Overview

Dataset statistics

Number of variables5
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory43.1 B

Variable types

Categorical1
Text4

Alerts

업종명 has constant value ""Constant
업소명 has unique valuesUnique
영업소 주소(도로명) has unique valuesUnique
영업소 주소(지번) has unique valuesUnique
소재지전화 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:35:46.488068
Analysis finished2024-01-09 22:35:46.838306
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
세탁업
43 

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 43
100.0%

Length

2024-01-10T07:35:46.891957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:46.994446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 43
100.0%

업소명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-01-10T07:35:47.140823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9302326
Min length2

Characters and Unicode

Total characters255
Distinct characters94
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

Unique43 ?
Unique (%)100.0%

Sample

1st row일광
2nd row성심
3rd row현대 컴퓨터
4th row백양
5th row백양 컴퓨터
ValueCountFrequency (%)
세탁소 10
 
15.2%
컴퓨터 8
 
12.1%
백양 3
 
4.5%
세탁 3
 
4.5%
현대 2
 
3.0%
성심 2
 
3.0%
일광 1
 
1.5%
d.c점 1
 
1.5%
런던 1
 
1.5%
뉴명동 1
 
1.5%
Other values (34) 34
51.5%
2024-01-10T07:35:47.429442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
10.2%
세 24
 
9.4%
탁 23
 
9.0%
소 18
 
7.1%
컴 8
 
3.1%
터 8
 
3.1%
퓨 8
 
3.1%
산 5
 
2.0%
백 4
 
1.6%
동 4
 
1.6%
Other values (84) 127
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
86.7%
Space Separator 26
 
10.2%
Other Punctuation 2
 
0.8%
Uppercase Letter 2
 
0.8%
Lowercase Letter 2
 
0.8%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 24
 
10.9%
탁 23
 
10.4%
소 18
 
8.1%
컴 8
 
3.6%
터 8
 
3.6%
퓨 8
 
3.6%
산 5
 
2.3%
백 4
 
1.8%
동 4
 
1.8%
대 4
 
1.8%
Other values (76) 115
52.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
D 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
d 1
50.0%
c 1
50.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
86.7%
Common 30
 
11.8%
Latin 4
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 24
 
10.9%
탁 23
 
10.4%
소 18
 
8.1%
컴 8
 
3.6%
터 8
 
3.6%
퓨 8
 
3.6%
산 5
 
2.3%
백 4
 
1.8%
동 4
 
1.8%
대 4
 
1.8%
Other values (76) 115
52.0%
Common
ValueCountFrequency (%)
26
86.7%
. 2
 
6.7%
( 1
 
3.3%
) 1
 
3.3%
Latin
ValueCountFrequency (%)
C 1
25.0%
D 1
25.0%
d 1
25.0%
c 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
86.7%
ASCII 34
 
13.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
76.5%
. 2
 
5.9%
( 1
 
2.9%
) 1
 
2.9%
C 1
 
2.9%
D 1
 
2.9%
d 1
 
2.9%
c 1
 
2.9%
Hangul
ValueCountFrequency (%)
세 24
 
10.9%
탁 23
 
10.4%
소 18
 
8.1%
컴 8
 
3.6%
터 8
 
3.6%
퓨 8
 
3.6%
산 5
 
2.3%
백 4
 
1.8%
동 4
 
1.8%
대 4
 
1.8%
Other values (76) 115
52.0%

영업소 주소(도로명)
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-01-10T07:35:47.642617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length25
Mean length21.116279
Min length19

Characters and Unicode

Total characters908
Distinct characters79
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row충청남도 예산군 고덕면 고덕중앙로 66-9
2nd row충청남도 예산군 고덕면 황금뜰로 209
3rd row충청남도 예산군 예산읍 신례원로 174
4th row충청남도 예산군 예산읍 예산로 216
5th row충청남도 예산군 예산읍 신례원로 198
ValueCountFrequency (%)
충청남도 43
19.7%
예산군 43
19.7%
예산읍 26
 
11.9%
덕산면 7
 
3.2%
예산로 7
 
3.2%
삽교읍 5
 
2.3%
삽교로 4
 
1.8%
주교로 3
 
1.4%
천변로 3
 
1.4%
고덕면 3
 
1.4%
Other values (66) 74
33.9%
2024-01-10T07:35:47.983138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
175
19.3%
산 83
 
9.1%
예 78
 
8.6%
충 43
 
4.7%
군 43
 
4.7%
청 43
 
4.7%
도 43
 
4.7%
남 43
 
4.7%
로 41
 
4.5%
1 32
 
3.5%
Other values (69) 284
31.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 594
65.4%
Space Separator 175
 
19.3%
Decimal Number 126
 
13.9%
Dash Punctuation 10
 
1.1%
Other Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 83
14.0%
예 78
13.1%
충 43
 
7.2%
군 43
 
7.2%
청 43
 
7.2%
도 43
 
7.2%
남 43
 
7.2%
로 41
 
6.9%
읍 31
 
5.2%
덕 14
 
2.4%
Other values (54) 132
22.2%
Decimal Number
ValueCountFrequency (%)
1 32
25.4%
2 15
11.9%
5 13
10.3%
6 13
10.3%
4 12
 
9.5%
9 11
 
8.7%
7 9
 
7.1%
3 8
 
6.3%
0 7
 
5.6%
8 6
 
4.8%
Space Separator
ValueCountFrequency (%)
175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 594
65.4%
Common 314
34.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 83
14.0%
예 78
13.1%
충 43
 
7.2%
군 43
 
7.2%
청 43
 
7.2%
도 43
 
7.2%
남 43
 
7.2%
로 41
 
6.9%
읍 31
 
5.2%
덕 14
 
2.4%
Other values (54) 132
22.2%
Common
ValueCountFrequency (%)
175
55.7%
1 32
 
10.2%
2 15
 
4.8%
5 13
 
4.1%
6 13
 
4.1%
4 12
 
3.8%
9 11
 
3.5%
- 10
 
3.2%
7 9
 
2.9%
3 8
 
2.5%
Other values (5) 16
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 594
65.4%
ASCII 314
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
55.7%
1 32
 
10.2%
2 15
 
4.8%
5 13
 
4.1%
6 13
 
4.1%
4 12
 
3.8%
9 11
 
3.5%
- 10
 
3.2%
7 9
 
2.9%
3 8
 
2.5%
Other values (5) 16
 
5.1%
Hangul
ValueCountFrequency (%)
산 83
14.0%
예 78
13.1%
충 43
 
7.2%
군 43
 
7.2%
청 43
 
7.2%
도 43
 
7.2%
남 43
 
7.2%
로 41
 
6.9%
읍 31
 
5.2%
덕 14
 
2.4%
Other values (54) 132
22.2%

영업소 주소(지번)
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-01-10T07:35:48.195498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length35
Mean length23.883721
Min length20

Characters and Unicode

Total characters1027
Distinct characters64
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

Unique43 ?
Unique (%)100.0%

Sample

1st row충청남도 예산군 고덕면 대천리 770-28
2nd row충청남도 예산군 고덕면 대천리 727-28
3rd row충청남도 예산군 예산읍 신례원리 292-15
4th row충청남도 예산군 예산읍 예산리 514-1
5th row충청남도 예산군 예산읍 신례원리 304-3
ValueCountFrequency (%)
충청남도 43
19.2%
예산군 43
19.2%
예산읍 26
 
11.6%
예산리 11
 
4.9%
덕산면 7
 
3.1%
주교리 6
 
2.7%
삽교읍 5
 
2.2%
신례원리 4
 
1.8%
읍내리 4
 
1.8%
고덕면 3
 
1.3%
Other values (65) 72
32.1%
2024-01-10T07:35:48.520535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
223
21.7%
산 89
 
8.7%
예 80
 
7.8%
충 43
 
4.2%
청 43
 
4.2%
군 43
 
4.2%
리 43
 
4.2%
도 43
 
4.2%
남 43
 
4.2%
- 36
 
3.5%
Other values (54) 341
33.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 579
56.4%
Space Separator 223
 
21.7%
Decimal Number 184
 
17.9%
Dash Punctuation 36
 
3.5%
Uppercase Letter 3
 
0.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 89
15.4%
예 80
13.8%
충 43
7.4%
청 43
7.4%
군 43
7.4%
리 43
7.4%
도 43
7.4%
남 43
7.4%
읍 35
 
6.0%
면 12
 
2.1%
Other values (38) 105
18.1%
Decimal Number
ValueCountFrequency (%)
2 32
17.4%
3 30
16.3%
1 24
13.0%
8 20
10.9%
7 15
8.2%
4 15
8.2%
0 14
7.6%
5 13
7.1%
6 13
7.1%
9 8
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
P 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
223
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 579
56.4%
Common 445
43.3%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 89
15.4%
예 80
13.8%
충 43
7.4%
청 43
7.4%
군 43
7.4%
리 43
7.4%
도 43
7.4%
남 43
7.4%
읍 35
 
6.0%
면 12
 
2.1%
Other values (38) 105
18.1%
Common
ValueCountFrequency (%)
223
50.1%
- 36
 
8.1%
2 32
 
7.2%
3 30
 
6.7%
1 24
 
5.4%
8 20
 
4.5%
7 15
 
3.4%
4 15
 
3.4%
0 14
 
3.1%
5 13
 
2.9%
Other values (3) 23
 
5.2%
Latin
ValueCountFrequency (%)
T 1
33.3%
P 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 579
56.4%
ASCII 448
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223
49.8%
- 36
 
8.0%
2 32
 
7.1%
3 30
 
6.7%
1 24
 
5.4%
8 20
 
4.5%
7 15
 
3.3%
4 15
 
3.3%
0 14
 
3.1%
5 13
 
2.9%
Other values (6) 26
 
5.8%
Hangul
ValueCountFrequency (%)
산 89
15.4%
예 80
13.8%
충 43
7.4%
청 43
7.4%
군 43
7.4%
리 43
7.4%
도 43
7.4%
남 43
7.4%
읍 35
 
6.0%
면 12
 
2.1%
Other values (38) 105
18.1%

소재지전화
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-01-10T07:35:48.744500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.023256
Min length11

Characters and Unicode

Total characters517
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

Unique43 ?
Unique (%)100.0%

Sample

1st row041-337-9305
2nd row041-337-8413
3rd row041-334-3076
4th row041-335-2151
5th row041-334-3423
ValueCountFrequency (%)
041-337-9305 1
 
2.3%
041-338-8989 1
 
2.3%
041-335-5910 1
 
2.3%
041-337-7837 1
 
2.3%
041-331-3443 1
 
2.3%
041-3780-4942 1
 
2.3%
041-333-3463 1
 
2.3%
041-334-2484 1
 
2.3%
041-337-0458 1
 
2.3%
041-334-4141 1
 
2.3%
Other values (33) 33
76.7%
2024-01-10T07:35:49.108166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 110
21.3%
- 86
16.6%
4 72
13.9%
1 61
11.8%
0 59
11.4%
7 30
 
5.8%
6 23
 
4.4%
5 22
 
4.3%
8 19
 
3.7%
2 18
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431
83.4%
Dash Punctuation 86
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 110
25.5%
4 72
16.7%
1 61
14.2%
0 59
13.7%
7 30
 
7.0%
6 23
 
5.3%
5 22
 
5.1%
8 19
 
4.4%
2 18
 
4.2%
9 17
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 110
21.3%
- 86
16.6%
4 72
13.9%
1 61
11.8%
0 59
11.4%
7 30
 
5.8%
6 23
 
4.4%
5 22
 
4.3%
8 19
 
3.7%
2 18
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 110
21.3%
- 86
16.6%
4 72
13.9%
1 61
11.8%
0 59
11.4%
7 30
 
5.8%
6 23
 
4.4%
5 22
 
4.3%
8 19
 
3.7%
2 18
 
3.5%

Correlations

2024-01-10T07:35:49.193597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명영업소 주소(도로명)영업소 주소(지번)소재지전화
업소명1.0001.0001.0001.000
영업소 주소(도로명)1.0001.0001.0001.000
영업소 주소(지번)1.0001.0001.0001.000
소재지전화1.0001.0001.0001.000

Missing values

2024-01-10T07:35:46.728368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:35:46.807357image/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)영업소 주소(지번)소재지전화
0세탁업일광충청남도 예산군 고덕면 고덕중앙로 66-9충청남도 예산군 고덕면 대천리 770-28041-337-9305
1세탁업성심충청남도 예산군 고덕면 황금뜰로 209충청남도 예산군 고덕면 대천리 727-28041-337-8413
2세탁업현대 컴퓨터충청남도 예산군 예산읍 신례원로 174충청남도 예산군 예산읍 신례원리 292-15041-334-3076
3세탁업백양충청남도 예산군 예산읍 예산로 216충청남도 예산군 예산읍 예산리 514-1041-335-2151
4세탁업백양 컴퓨터충청남도 예산군 예산읍 신례원로 198충청남도 예산군 예산읍 신례원리 304-3041-334-3423
5세탁업성심 컴퓨터충청남도 예산군 예산읍 신례원로212번길 16충청남도 예산군 예산읍 신례원리 246041-334-2159
6세탁업유진 컴퓨터충청남도 예산군 예산읍 주교로 57충청남도 예산군 예산읍 주교리 215-7041-333-1040
7세탁업삼공 컴퓨터충청남도 예산군 덕산면 예덕로 15-2충청남도 예산군 덕산면 읍내리 342-2041-337-3698
8세탁업동신충청남도 예산군 예산읍 예산로 114충청남도 예산군 예산읍 예산리 739-5041-335-5669
9세탁업대성충청남도 예산군 예산읍 아리랑로 7-4충청남도 예산군 예산읍 주교리 221-14041-334-5375
업종명업소명영업소 주소(도로명)영업소 주소(지번)소재지전화
33세탁업세탁나라충청남도 예산군 예산읍 주교로 66충청남도 예산군 예산읍 산성리 340-3041-331-0776
34세탁업하나세탁소충청남도 예산군 예산읍 예산로 210충청남도 예산군 예산읍 예산리 481-6041-332-0776
35세탁업(주)내셔널씨엘충청남도 예산군 덕산면 윤봉길로 368-28충청남도 예산군 덕산면 대치리 211041-334-2237
36세탁업주식회사그린산업충청남도 예산군 덕산면 온천단지3로 45-7충청남도 예산군 덕산면 사동리 364041-330-8024
37세탁업명품세탁전문점충청남도 예산군 예산읍 역전로 125충청남도 예산군 예산읍 산성리 312-4041-335-3615
38세탁업광시세탁소충청남도 예산군 광시면 광시소길 6충청남도 예산군 광시면 광시리 87-1, , 88041-6301-7629
39세탁업하이얀운동화빨래방충청남도 예산군 예산읍 벚꽃로155번길 50충청남도 예산군 예산읍 발연리 253-1 계룡아파트상가 102호041-331-6969
40세탁업현대세탁소충청남도 예산군 삽교읍 삽교로 79충청남도 예산군 삽교읍 두리 803-208041-337-2583
41세탁업예산군장애인보호작업장충청남도 예산군 신암면 추사고택로 496충청남도 예산군 신암면 신택리 174-3041-333-6606
42세탁업럭키세탁소충청남도 예산군 삽교읍 수암천로 11, 상가117동 106호 (이지더원아파트)충청남도 예산군 삽교읍 목리 894-1 이지더원아파트041-338-7364