Overview

Dataset statistics

Number of variables8
Number of observations1133
Missing cells3410
Missing cells (%)37.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory67.1 B

Variable types

Categorical2
Text3
Numeric3

Dataset

Description경상남도 양신시 관내 공중위생업소(목욕장업, 피부미용업, 일반미용업, 네일미용업, 화장, 세탁업, 숙박업 등) 업소소재지, 전화번호 정보 등
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15006926

Alerts

객실수 is highly overall correlated with 양실수 and 1 other fieldsHigh correlation
한실수 is highly overall correlated with 양실수 and 1 other fieldsHigh correlation
양실수 is highly overall correlated with 객실수 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 객실수 and 3 other fieldsHigh correlation
업태명 is highly overall correlated with 업종명High correlation
소재지전화 has 437 (38.6%) missing valuesMissing
객실수 has 985 (86.9%) missing valuesMissing
한실수 has 1000 (88.3%) missing valuesMissing
양실수 has 988 (87.2%) missing valuesMissing
한실수 has 30 (2.6%) zerosZeros

Reproduction

Analysis started2023-12-10 23:26:24.141292
Analysis finished2023-12-10 23:26:25.812257
Duration1.67 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
미용업
288 
미용업(일반)
268 
숙박업(일반)
148 
세탁업
126 
이용업
87 
Other values (11)
216 

Length

Max length31
Median length28
Mean length5.3186231
Min length3

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
미용업 288
25.4%
미용업(일반) 268
23.7%
숙박업(일반) 148
13.1%
세탁업 126
11.1%
이용업 87
 
7.7%
미용업(피부) 87
 
7.7%
목욕장업 58
 
5.1%
미용업(종합) 30
 
2.6%
미용업(손톱ㆍ발톱) 21
 
1.9%
미용업(일반), 미용업(손톱ㆍ발톱) 8
 
0.7%
Other values (6) 12
 
1.1%

Length

2023-12-11T08:26:25.869312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 288
25.0%
미용업(일반 281
24.4%
숙박업(일반 148
12.9%
세탁업 126
10.9%
미용업(피부 90
 
7.8%
이용업 87
 
7.6%
목욕장업 58
 
5.0%
미용업(손톱ㆍ발톱 33
 
2.9%
미용업(종합 30
 
2.6%
숙박업(생활 6
 
0.5%

업소명
Text

Distinct1082
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-11T08:26:26.077962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21
Mean length5.5525154
Min length1

Characters and Unicode

Total characters6291
Distinct characters537
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1035 ?
Unique (%)91.4%

Sample

1st row내원산장여관
2nd row경남여관
3rd row산장여관
4th row제일여관
5th row천우장여관
ValueCountFrequency (%)
미용실 11
 
0.9%
헤어 8
 
0.6%
모텔 7
 
0.6%
hair 6
 
0.5%
에스테틱 4
 
0.3%
민헤어 3
 
0.2%
예쁜얼굴 3
 
0.2%
여관 3
 
0.2%
이용원 3
 
0.2%
네일 3
 
0.2%
Other values (1140) 1209
96.0%
2023-12-11T08:26:26.439377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 369
 
5.9%
헤 352
 
5.6%
이 177
 
2.8%
미 177
 
2.8%
용 172
 
2.7%
스 134
 
2.1%
127
 
2.0%
실 109
 
1.7%
리 106
 
1.7%
원 106
 
1.7%
Other values (527) 4462
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5797
92.1%
Space Separator 127
 
2.0%
Lowercase Letter 114
 
1.8%
Uppercase Letter 84
 
1.3%
Close Punctuation 49
 
0.8%
Open Punctuation 49
 
0.8%
Other Punctuation 45
 
0.7%
Decimal Number 21
 
0.3%
Math Symbol 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 369
 
6.4%
헤 352
 
6.1%
이 177
 
3.1%
미 177
 
3.1%
용 172
 
3.0%
스 134
 
2.3%
실 109
 
1.9%
리 106
 
1.8%
원 106
 
1.8%
세 98
 
1.7%
Other values (465) 3997
68.9%
Uppercase Letter
ValueCountFrequency (%)
B 9
 
10.7%
H 9
 
10.7%
S 8
 
9.5%
M 6
 
7.1%
N 5
 
6.0%
C 5
 
6.0%
T 4
 
4.8%
W 4
 
4.8%
A 4
 
4.8%
I 4
 
4.8%
Other values (14) 26
31.0%
Lowercase Letter
ValueCountFrequency (%)
e 19
16.7%
i 12
10.5%
a 11
9.6%
t 9
 
7.9%
h 8
 
7.0%
s 7
 
6.1%
r 7
 
6.1%
y 6
 
5.3%
m 6
 
5.3%
o 6
 
5.3%
Other values (9) 23
20.2%
Decimal Number
ValueCountFrequency (%)
2 6
28.6%
4 3
14.3%
1 3
14.3%
7 2
 
9.5%
3 2
 
9.5%
6 2
 
9.5%
5 2
 
9.5%
9 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 13
28.9%
& 13
28.9%
? 9
20.0%
# 5
 
11.1%
' 4
 
8.9%
@ 1
 
2.2%
Space Separator
ValueCountFrequency (%)
127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5794
92.1%
Common 296
 
4.7%
Latin 198
 
3.1%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 369
 
6.4%
헤 352
 
6.1%
이 177
 
3.1%
미 177
 
3.1%
용 172
 
3.0%
스 134
 
2.3%
실 109
 
1.9%
리 106
 
1.8%
원 106
 
1.8%
세 98
 
1.7%
Other values (464) 3994
68.9%
Latin
ValueCountFrequency (%)
e 19
 
9.6%
i 12
 
6.1%
a 11
 
5.6%
B 9
 
4.5%
t 9
 
4.5%
H 9
 
4.5%
h 8
 
4.0%
S 8
 
4.0%
s 7
 
3.5%
r 7
 
3.5%
Other values (33) 99
50.0%
Common
ValueCountFrequency (%)
127
42.9%
) 49
 
16.6%
( 49
 
16.6%
. 13
 
4.4%
& 13
 
4.4%
? 9
 
3.0%
2 6
 
2.0%
# 5
 
1.7%
' 4
 
1.4%
4 3
 
1.0%
Other values (9) 18
 
6.1%
Han
ValueCountFrequency (%)
美 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5794
92.1%
ASCII 494
 
7.9%
CJK 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 369
 
6.4%
헤 352
 
6.1%
이 177
 
3.1%
미 177
 
3.1%
용 172
 
3.0%
스 134
 
2.3%
실 109
 
1.9%
리 106
 
1.8%
원 106
 
1.8%
세 98
 
1.7%
Other values (464) 3994
68.9%
ASCII
ValueCountFrequency (%)
127
25.7%
) 49
 
9.9%
( 49
 
9.9%
e 19
 
3.8%
. 13
 
2.6%
& 13
 
2.6%
i 12
 
2.4%
a 11
 
2.2%
B 9
 
1.8%
t 9
 
1.8%
Other values (52) 183
37.0%
CJK
ValueCountFrequency (%)
美 3
100.0%

업소소재지(도로명)
Text

Distinct1107
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-11T08:26:26.747521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length46
Mean length28.123566
Min length18

Characters and Unicode

Total characters31864
Distinct characters276
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1081 ?
Unique (%)95.4%

Sample

1st row경상남도 양산시 하북면 용연리 산 42번지 15호
2nd row경상남도 양산시 북안남4길 8-1 (북부동)
3rd row경상남도 양산시 하북면 신평강변로 84
4th row경상남도 양산시 북안남3길 7 (북부동)
5th row경상남도 양산시 하북면 신평중앙길 14-1
ValueCountFrequency (%)
경상남도 1133
 
16.6%
양산시 1133
 
16.6%
물금읍 219
 
3.2%
1층 205
 
3.0%
삼호동 124
 
1.8%
중부동 99
 
1.4%
북부동 83
 
1.2%
평산동 77
 
1.1%
하북면 76
 
1.1%
덕계동 70
 
1.0%
Other values (1257) 3612
52.9%
2023-12-11T08:26:27.180839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5940
18.6%
1 1550
 
4.9%
상 1402
 
4.4%
산 1389
 
4.4%
양 1270
 
4.0%
남 1261
 
4.0%
시 1188
 
3.7%
도 1175
 
3.7%
경 1144
 
3.6%
동 1100
 
3.5%
Other values (266) 14445
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18378
57.7%
Space Separator 5940
 
18.6%
Decimal Number 4983
 
15.6%
Close Punctuation 819
 
2.6%
Open Punctuation 819
 
2.6%
Other Punctuation 655
 
2.1%
Dash Punctuation 192
 
0.6%
Uppercase Letter 59
 
0.2%
Math Symbol 15
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1402
 
7.6%
산 1389
 
7.6%
양 1270
 
6.9%
남 1261
 
6.9%
시 1188
 
6.5%
도 1175
 
6.4%
경 1144
 
6.2%
동 1100
 
6.0%
호 645
 
3.5%
길 542
 
2.9%
Other values (235) 7262
39.5%
Uppercase Letter
ValueCountFrequency (%)
A 19
32.2%
B 11
18.6%
T 6
 
10.2%
P 6
 
10.2%
C 4
 
6.8%
L 4
 
6.8%
E 2
 
3.4%
D 2
 
3.4%
F 2
 
3.4%
N 2
 
3.4%
Decimal Number
ValueCountFrequency (%)
1 1550
31.1%
2 708
14.2%
0 525
 
10.5%
3 447
 
9.0%
4 347
 
7.0%
6 331
 
6.6%
5 329
 
6.6%
7 276
 
5.5%
8 266
 
5.3%
9 204
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 639
97.6%
. 8
 
1.2%
@ 6
 
0.9%
/ 2
 
0.3%
Space Separator
ValueCountFrequency (%)
5940
100.0%
Close Punctuation
ValueCountFrequency (%)
) 819
100.0%
Open Punctuation
ValueCountFrequency (%)
( 819
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18378
57.7%
Common 13423
42.1%
Latin 63
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1402
 
7.6%
산 1389
 
7.6%
양 1270
 
6.9%
남 1261
 
6.9%
시 1188
 
6.5%
도 1175
 
6.4%
경 1144
 
6.2%
동 1100
 
6.0%
호 645
 
3.5%
길 542
 
2.9%
Other values (235) 7262
39.5%
Common
ValueCountFrequency (%)
5940
44.3%
1 1550
 
11.5%
) 819
 
6.1%
( 819
 
6.1%
2 708
 
5.3%
, 639
 
4.8%
0 525
 
3.9%
3 447
 
3.3%
4 347
 
2.6%
6 331
 
2.5%
Other values (9) 1298
 
9.7%
Latin
ValueCountFrequency (%)
A 19
30.2%
B 11
17.5%
T 6
 
9.5%
P 6
 
9.5%
C 4
 
6.3%
L 4
 
6.3%
e 4
 
6.3%
E 2
 
3.2%
D 2
 
3.2%
F 2
 
3.2%
Other values (2) 3
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18378
57.7%
ASCII 13486
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5940
44.0%
1 1550
 
11.5%
) 819
 
6.1%
( 819
 
6.1%
2 708
 
5.2%
, 639
 
4.7%
0 525
 
3.9%
3 447
 
3.3%
4 347
 
2.6%
6 331
 
2.5%
Other values (21) 1361
 
10.1%
Hangul
ValueCountFrequency (%)
상 1402
 
7.6%
산 1389
 
7.6%
양 1270
 
6.9%
남 1261
 
6.9%
시 1188
 
6.5%
도 1175
 
6.4%
경 1144
 
6.2%
동 1100
 
6.0%
호 645
 
3.5%
길 542
 
2.9%
Other values (235) 7262
39.5%

소재지전화
Text

MISSING 

Distinct684
Distinct (%)98.3%
Missing437
Missing (%)38.6%
Memory size9.0 KiB
2023-12-11T08:26:27.430445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.008621
Min length8

Characters and Unicode

Total characters8358
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

Unique672 ?
Unique (%)96.6%

Sample

1st row055-375-6618
2nd row055-386-2678
3rd row055-382-6497
4th row055-385-3354
5th row055-385-9333
ValueCountFrequency (%)
055-363-0016 2
 
0.3%
055-388-3882 2
 
0.3%
055-388-4435 2
 
0.3%
055-366-4468 2
 
0.3%
055-382-0035 2
 
0.3%
055-364-5889 2
 
0.3%
055-365-6363 2
 
0.3%
055-365-7115 2
 
0.3%
055-382-7696 2
 
0.3%
055-382-8321 2
 
0.3%
Other values (674) 676
97.1%
2023-12-11T08:26:27.819976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1741
20.8%
- 1390
16.6%
0 1062
12.7%
3 1038
12.4%
8 747
8.9%
6 609
 
7.3%
7 400
 
4.8%
2 376
 
4.5%
1 366
 
4.4%
4 341
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6968
83.4%
Dash Punctuation 1390
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1741
25.0%
0 1062
15.2%
3 1038
14.9%
8 747
10.7%
6 609
 
8.7%
7 400
 
5.7%
2 376
 
5.4%
1 366
 
5.3%
4 341
 
4.9%
9 288
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 1390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1741
20.8%
- 1390
16.6%
0 1062
12.7%
3 1038
12.4%
8 747
8.9%
6 609
 
7.3%
7 400
 
4.8%
2 376
 
4.5%
1 366
 
4.4%
4 341
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1741
20.8%
- 1390
16.6%
0 1062
12.7%
3 1038
12.4%
8 747
8.9%
6 609
 
7.3%
7 400
 
4.8%
2 376
 
4.5%
1 366
 
4.4%
4 341
 
4.1%

업태명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
일반미용업
569 
여관업
138 
일반세탁업
111 
피부미용업
94 
일반이용업
87 
Other values (12)
134 

Length

Max length14
Median length5
Mean length4.7925861
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 569
50.2%
여관업 138
 
12.2%
일반세탁업 111
 
9.8%
피부미용업 94
 
8.3%
일반이용업 87
 
7.7%
공동탕업 46
 
4.1%
네일아트업 31
 
2.7%
기타 13
 
1.1%
공동탕업+찜질시설서비스영업 10
 
0.9%
숙박업 기타 7
 
0.6%
Other values (7) 27
 
2.4%

Length

2023-12-11T08:26:28.057068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 569
49.6%
여관업 138
 
12.0%
일반세탁업 111
 
9.7%
피부미용업 94
 
8.2%
일반이용업 87
 
7.6%
공동탕업 46
 
4.0%
네일아트업 31
 
2.7%
기타 28
 
2.4%
공동탕업+찜질시설서비스영업 10
 
0.9%
숙박업 7
 
0.6%
Other values (7) 27
 
2.4%

객실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)26.4%
Missing985
Missing (%)86.9%
Infinite0
Infinite (%)0.0%
Mean26.006757
Minimum8
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:26:28.258626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q118
median25
Q332.25
95-th percentile43.95
Maximum61
Range53
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation10.276118
Coefficient of variation (CV)0.39513261
Kurtosis0.046459995
Mean26.006757
Median Absolute Deviation (MAD)7
Skewness0.526208
Sum3849
Variance105.59859
MonotonicityNot monotonic
2023-12-11T08:26:28.404940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
19 9
 
0.8%
32 8
 
0.7%
35 8
 
0.7%
18 8
 
0.7%
29 7
 
0.6%
24 7
 
0.6%
15 7
 
0.6%
20 6
 
0.5%
17 6
 
0.5%
36 5
 
0.4%
Other values (29) 77
 
6.8%
(Missing) 985
86.9%
ValueCountFrequency (%)
8 1
 
0.1%
10 5
0.4%
11 4
0.4%
12 5
0.4%
13 1
 
0.1%
14 1
 
0.1%
15 7
0.6%
16 2
 
0.2%
17 6
0.5%
18 8
0.7%
ValueCountFrequency (%)
61 1
 
0.1%
52 2
0.2%
48 1
 
0.1%
46 1
 
0.1%
45 3
0.3%
42 1
 
0.1%
41 3
0.3%
40 3
0.3%
39 1
 
0.1%
38 3
0.3%

한실수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)19.5%
Missing1000
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean7.3383459
Minimum0
Maximum42
Zeros30
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:26:28.516781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q311
95-th percentile20.4
Maximum42
Range42
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4517511
Coefficient of variation (CV)1.0154538
Kurtosis4.7375956
Mean7.3383459
Median Absolute Deviation (MAD)5
Skewness1.7759268
Sum976
Variance55.528594
MonotonicityNot monotonic
2023-12-11T08:26:28.657608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 30
 
2.6%
5 10
 
0.9%
9 8
 
0.7%
2 8
 
0.7%
10 8
 
0.7%
7 8
 
0.7%
4 7
 
0.6%
12 7
 
0.6%
6 5
 
0.4%
11 5
 
0.4%
Other values (16) 37
 
3.3%
(Missing) 1000
88.3%
ValueCountFrequency (%)
0 30
2.6%
1 4
 
0.4%
2 8
 
0.7%
3 5
 
0.4%
4 7
 
0.6%
5 10
 
0.9%
6 5
 
0.4%
7 8
 
0.7%
8 5
 
0.4%
9 8
 
0.7%
ValueCountFrequency (%)
42 1
 
0.1%
36 1
 
0.1%
30 2
0.2%
26 1
 
0.1%
22 1
 
0.1%
21 1
 
0.1%
20 1
 
0.1%
18 1
 
0.1%
17 1
 
0.1%
16 3
0.3%

양실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)30.3%
Missing988
Missing (%)87.2%
Infinite0
Infinite (%)0.0%
Mean19.813793
Minimum0
Maximum52
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:26:28.804867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median17
Q330
95-th percentile38
Maximum52
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.648191
Coefficient of variation (CV)0.58788297
Kurtosis-0.70132171
Mean19.813793
Median Absolute Deviation (MAD)8
Skewness0.45241892
Sum2873
Variance135.68036
MonotonicityNot monotonic
2023-12-11T08:26:28.954903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
9 8
 
0.7%
14 8
 
0.7%
12 8
 
0.7%
19 7
 
0.6%
15 7
 
0.6%
10 6
 
0.5%
6 6
 
0.5%
32 5
 
0.4%
5 5
 
0.4%
38 4
 
0.4%
Other values (34) 81
 
7.1%
(Missing) 988
87.2%
ValueCountFrequency (%)
0 2
 
0.2%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 2
 
0.2%
5 5
0.4%
6 6
0.5%
7 2
 
0.2%
8 4
0.4%
9 8
0.7%
ValueCountFrequency (%)
52 1
 
0.1%
46 2
0.2%
45 1
 
0.1%
41 1
 
0.1%
40 2
0.2%
38 4
0.4%
37 3
0.3%
36 3
0.3%
35 4
0.4%
34 2
0.2%

Interactions

2023-12-11T08:26:25.260503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:24.743375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:24.986323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:25.351614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:24.825840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:25.071038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:25.429815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:24.900169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:25.161507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:26:29.047161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명객실수한실수양실수
업종명1.0000.937NaNNaNNaN
업태명0.9371.0000.3070.0000.438
객실수NaN0.3071.0000.8770.913
한실수NaN0.0000.8771.0000.612
양실수NaN0.4380.9130.6121.000
2023-12-11T08:26:29.159314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.663
업태명0.6631.000
2023-12-11T08:26:29.239886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수한실수양실수업종명업태명
객실수1.000-0.0620.8121.0000.187
한실수-0.0621.000-0.5431.0000.000
양실수0.812-0.5431.0001.0000.277
업종명1.0001.0001.0001.0000.663
업태명0.1870.0000.2770.6631.000

Missing values

2023-12-11T08:26:25.552979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:26:25.671134image/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.
2023-12-11T08:26:25.758996image/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

업종명업소명업소소재지(도로명)소재지전화업태명객실수한실수양실수
0숙박업(일반)내원산장여관경상남도 양산시 하북면 용연리 산 42번지 15호055-375-6618여관업1156
1숙박업(일반)경남여관경상남도 양산시 북안남4길 8-1 (북부동)055-386-2678여관업1010<NA>
2숙박업(일반)산장여관경상남도 양산시 하북면 신평강변로 84055-382-6497여관업1789
3숙박업(일반)제일여관경상남도 양산시 북안남3길 7 (북부동)055-385-3354여관업1266
4숙박업(일반)천우장여관경상남도 양산시 하북면 신평중앙길 14-1<NA>여관업1055
5숙박업(일반)삼복여관경상남도 양산시 물금읍 물금역2길 1<NA>여관업1073
6숙박업(일반)송원장여관경상남도 양산시 장터3길 16 (중부동)055-385-9333여관업11101
7숙박업(일반)백제장모텔경상남도 양산시 북안남3길 9 (북부동)<NA>여관업281414
8숙박업(일반)티파니경상남도 양산시 장터2길 14 (중부동)055-382-8321여관업231112
9숙박업(일반)제일장여관경상남도 양산시 북안남4길 15 (북부동)055-382-4849여관업1284
업종명업소명업소소재지(도로명)소재지전화업태명객실수한실수양실수
1123미용업(일반), 미용업(손톱ㆍ발톱)미뇽헤어경상남도 양산시 북정중앙로 53, 2층 202호 (북정동)<NA>일반미용업<NA><NA><NA>
1124미용업(일반), 미용업(손톱ㆍ발톱)네일아이경상남도 양산시 양주로 32, 112동 1층 105호 (남부동, 양산신도시동원로얄듀크)055-364-3421일반미용업<NA><NA><NA>
1125미용업(일반), 미용업(손톱ㆍ발톱)비비네일경상남도 양산시 동면 남양산1길 46, 2층 (형지리테일)<NA>일반미용업<NA><NA><NA>
1126미용업(일반), 미용업(손톱ㆍ발톱)미인만들기경상남도 양산시 황산로 919, 2층 (교동)<NA>일반미용업<NA><NA><NA>
1127미용업(일반), 미용업(손톱ㆍ발톱)린네일경상남도 양산시 신기서길 18, 1층 107호 (신기동, 한마음상가)<NA>일반미용업<NA><NA><NA>
1128미용업(일반), 미용업(화장ㆍ분장)헤어마니아경상남도 양산시 동면 금오2길 55, 1층 102호055-363-3101일반미용업<NA><NA><NA>
1129미용업(일반), 미용업(피부), 미용업(손톱ㆍ발톱)#네일 꿈꾸다경상남도 양산시 소주회야로 77 (소주동, 목원상가 1층)<NA>네일아트업<NA><NA><NA>
1130미용업(일반), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)다나헤어경상남도 양산시 물금읍 새실로 159, C동 110호 (나래메트로시티)<NA>일반미용업<NA><NA><NA>
1131미용업(일반), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)나연미용실(리즈본헤어점)경상남도 양산시 동면 금오8길 13-13, 1층<NA>일반미용업<NA><NA><NA>
1132미용업(피부), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)쁘띠네일경상남도 양산시 동면 금오16길 24, 1층<NA>네일아트업<NA><NA><NA>