Overview

Dataset statistics

Number of variables6
Number of observations755
Missing cells284
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.3 KiB
Average record size in memory49.2 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description대구광역시 서구 관내 공중위생업소(이·미용업)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/15052556/fileData.do

Alerts

연번 is highly overall correlated with 등급(구분)High correlation
등급(구분) is highly overall correlated with 연번High correlation
업종명 is highly imbalanced (60.4%)Imbalance
소재지전화 has 284 (37.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 23:24:28.879831
Analysis finished2024-03-14 23:24:30.741576
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct755
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378
Minimum1
Maximum755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-03-15T08:24:30.992559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38.7
Q1189.5
median378
Q3566.5
95-th percentile717.3
Maximum755
Range754
Interquartile range (IQR)377

Descriptive statistics

Standard deviation218.09402
Coefficient of variation (CV)0.5769683
Kurtosis-1.2
Mean378
Median Absolute Deviation (MAD)189
Skewness0
Sum285390
Variance47565
MonotonicityStrictly increasing
2024-03-15T08:24:31.738591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
509 1
 
0.1%
500 1
 
0.1%
501 1
 
0.1%
502 1
 
0.1%
503 1
 
0.1%
504 1
 
0.1%
505 1
 
0.1%
506 1
 
0.1%
507 1
 
0.1%
Other values (745) 745
98.7%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
755 1
0.1%
754 1
0.1%
753 1
0.1%
752 1
0.1%
751 1
0.1%
750 1
0.1%
749 1
0.1%
748 1
0.1%
747 1
0.1%
746 1
0.1%

업종명
Categorical

IMBALANCE 

Distinct14
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
일반미용업
551 
이용업
94 
네일미용업
 
39
피부미용업
 
31
네일미용업, 화장ㆍ분장 미용업
 
7
Other values (9)
 
33

Length

Max length23
Median length5
Mean length5.1350993
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 551
73.0%
이용업 94
 
12.5%
네일미용업 39
 
5.2%
피부미용업 31
 
4.1%
네일미용업, 화장ㆍ분장 미용업 7
 
0.9%
종합미용업 6
 
0.8%
화장ㆍ분장 미용업 6
 
0.8%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 4
 
0.5%
일반미용업, 네일미용업 4
 
0.5%
일반미용업, 피부미용업 4
 
0.5%
Other values (4) 9
 
1.2%

Length

2024-03-15T08:24:32.276689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 560
69.5%
이용업 94
 
11.7%
네일미용업 57
 
7.1%
피부미용업 45
 
5.6%
미용업 23
 
2.9%
화장ㆍ분장 21
 
2.6%
종합미용업 6
 
0.7%

업소명
Text

Distinct709
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-03-15T08:24:33.526937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length5.4980132
Min length1

Characters and Unicode

Total characters4151
Distinct characters444
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique667 ?
Unique (%)88.3%

Sample

1st row경운이용소
2nd row뉴대구이용소
3rd row새광명이용소
4th row샵케이대구01번지(#K-DG01st)
5th row엘이용소
ValueCountFrequency (%)
헤어 5
 
0.6%
hair 5
 
0.6%
진미용실 4
 
0.5%
미용실 4
 
0.5%
헤어샵 4
 
0.5%
헤어스케치 3
 
0.4%
3
 
0.4%
헤어스토리 3
 
0.4%
성화미용실 3
 
0.4%
수헤어샵 3
 
0.4%
Other values (730) 775
95.4%
2024-03-15T08:24:35.142617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 282
 
6.8%
헤 280
 
6.7%
용 276
 
6.6%
미 249
 
6.0%
실 206
 
5.0%
이 126
 
3.0%
소 85
 
2.0%
리 78
 
1.9%
샵 75
 
1.8%
스 62
 
1.5%
Other values (434) 2432
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3671
88.4%
Uppercase Letter 146
 
3.5%
Lowercase Letter 142
 
3.4%
Space Separator 57
 
1.4%
Open Punctuation 42
 
1.0%
Close Punctuation 42
 
1.0%
Other Punctuation 29
 
0.7%
Decimal Number 21
 
0.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 282
 
7.7%
헤 280
 
7.6%
용 276
 
7.5%
미 249
 
6.8%
실 206
 
5.6%
이 126
 
3.4%
소 85
 
2.3%
리 78
 
2.1%
샵 75
 
2.0%
스 62
 
1.7%
Other values (372) 1952
53.2%
Lowercase Letter
ValueCountFrequency (%)
a 21
14.8%
i 19
13.4%
e 15
10.6%
o 11
 
7.7%
s 10
 
7.0%
n 9
 
6.3%
r 8
 
5.6%
l 7
 
4.9%
u 6
 
4.2%
t 6
 
4.2%
Other values (12) 30
21.1%
Uppercase Letter
ValueCountFrequency (%)
A 14
 
9.6%
I 14
 
9.6%
N 13
 
8.9%
O 12
 
8.2%
S 10
 
6.8%
H 10
 
6.8%
L 9
 
6.2%
B 9
 
6.2%
J 8
 
5.5%
K 7
 
4.8%
Other values (12) 40
27.4%
Decimal Number
ValueCountFrequency (%)
7 4
19.0%
8 4
19.0%
5 4
19.0%
1 3
14.3%
0 3
14.3%
3 1
 
4.8%
2 1
 
4.8%
9 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
& 12
41.4%
# 6
20.7%
. 4
 
13.8%
' 3
 
10.3%
, 2
 
6.9%
: 2
 
6.9%
Space Separator
ValueCountFrequency (%)
57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3669
88.4%
Latin 288
 
6.9%
Common 192
 
4.6%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 282
 
7.7%
헤 280
 
7.6%
용 276
 
7.5%
미 249
 
6.8%
실 206
 
5.6%
이 126
 
3.4%
소 85
 
2.3%
리 78
 
2.1%
샵 75
 
2.0%
스 62
 
1.7%
Other values (370) 1950
53.1%
Latin
ValueCountFrequency (%)
a 21
 
7.3%
i 19
 
6.6%
e 15
 
5.2%
A 14
 
4.9%
I 14
 
4.9%
N 13
 
4.5%
O 12
 
4.2%
o 11
 
3.8%
S 10
 
3.5%
s 10
 
3.5%
Other values (34) 149
51.7%
Common
ValueCountFrequency (%)
57
29.7%
( 42
21.9%
) 42
21.9%
& 12
 
6.2%
# 6
 
3.1%
. 4
 
2.1%
7 4
 
2.1%
8 4
 
2.1%
5 4
 
2.1%
' 3
 
1.6%
Other values (8) 14
 
7.3%
Han
ValueCountFrequency (%)
林 1
50.0%
美 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3669
88.4%
ASCII 480
 
11.6%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 282
 
7.7%
헤 280
 
7.6%
용 276
 
7.5%
미 249
 
6.8%
실 206
 
5.6%
이 126
 
3.4%
소 85
 
2.3%
리 78
 
2.1%
샵 75
 
2.0%
스 62
 
1.7%
Other values (370) 1950
53.1%
ASCII
ValueCountFrequency (%)
57
 
11.9%
( 42
 
8.8%
) 42
 
8.8%
a 21
 
4.4%
i 19
 
4.0%
e 15
 
3.1%
A 14
 
2.9%
I 14
 
2.9%
N 13
 
2.7%
& 12
 
2.5%
Other values (52) 231
48.1%
CJK
ValueCountFrequency (%)
林 1
50.0%
美 1
50.0%

영업소 주소(도로명)
Text

Distinct745
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-03-15T08:24:36.310268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length50
Mean length27.344371
Min length21

Characters and Unicode

Total characters20645
Distinct characters123
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique735 ?
Unique (%)97.4%

Sample

1st row대구광역시 서구 서대구로3길 37 (내당동)
2nd row대구광역시 서구 서대구로 45, 3층 (내당동)
3rd row대구광역시 서구 국채보상로45길 6 (평리동)
4th row대구광역시 서구 달서로 39, 1층 (내당동)
5th row대구광역시 서구 국채보상로 232, 8층 (평리동)
ValueCountFrequency (%)
대구광역시 755
18.0%
서구 755
18.0%
평리동 233
 
5.6%
비산동 225
 
5.4%
1층 223
 
5.3%
내당동 196
 
4.7%
중리동 51
 
1.2%
평리로 26
 
0.6%
국채보상로 24
 
0.6%
달구벌대로 22
 
0.5%
Other values (610) 1682
40.1%
2024-03-15T08:24:38.036129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3438
 
16.7%
구 1690
 
8.2%
서 972
 
4.7%
대 968
 
4.7%
1 887
 
4.3%
동 796
 
3.9%
) 758
 
3.7%
( 758
 
3.7%
시 758
 
3.7%
역 758
 
3.7%
Other values (113) 8862
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11692
56.6%
Decimal Number 3467
 
16.8%
Space Separator 3438
 
16.7%
Close Punctuation 758
 
3.7%
Open Punctuation 758
 
3.7%
Other Punctuation 333
 
1.6%
Dash Punctuation 176
 
0.9%
Uppercase Letter 20
 
0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1690
14.5%
서 972
 
8.3%
대 968
 
8.3%
동 796
 
6.8%
시 758
 
6.5%
역 758
 
6.5%
광 758
 
6.5%
로 752
 
6.4%
길 573
 
4.9%
리 369
 
3.2%
Other values (88) 3298
28.2%
Decimal Number
ValueCountFrequency (%)
1 887
25.6%
2 437
12.6%
3 436
12.6%
6 310
 
8.9%
4 289
 
8.3%
5 284
 
8.2%
7 265
 
7.6%
0 221
 
6.4%
8 184
 
5.3%
9 154
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
A 7
35.0%
D 4
20.0%
B 3
15.0%
C 2
 
10.0%
E 1
 
5.0%
X 1
 
5.0%
K 1
 
5.0%
T 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 332
99.7%
/ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
3438
100.0%
Close Punctuation
ValueCountFrequency (%)
) 758
100.0%
Open Punctuation
ValueCountFrequency (%)
( 758
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11692
56.6%
Common 8930
43.3%
Latin 23
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1690
14.5%
서 972
 
8.3%
대 968
 
8.3%
동 796
 
6.8%
시 758
 
6.5%
역 758
 
6.5%
광 758
 
6.5%
로 752
 
6.4%
길 573
 
4.9%
리 369
 
3.2%
Other values (88) 3298
28.2%
Common
ValueCountFrequency (%)
3438
38.5%
1 887
 
9.9%
) 758
 
8.5%
( 758
 
8.5%
2 437
 
4.9%
3 436
 
4.9%
, 332
 
3.7%
6 310
 
3.5%
4 289
 
3.2%
5 284
 
3.2%
Other values (6) 1001
 
11.2%
Latin
ValueCountFrequency (%)
A 7
30.4%
D 4
17.4%
e 3
13.0%
B 3
13.0%
C 2
 
8.7%
E 1
 
4.3%
X 1
 
4.3%
K 1
 
4.3%
T 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11692
56.6%
ASCII 8953
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3438
38.4%
1 887
 
9.9%
) 758
 
8.5%
( 758
 
8.5%
2 437
 
4.9%
3 436
 
4.9%
, 332
 
3.7%
6 310
 
3.5%
4 289
 
3.2%
5 284
 
3.2%
Other values (15) 1024
 
11.4%
Hangul
ValueCountFrequency (%)
구 1690
14.5%
서 972
 
8.3%
대 968
 
8.3%
동 796
 
6.8%
시 758
 
6.5%
역 758
 
6.5%
광 758
 
6.5%
로 752
 
6.4%
길 573
 
4.9%
리 369
 
3.2%
Other values (88) 3298
28.2%

소재지전화
Text

MISSING 

Distinct470
Distinct (%)99.8%
Missing284
Missing (%)37.6%
Memory size6.0 KiB
2024-03-15T08:24:38.935415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.019108
Min length12

Characters and Unicode

Total characters5661
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

Unique469 ?
Unique (%)99.6%

Sample

1st row053-553-0441
2nd row053-566-5635
3rd row053-553-8427
4th row053-571-0924
5th row053-562-3340
ValueCountFrequency (%)
053-553-3367 2
 
0.4%
053-524-0895 1
 
0.2%
053-566-0032 1
 
0.2%
053-566-4281 1
 
0.2%
053-565-8808 1
 
0.2%
053-523-0918 1
 
0.2%
053-572-7766 1
 
0.2%
053-270-8006 1
 
0.2%
053-557-1619 1
 
0.2%
053-353-5411 1
 
0.2%
Other values (460) 460
97.7%
2024-03-15T08:24:40.072680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1292
22.8%
- 942
16.6%
3 804
14.2%
0 678
12.0%
6 378
 
6.7%
2 362
 
6.4%
7 317
 
5.6%
1 252
 
4.5%
8 232
 
4.1%
4 225
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4719
83.4%
Dash Punctuation 942
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1292
27.4%
3 804
17.0%
0 678
14.4%
6 378
 
8.0%
2 362
 
7.7%
7 317
 
6.7%
1 252
 
5.3%
8 232
 
4.9%
4 225
 
4.8%
9 179
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5661
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1292
22.8%
- 942
16.6%
3 804
14.2%
0 678
12.0%
6 378
 
6.7%
2 362
 
6.4%
7 317
 
5.6%
1 252
 
4.5%
8 232
 
4.1%
4 225
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1292
22.8%
- 942
16.6%
3 804
14.2%
0 678
12.0%
6 378
 
6.7%
2 362
 
6.4%
7 317
 
5.6%
1 252
 
4.5%
8 232
 
4.1%
4 225
 
4.0%

등급(구분)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
황색(우수)
515 
백색(일반)
176 
녹색(최우수)
64 

Length

Max length7
Median length6
Mean length6.0847682
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 (%)
황색(우수) 515
68.2%
백색(일반) 176
 
23.3%
녹색(최우수) 64
 
8.5%

Length

2024-03-15T08:24:40.373330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:24:40.641147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
황색(우수 515
68.2%
백색(일반 176
 
23.3%
녹색(최우수 64
 
8.5%

Interactions

2024-03-15T08:24:29.652192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:24:40.890938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명등급(구분)
연번1.0000.7380.915
업종명0.7381.0000.251
등급(구분)0.9150.2511.000
2024-03-15T08:24:41.039686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등급(구분)업종명
등급(구분)1.0000.142
업종명0.1421.000
2024-03-15T08:24:41.186531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명등급(구분)
연번1.0000.4100.874
업종명0.4101.0000.142
등급(구분)0.8740.1421.000

Missing values

2024-03-15T08:24:30.078264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:24:30.511341image/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)
01이용업경운이용소대구광역시 서구 서대구로3길 37 (내당동)<NA>녹색(최우수)
12이용업뉴대구이용소대구광역시 서구 서대구로 45, 3층 (내당동)<NA>녹색(최우수)
23이용업새광명이용소대구광역시 서구 국채보상로45길 6 (평리동)053-553-0441녹색(최우수)
34이용업샵케이대구01번지(#K-DG01st)대구광역시 서구 달서로 39, 1층 (내당동)<NA>녹색(최우수)
45이용업엘이용소대구광역시 서구 국채보상로 232, 8층 (평리동)<NA>녹색(최우수)
56이용업우정이용소대구광역시 서구 서대구로7길 10, 1동 105호 (내당동,대평아파트 상가)053-566-5635녹색(최우수)
67이용업훈이용소대구광역시 서구 북비산로47길 9 (평리동)<NA>녹색(최우수)
78이용업O땡큐천연머리염색방대구광역시 서구 평리로 236, 131호 (내당동)<NA>황색(우수)
89이용업가보자이용소대구광역시 서구 서대구로68길 10 (비산동)053-553-8427황색(우수)
910이용업골드대구광역시 서구 문화로66길 17, 1층 (비산동)<NA>황색(우수)
연번업종명업소명영업소 주소(도로명)소재지전화등급(구분)
745746일반미용업현대미용실대구광역시 서구 통학로49길 15 (비산동)053-553-6231백색(일반)
746747일반미용업현헤어샵대구광역시 서구 통학로46길 30 (평리동)<NA>백색(일반)
747748일반미용업혜원미용실대구광역시 서구 북비산로74길 38-3 (비산동)053-563-8802백색(일반)
748749일반미용업홍미용실대구광역시 서구 달서천로61안길 27 (비산동)053-352-5925백색(일반)
749750일반미용업홍미용실대구광역시 서구 달서천로57길 9-1 (비산동)053-354-1922백색(일반)
750751일반미용업홍실미용실대구광역시 서구 통학로 39, 상가동 105호 (내당동,홍실2차 아파트)053-551-7729백색(일반)
751752일반미용업황실미용타운대구광역시 서구 문화로63길 16 (비산동)053-553-1716백색(일반)
752753네일미용업Nail st35대구광역시 서구 달서천로55길 35, 1층 (비산동)<NA>백색(일반)
753754피부미용업바바뷰티대구광역시 서구 평리로73길 25, 1층 (평리동)<NA>백색(일반)
754755일반미용업, 네일미용업프로마녀 뷰티 작업실대구광역시 서구 국채보상로50길 38-15, 1층 101호 (평리동)<NA>백색(일반)