Overview

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

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

Reproduction

Analysis started2023-09-12 08:33:36.485123
Analysis finished2023-09-12 08:33:49.195128
Duration12.71 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
2023-09-12T09:33:49.398742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length17
Median length15
Mean length11.77027
Min length6

Characters and Unicode

Total characters871
Distinct characters59
Distinct categories6 ?
Distinct scripts2 ?
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%
chev 6
 
3.9%
pont 6
 
3.9%
plym 5
 
3.2%
datsun 4
 
2.6%
dodge 4
 
2.6%
vw 4
 
2.6%
cad 3
 
1.9%
Other values (93) 103
66.5%
2023-09-12T09:33:49.847046image/svg+xmlMatplotlib v3.7.3, 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%
. 30
 
3.4%
Other values (49) 392
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 568
65.2%
Uppercase Letter 161
 
18.5%
Space Separator 81
 
9.3%
Other Punctuation 30
 
3.4%
Decimal Number 30
 
3.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 62
10.9%
o 55
 
9.7%
e 53
 
9.3%
r 46
 
8.1%
i 41
 
7.2%
l 40
 
7.0%
t 37
 
6.5%
n 34
 
6.0%
d 30
 
5.3%
c 28
 
4.9%
Other values (15) 142
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 29
18.0%
M 20
12.4%
P 15
9.3%
D 13
8.1%
S 12
 
7.5%
B 9
 
5.6%
O 9
 
5.6%
V 8
 
5.0%
A 7
 
4.3%
L 7
 
4.3%
Other values (11) 32
19.9%
Decimal Number
ValueCountFrequency (%)
0 11
36.7%
2 4
 
13.3%
8 4
 
13.3%
1 3
 
10.0%
6 2
 
6.7%
5 2
 
6.7%
4 1
 
3.3%
3 1
 
3.3%
9 1
 
3.3%
7 1
 
3.3%
Space Separator
ValueCountFrequency (%)
81
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 729
83.7%
Common 142
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 62
 
8.5%
o 55
 
7.5%
e 53
 
7.3%
r 46
 
6.3%
i 41
 
5.6%
l 40
 
5.5%
t 37
 
5.1%
n 34
 
4.7%
d 30
 
4.1%
C 29
 
4.0%
Other values (36) 302
41.4%
Common
ValueCountFrequency (%)
81
57.0%
. 30
 
21.1%
0 11
 
7.7%
2 4
 
2.8%
8 4
 
2.8%
1 3
 
2.1%
6 2
 
1.4%
5 2
 
1.4%
4 1
 
0.7%
3 1
 
0.7%
Other values (3) 3
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 871
100.0%

Most frequent character per block

ASCII
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%
. 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
2023-09-12T09:33:50.198942image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:50.390869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4099 1
 
1.4%
6229 1
 
1.4%
6295 1
 
1.4%
9690 1
 
1.4%
4172 1
 
1.4%
4424 1
 
1.4%
4723 1
 
1.4%
5222 1
 
1.4%
4934 1
 
1.4%
5798 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
2023-09-12T09:33:50.543322image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:50.691841image/svg+xmlMatplotlib v3.7.3, 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%
25 5
 
6.8%
21 5
 
6.8%
16 4
 
5.4%
17 4
 
5.4%
24 4
 
5.4%
26 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
8 
Poor
 
2

Length

Max length9
Median length7
Mean length6.1014493
Min length4

Characters and Unicode

Total characters421
Distinct characters18
Distinct categories2 ?
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

2023-09-12T09:33:50.839761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T09:33:51.155841image/svg+xmlMatplotlib v3.7.3, 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%
A 30
 
7.1%
g 30
 
7.1%
v 30
 
7.1%
l 22
 
5.2%
G 18
 
4.3%
d 18
 
4.3%
Other values (8) 73
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 352
83.6%
Uppercase Letter 69
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 82
23.3%
r 40
11.4%
o 40
11.4%
a 38
10.8%
g 30
 
8.5%
v 30
 
8.5%
l 22
 
6.2%
d 18
 
5.1%
t 11
 
3.1%
n 11
 
3.1%
Other values (3) 30
 
8.5%
Uppercase Letter
ValueCountFrequency (%)
A 30
43.5%
G 18
26.1%
E 11
 
15.9%
F 8
 
11.6%
P 2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 421
100.0%

Most frequent character per script

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

Most occurring blocks

ValueCountFrequency (%)
ASCII 421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 82
19.5%
r 40
9.5%
o 40
9.5%
a 38
9.0%
A 30
 
7.1%
g 30
 
7.1%
v 30
 
7.1%
l 22
 
5.2%
G 18
 
4.3%
d 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
2023-09-12T09:33:51.299848image/svg+xmlMatplotlib v3.7.3, 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
Skewness0.14379655
Sum221.5
Variance0.71570712
MonotonicityNot monotonic
2023-09-12T09:33:51.474423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5 15
20.3%
2.5 14
18.9%
3 13
17.6%
2 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
2023-09-12T09:33:51.643396image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:51.819830image/svg+xmlMatplotlib v3.7.3, 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%
8 5
 
6.8%
10 5
 
6.8%
15 5
 
6.8%
14 4
 
5.4%
13 4
 
5.4%
9 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
2023-09-12T09:33:52.008123image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:52.223146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2830 2
 
2.7%
3690 2
 
2.7%
2200 2
 
2.7%
3370 2
 
2.7%
4060 2
 
2.7%
3420 2
 
2.7%
2650 2
 
2.7%
1800 2
 
2.7%
3600 2
 
2.7%
2750 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
2023-09-12T09:33:52.449630image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:52.674003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
198 4
 
5.4%
170 4
 
5.4%
200 4
 
5.4%
206 3
 
4.1%
179 3
 
4.1%
201 3
 
4.1%
165 3
 
4.1%
218 2
 
2.7%
221 2
 
2.7%
204 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
2023-09-12T09:33:52.837295image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:53.030626image/svg+xmlMatplotlib v3.7.3, 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%
38 3
 
4.1%
46 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
2023-09-12T09:33:53.233272image/svg+xmlMatplotlib v3.7.3, 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
2023-09-12T09:33:53.427409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
231 13
17.6%
97 5
 
6.8%
350 5
 
6.8%
302 4
 
5.4%
151 3
 
4.1%
119 3
 
4.1%
140 3
 
4.1%
121 3
 
4.1%
250 3
 
4.1%
200 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
2023-09-12T09:33:53.624665image/svg+xmlMatplotlib v3.7.3, 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-0.87628728
Mean3.0148649
Median Absolute Deviation (MAD)0.2650001
Skewness0.2237262
Sum223.1
Variance0.20819794
MonotonicityNot monotonic
2023-09-12T09:33:53.794265image/svg+xmlMatplotlib v3.7.3, 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%
3.539999962 3
 
4.1%
2.410000086 3
 
4.1%
3.779999971 3
 
4.1%
3.700000048 2
 
2.7%
3.369999886 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 categories2 ?
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

2023-09-12T09:33:53.971644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T09:33:54.108186image/svg+xmlMatplotlib v3.7.3, 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 (%)
Lowercase Letter 496
87.0%
Uppercase Letter 74
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 74
14.9%
e 74
14.9%
i 74
14.9%
m 52
10.5%
s 52
10.5%
t 52
10.5%
c 52
10.5%
r 22
 
4.4%
g 22
 
4.4%
n 22
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
D 52
70.3%
F 22
29.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 570
100.0%

Most frequent character per script

Latin
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 (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
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

2023-09-12T09:33:47.852952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:38.409310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.506946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.637610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.940444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.262638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.308570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.421512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.780626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.957654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:38.529041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.636932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.757710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:42.153522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.388221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.410933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.537544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.897851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.059652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:38.642766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.741633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.877469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:42.287606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.500225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.528444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.809197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.016166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.172785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:38.770802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.974729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.075157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:42.417024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.620832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.657945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.948194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.159767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.284499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:38.883544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.090955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.219426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:42.563320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.747308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.785016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.073374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.285014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.387493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:38.993431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.200131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.380352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:42.710721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.863136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.906172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.211303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.413057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.488296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.113222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.298100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.513318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:42.860272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.975545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.015862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.323926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.518130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.605512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.244010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.417632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.654699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.002810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.089053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.159012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.489683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.637545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:48.715440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:39.368474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:40.525835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:41.800303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:43.128569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:44.202885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:45.293891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:46.635942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-12T09:33:47.741539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-09-12T09:33:54.230429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
pricempgheadroomtrunkweightlengthturndisplacementgear_ratiorep78foreign
price1.000-0.5420.0970.4000.4870.4870.3060.374-0.2530.0000.000
mpg-0.5421.000-0.487-0.650-0.858-0.831-0.758-0.7710.6100.2200.295
headroom0.097-0.4871.0000.6770.5280.5320.4500.478-0.3840.2860.451
trunk0.400-0.6500.6771.0000.6560.7190.6200.577-0.5090.2020.369
weight0.487-0.8580.5280.6561.0000.9490.8600.905-0.7530.2450.541
length0.487-0.8310.5320.7190.9491.0000.8820.852-0.7060.2740.591
turn0.306-0.7580.4500.6200.8600.8821.0000.779-0.6540.3620.657
displacement0.374-0.7710.4780.5770.9050.8520.7791.000-0.8550.2290.635
gear_ratio-0.2530.610-0.384-0.509-0.753-0.706-0.654-0.8551.0000.2100.658
rep780.0000.2200.2860.2020.2450.2740.3620.2290.2101.0000.584
foreign0.0000.2950.4510.3690.5410.5910.6570.6350.6580.5841.000

Missing values

2023-09-12T09:33:48.890373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-12T09:33:49.106284image/svg+xmlMatplotlib v3.7.3, 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