Overview

Dataset statistics

Number of variables17
Number of observations557
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.3 KiB
Average record size in memory140.2 B

Variable types

Numeric4
Categorical6
Text5
DateTime1
Boolean1

Alerts

bsns_sector has constant value ""Constant
apr_at has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with ovrd_date and 2 other fieldsHigh correlation
instt_code is highly overall correlated with ovrd_date and 2 other fieldsHigh correlation
lat is highly overall correlated with lng and 1 other fieldsHigh correlation
lng is highly overall correlated with lat and 3 other fieldsHigh correlation
ovrd_date is highly overall correlated with skey and 4 other fieldsHigh correlation
gugun is highly overall correlated with skey and 5 other fieldsHigh correlation
data_day is highly overall correlated with skey and 4 other fieldsHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-17 00:47:28.050354
Analysis finished2024-04-17 00:47:30.521326
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct557
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5301.9874
Minimum4238
Maximum5833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-17T09:47:30.576480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4238
5-th percentile4317.8
Q14805
median5493
Q35632
95-th percentile5790.2
Maximum5833
Range1595
Interquartile range (IQR)827

Descriptive statistics

Standard deviation477.31558
Coefficient of variation (CV)0.090025785
Kurtosis-0.46803016
Mean5301.9874
Median Absolute Deviation (MAD)174
Skewness-0.97281293
Sum2953207
Variance227830.16
MonotonicityNot monotonic
2024-04-17T09:47:30.697422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5478 1
 
0.2%
5826 1
 
0.2%
5820 1
 
0.2%
5821 1
 
0.2%
5822 1
 
0.2%
5823 1
 
0.2%
5824 1
 
0.2%
5825 1
 
0.2%
4367 1
 
0.2%
4375 1
 
0.2%
Other values (547) 547
98.2%
ValueCountFrequency (%)
4238 1
0.2%
4239 1
0.2%
4240 1
0.2%
4241 1
0.2%
4242 1
0.2%
4243 1
0.2%
4244 1
0.2%
4245 1
0.2%
4246 1
0.2%
4247 1
0.2%
ValueCountFrequency (%)
5833 1
0.2%
5832 1
0.2%
5831 1
0.2%
5830 1
0.2%
5829 1
0.2%
5828 1
0.2%
5827 1
0.2%
5826 1
0.2%
5825 1
0.2%
5824 1
0.2%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3321400.4
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-17T09:47:30.808769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3250000
Q13270000
median3330000
Q33370000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation46062.745
Coefficient of variation (CV)0.013868471
Kurtosis-1.3036379
Mean3321400.4
Median Absolute Deviation (MAD)40000
Skewness0.042855437
Sum1.85002 × 109
Variance2.1217765 × 109
MonotonicityNot monotonic
2024-04-17T09:47:30.902892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3270000 86
15.4%
3290000 61
11.0%
3370000 58
10.4%
3340000 56
10.1%
3350000 50
9.0%
3250000 43
7.7%
3330000 39
7.0%
3300000 36
6.5%
3390000 33
 
5.9%
3380000 30
 
5.4%
Other values (6) 65
11.7%
ValueCountFrequency (%)
3250000 43
7.7%
3260000 12
 
2.2%
3270000 86
15.4%
3280000 9
 
1.6%
3290000 61
11.0%
3300000 36
6.5%
3310000 14
 
2.5%
3320000 5
 
0.9%
3330000 39
7.0%
3340000 56
10.1%
ValueCountFrequency (%)
3400000 21
 
3.8%
3390000 33
5.9%
3380000 30
5.4%
3370000 58
10.4%
3360000 4
 
0.7%
3350000 50
9.0%
3340000 56
10.1%
3330000 39
7.0%
3320000 5
 
0.9%
3310000 14
 
2.5%

bsns_sector
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
일반음식점
557 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 557
100.0%

Length

2024-04-17T09:47:31.029874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:47:31.118848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 557
100.0%

bsns_cond
Categorical

Distinct15
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
한식
331 
식육(숯불구이)
46 
회집
41 
일식
 
32
중국식
 
22
Other values (10)
85 

Length

Max length15
Median length2
Mean length2.8114901
Min length2

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row회집
2nd row한식
3rd row일식
4th row한식
5th row일식

Common Values

ValueCountFrequency (%)
한식 331
59.4%
식육(숯불구이) 46
 
8.3%
회집 41
 
7.4%
일식 32
 
5.7%
중국식 22
 
3.9%
분식 22
 
3.9%
복어취급 16
 
2.9%
탕류(보신용) 15
 
2.7%
뷔페식 13
 
2.3%
경양식 7
 
1.3%
Other values (5) 12
 
2.2%

Length

2024-04-17T09:47:31.208148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 331
59.4%
식육(숯불구이 46
 
8.3%
회집 41
 
7.4%
일식 32
 
5.7%
중국식 22
 
3.9%
분식 22
 
3.9%
복어취급 16
 
2.9%
탕류(보신용 15
 
2.7%
뷔페식 13
 
2.3%
경양식 7
 
1.3%
Other values (5) 12
 
2.2%

bsns_nm
Text

Distinct454
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-04-17T09:47:31.442039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length5.524237
Min length2

Characters and Unicode

Total characters3077
Distinct characters384
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique388 ?
Unique (%)69.7%

Sample

1st row청해
2nd row서울식당
3rd row동경식당
4th row합천돼지국밥
5th row대성관
ValueCountFrequency (%)
서울깍두기 9
 
1.4%
국제밀면 6
 
0.9%
조방낙지 5
 
0.8%
청담 4
 
0.6%
우미장꼬리곰탕 3
 
0.5%
가야골갈비 3
 
0.5%
영덕대게시청점 3
 
0.5%
돌집 3
 
0.5%
오륙도낙지 3
 
0.5%
동원숯불갈비 3
 
0.5%
Other values (503) 601
93.5%
2024-04-17T09:47:32.136535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
 
2.9%
집 67
 
2.2%
점 66
 
2.1%
식 55
 
1.8%
장 54
 
1.8%
탕 51
 
1.7%
대 50
 
1.6%
산 50
 
1.6%
가 49
 
1.6%
국 46
 
1.5%
Other values (374) 2501
81.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2916
94.8%
Space Separator 88
 
2.9%
Uppercase Letter 18
 
0.6%
Close Punctuation 16
 
0.5%
Open Punctuation 16
 
0.5%
Lowercase Letter 10
 
0.3%
Decimal Number 7
 
0.2%
Other Punctuation 5
 
0.2%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 67
 
2.3%
점 66
 
2.3%
식 55
 
1.9%
장 54
 
1.9%
탕 51
 
1.7%
대 50
 
1.7%
산 50
 
1.7%
가 49
 
1.7%
국 46
 
1.6%
당 42
 
1.4%
Other values (345) 2386
81.8%
Uppercase Letter
ValueCountFrequency (%)
H 3
16.7%
T 2
11.1%
C 2
11.1%
S 2
11.1%
B 2
11.1%
P 2
11.1%
U 1
 
5.6%
Y 1
 
5.6%
R 1
 
5.6%
A 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
n 2
20.0%
a 2
20.0%
y 1
10.0%
p 1
10.0%
m 1
10.0%
o 1
10.0%
r 1
10.0%
k 1
10.0%
Decimal Number
ValueCountFrequency (%)
3 2
28.6%
1 2
28.6%
2 2
28.6%
8 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
& 2
40.0%
Space Separator
ValueCountFrequency (%)
88
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2917
94.8%
Common 132
 
4.3%
Latin 28
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 67
 
2.3%
점 66
 
2.3%
식 55
 
1.9%
장 54
 
1.9%
탕 51
 
1.7%
대 50
 
1.7%
산 50
 
1.7%
가 49
 
1.7%
국 46
 
1.6%
당 42
 
1.4%
Other values (346) 2387
81.8%
Latin
ValueCountFrequency (%)
H 3
 
10.7%
n 2
 
7.1%
T 2
 
7.1%
C 2
 
7.1%
S 2
 
7.1%
B 2
 
7.1%
a 2
 
7.1%
P 2
 
7.1%
y 1
 
3.6%
p 1
 
3.6%
Other values (9) 9
32.1%
Common
ValueCountFrequency (%)
88
66.7%
) 16
 
12.1%
( 16
 
12.1%
. 3
 
2.3%
3 2
 
1.5%
& 2
 
1.5%
1 2
 
1.5%
2 2
 
1.5%
8 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2916
94.8%
ASCII 160
 
5.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
55.0%
) 16
 
10.0%
( 16
 
10.0%
. 3
 
1.9%
H 3
 
1.9%
3 2
 
1.2%
n 2
 
1.2%
T 2
 
1.2%
C 2
 
1.2%
S 2
 
1.2%
Other values (18) 24
 
15.0%
Hangul
ValueCountFrequency (%)
집 67
 
2.3%
점 66
 
2.3%
식 55
 
1.9%
장 54
 
1.9%
탕 51
 
1.7%
대 50
 
1.7%
산 50
 
1.7%
가 49
 
1.7%
국 46
 
1.6%
당 42
 
1.4%
Other values (345) 2386
81.8%
None
ValueCountFrequency (%)
㈜ 1
100.0%

addr_road
Text

Distinct468
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-04-17T09:47:32.434095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length48
Mean length28.308797
Min length16

Characters and Unicode

Total characters15768
Distinct characters253
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

Unique408 ?
Unique (%)73.2%

Sample

1st row부산광역시 금정구 서동로 159-1 (서동)
2nd row부산광역시 금정구 서부로16번길 10 (서동)
3rd row부산광역시 금정구 서동로175번길 32 (서동)
4th row부산광역시 금정구 서금로 17 (서동)
5th row부산광역시 금정구 금강로 179 (장전동)
ValueCountFrequency (%)
부산광역시 557
 
18.4%
연제구 98
 
3.2%
수영구 62
 
2.1%
부산진구 61
 
2.0%
1층 58
 
1.9%
사하구 56
 
1.9%
금정구 50
 
1.7%
중구 43
 
1.4%
연산동 43
 
1.4%
해운대구 39
 
1.3%
Other values (785) 1953
64.7%
2024-04-17T09:47:32.829717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2484
 
15.8%
산 707
 
4.5%
부 690
 
4.4%
동 668
 
4.2%
1 634
 
4.0%
광 621
 
3.9%
시 570
 
3.6%
구 560
 
3.6%
역 557
 
3.5%
( 549
 
3.5%
Other values (243) 7728
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9303
59.0%
Space Separator 2484
 
15.8%
Decimal Number 2436
 
15.4%
Open Punctuation 549
 
3.5%
Close Punctuation 548
 
3.5%
Other Punctuation 289
 
1.8%
Dash Punctuation 109
 
0.7%
Math Symbol 30
 
0.2%
Uppercase Letter 18
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 707
 
7.6%
부 690
 
7.4%
동 668
 
7.2%
광 621
 
6.7%
시 570
 
6.1%
구 560
 
6.0%
역 557
 
6.0%
로 535
 
5.8%
길 286
 
3.1%
번 273
 
2.9%
Other values (219) 3836
41.2%
Decimal Number
ValueCountFrequency (%)
1 634
26.0%
2 392
16.1%
3 286
11.7%
5 201
 
8.3%
4 195
 
8.0%
0 170
 
7.0%
6 159
 
6.5%
7 140
 
5.7%
9 140
 
5.7%
8 119
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 13
72.2%
C 3
 
16.7%
A 1
 
5.6%
D 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 287
99.3%
/ 1
 
0.3%
. 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
c 1
50.0%
Space Separator
ValueCountFrequency (%)
2484
100.0%
Open Punctuation
ValueCountFrequency (%)
( 549
100.0%
Close Punctuation
ValueCountFrequency (%)
) 548
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Math Symbol
ValueCountFrequency (%)
~ 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9303
59.0%
Common 6445
40.9%
Latin 20
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 707
 
7.6%
부 690
 
7.4%
동 668
 
7.2%
광 621
 
6.7%
시 570
 
6.1%
구 560
 
6.0%
역 557
 
6.0%
로 535
 
5.8%
길 286
 
3.1%
번 273
 
2.9%
Other values (219) 3836
41.2%
Common
ValueCountFrequency (%)
2484
38.5%
1 634
 
9.8%
( 549
 
8.5%
) 548
 
8.5%
2 392
 
6.1%
, 287
 
4.5%
3 286
 
4.4%
5 201
 
3.1%
4 195
 
3.0%
0 170
 
2.6%
Other values (8) 699
 
10.8%
Latin
ValueCountFrequency (%)
B 13
65.0%
C 3
 
15.0%
A 1
 
5.0%
e 1
 
5.0%
D 1
 
5.0%
c 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9303
59.0%
ASCII 6465
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2484
38.4%
1 634
 
9.8%
( 549
 
8.5%
) 548
 
8.5%
2 392
 
6.1%
, 287
 
4.4%
3 286
 
4.4%
5 201
 
3.1%
4 195
 
3.0%
0 170
 
2.6%
Other values (14) 719
 
11.1%
Hangul
ValueCountFrequency (%)
산 707
 
7.6%
부 690
 
7.4%
동 668
 
7.2%
광 621
 
6.7%
시 570
 
6.1%
구 560
 
6.0%
역 557
 
6.0%
로 535
 
5.8%
길 286
 
3.1%
번 273
 
2.9%
Other values (219) 3836
41.2%

addr_jibun
Text

Distinct466
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-04-17T09:47:33.123686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length41
Mean length23.587074
Min length17

Characters and Unicode

Total characters13138
Distinct characters186
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

Unique404 ?
Unique (%)72.5%

Sample

1st row부산광역시 금정구 서동 195 - 31
2nd row부산광역시 금정구 서동 196 - 25
3rd row부산광역시 금정구 서동 206 - 13
4th row부산광역시 금정구 서동 442 - 6
5th row부산광역시 금정구 장전동 598 - 8
ValueCountFrequency (%)
부산광역시 557
 
19.0%
203
 
6.9%
연제구 98
 
3.3%
수영구 62
 
2.1%
부산진구 61
 
2.1%
연산동 58
 
2.0%
사하구 56
 
1.9%
1층 52
 
1.8%
금정구 50
 
1.7%
중구 43
 
1.5%
Other values (640) 1698
57.8%
2024-04-17T09:47:33.531164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2961
22.5%
산 691
 
5.3%
부 675
 
5.1%
1 609
 
4.6%
동 603
 
4.6%
광 580
 
4.4%
시 560
 
4.3%
역 557
 
4.2%
구 546
 
4.2%
- 413
 
3.1%
Other values (176) 4943
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6895
52.5%
Space Separator 2961
22.5%
Decimal Number 2708
 
20.6%
Dash Punctuation 413
 
3.1%
Close Punctuation 45
 
0.3%
Open Punctuation 45
 
0.3%
Other Punctuation 41
 
0.3%
Math Symbol 18
 
0.1%
Uppercase Letter 11
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 691
 
10.0%
부 675
 
9.8%
동 603
 
8.7%
광 580
 
8.4%
시 560
 
8.1%
역 557
 
8.1%
구 546
 
7.9%
연 166
 
2.4%
제 140
 
2.0%
지 138
 
2.0%
Other values (154) 2239
32.5%
Decimal Number
ValueCountFrequency (%)
1 609
22.5%
2 359
13.3%
3 281
10.4%
4 271
10.0%
5 267
9.9%
8 207
 
7.6%
0 181
 
6.7%
7 180
 
6.6%
9 177
 
6.5%
6 176
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 38
92.7%
. 2
 
4.9%
/ 1
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 8
72.7%
C 2
 
18.2%
D 1
 
9.1%
Space Separator
ValueCountFrequency (%)
2961
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 413
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6895
52.5%
Common 6231
47.4%
Latin 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 691
 
10.0%
부 675
 
9.8%
동 603
 
8.7%
광 580
 
8.4%
시 560
 
8.1%
역 557
 
8.1%
구 546
 
7.9%
연 166
 
2.4%
제 140
 
2.0%
지 138
 
2.0%
Other values (154) 2239
32.5%
Common
ValueCountFrequency (%)
2961
47.5%
1 609
 
9.8%
- 413
 
6.6%
2 359
 
5.8%
3 281
 
4.5%
4 271
 
4.3%
5 267
 
4.3%
8 207
 
3.3%
0 181
 
2.9%
7 180
 
2.9%
Other values (8) 502
 
8.1%
Latin
ValueCountFrequency (%)
B 8
66.7%
C 2
 
16.7%
D 1
 
8.3%
c 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6895
52.5%
ASCII 6243
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2961
47.4%
1 609
 
9.8%
- 413
 
6.6%
2 359
 
5.8%
3 281
 
4.5%
4 271
 
4.3%
5 267
 
4.3%
8 207
 
3.3%
0 181
 
2.9%
7 180
 
2.9%
Other values (12) 514
 
8.2%
Hangul
ValueCountFrequency (%)
산 691
 
10.0%
부 675
 
9.8%
동 603
 
8.7%
광 580
 
8.4%
시 560
 
8.1%
역 557
 
8.1%
구 546
 
7.9%
연 166
 
2.4%
제 140
 
2.0%
지 138
 
2.0%
Other values (154) 2239
32.5%

menu
Text

Distinct177
Distinct (%)31.8%
Missing1
Missing (%)0.2%
Memory size4.5 KiB
2024-04-17T09:47:33.716630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length3.6223022
Min length1

Characters and Unicode

Total characters2014
Distinct characters157
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)18.2%

Sample

1st row생선회
2nd row갈비
3rd row초밥
4th row돼지국밥
5th row생선회
ValueCountFrequency (%)
생선회 52
 
8.9%
돼지갈비 20
 
3.4%
복국 18
 
3.1%
아구찜 17
 
2.9%
밀면 15
 
2.6%
소갈비 15
 
2.6%
설렁탕 14
 
2.4%
돼지국밥 13
 
2.2%
삼계탕 13
 
2.2%
낙지볶음 12
 
2.1%
Other values (161) 394
67.6%
2024-04-17T09:47:34.039625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 85
 
4.2%
비 75
 
3.7%
생 74
 
3.7%
회 74
 
3.7%
갈 72
 
3.6%
지 60
 
3.0%
선 60
 
3.0%
구 58
 
2.9%
국 54
 
2.7%
, 54
 
2.7%
Other values (147) 1348
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1929
95.8%
Other Punctuation 56
 
2.8%
Space Separator 27
 
1.3%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 85
 
4.4%
비 75
 
3.9%
생 74
 
3.8%
회 74
 
3.8%
갈 72
 
3.7%
지 60
 
3.1%
선 60
 
3.1%
구 58
 
3.0%
국 54
 
2.8%
돼 41
 
2.1%
Other values (142) 1276
66.1%
Other Punctuation
ValueCountFrequency (%)
, 54
96.4%
. 2
 
3.6%
Space Separator
ValueCountFrequency (%)
27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1929
95.8%
Common 85
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 85
 
4.4%
비 75
 
3.9%
생 74
 
3.8%
회 74
 
3.8%
갈 72
 
3.7%
지 60
 
3.1%
선 60
 
3.1%
구 58
 
3.0%
국 54
 
2.8%
돼 41
 
2.1%
Other values (142) 1276
66.1%
Common
ValueCountFrequency (%)
, 54
63.5%
27
31.8%
. 2
 
2.4%
( 1
 
1.2%
) 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1929
95.8%
ASCII 85
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 85
 
4.4%
비 75
 
3.9%
생 74
 
3.8%
회 74
 
3.8%
갈 72
 
3.7%
지 60
 
3.1%
선 60
 
3.1%
구 58
 
3.0%
국 54
 
2.8%
돼 41
 
2.1%
Other values (142) 1276
66.1%
ASCII
ValueCountFrequency (%)
, 54
63.5%
27
31.8%
. 2
 
2.4%
( 1
 
1.2%
) 1
 
1.2%

tel
Text

Distinct468
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-04-17T09:47:34.266568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.001795
Min length12

Characters and Unicode

Total characters6685
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

Unique408 ?
Unique (%)73.2%

Sample

1st row051-524-1887
2nd row051-527-9433
3rd row051-526-8488
4th row051-527-1302
5th row051-518-4001
ValueCountFrequency (%)
051-759-8202 3
 
0.5%
051-865-0160 3
 
0.5%
051-507-7762 3
 
0.5%
051-866-5295 3
 
0.5%
051-503-1567 3
 
0.5%
051-868-6133 3
 
0.5%
051-863-5000 3
 
0.5%
051-863-3572 3
 
0.5%
051-865-3141 3
 
0.5%
051-501-2208 3
 
0.5%
Other values (458) 527
94.6%
2024-04-17T09:47:34.594696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1114
16.7%
5 1103
16.5%
0 1054
15.8%
1 924
13.8%
2 479
7.2%
8 406
 
6.1%
6 367
 
5.5%
3 366
 
5.5%
7 362
 
5.4%
4 269
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5571
83.3%
Dash Punctuation 1114
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1103
19.8%
0 1054
18.9%
1 924
16.6%
2 479
8.6%
8 406
 
7.3%
6 367
 
6.6%
3 366
 
6.6%
7 362
 
6.5%
4 269
 
4.8%
9 241
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1114
16.7%
5 1103
16.5%
0 1054
15.8%
1 924
13.8%
2 479
7.2%
8 406
 
6.1%
6 367
 
5.5%
3 366
 
5.5%
7 362
 
5.4%
4 269
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1114
16.7%
5 1103
16.5%
0 1054
15.8%
1 924
13.8%
2 479
7.2%
8 406
 
6.1%
6 367
 
5.5%
3 366
 
5.5%
7 362
 
5.4%
4 269
 
4.0%

spec_date
Date

Distinct117
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum1995-08-11 00:00:00
Maximum2020-11-26 00:00:00
2024-04-17T09:47:34.729510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:34.874588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ovrd_date
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2020-11-09
146 
2020-10-30
67 
2019-10-24
56 
2019-12-30
43 
2019-10-22
39 
Other values (12)
206 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)0.7%

Sample

1st row2020-11-09
2nd row2020-11-09
3rd row2020-11-09
4th row2020-11-09
5th row2020-11-09

Common Values

ValueCountFrequency (%)
2020-11-09 146
26.2%
2020-10-30 67
12.0%
2019-10-24 56
 
10.1%
2019-12-30 43
 
7.7%
2019-10-22 39
 
7.0%
2020-11-26 39
 
7.0%
2020-11-01 34
 
6.1%
2020-11-04 33
 
5.9%
2019-11-01 31
 
5.6%
2020-10-27 21
 
3.8%
Other values (7) 48
 
8.6%

Length

2024-04-17T09:47:34.989145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-11-09 146
26.2%
2020-10-30 67
12.0%
2019-10-24 56
 
10.1%
2019-12-30 43
 
7.7%
2019-10-22 39
 
7.0%
2020-11-26 39
 
7.0%
2020-11-01 34
 
6.1%
2020-11-04 33
 
5.9%
2019-11-01 31
 
5.6%
2020-10-27 21
 
3.8%
Other values (7) 48
 
8.6%

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
부산광역시 연제구
98 
부산광역시 수영구
62 
부산광역시 부산진구
61 
부산광역시 사하구
56 
부산광역시 금정구
50 
Other values (11)
230 

Length

Max length10
Median length9
Mean length9.021544
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 금정구
2nd row부산광역시 금정구
3rd row부산광역시 금정구
4th row부산광역시 금정구
5th row부산광역시 금정구

Common Values

ValueCountFrequency (%)
부산광역시 연제구 98
17.6%
부산광역시 수영구 62
11.1%
부산광역시 부산진구 61
11.0%
부산광역시 사하구 56
10.1%
부산광역시 금정구 50
9.0%
부산광역시 중구 43
7.7%
부산광역시 해운대구 39
 
7.0%
부산광역시 동래구 36
 
6.5%
부산광역시 사상구 33
 
5.9%
부산광역시 기장군 21
 
3.8%
Other values (6) 58
10.4%

Length

2024-04-17T09:47:35.101568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 557
50.0%
연제구 98
 
8.8%
수영구 62
 
5.6%
부산진구 61
 
5.5%
사하구 56
 
5.0%
금정구 50
 
4.5%
중구 43
 
3.9%
해운대구 39
 
3.5%
동래구 36
 
3.2%
사상구 33
 
3.0%
Other values (7) 79
 
7.1%

data_day
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2020-12-31
297 
2020-08-31
69 
2020-07-31
43 
2020-04-28
39 
2020-03-31
32 
Other values (4)
77 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 297
53.3%
2020-08-31 69
 
12.4%
2020-07-31 43
 
7.7%
2020-04-28 39
 
7.0%
2020-03-31 32
 
5.7%
2021-01-08 30
 
5.4%
2021-01-12 29
 
5.2%
2020-09-09 14
 
2.5%
2021-01-21 4
 
0.7%

Length

2024-04-17T09:47:35.219865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:47:35.314663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 297
53.3%
2020-08-31 69
 
12.4%
2020-07-31 43
 
7.7%
2020-04-28 39
 
7.0%
2020-03-31 32
 
5.7%
2021-01-08 30
 
5.4%
2021-01-12 29
 
5.2%
2020-09-09 14
 
2.5%
2021-01-21 4
 
0.7%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct462
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.164399
Minimum35.022835
Maximum35.369941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-17T09:47:35.442011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.022835
5-th percentile35.090023
Q135.130439
median35.161912
Q335.190583
95-th percentile35.260466
Maximum35.369941
Range0.3471064
Interquartile range (IQR)0.06014386

Descriptive statistics

Standard deviation0.052401484
Coefficient of variation (CV)0.0014901857
Kurtosis0.79149936
Mean35.164399
Median Absolute Deviation (MAD)0.02944367
Skewness0.43045833
Sum19586.57
Variance0.0027459155
MonotonicityNot monotonic
2024-04-17T09:47:35.583482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1483313 4
 
0.7%
35.184365 3
 
0.5%
35.188567 3
 
0.5%
35.1939 3
 
0.5%
35.19105 3
 
0.5%
35.18917 3
 
0.5%
35.187505 3
 
0.5%
35.182082 3
 
0.5%
35.183251 3
 
0.5%
35.179309 3
 
0.5%
Other values (452) 526
94.4%
ValueCountFrequency (%)
35.0228346 1
0.2%
35.047305 1
0.2%
35.049139 1
0.2%
35.049184 1
0.2%
35.04937 1
0.2%
35.054605 1
0.2%
35.057758 1
0.2%
35.057955 1
0.2%
35.060213 1
0.2%
35.060292 2
0.4%
ValueCountFrequency (%)
35.369941 1
0.2%
35.346826 1
0.2%
35.346599 1
0.2%
35.345798 1
0.2%
35.341533 1
0.2%
35.329756 1
0.2%
35.2814816418 1
0.2%
35.2790145782 1
0.2%
35.2789153424 1
0.2%
35.277578 1
0.2%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct462
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.06898
Minimum128.8078
Maximum129.28192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-17T09:47:35.716838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.8078
5-th percentile128.96745
Q1129.0324
median129.07389
Q3129.10266
95-th percentile129.17404
Maximum129.28192
Range0.474119
Interquartile range (IQR)0.0702633

Descriptive statistics

Standard deviation0.062064942
Coefficient of variation (CV)0.00048086644
Kurtosis1.3306247
Mean129.06898
Median Absolute Deviation (MAD)0.036429
Skewness0.075654949
Sum71891.422
Variance0.003852057
MonotonicityNot monotonic
2024-04-17T09:47:35.847443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1078662 4
 
0.7%
129.107464 3
 
0.5%
129.097184 3
 
0.5%
129.067323 3
 
0.5%
129.074898 3
 
0.5%
129.100702 3
 
0.5%
129.07679 3
 
0.5%
129.075189 3
 
0.5%
129.073847 3
 
0.5%
129.071592 3
 
0.5%
Other values (452) 526
94.4%
ValueCountFrequency (%)
128.807799 1
0.2%
128.833221 1
0.2%
128.868901 1
0.2%
128.893126 1
0.2%
128.957479 1
0.2%
128.957492 1
0.2%
128.957644 1
0.2%
128.958145 1
0.2%
128.95916 1
0.2%
128.961052 1
0.2%
ValueCountFrequency (%)
129.281918 1
0.2%
129.254918 1
0.2%
129.254168 1
0.2%
129.253206 1
0.2%
129.251745 1
0.2%
129.244394 1
0.2%
129.233506 1
0.2%
129.227782 1
0.2%
129.226977 1
0.2%
129.226838 1
0.2%

apr_at
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size689.0 B
False
557 
ValueCountFrequency (%)
False 557
100.0%
2024-04-17T09:47:35.933848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2021-03-01 05:51:03
557 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01 05:51:03
2nd row2021-03-01 05:51:03
3rd row2021-03-01 05:51:03
4th row2021-03-01 05:51:03
5th row2021-03-01 05:51:03

Common Values

ValueCountFrequency (%)
2021-03-01 05:51:03 557
100.0%

Length

2024-04-17T09:47:36.010255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:47:36.093078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 557
50.0%
05:51:03 557
50.0%

Interactions

2024-04-17T09:47:29.890597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:28.830777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.210558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.547198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.982624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:28.936187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.304918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.638847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:30.062068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.030088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.390393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.725168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:30.153223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.121684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.473344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:29.806725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T09:47:36.151401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codebsns_condovrd_dategugundata_daylatlng
skey1.0000.8620.3050.9170.8910.7820.5560.681
instt_code0.8621.0000.2520.9510.9940.8450.8600.753
bsns_cond0.3050.2521.0000.3130.3740.3480.2130.194
ovrd_date0.9170.9510.3131.0000.9750.9760.7890.914
gugun0.8910.9940.3740.9751.0000.9660.8760.937
data_day0.7820.8450.3480.9760.9661.0000.6470.936
lat0.5560.8600.2130.7890.8760.6471.0000.752
lng0.6810.7530.1940.9140.9370.9360.7521.000
2024-04-17T09:47:36.240514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bsns_conddata_daygugunovrd_date
bsns_cond1.0000.1480.1330.109
data_day0.1481.0000.8560.874
gugun0.1330.8561.0000.816
ovrd_date0.1090.8740.8161.000
2024-04-17T09:47:36.327012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codelatlngbsns_condovrd_dategugundata_day
skey1.0000.132-0.343-0.4840.1430.7300.6800.568
instt_code0.1321.0000.3680.1510.1330.7990.9170.655
lat-0.3430.3681.0000.5060.0800.4540.5920.363
lng-0.4840.1510.5061.0000.0800.6810.7600.593
bsns_cond0.1430.1330.0800.0801.0000.1090.1330.148
ovrd_date0.7300.7990.4540.6810.1091.0000.8160.874
gugun0.6800.9170.5920.7600.1330.8161.0000.856
data_day0.5680.6550.3630.5930.1480.8740.8561.000

Missing values

2024-04-17T09:47:30.273104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T09:47:30.457670image/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

skeyinstt_codebsns_sectorbsns_condbsns_nmaddr_roadaddr_jibunmenutelspec_dateovrd_dategugundata_daylatlngapr_atlast_load_dttm
054783350000일반음식점회집청해부산광역시 금정구 서동로 159-1 (서동)부산광역시 금정구 서동 195 - 31생선회051-524-18872003-07-052020-11-09부산광역시 금정구2020-12-3135.214933129.103895N2021-03-01 05:51:03
154793350000일반음식점한식서울식당부산광역시 금정구 서부로16번길 10 (서동)부산광역시 금정구 서동 196 - 25갈비051-527-94332001-07-052020-11-09부산광역시 금정구2020-12-3135.215891129.104367N2021-03-01 05:51:03
254803350000일반음식점일식동경식당부산광역시 금정구 서동로175번길 32 (서동)부산광역시 금정구 서동 206 - 13초밥051-526-84882001-07-232020-11-09부산광역시 금정구2020-12-3135.215069129.107493N2021-03-01 05:51:03
354813350000일반음식점한식합천돼지국밥부산광역시 금정구 서금로 17 (서동)부산광역시 금정구 서동 442 - 6돼지국밥051-527-13022008-07-072020-11-09부산광역시 금정구2020-12-3135.218155129.103627N2021-03-01 05:51:03
454823350000일반음식점일식대성관부산광역시 금정구 금강로 179 (장전동)부산광역시 금정구 장전동 598 - 8생선회051-518-40012001-07-052020-11-09부산광역시 금정구2020-12-3135.224745129.081812N2021-03-01 05:51:03
554833350000일반음식점한식참나무숯불구이부산광역시 금정구 금정로 25 (장전동)부산광역시 금정구 장전동 342 - 13생갈비051-582-53922001-07-052020-11-09부산광역시 금정구2020-12-3135.227052129.085662N2021-03-01 05:51:03
654843350000일반음식점한식서울깍두기부산광역시 금정구 장전온천천로 75 (장전동)부산광역시 금정구 장전동 412 - 11설렁탕051-582-90052013-10-212020-11-09부산광역시 금정구2020-12-3135.231703129.088681N2021-03-01 05:51:03
754853350000일반음식점한식늘아침부산광역시 금정구 부산대학로64번길 155 (장전동)부산광역시 금정구 장전동 157 - 18오리고기051-518-92002006-07-052020-11-09부산광역시 금정구2020-12-3135.238359129.085986N2021-03-01 05:51:03
855153250000일반음식점경양식한우뭉치부산광역시 중구 보수대로 106, 2~3층 (보수동3가)부산광역시 중구 보수동3가 5 - 16쇠고기051-254-32502016-11-042019-12-30부산광역시 중구2020-07-3135.105279129.021568N2021-03-01 05:51:03
955163250000일반음식점한식서울순두부부산광역시 중구 남포길 25-3, 3층 (남포동2가)부산광역시 중구 남포동2가 15 - 1 (3층)순두부051-244-58882009-12-232019-12-30부산광역시 중구2020-07-3135.09885129.031458N2021-03-01 05:51:03
skeyinstt_codebsns_sectorbsns_condbsns_nmaddr_roadaddr_jibunmenutelspec_dateovrd_dategugundata_daylatlngapr_atlast_load_dttm
54755523330000일반음식점회집동백섬 횟집부산광역시 해운대구 해운대해변로209번나길 17 (우동, 지상1,2,3층)부산광역시 해운대구 우동 655-7생선회051-741-38882020-11-262020-11-26부산광역시 해운대구2020-04-2835.160411129.154664N2021-03-01 05:51:03
54855533330000일반음식점한식맛나감자탕부산광역시 해운대구 좌동로91번길 17 (좌동, 지상2층 )부산광역시 해운대구 좌동 985-4감자탕051-703-74712020-11-262020-11-26부산광역시 해운대구2020-04-2835.172777129.174357N2021-03-01 05:51:03
54955543330000일반음식점한식배비장보쌈부산광역시 해운대구 해운대로483번길 1-1 (우동, 외1필지 지상2층)부산광역시 해운대구 우동 959-1보쌈051-742-77552020-11-262020-11-26부산광역시 해운대구2020-04-2835.163319129.145539N2021-03-01 05:51:03
55055553330000일반음식점식육(숯불구이)오발탄부산광역시 해운대구 해운대로570번길 10 (우동, 지상1,2층일부)부산광역시 해운대구 우동 646-1양곱창051-746-60802020-11-262020-11-26부산광역시 해운대구2020-04-2835.160352129.155135N2021-03-01 05:51:03
55155563330000일반음식점경양식오페라부산광역시 해운대구 달맞이길117번길 29 (중동, 3층)부산광역시 해운대구 중동 1510-12스테이크051-746-66702020-11-262020-11-26부산광역시 해운대구2020-04-2835.158976129.175465N2021-03-01 05:51:03
55255573330000일반음식점식육(숯불구이)한우 뭉치부산광역시 해운대구 대천로106번길 16 (좌동)부산광역시 해운대구 좌동 908-4식육전문051-702-66922020-11-262020-11-26부산광역시 해운대구2020-04-2835.174146129.174519N2021-03-01 05:51:03
55355583330000일반음식점한식할매복국부산광역시 해운대구 달맞이길62번길 1 (중동, 2,3층)부산광역시 해운대구 중동 940-14복국051-742-27892020-11-262020-11-26부산광역시 해운대구2020-04-2835.160907129.171508N2021-03-01 05:51:03
55455593330000일반음식점한식해운대소문난삼계탕부산광역시 해운대구 중동2로 6 (중동, 외1필지 1층)부산광역시 해운대구 중동 1394-39탕류051-741-45452020-11-262020-11-26부산광역시 해운대구2020-04-2835.16261129.16499N2021-03-01 05:51:03
55555603330000일반음식점한식해운대암소갈비집부산광역시 해운대구 중동2로10번길 32-10 (중동)부산광역시 해운대구 중동 1225-1소갈비051-746-33332020-11-262020-11-26부산광역시 해운대구2020-04-2835.163229129.166273N2021-03-01 05:51:03
55657163390000일반음식점한식(주)합천일류돼지국밥부산광역시 사상구 광장로 34 (괘법동)부산광역시 사상구 괘법동 565-6돼지국밥051-317-24782011-10-312020-11-04부산광역시 사상구2020-12-3135.162142128.98033N2021-03-01 05:51:03