Overview

Dataset statistics

Number of variables19
Number of observations591
Missing cells614
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory92.5 KiB
Average record size in memory160.2 B

Variable types

Numeric5
Categorical7
Text4
DateTime2
Unsupported1

Alerts

last_load_dttm has constant value ""Constant
gugun is highly overall correlated with skey and 9 other fieldsHigh correlation
atchm_limit_date is highly overall correlated with lng and 5 other fieldsHigh correlation
inst_nm is highly overall correlated with skey and 9 other fieldsHigh correlation
tel is highly overall correlated with skey and 9 other fieldsHigh correlation
spec is highly overall correlated with skey and 9 other fieldsHigh correlation
ftrs is highly overall correlated with skey and 9 other fieldsHigh correlation
skey is highly overall correlated with inst_nm and 4 other fieldsHigh correlation
instt_code is highly overall correlated with inst_nm and 4 other fieldsHigh correlation
atchm_nmbr is highly overall correlated with occ_use_feeHigh correlation
occ_use_fee is highly overall correlated with atchm_nmbr and 5 other fieldsHigh correlation
lng is highly overall correlated with inst_nm and 5 other fieldsHigh correlation
civil_svc_fee is highly overall correlated with inst_nm and 4 other fieldsHigh correlation
atchm_limit_date is highly imbalanced (98.2%)Imbalance
civil_svc_fee is highly imbalanced (57.3%)Imbalance
addr_road has 18 (3.0%) missing valuesMissing
apr_at has 591 (100.0%) missing valuesMissing
skey has unique valuesUnique
apr_at is an unsupported type, check if it needs cleaning or further analysisUnsupported
occ_use_fee has 78 (13.2%) zerosZeros

Reproduction

Analysis started2024-04-16 23:11:17.868944
Analysis finished2024-04-16 23:11:21.564608
Duration3.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct591
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5803
Minimum5508
Maximum6098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-17T08:11:21.621943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5508
5-th percentile5537.5
Q15655.5
median5803
Q35950.5
95-th percentile6068.5
Maximum6098
Range590
Interquartile range (IQR)295

Descriptive statistics

Standard deviation170.75128
Coefficient of variation (CV)0.029424656
Kurtosis-1.2
Mean5803
Median Absolute Deviation (MAD)148
Skewness0
Sum3429573
Variance29156
MonotonicityNot monotonic
2024-04-17T08:11:21.727919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5949 1
 
0.2%
5842 1
 
0.2%
5621 1
 
0.2%
5622 1
 
0.2%
5623 1
 
0.2%
5624 1
 
0.2%
5625 1
 
0.2%
5626 1
 
0.2%
5627 1
 
0.2%
5628 1
 
0.2%
Other values (581) 581
98.3%
ValueCountFrequency (%)
5508 1
0.2%
5509 1
0.2%
5510 1
0.2%
5511 1
0.2%
5512 1
0.2%
5513 1
0.2%
5514 1
0.2%
5515 1
0.2%
5516 1
0.2%
5517 1
0.2%
ValueCountFrequency (%)
6098 1
0.2%
6097 1
0.2%
6096 1
0.2%
6095 1
0.2%
6094 1
0.2%
6093 1
0.2%
6092 1
0.2%
6091 1
0.2%
6090 1
0.2%
6089 1
0.2%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3336683.6
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-17T08:11:21.824780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13310000
median3340000
Q33360000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation40436.178
Coefficient of variation (CV)0.012118673
Kurtosis-0.68915486
Mean3336683.6
Median Absolute Deviation (MAD)30000
Skewness-0.2605776
Sum1.97198 × 109
Variance1.6350845 × 109
MonotonicityNot monotonic
2024-04-17T08:11:21.916443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3340000 117
19.8%
3310000 62
10.5%
3400000 56
9.5%
3360000 54
9.1%
3350000 36
 
6.1%
3390000 31
 
5.2%
3380000 31
 
5.2%
3270000 30
 
5.1%
3320000 30
 
5.1%
3370000 29
 
4.9%
Other values (6) 115
19.5%
ValueCountFrequency (%)
3250000 13
 
2.2%
3260000 19
 
3.2%
3270000 30
 
5.1%
3280000 13
 
2.2%
3290000 28
 
4.7%
3300000 19
 
3.2%
3310000 62
10.5%
3320000 30
 
5.1%
3330000 23
 
3.9%
3340000 117
19.8%
ValueCountFrequency (%)
3400000 56
9.5%
3390000 31
 
5.2%
3380000 31
 
5.2%
3370000 29
 
4.9%
3360000 54
9.1%
3350000 36
 
6.1%
3340000 117
19.8%
3330000 23
 
3.9%
3320000 30
 
5.1%
3310000 62
10.5%

inst_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
부산광역시 사하구청
117 
부산광역시 남구청
62 
부산광역시 기장군청
56 
부산광역시 강서구청
54 
부산광역시 사상구청
31 
Other values (13)
271 

Length

Max length11
Median length10
Mean length9.786802
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 사하구청 117
19.8%
부산광역시 남구청 62
10.5%
부산광역시 기장군청 56
9.5%
부산광역시 강서구청 54
9.1%
부산광역시 사상구청 31
 
5.2%
부산광역시 수영구청 31
 
5.2%
부산광역시 동구청 30
 
5.1%
부산광역시 북구청 30
 
5.1%
부산광역시 부산진구청 28
 
4.7%
부산광역시 연제구청 28
 
4.7%
Other values (8) 124
21.0%

Length

2024-04-17T08:11:22.019805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 573
48.5%
사하구청 117
 
9.9%
남구청 62
 
5.2%
기장군청 56
 
4.7%
강서구청 54
 
4.6%
사상구청 31
 
2.6%
수영구청 31
 
2.6%
동구청 30
 
2.5%
북구청 30
 
2.5%
연제구청 28
 
2.4%
Other values (10) 169
 
14.3%

admns_nm
Text

Distinct144
Distinct (%)24.4%
Missing1
Missing (%)0.2%
Memory size4.7 KiB
2024-04-17T08:11:22.251119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.5338983
Min length3

Characters and Unicode

Total characters2085
Distinct characters106
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)5.3%

Sample

1st row장림동
2nd row장림동
3rd row신평동
4th row신평동
5th row감천동
ValueCountFrequency (%)
녹산동 28
 
4.7%
신평동 26
 
4.4%
하단동 24
 
4.1%
정관읍 23
 
3.9%
기장읍 18
 
3.1%
다대동 18
 
3.1%
괴정동 17
 
2.9%
명지1동 15
 
2.5%
당리동 12
 
2.0%
대연제3동 12
 
2.0%
Other values (134) 397
67.3%
2024-04-17T08:11:22.608725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 545
26.1%
1 90
 
4.3%
제 81
 
3.9%
2 61
 
2.9%
대 60
 
2.9%
산 50
 
2.4%
읍 48
 
2.3%
정 46
 
2.2%
연 46
 
2.2%
장 45
 
2.2%
Other values (96) 1013
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1848
88.6%
Decimal Number 234
 
11.2%
Space Separator 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 545
29.5%
제 81
 
4.4%
대 60
 
3.2%
산 50
 
2.7%
읍 48
 
2.6%
정 46
 
2.5%
연 46
 
2.5%
장 45
 
2.4%
천 38
 
2.1%
신 36
 
1.9%
Other values (87) 853
46.2%
Decimal Number
ValueCountFrequency (%)
1 90
38.5%
2 61
26.1%
3 39
16.7%
4 23
 
9.8%
6 8
 
3.4%
9 8
 
3.4%
8 3
 
1.3%
5 2
 
0.9%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1848
88.6%
Common 237
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 545
29.5%
제 81
 
4.4%
대 60
 
3.2%
산 50
 
2.7%
읍 48
 
2.6%
정 46
 
2.5%
연 46
 
2.5%
장 45
 
2.4%
천 38
 
2.1%
신 36
 
1.9%
Other values (87) 853
46.2%
Common
ValueCountFrequency (%)
1 90
38.0%
2 61
25.7%
3 39
16.5%
4 23
 
9.7%
6 8
 
3.4%
9 8
 
3.4%
3
 
1.3%
8 3
 
1.3%
5 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1848
88.6%
ASCII 237
 
11.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 545
29.5%
제 81
 
4.4%
대 60
 
3.2%
산 50
 
2.7%
읍 48
 
2.6%
정 46
 
2.5%
연 46
 
2.5%
장 45
 
2.4%
천 38
 
2.1%
신 36
 
1.9%
Other values (87) 853
46.2%
ASCII
ValueCountFrequency (%)
1 90
38.0%
2 61
25.7%
3 39
16.5%
4 23
 
9.7%
6 8
 
3.4%
9 8
 
3.4%
3
 
1.3%
8 3
 
1.3%
5 2
 
0.8%

spot
Text

Distinct539
Distinct (%)91.4%
Missing1
Missing (%)0.2%
Memory size4.7 KiB
2024-04-17T08:11:22.808578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length11.898305
Min length3

Characters and Unicode

Total characters7020
Distinct characters395
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)84.7%

Sample

1st row장림동원아파트사거리 교통섬(화진볼트)
2nd row장림동원아파트사거리 교통섬(Speed mate)
3rd row하남초교
4th row부산은행 앞 교통섬
5th row감천삼거리
ValueCountFrequency (%)
앞 125
 
9.2%
옆 52
 
3.8%
맞은편 30
 
2.2%
입구 29
 
2.1%
사거리 26
 
1.9%
삼거리 18
 
1.3%
쪽 14
 
1.0%
건너편 13
 
1.0%
횡단보도 13
 
1.0%
옹벽 9
 
0.7%
Other values (726) 1036
75.9%
2024-04-17T08:11:23.114665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
827
 
11.8%
) 205
 
2.9%
( 205
 
2.9%
앞 171
 
2.4%
동 168
 
2.4%
리 154
 
2.2%
구 134
 
1.9%
거 132
 
1.9%
대 129
 
1.8%
로 110
 
1.6%
Other values (385) 4785
68.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5465
77.8%
Space Separator 827
 
11.8%
Decimal Number 219
 
3.1%
Close Punctuation 205
 
2.9%
Open Punctuation 205
 
2.9%
Uppercase Letter 55
 
0.8%
Dash Punctuation 16
 
0.2%
Lowercase Letter 16
 
0.2%
Other Punctuation 12
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 171
 
3.1%
동 168
 
3.1%
리 154
 
2.8%
구 134
 
2.5%
거 132
 
2.4%
대 129
 
2.4%
로 110
 
2.0%
교 109
 
2.0%
아 100
 
1.8%
사 98
 
1.8%
Other values (341) 4160
76.1%
Uppercase Letter
ValueCountFrequency (%)
G 8
14.5%
K 7
12.7%
L 6
10.9%
S 6
10.9%
I 5
9.1%
T 5
9.1%
P 3
 
5.5%
A 3
 
5.5%
C 3
 
5.5%
B 2
 
3.6%
Other values (5) 7
12.7%
Decimal Number
ValueCountFrequency (%)
2 66
30.1%
1 51
23.3%
3 28
12.8%
0 19
 
8.7%
4 13
 
5.9%
6 10
 
4.6%
9 10
 
4.6%
5 9
 
4.1%
7 8
 
3.7%
8 5
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
e 4
25.0%
t 3
18.8%
m 2
12.5%
h 1
 
6.2%
d 1
 
6.2%
k 1
 
6.2%
c 1
 
6.2%
n 1
 
6.2%
a 1
 
6.2%
p 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
@ 5
41.7%
. 3
25.0%
, 2
 
16.7%
/ 1
 
8.3%
: 1
 
8.3%
Space Separator
ValueCountFrequency (%)
827
100.0%
Close Punctuation
ValueCountFrequency (%)
) 205
100.0%
Open Punctuation
ValueCountFrequency (%)
( 205
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5463
77.8%
Common 1484
 
21.1%
Latin 71
 
1.0%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 171
 
3.1%
동 168
 
3.1%
리 154
 
2.8%
구 134
 
2.5%
거 132
 
2.4%
대 129
 
2.4%
로 110
 
2.0%
교 109
 
2.0%
아 100
 
1.8%
사 98
 
1.8%
Other values (340) 4158
76.1%
Latin
ValueCountFrequency (%)
G 8
 
11.3%
K 7
 
9.9%
L 6
 
8.5%
S 6
 
8.5%
I 5
 
7.0%
T 5
 
7.0%
e 4
 
5.6%
t 3
 
4.2%
P 3
 
4.2%
A 3
 
4.2%
Other values (15) 21
29.6%
Common
ValueCountFrequency (%)
827
55.7%
) 205
 
13.8%
( 205
 
13.8%
2 66
 
4.4%
1 51
 
3.4%
3 28
 
1.9%
0 19
 
1.3%
- 16
 
1.1%
4 13
 
0.9%
6 10
 
0.7%
Other values (9) 44
 
3.0%
Han
ValueCountFrequency (%)
區 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5463
77.8%
ASCII 1555
 
22.2%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
827
53.2%
) 205
 
13.2%
( 205
 
13.2%
2 66
 
4.2%
1 51
 
3.3%
3 28
 
1.8%
0 19
 
1.2%
- 16
 
1.0%
4 13
 
0.8%
6 10
 
0.6%
Other values (34) 115
 
7.4%
Hangul
ValueCountFrequency (%)
앞 171
 
3.1%
동 168
 
3.1%
리 154
 
2.8%
구 134
 
2.5%
거 132
 
2.4%
대 129
 
2.4%
로 110
 
2.0%
교 109
 
2.0%
아 100
 
1.8%
사 98
 
1.8%
Other values (340) 4158
76.1%
CJK
ValueCountFrequency (%)
區 2
100.0%

addr_road
Text

MISSING 

Distinct431
Distinct (%)75.2%
Missing18
Missing (%)3.0%
Memory size4.7 KiB
2024-04-17T08:11:23.374945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length250
Median length249
Mean length125.10471
Min length6

Characters and Unicode

Total characters71685
Distinct characters212
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

Unique338 ?
Unique (%)59.0%

Sample

1st row부산광역시 사하구 장림동 1118-32
2nd row부산광역시 사하구 장림동 1118-32
3rd row부산광역시 사하구 신평동 1181-25
4th row부산광역시 사하구 신평동 1181-25
5th row부산광역시 사하구 감천동 824-13
ValueCountFrequency (%)
부산광역시 562
 
23.2%
사하구 117
 
4.8%
기장군 56
 
2.3%
강서구 54
 
2.2%
남구 49
 
2.0%
금정구 32
 
1.3%
수영구 31
 
1.3%
사상구 31
 
1.3%
북구 30
 
1.2%
동구 30
 
1.2%
Other values (641) 1434
59.1%
2024-04-17T08:11:23.725064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62172
86.7%
산 586
 
0.8%
광 583
 
0.8%
부 576
 
0.8%
시 570
 
0.8%
역 565
 
0.8%
구 534
 
0.7%
1 508
 
0.7%
동 436
 
0.6%
2 330
 
0.5%
Other values (202) 4825
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 62172
86.7%
Other Letter 6908
 
9.6%
Decimal Number 2232
 
3.1%
Dash Punctuation 280
 
0.4%
Close Punctuation 42
 
0.1%
Open Punctuation 42
 
0.1%
Other Punctuation 7
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 586
 
8.5%
광 583
 
8.4%
부 576
 
8.3%
시 570
 
8.3%
역 565
 
8.2%
구 534
 
7.7%
동 436
 
6.3%
로 223
 
3.2%
사 161
 
2.3%
대 160
 
2.3%
Other values (186) 2514
36.4%
Decimal Number
ValueCountFrequency (%)
1 508
22.8%
2 330
14.8%
3 247
11.1%
7 209
9.4%
5 195
 
8.7%
4 186
 
8.3%
8 161
 
7.2%
0 152
 
6.8%
6 125
 
5.6%
9 119
 
5.3%
Space Separator
ValueCountFrequency (%)
62172
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 280
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64777
90.4%
Hangul 6908
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 586
 
8.5%
광 583
 
8.4%
부 576
 
8.3%
시 570
 
8.3%
역 565
 
8.2%
구 534
 
7.7%
동 436
 
6.3%
로 223
 
3.2%
사 161
 
2.3%
대 160
 
2.3%
Other values (186) 2514
36.4%
Common
ValueCountFrequency (%)
62172
96.0%
1 508
 
0.8%
2 330
 
0.5%
- 280
 
0.4%
3 247
 
0.4%
7 209
 
0.3%
5 195
 
0.3%
4 186
 
0.3%
8 161
 
0.2%
0 152
 
0.2%
Other values (6) 337
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64777
90.4%
Hangul 6908
 
9.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62172
96.0%
1 508
 
0.8%
2 330
 
0.5%
- 280
 
0.4%
3 247
 
0.4%
7 209
 
0.3%
5 195
 
0.3%
4 186
 
0.3%
8 161
 
0.2%
0 152
 
0.2%
Other values (6) 337
 
0.5%
Hangul
ValueCountFrequency (%)
산 586
 
8.5%
광 583
 
8.4%
부 576
 
8.3%
시 570
 
8.3%
역 565
 
8.2%
구 534
 
7.7%
동 436
 
6.3%
로 223
 
3.2%
사 161
 
2.3%
대 160
 
2.3%
Other values (186) 2514
36.4%

ftrs
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
탱탱걸이(우측고정)
117 
탱탱이 걸이식(좌측고정)
62 
접철식
56 
탱탱이걸이형(좌측고정)
52 
탱탱이
44 
Other values (20)
260 

Length

Max length13
Median length11
Mean length7.6818951
Min length2

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row탱탱걸이(우측고정)
2nd row탱탱걸이(우측고정)
3rd row탱탱걸이(우측고정)
4th row탱탱걸이(우측고정)
5th row탱탱걸이(우측고정)

Common Values

ValueCountFrequency (%)
탱탱걸이(우측고정) 117
19.8%
탱탱이 걸이식(좌측고정) 62
10.5%
접철식 56
9.5%
탱탱이걸이형(좌측고정) 52
8.8%
탱탱이 44
 
7.4%
탱탱걸이식 31
 
5.2%
탱탱걸이방식 31
 
5.2%
반자동접이형 30
 
5.1%
탱탱걸이 28
 
4.7%
탱탱걸이식(우측고정) 28
 
4.7%
Other values (15) 112
19.0%

Length

2024-04-17T08:11:23.833487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
탱탱이 119
17.9%
탱탱걸이(우측고정 117
17.6%
걸이식(좌측고정 62
9.3%
접철식 56
8.4%
탱탱이걸이형(좌측고정 52
7.8%
탱탱걸이식 31
 
4.7%
탱탱걸이방식 31
 
4.7%
반자동접이형 30
 
4.5%
탱탱걸이 28
 
4.2%
탱탱걸이식(우측고정 28
 
4.2%
Other values (15) 112
16.8%

spec
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
5.5*0.7
108 
6*0.9
105 
6.2*0.9
56 
6*0.8
56 
6.0*0.7
55 
Other values (32)
211 

Length

Max length10
Median length7
Mean length6.4247039
Min length4

Unique

Unique10 ?
Unique (%)1.7%

Sample

1st row5.5*0.7
2nd row5.5*0.7
3rd row5.5*0.7
4th row5.5*0.7
5th row6*0.9

Common Values

ValueCountFrequency (%)
5.5*0.7 108
18.3%
6*0.9 105
17.8%
6.2*0.9 56
9.5%
6*0.8 56
9.5%
6.0*0.7 55
9.3%
6*0.7 28
 
4.7%
7.0*0.9 22
 
3.7%
5.9*0.9 16
 
2.7%
6.5*0.9 15
 
2.5%
4.7*0.7 15
 
2.5%
Other values (27) 115
19.5%

Length

2024-04-17T08:11:23.933297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.5*0.7 108
18.3%
6*0.9 105
17.8%
6.2*0.9 56
9.5%
6*0.8 56
9.5%
6.0*0.7 55
9.3%
6*0.7 28
 
4.7%
7.0*0.9 22
 
3.7%
5.9*0.9 16
 
2.7%
5.8*0.9 15
 
2.5%
6.5*0.9 15
 
2.5%
Other values (27) 115
19.5%

atchm_nmbr
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3688663
Minimum0
Maximum18
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-17T08:11:24.017863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6068967
Coefficient of variation (CV)0.59669866
Kurtosis3.2127051
Mean4.3688663
Median Absolute Deviation (MAD)1
Skewness1.0142194
Sum2582
Variance6.7959104
MonotonicityNot monotonic
2024-04-17T08:11:24.111269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5 222
37.6%
1 134
22.7%
6 111
18.8%
2 30
 
5.1%
3 29
 
4.9%
4 23
 
3.9%
10 19
 
3.2%
7 6
 
1.0%
15 6
 
1.0%
12 5
 
0.8%
Other values (4) 6
 
1.0%
ValueCountFrequency (%)
0 1
 
0.2%
1 134
22.7%
2 30
 
5.1%
3 29
 
4.9%
4 23
 
3.9%
5 222
37.6%
6 111
18.8%
7 6
 
1.0%
8 3
 
0.5%
9 1
 
0.2%
ValueCountFrequency (%)
18 1
 
0.2%
15 6
 
1.0%
12 5
 
0.8%
10 19
 
3.2%
9 1
 
0.2%
8 3
 
0.5%
7 6
 
1.0%
6 111
18.8%
5 222
37.6%
4 23
 
3.9%

atchm_limit_date
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
10
590 
0
 
1

Length

Max length2
Median length2
Mean length1.998308
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 590
99.8%
0 1
 
0.2%

Length

2024-04-17T08:11:24.210071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:11:24.283863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 590
99.8%
0 1
 
0.2%

civil_svc_fee
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
10000
478 
0
68 
5000
 
28
28900
 
10
21500
 
7

Length

Max length5
Median length5
Mean length4.4923858
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10000
2nd row10000
3rd row10000
4th row10000
5th row10000

Common Values

ValueCountFrequency (%)
10000 478
80.9%
0 68
 
11.5%
5000 28
 
4.7%
28900 10
 
1.7%
21500 7
 
1.2%

Length

2024-04-17T08:11:24.372316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:11:24.460034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 478
80.9%
0 68
 
11.5%
5000 28
 
4.7%
28900 10
 
1.7%
21500 7
 
1.2%

occ_use_fee
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2183.6887
Minimum0
Maximum16200
Zeros78
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-17T08:11:24.554980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11100
median1560
Q31620
95-th percentile12600
Maximum16200
Range16200
Interquartile range (IQR)520

Descriptive statistics

Standard deviation3612.6384
Coefficient of variation (CV)1.6543743
Kurtosis8.9546468
Mean2183.6887
Median Absolute Deviation (MAD)410
Skewness3.2147012
Sum1290560
Variance13051156
MonotonicityNot monotonic
2024-04-17T08:11:24.650988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1620 178
30.1%
1150 108
18.3%
0 78
13.2%
720 48
 
8.1%
1260 36
 
6.1%
1560 31
 
5.2%
1890 26
 
4.4%
16200 25
 
4.2%
12600 19
 
3.2%
980 15
 
2.5%
Other values (9) 27
 
4.6%
ValueCountFrequency (%)
0 78
13.2%
720 48
8.1%
980 15
 
2.5%
1050 7
 
1.2%
1150 108
18.3%
1260 36
 
6.1%
1420 1
 
0.2%
1470 1
 
0.2%
1530 1
 
0.2%
1560 31
 
5.2%
ValueCountFrequency (%)
16200 25
 
4.2%
12600 19
 
3.2%
1890 26
 
4.4%
1830 1
 
0.2%
1780 1
 
0.2%
1755 2
 
0.3%
1750 12
 
2.0%
1620 178
30.1%
1590 1
 
0.2%
1560 31
 
5.2%

tel
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
051-220-4715
117 
051-607-4626
62 
051-709-4626
56 
051-970-4281
54 
051-310-4621
31 
Other values (14)
271 

Length

Max length12
Median length12
Mean length11.986464
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row051-220-4715
2nd row051-220-4715
3rd row051-220-4715
4th row051-220-4715
5th row051-220-4715

Common Values

ValueCountFrequency (%)
051-220-4715 117
19.8%
051-607-4626 62
10.5%
051-709-4626 56
9.5%
051-970-4281 54
9.1%
051-310-4621 31
 
5.2%
051-610-4625 31
 
5.2%
051-440-4625 30
 
5.1%
051-309-4625 30
 
5.1%
051-605-4629 28
 
4.7%
051-665-4621 28
 
4.7%
Other values (9) 124
21.0%

Length

2024-04-17T08:11:24.757540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-220-4715 117
19.8%
051-607-4626 62
10.5%
051-709-4626 56
9.5%
051-970-4281 54
9.1%
051-310-4621 31
 
5.2%
051-610-4625 31
 
5.2%
051-440-4625 30
 
5.1%
051-309-4625 30
 
5.1%
051-665-4621 28
 
4.7%
051-605-4629 28
 
4.7%
Other values (9) 124
21.0%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
부산광역시 사하구
117 
부산광역시 남구
62 
부산광역시 기장군
56 
부산광역시 강서구
54 
부산광역시 금정구
36 
Other values (12)
266 

Length

Max length10
Median length9
Mean length8.8172589
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 사하구 117
19.8%
부산광역시 남구 62
10.5%
부산광역시 기장군 56
9.5%
부산광역시 강서구 54
9.1%
부산광역시 금정구 36
 
6.1%
부산광역시 사상구 31
 
5.2%
부산광역시 수영구 31
 
5.2%
부산광역시 북구 30
 
5.1%
부산광역시 동구 30
 
5.1%
부산광역시 연제구 28
 
4.7%
Other values (7) 116
19.6%

Length

2024-04-17T08:11:24.856895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 590
50.0%
사하구 117
 
9.9%
남구 62
 
5.2%
기장군 56
 
4.7%
강서구 54
 
4.6%
금정구 36
 
3.0%
사상구 31
 
2.6%
수영구 31
 
2.6%
동구 30
 
2.5%
북구 30
 
2.5%
Other values (8) 144
 
12.2%

data_day
Date

Distinct4
Distinct (%)0.7%
Missing1
Missing (%)0.2%
Memory size4.7 KiB
Minimum2020-07-31 00:00:00
Maximum2020-08-27 00:00:00
2024-04-17T08:11:24.931484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:25.002988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

lat
Text

Distinct446
Distinct (%)75.6%
Missing1
Missing (%)0.2%
Memory size4.7 KiB
2024-04-17T08:11:25.241355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length9.2915254
Min length6

Characters and Unicode

Total characters5482
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

Unique345 ?
Unique (%)58.5%

Sample

1st row35.083096
2nd row35.083096
3rd row35.094272
4th row35.094272
5th row35.093709
ValueCountFrequency (%)
35.115393 5
 
0.8%
35.098895 4
 
0.7%
35.085483 4
 
0.7%
35.103451 4
 
0.7%
35.100625 4
 
0.7%
35.094272 4
 
0.7%
35.083096 4
 
0.7%
35.057293 4
 
0.7%
35.1288896 4
 
0.7%
35.115294 4
 
0.7%
Other values (436) 549
93.1%
2024-04-17T08:11:25.856130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 948
17.3%
5 877
16.0%
1 628
11.5%
. 591
10.8%
2 396
7.2%
0 390
7.1%
9 346
 
6.3%
8 342
 
6.2%
6 329
 
6.0%
4 295
 
5.4%
Other values (2) 340
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4838
88.3%
Other Punctuation 591
 
10.8%
Space Separator 53
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 948
19.6%
5 877
18.1%
1 628
13.0%
2 396
8.2%
0 390
8.1%
9 346
 
7.2%
8 342
 
7.1%
6 329
 
6.8%
4 295
 
6.1%
7 287
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 591
100.0%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 948
17.3%
5 877
16.0%
1 628
11.5%
. 591
10.8%
2 396
7.2%
0 390
7.1%
9 346
 
6.3%
8 342
 
6.2%
6 329
 
6.0%
4 295
 
5.4%
Other values (2) 340
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 948
17.3%
5 877
16.0%
1 628
11.5%
. 591
10.8%
2 396
7.2%
0 390
7.1%
9 346
 
6.3%
8 342
 
6.2%
6 329
 
6.0%
4 295
 
5.4%
Other values (2) 340
 
6.2%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct446
Distinct (%)75.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean129.04211
Minimum127.46873
Maximum129.28274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-17T08:11:25.972678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.46873
5-th percentile128.88135
Q1128.9806
median129.04841
Q3129.09972
95-th percentile129.20134
Maximum129.28274
Range1.814011
Interquartile range (IQR)0.11912855

Descriptive statistics

Standard deviation0.10820848
Coefficient of variation (CV)0.00083855166
Kurtosis74.928633
Mean129.04211
Median Absolute Deviation (MAD)0.0591885
Skewness-5.1898689
Sum76134.848
Variance0.011709075
MonotonicityNot monotonic
2024-04-17T08:11:26.086677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.961535 5
 
0.8%
128.965242 4
 
0.7%
129.1143634 4
 
0.7%
128.967647 4
 
0.7%
128.976001 4
 
0.7%
128.963693 4
 
0.7%
128.993993 4
 
0.7%
128.974553 4
 
0.7%
128.959648 4
 
0.7%
128.976554 4
 
0.7%
Other values (436) 549
92.9%
ValueCountFrequency (%)
127.468726 1
0.2%
128.812243 1
0.2%
128.830716 1
0.2%
128.831874 1
0.2%
128.832948 1
0.2%
128.836211 1
0.2%
128.839459 1
0.2%
128.839491 1
0.2%
128.839518 1
0.2%
128.842772 1
0.2%
ValueCountFrequency (%)
129.282737 1
0.2%
129.282694 1
0.2%
129.282624 1
0.2%
129.258162 1
0.2%
129.257847 1
0.2%
129.257346 1
0.2%
129.240175 1
0.2%
129.240064 1
0.2%
129.235056 1
0.2%
129.232624 1
0.2%

apr_at
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing591
Missing (%)100.0%
Memory size5.3 KiB

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum2020-12-22 14:36:41
Maximum2020-12-22 14:36:41
2024-04-17T08:11:26.184063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:26.254955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T08:11:20.705797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:18.851861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.235842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.677481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.294154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.783898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:18.921935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.311414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.752356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.369302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.863380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:18.995059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.412552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.832069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.450185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.939178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.071356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.500339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.133168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.544917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:21.023343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.158029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:19.599027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.214596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:20.626878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T08:11:26.324889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codeinst_nmftrsspecatchm_nmbratchm_limit_datecivil_svc_feeocc_use_feetelgugundata_daylng
skey1.0000.9590.9540.9520.9100.5790.0070.7350.6490.9560.9540.7290.608
instt_code0.9591.0001.0000.9970.9510.6600.0000.8390.7411.0001.0000.8420.725
inst_nm0.9541.0001.0000.9910.9710.791NaN0.9200.9341.0001.0001.0000.877
ftrs0.9520.9970.9911.0000.9700.800NaN0.8620.9250.9940.9981.0000.931
spec0.9100.9510.9710.9701.0000.841NaN0.8480.9410.9840.9710.9360.901
atchm_nmbr0.5790.6600.7910.8000.8411.0000.0000.2920.4210.8540.7780.4040.341
atchm_limit_date0.0070.000NaNNaNNaN0.0001.0000.0650.000NaNNaNNaNNaN
civil_svc_fee0.7350.8390.9200.8620.8480.2920.0651.0000.3370.9220.8650.6490.071
occ_use_fee0.6490.7410.9340.9250.9410.4210.0000.3371.0000.9390.9760.1800.189
tel0.9561.0001.0000.9940.9840.854NaN0.9220.9391.0001.0001.0000.883
gugun0.9541.0001.0000.9980.9710.778NaN0.8650.9761.0001.0001.0000.941
data_day0.7290.8421.0001.0000.9360.404NaN0.6490.1801.0001.0001.0000.676
lng0.6080.7250.8770.9310.9010.341NaN0.0710.1890.8830.9410.6761.000
2024-04-17T08:11:26.448245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugunatchm_limit_datecivil_svc_feeinst_nmtelspecftrs
gugun1.0001.0000.6610.9990.9980.7310.933
atchm_limit_date1.0001.0000.0791.0001.0001.0001.000
civil_svc_fee0.6610.0791.0000.7760.7740.5770.623
inst_nm0.9991.0000.7761.0000.9990.7300.907
tel0.9981.0000.7740.9991.0000.7140.908
spec0.7311.0000.5770.7300.7141.0000.635
ftrs0.9331.0000.6230.9070.9080.6351.000
2024-04-17T08:11:26.543075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codeatchm_nmbrocc_use_feelnginst_nmftrsspecatchm_limit_datecivil_svc_feetelgugun
skey1.0000.338-0.280-0.3250.0760.7930.7530.6050.0050.3930.7920.794
instt_code0.3381.000-0.004-0.3260.1110.9940.9680.7170.0000.4940.9930.995
atchm_nmbr-0.280-0.0041.0000.5140.1660.4640.4550.4490.0000.1720.4660.461
occ_use_fee-0.325-0.3260.5141.000-0.2630.8140.6820.7150.0000.2800.8120.798
lng0.0760.1110.166-0.2631.0000.7020.6940.6381.0000.0580.7010.703
inst_nm0.7930.9940.4640.8140.7021.0000.9070.7301.0000.7760.9990.999
ftrs0.7530.9680.4550.6820.6940.9071.0000.6351.0000.6230.9080.933
spec0.6050.7170.4490.7150.6380.7300.6351.0001.0000.5770.7140.731
atchm_limit_date0.0050.0000.0000.0001.0001.0001.0001.0001.0000.0791.0001.000
civil_svc_fee0.3930.4940.1720.2800.0580.7760.6230.5770.0791.0000.7740.661
tel0.7920.9930.4660.8120.7010.9990.9080.7141.0000.7741.0000.998
gugun0.7940.9950.4610.7980.7030.9990.9330.7311.0000.6610.9981.000

Missing values

2024-04-17T08:11:21.142231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T08:11:21.325878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-17T08:11:21.460329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

skeyinstt_codeinst_nmadmns_nmspotaddr_roadftrsspecatchm_nmbratchm_limit_datecivil_svc_feeocc_use_feetelgugundata_daylatlngapr_atlast_load_dttm
059493340000부산광역시 사하구청장림동장림동원아파트사거리 교통섬(화진볼트)부산광역시 사하구 장림동 1118-32탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-07-3135.083096128.965242<NA>2020-12-22 14:36:41
159503340000부산광역시 사하구청장림동장림동원아파트사거리 교통섬(Speed mate)부산광역시 사하구 장림동 1118-32탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-07-3135.083096128.965242<NA>2020-12-22 14:36:41
259513340000부산광역시 사하구청신평동하남초교부산광역시 사하구 신평동 1181-25탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-07-3135.094272128.976001<NA>2020-12-22 14:36:41
359523340000부산광역시 사하구청신평동부산은행 앞 교통섬부산광역시 사하구 신평동 1181-25탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-07-3135.094272128.976001<NA>2020-12-22 14:36:41
459533340000부산광역시 사하구청감천동감천삼거리부산광역시 사하구 감천동 824-13탱탱걸이(우측고정)6*0.91210100001620051-220-4715부산광역시 사하구2020-07-3135.093709128.993778<NA>2020-12-22 14:36:41
559543340000부산광역시 사하구청감천동감천전원빌라옹벽부산광역시 사하구 감천동 764탱탱걸이(우측고정)6*0.91010100001620051-220-4715부산광역시 사하구2020-07-3135.087593128.997394<NA>2020-12-22 14:36:41
659553340000부산광역시 사하구청구평동구평남영자동차운전학원부산광역시 사하구 신평1동 569-54탱탱걸이(우측고정)6*0.9510100001620051-220-4715부산광역시 사하구2020-07-3135.085763128.982077<NA>2020-12-22 14:36:41
759563340000부산광역시 사하구청구평동구평대림맨션부산광역시 사하구 구평동 29-9탱탱걸이(우측고정)6.2*0.91010100001620051-220-4715부산광역시 사하구2020-07-3135.087734128.995041<NA>2020-12-22 14:36:41
859573340000부산광역시 사하구청다대동현대아파트 맘모스 프라자 앞부산광역시 사하구 다대동 120-1탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-07-3135.061020128.978395<NA>2020-12-22 14:36:41
959583340000부산광역시 사하구청다대동다대해송아파트부산광역시 사하구 다대동 113-11탱탱걸이(우측고정)5.6*0.91010100001620051-220-4715부산광역시 사하구2020-07-3135.062615128.976517<NA>2020-12-22 14:36:41
skeyinstt_codeinst_nmadmns_nmspotaddr_roadftrsspecatchm_nmbratchm_limit_datecivil_svc_feeocc_use_feetelgugundata_daylatlngapr_atlast_load_dttm
58157503370000부산광역시 연제구청연산9동주공아파트(좌)부산광역시 연제구 토현로 10탱탱걸이식(우측고정)6*0.961050001620051-665-4621부산광역시 연제구2020-08-2735.179097129.111023<NA>2020-12-22 14:36:41
58257513370000부산광역시 연제구청연산9동주공아파트(우)부산광역시 연제구 토현로 10탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.179097129.111023<NA>2020-12-22 14:36:41
58357523370000부산광역시 연제구청연산9동LG아파트(좌)부산광역시 연제구 고분로 200탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.184255129.103471<NA>2020-12-22 14:36:41
58457533370000부산광역시 연제구청연산9동LG아파트(우)부산광역시 연제구 고분로 200탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.184255129.103471<NA>2020-12-22 14:36:41
58557543370000부산광역시 연제구청연산9동부산제일교회 앞(좌)부산광역시 연제구 좌수영로 291탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.187712129.110106<NA>2020-12-22 14:36:41
58657553370000부산광역시 연제구청연산9동부산제일교회 앞(우)부산광역시 연제구 좌수영로 291탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.187712129.110106<NA>2020-12-22 14:36:41
58757563370000부산광역시 연제구청거제1동롯데캐슬아파트앞부산광역시 연제구 세병로 44탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.176218129.079591<NA>2020-12-22 14:36:41
58857573370000부산광역시 연제구청거제1동교대테니스장부산광역시 연제구 황새알로 3-2탱탱걸이식(우측고정)6*0.951050001620051-665-4621부산광역시 연제구2020-08-2735.194662129.073013<NA>2020-12-22 14:36:41
58957583370000<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA><NA><NA><NA>2020-12-22 14:36:41
59058203310000부산광역시 남구청용호제2동오륙도 SK VIEW 아파트 입구(늘빛교회 맞은편)부산광역시 남구 용호동 894-3탱탱이 걸이식(좌측고정)7.0*0.9510100001890051-607-4626부산광역시 남구2020-07-3135.1078495129.111662<NA>2020-12-22 14:36:41