Overview

Dataset statistics

Number of variables7
Number of observations134
Missing cells69
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory58.0 B

Variable types

Numeric1
Categorical2
Text4

Dataset

Description전라남도 영암군 관내에 위치한 미용업소 현황에 대한 업소명, 도로명 주소, 지번주소, 전화번호 등의 항목을 제공한 데이터 입니다.
Author전라남도 영암군
URLhttps://www.data.go.kr/data/15054266/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
소재지전화 has 68 (50.7%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-21 01:20:34.620598
Analysis finished2024-04-21 01:20:36.447766
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.5
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-21T10:20:36.521892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.65
Q134.25
median67.5
Q3100.75
95-th percentile127.35
Maximum134
Range133
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation38.826537
Coefficient of variation (CV)0.57520796
Kurtosis-1.2
Mean67.5
Median Absolute Deviation (MAD)33.5
Skewness0
Sum9045
Variance1507.5
MonotonicityStrictly increasing
2024-04-21T10:20:36.657275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
86 1
 
0.7%
100 1
 
0.7%
99 1
 
0.7%
98 1
 
0.7%
97 1
 
0.7%
96 1
 
0.7%
95 1
 
0.7%
94 1
 
0.7%
93 1
 
0.7%
Other values (124) 124
92.5%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%
126 1
0.7%
125 1
0.7%

업종명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
일반미용업
49 
종합미용업
46 
피부미용업
11 
미용업
8 
네일미용업
7 
Other values (7)
13 

Length

Max length23
Median length5
Mean length5.8955224
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 49
36.6%
종합미용업 46
34.3%
피부미용업 11
 
8.2%
미용업 8
 
6.0%
네일미용업 7
 
5.2%
피부미용업, 네일미용업 3
 
2.2%
일반미용업, 피부미용업 2
 
1.5%
일반미용업, 네일미용업 2
 
1.5%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 2
 
1.5%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 2
 
1.5%
Other values (2) 2
 
1.5%

Length

2024-04-21T10:20:36.794747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 56
35.9%
종합미용업 46
29.5%
피부미용업 18
 
11.5%
네일미용업 16
 
10.3%
미용업 14
 
9.0%
화장ㆍ분장 6
 
3.8%

업소명
Text

Distinct132
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-21T10:20:37.040685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length5.8283582
Min length2

Characters and Unicode

Total characters781
Distinct characters226
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)97.0%

Sample

1st row황미용실
2nd row최설희 헤어러브
3rd row매혹미장
4th row은하미용실
5th rowMS헤어샵
ValueCountFrequency (%)
hair 6
 
3.7%
브릭스뷰티샵 2
 
1.2%
by 2
 
1.2%
the 2
 
1.2%
보라미용실 2
 
1.2%
sj프로헤어 1
 
0.6%
란미용실 1
 
0.6%
윤헤어스케치 1
 
0.6%
미화미용실 1
 
0.6%
이희자미용실 1
 
0.6%
Other values (144) 144
88.3%
2024-04-21T10:20:37.406778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
헤 51
 
6.5%
어 49
 
6.3%
미 42
 
5.4%
실 33
 
4.2%
용 32
 
4.1%
29
 
3.7%
리 21
 
2.7%
스 16
 
2.0%
일 12
 
1.5%
네 11
 
1.4%
Other values (216) 485
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 641
82.1%
Lowercase Letter 56
 
7.2%
Uppercase Letter 33
 
4.2%
Space Separator 29
 
3.7%
Other Punctuation 10
 
1.3%
Close Punctuation 5
 
0.6%
Open Punctuation 5
 
0.6%
Decimal Number 1
 
0.1%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
헤 51
 
8.0%
어 49
 
7.6%
미 42
 
6.6%
실 33
 
5.1%
용 32
 
5.0%
리 21
 
3.3%
스 16
 
2.5%
일 12
 
1.9%
네 11
 
1.7%
머 11
 
1.7%
Other values (176) 363
56.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
15.2%
J 4
12.1%
S 3
9.1%
T 3
9.1%
I 2
 
6.1%
H 2
 
6.1%
R 2
 
6.1%
E 2
 
6.1%
N 2
 
6.1%
M 2
 
6.1%
Other values (6) 6
18.2%
Lowercase Letter
ValueCountFrequency (%)
a 10
17.9%
i 10
17.9%
r 7
12.5%
h 7
12.5%
e 4
 
7.1%
n 4
 
7.1%
y 3
 
5.4%
l 2
 
3.6%
o 2
 
3.6%
b 2
 
3.6%
Other values (5) 5
8.9%
Other Punctuation
ValueCountFrequency (%)
. 3
30.0%
, 3
30.0%
; 2
20.0%
& 2
20.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 641
82.1%
Latin 89
 
11.4%
Common 51
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
헤 51
 
8.0%
어 49
 
7.6%
미 42
 
6.6%
실 33
 
5.1%
용 32
 
5.0%
리 21
 
3.3%
스 16
 
2.5%
일 12
 
1.9%
네 11
 
1.7%
머 11
 
1.7%
Other values (176) 363
56.6%
Latin
ValueCountFrequency (%)
a 10
 
11.2%
i 10
 
11.2%
r 7
 
7.9%
h 7
 
7.9%
A 5
 
5.6%
e 4
 
4.5%
n 4
 
4.5%
J 4
 
4.5%
S 3
 
3.4%
y 3
 
3.4%
Other values (21) 32
36.0%
Common
ValueCountFrequency (%)
29
56.9%
) 5
 
9.8%
( 5
 
9.8%
. 3
 
5.9%
, 3
 
5.9%
; 2
 
3.9%
& 2
 
3.9%
3 1
 
2.0%
_ 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 641
82.1%
ASCII 140
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
헤 51
 
8.0%
어 49
 
7.6%
미 42
 
6.6%
실 33
 
5.1%
용 32
 
5.0%
리 21
 
3.3%
스 16
 
2.5%
일 12
 
1.9%
네 11
 
1.7%
머 11
 
1.7%
Other values (176) 363
56.6%
ASCII
ValueCountFrequency (%)
29
20.7%
a 10
 
7.1%
i 10
 
7.1%
r 7
 
5.0%
h 7
 
5.0%
A 5
 
3.6%
) 5
 
3.6%
( 5
 
3.6%
e 4
 
2.9%
n 4
 
2.9%
Other values (30) 54
38.6%

영업소주소(도로명)
Text

Distinct128
Distinct (%)96.2%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
2024-04-21T10:20:37.752330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length43
Mean length25.443609
Min length18

Characters and Unicode

Total characters3384
Distinct characters124
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

Unique123 ?
Unique (%)92.5%

Sample

1st row전라남도 영암군 서호면 선사주거로 353
2nd row전라남도 영암군 삼호읍 대불주거1로3길 12
3rd row전라남도 영암군 미암면 흑석로 1569-39
4th row전라남도 영암군 영암읍 남문로 62
5th row전라남도 영암군 삼호읍 방아제로 22
ValueCountFrequency (%)
전라남도 133
17.7%
영암군 133
17.7%
삼호읍 69
 
9.2%
영암읍 28
 
3.7%
1층 27
 
3.6%
신항로 13
 
1.7%
중앙로 11
 
1.5%
학산면 11
 
1.5%
대불주거로 11
 
1.5%
삼호중앙로 10
 
1.3%
Other values (182) 306
40.7%
2024-04-21T10:20:38.180678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
619
18.3%
1 182
 
5.4%
암 169
 
5.0%
영 168
 
5.0%
남 140
 
4.1%
도 136
 
4.0%
군 136
 
4.0%
전 133
 
3.9%
라 133
 
3.9%
호 118
 
3.5%
Other values (114) 1450
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2066
61.1%
Space Separator 619
 
18.3%
Decimal Number 541
 
16.0%
Dash Punctuation 49
 
1.4%
Other Punctuation 45
 
1.3%
Close Punctuation 30
 
0.9%
Open Punctuation 30
 
0.9%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
암 169
 
8.2%
영 168
 
8.1%
남 140
 
6.8%
도 136
 
6.6%
군 136
 
6.6%
전 133
 
6.4%
라 133
 
6.4%
호 118
 
5.7%
로 112
 
5.4%
읍 97
 
4.7%
Other values (97) 724
35.0%
Decimal Number
ValueCountFrequency (%)
1 182
33.6%
2 81
15.0%
0 53
 
9.8%
3 51
 
9.4%
6 39
 
7.2%
5 34
 
6.3%
7 27
 
5.0%
4 25
 
4.6%
9 25
 
4.6%
8 24
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 44
97.8%
@ 1
 
2.2%
Space Separator
ValueCountFrequency (%)
619
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2066
61.1%
Common 1314
38.8%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
암 169
 
8.2%
영 168
 
8.1%
남 140
 
6.8%
도 136
 
6.6%
군 136
 
6.6%
전 133
 
6.4%
라 133
 
6.4%
호 118
 
5.7%
로 112
 
5.4%
읍 97
 
4.7%
Other values (97) 724
35.0%
Common
ValueCountFrequency (%)
619
47.1%
1 182
 
13.9%
2 81
 
6.2%
0 53
 
4.0%
3 51
 
3.9%
- 49
 
3.7%
, 44
 
3.3%
6 39
 
3.0%
5 34
 
2.6%
) 30
 
2.3%
Other values (6) 132
 
10.0%
Latin
ValueCountFrequency (%)
S 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2066
61.1%
ASCII 1318
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
619
47.0%
1 182
 
13.8%
2 81
 
6.1%
0 53
 
4.0%
3 51
 
3.9%
- 49
 
3.7%
, 44
 
3.3%
6 39
 
3.0%
5 34
 
2.6%
) 30
 
2.3%
Other values (7) 136
 
10.3%
Hangul
ValueCountFrequency (%)
암 169
 
8.2%
영 168
 
8.1%
남 140
 
6.8%
도 136
 
6.6%
군 136
 
6.6%
전 133
 
6.4%
라 133
 
6.4%
호 118
 
5.7%
로 112
 
5.4%
읍 97
 
4.7%
Other values (97) 724
35.0%

영업소주소(지번)
Text

Distinct124
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-21T10:20:38.489186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length43
Mean length25.977612
Min length20

Characters and Unicode

Total characters3481
Distinct characters112
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

Unique117 ?
Unique (%)87.3%

Sample

1st row전라남도 영암군 서호면 장천리 539-8
2nd row전라남도 영암군 삼호읍 용앙리 1686-12
3rd row전라남도 영암군 미암면 춘동리 171-34
4th row전라남도 영암군 영암읍 남풍리 116
5th row전라남도 영암군 삼호읍 용앙리 230-3
ValueCountFrequency (%)
전라남도 134
18.3%
영암군 134
18.3%
삼호읍 70
 
9.5%
용앙리 54
 
7.4%
영암읍 28
 
3.8%
용당리 16
 
2.2%
독천리 11
 
1.5%
학산면 11
 
1.5%
동무리 9
 
1.2%
월평리 8
 
1.1%
Other values (170) 258
35.2%
2024-04-21T10:20:38.921120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
722
20.7%
1 176
 
5.1%
암 170
 
4.9%
영 166
 
4.8%
남 145
 
4.2%
리 138
 
4.0%
군 137
 
3.9%
도 135
 
3.9%
라 134
 
3.8%
전 134
 
3.8%
Other values (102) 1424
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1946
55.9%
Space Separator 722
 
20.7%
Decimal Number 669
 
19.2%
Dash Punctuation 120
 
3.4%
Close Punctuation 9
 
0.3%
Open Punctuation 9
 
0.3%
Uppercase Letter 5
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
암 170
 
8.7%
영 166
 
8.5%
남 145
 
7.5%
리 138
 
7.1%
군 137
 
7.0%
도 135
 
6.9%
라 134
 
6.9%
전 134
 
6.9%
호 99
 
5.1%
읍 98
 
5.0%
Other values (85) 590
30.3%
Decimal Number
ValueCountFrequency (%)
1 176
26.3%
2 93
13.9%
3 77
11.5%
6 64
 
9.6%
7 55
 
8.2%
5 48
 
7.2%
0 48
 
7.2%
4 41
 
6.1%
9 36
 
5.4%
8 31
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 4
80.0%
B 1
 
20.0%
Space Separator
ValueCountFrequency (%)
722
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1946
55.9%
Common 1530
44.0%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
암 170
 
8.7%
영 166
 
8.5%
남 145
 
7.5%
리 138
 
7.1%
군 137
 
7.0%
도 135
 
6.9%
라 134
 
6.9%
전 134
 
6.9%
호 99
 
5.1%
읍 98
 
5.0%
Other values (85) 590
30.3%
Common
ValueCountFrequency (%)
722
47.2%
1 176
 
11.5%
- 120
 
7.8%
2 93
 
6.1%
3 77
 
5.0%
6 64
 
4.2%
7 55
 
3.6%
5 48
 
3.1%
0 48
 
3.1%
4 41
 
2.7%
Other values (5) 86
 
5.6%
Latin
ValueCountFrequency (%)
S 4
80.0%
B 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1946
55.9%
ASCII 1535
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
722
47.0%
1 176
 
11.5%
- 120
 
7.8%
2 93
 
6.1%
3 77
 
5.0%
6 64
 
4.2%
7 55
 
3.6%
5 48
 
3.1%
0 48
 
3.1%
4 41
 
2.7%
Other values (7) 91
 
5.9%
Hangul
ValueCountFrequency (%)
암 170
 
8.7%
영 166
 
8.5%
남 145
 
7.5%
리 138
 
7.1%
군 137
 
7.0%
도 135
 
6.9%
라 134
 
6.9%
전 134
 
6.9%
호 99
 
5.1%
읍 98
 
5.0%
Other values (85) 590
30.3%

소재지전화
Text

MISSING 

Distinct65
Distinct (%)98.5%
Missing68
Missing (%)50.7%
Memory size1.2 KiB
2024-04-21T10:20:39.133846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.863636
Min length1

Characters and Unicode

Total characters783
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

Unique64 ?
Unique (%)97.0%

Sample

1st row061-462-4555
2nd row
3rd row061-462-4621
4th row061-471-0118
5th row061-462-4004
ValueCountFrequency (%)
070-8811-4725 2
 
3.1%
061-472-4231 1
 
1.5%
061-472-3289 1
 
1.5%
061-473-9190 1
 
1.5%
061-472-2444 1
 
1.5%
061-471-3227 1
 
1.5%
061-471-2825 1
 
1.5%
061-472-3620 1
 
1.5%
061-472-9618 1
 
1.5%
061-471-0557 1
 
1.5%
Other values (54) 54
83.1%
2024-04-21T10:20:39.462040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 130
16.6%
1 112
14.3%
6 104
13.3%
0 99
12.6%
4 96
12.3%
7 70
8.9%
2 59
7.5%
3 35
 
4.5%
8 28
 
3.6%
9 25
 
3.2%
Other values (2) 25
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 652
83.3%
Dash Punctuation 130
 
16.6%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 112
17.2%
6 104
16.0%
0 99
15.2%
4 96
14.7%
7 70
10.7%
2 59
9.0%
3 35
 
5.4%
8 28
 
4.3%
9 25
 
3.8%
5 24
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 130
16.6%
1 112
14.3%
6 104
13.3%
0 99
12.6%
4 96
12.3%
7 70
8.9%
2 59
7.5%
3 35
 
4.5%
8 28
 
3.6%
9 25
 
3.2%
Other values (2) 25
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 130
16.6%
1 112
14.3%
6 104
13.3%
0 99
12.6%
4 96
12.3%
7 70
8.9%
2 59
7.5%
3 35
 
4.5%
8 28
 
3.6%
9 25
 
3.2%
Other values (2) 25
 
3.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-03
134 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-03
2nd row2024-04-03
3rd row2024-04-03
4th row2024-04-03
5th row2024-04-03

Common Values

ValueCountFrequency (%)
2024-04-03 134
100.0%

Length

2024-04-21T10:20:39.591085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:20:39.686126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-03 134
100.0%

Interactions

2024-04-21T10:20:36.020187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:20:39.755831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명소재지전화
연번1.0000.8450.000
업종명0.8451.0000.000
소재지전화0.0000.0001.000
2024-04-21T10:20:39.837003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.563
업종명0.5631.000

Missing values

2024-04-21T10:20:36.180652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:20:36.299398image/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.
2024-04-21T10:20:36.403220image/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

연번업종명업소명영업소주소(도로명)영업소주소(지번)소재지전화데이터기준일자
01미용업황미용실전라남도 영암군 서호면 선사주거로 353전라남도 영암군 서호면 장천리 539-8<NA>2024-04-03
12미용업최설희 헤어러브전라남도 영암군 삼호읍 대불주거1로3길 12전라남도 영암군 삼호읍 용앙리 1686-12061-462-45552024-04-03
23미용업매혹미장전라남도 영암군 미암면 흑석로 1569-39전라남도 영암군 미암면 춘동리 171-342024-04-03
34미용업은하미용실전라남도 영암군 영암읍 남문로 62전라남도 영암군 영암읍 남풍리 116<NA>2024-04-03
45미용업MS헤어샵전라남도 영암군 삼호읍 방아제로 22전라남도 영암군 삼호읍 용앙리 230-3061-462-46212024-04-03
56미용업선미용실전라남도 영암군 시종면 마한로 1274 ((1층))전라남도 영암군 시종면 만수리 950-1 (1층)061-471-01182024-04-03
67미용업헤븐헤어전라남도 영암군 삼호읍 신항로 63-12 ((1층))전라남도 영암군 삼호읍 용당리 2178-2 (1층)061-462-40042024-04-03
78미용업강남미용실전라남도 영암군 학산면 영산로 6-1전라남도 영암군 학산면 독천리 27-2<NA>2024-04-03
89일반미용업스카이헤어샵전라남도 영암군 삼호읍 삼호중앙로 139 ((1층 3호))전라남도 영암군 삼호읍 용앙리 313-27 (1층 3호)<NA>2024-04-03
910일반미용업모네의정원전라남도 영암군 삼호읍 삼호중앙로 204-8전라남도 영암군 삼호읍 용앙리 180-1061-464-77032024-04-03
연번업종명업소명영업소주소(도로명)영업소주소(지번)소재지전화데이터기준일자
124125일반미용업, 네일미용업네일,숲전라남도 영암군 삼호읍 대불주거2로2길 43-10전라남도 영암군 삼호읍 용앙리 1676-8<NA>2024-04-03
125126피부미용업, 네일미용업언니네일전라남도 영암군 삼호읍 대불주거6로 16, 126동 104호 (중흥S클래스리버티아파트)전라남도 영암군 삼호읍 용앙리 1669-1 중흥S클래스리버티아파트<NA>2024-04-03
126127피부미용업, 네일미용업오네일전라남도 영암군 영암읍 동문밖길 12-1, 1층전라남도 영암군 영암읍 동무리 95-3<NA>2024-04-03
127128피부미용업, 네일미용업인셀덤 앤 토탈뷰티전라남도 영암군 삼호읍 대불주거로 113, 103호전라남도 영암군 삼호읍 용앙리 427<NA>2024-04-03
128129화장ㆍ분장 미용업쥬쥬눈썹전라남도 영암군 삼호읍 대불주거2로2길 37-10, 1층전라남도 영암군 삼호읍 용앙리 1675-8<NA>2024-04-03
129130일반미용업, 화장ㆍ분장 미용업지나헤어(Jina hair)전라남도 영암군 삼호읍 삼호중앙로 225, 1층전라남도 영암군 삼호읍 용앙리 207-1<NA>2024-04-03
130131일반미용업, 네일미용업, 화장ㆍ분장 미용업금화미용실전라남도 영암군 삼호읍 대불주거7로3길 7전라남도 영암군 삼호읍 용앙리 1640-10<NA>2024-04-03
131132일반미용업, 네일미용업, 화장ㆍ분장 미용업봉쥬르헤어전라남도 영암군 삼호읍 삼호중앙로 238전라남도 영암군 삼호읍 용앙리 13-2061-462-15152024-04-03
132133피부미용업, 네일미용업, 화장ㆍ분장 미용업네일은 맑음전라남도 영암군 삼호읍 세가래2길 26-9, 1층전라남도 영암군 삼호읍 용앙리 222-32<NA>2024-04-03
133134피부미용업, 네일미용업, 화장ㆍ분장 미용업늘솜전라남도 영암군 영암읍 농암로 36 (우진레디앙스)전라남도 영암군 영암읍 서남리 125 우진레디앙스 상가<NA>2024-04-03