Overview

Dataset statistics

Number of variables15
Number of observations6574
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory770.5 KiB
Average record size in memory120.0 B

Variable types

NUM15

Reproduction

Analysis started2020-08-24 23:59:39.504569
Analysis finished2020-08-25 00:00:14.941242
Duration35.44 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

BIR is highly correlated with SHAHigh correlation
SHA is highly correlated with BIRHigh correlation
year is uniformly distributed Uniform

Variables

year
Real number (ℝ≥0)

UNIFORM

Distinct count18
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.50030422878004
Minimum61.0
Maximum78.0
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:14.996913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile61
Q165
median69.5
Q374
95-th percentile78
Maximum78
Range17
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.188131129
Coefficient of variation (CV)0.07464904199
Kurtosis-1.207410196
Mean69.50030423
Median Absolute Deviation (MAD)4.5
Skewness-4.465413197e-05
Sum456895
Variance26.91670461
2020-08-25T00:00:15.112444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
643665.6%
 
683665.6%
 
723665.6%
 
763665.6%
 
773655.6%
 
783655.6%
 
653655.6%
 
663655.6%
 
733655.6%
 
673655.6%
 
713655.6%
 
623655.6%
 
703655.6%
 
693655.6%
 
633655.6%
 
753655.6%
 
743655.6%
 
613655.6%
 
ValueCountFrequency (%) 
613655.6%
 
623655.6%
 
633655.6%
 
643665.6%
 
653655.6%
 
663655.6%
 
673655.6%
 
683665.6%
 
693655.6%
 
703655.6%
 
ValueCountFrequency (%) 
783655.6%
 
773655.6%
 
763665.6%
 
753655.6%
 
743655.6%
 
733655.6%
 
723665.6%
 
713655.6%
 
703655.6%
 
693655.6%
 

month
Real number (ℝ≥0)

Distinct count12
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.523273501673258
Minimum1.0
Maximum12.0
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:15.233254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.448871057
Coefficient of variation (CV)0.5287025075
Kurtosis-1.207951276
Mean6.523273502
Median Absolute Deviation (MAD)3
Skewness-0.009425117038
Sum42884
Variance11.89471157
2020-08-25T00:00:15.330625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
125588.5%
 
105588.5%
 
85588.5%
 
75588.5%
 
55588.5%
 
35588.5%
 
15588.5%
 
115408.2%
 
95408.2%
 
65408.2%
 
45408.2%
 
25087.7%
 
ValueCountFrequency (%) 
15588.5%
 
25087.7%
 
35588.5%
 
45408.2%
 
55588.5%
 
65408.2%
 
75588.5%
 
85588.5%
 
95408.2%
 
105588.5%
 
ValueCountFrequency (%) 
125588.5%
 
115408.2%
 
105588.5%
 
95408.2%
 
85588.5%
 
75588.5%
 
65408.2%
 
55588.5%
 
45408.2%
 
35588.5%
 

day
Real number (ℝ≥0)

Distinct count31
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.728627928202007
Minimum1.0
Maximum31.0
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:15.437487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.800334975
Coefficient of variation (CV)0.5595106588
Kurtosis-1.193928507
Mean15.72862793
Median Absolute Deviation (MAD)8
Skewness0.006845820734
Sum103400
Variance77.44589567
2020-08-25T00:00:15.541991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
162163.3%
 
152163.3%
 
22163.3%
 
32163.3%
 
42163.3%
 
52163.3%
 
62163.3%
 
72163.3%
 
82163.3%
 
92163.3%
 
102163.3%
 
112163.3%
 
122163.3%
 
132163.3%
 
142163.3%
 
12163.3%
 
182163.3%
 
202163.3%
 
222163.3%
 
242163.3%
 
262163.3%
 
282163.3%
 
172163.3%
 
192163.3%
 
212163.3%
 
Other values (6)117417.9%
 
ValueCountFrequency (%) 
12163.3%
 
22163.3%
 
32163.3%
 
42163.3%
 
52163.3%
 
62163.3%
 
72163.3%
 
82163.3%
 
92163.3%
 
102163.3%
 
ValueCountFrequency (%) 
311261.9%
 
301983.0%
 
292023.1%
 
282163.3%
 
272163.3%
 
262163.3%
 
252163.3%
 
242163.3%
 
232163.3%
 
222163.3%
 

RPT
Real number (ℝ≥0)

Distinct count671
Unique (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.363714631672938
Minimum0.6700000166893005
Maximum35.79999923706055
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:15.658049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6700000167
5-th percentile4.420000076
Q18.119999886
median11.71000004
Q315.92000008
95-th percentile22.67000008
Maximum35.79999924
Range35.12999922
Interquartile range (IQR)7.800000191

Descriptive statistics

Standard deviation5.61961043
Coefficient of variation (CV)0.4545244368
Kurtosis0.2378693306
Mean12.36371463
Median Absolute Deviation (MAD)3.829999924
Skewness0.6346030409
Sum81279.05999
Variance31.58002138
2020-08-25T00:00:15.777967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.789999962300.5%
 
12.82999992290.4%
 
12.67000008270.4%
 
9270.4%
 
7.5270.4%
 
10270.4%
 
10.36999989260.4%
 
16.87999916260.4%
 
7.170000076250.4%
 
13.07999992250.4%
 
13.61999989250.4%
 
10.53999996250.4%
 
12.78999996240.4%
 
9.960000038240.4%
 
10.07999992240.4%
 
8240.4%
 
12.86999989240.4%
 
10.21000004240.4%
 
12.17000008240.4%
 
7.960000038240.4%
 
9.829999924240.4%
 
11.38000011240.4%
 
8.5230.3%
 
13.88000011230.3%
 
13.13000011230.3%
 
Other values (646)594690.4%
 
ValueCountFrequency (%) 
0.67000001672< 0.1%
 
0.79000002151< 0.1%
 
0.95999997851< 0.1%
 
11< 0.1%
 
1.2100000381< 0.1%
 
1.251< 0.1%
 
1.2899999621< 0.1%
 
1.4199999571< 0.1%
 
1.4600000381< 0.1%
 
1.52< 0.1%
 
ValueCountFrequency (%) 
35.799999241< 0.1%
 
35.380001071< 0.1%
 
34.369998931< 0.1%
 
33.840000152< 0.1%
 
33.340000151< 0.1%
 
33.119998931< 0.1%
 
33.040000921< 0.1%
 
32.959999081< 0.1%
 
32.580001831< 0.1%
 
32.51< 0.1%
 

VAL
Real number (ℝ≥0)

Distinct count607
Unique (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.646448132454225
Minimum0.20999999344348907
Maximum33.36999893188477
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:15.898032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.2099999934
5-th percentile3
Q16.670000076
median10.17000008
Q314.03999996
95-th percentile20.13750057
Maximum33.36999893
Range33.15999894
Interquartile range (IQR)7.369999886

Descriptive statistics

Standard deviation5.268601536
Coefficient of variation (CV)0.4948694129
Kurtosis-0.01125012083
Mean10.64644813
Median Absolute Deviation (MAD)3.659999847
Skewness0.5250119781
Sum69989.75002
Variance27.75816215
2020-08-25T00:00:16.016313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.829999924340.5%
 
11.53999996300.5%
 
6.630000114280.4%
 
10.13000011270.4%
 
10.07999992270.4%
 
6.920000076270.4%
 
7.579999924260.4%
 
9.289999962260.4%
 
10.88000011260.4%
 
13.67000008260.4%
 
7.829999924260.4%
 
7.5260.4%
 
9.960000038260.4%
 
12.07999992260.4%
 
11.67000008260.4%
 
6.210000038260.4%
 
12.17000008250.4%
 
8.75250.4%
 
9.590000153250.4%
 
6.5240.4%
 
12.46000004240.4%
 
9.539999962240.4%
 
10.57999992240.4%
 
3.960000038240.4%
 
8.079999924240.4%
 
Other values (582)592290.1%
 
ValueCountFrequency (%) 
0.20999999341< 0.1%
 
0.37000000481< 0.1%
 
0.41999998691< 0.1%
 
0.54000002153< 0.1%
 
0.62999999521< 0.1%
 
0.67000001672< 0.1%
 
0.70999997851< 0.1%
 
0.753< 0.1%
 
0.79000002152< 0.1%
 
0.82999998332< 0.1%
 
ValueCountFrequency (%) 
33.369998931< 0.1%
 
33.040000921< 0.1%
 
31.629999161< 0.1%
 
30.959999081< 0.1%
 
30.340000151< 0.1%
 
29.879999161< 0.1%
 
29.51< 0.1%
 
29.379999161< 0.1%
 
29.329999921< 0.1%
 
28.52< 0.1%
 

ROS
Real number (ℝ≥0)

Distinct count611
Unique (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.660103444582356
Minimum1.5
Maximum33.84000015258789
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:16.136607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile4.789999962
Q18
median10.92000008
Q314.67000008
95-th percentile20.90999985
Maximum33.84000015
Range32.34000015
Interquartile range (IQR)6.670000076

Descriptive statistics

Standard deviation5.007764673
Coefficient of variation (CV)0.4294785803
Kurtosis0.5936156658
Mean11.66010344
Median Absolute Deviation (MAD)3.25
Skewness0.7801591705
Sum76653.52004
Variance25.07770702
2020-08-25T00:00:16.246994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8.670000076340.5%
 
8.119999886340.5%
 
8.75330.5%
 
9.25320.5%
 
9.539999962320.5%
 
8.460000038320.5%
 
10.53999996310.5%
 
9.590000153300.5%
 
9.170000076300.5%
 
10.63000011300.5%
 
8.329999924290.4%
 
10.36999989290.4%
 
9.380000114290.4%
 
8.25290.4%
 
8.039999962280.4%
 
12.67000008280.4%
 
10.03999996280.4%
 
10.92000008280.4%
 
7.960000038280.4%
 
11.78999996280.4%
 
7.619999886270.4%
 
9.710000038270.4%
 
6.960000038270.4%
 
8.170000076270.4%
 
10.78999996260.4%
 
Other values (586)583888.8%
 
ValueCountFrequency (%) 
1.51< 0.1%
 
1.751< 0.1%
 
1.8300000432< 0.1%
 
1.8700000051< 0.1%
 
1.9600000381< 0.1%
 
2.1300001141< 0.1%
 
2.1700000762< 0.1%
 
2.2100000381< 0.1%
 
2.251< 0.1%
 
2.2899999622< 0.1%
 
ValueCountFrequency (%) 
33.840000151< 0.1%
 
33.251< 0.1%
 
32.751< 0.1%
 
32.709999082< 0.1%
 
32.251< 0.1%
 
31.829999921< 0.1%
 
31.629999161< 0.1%
 
30.840000151< 0.1%
 
30.540000921< 0.1%
 
30.370000841< 0.1%
 

KIL
Real number (ℝ≥0)

Distinct count450
Unique (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.306274719855907
Minimum0.0
Maximum28.459999084472656
Zeros1
Zeros (%)< 0.1%
Memory size51.5 KiB
2020-08-25T00:00:16.364585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.539999962
Q13.579999924
median5.75
Q38.420000076
95-th percentile13.01399999
Maximum28.45999908
Range28.45999908
Interquartile range (IQR)4.840000153

Descriptive statistics

Standard deviation3.605407315
Coefficient of variation (CV)0.5717174521
Kurtosis1.054855367
Mean6.30627472
Median Absolute Deviation (MAD)2.329999924
Skewness0.9032573382
Sum41457.45001
Variance12.99896191
2020-08-25T00:00:16.473006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.579999924460.7%
 
3.960000038430.7%
 
5.039999962410.6%
 
3.210000038410.6%
 
4.789999962400.6%
 
4.210000038400.6%
 
3.880000114390.6%
 
5.829999924390.6%
 
6.039999962380.6%
 
6.420000076380.6%
 
5.880000114380.6%
 
4.75380.6%
 
4.039999962370.6%
 
4.25370.6%
 
3.369999886370.6%
 
3370.6%
 
3.829999924370.6%
 
3.670000076360.5%
 
4.119999886360.5%
 
4.380000114360.5%
 
4.420000076360.5%
 
5.409999847360.5%
 
4.829999924360.5%
 
4.5360.5%
 
5.460000038350.5%
 
Other values (425)562185.5%
 
ValueCountFrequency (%) 
01< 0.1%
 
0.079999998211< 0.1%
 
0.12999999521< 0.1%
 
0.170000001840.1%
 
0.209999993450.1%
 
0.2540.1%
 
0.28999999171< 0.1%
 
0.330000013170.1%
 
0.370000004850.1%
 
0.419999986980.1%
 
ValueCountFrequency (%) 
28.459999081< 0.1%
 
24.329999921< 0.1%
 
24.170000081< 0.1%
 
23.540000921< 0.1%
 
22.879999161< 0.1%
 
22.629999161< 0.1%
 
21.590000151< 0.1%
 
21.090000151< 0.1%
 
20.790000921< 0.1%
 
20.620000841< 0.1%
 

SHA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count596
Unique (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.456880129352417
Minimum0.12999999523162842
Maximum37.540000915527344
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:16.585810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1299999952
5-th percentile3.460000038
Q16.75
median9.960000038
Q313.53999996
95-th percentile19.37999916
Maximum37.54000092
Range37.41000092
Interquartile range (IQR)6.789999962

Descriptive statistics

Standard deviation4.935738734
Coefficient of variation (CV)0.4720087324
Kurtosis0.4679035169
Mean10.45688013
Median Absolute Deviation (MAD)3.329999924
Skewness0.6505155202
Sum68743.52997
Variance24.36151685
2020-08-25T00:00:16.695747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.460000038340.5%
 
9.920000076340.5%
 
10320.5%
 
10.53999996310.5%
 
12.61999989300.5%
 
8.960000038300.5%
 
9.960000038290.4%
 
8.380000114290.4%
 
10.96000004290.4%
 
6.960000038290.4%
 
7280.4%
 
7.920000076280.4%
 
6280.4%
 
8.829999924280.4%
 
9.710000038270.4%
 
9.170000076270.4%
 
9.75270.4%
 
8.289999962270.4%
 
13.88000011270.4%
 
10.03999996270.4%
 
11.11999989260.4%
 
9.380000114260.4%
 
6.170000076260.4%
 
8.119999886260.4%
 
10.57999992260.4%
 
Other values (571)586389.2%
 
ValueCountFrequency (%) 
0.12999999521< 0.1%
 
0.17000000181< 0.1%
 
0.251< 0.1%
 
0.41999998691< 0.1%
 
0.51< 0.1%
 
0.54000002151< 0.1%
 
0.751< 0.1%
 
0.87000000482< 0.1%
 
0.92000001672< 0.1%
 
0.95999997851< 0.1%
 
ValueCountFrequency (%) 
37.540000921< 0.1%
 
33.630001071< 0.1%
 
33.040000921< 0.1%
 
32.419998171< 0.1%
 
30.090000151< 0.1%
 
302< 0.1%
 
29.540000921< 0.1%
 
29.459999081< 0.1%
 
28.620000841< 0.1%
 
28.51< 0.1%
 

BIR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count461
Unique (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.092254336472291
Minimum0.0
Maximum26.15999984741211
Zeros7
Zeros (%)0.1%
Memory size51.5 KiB
2020-08-25T00:00:16.977660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.210000038
Q14
median6.829999924
Q39.670000076
95-th percentile13.92000008
Maximum26.15999985
Range26.15999985
Interquartile range (IQR)5.670000076

Descriptive statistics

Standard deviation3.968683114
Coefficient of variation (CV)0.55957992
Kurtosis0.1000508838
Mean7.092254336
Median Absolute Deviation (MAD)2.829999924
Skewness0.5164256173
Sum46624.48001
Variance15.75044566
2020-08-25T00:00:17.084895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.039999962380.6%
 
8.289999962350.5%
 
7.170000076350.5%
 
4.289999962340.5%
 
8.670000076330.5%
 
5.289999962330.5%
 
5.789999962320.5%
 
4.039999962320.5%
 
6.789999962310.5%
 
8.079999924310.5%
 
4.5310.5%
 
3.710000038310.5%
 
5.090000153310.5%
 
6.25310.5%
 
8.710000038300.5%
 
6.960000038300.5%
 
5.659999847300.5%
 
9.590000153290.4%
 
6.630000114290.4%
 
7.5290.4%
 
5.460000038290.4%
 
4.75290.4%
 
4.170000076290.4%
 
6.539999962290.4%
 
5.880000114290.4%
 
Other values (436)579488.1%
 
ValueCountFrequency (%) 
070.1%
 
0.0399999991170.1%
 
0.0799999982140.1%
 
0.1299999952100.2%
 
0.170000001850.1%
 
0.209999993470.1%
 
0.25100.2%
 
0.2899999917160.2%
 
0.3300000131120.2%
 
0.370000004880.1%
 
ValueCountFrequency (%) 
26.159999851< 0.1%
 
26.040000921< 0.1%
 
24.251< 0.1%
 
231< 0.1%
 
22.829999921< 0.1%
 
22.51< 0.1%
 
21.340000151< 0.1%
 
20.670000082< 0.1%
 
20.579999922< 0.1%
 
20.329999921< 0.1%
 

DUB
Real number (ℝ≥0)

Distinct count580
Unique (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.79683450566832
Minimum0.0
Maximum30.3700008392334
Zeros1
Zeros (%)< 0.1%
Memory size51.5 KiB
2020-08-25T00:00:17.199536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.789999962
Q16
median9.210000038
Q312.96000004
95-th percentile19.05400057
Maximum30.37000084
Range30.37000084
Interquartile range (IQR)6.960000038

Descriptive statistics

Standard deviation4.97727232
Coefficient of variation (CV)0.5080490353
Kurtosis0.2128562254
Mean9.796834506
Median Absolute Deviation (MAD)3.380000114
Skewness0.6457931355
Sum64404.39004
Variance24.77323975
2020-08-25T00:00:17.318053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8.380000114350.5%
 
9.420000076320.5%
 
8.5320.5%
 
7.869999886310.5%
 
10.88000011300.5%
 
10300.5%
 
8.289999962300.5%
 
8.710000038290.4%
 
11.07999992290.4%
 
8.579999924290.4%
 
4.789999962280.4%
 
5.960000038280.4%
 
8.869999886280.4%
 
7.119999886270.4%
 
7.409999847270.4%
 
7.960000038270.4%
 
9.960000038270.4%
 
5.909999847270.4%
 
6.5270.4%
 
6.579999924260.4%
 
7.170000076260.4%
 
5.5260.4%
 
11.28999996260.4%
 
9.210000038260.4%
 
9.329999924260.4%
 
Other values (555)586589.2%
 
ValueCountFrequency (%) 
01< 0.1%
 
0.20999999341< 0.1%
 
0.37000000481< 0.1%
 
0.41999998692< 0.1%
 
0.46000000831< 0.1%
 
0.54000002153< 0.1%
 
0.57999998331< 0.1%
 
0.62999999522< 0.1%
 
0.70999997852< 0.1%
 
0.751< 0.1%
 
ValueCountFrequency (%) 
30.370000841< 0.1%
 
29.579999921< 0.1%
 
29.540000921< 0.1%
 
29.170000081< 0.1%
 
28.909999851< 0.1%
 
28.840000151< 0.1%
 
28.790000921< 0.1%
 
28.620000841< 0.1%
 
28.51< 0.1%
 
28.159999852< 0.1%
 

CLA
Real number (ℝ≥0)

Distinct count534
Unique (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.494420452017915
Minimum0.0
Maximum31.07999992370605
Zeros6
Zeros (%)0.1%
Memory size51.5 KiB
2020-08-25T00:00:17.434645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.919999957
Q15.090000153
median8.079999924
Q311.42000008
95-th percentile16.48799973
Maximum31.07999992
Range31.07999992
Interquartile range (IQR)6.329999924

Descriptive statistics

Standard deviation4.499000038
Coefficient of variation (CV)0.5296417882
Kurtosis0.2220181425
Mean8.494420452
Median Absolute Deviation (MAD)3.130000114
Skewness0.5572132679
Sum55842.32005
Variance20.24100134
2020-08-25T00:00:17.555038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7400.6%
 
7.579999924350.5%
 
5.75320.5%
 
4.5320.5%
 
9.829999924320.5%
 
5.659999847320.5%
 
7.75320.5%
 
6.579999924310.5%
 
10.63000011300.5%
 
5.539999962290.4%
 
6.170000076290.4%
 
6.380000114280.4%
 
6.289999962280.4%
 
5.880000114280.4%
 
8.210000038280.4%
 
6.75280.4%
 
8.039999962280.4%
 
5.409999847280.4%
 
7.619999886280.4%
 
10.07999992280.4%
 
9.039999962280.4%
 
4.75270.4%
 
11.82999992270.4%
 
9.210000038270.4%
 
4.039999962270.4%
 
Other values (509)583288.7%
 
ValueCountFrequency (%) 
060.1%
 
0.039999999112< 0.1%
 
0.079999998213< 0.1%
 
0.12999999521< 0.1%
 
0.17000000183< 0.1%
 
0.209999993440.1%
 
0.253< 0.1%
 
0.28999999171< 0.1%
 
0.33000001313< 0.1%
 
0.370000004850.1%
 
ValueCountFrequency (%) 
31.079999921< 0.1%
 
30.629999161< 0.1%
 
28.209999081< 0.1%
 
26.670000081< 0.1%
 
26.340000151< 0.1%
 
26.079999921< 0.1%
 
25.920000081< 0.1%
 
25.209999081< 0.1%
 
25.040000921< 0.1%
 
24.370000841< 0.1%
 

MUL
Real number (ℝ≥0)

Distinct count503
Unique (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.495818376903806
Minimum0.0
Maximum25.8799991607666
Zeros1
Zeros (%)< 0.1%
Memory size51.5 KiB
2020-08-25T00:00:17.672847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.329999924
Q15.369999886
median8.170000076
Q311.21000004
95-th percentile15.86999989
Maximum25.87999916
Range25.87999916
Interquartile range (IQR)5.840000153

Descriptive statistics

Standard deviation4.167778006
Coefficient of variation (CV)0.490568162
Kurtosis0.1078950695
Mean8.495818377
Median Absolute Deviation (MAD)2.909999847
Skewness0.4981264277
Sum55851.51001
Variance17.37037351
2020-08-25T00:00:17.778473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9350.5%
 
7.210000038350.5%
 
9.380000114340.5%
 
7.119999886330.5%
 
6.789999962330.5%
 
7.829999924330.5%
 
7.460000038330.5%
 
8.829999924320.5%
 
6.579999924320.5%
 
6.869999886320.5%
 
4.460000038320.5%
 
5.75310.5%
 
8.75310.5%
 
5.710000038310.5%
 
4.380000114310.5%
 
7.170000076310.5%
 
9.539999962300.5%
 
6.210000038300.5%
 
8.789999962300.5%
 
7.869999886300.5%
 
6.5300.5%
 
6.130000114300.5%
 
9.329999924300.5%
 
10.21000004300.5%
 
8290.4%
 
Other values (478)578688.0%
 
ValueCountFrequency (%) 
01< 0.1%
 
0.17000000181< 0.1%
 
0.20999999341< 0.1%
 
0.28999999173< 0.1%
 
0.33000001311< 0.1%
 
0.37000000483< 0.1%
 
0.41999998692< 0.1%
 
0.460000008340.1%
 
0.540.1%
 
0.540000021540.1%
 
ValueCountFrequency (%) 
25.879999161< 0.1%
 
25.799999241< 0.1%
 
25.620000841< 0.1%
 
25.290000921< 0.1%
 
251< 0.1%
 
24.870000841< 0.1%
 
24.790000921< 0.1%
 
24.459999081< 0.1%
 
23.629999161< 0.1%
 
23.579999921< 0.1%
 

CLO
Real number (ℝ≥0)

Distinct count533
Unique (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.707268028541298
Minimum0.03999999910593033
Maximum28.209999084472656
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:17.887688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.03999999911
5-th percentile2.079999924
Q15.329999924
median8.289999962
Q311.63000011
95-th percentile16.75
Maximum28.20999908
Range28.16999909
Interquartile range (IQR)6.300000191

Descriptive statistics

Standard deviation4.503614765
Coefficient of variation (CV)0.5172247771
Kurtosis0.0504678995
Mean8.707268029
Median Absolute Deviation (MAD)3.119999886
Skewness0.545931601
Sum57241.58002
Variance20.28254595
2020-08-25T00:00:17.994403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8.579999924400.6%
 
10.82999992350.5%
 
6.869999886330.5%
 
8.119999886330.5%
 
4.380000114320.5%
 
7.289999962320.5%
 
7310.5%
 
7.579999924310.5%
 
8.539999962310.5%
 
5.75300.5%
 
10.34000015300.5%
 
6.039999962290.4%
 
8.75290.4%
 
8.630000114290.4%
 
5.369999886290.4%
 
7.380000114290.4%
 
7.329999924280.4%
 
7.119999886280.4%
 
7.710000038280.4%
 
10.17000008280.4%
 
6.079999924280.4%
 
5.829999924280.4%
 
4.079999924280.4%
 
6.170000076270.4%
 
7.210000038270.4%
 
Other values (508)582188.5%
 
ValueCountFrequency (%) 
0.039999999113< 0.1%
 
0.129999995250.1%
 
0.20999999341< 0.1%
 
0.251< 0.1%
 
0.28999999173< 0.1%
 
0.33000001312< 0.1%
 
0.37000000483< 0.1%
 
0.460000008340.1%
 
0.53< 0.1%
 
0.54000002151< 0.1%
 
ValueCountFrequency (%) 
28.209999081< 0.1%
 
27.290000921< 0.1%
 
26.379999161< 0.1%
 
25.370000841< 0.1%
 
25.209999081< 0.1%
 
251< 0.1%
 
24.579999921< 0.1%
 
24.409999851< 0.1%
 
24.251< 0.1%
 
24.170000081< 0.1%
 

BEL
Real number (ℝ≥0)

Distinct count687
Unique (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.121006990434697
Minimum0.12999999523162842
Maximum42.38000106811523
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:18.109891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1299999952
5-th percentile4.710000038
Q18.710000038
median12.5
Q316.87999916
95-th percentile23.55400057
Maximum42.38000107
Range42.25000107
Interquartile range (IQR)8.169999123

Descriptive statistics

Standard deviation5.83503699
Coefficient of variation (CV)0.4447095406
Kurtosis0.1549214954
Mean13.12100699
Median Absolute Deviation (MAD)4.079999924
Skewness0.5559510159
Sum86257.49996
Variance34.04765668
2020-08-25T00:00:18.212402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10.21000004280.4%
 
12.86999989270.4%
 
11.42000008270.4%
 
16.37999916270.4%
 
9.130000114260.4%
 
10.25260.4%
 
12.5250.4%
 
8.630000114250.4%
 
12.32999992250.4%
 
12.96000004240.4%
 
11.07999992240.4%
 
7.670000076240.4%
 
10.71000004240.4%
 
12.07999992240.4%
 
7.210000038230.3%
 
9.170000076230.3%
 
9.289999962230.3%
 
13230.3%
 
11.71000004230.3%
 
11.92000008230.3%
 
12.53999996230.3%
 
15.34000015230.3%
 
7.170000076230.3%
 
12.11999989230.3%
 
7.710000038220.3%
 
Other values (662)596690.8%
 
ValueCountFrequency (%) 
0.12999999521< 0.1%
 
0.57999998331< 0.1%
 
0.67000001671< 0.1%
 
0.70999997851< 0.1%
 
1.0800000431< 0.1%
 
1.2100000381< 0.1%
 
1.251< 0.1%
 
1.51< 0.1%
 
1.5800000431< 0.1%
 
1.6299999952< 0.1%
 
ValueCountFrequency (%) 
42.380001071< 0.1%
 
39.040000921< 0.1%
 
38.959999081< 0.1%
 
38.200000761< 0.1%
 
36.950000761< 0.1%
 
35.299999241< 0.1%
 
35.080001831< 0.1%
 
34.919998171< 0.1%
 
34.830001831< 0.1%
 
34.540000921< 0.1%
 

target
Real number (ℝ≥0)

Distinct count779
Unique (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.59946153023013
Minimum0.6700000166893005
Maximum42.540000915527344
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2020-08-25T00:00:18.327067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6700000167
5-th percentile5.612500048
Q110.71000004
median15
Q319.82999992
95-th percentile27.67000008
Maximum42.54000092
Range41.8700009
Interquartile range (IQR)9.119999886

Descriptive statistics

Standard deviation6.697856647
Coefficient of variation (CV)0.4293646056
Kurtosis0.06296712637
Mean15.59946153
Median Absolute Deviation (MAD)4.56499958
Skewness0.5121167333
Sum102550.8601
Variance44.86128367
2020-08-25T00:00:18.430269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
14.67000008270.4%
 
11.96000004260.4%
 
12.92000008250.4%
 
13.21000004250.4%
 
13.78999996240.4%
 
16.07999992240.4%
 
17.75230.3%
 
14.42000008230.3%
 
15.75230.3%
 
18.62999916220.3%
 
10.78999996220.3%
 
15.82999992220.3%
 
11.42000008220.3%
 
12.96000004220.3%
 
13.67000008220.3%
 
10.34000015210.3%
 
14.5210.3%
 
14.92000008210.3%
 
16.79000092210.3%
 
16.17000008210.3%
 
15.78999996210.3%
 
12.38000011210.3%
 
17.87999916210.3%
 
8.710000038210.3%
 
16.70999908210.3%
 
Other values (754)601291.5%
 
ValueCountFrequency (%) 
0.67000001671< 0.1%
 
1.751< 0.1%
 
21< 0.1%
 
2.0399999622< 0.1%
 
2.0799999241< 0.1%
 
2.1300001141< 0.1%
 
2.1700000761< 0.1%
 
2.2100000382< 0.1%
 
2.251< 0.1%
 
2.2899999622< 0.1%
 
ValueCountFrequency (%) 
42.540000921< 0.1%
 
41.459999081< 0.1%
 
41.251< 0.1%
 
40.409999851< 0.1%
 
40.369998931< 0.1%
 
40.119998931< 0.1%
 
40.080001831< 0.1%
 
38.790000921< 0.1%
 
38.659999851< 0.1%
 
38.251< 0.1%
 

Interactions

2020-08-24T23:59:40.629292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:40.791682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:40.936903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.086015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.238504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.389059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.542727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.689055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.840027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:41.980846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.124775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.272285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.412762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.553594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.691061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.832606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:42.972888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:43.278515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:43.414883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:43.557431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:43.697833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:43.843863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:43.987598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.131312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.269690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.413987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.553138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.689935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.828704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:44.962486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.095741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.245085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.387461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.535982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.681453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.831243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:45.979168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.122067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.270551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.413969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.560385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.707553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.848847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:46.988788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:47.126165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:47.265273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:47.604940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:47.750346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:47.902192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.054461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.206021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.359457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.503882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.656765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.800029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:48.959599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:49.118171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:49.267576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:49.421224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:49.562959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:49.706338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:49.858515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.005033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.153296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.308025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.469527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.627186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.770741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:50.924836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:51.067925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:51.214801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:51.362826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:51.516421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:51.668241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:51.999229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:52.140058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:52.295661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:52.444614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:52.604507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:52.755546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:52.907460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.067618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.212951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.373026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.519188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.666308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.822361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:53.968307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.112397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.252255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.392652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.537529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.672704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.811793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:54.954661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.098984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.238944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.371985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.516588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.649689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.786366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:55.922170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:56.237590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:56.371873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:56.548372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:56.714422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:56.886483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.034782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.188340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.338836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.501586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.660644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.803420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:57.954291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.100222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.248476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.394497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.541675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.685790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.826581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:58.967912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.110160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.246877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.385746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.533988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.676038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.824986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:59:59.958657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:00.102459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:00.236304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:00.373224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:00.689109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:00.821211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:00.953276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.087249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.224248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.378203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.521386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.677482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.826314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:01.971636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.119432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.256594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.401145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.543814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.688589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.830973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:02.969283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.111511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.248132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.383252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.528754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.673604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.818106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:03.966075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:04.114357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:04.259400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:04.397031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:04.542653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:04.683195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.006111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.155515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.300661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.440730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.575921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.717463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.859364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:05.994039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:06.137146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:06.288040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:06.440215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:06.581435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:06.717739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:06.866734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.004421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.148556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.285947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.419462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.553586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.685300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.815892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:07.954838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.092275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.232847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.373052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.512703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.656362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.799989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:08.946340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:09.266878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:09.411821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:09.553458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:09.690109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:09.824305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:09.957816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.090070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.228215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.360921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.499717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.637815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.775541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:10.913022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.052737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.198435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.332808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.467330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.600293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.731639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.859674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:11.984196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.109011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.248176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.387305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.532134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.682482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.822869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:12.964772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:13.093799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:13.233217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:13.539506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:13.673731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:13.809603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:13.941106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:14.071822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:14.199488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:00:18.565316image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T00:00:18.822601image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T00:00:19.081254image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T00:00:19.344373image/svg+xmlMatplotlib v3.3.1, 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

2020-08-25T00:00:14.461678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:00:14.809556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

yearmonthdayRPTVALROSKILSHABIRDUBCLAMULCLOBELtarget
061.01.01.015.0414.96000013.179.2913.969.8713.6710.2510.8312.5818.50000015.040000
161.01.02.014.7116.87999910.836.5012.627.6711.5010.049.799.6717.54000113.830000
261.01.03.018.5016.87999912.3310.1311.176.1711.258.048.507.6712.75000012.710000
361.01.04.010.586.63000011.754.584.542.888.631.795.835.885.46000010.880000
461.01.05.013.3313.25000011.426.1710.718.2111.926.5410.9210.3412.92000011.830000
561.01.06.013.218.1200009.966.675.374.5010.674.427.177.508.12000013.170000
661.01.07.013.5014.2900009.504.9612.298.339.179.297.587.9613.96000013.790000
761.01.08.010.969.7500007.625.919.627.2914.297.629.2510.4616.62000116.459999
861.01.09.012.5810.83000010.004.7510.376.798.0410.137.799.0813.04000015.370000
961.01.010.013.3711.12000019.508.339.716.5411.427.798.549.008.58000011.830000

Last rows

yearmonthdayRPTVALROSKILSHABIRDUBCLAMULCLOBELtarget
656478.012.022.06.2100007.38000013.0800002.547.585.332.468.385.095.0400009.92000011.000000
656578.012.023.016.62000113.29000022.2099999.5014.2913.0816.5017.1612.7112.00000018.50000021.500000
656678.012.024.08.6700005.63000012.1200004.795.095.9112.259.2510.8311.71000011.92000031.709999
656778.012.025.07.2100006.5800007.8300002.674.794.588.710.755.215.2500001.21000013.960000
656878.012.026.013.83000011.87000010.3400002.376.964.291.963.793.043.0800004.79000011.960000
656978.012.027.017.58000016.95999917.6200018.0813.2111.6714.4615.5914.0414.00000017.20999940.080002
657078.012.028.013.2100005.46000013.4600005.008.129.4214.3316.2515.2518.04999921.79000141.459999
657178.012.029.014.00000010.29000014.4200008.719.7110.5419.1712.4614.5016.42000018.87999929.580000
657278.012.030.018.50000014.04000021.2900019.1312.759.7118.0812.8712.4612.12000014.67000028.790001
657378.012.031.020.33000017.41000027.2900019.5912.0810.1319.2511.6311.5811.38000012.08000022.080000