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 23:51:14.228402
Analysis finished2020-08-24 23:51:51.151448
Duration36.92 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

Variables

symboling
Real number (ℝ≥0)

Distinct count6
Unique (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7358490566037736
Minimum1.0
Maximum6.0
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-08-24T23:51:51.210900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.193085725
Coefficient of variation (CV)0.3193613304
Kurtosis-0.5282326292
Mean3.735849057
Median Absolute Deviation (MAD)1
Skewness0.09495033424
Sum594
Variance1.423453547
2020-08-24T23:51:51.326766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
34830.2%
 
44628.9%
 
52918.2%
 
22012.6%
 
6138.2%
 
131.9%
 
ValueCountFrequency (%) 
131.9%
 
22012.6%
 
34830.2%
 
44628.9%
 
52918.2%
 
6138.2%
 
ValueCountFrequency (%) 
6138.2%
 
52918.2%
 
44628.9%
 
34830.2%
 
22012.6%
 
131.9%
 

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
2020-08-24T23:51:51.436023image/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
2020-08-24T23:51:51.538026image/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
2020-08-24T23:51:51.654830image/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
2020-08-24T23:51:51.757700image/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
2020-08-24T23:51:51.868426image/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
2020-08-24T23:51:51.979417image/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
2020-08-24T23:51:52.093342image/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
2020-08-24T23:51:52.199818image/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
2020-08-24T23:51:52.321150image/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
2020-08-24T23:51:52.427379image/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
2020-08-24T23:51:52.542974image/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
2020-08-24T23:51:52.826866image/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
2020-08-24T23:51:52.944308image/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
2020-08-24T23:51:53.048950image/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
2020-08-24T23:51:53.172664image/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
2020-08-24T23:51:53.288649image/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
2020-08-24T23:51:53.414786image/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
2020-08-24T23:51:53.527173image/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
2020-08-24T23:51:53.637295image/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
2020-08-24T23:51:53.751802image/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
2020-08-24T23:51:53.869411image/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
2020-08-24T23:51:53.972363image/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
2020-08-24T23:51:54.092658image/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
2020-08-24T23:51:54.208266image/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
2020-08-24T23:51:54.326778image/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
2020-08-24T23:51:54.449627image/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
2020-08-24T23:51:54.572873image/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
2020-08-24T23:51:54.686444image/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
2020-08-24T23:51:54.822086image/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
2020-08-24T23:51:54.937916image/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

2020-08-24T23:51:15.093183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.219524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.352711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.472199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.596070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.718610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.837013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:15.959893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.087504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.215174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.332950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.460574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.588319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.705843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.832916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:16.975303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:17.106582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:17.239165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:17.380457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:17.512482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:17.844657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:17.998516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.147319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.284286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.424603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.564069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.692780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.831380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:18.972755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.101751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.240617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.379794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.516272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.635873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.773442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:19.897673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.024471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.149049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.276421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.399822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.529848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.659811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.779310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:20.915280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.053968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.180615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.308916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.437973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.736663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.864048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:21.998495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.135113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.275154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.403493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.527497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.655209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.789999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:22.922179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.043577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.177270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.312895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.442528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.577761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.712119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.855907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:23.979782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.114850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.250983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.380817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.507878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.630644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.757902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:24.891438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.023451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.145048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.280596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.603187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.728244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.861561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:25.993500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.122961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.241637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.371092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.491804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.614903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.737675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.858412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:26.979103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.109650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.241627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.358078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.485429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.612216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.733646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.864740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:27.992049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.116679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.234940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.366410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.488246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.617572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.739392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.858915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:28.979447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:29.128126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:29.428883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:29.545426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:29.677472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:29.807757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:29.932282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.059802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.188288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.318341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.450922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.589185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.727561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:30.862188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.004704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.154028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.287766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.427972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.567593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.694204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.833718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:31.974964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.102594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.254844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.405818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.552176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.682006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.825668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:32.967915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:33.102447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:33.435069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:33.567847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:33.701635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:33.840322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:33.979026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.104821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.243844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.380678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.511484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.649408image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.797733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:34.939046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.064605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.200232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.317259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.438150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.562501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.678446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.796983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:35.933227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.059800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.177078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.301160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.429247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.544098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.668982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.795704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:36.920623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:37.056418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:37.392540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:37.537926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:37.686430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:37.831596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:37.981653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.118610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.260478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.402459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.529325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.669444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.808203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:38.934450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.071924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.216224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.363026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.491682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.630452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.760343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:39.896433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.031936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.166969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.300380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.438248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.579032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.706157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.844361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:40.981774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:41.110432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:41.424660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:41.569351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:41.703943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:41.816544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:41.939124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.057855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.176205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.293330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.406966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.523133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.656638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.779446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:42.907958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.039666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.166420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.278234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.399889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.524078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.648646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.779473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:43.942498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.076505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.217732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.353563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.483546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.616194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.755020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:44.893018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:45.020510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:45.367303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:45.514590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:45.655790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:45.798502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:45.959872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.094047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.223115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.361852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.493666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.630743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.762944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:46.892111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.023232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.160582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.298238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.422887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.559353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.696785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.820998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:47.956745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.090429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.222463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.347252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.481031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.607144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.737442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.865944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:48.991558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:49.296471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:49.430098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:49.563609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:49.688728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:49.824283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:49.959599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:50.082834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:50.222889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:50.362967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:51:55.094434image/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.
2020-08-24T23:51:55.415426image/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.
2020-08-24T23:51:55.712783image/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.
2020-08-24T23:51:56.229579image/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

2020-08-24T23:51:50.632184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:51:51.012484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgtarget
05.0164.099.800003176.60000666.19999754.2999992337.0109.03.193.4010.0102.05500.024.030.013950.0
15.0164.099.400002176.60000666.40000254.2999992824.0136.03.193.408.0115.05500.018.022.017450.0
24.0158.0105.800003192.69999771.40000255.7000012844.0136.03.193.408.5110.05500.019.025.017710.0
34.0158.0105.800003192.69999771.40000255.9000023086.0131.03.133.408.3140.05500.017.020.023875.0
45.0192.0101.199997176.80000364.80000354.2999992395.0108.03.502.808.8101.05800.023.029.016430.0
53.0192.0101.199997176.80000364.80000354.2999992395.0108.03.502.808.8101.05800.023.029.016925.0
63.0188.0101.199997176.80000364.80000354.2999992710.0164.03.313.199.0121.04250.021.028.020970.0
73.0188.0101.199997176.80000364.80000354.2999992765.0164.03.313.199.0121.04250.021.028.021105.0
85.0121.088.400002141.10000660.29999953.2000011488.061.02.913.039.548.05100.047.053.05151.0
94.098.094.500000155.89999463.59999852.0000001874.090.03.033.119.670.05400.038.043.06295.0

Last rows

symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgtarget
1492.074.0104.300003188.80000367.19999757.5000003034.0141.03.783.159.5114.05400.023.028.013415.0
1501.0103.0104.300003188.80000367.19999756.2000012935.0141.03.783.159.5114.05400.024.028.015985.0
1512.074.0104.300003188.80000367.19999757.5000003042.0141.03.783.159.5114.05400.024.028.016515.0
1521.0103.0104.300003188.80000367.19999756.2000013045.0130.03.623.157.5162.05100.017.022.018420.0
1532.074.0104.300003188.80000367.19999757.5000003157.0130.03.623.157.5162.05100.017.022.018950.0
1542.095.0109.099998188.80000368.90000255.5000002952.0141.03.783.159.5114.05400.023.028.016845.0
1552.095.0109.099998188.80000368.80000355.5000003049.0141.03.783.158.7160.05300.019.025.019045.0
1562.095.0109.099998188.80000368.90000255.5000003012.0173.03.582.878.8134.05500.018.023.021485.0
1572.095.0109.099998188.80000368.90000255.5000003217.0145.03.013.4023.0106.04800.026.027.022470.0
1582.095.0109.099998188.80000368.90000255.5000003062.0141.03.783.159.5114.05400.019.025.022625.0