Overview

Dataset statistics

Number of variables19
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory156.3 B

Variable types

Numeric3
Text5
Categorical11

Alerts

induty_nm has constant value ""Constant
svc_trget_se has constant value ""Constant
provd_instt_nm has constant value ""Constant
data_stdde is highly overall correlated with skey and 7 other fieldsHigh correlation
hmpg is highly overall correlated with skey and 7 other fieldsHigh correlation
setle_mth is highly overall correlated with skey and 7 other fieldsHigh correlation
sbrs is highly overall correlated with skey and 7 other fieldsHigh correlation
arnd_tursm_info is highly overall correlated with skey and 7 other fieldsHigh correlation
skey is highly overall correlated with sbrs and 5 other fieldsHigh correlation
provd_instt_code is highly overall correlated with fggg_guidance_sbc and 6 other fieldsHigh correlation
fggg_guidance_sbc is highly overall correlated with provd_instt_code and 6 other fieldsHigh correlation
prkplce_hold_at is highly overall correlated with skey and 7 other fieldsHigh correlation
telno is highly imbalanced (82.8%)Imbalance
skey has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:06:34.077451
Analysis finished2023-12-10 10:06:40.151361
Duration6.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.44
Minimum1
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:40.310422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum213
Range212
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation39.382178
Coefficient of variation (CV)0.6977707
Kurtosis5.317571
Mean56.44
Median Absolute Deviation (MAD)25.5
Skewness1.700625
Sum5644
Variance1550.956
MonotonicityNot monotonic
2023-12-10T19:06:40.576173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
213 1
1.0%
212 1
1.0%
211 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

bssh_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:40.997853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length21
Mean length9
Min length1

Characters and Unicode

Total characters900
Distinct characters199
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st rowSUITE
2nd rowJG하우스
3rd rowPENTHOUSE
4th row광안하우스(GWANGAN HOUSE)
5th row게스트하우스 은가비
ValueCountFrequency (%)
house 13
 
7.4%
하우스 10
 
5.7%
게스트하우스 6
 
3.4%
홈 4
 
2.3%
guest 4
 
2.3%
casa 2
 
1.1%
홈스테이 2
 
1.1%
스위트 2
 
1.1%
부산 2
 
1.1%
단디하우스 2
 
1.1%
Other values (128) 129
73.3%
2023-12-10T19:06:41.676961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76
 
8.4%
스 64
 
7.1%
하 41
 
4.6%
우 40
 
4.4%
e 30
 
3.3%
a 24
 
2.7%
s 23
 
2.6%
트 17
 
1.9%
S 17
 
1.9%
u 16
 
1.8%
Other values (189) 552
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 440
48.9%
Lowercase Letter 194
21.6%
Uppercase Letter 152
 
16.9%
Space Separator 76
 
8.4%
Other Punctuation 14
 
1.6%
Open Punctuation 8
 
0.9%
Close Punctuation 8
 
0.9%
Decimal Number 5
 
0.6%
Dash Punctuation 2
 
0.2%
Final Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 64
 
14.5%
하 41
 
9.3%
우 40
 
9.1%
트 17
 
3.9%
이 14
 
3.2%
게 11
 
2.5%
홈 7
 
1.6%
테 7
 
1.6%
리 6
 
1.4%
시 6
 
1.4%
Other values (129) 227
51.6%
Uppercase Letter
ValueCountFrequency (%)
S 17
 
11.2%
E 16
 
10.5%
O 13
 
8.6%
H 12
 
7.9%
U 12
 
7.9%
B 10
 
6.6%
N 9
 
5.9%
G 8
 
5.3%
C 6
 
3.9%
J 5
 
3.3%
Other values (14) 44
28.9%
Lowercase Letter
ValueCountFrequency (%)
e 30
15.5%
a 24
12.4%
s 23
11.9%
u 16
8.2%
n 14
 
7.2%
o 14
 
7.2%
i 10
 
5.2%
h 10
 
5.2%
t 8
 
4.1%
l 6
 
3.1%
Other values (12) 39
20.1%
Other Punctuation
ValueCountFrequency (%)
' 8
57.1%
; 2
 
14.3%
& 2
 
14.3%
: 1
 
7.1%
. 1
 
7.1%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
0 1
20.0%
3 1
20.0%
8 1
20.0%
Space Separator
ValueCountFrequency (%)
76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 437
48.6%
Latin 346
38.4%
Common 114
 
12.7%
Han 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 64
 
14.6%
하 41
 
9.4%
우 40
 
9.2%
트 17
 
3.9%
이 14
 
3.2%
게 11
 
2.5%
홈 7
 
1.6%
테 7
 
1.6%
리 6
 
1.4%
시 6
 
1.4%
Other values (126) 224
51.3%
Latin
ValueCountFrequency (%)
e 30
 
8.7%
a 24
 
6.9%
s 23
 
6.6%
S 17
 
4.9%
u 16
 
4.6%
E 16
 
4.6%
n 14
 
4.0%
o 14
 
4.0%
O 13
 
3.8%
H 12
 
3.5%
Other values (36) 167
48.3%
Common
ValueCountFrequency (%)
76
66.7%
' 8
 
7.0%
( 8
 
7.0%
) 8
 
7.0%
; 2
 
1.8%
& 2
 
1.8%
2 2
 
1.8%
- 2
 
1.8%
: 1
 
0.9%
’ 1
 
0.9%
Other values (4) 4
 
3.5%
Han
ValueCountFrequency (%)
美 1
33.3%
和 1
33.3%
智 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
51.0%
Hangul 437
48.6%
CJK 3
 
0.3%
Punctuation 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76
 
16.6%
e 30
 
6.5%
a 24
 
5.2%
s 23
 
5.0%
S 17
 
3.7%
u 16
 
3.5%
E 16
 
3.5%
n 14
 
3.1%
o 14
 
3.1%
O 13
 
2.8%
Other values (49) 216
47.1%
Hangul
ValueCountFrequency (%)
스 64
 
14.6%
하 41
 
9.4%
우 40
 
9.2%
트 17
 
3.9%
이 14
 
3.2%
게 11
 
2.5%
홈 7
 
1.6%
테 7
 
1.6%
리 6
 
1.4%
시 6
 
1.4%
Other values (126) 224
51.3%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%
CJK
ValueCountFrequency (%)
美 1
33.3%
和 1
33.3%
智 1
33.3%

induty_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
민박
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
민박 100
100.0%

Length

2023-12-10T19:06:41.914587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:42.069705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민박 100
100.0%

svc_trget_se
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
외국인
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
외국인 100
100.0%

Length

2023-12-10T19:06:42.241821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:42.421205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
외국인 100
100.0%

fggg_guidance_sbc
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가능
53 
영어
20 
Y
8 
-
8 
유
8 
Other values (3)
 
3

Length

Max length7
Median length2
Mean length1.84
Min length1

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row영어, 중국어
2nd row가능
3rd row영어
4th row영어
5th row일어

Common Values

ValueCountFrequency (%)
가능 53
53.0%
영어 20
 
20.0%
Y 8
 
8.0%
- 8
 
8.0%
유 8
 
8.0%
영어, 중국어 1
 
1.0%
일어 1
 
1.0%
영어,일어 1
 
1.0%

Length

2023-12-10T19:06:42.599919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:42.839483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가능 53
52.5%
영어 21
 
20.8%
y 8
 
7.9%
8
 
7.9%
유 8
 
7.9%
중국어 1
 
1.0%
일어 1
 
1.0%
영어,일어 1
 
1.0%

rdnmadr
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:43.251958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length46
Mean length33.32
Min length18

Characters and Unicode

Total characters3332
Distinct characters171
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row부산광역시 수영구 광안동 130-43
2nd row부산광역시 영도구 태종로 40, 1401호 (대교동1가, 브릿지타워)
3rd row부산광역시 수영구 광안동 130-43
4th row부산광역시 수영구 민락동 33-9
5th row부산광역시 수영구 광안동 693-33
ValueCountFrequency (%)
부산광역시 100
 
16.5%
해운대구 42
 
6.9%
수영구 18
 
3.0%
광안동 14
 
2.3%
서구 8
 
1.3%
중구 8
 
1.3%
남구 8
 
1.3%
중동 7
 
1.2%
좌동 7
 
1.2%
우동 6
 
1.0%
Other values (291) 387
64.0%
2023-12-10T19:06:43.987565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
505
 
15.2%
1 184
 
5.5%
동 146
 
4.4%
광 118
 
3.5%
산 112
 
3.4%
구 106
 
3.2%
부 104
 
3.1%
시 104
 
3.1%
, 104
 
3.1%
역 101
 
3.0%
Other values (161) 1748
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1861
55.9%
Decimal Number 665
 
20.0%
Space Separator 505
 
15.2%
Other Punctuation 105
 
3.2%
Open Punctuation 80
 
2.4%
Close Punctuation 80
 
2.4%
Dash Punctuation 35
 
1.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 146
 
7.8%
광 118
 
6.3%
산 112
 
6.0%
구 106
 
5.7%
부 104
 
5.6%
시 104
 
5.6%
역 101
 
5.4%
대 80
 
4.3%
로 77
 
4.1%
운 57
 
3.1%
Other values (144) 856
46.0%
Decimal Number
ValueCountFrequency (%)
1 184
27.7%
0 84
12.6%
2 83
12.5%
3 70
 
10.5%
4 60
 
9.0%
5 47
 
7.1%
7 40
 
6.0%
9 37
 
5.6%
6 34
 
5.1%
8 26
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 104
99.0%
. 1
 
1.0%
Space Separator
ValueCountFrequency (%)
505
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1861
55.9%
Common 1471
44.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 146
 
7.8%
광 118
 
6.3%
산 112
 
6.0%
구 106
 
5.7%
부 104
 
5.6%
시 104
 
5.6%
역 101
 
5.4%
대 80
 
4.3%
로 77
 
4.1%
운 57
 
3.1%
Other values (144) 856
46.0%
Common
ValueCountFrequency (%)
505
34.3%
1 184
 
12.5%
, 104
 
7.1%
0 84
 
5.7%
2 83
 
5.6%
( 80
 
5.4%
) 80
 
5.4%
3 70
 
4.8%
4 60
 
4.1%
5 47
 
3.2%
Other values (7) 174
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1861
55.9%
ASCII 1471
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
505
34.3%
1 184
 
12.5%
, 104
 
7.1%
0 84
 
5.7%
2 83
 
5.6%
( 80
 
5.4%
) 80
 
5.4%
3 70
 
4.8%
4 60
 
4.1%
5 47
 
3.2%
Other values (7) 174
 
11.8%
Hangul
ValueCountFrequency (%)
동 146
 
7.8%
광 118
 
6.3%
산 112
 
6.0%
구 106
 
5.7%
부 104
 
5.6%
시 104
 
5.6%
역 101
 
5.4%
대 80
 
4.3%
로 77
 
4.1%
운 57
 
3.1%
Other values (144) 856
46.0%

lnm_adres
Text

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:44.697498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length20.79
Min length1

Characters and Unicode

Total characters2079
Distinct characters102
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)87.0%

Sample

1st row부산광역시 수영구 수영로606번길 113, 1층 (광안동)
2nd row부산광역시 영도구 대교동1가 1( 브릿지타워 1401호)
3rd row부산광역시 수영구 수영로606번길 113, 2층 (광안동)
4th row부산광역시 수영구 민락본동로19번길 66, 4층 (민락동)
5th row부산광역시 수영구 광서로 5-12 (광안동)
ValueCountFrequency (%)
부산광역시 91
21.5%
해운대구 42
 
9.9%
수영구 18
 
4.2%
광안동 14
 
3.3%
좌동 10
 
2.4%
중동 10
 
2.4%
우동 10
 
2.4%
9
 
2.1%
서구 8
 
1.9%
남구 8
 
1.9%
Other values (152) 204
48.1%
2023-12-10T19:06:45.688510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
324
 
15.6%
1 119
 
5.7%
광 115
 
5.5%
동 99
 
4.8%
부 94
 
4.5%
산 92
 
4.4%
역 91
 
4.4%
시 91
 
4.4%
구 91
 
4.4%
- 61
 
2.9%
Other values (92) 902
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1175
56.5%
Decimal Number 450
 
21.6%
Space Separator 324
 
15.6%
Dash Punctuation 61
 
2.9%
Open Punctuation 21
 
1.0%
Close Punctuation 21
 
1.0%
Other Punctuation 19
 
0.9%
Uppercase Letter 8
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 115
 
9.8%
동 99
 
8.4%
부 94
 
8.0%
산 92
 
7.8%
역 91
 
7.7%
시 91
 
7.7%
구 91
 
7.7%
대 54
 
4.6%
운 42
 
3.6%
해 42
 
3.6%
Other values (71) 364
31.0%
Decimal Number
ValueCountFrequency (%)
1 119
26.4%
4 50
11.1%
2 49
10.9%
3 47
 
10.4%
9 36
 
8.0%
0 35
 
7.8%
5 35
 
7.8%
8 34
 
7.6%
6 29
 
6.4%
7 16
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
O 2
25.0%
F 2
25.0%
H 1
12.5%
U 1
12.5%
S 1
12.5%
E 1
12.5%
Space Separator
ValueCountFrequency (%)
324
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1175
56.5%
Common 896
43.1%
Latin 8
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 115
 
9.8%
동 99
 
8.4%
부 94
 
8.0%
산 92
 
7.8%
역 91
 
7.7%
시 91
 
7.7%
구 91
 
7.7%
대 54
 
4.6%
운 42
 
3.6%
해 42
 
3.6%
Other values (71) 364
31.0%
Common
ValueCountFrequency (%)
324
36.2%
1 119
 
13.3%
- 61
 
6.8%
4 50
 
5.6%
2 49
 
5.5%
3 47
 
5.2%
9 36
 
4.0%
0 35
 
3.9%
5 35
 
3.9%
8 34
 
3.8%
Other values (5) 106
 
11.8%
Latin
ValueCountFrequency (%)
O 2
25.0%
F 2
25.0%
H 1
12.5%
U 1
12.5%
S 1
12.5%
E 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1175
56.5%
ASCII 904
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
324
35.8%
1 119
 
13.2%
- 61
 
6.7%
4 50
 
5.5%
2 49
 
5.4%
3 47
 
5.2%
9 36
 
4.0%
0 35
 
3.9%
5 35
 
3.9%
8 34
 
3.8%
Other values (11) 114
 
12.6%
Hangul
ValueCountFrequency (%)
광 115
 
9.8%
동 99
 
8.4%
부 94
 
8.0%
산 92
 
7.8%
역 91
 
7.7%
시 91
 
7.7%
구 91
 
7.7%
대 54
 
4.6%
운 42
 
3.6%
해 42
 
3.6%
Other values (71) 364
31.0%

telno
Categorical

IMBALANCE 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
94 
051-611-9250
 
1
051-333-9009
 
1
070-8823-6137
 
1
051-525-6849
 
1
Other values (2)
 
2

Length

Max length13
Median length1
Mean length1.67
Min length1

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 94
94.0%
051-611-9250 1
 
1.0%
051-333-9009 1
 
1.0%
070-8823-6137 1
 
1.0%
051-525-6849 1
 
1.0%
051-744-4474 1
 
1.0%
051-746-4332 1
 
1.0%

Length

2023-12-10T19:06:46.179993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:46.477682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
94
94.0%
051-611-9250 1
 
1.0%
051-333-9009 1
 
1.0%
070-8823-6137 1
 
1.0%
051-525-6849 1
 
1.0%
051-744-4474 1
 
1.0%
051-746-4332 1
 
1.0%

rum_co
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.93
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:46.725319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0848358
Coefficient of variation (CV)0.56209108
Kurtosis3.2366527
Mean1.93
Median Absolute Deviation (MAD)1
Skewness1.594472
Sum193
Variance1.1768687
MonotonicityNot monotonic
2023-12-10T19:06:46.952628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 42
42.0%
2 35
35.0%
3 17
17.0%
5 2
 
2.0%
6 2
 
2.0%
4 2
 
2.0%
ValueCountFrequency (%)
1 42
42.0%
2 35
35.0%
3 17
17.0%
4 2
 
2.0%
5 2
 
2.0%
6 2
 
2.0%
ValueCountFrequency (%)
6 2
 
2.0%
5 2
 
2.0%
4 2
 
2.0%
3 17
17.0%
2 35
35.0%
1 42
42.0%

sbrs
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
58 
없음
42 

Length

Max length2
Median length1
Mean length1.42
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 58
58.0%
없음 42
42.0%

Length

2023-12-10T19:06:47.202868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:47.456184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
58
58.0%
없음 42
42.0%

prkplce_hold_at
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
48 
Y
32 
N
20 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd rowY
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 48
48.0%
Y 32
32.0%
N 20
20.0%

Length

2023-12-10T19:06:47.638741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:47.801604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48
48.0%
y 32
32.0%
n 20
20.0%

setle_mth
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
49 
현금
48 
현금, 신용카드
 
3

Length

Max length8
Median length2
Mean length1.69
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row현금, 신용카드
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 49
49.0%
현금 48
48.0%
현금, 신용카드 3
 
3.0%

Length

2023-12-10T19:06:48.037044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:48.224840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
현금 51
49.5%
49
47.6%
신용카드 3
 
2.9%

hmpg
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
50 
없음
42 
N
8 

Length

Max length2
Median length1
Mean length1.42
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 50
50.0%
없음 42
42.0%
N 8
 
8.0%

Length

2023-12-10T19:06:48.412196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:49.000859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50
50.0%
없음 42
42.0%
n 8
 
8.0%

arnd_tursm_info
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
없음
42 
-
25 
광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터
18 
송도해수욕장, 암남공원 등
5 
범어사+금정산성
 
4
Other values (5)
6 

Length

Max length36
Median length29
Mean length9.79
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터
2nd row영도대교, 영도웰컴센터, 깡깡이예술마을, 삼진어묵 등
3rd row광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터
4th row광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터
5th row광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터

Common Values

ValueCountFrequency (%)
없음 42
42.0%
- 25
25.0%
광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터 18
18.0%
송도해수욕장, 암남공원 등 5
 
5.0%
범어사+금정산성 4
 
4.0%
임시수도기념관, 동아대 석당박물관 등 2
 
2.0%
영도대교, 영도웰컴센터, 깡깡이예술마을, 삼진어묵 등 1
 
1.0%
흰여울문화마을, 절영해안산책로 등 1
 
1.0%
엄광산, 구덕산 등 1
 
1.0%
흰여울문화마을, 절영해안산책로, 태종대 등 1
 
1.0%

Length

2023-12-10T19:06:49.249644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:49.491120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 42
33.1%
25
19.7%
광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터 18
14.2%
등 11
 
8.7%
송도해수욕장 5
 
3.9%
암남공원 5
 
3.9%
범어사+금정산성 4
 
3.1%
절영해안산책로 2
 
1.6%
흰여울문화마을 2
 
1.6%
석당박물관 2
 
1.6%
Other values (9) 11
 
8.7%

la
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:49.967772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.19
Min length1

Characters and Unicode

Total characters1019
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)87.0%

Sample

1st row35.15895118
2nd row35.09444412
3rd row35.15895118
4th row35.15775546
5th row35.16358763
ValueCountFrequency (%)
5
 
5.0%
35.15939477 2
 
2.0%
35.17870863 2
 
2.0%
35.15895118 2
 
2.0%
35.070254 2
 
2.0%
35.1764112 1
 
1.0%
35.18044679 1
 
1.0%
35.18221917 1
 
1.0%
35.16430884 1
 
1.0%
35.16174348 1
 
1.0%
Other values (82) 82
82.0%
2023-12-10T19:06:50.651826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 167
16.4%
3 152
14.9%
1 134
13.2%
. 95
9.3%
7 82
8.0%
4 74
7.3%
8 74
7.3%
6 66
 
6.5%
9 64
 
6.3%
0 54
 
5.3%
Other values (2) 57
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 919
90.2%
Other Punctuation 95
 
9.3%
Dash Punctuation 5
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 167
18.2%
3 152
16.5%
1 134
14.6%
7 82
8.9%
4 74
8.1%
8 74
8.1%
6 66
 
7.2%
9 64
 
7.0%
0 54
 
5.9%
2 52
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 167
16.4%
3 152
14.9%
1 134
13.2%
. 95
9.3%
7 82
8.0%
4 74
7.3%
8 74
7.3%
6 66
 
6.5%
9 64
 
6.3%
0 54
 
5.3%
Other values (2) 57
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 167
16.4%
3 152
14.9%
1 134
13.2%
. 95
9.3%
7 82
8.0%
4 74
7.3%
8 74
7.3%
6 66
 
6.5%
9 64
 
6.3%
0 54
 
5.3%
Other values (2) 57
 
5.6%

lo
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:51.144880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length10.37
Min length1

Characters and Unicode

Total characters1037
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)85.0%

Sample

1st row129.119716
2nd row129.0365028
3rd row129.119716
4th row129.125437
5th row129.1136887
ValueCountFrequency (%)
5
 
5.0%
129.1768068 2
 
2.0%
129.1699419 2
 
2.0%
129.1475476 2
 
2.0%
129.119716 2
 
2.0%
129.018554 2
 
2.0%
129.1740453 1
 
1.0%
129.1773359 1
 
1.0%
129.1992979 1
 
1.0%
129.200546 1
 
1.0%
Other values (81) 81
81.0%
2023-12-10T19:06:51.868929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 223
21.5%
9 154
14.9%
2 152
14.7%
. 95
9.2%
6 68
 
6.6%
3 68
 
6.6%
7 61
 
5.9%
0 55
 
5.3%
5 55
 
5.3%
8 54
 
5.2%
Other values (2) 52
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 937
90.4%
Other Punctuation 95
 
9.2%
Dash Punctuation 5
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 223
23.8%
9 154
16.4%
2 152
16.2%
6 68
 
7.3%
3 68
 
7.3%
7 61
 
6.5%
0 55
 
5.9%
5 55
 
5.9%
8 54
 
5.8%
4 47
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 223
21.5%
9 154
14.9%
2 152
14.7%
. 95
9.2%
6 68
 
6.6%
3 68
 
6.6%
7 61
 
5.9%
0 55
 
5.3%
5 55
 
5.3%
8 54
 
5.2%
Other values (2) 52
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 223
21.5%
9 154
14.9%
2 152
14.7%
. 95
9.2%
6 68
 
6.6%
3 68
 
6.6%
7 61
 
5.9%
0 55
 
5.3%
5 55
 
5.3%
8 54
 
5.2%
Other values (2) 52
 
5.0%

data_stdde
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-04-28
42 
2020-02-19
18 
2020-03-12
11 
2020-03-11
8 
2019-12-19
8 
Other values (3)
13 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-02-19
2nd row2020-03-12
3rd row2020-02-19
4th row2020-02-19
5th row2020-02-19

Common Values

ValueCountFrequency (%)
2020-04-28 42
42.0%
2020-02-19 18
18.0%
2020-03-12 11
 
11.0%
2020-03-11 8
 
8.0%
2019-12-19 8
 
8.0%
2019-08-22 5
 
5.0%
2020-03-20 4
 
4.0%
2020-05-11 4
 
4.0%

Length

2023-12-10T19:06:52.118084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:52.308879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-04-28 42
42.0%
2020-02-19 18
18.0%
2020-03-12 11
 
11.0%
2020-03-11 8
 
8.0%
2019-12-19 8
 
8.0%
2019-08-22 5
 
5.0%
2020-03-20 4
 
4.0%
2020-05-11 4
 
4.0%

provd_instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3329100
Minimum3250000
Maximum3390000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:52.508005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3250000
Q13310000
median3330000
Q33370000
95-th percentile3380000
Maximum3390000
Range140000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation41441.086
Coefficient of variation (CV)0.012448135
Kurtosis-0.52891363
Mean3329100
Median Absolute Deviation (MAD)20000
Skewness-0.48572707
Sum3.3291 × 108
Variance1.7173636 × 109
MonotonicityNot monotonic
2023-12-10T19:06:52.709650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3330000 42
42.0%
3380000 18
18.0%
3260000 8
 
8.0%
3310000 8
 
8.0%
3250000 8
 
8.0%
3370000 5
 
5.0%
3390000 4
 
4.0%
3350000 4
 
4.0%
3280000 3
 
3.0%
ValueCountFrequency (%)
3250000 8
 
8.0%
3260000 8
 
8.0%
3280000 3
 
3.0%
3310000 8
 
8.0%
3330000 42
42.0%
3350000 4
 
4.0%
3370000 5
 
5.0%
3380000 18
18.0%
3390000 4
 
4.0%
ValueCountFrequency (%)
3390000 4
 
4.0%
3380000 18
18.0%
3370000 5
 
5.0%
3350000 4
 
4.0%
3330000 42
42.0%
3310000 8
 
8.0%
3280000 3
 
3.0%
3260000 8
 
8.0%
3250000 8
 
8.0%

provd_instt_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2023-12-10T19:06:52.949607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:53.111945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

Interactions

2023-12-10T19:06:38.981297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:38.068514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:38.497718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:39.123175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:38.202406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:38.657019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:39.269342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:38.351460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:38.824279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:53.236322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeybssh_nmfggg_guidance_sbcrdnmadrlnm_adrestelnorum_cosbrsprkplce_hold_atsetle_mthhmpgarnd_tursm_infolalodata_stddeprovd_instt_code
skey1.0000.8440.5961.0000.9490.0000.4040.9250.8460.9910.8470.8560.9330.9200.8070.837
bssh_nm0.8441.0000.8360.9970.9881.0000.9790.6900.0000.7970.9460.8980.9890.9880.0000.986
fggg_guidance_sbc0.5960.8361.0000.0000.9410.0000.0000.9390.6750.6590.9050.8080.7720.8410.9660.933
rdnmadr1.0000.9970.0001.0000.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
lnm_adres0.9490.9880.9410.9961.0000.0000.9651.0000.9200.9721.0001.0000.9990.9990.0000.819
telno0.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.4360.457
rum_co0.4040.9790.0001.0000.9650.0001.0000.3750.6260.3780.4670.4730.9840.9800.0320.113
sbrs0.9250.6900.9391.0001.0000.0000.3751.0000.5480.6151.0001.0001.0001.0001.0001.000
prkplce_hold_at0.8460.0000.6751.0000.9200.0000.6260.5481.0000.9340.8760.8081.0000.9800.7790.755
setle_mth0.9910.7970.6591.0000.9720.0000.3780.6150.9341.0000.9040.9721.0001.0000.8170.954
hmpg0.8470.9460.9051.0001.0000.0000.4671.0000.8760.9041.0001.0001.0001.0001.0001.000
arnd_tursm_info0.8560.8980.8081.0001.0000.0000.4731.0000.8080.9721.0001.0001.0001.0000.9100.947
la0.9330.9890.7721.0000.9990.0000.9841.0001.0001.0001.0001.0001.0001.0001.0001.000
lo0.9200.9880.8411.0000.9990.0000.9801.0000.9801.0001.0001.0001.0001.0001.0001.000
data_stdde0.8070.0000.9661.0000.0000.4360.0321.0000.7790.8171.0000.9101.0001.0001.0001.000
provd_instt_code0.8370.9860.9331.0000.8190.4570.1131.0000.7550.9541.0000.9471.0001.0001.0001.000
2023-12-10T19:06:53.547172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
data_stddehmpgfggg_guidance_sbcprkplce_hold_atsetle_mthsbrsarnd_tursm_infotelno
data_stdde1.0000.9740.7050.6750.7300.9690.7390.249
hmpg0.9741.0000.8680.5730.6220.9950.9630.000
fggg_guidance_sbc0.7050.8681.0000.5400.5220.7600.5550.000
prkplce_hold_at0.6750.5730.5401.0000.6860.8120.6770.000
setle_mth0.7300.6220.5220.6861.0000.8790.9430.000
sbrs0.9690.9950.7600.8120.8791.0000.9580.000
arnd_tursm_info0.7390.9630.5550.6770.9430.9581.0000.000
telno0.2490.0000.0000.0000.0000.0000.0001.000
2023-12-10T19:06:53.759592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyrum_coprovd_instt_codefggg_guidance_sbctelnosbrsprkplce_hold_atsetle_mthhmpgarnd_tursm_infodata_stdde
skey1.0000.038-0.3980.3780.0000.7410.5300.8670.5310.6600.613
rum_co0.0381.0000.0430.0000.0000.2640.3170.1650.2140.2640.000
provd_instt_code-0.3980.0431.0000.6050.1890.9740.6800.9590.8140.7860.914
fggg_guidance_sbc0.3780.0000.6051.0000.0000.7600.5400.5220.8680.5550.705
telno0.0000.0000.1890.0001.0000.0000.0000.0000.0000.0000.249
sbrs0.7410.2640.9740.7600.0001.0000.8120.8790.9950.9580.969
prkplce_hold_at0.5300.3170.6800.5400.0000.8121.0000.6860.5730.6770.675
setle_mth0.8670.1650.9590.5220.0000.8790.6861.0000.6220.9430.730
hmpg0.5310.2140.8140.8680.0000.9950.5730.6221.0000.9630.974
arnd_tursm_info0.6600.2640.7860.5550.0000.9580.6770.9430.9631.0000.739
data_stdde0.6130.0000.9140.7050.2490.9690.6750.7300.9740.7391.000

Missing values

2023-12-10T19:06:39.525574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:39.992509image/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

skeybssh_nminduty_nmsvc_trget_sefggg_guidance_sbcrdnmadrlnm_adrestelnorum_cosbrsprkplce_hold_atsetle_mthhmpgarnd_tursm_infolalodata_stddeprovd_instt_codeprovd_instt_nm
01SUITE민박외국인영어, 중국어부산광역시 수영구 광안동 130-43부산광역시 수영구 수영로606번길 113, 1층 (광안동)-1----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.15895118129.1197162020-02-193380000-
1211JG하우스민박외국인가능부산광역시 영도구 태종로 40, 1401호 (대교동1가, 브릿지타워)부산광역시 영도구 대교동1가 1( 브릿지타워 1401호)-3-Y현금, 신용카드-영도대교, 영도웰컴센터, 깡깡이예술마을, 삼진어묵 등35.09444412129.03650282020-03-123280000-
23PENTHOUSE민박외국인영어부산광역시 수영구 광안동 130-43부산광역시 수영구 수영로606번길 113, 2층 (광안동)-1----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.15895118129.1197162020-02-193380000-
34광안하우스(GWANGAN HOUSE)민박외국인영어부산광역시 수영구 민락동 33-9부산광역시 수영구 민락본동로19번길 66, 4층 (민락동)-2----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.15775546129.1254372020-02-193380000-
45게스트하우스 은가비민박외국인일어부산광역시 수영구 광안동 693-33부산광역시 수영구 광서로 5-12 (광안동)-2----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.16358763129.11368872020-02-193380000-
56M8 HOUSE민박외국인영어부산광역시 수영구 광안동 186-9부산광역시 수영구 광남로83번길 32-9, 102호 (광안동, 부성베스트빌라2차)-1----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.15074812129.11244362020-02-193380000-
67Jenny house민박외국인영어부산광역시 수영구 광안동 774-3부산광역시 수영구 호암로 5, 3층 (광안동)-3----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.156337129.1123232020-02-193380000-
7212와치민박외국인가능부산광역시 영도구 청학남로 19-3, 1층 (청학동)부산광역시 영도구 청학동 457-32-2-N현금, 신용카드-흰여울문화마을, 절영해안산책로 등35.09007782129.05976232020-03-123280000-
89힐링하우스민박외국인영어부산광역시 수영구 민락동 683-14부산광역시 수영구 민락로12번길 5, 2층 (민락동)-1----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.15663849129.1227262020-02-193380000-
910CASA DE NIEVES(니에베스의 집)민박외국인영어,일어부산광역시 수영구 광안동 186-15부산광역시 수영구 광남로83번길 34, 건왕파크2 401호 (광안동)-1----광안리해수욕장,광안대교,수영사적공원,민락수변공원,빵천동,민락회센터35.150474129.1123642020-02-193380000-
skeybssh_nminduty_nmsvc_trget_sefggg_guidance_sbcrdnmadrlnm_adrestelnorum_cosbrsprkplce_hold_atsetle_mthhmpgarnd_tursm_infolalodata_stddeprovd_instt_codeprovd_instt_nm
9091Live Like korean house민박외국인가능부산광역시 해운대구 재송1로60번길 29, 5층부산광역시 해운대구 재송동 1089-1-4없음N현금없음없음35.18627902129.12418822020-04-283330000-
9192휴민박외국인가능부산광역시 해운대구 송정중앙로6번길 54, 2층(송정동)부산광역시 해운대구 송정동 158-3-1없음N현금없음없음35.18154836129.20002012020-04-283330000-
9293해운대 joo하우스민박외국인가능부산광역시 해운대구 좌동순환로 47, 2층(좌동)부산광역시 해운대구 좌동 1341-19-2없음N현금없음없음35.1706934129.16615972020-04-283330000-
9394Chez June민박외국인가능부산광역시 해운대구 좌동순환로 401, 202호(중동, 해운아파트)부산광역시 해운대구 중동 1521-35-2없음N현금없음없음35.16210096129.17685942020-04-283330000-
9495부산 다온 하우스민박외국인가능부산광역시 해운대구 우동1로38번길 12, 3층(우동)부산광역시 해운대구 우동 517-12-2없음N현금없음없음35.1652611129.15833252020-04-283330000-
9596스테이모어 빌리지민박외국인가능부산광역시 해운대구 송정광어골로 90, 1~2층(송정동)부산광역시 해운대구 송정동 445-4-4없음N현금없음없음35.17346999129.19639682020-04-283330000-
9697아리랑스테이민박외국인가능부산광역시 해운대구 좌동순환로 275, 202동 2601호 (좌동, 해운대대우2차아파트)부산광역시 해운대구 좌동 1438-1없음Y현금없음없음35.17245382129.18322622020-04-283330000-
9798유네뜨 홈스테이민박외국인가능부산광역시 해운대구 좌동순환로217번길 7, 102동 104호 (좌동, 동신아파트)부산광역시 해운대구 좌동 1302-1없음Y현금없음없음35.17750411129.17900632020-04-283330000-
9899롯데민박민박외국인가능부산광역시 해운대구 해운대로483번길 10, 10동 503호 (우동, 롯데아파트)부산광역시 해운대구 우동 938-2없음Y현금없음없음35.16344479129.14761982020-04-283330000-
99100시크릿가든B민박외국인가능부산광역시 해운대구 달맞이길117번가길 31-2, 101호 (중동,마리나빌라)부산광역시 해운대구 중동 1504-5-3없음Y현금없음없음35.15939477129.17680682020-04-283330000-