Overview

Dataset statistics

Number of variables6
Number of observations734
Missing cells230
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 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 201 (27.4%) missing valuesMissing
데이터기준일자 has 29 (4.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:44:47.349741
Analysis finished2023-12-10 23:44:48.265191
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct734
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean367.5
Minimum1
Maximum734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T08:44:48.334460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.65
Q1184.25
median367.5
Q3550.75
95-th percentile697.35
Maximum734
Range733
Interquartile range (IQR)366.5

Descriptive statistics

Standard deviation212.03184
Coefficient of variation (CV)0.57695738
Kurtosis-1.2
Mean367.5
Median Absolute Deviation (MAD)183.5
Skewness0
Sum269745
Variance44957.5
MonotonicityStrictly increasing
2023-12-11T08:44:48.489299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
484 1
 
0.1%
486 1
 
0.1%
487 1
 
0.1%
488 1
 
0.1%
489 1
 
0.1%
490 1
 
0.1%
491 1
 
0.1%
492 1
 
0.1%
493 1
 
0.1%
Other values (724) 724
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 (%)
734 1
0.1%
733 1
0.1%
732 1
0.1%
731 1
0.1%
730 1
0.1%
729 1
0.1%
728 1
0.1%
727 1
0.1%
726 1
0.1%
725 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
일반미용업
283 
숙박업(일반)
151 
피부미용업
57 
이용업
56 
세탁업
44 
Other values (14)
143 

Length

Max length23
Median length5
Mean length5.6498638
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 283
38.6%
숙박업(일반) 151
20.6%
피부미용업 57
 
7.8%
이용업 56
 
7.6%
세탁업 44
 
6.0%
네일미용업 39
 
5.3%
목욕장업 38
 
5.2%
종합미용업 13
 
1.8%
숙박업(생활) 12
 
1.6%
네일미용업 화장ㆍ분장 미용업 9
 
1.2%
Other values (9) 32
 
4.4%

Length

2023-12-11T08:44:48.692947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 300
37.5%
숙박업(일반 151
18.9%
피부미용업 69
 
8.6%
네일미용업 65
 
8.1%
이용업 56
 
7.0%
세탁업 44
 
5.5%
목욕장업 38
 
4.7%
미용업 27
 
3.4%
화장ㆍ분장 26
 
3.2%
종합미용업 13
 
1.6%

업소명
Text

Distinct728
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-11T08:44:48.986821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length5.6321526
Min length1

Characters and Unicode

Total characters4134
Distinct characters500
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

Unique722 ?
Unique (%)98.4%

Sample

1st row금성여관
2nd row남일여인숙
3rd row신진여인숙
4th row한려장여관
5th row산수장여관
ValueCountFrequency (%)
미용실 29
 
3.3%
헤어 6
 
0.7%
hair 5
 
0.6%
모텔 5
 
0.6%
네일 5
 
0.6%
남일대리조트 4
 
0.5%
nail 4
 
0.5%
호텔 3
 
0.3%
이용원 3
 
0.3%
헤어샵 2
 
0.2%
Other values (793) 820
92.6%
2023-12-11T08:44:49.558850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 175
 
4.2%
헤 166
 
4.0%
153
 
3.7%
용 137
 
3.3%
미 118
 
2.9%
텔 116
 
2.8%
이 101
 
2.4%
모 90
 
2.2%
실 80
 
1.9%
원 73
 
1.8%
Other values (490) 2925
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3599
87.1%
Space Separator 153
 
3.7%
Lowercase Letter 137
 
3.3%
Uppercase Letter 133
 
3.2%
Open Punctuation 33
 
0.8%
Close Punctuation 33
 
0.8%
Decimal Number 22
 
0.5%
Other Punctuation 20
 
0.5%
Dash Punctuation 2
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 175
 
4.9%
헤 166
 
4.6%
용 137
 
3.8%
미 118
 
3.3%
텔 116
 
3.2%
이 101
 
2.8%
모 90
 
2.5%
실 80
 
2.2%
원 73
 
2.0%
리 70
 
1.9%
Other values (428) 2473
68.7%
Uppercase Letter
ValueCountFrequency (%)
A 17
12.8%
O 11
 
8.3%
S 10
 
7.5%
N 9
 
6.8%
L 8
 
6.0%
I 8
 
6.0%
M 8
 
6.0%
B 7
 
5.3%
H 7
 
5.3%
E 6
 
4.5%
Other values (14) 42
31.6%
Lowercase Letter
ValueCountFrequency (%)
a 16
11.7%
i 15
10.9%
e 15
10.9%
h 12
8.8%
o 12
8.8%
n 11
8.0%
r 10
7.3%
l 9
 
6.6%
y 7
 
5.1%
u 6
 
4.4%
Other values (9) 24
17.5%
Decimal Number
ValueCountFrequency (%)
1 5
22.7%
2 5
22.7%
3 4
18.2%
0 3
13.6%
4 2
 
9.1%
5 2
 
9.1%
6 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
& 6
30.0%
# 5
25.0%
. 5
25.0%
' 2
 
10.0%
: 1
 
5.0%
; 1
 
5.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Space Separator
ValueCountFrequency (%)
153
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3596
87.0%
Latin 272
 
6.6%
Common 263
 
6.4%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 175
 
4.9%
헤 166
 
4.6%
용 137
 
3.8%
미 118
 
3.3%
텔 116
 
3.2%
이 101
 
2.8%
모 90
 
2.5%
실 80
 
2.2%
원 73
 
2.0%
리 70
 
1.9%
Other values (427) 2470
68.7%
Latin
ValueCountFrequency (%)
A 17
 
6.2%
a 16
 
5.9%
i 15
 
5.5%
e 15
 
5.5%
h 12
 
4.4%
o 12
 
4.4%
n 11
 
4.0%
O 11
 
4.0%
r 10
 
3.7%
S 10
 
3.7%
Other values (35) 143
52.6%
Common
ValueCountFrequency (%)
153
58.2%
( 33
 
12.5%
) 33
 
12.5%
& 6
 
2.3%
# 5
 
1.9%
1 5
 
1.9%
. 5
 
1.9%
2 5
 
1.9%
3 4
 
1.5%
0 3
 
1.1%
Other values (7) 11
 
4.2%
Han
ValueCountFrequency (%)
美 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3596
87.0%
ASCII 533
 
12.9%
CJK 3
 
0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 175
 
4.9%
헤 166
 
4.6%
용 137
 
3.8%
미 118
 
3.3%
텔 116
 
3.2%
이 101
 
2.8%
모 90
 
2.5%
실 80
 
2.2%
원 73
 
2.0%
리 70
 
1.9%
Other values (427) 2470
68.7%
ASCII
ValueCountFrequency (%)
153
28.7%
( 33
 
6.2%
) 33
 
6.2%
A 17
 
3.2%
a 16
 
3.0%
i 15
 
2.8%
e 15
 
2.8%
h 12
 
2.3%
o 12
 
2.3%
n 11
 
2.1%
Other values (50) 216
40.5%
CJK
ValueCountFrequency (%)
美 3
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%

업소소재지(도로명)
Text

Distinct692
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-11T08:44:50.040236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length42
Mean length24.02861
Min length16

Characters and Unicode

Total characters17637
Distinct characters253
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

Unique654 ?
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 734
 
18.2%
사천시 734
 
18.2%
사천읍 191
 
4.7%
벌리동 113
 
2.8%
1층 86
 
2.1%
동금동 71
 
1.8%
진삼로 58
 
1.4%
선구동 45
 
1.1%
사남면 40
 
1.0%
정동면 36
 
0.9%
Other values (692) 1935
47.9%
2023-12-11T08:44:50.701519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3309
18.8%
사 1028
 
5.8%
천 973
 
5.5%
남 816
 
4.6%
상 785
 
4.5%
시 766
 
4.3%
경 751
 
4.3%
도 740
 
4.2%
1 709
 
4.0%
동 676
 
3.8%
Other values (243) 7084
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10629
60.3%
Space Separator 3309
 
18.8%
Decimal Number 2611
 
14.8%
Open Punctuation 429
 
2.4%
Close Punctuation 429
 
2.4%
Dash Punctuation 217
 
1.2%
Uppercase Letter 11
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1028
 
9.7%
천 973
 
9.2%
남 816
 
7.7%
상 785
 
7.4%
시 766
 
7.2%
경 751
 
7.1%
도 740
 
7.0%
동 676
 
6.4%
길 433
 
4.1%
로 302
 
2.8%
Other values (219) 3359
31.6%
Decimal Number
ValueCountFrequency (%)
1 709
27.2%
2 371
14.2%
4 244
 
9.3%
3 239
 
9.2%
0 216
 
8.3%
5 212
 
8.1%
6 186
 
7.1%
7 182
 
7.0%
9 129
 
4.9%
8 123
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
C 4
36.4%
K 2
18.2%
O 1
 
9.1%
H 1
 
9.1%
L 1
 
9.1%
E 1
 
9.1%
T 1
 
9.1%
Open Punctuation
ValueCountFrequency (%)
( 427
99.5%
[ 2
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 427
99.5%
] 2
 
0.5%
Space Separator
ValueCountFrequency (%)
3309
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 217
100.0%
Other Punctuation
ValueCountFrequency (%)
* 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10629
60.3%
Common 6997
39.7%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1028
 
9.7%
천 973
 
9.2%
남 816
 
7.7%
상 785
 
7.4%
시 766
 
7.2%
경 751
 
7.1%
도 740
 
7.0%
동 676
 
6.4%
길 433
 
4.1%
로 302
 
2.8%
Other values (219) 3359
31.6%
Common
ValueCountFrequency (%)
3309
47.3%
1 709
 
10.1%
( 427
 
6.1%
) 427
 
6.1%
2 371
 
5.3%
4 244
 
3.5%
3 239
 
3.4%
- 217
 
3.1%
0 216
 
3.1%
5 212
 
3.0%
Other values (7) 626
 
8.9%
Latin
ValueCountFrequency (%)
C 4
36.4%
K 2
18.2%
O 1
 
9.1%
H 1
 
9.1%
L 1
 
9.1%
E 1
 
9.1%
T 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10629
60.3%
ASCII 7008
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3309
47.2%
1 709
 
10.1%
( 427
 
6.1%
) 427
 
6.1%
2 371
 
5.3%
4 244
 
3.5%
3 239
 
3.4%
- 217
 
3.1%
0 216
 
3.1%
5 212
 
3.0%
Other values (14) 637
 
9.1%
Hangul
ValueCountFrequency (%)
사 1028
 
9.7%
천 973
 
9.2%
남 816
 
7.7%
상 785
 
7.4%
시 766
 
7.2%
경 751
 
7.1%
도 740
 
7.0%
동 676
 
6.4%
길 433
 
4.1%
로 302
 
2.8%
Other values (219) 3359
31.6%

소재지전화
Text

MISSING 

Distinct524
Distinct (%)98.3%
Missing201
Missing (%)27.4%
Memory size5.9 KiB
2023-12-11T08:44:51.030676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.746717
Min length12

Characters and Unicode

Total characters7327
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

Unique518 ?
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 (%)
055 449
36.3%
835 39
 
3.2%
832 37
 
3.0%
855 33
 
2.7%
852 32
 
2.6%
833 27
 
2.2%
853 24
 
1.9%
854 23
 
1.9%
834 11
 
0.9%
070 9
 
0.7%
Other values (530) 552
44.7%
2023-12-11T08:44:51.516437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1655
22.6%
- 1066
14.5%
921
12.6%
0 839
11.5%
8 763
10.4%
3 649
 
8.9%
2 390
 
5.3%
4 250
 
3.4%
7 214
 
2.9%
1 206
 
2.8%
Other values (2) 374
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5340
72.9%
Dash Punctuation 1066
 
14.5%
Space Separator 921
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1655
31.0%
0 839
15.7%
8 763
14.3%
3 649
 
12.2%
2 390
 
7.3%
4 250
 
4.7%
7 214
 
4.0%
1 206
 
3.9%
9 196
 
3.7%
6 178
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 1066
100.0%
Space Separator
ValueCountFrequency (%)
921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7327
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1655
22.6%
- 1066
14.5%
921
12.6%
0 839
11.5%
8 763
10.4%
3 649
 
8.9%
2 390
 
5.3%
4 250
 
3.4%
7 214
 
2.9%
1 206
 
2.8%
Other values (2) 374
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1655
22.6%
- 1066
14.5%
921
12.6%
0 839
11.5%
8 763
10.4%
3 649
 
8.9%
2 390
 
5.3%
4 250
 
3.4%
7 214
 
2.9%
1 206
 
2.8%
Other values (2) 374
 
5.1%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing29
Missing (%)4.0%
Memory size5.9 KiB
Minimum2023-10-27 00:00:00
Maximum2023-10-27 00:00:00
2023-12-11T08:44:51.654414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:51.770799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T08:44:47.825148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:44:51.839981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.921
업종명0.9211.000
2023-12-11T08:44:51.945330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.671
업종명0.6711.000

Missing values

2023-12-11T08:44:47.977957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:44:48.105604image/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.
2023-12-11T08:44:48.218832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
01숙박업(일반)금성여관경상남도 사천시 한내6길 80 (동금동)055-833-22382023-10-27
12숙박업(일반)남일여인숙경상남도 사천시 망산공원길 6-8 (선구동)055-834-33332023-10-27
23숙박업(일반)신진여인숙경상남도 사천시 한내5길 101-3 (선구동)055-833-33312023-10-27
34숙박업(일반)한려장여관경상남도 사천시 한내5길 85 (동금동)055-833-27732023-10-27
45숙박업(일반)산수장여관경상남도 사천시 벌리한들길 84 (벌리동)055-833-67122023-10-27
56숙박업(일반)행운장여관경상남도 사천시 팔포1길 6-3 (선구동)055-833-26442023-10-27
67숙박업(일반)브라운도트(삼천포항점)경상남도 사천시 수남길 33 (선구동)055-833-53052023-10-27
78숙박업(일반)신 유일장여관경상남도 사천시 수남길 27 (선구동)055-832-90072023-10-27
89숙박업(일반)우리모텔경상남도 사천시 팔포1길 58 (서금동)055-832-77682023-10-27
910숙박업(일반)신세계모텔경상남도 사천시 노산공원길 45 (서금동)055-832-02672023-10-27
연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
724725네일미용업 화장ㆍ분장 미용업티블뷰티(T-BLE beauty)경상남도 사천시 사남면 조동길 60 상가동 203호 (진사주공아파트)<NA><NA>
725726네일미용업 화장ㆍ분장 미용업나곰네일(NAGOM)경상남도 사천시 정동면 정동중앙로 7-17 1층 106호<NA><NA>
726727네일미용업 화장ㆍ분장 미용업네일;이슬경상남도 사천시 사남면 진삼로 1225<NA><NA>
727728일반미용업 네일미용업 화장ㆍ분장 미용업미즈 헤어숍경상남도 사천시 나무전길 12-14 (선구동)055- 832-9998<NA>
728729일반미용업 네일미용업 화장ㆍ분장 미용업장미네일경상남도 사천시 주공로 16 (벌리동)055 -832 -7890<NA>
729730일반미용업 네일미용업 화장ㆍ분장 미용업살롱드베이경상남도 사천시 사천읍 옥산로 63 2층<NA><NA>
730731일반미용업 네일미용업 화장ㆍ분장 미용업슬기로운미용생활경상남도 사천시 사천읍 평화1길 40 1층<NA><NA>
731732일반미용업 네일미용업 화장ㆍ분장 미용업융헤어경상남도 사천시 사천읍 읍내로 50 1층<NA><NA>
732733피부미용업 네일미용업 화장ㆍ분장 미용업서꽃잎 Beauty Salon경상남도 사천시 통창동길 4-1 (동금동)<NA><NA>
733734피부미용업 네일미용업 화장ㆍ분장 미용업금손언니경상남도 사천시 정동면 정동중앙로 16-5<NA><NA>