Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.7 KiB
Average record size in memory83.3 B

Variable types

Text5
Numeric2
Boolean1
Categorical2

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템메뉴상의 자주가기 그룹정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093184/fileData.do

Alerts

자주가기그룹일련번호 is highly overall correlated with 정렬순서값High correlation
정렬순서값 is highly overall correlated with 자주가기그룹일련번호High correlation
삭제여부 is highly overall correlated with 최종수정수High correlation
최종수정수 is highly overall correlated with 삭제여부High correlation
삭제여부 is highly imbalanced (84.7%)Imbalance
최종수정수 is highly imbalanced (84.0%)Imbalance
체크여부 is highly imbalanced (57.2%)Imbalance
자주가기그룹일련번호 has 79 (15.8%) zerosZeros

Reproduction

Analysis started2023-12-12 00:19:57.966015
Analysis finished2023-12-12 00:19:58.901971
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직원번호
Text

Distinct103
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:19:59.101965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.27
Min length4

Characters and Unicode

Total characters2135
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)6.0%

Sample

1st row5616
2nd row5616
3rd row5616
4th row5616
5th row5616
ValueCountFrequency (%)
6136 29
 
5.8%
4207 25
 
5.0%
6185 19
 
3.8%
4531 18
 
3.6%
4322 16
 
3.2%
5920 13
 
2.6%
6045 13
 
2.6%
6103 12
 
2.4%
3703 11
 
2.2%
5901 11
 
2.2%
Other values (93) 333
66.6%
2023-12-12T09:19:59.458171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 378
17.7%
1 237
11.1%
0 223
10.4%
9 216
10.1%
5 182
8.5%
3 178
8.3%
7 174
8.1%
2 172
8.1%
4 160
7.5%
C 135
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
93.7%
Uppercase Letter 135
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 378
18.9%
1 237
11.8%
0 223
11.2%
9 216
10.8%
5 182
9.1%
3 178
8.9%
7 174
8.7%
2 172
8.6%
4 160
8.0%
8 80
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
93.7%
Latin 135
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
6 378
18.9%
1 237
11.8%
0 223
11.2%
9 216
10.8%
5 182
9.1%
3 178
8.9%
7 174
8.7%
2 172
8.6%
4 160
8.0%
8 80
 
4.0%
Latin
ValueCountFrequency (%)
C 135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 378
17.7%
1 237
11.1%
0 223
10.4%
9 216
10.1%
5 182
8.5%
3 178
8.3%
7 174
8.1%
2 172
8.1%
4 160
7.5%
C 135
 
6.3%

자주가기그룹일련번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.686
Minimum0
Maximum31
Zeros79
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:19:59.587291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile20.05
Maximum31
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.4577713
Coefficient of variation (CV)1.1357318
Kurtosis2.5353147
Mean5.686
Median Absolute Deviation (MAD)2
Skewness1.6924819
Sum2843
Variance41.70281
MonotonicityNot monotonic
2023-12-12T09:19:59.698522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 79
15.8%
1 65
13.0%
2 59
11.8%
3 54
10.8%
4 42
8.4%
5 31
 
6.2%
6 23
 
4.6%
7 19
 
3.8%
8 15
 
3.0%
9 13
 
2.6%
Other values (22) 100
20.0%
ValueCountFrequency (%)
0 79
15.8%
1 65
13.0%
2 59
11.8%
3 54
10.8%
4 42
8.4%
5 31
 
6.2%
6 23
 
4.6%
7 19
 
3.8%
8 15
 
3.0%
9 13
 
2.6%
ValueCountFrequency (%)
31 1
 
0.2%
30 1
 
0.2%
29 2
0.4%
28 3
0.6%
27 1
 
0.2%
26 2
0.4%
25 3
0.6%
24 2
0.4%
23 2
0.4%
22 4
0.8%

정렬순서값
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.924
Minimum1
Maximum296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:19:59.841112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median21
Q350
95-th percentile133.25
Maximum296
Range295
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.355021
Coefficient of variation (CV)1.3014192
Kurtosis7.6336752
Mean37.924
Median Absolute Deviation (MAD)20
Skewness2.5366472
Sum18962
Variance2435.9181
MonotonicityNot monotonic
2023-12-12T09:19:59.985670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 104
 
20.8%
13 15
 
3.0%
19 14
 
2.8%
11 12
 
2.4%
18 11
 
2.2%
17 10
 
2.0%
22 10
 
2.0%
12 10
 
2.0%
15 9
 
1.8%
35 9
 
1.8%
Other values (118) 296
59.2%
ValueCountFrequency (%)
1 104
20.8%
2 2
 
0.4%
3 4
 
0.8%
4 5
 
1.0%
5 5
 
1.0%
6 6
 
1.2%
7 6
 
1.2%
8 6
 
1.2%
9 7
 
1.4%
10 6
 
1.2%
ValueCountFrequency (%)
296 1
0.2%
295 1
0.2%
279 1
0.2%
265 1
0.2%
257 1
0.2%
241 1
0.2%
232 1
0.2%
230 1
0.2%
223 2
0.4%
213 1
0.2%

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
489 
True
 
11
ValueCountFrequency (%)
False 489
97.8%
True 11
 
2.2%
2023-12-12T09:20:00.123789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
480 
2
 
19
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 480
96.0%
2 19
 
3.8%
3 1
 
0.2%

Length

2023-12-12T09:20:00.216424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:20:00.308577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 480
96.0%
2 19
 
3.8%
3 1
 
0.2%

처리시각
Text

Distinct112
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:20:00.567020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique39 ?
Unique (%)7.8%

Sample

1st row12:17.9
2nd row12:17.9
3rd row12:17.9
4th row12:17.9
5th row12:17.9
ValueCountFrequency (%)
05:17.5 29
 
5.8%
36:05.1 25
 
5.0%
11:47.0 18
 
3.6%
05:26.7 18
 
3.6%
31:11.5 16
 
3.2%
41:47.5 13
 
2.6%
18:34.4 13
 
2.6%
53:34.3 12
 
2.4%
14:37.6 11
 
2.2%
49:39.2 11
 
2.2%
Other values (102) 334
66.8%
2023-12-12T09:20:00.932058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 406
11.6%
0 345
9.9%
3 343
9.8%
5 318
9.1%
4 297
8.5%
7 251
7.2%
2 211
6.0%
9 139
 
4.0%
Other values (2) 190
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 406
16.2%
0 345
13.8%
3 343
13.7%
5 318
12.7%
4 297
11.9%
7 251
10.0%
2 211
8.4%
9 139
 
5.6%
6 127
 
5.1%
8 63
 
2.5%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 406
11.6%
0 345
9.9%
3 343
9.8%
5 318
9.1%
4 297
8.5%
7 251
7.2%
2 211
6.0%
9 139
 
4.0%
Other values (2) 190
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 406
11.6%
0 345
9.9%
3 343
9.8%
5 318
9.1%
4 297
8.5%
7 251
7.2%
2 211
6.0%
9 139
 
4.0%
Other values (2) 190
 
5.4%

처리직원번호
Text

Distinct103
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:20:01.183701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.27
Min length4

Characters and Unicode

Total characters2135
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)6.0%

Sample

1st row5616
2nd row5616
3rd row5616
4th row5616
5th row5616
ValueCountFrequency (%)
6136 29
 
5.8%
4207 25
 
5.0%
6185 19
 
3.8%
4531 18
 
3.6%
4322 16
 
3.2%
5920 13
 
2.6%
6045 13
 
2.6%
6103 12
 
2.4%
3703 11
 
2.2%
5901 11
 
2.2%
Other values (93) 333
66.6%
2023-12-12T09:20:01.615229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 378
17.7%
1 237
11.1%
0 223
10.4%
9 216
10.1%
5 182
8.5%
3 178
8.3%
7 174
8.1%
2 172
8.1%
4 160
7.5%
C 135
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
93.7%
Uppercase Letter 135
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 378
18.9%
1 237
11.8%
0 223
11.2%
9 216
10.8%
5 182
9.1%
3 178
8.9%
7 174
8.7%
2 172
8.6%
4 160
8.0%
8 80
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
93.7%
Latin 135
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
6 378
18.9%
1 237
11.8%
0 223
11.2%
9 216
10.8%
5 182
9.1%
3 178
8.9%
7 174
8.7%
2 172
8.6%
4 160
8.0%
8 80
 
4.0%
Latin
ValueCountFrequency (%)
C 135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 378
17.7%
1 237
11.1%
0 223
10.4%
9 216
10.1%
5 182
8.5%
3 178
8.3%
7 174
8.1%
2 172
8.1%
4 160
7.5%
C 135
 
6.3%

최초처리시각
Text

Distinct464
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:20:02.005072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique448 ?
Unique (%)89.6%

Sample

1st row07:07.2
2nd row36:14.4
3rd row13:45.3
4th row12:17.9
5th row38:10.6
ValueCountFrequency (%)
00:59.4 15
 
3.0%
41:49.1 4
 
0.8%
00:00.0 4
 
0.8%
03:21.8 3
 
0.6%
55:09.5 3
 
0.6%
36:47.9 3
 
0.6%
50:43.9 2
 
0.4%
11:11.2 2
 
0.4%
12:59.0 2
 
0.4%
51:22.1 2
 
0.4%
Other values (454) 460
92.0%
2023-12-12T09:20:02.491786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 341
9.7%
5 341
9.7%
4 319
9.1%
1 317
9.1%
3 302
8.6%
2 285
8.1%
6 159
 
4.5%
9 151
 
4.3%
Other values (2) 285
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 341
13.6%
5 341
13.6%
4 319
12.8%
1 317
12.7%
3 302
12.1%
2 285
11.4%
6 159
6.4%
9 151
6.0%
7 145
5.8%
8 140
5.6%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 341
9.7%
5 341
9.7%
4 319
9.1%
1 317
9.1%
3 302
8.6%
2 285
8.1%
6 159
 
4.5%
9 151
 
4.3%
Other values (2) 285
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 341
9.7%
5 341
9.7%
4 319
9.1%
1 317
9.1%
3 302
8.6%
2 285
8.1%
6 159
 
4.5%
9 151
 
4.3%
Other values (2) 285
8.1%

최초처리직원번호
Text

Distinct102
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:20:02.797621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.278
Min length4

Characters and Unicode

Total characters2139
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)5.8%

Sample

1st row5616
2nd row5616
3rd row5616
4th row5616
5th row5616
ValueCountFrequency (%)
6136 29
 
5.8%
4207 25
 
5.0%
6185 19
 
3.8%
4531 18
 
3.6%
4322 16
 
3.2%
5920 13
 
2.6%
6045 13
 
2.6%
6103 12
 
2.4%
3703 11
 
2.2%
5901 11
 
2.2%
Other values (92) 333
66.6%
2023-12-12T09:20:03.246922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 375
17.5%
1 235
11.0%
0 223
10.4%
9 215
10.1%
5 181
8.5%
3 177
8.3%
7 172
8.0%
2 168
7.9%
4 159
7.4%
C 139
 
6.5%
Other values (5) 95
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1984
92.8%
Uppercase Letter 155
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 375
18.9%
1 235
11.8%
0 223
11.2%
9 215
10.8%
5 181
9.1%
3 177
8.9%
7 172
8.7%
2 168
8.5%
4 159
8.0%
8 79
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 139
89.7%
B 4
 
2.6%
A 4
 
2.6%
T 4
 
2.6%
H 4
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1984
92.8%
Latin 155
 
7.2%

Most frequent character per script

Common
ValueCountFrequency (%)
6 375
18.9%
1 235
11.8%
0 223
11.2%
9 215
10.8%
5 181
9.1%
3 177
8.9%
7 172
8.7%
2 168
8.5%
4 159
8.0%
8 79
 
4.0%
Latin
ValueCountFrequency (%)
C 139
89.7%
B 4
 
2.6%
A 4
 
2.6%
T 4
 
2.6%
H 4
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 375
17.5%
1 235
11.0%
0 223
10.4%
9 215
10.1%
5 181
8.5%
3 177
8.3%
7 172
8.0%
2 168
7.9%
4 159
7.4%
C 139
 
6.5%
Other values (5) 95
 
4.4%

체크여부
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Y
414 
N
85 
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
Y 414
82.8%
N 85
 
17.0%
1
 
0.2%

Length

2023-12-12T09:20:03.441064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:20:03.571447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 414
83.0%
n 85
 
17.0%

Interactions

2023-12-12T09:19:58.417009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:58.252712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:58.499592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:58.332251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:20:03.661194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자주가기그룹일련번호정렬순서값삭제여부최종수정수체크여부
자주가기그룹일련번호1.0000.6890.0000.1600.463
정렬순서값0.6891.0000.0000.4170.554
삭제여부0.0000.0001.0000.4940.182
최종수정수0.1600.4170.4941.0000.432
체크여부0.4630.5540.1820.4321.000
2023-12-12T09:20:03.793102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삭제여부체크여부최종수정수
삭제여부1.0000.2980.753
체크여부0.2981.0000.163
최종수정수0.7530.1631.000
2023-12-12T09:20:03.893530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자주가기그룹일련번호정렬순서값삭제여부최종수정수체크여부
자주가기그룹일련번호1.0000.7000.0000.0950.312
정렬순서값0.7001.0000.0000.2740.395
삭제여부0.0000.0001.0000.7530.298
최종수정수0.0950.2740.7531.0000.163
체크여부0.3120.3950.2980.1631.000

Missing values

2023-12-12T09:19:58.607719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:19:58.822670image/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

직원번호자주가기그룹일련번호정렬순서값삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호체크여부
0561601N212:17.9561607:07.25616Y
156167103N112:17.9561636:14.45616Y
256166101N112:17.9561613:45.35616Y
356168107N112:17.9561612:17.95616Y
45616497N112:17.9561638:10.65616Y
556165100N112:17.9561651:37.15616Y
65616293N112:17.9561656:21.35616Y
75616395N112:17.9561658:24.65616Y
85616188N112:17.9561623:12.35616Y
94531455N111:47.0453105:51.54531N
직원번호자주가기그룹일련번호정렬순서값삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호체크여부
4909C701218N153:50.09C70133:17.29C701Y
4919C701110N153:50.09C70132:26.19C701Y
4929C701322N153:50.09C70133:43.69C701Y
4939C72901N146:35.49C72910:54.39C729Y
4949C729418N146:35.49C72907:29.89C729Y
4959C72936N146:35.49C72903:21.89C729Y
4969C729210N146:35.49C72903:21.89C729Y
4979C729113N146:35.49C72903:21.89C729Y
498615801N141:56.5615827:56.16158Y
4996158139N141:56.5615806:15.36158Y