Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.4 KiB
Average record size in memory107.3 B

Variable types

Categorical11
Numeric1
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 재물기타에너지절약점검상세에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092934/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 is highly overall correlated with 에너지절약추진계획실적ID and 4 other fieldsHigh correlation
처리시각 is highly overall correlated with 에너지절약추진계획실적ID and 8 other fieldsHigh correlation
3주에너지점검결과코드 is highly overall correlated with 에너지절약추진계획실적ID and 6 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 에너지절약추진계획실적ID and 5 other fieldsHigh correlation
4주에너지점검결과코드 is highly overall correlated with 에너지절약추진계획실적ID and 6 other fieldsHigh correlation
에너지절약추진계획실적ID is highly overall correlated with 2주에너지점검결과코드 and 8 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 에너지절약추진계획실적ID and 5 other fieldsHigh correlation
최초처리시각 is highly overall correlated with 에너지절약추진계획실적ID and 8 other fieldsHigh correlation
1주에너지점검결과코드 is highly overall correlated with 3주에너지점검결과코드 and 2 other fieldsHigh correlation
2주에너지점검결과코드 is highly overall correlated with 에너지절약추진계획실적ID and 5 other fieldsHigh correlation
5주에너지점검결과코드 is highly overall correlated with 에너지절약추진계획실적ID and 8 other fieldsHigh correlation
1주에너지점검결과코드 is highly imbalanced (94.7%)Imbalance
2주에너지점검결과코드 is highly imbalanced (81.2%)Imbalance
3주에너지점검결과코드 is highly imbalanced (83.8%)Imbalance
4주에너지점검결과코드 is highly imbalanced (84.7%)Imbalance
5주에너지점검결과코드 is highly imbalanced (68.9%)Imbalance
최종수정수 is highly imbalanced (56.7%)Imbalance

Reproduction

Analysis started2023-12-12 21:16:28.007149
Analysis finished2023-12-12 21:16:29.444760
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

에너지절약추진계획실적ID
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
9dhc12N5af
 
16
9dmJbHwndu
 
16
9dm0ktwClz
 
16
9dnLtsk0Xr
 
16
9dnp0233mV
 
16
Other values (27)
420 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9dhc12N5af
2nd row9dhc12N5af
3rd row9dhc12N5af
4th row9dhc12N5af
5th row9dhc12N5af

Common Values

ValueCountFrequency (%)
9dhc12N5af 16
 
3.2%
9dmJbHwndu 16
 
3.2%
9dm0ktwClz 16
 
3.2%
9dnLtsk0Xr 16
 
3.2%
9dnp0233mV 16
 
3.2%
9dnpJAXe2l 16
 
3.2%
9dnlgVh1gt 16
 
3.2%
9dmutFjmzP 16
 
3.2%
9dhc12N4UR 16
 
3.2%
9dm3mRqhrt 16
 
3.2%
Other values (22) 340
68.0%

Length

2023-12-13T06:16:29.504969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9dhc12n5af 16
 
3.2%
9dherlkcaf 16
 
3.2%
9dmxlm7u6o 16
 
3.2%
9dimrgixsg 16
 
3.2%
9dmyxr2v22 16
 
3.2%
9dmy0lwvvs 16
 
3.2%
9dmgtgjvfy 16
 
3.2%
9dmgtgjyna 16
 
3.2%
9dj9wki4nc 16
 
3.2%
9djxfdm1mu 16
 
3.2%
Other values (22) 340
68.0%

점검내용항목코드
Real number (ℝ)

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.42
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T06:16:29.595955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile16
Maximum16
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6171676
Coefficient of variation (CV)0.54835719
Kurtosis-1.2041644
Mean8.42
Median Absolute Deviation (MAD)4
Skewness0.027214369
Sum4210
Variance21.318236
MonotonicityNot monotonic
2023-12-13T06:16:29.690959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 32
 
6.4%
8 32
 
6.4%
2 32
 
6.4%
3 32
 
6.4%
4 32
 
6.4%
5 32
 
6.4%
7 32
 
6.4%
9 32
 
6.4%
10 32
 
6.4%
16 31
 
6.2%
Other values (6) 181
36.2%
ValueCountFrequency (%)
1 32
6.4%
2 32
6.4%
3 32
6.4%
4 32
6.4%
5 32
6.4%
6 30
6.0%
7 32
6.4%
8 32
6.4%
9 32
6.4%
10 32
6.4%
ValueCountFrequency (%)
16 31
6.2%
15 31
6.2%
14 31
6.2%
13 29
5.8%
12 30
6.0%
11 30
6.0%
10 32
6.4%
9 32
6.4%
8 32
6.4%
7 32
6.4%

1주에너지점검결과코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 497
99.4%
2 3
 
0.6%

Length

2023-12-13T06:16:29.800374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:29.878213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.4%
2 3
 
0.6%

2주에너지점검결과코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
478 
 
16
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 478
95.6%
16
 
3.2%
2 6
 
1.2%

Length

2023-12-13T06:16:29.969788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:30.055454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 478
98.8%
2 6
 
1.2%

3주에너지점검결과코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
481 
 
16
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 481
96.2%
16
 
3.2%
2 3
 
0.6%

Length

2023-12-13T06:16:30.138117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:30.221751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 481
99.4%
2 3
 
0.6%

4주에너지점검결과코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
482 
 
16
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 482
96.4%
16
 
3.2%
2 2
 
0.4%

Length

2023-12-13T06:16:30.314485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:30.409428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 482
99.6%
2 2
 
0.4%

5주에너지점검결과코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
450 
48 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 450
90.0%
48
 
9.6%
2 2
 
0.4%

Length

2023-12-13T06:16:30.522950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:30.626463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 450
99.6%
2 2
 
0.4%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T06:16:30.722572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
420 
3
43 
2
 
21
4
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 420
84.0%
3 43
 
8.6%
2 21
 
4.2%
4 16
 
3.2%

Length

2023-12-13T06:16:30.830123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:30.918308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 420
84.0%
3 43
 
8.6%
2 21
 
4.2%
4 16
 
3.2%

처리시각
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
18:30.4
 
16
47:06.8
 
16
31:10.5
 
16
46:35.6
 
16
12:38.3
 
16
Other values (27)
420 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18:30.4
2nd row18:30.4
3rd row18:30.4
4th row18:30.4
5th row18:30.4

Common Values

ValueCountFrequency (%)
18:30.4 16
 
3.2%
47:06.8 16
 
3.2%
31:10.5 16
 
3.2%
46:35.6 16
 
3.2%
12:38.3 16
 
3.2%
46:07.1 16
 
3.2%
22:47.9 16
 
3.2%
40:14.6 16
 
3.2%
05:34.7 16
 
3.2%
52:48.0 16
 
3.2%
Other values (22) 340
68.0%

Length

2023-12-13T06:16:31.023270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:30.4 16
 
3.2%
50:29.7 16
 
3.2%
14:53.0 16
 
3.2%
10:05.3 16
 
3.2%
15:37.4 16
 
3.2%
55:57.2 16
 
3.2%
42:08.3 16
 
3.2%
44:13.7 16
 
3.2%
39:06.3 16
 
3.2%
05:22.3 16
 
3.2%
Other values (22) 340
68.0%

처리직원번호
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
6109
64 
6007
64 
9A071
48 
5768
48 
2529
32 
Other values (10)
244 

Length

Max length5
Median length4
Mean length4.24
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2529
2nd row2529
3rd row2529
4th row2529
5th row2529

Common Values

ValueCountFrequency (%)
6109 64
12.8%
6007 64
12.8%
9A071 48
9.6%
5768 48
9.6%
2529 32
 
6.4%
9A079 32
 
6.4%
5564 32
 
6.4%
6003 32
 
6.4%
6022 32
 
6.4%
6054 28
 
5.6%
Other values (5) 88
17.6%

Length

2023-12-13T06:16:31.167840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6109 64
12.8%
6007 64
12.8%
9a071 48
9.6%
5768 48
9.6%
2529 32
 
6.4%
9a079 32
 
6.4%
5564 32
 
6.4%
6003 32
 
6.4%
6022 32
 
6.4%
6054 28
 
5.6%
Other values (5) 88
17.6%

최초처리시각
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
29:52.6
 
16
50:29.7
 
16
41:05.1
 
16
42:47.3
 
16
04:17.8
 
16
Other values (28)
420 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29:52.6
2nd row29:52.6
3rd row29:52.6
4th row29:52.6
5th row29:52.6

Common Values

ValueCountFrequency (%)
29:52.6 16
 
3.2%
50:29.7 16
 
3.2%
41:05.1 16
 
3.2%
42:47.3 16
 
3.2%
04:17.8 16
 
3.2%
52:48.0 16
 
3.2%
40:14.6 16
 
3.2%
00:16.5 16
 
3.2%
22:47.9 16
 
3.2%
46:07.1 16
 
3.2%
Other values (23) 340
68.0%

Length

2023-12-13T06:16:31.307024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29:52.6 16
 
3.2%
47:06.8 16
 
3.2%
44:01.9 16
 
3.2%
08:14.8 16
 
3.2%
07:15.6 16
 
3.2%
06:25.8 16
 
3.2%
39:06.3 16
 
3.2%
55:57.2 16
 
3.2%
42:08.3 16
 
3.2%
33:15.1 16
 
3.2%
Other values (23) 340
68.0%

최초처리직원번호
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
6109
64 
6007
64 
9A071
48 
5768
48 
2529
32 
Other values (10)
244 

Length

Max length5
Median length4
Mean length4.24
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2529
2nd row2529
3rd row2529
4th row2529
5th row2529

Common Values

ValueCountFrequency (%)
6109 64
12.8%
6007 64
12.8%
9A071 48
9.6%
5768 48
9.6%
2529 32
 
6.4%
9A079 32
 
6.4%
5564 32
 
6.4%
6003 32
 
6.4%
6022 32
 
6.4%
6054 28
 
5.6%
Other values (5) 88
17.6%

Length

2023-12-13T06:16:31.429941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6109 64
12.8%
6007 64
12.8%
9a071 48
9.6%
5768 48
9.6%
2529 32
 
6.4%
9a079 32
 
6.4%
5564 32
 
6.4%
6003 32
 
6.4%
6022 32
 
6.4%
6054 28
 
5.6%
Other values (5) 88
17.6%

Interactions

2023-12-13T06:16:29.051417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:16:31.516443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
에너지절약추진계획실적ID점검내용항목코드1주에너지점검결과코드2주에너지점검결과코드3주에너지점검결과코드4주에너지점검결과코드5주에너지점검결과코드최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
에너지절약추진계획실적ID1.0000.0000.2490.8910.8870.8920.8920.9991.0001.0001.0001.000
점검내용항목코드0.0001.0000.0800.0360.0000.0000.0000.0000.0000.0000.0000.000
1주에너지점검결과코드0.2490.0801.0000.2870.4240.5470.5510.0000.2490.2650.2270.265
2주에너지점검결과코드0.8910.0360.2871.0000.9680.9770.8850.1610.8910.6950.9230.695
3주에너지점검결과코드0.8870.0000.4240.9681.0000.9950.9400.0000.8870.6910.9200.691
4주에너지점검결과코드0.8920.0000.5470.9770.9951.0000.9760.0000.8920.7140.9250.714
5주에너지점검결과코드0.8920.0000.5510.8850.9400.9761.0000.4760.8920.9430.9250.943
최종수정수0.9990.0000.0000.1610.0000.0000.4761.0000.9990.8461.0000.846
처리시각1.0000.0000.2490.8910.8870.8920.8920.9991.0001.0001.0001.000
처리직원번호1.0000.0000.2650.6950.6910.7140.9430.8461.0001.0001.0001.000
최초처리시각1.0000.0000.2270.9230.9200.9250.9251.0001.0001.0001.0001.000
최초처리직원번호1.0000.0000.2650.6950.6910.7140.9430.8461.0001.0001.0001.000
2023-12-13T06:16:31.679711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2주에너지점검결과코드최종수정수처리시각5주에너지점검결과코드3주에너지점검결과코드처리직원번호4주에너지점검결과코드에너지절약추진계획실적ID1주에너지점검결과코드최초처리직원번호최초처리시각
2주에너지점검결과코드1.0000.1520.7050.5880.7790.4170.8150.7050.4630.4170.703
최종수정수0.1521.0000.9280.4730.0000.6610.0000.9280.0000.6610.970
처리시각0.7050.9281.0000.7070.6990.9820.7081.0000.1920.9820.999
5주에너지점검결과코드0.5880.4730.7071.0000.7000.7310.8090.7070.8180.7310.706
3주에너지점검결과코드0.7790.0000.6990.7001.0000.4140.9120.6990.6620.4140.698
처리직원번호0.4170.6610.9820.7310.4141.0000.4350.9820.2381.0000.981
4주에너지점검결과코드0.8150.0000.7080.8090.9120.4351.0000.7080.8140.4350.706
에너지절약추진계획실적ID0.7050.9281.0000.7070.6990.9820.7081.0000.1920.9820.999
1주에너지점검결과코드0.4630.0000.1920.8180.6620.2380.8140.1921.0000.2380.186
최초처리직원번호0.4170.6610.9820.7310.4141.0000.4350.9820.2381.0000.981
최초처리시각0.7030.9700.9990.7060.6980.9810.7060.9990.1860.9811.000
2023-12-13T06:16:31.817720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
점검내용항목코드에너지절약추진계획실적ID1주에너지점검결과코드2주에너지점검결과코드3주에너지점검결과코드4주에너지점검결과코드5주에너지점검결과코드최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
점검내용항목코드1.0000.0000.0640.0210.0000.0000.0000.0000.0000.0000.0000.000
에너지절약추진계획실적ID0.0001.0000.1920.7050.6990.7080.7070.9281.0000.9820.9990.982
1주에너지점검결과코드0.0640.1921.0000.4630.6620.8140.8180.0000.1920.2380.1860.238
2주에너지점검결과코드0.0210.7050.4631.0000.7790.8150.5880.1520.7050.4170.7030.417
3주에너지점검결과코드0.0000.6990.6620.7791.0000.9120.7000.0000.6990.4140.6980.414
4주에너지점검결과코드0.0000.7080.8140.8150.9121.0000.8090.0000.7080.4350.7060.435
5주에너지점검결과코드0.0000.7070.8180.5880.7000.8091.0000.4730.7070.7310.7060.731
최종수정수0.0000.9280.0000.1520.0000.0000.4731.0000.9280.6610.9700.661
처리시각0.0001.0000.1920.7050.6990.7080.7070.9281.0000.9820.9990.982
처리직원번호0.0000.9820.2380.4170.4140.4350.7310.6610.9821.0000.9811.000
최초처리시각0.0000.9990.1860.7030.6980.7060.7060.9700.9990.9811.0000.981
최초처리직원번호0.0000.9820.2380.4170.4140.4350.7310.6610.9821.0000.9811.000

Missing values

2023-12-13T06:16:29.200692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:16:29.371126image/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

에너지절약추진계획실적ID점검내용항목코드1주에너지점검결과코드2주에너지점검결과코드3주에너지점검결과코드4주에너지점검결과코드5주에너지점검결과코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dhc12N5af111111N218:30.4252929:52.62529
19dhc12N5af1611111N218:30.4252929:52.62529
29dhc12N5af1511111N218:30.4252929:52.62529
39dhc12N5af1411111N218:30.4252929:52.62529
49dhc12N5af1311111N218:30.4252929:52.62529
59dhc12N5af1211111N218:30.4252929:52.62529
69dhc12N5af1111111N218:30.4252929:52.62529
79dhc12N5af1011111N218:30.4252929:52.62529
89dhc12N5af911111N218:30.4252929:52.62529
99dhc12N5af811111N218:30.4252929:52.62529
에너지절약추진계획실적ID점검내용항목코드1주에너지점검결과코드2주에너지점검결과코드3주에너지점검결과코드4주에너지점검결과코드5주에너지점검결과코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dmxlM7URg1111111N101:56.2605401:56.26054
4919dmxlM7URg1011111N101:56.2605401:56.26054
4929dmxlM7URg911111N101:56.2605401:56.26054
4939dmxlM7URg811111N101:56.2605401:56.26054
4949dmxlM7URg711111N101:56.2605401:56.26054
4959dmxlM7URg611111N101:56.2605401:56.26054
4969dmxlM7URg511111N101:56.2605401:56.26054
4979dmxlM7URg411111N101:56.2605401:56.26054
4989dmxlM7URg311111N101:56.2605401:56.26054
4999dmxlM7URg211111N101:56.2605401:56.26054