Overview

Dataset statistics

Number of variables16
Number of observations159
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.0 KiB
Average record size in memory128.8 B

Variable types

NUM16

Reproduction

Analysis started2020-08-24 23:54:04.320930
Analysis finished2020-08-24 23:54:40.843799
Duration36.52 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

highway-mpg is highly correlated with city-mpgHigh correlation
city-mpg is highly correlated with highway-mpgHigh correlation
symboling has 48 (30.2%) zeros Zeros

Variables

symboling
Real number (ℝ)

ZEROS

Distinct count6
Unique (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7358490566037735
Minimum-2.0
Maximum3.0
Zeros48
Zeros (%)30.2%
Memory size1.4 KiB
2020-08-24T23:54:40.890666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.193085725
Coefficient of variation (CV)1.621372908
Kurtosis-0.5282326292
Mean0.7358490566
Median Absolute Deviation (MAD)1
Skewness0.09495033424
Sum117
Variance1.423453547
2020-08-24T23:54:40.992853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
04830.2%
 
14628.9%
 
22918.2%
 
-12012.6%
 
3138.2%
 
-231.9%
 
ValueCountFrequency (%) 
-231.9%
 
-12012.6%
 
04830.2%
 
14628.9%
 
22918.2%
 
3138.2%
 
ValueCountFrequency (%) 
3138.2%
 
22918.2%
 
14628.9%
 
04830.2%
 
-12012.6%
 
-231.9%
 

normalized-losses
Real number (ℝ≥0)

Distinct count51
Unique (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.13207547169812
Minimum65.0
Maximum256.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:41.105261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile74
Q194
median113
Q3148
95-th percentile188.4
Maximum256
Range191
Interquartile range (IQR)54

Descriptive statistics

Standard deviation35.65128457
Coefficient of variation (CV)0.2943174583
Kurtosis0.6231702076
Mean121.1320755
Median Absolute Deviation (MAD)22
Skewness0.8357663846
Sum19260
Variance1271.014091
2020-08-24T23:54:41.208516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
161116.9%
 
9185.0%
 
12863.8%
 
10463.8%
 
13463.8%
 
9553.1%
 
16853.1%
 
7453.1%
 
8553.1%
 
10253.1%
 
10353.1%
 
6553.1%
 
9453.1%
 
9342.5%
 
11842.5%
 
12242.5%
 
10642.5%
 
10131.9%
 
13731.9%
 
11531.9%
 
14831.9%
 
12531.9%
 
8331.9%
 
15431.9%
 
15031.9%
 
Other values (26)4226.4%
 
ValueCountFrequency (%) 
6553.1%
 
7453.1%
 
7710.6%
 
7810.6%
 
8121.3%
 
8331.9%
 
8553.1%
 
8721.3%
 
8921.3%
 
9010.6%
 
ValueCountFrequency (%) 
25610.6%
 
23110.6%
 
19721.3%
 
19421.3%
 
19221.3%
 
18821.3%
 
18610.6%
 
16853.1%
 
16421.3%
 
161116.9%
 

wheel-base
Real number (ℝ≥0)

Distinct count40
Unique (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.26415079944539
Minimum86.5999984741211
Maximum115.5999984741211
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:41.327511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum86.59999847
5-th percentile93.09999847
Q194.5
median96.90000153
Q3100.7999992
95-th percentile109.0999985
Maximum115.5999985
Range29
Interquartile range (IQR)6.299999237

Descriptive statistics

Standard deviation5.167416579
Coefficient of variation (CV)0.05258699675
Kurtosis0.6367818439
Mean98.2641508
Median Absolute Deviation (MAD)2.400001526
Skewness0.9147496948
Sum15623.99998
Variance26.7021941
2020-08-24T23:54:41.430742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
93.699996951911.9%
 
94.51710.7%
 
95.69999695138.2%
 
96.585.0%
 
97.3000030574.4%
 
107.900001563.8%
 
98.4000015363.8%
 
99.0999984763.8%
 
104.300003163.8%
 
96.3000030563.8%
 
98.8000030553.1%
 
109.099998553.1%
 
93.0999984753.1%
 
97.1999969553.1%
 
102.400001553.1%
 
9742.5%
 
101.199996942.5%
 
100.400001531.9%
 
86.5999984721.3%
 
102.900001521.3%
 
103.300003121.3%
 
11021.3%
 
91.3000030521.3%
 
105.800003121.3%
 
96.9000015321.3%
 
Other values (15)159.4%
 
ValueCountFrequency (%) 
86.5999984721.3%
 
88.4000015310.6%
 
91.3000030521.3%
 
9310.6%
 
93.0999984753.1%
 
93.3000030510.6%
 
93.699996951911.9%
 
94.51710.7%
 
95.0999984710.6%
 
95.69999695138.2%
 
ValueCountFrequency (%) 
115.599998510.6%
 
11310.6%
 
11021.3%
 
109.099998553.1%
 
10810.6%
 
107.900001563.8%
 
106.699996910.6%
 
105.800003121.3%
 
104.900001510.6%
 
104.510.6%
 

length
Real number (ℝ≥0)

Distinct count56
Unique (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.41383726491867
Minimum141.10000610351562
Maximum202.6000061035156
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:41.543242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum141.1000061
5-th percentile157.0800049
Q1165.6500015
median172.3999939
Q3177.8000031
95-th percentile188.8000031
Maximum202.6000061
Range61.5
Interquartile range (IQR)12.15000153

Descriptive statistics

Standard deviation11.52317679
Coefficient of variation (CV)0.06683440824
Kurtosis-0.2076848768
Mean172.4138373
Median Absolute Deviation (MAD)6.100006104
Skewness-0.0659752615
Sum27413.80013
Variance132.7836033
2020-08-24T23:54:41.659839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
157.3000031148.8%
 
188.8000031116.9%
 
186.699996974.4%
 
171.699996974.4%
 
166.300003174.4%
 
165.300003163.8%
 
176.199996963.8%
 
186.600006163.8%
 
17253.1%
 
177.800003153.1%
 
175.600006153.1%
 
172.399993942.5%
 
176.800003142.5%
 
168.699996942.5%
 
158.699996931.9%
 
169.699996931.9%
 
175.399993931.9%
 
15031.9%
 
159.100006131.9%
 
170.199996921.3%
 
174.600006121.3%
 
170.699996921.3%
 
173.600006121.3%
 
176.600006121.3%
 
192.699996921.3%
 
Other values (31)4125.8%
 
ValueCountFrequency (%) 
141.100006110.6%
 
144.600006121.3%
 
15031.9%
 
155.899993910.6%
 
156.899993910.6%
 
157.100006110.6%
 
157.3000031148.8%
 
157.899993910.6%
 
158.699996931.9%
 
158.800003110.6%
 
ValueCountFrequency (%) 
202.600006110.6%
 
199.600006110.6%
 
192.699996921.3%
 
190.899993921.3%
 
188.8000031116.9%
 
187.800003110.6%
 
187.510.6%
 
186.699996974.4%
 
186.600006163.8%
 
184.600006121.3%
 

width
Real number (ℝ≥0)

Distinct count33
Unique (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.60754711223099
Minimum60.29999923706055
Maximum71.69999694824219
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:41.773410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum60.29999924
5-th percentile63.59999847
Q164
median65.40000153
Q366.5
95-th percentile68.97000122
Maximum71.69999695
Range11.39999771
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.947882979
Coefficient of variation (CV)0.02968992235
Kurtosis0.8514330528
Mean65.60754711
Median Absolute Deviation (MAD)1.200004578
Skewness0.9168458778
Sum10431.59999
Variance3.794248101
2020-08-24T23:54:41.883591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
63.799999242314.5%
 
66.52012.6%
 
65.40000153159.4%
 
64.40000153106.3%
 
63.5999984795.7%
 
6495.7%
 
65.574.4%
 
65.5999984763.8%
 
68.4000015363.8%
 
67.1999969563.8%
 
65.1999969563.8%
 
64.1999969553.1%
 
68.9000015342.5%
 
64.8000030542.5%
 
63.9000015331.9%
 
67.9000015331.9%
 
70.3000030531.9%
 
64.5999984721.3%
 
71.4000015321.3%
 
67.6999969521.3%
 
68.3000030521.3%
 
66.4000015310.6%
 
63.4000015310.6%
 
71.6999969510.6%
 
66.1999969510.6%
 
Other values (8)85.0%
 
ValueCountFrequency (%) 
60.2999992410.6%
 
62.510.6%
 
63.4000015310.6%
 
63.5999984795.7%
 
63.799999242314.5%
 
63.9000015331.9%
 
6495.7%
 
64.1999969553.1%
 
64.40000153106.3%
 
64.5999984721.3%
 
ValueCountFrequency (%) 
71.6999969510.6%
 
71.4000015321.3%
 
70.510.6%
 
70.3000030531.9%
 
69.5999984710.6%
 
68.9000015342.5%
 
68.8000030510.6%
 
68.4000015363.8%
 
68.3000030521.3%
 
67.9000015331.9%
 

height
Real number (ℝ≥0)

Distinct count39
Unique (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.89937082926432
Minimum49.4000015258789
Maximum59.79999923706055
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:42.002035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum49.40000153
5-th percentile50.59999847
Q152.25
median54.09999847
Q355.5
95-th percentile57.5
Maximum59.79999924
Range10.39999771
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.268761441
Coefficient of variation (CV)0.04209254035
Kurtosis-0.2797115717
Mean53.89937083
Median Absolute Deviation (MAD)1.599998474
Skewness0.1684023378
Sum8569.999962
Variance5.147278478
2020-08-24T23:54:42.113128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50.79999924148.8%
 
54.5106.3%
 
55.7000007695.7%
 
54.0999984795.7%
 
5295.7%
 
54.2999992485.0%
 
55.585.0%
 
52.5999984774.4%
 
56.0999984774.4%
 
56.7000007663.8%
 
5363.8%
 
54.9000015363.8%
 
52.7999992453.1%
 
50.5999984742.5%
 
51.5999984742.5%
 
53.2999992442.5%
 
53.7000007642.5%
 
49.7000007631.9%
 
52.531.9%
 
57.531.9%
 
59.0999984731.9%
 
56.2000007631.9%
 
54.7000007621.3%
 
50.2000007621.3%
 
59.7999992421.3%
 
Other values (14)1811.3%
 
ValueCountFrequency (%) 
49.4000015321.3%
 
49.7000007631.9%
 
50.2000007621.3%
 
50.5999984742.5%
 
50.79999924148.8%
 
5110.6%
 
51.4000015310.6%
 
51.5999984742.5%
 
5295.7%
 
52.531.9%
 
ValueCountFrequency (%) 
59.7999992421.3%
 
59.0999984731.9%
 
58.7000007610.6%
 
58.2999992410.6%
 
57.531.9%
 
56.7000007663.8%
 
56.510.6%
 
56.2999992410.6%
 
56.2000007631.9%
 
56.0999984774.4%
 

curb-weight
Real number (ℝ≥0)

Distinct count136
Unique (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2461.1383647798743
Minimum1488.0
Maximum4066.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:42.231704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1889.9
Q12065.5
median2340
Q32809.5
95-th percentile3252
Maximum4066
Range2578
Interquartile range (IQR)744

Descriptive statistics

Standard deviation481.9413205
Coefficient of variation (CV)0.19582049
Kurtosis0.1534114134
Mean2461.138365
Median Absolute Deviation (MAD)332
Skewness0.7820354281
Sum391321
Variance232267.4364
2020-08-24T23:54:42.339801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
227531.9%
 
191831.9%
 
198931.9%
 
238531.9%
 
313921.3%
 
214521.3%
 
196721.3%
 
307521.3%
 
325221.3%
 
202421.3%
 
241021.3%
 
253521.3%
 
239521.3%
 
240321.3%
 
212821.3%
 
187621.3%
 
229021.3%
 
230021.3%
 
241421.3%
 
297510.6%
 
222110.6%
 
295210.6%
 
216910.6%
 
219110.6%
 
368510.6%
 
Other values (111)11169.8%
 
ValueCountFrequency (%) 
148810.6%
 
171310.6%
 
181910.6%
 
183710.6%
 
187410.6%
 
187621.3%
 
188910.6%
 
189010.6%
 
190010.6%
 
190510.6%
 
ValueCountFrequency (%) 
406610.6%
 
377010.6%
 
375010.6%
 
368510.6%
 
351510.6%
 
349510.6%
 
329610.6%
 
325221.3%
 
321710.6%
 
319710.6%
 

engine-size
Real number (ℝ≥0)

Distinct count32
Unique (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.22641509433963
Minimum61.0
Maximum258.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:42.455148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median110
Q3135
95-th percentile181
Maximum258
Range197
Interquartile range (IQR)38

Descriptive statistics

Standard deviation30.46079126
Coefficient of variation (CV)0.2554869341
Kurtosis2.950273357
Mean119.2264151
Median Absolute Deviation (MAD)13
Skewness1.490609644
Sum18957
Variance927.8598042
2020-08-24T23:54:42.567765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
92159.4%
 
122148.8%
 
98138.2%
 
97138.2%
 
108138.2%
 
110127.5%
 
90106.3%
 
14174.4%
 
18163.8%
 
10963.8%
 
12163.8%
 
14663.8%
 
9153.1%
 
12053.1%
 
18342.5%
 
15231.9%
 
17131.9%
 
13621.3%
 
16421.3%
 
13021.3%
 
13110.6%
 
6110.6%
 
14510.6%
 
15610.6%
 
7910.6%
 
Other values (7)74.4%
 
ValueCountFrequency (%) 
6110.6%
 
7910.6%
 
90106.3%
 
9153.1%
 
92159.4%
 
97138.2%
 
98138.2%
 
10310.6%
 
108138.2%
 
10963.8%
 
ValueCountFrequency (%) 
25810.6%
 
23410.6%
 
18342.5%
 
18163.8%
 
17310.6%
 
17131.9%
 
16421.3%
 
15610.6%
 
15231.9%
 
15110.6%
 

bore
Real number (ℝ≥0)

Distinct count33
Unique (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3001257863434605
Minimum2.539999961853028
Maximum3.940000057220459
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:42.684994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.539999962
5-th percentile2.919000077
Q13.049999952
median3.269999981
Q33.559999943
95-th percentile3.761999989
Maximum3.940000057
Range1.400000095
Interquartile range (IQR)0.5099999905

Descriptive statistics

Standard deviation0.2673355968
Coefficient of variation (CV)0.08100769912
Kurtosis-0.8231749171
Mean3.300125786
Median Absolute Deviation (MAD)0.2400000095
Skewness0.1564222657
Sum524.72
Variance0.0714683213
2020-08-24T23:54:42.793766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.6199998862012.6%
 
3.190000057159.4%
 
3.150000095159.4%
 
2.970000029127.5%
 
3.02999997195.7%
 
3.77999997174.4%
 
2.91000008674.4%
 
3.43000006763.8%
 
3.04999995263.8%
 
3.30999994363.8%
 
3.26999998163.8%
 
3.57999992453.1%
 
3.39000010553.1%
 
3.53999996253.1%
 
3.34999990542.5%
 
3.0099999942.5%
 
3.46000003842.5%
 
3.17000007631.9%
 
3.70000004831.9%
 
3.2400000121.3%
 
3.521.3%
 
3.32999992421.3%
 
2.9900000110.6%
 
3.59999990510.6%
 
2.53999996210.6%
 
Other values (8)85.0%
 
ValueCountFrequency (%) 
2.53999996210.6%
 
2.91000008674.4%
 
2.92000007610.6%
 
2.970000029127.5%
 
2.9900000110.6%
 
3.0099999942.5%
 
3.02999997195.7%
 
3.04999995263.8%
 
3.07999992410.6%
 
3.13000011410.6%
 
ValueCountFrequency (%) 
3.94000005710.6%
 
3.77999997174.4%
 
3.7599999910.6%
 
3.70000004831.9%
 
3.63000011410.6%
 
3.6199998862012.6%
 
3.60999989510.6%
 
3.59999990510.6%
 
3.57999992453.1%
 
3.53999996253.1%
 

stroke
Real number (ℝ≥0)

Distinct count31
Unique (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2363522217708565
Minimum2.0699999332427983
Maximum4.170000076293944
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:42.919577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.069999933
5-th percentile2.640000105
Q13.1049999
median3.269999981
Q33.410000086
95-th percentile3.579999924
Maximum4.170000076
Range2.100000143
Interquartile range (IQR)0.305000186

Descriptive statistics

Standard deviation0.2948877118
Coefficient of variation (CV)0.09111731096
Kurtosis2.531782477
Mean3.236352222
Median Absolute Deviation (MAD)0.1400001049
Skewness-0.9927430841
Sum514.5800033
Variance0.08695876254
2020-08-24T23:54:43.018964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.029999971148.8%
 
3.150000095148.8%
 
3.400000095138.2%
 
3.230000019127.5%
 
2.640000105116.9%
 
3.28999996295.7%
 
3.46000003885.0%
 
3.39000010585.0%
 
3.563.8%
 
3.57999992463.8%
 
3.41000008663.8%
 
3.34999990563.8%
 
3.26999998163.8%
 
3.06999993363.8%
 
3.64000010542.5%
 
3.19000005742.5%
 
3.53999996242.5%
 
3.10999989531.9%
 
3.47000002931.9%
 
3.51999998131.9%
 
3.07999992421.3%
 
2.79999995221.3%
 
3.90000009510.6%
 
3.16000008610.6%
 
2.35999989510.6%
 
Other values (6)63.8%
 
ValueCountFrequency (%) 
2.06999993310.6%
 
2.19000005710.6%
 
2.35999989510.6%
 
2.640000105116.9%
 
2.79999995221.3%
 
2.86999988610.6%
 
3.029999971148.8%
 
3.06999993363.8%
 
3.07999992421.3%
 
3.09999990510.6%
 
ValueCountFrequency (%) 
4.17000007610.6%
 
3.90000009510.6%
 
3.64000010542.5%
 
3.57999992463.8%
 
3.53999996242.5%
 
3.51999998131.9%
 
3.563.8%
 
3.47000002931.9%
 
3.46000003885.0%
 
3.41000008663.8%
 

compression-ratio
Real number (ℝ≥0)

Distinct count29
Unique (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.16113204176321
Minimum7.0
Maximum23.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:43.129170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.599999905
Q18.699999809
median9
Q39.399999619
95-th percentile21.53999996
Maximum23
Range16
Interquartile range (IQR)0.6999998093

Descriptive statistics

Standard deviation3.889474596
Coefficient of variation (CV)0.3827796529
Kurtosis5.725789822
Mean10.16113204
Median Absolute Deviation (MAD)0.3999996185
Skewness2.710241708
Sum1615.619995
Variance15.12801263
2020-08-24T23:54:43.238801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
94125.8%
 
9.3999996192213.8%
 
9.300000191116.9%
 
9.5106.3%
 
8.595.7%
 
8.69999980974.4%
 
9.19999980963.8%
 
7.553.1%
 
8.60000038153.1%
 
21.542.5%
 
2342.5%
 
8.80000019131.9%
 
22.531.9%
 
2131.9%
 
8.39999961931.9%
 
9.60000038131.9%
 
7.59999990531.9%
 
721.3%
 
821.3%
 
8.30000019121.3%
 
1021.3%
 
7.69999980921.3%
 
21.8999996210.6%
 
9.3100004210.6%
 
7.80000019110.6%
 
Other values (4)42.5%
 
ValueCountFrequency (%) 
721.3%
 
7.553.1%
 
7.59999990531.9%
 
7.69999980921.3%
 
7.80000019110.6%
 
821.3%
 
8.10000038110.6%
 
8.30000019121.3%
 
8.39999961931.9%
 
8.595.7%
 
ValueCountFrequency (%) 
2342.5%
 
22.531.9%
 
21.8999996210.6%
 
21.542.5%
 
2131.9%
 
10.1000003810.6%
 
1021.3%
 
9.60000038131.9%
 
9.5106.3%
 
9.40999984710.6%
 

horsepower
Real number (ℝ≥0)

Distinct count48
Unique (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.83647798742139
Minimum48.0
Maximum200.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:43.354233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile61.8
Q169
median88
Q3114
95-th percentile160
Maximum200
Range152
Interquartile range (IQR)45

Descriptive statistics

Standard deviation30.71858264
Coefficient of variation (CV)0.320531214
Kurtosis0.2988772307
Mean95.83647799
Median Absolute Deviation (MAD)20
Skewness0.9166625562
Sum15238
Variance943.6313192
2020-08-24T23:54:43.456133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
681811.3%
 
69106.3%
 
7095.7%
 
11695.7%
 
11463.8%
 
6263.8%
 
7653.1%
 
8853.1%
 
8453.1%
 
11053.1%
 
16053.1%
 
8253.1%
 
9542.5%
 
8642.5%
 
12342.5%
 
9742.5%
 
10242.5%
 
9242.5%
 
10131.9%
 
7331.9%
 
15231.9%
 
8531.9%
 
16221.3%
 
10021.3%
 
5221.3%
 
Other values (23)2918.2%
 
ValueCountFrequency (%) 
4810.6%
 
5221.3%
 
5510.6%
 
5621.3%
 
5810.6%
 
6010.6%
 
6263.8%
 
681811.3%
 
69106.3%
 
7095.7%
 
ValueCountFrequency (%) 
20010.6%
 
17610.6%
 
16221.3%
 
16121.3%
 
16053.1%
 
15610.6%
 
15510.6%
 
15231.9%
 
14510.6%
 
14310.6%
 

peak-rpm
Real number (ℝ≥0)

Distinct count20
Unique (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5113.836477987421
Minimum4150.0
Maximum6600.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:43.745830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5800
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation465.754864
Coefficient of variation (CV)0.09107738701
Kurtosis0.4002570935
Mean5113.836478
Median Absolute Deviation (MAD)300
Skewness0.1482521885
Sum813100
Variance216927.5933
2020-08-24T23:54:43.848569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
48003522.0%
 
55003018.9%
 
52002213.8%
 
5000159.4%
 
540085.0%
 
525074.4%
 
580074.4%
 
600053.1%
 
435042.5%
 
420042.5%
 
450042.5%
 
440031.9%
 
415031.9%
 
510031.9%
 
660021.3%
 
425021.3%
 
475021.3%
 
490010.6%
 
560010.6%
 
530010.6%
 
ValueCountFrequency (%) 
415031.9%
 
420042.5%
 
425021.3%
 
435042.5%
 
440031.9%
 
450042.5%
 
475021.3%
 
48003522.0%
 
490010.6%
 
5000159.4%
 
ValueCountFrequency (%) 
660021.3%
 
600053.1%
 
580074.4%
 
560010.6%
 
55003018.9%
 
540085.0%
 
530010.6%
 
525074.4%
 
52002213.8%
 
510031.9%
 

city-mpg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count25
Unique (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.52201257861635
Minimum15.0
Maximum49.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:43.969379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17.9
Q123
median26
Q331
95-th percentile37.1
Maximum49
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.097142387
Coefficient of variation (CV)0.2298898837
Kurtosis1.149275634
Mean26.52201258
Median Absolute Deviation (MAD)4
Skewness0.7336655532
Sum4217
Variance37.17514529
2020-08-24T23:54:44.095637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
312717.0%
 
241811.3%
 
19159.4%
 
27148.8%
 
26127.5%
 
23106.3%
 
3085.0%
 
2874.4%
 
2163.8%
 
1763.8%
 
3763.8%
 
2553.1%
 
3853.1%
 
2242.5%
 
1831.9%
 
2931.9%
 
2021.3%
 
3410.6%
 
3210.6%
 
4710.6%
 
3510.6%
 
1510.6%
 
1610.6%
 
4510.6%
 
4910.6%
 
ValueCountFrequency (%) 
1510.6%
 
1610.6%
 
1763.8%
 
1831.9%
 
19159.4%
 
2021.3%
 
2163.8%
 
2242.5%
 
23106.3%
 
241811.3%
 
ValueCountFrequency (%) 
4910.6%
 
4710.6%
 
4510.6%
 
3853.1%
 
3763.8%
 
3510.6%
 
3410.6%
 
3210.6%
 
312717.0%
 
3085.0%
 

highway-mpg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count28
Unique (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.081761006289305
Minimum18.0
Maximum54.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:44.218247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22.9
Q128
median32
Q337
95-th percentile43
Maximum54
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.459188876
Coefficient of variation (CV)0.2013352345
Kurtosis0.8291311169
Mean32.08176101
Median Absolute Deviation (MAD)4
Skewness0.6010520289
Sum5101
Variance41.72112093
2020-08-24T23:54:44.333144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
321610.1%
 
381610.1%
 
30159.4%
 
34148.8%
 
37138.2%
 
28127.5%
 
25116.9%
 
3395.7%
 
2485.0%
 
2974.4%
 
2253.1%
 
3153.1%
 
2731.9%
 
4131.9%
 
2331.9%
 
2621.3%
 
4321.3%
 
4621.3%
 
4221.3%
 
4721.3%
 
3621.3%
 
3910.6%
 
5310.6%
 
1910.6%
 
2010.6%
 
Other values (3)31.9%
 
ValueCountFrequency (%) 
1810.6%
 
1910.6%
 
2010.6%
 
2253.1%
 
2331.9%
 
2485.0%
 
25116.9%
 
2621.3%
 
2731.9%
 
28127.5%
 
ValueCountFrequency (%) 
5410.6%
 
5310.6%
 
5010.6%
 
4721.3%
 
4621.3%
 
4321.3%
 
4221.3%
 
4131.9%
 
3910.6%
 
381610.1%
 

target
Real number (ℝ≥0)

Distinct count145
Unique (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11445.729559748428
Minimum5118.0
Maximum35056.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:54:44.452506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile5572
Q17372
median9233
Q314719.5
95-th percentile22485.5
Maximum35056
Range29938
Interquartile range (IQR)7347.5

Descriptive statistics

Standard deviation5877.856195
Coefficient of variation (CV)0.5135414186
Kurtosis2.599731776
Mean11445.72956
Median Absolute Deviation (MAD)2384
Skewness1.591601023
Sum1819871
Variance34549193.45
2020-08-24T23:54:44.565917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
557221.3%
 
789821.3%
 
1349921.3%
 
760921.3%
 
884521.3%
 
669221.3%
 
927921.3%
 
729521.3%
 
795721.3%
 
622921.3%
 
777521.3%
 
849521.3%
 
1815021.3%
 
892121.3%
 
729910.6%
 
2247010.6%
 
998010.6%
 
996010.6%
 
2201810.6%
 
894810.6%
 
1125910.6%
 
925810.6%
 
538910.6%
 
648810.6%
 
923310.6%
 
Other values (120)12075.5%
 
ValueCountFrequency (%) 
511810.6%
 
515110.6%
 
519510.6%
 
534810.6%
 
538910.6%
 
539910.6%
 
549910.6%
 
557221.3%
 
609510.6%
 
618910.6%
 
ValueCountFrequency (%) 
3505610.6%
 
3225010.6%
 
3160010.6%
 
2824810.6%
 
2817610.6%
 
2555210.6%
 
2387510.6%
 
2262510.6%
 
2247010.6%
 
2201810.6%
 

Interactions

2020-08-24T23:54:05.213030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:05.333202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:05.624341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:05.743070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:05.866450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:05.994292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.112031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.231362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.356160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.492145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.606246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.731115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.856235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:06.973855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.099285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.225395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.349432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.482166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.621598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.754845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:07.887511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.021770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.149781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.279861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.416849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.562170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.687011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.824051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:08.962976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:09.096155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:09.392615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:09.534246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:09.668107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:09.786026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:09.913602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.035064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.163470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.285124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.402932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.527060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.655090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.782093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:10.897394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.030679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.157887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.273210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.398035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.525542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.649787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.772977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:11.907025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.031568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.165228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.290599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.412979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.541418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.673734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.804628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:12.924320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:13.236437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:13.369434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:13.492771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:13.633045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:13.770172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:13.900037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.021673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.156812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.279382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.406488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.533337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.661304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.789587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:14.920819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.055212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.178908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.312762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.444695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.566021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.698152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.831328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:15.963275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.080181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.211234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.330885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.453369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.574986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.695836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:16.816537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.108816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.234948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.348108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.471211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.595871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.714934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.839692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:17.964688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.088615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.213621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.358699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.484912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.607548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.731688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.848336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:18.966690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.092282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.221022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.335596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.462494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.589527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.711390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.838931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:19.966251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:20.093604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:20.225695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:20.363158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:20.492261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:20.625262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:20.940253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.072752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.209509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.348463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.484843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.609817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.751754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:21.888091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.012977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.149098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.291116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.425773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.556451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.696416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.830786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:22.964716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.097365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.228582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.370985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.508304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.647702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.775086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:23.911616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.048043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.173490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.310663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.447505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.581507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.867218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:24.990131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.104807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.227530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.346477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.458602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.580588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.702747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.828585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:25.939090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.061076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.184832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.297825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.418245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.539031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.658376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.787077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:26.926204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.063382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.197304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.332551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.459292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.588432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.723943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.862167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:27.988363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:28.125421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:28.263533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:28.408900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:28.729058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:28.872974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.015658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.144229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.283095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.420931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.561130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.701734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.845073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:29.996481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.145778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.293529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.426776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.566384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.703956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.831031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:30.978645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.121704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.259400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.377391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.506273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.621430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.738927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.860986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:31.977640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.094899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.219418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.341856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.626055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.754650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.881328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:32.994717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.117484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.248790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.369596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.501376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.643966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.772015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:33.907206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.038939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.167531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.296254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.435930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.574842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.702839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.840715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:34.980777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.108177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.249766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.387496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.524073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.652496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.792641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:35.924546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:36.061263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:36.195642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:36.512110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:36.644746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:36.784197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:36.924291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.057725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.198385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.338727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.473390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.617294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.755187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:37.893319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.025377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.168028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.304242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.435620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.569219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.696452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.824767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:38.959239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.095267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.218295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.351218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.490747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.620495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.753549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:39.889634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:54:44.712625image/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.
2020-08-24T23:54:45.008364image/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.
2020-08-24T23:54:45.316110image/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.
2020-08-24T23:54:45.620093image/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

2020-08-24T23:54:40.340562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:40.709343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgtarget
02.0164.099.800003176.60000666.19999754.2999992337.0109.03.193.4010.0102.05500.024.030.013950.0
12.0164.099.400002176.60000666.40000254.2999992824.0136.03.193.408.0115.05500.018.022.017450.0
21.0158.0105.800003192.69999771.40000255.7000012844.0136.03.193.408.5110.05500.019.025.017710.0
31.0158.0105.800003192.69999771.40000255.9000023086.0131.03.133.408.3140.05500.017.020.023875.0
42.0192.0101.199997176.80000364.80000354.2999992395.0108.03.502.808.8101.05800.023.029.016430.0
50.0192.0101.199997176.80000364.80000354.2999992395.0108.03.502.808.8101.05800.023.029.016925.0
60.0188.0101.199997176.80000364.80000354.2999992710.0164.03.313.199.0121.04250.021.028.020970.0
70.0188.0101.199997176.80000364.80000354.2999992765.0164.03.313.199.0121.04250.021.028.021105.0
82.0121.088.400002141.10000660.29999953.2000011488.061.02.913.039.548.05100.047.053.05151.0
91.098.094.500000155.89999463.59999852.0000001874.090.03.033.119.670.05400.038.043.06295.0

Last rows

symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgtarget
149-1.074.0104.300003188.80000367.19999757.5000003034.0141.03.783.159.5114.05400.023.028.013415.0
150-2.0103.0104.300003188.80000367.19999756.2000012935.0141.03.783.159.5114.05400.024.028.015985.0
151-1.074.0104.300003188.80000367.19999757.5000003042.0141.03.783.159.5114.05400.024.028.016515.0
152-2.0103.0104.300003188.80000367.19999756.2000013045.0130.03.623.157.5162.05100.017.022.018420.0
153-1.074.0104.300003188.80000367.19999757.5000003157.0130.03.623.157.5162.05100.017.022.018950.0
154-1.095.0109.099998188.80000368.90000255.5000002952.0141.03.783.159.5114.05400.023.028.016845.0
155-1.095.0109.099998188.80000368.80000355.5000003049.0141.03.783.158.7160.05300.019.025.019045.0
156-1.095.0109.099998188.80000368.90000255.5000003012.0173.03.582.878.8134.05500.018.023.021485.0
157-1.095.0109.099998188.80000368.90000255.5000003217.0145.03.013.4023.0106.04800.026.027.022470.0
158-1.095.0109.099998188.80000368.90000255.5000003062.0141.03.783.159.5114.05400.019.025.022625.0