Overview

Dataset statistics

Number of variables4
Number of observations113
Missing cells14
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory33.2 B

Variable types

Categorical1
Text3

Dataset

Description충청남도 아산시 세탁업소 현황으로 업소명, 업종명, 주소, 업소현황 등의 정보를 제공합니다.--------------------------
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=417&beforeMenuCd=DOM_000000201001001000&publicdatapk=15055143

Alerts

업종명 has constant value ""Constant
소재지전화 has 14 (12.4%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:34:50.385915
Analysis finished2024-01-09 21:34:50.725439
Duration0.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
세탁업
113 

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 113
100.0%

Length

2024-01-10T06:34:50.772978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:34:50.840076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 113
100.0%

업소명
Text

Distinct112
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-01-10T06:34:51.039251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length6.7168142
Min length4

Characters and Unicode

Total characters759
Distinct characters179
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

Unique111 ?
Unique (%)98.2%

Sample

1st row백양세탁소
2nd row미광세탁소
3rd row비봉세탁소
4th row남산세탁소
5th row신일세탁소
ValueCountFrequency (%)
세탁소 10
 
7.4%
신일세탁소 3
 
2.2%
한라세탁소 2
 
1.5%
일류세탁 1
 
0.7%
주)청정드라이크리닝 1
 
0.7%
김병민보석크리닝 1
 
0.7%
주)새하얀크린세탁 1
 
0.7%
y세탁앤크린샵 1
 
0.7%
세광클리닝 1
 
0.7%
송림산업 1
 
0.7%
Other values (113) 113
83.7%
2024-01-10T06:34:51.358326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 88
 
11.6%
탁 87
 
11.5%
소 69
 
9.1%
53
 
7.0%
크 22
 
2.9%
리 17
 
2.2%
이 14
 
1.8%
린 11
 
1.4%
닝 10
 
1.3%
아 9
 
1.2%
Other values (169) 379
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 676
89.1%
Space Separator 53
 
7.0%
Lowercase Letter 11
 
1.4%
Close Punctuation 7
 
0.9%
Open Punctuation 7
 
0.9%
Uppercase Letter 3
 
0.4%
Other Punctuation 1
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 88
 
13.0%
탁 87
 
12.9%
소 69
 
10.2%
크 22
 
3.3%
리 17
 
2.5%
이 14
 
2.1%
린 11
 
1.6%
닝 10
 
1.5%
아 9
 
1.3%
래 8
 
1.2%
Other values (152) 341
50.4%
Lowercase Letter
ValueCountFrequency (%)
l 2
18.2%
a 2
18.2%
y 1
9.1%
r 1
9.1%
d 1
9.1%
n 1
9.1%
g 1
9.1%
i 1
9.1%
e 1
9.1%
Uppercase Letter
ValueCountFrequency (%)
Y 1
33.3%
V 1
33.3%
L 1
33.3%
Space Separator
ValueCountFrequency (%)
53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Decimal Number
ValueCountFrequency (%)
7 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 676
89.1%
Common 69
 
9.1%
Latin 14
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 88
 
13.0%
탁 87
 
12.9%
소 69
 
10.2%
크 22
 
3.3%
리 17
 
2.5%
이 14
 
2.1%
린 11
 
1.6%
닝 10
 
1.5%
아 9
 
1.3%
래 8
 
1.2%
Other values (152) 341
50.4%
Latin
ValueCountFrequency (%)
l 2
14.3%
a 2
14.3%
Y 1
7.1%
V 1
7.1%
y 1
7.1%
r 1
7.1%
d 1
7.1%
n 1
7.1%
L 1
7.1%
g 1
7.1%
Other values (2) 2
14.3%
Common
ValueCountFrequency (%)
53
76.8%
) 7
 
10.1%
( 7
 
10.1%
& 1
 
1.4%
7 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 676
89.1%
ASCII 83
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 88
 
13.0%
탁 87
 
12.9%
소 69
 
10.2%
크 22
 
3.3%
리 17
 
2.5%
이 14
 
2.1%
린 11
 
1.6%
닝 10
 
1.5%
아 9
 
1.3%
래 8
 
1.2%
Other values (152) 341
50.4%
ASCII
ValueCountFrequency (%)
53
63.9%
) 7
 
8.4%
( 7
 
8.4%
l 2
 
2.4%
a 2
 
2.4%
Y 1
 
1.2%
& 1
 
1.2%
7 1
 
1.2%
V 1
 
1.2%
y 1
 
1.2%
Other values (7) 7
 
8.4%

영업소 주소(도로명)
Text

Distinct112
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-01-10T06:34:51.581137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length45
Mean length30.610619
Min length17

Characters and Unicode

Total characters3459
Distinct characters172
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

Unique111 ?
Unique (%)98.2%

Sample

1st row충청남도 아산시 둔포면 둔포면로 16-1
2nd row충청남도 아산시 신창면 순천향로 47-25, 1층
3rd row충청남도 아산시 시민로405번길 6, 비봉세탁소 (온천동)
4th row충청남도 아산시 남산로 48 (온천동)
5th row충청남도 아산시 시민로 321-4 (용화동)
ValueCountFrequency (%)
충청남도 113
 
16.0%
아산시 113
 
16.0%
1층 41
 
5.8%
배방읍 21
 
3.0%
온천동 14
 
2.0%
둔포면 9
 
1.3%
탕정면 8
 
1.1%
상가동 8
 
1.1%
용화동 7
 
1.0%
모종동 6
 
0.8%
Other values (274) 366
51.8%
2024-01-10T06:34:52.176030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
597
 
17.3%
1 197
 
5.7%
아 144
 
4.2%
산 132
 
3.8%
시 125
 
3.6%
남 121
 
3.5%
도 118
 
3.4%
청 117
 
3.4%
충 114
 
3.3%
로 97
 
2.8%
Other values (162) 1697
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1943
56.2%
Decimal Number 627
 
18.1%
Space Separator 597
 
17.3%
Close Punctuation 82
 
2.4%
Open Punctuation 82
 
2.4%
Other Punctuation 74
 
2.1%
Dash Punctuation 44
 
1.3%
Uppercase Letter 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 144
 
7.4%
산 132
 
6.8%
시 125
 
6.4%
남 121
 
6.2%
도 118
 
6.1%
청 117
 
6.0%
충 114
 
5.9%
로 97
 
5.0%
동 90
 
4.6%
층 53
 
2.7%
Other values (139) 832
42.8%
Decimal Number
ValueCountFrequency (%)
1 197
31.4%
2 91
14.5%
3 78
 
12.4%
0 64
 
10.2%
6 40
 
6.4%
8 36
 
5.7%
5 36
 
5.7%
4 35
 
5.6%
7 34
 
5.4%
9 16
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
A 3
30.0%
B 2
20.0%
D 1
 
10.0%
C 1
 
10.0%
T 1
 
10.0%
E 1
 
10.0%
P 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 73
98.6%
@ 1
 
1.4%
Space Separator
ValueCountFrequency (%)
597
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1943
56.2%
Common 1506
43.5%
Latin 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 144
 
7.4%
산 132
 
6.8%
시 125
 
6.4%
남 121
 
6.2%
도 118
 
6.1%
청 117
 
6.0%
충 114
 
5.9%
로 97
 
5.0%
동 90
 
4.6%
층 53
 
2.7%
Other values (139) 832
42.8%
Common
ValueCountFrequency (%)
597
39.6%
1 197
 
13.1%
2 91
 
6.0%
) 82
 
5.4%
( 82
 
5.4%
3 78
 
5.2%
, 73
 
4.8%
0 64
 
4.2%
- 44
 
2.9%
6 40
 
2.7%
Other values (6) 158
 
10.5%
Latin
ValueCountFrequency (%)
A 3
30.0%
B 2
20.0%
D 1
 
10.0%
C 1
 
10.0%
T 1
 
10.0%
E 1
 
10.0%
P 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1943
56.2%
ASCII 1516
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
597
39.4%
1 197
 
13.0%
2 91
 
6.0%
) 82
 
5.4%
( 82
 
5.4%
3 78
 
5.1%
, 73
 
4.8%
0 64
 
4.2%
- 44
 
2.9%
6 40
 
2.6%
Other values (13) 168
 
11.1%
Hangul
ValueCountFrequency (%)
아 144
 
7.4%
산 132
 
6.8%
시 125
 
6.4%
남 121
 
6.2%
도 118
 
6.1%
청 117
 
6.0%
충 114
 
5.9%
로 97
 
5.0%
동 90
 
4.6%
층 53
 
2.7%
Other values (139) 832
42.8%

소재지전화
Text

MISSING 

Distinct98
Distinct (%)99.0%
Missing14
Missing (%)12.4%
Memory size1.0 KiB
2024-01-10T06:34:52.371253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1188
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

Unique97 ?
Unique (%)98.0%

Sample

1st row041-532-3390
2nd row041-542-7452
3rd row041-545-6003
4th row041-545-5906
5th row041-546-0840
ValueCountFrequency (%)
041-911-3332 2
 
2.0%
041-910-7555 1
 
1.0%
041-548-2244 1
 
1.0%
041-549-3669 1
 
1.0%
041-541-2288 1
 
1.0%
041-911-8939 1
 
1.0%
041-546-8136 1
 
1.0%
041-532-0161 1
 
1.0%
041-548-1992 1
 
1.0%
041-544-5441 1
 
1.0%
Other values (88) 88
88.9%
2024-01-10T06:34:52.668554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 216
18.2%
- 198
16.7%
1 170
14.3%
5 149
12.5%
0 133
11.2%
3 77
 
6.5%
2 61
 
5.1%
9 52
 
4.4%
8 46
 
3.9%
6 46
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 990
83.3%
Dash Punctuation 198
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 216
21.8%
1 170
17.2%
5 149
15.1%
0 133
13.4%
3 77
 
7.8%
2 61
 
6.2%
9 52
 
5.3%
8 46
 
4.6%
6 46
 
4.6%
7 40
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1188
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 216
18.2%
- 198
16.7%
1 170
14.3%
5 149
12.5%
0 133
11.2%
3 77
 
6.5%
2 61
 
5.1%
9 52
 
4.4%
8 46
 
3.9%
6 46
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 216
18.2%
- 198
16.7%
1 170
14.3%
5 149
12.5%
0 133
11.2%
3 77
 
6.5%
2 61
 
5.1%
9 52
 
4.4%
8 46
 
3.9%
6 46
 
3.9%

Missing values

2024-01-10T06:34:50.628580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:34:50.699117image/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
0세탁업백양세탁소충청남도 아산시 둔포면 둔포면로 16-1041-532-3390
1세탁업미광세탁소충청남도 아산시 신창면 순천향로 47-25, 1층041-542-7452
2세탁업비봉세탁소충청남도 아산시 시민로405번길 6, 비봉세탁소 (온천동)041-545-6003
3세탁업남산세탁소충청남도 아산시 남산로 48 (온천동)041-545-5906
4세탁업신일세탁소충청남도 아산시 시민로 321-4 (용화동)041-546-0840
5세탁업광복세탁소충청남도 아산시 어의정로65번길 26 (용화동)041-543-5488
6세탁업신광세탁소충청남도 아산시 남산로 57-10 (온천동,(1층))041-545-3654
7세탁업남양상사충청남도 아산시 고불로 706, 3동 1층 (좌부동)041-545-8445
8세탁업용화세탁소충청남도 아산시 온여고길 24 (용화동,1층)041-545-6354
9세탁업대우세탁소충청남도 아산시 시민로 277-1 (용화동)041-543-6174
업종명업소명영업소 주소(도로명)소재지전화
103세탁업부영세탁수선충청남도 아산시 온천대로 1105-17, 상가동 1층 108호 (득산동, 부영아파트)<NA>
104세탁업세탁팩토리충청남도 아산시 둔포면 둔포로 130-39, 1층<NA>
105세탁업세탁7번가충청남도 아산시 탕정면 선문로 387, 120동 1층 109호041-543-1142
106세탁업매직크린팩토리충청남도 아산시 탕정면 꾀꼴성길 130-1, 1동 1층<NA>
107세탁업서들나래 세탁소충청남도 아산시 선장면 아산만로 368, 특별교실동 2층041-541-8794
108세탁업크린에이드(신아산지사)충청남도 아산시 둔포면 둔포중앙로133번길 20<NA>
109세탁업워시피플 셀프 빨래방 천안아산역점충청남도 아산시 배방읍 광장로 210, 와이몰동 1층 E103호 (요진 와이시티)<NA>
110세탁업라헬 크린컴퍼니충청남도 아산시 탕정면 꾀꼴성길 88-11, 2동 1층<NA>
111세탁업런드리웰충청남도 아산시 탕정면 매곡한들3길 5-7, 1층 101호041-532-8307
112세탁업비제이크린텍충청남도 아산시 둔포면 봉재길63번길 31-1, 1층041-544-2420