Overview

Dataset statistics

Number of variables18
Number of observations598
Missing cells1349
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.5 KiB
Average record size in memory153.2 B

Variable types

Categorical4
DateTime2
Text5
Numeric6
Boolean1

Dataset

Description경기도 여주시 관내 공중위생업소에 대한 현황정보를 제공합니다. 제공하는 항목에는 업종명, 신고일자, 업소명, 법인여부, 영업소주소(도로명), 영업소주소(지번), 영업장면적, 소재지전화, 영업자시작일, 객실수, 한실수, 양실수, 침대수, 욕실수, 발한실, 세탁기수, 회수건조기수 정보가 있습니다.
Author경기도 여주시
URLhttps://www.data.go.kr/data/15038658/fileData.do

Alerts

영업장면적 is highly overall correlated with 객실수 and 2 other fieldsHigh correlation
객실수 is highly overall correlated with 영업장면적 and 2 other fieldsHigh correlation
한실수 is highly overall correlated with 영업장면적 and 2 other fieldsHigh correlation
양실수 is highly overall correlated with 영업장면적 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 발한실High correlation
욕실수 is highly overall correlated with 발한실High correlation
발한실 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
세탁기수 is highly overall correlated with 회수건조기수High correlation
회수건조기수 is highly overall correlated with 세탁기수High correlation
욕실수 is highly imbalanced (58.2%)Imbalance
발한실 is highly imbalanced (90.8%)Imbalance
세탁기수 is highly imbalanced (58.0%)Imbalance
회수건조기수 is highly imbalanced (51.1%)Imbalance
법인명 has 552 (92.3%) missing valuesMissing
소재지전화 has 247 (41.3%) missing valuesMissing
한실수 has 163 (27.3%) missing valuesMissing
양실수 has 163 (27.3%) missing valuesMissing
의자수 has 47 (7.9%) missing valuesMissing
침대수 has 172 (28.8%) missing valuesMissing
객실수 has 504 (84.3%) zerosZeros
한실수 has 373 (62.4%) zerosZeros
양실수 has 350 (58.5%) zerosZeros
의자수 has 217 (36.3%) zerosZeros
침대수 has 353 (59.0%) zerosZeros

Reproduction

Analysis started2024-03-16 04:18:08.244884
Analysis finished2024-03-16 04:18:18.593496
Duration10.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
일반미용업
203 
숙박업(일반)
86 
세탁업
55 
건물위생관리업
49 
피부미용업
42 
Other values (16)
163 

Length

Max length23
Median length5
Mean length5.7959866
Min length3

Unique

Unique4 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 203
33.9%
숙박업(일반) 86
14.4%
세탁업 55
 
9.2%
건물위생관리업 49
 
8.2%
피부미용업 42
 
7.0%
이용업 40
 
6.7%
네일미용업 35
 
5.9%
종합미용업 25
 
4.2%
목욕장업 13
 
2.2%
피부미용업, 화장ㆍ분장 미용업 9
 
1.5%
Other values (11) 41
 
6.9%

Length

2024-03-16T13:18:18.731749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 213
31.9%
숙박업(일반 86
12.9%
피부미용업 61
 
9.1%
세탁업 55
 
8.2%
네일미용업 54
 
8.1%
건물위생관리업 49
 
7.3%
이용업 40
 
6.0%
미용업 33
 
4.9%
화장ㆍ분장 31
 
4.6%
종합미용업 25
 
3.7%
Other values (2) 21
 
3.1%

신고일자
Date

Distinct522
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum1963-09-16 00:00:00
Maximum2024-02-29 00:00:00
2024-03-16T13:18:18.963274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:19.199409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct593
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-03-16T13:18:19.573045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length6.2391304
Min length1

Characters and Unicode

Total characters3731
Distinct characters481
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

Unique588 ?
Unique (%)98.3%

Sample

1st row옥천유스텔
2nd row신륵여관
3rd row유성여인숙
4th row씨티모텔
5th row명성장여관
ValueCountFrequency (%)
헤어 11
 
1.4%
네일 9
 
1.2%
hair 8
 
1.1%
미용실 6
 
0.8%
주식회사 6
 
0.8%
the 5
 
0.7%
에스테틱 4
 
0.5%
nail 4
 
0.5%
살롱드몽 3
 
0.4%
무인텔 3
 
0.4%
Other values (680) 701
92.2%
2024-03-16T13:18:20.079655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163
 
4.4%
어 142
 
3.8%
헤 134
 
3.6%
이 91
 
2.4%
리 75
 
2.0%
미 62
 
1.7%
주 60
 
1.6%
스 59
 
1.6%
) 58
 
1.6%
( 57
 
1.5%
Other values (471) 2830
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2987
80.1%
Lowercase Letter 281
 
7.5%
Space Separator 163
 
4.4%
Uppercase Letter 134
 
3.6%
Close Punctuation 58
 
1.6%
Open Punctuation 57
 
1.5%
Other Punctuation 36
 
1.0%
Decimal Number 9
 
0.2%
Dash Punctuation 3
 
0.1%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 142
 
4.8%
헤 134
 
4.5%
이 91
 
3.0%
리 75
 
2.5%
미 62
 
2.1%
주 60
 
2.0%
스 59
 
2.0%
세 53
 
1.8%
관 52
 
1.7%
용 51
 
1.7%
Other values (405) 2208
73.9%
Lowercase Letter
ValueCountFrequency (%)
a 40
14.2%
e 34
12.1%
i 31
11.0%
o 23
8.2%
n 21
7.5%
r 21
7.5%
h 17
 
6.0%
l 16
 
5.7%
y 14
 
5.0%
u 12
 
4.3%
Other values (13) 52
18.5%
Uppercase Letter
ValueCountFrequency (%)
J 12
 
9.0%
H 12
 
9.0%
S 12
 
9.0%
T 11
 
8.2%
E 11
 
8.2%
L 10
 
7.5%
U 9
 
6.7%
O 9
 
6.7%
D 5
 
3.7%
C 5
 
3.7%
Other values (13) 38
28.4%
Decimal Number
ValueCountFrequency (%)
2 2
22.2%
1 2
22.2%
3 1
11.1%
7 1
11.1%
9 1
11.1%
8 1
11.1%
0 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 12
33.3%
# 7
19.4%
& 6
16.7%
, 5
13.9%
' 3
 
8.3%
: 3
 
8.3%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
163
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2983
80.0%
Latin 415
 
11.1%
Common 329
 
8.8%
Han 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 142
 
4.8%
헤 134
 
4.5%
이 91
 
3.1%
리 75
 
2.5%
미 62
 
2.1%
주 60
 
2.0%
스 59
 
2.0%
세 53
 
1.8%
관 52
 
1.7%
용 51
 
1.7%
Other values (402) 2204
73.9%
Latin
ValueCountFrequency (%)
a 40
 
9.6%
e 34
 
8.2%
i 31
 
7.5%
o 23
 
5.5%
n 21
 
5.1%
r 21
 
5.1%
h 17
 
4.1%
l 16
 
3.9%
y 14
 
3.4%
J 12
 
2.9%
Other values (36) 186
44.8%
Common
ValueCountFrequency (%)
163
49.5%
) 58
 
17.6%
( 57
 
17.3%
. 12
 
3.6%
# 7
 
2.1%
& 6
 
1.8%
, 5
 
1.5%
' 3
 
0.9%
- 3
 
0.9%
: 3
 
0.9%
Other values (10) 12
 
3.6%
Han
ValueCountFrequency (%)
美 2
50.0%
月 1
25.0%
文 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2977
79.8%
ASCII 744
 
19.9%
Compat Jamo 6
 
0.2%
CJK 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
21.9%
) 58
 
7.8%
( 57
 
7.7%
a 40
 
5.4%
e 34
 
4.6%
i 31
 
4.2%
o 23
 
3.1%
n 21
 
2.8%
r 21
 
2.8%
h 17
 
2.3%
Other values (56) 279
37.5%
Hangul
ValueCountFrequency (%)
어 142
 
4.8%
헤 134
 
4.5%
이 91
 
3.1%
리 75
 
2.5%
미 62
 
2.1%
주 60
 
2.0%
스 59
 
2.0%
세 53
 
1.8%
관 52
 
1.7%
용 51
 
1.7%
Other values (396) 2198
73.8%
CJK
ValueCountFrequency (%)
美 2
50.0%
月 1
25.0%
文 1
25.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
16.7%
ㄹ 1
16.7%
ㅇ 1
16.7%
ㅅ 1
16.7%
ㅛ 1
16.7%
ㅁ 1
16.7%

법인명
Text

MISSING 

Distinct42
Distinct (%)91.3%
Missing552
Missing (%)92.3%
Memory size4.8 KiB
2024-03-16T13:18:20.311301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14
Mean length10.456522
Min length5

Characters and Unicode

Total characters481
Distinct characters137
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)82.6%

Sample

1st row동광레저 주식회사
2nd row일성레저산업주식회사
3rd row농업회사법인 엠알아이농업 주식회사
4th row주식회사 썬밸리
5th row주식회사 라온아이엔씨
ValueCountFrequency (%)
주식회사 28
32.9%
합자회사 3
 
3.5%
동광레저 2
 
2.4%
썬밸리 2
 
2.4%
씨제이대한통운 2
 
2.4%
일성레저산업주식회사 2
 
2.4%
사단법인 2
 
2.4%
inc 1
 
1.2%
세원솔루션 1
 
1.2%
가드텍 1
 
1.2%
Other values (41) 41
48.2%
2024-03-16T13:18:20.747492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 43
 
8.9%
사 41
 
8.5%
39
 
8.1%
회 38
 
7.9%
식 31
 
6.4%
이 13
 
2.7%
인 10
 
2.1%
( 8
 
1.7%
스 8
 
1.7%
) 8
 
1.7%
Other values (127) 242
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 415
86.3%
Space Separator 39
 
8.1%
Open Punctuation 8
 
1.7%
Close Punctuation 8
 
1.7%
Uppercase Letter 8
 
1.7%
Lowercase Letter 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 43
 
10.4%
사 41
 
9.9%
회 38
 
9.2%
식 31
 
7.5%
이 13
 
3.1%
인 10
 
2.4%
스 8
 
1.9%
합 8
 
1.9%
에 7
 
1.7%
자 7
 
1.7%
Other values (114) 209
50.4%
Uppercase Letter
ValueCountFrequency (%)
E 2
25.0%
T 1
12.5%
H 1
12.5%
B 1
12.5%
I 1
12.5%
L 1
12.5%
U 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
c 1
50.0%
n 1
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 415
86.3%
Common 56
 
11.6%
Latin 10
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 43
 
10.4%
사 41
 
9.9%
회 38
 
9.2%
식 31
 
7.5%
이 13
 
3.1%
인 10
 
2.4%
스 8
 
1.9%
합 8
 
1.9%
에 7
 
1.7%
자 7
 
1.7%
Other values (114) 209
50.4%
Latin
ValueCountFrequency (%)
E 2
20.0%
T 1
10.0%
H 1
10.0%
B 1
10.0%
I 1
10.0%
L 1
10.0%
U 1
10.0%
c 1
10.0%
n 1
10.0%
Common
ValueCountFrequency (%)
39
69.6%
( 8
 
14.3%
) 8
 
14.3%
. 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 415
86.3%
ASCII 66
 
13.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 43
 
10.4%
사 41
 
9.9%
회 38
 
9.2%
식 31
 
7.5%
이 13
 
3.1%
인 10
 
2.4%
스 8
 
1.9%
합 8
 
1.9%
에 7
 
1.7%
자 7
 
1.7%
Other values (114) 209
50.4%
ASCII
ValueCountFrequency (%)
39
59.1%
( 8
 
12.1%
) 8
 
12.1%
E 2
 
3.0%
T 1
 
1.5%
H 1
 
1.5%
B 1
 
1.5%
I 1
 
1.5%
L 1
 
1.5%
U 1
 
1.5%
Other values (3) 3
 
4.5%

영업소 주소(도로명)
Text

Distinct560
Distinct (%)94.3%
Missing4
Missing (%)0.7%
Memory size4.8 KiB
2024-03-16T13:18:21.228829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length41
Mean length23.622896
Min length13

Characters and Unicode

Total characters14032
Distinct characters198
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

Unique533 ?
Unique (%)89.7%

Sample

1st row경기도 여주시 신륵사길 29
2nd row경기도 여주시 신륵사길 13
3rd row경기도 여주시 여흥로69번길 21
4th row경기도 여주시 청심로 162
5th row경기도 여주시 청심로 165-48
ValueCountFrequency (%)
경기도 594
 
18.3%
여주시 594
 
18.3%
1층 114
 
3.5%
세종로 84
 
2.6%
가남읍 64
 
2.0%
홍문동 49
 
1.5%
하동 43
 
1.3%
2층 41
 
1.3%
청심로 38
 
1.2%
여양로 37
 
1.1%
Other values (584) 1595
49.0%
2024-03-16T13:18:22.081131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2659
18.9%
1 809
 
5.8%
여 743
 
5.3%
주 631
 
4.5%
도 621
 
4.4%
경 615
 
4.4%
시 596
 
4.2%
기 595
 
4.2%
로 496
 
3.5%
2 377
 
2.7%
Other values (188) 5890
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7702
54.9%
Space Separator 2659
 
18.9%
Decimal Number 2603
 
18.6%
Other Punctuation 296
 
2.1%
Close Punctuation 271
 
1.9%
Open Punctuation 271
 
1.9%
Dash Punctuation 197
 
1.4%
Uppercase Letter 26
 
0.2%
Math Symbol 4
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
여 743
 
9.6%
주 631
 
8.2%
도 621
 
8.1%
경 615
 
8.0%
시 596
 
7.7%
기 595
 
7.7%
로 496
 
6.4%
동 354
 
4.6%
길 204
 
2.6%
층 179
 
2.3%
Other values (165) 2668
34.6%
Decimal Number
ValueCountFrequency (%)
1 809
31.1%
2 377
14.5%
3 260
 
10.0%
0 228
 
8.8%
4 201
 
7.7%
6 185
 
7.1%
5 170
 
6.5%
7 134
 
5.1%
8 134
 
5.1%
9 105
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 13
50.0%
K 6
23.1%
B 5
 
19.2%
A 1
 
3.8%
D 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 294
99.3%
@ 2
 
0.7%
Space Separator
ValueCountFrequency (%)
2659
100.0%
Close Punctuation
ValueCountFrequency (%)
) 271
100.0%
Open Punctuation
ValueCountFrequency (%)
( 271
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 197
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7702
54.9%
Common 6301
44.9%
Latin 29
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
여 743
 
9.6%
주 631
 
8.2%
도 621
 
8.1%
경 615
 
8.0%
시 596
 
7.7%
기 595
 
7.7%
로 496
 
6.4%
동 354
 
4.6%
길 204
 
2.6%
층 179
 
2.3%
Other values (165) 2668
34.6%
Common
ValueCountFrequency (%)
2659
42.2%
1 809
 
12.8%
2 377
 
6.0%
, 294
 
4.7%
) 271
 
4.3%
( 271
 
4.3%
3 260
 
4.1%
0 228
 
3.6%
4 201
 
3.2%
- 197
 
3.1%
Other values (7) 734
 
11.6%
Latin
ValueCountFrequency (%)
C 13
44.8%
K 6
20.7%
B 5
 
17.2%
e 3
 
10.3%
A 1
 
3.4%
D 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7702
54.9%
ASCII 6330
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2659
42.0%
1 809
 
12.8%
2 377
 
6.0%
, 294
 
4.6%
) 271
 
4.3%
( 271
 
4.3%
3 260
 
4.1%
0 228
 
3.6%
4 201
 
3.2%
- 197
 
3.1%
Other values (13) 763
 
12.1%
Hangul
ValueCountFrequency (%)
여 743
 
9.6%
주 631
 
8.2%
도 621
 
8.1%
경 615
 
8.0%
시 596
 
7.7%
기 595
 
7.7%
로 496
 
6.4%
동 354
 
4.6%
길 204
 
2.6%
층 179
 
2.3%
Other values (165) 2668
34.6%

영업소 주소(지번)
Text

Distinct527
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-03-16T13:18:22.431093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length19.729097
Min length14

Characters and Unicode

Total characters11798
Distinct characters175
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

Unique472 ?
Unique (%)78.9%

Sample

1st row경기도 여주시 천송동 290-2
2nd row경기도 여주시 천송동 296-1
3rd row경기도 여주시 하동 184-3
4th row경기도 여주시 홍문동 124-4
5th row경기도 여주시 상동 215-1
ValueCountFrequency (%)
경기도 598
22.2%
여주시 598
22.2%
홍문동 90
 
3.3%
하동 67
 
2.5%
가남읍 64
 
2.4%
창동 51
 
1.9%
현암동 38
 
1.4%
태평리 38
 
1.4%
오학동 38
 
1.4%
상동 36
 
1.3%
Other values (609) 1079
40.0%
2024-03-16T13:18:23.064389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2678
22.7%
주 625
 
5.3%
여 623
 
5.3%
도 604
 
5.1%
경 601
 
5.1%
기 599
 
5.1%
시 598
 
5.1%
1 531
 
4.5%
- 487
 
4.1%
동 460
 
3.9%
Other values (165) 3992
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6156
52.2%
Space Separator 2678
22.7%
Decimal Number 2442
 
20.7%
Dash Punctuation 487
 
4.1%
Uppercase Letter 18
 
0.2%
Lowercase Letter 5
 
< 0.1%
Other Punctuation 4
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 625
 
10.2%
여 623
 
10.1%
도 604
 
9.8%
경 601
 
9.8%
기 599
 
9.7%
시 598
 
9.7%
동 460
 
7.5%
리 177
 
2.9%
면 107
 
1.7%
문 92
 
1.5%
Other values (142) 1670
27.1%
Decimal Number
ValueCountFrequency (%)
1 531
21.7%
2 340
13.9%
3 279
11.4%
4 229
9.4%
6 202
 
8.3%
5 185
 
7.6%
9 178
 
7.3%
7 178
 
7.3%
0 162
 
6.6%
8 158
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
e 3
60.0%
c 1
 
20.0%
m 1
 
20.0%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
. 1
25.0%
@ 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 12
66.7%
K 6
33.3%
Space Separator
ValueCountFrequency (%)
2678
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 487
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6156
52.2%
Common 5619
47.6%
Latin 23
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 625
 
10.2%
여 623
 
10.1%
도 604
 
9.8%
경 601
 
9.8%
기 599
 
9.7%
시 598
 
9.7%
동 460
 
7.5%
리 177
 
2.9%
면 107
 
1.7%
문 92
 
1.5%
Other values (142) 1670
27.1%
Common
ValueCountFrequency (%)
2678
47.7%
1 531
 
9.5%
- 487
 
8.7%
2 340
 
6.1%
3 279
 
5.0%
4 229
 
4.1%
6 202
 
3.6%
5 185
 
3.3%
9 178
 
3.2%
7 178
 
3.2%
Other values (8) 332
 
5.9%
Latin
ValueCountFrequency (%)
C 12
52.2%
K 6
26.1%
e 3
 
13.0%
c 1
 
4.3%
m 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6156
52.2%
ASCII 5642
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2678
47.5%
1 531
 
9.4%
- 487
 
8.6%
2 340
 
6.0%
3 279
 
4.9%
4 229
 
4.1%
6 202
 
3.6%
5 185
 
3.3%
9 178
 
3.2%
7 178
 
3.2%
Other values (13) 355
 
6.3%
Hangul
ValueCountFrequency (%)
주 625
 
10.2%
여 623
 
10.1%
도 604
 
9.8%
경 601
 
9.8%
기 599
 
9.7%
시 598
 
9.7%
동 460
 
7.5%
리 177
 
2.9%
면 107
 
1.7%
문 92
 
1.5%
Other values (142) 1670
27.1%

영업장면적
Real number (ℝ)

HIGH CORRELATION 

Distinct457
Distinct (%)76.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean200.87002
Minimum3.7
Maximum10840.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-16T13:18:23.297542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile15.16
Q129.75
median41.3
Q383.51
95-th percentile873.32
Maximum10840.72
Range10837.02
Interquartile range (IQR)53.76

Descriptive statistics

Standard deviation659.64546
Coefficient of variation (CV)3.2839419
Kurtosis143.98424
Mean200.87002
Median Absolute Deviation (MAD)16.8
Skewness10.550784
Sum119919.4
Variance435132.13
MonotonicityNot monotonic
2024-03-16T13:18:23.559462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 26
 
4.3%
26.4 10
 
1.7%
16.5 7
 
1.2%
66.0 7
 
1.2%
30.0 7
 
1.2%
19.8 7
 
1.2%
42.9 5
 
0.8%
31.5 5
 
0.8%
44.0 4
 
0.7%
23.1 4
 
0.7%
Other values (447) 515
86.1%
ValueCountFrequency (%)
3.7 1
0.2%
4.08 1
0.2%
4.32 1
0.2%
5.71 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
6.8 1
0.2%
8.25 1
0.2%
9.0 2
0.3%
9.91 1
0.2%
ValueCountFrequency (%)
10840.72 1
0.2%
7513.0 1
0.2%
5091.94 1
0.2%
2979.07 1
0.2%
2615.91 1
0.2%
2544.53 1
0.2%
2050.4 1
0.2%
1908.15 1
0.2%
1794.27 1
0.2%
1495.62 1
0.2%

소재지전화
Text

MISSING 

Distinct344
Distinct (%)98.0%
Missing247
Missing (%)41.3%
Memory size4.8 KiB
2024-03-16T13:18:23.951345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.017094
Min length11

Characters and Unicode

Total characters4218
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

Unique338 ?
Unique (%)96.3%

Sample

1st row031-885-2476
2nd row031-885-2508
3rd row031-884-2515
4th row031-884-2662
5th row031-884-0191
ValueCountFrequency (%)
031-883-1199 3
 
0.9%
031-883-1157 2
 
0.6%
031-885-4800 2
 
0.6%
031-880-3889 2
 
0.6%
031-884-0552 2
 
0.6%
031-881-3325 2
 
0.6%
031-881-1873 1
 
0.3%
031-885-2476 1
 
0.3%
031-886-2012 1
 
0.3%
031-883-9988 1
 
0.3%
Other values (334) 334
95.2%
2024-03-16T13:18:24.549708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 828
19.6%
- 702
16.6%
3 551
13.1%
0 538
12.8%
1 525
12.4%
5 221
 
5.2%
2 203
 
4.8%
4 197
 
4.7%
6 179
 
4.2%
9 142
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3516
83.4%
Dash Punctuation 702
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 828
23.5%
3 551
15.7%
0 538
15.3%
1 525
14.9%
5 221
 
6.3%
2 203
 
5.8%
4 197
 
5.6%
6 179
 
5.1%
9 142
 
4.0%
7 132
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4218
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 828
19.6%
- 702
16.6%
3 551
13.1%
0 538
12.8%
1 525
12.4%
5 221
 
5.2%
2 203
 
4.8%
4 197
 
4.7%
6 179
 
4.2%
9 142
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 828
19.6%
- 702
16.6%
3 551
13.1%
0 538
12.8%
1 525
12.4%
5 221
 
5.2%
2 203
 
4.8%
4 197
 
4.7%
6 179
 
4.2%
9 142
 
3.4%

영업자시작일
Date

Distinct520
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum1977-08-18 00:00:00
Maximum2024-02-29 00:00:00
2024-03-16T13:18:24.828919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:25.074562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

객실수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6772575
Minimum0
Maximum203
Zeros504
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-16T13:18:25.378394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22
Maximum203
Range203
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.226079
Coefficient of variation (CV)3.5967236
Kurtosis125.75175
Mean3.6772575
Median Absolute Deviation (MAD)0
Skewness9.5084076
Sum2199
Variance174.92916
MonotonicityNot monotonic
2024-03-16T13:18:25.627973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 504
84.3%
19 13
 
2.2%
16 11
 
1.8%
18 7
 
1.2%
28 6
 
1.0%
20 5
 
0.8%
15 4
 
0.7%
36 4
 
0.7%
24 4
 
0.7%
32 3
 
0.5%
Other values (23) 37
 
6.2%
ValueCountFrequency (%)
0 504
84.3%
4 2
 
0.3%
5 1
 
0.2%
6 1
 
0.2%
7 2
 
0.3%
8 1
 
0.2%
9 1
 
0.2%
10 3
 
0.5%
11 2
 
0.3%
12 3
 
0.5%
ValueCountFrequency (%)
203 1
 
0.2%
168 1
 
0.2%
48 1
 
0.2%
40 1
 
0.2%
36 4
0.7%
35 1
 
0.2%
34 2
 
0.3%
32 3
0.5%
31 2
 
0.3%
28 6
1.0%

한실수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)5.1%
Missing163
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean1.262069
Minimum0
Maximum109
Zeros373
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-16T13:18:25.905590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum109
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.129577
Coefficient of variation (CV)4.8567687
Kurtosis221.65907
Mean1.262069
Median Absolute Deviation (MAD)0
Skewness13.170317
Sum549
Variance37.571715
MonotonicityNot monotonic
2024-03-16T13:18:26.130437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 373
62.4%
3 7
 
1.2%
4 7
 
1.2%
2 7
 
1.2%
5 5
 
0.8%
10 5
 
0.8%
1 5
 
0.8%
6 4
 
0.7%
7 4
 
0.7%
8 3
 
0.5%
Other values (12) 15
 
2.5%
(Missing) 163
27.3%
ValueCountFrequency (%)
0 373
62.4%
1 5
 
0.8%
2 7
 
1.2%
3 7
 
1.2%
4 7
 
1.2%
5 5
 
0.8%
6 4
 
0.7%
7 4
 
0.7%
8 3
 
0.5%
9 2
 
0.3%
ValueCountFrequency (%)
109 1
0.2%
25 1
0.2%
20 2
0.3%
19 1
0.2%
18 1
0.2%
17 1
0.2%
16 1
0.2%
15 2
0.3%
14 1
0.2%
13 1
0.2%

양실수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)7.6%
Missing163
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean3.7931034
Minimum0
Maximum178
Zeros350
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-16T13:18:26.370969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.6
Maximum178
Range178
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.621268
Coefficient of variation (CV)3.0637888
Kurtosis117.74314
Mean3.7931034
Median Absolute Deviation (MAD)0
Skewness8.6952885
Sum1650
Variance135.05387
MonotonicityNot monotonic
2024-03-16T13:18:26.606066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 350
58.5%
16 9
 
1.5%
14 5
 
0.8%
18 5
 
0.8%
12 5
 
0.8%
20 5
 
0.8%
17 4
 
0.7%
13 4
 
0.7%
15 4
 
0.7%
10 4
 
0.7%
Other values (23) 40
 
6.7%
(Missing) 163
27.3%
ValueCountFrequency (%)
0 350
58.5%
3 1
 
0.2%
4 3
 
0.5%
5 2
 
0.3%
6 2
 
0.3%
7 2
 
0.3%
8 1
 
0.2%
9 1
 
0.2%
10 4
 
0.7%
11 3
 
0.5%
ValueCountFrequency (%)
178 1
0.2%
59 1
0.2%
48 1
0.2%
36 2
0.3%
35 1
0.2%
31 2
0.3%
30 1
0.2%
29 1
0.2%
28 2
0.3%
26 2
0.3%

의자수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)2.2%
Missing47
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean2.0036298
Minimum0
Maximum18
Zeros217
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-16T13:18:26.818524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0714478
Coefficient of variation (CV)1.0338476
Kurtosis6.5745518
Mean2.0036298
Median Absolute Deviation (MAD)2
Skewness1.5078047
Sum1104
Variance4.2908959
MonotonicityNot monotonic
2024-03-16T13:18:27.035151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 217
36.3%
3 132
22.1%
2 79
 
13.2%
4 57
 
9.5%
5 25
 
4.2%
1 17
 
2.8%
6 14
 
2.3%
8 4
 
0.7%
10 3
 
0.5%
9 1
 
0.2%
Other values (2) 2
 
0.3%
(Missing) 47
 
7.9%
ValueCountFrequency (%)
0 217
36.3%
1 17
 
2.8%
2 79
 
13.2%
3 132
22.1%
4 57
 
9.5%
5 25
 
4.2%
6 14
 
2.3%
7 1
 
0.2%
8 4
 
0.7%
9 1
 
0.2%
ValueCountFrequency (%)
18 1
 
0.2%
10 3
 
0.5%
9 1
 
0.2%
8 4
 
0.7%
7 1
 
0.2%
6 14
 
2.3%
5 25
 
4.2%
4 57
9.5%
3 132
22.1%
2 79
13.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)1.4%
Missing172
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean0.3286385
Minimum0
Maximum5
Zeros353
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-16T13:18:27.244037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80876174
Coefficient of variation (CV)2.4609464
Kurtosis7.0464829
Mean0.3286385
Median Absolute Deviation (MAD)0
Skewness2.6580743
Sum140
Variance0.65409555
MonotonicityNot monotonic
2024-03-16T13:18:27.431496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 353
59.0%
2 34
 
5.7%
1 25
 
4.2%
3 10
 
1.7%
4 3
 
0.5%
5 1
 
0.2%
(Missing) 172
28.8%
ValueCountFrequency (%)
0 353
59.0%
1 25
 
4.2%
2 34
 
5.7%
3 10
 
1.7%
4 3
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 3
 
0.5%
3 10
 
1.7%
2 34
 
5.7%
1 25
 
4.2%
0 353
59.0%

욕실수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
0
427 
<NA>
161 
2
 
8
6
 
1
1
 
1

Length

Max length4
Median length1
Mean length1.8076923
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 427
71.4%
<NA> 161
 
26.9%
2 8
 
1.3%
6 1
 
0.2%
1 1
 
0.2%

Length

2024-03-16T13:18:27.631775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:18:27.839995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 427
71.4%
na 161
 
26.9%
2 8
 
1.3%
6 1
 
0.2%
1 1
 
0.2%

발한실
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size730.0 B
False
591 
True
 
7
ValueCountFrequency (%)
False 591
98.8%
True 7
 
1.2%
2024-03-16T13:18:27.994687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

세탁기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
0
417 
<NA>
162 
2
 
8
1
 
7
3
 
3

Length

Max length4
Median length1
Mean length1.812709
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 417
69.7%
<NA> 162
 
27.1%
2 8
 
1.3%
1 7
 
1.2%
3 3
 
0.5%
4 1
 
0.2%

Length

2024-03-16T13:18:28.192901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:18:28.355174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 417
69.7%
na 162
 
27.1%
2 8
 
1.3%
1 7
 
1.2%
3 3
 
0.5%
4 1
 
0.2%

회수건조기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
0
417 
<NA>
146 
1
 
31
2
 
3
3
 
1

Length

Max length4
Median length1
Mean length1.7324415
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 417
69.7%
<NA> 146
 
24.4%
1 31
 
5.2%
2 3
 
0.5%
3 1
 
0.2%

Length

2024-03-16T13:18:28.556645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:18:28.761630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 417
69.7%
na 146
 
24.4%
1 31
 
5.2%
2 3
 
0.5%
3 1
 
0.2%

Interactions

2024-03-16T13:18:16.359208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:10.901708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:11.985115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:13.192593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:14.149909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:15.324455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:16.502180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:11.090299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:12.198685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:13.348209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:14.319000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:15.469037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:16.677204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:11.322024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:12.520396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:13.515364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:14.520585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:15.625621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:16.859688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:11.516610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:12.691309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:13.684417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:14.720695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:15.792081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:17.071101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:11.684737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:12.860891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:13.852251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:14.983243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:15.974775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:17.207286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:11.835953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:13.023598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:14.007505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:15.156250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:16.185053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:18:28.920930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명법인명영업장면적객실수한실수양실수의자수침대수욕실수발한실세탁기수회수건조기수
업종명1.0000.0000.3690.5120.0000.4250.7210.7280.7070.7900.6860.742
법인명0.0001.0000.0000.0000.0000.000NaNNaN0.0000.0001.0001.000
영업장면적0.3690.0001.0000.9190.9230.8620.0000.0000.4850.2970.0000.000
객실수0.5120.0000.9191.0000.8430.9900.1510.0000.0000.0000.0000.000
한실수0.0000.0000.9230.8431.0000.8400.0000.0000.0000.0000.0000.000
양실수0.4250.0000.8620.9900.8401.0000.1270.0000.0000.0000.0000.000
의자수0.721NaN0.0000.1510.0000.1271.0000.1290.0000.0370.0000.000
침대수0.728NaN0.0000.0000.0000.0000.1291.0000.0000.0000.0000.000
욕실수0.7070.0000.4850.0000.0000.0000.0000.0001.0000.9300.0000.000
발한실0.7900.0000.2970.0000.0000.0000.0370.0000.9301.0000.0000.000
세탁기수0.6861.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.627
회수건조기수0.7421.0000.0000.0000.0000.0000.0000.0000.0000.0000.6271.000
2024-03-16T13:18:29.582453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회수건조기수업종명세탁기수발한실욕실수
회수건조기수1.0000.4950.5550.0000.000
업종명0.4951.0000.4120.7070.458
세탁기수0.5550.4121.0000.0000.000
발한실0.0000.7070.0001.0000.760
욕실수0.0000.4580.0000.7601.000
2024-03-16T13:18:29.764186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업장면적객실수한실수양실수의자수침대수업종명욕실수발한실세탁기수회수건조기수
영업장면적1.0000.6180.5290.666-0.265-0.1340.1730.3320.2130.0000.000
객실수0.6181.0000.7930.948-0.493-0.2380.2770.0000.0000.0000.000
한실수0.5290.7931.0000.660-0.378-0.1860.0000.0000.0000.0000.000
양실수0.6660.9480.6601.000-0.456-0.2240.2200.0000.0000.0000.000
의자수-0.265-0.493-0.378-0.4561.000-0.0930.3530.0000.0390.0000.000
침대수-0.134-0.238-0.186-0.224-0.0931.0000.4250.0000.0000.0000.000
업종명0.1730.2770.0000.2200.3530.4251.0000.4580.7070.4120.495
욕실수0.3320.0000.0000.0000.0000.0000.4581.0000.7600.0000.000
발한실0.2130.0000.0000.0000.0390.0000.7070.7601.0000.0000.000
세탁기수0.0000.0000.0000.0000.0000.0000.4120.0000.0001.0000.555
회수건조기수0.0000.0000.0000.0000.0000.0000.4950.0000.0000.5551.000

Missing values

2024-03-16T13:18:17.383392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:18:17.749082image/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.
2024-03-16T13:18:18.391231image/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

업종명신고일자업소명법인명영업소 주소(도로명)영업소 주소(지번)영업장면적소재지전화영업자시작일객실수한실수양실수의자수침대수욕실수발한실세탁기수회수건조기수
0숙박업(일반)1963-09-16옥천유스텔<NA>경기도 여주시 신륵사길 29경기도 여주시 천송동 290-2340.2031-885-24762021-11-0519190000N00
1숙박업(일반)1969-06-28신륵여관<NA>경기도 여주시 신륵사길 13경기도 여주시 천송동 296-1445.12031-885-25082001-12-3119109000N00
2숙박업(일반)1970-10-29유성여인숙<NA>경기도 여주시 여흥로69번길 21경기도 여주시 하동 184-3100.3<NA>2019-07-10633000N00
3숙박업(일반)1971-10-18씨티모텔<NA>경기도 여주시 청심로 162경기도 여주시 홍문동 124-4302.44<NA>2023-08-0118414000N00
4숙박업(일반)1972-07-10명성장여관<NA>경기도 여주시 청심로 165-48경기도 여주시 상동 215-1260.13031-884-25152021-09-2713013000N00
5숙박업(일반)1972-05-03한양여관<NA>경기도 여주시 신륵사길 17경기도 여주시 천송동 290-12460.1031-884-26622015-04-0615150000N00
6숙박업(일반)1974-11-09동해여관<NA>경기도 여주시 가남읍 태평로 27경기도 여주시 가남읍 태평리 141-14222.5031-884-01912015-01-14808000N00
7숙박업(일반)1974-06-12대성여관<NA>경기도 여주시 신륵사길 19경기도 여주시 천송동 290-9453.75031-885-23092021-10-1915105000N00
8숙박업(일반)1980-12-04성원여관<NA>경기도 여주시 세종로 30-1경기도 여주시 홍문동 66-4259.7031-882-41002022-10-121064000N00
9숙박업(일반)1980-10-23리버하우스여관<NA>경기도 여주시 금사면 이포로 72경기도 여주시 금사면 이포리 182-11399.44031-881-49582020-01-2114311000N00
업종명신고일자업소명법인명영업소 주소(도로명)영업소 주소(지번)영업장면적소재지전화영업자시작일객실수한실수양실수의자수침대수욕실수발한실세탁기수회수건조기수
588네일미용업, 화장ㆍ분장 미용업2023-04-06네일츄르<NA>경기도 여주시 현암3길 8, 1동 2층 218호 (현암동)경기도 여주시 현암동 191-8149.5<NA>2023-04-06000410N00
589네일미용업, 화장ㆍ분장 미용업2023-08-25아람다움<NA>경기도 여주시 향교로 117, 2층 (월송동)경기도 여주시 월송동 118-414.61<NA>2023-08-25000410N00
590네일미용업, 화장ㆍ분장 미용업2023-09-05라인뷰티<NA>경기도 여주시 여양로 231, 1층 3호 (천송동)경기도 여주시 천송동 590-184.08<NA>2023-09-05000010N00
591일반미용업, 피부미용업, 화장ㆍ분장 미용업2022-03-07첫눈에 반하다<NA>경기도 여주시 세종로 394-10, 102호 (점봉동, 부영아파트)경기도 여주시 점봉동 430-6 부영아파트24.75<NA>2022-03-07000110N00
592일반미용업, 네일미용업, 화장ㆍ분장 미용업2016-06-22쥬헤어<NA>경기도 여주시 여흥로 166 (상동)경기도 여주시 상동 324-1133.0<NA>2016-06-22000200N00
593일반미용업, 네일미용업, 화장ㆍ분장 미용업2017-06-28현 헤어<NA>경기도 여주시 세종로 62-1 (창동)경기도 여주시 창동 159-967.1031-886-70832017-06-28000000N00
594일반미용업, 네일미용업, 화장ㆍ분장 미용업2017-08-04Oic<NA>경기도 여주시 도예로 83-12 (오학동)경기도 여주시 오학동 279-4118.92<NA>2017-08-04000300N00
595일반미용업, 네일미용업, 화장ㆍ분장 미용업2017-11-15더다온헤어.hair Salon<NA>경기도 여주시 가남읍 경충대로 1468경기도 여주시 가남읍 신해리 635-3665.8031-882-68802023-07-06000800N00
596일반미용업, 네일미용업, 화장ㆍ분장 미용업2022-12-14헤어 13월<NA>경기도 여주시 세종로 173-62, 102호 (홍문동)경기도 여주시 홍문동 377-1347.97<NA>2024-02-13000400N00
597피부미용업, 네일미용업, 화장ㆍ분장 미용업2018-07-09백경뷰티<NA>경기도 여주시 세종로 246, 1층 (교동)경기도 여주시 교동 84-696.39<NA>2018-07-09000230N00