Overview

Dataset statistics

Number of variables9
Number of observations846
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.6 KiB
Average record size in memory72.2 B

Variable types

NUM7
DATE1
CAT1

Warnings

df_index has unique values Unique
data has unique values Unique

Reproduction

Analysis started2020-12-02 17:48:23.786745
Analysis finished2020-12-02 17:48:32.003231
Duration8.22 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Date

UNIQUE

Distinct846
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Minimum2020-10-11 00:00:00
Maximum2020-11-30 23:00:00
2020-12-02T18:48:32.119400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:32.262523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

data
Categorical

UNIQUE

Distinct846
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
16/11/2020 16:00
 
1
19/11/2020 23:00
 
1
19/11/2020 15:00
 
1
13/11/2020 01:00
 
1
20/11/2020 12:00
 
1
Other values (841)
841 
ValueCountFrequency (%) 
16/11/2020 16:0010.1%
 
19/11/2020 23:0010.1%
 
19/11/2020 15:0010.1%
 
13/11/2020 01:0010.1%
 
20/11/2020 12:0010.1%
 
24/11/2020 03:0010.1%
 
24/10/2020 06:0010.1%
 
19/10/2020 21:0010.1%
 
16/11/2020 10:0010.1%
 
17/11/2020 10:0010.1%
 
Other values (836)83698.8%
 
2020-12-02T18:48:32.642797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique846 ?
Unique (%)100.0%
2020-12-02T18:48:32.791009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length16
Mean length16
Min length16

NO2Arpat
Real number (ℝ≥0)

Distinct92
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.90661939
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:32.922175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q344
95-th percentile69.75
Maximum100
Range99
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.21265679
Coefficient of variation (CV)0.5838538613
Kurtosis0.2337327023
Mean32.90661939
Median Absolute Deviation (MAD)12
Skewness0.7911693392
Sum27839
Variance369.126181
MonotocityNot monotonic
2020-12-02T18:48:33.067937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18263.1%
 
22232.7%
 
16222.6%
 
24222.6%
 
23222.6%
 
41212.5%
 
25212.5%
 
28212.5%
 
12202.4%
 
26202.4%
 
Other values (82)62874.2%
 
ValueCountFrequency (%) 
150.6%
 
210.1%
 
360.7%
 
460.7%
 
550.6%
 
ValueCountFrequency (%) 
10010.1%
 
9810.1%
 
9410.1%
 
9220.2%
 
9110.1%
 

tair
Real number (ℝ≥0)

Distinct843
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.481751
Minimum1.176315789
Maximum26.43589744
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:33.208863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.176315789
5-th percentile5.916666667
Q111.1974359
median14.28471154
Q317.85675676
95-th percentile22.74381757
Maximum26.43589744
Range25.25958165
Interquartile range (IQR)6.659320859

Descriptive statistics

Standard deviation4.954726645
Coefficient of variation (CV)0.3421358815
Kurtosis-0.3295811056
Mean14.481751
Median Absolute Deviation (MAD)3.369230769
Skewness-0.03068165098
Sum12251.56135
Variance24.54931612
MonotocityNot monotonic
2020-12-02T18:48:33.360659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15.2692307720.2%
 
19.3820512820.2%
 
10.8230769220.2%
 
13.6666666710.1%
 
3.0510.1%
 
12.6110.1%
 
5.0710.1%
 
14.4351351410.1%
 
17.9333333310.1%
 
14.7611111110.1%
 
Other values (833)83398.5%
 
ValueCountFrequency (%) 
1.17631578910.1%
 
1.44615384610.1%
 
1.70256410310.1%
 
2.12510.1%
 
2.187510.1%
 
ValueCountFrequency (%) 
26.4358974410.1%
 
26.2540540510.1%
 
26.2153846210.1%
 
26.1552631610.1%
 
25.7487179510.1%
 

rad
Real number (ℝ≥0)

Distinct821
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.88133125
Minimum19.33684211
Maximum99.9
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:33.514700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum19.33684211
5-th percentile36.62845816
Q160.97348739
median82.33996795
Q393.71083333
95-th percentile99.3381891
Maximum99.9
Range80.56315789
Interquartile range (IQR)32.73734594

Descriptive statistics

Standard deviation20.45013106
Coefficient of variation (CV)0.2695014798
Kurtosis-0.5675791449
Mean75.88133125
Median Absolute Deviation (MAD)14.06887821
Skewness-0.7123314644
Sum64195.60624
Variance418.2078604
MonotocityNot monotonic
2020-12-02T18:48:33.673772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99.9192.2%
 
99.960.7%
 
97.420.2%
 
98.577520.2%
 
62.3888888910.1%
 
95.342510.1%
 
99.310.1%
 
91.8102564110.1%
 
50.1846153810.1%
 
90.6307692310.1%
 
Other values (811)81195.9%
 
ValueCountFrequency (%) 
19.3368421110.1%
 
19.610.1%
 
20.212510.1%
 
22.2076923110.1%
 
23.0459459510.1%
 
ValueCountFrequency (%) 
99.9192.2%
 
99.960.7%
 
99.910.1%
 
99.8710.1%
 
99.8257142910.1%
 

o3
Real number (ℝ≥0)

Distinct836
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean761.3314002
Minimum46.66666667
Maximum914.5384615
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:33.834474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum46.66666667
5-th percentile292.9838141
Q1750.3397436
median827.6076923
Q3864.3012821
95-th percentile893.8853695
Maximum914.5384615
Range867.8717949
Interquartile range (IQR)113.9615385

Descriptive statistics

Standard deviation179.4971704
Coefficient of variation (CV)0.23576746
Kurtosis4.654643893
Mean761.3314002
Median Absolute Deviation (MAD)46.1625
Skewness-2.269225402
Sum644086.3646
Variance32219.23419
MonotocityNot monotonic
2020-12-02T18:48:33.978382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
890.3530.4%
 
872.720.2%
 
88320.2%
 
874.897435920.2%
 
85820.2%
 
838.948717920.2%
 
840.320.2%
 
69920.2%
 
892.538461520.2%
 
618.270270310.1%
 
Other values (826)82697.6%
 
ValueCountFrequency (%) 
46.6666666710.1%
 
46.97510.1%
 
51.4358974410.1%
 
51.5128205110.1%
 
62.22510.1%
 
ValueCountFrequency (%) 
914.538461510.1%
 
914.110.1%
 
911.538461510.1%
 
910.512820510.1%
 
910.307692310.1%
 

no2
Real number (ℝ≥0)

Distinct831
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.2322978
Minimum40.35
Maximum282.974359
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:34.128008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum40.35
5-th percentile85.65144231
Q1155.6032095
median200.0259784
Q3232.9038462
95-th percentile262.9153418
Maximum282.974359
Range242.624359
Interquartile range (IQR)77.30063669

Descriptive statistics

Standard deviation53.91284455
Coefficient of variation (CV)0.2834053164
Kurtosis-0.09057386816
Mean190.2322978
Median Absolute Deviation (MAD)37.14102564
Skewness-0.679053604
Sum160936.524
Variance2906.594808
MonotocityNot monotonic
2020-12-02T18:48:34.317058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
146.384615420.2%
 
154.384615420.2%
 
92.6923076920.2%
 
215.230769220.2%
 
232.410256420.2%
 
16320.2%
 
17520.2%
 
243.7520.2%
 
238.82520.2%
 
208.120.2%
 
Other values (821)82697.6%
 
ValueCountFrequency (%) 
40.3510.1%
 
41.310.1%
 
42.2051282110.1%
 
42.4358974410.1%
 
43.4615384610.1%
 
ValueCountFrequency (%) 
282.97435910.1%
 
281.97510.1%
 
277.717948710.1%
 
27610.1%
 
275.3510.1%
 

noAlpha
Real number (ℝ≥0)

Distinct449
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.14762683
Minimum89.05263158
Maximum93.81081081
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:34.457633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum89.05263158
5-th percentile89.81205548
Q190.51761364
median91.025
Q391.69230769
95-th percentile92.8872549
Maximum93.81081081
Range4.758179232
Interquartile range (IQR)1.174694056

Descriptive statistics

Standard deviation0.9204398679
Coefficient of variation (CV)0.01009834156
Kurtosis-0.1639007852
Mean91.14762683
Median Absolute Deviation (MAD)0.575
Skewness0.4515707662
Sum77110.89229
Variance0.8472095505
MonotocityNot monotonic
2020-12-02T18:48:34.603828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
91111.3%
 
90.8974359101.2%
 
91.0256410380.9%
 
90.8461538580.9%
 
90.8717948770.8%
 
91.6923076970.8%
 
90.8205128260.7%
 
90.4102564160.7%
 
90.7435897460.7%
 
90.960.7%
 
Other values (439)77191.1%
 
ValueCountFrequency (%) 
89.0526315810.1%
 
89.0540540510.1%
 
89.1052631610.1%
 
89.1111111110.1%
 
89.1212121210.1%
 
ValueCountFrequency (%) 
93.8108108110.1%
 
93.702702710.1%
 
93.6756756810.1%
 
93.6666666710.1%
 
93.6052631610.1%
 

no2Alpha
Real number (ℝ≥0)

Distinct560
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.17862518
Minimum85.71794872
Maximum99.81081081
Zeros0
Zeros (%)0.0%
Memory size6.6 KiB
2020-12-02T18:48:34.752042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum85.71794872
5-th percentile86.64326923
Q187.51370656
median88.64102564
Q390.42291667
95-th percentile93.09615385
Maximum99.81081081
Range14.09286209
Interquartile range (IQR)2.909210103

Descriptive statistics

Standard deviation2.14911548
Coefficient of variation (CV)0.02409899767
Kurtosis1.640512446
Mean89.17862518
Median Absolute Deviation (MAD)1.331175131
Skewness1.148867842
Sum75445.1169
Variance4.618697347
MonotocityNot monotonic
2020-12-02T18:48:34.897774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
87.9230769270.8%
 
87.6923076970.8%
 
87.7692307770.8%
 
87.560.7%
 
87.150.6%
 
87.850.6%
 
87.1794871850.6%
 
87.27550.6%
 
86.6923076940.5%
 
87.2307692340.5%
 
Other values (550)79193.5%
 
ValueCountFrequency (%) 
85.7179487210.1%
 
85.8461538510.1%
 
85.87510.1%
 
85.97510.1%
 
8610.1%
 
ValueCountFrequency (%) 
99.8108108110.1%
 
98.297297310.1%
 
98.2564102610.1%
 
98.1282051310.1%
 
97.310.1%
 

Interactions

2020-12-02T18:48:24.296206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:24.431135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:24.561342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:24.709482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:24.839033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:24.971831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.114038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.273529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.404721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.537763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.675892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.797393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:25.921421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.055112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.198014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.338484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.501384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.654992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.790115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:26.938750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:27.088941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:27.734142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:27.867280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:27.998996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.134530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.249475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.371998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.501739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.633286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.761193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:28.884987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.022039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.141694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.266428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.409357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.558620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.705358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.840518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:29.985806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.107508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.234662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.363215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.508663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.647406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.782527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:30.933380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:31.066656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:31.200289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:31.343305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-02T18:48:35.034550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-02T18:48:35.204557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-02T18:48:35.387745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-02T18:48:35.563325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-02T18:48:31.618327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T18:48:31.868952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexdataNO2Arpattairrado3no2noAlphano2Alpha
02020-10-16 13:00:0016/10/2020 13:0032.016.12894778.426316829.973684195.50000091.15789589.210526
12020-10-16 14:00:0016/10/2020 14:0026.017.79210570.884211847.657895211.57894791.05263290.763158
22020-10-16 15:00:0016/10/2020 15:0034.020.61111154.911111870.944444194.05555689.86111191.666667
32020-10-16 16:00:0016/10/2020 16:0033.020.52250053.110000897.400000205.15000091.00000091.175000
42020-10-16 17:00:0016/10/2020 17:0054.018.32432463.116216870.513514226.67567692.24324391.864865
52020-10-16 18:00:0016/10/2020 18:0058.015.18888977.044444859.194444242.94444492.41666789.583333
62020-10-16 19:00:0016/10/2020 19:0056.013.64324386.251351869.459459255.86486593.13513591.108108
72020-10-16 20:00:0016/10/2020 20:0041.012.32368492.392105874.552632257.47368491.65789588.421053
82020-10-16 21:00:0016/10/2020 21:0038.011.34473795.597368869.842105251.73684291.21052689.052632
92020-10-16 22:00:0016/10/2020 22:0039.010.90263296.413158866.000000249.44736891.05263288.947368

Last rows

df_indexdataNO2Arpattairrado3no2noAlphano2Alpha
8362020-11-30 14:00:0030/11/2020 14:0020.019.25641030.951282725.000000106.82051389.82051389.384615
8372020-11-30 15:00:0030/11/2020 15:0039.019.06315833.521053719.263158126.42105391.97368488.342105
8382020-11-30 16:00:0030/11/2020 16:0074.014.21891946.145946733.702703154.18918992.21621688.702703
8392020-11-30 17:00:0030/11/2020 17:0057.011.55526356.257895776.736842185.60526393.10526388.394737
8402020-11-30 18:00:0030/11/2020 18:0080.010.29444463.538889839.583333204.55555692.88888989.500000
8412020-11-30 19:00:0030/11/2020 19:0062.08.97750071.537500861.275000211.17500092.95000091.050000
8422020-11-30 20:00:0030/11/2020 20:0073.08.06111177.361111887.583333215.38888992.72222293.555556
8432020-11-30 21:00:0030/11/2020 21:0060.07.38500072.305000891.875000211.27500091.57500088.850000
8442020-11-30 22:00:0030/11/2020 22:0051.07.96842166.963158872.368421160.60526391.05263288.131579
8452020-11-30 23:00:0030/11/2020 23:0030.08.85294163.150000836.411765154.32352990.32352987.411765