Overview

Dataset statistics

Number of variables5
Number of observations44
Missing cells3
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory43.0 B

Variable types

Categorical1
Text4

Alerts

업종명 has constant value ""Constant
소재지전화 has 3 (6.8%) missing valuesMissing
업소명 has unique valuesUnique
업소소재지(도로명) has unique valuesUnique
업소소재지(지번) has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:35:49.696366
Analysis finished2024-01-09 22:35:50.061797
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
세탁업
44 

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 44
100.0%

Length

2024-01-10T07:35:50.114064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:50.200311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 44
100.0%

업소명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-01-10T07:35:50.373653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.8409091
Min length2

Characters and Unicode

Total characters257
Distinct characters96
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

Unique44 ?
Unique (%)100.0%

Sample

1st row일광
2nd row성심
3rd row현대 컴퓨터
4th row백양
5th row백양 컴퓨터
ValueCountFrequency (%)
세탁소 10
 
14.9%
컴퓨터 8
 
11.9%
백양 3
 
4.5%
세탁 3
 
4.5%
현대 2
 
3.0%
성심 2
 
3.0%
예산군장애인보호작업장 1
 
1.5%
화산드라이크리닝 1
 
1.5%
신신세탁소 1
 
1.5%
중앙 1
 
1.5%
Other values (35) 35
52.2%
2024-01-10T07:35:50.666982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
10.1%
세 24
 
9.3%
탁 23
 
8.9%
소 18
 
7.0%
컴 8
 
3.1%
퓨 8
 
3.1%
터 8
 
3.1%
산 5
 
1.9%
백 4
 
1.6%
동 4
 
1.6%
Other values (86) 129
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 223
86.8%
Space Separator 26
 
10.1%
Other Punctuation 2
 
0.8%
Lowercase Letter 2
 
0.8%
Uppercase Letter 2
 
0.8%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 24
 
10.8%
탁 23
 
10.3%
소 18
 
8.1%
컴 8
 
3.6%
퓨 8
 
3.6%
터 8
 
3.6%
산 5
 
2.2%
백 4
 
1.8%
동 4
 
1.8%
대 4
 
1.8%
Other values (78) 117
52.5%
Lowercase Letter
ValueCountFrequency (%)
d 1
50.0%
c 1
50.0%
Uppercase 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 223
86.8%
Common 30
 
11.7%
Latin 4
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 24
 
10.8%
탁 23
 
10.3%
소 18
 
8.1%
컴 8
 
3.6%
퓨 8
 
3.6%
터 8
 
3.6%
산 5
 
2.2%
백 4
 
1.8%
동 4
 
1.8%
대 4
 
1.8%
Other values (78) 117
52.5%
Common
ValueCountFrequency (%)
26
86.7%
. 2
 
6.7%
( 1
 
3.3%
) 1
 
3.3%
Latin
ValueCountFrequency (%)
d 1
25.0%
c 1
25.0%
D 1
25.0%
C 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 223
86.8%
ASCII 34
 
13.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
76.5%
. 2
 
5.9%
( 1
 
2.9%
) 1
 
2.9%
d 1
 
2.9%
c 1
 
2.9%
D 1
 
2.9%
C 1
 
2.9%
Hangul
ValueCountFrequency (%)
세 24
 
10.8%
탁 23
 
10.3%
소 18
 
8.1%
컴 8
 
3.6%
퓨 8
 
3.6%
터 8
 
3.6%
산 5
 
2.2%
백 4
 
1.8%
동 4
 
1.8%
대 4
 
1.8%
Other values (78) 117
52.5%

업소소재지(도로명)
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-01-10T07:35:50.887857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length25
Mean length21.068182
Min length19

Characters and Unicode

Total characters927
Distinct characters80
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

Unique44 ?
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 44
19.7%
예산군 44
19.7%
예산읍 27
 
12.1%
덕산면 7
 
3.1%
예산로 7
 
3.1%
삽교읍 5
 
2.2%
삽교로 4
 
1.8%
주교로 3
 
1.3%
천변로 3
 
1.3%
고덕면 3
 
1.3%
Other values (68) 76
34.1%
2024-01-10T07:35:51.209855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179
19.3%
산 85
 
9.2%
예 80
 
8.6%
충 44
 
4.7%
남 44
 
4.7%
도 44
 
4.7%
군 44
 
4.7%
청 44
 
4.7%
로 41
 
4.4%
1 32
 
3.5%
Other values (70) 290
31.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 607
65.5%
Space Separator 179
 
19.3%
Decimal Number 128
 
13.8%
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 85
14.0%
예 80
13.2%
충 44
 
7.2%
남 44
 
7.2%
도 44
 
7.2%
군 44
 
7.2%
청 44
 
7.2%
로 41
 
6.8%
읍 32
 
5.3%
덕 14
 
2.3%
Other values (55) 135
22.2%
Decimal Number
ValueCountFrequency (%)
1 32
25.0%
2 15
11.7%
6 14
10.9%
4 13
10.2%
5 13
10.2%
9 11
 
8.6%
7 9
 
7.0%
3 8
 
6.2%
0 7
 
5.5%
8 6
 
4.7%
Space Separator
ValueCountFrequency (%)
179
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 607
65.5%
Common 320
34.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 85
14.0%
예 80
13.2%
충 44
 
7.2%
남 44
 
7.2%
도 44
 
7.2%
군 44
 
7.2%
청 44
 
7.2%
로 41
 
6.8%
읍 32
 
5.3%
덕 14
 
2.3%
Other values (55) 135
22.2%
Common
ValueCountFrequency (%)
179
55.9%
1 32
 
10.0%
2 15
 
4.7%
6 14
 
4.4%
4 13
 
4.1%
5 13
 
4.1%
9 11
 
3.4%
- 10
 
3.1%
7 9
 
2.8%
3 8
 
2.5%
Other values (5) 16
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 607
65.5%
ASCII 320
34.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179
55.9%
1 32
 
10.0%
2 15
 
4.7%
6 14
 
4.4%
4 13
 
4.1%
5 13
 
4.1%
9 11
 
3.4%
- 10
 
3.1%
7 9
 
2.8%
3 8
 
2.5%
Other values (5) 16
 
5.0%
Hangul
ValueCountFrequency (%)
산 85
14.0%
예 80
13.2%
충 44
 
7.2%
남 44
 
7.2%
도 44
 
7.2%
군 44
 
7.2%
청 44
 
7.2%
로 41
 
6.8%
읍 32
 
5.3%
덕 14
 
2.3%
Other values (55) 135
22.2%

업소소재지(지번)
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-01-10T07:35:51.419822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length35
Mean length23.863636
Min length20

Characters and Unicode

Total characters1050
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

Unique44 ?
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 44
19.2%
예산군 44
19.2%
예산읍 27
 
11.8%
예산리 12
 
5.2%
덕산면 7
 
3.1%
주교리 6
 
2.6%
삽교읍 5
 
2.2%
신례원리 4
 
1.7%
읍내리 4
 
1.7%
고덕면 3
 
1.3%
Other values (66) 73
31.9%
2024-01-10T07:35:51.745728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
228
21.7%
산 92
 
8.8%
예 83
 
7.9%
충 44
 
4.2%
청 44
 
4.2%
군 44
 
4.2%
리 44
 
4.2%
도 44
 
4.2%
남 44
 
4.2%
- 37
 
3.5%
Other values (54) 346
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 592
56.4%
Space Separator 228
 
21.7%
Decimal Number 188
 
17.9%
Dash Punctuation 37
 
3.5%
Uppercase Letter 3
 
0.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 92
15.5%
예 83
14.0%
충 44
7.4%
청 44
7.4%
군 44
7.4%
리 44
7.4%
도 44
7.4%
남 44
7.4%
읍 36
 
6.1%
면 12
 
2.0%
Other values (38) 105
17.7%
Decimal Number
ValueCountFrequency (%)
2 32
17.0%
3 31
16.5%
1 24
12.8%
8 20
10.6%
4 16
8.5%
7 16
8.5%
0 14
7.4%
6 13
6.9%
5 13
6.9%
9 9
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
P 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 592
56.4%
Common 455
43.3%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 92
15.5%
예 83
14.0%
충 44
7.4%
청 44
7.4%
군 44
7.4%
리 44
7.4%
도 44
7.4%
남 44
7.4%
읍 36
 
6.1%
면 12
 
2.0%
Other values (38) 105
17.7%
Common
ValueCountFrequency (%)
228
50.1%
- 37
 
8.1%
2 32
 
7.0%
3 31
 
6.8%
1 24
 
5.3%
8 20
 
4.4%
4 16
 
3.5%
7 16
 
3.5%
0 14
 
3.1%
6 13
 
2.9%
Other values (3) 24
 
5.3%
Latin
ValueCountFrequency (%)
T 1
33.3%
P 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 592
56.4%
ASCII 458
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228
49.8%
- 37
 
8.1%
2 32
 
7.0%
3 31
 
6.8%
1 24
 
5.2%
8 20
 
4.4%
4 16
 
3.5%
7 16
 
3.5%
0 14
 
3.1%
6 13
 
2.8%
Other values (6) 27
 
5.9%
Hangul
ValueCountFrequency (%)
산 92
15.5%
예 83
14.0%
충 44
7.4%
청 44
7.4%
군 44
7.4%
리 44
7.4%
도 44
7.4%
남 44
7.4%
읍 36
 
6.1%
면 12
 
2.0%
Other values (38) 105
17.7%

소재지전화
Text

MISSING 

Distinct41
Distinct (%)100.0%
Missing3
Missing (%)6.8%
Memory size484.0 B
2024-01-10T07:35:51.930465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.073171
Min length13

Characters and Unicode

Total characters577
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

Unique41 ?
Unique (%)100.0%

Sample

1st row041 -337 -9305
2nd row041 -337 -8413
3rd row041 -334 -3076
4th row041 -335 -2151
5th row 041- 334-3423
ValueCountFrequency (%)
041 40
34.8%
335 10
 
8.7%
337 6
 
5.2%
334 5
 
4.3%
333 4
 
3.5%
331 3
 
2.6%
338 3
 
2.6%
0776 2
 
1.7%
2484 1
 
0.9%
8989 1
 
0.9%
Other values (40) 40
34.8%
2024-01-10T07:35:52.200771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 109
18.9%
85
14.7%
- 82
14.2%
4 71
12.3%
1 57
9.9%
0 55
9.5%
7 28
 
4.9%
5 21
 
3.6%
6 21
 
3.6%
8 18
 
3.1%
Other values (2) 30
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 410
71.1%
Space Separator 85
 
14.7%
Dash Punctuation 82
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 109
26.6%
4 71
17.3%
1 57
13.9%
0 55
13.4%
7 28
 
6.8%
5 21
 
5.1%
6 21
 
5.1%
8 18
 
4.4%
2 16
 
3.9%
9 14
 
3.4%
Space Separator
ValueCountFrequency (%)
85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 109
18.9%
85
14.7%
- 82
14.2%
4 71
12.3%
1 57
9.9%
0 55
9.5%
7 28
 
4.9%
5 21
 
3.6%
6 21
 
3.6%
8 18
 
3.1%
Other values (2) 30
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 109
18.9%
85
14.7%
- 82
14.2%
4 71
12.3%
1 57
9.9%
0 55
9.5%
7 28
 
4.9%
5 21
 
3.6%
6 21
 
3.6%
8 18
 
3.1%
Other values (2) 30
 
5.2%

Correlations

2024-01-10T07:35:52.286047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명업소소재지(도로명)업소소재지(지번)소재지전화
업소명1.0001.0001.0001.000
업소소재지(도로명)1.0001.0001.0001.000
업소소재지(지번)1.0001.0001.0001.000
소재지전화1.0001.0001.0001.000

Missing values

2024-01-10T07:35:49.943079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:35:50.028683image/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)업소소재지(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
업종명업소명업소소재지(도로명)업소소재지(지번)소재지전화
34세탁업세탁나라충청남도 예산군 예산읍 주교로 66충청남도 예산군 예산읍 산성리 340-3041 -331 -0776
35세탁업하나세탁소충청남도 예산군 예산읍 예산로 210충청남도 예산군 예산읍 예산리 481-6041-332 -0776
36세탁업(주)내셔널씨엘충청남도 예산군 덕산면 윤봉길로 368-28충청남도 예산군 덕산면 대치리 211041- 334-2237
37세탁업주식회사그린산업충청남도 예산군 덕산면 온천단지3로 45-7충청남도 예산군 덕산면 사동리 364041 -330 -8024
38세탁업명품세탁전문점충청남도 예산군 예산읍 역전로 125충청남도 예산군 예산읍 산성리 312-4041 -335 -3615
39세탁업광시세탁소충청남도 예산군 광시면 광시소길 6충청남도 예산군 광시면 광시리 87-1, , 88<NA>
40세탁업하이얀운동화빨래방충청남도 예산군 예산읍 벚꽃로155번길 50충청남도 예산군 예산읍 발연리 253-1 계룡아파트상가 102호041 -331 -6969
41세탁업현대세탁소충청남도 예산군 삽교읍 삽교로 79충청남도 예산군 삽교읍 두리 803-208041 -337 -2583
42세탁업예산군장애인보호작업장충청남도 예산군 신암면 추사고택로 496충청남도 예산군 신암면 신택리 174-3041 -333 -6606
43세탁업럭키세탁소충청남도 예산군 삽교읍 수암천로 11, 상가117동 106호 (이지더원아파트)충청남도 예산군 삽교읍 목리 894-1 이지더원아파트041 -338 -7364