Overview

Dataset statistics

Number of variables8
Number of observations1460
Missing cells4675
Missing cells (%)40.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory95.7 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

Dataset has 1 (0.1%) duplicate rowsDuplicates
객실수 is highly overall correlated with 양실수High correlation
한실수 is highly overall correlated with 업종명High correlation
양실수 is highly overall correlated with 객실수High correlation
업종명 is highly overall correlated with 한실수 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 업종명High correlation
소재지전화 has 670 (45.9%) missing valuesMissing
객실수 has 1315 (90.1%) missing valuesMissing
한실수 has 1370 (93.8%) missing valuesMissing
양실수 has 1320 (90.4%) missing valuesMissing

Reproduction

Analysis started2024-04-17 18:38:15.184406
Analysis finished2024-04-17 18:38:16.779559
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
일반미용업
478 
미용업
235 
피부미용업
149 
숙박업(일반)
138 
네일미용업
110 
Other values (15)
350 

Length

Max length23
Median length5
Mean length5.5890411
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 478
32.7%
미용업 235
16.1%
피부미용업 149
 
10.2%
숙박업(일반) 138
 
9.5%
네일미용업 110
 
7.5%
이용업 91
 
6.2%
종합미용업 66
 
4.5%
목욕장업 60
 
4.1%
화장ㆍ분장 미용업 30
 
2.1%
네일미용업, 화장ㆍ분장 미용업 17
 
1.2%
Other values (10) 86
 
5.9%

Length

2024-04-18T03:38:16.838772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 528
31.4%
미용업 328
19.5%
피부미용업 187
 
11.1%
네일미용업 185
 
11.0%
숙박업(일반 138
 
8.2%
화장ㆍ분장 93
 
5.5%
이용업 91
 
5.4%
종합미용업 66
 
3.9%
목욕장업 60
 
3.6%
숙박업(생활 7
 
0.4%

업소명
Text

Distinct1415
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2024-04-18T03:38:17.054704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length5.8047945
Min length1

Characters and Unicode

Total characters8475
Distinct characters603
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1372 ?
Unique (%)94.0%

Sample

1st row물조은탕
2nd row천성탕
3rd row한림탕
4th row힐링온천스포렉스
5th row경보목욕탕
ValueCountFrequency (%)
헤어 17
 
1.0%
hair 11
 
0.6%
미용실 11
 
0.6%
에스테틱 10
 
0.6%
네일 10
 
0.6%
양산점 7
 
0.4%
nail 7
 
0.4%
모텔 6
 
0.4%
더 6
 
0.4%
헤어살롱 4
 
0.2%
Other values (1514) 1615
94.8%
2024-04-18T03:38:17.400537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 539
 
6.4%
헤 511
 
6.0%
244
 
2.9%
이 236
 
2.8%
스 193
 
2.3%
미 176
 
2.1%
일 155
 
1.8%
네 150
 
1.8%
용 143
 
1.7%
리 131
 
1.5%
Other values (593) 5997
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7316
86.3%
Lowercase Letter 321
 
3.8%
Uppercase Letter 263
 
3.1%
Space Separator 244
 
2.9%
Open Punctuation 111
 
1.3%
Close Punctuation 111
 
1.3%
Other Punctuation 54
 
0.6%
Decimal Number 49
 
0.6%
Math Symbol 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 539
 
7.4%
헤 511
 
7.0%
이 236
 
3.2%
스 193
 
2.6%
미 176
 
2.4%
일 155
 
2.1%
네 150
 
2.1%
용 143
 
2.0%
리 131
 
1.8%
라 108
 
1.5%
Other values (523) 4974
68.0%
Lowercase Letter
ValueCountFrequency (%)
a 35
10.9%
i 35
10.9%
e 33
10.3%
o 23
 
7.2%
n 23
 
7.2%
l 23
 
7.2%
r 22
 
6.9%
t 17
 
5.3%
h 16
 
5.0%
y 15
 
4.7%
Other values (13) 79
24.6%
Uppercase Letter
ValueCountFrequency (%)
A 30
 
11.4%
N 22
 
8.4%
L 19
 
7.2%
S 19
 
7.2%
I 19
 
7.2%
H 17
 
6.5%
J 16
 
6.1%
E 16
 
6.1%
B 13
 
4.9%
O 11
 
4.2%
Other values (13) 81
30.8%
Other Punctuation
ValueCountFrequency (%)
. 18
33.3%
# 9
16.7%
& 9
16.7%
, 8
14.8%
' 5
 
9.3%
: 2
 
3.7%
· 1
 
1.9%
@ 1
 
1.9%
＃ 1
 
1.9%
Decimal Number
ValueCountFrequency (%)
2 11
22.4%
1 9
18.4%
3 8
16.3%
0 6
12.2%
6 5
10.2%
9 4
 
8.2%
5 2
 
4.1%
7 2
 
4.1%
8 2
 
4.1%
Space Separator
ValueCountFrequency (%)
244
100.0%
Open Punctuation
ValueCountFrequency (%)
( 111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 111
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7313
86.3%
Latin 584
 
6.9%
Common 575
 
6.8%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 539
 
7.4%
헤 511
 
7.0%
이 236
 
3.2%
스 193
 
2.6%
미 176
 
2.4%
일 155
 
2.1%
네 150
 
2.1%
용 143
 
2.0%
리 131
 
1.8%
라 108
 
1.5%
Other values (521) 4971
68.0%
Latin
ValueCountFrequency (%)
a 35
 
6.0%
i 35
 
6.0%
e 33
 
5.7%
A 30
 
5.1%
o 23
 
3.9%
n 23
 
3.9%
l 23
 
3.9%
r 22
 
3.8%
N 22
 
3.8%
L 19
 
3.3%
Other values (36) 319
54.6%
Common
ValueCountFrequency (%)
244
42.4%
( 111
19.3%
) 111
19.3%
. 18
 
3.1%
2 11
 
1.9%
# 9
 
1.6%
1 9
 
1.6%
& 9
 
1.6%
3 8
 
1.4%
, 8
 
1.4%
Other values (14) 37
 
6.4%
Han
ValueCountFrequency (%)
美 2
66.7%
花 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7313
86.3%
ASCII 1157
 
13.7%
CJK 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 539
 
7.4%
헤 511
 
7.0%
이 236
 
3.2%
스 193
 
2.6%
미 176
 
2.4%
일 155
 
2.1%
네 150
 
2.1%
용 143
 
2.0%
리 131
 
1.8%
라 108
 
1.5%
Other values (521) 4971
68.0%
ASCII
ValueCountFrequency (%)
244
21.1%
( 111
 
9.6%
) 111
 
9.6%
a 35
 
3.0%
i 35
 
3.0%
e 33
 
2.9%
A 30
 
2.6%
o 23
 
2.0%
n 23
 
2.0%
l 23
 
2.0%
Other values (58) 489
42.3%
CJK
ValueCountFrequency (%)
美 2
66.7%
花 1
33.3%
None
ValueCountFrequency (%)
· 1
50.0%
＃ 1
50.0%

영업소 주소(도로명)
Text

Distinct1434
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2024-04-18T03:38:17.632171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length50
Mean length30.293151
Min length18

Characters and Unicode

Total characters44228
Distinct characters308
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

Unique1408 ?
Unique (%)96.4%

Sample

1st row경상남도 양산시 삼호동부7길 26 (삼호동)
2nd row경상남도 양산시 소주회야로 45-203 (소주동)
3rd row경상남도 양산시 연호로 23 (삼호동)
4th row경상남도 양산시 대운로 172 (명동)
5th row경상남도 양산시 내연1길 7 (평산동)
ValueCountFrequency (%)
경상남도 1460
 
15.3%
양산시 1460
 
15.3%
물금읍 458
 
4.8%
1층 434
 
4.5%
삼호동 146
 
1.5%
중부동 134
 
1.4%
2층 118
 
1.2%
동면 105
 
1.1%
상가동 95
 
1.0%
평산동 93
 
1.0%
Other values (1395) 5042
52.8%
2024-04-18T03:38:17.986161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8113
 
18.3%
1 2395
 
5.4%
산 1862
 
4.2%
상 1751
 
4.0%
양 1729
 
3.9%
남 1583
 
3.6%
시 1559
 
3.5%
도 1526
 
3.5%
경 1469
 
3.3%
동 1352
 
3.1%
Other values (298) 20889
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25102
56.8%
Space Separator 8113
 
18.3%
Decimal Number 7367
 
16.7%
Other Punctuation 1177
 
2.7%
Open Punctuation 1012
 
2.3%
Close Punctuation 1012
 
2.3%
Dash Punctuation 333
 
0.8%
Uppercase Letter 83
 
0.2%
Math Symbol 20
 
< 0.1%
Lowercase Letter 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1862
 
7.4%
상 1751
 
7.0%
양 1729
 
6.9%
남 1583
 
6.3%
시 1559
 
6.2%
도 1526
 
6.1%
경 1469
 
5.9%
동 1352
 
5.4%
호 967
 
3.9%
로 778
 
3.1%
Other values (264) 10526
41.9%
Uppercase Letter
ValueCountFrequency (%)
B 24
28.9%
A 17
20.5%
N 10
12.0%
E 6
 
7.2%
C 6
 
7.2%
I 4
 
4.8%
T 3
 
3.6%
P 3
 
3.6%
D 3
 
3.6%
L 2
 
2.4%
Other values (3) 5
 
6.0%
Decimal Number
ValueCountFrequency (%)
1 2395
32.5%
2 1077
14.6%
0 818
 
11.1%
3 686
 
9.3%
4 500
 
6.8%
5 475
 
6.4%
6 425
 
5.8%
7 401
 
5.4%
8 320
 
4.3%
9 270
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 1167
99.2%
. 7
 
0.6%
/ 2
 
0.2%
@ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 8
88.9%
c 1
 
11.1%
Space Separator
ValueCountFrequency (%)
8113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1012
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1012
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 333
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25102
56.8%
Common 19034
43.0%
Latin 92
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1862
 
7.4%
상 1751
 
7.0%
양 1729
 
6.9%
남 1583
 
6.3%
시 1559
 
6.2%
도 1526
 
6.1%
경 1469
 
5.9%
동 1352
 
5.4%
호 967
 
3.9%
로 778
 
3.1%
Other values (264) 10526
41.9%
Common
ValueCountFrequency (%)
8113
42.6%
1 2395
 
12.6%
, 1167
 
6.1%
2 1077
 
5.7%
( 1012
 
5.3%
) 1012
 
5.3%
0 818
 
4.3%
3 686
 
3.6%
4 500
 
2.6%
5 475
 
2.5%
Other values (9) 1779
 
9.3%
Latin
ValueCountFrequency (%)
B 24
26.1%
A 17
18.5%
N 10
10.9%
e 8
 
8.7%
E 6
 
6.5%
C 6
 
6.5%
I 4
 
4.3%
T 3
 
3.3%
P 3
 
3.3%
D 3
 
3.3%
Other values (5) 8
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25102
56.8%
ASCII 19126
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8113
42.4%
1 2395
 
12.5%
, 1167
 
6.1%
2 1077
 
5.6%
( 1012
 
5.3%
) 1012
 
5.3%
0 818
 
4.3%
3 686
 
3.6%
4 500
 
2.6%
5 475
 
2.5%
Other values (24) 1871
 
9.8%
Hangul
ValueCountFrequency (%)
산 1862
 
7.4%
상 1751
 
7.0%
양 1729
 
6.9%
남 1583
 
6.3%
시 1559
 
6.2%
도 1526
 
6.1%
경 1469
 
5.9%
동 1352
 
5.4%
호 967
 
3.9%
로 778
 
3.1%
Other values (264) 10526
41.9%

소재지전화
Text

MISSING 

Distinct778
Distinct (%)98.5%
Missing670
Missing (%)45.9%
Memory size11.5 KiB
2024-04-18T03:38:18.201087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.007595
Min length9

Characters and Unicode

Total characters9486
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

Unique766 ?
Unique (%)97.0%

Sample

1st row055-362-1126
2nd row055-363-2430
3rd row055-365-1039
4th row055-365-5533
5th row055-366-3769
ValueCountFrequency (%)
055-365-7115 2
 
0.3%
055-382-7696 2
 
0.3%
055-388-3882 2
 
0.3%
055-382-8321 2
 
0.3%
055-362-8891 2
 
0.3%
055-383-0671 2
 
0.3%
055-384-0061 2
 
0.3%
055-365-7615 2
 
0.3%
055-382-0035 2
 
0.3%
055-384-0925 2
 
0.3%
Other values (768) 770
97.5%
2024-04-18T03:38:18.552708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1963
20.7%
- 1580
16.7%
0 1234
13.0%
3 1198
12.6%
8 803
8.5%
6 685
 
7.2%
7 508
 
5.4%
2 452
 
4.8%
1 404
 
4.3%
4 349
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7906
83.3%
Dash Punctuation 1580
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1963
24.8%
0 1234
15.6%
3 1198
15.2%
8 803
10.2%
6 685
 
8.7%
7 508
 
6.4%
2 452
 
5.7%
1 404
 
5.1%
4 349
 
4.4%
9 310
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 1580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9486
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1963
20.7%
- 1580
16.7%
0 1234
13.0%
3 1198
12.6%
8 803
8.5%
6 685
 
7.2%
7 508
 
5.4%
2 452
 
4.8%
1 404
 
4.3%
4 349
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1963
20.7%
- 1580
16.7%
0 1234
13.0%
3 1198
12.6%
8 803
8.5%
6 685
 
7.2%
7 508
 
5.4%
2 452
 
4.8%
1 404
 
4.3%
4 349
 
3.7%

업태명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
일반미용업
787 
피부미용업
168 
네일아트업
151 
여관업
119 
일반이용업
90 
Other values (11)
145 

Length

Max length14
Median length5
Mean length4.839726
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 787
53.9%
피부미용업 168
 
11.5%
네일아트업 151
 
10.3%
여관업 119
 
8.2%
일반이용업 90
 
6.2%
공동탕업 48
 
3.3%
메이크업업 38
 
2.6%
기타 19
 
1.3%
공동탕업+찜질시설서비스영업 10
 
0.7%
일반호텔 10
 
0.7%
Other values (6) 20
 
1.4%

Length

2024-04-18T03:38:18.666765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 787
53.6%
피부미용업 168
 
11.4%
네일아트업 151
 
10.3%
여관업 119
 
8.1%
일반이용업 90
 
6.1%
공동탕업 48
 
3.3%
메이크업업 38
 
2.6%
기타 28
 
1.9%
공동탕업+찜질시설서비스영업 10
 
0.7%
일반호텔 10
 
0.7%
Other values (6) 20
 
1.4%

객실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)27.6%
Missing1315
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean26.737931
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-18T03:38:18.761018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q117
median25
Q335
95-th percentile45
Maximum255
Range254
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.220866
Coefficient of variation (CV)0.86846159
Kurtosis65.010733
Mean26.737931
Median Absolute Deviation (MAD)8
Skewness6.7089169
Sum3877
Variance539.20862
MonotonicityNot monotonic
2024-04-18T03:38:18.859806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
19 10
 
0.7%
18 8
 
0.5%
32 8
 
0.5%
24 8
 
0.5%
29 7
 
0.5%
35 7
 
0.5%
15 7
 
0.5%
17 6
 
0.4%
36 5
 
0.3%
28 5
 
0.3%
Other values (30) 74
 
5.1%
(Missing) 1315
90.1%
ValueCountFrequency (%)
1 4
0.3%
2 4
0.3%
3 4
0.3%
4 3
0.2%
6 1
 
0.1%
8 1
 
0.1%
11 2
 
0.1%
12 4
0.3%
14 1
 
0.1%
15 7
0.5%
ValueCountFrequency (%)
255 1
 
0.1%
82 1
 
0.1%
61 1
 
0.1%
52 2
0.1%
48 1
 
0.1%
46 1
 
0.1%
45 3
0.2%
42 3
0.2%
41 3
0.2%
39 4
0.3%

한실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)26.7%
Missing1370
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean10.011111
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-18T03:38:18.949940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q312
95-th percentile21.55
Maximum135
Range134
Interquartile range (IQR)9

Descriptive statistics

Standard deviation15.120857
Coefficient of variation (CV)1.5104075
Kurtosis53.411033
Mean10.011111
Median Absolute Deviation (MAD)4.5
Skewness6.637891
Sum901
Variance228.64032
MonotonicityNot monotonic
2024-04-18T03:38:19.040655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 10
 
0.7%
1 8
 
0.5%
3 7
 
0.5%
12 7
 
0.5%
7 7
 
0.5%
9 7
 
0.5%
5 6
 
0.4%
8 5
 
0.3%
11 5
 
0.3%
4 4
 
0.3%
Other values (14) 24
 
1.6%
(Missing) 1370
93.8%
ValueCountFrequency (%)
1 8
0.5%
2 10
0.7%
3 7
0.5%
4 4
 
0.3%
5 6
0.4%
6 4
 
0.3%
7 7
0.5%
8 5
0.3%
9 7
0.5%
11 5
0.3%
ValueCountFrequency (%)
135 1
0.1%
42 1
0.1%
36 1
0.1%
26 1
0.1%
22 1
0.1%
21 1
0.1%
19 1
0.1%
18 1
0.1%
17 1
0.1%
16 2
0.1%

양실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)29.3%
Missing1320
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean19.328571
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-18T03:38:19.152451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q329.5
95-th percentile41
Maximum52
Range51
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.673227
Coefficient of variation (CV)0.65567325
Kurtosis-0.85966372
Mean19.328571
Median Absolute Deviation (MAD)10
Skewness0.41197788
Sum2706
Variance160.61069
MonotonicityNot monotonic
2024-04-18T03:38:19.251919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
12 9
 
0.6%
14 8
 
0.5%
9 7
 
0.5%
15 7
 
0.5%
1 6
 
0.4%
19 6
 
0.4%
8 6
 
0.4%
2 5
 
0.3%
32 5
 
0.3%
6 4
 
0.3%
Other values (31) 77
 
5.3%
(Missing) 1320
90.4%
ValueCountFrequency (%)
1 6
0.4%
2 5
0.3%
3 4
0.3%
4 3
0.2%
5 3
0.2%
6 4
0.3%
7 2
 
0.1%
8 6
0.4%
9 7
0.5%
11 1
 
0.1%
ValueCountFrequency (%)
52 1
 
0.1%
46 2
0.1%
45 1
 
0.1%
42 2
0.1%
41 2
0.1%
39 2
0.1%
38 4
0.3%
37 4
0.3%
36 2
0.1%
35 4
0.3%

Interactions

2024-04-18T03:38:16.119512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:15.721935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:15.924530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:16.181206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:15.787974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:15.985128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:16.256520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:15.858569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:16.049911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:38:19.319861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명객실수한실수양실수
업종명1.0000.9440.3130.4680.000
업태명0.9441.0000.7890.6620.539
객실수0.3130.7891.0000.8550.783
한실수0.4680.6620.8551.0000.000
양실수0.0000.5390.7830.0001.000
2024-04-18T03:38:19.392624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.664
업종명0.6641.000
2024-04-18T03:38:19.457213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수한실수양실수업종명업태명
객실수1.0000.2030.6840.3780.411
한실수0.2031.000-0.0060.5580.301
양실수0.684-0.0061.0000.0000.247
업종명0.3780.5580.0001.0000.664
업태명0.4110.3010.2470.6641.000

Missing values

2024-04-18T03:38:16.548172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:38:16.641537image/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.
2024-04-18T03:38:16.732566image/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

업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수
0목욕장업물조은탕경상남도 양산시 삼호동부7길 26 (삼호동)055-362-1126공동탕업<NA><NA><NA>
1목욕장업천성탕경상남도 양산시 소주회야로 45-203 (소주동)055-363-2430공동탕업<NA><NA><NA>
2목욕장업한림탕경상남도 양산시 연호로 23 (삼호동)055-365-1039공동탕업<NA><NA><NA>
3목욕장업힐링온천스포렉스경상남도 양산시 대운로 172 (명동)055-365-5533공동탕업<NA><NA><NA>
4목욕장업경보목욕탕경상남도 양산시 내연1길 7 (평산동)055-366-3769공동탕업<NA><NA><NA>
5목욕장업한양탕경상남도 양산시 일동3길 3 (중부동)055-366-4468공동탕업<NA><NA><NA>
6목욕장업약수탕경상남도 양산시 평산14길 5 (평산동)055-366-6318공동탕업<NA><NA><NA>
7목욕장업한울황토탕경상남도 양산시 양주로 42, 한울빌딩 4층 (남부동)055-367-0307공동탕업<NA><NA><NA>
8목욕장업동광탕경상남도 양산시 물금읍 가촌로 115055-381-6990공동탕업<NA><NA><NA>
9목욕장업황제탕경상남도 양산시 물금읍 서남1길 42055-381-8412공동탕업<NA><NA><NA>
업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수
1450종합미용업꽃피는스킨바디경상남도 양산시 물금읍 신주로 73, 신창비바패밀리 상가 2층 202호<NA>피부미용업<NA><NA><NA>
1451종합미용업라디앙(Radi.Ang)에스테틱경상남도 양산시 신명동4길 21, 2층 (명동)<NA>피부미용업<NA><NA><NA>
1452종합미용업토탈뷰티하라경상남도 양산시 양주3길 25-1, 1층 (중부동)055-384-0113피부미용업<NA><NA><NA>
1453일반미용업, 피부미용업, 화장ㆍ분장 미용업뷰티톡경상남도 양산시 덕계12길 11, 1층 (덕계동)<NA>피부미용업<NA><NA><NA>
1454미용업비앤비뷰티샵경상남도 양산시 중앙로 141-1, 3층 (중부동)<NA>피부미용업<NA><NA><NA>
1455미용업민에스테틱경상남도 양산시 신명동6길 21 (명동)055-362-7185피부미용업<NA><NA><NA>
1456미용업라시아에스테틱경상남도 양산시 물금읍 황산로 643, 4층055-381-3348피부미용업<NA><NA><NA>
1457미용업오즈스킨케어경상남도 양산시 물금읍 신주로 45, 3층<NA>피부미용업<NA><NA><NA>
1458미용업김경순에스테틱경상남도 양산시 삼일로 90 (중부동,(3층))055-366-2403피부미용업<NA><NA><NA>
1459미용업보떼경상남도 양산시 물금읍 백호로 156, 202동 2층 204호 (우성스마트시티뷰)055-383-5658피부미용업<NA><NA><NA>

Duplicate rows

Most frequently occurring

업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수# duplicates
0이용업젠틀맨경상남도 양산시 서창서1길 5-2 (삼호동)<NA>일반이용업<NA><NA><NA>2