Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells8313
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory151.0 B

Variable types

Numeric11
Categorical4
Text1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본 정보를 가지고 있으며, 도로대장의 측구 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091936

Alerts

식별번호 is highly overall correlated with 노선번호High correlation
관리번호 is highly overall correlated with 노선번호High correlation
노선번호 is highly overall correlated with 식별번호 and 1 other fieldsHigh correlation
구간번호 is highly overall correlated with 도로종류High correlation
위치_시점 is highly overall correlated with 위치_종점High correlation
위치_종점 is highly overall correlated with 위치_시점High correlation
종류 is highly overall correlated with 높이_최대 and 1 other fieldsHigh correlation
높이_최대 is highly overall correlated with 종류 and 1 other fieldsHigh correlation
높이_최소 is highly overall correlated with 종류 and 1 other fieldsHigh correlation
도로종류 is highly overall correlated with 구간번호High correlation
관리기관 is highly imbalanced (98.4%)Imbalance
도로종류 is highly imbalanced (97.8%)Imbalance
이력코드 is highly imbalanced (96.7%)Imbalance
비고 has 8311 (83.1%) missing valuesMissing
식별번호 has unique valuesUnique
관리번호 has 395 (4.0%) zerosZeros
높이_최소 has 296 (3.0%) zerosZeros
has 772 (7.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:56:47.076141
Analysis finished2023-12-10 23:57:06.483987
Duration19.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8191.3625
Minimum3
Maximum16446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:06.607769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile811.85
Q14080.5
median8166.5
Q312323.25
95-th percentile15640.05
Maximum16446
Range16443
Interquartile range (IQR)8242.75

Descriptive statistics

Standard deviation4759.7259
Coefficient of variation (CV)0.58106644
Kurtosis-1.202434
Mean8191.3625
Median Absolute Deviation (MAD)4118.5
Skewness0.0097621129
Sum81913625
Variance22654990
MonotonicityNot monotonic
2023-12-11T08:57:06.816200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4093 1
 
< 0.1%
15366 1
 
< 0.1%
6232 1
 
< 0.1%
7970 1
 
< 0.1%
10768 1
 
< 0.1%
3783 1
 
< 0.1%
7158 1
 
< 0.1%
7085 1
 
< 0.1%
2677 1
 
< 0.1%
681 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
8 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
16446 1
< 0.1%
16445 1
< 0.1%
16443 1
< 0.1%
16442 1
< 0.1%
16441 1
< 0.1%
16440 1
< 0.1%
16439 1
< 0.1%
16438 1
< 0.1%
16436 1
< 0.1%
16434 1
< 0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9532
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8311322.6
Minimum0
Maximum10990372
Zeros395
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:06.958438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300177.95
Q110010696
median10140008
Q310340652
95-th percentile10890140
Maximum10990372
Range10990372
Interquartile range (IQR)329956

Descriptive statistics

Standard deviation3961452.7
Coefficient of variation (CV)0.47663324
Kurtosis0.21145282
Mean8311322.6
Median Absolute Deviation (MAD)150298.5
Skewness-1.4751433
Sum8.3113226 × 1010
Variance1.5693107 × 1013
MonotonicityNot monotonic
2023-12-11T08:57:07.088101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 395
 
4.0%
690031 3
 
< 0.1%
600008 3
 
< 0.1%
690032 3
 
< 0.1%
690033 3
 
< 0.1%
10220013 2
 
< 0.1%
10490021 2
 
< 0.1%
690034 2
 
< 0.1%
10240354 2
 
< 0.1%
10490054 2
 
< 0.1%
Other values (9522) 9583
95.8%
ValueCountFrequency (%)
0 395
4.0%
1 1
 
< 0.1%
2 1
 
< 0.1%
300002 1
 
< 0.1%
300004 1
 
< 0.1%
300005 1
 
< 0.1%
300006 1
 
< 0.1%
300010 1
 
< 0.1%
300011 1
 
< 0.1%
300013 1
 
< 0.1%
ValueCountFrequency (%)
10990372 1
< 0.1%
10990370 1
< 0.1%
10990369 1
< 0.1%
10990368 1
< 0.1%
10990367 1
< 0.1%
10990365 1
< 0.1%
10990364 1
< 0.1%
10990361 1
< 0.1%
10990360 1
< 0.1%
10990358 1
< 0.1%

관리기관
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1683
9977 
1684
 
19
1682
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 9977
99.8%
1684 19
 
0.2%
1682 4
 
< 0.1%

Length

2023-12-11T08:57:07.213595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:57:07.300613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 9977
99.8%
1684 19
 
0.2%
1682 4
 
< 0.1%

도로종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1504
9979 
1507
 
21

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1504
2nd row1504
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 9979
99.8%
1507 21
 
0.2%

Length

2023-12-11T08:57:07.384888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:57:07.467045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 9979
99.8%
1507 21
 
0.2%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean863.4602
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:07.578879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11002
median1018
Q31037
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)35

Descriptive statistics

Standard deviation368.30783
Coefficient of variation (CV)0.4265487
Kurtosis1.0174515
Mean863.4602
Median Absolute Deviation (MAD)16
Skewness-1.7247197
Sum8634602
Variance135650.66
MonotonicityNot monotonic
2023-12-11T08:57:07.750432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
60 676
 
6.8%
1001 474
 
4.7%
1034 462
 
4.6%
1018 415
 
4.2%
1021 400
 
4.0%
1024 390
 
3.9%
1007 383
 
3.8%
1077 377
 
3.8%
1089 360
 
3.6%
1010 342
 
3.4%
Other values (33) 5721
57.2%
ValueCountFrequency (%)
30 126
 
1.3%
37 315
3.1%
58 207
 
2.1%
60 676
6.8%
67 79
 
0.8%
69 308
3.1%
907 65
 
0.7%
1001 474
4.7%
1002 304
3.0%
1003 283
2.8%
ValueCountFrequency (%)
1099 226
2.3%
1089 360
3.6%
1084 338
3.4%
1080 204
2.0%
1077 377
3.8%
1051 133
 
1.3%
1049 172
1.7%
1047 120
 
1.2%
1042 206
2.1%
1041 91
 
0.9%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3827
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:07.870854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6349913
Coefficient of variation (CV)0.67531004
Kurtosis0.63322291
Mean5.3827
Median Absolute Deviation (MAD)2
Skewness0.95941325
Sum53827
Variance13.213162
MonotonicityNot monotonic
2023-12-11T08:57:08.001170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 1374
13.7%
4 1291
12.9%
2 1117
11.2%
3 1079
10.8%
7 1066
10.7%
5 1025
10.2%
6 737
7.4%
9 570
5.7%
8 445
 
4.5%
11 313
 
3.1%
Other values (7) 983
9.8%
ValueCountFrequency (%)
1 1374
13.7%
2 1117
11.2%
3 1079
10.8%
4 1291
12.9%
5 1025
10.2%
6 737
7.4%
7 1066
10.7%
8 445
 
4.5%
9 570
5.7%
10 251
 
2.5%
ValueCountFrequency (%)
19 41
 
0.4%
16 89
 
0.9%
15 113
 
1.1%
14 102
 
1.0%
13 196
 
2.0%
12 191
 
1.9%
11 313
3.1%
10 251
2.5%
9 570
5.7%
8 445
4.5%

이력코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9966 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9966
99.7%
1 34
 
0.3%

Length

2023-12-11T08:57:08.146983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:57:08.273739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9966
99.7%
1 34
 
0.3%

위치_시점
Real number (ℝ)

HIGH CORRELATION 

Distinct6452
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5175885
Minimum-0.3
Maximum28.725
Zeros80
Zeros (%)0.8%
Negative6
Negative (%)0.1%
Memory size166.0 KiB
2023-12-11T08:57:08.382632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3
5-th percentile0.3239
Q12.248
median4.8815
Q37.987
95-th percentile13.0962
Maximum28.725
Range29.025
Interquartile range (IQR)5.739

Descriptive statistics

Standard deviation4.0753535
Coefficient of variation (CV)0.73861134
Kurtosis1.4694873
Mean5.5175885
Median Absolute Deviation (MAD)2.8135
Skewness0.98829989
Sum55175.885
Variance16.608506
MonotonicityNot monotonic
2023-12-11T08:57:08.524884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 80
 
0.8%
8.0 15
 
0.1%
0.02 15
 
0.1%
7.0 13
 
0.1%
0.01 11
 
0.1%
4.76 9
 
0.1%
3.2 8
 
0.1%
5.24 8
 
0.1%
6.24 7
 
0.1%
4.54 7
 
0.1%
Other values (6442) 9827
98.3%
ValueCountFrequency (%)
-0.3 2
 
< 0.1%
-0.26 1
 
< 0.1%
-0.11 2
 
< 0.1%
-0.046 1
 
< 0.1%
0.0 80
0.8%
0.001 5
 
0.1%
0.002 4
 
< 0.1%
0.003 4
 
< 0.1%
0.004 4
 
< 0.1%
0.005 5
 
0.1%
ValueCountFrequency (%)
28.725 1
< 0.1%
28.65 1
< 0.1%
28.385 1
< 0.1%
28.268 1
< 0.1%
28.253 1
< 0.1%
28.23 1
< 0.1%
28.13 1
< 0.1%
27.375 1
< 0.1%
27.02 1
< 0.1%
26.866 1
< 0.1%

위치_종점
Real number (ℝ)

HIGH CORRELATION 

Distinct6441
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6677851
Minimum-0.27
Maximum254
Zeros24
Zeros (%)0.2%
Negative3
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T08:57:09.034331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.27
5-th percentile0.435
Q12.38925
median5.0085
Q38.09075
95-th percentile13.2262
Maximum254
Range254.27
Interquartile range (IQR)5.7015

Descriptive statistics

Standard deviation4.7717217
Coefficient of variation (CV)0.84190238
Kurtosis733.43136
Mean5.6677851
Median Absolute Deviation (MAD)2.8295
Skewness14.702466
Sum56677.851
Variance22.769328
MonotonicityNot monotonic
2023-12-11T08:57:09.196217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
 
0.2%
7.0 12
 
0.1%
8.0 12
 
0.1%
1.94 8
 
0.1%
1.91 8
 
0.1%
5.48 8
 
0.1%
1.215 8
 
0.1%
0.625 8
 
0.1%
4.76 8
 
0.1%
0.3 7
 
0.1%
Other values (6431) 9897
99.0%
ValueCountFrequency (%)
-0.27 1
 
< 0.1%
-0.21 1
 
< 0.1%
-0.053 1
 
< 0.1%
0.0 24
0.2%
0.008 1
 
< 0.1%
0.01 2
 
< 0.1%
0.012 1
 
< 0.1%
0.018 1
 
< 0.1%
0.02 3
 
< 0.1%
0.022 1
 
< 0.1%
ValueCountFrequency (%)
254.0 1
< 0.1%
28.755 1
< 0.1%
28.692 1
< 0.1%
28.65 1
< 0.1%
28.385 1
< 0.1%
28.315 1
< 0.1%
28.253 1
< 0.1%
28.23 1
< 0.1%
27.6 1
< 0.1%
27.06 1
< 0.1%

위치_방향
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5274 
0
4724 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.0006
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 5274
52.7%
0 4724
47.2%
<NA> 2
 
< 0.1%

Length

2023-12-11T08:57:09.334512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:57:09.451297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5274
52.7%
0 4724
47.2%
na 2
 
< 0.1%

연장
Real number (ℝ)

Distinct700
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.19329
Minimum0
Maximum2460
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:09.615496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q143
median85
Q3155
95-th percentile371.05
Maximum2460
Range2460
Interquartile range (IQR)112

Descriptive statistics

Standard deviation148.48071
Coefficient of variation (CV)1.1766133
Kurtosis37.097573
Mean126.19329
Median Absolute Deviation (MAD)49
Skewness4.5318661
Sum1261932.9
Variance22046.522
MonotonicityNot monotonic
2023-12-11T08:57:09.806332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 163
 
1.6%
60.0 140
 
1.4%
100.0 124
 
1.2%
30.0 115
 
1.1%
20.0 110
 
1.1%
80.0 107
 
1.1%
50.0 95
 
0.9%
25.0 95
 
0.9%
90.0 90
 
0.9%
45.0 88
 
0.9%
Other values (690) 8873
88.7%
ValueCountFrequency (%)
0.0 23
0.2%
1.54 1
 
< 0.1%
2.0 4
 
< 0.1%
3.0 7
 
0.1%
4.0 17
0.2%
4.5 1
 
< 0.1%
5.0 20
0.2%
6.0 39
0.4%
7.0 41
0.4%
7.3 1
 
< 0.1%
ValueCountFrequency (%)
2460.0 1
< 0.1%
2315.0 1
< 0.1%
2305.0 1
< 0.1%
2190.0 1
< 0.1%
1962.0 1
< 0.1%
1737.0 1
< 0.1%
1529.0 1
< 0.1%
1528.0 1
< 0.1%
1433.0 1
< 0.1%
1420.0 1
< 0.1%

종류
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1305.0356
Minimum1300
Maximum1399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:09.933995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile1301
Q11301
median1302
Q31304
95-th percentile1309
Maximum1399
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation13.847666
Coefficient of variation (CV)0.010610949
Kurtosis40.715262
Mean1305.0356
Median Absolute Deviation (MAD)1
Skewness6.4296271
Sum13047746
Variance191.75786
MonotonicityNot monotonic
2023-12-11T08:57:10.050311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1301 3581
35.8%
1302 2356
23.6%
1306 1205
 
12.0%
1304 930
 
9.3%
1309 718
 
7.2%
1303 625
 
6.2%
1399 206
 
2.1%
1308 191
 
1.9%
1305 166
 
1.7%
1300 18
 
0.2%
ValueCountFrequency (%)
1300 18
 
0.2%
1301 3581
35.8%
1302 2356
23.6%
1303 625
 
6.2%
1304 930
 
9.3%
1305 166
 
1.7%
1306 1205
 
12.0%
1307 2
 
< 0.1%
1308 191
 
1.9%
1309 718
 
7.2%
ValueCountFrequency (%)
1399 206
 
2.1%
1309 718
 
7.2%
1308 191
 
1.9%
1307 2
 
< 0.1%
1306 1205
 
12.0%
1305 166
 
1.7%
1304 930
 
9.3%
1303 625
 
6.2%
1302 2356
23.6%
1301 3581
35.8%

높이_최대
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.529539
Minimum0
Maximum6
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:10.170570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q10.2
median0.6
Q30.7
95-th percentile1.05
Maximum6
Range6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.35111178
Coefficient of variation (CV)0.6630518
Kurtosis20.736117
Mean0.529539
Median Absolute Deviation (MAD)0.3
Skewness2.2945939
Sum5295.39
Variance0.12327949
MonotonicityNot monotonic
2023-12-11T08:57:10.335377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 3115
31.1%
0.2 3039
30.4%
0.6 684
 
6.8%
0.5 639
 
6.4%
0.15 516
 
5.2%
1.0 465
 
4.7%
0.3 209
 
2.1%
0.8 208
 
2.1%
0.4 165
 
1.7%
1.2 133
 
1.3%
Other values (44) 827
 
8.3%
ValueCountFrequency (%)
0.0 6
 
0.1%
0.05 4
 
< 0.1%
0.1 75
 
0.8%
0.14 1
 
< 0.1%
0.15 516
 
5.2%
0.18 1
 
< 0.1%
0.2 3039
30.4%
0.23 11
 
0.1%
0.25 115
 
1.1%
0.3 209
 
2.1%
ValueCountFrequency (%)
6.0 3
 
< 0.1%
3.0 4
 
< 0.1%
2.8 1
 
< 0.1%
2.7 2
 
< 0.1%
2.6 2
 
< 0.1%
2.5 4
 
< 0.1%
2.3 4
 
< 0.1%
2.1 9
 
0.1%
2.0 42
0.4%
1.9 2
 
< 0.1%

높이_최소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.491565
Minimum0
Maximum3
Zeros296
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:10.542247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q10.2
median0.5
Q30.7
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.32162958
Coefficient of variation (CV)0.65429715
Kurtosis3.6635033
Mean0.491565
Median Absolute Deviation (MAD)0.25
Skewness1.1366718
Sum4915.65
Variance0.10344559
MonotonicityNot monotonic
2023-12-11T08:57:10.720324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 3116
31.2%
0.2 3058
30.6%
0.6 710
 
7.1%
0.5 642
 
6.4%
0.15 506
 
5.1%
1.0 390
 
3.9%
0.0 296
 
3.0%
0.3 221
 
2.2%
0.8 186
 
1.9%
0.4 155
 
1.6%
Other values (43) 720
 
7.2%
ValueCountFrequency (%)
0.0 296
 
3.0%
0.04 1
 
< 0.1%
0.05 4
 
< 0.1%
0.1 84
 
0.8%
0.14 1
 
< 0.1%
0.15 506
 
5.1%
0.18 1
 
< 0.1%
0.2 3058
30.6%
0.23 11
 
0.1%
0.25 113
 
1.1%
ValueCountFrequency (%)
3.0 3
 
< 0.1%
2.7 1
 
< 0.1%
2.6 1
 
< 0.1%
2.5 3
 
< 0.1%
2.3 3
 
< 0.1%
2.1 6
 
0.1%
2.0 33
0.3%
1.9 1
 
< 0.1%
1.85 1
 
< 0.1%
1.8 2
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.693017
Minimum0
Maximum10.51
Zeros772
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:57:10.886632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median0.7
Q30.7
95-th percentile1.5
Maximum10.51
Range10.51
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.44573918
Coefficient of variation (CV)0.6431865
Kurtosis106.72397
Mean0.693017
Median Absolute Deviation (MAD)0.05
Skewness7.4472015
Sum6930.17
Variance0.19868342
MonotonicityNot monotonic
2023-12-11T08:57:11.041184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 4762
47.6%
0.5 1107
 
11.1%
0.6 780
 
7.8%
0.0 772
 
7.7%
0.75 689
 
6.9%
1.5 477
 
4.8%
1.0 432
 
4.3%
0.8 364
 
3.6%
0.4 179
 
1.8%
0.3 83
 
0.8%
Other values (45) 355
 
3.5%
ValueCountFrequency (%)
0.0 772
7.7%
0.1 5
 
0.1%
0.14 1
 
< 0.1%
0.15 1
 
< 0.1%
0.2 17
 
0.2%
0.25 34
 
0.3%
0.3 83
 
0.8%
0.35 1
 
< 0.1%
0.4 179
 
1.8%
0.45 20
 
0.2%
ValueCountFrequency (%)
10.51 1
 
< 0.1%
8.0 6
 
0.1%
7.0 4
 
< 0.1%
6.0 4
 
< 0.1%
5.0 19
0.2%
4.0 1
 
< 0.1%
3.8 1
 
< 0.1%
3.5 1
 
< 0.1%
3.0 3
 
< 0.1%
2.8 1
 
< 0.1%

비고
Text

MISSING 

Distinct127
Distinct (%)7.5%
Missing8311
Missing (%)83.1%
Memory size156.2 KiB
2023-12-11T08:57:11.318342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length4.4339846
Min length1

Characters and Unicode

Total characters7489
Distinct characters99
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

Unique51 ?
Unique (%)3.0%

Sample

1st rowLU형측구(0.7*0.8
2nd rowJ형측구
3rd rowL형측구
4th row토사형측구
5th rowLU형측구
ValueCountFrequency (%)
l형측구 171
 
9.8%
u형측구 153
 
8.8%
덮개 127
 
7.3%
옹벽형l형 111
 
6.4%
유개 100
 
5.7%
옹벽형l형측구 87
 
5.0%
토사형측구 75
 
4.3%
공사중 71
 
4.1%
lu형측구 68
 
3.9%
옹벽형측구 64
 
3.7%
Other values (112) 721
41.2%
2023-12-11T08:57:11.741126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1302
17.4%
872
11.6%
864
11.5%
L 600
 
8.0%
U 358
 
4.8%
337
 
4.5%
337
 
4.5%
328
 
4.4%
186
 
2.5%
162
 
2.2%
Other values (89) 2143
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5571
74.4%
Uppercase Letter 1398
 
18.7%
Decimal Number 173
 
2.3%
Open Punctuation 90
 
1.2%
Close Punctuation 83
 
1.1%
Dash Punctuation 71
 
0.9%
Space Separator 59
 
0.8%
Other Punctuation 40
 
0.5%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1302
23.4%
872
15.7%
864
15.5%
337
 
6.0%
337
 
6.0%
328
 
5.9%
186
 
3.3%
162
 
2.9%
143
 
2.6%
102
 
1.8%
Other values (53) 938
16.8%
Uppercase Letter
ValueCountFrequency (%)
L 600
42.9%
U 358
25.6%
E 74
 
5.3%
P 73
 
5.2%
J 70
 
5.0%
T 69
 
4.9%
Y 69
 
4.9%
O 50
 
3.6%
V 9
 
0.6%
K 5
 
0.4%
Other values (7) 21
 
1.5%
Decimal Number
ValueCountFrequency (%)
0 72
41.6%
1 36
20.8%
2 26
 
15.0%
6 13
 
7.5%
7 9
 
5.2%
9 5
 
2.9%
8 4
 
2.3%
3 4
 
2.3%
4 3
 
1.7%
5 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 20
50.0%
, 10
25.0%
* 9
22.5%
: 1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Space Separator
ValueCountFrequency (%)
59
100.0%
Lowercase Letter
ValueCountFrequency (%)
φ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5571
74.4%
Latin 1396
 
18.6%
Common 516
 
6.9%
Greek 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1302
23.4%
872
15.7%
864
15.5%
337
 
6.0%
337
 
6.0%
328
 
5.9%
186
 
3.3%
162
 
2.9%
143
 
2.6%
102
 
1.8%
Other values (53) 938
16.8%
Common
ValueCountFrequency (%)
( 90
17.4%
) 83
16.1%
0 72
14.0%
- 71
13.8%
59
11.4%
1 36
 
7.0%
2 26
 
5.0%
. 20
 
3.9%
6 13
 
2.5%
, 10
 
1.9%
Other values (8) 36
 
7.0%
Latin
ValueCountFrequency (%)
L 600
43.0%
U 358
25.6%
E 74
 
5.3%
P 73
 
5.2%
J 70
 
5.0%
T 69
 
4.9%
Y 69
 
4.9%
O 50
 
3.6%
V 9
 
0.6%
K 5
 
0.4%
Other values (6) 19
 
1.4%
Greek
ValueCountFrequency (%)
φ 4
66.7%
Φ 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5571
74.4%
ASCII 1912
 
25.5%
None 6
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1302
23.4%
872
15.7%
864
15.5%
337
 
6.0%
337
 
6.0%
328
 
5.9%
186
 
3.3%
162
 
2.9%
143
 
2.6%
102
 
1.8%
Other values (53) 938
16.8%
ASCII
ValueCountFrequency (%)
L 600
31.4%
U 358
18.7%
( 90
 
4.7%
) 83
 
4.3%
E 74
 
3.9%
P 73
 
3.8%
0 72
 
3.8%
- 71
 
3.7%
J 70
 
3.7%
T 69
 
3.6%
Other values (24) 352
18.4%
None
ValueCountFrequency (%)
φ 4
66.7%
Φ 2
33.3%

Interactions

2023-12-11T08:57:04.838964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.508822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.954681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.340134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:55.911156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:57.242356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:58.588757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:59.849779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:01.155699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:02.389215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:56:52.018290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:56:54.819532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:56.323220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:57.771783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:59.035703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:00.361960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:01.644283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:03.081417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:04.167863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:05.277232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:57:03.309005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:57:05.481510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:56:53.817717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:55.188826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:56.718260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:58.119257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:59.360487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:00.694591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:01.950655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:03.405699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:04.451910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:05.567111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.553470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.943501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:55.610184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:56.839411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:58.216916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:59.481752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:00.821391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:02.052412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:03.498458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:04.538475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:05.657372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.698670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.082737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:55.716433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:56.998950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:58.327908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:59.602109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:00.945276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:02.166621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:03.619433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:04.629994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:05.765578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.830856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.221938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:55.816882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:57.130287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:58.460829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:59.732372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:01.070116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:02.267592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:03.717511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:57:04.719091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:57:11.899758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소
식별번호1.0000.7230.1620.1760.7330.5920.2260.2890.1330.0800.1060.1690.2720.5600.172
관리번호0.7231.0000.0980.0540.9990.2890.0160.0940.0480.0090.0260.0060.0690.1260.154
관리기관0.1620.0981.0000.0000.0240.1760.0000.0800.0000.0000.0000.0000.0370.0470.000
도로종류0.1760.0540.0001.0000.0600.8800.0000.0720.0000.0000.0000.0000.0360.0610.000
노선번호0.7330.9990.0240.0601.0000.2830.0140.1020.0000.0000.0090.0140.1150.1460.134
구간번호0.5920.2890.1760.8800.2831.0000.1260.2170.0690.0660.0000.0770.1140.2240.060
이력코드0.2260.0160.0000.0000.0140.1261.0000.0590.0000.0190.0000.0000.0000.0830.000
위치_시점0.2890.0940.0800.0720.1020.2170.0591.0000.7960.0410.0480.0400.0400.0930.071
위치_종점0.1330.0480.0000.0000.0000.0690.0000.7961.0000.0070.0000.0000.0000.0000.000
위치_방향0.0800.0090.0000.0000.0000.0660.0190.0410.0071.0000.0000.0190.0710.0940.010
연장0.1060.0260.0000.0000.0090.0000.0000.0480.0000.0001.0000.0000.0000.0000.071
종류0.1690.0060.0000.0000.0140.0770.0000.0400.0000.0190.0001.0000.1140.0840.000
높이_최대0.2720.0690.0370.0360.1150.1140.0000.0400.0000.0710.0000.1141.0000.8850.478
높이_최소0.5600.1260.0470.0610.1460.2240.0830.0930.0000.0940.0000.0840.8851.0000.601
0.1720.1540.0000.0000.1340.0600.0000.0710.0000.0100.0710.0000.4780.6011.000
2023-12-11T08:57:12.135972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치_방향이력코드관리기관도로종류
위치_방향1.0000.0120.0000.000
이력코드0.0121.0000.0000.000
관리기관0.0000.0001.0000.000
도로종류0.0000.0000.0001.000
2023-12-11T08:57:12.271002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점연장종류높이_최대높이_최소관리기관도로종류이력코드위치_방향
식별번호1.0000.4400.6030.068-0.098-0.0970.015-0.087-0.074-0.165-0.1480.0980.1350.1730.061
관리번호0.4401.0000.886-0.115-0.009-0.0090.021-0.039-0.0330.027-0.0890.0290.0900.0260.015
노선번호0.6030.8861.000-0.124-0.084-0.0840.007-0.055-0.069-0.062-0.0780.0070.1000.0230.000
구간번호0.068-0.115-0.1241.0000.0500.048-0.0200.0310.0240.0110.0050.1060.7140.0970.050
위치_시점-0.098-0.009-0.0840.0501.0000.9980.002-0.0050.0060.0000.0060.0470.0550.0450.031
위치_종점-0.097-0.009-0.0840.0480.9981.0000.031-0.0070.003-0.0030.0040.0000.0000.0000.012
연장0.0150.0210.007-0.0200.0020.0311.0000.0390.004-0.009-0.0370.0000.0000.0000.000
종류-0.087-0.039-0.0550.031-0.005-0.0070.0391.0000.6170.584-0.0230.0000.0000.0000.012
높이_최대-0.074-0.033-0.0690.0240.0060.0030.0040.6171.0000.8950.2480.0190.0230.0310.081
높이_최소-0.1650.027-0.0620.0110.000-0.003-0.0090.5840.8951.0000.2420.0250.0440.0600.073
-0.148-0.089-0.0780.0050.0060.004-0.037-0.0230.2480.2421.0000.0000.0000.0000.010
관리기관0.0980.0290.0070.1060.0470.0000.0000.0000.0190.0250.0001.0000.0000.0000.000
도로종류0.1350.0900.1000.7140.0550.0000.0000.0000.0230.0440.0000.0001.0000.0000.000
이력코드0.1730.0260.0230.0970.0450.0000.0000.0000.0310.0600.0000.0000.0001.0000.012
위치_방향0.0610.0150.0000.0500.0310.0120.0000.0120.0810.0730.0100.0000.0000.0121.000

Missing values

2023-12-11T08:57:05.929802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:57:06.135170image/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.
2023-12-11T08:57:06.384720image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소비고
411640936006521683150460706.1566.2621106.013010.20.20.7<NA>
9706968310370169168315041037304.4535.1211668.013990.70.70.7LU형측구(0.7*0.8
7921789810180305168315041018702.9212.959138.013090.70.70.7J형측구
14412110030366168315041003904.2484.3891141.013010.10.10.7<NA>
2326230310110024168315041011101.5381.575037.013090.70.50.25<NA>
130321300910210373168315041021902.7982.90102.013030.60.60.6<NA>
9672964910370040168315041037102.5012.526125.013040.70.71.5<NA>
9663964010370025168315041037101.7761.78418.013040.70.71.5<NA>
69796956101305341683150410135010.8110.85140.013060.70.70.7<NA>
146181459510840535168315041084908.879.11230.013010.250.250.5<NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소비고
9033901010340258168315041034602.052.13180.013020.70.70.7<NA>
143541433110840188168315041084406.2626.34078.013030.60.60.7<NA>
148121478910890044168315041089304.1364.3011165.013010.20.20.5<NA>
93391010100093168315041010207.4177.461044.013020.40.40.4<NA>
126441262110060130168315041006404.2754.4090135.013040.20.20.0토사형 측구
12781255100502481683150410056011.97812.1380160.013010.20.20.75<NA>
6214619110070286168315041007608.8368.940104.013010.20.20.7<NA>
142191419610840101168315041084306.3216.4710150.013010.150.150.3<NA>
459645735801651683150458304.0754.155180.013010.20.20.7<NA>
6479645610080110168315041008306.7167.6850969.013080.20.20.0<NA>