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 00:03:41.578884
Analysis finished2020-08-25 00:03:51.391763
Duration9.81 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 49 (9.8%) zeros Zeros

Variables

cars_per_hour
Real number (ℝ≥0)

Distinct count464
Unique (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.973342054367065
Minimum4.127130031585693
Maximum8.348540306091309
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:03:51.443913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4.127130032
5-th percentile4.812180042
Q16.175842404
median7.425360203
Q37.793175101
95-th percentile8.196201801
Maximum8.348540306
Range4.221410275
Interquartile range (IQR)1.617332697

Descriptive statistics

Standard deviation1.087166381
Coefficient of variation (CV)0.1559032058
Kurtosis-0.5827549183
Mean6.973342054
Median Absolute Deviation (MAD)0.5599648952
Skewness-0.8173996438
Sum3486.671027
Variance1.181930741
2020-08-25T00:03:51.546412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4.81218004230.6%
 
6.54965019220.4%
 
7.42536020320.4%
 
7.23634004620.4%
 
6.57507991820.4%
 
5.66642999620.4%
 
5.50533008620.4%
 
5.09987020520.4%
 
7.67554998420.4%
 
7.8272399920.4%
 
8.19589042720.4%
 
7.99563980120.4%
 
4.6821298620.4%
 
7.74803018620.4%
 
5.17614984520.4%
 
5.11198997520.4%
 
7.75319004120.4%
 
7.71244001420.4%
 
4.98361015320.4%
 
6.53087997420.4%
 
5.14749002520.4%
 
5.50938987720.4%
 
4.45434999520.4%
 
7.64826011720.4%
 
6.39358997320.4%
 
Other values (439)44989.8%
 
ValueCountFrequency (%) 
4.12713003210.2%
 
4.31749010110.2%
 
4.39445018810.2%
 
4.45434999520.4%
 
4.49981021910.2%
 
4.53259992610.2%
 
4.54329013810.2%
 
4.55388021510.2%
 
4.56435012810.2%
 
4.58496999710.2%
 
ValueCountFrequency (%) 
8.34854030610.2%
 
8.34640026110.2%
 
8.31898975410.2%
 
8.31630039210.2%
 
8.31385040310.2%
 
8.31066036210.2%
 
8.30251026210.2%
 
8.27052974710.2%
 
8.26461982710.2%
 
8.26255989110.2%
 

temperature_at_2m
Real number (ℝ)

Distinct count223
Unique (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8473999881744385
Minimum-18.600000381469727
Maximum21.100000381469727
Zeros3
Zeros (%)0.6%
Memory size4.0 KiB
2020-08-25T00:03:51.656604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-18.60000038
5-th percentile-9.600000381
Q1-3.900000095
median1.100000024
Q34.900000095
95-th percentile11.31000018
Maximum21.10000038
Range39.70000076
Interquartile range (IQR)8.800000191

Descriptive statistics

Standard deviation6.524636009
Coefficient of variation (CV)7.699594171
Kurtosis0.3137769745
Mean0.8473999882
Median Absolute Deviation (MAD)4.5
Skewness0.1388416772
Sum423.6999941
Variance42.57087505
2020-08-25T00:03:51.766839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.10000002471.4%
 
-4.09999990571.4%
 
-2.79999995261.2%
 
-4.19999980961.2%
 
6.40000009561.2%
 
4.90000009561.2%
 
-1.60000002461.2%
 
2.09999990561.2%
 
1.29999995261.2%
 
6.69999980951.0%
 
-5.09999990551.0%
 
3.40000009551.0%
 
0.300000011951.0%
 
3.59999990551.0%
 
2.70000004851.0%
 
2.20000004851.0%
 
-5.59999990551.0%
 
4.80000019151.0%
 
1.79999995251.0%
 
1.60000002451.0%
 
-0.899999976240.8%
 
4.19999980940.8%
 
2.59999990540.8%
 
-6.40000009540.8%
 
-2.09999990540.8%
 
Other values (198)36973.8%
 
ValueCountFrequency (%) 
-18.6000003810.2%
 
-16.2000007610.2%
 
-14.6999998110.2%
 
-14.3000001910.2%
 
-13.8000001930.6%
 
-13.6000003820.4%
 
-13.510.2%
 
-13.3999996210.2%
 
-12.8999996210.2%
 
-12.6999998110.2%
 
ValueCountFrequency (%) 
21.1000003810.2%
 
20.7999992410.2%
 
20.2999992410.2%
 
19.6000003810.2%
 
19.1000003810.2%
 
18.1000003810.2%
 
17.7999992410.2%
 
16.7999992410.2%
 
16.6000003810.2%
 
16.1000003820.4%
 

wind_speed
Real number (ℝ≥0)

Distinct count78
Unique (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0560000032186507
Minimum0.30000001192092896
Maximum9.899999618530273
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:03:51.889397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3000000119
5-th percentile0.6949999899
Q11.675000042
median2.799999952
Q34.199999809
95-th percentile6.405000091
Maximum9.899999619
Range9.599999607
Interquartile range (IQR)2.524999768

Descriptive statistics

Standard deviation1.784172238
Coefficient of variation (CV)0.5838259935
Kurtosis0.6021474867
Mean3.056000003
Median Absolute Deviation (MAD)1.200000048
Skewness0.8258422274
Sum1528.000002
Variance3.183270574
2020-08-25T00:03:51.993401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.299999952163.2%
 
2.099999905163.2%
 
2.900000095153.0%
 
3.5142.8%
 
1.600000024142.8%
 
2.799999952142.8%
 
4.199999809142.8%
 
1.700000048132.6%
 
0.8000000119122.4%
 
1.5122.4%
 
2.5122.4%
 
2112.2%
 
3112.2%
 
2.599999905112.2%
 
4.300000191112.2%
 
3.900000095102.0%
 
2.200000048102.0%
 
1.100000024102.0%
 
3.400000095102.0%
 
0.6000000238102.0%
 
3.09999990591.8%
 
0.899999976291.8%
 
1.79999995291.8%
 
3.79999995291.8%
 
2.40000009591.8%
 
Other values (53)20941.8%
 
ValueCountFrequency (%) 
0.300000011920.4%
 
0.40000000661.2%
 
0.571.4%
 
0.6000000238102.0%
 
0.699999988140.8%
 
0.8000000119122.4%
 
0.899999976291.8%
 
181.6%
 
1.100000024102.0%
 
1.20000004891.8%
 
ValueCountFrequency (%) 
9.89999961910.2%
 
9.60000038110.2%
 
9.30000019110.2%
 
8.39999961910.2%
 
8.30000019110.2%
 
8.10000038120.4%
 
7.80000019110.2%
 
7.69999980910.2%
 
7.520.4%
 
7.30000019110.2%
 

temperature_diff_2m_25m
Real number (ℝ)

ZEROS

Distinct count61
Unique (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14940000182390212
Minimum-5.400000095367432
Maximum4.300000190734863
Zeros49
Zeros (%)9.8%
Memory size4.0 KiB
2020-08-25T00:03:52.099059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-5.400000095
5-th percentile-1.510000002
Q1-0.200000003
median0
Q30.6000000238
95-th percentile1.799999952
Maximum4.300000191
Range9.700000286
Interquartile range (IQR)0.8000000268

Descriptive statistics

Standard deviation1.065236639
Coefficient of variation (CV)7.130097901
Kurtosis4.634905178
Mean0.1494000018
Median Absolute Deviation (MAD)0.3000000119
Skewness-0.8379516128
Sum74.70000091
Variance1.134729098
2020-08-25T00:03:52.204422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.10000000156513.0%
 
0499.8%
 
-0.200000003428.4%
 
0.3000000119316.2%
 
0.1000000015275.4%
 
0.200000003234.6%
 
0.6000000238214.2%
 
0.400000006193.8%
 
-0.3000000119163.2%
 
0.5132.6%
 
1122.4%
 
0.6999999881112.2%
 
1.100000024112.2%
 
1.299999952102.0%
 
-0.40000000691.8%
 
1.70000004881.6%
 
1.571.4%
 
-0.600000023871.4%
 
1.20000004871.4%
 
-1.10000002461.2%
 
-0.899999976261.2%
 
-2.09999990561.2%
 
0.800000011961.2%
 
1.39999997661.2%
 
-0.551.0%
 
Other values (36)7715.4%
 
ValueCountFrequency (%) 
-5.40000009510.2%
 
-5.09999990510.2%
 
-4.09999990510.2%
 
-3.90000009510.2%
 
-3.70000004810.2%
 
-3.59999990510.2%
 
-3.510.2%
 
-2.79999995220.4%
 
-2.70000004820.4%
 
-2.59999990520.4%
 
ValueCountFrequency (%) 
4.30000019110.2%
 
3.20000004810.2%
 
320.4%
 
2.90000009510.2%
 
2.79999995210.2%
 
2.70000004810.2%
 
2.59999990510.2%
 
2.29999995240.8%
 
2.20000004830.6%
 
2.09999990520.4%
 

wind_direction
Real number (ℝ≥0)

Distinct count373
Unique (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.37039994049073
Minimum2.0
Maximum359.0
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:03:52.316361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile42.06999855
Q172
median97
Q3220
95-th percentile282.4349945
Maximum359
Range357
Interquartile range (IQR)148

Descriptive statistics

Standard deviation86.51021318
Coefficient of variation (CV)0.6034035841
Kurtosis-1.203404395
Mean143.3703999
Median Absolute Deviation (MAD)53.75
Skewness0.4029950539
Sum71685.19997
Variance7484.016985
2020-08-25T00:03:52.435528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7791.8%
 
7381.6%
 
8271.4%
 
8061.2%
 
7861.2%
 
7961.2%
 
8551.0%
 
7651.0%
 
21240.8%
 
21540.8%
 
22040.8%
 
19430.6%
 
21930.6%
 
5630.6%
 
24930.6%
 
8130.6%
 
24830.6%
 
22930.6%
 
21630.6%
 
8630.6%
 
8830.6%
 
7430.6%
 
5930.6%
 
6730.6%
 
6530.6%
 
Other values (348)39478.8%
 
ValueCountFrequency (%) 
210.2%
 
310.2%
 
5.80000019110.2%
 
810.2%
 
910.2%
 
9.30000019110.2%
 
9.39999961910.2%
 
10.510.2%
 
1610.2%
 
1710.2%
 
ValueCountFrequency (%) 
35910.2%
 
35410.2%
 
35210.2%
 
33810.2%
 
33710.2%
 
33610.2%
 
33410.2%
 
325.600006110.2%
 
32210.2%
 
31710.2%
 

hour_of_day
Real number (ℝ≥0)

Distinct count24
Unique (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.382
Minimum1.0
Maximum24.0
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:03:52.556648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12.5
Q318
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.802692795
Coefficient of variation (CV)0.5494017763
Kurtosis-1.207816404
Mean12.382
Median Absolute Deviation (MAD)5.5
Skewness0.01072258922
Sum6191
Variance46.27662926
2020-08-25T00:03:52.660065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
17265.2%
 
5265.2%
 
15255.0%
 
6255.0%
 
2244.8%
 
14234.6%
 
21234.6%
 
10224.4%
 
11224.4%
 
9224.4%
 
8224.4%
 
16224.4%
 
20224.4%
 
22214.2%
 
18204.0%
 
4204.0%
 
7193.8%
 
23183.6%
 
1173.4%
 
13173.4%
 
3173.4%
 
19173.4%
 
24163.2%
 
12142.8%
 
ValueCountFrequency (%) 
1173.4%
 
2244.8%
 
3173.4%
 
4204.0%
 
5265.2%
 
6255.0%
 
7193.8%
 
8224.4%
 
9224.4%
 
10224.4%
 
ValueCountFrequency (%) 
24163.2%
 
23183.6%
 
22214.2%
 
21234.6%
 
20224.4%
 
19173.4%
 
18204.0%
 
17265.2%
 
16224.4%
 
15255.0%
 

day
Real number (ℝ≥0)

Distinct count287
Unique (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.474
Minimum32.0
Maximum608.0
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:03:52.955134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile48
Q1118.75
median212
Q3513
95-th percentile591.05
Maximum608
Range576
Interquartile range (IQR)394.25

Descriptive statistics

Standard deviation200.9777536
Coefficient of variation (CV)0.6473255525
Kurtosis-1.685810177
Mean310.474
Median Absolute Deviation (MAD)180
Skewness0.06303856518
Sum155237
Variance40392.05744
2020-08-25T00:03:53.075864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
57651.0%
 
41740.8%
 
12840.8%
 
14140.8%
 
17440.8%
 
57340.8%
 
52040.8%
 
44540.8%
 
3340.8%
 
9540.8%
 
52940.8%
 
40940.8%
 
19640.8%
 
12630.6%
 
40030.6%
 
5030.6%
 
58230.6%
 
18430.6%
 
9130.6%
 
11630.6%
 
10130.6%
 
11930.6%
 
40630.6%
 
54030.6%
 
19130.6%
 
Other values (262)41182.2%
 
ValueCountFrequency (%) 
3220.4%
 
3340.8%
 
3430.6%
 
3520.4%
 
3620.4%
 
4020.4%
 
4110.2%
 
4220.4%
 
4320.4%
 
4420.4%
 
ValueCountFrequency (%) 
60810.2%
 
60720.4%
 
60530.6%
 
60410.2%
 
60320.4%
 
60220.4%
 
60110.2%
 
60020.4%
 
59910.2%
 
59820.4%
 

target
Real number (ℝ≥0)

Distinct count385
Unique (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6983678576946257
Minimum1.2237800359725952
Maximum6.395090103149414
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:03:53.194066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.223780036
5-th percentile2.415462005
Q13.213862479
median3.848020077
Q34.216929913
95-th percentile4.623990059
Maximum6.395090103
Range5.171310067
Interquartile range (IQR)1.003067434

Descriptive statistics

Standard deviation0.7505966217
Coefficient of variation (CV)0.2029534785
Kurtosis0.7673830733
Mean3.698367858
Median Absolute Deviation (MAD)0.4661297798
Skewness-0.5509190525
Sum1849.183929
Variance0.5633952885
2020-08-25T00:03:53.299891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.81330990840.8%
 
2.42479991940.8%
 
2.9549100430.6%
 
4.26127004630.6%
 
2.73436999330.6%
 
4.04829978930.6%
 
3.71601009430.6%
 
2.87355995230.6%
 
3.91201996830.6%
 
3.173880130.6%
 
3.79549002630.6%
 
3.96651005730.6%
 
3.81551003530.6%
 
3.44041991230.6%
 
3.48430991230.6%
 
3.45632004730.6%
 
3.19458007830.6%
 
4.00186014220.4%
 
3.81110000620.4%
 
2.36085009620.4%
 
4.14155006420.4%
 
2.92851996420.4%
 
4.29728984820.4%
 
4.09101009420.4%
 
3.16968989420.4%
 
Other values (360)43186.2%
 
ValueCountFrequency (%) 
1.22378003610.2%
 
1.28093004210.2%
 
1.33500003810.2%
 
1.36098003410.2%
 
1.50408005710.2%
 
1.54755997710.2%
 
1.58923995510.2%
 
1.62924003610.2%
 
1.70474994210.2%
 
1.72276997610.2%
 
ValueCountFrequency (%) 
6.39509010310.2%
 
5.78105020510.2%
 
5.58236980410.2%
 
5.54713010810.2%
 
5.37388992310.2%
 
5.32932996710.2%
 
5.23644018210.2%
 
5.13048982610.2%
 
5.11499977110.2%
 
5.11258983610.2%
 

Interactions

2020-08-25T00:03:41.902272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.028818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.170503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.298152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.425310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.565358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.694336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.821120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:42.947318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:43.091597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:43.251959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:43.406396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:43.551917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:43.708771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:43.860365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.007263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.151875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.284642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.434723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.565269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.695344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.833222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:44.965130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:45.265208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:45.395520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:45.519740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:45.660207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:45.789472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:45.913375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.048290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.175109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.311303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.438412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.579394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.740572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:46.882112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.021384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.170683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.313892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.458453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.603486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.731732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:47.884263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.015502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.145889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.285999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.414712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.546311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.677082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.807915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:48.950270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:49.082365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:49.369248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:49.510054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:49.640338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:49.770676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:49.900238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.026686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.168641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.301644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.431444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.570817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.702736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:50.835670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:03:53.419186image/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.
2020-08-25T00:03:53.640516image/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.
2020-08-25T00:03:53.855783image/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.
2020-08-25T00:03:54.069766image/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

2020-08-25T00:03:51.071460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:03:51.301154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

cars_per_hourtemperature_at_2mwind_speedtemperature_diff_2m_25mwind_directionhour_of_daydaytarget
07.691209.24.8-0.174.40000220.0600.03.71844
17.698946.43.5-0.356.00000014.0196.03.10009
24.81218-3.70.9-0.1281.2999884.0513.03.31419
36.95177-7.21.71.274.00000023.0143.04.38826
47.51806-1.32.6-0.165.00000011.0115.04.34640
57.671832.61.60.3224.19999719.0527.04.16044
65.52545-7.91.60.3211.8999945.0502.04.01277
74.68213-4.13.8-0.163.0999984.0453.02.15176
87.15618-12.75.2-0.164.50000012.0462.03.15700
94.74493-1.63.00.458.2999993.0554.02.37955

Last rows

cars_per_hourtemperature_at_2mwind_speedtemperature_diff_2m_25mwind_directionhour_of_daydaytarget
4905.14749-6.51.70.8184.0000005.0154.04.31615
4915.666436.72.4-0.166.0999985.0592.03.33932
4927.34923-0.41.91.077.00000021.072.03.96651
4934.45435-13.84.20.280.0000004.095.03.86073
4944.584971.82.30.159.0000004.0164.02.56495
4957.682023.55.0-1.078.00000011.0166.04.30946
4966.529429.56.5-0.8210.00000010.035.02.94444
4977.757915.24.6-0.8214.00000014.0176.04.17439
4985.789968.40.5-2.6108.5000007.0588.02.95491
4998.162234.75.90.4207.00000017.0128.04.03247