Overview

Dataset statistics

Number of variables12
Number of observations74
Missing cells5
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory110.5 B

Variable types

Categorical3
Numeric9

Alerts

make has a high cardinality: 74 distinct values High cardinality
price is highly correlated with mpgHigh correlation
mpg is highly correlated with price and 6 other fieldsHigh correlation
headroom is highly correlated with trunk and 2 other fieldsHigh correlation
trunk is highly correlated with mpg and 6 other fieldsHigh correlation
weight is highly correlated with mpg and 6 other fieldsHigh correlation
length is highly correlated with mpg and 6 other fieldsHigh correlation
turn is highly correlated with mpg and 5 other fieldsHigh correlation
displacement is highly correlated with mpg and 5 other fieldsHigh correlation
gear_ratio is highly correlated with mpg and 5 other fieldsHigh correlation
price is highly correlated with weightHigh correlation
mpg is highly correlated with trunk and 5 other fieldsHigh correlation
headroom is highly correlated with trunk and 1 other fieldsHigh correlation
trunk is highly correlated with mpg and 6 other fieldsHigh correlation
weight is highly correlated with price and 6 other fieldsHigh correlation
length is highly correlated with mpg and 6 other fieldsHigh correlation
turn is highly correlated with mpg and 5 other fieldsHigh correlation
displacement is highly correlated with mpg and 5 other fieldsHigh correlation
gear_ratio is highly correlated with mpg and 5 other fieldsHigh correlation
mpg is highly correlated with weight and 3 other fieldsHigh correlation
headroom is highly correlated with trunkHigh correlation
trunk is highly correlated with headroom and 1 other fieldsHigh correlation
weight is highly correlated with mpg and 4 other fieldsHigh correlation
length is highly correlated with mpg and 5 other fieldsHigh correlation
turn is highly correlated with mpg and 3 other fieldsHigh correlation
displacement is highly correlated with mpg and 4 other fieldsHigh correlation
gear_ratio is highly correlated with weight and 2 other fieldsHigh correlation
make is highly correlated with foreign and 1 other fieldsHigh correlation
foreign is highly correlated with make and 1 other fieldsHigh correlation
rep78 is highly correlated with make and 1 other fieldsHigh correlation
make is highly correlated with price and 10 other fieldsHigh correlation
price is highly correlated with make and 4 other fieldsHigh correlation
mpg is highly correlated with make and 4 other fieldsHigh correlation
rep78 is highly correlated with make and 4 other fieldsHigh correlation
headroom is highly correlated with make and 3 other fieldsHigh correlation
trunk is highly correlated with make and 9 other fieldsHigh correlation
weight is highly correlated with make and 9 other fieldsHigh correlation
length is highly correlated with make and 10 other fieldsHigh correlation
turn is highly correlated with make and 8 other fieldsHigh correlation
displacement is highly correlated with make and 8 other fieldsHigh correlation
gear_ratio is highly correlated with make and 6 other fieldsHigh correlation
foreign is highly correlated with make and 7 other fieldsHigh correlation
rep78 has 5 (6.8%) missing values Missing
make is uniformly distributed Uniform
make has unique values Unique
price has unique values Unique

Reproduction

Analysis started2022-09-06 19:03:47.147722
Analysis finished2022-09-06 19:03:56.769335
Duration9.62 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

make
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
AMC Concord
 
1
Datsun 200
 
1
Audi Fox
 
1
Audi 5000
 
1
Pont. Sunbird
 
1
Other values (69)
69 

Length

Max length17
Median length15
Mean length11.77027027
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

Common Values

ValueCountFrequency (%)
AMC Concord1
 
1.4%
Datsun 2001
 
1.4%
Audi Fox1
 
1.4%
Audi 50001
 
1.4%
Pont. Sunbird1
 
1.4%
Pont. Phoenix1
 
1.4%
Pont. Le Mans1
 
1.4%
Pont. Grand Prix1
 
1.4%
Pont. Firebird1
 
1.4%
Pont. Catalina1
 
1.4%
Other values (64)64
86.5%

Length

2022-09-06T19:03:56.939665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buick7
 
4.5%
olds7
 
4.5%
merc6
 
3.9%
chev6
 
3.9%
pont6
 
3.9%
plym5
 
3.2%
datsun4
 
2.6%
dodge4
 
2.6%
vw4
 
2.6%
cad3
 
1.9%
Other values (93)103
66.5%

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%
.30
 
3.4%
Other values (49)392
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter568
65.2%
Uppercase Letter161
 
18.5%
Space Separator81
 
9.3%
Other Punctuation30
 
3.4%
Decimal Number30
 
3.4%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a62
10.9%
o55
 
9.7%
e53
 
9.3%
r46
 
8.1%
i41
 
7.2%
l40
 
7.0%
t37
 
6.5%
n34
 
6.0%
d30
 
5.3%
c28
 
4.9%
Other values (15)142
25.0%
Uppercase Letter
ValueCountFrequency (%)
C29
18.0%
M20
12.4%
P15
9.3%
D13
8.1%
S12
 
7.5%
B9
 
5.6%
O9
 
5.6%
V8
 
5.0%
A7
 
4.3%
L7
 
4.3%
Other values (11)32
19.9%
Decimal Number
ValueCountFrequency (%)
011
36.7%
24
 
13.3%
84
 
13.3%
13
 
10.0%
62
 
6.7%
52
 
6.7%
41
 
3.3%
31
 
3.3%
91
 
3.3%
71
 
3.3%
Space Separator
ValueCountFrequency (%)
81
100.0%
Other Punctuation
ValueCountFrequency (%)
.30
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin729
83.7%
Common142
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a62
 
8.5%
o55
 
7.5%
e53
 
7.3%
r46
 
6.3%
i41
 
5.6%
l40
 
5.5%
t37
 
5.1%
n34
 
4.7%
d30
 
4.1%
C29
 
4.0%
Other values (36)302
41.4%
Common
ValueCountFrequency (%)
81
57.0%
.30
 
21.1%
011
 
7.7%
24
 
2.8%
84
 
2.8%
13
 
2.1%
62
 
1.4%
52
 
1.4%
41
 
0.7%
31
 
0.7%
Other values (3)3
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII871
100.0%

Most frequent character per block

ASCII
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%
.30
 
3.4%
Other values (49)392
45.0%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6165.256757
Minimum3291
Maximum15906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:57.052786image/svg+xmlMatplotlib v3.5.3, 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 deviation2949.495885
Coefficient of variation (CV)0.4784060099
Kurtosis2.034047676
Mean6165.256757
Median Absolute Deviation (MAD)916
Skewness1.687840988
Sum456229
Variance8699525.974
MonotonicityNot monotonic
2022-09-06T19:03:57.180031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40991
 
1.4%
62291
 
1.4%
62951
 
1.4%
96901
 
1.4%
41721
 
1.4%
44241
 
1.4%
47231
 
1.4%
52221
 
1.4%
49341
 
1.4%
57981
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2972973
Minimum12
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:57.292348image/svg+xmlMatplotlib v3.5.3, 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 deviation5.78550321
Coefficient of variation (CV)0.2716543385
Kurtosis1.129919829
Mean21.2972973
Median Absolute Deviation (MAD)3.5
Skewness0.9684601369
Sum1576
Variance33.47204739
MonotonicityNot monotonic
2022-09-06T19:03:57.379684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
189
12.2%
198
 
10.8%
146
 
8.1%
225
 
6.8%
255
 
6.8%
215
 
6.8%
164
 
5.4%
174
 
5.4%
244
 
5.4%
263
 
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
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)7.2%
Missing5
Missing (%)6.8%
Memory size1.1 KiB
Average
30 
Good
18 
Excellent
11 
Fair
Poor
 
2

Length

Max length9
Median length7
Mean length6.101449275
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 (%)
Average30
40.5%
Good18
24.3%
Excellent11
 
14.9%
Fair8
 
10.8%
Poor2
 
2.7%
(Missing)5
 
6.8%

Length

2022-09-06T19:03:57.479226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:03:57.587313image/svg+xmlMatplotlib v3.5.3, 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%
A30
 
7.1%
g30
 
7.1%
v30
 
7.1%
l22
 
5.2%
G18
 
4.3%
d18
 
4.3%
Other values (8)73
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter352
83.6%
Uppercase Letter69
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e82
23.3%
r40
11.4%
o40
11.4%
a38
10.8%
g30
 
8.5%
v30
 
8.5%
l22
 
6.2%
d18
 
5.1%
t11
 
3.1%
n11
 
3.1%
Other values (3)30
 
8.5%
Uppercase Letter
ValueCountFrequency (%)
A30
43.5%
G18
26.1%
E11
 
15.9%
F8
 
11.6%
P2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin421
100.0%

Most frequent character per script

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

Most occurring blocks

ValueCountFrequency (%)
ASCII421
100.0%

Most frequent character per block

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

headroom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.993243243
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-09-06T19:03:57.677697image/svg+xmlMatplotlib v3.5.3, 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 deviation0.8459947705
Coefficient of variation (CV)0.2826348218
Kurtosis-0.7620739341
Mean2.993243243
Median Absolute Deviation (MAD)0.5
Skewness0.1437965482
Sum221.5
Variance0.7157071233
MonotonicityNot monotonic
2022-09-06T19:03:57.760746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.515
20.3%
2.514
18.9%
313
17.6%
213
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.75675676
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:57.857581image/svg+xmlMatplotlib v3.5.3, 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 deviation4.277404189
Coefficient of variation (CV)0.3109311493
Kurtosis-0.7796393143
Mean13.75675676
Median Absolute Deviation (MAD)3
Skewness0.02981113321
Sum1018
Variance18.2961866
MonotonicityNot monotonic
2022-09-06T19:03:57.948738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1612
16.2%
118
10.8%
178
10.8%
206
8.1%
85
 
6.8%
105
 
6.8%
155
 
6.8%
144
 
5.4%
134
 
5.4%
94
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3019.459459
Minimum1760
Maximum4840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:58.062569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation777.1935671
Coefficient of variation (CV)0.2573949336
Kurtosis-0.8585177502
Mean3019.459459
Median Absolute Deviation (MAD)550
Skewness0.1511986317
Sum223440
Variance604029.8408
MonotonicityNot monotonic
2022-09-06T19:03:58.176926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28302
 
2.7%
36902
 
2.7%
22002
 
2.7%
33702
 
2.7%
40602
 
2.7%
34202
 
2.7%
26502
 
2.7%
18002
 
2.7%
36002
 
2.7%
27502
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.9324324
Minimum142
Maximum233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:58.299294image/svg+xmlMatplotlib v3.5.3, 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 deviation22.2663399
Coefficient of variation (CV)0.1184805603
Kurtosis-0.9408177208
Mean187.9324324
Median Absolute Deviation (MAD)19
Skewness-0.0418272235
Sum13907
Variance495.7898926
MonotonicityNot monotonic
2022-09-06T19:03:58.415735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1984
 
5.4%
1704
 
5.4%
2004
 
5.4%
2063
 
4.1%
1793
 
4.1%
2013
 
4.1%
1653
 
4.1%
2182
 
2.7%
2212
 
2.7%
2042
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.64864865
Minimum31
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:58.520494image/svg+xmlMatplotlib v3.5.3, 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 deviation4.399353727
Coefficient of variation (CV)0.1109584785
Kurtosis-0.7395773616
Mean39.64864865
Median Absolute Deviation (MAD)3.5
Skewness0.1264026823
Sum2934
Variance19.35431322
MonotonicityNot monotonic
2022-09-06T19:03:58.614710image/svg+xmlMatplotlib v3.5.3, 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%
383
 
4.1%
463
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.2972973
Minimum79
Maximum425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2022-09-06T19:03:58.723620image/svg+xmlMatplotlib v3.5.3, 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 deviation91.83721896
Coefficient of variation (CV)0.4654763153
Kurtosis-0.5830817597
Mean197.2972973
Median Absolute Deviation (MAD)75
Skewness0.6039687276
Sum14600
Variance8434.074787
MonotonicityNot monotonic
2022-09-06T19:03:58.939613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
23113
17.6%
975
 
6.8%
3505
 
6.8%
3024
 
5.4%
1513
 
4.1%
1193
 
4.1%
1403
 
4.1%
1213
 
4.1%
2503
 
4.1%
2002
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.014864867
Minimum2.190000057
Maximum3.890000105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-09-06T19:03:59.050900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.190000057
5-th percentile2.364500046
Q12.730000019
median2.955000043
Q33.352499902
95-th percentile3.779999971
Maximum3.890000105
Range1.700000048
Interquartile range (IQR)0.6224998832

Descriptive statistics

Standard deviation0.4562871158
Coefficient of variation (CV)0.1513457936
Kurtosis-0.8762872815
Mean3.014864867
Median Absolute Deviation (MAD)0.2650001049
Skewness0.2237261981
Sum223.1000001
Variance0.2081979364
MonotonicityNot monotonic
2022-09-06T19:03:59.155060image/svg+xmlMatplotlib v3.5.3, 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%
3.5399999623
 
4.1%
2.4100000863
 
4.1%
3.7799999713
 
4.1%
3.7000000482
 
2.7%
3.3699998862
 
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
HIGH CORRELATION

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size903.0 B
Domestic
52 
Foreign
22 

Length

Max length8
Median length8
Mean length7.702702703
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 (%)
Domestic52
70.3%
Foreign22
29.7%

Length

2022-09-06T19:03:59.262754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:03:59.361055image/svg+xmlMatplotlib v3.5.3, 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 (%)
Lowercase Letter496
87.0%
Uppercase Letter74
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o74
14.9%
e74
14.9%
i74
14.9%
m52
10.5%
s52
10.5%
t52
10.5%
c52
10.5%
r22
 
4.4%
g22
 
4.4%
n22
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
D52
70.3%
F22
29.7%

Most occurring scripts

ValueCountFrequency (%)
Latin570
100.0%

Most frequent character per script

Latin
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 (%)
ASCII570
100.0%

Most frequent character per block

ASCII
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

2022-09-06T19:03:55.525055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:48.695793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:49.526013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:50.434598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:51.258213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:52.091371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:52.989151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:53.753601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:54.719364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:55.614083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:48.792319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:49.614365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:50.524530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:51.350921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:52.180391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:53.073363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:53.846410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:03:54.809047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/