Overview

Dataset statistics

Number of variables5
Number of observations297
Missing cells47
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory41.4 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description남해군의 현재 등록된 목욕장업 현황입니다. 등록된 목용장업의 업소명, 업소소재지(도로명주소), 전화번호 등을 포함한 정보입니다.
Author경상남도 남해군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15006888

Alerts

연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
소재지전화 has 47 (15.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:10:32.089822
Analysis finished2023-12-11 00:10:32.656770
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149
Minimum1
Maximum297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-11T09:10:32.724682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.8
Q175
median149
Q3223
95-th percentile282.2
Maximum297
Range296
Interquartile range (IQR)148

Descriptive statistics

Standard deviation85.880731
Coefficient of variation (CV)0.57638075
Kurtosis-1.2
Mean149
Median Absolute Deviation (MAD)74
Skewness0
Sum44253
Variance7375.5
MonotonicityStrictly increasing
2023-12-11T09:10:32.856793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
205 1
 
0.3%
203 1
 
0.3%
202 1
 
0.3%
201 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
198 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
Other values (287) 287
96.6%
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 (%)
297 1
0.3%
296 1
0.3%
295 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%
289 1
0.3%
288 1
0.3%

업종명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
숙박업(일반)
67 
숙박업(생활)
64 
미용업
62 
이용업
31 
목욕장업
20 
Other values (10)
53 

Length

Max length22
Median length7
Mean length5.5656566
Min length3

Unique

Unique4 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 67
22.6%
숙박업(생활) 64
21.5%
미용업 62
20.9%
이용업 31
10.4%
목욕장업 20
 
6.7%
미용업(일반) 18
 
6.1%
세탁업 12
 
4.0%
미용업(피부) 9
 
3.0%
건물위생관리업 6
 
2.0%
미용업(종합) 2
 
0.7%
Other values (5) 6
 
2.0%

Length

2023-12-11T09:10:32.996927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 67
22.3%
숙박업(생활 64
21.3%
미용업 62
20.6%
이용업 31
10.3%
목욕장업 20
 
6.6%
미용업(일반 20
 
6.6%
세탁업 12
 
4.0%
미용업(피부 11
 
3.7%
건물위생관리업 6
 
2.0%
미용업(손톱ㆍ발톱 5
 
1.7%
Other values (2) 3
 
1.0%

업소명
Text

Distinct292
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-11T09:10:33.267599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length5.4713805
Min length2

Characters and Unicode

Total characters1625
Distinct characters320
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique287 ?
Unique (%)96.6%

Sample

1st row재두장여관
2nd row해주여관
3rd row한려산장
4th rowK모텔
5th row영남장여관
ValueCountFrequency (%)
펜션 3
 
0.9%
현대이용원 2
 
0.6%
행복미용실 2
 
0.6%
남해 2
 
0.6%
벨비앙펜션 2
 
0.6%
eg미조힐링리조트 2
 
0.6%
영미용실 2
 
0.6%
신흥이용원 2
 
0.6%
서울미용실 2
 
0.6%
가든미용실 1
 
0.3%
Other values (303) 303
93.8%
2023-12-11T09:10:33.623212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 75
 
4.6%
미 64
 
3.9%
텔 47
 
2.9%
이 46
 
2.8%
실 46
 
2.8%
모 44
 
2.7%
션 40
 
2.5%
펜 40
 
2.5%
원 36
 
2.2%
스 35
 
2.2%
Other values (310) 1152
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1512
93.0%
Lowercase Letter 31
 
1.9%
Space Separator 26
 
1.6%
Uppercase Letter 25
 
1.5%
Close Punctuation 9
 
0.6%
Open Punctuation 9
 
0.6%
Decimal Number 7
 
0.4%
Dash Punctuation 3
 
0.2%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 75
 
5.0%
미 64
 
4.2%
텔 47
 
3.1%
이 46
 
3.0%
실 46
 
3.0%
모 44
 
2.9%
션 40
 
2.6%
펜 40
 
2.6%
원 36
 
2.4%
스 35
 
2.3%
Other values (268) 1039
68.7%
Uppercase Letter
ValueCountFrequency (%)
I 3
12.0%
A 2
 
8.0%
N 2
 
8.0%
G 2
 
8.0%
E 2
 
8.0%
J 2
 
8.0%
C 2
 
8.0%
Y 1
 
4.0%
H 1
 
4.0%
R 1
 
4.0%
Other values (7) 7
28.0%
Lowercase Letter
ValueCountFrequency (%)
a 5
16.1%
e 4
12.9%
l 4
12.9%
r 3
9.7%
t 2
 
6.5%
u 2
 
6.5%
h 2
 
6.5%
i 2
 
6.5%
o 2
 
6.5%
n 1
 
3.2%
Other values (4) 4
12.9%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
1 2
28.6%
5 1
 
14.3%
9 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
& 1
33.3%
· 1
33.3%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1510
92.9%
Common 57
 
3.5%
Latin 56
 
3.4%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 75
 
5.0%
미 64
 
4.2%
텔 47
 
3.1%
이 46
 
3.0%
실 46
 
3.0%
모 44
 
2.9%
션 40
 
2.6%
펜 40
 
2.6%
원 36
 
2.4%
스 35
 
2.3%
Other values (266) 1037
68.7%
Latin
ValueCountFrequency (%)
a 5
 
8.9%
e 4
 
7.1%
l 4
 
7.1%
I 3
 
5.4%
r 3
 
5.4%
A 2
 
3.6%
N 2
 
3.6%
t 2
 
3.6%
u 2
 
3.6%
h 2
 
3.6%
Other values (21) 27
48.2%
Common
ValueCountFrequency (%)
26
45.6%
) 9
 
15.8%
( 9
 
15.8%
2 3
 
5.3%
- 3
 
5.3%
1 2
 
3.5%
& 1
 
1.8%
· 1
 
1.8%
. 1
 
1.8%
5 1
 
1.8%
Han
ValueCountFrequency (%)
美 1
50.0%
作 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1510
92.9%
ASCII 112
 
6.9%
CJK 2
 
0.1%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 75
 
5.0%
미 64
 
4.2%
텔 47
 
3.1%
이 46
 
3.0%
실 46
 
3.0%
모 44
 
2.9%
션 40
 
2.6%
펜 40
 
2.6%
원 36
 
2.4%
스 35
 
2.3%
Other values (266) 1037
68.7%
ASCII
ValueCountFrequency (%)
26
23.2%
) 9
 
8.0%
( 9
 
8.0%
a 5
 
4.5%
e 4
 
3.6%
l 4
 
3.6%
I 3
 
2.7%
r 3
 
2.7%
2 3
 
2.7%
- 3
 
2.7%
Other values (31) 43
38.4%
CJK
ValueCountFrequency (%)
美 1
50.0%
作 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

업소소재지(도로명)
Text

Distinct283
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-11T09:10:33.830762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length40
Mean length24.316498
Min length18

Characters and Unicode

Total characters7222
Distinct characters128
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique269 ?
Unique (%)90.6%

Sample

1st row경상남도 남해군 상주면 남해대로 918-6
2nd row경상남도 남해군 상주면 상주로 17-6
3rd row경상남도 남해군 상주면 남해대로 591-56
4th row경상남도 남해군 남해읍 화전로 52-9
5th row경상남도 남해군 남해읍 화전로38번길 28
ValueCountFrequency (%)
경상남도 297
19.0%
남해군 297
19.0%
남해읍 113
 
7.2%
화전로 46
 
2.9%
창선면 35
 
2.2%
미조면 33
 
2.1%
삼동면 29
 
1.9%
이동면 23
 
1.5%
남해대로 22
 
1.4%
2층 21
 
1.3%
Other values (339) 646
41.4%
2023-12-11T09:10:34.140095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1265
17.5%
남 792
 
11.0%
해 445
 
6.2%
상 324
 
4.5%
경 297
 
4.1%
도 297
 
4.1%
군 297
 
4.1%
로 293
 
4.1%
1 283
 
3.9%
2 209
 
2.9%
Other values (118) 2720
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4361
60.4%
Decimal Number 1320
 
18.3%
Space Separator 1265
 
17.5%
Dash Punctuation 127
 
1.8%
Open Punctuation 51
 
0.7%
Close Punctuation 51
 
0.7%
Other Punctuation 44
 
0.6%
Other Symbol 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 792
18.2%
해 445
 
10.2%
상 324
 
7.4%
경 297
 
6.8%
도 297
 
6.8%
군 297
 
6.8%
로 293
 
6.7%
면 193
 
4.4%
길 120
 
2.8%
번 117
 
2.7%
Other values (100) 1186
27.2%
Decimal Number
ValueCountFrequency (%)
1 283
21.4%
2 209
15.8%
3 143
10.8%
6 114
8.6%
5 107
 
8.1%
7 99
 
7.5%
4 95
 
7.2%
9 95
 
7.2%
8 92
 
7.0%
0 83
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 42
95.5%
. 2
 
4.5%
Space Separator
ValueCountFrequency (%)
1265
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4361
60.4%
Common 2861
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 792
18.2%
해 445
 
10.2%
상 324
 
7.4%
경 297
 
6.8%
도 297
 
6.8%
군 297
 
6.8%
로 293
 
6.7%
면 193
 
4.4%
길 120
 
2.8%
번 117
 
2.7%
Other values (100) 1186
27.2%
Common
ValueCountFrequency (%)
1265
44.2%
1 283
 
9.9%
2 209
 
7.3%
3 143
 
5.0%
- 127
 
4.4%
6 114
 
4.0%
5 107
 
3.7%
7 99
 
3.5%
4 95
 
3.3%
9 95
 
3.3%
Other values (8) 324
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4361
60.4%
ASCII 2859
39.6%
CJK Compat 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1265
44.2%
1 283
 
9.9%
2 209
 
7.3%
3 143
 
5.0%
- 127
 
4.4%
6 114
 
4.0%
5 107
 
3.7%
7 99
 
3.5%
4 95
 
3.3%
9 95
 
3.3%
Other values (7) 322
 
11.3%
Hangul
ValueCountFrequency (%)
남 792
18.2%
해 445
 
10.2%
상 324
 
7.4%
경 297
 
6.8%
도 297
 
6.8%
군 297
 
6.8%
로 293
 
6.7%
면 193
 
4.4%
길 120
 
2.8%
번 117
 
2.7%
Other values (100) 1186
27.2%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%

소재지전화
Text

MISSING 

Distinct245
Distinct (%)98.0%
Missing47
Missing (%)15.8%
Memory size2.4 KiB
2023-12-11T09:10:34.336319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.008
Min length12

Characters and Unicode

Total characters3002
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

Unique241 ?
Unique (%)96.4%

Sample

1st row055-862-6022
2nd row055-862-6042
3rd row055-864-2981
4th row055-864-2478
5th row055-867-6078
ValueCountFrequency (%)
055-864-6672 3
 
1.2%
055-863-0020 2
 
0.8%
055-867-6543 2
 
0.8%
055-863-0807 2
 
0.8%
055-863-2236 1
 
0.4%
055-863-3181 1
 
0.4%
055-864-4917 1
 
0.4%
055-862-4327 1
 
0.4%
055-867-8048 1
 
0.4%
055-864-5645 1
 
0.4%
Other values (235) 235
94.0%
2023-12-11T09:10:34.734017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 614
20.5%
- 500
16.7%
0 368
12.3%
8 351
11.7%
6 337
11.2%
7 188
 
6.3%
3 167
 
5.6%
4 147
 
4.9%
2 139
 
4.6%
1 112
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2502
83.3%
Dash Punctuation 500
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 614
24.5%
0 368
14.7%
8 351
14.0%
6 337
13.5%
7 188
 
7.5%
3 167
 
6.7%
4 147
 
5.9%
2 139
 
5.6%
1 112
 
4.5%
9 79
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3002
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 614
20.5%
- 500
16.7%
0 368
12.3%
8 351
11.7%
6 337
11.2%
7 188
 
6.3%
3 167
 
5.6%
4 147
 
4.9%
2 139
 
4.6%
1 112
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 614
20.5%
- 500
16.7%
0 368
12.3%
8 351
11.7%
6 337
11.2%
7 188
 
6.3%
3 167
 
5.6%
4 147
 
4.9%
2 139
 
4.6%
1 112
 
3.7%

Interactions

2023-12-11T09:10:32.385549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:10:34.872882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.934
업종명0.9341.000
2023-12-11T09:10:35.016952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.690
업종명0.6901.000

Missing values

2023-12-11T09:10:32.526299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:10:32.622626image/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

연번업종명업소명업소소재지(도로명)소재지전화
01숙박업(일반)재두장여관경상남도 남해군 상주면 남해대로 918-6055-862-6022
12숙박업(일반)해주여관경상남도 남해군 상주면 상주로 17-6055-862-6042
23숙박업(일반)한려산장경상남도 남해군 상주면 남해대로 591-56<NA>
34숙박업(일반)K모텔경상남도 남해군 남해읍 화전로 52-9055-864-2981
45숙박업(일반)영남장여관경상남도 남해군 남해읍 화전로38번길 28055-864-2478
56숙박업(일반)미송여관경상남도 남해군 미조면 미조로 232-4055-867-6078
67숙박업(일반)진주장여관경상남도 남해군 남해읍 화전로 52055-864-2232
78숙박업(일반)금화여관경상남도 남해군 미조면 미조로 248055-867-7001
89숙박업(일반)J모텔경상남도 남해군 남해읍 화전로38번길 25055-862-1501
910숙박업(일반)남해장여관경상남도 남해군 남해읍 화전로96번길 6-3055-864-2273
연번업종명업소명업소소재지(도로명)소재지전화
287288세탁업정명사경상남도 남해군 남해읍 화전로 50055-863-1832
288289세탁업서울세탁소경상남도 남해군 미조면 미조로236번길 21-2055-867-6450
289290세탁업하얀세탁소경상남도 남해군 남해읍 화전로 57-1055-863-1078
290291세탁업서울종합컴퓨터세탁소경상남도 남해군 남해읍 망운로19번길 3055-863-5877
291292세탁업미광컴퓨터세탁소경상남도 남해군 남해읍 화전로 95055-864-2987
292293세탁업은혜세탁소경상남도 남해군 남해읍 화전로78번가길 10-2055-863-1176
293294세탁업미조세탁소경상남도 남해군 미조면 미조로236번안길 21055-867-7645
294295세탁업대일세탁소경상남도 남해군 남해읍 화전로38번길 11055-864-7751
295296세탁업세탁마을경상남도 남해군 남해읍 화전로122번가길 8055-864-7574
296297세탁업한아름크리닝경상남도 남해군 남해읍 에코파크길 7055-864-2207