Overview

Dataset statistics

Number of variables12
Number of observations74
Missing cells5
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory110.0 B

Variable types

NUM9
CAT3

Warnings

length is highly correlated with weightHigh correlation
weight is highly correlated with lengthHigh correlation
rep78 has 5 (6.8%) missing values Missing
make has unique values Unique
price has unique values Unique

Reproduction

Analysis started2020-10-25 20:12:12.481658
Analysis finished2020-10-25 20:12:29.385221
Duration16.9 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

make
Categorical

UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Cad. Eldorado
 
1
Merc. Zephyr
 
1
AMC Concord
 
1
VW Scirocco
 
1
Olds Delta 88
 
1
Other values (69)
69 
ValueCountFrequency (%) 
Cad. Eldorado11.4%
 
Merc. Zephyr11.4%
 
AMC Concord11.4%
 
VW Scirocco11.4%
 
Olds Delta 8811.4%
 
Pont. Catalina11.4%
 
Pont. Phoenix11.4%
 
Olds Starfire11.4%
 
Dodge Magnum11.4%
 
Toyota Celica11.4%
 
AMC Spirit11.4%
 
Merc. Monarch11.4%
 
Pont. Grand Prix11.4%
 
Subaru11.4%
 
Olds Omega11.4%
 
Plym. Champ11.4%
 
Chev. Monte Carlo11.4%
 
Ford Mustang11.4%
 
BMW 320i11.4%
 
Toyota Corolla11.4%
 
Fiat Strada11.4%
 
Chev. Impala11.4%
 
Olds 9811.4%
 
Chev. Chevette11.4%
 
VW Diesel11.4%
 
Other values (49)4966.2%
 
2020-10-25T20:12:29.509864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique74 ?
Unique (%)100.0%
2020-10-25T20:12:29.720350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length12
Mean length11.77027027
Min length6

Overview of Unicode Properties

Unique unicode characters59
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
819.3%
 
a627.1%
 
o556.3%
 
e536.1%
 
r465.3%
 
i414.7%
 
l404.6%
 
t374.2%
 
n343.9%
 
d303.4%
 
.303.4%
 
C293.3%
 
c283.2%
 
u273.1%
 
s222.5%
 
M202.3%
 
P151.7%
 
v131.5%
 
D131.5%
 
S121.4%
 
h121.4%
 
y111.3%
 
g111.3%
 
0111.3%
 
k101.1%
 
Other values (34)12814.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter56865.2%
 
Uppercase Letter16118.5%
 
Space Separator819.3%
 
Other Punctuation303.4%
 
Decimal Number303.4%
 
Dash Punctuation10.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C2918.0%
 
M2012.4%
 
P159.3%
 
D138.1%
 
S127.5%
 
B95.6%
 
O95.6%
 
V85.0%
 
A74.3%
 
L74.3%
 
R63.7%
 
F63.7%
 
W53.1%
 
T42.5%
 
H31.9%
 
E21.2%
 
G21.2%
 
I10.6%
 
N10.6%
 
X10.6%
 
Z10.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
81100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6210.9%
 
o559.7%
 
e539.3%
 
r468.1%
 
i417.2%
 
l407.0%
 
t376.5%
 
n346.0%
 
d305.3%
 
c284.9%
 
u274.8%
 
s223.9%
 
v132.3%
 
h122.1%
 
y111.9%
 
g111.9%
 
k101.8%
 
m101.8%
 
p91.6%
 
b81.4%
 
z30.5%
 
x30.5%
 
q10.2%
 
f10.2%
 
w10.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.30100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01136.7%
 
8413.3%
 
2413.3%
 
1310.0%
 
526.7%
 
626.7%
 
713.3%
 
913.3%
 
313.3%
 
413.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin72983.7%
 
Common14216.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a628.5%
 
o557.5%
 
e537.3%
 
r466.3%
 
i415.6%
 
l405.5%
 
t375.1%
 
n344.7%
 
d304.1%
 
C294.0%
 
c283.8%
 
u273.7%
 
s223.0%
 
M202.7%
 
P152.1%
 
v131.8%
 
D131.8%
 
S121.6%
 
h121.6%
 
y111.5%
 
g111.5%
 
k101.4%
 
m101.4%
 
p91.2%
 
B91.2%
 
Other values (21)8011.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
8157.0%
 
.3021.1%
 
0117.7%
 
842.8%
 
242.8%
 
132.1%
 
521.4%
 
621.4%
 
-10.7%
 
710.7%
 
910.7%
 
310.7%
 
410.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII871100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
819.3%
 
a627.1%
 
o556.3%
 
e536.1%
 
r465.3%
 
i414.7%
 
l404.6%
 
t374.2%
 
n343.9%
 
d303.4%
 
.303.4%
 
C293.3%
 
c283.2%
 
u273.1%
 
s222.5%
 
M202.3%
 
P151.7%
 
v131.5%
 
D131.5%
 
S121.4%
 
h121.4%
 
y111.3%
 
g111.3%
 
0111.3%
 
k101.1%
 
Other values (34)12814.7%
 

price
Real number (ℝ≥0)

UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6165.256757
Minimum3291
Maximum15906
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:29.911870image/svg+xmlMatplotlib v3.3.2, 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 deviation2949.495885
Coefficient of variation (CV)0.4784060099
Kurtosis2.034047676
Mean6165.256757
Median Absolute Deviation (MAD)916
Skewness1.687840988
Sum456229
Variance8699525.974
MonotocityNot monotonic
2020-10-25T20:12:30.161303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
588611.4%
 
630311.4%
 
429611.4%
 
1590611.4%
 
1450011.4%
 
438911.4%
 
579811.4%
 
464711.4%
 
401011.4%
 
539711.4%
 
379811.4%
 
1299011.4%
 
517211.4%
 
389511.4%
 
451611.4%
 
537911.4%
 
571911.4%
 
507911.4%
 
329111.4%
 
473311.4%
 
578811.4%
 
409911.4%
 
1037211.4%
 
973511.4%
 
442511.4%
 
Other values (49)4966.2%
 
ValueCountFrequency (%) 
329111.4%
 
329911.4%
 
366711.4%
 
374811.4%
 
379811.4%
 
379911.4%
 
382911.4%
 
389511.4%
 
395511.4%
 
398411.4%
 
ValueCountFrequency (%) 
1590611.4%
 
1450011.4%
 
1359411.4%
 
1346611.4%
 
1299011.4%
 
1199511.4%
 
1149711.4%
 
1138511.4%
 
1037211.4%
 
1037111.4%
 

mpg
Real number (ℝ≥0)

Distinct21
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2972973
Minimum12
Maximum41
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:30.365861image/svg+xmlMatplotlib v3.3.2, 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 deviation5.78550321
Coefficient of variation (CV)0.2716543385
Kurtosis1.129919829
Mean21.2972973
Median Absolute Deviation (MAD)3.5
Skewness0.9684601369
Sum1576
Variance33.47204739
MonotocityNot monotonic
2020-10-25T20:12:30.520566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
18912.2%
 
19810.8%
 
1468.1%
 
2556.8%
 
2256.8%
 
2156.8%
 
1645.4%
 
1745.4%
 
2445.4%
 
2334.1%
 
2834.1%
 
2634.1%
 
2034.1%
 
1522.7%
 
1222.7%
 
3522.7%
 
3022.7%
 
2911.4%
 
3111.4%
 
3411.4%
 
4111.4%
 
ValueCountFrequency (%) 
1222.7%
 
1468.1%
 
1522.7%
 
1645.4%
 
1745.4%
 
18912.2%
 
19810.8%
 
2034.1%
 
2156.8%
 
2256.8%
 
ValueCountFrequency (%) 
4111.4%
 
3522.7%
 
3411.4%
 
3111.4%
 
3022.7%
 
2911.4%
 
2834.1%
 
2634.1%
 
2556.8%
 
2445.4%
 

rep78
Categorical

MISSING

Distinct5
Distinct (%)7.2%
Missing5
Missing (%)6.8%
Memory size866.0 B
Average
30 
Good
18 
Excellent
11 
Fair
Poor
 
2
ValueCountFrequency (%) 
Average3040.5%
 
Good1824.3%
 
Excellent1114.9%
 
Fair810.8%
 
Poor22.7%
 
(Missing)56.8%
 
2020-10-25T20:12:30.703360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-25T20:12:30.827098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:30.984624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length5.891891892
Min length3

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e8218.8%
 
a439.9%
 
r409.2%
 
o409.2%
 
A306.9%
 
v306.9%
 
g306.9%
 
l225.0%
 
n214.8%
 
G184.1%
 
d184.1%
 
E112.5%
 
x112.5%
 
c112.5%
 
t112.5%
 
F81.8%
 
i81.8%
 
P20.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter36784.2%
 
Uppercase Letter6915.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A3043.5%
 
G1826.1%
 
E1115.9%
 
F811.6%
 
P22.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e8222.3%
 
a4311.7%
 
r4010.9%
 
o4010.9%
 
v308.2%
 
g308.2%
 
l226.0%
 
n215.7%
 
d184.9%
 
x113.0%
 
c113.0%
 
t113.0%
 
i82.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin436100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e8218.8%
 
a439.9%
 
r409.2%
 
o409.2%
 
A306.9%
 
v306.9%
 
g306.9%
 
l225.0%
 
n214.8%
 
G184.1%
 
d184.1%
 
E112.5%
 
x112.5%
 
c112.5%
 
t112.5%
 
F81.8%
 
i81.8%
 
P20.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII436100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e8218.8%
 
a439.9%
 
r409.2%
 
o409.2%
 
A306.9%
 
v306.9%
 
g306.9%
 
l225.0%
 
n214.8%
 
G184.1%
 
d184.1%
 
E112.5%
 
x112.5%
 
c112.5%
 
t112.5%
 
F81.8%
 
i81.8%
 
P20.5%
 

headroom
Real number (ℝ≥0)

Distinct8
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.993243217
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Memory size888.0 B
2020-10-25T20:12:31.134485image/svg+xmlMatplotlib v3.3.2, 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 deviation0.8459947705
Coefficient of variation (CV)0.2826348245
Kurtosis-0.7620739341
Mean2.993243217
Median Absolute Deviation (MAD)0.5
Skewness0.1437965482
Sum221.5
Variance0.7157071233
MonotocityNot monotonic
2020-10-25T20:12:31.279050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
3.51520.3%
 
2.51418.9%
 
21317.6%
 
31317.6%
 
41013.5%
 
1.545.4%
 
4.545.4%
 
511.4%
 
ValueCountFrequency (%) 
1.545.4%
 
21317.6%
 
2.51418.9%
 
31317.6%
 
3.51520.3%
 
41013.5%
 
4.545.4%
 
511.4%
 
ValueCountFrequency (%) 
511.4%
 
4.545.4%
 
41013.5%
 
3.51520.3%
 
31317.6%
 
2.51418.9%
 
21317.6%
 
1.545.4%
 

trunk
Real number (ℝ≥0)

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.75675676
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:31.443815image/svg+xmlMatplotlib v3.3.2, 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 deviation4.277404189
Coefficient of variation (CV)0.3109311493
Kurtosis-0.7796393143
Mean13.75675676
Median Absolute Deviation (MAD)3
Skewness0.02981113321
Sum1018
Variance18.2961866
MonotocityNot monotonic
2020-10-25T20:12:31.609044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
161216.2%
 
17810.8%
 
11810.8%
 
2068.1%
 
856.8%
 
1556.8%
 
1056.8%
 
1345.4%
 
1445.4%
 
945.4%
 
734.1%
 
1234.1%
 
2122.7%
 
611.4%
 
2311.4%
 
2211.4%
 
1811.4%
 
511.4%
 
ValueCountFrequency (%) 
511.4%
 
611.4%
 
734.1%
 
856.8%
 
945.4%
 
1056.8%
 
11810.8%
 
1234.1%
 
1345.4%
 
1445.4%
 
ValueCountFrequency (%) 
2311.4%
 
2211.4%
 
2122.7%
 
2068.1%
 
1811.4%
 
17810.8%
 
161216.2%
 
1556.8%
 
1445.4%
 
1345.4%
 

weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct64
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3019.459459
Minimum1760
Maximum4840
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:31.813397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation777.1935671
Coefficient of variation (CV)0.2573949336
Kurtosis-0.8585177502
Mean3019.459459
Median Absolute Deviation (MAD)550
Skewness0.1511986317
Sum223440
Variance604029.8408
MonotocityNot monotonic
2020-10-25T20:12:32.171636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
406022.7%
 
342022.7%
 
369022.7%
 
180022.7%
 
283022.7%
 
360022.7%
 
337022.7%
 
275022.7%
 
265022.7%
 
220022.7%
 
273011.4%
 
388011.4%
 
267011.4%
 
228011.4%
 
325011.4%
 
223011.4%
 
390011.4%
 
211011.4%
 
472011.4%
 
326011.4%
 
216011.4%
 
183011.4%
 
413011.4%
 
202011.4%
 
374011.4%
 
Other values (39)3952.7%
 
ValueCountFrequency (%) 
176011.4%
 
180022.7%
 
183011.4%
 
193011.4%
 
198011.4%
 
199011.4%
 
202011.4%
 
204011.4%
 
205011.4%
 
207011.4%
 
ValueCountFrequency (%) 
484011.4%
 
472011.4%
 
433011.4%
 
429011.4%
 
413011.4%
 
408011.4%
 
406022.7%
 
403011.4%
 
390011.4%
 
388011.4%
 

length
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.9324324
Minimum142
Maximum233
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:32.389446image/svg+xmlMatplotlib v3.3.2, 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 deviation22.2663399
Coefficient of variation (CV)0.1184805603
Kurtosis-0.9408177208
Mean187.9324324
Median Absolute Deviation (MAD)19
Skewness-0.0418272235
Sum13907
Variance495.7898926
MonotocityNot monotonic
2020-10-25T20:12:32.608153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%) 
20045.4%
 
17045.4%
 
19845.4%
 
17934.1%
 
16534.1%
 
20134.1%
 
20634.1%
 
20422.7%
 
17222.7%
 
19322.7%
 
16322.7%
 
17422.7%
 
21222.7%
 
22122.7%
 
22022.7%
 
15522.7%
 
21822.7%
 
15611.4%
 
16811.4%
 
14711.4%
 
17311.4%
 
16911.4%
 
14911.4%
 
16411.4%
 
15411.4%
 
Other values (22)2229.7%
 
ValueCountFrequency (%) 
14211.4%
 
14711.4%
 
14911.4%
 
15411.4%
 
15522.7%
 
15611.4%
 
15711.4%
 
16111.4%
 
16322.7%
 
16411.4%
 
ValueCountFrequency (%) 
23311.4%
 
23011.4%
 
22211.4%
 
22122.7%
 
22022.7%
 
21822.7%
 
21711.4%
 
21411.4%
 
21222.7%
 
20711.4%
 

turn
Real number (ℝ≥0)

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.64864865
Minimum31
Maximum51
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:32.806292image/svg+xmlMatplotlib v3.3.2, 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 deviation4.399353727
Coefficient of variation (CV)0.1109584785
Kurtosis-0.7395773616
Mean39.64864865
Median Absolute Deviation (MAD)3.5
Skewness0.1264026823
Sum2934
Variance19.35431322
MonotocityNot monotonic
2020-10-25T20:12:32.978086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
431216.2%
 
36912.2%
 
4279.5%
 
4068.1%
 
3568.1%
 
3468.1%
 
4145.4%
 
3745.4%
 
4634.1%
 
4534.1%
 
4434.1%
 
3834.1%
 
4822.7%
 
3322.7%
 
5111.4%
 
3211.4%
 
3911.4%
 
3111.4%
 
ValueCountFrequency (%) 
3111.4%
 
3211.4%
 
3322.7%
 
3468.1%
 
3568.1%
 
36912.2%
 
3745.4%
 
3834.1%
 
3911.4%
 
4068.1%
 
ValueCountFrequency (%) 
5111.4%
 
4822.7%
 
4634.1%
 
4534.1%
 
4434.1%
 
431216.2%
 
4279.5%
 
4145.4%
 
4068.1%
 
3911.4%
 

displacement
Real number (ℝ≥0)

Distinct31
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.2972973
Minimum79
Maximum425
Zeros0
Zeros (%)0.0%
Memory size740.0 B
2020-10-25T20:12:33.171621image/svg+xmlMatplotlib v3.3.2, 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 deviation91.83721896
Coefficient of variation (CV)0.4654763153
Kurtosis-0.5830817597
Mean197.2972973
Median Absolute Deviation (MAD)75
Skewness0.6039687276
Sum14600
Variance8434.074787
MonotocityNot monotonic
2020-10-25T20:12:33.356420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
2311317.6%
 
35056.8%
 
9756.8%
 
30245.4%
 
25034.1%
 
15134.1%
 
12134.1%
 
14034.1%
 
11934.1%
 
22522.7%
 
9822.7%
 
13422.7%
 
16322.7%
 
40022.7%
 
8622.7%
 
10522.7%
 
20022.7%
 
19622.7%
 
31822.7%
 
14611.4%
 
13111.4%
 
15611.4%
 
7911.4%
 
42511.4%
 
30411.4%
 
Other values (6)68.1%
 
ValueCountFrequency (%) 
7911.4%
 
8511.4%
 
8622.7%
 
8911.4%
 
9011.4%
 
9111.4%
 
9756.8%
 
9822.7%
 
10522.7%
 
10711.4%
 
ValueCountFrequency (%) 
42511.4%
 
40022.7%
 
35056.8%
 
31822.7%
 
30411.4%
 
30245.4%
 
25811.4%
 
25034.1%
 
2311317.6%
 
22522.7%
 

gear_ratio
Real number (ℝ≥0)

Distinct36
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.014864683
Minimum2.190000057
Maximum3.890000105
Zeros0
Zeros (%)0.0%
Memory size888.0 B
2020-10-25T20:12:33.557997image/svg+xmlMatplotlib v3.3.2, 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)0.1513458043
Kurtosis-0.8762872815
Mean3.014864683
Median Absolute Deviation (MAD)0.2650001049
Skewness0.2237261981
Sum223.0999908
Variance0.2081979364
MonotocityNot monotonic
2020-10-25T20:12:33.742271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
2.730000019912.2%
 
2.930000067810.8%
 
3.07999992479.5%
 
2.47000002956.8%
 
3.53999996234.1%
 
2.41000008634.1%
 
3.04999995234.1%
 
3.77999997134.1%
 
3.36999988622.7%
 
2.55999994322.7%
 
3.70000004822.7%
 
2.7522.7%
 
3.57999992422.7%
 
3.89000010511.4%
 
2.94000005711.4%
 
2.19000005711.4%
 
2.98000001911.4%
 
3.7400000111.4%
 
3.15000009511.4%
 
3.80999994311.4%
 
3.29999995211.4%
 
3.54999995211.4%
 
3.72000002911.4%
 
2.43000006711.4%
 
2.97000002911.4%
 
Other values (11)1114.9%
 
ValueCountFrequency (%) 
2.19000005711.4%
 
2.2400000111.4%
 
2.2599999911.4%
 
2.27999997111.4%
 
2.41000008634.1%
 
2.43000006711.4%
 
2.47000002956.8%
 
2.52999997111.4%
 
2.55999994322.7%
 
2.730000019912.2%
 
ValueCountFrequency (%) 
3.89000010511.4%
 
3.80999994311.4%
 
3.77999997134.1%
 
3.7400000111.4%
 
3.73000001911.4%
 
3.72000002911.4%
 
3.70000004822.7%
 
3.64000010511.4%
 
3.57999992422.7%
 
3.54999995211.4%
 

foreign
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size762.0 B
Domestic
52 
Foreign
22 
ValueCountFrequency (%) 
Domestic5270.3%
 
Foreign2229.7%
 
2020-10-25T20:12:33.939307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-25T20:12:34.051677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:34.168381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.702702703
Min length7

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7413.0%
 
e7413.0%
 
i7413.0%
 
D529.1%
 
m529.1%
 
s529.1%
 
t529.1%
 
c529.1%
 
F223.9%
 
r223.9%
 
g223.9%
 
n223.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter49687.0%
 
Uppercase Letter7413.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D5270.3%
 
F2229.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7414.9%
 
e7414.9%
 
i7414.9%
 
m5210.5%
 
s5210.5%
 
t5210.5%
 
c5210.5%
 
r224.4%
 
g224.4%
 
n224.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin570100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7413.0%
 
e7413.0%
 
i7413.0%
 
D529.1%
 
m529.1%
 
s529.1%
 
t529.1%
 
c529.1%
 
F223.9%
 
r223.9%
 
g223.9%
 
n223.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII570100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7413.0%
 
e7413.0%
 
i7413.0%
 
D529.1%
 
m529.1%
 
s529.1%
 
t529.1%
 
c529.1%
 
F223.9%
 
r223.9%
 
g223.9%
 
n223.9%
 

Interactions

2020-10-25T20:12:14.828058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:14.997353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:15.161139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:15.336053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:15.508898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:15.667950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:15.821480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:15.997755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:16.155038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T20:12:16.324583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/