Overview

Dataset statistics

Number of variables5
Number of observations108
Missing cells8
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory41.2 B

Variable types

Categorical1
Text3
DateTime1

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
데이터기준일 has constant value ""Constant
소재지전화 has 8 (7.4%) missing valuesMissing
업소소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:32:15.349275
Analysis finished2024-01-09 21:32:15.911706
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size996.0 B
세탁업
108 

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 108
100.0%

Length

2024-01-10T06:32:15.973729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:32:16.042580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 108
100.0%

업소명
Text

Distinct104
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T06:32:16.237762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length6.0462963
Min length4

Characters and Unicode

Total characters653
Distinct characters154
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

Unique100 ?
Unique (%)92.6%

Sample

1st row서산세탁소
2nd row금성세탁소
3rd row보은가죽피혁세탁전문
4th row송옥세탁소
5th row컴퓨터세탁소
ValueCountFrequency (%)
세탁소 3
 
2.5%
금성세탁소 2
 
1.7%
대광세탁소 2
 
1.7%
백조세탁소 2
 
1.7%
현대세탁소 2
 
1.7%
운동화 2
 
1.7%
형제세탁소 1
 
0.8%
아진세탁소 1
 
0.8%
기쁜날크리닝 1
 
0.8%
성미 1
 
0.8%
Other values (103) 103
85.8%
2024-01-10T06:32:16.569665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 91
 
13.9%
탁 90
 
13.8%
소 75
 
11.5%
크 19
 
2.9%
성 14
 
2.1%
12
 
1.8%
닝 11
 
1.7%
리 11
 
1.7%
터 11
 
1.7%
컴 10
 
1.5%
Other values (144) 309
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 629
96.3%
Space Separator 12
 
1.8%
Decimal Number 3
 
0.5%
Lowercase Letter 3
 
0.5%
Other Punctuation 2
 
0.3%
Uppercase Letter 2
 
0.3%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 91
 
14.5%
탁 90
 
14.3%
소 75
 
11.9%
크 19
 
3.0%
성 14
 
2.2%
닝 11
 
1.7%
리 11
 
1.7%
터 11
 
1.7%
컴 10
 
1.6%
퓨 10
 
1.6%
Other values (132) 287
45.6%
Lowercase Letter
ValueCountFrequency (%)
i 1
33.3%
v 1
33.3%
p 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 629
96.3%
Common 19
 
2.9%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 91
 
14.5%
탁 90
 
14.3%
소 75
 
11.9%
크 19
 
3.0%
성 14
 
2.2%
닝 11
 
1.7%
리 11
 
1.7%
터 11
 
1.7%
컴 10
 
1.6%
퓨 10
 
1.6%
Other values (132) 287
45.6%
Common
ValueCountFrequency (%)
12
63.2%
1 2
 
10.5%
, 1
 
5.3%
9 1
 
5.3%
) 1
 
5.3%
. 1
 
5.3%
( 1
 
5.3%
Latin
ValueCountFrequency (%)
U 1
20.0%
P 1
20.0%
i 1
20.0%
v 1
20.0%
p 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 629
96.3%
ASCII 24
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 91
 
14.5%
탁 90
 
14.3%
소 75
 
11.9%
크 19
 
3.0%
성 14
 
2.2%
닝 11
 
1.7%
리 11
 
1.7%
터 11
 
1.7%
컴 10
 
1.6%
퓨 10
 
1.6%
Other values (132) 287
45.6%
ASCII
ValueCountFrequency (%)
12
50.0%
1 2
 
8.3%
, 1
 
4.2%
9 1
 
4.2%
U 1
 
4.2%
) 1
 
4.2%
. 1
 
4.2%
( 1
 
4.2%
P 1
 
4.2%
i 1
 
4.2%
Other values (2) 2
 
8.3%

업소소재지(도로명)
Text

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T06:32:16.816916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length46
Mean length27.537037
Min length19

Characters and Unicode

Total characters2974
Distinct characters147
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

Unique108 ?
Unique (%)100.0%

Sample

1st row충청남도 서산시 시장6로 23-1 (동문동)
2nd row충청남도 서산시 고운로 103 (읍내동)
3rd row충청남도 서산시 안견로 277 (읍내동)
4th row충청남도 서산시 양대동 421-6번지
5th row충청남도 서산시 대산읍 대산리 128번지 2호
ValueCountFrequency (%)
충청남도 108
 
17.5%
서산시 108
 
17.5%
동문동 27
 
4.4%
1층 21
 
3.4%
읍내동 21
 
3.4%
상가동 10
 
1.6%
석림동 9
 
1.5%
해미면 6
 
1.0%
예천동 6
 
1.0%
서령로 6
 
1.0%
Other values (214) 294
47.7%
2024-01-10T06:32:17.185656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
546
18.4%
동 148
 
5.0%
1 139
 
4.7%
산 123
 
4.1%
서 121
 
4.1%
남 116
 
3.9%
충 111
 
3.7%
시 111
 
3.7%
청 109
 
3.7%
도 108
 
3.6%
Other values (137) 1342
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1684
56.6%
Space Separator 546
 
18.4%
Decimal Number 473
 
15.9%
Close Punctuation 93
 
3.1%
Open Punctuation 93
 
3.1%
Other Punctuation 62
 
2.1%
Dash Punctuation 21
 
0.7%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 148
 
8.8%
산 123
 
7.3%
서 121
 
7.2%
남 116
 
6.9%
충 111
 
6.6%
시 111
 
6.6%
청 109
 
6.5%
도 108
 
6.4%
로 82
 
4.9%
읍 40
 
2.4%
Other values (119) 615
36.5%
Decimal Number
ValueCountFrequency (%)
1 139
29.4%
2 68
14.4%
3 48
 
10.1%
0 46
 
9.7%
4 43
 
9.1%
6 31
 
6.6%
7 27
 
5.7%
5 26
 
5.5%
8 24
 
5.1%
9 21
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 57
91.9%
@ 5
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
F 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
546
100.0%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1684
56.6%
Common 1288
43.3%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 148
 
8.8%
산 123
 
7.3%
서 121
 
7.2%
남 116
 
6.9%
충 111
 
6.6%
시 111
 
6.6%
청 109
 
6.5%
도 108
 
6.4%
로 82
 
4.9%
읍 40
 
2.4%
Other values (119) 615
36.5%
Common
ValueCountFrequency (%)
546
42.4%
1 139
 
10.8%
) 93
 
7.2%
( 93
 
7.2%
2 68
 
5.3%
, 57
 
4.4%
3 48
 
3.7%
0 46
 
3.6%
4 43
 
3.3%
6 31
 
2.4%
Other values (6) 124
 
9.6%
Latin
ValueCountFrequency (%)
F 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1684
56.6%
ASCII 1290
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
546
42.3%
1 139
 
10.8%
) 93
 
7.2%
( 93
 
7.2%
2 68
 
5.3%
, 57
 
4.4%
3 48
 
3.7%
0 46
 
3.6%
4 43
 
3.3%
6 31
 
2.4%
Other values (8) 126
 
9.8%
Hangul
ValueCountFrequency (%)
동 148
 
8.8%
산 123
 
7.3%
서 121
 
7.2%
남 116
 
6.9%
충 111
 
6.6%
시 111
 
6.6%
청 109
 
6.5%
도 108
 
6.4%
로 82
 
4.9%
읍 40
 
2.4%
Other values (119) 615
36.5%

소재지전화
Text

MISSING 

Distinct100
Distinct (%)100.0%
Missing8
Missing (%)7.4%
Memory size996.0 B
2024-01-10T06:32:17.397321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1200
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

Unique100 ?
Unique (%)100.0%

Sample

1st row041-662-0824
2nd row041-662-1815
3rd row041-665-3686
4th row041-665-8342
5th row041-663-8260
ValueCountFrequency (%)
041-663-7763 1
 
1.0%
041-668-6061 1
 
1.0%
041-662-6546 1
 
1.0%
041-669-2503 1
 
1.0%
041-669-1051 1
 
1.0%
041-669-8989 1
 
1.0%
041-666-1119 1
 
1.0%
041-669-3345 1
 
1.0%
041-665-0877 1
 
1.0%
041-663-8070 1
 
1.0%
Other values (90) 90
90.0%
2024-01-10T06:32:17.695081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 228
19.0%
- 200
16.7%
4 162
13.5%
0 148
12.3%
1 142
11.8%
5 69
 
5.8%
8 60
 
5.0%
3 52
 
4.3%
2 51
 
4.2%
9 45
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
83.3%
Dash Punctuation 200
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 228
22.8%
4 162
16.2%
0 148
14.8%
1 142
14.2%
5 69
 
6.9%
8 60
 
6.0%
3 52
 
5.2%
2 51
 
5.1%
9 45
 
4.5%
7 43
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 228
19.0%
- 200
16.7%
4 162
13.5%
0 148
12.3%
1 142
11.8%
5 69
 
5.8%
8 60
 
5.0%
3 52
 
4.3%
2 51
 
4.2%
9 45
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 228
19.0%
- 200
16.7%
4 162
13.5%
0 148
12.3%
1 142
11.8%
5 69
 
5.8%
8 60
 
5.0%
3 52
 
4.3%
2 51
 
4.2%
9 45
 
3.8%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size996.0 B
Minimum2016-05-16 00:00:00
Maximum2016-05-16 00:00:00
2024-01-10T06:32:17.788632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:32:17.855856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-01-10T06:32:15.801128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:32:15.877812image/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)소재지전화데이터기준일
0세탁업서산세탁소충청남도 서산시 시장6로 23-1 (동문동)041-662-08242016-05-16
1세탁업금성세탁소충청남도 서산시 고운로 103 (읍내동)041-662-18152016-05-16
2세탁업보은가죽피혁세탁전문충청남도 서산시 안견로 277 (읍내동)041-665-36862016-05-16
3세탁업송옥세탁소충청남도 서산시 양대동 421-6번지041-665-83422016-05-16
4세탁업컴퓨터세탁소충청남도 서산시 대산읍 대산리 128번지 2호041-663-82602016-05-16
5세탁업대광세탁소충청남도 서산시 해미면 읍성마을1길 16041-688-26132016-05-16
6세탁업일류세탁소충청남도 서산시 해미면 읍성마을1길 8 ((1층))041-688-31712016-05-16
7세탁업한라일류세탁소충청남도 서산시 고운로 276, 상가동 101호 (동문동, 한라비발디)041-665-86592016-05-16
8세탁업오시오세탁소충청남도 서산시 한마음15로 26 (석림동)041-664-05602016-05-16
9세탁업명성세탁소충청남도 서산시 운산면 운암로 1049041-662-05122016-05-16
업종명업소명업소소재지(도로명)소재지전화데이터기준일
98세탁업풀세탁소충청남도 서산시 예천3로 10, 상가동 1층 103호 (예천동, 예천주공2단지아파트)041-663-98872016-05-16
99세탁업하나세탁충청남도 서산시 예천1로 3-45, 1층 (예천동)<NA>2016-05-16
100세탁업호수세탁충청남도 서산시 호수공원10로 8, 1층 (예천동)041-681-79422016-05-16
101세탁업크린토피아코인워시 서산동문점충청남도 서산시 고운로 267, 1층 (동문동)041-665-22382016-05-16
102세탁업크린토피아 서산호수공원점충청남도 서산시 호수공원6로 41, 1층 (예천동)041-669-09522016-05-16
103세탁업슈즈크린충청남도 서산시 대산읍 기은아섬길 22, 1층<NA>2016-05-16
104세탁업임마누엘세탁소충청남도 서산시 지곡면 충의로 762-78, 상가동 138호 (늘푸른오스카빌)041-663-39352016-05-16
105세탁업세탁나라충청남도 서산시 문화로 34, 1층 (읍내동)041-664-43632016-05-16
106세탁업메이택세탁,빨래방충청남도 서산시 서령로 48, 1층 (동문동)041-666-00572016-05-16
107세탁업한성세탁소충청남도 서산시 음암면 서령로 325-23, 상가동 2층 206호 (한성스위트빌)<NA>2016-05-16