Overview

Dataset statistics

Number of variables6
Number of observations104
Missing cells7
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory50.3 B

Variable types

Categorical1
Text4
Numeric1

Dataset

Description대전광역시 동구 세탁업 현황 2023.7.24일 기준 자료로 업종명 ,업소명, 영업소 주소(도로명,지번), 우편번호, 소재지전화를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15067192/fileData.do

Alerts

업종명 has constant value ""Constant
소재지전화 has 7 (6.7%) missing valuesMissing
영업소 주소(도로명) has unique valuesUnique
영업소 주소(지번) has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:42:51.590711
Analysis finished2023-12-12 22:42:52.295812
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size964.0 B
세탁업
104 

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 104
100.0%

Length

2023-12-13T07:42:52.406408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:42:52.510881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 104
100.0%

업소명
Text

Distinct100
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size964.0 B
2023-12-13T07:42:52.839717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.4326923
Min length2

Characters and Unicode

Total characters461
Distinct characters160
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

Unique97 ?
Unique (%)93.3%

Sample

1st row영광
2nd row충남
3rd row형제
4th row영신
5th row청호
ValueCountFrequency (%)
제일 3
 
2.8%
우리세탁소 2
 
1.8%
현대 2
 
1.8%
솔랑크리닝세탁소 1
 
0.9%
삼익세탁소 1
 
0.9%
주은세탁전문점 1
 
0.9%
영광 1
 
0.9%
아이파크세탁소 1
 
0.9%
크린화이트 1
 
0.9%
개울가빨래방 1
 
0.9%
Other values (95) 95
87.2%
2023-12-13T07:42:53.361892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 49
 
10.6%
탁 47
 
10.2%
소 31
 
6.7%
아 9
 
2.0%
신 8
 
1.7%
대 8
 
1.7%
동 8
 
1.7%
광 8
 
1.7%
이 8
 
1.7%
리 7
 
1.5%
Other values (150) 278
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 449
97.4%
Space Separator 5
 
1.1%
Uppercase Letter 2
 
0.4%
Decimal Number 2
 
0.4%
Open Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 49
 
10.9%
탁 47
 
10.5%
소 31
 
6.9%
아 9
 
2.0%
신 8
 
1.8%
대 8
 
1.8%
동 8
 
1.8%
광 8
 
1.8%
이 8
 
1.8%
리 7
 
1.6%
Other values (142) 266
59.2%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
K 1
50.0%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 449
97.4%
Common 10
 
2.2%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 49
 
10.9%
탁 47
 
10.5%
소 31
 
6.9%
아 9
 
2.0%
신 8
 
1.8%
대 8
 
1.8%
동 8
 
1.8%
광 8
 
1.8%
이 8
 
1.8%
리 7
 
1.6%
Other values (142) 266
59.2%
Common
ValueCountFrequency (%)
5
50.0%
( 1
 
10.0%
- 1
 
10.0%
1 1
 
10.0%
2 1
 
10.0%
) 1
 
10.0%
Latin
ValueCountFrequency (%)
O 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 449
97.4%
ASCII 12
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 49
 
10.9%
탁 47
 
10.5%
소 31
 
6.9%
아 9
 
2.0%
신 8
 
1.8%
대 8
 
1.8%
동 8
 
1.8%
광 8
 
1.8%
이 8
 
1.8%
리 7
 
1.6%
Other values (142) 266
59.2%
ASCII
ValueCountFrequency (%)
5
41.7%
( 1
 
8.3%
O 1
 
8.3%
K 1
 
8.3%
- 1
 
8.3%
1 1
 
8.3%
2 1
 
8.3%
) 1
 
8.3%

영업소 주소(도로명)
Text

UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size964.0 B
2023-12-13T07:42:53.688801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length43
Mean length28.596154
Min length16

Characters and Unicode

Total characters2974
Distinct characters130
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

Unique104 ?
Unique (%)100.0%

Sample

1st row대전광역시 동구 태전로 172 (삼성동)
2nd row대전광역시 동구 선화로 218 (정동)
3rd row대전광역시 동구 우암로85번길 19 (삼성동)
4th row대전광역시 동구 충무로 223 (인동)
5th row대전광역시 동구 우암로246번길 3 (가양동)
ValueCountFrequency (%)
대전광역시 104
 
17.7%
동구 104
 
17.7%
가양동 18
 
3.1%
용전동 12
 
2.0%
자양동 8
 
1.4%
삼성동 8
 
1.4%
상가동 8
 
1.4%
낭월동 7
 
1.2%
1층 6
 
1.0%
용운동 5
 
0.9%
Other values (228) 306
52.2%
2023-12-13T07:42:54.198127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
483
 
16.2%
동 240
 
8.1%
대 137
 
4.6%
전 133
 
4.5%
1 125
 
4.2%
시 109
 
3.7%
구 106
 
3.6%
광 105
 
3.5%
역 104
 
3.5%
( 103
 
3.5%
Other values (120) 1329
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1680
56.5%
Decimal Number 534
 
18.0%
Space Separator 483
 
16.2%
Open Punctuation 103
 
3.5%
Close Punctuation 103
 
3.5%
Other Punctuation 54
 
1.8%
Dash Punctuation 16
 
0.5%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 240
14.3%
대 137
 
8.2%
전 133
 
7.9%
시 109
 
6.5%
구 106
 
6.3%
광 105
 
6.2%
역 104
 
6.2%
로 99
 
5.9%
길 64
 
3.8%
번 59
 
3.5%
Other values (103) 524
31.2%
Decimal Number
ValueCountFrequency (%)
1 125
23.4%
2 77
14.4%
0 59
11.0%
3 58
10.9%
4 52
9.7%
5 41
 
7.7%
8 32
 
6.0%
9 32
 
6.0%
6 30
 
5.6%
7 28
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 50
92.6%
@ 4
 
7.4%
Space Separator
ValueCountFrequency (%)
483
100.0%
Open Punctuation
ValueCountFrequency (%)
( 103
100.0%
Close Punctuation
ValueCountFrequency (%)
) 103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1680
56.5%
Common 1293
43.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 240
14.3%
대 137
 
8.2%
전 133
 
7.9%
시 109
 
6.5%
구 106
 
6.3%
광 105
 
6.2%
역 104
 
6.2%
로 99
 
5.9%
길 64
 
3.8%
번 59
 
3.5%
Other values (103) 524
31.2%
Common
ValueCountFrequency (%)
483
37.4%
1 125
 
9.7%
( 103
 
8.0%
) 103
 
8.0%
2 77
 
6.0%
0 59
 
4.6%
3 58
 
4.5%
4 52
 
4.0%
, 50
 
3.9%
5 41
 
3.2%
Other values (6) 142
 
11.0%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1680
56.5%
ASCII 1294
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
483
37.3%
1 125
 
9.7%
( 103
 
8.0%
) 103
 
8.0%
2 77
 
6.0%
0 59
 
4.6%
3 58
 
4.5%
4 52
 
4.0%
, 50
 
3.9%
5 41
 
3.2%
Other values (7) 143
 
11.1%
Hangul
ValueCountFrequency (%)
동 240
14.3%
대 137
 
8.2%
전 133
 
7.9%
시 109
 
6.5%
구 106
 
6.3%
광 105
 
6.2%
역 104
 
6.2%
로 99
 
5.9%
길 64
 
3.8%
번 59
 
3.5%
Other values (103) 524
31.2%

우편번호(도로명)
Real number (ℝ)

Distinct81
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34601.423
Minimum34506
Maximum34712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T07:42:54.381480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34506
5-th percentile34513
Q134546.75
median34593.5
Q334657.25
95-th percentile34703.85
Maximum34712
Range206
Interquartile range (IQR)110.5

Descriptive statistics

Standard deviation61.985074
Coefficient of variation (CV)0.0017914024
Kurtosis-1.2160991
Mean34601.423
Median Absolute Deviation (MAD)50.5
Skewness0.2187889
Sum3598548
Variance3842.1494
MonotonicityNot monotonic
2023-12-13T07:42:54.551356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34625 4
 
3.8%
34544 3
 
2.9%
34580 2
 
1.9%
34686 2
 
1.9%
34704 2
 
1.9%
34680 2
 
1.9%
34508 2
 
1.9%
34705 2
 
1.9%
34532 2
 
1.9%
34515 2
 
1.9%
Other values (71) 81
77.9%
ValueCountFrequency (%)
34506 2
1.9%
34508 2
1.9%
34512 1
1.0%
34513 2
1.9%
34515 2
1.9%
34516 1
1.0%
34524 1
1.0%
34528 1
1.0%
34531 1
1.0%
34532 2
1.9%
ValueCountFrequency (%)
34712 1
1.0%
34711 1
1.0%
34705 2
1.9%
34704 2
1.9%
34703 1
1.0%
34702 1
1.0%
34701 1
1.0%
34698 1
1.0%
34696 1
1.0%
34686 2
1.9%

영업소 주소(지번)
Text

UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size964.0 B
2023-12-13T07:42:54.838531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length37
Mean length21.644231
Min length16

Characters and Unicode

Total characters2251
Distinct characters93
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)100.0%

Sample

1st row대전광역시 동구 삼성동 375-28
2nd row대전광역시 동구 정동 39-27
3rd row대전광역시 동구 삼성동 288-2
4th row대전광역시 동구 인동 142-6
5th row대전광역시 동구 가양동 412-23
ValueCountFrequency (%)
대전광역시 104
21.8%
동구 104
21.8%
가양동 18
 
3.8%
용전동 12
 
2.5%
삼성동 9
 
1.9%
자양동 8
 
1.7%
낭월동 7
 
1.5%
홍도동 6
 
1.3%
성남동 6
 
1.3%
용운동 6
 
1.3%
Other values (164) 196
41.2%
2023-12-13T07:42:55.313386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
465
20.7%
동 217
 
9.6%
전 116
 
5.2%
대 115
 
5.1%
시 107
 
4.8%
광 104
 
4.6%
역 104
 
4.6%
구 104
 
4.6%
1 98
 
4.4%
- 80
 
3.6%
Other values (83) 741
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1203
53.4%
Decimal Number 496
22.0%
Space Separator 465
 
20.7%
Dash Punctuation 80
 
3.6%
Other Punctuation 6
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 217
18.0%
전 116
9.6%
대 115
9.6%
시 107
8.9%
광 104
8.6%
역 104
8.6%
구 104
8.6%
가 39
 
3.2%
양 26
 
2.2%
호 22
 
1.8%
Other values (68) 249
20.7%
Decimal Number
ValueCountFrequency (%)
1 98
19.8%
2 79
15.9%
3 61
12.3%
0 55
11.1%
4 41
8.3%
5 38
 
7.7%
8 35
 
7.1%
9 33
 
6.7%
6 31
 
6.2%
7 25
 
5.0%
Other Punctuation
ValueCountFrequency (%)
@ 5
83.3%
, 1
 
16.7%
Space Separator
ValueCountFrequency (%)
465
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1203
53.4%
Common 1047
46.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 217
18.0%
전 116
9.6%
대 115
9.6%
시 107
8.9%
광 104
8.6%
역 104
8.6%
구 104
8.6%
가 39
 
3.2%
양 26
 
2.2%
호 22
 
1.8%
Other values (68) 249
20.7%
Common
ValueCountFrequency (%)
465
44.4%
1 98
 
9.4%
- 80
 
7.6%
2 79
 
7.5%
3 61
 
5.8%
0 55
 
5.3%
4 41
 
3.9%
5 38
 
3.6%
8 35
 
3.3%
9 33
 
3.2%
Other values (4) 62
 
5.9%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1203
53.4%
ASCII 1048
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
465
44.4%
1 98
 
9.4%
- 80
 
7.6%
2 79
 
7.5%
3 61
 
5.8%
0 55
 
5.2%
4 41
 
3.9%
5 38
 
3.6%
8 35
 
3.3%
9 33
 
3.1%
Other values (5) 63
 
6.0%
Hangul
ValueCountFrequency (%)
동 217
18.0%
전 116
9.6%
대 115
9.6%
시 107
8.9%
광 104
8.6%
역 104
8.6%
구 104
8.6%
가 39
 
3.2%
양 26
 
2.2%
호 22
 
1.8%
Other values (68) 249
20.7%

소재지전화
Text

MISSING 

Distinct97
Distinct (%)100.0%
Missing7
Missing (%)6.7%
Memory size964.0 B
2023-12-13T07:42:55.612902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1164
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 (%)100.0%

Sample

1st row042-673-6312
2nd row042-256-8319
3rd row042-622-7602
4th row042-284-3548
5th row042-622-2435
ValueCountFrequency (%)
042-284-8708 1
 
1.0%
042-673-2202 1
 
1.0%
042-252-6535 1
 
1.0%
042-622-9252 1
 
1.0%
042-621-0205 1
 
1.0%
042-286-5700 1
 
1.0%
042-272-7091 1
 
1.0%
042-635-8788 1
 
1.0%
042-284-1444 1
 
1.0%
042-621-1610 1
 
1.0%
Other values (87) 87
89.7%
2023-12-13T07:42:56.035329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 232
19.9%
- 194
16.7%
0 151
13.0%
4 144
12.4%
6 101
8.7%
3 71
 
6.1%
7 67
 
5.8%
5 58
 
5.0%
8 56
 
4.8%
1 49
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 970
83.3%
Dash Punctuation 194
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 232
23.9%
0 151
15.6%
4 144
14.8%
6 101
10.4%
3 71
 
7.3%
7 67
 
6.9%
5 58
 
6.0%
8 56
 
5.8%
1 49
 
5.1%
9 41
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 232
19.9%
- 194
16.7%
0 151
13.0%
4 144
12.4%
6 101
8.7%
3 71
 
6.1%
7 67
 
5.8%
5 58
 
5.0%
8 56
 
4.8%
1 49
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 232
19.9%
- 194
16.7%
0 151
13.0%
4 144
12.4%
6 101
8.7%
3 71
 
6.1%
7 67
 
5.8%
5 58
 
5.0%
8 56
 
4.8%
1 49
 
4.2%

Interactions

2023-12-13T07:42:51.949435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:42:56.166240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명우편번호(도로명)소재지전화
업소명1.0000.4881.000
우편번호(도로명)0.4881.0001.000
소재지전화1.0001.0001.000

Missing values

2023-12-13T07:42:52.080801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:42:52.227931image/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)우편번호(도로명)영업소 주소(지번)소재지전화
0세탁업영광대전광역시 동구 태전로 172 (삼성동)34563대전광역시 동구 삼성동 375-28042-673-6312
1세탁업충남대전광역시 동구 선화로 218 (정동)34625대전광역시 동구 정동 39-27042-256-8319
2세탁업형제대전광역시 동구 우암로85번길 19 (삼성동)34620대전광역시 동구 삼성동 288-2042-622-7602
3세탁업영신대전광역시 동구 충무로 223 (인동)34638대전광역시 동구 인동 142-6042-284-3548
4세탁업청호대전광역시 동구 우암로246번길 3 (가양동)34595대전광역시 동구 가양동 412-23042-622-2435
5세탁업두꺼비세탁소대전광역시 동구 비래서로42번안길 31 (가양동)34528대전광역시 동구 가양동 31-14042-636-2249
6세탁업백조세탁대전광역시 동구 백룡로 19 (자양동)34516대전광역시 동구 자양동 104-29042-634-1321
7세탁업광신대전광역시 동구 태전로114번길 1 (삼성동)34570대전광역시 동구 삼성동 318-17042-673-6961
8세탁업은경대전광역시 동구 계족로446번길 15 (용전동)34547대전광역시 동구 용전동 133-15042-623-2009
9세탁업실로암세탁소대전광역시 동구 충정로 18 (가양동)34506대전광역시 동구 가양동 146-37042-623-8775
업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)소재지전화
94세탁업삼광세탁소대전광역시 동구 동대전로284번길 60, A동 101호 (가양동)34535대전광역시 동구 가양동 86-9 A동 101호042-623-7948
95세탁업백양세탁소대전광역시 동구 옥천로180번길 47-30, 104호 (판암동)34677대전광역시 동구 판암동 179 복합상가 104호042-286-6121
96세탁업솔로몬세탁대전광역시 동구 대전로542번길 78, 상가동 202호 (천동, 휴먼시아1단지아파트)34686대전광역시 동구 천동 529 휴먼시아1단지아파트 상가동 202호<NA>
97세탁업세탁향기대전광역시 동구 산내로1257번길 88 (낭월동)34705대전광역시 동구 낭월동 534042-273-5670
98세탁업워시테리아 가양점대전광역시 동구 우암로 292-1 (가양동)34508대전광역시 동구 가양동 393-2<NA>
99세탁업신흥마을세탁대전광역시 동구 옥천로 38, 상가동 203호 (신흥동, 신흥마을아파트)34669대전광역시 동구 신흥동 212 신흥마을아파트042-282-5067
100세탁업신동아 세탁소대전광역시 동구 동산초교로 41, 106호 (홍도동)34556대전광역시 동구 홍도동 47-2042-633-2122
101세탁업삼정세탁소대전광역시 동구 동부로10번길 55, 401동 103호 (판암동)34675대전광역시 동구 판암동 839<NA>
102세탁업클린 원대전광역시 동구 산내로1315번길 48, 1층 (낭월동)34704대전광역시 동구 낭월동 397<NA>
103세탁업오케이기업(OK기업)대전광역시 동구 산서로 1593, 1층 (대별동)34711대전광역시 동구 대별동 119<NA>