Overview

Dataset statistics

Number of variables20
Number of observations202
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)3.5%
Total size in memory31.7 KiB
Average record size in memory160.6 B

Variable types

NUM17
BOOL2
CAT1

Reproduction

Analysis started2020-08-25 01:07:58.819664
Analysis finished2020-08-25 01:08:40.388972
Duration41.57 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 7 (3.5%) duplicate rows Duplicates
city-mpg is highly correlated with highway-mpgHigh correlation
highway-mpg is highly correlated with city-mpgHigh correlation
fuel-system has 11 (5.4%) zeros Zeros
normalized-losses has 3 (1.5%) zeros Zeros
engine-type has 12 (5.9%) zeros Zeros
make has 3 (1.5%) zeros Zeros
body-style has 6 (3.0%) zeros Zeros
peak-rpm has 5 (2.5%) zeros Zeros
target has 67 (33.2%) zeros Zeros

Variables

wheel-base
Real number (ℝ≥0)

Distinct count53
Unique (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.67425742574258
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:40.431572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.005
Q194.5
median96.9
Q3101.2
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation6.028062954
Coefficient of variation (CV)0.06109053274
Kurtosis1.110343462
Mean98.67425743
Median Absolute Deviation (MAD)2.55
Skewness1.092373885
Sum19932.2
Variance36.33754298
2020-08-25T01:08:40.549775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
94.52110.4%
 
93.7209.9%
 
95.7136.4%
 
96.584.0%
 
97.373.5%
 
98.473.5%
 
100.463.0%
 
98.863.0%
 
96.363.0%
 
99.163.0%
 
107.963.0%
 
93.152.5%
 
109.152.5%
 
97.252.5%
 
95.952.5%
 
102.452.5%
 
101.242.0%
 
114.242.0%
 
9742.0%
 
95.342.0%
 
11031.5%
 
104.331.5%
 
103.531.5%
 
105.831.5%
 
89.531.5%
 
Other values (28)4019.8%
 
ValueCountFrequency (%) 
86.621.0%
 
88.410.5%
 
88.621.0%
 
89.531.5%
 
91.321.0%
 
9310.5%
 
93.152.5%
 
93.310.5%
 
93.7209.9%
 
94.310.5%
 
ValueCountFrequency (%) 
120.910.5%
 
115.621.0%
 
114.242.0%
 
11321.0%
 
11210.5%
 
11031.5%
 
109.152.5%
 
10810.5%
 
107.963.0%
 
106.710.5%
 

fuel-system
Real number (ℝ≥0)

ZEROS

Distinct count8
Unique (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.227722772277228
Minimum0
Maximum7
Zeros11
Zeros (%)5.4%
Memory size1.7 KiB
2020-08-25T01:08:40.669023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q11
median4.5
Q35
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.016751677
Coefficient of variation (CV)0.6248218385
Kurtosis-1.663802303
Mean3.227722772
Median Absolute Deviation (MAD)1.5
Skewness-0.2161774638
Sum652
Variance4.067287326
2020-08-25T01:08:40.777329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
59145.0%
 
16632.7%
 
3209.9%
 
0115.4%
 
694.5%
 
231.5%
 
710.5%
 
410.5%
 
ValueCountFrequency (%) 
0115.4%
 
16632.7%
 
231.5%
 
3209.9%
 
410.5%
 
59145.0%
 
694.5%
 
710.5%
 
ValueCountFrequency (%) 
710.5%
 
694.5%
 
59145.0%
 
410.5%
 
3209.9%
 
231.5%
 
16632.7%
 
0115.4%
 

normalized-losses
Real number (ℝ≥0)

ZEROS

Distinct count52
Unique (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.26732673267327
Minimum0
Maximum51
Zeros3
Zeros (%)1.5%
Memory size1.7 KiB
2020-08-25T01:08:40.888161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median29.5
Q348
95-th percentile51
Maximum51
Range51
Interquartile range (IQR)32

Descriptive statistics

Standard deviation16.98280728
Coefficient of variation (CV)0.5610937308
Kurtosis-1.32768772
Mean30.26732673
Median Absolute Deviation (MAD)16.5
Skewness-0.2077181819
Sum6114
Variance288.4157431
2020-08-25T01:08:40.988788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
514120.3%
 
26115.4%
 
4684.0%
 
2273.5%
 
363.0%
 
1763.0%
 
1563.0%
 
4952.5%
 
3652.5%
 
152.5%
 
4252.5%
 
2852.5%
 
4852.5%
 
3752.5%
 
2142.0%
 
1342.0%
 
442.0%
 
4742.0%
 
1042.0%
 
1431.5%
 
931.5%
 
1831.5%
 
2431.5%
 
031.5%
 
4131.5%
 
Other values (27)4421.8%
 
ValueCountFrequency (%) 
031.5%
 
152.5%
 
221.0%
 
363.0%
 
442.0%
 
510.5%
 
621.0%
 
721.0%
 
821.0%
 
931.5%
 
ValueCountFrequency (%) 
514120.3%
 
5010.5%
 
4952.5%
 
4852.5%
 
4742.0%
 
4684.0%
 
4510.5%
 
4421.0%
 
4321.0%
 
4252.5%
 

highway-mpg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count30
Unique (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.821782178217823
Minimum16.0
Maximum54.0
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:41.269586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q335.5
95-th percentile42.95
Maximum54
Range38
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.904315529
Coefficient of variation (CV)0.2240076673
Kurtosis0.420488221
Mean30.82178218
Median Absolute Deviation (MAD)5
Skewness0.5237915089
Sum6226
Variance47.66957293
2020-08-25T01:08:41.369319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25199.4%
 
38178.4%
 
24178.4%
 
30167.9%
 
32167.9%
 
34146.9%
 
37136.4%
 
28115.4%
 
29105.0%
 
3394.5%
 
3184.0%
 
2273.5%
 
2373.5%
 
2752.5%
 
4342.0%
 
4231.5%
 
4131.5%
 
2631.5%
 
3921.0%
 
4621.0%
 
2021.0%
 
1621.0%
 
4721.0%
 
3621.0%
 
1821.0%
 
Other values (5)63.0%
 
ValueCountFrequency (%) 
1621.0%
 
1710.5%
 
1821.0%
 
1921.0%
 
2021.0%
 
2273.5%
 
2373.5%
 
24178.4%
 
25199.4%
 
2631.5%
 
ValueCountFrequency (%) 
5410.5%
 
5310.5%
 
5010.5%
 
4721.0%
 
4621.0%
 
4342.0%
 
4231.5%
 
4131.5%
 
3921.0%
 
38178.4%
 

height
Real number (ℝ≥0)

Distinct count48
Unique (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.68811881188119
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:41.484182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.442769293
Coefficient of variation (CV)0.04549925284
Kurtosis-0.4130862831
Mean53.68811881
Median Absolute Deviation (MAD)1.6
Skewness0.09405138403
Sum10845
Variance5.967121817
2020-08-25T01:08:41.590695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50.8146.9%
 
55.7125.9%
 
52125.9%
 
54.5105.0%
 
54.1105.0%
 
55.594.5%
 
54.384.0%
 
56.784.0%
 
56.173.5%
 
51.673.5%
 
52.673.5%
 
52.863.0%
 
5363.0%
 
54.963.0%
 
50.263.0%
 
53.752.5%
 
55.152.5%
 
50.652.5%
 
49.642.0%
 
53.342.0%
 
58.742.0%
 
52.531.5%
 
53.531.5%
 
57.531.5%
 
59.131.5%
 
Other values (23)3517.3%
 
ValueCountFrequency (%) 
47.810.5%
 
48.821.0%
 
49.421.0%
 
49.642.0%
 
49.731.5%
 
50.263.0%
 
50.521.0%
 
50.652.5%
 
50.8146.9%
 
5110.5%
 
ValueCountFrequency (%) 
59.821.0%
 
59.131.5%
 
58.742.0%
 
58.310.5%
 
57.531.5%
 
56.784.0%
 
56.521.0%
 
56.321.0%
 
56.173.5%
 
5610.5%
 

length
Real number (ℝ≥0)

Distinct count75
Unique (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.83019801980197
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:41.709368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.11
Q1166.3
median173.1
Q3181.2
95-th percentile196.84
Maximum208.1
Range67
Interquartile range (IQR)14.9

Descriptive statistics

Standard deviation12.29571899
Coefficient of variation (CV)0.07073407917
Kurtosis-0.01732428314
Mean173.830198
Median Absolute Deviation (MAD)6.8
Skewness0.1878791723
Sum35113.7
Variance151.1847054
2020-08-25T01:08:41.819518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
157.3157.4%
 
188.884.0%
 
186.773.5%
 
171.773.5%
 
166.373.5%
 
176.263.0%
 
177.863.0%
 
165.363.0%
 
186.663.0%
 
176.852.5%
 
17252.5%
 
173.252.5%
 
175.652.5%
 
172.442.0%
 
168.742.0%
 
168.942.0%
 
198.942.0%
 
16942.0%
 
159.131.5%
 
192.731.5%
 
155.931.5%
 
170.731.5%
 
175.431.5%
 
15031.5%
 
158.731.5%
 
Other values (50)7336.1%
 
ValueCountFrequency (%) 
141.110.5%
 
144.621.0%
 
15031.5%
 
155.931.5%
 
156.910.5%
 
157.110.5%
 
157.3157.4%
 
157.910.5%
 
158.731.5%
 
158.810.5%
 
ValueCountFrequency (%) 
208.110.5%
 
202.621.0%
 
199.621.0%
 
199.210.5%
 
198.942.0%
 
19710.5%
 
193.810.5%
 
192.731.5%
 
191.710.5%
 
190.921.0%
 

engine-type
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.014851485148515
Minimum0
Maximum6
Zeros12
Zeros (%)5.9%
Memory size1.7 KiB
2020-08-25T01:08:41.934877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.062606028
Coefficient of variation (CV)0.3524571717
Kurtosis3.203551353
Mean3.014851485
Median Absolute Deviation (MAD)0
Skewness-0.5323495376
Sum609
Variance1.12913157
2020-08-25T01:08:42.044530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
314571.8%
 
4157.4%
 
5136.4%
 
2125.9%
 
0125.9%
 
642.0%
 
110.5%
 
ValueCountFrequency (%) 
0125.9%
 
110.5%
 
2125.9%
 
314571.8%
 
4157.4%
 
5136.4%
 
642.0%
 
ValueCountFrequency (%) 
642.0%
 
5136.4%
 
4157.4%
 
314571.8%
 
2125.9%
 
110.5%
 
0125.9%
 

make
Real number (ℝ≥0)

ZEROS

Distinct count22
Unique (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.064356435643564
Minimum0
Maximum21
Zeros3
Zeros (%)1.5%
Memory size1.7 KiB
2020-08-25T01:08:42.159675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median12
Q319
95-th percentile20
Maximum21
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.227911562
Coefficient of variation (CV)0.5162241015
Kurtosis-1.184808031
Mean12.06435644
Median Absolute Deviation (MAD)6
Skewness-0.232983876
Sum2437
Variance38.78688242
2020-08-25T01:08:42.258383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
193215.8%
 
12188.9%
 
8178.4%
 
5136.4%
 
11136.4%
 
18125.9%
 
20125.9%
 
13115.4%
 
494.5%
 
984.0%
 
284.0%
 
2184.0%
 
1473.5%
 
173.5%
 
1763.0%
 
1552.5%
 
642.0%
 
331.5%
 
031.5%
 
731.5%
 
1621.0%
 
1010.5%
 
ValueCountFrequency (%) 
031.5%
 
173.5%
 
284.0%
 
331.5%
 
494.5%
 
5136.4%
 
642.0%
 
731.5%
 
8178.4%
 
984.0%
 
ValueCountFrequency (%) 
2184.0%
 
20125.9%
 
193215.8%
 
18125.9%
 
1763.0%
 
1621.0%
 
1552.5%
 
1473.5%
 
13115.4%
 
12188.9%
 

city-mpg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count29
Unique (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.277227722772277
Minimum13.0
Maximum49.0
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:42.360933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24.5
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.562548364
Coefficient of variation (CV)0.2596229474
Kurtosis0.5515973943
Mean25.27722772
Median Absolute Deviation (MAD)5.5
Skewness0.650894311
Sum5106
Variance43.06704103
2020-08-25T01:08:42.471974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
312813.9%
 
192713.4%
 
242110.4%
 
27146.9%
 
26125.9%
 
17125.9%
 
23115.4%
 
3084.0%
 
2184.0%
 
2584.0%
 
2873.5%
 
3873.5%
 
1663.0%
 
3763.0%
 
2242.0%
 
2031.5%
 
2931.5%
 
1831.5%
 
1531.5%
 
1421.0%
 
3310.5%
 
4510.5%
 
3610.5%
 
3210.5%
 
4910.5%
 
Other values (4)42.0%
 
ValueCountFrequency (%) 
1310.5%
 
1421.0%
 
1531.5%
 
1663.0%
 
17125.9%
 
1831.5%
 
192713.4%
 
2031.5%
 
2184.0%
 
2242.0%
 
ValueCountFrequency (%) 
4910.5%
 
4710.5%
 
4510.5%
 
3873.5%
 
3763.0%
 
3610.5%
 
3510.5%
 
3410.5%
 
3310.5%
 
3210.5%
 

body-style
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.608910891089109
Minimum0
Maximum4
Zeros6
Zeros (%)3.0%
Memory size1.7 KiB
2020-08-25T01:08:42.584239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8641677341
Coefficient of variation (CV)0.3312369683
Kurtosis0.9711705671
Mean2.608910891
Median Absolute Deviation (MAD)1
Skewness-0.6447432581
Sum527
Variance0.7467858726
2020-08-25T01:08:42.689165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
39346.0%
 
27034.7%
 
42512.4%
 
184.0%
 
063.0%
 
ValueCountFrequency (%) 
063.0%
 
184.0%
 
27034.7%
 
39346.0%
 
42512.4%
 
ValueCountFrequency (%) 
42512.4%
 
39346.0%
 
27034.7%
 
184.0%
 
063.0%
 

fuel-type
Boolean

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
182
0
 
20
ValueCountFrequency (%) 
118290.1%
 
0209.9%
 

curb-weight
Real number (ℝ≥0)

Distinct count168
Unique (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2549.5
Minimum1488.0
Maximum4066.0
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:42.806948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1900.25
Q12145
median2412
Q32924.75
95-th percentile3504.5
Maximum4066
Range2578
Interquartile range (IQR)779.75

Descriptive statistics

Standard deviation522.089242
Coefficient of variation (CV)0.2047810324
Kurtosis-0.003663282621
Mean2549.5
Median Absolute Deviation (MAD)379.5
Skewness0.7136223673
Sum514999
Variance272577.1766
2020-08-25T01:08:42.905874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
238542.0%
 
191831.5%
 
198931.5%
 
227531.5%
 
233721.0%
 
240321.0%
 
230021.0%
 
202421.0%
 
307521.0%
 
238021.0%
 
187421.0%
 
254821.0%
 
253521.0%
 
229021.0%
 
406621.0%
 
275621.0%
 
241021.0%
 
323021.0%
 
187621.0%
 
214521.0%
 
313921.0%
 
325221.0%
 
241421.0%
 
212821.0%
 
196721.0%
 
Other values (143)14772.8%
 
ValueCountFrequency (%) 
148810.5%
 
171310.5%
 
181910.5%
 
183710.5%
 
187421.0%
 
187621.0%
 
188910.5%
 
189010.5%
 
190010.5%
 
190510.5%
 
ValueCountFrequency (%) 
406621.0%
 
395010.5%
 
390010.5%
 
377010.5%
 
375010.5%
 
374010.5%
 
371510.5%
 
368510.5%
 
351510.5%
 
350510.5%
 

engine-size
Real number (ℝ≥0)

Distinct count44
Unique (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.75247524752476
Minimum61.0
Maximum326.0
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:08:43.012199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median115
Q3144
95-th percentile202.55
Maximum326
Range265
Interquartile range (IQR)47

Descriptive statistics

Standard deviation41.92789497
Coefficient of variation (CV)0.330785611
Kurtosis5.234145968
Mean126.7524752
Median Absolute Deviation (MAD)20
Skewness1.947749892
Sum25604
Variance1757.948377
2020-08-25T01:08:43.109515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
92157.4%
 
122157.4%
 
97146.9%
 
98146.9%
 
108136.4%
 
90125.9%
 
110125.9%
 
10984.0%
 
12073.5%
 
12163.0%
 
18163.0%
 
15263.0%
 
14663.0%
 
15652.5%
 
9152.5%
 
13652.5%
 
14152.5%
 
18342.0%
 
13031.5%
 
17131.5%
 
19431.5%
 
20931.5%
 
16431.5%
 
7031.5%
 
23421.0%
 
Other values (19)2411.9%
 
ValueCountFrequency (%) 
6110.5%
 
7031.5%
 
7910.5%
 
8010.5%
 
90125.9%
 
9152.5%
 
92157.4%
 
97146.9%
 
98146.9%
 
10310.5%
 
ValueCountFrequency (%) 
32610.5%
 
30810.5%
 
30410.5%
 
25821.0%
 
23421.0%
 
20931.5%
 
20310.5%
 
19431.5%
 
18342.0%
 
18163.0%
 

aspiration
Boolean

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
166
1
36
ValueCountFrequency (%) 
016682.2%
 
13617.8%
 

num-of-doors
Categorical

Distinct count3
Unique (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
111
2
89
0
 
2
ValueCountFrequency (%) 
111155.0%
 
28944.1%
 
021.0%
 
2020-08-25T01:08:43.251162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
111155.0%
 
28944.1%
 
021.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number202100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
111155.0%
 
28944.1%
 
021.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common202100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
111155.0%
 
28944.1%
 
021.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII202100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
111155.0%
 
28944.1%
 
021.0%
 

horsepower
Real number (ℝ≥0)

Distinct count60
Unique (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.004950495049506
Minimum0
Maximum59
Zeros2
Zeros (%)1.0%
Memory size1.7 KiB
2020-08-25T01:08:43.366996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113.25
median40
Q347
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)33.75

Descriptive statistics

Standard deviation18.53958989
Coefficient of variation (CV)0.5792725689
Kurtosis-1.316768742
Mean32.0049505
Median Absolute Deviation (MAD)13.5
Skewness-0.3268436855
Sum6465
Variance343.7163933
2020-08-25T01:08:43.475009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
42199.4%
 
44115.4%
 
43105.0%
 
994.5%
 
484.0%
 
5773.5%
 
4063.0%
 
2363.0%
 
5363.0%
 
163.0%
 
4952.5%
 
252.5%
 
4752.5%
 
5852.5%
 
1852.5%
 
5052.5%
 
542.0%
 
5242.0%
 
1242.0%
 
5542.0%
 
742.0%
 
1131.5%
 
1931.5%
 
5131.5%
 
5431.5%
 
Other values (35)5225.7%
 
ValueCountFrequency (%) 
021.0%
 
163.0%
 
252.5%
 
310.5%
 
484.0%
 
542.0%
 
621.0%
 
742.0%
 
810.5%
 
994.5%
 
ValueCountFrequency (%) 
5921.0%
 
5852.5%
 
5773.5%
 
5621.0%
 
5542.0%
 
5431.5%
 
5363.0%
 
5242.0%
 
5131.5%
 
5052.5%
 

bore
Real number (ℝ≥0)

Distinct count39
Unique (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.113861386138613
Minimum0
Maximum38
Zeros1
Zeros (%)0.5%
Memory size1.7 KiB
2020-08-25T01:08:43.582311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111
median16
Q327
95-th percentile35
Maximum38
Range38
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.27734338
Coefficient of variation (CV)0.5673745185
Kurtosis-1.116028417
Mean18.11386139
Median Absolute Deviation (MAD)9
Skewness0.1952248635
Sum3659
Variance105.623787
2020-08-25T01:08:43.683050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
302210.9%
 
13209.9%
 
11157.4%
 
4125.9%
 
7125.9%
 
2294.5%
 
2184.0%
 
1684.0%
 
273.5%
 
1573.5%
 
2663.0%
 
2563.0%
 
863.0%
 
2063.0%
 
3563.0%
 
3252.5%
 
652.5%
 
3842.0%
 
1942.0%
 
2731.5%
 
1231.5%
 
3331.5%
 
3721.0%
 
1721.0%
 
2321.0%
 
Other values (14)199.4%
 
ValueCountFrequency (%) 
010.5%
 
110.5%
 
273.5%
 
310.5%
 
4125.9%
 
510.5%
 
652.5%
 
7125.9%
 
863.0%
 
910.5%
 
ValueCountFrequency (%) 
3842.0%
 
3721.0%
 
3621.0%
 
3563.0%
 
3410.5%
 
3331.5%
 
3252.5%
 
3121.0%
 
302210.9%
 
2910.5%
 

peak-rpm
Real number (ℝ≥0)

ZEROS

Distinct count24
Unique (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.574257425742575
Minimum0
Maximum23
Zeros5
Zeros (%)2.5%
Memory size1.7 KiB
2020-08-25T01:08:43.809623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median12
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.347332128
Coefficient of variation (CV)0.462002177
Kurtosis-0.4477412272
Mean11.57425743
Median Absolute Deviation (MAD)4
Skewness-0.0945052767
Sum2338
Variance28.59396089
2020-08-25T01:08:43.908851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
163718.3%
 
83617.8%
 
102713.4%
 
122311.4%
 
15115.4%
 
2194.5%
 
1973.5%
 
1373.5%
 
573.5%
 
052.5%
 
152.5%
 
742.0%
 
342.0%
 
2031.5%
 
231.5%
 
431.5%
 
2321.0%
 
2221.0%
 
1121.0%
 
610.5%
 
910.5%
 
1410.5%
 
1710.5%
 
1810.5%
 
ValueCountFrequency (%) 
052.5%
 
152.5%
 
231.5%
 
342.0%
 
431.5%
 
573.5%
 
610.5%
 
742.0%
 
83617.8%
 
910.5%
 
ValueCountFrequency (%) 
2321.0%
 
2221.0%
 
2194.5%
 
2031.5%
 
1973.5%
 
1810.5%
 
1710.5%
 
163718.3%
 
15115.4%
 
1410.5%
 

stroke
Real number (ℝ≥0)

Distinct count37
Unique (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.61881188118812
Minimum0
Maximum36
Zeros1
Zeros (%)0.5%
Memory size1.7 KiB
2020-08-25T01:08:44.013142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q113
median21
Q325.75
95-th percentile33
Maximum36
Range36
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation8.947510984
Coefficient of variation (CV)0.4560679331
Kurtosis-0.7007201048
Mean19.61881188
Median Absolute Deviation (MAD)6
Skewness-0.3196666976
Sum3963
Variance80.05795281
2020-08-25T01:08:44.127598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
24209.9%
 
19146.9%
 
9146.9%
 
23136.4%
 
3115.4%
 
15115.4%
 
2294.5%
 
2194.5%
 
2684.0%
 
2863.0%
 
1763.0%
 
2563.0%
 
1063.0%
 
3163.0%
 
2063.0%
 
1363.0%
 
2952.5%
 
3252.5%
 
3642.0%
 
2742.0%
 
3042.0%
 
3342.0%
 
831.5%
 
3431.5%
 
3521.0%
 
Other values (12)178.4%
 
ValueCountFrequency (%) 
010.5%
 
121.0%
 
210.5%
 
3115.4%
 
421.0%
 
510.5%
 
621.0%
 
710.5%
 
831.5%
 
9146.9%
 
ValueCountFrequency (%) 
3642.0%
 
3521.0%
 
3431.5%
 
3342.0%
 
3252.5%
 
3163.0%
 
3042.0%
 
2952.5%
 
2863.0%
 
2742.0%
 

target
Real number (ℝ)

ZEROS

Distinct count5
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8762376237623762
Minimum-1
Maximum3
Zeros67
Zeros (%)33.2%
Memory size1.7 KiB
2020-08-25T01:08:44.401977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.205100976
Coefficient of variation (CV)1.375312978
Kurtosis-0.8277131735
Mean0.8762376238
Median Absolute Deviation (MAD)1
Skewness0.3266324066
Sum177
Variance1.452268361
2020-08-25T01:08:44.506383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
06733.2%
 
15426.7%
 
23215.8%
 
32713.4%
 
-12210.9%
 
ValueCountFrequency (%) 
-12210.9%
 
06733.2%
 
15426.7%
 
23215.8%
 
32713.4%
 
ValueCountFrequency (%) 
32713.4%
 
23215.8%
 
15426.7%
 
06733.2%
 
-12210.9%
 

Interactions

2020-08-25T01:07:59.956235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:00.104306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:00.241839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:00.374047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:00.518482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:00.856975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.007084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.144516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.271549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.411450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.563359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.699848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.834666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:01.971841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.110938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.240908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.380047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.525601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.672051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.806597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:02.938687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.080559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.213935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.357736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.501155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.626170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.765952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:03.909796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:04.040368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:04.172029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:04.303197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:04.442117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:04.573475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:04.708226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.053658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.183771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.307703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.428254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.557991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.679748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.805101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:05.925489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.035326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.157912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.290651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.406775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.520800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.640600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.764104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:06.891598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.016717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.144320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.284222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.421365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.553154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.693846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.828857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:07.968436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:08.101262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:08.226220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:08.369058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:08.513858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:08.651356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:08.794961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.146436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.292507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.423448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.562296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.706265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.839402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:09.978053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.098277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.227374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.353365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.485496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.605208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.720513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.847952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:10.979012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.102276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.221725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.348284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.472999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.593644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.727525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.860382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:11.994248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:12.130042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:12.259595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:12.396336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:12.524572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:12.657484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:12.784134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:13.084949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:13.218517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:13.357356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:08:39.320031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:39.457125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

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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.
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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.
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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.
2020-08-25T01:08:45.633839image/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

2020-08-25T01:08:39.747350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:08:40.221465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

wheel-basefuel-systemnormalized-losseshighway-mpgheightlengthengine-typemakecity-mpgbody-stylefuel-typecurb-weightengine-sizeaspirationnum-of-doorshorsepowerborepeak-rpmstroketarget
093.712638.050.8157.331131.0212004.092.00242416192
199.15326.056.1186.601719.0312847.0121.011232516102
298.451730.053.0176.231924.0012975.0146.0029308282
396.651918.050.8180.35916.0013685.0234.00221227123
494.555129.055.6159.332024.0012254.0109.002541316243
589.555125.051.6168.941517.0112756.0194.00231332083
694.51237.053.5170.231231.0412024.097.001431112211
794.511337.054.5165.331231.0311938.097.001431112211
8112.055116.055.4199.25914.0113715.0304.00229365221
993.062630.050.8157.331124.0212145.098.0122716231

Last rows

wheel-basefuel-systemnormalized-losseshighway-mpgheightlengthengine-typemakecity-mpgbody-stylefuel-typecurb-weightengine-sizeaspirationnum-of-doorshorsepowerborepeak-rpmstroketarget
19294.511537.054.5165.331231.0311889.097.002431112211
193113.052019.052.8199.60715.0314066.0258.00127317350
19496.50433.053.3167.53527.0212236.0110.002521119310
195103.555122.053.7193.83216.0313380.0209.002283015230
196107.952624.056.7186.721319.0313020.0120.001582210170
197100.435138.055.1180.232033.0302579.097.0114265240
198102.055117.047.8191.75713.0313950.0326.00232251050
199103.555122.055.7189.03216.0313230.0209.001283015230
200110.034725.056.5190.93922.0303515.0183.0111226332-1
201104.353728.057.5188.832124.0413042.0141.0017351515-1

Duplicate rows

Most frequent

wheel-basefuel-systemnormalized-losseshighway-mpgheightlengthengine-typemakecity-mpgbody-stylefuel-typecurb-weightengine-sizeaspirationnum-of-doorshorsepowerborepeak-rpmstroketargetcount
088.655127.048.8168.80021.0012548.0130.00252310432
189.555125.051.6168.941517.0112756.0194.002313320832
293.712138.050.6157.33431.0311989.090.001424161912
395.322223.049.6169.06817.0212380.070.002138213632
498.81932.055.5177.83826.0312410.0122.001502082302
598.811632.053.7177.83826.0212385.0122.002502082312
6107.932633.056.7186.721328.0303252.0152.011573202902