25
 
14.5%
2 9
 
5.2%
3 9
 
5.2%
5 9
 
5.2%
4 8
 
4.6%
6 7
 
4.0%
8 7
 
4.0%
7 7
 
4.0%
9 6
 
3.5%
Other values (2) 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82
47.4%
1 25
 
14.5%
2 9
 
5.2%
3 9
 
5.2%
5 9
 
5.2%
4 8
 
4.6%
6 7
 
4.0%
8 7
 
4.0%
7 7
 
4.0%
9 6
 
3.5%
Other values (2) 4
 
2.3%

중구
Text

MISSING 

Distinct37
Distinct (%)92.5%
Missing4
Missing (%)9.1%
Memory size484.0 B
2023-12-12T22:05:24.537942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.275
Min length3

Characters and Unicode

Total characters171
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)85.0%

Sample

1st row 3,304
2nd row 57
3rd row 69
4th row 74
5th row 33
ValueCountFrequency (%)
118 2
 
5.0%
17 2
 
5.0%
7 2
 
5.0%
194 1
 
2.5%
29 1
 
2.5%
6 1
 
2.5%
142 1
 
2.5%
4 1
 
2.5%
18 1
 
2.5%
1 1
 
2.5%
Other values (27) 27
67.5%
2023-12-12T22:05:24.884364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
46.8%
1 18
 
10.5%
7 13
 
7.6%
4 11
 
6.4%
2 10
 
5.8%
5 10
 
5.8%
3 8
 
4.7%
9 6
 
3.5%
6 6
 
3.5%
0 5
 
2.9%
Other values (2) 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
52.6%
Space Separator 80
46.8%
Other Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
20.0%
7 13
14.4%
4 11
12.2%
2 10
11.1%
5 10
11.1%
3 8
8.9%
9 6
 
6.7%
6 6
 
6.7%
0 5
 
5.6%
8 3
 
3.3%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
80
46.8%
1 18
 
10.5%
7 13
 
7.6%
4 11
 
6.4%
2 10
 
5.8%
5 10
 
5.8%
3 8
 
4.7%
9 6
 
3.5%
6 6
 
3.5%
0 5
 
2.9%
Other values (2) 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80
46.8%
1 18
 
10.5%
7 13
 
7.6%
4 11
 
6.4%
2 10
 
5.8%
5 10
 
5.8%
3 8
 
4.7%
9 6
 
3.5%
6 6
 
3.5%
0 5
 
2.9%
Other values (2) 4
 
2.3%

서구
Text

MISSING 

Distinct40
Distinct (%)93.0%
Missing1
Missing (%)2.3%
Memory size484.0 B
2023-12-12T22:05:25.110249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.4883721
Min length3

Characters and Unicode

Total characters193
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)86.0%

Sample

1st row 6,180
2nd row 18
3rd row 103
4th row 201
5th row 16
ValueCountFrequency (%)
3 2
 
4.7%
4 2
 
4.7%
1 2
 
4.7%
5 1
 
2.3%
63 1
 
2.3%
22 1
 
2.3%
328 1
 
2.3%
6,180 1
 
2.3%
10 1
 
2.3%
137 1
 
2.3%
Other values (30) 30
69.8%
2023-12-12T22:05:25.445349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
44.6%
1 22
 
11.4%
2 13
 
6.7%
3 12
 
6.2%
8 11
 
5.7%
4 10
 
5.2%
0 9
 
4.7%
6 8
 
4.1%
7 7
 
3.6%
5 6
 
3.1%
Other values (2) 9
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102
52.8%
Space Separator 86
44.6%
Other Punctuation 5
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
21.6%
2 13
12.7%
3 12
11.8%
8 11
10.8%
4 10
9.8%
0 9
8.8%
6 8
 
7.8%
7 7
 
6.9%
5 6
 
5.9%
9 4
 
3.9%
Space Separator
ValueCountFrequency (%)
86
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
86
44.6%
1 22
 
11.4%
2 13
 
6.7%
3 12
 
6.2%
8 11
 
5.7%
4 10
 
5.2%
0 9
 
4.7%
6 8
 
4.1%
7 7
 
3.6%
5 6
 
3.1%
Other values (2) 9
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
44.6%
1 22
 
11.4%
2 13
 
6.7%
3 12
 
6.2%
8 11
 
5.7%
4 10
 
5.2%
0 9
 
4.7%
6 8
 
4.1%
7 7
 
3.6%
5 6
 
3.1%
Other values (2) 9
 
4.7%

유성구
Text

MISSING 

Distinct38
Distinct (%)92.7%
Missing3
Missing (%)6.8%
Memory size484.0 B
2023-12-12T22:05:25.669854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.3658537
Min length3

Characters and Unicode

Total characters179
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)85.4%

Sample

1st row 5,197
2nd row 69
3rd row 64
4th row 156
5th row 21
ValueCountFrequency (%)
4 2
 
4.9%
2 2
 
4.9%
31 2
 
4.9%
11 1
 
2.4%
102 1
 
2.4%
98 1
 
2.4%
139 1
 
2.4%
233 1
 
2.4%
20 1
 
2.4%
77 1
 
2.4%
Other values (28) 28
68.3%
2023-12-12T22:05:26.121470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82
45.8%
1 17
 
9.5%
9 16
 
8.9%
3 13
 
7.3%
2 12
 
6.7%
8 8
 
4.5%
6 7
 
3.9%
4 6
 
3.4%
0 6
 
3.4%
5 5
 
2.8%
Other values (2) 7
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95
53.1%
Space Separator 82
45.8%
Other Punctuation 2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
17.9%
9 16
16.8%
3 13
13.7%
2 12
12.6%
8 8
8.4%
6 7
7.4%
4 6
 
6.3%
0 6
 
6.3%
5 5
 
5.3%
7 5
 
5.3%
Space Separator
ValueCountFrequency (%)
82
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 179
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
82
45.8%
1 17
 
9.5%
9 16
 
8.9%
3 13
 
7.3%
2 12
 
6.7%
8 8
 
4.5%
6 7
 
3.9%
4 6
 
3.4%
0 6
 
3.4%
5 5
 
2.8%
Other values (2) 7
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82
45.8%
1 17
 
9.5%
9 16
 
8.9%
3 13
 
7.3%
2 12
 
6.7%
8 8
 
4.5%
6 7
 
3.9%
4 6
 
3.4%
0 6
 
3.4%
5 5
 
2.8%
Other values (2) 7
 
3.9%

대덕구
Text

MISSING 

Distinct36
Distinct (%)83.7%
Missing1
Missing (%)2.3%
Memory size484.0 B
2023-12-12T22:05:26.339432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.1395349
Min length3

Characters and Unicode

Total characters178
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)72.1%

Sample

1st row 2,362
2nd row 53
3rd row 19
4th row 49
5th row 17
ValueCountFrequency (%)
53 3
 
7.0%
1 3
 
7.0%
3 2
 
4.7%
41 2
 
4.7%
10 2
 
4.7%
13 1
 
2.3%
85 1
 
2.3%
147 1
 
2.3%
45 1
 
2.3%
87 1
 
2.3%
Other values (26) 26
60.5%
2023-12-12T22:05:26.721764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
48.3%
1 22
 
12.4%
5 12
 
6.7%
3 12
 
6.7%
4 9
 
5.1%
2 8
 
4.5%
6 7
 
3.9%
0 6
 
3.4%
7 6
 
3.4%
9 6
 
3.4%
Other values (2) 4
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91
51.1%
Space Separator 86
48.3%
Other Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
24.2%
5 12
13.2%
3 12
13.2%
4 9
9.9%
2 8
 
8.8%
6 7
 
7.7%
0 6
 
6.6%
7 6
 
6.6%
9 6
 
6.6%
8 3
 
3.3%
Space Separator
ValueCountFrequency (%)
86
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
86
48.3%
1 22
 
12.4%
5 12
 
6.7%
3 12
 
6.7%
4 9
 
5.1%
2 8
 
4.5%
6 7
 
3.9%
0 6
 
3.4%
7 6
 
3.4%
9 6
 
3.4%
Other values (2) 4
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
48.3%
1 22
 
12.4%
5 12
 
6.7%
3 12
 
6.7%
4 9
 
5.1%
2 8
 
4.5%
6 7
 
3.9%
0 6
 
3.4%
7 6
 
3.4%
9 6
 
3.4%
Other values (2) 4
 
2.2%

Correlations

2023-12-12T22:05:26.828768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업종동구중구서구유성구대덕구
구분1.0001.0000.9390.9540.9420.5790.853
업종1.0001.0001.0001.0001.0001.0001.000
동구0.9391.0001.0000.9810.9950.9900.990
중구0.9541.0000.9811.0000.9900.9830.949
서구0.9421.0000.9950.9901.0000.9710.943
유성구0.5791.0000.9900.9830.9711.0000.952
대덕구0.8531.0000.9900.9490.9430.9521.000

Missing values

2023-12-12T22:05:22.678973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:05:22.844479image/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.
2023-12-12T22:05:22.983417image/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

구분업종동구중구서구유성구대덕구
0식품위생업소일반음식점2,8053,3046,1805,1972,362
1식품위생업소유흥주점1557186953
2식품위생업소단란주점17691036419
3식품위생업소제과점817420115649
4식품위생업소다방7233162117
5식품위생업소기타 휴게음식점7247741,6641,498535
6식품위생업소위탁급식영업19204815070
7식품위생업소집단급식(학교)4849949941
8식품위생업소집단급식(기업체등)126123237298161
9식품위생업소식품제조 및 가공업113627290114
구분업종동구중구서구유성구대덕구
34공중위생업소위생물수건처리업<NA><NA>312
35의약업소약국11914224813687
36의약업소한약국9613113
37의약업소한약도매137821
38의약업소양약도매3829633145
39의약업소약업사<NA><NA><NA><NA><NA>
40의약업소한약업사11<NA>5<NA>1
41의약업소안전상비의약품판매업183194408339147
42의약업소의료기기판매업4695351,517968435
43의약업소의료기기수리업2422202521