Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells90000
Missing cells (%)50.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory170.0 B

Variable types

Categorical4
Numeric5
Unsupported9

Dataset

DescriptionSample
Author인하대학교 산학협력단
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT03IHU005

Alerts

WTCH_YR has constant value ""Constant
WTCH_MNTH has constant value ""Constant
WTCH_DD has constant value ""Constant
WTCH_SEC has constant value ""Constant
WTCH_LA is highly overall correlated with WTCH_SLNTYHigh correlation
WTCH_SLNTY is highly overall correlated with WTCH_LAHigh correlation
WTCH_WTRLV has 10000 (100.0%) missing valuesMissing
WTCH_WTDP has 10000 (100.0%) missing valuesMissing
WTCH_CRSPD has 10000 (100.0%) missing valuesMissing
WTCH_CRDRC has 10000 (100.0%) missing valuesMissing
WTCH_WTEM has 10000 (100.0%) missing valuesMissing
SSC_CCTR has 10000 (100.0%) missing valuesMissing
MVMN_DRC has 10000 (100.0%) missing valuesMissing
MVMN_VE has 10000 (100.0%) missing valuesMissing
WTDP_VARTION_QY has 10000 (100.0%) missing valuesMissing
WTCH_WTRLV is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_WTDP is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_CRSPD is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_CRDRC is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_WTEM is an unsupported type, check if it needs cleaning or further analysisUnsupported
SSC_CCTR is an unsupported type, check if it needs cleaning or further analysisUnsupported
MVMN_DRC is an unsupported type, check if it needs cleaning or further analysisUnsupported
MVMN_VE is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTDP_VARTION_QY is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_HHS has 431 (4.3%) zerosZeros
WTCH_MIN has 1626 (16.3%) zerosZeros

Reproduction

Analysis started2024-03-13 12:30:43.336545
Analysis finished2024-03-13 12:30:51.524572
Duration8.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_YR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 10000
100.0%

Length

2024-03-13T21:30:51.687236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:30:51.928038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10000
100.0%

WTCH_MNTH
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
8
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 10000
100.0%

Length

2024-03-13T21:30:52.128870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:30:52.303529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8 10000
100.0%

WTCH_DD
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

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 10000
100.0%

Length

2024-03-13T21:30:52.497165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:30:52.660787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

WTCH_HHS
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4377
Minimum0
Maximum23
Zeros431
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:30:52.846320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9335363
Coefficient of variation (CV)0.60620023
Kurtosis-1.2092427
Mean11.4377
Median Absolute Deviation (MAD)6
Skewness0.0072482692
Sum114377
Variance48.073926
MonotonicityNot monotonic
2024-03-13T21:30:53.008333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7 439
 
4.4%
1 436
 
4.4%
15 432
 
4.3%
11 431
 
4.3%
0 431
 
4.3%
20 430
 
4.3%
19 428
 
4.3%
9 425
 
4.2%
3 423
 
4.2%
16 423
 
4.2%
Other values (14) 5702
57.0%
ValueCountFrequency (%)
0 431
4.3%
1 436
4.4%
2 407
4.1%
3 423
4.2%
4 412
4.1%
5 419
4.2%
6 416
4.2%
7 439
4.4%
8 410
4.1%
9 425
4.2%
ValueCountFrequency (%)
23 415
4.2%
22 386
3.9%
21 422
4.2%
20 430
4.3%
19 428
4.3%
18 400
4.0%
17 408
4.1%
16 423
4.2%
15 432
4.3%
14 405
4.0%

WTCH_MIN
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.077
Minimum0
Maximum50
Zeros1626
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:30:53.205163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median30
Q340
95-th percentile50
Maximum50
Range50
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.931117
Coefficient of variation (CV)0.67516519
Kurtosis-1.2433527
Mean25.077
Median Absolute Deviation (MAD)10
Skewness-0.0090452068
Sum250770
Variance286.66274
MonotonicityNot monotonic
2024-03-13T21:30:53.433464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 1746
17.5%
20 1696
17.0%
40 1657
16.6%
10 1639
16.4%
50 1636
16.4%
0 1626
16.3%
ValueCountFrequency (%)
0 1626
16.3%
10 1639
16.4%
20 1696
17.0%
30 1746
17.5%
40 1657
16.6%
50 1636
16.4%
ValueCountFrequency (%)
50 1636
16.4%
40 1657
16.6%
30 1746
17.5%
20 1696
17.0%
10 1639
16.4%
0 1626
16.3%

WTCH_SEC
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

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 10000
100.0%

Length

2024-03-13T21:30:53.705955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:30:53.900136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

WTCH_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549988
Minimum37.138273
Maximum37.769406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:30:54.169269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.138273
5-th percentile37.257687
Q137.451347
median37.581926
Q337.671441
95-th percentile37.74952
Maximum37.769406
Range0.63113306
Interquartile range (IQR)0.22009406

Descriptive statistics

Standard deviation0.15403548
Coefficient of variation (CV)0.0041021446
Kurtosis-0.41917585
Mean37.549988
Median Absolute Deviation (MAD)0.10343965
Skewness-0.67062731
Sum375499.88
Variance0.023726929
MonotonicityNot monotonic
2024-03-13T21:30:54.456311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.63215118753005 65
 
0.7%
37.603235670449855 64
 
0.6%
37.55928997730639 62
 
0.6%
37.361865358628286 61
 
0.6%
37.20514549130051 60
 
0.6%
37.49423252812879 59
 
0.6%
37.61002908135318 59
 
0.6%
37.73871471643864 57
 
0.6%
37.19236112702096 56
 
0.6%
37.753000671426186 56
 
0.6%
Other values (203) 9401
94.0%
ValueCountFrequency (%)
37.13827332290607 38
0.4%
37.15234160385118 40
0.4%
37.16581676130568 48
0.5%
37.17954831656238 48
0.5%
37.19236112702096 56
0.6%
37.20514549130051 60
0.6%
37.217753852332486 45
0.4%
37.22977085369588 46
0.5%
37.24022468515884 45
0.4%
37.249147266928695 43
0.4%
ValueCountFrequency (%)
37.76940638126019 46
0.5%
37.76692319196192 48
0.5%
37.7645939798359 48
0.5%
37.76250951230154 48
0.5%
37.760586096697594 52
0.5%
37.758448693957845 54
0.5%
37.7564610728098 47
0.5%
37.754691202991275 48
0.5%
37.753000671426186 56
0.6%
37.75137314027568 45
0.4%

WTCH_LO
Real number (ℝ)

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.53178
Minimum126.3452
Maximum126.59576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:30:54.701511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.3452
5-th percentile126.37448
Q1126.5214
median126.5361
Q3126.57639
95-th percentile126.59245
Maximum126.59576
Range0.2505551
Interquartile range (IQR)0.054990927

Descriptive statistics

Standard deviation0.057747136
Coefficient of variation (CV)0.00045638444
Kurtosis2.8714527
Mean126.53178
Median Absolute Deviation (MAD)0.021415153
Skewness-1.7268831
Sum1265317.8
Variance0.0033347318
MonotonicityNot monotonic
2024-03-13T21:30:54.976461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.54045242243552 65
 
0.7%
126.55397764854258 64
 
0.6%
126.58166645477382 62
 
0.6%
126.5212847466301 61
 
0.6%
126.3452047241124 60
 
0.6%
126.59563115876188 59
 
0.6%
126.55044742347144 59
 
0.6%
126.52693211635136 57
 
0.6%
126.34654256978916 56
 
0.6%
126.52408441536704 56
 
0.6%
Other values (203) 9401
94.0%
ValueCountFrequency (%)
126.3452047241124 60
0.6%
126.34566406904896 45
0.4%
126.34654256978916 56
0.6%
126.3486241342786 48
0.5%
126.35110285854572 46
0.5%
126.35113106071579 48
0.5%
126.35175464476833 38
0.4%
126.35261474666328 40
0.4%
126.35919029909392 45
0.4%
126.36733321875622 43
0.4%
ValueCountFrequency (%)
126.59575982692452 41
0.4%
126.59563115876188 59
0.6%
126.59536850671108 52
0.5%
126.59528817205955 50
0.5%
126.59497856511936 41
0.4%
126.59475622577511 50
0.5%
126.59446024549825 47
0.5%
126.59368222727154 46
0.5%
126.5935425956504 46
0.5%
126.59292960661892 52
0.5%

WTCH_WTRLV
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

WTCH_WTDP
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

WTCH_CRSPD
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

WTCH_CRDRC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

WTCH_WTEM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

WTCH_SLNTY
Real number (ℝ)

HIGH CORRELATION 

Distinct7166
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.424812
Minimum0.051
Maximum31.942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:30:55.255872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.051
5-th percentile0.29195
Q12.0075
median17.0885
Q326.087
95-th percentile31.741
Maximum31.942
Range31.891
Interquartile range (IQR)24.0795

Descriptive statistics

Standard deviation11.643202
Coefficient of variation (CV)0.75483592
Kurtosis-1.5617329
Mean15.424812
Median Absolute Deviation (MAD)11.1915
Skewness-0.060619666
Sum154248.12
Variance135.56415
MonotonicityNot monotonic
2024-03-13T21:30:55.540425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.735 14
 
0.1%
31.692 13
 
0.1%
31.695 12
 
0.1%
31.693 12
 
0.1%
31.829 11
 
0.1%
31.933 11
 
0.1%
31.732 11
 
0.1%
31.734 11
 
0.1%
31.697 10
 
0.1%
0.452 10
 
0.1%
Other values (7156) 9885
98.9%
ValueCountFrequency (%)
0.051 1
 
< 0.1%
0.053 1
 
< 0.1%
0.057 2
 
< 0.1%
0.062 1
 
< 0.1%
0.063 1
 
< 0.1%
0.064 2
 
< 0.1%
0.065 1
 
< 0.1%
0.066 3
< 0.1%
0.067 3
< 0.1%
0.068 5
0.1%
ValueCountFrequency (%)
31.942 2
 
< 0.1%
31.941 1
 
< 0.1%
31.94 3
 
< 0.1%
31.939 1
 
< 0.1%
31.937 4
 
< 0.1%
31.936 5
0.1%
31.934 6
0.1%
31.933 11
0.1%
31.932 2
 
< 0.1%
31.931 2
 
< 0.1%

SSC_CCTR
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

MVMN_DRC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

MVMN_VE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

WTDP_VARTION_QY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2024-03-13T21:30:49.059777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:44.714637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:45.646545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:46.720388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:47.911259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:49.293198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:44.894302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:45.786805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:46.944865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:48.098030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:49.555887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:45.107488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:46.027129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:47.200123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:48.290666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:50.319417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:45.303881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:46.239658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:47.466908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:48.513566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:50.490701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:45.497827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:46.444903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:47.660746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:48.727583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:30:55.752124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_HHSWTCH_MINWTCH_LAWTCH_LOWTCH_SLNTY
WTCH_HHS1.0000.0000.0000.0000.366
WTCH_MIN0.0001.0000.0000.0000.000
WTCH_LA0.0000.0001.0000.9560.894
WTCH_LO0.0000.0000.9561.0000.830
WTCH_SLNTY0.3660.0000.8940.8301.000
2024-03-13T21:30:55.953787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_HHSWTCH_MINWTCH_LAWTCH_LOWTCH_SLNTY
WTCH_HHS1.0000.0060.018-0.000-0.134
WTCH_MIN0.0061.000-0.0140.0070.010
WTCH_LA0.018-0.0141.000-0.122-0.940
WTCH_LO-0.0000.007-0.1221.0000.063
WTCH_SLNTY-0.1340.010-0.9400.0631.000

Missing values

2024-03-13T21:30:50.791365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:30:51.264682image/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

WTCH_YRWTCH_MNTHWTCH_DDWTCH_HHSWTCH_MINWTCH_SECWTCH_LAWTCH_LOWTCH_WTRLVWTCH_WTDPWTCH_CRSPDWTCH_CRDRCWTCH_WTEMWTCH_SLNTYSSC_CCTRMVMN_DRCMVMN_VEWTDP_VARTION_QY
901520208170037.64628126.531351<NA><NA><NA><NA><NA>11.636<NA><NA><NA><NA>
11174202081840037.594558126.558781<NA><NA><NA><NA><NA>12.196<NA><NA><NA><NA>
275472020812130037.644368126.532128<NA><NA><NA><NA><NA>0.866<NA><NA><NA><NA>
182802020811410037.38264126.5495<NA><NA><NA><NA><NA>27.706<NA><NA><NA><NA>
20572202081160037.548852126.581529<NA><NA><NA><NA><NA>24.226<NA><NA><NA><NA>
10503202081810037.652006126.52937<NA><NA><NA><NA><NA>4.759<NA><NA><NA><NA>
9304202081710037.494233126.595631<NA><NA><NA><NA><NA>23.647<NA><NA><NA><NA>
251082020811930037.329029126.480035<NA><NA><NA><NA><NA>29.936<NA><NA><NA><NA>
8472202081630037.424467126.572826<NA><NA><NA><NA><NA>27.574<NA><NA><NA><NA>
165912020811250037.314497126.456206<NA><NA><NA><NA><NA>31.641<NA><NA><NA><NA>
WTCH_YRWTCH_MNTHWTCH_DDWTCH_HHSWTCH_MINWTCH_SECWTCH_LAWTCH_LOWTCH_WTRLVWTCH_WTDPWTCH_CRSPDWTCH_CRDRCWTCH_WTEMWTCH_SLNTYSSC_CCTRMVMN_DRCMVMN_VEWTDP_VARTION_QY
277882020812140037.594558126.558781<NA><NA><NA><NA><NA>3.096<NA><NA><NA><NA>
2924202081210037.462369126.583369<NA><NA><NA><NA><NA>27.569<NA><NA><NA><NA>
251712020811940037.701348126.527483<NA><NA><NA><NA><NA>0.627<NA><NA><NA><NA>
925202081040037.639168126.534691<NA><NA><NA><NA><NA>2.738<NA><NA><NA><NA>
194812020811510037.594558126.558781<NA><NA><NA><NA><NA>19.635<NA><NA><NA><NA>
334202081010037.554347126.580977<NA><NA><NA><NA><NA>12.711<NA><NA><NA><NA>
7960202081610037.629135126.543158<NA><NA><NA><NA><NA>16.916<NA><NA><NA><NA>
290742020812240037.581926126.571558<NA><NA><NA><NA><NA>2.612<NA><NA><NA><NA>
175322020811340037.652006126.52937<NA><NA><NA><NA><NA>2.963<NA><NA><NA><NA>
135112020811030037.604896126.553221<NA><NA><NA><NA><NA>2.762<NA><NA><NA><NA>