Overview

Dataset statistics

Number of variables8
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory73.3 B

Variable types

Text4
Numeric3
DateTime1

Dataset

Description경상남도 거창군 관내 목욕장업 현황 자료로 업소명, 소재지 도로명주소, 소재지 지번주소, 면적, 전화번호, 위도, 경도 데이터가 있음
Author경상남도 거창군
URLhttps://www.data.go.kr/data/15113010/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
업소명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
전화번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:34:17.259048
Analysis finished2023-12-12 23:34:18.459821
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:34:18.578614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.047619
Min length3

Characters and Unicode

Total characters127
Distinct characters78
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

Unique21 ?
Unique (%)100.0%

Sample

1st row옥유탕
2nd row동아목욕탕
3rd row대성탕
4th row위천탕
5th row정하건강나라
ValueCountFrequency (%)
옥유탕 1
 
4.2%
동아목욕탕 1
 
4.2%
나리안길 1
 
4.2%
호텔가조(hotelgajo 1
 
4.2%
금강리플레사우나 1
 
4.2%
하나로휘트니스 1
 
4.2%
녹천탕 1
 
4.2%
금원산참숯가마 1
 
4.2%
사우나 1
 
4.2%
갠지스 1
 
4.2%
Other values (14) 14
58.3%
2023-12-13T08:34:18.842274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 9
 
7.1%
나 8
 
6.3%
천 7
 
5.5%
우 5
 
3.9%
사 5
 
3.9%
스 4
 
3.1%
지 3
 
2.4%
금 3
 
2.4%
강 3
 
2.4%
3
 
2.4%
Other values (68) 77
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
89.0%
Uppercase Letter 7
 
5.5%
Space Separator 3
 
2.4%
Lowercase Letter 2
 
1.6%
Open Punctuation 1
 
0.8%
Close Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 9
 
8.0%
나 8
 
7.1%
천 7
 
6.2%
우 5
 
4.4%
사 5
 
4.4%
스 4
 
3.5%
지 3
 
2.7%
금 3
 
2.7%
강 3
 
2.7%
가 3
 
2.7%
Other values (56) 63
55.8%
Uppercase Letter
ValueCountFrequency (%)
O 1
14.3%
H 1
14.3%
J 1
14.3%
T 1
14.3%
E 1
14.3%
L 1
14.3%
G 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
o 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
89.0%
Latin 9
 
7.1%
Common 5
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 9
 
8.0%
나 8
 
7.1%
천 7
 
6.2%
우 5
 
4.4%
사 5
 
4.4%
스 4
 
3.5%
지 3
 
2.7%
금 3
 
2.7%
강 3
 
2.7%
가 3
 
2.7%
Other values (56) 63
55.8%
Latin
ValueCountFrequency (%)
O 1
11.1%
H 1
11.1%
J 1
11.1%
T 1
11.1%
E 1
11.1%
L 1
11.1%
G 1
11.1%
a 1
11.1%
o 1
11.1%
Common
ValueCountFrequency (%)
3
60.0%
( 1
 
20.0%
) 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
89.0%
ASCII 14
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 9
 
8.0%
나 8
 
7.1%
천 7
 
6.2%
우 5
 
4.4%
사 5
 
4.4%
스 4
 
3.5%
지 3
 
2.7%
금 3
 
2.7%
강 3
 
2.7%
가 3
 
2.7%
Other values (56) 63
55.8%
ASCII
ValueCountFrequency (%)
3
21.4%
( 1
 
7.1%
O 1
 
7.1%
H 1
 
7.1%
J 1
 
7.1%
T 1
 
7.1%
E 1
 
7.1%
L 1
 
7.1%
G 1
 
7.1%
a 1
 
7.1%
Other values (2) 2
14.3%

소재지도로명주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:34:19.016112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length20
Mean length23.095238
Min length19

Characters and Unicode

Total characters485
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row경상남도 거창군 거창읍 강남로 150
2nd row경상남도 거창군 가조면 지산로 1482
3rd row경상남도 거창군 거창읍 강남로 140
4th row경상남도 거창군 위천면 금원산길 9
5th row경상남도 거창군 거창읍 거창대로 93
ValueCountFrequency (%)
경상남도 21
19.1%
거창군 21
19.1%
거창읍 15
 
13.6%
가조면 4
 
3.6%
강남로 2
 
1.8%
온천길 2
 
1.8%
2,3층 2
 
1.8%
9 2
 
1.8%
거열로1길 2
 
1.8%
하동1길 1
 
0.9%
Other values (38) 38
34.5%
2023-12-13T08:34:19.503730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
18.4%
거 42
 
8.7%
창 38
 
7.8%
남 23
 
4.7%
상 23
 
4.7%
1 22
 
4.5%
군 21
 
4.3%
경 21
 
4.3%
도 21
 
4.3%
읍 15
 
3.1%
Other values (56) 170
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 305
62.9%
Space Separator 89
 
18.4%
Decimal Number 78
 
16.1%
Other Punctuation 5
 
1.0%
Dash Punctuation 5
 
1.0%
Close Punctuation 1
 
0.2%
Math Symbol 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
거 42
13.8%
창 38
12.5%
남 23
 
7.5%
상 23
 
7.5%
군 21
 
6.9%
경 21
 
6.9%
도 21
 
6.9%
읍 15
 
4.9%
로 13
 
4.3%
길 13
 
4.3%
Other values (40) 75
24.6%
Decimal Number
ValueCountFrequency (%)
1 22
28.2%
2 12
15.4%
3 9
11.5%
0 6
 
7.7%
6 6
 
7.7%
4 6
 
7.7%
9 6
 
7.7%
5 5
 
6.4%
8 4
 
5.1%
7 2
 
2.6%
Space Separator
ValueCountFrequency (%)
89
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 305
62.9%
Common 180
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
거 42
13.8%
창 38
12.5%
남 23
 
7.5%
상 23
 
7.5%
군 21
 
6.9%
경 21
 
6.9%
도 21
 
6.9%
읍 15
 
4.9%
로 13
 
4.3%
길 13
 
4.3%
Other values (40) 75
24.6%
Common
ValueCountFrequency (%)
89
49.4%
1 22
 
12.2%
2 12
 
6.7%
3 9
 
5.0%
0 6
 
3.3%
6 6
 
3.3%
4 6
 
3.3%
9 6
 
3.3%
, 5
 
2.8%
- 5
 
2.8%
Other values (6) 14
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 305
62.9%
ASCII 180
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
49.4%
1 22
 
12.2%
2 12
 
6.7%
3 9
 
5.0%
0 6
 
3.3%
6 6
 
3.3%
4 6
 
3.3%
9 6
 
3.3%
, 5
 
2.8%
- 5
 
2.8%
Other values (6) 14
 
7.8%
Hangul
ValueCountFrequency (%)
거 42
13.8%
창 38
12.5%
남 23
 
7.5%
상 23
 
7.5%
군 21
 
6.9%
경 21
 
6.9%
도 21
 
6.9%
읍 15
 
4.9%
로 13
 
4.3%
길 13
 
4.3%
Other values (40) 75
24.6%

소재지지번주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:34:19.670928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length23.52381
Min length20

Characters and Unicode

Total characters494
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row경상남도 거창군 거창읍 김천리 17-13
2nd row경상남도 거창군 가조면 마상리 176-6
3rd row경상남도 거창군 거창읍 김천리 83-1
4th row경상남도 거창군 위천면 장기리 462-5
5th row경상남도 거창군 거창읍 대동리 702
ValueCountFrequency (%)
경상남도 21
19.4%
거창군 21
19.4%
거창읍 15
13.9%
대동리 6
 
5.6%
중앙리 4
 
3.7%
가조면 4
 
3.7%
김천리 3
 
2.8%
상림리 2
 
1.9%
일부리 2
 
1.9%
88-18 1
 
0.9%
Other values (29) 29
26.9%
2023-12-13T08:34:19.939904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
21.7%
창 36
 
7.3%
거 36
 
7.3%
상 25
 
5.1%
리 23
 
4.7%
군 21
 
4.3%
경 21
 
4.3%
도 21
 
4.3%
남 21
 
4.3%
1 18
 
3.6%
Other values (46) 165
33.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
59.1%
Space Separator 107
 
21.7%
Decimal Number 81
 
16.4%
Dash Punctuation 13
 
2.6%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 36
12.3%
거 36
12.3%
상 25
 
8.6%
리 23
 
7.9%
군 21
 
7.2%
경 21
 
7.2%
도 21
 
7.2%
남 21
 
7.2%
읍 15
 
5.1%
동 6
 
2.1%
Other values (33) 67
22.9%
Decimal Number
ValueCountFrequency (%)
1 18
22.2%
3 13
16.0%
5 11
13.6%
4 8
9.9%
6 7
 
8.6%
7 7
 
8.6%
2 6
 
7.4%
8 6
 
7.4%
0 3
 
3.7%
9 2
 
2.5%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
59.1%
Common 202
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 36
12.3%
거 36
12.3%
상 25
 
8.6%
리 23
 
7.9%
군 21
 
7.2%
경 21
 
7.2%
도 21
 
7.2%
남 21
 
7.2%
읍 15
 
5.1%
동 6
 
2.1%
Other values (33) 67
22.9%
Common
ValueCountFrequency (%)
107
53.0%
1 18
 
8.9%
- 13
 
6.4%
3 13
 
6.4%
5 11
 
5.4%
4 8
 
4.0%
6 7
 
3.5%
7 7
 
3.5%
2 6
 
3.0%
8 6
 
3.0%
Other values (3) 6
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
59.1%
ASCII 202
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
53.0%
1 18
 
8.9%
- 13
 
6.4%
3 13
 
6.4%
5 11
 
5.4%
4 8
 
4.0%
6 7
 
3.5%
7 7
 
3.5%
2 6
 
3.0%
8 6
 
3.0%
Other values (3) 6
 
3.0%
Hangul
ValueCountFrequency (%)
창 36
12.3%
거 36
12.3%
상 25
 
8.6%
리 23
 
7.9%
군 21
 
7.2%
경 21
 
7.2%
도 21
 
7.2%
남 21
 
7.2%
읍 15
 
5.1%
동 6
 
2.1%
Other values (33) 67
22.9%

면적(제곱미터)
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean708.62714
Minimum55
Maximum2250.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:34:20.046407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile123.76
Q1322
median526.09
Q3858.39
95-th percentile1838.7
Maximum2250.51
Range2195.51
Interquartile range (IQR)536.39

Descriptive statistics

Standard deviation571.09338
Coefficient of variation (CV)0.8059152
Kurtosis1.6644932
Mean708.62714
Median Absolute Deviation (MAD)253.27
Skewness1.3760724
Sum14881.17
Variance326147.65
MonotonicityNot monotonic
2023-12-13T08:34:20.134666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
312.8 1
 
4.8%
169.15 1
 
4.8%
1458.02 1
 
4.8%
2250.51 1
 
4.8%
1009.32 1
 
4.8%
858.39 1
 
4.8%
123.76 1
 
4.8%
402.21 1
 
4.8%
765.6 1
 
4.8%
1102.57 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
55.0 1
4.8%
123.76 1
4.8%
169.15 1
4.8%
288.48 1
4.8%
312.8 1
4.8%
322.0 1
4.8%
329.55 1
4.8%
334.15 1
4.8%
402.21 1
4.8%
469.6 1
4.8%
ValueCountFrequency (%)
2250.51 1
4.8%
1838.7 1
4.8%
1458.02 1
4.8%
1102.57 1
4.8%
1009.32 1
4.8%
858.39 1
4.8%
835.04 1
4.8%
779.36 1
4.8%
765.6 1
4.8%
650.87 1
4.8%

전화번호
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:34:20.341303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters252
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

Unique21 ?
Unique (%)100.0%

Sample

1st row055-944-3197
2nd row055-942-0065
3rd row055-944-2516
4th row055-943-0190
5th row055-944-8007
ValueCountFrequency (%)
055-944-3197 1
 
4.8%
055-941-0721 1
 
4.8%
055-944-0112 1
 
4.8%
055-945-5956 1
 
4.8%
055-944-7080 1
 
4.8%
055-944-6513 1
 
4.8%
055-943-9199 1
 
4.8%
055-945-3166 1
 
4.8%
055-944-5252 1
 
4.8%
055-944-1669 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T08:34:20.611904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 52
20.6%
- 42
16.7%
0 34
13.5%
4 32
12.7%
9 28
11.1%
1 18
 
7.1%
2 11
 
4.4%
6 11
 
4.4%
3 9
 
3.6%
8 8
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
83.3%
Dash Punctuation 42
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 52
24.8%
0 34
16.2%
4 32
15.2%
9 28
13.3%
1 18
 
8.6%
2 11
 
5.2%
6 11
 
5.2%
3 9
 
4.3%
8 8
 
3.8%
7 7
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 52
20.6%
- 42
16.7%
0 34
13.5%
4 32
12.7%
9 28
11.1%
1 18
 
7.1%
2 11
 
4.4%
6 11
 
4.4%
3 9
 
3.6%
8 8
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 52
20.6%
- 42
16.7%
0 34
13.5%
4 32
12.7%
9 28
11.1%
1 18
 
7.1%
2 11
 
4.4%
6 11
 
4.4%
3 9
 
3.6%
8 8
 
3.2%

위도
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.696909
Minimum35.680812
Maximum35.750563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:34:20.716390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.680812
5-th percentile35.683388
Q135.687117
median35.691289
Q335.698758
95-th percentile35.737029
Maximum35.750563
Range0.069751
Interquartile range (IQR)0.011641

Descriptive statistics

Standard deviation0.017445034
Coefficient of variation (CV)0.00048869872
Kurtosis4.6007084
Mean35.696909
Median Absolute Deviation (MAD)0.004938
Skewness2.1715102
Sum749.63508
Variance0.0003043292
MonotonicityNot monotonic
2023-12-13T08:34:20.815445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
35.683657 1
 
4.8%
35.708934 1
 
4.8%
35.694543 1
 
4.8%
35.698951 1
 
4.8%
35.693517 1
 
4.8%
35.692996 1
 
4.8%
35.689151 1
 
4.8%
35.737029 1
 
4.8%
35.685423 1
 
4.8%
35.680812 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
35.680812 1
4.8%
35.683388 1
4.8%
35.683657 1
4.8%
35.685423 1
4.8%
35.686351 1
4.8%
35.687117 1
4.8%
35.687876 1
4.8%
35.689151 1
4.8%
35.689912 1
4.8%
35.690791 1
4.8%
ValueCountFrequency (%)
35.750563 1
4.8%
35.737029 1
4.8%
35.710525 1
4.8%
35.708934 1
4.8%
35.698951 1
4.8%
35.698758 1
4.8%
35.694543 1
4.8%
35.693517 1
4.8%
35.693498 1
4.8%
35.692996 1
4.8%

경도
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.92716
Minimum127.82979
Maximum128.02548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:34:20.950673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.82979
5-th percentile127.85328
Q1127.91155
median127.91384
Q3127.92304
95-th percentile128.0231
Maximum128.02548
Range0.195689
Interquartile range (IQR)0.01149

Descriptive statistics

Standard deviation0.051589838
Coefficient of variation (CV)0.00040327511
Kurtosis0.59341109
Mean127.92716
Median Absolute Deviation (MAD)0.006195
Skewness0.76841986
Sum2686.4703
Variance0.0026615113
MonotonicityNot monotonic
2023-12-13T08:34:21.105177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
127.913697 1
 
4.8%
128.016181 1
 
4.8%
127.911552 1
 
4.8%
128.025481 1
 
4.8%
127.923632 1
 
4.8%
127.907913 1
 
4.8%
127.911843 1
 
4.8%
127.853277 1
 
4.8%
127.901134 1
 
4.8%
127.912603 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
127.829792 1
4.8%
127.853277 1
4.8%
127.901134 1
4.8%
127.907643 1
4.8%
127.907913 1
4.8%
127.911552 1
4.8%
127.911843 1
4.8%
127.912603 1
4.8%
127.912688 1
4.8%
127.913697 1
4.8%
ValueCountFrequency (%)
128.025481 1
4.8%
128.023103 1
4.8%
128.018114 1
4.8%
128.016181 1
4.8%
127.923632 1
4.8%
127.923042 1
4.8%
127.9203501 1
4.8%
127.914998 1
4.8%
127.914732 1
4.8%
127.914654 1
4.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2023-03-27 00:00:00
Maximum2023-03-27 00:00:00
2023-12-13T08:34:21.206271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:21.281625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:34:17.996238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:17.535135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:17.751415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:18.072514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:17.596139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:17.829032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:18.178060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:17.671380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:34:17.913759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:34:21.343753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명소재지도로명주소소재지지번주소면적(제곱미터)전화번호위도경도
업소명1.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.000
면적(제곱미터)1.0001.0001.0001.0001.0000.4180.543
전화번호1.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.4181.0001.0000.895
경도1.0001.0001.0000.5431.0000.8951.000
2023-12-13T08:34:21.444088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)위도경도
면적(제곱미터)1.0000.0580.227
위도0.0581.0000.204
경도0.2270.2041.000

Missing values

2023-12-13T08:34:18.300936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:34:18.417375image/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

업소명소재지도로명주소소재지지번주소면적(제곱미터)전화번호위도경도데이터기준일자
0옥유탕경상남도 거창군 거창읍 강남로 150경상남도 거창군 거창읍 김천리 17-13312.8055-944-319735.683657127.9136972023-03-27
1동아목욕탕경상남도 거창군 가조면 지산로 1482경상남도 거창군 가조면 마상리 176-6169.15055-942-006535.708934128.0161812023-03-27
2대성탕경상남도 거창군 거창읍 강남로 140경상남도 거창군 거창읍 김천리 83-1322.0055-944-251635.683388127.9126882023-03-27
3위천탕경상남도 거창군 위천면 금원산길 9경상남도 거창군 위천면 장기리 462-555.0055-943-019035.750563127.8297922023-03-27
4정하건강나라경상남도 거창군 거창읍 거창대로 93경상남도 거창군 거창읍 대동리 702329.55055-944-800735.689912127.9147322023-03-27
5천일탕경상남도 거창군 거창읍 시장3길 15경상남도 거창군 거창읍 중앙리 164288.48055-942-271435.686351127.9146542023-03-27
6화성탕경상남도 거창군 거창읍 거열로2길 9경상남도 거창군 거창읍 대동리 683-17469.6055-943-663835.691289127.9149982023-03-27
7천지연사우나경상남도 거창군 거창읍 동동6길 67경상남도 거창군 거창읍 대동리 111334.15055-944-111735.690791127.9230422023-03-27
8그린파크목욕탕경상남도 거창군 거창읍 거열로1길 62-6경상남도 거창군 거창읍 대동리 576-5650.87055-943-028235.693498127.9138382023-03-27
9본사우나경상남도 거창군 가조면 가조가야로 1125경상남도 거창군 가조면 수월리 455-4526.09055-941-128035.710525128.0181142023-03-27
업소명소재지도로명주소소재지지번주소면적(제곱미터)전화번호위도경도데이터기준일자
11백두산천지온천경상남도 거창군 가조면 온천길 161경상남도 거창군 가조면 일부리 13011838.7055-941-072135.698758128.0231032023-03-27
12한흥탕경상남도 거창군 거창읍 창동로 119경상남도 거창군 거창읍 대동리 88-18779.36055-944-166935.687876127.920352023-03-27
13금천헬스사우나경상남도 거창군 거창읍 거함대로 3205경상남도 거창군 거창읍 김천리 3511102.57055-944-525235.680812127.9126032023-03-27
14갠지스 사우나경상남도 거창군 거창읍 공수들1길 24-18경상남도 거창군 거창읍 상림리 446-3765.6055-945-316635.685423127.9011342023-03-27
15금원산참숯가마경상남도 거창군 마리면 빼재로 423경상남도 거창군 마리면 율리 735402.21055-943-919935.737029127.8532772023-03-27
16녹천탕경상남도 거창군 거창읍 하동1길 52경상남도 거창군 거창읍 중앙리 324-10123.76055-944-651335.689151127.9118432023-03-27
17하나로휘트니스경상남도 거창군 거창읍 거열로4길 98 (하나로3차아파트 지하1층~지상1층)경상남도 거창군 거창읍 중앙리 455 하나로3차아파트 지하1층~지상1층858.39055-944-708035.692996127.9079132023-03-27
18금강리플레사우나경상남도 거창군 거창읍 소만4길 31-7, 2,3층경상남도 거창군 거창읍 대동리 995-31009.32055-945-595635.693517127.9236322023-03-27
19호텔가조(HOTELGaJo)경상남도 거창군 가조면 온천길 108-11, 패밀리관광호텔경상남도 거창군 가조면 일부리 1275 패밀리관광호텔2250.51055-944-011235.698951128.0254812023-03-27
20나리안길 군민스포츠센터경상남도 거창군 거창읍 거열로1길 100-26, 2,3층경상남도 거창군 거창읍 중앙리 2-231458.02055-943-888735.694543127.9115522023-03-27