Overview

Dataset statistics

Number of variables5
Number of observations322
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory41.4 B

Variable types

Numeric1
Categorical1
DateTime1
Text2

Dataset

Description영광군 공중위생업소에 관한 데이터입니다. 내용으로는 미용업, 이용업, 위생관리용역업의 업소명 소재지 신고일자 등 현황 정보를 제공합니다
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15006991/fileData.do

Alerts

연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:18:23.157191
Analysis finished2023-12-12 19:18:23.738199
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct322
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.5
Minimum1
Maximum322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T04:18:23.813228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.05
Q181.25
median161.5
Q3241.75
95-th percentile305.95
Maximum322
Range321
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation93.097619
Coefficient of variation (CV)0.57645585
Kurtosis-1.2
Mean161.5
Median Absolute Deviation (MAD)80.5
Skewness0
Sum52003
Variance8667.1667
MonotonicityStrictly increasing
2023-12-13T04:18:24.005421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
243 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
214 1
 
0.3%
Other values (312) 312
96.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
322 1
0.3%
321 1
0.3%
320 1
0.3%
319 1
0.3%
318 1
0.3%
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%
313 1
0.3%

업종명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
미용업(일반)
60 
미용업
59 
건물위생관리업
42 
숙박업(일반)
39 
이용업
32 
Other values (8)
90 

Length

Max length19
Median length7
Mean length5.484472
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
미용업(일반) 60
18.6%
미용업 59
18.3%
건물위생관리업 42
13.0%
숙박업(일반) 39
12.1%
이용업 32
9.9%
세탁업 28
8.7%
목욕장업 19
 
5.9%
미용업(피부) 18
 
5.6%
숙박업(생활) 9
 
2.8%
미용업(손톱ㆍ발톱) 7
 
2.2%
Other values (3) 9
 
2.8%

Length

2023-12-13T04:18:24.168016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업(일반 60
18.6%
미용업 59
18.3%
건물위생관리업 42
13.0%
숙박업(일반 39
12.1%
이용업 32
9.9%
세탁업 28
8.7%
목욕장업 19
 
5.9%
미용업(피부 19
 
5.9%
숙박업(생활 9
 
2.8%
미용업(손톱ㆍ발톱 8
 
2.5%
Other values (2) 8
 
2.5%

신고일자
Date

Distinct297
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum1966-03-17 00:00:00
Maximum2022-09-05 00:00:00
2023-12-13T04:18:24.318808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:24.490152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct315
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-13T04:18:24.808623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length6.0714286
Min length3

Characters and Unicode

Total characters1955
Distinct characters343
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

Unique308 ?
Unique (%)95.7%

Sample

1st row궁전파크여관
2nd row금수장여관
3rd row서해장모텔
4th row휴 모텔
5th row세종모텔
ValueCountFrequency (%)
헤어 8
 
2.0%
hair 6
 
1.5%
무인호텔 3
 
0.7%
by 3
 
0.7%
모텔 3
 
0.7%
터미널이발관 2
 
0.5%
중앙목욕탕 2
 
0.5%
미용실 2
 
0.5%
영무파라드 2
 
0.5%
헤어샵 2
 
0.5%
Other values (352) 369
91.8%
2023-12-13T04:18:25.353718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
 
4.1%
미 61
 
3.1%
어 59
 
3.0%
헤 53
 
2.7%
용 51
 
2.6%
이 51
 
2.6%
실 41
 
2.1%
발 35
 
1.8%
텔 35
 
1.8%
리 33
 
1.7%
Other values (333) 1456
74.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1682
86.0%
Space Separator 80
 
4.1%
Lowercase Letter 63
 
3.2%
Uppercase Letter 56
 
2.9%
Close Punctuation 26
 
1.3%
Open Punctuation 26
 
1.3%
Decimal Number 12
 
0.6%
Other Punctuation 10
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 61
 
3.6%
어 59
 
3.5%
헤 53
 
3.2%
용 51
 
3.0%
이 51
 
3.0%
실 41
 
2.4%
발 35
 
2.1%
텔 35
 
2.1%
리 33
 
2.0%
소 32
 
1.9%
Other values (284) 1231
73.2%
Uppercase Letter
ValueCountFrequency (%)
H 11
19.6%
C 7
12.5%
R 5
8.9%
T 5
8.9%
O 4
 
7.1%
A 4
 
7.1%
I 3
 
5.4%
S 3
 
5.4%
M 3
 
5.4%
K 2
 
3.6%
Other values (8) 9
16.1%
Lowercase Letter
ValueCountFrequency (%)
a 10
15.9%
i 7
11.1%
r 7
11.1%
y 6
9.5%
b 5
7.9%
o 5
7.9%
s 4
 
6.3%
e 4
 
6.3%
m 3
 
4.8%
n 3
 
4.8%
Other values (6) 9
14.3%
Decimal Number
ValueCountFrequency (%)
2 2
16.7%
4 2
16.7%
9 2
16.7%
7 2
16.7%
1 1
8.3%
5 1
8.3%
3 1
8.3%
0 1
8.3%
Other Punctuation
ValueCountFrequency (%)
# 4
40.0%
. 3
30.0%
& 2
20.0%
' 1
 
10.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1682
86.0%
Common 154
 
7.9%
Latin 119
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 61
 
3.6%
어 59
 
3.5%
헤 53
 
3.2%
용 51
 
3.0%
이 51
 
3.0%
실 41
 
2.4%
발 35
 
2.1%
텔 35
 
2.1%
리 33
 
2.0%
소 32
 
1.9%
Other values (284) 1231
73.2%
Latin
ValueCountFrequency (%)
H 11
 
9.2%
a 10
 
8.4%
C 7
 
5.9%
i 7
 
5.9%
r 7
 
5.9%
y 6
 
5.0%
b 5
 
4.2%
o 5
 
4.2%
R 5
 
4.2%
T 5
 
4.2%
Other values (24) 51
42.9%
Common
ValueCountFrequency (%)
80
51.9%
) 26
 
16.9%
( 26
 
16.9%
# 4
 
2.6%
. 3
 
1.9%
2 2
 
1.3%
4 2
 
1.3%
9 2
 
1.3%
& 2
 
1.3%
7 2
 
1.3%
Other values (5) 5
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1682
86.0%
ASCII 273
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80
29.3%
) 26
 
9.5%
( 26
 
9.5%
H 11
 
4.0%
a 10
 
3.7%
C 7
 
2.6%
i 7
 
2.6%
r 7
 
2.6%
y 6
 
2.2%
b 5
 
1.8%
Other values (39) 88
32.2%
Hangul
ValueCountFrequency (%)
미 61
 
3.6%
어 59
 
3.5%
헤 53
 
3.2%
용 51
 
3.0%
이 51
 
3.0%
실 41
 
2.4%
발 35
 
2.1%
텔 35
 
2.1%
리 33
 
2.0%
소 32
 
1.9%
Other values (284) 1231
73.2%

업소소재지(도로명)
Text

Distinct293
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-13T04:18:25.669169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length22.363354
Min length9

Characters and Unicode

Total characters7201
Distinct characters132
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

Unique271 ?
Unique (%)84.2%

Sample

1st row전라남도 영광군 영광읍 신남로 245
2nd row전라남도 영광군 영광읍 신남로 231
3rd row전라남도 영광군 염산면 천년로1길 15
4th row전라남도 영광군 홍농읍 연우로 328
5th row전라남도 영광군 영광읍 중앙로 147-2
ValueCountFrequency (%)
전라남도 319
18.6%
영광군 319
18.6%
영광읍 205
 
12.0%
1층 54
 
3.2%
홍농읍 50
 
2.9%
신남로 34
 
2.0%
홍농로 28
 
1.6%
법성면 26
 
1.5%
옥당로 21
 
1.2%
백산길 19
 
1.1%
Other values (330) 637
37.2%
2023-12-13T04:18:26.133749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1390
19.3%
영 529
 
7.3%
광 526
 
7.3%
남 371
 
5.2%
군 328
 
4.6%
라 322
 
4.5%
전 319
 
4.4%
도 319
 
4.4%
1 302
 
4.2%
읍 268
 
3.7%
Other values (122) 2527
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4426
61.5%
Space Separator 1390
 
19.3%
Decimal Number 1113
 
15.5%
Dash Punctuation 114
 
1.6%
Other Punctuation 76
 
1.1%
Open Punctuation 26
 
0.4%
Close Punctuation 26
 
0.4%
Uppercase Letter 22
 
0.3%
Math Symbol 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 529
12.0%
광 526
11.9%
남 371
 
8.4%
군 328
 
7.4%
라 322
 
7.3%
전 319
 
7.2%
도 319
 
7.2%
읍 268
 
6.1%
로 236
 
5.3%
길 163
 
3.7%
Other values (97) 1045
23.6%
Decimal Number
ValueCountFrequency (%)
1 302
27.1%
2 147
13.2%
3 114
 
10.2%
4 112
 
10.1%
5 91
 
8.2%
6 83
 
7.5%
7 83
 
7.5%
8 62
 
5.6%
9 60
 
5.4%
0 59
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
E 6
27.3%
C 4
18.2%
A 3
13.6%
P 3
13.6%
R 3
13.6%
L 3
13.6%
Math Symbol
ValueCountFrequency (%)
> 3
37.5%
< 3
37.5%
~ 2
25.0%
Other Punctuation
ValueCountFrequency (%)
, 75
98.7%
: 1
 
1.3%
Space Separator
ValueCountFrequency (%)
1390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4426
61.5%
Common 2753
38.2%
Latin 22
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 529
12.0%
광 526
11.9%
남 371
 
8.4%
군 328
 
7.4%
라 322
 
7.3%
전 319
 
7.2%
도 319
 
7.2%
읍 268
 
6.1%
로 236
 
5.3%
길 163
 
3.7%
Other values (97) 1045
23.6%
Common
ValueCountFrequency (%)
1390
50.5%
1 302
 
11.0%
2 147
 
5.3%
- 114
 
4.1%
3 114
 
4.1%
4 112
 
4.1%
5 91
 
3.3%
6 83
 
3.0%
7 83
 
3.0%
, 75
 
2.7%
Other values (9) 242
 
8.8%
Latin
ValueCountFrequency (%)
E 6
27.3%
C 4
18.2%
A 3
13.6%
P 3
13.6%
R 3
13.6%
L 3
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4426
61.5%
ASCII 2775
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1390
50.1%
1 302
 
10.9%
2 147
 
5.3%
- 114
 
4.1%
3 114
 
4.1%
4 112
 
4.0%
5 91
 
3.3%
6 83
 
3.0%
7 83
 
3.0%
, 75
 
2.7%
Other values (15) 264
 
9.5%
Hangul
ValueCountFrequency (%)
영 529
12.0%
광 526
11.9%
남 371
 
8.4%
군 328
 
7.4%
라 322
 
7.3%
전 319
 
7.2%
도 319
 
7.2%
읍 268
 
6.1%
로 236
 
5.3%
길 163
 
3.7%
Other values (97) 1045
23.6%

Interactions

2023-12-13T04:18:23.486137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:18:26.225282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.947
업종명0.9471.000
2023-12-13T04:18:26.304216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.795
업종명0.7951.000

Missing values

2023-12-13T04:18:23.597086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:18:23.693869image/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

연번업종명신고일자업소명업소소재지(도로명)
01숙박업(일반)1987-03-12궁전파크여관전라남도 영광군 영광읍 신남로 245
12숙박업(일반)1988-09-24금수장여관전라남도 영광군 영광읍 신남로 231
23숙박업(일반)1989-07-24서해장모텔전라남도 영광군 염산면 천년로1길 15
34숙박업(일반)1995-09-20휴 모텔전라남도 영광군 홍농읍 연우로 328
45숙박업(일반)1995-12-30세종모텔전라남도 영광군 영광읍 중앙로 147-2
56숙박업(일반)1996-12-30로얄모텔전라남도 영광군 영광읍 천년로11길 16
67숙박업(일반)1997-07-10백제모텔전라남도 영광군 영광읍 함영로 3492
78숙박업(일반)1997-10-10태정모텔전라남도 영광군 영광읍 현암길 45-7
89숙박업(일반)1997-12-05샤인모텔전라남도 영광군 영광읍 천년로12길 51-1
910숙박업(일반)1998-01-09도원모텔전라남도 영광군 영광읍 대하길 23-7
연번업종명신고일자업소명업소소재지(도로명)
312313미용업(손톱ㆍ발톱)2020-09-22예진네일전라남도 영광군 영광읍 신남로 246-2, 1층 107호
313314미용업(손톱ㆍ발톱)2020-12-17Nail by mi전라남도 영광군 영광읍 중앙로 174, 주1동
314315미용업(손톱ㆍ발톱)2022-09-05설레는 네일전라남도 영광군 영광읍 천년로13길 14
315316미용업(피부)2019-10-28하얀눈꽃가루 by 오선정전라남도 영광군 영광읍 현암길 36
316317미용업(피부)2021-03-04아이리스 뷰티#전라남도 영광군 영광읍 백수로 1640, 2층
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