Overview

Dataset statistics

Number of variables8
Number of observations176
Missing cells64
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 KiB
Average record size in memory66.8 B

Variable types

Categorical1
Text4
Numeric2
DateTime1

Dataset

Description장성군 공중위생업소 현황에 대한 업종구분, 업소명, 연락처, 소재지도로명주소, 소재지 지번, 위도, 경도, 데이터 기준일 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15011985/fileData.do

Alerts

데이터기준일 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
소재지 도로명주소 has 18 (10.2%) missing valuesMissing
연락처 has 46 (26.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 07:00:51.677360
Analysis finished2023-12-12 07:00:52.609280
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종구분
Categorical

Distinct13
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
미용업
54 
숙박업(일반)
24 
이용업
21 
세탁업
16 
건물위생관리업
15 
Other values (8)
46 

Length

Max length23
Median length3
Mean length4.5511364
Min length3

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 54
30.7%
숙박업(일반) 24
13.6%
이용업 21
 
11.9%
세탁업 16
 
9.1%
건물위생관리업 15
 
8.5%
일반미용업 15
 
8.5%
피부미용업 9
 
5.1%
목욕장업 7
 
4.0%
네일미용업 6
 
3.4%
숙박업(생활) 5
 
2.8%
Other values (3) 4
 
2.3%

Length

2023-12-12T16:00:52.698162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 56
31.1%
숙박업(일반 24
13.3%
이용업 21
 
11.7%
세탁업 16
 
8.9%
일반미용업 16
 
8.9%
건물위생관리업 15
 
8.3%
피부미용업 10
 
5.6%
목욕장업 7
 
3.9%
네일미용업 6
 
3.3%
숙박업(생활 5
 
2.8%
Other values (2) 4
 
2.2%

업소명
Text

Distinct170
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T16:00:53.336301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length5.8693182
Min length2

Characters and Unicode

Total characters1033
Distinct characters264
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)93.2%

Sample

1st row(주)푸른종합건설
2nd row유한회사 대광개발
3rd row(주)건승
4th row(유)다연
5th row금송건설 주식회사
ValueCountFrequency (%)
헤어 6
 
2.8%
유한회사 3
 
1.4%
미 3
 
1.4%
미용실 3
 
1.4%
주식회사 3
 
1.4%
서울미용실 2
 
0.9%
헤어샵 2
 
0.9%
주)꿈앤들 2
 
0.9%
뷰티샵 2
 
0.9%
현대이발관 2
 
0.9%
Other values (187) 190
87.2%
2023-12-12T16:00:53.893460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 48
 
4.6%
42
 
4.1%
어 29
 
2.8%
용 29
 
2.8%
헤 28
 
2.7%
실 27
 
2.6%
관 27
 
2.6%
이 26
 
2.5%
장 24
 
2.3%
발 19
 
1.8%
Other values (254) 734
71.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 913
88.4%
Space Separator 42
 
4.1%
Uppercase Letter 27
 
2.6%
Close Punctuation 14
 
1.4%
Open Punctuation 14
 
1.4%
Lowercase Letter 13
 
1.3%
Decimal Number 4
 
0.4%
Other Punctuation 4
 
0.4%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 48
 
5.3%
어 29
 
3.2%
용 29
 
3.2%
헤 28
 
3.1%
실 27
 
3.0%
관 27
 
3.0%
이 26
 
2.8%
장 24
 
2.6%
발 19
 
2.1%
원 15
 
1.6%
Other values (221) 641
70.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
14.8%
E 3
11.1%
I 3
11.1%
L 3
11.1%
U 2
7.4%
C 2
7.4%
N 2
7.4%
O 2
7.4%
B 1
 
3.7%
T 1
 
3.7%
Other values (4) 4
14.8%
Lowercase Letter
ValueCountFrequency (%)
o 3
23.1%
a 2
15.4%
n 2
15.4%
e 1
 
7.7%
m 1
 
7.7%
s 1
 
7.7%
r 1
 
7.7%
i 1
 
7.7%
h 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
9 2
50.0%
8 1
25.0%
7 1
25.0%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
/ 1
25.0%
, 1
25.0%
Space Separator
ValueCountFrequency (%)
42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 912
88.3%
Common 80
 
7.7%
Latin 40
 
3.9%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 48
 
5.3%
어 29
 
3.2%
용 29
 
3.2%
헤 28
 
3.1%
실 27
 
3.0%
관 27
 
3.0%
이 26
 
2.9%
장 24
 
2.6%
발 19
 
2.1%
원 15
 
1.6%
Other values (220) 640
70.2%
Latin
ValueCountFrequency (%)
A 4
 
10.0%
E 3
 
7.5%
o 3
 
7.5%
I 3
 
7.5%
L 3
 
7.5%
U 2
 
5.0%
C 2
 
5.0%
a 2
 
5.0%
n 2
 
5.0%
N 2
 
5.0%
Other values (13) 14
35.0%
Common
ValueCountFrequency (%)
42
52.5%
) 14
 
17.5%
( 14
 
17.5%
9 2
 
2.5%
- 2
 
2.5%
& 2
 
2.5%
/ 1
 
1.2%
8 1
 
1.2%
7 1
 
1.2%
, 1
 
1.2%
Han
ValueCountFrequency (%)
秀 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 912
88.3%
ASCII 120
 
11.6%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 48
 
5.3%
어 29
 
3.2%
용 29
 
3.2%
헤 28
 
3.1%
실 27
 
3.0%
관 27
 
3.0%
이 26
 
2.9%
장 24
 
2.6%
발 19
 
2.1%
원 15
 
1.6%
Other values (220) 640
70.2%
ASCII
ValueCountFrequency (%)
42
35.0%
) 14
 
11.7%
( 14
 
11.7%
A 4
 
3.3%
E 3
 
2.5%
o 3
 
2.5%
I 3
 
2.5%
L 3
 
2.5%
9 2
 
1.7%
U 2
 
1.7%
Other values (23) 30
25.0%
CJK
ValueCountFrequency (%)
秀 1
100.0%

소재지 도로명주소
Text

MISSING 

Distinct147
Distinct (%)93.0%
Missing18
Missing (%)10.2%
Memory size1.5 KiB
2023-12-12T16:00:54.369597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length20.791139
Min length18

Characters and Unicode

Total characters3285
Distinct characters84
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)86.7%

Sample

1st row전라남도 장성군 장성읍 영천로 193
2nd row전라남도 장성군 남면 시목3길 30-16
3rd row전라남도 장성군 진원면 노사로 489-1
4th row전라남도 장성군 삼서면 금산로 147-9
5th row전라남도 장성군 장성읍 단풍로 257
ValueCountFrequency (%)
전라남도 158
19.8%
장성군 158
19.8%
장성읍 92
 
11.6%
영천로 36
 
4.5%
삼계면 22
 
2.8%
백양로 12
 
1.5%
역전로 12
 
1.5%
사창로 12
 
1.5%
황룡면 11
 
1.4%
북하면 10
 
1.3%
Other values (179) 273
34.3%
2023-12-12T16:00:55.076226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
646
19.7%
성 259
 
7.9%
장 256
 
7.8%
전 170
 
5.2%
남 163
 
5.0%
도 158
 
4.8%
군 158
 
4.8%
라 158
 
4.8%
1 151
 
4.6%
로 120
 
3.7%
Other values (74) 1046
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2086
63.5%
Space Separator 646
 
19.7%
Decimal Number 501
 
15.3%
Dash Punctuation 52
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 259
12.4%
장 256
12.3%
전 170
 
8.1%
남 163
 
7.8%
도 158
 
7.6%
군 158
 
7.6%
라 158
 
7.6%
로 120
 
5.8%
읍 92
 
4.4%
면 66
 
3.2%
Other values (62) 486
23.3%
Decimal Number
ValueCountFrequency (%)
1 151
30.1%
2 78
15.6%
3 46
 
9.2%
8 41
 
8.2%
5 36
 
7.2%
4 36
 
7.2%
6 31
 
6.2%
9 29
 
5.8%
0 27
 
5.4%
7 26
 
5.2%
Space Separator
ValueCountFrequency (%)
646
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2086
63.5%
Common 1199
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 259
12.4%
장 256
12.3%
전 170
 
8.1%
남 163
 
7.8%
도 158
 
7.6%
군 158
 
7.6%
라 158
 
7.6%
로 120
 
5.8%
읍 92
 
4.4%
면 66
 
3.2%
Other values (62) 486
23.3%
Common
ValueCountFrequency (%)
646
53.9%
1 151
 
12.6%
2 78
 
6.5%
- 52
 
4.3%
3 46
 
3.8%
8 41
 
3.4%
5 36
 
3.0%
4 36
 
3.0%
6 31
 
2.6%
9 29
 
2.4%
Other values (2) 53
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2086
63.5%
ASCII 1199
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
646
53.9%
1 151
 
12.6%
2 78
 
6.5%
- 52
 
4.3%
3 46
 
3.8%
8 41
 
3.4%
5 36
 
3.0%
4 36
 
3.0%
6 31
 
2.6%
9 29
 
2.4%
Other values (2) 53
 
4.4%
Hangul
ValueCountFrequency (%)
성 259
12.4%
장 256
12.3%
전 170
 
8.1%
남 163
 
7.8%
도 158
 
7.6%
군 158
 
7.6%
라 158
 
7.6%
로 120
 
5.8%
읍 92
 
4.4%
면 66
 
3.2%
Other values (62) 486
23.3%

소재지 지번주소
Text

Distinct161
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T16:00:55.523599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length23.204545
Min length20

Characters and Unicode

Total characters4084
Distinct characters69
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)84.7%

Sample

1st row전라남도 장성군 장성읍 영천리 1058-9
2nd row전라남도 장성군 남면 분향리 790-7
3rd row전라남도 장성군 진원면 선적리 117-8
4th row전라남도 장성군 삼서면 금산리 488
5th row전라남도 장성군 장성읍 수산리 162-2
ValueCountFrequency (%)
전라남도 176
20.0%
장성군 176
20.0%
장성읍 97
 
11.0%
영천리 78
 
8.9%
삼계면 26
 
3.0%
사창리 18
 
2.0%
황룡면 13
 
1.5%
사거리 12
 
1.4%
북이면 12
 
1.4%
월평리 11
 
1.2%
Other values (188) 261
29.7%
2023-12-12T16:00:56.011343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
875
21.4%
성 278
 
6.8%
장 274
 
6.7%
1 183
 
4.5%
남 180
 
4.4%
전 177
 
4.3%
라 176
 
4.3%
리 176
 
4.3%
도 176
 
4.3%
군 176
 
4.3%
Other values (59) 1413
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2284
55.9%
Space Separator 875
 
21.4%
Decimal Number 779
 
19.1%
Dash Punctuation 146
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 278
12.2%
장 274
12.0%
남 180
 
7.9%
전 177
 
7.7%
라 176
 
7.7%
리 176
 
7.7%
도 176
 
7.7%
군 176
 
7.7%
읍 97
 
4.2%
면 79
 
3.5%
Other values (47) 495
21.7%
Decimal Number
ValueCountFrequency (%)
1 183
23.5%
8 81
10.4%
3 80
10.3%
2 75
9.6%
4 73
 
9.4%
0 67
 
8.6%
7 66
 
8.5%
6 55
 
7.1%
9 53
 
6.8%
5 46
 
5.9%
Space Separator
ValueCountFrequency (%)
875
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2284
55.9%
Common 1800
44.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 278
12.2%
장 274
12.0%
남 180
 
7.9%
전 177
 
7.7%
라 176
 
7.7%
리 176
 
7.7%
도 176
 
7.7%
군 176
 
7.7%
읍 97
 
4.2%
면 79
 
3.5%
Other values (47) 495
21.7%
Common
ValueCountFrequency (%)
875
48.6%
1 183
 
10.2%
- 146
 
8.1%
8 81
 
4.5%
3 80
 
4.4%
2 75
 
4.2%
4 73
 
4.1%
0 67
 
3.7%
7 66
 
3.7%
6 55
 
3.1%
Other values (2) 99
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2284
55.9%
ASCII 1800
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
875
48.6%
1 183
 
10.2%
- 146
 
8.1%
8 81
 
4.5%
3 80
 
4.4%
2 75
 
4.2%
4 73
 
4.1%
0 67
 
3.7%
7 66
 
3.7%
6 55
 
3.1%
Other values (2) 99
 
5.5%
Hangul
ValueCountFrequency (%)
성 278
12.2%
장 274
12.0%
남 180
 
7.9%
전 177
 
7.7%
라 176
 
7.7%
리 176
 
7.7%
도 176
 
7.7%
군 176
 
7.7%
읍 97
 
4.2%
면 79
 
3.5%
Other values (47) 495
21.7%

연락처
Text

MISSING 

Distinct130
Distinct (%)100.0%
Missing46
Missing (%)26.1%
Memory size1.5 KiB
2023-12-12T16:00:56.262207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.646154
Min length12

Characters and Unicode

Total characters1774
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

Unique130 ?
Unique (%)100.0%

Sample

1st row063 -531 -7388
2nd row061- 392-9659
3rd row061 -394 -9006
4th row062 -366 -9990
5th row061 -394 -0072
ValueCountFrequency (%)
061 121
35.2%
393 31
 
9.0%
392 29
 
8.4%
394 14
 
4.1%
395 7
 
2.0%
061-392 3
 
0.9%
7388 2
 
0.6%
9006 2
 
0.6%
9525 2
 
0.6%
062 2
 
0.6%
Other values (131) 131
38.1%
2023-12-12T16:00:56.678324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 260
14.7%
3 231
13.0%
214
12.1%
0 201
11.3%
1 190
10.7%
6 181
10.2%
9 164
9.2%
2 111
6.3%
5 61
 
3.4%
4 56
 
3.2%
Other values (2) 105
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1300
73.3%
Dash Punctuation 260
 
14.7%
Space Separator 214
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 231
17.8%
0 201
15.5%
1 190
14.6%
6 181
13.9%
9 164
12.6%
2 111
8.5%
5 61
 
4.7%
4 56
 
4.3%
7 53
 
4.1%
8 52
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 260
100.0%
Space Separator
ValueCountFrequency (%)
214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 260
14.7%
3 231
13.0%
214
12.1%
0 201
11.3%
1 190
10.7%
6 181
10.2%
9 164
9.2%
2 111
6.3%
5 61
 
3.4%
4 56
 
3.2%
Other values (2) 105
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 260
14.7%
3 231
13.0%
214
12.1%
0 201
11.3%
1 190
10.7%
6 181
10.2%
9 164
9.2%
2 111
6.3%
5 61
 
3.4%
4 56
 
3.2%
Other values (2) 105
5.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct166
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.309739
Minimum35.206813
Maximum35.431207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T16:00:56.885338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.206813
5-th percentile35.25
Q135.290524
median35.300538
Q335.307307
95-th percentile35.428317
Maximum35.431207
Range0.22439448
Interquartile range (IQR)0.01678353

Descriptive statistics

Standard deviation0.050788674
Coefficient of variation (CV)0.0014383758
Kurtosis0.85592893
Mean35.309739
Median Absolute Deviation (MAD)0.01001428
Skewness1.171487
Sum6214.514
Variance0.0025794894
MonotonicityNot monotonic
2023-12-12T16:00:57.073064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.29641915 3
 
1.7%
35.3222559 2
 
1.1%
35.29052382 2
 
1.1%
35.3118339 2
 
1.1%
35.42875266 2
 
1.1%
35.30000385 2
 
1.1%
35.26059981 2
 
1.1%
35.30116637 2
 
1.1%
35.4281719 2
 
1.1%
35.3345628 1
 
0.6%
Other values (156) 156
88.6%
ValueCountFrequency (%)
35.206813 1
0.6%
35.22990105 1
0.6%
35.2302778 1
0.6%
35.23226696 1
0.6%
35.2401434 1
0.6%
35.24224192 1
0.6%
35.243336 1
0.6%
35.2476935 1
0.6%
35.2479167 1
0.6%
35.2506939 1
0.6%
ValueCountFrequency (%)
35.43120748 1
0.6%
35.43116608 1
0.6%
35.4311345 1
0.6%
35.4310941 1
0.6%
35.43018734 1
0.6%
35.42964421 1
0.6%
35.42949036 1
0.6%
35.42875266 2
1.1%
35.4281719 2
1.1%
35.4276351 1
0.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct166
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.77192
Minimum126.60946
Maximum126.90297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T16:00:57.253475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.60946
5-th percentile126.66368
Q1126.77426
median126.78197
Q3126.80088
95-th percentile126.87205
Maximum126.90297
Range0.2935076
Interquartile range (IQR)0.0266273

Descriptive statistics

Standard deviation0.056537643
Coefficient of variation (CV)0.00044597923
Kurtosis0.6101396
Mean126.77192
Median Absolute Deviation (MAD)0.0084621
Skewness-0.77386508
Sum22311.858
Variance0.0031965051
MonotonicityNot monotonic
2023-12-12T16:00:57.431626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7811766 3
 
1.7%
126.8010261 2
 
1.1%
126.7737582 2
 
1.1%
126.7432645 2
 
1.1%
126.8091331 2
 
1.1%
126.7814734 2
 
1.1%
126.6771538 2
 
1.1%
126.7848302 2
 
1.1%
126.808431 2
 
1.1%
126.8117844 1
 
0.6%
Other values (156) 156
88.6%
ValueCountFrequency (%)
126.6094642 1
0.6%
126.6439436 1
0.6%
126.6463373 1
0.6%
126.6467303 1
0.6%
126.646735 1
0.6%
126.6626385 1
0.6%
126.6634537 1
0.6%
126.6634949 1
0.6%
126.6635715 1
0.6%
126.6637146 1
0.6%
ValueCountFrequency (%)
126.9029718 1
0.6%
126.8859383 1
0.6%
126.8843235 1
0.6%
126.8828294 1
0.6%
126.8817077 1
0.6%
126.8810527 1
0.6%
126.8786853 1
0.6%
126.8783861 1
0.6%
126.8772135 1
0.6%
126.8703324 1
0.6%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2023-06-29 00:00:00
Maximum2023-06-29 00:00:00
2023-12-12T16:00:57.559545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:00:57.670270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:00:52.151291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:00:52.013033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:00:52.258043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:00:52.077080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:00:57.754557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분위도경도
업종구분1.0000.2450.448
위도0.2451.0000.773
경도0.4480.7731.000
2023-12-12T16:00:57.856601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업종구분
위도1.0000.7610.101
경도0.7611.0000.207
업종구분0.1010.2071.000

Missing values

2023-12-12T16:00:52.364193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:00:52.469148image/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.
2023-12-12T16:00:52.556558image/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

업종구분업소명소재지 도로명주소소재지 지번주소연락처위도경도데이터기준일
0건물위생관리업(주)푸른종합건설전라남도 장성군 장성읍 영천로 193전라남도 장성군 장성읍 영천리 1058-9063 -531 -738835.302109126.7842672023-06-29
1건물위생관리업유한회사 대광개발전라남도 장성군 남면 시목3길 30-16전라남도 장성군 남면 분향리 790-7061- 392-965935.255978126.8060952023-06-29
2건물위생관리업(주)건승전라남도 장성군 진원면 노사로 489-1전라남도 장성군 진원면 선적리 117-8<NA>35.273627126.8320042023-06-29
3건물위생관리업(유)다연전라남도 장성군 삼서면 금산로 147-9전라남도 장성군 삼서면 금산리 488<NA>35.206813126.6439442023-06-29
4건물위생관리업금송건설 주식회사전라남도 장성군 장성읍 단풍로 257전라남도 장성군 장성읍 수산리 162-2061 -394 -900635.324973126.8024792023-06-29
5건물위생관리업(주)서림전라남도 장성군 남면 나노산단5로 31전라남도 장성군 남면 삼태리 861-1<NA>35.240143126.8330712023-06-29
6건물위생관리업(주)나인스타전라남도 장성군 북하면 백양로 1115-7전라남도 장성군 북하면 약수리 232-6062 -366 -999035.426267126.8783862023-06-29
7건물위생관리업청소반장(주)전라남도 장성군 장성읍 단풍로 243전라남도 장성군 장성읍 수산리 206-2061 -394 -007235.323918126.8017472023-06-29
8건물위생관리업전남장성지역자활센터 장성크린전라남도 장성군 장성읍 영천로 212전라남도 장성군 장성읍 영천리 1087-1061-394-007235.303017126.7859012023-06-29
9건물위생관리업주식회사 그린청소방역전라남도 장성군 장성읍 영천로 229전라남도 장성군 장성읍 영천리 787-3<NA>35.304372126.7869112023-06-29
업종구분업소명소재지 도로명주소소재지 지번주소연락처위도경도데이터기준일
166피부미용업강민주 뷰티샵전라남도 장성군 장성읍 매화7길 61전라남도 장성군 장성읍 영천리 854-4061 -392 -131835.302771126.7873492023-06-29
167피부미용업피부공주전라남도 장성군 장성읍 충무길 9전라남도 장성군 장성읍 영천리 1033-4061 -393 -877735.300137126.7813852023-06-29
168피부미용업다흰 뷰티샵전라남도 장성군 장성읍 영천로 120-1전라남도 장성군 장성읍 영천리 908-1<NA>35.296419126.7811772023-06-29
169피부미용업피부나라전라남도 장성군 삼계면 능성로 548전라남도 장성군 삼계면 사창리 70061 -395 -216135.2606126.6771542023-06-29
170피부미용업피부미인전라남도 장성군 북이면 백양로 15-1전라남도 장성군 북이면 사거리 640-1061 -392 -952535.42949126.8095172023-06-29
171피부미용업피부의 차이전라남도 장성군 장성읍 충무길 9-2전라남도 장성군 장성읍 영천리 1033-12061-392 -059535.300004126.7814732023-06-29
172피부미용업바른체형뷰티샵전라남도 장성군 삼계면 사창로 57전라남도 장성군 삼계면 사창리 387<NA>35.25878126.6654832023-06-29
173피부미용업눈썸(noon some)전라남도 장성군 장성읍 영천로 223전라남도 장성군 장성읍 영천리 788-4<NA>35.304007126.786582023-06-29
174피부미용업피부만들기전라남도 장성군 장성읍 역전로 181 2호전라남도 장성군 장성읍 영천리 1482-42<NA>35.3055126.78462023-06-29
175화장ㆍ분장 미용업라뷰티(La BEAUTY)전라남도 장성군 장성읍 역전로 191전라남도 장성군 장성읍 영천리 1662<NA>35.305551126.7856452023-06-29