Overview

Dataset statistics

Number of variables17
Number of observations525
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.9 KiB
Average record size in memory140.3 B

Variable types

Numeric4
Categorical4
Text5
DateTime4

Alerts

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

Reproduction

Analysis started2024-04-17 00:47:47.328616
Analysis finished2024-04-17 00:47:49.668275
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3857.7276
Minimum2655
Maximum4213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T09:47:49.724865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2655
5-th percentile3112.2
Q13820
median3951
Q34082
95-th percentile4186.8
Maximum4213
Range1558
Interquartile range (IQR)262

Descriptive statistics

Standard deviation356.30722
Coefficient of variation (CV)0.092361942
Kurtosis2.8355537
Mean3857.7276
Median Absolute Deviation (MAD)131
Skewness-1.8623222
Sum2025307
Variance126954.83
MonotonicityNot monotonic
2024-04-17T09:47:49.837408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3863 1
 
0.2%
3819 1
 
0.2%
3817 1
 
0.2%
3803 1
 
0.2%
3802 1
 
0.2%
3801 1
 
0.2%
3800 1
 
0.2%
3799 1
 
0.2%
3798 1
 
0.2%
3797 1
 
0.2%
Other values (515) 515
98.1%
ValueCountFrequency (%)
2655 1
0.2%
2656 1
0.2%
2657 1
0.2%
2658 1
0.2%
2659 1
0.2%
2660 1
0.2%
2661 1
0.2%
2662 1
0.2%
2663 1
0.2%
2664 1
0.2%
ValueCountFrequency (%)
4213 1
0.2%
4212 1
0.2%
4211 1
0.2%
4210 1
0.2%
4209 1
0.2%
4208 1
0.2%
4207 1
0.2%
4206 1
0.2%
4205 1
0.2%
4204 1
0.2%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3327466.7
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T09:47:49.940889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3250000
Q13290000
median3330000
Q33370000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)80000

Descriptive statistics

Standard deviation44088.103
Coefficient of variation (CV)0.01324975
Kurtosis-1.0252026
Mean3327466.7
Median Absolute Deviation (MAD)40000
Skewness-0.13621918
Sum1.74692 × 109
Variance1.9437608 × 109
MonotonicityNot monotonic
2024-04-17T09:47:50.039522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3290000 65
12.4%
3340000 59
11.2%
3350000 53
10.1%
3330000 49
9.3%
3250000 43
8.2%
3390000 42
8.0%
3370000 40
7.6%
3300000 40
7.6%
3380000 32
 
6.1%
3400000 22
 
4.2%
Other values (6) 80
15.2%
ValueCountFrequency (%)
3250000 43
8.2%
3260000 15
 
2.9%
3270000 16
 
3.0%
3280000 13
 
2.5%
3290000 65
12.4%
3300000 40
7.6%
3310000 18
 
3.4%
3320000 13
 
2.5%
3330000 49
9.3%
3340000 59
11.2%
ValueCountFrequency (%)
3400000 22
 
4.2%
3390000 42
8.0%
3380000 32
6.1%
3370000 40
7.6%
3360000 5
 
1.0%
3350000 53
10.1%
3340000 59
11.2%
3330000 49
9.3%
3320000 13
 
2.5%
3310000 18
 
3.4%

bsns_sector
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
일반음식점
525 

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 525
100.0%

Length

2024-04-17T09:47:50.140060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:47:50.211207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 525
100.0%

bsns_cond
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
한식
338 
식육(숯불구이)
37 
회집
36 
일식
 
28
중국식
 
20
Other values (9)
66 

Length

Max length15
Median length2
Mean length2.6819048
Min length2

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row일식
2nd row한식
3rd row한식
4th row한식
5th row식육(숯불구이)

Common Values

ValueCountFrequency (%)
한식 338
64.4%
식육(숯불구이) 37
 
7.0%
회집 36
 
6.9%
일식 28
 
5.3%
중국식 20
 
3.8%
분식 14
 
2.7%
복어취급 14
 
2.7%
뷔페식 11
 
2.1%
탕류(보신용) 11
 
2.1%
경양식 8
 
1.5%
Other values (4) 8
 
1.5%

Length

2024-04-17T09:47:50.330887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 338
64.4%
식육(숯불구이 37
 
7.0%
회집 36
 
6.9%
일식 28
 
5.3%
중국식 20
 
3.8%
분식 14
 
2.7%
복어취급 14
 
2.7%
뷔페식 11
 
2.1%
탕류(보신용 11
 
2.1%
경양식 8
 
1.5%
Other values (4) 8
 
1.5%

bsns_nm
Text

Distinct509
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T09:47:50.558080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length5.5638095
Min length2

Characters and Unicode

Total characters2921
Distinct characters392
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique497 ?
Unique (%)94.7%

Sample

1st row여송제
2nd row바래
3rd row거창순두부
4th row궁중식당
5th row태백산 참숯마을
ValueCountFrequency (%)
서울깍두기 5
 
0.8%
조방낙지 5
 
0.8%
샤브향 3
 
0.5%
주식회사 3
 
0.5%
더파티 3
 
0.5%
오륙도낙지 3
 
0.5%
부산본점 2
 
0.3%
돼지 2
 
0.3%
개미집 2
 
0.3%
부산횟집 2
 
0.3%
Other values (563) 578
95.1%
2024-04-17T09:47:50.899568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
2.9%
집 71
 
2.4%
점 61
 
2.1%
식 51
 
1.7%
대 46
 
1.6%
장 46
 
1.6%
탕 46
 
1.6%
산 45
 
1.5%
국 42
 
1.4%
가 42
 
1.4%
Other values (382) 2386
81.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2754
94.3%
Space Separator 85
 
2.9%
Open Punctuation 20
 
0.7%
Close Punctuation 20
 
0.7%
Uppercase Letter 18
 
0.6%
Lowercase Letter 10
 
0.3%
Decimal Number 7
 
0.2%
Other Punctuation 6
 
0.2%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 71
 
2.6%
점 61
 
2.2%
식 51
 
1.9%
대 46
 
1.7%
장 46
 
1.7%
탕 46
 
1.7%
산 45
 
1.6%
국 42
 
1.5%
가 42
 
1.5%
당 41
 
1.5%
Other values (353) 2263
82.2%
Uppercase Letter
ValueCountFrequency (%)
H 3
16.7%
S 2
11.1%
T 2
11.1%
P 2
11.1%
C 2
11.1%
B 2
11.1%
U 1
 
5.6%
Y 1
 
5.6%
R 1
 
5.6%
A 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
a 2
20.0%
n 2
20.0%
k 1
10.0%
r 1
10.0%
y 1
10.0%
p 1
10.0%
m 1
10.0%
o 1
10.0%
Decimal Number
ValueCountFrequency (%)
3 2
28.6%
2 2
28.6%
1 2
28.6%
8 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
& 2
33.3%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2754
94.3%
Common 138
 
4.7%
Latin 28
 
1.0%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 71
 
2.6%
점 61
 
2.2%
식 51
 
1.9%
대 46
 
1.7%
장 46
 
1.7%
탕 46
 
1.7%
산 45
 
1.6%
국 42
 
1.5%
가 42
 
1.5%
당 41
 
1.5%
Other values (353) 2263
82.2%
Latin
ValueCountFrequency (%)
H 3
 
10.7%
S 2
 
7.1%
T 2
 
7.1%
P 2
 
7.1%
a 2
 
7.1%
n 2
 
7.1%
C 2
 
7.1%
B 2
 
7.1%
k 1
 
3.6%
r 1
 
3.6%
Other values (9) 9
32.1%
Common
ValueCountFrequency (%)
85
61.6%
( 20
 
14.5%
) 20
 
14.5%
. 4
 
2.9%
3 2
 
1.4%
2 2
 
1.4%
1 2
 
1.4%
& 2
 
1.4%
8 1
 
0.7%
Han
ValueCountFrequency (%)
好 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2753
94.2%
ASCII 166
 
5.7%
None 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
51.2%
( 20
 
12.0%
) 20
 
12.0%
. 4
 
2.4%
H 3
 
1.8%
S 2
 
1.2%
T 2
 
1.2%
P 2
 
1.2%
3 2
 
1.2%
2 2
 
1.2%
Other values (18) 24
 
14.5%
Hangul
ValueCountFrequency (%)
집 71
 
2.6%
점 61
 
2.2%
식 51
 
1.9%
대 46
 
1.7%
장 46
 
1.7%
탕 46
 
1.7%
산 45
 
1.6%
국 42
 
1.5%
가 42
 
1.5%
당 41
 
1.5%
Other values (352) 2262
82.2%
None
ValueCountFrequency (%)
㈜ 1
100.0%
CJK
ValueCountFrequency (%)
好 1
100.0%

addr_road
Text

Distinct524
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T09:47:51.180438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length48
Mean length28.059048
Min length16

Characters and Unicode

Total characters14731
Distinct characters261
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

Unique523 ?
Unique (%)99.6%

Sample

1st row부산광역시 중구 광복로18번길 5, 1~2층 (부평동1가)
2nd row부산광역시 중구 중앙대로81번길 4, 1층 (중앙동4가, 4,5,20)
3rd row부산광역시 해운대구 좌동순환로 55-1 (좌동, 지상1층)
4th row부산광역시 해운대구 좌동로63번길 21 (중동, 지상1층)
5th row부산광역시 해운대구 재반로60번길 2 (재송동, 2,3층)
ValueCountFrequency (%)
부산광역시 525
 
18.5%
부산진구 65
 
2.3%
사하구 59
 
2.1%
금정구 53
 
1.9%
해운대구 49
 
1.7%
1층 44
 
1.6%
중구 43
 
1.5%
사상구 42
 
1.5%
동래구 40
 
1.4%
연제구 40
 
1.4%
Other values (850) 1875
66.1%
2024-04-17T09:47:51.564196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2332
 
15.8%
부 667
 
4.5%
산 645
 
4.4%
동 642
 
4.4%
광 572
 
3.9%
1 568
 
3.9%
시 539
 
3.7%
구 529
 
3.6%
역 525
 
3.6%
) 516
 
3.5%
Other values (251) 7196
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8704
59.1%
Space Separator 2332
 
15.8%
Decimal Number 2273
 
15.4%
Close Punctuation 516
 
3.5%
Open Punctuation 516
 
3.5%
Other Punctuation 252
 
1.7%
Dash Punctuation 98
 
0.7%
Math Symbol 21
 
0.1%
Uppercase Letter 15
 
0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 667
 
7.7%
산 645
 
7.4%
동 642
 
7.4%
광 572
 
6.6%
시 539
 
6.2%
구 529
 
6.1%
역 525
 
6.0%
로 499
 
5.7%
길 254
 
2.9%
번 237
 
2.7%
Other values (224) 3595
41.3%
Decimal Number
ValueCountFrequency (%)
1 568
25.0%
2 365
16.1%
3 261
11.5%
5 186
 
8.2%
4 179
 
7.9%
0 169
 
7.4%
6 168
 
7.4%
7 140
 
6.2%
8 120
 
5.3%
9 117
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 10
66.7%
D 1
 
6.7%
A 1
 
6.7%
C 1
 
6.7%
O 1
 
6.7%
T 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 249
98.8%
/ 2
 
0.8%
. 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
c 1
50.0%
Space Separator
ValueCountFrequency (%)
2332
100.0%
Close Punctuation
ValueCountFrequency (%)
) 516
100.0%
Open Punctuation
ValueCountFrequency (%)
( 516
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%
Math Symbol
ValueCountFrequency (%)
~ 21
100.0%
Other Symbol
ValueCountFrequency (%)
� 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8703
59.1%
Common 6010
40.8%
Latin 17
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 667
 
7.7%
산 645
 
7.4%
동 642
 
7.4%
광 572
 
6.6%
시 539
 
6.2%
구 529
 
6.1%
역 525
 
6.0%
로 499
 
5.7%
길 254
 
2.9%
번 237
 
2.7%
Other values (223) 3594
41.3%
Common
ValueCountFrequency (%)
2332
38.8%
1 568
 
9.5%
) 516
 
8.6%
( 516
 
8.6%
2 365
 
6.1%
3 261
 
4.3%
, 249
 
4.1%
5 186
 
3.1%
4 179
 
3.0%
0 169
 
2.8%
Other values (9) 669
 
11.1%
Latin
ValueCountFrequency (%)
B 10
58.8%
D 1
 
5.9%
A 1
 
5.9%
e 1
 
5.9%
C 1
 
5.9%
O 1
 
5.9%
T 1
 
5.9%
c 1
 
5.9%
Han
ValueCountFrequency (%)
氷 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8703
59.1%
ASCII 6025
40.9%
Specials 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2332
38.7%
1 568
 
9.4%
) 516
 
8.6%
( 516
 
8.6%
2 365
 
6.1%
3 261
 
4.3%
, 249
 
4.1%
5 186
 
3.1%
4 179
 
3.0%
0 169
 
2.8%
Other values (16) 684
 
11.4%
Hangul
ValueCountFrequency (%)
부 667
 
7.7%
산 645
 
7.4%
동 642
 
7.4%
광 572
 
6.6%
시 539
 
6.2%
구 529
 
6.1%
역 525
 
6.0%
로 499
 
5.7%
길 254
 
2.9%
번 237
 
2.7%
Other values (223) 3594
41.3%
Specials
ValueCountFrequency (%)
� 2
100.0%
CJK
ValueCountFrequency (%)
氷 1
100.0%

addr_jibun
Text

Distinct522
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T09:47:51.810771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length43
Mean length23.628571
Min length16

Characters and Unicode

Total characters12405
Distinct characters200
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

Unique519 ?
Unique (%)98.9%

Sample

1st row부산광역시 중구 부평동1가 37 - 10 1~2층
2nd row부산광역시 중구 중앙동4가 53 - 1 4,5,20 (1층)
3rd row부산광역시 해운대구 좌동 1341-11
4th row부산광역시 해운대구 중동 218-7
5th row부산광역시 해운대구 재송동 480
ValueCountFrequency (%)
부산광역시 525
 
18.6%
253
 
9.0%
부산진구 65
 
2.3%
사하구 59
 
2.1%
금정구 53
 
1.9%
해운대구 49
 
1.7%
중구 43
 
1.5%
사상구 42
 
1.5%
연제구 40
 
1.4%
동래구 40
 
1.4%
Other values (697) 1653
58.6%
2024-04-17T09:47:52.183712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2947
23.8%
부 651
 
5.2%
산 629
 
5.1%
동 580
 
4.7%
1 564
 
4.5%
광 540
 
4.4%
시 528
 
4.3%
역 525
 
4.2%
구 513
 
4.1%
- 431
 
3.5%
Other values (190) 4497
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6370
51.4%
Space Separator 2947
23.8%
Decimal Number 2516
 
20.3%
Dash Punctuation 431
 
3.5%
Other Punctuation 40
 
0.3%
Close Punctuation 38
 
0.3%
Open Punctuation 38
 
0.3%
Math Symbol 12
 
0.1%
Uppercase Letter 10
 
0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 651
 
10.2%
산 629
 
9.9%
동 580
 
9.1%
광 540
 
8.5%
시 528
 
8.3%
역 525
 
8.2%
구 513
 
8.1%
사 107
 
1.7%
대 89
 
1.4%
지 87
 
1.4%
Other values (165) 2121
33.3%
Decimal Number
ValueCountFrequency (%)
1 564
22.4%
2 329
13.1%
3 269
10.7%
5 263
10.5%
4 233
9.3%
8 181
 
7.2%
6 175
 
7.0%
7 172
 
6.8%
0 167
 
6.6%
9 163
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
B 6
60.0%
D 1
 
10.0%
T 1
 
10.0%
C 1
 
10.0%
O 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 36
90.0%
. 2
 
5.0%
/ 2
 
5.0%
Space Separator
ValueCountFrequency (%)
2947
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 431
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%
Other Symbol
ValueCountFrequency (%)
� 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6369
51.3%
Common 6024
48.6%
Latin 11
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 651
 
10.2%
산 629
 
9.9%
동 580
 
9.1%
광 540
 
8.5%
시 528
 
8.3%
역 525
 
8.2%
구 513
 
8.1%
사 107
 
1.7%
대 89
 
1.4%
지 87
 
1.4%
Other values (164) 2120
33.3%
Common
ValueCountFrequency (%)
2947
48.9%
1 564
 
9.4%
- 431
 
7.2%
2 329
 
5.5%
3 269
 
4.5%
5 263
 
4.4%
4 233
 
3.9%
8 181
 
3.0%
6 175
 
2.9%
7 172
 
2.9%
Other values (9) 460
 
7.6%
Latin
ValueCountFrequency (%)
B 6
54.5%
D 1
 
9.1%
T 1
 
9.1%
C 1
 
9.1%
c 1
 
9.1%
O 1
 
9.1%
Han
ValueCountFrequency (%)
氷 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6369
51.3%
ASCII 6033
48.6%
Specials 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2947
48.8%
1 564
 
9.3%
- 431
 
7.1%
2 329
 
5.5%
3 269
 
4.5%
5 263
 
4.4%
4 233
 
3.9%
8 181
 
3.0%
6 175
 
2.9%
7 172
 
2.9%
Other values (14) 469
 
7.8%
Hangul
ValueCountFrequency (%)
부 651
 
10.2%
산 629
 
9.9%
동 580
 
9.1%
광 540
 
8.5%
시 528
 
8.3%
역 525
 
8.2%
구 513
 
8.1%
사 107
 
1.7%
대 89
 
1.4%
지 87
 
1.4%
Other values (164) 2120
33.3%
Specials
ValueCountFrequency (%)
� 2
100.0%
CJK
ValueCountFrequency (%)
氷 1
100.0%

menu
Text

Distinct191
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T09:47:52.359282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length3.6380952
Min length1

Characters and Unicode

Total characters1910
Distinct characters168
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

Unique127 ?
Unique (%)24.2%

Sample

1st row족발
2nd row해물탕
3rd row순두부
4th row아구찜
5th row통돼지갈비
ValueCountFrequency (%)
생선회 49
 
8.9%
돼지갈비 18
 
3.3%
복국 16
 
2.9%
아구찜 15
 
2.7%
한정식 15
 
2.7%
샤브샤브 14
 
2.5%
삼겹살 14
 
2.5%
돼지국밥 13
 
2.4%
삼계탕 13
 
2.4%
소갈비 13
 
2.4%
Other values (174) 370
67.3%
2024-04-17T09:47:52.672649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 76
 
4.0%
비 72
 
3.8%
생 70
 
3.7%
갈 69
 
3.6%
회 65
 
3.4%
선 57
 
3.0%
지 57
 
3.0%
국 56
 
2.9%
, 52
 
2.7%
구 50
 
2.6%
Other values (158) 1286
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1829
95.8%
Other Punctuation 54
 
2.8%
Space Separator 25
 
1.3%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 76
 
4.2%
비 72
 
3.9%
생 70
 
3.8%
갈 69
 
3.8%
회 65
 
3.6%
선 57
 
3.1%
지 57
 
3.1%
국 56
 
3.1%
구 50
 
2.7%
밥 43
 
2.4%
Other values (153) 1214
66.4%
Other Punctuation
ValueCountFrequency (%)
, 52
96.3%
. 2
 
3.7%
Space Separator
ValueCountFrequency (%)
25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1829
95.8%
Common 81
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 76
 
4.2%
비 72
 
3.9%
생 70
 
3.8%
갈 69
 
3.8%
회 65
 
3.6%
선 57
 
3.1%
지 57
 
3.1%
국 56
 
3.1%
구 50
 
2.7%
밥 43
 
2.4%
Other values (153) 1214
66.4%
Common
ValueCountFrequency (%)
, 52
64.2%
25
30.9%
. 2
 
2.5%
) 1
 
1.2%
( 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1829
95.8%
ASCII 81
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 76
 
4.2%
비 72
 
3.9%
생 70
 
3.8%
갈 69
 
3.8%
회 65
 
3.6%
선 57
 
3.1%
지 57
 
3.1%
국 56
 
3.1%
구 50
 
2.7%
밥 43
 
2.4%
Other values (153) 1214
66.4%
ASCII
ValueCountFrequency (%)
, 52
64.2%
25
30.9%
. 2
 
2.5%
) 1
 
1.2%
( 1
 
1.2%

tel
Text

Distinct524
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T09:47:52.881036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.00381
Min length12

Characters and Unicode

Total characters6302
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

Unique523 ?
Unique (%)99.6%

Sample

1st row051-246-2111
2nd row051-463-7005
3rd row051-731-4988
4th row051-702-1801
5th row051-784-5522
ValueCountFrequency (%)
051-266-1023 2
 
0.4%
051-973-7008 1
 
0.2%
051-941-5030 1
 
0.2%
051-245-3330 1
 
0.2%
051-256-6664 1
 
0.2%
051-243-6973 1
 
0.2%
051-231-1076 1
 
0.2%
051-254-7639 1
 
0.2%
051-246-2452 1
 
0.2%
051-244-6143 1
 
0.2%
Other values (514) 514
97.9%
2024-04-17T09:47:53.190210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1050
16.7%
5 1003
15.9%
0 984
15.6%
1 872
13.8%
2 462
7.3%
3 380
 
6.0%
8 356
 
5.6%
7 333
 
5.3%
6 325
 
5.2%
4 298
 
4.7%
Other values (2) 239
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5251
83.3%
Dash Punctuation 1050
 
16.7%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1003
19.1%
0 984
18.7%
1 872
16.6%
2 462
8.8%
3 380
 
7.2%
8 356
 
6.8%
7 333
 
6.3%
6 325
 
6.2%
4 298
 
5.7%
9 238
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 1050
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1050
16.7%
5 1003
15.9%
0 984
15.6%
1 872
13.8%
2 462
7.3%
3 380
 
6.0%
8 356
 
5.6%
7 333
 
5.3%
6 325
 
5.2%
4 298
 
4.7%
Other values (2) 239
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1050
16.7%
5 1003
15.9%
0 984
15.6%
1 872
13.8%
2 462
7.3%
3 380
 
6.0%
8 356
 
5.6%
7 333
 
5.3%
6 325
 
5.2%
4 298
 
4.7%
Other values (2) 239
 
3.8%

spec_date
Date

Distinct121
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum1995-08-11 00:00:00
Maximum2019-12-13 00:00:00
2024-04-17T09:47:53.314737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:53.438717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ovrd_date
Date

Distinct17
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum2018-11-05 00:00:00
Maximum2019-12-31 00:00:00
2024-04-17T09:47:53.589165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:53.689672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
부산광역시 부산진구
65 
부산광역시 사하구
59 
부산광역시 금정구
53 
부산광역시 해운대구
49 
부산광역시 중구
43 
Other values (11)
256 

Length

Max length10
Median length9
Mean length9.0171429
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 중구
2nd row부산광역시 중구
3rd row부산광역시 해운대구
4th row부산광역시 해운대구
5th row부산광역시 해운대구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 65
12.4%
부산광역시 사하구 59
11.2%
부산광역시 금정구 53
10.1%
부산광역시 해운대구 49
9.3%
부산광역시 중구 43
8.2%
부산광역시 사상구 42
8.0%
부산광역시 연제구 40
7.6%
부산광역시 동래구 40
7.6%
부산광역시 수영구 32
 
6.1%
부산광역시 기장군 22
 
4.2%
Other values (6) 80
15.2%

Length

2024-04-17T09:47:54.073500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 525
50.0%
부산진구 65
 
6.2%
사하구 59
 
5.6%
금정구 53
 
5.0%
해운대구 49
 
4.7%
중구 43
 
4.1%
사상구 42
 
4.0%
동래구 40
 
3.8%
연제구 40
 
3.8%
수영구 32
 
3.0%
Other values (7) 102
 
9.7%

data_day
Date

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum2020-03-31 00:00:00
Maximum2020-09-09 00:00:00
2024-04-17T09:47:54.155442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:54.236574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct517
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.159469
Minimum33.522788
Maximum35.369941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T09:47:54.343897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.522788
5-th percentile35.082336
Q135.112711
median35.159693
Q335.195542
95-th percentile35.262069
Maximum35.369941
Range1.8471531
Interquartile range (IQR)0.08283087

Descriptive statistics

Standard deviation0.090521541
Coefficient of variation (CV)0.0025745992
Kurtosis203.77673
Mean35.159469
Median Absolute Deviation (MAD)0.04013829
Skewness-11.172946
Sum18458.721
Variance0.0081941494
MonotonicityNot monotonic
2024-04-17T09:47:54.490479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.140092 2
 
0.4%
35.1546303 2
 
0.4%
35.060292 2
 
0.4%
35.103203 2
 
0.4%
35.1483313 2
 
0.4%
35.1588832 2
 
0.4%
35.177812 2
 
0.4%
35.1514508 2
 
0.4%
35.1150254422 1
 
0.2%
35.0959336637 1
 
0.2%
Other values (507) 507
96.6%
ValueCountFrequency (%)
33.5227879392 1
0.2%
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%
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%
35.281468 1
0.2%
35.279012 1
0.2%
35.278908 1
0.2%
35.277991 1
0.2%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct517
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.0587
Minimum126.56202
Maximum129.28192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T09:47:54.632045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.56202
5-th percentile128.96693
Q1129.02345
median129.06103
Q3129.09525
95-th percentile129.17575
Maximum129.28192
Range2.7198956
Interquartile range (IQR)0.071802452

Descriptive statistics

Standard deviation0.12767786
Coefficient of variation (CV)0.0009893007
Kurtosis279.51653
Mean129.0587
Median Absolute Deviation (MAD)0.0373575
Skewness-14.250375
Sum67755.816
Variance0.016301636
MonotonicityNot monotonic
2024-04-17T09:47:54.759072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.088768 2
 
0.4%
129.1078662 2
 
0.4%
129.054471 2
 
0.4%
129.075569 2
 
0.4%
129.064284 2
 
0.4%
129.054079 2
 
0.4%
129.002523 2
 
0.4%
128.97982 2
 
0.4%
129.084231 1
 
0.2%
129.0156621565 1
 
0.2%
Other values (507) 507
96.6%
ValueCountFrequency (%)
126.5620223692 1
0.2%
128.807799 1
0.2%
128.833221 1
0.2%
128.868901 1
0.2%
128.893126 1
0.2%
128.945753 1
0.2%
128.957479 1
0.2%
128.957492 1
0.2%
128.957644 1
0.2%
128.958145 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
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
<NA>
456 
69 

Length

Max length4
Median length4
Mean length3.6057143
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 456
86.9%
69
 
13.1%

Length

2024-04-17T09:47:54.866984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:47:54.946662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 456
100.0%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum2021-01-05 15:40:15
Maximum2021-01-05 15:40:15
2024-04-17T09:47:55.012313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:55.083330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T09:47:49.058115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.052379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.383627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.736909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:49.142575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.130435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.487996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.817467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:49.225626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.214355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.574966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.901918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:49.303614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.296490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.655992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:47:48.979929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T09:47:55.144949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codebsns_condovrd_dategugundata_daylatlng
skey1.0000.8560.0960.9290.9450.7530.4940.313
instt_code0.8561.0000.2320.9851.0000.9860.6830.603
bsns_cond0.0960.2321.0000.3260.3360.2960.0000.000
ovrd_date0.9290.9850.3261.0000.9910.9850.8060.799
gugun0.9451.0000.3360.9911.0001.0000.8050.838
data_day0.7530.9860.2960.9851.0001.0000.2530.257
lat0.4940.6830.0000.8060.8050.2531.0000.947
lng0.3130.6030.0000.7990.8380.2570.9471.000
2024-04-17T09:47:55.234280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bsns_condgugunapr_at
bsns_cond1.0000.1221.000
gugun0.1221.0001.000
apr_at1.0001.0001.000
2024-04-17T09:47:55.312176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codelatlngbsns_condgugunapr_at
skey1.0000.267-0.0630.0590.0310.8111.000
instt_code0.2671.0000.4170.1980.1280.9941.000
lat-0.0630.4171.0000.5510.0000.6351.000
lng0.0590.1980.5511.0000.0000.6821.000
bsns_cond0.0310.1280.0000.0001.0000.1221.000
gugun0.8110.9940.6350.6820.1221.0001.000
apr_at1.0001.0001.0001.0001.0001.0001.000

Missing values

2024-04-17T09:47:49.412317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T09:47:49.595168image/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
038633250000일반음식점일식여송제부산광역시 중구 광복로18번길 5, 1~2층 (부평동1가)부산광역시 중구 부평동1가 37 - 10 1~2층족발051-246-21112015-11-032019-12-30부산광역시 중구2020-07-3135.099165129.027206<NA>2021-01-05 15:40:15
138643250000일반음식점한식바래부산광역시 중구 중앙대로81번길 4, 1층 (중앙동4가, 4,5,20)부산광역시 중구 중앙동4가 53 - 1 4,5,20 (1층)해물탕051-463-70052003-07-162019-12-30부산광역시 중구2020-07-3135.105123129.035727<NA>2021-01-05 15:40:15
239873330000일반음식점한식거창순두부부산광역시 해운대구 좌동순환로 55-1 (좌동, 지상1층)부산광역시 해운대구 좌동 1341-11순두부051-731-49882019-12-132019-12-13부산광역시 해운대구2020-04-2835.171457129.166423<NA>2021-01-05 15:40:15
339883330000일반음식점한식궁중식당부산광역시 해운대구 좌동로63번길 21 (중동, 지상1층)부산광역시 해운대구 중동 218-7아구찜051-702-18012019-12-132019-12-13부산광역시 해운대구2020-04-2835.171423129.171824<NA>2021-01-05 15:40:15
439893330000일반음식점식육(숯불구이)태백산 참숯마을부산광역시 해운대구 재반로60번길 2 (재송동, 2,3층)부산광역시 해운대구 재송동 480통돼지갈비051-784-55222019-12-132019-12-13부산광역시 해운대구2020-04-2835.18386129.126504<NA>2021-01-05 15:40:15
539903330000일반음식점회집동백섬 횟집부산광역시 해운대구 해운대해변로209번나길 17 (우동, 지상1,2,3층)부산광역시 해운대구 우동 655-7생선회051-741-38882019-12-132019-12-13부산광역시 해운대구2020-04-2835.160411129.154664<NA>2021-01-05 15:40:15
639913330000일반음식점한식맛나감자탕부산광역시 해운대구 좌동로91번길 17 (좌동, 지상2층 )부산광역시 해운대구 좌동 985-4감자탕051-703-74712019-12-132019-12-13부산광역시 해운대구2020-04-2835.172777129.174357<NA>2021-01-05 15:40:15
739923330000일반음식점한식맛나감자탕부산광역시 해운대구 명장로67번길 53 (반여동, 지상2층 )부산광역시 해운대구 반여동 1160감자탕051-527-62582019-12-132019-12-13부산광역시 해운대구2020-04-2835.204848129.112397<NA>2021-01-05 15:40:15
839933330000일반음식점한식배비장보쌈부산광역시 해운대구 해운대로483번길 1-1 (우동, 외1필지 지상2층)부산광역시 해운대구 우동 959-1보쌈051-742-77552019-12-132019-12-13부산광역시 해운대구2020-04-2835.163319129.145539<NA>2021-01-05 15:40:15
939943330000일반음식점식육(숯불구이)삼겹속에 삼겹부산광역시 해운대구 반여로27번길 28 (반여동, 1364-4 지상2층)부산광역시 해운대구 반여동 1364-4삼겹살051-527-37882019-12-132019-12-13부산광역시 해운대구2020-04-2835.195965129.118809<NA>2021-01-05 15:40:15
skeyinstt_codebsns_sectorbsns_condbsns_nmaddr_roadaddr_jibunmenutelspec_dateovrd_dategugundata_daylatlngapr_atlast_load_dttm
51537603310000일반음식점한식가연장 대연점부산광역시 남구 유엔평화로 47(대연동)부산광역시 남구 대연동 881-6미역국051-627-53772015-11-052019-11-20부산광역시 남구2020-07-3135.13063129.092415<NA>2021-01-05 15:40:15
51637733280000일반음식점한식포항물회부산광역시 영도구 절영로 478 (동삼동)부산광역시 영도구 동삼동 570-1번지물회051-405-90772001-07-212019-10-30부산광역시 영도구2020-07-3135.070529129.068675<NA>2021-01-05 15:40:15
51738463250000일반음식점한식부산명물횟집부산광역시 중구 자갈치해안로 55 (남포동4가)부산광역시 중구 남포동4가 38생선회051-245-49952003-07-162019-12-30부산광역시 중구2020-07-3135.097113129.031118<NA>2021-01-05 15:40:15
51838473250000일반음식점한식무궁화부산광역시 중구 충장대로5번길 46, 1층 (중앙동4가)부산광역시 중구 중앙동4가 78 - 23 (1층)한정식051-463-22232003-07-162019-12-30부산광역시 중구2020-07-3135.109152129.037906<NA>2021-01-05 15:40:15
51938483250000일반음식점한식녹두밭부산광역시 중구 광복로6번길 9, 1층 (부평동2가)부산광역시 중구 부평동2가 37 - 6 (1층)녹두전051-246-07902003-07-162019-12-30부산광역시 중구2020-07-3135.098753129.025797<NA>2021-01-05 15:40:15
52038493250000일반음식점한식개미집 본점부산광역시 중구 중구로30번길 20-2 (신창동1가)부산광역시 중구 신창동1가 14 - 4낙지볶음051-246-31862009-07-012019-12-30부산광역시 중구2020-07-3135.100521129.029987<NA>2021-01-05 15:40:15
52138503250000일반음식점한식㈜팬스타트리(담)부산광역시 중구 중앙대로41번길 12 (중앙동2가)부산광역시 중구 중앙동2가 20 - 0등심구이051-241-69992007-07-062019-12-30부산광역시 중구2020-07-3135.101287129.035229<NA>2021-01-05 15:40:15
52241603380000일반음식점탕류(보신용)조마루감자탕부산광역시 수영구 수영로 723, 1층 8,9호 (수영동)부산광역시 수영구 수영동 444 - 8 ,9(1층)감자탕051-751-36512010-06-302019-11-01부산광역시 수영구2020-03-3135.168581129.118303<NA>2021-01-05 15:40:15
52341613380000일반음식점회집진미횟집부산광역시 수영구 광안해변로 207, 1~2층 (광안동)부산광역시 수영구 광안동 196 - 2 로즈모텔생선회051-758-87002010-06-302019-11-01부산광역시 수영구2020-03-3135.152664129.11747<NA>2021-01-05 15:40:15
52441623380000일반음식점식육(숯불구이)진주양곱창부산광역시 수영구 무학로9번길 138, 1층 (광안동)부산광역시 수영구 광안동 207 - 5 1층양곱창051-758-14602002-03-252019-11-01부산광역시 수영구2020-03-3135.167142129.117358<NA>2021-01-05 15:40:15