Overview

Dataset statistics

Number of variables6
Number of observations234
Missing cells12
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory49.5 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description대구광역시 서구 관내 공중위생업소(숙박·목욕·세탁업)의 위생서비스평가등급현황에 대한 데이터(업종,업소명,소재지,소재지전화,평가구분)로써 전반적인 위생관리수준을 2년마다 평가하며 이에 따라 최우수(녹색등급), 우수(황색등급), 일반관리대상(백색등급)으로 등급을 구분합니다.
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15052557/fileData.do

Alerts

연번 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 연번High correlation
등급(구분) is highly overall correlated with 연번High correlation
소재지전화 has 12 (5.1%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:40:48.187754
Analysis finished2024-03-15 01:40:49.667005
Duration1.48 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.5
Minimum1
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-15T10:40:49.869678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.65
Q159.25
median117.5
Q3175.75
95-th percentile222.35
Maximum234
Range233
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation67.694165
Coefficient of variation (CV)0.57612055
Kurtosis-1.2
Mean117.5
Median Absolute Deviation (MAD)58.5
Skewness0
Sum27495
Variance4582.5
MonotonicityStrictly increasing
2024-03-15T10:40:50.321782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
162 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
152 1
 
0.4%
153 1
 
0.4%
154 1
 
0.4%
155 1
 
0.4%
156 1
 
0.4%
157 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
234 1
0.4%
233 1
0.4%
232 1
0.4%
231 1
0.4%
230 1
0.4%
229 1
0.4%
228 1
0.4%
227 1
0.4%
226 1
0.4%
225 1
0.4%

업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
세탁업
105 
숙박업
104 
목욕장업
25 

Length

Max length4
Median length3
Mean length3.1068376
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 105
44.9%
숙박업 104
44.4%
목욕장업 25
 
10.7%

Length

2024-03-15T10:40:50.800679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:40:51.026701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 105
44.9%
숙박업 104
44.4%
목욕장업 25
 
10.7%

업소명
Text

Distinct222
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T10:40:52.105153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length5.2820513
Min length2

Characters and Unicode

Total characters1236
Distinct characters265
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

Unique213 ?
Unique (%)91.0%

Sample

1st rowF2 모텔
2nd row가나다모텔
3rd row기키모텔
4th row대구호텔 여기어때
5th row모텔마이더스
ValueCountFrequency (%)
여관 6
 
2.4%
백광세탁소 4
 
1.6%
모텔 4
 
1.6%
현대세탁소 3
 
1.2%
경북세탁소 2
 
0.8%
그린장 2
 
0.8%
호텔센텀 2
 
0.8%
세화컴퓨터크리닝 2
 
0.8%
서부세탁소 2
 
0.8%
백양세탁소 2
 
0.8%
Other values (221) 223
88.5%
2024-03-15T10:40:53.351075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 93
 
7.5%
탁 86
 
7.0%
소 80
 
6.5%
텔 57
 
4.6%
모 48
 
3.9%
여 39
 
3.2%
관 33
 
2.7%
장 27
 
2.2%
대 20
 
1.6%
19
 
1.5%
Other values (255) 734
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1166
94.3%
Uppercase Letter 25
 
2.0%
Space Separator 19
 
1.5%
Decimal Number 7
 
0.6%
Lowercase Letter 7
 
0.6%
Open Punctuation 6
 
0.5%
Close Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 93
 
8.0%
탁 86
 
7.4%
소 80
 
6.9%
텔 57
 
4.9%
모 48
 
4.1%
여 39
 
3.3%
관 33
 
2.8%
장 27
 
2.3%
대 20
 
1.7%
리 17
 
1.5%
Other values (227) 666
57.1%
Uppercase Letter
ValueCountFrequency (%)
T 4
16.0%
O 3
12.0%
H 3
12.0%
E 3
12.0%
L 3
12.0%
M 2
8.0%
F 1
 
4.0%
Y 1
 
4.0%
P 1
 
4.0%
A 1
 
4.0%
Other values (3) 3
12.0%
Lowercase Letter
ValueCountFrequency (%)
t 1
14.3%
k 1
14.3%
e 1
14.3%
n 1
14.3%
s 1
14.3%
y 1
14.3%
i 1
14.3%
Decimal Number
ValueCountFrequency (%)
5 2
28.6%
2 2
28.6%
8 1
14.3%
3 1
14.3%
6 1
14.3%
Space Separator
ValueCountFrequency (%)
19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1166
94.3%
Common 38
 
3.1%
Latin 32
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 93
 
8.0%
탁 86
 
7.4%
소 80
 
6.9%
텔 57
 
4.9%
모 48
 
4.1%
여 39
 
3.3%
관 33
 
2.8%
장 27
 
2.3%
대 20
 
1.7%
리 17
 
1.5%
Other values (227) 666
57.1%
Latin
ValueCountFrequency (%)
T 4
 
12.5%
O 3
 
9.4%
H 3
 
9.4%
E 3
 
9.4%
L 3
 
9.4%
M 2
 
6.2%
F 1
 
3.1%
t 1
 
3.1%
Y 1
 
3.1%
P 1
 
3.1%
Other values (10) 10
31.2%
Common
ValueCountFrequency (%)
19
50.0%
( 6
 
15.8%
) 6
 
15.8%
5 2
 
5.3%
2 2
 
5.3%
8 1
 
2.6%
3 1
 
2.6%
6 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1166
94.3%
ASCII 70
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 93
 
8.0%
탁 86
 
7.4%
소 80
 
6.9%
텔 57
 
4.9%
모 48
 
4.1%
여 39
 
3.3%
관 33
 
2.8%
장 27
 
2.3%
대 20
 
1.7%
리 17
 
1.5%
Other values (227) 666
57.1%
ASCII
ValueCountFrequency (%)
19
27.1%
( 6
 
8.6%
) 6
 
8.6%
T 4
 
5.7%
O 3
 
4.3%
H 3
 
4.3%
E 3
 
4.3%
L 3
 
4.3%
5 2
 
2.9%
2 2
 
2.9%
Other values (18) 19
27.1%

영업소 주소(도로명)
Text

Distinct232
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T10:40:54.409808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length40
Mean length25.474359
Min length20

Characters and Unicode

Total characters5961
Distinct characters76
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

Unique230 ?
Unique (%)98.3%

Sample

1st row대구광역시 서구 국채보상로46길 43-6 (평리동)
2nd row대구광역시 서구 팔달로 96 (비산동)
3rd row대구광역시 서구 국채보상로42길 41 (평리동)
4th row대구광역시 서구 달서로 151 (비산동)
5th row대구광역시 서구 서대구로 370-1 (비산동)
ValueCountFrequency (%)
대구광역시 234
19.5%
서구 234
19.5%
평리동 85
 
7.1%
비산동 80
 
6.7%
내당동 40
 
3.3%
서대구로 29
 
2.4%
국채보상로46길 15
 
1.3%
1층 15
 
1.3%
국채보상로42길 11
 
0.9%
중리동 11
 
0.9%
Other values (276) 445
37.1%
2024-03-15T10:40:55.655990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
965
 
16.2%
구 532
 
8.9%
서 321
 
5.4%
대 314
 
5.3%
시 236
 
4.0%
동 236
 
4.0%
광 234
 
3.9%
역 234
 
3.9%
로 234
 
3.9%
( 233
 
3.9%
Other values (66) 2422
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3494
58.6%
Space Separator 965
 
16.2%
Decimal Number 939
 
15.8%
Open Punctuation 233
 
3.9%
Close Punctuation 233
 
3.9%
Dash Punctuation 69
 
1.2%
Other Punctuation 27
 
0.5%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 532
15.2%
서 321
 
9.2%
대 314
 
9.0%
시 236
 
6.8%
동 236
 
6.8%
광 234
 
6.7%
역 234
 
6.7%
로 234
 
6.7%
길 165
 
4.7%
리 118
 
3.4%
Other values (49) 870
24.9%
Decimal Number
ValueCountFrequency (%)
1 197
21.0%
2 135
14.4%
3 118
12.6%
4 110
11.7%
5 95
10.1%
6 87
9.3%
7 69
 
7.3%
0 47
 
5.0%
8 46
 
4.9%
9 35
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 26
96.3%
. 1
 
3.7%
Space Separator
ValueCountFrequency (%)
965
100.0%
Open Punctuation
ValueCountFrequency (%)
( 233
100.0%
Close Punctuation
ValueCountFrequency (%)
) 233
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3494
58.6%
Common 2466
41.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 532
15.2%
서 321
 
9.2%
대 314
 
9.0%
시 236
 
6.8%
동 236
 
6.8%
광 234
 
6.7%
역 234
 
6.7%
로 234
 
6.7%
길 165
 
4.7%
리 118
 
3.4%
Other values (49) 870
24.9%
Common
ValueCountFrequency (%)
965
39.1%
( 233
 
9.4%
) 233
 
9.4%
1 197
 
8.0%
2 135
 
5.5%
3 118
 
4.8%
4 110
 
4.5%
5 95
 
3.9%
6 87
 
3.5%
7 69
 
2.8%
Other values (6) 224
 
9.1%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3494
58.6%
ASCII 2467
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
965
39.1%
( 233
 
9.4%
) 233
 
9.4%
1 197
 
8.0%
2 135
 
5.5%
3 118
 
4.8%
4 110
 
4.5%
5 95
 
3.9%
6 87
 
3.5%
7 69
 
2.8%
Other values (7) 225
 
9.1%
Hangul
ValueCountFrequency (%)
구 532
15.2%
서 321
 
9.2%
대 314
 
9.0%
시 236
 
6.8%
동 236
 
6.8%
광 234
 
6.7%
역 234
 
6.7%
로 234
 
6.7%
길 165
 
4.7%
리 118
 
3.4%
Other values (49) 870
24.9%

소재지전화
Text

MISSING 

Distinct222
Distinct (%)100.0%
Missing12
Missing (%)5.1%
Memory size2.0 KiB
2024-03-15T10:40:56.585720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2664
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

Unique222 ?
Unique (%)100.0%

Sample

1st row053-552-2400
2nd row053-359-2020
3rd row053-557-6699
4th row053-557-5667
5th row053-354-9055
ValueCountFrequency (%)
053-523-9918 1
 
0.5%
053-527-8572 1
 
0.5%
053-563-3972 1
 
0.5%
053-552-4249 1
 
0.5%
053-557-3732 1
 
0.5%
053-567-5604 1
 
0.5%
053-358-1318 1
 
0.5%
053-566-9672 1
 
0.5%
053-558-8362 1
 
0.5%
053-551-2231 1
 
0.5%
Other values (212) 212
95.5%
2024-03-15T10:40:57.757400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 642
24.1%
- 444
16.7%
3 400
15.0%
0 330
12.4%
6 163
 
6.1%
2 144
 
5.4%
7 135
 
5.1%
1 117
 
4.4%
8 102
 
3.8%
9 94
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2220
83.3%
Dash Punctuation 444
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 642
28.9%
3 400
18.0%
0 330
14.9%
6 163
 
7.3%
2 144
 
6.5%
7 135
 
6.1%
1 117
 
5.3%
8 102
 
4.6%
9 94
 
4.2%
4 93
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 642
24.1%
- 444
16.7%
3 400
15.0%
0 330
12.4%
6 163
 
6.1%
2 144
 
5.4%
7 135
 
5.1%
1 117
 
4.4%
8 102
 
3.8%
9 94
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 642
24.1%
- 444
16.7%
3 400
15.0%
0 330
12.4%
6 163
 
6.1%
2 144
 
5.4%
7 135
 
5.1%
1 117
 
4.4%
8 102
 
3.8%
9 94
 
3.5%

등급(구분)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
백색(일반)
160 
황색(우수)
47 
녹색(최우수)
27 

Length

Max length7
Median length6
Mean length6.1153846
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row녹색(최우수)
2nd row녹색(최우수)
3rd row녹색(최우수)
4th row녹색(최우수)
5th row녹색(최우수)

Common Values

ValueCountFrequency (%)
백색(일반) 160
68.4%
황색(우수) 47
 
20.1%
녹색(최우수) 27
 
11.5%

Length

2024-03-15T10:40:58.238285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:40:58.551957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
백색(일반 160
68.4%
황색(우수 47
 
20.1%
녹색(최우수 27
 
11.5%

Interactions

2024-03-15T10:40:48.732155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:40:58.756265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명등급(구분)
연번1.0000.8930.855
업종명0.8931.0000.740
등급(구분)0.8550.7401.000
2024-03-15T10:40:58.993261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등급(구분)업종명
등급(구분)1.0000.397
업종명0.3971.000
2024-03-15T10:40:59.236595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명등급(구분)
연번1.0000.8280.764
업종명0.8281.0000.397
등급(구분)0.7640.3971.000

Missing values

2024-03-15T10:40:49.061706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:40:49.437835image/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

연번업종명업소명영업소 주소(도로명)소재지전화등급(구분)
01숙박업F2 모텔대구광역시 서구 국채보상로46길 43-6 (평리동)053-552-2400녹색(최우수)
12숙박업가나다모텔대구광역시 서구 팔달로 96 (비산동)053-359-2020녹색(최우수)
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