Overview

Dataset statistics

Number of variables8
Number of observations64
Missing cells27
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory67.1 B

Variable types

Numeric1
Text4
Categorical2
DateTime1

Dataset

Description경상남도 의령군에 소재한 이미용업소의 업소명, 소재지의 도로명과 지번주소, 전화번호, 업태명, 업체구분 데이터를 제공합니다.
Author경상남도 의령군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15034257

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 업태명 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
업체구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
업소소재지(지번) has 5 (7.8%) missing valuesMissing
소재지전화 has 22 (34.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:38:45.838513
Analysis finished2023-12-11 00:38:46.875199
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T09:38:46.969099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q116.75
median32.5
Q348.25
95-th percentile60.85
Maximum64
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.5728919
Kurtosis-1.2
Mean32.5
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance346.66667
MonotonicityStrictly increasing
2023-12-11T09:38:47.164254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
34 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
64 1
1.6%
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%

업소명
Text

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-11T09:38:47.434391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.171875
Min length2

Characters and Unicode

Total characters331
Distinct characters131
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

Unique62 ?
Unique (%)96.9%

Sample

1st row동창이용원
2nd row대성이용원
3rd row대의이용원
4th row신신이용원
5th row태양이용원
ValueCountFrequency (%)
마산미용실 2
 
2.9%
피부관리실 2
 
2.9%
네일 2
 
2.9%
쁄라코스메틱 1
 
1.4%
세븐헤어샵 1
 
1.4%
제이엠헤어 1
 
1.4%
이윤헤어 1
 
1.4%
이수정헤어 1
 
1.4%
유니뷰티 1
 
1.4%
수헤어샵 1
 
1.4%
Other values (57) 57
81.4%
2023-12-11T09:38:47.928659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 23
 
6.9%
미 19
 
5.7%
헤 18
 
5.4%
어 17
 
5.1%
실 16
 
4.8%
이 15
 
4.5%
원 12
 
3.6%
리 8
 
2.4%
스 7
 
2.1%
6
 
1.8%
Other values (121) 190
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 316
95.5%
Space Separator 6
 
1.8%
Uppercase Letter 5
 
1.5%
Other Punctuation 2
 
0.6%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 23
 
7.3%
미 19
 
6.0%
헤 18
 
5.7%
어 17
 
5.4%
실 16
 
5.1%
이 15
 
4.7%
원 12
 
3.8%
리 8
 
2.5%
스 7
 
2.2%
네 6
 
1.9%
Other values (112) 175
55.4%
Uppercase Letter
ValueCountFrequency (%)
S 2
40.0%
G 1
20.0%
O 1
20.0%
N 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
# 1
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 316
95.5%
Common 10
 
3.0%
Latin 5
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 23
 
7.3%
미 19
 
6.0%
헤 18
 
5.7%
어 17
 
5.4%
실 16
 
5.1%
이 15
 
4.7%
원 12
 
3.8%
리 8
 
2.5%
스 7
 
2.2%
네 6
 
1.9%
Other values (112) 175
55.4%
Common
ValueCountFrequency (%)
6
60.0%
) 1
 
10.0%
( 1
 
10.0%
, 1
 
10.0%
# 1
 
10.0%
Latin
ValueCountFrequency (%)
S 2
40.0%
G 1
20.0%
O 1
20.0%
N 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 316
95.5%
ASCII 15
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 23
 
7.3%
미 19
 
6.0%
헤 18
 
5.7%
어 17
 
5.4%
실 16
 
5.1%
이 15
 
4.7%
원 12
 
3.8%
리 8
 
2.5%
스 7
 
2.2%
네 6
 
1.9%
Other values (112) 175
55.4%
ASCII
ValueCountFrequency (%)
6
40.0%
S 2
 
13.3%
) 1
 
6.7%
( 1
 
6.7%
G 1
 
6.7%
, 1
 
6.7%
O 1
 
6.7%
N 1
 
6.7%
# 1
 
6.7%

업소소재지(도로명)
Text

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-11T09:38:48.267331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length43
Mean length24.234375
Min length19

Characters and Unicode

Total characters1551
Distinct characters88
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

Unique62 ?
Unique (%)96.9%

Sample

1st row경상남도 의령군 의령읍 백산로1길 6
2nd row경상남도 의령군 의령읍 의병로21길 25-8
3rd row경상남도 의령군 대의면 대의로 57
4th row경상남도 의령군 부림면 대한로 1774
5th row경상남도 의령군 의령읍 의병로23길 6-10
ValueCountFrequency (%)
경상남도 64
18.6%
의령군 64
18.6%
의령읍 48
14.0%
의병로 11
 
3.2%
부림면 10
 
2.9%
충익로 7
 
2.0%
의병로14길 6
 
1.7%
의병로22길 6
 
1.7%
대한로 5
 
1.5%
2층 5
 
1.5%
Other values (95) 118
34.3%
2023-12-11T09:38:48.730748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
280
18.1%
의 154
 
9.9%
령 112
 
7.2%
1 72
 
4.6%
상 67
 
4.3%
로 65
 
4.2%
남 65
 
4.2%
경 64
 
4.1%
도 64
 
4.1%
군 64
 
4.1%
Other values (78) 544
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 942
60.7%
Space Separator 280
 
18.1%
Decimal Number 265
 
17.1%
Dash Punctuation 30
 
1.9%
Other Punctuation 14
 
0.9%
Uppercase Letter 12
 
0.8%
Close Punctuation 4
 
0.3%
Open Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 154
16.3%
령 112
11.9%
상 67
 
7.1%
로 65
 
6.9%
남 65
 
6.9%
경 64
 
6.8%
도 64
 
6.8%
군 64
 
6.8%
읍 48
 
5.1%
병 39
 
4.1%
Other values (59) 200
21.2%
Decimal Number
ValueCountFrequency (%)
1 72
27.2%
2 51
19.2%
7 29
10.9%
4 25
 
9.4%
3 22
 
8.3%
9 17
 
6.4%
0 16
 
6.0%
6 15
 
5.7%
5 12
 
4.5%
8 6
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
V 3
25.0%
I 3
25.0%
E 3
25.0%
W 3
25.0%
Space Separator
ValueCountFrequency (%)
280
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 942
60.7%
Common 597
38.5%
Latin 12
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 154
16.3%
령 112
11.9%
상 67
 
7.1%
로 65
 
6.9%
남 65
 
6.9%
경 64
 
6.8%
도 64
 
6.8%
군 64
 
6.8%
읍 48
 
5.1%
병 39
 
4.1%
Other values (59) 200
21.2%
Common
ValueCountFrequency (%)
280
46.9%
1 72
 
12.1%
2 51
 
8.5%
- 30
 
5.0%
7 29
 
4.9%
4 25
 
4.2%
3 22
 
3.7%
9 17
 
2.8%
0 16
 
2.7%
6 15
 
2.5%
Other values (5) 40
 
6.7%
Latin
ValueCountFrequency (%)
V 3
25.0%
I 3
25.0%
E 3
25.0%
W 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 942
60.7%
ASCII 609
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
280
46.0%
1 72
 
11.8%
2 51
 
8.4%
- 30
 
4.9%
7 29
 
4.8%
4 25
 
4.1%
3 22
 
3.6%
9 17
 
2.8%
0 16
 
2.6%
6 15
 
2.5%
Other values (9) 52
 
8.5%
Hangul
ValueCountFrequency (%)
의 154
16.3%
령 112
11.9%
상 67
 
7.1%
로 65
 
6.9%
남 65
 
6.9%
경 64
 
6.8%
도 64
 
6.8%
군 64
 
6.8%
읍 48
 
5.1%
병 39
 
4.1%
Other values (59) 200
21.2%

업소소재지(지번)
Text

MISSING 

Distinct58
Distinct (%)98.3%
Missing5
Missing (%)7.8%
Memory size644.0 B
2023-12-11T09:38:49.026226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length35
Mean length24.576271
Min length16

Characters and Unicode

Total characters1450
Distinct characters83
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)96.6%

Sample

1st row경상남도 의령군 의령읍 동동리 1030-2
2nd row경상남도 의령군 의령읍 중동리 411-14
3rd row경상남도 의령군 대의면 마쌍리 441-3
4th row경상남도 의령군 의령읍 동동리 1481-2
5th row경상남도 의령군 낙서면 정곡리 1160-4
ValueCountFrequency (%)
경상남도 59
19.1%
의령군 59
19.1%
의령읍 43
13.9%
중동리 24
 
7.8%
서동리 16
 
5.2%
신반리 8
 
2.6%
부림면 8
 
2.6%
동동리 5
 
1.6%
진승view주상복합아파트 3
 
1.0%
395 3
 
1.0%
Other values (81) 81
26.2%
2023-12-11T09:38:49.496561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
301
20.8%
의 103
 
7.1%
령 102
 
7.0%
상 62
 
4.3%
경 59
 
4.1%
남 59
 
4.1%
도 59
 
4.1%
군 59
 
4.1%
리 58
 
4.0%
동 54
 
3.7%
Other values (73) 534
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 830
57.2%
Space Separator 301
 
20.8%
Decimal Number 258
 
17.8%
Dash Punctuation 49
 
3.4%
Uppercase Letter 12
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 103
12.4%
령 102
12.3%
상 62
 
7.5%
경 59
 
7.1%
남 59
 
7.1%
도 59
 
7.1%
군 59
 
7.1%
리 58
 
7.0%
동 54
 
6.5%
읍 43
 
5.2%
Other values (57) 172
20.7%
Decimal Number
ValueCountFrequency (%)
1 38
14.7%
3 36
14.0%
4 34
13.2%
8 30
11.6%
5 26
10.1%
9 23
8.9%
6 23
8.9%
0 19
7.4%
2 18
7.0%
7 11
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
W 3
25.0%
E 3
25.0%
I 3
25.0%
V 3
25.0%
Space Separator
ValueCountFrequency (%)
301
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 830
57.2%
Common 608
41.9%
Latin 12
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 103
12.4%
령 102
12.3%
상 62
 
7.5%
경 59
 
7.1%
남 59
 
7.1%
도 59
 
7.1%
군 59
 
7.1%
리 58
 
7.0%
동 54
 
6.5%
읍 43
 
5.2%
Other values (57) 172
20.7%
Common
ValueCountFrequency (%)
301
49.5%
- 49
 
8.1%
1 38
 
6.2%
3 36
 
5.9%
4 34
 
5.6%
8 30
 
4.9%
5 26
 
4.3%
9 23
 
3.8%
6 23
 
3.8%
0 19
 
3.1%
Other values (2) 29
 
4.8%
Latin
ValueCountFrequency (%)
W 3
25.0%
E 3
25.0%
I 3
25.0%
V 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 830
57.2%
ASCII 620
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301
48.5%
- 49
 
7.9%
1 38
 
6.1%
3 36
 
5.8%
4 34
 
5.5%
8 30
 
4.8%
5 26
 
4.2%
9 23
 
3.7%
6 23
 
3.7%
0 19
 
3.1%
Other values (6) 41
 
6.6%
Hangul
ValueCountFrequency (%)
의 103
12.4%
령 102
12.3%
상 62
 
7.5%
경 59
 
7.1%
남 59
 
7.1%
도 59
 
7.1%
군 59
 
7.1%
리 58
 
7.0%
동 54
 
6.5%
읍 43
 
5.2%
Other values (57) 172
20.7%

소재지전화
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing22
Missing (%)34.4%
Memory size644.0 B
2023-12-11T09:38:49.712713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters588
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

Unique42 ?
Unique (%)100.0%

Sample

1st row055 -573 -3265
2nd row 055- 573-3657
3rd row055 -572 -9042
4th row055 -574 -1912
5th row 055- 573-2155
ValueCountFrequency (%)
055 41
36.9%
573 9
 
8.1%
572 8
 
7.2%
574 7
 
6.3%
2
 
1.8%
8923 1
 
0.9%
573-3222 1
 
0.9%
2110 1
 
0.9%
573-1909 1
 
0.9%
574-2244 1
 
0.9%
Other values (39) 39
35.1%
2023-12-11T09:38:50.007514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 148
25.2%
84
14.3%
- 84
14.3%
7 62
10.5%
0 56
 
9.5%
2 38
 
6.5%
3 34
 
5.8%
4 22
 
3.7%
1 17
 
2.9%
9 16
 
2.7%
Other values (2) 27
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 420
71.4%
Space Separator 84
 
14.3%
Dash Punctuation 84
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 148
35.2%
7 62
14.8%
0 56
 
13.3%
2 38
 
9.0%
3 34
 
8.1%
4 22
 
5.2%
1 17
 
4.0%
9 16
 
3.8%
6 14
 
3.3%
8 13
 
3.1%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 148
25.2%
84
14.3%
- 84
14.3%
7 62
10.5%
0 56
 
9.5%
2 38
 
6.5%
3 34
 
5.8%
4 22
 
3.7%
1 17
 
2.9%
9 16
 
2.7%
Other values (2) 27
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 148
25.2%
84
14.3%
- 84
14.3%
7 62
10.5%
0 56
 
9.5%
2 38
 
6.5%
3 34
 
5.8%
4 22
 
3.7%
1 17
 
2.9%
9 16
 
2.7%
Other values (2) 27
 
4.6%

업태명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size644.0 B
일반미용업
34 
일반이용업
12 
네일아트업
8 
피부미용업
7 
기타
 
1
Other values (2)
 
2

Length

Max length5
Median length5
Mean length4.90625
Min length2

Unique

Unique3 ?
Unique (%)4.7%

Sample

1st row일반이용업
2nd row일반이용업
3rd row일반이용업
4th row일반이용업
5th row일반이용업

Common Values

ValueCountFrequency (%)
일반미용업 34
53.1%
일반이용업 12
 
18.8%
네일아트업 8
 
12.5%
피부미용업 7
 
10.9%
기타 1
 
1.6%
메이크업업 1
 
1.6%
피부 1
 
1.6%

Length

2023-12-11T09:38:50.416573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:50.519994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 34
53.1%
일반이용업 12
 
18.8%
네일아트업 8
 
12.5%
피부미용업 7
 
10.9%
기타 1
 
1.6%
메이크업업 1
 
1.6%
피부 1
 
1.6%

업체구분
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size644.0 B
미용업
23 
이용업
12 
일반미용업
10 
피부미용업
8 
네일미용업
6 
Other values (3)
5 

Length

Max length16
Median length3
Mean length4.1875
Min length3

Unique

Unique2 ?
Unique (%)3.1%

Sample

1st row이용업
2nd row이용업
3rd row이용업
4th row이용업
5th row이용업

Common Values

ValueCountFrequency (%)
미용업 23
35.9%
이용업 12
18.8%
일반미용업 10
15.6%
피부미용업 8
 
12.5%
네일미용업 6
 
9.4%
종합미용업 3
 
4.7%
피부미용업, 네일미용업 1
 
1.6%
네일미용업, 화장ㆍ분장 미용업 1
 
1.6%

Length

2023-12-11T09:38:50.673453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:50.810824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 24
35.8%
이용업 12
17.9%
일반미용업 10
14.9%
피부미용업 9
 
13.4%
네일미용업 8
 
11.9%
종합미용업 3
 
4.5%
화장ㆍ분장 1
 
1.5%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
Minimum2023-09-11 00:00:00
Maximum2023-09-11 00:00:00
2023-12-11T09:38:50.903554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:50.973782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T09:38:46.452946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:38:51.032638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소명업소소재지(도로명)업소소재지(지번)소재지전화업태명업체구분
연번1.0000.9370.9370.9471.0000.7990.839
업소명0.9371.0000.9980.9971.0001.0001.000
업소소재지(도로명)0.9370.9981.0001.0001.0000.9830.978
업소소재지(지번)0.9470.9971.0001.0001.0000.9500.934
소재지전화1.0001.0001.0001.0001.0001.0001.000
업태명0.7991.0000.9830.9501.0001.0000.877
업체구분0.8391.0000.9780.9341.0000.8771.000
2023-12-11T09:38:51.116700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업체구분
업태명1.0000.708
업체구분0.7081.000
2023-12-11T09:38:51.184942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업태명업체구분
연번1.0000.5570.597
업태명0.5571.0000.708
업체구분0.5970.7081.000

Missing values

2023-12-11T09:38:46.575035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:38:46.710351image/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.
2023-12-11T09:38:46.819357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번업소명업소소재지(도로명)업소소재지(지번)소재지전화업태명업체구분데이터기준일
01동창이용원경상남도 의령군 의령읍 백산로1길 6경상남도 의령군 의령읍 동동리 1030-2055 -573 -3265일반이용업이용업2023-09-11
12대성이용원경상남도 의령군 의령읍 의병로21길 25-8경상남도 의령군 의령읍 중동리 411-14055- 573-3657일반이용업이용업2023-09-11
23대의이용원경상남도 의령군 대의면 대의로 57경상남도 의령군 대의면 마쌍리 441-3055 -572 -9042일반이용업이용업2023-09-11
34신신이용원경상남도 의령군 부림면 대한로 1774<NA>055 -574 -1912일반이용업이용업2023-09-11
45태양이용원경상남도 의령군 의령읍 의병로23길 6-10경상남도 의령군 의령읍 동동리 1481-2055- 573-2155일반이용업이용업2023-09-11
56정곡이용원경상남도 의령군 낙서면 낙동강로3길 4경상남도 의령군 낙서면 정곡리 1160-4055 -572 -7050일반이용업이용업2023-09-11
67신라이용원경상남도 의령군 의령읍 의병로 203경상남도 의령군 의령읍 서동리 480-14055 -573 -3589일반이용업이용업2023-09-11
78행복이용원경상남도 의령군 의령읍 의병로 195-1경상남도 의령군 의령읍 서동리 303-1055 -574 -7695일반이용업이용업2023-09-11
89혜성이용원경상남도 의령군 의령읍 의병로24길 16경상남도 의령군 의령읍 중동리 400-8<NA>일반이용업이용업2023-09-11
910서울이용원경상남도 의령군 부림면 신번로 163-1<NA><NA>일반이용업이용업2023-09-11
연번업소명업소소재지(도로명)업소소재지(지번)소재지전화업태명업체구분데이터기준일
5455다정한 네일경상남도 의령군 의령읍 충익로 47-16<NA>055 -573 -1577네일아트업네일미용업2023-09-11
5556봉숭아경상남도 의령군 의령읍 의병로14길 16-15, 꿈에그린빌 1층경상남도 의령군 의령읍 서동리 598-4 꿈에그린빌<NA>네일아트업네일미용업2023-09-11
5657네일#경상남도 의령군 의령읍 의병로19길 8경상남도 의령군 의령읍 중동리 334<NA>네일아트업네일미용업2023-09-11
5758네일의모든것경상남도 의령군 의령읍 의병로22길 7-7, 2층 (진승VIEW주상복합아파트)경상남도 의령군 의령읍 중동리 395 진승VIEW주상복합아파트<NA>네일아트업네일미용업2023-09-11
5859수지탑스킨경상남도 의령군 부림면 대한로 1741경상남도 의령군 부림면 신반리 168-2<NA>네일아트업피부미용업, 네일미용업2023-09-11
5960손톱공쥬경상남도 의령군 의령읍 의병로14길 16-13경상남도 의령군 의령읍 서동리 598-5<NA>네일아트업네일미용업, 화장ㆍ분장 미용업2023-09-11
6061연이,네일경상남도 의령군 행복로 7, 204동 103호(신우희가로 아파트)경상남도 의령군 의령읍 동동리 1556 신우희가로아파트<NA>네일아트업네일미용업2023-09-11
6162SSONG(쏭)경상남도 의령군 의령읍 의병로19길 23-2, 2층경상남도 의령군 중동리 258<NA>메이크업업일반미용업2023-09-11
6263나봄뷰티경상남도 의령군 의령읍 의병로22길 19-16, 202호경상남도 의령군 중동리 434-11<NA>피부미용업피부미용업2023-09-11
6364지안뷰티경상남도 의령군 의령읍 의병로14길 16-17, 노블레스 101호경상남도 의령군 서동리 598-3 노블레스 101호<NA>피부피부미용업2023-09-11