Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations74
Missing cells5
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory104.3 B

Variable types

Text1
Numeric9
Categorical2

Alerts

displacement is highly overall correlated with foreign and 6 other fieldsHigh correlation
foreign is highly overall correlated with displacement and 5 other fieldsHigh correlation
gear_ratio is highly overall correlated with displacement and 6 other fieldsHigh correlation
headroom is highly overall correlated with length and 2 other fieldsHigh correlation
length is highly overall correlated with displacement and 7 other fieldsHigh correlation
mpg is highly overall correlated with displacement and 6 other fieldsHigh correlation
price is highly overall correlated with mpgHigh correlation
rep78 is highly overall correlated with foreignHigh correlation
trunk is highly overall correlated with displacement and 6 other fieldsHigh correlation
turn is highly overall correlated with displacement and 6 other fieldsHigh correlation
weight is highly overall correlated with displacement and 7 other fieldsHigh correlation
rep78 has 5 (6.8%) missing valuesMissing
make has unique valuesUnique
price has unique valuesUnique

Reproduction

Analysis started2025-09-23 16:06:10.650078
Analysis finished2025-09-23 16:06:15.923953
Duration5.27 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

make
Text

Unique 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2025-09-23T16:06:16.087065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length15
Mean length11.77027
Min length6

Characters and Unicode

Total characters871
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st rowAMC Concord
2nd rowAMC Pacer
3rd rowAMC Spirit
4th rowBuick Century
5th rowBuick Electra
ValueCountFrequency (%)
buick7
 
4.5%
olds7
 
4.5%
merc6
 
3.9%
pont6
 
3.9%
chev6
 
3.9%
plym5
 
3.2%
dodge4
 
2.6%
datsun4
 
2.6%
vw4
 
2.6%
toyota3
 
1.9%
Other values (93)103
66.5%
2025-09-23T16:06:16.361355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
 
9.3%
a62
 
7.1%
o55
 
6.3%
e53
 
6.1%
r46
 
5.3%
i41
 
4.7%
l40
 
4.6%
t37
 
4.2%
n34
 
3.9%
d30
 
3.4%
Other values (49)392
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81
 
9.3%
a62
 
7.1%
o55
 
6.3%
e53
 
6.1%
r46
 
5.3%
i41
 
4.7%
l40
 
4.6%
t37
 
4.2%
n34
 
3.9%
d30
 
3.4%
Other values (49)392
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81
 
9.3%
a62
 
7.1%
o55
 
6.3%
e53
 
6.1%
r46
 
5.3%
i41
 
4.7%
l40
 
4.6%
t37
 
4.2%
n34
 
3.9%
d30
 
3.4%
Other values (49)392
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81
 
9.3%
a62
 
7.1%
o55
 
6.3%
e53
 
6.1%
r46
 
5.3%
i41
 
4.7%
l40
 
4.6%
t37
 
4.2%
n34
 
3.9%
d30
 
3.4%
Other values (49)392
45.0%

price
Real number (ℝ)

High correlation  Unique 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6165.2568
Minimum3291
Maximum15906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:16.446937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3291
5-th percentile3780.5
Q14220.25
median5006.5
Q36332.25
95-th percentile13156.6
Maximum15906
Range12615
Interquartile range (IQR)2112

Descriptive statistics

Standard deviation2949.4959
Coefficient of variation (CV)0.47840601
Kurtosis2.0340477
Mean6165.2568
Median Absolute Deviation (MAD)916
Skewness1.687841
Sum456229
Variance8699526
MonotonicityNot monotonic
2025-09-23T16:06:16.541917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40991
 
1.4%
47491
 
1.4%
37991
 
1.4%
48161
 
1.4%
78271
 
1.4%
57881
 
1.4%
44531
 
1.4%
51891
 
1.4%
103721
 
1.4%
40821
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
32911
1.4%
32991
1.4%
36671
1.4%
37481
1.4%
37981
1.4%
37991
1.4%
38291
1.4%
38951
1.4%
39551
1.4%
39841
1.4%
ValueCountFrequency (%)
159061
1.4%
145001
1.4%
135941
1.4%
134661
1.4%
129901
1.4%
119951
1.4%
114971
1.4%
113851
1.4%
103721
1.4%
103711
1.4%

mpg
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.297297
Minimum12
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:16.614665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14
Q118
median20
Q324.75
95-th percentile32.05
Maximum41
Range29
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation5.7855032
Coefficient of variation (CV)0.27165434
Kurtosis1.1299198
Mean21.297297
Median Absolute Deviation (MAD)3.5
Skewness0.96846014
Sum1576
Variance33.472047
MonotonicityNot monotonic
2025-09-23T16:06:16.806443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
189
12.2%
198
 
10.8%
146
 
8.1%
225
 
6.8%
215
 
6.8%
255
 
6.8%
244
 
5.4%
174
 
5.4%
164
 
5.4%
233
 
4.1%
Other values (11)21
28.4%
ValueCountFrequency (%)
122
 
2.7%
146
8.1%
152
 
2.7%
164
5.4%
174
5.4%
189
12.2%
198
10.8%
203
 
4.1%
215
6.8%
225
6.8%
ValueCountFrequency (%)
411
 
1.4%
352
 
2.7%
341
 
1.4%
311
 
1.4%
302
 
2.7%
291
 
1.4%
283
4.1%
263
4.1%
255
6.8%
244
5.4%

rep78
Categorical

High correlation  Missing 

Distinct5
Distinct (%)7.2%
Missing5
Missing (%)6.8%
Memory size691.0 B
Average
30 
Good
18 
Excellent
11 
Fair
Poor
 
2

Length

Max length9
Median length7
Mean length6.1014493
Min length4

Characters and Unicode

Total characters421
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage
2nd rowAverage
3rd rowAverage
4th rowGood
5th rowAverage

Common Values

ValueCountFrequency (%)
Average30
40.5%
Good18
24.3%
Excellent11
 
14.9%
Fair8
 
10.8%
Poor2
 
2.7%
(Missing)5
 
6.8%

Length

2025-09-23T16:06:16.878850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T16:06:16.935620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
average30
43.5%
good18
26.1%
excellent11
 
15.9%
fair8
 
11.6%
poor2
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e82
19.5%
r40
9.5%
o40
9.5%
a38
9.0%
v30
 
7.1%
A30
 
7.1%
g30
 
7.1%
l22
 
5.2%
d18
 
4.3%
G18
 
4.3%
Other values (8)73
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e82
19.5%
r40
9.5%
o40
9.5%
a38
9.0%
v30
 
7.1%
A30
 
7.1%
g30
 
7.1%
l22
 
5.2%
d18
 
4.3%
G18
 
4.3%
Other values (8)73
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e82
19.5%
r40
9.5%
o40
9.5%
a38
9.0%
v30
 
7.1%
A30
 
7.1%
g30
 
7.1%
l22
 
5.2%
d18
 
4.3%
G18
 
4.3%
Other values (8)73
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e82
19.5%
r40
9.5%
o40
9.5%
a38
9.0%
v30
 
7.1%
A30
 
7.1%
g30
 
7.1%
l22
 
5.2%
d18
 
4.3%
G18
 
4.3%
Other values (8)73
17.3%

headroom
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9932432
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.0 B
2025-09-23T16:06:16.994244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.825
Q12.5
median3
Q33.5
95-th percentile4.5
Maximum5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84599477
Coefficient of variation (CV)0.28263482
Kurtosis-0.76207393
Mean2.9932432
Median Absolute Deviation (MAD)0.5
Skewness0.14379646
Sum221.5
Variance0.71570712
MonotonicityNot monotonic
2025-09-23T16:06:17.056983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.515
20.3%
2.514
18.9%
213
17.6%
313
17.6%
410
13.5%
4.54
 
5.4%
1.54
 
5.4%
51
 
1.4%
ValueCountFrequency (%)
1.54
 
5.4%
213
17.6%
2.514
18.9%
313
17.6%
3.515
20.3%
410
13.5%
4.54
 
5.4%
51
 
1.4%
ValueCountFrequency (%)
51
 
1.4%
4.54
 
5.4%
410
13.5%
3.515
20.3%
313
17.6%
2.514
18.9%
213
17.6%
1.54
 
5.4%

trunk
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.756757
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:17.121808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q110.25
median14
Q316.75
95-th percentile20.35
Maximum23
Range18
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.2774042
Coefficient of variation (CV)0.31093115
Kurtosis-0.77963931
Mean13.756757
Median Absolute Deviation (MAD)3
Skewness0.029811133
Sum1018
Variance18.296187
MonotonicityNot monotonic
2025-09-23T16:06:17.193474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1612
16.2%
118
10.8%
178
10.8%
206
8.1%
155
 
6.8%
105
 
6.8%
85
 
6.8%
144
 
5.4%
94
 
5.4%
134
 
5.4%
Other values (8)13
17.6%
ValueCountFrequency (%)
51
 
1.4%
61
 
1.4%
73
 
4.1%
85
6.8%
94
5.4%
105
6.8%
118
10.8%
123
 
4.1%
134
5.4%
144
5.4%
ValueCountFrequency (%)
231
 
1.4%
221
 
1.4%
212
 
2.7%
206
8.1%
181
 
1.4%
178
10.8%
1612
16.2%
155
6.8%
144
 
5.4%
134
 
5.4%

weight
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3019.4595
Minimum1760
Maximum4840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:17.274913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1760
5-th percentile1895
Q12250
median3190
Q33600
95-th percentile4186
Maximum4840
Range3080
Interquartile range (IQR)1350

Descriptive statistics

Standard deviation777.19357
Coefficient of variation (CV)0.25739493
Kurtosis-0.85851775
Mean3019.4595
Median Absolute Deviation (MAD)550
Skewness0.15119863
Sum223440
Variance604029.84
MonotonicityNot monotonic
2025-09-23T16:06:17.365648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36902
 
2.7%
18002
 
2.7%
33702
 
2.7%
40602
 
2.7%
36002
 
2.7%
27502
 
2.7%
26502
 
2.7%
28302
 
2.7%
22002
 
2.7%
34202
 
2.7%
Other values (54)54
73.0%
ValueCountFrequency (%)
17601
1.4%
18002
2.7%
18301
1.4%
19301
1.4%
19801
1.4%
19901
1.4%
20201
1.4%
20401
1.4%
20501
1.4%
20701
1.4%
ValueCountFrequency (%)
48401
1.4%
47201
1.4%
43301
1.4%
42901
1.4%
41301
1.4%
40801
1.4%
40602
2.7%
40301
1.4%
39001
1.4%
38801
1.4%

length
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.93243
Minimum142
Maximum233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:17.455933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum142
5-th percentile154.65
Q1170
median192.5
Q3203.75
95-th percentile221
Maximum233
Range91
Interquartile range (IQR)33.75

Descriptive statistics

Standard deviation22.26634
Coefficient of variation (CV)0.11848056
Kurtosis-0.94081772
Mean187.93243
Median Absolute Deviation (MAD)19
Skewness-0.041827224
Sum13907
Variance495.78989
MonotonicityNot monotonic
2025-09-23T16:06:17.544728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1704
 
5.4%
2004
 
5.4%
1984
 
5.4%
2013
 
4.1%
1653
 
4.1%
2063
 
4.1%
1793
 
4.1%
1932
 
2.7%
1632
 
2.7%
1552
 
2.7%
Other values (37)44
59.5%
ValueCountFrequency (%)
1421
1.4%
1471
1.4%
1491
1.4%
1541
1.4%
1552
2.7%
1561
1.4%
1571
1.4%
1611
1.4%
1632
2.7%
1641
1.4%
ValueCountFrequency (%)
2331
1.4%
2301
1.4%
2221
1.4%
2212
2.7%
2202
2.7%
2182
2.7%
2171
1.4%
2141
1.4%
2122
2.7%
2071
1.4%

turn
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.648649
Minimum31
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:17.615888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile33.65
Q136
median40
Q343
95-th percentile46
Maximum51
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3993537
Coefficient of variation (CV)0.11095848
Kurtosis-0.73957736
Mean39.648649
Median Absolute Deviation (MAD)3.5
Skewness0.12640268
Sum2934
Variance19.354313
MonotonicityNot monotonic
2025-09-23T16:06:17.687295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4312
16.2%
369
12.2%
427
9.5%
406
8.1%
356
8.1%
346
8.1%
374
 
5.4%
414
 
5.4%
443
 
4.1%
453
 
4.1%
Other values (8)14
18.9%
ValueCountFrequency (%)
311
 
1.4%
321
 
1.4%
332
 
2.7%
346
8.1%
356
8.1%
369
12.2%
374
5.4%
383
 
4.1%
391
 
1.4%
406
8.1%
ValueCountFrequency (%)
511
 
1.4%
482
 
2.7%
463
 
4.1%
453
 
4.1%
443
 
4.1%
4312
16.2%
427
9.5%
414
 
5.4%
406
8.1%
391
 
1.4%

displacement
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.2973
Minimum79
Maximum425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 B
2025-09-23T16:06:17.761695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile87.95
Q1119
median196
Q3245.25
95-th percentile350
Maximum425
Range346
Interquartile range (IQR)126.25

Descriptive statistics

Standard deviation91.837219
Coefficient of variation (CV)0.46547632
Kurtosis-0.58308176
Mean197.2973
Median Absolute Deviation (MAD)75
Skewness0.60396873
Sum14600
Variance8434.0748
MonotonicityNot monotonic
2025-09-23T16:06:17.842790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
23113
17.6%
3505
 
6.8%
975
 
6.8%
3024
 
5.4%
1403
 
4.1%
1513
 
4.1%
1213
 
4.1%
2503
 
4.1%
1193
 
4.1%
982
 
2.7%
Other values (21)30
40.5%
ValueCountFrequency (%)
791
 
1.4%
851
 
1.4%
862
 
2.7%
891
 
1.4%
901
 
1.4%
911
 
1.4%
975
6.8%
982
 
2.7%
1052
 
2.7%
1071
 
1.4%
ValueCountFrequency (%)
4251
 
1.4%
4002
 
2.7%
3505
 
6.8%
3182
 
2.7%
3041
 
1.4%
3024
 
5.4%
2581
 
1.4%
2503
 
4.1%
23113
17.6%
2252
 
2.7%

gear_ratio
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0148649
Minimum2.1900001
Maximum3.8900001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.0 B
2025-09-23T16:06:17.919987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.1900001
5-th percentile2.3645
Q12.73
median2.955
Q33.3524999
95-th percentile3.78
Maximum3.8900001
Range1.7
Interquartile range (IQR)0.62249988

Descriptive statistics

Standard deviation0.45628712
Coefficient of variation (CV)0.15134579
Kurtosis-0.87628722
Mean3.0148649
Median Absolute Deviation (MAD)0.2650001
Skewness0.22372657
Sum223.1
Variance0.20819794
MonotonicityNot monotonic
2025-09-23T16:06:17.999656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.7300000199
 
12.2%
2.9300000678
 
10.8%
3.0799999247
 
9.5%
2.4700000295
 
6.8%
3.0499999523
 
4.1%
3.7799999713
 
4.1%
3.5399999623
 
4.1%
2.4100000863
 
4.1%
3.3699998862
 
2.7%
3.5799999242
 
2.7%
Other values (26)29
39.2%
ValueCountFrequency (%)
2.1900000571
 
1.4%
2.240000011
 
1.4%
2.259999991
 
1.4%
2.2799999711
 
1.4%
2.4100000863
 
4.1%
2.4300000671
 
1.4%
2.4700000295
6.8%
2.5299999711
 
1.4%
2.5599999432
 
2.7%
2.7300000199
12.2%
ValueCountFrequency (%)
3.8900001051
 
1.4%
3.8099999431
 
1.4%
3.7799999713
4.1%
3.740000011
 
1.4%
3.7300000191
 
1.4%
3.7200000291
 
1.4%
3.7000000482
2.7%
3.6400001051
 
1.4%
3.5799999242
2.7%
3.5499999521
 
1.4%

foreign
Categorical

High correlation 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size443.0 B
Domestic
52 
Foreign
22 

Length

Max length8
Median length8
Mean length7.7027027
Min length7

Characters and Unicode

Total characters570
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDomestic
2nd rowDomestic
3rd rowDomestic
4th rowDomestic
5th rowDomestic

Common Values

ValueCountFrequency (%)
Domestic52
70.3%
Foreign22
29.7%

Length

2025-09-23T16:06:18.079711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T16:06:18.124783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
domestic52
70.3%
foreign22
29.7%

Most occurring characters

ValueCountFrequency (%)
o74
13.0%
e74
13.0%
i74
13.0%
D52
9.1%
m52
9.1%
s52
9.1%
t52
9.1%
c52
9.1%
F22
 
3.9%
r22
 
3.9%
Other values (2)44
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o74
13.0%
e74
13.0%
i74
13.0%
D52
9.1%
m52
9.1%
s52
9.1%
t52
9.1%
c52
9.1%
F22
 
3.9%
r22
 
3.9%
Other values (2)44
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o74
13.0%
e74
13.0%
i74
13.0%
D52
9.1%
m52
9.1%
s52
9.1%
t52
9.1%
c52
9.1%
F22
 
3.9%
r22
 
3.9%
Other values (2)44
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o74
13.0%
e74
13.0%
i74
13.0%
D52
9.1%
m52
9.1%
s52
9.1%
t52
9.1%
c52
9.1%
F22
 
3.9%
r22
 
3.9%
Other values (2)44
7.7%

Interactions

2025-09-23T16:06:15.242413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-09-23T16:06:18.170992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
displacementforeigngear_ratioheadroomlengthmpgpricerep78trunkturnweight
displacement1.0000.635-0.8550.4780.852-0.7710.3740.2290.5770.7790.905
foreign0.6351.0000.6580.4510.5910.2950.0000.5840.3690.6570.541
gear_ratio-0.8550.6581.000-0.384-0.7060.610-0.2530.210-0.509-0.654-0.753
headroom0.4780.451-0.3841.0000.532-0.4870.0970.2860.6770.4500.528
length0.8520.591-0.7060.5321.000-0.8310.4870.2740.7190.8820.949
mpg-0.7710.2950.610-0.487-0.8311.000-0.5420.220-0.650-0.758-0.858
price0.3740.000-0.2530.0970.487-0.5421.0000.0000.4000.3060.487
rep780.2290.5840.2100.2860.2740.2200.0001.0000.2020.3620.245
trunk0.5770.369-0.5090.6770.719-0.6500.4000.2021.0000.6200.656
turn0.7790.657-0.6540.4500.882-0.7580.3060.3620.6201.0000.860
weight0.9050.541-0.7530.5280.949-0.8580.4870.2450.6560.8601.000

Missing values

2025-09-23T16:06:15.802858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-23T16:06:15.879496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

makepricempgrep78headroomtrunkweightlengthturndisplacementgear_ratioforeign
0AMC Concord409922Average2.5112930186401213.58Domestic
1AMC Pacer474917Average3.0113350173402582.53Domestic
2AMC Spirit379922NaN3.0122640168351213.08Domestic
3Buick Century481620Average4.5163250196401962.93Domestic
4Buick Electra782715Good4.0204080222433502.41Domestic
5Buick LeSabre578818Average4.0213670218432312.73Domestic
6Buick Opel445326NaN3.0102230170343042.87Domestic
7Buick Regal518920Average2.0163280200421962.93Domestic
8Buick Riviera1037216Average3.5173880207432312.93Domestic
9Buick Skylark408219Average3.5133400200422313.08Domestic
makepricempgrep78headroomtrunkweightlengthturndisplacementgear_ratioforeign
64Renault Le Car389526Average3.010183014234793.72Foreign
65Subaru379835Excellent2.511205016436973.81Foreign
66Toyota Celica589918Excellent2.5142410174361343.06Foreign
67Toyota Corolla374831Excellent3.09220016535973.21Foreign
68Toyota Corona571918Excellent2.0112670175361343.05Foreign
69VW Dasher714023Good2.512216017236973.74Foreign
70VW Diesel539741Excellent3.015204015535903.78Foreign
71VW Rabbit469725Good3.015193015535893.78Foreign
72VW Scirocco685025Good2.016199015636973.78Foreign
73Volvo 2601199517Excellent2.5143170193371632.98Foreign