Overview

Dataset statistics

Number of variables6
Number of observations727
Missing cells193
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.9 KiB
Average record size in memory49.2 B

Variable types

Numeric1
Categorical1
Text3
DateTime1

Dataset

Description경상남도 사천시 공중위생업소 현황 (연번, 업종명, 업소명, 업소소재지, 소재지 전화번호, 기준일자 )자료 입니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15045276

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
소재지전화 has 193 (26.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:44:41.812649
Analysis finished2023-12-10 23:44:42.581944
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct727
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364
Minimum1
Maximum727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-11T08:44:42.667150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.3
Q1182.5
median364
Q3545.5
95-th percentile690.7
Maximum727
Range726
Interquartile range (IQR)363

Descriptive statistics

Standard deviation210.01111
Coefficient of variation (CV)0.5769536
Kurtosis-1.2
Mean364
Median Absolute Deviation (MAD)182
Skewness0
Sum264628
Variance44104.667
MonotonicityNot monotonic
2023-12-11T08:44:42.814989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
479 1
 
0.1%
481 1
 
0.1%
482 1
 
0.1%
483 1
 
0.1%
484 1
 
0.1%
485 1
 
0.1%
486 1
 
0.1%
487 1
 
0.1%
488 1
 
0.1%
Other values (717) 717
98.6%
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 (%)
727 1
0.1%
726 1
0.1%
725 1
0.1%
724 1
0.1%
723 1
0.1%
722 1
0.1%
721 1
0.1%
720 1
0.1%
719 1
0.1%
718 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
일반미용업
281 
숙박업(일반)
151 
피부미용업
58 
이용업
56 
세탁업
45 
Other values (13)
136 

Length

Max length23
Median length5
Mean length5.6231087
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 281
38.7%
숙박업(일반) 151
20.8%
피부미용업 58
 
8.0%
이용업 56
 
7.7%
세탁업 45
 
6.2%
네일미용업 40
 
5.5%
목욕장업 38
 
5.2%
숙박업(생활) 11
 
1.5%
종합미용업 9
 
1.2%
네일미용업, 화장ㆍ분장 미용업 9
 
1.2%
Other values (8) 29
 
4.0%

Length

2023-12-11T08:44:43.035270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 297
37.6%
숙박업(일반 151
19.1%
피부미용업 70
 
8.9%
네일미용업 65
 
8.2%
이용업 56
 
7.1%
세탁업 45
 
5.7%
목욕장업 38
 
4.8%
화장ㆍ분장 24
 
3.0%
미용업 24
 
3.0%
숙박업(생활 11
 
1.4%

업소명
Text

Distinct721
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-11T08:44:43.433846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length5.6258597
Min length1

Characters and Unicode

Total characters4090
Distinct characters490
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique715 ?
Unique (%)98.3%

Sample

1st row금성여관
2nd row남일여인숙
3rd row신진여인숙
4th row한려장여관
5th row산수장여관
ValueCountFrequency (%)
미용실 30
 
3.4%
헤어 5
 
0.6%
모텔 5
 
0.6%
hair 4
 
0.5%
네일 3
 
0.3%
nail 3
 
0.3%
이용원 3
 
0.3%
삼천포 2
 
0.2%
영 2
 
0.2%
결 2
 
0.2%
Other values (787) 813
93.2%
2023-12-11T08:44:43.862520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 169
 
4.1%
헤 162
 
4.0%
145
 
3.5%
용 137
 
3.3%
미 121
 
3.0%
텔 116
 
2.8%
이 103
 
2.5%
모 91
 
2.2%
실 79
 
1.9%
원 74
 
1.8%
Other values (480) 2893
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3530
86.3%
Lowercase Letter 149
 
3.6%
Space Separator 145
 
3.5%
Uppercase Letter 139
 
3.4%
Open Punctuation 36
 
0.9%
Close Punctuation 36
 
0.9%
Other Punctuation 26
 
0.6%
Decimal Number 25
 
0.6%
Dash Punctuation 2
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 169
 
4.8%
헤 162
 
4.6%
용 137
 
3.9%
미 121
 
3.4%
텔 116
 
3.3%
이 103
 
2.9%
모 91
 
2.6%
실 79
 
2.2%
원 74
 
2.1%
리 66
 
1.9%
Other values (418) 2412
68.3%
Uppercase Letter
ValueCountFrequency (%)
A 17
 
12.2%
O 11
 
7.9%
S 10
 
7.2%
E 9
 
6.5%
N 8
 
5.8%
M 8
 
5.8%
I 8
 
5.8%
B 8
 
5.8%
H 8
 
5.8%
L 7
 
5.0%
Other values (13) 45
32.4%
Lowercase Letter
ValueCountFrequency (%)
e 18
12.1%
a 17
11.4%
i 16
10.7%
h 13
8.7%
n 12
8.1%
o 10
 
6.7%
y 9
 
6.0%
l 9
 
6.0%
r 9
 
6.0%
u 7
 
4.7%
Other values (9) 29
19.5%
Other Punctuation
ValueCountFrequency (%)
, 6
23.1%
& 6
23.1%
# 5
19.2%
. 5
19.2%
' 2
 
7.7%
: 1
 
3.8%
; 1
 
3.8%
Decimal Number
ValueCountFrequency (%)
3 6
24.0%
1 6
24.0%
2 5
20.0%
0 3
12.0%
4 2
 
8.0%
5 2
 
8.0%
6 1
 
4.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3527
86.2%
Latin 290
 
7.1%
Common 270
 
6.6%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 169
 
4.8%
헤 162
 
4.6%
용 137
 
3.9%
미 121
 
3.4%
텔 116
 
3.3%
이 103
 
2.9%
모 91
 
2.6%
실 79
 
2.2%
원 74
 
2.1%
리 66
 
1.9%
Other values (417) 2409
68.3%
Latin
ValueCountFrequency (%)
e 18
 
6.2%
A 17
 
5.9%
a 17
 
5.9%
i 16
 
5.5%
h 13
 
4.5%
n 12
 
4.1%
O 11
 
3.8%
S 10
 
3.4%
o 10
 
3.4%
E 9
 
3.1%
Other values (34) 157
54.1%
Common
ValueCountFrequency (%)
145
53.7%
( 36
 
13.3%
) 36
 
13.3%
, 6
 
2.2%
3 6
 
2.2%
1 6
 
2.2%
& 6
 
2.2%
# 5
 
1.9%
. 5
 
1.9%
2 5
 
1.9%
Other values (8) 14
 
5.2%
Han
ValueCountFrequency (%)
美 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3527
86.2%
ASCII 558
 
13.6%
CJK 3
 
0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 169
 
4.8%
헤 162
 
4.6%
용 137
 
3.9%
미 121
 
3.4%
텔 116
 
3.3%
이 103
 
2.9%
모 91
 
2.6%
실 79
 
2.2%
원 74
 
2.1%
리 66
 
1.9%
Other values (417) 2409
68.3%
ASCII
ValueCountFrequency (%)
145
26.0%
( 36
 
6.5%
) 36
 
6.5%
e 18
 
3.2%
A 17
 
3.0%
a 17
 
3.0%
i 16
 
2.9%
h 13
 
2.3%
n 12
 
2.2%
O 11
 
2.0%
Other values (50) 237
42.5%
CJK
ValueCountFrequency (%)
美 3
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%

업소소재지(도로명)
Text

Distinct685
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-11T08:44:44.291067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length42
Mean length24.299862
Min length16

Characters and Unicode

Total characters17666
Distinct characters249
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

Unique648 ?
Unique (%)89.1%

Sample

1st row경상남도 사천시 한내6길 80 (동금동)
2nd row경상남도 사천시 망산공원길 6-8 (선구동)
3rd row경상남도 사천시 한내5길 101-3 (선구동)
4th row경상남도 사천시 한내5길 85 (동금동)
5th row경상남도 사천시 벌리한들길 84 (벌리동)
ValueCountFrequency (%)
경상남도 727
 
18.2%
사천시 727
 
18.2%
사천읍 193
 
4.8%
벌리동 110
 
2.8%
1층 75
 
1.9%
동금동 71
 
1.8%
진삼로 58
 
1.5%
선구동 45
 
1.1%
사남면 38
 
1.0%
정동면 36
 
0.9%
Other values (689) 1916
47.9%
2023-12-11T08:44:45.089365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3269
18.5%
사 1023
 
5.8%
천 969
 
5.5%
남 808
 
4.6%
상 777
 
4.4%
시 759
 
4.3%
경 745
 
4.2%
도 731
 
4.1%
1 699
 
4.0%
동 669
 
3.8%
Other values (239) 7217
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10523
59.6%
Space Separator 3269
 
18.5%
Decimal Number 2577
 
14.6%
Close Punctuation 421
 
2.4%
Open Punctuation 421
 
2.4%
Other Punctuation 229
 
1.3%
Dash Punctuation 215
 
1.2%
Uppercase Letter 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1023
 
9.7%
천 969
 
9.2%
남 808
 
7.7%
상 777
 
7.4%
시 759
 
7.2%
경 745
 
7.1%
도 731
 
6.9%
동 669
 
6.4%
길 430
 
4.1%
로 299
 
2.8%
Other values (214) 3313
31.5%
Decimal Number
ValueCountFrequency (%)
1 699
27.1%
2 363
14.1%
4 244
 
9.5%
3 235
 
9.1%
0 219
 
8.5%
5 203
 
7.9%
6 188
 
7.3%
7 182
 
7.1%
9 123
 
4.8%
8 121
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
C 4
36.4%
K 2
18.2%
H 1
 
9.1%
T 1
 
9.1%
O 1
 
9.1%
E 1
 
9.1%
L 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 419
99.5%
] 2
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 419
99.5%
[ 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
, 227
99.1%
* 2
 
0.9%
Space Separator
ValueCountFrequency (%)
3269
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10523
59.6%
Common 7132
40.4%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1023
 
9.7%
천 969
 
9.2%
남 808
 
7.7%
상 777
 
7.4%
시 759
 
7.2%
경 745
 
7.1%
도 731
 
6.9%
동 669
 
6.4%
길 430
 
4.1%
로 299
 
2.8%
Other values (214) 3313
31.5%
Common
ValueCountFrequency (%)
3269
45.8%
1 699
 
9.8%
) 419
 
5.9%
( 419
 
5.9%
2 363
 
5.1%
4 244
 
3.4%
3 235
 
3.3%
, 227
 
3.2%
0 219
 
3.1%
- 215
 
3.0%
Other values (8) 823
 
11.5%
Latin
ValueCountFrequency (%)
C 4
36.4%
K 2
18.2%
H 1
 
9.1%
T 1
 
9.1%
O 1
 
9.1%
E 1
 
9.1%
L 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10523
59.6%
ASCII 7143
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3269
45.8%
1 699
 
9.8%
) 419
 
5.9%
( 419
 
5.9%
2 363
 
5.1%
4 244
 
3.4%
3 235
 
3.3%
, 227
 
3.2%
0 219
 
3.1%
- 215
 
3.0%
Other values (15) 834
 
11.7%
Hangul
ValueCountFrequency (%)
사 1023
 
9.7%
천 969
 
9.2%
남 808
 
7.7%
상 777
 
7.4%
시 759
 
7.2%
경 745
 
7.1%
도 731
 
6.9%
동 669
 
6.4%
길 430
 
4.1%
로 299
 
2.8%
Other values (214) 3313
31.5%

소재지전화
Text

MISSING 

Distinct525
Distinct (%)98.3%
Missing193
Missing (%)26.5%
Memory size5.8 KiB
2023-12-11T08:44:45.393363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.588015
Min length13

Characters and Unicode

Total characters7256
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique519 ?
Unique (%)97.2%

Sample

1st row055 -833 -2238
2nd row055 -834 -3333
3rd row055 -833 -3331
4th row055 -833 -2773
5th row055 -833 -6712
ValueCountFrequency (%)
055 516
37.6%
835 50
 
3.6%
832 47
 
3.4%
855 42
 
3.1%
833 42
 
3.1%
852 40
 
2.9%
853 29
 
2.1%
854 27
 
2.0%
834 16
 
1.2%
070 10
 
0.7%
Other values (523) 552
40.3%
2023-12-11T08:44:45.795184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1659
22.9%
- 1068
14.7%
0 841
11.6%
837
11.5%
8 765
10.5%
3 648
 
8.9%
2 389
 
5.4%
4 250
 
3.4%
7 215
 
3.0%
1 208
 
2.9%
Other values (2) 376
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5351
73.7%
Dash Punctuation 1068
 
14.7%
Space Separator 837
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1659
31.0%
0 841
15.7%
8 765
14.3%
3 648
 
12.1%
2 389
 
7.3%
4 250
 
4.7%
7 215
 
4.0%
1 208
 
3.9%
9 199
 
3.7%
6 177
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 1068
100.0%
Space Separator
ValueCountFrequency (%)
837
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1659
22.9%
- 1068
14.7%
0 841
11.6%
837
11.5%
8 765
10.5%
3 648
 
8.9%
2 389
 
5.4%
4 250
 
3.4%
7 215
 
3.0%
1 208
 
2.9%
Other values (2) 376
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1659
22.9%
- 1068
14.7%
0 841
11.6%
837
11.5%
8 765
10.5%
3 648
 
8.9%
2 389
 
5.4%
4 250
 
3.4%
7 215
 
3.0%
1 208
 
2.9%
Other values (2) 376
 
5.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum2023-06-01 00:00:00
Maximum2023-06-01 00:00:00
2023-12-11T08:44:45.939145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:46.039093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T08:44:42.282640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:44:46.116330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.897
업종명0.8971.000
2023-12-11T08:44:46.204523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.625
업종명0.6251.000

Missing values

2023-12-11T08:44:42.415977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:44:42.531019image/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)소재지전화데이터기준일자
01숙박업(일반)금성여관경상남도 사천시 한내6길 80 (동금동)055 -833 -22382023-06-01
12숙박업(일반)남일여인숙경상남도 사천시 망산공원길 6-8 (선구동)055 -834 -33332023-06-01
23숙박업(일반)신진여인숙경상남도 사천시 한내5길 101-3 (선구동)055 -833 -33312023-06-01
34숙박업(일반)한려장여관경상남도 사천시 한내5길 85 (동금동)055 -833 -27732023-06-01
45숙박업(일반)산수장여관경상남도 사천시 벌리한들길 84 (벌리동)055 -833 -67122023-06-01
56숙박업(일반)행운장여관경상남도 사천시 팔포1길 6-3 (선구동)055 -833 -26442023-06-01
67숙박업(일반)브라운도트(삼천포항점)경상남도 사천시 수남길 33 (선구동)055 -833 -53052023-06-01
78숙박업(일반)신 유일장여관경상남도 사천시 수남길 27 (선구동)055 -832 -90072023-06-01
89숙박업(일반)우리모텔경상남도 사천시 팔포1길 58 (서금동)055 -832 -77682023-06-01
910숙박업(일반)신세계모텔경상남도 사천시 노산공원길 45 (서금동)055 -832 -02672023-06-01
연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
717718네일미용업, 화장ㆍ분장 미용업그리다경상남도 사천시 벌리7길 62 (벌리동)<NA>2023-06-01
718719네일미용업, 화장ㆍ분장 미용업티블뷰티(T-BLE beauty)경상남도 사천시 사남면 조동길 60, 상가동 203호 (진사주공아파트)<NA>2023-06-01
719720네일미용업, 화장ㆍ분장 미용업나곰네일(NAGOM)경상남도 사천시 정동면 정동중앙로 7-17, 1층 106호<NA>2023-06-01
720721네일미용업, 화장ㆍ분장 미용업네일;이슬경상남도 사천시 사남면 진삼로 1225<NA>2023-06-01
721722일반미용업, 네일미용업, 화장ㆍ분장 미용업미즈 헤어숍경상남도 사천시 나무전길 12-14 (선구동)055- 832-99982023-06-01
722723일반미용업, 네일미용업, 화장ㆍ분장 미용업장미네일경상남도 사천시 주공로 16 (벌리동)055 -832 -78902023-06-01
723724일반미용업, 네일미용업, 화장ㆍ분장 미용업살롱드베이경상남도 사천시 사천읍 옥산로 63, 2층<NA>2023-06-01
724725일반미용업, 네일미용업, 화장ㆍ분장 미용업슬기로운미용생활경상남도 사천시 사천읍 평화1길 40, 1층<NA>2023-06-01
725726피부미용업, 네일미용업, 화장ㆍ분장 미용업서꽃잎 Beauty Salon경상남도 사천시 통창동길 4-1 (동금동)<NA>2023-06-01
726727피부미용업, 네일미용업, 화장ㆍ분장 미용업금손언니경상남도 사천시 정동면 정동중앙로 16-5<NA>2023-06-01