Overview

Dataset statistics

Number of variables4
Number of observations102
Missing cells9
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory33.3 B

Variable types

Categorical1
Text3

Dataset

Description서산시에 영업허가된 공중 위생업소(이발소. 미용실, 세탁소, 목욕탕, 사우나, 네일아트, 피부관리샵)정보로 업종명, 업소명, 업소소재지, 소재지에 대한 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=445&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000677

Alerts

업종명 has constant value ""Constant
소재지전화 has 9 (8.8%) missing valuesMissing
업소소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:32:00.445656
Analysis finished2024-01-09 21:32:00.750833
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
세탁업
102 

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 102
100.0%

Length

2024-01-10T06:32:00.806250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:32:00.874593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 102
100.0%

업소명
Text

Distinct100
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
2024-01-10T06:32:01.073852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length6.1960784
Min length4

Characters and Unicode

Total characters632
Distinct characters147
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

Unique98 ?
Unique (%)96.1%

Sample

1st row119세탁크리닝
2nd row가나세탁소
3rd row광성세탁소
4th row궁전세탁소
5th row그린세탁소
ValueCountFrequency (%)
대광세탁소 2
 
1.8%
운동화 2
 
1.8%
세탁소 2
 
1.8%
현대세탁소 2
 
1.8%
아진세탁소 1
 
0.9%
엄마손 1
 
0.9%
빨래터 1
 
0.9%
중앙컴퓨터크리닝 1
 
0.9%
주공컴퓨터세탁소 1
 
0.9%
제일세탁소 1
 
0.9%
Other values (100) 100
87.7%
2024-01-10T06:32:01.400661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 83
 
13.1%
탁 83
 
13.1%
소 66
 
10.4%
크 20
 
3.2%
성 13
 
2.1%
12
 
1.9%
닝 12
 
1.9%
리 12
 
1.9%
터 11
 
1.7%
컴 10
 
1.6%
Other values (137) 310
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 608
96.2%
Space Separator 12
 
1.9%
Decimal Number 3
 
0.5%
Other Punctuation 3
 
0.5%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 83
 
13.7%
탁 83
 
13.7%
소 66
 
10.9%
크 20
 
3.3%
성 13
 
2.1%
닝 12
 
2.0%
리 12
 
2.0%
터 11
 
1.8%
컴 10
 
1.6%
퓨 10
 
1.6%
Other values (127) 288
47.4%
Other Punctuation
ValueCountFrequency (%)
& 1
33.3%
, 1
33.3%
. 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 608
96.2%
Common 22
 
3.5%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 83
 
13.7%
탁 83
 
13.7%
소 66
 
10.9%
크 20
 
3.3%
성 13
 
2.1%
닝 12
 
2.0%
리 12
 
2.0%
터 11
 
1.8%
컴 10
 
1.6%
퓨 10
 
1.6%
Other values (127) 288
47.4%
Common
ValueCountFrequency (%)
12
54.5%
1 2
 
9.1%
( 2
 
9.1%
) 2
 
9.1%
& 1
 
4.5%
9 1
 
4.5%
, 1
 
4.5%
. 1
 
4.5%
Latin
ValueCountFrequency (%)
U 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 608
96.2%
ASCII 24
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 83
 
13.7%
탁 83
 
13.7%
소 66
 
10.9%
크 20
 
3.3%
성 13
 
2.1%
닝 12
 
2.0%
리 12
 
2.0%
터 11
 
1.8%
컴 10
 
1.6%
퓨 10
 
1.6%
Other values (127) 288
47.4%
ASCII
ValueCountFrequency (%)
12
50.0%
1 2
 
8.3%
( 2
 
8.3%
) 2
 
8.3%
U 1
 
4.2%
& 1
 
4.2%
P 1
 
4.2%
9 1
 
4.2%
, 1
 
4.2%
. 1
 
4.2%

업소소재지(도로명)
Text

UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
2024-01-10T06:32:01.625908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44.5
Mean length27.019608
Min length19

Characters and Unicode

Total characters2756
Distinct characters144
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row충청남도 서산시 부춘1로 32 (읍내동)
2nd row충청남도 서산시 동문동 322번지 2호
3rd row충청남도 서산시 동서1로 101 (석남동)
4th row충청남도 서산시 안견로 151, 가동 1층 (동문동)
5th row충청남도 서산시 부춘1로 19 (읍내동)
ValueCountFrequency (%)
충청남도 102
 
17.0%
서산시 102
 
17.0%
1층 27
 
4.5%
동문동 27
 
4.5%
읍내동 21
 
3.5%
상가동 15
 
2.5%
석림동 12
 
2.0%
서령로 7
 
1.2%
안견로 6
 
1.0%
해미면 6
 
1.0%
Other values (190) 276
45.9%
2024-01-10T06:32:01.971641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
502
18.2%
동 146
 
5.3%
1 132
 
4.8%
서 117
 
4.2%
남 114
 
4.1%
산 114
 
4.1%
시 106
 
3.8%
충 104
 
3.8%
청 102
 
3.7%
도 102
 
3.7%
Other values (134) 1217
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1599
58.0%
Space Separator 502
 
18.2%
Decimal Number 410
 
14.9%
Open Punctuation 82
 
3.0%
Close Punctuation 82
 
3.0%
Other Punctuation 63
 
2.3%
Dash Punctuation 15
 
0.5%
Math Symbol 2
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 146
 
9.1%
서 117
 
7.3%
남 114
 
7.1%
산 114
 
7.1%
시 106
 
6.6%
충 104
 
6.5%
청 102
 
6.4%
도 102
 
6.4%
로 89
 
5.6%
읍 32
 
2.0%
Other values (117) 573
35.8%
Decimal Number
ValueCountFrequency (%)
1 132
32.2%
2 64
15.6%
3 42
 
10.2%
0 41
 
10.0%
4 31
 
7.6%
7 26
 
6.3%
6 25
 
6.1%
5 19
 
4.6%
8 17
 
4.1%
9 13
 
3.2%
Space Separator
ValueCountFrequency (%)
502
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Other Punctuation
ValueCountFrequency (%)
, 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1599
58.0%
Common 1156
41.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 146
 
9.1%
서 117
 
7.3%
남 114
 
7.1%
산 114
 
7.1%
시 106
 
6.6%
충 104
 
6.5%
청 102
 
6.4%
도 102
 
6.4%
로 89
 
5.6%
읍 32
 
2.0%
Other values (117) 573
35.8%
Common
ValueCountFrequency (%)
502
43.4%
1 132
 
11.4%
( 82
 
7.1%
) 82
 
7.1%
2 64
 
5.5%
, 63
 
5.4%
3 42
 
3.6%
0 41
 
3.5%
4 31
 
2.7%
7 26
 
2.2%
Other values (6) 91
 
7.9%
Latin
ValueCountFrequency (%)
F 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1599
58.0%
ASCII 1157
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
502
43.4%
1 132
 
11.4%
( 82
 
7.1%
) 82
 
7.1%
2 64
 
5.5%
, 63
 
5.4%
3 42
 
3.6%
0 41
 
3.5%
4 31
 
2.7%
7 26
 
2.2%
Other values (7) 92
 
8.0%
Hangul
ValueCountFrequency (%)
동 146
 
9.1%
서 117
 
7.3%
남 114
 
7.1%
산 114
 
7.1%
시 106
 
6.6%
충 104
 
6.5%
청 102
 
6.4%
도 102
 
6.4%
로 89
 
5.6%
읍 32
 
2.0%
Other values (117) 573
35.8%

소재지전화
Text

MISSING 

Distinct93
Distinct (%)100.0%
Missing9
Missing (%)8.8%
Memory size948.0 B
2024-01-10T06:32:02.197908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1302
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

Unique93 ?
Unique (%)100.0%

Sample

1st row 041- 666-1119
2nd row 041- 666-0105
3rd row041 -668 -1668
4th row 041- 667-1666
5th row 041- 667-5535
ValueCountFrequency (%)
041 92
41.4%
665 8
 
3.6%
666 5
 
2.3%
667 4
 
1.8%
663 4
 
1.8%
668 4
 
1.8%
669 3
 
1.4%
688 3
 
1.4%
681 3
 
1.4%
5042 2
 
0.9%
Other values (92) 94
42.3%
2024-01-10T06:32:02.502263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 215
16.5%
186
14.3%
- 186
14.3%
4 148
11.4%
0 137
10.5%
1 134
10.3%
5 68
 
5.2%
8 55
 
4.2%
2 47
 
3.6%
3 47
 
3.6%
Other values (2) 79
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 930
71.4%
Space Separator 186
 
14.3%
Dash Punctuation 186
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 215
23.1%
4 148
15.9%
0 137
14.7%
1 134
14.4%
5 68
 
7.3%
8 55
 
5.9%
2 47
 
5.1%
3 47
 
5.1%
7 42
 
4.5%
9 37
 
4.0%
Space Separator
ValueCountFrequency (%)
186
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 215
16.5%
186
14.3%
- 186
14.3%
4 148
11.4%
0 137
10.5%
1 134
10.3%
5 68
 
5.2%
8 55
 
4.2%
2 47
 
3.6%
3 47
 
3.6%
Other values (2) 79
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 215
16.5%
186
14.3%
- 186
14.3%
4 148
11.4%
0 137
10.5%
1 134
10.3%
5 68
 
5.2%
8 55
 
4.2%
2 47
 
3.6%
3 47
 
3.6%
Other values (2) 79
 
6.1%

Correlations

2024-01-10T06:32:02.583897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명소재지전화
업소명1.0001.000
소재지전화1.0001.000

Missing values

2024-01-10T06:32:00.661001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:32:00.724530image/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

업종명업소명업소소재지(도로명)소재지전화
0세탁업119세탁크리닝충청남도 서산시 부춘1로 32 (읍내동)041- 666-1119
1세탁업가나세탁소충청남도 서산시 동문동 322번지 2호041- 666-0105
2세탁업광성세탁소충청남도 서산시 동서1로 101 (석남동)041 -668 -1668
3세탁업궁전세탁소충청남도 서산시 안견로 151, 가동 1층 (동문동)041- 667-1666
4세탁업그린세탁소충청남도 서산시 부춘1로 19 (읍내동)041- 667-5535
5세탁업금성세탁소충청남도 서산시 고운로 103 (읍내동)041- 662-1815
6세탁업기쁜날크리닝충청남도 서산시 한마음16로 70-4, 1층 (석림동)041- 662-9694
7세탁업기쁜날크리닝(동남점)충청남도 서산시 한성지3로 28 (수석동, 동남2차 상가)<NA>
8세탁업까치세탁소충청남도 서산시 동헌로 117 (읍내동)041- 667-4650
9세탁업낙원세탁소충청남도 서산시 읍내1로 12 (읍내동)041- 666-2733
업종명업소명업소소재지(도로명)소재지전화
92세탁업한성세탁소충청남도 서산시 음암면 서령로 325-23, 상가동 2층 206호 (한성스위트빌)<NA>
93세탁업한성크리닝충청남도 서산시 동서1로 182, 상가동 102호 (예천동, 예천한성필하우스)041- 669-9034
94세탁업한성필크리닝충청남도 서산시 동서2로 15, 상가동 1층 101호 (석림동, 한성필하우스아파트)041- 663-8965
95세탁업행운세탁소충청남도 서산시 서령로 104 (동문동)041- 667-3428
96세탁업현대세탁소충청남도 서산시 시장6로 19 (동문동)041- 662-1936
97세탁업현대세탁소충청남도 서산시 안견로 721-14 (갈산동)<NA>
98세탁업현창크리닝충청남도 서산시 안견로 585, 서산농,수,축산물 유통센터 5동 101~104호 (갈산동)041 -666 -0411
99세탁업형제세탁소충청남도 서산시 대산읍 구진로 52041- 663-8070
100세탁업호수세탁충청남도 서산시 호수공원10로 8, 1층 (예천동)041 -681 -7942
101세탁업화이트크리닝충청남도 서산시 한마음6로 30, 1층 (석림동)041 -681 -8939