Overview

Dataset statistics

Number of variables15
Number of observations641
Missing cells645
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.4 KiB
Average record size in memory133.2 B

Variable types

Numeric12
Unsupported1
Categorical1
DateTime1

Alerts

inspec_area has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with inspec_ym and 2 other fieldsHigh correlation
inspec_ym is highly overall correlated with skey and 3 other fieldsHigh correlation
virus02 is highly overall correlated with inspec_ym and 4 other fieldsHigh correlation
virus03 is highly overall correlated with skey and 3 other fieldsHigh correlation
virus04 is highly overall correlated with virus02 and 1 other fieldsHigh correlation
virus07 is highly overall correlated with skey and 2 other fieldsHigh correlation
virus08 is highly overall correlated with virus02 and 1 other fieldsHigh correlation
virus09 is highly overall correlated with virus02High correlation
virus10 has 641 (100.0%) missing valuesMissing
virus06 is highly skewed (γ1 = 21.90980324)Skewed
skey has unique valuesUnique
virus10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
virus01 has 87 (13.6%) zerosZeros
virus02 has 304 (47.4%) zerosZeros
virus03 has 407 (63.5%) zerosZeros
virus04 has 159 (24.8%) zerosZeros
virus05 has 388 (60.5%) zerosZeros
virus06 has 470 (73.3%) zerosZeros
virus07 has 445 (69.4%) zerosZeros
virus08 has 197 (30.7%) zerosZeros
virus09 has 474 (73.9%) zerosZeros

Reproduction

Analysis started2024-04-17 00:57:47.086510
Analysis finished2024-04-17 00:57:59.923817
Duration12.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct641
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308432
Minimum308112
Maximum308752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:57:59.992307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum308112
5-th percentile308144
Q1308272
median308432
Q3308592
95-th percentile308720
Maximum308752
Range640
Interquartile range (IQR)320

Descriptive statistics

Standard deviation185.18504
Coefficient of variation (CV)0.00060040801
Kurtosis-1.2
Mean308432
Median Absolute Deviation (MAD)160
Skewness0
Sum1.9770491 × 108
Variance34293.5
MonotonicityNot monotonic
2024-04-17T09:58:00.128340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
308609 1
 
0.2%
308353 1
 
0.2%
308295 1
 
0.2%
308296 1
 
0.2%
308297 1
 
0.2%
308298 1
 
0.2%
308299 1
 
0.2%
308300 1
 
0.2%
308301 1
 
0.2%
308302 1
 
0.2%
Other values (631) 631
98.4%
ValueCountFrequency (%)
308112 1
0.2%
308113 1
0.2%
308114 1
0.2%
308115 1
0.2%
308116 1
0.2%
308117 1
0.2%
308118 1
0.2%
308119 1
0.2%
308120 1
0.2%
308121 1
0.2%
ValueCountFrequency (%)
308752 1
0.2%
308751 1
0.2%
308750 1
0.2%
308749 1
0.2%
308748 1
0.2%
308747 1
0.2%
308746 1
0.2%
308745 1
0.2%
308744 1
0.2%
308743 1
0.2%

inspec_ym
Real number (ℝ)

HIGH CORRELATION 

Distinct148
Distinct (%)23.2%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean201135.14
Minimum200204
Maximum202011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:00.247961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200204
5-th percentile200305
Q1200706
median201111
Q3201605
95-th percentile202004
Maximum202011
Range1807
Interquartile range (IQR)899

Descriptive statistics

Standard deviation532.66362
Coefficient of variation (CV)0.0026482872
Kurtosis-1.1344944
Mean201135.14
Median Absolute Deviation (MAD)407
Skewness-0.059252582
Sum1.2812308 × 108
Variance283730.53
MonotonicityNot monotonic
2024-04-17T09:58:00.359723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201405 8
 
1.2%
201404 7
 
1.1%
200209 5
 
0.8%
201407 5
 
0.8%
200208 5
 
0.8%
201409 5
 
0.8%
201504 5
 
0.8%
200610 5
 
0.8%
201005 5
 
0.8%
201509 5
 
0.8%
Other values (138) 582
90.8%
ValueCountFrequency (%)
200204 2
 
0.3%
200205 5
0.8%
200206 4
0.6%
200207 4
0.6%
200208 5
0.8%
200209 5
0.8%
200210 4
0.6%
200304 2
 
0.3%
200305 4
0.6%
200306 4
0.6%
ValueCountFrequency (%)
202011 3
0.5%
202010 4
0.6%
202009 4
0.6%
202008 5
0.8%
202007 4
0.6%
202006 5
0.8%
202005 4
0.6%
202004 4
0.6%
201911 4
0.6%
201910 5
0.8%

inspec_wk
Real number (ℝ)

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7566303
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:00.455712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3393295
Coefficient of variation (CV)0.48585752
Kurtosis0.040735473
Mean2.7566303
Median Absolute Deviation (MAD)1
Skewness0.41586027
Sum1767
Variance1.7938036
MonotonicityNot monotonic
2024-04-17T09:58:00.543797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 148
23.1%
4 147
22.9%
1 144
22.5%
2 144
22.5%
5 52
 
8.1%
6 2
 
0.3%
7 2
 
0.3%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
1 144
22.5%
2 144
22.5%
3 148
23.1%
4 147
22.9%
5 52
 
8.1%
6 2
 
0.3%
7 2
 
0.3%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 1
 
0.2%
7 2
 
0.3%
6 2
 
0.3%
5 52
 
8.1%
4 147
22.9%
3 148
23.1%
2 144
22.5%
1 144
22.5%

virus01
Real number (ℝ)

ZEROS 

Distinct341
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.9766
Minimum0
Maximum7022
Zeros87
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:00.658030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median44
Q3255
95-th percentile1960
Maximum7022
Range7022
Interquartile range (IQR)252.5

Descriptive statistics

Standard deviation866.55351
Coefficient of variation (CV)2.3807946
Kurtosis18.805574
Mean363.9766
Median Absolute Deviation (MAD)44
Skewness4.0008449
Sum233309
Variance750914.99
MonotonicityNot monotonic
2024-04-17T09:58:00.770685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
 
13.6%
1.0 25
 
3.9%
0.5 18
 
2.8%
2.0 14
 
2.2%
2.5 11
 
1.7%
4.5 10
 
1.6%
3.0 9
 
1.4%
4.0 8
 
1.2%
1.5 8
 
1.2%
7.5 7
 
1.1%
Other values (331) 444
69.3%
ValueCountFrequency (%)
0.0 87
13.6%
0.5 18
 
2.8%
1.0 25
 
3.9%
1.5 8
 
1.2%
2.0 14
 
2.2%
2.5 11
 
1.7%
3.0 9
 
1.4%
3.5 4
 
0.6%
4.0 8
 
1.2%
4.5 10
 
1.6%
ValueCountFrequency (%)
7022.0 1
0.2%
6613.0 1
0.2%
5708.0 1
0.2%
5351.0 1
0.2%
4696.0 1
0.2%
4563.0 1
0.2%
4338.0 1
0.2%
4271.0 2
0.3%
4193.0 1
0.2%
4056.0 1
0.2%

virus02
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.581903
Minimum0
Maximum259.5
Zeros304
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:00.882925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q39
95-th percentile68.5
Maximum259.5
Range259.5
Interquartile range (IQR)9

Descriptive statistics

Standard deviation32.310093
Coefficient of variation (CV)2.5679814
Kurtosis21.734855
Mean12.581903
Median Absolute Deviation (MAD)0.5
Skewness4.345254
Sum8065
Variance1043.9421
MonotonicityNot monotonic
2024-04-17T09:58:01.026597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 304
47.4%
1.0 33
 
5.1%
0.5 23
 
3.6%
2.0 19
 
3.0%
3.0 16
 
2.5%
1.5 11
 
1.7%
3.5 10
 
1.6%
4.5 8
 
1.2%
2.5 8
 
1.2%
6.0 7
 
1.1%
Other values (102) 202
31.5%
ValueCountFrequency (%)
0.0 304
47.4%
0.5 23
 
3.6%
1.0 33
 
5.1%
1.5 11
 
1.7%
2.0 19
 
3.0%
2.5 8
 
1.2%
3.0 16
 
2.5%
3.5 10
 
1.6%
4.0 7
 
1.1%
4.5 8
 
1.2%
ValueCountFrequency (%)
259.5 1
0.2%
227.0 1
0.2%
224.0 2
0.3%
206.0 1
0.2%
180.0 1
0.2%
178.0 1
0.2%
175.0 1
0.2%
172.0 1
0.2%
168.0 1
0.2%
160.0 1
0.2%

virus03
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388.56552
Minimum0
Maximum9718
Zeros407
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:01.170193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318.5
95-th percentile2673
Maximum9718
Range9718
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation1213.9102
Coefficient of variation (CV)3.124081
Kurtosis24.161037
Mean388.56552
Median Absolute Deviation (MAD)0
Skewness4.526345
Sum249070.5
Variance1473577.9
MonotonicityNot monotonic
2024-04-17T09:58:01.531397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 407
63.5%
0.5 14
 
2.2%
1.0 8
 
1.2%
5.0 7
 
1.1%
2.0 5
 
0.8%
7.0 4
 
0.6%
3.0 4
 
0.6%
12.0 4
 
0.6%
17.0 3
 
0.5%
4.0 3
 
0.5%
Other values (166) 182
28.4%
ValueCountFrequency (%)
0.0 407
63.5%
0.5 14
 
2.2%
1.0 8
 
1.2%
1.5 1
 
0.2%
2.0 5
 
0.8%
3.0 4
 
0.6%
3.5 2
 
0.3%
4.0 3
 
0.5%
4.5 2
 
0.3%
5.0 7
 
1.1%
ValueCountFrequency (%)
9718.0 1
0.2%
9207.0 1
0.2%
9036.0 1
0.2%
8808.0 1
0.2%
8541.0 1
0.2%
7024.0 1
0.2%
7003.0 1
0.2%
5282.0 1
0.2%
5075.0 1
0.2%
5064.0 1
0.2%

virus04
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct225
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.64384
Minimum0
Maximum4765.5
Zeros159
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:01.637149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median4
Q365.5
95-th percentile1199.5
Maximum4765.5
Range4765.5
Interquartile range (IQR)65

Descriptive statistics

Standard deviation536.33851
Coefficient of variation (CV)2.8582794
Kurtosis27.021425
Mean187.64384
Median Absolute Deviation (MAD)4
Skewness4.6787892
Sum120279.7
Variance287659
MonotonicityNot monotonic
2024-04-17T09:58:01.752363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 159
24.8%
2.0 41
 
6.4%
1.0 35
 
5.5%
0.5 33
 
5.1%
4.0 18
 
2.8%
1.5 17
 
2.7%
2.5 16
 
2.5%
5.0 11
 
1.7%
9.0 11
 
1.7%
3.0 11
 
1.7%
Other values (215) 289
45.1%
ValueCountFrequency (%)
0.0 159
24.8%
0.2 1
 
0.2%
0.5 33
 
5.1%
1.0 35
 
5.5%
1.5 17
 
2.7%
2.0 41
 
6.4%
2.5 16
 
2.5%
3.0 11
 
1.7%
3.5 7
 
1.1%
4.0 18
 
2.8%
ValueCountFrequency (%)
4765.5 2
0.3%
3833.5 1
0.2%
3747.0 1
0.2%
2839.0 1
0.2%
2646.0 1
0.2%
2621.0 2
0.3%
2604.0 1
0.2%
2538.0 1
0.2%
2429.5 1
0.2%
2154.0 1
0.2%

virus05
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0460218
Minimum0
Maximum112
Zeros388
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:01.867355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile18
Maximum112
Range112
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation9.7012719
Coefficient of variation (CV)3.184899
Kurtosis41.006612
Mean3.0460218
Median Absolute Deviation (MAD)0
Skewness5.6501578
Sum1952.5
Variance94.114676
MonotonicityNot monotonic
2024-04-17T09:58:01.976644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 388
60.5%
1.0 44
 
6.9%
0.5 41
 
6.4%
2.0 28
 
4.4%
1.5 19
 
3.0%
3.0 15
 
2.3%
4.0 14
 
2.2%
2.5 9
 
1.4%
8.0 9
 
1.4%
6.0 7
 
1.1%
Other values (38) 67
 
10.5%
ValueCountFrequency (%)
0.0 388
60.5%
0.5 41
 
6.4%
1.0 44
 
6.9%
1.5 19
 
3.0%
2.0 28
 
4.4%
2.5 9
 
1.4%
3.0 15
 
2.3%
3.5 6
 
0.9%
4.0 14
 
2.2%
4.5 3
 
0.5%
ValueCountFrequency (%)
112.0 1
0.2%
77.0 1
0.2%
68.0 1
0.2%
64.0 1
0.2%
55.0 1
0.2%
50.0 1
0.2%
49.0 2
0.3%
48.0 2
0.3%
47.0 1
0.2%
45.0 1
0.2%

virus06
Real number (ℝ)

SKEWED  ZEROS 

Distinct35
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0616225
Minimum0
Maximum396
Zeros470
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:02.081964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile9
Maximum396
Range396
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation16.394493
Coefficient of variation (CV)7.9522285
Kurtosis523.1553
Mean2.0616225
Median Absolute Deviation (MAD)0
Skewness21.909803
Sum1321.5
Variance268.7794
MonotonicityNot monotonic
2024-04-17T09:58:02.200969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 470
73.3%
0.5 32
 
5.0%
1.0 31
 
4.8%
2.0 19
 
3.0%
1.5 12
 
1.9%
2.5 10
 
1.6%
3.0 9
 
1.4%
4.0 8
 
1.2%
5.0 6
 
0.9%
10.0 4
 
0.6%
Other values (25) 40
 
6.2%
ValueCountFrequency (%)
0.0 470
73.3%
0.5 32
 
5.0%
1.0 31
 
4.8%
1.5 12
 
1.9%
2.0 19
 
3.0%
2.5 10
 
1.6%
3.0 9
 
1.4%
4.0 8
 
1.2%
4.5 3
 
0.5%
5.0 6
 
0.9%
ValueCountFrequency (%)
396.0 1
0.2%
48.5 1
0.2%
47.0 1
0.2%
44.0 1
0.2%
41.0 1
0.2%
32.0 1
0.2%
31.0 1
0.2%
28.0 1
0.2%
25.0 1
0.2%
22.0 2
0.3%

virus07
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.630265
Minimum0
Maximum1308
Zeros445
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:02.332577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile88
Maximum1308
Range1308
Interquartile range (IQR)3

Descriptive statistics

Standard deviation73.115506
Coefficient of variation (CV)4.1471586
Kurtosis162.51281
Mean17.630265
Median Absolute Deviation (MAD)0
Skewness10.771123
Sum11301
Variance5345.8771
MonotonicityNot monotonic
2024-04-17T09:58:02.456912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 445
69.4%
1.0 16
 
2.5%
0.5 9
 
1.4%
4.0 9
 
1.4%
2.0 7
 
1.1%
8.0 6
 
0.9%
9.0 5
 
0.8%
13.0 5
 
0.8%
33.0 5
 
0.8%
7.0 4
 
0.6%
Other values (90) 130
 
20.3%
ValueCountFrequency (%)
0.0 445
69.4%
0.5 9
 
1.4%
1.0 16
 
2.5%
1.5 1
 
0.2%
2.0 7
 
1.1%
2.5 1
 
0.2%
3.0 3
 
0.5%
3.5 2
 
0.3%
4.0 9
 
1.4%
5.0 4
 
0.6%
ValueCountFrequency (%)
1308.0 1
0.2%
566.0 1
0.2%
537.5 1
0.2%
364.5 1
0.2%
344.0 1
0.2%
342.0 1
0.2%
294.0 1
0.2%
291.0 1
0.2%
282.0 1
0.2%
258.0 1
0.2%

virus08
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct249
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.575663
Minimum0
Maximum1214
Zeros197
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:02.599488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q375
95-th percentile440
Maximum1214
Range1214
Interquartile range (IQR)75

Descriptive statistics

Standard deviation187.10549
Coefficient of variation (CV)2.1611788
Kurtosis13.961217
Mean86.575663
Median Absolute Deviation (MAD)14
Skewness3.5871842
Sum55495
Variance35008.465
MonotonicityNot monotonic
2024-04-17T09:58:02.731664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 197
30.7%
1.0 15
 
2.3%
0.5 13
 
2.0%
7.5 10
 
1.6%
2.0 10
 
1.6%
6.0 8
 
1.2%
7.0 6
 
0.9%
2.5 6
 
0.9%
5.5 5
 
0.8%
11.0 5
 
0.8%
Other values (239) 366
57.1%
ValueCountFrequency (%)
0.0 197
30.7%
0.5 13
 
2.0%
1.0 15
 
2.3%
1.5 5
 
0.8%
2.0 10
 
1.6%
2.5 6
 
0.9%
3.0 3
 
0.5%
3.5 2
 
0.3%
4.0 4
 
0.6%
4.5 2
 
0.3%
ValueCountFrequency (%)
1214.0 1
0.2%
1196.0 1
0.2%
1114.0 1
0.2%
1076.0 1
0.2%
1069.0 1
0.2%
1054.0 1
0.2%
1004.0 1
0.2%
1000.0 1
0.2%
969.0 1
0.2%
959.0 1
0.2%

virus09
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3291732
Minimum0
Maximum168
Zeros474
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-17T09:58:02.862619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile14
Maximum168
Range168
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation9.298147
Coefficient of variation (CV)3.9920377
Kurtosis169.54868
Mean2.3291732
Median Absolute Deviation (MAD)0
Skewness10.949909
Sum1493
Variance86.455538
MonotonicityNot monotonic
2024-04-17T09:58:02.986131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 474
73.9%
0.5 23
 
3.6%
1.0 18
 
2.8%
6.0 10
 
1.6%
1.5 9
 
1.4%
2.5 9
 
1.4%
4.5 9
 
1.4%
2.0 9
 
1.4%
3.0 8
 
1.2%
14.5 5
 
0.8%
Other values (33) 67
 
10.5%
ValueCountFrequency (%)
0.0 474
73.9%
0.5 23
 
3.6%
1.0 18
 
2.8%
1.5 9
 
1.4%
2.0 9
 
1.4%
2.5 9
 
1.4%
3.0 8
 
1.2%
3.5 4
 
0.6%
4.0 5
 
0.8%
4.5 9
 
1.4%
ValueCountFrequency (%)
168.0 1
0.2%
86.0 1
0.2%
47.0 1
0.2%
38.0 2
0.3%
37.5 2
0.3%
36.0 1
0.2%
33.0 1
0.2%
28.0 1
0.2%
27.5 2
0.3%
24.5 1
0.2%

virus10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing641
Missing (%)100.0%
Memory size5.8 KiB

inspec_area
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
부산지역축사
641 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산지역축사 641
100.0%

Length

2024-04-17T09:58:03.101251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:58:03.176152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산지역축사 641
100.0%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Minimum2020-12-21 17:38:45
Maximum2020-12-21 17:38:45
2024-04-17T09:58:03.238648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:58:03.312847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T09:57:58.656048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:47.551901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.491786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.468051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.453578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.450615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.623020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.560129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.514249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.461142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.429673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.594582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.744869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:47.625252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.575788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.547481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.531211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.527298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.703176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.640387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.602585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.543998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.512790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.685256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.834768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:47.703579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.663691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.630609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.613547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.605365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.791317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.719197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.681142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.625989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.590949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.765635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.921095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:47.790694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.752641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.715712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.700500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.691742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.885885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.799921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.759266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.714706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.669705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.860048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.007637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:47.869563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.834657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.802453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.785240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.766238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.962244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.878242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.840064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.793197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.748346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.940794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.097256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:47.946986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.907826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.875864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.876451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.836967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.032341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.964480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.910854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.867854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.819709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.016047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.189361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.020324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.982822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.954127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.968366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.905667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.104198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.053799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.995545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.944024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.892703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.092524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.276186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.093832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.073985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.030481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.048085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.986941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.186709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.126769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.079826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.019439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.964951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.164351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.352657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.166012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.149575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.109169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.141734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.294971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.264346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.196309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.160506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.113029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.276316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.256538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.435244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.257273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.231728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.205551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.223729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.372306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.342494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.276070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.240132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.196867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.352806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.358284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.512707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.333975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.311622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.289567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.298271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.462857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.413630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.358993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.311178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.272420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.438652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.458625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:59.592320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:48.408423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:49.388132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:50.372087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:51.374060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:52.533938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:53.484346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:54.436373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:55.387071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:56.350410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:57.509756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:57:58.560814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T09:58:03.384148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinspec_yminspec_wkvirus01virus02virus03virus04virus05virus06virus07virus08virus09
skey1.0000.9950.0000.3280.3130.3610.2320.3980.0700.3680.3840.432
inspec_ym0.9951.0000.0000.3620.3040.3840.2320.4200.0000.3540.3700.473
inspec_wk0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
virus010.3280.3620.0001.0000.5900.6940.5690.3390.4290.2620.3480.602
virus020.3130.3040.0000.5901.0000.0000.7180.0000.6130.0000.6230.745
virus030.3610.3840.0000.6940.0001.0000.0000.5030.3260.6010.0000.000
virus040.2320.2320.0000.5690.7180.0001.0000.0000.6020.0000.5000.767
virus050.3980.4200.0000.3390.0000.5030.0001.0000.0960.3720.2380.000
virus060.0700.0000.0000.4290.6130.3260.6020.0961.0000.1540.2590.000
virus070.3680.3540.0000.2620.0000.6010.0000.3720.1541.0000.0000.000
virus080.3840.3700.0000.3480.6230.0000.5000.2380.2590.0001.0000.522
virus090.4320.4730.0000.6020.7450.0000.7670.0000.0000.0000.5221.000
2024-04-17T09:58:03.511440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinspec_yminspec_wkvirus01virus02virus03virus04virus05virus06virus07virus08virus09
skey1.0001.000-0.007-0.3340.499-0.7500.333-0.0200.197-0.5450.3590.202
inspec_ym1.0001.000-0.007-0.3230.526-0.7660.358-0.0050.200-0.5580.3730.231
inspec_wk-0.007-0.0071.000-0.0020.005-0.018-0.022-0.026-0.024-0.019-0.021-0.006
virus01-0.334-0.323-0.0021.0000.2080.4100.4890.4380.2350.3720.3860.156
virus020.4990.5260.0050.2081.000-0.5910.7390.2130.340-0.4680.6830.559
virus03-0.750-0.766-0.0180.410-0.5911.000-0.3140.135-0.0480.822-0.273-0.382
virus040.3330.358-0.0220.4890.739-0.3141.0000.3420.417-0.1720.7330.471
virus05-0.020-0.005-0.0260.4380.2130.1350.3421.0000.1910.2340.2980.074
virus060.1970.200-0.0240.2350.340-0.0480.4170.1911.000-0.0190.232-0.013
virus07-0.545-0.558-0.0190.372-0.4680.822-0.1720.234-0.0191.000-0.104-0.307
virus080.3590.373-0.0210.3860.683-0.2730.7330.2980.232-0.1041.0000.418
virus090.2020.231-0.0060.1560.559-0.3820.4710.074-0.013-0.3070.4181.000

Missing values

2024-04-17T09:57:59.708364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T09:57:59.861263image/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

skeyinspec_yminspec_wkvirus01virus02virus03virus04virus05virus06virus07virus08virus09virus10inspec_arealast_load_dttm
03086092016093161.010.50.530.50.00.50.034.00.0<NA>부산지역축사2020-12-21 17:38:45
13086102016094139.51.50.021.52.50.51.0136.00.0<NA>부산지역축사2020-12-21 17:38:45
2308611201610159.00.50.05.00.00.00.092.50.0<NA>부산지역축사2020-12-21 17:38:45
330861220161027.51.50.09.01.00.00.093.00.0<NA>부산지역축사2020-12-21 17:38:45
430861320161034.00.50.01.51.50.50.036.50.0<NA>부산지역축사2020-12-21 17:38:45
530861420161043.51.50.01.00.00.00.022.50.0<NA>부산지역축사2020-12-21 17:38:45
630861520161110.00.00.00.00.00.00.00.00.0<NA>부산지역축사2020-12-21 17:38:45
730861620161120.00.00.00.00.00.00.00.00.0<NA>부산지역축사2020-12-21 17:38:45
830861720161130.00.00.00.00.00.00.00.00.0<NA>부산지역축사2020-12-21 17:38:45
930861820161140.01.00.00.00.00.00.00.00.0<NA>부산지역축사2020-12-21 17:38:45
skeyinspec_yminspec_wkvirus01virus02virus03virus04virus05virus06virus07virus08virus09virus10inspec_arealast_load_dttm
6313082292005104116.00.029.04.00.01.00.016.00.0<NA>부산지역축사2020-12-21 17:38:45
632308230200604111.51.03.50.00.00.00.00.00.0<NA>부산지역축사2020-12-21 17:38:45
63330823120060423.00.07.50.00.00.00.00.50.0<NA>부산지역축사2020-12-21 17:38:45
63430823220060436.50.54.50.00.50.00.00.50.0<NA>부산지역축사2020-12-21 17:38:45
63530823320060444.52.50.00.01.00.00.00.50.0<NA>부산지역축사2020-12-21 17:38:45
63630823420060512.54.01.50.00.00.00.01.00.0<NA>부산지역축사2020-12-21 17:38:45
63730823520060527.00.57.00.00.50.05.54.50.0<NA>부산지역축사2020-12-21 17:38:45
63830823620060537.57.014.50.50.00.04.08.00.0<NA>부산지역축사2020-12-21 17:38:45
639308237200605411.03.016.03.50.00.09.510.00.0<NA>부산지역축사2020-12-21 17:38:45
640308238200605525.03.016.51.51.50.08.517.50.0<NA>부산지역축사2020-12-21 17:38:45