Overview

Dataset statistics

Number of variables4
Number of observations122
Missing cells20
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory33.1 B

Variable types

Categorical1
Text3

Dataset

Description해당 자료는 부산광역시 사상구에 위치한 세탁업 현황(업종명,업소명,소재지, 연락처)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3078906/fileData.do

Alerts

업종명 has constant value ""Constant
소재지전화 has 20 (16.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 11:51:02.839431
Analysis finished2023-12-12 11:51:03.635695
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
세탁업
122 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 122
100.0%

Length

2023-12-12T20:51:03.722719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:03.849704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 122
100.0%

업소명
Text

Distinct113
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T20:51:04.137423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.2459016
Min length1

Characters and Unicode

Total characters518
Distinct characters161
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)86.9%

Sample

1st row태화
2nd row부일사
3rd row영락사세탁
4th row현대사
5th row신흥
ValueCountFrequency (%)
제일 3
 
2.3%
백조 3
 
2.3%
원빨래방 2
 
1.5%
월드크리닝 2
 
1.5%
크린 2
 
1.5%
세탁 2
 
1.5%
미광 2
 
1.5%
진주 2
 
1.5%
동양 2
 
1.5%
삼성세탁소 2
 
1.5%
Other values (111) 111
83.5%
2023-12-12T20:51:04.643383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 43
 
8.3%
탁 43
 
8.3%
소 23
 
4.4%
성 13
 
2.5%
크 13
 
2.5%
동 12
 
2.3%
사 11
 
2.1%
11
 
2.1%
린 9
 
1.7%
화 9
 
1.7%
Other values (151) 331
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 485
93.6%
Space Separator 11
 
2.1%
Uppercase Letter 10
 
1.9%
Close Punctuation 5
 
1.0%
Open Punctuation 5
 
1.0%
Decimal Number 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 43
 
8.9%
탁 43
 
8.9%
소 23
 
4.7%
성 13
 
2.7%
크 13
 
2.7%
동 12
 
2.5%
사 11
 
2.3%
린 9
 
1.9%
화 9
 
1.9%
일 9
 
1.9%
Other values (138) 300
61.9%
Uppercase Letter
ValueCountFrequency (%)
M 2
20.0%
C 2
20.0%
A 1
10.0%
B 1
10.0%
S 1
10.0%
R 1
10.0%
E 1
10.0%
T 1
10.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 485
93.6%
Common 23
 
4.4%
Latin 10
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 43
 
8.9%
탁 43
 
8.9%
소 23
 
4.7%
성 13
 
2.7%
크 13
 
2.7%
동 12
 
2.5%
사 11
 
2.3%
린 9
 
1.9%
화 9
 
1.9%
일 9
 
1.9%
Other values (138) 300
61.9%
Latin
ValueCountFrequency (%)
M 2
20.0%
C 2
20.0%
A 1
10.0%
B 1
10.0%
S 1
10.0%
R 1
10.0%
E 1
10.0%
T 1
10.0%
Common
ValueCountFrequency (%)
11
47.8%
) 5
21.7%
( 5
21.7%
2 1
 
4.3%
& 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 485
93.6%
ASCII 33
 
6.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 43
 
8.9%
탁 43
 
8.9%
소 23
 
4.7%
성 13
 
2.7%
크 13
 
2.7%
동 12
 
2.5%
사 11
 
2.3%
린 9
 
1.9%
화 9
 
1.9%
일 9
 
1.9%
Other values (138) 300
61.9%
ASCII
ValueCountFrequency (%)
11
33.3%
) 5
15.2%
( 5
15.2%
M 2
 
6.1%
C 2
 
6.1%
A 1
 
3.0%
B 1
 
3.0%
2 1
 
3.0%
S 1
 
3.0%
R 1
 
3.0%
Other values (3) 3
 
9.1%

영업소 주소(도로명)
Text

Distinct121
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T20:51:04.995443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length49
Mean length32.565574
Min length21

Characters and Unicode

Total characters3973
Distinct characters118
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

Unique120 ?
Unique (%)98.4%

Sample

1st row부산광역시 사상구 광장로37번길 36 (괘법동)
2nd row부산광역시 사상구 새벽로202번길 20 (괘법동)
3rd row부산광역시 사상구 강선로 21 (덕포동)
4th row부산광역시 사상구 사상로137번길 8 (감전동)
5th row부산광역시 사상구 주감로7번길 10 (감전동)
ValueCountFrequency (%)
부산광역시 122
 
16.2%
사상구 122
 
16.2%
학장동 21
 
2.8%
주례동 21
 
2.8%
1층 20
 
2.7%
모라동 19
 
2.5%
덕포동 17
 
2.3%
괘법동 16
 
2.1%
감전동 14
 
1.9%
상가 11
 
1.5%
Other values (246) 370
49.1%
2023-12-12T20:51:05.583158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
631
 
15.9%
상 177
 
4.5%
동 172
 
4.3%
1 163
 
4.1%
사 141
 
3.5%
광 133
 
3.3%
산 127
 
3.2%
구 125
 
3.1%
시 125
 
3.1%
) 123
 
3.1%
Other values (108) 2056
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2275
57.3%
Decimal Number 690
 
17.4%
Space Separator 631
 
15.9%
Close Punctuation 123
 
3.1%
Open Punctuation 123
 
3.1%
Other Punctuation 101
 
2.5%
Dash Punctuation 27
 
0.7%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 177
 
7.8%
동 172
 
7.6%
사 141
 
6.2%
광 133
 
5.8%
산 127
 
5.6%
구 125
 
5.5%
시 125
 
5.5%
부 123
 
5.4%
로 123
 
5.4%
역 122
 
5.4%
Other values (90) 907
39.9%
Decimal Number
ValueCountFrequency (%)
1 163
23.6%
2 110
15.9%
0 78
11.3%
4 65
 
9.4%
3 62
 
9.0%
9 48
 
7.0%
6 46
 
6.7%
5 45
 
6.5%
7 44
 
6.4%
8 29
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 98
97.0%
@ 3
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%
Space Separator
ValueCountFrequency (%)
631
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2275
57.3%
Common 1695
42.7%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 177
 
7.8%
동 172
 
7.6%
사 141
 
6.2%
광 133
 
5.8%
산 127
 
5.6%
구 125
 
5.5%
시 125
 
5.5%
부 123
 
5.4%
로 123
 
5.4%
역 122
 
5.4%
Other values (90) 907
39.9%
Common
ValueCountFrequency (%)
631
37.2%
1 163
 
9.6%
) 123
 
7.3%
( 123
 
7.3%
2 110
 
6.5%
, 98
 
5.8%
0 78
 
4.6%
4 65
 
3.8%
3 62
 
3.7%
9 48
 
2.8%
Other values (6) 194
 
11.4%
Latin
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2275
57.3%
ASCII 1698
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
631
37.2%
1 163
 
9.6%
) 123
 
7.2%
( 123
 
7.2%
2 110
 
6.5%
, 98
 
5.8%
0 78
 
4.6%
4 65
 
3.8%
3 62
 
3.7%
9 48
 
2.8%
Other values (8) 197
 
11.6%
Hangul
ValueCountFrequency (%)
상 177
 
7.8%
동 172
 
7.6%
사 141
 
6.2%
광 133
 
5.8%
산 127
 
5.6%
구 125
 
5.5%
시 125
 
5.5%
부 123
 
5.4%
로 123
 
5.4%
역 122
 
5.4%
Other values (90) 907
39.9%

소재지전화
Text

MISSING 

Distinct102
Distinct (%)100.0%
Missing20
Missing (%)16.4%
Memory size1.1 KiB
2023-12-12T20:51:05.930254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1224
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

Unique102 ?
Unique (%)100.0%

Sample

1st row051-324-2559
2nd row051-328-3928
3rd row051-303-8607
4th row051-325-6384
5th row051-313-7924
ValueCountFrequency (%)
051-315-9036 1
 
1.0%
051-322-5464 1
 
1.0%
051-323-1527 1
 
1.0%
051-301-8736 1
 
1.0%
051-301-7377 1
 
1.0%
051-311-5257 1
 
1.0%
051-311-9244 1
 
1.0%
051-324-0383 1
 
1.0%
051-302-2461 1
 
1.0%
051-322-7444 1
 
1.0%
Other values (92) 92
90.2%
2023-12-12T20:51:06.386893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 204
16.7%
1 180
14.7%
3 159
13.0%
0 157
12.8%
5 155
12.7%
2 110
9.0%
4 57
 
4.7%
8 52
 
4.2%
7 52
 
4.2%
6 51
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1020
83.3%
Dash Punctuation 204
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 180
17.6%
3 159
15.6%
0 157
15.4%
5 155
15.2%
2 110
10.8%
4 57
 
5.6%
8 52
 
5.1%
7 52
 
5.1%
6 51
 
5.0%
9 47
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1224
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 204
16.7%
1 180
14.7%
3 159
13.0%
0 157
12.8%
5 155
12.7%
2 110
9.0%
4 57
 
4.7%
8 52
 
4.2%
7 52
 
4.2%
6 51
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 204
16.7%
1 180
14.7%
3 159
13.0%
0 157
12.8%
5 155
12.7%
2 110
9.0%
4 57
 
4.7%
8 52
 
4.2%
7 52
 
4.2%
6 51
 
4.2%

Missing values

2023-12-12T20:51:03.476477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:51:03.593282image/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

업종명업소명영업소 주소(도로명)소재지전화
0세탁업태화부산광역시 사상구 광장로37번길 36 (괘법동)051-324-2559
1세탁업부일사부산광역시 사상구 새벽로202번길 20 (괘법동)051-328-3928
2세탁업영락사세탁부산광역시 사상구 강선로 21 (덕포동)051-303-8607
3세탁업현대사부산광역시 사상구 사상로137번길 8 (감전동)051-325-6384
4세탁업신흥부산광역시 사상구 주감로7번길 10 (감전동)051-313-7924
5세탁업동성부산광역시 사상구 광장로97번길 4 (괘법동)051-328-2371
6세탁업동보부산광역시 사상구 사상로161번길 54 (괘법동)051-322-9467
7세탁업대성부산광역시 사상구 백양대로 430, 2동 111호 (주례동, 대성상가)051-326-9604
8세탁업성일부산광역시 사상구 낙동대로1250번길 29 (삼락동)051-305-0725
9세탁업유성부산광역시 사상구 새벽시장로56번길 5 (감전동)051-326-6675
업종명업소명영업소 주소(도로명)소재지전화
112세탁업CM(크린마스터)운동화세탁부산광역시 사상구 백양대로 372-16, 상가동 4층 303호 (주례동, 반도보라메머드타운)051-314-5252
113세탁업꼭 하이트세탁소부산광역시 사상구 주례로9번길 53, 1층 (주례동)<NA>
114세탁업월드크리닝부산광역시 사상구 사상로 404 (주)원광 2층 (덕포동)<NA>
115세탁업동해사부산광역시 사상구 대동로 121-10 (학장동)<NA>
116세탁업씨앤비(C&B)부산광역시 사상구 대동로 121-10, 1층 (학장동)<NA>
117세탁업샘터세탁부산광역시 사상구 삼덕로89번길 27, 1층 (덕포동, 화신식품)<NA>
118세탁업(주)위드아스 뚱보빨래방부산광역시 사상구 대동로 94, 학장반도보라타운 상가동 101호 (학장동)051-316-0579
119세탁업부경산업부산광역시 사상구 학장로 261, 1층 (학장동)051-325-1179
120세탁업한결부산광역시 사상구 새벽로121번길 83, 1층 (감전동)<NA>
121세탁업맡기는 빨래방부산광역시 사상구 진사로 24, 1층 (주례동)<NA>