Overview

Dataset statistics

Number of variables7
Number of observations73
Missing cells14
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory58.8 B

Variable types

Categorical3
Text3
DateTime1

Dataset

Description공중위생관리
Author경상북도 성주군
URLhttps://www.data.go.kr/data/15006818/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
업태명 is highly overall correlated with 업종코드 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 업종코드 and 1 other fieldsHigh correlation
업종코드 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly imbalanced (65.5%)Imbalance
업태명 is highly imbalanced (65.5%)Imbalance
소재지전화 has 14 (19.2%) missing valuesMissing
업소명 has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:55:29.547924
Analysis finished2023-12-13 00:55:29.995162
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size716.0 B
211
61 
212
7 
<NA>
 
5

Length

Max length4
Median length3
Mean length3.0684932
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row211
2nd row211
3rd row211
4th row211
5th row211

Common Values

ValueCountFrequency (%)
211 61
83.6%
212 7
 
9.6%
<NA> 5
 
6.8%

Length

2023-12-13T09:55:30.048270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:55:30.126821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
211 61
83.6%
212 7
 
9.6%
na 5
 
6.8%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size716.0 B
미용업(일반)
64 
미용업(피부)
 
6
미용업(종합)
 
2
미용업(손톱ㆍ발톱)
 
1

Length

Max length10
Median length7
Mean length7.0410959
Min length7

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row미용업(일반)
2nd row미용업(일반)
3rd row미용업(일반)
4th row미용업(일반)
5th row미용업(일반)

Common Values

ValueCountFrequency (%)
미용업(일반) 64
87.7%
미용업(피부) 6
 
8.2%
미용업(종합) 2
 
2.7%
미용업(손톱ㆍ발톱) 1
 
1.4%

Length

2023-12-13T09:55:30.210188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:55:30.292821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업(일반 64
87.7%
미용업(피부 6
 
8.2%
미용업(종합 2
 
2.7%
미용업(손톱ㆍ발톱 1
 
1.4%

업소명
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-13T09:55:30.485301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.3424658
Min length3

Characters and Unicode

Total characters390
Distinct characters136
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

Unique73 ?
Unique (%)100.0%

Sample

1st row해일미용소
2nd row부용미용소
3rd row성화미용실
4th row성주연화미용실
5th row유진미용실
ValueCountFrequency (%)
2
 
2.5%
다인헤어 1
 
1.2%
사랑방미용실 1
 
1.2%
도도헤어샵 1
 
1.2%
헤어 1
 
1.2%
happy 1
 
1.2%
머리하는날 1
 
1.2%
119헤어 1
 
1.2%
헤어스타일 1
 
1.2%
드림미용실 1
 
1.2%
Other values (69) 69
86.2%
2023-12-13T09:55:30.824209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 39
 
10.0%
용 36
 
9.2%
실 32
 
8.2%
헤 23
 
5.9%
어 23
 
5.9%
리 9
 
2.3%
아 7
 
1.8%
7
 
1.8%
화 6
 
1.5%
소 5
 
1.3%
Other values (126) 203
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 360
92.3%
Lowercase Letter 10
 
2.6%
Space Separator 7
 
1.8%
Other Punctuation 6
 
1.5%
Decimal Number 3
 
0.8%
Uppercase Letter 2
 
0.5%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 39
 
10.8%
용 36
 
10.0%
실 32
 
8.9%
헤 23
 
6.4%
어 23
 
6.4%
리 9
 
2.5%
아 7
 
1.9%
화 6
 
1.7%
소 5
 
1.4%
장 5
 
1.4%
Other values (110) 175
48.6%
Lowercase Letter
ValueCountFrequency (%)
p 2
20.0%
a 2
20.0%
h 2
20.0%
i 1
10.0%
s 1
10.0%
y 1
10.0%
r 1
10.0%
Other Punctuation
ValueCountFrequency (%)
? 4
66.7%
' 1
 
16.7%
# 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 360
92.3%
Common 18
 
4.6%
Latin 12
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 39
 
10.8%
용 36
 
10.0%
실 32
 
8.9%
헤 23
 
6.4%
어 23
 
6.4%
리 9
 
2.5%
아 7
 
1.9%
화 6
 
1.7%
소 5
 
1.4%
장 5
 
1.4%
Other values (110) 175
48.6%
Common
ValueCountFrequency (%)
7
38.9%
? 4
22.2%
1 2
 
11.1%
( 1
 
5.6%
' 1
 
5.6%
9 1
 
5.6%
# 1
 
5.6%
) 1
 
5.6%
Latin
ValueCountFrequency (%)
J 2
16.7%
p 2
16.7%
a 2
16.7%
h 2
16.7%
i 1
8.3%
s 1
8.3%
y 1
8.3%
r 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 360
92.3%
ASCII 30
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 39
 
10.8%
용 36
 
10.0%
실 32
 
8.9%
헤 23
 
6.4%
어 23
 
6.4%
리 9
 
2.5%
아 7
 
1.9%
화 6
 
1.7%
소 5
 
1.4%
장 5
 
1.4%
Other values (110) 175
48.6%
ASCII
ValueCountFrequency (%)
7
23.3%
? 4
13.3%
J 2
 
6.7%
p 2
 
6.7%
a 2
 
6.7%
h 2
 
6.7%
1 2
 
6.7%
( 1
 
3.3%
i 1
 
3.3%
s 1
 
3.3%
Other values (6) 6
20.0%

업소소재지(도로명)
Text

Distinct71
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-13T09:55:31.054305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length21.69863
Min length18

Characters and Unicode

Total characters1584
Distinct characters68
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

Unique69 ?
Unique (%)94.5%

Sample

1st row경상북도 성주군 초전면 대장길 98-1
2nd row경상북도 성주군 가천면 가천로 74-2
3rd row경상북도 성주군 성주읍 성주로 3181-1
4th row경상북도 성주군 성주읍 시장길 18
5th row경상북도 성주군 초전면 대장길 97-2
ValueCountFrequency (%)
경상북도 73
19.7%
성주군 73
19.7%
성주읍 47
 
12.7%
시장길 16
 
4.3%
성주로 6
 
1.6%
성주읍1길 6
 
1.6%
초전면 6
 
1.6%
성주읍3길 5
 
1.3%
성주읍4길 4
 
1.1%
대장길 4
 
1.1%
Other values (101) 131
35.3%
2023-12-13T09:55:31.366696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
301
19.0%
성 148
 
9.3%
주 147
 
9.3%
경 78
 
4.9%
도 74
 
4.7%
북 73
 
4.6%
군 73
 
4.6%
상 73
 
4.6%
읍 65
 
4.1%
1 62
 
3.9%
Other values (58) 490
30.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 988
62.4%
Space Separator 301
 
19.0%
Decimal Number 253
 
16.0%
Dash Punctuation 35
 
2.2%
Other Punctuation 3
 
0.2%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 148
15.0%
주 147
14.9%
경 78
7.9%
도 74
7.5%
북 73
7.4%
군 73
7.4%
상 73
7.4%
읍 65
 
6.6%
길 53
 
5.4%
면 26
 
2.6%
Other values (43) 178
18.0%
Decimal Number
ValueCountFrequency (%)
1 62
24.5%
3 44
17.4%
2 39
15.4%
4 19
 
7.5%
8 18
 
7.1%
6 17
 
6.7%
9 15
 
5.9%
0 14
 
5.5%
7 14
 
5.5%
5 11
 
4.3%
Space Separator
ValueCountFrequency (%)
301
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 988
62.4%
Common 596
37.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 148
15.0%
주 147
14.9%
경 78
7.9%
도 74
7.5%
북 73
7.4%
군 73
7.4%
상 73
7.4%
읍 65
 
6.6%
길 53
 
5.4%
면 26
 
2.6%
Other values (43) 178
18.0%
Common
ValueCountFrequency (%)
301
50.5%
1 62
 
10.4%
3 44
 
7.4%
2 39
 
6.5%
- 35
 
5.9%
4 19
 
3.2%
8 18
 
3.0%
6 17
 
2.9%
9 15
 
2.5%
0 14
 
2.3%
Other values (5) 32
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 988
62.4%
ASCII 596
37.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301
50.5%
1 62
 
10.4%
3 44
 
7.4%
2 39
 
6.5%
- 35
 
5.9%
4 19
 
3.2%
8 18
 
3.0%
6 17
 
2.9%
9 15
 
2.5%
0 14
 
2.3%
Other values (5) 32
 
5.4%
Hangul
ValueCountFrequency (%)
성 148
15.0%
주 147
14.9%
경 78
7.9%
도 74
7.5%
북 73
7.4%
군 73
7.4%
상 73
7.4%
읍 65
 
6.6%
길 53
 
5.4%
면 26
 
2.6%
Other values (43) 178
18.0%

소재지전화
Text

MISSING 

Distinct59
Distinct (%)100.0%
Missing14
Missing (%)19.2%
Memory size716.0 B
2023-12-13T09:55:31.560660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters708
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

Unique59 ?
Unique (%)100.0%

Sample

1st row054-932-9755
2nd row054-932-4041
3rd row054-933-2918
4th row054-933-2468
5th row054-932-9875
ValueCountFrequency (%)
054-932-9755 1
 
1.7%
054-933-5879 1
 
1.7%
054-933-4072 1
 
1.7%
053-983-9096 1
 
1.7%
054-932-5707 1
 
1.7%
054-931-3532 1
 
1.7%
054-933-3088 1
 
1.7%
054-931-3500 1
 
1.7%
054-931-1441 1
 
1.7%
054-931-8618 1
 
1.7%
Other values (49) 49
83.1%
2023-12-13T09:55:31.840736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 118
16.7%
3 111
15.7%
0 90
12.7%
9 85
12.0%
4 82
11.6%
5 75
10.6%
1 40
 
5.6%
2 32
 
4.5%
6 27
 
3.8%
7 24
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 590
83.3%
Dash Punctuation 118
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 111
18.8%
0 90
15.3%
9 85
14.4%
4 82
13.9%
5 75
12.7%
1 40
 
6.8%
2 32
 
5.4%
6 27
 
4.6%
7 24
 
4.1%
8 24
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 118
16.7%
3 111
15.7%
0 90
12.7%
9 85
12.0%
4 82
11.6%
5 75
10.6%
1 40
 
5.6%
2 32
 
4.5%
6 27
 
3.8%
7 24
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 118
16.7%
3 111
15.7%
0 90
12.7%
9 85
12.0%
4 82
11.6%
5 75
10.6%
1 40
 
5.6%
2 32
 
4.5%
6 27
 
3.8%
7 24
 
3.4%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size716.0 B
일반미용업
64 
피부미용업
 
6
종합미용업
 
2
손톱.발톱미용업
 
1

Length

Max length8
Median length5
Mean length5.0410959
Min length5

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 64
87.7%
피부미용업 6
 
8.2%
종합미용업 2
 
2.7%
손톱.발톱미용업 1
 
1.4%

Length

2023-12-13T09:55:31.947357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:55:32.028422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 64
87.7%
피부미용업 6
 
8.2%
종합미용업 2
 
2.7%
손톱.발톱미용업 1
 
1.4%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
Minimum2017-11-03 00:00:00
Maximum2017-11-03 00:00:00
2023-12-13T09:55:32.110348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:55:32.196818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2023-12-13T09:55:32.252003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종코드업종명업소명업소소재지(도로명)소재지전화업태명
업종코드1.0000.8341.0001.0001.0000.834
업종명0.8341.0001.0000.7821.0001.000
업소명1.0001.0001.0001.0001.0001.000
업소소재지(도로명)1.0000.7821.0001.0001.0000.782
소재지전화1.0001.0001.0001.0001.0001.000
업태명0.8341.0001.0000.7821.0001.000
2023-12-13T09:55:32.327214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종코드업종명
업태명1.0000.6281.000
업종코드0.6281.0000.628
업종명1.0000.6281.000
2023-12-13T09:55:32.388497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종코드업종명업태명
업종코드1.0000.6280.628
업종명0.6281.0001.000
업태명0.6281.0001.000

Missing values

2023-12-13T09:55:29.877629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:55:29.961991image/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

업종코드업종명업소명업소소재지(도로명)소재지전화업태명데이터기준일자
0211미용업(일반)해일미용소경상북도 성주군 초전면 대장길 98-1054-932-9755일반미용업2017-11-03
1211미용업(일반)부용미용소경상북도 성주군 가천면 가천로 74-2054-932-4041일반미용업2017-11-03
2211미용업(일반)성화미용실경상북도 성주군 성주읍 성주로 3181-1054-933-2918일반미용업2017-11-03
3211미용업(일반)성주연화미용실경상북도 성주군 성주읍 시장길 18054-933-2468일반미용업2017-11-03
4211미용업(일반)유진미용실경상북도 성주군 초전면 대장길 97-2054-932-9875일반미용업2017-11-03
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