Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.4 KiB
Average record size in memory64.3 B

Variable types

NUM8

Reproduction

Analysis started2020-08-25 00:01:38.197097
Analysis finished2020-08-25 00:01:48.301436
Duration10.1 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

temperature_diff_2m_25m has 39 (7.8%) zeros Zeros

Variables

cars_per_hour
Real number (ℝ≥0)

Distinct count456
Unique (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.013101393699646
Minimum3.8066599369049072
Maximum8.352080345153809
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:01:48.351917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3.806659937
5-th percentile4.819875073
Q16.260525107
median7.523750067
Q37.82784009
95-th percentile8.233010244
Maximum8.352080345
Range4.545420408
Interquartile range (IQR)1.567314982

Descriptive statistics

Standard deviation1.094036897
Coefficient of variation (CV)0.1559990132
Kurtosis-0.346298305
Mean7.013101394
Median Absolute Deviation (MAD)0.4936699867
Skewness-0.913561164
Sum3506.550697
Variance1.196916732
2020-08-25T00:01:48.462781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6.43294000630.6%
 
7.90175008830.6%
 
5.48479986230.6%
 
5.12989997930.6%
 
7.85516023620.4%
 
7.65112018620.4%
 
6.6398801820.4%
 
4.54329013820.4%
 
4.47734022120.4%
 
5.31321001120.4%
 
7.52510023120.4%
 
5.74300003120.4%
 
7.54327011120.4%
 
7.64347982420.4%
 
7.59336996120.4%
 
4.95583009720.4%
 
7.43602991120.4%
 
5.71043014520.4%
 
5.6276202220.4%
 
7.76683998120.4%
 
7.55276012420.4%
 
7.29028987920.4%
 
6.1612100620.4%
 
5.42495012320.4%
 
4.70047998420.4%
 
Other values (431)44689.2%
 
ValueCountFrequency (%) 
3.80665993710.2%
 
3.97028994610.2%
 
4.11086988410.2%
 
4.30406999620.4%
 
4.36945009210.2%
 
4.45434999510.2%
 
4.47734022120.4%
 
4.54329013820.4%
 
4.56435012810.2%
 
4.59511995310.2%
 
ValueCountFrequency (%) 
8.35208034510.2%
 
8.34068965910.2%
 
8.33471012110.2%
 
8.32579040510.2%
 
8.31532001510.2%
 
8.31385040310.2%
 
8.31361007710.2%
 
8.30720996910.2%
 
8.30597972910.2%
 
8.29255008710.2%
 

temperature_at_2m
Real number (ℝ)

Distinct count218
Unique (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7869999919235706
Minimum-19.0
Maximum21.899999618530273
Zeros4
Zeros (%)0.8%
Memory size4.0 KiB
2020-08-25T00:01:48.592301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile-10.82000017
Q1-3.12499994
median1
Q34.224999905
95-th percentile11.60000038
Maximum21.89999962
Range40.89999962
Interquartile range (IQR)7.349999845

Descriptive statistics

Standard deviation6.386757583
Coefficient of variation (CV)8.115321027
Kurtosis0.5215847788
Mean0.7869999919
Median Absolute Deviation (MAD)3.799999952
Skewness-0.07115680333
Sum393.499996
Variance40.79067242
2020-08-25T00:01:48.715288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.100000001591.8%
 
3.40000009591.8%
 
0.20000000371.4%
 
2.09999990571.4%
 
-2.79999995261.2%
 
161.2%
 
4.19999980961.2%
 
1.561.2%
 
-4.40000009561.2%
 
3.59999990561.2%
 
3.29999995261.2%
 
6.551.0%
 
-4.80000019151.0%
 
1.39999997651.0%
 
4.09999990551.0%
 
1.70000004851.0%
 
0.699999988151.0%
 
0.800000011951.0%
 
2.40000009551.0%
 
-1.29999995251.0%
 
1.60000002451.0%
 
251.0%
 
-0.20000000351.0%
 
2.20000004851.0%
 
340.8%
 
Other values (193)35771.4%
 
ValueCountFrequency (%) 
-1910.2%
 
-18.6000003810.2%
 
-15.510.2%
 
-1510.2%
 
-14.8999996210.2%
 
-14.6000003810.2%
 
-14.1999998110.2%
 
-13.510.2%
 
-13.3999996220.4%
 
-13.1000003830.6%
 
ValueCountFrequency (%) 
21.8999996210.2%
 
19.7999992410.2%
 
18.2000007610.2%
 
17.7999992410.2%
 
16.7999992410.2%
 
16.2999992410.2%
 
15.8000001920.4%
 
15.1000003810.2%
 
1510.2%
 
14.8999996210.2%
 

wind_speed
Real number (ℝ≥0)

Distinct count79
Unique (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2111999925971033
Minimum0.30000001192092896
Maximum9.899999618530273
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:01:48.845341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3000000119
5-th percentile0.8000000119
Q11.700000048
median3
Q34.300000191
95-th percentile6.609999919
Maximum9.899999619
Range9.599999607
Interquartile range (IQR)2.600000143

Descriptive statistics

Standard deviation1.86061234
Coefficient of variation (CV)0.5794134106
Kurtosis0.5937840127
Mean3.211199993
Median Absolute Deviation (MAD)1.299999952
Skewness0.8238986084
Sum1605.599996
Variance3.461878279
2020-08-25T00:01:48.946970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.5183.6%
 
0.8000000119163.2%
 
2.799999952142.8%
 
3.900000095132.6%
 
4.199999809132.6%
 
3.099999905132.6%
 
4.300000191122.4%
 
1.899999976122.4%
 
2.200000048122.4%
 
2.099999905122.4%
 
4122.4%
 
3.599999905112.2%
 
2.5112.2%
 
1.799999952112.2%
 
2.599999905112.2%
 
3.200000048102.0%
 
1.100000024102.0%
 
1.399999976102.0%
 
1.700000048102.0%
 
3.5102.0%
 
1.600000024102.0%
 
2102.0%
 
4.90000009591.8%
 
2.40000009581.6%
 
181.6%
 
Other values (54)21442.8%
 
ValueCountFrequency (%) 
0.300000011910.2%
 
0.40000000610.2%
 
0.571.4%
 
0.600000023871.4%
 
0.699999988181.6%
 
0.8000000119163.2%
 
0.899999976271.4%
 
181.6%
 
1.100000024102.0%
 
1.20000004861.2%
 
ValueCountFrequency (%) 
9.89999961910.2%
 
9.39999961940.8%
 
8.89999961910.2%
 
8.60000038110.2%
 
8.30000019110.2%
 
8.10000038110.2%
 
820.4%
 
7.90000009510.2%
 
7.69999980910.2%
 
7.59999990510.2%
 

temperature_diff_2m_25m
Real number (ℝ)

ZEROS

Distinct count62
Unique (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15480000133812427
Minimum-5.0
Maximum4.0
Zeros39
Zeros (%)7.8%
Memory size4.0 KiB
2020-08-25T00:01:49.054454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-1.299999952
Q1-0.200000003
median0.1000000015
Q30.6000000238
95-th percentile1.700000048
Maximum4
Range9
Interquartile range (IQR)0.8000000268

Descriptive statistics

Standard deviation0.9886312497
Coefficient of variation (CV)6.386506726
Kurtosis4.622818138
Mean0.1548000013
Median Absolute Deviation (MAD)0.3000000045
Skewness-0.3071873536
Sum77.40000067
Variance0.9773917479
2020-08-25T00:01:49.160477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.10000000156913.8%
 
0.1000000015448.8%
 
-0.200000003438.6%
 
0397.8%
 
0.200000003346.8%
 
-0.3000000119193.8%
 
0.400000006163.2%
 
1.100000024163.2%
 
0.3000000119163.2%
 
0.6000000238153.0%
 
0.5142.8%
 
0.6999999881132.6%
 
1.200000048112.2%
 
0.8000000119102.0%
 
-0.40000000691.8%
 
1.29999995291.8%
 
-0.600000023881.6%
 
-0.581.6%
 
1.39999997671.4%
 
1.70000004871.4%
 
-0.899999976271.4%
 
-0.800000011961.2%
 
-0.699999988161.2%
 
0.899999976251.0%
 
151.0%
 
Other values (37)6412.8%
 
ValueCountFrequency (%) 
-510.2%
 
-4.510.2%
 
-3.40000009510.2%
 
-3.20000004810.2%
 
-3.09999990520.4%
 
-2.79999995210.2%
 
-2.70000004820.4%
 
-2.530.6%
 
-2.20000004810.2%
 
-220.4%
 
ValueCountFrequency (%) 
410.2%
 
3.70000004820.4%
 
3.59999990510.2%
 
3.20000004810.2%
 
3.09999990510.2%
 
2.79999995210.2%
 
2.70000004810.2%
 
2.59999990510.2%
 
2.510.2%
 
2.40000009520.4%
 

wind_direction
Real number (ℝ≥0)

Distinct count354
Unique (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.0909999256134
Minimum5.0
Maximum358.0
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:01:49.263323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile38.09499855
Q171.27500153
median101.8499985
Q3216.8500023
95-th percentile276.0399994
Maximum358
Range353
Interquartile range (IQR)145.5750008

Descriptive statistics

Standard deviation85.51077345
Coefficient of variation (CV)0.6060682361
Kurtosis-1.002653125
Mean141.0909999
Median Absolute Deviation (MAD)57.84999847
Skewness0.4614759921
Sum70545.49996
Variance7312.092376
2020-08-25T00:01:49.385657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8071.4%
 
7871.4%
 
7771.4%
 
7561.2%
 
6061.2%
 
7961.2%
 
7351.0%
 
8151.0%
 
8251.0%
 
6651.0%
 
20640.8%
 
5540.8%
 
5940.8%
 
8640.8%
 
23040.8%
 
8540.8%
 
26030.6%
 
24330.6%
 
17230.6%
 
20030.6%
 
22830.6%
 
23230.6%
 
22030.6%
 
4430.6%
 
8330.6%
 
Other values (329)39078.0%
 
ValueCountFrequency (%) 
510.2%
 
910.2%
 
9.39999961910.2%
 
1010.2%
 
1110.2%
 
14.3000001910.2%
 
1510.2%
 
1620.4%
 
16.6000003810.2%
 
1710.2%
 
ValueCountFrequency (%) 
35810.2%
 
357.510.2%
 
35410.2%
 
35310.2%
 
35010.2%
 
34510.2%
 
344.700012210.2%
 
341.100006110.2%
 
34010.2%
 
337.799987810.2%
 

hour_of_day
Real number (ℝ≥0)

Distinct count24
Unique (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.446
Minimum1.0
Maximum24.0
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:01:49.514050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q318
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.85748746
Coefficient of variation (CV)0.5509792271
Kurtosis-1.167314917
Mean12.446
Median Absolute Deviation (MAD)6
Skewness-0.01976810176
Sum6223
Variance47.02513427
2020-08-25T00:01:49.626517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8285.6%
 
14265.2%
 
19265.2%
 
3244.8%
 
15244.8%
 
13234.6%
 
2234.6%
 
21224.4%
 
17224.4%
 
16214.2%
 
11214.2%
 
24214.2%
 
1204.0%
 
7204.0%
 
4204.0%
 
18193.8%
 
22193.8%
 
5193.8%
 
9193.8%
 
10183.6%
 
12183.6%
 
23173.4%
 
20163.2%
 
6142.8%
 
ValueCountFrequency (%) 
1204.0%
 
2234.6%
 
3244.8%
 
4204.0%
 
5193.8%
 
6142.8%
 
7204.0%
 
8285.6%
 
9193.8%
 
10183.6%
 
ValueCountFrequency (%) 
24214.2%
 
23173.4%
 
22193.8%
 
21224.4%
 
20163.2%
 
19265.2%
 
18193.8%
 
17224.4%
 
16214.2%
 
15244.8%
 

day
Real number (ℝ≥0)

Distinct count273
Unique (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.426
Minimum32.0
Maximum608.0
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:01:49.747731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile51
Q1119.5
median212
Q3511.25
95-th percentile583
Maximum608
Range576
Interquartile range (IQR)391.75

Descriptive statistics

Standard deviation200.5807713
Coefficient of variation (CV)0.6440720149
Kurtosis-1.719374817
Mean311.426
Median Absolute Deviation (MAD)178.5
Skewness0.05382143689
Sum155713
Variance40232.64582
2020-08-25T00:01:49.870112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5161.2%
 
15561.2%
 
21251.0%
 
50751.0%
 
39740.8%
 
6140.8%
 
57440.8%
 
12840.8%
 
19640.8%
 
8240.8%
 
47340.8%
 
9040.8%
 
46040.8%
 
55740.8%
 
12440.8%
 
17340.8%
 
53340.8%
 
48040.8%
 
47040.8%
 
44140.8%
 
44530.6%
 
8530.6%
 
45030.6%
 
52530.6%
 
40930.6%
 
Other values (248)39979.8%
 
ValueCountFrequency (%) 
3220.4%
 
3320.4%
 
3420.4%
 
3820.4%
 
3910.2%
 
4010.2%
 
4220.4%
 
4320.4%
 
4420.4%
 
4510.2%
 
ValueCountFrequency (%) 
60810.2%
 
60610.2%
 
60510.2%
 
60420.4%
 
60230.6%
 
60010.2%
 
59910.2%
 
59520.4%
 
59410.2%
 
59310.2%
 

target
Real number (ℝ≥0)

Distinct count117
Unique (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.270929131269455
Minimum0.6931499838829039
Maximum5.393630027770996
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:01:50.000570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6931499839
5-th percentile1.791759968
Q12.708050013
median3.295840025
Q33.871200085
95-th percentile4.683047366
Maximum5.393630028
Range4.700480044
Interquartile range (IQR)1.163150072

Descriptive statistics

Standard deviation0.8868084459
Coefficient of variation (CV)0.2711182084
Kurtosis0.2710703101
Mean3.270929131
Median Absolute Deviation (MAD)0.5753600597
Skewness-0.2930626529
Sum1635.464566
Variance0.7864292197
2020-08-25T00:01:50.100912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.044519901173.4%
 
3.401200056163.2%
 
2.995729923153.0%
 
2.564949989142.8%
 
2.484910011142.8%
 
2.079440117142.8%
 
2.302589893132.6%
 
3.295840025132.6%
 
3.135489941132.6%
 
3.258100033132.6%
 
1.945909977132.6%
 
3.218879938122.4%
 
3.178050041122.4%
 
2.63906002112.2%
 
3.091039896102.0%
 
2.944439888102.0%
 
3.33220005102.0%
 
2.19722008791.8%
 
3.36730003491.8%
 
0.693149983981.6%
 
2.70805001381.6%
 
3.73766994581.6%
 
2.83320999171.4%
 
3.87120008571.4%
 
3.43399000271.4%
 
Other values (92)21743.4%
 
ValueCountFrequency (%) 
0.693149983981.6%
 
1.09861004430.6%
 
1.38628995440.8%
 
1.60943996961.2%
 
1.79175996861.2%
 
1.945909977132.6%
 
2.079440117142.8%
 
2.19722008791.8%
 
2.302589893132.6%
 
2.39790010551.0%
 
ValueCountFrequency (%) 
5.39363002810.2%
 
5.38450002710.2%
 
5.26785993610.2%
 
5.25227022220.4%
 
5.17047977410.2%
 
5.15328979510.2%
 
5.08760023110.2%
 
5.05625009510.2%
 
4.95583009710.2%
 
4.941639910.2%
 

Interactions

2020-08-25T00:01:38.552856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:38.678927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:38.818008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.130796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.262936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.398211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.525174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.653394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.778323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:39.921945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.080375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.228210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.377038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.536678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.685463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.833150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:40.977066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.107774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.252026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.381552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.510660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.651924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.782889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:41.913553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.043756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.171618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.311549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.439889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.564626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.703389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.831235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:42.959738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:43.251084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:43.396478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:43.552977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:43.698259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:43.838493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:43.990789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.133682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.276970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.419257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.549324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.695762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.827342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:44.956026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.096310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.228495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.360594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.493557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.630648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.785624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:45.919791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.051556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.196169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.331000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.468360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.597986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.722414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.860282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:46.989612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:47.117950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:47.448283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:47.586713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:47.726246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:01:50.219937image/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.
2020-08-25T00:01:50.446089image/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.
2020-08-25T00:01:50.841548image/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.
2020-08-25T00:01:51.061456image/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

2020-08-25T00:01:47.958872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:48.199398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

cars_per_hourtemperature_at_2mwind_speedtemperature_diff_2m_25mwind_directionhour_of_daydaytarget
07.74414-4.44.20.018.00000019.0116.03.66356
18.03398-5.74.8-0.369.0999989.0506.03.04452
24.70048-13.54.30.280.0000003.095.03.71357
37.525101.43.00.1177.00000022.0161.02.94444
47.762604.15.61.1287.0000007.080.04.06044
57.886835.82.3-0.1200.0000009.033.03.68888
67.815212.71.90.4228.0000007.0129.03.33220
77.777797.18.90.2220.00000015.0155.03.36730
86.891634.12.00.1183.0000009.0132.02.07944
97.677401.15.20.143.09999810.0480.01.94591

Last rows

cars_per_hourtemperature_at_2mwind_speedtemperature_diff_2m_25mwind_directionhour_of_daydaytarget
4907.545396.59.4-0.9250.00000011.0160.01.94591
4916.23832-0.43.30.3215.10000624.0470.01.94591
4924.56435-6.32.02.3223.0000003.0148.02.77259
4936.582032.21.80.164.1999976.0549.02.70805
4945.31321-4.94.20.3353.0000002.0117.01.79176
4955.61677-1.32.8-0.165.1999971.0486.02.30259
4967.71110-5.10.70.360.00000010.099.04.11087
4976.251900.11.00.287.00000024.0111.03.40120
4987.855166.55.2-0.269.00000019.0196.03.68888
4998.245128.61.6-1.0258.79998815.0530.04.17439