Overview

Dataset statistics

Number of variables9
Number of observations1796
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory126.4 KiB
Average record size in memory72.1 B

Variable types

Categorical6
Text3

Dataset

Description경상남도 양신시 관내 공중위생업소(목욕장업, 피부미용업, 일반미용업, 네일미용업, 화장, 세탁업, 숙박업 등) 업소소재지, 전화번호 정보 등
Author경상남도 양산시
URLhttps://www.data.go.kr/data/15006926/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.1%) duplicate rowsDuplicates
업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
객실수 is highly overall correlated with 한실수 and 1 other fieldsHigh correlation
한실수 is highly overall correlated with 객실수 and 1 other fieldsHigh correlation
양실수 is highly overall correlated with 객실수 and 1 other fieldsHigh correlation
객실수 is highly imbalanced (84.8%)Imbalance
한실수 is highly imbalanced (85.5%)Imbalance
양실수 is highly imbalanced (85.0%)Imbalance

Reproduction

Analysis started2024-03-16 06:33:24.383351
Analysis finished2024-03-16 06:33:34.628551
Duration10.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
일반미용업
546 
미용업
215 
피부미용업
169 
숙박업(일반)
140 
네일미용업
139 
Other values (17)
587 

Length

Max length23
Median length5
Mean length5.7399777
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 546
30.4%
미용업 215
 
12.0%
피부미용업 169
 
9.4%
숙박업(일반) 140
 
7.8%
네일미용업 139
 
7.7%
세탁업 99
 
5.5%
이용업 95
 
5.3%
건물위생관리업 91
 
5.1%
종합미용업 62
 
3.5%
목욕장업 57
 
3.2%
Other values (12) 183
 
10.2%

Length

2024-03-16T06:33:35.216219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 602
28.7%
미용업 346
16.5%
피부미용업 239
 
11.4%
네일미용업 230
 
11.0%
숙박업(일반 140
 
6.7%
화장ㆍ분장 131
 
6.2%
세탁업 99
 
4.7%
이용업 95
 
4.5%
건물위생관리업 91
 
4.3%
종합미용업 62
 
3.0%
Other values (2) 64
 
3.0%

업소명
Text

Distinct1744
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-16T06:33:36.522373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length6.0272829
Min length1

Characters and Unicode

Total characters10825
Distinct characters643
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1696 ?
Unique (%)94.4%

Sample

1st row경남여관
2nd row산장여관
3rd row송원장여관
4th row꼭지모텔
5th row티파니
ValueCountFrequency (%)
헤어 20
 
0.9%
hair 15
 
0.7%
네일 14
 
0.7%
주식회사 12
 
0.6%
미용실 12
 
0.6%
에스테틱 11
 
0.5%
양산점 10
 
0.5%
더 7
 
0.3%
모텔 6
 
0.3%
브라운트리 6
 
0.3%
Other values (1892) 2031
94.7%
2024-03-16T06:33:37.990662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 585
 
5.4%
헤 553
 
5.1%
348
 
3.2%
이 290
 
2.7%
스 226
 
2.1%
일 189
 
1.7%
네 182
 
1.7%
미 176
 
1.6%
) 169
 
1.6%
리 169
 
1.6%
Other values (633) 7938
73.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9192
84.9%
Lowercase Letter 463
 
4.3%
Space Separator 348
 
3.2%
Uppercase Letter 336
 
3.1%
Close Punctuation 169
 
1.6%
Open Punctuation 169
 
1.6%
Other Punctuation 80
 
0.7%
Decimal Number 58
 
0.5%
Dash Punctuation 4
 
< 0.1%
Math Symbol 3
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 585
 
6.4%
헤 553
 
6.0%
이 290
 
3.2%
스 226
 
2.5%
일 189
 
2.1%
네 182
 
2.0%
미 176
 
1.9%
리 169
 
1.8%
용 132
 
1.4%
라 132
 
1.4%
Other values (563) 6558
71.3%
Uppercase Letter
ValueCountFrequency (%)
A 34
 
10.1%
N 28
 
8.3%
J 24
 
7.1%
I 24
 
7.1%
S 23
 
6.8%
H 22
 
6.5%
L 21
 
6.2%
B 20
 
6.0%
E 20
 
6.0%
T 18
 
5.4%
Other values (14) 102
30.4%
Lowercase Letter
ValueCountFrequency (%)
e 54
11.7%
a 53
11.4%
i 52
11.2%
l 39
8.4%
o 34
 
7.3%
n 32
 
6.9%
r 30
 
6.5%
h 30
 
6.5%
t 25
 
5.4%
s 19
 
4.1%
Other values (12) 95
20.5%
Decimal Number
ValueCountFrequency (%)
1 14
24.1%
2 11
19.0%
0 6
10.3%
3 6
10.3%
7 5
 
8.6%
6 5
 
8.6%
8 4
 
6.9%
9 4
 
6.9%
5 3
 
5.2%
Other Punctuation
ValueCountFrequency (%)
& 19
23.8%
, 19
23.8%
. 18
22.5%
# 12
15.0%
' 6
 
7.5%
: 4
 
5.0%
· 1
 
1.2%
@ 1
 
1.2%
Space Separator
ValueCountFrequency (%)
348
100.0%
Close Punctuation
ValueCountFrequency (%)
) 169
100.0%
Open Punctuation
ValueCountFrequency (%)
( 169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9190
84.9%
Common 834
 
7.7%
Latin 799
 
7.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 585
 
6.4%
헤 553
 
6.0%
이 290
 
3.2%
스 226
 
2.5%
일 189
 
2.1%
네 182
 
2.0%
미 176
 
1.9%
리 169
 
1.8%
용 132
 
1.4%
라 132
 
1.4%
Other values (561) 6556
71.3%
Latin
ValueCountFrequency (%)
e 54
 
6.8%
a 53
 
6.6%
i 52
 
6.5%
l 39
 
4.9%
o 34
 
4.3%
A 34
 
4.3%
n 32
 
4.0%
r 30
 
3.8%
h 30
 
3.8%
N 28
 
3.5%
Other values (36) 413
51.7%
Common
ValueCountFrequency (%)
348
41.7%
) 169
20.3%
( 169
20.3%
& 19
 
2.3%
, 19
 
2.3%
. 18
 
2.2%
1 14
 
1.7%
# 12
 
1.4%
2 11
 
1.3%
' 6
 
0.7%
Other values (14) 49
 
5.9%
Han
ValueCountFrequency (%)
在 1
50.0%
美 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9190
84.9%
ASCII 1632
 
15.1%
CJK 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 585
 
6.4%
헤 553
 
6.0%
이 290
 
3.2%
스 226
 
2.5%
일 189
 
2.1%
네 182
 
2.0%
미 176
 
1.9%
리 169
 
1.8%
용 132
 
1.4%
라 132
 
1.4%
Other values (561) 6556
71.3%
ASCII
ValueCountFrequency (%)
348
21.3%
) 169
 
10.4%
( 169
 
10.4%
e 54
 
3.3%
a 53
 
3.2%
i 52
 
3.2%
l 39
 
2.4%
o 34
 
2.1%
A 34
 
2.1%
n 32
 
2.0%
Other values (59) 648
39.7%
CJK
ValueCountFrequency (%)
在 1
50.0%
美 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

영업소 주소(도로명)
Text

Distinct1723
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-16T06:33:38.652050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length49
Mean length29.942094
Min length7

Characters and Unicode

Total characters53776
Distinct characters330
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

Unique1695 ?
Unique (%)94.4%

Sample

1st row경상남도 양산시 북안남4길 8-1 (북부동)
2nd row경상남도 양산시 하북면 신평강변로 84
3rd row경상남도 양산시 장터3길 16 (중부동)
4th row경상남도 양산시 북안남3길 9 (북부동)
5th row경상남도 양산시 장터2길 14 (중부동)
ValueCountFrequency (%)
경상남도 1749
 
15.0%
양산시 1749
 
15.0%
물금읍 578
 
5.0%
1층 502
 
4.3%
삼호동 164
 
1.4%
중부동 156
 
1.3%
동면 155
 
1.3%
2층 148
 
1.3%
일부 111
 
1.0%
상가동 105
 
0.9%
Other values (1540) 6247
53.6%
2024-03-16T06:33:39.694203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9869
 
18.4%
1 2923
 
5.4%
산 2244
 
4.2%
양 2067
 
3.8%
상 2052
 
3.8%
남 1903
 
3.5%
시 1888
 
3.5%
도 1824
 
3.4%
경 1761
 
3.3%
동 1592
 
3.0%
Other values (320) 25653
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30568
56.8%
Space Separator 9869
 
18.4%
Decimal Number 9000
 
16.7%
Other Punctuation 1517
 
2.8%
Open Punctuation 1151
 
2.1%
Close Punctuation 1151
 
2.1%
Dash Punctuation 395
 
0.7%
Uppercase Letter 90
 
0.2%
Math Symbol 23
 
< 0.1%
Lowercase Letter 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2244
 
7.3%
양 2067
 
6.8%
상 2052
 
6.7%
남 1903
 
6.2%
시 1888
 
6.2%
도 1824
 
6.0%
경 1761
 
5.8%
동 1592
 
5.2%
호 1203
 
3.9%
로 945
 
3.1%
Other values (283) 13089
42.8%
Uppercase Letter
ValueCountFrequency (%)
B 23
25.6%
A 18
20.0%
N 11
12.2%
C 8
 
8.9%
E 8
 
8.9%
D 6
 
6.7%
L 4
 
4.4%
G 3
 
3.3%
I 3
 
3.3%
F 2
 
2.2%
Other values (2) 4
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 2923
32.5%
2 1340
14.9%
0 1011
 
11.2%
3 844
 
9.4%
4 623
 
6.9%
5 587
 
6.5%
7 494
 
5.5%
6 470
 
5.2%
8 376
 
4.2%
9 332
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 1506
99.3%
. 7
 
0.5%
/ 2
 
0.1%
& 1
 
0.1%
@ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 8
66.7%
c 1
 
8.3%
i 1
 
8.3%
t 1
 
8.3%
y 1
 
8.3%
Space Separator
ValueCountFrequency (%)
9869
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 395
100.0%
Math Symbol
ValueCountFrequency (%)
~ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30568
56.8%
Common 23106
43.0%
Latin 102
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2244
 
7.3%
양 2067
 
6.8%
상 2052
 
6.7%
남 1903
 
6.2%
시 1888
 
6.2%
도 1824
 
6.0%
경 1761
 
5.8%
동 1592
 
5.2%
호 1203
 
3.9%
로 945
 
3.1%
Other values (283) 13089
42.8%
Common
ValueCountFrequency (%)
9869
42.7%
1 2923
 
12.7%
, 1506
 
6.5%
2 1340
 
5.8%
( 1151
 
5.0%
) 1151
 
5.0%
0 1011
 
4.4%
3 844
 
3.7%
4 623
 
2.7%
5 587
 
2.5%
Other values (10) 2101
 
9.1%
Latin
ValueCountFrequency (%)
B 23
22.5%
A 18
17.6%
N 11
10.8%
e 8
 
7.8%
C 8
 
7.8%
E 8
 
7.8%
D 6
 
5.9%
L 4
 
3.9%
G 3
 
2.9%
I 3
 
2.9%
Other values (7) 10
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30568
56.8%
ASCII 23208
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9869
42.5%
1 2923
 
12.6%
, 1506
 
6.5%
2 1340
 
5.8%
( 1151
 
5.0%
) 1151
 
5.0%
0 1011
 
4.4%
3 844
 
3.6%
4 623
 
2.7%
5 587
 
2.5%
Other values (27) 2203
 
9.5%
Hangul
ValueCountFrequency (%)
산 2244
 
7.3%
양 2067
 
6.8%
상 2052
 
6.7%
남 1903
 
6.2%
시 1888
 
6.2%
도 1824
 
6.0%
경 1761
 
5.8%
동 1592
 
5.2%
호 1203
 
3.9%
로 945
 
3.1%
Other values (283) 13089
42.8%

소재지전화
Text

Distinct804
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-16T06:33:40.359337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length9.2817372
Min length7

Characters and Unicode

Total characters16670
Distinct characters18
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

Unique792 ?
Unique (%)44.1%

Sample

1st row055-386-2678
2nd row055-382-6723
3rd row055-385-9333
4th row데이터 미집계
5th row055-382-8321
ValueCountFrequency (%)
데이터 982
35.0%
미집계 982
35.0%
055 14
 
0.5%
055-388-3882 2
 
0.1%
367 2
 
0.1%
055-384-0061 2
 
0.1%
055-384-0925 2
 
0.1%
055-382-8321 2
 
0.1%
055-383-0671 2
 
0.1%
055-375-1966 2
 
0.1%
Other values (808) 813
29.0%
2024-03-16T06:33:41.417734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2071
12.4%
- 1628
 
9.8%
3 1245
 
7.5%
0 1244
 
7.5%
1009
 
6.1%
이 982
 
5.9%
데 982
 
5.9%
계 982
 
5.9%
집 982
 
5.9%
미 982
 
5.9%
Other values (8) 4563
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8141
48.8%
Other Letter 5892
35.3%
Dash Punctuation 1628
 
9.8%
Space Separator 1009
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2071
25.4%
3 1245
15.3%
0 1244
15.3%
8 839
10.3%
6 689
 
8.5%
2 474
 
5.8%
7 469
 
5.8%
1 419
 
5.1%
4 359
 
4.4%
9 332
 
4.1%
Other Letter
ValueCountFrequency (%)
이 982
16.7%
데 982
16.7%
계 982
16.7%
집 982
16.7%
미 982
16.7%
터 982
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 1628
100.0%
Space Separator
ValueCountFrequency (%)
1009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10778
64.7%
Hangul 5892
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2071
19.2%
- 1628
15.1%
3 1245
11.6%
0 1244
11.5%
1009
9.4%
8 839
7.8%
6 689
 
6.4%
2 474
 
4.4%
7 469
 
4.4%
1 419
 
3.9%
Other values (2) 691
 
6.4%
Hangul
ValueCountFrequency (%)
이 982
16.7%
데 982
16.7%
계 982
16.7%
집 982
16.7%
미 982
16.7%
터 982
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10778
64.7%
Hangul 5892
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2071
19.2%
- 1628
15.1%
3 1245
11.6%
0 1244
11.5%
1009
9.4%
8 839
7.8%
6 689
 
6.4%
2 474
 
4.4%
7 469
 
4.4%
1 419
 
3.9%
Other values (2) 691
 
6.4%
Hangul
ValueCountFrequency (%)
이 982
16.7%
데 982
16.7%
계 982
16.7%
집 982
16.7%
미 982
16.7%
터 982
16.7%

업태명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
일반미용업
841 
피부미용업
201 
네일아트업
185 
여관업
117 
일반이용업
93 
Other values (16)
359 

Length

Max length14
Median length5
Mean length4.9860802
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 841
46.8%
피부미용업 201
 
11.2%
네일아트업 185
 
10.3%
여관업 117
 
6.5%
일반이용업 93
 
5.2%
건물위생관리업 90
 
5.0%
일반세탁업 89
 
5.0%
메이크업업 62
 
3.5%
공동탕업 45
 
2.5%
기타 17
 
0.9%
Other values (11) 56
 
3.1%

Length

2024-03-16T06:33:41.904910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 841
46.3%
피부미용업 201
 
11.1%
네일아트업 185
 
10.2%
여관업 117
 
6.4%
일반이용업 93
 
5.1%
건물위생관리업 91
 
5.0%
일반세탁업 89
 
4.9%
메이크업업 62
 
3.4%
공동탕업 45
 
2.5%
기타 36
 
2.0%
Other values (10) 55
 
3.0%

객실수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct43
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
데이터 미집계
1649 
19
 
10
18
 
9
24
 
8
32
 
8
Other values (38)
 
112

Length

Max length7
Median length7
Mean length6.5907572
Min length1

Unique

Unique13 ?
Unique (%)0.7%

Sample

1st row10
2nd row17
3rd row11
4th row28
5th row23

Common Values

ValueCountFrequency (%)
데이터 미집계 1649
91.8%
19 10
 
0.6%
18 9
 
0.5%
24 8
 
0.4%
32 8
 
0.4%
29 7
 
0.4%
35 7
 
0.4%
15 7
 
0.4%
17 6
 
0.3%
36 5
 
0.3%
Other values (33) 80
 
4.5%

Length

2024-03-16T06:33:42.386332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
데이터 1649
47.9%
미집계 1649
47.9%
19 10
 
0.3%
18 9
 
0.3%
24 8
 
0.2%
32 8
 
0.2%
29 7
 
0.2%
35 7
 
0.2%
15 7
 
0.2%
17 6
 
0.2%
Other values (34) 85
 
2.5%

한실수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct30
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
데이터 미집계
1649 
0
 
55
2
 
9
9
 
7
7
 
7
Other values (25)
 
69

Length

Max length7
Median length7
Mean length6.5339644
Min length1

Unique

Unique11 ?
Unique (%)0.6%

Sample

1st row10
2nd row8
3rd row10
4th row14
5th row11

Common Values

ValueCountFrequency (%)
데이터 미집계 1649
91.8%
0 55
 
3.1%
2 9
 
0.5%
9 7
 
0.4%
7 7
 
0.4%
12 7
 
0.4%
5 6
 
0.3%
10 5
 
0.3%
8 5
 
0.3%
11 5
 
0.3%
Other values (20) 41
 
2.3%

Length

2024-03-16T06:33:42.794008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
데이터 1649
47.9%
미집계 1649
47.9%
0 55
 
1.6%
2 9
 
0.3%
9 7
 
0.2%
7 7
 
0.2%
12 7
 
0.2%
5 6
 
0.2%
10 5
 
0.1%
8 5
 
0.1%
Other values (21) 46
 
1.3%

양실수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct50
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
데이터 미집계
1649 
12
 
8
14
 
8
15
 
7
9
 
7
Other values (45)
 
117

Length

Max length7
Median length7
Mean length6.575167
Min length1

Unique

Unique13 ?
Unique (%)0.7%

Sample

1st row0
2nd row9
3rd row1
4th row14
5th row12

Common Values

ValueCountFrequency (%)
데이터 미집계 1649
91.8%
12 8
 
0.4%
14 8
 
0.4%
15 7
 
0.4%
9 7
 
0.4%
0 6
 
0.3%
19 6
 
0.3%
8 5
 
0.3%
32 5
 
0.3%
35 4
 
0.2%
Other values (40) 91
 
5.1%

Length

2024-03-16T06:33:43.399779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
데이터 1649
47.9%
미집계 1649
47.9%
12 8
 
0.2%
14 8
 
0.2%
15 7
 
0.2%
9 7
 
0.2%
0 6
 
0.2%
19 6
 
0.2%
8 5
 
0.1%
32 5
 
0.1%
Other values (41) 95
 
2.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-06
1796 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-06
2nd row2024-03-06
3rd row2024-03-06
4th row2024-03-06
5th row2024-03-06

Common Values

ValueCountFrequency (%)
2024-03-06 1796
100.0%

Length

2024-03-16T06:33:43.836273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:33:44.163043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-06 1796
100.0%

Correlations

2024-03-16T06:33:44.355398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명객실수한실수양실수
업종명1.0000.9530.7030.6550.672
업태명0.9531.0000.8500.7340.847
객실수0.7030.8501.0000.9720.992
한실수0.6550.7340.9721.0000.977
양실수0.6720.8470.9920.9771.000
2024-03-16T06:33:44.658805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수업태명한실수양실수업종명
객실수1.0000.3710.6320.7550.233
업태명0.3711.0000.2760.3620.652
한실수0.6320.2761.0000.6500.220
양실수0.7550.3620.6501.0000.212
업종명0.2330.6520.2200.2121.000
2024-03-16T06:33:44.993151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명객실수한실수양실수
업종명1.0000.6520.2330.2200.212
업태명0.6521.0000.3710.2760.362
객실수0.2330.3711.0000.6320.755
한실수0.2200.2760.6321.0000.650
양실수0.2120.3620.7550.6501.000

Missing values

2024-03-16T06:33:32.961786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T06:33:34.201612image/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

업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수데이터기준일자
0숙박업(일반)경남여관경상남도 양산시 북안남4길 8-1 (북부동)055-386-2678여관업101002024-03-06
1숙박업(일반)산장여관경상남도 양산시 하북면 신평강변로 84055-382-6723여관업17892024-03-06
2숙박업(일반)송원장여관경상남도 양산시 장터3길 16 (중부동)055-385-9333여관업111012024-03-06
3숙박업(일반)꼭지모텔경상남도 양산시 북안남3길 9 (북부동)데이터 미집계여관업2814142024-03-06
4숙박업(일반)티파니경상남도 양산시 장터2길 14 (중부동)055-382-8321여관업2311122024-03-06
5숙박업(일반)제일장여관경상남도 양산시 북안남4길 15 (북부동)055-382-4849여관업12842024-03-06
6숙박업(일반)향촌장여관경상남도 양산시 북안남4길 13 (북부동)055-382-3208여관업10552024-03-06
7숙박업(일반)동원장여관경상남도 양산시 서창서1길 18-16, 동원장여관 (삼호동)055-365-0218여관업199102024-03-06
8숙박업(일반)보미모텔경상남도 양산시 장터3길 13 (중부동)055-385-6547여관업172152024-03-06
9숙박업(일반)연호장여관경상남도 양산시 연호2길 9-8 (삼호동)055-366-2217여관업15782024-03-06
업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수데이터기준일자
1786피부미용업, 네일미용업, 화장ㆍ분장 미용업니즈네일뷰티경상남도 양산시 번영로 170, 1층 (평산동)051-292-5585네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1787피부미용업, 네일미용업, 화장ㆍ분장 미용업안녕, 소중한 네일경상남도 양산시 물금읍 화합길 37, 나래월드빌딩 111호데이터 미집계네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1788피부미용업, 네일미용업, 화장ㆍ분장 미용업제시아카데미경상남도 양산시 물금읍 서들로 148, 채움 307호데이터 미집계피부미용업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1789피부미용업, 네일미용업, 화장ㆍ분장 미용업글램뷰티경상남도 양산시 물금읍 백호로 155, 411동 129호 (양산신도시4차동원로얄듀크비스타)데이터 미집계네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1790피부미용업, 네일미용업, 화장ㆍ분장 미용업피우다,네일경상남도 양산시 동면 금오로 253, 101호데이터 미집계네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1791피부미용업, 네일미용업, 화장ㆍ분장 미용업루비&네일경상남도 양산시 동면 금오7길 37-12, 1층일부데이터 미집계네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1792피부미용업, 네일미용업, 화장ㆍ분장 미용업예뻐, 영경상남도 양산시 대운로 145, 301동 114호 (명동, 명동 삼한 사랑채 아파트)데이터 미집계피부미용업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1793피부미용업, 네일미용업, 화장ㆍ분장 미용업로얄라인(Royal Line)경상남도 양산시 물금읍 야리1길 22, 미래타워 201호데이터 미집계피부미용업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1794피부미용업, 네일미용업, 화장ㆍ분장 미용업네일, 달아경상남도 양산시 물금읍 새실로 53, 108동 105호 (양산물금 이지더원 1차)데이터 미집계네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06
1795피부미용업, 네일미용업, 화장ㆍ분장 미용업네일드블링경상남도 양산시 평산남로 60, 102호 (평산동)데이터 미집계네일아트업데이터 미집계데이터 미집계데이터 미집계2024-03-06

Duplicate rows

Most frequently occurring

업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수데이터기준일자# duplicates
0이용업젠틀맨경상남도 양산시 서창서1길 5-2 (삼호동)데이터 미집계일반이용업데이터 미집계데이터 미집계데이터 미집계2024-03-062