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 values Missing
make has unique values Unique
price has unique values Unique

Reproduction

Analysis started2024-10-16 08:55:58.932477
Analysis finished2024-10-16 08:56:07.148737
Duration8.22 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
2024-10-16T08:56:07.341450image/svg+xmlMatplotlib v3.9.2, 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 (%)
buick 7
 
4.5%
olds 7
 
4.5%
merc 6
 
3.9%
pont 6
 
3.9%
chev 6
 
3.9%
plym 5
 
3.2%
dodge 4
 
2.6%
datsun 4
 
2.6%
vw 4
 
2.6%
toyota 3
 
1.9%
Other values (93) 103
66.5%
2024-10-16T08:56:07.731453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
 
9.3%
a 62
 
7.1%
o 55
 
6.3%
e 53
 
6.1%
r 46
 
5.3%
i 41
 
4.7%
l 40
 
4.6%
t 37
 
4.2%
n 34
 
3.9%
d 30
 
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%
a 62
 
7.1%
o 55
 
6.3%
e 53
 
6.1%
r 46
 
5.3%
i 41
 
4.7%
l 40
 
4.6%
t 37
 
4.2%
n 34
 
3.9%
d 30
 
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%
a 62
 
7.1%
o 55
 
6.3%
e 53
 
6.1%
r 46
 
5.3%
i 41
 
4.7%
l 40
 
4.6%
t 37
 
4.2%
n 34
 
3.9%
d 30
 
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%
a 62
 
7.1%
o 55
 
6.3%
e 53
 
6.1%
r 46
 
5.3%
i 41
 
4.7%
l 40
 
4.6%
t 37
 
4.2%
n 34
 
3.9%
d 30
 
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 size276.0 B
2024-10-16T08:56:07.872442image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:08.021228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4099 1
 
1.4%
4749 1
 
1.4%
3799 1
 
1.4%
4816 1
 
1.4%
7827 1
 
1.4%
5788 1
 
1.4%
4453 1
 
1.4%
5189 1
 
1.4%
10372 1
 
1.4%
4082 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
3291 1
1.4%
3299 1
1.4%
3667 1
1.4%
3748 1
1.4%
3798 1
1.4%
3799 1
1.4%
3829 1
1.4%
3895 1
1.4%
3955 1
1.4%
3984 1
1.4%
ValueCountFrequency (%)
15906 1
1.4%
14500 1
1.4%
13594 1
1.4%
13466 1
1.4%
12990 1
1.4%
11995 1
1.4%
11497 1
1.4%
11385 1
1.4%
10372 1
1.4%
10371 1
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 size276.0 B
2024-10-16T08:56:08.147846image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:08.262302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
18 9
12.2%
19 8
 
10.8%
14 6
 
8.1%
22 5
 
6.8%
21 5
 
6.8%
25 5
 
6.8%
24 4
 
5.4%
17 4
 
5.4%
16 4
 
5.4%
23 3
 
4.1%
Other values (11) 21
28.4%
ValueCountFrequency (%)
12 2
 
2.7%
14 6
8.1%
15 2
 
2.7%
16 4
5.4%
17 4
5.4%
18 9
12.2%
19 8
10.8%
20 3
 
4.1%
21 5
6.8%
22 5
6.8%
ValueCountFrequency (%)
41 1
 
1.4%
35 2
 
2.7%
34 1
 
1.4%
31 1
 
1.4%
30 2
 
2.7%
29 1
 
1.4%
28 3
4.1%
26 3
4.1%
25 5
6.8%
24 4
5.4%

rep78
Categorical

High correlation  Missing 

Distinct5
Distinct (%)7.2%
Missing5
Missing (%)6.8%
Memory size687.0 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 (%)
Average 30
40.5%
Good 18
24.3%
Excellent 11
 
14.9%
Fair 8
 
10.8%
Poor 2
 
2.7%
(Missing) 5
 
6.8%

Length

2024-10-16T08:56:08.389208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T08:56:08.506820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
average 30
43.5%
good 18
26.1%
excellent 11
 
15.9%
fair 8
 
11.6%
poor 2
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 82
19.5%
r 40
9.5%
o 40
9.5%
a 38
9.0%
v 30
 
7.1%
A 30
 
7.1%
g 30
 
7.1%
l 22
 
5.2%
d 18
 
4.3%
G 18
 
4.3%
Other values (8) 73
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 82
19.5%
r 40
9.5%
o 40
9.5%
a 38
9.0%
v 30
 
7.1%
A 30
 
7.1%
g 30
 
7.1%
l 22
 
5.2%
d 18
 
4.3%
G 18
 
4.3%
Other values (8) 73
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 82
19.5%
r 40
9.5%
o 40
9.5%
a 38
9.0%
v 30
 
7.1%
A 30
 
7.1%
g 30
 
7.1%
l 22
 
5.2%
d 18
 
4.3%
G 18
 
4.3%
Other values (8) 73
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 82
19.5%
r 40
9.5%
o 40
9.5%
a 38
9.0%
v 30
 
7.1%
A 30
 
7.1%
g 30
 
7.1%
l 22
 
5.2%
d 18
 
4.3%
G 18
 
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 size424.0 B
2024-10-16T08:56:08.615499image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:08.730482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5 15
20.3%
2.5 14
18.9%
2 13
17.6%
3 13
17.6%
4 10
13.5%
4.5 4
 
5.4%
1.5 4
 
5.4%
5 1
 
1.4%
ValueCountFrequency (%)
1.5 4
 
5.4%
2 13
17.6%
2.5 14
18.9%
3 13
17.6%
3.5 15
20.3%
4 10
13.5%
4.5 4
 
5.4%
5 1
 
1.4%
ValueCountFrequency (%)
5 1
 
1.4%
4.5 4
 
5.4%
4 10
13.5%
3.5 15
20.3%
3 13
17.6%
2.5 14
18.9%
2 13
17.6%
1.5 4
 
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 size276.0 B
2024-10-16T08:56:08.847109image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:08.972538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
16 12
16.2%
11 8
10.8%
17 8
10.8%
20 6
8.1%
15 5
 
6.8%
10 5
 
6.8%
8 5
 
6.8%
14 4
 
5.4%
9 4
 
5.4%
13 4
 
5.4%
Other values (8) 13
17.6%
ValueCountFrequency (%)
5 1
 
1.4%
6 1
 
1.4%
7 3
 
4.1%
8 5
6.8%
9 4
5.4%
10 5
6.8%
11 8
10.8%
12 3
 
4.1%
13 4
5.4%
14 4
5.4%
ValueCountFrequency (%)
23 1
 
1.4%
22 1
 
1.4%
21 2
 
2.7%
20 6
8.1%
18 1
 
1.4%
17 8
10.8%
16 12
16.2%
15 5
6.8%
14 4
 
5.4%
13 4
 
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 size276.0 B
2024-10-16T08:56:09.109739image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:09.251365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3690 2
 
2.7%
1800 2
 
2.7%
3370 2
 
2.7%
4060 2
 
2.7%
3600 2
 
2.7%
2750 2
 
2.7%
2650 2
 
2.7%
2830 2
 
2.7%
2200 2
 
2.7%
3420 2
 
2.7%
Other values (54) 54
73.0%
ValueCountFrequency (%)
1760 1
1.4%
1800 2
2.7%
1830 1
1.4%
1930 1
1.4%
1980 1
1.4%
1990 1
1.4%
2020 1
1.4%
2040 1
1.4%
2050 1
1.4%
2070 1
1.4%
ValueCountFrequency (%)
4840 1
1.4%
4720 1
1.4%
4330 1
1.4%
4290 1
1.4%
4130 1
1.4%
4080 1
1.4%
4060 2
2.7%
4030 1
1.4%
3900 1
1.4%
3880 1
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 size276.0 B
2024-10-16T08:56:09.395460image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:09.666771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
170 4
 
5.4%
200 4
 
5.4%
198 4
 
5.4%
201 3
 
4.1%
165 3
 
4.1%
206 3
 
4.1%
179 3
 
4.1%
193 2
 
2.7%
163 2
 
2.7%
155 2
 
2.7%
Other values (37) 44
59.5%
ValueCountFrequency (%)
142 1
1.4%
147 1
1.4%
149 1
1.4%
154 1
1.4%
155 2
2.7%
156 1
1.4%
157 1
1.4%
161 1
1.4%
163 2
2.7%
164 1
1.4%
ValueCountFrequency (%)
233 1
1.4%
230 1
1.4%
222 1
1.4%
221 2
2.7%
220 2
2.7%
218 2
2.7%
217 1
1.4%
214 1
1.4%
212 2
2.7%
207 1
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 size276.0 B
2024-10-16T08:56:09.788879image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:09.918499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
43 12
16.2%
36 9
12.2%
42 7
9.5%
40 6
8.1%
35 6
8.1%
34 6
8.1%
37 4
 
5.4%
41 4
 
5.4%
44 3
 
4.1%
45 3
 
4.1%
Other values (8) 14
18.9%
ValueCountFrequency (%)
31 1
 
1.4%
32 1
 
1.4%
33 2
 
2.7%
34 6
8.1%
35 6
8.1%
36 9
12.2%
37 4
5.4%
38 3
 
4.1%
39 1
 
1.4%
40 6
8.1%
ValueCountFrequency (%)
51 1
 
1.4%
48 2
 
2.7%
46 3
 
4.1%
45 3
 
4.1%
44 3
 
4.1%
43 12
16.2%
42 7
9.5%
41 4
 
5.4%
40 6
8.1%
39 1
 
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 size276.0 B
2024-10-16T08:56:10.050009image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:10.186759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
231 13
17.6%
350 5
 
6.8%
97 5
 
6.8%
302 4
 
5.4%
140 3
 
4.1%
151 3
 
4.1%
121 3
 
4.1%
250 3
 
4.1%
119 3
 
4.1%
98 2
 
2.7%
Other values (21) 30
40.5%
ValueCountFrequency (%)
79 1
 
1.4%
85 1
 
1.4%
86 2
 
2.7%
89 1
 
1.4%
90 1
 
1.4%
91 1
 
1.4%
97 5
6.8%
98 2
 
2.7%
105 2
 
2.7%
107 1
 
1.4%
ValueCountFrequency (%)
425 1
 
1.4%
400 2
 
2.7%
350 5
 
6.8%
318 2
 
2.7%
304 1
 
1.4%
302 4
 
5.4%
258 1
 
1.4%
250 3
 
4.1%
231 13
17.6%
225 2
 
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 size424.0 B
2024-10-16T08:56:10.315972image/svg+xmlMatplotlib v3.9.2, 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
2024-10-16T08:56:10.450448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.730000019 9
 
12.2%
2.930000067 8
 
10.8%
3.079999924 7
 
9.5%
2.470000029 5
 
6.8%
3.049999952 3
 
4.1%
3.779999971 3
 
4.1%
3.539999962 3
 
4.1%
2.410000086 3
 
4.1%
3.369999886 2
 
2.7%
3.579999924 2
 
2.7%
Other values (26) 29
39.2%
ValueCountFrequency (%)
2.190000057 1
 
1.4%
2.24000001 1
 
1.4%
2.25999999 1
 
1.4%
2.279999971 1
 
1.4%
2.410000086 3
 
4.1%
2.430000067 1
 
1.4%
2.470000029 5
6.8%
2.529999971 1
 
1.4%
2.559999943 2
 
2.7%
2.730000019 9
12.2%
ValueCountFrequency (%)
3.890000105 1
 
1.4%
3.809999943 1
 
1.4%
3.779999971 3
4.1%
3.74000001 1
 
1.4%
3.730000019 1
 
1.4%
3.720000029 1
 
1.4%
3.700000048 2
2.7%
3.640000105 1
 
1.4%
3.579999924 2
2.7%
3.549999952 1
 
1.4%

foreign
Categorical

High correlation 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size439.0 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 (%)
Domestic 52
70.3%
Foreign 22
29.7%

Length

2024-10-16T08:56:10.585510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T08:56:10.691984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
domestic 52
70.3%
foreign 22
29.7%

Most occurring characters

ValueCountFrequency (%)
o 74
13.0%
e 74
13.0%
i 74
13.0%
D 52
9.1%
m 52
9.1%
s 52
9.1%
t 52
9.1%
c 52
9.1%
F 22
 
3.9%
r 22
 
3.9%
Other values (2) 44
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 74
13.0%
e 74
13.0%
i 74
13.0%
D 52
9.1%
m 52
9.1%
s 52
9.1%
t 52
9.1%
c 52
9.1%
F 22
 
3.9%
r 22
 
3.9%
Other values (2) 44
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 74
13.0%
e 74
13.0%
i 74
13.0%
D 52
9.1%
m 52
9.1%
s 52
9.1%
t 52
9.1%
c 52
9.1%
F 22
 
3.9%
r 22
 
3.9%
Other values (2) 44
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 74
13.0%
e 74
13.0%
i 74
13.0%
D 52
9.1%
m 52
9.1%
s 52
9.1%
t 52
9.1%
c 52
9.1%
F 22
 
3.9%
r 22
 
3.9%
Other values (2) 44
7.7%

Interactions

2024-10-16T08:56:05.915403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.200963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.019842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.895737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.724789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.575319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.490246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.259976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.092624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.005252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.294118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.109685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.988444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.819897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.664536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.575157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.354042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.182387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.091072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.381209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.190974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.078830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.908620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.750301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.656671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.441355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.268892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.185306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.475098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.374725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.174300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.009610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.842705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.746328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.539337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.363665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.280223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.570648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.466051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.270206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.106688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.935873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.836330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.634833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.459465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.364464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.655850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.548902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.358407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.198773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.017744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.916979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.723668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.551048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.570025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.739499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.626809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.441664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.283892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.098197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.992910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.807936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.635442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.667430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.836469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.721413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.540291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.385149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.193745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.085631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.905845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.734734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:06.757354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:59.928211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:00.808679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:01.632153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:02.479528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:03.402518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.172985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:04.998977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:56:05.823633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-16T08:56:10.774439image/svg+xmlMatplotlib v3.9.2, 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

2024-10-16T08:56:06.888657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T08:56:07.075585image/svg+xmlMatplotlib v3.9.2, 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